From 0190e2286bab42e9ad978ddccc9b8411983cb067 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Tue, 15 Aug 2023 01:17:32 +0300 Subject: [PATCH 001/148] Added extra tests --- .../test_protected_groups_partitioning.py | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/tests/utils/test_protected_groups_partitioning.py b/tests/utils/test_protected_groups_partitioning.py index 2177518c..ed9b6f57 100644 --- a/tests/utils/test_protected_groups_partitioning.py +++ b/tests/utils/test_protected_groups_partitioning.py @@ -79,3 +79,48 @@ def test_create_test_protected_groups_true2(compas_without_sensitive_attrs_datas assert actual_test_protected_groups['sex_dis'].shape[0] == 845 assert actual_test_protected_groups['race_priv'].shape[0] == 414 assert actual_test_protected_groups['race_dis'].shape[0] == 642 + + +def test_create_test_protected_groups_folk_true1(folk_emp_config_params): + data_loader = ACSEmploymentDataset(state=['NY'], year=2018, with_nulls=False, + subsample_size=20_000, subsample_seed=42) + + seed = 100 + X_train, X_test, y_train, y_test = train_test_split(data_loader.X_data, + data_loader.y_data, + test_size=folk_emp_config_params.test_set_fraction, + random_state=seed) + actual_test_protected_groups = create_test_protected_groups(X_test, + data_loader.full_df, + folk_emp_config_params.sensitive_attributes_dct) + + assert len(actual_test_protected_groups) == len(folk_emp_config_params.sensitive_attributes_dct.keys()) * 2 + + assert actual_test_protected_groups['SEX_priv'].shape[0] == X_test[X_test.SEX == '1'].shape[0] + assert actual_test_protected_groups['SEX_dis'].shape[0] == X_test[X_test.SEX == '2'].shape[0] + assert actual_test_protected_groups['RAC1P_priv'].shape[0] == X_test[X_test.RAC1P != '2'].shape[0] + assert actual_test_protected_groups['RAC1P_dis'].shape[0] == X_test[X_test.RAC1P == '2'].shape[0] + assert actual_test_protected_groups['SEX&RAC1P_priv'].shape[0] == X_test[(X_test.SEX != '2') | (X_test.RAC1P != '2')].shape[0] + assert actual_test_protected_groups['SEX&RAC1P_dis'].shape[0] == X_test[(X_test.SEX == '2') & (X_test.RAC1P == '2')].shape[0] + + +def test_create_test_protected_groups_folk_true2(folk_emp_config_params): + data_loader = ACSEmploymentDataset(state=['NY'], year=2018, with_nulls=False, + subsample_size=20_000, subsample_seed=42) + new_sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9']} + + seed = 100 + X_train, X_test, y_train, y_test = train_test_split(data_loader.X_data, + data_loader.y_data, + test_size=folk_emp_config_params.test_set_fraction, + random_state=seed) + actual_test_protected_groups = create_test_protected_groups(X_test, + data_loader.full_df, + new_sensitive_attributes_dct) + + assert len(actual_test_protected_groups) == len(new_sensitive_attributes_dct.keys()) * 2 + + assert actual_test_protected_groups['SEX_priv'].shape[0] == X_test[X_test.SEX == '1'].shape[0] + assert actual_test_protected_groups['SEX_dis'].shape[0] == X_test[X_test.SEX == '2'].shape[0] + assert actual_test_protected_groups['RAC1P_priv'].shape[0] == X_test[X_test.RAC1P == '1'].shape[0] + assert actual_test_protected_groups['RAC1P_dis'].shape[0] == X_test[X_test.RAC1P.isin(['2', '3', '4', '5', '6', '7', '8', '9'])].shape[0] From e65dcc140877770c04191a54549d7180a0ecf71a Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sun, 1 Oct 2023 01:23:51 +0300 Subject: [PATCH 002/148] Added plot 1 to a gradio app --- .gitignore | 1 + .../Multiple_Models_Interface_Use_Case.ipynb | 86 ++-- .../Multiple_Models_Interface_Vis.ipynb | 480 ++++++++++++++++++ docs/examples/experiment_config.yaml | 2 +- requirements.txt | 2 +- .../metrics_interactive_visualizer.py | 120 +++++ virny/custom_classes/metrics_visualizer.py | 16 +- virny/utils/data_viz_utils.py | 132 +++-- 8 files changed, 701 insertions(+), 138 deletions(-) create mode 100644 docs/examples/Multiple_Models_Interface_Vis.ipynb create mode 100644 virny/custom_classes/metrics_interactive_visualizer.py diff --git a/.gitignore b/.gitignore index cf2ccb41..375238bf 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ *_venv +virny_env notebooks *.env .DS_Store diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 7c056ceb..d0bb62a5 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -152,7 +152,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8d7b3af66d9484a6" }, { "cell_type": "code", @@ -200,7 +201,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "d8e5cb3ff6e3941a" }, { "cell_type": "markdown", @@ -209,7 +211,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "fc98f09ac0fc8ded" }, { "cell_type": "markdown", @@ -226,7 +229,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "e6c314a1af8c4fe5" }, { "cell_type": "code", @@ -247,7 +251,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "4955f140ad45254e" }, { "cell_type": "code", @@ -259,7 +264,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "b64f9dbcbfa9cdc2" }, { "cell_type": "markdown", @@ -359,7 +365,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "c6719c1b6b5748a9" }, { "cell_type": "code", @@ -370,7 +377,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "de4db3f6b82c8f05" }, { "cell_type": "markdown", @@ -379,7 +387,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "7317a756231dc58a" }, { "cell_type": "code", @@ -403,8 +412,7 @@ "\n", "2023/08/13, 01:39:23: Tuning XGBClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:39:27: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n", - "\n" + "2023/08/13, 01:39:27: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" ] }, { @@ -423,7 +431,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "d76c04f902e8548a" }, { "cell_type": "code", @@ -437,7 +446,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "dc00b584001630d3" }, { "cell_type": "markdown", @@ -446,7 +456,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "9a3bc8180fc00ea2" }, { "cell_type": "code", @@ -480,7 +491,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "9b04064fae7867f4" }, { "cell_type": "markdown", @@ -520,9 +532,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "############################## [Model 1 / 4] Analyze DecisionTreeClassifier ##############################\n", - "\n", - "\n" + "############################## [Model 1 / 4] Analyze DecisionTreeClassifier ##############################\n" ] }, { @@ -547,10 +557,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "\n", - "\n" - ] + "text": [] }, { "name": "stderr", @@ -568,9 +575,7 @@ "\n", "\n", "\n", - "############################## [Model 2 / 4] Analyze LogisticRegression ##############################\n", - "\n", - "\n" + "############################## [Model 2 / 4] Analyze LogisticRegression ##############################\n" ] }, { @@ -595,10 +600,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "\n", - "\n" - ] + "text": [] }, { "name": "stderr", @@ -616,9 +618,7 @@ "\n", "\n", "\n", - "############################## [Model 3 / 4] Analyze RandomForestClassifier ##############################\n", - "\n", - "\n" + "############################## [Model 3 / 4] Analyze RandomForestClassifier ##############################\n" ] }, { @@ -643,10 +643,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "\n", - "\n" - ] + "text": [] }, { "name": "stderr", @@ -664,9 +661,7 @@ "\n", "\n", "\n", - "############################## [Model 4 / 4] Analyze XGBClassifier ##############################\n", - "\n", - "\n" + "############################## [Model 4 / 4] Analyze XGBClassifier ##############################\n" ] }, { @@ -691,10 +686,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "\n", - "\n" - ] + "text": [] }, { "name": "stderr", @@ -708,8 +700,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", "\n", "\n" ] @@ -886,7 +876,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "de72ce340642702f" }, { "cell_type": "code", @@ -907,7 +898,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "b08c56d7c4bd0096" }, { "cell_type": "code", diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb new file mode 100644 index 00000000..e75f0a48 --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:16.932083Z", + "start_time": "2023-09-29T20:56:16.278169Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:16.940086Z", + "start_time": "2023-09-29T20:56:16.931485Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:16.951831Z", + "start_time": "2023-09-29T20:56:16.940588Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": [ + "# Multiple Models Interface Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:30.072450Z", + "start_time": "2023-09-29T20:56:22.772584Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from virny.utils.custom_initializers import read_model_metric_dfs, create_config_obj\n", + "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer\n", + "from virny.custom_classes.metrics_composer import MetricsComposer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", + "config_yaml_content = \"\"\"\n", + "dataset_name: COMPAS_Without_Sensitive_Attributes\n", + "bootstrap_fraction: 0.8\n", + "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", + "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", + "\"\"\"\n", + "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", + " f.write(config_yaml_content)\n", + "\n", + "config = create_config_obj(config_yaml_path=config_yaml_path)\n", + "model_names = ['DecisionTreeClassifier', 'LogisticRegression', 'RandomForestClassifier', 'XGBClassifier']\n", + "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', 'COMPAS_Without_Sensitive_Attributes_Metrics_20230812__224136')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-29T20:56:30.095448Z", + "start_time": "2023-09-29T20:56:30.073873Z" + } + }, + "id": "d777610462304f63" + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f94a20dc", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:30.121865Z", + "start_time": "2023-09-29T20:56:30.094816Z" + } + }, + "outputs": [], + "source": [ + "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=model_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b04d06cf", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:30.139696Z", + "start_time": "2023-09-29T20:56:30.121071Z" + } + }, + "outputs": [], + "source": [ + "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" + ] + }, + { + "cell_type": "markdown", + "id": "e1a23ece", + "metadata": {}, + "source": [ + "Compute composed metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "be6ace22", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T20:56:30.169575Z", + "start_time": "2023-09-29T20:56:30.138633Z" + } + }, + "outputs": [], + "source": [ + "models_composed_metrics_df = metrics_composer.compose_metrics()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/urllib3/__init__.py:34: NotOpenSSLWarning: urllib3 v2.0 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import altair as alt\n", + "import gradio as gr\n", + "import numpy as np\n", + "import pandas as pd\n", + "from vega_datasets import data\n", + "\n", + "\n", + "def make_plot(plot_type):\n", + " if plot_type == \"scatter_plot\":\n", + " cars = data.cars()\n", + " return alt.Chart(cars).mark_point().encode(\n", + " x='Horsepower',\n", + " y='Miles_per_Gallon',\n", + " color='Origin',\n", + " )\n", + " elif plot_type == \"heatmap\":\n", + " # Compute x^2 + y^2 across a 2D grid\n", + " x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n", + " z = x ** 2 + y ** 2\n", + "\n", + " # Convert this grid to columnar data expected by Altair\n", + " source = pd.DataFrame({'x': x.ravel(),\n", + " 'y': y.ravel(),\n", + " 'z': z.ravel()})\n", + " return alt.Chart(source).mark_rect().encode(\n", + " x='x:O',\n", + " y='y:O',\n", + " color='z:Q'\n", + " )\n", + "\n", + "\n", + "with gr.Blocks() as demo:\n", + " button = gr.Radio(label=\"Plot type\",\n", + " choices=['scatter_plot', 'heatmap'], value='scatter_plot')\n", + " plot = gr.Plot(label=\"Plot\")\n", + " button.change(make_plot, inputs=button, outputs=[plot])\n", + " demo.load(make_plot, inputs=[button], outputs=[plot])\n", + "\n", + "\n", + "demo.launch(inline=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T22:25:40.759154Z", + "start_time": "2023-09-28T22:25:39.629263Z" + } + }, + "id": "b9dad21b662edd59" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], + "source": [ + "demo.close()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T22:26:12.203639Z", + "start_time": "2023-09-28T22:26:12.019693Z" + } + }, + "id": "920e2c1a81d4e810" + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": {}, + "source": [ + "## Metrics Visualization and Reporting" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-30T22:20:33.545960Z", + "start_time": "2023-09-30T22:20:33.514242Z" + } + }, + "outputs": [], + "source": [ + "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", + " model_names=model_names,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + } + ], + "source": [ + "visualizer.start_web_app()" + ], + "metadata": { + "collapsed": false, + "is_executing": true, + "ExecuteTime": { + "start_time": "2023-09-30T22:20:33.605579Z" + } + }, + "id": "678a9dc8d51243f4" + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], + "source": [ + "visualizer.stop_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-29T21:41:49.927075Z", + "start_time": "2023-09-29T21:41:49.639933Z" + } + }, + "id": "277b6d1de837dab7" + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer.create_model_rank_heatmap(\n", + " metrics_lst=[\n", + " # Group fairness metrics\n", + " 'Equalized_Odds_TPR',\n", + " 'Equalized_Odds_FPR',\n", + " 'Disparate_Impact',\n", + " 'Statistical_Parity_Difference',\n", + " 'Accuracy_Parity',\n", + " # Group stability metrics\n", + " 'Label_Stability_Ratio',\n", + " 'IQR_Parity',\n", + " 'Std_Parity',\n", + " 'Std_Ratio',\n", + " 'Jitter_Parity',\n", + " ],\n", + " groups_lst=config.sensitive_attributes_dct.keys(),\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-29T20:57:58.777407Z", + "start_time": "2023-09-29T20:57:58.303858Z" + } + }, + "id": "43fca999faac66af" + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "5efb1bf2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": "\n
\n", + "text/plain": "alt.Chart(...)" + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualizer.create_overall_metrics_bar_char(\n", + " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", + " metrics_title=\"Error Metrics\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "0eb8528e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": "\n
\n", + "text/plain": "alt.Chart(...)" + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualizer.create_overall_metrics_bar_char(\n", + " metrics_names=['Label_Stability'],\n", + " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", + " metrics_title=\"Variance Metrics\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "2326c129", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 555d36db..44efa1b1 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,5 +1,5 @@ + dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -computation_mode: error_analysis sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} diff --git a/requirements.txt b/requirements.txt index 6e55b6af..4b2154bf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ wheel~=0.38.4 twine~=4.0.2 requests-toolbelt==1.0.0 numpy~=1.24.2 -datapane~=0.15.5 +datapane~=0.16.0 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py new file mode 100644 index 00000000..574d0a91 --- /dev/null +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -0,0 +1,120 @@ +import pandas as pd +import gradio as gr + +from virny.utils.data_viz_utils import create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank + + +class MetricsInteractiveVisualizer: + """ + Class to create useful visualizations of models metrics. + + Parameters + ---------- + models_metrics_dct + Dictionary where keys are model names and values are dataframes of subgroup metrics for each model + models_composed_metrics_df + Dataframe of all model composed metrics + dataset_name + Name of a dataset that was included in metric filenames and was used for the metrics computation + model_names + Metrics for what model names to visualize + sensitive_attributes_dct + A dictionary where keys are sensitive attributes names (including attributes intersections), + and values are privilege values for these attributes + + """ + def __init__(self, models_metrics_dct: dict, models_composed_metrics_df: pd.DataFrame, + dataset_name: str, model_names: list, sensitive_attributes_dct: dict): + self.demo = None + self.dataset_name = dataset_name + self.model_names = model_names + self.sensitive_attributes_dct = sensitive_attributes_dct + + # Create one metrics df with all model_dfs + all_models_metrics_df = pd.DataFrame() + for model_name in models_metrics_dct.keys(): + model_metrics_df = models_metrics_dct[model_name] + all_models_metrics_df = pd.concat([all_models_metrics_df, model_metrics_df]) + + all_models_metrics_df = all_models_metrics_df.reset_index(drop=True) + + self.models_metrics_dct = models_metrics_dct + self.all_models_metrics_df = all_models_metrics_df + self.models_composed_metrics_df = models_composed_metrics_df + self.melted_models_composed_metrics_df = self.models_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"], + var_name="Subgroup", + value_name="Value") + self.sorted_models_composed_metrics_df = self.melted_models_composed_metrics_df.sort_values(by=['Value']) + + def start_web_app(self): + css = """ + .plot_output1 {position: right !important} + """ + with gr.Blocks(css=css) as demo: + with gr.Row(): + with gr.Column(scale=1): + fairness_metrics = gr.Dropdown( + ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], + value=['Equalized_Odds_TPR', 'Equalized_Odds_FPR'], multiselect=True, label="Fairness Metrics", info="Select fairness metrics to display on the heatmap:", + ) + group_stability_metrics = gr.Dropdown( + ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], + value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Group Stability Metrics", info="Select group stability metrics to display on the heatmap:", + ) + btn = gr.Button("Submit") + with gr.Column(scale=2): + model_ranking_heatmap = gr.Plot(label="Plot") + + btn.click(self._create_model_rank_heatmap, + inputs=[fairness_metrics, group_stability_metrics], + outputs=[model_ranking_heatmap]) + + self.demo = demo + self.demo.launch(inline=False, debug=True, show_error=True) + + def stop_web_app(self): + self.demo.close() + + def _create_model_rank_heatmap(self, group_fairness_metrics_lst: list, group_stability_metrics_lst: list): + """ + Create a model rank heatmap. + + Parameters + ---------- + group_fairness_metrics_lst + A list of group fairness metrics to visualize + group_stability_metrics_lst + A list of group stability metrics to visualize + + """ + groups_lst = self.sensitive_attributes_dct.keys() + metrics_lst = group_fairness_metrics_lst + group_stability_metrics_lst + + # Find metric values for each model based on metric, group, and model names. + # Add the values to a results dict. + results = {} + num_models = len(self.model_names) + for metric in metrics_lst: + for group in groups_lst: + group_metric = metric + '_' + group + results[group_metric] = dict() + # Get distinct sorted model names + sorted_model_names_arr = self.sorted_models_composed_metrics_df[ + (self.sorted_models_composed_metrics_df.Metric == metric) & + (self.sorted_models_composed_metrics_df.Subgroup == group) + ]['Model_Name'].values + # Add values to a results dict + for idx, model_name in enumerate(sorted_model_names_arr): + metric_value = self.sorted_models_composed_metrics_df[ + (self.sorted_models_composed_metrics_df.Metric == metric) & + (self.sorted_models_composed_metrics_df.Subgroup == group) & + (self.sorted_models_composed_metrics_df.Model_Name == model_name) + ]['Value'].values[0] + metric_value = round(metric_value, 3) + results[group_metric][model_name] = metric_value + + model_metrics_matrix = pd.DataFrame(results).T + sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) + + return model_rank_heatmap diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index e895b493..377bcdf9 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -8,6 +8,7 @@ from datetime import datetime, timezone from virny.configs.constants import ReportType +from virny.utils.data_viz_utils import create_sorted_matrix_by_rank class MetricsVisualizer: @@ -252,19 +253,6 @@ def create_fairness_variance_interactive_bar_chart(self): ) ) - @staticmethod - def _create_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: - models_distances_matrix = model_metrics_matrix.copy(deep=True).T - metric_names = models_distances_matrix.columns - for metric_name in metric_names: - if 'impact' in metric_name.lower() or 'ratio' in metric_name.lower(): - models_distances_matrix[metric_name] = models_distances_matrix[metric_name] - 1 - models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() - - models_distances_matrix = models_distances_matrix.T - sorted_matrix_by_rank = np.argsort(np.argsort(models_distances_matrix, axis=1), axis=1) - return sorted_matrix_by_rank - def create_model_rank_heatmap(self, model_metrics_matrix, sorted_matrix_by_rank, num_models: int): """ This heatmap includes all group fairness and stability metrics and all defined models. @@ -385,7 +373,7 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): results[group_metric][model_name] = metric_value model_metrics_matrix = pd.DataFrame(results).T - sorted_matrix_by_rank = MetricsVisualizer._create_sorted_matrix_by_rank(model_metrics_matrix) + sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) model_rank_heatmap = self.create_model_rank_heatmap(model_metrics_matrix, sorted_matrix_by_rank, num_models) total_model_rank_heatmap = self.create_total_model_rank_heatmap(sorted_matrix_by_rank, num_models) if self.__create_report: diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index ddf397e0..9895d84a 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -1,6 +1,5 @@ -import os -import pandas as pd import seaborn as sns +import numpy as np from matplotlib import pyplot as plt @@ -29,76 +28,59 @@ def plot_generic(x, y, xlabel, ylabel, x_lim, y_lim, plot_title): plt.show() -def create_average_metrics_df(dataset_name, model_names, metrics_path): - results_filenames = [filename for filename in os.listdir(metrics_path)] - models_average_results_dct = dict() - for model_name in model_names: - model_results_filenames = [filename for filename in results_filenames if 'Average_Metrics' not in filename - and dataset_name in filename - and model_name in filename] - - if len(model_results_filenames) == 0: - continue - - model_results_dfs = [] - for model_results_filename in model_results_filenames: - model_results_df = pd.read_csv(f'{metrics_path}/{model_results_filename}') - model_results_df.set_index('index', inplace = True) - model_results_dfs.append(model_results_df) - - model_average_results_df = None - for model_results_df in model_results_dfs: - if model_average_results_df is None: - model_average_results_df = model_results_df - else: - model_average_results_df += model_results_df - - model_average_results_df = model_average_results_df / len(model_results_dfs) - models_average_results_dct[model_name] = model_average_results_df - - filename = f'Average_Metrics_{dataset_name}_{model_name}.csv' - model_average_results_df.reset_index().to_csv(f'{metrics_path}/{filename}', index=False) - print(f'File with average metrics for {model_name} is created') - - return models_average_results_dct - - -def visualize_fairness_metrics_for_prediction_metric(models_average_results_dct, x_metric, y_metrics: list): - sns.set_style("darkgrid") - x_lim = 0.5 - y_lim = 0.22 - priv_dis_pairs = [('SEX_RAC1P_priv', 'SEX_RAC1P_dis'), - ('SEX_priv', 'SEX_dis'), - ('RAC1P_priv', 'RAC1P_dis')] - for y_metric in y_metrics: - for fairness_metric_priv, fairness_metric_dis in priv_dis_pairs: - display_fairness_plot(models_average_results_dct, x_metric, y_metric, - fairness_metric_priv, fairness_metric_dis, x_lim, y_lim) - - -def display_fairness_plot(models_average_results_dct, x_metric, y_metric, - fairness_metric_priv, fairness_metric_dis, x_lim, y_lim): - fig, ax = plt.subplots() - set_size(15, 8, ax) - - # List of all markers -- https://matplotlib.org/stable/api/markers_api.html - markers = ['o', '*', '|', '<', '>', '^', 'v', '1', 's', 'x', 'D', 'P', 'H'] - model_names = models_average_results_dct.keys() - shapes = [] - for idx, model_name in enumerate(model_names): - x_val = abs(models_average_results_dct[model_name][fairness_metric_priv].loc[x_metric] - \ - models_average_results_dct[model_name][fairness_metric_dis].loc[x_metric]) - y_val = abs(models_average_results_dct[model_name][fairness_metric_priv].loc[y_metric] - \ - models_average_results_dct[model_name][fairness_metric_dis].loc[y_metric]) - a = ax.scatter(x_val, y_val, marker=markers[idx], s=100) - shapes.append(a) - - plt.axhline(y=0.0, color='r', linestyle='-') - plt.xlabel(f'{x_metric} Difference') - plt.ylabel(f'{y_metric} Difference') - plt.xlim(-0.01, x_lim) - plt.ylim(-0.01, y_lim) - plt.title(f'{fairness_metric_priv}-{fairness_metric_dis} difference for {x_metric} and {y_metric}', fontsize=20) - ax.legend(shapes, model_names, fontsize=12, title='Markers') - - plt.show() +def create_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: + models_distances_matrix = model_metrics_matrix.copy(deep=True).T + metric_names = models_distances_matrix.columns + for metric_name in metric_names: + if 'impact' in metric_name.lower() or 'ratio' in metric_name.lower(): + models_distances_matrix[metric_name] = models_distances_matrix[metric_name] - 1 + models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() + + models_distances_matrix = models_distances_matrix.T + sorted_matrix_by_rank = np.argsort(np.argsort(models_distances_matrix, axis=1), axis=1) + return sorted_matrix_by_rank + + +def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int): + """ + This heatmap includes group fairness and stability metrics and defined models. + Using it, you can visually compare the models across defined group metrics. On this plot, + colors display ranks where 1 is the best model for the metric. These ranks are conditioned + on difference or ratio operations used to create these group metrics: + + 1) if the metric is created based on the difference operation, closer values to zero have ranks that are closer to the first rank + + 2) if the metric is created based on the ratio operation, closer values to one have ranks that are closer to the first rank + + Parameters + ---------- + model_metrics_matrix + Matrix of model metrics values where indexes are group metric names and columns are model names + sorted_matrix_by_rank + Matrix of model ranks per metric where indexes are group metric names and columns are model names + num_models + Number of models to visualize + + """ + font_increase = 2 + matrix_width = num_models * 5 + matrix_height = model_metrics_matrix.shape[0] // 1.5 + fig = plt.figure(figsize=(matrix_width, matrix_height)) + rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex()[::-1] + ax = sns.heatmap(sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, + fmt='', annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 16 + font_increase}) + ax.set(xlabel="", ylabel="") + ax.xaxis.tick_top() + ax.tick_params(labelsize=16 + font_increase) + fig.subplots_adjust(left=0.25, top=0.9) + + cbar = ax.collections[0].colorbar + model_ranks = [idx for idx in range(num_models)] + cbar.set_ticks([float(idx) for idx in model_ranks]) + tick_labels = [str(idx + 1) for idx in model_ranks] + tick_labels[0] = tick_labels[0] + ', best' + tick_labels[-1] = tick_labels[-1] + ', worst' + cbar.set_ticklabels(tick_labels, fontsize=16 + font_increase) + cbar.set_label('Model Ranks', fontsize=18 + font_increase) + + return fig, ax From c84456ef7d80660f696f985dd9b3375122fb94cb Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sun, 1 Oct 2023 22:46:45 +0300 Subject: [PATCH 003/148] Created subgroup and group heatmaps --- .../Multiple_Models_Interface_Vis.ipynb | 63 +++++- .../metrics_interactive_visualizer.py | 207 ++++++++++++++---- virny/utils/common_helpers.py | 9 + virny/utils/data_viz_utils.py | 22 +- 4 files changed, 246 insertions(+), 55 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index e75f0a48..57d67263 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -276,6 +276,58 @@ }, "id": "920e2c1a81d4e810" }, + { + "cell_type": "code", + "execution_count": 133, + "outputs": [ + { + "data": { + "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Mean 0.524270 0.578645 0.600790 \n1 Std 0.067963 0.073618 0.072201 \n2 IQR 0.090596 0.099782 0.098402 \n3 Aleatoric_Uncertainty 0.834874 0.846689 0.826891 \n4 Overall_Uncertainty 0.859083 0.876581 0.856843 \n5 Statistical_Bias 0.405041 0.395811 0.314809 \n6 Jitter 0.106917 0.132090 0.112864 \n7 Per_Sample_Accuracy 0.691061 0.711090 0.918452 \n8 Label_Stability 0.851667 0.807393 0.836903 \n9 TPR 0.679406 0.613333 1.000000 \n10 TNR 0.738462 0.801471 1.000000 \n11 PPV 0.676533 0.630137 1.000000 \n12 FNR 0.320594 0.386667 0.000000 \n13 FPR 0.261538 0.198529 0.000000 \n14 Accuracy 0.712121 0.734597 1.000000 \n15 F1 0.677966 0.621622 1.000000 \n16 Selection-Rate 0.447917 0.345972 0.296774 \n17 Positive-Rate 1.004246 0.973333 1.000000 \n18 Sample_Size 1056.000000 211.000000 155.000000 \n\n sex_priv_incorrect sex_dis sex_dis_correct sex_dis_incorrect \\\n0 0.517352 0.510692 0.514399 0.501767 \n1 0.077539 0.066551 0.064791 0.070788 \n2 0.103600 0.088303 0.085977 0.093900 \n3 0.901488 0.831924 0.817170 0.867440 \n4 0.931213 0.854713 0.839203 0.892051 \n5 0.620012 0.407346 0.301656 0.661771 \n6 0.185306 0.100631 0.091351 0.122972 \n7 0.137143 0.686059 0.936918 0.082177 \n8 0.725714 0.862722 0.873970 0.835645 \n9 0.000000 0.691919 1.000000 0.000000 \n10 0.000000 0.719376 1.000000 0.000000 \n11 0.000000 0.685000 1.000000 0.000000 \n12 1.000000 0.308081 0.000000 1.000000 \n13 1.000000 0.280624 0.000000 1.000000 \n14 0.000000 0.706509 1.000000 0.000000 \n15 0.000000 0.688442 1.000000 0.000000 \n16 0.482143 0.473373 0.458961 0.508065 \n17 0.931034 1.010101 1.000000 1.032787 \n18 56.000000 845.000000 597.000000 248.000000 \n\n race_priv race_priv_correct ... race_dis_correct race_dis_incorrect \\\n0 0.597526 0.618185 ... 0.473863 0.484344 \n1 0.069162 0.066865 ... 0.065947 0.070060 \n2 0.093184 0.089451 ... 0.087919 0.091258 \n3 0.821672 0.807043 ... 0.827404 0.880296 \n4 0.847778 0.832001 ... 0.850193 0.903737 \n5 0.393484 0.296788 ... 0.309510 0.650314 \n6 0.107225 0.097218 ... 0.094812 0.134214 \n7 0.708261 0.930526 ... 0.934866 0.091340 \n8 0.848213 0.861316 ... 0.869732 0.817320 \n9 0.585034 1.000000 ... 1.000000 0.000000 \n10 0.816479 1.000000 ... 1.000000 0.000000 \n11 0.637037 1.000000 ... 1.000000 0.000000 \n12 0.414966 0.000000 ... 0.000000 1.000000 \n13 0.183521 0.000000 ... 0.000000 1.000000 \n14 0.734300 1.000000 ... 1.000000 0.000000 \n15 0.609929 1.000000 ... 1.000000 0.000000 \n16 0.326087 0.282895 ... 0.522321 0.536082 \n17 0.918367 1.000000 ... 1.000000 1.155556 \n18 414.000000 304.000000 ... 448.000000 194.000000 \n\n sex&race_priv sex&race_priv_correct sex&race_priv_incorrect \\\n0 0.586391 0.607290 0.529874 \n1 0.068718 0.066018 0.076019 \n2 0.092020 0.088338 0.101975 \n3 0.832383 0.817398 0.872906 \n4 0.857995 0.841790 0.901818 \n5 0.396398 0.302520 0.650263 \n6 0.108871 0.095304 0.145559 \n7 0.708783 0.933073 0.102254 \n8 0.847224 0.866354 0.795493 \n9 0.595745 1.000000 0.000000 \n10 0.804734 1.000000 0.000000 \n11 0.629213 1.000000 0.000000 \n12 0.404255 0.000000 1.000000 \n13 0.195266 0.000000 1.000000 \n14 0.730038 1.000000 0.000000 \n15 0.612022 1.000000 0.000000 \n16 0.338403 0.291667 0.464789 \n17 0.946809 1.000000 0.868421 \n18 526.000000 384.000000 142.000000 \n\n sex&race_dis sex&race_dis_correct sex&race_dis_incorrect \\\n0 0.462617 0.453857 0.482517 \n1 0.067213 0.066631 0.068536 \n2 0.089184 0.088747 0.090175 \n3 0.837346 0.821026 0.874418 \n4 0.860162 0.843933 0.897027 \n5 0.413620 0.306294 0.657422 \n6 0.104978 0.096287 0.124722 \n7 0.673472 0.933152 0.083580 \n8 0.856075 0.866304 0.832840 \n9 0.734982 1.000000 0.000000 \n10 0.647773 1.000000 0.000000 \n11 0.705085 1.000000 0.000000 \n12 0.265018 0.000000 1.000000 \n13 0.352227 0.000000 1.000000 \n14 0.694340 1.000000 0.000000 \n15 0.719723 1.000000 0.000000 \n16 0.556604 0.565217 0.537037 \n17 1.042403 1.000000 1.160000 \n18 530.000000 368.000000 162.000000 \n\n Model_Name Model_Params \n0 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n1 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n2 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n3 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n4 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n5 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n6 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n7 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n8 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n9 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n10 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n11 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n12 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n13 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n14 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n15 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n16 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n17 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n18 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n\n[19 rows x 22 columns]", + "text/html": "
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Metricoverallsex_privsex_priv_correctsex_priv_incorrectsex_dissex_dis_correctsex_dis_incorrectrace_privrace_priv_correct...race_dis_correctrace_dis_incorrectsex&race_privsex&race_priv_correctsex&race_priv_incorrectsex&race_dissex&race_dis_correctsex&race_dis_incorrectModel_NameModel_Params
0Mean0.5242700.5786450.6007900.5173520.5106920.5143990.5017670.5975260.618185...0.4738630.4843440.5863910.6072900.5298740.4626170.4538570.482517RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
1Std0.0679630.0736180.0722010.0775390.0665510.0647910.0707880.0691620.066865...0.0659470.0700600.0687180.0660180.0760190.0672130.0666310.068536RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
2IQR0.0905960.0997820.0984020.1036000.0883030.0859770.0939000.0931840.089451...0.0879190.0912580.0920200.0883380.1019750.0891840.0887470.090175RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
3Aleatoric_Uncertainty0.8348740.8466890.8268910.9014880.8319240.8171700.8674400.8216720.807043...0.8274040.8802960.8323830.8173980.8729060.8373460.8210260.874418RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
4Overall_Uncertainty0.8590830.8765810.8568430.9312130.8547130.8392030.8920510.8477780.832001...0.8501930.9037370.8579950.8417900.9018180.8601620.8439330.897027RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
5Statistical_Bias0.4050410.3958110.3148090.6200120.4073460.3016560.6617710.3934840.296788...0.3095100.6503140.3963980.3025200.6502630.4136200.3062940.657422RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
6Jitter0.1069170.1320900.1128640.1853060.1006310.0913510.1229720.1072250.097218...0.0948120.1342140.1088710.0953040.1455590.1049780.0962870.124722RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
7Per_Sample_Accuracy0.6910610.7110900.9184520.1371430.6860590.9369180.0821770.7082610.930526...0.9348660.0913400.7087830.9330730.1022540.6734720.9331520.083580RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
8Label_Stability0.8516670.8073930.8369030.7257140.8627220.8739700.8356450.8482130.861316...0.8697320.8173200.8472240.8663540.7954930.8560750.8663040.832840RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
9TPR0.6794060.6133331.0000000.0000000.6919191.0000000.0000000.5850341.000000...1.0000000.0000000.5957451.0000000.0000000.7349821.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
10TNR0.7384620.8014711.0000000.0000000.7193761.0000000.0000000.8164791.000000...1.0000000.0000000.8047341.0000000.0000000.6477731.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
11PPV0.6765330.6301371.0000000.0000000.6850001.0000000.0000000.6370371.000000...1.0000000.0000000.6292131.0000000.0000000.7050851.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
12FNR0.3205940.3866670.0000001.0000000.3080810.0000001.0000000.4149660.000000...0.0000001.0000000.4042550.0000001.0000000.2650180.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
13FPR0.2615380.1985290.0000001.0000000.2806240.0000001.0000000.1835210.000000...0.0000001.0000000.1952660.0000001.0000000.3522270.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
14Accuracy0.7121210.7345971.0000000.0000000.7065091.0000000.0000000.7343001.000000...1.0000000.0000000.7300381.0000000.0000000.6943401.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
15F10.6779660.6216221.0000000.0000000.6884421.0000000.0000000.6099291.000000...1.0000000.0000000.6120221.0000000.0000000.7197231.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
16Selection-Rate0.4479170.3459720.2967740.4821430.4733730.4589610.5080650.3260870.282895...0.5223210.5360820.3384030.2916670.4647890.5566040.5652170.537037RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
17Positive-Rate1.0042460.9733331.0000000.9310341.0101011.0000001.0327870.9183671.000000...1.0000001.1555560.9468091.0000000.8684211.0424031.0000001.160000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
18Sample_Size1056.000000211.000000155.00000056.000000845.000000597.000000248.000000414.000000304.000000...448.000000194.000000526.000000384.000000142.000000530.000000368.000000162.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
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" + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_metrics_dct['RandomForestClassifier'].head(20)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-01T11:25:45.963770Z", + "start_time": "2023-10-01T11:25:45.421681Z" + } + }, + "id": "54a73b4d053334b4" + }, + { + "cell_type": "code", + "execution_count": 135, + "outputs": [ + { + "data": { + "text/plain": " Metric sex race sex&race \\\n0 Equalized_Odds_TPR 0.211919 0.195326 0.183576 \n1 Equalized_Odds_FPR 0.098356 0.104728 0.141078 \n2 Equalized_Odds_FNR -0.211919 -0.195326 -0.183576 \n3 Disparate_Impact 1.234115 1.135965 1.125105 \n4 Statistical_Parity_Difference 0.193535 0.123016 0.115123 \n5 Accuracy_Parity 0.009832 0.006840 -0.010984 \n6 Label_Stability_Ratio 1.024740 0.997454 0.995869 \n7 IQR_Parity 0.000768 -0.004804 -0.003282 \n8 Std_Parity -0.005106 -0.000927 -0.001976 \n9 Std_Ratio 0.931699 0.986984 0.972422 \n10 Jitter_Parity -0.013818 0.007192 0.005364 \n11 Equalized_Odds_TPR 0.166465 0.258440 0.226205 \n12 Equalized_Odds_FPR 0.096129 0.156703 0.186079 \n13 Equalized_Odds_FNR -0.166465 -0.258440 -0.226205 \n14 Disparate_Impact 1.176075 1.341036 1.263916 \n15 Statistical_Parity_Difference 0.145556 0.262157 0.216187 \n16 Accuracy_Parity -0.010286 -0.003747 -0.024119 \n17 Label_Stability_Ratio 1.021988 0.988991 1.003152 \n18 IQR_Parity 0.001712 0.001225 0.001058 \n19 Std_Parity 0.000822 0.000278 0.000170 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 LogisticRegression \n12 LogisticRegression \n13 LogisticRegression \n14 LogisticRegression \n15 LogisticRegression \n16 LogisticRegression \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression ", + "text/html": "
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Metricsexracesex&raceModel_Name
0Equalized_Odds_TPR0.2119190.1953260.183576DecisionTreeClassifier
1Equalized_Odds_FPR0.0983560.1047280.141078DecisionTreeClassifier
2Equalized_Odds_FNR-0.211919-0.195326-0.183576DecisionTreeClassifier
3Disparate_Impact1.2341151.1359651.125105DecisionTreeClassifier
4Statistical_Parity_Difference0.1935350.1230160.115123DecisionTreeClassifier
5Accuracy_Parity0.0098320.006840-0.010984DecisionTreeClassifier
6Label_Stability_Ratio1.0247400.9974540.995869DecisionTreeClassifier
7IQR_Parity0.000768-0.004804-0.003282DecisionTreeClassifier
8Std_Parity-0.005106-0.000927-0.001976DecisionTreeClassifier
9Std_Ratio0.9316990.9869840.972422DecisionTreeClassifier
10Jitter_Parity-0.0138180.0071920.005364DecisionTreeClassifier
11Equalized_Odds_TPR0.1664650.2584400.226205LogisticRegression
12Equalized_Odds_FPR0.0961290.1567030.186079LogisticRegression
13Equalized_Odds_FNR-0.166465-0.258440-0.226205LogisticRegression
14Disparate_Impact1.1760751.3410361.263916LogisticRegression
15Statistical_Parity_Difference0.1455560.2621570.216187LogisticRegression
16Accuracy_Parity-0.010286-0.003747-0.024119LogisticRegression
17Label_Stability_Ratio1.0219880.9889911.003152LogisticRegression
18IQR_Parity0.0017120.0012250.001058LogisticRegression
19Std_Parity0.0008220.0002780.000170LogisticRegression
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" + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_composed_metrics_df.head(20)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-01T11:29:37.410638Z", + "start_time": "2023-10-01T11:29:37.382980Z" + } + }, + "id": "5798eb95fbeaea54" + }, { "cell_type": "markdown", "id": "deb45226", @@ -286,18 +338,17 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 169, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-09-30T22:20:33.545960Z", - "start_time": "2023-09-30T22:20:33.514242Z" + "end_time": "2023-10-01T19:42:30.098766Z", + "start_time": "2023-10-01T19:42:30.039734Z" } }, "outputs": [], "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", - " model_names=model_names,\n", + "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", " sensitive_attributes_dct=config.sensitive_attributes_dct)" ] }, @@ -322,7 +373,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-09-30T22:20:33.605579Z" + "start_time": "2023-10-01T19:42:30.126790Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 574d0a91..40a2beab 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -1,7 +1,8 @@ import pandas as pd import gradio as gr -from virny.utils.data_viz_utils import create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank +from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, + create_subgroup_sorted_matrix_by_rank) class MetricsInteractiveVisualizer: @@ -10,64 +11,118 @@ class MetricsInteractiveVisualizer: Parameters ---------- - models_metrics_dct + model_metrics_dct Dictionary where keys are model names and values are dataframes of subgroup metrics for each model - models_composed_metrics_df + model_composed_metrics_df Dataframe of all model composed metrics - dataset_name - Name of a dataset that was included in metric filenames and was used for the metrics computation - model_names - Metrics for what model names to visualize sensitive_attributes_dct A dictionary where keys are sensitive attributes names (including attributes intersections), and values are privilege values for these attributes """ - def __init__(self, models_metrics_dct: dict, models_composed_metrics_df: pd.DataFrame, - dataset_name: str, model_names: list, sensitive_attributes_dct: dict): + def __init__(self, model_metrics_dct: dict, model_composed_metrics_df: pd.DataFrame, + sensitive_attributes_dct: dict): self.demo = None - self.dataset_name = dataset_name - self.model_names = model_names + self.model_names = list(model_metrics_dct.keys()) self.sensitive_attributes_dct = sensitive_attributes_dct # Create one metrics df with all model_dfs - all_models_metrics_df = pd.DataFrame() - for model_name in models_metrics_dct.keys(): - model_metrics_df = models_metrics_dct[model_name] - all_models_metrics_df = pd.concat([all_models_metrics_df, model_metrics_df]) - - all_models_metrics_df = all_models_metrics_df.reset_index(drop=True) - - self.models_metrics_dct = models_metrics_dct - self.all_models_metrics_df = all_models_metrics_df - self.models_composed_metrics_df = models_composed_metrics_df - self.melted_models_composed_metrics_df = self.models_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"], - var_name="Subgroup", - value_name="Value") - self.sorted_models_composed_metrics_df = self.melted_models_composed_metrics_df.sort_values(by=['Value']) - + models_metrics_df = pd.DataFrame() + for model_name in model_metrics_dct.keys(): + model_metrics_df = model_metrics_dct[model_name] + models_metrics_df = pd.concat([models_metrics_df, model_metrics_df]) + + models_metrics_df = models_metrics_df.reset_index(drop=True) + + self.models_metrics_dct = model_metrics_dct + self.models_metrics_df = self._align_input_metric_df(models_metrics_df, allowed_cols=["Metric", "Model_Name"], + sensitive_attrs=list(self.sensitive_attributes_dct.keys())) + self.model_composed_metrics_df = self._align_input_metric_df(model_composed_metrics_df, allowed_cols=["Metric", "Model_Name"], + sensitive_attrs=list(self.sensitive_attributes_dct.keys())) + + melted_model_metrics_df = self.models_metrics_df.melt(id_vars=["Metric", "Model_Name"], + var_name="Subgroup", + value_name="Value") + self.sorted_model_metrics_df = melted_model_metrics_df.sort_values(by=['Value']) + melted_model_composed_metrics_df = self.model_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"], + var_name="Subgroup", + value_name="Value") + self.sorted_model_composed_metrics_df = melted_model_composed_metrics_df.sort_values(by=['Value']) + + def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: list, sensitive_attrs: list): + # Filter columns in the input dataframe based on allowed_cols and sensitive_attrs + filtered_cols = allowed_cols + for col in model_metrics_df.columns: + for sensitive_attr in sensitive_attrs: + if sensitive_attr in col: + filtered_cols.append(col) + break + + return model_metrics_df[filtered_cols] + def start_web_app(self): - css = """ - .plot_output1 {position: right !important} - """ - with gr.Blocks(css=css) as demo: + # css = """ + # .plot_output1 {position: right !important} + # """ + with gr.Blocks(theme=gr.themes.Soft()) as demo: + # ======================================= Subgroup Metrics Heatmap ======================================= + gr.Markdown( + """ + ## Subgroup Metrics Heatmap + Select input arguments to create a subgroup metrics heatmap. + """) + with gr.Row(): + with gr.Column(scale=1): + model_names = gr.Dropdown( + self.model_names, value=self.model_names[:4], max_choices=5, multiselect=True, + label="Model Names", info="Select model names to display on the heatmap:", + ) + accuracy_metrics = gr.Dropdown( + ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], + value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", + ) + uncertainty_metrics = gr.Dropdown( + ['Aleatoric_Uncertainty', 'Overall_Uncertainty'], + value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:", + ) + subgroup_stability_metrics = gr.Dropdown( + ['Std', 'IQR', 'Jitter', 'Label_Stability'], + value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", + ) + btn = gr.Button("Submit") + with gr.Column(scale=2): + subgroup_model_ranking_heatmap = gr.Plot(label="Plot") + + btn.click(self._create_subgroup_model_rank_heatmap, + inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], + outputs=[subgroup_model_ranking_heatmap]) + # ======================================== Group Metrics Heatmap ======================================== + gr.Markdown( + """ + ## Group Metrics Heatmap + Select input arguments to create a group metrics heatmap. + """) with gr.Row(): with gr.Column(scale=1): + model_names = gr.Dropdown( + self.model_names, value=self.model_names[:4], max_choices=5, multiselect=True, + label="Model Names", info="Select model names to display on the heatmap:", + ) fairness_metrics = gr.Dropdown( ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], - value=['Equalized_Odds_TPR', 'Equalized_Odds_FPR'], multiselect=True, label="Fairness Metrics", info="Select fairness metrics to display on the heatmap:", + value=['Equalized_Odds_TPR', 'Equalized_Odds_FPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], - value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Group Stability Metrics", info="Select group stability metrics to display on the heatmap:", + value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", ) btn = gr.Button("Submit") with gr.Column(scale=2): - model_ranking_heatmap = gr.Plot(label="Plot") + group_model_ranking_heatmap = gr.Plot(label="Plot") - btn.click(self._create_model_rank_heatmap, - inputs=[fairness_metrics, group_stability_metrics], - outputs=[model_ranking_heatmap]) + btn.click(self._create_group_model_rank_heatmap, + inputs=[model_names, fairness_metrics, group_stability_metrics], + outputs=[group_model_ranking_heatmap]) self.demo = demo self.demo.launch(inline=False, debug=True, show_error=True) @@ -75,12 +130,69 @@ def start_web_app(self): def stop_web_app(self): self.demo.close() - def _create_model_rank_heatmap(self, group_fairness_metrics_lst: list, group_stability_metrics_lst: list): + def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accuracy_metrics_lst: list, + subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list): """ - Create a model rank heatmap. + Create a group model rank heatmap. Parameters ---------- + model_names + A list of selected model names to display on the heatmap + subgroup_accuracy_metrics_lst + A list of subgroup accuracy metrics to visualize + subgroup_uncertainty_metrics + A list of subgroup uncertainty metrics to visualize + subgroup_stability_metrics_lst + A list of subgroup stability metrics to visualize + + """ + groups_lst = self.sensitive_attributes_dct.keys() + metrics_lst = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst + + # Find metric values for each model based on metric, subgroup, and model names. + # Add the values to a results dict. + results = {} + num_models = len(model_names) + for metric in metrics_lst: + for group in groups_lst: + for prefix in ['priv', 'dis']: + subgroup = group + '_' + prefix + subgroup_metric = metric + '_' + subgroup + results[subgroup_metric] = dict() + + # Get distinct sorted model names + sorted_model_names_arr = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == metric) & + (self.sorted_model_metrics_df.Subgroup == subgroup) + ]['Model_Name'].values + sorted_model_names_arr = [model for model in sorted_model_names_arr if model in model_names] + + # Add values to a results dict + for idx, model_name in enumerate(sorted_model_names_arr): + metric_value = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == metric) & + (self.sorted_model_metrics_df.Subgroup == subgroup) & + (self.sorted_model_metrics_df.Model_Name == model_name) + ]['Value'].values[0] + metric_value = round(metric_value, 3) + results[subgroup_metric][model_name] = metric_value + + model_metrics_matrix = pd.DataFrame(results).T + sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) + + return model_rank_heatmap + + def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_metrics_lst: list, + group_stability_metrics_lst: list): + """ + Create a group model rank heatmap. + + Parameters + ---------- + model_names + A list of selected model names to display on the heatmap group_fairness_metrics_lst A list of group fairness metrics to visualize group_stability_metrics_lst @@ -93,22 +205,25 @@ def _create_model_rank_heatmap(self, group_fairness_metrics_lst: list, group_sta # Find metric values for each model based on metric, group, and model names. # Add the values to a results dict. results = {} - num_models = len(self.model_names) + num_models = len(model_names) for metric in metrics_lst: for group in groups_lst: group_metric = metric + '_' + group results[group_metric] = dict() + # Get distinct sorted model names - sorted_model_names_arr = self.sorted_models_composed_metrics_df[ - (self.sorted_models_composed_metrics_df.Metric == metric) & - (self.sorted_models_composed_metrics_df.Subgroup == group) + sorted_model_names_arr = self.sorted_model_composed_metrics_df[ + (self.sorted_model_composed_metrics_df.Metric == metric) & + (self.sorted_model_composed_metrics_df.Subgroup == group) ]['Model_Name'].values + sorted_model_names_arr = [model for model in sorted_model_names_arr if model in model_names] + # Add values to a results dict for idx, model_name in enumerate(sorted_model_names_arr): - metric_value = self.sorted_models_composed_metrics_df[ - (self.sorted_models_composed_metrics_df.Metric == metric) & - (self.sorted_models_composed_metrics_df.Subgroup == group) & - (self.sorted_models_composed_metrics_df.Model_Name == model_name) + metric_value = self.sorted_model_composed_metrics_df[ + (self.sorted_model_composed_metrics_df.Metric == metric) & + (self.sorted_model_composed_metrics_df.Subgroup == group) & + (self.sorted_model_composed_metrics_df.Model_Name == model_name) ]['Value'].values[0] metric_value = round(metric_value, 3) results[group_metric][model_name] = metric_value diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index dbaac29f..f99f227a 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -93,6 +93,15 @@ def save_metrics_to_file(metrics_df, result_filename, save_dir_path): metrics_df.to_csv(f'{save_dir_path}/{filename}', index=False) +def check_substring_in_list(val_to_check: str, allowed_lst: list): + # Case-insensitive check if a val_to_check substring is in allowed_lst + val_to_check = val_to_check.lower() + for allowed_val in allowed_lst: + if allowed_val.lower() in val_to_check: + return True + return False + + def confusion_matrix_metrics(y_true, y_preds): metrics = {} TN, FP, FN, TP = confusion_matrix(y_true, y_preds).ravel() diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 9895d84a..057f85f4 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -3,6 +3,8 @@ from matplotlib import pyplot as plt +from virny.utils.common_helpers import check_substring_in_list + def set_size(w,h, ax=None): """ w, h: width, height in inches """ @@ -41,6 +43,20 @@ def create_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: return sorted_matrix_by_rank +def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: + models_distances_matrix = model_metrics_matrix.copy(deep=True).T + metric_names = models_distances_matrix.columns + for metric_name in metric_names: + if check_substring_in_list(metric_name, ['TPR', 'TNR', 'PPV', 'Accuracy', 'F1', 'Label_Stability']): + # Cast a metric to a case when the closer value to zero is the better + models_distances_matrix[metric_name] = 1 - models_distances_matrix[metric_name] + models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() + + models_distances_matrix = models_distances_matrix.T + sorted_matrix_by_rank = np.argsort(np.argsort(models_distances_matrix, axis=1), axis=1) + return sorted_matrix_by_rank + + def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int): """ This heatmap includes group fairness and stability metrics and defined models. @@ -63,8 +79,8 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ """ font_increase = 2 - matrix_width = num_models * 5 - matrix_height = model_metrics_matrix.shape[0] // 1.5 + matrix_width = 20 + matrix_height = model_metrics_matrix.shape[0] // 2 fig = plt.figure(figsize=(matrix_width, matrix_height)) rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex()[::-1] ax = sns.heatmap(sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, @@ -72,7 +88,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ ax.set(xlabel="", ylabel="") ax.xaxis.tick_top() ax.tick_params(labelsize=16 + font_increase) - fig.subplots_adjust(left=0.25, top=0.9) + fig.subplots_adjust(left=0.25, right=1., top=0.9) cbar = ax.collections[0].colorbar model_ranks = [idx for idx in range(num_models)] From 21f783fef7197228bff5f145d2598f69bdf95775 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Mon, 2 Oct 2023 00:05:56 +0300 Subject: [PATCH 004/148] Added bar charts to a web app --- .../Multiple_Models_Interface_Vis.ipynb | 18 +-- .../metrics_interactive_visualizer.py | 146 ++++++++++++++++-- 2 files changed, 138 insertions(+), 26 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index 57d67263..ab7c8ca4 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -278,26 +278,26 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 185, "outputs": [ { "data": { "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Mean 0.524270 0.578645 0.600790 \n1 Std 0.067963 0.073618 0.072201 \n2 IQR 0.090596 0.099782 0.098402 \n3 Aleatoric_Uncertainty 0.834874 0.846689 0.826891 \n4 Overall_Uncertainty 0.859083 0.876581 0.856843 \n5 Statistical_Bias 0.405041 0.395811 0.314809 \n6 Jitter 0.106917 0.132090 0.112864 \n7 Per_Sample_Accuracy 0.691061 0.711090 0.918452 \n8 Label_Stability 0.851667 0.807393 0.836903 \n9 TPR 0.679406 0.613333 1.000000 \n10 TNR 0.738462 0.801471 1.000000 \n11 PPV 0.676533 0.630137 1.000000 \n12 FNR 0.320594 0.386667 0.000000 \n13 FPR 0.261538 0.198529 0.000000 \n14 Accuracy 0.712121 0.734597 1.000000 \n15 F1 0.677966 0.621622 1.000000 \n16 Selection-Rate 0.447917 0.345972 0.296774 \n17 Positive-Rate 1.004246 0.973333 1.000000 \n18 Sample_Size 1056.000000 211.000000 155.000000 \n\n sex_priv_incorrect sex_dis sex_dis_correct sex_dis_incorrect \\\n0 0.517352 0.510692 0.514399 0.501767 \n1 0.077539 0.066551 0.064791 0.070788 \n2 0.103600 0.088303 0.085977 0.093900 \n3 0.901488 0.831924 0.817170 0.867440 \n4 0.931213 0.854713 0.839203 0.892051 \n5 0.620012 0.407346 0.301656 0.661771 \n6 0.185306 0.100631 0.091351 0.122972 \n7 0.137143 0.686059 0.936918 0.082177 \n8 0.725714 0.862722 0.873970 0.835645 \n9 0.000000 0.691919 1.000000 0.000000 \n10 0.000000 0.719376 1.000000 0.000000 \n11 0.000000 0.685000 1.000000 0.000000 \n12 1.000000 0.308081 0.000000 1.000000 \n13 1.000000 0.280624 0.000000 1.000000 \n14 0.000000 0.706509 1.000000 0.000000 \n15 0.000000 0.688442 1.000000 0.000000 \n16 0.482143 0.473373 0.458961 0.508065 \n17 0.931034 1.010101 1.000000 1.032787 \n18 56.000000 845.000000 597.000000 248.000000 \n\n race_priv race_priv_correct ... race_dis_correct race_dis_incorrect \\\n0 0.597526 0.618185 ... 0.473863 0.484344 \n1 0.069162 0.066865 ... 0.065947 0.070060 \n2 0.093184 0.089451 ... 0.087919 0.091258 \n3 0.821672 0.807043 ... 0.827404 0.880296 \n4 0.847778 0.832001 ... 0.850193 0.903737 \n5 0.393484 0.296788 ... 0.309510 0.650314 \n6 0.107225 0.097218 ... 0.094812 0.134214 \n7 0.708261 0.930526 ... 0.934866 0.091340 \n8 0.848213 0.861316 ... 0.869732 0.817320 \n9 0.585034 1.000000 ... 1.000000 0.000000 \n10 0.816479 1.000000 ... 1.000000 0.000000 \n11 0.637037 1.000000 ... 1.000000 0.000000 \n12 0.414966 0.000000 ... 0.000000 1.000000 \n13 0.183521 0.000000 ... 0.000000 1.000000 \n14 0.734300 1.000000 ... 1.000000 0.000000 \n15 0.609929 1.000000 ... 1.000000 0.000000 \n16 0.326087 0.282895 ... 0.522321 0.536082 \n17 0.918367 1.000000 ... 1.000000 1.155556 \n18 414.000000 304.000000 ... 448.000000 194.000000 \n\n sex&race_priv sex&race_priv_correct sex&race_priv_incorrect \\\n0 0.586391 0.607290 0.529874 \n1 0.068718 0.066018 0.076019 \n2 0.092020 0.088338 0.101975 \n3 0.832383 0.817398 0.872906 \n4 0.857995 0.841790 0.901818 \n5 0.396398 0.302520 0.650263 \n6 0.108871 0.095304 0.145559 \n7 0.708783 0.933073 0.102254 \n8 0.847224 0.866354 0.795493 \n9 0.595745 1.000000 0.000000 \n10 0.804734 1.000000 0.000000 \n11 0.629213 1.000000 0.000000 \n12 0.404255 0.000000 1.000000 \n13 0.195266 0.000000 1.000000 \n14 0.730038 1.000000 0.000000 \n15 0.612022 1.000000 0.000000 \n16 0.338403 0.291667 0.464789 \n17 0.946809 1.000000 0.868421 \n18 526.000000 384.000000 142.000000 \n\n sex&race_dis sex&race_dis_correct sex&race_dis_incorrect \\\n0 0.462617 0.453857 0.482517 \n1 0.067213 0.066631 0.068536 \n2 0.089184 0.088747 0.090175 \n3 0.837346 0.821026 0.874418 \n4 0.860162 0.843933 0.897027 \n5 0.413620 0.306294 0.657422 \n6 0.104978 0.096287 0.124722 \n7 0.673472 0.933152 0.083580 \n8 0.856075 0.866304 0.832840 \n9 0.734982 1.000000 0.000000 \n10 0.647773 1.000000 0.000000 \n11 0.705085 1.000000 0.000000 \n12 0.265018 0.000000 1.000000 \n13 0.352227 0.000000 1.000000 \n14 0.694340 1.000000 0.000000 \n15 0.719723 1.000000 0.000000 \n16 0.556604 0.565217 0.537037 \n17 1.042403 1.000000 1.160000 \n18 530.000000 368.000000 162.000000 \n\n Model_Name Model_Params \n0 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n1 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n2 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n3 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n4 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n5 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n6 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n7 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n8 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n9 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n10 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n11 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n12 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n13 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n14 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n15 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n16 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n17 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n18 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n\n[19 rows x 22 columns]", "text/html": "
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Metricoverallsex_privsex_priv_correctsex_priv_incorrectsex_dissex_dis_correctsex_dis_incorrectrace_privrace_priv_correct...race_dis_correctrace_dis_incorrectsex&race_privsex&race_priv_correctsex&race_priv_incorrectsex&race_dissex&race_dis_correctsex&race_dis_incorrectModel_NameModel_Params
0Mean0.5242700.5786450.6007900.5173520.5106920.5143990.5017670.5975260.618185...0.4738630.4843440.5863910.6072900.5298740.4626170.4538570.482517RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
1Std0.0679630.0736180.0722010.0775390.0665510.0647910.0707880.0691620.066865...0.0659470.0700600.0687180.0660180.0760190.0672130.0666310.068536RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
2IQR0.0905960.0997820.0984020.1036000.0883030.0859770.0939000.0931840.089451...0.0879190.0912580.0920200.0883380.1019750.0891840.0887470.090175RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
3Aleatoric_Uncertainty0.8348740.8466890.8268910.9014880.8319240.8171700.8674400.8216720.807043...0.8274040.8802960.8323830.8173980.8729060.8373460.8210260.874418RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
4Overall_Uncertainty0.8590830.8765810.8568430.9312130.8547130.8392030.8920510.8477780.832001...0.8501930.9037370.8579950.8417900.9018180.8601620.8439330.897027RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
5Statistical_Bias0.4050410.3958110.3148090.6200120.4073460.3016560.6617710.3934840.296788...0.3095100.6503140.3963980.3025200.6502630.4136200.3062940.657422RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
6Jitter0.1069170.1320900.1128640.1853060.1006310.0913510.1229720.1072250.097218...0.0948120.1342140.1088710.0953040.1455590.1049780.0962870.124722RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
7Per_Sample_Accuracy0.6910610.7110900.9184520.1371430.6860590.9369180.0821770.7082610.930526...0.9348660.0913400.7087830.9330730.1022540.6734720.9331520.083580RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
8Label_Stability0.8516670.8073930.8369030.7257140.8627220.8739700.8356450.8482130.861316...0.8697320.8173200.8472240.8663540.7954930.8560750.8663040.832840RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
9TPR0.6794060.6133331.0000000.0000000.6919191.0000000.0000000.5850341.000000...1.0000000.0000000.5957451.0000000.0000000.7349821.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
10TNR0.7384620.8014711.0000000.0000000.7193761.0000000.0000000.8164791.000000...1.0000000.0000000.8047341.0000000.0000000.6477731.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
11PPV0.6765330.6301371.0000000.0000000.6850001.0000000.0000000.6370371.000000...1.0000000.0000000.6292131.0000000.0000000.7050851.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
12FNR0.3205940.3866670.0000001.0000000.3080810.0000001.0000000.4149660.000000...0.0000001.0000000.4042550.0000001.0000000.2650180.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
13FPR0.2615380.1985290.0000001.0000000.2806240.0000001.0000000.1835210.000000...0.0000001.0000000.1952660.0000001.0000000.3522270.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
14Accuracy0.7121210.7345971.0000000.0000000.7065091.0000000.0000000.7343001.000000...1.0000000.0000000.7300381.0000000.0000000.6943401.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
15F10.6779660.6216221.0000000.0000000.6884421.0000000.0000000.6099291.000000...1.0000000.0000000.6120221.0000000.0000000.7197231.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
16Selection-Rate0.4479170.3459720.2967740.4821430.4733730.4589610.5080650.3260870.282895...0.5223210.5360820.3384030.2916670.4647890.5566040.5652170.537037RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
17Positive-Rate1.0042460.9733331.0000000.9310341.0101011.0000001.0327870.9183671.000000...1.0000001.1555560.9468091.0000000.8684211.0424031.0000001.160000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
18Sample_Size1056.000000211.000000155.00000056.000000845.000000597.000000248.000000414.000000304.000000...448.000000194.000000526.000000384.000000142.000000530.000000368.000000162.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
\n

19 rows × 22 columns

\n
" }, - "execution_count": 133, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "models_metrics_dct['RandomForestClassifier'].head(20)" + "models_metrics_dct['RandomForestClassifier'].head(100)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-01T11:25:45.963770Z", - "start_time": "2023-10-01T11:25:45.421681Z" + "end_time": "2023-10-01T20:57:20.233976Z", + "start_time": "2023-10-01T20:57:20.133369Z" } }, "id": "54a73b4d053334b4" @@ -338,12 +338,12 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 186, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-01T19:42:30.098766Z", - "start_time": "2023-10-01T19:42:30.039734Z" + "end_time": "2023-10-01T21:02:36.301716Z", + "start_time": "2023-10-01T21:02:33.017804Z" } }, "outputs": [], @@ -373,7 +373,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-01T19:42:30.126790Z" + "start_time": "2023-10-01T21:02:36.296642Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 40a2beab..fe59f488 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -1,5 +1,6 @@ import pandas as pd import gradio as gr +import altair as alt from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank) @@ -35,19 +36,19 @@ def __init__(self, model_metrics_dct: dict, model_composed_metrics_df: pd.DataFr models_metrics_df = models_metrics_df.reset_index(drop=True) self.models_metrics_dct = model_metrics_dct - self.models_metrics_df = self._align_input_metric_df(models_metrics_df, allowed_cols=["Metric", "Model_Name"], + self.models_metrics_df = self._align_input_metric_df(models_metrics_df, allowed_cols=["Metric", "Model_Name", "overall"], sensitive_attrs=list(self.sensitive_attributes_dct.keys())) self.model_composed_metrics_df = self._align_input_metric_df(model_composed_metrics_df, allowed_cols=["Metric", "Model_Name"], sensitive_attrs=list(self.sensitive_attributes_dct.keys())) - melted_model_metrics_df = self.models_metrics_df.melt(id_vars=["Metric", "Model_Name"], - var_name="Subgroup", - value_name="Value") - self.sorted_model_metrics_df = melted_model_metrics_df.sort_values(by=['Value']) - melted_model_composed_metrics_df = self.model_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"], - var_name="Subgroup", - value_name="Value") - self.sorted_model_composed_metrics_df = melted_model_composed_metrics_df.sort_values(by=['Value']) + self.melted_model_metrics_df = self.models_metrics_df.melt(id_vars=["Metric", "Model_Name"], + var_name="Subgroup", + value_name="Value") + self.sorted_model_metrics_df = self.melted_model_metrics_df.sort_values(by=['Value']) + self.melted_model_composed_metrics_df = self.model_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"], + var_name="Subgroup", + value_name="Value") + self.sorted_model_composed_metrics_df = self.melted_model_composed_metrics_df.sort_values(by=['Value']) def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: list, sensitive_attrs: list): # Filter columns in the input dataframe based on allowed_cols and sensitive_attrs @@ -89,13 +90,13 @@ def start_web_app(self): ['Std', 'IQR', 'Jitter', 'Label_Stability'], value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) - btn = gr.Button("Submit") + subgroup_btn_view1 = gr.Button("Submit") with gr.Column(scale=2): subgroup_model_ranking_heatmap = gr.Plot(label="Plot") - btn.click(self._create_subgroup_model_rank_heatmap, - inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], - outputs=[subgroup_model_ranking_heatmap]) + subgroup_btn_view1.click(self._create_subgroup_model_rank_heatmap, + inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], + outputs=[subgroup_model_ranking_heatmap]) # ======================================== Group Metrics Heatmap ======================================== gr.Markdown( """ @@ -116,13 +117,67 @@ def start_web_app(self): ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", ) - btn = gr.Button("Submit") + group_btn_view1 = gr.Button("Submit") with gr.Column(scale=2): group_model_ranking_heatmap = gr.Plot(label="Plot") - btn.click(self._create_group_model_rank_heatmap, - inputs=[model_names, fairness_metrics, group_stability_metrics], - outputs=[group_model_ranking_heatmap]) + group_btn_view1.click(self._create_group_model_rank_heatmap, + inputs=[model_names, fairness_metrics, group_stability_metrics], + outputs=[group_model_ranking_heatmap]) + # =============================== Subgroup and Group Metrics Bar Chart =============================== + with gr.Row(): + with gr.Column(): + gr.Markdown( + """ + ## Subgroup Metrics Bar Chart + """) + subgroup_model_names = gr.Dropdown( + self.model_names, value=self.model_names[0], multiselect=False, + label="Model Names", info="Select one model to display on the bar chart:", + ) + accuracy_metrics = gr.Dropdown( + ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], + value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", + ) + uncertainty_metrics = gr.Dropdown( + ['Aleatoric_Uncertainty', 'Overall_Uncertainty'], + value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:", + ) + subgroup_stability_metrics = gr.Dropdown( + ['Std', 'IQR', 'Jitter', 'Label_Stability'], + value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", + ) + subgroup_btn_view2 = gr.Button("Submit") + with gr.Column(): + gr.Markdown( + """ + ## Group Metrics Bar Chart + """) + group_model_names = gr.Dropdown( + self.model_names, value=self.model_names[0], multiselect=False, + label="Model Names", info="Select one model to display on the bar chart:", + ) + fairness_metrics = gr.Dropdown( + ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], + value=['Equalized_Odds_TPR', 'Equalized_Odds_FPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", + ) + group_stability_metrics = gr.Dropdown( + ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], + value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", + ) + group_btn_view2 = gr.Button("Submit") + with gr.Row(): + with gr.Column(): + subgroup_metrics_bar_chart = gr.Plot(label="Plot") + with gr.Column(): + group_metrics_bar_chart = gr.Plot(label="Plot") + + subgroup_btn_view2.click(self._create_subgroup_metrics_bar_chart_per_one_model, + inputs=[subgroup_model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], + outputs=[subgroup_metrics_bar_chart]) + group_btn_view2.click(self._create_group_metrics_bar_chart_per_one_model, + inputs=[group_model_names, fairness_metrics, group_stability_metrics], + outputs=[group_metrics_bar_chart]) self.demo = demo self.demo.launch(inline=False, debug=True, show_error=True) @@ -233,3 +288,60 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) return model_rank_heatmap + + def _create_subgroup_metrics_bar_chart_per_one_model(self, model_name: str, subgroup_accuracy_metrics_lst: list, + subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list): + metrics_names = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst + return self._create_metrics_bar_chart_per_one_model(model_name, metrics_names, metrics_type='subgroup') + + def _create_group_metrics_bar_chart_per_one_model(self, model_name: str, group_fairness_metrics_lst: list, + group_stability_metrics_lst: list): + metrics_names = group_fairness_metrics_lst + group_stability_metrics_lst + return self._create_metrics_bar_chart_per_one_model(model_name, metrics_names, metrics_type='group') + + def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names: list, metrics_type: str): + """ + This bar chart displays metrics for different groups and one specific model. + + Parameters + ---------- + model_name + A model name to display metrics + metrics_names + A list of metric names to visualize + metrics_type + A metrics type ('subgroup' or 'group') to visualize + + """ + metrics_title = f'{metrics_type.capitalize()} Metrics' + metrics_df = self.melted_model_composed_metrics_df if metrics_type == "group" else self.melted_model_metrics_df + filtered_groups = [grp for grp in metrics_df.Subgroup.unique() if '_correct' not in grp and '_incorrect' not in grp] + filtered_metrics_df = metrics_df[(metrics_df['Metric'].isin(metrics_names)) & + (metrics_df['Model_Name'] == model_name) & + (metrics_df['Subgroup'].isin(filtered_groups))] + + models_metrics_chart = ( + alt.Chart(filtered_metrics_df).mark_bar().encode( + alt.Row('Metric:N', title=metrics_title), + alt.Y('Subgroup:N', axis=None), + alt.X('Value:Q', axis=alt.Axis(grid=True), title=''), + alt.Color('Subgroup:N', + scale=alt.Scale(scheme="tableau20"), + legend=alt.Legend(title=metrics_type.capitalize(), + labelFontSize=14, + titleFontSize=14) + ) + ) + ).properties( + width=500, height=80 + ).configure_headerRow( + labelAngle=0, + labelPadding=10, + labelAlign='left', + labelFontSize=14, + titleFontSize=18 + ).configure_axis( + labelFontSize=14, titleFontSize=18 + ) + + return models_metrics_chart From e0113e1e460539a7dde6404685449b3fb2e75b00 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 2 Oct 2023 00:10:27 +0300 Subject: [PATCH 005/148] Added bar charts to a web app --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 7414280a..66119d84 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,6 @@

- ## 📜 Description **Virny** is a Python library for auditing model stability and fairness. The Virny library was From 8580bbc49e2399f529bcfbbfac0f5c41fbf3d982 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 2 Oct 2023 02:06:05 +0300 Subject: [PATCH 006/148] Added init for view 1 --- .../Multiple_Models_Interface_Vis.ipynb | 8 +- .../metrics_interactive_visualizer.py | 103 +++++++++++-- virny/utils/data_viz_utils.py | 143 +++++++++++++++++- 3 files changed, 240 insertions(+), 14 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index ab7c8ca4..2382c421 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -338,12 +338,12 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 212, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-01T21:02:36.301716Z", - "start_time": "2023-10-01T21:02:33.017804Z" + "end_time": "2023-10-01T23:03:56.089028Z", + "start_time": "2023-10-01T23:03:56.019414Z" } }, "outputs": [], @@ -373,7 +373,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-01T21:02:36.296642Z" + "start_time": "2023-10-01T23:03:56.113686Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index fe59f488..567b0d33 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -3,7 +3,7 @@ import altair as alt from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, - create_subgroup_sorted_matrix_by_rank) + create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection) class MetricsInteractiveVisualizer: @@ -66,6 +66,56 @@ def start_web_app(self): # .plot_output1 {position: right !important} # """ with gr.Blocks(theme=gr.themes.Soft()) as demo: + # ==================================== Bar Chart for Model Selection ==================================== + gr.Markdown( + """ + ## Bar Chart for Model Selection + Select input arguments to create a bar chart for model selection. + """) + with gr.Row(): + with gr.Column(scale=2): + with gr.Row(): + accuracy_metric = gr.Dropdown( + ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], + value='Accuracy', multiselect=False, label="Constraint 1 (C1)", + scale=2 + ) + acc_min_val = gr.Number(value=0.815, label="Min value", scale=1) + acc_max_val = gr.Number(value=0.85, label="Max value", scale=1) + with gr.Row(): + fairness_metric = gr.Dropdown( + ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], + value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", + scale=2 + ) + fairness_min_val = gr.Number(value=-0.03, label="Min value", scale=1) + fairness_max_val = gr.Number(value=0.03, label="Max value", scale=1) + with gr.Row(): + subgroup_stability_metric = gr.Dropdown( + ['Std', 'IQR', 'Jitter', 'Label_Stability'], + value='Label_Stability', multiselect=False, label="Constraint 3 (C3)", + scale=2 + ) + subgroup_stab_min_val = gr.Number(value=0.9, label="Min value", scale=1) + subgroup_stab_max_val = gr.Number(value=0.94, label="Max value", scale=1) + with gr.Row(): + group_stability_metrics = gr.Dropdown( + ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], + value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", + scale=2 + ) + group_stab_min_val = gr.Number(value=1.0, label="Min value", scale=1) + group_stab_max_val = gr.Number(value=1.03, label="Max value", scale=1) + btn_view1 = gr.Button("Submit") + with gr.Column(scale=3): + bar_plot_for_model_selection = gr.Plot(label="Plot") + + btn_view1.click(self._create_bar_plot_for_model_selection, + inputs=[accuracy_metric, acc_min_val, acc_max_val, + fairness_metric, fairness_min_val, fairness_max_val, + subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, + group_stability_metrics, group_stab_min_val, group_stab_max_val], + outputs=[bar_plot_for_model_selection]) # ======================================= Subgroup Metrics Heatmap ======================================= gr.Markdown( """ @@ -90,11 +140,11 @@ def start_web_app(self): ['Std', 'IQR', 'Jitter', 'Label_Stability'], value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) - subgroup_btn_view1 = gr.Button("Submit") + subgroup_btn_view2 = gr.Button("Submit") with gr.Column(scale=2): subgroup_model_ranking_heatmap = gr.Plot(label="Plot") - subgroup_btn_view1.click(self._create_subgroup_model_rank_heatmap, + subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap, inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], outputs=[subgroup_model_ranking_heatmap]) # ======================================== Group Metrics Heatmap ======================================== @@ -117,11 +167,11 @@ def start_web_app(self): ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", ) - group_btn_view1 = gr.Button("Submit") + group_btn_view2 = gr.Button("Submit") with gr.Column(scale=2): group_model_ranking_heatmap = gr.Plot(label="Plot") - group_btn_view1.click(self._create_group_model_rank_heatmap, + group_btn_view2.click(self._create_group_model_rank_heatmap, inputs=[model_names, fairness_metrics, group_stability_metrics], outputs=[group_model_ranking_heatmap]) # =============================== Subgroup and Group Metrics Bar Chart =============================== @@ -147,7 +197,7 @@ def start_web_app(self): ['Std', 'IQR', 'Jitter', 'Label_Stability'], value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) - subgroup_btn_view2 = gr.Button("Submit") + subgroup_btn_view3 = gr.Button("Submit") with gr.Column(): gr.Markdown( """ @@ -165,17 +215,17 @@ def start_web_app(self): ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", ) - group_btn_view2 = gr.Button("Submit") + group_btn_view3 = gr.Button("Submit") with gr.Row(): with gr.Column(): subgroup_metrics_bar_chart = gr.Plot(label="Plot") with gr.Column(): group_metrics_bar_chart = gr.Plot(label="Plot") - subgroup_btn_view2.click(self._create_subgroup_metrics_bar_chart_per_one_model, + subgroup_btn_view3.click(self._create_subgroup_metrics_bar_chart_per_one_model, inputs=[subgroup_model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], outputs=[subgroup_metrics_bar_chart]) - group_btn_view2.click(self._create_group_metrics_bar_chart_per_one_model, + group_btn_view3.click(self._create_group_metrics_bar_chart_per_one_model, inputs=[group_model_names, fairness_metrics, group_stability_metrics], outputs=[group_metrics_bar_chart]) @@ -185,6 +235,41 @@ def start_web_app(self): def stop_web_app(self): self.demo.close() + def _create_bar_plot_for_model_selection(self, accuracy_metric, acc_min_val, acc_max_val, + fairness_metric, fairness_min_val, fairness_max_val, + subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, + group_stability_metrics, group_stab_min_val, group_stab_max_val): + accuracy_constraint = (accuracy_metric, acc_min_val, acc_max_val) + subgroup_stability_constraint = (subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val) + fairness_constraint = (fairness_metric, fairness_min_val, fairness_max_val) + group_stability_constraint = (group_stability_metrics, group_stab_min_val, group_stab_max_val) + + # Create individual constraints + metrics_value_range_dct = dict() + for constraint in [accuracy_constraint, subgroup_stability_constraint, fairness_constraint, group_stability_constraint]: + metrics_value_range_dct[constraint[0]] = [constraint[1], constraint[2]] + # Create intersectional constraints + metrics_value_range_dct[f'{accuracy_constraint[0]}&{subgroup_stability_constraint[0]}'] = None + metrics_value_range_dct[f'{accuracy_constraint[0]}&{fairness_constraint[0]}'] = None + metrics_value_range_dct[f'{accuracy_constraint[0]}&{group_stability_constraint[0]}'] = None + metrics_value_range_dct[(f'{accuracy_constraint[0]}&{subgroup_stability_constraint[0]}' + f'&{fairness_constraint[0]}&{group_stability_constraint[0]}')] = None + + melted_all_subgroup_metrics_per_model_dct = dict() + for model_name in self.melted_model_metrics_df['Model_Name'].unique(): + melted_all_subgroup_metrics_per_model_dct[model_name] = ( + self.melted_model_metrics_df)[self.melted_model_metrics_df.Model_Name == model_name] + + melted_all_group_metrics_per_model_dct = dict() + for model_name in self.melted_model_composed_metrics_df['Model_Name'].unique(): + melted_all_group_metrics_per_model_dct[model_name] = ( + self.melted_model_composed_metrics_df)[self.melted_model_composed_metrics_df.Model_Name == model_name] + + return create_bar_plot_for_model_selection(melted_all_subgroup_metrics_per_model_dct, + melted_all_group_metrics_per_model_dct, + metrics_value_range_dct, + group='sex&race') + def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accuracy_metrics_lst: list, subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list): """ diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 057f85f4..877979f9 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -1,5 +1,7 @@ -import seaborn as sns import numpy as np +import pandas as pd +import altair as alt +import seaborn as sns from matplotlib import pyplot as plt @@ -100,3 +102,142 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ cbar.set_label('Model Ranks', fontsize=18 + font_increase) return fig, ax + + +def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, + metrics_value_range_dct: dict, group: str): + # Compute the number of models that satisfy the conditions + models_in_range_df = create_models_in_range_dct(all_subgroup_metrics_per_model_dct, all_group_metrics_per_model_dct, + metrics_value_range_dct, group) + # Replace metric groups on their aliases + metric_name_to_alias_dct = { + # C1 + 'TPR': 'C1', + 'TNR': 'C1', + 'FNR': 'C1', + 'FPR': 'C1', + 'PPV': 'C1', + 'Accuracy': 'C1', + 'F1': 'C1', + # C2 + 'Equalized_Odds_TPR': 'C2', + 'Equalized_Odds_FPR': 'C2', + 'Equalized_Odds_FNR': 'C2', + 'Disparate_Impact': 'C2', + 'Statistical_Parity_Difference': 'C2', + # C3 + 'Std': 'C3', + 'IQR': 'C3', + 'Jitter': 'C3', + 'Label_Stability': 'C3', + # C4 + 'IQR_Parity': 'C4', + 'Label_Stability_Ratio': 'C4', + 'Std_Parity': 'C4', + 'Std_Ratio': 'C4', + 'Jitter_Parity': 'C4', + } + + def get_column_alias(metric_group): + if '&' not in metric_group: + alias = metric_name_to_alias_dct[metric_group] + else: + metrics = metric_group.split('&') + alias = None + for idx, metric in enumerate(metrics): + if idx == 0: + alias = metric_name_to_alias_dct[metric] + else: + alias += ' & ' + metric_name_to_alias_dct[metric] + + return alias + + models_in_range_df['Alias'] = models_in_range_df['Metric_Group'].apply(get_column_alias) + models_in_range_df['Title'] = models_in_range_df['Alias'] + + base_font_size = 25 + bar_plot = alt.Chart(models_in_range_df).mark_bar().encode( + x=alt.X("Title", type="nominal", title='Metric Group', axis=alt.Axis(labelAngle=-30), + sort=alt.Sort(order='ascending')), + y=alt.Y("Number_of_Models", title="Number of Models", type="quantitative"), + color=alt.Color('Model_Name', legend=alt.Legend(title='Model Name')) + ).configure_axis( + labelFontSize=base_font_size + 2, + titleFontSize=base_font_size + 4, + labelFontWeight='normal', + titleFontWeight='normal', + labelLimit=300, + ).configure_title( + fontSize=base_font_size + 2 + ).configure_legend( + titleFontSize=base_font_size + 2, + labelFontSize=base_font_size, + symbolStrokeWidth=4, + labelLimit=300, + titleLimit=220, + orient='none', + legendX=345, legendY=10, + ).properties(width=650, height=450) + + return bar_plot + + +def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, + metrics_value_range_dct: dict, group: str): + # Merge subgroup and group metrics for each model and align their columns + all_metrics_for_all_models_df = pd.DataFrame() + for model_name in all_subgroup_metrics_per_model_dct.keys(): + group_metrics_per_model_df = all_group_metrics_per_model_dct[model_name][ + (all_group_metrics_per_model_dct[model_name]['Subgroup'] == group) + ] + subgroup_metrics_per_model_df = all_subgroup_metrics_per_model_dct[model_name][ + (all_subgroup_metrics_per_model_dct[model_name]['Subgroup'] == 'overall') + ] + subgroup_metrics_per_model_df['Subgroup'] = subgroup_metrics_per_model_df['Subgroup'] + aligned_subgroup_metrics_per_model_df = subgroup_metrics_per_model_df[group_metrics_per_model_df.columns] + + combined_metrics_per_model_df = pd.concat([group_metrics_per_model_df, aligned_subgroup_metrics_per_model_df]).reset_index(drop=True) + all_metrics_for_all_models_df = pd.concat([all_metrics_for_all_models_df, combined_metrics_per_model_df]) + + all_metrics_for_all_models_df = all_metrics_for_all_models_df.reset_index(drop=True) + all_metrics_for_all_models_df = all_metrics_for_all_models_df.drop(['Subgroup'], axis=1) + + # Create new columns based on values in Metric and Value columns + pivoted_model_metrics_df = all_metrics_for_all_models_df.pivot(columns='Metric', values='Value', + index=[col for col in all_metrics_for_all_models_df.columns + if col not in ('Metric', 'Value')]).reset_index() + + # Create a pandas condition for filtering based on the input value ranges + models_in_range_df = pd.DataFrame() + for idx, (metric_group, value_range) in enumerate(metrics_value_range_dct.items()): + pd_condition = None + if '&' not in metric_group: + min_range_val, max_range_val = value_range + if max_range_val < min_range_val: + raise ValueError('The second element in the input range must be greater than the first element, ' + 'so to be in the following format -- (min_range_val, max_range_val)') + metric = metric_group + pd_condition = (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val) + else: + metrics = metric_group.split('&') + for idx, metric in enumerate(metrics): + min_range_val, max_range_val = metrics_value_range_dct[metric] + if max_range_val < min_range_val: + raise ValueError('The second element in the input range must be greater than the first element, ' + 'so to be in the following format -- (min_range_val, max_range_val)') + if idx == 0: + pd_condition = (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val) + else: + pd_condition &= (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val) + + num_satisfied_models_df = pivoted_model_metrics_df[pd_condition]['Model_Name'].value_counts().reset_index() + num_satisfied_models_df.rename(columns = {'Model_Name': 'Number_of_Models'}, inplace = True) + num_satisfied_models_df.rename(columns = {'index': 'Model_Name'}, inplace = True) + num_satisfied_models_df['Metric_Group'] = metric_group + if idx == 0: + models_in_range_df = num_satisfied_models_df + else: + # Concatenate based on rows + models_in_range_df = pd.concat([models_in_range_df, num_satisfied_models_df], ignore_index=True, sort=False) + + return models_in_range_df From de6b7d7bd02667b568a79c0f8d5ad9e73a959bec Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 2 Oct 2023 17:27:52 +0300 Subject: [PATCH 007/148] Improved a metrics bar chart --- .../Multiple_Models_Interface_Vis.ipynb | 218 +----------------- .../metrics_interactive_visualizer.py | 84 ++++--- virny/utils/data_viz_utils.py | 21 +- 3 files changed, 76 insertions(+), 247 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index 2382c421..dad3d0d5 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -148,14 +148,6 @@ "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" ] }, - { - "cell_type": "markdown", - "id": "e1a23ece", - "metadata": {}, - "source": [ - "Compute composed metrics" - ] - }, { "cell_type": "code", "execution_count": 8, @@ -168,114 +160,10 @@ }, "outputs": [], "source": [ + "# Compute composed metrics\n", "models_composed_metrics_df = metrics_composer.compose_metrics()" ] }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/urllib3/__init__.py:34: NotOpenSSLWarning: urllib3 v2.0 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import altair as alt\n", - "import gradio as gr\n", - "import numpy as np\n", - "import pandas as pd\n", - "from vega_datasets import data\n", - "\n", - "\n", - "def make_plot(plot_type):\n", - " if plot_type == \"scatter_plot\":\n", - " cars = data.cars()\n", - " return alt.Chart(cars).mark_point().encode(\n", - " x='Horsepower',\n", - " y='Miles_per_Gallon',\n", - " color='Origin',\n", - " )\n", - " elif plot_type == \"heatmap\":\n", - " # Compute x^2 + y^2 across a 2D grid\n", - " x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n", - " z = x ** 2 + y ** 2\n", - "\n", - " # Convert this grid to columnar data expected by Altair\n", - " source = pd.DataFrame({'x': x.ravel(),\n", - " 'y': y.ravel(),\n", - " 'z': z.ravel()})\n", - " return alt.Chart(source).mark_rect().encode(\n", - " x='x:O',\n", - " y='y:O',\n", - " color='z:Q'\n", - " )\n", - "\n", - "\n", - "with gr.Blocks() as demo:\n", - " button = gr.Radio(label=\"Plot type\",\n", - " choices=['scatter_plot', 'heatmap'], value='scatter_plot')\n", - " plot = gr.Plot(label=\"Plot\")\n", - " button.change(make_plot, inputs=button, outputs=[plot])\n", - " demo.load(make_plot, inputs=[button], outputs=[plot])\n", - "\n", - "\n", - "demo.launch(inline=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T22:25:40.759154Z", - "start_time": "2023-09-28T22:25:39.629263Z" - } - }, - "id": "b9dad21b662edd59" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "demo.close()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T22:26:12.203639Z", - "start_time": "2023-09-28T22:26:12.019693Z" - } - }, - "id": "920e2c1a81d4e810" - }, { "cell_type": "code", "execution_count": 185, @@ -338,12 +226,12 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 320, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-01T23:03:56.089028Z", - "start_time": "2023-10-01T23:03:56.019414Z" + "end_time": "2023-10-02T14:23:41.153446Z", + "start_time": "2023-10-02T14:23:37.215399Z" } }, "outputs": [], @@ -373,7 +261,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-01T23:03:56.113686Z" + "start_time": "2023-10-02T14:23:41.153322Z" } }, "id": "678a9dc8d51243f4" @@ -402,102 +290,6 @@ }, "id": "277b6d1de837dab7" }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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+Pj5wdXVlarqlp6fjxo0b2Lx5M/Lz8zFo0CDcv38f1tbWYreXlZWFnj17QkpKCsuXL0erVq3A4XBw/fp1LFy4EDk5OZgzZw46d+4MW1tbiffj7du36Ny5M+7duwcOh4Pdu3ejX79+QmGCgoJw7do1AEC3bt0wePBgGBsbQ1ZWFtnZ2YiNjcXp06e/+ShToaGhTO2YHj16iAxTWloKNTU19OjRA23atEHDhg2hoKCAzMxM3L9/H3///TfevXuHiRMnolGjRpWGFX/z5g2GDx+O4uJiaGtrY+LEiWjRogU0NTXx5csXPHv2DFFRUWJHPj106BCGDh0KQgjMzc0xceJE2NjYQEtLC2lpadi5cyfOnj2LnTt3QllZGWvWrKkUR0BAAA4fPgwAsLKywqxZs+Dg4IDc3FwcPnwYO3bsQP/+/b/iSJZda4Jaml26dKmymU5F3bt3x9ixYwGAyReixMfH4+DBg9DT08OMGTPg4uICQgjOnz+P5cuXo7CwEP7+/mjXrh20tLRqlP7c3Fw8ePAAAGBtbQ0TE5MarS+JY8eOIT4+Hvb29pg2bRrs7Ozw5csXoXvq//73P2YUUDMzM8yePRtNmjTB58+fcfLkSWzcuBG5ubno1q0bbt++DUdHxzpPZ3kFBQXo27cvcnNz8b///Q9dunQBl8vFrVu3sGzZMmRlZWHNmjUwNjbGlClTvmlayvva67Im7ty5gz179sDIyAgBAQFwcXFBaWmpyLz6tXl05cqVmDVrFgDAwcEB48aNQ8OGDaGqqork5GRs3LgRMTExWLRoETQ1NTF58mSh9SdNmsTUZBsyZAh69eoFfX19SEtLIysrC3fv3hXZF9DDhw8REBAAPp8PMzMzTJw4EY0bN4a6ujo+ffqE5ORkXLlypdb9CPF4PHA4HHh6eqJjx46wsbGBuro6Pnz4gCdPnmDTpk1ISEjAnj17YG5uLlSLSZTRo0fj5s2b8PX1Rb9+/aCrq4uXL1/ir7/+QkxMDG7fvo3Fixdj2bJllda9c+cOBg8ejNLSUnC5XEybNk0oXy9duhTjxo2DjY1NrfZVoEmTJoiPjwchBKNHj0ZoaCgUFRUlXr9nz55wcXHB5s2bsWXLFgBg+vwqz8DAgPm7Lt8RMjMzMWjQIMjIyGDp0qVMLfArV65gxYoVyMvLw7Rp02BqaoqePXtKvF9fY8mSJQgICMDw4cNx9+5duLi4IDg4WCgMh8MBAKirq6NXr17Yv38/Dhw4gLVr10JOTk5kvHv37mUGHxH02/k1Bg0ahJkzZ+L169eIiIiAl5eX0HLBe/WAAQPAZrOrje/9+/do1aoV0tPTweVyMXr0aLi7u8PU1BT5+fm4cOEC1q9fj2fPnqFz5864f/8+VFRUAADjx49Hnz59MG/ePJw4cUKiQT8EanLvE6egoABt27Zl8q6zszP8/f1hZ2cHLpeL9PR0XL16VWTfv0OHDkVmZibYbDZGjx6Nzp07Q1dXF3w+HxkZGbh58yaOHz8ucVooilG/5WwURX0rFWt0EULIihUrCABiZGRE+Hw+E5bP5xMjIyMCgPz111+EEFJtja6AgABm+bx58yot5/P5ZPDgwUyYzZs3VwrTp08fZvm+ffsqLc/Ly6v0Zaui4uJiYmpqSgAQLy8v8vnzZ5HHY/v27UwcompJfK8aXd7e3ky43bt3i42ruLhYbBzp6enEwMCA4P+/jIsi+FqL///ymZGRUSnMtWvXCIvFIgDI5MmTq9yn8l/gXr58SaysrJivxuHh4SLTIMhTffr0EbsvhJBafQUnRPgL5q5du0h8fDwzxcXFkfPnz5OpU6cytaxsbW3J27dvRcaVkZEhNu8QQkhOTg5xcHAgQFktxYp27tzJpEVcjS1CCCkoKCAFBQVC896+fUtUVFQIADJixAixNR0EtYSkpKRIUlKS0LKHDx8ytTSaNGkisjZGaGio0LVUmxpdixcvZtbfunVrjdfX1dUlAAiHw6m0THANAiDOzs4kNze3Upg9e/YwYdasWVPj7V+/fp1ZX9y1U1vlj2379u1JYWGhyHDlz5WdnR35+PFjpTDnzp1jwjRr1qzS8rqu0QWAsNlsEhUVVSnMq1eviKGhIQFAFBQUSHZ2ttjtVUVwfvX19YWu1YpT+ZoKX3tdEiJ5jS4AxN7eXuT5EBW2tnk0ISGBqVUSGBgo9CwW4PF4ZMiQIQQAUVRUJB8+fGCWffnyhVlfVI2t8ireW//880/mPL5+/Vrsejk5OTWqrSmQn59PMjMzxS7n8/nEz8+PSUNOTk6lMBVrOIl6ThYWFhI7OzsCgGhoaIi8Z7q4uFSZrzMyMph8LXhO1satW7eEasipqqqSoUOHku3bt5MHDx6IrOkjiiQ11wXq+h1BRUWFPH78uFKYR48eMTXuDQwMRG6zqudJbWt0VUyju7u72H0lhJBLly4x8ezdu1dsOMH7pLja9NWpWKOLEEI6d+5MAJBBgwYJhX369CkT9vbt24QQUm2NrkGDBjF5MSUlRWSY+/fvM7Xv5s6dW2l5TWoo1uTeV925nDZtGrN8woQJIu9rhBBSVFQkdO95/vw5s15VNbb4fL7QfZCiJEELuijqFyWqoCsjI4N5Ibt8+TIT9vLly8wPaEGBSFUFXYWFhURVVZUpPBD3Ipebm8s0J7SxsRFalpWVRaSlpQkA0q1bN7H7cevWrSoLuv79918CgMjKylb7A6xZs2YiX0gI+bYFXR8/fiQ3btwQKuRydXWt8kW1OuvWrSMAiLKyssgXivIvsSdPnhQbT4sWLQgA4uTkVOU+CfJQUlISMTY2Zl7oo6OjxcYt+DG2fv36mu+gBCStqq+iokKWLFlS5UucJMLCwpg43717J7RsyZIlBABRU1OrcbwLFy5kfkiIKxwhpKxpouDHS8UX3PHjxzNpu3v3rtg4BC/ltS3omjp1KrN+WFhYjdcvX3Cdl5cntKz8S/eDBw9Ers/n85kmDj4+PjXefvlzKKr5ZHmPHz8WWxgjqrmUIF4pKakqC5/GjRvHhL1586bYcKNGjar0Q0ngWxR0TZw4UWw8Bw8eZMKJa+pcnfLnt6qppgUOVV2XhNSsoOvq1asS70Nt8+iIESMIAOLi4iL2xyAhZc8NQZOs8s3CX716xaThxIkTVaa3otGjR4u9338v79+/Z579R44cqbS8fN7u1auX2HjKN4WueC5u375d43xd24IuQgjZsWOH2GaoCgoKpGPHjmT79u1VNrOsSUGXJGryjrBq1Sqx8Qg+kAIghw8frrS8qufJ9yro4vP5pEGDBgQA6dChg8gw9+7dY7ZV23cSUQVd+/btI0DZR7/yH5jmz59PABArKytmXlUFXampqcx1cerUqSrTMWvWLAKUfTSoqLYFXdXd+6o6lx8/fmSacTs7O0tcuEsIIdHR0dXeUymqtmhn9BT1GzEwMEDbtm0BCHeeKfi7Xbt2QtXjxbl37x7TsbWfn5/YjuuVlZWZ5myPHz9GVlYWs+zKlSvg8XgAykbFE6dZs2ZVNqk7efIkAMDd3b3aZkxt2rQBAMTExFQZ7muFhoYKdf6ppqaGli1b4tSpU2Cz2fDz80N4eLhEVdkBIC8vD6mpqUhISMCjR4/w6NEjyMvLCy0TR1VVFV27dhW73NnZGQCYJqhVuX//Plq3bo2XL19CV1cXUVFRVXbkraenBwA4ePCgRJ0Gfyu5ubn4559/RFaZF+fz589IS0sTOublz5eg+ZuAYF8/fvxY42Y/gjzcrVs3cLlcseFkZGTg6uoKoHIeFnRUb29vz5xTUb62ucanT5+Yv2vSPEfUOnl5eSLD2Nvbw8HBQeQyFovFdAAsSZ6tqHz6FRQUqgzr6uoKe3t7kVNVHVm7ubmJbZYM/HeubG1t0bx5c7HhRo8eXWmdb6mq+7CPjw/T0XZ9jhZb0+uyJoyMjNC6dWuJwn5NHj116hSA/zrFFkdVVZUZvKT89a6hocE029q9ezdKS0slSjPw333q8ePHuH37tsTr1VZJSQkyMjKQmJjInK/MzExoaGgAqP58DR48WOyy8ve5ise5fB6VNF9/jVGjRiE+Ph7Dhw+HkpKS0LLPnz8jIiIC/v7+aNiw4TcZ2OFr3hFYLBZ8fX3FLh8+fDiTT3/UkaJZLBbzbLt8+bLIjvgFTR85HE6V+aqmevbsCSUlJRQUFODYsWPM/D179gCQvBP6M2fOgMfjQV5eHp07d64yrOBdNjMzs8aDDohSk3ufKJcvX2be8SZPnizxYFbAf/ckACIHTKGor0ELuijqNyMYEvno0aP48uULvnz5giNHjggtq86jR4+Yv6v6oVZxefn1yvdB0bRp0yrjaNasmdhlgpGpzp8/L3KEmfLTqlWrAJSNbFdfGjZsiGnTpkFZWbnKcC9evMCkSZNgamoKFRUVmJubw87Ojvmh7e/vz4R99+5dldurqh8ldXV1AMIFAKJcu3YNbdu2xdu3b2Fqaorr16+L/aEnIHh5vnHjBtMfzPHjx6scTa62rly5AlJWSxmEEPD5fOTk5CAqKgo+Pj5ITU3F2LFjMX36dLFxvHv3DnPnzoWVlRWUlJRgZmYmdMzLFxhWPObdu3dnfjD5+PigXbt2WLt2Le7du8cU6IrC4/GYvpu2bdtWbR4WXKvl83BRURGePn0K4OuuJUmU/xGXn59f4/XLryPuGmjUqFGVcUiaZ0Upn/7Pnz/XeH1JVHVdlD9X1d07nZycmEKc8vfOb4HD4VTZDxibzWYKb0T1H1QTJiYmQtdqxaniSIFfc13WRHX3s/Jqm0dfvHjB3P/mzJlT7fUueL6Vv965XC7T196RI0dgYWGBWbNm4ezZs9WOrDpw4ECw2WwUFRXBzc0N3t7e2Lp1Kx49elRnoxKXlJRg06ZNaNGiBRQVFWFkZAQbGxuhgmLBqKfVna+qjrPgGAOVj7Mgj9YkX38tKysr7Nq1C+/fv8eNGzewZs0aDB48GIaGhkyYrKwsdOvWrU4KjOrqHcHMzAyamppil2tpaTEF91977X9Lgo+ufD6fGXlQoKioCPv27QNQ1k+noKC1LsjJyaFPnz4A/vtofP36daSkpIDFYmHIkCESxSO41gsKCiAjI1PlfaFbt27MenXxPluTe58osbGxzN81LTAzMzNj1lm7di1sbW0xf/58ocIziqotWtBFUb+ZXr16QV5eHnl5eThx4gTCwsLw6dMnKCgooFevXhLF8eHDB+ZvbW3tKsPq6uqKXK8mcejo6IhdJnhhrglRQzPXpR49eiA+Ph7x8fF48OABzp07hylTpkBWVhaPHz+Gh4cHkpOTxa5/7tw52NjYYOPGjXjx4kW126tqfwRfdcURFIIJOhgXZ9euXUwNnIMHD6JBgwbVpuvPP//EiBEjwGKxkJ2djU2bNqFXr17Q1taGnZ0dAgMDv9lw0SwWCyoqKmjTpg2OHTuGQYMGASh7kRL1I+PevXto1KgRli1bhidPnlT7o6/iMdfQ0MDJkydhYGAAQgiuXLmC6dOnw8XFheks9/Tp05Xi+fDhQ41qZAiUfwH8+PEjk96vuZYkUf4HUW1esAXnm8PhVKr5ICBpnq2qAFGc8j9wqitwzcnJESqACQwMlGgbampqYpd9/PiR+bu6c8Vms5n0lr9ffgvq6urVfoUX5J1vnZbyvva6rImqzltFtc2jtXleAaj0g2/jxo3w9vYGUFbgsXLlSnTt2hUaGhpo2rQpVq5cidzc3ErxNGrUCPv374eamhpKS0tx+vRpjBs3Dvb29tDW1sbQoUNr1AF1RR8+fICrqysmTpyIW7duobi4uMrw1Z2vqo5z+Q84FY+zII/WJF/XFTabDVdXV0ybNg179uxBeno6Ll26xNRM5/F4GD9+/FcVLNblO0J19yGgfq79mtLX10eXLl0AlNUMKn98T5w4waS9Ljqhr0hQa+vy5ct49eoVU+DVpk0biQc8qat7Q23U5N4nSvmC1PI1tCS1f/9+prb648ePsWjRIrRv3x6qqqpo06YNtm7disLCwq9KI/V7ogVdFPWbUVRUhI+PD4Cyr0+CB7KPj0+1TXlEqYsR874mDsELbufOnZnCJUmmb0lVVRV2dnaws7ODg4MDvLy8sG7dOpw+fRoyMjL4+PEjBg0aJPKH+rt37zBo0CAUFBRAUVERQUFBiImJQXZ2NoqKipgf3ZcuXWLWqasv8VXp0aMH84Nh6NChQs1QxWGz2di5cycePXqEefPmoWXLlkyTm4SEBCxcuBAWFha1HuGrJmbOnMn8vWvXLqFlxcXF6NevH96/fw82m43p06cjKioKWVlZKCwsZI758+fPmXVEHfPWrVvj2bNn2LNnDwYNGsR8yc/Ly8Px48fh7e0NLy8voRfT8nlA0PxFkunChQsi9/Nbj2BZ/stv+a+4ksjKymIKx772C3Jt2dnZMT+Qa5p+SUnabONbn6ua+JHSIlBX16WkatLcprbKX+/z58+X+HqvOOKcsrIyTp48iVu3bmHGjBlwdnZmarPcvXsXs2bNgqWlpchm+r1790Zqaiq2bduGXr16MU3+3717hz179qBNmzbw8/Or9uOHKFOmTMG9e/cAlDXpOnnyJNLS0lBQUAA+n8+cMyMjIwDf/tn1o+Trdu3aISIigqmF9vTp02pHthanrt8RfpRjVBdGjRoFoKwp69WrV5n5guvH0NAQnTp1qvPtenh4wMjICHw+H8HBwTh06BAAyZstAv/dGzQ1NWv0LltdLW5JfI97X1UMDAxw48YNXLx4EePHj4etrS1YLBZKSkpw7do1jBs3DnZ2dnjy5Em9ppP6+cjUdwIoivr+hg0bhr179wr9WJa02SIg3GTgzZs3sLS0FBu2fK2P8uuV/4L05s0b5sVXlKpq/WhoaCAzMxPFxcWws7OrNu31qX379pgyZQpWr16N+/fvIyQkBCNHjhQKc+TIEab5yfHjx9GhQweRcX3vL6s9e/bEgAEDMGTIEDx58gTt2rVDZGSkRF/DbWxssGjRIixatAiFhYW4fv069u3bh3///Rf5+fkYOHAgnj9/XqsvgZIq3wSmYkHn5cuXmT5eNm/ezLwsVyTJMZeVlcXgwYOZPkBSU1Nx5swZbNiwAU+ePMH58+fxxx9/YO3atQCErwlCSK3ycPk+ZqqrIfe1NehatWoFKSkp8Pl8nDlzBnw+v8qmseUJ+iID/utj5HtTVVWFo6MjYmNjkZiYiJcvX8LY2Pi7bb/ifa8qpaWleP/+PQDhfAII12apqkBC0uaZ79+/B4/Hq/IHjyC9FdPyrdTldfmjKF+jkM1mf/Uzq1mzZkxz5E+fPiEyMhIhISE4duwYsrOz0bt3bzx//hxycnJC66moqMDf359p3paYmIgTJ05gw4YNyMzMRGhoKJycnDBlyhSJ05KXl8f0gzh48GCmjyJRytds/BYE11lN8vW3pqenh65duzIfF589e1arZpN1/Y4gyf5/72u/trp27Qo9PT1kZWUhODgY7u7uePXqFfOu6+vrK/HzqiYETRSXLVuGJUuWoLCwELKysujbt6/EcQjuDZ8+fYK1tXW9Fz7VRPma3llZWTAzM6tVPO3bt0f79u0BlF27Fy9exPbt23H58mU8f/4c/fv3/2YfqKhfE63RRVG/ofbt20NPTw+lpaUoLS2Fvr4+83CRRPmX81u3blUZtnyHt+XXE3SyC6DKjp2rWy54Ubx79261zSR+BHPnzmX6JlqwYEGlNCckJAAoe6EU9wIL/Nefw/c0YMAAhIaGQkpKCklJSWjXrl2Nq9vLysqiQ4cO2LVrF1auXAmgrFmFqGZ9dal888CKTQUFxxwA0/eNKLU55oK+ye7cucPU8BJ87QXKmvAJmrRER0fXOH6g7Jg2bNgQwNddS5JQU1ND9+7dAQAZGRlCne9WhcfjYfPmzcz/fn5+X5WOryEo1Ofz+di0adN33TaXy2XOVXX3ztjYWJSUlABApQKR8s0+qyo0kPQLeHFxcZUdg5eWljI1UL7XB4VveV3WF3Nzc6ioqACo/fUujpKSEry9vXH06FFMnjwZQNmPzuvXr1e7rrW1Nf73v//h5s2bTM3u8vcpSTx9+pTJr1Wdr6SkpFr171cTgveLmuTr70FfX5/5u2JNKklrVtX1O0JqaipToC7K27dvmX7zvvfHxJrWNpOWlmaeLUeOHEF+fj5CQ0PB5/PBYrGqHJjgawlqbwma2PXo0aPavljLE7zLFhUVfdU9rT5q6DVp0oT5u3xNuq+hoaGB/v3749KlS8w7R1xcHNPHJUVJghZ0UdRvSFpaGkOHDgWXywWXy8XQoUNr9JXL2dmZqUUieIkQ5dOnT8zLso2NjVCNnbZt2zJfrCp2HFrenTt3quyIWfAAzM3NrdS840ekrq6OCRMmAADS09Mr7bugEKawsFDscS0oKBAaNfN7Gjx4MIKDgyElJYXHjx+jffv2te4Aunzh6td0Ii2J8i+OFWsPli/4ElcDhs/nY8eOHbXevrKyMtPEQFRH9kDZD8Dz58/XKn7BD574+Pgqv3hWbLZZGzNnzmRepqdNmybRuVu5ciUePnwIAOjSpYtQQff3Nnr0aKa51tq1a6stcKprgnOVkJBQ5ch3//zzT6V1BMp/Ma/qR9H+/fslTldV9+Hjx48zBWpV/biuS9/juvzepKWlmX6ELly4gMTExG+yndreW42MjJga2jW9J0tyvgBg69atNYq3NsrnUUnzdW3VpPll+WvV3NxcaJmsrCzzd1FRkdg46vodgRCCf//9V+zy8v1dfa9rX0BwTKo6HhWNHDkSLBYLnz9/xsGDB5mR/Nq0aSNR36K1ZW1tjRYtWjDv1TVpJQEA3t7ezHN13bp1tU5HbY7Z12rbti1TQL5hw4Za9Z9Zle/5rkj9WmhBF0X9plasWIHCwkIUFhZi+fLlNVqXy+UyzUgePXqERYsWVQpDCMHEiROZh9LEiROFluvp6aFHjx4Aypo0ifp6nJ+fjzFjxlSZFl9fX6bgIiAgoNqvSdevX0dUVFSVYb61adOmMZ3sLl++XOilQFDbo6CgQOQx4fF4GDVqFDIzM79PYkUYNmwY/vnnH7BYLDx69Ajt27ev9EX4w4cPOHXqVJU/Aso3na1tVXdJFBUV4c8//2T+Lz9KG/DfMQfED289Z84c3L9/X+w2zp8/X2W/Zbm5uUyhRsV9nTJlChQVFQGUDeVeviaLKGfOnGEKjQTGjBnDvCT7+/uL/KG5d+9enD17tsq4JdGyZUtMmzYNQFmtrg4dOlQaKU+AEIJ169Zh7ty5AMoKerdt2/bVafgaCgoK2L17N6SkpFBSUoJOnTohLCys2vXqqrnVuHHjmA8L/v7+zCAP5V24cAE7d+4EUNY8rWI/LHZ2dkwzoo0bN4r8UXPo0CEcPnxY4nRt2bJFZO2f169fIyAgAEBZ5+CC0VS/tbq4Ln9Ec+bMYfrT6tOnDzIyMsSG5fF42Lt3r1CYlJSUap9h4u6tYWFhVY7MmJ6ejqSkpErrScLCwoK5B4WGhoq89586dQobN26sUby10axZM6aWibh8nZWVxeTrr9GrVy9s3ry52mbCISEhTL9ZxsbGlZotlv8QWL7fuYq+xTvCokWLRA6Qk5iYiCVLljDpE7yzfS+CY5KSkiJxgWKDBg3g4eEBAJg3bx5TA+hbdEJfUUxMDPNeLSjQlpSVlRXT1PHAgQNYs2ZNleFTU1NFfsgQHLPs7OxajUxcG6qqqsy7+r179zB16lSx56ukpESoJUBcXFyVtSoJIcwAQiwWixkBlKIkQfvooiiqVubPn49jx44hJSUFQUFBiI+Px/Dhw6Gnp4fU1FRs3LgRkZGRAABXV1ehoa4FVq9ejYiICHz69AmDBg1CVFQU+vTpA2VlZTx8+BDLly/HkydP4OLiIrbWApfLxaFDh+Dh4YH8/Hy0a9cOAwYMQM+ePWFmZgY+n4+srCzcu3cPx48fR3x8PDZs2AB3d/dveXiqpKWlhdGjR2P9+vVISUnBvn37mGrv/fr1w9y5c1FUVIThw4cjLi4OHTt2hIqKChISErBhwwbcu3cPbm5udd70pSaGDx+O0tJSjBkzBg8fPkTHjh1x6dIlpm+UvLw8dO/eHaampujVqxeaN28OExMTyMjIICsrC6dOnWJqrBgYGAgNl10bqamplYZIz8vLQ1xcHDZv3swUHllYWFTKi56entDW1kZ2djbmzZuHtLQ0+Pj4QFNTE8+ePcOOHTtw6dKlKo/5/v374e3tjY4dO6JTp05MQcSnT5/w6NEjbNy4Ea9evQIAjB07VmhdHR0dhIaGok+fPsjKyoKLiwv8/PzQuXNnGBoaoqSkBBkZGbh9+zaOHDmClJQUnDp1SqhDd0dHR0yYMAEbN27E3bt34eLigtmzZ8Pe3h65ubk4fPgwtm/fXuW1VBPLli1Deno6Dh8+jAcPHsDW1ha+vr7w8vKCgYEBCgoK8PDhQ4SGhjLNJVVUVBAWFsY04axPnp6e2LZtG8aPH4+8vDz4+PigadOm8PHxgbOzM9Nfyrt37/DgwQMcO3ZMqOZXxT6PasLe3h4zZszAypUr8eDBAzRp0gSzZ8+Gk5MTPn/+jFOnTuHvv/8Gj8cDh8MRWTAoIyODMWPGYNmyZXj06BHatWuHWbNmwdjYGG/evMHhw4cREhKCli1b4saNG9WmSUtLC/Ly8ujYsSOmTZuGLl26gMvl4vbt21i6dCnzo3nRokUSjdJWF+riuvwR2dvbY9WqVZg2bRoeP34MOzs7+Pv7o127dtDR0UFhYSHS0tIQExODI0eOICsrC/Hx8cx18/LlS7Rt2xY2Njbw8fGBi4sLDAwMAJQVVB08eJApAGncuDGaN2/ObHvdunUYPHgwunbtinbt2sHa2hoqKir4+PEj7t69iw0bNjAj9FW8T1VHQ0MDXbp0wZkzZxAeHo5OnTph3LhxMDExQXZ2No4ePYqQkBCYm5sjJyen2lFPv9bmzZvRqlUrlJSUVMrXt27dwtKlS/Hu3Ts4OjpW2byxOunp6ZgwYQJmz54Nb29vtGnTBlZWVlBTU0NhYSGSkpJw+PBh5iMDi8XC2rVrKzUxa9myJfP3tGnT8Mcff0BPT48JZ2pqChkZmTp/R7CwsMDbt2/RokULzJ49mykkioyMxPLly5nROzds2MAMJvO9tGzZEsHBwcjOzsb06dMxZMgQpukvm80WO6LhqFGjcOXKFaaPWGVlZfTp0+e7pbu2tmzZgrt37yIlJQUzZszAiRMnMGzYMNja2oLL5eL9+/d48OABwsPDcfnyZfj4+GDgwIFCcQjyEZ/Px9ixYzFp0iShdyMLC4tvkvZFixYhIiIC8fHx2LhxI2JiYjBmzBjY29uDw+EgIyMD165dw/79+7F48WKmiWlcXByGDx+Opk2bwtvbG02aNIGuri5KSkqQmpqK4OBgREREACir/f4t+3KlfkGEoqhf0pUrVwgAAoAEBwfXeP3g4GBm/StXrogMk5qaSho1asSEEzW5ubmR9+/fV5lOJSUlsevPnz+fBAYGMv+LExMTQ4yMjKpMi2AKDQ2ttL6JiQkBQHx9fWt6qJhjIYhfkjjS09MJh8MhAIi1tTXh8XjMsl27dhEpKSmx6e/fvz+5ePFilefH3d2dACDu7u5VpqOqY1t+n8TloS1bthAWi0UAEGdnZ/Lx48dK61Y16enpkbt371Z7vKpLuySTo6MjSU1NFRlXeHg4kZWVFbuuh4cHefTokdjj4evrK1Eaxo4dK3Suyzt58iRRV1evNg4pKSly+fLlSusXFxeTXr16iV3PzMyMPH/+nPk/MDCwVsddgMfjkQULFhB5eflq09yiRQsSHx9fZXySXoOCY21iYvJV6SeEkKioKOLo6ChxHnJ1dSXXrl0TGVdNjiuPxyPjx4+vclsqKirk/PnzYuP4/PkzadGiRa3zLCHCx/LOnTtEU1NTbHyTJ0+W9LCKJDi/NTlvX3tdElL1Pa58uiS5b9dlHt2+fbtE1w6HwyFPnz5l1iv/bK9qatSoEUlJSRHapuC5UN39ZdGiRdUeC1FevnxJjI2NxcZtbGxMEhISqjyO5d89xN2vCZHs+bRv3z7mOVtxkpGRIdu3b//q+0mPHj0kvn+oqKiQf//9V2xc/fr1E7tu+WNR1+8Ip0+fFpsXpaSkyKpVq8Smuar7Xvm8KioN1V2bnz59Iubm5iLTVdX5+vLlC1FTU2PCjh49WmzYmiif3qrypjjl3xPEycrKIq1bt5YoPw0fPrzS+jwer8rnQnk1ufdVdy4JIeTt27ekTZs21aa7/PVa/nqvamrZsiV59+5dtemkqPJo00WKomrN1NQUDx48wMaNG+Hu7g4NDQ2w2Wzo6OjAy8sLu3fvxtWrV6scqcfDwwMJCQnMl18OhwMdHR107doV4eHhWLBggURpadGiBZ4+fYqtW7eia9eu0NfXB4fDgaysLIyMjNCpUycsWbIESUlJNe474VswNDRkmgAlJibi6NGjzLLhw4fj2rVr6NmzJ7S0tMBms6GnpwcvLy8cPHgQBw4c+GFG5Bk7diw2bNgAoKzKeqdOnZCbmwsTExPcvn0bQUFB6NSpE6ysrKCqqgoZGRloamqiTZs2WLlyJZKSkuDs7PxN0iYvL8/UKNu3bx/u3r0rttq7p6cn7t69iyFDhkBfXx9sNhtaWlpwd3fH9u3bcenSJaYPClHWrl2LPXv2YMSIEUwNCw6HAzk5OVhaWsLX1xfXrl3Dli1bxPaH5+3tjdTUVKxatYqp3cFmsyEnJwczMzN069YNa9asQVpaGtq2bVtpfTabjaNHj2L37t1o3bo1VFRUIC8vD2tra8ydOxf37t2r1C/M15CSksL8+fPx9OlTrFixAh4eHsx+q6iowMrKCiNGjMDp06cRExPzQ46K2qZNG8TGxuLs2bOYMGECHB0doa2tDTabDUVFRRgbG8PLywvz589HfHw8bty4gVatWn31dqWkpLBp0yZcvXoVgwcPhrGxMbhcLpSVldG4cWPMnTsXT58+RadOncTGIS8vj8uXL2PJkiWwt7eHnJwc0xfcxo0bcfHixSrzbEUuLi64f/8+Jk+ejAYNGkBWVhYaGhrw8vLC2bNnsX79+q/e75r62uvyRzZ69GikpKRgwYIFcHNzg6amJmRkZKCgoABLS0v07t0bW7duxatXr4RqYbRu3RqRkZGYM2cO2rZtCwsLCygpKTHP3k6dOmHr1q2Ii4ur1Pxw//792L59OwYNGoTGjRtDV1cXMjIyUFRUhK2tLcaNG4fY2FjMmzevVvtkZGSE+/fvY+bMmbC0tASXy4WKigocHR0RGBiIuLg42NjYfNVxq4mBAwciNjYWQ4cOZd4JDAwM0K9fP1y/fh2jR4/+6m2EhYUhKSkJ69evR79+/WBrawsVFRVIS0tDQUEBxsbG6NKlC9atW4dnz54xtbdF2bNnD/766y80a9YMKioqYp8Vdf2O0LVrV9y9exfDhw9n3sO0tbXRu3dvXL9+HTNmzKjxcakLioqKuHHjBqZMmQJra2umy4fqVBzx8Hs0W6wrurq6uHr1Kk6fPo3BgwfD3Nwc8vLyzL2vZcuWmDFjBqKiokT2uSklJYULFy5g3rx5cHR0hKKi4nfroF5TUxNRUVE4duwY+vTpA0NDQ3C5XMjKysLc3Bx9+/bF3r17hWqhDRw4EGfPnsW0adPQqlUrmJmZQV5eHhwOB4aGhujevTv27t2La9euCY1aS1GSYBFSg14UKYqiKIqiqF+Cn58fQkNDYWJiIrafNYqiqJ+Nm5sbbty4ARsbm2r7vaQo6tdEa3RRFEVRFEVRFEVRP73k5GSmb8KfqTYXRVF1ixZ0URRFURRFURRFUT+9FStWAChrwijo9JyiqN8PHXWRoiiKoiiKoiiK+ul8+fIFr169QkFBAcLCwhASEgIA8Pf3p/06UdRvjBZ0URRFURRFURRFUT+dW7duVRqgxcjICEFBQfWTIIqifgi06SJFURRFURRFURT102KxWNDX18eQIUNw/fp1qKmp1XeSKIqqR3TURYqiKIqiKIqiKIqiKOqXQGt0URRFURRFURRFURRFUb8EWtBFURRVDRaLhbCwsPpOxk/Hw8MDU6dO/S7bqniOkpKS0KJFC8jKyqJx48ZIS0sDi8VCXFzcd0kP9e2EhIRAVVX1u2zLz88PPXv2ZP4nhMDf3x/q6upMfvqe+Zz6fkxNTbFu3bpar/898+mPLDIyEiwWCzk5OfWdlN9OTfLw1+b3n833eq8Tlf/DwsJgYWEBaWlpTJ06ld4rKOoboQVdFEX9FPz8/MBiscBiscBms2FmZoZZs2ahsLCwvpNWZwT7V35q1apVvadJ1MtgcXEx/vrrLzg6OkJeXh6amppwc3NDcHAwSkpKvns6s7Ky0LlzZ+b/wMBAKCgoIDk5GZcuXYKRkRGysrJgZ2f33dP2M+LxeGjZsiV69eolND83NxdGRkb4448/mHlHjx5Fu3btoKamBjk5OVhZWWHEiBGIjY1lwoSEhAjla0VFRTg7O+PYsWOVtn3lyhV06dIFGhoakJeXh42NDWbMmIFXr159ux0WY/369cwIXgAQHh6OkJAQnD59mslPx44dw6JFi7572n53FQsh69qdO3fg7+8vUVhRhQT9+/fHkydPJN6eh4cHc33IysrC0tISy5Ytw8/ew0jLli2RlZUFFRWV+k7KD6Pi+4yOjg46duyIXbt2gc/n19l2apKHaxJWEqLeZ8pP37Kj+NevX2PSpEkwNzcHl8uFkZERvL29cenSpW+2TXFE5f8xY8agT58+SE9Px6JFi2p8r6AoSjK0oIuiqJ+Gl5cXsrKykJKSgrVr12Lbtm0IDAys72TVqeDgYGRlZTHTyZMnax3XtypwKi4uhqenJ5YvXw5/f3/cuHEDt2/fxoQJE7BhwwYkJCR8k+1WRVdXF1wul/n/+fPnaNWqFUxMTKChoQFpaWno6upCRqb2gw0XFxfXRVJ/CtLS0ggJCUF4eDj27t3LzJ80aRLU1dWZ62727Nno378/GjdujJMnTyI5ORn79u2Dubk55syZIxSnsrIyk69jY2Ph6emJfv36ITk5mQmzbds2dOjQAbq6ujh69CgeP36MrVu3Ijc3F6tXr/4+O1+OioqK0Jf258+fQ09PDy1btmTyk7q6OpSUlGq9DR6PV6c/bqm6oaWlBXl5+VqvLycnB21t7RqtM3r0aGRlZSE5ORlz5szB/PnzsXXr1lqnQRLf+r7G4XCgq6sLFov1TbfzsxG8z6SlpeHcuXNo27YtpkyZgm7duqG0tLROtlGTPPy1+b2i8u8x69atE7r/Z2VlISAggAlLCKmzfU5LS4OzszMuX76MlStXIj4+HuHh4Wjbti0mTJhQJ9uoiYr5Pz8/H9nZ2fD09IS+vj6UlJRqda+oqD4+MFLUD49QFEX9BHx9fUmPHj2E5vXq1Ys4OTkRQgh59+4dGTBgANHX1ydycnLEzs6O7Nu3Tyi8u7s7mTRpEpk5cyZRU1MjOjo6JDAwUCjMkydPSOvWrQmXyyXW1tbkwoULBAA5fvw4E+bhw4ekbdu2RFZWlqirq5PRo0eTT58+VUrrkiVLiLa2NlFRUSELFiwgJSUlJCAggKipqREDAwOya9cuoW1X3E55PB6PLFiwgBgYGBAOh0McHR3JuXPnmOWpqakEADlw4ABp06YN4XK5JDg4mBBCyI4dO0ijRo0Il8slVlZWZNOmTcx6RUVFZMKECURXV5dwuVxibGxMli5dSgghxMTEhABgJhMTE0IIIStWrCBSUlLk/v37ldJZXFxM8vPzmeM9ZcoUZtm///5LnJ2diaKiItHR0SEDBw4kb968YZZ/+PCBDBo0iGhqahJZWVliYWHBHKOq0lnx2JVPMwASGBjIHJ/Y2Fhmnfj4eOLl5UUUFBSItrY2GTJkCHn79i2z3N3dnUyYMIFMmTKFaGhoEA8PD5Hn5le2fv16oqamRjIzM0lYWBhhs9kkLi6OEEJITEwMAUDWr18vcl0+n8/8HRwcTFRUVISW83g8wmazyaFDhwghhKSnpxMOh0OmTp0qMr6PHz+KjOvZs2eke/fuRFtbmygoKBAXFxcSEREhtO6mTZuIhYUF4XK5RFtbm/Tu3ZtZdvjwYWJnZ8dcz+3bt2fycPn7jq+vr8jroWI+LywsJDNmzCD6+vpEXl6eNGvWjFy5cqXSsThx4gSxtrYm0tLSJDU1VeQ+U+KJeiYIREZGkqZNmxIOh0N0dXXJ7NmzSUlJCbM8Ly+PDBo0iMjLyxNdXV2yZs2aSufRxMSErF27lhBSlpcDAwOJkZER4XA4RE9Pj0yaNIkQUnb+K95zCBGd50+ePElcXFwIl8slGhoapGfPnsyyitsnhJAmTZoQHx8f5v/q8hYhhGzfvp0YGhoSOTk50rNnT7J69WqhdAQGBhJHR0eyY8cOYmpqSlgsFiGk7PoaOXIk0dTUJEpKSqRt27bMtU4IIXFxccTDw4MoKioSJSUl0qRJE3Lnzh1CCCFpaWmkW7duRFVVlcjLyxMbGxty5swZQgghV65cIQCY65cQQo4cOUJsbGwIh8MhJiYmZNWqVUL7YGJiQpYsWUKGDx9OFBUViZGREdm2bRv5VYjLu5cuXSIAyI4dOwgh1Z8TQqrOU5Lm4YphCSHkxYsXpHv37kRBQYEoKSmRvn37ktevXzPLBfno33//JSYmJkRZWZn079+f5OXlVdqviteCIE+cPXuWNGnShLDZbHLlyhXC4/HI0qVLiampKZGVlSUODg7k8OHDQnFV99zu3LkzMTAwYO7h5ZXPgxXft2bNmkUaNmxI5OTkiJmZGZk3bx4pLi5mltdF/hf8XX66cuWKyHtFWFgYcXJyIlwul5iZmZGgoCChexgAsnnzZuLt7U3k5eUrvctSFEUIrdFFUdRP6dGjR7hx4wY4HA4AoLCwEM7Ozjhz5gwePXoEf39/DB06FLdv3xZaLzQ0FAoKCrh16xb++usvLFy4EBEREQAAPp+PXr16gcPh4NatW9i6dStmz54ttP7nz5/h6ekJNTU13LlzB4cPH8bFixcxceJEoXCXL19GZmYmrl69ijVr1iAwMBDdunWDmpoabt26hbFjx2LMmDHIyMiQaH/Xr1+P1atXY9WqVXj48CE8PT3RvXt3PH36VCjc//73P0yZMgWJiYnw9PTE3r17MX/+fCxZsgSJiYlYunQp/vzzT4SGhgIA/v77b5w8eRKHDh1CcnIy9u7dC1NTUwBlTRmA/2qZCf7fu3cvOnToACcnp0rpZLPZUFBQELkPJSUlWLRoER48eICwsDCkpaXBz8+PWf7nn3/i8ePHOHfuHBITE7FlyxZoampWm86KsrKyYGtrixkzZlT6ciyQk5ODdu3awcnJCXfv3kV4eDjevHmDfv36CYULDQ0Fh8NBdHT0N69Z8SOaNGkSHB0dMXToUPj7+2P+/PlwdHQEAOzfvx+KiooYP368yHWrqsHB4/GYPNikSRMAwOHDh1FcXIxZs2aJXEdcHyb5+fno0qULLl26hNjYWHh5ecHb2xsvX74EANy9exeTJ0/GwoULkZycjPDwcLRp0wZAWV4ZOHAgRowYgcTERERGRqJXr14im4utX78eCxcuhKGhodD1UNHEiRMRExODAwcO4OHDh+jbty+8vLyErtWCggKsWLEC//zzDxISEr76az71n1evXqFLly5o2rQpHjx4gC1btmDnzp1YvHgxE2b69OmIjo7GyZMnERERgWvXruH+/fti4zx69ChTi/jp06cICwuDvb09AODYsWMwNDTEwoULmdoqopw5cwY+Pj7o0qULYmNjcenSJTRr1kxkWEIIrl27hqSkJOYZB1Sft6KjozF27FhMmTIFcXFx6NixI5YsWVIp/mfPnuHo0aM4duwY029h3759kZ2djXPnzuHevXto0qQJ2rdvjw8fPgAABg8eDENDQ9y5cwf37t3D//73P7DZbADAhAkTUFRUhKtXryI+Ph4rVqyAoqKiyH27d+8e+vXrhwEDBiA+Ph5BQUH4888/hZoIA8Dq1avh4uKC2NhYjB8/HuPGjROq/fkrateuHRwdHZkm3dWdk5rkqarycEV8Ph89evTAhw8fEBUVhYiICKSkpKB///5C4Z4/f46wsDCcPn0ap0+fRlRUFJYvXy7x/v7vf//D8uXLkZiYCAcHByxbtgz//vsvtm7dioSEBEybNg1DhgxBVFQUgOqf2x8+fEB4eDgmTJgg8j2kqn6wlJSUEBISgsePH2P9+vXYsWMH1q5dyyyvi/zfsmVLJg8fPXoUWVlZaNmyZaVw165dw7BhwzBlyhQ8fvwY27ZtQ0hISKVrOSgoCD4+PoiPj8eIESOqOdoU9Ruq75I2iqIoSfj6+hJpaWmioKBAuFwuAUCkpKTIkSNHxK7TtWtXMmPGDOZ/d3d30qpVK6EwTZs2JbNnzyaEEHL+/HkiIyNDXr16xSw/d+6c0Je/7du3EzU1NaGvhWfOnCFSUlLM105fX19iYmJCeDweE8bKyoq0bt2a+b+0tJQoKCiQ/fv3M/MAEFlZWaKgoMBMgu3q6+uTJUuWVEr7+PHjCSH/1ehat26dUJgGDRpUqtm2aNEi4urqSgghZNKkSaRdu3ZCtW/KAyrXMpOTkyOTJ08WGb48UTUUyrtz5w4BwNSG8/b2JsOHDxcZtqbpdHR0FPrCWbFG16JFi0inTp2E4khPTycASHJyMpN+QY3B31liYiIBQOzt7YW+KHt5eREHBwehsKtXrxbKvzk5OYSQsi/6AJj5UlJSQrUOCSFk3LhxRFlZudr0iPr6XZGtrS3ZsGEDIYSQo0ePEmVlZZE1De7du0cAkLS0NJHxVKx5sXbtWqYml0D5fP7ixQsiLS0tdA8hhJD27duTOXPmMOkHUKlmBlUz4mrFzJ07l1hZWQndKzZt2kQUFRUJj8cjeXl5hM1mC9UUycnJIfLy8mJrdK1evZpYWloK1fAor2JtGEIq51NXV1cyePBgsfvj7u5O2Gw2UVBQIGw2m3keREdHE0Iky1v9+/cnXbt2FVo+ePDgSjW62Gw2yc7OZuZdu3aNKCsrk8LCQqF1GzRowNSkUlJSIiEhISLTbm9vT4KCgkQuq1ija9CgQaRjx45CYWbOnElsbGyY/01MTMiQIUOY//l8PtHW1iZbtmwRuY2fTVW1Efv370+sra0lOifV5ana5uELFy4QaWlp8vLlS2Z5QkICAUBu375NCCnLR/Ly8kL31ZkzZ5LmzZtXiltcja6wsDBmXmFhIZGXlyc3btwQWnfkyJFk4MCBhJDqn9u3bt0iAMixY8fEHhMBUe825a1cuZI4Ozsz/9dV/v/48SNTk0ug4vFp3769UI11QgjZvXs30dPTE0q/uNrPFEWVoTW6KIr6abRt2xZxcXG4desWfH19MXz4cPTu3RtAWQ2RRYsWwd7eHurq6lBUVMT58+eZWh0CDg4OQv/r6ekhOzsbAJCYmAgjIyPo6+szy11dXYXCJyYmwtHRUehroZubG/h8vtDXZltbW0hJ/XeL1dHREfp6Ki0tDQ0NDWbbAmvXrkVcXBwzdezYEXl5ecjMzISbm5tQWDc3NyQmJgrNc3FxYf7+/Pkznj9/jpEjR0JRUZGZFi9ejOfPnwMo6xQ3Li4OVlZWmDx5Mi5cuIDqkFp2jnzv3j14e3vD2NgYSkpKcHd3BwDmHI0bNw4HDhxA48aNMWvWLNy4cYNZtzbprMqDBw9w5coVoePSqFEjAGCODQA4Ozt/1XZ+Bbt27YK8vDxSU1OrrYE4YsQIxMXFYdu2bfj8+bNQXlFSUmLydWxsLJYuXYqxY8fi1KlTAMryVW368cnPz0dAQACsra2hqqoKRUVFJCYmMvmqY8eOMDExgbm5OYYOHYq9e/eioKAAAODo6Ij27dvD3t4effv2xY4dO/Dx48cap0EgPj4ePB4PlpaWQnkrKipKKF9xOJxK9yKqbiQmJsLV1VUoL7m5uSE/Px8ZGRlISUlBSUmJUM0XFRUVWFlZiY2zb9+++PLlC8zNzTF69GgcP368xn0KxcXFoX379lWGGTx4MOLi4hAdHY3OnTvjjz/+YGp8SJK3kpOTK9XoEVXDx8TEBFpaWsz/Dx48QH5+PjQ0NITiTk1NZeKePn06Ro0ahQ4dOmD58uVC+Xny5MlYvHgx3NzcEBgYiIcPH4rdx8TERJHPsqdPn4LH4zHzyl8fLBYLurq6lZ6XvyLBfVCScyJJnhKoSR4WvAsZGRkx82xsbKCqqir0zmFqairUP2H59ylJlH9fefbsGQoKCtCxY0eh/f3333+Z/a3uuV3bdxMAOHjwINzc3KCrqwtFRUXMmzdP6P2xrvK/JB48eICFCxcK7aeg/z7BswsQPn4URVVW+155KYqivjMFBQVYWFgAKPvx7ejoiJ07d2LkyJFYuXIl1q9fj3Xr1sHe3h4KCgqYOnVqpY52BVXNBVgs1jfpCFrUdiTZtq6uLrOPAnl5eRJvt3wBXH5+PgBgx44daN68uVA4aWlpAGXNxlJTU3Hu3DlcvHgR/fr1Q4cOHXDkyBGx27C0tERSUpLEaQL+a/IpaE6ppaWFly9fwtPTkzlHnTt3xosXL3D27FlERESgffv2mDBhAlatWlWrdFYlPz8f3t7eWLFiRaVlenp6zN/immH+Lm7cuIG1a9fiwoULWLx4MUaOHImLFy+CxWKhYcOGuH79OkpKSpi8raqqClVVVZEFYlJSUkJ528HBARcuXMCKFSvg7e0NS0tL5ObmIisrS+gcVCcgIAARERFYtWoVLCwsICcnhz59+jD5SklJCffv30dkZCQuXLiA+fPnIygoCHfu3IGqqioiIiJw48YNXLhwARs2bMAff/yBW7duwczMrMbHKz8/H9LS0rh37x5zjQmUb8oiJydHO+f+iRgZGSE5ORkXL15EREQExo8fj5UrVyIqKqrSfV0cOTm5asOoqKgw18ihQ4dgYWGBFi1aoEOHDhLnLUlUvK/l5+dDT08PkZGRlcIKmnsFBQVh0KBBOHPmDM6dO4fAwEAcOHAAPj4+GDVqFDw9PXHmzBlcuHABy5Ytw+rVqzFp0qQapau87/Ws/tEkJibCzMxMonMiSZ4SqIs8XNHXniNR7ytnzpyBgYGBUDjBQDPVPbeLiorAYrFq/H4SExODwYMHY8GCBfD09ISKigoOHDggNADK98z/+fn5WLBgQaVRjwFAVlaW+ft3fz+hqOrQGl0URf2UpKSkMHfuXMybNw9fvnxBdHQ0evTogSFDhsDR0RHm5uY1Hq7Z2toa6enpQn2s3Lx5s1KYBw8e4PPnz8y86OhoSElJVVkj4GsoKytDX18f0dHRQvOjo6NhY2Mjdj0dHR3o6+sjJSUFFhYWQlP5H/HKysro378/duzYgYMHD+Lo0aNMHyBsNlvoKzsADBo0CBcvXkRsbGylbZaUlAgdG4GkpCS8f/8ey5cvR+vWrdGoUSORX361tLTg6+uLPXv2YN26ddi+fbtE6aypJk2aICEhAaamppWODX15LFNQUAA/Pz+MGzcObdu2xc6dO3H79m2mr7KBAwciPz8fmzdvrvU2pKWl8eXLFwBAnz59wOFw8Ndff4kMm5OTI3J+dHQ0/Pz84OPjA3t7e+jq6iItLU0ojIyMDDp06IC//voLDx8+RFpaGi5fvgyg7MeZm5sbFixYgNjYWHA4HBw/frxW++Pk5AQej4fs7OxK+UpXV7dWcVI1Y21tjZiYGKHaHdHR0VBSUoKhoSHMzc3BZrOF+ljLzc2t9nkhJycHb29v/P3334iMjERMTAzi4+MBlNXQq3ifrMjBwQGXLl2SeD8UFRUxZcoUBAQEgBAiUd6ysrKq1HecuL7kymvSpAlev34NGRmZSnEL+kkEyj5yTJs2DRcuXECvXr0QHBzMLDMyMsLYsWNx7NgxzJgxAzt27BC5LWtra5HPMktLy0oFeL+by5cvIz4+Hr1795bonNQ0T1WVh8sTvAulp6cz8x4/foycnJwq3zm+ho2NDbhcLl6+fFlpfwU1y6p7bqurq8PT0xObNm0S+R4i7hly48YNmJiY4I8//oCLiwsaNmyIFy9eVApXF/lfEk2aNEFycnKlfbSwsBBqKUBRVNVojS6Kon5affv2xcyZM7Fp0yY0bNgQR44cwY0bN6CmpoY1a9bgzZs3NXop69ChAywtLeHr64uVK1ciLy8Pf/zxh1CYwYMHIzAwEL6+vggKCsLbt28xadIkDB06FDo6OnW9i4yZM2ciMDAQDRo0QOPGjREcHIy4uDjs3bu3yvUWLFiAyZMnQ0VFBV5eXigqKsLdu3fx8eNHTJ8+HWvWrIGenh6cnJwgJSWFw4cPQ1dXl/libGpqikuXLsHNzQ1cLhdqamqYOnUqzpw5g/bt22PRokVo1aoVlJSUcPfuXaxYsQI7d+5E48aNhdJhbGwMDoeDDRs2YOzYsXj06BEWLVokFGb+/PlwdnaGra0tioqKcPr0aVhbWwNAtemsqQkTJmDHjh0YOHAgZs2aBXV1dTx79gwHDhzAP//889v/4AKAOXPmgBDCdC5samqKVatWISAgAJ07d4arqytmzJiBGTNm4MWLF+jVqxeMjIyQlZWFnTt3gsViCb2UE0Lw+vVrAMCXL18QERGB8+fPY/78+QDKfiisXbsWEydORF5eHoYNGwZTU1NkZGTg33//haKiotAXdoGGDRvi2LFj8Pb2BovFwp9//ilUq+D06dNISUlBmzZtoKamhrNnz4LP58PKygq3bt3CpUuX0KlTJ2hra+PWrVt4+/Ytk+9qytLSEoMHD8awYcOwevVqODk54e3bt7h06RIcHBzQtWvXWsVLiZabm8t0pi7g7++PdevWYdKkSZg4cSKSk5MRGBiI6dOnQ0pKCkpKSvD19cXMmTOhrq4ObW1tBAYGQkpKSmwtu5CQEPB4PDRv3hzy8vLYs2cP5OTkYGJiAqDs2rh69SoGDBgALpcrVDgkEBgYiPbt26NBgwYYMGAASktLcfbs2UoDnpQ3ZswYLFq0CEePHkWfPn2qzVuTJk1CmzZtsGbNGnh7e+Py5cs4d+5ctbUHO3ToAFdXV/Ts2RN//fUXLC0tkZmZyXR2bmtri5kzZ6JPnz4wMzNDRkYG7ty5w3QdMHXqVHTu3BmWlpb4+PEjrly5IvYamjFjBpo2bYpFixahf//+iImJwcaNG7+qwPxnVFRUhNevX4PH4+HNmzcIDw/HsmXL0K1bNwwbNgxSUlJVnhMXF5ca5anq8nB5HTp0gL29PQYPHox169ahtLQU48ePh7u7+zdrLqekpISAgABMmzYNfD4frVq1Qm5uLqKjo6GsrAxfX1+JntubNm2Cm5sbmjVrhoULF8LBwQGlpaWIiIjAli1bKnX3AJQ9Q16+fIkDBw6gadOmOHPmjNDHji9fvtRZ/pfE/Pnz0a1bNxgbG6NPnz6QkpLCgwcP8OjRI6FBNSiKqka99Q5GURRVA+I6b122bBnR0tIiGRkZpEePHkRRUZFoa2uTefPmkWHDhgmtI6pz9B49ehBfX1/m/+TkZNKqVSvC4XCIpaUlCQ8Pr9Rp6cOHD0nbtm2JrKwsUVdXJ6NHj2Y6VBeXVlHbrtiBccXtlMfj8UhQUBAxMDAgbDabODo6knPnzjHLK3a2Xt7evXtJ48aNCYfDIWpqaqRNmzZMZ63bt28njRs3JgoKCkRZWZm0b9+e3L9/n1n35MmTxMLCgsjIyAh1wl1YWEiWLVtG7O3tmePg5uZGQkJCmA7LK+7zvn37iKmpKeFyucTV1ZWcPHmyUgfx1tbWRE5Ojqirq5MePXqQlJQUidJZ8dhV1xk9IYQ8efKE+Pj4EFVVVSInJ0caNWpEpk6dynRiXV1n+r+yyMhIIi0tTa5du1ZpWadOnYQGBjh48CDx8PAgKioqhM1mE0NDQzJo0CBy8+ZNZh1BB+yCicvlEktLS7JkyRJSWloqFH9ERATx9PQkampqRFZWljRq1IgEBASQzMxMJq7yHfempqaStm3bEjk5OWJkZEQ2btwodO6uXbtG3N3diZqaGpGTkyMODg7k4MGDhBBCHj9+TDw9PYmWlhaTJkEn9oTUvDN6QggpLi4m8+fPJ6ampoTNZhM9PT3i4+NDHj58KDL9VO34+voK5SnBNHLkSBIZGUmaNm1KOBwO0dXVJbNnzxYaSCEvL48MGjSIyMvLE11dXbJmzRrSrFkz8r///Y8JU/7+fPz4cdK8eXOirKxMFBQUSIsWLcjFixeZsDExMcTBwYEZKIUQ0ef56NGjzL1YU1OT9OrVi1km7n4zZswYYmtrS3g8XrV5i5Cye6WBgQGRk5MjPXv2JIsXLya6urrM8sDAQOLo6FhpO3l5eWTSpElEX1+fsNlsYmRkRAYPHkxevnxJioqKyIABA4iRkRHhcDhEX1+fTJw4kXz58oUQQsjEiRNJgwYNCJfLJVpaWmTo0KHk3bt3hJDKnXETQsiRI0eIjY0NYbPZxNjYmKxcuVIoLaI69694T/+Zlc+7MjIyREtLi3To0IHs2rVLaBCbqs6JQFV5qiZ5uOIxf/HiBenevTtRUFAgSkpKpG/fvsyAO4SIzkei7o+EiO+MvnyeIKRs0IF169YRKysrwmaziZaWFvH09CRRUVFMmOqe24QQkpmZSSZMmEBMTEwIh8MhBgYGpHv37kIdwFd8Z5g5cybR0NAgioqKpH///mTt2rVMmusy/0vSGT0hhISHh5OWLVsSOTk5oqysTJo1a0a2b98uNv0URVXGIuQreu6jKIqiKIqiqJ/Y58+fYWBggNWrV2PkyJH1nZw6NXr0aCQlJeHatWv1nRSKoiiK+m5o00WKoiiKoijqtxEbG4ukpCQ0a9YMubm5WLhwIQCgR48e9Zyyr7dq1Sp07NgRCgoKOHfuHEJDQ3+7ZoEURVEURQu6KIqiKIqiqN/KqlWrkJycDA6HA2dnZ1y7dk1k31o/m9u3b+Ovv/7Cp0+fYG5ujr///hujRo2q72RRFEVR1HdFmy5SFEVRFEVRFEVRFEVRvwQ6RilFURRFURRFURRFURT1S6AFXRRFURRFURRFURRFUdQvgRZ0URRFURRFURRFURRFUb8EWtBFURRFURRFURRFURRF/RJoQRdFUdQvrqioCEFBQSgqKqrvpFDUN0PzOfU7oPmc+h3QfE5R1Neioy5SFEX94vLy8qCiooLc3FwoKyvXd3Io6pug+Zz6HdB8Tv0OaD6nfnfLly/HnDlzMGXKFKxbt66+k1MjQUFBCAsLQ1xcXL2mg9booiiKoiiKoiiKoiiKqmd37tzBtm3b4ODgUN9JqYTH44HP59d3MiRCC7ooiqIoiqIoiqIoiqLqUX5+PgYPHowdO3ZATU2txuu7uLhg1apVzP89e/YEm81Gfn4+ACAjIwMsFgvPnj0DAHz8+BHDhg2Dmpoa5OXl0blzZzx9+pRZPyQkBKqqqjh58iRsbGzA5XLx8uVLREZGolmzZlBQUICqqirc3Nzw4sULhISEYMGCBXjw4AFYLBZYLBZCQkK+7qDUEi3ooiiKoiiKoiiKoiiKqkNFRUXIy8sTmqrqe27ChAno2rUrOnToUKvtubu7IzIyEgBACMG1a9egqqqK69evAwCioqJgYGAACwsLAICfnx/u3r2LkydPIiYmBoQQdOnSBSUlJUycBQUFWLFiBf755x8kJCRAXV0dPXv2hLu7Ox4+fIiYmBj4+/uDxWKhf//+mDFjBmxtbZGVlYWsrCz079+/VvvytWTqZasURf3WPi4ZV99J+K0UlfIwq60zCtYGgCcjXd/J+W3IePao7yT8VoqLi/G/0YNR/CgSnzic+k7Ob+O+XJv6TsJvpbiYB99xc3HrOQ8cTkF9J+e3EXGH/mT6nkpLWGjdYx6WHmRBhl1c38n5bSz2+3mfnfNCfsx8IpO2DAsWLBCaFxgYiKCgoEphDxw4gPv37+POnTu13p6Hhwd27twJHo+HR48egcPhoH///oiMjISXlxciIyPh7u4OAHj69ClOnjyJ6OhotGzZEgCwd+9eGBkZISwsDH379gUAlJSUYPPmzXB0dAQAfPjwAbm5uejWrRsaNGgAALC2tmbSoKioCBkZGejq6tZ6P+oCrdFFURT1i+PKSON/7ZuCSwu5qF8Yl8PBXP8h4NJCLuoXxuFwMXzCH+BwuPWdFIr6ZmTYXLTp+Sdk2DSfUz+3OXPmIDc3V2iaM2dOpXDp6emYMmUK9u7dC1lZ2Vpvr3Xr1vj06RNiY2MRFRUFd3d3eHh4MLW8oqKi4OHhAQBITEyEjIwMmjdvzqyvoaEBKysrJCYmMvM4HI5Qf2Hq6urw8/ODp6cnvL29sX79emRlZdU6zd8KLeiiKIqiKIqiKIqiKIqqQ1wuF8rKykITl1u5APfevXvIzs5GkyZNICMjAxkZGURFReHvv/+GjIwMeDyeRNtTVVWFo6MjIiMjmUKtNm3aIDY2Fk+ePMHTp0+ZGl2SkpOTA4vFEpoXHByMmJgYtGzZEgcPHoSlpSVu3rxZo3i/NVrQRVEURVEURVEURVEUVQ/at2+P+Ph4xMXFMZOLiwsGDx6MuLg4SEtL3irD3d0dV65cwdWrV+Hh4QF1dXVYW1tjyZIl0NPTg6WlJYCy5oalpaW4desWs+779++RnJwMGxubarfj5OSEOXPm4MaNG7Czs8O+ffsAlNUAk7Rg7luiBV0URVEURVEURVEURVH1QElJCXZ2dkKTgoICNDQ0YGdnV6O4PDw8cP78ecjIyKBRo0bMvL179wrV5mrYsCF69OiB0aNH4/r163jw4AGGDBkCAwMD9Oghvp/Z1NRUzJkzBzExMXjx4gUuXLiAp0+fMv10mZqaIjU1FXFxcXj37l2Vne9/S7Sgi6IoiqIoiqIoiqIo6gfm5+fH9LElTuvWrcHn84UKtTw8PMDj8SqtGxwcDGdnZ3Tr1g2urq4ghODs2bNgs9li45eXl0dSUhJ69+4NS0tL+Pv7Y8KECRgzZgwAoHfv3vDy8kLbtm2hpaWF/fv313p/vwaLEELqZcvUT8XDwwONGzfGunXrAJSV1E6dOhVTp079JttLS0uDmZkZYmNj0bhx42+yDYGQkBBMnToVOTk5YsMEBQUhLCwMcXFx3zQtvws66iL1O6CjLlK/AzrqIvU7oKMuUr8DOupi3avrY+ru7o62bduKHLWREkbv2j84Pz8/hIaGVprv6emJ8PDwekhRmTt37kBBQaHeti9KaGgoNm7ciISEBEhLS6NJkyaYOXMmunXrVt9JA1BWWBgVFSV2ubu7OyIjI2FqaooXL14AKCsxt7Kywpw5c5ghXoOCgphhaqWkpKCvr4/OnTtj+fLlUFdX//Y7Qn13F5Jf4FRCKnILi2CspgS/pjaw0FQVG/5mWhYOPXiKd5+/QFdJHgObWMHJQJtZPnD3OZHrDWpiBW9bcwBA6vtc7ItNRsr7XEixWGhmrIuhzo0gy6aPDerbCL9+G6euRCMnLx8m+roY3qszGpoYigyb/jobh85dQUpGJt5+yIFvTy90dXcVG3fYpWvYd/oiurRpAT+fzsz87YdOIf5JCj7mfQKXw4aVmTEGd+sAAx2tOt8/igKAmKhwXLt4Ap/ycqBnaALvviNhZNpQZNg3Wem4ePoAXr1MwccPb9G1tx9atav8TpOb8x7hYXvw5HEsSoqLoa6liz5DxsPQxIIJk52VgfATe5D69DF4fB509AwxeFQAVNVpXqfq3tMHZ5F87wQKP3+EqpYpnDxGQUPXUmTY5/EX8CIxErnvXwIA1LQbwN5tsFD4jKcxeBZ/HjnZKSgq/IROg1ZDTducWV705RMSbh7A65dxKPj0Dlw5ZRg0aA4714HgcH+s3ysUVVu5ubl4/vw5zpw5U99J+SnQXyw/AS8vLwQHBwvNEzVaw/ekpfVjvRgFBARg48aNWLx4MXr27ImSkhLs2bMHPXr0wPr16zFx4sT6TiKOHTuG4uKyrw3p6elo1qwZLl68CFtbWwBlHfcJLFy4EKNHj0ZeXh5Wr16N/v37w8DAAC1btgQA2Nra4uLFi+DxeEhMTMSIESOQm5uLgwcPfv8do76pmLQs7L6biJHN7WChqYpzSWlYdukO1vZoA2XZyveB5OyP2HD9AQY4WcLJQBs30jKxJvI+lnZxg5GaEgBgS592QuvEvXqL7Tfj0cxYFwDwsaAQSy7egaupLoY3s8GX4lL8ezcRW248xDT3Jt9+p6nfzo3YR/j3RDhG9/FGQxNDnLkagyXbdmP93MlQUaz8I6WouATaGqpo4WiD0BPnq4z72ctXiLhxF8b6OpWWmRnqoVUTe2ipq+JTQQEOh0di8dbd2PTnVEhJ0d4dqLr18F40zhwNQc+B/jA2tcT1K6exa+NizAj8G4pKKpXClxQXQU1DG3ZOrjhzNERknAUF+di2eh7MLG3hN/4PKCgq4/3bLMjJ/3fdvH+bhW1r58HFtR06dO0Prqws3mRlQIb989beoH5cL59cR9zVYLi0GwsNXUskx57C1eML0dl3E2TlK+fztxmPYGzVGhp6VpCW4SDp7jFcPb4QnkPXQ15RAwBQWloELQMbGFu64c7FzZXiKPz8AV8+f4Rjaz8oqxui4NM73Lu0BV/yP8Ct26xvvs8U9T2oqKggIyOjvpPx06BvcT8BLpcLXV1doUlNTQ0A8PTpU7Rp0waysrKwsbFBREQEWCwWwsLCAACRkZFgsVhCzfLi4uLAYrGQlpYGoGx0hYEDB8LAwADy8vKwt7evti2tqakp04wxJCQELBar0lS+SuU///wDa2tryMrKolGjRti8Wfghdfv2bTg5OUFWVhYuLi6IjY2V+PjcvHkTq1evxsqVKxEQEAALCwtmZImpU6di+vTpSE9PZ8KHhITA2NgY8vLy8PHxwfv37yvFuXz5cujo6EBJSQkjR45EYWGh0PLIyEg0a9YMCgoKUFVVhZubG1MLSxx1dXXm/AkKCjU0NJh55WtjKSkpQVdXF5aWlti0aRPk5ORw6tQpZrmMjAx0dXVhYGCADh06oG/fvoiIiJDoeBFCEBQUBGNjY3C5XOjr62Py5MnM8qKiIgQEBMDAwAAKCgpo3rw5IiMjAQCFhYWwtbWFv78/E/758+dQUlLCrl27JNo+VTNnElPRrqERPCwMYaiqiFHNbcGVlsaVZ6IfdOFJaXDU14S3rTkMVRXRr7ElTNVVcD75v/ypKscVmu6mv4GNjjp0lOQBAPcysiEtxcKIZrbQV1ZEA01VjGxui9sv3+D1p8/fZb+p38vpyBi0b+GMts2dYKirBf++3uBy2Lhy677I8BbGBhja3RNuTezBlhE/ElFhUTE27DmKMf26Q1FOrtLyji1dYGNhCi11VZgb6mNAl/Z4n5OL7A85dbVrFMW4fvkUmrp1gItrO2jrGcJn4BhwOBzcjbksMryhiQW69PKFo0sryMiI7i/lakQYVNQ00HfoRBiZNoS6pg4aWjeGhpYeE+bCqf2wsm2Czj7DoG9kBg0tPdg4NBVZuEZRX+vJ/ZMwt+sIM9v2UNYwgkv7cZCW4SI14ZLI8C06T4eFY2eoaZtDWd0QTTtMBCF8ZL98yIQxtfaAbfN+0DF2FBmHiqYJ3LrNgoF5Uyip6kHHyB72LQcjM/UO+Pz6H/2NoqjvjxZ0/cT4fD569eoFDoeDW7duYevWrZg9e3aN4yksLISzszPOnDmDR48ewd/fH0OHDsXt27clWr9///7Iyspipv3790NGRgZubm4AgL1792L+/PlYsmQJEhMTsXTpUvz5559Mk8z8/Hx069YNNjY2uHfvHoKCghAQECBx+vfv3w9FRUWmA7zyZsyYgZKSEhw9ehQAcOvWLYwcORITJ05EXFwc2rZti8WLFwutc+jQIQQFBWHp0qW4e/cu9PT0hArmSktL0bNnT7i7u+Phw4eIiYmBv78/WCyWxGmuCRkZGbDZbKY2WEVpaWk4f/68UI2wqhw9ehRr167Ftm3b8PTpU4SFhcHe3p5ZPnHiRMTExODAgQN4+PAh+vbtCy8vLzx9+hSysrLYu3cvQkNDceLECfB4PAwZMgQdO3bEiBEj6mR/qf+U8vhIfZ8HOz1NZh6LxYKdngaevs0Ruc6Ttzmw09MQmuegp4Gn70SHz/1ShLjMt2hrYfTfdvl8yEhJCeVpzv8XJiRnf6zl3lCUaKWlPKRkZMLe8r9mKCwWC/aWDfAk7eu+XP5z9Aya2FjCwapBtWELi4oReTsW2hpq0FSlBQBU3SotLcWrlymwaOTAzGOxWLBo5ICXqU9qHW/iw7swMLbA3n9WYfHsEdiwLAC3o//78EUIQdKj+9DQ0sOujYuwePYIbF75PyQ8kOwdj6Jqgs8rxYc3z6FbrkCKxWJBx9gB77OSJYqjtLQIfD4PHFmlr0pLSXEB2Bx5SEmJ/xhCUdSvizZd/AmcPn0aioqKQvPmzp0LFxcXJCUl4fz589DX1wcALF26FJ07dxYVjVgGBgZCBUuTJk3C+fPncejQITRr1qza9eXk5CD3/1/Knz9/jgkTJmDp0qXo2LEjACAwMBCrV69Gr169AABmZmZ4/Pgxtm3bBl9fX+zbtw98Ph87d+6ErKwsbG1tkZGRgXHjJOuw/MmTJ2jQoIHIgh59fX0oKyvjyZOyl8j169fDy8sLs2aVVWO2tLTEjRs3hPo7W7duHUaOHImRI0cCABYvXoyLFy8ytbry8vKQm5uLbt26oUGDsh9PguFU61pxcTFWr16N3NxctGv3X3Oz+Ph4KCoqgsfjMelas2aNRHG+fPkSurq66NChA9hsNoyNjZnz/PLlSwQHB+Ply5dMngoICEB4eDiCg4OxdOlSNG7cGIsXL8aoUaMwYMAAvHjxAqdPnxa7vaKiokrDyhaV8sCtohYGVeZTUTH4hEBFVjhvq8hykZknumZVXmERVCs0aVSV4yLni+ihfaNSXkFWRgZNjf5r1mWnq4E995JwKiEFnRuZorCUh/33y15QP4qJh6JqK+9zAfh8PlSVhJ9zqooKyHzzrtbxRt+PR2pGFpZNG11luPPRt7H3VAQKi4qhr62JeWOHQYben6g6VvA5D3w+v1ItKkUlFbx986rW8X549wa3rp1Hq3bd0NazN9JfPMXpw8GQlpaBc4u2yM/LQXFRIaIiwtDJeyC8egzBk8Q47N2xEqOmBMG8oe3X7hpFMYq+5IEQPrgVmijKyqvi00fJ8vnD6/9CTkEdOsYO1QeuIh0Jtw6jgX2nWsdBUdTPjdbo+gm0bdsWcXFxQtPYsWORmJgIIyMjpkACAFxdxXfGKw6Px8OiRYtgb28PdXV1KCoq4vz583j58mWN4hEU/nTt2hUzZ84EAHz+/BnPnz/HyJEjoaioyEyLFy/G8+fPAQCJiYlwcHCArKxsrfdD0sFDExMT0bx5c6F5FbdVXRh1dXX4+fnB09MT3t7eWL9+PbKysmqU3urMnj0bioqKkJeXx4oVK7B8+XJ07dqVWW5lZYW4uDjcuXMHs2fPhqenJyZNmiRR3H379sWXL19gbm6O0aNH4/jx4ygtLQVQVoDG4/FgaWkpdL6ioqKY8wWU1ZSztLTExo0bsWvXLmhoaIjbHJYtWwYVFRWhaW2U6OZI1PcX+SwDrcz0mRpbAGCoqoRxLR1w5nEqfPdfwLgjl6ClKA8VWQ6+Tb1Fiqpb73NyEXz8HCYP6Q1OFUNkA0CrJg5YMWMsgiYMh56WBtaGHkJxScl3SilFfR1CCPSNzODZYzD0jczQvFUnNHXrgFvXLpQtR9n7kY29C1q16wZ9IzN4dPJBIztn3P7/MBT1o0i8cwwvn1yHm/dsSMvUrg+5kqICXDuxGCoaRrBt3r+OU0hR1M+C1uj6CSgoKMDCwqL6gCIIOtMtXxBUUuEFfuXKlVi/fj3WrVsHe3t7KCgoYOrUqWKbyonC4/HQv39/KCsrY/v27cz8/Px8AMCOHTsqFR5JS9fNF3NLS0tcv34dxcXFlWp1ZWZmIi8vD5aWokd6qa3g4GBMnjwZ4eHhOHjwIObNm4eIiAi0aNGiTuKfOXMm/Pz8oKioCB0dnUrNIjkcDpMnBIVgCxYswKJFi6qN28jICMnJybh48SIiIiIwfvx4rFy5ElFRUcjPz4e0tDTu3btX6fyUr1WYnZ2NJ0+eQFpaGk+fPoWXl5fY7c2ZMwfTp08XmlewVvKmqb8zJS4HUiwWcguFr8VcEbW2BJRlucgpFK51lfOlCKpylcMnvvmArLzPmNK6caVlbmb6cDPTR+6XIqb23dnEVOgoytdybyhKNGUFeUhJSSHnU77Q/Jz8z1BVVhSzVtWep2ciL/8zZq/eyszj8/lITHmB8Ou3sW/ln8zzUUFOFgpystDT0oClqRGG/7EMd+KT4NbEXlz0FFVj8grKkJKSQv6nXKH5+Z9yoaisVut4lZRVoa0rPDqplo4+HsXG/LddaWlo6xkJh9E1wIvnSbXeLkWJwpVTBoslhaIC4XxeWJADWfmq83nSvRNIunsM7j6BUNU0rdX2S4q/4GrYQsiw5eDWbTakpOlPXYr6XdEaXT8xa2trpKenC9UmunnzplAYQafn5cPExcUJhYmOjkaPHj0wZMgQODo6wtzcnGnqJ6lp06YhPj4eYWFhQjWzdHR0oK+vj5SUFFhYWAhNZmZmzH48fPhQqMP3ivtRlQEDBiA/Px/btm2rtGzVqlVgs9no3bs3s61bt24Jham4LUnCAICTkxPmzJmDGzduwM7ODvv27ZM4zdXR1NSEhYUFdHV1Jer7a968eVi1ahUyMzMlil9OTg7e3t74+++/ERkZiZiYGMTHx8PJyQk8Hg/Z2dmVzpeuri6z/ogRI2Bvb4/Q0FDMnj0biYmJYrfF5XKhrKwsNNFmi5KRkZaCmYYyErL+GzCBEIKE1+/RUEtV5DqWWqp4lCU8wEL86/doqFk5fOTzDJhpKMNEXVlsGlTkuJBlyyDmRRbY0tKw19cUG5aiakNGRhrmhvp49DSVmUcIwaMnKbA0NaxiTfHsG5pj1azx+CtgLDM1MDZAa2cH/BUwVuyIigQEhAAl/1/LlaLqioyMDAyMzfE8OZ6ZRwjBs+R4GJvV/mOcibkV3mULP/vfZb+GqroWs11D4waVw7zJgqoavZ9TdUtKWgbqOg3wplxH8oQQZKfHQ0PPSux6SXeP4/GtQ2jT80+o6zas1bZLigoQdSwIUlIyaNV9bq1rhFEU9Wugxdw/gaKiIrx+/VponoyMDDp06ABLS0v4+vpi5cqVyMvLwx9//CEUzsLCAkZGRggKCsKSJUvw5MkTrF69WihMw4YNceTIEdy4cQNqampYs2YN3rx5AxsbG4nSFxwcjM2bN+P48eNgsVhMWgXN3hYsWIDJkydDRUUFXl5eKCoqwt27d/Hx40dMnz4dgwYNwh9//IHRo0djzpw5SEtLw6pVqyQ+Pq6urpgyZQpmzpyJ4uJi9OzZEyUlJdizZw9TU83IqOxL5uTJk+Hm5oZVq1ahR48eOH/+vFD/XAAwZcoU+Pn5wcXFBW5ubti7dy8SEhJgbl7WUXJqaiq2b9+O7t27Q19fH8nJyXj69CmGDRsmcZrrmqurKxwcHLB06VJs3LixyrAhISHg8Xho3rw55OXlsWfPHsjJycHExAQaGhoYPHgwhg0bhtWrV8PJyQlv377FpUuX4ODggK5du2LTpk2IiYnBw4cPYWRkhDNnzmDw4MG4efOmxB3iU5Lram2GLTcewkxDGQ00VBGelIbCUh48GpQVAGyKfgB1OVkMbFL2AunVyBSLIm7h9ONUOBloISYtC6nvczG6uZ1QvAXFJbj54jWGODcSud3zSS9gqaUKLlsaj7LeY8+9JAxqYgUFTtXNwCiqNrp5uGLTvuMwN9SDhbEhzl69icLiYng0cwIAbNx7DGoqShjcrazvx9JSHtJfZ5f9zePhQ24eUjOyICfLha6mOuRkuTDW0xHaBpfNhqK8HDP/zbsPuBGXAAerBlBRlMf7nDyEXboODocNJ5u6rQVMUQDQqp03Dv+7EQbG5jAyaYjoyDMoLiqCc4u2AIBDoX9DWVUdXj2GACjrwD47q2zUaB6vFHk575GZngqurCwzqmKr9t7YsvoPXAk/CgfnlkhPe4bb0RHwGfjfAD1tOvTA/l1rYWphjQaWdkhOiEPSo7sYPXXBdz4C1O/Askl33D7/N9R0GkBDpyGexJ1GaUkhzGzL+pq9dX4d5BQ04NBqKICy5oqPbu6Hq9d0yCtr48vnskFv2Bw5yLDLPp4XffmEgk9vmWWfcsoKbmUV1CCnoFZWyHV8AXilRWjuNRUlxQUoKS4oCyOnApaYjxsURf26aEHXTyA8PBx6enpC86ysrJCUlITjx49j5MiRaNasGUxNTfH3338LNSNjs9nYv38/xo0bBwcHBzRt2hSLFy9G3759mTDz5s1DSkoKPD09IS8vD39/f/Ts2RO5ucLVjsWJiooCj8dD9+7dheYHBgYiKCgIo0aNgry8PFauXImZM2dCQUEB9vb2mDp1KoCyArFTp05h7NixcHJygo2NDVasWMHUwpLEunXr4ODggM2bN2PevHmQlpZGkyZNEBYWBm9vbyZcixYtsGPHDgQGBmL+/Pno0KED5s2bJ9Tkr3///nj+/DlmzZqFwsJC9O7dG+PGjcP58+cBAPLy8khKSkJoaCjev38PPT09TJgwQeSoj9/TtGnT4Ofnh9mzZzMFe6Koqqpi+fLlmD59Ong8Huzt7XHq1Cmmn63g4GAsXrwYM2bMwKtXr6CpqYkWLVqgW7duSEpKwsyZM7Fz505mG5s3b4aDgwP+/PNPrFix4rvs6+/E1VQPeYXFOPzgKXK/FMFEXRn/a9cUKv/fFPH950JIlav1Z6Wtholujjj04CkOxiZDV1kB0z2awEhNePSiG2lZIISgpanwvUXg2fscHHn4FIUlpdBXUcToFnZobW7w7XaU+q21dLJDbv5nHAq/gpxP+TDV18Mf/kOZDurffcwVqt36Me+TULPEU1du4NSVG7BpYIqgicMl2iabzUZiyguciYpBQWEhVBQVYW1ujMWTR0JFUaFud5CiADg4uyH/Ux4unj6IT3k50DcyxfAJf0BJWRUAkPPhndAP8k+5H7Bh+Uzm/2uXTuHapVMws7CB/7SFAABDEwsMHT0L4Sf34vK5I1DT0Ea3Pn5wataGWc+2cXP0HDAakReO4/ThYGhq62Pw6JkwbfBtBtKhfm/Glq1QVJCLRzEHUFjwEWpaZmjT80/IyqsCAAry3oHF+i+fP48/Dz6vFNFn/hKKx7Z5f9i5DgAAZKbewe0LG5hlMWdXC4X5mJ2C96/LWqOcDRkvFE+3EdugoKxd5/tJUdSPjUUk7cWb+mmwWCwcP34cPXv2rO+kUJRIH5dINqImRf3MZDx71HcSKOqbuy/XpvpAFPWTi7hD6wZQv77Ffj9vy4x5IZL3Lf09/czH9GdH63FSFEVRFEVRFEVRFEVRvwRa0EX98MaOHcv091VxGjt2bH0nT4itra3YtO7du/e7pGHv3r1i02Bra/td0kBRFEVRFEVRFEVR9YHWw/0F/WqtURcuXIiAgACRy5SVxY8WVx/Onj2LkpISkct0dHREzq9r3bt3R/PmzUUuY7NpR+IURVEURVEURVHUr4sWdFE/PG1tbWhr/xydSJqYmNR3EqCkpAQlJaXqA1IURVEURVEURVHUL4Y2XaQoiqIoiqIoiqIoiqJ+CbSgi6IoiqIoiqIoiqIoivol0IIuiqIoiqIoiqIoiqIo6pdA++iiKIqiKIqiaiXiDn2VpH59HZuW1ncSKOo74NR3AiiqztAaXRRFURRFURRFURRFUdQvgRZ0URRFURRFURRFURRFUb8EWtBFURRFURRFURRFURRF/RJoQRdFURRFURRFURRFURT1S6AFXRRFURRFURRFURRFUdQvgRZ0URRFURRFURRFURRFUb8EWtBFMTw8PDB16lTmf1NTU6xbt+6bbS8tLQ0sFgtxcXHfbBsCISEhUFVVrTJMUFAQGjdu/M3TQlEURVEURVEURVHUtyFT3wmgAD8/P4SGhlaa7+npifDw8HpIUZk7d+5AQUGh3rYvSmhoKDZu3IiEhARIS0ujSZMmmDlzJrp161bfSQNQVlgYFRUldrm7uzsiIyNhamqKFy9eAADk5eVhZWWFOXPmoG/fvgDKCt0WLFgAAJCSkoK+vj46d+6M5cuXQ11d/dvvCPVDuZD8AqcSUpFbWARjNSX4NbWBhaaqyLAZOZ9w+MFTpLzPw7vPXzDUpRG6WJsJhYlIfoGLT9ORnV8AADBUUUJvBws0NtACALzN/4LJxyNFxj+ldWO0MNWrs32jKIHw67dx6ko0cvLyYaKvi+G9OqOhiaHIsOmvs3Ho3BWkZGTi7Ycc+Pb0Qld3V6Ewh8Kv4Mj5SKF5+tqaWDdnEvP/x7xP2HsqAg+Sn6OwqAh6Wpro3bENmjva1Pn+URQAPH1wFsn3TqDw80eoapnCyWMUNHQtRYZ9Hn8BLxIjkfv+JQBATbsB7N0GC4W/feFvpD6+IrSerokT3H3mM//nfXyFh9f+xbvMRPD5pVDRNIGd6yDoGNl/gz2kKCAmKhzXLp7Ap7wc6BmawLvvSBiZNhQZ9k1WOi6ePoBXL1Pw8cNbdO3th1bthN/riwq/IOL0ASTE3cLn/DzoGZrCu+8IGJpYMGEO796I+zcjhdaztG6M4RPn1fn+URT146MFXT8ILy8vBAcHC83jcrn1lJoyWlpa9br9igICArBx40YsXrwYPXv2RElJCfbs2YMePXpg/fr1mDhxYn0nEceOHUNxcTEAID09Hc2aNcPFixdha2sLAOBwOEzYhQsXYvTo0cjLy8Pq1avRv39/GBgYoGXLlgAAW1tbXLx4ETweD4mJiRgxYgRyc3Nx8ODBWqWtpKQEbDb7K/eQ+t5i0rKw+24iRja3g4WmKs4lpWHZpTtY26MNlGUr3yOKSnnQUpRHc2Nd7L6XJDJOdQVZDHCyhK6SAggIrj3PxOrIe1jW1Q2GqkrQkJfFlj7thNa59DQdpxJSmMIwiqpLN2If4d8T4RjdxxsNTQxx5moMlmzbjfVzJ0NFsfIHl6LiEmhrqKKFow1CT5wXG6+hrjb+HDeM+V9aSrgi+8a9x1FQWIhZIwdCWUEe1+/HY03oISyfPgZmhrRAl6pbL59cR9zVYLi0GwsNXUskx57C1eML0dl3E2TlVSqFf5vxCMZWraGhZwVpGQ6S7h7D1eML4Tl0PeQVNZhweqZN0LTjf+9A0tLCz/rrJ5ZAUVUfHn0WQlqagyexp3D95BJ08dsCOQW1b7fD1G/p4b1onDkagp4D/WFsaonrV05j18bFmBH4NxSVKufzkuIiqGlow87JFWeOhoiM8+jezXiTmY6+vpOgoqqO2NtXsfPvhZj651qoqP53LVjaOKHPkPHM/9L0vZeiflu06eIPgsvlQldXV2hSUyt7+Xj69CnatGkDWVlZ2NjYICIiAiwWC2FhYQCAyMhIsFgs5OTkMPHFxcWBxWIhLS0NAPD+/XsMHDgQBgYGkJeXh729Pfbv319lmso3XQwJCQGLxao0BQUFMeH/+ecfWFtbQ1ZWFo0aNcLmzZuF4rt9+zacnJwgKysLFxcXxMbGSnx8bt68idWrV2PlypUICAiAhYUFrK2tsWTJEkydOhXTp09Heno6Ez4kJATGxsaQl5eHj48P3r9/XynO5cuXQ0dHB0pKShg5ciQKCwuFlkdGRqJZs2ZQUFCAqqoq3NzcmFpY4qirqzPnT1BQqKGhwcwrXxtLSUkJurq6sLS0xKZNmyAnJ4dTp04xy2VkZKCrqwsDAwN06NABffv2RUREhMTHjMViYcuWLejevTsUFBSwZMkS8Hg8jBw5EmZmZpCTk4OVlRXWr19fad1du3bB1tYWXC4Xenp6QoWIOTk5GDVqFLS0tKCsrIx27drhwYMHEqeLqpkzialo19AIHhaGMFRVxKjmtuBKS+PKswyR4RtoqmKIcyO0NNOHjJToW7yzoQ6cDLShp6wAfWVF9HeyBFdGBk/f5QAApKRYUJXjCk130l/D1VQPsmz6fYSqe6cjY9C+hTPaNneCoa4W/Pt6g8th48qt+yLDWxgbYGh3T7g1sQdbRlpsvNLSUlBTVmIm5QqFZk9fpKNz6+ZoaGIIHU119O7kDgV5OaRkZNbp/lEUADy5fxLmdh1hZtseyhpGcGk/DtIyXKQmXBIZvkXn6bBw7Aw1bXMoqxuiaYeJIISP7JcPhcJJSclATkGNmTiyisyyoi95+JSTBeumvaCqaQolNX04tBqG0pIipqYYRdWl65dPoalbB7i4toO2niF8Bo4Bh8PB3ZjLIsMbmligSy9fOLq0goxM5YKpkpJiJMTdQueeQ2He0BYaWnro0LU/1LV0cOuq8IcOaRkZKKmoMZO8vGKl+CiK+j3Qgq4fHJ/PR69evcDhcHDr1i1s3boVs2fPrnE8hYWFcHZ2xpkzZ/Do0SP4+/tj6NChuH37tkTr9+/fH1lZWcy0f/9+yMjIwM3NDQCwd+9ezJ8/H0uWLEFiYiKWLl2KP//8k2mSmZ+fj27dusHGxgb37t1DUFAQAgICJE7//v37oaioiDFjxlRaNmPGDJSUlODo0aMAgFu3bmHkyJGYOHEi4uLi0LZtWyxevFhonUOHDiEoKAhLly7F3bt3oaenJ1QwV1paip49e8Ld3R0PHz5ETEwM/P39wWKxJE5zTcjIyIDNZjO1wSpKS0vD+fPnhWqESSIoKAg+Pj6Ij4/HiBEjwOfzYWhoiMOHD+Px48eYP38+5s6di0OHDjHrbNmyBRMmTIC/vz/i4+Nx8uRJWFj8VzW8b9++yM7Oxrlz53Dv3j00adIE7du3x4cPH2q385RYpTw+Ut/nwU5Pk5nHYrFgp6eBp29z6mQbfD7BjdRMFJWWoqGm6C/7Ke9z8eLDJ3g0EN2MjKK+RmkpDykZmbC3NGfmsVgs2Fs2wJM00QW6knr99j3GBK3CxMXr8PfuI3j3MUdoeUMTI9yIfYRPnwtACEH0/XiUlJTA1sJMdIQUVUt8Xik+vHkOXWNHZh6LxYKOsQPeZyVLFEdpaRH4fB44skpC89++SsCJbX44GzoBdy9vRdGXT8wyjqwSlNT0kZZ4BaUlheDzeXgefwGycipQ125QNztHUf+vtLQUr16mwKKRAzOPxWLBopEDXqY+qVWcfB4PfD4fMhVqZ7HZHKQ9F665nvr0MRbPHoHVCyYj7MB2FHz+BIqifk/00/wP4vTp01BUFP7qMHfuXLi4uCApKQnnz5+Hvr4+AGDp0qXo3LlzjeI3MDAQKliaNGkSzp8/j0OHDqFZs2bVri8nJwc5OTkAwPPnzzFhwgQsXboUHTt2BAAEBgZi9erV6NWrFwDAzMwMjx8/xrZt2+Dr64t9+/aBz+dj586dkJWVha2tLTIyMjBu3DiJ0v/kyRM0aNBAZEGPvr4+lJWV8eRJ2QN0/fr18PLywqxZswAAlpaWuHHjhlB/Z+vWrcPIkSMxcuRIAMDixYtx8eJFplZXXl4ecnNz0a1bNzRoUPYiaG1tLVFaa6q4uBirV69Gbm4u2rX7r7lYfHw8FBUVwePxmHStWbOmRnEPGjQIw4cPF5on6PsLKDtPMTExOHToEPr16weg7FjMmDEDU6ZMYcI1bdoUAHD9+nXcvn0b2dnZTNPaVatWISwsDEeOHIG/v3+lNBQVFaGoqEh4XikP3CpqYVBlPhUVg08IVGSF872KLBeZeZ+/Ku6XH/MwP/wmSnh8yLKlMd2jCQxVRX/5jHyWAQMVBVhp0yYuVN3L+1wAPp8PVSXh/KeqqIDMN+9qHW9DE0OMH9gT+tpa+Jj3CUfOR2L+hl1YPWsC5P6/2e90v35YG3oII+etgLS0NLhsNgJGDICuJu0LkapbRV/yQAgf3ApNFGXlVfHp4yuJ4nh4/V/IKahDx/i/QgRdEycYNGgBBRVtfM59g4fRe3A1bCE69F8BlpQUWCwWPHotQPTp5Ti2eRAAFmTlVdHGZ75QzS+KqgsFn/PA5/MrNVFUVFLB2zeS5fOKuLJyMDazxOVzR6CtZwhFJVU8uHsNL1OfQEPrvybmltaNYevYHGoa2vjw7g0unNyH4E2LMS5gGaTE1HCnKOrXRQu6fhBt27bFli1bhOapq6tj9+7dMDIyYgq5AMDV1bXi6tXi8XhYunQpDh06hFevXqG4uBhFRUWQl5evUTyCwp+uXbti5syZAIDPnz/j+fPnGDlyJEaPHs2ELS0thYpK2YMuMTERDg4OkJWVrfV+EEIkCpeYmAgfHx+hea6urkIFXYmJiRg7dmylMFeulHXoqq6uDj8/P3h6eqJjx47o0KED+vXrBz29uuuzZfbs2Zg3bx4KCwuhqKiI5cuXo2vXrsxyKysrnDx5EoWFhdizZw/i4uIwadKkKmKszMXFpdK8TZs2YdeuXXj58iW+fPmC4uJiZrTJ7OxsZGZmon379iLje/DgAfLz86GhoSE0/8uXL3j+/LnIdZYtWyZUuAYAs9o643/tm9ZoX6i6pa+siOXd3FBQXIpbL15jS/RDzO/UolJhV3EpD9FpmfCxtxATE0X9mJys/+v42ERfBw1NDDFh4VrEPEhAu+ZNAAAHz11GwZci/DnOF0oK8rjzKAlrQw9jwcQRMNHXqa+kU1QliXeO4eWT62jbZxGkZf77+GFs1Zr5W1XTFCqaJjgTPA7ZGY+gY+wAQgjuX9kBrpwK2vVdAmkZDlIeXcT1k0vRYcBfkFOkhbrUj6+f7yQc3bMFy+b6lw3SZGQGR5dWePUyhQnj6NKK+VvPwAS6BsZYFTgRKU8eCdUwoyjq90ALun4QCgoKQs3DakLwlaJ8QVBJSYlQmJUrV2L9+vVYt24d7O3toaCggKlTp4ptKicKj8dD//79oaysjO3btzPz8/PzAQA7duxA8+bNhdaRlq6bWjuWlpa4fv06iouLK9XqyszMRF5eHiwtRY9aVFvBwcGYPHkywsPDcfDgQcybNw8RERFo0aJFncQ/c+ZM+Pn5QVFRETo6OpWaRXI4HCZPCArBFixYgEWLFkm8jYqjZh44cAABAQFYvXo1XF1doaSkhJUrV+LWrVsAwNTaEyc/Px96enqIjIystExVVVXkOnPmzMH06dOF5hWslbzZ6u9MicuBFIuF3ELh6zS3sAiqIjqirwkZaSnoKpXlD3MNFaS8z0V4UhpGtbATCnfr5WsUl/LQxlxfVDQU9dWUFeQhJSWFnE/5QvNz8j9DVbnuapwoyMlCT1sDWW/L+mx8/e4Dwq/dwurZE2Ckqw0AMDXQRWLKC5y/fhv+/bzrbNsUxZVTBoslhaKCXKH5hQU5kJWvurZs0r0TSLp7DO4+gVDVNK0yrKKKLrhyysjPzYIOHJCdHo/M1DvwGbsHbG7Zx03ndg3w+mUc0hIjYd2011ftF0WVJ6+gDCkpKeR/Es7n+Z9yoahc+1rhGlp68J+2EEVFhSgqLICyijr27VwNdU3xHyQ0NHWhoKiE9+9ewwK0oIuifje0HucPztraGunp6cjKymLm3bx5UyiMoNPz8mHi4uKEwkRHR6NHjx4YMmQIHB0dYW5uzjT1k9S0adMQHx+PsLAwoZpZOjo60NfXR0pKCiwsLIQmMzMzZj8ePnwo1OF7xf2oyoABA5Cfn49t27ZVWrZq1Sqw2Wz07t2b2Zag4EbctiQJAwBOTk6YM2cObty4ATs7O+zbt0/iNFdHU1MTFhYW0NXVlajvr3nz5mHVqlXIzKx9J8nR0dFo2bIlxo8fDycnJ1hYWAjVxFJSUoKpqSkuXRLdMW6TJk3w+vVryMjIVDrXmpqaItfhcrlQVlYWmmizRcnISEvBTEMZCVn/DaZACEHC6/doqKVap9siICjh8yvNv/IsA00MtUWO8EhRdUFGRhrmhvp49DSVmUcIwaMnKbA0rbt+4QqLivH63QeoK5f1b1RUXPZBiAXh+68UiwUCyWoQU5SkpKRloK7TAG/KdSRPCEF2ejw09KzErpd09zge3zqENj3/hLpuQ7HhBAo+vUNx4Sem8Ky0tKzrAFaFplsslhQIqXzPp6ivISMjAwNjczxPjmfmEULwLDkexmZf/0Gay5WFsoo6Cgry8TTxIawdKrdcEMj5+A4Fn/Oh9BUFbBRF/bxoja4fRFFREV6/fi00T0ZGBh06dIClpSV8fX2xcuVK5OXl4Y8//hAKZ2FhASMjIwQFBWHJkiV48uQJVq9eLRSmYcOGOHLkCG7cuAE1NTWsWbMGb968gY2NjUTpCw4OxubNm3H8+HGwWCwmrYqKilBUVMSCBQswefJkqKio4P/Yu/P4mK7+geOfyb5ONtllI4JEQiKW2CkVagmqqLb0iVqeKqqWaoMooh48xaNapQ3V2FpFLU2KovY9sWUTiTW2yC6ZLJPfH/kZnSZIFWn5vl+veb3Mud977jkz12Tme885Nzg4GJVKxbFjx8jMzGTs2LG8/vrrfPzxx7zzzjtMmjSJtLQ05s6dW+XXJygoiNGjRzN+/HiKiooICQmhuLiY7777TjNSzcXFBYBRo0bRsmVL5s6dS8+ePYmJidGatggwevRoBg8eTGBgIC1btiQqKoqzZ89Sq1b5Ysipqal89dVX9OjRAycnJxITE0lOTuatt96q0LZnJSgoCD8/PyIiIli0aNFj1VGnTh2+/fZbYmJi8PDwYOXKlRw9elSTkITyBeyHDx+OnZ0dXbp0ITc3l/379/Pee+/RsWNHgoKCCAkJ4T//+Q9eXl5cu3aNrVu30qtXr0qnSoq/5pX6Hnxx4BQeNkpq21gSnZBGYUmpZmH4z/fHYW1sxICA8h9KJaVqrmSXj4wpVau5c1dF2p0cjPR1NSO4Vp9IpKGzLbamxhQUF7M/NZ1zN+4w6Q/TSa/n5pNw8w4T2sv7Kp6ubu2C+HzVBmrVdMTTtSbbfjtEYVER7Zr6A7Ao6kesLMwZ2K18XciSklIuX79Z/u/SUu5k55B6JR1jI0PN+lorf4qhsXddbK0tuZOdw7ro3egoFLQI8AXA2a4GDjWsWfr9Zt7o8XL51MXT8ZxOusDEIa9Xw6sgnndeAT04ErMQK/va2NjXISl2CyXFhXj4lK/PeThmPsamNvi1ehMon6545tBqgoLHYqK0oyA/EwB9A2P09I0oKS7kzME1uNQJwsjUirys68TtW4GZhQMObuX/d2o41sXA0IzDMQvxadYXHT0DLpzeTn7OTZw8GlfPCyGea606dOf7bxfh7FoLF7c67N+9lSKVisbN2wOwbsVClJbWBPd8Ayhf6uRmevmd00tLS8jJyuDa5VQMjYw0a3AlnTtJWRnY2juRcfs6P/+4Elt7Jxo3L/+/o1IVsnPrWhr4B2GutCTj1nV+3rgSG1sHvOo3evYvghCi2kmi628iOjq6wvpPdevWJSEhgQ0bNhAaGkrTpk1xd3dn4cKFBAcHa+L09fVZvXo1I0aMwM/PjyZNmjBjxgz69u2riQkLC+PChQt07twZExMThg4dSkhICNnZ2kOLH2TPnj2UlpbSo0cPrfKpU6cSHh7OkCFDMDExYc6cOYwfPx5TU1N8fX0ZM2YMUJ4Q27x5M8OHD8ff3x9vb29mz56tGYVVFfPnz8fPz4/FixcTFhaGrq4uAQEBbNy4ke7d708xad68OUuXLmXq1KlMmTKFjh07EhYWpjXlr1+/fqSkpDBhwgQKCwvp06cPI0aMICam/DbFJiYmJCQksGLFCjIyMnB0dOTdd9+t9K6Pz9L777/P4MGDmThxoiax92cMGzaMkydP0q9fPxQKBQMGDODf//43P//8syZm0KBBFBYW8tlnnzFu3Dhq1KjBq6++CpTfOWfbtm18/PHHvP3229y6dQsHBwfatGmDvb2sZ/M0BLk7klNYxPdxyWQXqHCzVvJhhyZYGJePsMrIL0TndyMCMwtUTNq6X/N867lUtp5Lpb69NVNeLp9anKMq4ov9p8gqKMTEQB9XS3MmvdQEX0ftUXm7z1/BytiIhk6Vj9YT4klp4d+A7Lx81kXvIis3D3cnRz4e+qZmgfrbmdlaI18zc3KZOO9LzfPNuw6wedcBvGu7Ez6y/AYcGVk5LPjuB3Lz76I0NaVeLVdmjnkHC7PyhK+eni6Thr7Bqi07mL1sFaqiIuxrWPPu670I8H6yU+GFAHD1aoXqbjZnDq6h8G4mVrYetAmZjJGJJQB3c26jUNwfeZVyOgZ1aQn7t/5Hqx6fZv1oENQfhUKH7IyLXIzfTVFR/v8vVN8Q3xavo6tXfoc6Q2MlbUImc/rgKnavn4paXYLS2oVW3SdhaSt3FxVPnl/jluTl5rBjy1pyc7JwcnHn7Xc/xlxpCUDWndtaIwxzs+/wv0/Ha57v3bmZvTs34+HpzdD3PwGgsLCAmE1RZGdmYGJqhk+j5rzcfQB6euU/ZXV0dLh+7RInDu+hsOAuSgsrPOs3pFO3/hXu1iiEeDEoyqq6wrf4W1EoFGzYsIGQkJDqbooQf1rmzKrdbVOIfzK9zj2ruwlCPHWzz3R4dJAQ/3CdmpRUdxOEeOra+vy5m5T9nYQtr/q608/SjMEGjw4ST4Ws0SWEEEIIIYQQQgghnguS6BJ/C8OHD9es9/XHx/Dhw6u7eVp8fHwe2NaoqKhn0oaoqKgHtsHHx+eZtEEIIYQQQgghhPi7kTW6/qGetxmnn3zyCePGjat0m1KpfMatebht27ZRXFxc6bZntU5Vjx49aNasWaXb9GUtAiGEEEIIIYQQLyhJdIm/BTs7O+zs7Kq7GVXi5uZW3U3A3Nwcc3Pz6m6GEEIIIYQQQgjxtyJTF4UQQgghhBBCCCHEc0ESXUIIIYQQQgghhBDiuSCJLiGEEEIIIYQQQgjxXJBElxBCCCGEEEIIIYR4LkiiSwghhBBCCCGEEEI8FyTRJYQQQgghhBBCCCGeC5LoEkIIIYQQQgghhBDPBUl0CSGEEEIIIYQQQojngiS6hBBCCCGEEEIIIcRzQRJd1axdu3aMGTNG89zd3Z358+c/teOlpaWhUCiIjY19ase4Z/ny5VhaWj40Jjw8nEaNGj31tgghhBBCCCGEEOL5p1fdDagugwcPZsWKFRXKO3fuTHR0dDW0qNzRo0cxNTWttuNXZsWKFSxatIizZ8+iq6tLQEAA48ePp1u3btXdNKA8Wbhnz54Hbm/bti27d+/G3d2dixcvAmBiYkLdunWZNGkSffv2BcqTbtOmTQNAR0cHJycnunTpwqeffoq1tfXT70gVFBQU8N5777Fp0yYKCgrw9vZm/vz5tGjRorqbJp6iXxIvsvlsKtmFKlytzBncxBvPGpaVxl7JyuX7uGQuZORwO7+ANwPr0bW+h1bM9sSL7Ei+zM28uwDUtDCnj58njZxtNTHXc/OJOp5A4s1MitVqGjnZMriJNxbGhk+tn+LFFr3vCJt37ScrJw83Jwfe7t2FOm41K429fP0m637exYUr17h1J4tBIcG80jZIK2Zd9C5+iNmtVeZkV4P5k97TPP9q3WZOJ10gMycXQwN96nq4MrBbR5ztbRHiaUiO20bi8U0U5mdiaeuOf7sh2Dh4VRqbcvoXLsbvJjvjEgBWdrXxbTlQE68uLeH0wVWkpx4nP+cG+gam2Lv64tfyLYzNtL+3XLtwjHNH1pF1+yK6uvrYOvvQqsekp9tZ8cI6uCeavTs2kZuThWNNN7r3DcXFvU6lsTfSL7NjyxquXrpA5p1bvNJnMK06aP/GUKvV7Ni6ltgjv5GXm425hRUBzdrRocurKBSKCnVuWL2EI/u2V1qXEOLF8MImugCCg4OJjIzUKjM0rN4fcba2f68v1+PGjWPRokXMmDGDkJAQiouL+e677+jZsycLFixg5MiR1d1EfvzxR4qKigC4fPkyTZs2ZceOHfj4+ABgYGCgif3kk0945513yMnJYd68efTr1w9nZ2dNosjHx4cdO3ZQWlpKfHw8//rXv8jOzmbt2rXPvmOVmDNnDj/88ANr167Fy8uLM2fOoKf36P/GRUVFWq+D+Oc4mJbOymPxhDZrgGcNS35OSGPWzqN81rMNSqOKn1eqklJszUxo5urAyuMJldZpbWpEf38vHMxNKaOMvSnXmLf7OLNeaUlNS3MKi0uYteMoblZKwjo1BWBdbDL/2XWcGV2CKv1SKcRfceDkGb7dFM07r3anjltNtv52kJlLVrLgo1FYmFW8+KMqKsbOxpLmDb1ZsSnmgfXWdLBj8oi3NM91dbQHsnvUdKRVgC+21pbk3r3L99G7mfHlSj6fPAYdHRn0Lp6sS0n7iP0tksAOw7Fx8CLx5GZ+2/AJXQZ9jpGJRYX4W1fO4Fq3NTaOddHVMyDh2I/8tuETOr+5ABMzG0pKVGTevIBPs9ewsHWnuDCPk3u+Zt/mCDoNmKup50ryQY7uXIxvi4HYufiCWq1JngnxpJ06vp+t65cTMmAoru5e7Nu1hW8WzeCDqQsxM694nhcXqbCysaOBfxBb1y+vtM49v2zg8N4Y+r75HvZOLly5mMIP332OsYkpLdp11Yo9G3uYy6nJKC2snkb3hBD/EC/0tzhDQ0McHBy0HlZW5R+KycnJtGnTBiMjI7y9vdm+fTsKhYKNGzcCsHv3bhQKBVlZWZr6YmNjUSgUpKWlAZCRkcGAAQNwdnbGxMQEX19fVq9e/dA2/X7q4vLly1EoFBUe4eHhmvhly5ZRv359jIyMqFevHosXL9aq78iRI/j7+2NkZERgYCAnT56s8utz6NAh5s2bx5w5cxg3bhyenp7Ur1+fmTNnMmbMGMaOHcvly5c18cuXL8fV1RUTExN69epFRkZGhTo//fRT7O3tMTc3JzQ0lMLCQq3tu3fvpmnTppiammJpaUnLli01o7AexNraWvP+3UsU2tjYaMp+PxrL3NwcBwcHvLy8+PzzzzE2Nmbz5s2a7Xp6ejg4OODs7EzHjh3p27cv27dvr9LrVVZWRnh4OK6urhgaGuLk5MSoUaM021UqFePGjcPZ2RlTU1OaNWvG7t27ASgsLMTHx4ehQ4dq4lNSUjA3N+ebb77RlOno6ODt7U3nzp3x8PCge/fuNG3atEJbBg8eTEhICDNnzsTJyYm6desCsHLlSgIDAzWvw+uvv87Nmze19j179izdunVDqVRibm5O69atSUlJ0Wx/1Dknnqyt8al0qONCO8+a1LQ0Y0gzHwx1ddl1/kql8bVrWPJG43q08HBC7wE/1BvXtMff2Q5HpSlOSjP6+XthqKdH8u0sAJJuZXErv4DhLXxxtVLiaqVkREs/Uu9kc+Z6xf/XQvxVW3Yf5KXmjWnfzJ+aDrYM7dsdQwN9dh0+UWm8p6szb/boTMsAX/T1dB9Yr66uDlZKc81D+YekWacWgXh7umNrbUmtmk707/oSGVnZ3LyT9SS7JwQASSd+olaDTnj4vITSxoXAl0agq2dI6tmdlcY37zIWz4ZdsLKrhdK6Jk06jqSsTM3NS6cAMDA0pV3vcFy8WqK0csbGsS4B7d7hzo0U8nNuAaBWl3Jyz9c0bD0IT79glFbOKG1ccPFq+ay6LV4w+37dTJOWHQkM6oCdY016DRiGgYEBxw7+Wml8TTdPuvYeRMPAVujp6Vcacyk1CW+/ptTzbYyVjR2+AUF41W/I5bRkrbjsrAx++v5r+r09Gl3dF3o8hxAvvBc60fUgarWa3r17Y2BgwOHDh/nyyy+ZOHHin66nsLCQxo0bs3XrVs6cOcPQoUN58803OXLkSJX279evH+np6ZrH6tWr0dPTo2XL8i8nUVFRTJkyhZkzZxIfH09ERASTJ0/WTMnMy8ujW7dueHt7c/z4ccLDwxk3blyV27969WrMzMwYNmxYhW0ffPABxcXFrF+/HoDDhw8TGhrKyJEjiY2NpX379syYMUNrn3Xr1hEeHk5ERATHjh3D0dFRK0lSUlJCSEgIbdu25dSpUxw8eJChQ4c+tdEjenp66Ovra0aD/VFaWhoxMTFVHgm1fv16PvvsM5YsWUJycjIbN27E19dXs33kyJEcPHiQNWvWcOrUKfr27UtwcDDJyckYGRkRFRXFihUr2LRpE6Wlpbzxxht06tSJf/3rX5o6unfvzuHDh/n6668f2Z6dO3eSmJjI9u3b2bJlCwDFxcVMnz6duLg4Nm7cSFpaGoMHD9bsc/XqVdq0aYOhoSG//vorx48f51//+hclJSXAo8858WSVlKpJzcihgWMNTZlCoaCBow3Jt7KeyDHU6jIOpF5DVVJCnRrlif5idSkKFOjr3v8TYaCrgwIFiTczn8hxhbinpKSUC1eu4etVS1OmUCjw9apNUlrlCd2qun4rg2Hhcxk5Yz4LV/7A7cysB8YWqorYfeQkdjZW1LCsOOpAiL9CXVrCnRspOLg21JQpFArsXf3ISE+sUh0lJSrU6lIMjMwfGFNUdBeFQoGBYXlSN/NmCnfzMlCg4Jeosfy09F/8tnE62bcffhFRiMdRUlLC1UsX8KznpylTKBR41vPjUmrSY9fr6uFFSuJpbt24BkD6lTRSz8fj5e2viSkrK+P7Ff+jTcee2Du6PH4nhBDPhRc61b1lyxbMzMy0yj766CMCAwNJSEggJiYGJycnACIiIujSpcufqt/Z2VkrsfTee+8RExPDunXrKh2F80fGxsYYGxsD5aN73n33XSIiIujUqRMAU6dOZd68efTu3RsADw8Pzp07x5IlSxg0aBCrVq1CrVbz9ddfY2RkhI+PD1euXGHEiBFVan9SUhK1a9euNNHj5OSEUqkkKan8j9aCBQsIDg5mwoQJAHh5eXHgwAGt9c7mz59PaGgooaGhAMyYMYMdO3ZoRnXl5OSQnZ1Nt27dqF27NgD169evUlv/rKKiIubNm0d2djYdOnTQlJ8+fRozMzNKS0s17frvf/9bpTovXbqEg4MDHTt2RF9fH1dXV837fOnSJSIjI7l06ZLmnBo3bhzR0dFERkYSERFBo0aNmDFjBkOGDKF///5cvHhRk6ACuHHjBsHBwUycOJHZs2eTl5fH6NGjgfLRgzVq1ODo0aMEBgYCYGpqyrJly7Tev98nzWrVqsXChQtp0qQJeXl5mJmZ8fnnn2NhYcGaNWvQ1y+/qubldX/tkEedc+LJylUVoS4rw8JI+/+ghZEh13Ly/1LdlzJzmBJ9iOJSNUb6uoxtF0BNy/LPwzo1LDHU02XViUT6+9elrKyMNSeTUJeVkVWg+kvHFeKPcvLvolarsTTX/ntsaWbKtRu3H7veOm41+feAEJzsbMnMyeWHmN1M+d83zJvwLsa/m/Ybs/8IUZu3U6gqwsmuBmHD30LvIaPEhHgcqoIcysrUGP5hiqKRiSW5mVerVMepfd9ibGqNvatfpdtLS4o4te9bXL1aoW9oAkB+9g0Azhxai3+btzFR2pJ4YhO7fphMl0GfY2j84KSZEH/W3fwc1Gp1hSmKZuYW3LpRtfO8Mu0690alKuCz6aNR6OhQplbzcvcB+Ddto4nZ88sGFDo6FaYyCiFeTC90oqt9+/Z88cUXWmXW1tasXLkSFxcXTUICICgo6I+7P1JpaSkRERGsW7eOq1evUlRUhEqlwsTE5E/Vcy/588orrzB+/HgA8vPzSUlJITQ0lHfeeUcTW1JSgoVF+R+X+Ph4/Pz8MDIyeux+lJWVVSkuPj6eXr16aZUFBQVpJbri4+MZPnx4hZhdu3YB5a/94MGD6dy5M506daJjx4689tprODo6/qk2P8zEiRMJCwujsLAQMzMzPv30U1555RXN9rp16/LTTz9RWFjId999R2xsLO+9995Daryvb9++zJ8/n1q1ahEcHEzXrl3p3r07enp6nD59mtLSUq2kEZRPZ7SxsdE8/+CDD9i4cSOLFi3i559/1to2b948XF1diYiIYNiwYbRu3Zpbt24xY8YMTp8+jbm5OQ0b3r9S7OvrWyFJeW9kX1xcHJmZmajVaqA8Eeft7U1sbCytW7fWJLl+ryrnXGVUKhUqlXZyRFVSiqH8kKxWTkozPu3WkrtFJRy+eJ0v9p9iysvNqWlphtLIkDFt/Pn68BliEi+iQEELD0fcrZXI8lzin8K//v2Fj92c7KnjVpN3P/mMg3Fn6dAsQLOtVYAffl61yczOZfPuA3y2Yh3TR4ViUMnnoBDVJf7oj1xK2kf7V6ejq1fxAqS6tIQD2+ZCWRmNO9z/rnXve5x30z7UrFP+HbBpp1Fs/noIV84foLZv52fTASH+glPH9xN7dC/9Bo/GztGF61fT2PJDJOYWVjRu3p4rl1LYv2sr702aI+uICiGAFzzRZWpqiqen52Pte2+R2t8ngoqLi7Vi5syZw4IFC5g/fz6+vr6YmpoyZsyYB06Vq0xpaSn9+vVDqVTy1Vdfacrz8vIAWLp0Kc2aNdPaR1f3ySQQvLy82LdvX6ULmV+7do2cnJwKiZu/KjIyklGjRhEdHc3atWsJCwtj+/btNG/e/InUP378eAYPHoyZmRn29vYV/hgaGBhozol7SbBp06Yxffr0R9bt4uJCYmIiO3bsYPv27fz73/9mzpw57Nmzh7y8PHR1dTl+/HiF9+f3owpv3rxJUlISurq6JCcnExwcrNl26tQpGjVqBICbmxs7duzQJLtycnJ44403tBJUf7x7Z35+Pp07d6Zz585ERUVha2vLpUuX6Ny5s+acvDeCsDKPe87NmjVLczfLeya0b8yHLzV54D6inLmhAToKBdmF2p8Z2YUqLCtZiP7P0NPVwcG8/BypZWPBhYxsohPSGNK8AQB+TjVY0KsdOYVF6OooMDXQZ/j3O7Eze3KJZyEAlKYm6OjokJWbp1WelZePpdLsAXv9eabGRjja2ZB+K6NCuamxEY62Nni5u/D2x7M4ejqBlgG+D6hJiD/P0FiJQqGD6m62Vnnh3SyMTB6+aHbC8U0kHPuRtr2mYlnDvcL2e0muuzk3addnumY0F4CRaXndSmtXTZmunj5mFvbczX38EZNCVMbEVImOjg55udrneV5uNmbKx18c/ueNK2nbqRcNA1sB4OjsRmbGLXbHbKBx8/aknY8nPy+H2WH3k7xqtZptP67gwK6tTJj+xYOqFkI8p2SNrkrUr1+fy5cvk56erik7dOiQVsy9Rc9/HxMbG6sVs3//fnr27Mkbb7xBw4YNqVWrlmaqX1W9//77nD59mo0bN2qNzLK3t8fJyYkLFy7g6emp9fDw8ND049SpU1oLvv+xHw/Tv39/8vLyWLJkSYVtc+fORV9fnz59+miOdfjwYa2YPx6rKjEA/v7+TJo0iQMHDtCgQQNWrVpV5TY/So0aNfD09MTBwaFKV3zCwsKYO3cu165dq1L9xsbGdO/enYULF7J7924OHjzI6dOn8ff3p7S0lJs3b1Z4vxwcHDT7/+tf/8LX15cVK1YwceJE4uPjNducnZ05cOAApaWlQHki8pdffmHdunVs2LCByZMnP7RtCQkJZGRk8Omnn9K6dWvq1atXYSF6Pz8/9u7dWyFpC1U75yozadIksrOztR7vtw14YLy4T09XBw8bJWfT7/8wLysr4+z1DOrYWj7RY5VRRvH/j/D7PaWRAaYG+py9nkGOqojGLnZP9LhC6OnpUqumE2eSUzVlZWVlnEm6gJd7zSd2nEJVEddv38Fa+eCpWmWUUVYGxf+/LqEQT4qOrh7W9rW58f8LyUP5eX7z8mlsHOs+cL+EYxs4d3gdbUImY+1Qp8L2e0muvKxrtO09rcJURGu72ujq6pObdVVrn/zsm5iY1/hjdUL8JXp6eji71iIl8bSmrKysjPOJp3H1ePyL48VFqgojyssHHZQPOAho1pZRH83jvUlzNQ+lhRVtOvXk7ZEP/34shHg+vdAjulQqFdevX9cq09PTo2PHjnh5eTFo0CDmzJlDTk4OH3/8sVacp6cnLi4uhIeHM3PmTJKSkpg3b55WTJ06dfjhhx84cOAAVlZW/Pe//+XGjRt4e3tXqX2RkZEsXryYDRs2oFAoNG01MzPDzMyMadOmMWrUKCwsLAgODkalUnHs2DEyMzMZO3Ysr7/+Oh9//DHvvPMOkyZNIi0tjblz5z7iqPcFBQUxevRoxo8fT1FRESEhIRQXF/Pdd99pRqq5uJQv9jhq1ChatmzJ3Llz6dmzJzExMVrTFgFGjx7N4MGDCQwMpGXLlkRFRXH27Flq1SpfgDg1NZWvvvqKHj164OTkRGJiIsnJybz11lsV2vasBAUF4efnR0REBIsWLXpo7PLlyyktLaVZs2aYmJjw3XffYWxsjJubGzY2NgwcOJC33nqLefPm4e/vz61bt9i5cyd+fn688sorfP755xw8eJBTp07h4uLC1q1bGThwIIcOHcLAwIBRo0bRvHlz+vfvz6RJkzA0NGTHjh2aheJXrlypWSOtMq6urhgYGPC///2P4cOHc+bMmQoj1UaOHMn//vc/zTEsLCw4dOgQTZs2pW7duo885ypjaGiIoaH26KNSmbZYZa/U9+CLA6fwsFFS28aS6IQ0CktKaVe7PAHw+f44rI2NGBBQ/kOppFTNlezykTGlajV37qpIu5ODkb6uZgTX6hOJNHS2xdbUmILiYvanpnPuxh0m/W6U3e7zV3C2MMPcSJ/kW1l8eyyeLvXccXqCI2yEuKdbuyA+X7WBWjUd8XStybbfDlFYVES7puULDS+K+hErC3MGditfo7KkpJTL18sT9SWlpdzJziH1SjrGRoY41Ci/0+7Kn2Jo7F0XW2tL7mTnsC56NzoKBS3+f6TWjdt3OBB7Fr+6tbEwMyEjK4eNO/dhYKCPv/eTHa0sBIBXQA+OxCzEyr42NvZ1SIrdQklxIR4+5WuFHo6Zj7GpDX6t3gTKpyueObSaoOCxmCjtKMgvvxmIvoExevpG5UmurXPIvHWB1j0+oqxMrYkxNDJHR1cPfUMTavt15uzBNZiY1Shfo+vYBgBc6sidF8WT16pDd77/dhHOrrVwcavD/t1bKVKpaNy8PQDrVixEaWlNcM83gPIlMG6ml9/FvbS0hJysDK5dTsXQyAgb2/JR5PV8A9kV8yOWVrbYO7lw7XIq+37dTOOg8v87JqbmmJhqJ3l1dfUwM7fE1t4JIcSL54VOdEVHR1dY/6lu3bokJCSwYcMGQkNDadq0Ke7u7ixcuFBrGpm+vj6rV69mxIgR+Pn50aRJE2bMmEHfvn01MWFhYVy4cIHOnTtjYmLC0KFDCQkJITtbezjvg+zZs4fS0lJ69OihVT516lTCw8MZMmQIJiYmzJkzh/Hjx2Nqaoqvry9jxowByhNimzdvZvjw4fj7++Pt7c3s2bM1o7CqYv78+fj5+bF48WLCwsLQ1dUlICCAjRs30r17d01c8+bNWbp0KVOnTmXKlCl07NiRsLAwrURKv379SElJYcKECRQWFtKnTx9GjBhBTEwMACYmJiQkJLBixQoyMjJwdHTk3XffrfSuj8/S+++/z+DBg5k4caImsVcZS0tLPv30U8aOHUtpaSm+vr5s3rxZs85WZGQkM2bM4IMPPuDq1avUqFGD5s2b061bNxISEhg/fjxff/215hiLFy/Gz8+PyZMnM3v2bBo2bMjBgweZNGkSnTp1oqioiFatWrF9+3bOnz/PoEGDqF279gPfX1tbW5YvX85HH33EwoULCQgIYO7cuVrnl42NDb/++ivjx4+nbdu26Orq0qhRI82dPh91zoknL8jdkZzCIr6PSya7QIWbtZIPOzTBwrg8eZiRX4jO7y5zZhaomLR1v+b51nOpbD2XSn17a6a8XD7lNEdVxBf7T5FVUIiJgT6uluZMeqkJvr+7u2N6Tj5rTiaSX1RMDVNjQhrUpmt992fTafHCaeHfgOy8fNZF7yIrNw93J0c+HvqmZoH625nZWqNwM3NymTjvS83zzbsOsHnXAbxruxM+8m0AMrJyWPDdD+Tm30Vpakq9Wq7MHPMOFmblCV99fX3iL1xk656D3C0sxMLMjPq1XJkxKlQTI8ST5OrVCtXdbM4cXEPh3UysbD1oEzIZIxNLAO7m3EahuD/ZIuV0DOrSEvZv/Y9WPT7N+tEgqD8F+Xe4eqH8Tt4xUdoXm9r3mY6dS/lU9IatBqFQ6HI4ej6lpUVYO3jR7tVPMDCSCxfiyfNr3JK83Bx2bFlLbk4WTi7uvP3ux5grLQHIunMbhc798zw3+w7/+3S85vnenZvZu3MzHp7eDH3/EwB69A1l+5Y1bFq7lPy8HMwtrGjashMdur72TPsmhPjnUJRVdbVxgUKhYMOGDYSEhFR3U4T4R8ucWbU7fwrxT6bXuWd1N0GIp272mQ6PDhLiH65TE5nOLJ5/bX3+3A3T/k7Clld9DexnacbgijcPEc+GrNElhBBCCCGEEEIIIZ4Lkuh6gQ0fPlyz3tcfH8OHD390Bc+Qj4/PA9saFRX1TNoQFRX1wDb4+Pg8kzYIIYQQQgghhBDiwV7oNbr+rOdtlucnn3zCuHHjKt2mVCqfcWsebtu2bZXeCRDK7wb4LPTo0YNmzZpVuk1fX/+ZtEEIIYQQQgghhBAPJomuF5idnR12dnbV3YwqcXNzq+4mYG5ujrn5g29LL4QQQgghhBBCiOolUxeFEEIIIYQQQgghxHNBEl1CCCGEEEIIIYQQ4rkgiS4hhBBCCCGEEEII8VyQRJcQQgghhBBCCCGEeC7IYvRCiGdOr3PP6m6CEE9dScym6m6CEE9dp5A21d0EIZ667UflJ5N4/rX1qe4WCPHkyIguIYQQQgghhBBCCPFckESXEEIIIYQQQgghhHguSKJLCCGEEEIIIYQQQjwXJNElhBBCCCGEEEIIIZ4LkugSQgghhBBCCCGEEM8FSXSJKmnXrh1jxozRPHd3d2f+/PlP7XhpaWkoFApiY2Of2jHuWb58OZaWlg+NCQ8Pp1GjRk+9LUIIIYQQQgghhHh8cq/cv7nBgwezYsWKCuWdO3cmOjq6GlpU7ujRo5iamlbb8SuzYsUKFi1axNmzZ9HV1SUgIIDx48fTrVu36m6axvLly3n77bcrlC9dupQhQ4ZobVcoFDg5OdGpUydmz56NnZ2dpvwec3Nz6tatS1hYGD179nw2nRDPXPS+I2zetZ+snDzcnBx4u3cX6rjVfGD8wdgzrP15F7fuZOFga8PAbh0J8PbSbC9UFRG1ZTtHTyeQd7cAW2tLurRuxsstmwCQm3+X72N2cyoxhduZ2ZibmtDUtx6vdemAqbHRU++veDH9kniRzWdTyS5U4WplzuAm3njWsHxg/KG0dNbFJXM7vwAHcxMGBNTF39lOs33Ayp8r3e/1gLp096nFuesZTN9+pNKYGV2CqP2QYwvxuA7uiWbvjk3k5mThWNON7n1DcXGv88D4UycOsH3zGrLu3MLGzpHgnm9Qr0GAZntuThbRm77jfHwcBXfz8fD0pvtrodSwc9Sq5+KFRLZvXs2ltGR0dHRwrOnOv0ZORl/f4Kn1Vby4kuO2kXh8E4X5mVjauuPfbgg2Dl4PjL+ctJ/TB1dxN+cWZpaO+LV6CyePxprthXezOLXvW65fjKNIlYetsw8B7YZgbuWkiSnIz+TUvhVcvxhHSXEB5pZOeDftS806QU+1r0KIvydJdP0DBAcHExkZqVVmaGhYTa0pZ2trW63H/6Nx48axaNEiZsyYQUhICMXFxXz33Xf07NmTBQsWMHLkyOpuooZSqSQxMVGrzMLCosJ2tVpNXFwcb7/9NteuXSMmJkYTExkZSXBwMDk5OSxevJhXX32VEydO4Ovr+8z6IZ6NAyfP8O2maN55tTt13Gqy9beDzFyykgUfjcLCrGKyOTH1EgtWruf1V8qTW/tOnGZu5Bo+HTsMV0d7AFZsiuZscirvvdEbO2srYhPO8/X6rVhZmNOkQT0yc3LJzM7ljR4vU9PeltuZ2Sz9fjN3snP54O1+z/olEC+Ag2nprDwWT2izBnjWsOTnhDRm7TzKZz3boDSq+Pcu8WYm/9sXR39/L/yd7TiQdo3/7j5BRNeWuFiZA/DFqx209om9eouvDp2mqasDAF62VhVi1sUmceZ6BrVsLBDiSTt1fD9b1y8nZMBQXN292LdrC98smsEHUxdiZl7xnLuYksDayPl07jGQeg0aE3tsL9999R9GfvgfHJxcKSsrY+WS2ejq6fHmsIkYGpmw79fNfL1wGmMmz8fQsPzCxMULiSz/fCZtO/ei+2uh6Ojokn41DQWKCscU4q+6lLSP2N8iCewwHBsHLxJPbua3DZ/QZdDnGJlUPM9vX4vn4M//xa/lmzh5NOZi4l72b/mUlwfMxaKGG2VlZez7aRY6unq06v4heoYmJJ34id0/htPlrYXo6Zef54djFlCsyqdVj0kYGiu5lLCXA9vm0GnAXKzsaj3rl0EIUc1k6uI/gKGhIQ4ODloPKysrAJKTk2nTpg1GRkZ4e3uzfft2FAoFGzduBGD37t0oFAqysrI09cXGxqJQKEhLSwMgIyODAQMG4OzsjImJCb6+vqxevfqhbfr91MXly5ejUCgqPMLDwzXxy5Yto379+hgZGVGvXj0WL16sVd+RI0fw9/fHyMiIwMBATp48WeXX59ChQ8ybN485c+Ywbtw4PD09qV+/PjNnzmTMmDGMHTuWy5cva+KXL1+Oq6srJiYm9OrVi4yMjAp1fvrpp9jb22Nubk5oaCiFhYVa23fv3k3Tpk0xNTXF0tKSli1bcvHixSq1V6FQVHg/jY2NK2x3cnKiS5cujBo1ih07dlBQUKCJsbS0xMHBAS8vL6ZPn05JSQm7du2q0vHj4uJo37495ubmKJVKGjduzLFjxzTb9+3bR+vWrTE2NsbFxYVRo0aRn58PwLfffouZmRnJycma+H//+9/Uq1ePu3fvVun44s/ZsvsgLzVvTPtm/tR0sGVo3+4YGuiz6/CJSuO3/XaIRvU86dGhJTUdbOnftQMezo5E770/ciUx9TJtmjTEx9MDW2tLOrUIxM3JnvOXrgLg6mjPB2/3I9CnLg41rGlQx4P+XTtw/FwipaWlz6Tf4sWyNT6VDnVcaOdZk5qWZgxp5oOhri67zl+pND46IY2GTjXo7lOLmpZmvNbIC3drC2IS738OWxobaj2OXb6Bt7019uYmAOjp6mhtNzPQ5/iVm7StXVNr5KwQT8q+XzfTpGVHAoM6YOdYk14DhmFgYMCxg79WGr9/91a8vP1p06kndo41ebn7AJxcPDi4p3y04u2b6VxOSyak/1Bqunlia+9ESP+hFBcXcerYPk09W9cvJ6hdF9q93At7Rxds7Z3wC2iBnr7+M+m3eLEknfiJWg064eHzEkobFwJfGoGuniGpZ3dWHn9yK47uAdQLDEFp44Jvi9exsq1Fctw2AHKzrpFxPYnGHYZh7VAHpZUzjTsMR11SxKXEvZp6MtITqdPoFWwcvDCzcMC7WV8MDM3IvJnyTPothPh7kUTXP5haraZ3794YGBhw+PBhvvzySyZOnPin6yksLKRx48Zs3bqVM2fOMHToUN58802OHKl8Sscf9evXj/T0dM1j9erV6Onp0bJlSwCioqKYMmUKM2fOJD4+noiICCZPnqyZkpmXl0e3bt3w9vbm+PHjhIeHM27cuCq3f/Xq1ZiZmTFs2LAK2z744AOKi4tZv349AIcPHyY0NJSRI0cSGxtL+/btmTFjhtY+69atIzw8nIiICI4dO4ajo6NWYq6kpISQkBDatm3LqVOnOHjwIEOHDn1qP4yMjY1Rq9WUlJRU2FZSUsLXX38NgIFB1aYfDBw4kJo1a3L06FGOHz/Ohx9+iP7/f9lNSUkhODiYPn36cOrUKdauXcu+ffs0I+LeeustunbtysCBAykpKWHr1q0sW7aMqKgoTExMnlCPxT0lJaVcuHINX6/7VyIVCgW+XrVJSqs8AZCUdkUrHsCvbm2SLt5P9tb1cOH42SQysnIoKyvjTHIq6bcyaOhV+4FtuVuowtjQEF1d3b/YKyG0lZSqSc3IoYFjDU2ZQqGggaMNybeyKt0n6VYWDRxttMr8HG1Ivl15fHaBithrt2jv6fLAdhy/epNcVRHtajv/6T4I8SglJSVcvXQBz3p+mjKFQoFnPT8upSZVus+l1CRq19UeqV2nfkNNfElJMQB6evcnaCgUCvT09Um7kABAXm42l9OSMTO34Iu5HzFj4r/46rMppKXEP9H+CQGgLi3hzo0UHFwbasoUCgX2rn5kpCdWuk/G9UTsXLTPcwe3Rpp4dWn5ea6jez8xq1Ao0NHT5/a1++exjWNdLiftR1WQS1lZGZcS91JaWoRtzQZPrH9CiH8Ombr4D7BlyxbMzMy0yj766CMCAwNJSEggJiYGJ6fyOeoRERF06dLlT9Xv7OyslVh67733iImJYd26dTRt2vSR+xsbG2tGJKWkpPDuu+8SERFBp06dAJg6dSrz5s2jd+/eAHh4eHDu3DmWLFnCoEGDWLVqFWq1mq+//hojIyN8fHy4cuUKI0aMqFL7k5KSqF27dqWJHicnJ5RKJUlJ5V8KFyxYQHBwMBMmTADAy8uLAwcOaK13Nn/+fEJDQwkNDQVgxowZ7NixQzOqKycnh+zsbLp160bt2uWJgfr161eprQDZ2dla76eZmRnXr1+vNDY5OZkvv/ySwMBAzM3NNeUDBgxAV1eXgoIC1Go17u7uvPbaa1U6/qVLlxg/fjz16tUDoE6d+2uDzJo1i4EDB2puPFCnTh0WLlxI27Zt+eKLLzAyMmLJkiX4+fkxatQofvzxR8LDw2ncuHFlhwJApVKhUqm0yoqKijCsYmLuRZaTfxe1Wo2lufb/f0szU67duF3pPtl5eRXjlWZk5eRpnv+rd1eWrNvMiGnz0NXVRaGAYa/1wNvTvfJ25OWz/pc9dGwR+Nc6JEQlclVFqMvKsDDS/kywMDLkWk5+pfvkFKqw/MOURktjQ7IKVJXG77lwFSM9PZq42D+wHbuSL+PnWAMbU+MHxgjxuO7m56BWqytMUTQzt+DWjauV7pOXk4250lKrzFxpSW52JgC29s5YWtUg5qdVhAwYhoGBEft+3Ux2ZoYm5s7tGwDs3LaOLr3ewqmmBycO72HZgmmMCfuswlpeQvwVqoIcysrUGP5hiqKRiSW5mZWf54V3szA2taoQX5ifBYDSqiYm5rac3h9F4EvD0dM3IvHET9zNvU1BfqZmnxavjOfgtrlsXPIWOjq66OoZ0rLbh5hbyjkuxItIEl3/AO3bt+eLL77QKrO2tmblypW4uLhoklwAQUF/fsHF0tJSIiIiWLduHVevXqWoqAiVSvWnR+jcS/688sorjB8/HoD8/HxSUlIIDQ3lnXfe0cSWlJRo1qWKj4/Hz88PI6P7i1z/2X6UlZVVKS4+Pp5evXpplQUFBWkluuLj4xk+fHiFmHtTA62trRk8eDCdO3emU6dOdOzYkddeew1Hx6r9ITU3N+fEifvTznR0tAdW3kuEqdVqCgsLadWqFcuWLdOK+eyzz+jYsSMXLlzg/fffZ+HChVhbW1fp+GPHjmXIkCGsXLmSjh070rdvX03CLi4ujlOnThEVFaWJLysrQ61Wk5qaSv369bGysuLrr7+mc+fOtGjRgg8//PChx5s1axbTpk3TKvvwnYF8NPSNKrVXPHnRew9z/uIVJgx5HVsrS86lpJWv0aU0x6+u9qiuu4WFfLp0FTUd7Oj7crvqabAQf9Hu81do5eGEgV7lIxIz8gs4lX6b0W38n3HLhHh8enp6DBw6nh+/+4Lp4wejo6ODZ10/vLzvn8dlajUATVt2IjCofE06JxcPUpJOc+zgToJ7yt9i8femo6tHy24TOLrjczZ8+SYKhQ72rg1xdA/QijtzYBXFqnza9Z6GobE5V1OOcHDbXNr3nYFlDffqabwQotpIousfwNTUFE9Pz8fa914S5feJoOLiYq2YOXPmsGDBAubPn4+vry+mpqaMGTOGoqKiKh+ntLSUfv36oVQq+eqrrzTleXnlo0iWLl1Ks2bNtPZ5UlOgvLy82LdvH0VFRRVGdV27do2cnBy8vB58p5fHERkZyahRo4iOjmbt2rWEhYWxfft2mjdv/sh9dXR0Hvp+3kuE6ejo4OjoqLV+1z0ODg54enri6elJZGQkXbt25dy5c5o7Mz5MeHg4r7/+Olu3buXnn39m6tSprFmzhl69epGXl8ewYcMYNWpUhf1cXV01//7tt9/Q1dUlPT2d/Px8rdFmfzRp0iTGjh2rVVZ0Zvcj2ylAaWqCjo4OWbl5WuVZeflYKs0q3cfCzKxifE6eJr6ouJjV23byweB+NPapC4Cbkz1pV6+zefcBrURXQaGKiCXfYWRkwLi3+6H3gCSBEH+FuaEBOgoF2YXaf3OyKxm1dY/SyJCsQu3RW1kFKiyNK8bH37hDek4+o1s3emAbdqdcxdzQgMbOj/4MFeJxmJgq0dHRIS83W6s8LzcbM6VVpfuYKS3IzcnSKsvNycLc4n58TdfajPpoLgV38yktLcHM3ILFcz7E2aX8s/xerJ2j9p16be2dyLpT+chgIR6XobEShUIH1V3t87zwbhZGJpWf50YmllojszTxppaa59b2nnQe+BlFqnzUpSUYmViwY80ErOzKz/PcrHSS47YR/OYCLGzKv69a2npw6+o5zsf9TOBLVZslIoR4fsgaXf9g9evX5/Lly6Snp2vKDh06pBVz7+6Iv4+JjY3Vitm/fz89e/bkjTfeoGHDhtSqVUsz1a+q3n//fU6fPs3GjRu1RmbZ29vj5OTEhQsXNImZew8PDw9NP06dOqW14Psf+/Ew/fv3Jy8vjyVLllTYNnfuXPT19enTp4/mWIcPH9aK+eOxqhID4O/vz6RJkzhw4AANGjRg1apVVW7zw9xLhNWqVavSJNcfNW3alMaNGzNz5swqH8PLy4v333+fX375hd69e2vu6hkQEMC5c+cqvFeenp6aJOKBAweYPXs2mzdvxszM7JF3tDQ0NESpVGo9ZNpi1ejp6VKrphNnklM1ZWVlZZxJuoCXe81K9/Fyr8nppAtaZaeTLuDlVr42UUlpKSUlpRVGEurq6KBW30+I3y0sZMaX36Knq8vE0AEYyKLF4inR09XBw0bJ2fT7NwYpKyvj7PUM6thaVrqPl60lZ9K1byRy+noGdWpUjN+dcgUPGyVu1spK6yorK2NPyhVa13JGT1e+FomnQ09PD2fXWqQkntaUlZWVcT7xNK4elV+Mc/Xw0ooHOJ9wqtJ4YxNTzMwtuH0znSsXU/Bu2AQAKxs7lBZW3LpxTSs+42Y6VtZ/rztoi38+HV09rO1rc+PSKU1ZWVkZNy+fxsaxbqX72DjU5eZl7fP8xqW4SuMNDE0xMrEgN/Mad26cx7l2+UX00pLyCx9/vJOoQkenyrM+hBDPF/lG9w+gUqm4fv261uP27dt07NgRLy8vBg0aRFxcHHv37uXjjz/W2tfT0xMXFxfCw8NJTk5m69atzJs3TyumTp06bN++nQMHDhAfH8+wYcO4ceNGldsXGRnJ4sWL+fLLL1EoFJo23hvNNW3aNGbNmsXChQtJSkri9OnTREZG8t///heA119/HYVCwTvvvMO5c+fYtm0bc+fOrfLxg4KCGD16NOPHj2fevHmkpKSQkJBAWFgYCxYsYN68ebi4lP/IvzcKa+7cuSQnJ7No0SKtaYsAo0eP5ptvviEyMpKkpCSmTp3K2bNnNdtTU1OZNGkSBw8e5OLFi/zyyy8kJyf/qXW6nrQxY8awZMkSrl6tfP2DewoKChg5ciS7d+/m4sWL7N+/n6NHj2raPnHiRA4cOKBZrD85OZlNmzZpklm5ubm8+eabjBo1ii5duhAVFcXatWv54YcfnnofX1Td2gWx8+Bxdh85yZXrt1j6/RYKi4po17R8asqiqB+J2rJdE9+1TXPiElPYvGs/V2/cYl30Li5cuUZw6/L19kyMjPCu7c7Kn37h7PlUbmZksvvISX47FkdTv/J12+4WFjLzy5WoiosZ3r8ndwtVZObkkpmTi/r/p8EI8SS9Ut+DX89fZk/KFa5k5fH14bMUlpTSrnZ5Qvfz/XGsPnF/IePgeu6cSr/NlnOpXM3O44e4ZFIzsulc102r3rtFxRy6eP2hi9CfvZ7BrbwC2ntWnjwW4klp1aE7R/fv4PihXdxMv8LGNV9RpFLRuHl7ANatWEj0pu808S3bvUJSfCx7d/zEzetX2bF1LVcvXSCo7f21WE+dOMCFpDNk3L7OuVNH+eZ/n+DTsCl16jcCyhftbt2xJwd3/8zpEwfJuJXOL5tXc+vGNQJbdHim/RcvBq+AHlw4s53Uc7+Sk3GZ479+SUlxIR4+5efb4Zj5nNq38n68/ytcv3iShOObyLlzhTMH13DnZgp1GnbVxFxO2s/Ny2fIy77O1ZQj7NkwDefazXFwawSUr+NlZunAsV+/JON6ErlZ6SQc38SNS3GaZJgQ4sUiUxf/AaKjoyus/1S3bl0SEhLYsGEDoaGhNG3aFHd3dxYuXEhwcLAmTl9fn9WrVzNixAj8/Pxo0qQJM2bMoG/fvpqYsLAwLly4QOfOnTExMWHo0KGEhISQna097PhB9uzZQ2lpKT169NAqnzp1KuHh4QwZMgQTExPmzJnD+PHjMTU1xdfXV7PguZmZGZs3b2b48OH4+/vj7e3N7NmzNaOwqmL+/Pn4+fmxePFiwsLC0NXVJSAggI0bN9K9e3dNXPPmzVm6dClTp05lypQpdOzYkbCwMKZPn66J6devHykpKUyYMIHCwkL69OnDiBEjiImJAcDExISEhARWrFhBRkYGjo6OvPvuu5Xe9fFZCQ4OxsPDg5kzZ2rdIfKPdHV1ycjI4K233uLGjRvUqFGD3r17a9bQ8vPzY8+ePXz88ce0bt2asrIyateuTb9+/YDyJKCpqSkREREA+Pr6EhERwbBhwwgKCsLZWe5W9qS18G9Adl4+66J3kZWbh7uTIx8PfVOz4PztzGytO37W9XBl1Bu9WfvzLlZv24lDDRvGvd0fV8f7i3CPfutVVm3ZwcLv1pN/t5AaVhb079qBl1uUjwBIvZJO8sXyuzqOmrlAqz2fT34fW2vLp9xr8aIJcnckp7CI7+OSyS5Q4Wat5MMOTbD4/6mIGfmF6Pz+PLezYmTLhqyLS2btyUQclKaMbReAi5X2NOoDaemUlZXRwv3Bayj+ev4KXraWOFtUPh1YiCfFr3FL8nJz2LFlLbk5WTi5uPP2ux9rFpzPunMbxe9G27rVrke/waPZvnkNMT+tooadI28MnYCD0/2lBPJystj24wrNwvX+zdrSoUtfreO26tCNkpJitv64nLv5eTg6u/Gv9yZjYyuLdIsnz9WrFaq72Zw5uIbCu5lY2XrQJmQyRiaWANzNuY1Ccf88r+FUn+bB73P64CpOH/gOc0tHWnb7EIsa9y9cFNzNJHbvcs0USPf67fBpdv8mTDq6erTpOZlT+79j36YISkoKMbNwoOnLo3DyePANk4QQzy9FmYznfO4oFAo2bNhASEhIdTdFiErlHot+dJAQ/3AlMZuquwlCPHWnQuY9OkiIf7jtR2VsgHj+zRj8z11aJGx51deWfpb+ya/pP51MXRRCCCGEEEIIIYQQzwVJdIm/veHDh2NmZlbpY/jw4dXdPC0+Pj4PbGtUVNQL0wYhhBBCCCGEEKI6yDjc59DzNhv1k08+Ydy4cZVuUyorv4tWddm2bRvFxcWVbrO3t6+0/HlsgxBCCCGEEEIIUR0k0SX+9uzs7LCzs6vuZlSJm5vbo4NegDYIIYQQQgghhBDVQaYuCiGEEEIIIYQQQojngiS6hBBCCCGEEEIIIcRzQRJdQgghhBBCCCGEEOK5IGt0CSGeuRPGbaq7CUI8dX5squ4mCPHUbT8qXyXF829ig1+ruwlCPAPB1d0AIZ4YGdElhBBCCCGEEEIIIZ4LkugSQgghhBBCCCGEEM8FSXQJIYQQQgghhBBCiOeCJLqEEEIIIYQQQgghxHNBEl1CCCGEEEIIIYQQ4rkgiS4hhBBCCCGEEEII8VyQRJfQaNeuHWPGjNE8d3d3Z/78+U/teGlpaSgUCmJjY5/aMe5Zvnw5lpaWD40JDw+nUaNGT70tQgghhBBCCCGEeDr0qrsBAgYPHsyKFSsqlHfu3Jno6OhqaFG5o0ePYmpqWm3Hr8yKFStYtGgRZ8+eRVdXl4CAAMaPH0+3bt2qu2kay5cv5+23365QvnTpUoYMGaK1XaFQ4OTkRKdOnZg9ezZ2dnaa8nvMzc2pW7cuYWFh9OzZ89l0QvytHNwTzd4dm8jNycKxphvd+4bi4l6n0tgb6ZfZsWUNVy9dIPPOLV7pM5hWHR78/2P3LxuI2RRFy/av0O3ViudtWVkZyxdHkHTuJG8MnYBPw6ZPrF9C/N4viRfZfDaV7EIVrlbmDG7ijWcNy0pjr2Tl8n1cMhcycridX8CbgfXoWt/jT9WZqypifdx5TqXf5nZ+AeaGBjRxtadvwzqYGug/xZ6KF1ly3DYSj2+iMD8TS1t3/NsNwcbBq9LY7IxLnDm4hsybKeTn3KRRm7epG9BDK+bMwTWcPbxWq8zcyomugz4HID/nJlu+GVZp/S26jsPFq+UT6JUQ2qL3HWHzrv1k5eTh5uTA2727UMetZqWxl6/fZN3Pu7hw5Rq37mQxKCSYV9oG/ek6wxdFci4lTWufjkGBDH2t+xPtmxDin0ESXX8TwcHBREZGapUZGhpWU2vK2draVuvx/2jcuHEsWrSIGTNmEBISQnFxMd999x09e/ZkwYIFjBw5srqbqKFUKklMTNQqs7CwqLBdrVYTFxfH22+/zbVr14iJidHEREZGEhwcTE5ODosXL+bVV1/lxIkT+Pr6/un2FBUVYWBg8PgdEtXm1PH9bF2/nJABQ3F192Lfri18s2gGH0xdiJm5RYX44iIVVjZ2NPAPYuv65Q+t+8rF8xzZ+wsOzm4PjNm/a+tf7YIQj3QwLZ2Vx+IJbdYAzxqW/JyQxqydR/msZxuURhX/FqpKSrE1M6GZqwMrjyc8Vp1Zd1VkFhQysHFdnC3MyMgvZNmhM2TeLeT9tgFPu8viBXQpaR+xv0US2GE4Ng5eJJ7czG8bPqHLoM8xMqn4eV5arMJUaYdLnSBif4uspMZyFjYutO09TfNcR0dX828Tsxr0eOcbrfgLZ7aTcGwDjh6Nn0CvhNB24OQZvt0UzTuvdqeOW022/naQmUtWsuCjUViYVbyArioqxs7GkuYNvVmxKaaSGqte50tBjXktuL3muaFctBDihSVTF/8mDA0NcXBw0HpYWVkBkJycTJs2bTAyMsLb25vt27ejUCjYuHEjALt370ahUJCVlaWpLzY2FoVCQVpaGgAZGRkMGDAAZ2dnTExM8PX1ZfXq1Q9t0++nLi5fvhyFQlHhER4erolftmwZ9evXx8jIiHr16rF48WKt+o4cOYK/vz9GRkYEBgZy8uTJKr8+hw4dYt68ecyZM4dx48bh6elJ/fr1mTlzJmPGjGHs2LFcvnxZE798+XJcXV0xMTGhV69eZGRkVKjz008/xd7eHnNzc0JDQyksLNTavnv3bpo2bYqpqSmWlpa0bNmSixcvVqm9CoWiwvtpbGxcYbuTkxNdunRh1KhR7Nixg4KCAk2MpaUlDg4OeHl5MX36dEpKSti1a1eVjn9vGuayZcvw8PDAyMgIgOjoaFq1aoWlpSU2NjZ069aNlJQUrX2vXLnCgAEDsLa2xtTUlMDAQA4fPqzZvmnTJgICAjAyMqJWrVpMmzaNkpKSKrVL/Hn7ft1Mk5YdCQzqgJ1jTXoNGIaBgQHHDv5aaXxNN0+69h5Ew8BW6Ok9+AueSlXI2uUL6DVwOMbGlY/cvHY5lb07f+LVN/79RPoixINsjU+lQx0X2nnWpKalGUOa+WCoq8uu81cqja9dw5I3GtejhYcTejqVf5V5VJ0uVua83zaAxjXtcTA3xcfBhn6NvDhx5SalavVT66t4cSWd+IlaDTrh4fMSShsXAl8aga6eIalnd1Yab+1Qh0ZtBuNatzU6ug/+PFfo6GJsaqV5GBorf7dNR2ubsakVV88fwtWrJXr6Rk+8j0Js2X2Ql5o3pn0zf2o62DK0b3cMDfTZdfhEpfGers682aMzLQN80dfTrTSmqnUa6utjpTTXPEyM5BwX4kUlia6/ObVaTe/evTEwMODw4cN8+eWXTJw48U/XU1hYSOPGjdm6dStnzpxh6NChvPnmmxw5cqRK+/fr14/09HTNY/Xq1ejp6dGyZfmQ96ioKKZMmcLMmTOJj48nIiKCyZMna6Zk5uXl0a1bN7y9vTl+/Djh4eGMGzeuyu1fvXo1ZmZmDBtWcfj9Bx98QHFxMevXrwfg8OHDhIaGMnLkSGJjY2nfvj0zZszQ2mfdunWEh4cTERHBsWPHcHR01ErMlZSUEBISQtu2bTl16hQHDx5k6NChWlMKnyRjY2PUanWlCaOSkhK+/vprgD81Kuv8+fOsX7+eH3/8UbMOWn5+PmPHjuXYsWPs3LkTHR0devXqhfr/f9Tl5eXRtm1brl69yk8//URcXBwTJkzQbN+7dy9vvfUWo0eP5ty5cyxZsoTly5czc+bMv/gKiMqUlJRw9dIFPOv5acoUCgWe9fy4lJr0l+r+ae1S6vo0pk69hpVuLypSsXb5Anr2ewdzC6u/dCwhHqakVE1qRg4NHGtoyhQKBQ0cbUi+lfVM67xbXIKxvh66D0ieCfG41KUl3LmRgoPr/c9chUKBvasfGemJD9nz0fKy0vlpaShbvhnOoZ//S37OrQfG3rlxnsxbqXj4vPSXjilEZUpKSrlw5Rq+XrU0ZQqFAl+v2iSlVX7h4knWuff4Kf4VNpuxsz8nast2VEVFj9cRIcQ/nkxd/JvYsmULZmZmWmUfffQRgYGBJCQkEBMTg5OTEwARERF06dLlT9Xv7OyslVh67733iImJYd26dTRt+ug1d4yNjTUjklJSUnj33XeJiIigU6dOAEydOpV58+bRu3dvADw8PDSJkEGDBrFq1SrUajVff/01RkZG+Pj4cOXKFUaMGFGl9iclJVG7du1KEz1OTk4olUqSksp/+C9YsIDg4GAmTJgAgJeXFwcOHNBa72z+/PmEhoYSGhoKwIwZM9ixY4dmVFdOTg7Z2dl069aN2rVrA1C/fv0qtRUgOztb6/00MzPj+vXrlcYmJyfz5ZdfEhgYiLm5uaZ8wIAB6OrqUlBQgFqtxt3dnddee63KbSgqKuLbb7/VmoLap08frZhvvvkGW1tbzp07R4MGDVi1ahW3bt3i6NGjWFtbA+Dp6amJnzZtGh9++CGDBg0CoFatWkyfPp0JEyYwderUStuhUqlQqVR/aFspBgbVOzX3n+Bufg5qtbrCFEUzcwtu3bj62PXGHdvH1cupvDvh0wfGbF2/HFcPL7z9mjz2cYSoilxVEeqyMiyMtD/fLYwMuZaT/8zqzCks4sfT53mpjutjHVOIh1EV5FBWpsbwD1MUjUwsyc18/M9zG0cvmnZ6D3NrZwryMjl3eC2/fv8xwW8uQN/AuEJ86tmdKK1rUsOp6t9phKiqnPy7qNVqLM21f9NYmply7cbtp1pnywBfbK0tsVKacyn9BlGbt5N+K4Nxb/d/rOMKIf7Z5JLl30T79u2JjY3VegwfPpz4+HhcXFw0SS6AoKCKCzQ+SmlpKdOnT8fX1xdra2vMzMyIiYnh0qVLf6qee8mfV155hfHjxwPlo4RSUlIIDQ3FzMxM85gxY4ZmWlx8fDx+fn6aKXSP04+ysrIqxcXHx9OsWTOtsj8e61Ex1tbWDB48mM6dO9O9e3cWLFhAenp6ldtqbm6u9V4eOHBAa/u9RJiJiQl169bF3t6eqKgorZjPPvuM2NhYfv75Z7y9vVm2bJkm+VQVbm5uFdZZS05OZsCAAdSqVQulUom7uzuA5jyIjY3F39//gceJi4vjk08+0Xqf33nnHdLT07l7926l+8yaNQsLCwutR9TSuVXuh3iysjJvs/n7b+g3eDT6+pWPEDx36igpiafp1vdfz7h1QlSPu0XF/GfXMWpamNHHz/PROwjxN+HoHoCLV0ssa7jj6O5P65DJFKvyuZy8v0JsaUkRlxL34uHTsRpaKsTT1alFII3qeeLmZE/rxn6MHNibI6fiuX77TnU3TQhRDWRE19+Eqamp1siZP0Pn/6dY/D4RVFxcrBUzZ84cFixYwPz58/H19cXU1JQxY8ZQ9CeG9JaWltKvXz+USiVfffWVpjwvLw8ov6vgH5NHurqVz7X/s7y8vNi3b1+li6pfu3aNnJwcvLwqv2vR44qMjGTUqFFER0ezdu1awsLC2L59O82bN3/kvjo6Og99P83NzTlx4gQ6Ojo4Ojpqrd91j4ODA56ennh6ehIZGUnXrl05d+6c5s6Mj1LZHTO7d++Om5sbS5cuxcnJCbVaTYMGDTTnQWXt+L28vDymTZumGbn3e0YPWAdh0qRJjB07VqvscEpplfrwojMxVaKjo0NebrZWeV5uNmbKx5tOePXSBfLzclj06XhNmVqtJi0lnoN7fmb6gjVcSDrDnds3+GTcW1r7Ri2dg3vt+gx9/5PHOrYQlTE3NEBHoSC7UPvvUXahCstKFqJ/0nUWFJfw6a/HMNbTY2zbAPR05RqgePIMjZUoFDqo7mp/nhfezcLI5MlNDzcwNMXcyom8zIoX5y4nH6CkRIV7/XZP7HhC/J7S1AQdHR2ycvO0yrPy8rFUmj1gr6dTp6erMwDXb9/BoUbVLxQLIZ4Pkuj6m6tfvz6XL18mPT0dR0dHoHxh9t+7N2onPT1ds4D9vTWZ7tm/fz89e/bkjTfeAMp/2CYlJeHt7V3ltrz//vucPn2aY8eOaSU17O3tcXJy4sKFCwwcOPCB/Vi5ciWFhYWaff/Yj4fp378/CxcuZMmSJbz33nta2+bOnYu+vr5mWl79+vW1Fk+v7Fj3Yt56660HxgD4+/vj7+/PpEmTCAoKYtWqVVVKdD3KoxJhf9S0aVMaN27MzJkzWbBgwWMdMyMjg8TERJYuXUrr1q0B2Ldvn1aMn58fy5Yt486dO5WO6goICCAxMfFPtd3Q0LDCHUQNDCof/SW06enp4exai5TE0/g0LJ9iXFZWxvnE0wS1/XPTl+/xrOvL6I//q1W2/rvPsbV3pk2nEHR0dGjbKYTAFtrrtyyYOZZur75NvQZyly7xZOnp6uBho+RsegZNXOyB8vP87PUMXq774DuCPok67xYVM2vnMfR1dRjXvjEGD1gIWYi/SkdXD2v72ty4dArn2uUXBcvKyrh5+TSeDbs+seOUFBeSl30dt/ptK2xLPbMDJ48mld7hUYgnQU9Pl1o1nTiTnEpT3/LpsWVlZZxJukBw60cvlfIk67x4rXzJECul+QNjhBDPL0l0/U2oVKoKazjp6enRsWNHvLy8GDRoEHPmzCEnJ4ePP/5YK87T0xMXFxfCw8OZOXMmSUlJzJs3TyumTp06/PDDDxw4cAArKyv++9//cuPGjSonuiIjI1m8eDEbNmxAoVBo2npv+tq0adMYNWoUFhYWBAcHo1KpOHbsGJmZmYwdO5bXX3+djz/+mHfeeYdJkyaRlpbG3LlVn74WFBTE6NGjGT9+PEVFRYSEhFBcXMx3332nGanm4uICwKhRo2jZsiVz586lZ8+exMTEaK3PBTB69GgGDx5MYGAgLVu2JCoqirNnz1KrVvlCl6mpqXz11Vf06NEDJycnEhMTSU5O1kqMPWtjxoyhV69eTJgwAWdn5z+9v5WVFTY2Nnz11Vc4Ojpy6dIlPvzwQ62YAQMGEBERQUhICLNmzcLR0ZGTJ0/i5OREUFAQU6ZMoVu3bri6uvLqq6+io6NDXFwcZ86cqbDgv3gyWnXozvffLsLZtRYubnXYv3srRSoVjZuX3z573YqFKC2tCe5ZnsQuKSnhZnr5HUhLS0vIycrg2uVUDI2MsLF1xNDIGAcn7TWI9PUNMTE115SbW1hVugC9hVUNrGvYP83uihfUK/U9+OLAKTxslNS2sSQ6IY3CklLa1a4JwOf747A2NmJAQF2gfLH5K9nlV/dL1Wru3FWRdicHI31dHMxNq1TnvSRXUWkp77byo6C4hILi8huCKA0N0NF5OjcfES8ur4AeHIlZiJV9bWzs65AUu4WS4kI8fDoAcDhmPsamNvi1ehMoX8A+O+OS5t8FeXfIvHkBPQNjzC3LL37G/rYcp1pNMFXaUpCXwZlDa1EodHD1aq117NysdG5dO0frnmHPsMfiRdStXRCfr9pArZqOeLrWZNtvhygsKqJdU38AFkX9iJWFOQO7la/zW1JSyuXrN8v/XVrKnewcUq+kY2xkqBmJ9ag6r9++w74Tp/Cv74XS1IS0a9f5dmMM3rXdcXOS7y1CvIgk0fU3ER0drRmxdU/dunVJSEhgw4YNhIaG0rRpU9zd3Vm4cCHBwcGaOH19fVavXs2IESPw8/OjSZMmzJgxg759+2piwsLCuHDhAp07d8bExIShQ4cSEhJCdrb2EPoH2bNnD6WlpfTo0UOrfOrUqYSHhzNkyBBMTEyYM2cO48ePx9TUFF9fX8aMGQOUJ8Q2b97M8OHD8ff3x9vbm9mzZ1dYHP1h5s+fj5+fH4sXLyYsLAxdXV0CAgLYuHEj3bt318Q1b96cpUuXMnXqVKZMmULHjh0JCwtj+vTpmph+/fqRkpLChAkTKCwspE+fPowYMYKYmBgATExMSEhIYMWKFWRkZODo6Mi7775b6V0fn5Xg4GA8PDyYOXOm1h0iq0pHR4c1a9YwatQoGjRoQN26dVm4cCHt2rXTxBgYGPDLL7/wwQcf0LVrV0pKSvD29ubzzz8HoHPnzmzZsoVPPvmE2bNno6+vT7169RgyZMiT6qb4A7/GLcnLzWHHlrXk5mTh5OLO2+9+jLnSEoCsO7dR/O4OcbnZd/jf76Yl7t25mb07N+Ph6S1TDsXfVpC7IzmFRXwfl0x2gQo3ayUfdmiChXH5aNCM/EJ0fnfX28wCFZO23l+DaOu5VLaeS6W+vTVTXm5WpTrT7uRw/nYWAO9v/E2rPQt7tcPW7OFTuYX4s1y9WqG6m82Zg2sovJuJla0HbUImY2RiCcDdnNsoFPc/zwvy7/DLqg80zxNPbCLxxCbsnH1o37f84lJB3m0O/fxfVIU5GBlbUMOpHi/1+7TCqK3UszsxNrPBwc3/6XdUvNBa+DcgOy+fddG7yMrNw93JkY+HvqlZTP52ZrbWXcwzc3KZOO9LzfPNuw6wedcBvGu7Ez7y7SrVqa+ny5mkVLb9dhiVqggbSyXN/OrT++U2z7DnQoi/E0VZVVf4Fn8rCoWCDRs2EBISUt1NEeJP23NWpi6K55/fxg8eHSTEP9w858ebTi/EP8nEBr9WdxOEeOrMA4MfHfQ3Fba86utOP0szBld+4yfx9MmKq0IIIYQQQgghhBDiuSCJLvG3MHz4cM16X398DB8+vLqbp8XHx+eBbY2Kinph2iCEEEIIIYQQQvzdyBpd/1DP24zTTz75hHHjxlW6TalUPuPWPNy2bdsoLi6udJu9/bNZ8PLv0AYhhBBCCCGEEOLvRhJd4m/Bzs4OOzu76m5Glbi5Pd7t7p+3NgghhBBCCCGEEH83MnVRCCGEEEIIIYQQQjwXJNElhBBCCCGEEEIIIZ4LkugSQgghhBBCCCGEEM8FSXQJIYQQQgghhBBCiOeCJLqEEEIIIYQQQgghxHNBEl1CCCGEEEIIIYQQ4rkgiS4hhBBCCCGEEEII8VyQRJcQQgghhBBCCCGEeC5IoksIIYQQQgghhBBCPBck0fU30K5dO8aMGaN57u7uzvz585/a8dLS0lAoFMTGxj61Y9yzfPlyLC0tHxoTHh5Oo0aNnnpbhBBCCCGEEEII8XzTq+4GVKfBgwezYsWKCuWdO3cmOjq6GlpU7ujRo5iamlbb8SuzYsUKFi1axNmzZ9HV1SUgIIDx48fTrVu36m6axvLly3n77bcrlC9dupQhQ4ZobVcoFDg5OdGpUydmz56NnZ2dpvwec3Nz6tatS1hYGD179nw2naiC6OhoJk2aREJCAtbW1vTs2ZPFixdXd7PEU3RwTzR7d2wiNycLx5pudO8biot7nUpjb6RfZseWNVy9dIHMO7d4pc9gWnXQ/n+6Y+tadm77Xqushp0TH0xdCEBmxk3+M+XfldY/IHQsfgEtnkCvhND2S+JFNp9NJbtQhauVOYObeONZw7LS2CtZuXwfl8yFjBxu5xfwZmA9utb30IrZeCaFo5ducDU7D0M9XerUsOT1xnVxUpppYpYdOsPp9NtkFqgw0tPFy9aKAQF1cbYw++MhhXgikuO2kXh8E4X5mVjauuPfbgg2Dl6VxmZnXOLMwTVk3kwhP+cmjdq8Td2AHloxZWo1Zw6t4WLCHgrvZmFsao27d3u8m/bV+k6Tk3GZuP0ruXXlLGVlpSitXWjxygRMlbZPtb/ixRS97wibd+0nKycPNycH3u7dhTpuNSuNvXz9Jut+3sWFK9e4dSeLQSHBvNI26IF1b9y5l1VbdtC1TXMG9+qiKd9+4Bj7T5wm9Wo6BYUqIiMmYWps9MT7JoT4Z3ihE10AwcHBREZGapUZGhpWU2vK2dr+vb50jBs3jkWLFjFjxgxCQkIoLi7mu+++o2fPnixYsICRI0dWdxM1lEoliYmJWmUWFhYVtqvVauLi4nj77be5du0aMTExmpjIyEiCg4PJyclh8eLFvPrqq5w4cQJfX99n1o8HKSwspHfv3vTt25fvv/+evLw8jhw5UqV9i4uL0dfXf8otFE/aqeP72bp+OSEDhuLq7sW+XVv4ZtEMPpi6EDNziwrxxUUqrGzsaOAfxNb1yx9Yr72jC6HvTdE819HV1fzbwqoGH0Us1Yo/sn8Hv+3YRF2fgL/eKSH+4GBaOiuPxRParAGeNSz5OSGNWTuP8lnPNiiNKv5NVpWUYmtmQjNXB1YeT6i0zvgbd3i5riu1bSwoVZexJjaJiB1Hmdu9NUb65V9/3K2VtPBwwtbUmDxVET+cOk/EjqP8r1c7dHQUldYrxOO6lLSP2N8iCewwHBsHLxJPbua3DZ/QZdDnGJlU/DwvLVZhqrTDpU4Qsb9FVlIjxB/7kZRT0TR9eRQWNVy5c+M8R375H/qGJng1Kr/IkZuVzq/ff4yHz0s0aN4fPQNjcjIuo6tn8FT7K15MB06e4dtN0bzzanfquNVk628HmblkJQs+GoWFWcUL+aqiYuxsLGne0JsVm2IqqfG+85eusv3AMVyd7CtsKyouplF9TxrV92TVlh1PrD9CiH+mF37qoqGhIQ4ODloPKysrAJKTk2nTpg1GRkZ4e3uzfft2FAoFGzduBGD37t0oFAqysrI09cXGxqJQKEhLSwMgIyODAQMG4OzsjImJCb6+vqxevfqhbfr91MXly5ejUCgqPMLDwzXxy5Yto379+hgZGVGvXr0Ko3uOHDmCv78/RkZGBAYGcvLkySq/PocOHWLevHnMmTOHcePG4enpSf369Zk5cyZjxoxh7NixXL58WRO/fPlyXF1dMTExoVevXmRkZFSo89NPP8Xe3h5zc3NCQ0MpLCzU2r57926aNm2KqakplpaWtGzZkosXL1apvQqFosL7aWxsXGG7k5MTXbp0YdSoUezYsYOCggJNjKWlJQ4ODnh5eTF9+nRKSkrYtWtXlY4fFxdH+/btMTc3R6lU0rhxY44dO6bZvm/fPlq3bo2xsTEuLi6MGjWK/Px8AL799lvMzMxITk7WxP/73/+mXr163L17V1Omq6vLwIED8fT0pFGjRgwdOrRCO+5NT127di1t27bFyMiIqKioKp2ParWa//znP3h6emJoaIirqyszZ87UbL98+TKvvfYalpaWmhFl98538eTt+3UzTVp2JDCoA3aONek1YBgGBgYcO/hrpfE13Tzp2nsQDQNboaf34MSmjo4u5hZWmoepmfJ323S0tplbWHEu7jB+AS0wNJSro+LJ2xqfSoc6LrTzrElNSzOGNPPBUFeXXeevVBpfu4YlbzSuRwsPJ/R0Kv8qM+mlJrStXZOalua4WSsZ0cKXjPxCLtzJ0cR09HLF294aWzNjPGws6NeoDnfuFnIz/26ldQrxVySd+IlaDTrh4fMSShsXAl8aga6eIalnd1Yab+1Qh0ZtBuNatzU6upV/nmekJ+JcuylOtQL/PynWAgc3f+5cv/9d4syBKBw9AmjYehBWdrUwt3TEuXbTSpNrQvxVW3Yf5KXmjWnfzJ+aDrYM7dsdQwN9dh0+UWm8p6szb/boTMsAX/T1dCuNAShUFfG/79Yz7LUemP3uu/09r7QNIuSl1g8cOSaEeLG88ImuB1Gr1fTu3RsDAwMOHz7Ml19+ycSJE/90PYWFhTRu3JitW7dy5swZhg4dyptvvlnlUTj9+vUjPT1d81i9ejV6enq0bNkSgKioKKZMmcLMmTOJj48nIiKCyZMna6Zk5uXl0a1bN7y9vTl+/Djh4eGMGzeuyu1fvXo1ZmZmDBs2rMK2Dz74gOLiYtavXw/A4cOHCQ0NZeTIkcTGxtK+fXtmzJihtc+6desIDw8nIiKCY8eO4ejoqJWYKykpISQkhLZt23Lq1CkOHjzI0KFDtYbfP0nGxsao1WpKSkoqbCspKeHrr78GwMCgalc9Bw4cSM2aNTl69CjHjx/nww8/1IyiSklJITg4mD59+nDq1CnWrl3Lvn37NCPi3nrrLbp27crAgQMpKSlh69atLFu2jKioKExMTAAwMjKic+fOTJgwgTt37jyyPR9++CGjR48mPj6ezp07V+l8nDRpEp9++imTJ0/m3LlzrFq1Cnv78itnxcXFdO7cGXNzc/bu3cv+/fsxMzMjODiYoqKiKr1GoupKSkq4eukCnvX8NGUKhQLPen5cSk36S3XfvpXOrI/e4T9T/s2ayPlk3bn1wNgrl1K4diWNwKAOf+mYQlSmpFRNakYODRxraMoUCgUNHG1IvpX1xI5zt7j8c97coPKEQWFxCbtTrmJrZkwNk4o/ooT4K9SlJdy5kYKDa0NNmUKhwN7Vj4z0xIfs+XA2jnW5cfk0OZlXAci6lcrtq+dwdC8ffVtWVsa11OOYWTqxZ8M0Ni0ZzI41E7iacvivdUiISpSUlHLhyjV8vWppyhQKBb5etUlKq/zCRVUtW7+VAG8v/OrW/qvNFEK8AF74qYtbtmzBzEx7LY6PPvqIwMBAEhISiImJwcnJCYCIiAi6dOlSWTUP5OzsrJVYeu+994iJiWHdunU0bdr0kfsbGxtrRiSlpKTw7rvvEhERQadOnQCYOnUq8+bNo3fv3gB4eHhw7tw5lixZwqBBg1i1ahVqtZqvv/4aIyMjfHx8uHLlCiNGjKhS+5OSkqhdu3aliR4nJyeUSiVJSeU/uBcsWEBwcDATJkwAwMvLiwMHDmitdzZ//nxCQ0MJDQ0FYMaMGezYsUMzqisnJ4fs7Gy6detG7drlf8jq169fpbYCZGdna72fZmZmXL9+vdLY5ORkvvzySwIDAzE3N9eUDxgwAF1dXQoKClCr1bi7u/Paa69V6fiXLl1i/Pjx1KtXD4A6de6vozRr1iwGDhyoufFAnTp1WLhwIW3btuWLL77AyMiIJUuW4Ofnx6hRo/jxxx8JDw+ncePGmjqmTZvGyZMn6d+/P23bttU6P9977z1SU1PZsmWLJn7MmDGac+Oeh52Pubm5LFiwgEWLFjFo0CAAateuTatWrQBYu3YtarWaZcuWaZKPkZGRWFpasnv3bl5++eUqvU6iau7m56BWqytMUTQzt+DWjauPXa+LuxevvvkutvbO5GbfYee271ny38mMCfsMQ6OKP/CPH/gVO4eauNWu99jHFOJBclVFqMvKsDDS/jtjYWTItZz8J3KMsrIyvj0aj5etJS5W5lrbtideJOpEIqqSUhyVpnzUsQl6unIdUDxZqoIcysrUGP5hFJWRiSW5mY//eV6/SR9KigqI/vY9FAodysrU+LYYiFu9tgAU3s2ipLiQhGM/0iDodfxavsn1i7Hs3zKbdn2mY1fT5y/1S4jfy8m/i1qtxtJc+7eVpZkp127cfux69584TeqVdGa9/85fbaIQ4gXxwie62rdvzxdffKFVZm1tzcqVK3FxcdEkEQCCgh68MOKDlJaWEhERwbp167h69SpFRUWoVCrNCJ2qupf8eeWVVxg/fjwA+fn5pKSkEBoayjvv3P/gLykp0axLFR8fj5+fH0ZG96cb/dl+lJWVVSkuPj6eXr16aZUFBQVpJbri4+MZPnx4hZh7UwOtra0ZPHgwnTt3plOnTnTs2JHXXnsNR0fHKrXB3NycEyfuD43W+cOUlnuJMLVaTWFhIa1atWLZsmVaMZ999hkdO3bkwoULvP/++yxcuBBra+sqHX/s2LEMGTKElStX0rFjR/r27atJ2MXFxXHq1CmioqI08WVlZajValJTU6lfvz5WVlZ8/fXXdO7cmRYtWvDhhx9qYjMzM5k1axY//vgjXbt2RVdXl5YtW/LLL79Qp04dTp8+XSERGxgYqPX8UedjfHw8KpWKl156qdL+xcXFcf78ea3EIJSPXExJSal0H5VKhUql0iorKirFwKB618J7kdX18df829HZDRd3L/4zeQSnTxwgsIX2e19cXETssX10CO7zrJspxBPzzZGzXM7KI7xzswrbWng40cCxBpkFKraeS2XBb7FM69wcg4dMoRHi7+Jy0j4uJv5G8+D3Udq4kHUrjdg932BkaoWHdwf4/+9wTrWaaBayt7Krxe30BFJOR0uiS/ztZWRlE7nhZyaPGISBrDUrhKiiFz7RZWpqiqen52Ptey+J8vtEUHFxsVbMnDlzWLBgAfPnz8fX1xdTU1PGjBnzp6Z5lZaW0q9fP5RKJV999ZWmPC8vDyi/q2CzZtpf3nV1n8wXdC8vL/bt20dRUVGFUV3Xrl0jJycHL6/K7xb0uCIjIxk1ahTR0dGsXbuWsLAwtm/fTvPmzR+5r46OzkPfz3uJMB0dHRwdHbXW77rHwcEBT09PPD09iYyMpGvXrpw7d05zZ8aHCQ8P5/XXX2fr1q38/PPPTJ06lTVr1tCrVy/y8vIYNmwYo0aNqrCfq6ur5t+//fYburq6pKenk5+fr0kqJSYmolKp8PcvT1J88skn5OTk0KpVK+bPn8+hQ4e0kmhAhbt3Pup8rOz1+L28vDwaN25c4Tjw4JsozJo1i2nTpmmVDRrxEW+/+/FDjyXAxFSJjo4OebnZWuV5udmYKa2e2HGMTUypYe/I7VvpFbadOXmQ4iIV/s3aPrHjCfF75oYG6CgUZBdq/13MLlRhWclC9H9W5JGznLhyi6mdm2FjWvEzztRAH1MDfRyVpnjVsCR03Q6OXb5BCw+nSmoT4vEYGitRKHRQ3dX+PC+8m4WRyeN/nsft+5Z6gb1wrdsaAMsa7tzNuUX80fV4eHfA0FiJjo4uFtauWvsprWty+1r8Yx9XiMooTU3Q0dEhKzdPqzwrLx9L5ePdzTbl8jVy8vKZOO9LTZlarSb+wkWi9x1h1ZzJFS5sCyGEfCo8QP369bl8+TLp6fd/+B06dEgr5t4P+9/HxMbGasXs37+fnj178sYbb9CwYUNq1aqlmepXVe+//z6nT59m48aNWiOz7O3tcXJy4sKFC5rEzL2Hh4eHph+nTp3SWvD9j/14mP79+5OXl8eSJUsqbJs7dy76+vr06dNHc6zDh7XXfPjjsaoSA+Dv78+kSZM4cOAADRo0YNWqVVVu88PcS4TVqlXrkUkdgKZNm9K4cWOtxdgfxcvLi/fff59ffvmF3r17a+7qGRAQwLlz5yq8V56enpok4oEDB5g9ezabN2/GzMxM646Wzs7OQHki7J7PPvuMbt268frrrzNs2DBNzIM86nysU6cOxsbG7NxZ+cK4AQEBJCcnY2dnV6EPv7+75e9NmjSJ7OxsrcfAd6q+TtyLTE9PD2fXWqQkntaUlZWVcT7xNK4eTy7BrFIVknHrOkqLiiMXj+7fSX3fxpXe4VGIJ0FPVwcPGyVn0+/fvKSsrIyz1zOoY2v52PWWlZUReeQsRy/dIKxTU+zMHj2SuowyysrKKFarH/u4QlRGR1cPa/va3Lh0SlNWVlbGzcunsXGs+9j1lharUKC9jqlCR0czkqv8uJ7kZmlPj8zNvIaJeQ2EeJL09HSpVdOJM8mpmrKysjLOJF3Ay/3xFon3rVOLuRP+zX/GDdc8ars607qxH/8ZN1ySXEKISr3wnwwqlYrr169rPW7fvk3Hjh3x8vJi0KBBxMXFsXfvXj7+WHsEiqenJy4uLoSHh5OcnMzWrVuZN2+eVkydOnXYvn07Bw4cID4+nmHDhnHjxo0qty8yMpLFixfz5ZdfolAoNG28N5pr2rRpzJo1i4ULF5KUlMTp06eJjIzkv//9LwCvv/46CoWCd955h3PnzrFt2zbmzp1b5eMHBQUxevRoxo8fz7x580hJSSEhIYGwsDAWLFjAvHnzcHFxAdCMwpo7dy7JycksWrRIa9oiwOjRo/nmm2+IjIwkKSmJqVOncvbsWc321NRUJk2axMGDB7l48SK//PILycnJf2qdridtzJgxLFmyhKtXH76GRkFBASNHjmT37t1cvHiR/fv3c/ToUU3bJ06cyIEDBzSL9ScnJ7Np0yZNMis3N5c333yTUaNG0aVLF6Kioli7di0//PADAC4uLvTv3593332Xb7/9lpSUFHbu3ElKSgqmpqb89NNP3Lx586FtfNT5aGRkxMSJE5kwYYLmGIcOHdIsyj9w4EBq1KhBz5492bt3L6mpqezevZtRo0Zx5Urli4waGhqiVCq1HjJtsepadejO0f07OH5oFzfTr7BxzVcUqVQ0bt4egHUrFhK96TtNfElJCdcup3LtciqlpSXkZGVw7XIqGb8brbXtxxVcSD5LZsZNLqYk8N1X/0FHRwe/xi21jp1xK520lHgCW3R8Np0VL6xX6nvw6/nL7Em5wpWsPL4+fJbCklLa1S7/YfT5/jhWn7i/YHdJqZq0Ozmk3cmhVK3mzl0VaXdyuJ57f02vyCPn2Jd6jZGtG2Gsr0dWgYqsAhVFJaUA3Mi9y8YzKVzIyOZ2fgGJNzOZ/1sshnq6+DtXPkJViL/CK6AHF85sJ/Xcr+RkXOb4r19SUlyIh0/5jT4Ox8zn1L6Vmnh1aQmZNy+QefMC6tISCvLukHnzArlZ9z/PnWoFEn90PdcuHCM/5yZXzh8i8cRPOHveHwVft3EIl5L2k3L6F3Kz0kmO3cq1C0fx9Ov67DovXhjd2gWx8+Bxdh85yZXrt1j6/RYKi4po17R8RsKiqB+J2rJdE19SUkrqlXRSr6RTUlrKnewcUq+kc/12+U2XjI0McXW013oY6utjZmKMq6O9pp7MnFyt/S5du0HqlXRy5S66QryQXvipi9HR0RXWf6pbty4JCQls2LCB0NBQmjZtiru7OwsXLiQ4OFgTp6+vz+rVqxkxYgR+fn40adKEGTNm0LdvX01MWFgYFy5coHPnzpiYmDB06FBCQkLIztYeuv4ge/bsobS0lB49emiVT506lfDwcIYMGYKJiQlz5sxh/PjxmJqa4uvrq1nw3MzMjM2bNzN8+HD8/f3x9vZm9uzZmlFYVTF//nz8/PxYvHgxYWFh6OrqEhAQwMaNG+nevbsmrnnz5ixdupSpU6cyZcoUOnbsSFhYGNOnT9fE9OvXj5SUFCZMmEBhYSF9+vRhxIgRxMTEAGBiYkJCQgIrVqwgIyMDR0dH3n333Urv+visBAcH4+HhwcyZM7XuEPlHurq6ZGRk8NZbb3Hjxg1q1KhB7969NdP2/Pz82LNnDx9//DGtW7emrKyM2rVr069fP6A8CWhqakpERAQAvr6+REREMGzYMIKCgnB2dmbFihXMnTuXmTNncvHiRZydnXnjjTdYv349L730Ej169NCsd1aZqpyPkydPRk9PjylTpnDt2jUcHR0166qZmJjw22+/MXHiRHr37k1ubi7Ozs689NJLKJXKv/xai4r8GrckLzeHHVvWkpuThZOLO2+/+zHmSksAsu7cLr96//9ys+/wv0/Ha57v3bmZvTs34+HpzdD3PwEgOyuDtZHzyc/LxdRciXvteowYF1Fh1NaxA7+itLTGy7vRU++neLEFuTuSU1jE93HJZBeocLNW8mGHJlgYlyfFM/IL0fnd3XczC1RM2rpf83zruVS2nkulvr01U14un8q/PekSANN/0R5FPLyFL21r18RAV4eEG3f4OT6Nu0XFKI0MqWdvxbTg5iifwJRJIf7I1asVqrvZnDm4hsK7mVjZetAmZDJGJpYA3M25jUJx//O8IP8Ov6z6QPM88cQmEk9sws7Zh/Z9y+9q7d/uHc4cXMXxXV+hKsjG2NSa2r4v49Osn2a/mp7NadxhGPFH13Nyz9eYWznRsttEbJ2r7yKieH618G9Adl4+66J3kZWbh7uTIx8PfVOzQP3tzGytu6ln5uRqTUvcvOsAm3cdwLu2O+Ej367ycbcfOMYPMbs1z6cu+gaAfw8I0STZhBAvDkVZVVcaF0D5LXI3bNhASEhIdTdFiH+sPWfl6pp4/vlt/ODRQUL8w81zXlDdTRDiqZvY4NfqboIQT515YPCjg/6mwpZXff3rZ2nGYINHB4mn4oWfuiiEEEIIIYQQQgghng+S6HrBDR8+HDMzs0of96ar/V34+Pg8sK2V3QXweW2DEEIIIYQQQgghKvfCr9H1Zz1vMz0/+eQTxo2r/A54f7c1l7Zt20ZxcXGl2+zt7Sstfx7bIIQQQgghhBBCiMpJousFZ2dnh52dXXU3o0rc3Nyquwl/izYIIYQQQgghhBCicjJ1UQghhBBCCCGEEEI8FyTRJYQQQgghhBBCCCGeC5LoEkIIIYQQQgghhBDPBUl0CSGEEEIIIYQQQojngixGL4R45vw2flDdTRDiqdPr3LO6myDEU9fJuKS6myDEUzf7aIfqboIQT92MwOpugRBPjozoEkIIIYQQQgghhBDPBUl0CSGEEEIIIYQQQojngiS6hBBCCCGEEEIIIcRzQRJdQgghhBBCCCGEEOK5IIkuIYQQQgghhBBCCPFckESXeCCFQsHGjRuruxlCCCGEEEIIIYQQVaJX3Q0Qz97gwYNZsWIFAHp6elhbW+Pn58eAAQMYPHgwOjrl+c/09HSsrKyqs6lVkpaWhoeHBydPnqRRo0ZPrF6FQsGGDRsICQl5YnU+DeHh4WzcuJHY2Njqbop4Cs7duMOWsxdIu5NDZoGKse0CaOJi//B9rmfw7fF4rmblYWNqTC/f2rStXVOzfXviRXYkX+Zm3l0AalqY08fPk0bOtpqYrAIVUScSOH3tNgUlpTgqTejl60kzV4en01HxQjt3Po2fdu0n9Wo6mdm5jPtXf5r61n/oPmfPp7JiYwxXbtykhqUFvTu1oV1Tf832gkIVa3/+lSOn48nJu4u7swODe3XB09VZE1OoKiJqy3aOnk4g724BttaWdGndjJdbNnlqfRUvrgvJZ9m74yeuXb5ATnYmbwydgE/Dpg+Mz8m+w7b1K7h6+QIZt67Tol1Xur36tlbMV59NIfX8uQr71vUJYPC/P9I8v5l+hehN35GafI5SdSn2jjUZOGQclta2FfYV4q+4eeUsicc3knnzAgX5d2jV/UOcazd7YPyV5IOcPx1D1q1U1KXFKK1d8GneH0f3+5/n5+N+JuV0DPk5NwH+P6Yfju4BFeorKytj76YZpKedeOSxhRDPL0l0vaCCg4OJjIyktLSUGzduEB0dzejRo/nhhx/46aef0NPTw8Ghen/QFhUVYWBgUK1tEKK6FZWU4malpJ1nTT7bc/KR8Tfz7jJ713E61nFhZMtGnL2ewVcHz2BpbEhDp/IfNNamRvT398LB3JQyytibco15u48z65WW1LQ0B+Dz/XHcLSphXPvGmBsasD/tGgt+O0lE15a4Wyufap/Fi0dVXIybswPtmwUwL3LNI+NvZmTy6dIoOrVowqg3+nA6+QJfrv0JS6U5jep5AvDl2k1cSr/JyNd7Y22p5Ldjccz44lvmTXwXG8vyc3jFpmjOJqfy3hu9sbO2IjbhPF+v34qVhTlNGtR7qn0WL57ioiIcnd0IDOrAd0vnPDK+tKQEU3ML2gf3Yf+vWyqNeWPoeEpLSjTP797NY2HEB/gGBGnKMm6ls+SzMAKDOtDxlX4YGhlxI/0KevryHUs8eaUlKixt3fHweYn9W2Y/Mv7WtXM4uDbEr8VA9A1NST33K/s2R9Cx32ys7GoBYGxeA9+Wb2Ju6UgZZVyM382+zbN4+fV5WNi4atWXdHLzU+mXEOKfRaYuvqAMDQ1xcHDA2dmZgIAAPvroIzZt2sTPP//M8uXLAe2pi0VFRYwcORJHR0eMjIxwc3Nj1qxZmvoUCgVffPEFXbp0wdjYmFq1avHDDz9oHXPixIl4eXlhYmJCrVq1mDx5MsXFxZrt4eHhNGrUiGXLluHh4YGRkREA0dHRtGrVCktLS2xsbOjWrRspKSma/Tw8PADw9/dHoVDQrl07zbZly5ZRv359jIyMqFevHosXL36s1ystLQ2FQsG6deto3bo1xsbGNGnShKSkJI4ePUpgYCBmZmZ06dKFW7duafYbPHgwISEhTJs2DVtbW5RKJcOHD6eoqEgT86j+AVy5coUBAwZgbW2NqakpgYGBHD58mOXLlzNt2jTi4uJQKBQoFArN+/cgZWVlhIeH4+rqiqGhIU5OTowaNUqzXaVSDQidUgABAABJREFUMW7cOJydnTE1NaVZs2bs3r0bgMLCQnx8fBg6dKgmPiUlBXNzc7755pvHem3FwzVytqWfvxdNqziSakfSJezMjHkzsD41Lc3oXM+NZm72bItP08Q0rmmPv7MdjkpTnJRm9PP3wlBPj+TbWZqY5FtZBNdzw7OGJfbmJvT29cTUQJ8LGdlPuIdCgH/9Ogzo+hLN/B4+iuue7QeOYWttxVs9O1PTwZYurZvRvKE3W/ccBKCouJjDp+J5o3snvD3dcahhzWvB7bGvYcUvB45q6klMvUybJg3x8fTA1tqSTi0CcXOy5/ylq0+ln+LFVtfHn5d7vI5Po6qNMLGysaN7338R0KwdhkYmlcaYmJpjbmGleSTHx6FvYEgD//uJrl82r6auTwBder2Fk4sHNraOePs1wczc4on0S4jfc3QPwLfFQGp6Nq9SvH/bUOoF9sLaoQ7mVk74tXwDMwsHrqUe08Q412qCk0djzK2cUFo549tiIHr6RmRcT9KqK/PmBZJO/ESTTiOfaJ+EEP88kugSGh06dKBhw4b8+OOPFbYtXLiQn376iXXr1pGYmEhUVBTu7u5aMZMnT6ZPnz7ExcUxcOBA+vfvT3x8vGa7ubk5y5cv59y5cyxYsIClS5fy2WefadVx/vx51q9fz48//qiZipefn8/YsWM5duwYO3fuREdHh169eqFWqwE4cuQIADt27CA9PV3T/qioKKZMmcLMmTOJj48nIiKCyZMna6ZtPo6pU6cSFhbGiRMn0NPT4/XXX2fChAksWLCAvXv3cv78eaZMmaK1z86dO4mPj2f37t2sXr2aH3/8kWnTpmm2P6p/eXl5tG3blqtXr/LTTz8RFxfHhAkTUKvV9OvXjw8++AAfHx/S09NJT0+nX79+D+3D+vXr+eyzz1iyZAnJycls3LgRX19fzfaRI0dy8OBB1qxZw6lTp+jbty/BwcEkJydjZGREVFQUK1asYNOmTZSWlvLGG2/QqVMn/vWvfz326yqenORbWTRwsNEq83O0JflWVqXxanUZB1KvoSopoU6N+1OV69hacjAtnVxVEWVl5TFFpWq8HayfZvOFqJKktMv4edXSKmtY15PktCsAlJaqUavVGOjra8UY6OuTeOGS5nldDxeOn00iIyuHsrIyziSnkn4rg4ZetZ9+J4R4Co4d/BW/xi0xNCy/WFhWVkbCmRPY2DryzaLpzJj4LxbP+ZCzcUequaVCVK6srIyS4kIMDE0r365WcylxLyXFhdRwqKspLylWcSj6MwLaD8XY9O+/9IoQ4umSqYtCS7169Th16lSF8kuXLlGnTh1atWqFQqHAzc2tQkzfvn0ZMmQIANOnT2f79u3873//04yiCgsL08S6u7szbtw41qxZw4QJEzTlRUVFfPvtt9ja3l8zok+fPlrH+eabb7C1teXcuXM0aNBAE2tjY6M13XLq1KnMmzeP3r17A+Ujv86dO8eSJUsYNGjQn35tAMaNG0fnzp0BGD16NAMGDGDnzp20bNkSgNDQ0AojqgwMDPjmm28wMTHBx8eHTz75hPHjxzN9+nR0dHQe2b9Vq1Zx69Ytjh49irV1eZLB09NTE29mZvanpppeunQJBwcHOnbsiL6+Pq6urjRt2lSzLTIykkuXLuHk5KTpc3R0NJGRkURERNCoUSNmzJjBkCFD6N+/PxcvXmTLlsqnVED5CDGVSqVdVlKKoZ5uldor/pysAhUWxoZaZRbGBhQUl1BUUorB/7/ulzJzmBJ9iOJSNUb6uoxtF0BNSzPNPmPa+LPgt1iGrtuJro4CA11dPmgbgIN55V88hXiWsnLzsDA30yqzMDflbmEhRcXFGBsZ4uXuwvpf9uBsb4uluSn7TpwmKe0yjrb3E8H/6t2VJes2M2LaPHR1dVEoYNhrPfD2dH/GPRLir7uclsyNa5foM3CEpiwvJ4siVSF7tm/k5e4DCO75BknxsUQtncOQ0eHUquNTjS0WoqLE4xspKS7ExauVVnnW7TR2rp1EaUkR+gbGtOz2IUobF8322N++oYZjXZxrP3jdOyHEi0NGdAktZWVlKBSKCuWDBw8mNjaWunXrMmrUKH755ZcKMUFBQRWe/35E19q1a2nZsiUODg6YmZkRFhbGpUuXtPZxc3PTSnIBJCcnM2DAAGrVqoVSqdSMJPvjvr+Xn59PSkoKoaGhmJmZaR4zZsyoMC3wz/Dz89P8296+fEHw34+Gsre35+bNm1r7NGzYEBOT+1MOgoKCyMvL4/Lly1XqX2xsLP7+/pok11/Vt29fCgoKqFWrFu+88w4bNmyg5P/X9zh9+jSlpaV4eXlpvW579uzRet0++OADvLy8WLRoEd988w02NjYPOhyzZs3CwsJC6/HZnhNPpC/i8Tkpzfi0W0umdwmiYx1Xvth/iitZeZrt38cmc7e4mI87NmFmlxa8Ut+dBXtPcikzpxpbLUTVjRxYfpFjePhcXh8/nZ/3HqZVgC+//xMXvfcw5y9eYcKQ1/l07DDe7NGZr9dv5VTi4/+dEKK6HDv4Kw7Obri419GUlVEGgLdvIK06dMPJxYN2L/eiXoPGHNlb8bucENXpYsJvnD28lqCu4zAy0Z5aq7SqycsD/0vH/rOp7duZI78sJCej/Lv01ZQj3Lh8Cv92Q6qj2UKIvyEZ0SW0xMfHa9a8+r2AgABSU1P5+eef2bFjB6+99hodO3assA7Xgxw8eJCBAwcybdo0OnfujIWFBWvWrGHevHlacaamFUeLdO/eHTc3N5YuXYqTkxNqtZoGDRporXP1R3l55T/Yly5dSrNm2mth6Oo+/kgi/d9Ng7mXEPxj2b0ph1X1qP4ZGxs/dnsr4+LiQmJiIjt27GD79u38+9//Zs6cOezZs4e8vDx0dXU5fvx4hdfJzOz+6ImbN2+SlJSErq4uycnJBAcHP/B4kyZNYuzYsVpldz8b90T7JO6zNDYku0B7BF12QRHG+nqa0VwAero6mtFZtWwsuJCRTXRCGkOaN+B6bj4xiReZ072VZnF6N2slCTcz+SXxEkOaN3h2HRKiEpbmZmTn5mmVZefmY2JkpJmu6FDDmvCRb1OoKuJuoQprC3M+W7EOO5vyiwZFxcWs3raTDwb3o7FP+fQXNyd70q5eZ/PuA/jVlemL4p9DpSrk1LH9dOymvXyBiakSHV1d7BxdtMptHZy5mJLwLJsoxENdStzL0R2f06LreBxcG1bYrqOrh7mlIwDW9p7cuXGepNgtBL40gptXTpOffYMfFw/U2mf/ltnYOnnTvu+MZ9IHIcTfhyS6hMavv/7K6dOnef/9/2PvvqOiOt4Gjn+XvrBLR3pVsCAg2GLHFrGXWEOKPY0YNWpMUUETNcUkmpj8Eo2ixkjU2GJv0VhjRxRFRRALCtJBOrx/8Lq6ASNRlKjP5xzOcec+Mztz97qwz87MHVPhcVNTUwYMGMCAAQPo27cvQUFBpKamamYaHTx4kFdeeUUTf/DgQfz9y24NvH//flxdXfnwww81xy9dunTfPqWkpBATE8O8efNo1aoVAHv37tWKuX1nxuLiYk2Zra0tDg4OXLx4keBg7V96j1tkZCS5ubmahNXBgwdRqVQ4OztXany+vr7Mnz9f61zfzcDAQGvslaFUKunevTvdu3fnrbfeok6dOkRFReHv709xcTFJSUma/lRk6NCh+Pj4MGzYMEaMGEGHDh2oW7fiTaQNDQ0xNNReSlcsyxYfGU8bc05cTdYqO5l4E08b83+sV0ophf+fpC0oun09ac/uVCgU/z83QIjq5eXmzLEz57XKTp6LxdPNqVyskaEBRoYGZN/KJTImluBuHQEoKi6mqKgYHR3tye26OjqUlMiVLp4sp44foKioEP8mrbXK9fT0cHKpyc2ka1rlN28kYm5h/Ti7KMQ9JcTs4dC2b2nW+V0cPBpVqk4ppZQUl93Uqk6jPnh4d9A6vvnn0TRoMxQH98ZV3l8hxH+fJLqeUfn5+Vy/fp3i4mJu3LjB5s2bmTFjBt26ddNKVt325ZdfYm9vj7+/Pzo6OqxYsQI7OzvMzc01MStWrKBRo0a0bNmSpUuXcujQIX766ScAPD09SUhIICIigsaNG7NhwwZWr159335aWFhgZWXFjz/+iL29PQkJCUycOFErpkaNGiiVSjZv3oyTkxNGRkaYmZkRFhbGqFGjMDMzIygoiPz8fI4cOUJaWlq5GUaPUkFBAcOGDeOjjz4iPj6eKVOmEBISgo6OTqXGN2jQIKZPn06vXr2YMWMG9vb2HD9+HAcHB5o1a4abmxtxcXGcOHECJycn1Gp1ucTS3cLDwykuLqZp06YYGxvz888/o1QqcXV1xcrKiuDgYF555RVmzZqFv78/ycnJ7NixA19fX7p27crcuXM5cOAAJ0+exNnZmQ0bNhAcHMzBgwc1SUdRdfIKi7iedUvzODn7FvGpmagM9bE2UbLsWAypuXm81aLs288OXi5siUlg6dGzBNZyIvp6Cn9dus6Edg01bSw7FoOfow02JkpyCwvZF5dI9I1U3m9f9segg6kKW7UxP/11iuCGdVAZ6nPkchKnrt9kfNuGCFHV8vILSExO0TxOTk0n7koiahMl1hbmLF2/jbSMLM1yxI7NG7F571/8vG4rbZv6c+pCHAdOnGbiiDtfbJw4e4HS0lIca1hz/WYqS37fimMNa9o2KfsCxtjIiHo13ViybisG+nrYWJgTHRvPn0ciebnn84/3BIhnQn5+HilJiZrHaSlJXLsch7GJCnNLGzav/ZnM9FT6v3rnTsjXLscBUFCQR3ZWBtcux6Gnp08Ne+2k7pH9O6jn1xhjE3W5523doSfLFnyFW6261PSqT8zpE5w9dYQRo8PKxQrxsIoK88hKu5NYzc64QVrSRQyM1JiY2nBy7xJyc1Jo2mk0ULZc8dDWOfi3GYalnSe5OWkA6OoZaDakP7l3CXZuAZiY2lBYcIuEs3tIvnKKur2nAKA0sahwA3oTtQ0qM9tHPGIhxH+RJLqeUZs3b8be3h49PT0sLCzw8/Njzpw5vPrqq+W+3YayOyZ+9tlnnD9/Hl1dXRo3bszGjRu1YsPCwoiIiODNN9/E3t6eZcuWUa9ePQB69OjBmDFjCAkJIT8/n65duzJp0iRCQ0P/sZ86OjpEREQwatQo6tevT+3atZkzZw6BgYGaGD09PebMmcPUqVOZPHkyrVq1YteuXQwfPhxjY2M+//xzxo8fj4mJCT4+PowePboqTmGltW/fHk9PT1q3bk1+fj6DBg3SjLsy4zMwMGDr1q28++67dOnShaKiIurVq8fcuXOBss36V61aRdu2bUlPT2fhwoUMHjz4nv0xNzdn5syZjB07luLiYnx8fPj99981+2wtXLiQjz/+mHfffZerV69ibW3Nc889R7du3Th79izjx4/np59+wtm5bBnEd999h6+vL5MmTeLTTz99JOfwWXYxJYNp2+7cHWvJkbKlJq1rOvJGc1/S8/JJycnTHK+hMua9tg1ZfPQMm8/GY2lsxMhm9fFzuLP3XWZ+Ad/vO0l6bh7GBvq4mKt5v31jfOzLvt3X09XhvXaNiDgew+d/HCWvqBg7tTFvNPfF37HGYxq5eJbEXr5K2NxwzeNFazYD0KZxA956sTfpmdncTMvQHK9hZcHEEcEsWrOFjXsOYmVmxusDetCgzp0bdeTm5fHL+h2kZGSgMjamqW9dBnZpj95dM0rfeaUvv6zfzpyffyPnVh7WFmYM7NKO55vLDABR9a5eusC82aGaxxt+Cwcg4LlA+r0cQlZmOumpN7XqfDNz/J36CReJPLIXC0sbJkz7XlOefOMa8bFnGfr2pAqf17tBU3oNHMGuratZv2Ih1jUcCB4xHreaFc/EFuJhpF6/wB+/3bkWT/y5EAD3em1p8vwo8m6lcSvzznV+MWorJSXFHP3jR47+8aOm/HY8QH5uBoe2ziE3JxUDAxPMrF1p3XtKhUschRACQFFaWirz88VDUygUrF69ml69elV3V/5TBg8eTHp6OmvWrKnurvynpH3yxv2DhHjC6XXqWd1dEOKRO6Zsff8gIZ5w2w7L3ADx9Pt48JO7MuOj8Hvv3VydnuRz+qSTuy4KIYQQQgghhBBCiKeCJLrEM2n69OmoVKoKfzp37lzd3XsoS5cuvefYvL29q7t7QgghhBBCCCHEIyPzcEWVeNJWwL7++uv079+/wmO3745YFcLDw6usrcrq0aMHTZs2rfCYvr7+Y+6NEEIIIYQQQgjx+EiiSzyTLC0tsbS0rO5uPBJqtRq1uvxdl4QQQgghhBBCiKedLF0UQgghhBBCCCGEEE8FSXQJIYQQQgghhBBCiKeCJLqEEEIIIYQQQgghxFNB9ugSQjx2ep16VncXhHjkirasre4uCPHIbXNsV91dEOKR69i4qLq7IMRjYFDdHRCiysiMLiGEEEIIIYQQQgjxVJBElxBCCCGEEEIIIYR4KkiiSwghhBBCCCGEEEI8FSTRJYQQQgghhBBCCCGeCpLoEkIIIYQQQgghhBBPBUl0CSGEEEIIIYQQQoingiS6nnEKhYI1a9ZUdzeEEEIIIYQQQgghHppedXdAPBqDBw9m0aJFAOjp6WFpaYmvry+DBg1i8ODB6OiU5TgTExOxsLCozq5WSnx8PO7u7hw/fpwGDRpUWbsKhYLVq1fTq1evKmvzUQgNDWXNmjWcOHGiursiHrPoC/Gs+2MfcVcTScvIYtzQgTTxqXvP+NSMLJas28LFy9e4fjOVzq2aMrh3Z62YvyKjWb1jD9dvplJcXIydjRXdA5vTupGfVtyV68ksXb+NM7GXKC4pxsmuBu8O7o+1hfmjGKp4hkXfSGX96YvEp2aSlpvP2MAAGjvb/nOd6yksPnqGq+nZWJko6e1TkzY1nTTHS0pKWXnyPHsuXiMjLx8LpSGtazrRx6cmCoUCgLzCIpYdj+HI5Rtk5RdSQ6WkUx03Onq5PNLximdT0pXTxBxdQ1rSRXJzUmnZfSKONZveMz756hlO7ltMVupVioryMVZbU9OnE7UDelQYf+bwKk7uW4KXfzf82wzTlMdGbSHh7B7Ski9SWJBL7zd+xsDQpMrHJwTAxfOn2bN9HdcuXyQzI42XRk7A26/JPeMzM1LZ+Nsirl6+SErydZoHdqFb3yFaMaeOH2TX1lWkJN+guLgIaxt7WnXojn+TNlpxSYlX2Lz2Z+LOR1NcUoytvRPBw8dhbmnzSMYqhPjvkkTXUywoKIiFCxdSXFzMjRs32Lx5M++88w4rV65k3bp16OnpYWdnV619LCgowMDAoFr78KwoLCxEX1+/ursh/qX8wkJcHe1o2zSAWQsj7htfXFyMqYkxfTq2ZsPugxXGqEyU9O7QGsca1ujp6XLs9Dm+W7YGU5UJDerUAuD6zVQmf7uAdk386R/UFqWRIZevJ8k1JB6JgqJiXC1MCazlxFe7j983Pin7Fp/+cZQOns6EtGjA6esp/HjgFOZKQ/wcyj7QrD0dy7ZzCbzR3BdnczWxKen8b38UJgZ6BNVxA2DJ0bOcvp7CWy38sFEZE3ktmYWHorFQGtLoPok2If6t4qJ8zG3ccPduz771n943Xk/fEE+/LphZu6Knb0Ty1WiO7vwfevqG1PTppBWbev08sVFbMLd2LddOUWE+dm4B2LkFcHLfkiobjxAVKSwowN7RlUbN2vHzvM/vG19cVISJ2oy2QS+wb+f6CmOMTVS07fQCNraO6OrpcvbUMVYumYuJyhSvev4ApCQn8sNXH9GoWTs6dB2AoZERNxKvoKcvnzOEeBbJ0sWnmKGhIXZ2djg6OhIQEMAHH3zA2rVr2bRpE+Hh4YD20sWCggJCQkKwt7fHyMgIV1dXZsyYoWlPoVDw/fff07lzZ5RKJR4eHqxcuVLrOd977z28vLwwNjbGw8ODSZMmUVhYqDkeGhpKgwYNmD9/Pu7u7hgZGQGwefNmWrZsibm5OVZWVnTr1o3Y2FhNPXd3dwD8/f1RKBQEBgZqjs2fP5+6detiZGREnTp1+O677x7ofMXHx6NQKFi+fDmtWrVCqVTSuHFjzp07x+HDh2nUqBEqlYrOnTuTnJysqTd48GB69epFWFgYNjY2mJqa8vrrr1NQUKCJud/4AK5cucKgQYOwtLTExMSERo0a8ddffxEeHk5YWBiRkZEoFAoUCoXm9fsnt1+vHj16YGJiwieffEJxcTHDhg3D3d0dpVJJ7dq1mT17drm6CxYswNvbG0NDQ+zt7QkJCdEcS09PZ/jw4ZqxtmvXjsjIyAc446Iy/Ot6MqhLe5r63nsW191sLM0Z0qcLbRo3wNjIsMIY71ruNPWti5OdDXbWlnRp8xwu9racvZigiYnYuIOAup681ON53J3ssbO2pHH9OpipZBaAqHoNHG0Y4O9FE5fKffmy/VwCNVRKXm5UFydzFZ3quNLU1ZaNZ+I1MeeT02nkbEuAUw1sVEqec7XHz8GaCzczNDHnktNo5eFIPTsrbFRKOni54GKhJvauGCGqir1bAD7Ng3Gq9Vyl4i1qeOBSuxVmVi6YmNbArW4gdq7+3Lx2RiuuqDCPg5u/olGHNzEwVJVrp3ZAD+o27oOVnVeVjEOIf1Lb25/ne7yId4N7z1a8m4VVDbr3G0pA00AMjYwrjPHwqo93g6bUsHfCysaeFm27YufgQnzsWU3M1t+XUds7gM69X8HB2R0rG3vq+TZGpTarknEJIZ4skuh6xrRr1w4/Pz9WrVpV7ticOXNYt24dy5cvJyYmhqVLl+Lm5qYVM2nSJF544QUiIyMJDg5m4MCBnDlz5w8utVpNeHg40dHRzJ49m3nz5vHVV19ptXHhwgV+++03Vq1apVmKl5OTw9ixYzly5Ag7duxAR0eH3r17U1JSAsChQ4cA2L59O4mJiZr+L126lMmTJ/PJJ59w5swZpk+fzqRJkzTLNh/ElClT+Oijjzh27Bh6enq8+OKLTJgwgdmzZ7Nnzx4uXLjA5MmTters2LGDM2fOsGvXLpYtW8aqVasICwvTHL/f+LKzs2nTpg1Xr15l3bp1REZGMmHCBEpKShgwYADvvvsu3t7eJCYmkpiYyIABAyo1ltDQUHr37k1UVBRDhw6lpKQEJycnVqxYQXR0NJMnT+aDDz5g+fLlmjrff/89b731FiNHjiQqKop169ZRq1YtzfF+/fqRlJTEpk2bOHr0KAEBAbRv357U1NQHPuei+pSWlhJ17iKJyTepV9NVU3Ys+hx2NpZ8/L/FDJ/0GR98NY9DUWfu05oQj8f55HTq21lplfna23A+OV3z2NPGnFOJKVzLzAbgUmomZ5PSaOBgrYnxsrHg2JUkUm/lUVpayunrKSRm5uBzV4wQ/xVpSRe5ee0sNo7eWuVHd/6AvXsj7Fz87lFTiKdHaWkpF86eJDkpEXfPepqys6eOYWVjz4Jvp/Hxe0P57vOJnI48VM29FUJUF1m6+AyqU6cOJ0+eLFeekJCAp6cnLVu2RKFQ4Opafvp7v379GD58OADTpk1j27ZtfPPNN5pZVB999JEm1s3NjXHjxhEREcGECRM05QUFBSxevBgbmzvr5V944QWt51mwYAE2NjZER0dTv359TayVlZXWcsspU6Ywa9Ys+vTpA5TN/IqOjuaHH37g1Vdf/dfnBmDcuHF06lS2JOCdd95h0KBB7NixgxYtWgAwbNiwcjOqDAwMWLBgAcbGxnh7ezN16lTGjx/PtGnT0NHRue/4fvnlF5KTkzl8+DCWlpYAWskllUr1QEtNX3zxRYYM0d7n4O4EnLu7OwcOHGD58uX0798fgI8//ph3332Xd955RxPXuHFjAPbu3cuhQ4dISkrC0LBsttAXX3zBmjVrWLlyJSNHjizXh/z8fPLz87XKCgoKMJQlq9UqJzePN8JmUVhUjI5CwbC+XfGtXROA9Kxs8vILWLtjLwO7tCe4W0ciYy4wa+GvTHlzMPVquVVv58UzLz03HzOl9oxFM6UBuYVFFBQVY6CnS6/6NckrLGbcuj3oKBSUlJbSv4EXLT0cNXUGN67LvIOneOu3P9DVUaAARjznQz1by8c8IiHu7ff5w8nLzaC0pBjv5wbiUb+j5lhCzB7Ski7ScdD9l4gJ8STLvZXDzI9eo6ioEB2FDj0HDMezTllyNzsznYL8PHZvW8Pz3QcR1PMlzp05wdJ5nzP8nVA8PL3v07oQ4mkjia5nUGlpqWYj3rsNHjyYjh07Urt2bYKCgujWrRvPP/+8VkyzZs3KPb57g/Rff/2VOXPmEBsbS3Z2NkVFRZiammrVcXV11UpyAZw/f57Jkyfz119/cfPmTc1Mp4SEBOrXr1/hOHJycoiNjWXYsGGMGDFCU15UVISZ2YNPU/b19dX829a2bI8WHx8frbKkpCStOn5+fhgb35lu3axZM7Kzs7l8+TKurq73Hd+JEyfw9/fXJLmqSqNGjcqVzZ07lwULFpCQkEBubi4FBQWaDf6TkpK4du0a7du3r7C9yMhIsrOzsbLSnkmRm5tbbinmbTNmzNBKrgFMHBHMByNfeoARiapibGTIZ+PeIDcvn1PnL7J47RZsrSzwruVOaWlZTKP6dejapuz/vLuTPTFxl9l24IgkusQT4UB8InvjrhHSwg8ncxWX0rJYfOQMFkpDzab1W2ISuHAzg3FtA7AxUXLmRhoLDp3GwtgQH3uZ1SX+G9r2+4SiwlxSEs8Rte9n1Ob2uNRuxa2smxzf9RNtXghFV0++PBJPNyOlMaPe/5z8vDxiY6LYsGoxlta2eHjVp5SyP1zq+TSiZbtuADg4u5NwMYZDe7ZKousZ0LFxUXV34R7kvbm6SKLrGXTmzBnNnld3CwgIIC4ujk2bNrF9+3b69+9Phw4dyu3DdS8HDhwgODiYsLAwOnXqhJmZGREREcyaNUsrzsSk/B4/3bt3x9XVlXnz5uHg4EBJSQn169fX2ufq77Kzy5ajzJs3j6ZNtfcB0NXVrVSfK3L3Ztu3E4J/L7udqKqs+41PqVQ+cH//yd/PdUREBOPGjWPWrFk0a9YMtVrN559/zl9//VWpfmRnZ2Nvb8+uXbvKHTM3N6+wzvvvv8/YsWO1ygpOla8vHi+FQoGddVli1d3Jnis3brJ6+x68a7ljamKMrq4uTrbaCWknWxvOxiVU1JwQj5W50pCMXO2Zohm5BSj19TDQK3v/X3oshp71PWju7gCAi4UpyTm5rD11kTY1nSgoKubX4zGMaRNAgFMNTUx8Wibro+Mk0SX+M1RmZV+6mVu7kX8rg1MHI3Cp3Yq0pFjycjPYuvRdTWxpaQnJ16I5f2Ij/d5egUJHdikRTweFQoGVjT1QlsRKun6FXVtX4+FVH2MTU3R0dalh76xVx8bOkUt37eMlhHh2SKLrGbNz506ioqIYM2ZMhcdNTU0ZMGAAAwYMoG/fvgQFBZGamqqZaXTw4EFeeeUVTfzBgwfx9y+728n+/ftxdXXlww8/1By/dOnSffuUkpJCTEwM8+bNo1WrVkDZErm73b4zY3FxsabM1tYWBwcHLl68SHBwcGWG/8hERkaSm5urSRQdPHgQlUqFs7Nzpcbn6+vL/Pnztc713QwMDLTG/qD27dtH8+bNefPNNzVld8/EUqvVuLm5sWPHDtq2bVuufkBAANevX0dPT6/c/m33YmhoqFnmeFuWLFv8zyktLaWoqOwa09PTpaazA4nJKVox15JvYm0hm7qK6udpY86Jq8laZScTb+JpY655XFBczN/nLusoFJT+/5TFopISikpK0fnbDGfd/1/mKMR/USmllBSXzVyo4exL0Etfax0/tO1bTC0dqdOwtyS5xFOttLSUov+/4ZWenh5OLjW5mXRNK+bmjUTMLeRLCyGeRZLoeorl5+dz/fp1iouLuXHjBps3b2bGjBl069ZNK1l125dffom9vT3+/v7o6OiwYsUK7OzstGbqrFixgkaNGtGyZUuWLl3KoUOH+OmnnwDw9PQkISGBiIgIGjduzIYNG1i9evV9+2lhYYGVlRU//vgj9vb2JCQkMHHiRK2YGjVqoFQq2bx5M05OThgZGWFmZkZYWBijRo3CzMyMoKAg8vPzOXLkCGlpaeVmET1KBQUFDBs2jI8++oj4+HimTJlCSEgIOjo6lRrfoEGDmD59Or169WLGjBnY29tz/PhxHBwcaNasGW5ubsTFxXHixAmcnJxQq9XlkkeV4enpyeLFi9myZQvu7u4sWbKEw4cPa83wCw0N5fXXX6dGjRp07tyZrKws9u3bx9tvv02HDh1o1qwZvXr14rPPPsPLy4tr166xYcMGevfuXeFSSfFw8vILtBJOyanpxF1JRG2ixNrCnKXrt5GWkUVIcB9NTNyVxLK6BQVkZucQdyURfT09nOzKZmit3r6Hms4O2FpZUFhUzPEz59hz9CTD+3bVtNG9bQtmL1lBHQ9X6nu6c/zMeY6ePkfoW4Mfz8DFMyWvsIjrWbc0j5OzbxGfmonKUB9rEyXLjsWQmpvHWy3K9mPp4OXClpgElh49S2AtJ6Kvp/DXpetMaNdQ00aAUw3WnIrFykSJs7mauNQMNkbHEVirbNmisYE+dW0tWXrsLAa6OlirlJy5kcqfF6/ycsM6j/cEiGdCUWEeWWl3PohnZ9wgLekiBkZqTExtOLl3Cbk5KTTtNBqA85EbMVbbYGpRtq9c8rVoYo6uwbNB2Xu1voESM2vt/VT19AwxMFJrlefmpJGXk0Z2Rtnvhoybl9DTN8JYbYOhUv0ohyyeQfn5eaQkJWoep6Ukce1yHMYmKswtbdi89mcy01Pp/+ooTcy1y3EAFBTkkZ2VwbXLcejp6VPDvuz9eteWVTi61sTS2pbiwiJiTh/j+OE/6TXgztYlrTv0ZNmCr3CrVZeaXvWJOX2Cs6eOMGK09vYZQohngyS6nmKbN2/G3t4ePT09LCws8PPzY86cObz66qvoVPAtn1qt5rPPPuP8+fPo6urSuHFjNm7cqBUbFhZGREQEb775Jvb29ixbtox69crueNKjRw/GjBlDSEgI+fn5dO3alUmTJhEaGvqP/dTR0SEiIoJRo0ZRv359ateuzZw5cwgMDNTE6OnpMWfOHKZOncrkyZNp1aoVu3btYvjw4RgbG/P5558zfvx4TExM8PHxYfTo0VVxCiutffv2eHp60rp1a/Lz8xk0aJBm3JUZn4GBAVu3buXdd9+lS5cuFBUVUa9ePebOnQuUbda/atUq2rZtS3p6OgsXLmTw4MH/up+vvfYax48fZ8CAASgUCgYNGsSbb77Jpk2bNDGvvvoqeXl5fPXVV4wbNw5ra2v69u0LlE0b37hxIx9++CFDhgwhOTkZOzs7WrdurdnPTFSt2MtXCZsbrnm8aM1mANo0bsBbL/YmPTObm2kZWnXem/U/zb8vXr7G3mNR2FiaM3dS2UzOvIIC5q9cT2pGFvr6ejjWsObt4D4097+zH15T37oM79uNNdv3EL56E/Y1rHh3yADqeJS/SYUQD+tiSgbTtt25O9aSI2VLTVrXdOSN5r6k5+WTkpOnOV5DZcx7bRuy+OgZNp+Nx9LYiJHN6uPncGe57eDG9Vh+4hwLDp0mM68AC6Uh7T1deMH3zo0+RrVqwLLjMXy7N5LsgkKsTZQMaOBFBy+XxzBq8axJvX6BP36bpHl84s+FALjXa0uT50eRdyuNW5k3NcdLS0uI2reEnMwkFApdVGa2+LZ4mZq+Qf/qeWNPbuH0X79qHu9cUTbzvsnzb+Ner93DDEmIcq5eusC82aGaxxt+Cwcg4LlA+r0cQlZmOumpN7XqfDNz/J36CReJPLIXC0sbJkz7HoCC/DzWRswjMz0VPX19bGwdGfDqKHwbttDU827QlF4DR7Br62rWr1iIdQ0HgkeMx61m3Uc3WCHEf5aitFTm54vKUSgUrF69ml69elV3V/5TBg8eTHp6OmvWrKnurjwxso5sru4uCPHIFW1ZW91dEOKRm+U4u7q7IMQj99/d6FqIqtPG2/j+Qf9Ru0/fun9QNXiSz+mTThbvCyGEEEIIIYQQQoingiS6xFNr+vTpqFSqCn86d+5c3d17KEuXLr3n2Ly95RbKQgghhBBCCCGeTbJHl6i0J22V6+uvv07//v0rPHb77ohVITw8vMraqqwePXrQtGnTCo/p6+s/5t4IIYQQQgghhBD/DZLoEk8tS0tLLC0tq7sbj4RarUatljslCSGEEEIIIYQQd5Oli0IIIYQQQgghhBDiqSCJLiGEEEIIIYQQQgjxVJBElxBCCCGEEEIIIYR4KkiiSwghhBBCCCGEEEI8FSTRJYQQQgghhBBCCCGeCpLoEkIIIYQQQgghhBBPBUl0CSGEEEIIIYQQQoingiS6hBBCCCGEEEIIIcRTQRJdQgghhBBCCCGEEOKpIImuaqBQKFizZk11d0MIIYQQQgghhBDiqaJX3R14mgwePJhFixYBoKenh6WlJb6+vgwaNIjBgwejo1OWV0xMTMTCwqI6u1op8fHxuLu7c/z4cRo0aFBl7SoUClavXk2vXr2qrM1HITQ0lDVr1nDixInq7opGbm4ub7/9NmvXriU3N5d69erx9ddf07x58+rumnhEoi/Es+6PfcRdTSQtI4txQwfSxKfuPeNTM7JYsm4LFy9f4/rNVDq3asrg3p3LxR04cYpfN/1Bcmo6djZWBHfrQEA9L83xvyKj2br/CHFXE8nOucWn776Ou5P9IxmjENE3Ull/+iLxqZmk5eYzNjCAxs62/1znegqLj57hano2ViZKevvUpE1NJ62Y1Ft5/HIshshryeQXFWOnNua1Zj7UtDYHYNCSTRW2/WJAbbp7e1TJ2IS4LenKaWKOriEt6SK5Oam07D4Rx5pN7xl/5fwBLkRtIT05jpLiQkwtnfF+biD2bv5acbeyUzi5dzHX449TVJSPysyOJh1DsLTzLNfmkR3fExu1lQath1A7oEeVj1GIi+dPs2f7Oq5dvkhmRhovjZyAt1+Te8ZnZqSy8bdFXL18kZTk6zQP7EK3vkO0Yg7t28bxv3Zz/dplABxdPOjU40Wc3e5c4yuWfMuxg7u06nnVbcCQkI+qbnBCiCeGJLqqWFBQEAsXLqS4uJgbN26wefNm3nnnHVauXMm6devQ09PDzs6uWvtYUFCAgYFBtfZBPJjPP/+clStX8uuvv+Ll5cWpU6fQ07v/f2N5zZ9c+YWFuDra0bZpALMWRtw3vri4GFMTY/p0bM2G3QcrjImJS2D2kt94sWtZcmvvsSi+WBjBzLGv4WJvq3neujVdaO7vzQ+/rqvSMQnxdwVFxbhamBJYy4mvdh+/b3xS9i0+/eMoHTydCWnRgNPXU/jxwCnMlYb4OdgAkJ1fyJTNB6lnZ8l77RphamTA9cxbmBjqa9r5vm87rXZPXE3mx4NRNHGp3t/T4ulUXJSPuY0b7t7t2bf+0/vGJ1+Lxs7FD9/mwegbmhAXvZO9v0+nw4BPsahRlogtyMtm5/IPqOHkTateH2GoNCM7PRF9I1W59q5cOEjK9XMoTSyrfGxC3FZYUIC9oyuNmrXj53mf3ze+uKgIE7UZbYNeYN/O9RXGXDx3Gr9GLenuXhs9fQN2b1vNgm8/ZvRHX2JmbqWJ86rnT9+X3tQ81tXXr6g5IcQzQJYuVjFDQ0Ps7OxwdHQkICCADz74gLVr17Jp0ybCw8MB7aWLBQUFhISEYG9vj5GREa6ursyYMUPTnkKh4Pvvv6dz584olUo8PDxYuXKl1nO+9957eHl5YWxsjIeHB5MmTaKwsFBzPDQ0lAYNGjB//nzc3d0xMjICYPPmzbRs2RJzc3OsrKzo1q0bsbGxmnru7u4A+Pv7o1AoCAwM1BybP38+devWxcjIiDp16vDdd9890PmKj49HoVCwfPlyWrVqhVKppHHjxpw7d47Dhw/TqFEjVCoVnTt3Jjk5WVNv8ODB9OrVi7CwMGxsbDA1NeX111+noKBAE3O/8QFcuXKFQYMGYWlpiYmJCY0aNeKvv/4iPDycsLAwIiMjUSgUKBQKzet3L6WlpYSGhuLi4oKhoSEODg6MGjVKczw/P59x48bh6OiIiYkJTZs2ZdeuXQDk5eXh7e3NyJEjNfGxsbGo1WoWLFigKdPR0aFevXp06tQJd3d3unfvTpMm5b8lu31+PvnkExwcHKhduzYAS5YsoVGjRqjVauzs7HjxxRdJSkrSqnv69Gm6deuGqakparWaVq1aaZ23qnrtReX41/VkUJf2NPW99yyuu9lYmjOkTxfaNG6AsZFhhTEb/zxIgzq16NGuBU52Ngzs0g53R3s27zmkiWndyI++zwfi61WzSsYhxD9p4GjDAH+vSieYtp9LoIZKycuN6uJkrqJTHVeautqy8Uy8Jub30xexMjHijea+1LI2p4bKGF8Ha+zUJpoYc6Wh1s+RyzeoZ2uJrdq4qocoBPZuAfg0D8ap1nOVivdvM4w6jXpjaeeJ2sIB3xYvoTKz41rcEU3M2SOrMVZZ0eT5UVjZeaEys8XOtQFqc+0ZuLeyUzi+az7PBY1BR1e+5xaPTm1vf57v8SLeDe49W/FuFlY16N5vKAFNAzE0qvi9d+CQ0TzXOggHZ3dq2DnyQvCblJaWEBsTpRWnq6eH2sxC82NsXD7hK4R4NshvusegXbt2+Pn5sWrVKoYPH651bM6cOaxbt47ly5fj4uLC5cuXuXz5slbMpEmTmDlzJrNnz2bJkiUMHDiQqKgo6tYt++CrVqsJDw/HwcGBqKgoRowYgVqtZsKECZo2Lly4wG+//caqVavQ1dUFICcnh7Fjx+Lr60t2djaTJ0+md+/enDhxAh0dHQ4dOkSTJk3Yvn073t7emhlBS5cuZfLkyXz77bf4+/tz/PhxRowYgYmJCa+++uoDnaMpU6bw9ddf4+LiwtChQ3nxxRdRq9XMnj0bY2Nj+vfvz+TJk/n+++81dXbs2IGRkRG7du0iPj6eIUOGYGVlxSeffFKp8WVnZ9OmTRscHR1Zt24ddnZ2HDt2jJKSEgYMGMCpU6fYvHkz27dvB8DMzOwfx/Dbb7/x1VdfERERgbe3N9evXycyMlJzPCQkhOjoaCIiInBwcGD16tUEBQURFRWFp6cnS5cupWnTpnTt2pVu3brx0ksv0bFjR4YOHappo3v37kyZMoWffvqJYcOG/WN/duzYgampKdu2bdOUFRYWMm3aNGrXrk1SUhJjx45l8ODBbNy4EYCrV6/SunVrAgMD2blzJ6ampuzbt4+ioiLg0bz24vE7F3+FboHNtMp8a9fk8Kmz1dQjIf6d88np1Lez0irztbdh8ZEzmsdHr9zA18GGr3cf50xSKhbGhnT0cqW9p3OFbWbk5nPiWjJvNPd9pH0X4kGVlpZSVJiHgeGdZO3Vi4ewc/Vn/4bPSb5yGqXKkpq+QdT0eV6r3l9bZlO7YU/MrFyqo+tCVKnCgnxKiosxNlZrlcedj+bj94aiNFZRs3Z9nu8+CGMT9T1aEUI8zSTR9ZjUqVOHkydPlitPSEjA09OTli1bolAocHV1LRfTr18/TYJs2rRpbNu2jW+++UYzk+ajj+6sPXdzc2PcuHFERERoJboKCgpYvHgxNjY2mrIXXnhB63kWLFiAjY0N0dHR1K9fXxNrZWWltdxyypQpzJo1iz59+gBlM7+io6P54YcfHjjZMW7cODp16gTAO++8w6BBg9ixYwctWrQAYNiwYeVmVBkYGLBgwQKMjY3x9vZm6tSpjB8/nmnTpqGjo3Pf8f3yyy8kJydz+PBhLC3LpvHXqlVLE69Sqf7VUtOEhATs7Ozo0KED+vr6uLi4aGZbJSQksHDhQhISEnBwcNCMefPmzSxcuJDp06fToEEDPv74Y4YPH87AgQO5dOkS69ffmcJ948YNgoKCeO+99/j000/Jzs7mnXfeASAlJQVra2vNLDgAExMT5s+fr7Vk8e6kmYeHB3PmzKFx48ZkZ2ejUqmYO3cuZmZmREREoP//0729vO7s2/QoXnvx+GVkZ2Ou1v6W09xURXpmdjX1SIh/Jz03HzOl9oxFM6UBuYVFFBQVY6CnS1J2LtvPJdClrhu9fGpy4WY6iw5Ho6ejKLeXF8Dui1cx0tO7795gQlSXmKNrKCrMw9mrpaYsJ+MGsSc34xXQg7qNXyD1xnmO7/4JHV093OuVLc09e2QVCoUOng26VVfXhahSm9f8jNrMgpp1fDRlXnUb4O3XFAurGqTevMHWdb+wcO7HvDFuhmafZCHEs0P+1z8mpaWlKBSKcuWDBw/mxIkT1K5dm1GjRrF169ZyMc2aNSv3+MyZO99a//rrr7Ro0QI7OztUKhUfffQRCQkJWnVcXV21klwA58+fZ9CgQXh4eGBqaoqbmxtAubp3y8nJITY2lmHDhqFSqTQ/H3/8cbllgf+Gr++db9Btbcs+ZPj4+GiV/X2JnZ+fH8bGd6Y4N2vWjOzsbM2MuPuN78SJE/j7+2uSXA+rX79+5Obm4uHhwYgRI1i9erVmJlRUVBTFxcV4eXlpnbfdu3drnbd3330XLy8vvv32WxYsWICV1Z0ZC7NmzcLFxYXp06ezbds2Zs2apUlyRkVFoVar8fPz08T7+PiU25fr6NGjdO/eHRcXF9RqNW3atCl3Tlq1aqVJct3tQV/7/Px8MjMztX7y71piKoQQj0JpaSlulqYM9K+Nm6UpHbxcaOfpzPZzlyuM33XhCi3dHTDQ033MPRXi/i6d/ZPTf/1Ksy7jMDK+M8O8lFIsanjg2+IlLGp4UNOnEx71OxJ7cgsAqTcucO74epo+P6rCv0OFeNLs2rqayKN7eWnkBPT17/yd69eoJfV8G2Pv6Iq3XxNeeWMiVy7FcvHcqWrsrRCiusiMrsfkzJkzmj2v7hYQEEBcXBybNm1i+/bt9O/fnw4dOpTbh+teDhw4QHBwMGFhYXTq1EkzG2fWrFlacSYmJuXqdu/eHVdXV+bNm4eDgwMlJSXUr19fa5+rv8vOLpvxMW/ePJo21V57f3tJ5IO4O7Fy+w+xv5eVlJT8qzbvNz6lUvnA/a2Is7MzMTExbN++nW3btvHmm2/y+eefs3v3brKzs9HV1eXo0aPlzpNKdWdmTVJSEufOnUNXV5fz588TFBSkOXby5EnN3S9dXV3Zvn07rVq1Ijk5mczMTF566SWtc/b31zwnJ4dOnTrRqVMnli5dio2NDQkJCXTq1KlS5+RBX/sZM2YQFhamVTZxRDAfjHzpnnXEo2WmUpGepT17Kz0zG3NT2ctCPBnMlYZk5OZrlWXkFqDU19MkqsyUhjiaaV/TjqYmHLp0vVx7Z26kkpiZwzutGjyyPgvxoBJi9nB4+1yadxmPnYuf1jEjYwtMLbVnKJpaOHLl/AEAkq9Gk5+bwe8/jdAcLy0tIXJPOOdPbKDb0B8e/QCEqCJ7tq9j99Y1DHt7EvaO5VfB3M3K2g4TlZqUm9ephSxJF+JZI4mux2Dnzp1ERUUxZsyYCo+bmpoyYMAABgwYQN++fQkKCiI1NVUz0+jgwYO88sormviDBw/i7192a+n9+/fj6urKhx9+qDl+6dKl+/YpJSWFmJgY5s2bR6tWrQDYu3evVszt2UDFxcWaMltbWxwcHLh48SLBwcGVGf4jExkZSW5uriY5c/DgQVQqFc7OzpUan6+vL/Pnz9c613czMDDQGntlKJVKunfvTvfu3XnrrbeoU6cOUVFR+Pv7U1xcTFJSkqY/FRk6dCg+Pj4MGzaMESNG0KFDB81ebI6Ojuzfv5/i4mJ0dXXx8vJi69atBAYGkpubS1xc3D/27ezZs6SkpDBz5kycncv2qDly5IhWjK+vL4sWLaKwsLDcrK4Hfe3ff/99xo4dq1VWcGpXpeuLqufl5kTUuYt0bXNntmjUuYt4uVa8d5EQ/zWeNuacuJqsVXYy8SaeNuaax142FiRm5mjFJGbdwlpVPqG/K/YK7lamuFqaPpL+CvGgEmL2cGjbtzTr/C4OHo3KHbd2qE1W2jWtsqz0RExMy2bxu9VtWy45tnv1VNzqBuLmrX3XUSH+y3ZvW8OuzasYEvIRTq617hufnnaTWznZqE0tHkPvhBD/NZLoqmL5+flcv36d4uJibty4webNm5kxYwbdunXTSlbd9uWXX2Jvb4+/vz86OjqsWLECOzs7zM3NNTErVqygUaNGtGzZkqVLl3Lo0CF++uknADw9PUlISCAiIoLGjRuzYcMGVq9efd9+WlhYYGVlxY8//oi9vT0JCQlMnDhRK6ZGjRoolUo2b96Mk5MTRkZGmJmZERYWxqhRozAzMyMoKIj8/HyOHDlCWlpauYTGo1RQUMCwYcP46KOPiI+PZ8qUKYSEhKCjo1Op8Q0aNIjp06fTq1cvZsyYgb29PcePH8fBwYFmzZrh5uZGXFwcJ06cwMnJCbVajaFhxXexAwgPD6e4uJimTZtibGzMzz//jFKpxNXVFSsrK4KDg3nllVeYNWsW/v7+JCcns2PHDnx9fenatStz587lwIEDnDx5EmdnZzZs2EBwcDAHDx7EwMCAUaNG8dxzzzFw4EDef/99DA0N2b59u2Z55JIlS7T2Zfs7FxcXDAwM+Oabb3j99dc5deoU06ZN04oJCQnhm2++0TyHmZkZBw8epEmTJtSuXfuBXntDQ8Ny5y3rb0sqxb3l5ReQmJyieZycmk7clUTUJkqsLcxZun4baRlZhAT30cTEXUksq1tQQGZ2DnFXEtHX08PJruyDT5fWzxE6N5zf/9hHQD0v9h0/xcUr1xjZv7umjaycW9xMyyAtMwtA0wdzUxUWprKxq6haeYVFXM+6pXmcnH2L+NRMVIb6WJsoWXYshtTcPN5qUfaBvYOXC1tiElh69CyBtZyIvp7CX5euM6FdQ00bXeu6MWXLQVZHXaCZmz2xNzPYcf4yI5p6az33rYJCDl66zksN6zyewYpnVlFhnlZSKjvjBmlJFzEwUmNiasPJvUvIzUmhaafRQNlyxUNb5+DfZhiWdp7k5qQBoKtnoNmQvrZ/D3Ysf5/oQytx9mpB6vXzXDy1lUbt3wDAUKnGUKn9nq2jq4ehsRmmFo6PYdTiWZOfn0dKUqLmcVpKEtcux2FsosLc0obNa38mMz2V/q/euTP5tctlX9YWFOSRnZXBtctx6OnpU8O+bLbirq2r2b7+VwYOGY2FpQ1ZGWX/FwyMlBgaGpGfn8eODb9S378ZalNzUpKvs2nNEqxs7PCq2+DxDV4I8Z8hia4qtnnzZuzt7dHT08PCwgI/Pz/mzJnDq6++WuFGiGq1ms8++4zz58+jq6tL48aN2bhxo1ZsWFgYERERvPnmm9jb27Ns2TLq1asHQI8ePRgzZgwhISHk5+fTtWtXJk2aRGho6D/2U0dHh4iICEaNGkX9+vWpXbs2c+bMITAwUBOjp6fHnDlzmDp1KpMnT6ZVq1bs2rWL4cOHY2xszOeff8748eMxMTHBx8eH0aNHV8UprLT27dvj6elJ69atyc/PZ9CgQZpxV2Z8BgYGbN26lXfffZcuXbpQVFREvXr1mDt3LlC2Wf+qVato27Yt6enpLFy4kMGDB9+zP+bm5sycOZOxY8dSXFyMj48Pv//+u2afrYULF/Lxxx/z7rvvcvXqVaytrXnuuefo1q0bZ8+eZfz48fz000+a2Vbfffcdvr6+TJo0iU8//RQ/Pz8OHDjA+++/T8eOHSkoKKBly5Zs27aNCxcu8Oqrr1KzZs1ym/DfZmNjQ3h4OB988AFz5swhICCAL774gh49emhirKys2LlzJ+PHj6dNmzbo6urSoEEDzU0B/iuv/bMk9vJVwuaGax4vWrMZgDaNG/DWi71Jz8zmZlqGVp33Zv1P8++Ll6+x91gUNpbmzJ1UNqu0trsLo17qw6+b/mDZxh3YWVsxbshAXOzvbMJ99HQM3y1bo3n89eIVAPTtFEj/oLZVPUzxjLuYksG0bYc0j5ccKbsDaOuajrzR3Jf0vHxScvI0x2uojHmvbUMWHz3D5rPxWBobMbJZffwc7uxFWdPanLFtAog4HsPqqFhsVEpeaVSXlh7aH+73xydSWlpKczf7RzxK8axLvX6BP36bpHl84s+FALjXa0uT50eRdyuNW5k3NccvRm2lpKSYo3/8yNE/ftSU344HsLTzpEW3iZzct4Tov5ZjYlaDBq2H4lqnzWMalRDarl66wLzZoZrHG34LByDguUD6vRxCVmY66ak3tep8M3P8nfoJF4k8shcLSxsmTCu72/qhPVspLi5i6fwvtOq179KPDl0HoKOjw/VrCRz7azd5ubcwNbOgVl0/OnYbiF4F+84KIZ5+itLS0tLq7oS4N4VCwerVq+nVq1d1d+U/ZfDgwaSnp7NmzZrq7op4AFlHNld3F4R45Iq2rK3uLgjxyM1ynF3dXRDikevYuKi6uyDEI9fG2/j+Qf9Ru0/fun9QNXiSz+mTTu66KIQQQgghhBBCCCGeCpLoElVq+vTpqFSqCn86d+5c3d17KEuXLr3n2Ly9ve/fgBBCCCGEEEIIIR4p2aPrP+5JW1n6+uuv079//wqP3b47YlUIDw+vsrYqq0ePHjRt2rTCY3+/Q6EQQgghhBBCCCEeP0l0iSplaWmJpaVldXfjkVCr1ajVcrc5IYQQQgghhBDiv0qWLgohhBBCCCGEEEKIp4IkuoQQQgghhBBCCCHEU0ESXUIIIYQQQgghhBDiqSCJLiGEEEIIIYQQQgjxVJDN6IUQj13RlrXV3QUhHjm9Tj2ruwtCPHIdlUXV3QUhHrlth+Ujk3j6tfGu7h4IUXVkRpcQQgghhBBCCCGEeCpIoksIIYQQQgghhBBCPBUk0SWEEEIIIYQQQgghngqS6BJCCCGEEEIIIYQQTwVJdAkhhBBCCCGEEEKIp4Ikuh5SeHg45ubmD9WGm5sbX3/9dZX0ByAwMJDRo0dXWXtPah9uq+g1+vHHH3F2dkZHR0dz7isqE0IIIYQQQgghxJPjqbxXbnJyMpMnT2bDhg3cuHEDCwsL/Pz8mDx5Mi1atEChULB69Wp69er1r9p1c3Nj9OjRWgmcAQMG0KVLl0rVDw8PZ/To0aSnp2uVHz58GBMTk3/Vl6oSGBjI7t27ATA0NMTDw4OQkBDefPPNh2p31apV6Ovrax5XdO4elkKh0Pzb2NgYBwcHWrRowdtvv03Dhg01x/7+GmVmZhISEsKXX37JCy+8gJmZWYVlQvxXbI25xO+n48jIy8fFQs3gxvWoZW1+z/iD8YksjzzPzZxc7NTGDAqojb9jDc3xlZHn2R+fSMqtPPR0FHhYmtG/gReeNmVtRl9PYdq2QxW2/XHnZtT8h+cWoipt3nuI3//YR3pmNq4Odgzp0xlPV6d7xh84cYpfN/1Bcmo6djZWBHfrQEA9L83x9Kxslv6+jZPnYsm5lUfdmq4M7dMFexurxzEcITiwezN7tq8lKzMdeydXuvcbhrObZ4WxNxIvs319BFcTLpKWmkzXFwbTsl23e7a9a+tqtqxdSou2XenWd4jWsUsXY9j2+zIS4s+jo6ODvZMbQ0Mmoa9vUKXjEwLgfORGYo6uJS8nDXMbN/wDh2Nl51VhbEZKAqcORJCWFEtOZhINWg+hdkAPrZjCglxOHfiFKxf+Ij83Awsbd/zbDMPSruL/O0d2fE9s1NYK2xJCPBueyhldL7zwAsePH2fRokWcO3eOdevWERgYSEpKSpU/l1KppEaNGvcP/Ac2NjYYGxtXUY/+vREjRpCYmEh0dDT9+/fnrbfeYtmyZQ/UVkFBAQCWlpao1eqq7GaFFi5cSGJiIqdPn2bu3LlkZ2fTtGlTFi9erIn5+2uUkJBAYWEhXbt2xd7eHmNj4wrLHkRhYeFDj0mIux2IT2TJkTO84FuL6V1a4Gphyowdh8nMy68wPiYpjW/2RtK2lhPTu7SgkbMtX+46xuW0LE2MvakJQ5rU47NuLQnr9BzWKqVWm142Fnzft53WT9taTtiolHhYSRJYPB77j59i8drN9H0+kE/ffR1XR1s++WEJGdk5FcbHxCUwe8lvtGsawKfvvk7j+nX4YmEECYk3ACgtLeXzn5aRlJLG+KGD+Gzc69hYmDPt+0Xk5Rc8zqGJZ9TJo/vY8Fs47br04+2Jn2Pn6MaCbz8mOyujwvjCgnwsrGrQqWcwalPzf2z7yqULHNqzFTtH13LHLl2MIXzuJ9Sq68dbE2by1oRPadamMwoUFbQkxMNJOLeXE38uxLtpf55/cRZm1m78uXoqebcqvs6LC/MxMa2Bb4uXUJpYVBhzeNtcrl86QdNO79Dppa+xdWnArlWh3Mou/9nuyoWDpFw/h9LEskrHJYR4sjx1ia709HT27NnDp59+Stu2bXF1daVJkya8//779OjRAzc3NwB69+6NQqHQPI6NjaVnz57Y2tqiUqlo3Lgx27dv17QbGBjIpUuXGDNmDAqFQjOb6O/L4iIjI2nbti1qtRpTU1MaNmzIkSNH2LVrF0OGDCEjI0NTPzQ0FCi/dDE9PZ3XXnsNW1tbjIyMqF+/PuvXrwcgJSWFQYMG4ejoiLGxMT4+Pg+clLrN2NgYOzs7PDw8CA0NxdPTk3Xr1gHw3nvv4eXlhbGxMR4eHkyaNEkrmRMaGkqDBg2YP38+7u7uGBkZac7X7dlbFZ27nJwcTE1NWblypVZf1qxZg4mJCVlZWVSGubk5dnZ2uLm58fzzz7Ny5UqCg4MJCQkhLS0N0H6NwsPD8fHxAcDDwwOFQlFhWXx8PABr164lICAAIyMjPDw8CAsLo6ioSPP8CoWC77//nh49emBiYsInn3xS6Xrz58+nd+/eGBsba53z206fPk23bt0wNTVFrVbTqlUrYmNjNcfnz59P3bp1MTIyok6dOnz33XeVOmcFBQWEhIRgb2+PkZERrq6uzJgxQ3M8PT2d4cOHY2Njg6mpKe3atSMyMhIomy1pZ2fH9OnTNfH79+/HwMCAHTt2VOr5xb+z4Uwc7TydCazlhJO5iuFNvTHU1eWPC1cqjN98Nh4/B2u6e3vgZK6ifwMv3CzN2BJzSRPTwt0BH3trbNXGOJmreblhHXILi0j4/2SYnq4O5kpDzY/KQJ+jV5JoU9NJayalEI/S+l0HaP9cQ9o29cfJzoaR/bpjaKDPH38dqzB+458HaVCnFj3atcDJzoaBXdrh7mjP5j1lsxMTk1M4f+kKw/t1o5aLIw41rBnRrxsFhUXsOx71OIcmnlF7d/5O4xYdaNSsHTXsneg96DUMDAw4cmBnhfFOrrXo0udV/Bq1RE9Pv8IYgPz8PH4Nn03v4NdRKsuvENjwWzjNAjsT+HxvbO2dsbF1wDegOXr6925TiAd17tg6POp3xN27PaZWzjRq/wa6eobEna7470RLO08atB6MS+1W6OiWvyaLiwq4cuEAfi1fpYaTN2pze+o3G4jK3I7Yk5u1Ym9lp3B813yeCxqDju5TuXBJCFFJT12iS6VSoVKpWLNmDfn55Wc8HD58GLgzE+j24+zsbLp06cKOHTs4fvw4QUFBdO/enYSEBKBsKZ6TkxNTp04lMTGRxMTECp8/ODgYJycnDh8+zNGjR5k4cSL6+vo0b96cr7/+GlNTU039cePGlatfUlJC586d2bdvHz///DPR0dHMnDkTXV1dAPLy8mjYsCEbNmzg1KlTjBw5kpdffplDhypeZvQglEqlZmaWWq0mPDyc6OhoZs+ezbx58/jqq6+04i9cuMBvv/3GqlWrOHHiRLn2Kjp3JiYmDBw4kIULF2rFLly4kL59+z7UbLAxY8aQlZXFtm3byh0bMGCAJoF56NAhEhMT6devX7kyZ2dn9uzZwyuvvMI777xDdHQ0P/zwA+Hh4Zpk1m2hoaH07t2bqKgohg4dWul6YWFh9O/fn5MnT9KlSxeCg4NJTU0F4OrVq7Ru3RpDQ0N27tzJ0aNHGTp0qCZZtnTpUiZPnswnn3zCmTNnmD59OpMmTWLRokX3PT9z5sxh3bp1LF++nJiYGJYuXapJ+AL069ePpKQkNm3axNGjRwkICKB9+/akpqZiY2PDggULCA0N5ciRI2RlZfHyyy8TEhJC+/btK/8iiUopKi4hLiWT+vbWmjKFQkF9eyvOJ6dXWOdccjr17bWXYfnaW3H+ZsXxRcUl7LxwBWMDPVwsTCuMOXo1iaz8AgJrOj7QOIT4t4qKirl45Ro+Xh6aMoVCgY9XTc7FV5zkPRd/RSsewLd2Tc5dugxAYVExAPp6dz78KBQK9PX0OHsxoaqHIISWoqIiriZcpFYdX02ZQqGgVh1fEuLOPVTb636dR23vhnjW8St3LDsrg8vx51Gpzfj+iw/4+L2h/PjVZOJjzzzUcwpRkZLiIlJvxGLncudaVCgU2Lr4kpIY82BtlhRTWlqCrp72MltdPQNuXr1zHZeWlvLXltnUbtgTMyuXBxuAEOKp8dSluvX09AgPD2fEiBH873//IyAggDZt2jBw4EB8fX2xsbEB7swEus3Pzw8/vztvytOmTWP16tWsW7eOkJAQLC0t0dXVRa1Wa9X7u4SEBMaPH0+dOnUA8PS8s3bczMwMhULxj/W3b9/OoUOHOHPmDF5eZWvZPTzu/OHu6OiolSB7++232bJlC8uXL6dJkyaVPU0VKi4uZtmyZZw8eZKRI0cC8NFHH2mOu7m5MW7cOCIiIpgwYYKmvKCggMWLF2vO7d/d69wNHz6c5s2bk5iYiL29PUlJSWzcuFFrJt2DuH3ub8/KuptSqcTKqiwJYGNjo+lPRWVhYWFMnDiRV199FSh7HaZNm8aECROYMmWKps0XX3yRIUPu7IUxdOjQStUbPHgwgwYNAmD69OnMmTOHQ4cOERQUxNy5czEzMyMiIkKz19nt6wFgypQpzJo1iz59+gDg7u6uSardft57SUhIwNPTk5YtW6JQKHB1vbPMYe/evRw6dIikpCQMDQ0B+OKLL1izZg0rV65k5MiRdOnShREjRhAcHEyjRo0wMTHRmhH2d/n5+eWSzvlFxRjq6f5jPwVk5RdQUlqKmZH2H3dmRoZcy6x4+VZmXj7mRoZaZeZKQ9JztV+DY1eSmLPnBAXFxZgbGfJB+8aYGlW8V8sf5y/ja2+NlYnyIUYjROVl5tyipKQEc7VKq9xcZcK1GzcrrJORnV0+3lRFemY2AI41rLG2MGPZhu2M6NcdIwMD1u/eT0p6BmmZlZtFLMSDupWTSUlJCSq19vJvldqM5BtXH7jdyCN7uXo5jrcmzKzweOrNsqW7OzYup3PvV3BwcufYX7uZPzuM0R99hXUN+wd+biH+Lj83k9LSEgyNta9zI2NzstIe7DrXN1BibV+b6L+WY2rphJGxOZdi/iQlMQaV+Z3r9+yRVSgUOng2uPc+dkKIZ8dTN6MLyvbounbtGuvWrSMoKIhdu3YREBBAeHj4PetkZ2czbtw46tati7m5OSqVijNnzmhmdFXW2LFjGT58OB06dGDmzJlaS80q48SJEzg5OWklNe5WXFzMtGnT8PHxwdLSEpVKxZYtW/51P+/23XffoVKpUCqVjBgxgjFjxvDGG28A8Ouvv9KiRQvs7OxQqVR89NFH5Z7L1dX1nkmuf9KkSRO8vb01s5B+/vlnXF1dad269QOPBcq+0QEeeolVZGQkU6dO1cwSVKlUmv3Mbt26pYlr1KjRA9Xz9b3zra6JiQmmpqYkJSUBZddBq1attDb0vy0nJ4fY2FiGDRum9Rwff/xxpa63wYMHc+LECWrXrs2oUaPYunWrVt+zs7OxsrLSajsuLk6r7S+++IKioiJWrFjB0qVLNUmxisyYMQMzMzOtn692V7z0SDw+9WwtmdmtBWGdmuHnaMPXf56ocN+vlJxcTibepK2nczX0Uoiqo6eny7tDBnItOYWhH87kpfc+5vSFeBrU9URH56n8c0g85dLTbvL7igUMGPzOPTeVLy0pAaBJi440atYOB2d3uvUdjI2dI0cOyJYD4snQpNM7AKybP4wV3/Tj/IkNuNRupflbP/XGBc4dX0/T50fJFgtCCOApnNF1m5GRER07dqRjx45MmjSJ4cOHM2XKFAYPHlxh/Lhx49i2bRtffPEFtWrVQqlU0rdvX80SvsoKDQ3lxRdfZMOGDWzatIkpU6YQERFB7969K1VfqfznGROff/45s2fP5uuvv8bHxwcTExNGjx79r/t5t+DgYD788EOUSiX29vaaP/gPHDhAcHAwYWFhdOrUSTPDaNasWVr1H+aOkcOHD2fu3LlMnDiRhQsXMmTIkIf+BXXmTNk0Znd394dqJzs7m7CwMM2sqbvd3osMyo+/svX+nsRSKBSU/P8fpP90HWRnl81OmDdvHk2bNtU6dnuJ6z8JCAggLi6OTZs2sX37dvr370+HDh1YuXIl2dnZ2Nvbs2vXrnL17t6LLjY2lmvXrlFSUkJ8fLxmj7OKvP/++4wdO1ar7NZX5ZftivLUhgboKBRk5Gn//86oYNbWbaZGhqT/LWGVnpuPuVI73khfDzt9PezU4Gljzug1u9l54Qq96tfUitsVexW1oQENHR/uphtC/BumJsbo6OiQnpWtVZ6enYO5qarCOmYqVfn4zGyt+JrODnw+7g1ycvMoKi7GTGXCB1/Nw8NZZrWIR8vYxBQdHZ1yG89nZ2WgMq14A+77uZpwkZzsTL6dOV5TVlJSQnzsGQ7s3sS02RGozcrarmGvfbdSG1sH0lMrnh0pxIMyVJqiUOiQ/7eN5/NupWNk/GDXOYDa3J62/T6mqDCPwvxbKFWW7N/wBSamZaswkq9Gk5+bwe8/jdDUKS0tIXJPOOdPbKDb0B8e+LmFEE+mpzbR9Xf16tVjzZo1QFmCobi4WOv4vn37GDx4sCYhlZ2dXW7pm4GBQbl6FfHy8sLLy4sxY8YwaNAgFi5cSO/evStV39fXlytXrnDu3LkKZ3Xt27ePnj178tJLLwFlf9CcO3eOevXq3bdf92JmZkatWrXKle/fvx9XV1c+/PBDTdmlS5fKxVXGvcb+0ksvMWHCBObMmUN0dPR9l91Vxu290Dp06PBQ7QQEBBATE1PhuXkU9e7m6+vLokWLKCwsLJcQs7W1xcHBgYsXLxIcHPxA7ZuamjJgwAAGDBhA3759CQoKIjU1lYCAAK5fv46enp7Wvl13Kygo4KWXXmLAgAHUrl2b4cOHExUVdc+7jxoaGpab8VUsyxYrRU9XB3crU04nptDY2RYom7F4+noKz9cuf2ctAC8bc04lptCl7p1Eb9T1FDytzf/xuUpLSykqLilXtjv2Cq08HNHTlRkv4vHR09PFw8mBU+fjaOJTFyi7Hk+du0hQq4qX6Xu5ORF17iJd2zTTlEWdu4iXa/nZiCbKsi8dEpNTiL18lQFd2j6CUQhxh56eHo4uHsTGROHtV3YNl5aWciEmimZtOj9Qm7Vq+/DOh19qlf3281xsbB1p3bEXOjo6WFjVwNTMguQb17TiUpIS8arn/2CDEeIedHT1sLStyY2EkzjWLPsytrS0lKTLUdTy6/LQ7evpG6Gnb0RBXjY3Ek7g2/JlANzqttXaFwxg9+qpuNUNxM273UM/rxDiyfPUJbpSUlLo168fQ4cOxdfXF7VazZEjR/jss8/o2bMnULbX1I4dO2jRogWGhoZYWFjg6enJqlWr6N69OwqFgkmTJmlm19zm5ubGn3/+ycCBAzE0NMTa2lrreG5uLuPHj6dv3764u7tz5coVDh8+zAsvvKCpn52dzY4dO/Dz88PY2BhjY2OtNtq0aUPr1q154YUX+PLLL6lVqxZnz55FoVAQFBSEp6cnK1euZP/+/VhYWPDll19y48aNh0p03YunpycJCQlERETQuHFjNmzYwOrVqx+orXudOwsLC/r06cP48eN5/vnncXJyuk9L2tLT07l+/Tr5+fmcO3eOH374gTVr1rB48WKtGUgPYvLkyXTr1g0XFxf69u2Ljo4OkZGRnDp1io8//rjK690tJCSEb775hoEDB/L+++9jZmbGwYMHadKkCbVr1yYsLIxRo0ZhZmZGUFAQ+fn5HDlyhLS0tHKzp/7uyy+/xN7eHn9/f3R0dFixYgV2dnaYm5vToUMHmjVrRq9evfjss8/w8vLi2rVrbNiwgd69e9OoUSM+/PBDMjIymDNnDiqVio0bNzJ06FDNnUFF1epa153v95/E3cqUmlbmbD4bT15RMYE1y/6vzN0XiaXSiEEBtQEIquPGtG1/sT46Dn9HGw7EJxKXksGIpvUByCssYs2pWBo62WKuNCQrv4CtMZdIy83nOVft/QNPX08hOTuXtrX+3f9LIapCt8BmzP1lNR5O9tRycWLjnwfJKyggsEnZh/Nvl67CwkxNcLeOAHRp/Ryhc8P5/Y99BNTzYt/xU1y8co2R/btr2jxw4hSmKhOszc1IuJ5E+OpNNPGpi1/tB/9iQojKatmuOysWf4ujiwfOrp7s27WBgvx8Gj5XlmhdvmgOpuaWBPUs+zKzqKiIpMSymykUFxeRmZ7CtctxGBoZYWVjj6GREjsH7U239fUNMTZRa8oVCgWtOvRkx4bl2Du64eDsxtGDu0i+cY0Xh7/7GEcvnhVeAT04tGUOFrY1sbL15NyJ9RQV5uH+/wmnv7Z8jdLESpOkKikuIiMlQfPv3OxU0pIuomegRP3/e3Alxh8HSlFbOJKdcZ3IPeGoLRxxr1d2IyRDpRpDpfaNrHR09TA0NsPUQm6kI8Sz6KlLdKlUKpo2bcpXX31FbGwshYWFODs7M2LECD744AMAZs2axdixY5k3bx6Ojo7Ex8fz5ZdfMnToUJo3b461tTXvvfcemZmZWm1PnTqV1157jZo1a5Kfn6/ZC+o2XV1dUlJSeOWVV7hx4wbW1tb06dOHsLAwAJo3b87rr7/OgAEDSElJYcqUKYSGhpYbw2+//ca4ceMYNGgQOTk51KpVi5kzyzYZ/eijj7h48SKdOnXC2NiYkSNH0qtXLzIyMsq187B69OjBmDFjCAkJIT8/n65duzJp0qQK+3w//3Tuhg0bxi+//MLQoUP/dbu3N4E3MjLC0dGRli1bcujQIQICAv51W3/XqVMn1q9fz9SpU/n000/R19enTp06DB8+/JHUu5uVlRU7d+5k/PjxtGnTBl1dXRo0aECLFi2AsiWfxsbGfP7554wfPx4TExN8fHwYPXr0fdtWq9V89tlnnD9/Hl1dXRo3bszGjRs1S1Y3btzIhx9+yJAhQ0hOTsbOzo7WrVtja2vLrl27+Prrr/njjz8wNS27Q9+SJUvw8/Pj+++/1+ztJqpOMzd7MvMKWBF5nozcfFwtTZnYrjFm/78UMSUnD527lvvWrmFBSAs/lkee59fjMdiZmjA2MABni7I/AHUUCq5l5PBn7DGy8gtQGxrgYWXGlOeb4mSu/UfizgtX8LIxx9Gs4qViQjxKzf3rk5Gdw/LNf5CelY2bgz0fjnxZs+H8zbQMraXutd1dGPVSH37d9AfLNu7AztqKcUMG4mJvq4lJz8xm8dqtmo3rWzdqQN/n2zz2sYlnk2/DFmRnZbJ9/a9kZabj4OzGkLc+RG1qDkB66k0Ud+0Xl5WRyjd3LUvcs+N39uz4Hfda9Rg5Zmqln7dlu24UFRWyYVU4t3KysXd0Zejbk7CykSW7ouq5eLUk/1YGpw5EkHcrDQsbd1r3moSRsTkAtzJvolDcuc5zc1LZ+sudpGvMsbXEHFtLDUdv2vYr+4K4qOAWJ/f9zK3smxgaqXGs9Rw+zYPR0X3qPsoKIaqIovTv2RohHrMlS5YwZswYrl27hoFBxZupiqdL2ieSEBNPP71OPau7C0I8cseUD3cDGSGeBNsOS0JFPP0+Hvzkfg7bffrW/YOqQRtv4/sHiUdC3rVFtbl16xaJiYnMnDmT1157TZJcQgghhBBCCCGEeCiyu/BTbM+ePahUqnv+VLfPPvuMOnXqYGdnx/vvv691bPr06ffsd+fOD7Zp67NCzp0QQgghhBBCiGeVLF18iuXm5nL16tV7Hn+YuwI+aqmpqaSmplZ4TKlU4ugoG0vey5Nw7mTpongWyNJF8SyQpYviWSBLF8WzQJYuVj1Zulh95F37KaZUKv/Tyax/YmlpiaWlZXV344kk504IIYQQQgghnhzff/8933//PfHx8QB4e3szefJkWZHzgGTpohBCCCGEEEIIIUQ1cXJyYubMmRw9epQjR47Qrl07evbsyenTp6u7a08kmdElhBBCCCGEEEIIUU26d++u9fiTTz7h+++/5+DBg3h7e1dTr55ckugSQgghhBBCCCGEqEL5+fnk5+drlRkaGmJoaPiP9YqLi1mxYgU5OTk0a9bsUXbxqSWJLiHEY3ey16zq7oIQj5zvmneruwtCPHLbHNtVdxeEeOTeq7+zursgxGMQVN0deOrMmDGDsLAwrbIpU6YQGhpaYXxUVBTNmjUjLy8PlUrF6tWrqVev3mPo6dNHEl1CCCGEEEIIIYQQVej9999n7NixWmX/NJurdu3anDhxgoyMDFauXMmrr77K7t27Jdn1ACTRJYQQQgghhBBCCFGFKrNM8W4GBgbUqlULgIYNG3L48GFmz57NDz/88Ki6+NSSuy4KIYQQQgghhBBC/IeUlJSU2+NLVI7M6BJCCCGEEEIIIYSoJu+//z6dO3fGxcWFrKwsfvnlF3bt2sWWLVuqu2tPJEl0CSGEEEIIIYQQQlSTpKQkXnnlFRITEzEzM8PX15ctW7bQsWPH6u7aE0kSXUIIIYQQQgghhBDV5KeffqruLjxVZI+uSggPD8fc3Pyh2nBzc+Prr7+ukv4ABAYGMnr06Cpr70ntw20VvUY//vgjzs7O6OjoaM59RWVCCCGEEEIIIYR4OjyxM7qSk5OZPHkyGzZs4MaNG1hYWODn58fkyZNp0aIFCoWC1atX06tXr3/VrpubG6NHj9ZK4AwYMIAuXbpUqn54eDijR48mPT1dq/zw4cOYmJj8q75UlcDAQHbv3g2U3fnBw8ODkJAQ3nzzzYdqd9WqVejr62seV3TuHpZCodD829jYGAcHB1q0aMHbb79Nw4YNNcf+/hplZmYSEhLCl19+yQsvvICZmVmFZUI8CQ7s3sye7WvJykzH3smV7v2G4ezmWWHsjcTLbF8fwdWEi6SlJtP1hcG0bNdNK2bXllWcjvyLpOtX0dc3wMWjNp17vYyNrYMm5sevJhN3IVqrXpOWHek96LWqH6AQwNaYS/x+Oo6MvHxcLNQMblyPWtbm94w/GJ/I8sjz3MzJxU5tzKCA2vg71tAczyssYtnxGI5cvkFWfiE1VEo61XGjo5eLJmb+wVNEJd4kLTcfIz1dvGwsGBRQG0cz1aMcqniGnY/cSMzRteTlpGFu44Z/4HCs7LwqjI2N2sqlM7vISEkAwKJGTXxaBJeLz0y5TOS+JSRfOU1paTGmls407zoBE1Mb8nOzOH0wgusJJ7iVdRNDpSmONZtSv9kgDAyr5+9S8fTbvPcQv/+xj/TMbFwd7BjSpzOerk4Vxl6+nsTyTX9w8co1klPTebVXEF3bNNOKWb19D4eiznD1RjIG+vp4uTnzUveOONSw1sT8uPx3os5dJC0zC0MDfWq7uxDcrQOOtjaPdKxCiP+mJzbR9cILL1BQUMCiRYvw8PDgxo0b7Nixg5SUlCp/LqVSiVKpfKg2bGyq9012xIgRTJ06lVu3brF48WLeeustLCwsGDRo0L9uq6CgAAMDAywtLR9BT8tbuHAhQUFB5OXlce7cOX788UeaNm3KggULeOWVV4Dyr1FCQgKFhYV07doVe3t7AE6dOlWu7EEUFhZqJfieFE9qv591J4/uY8Nv4fQaNBIXNy/2/rGeBd9+zLtT5qBSl0/WFhbkY2FVg/r+zdjwW3iFbcadj+a51kE4udaipLiYLet+YcE3Uxk96WsMDY00cY1bdKBj1wGax/oGlb89shD/xoH4RJYcOcOwpvWpZW3OprPxzNhxmK96tsbUqPx1F5OUxjd7Ixno74W/Yw32x1/jy13HmN6lBc4WagCWHD3L6espvNXCDxuVMZHXkll4KBoLpSGNnG0BcLM0pbm7AzYmSrLzC1h58gLTtx/mm96B6Ogoyj2vEA8j4dxeTvy5kEbtXsfKzouY47/z5+qpdH51LkbG5d/Pk6+cwqV2K6zsa6OrZ8DZI6v4c/VUOr08G2OVFQBZ6YnsXPEh7t7tqf/cQPQMlGSmXEZXzwCAvJxUcnPS8Gs1GFNLJ25l3eToju/JzU6lRbcJj3X84tmw//gpFq/dzIi+3fF0dWLDnwf45IclzP5gFGaq8snV/IJCaliZ85xfPRatrXjT7ejYeDq1aExNF0eKi0tYtnEHH/9vMV++F4KRYdm17u5kT8sAH2wszcm6dYsVm3fx8f+WMHfSaHR0ZBGTEM+aJ/J/fXp6Onv27OHTTz+lbdu2uLq60qRJE95//3169OiBm5sbAL1790ahUGgex8bG0rNnT2xtbVGpVDRu3Jjt27dr2g0MDOTSpUuMGTMGhUKhmU3092VxkZGRtG3bFrVajampKQ0bNuTIkSPs2rWLIUOGkJGRoakfGhoKlF+6mJ6ezmuvvYatrS1GRkbUr1+f9evXA5CSksKgQYNwdHTE2NgYHx8fli1b9lDnzNjYGDs7Ozw8PAgNDcXT05N169YB8N577+Hl5YWxsTEeHh5MmjSJwsJCTd3Q0FAaNGjA/PnzcXd3x8jISHO+bs/equjc5eTkYGpqysqVK7X6smbNGkxMTMjKyqpU383NzbGzs8PNzY3nn3+elStXEhwcTEhICGlpaYD2axQeHo6Pjw8AHh4eKBSKCsvi4+MBWLt2LQEBARgZGeHh4UFYWBhFRUWa51coFHz//ff06NEDExMTPvnkk0rXmz9/Pr1798bY2FjrnN92+vRpunXrhqmpKWq1mlatWhEbG6s5Pn/+fOrWrYuRkRF16tThu+++q9Q5i4+PR6FQ8Ouvv9KmTRuMjIxYunRppa6tkpISPvvsM2rVqoWhoSEuLi6aMQNcvnyZ/v37Y25ujqWlJT179tScS1H19u78ncYtOtCoWTtq2DvRe9BrGBgYcOTAzgrjnVxr0aXPq/g1aomeXsWJzSEhH9HwubbY2jtj7+RG35ffIj3tJlcTYrXiDAwMUZtZaH6MlMZVPj4hADaciaOdpzOBtZxwMlcxvKk3hrq6/HHhSoXxm8/G4+dgTXdvD5zMVfRv4IWbpRlbYi5pYs4lp9HKw5F6dlbYqJR08HLBxUJN7M0MTUwHLxfq2Vpio1LibmXGgAaepN7KIynn1iMfs3j2nDu2Do/6HXH3bo+plTON2r+Brp4hcad3VBj/XOex1PLrjEUND0wtnWjcIYTS0hKSEk5qYk7tX4q9ewB+rV7FooYHanN7HGs20STOzKxdadFtAo4ejVGb22Pr7INP82CuxR2mpKT4sYxbPFvW7zpA++ca0rapP052Nozs1x1DA33++OtYhfG1XBx5uUcnWgT4oK+nW2HMh6+9TGATf5ztauDmaMebg3pxMy2Di5evaWI6Nm9EvVpu2Fia4+HkwMAu7UlJzyApNf1RDFMI8R/3RCa6VCoVKpWKNWvWkJ+fX+744cOHgbKZQImJiZrH2dnZdOnShR07dnD8+HGCgoLo3r07CQllU8JXrVqFk5MTU6dOJTExkcTExAqfPzg4GCcnJw4fPszRo0eZOHEi+vr6NG/enK+//hpTU1NN/XHjxpWrX1JSQufOndm3bx8///wz0dHRzJw5E13dsjf3vLw8GjZsyIYNGzh16hQjR47k5Zdf5tChQ1Vy/qBsBlRBQQEAarWa8PBwoqOjmT17NvPmzeOrr77Sir9w4QK//fYbq1at4sSJE+Xaq+jcmZiYMHDgQBYuXKgVu3DhQvr27YtarX7g/o8ZM4asrCy2bdtW7tiAAQM0CcxDhw6RmJhIv379ypU5OzuzZ88eXnnlFd555x2io6P54YcfCA8P10rsQFmyr3fv3kRFRTF06NBK1wsLC6N///6cPHmSLl26EBwcTGpqKgBXr16ldevWGBoasnPnTo4ePcrQoUM1ybKlS5cyefJkPvnkE86cOcP06dOZNGkSixYtqvR5mjhxIu+88w5nzpyhU6dOlbq23n//fWbOnMmkSZOIjo7ml19+wda2bPZDYWEhnTp1Qq1Ws2fPHvbt24dKpSIoKEhzPYmqU1RUxNWEi9Sq46spUygU1KrjS0LcuSp7nvy8sg/1xiba/ydPHP6TaRMG8/XHY9i89mcKCsq/3wrxsIqKS4hLyaS+/Z0lKAqFgvr2VpxPTq+wzrnkdOrbW2mV+dpbcf7mnXgvGwuOXUki9VYepaWlnL6eQmJmDj4O1lQkr7CIXbFXsVEpsTZ+uFncQvxdSXERqTdisXPx05QpFApsXXxJSYypVBtFRfmUlBRjYFT2Xl1aWsq1uKOozB3YvTqMtT8MZnvEBK7G/vWP7RQW3ELfwBgdnYqTCkI8qKKiYi5euYaPl4emTKFQ4ONVk3PxFX9x8SBy88r+HlGZVPxenZdfwK5Dx6lhZYG1uWxVIsSz6Ilcuqinp0d4eDgjRozgf//7HwEBAbRp04aBAwfi6+urWSZ4eybQbX5+fvj53fkDY9q0aaxevZp169YREhKCpaUlurq6qNVqrXp/l5CQwPjx46lTpw4Anp539soxMzNDoVD8Y/3t27dz6NAhzpw5g5dX2T4LHh53fiE4OjpqJcjefvtttmzZwvLly2nSpEllT1OFiouLWbZsGSdPnmTkyJEAfPTRR5rjbm5ujBs3joiICCZMuDOlvaCggMWLF99zCea9zt3w4cNp3rw5iYmJ2Nvbk5SUxMaNG7Vm0j2I2+e+oplESqUSK6uyD0A2Njaa/lRUFhYWxsSJE3n11VeBstdh2rRpTJgwgSlTpmjafPHFFxkyZIjm8dChQytVb/DgwZrlodOnT2fOnDkcOnSIoKAg5s6di5mZGREREZolhbevB4ApU6Ywa9Ys+vTpA4C7u7smqXb7ee9n9OjRmvq3/dO1lZWVxezZs/n22281z1GzZk1atmwJwK+//kpJSQnz58/XzHhcuHAh5ubm7Nq1i+eff75cH/Lz88slpAsKijGQZXD3dSsnk5KSknJLFFVqM5JvXK2S5ygtLWX9ynBcPWpj53Bn7yK/xi2xsLRBbWbJ9auX2Lz2Z27eSOSlkeOr5HmFuC0rv4CS0lLMjAy0ys2MDLmWmVNhncy8fMz/tqTRXGlIeu6d95rBjesy7+Ap3vrtD3R1FCiAEc/5UM9We9n9tphLLD0WQ35RMfamJnzQoTF6uk/k94DiPyw/N5PS0hIM/7ZE0cjYnKy0yr2fn9y7GKWJJbYuZV9+5N1Kp6gwj7NHVlG/2Yv4tniZ65dOsG/9pwS+MI0aTt4V9uP0Xyuo6VP+97UQDysz5xYlJSWYq7X3OTRXmXDtxs0qeY7S0lLCV2+itrsLLva2Wse27DvE0t+3kZdfgEMNaz56/RX07jFLTAjxdHsiE11QtkdX165d2bNnDwcPHmTTpk189tlnzJ8/n8GDB1dYJzs7m9DQUDZs2EBiYiJFRUXk5uZqZnRV1tixYxk+fDhLliyhQ4cO9OvXj5o1a1a6/okTJ3ByctJKatytuLiY6dOns3z5cq5evUpBQQH5+fkYGz/4sqHvvvuO+fPnU1BQgK6uLmPGjOGNN94AypIXc+bMITY2luzsbIqKijA1NdWq7+rq+kD7jDVp0gRvb28WLVrExIkT+fnnn3F1daV169YPPBYo+yUH2pvVP4jIyEj27dunNROruLiYvLw8bt26pTnnjRo1eqB6vr53ZuKYmJhgampKUlISUHYdtGrVqsJ9s3JycoiNjWXYsGGMGDFCU15UVPSvNtH/e7/vd22dOXOG/Px82rdvX2F7kZGRXLhwodxsvLy8PK0ll3ebMWMGYWFhWmWvvvEBQ976sNLjEI/O2l/nceNaAiPHTtMqb9ryzocge0dXTM0smD8njJTkRKxsHnyPOyEely0xCVy4mcG4tgHYmCg5cyONBYdOY2FsiM9ds8eauztQ396atNx8NkTHMfvPE4R1eg4D+XAk/kPOHF5Fwrm9tO07TbP/Fv//t5CDR2NqB/QAwKKGBzcTzxIbtblcoqsw/xZ71n6MmZUz3k0HIMSTaP7KDVy+nsTUt4eWO9YywBdfr5qkZWTx+679fLVoOdNGDcNA9qgV4pnzxCa6AIyMjOjYsSMdO3Zk0qRJDB8+nClTptwz0TVu3Di2bdvGF198Qa1atVAqlfTt2/dfL7kKDQ3lxRdfZMOGDWzatIkpU6YQERFB7969K1X/fhvbf/7558yePZuvv/4aHx8fTExMGD169EMtDQsODubDDz9EqVRib2+v2ZTxwIEDBAcHExYWRqdOnTQzjGbNmqVV/2HuGDl8+HDmzp3LxIkTWbhwIUOGDHnoBNWZM2eAsllODyM7O5uwsLBys54AzV5kUH78la339ySWQqGgpKQE+OfrIDs7G4B58+bRtGlTrWO3l7hWxt/7fb9r637XZnZ2Ng0bNmTp0qXljt0rEfr+++8zduxYrbK/YmVfkMowNjFFR0eH7KwMrfLsrAxUphYP3f7aX+dz9tRRRo6eirlFxcu5bnP6/7s8piRfl0SXqFJqQwN0FAoy8rR/x2VUMGvrNlMjQ9LztGeKpufmY64siy8oKubX4zGMaRNAgFPZnRhdLEyJT8tkfXScVqLLxEAfEwN97E1N8LI2Z9jy7Ry5fIPm7g4IUVUMlaYoFDrk39J+P8+7lY6R8T+/n589upazR1bRpvcUzK3dtNrU0dHFzNJFK97U0omb185olRUW5PLnmqno6Stp0e09dHSf6I8A4j/K1MQYHR0d0rOytcrTs3MwN334u9n+9NsGjkWfIyxkCFYVLEk0URphojTC3sYKLzdnhnw4g8NRZ2kR4PPQzy2EeLI8Vb/l6tWrx5o1a4CyBENxsfaH6X379jF48GBNQio7O7vc0jcDA4Ny9Sri5eWFl5cXY8aMYdCgQSxcuJDevXtXqr6vry9Xrlzh3LlzFc7q2rdvHz179uSll14Cyvb0OnfuHPXq1btvv+7FzMyMWrVqlSvfv38/rq6ufPjhndk1ly5dKhdXGfca+0svvcSECROYM2cO0dHRlV52909u74XWoUOHh2onICCAmJiYCs/No6h3N19fXxYtWlTh3RBtbW1xcHDg4sWLBAcHP/Bz/N39ri1PT0+USiU7duxg+PDh5eoHBATw66+/UqNGjXKz/u7F0NAQQ0PtD6sGBrLRc2Xo6enh6OJBbEwU3n5ly5ZLS0u5EBNFszadH7jd0tJS1i3/iejIvxgxeiqW1rb3rXP9SjwAarPHc7dV8ezQ09XB3cqU04kpNP7/uyHe3lPr+dquFdbxsjHnVGIKXere+bIj6noKntbmABSVlFBUUorO375U0VUoKPn/WTAVKaWU0tJSCv//CwkhqoqOrh6WtjW5kXASx5plX2CVlpaSdDmKWn5d7lnv7JHVRB9aSZvek7G089Q6VtZmLbLStZc+ZqVdw1h9J5lbmH+L3avD0NXVp2WPD+7MCBOiiunp6eLh5MCp83E08akLlF3np85dJKjVg2+/UlpayoJVGzkUdYbQt4ZQw+r+X/aVvZ9D4V03ihJCPDueyE0oUlJSaNeuHT///DMnT54kLi6OFStW8Nlnn9GzZ0+gbK+pHTt2cP36dc2d+Tw9PTWbqUdGRvLiiy9qZtfc5ubmxp9//snVq1e5ebP8WvLc3FxCQkLYtWsXly5dYt++fRw+fJi6detq6mdnZ7Njxw5u3rzJrVvlP9C3adOG1q1b88ILL7Bt2zbi4uLYtGkTmzdv1vRz27Zt7N+/nzNnzvDaa69x48aNKj2Ht3l6epKQkEBERASxsbHMmTOH1atXP1Bb9zp3FhYW9OnTh/Hjx/P888/j5OT0r9pNT0/n+vXrXLp0iW3bttG3b19++eUXvv/+e627YT6IyZMns3jxYsLCwjh9+jRnzpwhIiJCa9+yqqx3t5CQEDIzMxk4cCBHjhzh/PnzLFmyhJiYsk1pw8LCmDFjBnPmzOHcuXNERUWxcOFCvvzyywce7/2uLSMjI9577z0mTJjA4sWLiY2N5eDBg/z0009A2cxAa2trevbsyZ49e4iLi2PXrl2MGjWKK1eqbpNRcUfLdt05vG87Rw/+QVLiFdZE/EhBfj4Nn2sLwPJFc9i89mdNfFFREdcux3HtchzFxUVkpqdw7XIcKcl3bq6x7tf5nDi8hwFDRmNoaERWRhpZGWkUFpbNqElJTmTHxhVcSYglLSWJ6JOHWb74G9xr1cPeseLEgxAPo2tdd3ZeuMzu2CtcSc/mp79Ok1dUTGDNst8Xc/dFsuzYnQ27g+q4cTLxJuuj47iakc3KyPPEpWTQ6f8TY8YG+tS1tWTpsbNEX08hKfsWu2Ov8OfFqzT5/2TajaxbrDkVy8WUDG7m5BKTlMbXf57AUE8Xf8d/v1RfiPvxCujBxVPbiIveSWbKZY7u/B9FhXm4e7cD4K8tX3Ny7xJN/JnDq4g68AtNOoZgbFqD3Jw0cnPSKCrM08TUbtiLhHP7iI3aSlZ6IudPbODaxcPU8i1Lnt1OchUX5dOo41sUFtzStFMqCV3xCHQLbMaOA0fZdeg4V64nM2/FevIKCghs4g/At0tXsXT9nZtJFRUVE3clkbgriRQVF5OakUnclUSu30zVxPz02wb2HD3JOy/1xcjQgLTMLNIysyj4/7vE37iZyurte4i9fI2baenExCXwZfhyDAz08a9X8VYxQoin2xM5o0ulUtG0aVO++uorYmNjKSwsxNnZmREjRvDBBx8AMGvWLMaOHcu8efNwdHQkPj6eL7/8kqFDh9K8eXOsra157733yMzM1Gp76tSpvPbaa9SsWZP8/HzNXlC36erqkpKSwiuvvMKNGzewtramT58+mj2Imjdvzuuvv86AAQNISUlhypQphIaGlhvDb7/9xrhx4xg0aBA5OTnUqlWLmTNnAmWbw1+8eJFOnTphbGzMyJEj6dWrFxkZGeXaeVg9evRgzJgxhISEkJ+fT9euXZk0aVKFfb6ffzp3w4YN45dffmHo0PLr6e/n9ibwRkZGODo60rJlSw4dOkRAQMC/buvvOnXqxPr165k6dSqffvop+vr61KlTp8LZTFVR725WVlbs3LmT8ePH06ZNG3R1dWnQoAEtWrQAypZ8Ghsb8/nnnzN+/HhMTEzw8fFh9OjRDzzeylxbkyZNQk9Pj8mTJ3Pt2jXs7e15/fXXATA2NubPP//kvffeo0+fPmRlZeHo6Ej79u0rPcNL/Du+DVuQnZXJ9vW/kpWZjoOzG0Pe+hC1qTkA6ak3Uejc+c4iKyOVb2be2TB+z47f2bPjd9xr1WPkmKkAHNyzBYB5X9+5cQJA35ffouFzbdHV0yc2Jor9uzZQkJ+PmYUV9Rs0pW1Q30c8WvGsauZmT2ZeASsiz5ORm4+rpSkT2zXG7P+XIqbk5GnNzqpdw4KQFn4sjzzPr8djsDM1YWxgAM4Wd/YPHNWqAcuOx/Dt3kiyCwqxNlEyoIEXHbzKlnkZ6Opw9kYqm87Ec6ugEFMjQ+rYWhAW9Bym91gyKcTDcPFqSf6tDE4diCDvVhoWNu607jUJI2NzAG5l3kShuPN+Hhu1hZLiIvZt+EyrHe+mA6jfbCAATrWeo2G71zhz+DeO7/4JtYUDLbq9h41j2RewaUkXSbledpfejeFvarXTbegPmJjWeFTDFc+o5v71ycjOYfnmP0jPysbNwZ4PR76s2aD+ZlqG1hYmaZlZvDfrf5rHv/+xn9//2E+9mm6EhpR9Bti67zAAoXO17+T+5qBeBDbxR19fnzMXL7Fh9wFu5eVhplJR18OFj0cNw0z14NuvCCGeXIrSv2dyhHgElixZwpgxY7h27RoGBjJl/lm3+7QsXRRPP98171Z3F4R45GY5zq7uLgjxyL1Xf2d1d0GIR07dKKi6u/DA/qufLdp4P/jN5MTDeSJndIknx61bt0hMTGTmzJm89tprkuQSQgghhBBCCCHEI/NE7tEl7tizZw8qleqeP9Xts88+o06dOtjZ2fH+++9rHZs+ffo9+92584NvtP0skHMnhBBCCCGEEEKUJ0sXn3C5ublcvXr1nscf5q6Aj1pqaiqpqakVHlMqlTg6Oj7mHj05nvRz91+dXixEVZKli+JZIEsXxbNAli6KZ4EsXax6snSx+sjSxSecUqn8Tyez/omlpSWWlpbV3Y0nkpw7IYQQQgghhBCiPFm6KIQQQgghhBBCCCGeCpLoEkIIIYQQQgghhBBPBUl0CSGEEEIIIYQQQoinguzRJYR47AJy/6zuLgjxyBVVdweEEEJUiWPK1tXdBSEeuTbV3QEhqpDM6BJCCCGEEEIIIYQQTwVJdAkhhBBCCCGEEEKIp4IkuoQQQgghhBBCCCHEU0ESXUIIIYQQQgghhBDiqSCJLiGEEEIIIYQQQgjxVPhPJ7rCw8MxNzd/qDbc3Nz4+uuvq6Q/AIGBgYwePbrK2ntS+3BbRa/Rjz/+iLOzMzo6OppzX1GZEEIIIYQQQgghRFXS+7cVkpOTmTx5Mhs2bODGjRtYWFjg5+fH5MmTadGiBQqFgtWrV9OrV69/1a6bmxujR4/WSuAMGDCALl26VKp+eHg4o0ePJj09Xav88OHDmJiY/Ku+VJXAwEB2794NgKGhIR4eHoSEhPDmm28+VLurVq1CX19f87iic/ewFAqF5t/GxsY4ODjQokUL3n77bRo2bKg59vfXKDMzk5CQEL788kteeOEFzMzMKiwTDy45OZk33niDHTt2UFxcTEBAAD/88AO1a9eu7q6JR2Tz3kP8/sc+0jOzcXWwY0ifzni6Ot0z/sCJU/y66Q+SU9Oxs7EiuFsHAup5aY6nZ2Wz9PdtnDwXS86tPOrWdGVony7Y21hptXMu/jIRG3dw/tIVdHR0cHOw48PXX8bgrvcfIarK1phL/H46joy8fFws1AxuXI9a1ub3jD8Yn8jyyPPczMnFTm3MoIDa+DvW0Iq5kp7NsuMxnLmRSnFpKU5mKsa08cfaRKkVV1payqc7jxB57SZjAwNo7Gz7KIYoBOcjNxJzdC15OWmY27jhHzgcKzuvCmNjo7Zy6cwuMlISALCoUROfFsFa8VfOH+BC1BbSky6Sn5fF8y/OwqKGR7m2biaeJWr/L6ReP4dCoYO5jTttek9BV8/g0QxUPNMO7N7Mnu1rycpMx97Jle79huHs5llh7I3Ey2xfH8HVhIukpSbT9YXBtGzXTSvm4vnT7Nm+jmuXL5KZkcZLIyfg7ddEK2bFkm85dnCXVplX3QYMCfmoSscmhHgy/OtE1wsvvEBBQQGLFi3Cw8ODGzdusGPHDlJSUqq8c0qlEqVSef/Af2BjY1NFvXkwI0aMYOrUqdy6dYvFixfz1ltvYWFhwaBBg/51WwUFBRgYGGBpafkIelrewoULCQoKIi8vj3PnzvHjjz/StGlTFixYwCuvvAKUf40SEhIoLCyka9eu2NvbA3Dq1KlyZQ+isLBQK8H3rHrvvfc4cuQI69evx87OjmPHjlWq3u3rRzxZ9h8/xeK1mxnRtzuerk5s+PMAn/ywhNkfjMJMVT6JHxOXwOwlv/Fi17Lk1t5jUXyxMIKZY1/Dxd6W0tJSPv9pGXq6uowfOghjI0PW7zrAtO8X8eV7IRgZll0j5+IvM/2Hn+nVoSVD+nRBV0eHS9euayXBhagqB+ITWXLkDMOa1qeWtTmbzsYzY8dhvurZGlMjw3LxMUlpfLM3koH+Xvg71mB//DW+3HWM6V1a4GyhBuB6Vg5hWw8SWNOJfn6eGOnrciU9GwPd8pPZN52Nf9RDFIKEc3s58edCGrV7HSs7L2KO/86fq6fS+dW5GBmX/xIw+copXGq3wsq+Nrp6Bpw9soo/V0+l08uzMVaVfTFRVJSPjWM9XLxacHj7dxU+783Es/y5ehp1G79AQOBwFDq6ZCTHA/J+LqreyaP72PBbOL0GjcTFzYu9f6xnwbcf8+6UOajU5a/zwoJ8LKxqUN+/GRt+C6+wzcKCAuwdXWnUrB0/z/v8ns/tVc+fvi/dmVCgK58bhHhm/auli+np6ezZs4dPP/2Utm3b4urqSpMmTXj//ffp0aMHbm5uAPTu3RuFQqF5HBsbS8+ePbG1tUWlUtG4cWO2b9+uaTcwMJBLly4xZswYFAqF5oPU35fFRUZG0rZtW9RqNaampjRs2JAjR46wa9cuhgwZQkZGhqZ+aGgoUH7pYnp6Oq+99hq2trYYGRlRv3591q9fD0BKSgqDBg3C0dERY2NjfHx8WLZs2b88pdqMjY2xs7PDw8OD0NBQPD09WbduHVCWsPDy8sLY2BgPDw8mTZpEYWGhpm5oaCgNGjRg/vz5uLu7Y2RkpDlft2dvVXTucnJyMDU1ZeXKlVp9WbNmDSYmJmRlZVWq7+bm5tjZ2eHm5sbzzz/PypUrCQ4OJiQkhLS0NED7NQoPD8fHxwcADw8PFApFhWXx8fEArF27loCAAIyMjPDw8CAsLIyioiLN8ysUCr7//nt69OiBiYkJn3zySaXrzZ8/n969e2NsbKx1zm87ffo03bp1w9TUFLVaTatWrYiNjdUcnz9/PnXr1sXIyIg6derw3XcV//H4dwUFBYSEhGBvb4+RkRGurq7MmDFDczw9PZ3hw4djY2ODqakp7dq1IzIyEiibqWVnZ8f06dM18fv378fAwIAdO3ZoynR0dGjevDktWrSgZs2a9OvXr8LZXIGBgYSEhDB69Gisra3p1KkTAF9++SU+Pj6YmJjg7OzMm2++SXZ2tlbdffv2ERgYiLGxMRYWFnTq1EnzmpeUlDBjxgzc3d1RKpX4+fmVu9ZE1Vm/6wDtn2tI26b+ONnZMLJfdwwN9Pnjr4oTnBv/PEiDOrXo0a4FTnY2DOzSDndHezbvOQRAYnIK5y9dYXi/btRyccShhjUj+nWjoLCIfcejNO0sWrOZoFZN6dW+Fc52NXCoYU2zBvXR1/vX348IcV8bzsTRztOZwFpOOJmrGN7UG0NdXf64cKXC+M1n4/FzsKa7twdO5ir6N/DCzdKMLTGXNDHLT5yjgaMNwQ3r4GZpip3ahEbOtuUSZ/GpmWyIjuP15r6PdIxCnDu2Do/6HXH3bo+plTON2r+Brp4hcad3VBj/XOex1PLrjEUND0wtnWjcIYTS0hKSEk5qYtzqBuLdtD+2Ln73fN4Tuxfi2aArdRv3wczKBVMLR5y9WqCrJ0kAUfX27vydxi060KhZO2rYO9F70GsYGBhw5MDOCuOdXGvRpc+r+DVqid49rsna3v483+NFvBs0/cfn1tXTQ21mofkxNlY99HiEEE+mf5XoUqlUqFQq1qxZQ35+frnjhw8fBspmAiUmJmoeZ2dn06VLF3bs2MHx48cJCgqie/fuJCSUTcVetWoVTk5OTJ06lcTERBITEyt8/uDgYJycnDh8+DBHjx5l4sSJ6Ovr07x5c77++mtMTU019ceNG1eufklJCZ07d2bfvn38/PPPREdHM3PmTHR1dQHIy8ujYcOGbNiwgVOnTjFy5EhefvllDh069G9O0z9SKpUUFBQAoFarCQ8PJzo6mtmzZzNv3jy++uorrfgLFy7w22+/sWrVKk6cOFGuvYrOnYmJCQMHDmThwoVasQsXLqRv376o1eoH7v+YMWPIyspi27Zt5Y4NGDBAk8A8dOgQiYmJ9OvXr1yZs7Mze/bs4ZVXXuGdd94hOjqaH374gfDwcE0y67bQ0FB69+5NVFQUQ4cOrXS9sLAw+vfvz8mTJ+nSpQvBwcGkpqYCcPXqVVq3bo2hoSE7d+7k6NGjDB06VJMsW7p0KZMnT+aTTz7hzJkzTJ8+nUmTJrFo0aL7np85c+awbt06li9fTkxMDEuXLtUkfAH69etHUlISmzZt4ujRowQEBNC+fXtSU1OxsbFhwYIFhIaGcuTIEbKysnj55ZcJCQmhffv2mjZ69uzJypUr2bx58337s2jRIgwMDNi3bx//+9//gLJE2Zw5czh9+jSLFi1i586dTJgwQVPnxIkTtG/fnnr16nHgwAH27t1L9+7dKS4uBmDGjBksXryY//3vf5w+fZoxY8bw0ksvaZbpiqpTVFTMxSvX8PG6swxFoVDg41WTc/EVJwDOxV/RigfwrV2Tc5cuA1BYVPY63p2wUigU6OvpcfZi2XtyRnYO5y9dwUxlzEez5zN80meEfruQsxcvIURVKyouIS4lk/r21poyhUJBfXsrzienV1jnXHI69e21l9r62ltx/mZZfGlpKceuJGOnNmH69sO8tmIHH23az+HLN7Tq5BcV8+3eEwxp4o25svzMMSGqSklxEak3YrG7KyGlUCiwdfElJTGmUm0UFeVTUlKMgVHl/47Lu5VByvVzGBqbsuPXiaz9YTB/rPiI5Ktn/vUYhLifoqIiriZcpFadO18cKBQKatXxJSHu3CN//rjz0Xz83lBmhY1iTcSP3Mqp3Jf7Qoinz7/6al5PT4/w8HBGjBjB//73PwICAmjTpg0DBw7E19dXs0zw9kyg2/z8/PDzu/OLfdq0aaxevZp169YREhKCpaUlurq6qNVqrXp/l5CQwPjx46lTpw4Anp531nqbmZmhUCj+sf727ds5dOgQZ86cwcurbH8DD487HwgdHR21EmRvv/02W7ZsYfny5TRp0qRce/9GcXExy5Yt4+TJk4wcORKAjz66s2bczc2NcePGERERoZV0KCgoYPHixfdcgnmvczd8+HCaN29OYmIi9vb2JCUlsXHjRq2ZdA/i9rm/PSvrbkqlEiursg8eNjY2mv5UVBYWFsbEiRN59dVXgbLXYdq0aUyYMIEpU6Zo2nzxxRcZMmSI5vHQoUMrVW/w4MGa5aHTp09nzpw5HDp0iKCgIObOnYuZmRkRERGapZC3rweAKVOmMGvWLPr06QOAu7u7Jql2+3nvJSEhAU9PT/6PvfuOjqp4Gzj+3fS66b1CEggkhBZ6J3RBunQMVVBERar+lKYgCAiI+iogiKIC0gm9V+kJVRIgoYYA6ZuyySb7/hHdsCS00OH5nLPnsPfOzM5cNsnuc2eeqVu3LgqFAh8fH925vXv3cujQIW7evImpacEXqmnTprFq1Sr++usvBg4cSKtWrRgwYAA9evQgNDQUS0tLvRlhZ86coXv37kyYMIH+/fvzzTff0LlzZwCOHj1KaGgot27dwtGx4AtjQEAAU6dO1evjnbncfH19+eKLLxg0aJBu1trUqVMJDQ3Vm8UWFBQEgFqtZtKkSWzdupVatWrp/g/27t3Ljz/+SIMGDe57fcSjScvIJD8/H1tr/TuStlaWXE+4XWydVJWqaHmlFSlpBbP2PJwdcbSz4Y+IrQzo3AYzExPW7dpPYkoqyWkFHwhv3i6Yvbds8y56tWmGr4cru49EMeGHX5g+8r0iubyEeBzp6hzytVpszPSXVtuYmXI9LaPYOmnZamzvmplla25KSlbBTbjU7BzUmjzWnL5Il0oBdK9Slqjrt/hm1zH+17QG5V0KUgD8euQsAU52hEpOLvGUqbPS0GrzMb1riaKZhS3pydceqo0TexdhbmmPi/fDzz7MSL0BwJm/l1Kx3tvYOpUi7uxOdq74nBY9Z2Ft5/7wgxDiATIz0sjPzy+yRNHK2oZbCQ/3Pi+pMuUqEVSxBnYOziTdTmDzmt9Z8N0XDB4+GQODF3r/NSHEU1CiHF1vvPEGe/bs4e+//2bDhg1MnTqVefPmER4eXmwdlUrFuHHjiIiIID4+Ho1GQ1ZWlm5G18MaNmwY/fv359dff6VJkyZ07twZPz+/h64fGRmJp6enXlDjTnl5eUyaNImlS5dy7do1cnJyUKvVWFhYPFI/7/T9998zb948cnJyMDQ05KOPPmLw4MEALFmyhNmzZ3PhwgVUKhUajQalUqlX38fHp0R5xqpXr05QUBC//PILo0eP5rfffsPHx4f69euXeCxQcJcceOw8PVFRUezbt09vJlZeXh7Z2dlkZmbqrnloaGiJ6oWEFH4ItLS0RKlUcvPmTaDgfVCvXr1i831lZGRw4cIF+vXrx4ABA3THNRrNQyXRDw8Pp2nTppQtW5YWLVrQunVrmjVrpuu7SqXSBf7+k5WVpbdsctq0aQQHB7Ns2TKOHj2qC4pBwQy3li1bMnr0aJo1a0bTpk1JTExk0KBBnDx5ksDAQF2QC9DbOOA/W7duZfLkyfzzzz+kpaWh0Wj0rl9kZKQueHa38+fPk5mZSdOmTfWO5+TkULly5WLrqNXqIjNAc3JyMJV8Yc+FkZEhH/fpyv8tWU3fT7/CwMCACmVKU6lc4Y2DfG0+AE1qFSyZBCjl6cbJmItsP3iMHq2bFtu2EC+K//5WVfV0plW5UgD42iuJvpXC1ujLlHex58iVBE7dSOSrN+o8z64K8VDOHl7B5ei9NOo08ZESyGv//X1eukIzSgUVzA63cy7NzSsniD29jZC6vZ5Kf4V41iqG1tX9283DB1cPb6aNHcLF6FN6M8yEEK+HEiVbMTMzo2nTpjRt2pTPPvuM/v37M3bs2HsGuoYPH86WLVuYNm0a/v7+mJub06lTJ90Svoc1btw4unfvTkREBBs2bGDs2LH8+eeftG/f/qHqPyix/ddff82sWbOYOXOmLofRhx9++Mj9vFOPHj349NNPMTc3x83NTXdH4cCBA/To0YPx48fTvHlz3Qyj6dOn69V/nB0j+/fvz3fffcfo0aNZsGABffr0eewA1dmzBVPdS5Uq9VjtqFQqxo8fr5s1daf/cpFB0fE/bL27g1gKhYL8/IIPe/d7H/yXq2ru3LnUqKGfB+C/Ja73U6VKFWJjY9mwYQNbt27lrbfeokmTJvz111+oVCrc3NzYuXNnkXp35qK7cOEC169fJz8/n7i4OF2OM4ATJ07oZpVVqVKFNWvW0Lx5c27fvs3GjRv1Zr9B0esXFxdH69atGTx4MF9++SX29vbs3buXfv36kZOTg4WFxUNdn4iICDw8PPTO3RmQu9PkyZMZP3683rHRA3rwycCe93wdUUBpaYGBgQEp6fo51FJUGdgqi887YWNlVbR8mkqvvJ+XO18PH0xGVjaavDxsrCz55Ju5lPYq2CzCTlmwLMbTRT/I7uHsSGJy6mOPS4g7WZuaYKBQkJqt/7c2tZhZW/9RmpmSkq0fQE/JUuuWH1qbmmBooMDTVv/nxMPGknM3C2Ysnr6RyE1VJv2W6M90/mbXMQKd7fm82f1zwQjxKEzNlSgUBqgz9X+HZmemYGZhd9+6/xxdzT9HVtCg/VhsHX0f6XXNLAtmLyrt9XfqtbbzJDP91iO1JcSDWFgqMTAwQJWu/z5Xpadipbz/+/xJc3B0xdLKmsTbN/BHAl1CvG6eSFbh8uXLs2rVKqAgwPBfLp//7Nu3j/DwcF1ASqVSFVn6ZmJiUqReccqUKUOZMmX46KOP6NatGwsWLKB9+/YPVT8kJISrV68SHR1d7Kyuffv20bZtW3r2LPgCnp+fT3R0NOXLl39gv+7FxsYGf3//Isf379+Pj48Pn376qe7YpUsly39zr7H37NmTkSNHMnv2bM6cOfPAZXcP479caE2aNHmsdqpUqcK5c+eKvTZPo96dQkJC+OWXX4rdxdHFxQV3d3cuXrxIjx49StS+UqmkS5cudOnShU6dOtGiRQuSkpKoUqUKN27cwMjISC9v151ycnLo2bMnXbp0oWzZsvTv35+TJ0/i7OwMFCyv3bNnD2PGjAGgTp06rFy5ktatW2Nvb8+QIUPu27ejR4+Sn5/P9OnTdUHXpUuX6pUJCQlh27ZtRYJTUPCzbmpqyuXLlx96meKYMWMYNmyY/jhP7Xyouq87IyNDSnu6cyomluoVygEFM1VORV+kRb3il1OX8fXkZPRF3mhQS3fsZPRFyvh4FSlraV4QHI6/lciFK9fo0qoRAE72ttjZWHP9pv5uuvG3kqhUruQ/e0IUx8jQgFIOSk7HJ1Lt3yWEWq2W0zcSaVbWp9g6ZZxsORWfqJutBXDyRiIBjra6Nks72BB/19LH+LQMHCwLgvltg/1o7K//czFy3V56h5ajiqfzkxqeEAAYGBph7+JHwuUTePgVBFG1Wi03r5zEv2Kre9b758hKzhz6iwbtP8feNeCe5e7FUumMuaU96cnX9Y6rUq7j6lvlkdsT4n6MjIzw8C7NhXMnCapY8DlFq9Vy/txJajVo+Uz7kpJ8m8wMFdbPOMAmhHgxPFKgKzExkc6dO9O3b19CQkKwtrbmyJEjTJ06lbZt2wIFOX+2bdtGnTp1MDU1xc7OjoCAAFasWEGbNm1QKBR89tlnutk1//H19WX37t107doVU1NTveVXULC8a8SIEXTq1IlSpUpx9epVDh8+TMeOHXX1VSoV27Zto2LFilhYWBRZctigQQPq169Px44dmTFjBv7+/vzzzz8oFApatGhBQEAAf/31F/v378fOzo4ZM2aQkJDwWIGuewkICODy5cv8+eefVKtWjYiICFauXFmitu517ezs7OjQoQMjRoygWbNmeHp6PqAlfSkpKdy4cQO1Wk10dDQ//vgjq1atYtGiRXozkEri888/p3Xr1nh7e9OpUycMDAyIiori1KlTfPHFF0+83p2GDBnCt99+S9euXRkzZgw2Njb8/fffVK9enbJlyzJ+/HiGDh2KjY0NLVq0QK1Wc+TIEZKTk4sEbO42Y8YM3NzcqFy5MgYGBixbtgxXV1dsbW1p0qQJtWrVol27dkydOpUyZcpw/fp1IiIiaN++PaGhoXz66aekpqYye/ZsrKysWL9+PX379tXtDDpixAhatWrFe++9x+DBg8nNzWXXrl2YmJhw69Yt1q5dS5cuXe7ZP39/f3Jzc/n2229p06aNXpL6/4wZM4YKFSrw7rvvMmjQIExMTNixYwedO3fG0dGR4cOH89FHH5Gfn0/dunVJTU1l3759KJXKYoOppqamRWZ7pcuyxYfWumEtvvt9JaU93fD39mT97r/JzsmhYfWCJYVzFq/AzsZat5ywVf2ajPtuIWt37KNK+TLsO36Ki1evM/CtNro2D0SeQmlliaOtDZdv3GThyg1Ur1COimULglgKhYI3G9Vh2cad+Hq44Ovhxs5DkVy/eZth4W89+4sgXnlvlCvFD/tPUMpBiZ+DLRv/iSNbk0dDv4K/W9/ti8Le3IxuVQp2mG0R6MvELQdZdyaWyh5OHIiLJzYxlQE1gnVtti5fim/3RBLobE+Qqz1R125z7OotPmtW8OXL1ty02AT0DpbmOFuVPGWBEPdSpsqbHNo0GzsXPxxcAoiOXIcmN5tSQY0BOLhpJuaWDrrlhGcPr+DU339Qq8UwLJTOZGUUzEY0NjHHyLjgRoU6K53M9Fu6c+kpBQEtM0s7zC3tUCgUlK3altN/L8HWybcgR9eZHaQlX6PWGyOe9SUQr4G6jduwbNEcPLxL4+UTwL6dEeSo1VStWXAzbekvs1Ha2tOibcHEAo1Gw834gg1z8vI0pKUkcv1KLKZmZjg4Fcw0V6uzSbxZuFlZcuJNrl+JxcLSClt7J9TqbLZFLCG4ci2slbYk3rrBhlW/4uDkSplylZ7tBRBCvBAeKdBlZWVFjRo1+Oabb7hw4QK5ubl4eXkxYMAAPvnkEwCmT5/OsGHDmDt3Lh4eHsTFxTFjxgz69u1L7dq1cXR0ZNSoUaSlpem1PWHCBN555x38/PxQq9W6/Br/MTQ0JDExkd69e5OQkICjoyMdOnTQzTqpXbs2gwYNokuXLiQmJjJ27FjGjRtXZAzLly9n+PDhdOvWjYyMDPz9/fnqq6+AguTwFy9epHnz5lhYWDBw4EDatWtHauqTX6rz5ptv8tFHHzFkyBDUajVvvPEGn332WbF9fpD7Xbt+/frx+++/07dv30du979lcGZmZnh4eFC3bl0OHTpElSqPfwewefPmrFu3jgkTJjBlyhSMjY0JDAykf//+T6XenRwcHNi+fTsjRoygQYMGGBoaUqlSJerUKcjT0r9/fywsLPj6668ZMWIElpaWVKhQQS+J+71YW1szdepUYmJiMDQ0pFq1aqxfv143e2r9+vV8+umn9OnTh1u3buHq6kr9+vVxcXFh586dzJw5kx07duhytf36669UrFiRH374gcGDB9OiRQu2bdvG2LFjqVOnDgYGBjRp0oRDhw6xYsUKwsPD8fLyonbt2sX2r2LFisyYMYMpU6YwZswY6tevz+TJk+ndu7euTJkyZdi8eTOffPIJ1atXx9zcnBo1auiS+0+cOBEnJycmT57MxYsXsbW1pUqVKrrfAeLJql05mFRVBks37iAlXYWvuxufDuylSzh/OzlVb0ly2VLeDO3ZgSUbdvDH+m24OjowvE9XvN0Kk22npKlYtHqzLnF9/dBKdGqmP0PvjQa1yNVo+GXVJlSZmfi4u/K/Qb1wdbR/NgMXr5Vavm6kZeewLCqG1Cw1PvZKRjeuhs2/gajEjGwM7nyfO9sxpE5FlkbFsOT4OVyVlgxrWAUvu8Ld6Kp7u9K3RhCrT13kl8NncFda8lGDygQ6y3tYPB/eZeqizkzl1IE/yc5Mxs6pFPXbfYaZhS0AmWm3USgKk2ZfOLmJ/DwN+yL0N5UJqtGF4FpdAbgee5hDm7/VnTuwfnqRMmWrvEl+nobI3QvIyU7H1tGXBu3HYW3r9jSHK15TIVXroEpPY+u6JaSnpeDu5Uuf9z7FWmkLQErSbRR3JIdPT03i268Kg657tq1lz7a1lPIvz8CPJgBw7dJ55s4apysTsXwhAFVqNqRzryEYGBhw4/pljh3cRXZWJkobO/zLVaRp664YFZOTVwjx6lNo744oiVfKr7/+ykcffcT169cxkVk04gWRfmTj8+6CEE+dZtPq590FIZ666R6znncXhHjqmlbTPO8uCPHUNQh6eWcz7zqd+by7UKyX+Zq+7J5Iji7x4snMzCQ+Pp6vvvqKd955R4JcQgghhBBCCCGEeOUZPLiIKM6ePXuwsrK65+N5mzp1KoGBgbi6uuoSl/9n0qRJ9+x3y5bPNlHky0aunRBCCCGEEEII8eKSpYsllJWVxbVr1+55/nF2BXzakpKSSEpKKvacubk5Hh4ez7hHLw+5dk+GLF0UrwNZuiheB7J0UbwOZOmieB28zMvsZOmiuJssXSwhc3PzFzqYdT/29vbY20sy3pKQayeEEEIIIYQQQry4ZOmiEEIIIYQQQgghhHglSKBLCCGEEEIIIYQQQrwSJNAlhBBCCCGEEEIIIV4JEugSQgghhBBCCCGEEK8ESUYvhHjmppxq/Ly7IMRT9zGy66J49Y0K3v68uyDEU3eM+s+7C0IIIR6BzOgSQgghhBBCCCGEEK8ECXQJIYQQQgghhBBCiFeCBLqEEEIIIYQQQgghxCtBAl1CCCGEEEIIIYQQ4pUggS4hhBBCCCGEEEII8UqQQJcQL5C4uDgUCgWRkZHPuytCCCGEEEIIIcRLx+h5d0C8PA4cOEDdunVp0aIFERERz7s7z1VcXBylSpXSPbe3t6dq1apMmTKFypUrl7hdLy8v4uPjcXR0BGDnzp00atSI5ORkbG1tH7fb4iWm1Wo5/fefXDy1hRx1Bo5ugVRt/A7Wdu73rRcTtZ5zR1eTnZGMrZMvlRv2x8G1DADqrHRO//0nNy5Hkpl+G1NzJR5+NQiu1Q0TU0tdG0k3Yoja9yspNy8CYO8aQMW6vbF1KlXsawpRUlqtlr+iYth+/ioZObmUdbKjb40g3JSW9623+dwl1p6OJTVbjbedNeHVyuPvaKs7n6PJ47ej/3DgUjy5eflUdHekb/UgbMxNAbiUlMbq0xc5dzOZdHUOTlbmNAnwpmU536c4WiEKaLValm7cwba/j5GZlU2ZUl4M6NQaNyeH+9bbuPcQa3fsIyVNhY+7K306tCTAx1N3fsv+I+w7dpLYa/FkZatZMGkMluZmT3s4QhTrwK6N7Nm6mvS0FNw8fWjTuR9evgH3LH/i2H62rP2TlKRbODi70aJtTwKDq+jOnzr+Nwf3bub6lYtkZqh4f/TXuHvJ5xIhRAGZ0SUe2vz583n//ffZvXs3169ff279yMnJeW6vfbetW7cSHx/Ppk2bUKlUtGzZkpSUlBK1lZOTg6GhIa6urhgZSQxa6PvnyEpiIiOo2ngQTbpOwcjYjF0rJ5CnuffPw+XovUTuXkBQjbdo1n06No6+7F45gezMVACyM5LIykimYr1wmvecSfVmQ7kRd4zDW77TtaHJzWb3qolYWjsS1nUKjd+ahJGxObtWTiA/T/PUxy1eL2tOX2TjuUv0qxHEFy1rYWpkyORth8nR5N2zzoG4eH49cpaOIf5MalUHHzslk7cdJi1brSvz69GzHL16kw/rV+bzZjVIylQzY9cx3fmLSakozUx4r25Fvm5Tj3bBfvxx/Byb/rn0VMcrBMDq7XvZsOcgAzq35ssPB2BmYsKXP/5KTm7uPevsP36KRas30qlZQ6Z8PAgfDxe+/PFXUlUZujI5ublUKudP+yb1nsUwhLinE0f3EbF8IY1bdeb90V/j6uHLz3O+QJWeWmz5Sxf+YcmCmVSrHcb7o7+mfEg1fvtpKjeuX9aVyc1V4+tXjhZtez6rYQghXiIS6BIPRaVSsWTJEgYPHswbb7zBwoUL9c6vXbuWatWqYWZmhqOjI+3bt9edU6vVjBo1Ci8vL0xNTfH392f+/PkALFy4sMhMpVWrVqFQKHTPx40bR6VKlZg3bx6lSpXCzKzgbuTGjRupW7cutra2ODg40Lp1ay5cuKDX1tWrV+nWrRv29vZYWloSGhrKwYMHiYuLw8DAgCNHjuiVnzlzJj4+PuTn5z/UdXFwcMDV1ZXQ0FCmTZtGQkICBw8e5MKFC7Rt2xYXFxesrKyoVq0aW7du1avr6+vLxIkT6d27N0qlkoEDB+otXYyLi6NRo0YA2NnZoVAoCA8PZ9GiRTg4OKBWq/Xaa9euHb169Xpgn6OiomjUqBHW1tYolUqqVq2qdx327t1LvXr1MDc3x8vLi6FDh5KRUfDBedGiRVhZWRETE6Mr/+677xIYGEhmZuZDXTPx6LRaLTGR6yhXvTMeftWxdfSlevOhZGckce3CwXvWiz62htLBTSkVFIbSwYvQsMEYGpkSe3obADaOPtRpPRKP0tWwtnXDxasCFWr34HrsYfLzCwILaYlXUGenE1yrO0o7D2wcvAmq+RbZmSlkpN96JuMXrwetVsvGfy7RvoI/oV4ueNspGVwnhJQsNUeuJNyzXsTZWBoHeNHQ3xNPWyv61wjC1NCQHeevApCRk8vO81fpFRpIkKsDpR1sGFS7AtG3Uoi5lQJAI38vwquVp7yLPS7WFtQr7UFDP08OX7nxLIYuXmNarZb1uw/SsWl9qgUH4uPuwnvd25Ocms7hk//cs966nQcIq1mVRjUq4+nqxMDObTA1MWbHwcIA7hsNatEurJ7eLC8hnoe929dSrU4TQms1xtnNk/bd3sHExIQjB7YXW37fzgjKlK9M/aZtcXbzpFmbbrh7leLArg26MpWrNyCsVWf8A0Oe1TCEEC8RCXSJh7J06VICAwMpW7YsPXv25Oeff0ar1QIQERFB+/btadWqFcePH2fbtm1Ur15dV7d379788ccfzJ49m7Nnz/Ljjz9iZWX1SK9//vx5li9fzooVK3T5qzIyMhg2bBhHjhxh27ZtGBgY0L59e12QSqVS0aBBA65du8aaNWuIiopi5MiR5Ofn4+vrS5MmTViwYIHe6yxYsIDw8HAMDB79R8Pc3BwomJmlUqlo1aoV27Zt4/jx47Ro0YI2bdpw+fJlvTrTpk2jYsWKHD9+nM8++0zvnJeXF8uXLwfg3LlzxMfHM2vWLDp37kxeXh5r1qzRlb158yYRERH07dv3gf3s0aMHnp6eHD58mKNHjzJ69GiMjY0BuHDhAi1atKBjx46cOHGCJUuWsHfvXoYMGQIU/F+2atWKHj16oNFoiIiIYN68eSxevBgLC4tHvmbi4WSkJZCVkYyLVwXdMRNTSxxcy3A7/lyxdfLzNCQlXMDVu6LumEKhwMU7hMR71AHIzcnE2MQCAwNDAKztPTE1s+biqa3k52nI0+QQe3obSntPLJXOT2iEQsBNVRYpWWqCXQuXa1maGOPvaEv07ZRi62jy8olNTCPYzVF3TKFQEOzmoAtixSWlocnXUuGOMh42VjhYmhFzO/me/cnM1WBpYvx4gxLiAW4mJpOSlk5wQGndMUtzMwJ8PImOu1JsHY0mj4tXr1OhTGEdhUJBhTJ+RMddfep9FuJRaDQarl2+qBeQUigU+AeGcDk2utg6l2Oj8StbQe9YQLmK9ywvhBB3k/VR4qHMnz+fnj0Lpga3aNGC1NRUdu3aRcOGDfnyyy/p2rUr48eP15WvWLHgy3V0dDRLly5ly5YtNGnSBIDSpUsXfYEHyMnJYdGiRTg5OemOdezYUa/Mzz//jJOTE2fOnCE4OJjff/+dW7ducfjwYezt7QHw9/fXle/fvz+DBg1ixowZmJqacuzYMU6ePMnq1asfuX8pKSlMnDgRKysrqlevjouLi+4aAEycOJGVK1eyZs0aXdAIoHHjxnz88ce653Fxcbp/Gxoa6vrt7OysN/Ote/fuLFiwgM6dOwPw22+/4e3tTcOGDR/Y18uXLzNixAgCAwMBCAgozI8wefJkevTowYcffqg7N3v2bBo0aMAPP/yAmZkZP/74IyEhIQwdOpQVK1Ywbtw4qlates/XU6vVRWafaXIVGBmbPrCvokB2RsGXcTMLW73jpha2ZGekFFtHnZWGVpuPqYWN3nEzC1vSk6/ds87pg8vwq9BMd8zYxJxGnSayd91XnDm0FAArWzcatB+rC4YJ8SSkZBX8nrAxM9E7bmNmQmqWurgqpKtzyNdqi6ljyvW0DF27RgaKIkErGzNT3Wve7dzNZP6+FM/IRqElGosQDyslXQWAjbX+DUAba0vdubulZWSSn5+P7V11bK0suZ5w++l0VIgSysxIIz8/Hytr/c8jVtY23Eoo/vOIKi0Va6Wt3jFrpS3pqfe+OSGEEHeSGV3igc6dO8ehQ4fo1q0bAEZGRnTp0kW3/DAyMpKwsLBi60ZGRmJoaEiDBg0eqw8+Pj56QS6AmJgYunXrRunSpVEqlfj6+gLoZk1FRkZSuXJlXbDobu3atcPQ0JCVK1cCBcsoGzVqpGvnYdSuXRsrKyvs7OyIiopiyZIluLi4oFKpGD58OOXKlcPW1hYrKyvOnj1bZEZXaGjJvkQNGDCAzZs3c+3aNV3fw8PD9ZZ83suwYcPo378/TZo04auvvtJb7hkVFcXChQuxsrLSPZo3b05+fj6xsbFAwTLK+fPn88MPP+Dn58fo0aPv+3qTJ0/GxsZG77E/YmqJxv26uPTPLpZ/1033eNiltI8jV53JntVfYOPgRVCNLrrjeZocDm/9Dke3QJp0mULYW5OxcfBhz+ov7psfTIgH2XvxGuF/bNY98v6dJfy8XUlOZ/rOo3QI8SfE3fHBFYR4BHuOnqD36C91j7y8p//7XQghhHjdyIwu8UDz589Ho9Hg7l64u5tWq8XU1JQ5c+boluwV537nAAwMDHRLIP+TW0zyVUvLojtutWnTBh8fH+bOnYu7uzv5+fkEBwfrktU/6LVNTEzo3bs3CxYsoEOHDvz+++/MmjXrvnXutmTJEsqXL4+Dg4PejKvhw4ezZcsWpk2bhr+/P+bm5nTq1KlIIv3ixvUwKleuTMWKFVm0aBHNmjXj9OnTD70T5rhx4+jevTsRERFs2LCBsWPH8ueff9K+fXtUKhXvvPMOQ4cOLVLP29tb9+/du3djaGhIfHw8GRkZWFtb3/P1xowZw7Bhw/SOTVry4IDc68y9dHWa/bszIkB+XsHPRHZmCuZWhYFbdWYKNk6+xbZhaq5EoTBAnamf6DU7MwUzCzu9Y7k5WexeNQEjY3PqtB6FgWHhn4ZL53aTkXqTsC5TdIHUWi2HsfL/enLtwkG8y0qSY1EyVb1c8Hey1T3X/PuFPzU7BzuLwp3hUrNz8LYr/neMtakJBgoFqdn6v1tTs9XYmhXMGrU1N0WTryUjJ1dvVldqthpbc/2ZpVdTVHyx9RBhAd50qOCPEE9aaFBZvZxZuZqCTT1S01XY2xS+z1PTM/Bxdym2DaWlBQYGBkVmfKWoMrBVPlpqCCGeNgtLJQYGBkUSz6vSU7FS2hVbx0ppQ3pait6x9LQUrG2KLy+EEHeTGV3ivjQaDYsWLWL69OlERkbqHlFRUbi7u/PHH38QEhLCtm3biq1foUIF8vPz2bVrV7HnnZycSE9P1yU7B3Q5uO4nMTGRc+fO8b///Y+wsDDKlStHcrL+dOaQkBAiIyNJSkq6Zzv9+/dn69atfP/992g0Gjp06PDA176Tl5cXfn5+RRLq79u3j/DwcNq3b0+FChVwdXXVW5b4sExMCpbj5OUV3XGsf//+LFy4kAULFtCkSRO8vLweut0yZcrw0UcfsXnzZjp06KDLVValShXOnDmDv79/kcd/fdm/fz9Tpkxh7dq1WFlZ6S3FLI6pqSlKpVLvIcsW78/YxBxrWzfdQ2nvhbmlHQlXTurK5KozSbwRjaNb2WLbMDA0wt7Fj4TLJ3THtFotN6+cxOGOOrnqTHatGIeBgRF13/wEQyP9JWCa3GwUd+esUygARZEgtRCPwtzYCFdrS93Dw8YKW3NTTt1I1JXJzMnl/O0UyjjaFtuGkaEBpRyUnI4vrKPVajl9I5GAf4NovvZKjAwUeu1eT1ORmJFNgGPhl6arKelM3HKQ+qU96FK5MNAsxJNkbmaKq6O97uHp4oSt0ppTMRd1ZTKzs4m5dJUyvsX/XTcyMqS0pzunYmJ1x7RaLaeiL1LGVxLPixeLkZERHt6luXCu8DOMVqvl/LmTeJcq/netd6kyeuUBzv9z4p7lhRDibhLoEve1bt06kpOT6devH8HBwXqPjh07Mn/+fMaOHcsff/zB2LFjOXv2LCdPnmTKlClAwc6Cb7/9Nn379mXVqlXExsayc+dOli4tyPVTo0YNLCws+OSTT7hw4QK///57kR0di2NnZ4eDgwM//fQT58+fZ/v27UVmDXXr1g1XV1fatWvHvn37uHjxIsuXL+fAgQO6MuXKlaNmzZqMGjWKbt26PXAW2MMKCAjQJc6Pioqie/fuJVp+5uPjg0KhYN26ddy6dQuVqvDubffu3bl69Spz5859qCT0AFlZWQwZMoSdO3dy6dIl9u3bx+HDhylXrhwAo0aNYv/+/QwZMoTIyEhiYmJYvXq1LpiVnp5Or169GDp0KC1btmTx4sUsWbKEv/7665HHJh6eQqEgoFJrzh5axrULh0i5HcfBTbMws7THw6+GrtyO5Z8TE1k4s69MlTe5eGoLsWe2k5Z4haPb/w9NbjalghoD/wa5Vo4nT6MmtOl75OZkkpWRTFZGMtp/36+u3pXIyVZxbMdPpCVeITXxMoe3fIuBgSHOnsHP9kKIV5pCoaBFoA8rT57nyJUELien8f2+E9iamxLqVTiz5YstB9n0zyXd8zfKlWL7+SvsunCVqykq5h88TbYmj4Z+BV/4LU2Maejvya9HznL6RiIXE1P5v/0nCXCy1QXDriSnM3HLIULcHXmjfClSstSkZKlJyy4+h5cQT4pCoaBV/Ros37Kbw6f+4dL1BOYsXomdjTXVKgTqyk34fiEb9hTustu6YS22HTjKzkPHuXrjFnOXrSM7J4eG1SvryiSnpRN7NZ4btwtu+F2+nkDs1XjSM2SXZPFs1W3chsP7tnL07x3cjL/Kqj9/IketpmrNgt3Fl/4ym42rf9OVr9PwDaLPRrJn6xpu3rjG1oglXLt8kVoNWurKZGakc/1KLAnxBRsw3L55netXYiWPlxACkKWL4gHmz59PkyZNsLGxKXKuY8eOTJ06FXt7e5YtW8bEiRP56quvUCqV1K9fX1fuhx9+4JNPPuHdd98lMTERb29vPvnkEwDs7e357bffGDFiBHPnziUsLIxx48YxcODA+/bLwMCAP//8k6FDhxIcHEzZsmWZPXu2XjJ2ExMTNm/ezMcff0yrVq3QaDSUL1+e7777Tq+tfv36sX///ocOFj2MGTNm0LdvX2rXro2joyOjRo0iLS3tkdvx8PBg/PjxjB49mj59+tC7d29dINDGxoaOHTsSERFBu3btHqo9Q0NDEhMT6d27NwkJCTg6OtKhQwfdRgIhISHs2rWLTz/9lHr16qHVavHz86NLl4KcTR988AGWlpZMmjQJKJixN2nSJN555x1q1aqFh4fHI49RPJzA0PbkadQc2fYDueoMHN3LUb/9Z3ozsDJSE1Bnpeuee5epizozlVMH/iQ7Mxk7p1LUb/eZLql98s2LJN4o2MFo/cJ39V6vdd8fsVQ6o7T3pG7bTzn9959sWzoGFAps/23nzmWUQjwJbwaVJkeTx7y/T5GZq6Gskx2jw0IxMSrc+CAhPYt0deFSxVq+bqRl57AsKobULDU+9kpGN66GzR3LEntVLYcCBd/sOk5ufj4V3RzpWyNId/7g5RukZeew9+J19l68rjvuaGnOtx0aPt1Bi9de28Z1Uefk8tPStWRmZVO2tDefDOyJiXHhUtuExGS9AFXtysGkqjJYunEHKekqfN3d+HRgL70E9Vv2H+GvTTt1z8fO+RmAd7u10wuICfG0hVStgyo9ja3rlpCeloK7ly993vtUl3A+Jem23uxxH79AuoR/wJa1f7Jpze84OrvRc+BIXN0L02icPXmEv34t/Ez/x8/fABDWqjNN3ijMNSqEeD0ptLL2RLzmJk6cyLJlyzhx4sSDC79gwsLCCAoKYvbs2c+7K4/kfwslibl49X187YPn3QUhnjqj5m2fdxeEeOqOmdd/cCEhXnINgiyedxdKbNfpF3Om6st8TV92MqNLvLZUKhVxcXHMmTOHL7744nl355EkJyezc+dOdu7cyffff/+8uyOEEEIIIYQQQrwQJEeXeG0NGTKEqlWr0rBhwyLLFgcNGoSVlVWxj0GDBj2nHheqXLky4eHhTJkyhbJl9ZORBwUF3bPvixcvfk49FkIIIYQQQgghnj5ZuihEMW7evHnPnFpKpRJnZ+dn3KOHd+nSJXJzc4s95+LigrW1dbHnniVZuiheB7J0UbwOZOmieB3I0kXxOniZl9nJ0kVxN1m6KEQxnJ2dX+hg1v34+Pg87y4IIYQQQgghhBDPhSxdFEIIIYQQQgghhBCvBAl0CSGEEEIIIYQQQohXggS6hBBCCCGEEEIIIcQrQXJ0CSGEEE+BJOkWrwPNptXPuwtCPH3tJBm9EEK8TGRGlxBCCCGEEEIIIYR4JUigSwghhBBCCCGEEEK8EiTQJYQQQgghhBBCCCFeCRLoEkIIIYQQQgghhBCvBAl0CSGEEEIIIYQQQohXggS6hBBCCCGEEEIIIcQrQQJdQjxjcXFxKBQKIiMjn3dXhBBCCCGEEEKIV4rR8+6AeLEcOHCAunXr0qJFCyIiIp53d56ruLg4SpUqpXtub29P1apVmTJlCpUrVy5xu15eXsTHx+Po6AjAzp07adSoEcnJydja2j5ut8UrSqvVcvrvP7l4ags56gwc3QKp2vgdrO3c71svJmo9546uJjsjGVsnXyo37I+Daxnd+SPbfiDh8gmyMpIwMjbD0S2QkLq9UNp76rUTe2Y70cfWkJ58HWMTCzwDalG18TtPZazi9aXValm6cQfb/j5GZlY2ZUp5MaBTa9ycHO5bb+PeQ6zdsY+UNBU+7q706dCSAJ/C93BObi6LVm9if+RpNBoNFcv606/TG9haW+nKvPXR2CLtftCrE3WqVHhyAxSCgvf5X1ExbD9/lYycXMo62dG3RhBuSsv71tt87hJrT8eSmq3G286a8Grl8Xe01Z3P0eTx29F/OHApnty8fCq6O9K3ehA25qZF2kpX5zB63V6SMtXM69IESxPjJz1M8Ro7sGsje7auJj0tBTdPH9p07oeXb8A9y584tp8ta/8kJekWDs5utGjbk8DgKrrzWq2WrRFLOLxvK9lZmXiXLku7rgNxdHYD4GL0KebOGlds2++N/ApPH/8nOj4hxItPZnQJPfPnz+f9999n9+7dXL9+/bn1Iycn57m99t22bt1KfHw8mzZtQqVS0bJlS1JSUkrUVk5ODoaGhri6umJk9OzizC/S9RQl88+RlcRERlC18SCadJ2CkbEZu1ZOIE9z7//by9F7idy9gKAab9Gs+3RsHH3ZvXIC2ZmpujJ2zqWp1nQILXt/S4P2n6NFy66V49Hm5+vKnDu2mpP7FhMY2oHmvWbRoOM4XH1KHuwV4l5Wb9/Lhj0HGdC5NV9+OAAzExO+/PFXcnJz71ln//FTLFq9kU7NGjLl40H4eLjw5Y+/kqrK0JX5ZdUmjp6OZtjbbzH2vT4kpaYxfcGSIm29260dP44frntUqxD4VMYpXm9rTl9k47lL9KsRxBcta2FqZMjkbYfJ0eTds86BuHh+PXKWjiH+TGpVBx87JZO3HSYtW60r8+vRsxy9epMP61fm82Y1SMpUM2PXsWLb+3H/SbxsrZ/42IQ4cXQfEcsX0rhVZ94f/TWuHr78POcLVOmpxZa/dOEfliyYSbXaYbw/+mvKh1Tjt5+mcuP6ZV2ZXVtWsX/nBtp1e4fBIyZjYmrGz3Mmkptb8BnIu3Qgn0yaq/eoVjsMOwdnPLz9nsm4hRAlk5WVRWZmpu75pUuXmDlzJps3b36sdiXQJXRUKhVLlixh8ODBvPHGGyxcuFDv/Nq1a6lWrRpmZmY4OjrSvn173Tm1Ws2oUaPw8vLC1NQUf39/5s+fD8DChQuLzFRatWoVCoVC93zcuHFUqlSJefPmUapUKczMzADYuHEjdevWxdbWFgcHB1q3bs2FCxf02rp69SrdunXD3t4eS0tLQkNDOXjwIHFxcRgYGHDkyBG98jNnzsTHx4f8O77I34+DgwOurq6EhoYybdo0EhISOHjwIBcuXKBt27a4uLhgZWVFtWrV2Lp1q15dX19fJk6cSO/evVEqlQwcOFBv6WJcXByNGjUCwM7ODoVCQXh4OIsWLcLBwQG1Wq3XXrt27ejVq9cD+/w0rud/Vq9eTZUqVTAzM6N06dKMHz8ejUbzUNdSlIxWqyUmch3lqnfGw686to6+VG8+lOyMJK5dOHjPetHH1lA6uCmlgsJQOngRGjYYQyNTYk9v05Xxq9AcZ88gLJXO2Dn7UaF2dzLTb6NKSwAgJ1vFyf2/U6P5B/gE1sfa1g1bR188/Ko/9XGL14tWq2X97oN0bFqfasGB+Li78F739iSnpnP45D/3rLdu5wHCalalUY3KeLo6MbBzG0xNjNlxsOALfkZWNjsOHePtds0JDiiFn5c773Zrx7nYy8TEXdVry8LcDDulte5hYiyzXMSTpdVq2fjPJdpX8CfUywVvOyWD64SQkqXmyJWEe9aLOBtL4wAvGvp74mlrRf8aQZgaGrLjfMF7OCMnl53nr9IrNJAgVwdKO9gwqHYFom+lEHMrRa+tLecukZmroU1Q6ac5VPGa2rt9LdXqNCG0VmOc3Txp3+0dTExMOHJge7Hl9+2MoEz5ytRv2hZnN0+atemGu1cpDuzaABT8zOzfEUHjFh0pH1INNw8fOvcaQnpqMmeiDgFgZGSEtY2d7mFuac2ZE4epWrOR3vcNIcSLp23btixatAiAlJQUatSowfTp02nbti0//PBDiduVQJfQWbp0KYGBgZQtW5aePXvy888/o9VqAYiIiKB9+/a0atWK48ePs23bNqpXL/yi27t3b/744w9mz57N2bNn+fHHH7GysrrXSxXr/PnzLF++nBUrVujyV2VkZDBs2DCOHDnCtm3bMDAwoH379roglUqlokGDBly7do01a9YQFRXFyJEjyc/Px9fXlyZNmrBgwQK911mwYAHh4eEYGDz629/c3BwomCGlUqlo1aoV27Zt4/jx47Ro0YI2bdpw+fJlvTrTpk2jYsWKHD9+nM8++0zvnJeXF8uXLwfg3LlzxMfHM2vWLDp37kxeXh5r1qzRlb158yYRERH07dv3ofr6pK8nwJ49e+jduzcffPABZ86c4ccff2ThwoV8+eWXj3wtxcPLSEsgKyMZF6/CJVQmppY4uJbhdvy5Yuvk52lISriAq3dF3TGFQoGLdwiJ96ijyc0m9vR2LJUuWFo7AZBwOQq0WrJUiWxYNIS18/qzP2Iamem3n+AIhYCbicmkpKUTHFD45dvS3IwAH0+i464UW0ejyePi1etUKFNYR6FQUKGMH9H/BrFir8aj0eRRoUzhXX0PFycc7WyIvqTf7vzlEfT93xTGfPMT2w8e0/0NFOJJuanKIiVLTbBr4XJcSxNj/B1tib6dUmwdTV4+sYlpBLs56o4pFAqC3Rx0Qay4pDQ0+Voq3FHGw8YKB0szYm4n645dTVGx4uQF3q0Tgnz9F0+aRqPh2uWL+AeG6I4pFAr8A0O4HBtdbJ3LsdH4ldVfIh5QrqKufFJiAulpKXplzC0s8fINuGeb/5w8QmZGOlVrNXrcIQkhnrJjx45Rr149AP766y9cXFy4dOkSixYtYvbs2SVuV3J0CZ358+fTs2dPAFq0aEFqaiq7du2iYcOGfPnll3Tt2pXx48frylesWPAFOjo6mqVLl7JlyxaaNGkCQOnSj36XMCcnh0WLFuHk5KQ71rFjR70yP//8M05OTpw5c4bg4GB+//13bt26xeHDh7G3twfA379wHX7//v0ZNGgQM2bMwNTUlGPHjnHy5ElWr179yP1LSUlh4sSJWFlZUb16dVxcXHTXAGDixImsXLmSNWvWMGTIEN3xxo0b8/HHH+uex8XF6f5taGio67ezs7PezLfu3buzYMECOnfuDMBvv/2Gt7c3DRs2fKj+Po3rOX78eEaPHs3bb78NFPw/T5w4kZEjRzJ2bNH8NlAw2+/umWmaXAVGxkVzhojiZWcUfEkxs7DVO25qYUt2RkqxddRZaWi1+Zha2OgdN7OwJT35mt6x81EbiNq7CE1uNtZ27jToMBYDw4I/D6rUBLRoOXP4Lyo36I+JqQUn9//OzhVjadFzlq6cEI8rJV0FgI21/k0SG2tL3bm7pWVkkp+fr5drC8DWypLrCQXB2JS0dIyMDLE0N7urXStS0grbfatlIyoElMbE2Jioc+eZ/1cEanUOLevXfOyxCfGflKyCv4c2ZiZ6x23MTEjNUhdXhXR1DvlabTF1TLmelqFr18hAUSTXlo2Zqe41c/Py+HZvJN2rlMXR0pyb6ZkI8SRlZqSRn5+PlbX+Zw8raxtuJVwrto4qLRVrpa3eMWulLempBZ99VKkpBW0o72pTaUN6WjLFObx/GwHlKmFr51jseSHEiyMzMxNr64Kl9Js3b6ZDhw4YGBhQs2ZNLl26VOJ2ZUaXAApmEx06dIhu3boBBVOAu3Tpolt+GBkZSVhYWLF1IyMjMTQ0pEGDBo/VBx8fH72gDEBMTAzdunWjdOnSKJVKfH19AXSzpiIjI6lcubIuKHO3du3aYWhoyMqVK4GCZZSNGjXStfMwateujZWVFXZ2dkRFRbFkyRJcXFxQqVQMHz6ccuXKYWtri5WVFWfPni0yoys0NPShX+tOAwYMYPPmzVy7dk3X9/Dw8Ieegv00rmdUVBQTJkzAyspK9xgwYADx8fF6a6vvNHnyZGxsbPQe+yOmPtQYXleX/tnF8u+66R4Pu8y2pLwD69Os+3QadfoCazsPDqyfpsv9pdXmk5+noUrDAbj5VsbBrSw1Ww5DlRJPwpWTT7Vf4tW25+gJeo/+UvfIy3u67/MH6dSsIWVLeVPK0412YfV4s3Ed1uzY/1z7JF5+ey9eI/yPzbpH3nOcJfjH8Wg8bCypV9rjufVBiKctJfk2MWcjqVa7+O8tQogXi7+/P6tWreLKlSts2rSJZs2aAQWrmZRKZYnblVvxAiiYzaXRaHB3L9zBTavVYmpqypw5c3RL9opzv3MABgYGRZZ/5BaTWNjSsuhuQ23atMHHx4e5c+fi7u5Ofn4+wcHBuuTqD3ptExMTevfuzYIFC+jQoQO///47s2bNum+duy1ZsoTy5cvj4OCgN+Nq+PDhbNmyhWnTpuHv74+5uTmdOnUqkvi9uHE9jMqVK1OxYkUWLVpEs2bNOH369CPthPk0rqdKpWL8+PF06NChyLn/8oDdbcyYMQwbNkzv2KQlsmDiftxLV6fZHTsj5ucV/LxkZ6ZgblUYhFRnpmDj5FtsG6bmShQKA9SZ+slfszNTMLOw0ztmYmqJiakl1nbuOLqVZeX/9eTahYN4l62HuWVB2Tt3YTSzsMHUTElm+q3HGqd4vYUGldXbGTH331x/qekq7G0Kk2Snpmfg4+5SbBtKSwsMDAyKzPhKUWVgqyyY5WWrtEajySMjK1tvVldqukpXpjj+3h4s37yLXI0G42e4eYh4tVT1csHfyVb3XPNvQDc1Owc7izvej9k5eNsVnxze2tQEA4WC1Gz9zxep2WpszQpmR9uam6LJ15KRk6s3qys1W43tv7sunrmRyOWUdA5e2giAloLPZgOXbqNdcGk6VyqDEI/DwlKJgYFBkcTzqvRUrJR2xdYpmJmVoncsPS0Fa5uC8lY2tgVtpKWitCn8DKRKS8XN07dIe0cP7MDC0prACiW70SyEeLY+//xzunfvzkcffURYWBi1atUCCmZ3Va5c8s2v5JObQKPRsGjRIqZPn66LoP6nXbt2/PHHH4SEhLBt2zb69OlTpH6FChXIz89n165duqWLd3JyciI9PZ2MjAxd8OW/nFH3k5iYyLlz55g7d65u3e7evXv1yoSEhDBv3jySkpLuOQupf//+BAcH8/3336PRaIoN0tyPl5cXfn5Fd2zZt28f4eHhuqT8KpVKb1niwzIxKViKkJdXdLel/v37M3PmTK5du0aTJk3w8vJ65Pb/8ySuZ5UqVTh37pzecsYHMTU1xdRUf5mikbHsAnk/xibmGJsUBh21Wi3mlnYkXDmJnXPBsuBcdSaJN6LxC2lebBsGhkbYu/iRcPkEHn41dO3cvHIS/4qt7vnaWq0WrVZL3r/BNQf3gl3n0pOvY2FdsARAnZWOOjsNS6Xz4w9WvLbMzUwxNyv83aDVarFVWnMq5iKlPAu2jM/Mzibm0lWa1i7+C4uRkSGlPd05FRNL9QrldO2cir5Ii3oFeSRLebphZGTIqeiL1KhYHoDrN29zOzmVMj73/p166XoClhbmEuQSj8Xc2Ahz48L3kFarxdbclFM3EvG1L7hTnZmTy/nbKTQJKP79aGRoQCkHJafjE6nm5aJr5/SNRJqV9QHA116JkYGCUzcSqeHtCsD1NBWJGdkEOBYEDD5sUJlcTeHMyQuJqfx44CRjm9fA1driyQ9evHaMjIzw8C7NhXMnCapY8DtYq9Vy/txJajVoWWwd71JluHDuJHUbt9YdO//PCbxLFQRe7R1csFbacuHcSdy9SgGQnZXJlbgYatTT/96i1Wo5+vcOqtRo8Ex3NxdClFynTp2oW7cu8fHxemmBwsLC9Da/e1TyG0Cwbt06kpOT6devHzY2+uvfO3bsyPz58/n6668JCwvDz8+Prl27otFoWL9+PaNGjcLX15e3336bvn37Mnv2bCpWrMilS5e4efMmb731FjVq1MDCwoJPPvmEoUOHcvDgwSI7OhbHzs4OBwcHfvrpJ9zc3Lh8+TKjR4/WK9OtWzcmTZpEu3btmDx5Mm5ubhw/fhx3d3ddNLhcuXLUrFmTUaNG0bdv3wfOWnpYAQEBrFixgjZt2qBQKPjss89KtMTMx8cHhULBunXraNWqFebm5rpE/t27d2f48OHMnTtXtxtFST2J6/n555/TunVrvL296dSpEwYGBkRFRXHq1Cm++OKLx+qfuDeFQkFApdacPbQMa1s3LG2cObX/D8ws7XVBLIAdyz/H068GAZXeAKBMlTc5tGk2di5+OLgEEB25Dk1uNqWCGgOgSr3Bleh9uHhXxNTchizVbc4eWYmRkSluvlUBUNp54FG6Osd3zSM0bDDGJhac2Pcb1nYeOHsGP/uLIV5ZCoWCVvVrsHzLblydHHC2t2PJhu3Y2VhTrUKgrtyE7xdSrUI5WtYreO+3bliL735fSWlPN/y9PVm/+2+yc3JoWL3gLqCluRmNqlfhl9UbsbQwx9zMlAUr1lPG14sA34IZZUdOnyM1XUWAj6cuR9fKrbtp3bD2s78Q4pWmUChoEejDypPncbW2wNnKnKWRMdiamxLqVThz8YstB6nm5UrzwIJA1hvlSvHD/hOUclDi52DLxn/iyNbk0dCv4D1saWJMQ39Pfj1yFisTY8yNjVh4+AwBTrYE/DujzNVaf6Z3urrgppOHjVWR3F5ClFTdxm1YtmgOHt6l8fIJYN/OCHLUaqrWLEgMv/SX2Sht7WnRtiAvcJ2Gb/DTrLHs2bqGssFVOXF0L9cuX6R990FAwc9M7UZvsH3jchyc3bBzcGbLuj+xtrGjfEX9HaAvnDtJcuJNQmsXvfEuhHgx7dixg0aNGuHq6qp3vHr16nz33XcEBgbeo+b9SaBLMH/+fJo0aVIkyAUFga6pU6dib2/PsmXLmDhxIl999RVKpZL69evryv3www988sknvPvuuyQmJuLt7c0nn3wCgL29Pb/99hsjRoxg7ty5hIWFMW7cOAYOHHjffhkYGPDnn38ydOhQgoODKVu2LLNnz9ZLxm5iYsLmzZv5+OOPadWqFRqNhvLly/Pdd9/ptdWvXz/279//0DsWPowZM2bQt29fateujaOjI6NGjSItLe2R2/Hw8NAlee/Tpw+9e/fWBQJtbGzo2LEjERERtGvX7rH6+ySuZ/PmzVm3bh0TJkxgypQpGBsbExgYSP/+/R+rb+LBAkPbk6dRc2TbD+SqM3B0L0f99p9haFSYnDgjNQF1VrruuXeZuqgzUzl14E+yM5OxcypF/Xaf6ZLaGxqacOvaGaKPrSUnJwMzC1uc3MvRuMtkzO5IYl+j+QdE7v6ZPau/RKEwwMkziAbtx0oievHEtW1cF3VOLj8tXUtmVjZlS3vzycCemBgXfglPSEwmPaMwJ2DtysGkqjJYunEHKekqfN3d+HRgL70E9W+3a45CAdMXLkGj0VAx0J9+Hd/QnTc0MGDT3sP8sqpgSZeLoz2927agSa2qz2DU4nXzZlBpcjR5zPv7FJm5Gso62TE6LBQTI0NdmYT0LF0gCqCWrxtp2Tksi4ohNUuNj72S0Y2rYWNeOCuyV9VyKFDwza7j5ObnU9HNkb41gp7p2IQIqVoHVXoaW9ctIT0tBXcvX/q896ku4XxK0m0Ud+x87uMXSJfwD9iy9k82rfkdR2c3eg4ciau7t65Mg6btyM1Rs/L3/yM7KxMfv0D6vPc/jI31N2g4sn8bPqXL4uwqeeiEeFl06NCBrVu3UrWq/meuWbNm8dlnn/Hee++VqF2FVvbOFq+BiRMnsmzZMk6cOPG8u/LIwsLCCAoKeqztVV80/1soSxfFq29U8Pbn3QUhnjrNpkffxViIl82JdtOfdxeEeOoaBL28S5h3nX4xd5F9ma/pszJv3jw++eQTdu/erZu9NX36dCZMmMC6det0KXceldyOF6+0//JmzZkz56VbWpecnMzOnTvZuXMn33///fPujhBCCCGEEEII8cT079+fpKQkmjRpwt69e1myZAmTJk1i/fr11KlTp8TtSqBLvNKGDBnCH3/8Qbt27YosWxw0aBC//fZbsfV69uzJ//3f/z2LLt5T5cqVSU5OZsqUKZQtW1bvXFBQEJcuXSq23o8//kiPHj2eRReFEEIIIYQQQogSGzlyJImJiYSGhpKXl8emTZuoWbPmY7UpSxfFa+vmzZv3zKmlVCpxdn5xd5S7dOkSubm5xZ5zcXHB2rr4LcpfFLJ0UbwOZOmieB3I0kXxOpCli+J18DIvs5Oliy+Xe6XkmTZtGvXr16d69cKNJoYOHVqi15AZXeK15ezs/EIHs+7Hx8fneXdBCCGEEEIIIYR4JN98802xxw0NDdm3bx/79u0DCnZdlUCXEEIIIYQQQgghhHhhxcbGPvXXMHhwESGEEEIIIYQQQgghXnwyo0sIIYQQQgghhBBCPFN5eXksXLiQbdu2cfPmTfLz8/XOb99espy3EugSQgghhBAlIkm6xesgZNXHz7sLQjx9QT887x6I19AHH3zAwoULeeONNwgODkahUDyRdiXQJYQQQgghhBBCCCGeqT///JOlS5fSqlWrJ9qu5OgSQgghhBBCCCGEEM+UiYkJ/v7+T7xdCXQJIYQQQgghhBBCiGfq448/ZtasWWi12ifarixdFEIIIYQQQgghhBDP1N69e9mxYwcbNmwgKCgIY2NjvfMrVqwoUbsS6BJCCCGEEEIIIYQQz5StrS3t27d/4u1KoEu8FuLi4ihVqhTHjx+nUqVKz7s7QgghhBBCCCHEa23BggVPpV0JdL0ADhw4QN26dWnRogURERHPuzvP1X8Bqf/Y29tTtWpVpkyZQuXKlUvcrpeXF/Hx8Tg6OgKwc+dOGjVqRHJyMra2to/b7Wdm48aNjBkzhn/++Qd7e3vatm3L999//7y7JZ4BrVbL6b//5OKpLeSoM3B0C6Rq43ewtnO/b72YqPWcO7qa7IxkbJ18qdywPw6uZXTnj2z7gYTLJ8jKSMLI2AxHt0BC6vZCae+p107sme1EH1tDevJ1jE0s8AyoRdXG7zyVsYrXl1arZenGHWz7+xiZWdmUKeXFgE6tcXNyuG+9jXsPsXbHPlLSVPi4u9KnQ0sCfArfw1v2H2HfsZPEXosnK1vNgkljsDQ302tjyrzfibt2gzRVBpYW5oSUKU331k2xt7F+KmMVry+tVsvWiCUc3reV7KxMvEuXpV3XgTg6u9233oFdG9mzdTXpaSm4efrQpnM/vHwDAMjMSGdrxFJizkaRmnwbCytrgirWoMkbXTC3sCzSVmZGOrMmfUxaShKff/1LsWWEeBxarZa/omLYfv4qGTm5lHWyo2+NINyU93+vbT53ibWnY0nNVuNtZ014tfL4O9rqzm+Nvsz+uHhik1LJzs1jXpcmWJroL3VaefI8x6/dIi4pDWNDA+Z3afo0hiiEeIFJMvoXwPz583n//ffZvXs3169ff279yMnJeW6vfbetW7cSHx/Ppk2bUKlUtGzZkpSUlBK1lZOTg6GhIa6urhgZvbyx3ezsbDp06EBISAgnT54kIiLioWen5ebmPt3OiafunyMriYmMoGrjQTTpOgUjYzN2rZxAnubeP7eXo/cSuXsBQTXeoln36dg4+rJ75QSyM1N1ZeycS1Ot6RBa9v6WBu0/R4uWXSvHo83P15U5d2w1J/ctJjC0A817zaJBx3G4+pQ88CzEvazevpcNew4yoHNrvvxwAGYmJnz546/k3Od32P7jp1i0eiOdmjVkyseD8PFw4csffyVVlaErk5ObS6Vy/rRvUu+e7QQHlGLY228xc8z7fBz+FjduJzFj4ZInOj4hAHZtWcX+nRto1+0dBo+YjImpGT/PmUhu7r1/n584uo+I5Qtp3Koz74/+GlcPX36e8wWq9ILf52mpyaSnJtGqQ28++HQ6nXsNIfrMcVYsLv5m2F+/fY+ru89TGZ8QAGtOX2TjuUv0qxHEFy1rYWpkyORth8nR5N2zzoG4eH49cpaOIf5MalUHHzslk7cdJi1brSuTk5dHRXdH2gX73bMdTV4+NX1caVrG+4mOSQjxdPz111+89dZb1KxZkypVqug9SkoCXc+ZSqViyZIlDB48mDfeeIOFCxfqnV+7di3VqlXDzMwMR0dHvfWrarWaUaNG4eXlhampKf7+/syfPx+AhQsXFpmptGrVKhQKhe75uHHjqFSpEvPmzaNUqVKYmRXc3d64cSN169bF1tYWBwcHWrduzYULF/Taunr1Kt26dcPe3h5LS0tCQ0M5ePAgcXFxGBgYcOTIEb3yM2fOxMfHh/w7vjzfj4ODA66uroSGhjJt2jQSEhI4ePAgFy5coG3btri4uGBlZUW1atXYunWrXl1fX18mTpxI7969USqVDBw4kLi4OBQKBZGRkcTFxdGoUSMA7OzsUCgUhIeHs2jRIhwcHFCr1XrttWvXjl69ej2wz1FRUTRq1Ahra2uUSiVVq1bVuw579+6lXr16mJub4+XlxdChQ8nIKPgitmjRIqysrIiJidGVf/fddwkMDCQzM1N3zNDQkB49euDv70+lSpUYOHBgkX78N9YlS5bQoEEDzMzMWLx4MYmJiXTr1g0PDw8sLCyoUKECf/zxh17d/Px8pk6dir+/P6ampnh7e/Pll1/qzl+5coW33noLW1tb3YyyuLi4B14b8Xi0Wi0xkesoV70zHn7VsXX0pXrzoWRnJHHtwsF71os+tobSwU0pFRSG0sGL0LDBGBqZEnt6m66MX4XmOHsGYal0xs7Zjwq1u5OZfhtVWgIAOdkqTu7/nRrNP8AnsD7Wtm7YOvri4Vf9qY9bvF60Wi3rdx+kY9P6VAsOxMfdhfe6tyc5NZ3DJ/+5Z711Ow8QVrMqjWpUxtPViYGd22BqYsyOg8d0Zd5oUIt2YfX0Znnd7Y0GtQjw9cTJ3paypbxpG1aXmEtX0dznS5kQj0qr1bJ/RwSNW3SkfEg13Dx86NxrCOmpyZyJOnTPenu3r6VanSaE1mqMs5sn7bu9g4mJCUcObAfA1d2bHgNGUK5CKA5ObviVrUDTNt04e/IoeXn67+G/d28kOzOD+k3efKpjFa8vrVbLxn8u0b6CP6FeLnjbKRlcJ4SULDVHriTcs17E2VgaB3jR0N8TT1sr+tcIwtTQkB3nr+rKtCpXirbBfgTcMcvrbp0rlaFVuVJ428mMXCFedLNnz6ZPnz64uLhw/PhxqlevjoODAxcvXqRly5YlblcCXc/Z0qVLCQwMpGzZsvTs2ZOff/5Zt7VmREQE7du3p1WrVhw/fpxt27ZRvXrhl8vevXvzxx9/MHv2bM6ePcuPP/6IlZXVI73++fPnWb58OStWrCAyMhKAjIwMhg0bxpEjR9i2bRsGBga0b99eF6RSqVQ0aNCAa9eusWbNGqKiohg5ciT5+fn4+vrSpEmTImttFyxYQHh4OAYGj/6WMzc3BwpmZqlUKlq1asW2bds4fvw4LVq0oE2bNly+fFmvzrRp06hYsSLHjx/ns88+0zvn5eXF8uXLATh37hzx8fHMmjWLzp07k5eXx5o1a3Rlb968SUREBH379n1gP3v06IGnpyeHDx/m6NGjjB49WrdrxIULF2jRogUdO3bkxIkTLFmyhL179zJkyBCg4P+yVatW9OjRA41GQ0REBPPmzWPx4sVYWFgAYGZmRvPmzRk5ciRJSUkP7M/o0aP54IMPOHv2LM2bNyc7O5uqVasSERHBqVOnGDhwIL169eLQocIP1mPGjOGrr77is88+48yZM/z++++4uLgABbPCmjdvjrW1NXv27GHfvn1YWVnRokWLF2o24KsoIy2BrIxkXLwq6I6ZmFri4FqG2/Hniq2Tn6chKeECrt4VdccUCgUu3iEk3qOOJjeb2NPbsVS6YGntBEDC5SjQaslSJbJh0RDWzuvP/ohpZKbffoIjFAJuJiaTkpZOcEBp3TFLczMCfDyJjrtSbB2NJo+LV69ToUxhHYVCQYUyfkTHXS22zsNIz8hk79ETlPH1wsjIsMTtCHG3pMQE0tNS8Ctb+Pvc3MISL98ALsdGF1tHo9Fw7fJF/ANDdMcUCgX+gSH3rAOgzsrEzNwcQ8PC9/DN+Kts3/AXb739vt7NTyGepJuqLFKy1AS7Fi47tzQxxt/RlujbKcXW0eTlE5uYRrCbo+6YQqEg2M2BmFvF1xFCvPy+//57fvrpJ7799ltMTEwYOXIkW7ZsYejQoaSmpj64gXt4eddxvSLmz59Pz549AWjRogWpqans2rWLhg0b8uWXX9K1a1fGjx+vK1+xYsGX1ujoaJYuXcqWLVto0qQJAKVLly76Ag+Qk5PDokWLcHJy0h3r2LGjXpmff/4ZJycnzpw5Q3BwML///ju3bt3i8OHD2NvbA+Dv768r379/fwYNGsSMGTMwNTXl2LFjnDx5ktWrVz9y/1JSUpg4cSJWVlZUr14dFxcX3TUAmDhxIitXrmTNmjW6oBFA48aN+fjjj3XP75x1ZGhoqOu3s7Oz3sy37t27s2DBAjp37gzAb7/9hre3Nw0bNnxgXy9fvsyIESMIDAwEICAgQHdu8uTJ9OjRgw8//FB3bvbs2TRo0IAffvgBMzMzfvzxR0JCQhg6dCgrVqxg3LhxVK1aVdfG+PHjOX78OF27dqVBgwZs2rQJd/eC/Ezvv/8+sbGxrFu3Tlf+ww8/pEOHDnp9HD58uO7f77//Pps2bWLp0qVUr16d9PR0Zs2axZw5c3j77bcB8PPzo27dugAsWbKE/Px85s2bp/twvGDBAmxtbdm5cyfNmjV74DUSJZOdkQyAmYWt3nFTC1uyM1KKraPOSkOrzcfUwkbvuJmFLenJ1/SOnY/aQNTeRWhys7G2c6dBh7EYGBb8eVClJqBFy5nDf1G5QX9MTC04uf93dq4YS4ues3TlhHhcKekqAGys9W/Y2Fhb6s7dLS0jk/z8fGzvqmNrZcn1hEcPxi5eu4WNew+izsklwMeT0QN6PHIbQtyPKjUFACul/u9mK6UN6WnJxdbJzEgjPz8fK+u76ljbcCvhWrF1MlRpbN/wF9XrFOYm0uTm8seCb2jZvhe29k4k3b73zBohHkdKVsHqCBszE73jNmYmpGapi6tCujqHfK22mDqmXE/LKLaOEOLld/nyZWrXrg0UTHBJT08HoFevXtSsWZM5c+aUqF35hvIcnTt3jkOHDrFy5UoAjIyM6NKlC/Pnz6dhw4ZERkYyYMCAYutGRkZiaGhIgwYNHqsPPj4+ekEugJiYGD7//HMOHjzI7du3dTO5Ll++THBwMJGRkVSuXFkXLLpbu3bteO+991i5ciVdu3Zl4cKFNGrUCF9f34fuV+3atTEwMCAjI4PSpUuzZMkSXFxcUKlUjBs3joiICOLj49FoNGRlZRWZ0RUaGvpoF+JfAwYMoFq1aly7dg0PDw8WLlxIeHj4Q931HDZsGP379+fXX3+lSZMmdO7cGT+/gvwBUVFRnDhxgsWLF+vKa7Va8vPziY2NpVy5ctjZ2TF//nyaN29O7dq1GT16tK5scnIykydPZsWKFbRq1QpDQ0Pq1KnD5s2bCQgI4OTJk0Wmdt59DfLy8pg0aRJLly7l2rVr5OTkoFardTPGzp49i1qtJiwsrNjxRUVFcf78eayt9aeBZ2dnF1naeie1Wl1kOagmV4GRsek967zuLv2ziyPb/k/3vF7b/z3V1/MOrI+Ld0WyMpI5d2w1B9ZPI+ytyRgamaDV5pOfp6FKwwG4+lQCoGbLYayZ24eEKydx85VcXaJk9hw9wdxla3XPR/d//kGlNo1q06hGZW4np7Js007m/L6S0f27y8wXUWLHD+1m1Z8/6Z6/PXjMU3/N7KxMfvlhEs5uXjRu9Zbu+MbVi3F29aBy9cf77CjE3fZevMa8g6d1z0c2LtnncCHE68fV1ZWkpCR8fHzw9vbm77//pmLFisTGxupWupWEBLqeo/nz56PRaHSzcqAg+GFqasqcOXN0S/aKc79zAAYGBkXeGMUlJLe0LLrzSZs2bfDx8WHu3Lm4u7uTn59PcHCwbnnag17bxMSE3r17s2DBAjp06MDvv//OrFmz7lvnbkuWLKF8+fI4ODjozbgaPnw4W7ZsYdq0afj7+2Nubk6nTp2KLJ0rblwPo3LlylSsWJFFixbRrFkzTp8+/dA7YY4bN47u3bsTERHBhg0bGDt2LH/++Sft27dHpVLxzjvvMHTo0CL1vL0LE2Xu3r0bQ0ND4uPjycjI0AWVzp07h1qt1u08OWHCBNLS0qhbty4zZ87k77//1guiFXcNvv76a2bNmsXMmTOpUKEClpaWfPjhhw/9/6pSqahatWqR1wGKBEvvNHnyZL1ZiVAQuKnf7rN71BDupavT7I6dEfPzCn52szNTMLcqDDCrM1OwcfIttg1TcyUKhQHqTP0pv9mZKZhZ2OkdMzG1xMTUEms7dxzdyrLy/3py7cJBvMvWw9yyoOyduzCaWdhgaqYkM/3WY41TvN5Cg8rq5czK1WgASE1X6e10mJqegY+7S7FtKC0tMDAwKDLjK0WVga3y0ZbyAyitLFFaWeLu7IiHiyODx88g5tJVyvh6PXJbQgCUD6mGd6nCGd6af9/nqrRUlDaFv89Vaam4efoW24aFpRIDAwNd4nldnfRUrJT6v8/V2Vks+O4LTEzN6TlwhN4mPBejT3Hj+iVOHS8Ifv33OfGLUX1o2LwDTVt3LflAxWutqpcL/k62uueavIKb5KnZOdhZFO5wm5qdc8+8WdamJhgoFKRm63+mT81WY2smN0eFeFU1btyYNWvWULlyZfr06cNHH33EX3/9xZEjR4qsTnoUEuh6TjQaDYsWLWL69OlFlny1a9eOP/74g5CQELZt20afPn2K1K9QoQL5+fns2rVLt3TxTk5OTqSnp5ORkaELePyXg+t+EhMTOXfuHHPnzqVevYLdqfbu3atXJiQkhHnz5pGUlHTPWV39+/cnODiY77//Ho1G88hvUi8vL91sqDvt27eP8PBwXVJ+lUpVomToJiYF06LvTtD6X99nzpzJtWvXaNKkCV5eD/8Fp0yZMpQpU4aPPvqIbt26sWDBAtq3b0+VKlU4c+aM3hLPu+3fv58pU6awdu1aRo0axZAhQ/jll18A8PDwAAoCYV26dAHgm2++IT09ne7duzN06FBdmXvZt28fbdu21S2Vzc/PJzo6mvLlywMFyynNzc3Ztm0b/fv3L1K/SpUqLFmyBGdnZ5RK5UNfkzFjxjBs2DC9Y5OWyOyI+zE2McfYpDDwqNVqMbe0I+HKSeycC5Yo56ozSbwRjV9I82LbMDA0wt7Fj4TLJ/Dwq6Fr5+aVk/hXbHXP19ZqtWi1WvL+Da45uBcsxU1Pvo6FdUHeDHVWOursNCyVzo8/WPHaMjczxfyOLy9arRZbpTWnYi5SytMNgMzsbGIuXaVp7eJnBxgZGVLa051TMbFUr1BO186p6Iu0qPd4Gyb8FwT4LwAnREmYmpljaqb/+9xaacuFcydx9yoFFMzAuhIXQ416xacAMDIywsO7NBfOnSSoYnVdO+fPnaRWg8LZ3NlZmfw8ZyJGRsb0HjQaY2P9JWA9Bnysd9Pz6qXzLP/tewZ+NBEHJ9cnNmbx+jE3NsLcuPBrpVarxdbclFM3EvG1L/jMmJmTy/nbKTQJKP5ztZGhAaUclJyOT6Sal4uundM3EmlWVnYIFeJV9dNPP+lWkL333ns4ODiwf/9+3nzzTd55550StyuBrudk3bp1JCcn069fP2xs9HMudOzYkfnz5/P1118TFhaGn58fXbt2RaPRsH79ekaNGoWvry9vv/02ffv2Zfbs2VSsWJFLly5x8+ZN3nrrLWrUqIGFhQWffPIJQ4cO5eDBg0V2dCyOnZ0dDg4O/PTTT7i5uXH58mW9JXQA3bp1Y9KkSbRr147Jkyfj5ubG8ePHcXd3p1atWgCUK1eOmjVrMmrUKPr27fvA2UIPKyAggBUrVtCmTRsUCgWfffbZQ+/keCcfHx8UCgXr1q2jVatWmJub6xL5d+/eneHDhzN37lwWLVr0UO1lZWUxYsQIOnXqRKlSpbh69SqHDx/W5TsbNWoUNWvWZMiQIfTv3x9LS0vOnDnDli1bmDNnDunp6fTq1YuhQ4fSsmVLPD09qVatGm3atKFTp054eXnRtWtX3nvvPdRqNXXq1CE2NpYLFy5gaWnJmjVr+PTTT3F2vnfgISAggL/++ov9+/djZ2fHjBkzSEhI0AW6zMzMGDVqFCNHjsTExIQ6depw69YtTp8+Tb9+/ejRowdff/01bdu2ZcKECXh6enLp0iVWrFjByJEj8fQsfjczU1NTTE3178QZGUvy+kehUCgIqNSas4eWYW3rhqWNM6f2/4GZpb0uiAWwY/nnePrVIKDSGwCUqfImhzbNxs7FDweXAKIj16HJzaZUUGMAVKk3uBK9Dxfvipia25Clus3ZIysxMjLFzbcgP5zSzgOP0tU5vmseoWGDMTax4MS+37C288DZM/jZXwzxylIoFLSqX4PlW3bj6uSAs70dSzZsx87GmmoVAnXlJny/kGoVytGyXsF7v3XDWnz3+0pKe7rh7+3J+t1/k52TQ8Pqhctqk9PSSUlTceN2wUYel68nYGZqgqOdDdaWFsTEXeX8lWsElvLGysKchMRklmzYjqujvczmEk+UQqGgdqM32L5xOQ7Obtg5OLNl3Z9Y29hRvmJhcHberHGUr1iD2g0LAll1G7dh2aI5eHiXxssngH07I8hRq6las2AX6f+CXLm5OXQJH4o6KxN1VsGuzZbWNhgYGODg5KbXl0xVGgDOrp6YW5RsJrwQxVEoFLQI9GHlyfO4WlvgbGXO0sgYbM1NCfUqnKH7xZaDVPNypXlgQSDrjXKl+GH/CUo5KPFzsGXjP3Fka/Jo6Ff4GTMlS01Klpob6QXv78vJ6ZgbG+FgaYa1aUFw93ZGFip1LrczssnL1xKXVPBed7W2wMxYvv4K8SIxMDDQ27Cua9eudO1aMMP4v3RCJSE/6c/J/PnzadKkSZEgFxQEuqZOnYq9vT3Lli1j4sSJfPXVVyiVSurXr68r98MPP/DJJ5/w7rvvkpiYiLe3N5988gkA9vb2/Pbbb4wYMYK5c+cSFhbGuHHjGDhw4H37ZWBgwJ9//snQoUMJDg6mbNmyzJ49Wy8Zu4mJCZs3b+bjjz+mVatWaDQaypcvz3fffafXVr9+/di/f/9D7Vj4sGbMmEHfvn2pXbs2jo6OjBo1irS0tEdux8PDg/HjxzN69Gj69OlD7969dYFAGxsbOnbsSEREBO3atXuo9gwNDUlMTKR3794kJCTg6OhIhw4ddEv2QkJC2LVrF59++in16tVDq9Xi5+enm531wQcfYGlpyaRJk4CCGXuTJk3inXfeoVatWnh4ePDLL78wbdo0vvzySy5duoSHhwc9e/Zk+fLlhIWF8eabb7Jjx4579vF///sfFy9epHnz5lhYWDBw4EDatWunt5vFZ599hpGREZ9//jnXr1/Hzc2NQYMGAWBhYcHu3bsZNWoUHTp0ID09HQ8PD8LCwh5phpcomcDQ9uRp1BzZ9gO56gwc3ctRv/1nGBoV3rHPSE1AnZWue+5dpi7qzFROHfiT7Mxk7JxKUb/dZ7qk9oaGJty6doboY2vJycnAzMIWJ/dyNO4yGbM7ktjXaP4Bkbt/Zs/qL1EoDHDyDKJB+7GSiF48cW0b10Wdk8tPS9eSmZVN2dLefDKwJyb/7mALkJCYTHpGpu557crBpKoyWLpxBynpKnzd3fh0YC+9BPVb9h/hr007dc/HzvkZgHe7taNh9cqYmBhz+ORZlm3aiVqdg63SikqBAXTo3RljI3mfiyerQdN25OaoWfn7/5GdlYmPXyB93vuf3gyspNsJZGYUfr4JqVoHVXoaW9ctIT0tBXcvX/q89ynWSlsArl+5yJW4GACmjXtf7/VGTvgeOweZgSuerTeDSpOjyWPe36fIzNVQ1smO0WGhmNyxk21Cehbp6sKbn7V83UjLzmFZVAypWWp87JWMblwNG/PCG6Zboy+z/MR53fMJmw8CMKh2BRr8GxBbFhXD7guFGzWMidgHwGdNq1P+jp0ghRAvphs3bvDll18yf/58MjMzH1yhGArt42T4EuI+Jk6cyLJlyzhx4sTz7sojCwsLIygoiNmzZz/vrryS/rdQZnSJV9+o4O3PuwtCPHXHzOs/uJAQL7mQVR8/uJAQLzm7T3943l0osV2nSxYMedoaBFk87y68sJKTk3n33XfZsmULJiYmjB49miFDhjBu3DimTZtGSEgIH330kW5iyKOS25Tiifsvb9acOXP44osvnnd3HklycjI7d+5k586dfP/998+7O0IIIYQQQgghxCtl9OjR7N+/n/DwcDZt2sRHH33Exo0bMTAwYPv27dSsWfOx2jd4cBEhHs2QIUOoWrUqDRs2LLJscdCgQVhZWRX7+G+J3PNUuXJlwsPDmTJlCmXLltU7FxQUdM++F7cToRBCCCGEEEIIIfRt2LCBBQsWMG3aNNauXYtWq6VSpUqsW7fusYNcIEsXxTN28+bNe+bUUiqV902m/rxdunRJb7eiO7m4uGBtXfx2yaIoWbooXgeydFG8DmTpongdyNJF8TqQpYtPnixdvDcjIyOuXLmCm1vBRikWFhYcOXJEt1HaY7f/RFoR4iE5Ozu/0MGs+/Hxka2NhRBCCCGEEEKIx6HVajG6Y8MfQ0NDzM3Nn1j7EugSQgghhBBCCCGEEM+EVqslLCxMF+zKysqiTZs2mJiY6JU7duxYidqXQJcQQgghhBBCCCGEeCbGjh2r97xt27ZPtH0JdAkhhBBCCCGEEEKIZ+LuQNeTJrsuCiGEEEIIIYQQQohXgszoEkIIIZ4C2Y1OvA5kNzrxOjBq/mSX1AghnqwqWbufdxfuocXz7sBrS2Z0CSGEEEIIIYQQQohXggS6hBBCCCGEEEIIIcQrQQJdQgghhBBCCCGEEOKVIDm6hBBCCCGEEEIIIcRTN3v27IcuO3To0BK9hgS6hBBCCCGEEEIIIcRT98033zxUOYVCUeJAlyxdfEUtXLgQW1vbx25HoVCwatWqx27ncezcuROFQkFKSso9y9w93nHjxlGpUiXd8/DwcNq1a/fU+vi03T0eIYQQQgghhBDiZRMbG/tQj4sXL5b4NWRG1wssPDyclJSU5x5oeli7du1i/PjxREZGkp2djYeHB7Vr12bu3LmYmJiwcOFCPvzww/sGrEqqS5cutGrV6p7nZ82ahVar1T1v2LAhlSpVYubMmU+sD76+vly6dAkAc3Nz/Pz8+OCDD+jfv/8jtaNQKFi5cqVeYG748OG8//77T6yv4uVx8+ppzh1dRfLNi2RlJFG3zWg8/Grcv86VU0Tu/pnUpCtYWDtSvnpnSpVvrFcmU5XIib2LuBF3HI1GjZWNK9WbDsHeNQCA7MyUgvOXoshRq3DyCKJKw/5Y27k/tbEK8Z+LMafZs3UN169cJC01mZ4DRxJUsfr960SfImL5QhLir2Jr70ijFh2pWrORXpnUlEQ2rvqN6DPHyc3Jwd7JlU4938XTx/9pDkcIAM4kJLHu9EXiktJIzlIzrGEVqnm53L/OjUQWHT3LtRQVDpbmtK/gRwM/T935VacucPhyAtdSVZgaGRLgaEv3qmVxV1oBkK7OYXnUeU7E3+Z2RhbWpiZU83ahc8UALE2Mn+p4xevpzPk41uzYR+y1eJJT0xnetyvVK5S7b53T52P5ZdUmribcxNHWhg5N69OwemXd+ZVb93Do5FmuJdzCxNiYMr5e9GzTFHdnR12ZcXMWcOZCnF67TWqFMvCtNk90fEKIpyMnJ4fY2Fj8/PwwMnr8MJUEusQTcebMGVq0aMH777/P7NmzMTc3JyYmhuXLl5OXl/fUX9/c3Bxzc/N7nrexsXnqfQCYMGECAwYMIDMzk2XLljFgwAA8PDxo2bLlY7VrZWWFlZXVE+qleJnkadTYOvlSKiiMfeumPLC8KjWBPWu+wK9Cc2q2+IiEKyc4vOU7zCzscPMt+NCYk61i+9JPcPYMol67/2FqboMqJR5js4L3mFarZe+ayRgYGlG3zWiMTC2IPraGnSvG0bL3bIyMzZ7qmIXIzcnBzcOH0FqN+W3u1w8sn3Q7gYU/TKZG3WZ0Cf+Q8+dOsmLxD1grbSlTvuB9n5mp4sfp/6NUmSDC3/0USyslibfiMbewfNrDEQKAHE0ePnZKGvp78s2u4w8sf1OVyZQdR2kS4MWQOpU4fSORnw6cwtbclIruTgCcTUiiWVlv/BxsyMvX8mdkNJO2HmZam3qYGRuRkqkmOSubHlXL4mFjRWJGNvP+PkVyZjYfNajytIcsXkPq3Fx8PFxpVKMK0xf8+cDyNxOT+WruYprWrsbQnh05GXOR/1uyBlulNZUCC25CnLkQR/M61fDz9iAvL58/1m/ji/9bxIxRQzAzNdG1FVarKm+1KLzBYSrBXCFeeJmZmbz//vv88ssvAERHR1O6dGnef/99PDw8GD16dInalaWLL6kZM2ZQoUIFLC0t8fLy4t1330WlUhUpt2rVKgICAjAzM6N58+ZcuXJF7/zq1aupUqUKZmZmlC5dmvHjx6PRaB65P5s3b8bV1ZWpU6cSHByMn58fLVq0YO7cuZibm7Nz50769OlDamoqCoUChULBuHHjAPj1118JDQ3F2toaV1dXunfvzs2bN4u8xr59+wgJCcHMzIyaNWty6tQp3bkHLdW8c+lieHg4u3btYtasWbq+xMbG4u/vz7Rp0/TqRUZGolAoOH/+/ENdh//GULp0aUaNGoW9vT1btmzRnT98+DBNmzbF0dERGxsbGjRowLFjx3TnfX19AWjfvj0KhUL3/O6li/n5+UyYMAFPT09MTU2pVKkSGzdufKg+5uTkMGTIENzc3DAzM8PHx4fJkyfrzqekpNC/f3+cnJxQKpU0btyYqKgoAG7duoWrqyuTJk3Sld+/fz8mJiZs27btoV5fPBo33ypUqN0DT/+aD1X+wslNWCqdqVS/D0oHLwIqvYFXQC2ij6/VlfnnyEosrByo3mwoDq5lsLJxwdWnEta2bgCkp1wn8UY0VRu/g71rAEo7D6o2HkS+JofL5/Y8lXEKcaeyQZVp9mZ3girdf/bifw7u3Yy9gzNvdHwbZzdPajdsSXDlmuzbHqErs3vLKmzsHOjcawhevgHYO7oQUK4SDk5uT2sYQuip5OFEl8plqO7t+lDlt0ZfxtnKnF6h5fC0taJ5oA81fFxYfzZOV2ZMWDUa+HniaWuNj72SwbUrkJiRzcWkNAC87Kz5qEEVqnq64GptSZCrA10qleHY1Zvk5ec/jWGK11zlcgF0axVGjZD7z+L6z5b9R3Cyt6N32+Z4ujrRsl4NalYsT8SuA7oyn77Ti4bVK+Pl6oyvhyvvdmvH7eRULl65rteWqbExdkpr3cPCTG7MCfGiGzNmDFFRUezcuROzO35mmzRpwpIlS0rcrgS6XlIGBgbMnj2b06dP88svv7B9+3ZGjhypVyYzM5Mvv/ySRYsWsW/fPlJSUujatavu/J49e+jduzcffPABZ86c4ccff2ThwoV8+eWXj9wfV1dX4uPj2b17d7Hna9euzcyZM1EqlcTHxxMfH8/w4cMByM3NZeLEiURFRbFq1Sri4uIIDw8v0saIESOYPn06hw8fxsnJiTZt2pCbm/vIfZ01axa1atViwIABur54e3vTt29fFixYoFd2wYIF1K9fH3//R1vWkp+fz/Lly0lOTsbEpPBOU3p6Om+//TZ79+7l77//JiAggFatWpGeng4UBML+e934+Hjd8+LGMH36dKZNm8aJEydo3rw5b775JjExMQ/s2+zZs1mzZg1Lly7l3LlzLF68WBdQA+jcuTM3b95kw4YNHD16lCpVqhAWFkZSUhJOTk78/PPPjBs3jiNHjpCenk6vXr0YMmQIYWFhj3SNxNORGH8OF++KesdcfSqTGH9O9/zaxUPYufizP+JrVv8YzubFw7hwcrPufH5ewc+VgWHhnVCFQoGBkTG3r599yiMQ4tFdjo3GPzBE71hAuUpcjo3WPT974gge3v4snjeNL0b15dvJwzm0b8vdTQnxwoi5lUKwq4PesRA3J2JupdyzTmZuwc1K6/vMZMnM1WBubIShgXwNEM9fdNwVQsqU1jtWsaw/MXFX71knK1sNgJWl/mqOPUdP0Pd/Uxg25TsWr9uCOifnyXdYCPFErVq1ijlz5lC3bl0UCoXueFBQEBcuXChxu7J08SX14Ycf6v7t6+vLF198waBBg/j+++91x3Nzc5kzZw41ahTcEf/ll18oV64chw4donr16owfP57Ro0fz9ttvA1C6dGkmTpzIyJEjGTt27CP1p3PnzmzatIkGDRrg6upKzZo1CQsLo3fv3iiVSkxMTLCxsUGhUODqqn8ns2/fvrp/ly5dmtmzZ1OtWjVUKpXecr2xY8fStGlT3Vg8PT1ZuXIlb7311iP11cbGBhMTEywsLPT6Eh4ezueff667Prm5ufz+++9FZnndz6hRo/jf//6HWq1Go9Fgb2+vl6OrcWP9PEk//fQTtra27Nq1i9atW+PkVLAUwdbWtsh1utO0adMYNWqULnA5ZcoUduzYwcyZM/nuu+/u28fLly8TEBCg+2Xi4+OjO7d3714OHTrEzZs3MTU11b3WqlWr+Ouvvxg4cCCtWrViwIAB9OjRg9DQUCwtLfVmhN1NrVajVqv1jmlyFRgZm963n6JksjOSMbOw1TtmamFLbk4meZocDI1MyEhN4MKJjZSp8iblqnUkKSGG47vmY2BoRKnyjVHaeWJh7cTJfYsJDRuEkbEZ546tITP9NlkZyc9nYELchyotBStr/SXqVta2ZGdnkpubg7GxCUm3Ezi4ZxN1G7emUfOOXLkUw7plCzA0NCqSy0uIF0FKlhobc/2/lTbmJmTlasjR5GFiZKh3TqvVsujwWco42eJlZ11sm2nZOaw4eZ6wAO+n1m8hHkVKugoba/30HDbWlmRmZ5OTm4uJsX7QVqvVsnDlBsqW8sbbrTDHXZ0qFXCyt8VOac3l+AQWr91C/K1EhvfpihDixXXr1i2cnZ2LHM/IyNALfD0quZXzktq6dSthYWF4eHhgbW1Nr169SExMJDMzU1fGyMiIatWq6Z4HBgZia2vL2bMFMzKioqKYMGGCLv+TlZWVbpbTne08DENDQxYsWMDVq1eZOnUqHh4eTJo0iaCgIOLj4+9b9+jRo7Rp0wZvb2+sra1p0KABUBCQuVOtWrV0/7a3t6ds2bK6sTwJ7u7uvPHGG/z8888ArF27FrVaTefOnR+6jREjRhAZGcn27dupUaMG33zzjd5ssISEBAYMGEBAQAA2NjYolUpUKlWRsd5PWloa169fp06dOnrH69Sp81DXIzw8nMjISMqWLcvQoUPZvLlwJk9UVBQqlQoHBwe990VsbKxeRH3atGloNBqWLVvG4sWLdUGx4kyePBkbGxu9x/6IqQ89XvHkadFi51yakDo9sXMujV+F5pQObsqFE5sAMDA0ok7rkaSnXGPl//XirzlduHn1FG6+VVAo5M+GeDlptVrcvUrRvG0P3L1KUaNuM6rVacLBPZsfXFmIl8DPh05zJUXF0HqVij2fmZPL1B1H8LSxomOIbMAgXk7z/orgyo2bfNi7k97xprVDqRToj4+7C/WqhjCkRwcOnTjLjdtJz6mnQoiHERoaSkREYaqJ/4Jb8+bN0/v+/6hkRtdLKC4ujtatWzN48GC+/PJL7O3t2bt3L/369SMnJwcLC4uHakelUjF+/Hg6dOhQ5JxZCde0e3h40KtXL3r16sXEiRMpU6YM//d//8f48eOLLZ+RkUHz5s1p3rw5ixcvxsnJicuXL9O8eXNynsN04/79+9OrVy+++eYbFixYQJcuXR76egI4Ojri7++Pv78/y5Yto0KFCoSGhlK+fHkA3n77bRITE5k1axY+Pj6YmppSq1atZzrWKlWqEBsby4YNG9i6dStvvfUWTZo04a+//kKlUuHm5sbOnTuL1LszB9qFCxe4fv06+fn5xMXFUaFChXu+3pgxYxg2bJjesUlLSh6dF/dnZmlHdmaK3jF1ZgrGJhYYGhUsozWzsENp76lXRmnnwdWYwnwY9i7+NO/xDTnqDPLzNJhZ2LD1z5HYOfs99TEI8aislLao0lP1jqnSUzAzs8DYuOB9b620xdlV/33v5OLOqeMHEOJFZGtuSmqW/ozo1KwczI2NiszmWnDoNMeu3mJs8xo4WBbdnCcrV8NX249gbmTEsAZVMDKUmxbixWBrbUVqun6e4dT0DCzMzIrM5pq/PIJjZ6IZP6QPDrb332jK39sDgBu3k3B1tH+ynRZCPDGTJk2iZcuWnDlzBo1Gw6xZszhz5gz79+9n165dJW5XAl0voaNHj5Kfn8/06dMx+De/wtKlS4uU02g0HDlyhOrVC7ZkP3fuHCkpKZQrV5AcskqVKpw7d+6R8089LDs7O9zc3MjIyADAxMSkyA6M//zzD4mJiXz11Vd4eXkBcOTIkWLb+/vvv/H2Lphqn5ycTHR0tG4sj6q4vgC0atUKS0tLfvjhBzZu3HjPnGMPw8vLiy5dujBmzBhWr14NFCTU//7772nVqhUAV65c4fbt23r1jI2N77tTpVKpxN3dnX379ulmv/3X9n//1w+iVCrp0qULXbp0oVOnTrRo0YKkpCSqVKnCjRs3MDIy0svbdaecnBx69uxJly5dKFu2LP379+fkyZPFTjkFMDU1LTLjy8hYciY8LQ5uZYmPPap37MblSBzcyuqeO7qXJT1ZP4Freko8lkqnIu2ZmBbsSJeefJ2khPME1+r+FHotxOPxLlWGc6f1d7E7fzYK71JldM99Spfl9k399/3tmzewtS/6vhfiRRDgZEvktVt6x07E3ybAyVb3XKvVsvDwGQ5fTuCzZjVwtip6cy4zJ5fJ245gbGjA8EZViwTJhHieyvh6ceysfo7ZE9EXCPAtvDGh1Wr5ecV6Dp08y7j3+uDsYPfAdi9dvwGAnbL4ZbxCiBdD3bp1iYyM5KuvvqJChQps3ryZKlWqcODAgftOpngQCXS94FJTU4mMjNQ75ujoSG5uLt9++y1t2rRh3759/N///V+RusbGxrz//vvMnj0bIyMjhgwZQs2aNXXBkM8//5zWrVvj7e1Np06dMDAwICoqilOnTvHFF188Uj9//PFHIiMjad++PX5+fmRnZ7No0SJOnz7Nt99+CxTkElOpVGzbto2KFStiYWGBt7c3JiYmfPvttwwaNIhTp04xceLEYl9jwoQJODg44OLiwqeffoqjo6NuJ8VH5evry8GDB4mLi8PKygp7e3sMDAwwNDQkPDycMWPGEBAQ8FjTJQE++OADgoODOXLkCKGhoQQEBOh2mUxLS2PEiBGYm+vfefX19WXbtm3UqVMHU1NT7OyK/jEfMWIEY8eOxc/Pj0qVKrFgwQIiIyNZvHjxA/s0Y8YM3NzcqFy5MgYGBixbtgxXV1dsbW1p0qQJtWrVol27dkydOpUyZcpw/fp1IiIiaN++PaGhoXz66aekpqYye/ZsrKysWL9+PX379mXdunWPda1E8TS52XpBKVVqAsk3L2JiZo2l0okTe38lKyORGs0/BMCvQnPOR60nas8vlAoK4+aVk1yJ3k+9tv/TtVG28ptsWzqGM4f+wqtMHZJuxHDx1GZCwwbrylyJ3oepuQ0WSkdSb1/m+K75ePjVxNWn0rMauniNqdXZJN4sXPaenHiT61disbC0wtbeiY2rfyMtJYm33h4KQI26zTiwayMbVi6iaq0wLkaf5OTxA7w9eIyujbphbfhh+qfs2LickKq1uRJ3nkP7ttC+2zvPfMA1rjwAAQAASURBVHzi9ZSdq+FGemFqiFuqTOKS0rAyNcbR0pw/jp0jKSub9+oUbCjSpIw3m85dZvHRf2jo78mZG4kcvHSDkY2r6tpYcOgM++Ku83HDqpgbG5Hy7wwwi39nff0X5MrJy+O9uiFk5WrI+jdhvdLUBAMDmWEtnqxsdQ7xtxJ1z28lpRB7NR5rS3Mc7WxZvG4LyanpDOlRsKKkae1QNu49yG9rNtOoRmVOnY/lQORpRg/ooWtj/vII9h47yci+3TAzNSE5rWATJ0vzgllfN24nsffYCSqXK4PS0oK46zdYtGoT5f188XF3QQjxYvPz82Pu3LlPtE0JdL3gdu7cSeXKlfWO9evXjxkzZjBlyhTGjBlD/fr1mTx5Mr1799YrZ2FhwahRo+jevTvXrl2jXr16zJ8/X3e+efPmrFu3jgkTJjBlyhSMjY0JDAzUS57+sKpXr87evXsZNGgQ169fx8rKiqCgIFatWqWbdVS7dm0GDRpEly5dSExMZOzYsYwbN46FCxfyySefMHv2bKpUqcK0adN48803i7zGV199xQcffEBMTAyVKlVi7dq1ejsaPorhw4fz9ttvU758ebKysoiNjdXNYOrXrx+TJk2iT58+JWr7TuXLl6dZs2Z8/vnnrF+/nvnz5zNw4ECqVKmCl5cXkyZN0u0++Z/p06czbNgw5s6di4eHB3FxcUXaHTp0KKmpqXz88cfcvHmT8uXLs2bNGgICAh7YJ2tra6ZOnUpMTAyGhoZUq1aN9evX62YHrl+/nk8//ZQ+ffpw69YtXF1dqV+/Pi4uLuzcuZOZM2eyY8cOlEolAL/++isVK1bkhx9+YPDgwfd7aVECSTfOs2P5Z7rnkbsLdgYtVb4R1ZsNJTszmcy0wlmBVjYu1Hvzf0Tu/pnoyHVYWDlSrel7uPkW/h6xdw2gTuvRnNj3K2cOLsXSxplK9fviE1g4QzArM5nIPQvJzkzBzMIO33INCarxaBs/CFFS1y6dZ+6scbrnEcsXAlClZkM69xpCeloKKUmF73t7RxfCB48hYvlC9u1Yj42dAx16DKZM+cL3vaePP70GjGTjmsVs3/AXdg7OtO4UTuXq9Z/VsMRr7mJiKhO3HNI9//XIPwDU9/NgcO0QUrLVJGZk6847W1kwqlFVFh09y8Z/4rC3MGNgrWAquhfOQtwSXZDjc+Lmg3qvNah2BRr4eRKXlMb52ykAfLRKf5b67PYNcbIqusxRiMdx4co1xn+3UPf8l1UbAWhQrRLvdW9PSpqK28mFS82dHewYPaAHv6zaxPo9f+NgY8OgLm9SKbBwxcnmfQW7kI/7Tn939He7taNh9coYGxlyKjqW9bsPolbn4GCrpEZIOTo0k9/vQryI0tLSHrrsf985H5VCq9VqS1RTiFfUnj17CAsL48qVK7i4yF2gp+F/C2Xponj1Na2med5dEOKpC1n18fPughBPnVHzts+7C0I8ddahLZ53F0os/cjG592FYr3M1/RpMjAweOgdFe+X0ud+ZEaXEP9Sq9XcunWLcePG0blzZwlyCSGEEEIIIYQQT9COHTt0/46Li2P06NGEh4fr0gYdOHCAX375hcmTJ5f4NWTLFfFQJk2ahJWVVbGPli1bPu/uPRF//PEHPj4+pKSkMHXqVL1zixcvvuf4g4KCnlOPi/c6/F8JIYQQQgghhHj5NGjQQPdYtGgRM2bMYPLkybz55pu8+eabTJ48mWnTprFgwYIHN3YPsnRRPJSkpCSSkpKKPWdubo6Hh8cz7tGzlZ6eTkJCQrHnjI2N8fHxecY9ureX4f9Kli6K14EsXRSvA1m6KF4HsnRRvA5e5mV2snTx5WVhYUFUVFSRXNPR0dFUqlSJzMzMe9S8P1m6KB6Kvb099vb2z7sbz421tTXW1i/H9sSv+/+VEEIIIYQQQogXn5eXF3Pnzi2yomrevHl4eXmVuF0JdAkhhBBCCCGEEEKIZ+qbb76hY8eObNiwgRo1agBw6NAhYmJiWL58eYnblRxdQgghhBBCCCGEEOKZatWqFTExMbRp00aXgqdNmzZER0fTqlWrErcrM7qEEEIIIYQQQgghxDPn6enJpEmTnmibEugSQjxzo4K3P+8uCPHUTTnc+Hl3QYinr930590DIZ462XRBvBYkcbp4TlJSUpg/fz5nz54FICgoiL59+2JjY1PiNmXpohBCCCGEEEIIIYR4po4cOYKfnx/ffPONbunijBkz8PPz49ixYyVuV2Z0CSGEEEIIIYQQQohn6qOPPuLNN99k7ty5GBkVhKc0Gg39+/fnww8/ZPfu3SVqVwJdQgghhBBCCCGEEOKZOnLkiF6QC8DIyIiRI0cSGhpa4nZl6aIQQgghhBBCCCGEeKaUSiWXL18ucvzKlStYW1uXuF0JdAkhhBBCCCGEEEKIZ6pLly7069ePJUuWcOXKFa5cucKff/5J//796datW4nblaWLQgghhBBCCCGEEOKZmjZtGgqFgt69e6PRaAAwNjZm8ODBfPXVVyVuV2Z0vcQWLlyIra3tY7ejUChYtWrVY7fzOHbu3IlCoSAlJeWeZe4e77hx46hUqZLueXh4OO3atXtqfXza7h6PEEIIIYQQQgjxqjIxMWHWrFkkJycTGRlJZGQkSUlJfPPNN5iampa4XZnR9ZyFh4eTkpLy3ANND2vXrl2MHz+eyMhIsrOz8fDwoHbt2sydOxcTExMWLlzIhx9+eN+AVUl16dKFVq1a3fP8rFmz0Gq1uucNGzakUqVKzJw584n1wdfXl0uXLgFgbm6On58fH3zwAf3793+kdhQKBStXrtQLzA0fPpz333//ifVVvBrOnI9jzY59xF6LJzk1neF9u1K9Qrn71jl9PpZfVm3iasJNHG1t6NC0Pg2rV9Yrs3HvIdbu2EdKmgofd1f6dGhJgI8nALeSUnhv4jfFtv3R252pVSn4yQxOiH/dvHqac0dXkXzzIlkZSdRtMxoPvxr3r3PlFJG7fyY16QoW1o6Ur96ZUuUbP3Sb+XkaTh74nfjYo2SkJWBsYomLdwVC6vTG3Mr+qY1VvN4O7NrInq2rSU9Lwc3Thzad++HlG1BsWY1Gw67NKzl2cCdpKUk4urjTom1PygYV/j7Pz89na8QSIg/tRpWeirWNHVVqNKRxy04oFAoA0tNS2Lj6N86fjSIrM4NS/uVp81Y/HJ3dnsmYxetn87lLrD0dS2q2Gm87a8Krlcff0bbYspq8fFafvsjuC1dJzlLjprSkW+WyVPJw0pXJytWwNDKaw1cSSMvOwddeyduh5fC7o81uv24otv3uVcrSJqj0kxyeEOIpsLCwoEKFCk+sPQl0iYd25swZWrRowfvvv8/s2bMxNzcnJiaG5cuXk5eX99Rf39zcHHNz83uet7Gxeep9AJgwYQIDBgwgMzOTZcuWMWDAADw8/p+9+46ruvofOP667HG57CVTlgMnIq5yK2ruMssVlqs07VeWmuUszTJLK9OWpllpmRPFgeLAnAluQAFFRUTgsjf39wdfL97AHImkvp+Px33oPZ/3OZ/3uV7hc88953xc6N69+79qV6lUolQqH1CWlRUXF2NoaFht7YvqUVhcjIeLEx1aBPDpsl/vGH89LYOPvl1Fl9bNGT/kWU7GxbNk9UasVBY0qesDwIHjp1ixIYyRz/XC18OV0L1/8uHSlSx8dzyWSnNsrVQsnTlRp93wg8fYuCuSpvX8qqWf4slWWlKIlb0ntf07Ebl53h3jczJT2LfxA7wbBtOy2/+RknSCIzu+wsTMGmfPpnfVZklJIRnX4/Fv8TyW9p4UF+RwfM/37N80hy4vzn/gfRTixLFIQtcup++Lo3D39GP/7s388OUHvDV9EUqLytcwOzb/QtThvfQb9Cr2TrWIOxPFqm8/YcxbH1LLrTYAe7av49C+bQwY+jqOtdy4fPECv//0FaZm5rRu3wONRsPKpfPQNzBg6OhJGJuYsX/XJr5fNJM33v8cY2OTh/0yiMfcn4nJrDx6lldaNMDHzoqt5xKZG36Ez/q0RWVSeXbGmuhY9sdfZWSrBtRSKTlxNZUFe/5iVrdWeNqoAPjmz5MkqbN5rU1jbMyM2R9/lQ93HmF+76exMSt/D3/9XEeddqOupPLNwZMEuTtVf6eFEPfs5Zdfvqu4H3744b7al6WL/2ELFiygYcOGmJub4+bmxmuvvUZOTk6luPXr1+Pr64uJiQnBwcEkJSXpHN+wYQMBAQGYmJjg5eXFzJkztetf78X27dtxcnLi448/pkGDBnh7e9OtWze+/fZbTE1NiYiIYPjw4WRmZqJQKFAoFMyYMQOAlStXEhgYiIWFBU5OTgwaNIjr169XOkdkZCSNGjXCxMSEli1bcurUKe2xOy3VvHXpYkhICHv27GHhwoXaXBISEvDx8WH+fN0PMFFRUSgUCs6fP39Xr8PNPnh5eTFp0iRsbGzYsWOH9viRI0fo0qULdnZ2WFpa0q5dO/766y/tcU9PTwD69euHQqHQPv/70sWysjJmzZqFq6srxsbGNGnShLCwsLvKMTExEYVCwerVq2nXrh0mJiasWrWKtLQ0XnzxRVxcXLSj5r/88otO3bKyMj7++GN8fHwwNjbG3d2dDz/8UHs8KSmJ559/HisrK2xsbOjTpw+JiYl3lZe4d03r+fJij060aPTPs7hu2nHgKPY21gzrE4yrkz3dn25By8b1Cd3zpzZmc8SfdGrZjA4tmuLqZM+oAb0wNjJk96Hy96menh7WKgudx+ET52jVpAEmxkbV0k/xZHP2DKBh68G4+rS8q/gLJ7dhrnKgSdvhqGzd8G3yDG6+rYg9vumu2zQyNqd9/xm4+bVBZe2CrXMdAtqPJD3lArlZqQ+iW0Lo2L9rE83bdCawVUccnF3p9+JojIyMOPrnrirjow7vpX3ws9RtEICtnRMt23ajjn9T9oVv1MZcSoilfqMg6jZshrWtAw0DWuFXrzFJiXEA3LieTFJiHH1fGIWrhw/2jrXo+8IoiouLOHF0/0Ppt3iyhJ5NoKOvG+19XHG1UjKihT/G+vrsPn+5yvj98Vfp29Cbpi4OOFqY0aWOB01d7Nl8JgGAopJSDl9KYVBAXeo72uBkYc5zjX1xtDBjR0zFndqsTI11HkeTUqjvaIOjhdlD6bcQ4t4sX76c3bt3o1arycjIuO3jfslA13+Ynp4eixYt4vTp0/z444/s2rWLd955RycmLy+PDz/8kBUrVhAZGYlareaFF17QHt+3bx/Dhg1jwoQJnDlzhqVLl7J8+XKdgYu75eTkRHJyMnv37q3yeOvWrfn8889RqVQkJyeTnJzMxInls0KKi4uZPXs20dHRrF+/nsTEREJCQiq18fbbb/Ppp59y5MgR7O3t6dWrF8XFxfec68KFC2nVqhUjR47U5uLu7s7LL7/MsmXLdGKXLVtG27Zt8fHxuadzlJWVsXbtWjIyMjAyqvjwn52dzUsvvcT+/fs5ePAgvr6+9OjRg+zsbKB8IOzmeZOTk7XPq+rDp59+yvz58zlx4gTBwcH07t2buLi4u85x8uTJTJgwgbNnzxIcHExBQQHNmjUjNDSUU6dOMWrUKIYOHcrhw4e1daZMmcJHH33E+++/z5kzZ/j5559xdHQEyv8dg4ODsbCwYN++fURGRqJUKunWrRtFRUX39PqJ6hGbmEQjP90p+o3r+BCXWH6BWVJSSvzlqzS8JUahUNDQz5vYxKovQi8kXSXxSjIdWzSt8rgQD1tacgyO7o11ypw8mpKWHPOv2i0qykOhUGBkbP6v2hHi70pKSrhyKR6fuo20ZQqFAp+6jbiUEHubOpVnYhsYGnHxwjntc/faflyIOUlqylUAki8nknD+LH71m2rbADAwqFjEoVAoMDA0JDH+HEI8SCWlZSSkZdHA2U5bplAoaOBsS1yquso6xWVlGOrrfiQ11Ncn5nr5B9xSjYYyjQajv8UY6esRk1r1h+DM/EKirqbSwcftX/RGCFGdXn31VTIzM0lISKBDhw58//33rFu3rtLjfsnSxf+wN954Q/t3T09PPvjgA8aMGcPixYu15cXFxXz55Ze0aFG+78iPP/5IvXr1OHz4MEFBQcycOZPJkyfz0ksvAeDl5cXs2bN55513mD59+j3lM2DAALZt20a7du1wcnKiZcuWdOrUiWHDhqFSqTAyMsLS0hKFQoGTk+404VunJnp5ebFo0SKaN29OTk6OznK96dOn06VLF21fXF1dWbduHc8///w95WppaYmRkRFmZmY6uYSEhDBt2jTt61NcXMzPP/9caZbXP5k0aRLvvfcehYWFlJSUYGNjo7NHV8eOulOnv/nmG6ysrNizZw89e/bE3r58zwErK6tKr9Ot5s+fz6RJk7QDl/PmzWP37t18/vnnfPXVV3eV6xtvvEH//v11ym4OPgK8/vrrbNu2jTVr1hAUFER2djYLFy7kyy+/1L5nvL29eeqppwBYvXo1ZWVlfPfdd9q9P5YtW4aVlRURERF07dq1Ug6FhYUUFhbqlBUVFWFsJDODqoM6OwdLC90lsJYW5uQVFFBUXExOXgFlZWVY/S3GSmnO1ZQbVba5+9BfuDjaU6e2e7XlLcS9KMjNwMTMSqfM2MyK4qI8SkuK0De4958vpSVFnNi/Ane/pzA0lhkA4sHKy82irKys0hJFpYUlqSlXqqzjW7cx+8I34elTH1t7J87HnOB01CHKysq0Me2D+1NYmM9nsyeg0NNDU1ZG114v0jSoLQD2ji5YWduxbePP9H1xNEZGJuzftYnMjDSyM+//m3IhqpJdWESZRoOlie7PYEsTY65m5VZZp6GzHaFnEqjrYIOThRmnrqVx+NI17b67poYG+Npb8cfJC7hYKrE0MSYy8SpxN9Q4WVT9pcSe+CuYGBjQ3M3xwXZQCPHAfPXVVyxYsIA//viDH374gSlTpvDMM8/wyiuv0LVrV+1nzfslM7r+w3bu3EmnTp1wcXHBwsKCoUOHkpaWRl5enjbGwMCA5s2ba5/XrVsXKysrzp49C0B0dDSzZs3S7v+kVCq1s5xubedu6Ovrs2zZMi5fvszHH3+Mi4sLc+bMwd/fn+Tk5H+se+zYMXr16oW7uzsWFha0a9cOgEuXLunEtWrVSvt3Gxsb6tSpo+3Lg1CrVi2eeeYZ7VrfTZs2UVhYyIABA+66jbfffpuoqCh27dpFixYt+Oyzz3Rmg6WkpDBy5Eh8fX2xtLREpVKRk5NTqa//JCsri6tXr9KmTRud8jZt2tzT6xEYGKjzvLS0lNmzZ9OwYUNsbGxQKpVs27ZNm9vZs2cpLCykU6dOVbYXHR3N+fPnsbCw0L6fbGxsKCgo4MKFC1XWmTt3LpaWljqPT5evues+iJpVVFzM/r9Oymwu8VgrKy3hwJb5oNHQrOOYmk5HCAB6DngZWwdnFswaz3vjB7Jx9fc0a9kBPb2Ky/cTxyKJOrKPgSETGDfpYwYMG8e+8I0cO7gbKL9OHDzqbW6kJDP77RCm/98gEmJP41e/KQqFfAwQNS+keT2cLMx5a+NehqzaxrLDZ2jv7arzIfe1NuUzIV9bu5uhP28j7NxFWnvW4nafgyPOX+ap2rUwMtB/GF0QQtwnY2NjXnzxRXbs2MGZM2fw9/fntddew9PTs8otm+6FzOj6j0pMTKRnz568+uqrfPjhh9jY2LB//35eeeUVioqKMDO7u2+bc3JymDlzZqVZPQAmJve3AamLiwtDhw5l6NChzJ49Gz8/P5YsWcLMmTOrjM/NzSU4OJjg4GBWrVqFvb09ly5dIjg4uEaWu40YMYKhQ4fy2WefsWzZMgYOHHjXryeAnZ0dPj4++Pj48Ntvv9GwYUMCAwOpX78+AC+99BJpaWksXLgQDw8PjI2NadWqVY301dxc95uuTz75hIULF/L5559r93974403tLn902b/UP5+atasGatWrap07OZMtb+bMmUKb775pk5Z0amIe+iFuBdWFkoys3V/MWRm52JmYoKRoSEqcz309PRQ/y1GnZOLlaryzRAORp+hqLiYts2bVGfaQtwTE3NrCvLUOmWFeWoMjczueTbXzUGuvKzrtH92tszmEtXCzFyFnp4eOdmZOuU52ZkoVdZV1lFaWDJs9CSKi4vIy81GZWlD2IafsLZ10MZsXb+Sdl360TiwfOa1s4sHGWmpRGxbR7OWHQBwdfdm/Lvzyc/LpbS0BKWFJYs/mYyLm3c19VY8qSyMjdBTKMgs0L3mzSwoxKqKjegBVCbGTOzQjKKSUnKKirE2NeaX4zE4KCuuSZ0szJnWtQUFxSXkF5dgbWbCwr3HcVBW/nl9NiWd5KxcJjzd5IH2TQhRvfT09FAoFGg0mgdyozv5Kuc/6tixY5SVlfHpp5/SsmVL/Pz8uHr1aqW4kpISjh49qn0eExODWq2mXr3yjasDAgKIiYnRDszc+rj1G8H7ZW1tjbOzM7m55dORjYyMKr0xz507R1paGh999BFPP/00devWrXIjeoCDBw9q/56RkUFsbKy2L/eqqlwAevTogbm5OV9//TVhYWF3fceHqri5uTFw4ECmTJmiLYuMjGT8+PH06NEDf39/jI2NuXFDd0mYoaHhP/4HVqlU1KpVi8jISJ3yyMhI7YDa/YiMjKRPnz4MGTKExo0b4+XlRWxsxd4gvr6+mJqaEh4eXmX9gIAA4uLicHBwqPR+ut1dL42NjVGpVDoPWbZYffw83TgZl6BTdiL2Ar6ergAYGOjj5VqLU7fEaDQaTsXG4/e/mFvtOvgXzerXwVIpexaJ/w5b5zqkXDqhU3btUhS2znXuqZ2bg1w56qu06z8TY1OLB5mmEFoGBga4uHtxIeaktkyj0XA+5iTutf/5braGhkZYWtlSWlrK6ahD1G9UMZO/uKiw0qyW8us7TaV2TM3MUVpYcuN6MpcvXqB+4+aVYoT4Nwz09ahtq+J0cpq2TKPRcPpaGr72Vv9Y18hAHxszE0rLNBy+lEJgFcsOTQwNsDYzIaewmBPJN2jm6lApJuLCZWrbqvD43x0bhRD/XYWFhfzyyy906dIFPz8/Tp48yZdffsmlS5d0tje6HzKj6z8gMzOTqKgonTI7OzuKi4v54osv6NWrF5GRkSxZsqRSXUNDQ15//XUWLVqEgYEB48aNo2XLlgQFBQEwbdo0evbsibu7O8899xx6enpER0dz6tQpPvjgg3vKc+nSpURFRdGvXz+8vb0pKChgxYoVnD59mi+++AJAO80wPDycxo0bY2Zmhru7O0ZGRnzxxReMGTOGU6dOMXv27CrPMWvWLGxtbXF0dGTq1KnY2dlp76R4rzw9PTl06BCJiYnaJXZ6enro6+sTEhLClClT8PX11VkueT8mTJhAgwYNOHr0KIGBgfj6+mrvMpmVlcXbb79daaaUp6cn4eHhtGnTBmNjY6ytK3+b+/bbbzN9+nS8vb1p0qQJy5YtIyoqqsrZVHfL19eX33//nQMHDmBtbc2CBQtISUnRDp6ZmJgwadIk3nnnHYyMjGjTpg2pqamcPn2aV155hcGDB/PJJ5/Qp08f7R0hL168yB9//ME777yDq2vlgRLx7xQUFpGcWnHBmJquJuFyMhbmpthZW7Fq8w4yMrMZN7h81maX1oGE7T/ETxu306FFU06dT+DPqNNMHjlY20bP9q346ud1eLk64+Puypa9BykoKqJ9kO7yxGs30jkbf1GnrhDVoaS4gOyMii9zcjJTyLgej5GJBeYqe07sX0l+bhotgt8AwLthMOejtxC970dq+3fietJJkmIP8HSf9+66zbLSEg6EfkJGajxP934XjaaM/NzyPYuMTSzQ05dLJPFgPdWxF7+t+BIXdy/cPHyJjAilqLBQO/NqzY+LUFnZ0K3PEKD8jopZ6nSc3TzJUqcTHrqGsrIy2nbpq22zbsNAdm/7AytrexxruXE1KYH9uzbRrFXFfqEn/jqAUqnC0saOlKtJbP7tB/wbB+Fbr8nD7L54QjxTrzZfHzhBbVsV3rZWhJ1LpKCklPbe5deIX0VGY2NqwosB5V9MxKWqSc8vwNNaRXpeAWtPxFGm0dDLv+KmOdFXU9FooJalOSnZeaw6do5aKqW2zZvyioo5ePEaQ5rVfXgdFkLcl9dee41ff/0VNzc3Xn75ZX755Rfs7OzuXPEuyVXcf0BERARNm+p+wHzllVdYsGAB8+bNY8qUKbRt25a5c+cybNgwnTgzMzMmTZrEoEGDuHLlCk8//TTff/+99nhwcDCbN29m1qxZzJs3D0NDQ+rWrauzefrdCgoKYv/+/YwZM4arV6+iVCrx9/dn/fr12j23WrduzZgxYxg4cCBpaWlMnz6dGTNmsHz5ct59910WLVpEQEAA8+fPp3fv3pXO8dFHHzFhwgTi4uJo0qQJmzZt0rmj4b2YOHEiL730EvXr1yc/P5+EhAQ8PT2B8td3zpw5DB8+/L7avlX9+vXp2rUr06ZNY8uWLXz//feMGjWKgIAA3NzcmDNnjs4G8ACffvopb775Jt9++y0uLi4kJiZWanf8+PFkZmby1ltvcf36derXr8/GjRvx9fW971zfe+894uPjCQ4OxszMjFGjRtG3b18yMyuWUrz//vsYGBgwbdo0rl69irOzM2PGlO9ZY2Zmxt69e5k0aRL9+/cnOzsbFxcXOnXqhEol35xVhwtJV5j51XLt8x/XhwHQrnkTxg7qhzorhxsZFf9+DrbWTB45mB/Xb2PLvoPYWloyZmBvmtSt2EeuddMGZObksiZsN+rsHDxrOTN11NBKG9TvOvQXNpYqnbpCVIf0a+fZvfZ97fOoveV3x61dvwNBXcdTkJdBXlbFzFilpSNP936PqL0/EBu1GTOlHc27jMXZs+ldt5mfm86V+PI7zm5bpbu8usOzs3Fwa/DgOyqeaI2atSEnO4udm1eTnaWmlpsnw8dOxUJlBYA6/QaKW2bbl5QUs2Pzr6TfSMHI2Jg6/gE8HzIeU7OKGba9B7zCjs2/smH1t+TmZGFhaU1Qmy507FFxE5+cLDVb/viRnKxMLFRWNG3Rjo7d735vUiHuRStPZ7IKivgtOo7M/EI8bFRM7tgcS9PypYtpuQXo3TINsbisjN+iYrmek4+xgT5NXOwZ26Yx5kYVdxzNLyrhl+MxpOcVoDQ2Isjdkeeb+GHwtzsxHkhMRqPR0NrT+eF0Vghx35YsWYK7uzteXl7s2bOHPXv2VBn3xx9/3Ff7Cs3NW1oI8QTZt28fnTp1IikpCUdHuSPLw5Z9NKymUxCi2s071fHOQUI84ro0L6npFISodo3Wv1XTKQhR7aynfl3TKdy3/+pnC4vAbjWdwn9WSEjIXd1ZcdmyZffVvszoEk+UwsJCUlNTmTFjBgMGDJBBLiGEEEIIIYQQ4iFavnx5tbYvm9ELrTlz5qBUKqt8dO/evabTeyB++eUXPDw8UKvVfPzxxzrHVq1addv++/v711DGVXsS/q2EEEIIIYQQQoh7JUsXhVZ6ejrp6elVHjM1NcXFxeUhZ/RwZWdnk5KSUuUxQ0NDPDw8HnJGt/eo/1v9V6cXC/EgydJF8SSQpYviSSBLF8WTQJYuPniydLHmyNJFoWVjY4ONjU1Np1FjLCwssLB4NG4t/6T/WwkhhBBCCCGEEFWRpYtCCCGEEEIIIYQQ4rEgA11CCCGEEEIIIYQQ4rEgA11CCCGEEEIIIYQQ4rEge3QJIR66v0zb1nQKQgghHgDZpFs8CQyC+9R0CkIIIe6BzOgSQgghhBBCCCGEEI8FGegSQgghhBBCCCGEEI8FGegSQgghhBBCCCGEEI8FGegSQgghhBBCCCGEEI8FGegSQgghhBBCCCGEEI8FGei6R8uXL8fKyupft6NQKFi/fv2/buffiIiIQKFQoFarbxvz9/7OmDGDJk2aaJ+HhITQt2/fasuxuv29P0IIIYQQQgghhHh0GdR0AjUhJCQEtVpd4wNNd2vPnj3MnDmTqKgoCgoKcHFxoXXr1nz77bcYGRmxfPly3njjjX8csLpfAwcOpEePHrc9vnDhQjQajfZ5+/btadKkCZ9//vkDy8HT05OLFy8CYGpqire3NxMmTGDEiBH31I5CoWDdunU6A3MTJ07k9ddff2C5VrfU1FReffVVwsPDKS0tJSAggKVLl1KnTp2aTk08JPFxp9m3cyNXk+LJysxgyKh38G8c9M91Yk8RunY5KcmXsbKxo0O3Z2nWssO/alOIB+n65dPEHFtPxvV48nPTearXZFy8W/xznaRTRO39gcz0JMws7KgfNIDa9Ttqj589spbL5w+SlX4ZAwNjbJ3r0OjpYaisXbQxF05u49K5fWSkxlNclE+/V3/CyNi82vopnmxnUtLZfDqexPQsMvILebN9AM3dHP+5zrU0Vhw7yxV1DrbmpvRr6E07b1edmO0xF9l0OoHMgkLcrS0IaV4fHzsr7fHvDp7iZPINMvILMTHQx8/emhcD6uBiqayObgpB2P7DbNodiTorB49aTgzv3x1fD9cqY0tKSlm/ax97DkeTnpmFs4Mtg3t2oWk9X21MfkEhq7fu4vDJs2Tl5OHp4kRIv+74uLvotHX5WiqrNu/g7IWLlJaV4urkwFshz2NnbVWd3RVC/AfJjK7/uDNnztCtWzcCAwPZu3cvJ0+e5IsvvsDIyIjS0tJqP7+pqSkODg63PW5paflAZrjdyaxZs0hOTubUqVMMGTKEkSNHsnXr1n/drlKpxNbW9gFk+HBMmjSJo0ePsnnzZo4fP87YsWPvql5RUVE1ZyYeluKiIpxdPOj9/N0N9KbfSGH513Px8mvI+Cnzad3+Gf5Y9TWxZ47fd5tCPGilJYVY2XsS0GHkXcXnZKawb+MHOLg1JHjQAvya9OTIjq9ITqx4X1+/fBqfxt3p/MI82vWfTllZKXv+mElJcYE2pqS4ECfPAOo1f+6B90mIvysqKcXDWkVIUP27ir+ek8e83cfwd7Rl7jNP0b2uJ9/8eYroq6namD8Tk1l59CzPNvJhTo82eFirmBt+hKyCQm2Mp42K0a0b8Wnvtkzp1BwNMGfnEcrKNFWcVYh/58DxU6zYEMZzXdsz760xeLg48uHSlWTm5FYZv3rrLnYcOMrw/t1ZMGksXVoH8umy1SRcTtbGLFm9geiYC4wb1J/577xGozrefPD1CtLUWdqYazfSmfblD7g42DF9bAifvP0a/bu0xdDQsNr7LIT475GBrr9ZsGABDRs2xNzcHDc3N1577TVycnIqxa1fvx5fX19MTEwIDg4mKSlJ5/iGDRsICAjAxMQELy8vZs6cSUlJyT3ns337dpycnPj4449p0KAB3t7edOvWjW+//RZTU1MiIiIYPnw4mZmZKBQKFAoFM2bMAGDlypUEBgZiYWGBk5MTgwYN4vr165XOERkZSaNGjTAxMaFly5acOnVKe+xOSzVvXboYEhLCnj17WLhwoTaXhIQEfHx8mD9/vk69qKgoFAoF58+fv6vX4WYfvLy8mDRpEjY2NuzYsUN7/MiRI3Tp0gU7OzssLS1p164df/31l/a4p6cnAP369UOhUGif/33pYllZGbNmzcLV1RVjY2OaNGlCWFjYXeVYVFTEuHHjcHZ2xsTEBA8PD+bOnas9rlarGTFiBPb29qhUKjp27Eh0dDRQPlPLycmJOXPmaOMPHDiAkZER4eHh2jI9PT1at25NmzZt8Pb2ZsCAAVXO5mrfvj3jxo3jjTfewM7OjuDgYODu3t+RkZG0b98eMzMzrK2tCQ4OJiMjQ/v6zJ07l9q1a2Nqakrjxo35/fff7+r1EQ9GHf+mdO09CP8m/zzb5aZD+7djY+vAM8++hIOzK63bd6dB05ZE7gq97zaFeNCcPQNo2Howrj4t7yr+wsltmKscaNJ2OCpbN3ybPIObbytij2/SxrTrN43a9TtiaeuOlX1tgrq+Tl52KukpF7QxdQJ6U695f2yd/B54n4T4uyYu9gxs6keQu9Ndxe+MvYSD0pShgfVwtVISXNeDFh6ObDmbqI0JPZtAR1832vu44mqlZEQLf4z19dl9/rI2prOfO/UdbbBXmlLb1pKBTXxJzyvgem7eg+6iEGyO+JNOLZvRoUVTXJ3sGTWgF8ZGhuw+9FeV8XuPRdOvc1sC6vvhaGdDcJsgmtbzZXPEAQCKios5dOIsQ3p1ob6PJ052NjzfrQOOdtZsP3BE286vW8IJqOfLkN5dqe3qjJOdDc0b1MVSKbN0hXgSyUDX3+jp6bFo0SJOnz7Njz/+yK5du3jnnXd0YvLy8vjwww9ZsWIFkZGRqNVqXnjhBe3xffv2MWzYMCZMmMCZM2dYunQpy5cv58MPP7znfJycnEhOTmbv3r1VHm/dujWff/45KpWK5ORkkpOTmThxIgDFxcXMnj2b6Oho1q9fT2JiIiEhIZXaePvtt/n00085cuQI9vb29OrVi+Li4nvOdeHChbRq1YqRI0dqc3F3d+fll19m2bJlOrHLli2jbdu2+Pj43NM5ysrKWLt2LRkZGRgZGWnLs7Ozeemll9i/fz8HDx7E19eXHj16kJ2dDZQPhN08b3JysvZ5VX349NNPmT9/PidOnCA4OJjevXsTFxd3x9wWLVrExo0bWbNmDTExMaxatUo7oAYwYMAArl+/ztatWzl27BgBAQF06tSJ9PR07O3t+eGHH5gxYwZHjx4lOzuboUOHMm7cODp16qRto0+fPvz+++93Nfj2448/YmRkRGRkJEuWLAHu/P6OioqiU6dO1K9fnz///JP9+/fTq1cv7ezBuXPnsmLFCpYsWcLp06f5v//7P4YMGcKePXvumI+oGZcSYvGp20inzLdeEy4lxNZQRkL8e2nJMTi6N9Ypc/JoSlpyzG3rFBeVf6g3NpHlWuLREJeqpoGT7qzzRs72xKWqASgpLSMhLYsGznba4wqFggbOttqYvysoLiHiwhXslabYmZlWV+riCVVSUkr85as09PPSlikUChr6eRObeLnKOsUlpRgZ6u6mY2RowLmESwCUlpZRVlaG0d9mZhkZGhITXx6j0Wj460wsTvY2fLBkBSPe/5h3P/uWwyfPPsjuCSEeIU/kHl3/5I033tD+3dPTkw8++IAxY8awePFibXlxcTFffvklLVqUz3748ccfqVevHocPHyYoKIiZM2cyefJkXnrpJQC8vLyYPXs277zzDtOnT7+nfAYMGMC2bdto164dTk5OtGzZkk6dOjFs2DBUKhVGRkZYWlqiUChwctL9hvDll1/W/t3Ly4tFixbRvHlzcnJyUCorLvSnT59Oly5dtH1xdXVl3bp1PP/88/eUq6WlJUZGRpiZmenkEhISwrRp07SvT3FxMT///HOlWV7/ZNKkSbz33nsUFhZSUlKCjY2Nzh5dHTt21In/5ptvsLKyYs+ePfTs2RN7e3sArKysKr1Ot5o/fz6TJk3SDlzOmzeP3bt38/nnn/PVV1/9Y46XLl3C19eXp556CoVCgYeHh/bY/v37OXz4MNevX8fY2Fh7rvXr1/P7778zatQoevTowciRIxk8eDCBgYGYm5vrzAg7c+YMgwYNYtasWYwYMYLPPvuMAQMGAHDs2DECAwNJTU3Fzq78gtfX15ePP/5YJ8c7vb8//vhjAgMDdd7v/v7+ABQWFjJnzhx27txJq1atgPL31f79+1m6dCnt2rX7x9dH1IycLDVKC0udMqWFFQUFeRQXF2FoaHSbmkL8dxXkZmBiZqVTZmxmRXFRHqUlRegb6L6vNRoNUXt+wK5WXSztPBDiUaDOL8TS1FinzNLUiPziEopKSsktKqZMo8HSRPf9bmlizNUs3WViO2IusuqvGApLSnFWmfNu5+YY6Mv33eLBysrNo6ysDCsL3S8UrJTmXE25UWWdxnW82bT7APW8PHCys+FkbDyHTpylTFMGgKmJMX6ebqzdvgcXR3usLMzZ/9dJYhOTcLYvHwhWZ+dQUFjEhvD9vNCjE4N7diE65jyfLlvN9NdCqO/jWa39FkL898hvuL/ZuXMnnTp1wsXFBQsLC4YOHUpaWhp5eRXTuw0MDGjevLn2ed26dbGysuLs2fJvDaKjo5k1axZKpVL7uDnL6dZ27oa+vj7Lli3j8uXLfPzxx7i4uDBnzhz8/f1JTk7+x7rHjh2jV69euLu7Y2FhoR2IuHTpkk7czUELABsbG+rUqaPty4NQq1YtnnnmGX744QcANm3aRGFhoXaQ5m68/fbbREVFsWvXLlq0aMFnn32mMxssJSWFkSNH4uvri6WlJSqVipycnEp9/SdZWVlcvXqVNm3a6JS3adPmrl6PkJAQoqKiqFOnDuPHj2f79u3aY9HR0eTk5GBra6vzvkhISODChYplNPPnz6ekpITffvuNVatWaQfFoHyZZffu3Zk8eTIbN25kzJgx2plaJ0+epG7dutpBLoBmzZpVyvFO7++bM7qqcv78efLy8ujSpYtOH1asWKHTh78rLCwkKytL51FUVHjbeCGEeNCO7V5KZtolWnV/q6ZTEaJGtK5di7nPtOH9ri1wVpmzcG8URSXVv9erEHcS0q87zva2vDH3C16cOIsf/thC+6Am6CkqPqaOG9wfgDEz5jPo7dls3XeIpwIaolCUH795X6zABnV5pl0rars607fT0wTU92PHn0cfdpeEEP8BMqPrFomJifTs2ZNXX32VDz/8EBsbG/bv388rr7xCUVERZmZmd9VOTk4OM2fOpH///pWOmZiY3FduLi4uDB06lKFDhzJ79mz8/PxYsmQJM2fOrDI+NzeX4OBggoODWbVqFfb29ly6dIng4OAa2Zh8xIgRDB06lM8++4xly5YxcODAu349Aezs7PDx8cHHx4fffvuNhg0bEhgYSP365Ru6vvTSS6SlpbFw4UI8PDwwNjamVatWD7WvAQEBJCQksHXrVnbu3Mnzzz9P586d+f3338nJycHZ2ZmIiIhK9W7dA+3ChQtcvXqVsrIyEhMTadiwofbYiRMntLMEAwIC2LhxI8HBwdy4cYOwsDCGDx+u0665ue6eBHfz/jY1vf0yhpt7eYWGhuLionuXm1sH5P5u7ty5ld6nL736LsPHTr1tHfHgKFVW5GRn6pTlZKsxMTGT2VzikWVibk1BnlqnrDBPjaGRWaXZXMd2f0NywjE6PPcBZhZ2CPGosDI1JjNf94uhzPwiTA0NMDLQR0+hQE+hILNA91ons6AQKxPd38vmRoaYGxnirDLHz86KV9bs5GhSCq1r16r2fognh8rcDD09PdTZuvu/qnNysVJVvWzcUmnOO6+8SFFxMdm5+dhYWvDz5p3Y21hpY5zsbJgxbjgFhUXkFRRiY2nBZz+uwcHWRntefX19XB3tddp2dbTXLoEUQjxZZKDrFseOHaOsrIxPP/0UPb3ybxHWrFlTKa6kpISjR48SFBQEQExMDGq1mnr16gHlgxAxMTH3vP/U3bK2tsbZ2Znc3PJp6VXdgfHcuXOkpaXx0Ucf4ebmBsDRo1V/o3Hw4EHc3d0ByMjIIDY2VtuXe3W7u0H26NEDc3Nzvv76a8LCwm6759jdcHNzY+DAgUyZMoUNGzYA5RuoL168mB49egCQlJTEjRu6U6QNDQ3/8U6VKpWKWrVqERkZqbMMLzIyUvtvfScqlYqBAwcycOBAnnvuObp160Z6ejoBAQFcu3YNAwMDnX27blVUVMSQIUMYOHAgderUYcSIEZw8eVJ710sXFxf27dvHlClTgPKZZuvWraNnz57Y2Ngwbty4f8ztbt7fjRo1Ijw8vMoB1Pr162NsbMylS5fuaZnilClTePPNN3XKDl2Qb5EfFvfafsScPq5Tdv5sNO61ZfNt8eiyda5DcsIxnbJrl6Kwda64OYdGo+GviG+5cv4QHZ6bjdLS8WGnKcS/4mtvRdSVVJ2yE8k38LW3AsBAX4/atipOJ6fR3K38/a3RaDh9LY2udW6/RFeDBo1GQ3FZWbXlLp5MBgb6eLnW4lRcAkENyz9LaDQaTsXG0+3pf76WNjI0xNbKkJKSUg6dOEPLJv6VYkyMjTAxNiInL5/omAsM7tlFe15vt1okp6bpxF9NvYGdtWWldoQQj78ndqArMzOTqKgonTI7OzuKi4v54osv6NWrl84m3rcyNDTk9ddfZ9GiRRgYGDBu3DhatmypHQyZNm0aPXv2xN3dneeeew49PT2io6M5deoUH3zwwT3luXTpUqKioujXrx/e3t4UFBSwYsUKTp8+zRdffAGU77WUk5NDeHg4jRs3xszMDHd3d4yMjPjiiy8YM2YMp06dYvbs2VWeY9asWdja2uLo6MjUqVOxs7PT3knxXnl6enLo0CESExNRKpXY2Nigp6eHvr4+ISEhTJkyBV9fX53lkvdjwoQJNGjQgKNHjxIYGIivr6/2LpNZWVm8/fbblWYneXp6Eh4eTps2bTA2Nsba2rpSu2+//TbTp0/H29ubJk2asGzZMqKioli1atUdc1qwYAHOzs40bdoUPT09fvvtN5ycnLCysqJz5860atWKvn378vHHH+Pn58fVq1cJDQ2lX79+BAYGMnXqVDIzM1m0aBFKpZItW7bw8ssvs3nzZm1uPXr0YOzYsbz66qsUFxezZ88ejIyMSE1NZdOmTQwcOPC2+fn4+Nzx/T1lyhQaNmzIa6+9xpgxYzAyMmL37t0MGDAAOzs7Jk6cyP/93/9RVlbGU089RWZmJpGRkahUKu1ss78zNjauNOPLyEju9HS/CgsLSLtesWw5I+06V5MSMDNXYmVjT9iGn8hSp/P8S+MBaPFUV/7cE8bWdSto1qoT8bEnOXn8T156dcpdtylEdSspLiA746r2eU5mChnX4zEyscBcZc+J/SvJz02jRfAbAHg3DOZ89Bai9/1Ibf9OXE86SVLsAZ7u8562jb92f8OlmH206TUFAyNT8nPL7x5rZGyunfWVn5tBQW4GOZnl7//MGxcxMDTBzMIeY1OLh9R78aQoKC7hWnbF77/UnDwS07NQGhtiZ27KL3/FkJ5fwNg25Tda6OznzraYS6w6do72Pq6cuZbGoYvXeKdjxdYEz9SrzdcHTlDbVoW3rRVh5xIpKCmlvbcrACnZefx5MZlGznaoTIxIyy1g4+l4jA30aeoiP9/Fg9ezfSu++nkdXq7O+Li7smXvQQqKimgf1BSAL1f9gbWlhXaQKi7xMumZWXi6OJGemc1v23ZTptHQp+NT2jajzp1Ho9Hg4mDHtRvprNy0HRcHOzr8r02AXh3asHDlb9T18qCBb22On43j2OlYZowNeaj9F0L8NzyxA10RERE0bdpUp+yVV15hwYIFzJs3jylTptC2bVvmzp3LsGHDdOLMzMyYNGkSgwYN4sqVKzz99NN8//332uPBwcFs3ryZWbNmMW/ePAwNDalbt67O5ul3KygoiP379zNmzBiuXr2KUqnE39+f9evXa2fVtG7dmjFjxjBw4EDS0tKYPn06M2bMYPny5bz77rssWrSIgIAA5s+fT+/evSud46OPPmLChAnExcXRpEkTNm3apHNHw3sxceJEXnrpJerXr09+fj4JCQnaGUyvvPIKc+bMqbTE7n7Ur1+frl27Mm3aNLZs2cL333/PqFGjCAgIwM3NjTlz5mjvPnnTp59+yptvvsm3336Li4sLiYmJldodP348mZmZvPXWW1y/fp369euzceNGfH1975iThYUFH3/8MXFxcejr69O8eXO2bNminT21ZcsWpk6dyvDhw0lNTcXJyYm2bdvi6OhIREQEn3/+Obt370alUgGwcuVKGjduzNdff82rr75Kt27dCA8PZ/r06bRp0wY9PT06d+7M4cOH+eOPPwgJCcHNzY3WrVtXmV/jxo3v+P728/Nj+/btvPvuuwQFBWFqakqLFi148cUXAZg9ezb29vbMnTuX+Ph4rKysCAgI4N13372rfzfx7125eJ5vF87QPg9duxyAgJbtGTB0HNlZatTpFbMZbewcCXl1CqFrlxO5ewuW1rb0H/wqfvWb3nWbQlS39Gvn2b32fe3zqL3ld+qtXb8DQV3HU5CXQV5WxftaaenI073fI2rvD8RGbcZMaUfzLmNx9qx4X58/UX532t2/Vwx+AQR1fZ3a9ctvYHLhxDZOH1qtPbbrt6mVYoR4UOLTMpm947D2+cqj5wBo6+3Cq60boS4oJC23QHvcQWnGpA7NWHHsLGHnErExM2FUqwY0rlUxQNXK05msgiJ+i44jM78QDxsVkzs2125ib6Svx7mUdLaeTSSvqBiViTF1Ha2Z2a0lKpPbbzsgxP1q3bQBmTm5rAnbjTo7B89azkwdNVS7Qf2NjEwUNzfXAopLSvh16y6up2VgbGxEQD1fxg1+FnPTiu1e8gsK+HlzOGmZmSjNzGjRqB4v9OiEgYG+NqZFo3qMeK4n63fuY/m6rTg72PLW8IHU9ZIbkAjxJFJoNDe37xOieu3bt49OnTqRlJSEo6MsIXmS7TktM7rE42/HkSf2uyTxBHnryoSaTkGIamcQ3KemUxCi2lkEdqvpFO5b9tGwmk6hSo/ya/qok6twUe0KCwtJTU1lxowZDBgwQAa5hBBCCCGEEEIIUS307hwiqtOcOXNQKpVVPrp3717T6T0Qv/zyCx4eHqjVaj7++GOdY6tWrbpt//39K29CWZOehH8rIYQQQgghhBDiUSZLF2tYeno66enpVR4zNTXFxcXlIWf0cGVnZ5OSklLlMUNDQzw8/jvr6p/0f6sHSZYuiieBLF0UTwJZuiieBLJ0UTwJHuVldrJ0UfydXIXXMBsbG2xsbGo6jRpjYWGBhcWjcWerJ/3fSgghhBBCCCGE+K+TpYtCCCGEEEIIIYQQ4rEgA11CCCGEEEIIIYQQ4rEgA11CCCGEEEIIIYQQ4rEgA11CCCGEEEIIIYQQ4rEgm9ELIR46uRudeBLI3ejEk+BE309rOgUhql1A/t6aTkEIIcQ9kBldQgghhBBCCCGEEOKxIANdQgghhBBCCCGEEOKxIANdQgghhBBCCCGEEOKxIANdQgghhBBCCCGEEOKxIANdQgghhBBCCCGEEOKxIANdQvyHJCYmolAoiIqKqulUhBBCCCGEEEKIR45BTScgxL8REhKCWq1m/fr1ACQlJTF9+nTCwsK4ceMGzs7O9O3bl2nTpmFra6ut1759e/bs2QOAsbEx7u7uDB8+nMmTJ6NQKO543sTERGrXrq19bmNjQ7NmzZg3bx5Nmza97/64ubmRnJyMnZ0dABEREXTo0IGMjAysrKzuu13x6NNoNJw++Cvxp3ZQVJiLnXNdmnUcjYV1rX+sFxe9hZhjGyjIzcDK3pOm7Udg6+SnPX7h5DYundtHRmo8xUX59Hv1J4yMzXXaOHP4d5ITjpKRmoC+viH9Xv2pWvoonmzbYy6y6XQCmQWFuFtbENK8Pj52VreNP5iYzJroOG7k5uNkYcaLAXVo6uKgPa7RaPg9Oo5d5y+TW1RMHXtrXm7hj7Oq4v297uR5jl9JJTE9C0N9Pb4f2KU6uygEf+4JY9/ODWRnqXF29aDXgFdw8/S9bfyJvw6wY9OvqNNTsXVwplufIdRtEKA9rtFo2Bm6miOROynIz8Pdqw59XxiFnYMzABlp19m19XcuxJwkJzsTC0trmjR/mg7dnsPAQD4GiOoRtv8wm3ZHos7KwaOWE8P7d8fXw/W28X9GnWL11t2kpqtxsrdlcM/OBNSvuFbRaDSsCdtN+MG/yMsvwK+2GyOf64mzfcW1/R879vLXmVgSr1zDwECf5XOmVGsfhRD/bTKjSzw24uPjCQwMJC4ujl9++YXz58+zZMkSwsPDadWqFenp6TrxI0eOJDk5mZiYGKZMmcK0adNYsmTJPZ1z586dJCcns23bNnJycujevTtqtfq+8i8qKkJfXx8nJye5+BSVnDu6jrioUJp1HEPnF+ZhYGjCnnWzKC0pum2dS7H7idq7DP8Wz9N10KdY2nmyd90sCvIytTElxYU4eQZQr/lzt22nrLQYV9/W+DTq9kD7JMRNfyYms/LoWZ5t5MOcHm3wsFYxN/wIWQWFVcbHXM/gi/3RdPBxZU6PNgS6ObIg4i+SMrK1MRtPxxMWc5FXWvjzQfdWGBvoMzf8CEUlpdqYktIyWno40cXPvdr7KMSJY5GErl1Oxx4DeH3yJzi5ePLDlx+Qk51ZZfzFC+dYvexzmrfuxOuTP6F+o+b89M3HXLt6SRuzZ8d6DkRspe+Lo3n17bkYGZvww5ezKS4u/91w/doVyjRl9Bs0mgnvLeCZZ0M4tG872zeueih9Fk+eA8dPsWJDGM91bc+8t8bg4eLIh0tXkpmTW2V8TMIlFq5cS8cWAcx7awzNG9Rl/rJfuZScoo3ZsGs/W/cdYuSAnnz4xkhMjIz4cOlKioqLtTHFJSW0auJP1zbNq72PQoj/PhnoEo+NsWPHYmRkxPbt22nXrh3u7u50796dnTt3cuXKFaZOnaoTb2ZmhpOTEx4eHgwfPpxGjRqxY8eOezqnra0tTk5OBAYGMn/+fFJSUjh06BAXLlygT58+ODo6olQqad68OTt37tSp6+npyezZsxk2bBgqlYpRo0bpLF1MTEykQ4cOAFhbW6NQKAgJCWHFihXY2tpSWKj7AbBv374MHTr0jjlHR0fToUMHLCwsUKlUNGvWjKNHj2qP79+/n6effhpTU1Pc3NwYP348ubnlFycrVqxAqVQSFxenjX/ttdeoW7cueXl59/Taibun0WiIi9pMvaABuHgHYWXnSVDweApy07ly4dBt68X+tRGvBl2o7d8Jla0bgZ1eRd/AmITT4dqYOgG9qde8v84sr79r0OpF6gT0xtLO44H2S4ibQs8m0NHXjfY+rrhaKRnRwh9jfX12n79cZXzYuUQa17Kjl78XrlZKnm/ih6eNJdtiLgLl/2fCzl2kX0MfAt0ccbdW8WqbRqjzCzmaVPHhaUATP3rUq427tcVD6ad4su3ftYnmbToT2KojDs6u9HtxNEZGRhz9c1eV8ZERofjVb0rbLn1wcHala68XqeVWmz/3bAXK3+cHdofSsduz1G/UHGcXDwYMHUd2ZgZnog8DUMe/KQOGjsO3XhNs7Zyo36g5bTv34VTU7X93CPFvbI74k04tm9GhRVNcnewZNaAXxkaG7D70V5XxW/YepEldH3p3bIOrkz0v9OhIbRdnwvaVv4c1Gg1b9h7i2S5tad6gLh61HBk7qB8ZmdkcOXlO287A7h15pl0r3J0dqjyPEOLJIgNd4rGQnp7Otm3beO211zA1NdU55uTkxODBg1m9ejUajaZSXY1Gw759+zh37hxGRkb3ncPN8xYVFZGTk0OPHj0IDw/n+PHjdOvWjV69enHp0iWdOvPnz6dx48YcP36c999/X+eYm5sba9euBSAmJobk5GQWLlzIgAEDKC0tZePGjdrY69evExoayssvv3zHPAcPHoyrqytHjhzh2LFjTJ48GUNDQwAuXLhAt27dePbZZzlx4gSrV69m//79jBs3DoBhw4bRo0cPBg8eTElJCaGhoXz33XesWrUKMzOz+37txD/LzUohPzcDR7eG2jIjY3Nsnfy4kRxTZZ2y0hLSUy7g5N5YW6ZQKHB0b0TabeoIURNKSstISMuigbOdtkyhUNDA2Za4VHWVdWJT1TRwttUpa+RsS9yN8vjrOfmo8wtp4FQRY25kiI+dFbE3qm5TiOpUUlLClUvx+NRtpC1TKBT41G3EpYTYKutcSojFu05DnTLfeo218elpKWRnqXViTM3McfP0vW2bAAX5uZiZK/9Nd4SoUklJKfGXr9LQz0tbplAoaOjnTWxi1V9cxCZe1okHaFTHm9iLSQBcT8tAnZVNA9+KGHNTE3w9XIlNTKqGXgghHgcy0CUeC3FxcWg0GurVq1fl8Xr16pGRkUFqaqq2bPHixSiVSoyNjWnbti1lZWWMHz/+vs6vVquZPXs2SqWSoKAgGjduzOjRo2nQoAG+vr7Mnj0bb29vncEpgI4dO/LWW2/h7e2Nt7e3zjF9fX1sbGwAcHBwwMnJCUtLS0xNTRk0aBDLli3Txv7000+4u7vTvn37O+Z66dIlOnfuTN26dfH19WXAgAE0blw+GDJ37lwGDx7MG2+8ga+vL61bt2bRokWsWLGCgoICAJYuXUpycjLjx4/nlVdeYcaMGTRr1uy25yssLCQrK0vnUVJc9XIkUbWC3AwATMysdMqNzawoyFVXWacwPwuNpgxjM0udchMzKwryMqojTSHuS3ZhEWUaDZYmul80WJoYo77N0sWsgkKsTIx1yqxMjVHnl8ff/LNym0Zk5svPH/Hw5eVmUVZWhtJC92ey0sKSnKyqfybnZGViobLSKbNQWZGdWR6fk6kub0P1tzZVlmTfps201GT+3BNG0FOyH5148LJy8ygrK8PKQncg1Uppjjorp8o6mTk5leNVSm28Orv8T8u/xVhamGuPCSHE38lAl3isVDVj61a3ztgaPHgwUVFRREZG0r17d6ZOnUrr1q3v6XytW7dGqVRibW1NdHQ0q1evxtHRkZycHCZOnEi9evWwsrJCqVRy9uzZSjO6AgMD7+l8N40cOZLt27dz5coVAJYvX05ISMhdbaT/5ptvMmLECDp37sxHH33EhQsXtMeio6NZvnw5SqVS+wgODqasrIyEhASgfBnl999/z9dff423tzeTJ0/+x/PNnTsXS0tLnceB0I/vq99Piovn9rD2qxe1j7KysppOSQghxCMuU53Gsq8+pEHTlgS1kYEuIYQQjy/Z8Vo8Fnx8fFAoFJw9e5Z+/fpVOn727Fns7e117lxoaWmJj48PAGvWrMHHx4eWLVvSuXPnuz7v6tWrqV+/Pra2tjptT5w4kR07djB//nx8fHwwNTXlueeeo6hId+Nwc3Nz7kfTpk1p3LgxK1asoGvXrpw+fZrQ0NC7qjtjxgwGDRpEaGgoW7duZfr06fz666/069ePnJwcRo8eXeXMNnf3is2a9+7di76+PsnJyeTm5mJhcfv9baZMmcKbb76pUzZn9Z0H5J5ktbyC6HrLnlllpeWbrRbkqTFV2mjLC/PUWNp7VtmGsakKhUKPwjzdTY4L8tSYmFk/+KSFuE8WxkboKRRkFuj+fMysYtbWTaoqZnup8wuxMi2Pv/lnZkER1mYmt7RZJPtxiRphZq5CT0+v0sbzOdmZKFVV/0wun5ml1inLzlJjYVker7S0Km8jKxOVZcXvhpysTJxdPXXqZWWm893CGbjX9qP/4Ff/XWeEuA2VuRl6enqVZlqpc3KxUlW9XNZSqawcn5Wjjb852yszOwcby4qf35nZuXjUcnyQ6QshHiMyo0s8FmxtbenSpQuLFy8mPz9f59i1a9dYtWoVISEht62vVCqZMGECEydOvOOssFu5ubnh7e2tM8gFEBkZSUhICP369aNhw4Y4OTmRmJh4Dz0qd3MGWmlpaaVjI0aMYPny5SxbtozOnTvj5uZ21+36+fnxf//3f2zfvp3+/ftrl0EGBARw5swZfHx8Kj1u5nLgwAHmzZvHpk2bUCqV2v27bsfY2BiVSqXzMDCs+sOrKGdoZIqFlbP2obJxw9TcmpSkk9qY4sI80q7FYudcp8o29PQNsHH0JuXSCW2ZRqPhetJJbG9TR4iaYKCvR21bFaeT07RlGo2G09fS8LW3qrKOn70Vp26JBzh5LQ1fu/J4B6UpVqbGnLpWEZNXVMz5G2r87KpuU4jqZGBggIu7FxdiKn6OazQazsecxL121TcDca/tpxMPcP7cCW28ja0jFiornZiC/DySEuN02sxUp/Ht59NxcfPiuaHj7mr2txD3w8BAHy/XWpyKS9CWaTQaTsXG4+fpWmUdP09XTsbG65SdjI3Hz6P8utbB1horlQWn4ipi8goKiLt4GT/Pu7/2FUI8WWSgSzw2vvzySwoLCwkODmbv3r0kJSURFhZGly5d8PPzY9q0af9Yf/To0cTGxmo3gP83fH19+eOPP4iKiiI6OppBgwbd1/IzDw8PFAoFmzdvJjU1lZycim+8Bg0axOXLl/n222/vahN6gPz8fMaNG0dERAQXL14kMjKSI0eOaPc2mzRpEgcOHGDcuHFERUURFxfHhg0btINZ2dnZDB06lPHjx9O9e3dWrVrF6tWr+f333++5b+LuKRQKfJv05Ozh37hy4TDqG4kc2rYQE3MbXLxbaON2r51GXFTFzD6/gN7En9pBwpldZKUlcWzXEkqKC6jt31Ebk5+bQcb1eHIykwHIvHGRjOvxFOZna2Nys1LJuB5PXvYNyspKybgeT8b1eEqKCx5C78WT4Jl6tdl1Pok9Fy5zWZ3D94dOU1BSSnvv8g9GX0VG88tfFTdR6FbXkxPJN9h8JoErmTn8Hh1HQlomwXXK7wyqUCjoVteDdSfPczQphUsZWSyOPIGVqTGBbhUzAG7k5pOYnsWN3AJKyzQkpmeRmJ5FQXHJw30BxBPhqY69OBK5k2MHd3M9+TLrf/2GosJCmrUsv8Pymh8XEbbhJ218m/bPEHs2in07N3L92hV2hq7myqV4WrXrDpS/z1t3eIZdYWs5c+IIyVcusmbFF1hYWlO/cRBQMchlaW1H9/7DyM3OJDszQ7vPlxAPWs/2rQj/8xgRh49z+Voq3/62mYKiItoHNQXgy1V/sGpzxV3Oe7RtSXTMBTbtjuRKSiprwnYTf/kq3Z4ufw8rFAp6tG3B2h17OXLqHBevpvDlqnVYW1rQvGFdbTs3MtQkXE7mhjqzfMuNy8kkXE6moFB3trAQ4skgSxfFY8PX15cjR44wY8YMnn/+ea5fv45Go6F///6sXLnyjncFtLGxYdiwYcyYMYP+/fujp3f/48ALFizg5ZdfpnXr1tjZ2TFp0iSysrLuuR0XFxdmzpzJ5MmTGT58OMOGDWP58uVA+dLLZ599ltDQUPr27XtX7enr65OWlsawYcNISUnBzs6O/v37M3PmTAAaNWrEnj17mDp1Kk8//TQajQZvb28GDhwIwIQJEzA3N2fOnDkANGzYkDlz5jB69GhatWqFi4vLPfdR3J26gf0oLSnkaPjXFBfmYlerHm37vY++QcW+c7mZKToDVO5+T1GYl8mpP3+lIC8Da/vatO37vs6m9hdObOP0odXa57t+mwpAUNfXqV2/fEDs9MFfSDizWxuz/ee3AOjw7Gwc3BpUS3/Fk6WVpzNZBUX8Fh1HZn4hHjYqJndsjuX/liCm5Ragd8sslDoO1oxr05g10XGsPh6Dk8qcN9sH4HbLssTe/l4UlZTy3cFT5BWXUMfemsmdAjEy0NfG/BYdx94LV7TPp4RGAvB+lyDqO+ne1VGIf6tRszbkZGexc/NqsrPU1HLzZPjYqdoN59XpN1Dccu3h4V2XgSET2LHpV7Zt/Bk7B2eGjHoHp1oVWwm069KX4qJC1v28hIL8PDy86zJ87HsYGpb/bjh/7gRpqddIS73GR1NH6+Qz9yv5kko8eK2bNiAzJ5c1YbtRZ+fgWcuZqaOGapcg3sjI1JlVWKe2O+OH9Gf11t38siUcJztbJg5/AXfnii8l+nR8isKiYr5Zs4m8/ALqeLnz7qghGP3vruEAq7fuZs+RKO3zSZ8uAWD62BD8fWpXc6+FEP81Cs29rNMS4hEzffp0FixYwI4dO2jZsmVNp/PAderUCX9/fxYtWlTTqdyT95bLt2vi8ffWlQk1nYIQ1e5E309rOgUhql1A/t6aTkGIamcR2K2mU7hv2UfDajqFKj3Kr+mjTmZ0icfazJkz8fT05ODBgwQFBf2rWVr/JRkZGURERBAREcHixYtrOh0hhBBCCCGEEOI/QQa6xGNv+PDh91xnzJgx/PTTT1UeGzJkCEuWLPm3af0rTZs2JSMjg3nz5lGnju7G4v7+/ly8eLHKekuXLmXw4MEPI0UhhBBCCCGEEOKhk4EuIaowa9YsJk6cWOUxlUr1kLOp7J/u4LhlyxaKi4urPOboKLdhFkIIIYQQQgjx+JKBLiGq4ODggIODQ02ncV88PDxqOgUhhBBCCCGEEKJGPB4bFgkhhBBCCCGEEEKIJ54MdAkhhBBCCCGEEEKIx4IMdAkhhBBCCCGEEEKIx4Ls0SWEeOi6NC+p6RSEqHYGDfrUdApCVLtG69+q6RSEqHbzXBbWdApCVLsPAms6AyEeHJnRJYQQQgghhBBCCCEeCzLQJYQQQgghhBBCCCEeCzLQJYQQQgghhBBCCCEeCzLQJYQQQgghhBBCCCEeCzLQJYQQQgghhBBCCCEeCzLQJYQQQgghhBBCCCEeCzLQJcRDlpiYiEKhICoqqqZTEUIIIYQQQgghHisGNZ2AEHcSEhKCWq1m/fr1ACQlJTF9+nTCwsK4ceMGzs7O9O3bl2nTpmFra6ut1759e/bs2QOAsbEx7u7uDB8+nMmTJ6NQKO543sTERGrXrq19bmNjQ7NmzZg3bx5Nmza97/64ubmRnJyMnZ0dABEREXTo0IGMjAysrKzuu13xeNNoNOwMXc2RyJ0U5Ofh7lWHvi+Mws7B+R/r/bknjH07N5CdpcbZ1YNeA17BzdNXe7y4uIgta3/kxF+RlJSU4FevMb0HjsRCZVWprbzcbBbOeYssdTrTPvkRUzPzB91N8QQL23+YTbsjUWfl4FHLieH9u+Pr4Xrb+D+jTrF6625S09U42dsyuGdnAur7aY9rNBrWhO0m/OBf5OUX4FfbjZHP9cTZvvz3RGq6mt+37+FUXDyZ2blYqyx4qllDnu3SDgMD/Wrvr3gybY+5yKbTCWQWFOJubUFI8/r42FndNv5gYjJrouO4kZuPk4UZLwbUoamLg/a4RqPh9+g4dp2/TG5RMXXsrXm5hT/Oqoqfz+tOnuf4lVQS07Mw1Nfj+4FdqrOLQgDl783TB38l/tQOigpzsXOuS7OOo7GwrvWP9eKitxBzbAMFuRlY2XvStP0IbJ0qfraXlhQRtXcZSbGRlJYW4+TRlGYdR2FiZqWNWf15v0rttur+Ju51nn5g/RNC/LfJjC7xSImPjycwMJC4uDh++eUXzp8/z5IlSwgPD6dVq1akp6frxI8cOZLk5GRiYmKYMmUK06ZNY8mSJfd0zp07d5KcnMy2bdvIycmhe/fuqNXq+8q/qKgIfX19nJycMDB4eOPMRUVFD+1conrs2bGeAxFb6fviaF59ey5Gxib88OVsiotv/2974lgkoWuX07HHAF6f/AlOLp788OUH5GRnamNC1y7n7KmjDHrlLUa+MZNMdTqrvv2kyvZ+/2kxTrU8HnjfhDhw/BQrNoTxXNf2zHtrDB4ujny4dCWZOblVxsckXGLhyrV0bBHAvLfG0LxBXeYv+5VLySnamA279rN13yFGDujJh2+MxMTIiA+XrqSouBiAyymplJWVMer5Xnz6zmsM6xvMjgNH+WXLzofSZ/Hk+TMxmZVHz/JsIx/m9GiDh7WKueFHyCoorDI+5noGX+yPpoOPK3N6tCHQzZEFEX+RlJGtjdl4Op6wmIu80sKfD7q3wthAn7nhRygqKdXGlJSW0dLDiS5+7tXeRyFuOnd0HXFRoTTrOIbOL8zDwNCEPetmUVpy++uWS7H7idq7DP8Wz9N10KdY2nmyd90sCvIqrluO7/2BqwlHafXM23R4bjb5OWlEbp5Xqa2grq/Te+QP2oeLd4tq6acQ4r9JBrrEI2Xs2LEYGRmxfft22rVrh7u7O927d2fnzp1cuXKFqVOn6sSbmZnh5OSEh4cHw4cPp1GjRuzYseOezmlra4uTkxOBgYHMnz+flJQUDh06xIULF+jTpw+Ojo4olUqaN2/Ozp26H5A8PT2ZPXs2w4YNQ6VSMWrUKJ2li4mJiXTo0AEAa2trFAoFISEhrFixAltbWwoLdS9++/bty9ChQ++Y84wZM2jSpAnfffcdtWvXxsTEBICwsDCeeuoprKyssLW1pWfPnly4cEGn7uXLl3nxxRexsbHB3NycwMBADh06pD2+YcMGAgICMDExwcvLi5kzZ1JSUnJPr6m4NxqNhgO7Q+nY7VnqN2qOs4sHA4aOIzszgzPRh29bb/+uTTRv05nAVh1xcHal34ujMTIy4uifuwDIz8vl6IFdPNM/BO86DXF19+a5oeO4GB/DpYRYnbYO7g2jIC+Xtp17V2tfxZNpc8SfdGrZjA4tmuLqZM+oAb0wNjJk96G/qozfsvcgTer60LtjG1yd7HmhR0dquzgTtq/8/4NGo2HL3kM826UtzRvUxaOWI2MH9SMjM5sjJ88B0LSeL2MH9aNxHR8c7Wxo3qAuvTu04dCJsw+t3+LJEno2gY6+brT3ccXVSsmIFv4Y6+uz+/zlKuPDziXSuJYdvfy9cLVS8nwTPzxtLNkWcxEof5+HnbtIv4Y+BLo54m6t4tU2jVDnF3I0qWLQd0ATP3rUq427tcVD6acQGo2GuKjN1AsagIt3EFZ2ngQFj6cgN50rFw7dtl7sXxvxatCF2v6dUNm6EdjpVfQNjEk4HQ5AUWEuCafDadJ2OI5uDbFx9CGo6+vcuHqOtOQYnbaMjM0xNbfWPvQNjKq1z0KI/xYZ6BKPjPT0dLZt28Zrr72GqampzjEnJycGDx7M6tWr0Wg0lepqNBr27dvHuXPnMDK6/190N89bVFRETk4OPXr0IDw8nOPHj9OtWzd69erFpUuXdOrMnz+fxo0bc/z4cd5//32dY25ubqxduxaAmJgYkpOTWbhwIQMGDKC0tJSNGzdqY69fv05oaCgvv/zyXeV6/vx51q5dyx9//KHdDyw3N5c333yTo0ePEh4ejp6eHv369aOsrAyAnJwc2rVrx5UrV9i4cSPR0dG888472uP79u1j2LBhTJgwgTNnzrB06VKWL1/Ohx9+eO8vprhr6WkpZGep8a7TUFtmamaOm6dvpQGpm0pKSrhyKR6fuo20ZQqFAp+6jbR1ribFU1paohPj4OSClbWdTrvXky+za+vvPP/S63e17FeIe1FSUkr85as09PPSlikUChr6eRObWPUAQGziZZ14gEZ1vIm9mATA9bQM1FnZNPCtiDE3NcHXw5XYxKTb5pJbUIDSzPS2x4W4XyWlZSSkZdHA2U5bplAoaOBsS1yquso6salqGjjb6pQ1crYl7kZ5/PWcfNT5hTRwqogxNzLEx86K2BtVtynEw5CblUJ+bgaObhXXLUbG5tg6+XHjbwNSN5WVlpCecgEn98baMoVCgaN7I+0gVsb1eMpKS3C8JUZl44qZhX2ldo/t+ob1S4ax45e3iT+9s8rPB0KIx5fs0SUeGXFxcWg0GurVq1fl8Xr16pGRkUFqaioODuX7VyxevJjvvvuOoqIiiouLMTExYfz48fd1frVazezZs1EqlQQFBeHo6EjjxhW/aGfPns26devYuHEj48aN05Z37NiRt956S/s8MTFR+3d9fX1sbGwAcHBw0Nmja9CgQSxbtowBAwYA8NNPP+Hu7k779u3vKt+ioiJWrFiBvb29tuzZZ5/Vifnhhx+wt7fnzJkzNGjQgJ9//pnU1FSOHDmizcvHx0cbP3PmTCZPnsxLL70EgJeXF7Nnz+add95h+vTpVeZRWFhYaWZaUVEpRkbGd9UPATmZagCUKkudcqXKkuysjCrr5OVmUVZWhtLib3UsLElNuQJAdpYafX2DSntt3dpuSXExvyz7jO79hmJlY0/6jRSEeJCycvMoKyvDykKpU26lNOdqyo0q62Tm5FSOVylRZ+UAoM4u/9PybzGWFubaY3937UY62/YdZkjvrvfVDyH+SXZhEWUaDZYmul+2WZoYczWr6iW6WQWFWJno/q60MjVGnV/+O/Xmn5XbNCIzv+rlkEI8DAW55dcQt+6bBWBsZkVBrrrKOoX5WWg0ZRib6V63mJhZkZ1xRduunr4BRsbmf4uxpCCvot0GrV7E0a0h+gbGXLsYxV+7vqGkuAC/Jj3/XceEEI8MGegSj5w7fSNz64ytwYMHM3XqVDIyMpg+fTqtW7emdevW93S+1q1bo6enR25uLl5eXqxevRpHR0dycnKYMWMGoaGhJCcnU1JSQn5+fqUZXYGBgfd0vptGjhxJ8+bNuXLlCi4uLixfvpyQkJC7nlHj4eGhM8gF5YOF06ZN49ChQ9y4cUM7U+vSpUs0aNCAqKgomjZtqh3k+rvo6GgiIyN1ZnCVlpZSUFBAXl4eZmZmlerMnTuXmTNn6pS99Oq7DB87tVKsKHf88F7W//qN9vlLr06psVzCNqzCwcmFpkHtaiwHIapbmjqLOUtX0qJxfTq3albT6QghxCPl4rk9HA2v2AP36T7v1WA24N/iee3frR28KC0pJObYBhnoEuIJIgNd4pHh4+ODQqHg7Nmz9OtX+W4qZ8+exd7eXmdWlKWlpXZG0po1a/Dx8aFly5Z07tz5rs+7evVq6tevj62trU7bEydOZMeOHcyfPx8fHx9MTU157rnnKm38bm5+f3ema9q0KY0bN2bFihV07dqV06dPExoaetf1qzpvr1698PDw4Ntvv6VWrVqUlZXRoEEDbc5/XxL6dzk5OcycOZP+/ftXOnZzH7C/mzJlCm+++aZO2aELpVXGinL1GzXHvXbFnRFv7oGWk5WJyrJiEDInKxNnV88q2zAzV6Gnp6ez8TxATnYmSpU1ABYqK0pLS8jPy9WZ1ZWTlYnF/2LiY09x7epFTh0vv2i8OdD8waThtA/uT5eeL/zL3oonncrcDD09vUozrdQ5uViplFXWsVQqK8dn5Wjjb872yszOwcayYl+izOxcPGo56tRLz8xm1uLl+Hm6MWag7EEnqoeFsRF6CgWZBbrXCJlVzNq6SWVijPpvG9Wr8wuxMi2Pv/lnZkER1mYVv4MzC4pkPy7xUNXyCqLrLXdGLCstv+lHQZ4aU2XFdUthnhpLe88q2zA2VaFQ6FGYp3vdUpCnxsSs/JrExNyastISigpzdWZ1FeRlVpo9disbR19OH1pDaUkx+gaG99o9IcQjSPboEo8MW1tbunTpwuLFi8nPz9c5du3aNVatWkVISMht6yuVSiZMmMDEiRPvaZ2+m5sb3t7eOoNcAJGRkYSEhNCvXz8aNmyIk5OTzrLEu3VzBlppaeXBnxEjRrB8+XKWLVtG586dcXNzu+f2b0pLSyMmJob33nuPTp06aZd63qpRo0ZERUVVunvlTQEBAcTExODj41PpoadX9Y8TY2NjVCqVzkOWLf4zYxNTbO2dtQ8HJ1csVFZciDmpjSnIzyMpMQ732n5VtmFgYICLu5dOHY1Gw/mYk9o6tdy80Nc30IlJTbmKOuOGNmbwyLcY/+6nvD5lPq9PmU//wa8CMOr/ZtOqXfcH3nfx5DEw0MfLtRan4hK0ZRqNhlOx8fh5ulZZx8/TlZOx8TplJ2Pj8fMo/xnpYGuNlcqCU3EVMXkFBcRdvIyfZ8XP0TR1FjO/WoaXWy1ee7Gv7EEnqo2Bvh61bVWcTk7Tlmk0Gk5fS8PX3qrKOn72Vpy6JR7g5LU0fO3K4x2UpliZGnPqWkVMXlEx52+o8bOruk0hqoOhkSkWVs7ah8rGDVNza1KSKq4vigvzSLsWi51znSrb0NM3wMbRm5RLJ7RlGo2G60knsf1fHWsHL/T0Dbh+S0xWxhXyslNv2y6A+kYiRiZKGeQS4gkiA13ikfLll19SWFhIcHAwe/fuJSkpibCwMLp06YKfnx/Tpk37x/qjR48mNjZWuwH8v+Hr66vd6D06OppBgwZplwLeCw8PDxQKBZs3byY1NZWcnIpZCoMGDeLy5ct8++23d70J/e1YW1tja2vLN998w/nz59m1a1elmVYvvvgiTk5O9O3bl8jISOLj41m7di1//vknANOmTWPFihXMnDmT06dPc/bsWX799Vfee69mp6g/7hQKBa07PMOusLWcOXGE5CsXWbPiCywsranfOEgb993CGRyI2Kp9/lTHXhyJ3Mmxg7u5nnyZ9b9+Q1FhIc1alt/p09TMnMDWHQldu5wLMSe5fOkCv6/8EvfaftqBLlt7Z5xquWsfNrbl+985OLlW2v9LiPvVs30rwv88RsTh41y+lsq3v22moKiI9kFNAfhy1R+s2lxxx9webVsSHXOBTbsjuZKSypqw3cRfvkq3p8v/PygUCnq0bcHaHXs5cuocF6+m8OWqdVhbWtC8YV2gYpDL1tqSIb26kpmTS0ZWNhlZ2Q//BRBPhGfq1WbX+ST2XLjMZXUO3x86TUFJKe29ywd0v4qM5pe/KjbU7lbXkxPJN9h8JoErmTn8Hh1HQlomwXU8gPL3ebe6Hqw7eZ6jSSlcyshiceQJrEyNCXSrmLl4IzefxPQsbuQWUFqmITE9i8T0LAqK5Y7JonooFAp8m/Tk7OHfuHLhMOobiRzathATcxtcvFto43avnUZcVMVqBb+A3sSf2kHCmV1kpSVxbNcSSooLqO3fESjf0L62fyeO711GStJJ0lPOc2T7F9g519EOhl2JP0L8qR1k3rhItjqZ89FbOXvkd3wb93i4L4IQokbJ0kXxSPH19eXIkSPMmDGD559/nuvXr6PRaOjfvz8rV66sco+oW9nY2DBs2DBmzJhB//79bzsL6W4sWLCAl19+mdatW2NnZ8ekSZPIysq653ZcXFy0m7wPHz6cYcOGsXz5cqB86eWzzz5LaGgoffv2ve9cAfT09Pj1118ZP348DRo0oE6dOixatEhnc3sjIyO2b9/OW2+9RY8ePSgpKaF+/fp89dVXAAQHB7N582ZmzZrFvHnzMDQ0pG7duowYMeJf5SburF2XvhQXFbLu5yUU5Ofh4V2X4WPfw9CwYk+69Bsp5OVWvAcbNWtDTnYWOzevJjtLTS03T4aPnYqFykob88yzIShQsOq7+ZSUlOBXrwl9Bsq/p3i4WjdtQGZOLmvCdqPOzsGzljNTRw3VLkG8kZGpM9uqTm13xg/pz+qtu/llSzhOdrZMHP4C7s4VH+77dHyKwqJivlmzibz8Aup4ufPuqCEYGZZ/o38y9gLXbqRz7UY6r878VCefNZ/p7isoxIPQytOZrIIifouOIzO/EA8bFZM7Nsfyf0sQ03IL0Lv1fe5gzbg2jVkTHcfq4zE4qcx5s30AbrcsS+zt70VRSSnfHTxFXnEJdeytmdwpECMDfW3Mb9Fx7L1wRft8SmgkAO93CaK+k+5dHYV4UOoG9qO0pJCj4V9TXJiLXa16tO33PvoGFdctuZkpFOZXfLng7vcUhXmZnPrzVwryMrC2r03bvu/rLEts2vZlFCg4EPoxZaXFOHk0JaDDKO1xPT19zkdv5fieHwBQWjnRpO1wvBrIjUaEeJIoNHKvVfGImz59OgsWLGDHjh20bNmyptN54Dp16oS/vz+LFi2q6VQemD2n82o6BSGqXUD+3ppOQYhqV7JtQ02nIES1+9RlYU2nIES1+yDE6M5B/1HZR8NqOoUqWQR2q+kUnlgyo0s88mbOnImnpycHDx4kKCjoX83S+i/JyMggIiKCiIgIFi9eXNPpCCGEEEIIIYQQ/3ky0CUeC8OHD7/nOmPGjOGnn36q8tiQIUNYsmRJlccelqZNm5KRkcG8efOoU0d3g01/f38uXrxYZb2lS5cyePDgh5GiEEIIIYQQQgjxnyIDXeKJNWvWLCZOnFjlMZVK9ZCzqeyf7uC4ZcsWiouLqzzm6OhYZbkQQgghhBBCCPG4k4Eu8cRycHDAwcGhptO4Lx4eHjWdghBCCCGEEEKIB2Du3Ln88ccfnDt3DlNTU1q3bl3lyh5xdx6PzYyEEEIIIYQQQgghHkF79uxh7NixHDx4kB07dlBcXEzXrl3Jzc2t6dQeSTKjSwghhBBCCCGEEKKGhIXp3jly+fLlODg4cOzYMdq2bVtDWT26ZKBLCCGEEEIIIYQQ4gEqLCyksLBQp8zY2BhjY+M71s3MzATAxsamWnJ73MnSRSGEEEIIIYQQQogHaO7cuVhaWuo85s6de8d6ZWVlvPHGG7Rp04YGDRo8hEwfPzKjSwghhBBCCCGEEOIBmjJlCm+++aZO2d3M5ho7diynTp1i//791ZXaY08GuoQQQgghhBBCCCEeoLtdpnircePGsXnzZvbu3Yurq2s1Zfb4k4EuIYQQQgghhBBCiBqi0Wh4/fXXWbduHREREdSuXbumU3qkyUCXEEIIIYQQQgghRA0ZO3YsP//8Mxs2bMDCwoJr164BYGlpiampaQ1n9+iRzeiFEEIIIYQQQgghasjXX39NZmYm7du3x9nZWftYvXp1Taf2SJIZXeKJkZiYSO3atTl+/DhNmjSp6XSEEEIIIYQQQgg0Gk1Np/BYkYEucVshISGo1WrWr18PQFJSEtOnTycsLIwbN27g7OxM3759mTZtGra2ttp67du3Z8+ePUD5Bnzu7u4MHz6cyZMno1Ao7njemwNSN9nY2NCsWTPmzZtH06ZN77s/bm5uJCcnY2dnB0BERAQdOnQgIyMDKyur+273YQsLC2PKlCmcO3cOGxsb+vTpw+LFi2s6LVHNNBoNO0NXcyRyJwX5ebh71aHvC6Owc3D+x3p/7glj384NZGepcXb1oNeAV3Dz9NUeLy4uYsvaHznxVyQlJSX41WtM74EjsVBZAZCXm83qZQu5dvUiuTnZKFWW1GvYnODegzAxNavOLosnTNj+w2zaHYk6KwePWk4M798dX4/bb8L6Z9QpVm/dTWq6Gid7Wwb37ExAfT/tcY1Gw5qw3YQf/Iu8/AL8arsx8rmeONuX/75KTVfz+/Y9nIqLJzM7F2uVBU81a8izXdphYKBf7f0VT6btMRfZdDqBzIJC3K0tCGleHx87q9vGH0xMZk10HDdy83GyMOPFgDo0dXHQHtdoNPweHceu85fJLSqmjr01L7fwx1llro1Zd/I8x6+kkpiehaG+Ht8P7FKdXRQCKH9vnj74K/GndlBUmIudc12adRyNhXWtf6wXF72FmGMbKMjNwMrek6btR2DrVPGzvbSkiKi9y0iKjaS0tBgnj6Y06zgKEzMrAArzszkY9hmZNy5SWJCFiZkVtbya06j1EAyN5bpFiCeFLF0UdyU+Pp7AwEDi4uL45ZdfOH/+PEuWLCE8PJxWrVqRnp6uEz9y5EiSk5OJiYlhypQpTJs2jSVLltzTOXfu3ElycjLbtm0jJyeH7t27o1ar7yv/oqIi9PX1cXJywsDg0R3fLSgooH///jRq1IiTJ08SGhp617PTiouLqzc5Ua327FjPgYit9H1xNK++PRcjYxN++HI2xcVFt61z4lgkoWuX07HHAF6f/AlOLp788OUH5GRnamNC1y7n7KmjDHrlLUa+MZNMdTqrvv2kohGFgnqNAhk6ehJvTV/Ec0PGcv7cCdb/srQ6uyueMAeOn2LFhjCe69qeeW+NwcPFkQ+XriQzJ7fK+JiESyxcuZaOLQKY99YYmjeoy/xlv3IpOUUbs2HXfrbuO8TIAT358I2RmBgZ8eHSlRT972fh5ZRUysrKGPV8Lz595zWG9Q1mx4Gj/LJl50Pps3jy/JmYzMqjZ3m2kQ9zerTBw1rF3PAjZBUUVhkfcz2DL/ZH08HHlTk92hDo5siCiL9IysjWxmw8HU9YzEVeaeHPB91bYWygz9zwIxSVlGpjSkrLaOnhRBc/92rvoxA3nTu6jrioUJp1HEPnF+ZhYGjCnnWzKC25/XXLpdj9RO1dhn+L5+k66FMs7TzZu24WBXkV1y3H9/7A1YSjtHrmbTo8N5v8nDQiN8/THlcoFLh4NeepXpPp8dJXBHV5nZRL0Rzd9XW19lcI8d8iA13irowdOxYjIyO2b99Ou3btcHd3p3v37uzcuZMrV64wdepUnXgzMzOcnJzw8PBg+PDhNGrUiB07dtzTOW1tbXFyciIwMJD58+eTkpLCoUOHuHDhAn369MHR0RGlUknz5s3ZuVP3g4mnpyezZ89m2LBhqFQqRo0aRWJiIgqFgqioKBITE+nQoQMA1tbWKBQKQkJCWLFiBba2thQW6l509u3bl6FDh94x5+joaDp06ICFhQUqlYpmzZpx9OhR7fH9+/fz9NNPY2pqipubG+PHjyc3t/yD3IoVK1AqlcTFxWnjX3vtNerWrUteXp62TF9fn8GDB+Pj40OTJk0YNWpUpTxu9nX16tW0a9cOExMTVq1aRVpaGi+++CIuLi6YmZnRsGFDfvnlF526ZWVlfPzxx/j4+Ghn5H344Yfa40lJSTz//PNYWVlpZ5QlJibe8bUR90+j0XBgdygduz1L/UbNcXbxYMDQcWRnZnAm+vBt6+3ftYnmbToT2KojDs6u9HtxNEZGRhz9cxcA+Xm5HD2wi2f6h+BdpyGu7t48N3QcF+NjuJQQC4CZmZKWbbvh6uGDta0DPnUb0apdNxLjzz2Uvosnw+aIP+nUshkdWjTF1cmeUQN6YWxkyO5Df1UZv2XvQZrU9aF3xza4OtnzQo+O1HZxJmxf+f8HjUbDlr2HeLZLW5o3qItHLUfGDupHRmY2R06Wv3eb1vNl7KB+NK7jg6OdDc0b1KV3hzYcOnH2ofVbPFlCzybQ0deN9j6uuFopGdHCH2N9fXafv1xlfNi5RBrXsqOXvxeuVkqeb+KHp40l22IuAuXv87BzF+nX0IdAN0fcrVW82qYR6vxCjiZVDPoOaOJHj3q1cbe2eCj9FEKj0RAXtZl6QQNw8Q7Cys6ToODxFOSmc+XCodvWi/1rI14NulDbvxMqWzcCO72KvoExCafDASgqzCXhdDhN2g7H0a0hNo4+BHV9nRtXz5GWHAOAkYkSn8bdsXHyxVzlgKN7I3wad+fGVbluEeJJIgNd4o7S09PZtm0br732WqU7Pjg5OTF48GBWr15d5bpijUbDvn37OHfuHEZGRvedw83zFhUVkZOTQ48ePQgPD+f48eN069aNXr16cenSJZ068+fPp3Hjxhw/fpz3339f55ibmxtr164FICYmhuTkZBYuXMiAAQMoLS1l48aN2tjr168TGhrKyy+/fMc8Bw8ejKurK0eOHOHYsWNMnjwZQ0NDAC5cuEC3bt149tlnOXHiBKtXr2b//v2MGzcOgGHDhtGjRw8GDx5MSUkJoaGhfPfdd6xatQozs/Kp1iYmJgQHB/POO+9UmkVXlcmTJzNhwgTOnj1LcHAwBQUFNGvWjNDQUE6dOsWoUaMYOnQohw9XDJZMmTKFjz76iPfff58zZ87w888/4+joCJTPCgsODsbCwoJ9+/YRGRmJUqmkW7duFBXd/hs68e+kp6WQnaXGu05DbZmpmTlunr7aAam/Kykp4cqleHzqNtKWKRQKfOo20ta5mhRPaWmJToyDkwtW1na3bTcrM51Txw9S26f+g+iaEJSUlBJ/+SoN/by0ZQqFgoZ+3sQmVj0AEJt4WSceoFEdb2IvJgFwPS0DdVY2DXwrYsxNTfD1cCU2Mem2ueQWFKA0kzsbiQevpLSMhLQsGjjbacsUCgUNnG2JS1VXWSc2VU0DZ1udskbOtsTdKI+/npOPOr+QBk4VMeZGhvjYWRF7o+o2hXgYcrNSyM/NwNGt4rrFyNgcWyc/bvxvQOrvykpLSE+5gJN7Y22ZQqHA0b2RdhAr43o8ZaUlON4So7JxxczC/rbt5uekcyXuIPYuct0ixJPk0V3DJR6auLg4NBoN9erVq/J4vXr1yMjIIDU1FQeH8n0jFi9ezHfffUdRURHFxcWYmJgwfvz4+zq/Wq1m9uzZKJVKgoKCcHR0pHHjil9ws2fPZt26dWzcuFE7aATQsWNH3nrrLe3zW2cd6evrY2NjA4CDg4POHl2DBg1i2bJlDBgwAICffvoJd3d32rdvf8dcL126xNtvv03dunUB8PWt2Atp7ty5DB48mDfeeEN7bNGiRbRr146vv/4aExMTli5dSqNGjRg/fjx//PEHM2bMoFmzZto2Zs6cyfHjx3nhhRdo164d27Zto1at8r0OXn/9dRISEti8ebM2/o033qB///46OU6cOFH799dff51t27axZs0agoKCyM7OZuHChXz55Ze89NJLAHh7e/PUU08BsHr1asrKyvjuu++0+60tW7YMKysrIiIi6Nq16x1fI3HvcjLVAChVljrlSpUl2VkZVdbJy82irKwMpcXf6lhYkppyBYDsLDX6+gaYmpnrxlTR7q8/fMaZE0coLi6iXsNA+g9+9d90SQitrNw8ysrKsLJQ6pRbKc25mnKjyjqZOTmV41VK1Fk5AKizy/+0/FuMpYW59tjfXbuRzrZ9hxnSW36OiQcvu7CIMo0GSxPdL/0sTYy5mlX1Et2sgkKsTIx1yqxMjVHnl886v/ln5TaNyMyvejmkEA9DQW75NcTNfbNuMjazoiBXXWWdwvwsNJoyjM10r1tMzKzIzriibVdP3wAjY/O/xVhSkKfb7p9bP+XKhcOUlhRRy6s5zTuPvf8OCSEeOTLQJe7ane4EceuMrcGDBzN16lQyMjKYPn06rVu3pnXr1vd0vtatW6Onp0dubi5eXl6sXr0aR0dHcnJymDFjBqGhoSQnJ1NSUkJ+fn6lGV2BgYH3dL6bRo4cSfPmzbly5QouLi4sX76ckJCQu9pI/80332TEiBGsXLmSzp07M2DAALy9vYHyZY0nTpxg1apV2niNRkNZWRkJCQnUq1cPa2trvv/+e4KDg2ndujWTJ0/WxmZkZDB37lz++OMPevTogb6+Pm3atGH79u34+vpy8uRJunfv/o+vQWlpKXPmzGHNmjVcuXKFoqIiCgsLtTPGzp49S2FhIZ06daqyf9HR0Zw/fx4LC93lDwUFBVy4cKHKOoWFhZWWghYVlWJkZFxlvIDjh/ey/tdvtM9fenVKDWZT7plnQ+jY43luXL/Ktg2r2LL2R/q8MLKm0xLigUhTZzFn6UpaNK5P51bN7lxBCCGE1sVzezgaXrEX79N93qvBbMo1afsy/i0Gkp1xlRORK4nau4xmHUfXdFpCiIdEBrrEHfn4+KBQKDh79iz9+vWrdPzs2bPY29vrzIqytLTEx8cHgDVr1uDj40PLli3p3LnzXZ939erV1K9fH1tbW522J06cyI4dO5g/fz4+Pj6Ympry3HPPVVo6Z25uzv1o2rQpjRs3ZsWKFXTt2pXTp08TGhp6V3VnzJjBoEGDCA0NZevWrUyfPp1ff/2Vfv36kZOTw+jRo6uc2ebuXrFB7N69e9HX1yc5OZnc3FztoFJMTAyFhYXaO0/OmjWLrKwsnnrqKT7//HMOHjyoM4hW1WvwySefsHDhQj7//HMaNmyIubk5b7zxhva1+/vS1L/LycmhWbNmlc4DYG9vX2WduXPnMnPmTJ2yl159l+Fjp1YZL6B+o+a4166YDVhSUgJATlYmKksbbXlOVibOrp5VtmFmrkJPT09n43mAnOxMlCprACxUVpSWlpCfl6szqysnKxOL/8XcZGFpjYWlNQ5OLpiZK1m64H06dH9WJx8h7ofK3Aw9Pb1KM63UOblYqZRV1rFUKivHZ+Vo42/O9srMzsHGsmJgPjM7F49ajjr10jOzmbV4OX6ebowZ2Ptf90eIqlgYG6GnUJBZoHutklnFrK2bVCbGqP+2Ub06vxAr0/L4m39mFhRhbWZyS5tFsh+XeKhqeQXR9ZY7I5aVlt/0oyBPjamy4jqhME+Npb1nlW0Ym6pQKPQozNO9binIU2NiVn5NYmJuTVlpCUWFuTqzugryMivNHjM1t8bU3BqVjStGJhbs+u1d6gcN0MlHCPH4kj26xB3Z2trSpUsXFi9eTH5+vs6xa9eusWrVKkJCQm5bX6lUMmHCBCZOnHjHWWG3cnNzw9vbW2eQCyAyMpKQkBD69etHw4YNcXJyuq/N0G/OQCstLa10bMSIESxfvpxly5bRuXNn3Nzc7rpdPz8//u///o/t27fTv39/li1bBkBAQABnzpzBx8en0uNmLgcOHGDevHls2rQJpVKpsxTTxcUFKB8Iu+mzzz6jZ8+eDBo0iNGjR2tjbicyMpI+ffowZMgQGjdujJeXF7GxFXsx+fr6YmpqSnh4eJX1AwICiIuLw8HBoVIfLC0tq6wzZcoUMjMzdR6DR06sMlaUMzYxxdbeWftwcHLFQmXFhZiT2piC/DySEuNwr+1XZRsGBga4uHvp1NFoNJyPOamtU8vNC319A52Y1JSrqDNu3LZdKL9hAUDp/wbghPg3DAz08XKtxam4BG2ZRqPhVGw8fp6uVdbx83TlZGy8TtnJ2Hj8PMp/VjvYWmOlsuBUXEVMXkEBcRcv4+dZ8fM8TZ3FzK+W4eVWi9de7HtXM3eFuB8G+nrUtlVxOjlNW6bRaDh9LQ1fe6sq6/jZW3HqlniAk9fS8LUrj3dQmmJlasypaxUxeUXFnL+hxs+u6jaFqA6GRqZYWDlrHyobN0zNrUlJqri+KC7MI+1aLHbOdapsQ0/fABtHb1IundCWaTQariedxPZ/dawdvNDTN+D6LTFZGVfIy069bbvl7ZRft5SVyXWLEE8KGegSd+XLL7+ksLCQ4OBg9u7dS1JSEmFhYXTp0gU/Pz+mTZv2j/VHjx5NbGysdgP4f8PX15c//viDqKgooqOjGTRokPaD973w8PBAoVCwefNmUlNTycmpmB0waNAgLl++zLfffntXm9AD5OfnM27cOCIiIrh48SKRkZEcOXJEu7fZpEmTOHDgAOPGjSMqKoq4uDg2bNigHczKzs5m6NChjB8/nu7du7Nq1SpWr17N77//DpQP/L3wwguMHTuWFStWcOHCBcLDw7lw4QLm5uZs3LiR69ev3/G127FjBwcOHODs2bOMHj2alJSKOzOZmJgwadIk3nnnHe05Dh48yPfffw+UL0m1s7OjT58+7Nu3j4SEBCIiIhg/fjyXL1e9abSxsTEqlUrnIcsW741CoaB1h2fYFbaWMyeOkHzlImtWfIGFpTX1Gwdp475bOIMDEVu1z5/q2IsjkTs5dnA315Mvs/7XbygqLKRZy/I7jpqamRPYuiOha5dzIeYkly9d4PeVX+Je20870HXu1F8c/XMX165eIiPtOudOHmPDr9/i4VUHa1uHh/tCiMdWz/atCP/zGBGHj3P5Wirf/raZgqIi2geVz2D9ctUfrNpccefeHm1bEh1zgU27I7mSksqasN3EX75Kt6fL/z8oFAp6tG3B2h17OXLqHBevpvDlqnVYW1rQvGH5Hoo3B7lsrS0Z0qsrmTm5ZGRlk5GV/fBfAPFEeKZebXadT2LPhctcVufw/aHTFJSU0t67fED3q8hofvmrYkPtbnU9OZF8g81nEriSmcPv0XEkpGUSXMcDKH+fd6vrwbqT5zmalMKljCwWR57AytSYQLeKmYs3cvNJTM/iRm4BpWUaEtOzSEzPoqBYPvSL6qFQKPBt0pOzh3/jyoXDqG8kcmjbQkzMbXDxbqGN2712GnFRFasm/AJ6E39qBwlndpGVlsSxXUsoKS6gtn9HoHxD+9r+nTi+dxkpSSdJTznPke1fYOdcRzsYdjXhGAmnw8m8cZHcrOtcjT/KsV1LsKtVF3OVXLcI8aSQpYvirvj6+nLkyBFmzJjB888/z/Xr19FoNPTv35+VK1dq93i6HRsbG4YNG8aMGTPo378/enr3P8a6YMECXn75ZVq3bo2dnR2TJk0iKyvrnttxcXFh5syZTJ48meHDhzNs2DCWL18OlC+9fPbZZwkNDaVv37531Z6+vj5paWkMGzaMlJQU7Ozs6N+/v3bZXqNGjdizZw9Tp07l6aefRqPR4O3tzcCBAwGYMGEC5ubmzJkzB4CGDRsyZ84cRo8eTatWrXBxceHHH39k/vz5fPjhh1y8eBEXFxeGDBnC2rVr6dSpE71792b37t23zfG9994jPj6e4OBgzMzMGDVqFH379iUzs2Ka+Pvvv4+BgQHTpk3j6tWrODs7M2bMGADMzMzYu3cvkyZNon///mRnZ+Pi4kKnTp1QqVT3+k8g7kG7Ln0pLipk3c9LKMjPw8O7LsPHvoehYcXeeOk3UsjLrfi/0KhZG3Kys9i5eTXZWWpquXkyfOxULFRW2phnng1BgYJV382npKQEv3pN6DNwhPa4oaEhRyJ3Err2R0pLirG0tsW/SQvadam8jFmI+9W6aQMyc3JZE7YbdXYOnrWcmTpqqHYJ4o2MTJ3ZVnVquzN+SH9Wb93NL1vCcbKzZeLwF3B3rvhw36fjUxQWFfPNmk3k5RdQx8udd0cNweh/d8I9GXuBazfSuXYjnVdnfqqTz5rPdJdbC/EgtPJ0JqugiN+i48jML8TDRsXkjs2x/N8SxLTcAvRufZ87WDOuTWPWRMex+ngMTipz3mwfgNstyxJ7+3tRVFLKdwdPkVdcQh17ayZ3CsTIQF8b81t0HHsvXNE+nxIaCcD7XYKo76R7V0chHpS6gf0oLSnkaPjXFBfmYlerHm37vY++QcV1S25mCoX5FV8uuPs9RWFeJqf+/JWCvAys7WvTtu/7OssSm7Z9GQUKDoR+TFlpMU4eTQnoMEp7XN/AiPhTO4jau4zS0mLMlLa4+rSibnPdmzMJIR5vCs29rCUT4hbTp09nwYIF7Nixg5YtW9Z0Og9cp06d8Pf3Z9GiRTWdymNnz+m8mk5BiGoXkL/3zkFCPOJKtm2o6RSEqHafuiys6RSEqHYfhBjdOeg/KvtoWE2nUCWLwG41ncITS2Z0ifs2c+ZMPD09OXjwIEFBQf9qltZ/SUZGBhEREURERLB48eKaTkcIIYQQQgghhBB3SQa6xL8yfPjwe64zZswYfvrppyqPDRkyhCVLllR57GFp2rQpGRkZzJs3jzp1dDe29Pf35+LFi1XWW7p0KYMHD34YKQohhBBCCCGEEKIKMtAlHrpZs2YxcWLVd937L+zz9E93cNyyZQvFxcVVHnN0dKyyXAghhBBCCCGEEA+HDHSJh87BwQEHh0fzriceHh41nYIQQgghhBBCCCFu4/HYVEkIIYQQQgghhBBCPPFkoEsIIYQQQgghhBBCPBZkoEsIIYQQQgghhBBCPBZkoEsIIYQQQgghhBBCPBZkM3ohxEO344j86BGPv0ZXNtR0CkJUO4PgPjWdghDVrotpSU2nIMRDYFTTCQjxwMiMLiGEEEIIIYQQQgjxWJCBLiGEEEIIIYQQQgjxWJCBLiGEEEIIIYQQQgjxWJCBLiGEEEIIIYQQQgjxWJCBLiGEEEIIIYQQQgjxWJCBLiFuMWPGDJo0afLE5yCEEEIIIYQQQjyKDGo6ASEehNTUVKZNm0ZoaCgpKSlYW1vTuHFjpk2bRps2bVAoFKxbt46+ffs+sHPOmDGDmTNnAqCvr4+rqyv9+vVj9uzZKJXK+2534sSJvP7669rnISEhqNVq1q9f/29TFo8wjUbD6YO/En9qB0WFudg516VZx9FYWNf6x3px0VuIObaBgtwMrOw9adp+BLZOftrjpSVFRO1dRlJsJKWlxTh5NKVZx1GYmFkBkHBmF4e3f1Fl231GLcfEzPKB9VE82bbHXGTT6QQyCwpxt7YgpHl9fOysbht/MDGZNdFx3MjNx8nCjBcD6tDUxUF7XKPR8Ht0HLvOXya3qJg69ta83MIfZ5W5NmbdyfMcv5JKYnoWhvp6fD+wS3V2UQjC9h9m0+5I1Fk5eNRyYnj/7vh6uN42/s+oU6zeupvUdDVO9rYM7tmZgPoVP8M1Gg1rwnYTfvAv8vIL8KvtxsjneuJsb6uN+WPHXv46E0vilWsYGOizfM6Uau2jEFD+3twZupojkTspyM/D3asOfV8YhZ2D8z/W+3NPGPt2biA7S42zqwe9BryCm6ev9nhxcRFb1v7Iib8iKSkpwa9eY3oPHImFykobM2Xsc5XafWH4GzQOfOqB9U8I8d8mM7rEY+HZZ5/l+PHj/Pjjj8TGxrJx40bat29PWlpatZ7X39+f5ORkEhMTmTdvHt988w1vvfXWfbWl0WgoKSlBqVRia2t75wriiXLu6DriokJp1nEMnV+Yh4GhCXvWzaK0pOi2dS7F7idq7zL8WzxP10GfYmnnyd51syjIy9TGHN/7A1cTjtLqmbfp8Nxs8nPSiNw8T3vc3e8peo/8Qefh5NEUBxd/GeQSD8yficmsPHqWZxv5MKdHGzysVcwNP0JWQWGV8THXM/hifzQdfFyZ06MNgW6OLIj4i6SMbG3MxtPxhMVc5JUW/nzQvRXGBvrMDT9CUUmpNqaktIyWHk508XOv9j4KceD4KVZsCOO5ru2Z99YYPFwc+XDpSjJzcquMj0m4xMKVa+nYIoB5b42heYO6zF/2K5eSU7QxG3btZ+u+Q4wc0JMP3xiJiZERHy5dSVFxsTamuKSEVk386dqmebX3UYib9uxYz4GIrfR9cTSvvj0XI2MTfvhyNsXFt79uOXEsktC1y+nYYwCvT/4EJxdPfvjyA3KyK65bQtcu5+ypowx65S1GvjGTTHU6q779pFJbzw0dy7tzvtU+6jcOqpZ+CiH+m2SgSzzy1Go1+/btY968eXTo0AEPDw+CgoKYMmUKvXv3xtPTE4B+/fqhUCi0zwE++ugjHB0dsbCw4JVXXqGgoOCezm1gYICTkxOurq4MHDiQwYMHs3HjRgBWrlxJYGAgFhYWODk5MWjQIK5fv66tGxERgUKhYOvWrTRr1gxjY2P279+vs3RxxowZ/Pjjj2zYsAGFQoFCoSAiIoKOHTsybtw4nVxSU1MxMjIiPDz8jnkvXrwYX19fTExMcHR05LnnKr75KisrY+7cudSuXRtTU1MaN27M77//DpQPxnXu3Jng4GA0Gg0A6enpuLq6Mm3atHt67cTd02g0xEVtpl7QAFy8g7Cy8yQoeDwFuelcuXDotvVi/9qIV4Mu1PbvhMrWjcBOr6JvYEzC6fL3SFFhLgmnw2nSdjiObg2xcfQhqOvr3Lh6jrTkGAD0DYwwNbfWPhQKPa5fPkntBp0fSt/FkyH0bAIdfd1o7+OKq5WSES38MdbXZ/f5y1XGh51LpHEtO3r5e+FqpeT5Jn542liyLeYiUP5/5v/Zu++4qqv/geOvyx73spG9lxMUcSDlQEnT3KnZNFHLsqVWas40R2XDlmU5Kv2lfcuRMxcO3ANxICiIOBAR2ePChfv7g7x2BbdI6vv5eHwe3+89n/c5n/e5wfVyPuecz9rjp+nZyJ8wDyc8ba0YGhFMTrGafWeuDhL0aRxI53o+eNqq7ks/xaNtZcxO2rdsSrsWTXB3dmRIn66YmhizefeBauNXb91F47r+dIuMwN3ZkWc6R+Lj5sLabXuAyp/z1Vt30zuqNc0a1sXL1YnXn+1Jdm4+ew8f17XT78lIurQJx9OlTrXXEeJe02q17Ni8ishOvakf3AwXNy/6vDCM/Nxsjh3ac9162zf9RbOIDoSFR1LHxZ2e/V/BxMSEfTs3AVBcVMi+HZvo0msAfkGNcPf04+kXhnE6JZG0U0l6bZmZW6KyttUdxsYmNdpnIcR/iwx0iQeeUqlEqVSybNky1Oqqd//37t0LwLx580hPT9e9XrJkCRMnTmTq1Kns27cPFxcXvv3227vKxdzcnNLSyjtVZWVlTJ48mUOHDrFs2TJSU1MZMGBAlTqjRo1i+vTpJCQkEBwcrHdu5MiR9O3bl06dOpGenk56ejqtWrVi0KBBLFq0SK+/v/76K25ubkRGRt4wx3379vHmm2/y4YcfkpiYyNq1a2ndurXu/LRp0/j555+ZPXs2R48e5Z133uH5559ny5YtKBQKFixYwN69e5k1axYAr776Km5ubjLQVYMK8zIoLszGyaORrszE1BJ750Au/TMgda2Kcg2XM5Jx9gzRlSkUCpw8g3WDWNkXU6go1+D0rxgrO3csVI7XbTc1IQYjI1M8Alrdi64Jgaa8glNZeTR0cdCVKRQKGrrYcyIzp9o6SZk5NHTRn/ka7GLPiUuV8RcLiskpVtPQ+WqMpYkx/g42JF2qvk0hapJGU07K2fM0CvTVlSkUChoF+pGUWv2AblLqWb14gOAgP5JOnwHgYlY2OXn5NAy4GmNpbkaAlztJqWdqoBdC3JrLWRnk5+XgF3T1e4u5hSUe3gFVBqSu0Gg0nEtLwb/u1e/CCoUC/7rBujrnz6RQXq7Ri6nj7IaNrUOVdlcsnsPk9wbwzcfvs2/HRt0NWiHEo0H26BIPPCMjI+bPn8/gwYOZPXs2oaGhtGnThmeeeYbg4GAcHR0BsLGxwdnZWVfviy++IDo6mujoaACmTJnChg0bbntW1xX79+9n0aJFuoGmgQMH6s75+voya9YsmjVrRkFBgd4eXh9++CFRUdXvC6NUKjE3N0etVuvl3qtXL4YNG8by5cvp27cvAPPnz2fAgAEoFIob5pmWloalpSVPPfUUKpUKLy8vmjRpAoBarWbq1Kls2LCB8PBwXe7bt2/n+++/p02bNri5ufH999/z4osvcuHCBVavXs3BgwcxMqr+40StVlcZgNSUKTAyNr1hnuKqksJsAN2+WVeYWthQUphTbR11cR5abQWm1ywvNLOwIT/7nK5dA0MjTEwtr4mxpqSo+nZPHd2AZ9DjGBrJnVFxb+SrS6nQarE20/+ZsjYz5Xxe9Uu68krU2Jjpf4bYmJuSU1z5WXPlf6u2aUJucfXLIYWoSXmFRVRUVGCj0t/D00ZpyfmMS9XWyS0oqBpvpSQnrwCAnPzK/7W+JsZaZak7J0RtKMjNAUBppf8dRGllTX5edrV1igrzqKioQKm6po7KmsyMyu8t+Xk5GBoaYW6h/73l2nY7dOmHf1AjjE1MSUqIY/niHyktVdOqbee77ZoQ4gEhM7rEQ6F3796cP3+eFStW0KlTJ2JiYggNDWX+/PnXrZOQkECLFi30yq4M7tyqw4cP6wajmjdvTnh4OF9//TVQOfDVtWtXPD09UalUtGnTBqgcaPq3sLCw27omgJmZGS+88AJz584F4MCBAxw5cqTaGWPXioqKwsvLC19fX1544QUWLlxIUVERACdPnqSoqIioqCjdTDmlUsnPP/9McnKyro0+ffrQs2dPpk+fzqeffkpAQMD1Lse0adOwtrbWO3as+vi2+/woOX18C3980193VFRU1HZKAFxKP07e5bP4NGhf26kIIYQQ4j/i4J6tTBj+vO4oryi/eaUa1L5zH7z86uLq4UPbJ3rSOqo7Wzcsr9WchBD3l8zoEg8NMzMzoqKiiIqKYty4cQwaNIgJEybc0uDPnQoKCmLFihUYGRnh6uqKiUnl7IHCwkI6duxIx44dWbhwIY6OjqSlpdGxY0fd0sYrLC0tq2v6pgYNGkTjxo05e/Ys8+bNIzIyEi8vr5vWU6lUHDhwgJiYGP7++2/Gjx/PxIkT2bt3LwUFlXeAV61ahZubm149U9OrsyeKiorYv38/hoaGnDhx4obXGz16NMOHD9crm7r4xrPOHnWuvs154l9PRqwor9xUuKQoB3Olna5cXZSDtaN3tW2YmluhUBig/tfG81faMLOwBcDM0paKcg2l6kK9WV0lRblVZo8BnDqyAVtHH+yc/O+0a0JUoTI1wUChILdE/7Mxt5pZW1dYmZmSc81G9TnFamzMK+Ov/G9uSSm2Fmb/arNU9uMStcLK0gIDA4MqM61yCgqxsar+Sc3WSmXV+LwCXfyV2V65+QXYWV/9uc7NL8TL1elepi/EDdUPboanz9WbnhqNBoCCvFysrK9+bynIy8XF3bvaNiwsrTAwMNDbeB6gID8XpVXl9xaVlQ3l5RqKiwr1ZnUV5OWi+iemOh5eAWxa8z80ZWUYGRvfdv+EEA8emdElHlr169ensLBy2YuxsTHl5fp3l+rVq8fu3fobee/ateu2rmFiYoK/vz/e3t66QS6A48ePk5WVxfTp03n88cepW7eu3kb0t3uNa3MHaNSoEWFhYcyZM4dFixbpLZW8GSMjIzp06MDHH39MfHw8qampbNq0ifr162NqakpaWhr+/v56h4eHh67+iBEjMDAwYM2aNcyaNYtNmzZd91qmpqZYWVnpHbJs8caMTcxR2bjoDis7D8wtbck4c1gXU6YuIutCEg4uQdW2YWBohJ2THxlp8boyrVbLxTOHsf+njm0dXwwMjbj4r5i87HMU5WdWaVdTVkJaUqzM5hL3nJGhAT72VhxNv/qUXK1Wy9ELWQQ42lRbJ9DRhiPp+k/VPXwhiwCHyvg6SnNszE05cuFqTFFpGScv5RDoUH2bQtQkIyNDfN1dOXLilK5Mq9VyJCmFQG/3ausEertzOClFr+xwUgqBXpX/Htext8XGSsWRE1djikpKOHH6LIHeHghxv5iamWPv6KI76ji7o7KyITnx6veWkuIizqSewNMnsNo2jIyMcPP01auj1Wo5mXhYV8fVwxdDQyO9mMyM8+RkX7puuwDp51Ixt7CUQS4hHiEyo0s88LKysujTpw8DBw4kODgYlUrFvn37+Pjjj+nevTsA3t7ebNy4kYiICExNTbG1teWtt95iwIABhIWFERERwcKFCzl69Ci+vr43ueLNeXp6YmJiwldffcWrr77KkSNHmDx58h215e3tzbp160hMTMTe3h5ra2uM//mHetCgQQwbNgxLS0t69ux5S+2tXLmSlJQUWrduja2tLatXr6aiooKgoCBUKhUjR47knXfeoaKigscee4zc3FxiY2OxsrLipZdeYtWqVcydO5edO3cSGhrKu+++y0svvUR8fDy2tte/mybunEKhIKDxUyTs+R2VjQuW1nU4suP/MLO0w83v6vLbzX+Mx92vBQGNuwAQGNqNPetmYevkh71TAElxK9GUleDToHIfORNTS3watOfg1nkYmykxNjHnYMyPOLgE6QbDrkhL2o5WW4FXvbb3rd/i0dGlng/f7YjHx94KP3sb1h5PpURTTlu/ygGAb2IPYWduRv/Qyp/LTnW9mbx+NyuPnaKJmyM7U9M5lZXL4BYNgcrfmU51vVh6+CTOKgvqKM1ZEncCG3NTwjyuznS5VFhMgbqMS4UllFdoSb2cB4CzygIzY/mKJO6tp9qG882ipfi6u+Dv6c7qrbsoKS2lbfPKfTK/XvgnttYqnnuqct/Ozq1bMvGb+fy1OZbQ+oHEHjxCytnzDOnbFaj8Oe/cugV/rN+Ks6M9dexsWbxmE7bWKpo1qqu77qXsHPILi7mUk0tFRQWnzqYD4OJoj5mp7Lco7j2FQkGrdl3YtPYP7Ou4YGtfh/Urf0NlbUv9kOa6uB+/nEj9kBa0avskAI9FduX3n7/GzdMXD68AYmNWUapW07RlO6ByQ/uwVpGs+mM+5haWmJpb8NeSn/D0CdQNdCUc3kdBXg4ePoEYGxtzIuEQm9f9yePtu97/N0IIUWvkW5x44CmVSlq0aMHnn39OcnIyZWVleHh4MHjwYMaMGQPAzJkzGT58OHPmzMHNzY3U1FT69etHcnIy7733HiUlJfTu3ZuhQ4eybt26u87J0dGR+fPnM2bMGGbNmkVoaCiffvop3bp1u+22Bg8eTExMDGFhYRQUFLB582batm0LQP/+/Xn77bfp378/ZmZmN27oHzY2Nvz5559MnDiRkpISAgIC+L//+z8aNGgAwOTJk3F0dGTatGmkpKRgY2NDaGgoY8aMITMzk+joaCZOnEhoaCgAkyZN4u+//+bVV19l8eLFt90/cWvqhvWkXKNm38bvKFMX4uBaj9Y9x+ltCl+Ym4G6OF/32jPwMdRFuRzZ+RslRdnYOvrQusc4vWWJTVoPRIGCHas+pqK8DGevJoS2G1Ll+qeObMDdr0WVjeuFuBfCvV3IKynl90MnyC1W42VnxajIZlj/swQxq7AEg389aCOoji3DIkJYcugEiw8m4mxlyfC2oXj8a1litwa+lGrK+XHXEYrKNAQ52jKqfRgmRoa6mN8PnWBr8jnd69GrYgEYF9Wc+s76T3UU4m61atKQ3IJClqzdTE5+Ad6uLnww5AXdEsRL2bl6D5QJ8vHkzed7sXjNZv5v9UacHewZ+fIzeLpcHaztHvkY6tIyfljyF0XFJQT5ejJmyPOY/GvmyuI1m9myN073+v2ZswGY8PoAGvj71HCvxaOqTVQPykrVLF00m5LiIrz86vLy62MxNr76veXypQyKCvN0r4ObRlCQn8eGlYvJz8vB1cObl1//AJWVjS6mS+8BKFCw8MdP0Wg0BNZrTPd+g3TnDQwM2bV1LSv/mA+AvYMzXXq/RPOI6h/8JIR4OCm08qxVIR5Yqamp+Pn5sXfvXt3A04Ng7PzSmwcJ8YAbce6t2k5BiBpn1LF7bacgRI07YN66tlMQosa1aWBR2yncsfx9a2s7hWqpwjrVdgqPLJnRJcQDqKysjKysLMaOHUvLli0fqEEuIYQQQgghhBCipshm9EJch1KpvO6xbdu2Ws0tNjYWFxcX9u7dy+zZs/XObdu27Ya5CyGEEEIIIYQQDyuZ0SXEdcTFxV33nJub2/1LpBpt27blequOw8LCbpi7EEIIIYQQQgjxsJKBLiGuw9/fv7ZTuCPm5uYPbO5CCCGEEEIIIcTdkKWLQgghhBBCCCGEEOKhIANdQgghhBBCCCGEEOKhIANdQgghhBBCCCGEEOKhIHt0CSHuu6hmmtpOQYgaN5MvazsFIWrciHVv1XYKQtS40I61nYEQ90On2k5AiHtGZnQJIYQQQgghhBBCiIeCDHQJIYQQQgghhBBCiIeCDHQJIYQQQgghhBBCiIeCDHQJIYQQQgghhBBCiIeCDHQJIYQQQgghhBBCiIeCDHQJIYQQQgghhBBCiIeCDHSJR87EiRNp3LjxI5+DEEIIIYQQQgjxsDGq7QSEuFWZmZmMHz+eVatWkZGRga2tLSEhIYwfP56IiAgUCgVLly6lR48e9+yaEydOZNKkSQAYGhri7u5Oz549mTx5Mkql8o7bHTlyJG+88Ybu9YABA8jJyWHZsmV3m7J4SGm1WjasWsze2A2UFBfh6RtEj2eG4FDH5Yb1dm5Zy7YNy8nPy8HF3YuufaLx8A7QnS8rK2X1HwuIPxCLRqMhsF4I3foNRmVlA0D62VS2/L2U1JTjFBXkY2PnSIvHnyCiXZea7K54BJ04tJrE/cspKczGxtGbJm0HYe8ceN34M0mxHN65iKK8TJQ2LgQ/9iKuPk1157VaLUd3/UbKkfWUqgtxcKlL08hXUNm6AlCYd5Fju5eQceYwJUU5mFva4VW3NfWb98HAUL4eidrzd+Jp/jp6itwSNZ62KgY0q4+/g81143elprPk0AkuFRbjrLKgf2gQTdzq3L+EhaiGVqtlydrNbNx1gKLiEgJ9PBj89FO4ONrfsN7a7Xv4a3MsOXkFeLk683KvJwnwctedLy0r4+fl69gRdxSNRkNIkD/RT3fBRnX1e/m8P1dz/FQaZy5cxM3JkU9GDq2xfgoh/ptkRpd4YPTu3ZuDBw+yYMECkpKSWLFiBW3btiUrK6tGr9ugQQPS09NJTU1lxowZ/PDDD4wYMeKO2tJqtWg0GpRKJfb2N/6H/l4rLS29r9cT99aW9cvYEbOGHv1fYei70zAxNWPu15MpK7v+f9f4/bGs+mM+kZ378MaoT3B282bu11MoyM/Vxaz6Yz4JR/bxbPQIBr89idycyyyc84nu/Lm0ZCxV1vR96U3eGvsZ7Tr1Yt3yheyIWVOj/RWPlrSk7cRtnUeDFn154tmZWDt4s3Xph5QU5VYbf+l8AjvXfIZvgyieeHYmbn4tiF05ndxLp3Uxx/ct5UTcKppGvkqHZ2ZgZGzGlqUfUq6p/J3Ju3wWrbaCsPZD6fTClzRu/TLJ8euIj/31vvRZiOrsTE3nl30J9A72Z2rnCLxsrZi2cS95Jepq4xMvZvPV9kO083dnaucIwjyc+CzmAGey8+9z5kLoW75pO2u27WZwn6f46O3BmJmY8NH3v1BaVnbdOjsOHuHn5Wt5+om2zBjxKl5uTnz0/S/kFhTqYhYsW8f+o0kMf6kvE15/mcu5ecyct7hKW+1ahNKqccMa6ZsQ4r9PBrrEAyEnJ4dt27YxY8YM2rVrh5eXF82bN2f06NF069YNb29vAHr27IlCodC9Bpg+fTpOTk6oVCqio6MpKSm5rWsbGRnh7OyMu7s7/fr147nnnmPFihUA/PLLL4SFhaFSqXB2dubZZ5/l4sWLuroxMTEoFArWrFlD06ZNMTU1Zfv27XpLFydOnMiCBQtYvnw5CoUChUJBTEwMkZGRDBs2TC+XzMxMTExM2Lhx403z9vb2ZvLkybz44otYWVkxZMgQAN5//30CAwOxsLDA19eXcePGUXbNl46//vqLZs2aYWZmhoODAz179tSdU6vVjBw5Ejc3NywtLWnRogUxMTG39Z6K26PVatmxeRWRnXpTP7gZLm5e9HlhGPm52Rw7tOe69bZv+otmER0IC4+kjos7Pfu/gomJCft2bgKguKiQfTs20aXXAPyCGuHu6cfTLwzjdEoiaaeSAAhr1Z6ufQbiG9AAewdnmjRvQ9OW7Th2aPd96bt4NCQdWIFvwyh8GrTHyt6DsPZDMTQy5dTR6j/rkg6uwsU7lLphPbCy96BRq2exdfTlxKHVQOXvzIm4ldRr3gc3v+bYOHjTvOOblBRe5lxy5c+ui3cozZ94E2evxiitnXHza05Q0x6cPbnrvvVbiGutSjhFZIAHbf3dcbdRMqhFA0wNDdl88my18WuPpxLi6kDXBr642yjp2zgQbztr1iWerjZeiPtBq9Wyeutueke1plnDuni5OvH6sz3Jzs1n7+Hj1623MmYn7Vs2pV2LJrg7OzKkT1dMTYzZvPsAAIXFJWzec4CXenSkYYAPfh6uvNa/B4mn0jiRevV35OVenen0WHPq2NvWeF+FEP9NMtAlHghKpRKlUsmyZctQq6ve1dy7dy8A8+bNIz09Xfd6yZIlTJw4kalTp7Jv3z5cXFz49ttv7yoXc3Nz3eyosrIyJk+ezKFDh1i2bBmpqakMGDCgSp1Ro0Yxffp0EhISCA4O1js3cuRI+vbtS6dOnUhPTyc9PZ1WrVoxaNAgFi1apNffX3/9FTc3NyIjI28p108//ZSQkBAOHjzIuHHjAFCpVMyfP59jx47x5ZdfMmfOHD7//HNdnVWrVtGzZ086d+7MwYMH2bhxI82bN9edHzZsGDt37uS3334jPj6ePn360KlTJ06cOHHL76G4PZezMsjPy8EvqJGuzNzCEg/vAN2A1LU0Gg3n0lLwr3v1502hUOBfN1hX5/yZFMrLNXoxdZzdsLF1uG67ACUlRZhbWN5tt4QAoKJcw+WMZJw9Q3RlCoUCJ89gstITq62TdSGROh6N9MqcvRrr4gvzMiguzMbpXzEmppbYOwdy6TptApSVFmJqfufL0oW4G5ryCk5l5dHQxUFXplAoaOhiz4nMnGrrJGXm0NBFf4Z4sIs9Jy5VHy/E/XAxK5ucvHwaBvjqyizNzQjwcicp9Uy1dTSaclLOnqdR4NU6CoWCRoF+JP0ziHXqbDoaTTmNAv10MW5OjjjYWpN0uvp2hRCPJtmEQjwQjIyMmD9/PoMHD2b27NmEhobSpk0bnnnmGYKDg3F0dATAxsYGZ2dnXb0vvviC6OhooqOjAZgyZQobNmy47VldV+zfv59FixbpBpoGDhyoO+fr68usWbNo1qwZBQUFent4ffjhh0RFRVXbplKpxNzcHLVarZd7r169GDZsGMuXL6dv374AzJ8/nwEDBqBQKG4p38jIyCrLLMeOHav7/97e3owcOZLffvuN9957D4CPPvqIZ555Rrc3GUBISOUfoGlpacybN4+0tDRcXSv3uRk5ciRr165l3rx5TJ06tUoOarW6yuBkaWk5Jiamt9QHAQW5OQAoraz1ypVW1uTnZVdbp6gwj4qKCpSqa+qorMnMOAdAfl4OhoZGVQatbtTu6eTjxB/YwYCho++kK0JUoS7OQ6utwNRC/2fVzMKG/Oxz1dap3FPLtkp8SWFO5fnCbF3Zv5n+K+Za+TnpnIhbTcjjL91+J4S4B/LVpVRotVibmeiVW5uZcj6vsNo6eSVqbMz0/z21MTclp7j6pY5C3A85+QUAWKv0bxxYqyx1566VV1hERUWF3l5bADZKS85nXKpsNy8fIyNDLM3NrmlXSU5e9e0KIR5NMtAlHhi9e/emS5cubNu2jV27drFmzRo+/vhjfvzxx2pnUQEkJCTw6quv6pWFh4ezefPmW77u4cOHUSqVlJeXU1paSpcuXfj666+ByoGviRMncujQIbKzs6moqAAqB4Tq16+vayMsLOw2ewtmZma88MILzJ07l759+3LgwAGOHDmiWzZ5K6q77uLFi5k1axbJyckUFBSg0WiwsrLSnY+Li2Pw4MHVtnf48GHKy8sJDNTfIFqtVl93z7Fp06bpDZoBvDR0DC+//sEt9+NRc3DPVpb99oPu9Uv/kUGlC+fT+OWHGbR/sg8B9RrXdjpC3DNFBVlsXfYhHgHh+DV6orbTEUKIB8q2/fHM+f0v3etRg56rxWzEo0izbnltp1C9sE61ncEjSwa6xAPFzMyMqKgooqKiGDduHIMGDWLChAnXHei6F4KCglixYgVGRka4urpiYlJ5p7WwsJCOHTvSsWNHFi5ciKOjI2lpaXTs2LHKxu+Wlne2zGvQoEE0btyYs2fPMm/ePCIjI/Hy8rrl+tded+fOnTz33HNMmjSJjh07Ym1tzW+//cbMmTN1Mebm5tdtr6CgAENDQ/bv34+hoaHeues9hXL06NEMHz5cr2x3cvkt9+FRVD+4GZ4+V5+MqNFoACjIy8XK2k5XXpCXi4u7d7VtWFhaYWBgoLfxPEBBfi5Kq8qZMCorG8rLNRQXFerN6irIy0VlpT9b5mL6WX78ciLNI6KIfPLpu+qfEP9mam6FQmGA+pqN50uKcjCzqH5/FTMLG4oLs6vGW9pUnv9ntldJUQ7myqu/M+qiHKwdvfXqFRdcJuZ/43BwCSKsw+t32Rsh7pzK1AQDhYLcEv3vELnVzNq6wsrMlJxrNqrPKVZjYy6zpsX9E9YgSO/JiGX/fG/JzS/AzlqlK8/NL8TL1anaNqwsLTAwMKgy4yunoBAbq8rvmDZWKjSacgqLS/RmdeXmF+hihBACZI8u8YCrX78+hYWV0/mNjY0pL9cfQKlXrx67d+tvmr1r1+1tNGxiYoK/vz/e3t66QS6A48ePk5WVxfTp03n88cepW7eu3kb0t3uNa3MHaNSoEWFhYcyZM4dFixbpLZW8Ezt27MDLy4sPPviAsLAwAgICOH1af8Pa4ODg625236RJE8rLy7l48SL+/v56x7+XXf6bqakpVlZWeocsW7wxUzNz7B1ddEcdZ3dUVjYkJx7WxZQUF3Em9QSePoHVtmFkZISbp69eHa1Wy8nEw7o6rh6+GBoa6cVkZpwnJ/uSXrsZ6Wf44YvxNG3Zlie6PXuvuysecQaGRtg5+ZGRFq8r02q1XDxzGHuXoGrr2DsHcfHMYb2yjLRDunhLKyfMLW3J+FdMmbqIrAtJOPyrzaKCLDb/byy2Tn40j3rzlpeFC1ETjAwN8LG34mj61adJa7Vajl7IIsDRpto6gY42HEnXf/r04QtZBDhUHy9ETTA3M8XZwU53uDs5YmOl4siJFF1MUUkJJ06fJdDbo9o2jIwM8XV35ciJU7oyrVbLkaQUAr0rB9F83F0wMjLkSNLVds9fvMSl7FwCvapvVwjxaJIZXeKBkJWVRZ8+fRg4cCDBwcGoVCr27dvHxx9/TPfu3YHK/aY2btxIREQEpqam2Nra8tZbbzFgwADCwsKIiIhg4cKFHD16FF9f35tc8eY8PT0xMTHhq6++4tVXX+XIkSNMnjz5jtry9vZm3bp1JCYmYm9vj7W1NcbGxkDlrK5hw4ZhaWmp9/TDOxEQEEBaWhq//fYbzZo1Y9WqVSxdulQvZsKECbRv3x4/Pz+eeeYZNBoNq1ev1j2t8bnnnuPFF19k5syZNGnShMzMTDZu3EhwcDBdunS5q/xE9RQKBa3adWHT2j+wr+OCrX0d1q/8DZW1LfVDrj4o4McvJ1I/pAWt2j4JwGORXfn9569x8/TFwyuA2JhVlKrVNG3ZDqjc0D6sVSSr/piPuYUlpuYW/LXkJzx9AnUDXRfOp/HjlxMJrN+YxyK7kp9bOYtGYWBQZf8vIe5UYGg39qybha2TH/ZOASTFrURTVoJPg8r9EHev+wJzS3uCH3uhMr5JFzb/bxzH9y/H1acpaYnbuXwxmbD2Q4HK35mAxk+RsOd3VDYuWFrX4ciO/8PM0g43vxbA1UEuS1UdQh4fQEnx1Rll1+7/JcT90qWeD9/tiMfH3go/exvWHk+lRFNOW7/KP/S/iT2EnbkZ/UMrB2w71fVm8vrdrDx2iiZujuxMTedUVi6DWzSszW6IR5xCoaBz6xb8sX4rzo721LGzZfGaTdhaq2jWqK4u7sNv59OsUT2efLzyc/mptuF8s2gpvu4u+Hu6s3rrLkpKS2nbvAlQuaF9u+ahLFi+FksLc8zNTJn352oCvT0I8L46o+zCpcsUl6jJySugrEzDqbPpAHg418HISH9FghDi4SQDXeKBoFQqadGiBZ9//jnJycmUlZXh4eHB4MGDGTNmDAAzZ85k+PDhzJkzBzc3N1JTU+nXrx/Jycm89957lJSU0Lt3b4YOHcq6devuOidHR0fmz5/PmDFjmDVrFqGhoXz66ad069btttsaPHgwMTExhIWFUVBQwObNm2nbti0A/fv35+2336Z///6YmZnduKGb6NatG++88w7Dhg1DrVbTpUsXxo0bx8SJE3Uxbdu25ffff2fy5MlMnz4dKysrWrdurTs/b948pkyZwogRIzh37hwODg60bNmSp5566q5yEzfWJqoHZaVqli6aTUlxEV5+dXn59bEYG1+dZXj5UgZFhXm618FNIyjIz2PDysXk5+Xg6uHNy69/gMrKRhfTpfcAFChY+OOnaDQaAus1pnu/QbrzRw7upLAgj4N7tnJwz1Zdua2dI+9N/q5mOy0eGZ6Bj6EuyuXIzt8oKcrG1tGH1j3G6TaTL8q7hEJxdRK6g2s9WnZ6h8M7F3F4x6+obFyIeGoU1g5Xl3bXDetJuUbNvo3fUaYuxMG1Hq17jsPQqPJ3JiPtEAU5FyjIucBfPw7Sy6ff2/o3AIS4X8K9XcgrKeX3QyfILVbjZWfFqMhmWP+zFDGrsASDf808DKpjy7CIEJYcOsHig4k4W1kyvG0oHraq611CiPuie+RjqEvL+GHJXxQVlxDk68mYIc9j8s+NXICMrGzyC4t0r1s1aUhuQSFL1m4mJ78Ab1cXPhjygt4G9S/16IhCATPnL0aj0RBS15/o3vo3Wmf/tpxjyam61+/PnA3AN+PewdHOpmY6LIT4T1FotVptbSchhLi+1NRU/Pz82Lt3L6GhobWdzj2x5WjRzYOEeMCt3yv3ksTDb8S5t2o7BSFqnFHH7rWdghA1TvUAb5ye/dHQ2k6hWrYfyE3h2iLfwoX4jyorKyMrK4uxY8fSsmXLh2aQSwghhBBCCCGEqCmyGb14pCmVyuse27Ztq9XcYmNjcXFxYe/evcyePVvv3LZt226YuxBCCCGEEEII8SiSGV3ikRYXF3fdc25ubvcvkWq0bduW660sDgsLu2HuQgghhBBCCCHEo0gGusQjzd/fv7ZTuCPm5uYPbO5CCCGEEEIIIURNkaWLQgghhBBCCCGEEOKhIANdQgghhBBCCCGEEOKhIANdQgghhBBCCCGEEOKhIHt0CSHuu9DirbWdghA1r1nr2s5AiJp3rrYTEKLmzTgSWdspCFHjpoTVdgZC3Dsyo0sIIYQQQgghhBBCPBRkoEsIIYQQQgghhBBCPBRkoEsIIYQQQgghhBBCPBRkoEsIIYQQQgghhBBCPBRkoEsIIYQQQgghhBBCPBRkoEvcNxMnTqRx48aPfA5CCCGEEEIIIYSoGUa1nYD478vMzGT8+PGsWrWKjIwMbG1tCQkJYfz48URERKBQKFi6dCk9evS4Z9ecOHEikyZNAsDQ0BB3d3d69uzJ5MmTUSqVd9zuyJEjeeONN3SvBwwYQE5ODsuWLbvblO+biooKRo8ezS+//EJ2djYBAQFMnjyZ7t2713ZqogZptVqWrN3Mxl0HKCouIdDHg8FPP4WLo/0N663dvoe/NseSk1eAl6szL/d6kgAvd9350rIyfl6+jh1xR9FoNIQE+RP9dBdsVJW/Z6nnLrB803aOp6SRX1iEo501UeHN6NymZY32Vzx6dm5Zy7YNy8nPy8HF3YuufaLx8A64bnz8gR2s/+s3ci5nYl/HhU7dn6duw1Ddea1Wy4ZVi9kbu4GS4iI8fYPo8cwQHOq4AJCddZFNa/5HcuJhCvJzUVnb0rjZ47Tr9DRGRvL1SNSevxNP89fRU+SWqPG0VTGgWX38HWyuG78rNZ0lh05wqbAYZ5UF/UODaOJW5/4lLEQ1tFotR3f9RsqR9ZSqC3FwqUvTyFdQ2bresN6JQ6tJ3L+cksJsbBy9adJ2EPbOgbrz5ZpS4rbO40xSLOXlZTh7NaFp5BDMLGwAyMk8RcK+P7l0/jjq4jwsrRzxa9SRwCZda7K7Qoj/GJnRJW6qd+/eHDx4kAULFpCUlMSKFSto27YtWVlZNXrdBg0akJ6eTmpqKjNmzOCHH35gxIgRd9SWVqtFo9GgVCqxt7/xwMB/3a+//srnn3/OZ599RkJCAp999hmWlpY3rVdaWnofshM1Zfmm7azZtpvBfZ7io7cHY2Ziwkff/0JpWdl16+w4eISfl6/l6SfaMmPEq3i5OfHR97+QW1Coi1mwbB37jyYx/KW+THj9ZS7n5jFz3mLd+ZSz57GytOCN53ox873X6NmhNYtWbWDNtt012l/xaInfH8uqP+YT2bkPb4z6BGc3b+Z+PYWC/Nxq408nH2fxvC9o1qo9b4z6hPrBzfj1h4+5cD5NF7Nl/TJ2xKyhR/9XGPruNExMzZj79WTKyio/Cy9eOEeFtoKez77CW2M/o0vvAeze9jd/r1h4X/osRHV2pqbzy74Eegf7M7VzBF62VkzbuJe8EnW18YkXs/lq+yHa+bsztXMEYR5OfBZzgDPZ+fc5cyH0Hd+3lBNxq2ga+SodnpmBkbEZW5Z+SLnm+t9H05K2E7d1Hg1a9OWJZ2di7eDN1qUfUlJ09d+Cg1vncv7UPsK7vEu7pydTXJBF7MoZuvOXLyZjam5Ni45v0+mFL6nX7GniY3/lRNyqGu2vEOK/RQa6xA3l5OSwbds2ZsyYQbt27fDy8qJ58+aMHj2abt264e3tDUDPnj1RKBS61wDTp0/HyckJlUpFdHQ0JSUlt3VtIyMjnJ2dcXd3p1+/fjz33HOsWLECgF9++YWwsDBUKhXOzs48++yzXLx4UVc3JiYGhULBmjVraNq0Kaampmzfvl1v6eLEiRNZsGABy5cvR6FQoFAoiImJITIykmHDhunlkpmZiYmJCRs3brxp3t9++y0BAQGYmZnh5OTE008/rTtXUVHBtGnT8PHxwdzcnJCQEP73v/8BlYNxHTp0oGPHjmi1WgAuX76Mu7s748eP17VhYGCAo6MjzzzzDN7e3nTo0IEOHTpUyeNKX3/88Ud8fHwwMzMDYO3atTz22GPY2Nhgb2/PU089RXJysl7ds2fP0r9/f+zs7LC0tCQsLIzdu68ObCxfvpzQ0FDMzMzw9fVl0qRJaDSam7434s5otVpWb91N76jWNGtYFy9XJ15/tifZufnsPXz8uvVWxuykfcumtGvRBHdnR4b06YqpiTGbdx8AoLC4hM17DvBSj440DPDBz8OV1/r3IPFUGidSzwIQ2SKUl3t1pr6/N04OdrQOC6Ft88bsPZxwX/ouHg3bN/1Fs4gOhIVHUsfFnZ79X8HExIR9OzdVGx8bs4rA+k1oHdWdOi7uPNG1P64ePuzcsgao/J3ZsXkVkZ16Uz+4GS5uXvR5YRj5udkcO7QHgKAGTejzwjAC6jXG3sGZ+sHNaN2hO0fiZBBX1J5VCaeIDPCgrb877jZKBrVogKmhIZtPnq02fu3xVEJcHejawBd3GyV9GwfibWfNusTT9zlzIa7SarWciFtJveZ9cPNrjo2DN807vklJ4WXOJV//MzbpwAp8G0bh06A9VvYehLUfiqGRKaeOVn7/LlUXcuroRhq3fhknj0bYOfnT/Ik3uHT+OFnpiQD4NuhAaNtB1HFvgNLaGe96bfGpH8nZG1xXCPHwkYEucUNKpRKlUsmyZctQq6veTdy7dy8A8+bNIz09Xfd6yZIlTJw4kalTp7Jv3z5cXFz49ttv7yoXc3Nz3ayksrIyJk+ezKFDh1i2bBmpqakMGDCgSp1Ro0Yxffp0EhISCA4O1js3cuRI+vbtS6dOnUhPTyc9PZ1WrVoxaNAgFi1apNffX3/9FTc3NyIjI2+Y4759+3jzzTf58MMPSUxMZO3atbRu3Vp3ftq0afz888/Mnj2bo0eP8s477/D888+zZcsWFAoFCxYsYO/evcyaNQuAV199FTc3N72Brvbt25Obm8u4ceNu+p6dPHmSP/74gz///JO4uDgACgsLGT58OPv27WPjxo0YGBjQs2dPKioqACgoKKBNmzacO3eOFStWcOjQId577z3d+W3btvHiiy/y1ltvcezYMb7//nvmz5/PRx99dNN8xJ25mJVNTl4+DQN8dWWW5mYEeLmTlHqm2joaTTkpZ8/TKPBqHYVCQaNAP5L+GcQ6dTYdjaacRoF+uhg3J0ccbK1JOl19uwDFJWoszc3vtltCAKDRaDiXloJ/3auf0QqFAv+6waSdSqq2TtqpJPyCGumVBdQL0cVfzsogPy9HL8bcwhIP74DrtglQUlyIheWdL48X4m5oyis4lZVHQxcHXZlCoaChiz0nMnOqrZOUmUNDF/2Z6sEu9py4VH28EPdDYV4GxYXZOHlc/Qw2MbXE3jmQS/8MSF2rolzD5YxknD1DdGUKhQInz2DdIFb2xRQqyjU4/SvGys4dC5XjddsFKCstwsRUPtuFeJTIJhTihoyMjJg/fz6DBw9m9uzZhIaG0qZNG5555hmCg4NxdHQEwMbGBmdnZ129L774gujoaKKjowGYMmUKGzZsuO1ZXVfs37+fRYsW6QaaBg4cqDvn6+vLrFmzaNasGQUFBXp7eH344YdERUVV26ZSqcTc3By1Wq2Xe69evRg2bBjLly+nb9++AMyfP58BAwagUChumGdaWhqWlpY89dRTqFQqvLy8aNKkCQBqtZqpU6eyYcMGwsPDdblv376d77//njZt2uDm5sb333/Piy++yIULF1i9ejUHDx7U7RdTVFREVFQUzz77LOvXr6e4uJhPPvlEl5eVlRVz587VzSIrLS3l559/1v13gsqlqP82d+5cHB0dOXbsGA0bNmTRokVkZmayd+9e7OzsAPD399fFT5o0iVGjRvHSSy/p+jB58mTee+89JkyYcMP3R9yZnPwCAKxV+l/SrFWWunPXyissoqKiQrfX1hU2SkvOZ1yqbDcvHyMjQyzNza5pV0lOXvXtJp5KY0fcUUYNfvaO+iLEtYoK86ioqECpstYrV6qsycw4V22dgrxcVFY2emUqKxvyc7Mrz+fmVLZhdU2bVtbk52VX22ZWZjo7t6zlyV4v3EEvhLh7+epSKrRarM1M9MqtzUw5n1dYbZ28EjU2ZqZ6ZTbmpuQUV7/UUYj7oaSw8nP2yr5ZV5ha2FBSmFNtHXVxHlptBaYW+p/bZhY25Gef07VrYGiEianlNTHWlBRV3+6l8wmcSYrl8e5jb78jQogHlszoEjfVu3dvzp8/z4oVK+jUqRMxMTGEhoYyf/7869ZJSEigRYsWemVXBndu1eHDh3WDUc2bNyc8PJyvv/4aqBz46tq1K56enqhUKtq0aQNUDjT9W1hY2G1dE8DMzIwXXniBuXPnAnDgwAGOHDlS7Yyxa0VFReHl5YWvry8vvPACCxcupKioCKicXXVloOrKTDmlUsnPP/+st3SwT58+9OzZk+nTp/Ppp58SEHB1M+b58+eTk5PDN998w5o1a1i/fj0vv/wyGo2G1NRUCgoKiIiI0MV7eXnpDXIBnDhxgv79++Pr64uVlZVuuemV9y4uLo4mTZroBrmudejQIT788EO9PgwePJj09HRdX/9NrVaTl5end6hlv7Ab2rY/nhdHfaQ7yssrajslANLSM/j4p//j6SfaEBLkf/MKQjwgcnOymPfNRzRs0pLmEdXfHBFCCFG908e38Mc3/XXHlVUAtS330mm2/zWdBi364ezVuLbTEULcRzKjS9wSMzMzoqKiiIqKYty4cQwaNIgJEybc0uDPnQoKCmLFihUYGRnh6uqKiUnlHc7CwkI6duxIx44dWbhwIY6OjqSlpdGxY8cqG67fyibt1Rk0aBCNGzfm7NmzzJs3j8jISLy8vG5aT6VSceDAAWJiYvj7778ZP348EydOZO/evRQUVM6QWbVqFW5ubnr1TE2v3o0tKipi//79GBoacuLECb24+Ph4GjRogLGxMba2tqxfv57HH3+cnj17EhAQQKdOnXBxcblh/7t27YqXlxdz5szB1dWViooKGjZsqHvvzG+yJK2goIBJkybRq1evKueu7AP2b9OmTdM9QfOKUYOfY8yQ5294nUdZWIMgvScjlv2z/1lufgF21ipdeW5+IV6uTtW2YWVpgYGBQZUZXzkFhdhYVc7ysrFSodGUU1hcojerKze/QBdzxdkLmXz47QI6tAqj9xNt7q6DQvyLhaUVBgYGVTaeL8jPRWllW22dyplZOXpl+Xk5qKwr45XWNpVt5OViZX110L4gLxcXd2+9enm5l/nxy4l4+gTS67mhd9cZIe6CytQEA4WC3BL97zK51czausLKzJScazaqzylWY2NefbwQNcHVtzlP/OvJiBXllQ/KKSnKwVx59TNYXZSDtaN3tW2YmluhUBigLtL/t6CkKAczi8rPdjNLWyrKNZSqC/VmdZUU5VaZPZaXdYaYPybg1+gJ6rfoczfdE0I8gGRGl7gj9evXp7Cwchq9sbEx5eXleufr1aunt3k5wK5du27rGiYmJvj7++Pt7a0b5AI4fvw4WVlZTJ8+nccff5y6devqbUR/u9e4NneARo0aERYWxpw5c1i0aJHeUsmbMTIyokOHDnz88cfEx8eTmprKpk2bqF+/PqampqSlpeHv7693eHh46OqPGDECAwMD1qxZw6xZs9i06epmzG5ubsTFxZGfX/k0pTp16rBhwwYOHz7M559/zpQpU26YW1ZWFomJiYwdO5b27dtTr149srP1l/EEBwcTFxfH5cuXq20jNDSUxMTEKn3w9/fHwKDqR8ro0aPJzc3VO0YM6HvL7+ejyNzMFGcHO93h7uSIjZWKIydSdDFFJSWcOH2WQG+PatswMjLE192VIydO6cq0Wi1HklII9K4cRPNxd8HIyJAjSVfbPX/xEpeycwn0utrumQsXmfjNPNo2a0z/zu3vdXfFI87IyAg3T1+SEw/ryrRaLScTD+PpE1htHU+fQL14gJPH43XxdvZOqKxs9GJKios4k3pCr83cnCzmfDEBNw9fnn5h2E2XpwtRk4wMDfCxt+Jo+tWnWmu1Wo5eyCLA0abaOoGONhxJ138K9uELWQQ4VB8vRE0wNjFHZeOiO6zsPDC3tCXjzNXP4DJ1EVkXknBwCaq2DQNDI+yc/MhIi9eVabVaLp45jP0/dWzr+GJgaMTFf8XkZZ+jKD9Tr93crDQ2/28c3vXb0ajVc/e6u0KIB4DM6BI3lJWVRZ8+fRg4cCDBwcGoVCr27dvHxx9/TPfu3QHw9vZm48aNREREYGpqiq2tLW+99RYDBgwgLCyMiIgIFi5cyNGjR/H19b3JFW/O09MTExMTvvrqK1599VWOHDnC5MmT76gtb29v1q1bR2JiIvb29lhbW2NsbAxUzuoaNmwYlpaW9OzZ85baW7lyJSkpKbRu3RpbW1tWr15NRUUFQUFBqFQqRo4cyTvvvENFRQWPPfYYubm5xMbGYmVlxUsvvcSqVauYO3cuO3fuJDQ0lHfffZeXXnqJ+Ph4bG1tiY6O5ssvv6Rbt2589NFH2Nvbs23bNgoKCrCwsOCnn34iNDT0uvnZ2tpib2/PDz/8gIuLC2lpaYwaNUovpn///kydOpUePXowbdo0XFxcOHjwIK6uroSHhzN+/HieeuopPD09efrppzEwMODQoUMcOXKk2oE2U1NTvRlrAPkmJlXixPUpFAo6t27BH+u34uxoTx07Wxav2YSttYpmjerq4j78dj7NGtXjyccrlw0/1TacbxYtxdfdBX9Pd1Zv3UVJaSltm1fuG2dpbka75qEsWL4WSwtzzM1MmffnagK9PQj4ZzAsLT2DD79dQOO6/nRpG052XuUgq4GBAdbKO5sxKcS1Hovsyu8/f42bpy8eXgHExqyiVK2mact2ACxZMAsrGzs6da+cCRrRtgs/fDmBbRtWENSwKfH7t3MuLYWez74KVP7OtGrXhU1r/8C+jgu29nVYv/I3VNa21A9pDlwd5LKxc+TJXi9S+K8ZZVdmhglxv3Wp58N3O+LxsbfCz96GtcdTKdGU09av8jP5m9hD2Jmb0T+08o/6TnW9mbx+NyuPnaKJmyM7U9M5lZXL4BYNa7Mb4hGnUCgIaPwUCXt+R2XjgqV1HY7s+D/MLO1w87u6tcnmP8bj7teCgMZdAAgM7caedbOwdfLD3imApLiVaMpK8GlQuUeviaklPg3ac3DrPIzNlBibmHMw5kccXIJ0g2G5l04T88cEnL0bExjajeJ/9gtTKAwwu2b/LyHEw0sGusQNKZVKWrRoweeff05ycjJlZWV4eHgwePBgxowZA8DMmTMZPnw4c+bMwc3NjdTUVPr160dycjLvvfceJSUl9O7dm6FDh7Ju3bq7zsnR0ZH58+czZswYZs2aRWhoKJ9++indunW77bYGDx5MTEwMYWFhFBQUsHnzZtq2bQtUDvi8/fbb9O/fv9oledWxsbHhzz//ZOLEiZSUlBAQEMD//d//0aBBAwAmT56Mo6Mj06ZNIyUlBRsbG0JDQxkzZgyZmZlER0czceJE3WDVpEmT+Pvvv3n11VdZvHgxrq6u7Nmzh/fff59evXqRl5dHWFgYP//8MxYWFkRFReHn58fw4cOrzc/AwIDffvuNN998k4YNGxIUFMSsWbN0fYbKWW5///03I0aMoHPnzmg0GurXr88333wDQMeOHVm5ciUffvghM2bMwNjYmLp16zJo0KDbfv/Frese+Rjq0jJ+WPIXRcUlBPl6MmbI85j8MzALkJGVTX7h1X3SWjVpSG5BIUvWbiYnvwBvVxc+GPKC3gb1L/XoiEIBM+cvRqPREFLXn+jeXXTndx06Rl5BIVv3HWLrvkO6ckc7G74Z904N91o8KoKbRlCQn8eGlYvJz8vB1cObl1//QLfhfM7lSyj+NWPUy68u/Qa8xfq/fmPdikU41HHh+SHv4ezqqYtpE9WDslI1SxfNpqS4CC+/urz8+liMjSsH2k8ejycr8wJZmReY/sErevlM++Z/Nd9pIaoR7u1CXkkpvx86QW6xGi87K0ZFNsP6n6WIWYUlGPxr5mFQHVuGRYSw5NAJFh9MxNnKkuFtQ/GwVV3vEkLcF3XDelKuUbNv43eUqQtxcK1H657jMDS6erOzMDcDdXG+7rVn4GOoi3I5svM3SoqysXX0oXWPcXrLEpu0HogCBTtWfUxFeRnOXk0IbTdEd/7MiZ2UFOeSmrCF1IQtunJLqzo8NfD7mu20EOI/Q6HVarW1nYQQ/0Wpqan4+fmxd+/eG86SErcvf9/a2k5BiBp3wLx1bacgRI0LXjaitlMQosbNdPuytlMQosZNGfDgrrjI/ui/ucem7Qff1XYKjyyZ0SXENcrKysjKymLs2LG0bNlSBrmEEEIIIYQQQogHhGxGL2qFUqm87rFt27ZazS02NhYXFxf27t3L7Nmz9c5t27bthrkLIYQQQgghhBCi9siMLlEr4uLirnvOzc3t/iVSjbZt23K9Fb1hYWE3zF0IIYQQQgghhBC1Rwa6RK3w9/ev7RTuiLm5+QObuxBCCCGEEEII8bCTpYtCCCGEEEIIIYQQ4qEgA11CCCGEEEIIIYQQ4qEgA11CCCGEEEIIIYQQ4qEgA11CCCGEEEIIIYQQ4qEgm9ELIe67GUciazsFIWrciHNv1XYKQtQ4o47dazsFIWpclLmmtlMQ4j4wqe0EhLhnZEaXEEIIIYQQQgghhHgoyECXEEIIIYQQQgghhHgoyECXEEIIIYQQQgghhHgoyECXEEIIIYQQQgghhHgoyECXEEIIIYQQQgghhHgoyECXEDcwceJEGjduXNtpkJqaikKhIC4urrZTEUIIIYQQQggh/rOMajsBIWpCZmYm48ePZ9WqVWRkZGBra0tISAjjx48nIiIChULB0qVL6dGjxz275sSJE5k0aRIABgYGuLq68uSTTzJ9+nTs7OxuuZ0BAwaQk5PDsmXLdGUeHh6kp6fj4OBwz/IVD44Th1aTuH85JYXZ2Dh606TtIOydA6uNrSjXkLDvT1KPbaa4IAuVrSvBj72Ii3foHbWp1WrZtnwK6akHeKzrKNz8WtRIH8Wj7VjGZVYeTSH1ch7ZxWqGtw2lmYfTjetcyOLn/QmcyynA3tKcno38aOPnflttfrcjnq3J5/TKgl0dGN2+2b3rnBD/OHYylRWbYzl1Lp3s3HxGDnyG5o3q3bDO0ZOnWLBsHWczLuJgY02vqNa0bd5Ed35d7B7W79hH5uUcANydHHm6Y1ua1AsAIPNyDq9P/rzatt95qQ/hjRvem84J8S87t6xl24bl5Ofl4OLuRdc+0Xh4B1Qbq9Fo2PL3Ug7sjiEv5zIOTq506v48QQ2u/pzv2rqW3dvXk511EYA6zu6079xXF1NUmM+GVUs4kXCI3OxLWChVNAhpQYcu/TC3sKz5Dgsh/nNkoEs8lHr37k1paSkLFizA19eXjIwMNm7cSFZWVo1et0GDBmzYsIHy8nISEhIYOHAgubm5LF68+K7aNTQ0xNnZ+R5lKR4kaUnbids6j7DIV7F3DiTx4F9sXfohT770DWYW1lXiD+9cxOmELYR1eA0rOzcupB4kduUM2vedhm0d39tuM+ngX/eln+LRVqopx8vWirb+7ny+5eBN4y8WFDFj8346BHgwLKIxRy9k8cPOI9iYmxLi6nhbbYa4OvBqq2DdayMDmewuaoa6rAwvN2fatQhl5rzfbhp/MSub6XMWEtWqGW8+35vDJ1KYvXgFNlYqGtf1B8Dexppnu3TAxdEerVbLln2H+GTu/zFjxKt4ONfB3saK7yeN1Gt34679rNgUS5N61d8wEeJuxO+PZdUf8+nRfwie3oFs37ySuV9PYcSEWShVVb+3rF/5f8Tt2UrPZ4fi6OzKiWNxLJzzCa+O+AhXDx8ArG0d6NjtORzquKDVajm4O4Zfvp/BG6M/wcnFg7zcbPJzL9O514vUcXYj5/Illv32A3k5WTw3+N37/RYIIf4D5NuceOjk5OSwbds2ZsyYQbt27fDy8qJ58+aMHj2abt264e3tDUDPnj1RKBS61wDTp0/HyckJlUpFdHQ0JSUlt3VtIyMjnJ2dcXNzo0OHDvTp04f169frzpeXlxMdHY2Pjw/m5uYEBQXx5Zdf6s5PnDiRBQsWsHz5chQKBQqFgpiYmGqXLm7ZsoXmzZtjamqKi4sLo0aNQqPR3FKe//vf/2jUqBHm5ubY29vToUMHCgsLded//PFH6tWrh5mZGXXr1uXbb7/VnRs4cCDBwcGo1WoASktLadKkCS+++OJtvVfi1iQdWIFvwyh8GrTHyt6DsPZDMTQy5dTRjdXGn07YQr3mvXH1aYrS2hn/kCdx8W5K4oHlt91m9sUUkg6soFnUsBrtoxCN3Rzp1ySQ5p63NqC/ISmNOkpzXgirh7uNko51vWjh5cTqhNTbbtPIwAAbc1PdoTQ1vpuuCHFdTeoF0L9ze1oE33gW1xXrd+zD0c6WF7t3xN3ZkScfb0HLkPqs2rJTFxPWIIjQ+oG4ONrjWseB/p3bY2piwonTZ4HKGea2Viq9Y0/8ccIbN8TM1KRG+ikebds3/UWziA6EhUdSx8Wdnv1fwcTEhH07N1UbH7dnK2079qZuw1DsHZxp2boTQQ2asG3jCl1MvUZh1G0YikMdFxydXHmi27OYmJpy5lQSAM6unjw3+F3qNQrD3tEFv6BGRHXtT8Lh/ZSXl9+Xfgsh/ltkoEs8dJRKJUqlkmXLlukGY/5t7969AMybN4/09HTd6yVLljBx4kSmTp3Kvn37cHFx0RvguV2pqamsW7cOE5OrXyQrKipwd3fn999/59ixY4wfP54xY8awZMkSAEaOHEnfvn3p1KkT6enppKen06pVqyptnzt3js6dO9OsWTMOHTrEd999x08//cSUKVNumld6ejr9+/dn4MCBJCQkEBMTQ69evdBqtQAsXLiQ8ePH89FHH5GQkMDUqVMZN24cCxYsAGDWrFkUFhYyatQoAD744ANycnL4+uuv7/i9EtWrKNdwOSMZZ88QXZlCocDJM5is9MTr1CnD0Ej/jxdDI2MunT9+W21qytTsWvs5oe2GYG5pey+7JcRdO5GZQ0Nne72yYBdHTmTm3HZbCRcv88rvGxm+fCs/7T5Cvrr0HmUpxN1JSj1DcKCvXllIkD8nUs9WG19RUUHsgcOoS0sJ9PKoNib5zHlSz6UT2aJJteeFuBsajYZzaSn41706S1ahUOBfN5i0fwalqtYpw9hY/waDkbEJp5OPVxtfUVHBoX3bKVWr8fQJum4u6uIizMzNMTQ0vIOeCCEedLJ0UTx0jIyMmD9/PoMHD2b27NmEhobSpk0bnnnmGYKDg3F0rFzWYmNjo7cc8IsvviA6Opro6GgApkyZwoYNG25rVtfhw4dRKpWUl5fr6n322We688bGxrp9vAB8fHzYuXMnS5YsoW/fviiVSszNzVGr1Tdcqvjtt9/i4eHB119/jUKhoG7dupw/f57333+f8ePHY3CDpTfp6eloNBp69eqFl5cXAI0aNdKdnzBhAjNnzqRXr166HI8dO8b333/PSy+9hFKp5Ndff6VNmzaoVCq++OILNm/ejJWVVbXXU6vVVQYcNWUKjIxNr5ujqKQuzkOrrcD0muWEZhY25Gefq7aOk1cIifuX4+haH6WNCxln4jl7chdabcVttRm3dS4OLkG4+TW/x70S4u7lFKuxNtf/DLE2N6G4TEOpphwTo1v7wybExYFmHk7UUZqTUVDM4oOJTN+4j8mdwjEwUNRE6kLcspz8AqxVSr0ya5UlRSUllJaVYfLP4MDp8xmMm/UjpWUazExNGPnyM7g7O1bb5ubdB3BzciTIx7PG8xePnqLCPCoqKqosUVSqrMnMqP57S0DdELZt/Atv//rYOzpzMjGeo3G7qaio0ItLP3ea2TM/QFNWiompOc8PeY86Lu7VtllYkMemNf+jeUTUvemYEOKBIzO6xEOpd+/enD9/nhUrVtCpUydiYmIIDQ1l/vz5162TkJBAixb6G22Hh4ff1nWDgoKIi4tj7969vP/++3Ts2JE33nhDL+abb76hadOmODo6olQq+eGHH0hLS7ut6yQkJBAeHo5CcfUPsYiICAoKCjh7tvo7vVeEhITQvn17GjVqRJ8+fZgzZw7Z2dkAFBYWkpycTHR0tG5mnFKpZMqUKSQnJ+vaCA8PZ+TIkUyePJkRI0bw2GOPXfd606ZNw9raWu/Yserj2+qvuHVN2gxCZePKmp+H8fuspzmw+Qd86keiUNz6x/255D1knImnSdtBNZipELWvlY8rYR5OeNpa0czDiXfbhZGSlcuxjJrdz1GIe8mtjgMfjxzKR28PJqpVGN8sWsrZC5lV4krLyth+4LDM5hL/KU/1GYh9HRc++/BNxr7ZjxWLf6Jpy3ZVbto6Ornx5uhPGPruNFo8HsX/fvmKi+lVv/OWFBex4Lup1HHxILJz3/vVDSHEf4zM6BIPLTMzM6KiooiKimLcuHEMGjSICRMmMGDAgBq7pomJCf7+lRvETp8+nS5dujBp0iQmT54MwG+//cbIkSOZOXMm4eHhqFQqPvnkE3bv3l1jOV3L0NCQ9evXs2PHDv7++2+++uorPvjgA3bv3o2FhQUAc+bMqTLo9++p3xUVFcTGxmJoaMjJkydveL3Ro0czfPhwvbKpi2WmxK0wNbdCoTBAXZSrV15SlIOZRfXLCc0srHms22jKNaWoS/Ixt7QjPvYXLK3q3HKbF88epjA3gz+/fU4vJnblDBxd69Ouz82XyApRk2zMTckt1p8pmltcirmx0S3P5qqOk8oClakxF/KLaOhyt1kKcXdsVEpy8wv0ynLzC7EwM9PN5gIwMjLE2aHy6c5+Hq4knznP6q27GNK3q17dXYeOUVpWRutmjWs8d/FosrC0wsDAgIJ8/e8YBfm5KK2q/96iVFnz4ivvU1ZWSlFhPlbWdqxd/iu29nX04oyMjLB3rPxgdvf04+zpZGJjVtGz/yu6GHVJMfO+mfLPjK93MTKSP3WFeFTJjC7xyKhfv75uw3VjY+Mqm1PWq1evyoDTrl277uqaY8eO5dNPP+X8+fMAxMbG0qpVK1577TWaNGmCv7+/3kwpqBwsu9nGmfXq1WPnzp26fbWutK1SqXB3r34a978pFAoiIiKYNGkSBw8exMTEhKVLl+Lk5ISrqyspKSn4+/vrHT4+Prr6n3zyCcePH2fLli2sXbuWefPmXfdapqamWFlZ6R2ybPHWGBgaYefkR0ZavK5Mq9Vy8cxh7F2uvy8FgKGRCRZKe7QV5Zw9uRM3vxa33GbdsF50fO5zOj73me4AaNxmIM2eeKPqxYS4zwIcbTh6QX/WVXz6JQIcbe6q3azCYgpKy7Axl88oUfsCvT04fOKUXll8UjIB3jf5d16rpayah9Ns2nWApvWDsFZa3ss0hdAxMjLCzdOX5MTDujKtVsvJxMN4+tz4KZ/GxiZY29hTXl7O0bjd1A9uduOLabVoNGW6lyXFRfz01YcYGhrx4qujMDaWhy0I8SiTgS7x0MnKyiIyMpJff/2V+Ph4Tp06xe+//87HH39M9+7dAfD29mbjxo1cuHBBt2zvrbfeYu7cucybN4+kpCQmTJjA0aNH7yqX8PBwgoODmTp1KgABAQHs27ePdevWkZSUxLhx43Sb4V/h7e1NfHw8iYmJXLp0ibKysirtvvbaa5w5c4Y33niD48ePs3z5ciZMmMDw4cNvuD8XwO7du3Ub7qelpfHnn3+SmZlJvXqVT4GaNGkS06ZNY9asWSQlJXH48GHmzZun22vs4MGDjB8/nh9//JGIiAg+++wz3nrrLVJSUu7qvRLVCwztRsqR9Zw6tom8rDPs3zQbTVkJPg0iAdi97gvit/+ii89KT+TsiZ0U5F4g89wxtiz7EG2FlrphPW+5TXNLW6wdvPQOAEuVI0prp/vYe/GoKCnTkHo5j9TLeQBkFhSRejmPS4XFAPzfgUS+iT2ki+8Q6ElGQTEL9x/nXG4B6xNPs/v0BTrX877lNkvKNPy6/zgnMnPILCjmSPolPo05gJPSghBXh/vUc/EoKVGXcupsOqfOpgOQeTmHU2fTuZSdA8DClev5euGfuvioVmFczLrMryv+5lxGJuti97Az7ihd2lzdVmHhyvUcO5lK5uUcTp/PYOHK9Rw9mcrjTYP1rn3h0mUSUk4T2TK05jsqHmmPRXZlb+wG9u/azMX0syz77QdK1WqatmwHwJIFs1i7/FddfNqpJI4c3EXWpQucOnmM+d9MoaKigtZRPXQxa5f/SsqJo2RnXST93Gnd68bNHgcqB7nmfj2ZsrJSej8/FHVxEfm52eTnZlfZ60sI8WiQ+ZzioaNUKmnRogWff/45ycnJlJWV4eHhweDBgxkzZgwAM2fOZPjw4cyZMwc3NzdSU1Pp168fycnJvPfee5SUlNC7d2+GDh3KunXr7iqfd955hwEDBvD+++/zyiuvcPDgQfr164dCoaB///689tprrFmzRhc/ePBgYmJiCAsLo6CggM2bN+Pt7a3XppubG6tXr+bdd98lJCQEOzs7oqOjGTt27E3zsbKyYuvWrXzxxRfk5eXh5eXFzJkzefLJJwEYNGgQFhYWfPLJJ7z77rtYWlrSqFEj3n77bUpKSnj++ecZMGAAXbtWLokYMmQIq1at4oUXXmDr1q3ydJt7zDPwMdRFuRzZ+RslRdnYOvrQusc4zCxsACjKu6S3/1Z5uYbDOxdRmJuBkbEZLj6htOz4NiamlrfcphD3W0pWLpPX79G9/mVf5dO2Wvu5MbRVMDklarIKrz4YpI7SgvfbNeXn/QmsPZ6KnYUZQ8IbEuLqeMttGigUpGXnsy3lHEWlZdiYmxHs6kDfkACM5XNM1IDkM+eY9M183esFy9YC0KZZY15/tic5eQVcyr665KuOvS2jBj/HgmXrWL1tF/bW1rzarxuN6/rrYvIKivjm/5aSnZePhZkZnq5OfPDKCwQH+elde9PuA9hZW+nVFaImBDeNoCA/jw0rF5Ofl4Orhzcvv/4BKisbAHIuX0Lxr5uyGk0Z61f+xuVLGZiYmhLUIJS+A97E3OLq95bC/Dz+9/PX5OVmY2ZugbObFy8PG0tA3conSJ8/k8KZ1BMAfDpRf+b5ex9+W2UZpBDi4afQ/nvtkxBC3Adj55fWdgpC1LgR596q7RSEqHFGHbvXdgpC1LgD5q1rOwUhalybBha1ncIdy/5oaG2nUC3bD76r7RQeWbJ0UQghhBBCCCGEEEI8FGSgS4hbpFQqr3ts27atttPTSUtLu2GuaWlptZ2iEEIIIYQQQghRI2SPLiFuUVxc3HXPubm53b9EbsLV1fWGubq6ut6/ZIQQQgghhBBCiPtIBrqEuEX+/g/GBq5GRkYPTK5CCCGEEEIIIcS9JEsXhRBCCCGEEEIIIcRDQQa6hBBCCCGEEEIIIcRDQQa6hBBCCCGEEEIIIcRDQfboEkLcd1HNNLWdghA1biZf1nYKQtS4Eevequ0UhKhxoR1rOwMh7odOtZ2AEPeMzOgSQgghhBBCCCGEEA8FGegSQgghhBBCCCGEEA8FGegSQgghhBBCCCGEEA8FGegSQgghhBBCCCGEEA8FGegSQgghhBBCCCGEEA8FGegSQgghhBBCCCGEEA8FGegSj7yJEyfSuHHj2k6D1NRUFAoFcXFxtZ2KEEIIIYQQQgjxQDKq7QSEuFOZmZmMHz+eVatWkZGRga2tLSEhIYwfP56IiAgUCgVLly6lR48e9+yaEydOZNKkSQAYGBjg6urKk08+yfTp07Gzs7vldgYMGEBOTg7Lli3TlXl4eJCeno6Dg8M9y1c8HHZuWcu2DcvJz8vBxd2Lrn2i8fAOqDZWo9Gw5e+lHNgdQ17OZRycXOnU/XmCGjTRxXw8bijZlzOr1G35eEe6PzMYgN3b/+bQ3u2cP3sKdUkx4z9ZgLmFZc10UDzyLp49SuL+ZWRfTKG48DKPdR2Fm1+LG9c5c4S4rXPJvXwGC5UD9Zv3wad+pO78yUNrSD68jsK8iwBY2XnQoGU/XLxDASjMu8jKua9U23arziPxCIy4R70TotKxjMusPJpC6uU8sovVDG8bSjMPpxvXuZDFz/sTOJdTgL2lOT0b+dHGz/2W29SUV7DkUBJx5zLJKCjG0tiIBi72PNskCFsLsxrrq3i0rd2+h782x5KTV4CXqzMv93qSAC/3amM1mnKWbdrGlj2HuJybh0sde557Koom9a5+z6moqGDJ2hi27T9Ebn4htlYq2jQPoXdUGxQKBQB935lQbfvPd32CbpHyeS7Eo0YGusQDq3fv3pSWlrJgwQJ8fX3JyMhg48aNZGVl1eh1GzRowIYNGygvLychIYGBAweSm5vL4sWL76pdQ0NDnJ2d71GWVZWWlmJiYlJj7YuaEb8/llV/zKdH/yF4egeyffNK5n49hRETZqFUWVeJX7/y/4jbs5Wezw7F0dmVE8fiWDjnE14d8RGuHj4AvPbedLQVFbo6F9LTmPvVZBqFhuvKykpLCWzQhMAGTVi3fGHNd1Q80so1amwcvfFp0J7YlTNuGl+Qm8G2FVPwa9SRlp3eIeNMPHvXf4OZhS0u3pWDuuYqBxpFvIDKxgUtWk4nxLD9r2k88exMrO09sVA60G3wXL12U46s5/i+pbj4NK2RfopHW6mmHC9bK9r6u/P5loM3jb9YUMSMzfvpEODBsIjGHL2QxQ87j2BjbkqIq+MttakuL+fU5Tx6NfLH005FoVrDgn3H+CRmP1M7yx//4t7bcfAIPy9fy+CnuxLg5c6qrTv56Ptf+HLMm1grq94wW7xmE1v3H+KVvt1wq+NAXOJJZs5bzOQ3o/FxdwFg2cbt/L1jL6/374GnixPJZ87x7f8tw9LMjCdbtwTg+0kj9dqNSzjJ7MXLaRFcr+Y7LYT4z5Gli+KBlJOTw7Zt25gxYwbt2rXDy8uL5s2bM3r0aLp164a3tzcAPXv2RKFQ6F4DTJ8+HScnJ1QqFdHR0ZSUlNzWtY2MjHB2dsbNzY0OHTrQp08f1q9frztfXl5OdHQ0Pj4+mJubExQUxJdffqk7P3HiRBYsWMDy5ctRKBQoFApiYmKqXbq4ZcsWmjdvjqmpKS4uLowaNQqNRnNLebZt25Zhw4bx9ttv4+DgQMeOHQH47LPPaNSoEZaWlnh4ePDaa69RUFCgVzc2Npa2bdtiYWGBra0tHTt2JDs7G6i8qzZt2jRd/0JCQvjf//53W++huHXbN/1Fs4gOhIVHUsfFnZ79X8HExIR9OzdVGx+3ZyttO/ambsNQ7B2cadm6E0ENmrBt4wpdjFJljcraVnckHjmAvaMzPgENdDGPRT5F2yd64nmdmWNC3Esu3qE0avUc7v4tbyk++fA6LK3q0Lj1y1jZexDQuAseAeEkHfxLF+Pm2wxXn6aobF2xsnWjUavnMDI2I+tCEgAKAwPMLW31jnMnd+EZGIGRscx0EfdeYzdH+jUJpLnnrd3U2pCURh2lOS+E1cPdRknHul608HJidULqLbdpaWLMBx2a09LbBVcrJQGONrzcrD6nsvK4VFh8L7olhJ6VMTtp37Ip7Vo0wd3ZkSF9umJqYszm3Qeqjd+6/xA9O7QmtH4gTg52dIxoTpN6AayM2aGLSUo9Q7OGdWnaIAhHOxtahjQgJMifk2nndDG2Viq9Y++R4zTw98bJ4dZXXAghHh4y0CUeSEqlEqVSybJly1Cr1VXO7927F4B58+aRnp6ue71kyRImTpzI1KlT2bdvHy4uLnz77bd3nEdqairr1q3TmylVUVGBu7s7v//+O8eOHWP8+PGMGTOGJUuWADBy5Ej69u1Lp06dSE9PJz09nVatWlVp+9y5c3Tu3JlmzZpx6NAhvvvuO3766SemTJlyy/ktWLAAExMTYmNjmT17NlC55HLWrFkcPXqUBQsWsGnTJt577z1dnbi4ONq3b0/9+vXZuXMn27dvp2vXrpSXlwMwbdo0fv75Z2bPns3Ro0d55513eP7559myZcsdvYfi+jQaDefSUvCvG6wrUygU+NcNJu1U0nXqlGFsbKxXZmRswunk49e9xsE9W2jasp1u+r8Q/3VZ6Yk4eYbolTl7NSErPbHaeG1FBWmJ29CUleDgHFRtzOWMk2RnnsKnQft7nq8Qd+JEZg4Nne31yoJdHDmRmXNX7RaVaVAowMJYFnaIe0ujKSfl7HkaBfrqyhQKBY0C/UhKPVttnTJNOSbX/CyaGBtx/FSa7nWgtwdHTqRw/uIlAFLPXSAh5TSN61V/My4nv4CDCSeIbBF6t10SQjyg5F848UAyMjJi/vz5DB48mNmzZxMaGkqbNm145plnCA4OxtGxckq/jY2N3nLAL774gujoaKKjowGYMmUKGzZsuK1ZXYcPH0apVFJeXq6r99lnn+nOGxsb6/bxAvDx8WHnzp0sWbKEvn37olQqMTc3R61W33Cp4rfffouHhwdff/01CoWCunXrcv78ed5//33Gjx+PgcHNx6kDAgL4+OOP9crefvtt3f/39vZmypQpvPrqq7oBv48//piwsDC9AcAGDSpn+qjVaqZOncqGDRsID69c5ubr68v27dv5/vvvadOmTZUc1Gp1lcHI0tJyTExMb5r/o66oMI+KiooqSxSVKmsyM85VWyegbgjbNv6Ft3997B2dOZkYz9G43VT8a6nivx2L30NJcRFNw9vd8/yFqCklhdmYWdjolZla2FBWWkS5phRDo8qbDzmXUtm4eDTlmlKMTcyJeGoUVvYe1bZ56uhGrOzccXCVZS7ivyGnWI21uf6/ldbmJhSXaSjVlGNiZHjbbZZqyll0IJFwbxcsTIxvXkGI25BXWERFRQU2KqVeuY3SkvMZl6qtExLkx1+bd1DP1wtnBzsOJ6WwOz6BCu3V7y09OzxOsVrNO9O/xsDAgIqKCp7p3J7HmwZX2+aWvXGYmZrQXJYtCvHIkhld4oHVu3dvzp8/z4oVK+jUqRMxMTGEhoYyf/7869ZJSEigRQv9DY6vDNjcqqCgIOLi4ti7dy/vv/8+HTt25I033tCL+eabb2jatCmOjo4olUp++OEH0tLSrtPi9XMNDw/Xm2UTERFBQUEBZ89Wf1fsWk2bVt1nZsOGDbRv3x43NzdUKhUvvPACWVlZFBUVAVdndFXn5MmTFBUVERUVpZtVp1Qq+fnnn0lOTq62zrRp07C2ttY7Fs759JbyF7fvqT4Dsa/jwmcfvsnYN/uxYvFPNG3Z7roDo/t2bCSwfhOsrGVqv3j4WNm688Rzn9HhmRn4NerInr9nkZd1pkpcuaaUtMRt+DToUAtZCnF/aMor+HJbHFq0RDdvcPMKQtwHA3o+iYujPW9P+4r+Iz9k7p+radu8MQaKq99bdhw8wvb9h3nz+d5MH/4Krz/bk79idhCzp/q97jbvPsjjTYMxMZbBXCEeVTKjSzzQzMzMiIqKIioqinHjxjFo0CAmTJjAgAEDauyaJiYm+Pv7A5X7fXXp0oVJkyYxefJkAH777TdGjhzJzJkzCQ8PR6VS8cknn7B79+4ay+l6LC31N/1MTU3lqaeeYujQoXz00UfY2dmxfft2oqOjKS0txcLCAnNz8+u2d2Uvr1WrVuHm5qZ3ztS0+hlao0ePZvjw4Xplu5PL76Q7jxwLSysMDAwoyM/VKy/Iz0VpZVttHaXKmhdfeZ+yslKKCvOxsrZj7fJfsbWvUyU2O+siJ4/H8/yQ96ppSYj/LjNLW0qKcvTK1EU5GJtY6GZzARgYGqGyqdzM2M7Jn8sZJ0mKW0lY+6F6dc+c2IFGo8a7XtuaTl2IW2Zjbkpusf6M6NziUsyNjW57NlflINdBLhUWMzaquczmEjXCytICAwMDcvL1937NKSjExkpZbR1rpSXvRfentKyM/MJi7KxVLFq5AUc7G13Mr3+tp3v7x4gIbQSAl6sTmZdzWLZxO22bN9FrLyH5NOcvXuLtF/vc284JIR4oMqNLPFTq169PYWEhULmE8Mq+UlfUq1evyoDTrl277uqaY8eO5dNPP+X8+fNA5UburVq14rXXXqNJkyb4+/tXme1kYmJSJbdr1atXj507d6LVanVlsbGxqFQq3N2rf0Tzzezfv5+KigpmzpxJy5YtCQwM1OV9RXBwMBs3bqy2fv369TE1NSUtLQ1/f3+9w8Oj+uVApqamWFlZ6R2ybPHWGBkZ4ebpS3LiYV2ZVqvlZOJhPH0Cb1jX2NgEaxt7ysvLORq3m/rBzarE7N+1GaXKmqAGsoeFeLDYuwSRkRavV3YhLQ57l+r337pCi5aK8rIq5aeObMDVpxlmFlWfZCpEbQlwtOHoBf0nScenXyLA0ea22rkyyHUhv4gPOjRDZSpPYBY1w8jIEF93V46cOKUr02q1HElKIdD7xt9dTYyNsbexory8gt3xx2jWqK7unLqsjGu3ETUwMND7jnzFpt0H8PVwxdut5p5kLoT475OBLvFAysrKIjIykl9//ZX4+HhOnTrF77//zscff0z37t2Byv2nNm7cyIULF3RPDHzrrbeYO3cu8+bNIykpiQkTJnD06NG7yiU8PJzg4GCmTp0KVO6LtW/fPtatW0dSUhLjxo3TbYZ/hbe3N/Hx8SQmJnLp0iXKyqr+4fXaa69x5swZ3njjDY4fP87y5cuZMGECw4cPv6X9uarj7+9PWVkZX331FSkpKfzyyy+6TeqvGD16NHv37uW1114jPj6e48eP891333Hp0iVUKhUjR47knXfeYcGCBSQnJ3PgwAG++uorFixYcEc5iRt7LLIre2M3sH/XZi6mn2XZbz9QqlbTtGXlnlpLFsxi7fJfdfFpp5I4cnAXWZcucOrkMeZ/M4WKigpaR/XQa1er1bJ/52ZCW7bF0LDqzID83GzOnzlFVuYFAC6cP835M6coKsyvuc6KR5amrITsiylkX0wBoCA3g+yLKRTmZQIQv/0Xdq/7Qhfv16gjhXkZHNq2gLzLZzl5aA1nknYQ2KSrLiZ++y9cPHuUwryL5FxKJX77L2SePYJnXf29BPNz0sk8fwzfhrJsUdSskjINqZfzSL2cB0BmQRGpl68+/fD/DiTyTewhXXyHQE8yCopZuP8453ILWJ94mt2nL9C5nvctt6kpr+CLrQdJycpjWEQIFdrKvb9yitVoyqvfu1GIu/FU23A27txPzJ6DnL2QyZzfV1JSWqqbefX1wj9ZuPLq08pPpJ5l96FjZFy6TELyaab+8AsVWi3dIx/TxTStH8jS9dvYfzSRzMs57I5PYGXMDpo30t+Dq6ikhF2Hjsom9EIIWbooHkxKpZIWLVrw+eefk5ycTFlZGR4eHgwePJgxY8YAMHPmTIYPH86cOXNwc3MjNTWVfv36kZyczHvvvUdJSQm9e/dm6NChrFu37q7yeeeddxgwYADvv/8+r7zyCgcPHqRfv34oFAr69+/Pa6+9xpo1a3TxgwcPJiYmhrCwMAoKCti8eTPe3t56bbq5ubF69WreffddQkJCsLOzIzo6mrFjx95xniEhIXz22WfMmDGD0aNH07p1a6ZNm8aLL76oiwkMDOTvv/9mzJgxNG/eHHNzc1q0aEH//v0BmDx5Mo6OjkybNo2UlBRsbGwIDQ3Vve/i3gpuGkFBfh4bVi4mPy8HVw9vXn79A1RWNgDkXL6E4l8DnxpNGetX/sblSxmYmJoS1CCUvgPexNxCfxnryeOHyMm+RNOWkdVed/f2v9m4+nfd6x8+Hw/A0y+8rhtkE+JeuXzhJJv/GKd7Hbd1HgA+9dvR/Ik3KSnKpijv6kbGSmsnHu82lritc0mKW4mF0oFmUa/j4n11CYu6OJc9f8+iuPAyJiaWWDt40brnBJyveVrjqaMbMVfa4+ylv/xFiHstJSuXyev36F7/sq/yabit/dwY2iqYnBI1WYVXH45TR2nB++2a8vP+BNYeT8XOwowh4Q0JcXW85Tazi9XsP3sRgFGrYvXyGRfVnPrXPNVRiLvVqklDcgsKWbJ2Mzn5BXi7uvDBkBd0G9Rfys7V23+2TKPhtzWbuJiVjampCaH1Ahj2XG8szc10MQN7dWbxmk389Mcq8gqKsLVS0aFVGH2eaKt37dgDR9Bq0S1xFEI8uhTa6uZ8CiFEDdpytKi2UxCixq3fK/eSxMNvxLm3ajsFIWqcUcfutZ2CEDVOFdaptlO4Y9kfDb15UC2w/eC72k7hkSVLF4UQQgghhBBCCCHEQ0EGuoT4F6VSed1j27ZttZ2eTlpa2g1zTUtLq+0UhRBCCCGEEEKI+07WVQjxL3Fxcdc95+bmdv8SuQlXV9cb5urq6nr/khFCCCGEEEIIIf4jZKBLiH/x9/ev7RRuiZGR0QOTqxBCCCGEEEIIcb/I0kUhhBBCCCGEEEII8VCQgS4hhBBCCCGEEEII8VCQgS4hhBBCCCGEEEII8VCQPbqEEPddaPHW2k5BiBq3nsjaTkEIIcQ9cMC8dW2nIESNa1PbCQhxD8mMLiGEEEIIIYQQQgjxUJCBLiGEEEIIIYQQQgjxUJCBLiGEEEIIIYQQQgjxUJCBLiGEEEIIIYQQQgjxUJCBLiGEEEIIIYQQQgjxUJCBLlFrJk6cSOPGjWs7DVJTU1EoFMTFxdV2KkIIIYQQQgghhLgLRrWdgHjwZGZmMn78eFatWkVGRga2traEhIQwfvx4IiIiUCgULF26lB49etyza06cOJFJkyYBYGBggKurK08++STTp0/Hzs7ultsZMGAAOTk5LFu2TFfm4eFBeno6Dg4O9yzfmjZz5ky++uorMjIy8PT0ZMSIEQwZMqS20xI1ZO32Pfy1OZacvAK8XJ15udeTBHi5Vxur0ZSzbNM2tuw5xOXcPFzq2PPcU1E0qRegi3l98udkXs6pUveJiGYMevopALLz8ln413oOJSZTolbj4uhA76jWtAipXyN9FI+2i2ePkrh/GdkXUyguvMxjXUfh5tfixnXOHCFu61xyL5/BQuVA/eZ98KkfWW1swt4/iY/9hcAmT9GkTbSuvLgwm/jtC7hw+hCasmJUNq7Ub94H94Dwe9o/IQCOZVxm5dEUUi/nkV2sZnjbUJp5ON24zoUsft6fwLmcAuwtzenZyI82fvqf/38nnuavo6fILVHjaatiQLP6+DvYAJBZUMybS2OqbfutxxvT0tvlXnRNCD07t6xl24bl5Ofl4OLuRdc+0Xh4B1Qbq9Fo2PL3Ug7sjiEv5zIOTq506v48QQ2a6GI+HjeU7MuZVeq2fLwj3Z8ZTFFhPhtWLeFEwiFysy9hoVTRIKQFHbr0w9zCssb6KYT475KBLnHbevfuTWlpKQsWLMDX15eMjAw2btxIVlZWjV63QYMGbNiwgfLychISEhg4cCC5ubksXrz4rto1NDTE2dn5HmVZ87Zu3crIkSOZNWsWXbt25ezZs2RmVv3H/1rl5eUoFAoMDGQi54Nkx8Ej/Lx8LYOf7kqAlzurtu7ko+9/4csxb2KtrPrlbfGaTWzdf4hX+nbDrY4DcYknmTlvMZPfjMbHvfIPmqnvDKGiokJX50z6RabM/pnwxg10ZV8vXEpRSQnvRffHytKC7QcO89mCJUwf/oquHSHulXKNGhtHb3watCd25YybxhfkZrBtxRT8GnWkZad3yDgTz97132BmYYuLdxO92MsXTpB8eB02Dl5V2tm97kvK1IU81m00puZWpB3fxo7VnxDV/1Ns6/jes/4JAVCqKcfL1oq2/u58vuXgTeMvFhQxY/N+OgR4MCyiMUcvZPHDziPYmJsS4uoIwM7UdH7Zl0B0i4b4O9iw5ngq0zbu5fPurbEyM8XewozvntYfAN544gx/HU2hsZtjjfRTPNri98ey6o/59Og/BE/vQLZvXsncr6cwYsIslCrrKvHrV/4fcXu20vPZoTg6u3LiWBwL53zCqyM+wtXDB4DX3puO9l/fWy6kpzH3q8k0Cq28KZGXm01+7mU693qROs5u5Fy+xLLffiAvJ4vnBr97fzouhPhPkb94xW3Jyclh27ZtzJgxg3bt2uHl5UXz5s0ZPXo03bp1w9vbG4CePXuiUCh0rwGmT5+Ok5MTKpWK6OhoSkpKbuvaRkZGODs74+bmRocOHejTpw/r16/XnS8vLyc6OhofHx/Mzc0JCgriyy+/1J2fOHEiCxYsYPny5SgUChQKBTExMdUuXdyyZQvNmzfH1NQUFxcXRo0ahUajuaU8//e//9GoUSPMzc2xt7enQ4cOFBYW6s7/+OOP1KtXDzMzM+rWrcu3336rOzdw4ECCg4NRq9UAlJaW0qRJE1588UVdjIGBAYaGhkRHR+Pt7c1jjz1Gz549q+Qxf/58bGxsWLFiBfXr18fU1JS0tDT27t1LVFQUDg4OWFtb06ZNGw4cOKBXNycnh1deeQUnJyfMzMxo2LAhK1eu1J3fvn07jz/+OObm5nh4ePDmm2/q9VHcOytjdtK+ZVPatWiCu7MjQ/p0xdTEmM27D1Qbv3X/IXp2aE1o/UCcHOzoGNGcJvUCWBmzQxdjrbTE1kqlOw4cS8LZwY76ft66mBOnz/Dk4y0I8HLHycGO3k+0wdLCnJSz52u6y+IR5OIdSqNWz+Hu3/KW4pMPr8PSqg6NW7+Mlb0HAY274BEQTtLBv/TiNGUl7Fr7OWEdXsPEVFmlnaz0RAIad8HeORCltTP1W/TBxFRJ9sXke9IvIf6tsZsj/ZoE0tzz1m6ubUhKo47SnBfC6uFuo6RjXS9aeDmxOiFVF7Mq4RSRAR609XfH3UbJoBYNMDU0ZPPJswAYGCiwMTfVO/aeuUC4twtmxnK/W9x72zf9RbOIDoSFR1LHxZ2e/V/BxMSEfTs3VRsft2crbTv2pm7DUOwdnGnZuhNBDZqwbeMKXYxSZY3K2lZ3JB45gL2jMz4BlTfonF09eW7wu9RrFIa9owt+QY2I6tqfhMP7KS8vvy/9FkL8t8hAl7gtSqUSpVLJsmXLdIMx/7Z3714A5s2bR3p6uu71kiVLmDhxIlOnTmXfvn24uLjoDfDcrtTUVNatW4eJiYmurKKiAnd3d37//XeOHTvG+PHjGTNmDEuWLAFg5MiR9O3bl06dOpGenk56ejqtWrWq0va5c+fo3LkzzZo149ChQ3z33Xf89NNPTJky5aZ5paen079/fwYOHEhCQgIxMTH06tULrVYLwMKFCxk/fjwfffQRCQkJTJ06lXHjxrFgwQIAZs2aRWFhIaNGjQLggw8+ICcnh6+//lp3jcaNG+Pm5sZrr72mNyunOkVFRcyYMYMff/yRo0ePUqdOHfLz83nppZfYvn07u3btIiAggM6dO5Ofn697H5988kliY2P59ddfOXbsGNOnT8fQ0BCA5ORkOnXqRO/evYmPj2fx4sVs376dYcOG3fT9EbdHoykn5ex5GgVenVmiUChoFOhHUurZauuUacoxueaPFxNjI46fSrvuNbbuj6dt8yYoFApdeYCXBzsOHiG/sAitVkvsgcOUlZXRwN/nHvRMiLuTlZ6Ik2eIXpmzVxOy0hP1yvZv+h4XnzCcr4m9wt4liDNJsaiL89FqtaQlbqO8vBRH94Y1lrsQt+pEZg4Nne31yoJdHDmRmQOApryCU1l5NHS5uvWCQqGgoYu9LuZaKVm5nL6cT1u/6pe/C3E3NBoN59JS8K8brCtTKBT41w0m7VTSdeqUYWxsrFdmZGzC6eTj173GwT1baNqynd73lmupi4swMzfXfX8VQjxa5FaOuC1GRkbMnz+fwYMHM3v2bEJDQ2nTpg3PPPMMwcHBODpWToO3sbHRWw74xRdfEB0dTXR05d4oU6ZMYcOGDbc1q+vw4cMolUrKy8t19T777DPdeWNjY90+XgA+Pj7s3LmTJUuW0LdvX5RKJebm5qjV6hsuVfz222/x8PDg66+/RqFQULduXc6fP8/777/P+PHjb7j0Lz09HY1GQ69evfDyqlwm06hRI935CRMmMHPmTHr16qXL8dixY3z//fe89NJLKJVKfv31V9q0aYNKpeKLL75g8+bNWFlZAZWDUD169CAkJIScnByeffZZfv75Z92AX6NGjXjppZcYOXIkAGVlZXz77beEhFz9Iy8yUn8Jww8//ICNjQ1btmzhqaeeYsOGDezZs4eEhAQCAwMB8PW9OtAybdo0nnvuOd5++20AAgICmDVrFm3atOG7777DzMzsuu+PuD15hUVUVFRgo9KfiWKjtOR8xqVq64QE+fHX5h3U8/XC2cGOw0kp7I5PoEJb/aDo3iMJFBWX0La5/nKv4QP68vmCJUSPnYGhoSGmxsaMHPgMzg63vieeEDWlpDAbMwsbvTJTCxvKSoso15RiaGRCWuI2si+mENX/k+u206rLu+xc/SnLvn8RAwNDDI1MiXhqFCobWZ4ral9OsRprc1O9MmtzE4rLNJRqyiksLaNCq8XazEQ/xsyU83nVz7KOOXkWN2tLgurY1lje4tFVVJhHRUVFlSWKSpU1mRnnqq0TUDeEbRv/wtu/PvaOzpxMjOdo3O7r3sw9Fr+HkuIimoa3u24ehQV5bFrzP5pHRN15Z4QQDzSZ0SVuW+/evTl//jwrVqygU6dOxMTEEBoayvz5869bJyEhgRYt9DcWDg+/vc1+g4KCiIuLY+/evbz//vt07NiRN954Qy/mm2++oWnTpjg6OqJUKvnhhx9IS6t+JsuNcg0PD9e7SxQREUFBQQFnz1Y/i+aKkJAQ2rdvT6NGjejTpw9z5swhOzsbgMLCQpKTk4mOjtbNjFMqlUyZMoXk5KvLZMLDwxk5ciSTJ09mxIgRPPbYY7pza9euJTY2lvnz57N48WKysrLo2rUrhYWFlJSUcPLkSR5//HFdvImJCcHBV++qAWRkZDB48GACAgKwtrbGysqKgoIC3fsUFxeHu7u7bpDrWocOHWL+/Pl6fejYsSMVFRWcOnWqSrxarSYvL0/vUJeW3vB9FHduQM8ncXG05+1pX9F/5IfM/XM1bZs3xkBR/cf9xl0HaFzXHztrlV754jWbKCpWM27oS0x7Zwhd2obz+YLfOX0+4350Q4i7UpR/iYMxP9HyyXcwNDK5btyRHYsoUxfSttckovp/QlBoN3au/pScS6n3L1kh7pNSTTmxqedp6+9R26kIofNUn4HY13Hhsw/fZOyb/Vix+Ceatmx33RvL+3ZsJLB+E6ysq7/xVlJcxILvplLHxYPIzn1rMnUhxH+YzOgSd8TMzIyoqCiioqIYN24cgwYNYsKECQwYMKDGrmliYoK/vz9Qud9Xly5dmDRpEpMnTwbgt99+Y+TIkcycOZPw8HBUKhWffPIJu3fvrrGcrmVoaMj69evZsWMHf//9N1999RUffPABu3fvxsLCAoA5c+ZUGfT797TqiooKYmNjMTQ05OTJk3px8fHxeHp66p40uWzZMp544gnat29Pjx498PX11Wvb3Ny8yrTul156iaysLL788ku8vLwwNTUlPDyc0n8Gn8zNzW/Yx4KCAl555RXefPPNKuc8PT2rlE2bNk1vph3AqMHPMWbI8ze8jgArSwsMDAzIyS/QK88pKMTGqup+Q1C5/9Z70f0pLSsjv7AYO2sVi1ZuwNHOpkps5uUcDielMHLgM3rlFy5dZu223cx8/3U8nOsA4O3mTELKadZt38OQvl3vTQeFuENmlraUFOXolamLcjA2scDQyITsi8mUFOfy98IRuvNabQWZ549xIm41fd74nYK8DE4cWk2nF77E2r7ys8vG0YfMc8c4eWgNYe2H3s8uCVGFjbkpucX620TkFpdibmyEiZEhBgoFBgoFuSX6N49yS9TYmOnPBAPYnXaBUk05rX1dazRv8eiysLTCwMCAgvxcvfKC/FyUVtXPIlSqrHnxlfcpKyulqDAfK2s71i7/FVv7OlVis7MucvJ4PM8Pea/attQlxcz7ZgompuY8P+RdjIzkT10hHlUyo0vcE/Xr19dtRm5sbFxl48d69epVGXDatWvXXV1z7NixfPrpp5w/X7k5dmxsLK1ateK1116jSZMm+Pv7682UgsrBspttSlmvXj127typ21frStsqlQp395vvaaFQKIiIiGDSpEkcPHgQExMTli5dipOTE66urqSkpODv7693+Phc3ffok08+4fjx42zZsoW1a9cyb9483Tk3NzdOnTqlm1lmaWnJ6tWrKS0tZfTo0be0j1hsbCwXQ3y7AABSAElEQVRvvvkmnTt3pkGDBpiamnLp0tVlcMHBwZw9e5akpOr3UggNDeXYsWNV+uDv76+3Z9oVo0ePJjc3V+8YMUDusN0KIyNDfN1dOXLi6kw5rVbLkaQUAr1v/LNoYmyMvY0V5eUV7I4/RrNGdavEbN5zEGuVktB6+o/8VpeWAaBAf5DUQKFAixYhapu9SxAZafF6ZRfS4rB3CQKgjkcwnZ7/go7PfaY77Jz88arbmo7PfYbCwIByTeUAwrU/5woDA73PfyFqS4CjDUcv6D/ROj79EgGONgAYGRrgY2/F0fSrMVqtlqMXsnQx/7b55FlC3etgVc0gmBD3gpGREW6eviQnHtaVabVaTiYextOn+pUCVxgbm2BtY095eTlH43ZTP7hZlZj9uzajVFkT1CC0yrmS4iJ++upDDA2NePHVURgbX382rxDi4ScDXeK2ZGVlERkZya+//kp8fDynTp3i999/5+OPP6Z79+4AeHt7s3HjRi5cuKBbtvfWW28xd+5c5s2bR1JSEhMmTODo0aN3lUt4eDjBwcFMnToVqNwrat++faxbt46kpCTGjRun2wz/Cm9vb+Lj40lMTOTSpUuUlZVVafe1117jzJkzvPHGGxw/fpzly5czYcIEhg8ffsP9uQB2796t23A/LS2NP//8k8zMTOrVqwfApEmTmDZtGrNmzSIpKYnDhw8zb9483V5jBw8eZPz48fz4449ERETw2Wef8dZbb5GSkgJULhv19PSkS5cubNiwgZMnT7JmzRouX76MpaUl8+bNu+kG9QEBAfzyyy8kJCSwe/dunnvuOb1ZXG3atKF169b07t2b9evXc+rUKdasWcPatWsBeP/999mxYwfDhg0jLi6OEydOsHz58utuRm9qaoqVlZXeYVrNgJio3lNtw9m4cz8xew5y9kImc35fSUlpqW5Pra8X/snClVefPnoi9Sy7Dx0j49JlEpJPM/WHX6jQauke+Zheu1qtlpg9B2nTLKTKRq1udRxwdrBjzu9/ceL0WS5cusxfm2M5nJRCs4ZVB8yEuFuashKyL6aQfbHys64gN4PsiykU5mUCEL/9F3av+0IX79eoI4V5GRzatoC8y2c5eWgNZ5J2ENikcrahsYk51g5eeoeRkSkmZiqsHSr3T7SydUdp48y+TbPJupBEfk46x/cvJyPtEG5++rNuhbgXSso0pF7OI/VyHgCZBUWkXs7jUmExAP93IJFvYg/p4jsEepJRUMzC/cc5l1vA+sTT7D59gc71vHUxXer5sOnkGbYkn+VsTgE/7T5Kiaa8ymbzF/ILOX7xMu1k2aKoYY9FdmVv7Ab279rMxfSzLPvtB0rVapq2rNxTa8mCWaxd/qsuPu1UEkcO7iLr0gVOnTzG/G+mUFFRQeuoHnrtarVa9u/cTGjLtlW+t5QUFzH368mUlZXS+/mhqIuLyM/NJj83+6bfi4UQDyeZzylui1KppEWLFnz++eckJydTVlaGh4cHgwcPZsyYMQDMnDmT4cOHM2fOHNzc3EhNTaVfv34kJyfz3nvvUVJSQu/evRk6dCjr1q27q3zeeecdBgwYwPvvv88rr7zCwYMH6devHwqFgv79+/Paa6+xZs0aXfzgwYOJiYkhLCyMgoICNm/ejLe3t16bbm5urF69mnfffZeQkBDs7OyIjo5m7NixN83HysqKrVu38sUXX5CXl4eXlxczZ87kySefBGDQoEFYWFjwySef8O6772JpaUmjRo14++23KSkp4fnnn2fAgAF07Vr5x9qQIUNYtWoVL7zwAlu3bsXCwoIdO3YwZswYXn75ZTIzM2nQoAGTJ08mLCyMFi1a8PbbbzNr1qzr5vjTTz8xZMgQQkND8fDwYOrUqbrN66/4448/GDlyJP3796ewsBB/f3+mT58OVM742rJlCx988AGPP/44Wq0WPz8/+vXrd0v/zcTtadWkIbkFhSxZu5mc/AK8XV34YMgLug3qL2Xn6i1PLdNo+G3NJi5mZWNqakJovQCGPdcbS3P9hwTEJyVzKTuXdtdsQg+VM8lGD3meRSs3MOPHRahLS3FysOP1Z3sSWv/Gd2SFuBOXL5xk8x/jdK/jtlbOZPWp347mT7xJSVE2RXlXZ54qrZ14vNtY4rbOJSluJRZKB5pFvY6Ld9Wf5+sxMDSidfdxxMf+yvblU9FoSlBaO9P8iTdx9Wl67zonxD9SsnKZvH6P7vUv+yqfKtfaz42hrYLJKVGTVXj1IT11lBa8364pP+9PYO3xVOwszBgS3pAQV0ddTLi3C3klpfx+6AS5xWq87KwYFdmsyib2MSfPYmtuRoirA0LUpOCmERTk57Fh5WLy83Jw9fDm5dc/QPX/7d13WJPn/vjxdxgJYYYley8VFQfOOpCq4GrFVls71drWfmt7bKU9doqjjt9pz+mxexy141i7XNWqdeGu4harOBEHDpAAYQQC+f1B+2AUXAdE8fO6rlwXee6Rzx0eQvLJfd+Psw4A/cUcVJd8cWwylbNyyTwu5pxDrdEQFd2WocNfRGvvYNHvkYN70Ofl0K6T5UWVAM6cPMbJzMMAvJtiuX/vq5M+rnEZpBCicVOZZX6+EOIWK9y+vKFDEKLezUi/8s24EI3NuNN/a+gQhKh3ewe919AhCFHvekTbN3QINy3vndtzX03XNz5p6BDuWrJ0UQghhBBCCCGEEEI0CpLoErcFR0fHWm8bNmxo6PAUWVlZV401KyuroUMUQgghhBBCCCHuWrJHl7gt7N69u9YyPz+/WxfINfj6+l41Vl9fuWS3EEIIIYQQQgjRUCTRJW4L4eHhDR3CdbGxsbljYhVCCCGEEEIIIe42snRRCCGEEEIIIYQQQjQKkugSQgghhBBCCCGEEI2CJLqEEEIIIYQQQgghRKMgiS4hhBBCCCGEEEII0SjIZvRCiFtup7Z7Q4cghBCiDtgk3N/QIQhR71otHNfQIQhR/6I/aegIhKgzMqNLCCGEEEIIIYQQQjQKkugSQgghhBBCCCGEEI2CJLqEEEIIIYQQQgghRKMgiS4hhBBCCCGEEEII0ShIoksIIYQQQgghhBBCNAqS6BJ3hLi4OMaOHdvQYdS7lJQUWrdu3dBhCCGEEEIIIYQQdySbhg5AiNoMHz4cvV7PwoULmT9/Pra2tkpZcHAwY8eOtUh+zZkzh7Fjx6LX6+s9tpSUFCZOnAiAtbU1/v7+JCUlMXnyZBwdHW+63+TkZF544QXl/qXPgbi7mc1mVi39nrRNqygtKSYwNIpBDz+DRxOfq7bbsm45G1YtorBAj49/EAOHPEVAcIRSvuC7zzhycC+F+XmoNRoCQ5uSeP9jNPH2U+r88uMsMo8e4NyZkzTx9ufF19+tt3GKu5vZbGb/7/M4lr6SMmMRHj5NaRf/LE6uvldtd3jPr2TsWERpUR46z2DaxI3C3TtSKd+++hPOZe2lpOgiNrZ2ePg0pVXXx3F281fqfP9+0hX9du77MoFR3epugEJQdZ7/sHwtq3/fSXFJKZEhATz94AB8PN2v2m75xm38snYT+gIDQb7ejBjcl4ig6nN45ebtbNq5j+OnsykpNTJ76ms4aO2U8v1HjjPxozk19j31pWcID/SrsUyIm2E2m/lpz2HWHDlFUVk5UZ6ujOwYjY+zw1Xb/ZZxgl/2Hye/1EigqxPD2zcn3EOnlK86lMXmzGyOX8yntLyCLx/qhYPatsa+yisqeGvZFk7kFTKt/z0EuznX5RCFELcxmdEl7ghubm44OTndkseqqKigsrLymvWio6PJzs4mMzOTGTNm8PnnnzNu3Libekyz2YzJZMLR0RF396u/0RV3p3UrF7I5dRmDhj3Lc69MQ62xY9aHkykvL6u1zd4dm1j68xzi+w3hhfH/wNsvmFkfTsFQmK/U8Q0I4YHH/o+X3nqfEc+/BWYzsz6cdMXfQGzne2nVrku9jU8IgIPbF3B491LaxY+m18MzsLG1Y92CSVSYaj/Psw5tZPf62UR3HEqfR97DxSOY9QsmUVpcfZ67Ngmlfe8x9H3iA3okvY0ZM+sWTMR82Xneoc8L3Pf0LOXmF9ax3sYq7l6L1mxk2YatPD1kAO+MfRo7tZp3PvuGsvLyWtts3pXO14uW82CfOGaMG02QnxfvfPYN+YYipU5ZeTmtm4WT1Kvm5GxUcCCfTUy2uMV3aksTd1fCAq6eTBbiRi3ef4zlGSd4qmM0U/p2RmNjzbTVaZSZKmptsyUzm2+2H+CBVuFM7XcPQa7OTFudRkGpUalTVlFBjK8Hg1qEXTOG/+7MQKfV1Ml4hBB3Fkl0iTvCpUsX4+LiOHHiBC+99BIqlQqVSkVqaiojRowgPz9fOZaSkgKA0WgkOTkZPz8/HBwc6NixI6mpqUrfc+bMQafTsXjxYpo3b45GoyErK+uaMdnY2ODt7Y2/vz8PPfQQjz76KIsXLwbgm2++ITY2FicnJ7y9vXnkkUc4f/680jY1NRWVSsWyZcto164dGo2GjRs3WixdTElJ4auvvmLRokUW44yPj2fMmDEWsVy4cAG1Ws3q1auvGffHH39MREQEdnZ2eHl58eCDDypllZWVTJs2jZCQELRaLTExMfz0009AVTKuV69eJCQkYDabAbh48SL+/v68/fbb13xccfPMZjOb1y4lPvEBmrdqj49fEEMeH0Nhfh5/7NlWa7uNa36h/T29iO0cTxMff5KGPYtarWb7ljVKnY5d+xAaEY2rexP8AkPpPXAY+Xm55OWeU+oMHDKSzj0ScfPwqtdxirub2Wzm8O4lNOswBL+wDug8gumQ8CKlRRc5fXRrre0O7VxMaIvehETfi7N7ALH3Poe1jYbj+6tfD8NaJtDEPxoH5ya4NgmjZZdHKC7MwVBwzqIvtcYBrYOrcrO2UdfbeMXdyWw28+v6rTzQuzvtWzQlyNeL5x9JIi+/kLR9B2tttyR1C/d2akfPjm3w9/bkmSED0ahtWbt1p1Knf4/ODLq3m8Usr0vZ2Fjj6uyk3Jzs7dmenkFch9aoVKo6H6u4e5nNZpYfPEFSy3BiA7wIdHXmuXtaoS8xsv3kuVrbLT1wnPiIAOLC/fHXOTKqYzQaa2vWHjml1OnXLIT7W4QRccksr5rsPn2Bfdk5PNauWV0NSwhxB5FEl7jjzJ8/H39/fyZNmkR2djbZ2dl06dKF999/H2dnZ+VYcnIyAGPGjGHLli3MmzePvXv3MmTIEBITEzl8+LDSZ3FxMTNmzODLL79k//79NGnS5Ibj0mq1lJVVzTooLy9n8uTJ7Nmzh4ULF5KZmcnw4cOvaDN+/HimT5/OgQMHaNWqlUVZcnIyQ4cOJTEx0WKco0aNYu7cuRiN1d9uffvtt/j5+REfH3/VGLdv386LL77IpEmTyMjIYPny5XTv3l0pnzZtGl9//TWffvop+/fv56WXXuKxxx5j3bp1qFQqvvrqK9LS0pg5cyYAo0ePxs/PTxJd9exi7jkKC/SERbVUjmntHQgIjiDr+KEa25hMJk5nHSO8afV5pVKpCG/aqtY2RmMpO7asxdW9CS6unnU7CCGuoajgHCVFeXgFVJ/nao0D7t6R5GRn1NimssLExXNH8Q6MUY6pVCq8AluRW0sbU3kpx/evwcHZCwcny/N8x5rPWfjpE6z87hWO7V+lJPWFqCvnc/PQFxTSIiJUOeagtSMiyJ9DmSdrbGMyVXDs1BlaRla3UalUtIwM41DmqRrbXI8d+zMoLCqmZ4c2N92HEDU5byhBX2KkhXf1KgUHtS3hHjoO5ehrbGOqqOR4bgEtfDyUYyqVihY+7hy+UHOb2uSXGPni9338X5dWaGysb2YIQog7nOzRJe44bm5uWFtbK7Ol/uLi4oJKpbI4lpWVxezZs8nKysLXt2pafnJyMsuXL2f27NlMnToVqEpMffzxx8TExHAzduzYwdy5c5VE08iRI5Wy0NBQZs6cSfv27TEYDBZ7eE2aNInevXvX2KejoyNarRaj0WgxpsGDBzNmzBgWLVrE0KFDgapZacOHD7/mN7JZWVk4ODgwYMAAnJycCAoKok2bqje4RqORqVOnsmrVKjp37qzEvnHjRj777DN69OiBn58fn332GU888QRnz57l119/ZdeuXdjY1P5SYjQaLZJyAGVlFajVMpX8ehny9QA4OrtYHHd0dqGwIK/GNsVFBVRWVuLodFkbJxcunDttcez39ctZtvBbyoyleDTx5akX3rrq71SI+lBaVHUu29nrLI5r7HWUFulrbGMsKcBsrkRjb3me29nrKMyzPM+P7FnGno1fYyovxcnVlx6DJ2BlXX2et+g8DK+AlljbaDh7Yjc713yOqbyUyNYD/vfBCfEnfaEBABcny/08XZwclLLLFRQVU1lZie6yNjpHB86cy7npWFZv3UlM03DcdS7XrizEDdCXVL3vc7GznBXrYqcmv8RYUxMKjWVUms01tNFwpqCoxjY1MZvNfLJ5L/dGBhLmoeOCoeQGoxdCNAbySUY0avv27aOiooLIyEiL40aj0WIvLLVafcWMquvp29HRkYqKCsrKyujfvz8ffvghUJX4SklJYc+ePeTl5Sn7HWVlZdG8eXOlj9jY2Bsek52dHY8//jizZs1i6NCh7Ny5k/T0dGXZ5NX07t2boKAgQkNDSUxMJDExkaSkJOzt7Tly5AjFxcVXJN7KysqUZBjAkCFDWLBgAdOnT+eTTz4hIiLi8oexMG3aNGXj/r88+dzrjHj+jRsY9d1l17b1LJz3uXL/yedeq9fHi4ntRnjTGAryL7Jx9S/M/c8/GT3uHWxtZdmWqD8nDq5j++pPlfvd7n+zXh8vsGl3vAJjKCnKI2PnIrb8+i73Dp2mLE+M7jhUqevaJJQKk5GMHYsk0SX+Jxt27OWLH39R7o8f9WgDRlMtV5/PnoNHePnJodeuLMQ1bDx2mi+37lfuvxp/4+9v68qKjBOUlJsYFH3tPbyEEI2XJLpEo2YwGLC2tmbHjh1YW1tOXb50ZpVWq73h/SmioqJYvHgxNjY2+Pr6olZXfVgqKioiISGBhIQE/vvf/+Lp6UlWVhYJCQnK0sa/ODhc/coztRk1ahStW7fm1KlTzJ49m/j4eIKCgq7ZzsnJiZ07d5Kamspvv/3G22+/TUpKCmlpaRgMVd8kL126FD8/yysvaTTVs6+Ki4uV5/PS5Z+1ee2113j55Zctjm09WvtGpAKat2pPYEh1AtFkMgFgKMjH2cVNOW4oyMfHP7jGPuwdnLGysrLYeB7AUJiPo7OrxTGtvQNaewc8mvgQGBLFpFee5I8924iJ7VpHIxLiSr6hHehzyZURKyuqNuIuLdajdaw+z43Felw8g2vsQ6N1RqWywlhseZ6XFuuxs7c8z9UaB9QaB5xcffHwiWLBp49x+ujWWq+q6OYVwf6tP1BhKsfapuYreglxLbHRURZ7ZpX/+XqeX2jAzaX6Ijv5hUUE+da8D6Kzgz1WVlZXzPjSG4rQOd/clZ7XbtuFk4M97aKjbqq9EJdqF+BFuKdOuW+qqPqCN7+0DFf76it/5peWEeha88WlnDRqrFQq8kst3yvnlxrR2V3/KoD07FwO5+h5fO4Ki+Nv/LqZe0J8+L97bm71hhDiziKJLnFHUqvVVFRUXPNYmzZtqKio4Pz583TrVreXiFer1YSHh19x/ODBg+Tm5jJ9+nQCAgKAqr2xbvYxLh8TQMuWLYmNjeWLL75g7ty5ykyy62FjY0OvXr3o1asXEyZMQKfTsWbNGnr37q1sxN+jR49a248bNw4rKyuWLVtGv3796N+//1X3BtNoNBaJsqpxFV93vHcjjZ0WjZ1WuW82m3Fy1nE0Yx++ASEAlJYUczLzMB279amxDxsbG/wCQzmasY/omA5KP0cy9tG5R9/aH9xsBrMZk6n2q38JURds1Vps1ZbnudbBlXMn9+HapGovonJjMblnDxHWKqHGPqysbXDzCuNc1l7lColms5nzJ/cRHtOv1sc2m82YzWYqKmo/z/U5majtHCXJJf4nWjsN2ks+pJvNZnTOTqQfPkaIvw8AxaWlHD5xit5dap4FY2NjTai/L+mHj9OhZTOln/RDx0js1uGGYzKbzaRu202P9jHYyP5Fog5obW3Q2lZ/rDSbzei0GtLP5hLs5gxAcVk5R3L09IoIqLEPG2srQtyd2Z+dS/sAL6Wf/Wdz6RN17S9z/zK8Q3MeKqv+EiWvxMi01Wm82L014e6yTFeIu4UkusQdKTg4mPXr1/Pwww+j0Wjw8PAgODgYg8HA6tWriYmJwd7ensjISB599FGeeOIJ3nvvPdq0acOFCxdYvXo1rVq1on///nUeW2BgIGq1mg8++IDRo0eTnp7O5MmTb6qv4OBgVqxYQUZGBu7u7ri4uGBrW/Wha9SoUYwZMwYHBweSkpKuq78lS5Zw7NgxunfvjqurK7/++iuVlZVERUXh5OREcnIyL730EpWVlXTt2pX8/Hw2bdqEs7MzTz75JEuXLmXWrFls2bKFtm3b8sorr/Dkk0+yd+9eXF1drx2AuCkqlYouPfuzZvnPuDfxwdW9CSuXzMPJxZXmMdUfcr78dwrNYzrSJa4qkdU1fiA/fv0hfoGhBARFsCl1KWVGI+069QQgN+cs+3ZsJrxZDI6OzuTn5bJu5UJs1Wqiotsq/eZeyMZYWkphgZ7y8jLOnDwOQBOfANnLS9QZlUpFROsBHNj2I046HxxcmpC++TvsHNyUJBbA2p/fxj+sIxGtq16/I9vex7YVM3H1CsPdK4JDu5dgKi8lJLoqAW/IP8vJQ5vwCoxBo3WhxJDDge0LsLHR4BPcDoDTx9IwFutx947EykbNuRO7OZD2E1Ft77/1T4Ro1FQqFf26d+Tnlevx9nSniZsr3y9bg6uLE+1bNlXqTfp4Du1bNqNvt6pzf0BcZz6au4BQfx/CA/35df3vlJaVEXfJRvJ5BYXoCwyczbkIQNaZc9hp1Hi4uuDkYK/USz98nPO5ecR3rH6dF6IuqVQqEpsGsWDfEbyd7GniqOWH3YfRaTXEBlTPXJyycivtA7xJaFqVyOrfLIRPNu8lxN2ZMHcdyw9mUmqqIC6selakvsSIvsTI2cKqL02z8grR2trg7mCHk0aNh4MWLlkwYfdnAs7L0R53h+ovV4QQjZt8QhG3rcrKylo/RE+aNIlnn32WsLAwjEYjZrOZLl26MHr0aB566CFyc3OZMGECKSkpzJ49mylTpjBu3DhOnz6Nh4cHnTp1YsCA+tl3xdPTkzlz5vD6668zc+ZM2rZty7vvvst99913w309/fTTpKamEhsbi8FgYO3atcTFxQEwbNgwxo4dy7Bhw7Czs7t6R3/S6XTMnz+flJQUSktLiYiI4LvvviM6OhqAyZMn4+npybRp0zh27Bg6nY62bdvy+uuvc+HCBZ566ilSUlJo27bqzfHEiRP57bffGD16NN9///0Nj09cvx69B1FeZmTB3E8pLSkmKKwpI55/02IfrYs55yguKlDut2p3D4bCAlYt+Z7CAj2+AcGMeP4NnJx1ANjaqjl+5AAb1yyhtKQYR2cXgsOaMXrcVItN7H/+9hOOH/lDuf/B9FcAeHXSx7i63/gVSoWoTdPYJCpMRrav/oRyYxEevs3onvSWso8WQFH+OYwlhcr9wMiuGIvzSd8yj9LiPFw9Q+g+6C1lU3trazUXTv/BoZ2/UFZWhJ29Dk/fZsQ/NA27Pzext7Ky5sieZexaNwsAR503rbuPILRFzTMmhfhf3B/fFWNZOZ//8AvFJaVEhQby+jOPobatnj14LjePwqLq2c9d2rQg31DED8vXoi80EOzrwxvPPG6xQf3Kzdv5aUWqcn/Ch1Xn8/8NG2SREFuzdSdRIYH4ecnVdUX9uS86lDJTBV/+nk5xuYkoT1fG3xuL+pJZhOcKSyg0Vi9V7BzsQ0FpGT/uOUx+iZEgN2fGx7fHRVs9K3LVoSx+3ntEuT/pt60AjO7Skh6XJMSEEHc3lVmunS1uU4mJiYSHh9/Qsry7SWZmJmFhYaSlpSmJpzvFuv2ydFE0fivT5Lsk0fj9vcWahg5BiHpnWrGooUMQot65vvFJQ4dw0/Leea6hQ6jRnfyc3umsGjoAIS6Xl5fHkiVLSE1NpVevXg0dzm2nvLycs2fP8uabb9KpU6c7LsklhBBCCCGEEELUF/m6Wdx2Ro4cSVpaGuPGjeP++xtmf5RLr8h4uWXLltX5xvY3YtOmTfTs2ZPIyEh++ukni7INGzbQt2/tG43/dWVFIYQQQgghhBCiMZJEl7jtLFiwoKFDYPfu3bWW+fn53bpAahAXF0dtK45jY2OvGrsQQgghhBBCCNGYSaJLiBqEh4c3dAg3RavV3rGxCyGEEEIIIYQQ/yvZo0sIIYQQQgghhBBCNAqS6BJCCCGEEEIIIYQQjYIkuoQQQgghhBBCCCFEoyB7dAkhbrlWC8c1dAhC1Lu2CQ1z1VghbqUZ6fENHYIQ9a73oO4NHYIQ9a5HQwcgRB2SGV1CCCGEEEIIIYQQolGQRJcQQgghhBBCCCGEaBQk0SWEEEIIIYQQQgghGgVJdAkhhBBCCCGEEEKIRkESXUIIIYQQQgghhBCiUZBElxBCCCGEEEIIIYRoFCTRJW4bcXFxjB07tqHDqHcpKSm0bt26ocMQQgghhBBCCCEaHZuGDkDc3YYPH45er2fhwoXMnz8fW1tbpSw4OJixY8daJL/mzJnD2LFj0ev19R5bSkoKEydOBMDa2hp/f3+SkpKYPHkyjo6ON91vcnIyL7zwgnL/0udAiNqYzWZ+2nOYNUdOUVRWTpSnKyM7RuPj7HDVdr9lnOCX/cfJLzUS6OrE8PbNCffQKeVlpgq+3XGQLSeyKa+oJMbXg5EdonHRapQ6c9L+ION8Hqf0hfi5ODJ9QNf6Gqa4y5nNZn5YvpbVv++kuKSUyJAAnn5wAD6e7ldtt3zjNn5Zuwl9gYEgX29GDO5LRJC/Ul5WXs7Xi1awefd+TCYTMVHhPPVgf3RO1a/lQ1+acEW/f3v8Qe5p27LuBijueof3/ErGjkWUFuWh8wymTdwo3L0ja61/8tAm9m2ZS3HBBRx1PrTq+gS+Ie2UcrPZzP7f53EsfSVlxiI8fJrSLv5ZnFx9lToFeafZu+Frcs4coLLShItHEC06P4JXgJzbon5sWbecDasWUVigx8c/iIFDniIgOKLW+nt3bmblL/PQX7yAexMfEu9/jKYt2irlZrOZVUu/J23TKkpLigkMjWLQw8/g0cQHgGOH0vni3yk19v38q9PxDwqv0/EJIW5/MqNL3Dbc3NxwcnK6JY9VUVFBZWXlNetFR0eTnZ1NZmYmM2bM4PPPP2fcuHE39ZhmsxmTyYSjoyPu7lf/0FbXysrKbunjibq3eP8xlmec4KmO0Uzp2xmNjTXTVqdRZqqotc2WzGy+2X6AB1qFM7XfPQS5OjNtdRoFpUalzjc7DrDj1HnGdm/D2306crHYyD/X7byir57h/nQK9qmXsQnxl0VrNrJsw1aeHjKAd8Y+jZ1azTuffUNZeXmtbTbvSufrRct5sE8cM8aNJsjPi3c++4Z8Q5FS56uFK9ix/xAvPzmUCc+P4GJ+Ae/N/v6Kvv5v2CA+m5is3Nq3bFov4xR3p6xDG9m9fjbRHYfS55H3cPEIZv2CSZQW59dYP+fMAbYs+yeh0b3p88h7+IV1ZNOS6eTnnFDqHNy+gMO7l9IufjS9Hp6Bja0d6xZMosJU/X9/46J3qKysIO7BSfQe9i46j2A2Ln6HkqK8eh+zuPvs3bGJpT/PIb7fEF4Y/w+8/YKZ9eEUDIU1n+cnjh7k+9nv077Lvbww/h80b9Webz//f5w9k6XUWbdyIZtTlzFo2LM898o01Bo7Zn04mfLyqvM8MLQpr0/9wuLWvsu9uLo3wS8w7JaMWwhxe5FEl7htXLp0MS4ujhMnTvDSSy+hUqlQqVSkpqYyYsQI8vPzlWMpKSkAGI1GkpOT8fPzw8HBgY4dO5Kamqr0PWfOHHQ6HYsXL6Z58+ZoNBqysrKuDOIyNjY2eHt74+/vz0MPPcSjjz7K4sWLAfjmm2+IjY3FyckJb29vHnnkEc6fP6+0TU1NRaVSsWzZMtq1a4dGo2Hjxo0WSxdTUlL46quvWLRokcU44+PjGTNmjEUsFy5cQK1Ws3r16mvGHRwczOTJk3niiSdwdnbmmWeeAeDvf/87kZGR2NvbExoayltvvUX5ZR8gf/nlF9q3b4+dnR0eHh4kJSUpZdd6nkX9MJvNLD94gqSW4cQGeBHo6sxz97RCX2Jk+8lztbZbeuA48REBxIX7469zZFTHaDTW1qw9cgqAorJyUo+c4vHYpkR7uxPq7sLoLi05dEHP4Qt6pZ/h7ZvTJyoIL0f7+h6quIuZzWZ+Xb+VB3p3p32LpgT5evH8I0nk5ReStu9gre2WpG7h3k7t6NmxDf7enjwzZCAatS1rt1YlbItKSlm7bSdPDkqgRUQIYQG+/N+wQWQcz+Jw5imLvuy1drg6Oyk39SWzjIX4Xx3auZjQFr0Jib4XZ/cAYu99DmsbDcf31/x//dCupfgEt6Vp7CCc3QNo2eURXD1DObznV6Dqb+bw7iU06zAEv7AO6DyC6ZDwIqVFFzl9dCsAxpICCvXZNGs/GJ1HME6uvrTq+gSmciP5udd+HyTEjdq45hfa39OL2M7xNPHxJ2nYs6jVarZvWVNj/U2pS4ls3obuve+niY8/fQYOwzcghC3rlgFV5/nmtUuJT3yA5q3a4+MXxJDHx1CYn8cfe7YBVe/XnVxclZvWwYk/9qbRrlNPVCrVLRu7EOL2IYkucVuaP38+/v7+TJo0iezsbLKzs+nSpQvvv/8+zs7OyrHk5GQAxowZw5YtW5g3bx579+5lyJAhJCYmcvjwYaXP4uJiZsyYwZdffsn+/ftp0qTJDcel1WqV2VHl5eVMnjyZPXv2sHDhQjIzMxk+fPgVbcaPH8/06dM5cOAArVq1sihLTk5m6NChJCYmWoxz1KhRzJ07F6OxeubNt99+i5+fH/Hx8dcV67vvvktMTAy7du3irbfeAsDJyYk5c+bwxx9/8O9//5svvviCf/3rX0qbpUuXkpSURL9+/di1axerV6+mQ4cOSvn1PM+i7p03lKAvMdLCu3omoIPalnAPHYdy9DW2MVVUcjy3gBY+HsoxlUpFCx93JYmVebEAU6WZlpfU8XNxxN3BjsM58k2/uLXO5+ahLyikRUSocsxBa0dEkD+HMk/W2MZkquDYqTO0jKxuo1KpaBkZxqE/k1jHT2VjMlXQMrL6W30/L088XF04dMKy3//8vJSRb87gtX99zpqtOzGbzXU5RHEXq6wwcfHcUbwDY5RjKpUKr8BW5GZn1Ngm92wGTS5bXugd1FqpX1RwjpKiPIsliGqNA+7ekeT8WUdt54STqy+ZB9ZiKi+lsrKCo/t+w07rglsTmeki6pbJZOJ01jHCm1a/31WpVIQ3bUXW8UM1tsk6foiwKMvzPKJZjFL/Yu45Cgv0FnW09g4EBEfU2ufBfdspLiqkXeee/+uQhBB3KNmjS9yW3NzcsLa2VmZL/cXFxQWVSmVxLCsri9mzZ5OVlYWvb9WeFMnJySxfvpzZs2czdepUoCox9fHHHxMTE8PN2LFjB3PnzlUSTSNHjlTKQkNDmTlzJu3bt8dgMFjs4TVp0iR69+5dY5+Ojo5otVqMRqPFmAYPHsyYMWNYtGgRQ4cOBapmpQ0fPvy6v5mKj4+/Ypnlm2++qfwcHBxMcnIy8+bN49VXXwXgnXfe4eGHH1b2JgOU5+t6n+fLGY1Gi4QdgNFUgcbG+rrGIUBfUvX8udipLY672KnJLzHW1IRCYxmVZnMNbTScKShS+rWxUuGgtr2ijr6WfoWoL/pCAwAuTpZ7ILo4OShllysoKqaystJiry0AnaMDZ87lVPVbUIiNjTUOWrvL+nVEX1Dd79C+PWkZEYra1pY9GUf4z09LMRrL6Nu90/88NiGMJQWYzZVo7F0sjtvZ6yjMO11jm9JiPVoH1yvqlxbpq8r/XHpoZ6+zqKO5pI5KpSJu8EQ2LZnO/I8fAVTY2evonvQ2arub329UiJoUFxVQWVmJo5Plee7o5MKFczWf54aCfJycdRbHnJx1FOZXnd+GfH1VH86X9ensQmFBzV/KpW1eTUSz1uhcPWosF0I0fpLoEne8ffv2UVFRQWSk5WauRqPRYi8stVp9xYyq6+nb0dGRiooKysrK6N+/Px9++CFQlfhKSUlhz5495OXlKXt+ZWVl0bx5c6WP2NjYGx6TnZ0djz/+OLNmzWLo0KHs3LmT9PR0Zdnk9ajpcb///ntmzpzJ0aNHMRgMmEwmnJ2dlfLdu3fz9NNP19jf9T7Pl5s2bZpF4gzg1Z7tGH9v++sey91m47HTfLl1v3L/1fgbP4eEuN1t2LGXL378Rbk/ftSjDRgNPNgnTvk5xN8HY1k5i9dulkSXuKOZzWZ2rv0CjdaF+CHvYG2j5lj6KjYunkqvh/8fWke3hg5RiDqlz8vh8IHdPPLUze2pK4RoHCTRJe54BoMBa2trduzYgbW15SyhS2dWabXaG16nHxUVxeLFi7GxscHX1xe1ump2TFFREQkJCSQkJPDf//4XT09PsrKySEhIuGLjdweHq18VrzajRo2idevWnDp1itmzZxMfH09QUNB1t7/8cbds2cKjjz7KxIkTSUhIwMXFhXnz5vHee+8pdbRaba39Xe/zfLnXXnuNl19+2eJY8b+Sr3scd6N2AV6Ee+qU+6aKqiRqfmkZrvbVs1LyS8sIdK35Ag5OGjVWKhX5pZbnY36pEZ1d1RUVdVoNpkozRWXlFrO68kuN6C656qIQ9SE2OsriyojlJhMA+YUG3Fyqz+v8wiKCfL1q7MPZwR4rK6srZnzpDUXonKtel3TOTphMFRSVlFrM6sovNCh1ahIe6MfPv62j3GTC1kbeLon/jUbrjEplhfGyjedLi/XY2bvW2MbOXnfFhvGlxXrsHHRV5X/O9iot1lskrIzFelw8gwE4f3IfZ46nkTT6W2w1VfsstosP42zWbjIPpNKs/eC6GJ4QANg7OGNlZXXFxvOGwnwcnWs+z6tmZuktjhUW6HFyqarv6KKr6qMgH2eX6vPcUJCPj3/wFf3t2LIWewcnmraULwmFuJvJHl3itqVWq6moqLjmsTZt2lBRUcH58+cJDw+3uF26HPBmYwgPDyc4OFhJcgEcPHiQ3Nxcpk+fTrdu3WjatKnFRvQ3+hiXjwmgZcuWxMbG8sUXXzB37lyLpZI3Y/PmzQQFBfHGG28QGxtLREQEJ06csKjTqlWrWje7v9nnWaPR4OzsbHGTZYtXp7W1wdvJQbn5uTii02pIP5ur1CkuK+dIjp5ID12NfdhYWxHi7sz+7Oo2ZrOZ/WdzifgziRbs5oyNlcqi3zMFBnKLSonwqPkNqRB1RWunwdvDTbn5e3mic3Yi/fAxpU5xaSmHT5wiMjigxj5sbKwJ9fcl/fBx5ZjZbCb90DEig6uSaCH+PtjYWJN+qLrfM+dzyMnLJzKo5n4BTpw5h4O9VpJcok5YWdvg5hXGuay9yjGz2cz5k/tw94mqsY27dxTnT+6zOHYua49S38HZC62DK+cuqVNuLCb37CE8/qxjMlUtQ1dZWb7lV6msMJuvffVpIW6EjY0NfoGhHM2oPifNZjNHMvYRGBJZY5vAkEiL+gBHDu5V6ru5e+HkrLOoU1pSzMnMw1f0aTab2fH7Wtp27IGNvHYLcVeTRJe4bQUHB7N+/XpOnz5NTk6OcsxgMLB69WpycnIoLi4mMjKSRx99lCeeeIL58+dz/Phxtm3bxrRp01i6dGm9xBYYGIhareaDDz7g2LFjLF68mMmTJ99UX8HBwezdu5eMjAxycnIsroI4atQopk+fjtlstrj64c2IiIggKyuLefPmcfToUWbOnMmCBQss6kyYMIHvvvuOCRMmcODAAfbt28eMGTMAGuR5FlVUKhWJTYNYsO8I20+eIyuvgI837UWn1RAbUD3TZcrKraw4WJ287N8shDVHTrLu6ClO6Q38Z+t+Sk0VxIVVJQAc1LbEhfvzzfYD7D+by7HcfD7dvI8IT52SDAM4W1hE5sUC9CVGyioqybxYULWRfYV8SBJ1R6VS0a97R35euZ609IOcOHOOD/+7AFcXJ9q3bKrUm/TxHJZt2KrcHxDXmdVbdpC6bRenzl7gix+XUFpWRlyHNkDVhvY9O7Tlq0XLST98nKMnz/DxdwuJDA4g4s9k2Pb9Gaz+fQdZ2ec4m3ORFZu2sWDVehK7dkCIuhLZ9j6Opa/k+B9rKMg9yY41n2IqLyUkumrvz60r3mfvxm+q67fpz9kTuzi4YxEFF0+RvmUeF88fJSKmH1D1NxPRegAHtv3I6aPb0OdksnXFv7FzcMMvrCMAHj5RqDWObF0xE/2F4xTknWb3+jkUFZzHN6TdrX8SRKPXNX4gaZtWseP3tZzPPsXCeZ9TZjTSrlPVxvA/fDWT5Yu+VerfE9efQwd2s2HVYs6fPc2qpd9zOusYnXv0BarO8y49+7Nm+c/8sTeN7NMn+OHrD3BycaV5jOVr9NGMfeTlnie2S69bN2AhxG1JUt2iQVVWVtb6jcukSZN49tlnCQsLw2g0Yjab6dKlC6NHj+ahhx4iNzeXCRMmkJKSwuzZs5kyZQrjxo3j9OnTeHh40KlTJwYMGFAvcXt6ejJnzhxef/11Zs6cSdu2bXn33Xe57777brivp59+mtTUVGJjYzEYDKxdu5a4uDgAhg0bxtixYxk2bBh2dnZX7+ga7rvvPl566SXGjBmD0Wikf//+vPXWW6SkpCh14uLi+PHHH5k8eTLTp0/H2dmZ7t27K+W3+nkW1e6LDqXMVMGXv6dTXG4iytOV8ffGor5kdty5whIKjdVLFTsH+1BQWsaPew6TX2IkyM2Z8fHtcblkWeLj7ZqhQsW/1u2ivLKSGB8PRnaMtnjsz7ekc+DcReX+a0s3ATAzKQ5Px9qXuwpxo+6P74qxrJzPf/iF4pJSokIDef2Zx1DbVi+tPZebR2FRsXK/S5sW5BuK+GH5WvSFBoJ9fXjjmcctNqh/clACKhW8N+d7TCYTMU3DeeqB/kq5tZUVKzam8dXC5QB4ebjxxP2J9OosiQBRdwIju2Iszid9yzxKi/Nw9Qyh+6C3lM3kiwtyUKmqv4P28G1Gp8SX2LdlLvs2f4uTzod7BozHxaN6G4OmsUlUmIxsX/0J5cYiPHyb0T3pLaxtqmaha7TOdB/0Fvu2zCX15wlUVppwdgug68DX0HmG3NLxi7tDq3b3YCgsYNWS7yks0OMbEMyI599QNpzXX8yxmGEYFNaUh4b/jZW/zGPF4rl4NPHhsWdexds3UKnTo/cgysuMLJj7KaUlxQSFNWXE829ia2t5wZ3tm1cTFBpFE2+/WzJWIcTtS2WWa2eLBpSYmEh4eLiywbuwlJmZSVhYGGlpabRt27ahw6kzee8819AhCFHvbBLub+gQhKh3M9LjGzoEIepd7/amhg5BiHrXI9q+oUO4abfrZwvXNz5p6BDuWrJ0UTSIvLw8lixZQmpqKr16yfTiy5WXl3P27FnefPNNOnXq1KiSXEIIIYQQQgghRH2RpYuiQYwcOZK0tDTGjRvH/fc3zKyHq10pcNmyZXTr1u0WRmNp06ZN9OzZk8jISH766SeLsg0bNtC3b99a2xoMhlrLhBBCCCGEEEKIxkwSXaJBXL4JekPYvXt3rWV+fg27tj8uLo7aVhXHxsZeNXYhhBBCCCGEEOJuJYkucdcKDw9v6BBuilarvWNjF0IIIYQQQggh6pPs0SWEEEIIIYQQQgghGgVJdAkhhBBCCCGEEEKIRkESXUIIIYQQQgghhBCiUZA9uoQQt5xNQsNcaVOIW2lGenxDhyBEvRt3+m8NHYIQ9W5v+/caOgQhhBA3QGZ0CSGEEEIIIYQQQohGQRJdQgghhBBCCCGEEKJRkESXEEIIIYQQQgghhGgUJNElhBBCCCGEEEIIIRoFSXQJIYQQQgghhBBCiEZBEl2CuLg4xo4d29Bh1LuUlBRat27d0GEIIYQQQgghhBCintg0dACiYQwfPhy9Xs/ChQuZP38+tra2SllwcDBjx461SH7NmTOHsWPHotfr6z22lJQUJk6cCIC1tTX+/v4kJSUxefJkHB0db7rf5ORkXnjhBeX+pc/BnaKyspLXXnuNb775hry8PCIiIpg8eTL3339/Q4cm6pnZbOaH5WtZ/ftOiktKiQwJ4OkHB+Dj6X7Vdss3buOXtZvQFxgI8vVmxOC+RAT5K+Vl5eV8vWgFm3fvx2QyERMVzlMP9kfndOXfWmFRMa/84xMu5hcwe+prOGjt6nycQlzKbDaz//d5HEtfSZmxCA+fprSLfxYnV9+rtju851cydiyitCgPnWcwbeJG4e4dqZQf3beCrIMbyLtwjPKyEpKe+xa1xqG+hyMEUHVe/7TnMGuOnKKorJwoT1dGdozGx/nq5+BvGSf4Zf9x8kuNBLo6Mbx9c8I9dEp5mamCb3ccZMuJbMorKonx9WBkh2hctBoATlwsYNH+Y2Scz6PQWIano5ZeEYH0bRZcj6MVd6Mt65azYdUiCgv0+PgHMXDIUwQER9Raf+/Ozaz8ZR76ixdwb+JD4v2P0bRFW6XcbDazaun3pG1aRWlJMYGhUQx6+Bk8mvgodf7fW8+Rd/GCRb8J9z9KXJ+kuh+gEOK2JzO6BG5ubjg5Od2Sx6qoqKCysvKa9aKjo8nOziYzM5MZM2bw+eefM27cuJt6TLPZjMlkwtHREXf3qycFbnfffvst//rXv/jnP//JgQMH+Oc//4mDw7U/nJWVld2C6ER9WrRmI8s2bOXpIQN4Z+zT2KnVvPPZN5SVl9faZvOudL5etJwH+8QxY9xogvy8eOezb8g3FCl1vlq4gh37D/Hyk0OZ8PwILuYX8N7s72vs75N5iwj09arzsQlRm4PbF3B491LaxY+m18MzsLG1Y92CSVSYan9Nyzq0kd3rZxPdcSh9HnkPF49g1i+YRGlxvlLHVG7EO7gtzdo/eCuGIYSFxfuPsTzjBE91jGZK385obKyZtjqNMlNFrW22ZGbzzfYDPNAqnKn97iHI1Zlpq9MoKDUqdb7ZcYAdp84ztnsb3u7TkYvFRv65bqdSfuxiPs52ap7vGsM/BnZjUIswvtuVwYqDJ+p1vOLusnfHJpb+PIf4fkN4Yfw/8PYLZtaHUzAU5tdY/8TRg3w/+33ad7mXF8b/g+at2vPt5/+Ps2eylDrrVi5kc+oyBg17ludemYZaY8esDydTXm75v6BX/4d4feoXyq1zj771OlYhxO1LEl3CYuliXFwcJ06c4KWXXkKlUqFSqUhNTWXEiBHk5+crx1JSUgAwGo0kJyfj5+eHg4MDHTt2JDU1Vel7zpw56HQ6Fi9eTPPmzdFoNGRlZV0ZxGVsbGzw9vbG39+fhx56iEcffZTFixcD8M033xAbG4uTkxPe3t488sgjnD9/XmmbmpqKSqVi2bJltGvXDo1Gw8aNGy2WLqakpPDVV1+xaNEii3HGx8czZswYi1guXLiAWq1m9erV14z7448/JiIiAjs7O7y8vHjwweoPUZWVlUybNo2QkBC0Wi0xMTH89NNPQFUyrlevXiQkJGA2mwG4ePEi/v7+vP3220ofVlZWeHp68vDDDxMcHEyvXr3o1avXFXH8NdYvv/ySkJAQ7OyqZt4sX76crl27otPpcHd3Z8CAARw9etSi7alTpxg2bBhubm44ODgQGxvL1q1blfJFixbRtm1b7OzsCA0NZeLEiZhMpms+N+Lmmc1mfl2/lQd6d6d9i6YE+Xrx/CNJ5OUXkrbvYK3tlqRu4d5O7ejZsQ3+3p48M2QgGrUta7dWffApKill7badPDkogRYRIYQF+PJ/wwaRcTyLw5mnLPpasWkbxSWl3NfznnodqxB/MZvNHN69hGYdhuAX1gGdRzAdEl6ktOgip49urbXdoZ2LCW3Rm5Doe3F2DyD23uewttFwfH/1a3hU2/to1n6wxSwvIW4Fs9nM8oMnSGoZTmyAF4Guzjx3Tyv0JUa2nzxXa7ulB44THxFAXLg//jpHRnWMRmNtzdojVa/VRWXlpB45xeOxTYn2difU3YXRXVpy6IKewxf0APQMD2B4++Y093LDy8mebqF+xIX5k3by7K0YurhLbFzzC+3v6UVs53ia+PiTNOxZ1Go127esqbH+ptSlRDZvQ/fe99PEx58+A4fhGxDClnXLgKq/mc1rlxKf+ADNW7XHxy+IIY+PoTA/jz/2bLPoS2OnxcnFVblpNDLzXIi7lSS6hIX58+fj7+/PpEmTyM7OJjs7my5duvD+++/j7OysHEtOTgZgzJgxbNmyhXnz5rF3716GDBlCYmIihw8fVvosLi5mxowZfPnll+zfv58mTZrccFxarVaZlVReXs7kyZPZs2cPCxcuJDMzk+HDh1/RZvz48UyfPp0DBw7QqlUri7Lk5GSGDh1KYmKixThHjRrF3LlzMRqrvyH99ttv8fPzIz4+/qoxbt++nRdffJFJkyaRkZHB8uXL6d69u1I+bdo0vv76az799FP279/PSy+9xGOPPca6detQqVR89dVXpKWlMXPmTABGjx6Nn5+fRaLr3nvvJT8/n7feeuuaz9mRI0f4+eefmT9/Prt37wagqKiIl19+me3bt7N69WqsrKxISkpSZtkZDAZ69OjB6dOnWbx4MXv27OHVV19Vyjds2MATTzzB3/72N/744w8+++wz5syZwzvvvHPNeMTNO5+bh76gkBYRocoxB60dEUH+HMo8WWMbk6mCY6fO0DKyuo1KpaJlZBiH/kxiHT+VjclUQcvIMKWOn5cnHq4uHDpR3e+psxf4+bf1jHk0CZWqrkcnRM2KCs5RUpSHV0BL5Zha44C7dyQ52Rk1tqmsMHHx3FG8A2OUYyqVCq/AVuTW0kaIW+m8oQR9iZEW3tUzzB3UtoR76DiUo6+xjamikuO5BbTw8VCOqVQqWvi4K0mszIsFmCrNtLykjp+LI+4OdhzOyas1nuJyEw5q21rLhbgRJpOJ01nHCG9a/b5bpVIR3rQVWccP1dgm6/ghwqJaWhyLaBaj1L+Ye47CAr1FHa29AwHBEVf0ue63BUx+dTgfTEtm/cpFVFTUPktSCNG4yR5dwoKbmxvW1tbKbKm/uLi4oFKpLI5lZWUxe/ZssrKy8PWt2i8lOTmZ5cuXM3v2bKZOnQpUJaY+/vhjYmJiuBk7duxg7ty5SqJp5MiRSlloaCgzZ86kffv2GAwGiz28Jk2aRO/evWvs09HREa1Wi9FotBjT4MGDGTNmDIsWLWLo0KFA1ay04cOHo7rGJ/ysrCwcHBwYMGAATk5OBAUF0aZNG6Bq5tvUqVNZtWoVnTt3VmLfuHEjn332GT169MDPz4/PPvuMJ554grNnz/Lrr7+ya9cubGyq/kyLi4vp3bs3jzzyCCtXrqSkpIR//OMfSlzOzs7MmjVLmUVWVlbG119/jaenpxLjAw88YBHzrFmz8PT05I8//qBFixbMnTuXCxcukJaWhpubGwDh4eFK/YkTJzJ+/HiefPJJZQyTJ0/m1VdfZcKECVd9fsTN0xcaAHC5bN8sFycHpexyBUXFVFZWXrHXls7RgTPncqr6LSjExsb6ir22XJwc0RdU9VtuMvH+Nz/y2MDeeLjqOJdb+wcmIepSaVHVuWZnr7M4rrHXUVqkr7GNsaQAs7kSjb2LxXE7ex2FeafrI0whboi+pOqLNBc7tcVxFzs1+SXGmppQaCyj0myuoY2GMwVFSr82VqorklYudhrlMS+XcT6P309k82rP2JsaixCXKy4qoLKyEkcny9dgRycXLpyr+TXYUJCPk7PO4piTs47C/Kr/AYZ8fVUfzpf16exCYUH1e5LOcf3wCwhB6+BE1rGDLF80l4L8PAY8OPx/G5QQ4o4kiS5x0/bt20dFRQWRkZZLP4xGo8VeWGq1+ooZVdfTt6OjIxUVFZSVldG/f38+/PBDoCrxlZKSwp49e8jLy1NmG2VlZdG8eXOlj9jYG3/jZmdnx+OPP86sWbMYOnQoO3fuJD09XVk2eTW9e/cmKCiI0NBQEhMTSUxMJCkpCXt7e44cOaIkqi5VVlamJMMAhgwZwoIFC5g+fTqffPIJERHVG3fOmTMHvV7PRx99hMFgIC4ujhEjRvDll19y6tQpDAYD99xTvawsKCjIIskFcPjwYd5++222bt1KTk6OxXPXokULdu/eTZs2bZQk1+X27NnDpk2bLGZwVVRUUFpaSnFxMfb29le0MRqNFjPk/hq3Rq2+oq6osmHHXr748Rfl/vhRjzZYLHOXrMLfy5PusTeXqBbiep04uI7tqz9V7ne7/80GjEaIurHx2Gm+3Lpfuf9q/O2RVDqZV8h7qTsY3CqcVr4e124gxG2u270DlZ99/IKwtrZhwbzPSbz/UWxsZdaiEHcbSXSJm2YwGLC2tmbHjh1YW1tblF06s0qr1V5zNtTloqKiWLx4MTY2Nvj6+qL+MylSVFREQkICCQkJ/Pe//8XT05OsrCwSEhKu2HD9ejZpr8moUaNo3bo1p06dYvbs2cTHxxMUFHTNdk5OTuzcuZPU1FR+++033n77bVJSUkhLS8NgqJods3TpUvz8/CzaaTQa5efi4mLl+bx0+SfA3r17iY6OxtbWFldXV1auXEm3bt1ISkoiIiKCxMREfHyqrz5T0/gHDhxIUFAQX3zxBb6+vlRWVtKiRQvludNqtVcdo8FgYOLEiQwePPiKsr/2AbvctGnTlKto/mX804/y+jOPXfWx7max0VEWV0Ys/3MPtPxCA24u1ReOyC8sIqiWzeGdHeyxsrK6YsaX3lCEzrnq71Pn7ITJVEFRSanFrK78QoNSJ/3IcbLOnOP3PX8AKHvIPfXmDJJ6deOhvldf0ivE9fIN7UCfS/bMqqyoutBCabEerWN18t1YrMfFM7jGPjRaZ1QqK4zFlpselxbrsbN3rfughbiGdgFehHvqlPumiqovmPJLy3C1v+R1t7SMQNeaLwzkpFFjpVKRX2r5Pie/1IjOruo9hE6rwVRppqis3GJWV36pEZ1WY9HulN7AlFXbuDcikMEtwxGirtg7OGNlZXXFxvOGwnwcnWt+Da6amaW3OFZYoMfJpaq+o4uuqo+CfJxdqv8XGAry8fEPrjWWgJBIKisqyLt4AU+vq1+pVwjR+EiiS1xBrVZfsaa9pmNt2rShoqKC8+fP061btzqP4dIlc385ePAgubm5TJ8+nYCAAKBqb6ybfYya1u63bNmS2NhYvvjiC+bOnavMJLseNjY2ygbxEyZMQKfTsWbNGnr37q1sxN+jR49a248bNw4rKyuWLVtGv3796N+/v7Jk08/PjwULFlBYWIiTkxNNmjRh1apVdOvWjSVLlrBjx46rxpabm0tGRgZffPGF8vvauHGjRZ1WrVrx5ZdfcvHixRpndbVt25aMjIwafze1ee2113j55ZctjpWlp153+7uR1k6D1q76g4nZbEbn7ET64WOE+FclM4tLSzl84hS9u9Q8O8DGxppQf1/SDx+nQ8tmSj/ph46R2K0DACH+PtjYWJN+6BgdY6pmQ545n0NOXj6RQVV/X+OGP2RxZcejWWf4ZN5CJr0wEi+Pmmf+CXEzbNVabNXVyXaz2YzWwZVzJ/fh2qRqr7lyYzG5Zw8R1iqhxj6srG1w8wrjXNZe/MI6Kv2cP7mP8Jh+9T8IIS6jtbVBa1v9dttsNqPTakg/m0uwmzMAxWXlHMnR0ysioMY+bKytCHF3Zn92Lu0DvJR+9p/NpU9U1RdxwW7O2FipSD+bS8fAqi0ZzhQYyC0qJcKjOsFwSl/I5JXb6B7qx0Nt5GIMom7Z2NjgFxjK0Yx9RMdUvdcwm80cydhX6xUQA0MiOZqxj67xA5RjRw7uJTCk6vx0c/fCyVnH0Yx9+AaEAFBaUszJzMN07Nan1ljOnDyOSqXCwcm5roYnhLiDSKJLXCE4OJj169fz8MMPo9Fo8PDwIDg4GIPBwOrVq4mJicHe3p7IyEgeffRRnnjiCd577z3atGnDhQsXWL16Na1ataJ///51HltgYCBqtZoPPviA0aNHk56ezuTJk2+qr+DgYFasWEFGRgbu7u64uLhg++fU5lGjRjFmzBgcHBxISkq6rv6WLFnCsWPH6N69O66urvz6669UVlYSFRWFk5MTycnJvPTSS1RWVtK1a1fy8/PZtGkTzs7OPPnkkyxdupRZs2axZcsW2rZtyyuvvMKTTz7J3r17cXV15amnnuLf//439913H++88w7u7u5s2LABg8GAvb09//nPf2jbtm2t8bm6uuLu7s7nn3+Oj48PWVlZjB8/3qLOsGHDmDp1KoMGDWLatGn4+Piwa9cufH196dy5M2+//TYDBgwgMDCQBx98ECsrK/bs2UN6ejpTpkyp8XE1Go3FrDWAQlm2eENUKhX9unfk55Xr8fZ0p4mbK98vW4OrixPtWzZV6k36eA7tWzajb7eqD/gD4jrz0dwFhPr7EB7oz6/rf6e0rIy4DlXLZR20dvTs0JavFi3HwV6L1k7D7Pm/EhkcQERw1Ywy78uSWYVFxUDVpvWX7+0lRF1SqVREtB7AgW0/4qTzwcGlCembv8POwU1JYgGs/flt/MM6EtG66n9OZNv72LZiJq5eYbh7RXBo9xJM5aWERFfPPiwpyqO0KA9DfjYA+TknsLG1w97JE4225lk1QtQFlUpFYtMgFuw7greTPU0ctfyw+zA6rYbYgOoZulNWbqV9gDcJTasSWf2bhfDJ5r2EuDsT5q5j+cFMSk0VxIVVvVY7qG2JC/fnm+0HcFTborW1YU7aH0R46oj4c0bZybxCpqzaRitfD/o3D1H27rJSgbOd5f9pIW5W1/iB/Pj1h/gFhhIQFMGm1KWUGY2069QTgB++momzzo3E+6tm9t8T15/P/z2BDasWE9WiHXt3bOR01jGSHhkNVP3NdOnZnzXLf8a9iQ+u7k1YuWQeTi6uNP8zmXbiWAYnMw8TFtkCtZ0dWccOsfTnObTp0B17e8eaAxVCNGqS6LpLVVZWKpucX27SpEk8++yzhIWFYTQaMZvNdOnShdGjR/PQQw+Rm5vLhAkTSElJYfbs2UyZMoVx48Zx+vRpPDw86NSpEwMGDKix7/+Vp6cnc+bM4fXXX2fmzJm0bduWd999l/vuu++G+3r66adJTU0lNjYWg8HA2rVriYuLA6oSPmPHjmXYsGG1Lsm7nE6nY/78+aSkpFBaWkpERATfffcd0dHRAEyePBlPT0+mTZvGsWPH0Ol0tG3bltdff50LFy7w1FNPkZKSoiSrJk6cyG+//cbo0aP5/vvv8fX1Zdu2bfz9739n8ODBFBQUEBsby9dff429vT29e/cmLCzsitlTf7GysmLevHm8+OKLtGjRgqioKGbOnKmMGapmuf3222+MGzeOfv36YTKZaN68OR999BEACQkJLFmyhEmTJjFjxgxsbW1p2rQpo0aNuuHnX9yY++O7Yiwr5/MffqG4pJSo0EBef+Yx1JfsO3EuN09JRAF0adOCfEMRPyxfi77QQLCvD28887jFBvVPDkpApYL35nyPyWQipmk4Tz1Q90lqIW5G09gkKkxGtq/+hHJjER6+zeie9BbWNtXJ8qL8cxhLCpX7gZFdMRbnk75lHqXFebh6htB90FsWm9of3buC/Vu/V+6v+fENADr0eYGQ5rIcV9Sv+6JDKTNV8OXv6RSXm4jydGX8vbGobaq3gThXWEKhsXqpYudgHwpKy/hxz2HyS4wEuTkzPr49LpcsS3y8XTNUqPjXul2UV1YS4+PByI7RSvnWrLMUlJax8dgZNh47oxz3cNDyweC4+h20uGu0ancPhsICVi35nsICPb4BwYx4/g1lw3n9xRxUVlZK/aCwpjw0/G+s/GUeKxbPxaOJD4898yrevoFKnR69B1FeZmTB3E8pLSkmKKwpI55/E1vbqv8FNja27N2xidW//kiFqRxX9yZ0jR9A1/iBCCHuTirzXxuuiLtKYmIi4eHhN7Qs726SmZlJWFgYaWlpV50lJW5O4fblDR2CEPVuRrokTETjN+703xo6BCHq3d5B7zV0CELUux7RV15U6k6R985zDR1CjVzf+KShQ7hrWV27imhM8vLyWLJkCampqfTq1auhw7ntlJeXc/bsWd588006deokSS4hhBBCCCGEEOIOIksX7zIjR44kLS2NcePGcf/99zdIDJdekfFyy5Ytq/ON7W/Epk2b6NmzJ5GRkfz0008WZRs2bKBv35o30gSUKysKIYQQQgghhBCiYUii6y6zYMGChg6B3bt311rm5+d36wKpQVxcHLWt5o2Njb1q7EIIIYQQQgghhGhYkugSt1x4eHhDh3BTtFrtHRu7EEIIIYQQQojGKzMzk5CQEHbt2kXr1q0bOpwGJXt0CSGEEEIIIYQQQjSQ9evXM3DgQHx9fVGpVCxcuLChQ6rR8OHDGTRoUEOHcU2S6BJCCCGEEEIIIYRoIEVFRcTExPDRRx81dCiNgiS6hBBCCCGEEEIIIRpI3759mTJlCklJSf9zXwcPHqRLly7Y2dnRokUL1q1bZ1Genp5O3759cXR0xMvLi8cff5ycnByl/KeffqJly5ZotVrc3d3p1asXRUVFpKSk8NVXX7Fo0SJUKhUqlYrU1NT/Od76IIkuIYQQQgghhBBCiDpkNBopKCiwuBmNxnp/3FdeeYVx48axa9cuOnfuzMCBA8nNzQVAr9cTHx9PmzZt2L59O8uXL+fcuXMMHToUgOzsbIYNG8bIkSM5cOAAqampDB48GLPZTHJyMkOHDiUxMZHs7Gyys7Pp0qVLvY/nZqjMtV1iTgghhBBCCCGEEELcsJSUFCZOnGhxbMKECaSkpFy1nUqlYsGCBTe8F9Zfm9FPnz6dv//97wCYTCZCQkJ44YUXePXVV5kyZQobNmxgxYoVSrtTp04REBBARkYGBoOBdu3akZmZSVBQ0BWPMXz4cPR6/W27h9hf5KqLQgghhBBCCCGEEHXotdde4+WXX7Y4ptFo6v1xO3furPxsY2NDbGwsBw4cAGDPnj2sXbsWR0fHK9odPXqUPn36cO+999KyZUsSEhLo06cPDz74IK6urvUed12SRJcQQgghhBBCCCFEHdJoNLcksXUjDAYDAwcOZMaMGVeU+fj4YG1tzcqVK9m8eTO//fYbH3zwAW+88QZbt24lJCSkASK+ObJHlxBCCCGEEEIIIUQj8Pvvvys/m0wmduzYQbNmzQBo27Yt+/fvJzg4mPDwcIubg4MDULV08p577mHixIns2rULtVrNggULAFCr1VRUVNz6Qd0gSXQJIYQQQgghhBBCNBCDwcDu3bvZvXs3AMePH2f37t1kZWXdcF8fffQRCxYs4ODBgzz//PPk5eUxcuRIAJ5//nkuXrzIsGHDSEtL4+jRo6xYsYIRI0ZQUVHB1q1bmTp1Ktu3bycrK4v58+dz4cIFJVEWHBzM3r17ycjIICcnh/Ly8jp7DuqSJLqEEEIIIYQQQgghGsj27dtp06YNbdq0AeDll1+mTZs2vP3220qdlJQUgoODr9nX9OnTmT59OjExMWzcuJHFixfj4eEBgK+vL5s2baKiooI+ffrQsmVLxo4di06nw8rKCmdnZ9avX0+/fv2IjIzkzTff5L333qNv374APP3000RFRREbG4unpyebNm2q+yejDshVF4UQQgghhBBCCCFuY08++SQqlYo5c+Y0dCi3PUl0CSGEEEIIIYQQQtymzGYzwcHBbNy4kYCAgIYO57YniS4hhBBCCCGEEEII0SjIHl1CCCGEEEIIIYQQolGQRJcQQgghhBBCCCGEaBQk0SWEEEIIIYQQQgghGgVJdAkhhBBCCCGEEEKIRkESXUIIIYQQQgghhBCiUZBElxBCCCGEEEIIIYRoFCTRJYQQQgghhBBCCCEaBUl0CSGEEEIIIYQQQohGQRJdQgghhBBCCCGEEKJR+P+l6akO1+ToyQAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer.create_model_rank_heatmap(\n", - " metrics_lst=[\n", - " # Group fairness metrics\n", - " 'Equalized_Odds_TPR',\n", - " 'Equalized_Odds_FPR',\n", - " 'Disparate_Impact',\n", - " 'Statistical_Parity_Difference',\n", - " 'Accuracy_Parity',\n", - " # Group stability metrics\n", - " 'Label_Stability_Ratio',\n", - " 'IQR_Parity',\n", - " 'Std_Parity',\n", - " 'Std_Ratio',\n", - " 'Jitter_Parity',\n", - " ],\n", - " groups_lst=config.sensitive_attributes_dct.keys(),\n", - ")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T20:57:58.777407Z", - "start_time": "2023-09-29T20:57:58.303858Z" - } - }, - "id": "43fca999faac66af" - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "5efb1bf2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": "\n
\n", - "text/plain": "alt.Chart(...)" - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", - " metrics_title=\"Error Metrics\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "0eb8528e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": "\n
\n", - "text/plain": "alt.Chart(...)" - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['Label_Stability'],\n", - " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", - " metrics_title=\"Variance Metrics\"\n", - ")" - ] - }, { "cell_type": "code", "execution_count": 78, diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 567b0d33..007ff0c3 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -26,6 +26,7 @@ def __init__(self, model_metrics_dct: dict, model_composed_metrics_df: pd.DataFr self.demo = None self.model_names = list(model_metrics_dct.keys()) self.sensitive_attributes_dct = sensitive_attributes_dct + self.group_names = list(self.sensitive_attributes_dct.keys()) # Create one metrics df with all model_dfs models_metrics_df = pd.DataFrame() @@ -74,13 +75,17 @@ def start_web_app(self): """) with gr.Row(): with gr.Column(scale=2): + group_name = gr.Dropdown( + self.group_names, + value=self.group_names[0], multiselect=False, label="Group Name for Parity Metrics", + ) with gr.Row(): accuracy_metric = gr.Dropdown( ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], value='Accuracy', multiselect=False, label="Constraint 1 (C1)", scale=2 ) - acc_min_val = gr.Number(value=0.815, label="Min value", scale=1) + acc_min_val = gr.Number(value=0.7, label="Min value", scale=1) acc_max_val = gr.Number(value=0.85, label="Max value", scale=1) with gr.Row(): fairness_metric = gr.Dropdown( @@ -88,8 +93,8 @@ def start_web_app(self): value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", scale=2 ) - fairness_min_val = gr.Number(value=-0.03, label="Min value", scale=1) - fairness_max_val = gr.Number(value=0.03, label="Max value", scale=1) + fairness_min_val = gr.Number(value=-0.15, label="Min value", scale=1) + fairness_max_val = gr.Number(value=0.15, label="Max value", scale=1) with gr.Row(): subgroup_stability_metric = gr.Dropdown( ['Std', 'IQR', 'Jitter', 'Label_Stability'], @@ -111,7 +116,8 @@ def start_web_app(self): bar_plot_for_model_selection = gr.Plot(label="Plot") btn_view1.click(self._create_bar_plot_for_model_selection, - inputs=[accuracy_metric, acc_min_val, acc_max_val, + inputs=[group_name, + accuracy_metric, acc_min_val, acc_max_val, fairness_metric, fairness_min_val, fairness_max_val, subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, group_stability_metrics, group_stab_min_val, group_stab_max_val], @@ -235,25 +241,25 @@ def start_web_app(self): def stop_web_app(self): self.demo.close() - def _create_bar_plot_for_model_selection(self, accuracy_metric, acc_min_val, acc_max_val, + def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_min_val, acc_max_val, fairness_metric, fairness_min_val, fairness_max_val, subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, group_stability_metrics, group_stab_min_val, group_stab_max_val): accuracy_constraint = (accuracy_metric, acc_min_val, acc_max_val) - subgroup_stability_constraint = (subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val) fairness_constraint = (fairness_metric, fairness_min_val, fairness_max_val) + subgroup_stability_constraint = (subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val) group_stability_constraint = (group_stability_metrics, group_stab_min_val, group_stab_max_val) # Create individual constraints metrics_value_range_dct = dict() - for constraint in [accuracy_constraint, subgroup_stability_constraint, fairness_constraint, group_stability_constraint]: + for constraint in [accuracy_constraint, fairness_constraint, subgroup_stability_constraint, group_stability_constraint]: metrics_value_range_dct[constraint[0]] = [constraint[1], constraint[2]] # Create intersectional constraints - metrics_value_range_dct[f'{accuracy_constraint[0]}&{subgroup_stability_constraint[0]}'] = None metrics_value_range_dct[f'{accuracy_constraint[0]}&{fairness_constraint[0]}'] = None + metrics_value_range_dct[f'{accuracy_constraint[0]}&{subgroup_stability_constraint[0]}'] = None metrics_value_range_dct[f'{accuracy_constraint[0]}&{group_stability_constraint[0]}'] = None - metrics_value_range_dct[(f'{accuracy_constraint[0]}&{subgroup_stability_constraint[0]}' - f'&{fairness_constraint[0]}&{group_stability_constraint[0]}')] = None + metrics_value_range_dct[(f'{accuracy_constraint[0]}&{fairness_constraint[0]}' + f'&{subgroup_stability_constraint[0]}&{group_stability_constraint[0]}')] = None melted_all_subgroup_metrics_per_model_dct = dict() for model_name in self.melted_model_metrics_df['Model_Name'].unique(): @@ -268,7 +274,7 @@ def _create_bar_plot_for_model_selection(self, accuracy_metric, acc_min_val, acc return create_bar_plot_for_model_selection(melted_all_subgroup_metrics_per_model_dct, melted_all_group_metrics_per_model_dct, metrics_value_range_dct, - group='sex&race') + group=group_name) def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accuracy_metrics_lst: list, subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list): @@ -405,28 +411,52 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names (metrics_df['Model_Name'] == model_name) & (metrics_df['Subgroup'].isin(filtered_groups))] + base_font_size = 16 models_metrics_chart = ( - alt.Chart(filtered_metrics_df).mark_bar().encode( - alt.Row('Metric:N', title=metrics_title), - alt.Y('Subgroup:N', axis=None), + alt.Chart().mark_bar().encode( + alt.Y('Subgroup:N', axis=None, sort='descending'), alt.X('Value:Q', axis=alt.Axis(grid=True), title=''), alt.Color('Subgroup:N', + sort='descending', scale=alt.Scale(scheme="tableau20"), legend=alt.Legend(title=metrics_type.capitalize(), - labelFontSize=14, - titleFontSize=14) + labelFontSize=base_font_size, + titleFontSize=base_font_size + 2) ) ) - ).properties( - width=500, height=80 - ).configure_headerRow( - labelAngle=0, - labelPadding=10, - labelAlign='left', - labelFontSize=14, - titleFontSize=18 - ).configure_axis( - labelFontSize=14, titleFontSize=18 ) - return models_metrics_chart + text = ( + models_metrics_chart.mark_text( + align='left', + baseline='middle', + fontSize=base_font_size, + dx=10 + ).encode( + text=alt.Text('Value:Q', format=",.3f"), + color=alt.value("black") + ) + ) + + final_chart = ( + alt.layer( + models_metrics_chart, text, data=filtered_metrics_df + ).properties( + width=500, + height=100 + ).facet( + row=alt.Row('Metric:N', title=metrics_title) + ).configure( + padding={'top': 33}, + ).configure_headerRow( + labelAngle=0, + labelPadding=10, + labelAlign='left', + labelFontSize=base_font_size, + titleFontSize=base_font_size + 2 + ).configure_axis( + labelFontSize=base_font_size, titleFontSize=base_font_size + 2 + ) + ) + + return final_chart diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 877979f9..c6fbe759 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -4,6 +4,7 @@ import seaborn as sns from matplotlib import pyplot as plt +from IPython.display import display from virny.utils.common_helpers import check_substring_in_list @@ -80,7 +81,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ Number of models to visualize """ - font_increase = 2 + font_increase = 4 matrix_width = 20 matrix_height = model_metrics_matrix.shape[0] // 2 fig = plt.figure(figsize=(matrix_width, matrix_height)) @@ -90,7 +91,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ ax.set(xlabel="", ylabel="") ax.xaxis.tick_top() ax.tick_params(labelsize=16 + font_increase) - fig.subplots_adjust(left=0.25, right=1., top=0.9) + fig.subplots_adjust(left=0.27, right=0.99, top=0.92) cbar = ax.collections[0].colorbar model_ranks = [idx for idx in range(num_models)] @@ -155,28 +156,28 @@ def get_column_alias(metric_group): models_in_range_df['Alias'] = models_in_range_df['Metric_Group'].apply(get_column_alias) models_in_range_df['Title'] = models_in_range_df['Alias'] - base_font_size = 25 + base_font_size = 14 bar_plot = alt.Chart(models_in_range_df).mark_bar().encode( x=alt.X("Title", type="nominal", title='Metric Group', axis=alt.Axis(labelAngle=-30), sort=alt.Sort(order='ascending')), y=alt.Y("Number_of_Models", title="Number of Models", type="quantitative"), color=alt.Color('Model_Name', legend=alt.Legend(title='Model Name')) + ).configure(padding={'top': 33} ).configure_axis( labelFontSize=base_font_size + 2, titleFontSize=base_font_size + 4, labelFontWeight='normal', titleFontWeight='normal', labelLimit=300, + tickMinStep=1, ).configure_title( fontSize=base_font_size + 2 ).configure_legend( - titleFontSize=base_font_size + 2, - labelFontSize=base_font_size, + titleFontSize=base_font_size + 4, + labelFontSize=base_font_size + 2, symbolStrokeWidth=4, labelLimit=300, titleLimit=220, - orient='none', - legendX=345, legendY=10, ).properties(width=650, height=450) return bar_plot @@ -209,6 +210,7 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro # Create a pandas condition for filtering based on the input value ranges models_in_range_df = pd.DataFrame() + model_names = pivoted_model_metrics_df['Model_Name'].unique() for idx, (metric_group, value_range) in enumerate(metrics_value_range_dct.items()): pd_condition = None if '&' not in metric_group: @@ -233,6 +235,11 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro num_satisfied_models_df = pivoted_model_metrics_df[pd_condition]['Model_Name'].value_counts().reset_index() num_satisfied_models_df.rename(columns = {'Model_Name': 'Number_of_Models'}, inplace = True) num_satisfied_models_df.rename(columns = {'index': 'Model_Name'}, inplace = True) + # If a constraint for a metric group is not satisfied, add zeros for all model names + if num_satisfied_models_df.shape[0] == 0: + num_satisfied_models_df = pd.DataFrame({'Model_Name': model_names, + 'Number_of_Models': [0] * len(model_names)}) + num_satisfied_models_df['Metric_Group'] = metric_group if idx == 0: models_in_range_df = num_satisfied_models_df From 3afea56eece3c7c01743e255ec589cf77324beb0 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 2 Oct 2023 20:56:12 +0300 Subject: [PATCH 008/148] Tested a gradio app on a big metrics df --- .../Multiple_Models_Interface_Vis.ipynb | 15 +- ...ple_Models_Interface_Vis_Big_Example.ipynb | 292 ++++++++++++++++++ docs/examples/group_metrics_sample.csv | 133 ++++++++ docs/examples/subgroup_metrics_sample.csv | 221 +++++++++++++ .../metrics_interactive_visualizer.py | 9 +- virny/utils/data_viz_utils.py | 24 +- 6 files changed, 674 insertions(+), 20 deletions(-) create mode 100644 docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb create mode 100644 docs/examples/group_metrics_sample.csv create mode 100644 docs/examples/subgroup_metrics_sample.csv diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index dad3d0d5..14fb79b7 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -226,12 +226,12 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 322, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-02T14:23:41.153446Z", - "start_time": "2023-10-02T14:23:37.215399Z" + "end_time": "2023-10-02T17:55:10.703782Z", + "start_time": "2023-10-02T17:55:06.041613Z" } }, "outputs": [], @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 323, "outputs": [ { "name": "stdout", @@ -250,7 +250,8 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n" + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" ] } ], @@ -259,9 +260,9 @@ ], "metadata": { "collapsed": false, - "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-02T14:23:41.153322Z" + "end_time": "2023-10-02T17:55:47.535767Z", + "start_time": "2023-10-02T17:55:10.703964Z" } }, "id": "678a9dc8d51243f4" diff --git a/docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb new file mode 100644 index 00000000..abc20ff4 --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T17:17:48.735994Z", + "start_time": "2023-10-02T17:17:48.334219Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T17:17:48.745045Z", + "start_time": "2023-10-02T17:17:48.736330Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T17:17:48.756222Z", + "start_time": "2023-10-02T17:17:48.745173Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": [ + "# Multiple Models Interface Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T17:17:53.361336Z", + "start_time": "2023-10-02T17:17:48.754954Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T17:17:53.387585Z", + "start_time": "2023-10-02T17:17:53.364121Z" + } + }, + "id": "d3c53c7b72ecbcd0" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'subgroup_metrics_sample.csv'), header=0)\n", + "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'group_metrics_sample.csv'), header=0)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T17:19:48.959080Z", + "start_time": "2023-10-02T17:19:48.892728Z" + } + }, + "id": "2aab7c79ecdee914" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", + " subgroup_metrics_df['Intervention_Param'].astype(str))\n", + "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", + " + models_composed_metrics_df['Intervention_Param'].astype(str))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T17:20:12.234612Z", + "start_time": "2023-10-02T17:20:12.185239Z" + } + }, + "id": "2d922003e752a4b4" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "models_metrics_dct = dict()\n", + "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", + " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T17:20:13.514668Z", + "start_time": "2023-10-02T17:20:13.478758Z" + } + }, + "id": "833484748ed512e8" + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_metrics_dct.keys()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T17:20:14.063914Z", + "start_time": "2023-10-02T17:20:14.031614Z" + } + }, + "id": "15ed7d1ba1f22317" + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": {}, + "source": [ + "## Metrics Visualization and Reporting" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T17:53:01.433697Z", + "start_time": "2023-10-02T17:53:01.373046Z" + } + }, + "outputs": [], + "source": [ + "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", + " sensitive_attributes_dct=sensitive_attributes_dct)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" + ] + } + ], + "source": [ + "visualizer.start_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T17:54:57.507776Z", + "start_time": "2023-10-02T17:53:01.479901Z" + } + }, + "id": "678a9dc8d51243f4" + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], + "source": [ + "visualizer.stop_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-29T21:41:49.927075Z", + "start_time": "2023-09-29T21:41:49.639933Z" + } + }, + "id": "277b6d1de837dab7" + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "2326c129", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/group_metrics_sample.csv b/docs/examples/group_metrics_sample.csv new file mode 100644 index 00000000..25b06763 --- /dev/null +++ b/docs/examples/group_metrics_sample.csv @@ -0,0 +1,133 @@ +Metric,SEX,RAC1P,SEX&RAC1P,Model_Name,Intervention_Param +Equalized_Odds_TPR,-0.03079268292682924,0.11074514666563329,0.05249773566501781,LGBMClassifier,0.7 +Equalized_Odds_FPR,-0.02131701139721401,0.0009518370454978109,-0.00700793796337533,LGBMClassifier,0.7 +Equalized_Odds_FNR,0.030792682926829296,-0.11074514666563334,-0.05249773566501781,LGBMClassifier,0.7 +Disparate_Impact,1.0451749734888653,1.3342133960856337,1.248406706539857,LGBMClassifier,0.7 +Statistical_Parity_Difference,0.03246951219512195,0.22563645522532638,0.17651362084581623,LGBMClassifier,0.7 +Accuracy_Parity,0.04775641025641031,0.07497732132443469,0.0652173913043479,LGBMClassifier,0.7 +Label_Stability_Ratio,1.0095819811577007,1.0301209785116932,1.012842085178694,LGBMClassifier,0.7 +IQR_Parity,-0.0026551143311698278,-0.00967660527132716,-0.005313076583927184,LGBMClassifier,0.7 +Std_Parity,-0.002214117425894342,-0.00706110127509476,-0.004207550960833459,LGBMClassifier,0.7 +Std_Ratio,0.9581473862978338,0.8695701641666075,0.9199532195084829,LGBMClassifier,0.7 +Jitter_Parity,-0.007536566378903806,-0.019030010009223178,-0.009410112766584558,LGBMClassifier,0.7 +Equalized_Odds_TPR,-0.01097560975609757,-0.00598674778160968,-0.05549362502612687,LGBMClassifier,0.0 +Equalized_Odds_FPR,-0.025116082735331363,-0.014481520763645256,-0.019877655812003875,LGBMClassifier,0.0 +Equalized_Odds_FNR,0.01097560975609757,0.005986747781609625,0.055493625026126925,LGBMClassifier,0.0 +Disparate_Impact,1.0739042728773152,1.1095233662260287,1.0641057210867602,LGBMClassifier,0.0 +Statistical_Parity_Difference,0.061432926829268264,0.09117681249273446,0.05441371141921547,LGBMClassifier,0.0 +Accuracy_Parity,0.04294871794871791,0.037355018466921575,0.032355915065722995,LGBMClassifier,0.0 +Label_Stability_Ratio,1.013086132198451,1.0203793128013074,1.0132200761605896,LGBMClassifier,0.0 +IQR_Parity,-0.0030377716953829265,-0.007959024854970922,-0.005539567672418727,LGBMClassifier,0.0 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+Disparate_Impact,1.0570386018820819,1.15227520571032,1.1070776454221372,LogisticRegression,0.7 +Statistical_Parity_Difference,0.04527439024390245,0.11903747045375279,0.08580087786525459,LogisticRegression,0.7 +Accuracy_Parity,0.02147435897435901,0.07841152076718727,0.05596749428903114,LogisticRegression,0.7 +Label_Stability_Ratio,0.9984503821387735,1.0050781309776278,0.9984788609152078,LogisticRegression,0.7 +IQR_Parity,-0.0030463198184801366,-0.0007317941412861503,-0.0023944641804607703,LogisticRegression,0.7 +Std_Parity,-0.002576978265789877,-0.00016051358574650093,-0.0019729774914916606,LogisticRegression,0.7 +Std_Ratio,0.9398193735885796,0.9961448364837571,0.9529382123765405,LogisticRegression,0.7 +Jitter_Parity,0.0017610005153971056,-0.0049119190685485425,0.0009849212357710413,LogisticRegression,0.7 +Equalized_Odds_TPR,-0.062347560975609784,-0.0033518037741697704,-0.09653034208876188,LogisticRegression,0.4 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+Disparate_Impact,1.086494036746535,1.2810291207237774,1.2183023097164019,RandomForestClassifier,0.7 +Statistical_Parity_Difference,0.06135670731707321,0.19258340760258852,0.15606493416010592,RandomForestClassifier,0.7 +Accuracy_Parity,0.04049145299145296,0.06377567550055074,0.04785979103471516,RandomForestClassifier,0.7 +Label_Stability_Ratio,1.0042931659613945,1.0158033089241028,0.997701913713356,RandomForestClassifier,0.7 +IQR_Parity,-0.0021576183268891685,-0.0011512403501528212,-0.00012869147334378106,RandomForestClassifier,0.7 +Std_Parity,-0.002107574597948185,-0.0019021823908419097,-0.001451921355860343,RandomForestClassifier,0.7 +Std_Ratio,0.962673667602888,0.9660752793094682,0.9739339258916726,RandomForestClassifier,0.7 +Jitter_Parity,-0.004579489326979741,-0.010039410100458009,-0.0014495632866055874,RandomForestClassifier,0.7 +Equalized_Odds_TPR,-0.006478658536585358,0.034680513039097915,-0.010276597227060535,RandomForestClassifier,0.0 +Equalized_Odds_FPR,-0.018446601941747576,-0.00981751924070598,-0.010793534358544452,RandomForestClassifier,0.0 +Equalized_Odds_FNR,0.006478658536585358,-0.034680513039097915,0.010276597227060535,RandomForestClassifier,0.0 +Disparate_Impact,1.0696725293946165,1.149977548271217,1.1161027349228612,RandomForestClassifier,0.0 +Statistical_Parity_Difference,0.05464939024390236,0.11648002479947306,0.09228035950672331,RandomForestClassifier,0.0 +Accuracy_Parity,0.043910256410256476,0.04994168340568905,0.04126877129910489,RandomForestClassifier,0.0 +Label_Stability_Ratio,1.0018200544605445,1.031331519636685,1.0152022947420831,RandomForestClassifier,0.0 +IQR_Parity,-0.0014266839924084312,-0.005259735864872772,-0.003978617177466615,RandomForestClassifier,0.0 +Std_Parity,-0.0014799865759114808,-0.0045115073360025085,-0.003718279422753004,RandomForestClassifier,0.0 +Std_Ratio,0.9712188827286748,0.913538681205104,0.9275913116942456,RandomForestClassifier,0.0 +Jitter_Parity,-0.0018609822617384336,-0.017820314313740024,-0.008520772043575799,RandomForestClassifier,0.0 diff --git a/docs/examples/subgroup_metrics_sample.csv b/docs/examples/subgroup_metrics_sample.csv new file mode 100644 index 00000000..f9e45d09 --- /dev/null +++ b/docs/examples/subgroup_metrics_sample.csv @@ -0,0 +1,221 @@ +Metric,Model_Name,Model_Params,Dataset_Name,Intervention_Param,RAC1P_dis,RAC1P_priv,SEX&RAC1P_dis,SEX&RAC1P_priv,SEX_dis,SEX_priv,overall +Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 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False}",Folktables_GA_2018_Income,0.0,0.7016129032258065,0.7085714285714285,0.6666666666666666,0.7123947051744886,0.6896551724137931,0.7180385288966725,0.7067510548523207 +FNR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.33587786259541985,0.37055837563451777,0.3709677419354839,0.36069114470842334,0.36585365853658536,0.359375,0.3619047619047619 +FPR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.08403361344537816,0.09385113268608414,0.08163265306122448,0.09242618741976893,0.08155339805825243,0.1,0.09025641025641026 +IQR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.06625906669162206,0.07151880255649483,0.06651334013218053,0.07049195730964715,0.06906575947907051,0.07049244347147894,0.0698076351551229 +Jitter,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.05250175055605957,0.0703220648697996,0.05746932336098636,0.06599009540456216,0.0635568118369625,0.06541779409870094,0.06452452261306409 +Label_Stability,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9285040983606557,0.900296442687747,0.9208914728682169,0.9071014492753622,0.9103333333333334,0.9086794871794872,0.9094733333333334 +Mean,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7044707097166929,0.6383597499240815,0.7315249192761425,0.6449825636808952,0.6940397580188165,0.6283245480658572,0.6598678488432778 +Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6163162753250673,0.6810105385909122,0.6151568145209929,0.6692709736442993,0.6484095577353082,0.670628366465659,0.6599633382750907 +PPV,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7435897435897436,0.8104575163398693,0.7090909090909091,0.8043478260869565,0.7558139534883721,0.8167330677290837,0.7919621749408984 +Per_Sample_Accuracy,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8393954918032787,0.7940316205533596,0.8337403100775194,0.8036070853462158,0.8301944444444445,0.7890320512820512,0.80879 +Positive-Rate,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8931297709923665,0.7766497461928934,0.8870967741935484,0.7948164146868251,0.8390243902439024,0.784375,0.8057142857142857 +Sample_Size,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 +Selection-Rate,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.23975409836065573,0.30237154150197626,0.2131782945736434,0.2962962962962963,0.2388888888888889,0.3217948717948718,0.282 +Std,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.047667980542325225,0.052179487878327734,0.04763300879630876,0.051351288219061764,0.04994215113887428,0.05142213771478576,0.05071174415834825 +TNR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9159663865546218,0.9061488673139159,0.9183673469387755,0.9075738125802311,0.9184466019417475,0.9,0.9097435897435897 +TPR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6641221374045801,0.6294416243654822,0.6290322580645161,0.6393088552915767,0.6341463414634146,0.640625,0.638095238095238 diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 007ff0c3..9ca3cc71 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -326,7 +326,8 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura model_metrics_matrix = pd.DataFrame(results).T sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, + num_models, top_adjust=1.) return model_rank_heatmap @@ -376,7 +377,8 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met model_metrics_matrix = pd.DataFrame(results).T sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, + num_models, top_adjust=0.78) return model_rank_heatmap @@ -414,10 +416,9 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names base_font_size = 16 models_metrics_chart = ( alt.Chart().mark_bar().encode( - alt.Y('Subgroup:N', axis=None, sort='descending'), + alt.Y('Subgroup:N', axis=None), alt.X('Value:Q', axis=alt.Axis(grid=True), title=''), alt.Color('Subgroup:N', - sort='descending', scale=alt.Scale(scheme="tableau20"), legend=alt.Legend(title=metrics_type.capitalize(), labelFontSize=base_font_size, diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index c6fbe759..e90a3e71 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -60,7 +60,8 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: return sorted_matrix_by_rank -def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int): +def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int, + top_adjust: float = 0.92): """ This heatmap includes group fairness and stability metrics and defined models. Using it, you can visually compare the models across defined group metrics. On this plot, @@ -79,6 +80,8 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ Matrix of model ranks per metric where indexes are group metric names and columns are model names num_models Number of models to visualize + top_adjust + Percentage of a top padding for the heatmap """ font_increase = 4 @@ -90,8 +93,9 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ fmt='', annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 16 + font_increase}) ax.set(xlabel="", ylabel="") ax.xaxis.tick_top() + ax.tick_params(axis='x', rotation=10) ax.tick_params(labelsize=16 + font_increase) - fig.subplots_adjust(left=0.27, right=0.99, top=0.92) + fig.subplots_adjust(left=0.3, right=0.99, top=0.8) cbar = ax.collections[0].colorbar model_ranks = [idx for idx in range(num_models)] @@ -161,7 +165,7 @@ def get_column_alias(metric_group): x=alt.X("Title", type="nominal", title='Metric Group', axis=alt.Axis(labelAngle=-30), sort=alt.Sort(order='ascending')), y=alt.Y("Number_of_Models", title="Number of Models", type="quantitative"), - color=alt.Color('Model_Name', legend=alt.Legend(title='Model Name')) + color=alt.Color('Model_Type', legend=alt.Legend(title='Model Type')) ).configure(padding={'top': 33} ).configure_axis( labelFontSize=base_font_size + 2, @@ -207,10 +211,12 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro pivoted_model_metrics_df = all_metrics_for_all_models_df.pivot(columns='Metric', values='Value', index=[col for col in all_metrics_for_all_models_df.columns if col not in ('Metric', 'Value')]).reset_index() + # Create a Model_Type column to count the number of models that satisfied the constraints based on their model types + pivoted_model_metrics_df['Model_Type'] = pivoted_model_metrics_df['Model_Name'].str.split('__', expand=True)[0] + model_types = pivoted_model_metrics_df['Model_Type'].unique() # Create a pandas condition for filtering based on the input value ranges models_in_range_df = pd.DataFrame() - model_names = pivoted_model_metrics_df['Model_Name'].unique() for idx, (metric_group, value_range) in enumerate(metrics_value_range_dct.items()): pd_condition = None if '&' not in metric_group: @@ -232,13 +238,13 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro else: pd_condition &= (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val) - num_satisfied_models_df = pivoted_model_metrics_df[pd_condition]['Model_Name'].value_counts().reset_index() - num_satisfied_models_df.rename(columns = {'Model_Name': 'Number_of_Models'}, inplace = True) - num_satisfied_models_df.rename(columns = {'index': 'Model_Name'}, inplace = True) + num_satisfied_models_df = pivoted_model_metrics_df[pd_condition]['Model_Type'].value_counts().reset_index() + num_satisfied_models_df.rename(columns = {'Model_Type': 'Number_of_Models'}, inplace = True) + num_satisfied_models_df.rename(columns = {'index': 'Model_Type'}, inplace = True) # If a constraint for a metric group is not satisfied, add zeros for all model names if num_satisfied_models_df.shape[0] == 0: - num_satisfied_models_df = pd.DataFrame({'Model_Name': model_names, - 'Number_of_Models': [0] * len(model_names)}) + num_satisfied_models_df = pd.DataFrame({'Model_Type': model_types, + 'Number_of_Models': [0] * len(model_types)}) num_satisfied_models_df['Metric_Group'] = metric_group if idx == 0: From 6da26a2bfc7d9611bbb180a7be0113818a1bb9dd Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 5 Oct 2023 00:19:15 +0300 Subject: [PATCH 009/148] Added a gradio app for Law_School --- ...ultiple_Models_Interface_Vis_Income.ipynb} | 77 ++--- ...iple_Models_Interface_Vis_Law_School.ipynb | 297 ++++++++++++++++++ ...cs_sample.csv => income_group_metrics.csv} | 0 ...sample.csv => income_subgroup_metrics.csv} | 0 docs/examples/law_school_group_metrics.csv | 89 ++++++ docs/examples/law_school_subgroup_metrics.csv | 153 +++++++++ .../metrics_interactive_visualizer.py | 4 +- 7 files changed, 582 insertions(+), 38 deletions(-) rename docs/examples/{Multiple_Models_Interface_Vis_Big_Example.ipynb => Multiple_Models_Interface_Vis_Income.ipynb} (78%) create mode 100644 docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb rename docs/examples/{group_metrics_sample.csv => income_group_metrics.csv} (100%) rename docs/examples/{subgroup_metrics_sample.csv => income_subgroup_metrics.csv} (100%) create mode 100644 docs/examples/law_school_group_metrics.csv create mode 100644 docs/examples/law_school_subgroup_metrics.csv diff --git a/docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb similarity index 78% rename from docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb rename to docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index abc20ff4..7b653984 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Big_Example.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-02T17:17:48.735994Z", - "start_time": "2023-10-02T17:17:48.334219Z" + "end_time": "2023-10-04T21:08:30.999391Z", + "start_time": "2023-10-04T21:08:30.521174Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-02T17:17:48.745045Z", - "start_time": "2023-10-02T17:17:48.736330Z" + "end_time": "2023-10-04T21:08:31.008054Z", + "start_time": "2023-10-04T21:08:31.000071Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-02T17:17:48.756222Z", - "start_time": "2023-10-02T17:17:48.745173Z" + "end_time": "2023-10-04T21:08:31.018864Z", + "start_time": "2023-10-04T21:08:31.008657Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-02T17:17:53.361336Z", - "start_time": "2023-10-02T17:17:48.754954Z" + "end_time": "2023-10-04T21:08:33.567112Z", + "start_time": "2023-10-04T21:08:31.017655Z" } }, "outputs": [], @@ -98,33 +98,33 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-02T17:17:53.387585Z", - "start_time": "2023-10-02T17:17:53.364121Z" + "end_time": "2023-10-04T21:08:33.593962Z", + "start_time": "2023-10-04T21:08:33.567969Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'subgroup_metrics_sample.csv'), header=0)\n", - "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'group_metrics_sample.csv'), header=0)" + "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_subgroup_metrics.csv'), header=0)\n", + "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_group_metrics.csv'), header=0)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-02T17:19:48.959080Z", - "start_time": "2023-10-02T17:19:48.892728Z" + "end_time": "2023-10-04T21:08:33.619601Z", + "start_time": "2023-10-04T21:08:33.593364Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -135,15 +135,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-02T17:20:12.234612Z", - "start_time": "2023-10-02T17:20:12.185239Z" + "end_time": "2023-10-04T21:08:33.644681Z", + "start_time": "2023-10-04T21:08:33.620136Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -153,21 +153,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-02T17:20:13.514668Z", - "start_time": "2023-10-02T17:20:13.478758Z" + "end_time": "2023-10-04T21:08:33.669581Z", + "start_time": "2023-10-04T21:08:33.643533Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-02T17:20:14.063914Z", - "start_time": "2023-10-02T17:20:14.031614Z" + "end_time": "2023-10-04T21:08:33.691780Z", + "start_time": "2023-10-04T21:08:33.667179Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-02T17:53:01.433697Z", - "start_time": "2023-10-02T17:53:01.373046Z" + "end_time": "2023-10-04T21:08:33.716112Z", + "start_time": "2023-10-04T21:08:33.690511Z" } }, "outputs": [], @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "outputs": [ { "name": "stdout", @@ -229,15 +229,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-02T17:54:57.507776Z", - "start_time": "2023-10-02T17:53:01.479901Z" + "end_time": "2023-10-04T21:11:38.266786Z", + "start_time": "2023-10-04T21:08:33.716571Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "outputs": [ { "name": "stdout", @@ -253,17 +253,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-29T21:41:49.927075Z", - "start_time": "2023-09-29T21:41:49.639933Z" + "end_time": "2023-10-04T21:11:38.361088Z", + "start_time": "2023-10-04T21:11:38.269315Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 12, "id": "2326c129", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:11:38.363712Z", + "start_time": "2023-10-04T21:11:38.360139Z" + } + }, "outputs": [], "source": [] } diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb new file mode 100644 index 00000000..c88e03d5 --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:15:46.248933Z", + "start_time": "2023-10-04T21:15:45.908524Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:15:46.257749Z", + "start_time": "2023-10-04T21:15:46.249557Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:15:46.268273Z", + "start_time": "2023-10-04T21:15:46.257867Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": [ + "# Multiple Models Interface Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.510506Z", + "start_time": "2023-10-04T21:15:46.267180Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.534494Z", + "start_time": "2023-10-04T21:15:47.511483Z" + } + }, + "id": "d3c53c7b72ecbcd0" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_subgroup_metrics.csv'), header=0)\n", + "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_group_metrics.csv'), header=0)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.559988Z", + "start_time": "2023-10-04T21:15:47.534609Z" + } + }, + "id": "2aab7c79ecdee914" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", + " subgroup_metrics_df['Intervention_Param'].astype(str))\n", + "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", + " + models_composed_metrics_df['Intervention_Param'].astype(str))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.581842Z", + "start_time": "2023-10-04T21:15:47.560554Z" + } + }, + "id": "2d922003e752a4b4" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "models_metrics_dct = dict()\n", + "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", + " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.603973Z", + "start_time": "2023-10-04T21:15:47.582304Z" + } + }, + "id": "833484748ed512e8" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_metrics_dct.keys()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.625522Z", + "start_time": "2023-10-04T21:15:47.604575Z" + } + }, + "id": "15ed7d1ba1f22317" + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": {}, + "source": [ + "## Metrics Visualization and Reporting" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:15:47.653413Z", + "start_time": "2023-10-04T21:15:47.624966Z" + } + }, + "outputs": [], + "source": [ + "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", + " sensitive_attributes_dct=sensitive_attributes_dct)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" + ] + } + ], + "source": [ + "visualizer.start_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:17:37.487583Z", + "start_time": "2023-10-04T21:15:47.653522Z" + } + }, + "id": "678a9dc8d51243f4" + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], + "source": [ + "visualizer.stop_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:17:37.530553Z", + "start_time": "2023-10-04T21:17:37.492738Z" + } + }, + "id": "277b6d1de837dab7" + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2326c129", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-04T21:17:37.533378Z", + "start_time": "2023-10-04T21:17:37.530182Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/group_metrics_sample.csv b/docs/examples/income_group_metrics.csv similarity index 100% rename from docs/examples/group_metrics_sample.csv rename to docs/examples/income_group_metrics.csv diff --git a/docs/examples/subgroup_metrics_sample.csv b/docs/examples/income_subgroup_metrics.csv similarity index 100% rename from docs/examples/subgroup_metrics_sample.csv rename to docs/examples/income_subgroup_metrics.csv diff --git a/docs/examples/law_school_group_metrics.csv b/docs/examples/law_school_group_metrics.csv new file mode 100644 index 00000000..f39a023c --- /dev/null +++ b/docs/examples/law_school_group_metrics.csv @@ -0,0 +1,89 @@ +Metric,male,race,male&race,Model_Name,Experiment_Iteration,Intervention_Param,Test_Set_Index +Equalized_Odds_TPR,-0.006852677560728049,-0.08926010463166822,-0.09233449477351918,LGBMClassifier,Exp_iter_1,0.6,0 +Equalized_Odds_FPR,0.027310924369747913,-0.2892592592592593,-0.15657230634189157,LGBMClassifier,Exp_iter_1,0.6,0 +Equalized_Odds_FNR,0.006852677560727997,0.08926010463166825,0.09233449477351917,LGBMClassifier,Exp_iter_1,0.6,0 +Disparate_Impact,1.0155706946616037,1.0637883787525366,1.064060803474484,LGBMClassifier,Exp_iter_1,0.6,0 +Statistical_Parity_Difference,0.01661276831014935,0.06794241218413566,0.06852497096399524,LGBMClassifier,Exp_iter_1,0.6,0 +Accuracy_Parity,-0.02441327723235165,-0.15885561838018636,-0.16299790356394128,LGBMClassifier,Exp_iter_1,0.6,0 +Label_Stability_Ratio,1.0022336520605963,0.9215110409144575,0.9448800911879143,LGBMClassifier,Exp_iter_1,0.6,0 +IQR_Parity,-0.0019234170135528535,0.030752485425003136,0.026171410156253943,LGBMClassifier,Exp_iter_1,0.6,0 +Std_Parity,-0.0014947483480208142,0.022654697553924172,0.019420397675297765,LGBMClassifier,Exp_iter_1,0.6,0 +Std_Ratio,0.9362288387603354,2.1935697988767453,1.91757091989168,LGBMClassifier,Exp_iter_1,0.6,0 +Jitter_Parity,-0.0009008483276642908,0.05605017735541579,0.04049042442969653,LGBMClassifier,Exp_iter_1,0.6,0 +Equalized_Odds_TPR,-0.005171382474971176,-0.11004756903064217,-0.0987224157955865,LGBMClassifier,Exp_iter_1,0.0,0 +Equalized_Odds_FPR,-0.025282526803824923,-0.4292592592592593,-0.3133640552995392,LGBMClassifier,Exp_iter_1,0.0,0 +Equalized_Odds_FNR,0.005171382474971221,0.11004756903064213,0.09872241579558652,LGBMClassifier,Exp_iter_1,0.0,0 +Disparate_Impact,1.0093278423562047,0.9912768659262348,0.9923664122137406,LGBMClassifier,Exp_iter_1,0.0,0 +Statistical_Parity_Difference,0.009888779529904745,-0.009296665118064151,-0.008130081300812941,LGBMClassifier,Exp_iter_1,0.0,0 +Accuracy_Parity,-0.015605423094904092,-0.1335709166202118,-0.11753449368631463,LGBMClassifier,Exp_iter_1,0.0,0 +Label_Stability_Ratio,0.998577907316844,0.911696818570683,0.920586307756427,LGBMClassifier,Exp_iter_1,0.0,0 +IQR_Parity,-0.0018418992707291484,0.03070162807008311,0.02640029144412168,LGBMClassifier,Exp_iter_1,0.0,0 +Std_Parity,-0.001380608364266945,0.022270950149824872,0.01912573180911198,LGBMClassifier,Exp_iter_1,0.0,0 +Std_Ratio,0.9474740574271133,2.0150416987665265,1.7940775240850562,LGBMClassifier,Exp_iter_1,0.0,0 +Jitter_Parity,0.0009153720918688296,0.06001277824710762,0.05223850775990582,LGBMClassifier,Exp_iter_1,0.0,0 +Equalized_Odds_TPR,-0.005365607208478229,-0.07716333043811985,-0.08536585365853655,LogisticRegression,Exp_iter_1,0.6,0 +Equalized_Odds_FPR,0.0021008403361344463,-0.2592592592592593,-0.14691683124862842,LogisticRegression,Exp_iter_1,0.6,0 +Equalized_Odds_FNR,0.005365607208478207,0.07716333043811986,0.08536585365853659,LogisticRegression,Exp_iter_1,0.6,0 +Disparate_Impact,1.014185628316063,1.0865027213593523,1.0769230769230769,LogisticRegression,Exp_iter_1,0.6,0 +Statistical_Parity_Difference,0.015190042348141253,0.09213596057123241,0.0824622531939605,LogisticRegression,Exp_iter_1,0.6,0 +Accuracy_Parity,-0.020523609163160317,-0.15885561838018636,-0.16299790356394128,LogisticRegression,Exp_iter_1,0.6,0 +Label_Stability_Ratio,1.004335707649427,0.9552448804260418,0.9689971045213348,LogisticRegression,Exp_iter_1,0.6,0 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0.6]",0.0,0.8780487804878049,1.0,0.0,0.9796747967479674,1.0,0.0,0.9717444717444718,1.0,0.0,0.973811833171678,1.0,0.0,0.9728997289972899,0.8709677419354839,1.0,0.0,0.9887288666249218,1.0,0.0,0 diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 9ca3cc71..21e86f3d 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -86,7 +86,7 @@ def start_web_app(self): scale=2 ) acc_min_val = gr.Number(value=0.7, label="Min value", scale=1) - acc_max_val = gr.Number(value=0.85, label="Max value", scale=1) + acc_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): fairness_metric = gr.Dropdown( ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], @@ -102,7 +102,7 @@ def start_web_app(self): scale=2 ) subgroup_stab_min_val = gr.Number(value=0.9, label="Min value", scale=1) - subgroup_stab_max_val = gr.Number(value=0.94, label="Max value", scale=1) + subgroup_stab_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): group_stability_metrics = gr.Dropdown( ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], From cb207bb64bdc75ef6dc5dfa779261b80dbb098e8 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 5 Oct 2023 01:41:06 +0300 Subject: [PATCH 010/148] Added an overall subgrop to heatmaps --- ...Multiple_Models_Interface_Vis_Income.ipynb | 91 ++++++++++--------- .../metrics_interactive_visualizer.py | 51 +++++++---- 2 files changed, 81 insertions(+), 61 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 7b653984..086dfd4a 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -2,15 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:08:30.999391Z", - "start_time": "2023-10-04T21:08:30.521174Z" + "end_time": "2023-10-04T21:22:16.448256Z", + "start_time": "2023-10-04T21:22:16.399916Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -19,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:08:31.008054Z", - "start_time": "2023-10-04T21:08:31.000071Z" + "end_time": "2023-10-04T21:22:16.489117Z", + "start_time": "2023-10-04T21:22:16.447387Z" } }, "outputs": [], @@ -37,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:08:31.018864Z", - "start_time": "2023-10-04T21:08:31.008657Z" + "end_time": "2023-10-04T21:22:16.493213Z", + "start_time": "2023-10-04T21:22:16.472246Z" } }, "outputs": [ @@ -72,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.567112Z", - "start_time": "2023-10-04T21:08:31.017655Z" + "end_time": "2023-10-04T21:22:16.529742Z", + "start_time": "2023-10-04T21:22:16.494483Z" } }, "outputs": [], @@ -90,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "outputs": [], "source": [ "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" @@ -98,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.593962Z", - "start_time": "2023-10-04T21:08:33.567969Z" + "end_time": "2023-10-04T21:22:16.537318Z", + "start_time": "2023-10-04T21:22:16.516511Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -116,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.619601Z", - "start_time": "2023-10-04T21:08:33.593364Z" + "end_time": "2023-10-04T21:22:16.563352Z", + "start_time": "2023-10-04T21:22:16.537733Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -135,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.644681Z", - "start_time": "2023-10-04T21:08:33.620136Z" + "end_time": "2023-10-04T21:22:16.584758Z", + "start_time": "2023-10-04T21:22:16.563460Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -153,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.669581Z", - "start_time": "2023-10-04T21:08:33.643533Z" + "end_time": "2023-10-04T21:22:16.607231Z", + "start_time": "2023-10-04T21:22:16.584939Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -178,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.691780Z", - "start_time": "2023-10-04T21:08:33.667179Z" + "end_time": "2023-10-04T21:22:16.630707Z", + "start_time": "2023-10-04T21:22:16.608538Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:08:33.716112Z", - "start_time": "2023-10-04T21:08:33.690511Z" + "end_time": "2023-10-04T22:04:26.837638Z", + "start_time": "2023-10-04T22:04:26.735350Z" } }, "outputs": [], @@ -210,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "outputs": [ { "name": "stdout", @@ -229,15 +238,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:11:38.266786Z", - "start_time": "2023-10-04T21:08:33.716571Z" + "end_time": "2023-10-04T22:40:36.211694Z", + "start_time": "2023-10-04T22:04:27.009071Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "outputs": [ { "name": "stdout", @@ -253,20 +262,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:11:38.361088Z", - "start_time": "2023-10-04T21:11:38.269315Z" + "end_time": "2023-10-04T22:04:25.890691Z", + "start_time": "2023-10-04T22:04:25.777458Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:11:38.363712Z", - "start_time": "2023-10-04T21:11:38.360139Z" + "end_time": "2023-10-04T22:04:25.893162Z", + "start_time": "2023-10-04T22:04:25.889647Z" } }, "outputs": [], diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 21e86f3d..24edc520 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -109,8 +109,8 @@ def start_web_app(self): value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", scale=2 ) - group_stab_min_val = gr.Number(value=1.0, label="Min value", scale=1) - group_stab_max_val = gr.Number(value=1.03, label="Max value", scale=1) + group_stab_min_val = gr.Number(value=0.98, label="Min value", scale=1) + group_stab_max_val = gr.Number(value=1.02, label="Max value", scale=1) btn_view1 = gr.Button("Submit") with gr.Column(scale=3): bar_plot_for_model_selection = gr.Plot(label="Plot") @@ -241,6 +241,29 @@ def start_web_app(self): def stop_web_app(self): self.demo.close() + def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, + selected_metric: str, selected_subgroup: str, defined_model_names: list): + results[subgroup_metric] = dict() + + # Get distinct sorted model names + sorted_model_names_arr = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == selected_metric) & + (self.sorted_model_metrics_df.Subgroup == selected_subgroup) + ]['Model_Name'].values + sorted_model_names_arr = [model for model in sorted_model_names_arr if model in defined_model_names] + + # Add values to a results dict + for idx, model_name in enumerate(sorted_model_names_arr): + metric_value = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == selected_metric) & + (self.sorted_model_metrics_df.Subgroup == selected_subgroup) & + (self.sorted_model_metrics_df.Model_Name == model_name) + ]['Value'].values[0] + metric_value = round(metric_value, 3) + results[subgroup_metric][model_name] = metric_value + + return results + def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_min_val, acc_max_val, fairness_metric, fairness_min_val, fairness_max_val, subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, @@ -301,28 +324,16 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura results = {} num_models = len(model_names) for metric in metrics_lst: + # Add an overall metric + subgroup_metric = metric + '_overall' + results = self.__filter_subgroup_metrics_df(results, subgroup_metric, metric, + selected_subgroup='overall', defined_model_names=model_names) + # Add a subgroup metric for group in groups_lst: for prefix in ['priv', 'dis']: subgroup = group + '_' + prefix subgroup_metric = metric + '_' + subgroup - results[subgroup_metric] = dict() - - # Get distinct sorted model names - sorted_model_names_arr = self.sorted_model_metrics_df[ - (self.sorted_model_metrics_df.Metric == metric) & - (self.sorted_model_metrics_df.Subgroup == subgroup) - ]['Model_Name'].values - sorted_model_names_arr = [model for model in sorted_model_names_arr if model in model_names] - - # Add values to a results dict - for idx, model_name in enumerate(sorted_model_names_arr): - metric_value = self.sorted_model_metrics_df[ - (self.sorted_model_metrics_df.Metric == metric) & - (self.sorted_model_metrics_df.Subgroup == subgroup) & - (self.sorted_model_metrics_df.Model_Name == model_name) - ]['Value'].values[0] - metric_value = round(metric_value, 3) - results[subgroup_metric][model_name] = metric_value + results = self.__filter_subgroup_metrics_df(results, subgroup_metric, metric, subgroup, model_names) model_metrics_matrix = pd.DataFrame(results).T sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) From 498b0efa82636a53035ff1176aa13d2dd787d577 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 5 Oct 2023 16:20:49 +0300 Subject: [PATCH 011/148] Added a gradio app for Ricci --- ...iple_Models_Interface_Vis_Law_School.ipynb | 91 +++--- .../Multiple_Models_Interface_Vis_Ricci.ipynb | 297 ++++++++++++++++++ docs/examples/ricci_group_metrics.csv | 133 ++++++++ docs/examples/ricci_subgroup_metrics.csv | 229 ++++++++++++++ 4 files changed, 709 insertions(+), 41 deletions(-) create mode 100644 docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb create mode 100644 docs/examples/ricci_group_metrics.csv create mode 100644 docs/examples/ricci_subgroup_metrics.csv diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index c88e03d5..51f3eda9 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -2,15 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:15:46.248933Z", - "start_time": "2023-10-04T21:15:45.908524Z" + "end_time": "2023-10-04T22:41:38.880532Z", + "start_time": "2023-10-04T22:41:38.744525Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -19,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:15:46.257749Z", - "start_time": "2023-10-04T21:15:46.249557Z" + "end_time": "2023-10-04T22:41:38.897390Z", + "start_time": "2023-10-04T22:41:38.879544Z" } }, "outputs": [], @@ -37,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:15:46.268273Z", - "start_time": "2023-10-04T21:15:46.257867Z" + "end_time": "2023-10-04T22:41:38.905091Z", + "start_time": "2023-10-04T22:41:38.881727Z" } }, "outputs": [ @@ -72,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.510506Z", - "start_time": "2023-10-04T21:15:46.267180Z" + "end_time": "2023-10-04T22:41:38.938535Z", + "start_time": "2023-10-04T22:41:38.904769Z" } }, "outputs": [], @@ -90,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "outputs": [], "source": [ "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" @@ -98,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.534494Z", - "start_time": "2023-10-04T21:15:47.511483Z" + "end_time": "2023-10-04T22:41:38.946897Z", + "start_time": "2023-10-04T22:41:38.927198Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -116,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.559988Z", - "start_time": "2023-10-04T21:15:47.534609Z" + "end_time": "2023-10-04T22:41:38.973205Z", + "start_time": "2023-10-04T22:41:38.947863Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -135,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.581842Z", - "start_time": "2023-10-04T21:15:47.560554Z" + "end_time": "2023-10-04T22:41:38.994980Z", + "start_time": "2023-10-04T22:41:38.973852Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -153,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.603973Z", - "start_time": "2023-10-04T21:15:47.582304Z" + "end_time": "2023-10-04T22:41:39.019414Z", + "start_time": "2023-10-04T22:41:38.994888Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -178,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.625522Z", - "start_time": "2023-10-04T21:15:47.604575Z" + "end_time": "2023-10-04T22:41:39.040053Z", + "start_time": "2023-10-04T22:41:39.018488Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:15:47.653413Z", - "start_time": "2023-10-04T21:15:47.624966Z" + "end_time": "2023-10-04T22:41:39.066833Z", + "start_time": "2023-10-04T22:41:39.039759Z" } }, "outputs": [], @@ -210,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "outputs": [ { "name": "stdout", @@ -229,15 +238,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:17:37.487583Z", - "start_time": "2023-10-04T21:15:47.653522Z" + "end_time": "2023-10-04T23:04:24.847056Z", + "start_time": "2023-10-04T22:41:39.066921Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "outputs": [ { "name": "stdout", @@ -253,20 +262,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:17:37.530553Z", - "start_time": "2023-10-04T21:17:37.492738Z" + "end_time": "2023-10-04T23:04:24.902597Z", + "start_time": "2023-10-04T23:04:24.849984Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:17:37.533378Z", - "start_time": "2023-10-04T21:17:37.530182Z" + "end_time": "2023-10-04T23:04:24.904745Z", + "start_time": "2023-10-04T23:04:24.902886Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb new file mode 100644 index 00000000..b4d780d9 --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T10:39:55.272406Z", + "start_time": "2023-10-05T10:39:54.897985Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T10:39:55.281893Z", + "start_time": "2023-10-05T10:39:55.273119Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T10:39:55.292533Z", + "start_time": "2023-10-05T10:39:55.282026Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": [ + "# Multiple Models Interface Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.844327Z", + "start_time": "2023-10-05T10:39:55.291377Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "sensitive_attributes_dct = {'Race': 'Non-White'}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.870851Z", + "start_time": "2023-10-05T10:39:56.847550Z" + } + }, + "id": "d3c53c7b72ecbcd0" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_subgroup_metrics.csv'), header=0)\n", + "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_group_metrics.csv'), header=0)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.896386Z", + "start_time": "2023-10-05T10:39:56.868941Z" + } + }, + "id": "2aab7c79ecdee914" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", + " subgroup_metrics_df['Intervention_Param'].astype(str))\n", + "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", + " + models_composed_metrics_df['Intervention_Param'].astype(str))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.916837Z", + "start_time": "2023-10-05T10:39:56.894764Z" + } + }, + "id": "2d922003e752a4b4" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "models_metrics_dct = dict()\n", + "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", + " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.940693Z", + "start_time": "2023-10-05T10:39:56.916977Z" + } + }, + "id": "833484748ed512e8" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "dict_keys(['LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LGBMClassifier__alpha=0.7', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.4', 'LogisticRegression__alpha=0.7', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7'])" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_metrics_dct.keys()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.964580Z", + "start_time": "2023-10-05T10:39:56.941618Z" + } + }, + "id": "15ed7d1ba1f22317" + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": {}, + "source": [ + "## Metrics Visualization and Reporting" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T10:39:56.991119Z", + "start_time": "2023-10-05T10:39:56.962485Z" + } + }, + "outputs": [], + "source": [ + "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", + " sensitive_attributes_dct=sensitive_attributes_dct)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" + ] + } + ], + "source": [ + "visualizer.start_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:20:31.060413Z", + "start_time": "2023-10-05T10:39:56.991233Z" + } + }, + "id": "678a9dc8d51243f4" + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], + "source": [ + "visualizer.stop_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:20:31.101325Z", + "start_time": "2023-10-05T13:20:31.064318Z" + } + }, + "id": "277b6d1de837dab7" + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2326c129", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T13:20:31.104256Z", + "start_time": "2023-10-05T13:20:31.102380Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/ricci_group_metrics.csv b/docs/examples/ricci_group_metrics.csv new file mode 100644 index 00000000..31cfbb3f --- /dev/null +++ b/docs/examples/ricci_group_metrics.csv @@ -0,0 +1,133 @@ +Metric,Race,Model_Name,Experiment_Iteration,Intervention_Param,Test_Set_Index +Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.0,0 +Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.0,0 +Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.0,0 +Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.0,0 +Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.0,0 +IQR_Parity,2.7755575615628914e-17,LGBMClassifier,Exp_iter_1,0.0,0 +Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.0,0 +Label_Stability_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.0,0 +Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.0,0 +Std_Parity,-1.3877787807814457e-17,LGBMClassifier,Exp_iter_1,0.0,0 +Std_Ratio,0.9999999999999998,LGBMClassifier,Exp_iter_1,0.0,0 +Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.4,0 +Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.4,0 +Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.4,0 +Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.4,0 +Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.4,0 +IQR_Parity,2.7755575615628914e-17,LGBMClassifier,Exp_iter_1,0.4,0 +Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.4,0 +Label_Stability_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.4,0 +Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.4,0 +Std_Parity,0.0,LGBMClassifier,Exp_iter_1,0.4,0 +Std_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.4,0 +Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.7,0 +Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.7,0 +Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.7,0 +Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.7,0 +Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.7,0 +IQR_Parity,0.0,LGBMClassifier,Exp_iter_1,0.7,0 +Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.7,0 +Label_Stability_Ratio,1.0000000000000002,LGBMClassifier,Exp_iter_1,0.7,0 +Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.7,0 +Std_Parity,-1.3877787807814457e-17,LGBMClassifier,Exp_iter_1,0.7,0 +Std_Ratio,0.9999999999999998,LGBMClassifier,Exp_iter_1,0.7,0 +Accuracy_Parity,0.3529411764705882,LogisticRegression,Exp_iter_1,0.0,0 +Disparate_Impact,0.5384615384615384,LogisticRegression,Exp_iter_1,0.0,0 +Equalized_Odds_FNR,0.0,LogisticRegression,Exp_iter_1,0.0,0 +Equalized_Odds_FPR,-0.6,LogisticRegression,Exp_iter_1,0.0,0 +Equalized_Odds_TPR,0.0,LogisticRegression,Exp_iter_1,0.0,0 +IQR_Parity,-0.004259721871489895,LogisticRegression,Exp_iter_1,0.0,0 +Jitter_Parity,-0.0640968210033926,LogisticRegression,Exp_iter_1,0.0,0 +Label_Stability_Ratio,1.0935446085768203,LogisticRegression,Exp_iter_1,0.0,0 +Statistical_Parity_Difference,-0.8571428571428572,LogisticRegression,Exp_iter_1,0.0,0 +Std_Parity,-0.0011073640847593519,LogisticRegression,Exp_iter_1,0.0,0 +Std_Ratio,0.9758617440249395,LogisticRegression,Exp_iter_1,0.0,0 +Accuracy_Parity,0.0267379679144385,LogisticRegression,Exp_iter_1,0.4,0 +Disparate_Impact,1.1666666666666665,LogisticRegression,Exp_iter_1,0.4,0 +Equalized_Odds_FNR,0.0,LogisticRegression,Exp_iter_1,0.4,0 +Equalized_Odds_FPR,-0.0888888888888889,LogisticRegression,Exp_iter_1,0.4,0 +Equalized_Odds_TPR,0.0,LogisticRegression,Exp_iter_1,0.4,0 +IQR_Parity,-0.02897288592163355,LogisticRegression,Exp_iter_1,0.4,0 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a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 91, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.448256Z", - "start_time": "2023-10-04T21:22:16.399916Z" + "end_time": "2023-10-06T21:10:36.749502Z", + "start_time": "2023-10-06T21:10:36.493538Z" } }, "outputs": [ @@ -28,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 92, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.489117Z", - "start_time": "2023-10-04T21:22:16.447387Z" + "end_time": "2023-10-06T21:10:36.782386Z", + "start_time": "2023-10-06T21:10:36.747786Z" } }, "outputs": [], @@ -46,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 93, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.493213Z", - "start_time": "2023-10-04T21:22:16.472246Z" + "end_time": "2023-10-06T21:10:36.793383Z", + "start_time": "2023-10-06T21:10:36.770963Z" } }, "outputs": [ @@ -81,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 94, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.529742Z", - "start_time": "2023-10-04T21:22:16.494483Z" + "end_time": "2023-10-06T21:10:36.813210Z", + "start_time": "2023-10-06T21:10:36.791765Z" } }, "outputs": [], @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 95, "outputs": [], "source": [ "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" @@ -107,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.537318Z", - "start_time": "2023-10-04T21:22:16.516511Z" + "end_time": "2023-10-06T21:10:36.834736Z", + "start_time": "2023-10-06T21:10:36.814444Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 96, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -125,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.563352Z", - "start_time": "2023-10-04T21:22:16.537733Z" + "end_time": "2023-10-06T21:10:36.872573Z", + "start_time": "2023-10-06T21:10:36.835382Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 97, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -144,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.584758Z", - "start_time": "2023-10-04T21:22:16.563460Z" + "end_time": "2023-10-06T21:10:36.884390Z", + "start_time": "2023-10-06T21:10:36.860961Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 98, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -162,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.607231Z", - "start_time": "2023-10-04T21:22:16.584939Z" + "end_time": "2023-10-06T21:10:36.909891Z", + "start_time": "2023-10-06T21:10:36.885939Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 99, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 21, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -187,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T21:22:16.630707Z", - "start_time": "2023-10-04T21:22:16.608538Z" + "end_time": "2023-10-06T21:10:36.945035Z", + "start_time": "2023-10-06T21:10:36.910469Z" } }, "id": "15ed7d1ba1f22317" @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 119, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:04:26.837638Z", - "start_time": "2023-10-04T22:04:26.735350Z" + "end_time": "2023-10-06T21:29:47.511Z", + "start_time": "2023-10-06T21:29:47.468822Z" } }, "outputs": [], @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "outputs": [ { "name": "stdout", @@ -227,8 +227,7 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" + "To create a public link, set `share=True` in `launch()`.\n" ] } ], @@ -237,16 +236,16 @@ ], "metadata": { "collapsed": false, + "is_executing": true, "ExecuteTime": { - "end_time": "2023-10-04T22:40:36.211694Z", - "start_time": "2023-10-04T22:04:27.009071Z" + "start_time": "2023-10-06T21:29:47.543336Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 102, "outputs": [ { "name": "stdout", @@ -262,20 +261,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T22:04:25.890691Z", - "start_time": "2023-10-04T22:04:25.777458Z" + "end_time": "2023-10-06T21:14:23.138059Z", + "start_time": "2023-10-06T21:14:23.094188Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 102, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:04:25.893162Z", - "start_time": "2023-10-04T22:04:25.889647Z" + "end_time": "2023-10-06T21:14:23.140632Z", + "start_time": "2023-10-06T21:14:23.137188Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 51f3eda9..04753176 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.880532Z", - "start_time": "2023-10-04T22:41:38.744525Z" + "end_time": "2023-10-06T20:57:23.539739Z", + "start_time": "2023-10-06T20:57:23.403057Z" } }, "outputs": [ @@ -28,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 38, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.897390Z", - "start_time": "2023-10-04T22:41:38.879544Z" + "end_time": "2023-10-06T20:57:23.574022Z", + "start_time": "2023-10-06T20:57:23.538351Z" } }, "outputs": [], @@ -46,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 39, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.905091Z", - "start_time": "2023-10-04T22:41:38.881727Z" + "end_time": "2023-10-06T20:57:23.581730Z", + "start_time": "2023-10-06T20:57:23.560533Z" } }, "outputs": [ @@ -81,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.938535Z", - "start_time": "2023-10-04T22:41:38.904769Z" + "end_time": "2023-10-06T20:57:23.606204Z", + "start_time": "2023-10-06T20:57:23.581940Z" } }, "outputs": [], @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "outputs": [], "source": [ "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" @@ -107,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.946897Z", - "start_time": "2023-10-04T22:41:38.927198Z" + "end_time": "2023-10-06T20:57:23.625570Z", + "start_time": "2023-10-06T20:57:23.604454Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -125,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.973205Z", - "start_time": "2023-10-04T22:41:38.947863Z" + "end_time": "2023-10-06T20:57:23.653982Z", + "start_time": "2023-10-06T20:57:23.626330Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 43, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -144,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T22:41:38.994980Z", - "start_time": "2023-10-04T22:41:38.973852Z" + "end_time": "2023-10-06T20:57:23.679479Z", + "start_time": "2023-10-06T20:57:23.654567Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 44, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -162,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T22:41:39.019414Z", - "start_time": "2023-10-04T22:41:38.994888Z" + "end_time": "2023-10-06T20:57:23.700549Z", + "start_time": "2023-10-06T20:57:23.677916Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 21, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -187,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T22:41:39.040053Z", - "start_time": "2023-10-04T22:41:39.018488Z" + "end_time": "2023-10-06T20:57:23.724011Z", + "start_time": "2023-10-06T20:57:23.701125Z" } }, "id": "15ed7d1ba1f22317" @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 53, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T22:41:39.066833Z", - "start_time": "2023-10-04T22:41:39.039759Z" + "end_time": "2023-10-06T21:03:32.115564Z", + "start_time": "2023-10-06T21:03:31.937977Z" } }, "outputs": [], @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 54, "outputs": [ { "name": "stdout", @@ -238,15 +238,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T23:04:24.847056Z", - "start_time": "2023-10-04T22:41:39.066921Z" + "end_time": "2023-10-06T21:09:25.295447Z", + "start_time": "2023-10-06T21:03:32.116976Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 48, "outputs": [ { "name": "stdout", @@ -262,20 +262,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-04T23:04:24.902597Z", - "start_time": "2023-10-04T23:04:24.849984Z" + "end_time": "2023-10-06T21:00:49.188809Z", + "start_time": "2023-10-06T21:00:49.151061Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 48, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-04T23:04:24.904745Z", - "start_time": "2023-10-04T23:04:24.902886Z" + "end_time": "2023-10-06T21:00:49.189515Z", + "start_time": "2023-10-06T21:00:49.186479Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb index b4d780d9..176a30ed 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb @@ -2,15 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-05T10:39:55.272406Z", - "start_time": "2023-10-05T10:39:54.897985Z" + "end_time": "2023-10-06T21:09:29.050528Z", + "start_time": "2023-10-06T21:09:28.908204Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -19,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-05T10:39:55.281893Z", - "start_time": "2023-10-05T10:39:55.273119Z" + "end_time": "2023-10-06T21:09:29.086370Z", + "start_time": "2023-10-06T21:09:29.050228Z" } }, "outputs": [], @@ -37,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-05T10:39:55.292533Z", - "start_time": "2023-10-05T10:39:55.282026Z" + "end_time": "2023-10-06T21:09:29.094601Z", + "start_time": "2023-10-06T21:09:29.073102Z" } }, "outputs": [ @@ -72,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.844327Z", - "start_time": "2023-10-05T10:39:55.291377Z" + "end_time": "2023-10-06T21:09:29.128343Z", + "start_time": "2023-10-06T21:09:29.094781Z" } }, "outputs": [], @@ -90,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "outputs": [], "source": [ "sensitive_attributes_dct = {'Race': 'Non-White'}" @@ -98,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.870851Z", - "start_time": "2023-10-05T10:39:56.847550Z" + "end_time": "2023-10-06T21:09:29.135958Z", + "start_time": "2023-10-06T21:09:29.115755Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -116,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.896386Z", - "start_time": "2023-10-05T10:39:56.868941Z" + "end_time": "2023-10-06T21:09:29.163189Z", + "start_time": "2023-10-06T21:09:29.136609Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -135,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.916837Z", - "start_time": "2023-10-05T10:39:56.894764Z" + "end_time": "2023-10-06T21:09:29.187069Z", + "start_time": "2023-10-06T21:09:29.163967Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -153,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.940693Z", - "start_time": "2023-10-05T10:39:56.916977Z" + "end_time": "2023-10-06T21:09:29.210023Z", + "start_time": "2023-10-06T21:09:29.185859Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LGBMClassifier__alpha=0.7', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.4', 'LogisticRegression__alpha=0.7', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7'])" }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -178,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.964580Z", - "start_time": "2023-10-05T10:39:56.941618Z" + "end_time": "2023-10-06T21:09:29.231487Z", + "start_time": "2023-10-06T21:09:29.210107Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-05T10:39:56.991119Z", - "start_time": "2023-10-05T10:39:56.962485Z" + "end_time": "2023-10-06T21:09:29.260102Z", + "start_time": "2023-10-06T21:09:29.231557Z" } }, "outputs": [], @@ -210,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "outputs": [ { "name": "stdout", @@ -229,15 +238,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T13:20:31.060413Z", - "start_time": "2023-10-05T10:39:56.991233Z" + "end_time": "2023-10-06T21:10:28.861090Z", + "start_time": "2023-10-06T21:09:29.258554Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "outputs": [ { "name": "stdout", @@ -253,20 +262,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-05T13:20:31.101325Z", - "start_time": "2023-10-05T13:20:31.064318Z" + "end_time": "2023-10-06T21:10:28.893637Z", + "start_time": "2023-10-06T21:10:28.857995Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-05T13:20:31.104256Z", - "start_time": "2023-10-05T13:20:31.102380Z" + "end_time": "2023-10-06T21:10:28.896502Z", + "start_time": "2023-10-06T21:10:28.892359Z" } }, "outputs": [], diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 24edc520..0a151995 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -63,9 +63,6 @@ def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: l return model_metrics_df[filtered_cols] def start_web_app(self): - # css = """ - # .plot_output1 {position: right !important} - # """ with gr.Blocks(theme=gr.themes.Soft()) as demo: # ==================================== Bar Chart for Model Selection ==================================== gr.Markdown( @@ -81,7 +78,7 @@ def start_web_app(self): ) with gr.Row(): accuracy_metric = gr.Dropdown( - ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], + sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), value='Accuracy', multiselect=False, label="Constraint 1 (C1)", scale=2 ) @@ -89,7 +86,7 @@ def start_web_app(self): acc_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): fairness_metric = gr.Dropdown( - ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], + sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", scale=2 ) @@ -97,7 +94,7 @@ def start_web_app(self): fairness_max_val = gr.Number(value=0.15, label="Max value", scale=1) with gr.Row(): subgroup_stability_metric = gr.Dropdown( - ['Std', 'IQR', 'Jitter', 'Label_Stability'], + sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), value='Label_Stability', multiselect=False, label="Constraint 3 (C3)", scale=2 ) @@ -105,7 +102,7 @@ def start_web_app(self): subgroup_stab_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): group_stability_metrics = gr.Dropdown( - ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], + sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", scale=2 ) @@ -122,28 +119,28 @@ def start_web_app(self): subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, group_stability_metrics, group_stab_min_val, group_stab_max_val], outputs=[bar_plot_for_model_selection]) - # ======================================= Subgroup Metrics Heatmap ======================================= + # ======================================= Overall Metrics Heatmap ======================================= gr.Markdown( """ - ## Subgroup Metrics Heatmap - Select input arguments to create a subgroup metrics heatmap. + ## Overall Metrics Heatmap + Select input arguments to create an overall metrics heatmap. """) with gr.Row(): with gr.Column(scale=1): model_names = gr.Dropdown( - self.model_names, value=self.model_names[:4], max_choices=5, multiselect=True, + sorted(self.model_names), value=sorted(self.model_names)[:4], max_choices=5, multiselect=True, label="Model Names", info="Select model names to display on the heatmap:", ) accuracy_metrics = gr.Dropdown( - ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], + sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", ) uncertainty_metrics = gr.Dropdown( - ['Aleatoric_Uncertainty', 'Overall_Uncertainty'], + sorted(['Aleatoric_Uncertainty', 'Overall_Uncertainty']), value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:", ) subgroup_stability_metrics = gr.Dropdown( - ['Std', 'IQR', 'Jitter', 'Label_Stability'], + sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) subgroup_btn_view2 = gr.Button("Submit") @@ -153,24 +150,24 @@ def start_web_app(self): subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap, inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], outputs=[subgroup_model_ranking_heatmap]) - # ======================================== Group Metrics Heatmap ======================================== + # ======================================== Parity Metrics Heatmap ======================================== gr.Markdown( """ - ## Group Metrics Heatmap - Select input arguments to create a group metrics heatmap. + ## Parity Metrics Heatmap + Select input arguments to create a parity metrics heatmap. """) with gr.Row(): with gr.Column(scale=1): model_names = gr.Dropdown( - self.model_names, value=self.model_names[:4], max_choices=5, multiselect=True, + sorted(self.model_names), value=sorted(self.model_names)[:4], max_choices=5, multiselect=True, label="Model Names", info="Select model names to display on the heatmap:", ) fairness_metrics = gr.Dropdown( - ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], - value=['Equalized_Odds_TPR', 'Equalized_Odds_FPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", + sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), + value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( - ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], + sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", ) group_btn_view2 = gr.Button("Submit") @@ -188,19 +185,19 @@ def start_web_app(self): ## Subgroup Metrics Bar Chart """) subgroup_model_names = gr.Dropdown( - self.model_names, value=self.model_names[0], multiselect=False, + sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, label="Model Names", info="Select one model to display on the bar chart:", ) accuracy_metrics = gr.Dropdown( - ['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1'], + sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", ) uncertainty_metrics = gr.Dropdown( - ['Aleatoric_Uncertainty', 'Overall_Uncertainty'], + sorted(['Aleatoric_Uncertainty', 'Overall_Uncertainty']), value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:", ) subgroup_stability_metrics = gr.Dropdown( - ['Std', 'IQR', 'Jitter', 'Label_Stability'], + sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) subgroup_btn_view3 = gr.Button("Submit") @@ -210,15 +207,15 @@ def start_web_app(self): ## Group Metrics Bar Chart """) group_model_names = gr.Dropdown( - self.model_names, value=self.model_names[0], multiselect=False, + sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, label="Model Names", info="Select one model to display on the bar chart:", ) fairness_metrics = gr.Dropdown( - ['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity'], - value=['Equalized_Odds_TPR', 'Equalized_Odds_FPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", + sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), + value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( - ['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity'], + sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", ) group_btn_view3 = gr.Button("Submit") @@ -316,7 +313,6 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura A list of subgroup stability metrics to visualize """ - groups_lst = self.sensitive_attributes_dct.keys() metrics_lst = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst # Find metric values for each model based on metric, subgroup, and model names. @@ -325,20 +321,14 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura num_models = len(model_names) for metric in metrics_lst: # Add an overall metric - subgroup_metric = metric + '_overall' + subgroup_metric = metric results = self.__filter_subgroup_metrics_df(results, subgroup_metric, metric, selected_subgroup='overall', defined_model_names=model_names) - # Add a subgroup metric - for group in groups_lst: - for prefix in ['priv', 'dis']: - subgroup = group + '_' + prefix - subgroup_metric = metric + '_' + subgroup - results = self.__filter_subgroup_metrics_df(results, subgroup_metric, metric, subgroup, model_names) model_metrics_matrix = pd.DataFrame(results).T sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, - num_models, top_adjust=1.) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, + sorted_matrix_by_rank, num_models) return model_rank_heatmap @@ -388,8 +378,8 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met model_metrics_matrix = pd.DataFrame(results).T sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, - num_models, top_adjust=0.78) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, + sorted_matrix_by_rank, num_models) return model_rank_heatmap @@ -420,6 +410,7 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names metrics_title = f'{metrics_type.capitalize()} Metrics' metrics_df = self.melted_model_composed_metrics_df if metrics_type == "group" else self.melted_model_metrics_df filtered_groups = [grp for grp in metrics_df.Subgroup.unique() if '_correct' not in grp and '_incorrect' not in grp] + filtered_groups = [grp for grp in filtered_groups if grp.lower() != 'overall'] + ['overall'] filtered_metrics_df = metrics_df[(metrics_df['Metric'].isin(metrics_names)) & (metrics_df['Model_Name'] == model_name) & (metrics_df['Subgroup'].isin(filtered_groups))] @@ -427,14 +418,14 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names base_font_size = 16 models_metrics_chart = ( alt.Chart().mark_bar().encode( - alt.Y('Subgroup:N', axis=None), + alt.Y('Subgroup:N', axis=None, sort=filtered_groups), alt.X('Value:Q', axis=alt.Axis(grid=True), title=''), alt.Color('Subgroup:N', scale=alt.Scale(scheme="tableau20"), + sort=filtered_groups, legend=alt.Legend(title=metrics_type.capitalize(), labelFontSize=base_font_size, - titleFontSize=base_font_size + 2) - ) + titleFontSize=base_font_size + 2)) ) ) @@ -457,7 +448,7 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names width=500, height=100 ).facet( - row=alt.Row('Metric:N', title=metrics_title) + row=alt.Row('Metric:N', title=metrics_title, sort=metrics_names) ).configure( padding={'top': 33}, ).configure_headerRow( diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index e90a3e71..8039a757 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -60,8 +60,7 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: return sorted_matrix_by_rank -def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int, - top_adjust: float = 0.92): +def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int): """ This heatmap includes group fairness and stability metrics and defined models. Using it, you can visually compare the models across defined group metrics. On this plot, @@ -80,13 +79,12 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ Matrix of model ranks per metric where indexes are group metric names and columns are model names num_models Number of models to visualize - top_adjust - Percentage of a top padding for the heatmap """ font_increase = 4 matrix_width = 20 - matrix_height = model_metrics_matrix.shape[0] // 2 + matrix_height = model_metrics_matrix.shape[0] if model_metrics_matrix.shape[0] >= 3 \ + else model_metrics_matrix.shape[0] * 2.5 fig = plt.figure(figsize=(matrix_width, matrix_height)) rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex()[::-1] ax = sns.heatmap(sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, @@ -95,7 +93,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ ax.xaxis.tick_top() ax.tick_params(axis='x', rotation=10) ax.tick_params(labelsize=16 + font_increase) - fig.subplots_adjust(left=0.3, right=0.99, top=0.8) + fig.tight_layout() cbar = ax.collections[0].colorbar model_ranks = [idx for idx in range(num_models)] From 69306c5cdd7b640ec84a33f21ba2b1fb4bff0d4f Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 7 Oct 2023 12:17:00 +0300 Subject: [PATCH 013/148] Reveresed a color bar for heatmaps --- .../Multiple_Models_Interface_Vis_Income.ipynb | 8 ++++---- .../metrics_interactive_visualizer.py | 14 +++++++------- virny/utils/data_viz_utils.py | 8 +++++--- 3 files changed, 16 insertions(+), 14 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 8a24cd07..df14ea69 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 136, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:29:47.511Z", - "start_time": "2023-10-06T21:29:47.468822Z" + "end_time": "2023-10-07T09:06:44.773742Z", + "start_time": "2023-10-07T09:06:44.711157Z" } }, "outputs": [], @@ -238,7 +238,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-06T21:29:47.543336Z" + "start_time": "2023-10-07T09:06:45.327146Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 0a151995..68dd4e7a 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -68,7 +68,7 @@ def start_web_app(self): gr.Markdown( """ ## Bar Chart for Model Selection - Select input arguments to create a bar chart for model selection. + Select input arguments to create a bar chart for model selection. Default values display the lowest and greatest limits of constraints. """) with gr.Row(): with gr.Column(scale=2): @@ -82,7 +82,7 @@ def start_web_app(self): value='Accuracy', multiselect=False, label="Constraint 1 (C1)", scale=2 ) - acc_min_val = gr.Number(value=0.7, label="Min value", scale=1) + acc_min_val = gr.Number(value=0.0, label="Min value", scale=1) acc_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): fairness_metric = gr.Dropdown( @@ -90,15 +90,15 @@ def start_web_app(self): value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", scale=2 ) - fairness_min_val = gr.Number(value=-0.15, label="Min value", scale=1) - fairness_max_val = gr.Number(value=0.15, label="Max value", scale=1) + fairness_min_val = gr.Number(value=-1.0, label="Min value", scale=1) + fairness_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): subgroup_stability_metric = gr.Dropdown( sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), value='Label_Stability', multiselect=False, label="Constraint 3 (C3)", scale=2 ) - subgroup_stab_min_val = gr.Number(value=0.9, label="Min value", scale=1) + subgroup_stab_min_val = gr.Number(value=0.0, label="Min value", scale=1) subgroup_stab_max_val = gr.Number(value=1.0, label="Max value", scale=1) with gr.Row(): group_stability_metrics = gr.Dropdown( @@ -106,8 +106,8 @@ def start_web_app(self): value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", scale=2 ) - group_stab_min_val = gr.Number(value=0.98, label="Min value", scale=1) - group_stab_max_val = gr.Number(value=1.02, label="Max value", scale=1) + group_stab_min_val = gr.Number(value=0.1, label="Min value", scale=1) + group_stab_max_val = gr.Number(value=10.0, label="Max value", scale=1) btn_view1 = gr.Button("Submit") with gr.Column(scale=3): bar_plot_for_model_selection = gr.Plot(label="Plot") diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 8039a757..439739b4 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -86,8 +86,10 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ matrix_height = model_metrics_matrix.shape[0] if model_metrics_matrix.shape[0] >= 3 \ else model_metrics_matrix.shape[0] * 2.5 fig = plt.figure(figsize=(matrix_width, matrix_height)) - rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex()[::-1] - ax = sns.heatmap(sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, + rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex() + # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a coolwarm color scheme + reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1 + ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, fmt='', annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 16 + font_increase}) ax.set(xlabel="", ylabel="") ax.xaxis.tick_top() @@ -97,7 +99,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ cbar = ax.collections[0].colorbar model_ranks = [idx for idx in range(num_models)] - cbar.set_ticks([float(idx) for idx in model_ranks]) + cbar.set_ticks([float(idx) * -1 for idx in model_ranks]) tick_labels = [str(idx + 1) for idx in model_ranks] tick_labels[0] = tick_labels[0] + ', best' tick_labels[-1] = tick_labels[-1] + ', worst' From 6262752731cb70a8b9d73ec13f78b81670a7c812 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 7 Oct 2023 16:30:21 +0300 Subject: [PATCH 014/148] Added a table with model names that satisfy all 4 constraints --- .../Multiple_Models_Interface_Vis_Income.ipynb | 8 ++++---- .../metrics_interactive_visualizer.py | 15 +++++++++------ virny/utils/data_viz_utils.py | 13 +++++++++---- 3 files changed, 22 insertions(+), 14 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index df14ea69..7466ede7 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 148, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T09:06:44.773742Z", - "start_time": "2023-10-07T09:06:44.711157Z" + "end_time": "2023-10-07T13:23:24.513519Z", + "start_time": "2023-10-07T13:23:24.200188Z" } }, "outputs": [], @@ -238,7 +238,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-07T09:06:45.327146Z" + "start_time": "2023-10-07T13:23:24.514061Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 68dd4e7a..43a1173c 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -110,7 +110,8 @@ def start_web_app(self): group_stab_max_val = gr.Number(value=10.0, label="Max value", scale=1) btn_view1 = gr.Button("Submit") with gr.Column(scale=3): - bar_plot_for_model_selection = gr.Plot(label="Plot") + bar_plot_for_model_selection = gr.Plot(label="Bar Chart") + df_with_models_satisfied_all_constraints = gr.DataFrame(label='Models that satisfy all 4 constraints') btn_view1.click(self._create_bar_plot_for_model_selection, inputs=[group_name, @@ -118,7 +119,7 @@ def start_web_app(self): fairness_metric, fairness_min_val, fairness_max_val, subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, group_stability_metrics, group_stab_min_val, group_stab_max_val], - outputs=[bar_plot_for_model_selection]) + outputs=[bar_plot_for_model_selection, df_with_models_satisfied_all_constraints]) # ======================================= Overall Metrics Heatmap ======================================= gr.Markdown( """ @@ -145,7 +146,7 @@ def start_web_app(self): ) subgroup_btn_view2 = gr.Button("Submit") with gr.Column(scale=2): - subgroup_model_ranking_heatmap = gr.Plot(label="Plot") + subgroup_model_ranking_heatmap = gr.Plot(label="Heatmap") subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap, inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], @@ -172,7 +173,7 @@ def start_web_app(self): ) group_btn_view2 = gr.Button("Submit") with gr.Column(scale=2): - group_model_ranking_heatmap = gr.Plot(label="Plot") + group_model_ranking_heatmap = gr.Plot(label="Heatmap") group_btn_view2.click(self._create_group_model_rank_heatmap, inputs=[model_names, fairness_metrics, group_stability_metrics], @@ -221,9 +222,9 @@ def start_web_app(self): group_btn_view3 = gr.Button("Submit") with gr.Row(): with gr.Column(): - subgroup_metrics_bar_chart = gr.Plot(label="Plot") + subgroup_metrics_bar_chart = gr.Plot(label="Subgroup Bar Chart") with gr.Column(): - group_metrics_bar_chart = gr.Plot(label="Plot") + group_metrics_bar_chart = gr.Plot(label="Group Bar Chart") subgroup_btn_view3.click(self._create_subgroup_metrics_bar_chart_per_one_model, inputs=[subgroup_model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], @@ -326,6 +327,7 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura selected_subgroup='overall', defined_model_names=model_names) model_metrics_matrix = pd.DataFrame(results).T + model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) @@ -377,6 +379,7 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met results[group_metric][model_name] = metric_value model_metrics_matrix = pd.DataFrame(results).T + model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 439739b4..1ee8096c 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -112,8 +112,9 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, metrics_value_range_dct: dict, group: str): # Compute the number of models that satisfy the conditions - models_in_range_df = create_models_in_range_dct(all_subgroup_metrics_per_model_dct, all_group_metrics_per_model_dct, - metrics_value_range_dct, group) + models_in_range_df, df_with_models_satisfied_all_constraints = ( + create_models_in_range_dct(all_subgroup_metrics_per_model_dct, all_group_metrics_per_model_dct, + metrics_value_range_dct, group)) # Replace metric groups on their aliases metric_name_to_alias_dct = { # C1 @@ -184,7 +185,7 @@ def get_column_alias(metric_group): titleLimit=220, ).properties(width=650, height=450) - return bar_plot + return bar_plot, df_with_models_satisfied_all_constraints def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, @@ -217,6 +218,7 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro # Create a pandas condition for filtering based on the input value ranges models_in_range_df = pd.DataFrame() + df_with_models_satisfied_all_constraints = pd.DataFrame() for idx, (metric_group, value_range) in enumerate(metrics_value_range_dct.items()): pd_condition = None if '&' not in metric_group: @@ -253,4 +255,7 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro # Concatenate based on rows models_in_range_df = pd.concat([models_in_range_df, num_satisfied_models_df], ignore_index=True, sort=False) - return models_in_range_df + if metric_group.count('&') == 3: + df_with_models_satisfied_all_constraints = pivoted_model_metrics_df[pd_condition][['Model_Type', 'Model_Name']] + + return models_in_range_df, df_with_models_satisfied_all_constraints From 6146aae1bcfec042cb4219ddcb8af79cbcee8829 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 9 Oct 2023 01:08:05 +0300 Subject: [PATCH 015/148] Added tolerance to heatmaps --- ...Multiple_Models_Interface_Vis_Income.ipynb | 94 ++++++++----------- ...iple_Models_Interface_Vis_Law_School.ipynb | 80 ++++++++-------- .../Multiple_Models_Interface_Vis_Ricci.ipynb | 80 ++++++++-------- .../metrics_interactive_visualizer.py | 57 ++++++----- virny/utils/common_helpers.py | 15 +++ virny/utils/data_viz_utils.py | 56 ++++++++--- 6 files changed, 214 insertions(+), 168 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 7466ede7..2155fbb9 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -2,24 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 91, + "execution_count": 1, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.749502Z", - "start_time": "2023-10-06T21:10:36.493538Z" + "end_time": "2023-10-08T19:53:45.170627Z", + "start_time": "2023-10-08T19:53:44.682414Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -28,12 +19,12 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 2, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.782386Z", - "start_time": "2023-10-06T21:10:36.747786Z" + "end_time": "2023-10-08T19:53:45.179971Z", + "start_time": "2023-10-08T19:53:45.170956Z" } }, "outputs": [], @@ -46,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 3, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.793383Z", - "start_time": "2023-10-06T21:10:36.770963Z" + "end_time": "2023-10-08T19:53:45.190533Z", + "start_time": "2023-10-08T19:53:45.180261Z" } }, "outputs": [ @@ -81,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 4, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.813210Z", - "start_time": "2023-10-06T21:10:36.791765Z" + "end_time": "2023-10-08T19:53:47.366728Z", + "start_time": "2023-10-08T19:53:45.190219Z" } }, "outputs": [], @@ -99,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "outputs": [], "source": [ "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" @@ -107,15 +98,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.834736Z", - "start_time": "2023-10-06T21:10:36.814444Z" + "end_time": "2023-10-08T19:53:47.391686Z", + "start_time": "2023-10-08T19:53:47.369626Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 6, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -125,15 +116,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.872573Z", - "start_time": "2023-10-06T21:10:36.835382Z" + "end_time": "2023-10-08T19:53:47.419075Z", + "start_time": "2023-10-08T19:53:47.391397Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 7, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -144,15 +135,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.884390Z", - "start_time": "2023-10-06T21:10:36.860961Z" + "end_time": "2023-10-08T19:53:47.443543Z", + "start_time": "2023-10-08T19:53:47.419472Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 8, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -162,21 +153,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.909891Z", - "start_time": "2023-10-06T21:10:36.885939Z" + "end_time": "2023-10-08T19:53:47.469996Z", + "start_time": "2023-10-08T19:53:47.443240Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 9, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 99, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -187,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:36.945035Z", - "start_time": "2023-10-06T21:10:36.910469Z" + "end_time": "2023-10-08T19:53:47.513710Z", + "start_time": "2023-10-08T19:53:47.469016Z" } }, "id": "15ed7d1ba1f22317" @@ -203,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 66, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:23:24.513519Z", - "start_time": "2023-10-07T13:23:24.200188Z" + "end_time": "2023-10-08T22:06:28.762250Z", + "start_time": "2023-10-08T22:06:28.618558Z" } }, "outputs": [], @@ -238,14 +229,14 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-07T13:23:24.514061Z" + "start_time": "2023-10-08T22:06:28.762615Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 12, "outputs": [ { "name": "stdout", @@ -261,24 +252,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:14:23.138059Z", - "start_time": "2023-10-06T21:14:23.094188Z" + "end_time": "2023-10-08T20:42:21.447796Z", + "start_time": "2023-10-08T20:42:21.325905Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 102, - "id": "2326c129", + "execution_count": null, + "outputs": [], + "source": [], "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T21:14:23.140632Z", - "start_time": "2023-10-06T21:14:23.137188Z" - } + "collapsed": false }, - "outputs": [], - "source": [] + "id": "c207d4345ddca1db" } ], "metadata": { diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 04753176..a2a5a603 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 37, + "execution_count": 55, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.539739Z", - "start_time": "2023-10-06T20:57:23.403057Z" + "end_time": "2023-10-07T13:37:09.385430Z", + "start_time": "2023-10-07T13:37:09.127608Z" } }, "outputs": [ @@ -28,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 56, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.574022Z", - "start_time": "2023-10-06T20:57:23.538351Z" + "end_time": "2023-10-07T13:37:09.409539Z", + "start_time": "2023-10-07T13:37:09.385249Z" } }, "outputs": [], @@ -46,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 57, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.581730Z", - "start_time": "2023-10-06T20:57:23.560533Z" + "end_time": "2023-10-07T13:37:09.430322Z", + "start_time": "2023-10-07T13:37:09.408329Z" } }, "outputs": [ @@ -81,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 58, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.606204Z", - "start_time": "2023-10-06T20:57:23.581940Z" + "end_time": "2023-10-07T13:37:09.451279Z", + "start_time": "2023-10-07T13:37:09.431063Z" } }, "outputs": [], @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 59, "outputs": [], "source": [ "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" @@ -107,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.625570Z", - "start_time": "2023-10-06T20:57:23.604454Z" + "end_time": "2023-10-07T13:37:09.475696Z", + "start_time": "2023-10-07T13:37:09.453496Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 60, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -125,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.653982Z", - "start_time": "2023-10-06T20:57:23.626330Z" + "end_time": "2023-10-07T13:37:09.500877Z", + "start_time": "2023-10-07T13:37:09.474723Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 61, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -144,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.679479Z", - "start_time": "2023-10-06T20:57:23.654567Z" + "end_time": "2023-10-07T13:37:09.520270Z", + "start_time": "2023-10-07T13:37:09.500217Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 62, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -162,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.700549Z", - "start_time": "2023-10-06T20:57:23.677916Z" + "end_time": "2023-10-07T13:37:09.543689Z", + "start_time": "2023-10-07T13:37:09.521274Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 63, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 45, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -187,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T20:57:23.724011Z", - "start_time": "2023-10-06T20:57:23.701125Z" + "end_time": "2023-10-07T13:37:09.565841Z", + "start_time": "2023-10-07T13:37:09.543823Z" } }, "id": "15ed7d1ba1f22317" @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 64, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:03:32.115564Z", - "start_time": "2023-10-06T21:03:31.937977Z" + "end_time": "2023-10-07T13:37:09.593512Z", + "start_time": "2023-10-07T13:37:09.565293Z" } }, "outputs": [], @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 65, "outputs": [ { "name": "stdout", @@ -238,15 +238,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:09:25.295447Z", - "start_time": "2023-10-06T21:03:32.116976Z" + "end_time": "2023-10-07T13:42:17.431036Z", + "start_time": "2023-10-07T13:37:09.593677Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 66, "outputs": [ { "name": "stdout", @@ -262,20 +262,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:00:49.188809Z", - "start_time": "2023-10-06T21:00:49.151061Z" + "end_time": "2023-10-07T13:42:17.479914Z", + "start_time": "2023-10-07T13:42:17.432456Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 66, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:00:49.189515Z", - "start_time": "2023-10-06T21:00:49.186479Z" + "end_time": "2023-10-07T13:42:17.482254Z", + "start_time": "2023-10-07T13:42:17.478725Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb index 176a30ed..8e21b6bc 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": 37, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.050528Z", - "start_time": "2023-10-06T21:09:28.908204Z" + "end_time": "2023-10-07T13:42:22.642940Z", + "start_time": "2023-10-07T13:42:22.508015Z" } }, "outputs": [ @@ -28,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 38, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.086370Z", - "start_time": "2023-10-06T21:09:29.050228Z" + "end_time": "2023-10-07T13:42:22.677119Z", + "start_time": "2023-10-07T13:42:22.641937Z" } }, "outputs": [], @@ -46,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 39, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.094601Z", - "start_time": "2023-10-06T21:09:29.073102Z" + "end_time": "2023-10-07T13:42:22.689334Z", + "start_time": "2023-10-07T13:42:22.664188Z" } }, "outputs": [ @@ -81,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 40, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.128343Z", - "start_time": "2023-10-06T21:09:29.094781Z" + "end_time": "2023-10-07T13:42:22.711038Z", + "start_time": "2023-10-07T13:42:22.687552Z" } }, "outputs": [], @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 41, "outputs": [], "source": [ "sensitive_attributes_dct = {'Race': 'Non-White'}" @@ -107,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.135958Z", - "start_time": "2023-10-06T21:09:29.115755Z" + "end_time": "2023-10-07T13:42:22.732136Z", + "start_time": "2023-10-07T13:42:22.711244Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 42, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -125,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.163189Z", - "start_time": "2023-10-06T21:09:29.136609Z" + "end_time": "2023-10-07T13:42:22.759203Z", + "start_time": "2023-10-07T13:42:22.732607Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 43, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -144,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.187069Z", - "start_time": "2023-10-06T21:09:29.163967Z" + "end_time": "2023-10-07T13:42:22.784062Z", + "start_time": "2023-10-07T13:42:22.759791Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 44, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -162,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.210023Z", - "start_time": "2023-10-06T21:09:29.185859Z" + "end_time": "2023-10-07T13:42:22.809161Z", + "start_time": "2023-10-07T13:42:22.782462Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 45, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LGBMClassifier__alpha=0.7', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.4', 'LogisticRegression__alpha=0.7', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7'])" }, - "execution_count": 33, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -187,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.231487Z", - "start_time": "2023-10-06T21:09:29.210107Z" + "end_time": "2023-10-07T13:42:22.831140Z", + "start_time": "2023-10-07T13:42:22.806994Z" } }, "id": "15ed7d1ba1f22317" @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 46, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:09:29.260102Z", - "start_time": "2023-10-06T21:09:29.231557Z" + "end_time": "2023-10-07T13:42:22.859150Z", + "start_time": "2023-10-07T13:42:22.830292Z" } }, "outputs": [], @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 47, "outputs": [ { "name": "stdout", @@ -238,15 +238,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:28.861090Z", - "start_time": "2023-10-06T21:09:29.258554Z" + "end_time": "2023-10-07T13:45:45.222662Z", + "start_time": "2023-10-07T13:42:22.859325Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 48, "outputs": [ { "name": "stdout", @@ -262,20 +262,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-06T21:10:28.893637Z", - "start_time": "2023-10-06T21:10:28.857995Z" + "end_time": "2023-10-07T13:45:45.264959Z", + "start_time": "2023-10-07T13:45:45.221841Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 48, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-06T21:10:28.896502Z", - "start_time": "2023-10-06T21:10:28.892359Z" + "end_time": "2023-10-07T13:45:45.265758Z", + "start_time": "2023-10-07T13:45:45.264074Z" } }, "outputs": [], diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 43a1173c..e7b7efc9 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -2,6 +2,7 @@ import gradio as gr import altair as alt +from virny.utils.common_helpers import isfloat_regex, str_to_float from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection) @@ -82,32 +83,32 @@ def start_web_app(self): value='Accuracy', multiselect=False, label="Constraint 1 (C1)", scale=2 ) - acc_min_val = gr.Number(value=0.0, label="Min value", scale=1) - acc_max_val = gr.Number(value=1.0, label="Max value", scale=1) + acc_min_val = gr.Text(value="0.0", label="Min value", scale=1) + acc_max_val = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): fairness_metric = gr.Dropdown( sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", scale=2 ) - fairness_min_val = gr.Number(value=-1.0, label="Min value", scale=1) - fairness_max_val = gr.Number(value=1.0, label="Max value", scale=1) + fairness_min_val = gr.Text(value="-1.0", label="Min value", scale=1) + fairness_max_val = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): subgroup_stability_metric = gr.Dropdown( sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), value='Label_Stability', multiselect=False, label="Constraint 3 (C3)", scale=2 ) - subgroup_stab_min_val = gr.Number(value=0.0, label="Min value", scale=1) - subgroup_stab_max_val = gr.Number(value=1.0, label="Max value", scale=1) + subgroup_stab_min_val = gr.Text(value="0.0", label="Min value", scale=1) + subgroup_stab_max_val = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): group_stability_metrics = gr.Dropdown( sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", scale=2 ) - group_stab_min_val = gr.Number(value=0.1, label="Min value", scale=1) - group_stab_max_val = gr.Number(value=10.0, label="Max value", scale=1) + group_stab_min_val = gr.Text(value="0.1", label="Min value", scale=1) + group_stab_max_val = gr.Text(value="10.0", label="Max value", scale=1) btn_view1 = gr.Button("Submit") with gr.Column(scale=3): bar_plot_for_model_selection = gr.Plot(label="Bar Chart") @@ -132,6 +133,7 @@ def start_web_app(self): sorted(self.model_names), value=sorted(self.model_names)[:4], max_choices=5, multiselect=True, label="Model Names", info="Select model names to display on the heatmap:", ) + subgroup_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") accuracy_metrics = gr.Dropdown( sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", @@ -149,7 +151,7 @@ def start_web_app(self): subgroup_model_ranking_heatmap = gr.Plot(label="Heatmap") subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap, - inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], + inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics, subgroup_tolerance], outputs=[subgroup_model_ranking_heatmap]) # ======================================== Parity Metrics Heatmap ======================================== gr.Markdown( @@ -163,6 +165,7 @@ def start_web_app(self): sorted(self.model_names), value=sorted(self.model_names)[:4], max_choices=5, multiselect=True, label="Model Names", info="Select model names to display on the heatmap:", ) + group_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") fairness_metrics = gr.Dropdown( sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", @@ -176,7 +179,7 @@ def start_web_app(self): group_model_ranking_heatmap = gr.Plot(label="Heatmap") group_btn_view2.click(self._create_group_model_rank_heatmap, - inputs=[model_names, fairness_metrics, group_stability_metrics], + inputs=[model_names, fairness_metrics, group_stability_metrics, group_tolerance], outputs=[group_model_ranking_heatmap]) # =============================== Subgroup and Group Metrics Bar Chart =============================== with gr.Row(): @@ -187,7 +190,7 @@ def start_web_app(self): """) subgroup_model_names = gr.Dropdown( sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, - label="Model Names", info="Select one model to display on the bar chart:", + label="Model Name", info="Select one model to display on the bar chart:", ) accuracy_metrics = gr.Dropdown( sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), @@ -209,7 +212,7 @@ def start_web_app(self): """) group_model_names = gr.Dropdown( sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, - label="Model Names", info="Select one model to display on the bar chart:", + label="Model Name", info="Select one model to display on the bar chart:", ) fairness_metrics = gr.Dropdown( sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), @@ -266,10 +269,10 @@ def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_ fairness_metric, fairness_min_val, fairness_max_val, subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, group_stability_metrics, group_stab_min_val, group_stab_max_val): - accuracy_constraint = (accuracy_metric, acc_min_val, acc_max_val) - fairness_constraint = (fairness_metric, fairness_min_val, fairness_max_val) - subgroup_stability_constraint = (subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val) - group_stability_constraint = (group_stability_metrics, group_stab_min_val, group_stab_max_val) + accuracy_constraint = (accuracy_metric, str_to_float(acc_min_val, 'C1 min value'), str_to_float(acc_max_val, 'C2 max value')) + fairness_constraint = (fairness_metric, str_to_float(fairness_min_val, 'C2 min value'), str_to_float(fairness_max_val, 'C2 max value')) + subgroup_stability_constraint = (subgroup_stability_metric, str_to_float(subgroup_stab_min_val, 'C3 min value'), str_to_float(subgroup_stab_max_val, 'C3 max value')) + group_stability_constraint = (group_stability_metrics, str_to_float(group_stab_min_val, 'C4 min value'), str_to_float(group_stab_max_val, 'C4 max value')) # Create individual constraints metrics_value_range_dct = dict() @@ -298,7 +301,8 @@ def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_ group=group_name) def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accuracy_metrics_lst: list, - subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list): + subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list, + tolerance: str): """ Create a group model rank heatmap. @@ -312,8 +316,11 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura A list of subgroup uncertainty metrics to visualize subgroup_stability_metrics_lst A list of subgroup stability metrics to visualize + tolerance + An acceptable value difference for metrics dense ranking """ + tolerance = str_to_float(tolerance, 'Tolerance') metrics_lst = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst # Find metric values for each model based on metric, subgroup, and model names. @@ -328,14 +335,13 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura model_metrics_matrix = pd.DataFrame(results).T model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] - sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, - sorted_matrix_by_rank, num_models) + sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) return model_rank_heatmap def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_metrics_lst: list, - group_stability_metrics_lst: list): + group_stability_metrics_lst: list, tolerance: str): """ Create a group model rank heatmap. @@ -347,8 +353,12 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met A list of group fairness metrics to visualize group_stability_metrics_lst A list of group stability metrics to visualize + tolerance + An acceptable value difference for metrics dense ranking """ + tolerance = str_to_float(tolerance, 'Tolerance') + groups_lst = self.sensitive_attributes_dct.keys() metrics_lst = group_fairness_metrics_lst + group_stability_metrics_lst @@ -380,9 +390,8 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met model_metrics_matrix = pd.DataFrame(results).T model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] - sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, - sorted_matrix_by_rank, num_models) + sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) return model_rank_heatmap diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index f99f227a..eaadcd75 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -1,4 +1,5 @@ import os +import re from datetime import datetime, timezone from sklearn.metrics import confusion_matrix @@ -71,6 +72,20 @@ def validate_config(config_obj): return True +def isfloat_regex(string): + # We have defined a pattern for float value + pattern = r'^[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?$' + # Find the match and convert to boolean + return bool(re.match(pattern, string)) + + +def str_to_float(str_var: str, var_name: str): + if isfloat_regex(str_var): + return float(str_var) + else: + raise ValueError(f"{var_name} must be a float number with a '.' separator.") + + def reset_model_seed(model, new_seed, verbose): if isinstance(model, base.Classifier): # For incremental models model.seed = new_seed diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 1ee8096c..a4a6068e 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -4,7 +4,6 @@ import seaborn as sns from matplotlib import pyplot as plt -from IPython.display import display from virny.utils.common_helpers import check_substring_in_list @@ -33,7 +32,34 @@ def plot_generic(x, y, xlabel, ylabel, x_lim, y_lim, plot_title): plt.show() -def create_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: +def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: str = 'dense'): + """ + Rank a pandas series with defined tolerance. + Ref: https://stackoverflow.com/questions/72956450/pandas-ranking-with-tolerance + + Parameters + ---------- + pd_series + A pandas series to rank + tolerance + A float value for ranking + method + Ranking methods for numpy.rank() + + Returns + ------- + A pandas series with dense ranks for the input pd series. + + """ + tolerance += 1e-10 # Add 0.0000000001 for correct comparison of float numbers + vals = pd.Series(pd_series.unique()).sort_values() + vals.index = vals + vals = vals.mask(vals - vals.shift(1) <= tolerance, vals.shift(1)) + + return pd_series.map(vals).fillna(pd_series).rank(method=method) + + +def create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np.array: models_distances_matrix = model_metrics_matrix.copy(deep=True).T metric_names = models_distances_matrix.columns for metric_name in metric_names: @@ -42,11 +68,15 @@ def create_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() models_distances_matrix = models_distances_matrix.T - sorted_matrix_by_rank = np.argsort(np.argsort(models_distances_matrix, axis=1), axis=1) + models_distances_df = pd.DataFrame(models_distances_matrix) + sorted_matrix_by_rank = models_distances_df.apply( + lambda row : rank_with_tolerance(row, tolerance, method='dense'), axis = 1 + ) + return sorted_matrix_by_rank -def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: +def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np.array: models_distances_matrix = model_metrics_matrix.copy(deep=True).T metric_names = models_distances_matrix.columns for metric_name in metric_names: @@ -56,11 +86,15 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix) -> np.array: models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() models_distances_matrix = models_distances_matrix.T - sorted_matrix_by_rank = np.argsort(np.argsort(models_distances_matrix, axis=1), axis=1) + models_distances_df = pd.DataFrame(models_distances_matrix) + sorted_matrix_by_rank = models_distances_df.apply( + lambda row : rank_with_tolerance(row, tolerance, method='dense'), axis = 1 + ) + return sorted_matrix_by_rank -def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, num_models: int): +def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank): """ This heatmap includes group fairness and stability metrics and defined models. Using it, you can visually compare the models across defined group metrics. On this plot, @@ -77,16 +111,16 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ Matrix of model metrics values where indexes are group metric names and columns are model names sorted_matrix_by_rank Matrix of model ranks per metric where indexes are group metric names and columns are model names - num_models - Number of models to visualize """ font_increase = 4 matrix_width = 20 matrix_height = model_metrics_matrix.shape[0] if model_metrics_matrix.shape[0] >= 3 \ else model_metrics_matrix.shape[0] * 2.5 + num_ranks = int(sorted_matrix_by_rank.values.max()) + fig = plt.figure(figsize=(matrix_width, matrix_height)) - rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex() + rank_colors = sns.color_palette("coolwarm", n_colors=num_ranks).as_hex() # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a coolwarm color scheme reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1 ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, @@ -98,9 +132,9 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ fig.tight_layout() cbar = ax.collections[0].colorbar - model_ranks = [idx for idx in range(num_models)] + model_ranks = [idx + 1 for idx in range(num_ranks)] cbar.set_ticks([float(idx) * -1 for idx in model_ranks]) - tick_labels = [str(idx + 1) for idx in model_ranks] + tick_labels = [str(idx) for idx in model_ranks] tick_labels[0] = tick_labels[0] + ', best' tick_labels[-1] = tick_labels[-1] + ', worst' cbar.set_ticklabels(tick_labels, fontsize=16 + font_increase) From b8ea3414ad0d9dc9b59c69526cf8a0d91a53c1ee Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 9 Oct 2023 01:40:50 +0300 Subject: [PATCH 016/148] Added tolerance to heatmaps --- docs/examples/Multiple_Models_Interface_Vis_Income.ipynb | 8 ++++---- virny/custom_classes/metrics_interactive_visualizer.py | 4 ++-- virny/utils/data_viz_utils.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 2155fbb9..96262a8a 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 70, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-08T22:06:28.762250Z", - "start_time": "2023-10-08T22:06:28.618558Z" + "end_time": "2023-10-08T22:12:56.138844Z", + "start_time": "2023-10-08T22:12:56.085891Z" } }, "outputs": [], @@ -229,7 +229,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-08T22:06:28.762615Z" + "start_time": "2023-10-08T22:12:56.178820Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index e7b7efc9..aa2ef476 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -260,7 +260,7 @@ def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, (self.sorted_model_metrics_df.Subgroup == selected_subgroup) & (self.sorted_model_metrics_df.Model_Name == model_name) ]['Value'].values[0] - metric_value = round(metric_value, 3) + metric_value = metric_value results[subgroup_metric][model_name] = metric_value return results @@ -385,7 +385,7 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met (self.sorted_model_composed_metrics_df.Subgroup == group) & (self.sorted_model_composed_metrics_df.Model_Name == model_name) ]['Value'].values[0] - metric_value = round(metric_value, 3) + metric_value = metric_value results[group_metric][model_name] = metric_value model_metrics_matrix = pd.DataFrame(results).T diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index a4a6068e..d251cd1a 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -123,7 +123,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ rank_colors = sns.color_palette("coolwarm", n_colors=num_ranks).as_hex() # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a coolwarm color scheme reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1 - ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, + ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix.round(3), cmap=rank_colors, fmt='', annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 16 + font_increase}) ax.set(xlabel="", ylabel="") ax.xaxis.tick_top() From b9812181f4dacecdb7ed35c166b948fc86090c32 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 10 Oct 2023 00:38:19 +0300 Subject: [PATCH 017/148] Added a test sample for data stats panel --- ...Multiple_Models_Interface_Vis_Income.ipynb | 89 +++++++++++-------- .../metrics_interactive_visualizer.py | 46 ++++++++++ virny/utils/data_viz_utils.py | 2 +- 3 files changed, 98 insertions(+), 39 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 96262a8a..4285f1df 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -2,15 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 72, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-08T19:53:45.170627Z", - "start_time": "2023-10-08T19:53:44.682414Z" + "end_time": "2023-10-09T18:41:13.001910Z", + "start_time": "2023-10-09T18:41:12.938067Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -19,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 73, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-08T19:53:45.179971Z", - "start_time": "2023-10-08T19:53:45.170956Z" + "end_time": "2023-10-09T18:41:13.042184Z", + "start_time": "2023-10-09T18:41:13.000213Z" } }, "outputs": [], @@ -37,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 74, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-08T19:53:45.190533Z", - "start_time": "2023-10-08T19:53:45.180261Z" + "end_time": "2023-10-09T18:41:13.046945Z", + "start_time": "2023-10-09T18:41:13.024368Z" } }, "outputs": [ @@ -72,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 75, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-08T19:53:47.366728Z", - "start_time": "2023-10-08T19:53:45.190219Z" + "end_time": "2023-10-09T18:41:13.071700Z", + "start_time": "2023-10-09T18:41:13.047422Z" } }, "outputs": [], @@ -90,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 76, "outputs": [], "source": [ "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" @@ -98,15 +107,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-08T19:53:47.391686Z", - "start_time": "2023-10-08T19:53:47.369626Z" + "end_time": "2023-10-09T18:41:13.095787Z", + "start_time": "2023-10-09T18:41:13.071607Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 77, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -116,15 +125,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-08T19:53:47.419075Z", - "start_time": "2023-10-08T19:53:47.391397Z" + "end_time": "2023-10-09T18:41:13.134622Z", + "start_time": "2023-10-09T18:41:13.094182Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 78, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -135,15 +144,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-08T19:53:47.443543Z", - "start_time": "2023-10-08T19:53:47.419472Z" + "end_time": "2023-10-09T18:41:13.161705Z", + "start_time": "2023-10-09T18:41:13.134978Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 79, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -153,21 +162,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-08T19:53:47.469996Z", - "start_time": "2023-10-08T19:53:47.443240Z" + "end_time": "2023-10-09T18:41:13.190514Z", + "start_time": "2023-10-09T18:41:13.160460Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 80, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 9, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -178,8 +187,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-08T19:53:47.513710Z", - "start_time": "2023-10-08T19:53:47.469016Z" + "end_time": "2023-10-09T18:41:13.212492Z", + "start_time": "2023-10-09T18:41:13.189317Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 110, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-08T22:12:56.138844Z", - "start_time": "2023-10-08T22:12:56.085891Z" + "end_time": "2023-10-09T21:33:22.941196Z", + "start_time": "2023-10-09T21:33:22.653493Z" } }, "outputs": [], @@ -229,14 +238,14 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-08T22:12:56.178820Z" + "start_time": "2023-10-09T21:33:23.302903Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 83, "outputs": [ { "name": "stdout", @@ -252,19 +261,23 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-08T20:42:21.447796Z", - "start_time": "2023-10-08T20:42:21.325905Z" + "end_time": "2023-10-09T18:43:18.507269Z", + "start_time": "2023-10-09T18:43:18.460630Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "outputs": [], "source": [], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T18:43:18.509920Z", + "start_time": "2023-10-09T18:43:18.506670Z" + } }, "id": "c207d4345ddca1db" } diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index aa2ef476..65cbde38 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -25,6 +25,7 @@ class MetricsInteractiveVisualizer: def __init__(self, model_metrics_dct: dict, model_composed_metrics_df: pd.DataFrame, sensitive_attributes_dct: dict): self.demo = None + self.max_groups = 8 self.model_names = list(model_metrics_dct.keys()) self.sensitive_attributes_dct = sensitive_attributes_dct self.group_names = list(self.sensitive_attributes_dct.keys()) @@ -63,8 +64,53 @@ def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: l return model_metrics_df[filtered_cols] + def __variable_inputs(self, k): + k = int(k) + return [gr.Textbox(value='', visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) + + def _test(self, grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8, + grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8): + grp_names = [grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8] + grp_names = [grp for grp in grp_names if grp != '' and grp is not None] + grp_dis_values = [grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8] + grp_dis_values = [grp for grp in grp_dis_values if grp != '' and grp is not None] + + inp_str1 = ' '.join(grp_names) + '.' + inp_str2 = ' '.join(grp_dis_values) + '.' + + return inp_str1 + ' | ' + inp_str2 + def start_web_app(self): with gr.Blocks(theme=gr.themes.Soft()) as demo: + # ==================================== Dataset Statistics ==================================== + gr.Markdown( + """ + ## Dataset Statistics + """) + with gr.Row(): + with gr.Column(scale=2): + default_val = 5 + s = gr.Slider(1, self.max_groups, value=default_val, step=1, label="How many groups to show:") + grp_names = [] + grp_dis_values = [] + for i in range(self.max_groups): + visibility = True if i + 1 <= default_val else False + with gr.Row(): + grp_name = gr.Text(label=f"Group {i + 1}", interactive=True, visible=visibility) + grp_dis_value = gr.Text(label="Disadvantage value", interactive=True, visible=visibility) + grp_names.append(grp_name) + grp_dis_values.append(grp_dis_value) + + s.change(self.__variable_inputs, s, grp_names) + s.change(self.__variable_inputs, s, grp_dis_values) + btn_view0 = gr.Button("Submit") + with gr.Column(scale=3): + test_output = gr.Text(label="Test") + + btn_view0.click(self._test, + inputs=[grp_names[0], grp_names[1], grp_names[2], grp_names[3], grp_names[4], grp_names[5], grp_names[6], grp_names[7], + grp_dis_values[0], grp_dis_values[1], grp_dis_values[2], grp_dis_values[3], grp_dis_values[4], grp_dis_values[5], grp_dis_values[6], grp_dis_values[7]], + outputs=[test_output]) # ==================================== Bar Chart for Model Selection ==================================== gr.Markdown( """ diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index d251cd1a..bed7deda 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -54,7 +54,7 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: s tolerance += 1e-10 # Add 0.0000000001 for correct comparison of float numbers vals = pd.Series(pd_series.unique()).sort_values() vals.index = vals - vals = vals.mask(vals - vals.shift(1) <= tolerance, vals.shift(1)) + vals = vals.mask(vals - vals.shift(1) < tolerance, vals.shift(1)) return pd_series.map(vals).fillna(pd_series).rank(method=method) From 97ccb6fe20e4924813fc8124528f44223dd5ac40 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 10 Oct 2023 02:23:31 +0300 Subject: [PATCH 018/148] Added subgroup proportions and base rates --- ...Multiple_Models_Interface_Vis_Income.ipynb | 38 +++++---- .../metrics_interactive_visualizer.py | 84 ++++++++++++++----- virny/utils/data_viz_utils.py | 70 ++++++++++++++++ 3 files changed, 154 insertions(+), 38 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 4285f1df..88159598 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -81,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 113, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.071700Z", - "start_time": "2023-10-09T18:41:13.047422Z" + "end_time": "2023-10-09T22:39:14.946035Z", + "start_time": "2023-10-09T22:39:14.899470Z" } }, "outputs": [], @@ -94,21 +94,23 @@ "import os\n", "import pandas as pd\n", "\n", + "from virny.datasets import ACSIncomeDataset\n", "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 114, "outputs": [], "source": [ + "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n", "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.095787Z", - "start_time": "2023-10-09T18:41:13.071607Z" + "end_time": "2023-10-09T22:39:18.249850Z", + "start_time": "2023-10-09T22:39:16.778059Z" } }, "id": "d3c53c7b72ecbcd0" @@ -203,23 +205,24 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 175, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T21:33:22.941196Z", - "start_time": "2023-10-09T21:33:22.653493Z" + "end_time": "2023-10-09T23:21:39.540076Z", + "start_time": "2023-10-09T23:21:39.222249Z" } }, "outputs": [], "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", + "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", + " models_metrics_dct, models_composed_metrics_df,\n", " sensitive_attributes_dct=sensitive_attributes_dct)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 176, "outputs": [ { "name": "stdout", @@ -227,7 +230,8 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n" + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" ] } ], @@ -236,16 +240,16 @@ ], "metadata": { "collapsed": false, - "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-09T21:33:23.302903Z" + "end_time": "2023-10-09T23:23:14.184149Z", + "start_time": "2023-10-09T23:21:39.540354Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 130, "outputs": [ { "name": "stdout", @@ -261,8 +265,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:43:18.507269Z", - "start_time": "2023-10-09T18:43:18.460630Z" + "end_time": "2023-10-09T22:55:30.303832Z", + "start_time": "2023-10-09T22:55:29.989601Z" } }, "id": "277b6d1de837dab7" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 65cbde38..7fee74e1 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -2,9 +2,11 @@ import gradio as gr import altair as alt -from virny.utils.common_helpers import isfloat_regex, str_to_float +from virny.utils.common_helpers import str_to_float +from virny.utils.protected_groups_partitioning import create_test_protected_groups from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, - create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection) + create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection, + compute_proportions, compute_base_rates, create_col_facet_bar_chart) class MetricsInteractiveVisualizer: @@ -13,6 +15,10 @@ class MetricsInteractiveVisualizer: Parameters ---------- + X_data + An original features dataframe + y_data + An original target column pandas series model_metrics_dct Dictionary where keys are model names and values are dataframes of subgroup metrics for each model model_composed_metrics_df @@ -22,14 +28,18 @@ class MetricsInteractiveVisualizer: and values are privilege values for these attributes """ - def __init__(self, model_metrics_dct: dict, model_composed_metrics_df: pd.DataFrame, - sensitive_attributes_dct: dict): - self.demo = None - self.max_groups = 8 + def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics_dct: dict, + model_composed_metrics_df: pd.DataFrame, sensitive_attributes_dct: dict): + self.X_data = X_data + self.y_data = y_data self.model_names = list(model_metrics_dct.keys()) self.sensitive_attributes_dct = sensitive_attributes_dct self.group_names = list(self.sensitive_attributes_dct.keys()) + # Technical attributes + self.demo = None + self.max_groups = 8 + # Create one metrics df with all model_dfs models_metrics_df = pd.DataFrame() for model_name in model_metrics_dct.keys(): @@ -68,18 +78,6 @@ def __variable_inputs(self, k): k = int(k) return [gr.Textbox(value='', visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) - def _test(self, grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8, - grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8): - grp_names = [grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8] - grp_names = [grp for grp in grp_names if grp != '' and grp is not None] - grp_dis_values = [grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8] - grp_dis_values = [grp for grp in grp_dis_values if grp != '' and grp is not None] - - inp_str1 = ' '.join(grp_names) + '.' - inp_str2 = ' '.join(grp_dis_values) + '.' - - return inp_str1 + ' | ' + inp_str2 - def start_web_app(self): with gr.Blocks(theme=gr.themes.Soft()) as demo: # ==================================== Dataset Statistics ==================================== @@ -105,12 +103,12 @@ def start_web_app(self): s.change(self.__variable_inputs, s, grp_dis_values) btn_view0 = gr.Button("Submit") with gr.Column(scale=3): - test_output = gr.Text(label="Test") + dataset_proportions_bar_chart = gr.Plot(label="Subgroup Proportions and Base Rates") - btn_view0.click(self._test, + btn_view0.click(self._create_dataset_proportions_bar_chart, inputs=[grp_names[0], grp_names[1], grp_names[2], grp_names[3], grp_names[4], grp_names[5], grp_names[6], grp_names[7], grp_dis_values[0], grp_dis_values[1], grp_dis_values[2], grp_dis_values[3], grp_dis_values[4], grp_dis_values[5], grp_dis_values[6], grp_dis_values[7]], - outputs=[test_output]) + outputs=[dataset_proportions_bar_chart]) # ==================================== Bar Chart for Model Selection ==================================== gr.Markdown( """ @@ -311,6 +309,50 @@ def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, return results + def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8, + grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8): + grp_names = [grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8] + grp_names = [grp for grp in grp_names if grp != '' and grp is not None] + grp_dis_values = [grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8] + grp_dis_values = [grp for grp in grp_dis_values if grp != '' and grp is not None] + + # Create a sensitive attrs dict + input_sensitive_attrs_dct = dict() + for grp_name, grp_dis_val in zip(grp_names, grp_dis_values): + if '&' in grp_name: + input_sensitive_attrs_dct[grp_name] = None + else: + converted_grp_dis_val = eval(grp_dis_val) if '[' in grp_dis_val else grp_dis_val + input_sensitive_attrs_dct[grp_name] = converted_grp_dis_val + + # Partition on protected groups + protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct) + + # Create a df with group proportions and group base rates + subgroup_proportions_dct = compute_proportions(protected_groups, self.X_data) + subgroup_base_rates_dct = compute_base_rates(protected_groups, self.y_data) + subgroup_relative_base_rates_dct = dict() + for subgroup in subgroup_proportions_dct.keys(): + subgroup_relative_base_rates_dct[subgroup] = subgroup_base_rates_dct[subgroup] * subgroup_proportions_dct[subgroup] + + stats_df = pd.DataFrame(columns=['Subgroup', 'Value', 'Statistics_Type']) + for subgroup in subgroup_proportions_dct.keys(): + stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_proportions_dct[subgroup], 'Proportion'] + stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_relative_base_rates_dct[subgroup], 'Base_Rate'] + + # Create a row facet bar chart + col_facet_sort_by_lst = ['overall'] + [grp for grp in stats_df.Subgroup.unique() if grp.lower() != 'overall'] + col_facet_bar_chart = create_col_facet_bar_chart(stats_df, + x_col='Statistics_Type', + y_col='Value', + col_facet_by='Subgroup', + x_sort_by_lst=['Proportion', 'Base_Rate'], + col_facet_sort_by_lst=col_facet_sort_by_lst, + color_legend_title='Statistics Type', + facet_title='') + + return col_facet_bar_chart + def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_min_val, acc_max_val, fairness_metric, fairness_min_val, fairness_max_val, subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index bed7deda..6de8921c 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -4,6 +4,7 @@ import seaborn as sns from matplotlib import pyplot as plt +from altair.utils.schemapi import Undefined from virny.utils.common_helpers import check_substring_in_list @@ -59,6 +60,26 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: s return pd_series.map(vals).fillna(pd_series).rank(method=method) +def compute_proportions(protected_groups, X_data): + subgroup_proportions_dct = {'overall': 1.0} + for col_name in protected_groups.keys(): + proportion = protected_groups[col_name].shape[0] / X_data.shape[0] + subgroup_proportions_dct[col_name] = proportion + + return subgroup_proportions_dct + + +def compute_base_rates(protected_groups, y_data): + overall_base_rate = y_data[y_data == 1].shape[0] / y_data.shape[0] + subgroup_base_rates_dct = {'overall': overall_base_rate} + for col_name in protected_groups.keys(): + filtered_df = y_data.iloc[protected_groups[col_name].index].copy(deep=True) + base_rate = filtered_df[filtered_df == 1].shape[0] / filtered_df.shape[0] + subgroup_base_rates_dct[col_name] = base_rate + + return subgroup_base_rates_dct + + def create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np.array: models_distances_matrix = model_metrics_matrix.copy(deep=True).T metric_names = models_distances_matrix.columns @@ -94,6 +115,55 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np return sorted_matrix_by_rank +def create_col_facet_bar_chart(df, x_col, y_col, col_facet_by, x_sort_by_lst=Undefined, + col_facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined): + base_font_size = 16 + bar_chart = ( + alt.Chart().mark_bar().encode( + alt.X(f'{x_col}:N', axis=None, sort=x_sort_by_lst), + alt.Y(f'{y_col}:Q', axis=alt.Axis(grid=True), title=''), + alt.Color(f'{x_col}:N', + scale=alt.Scale(scheme="tableau20"), + sort=x_sort_by_lst, + legend=alt.Legend(title=color_legend_title, + labelFontSize=base_font_size, + titleFontSize=base_font_size + 2, + orient='top')) + ) + ) + + text_labels = ( + bar_chart.mark_text( + baseline='middle', + fontSize=base_font_size, + dy=-10 + ).encode( + text=alt.Text('Value:Q', format=",.3f"), + color=alt.value("black") + ) + ) + + final_chart = ( + alt.layer( + bar_chart, text_labels, data=df + ).properties( + width=100, + height=500 + ).facet( + column=alt.Column(f'{col_facet_by}:N', title=facet_title, sort=col_facet_sort_by_lst) + ).configure( + padding={'top': 33}, + ).configure_headerColumn( + labelFontSize=base_font_size, + titleFontSize=base_font_size + 2 + ).configure_axis( + labelFontSize=base_font_size, titleFontSize=base_font_size + 2 + ) + ) + + return final_chart + + def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank): """ This heatmap includes group fairness and stability metrics and defined models. From ab3410437de3dafa8469ba7ffaa35662b2ce544e Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 10 Oct 2023 22:27:53 +0300 Subject: [PATCH 019/148] Changed a default range for Label_Stability_Ratio --- ...Multiple_Models_Interface_Vis_Income.ipynb | 137 +++++++++++++++++- .../metrics_interactive_visualizer.py | 6 +- 2 files changed, 132 insertions(+), 11 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 88159598..08e31492 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -205,12 +205,12 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 179, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T23:21:39.540076Z", - "start_time": "2023-10-09T23:21:39.222249Z" + "end_time": "2023-10-10T14:50:17.837898Z", + "start_time": "2023-10-10T14:50:17.676305Z" } }, "outputs": [], @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": null, "outputs": [ { "name": "stdout", @@ -230,8 +230,129 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", + " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 73, in create_test_protected_groups\n", + " X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index]\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/pandas/core/frame.py\", line 3813, in __getitem__\n", + " indexer = self.columns._get_indexer_strict(key, \"columns\")[1]\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/pandas/core/indexes/base.py\", line 6070, in _get_indexer_strict\n", + " self._raise_if_missing(keyarr, indexer, axis_name)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/pandas/core/indexes/base.py\", line 6133, in _raise_if_missing\n", + " raise KeyError(f\"{not_found} not in index\")\n", + "KeyError: \"['DIS', 'AGE'] not in index\"\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", + " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", + " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", + "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", + " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", + " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", + "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", + " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", + " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", + "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", + " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", + " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", + "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n" ] } ], @@ -240,9 +361,9 @@ ], "metadata": { "collapsed": false, + "is_executing": true, "ExecuteTime": { - "end_time": "2023-10-09T23:23:14.184149Z", - "start_time": "2023-10-09T23:21:39.540354Z" + "start_time": "2023-10-10T14:50:17.852688Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 7fee74e1..50c2fadc 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -76,7 +76,7 @@ def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: l def __variable_inputs(self, k): k = int(k) - return [gr.Textbox(value='', visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) + return [gr.Textbox(visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) def start_web_app(self): with gr.Blocks(theme=gr.themes.Soft()) as demo: @@ -151,8 +151,8 @@ def start_web_app(self): value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", scale=2 ) - group_stab_min_val = gr.Text(value="0.1", label="Min value", scale=1) - group_stab_max_val = gr.Text(value="10.0", label="Max value", scale=1) + group_stab_min_val = gr.Text(value="0.7", label="Min value", scale=1) + group_stab_max_val = gr.Text(value="1.5", label="Max value", scale=1) btn_view1 = gr.Button("Submit") with gr.Column(scale=3): bar_plot_for_model_selection = gr.Plot(label="Bar Chart") From 84cf426e8c90b7dc601d10a39835ab11889febd6 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 11 Oct 2023 23:02:18 +0300 Subject: [PATCH 020/148] Restructured section 3 in the gradio app --- ...Multiple_Models_Interface_Vis_Income.ipynb | 209 ++++-------------- .../metrics_interactive_visualizer.py | 64 +++--- 2 files changed, 73 insertions(+), 200 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 08e31492..cd39444c 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -2,24 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 72, + "execution_count": 1, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.001910Z", - "start_time": "2023-10-09T18:41:12.938067Z" + "end_time": "2023-10-11T19:05:26.386191Z", + "start_time": "2023-10-11T19:05:25.944121Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -28,12 +19,12 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 2, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.042184Z", - "start_time": "2023-10-09T18:41:13.000213Z" + "end_time": "2023-10-11T19:05:26.394513Z", + "start_time": "2023-10-11T19:05:26.385903Z" } }, "outputs": [], @@ -46,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 3, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.046945Z", - "start_time": "2023-10-09T18:41:13.024368Z" + "end_time": "2023-10-11T19:05:26.404007Z", + "start_time": "2023-10-11T19:05:26.395039Z" } }, "outputs": [ @@ -81,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 4, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-09T22:39:14.946035Z", - "start_time": "2023-10-09T22:39:14.899470Z" + "end_time": "2023-10-11T19:05:28.926284Z", + "start_time": "2023-10-11T19:05:26.405380Z" } }, "outputs": [], @@ -100,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 5, "outputs": [], "source": [ "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n", @@ -109,15 +100,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T22:39:18.249850Z", - "start_time": "2023-10-09T22:39:16.778059Z" + "end_time": "2023-10-11T19:05:30.217781Z", + "start_time": "2023-10-11T19:05:28.929275Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 6, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -127,15 +118,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.134622Z", - "start_time": "2023-10-09T18:41:13.094182Z" + "end_time": "2023-10-11T19:05:30.244888Z", + "start_time": "2023-10-11T19:05:30.218209Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 7, "outputs": [], "source": [ "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", @@ -146,15 +137,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.161705Z", - "start_time": "2023-10-09T18:41:13.134978Z" + "end_time": "2023-10-11T19:05:30.270595Z", + "start_time": "2023-10-11T19:05:30.245746Z" } }, "id": "2d922003e752a4b4" }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 8, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -164,21 +155,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.190514Z", - "start_time": "2023-10-09T18:41:13.160460Z" + "end_time": "2023-10-11T19:05:30.292095Z", + "start_time": "2023-10-11T19:05:30.268258Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 9, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 80, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -189,8 +180,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:41:13.212492Z", - "start_time": "2023-10-09T18:41:13.189317Z" + "end_time": "2023-10-11T19:05:30.316436Z", + "start_time": "2023-10-11T19:05:30.292589Z" } }, "id": "15ed7d1ba1f22317" @@ -205,12 +196,12 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 49, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-10T14:50:17.837898Z", - "start_time": "2023-10-10T14:50:17.676305Z" + "end_time": "2023-10-11T19:56:39.234085Z", + "start_time": "2023-10-11T19:56:39.056500Z" } }, "outputs": [], @@ -232,128 +223,6 @@ "\n", "To create a public link, set `share=True` in `launch()`.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", - " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 73, in create_test_protected_groups\n", - " X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index]\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/pandas/core/frame.py\", line 3813, in __getitem__\n", - " indexer = self.columns._get_indexer_strict(key, \"columns\")[1]\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/pandas/core/indexes/base.py\", line 6070, in _get_indexer_strict\n", - " self._raise_if_missing(keyarr, indexer, axis_name)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/pandas/core/indexes/base.py\", line 6133, in _raise_if_missing\n", - " raise KeyError(f\"{not_found} not in index\")\n", - "KeyError: \"['DIS', 'AGE'] not in index\"\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", - " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", - " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", - "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", - " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", - " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", - "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", - " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", - " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", - "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 329, in _create_dataset_proportions_bar_chart\n", - " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 96, in create_test_protected_groups\n", - " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", - "ValueError: Protected group (AGEP_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n" - ] } ], "source": [ @@ -363,14 +232,14 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-10T14:50:17.852688Z" + "start_time": "2023-10-11T19:56:39.234618Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 12, "outputs": [ { "name": "stdout", @@ -386,22 +255,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T22:55:30.303832Z", - "start_time": "2023-10-09T22:55:29.989601Z" + "end_time": "2023-10-11T19:21:09.901993Z", + "start_time": "2023-10-11T19:21:09.806669Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 12, "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-09T18:43:18.509920Z", - "start_time": "2023-10-09T18:43:18.506670Z" + "end_time": "2023-10-11T19:21:09.902311Z", + "start_time": "2023-10-11T19:21:09.899330Z" } }, "id": "c207d4345ddca1db" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 50c2fadc..20d39276 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -119,7 +119,7 @@ def start_web_app(self): with gr.Column(scale=2): group_name = gr.Dropdown( self.group_names, - value=self.group_names[0], multiselect=False, label="Group Name for Parity Metrics", + value=self.group_names[0], multiselect=False, label="Group Name for Disparity Metrics", ) with gr.Row(): accuracy_metric = gr.Dropdown( @@ -197,11 +197,11 @@ def start_web_app(self): subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap, inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics, subgroup_tolerance], outputs=[subgroup_model_ranking_heatmap]) - # ======================================== Parity Metrics Heatmap ======================================== + # ======================================== Disparity Metrics Heatmap ======================================== gr.Markdown( """ - ## Parity Metrics Heatmap - Select input arguments to create a parity metrics heatmap. + ## Disparity Metrics Heatmap + Select input arguments to create a disparity metrics heatmap. """) with gr.Row(): with gr.Column(scale=1): @@ -212,11 +212,11 @@ def start_web_app(self): group_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") fairness_metrics = gr.Dropdown( sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), - value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", + value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), - value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", + value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:", ) group_btn_view2 = gr.Button("Submit") with gr.Column(scale=2): @@ -225,17 +225,26 @@ def start_web_app(self): group_btn_view2.click(self._create_group_model_rank_heatmap, inputs=[model_names, fairness_metrics, group_stability_metrics, group_tolerance], outputs=[group_model_ranking_heatmap]) - # =============================== Subgroup and Group Metrics Bar Chart =============================== + # ============================ Group Specific and Disparity Metrics Bar Charts ============================ with gr.Row(): + # Scale column 1 to a half of a screen with gr.Column(): gr.Markdown( """ - ## Subgroup Metrics Bar Chart + ## Group Specific and Disparity Metrics Bar Charts """) - subgroup_model_names = gr.Dropdown( - sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, - label="Model Name", info="Select one model to display on the bar chart:", + model_name_vw3 = gr.Dropdown( + sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, scale=1, + label="Model Name", info="Select one model to display on the bar charts:", ) + with gr.Column(): + pass + with gr.Row(): + with gr.Column(): + gr.Markdown( + """ + ### Group Specific Metrics + """) accuracy_metrics = gr.Dropdown( sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", @@ -248,37 +257,32 @@ def start_web_app(self): sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) - subgroup_btn_view3 = gr.Button("Submit") + btn_view3 = gr.Button("Submit") with gr.Column(): gr.Markdown( """ - ## Group Metrics Bar Chart + ### Disparity Metrics """) - group_model_names = gr.Dropdown( - sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, - label="Model Name", info="Select one model to display on the bar chart:", - ) fairness_metrics = gr.Dropdown( sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), - value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Parity Metrics", info="Select error parity metrics to display on the heatmap:", + value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), - value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Parity Metrics", info="Select stability parity metrics to display on the heatmap:", + value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:", ) - group_btn_view3 = gr.Button("Submit") with gr.Row(): with gr.Column(): - subgroup_metrics_bar_chart = gr.Plot(label="Subgroup Bar Chart") + subgroup_metrics_bar_chart = gr.Plot(label="Group Specific Bar Chart") with gr.Column(): - group_metrics_bar_chart = gr.Plot(label="Group Bar Chart") + group_metrics_bar_chart = gr.Plot(label="Disparity Bar Chart") - subgroup_btn_view3.click(self._create_subgroup_metrics_bar_chart_per_one_model, - inputs=[subgroup_model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], - outputs=[subgroup_metrics_bar_chart]) - group_btn_view3.click(self._create_group_metrics_bar_chart_per_one_model, - inputs=[group_model_names, fairness_metrics, group_stability_metrics], - outputs=[group_metrics_bar_chart]) + btn_view3.click(self._create_subgroup_metrics_bar_chart_per_one_model, + inputs=[model_name_vw3, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], + outputs=[subgroup_metrics_bar_chart]) + btn_view3.click(self._create_group_metrics_bar_chart_per_one_model, + inputs=[model_name_vw3, fairness_metrics, group_stability_metrics], + outputs=[group_metrics_bar_chart]) self.demo = demo self.demo.launch(inline=False, debug=True, show_error=True) @@ -507,7 +511,7 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names A metrics type ('subgroup' or 'group') to visualize """ - metrics_title = f'{metrics_type.capitalize()} Metrics' + metrics_title = 'Disparity Metrics' if metrics_type == "group" else 'Group Specific Metrics' metrics_df = self.melted_model_composed_metrics_df if metrics_type == "group" else self.melted_model_metrics_df filtered_groups = [grp for grp in metrics_df.Subgroup.unique() if '_correct' not in grp and '_incorrect' not in grp] filtered_groups = [grp for grp in filtered_groups if grp.lower() != 'overall'] + ['overall'] @@ -523,7 +527,7 @@ def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names alt.Color('Subgroup:N', scale=alt.Scale(scheme="tableau20"), sort=filtered_groups, - legend=alt.Legend(title=metrics_type.capitalize(), + legend=alt.Legend(title='Disparity' if metrics_type == 'group' else 'Group', labelFontSize=base_font_size, titleFontSize=base_font_size + 2)) ) From df44d29e1f9eefd73f1e7b2fa3b622a167216fa7 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 12 Oct 2023 11:46:46 +0300 Subject: [PATCH 021/148] Added dynamic variables for the stats bar chart --- ...Multiple_Models_Interface_Vis_Income.ipynb | 15 +-- .../metrics_interactive_visualizer.py | 27 +++-- virny/utils/data_viz_utils.py | 101 +++++++++++++++--- 3 files changed, 113 insertions(+), 30 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index cd39444c..14f4a7e9 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -196,12 +196,12 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 205, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-11T19:56:39.234085Z", - "start_time": "2023-10-11T19:56:39.056500Z" + "end_time": "2023-10-11T22:43:14.195509Z", + "start_time": "2023-10-11T22:43:14.152189Z" } }, "outputs": [], @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, "outputs": [ { "name": "stdout", @@ -221,7 +221,8 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n" + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" ] } ], @@ -230,9 +231,9 @@ ], "metadata": { "collapsed": false, - "is_executing": true, "ExecuteTime": { - "start_time": "2023-10-11T19:56:39.234618Z" + "end_time": "2023-10-11T23:08:56.632448Z", + "start_time": "2023-10-11T22:43:14.261225Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 20d39276..bdd13609 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -6,7 +6,8 @@ from virny.utils.protected_groups_partitioning import create_test_protected_groups from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection, - compute_proportions, compute_base_rates, create_col_facet_bar_chart) + compute_proportions, compute_base_rates, create_col_facet_bar_chart, + create_row_facet_bar_chart) class MetricsInteractiveVisualizer: @@ -102,8 +103,8 @@ def start_web_app(self): s.change(self.__variable_inputs, s, grp_names) s.change(self.__variable_inputs, s, grp_dis_values) btn_view0 = gr.Button("Submit") - with gr.Column(scale=3): - dataset_proportions_bar_chart = gr.Plot(label="Subgroup Proportions and Base Rates") + with gr.Column(scale=4): + dataset_proportions_bar_chart = gr.Plot(label="Group Proportions and Base Rates") btn_view0.click(self._create_dataset_proportions_bar_chart, inputs=[grp_names[0], grp_names[1], grp_names[2], grp_names[3], grp_names[4], grp_names[5], grp_names[6], grp_names[7], @@ -337,21 +338,25 @@ def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, subgroup_base_rates_dct = compute_base_rates(protected_groups, self.y_data) subgroup_relative_base_rates_dct = dict() for subgroup in subgroup_proportions_dct.keys(): - subgroup_relative_base_rates_dct[subgroup] = subgroup_base_rates_dct[subgroup] * subgroup_proportions_dct[subgroup] + pct = subgroup_base_rates_dct[subgroup]['percentage'] * subgroup_proportions_dct[subgroup]['percentage'] + subgroup_relative_base_rates_dct[subgroup] = {'percentage': pct, 'num_rows': subgroup_base_rates_dct[subgroup]['num_rows']} - stats_df = pd.DataFrame(columns=['Subgroup', 'Value', 'Statistics_Type']) + stats_df = pd.DataFrame(columns=['Subgroup', 'Percentage', 'Num_Rows', 'Statistics_Type']) for subgroup in subgroup_proportions_dct.keys(): - stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_proportions_dct[subgroup], 'Proportion'] - stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_relative_base_rates_dct[subgroup], 'Base_Rate'] + stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_proportions_dct[subgroup]['percentage'], + subgroup_proportions_dct[subgroup]['num_rows'], 'Proportion'] + stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_relative_base_rates_dct[subgroup]['percentage'], + subgroup_relative_base_rates_dct[subgroup]['num_rows'], 'Base_Rate'] # Create a row facet bar chart - col_facet_sort_by_lst = ['overall'] + [grp for grp in stats_df.Subgroup.unique() if grp.lower() != 'overall'] + facet_sort_by_lst = ['overall'] + [grp for grp in stats_df.Subgroup.unique() if grp.lower() != 'overall'] col_facet_bar_chart = create_col_facet_bar_chart(stats_df, x_col='Statistics_Type', - y_col='Value', - col_facet_by='Subgroup', + y_col='Num_Rows', + facet_column_name='Subgroup', + text_labels_column='Percentage', x_sort_by_lst=['Proportion', 'Base_Rate'], - col_facet_sort_by_lst=col_facet_sort_by_lst, + facet_sort_by_lst=facet_sort_by_lst, color_legend_title='Statistics Type', facet_title='') diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 6de8921c..f8b51640 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -61,21 +61,21 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: s def compute_proportions(protected_groups, X_data): - subgroup_proportions_dct = {'overall': 1.0} + subgroup_proportions_dct = {'overall': {'percentage': 1.0, 'num_rows': X_data.shape[0]}} for col_name in protected_groups.keys(): proportion = protected_groups[col_name].shape[0] / X_data.shape[0] - subgroup_proportions_dct[col_name] = proportion + subgroup_proportions_dct[col_name] = {'percentage': proportion, 'num_rows': protected_groups[col_name].shape[0]} return subgroup_proportions_dct def compute_base_rates(protected_groups, y_data): overall_base_rate = y_data[y_data == 1].shape[0] / y_data.shape[0] - subgroup_base_rates_dct = {'overall': overall_base_rate} + subgroup_base_rates_dct = {'overall': {'percentage': overall_base_rate, 'num_rows': y_data[y_data == 1].shape[0]}} for col_name in protected_groups.keys(): filtered_df = y_data.iloc[protected_groups[col_name].index].copy(deep=True) base_rate = filtered_df[filtered_df == 1].shape[0] / filtered_df.shape[0] - subgroup_base_rates_dct[col_name] = base_rate + subgroup_base_rates_dct[col_name] = {'percentage': base_rate, 'num_rows': filtered_df[filtered_df == 1].shape[0]} return subgroup_base_rates_dct @@ -115,16 +115,93 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np return sorted_matrix_by_rank -def create_col_facet_bar_chart(df, x_col, y_col, col_facet_by, x_sort_by_lst=Undefined, - col_facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined): +def create_col_facet_bar_chart(df, x_col, y_col, facet_column_name, text_labels_column, x_sort_by_lst=Undefined, + facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined): + num_facets = len(df[facet_column_name].unique()) + max_y_axis_limit = df[y_col].max() base_font_size = 16 + + # Set dynamic variables that adapt to the number of defined groups + dynamic_facet_width = 100 + dynamic_label_angle = -20 + dynamic_font_size = base_font_size + dynamic_top_padding = 40 + dynamic_legend_y_padding = -140 + if num_facets > 4 * 2 + 1 and num_facets <= 6 * 2 + 1: + dynamic_facet_width = 75 + dynamic_label_angle = -25 + dynamic_font_size -= 2 + dynamic_top_padding = 40 + dynamic_legend_y_padding = -160 + elif num_facets > 6 * 2 + 1: + dynamic_facet_width = 50 + dynamic_label_angle = -45 + dynamic_font_size -= 4 + dynamic_top_padding = 50 + dynamic_legend_y_padding = -200 + bar_chart = ( alt.Chart().mark_bar().encode( alt.X(f'{x_col}:N', axis=None, sort=x_sort_by_lst), - alt.Y(f'{y_col}:Q', axis=alt.Axis(grid=True), title=''), + alt.Y(f'{y_col}:Q', axis=alt.Axis(grid=True), title='', scale=alt.Scale(domain=[0, max_y_axis_limit])), alt.Color(f'{x_col}:N', scale=alt.Scale(scheme="tableau20"), sort=x_sort_by_lst, + legend=alt.Legend(title=color_legend_title, + labelFontSize=base_font_size, + titleFontSize=base_font_size + 2, + orient='none', + legendX=0, legendY=dynamic_legend_y_padding, + direction='horizontal')) + ) + ) + + text_labels = ( + bar_chart.mark_text( + baseline='middle', + fontSize=dynamic_font_size, + dy=-10 + ).encode( + text=alt.Text(f'{text_labels_column}:Q', format=",.2f"), + color=alt.value("black") + ) + ) + + final_chart = ( + alt.layer( + bar_chart, text_labels, data=df + ).properties( + width=dynamic_facet_width, + height=500 + ).facet( + column=alt.Column(f'{facet_column_name}:N', title=facet_title, + sort=facet_sort_by_lst, header=alt.Header(labelAngle=dynamic_label_angle, + labelAnchor='middle', + labelAlign='center', + labelPadding=-15)) + ).configure( + padding={'top': dynamic_top_padding}, + ).configure_headerColumn( + labelFontSize=base_font_size, + titleFontSize=base_font_size + 2, + ).configure_axis( + labelFontSize=base_font_size, titleFontSize=base_font_size + 2 + ) + ) + + return final_chart + + +def create_row_facet_bar_chart(df, x_col, y_col, facet_column_name, y_sort_by_lst=Undefined, + facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined): + base_font_size = 16 + bar_chart = ( + alt.Chart().mark_bar().encode( + alt.Y(f'{y_col}:N', axis=None, sort=y_sort_by_lst), + alt.X(f'{x_col}:Q', axis=alt.Axis(grid=True), title=''), + alt.Color(f'{y_col}:N', + scale=alt.Scale(scheme="tableau20"), + sort=y_sort_by_lst, legend=alt.Legend(title=color_legend_title, labelFontSize=base_font_size, titleFontSize=base_font_size + 2, @@ -136,7 +213,7 @@ def create_col_facet_bar_chart(df, x_col, y_col, col_facet_by, x_sort_by_lst=Und bar_chart.mark_text( baseline='middle', fontSize=base_font_size, - dy=-10 + dx=10 ).encode( text=alt.Text('Value:Q', format=",.3f"), color=alt.value("black") @@ -147,13 +224,13 @@ def create_col_facet_bar_chart(df, x_col, y_col, col_facet_by, x_sort_by_lst=Und alt.layer( bar_chart, text_labels, data=df ).properties( - width=100, - height=500 + width=500, + height=100 ).facet( - column=alt.Column(f'{col_facet_by}:N', title=facet_title, sort=col_facet_sort_by_lst) + row=alt.Row(f'{facet_column_name}:N', title=facet_title, sort=facet_sort_by_lst) ).configure( padding={'top': 33}, - ).configure_headerColumn( + ).configure_headerRow( labelFontSize=base_font_size, titleFontSize=base_font_size + 2 ).configure_axis( From 03f4c194055c23e6fd77f2366e1d8a07cc547197 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 12 Oct 2023 11:59:46 +0300 Subject: [PATCH 022/148] Added new uncertainty disparity metrics --- .gitignore | 1 + requirements.txt | 6 +++--- virny/custom_classes/metrics_composer.py | 9 +++++++-- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index cf2ccb41..375238bf 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ *_venv +virny_env notebooks *.env .DS_Store diff --git a/requirements.txt b/requirements.txt index 6e55b6af..12b6aa38 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,6 @@ wheel~=0.38.4 twine~=4.0.2 -requests-toolbelt==1.0.0 numpy~=1.24.2 -datapane~=0.15.5 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 @@ -17,4 +15,6 @@ PyYAML~=6.0 river==0.15.0 python-dotenv~=1.0.0 pytest~=7.2.2 -pymongo==4.3.3 \ No newline at end of file +pymongo==4.3.3 +datapane~=0.16.0 +requests-toolbelt==1.0.0 \ No newline at end of file diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index 4d9cdc10..b18f3866 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -46,19 +46,24 @@ def compose_metrics(self): priv_group = sensitive_attr + '_priv' groups_metrics_dct[sensitive_attr] = { - # Group fairness metrics + # Error disparity metrics 'Equalized_Odds_TPR': cfm[dis_group]['TPR'] - cfm[priv_group]['TPR'], 'Equalized_Odds_FPR': cfm[dis_group]['FPR'] - cfm[priv_group]['FPR'], 'Equalized_Odds_FNR': cfm[dis_group]['FNR'] - cfm[priv_group]['FNR'], 'Disparate_Impact': cfm[dis_group]['Positive-Rate'] / cfm[priv_group]['Positive-Rate'], 'Statistical_Parity_Difference': cfm[dis_group]['Positive-Rate'] - cfm[priv_group]['Positive-Rate'], 'Accuracy_Parity': cfm[dis_group]['Accuracy'] - cfm[priv_group]['Accuracy'], - # Group stability metrics + # Stability disparity metrics 'Label_Stability_Ratio': cfm[dis_group]['Label_Stability'] / cfm[priv_group]['Label_Stability'], 'IQR_Parity': cfm[dis_group]['IQR'] - cfm[priv_group]['IQR'], 'Std_Parity': cfm[dis_group]['Std'] - cfm[priv_group]['Std'], 'Std_Ratio': cfm[dis_group]['Std'] / cfm[priv_group]['Std'], 'Jitter_Parity': cfm[dis_group]['Jitter'] - cfm[priv_group]['Jitter'], + # Uncertainty disparity metrics + 'Overall_Uncertainty_Parity': cfm[dis_group]['Overall_Uncertainty'] - cfm[priv_group]['Overall_Uncertainty'], + 'Overall_Uncertainty_Ratio': cfm[dis_group]['Overall_Uncertainty'] / cfm[priv_group]['Overall_Uncertainty'], + 'Aleatoric_Uncertainty_Parity': cfm[dis_group]['Aleatoric_Uncertainty'] - cfm[priv_group]['Aleatoric_Uncertainty'], + 'Aleatoric_Uncertainty_Ratio': cfm[dis_group]['Aleatoric_Uncertainty'] / cfm[priv_group]['Aleatoric_Uncertainty'], } model_composed_metrics_df = pd.DataFrame(groups_metrics_dct).reset_index() From d26f528115ea75e78ec75293040d19b3b38753bd Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 12 Oct 2023 12:07:27 +0300 Subject: [PATCH 023/148] Added new dependencies --- lib_base_packages.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/lib_base_packages.txt b/lib_base_packages.txt index db5475f2..830ceeb0 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -1,5 +1,4 @@ numpy~=1.24.2 -datapane~=0.15.5 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 @@ -10,4 +9,6 @@ seaborn~=0.12.1 folktables~=0.0.11 munch~=2.5.0 PyYAML~=6.0 -river==0.15.0 \ No newline at end of file +river==0.15.0 +datapane~=0.16.0 +requests-toolbelt==1.0.0 \ No newline at end of file From 866f30f50c9b0706411ef42a0170657ff58b2474 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 13 Oct 2023 23:50:30 +0300 Subject: [PATCH 024/148] Added minor fixes to a visualization component --- ...Multiple_Models_Interface_Vis_Income.ipynb | 56 +++++++++---------- .../metrics_interactive_visualizer.py | 4 +- virny/utils/data_viz_utils.py | 10 ++-- 3 files changed, 34 insertions(+), 36 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 14f4a7e9..e19f415f 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-11T19:05:26.386191Z", - "start_time": "2023-10-11T19:05:25.944121Z" + "end_time": "2023-10-13T20:20:09.765631Z", + "start_time": "2023-10-13T20:20:09.381209Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-11T19:05:26.394513Z", - "start_time": "2023-10-11T19:05:26.385903Z" + "end_time": "2023-10-13T20:20:09.774183Z", + "start_time": "2023-10-13T20:20:09.765873Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-11T19:05:26.404007Z", - "start_time": "2023-10-11T19:05:26.395039Z" + "end_time": "2023-10-13T20:20:09.783681Z", + "start_time": "2023-10-13T20:20:09.774750Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-11T19:05:28.926284Z", - "start_time": "2023-10-11T19:05:26.405380Z" + "end_time": "2023-10-13T20:20:11.549308Z", + "start_time": "2023-10-13T20:20:09.784822Z" } }, "outputs": [], @@ -100,8 +100,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:05:30.217781Z", - "start_time": "2023-10-11T19:05:28.929275Z" + "end_time": "2023-10-13T20:20:12.860282Z", + "start_time": "2023-10-13T20:20:11.551544Z" } }, "id": "d3c53c7b72ecbcd0" @@ -118,8 +118,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:05:30.244888Z", - "start_time": "2023-10-11T19:05:30.218209Z" + "end_time": "2023-10-13T20:20:12.888990Z", + "start_time": "2023-10-13T20:20:12.860786Z" } }, "id": "2aab7c79ecdee914" @@ -137,8 +137,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:05:30.270595Z", - "start_time": "2023-10-11T19:05:30.245746Z" + "end_time": "2023-10-13T20:20:12.911932Z", + "start_time": "2023-10-13T20:20:12.888583Z" } }, "id": "2d922003e752a4b4" @@ -155,8 +155,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:05:30.292095Z", - "start_time": "2023-10-11T19:05:30.268258Z" + "end_time": "2023-10-13T20:20:12.937376Z", + "start_time": "2023-10-13T20:20:12.912368Z" } }, "id": "833484748ed512e8" @@ -180,8 +180,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:05:30.316436Z", - "start_time": "2023-10-11T19:05:30.292589Z" + "end_time": "2023-10-13T20:20:12.963217Z", + "start_time": "2023-10-13T20:20:12.935698Z" } }, "id": "15ed7d1ba1f22317" @@ -196,12 +196,12 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 23, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-11T22:43:14.195509Z", - "start_time": "2023-10-11T22:43:14.152189Z" + "end_time": "2023-10-13T20:49:19.030436Z", + "start_time": "2023-10-13T20:49:18.977199Z" } }, "outputs": [], @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 24, "outputs": [ { "name": "stdout", @@ -232,8 +232,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T23:08:56.632448Z", - "start_time": "2023-10-11T22:43:14.261225Z" + "end_time": "2023-10-13T20:50:05.536644Z", + "start_time": "2023-10-13T20:49:19.061199Z" } }, "id": "678a9dc8d51243f4" @@ -256,8 +256,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:21:09.901993Z", - "start_time": "2023-10-11T19:21:09.806669Z" + "end_time": "2023-10-13T20:23:04.989037Z", + "start_time": "2023-10-13T20:23:04.937593Z" } }, "id": "277b6d1de837dab7" @@ -270,8 +270,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-11T19:21:09.902311Z", - "start_time": "2023-10-11T19:21:09.899330Z" + "end_time": "2023-10-13T20:23:04.991061Z", + "start_time": "2023-10-13T20:23:04.988926Z" } }, "id": "c207d4345ddca1db" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index bdd13609..54588226 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -6,8 +6,7 @@ from virny.utils.protected_groups_partitioning import create_test_protected_groups from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection, - compute_proportions, compute_base_rates, create_col_facet_bar_chart, - create_row_facet_bar_chart) + compute_proportions, compute_base_rates, create_col_facet_bar_chart) class MetricsInteractiveVisualizer: @@ -423,7 +422,6 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura # Find metric values for each model based on metric, subgroup, and model names. # Add the values to a results dict. results = {} - num_models = len(model_names) for metric in metrics_lst: # Add an overall metric subgroup_metric = metric diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index f8b51640..32a3a9b2 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -267,7 +267,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ num_ranks = int(sorted_matrix_by_rank.values.max()) fig = plt.figure(figsize=(matrix_width, matrix_height)) - rank_colors = sns.color_palette("coolwarm", n_colors=num_ranks).as_hex() + rank_colors = sns.color_palette("coolwarm_r", n_colors=num_ranks).as_hex() # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a coolwarm color scheme reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1 ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix.round(3), cmap=rank_colors, @@ -281,11 +281,11 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ cbar = ax.collections[0].colorbar model_ranks = [idx + 1 for idx in range(num_ranks)] cbar.set_ticks([float(idx) * -1 for idx in model_ranks]) - tick_labels = [str(idx) for idx in model_ranks] - tick_labels[0] = tick_labels[0] + ', best' - tick_labels[-1] = tick_labels[-1] + ', worst' + tick_labels = ['' for _ in model_ranks] + if len(tick_labels) > 1: + tick_labels[0] = 'Best' + tick_labels[-1] = 'Worst' cbar.set_ticklabels(tick_labels, fontsize=16 + font_increase) - cbar.set_label('Model Ranks', fontsize=18 + font_increase) return fig, ax From 7e5714e4d5c622ac3d16943f1db43faeb4f2a1c5 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 14 Oct 2023 18:23:17 +0300 Subject: [PATCH 025/148] Added gc collect and fixed metrics computation issue for a correct/incorrect groups --- lib_base_packages.txt | 3 ++- requirements.txt | 3 ++- .../abstract_overall_variance_analyzer.py | 4 ++++ virny/analyzers/abstract_subgroup_analyzer.py | 17 ++++++++++++++++- .../analyzers/subgroup_variance_calculator.py | 19 +++++++++++++++++-- 5 files changed, 41 insertions(+), 5 deletions(-) diff --git a/lib_base_packages.txt b/lib_base_packages.txt index 830ceeb0..10cbab0f 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -11,4 +11,5 @@ munch~=2.5.0 PyYAML~=6.0 river==0.15.0 datapane~=0.16.0 -requests-toolbelt==1.0.0 \ No newline at end of file +requests-toolbelt==1.0.0 +colorama~=0.4.6 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 12b6aa38..0b5c8ab7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,4 +17,5 @@ python-dotenv~=1.0.0 pytest~=7.2.2 pymongo==4.3.3 datapane~=0.16.0 -requests-toolbelt==1.0.0 \ No newline at end of file +requests-toolbelt==1.0.0 +colorama~=0.4.6 \ No newline at end of file diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 93434d8b..af154605 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -1,4 +1,5 @@ import os +import gc import numpy as np import pandas as pd @@ -178,6 +179,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b classifier = self._fit_model(classifier, X_sample, y_sample) models_predictions[idx] = self._batch_predict_proba(classifier, self.X_test) self.models_lst[idx] = classifier + # Force garbage collection to avoid out of memory error + if with_fit and ((idx + 1) % 10 == 0 or (idx + 1) == self.n_estimators): + gc.collect() if self._verbose >= 1: print('\n', flush=True) diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index a47f19fa..65eefc21 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -1,6 +1,7 @@ import os import pandas as pd +from colorama import Fore from datetime import datetime, timezone from abc import ABCMeta, abstractmethod @@ -70,7 +71,21 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, results: di # Compute metrics for each group partition for group_partition_name, partition_indexes in partition_indexes_dct.items(): - metrics_dct = self._compute_metrics(self.y_test[partition_indexes], y_preds[partition_indexes]) + if partition_indexes.shape[0] == 0: + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET) + metrics_dct = { + 'TPR': None, + 'TNR': None, + 'PPV': None, + 'FNR': None, + 'FPR': None, + 'Accuracy': None, + 'F1': None, + 'Selection-Rate': None, + 'Positive-Rate': None, + } + else: + metrics_dct = self._compute_metrics(self.y_test[partition_indexes], y_preds[partition_indexes]) metrics_dct['Sample_Size'] = len(partition_indexes) results[group_partition_name] = metrics_dct diff --git a/virny/analyzers/subgroup_variance_calculator.py b/virny/analyzers/subgroup_variance_calculator.py index 2294a845..0d90e757 100644 --- a/virny/analyzers/subgroup_variance_calculator.py +++ b/virny/analyzers/subgroup_variance_calculator.py @@ -1,5 +1,6 @@ import numpy as np import pandas as pd +from colorama import Fore from virny.configs.constants import ComputationMode from virny.utils.stability_utils import count_prediction_stats, combine_bootstrap_predictions @@ -80,8 +81,22 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, model_idx: models_predictions[model_idx][partition_indexes].reset_index(drop=True) for model_idx in models_predictions.keys() } - metrics_dct = self._compute_metrics(self.y_test[partition_indexes].reset_index(drop=True), - group_models_predictions) + if partition_indexes.shape[0] == 0: + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET) + metrics_dct = { + 'Jitter': None, + 'Mean': None, + 'Std': None, + 'IQR': None, + 'Aleatoric_Uncertainty': None, + 'Overall_Uncertainty': None, + 'Statistical_Bias': None, + 'Per_Sample_Accuracy': None, + 'Label_Stability': None, + } + else: + metrics_dct = self._compute_metrics(self.y_test[partition_indexes].reset_index(drop=True), + group_models_predictions) results[group_partition_name] = metrics_dct return results From 0ee929a0dd2b7a69e34331a20da95111f85d083e Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 14 Oct 2023 23:49:20 +0300 Subject: [PATCH 026/148] Added flushing for warning prints --- virny/analyzers/abstract_subgroup_analyzer.py | 2 +- virny/analyzers/subgroup_variance_calculator.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 65eefc21..1a753074 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -72,7 +72,7 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, results: di # Compute metrics for each group partition for group_partition_name, partition_indexes in partition_indexes_dct.items(): if partition_indexes.shape[0] == 0: - print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET) + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET, flush=True) metrics_dct = { 'TPR': None, 'TNR': None, diff --git a/virny/analyzers/subgroup_variance_calculator.py b/virny/analyzers/subgroup_variance_calculator.py index 0d90e757..ac37cc59 100644 --- a/virny/analyzers/subgroup_variance_calculator.py +++ b/virny/analyzers/subgroup_variance_calculator.py @@ -82,7 +82,7 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, for model_idx in models_predictions.keys() } if partition_indexes.shape[0] == 0: - print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET) + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) metrics_dct = { 'Jitter': None, 'Mean': None, From 5130cceb99ca0ef39bada8ae528c3b8d24f9bf57 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 18 Oct 2023 14:00:19 +0300 Subject: [PATCH 027/148] Fixed a bug with group partitioning for extra test sets --- virny/user_interfaces/metrics_computation_interfaces.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index 21631e66..f1de523d 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -502,7 +502,8 @@ def compute_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, dataset BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. extra_test_sets_lst - List of extra test sets like [(X_test1, y_test1), (X_test2, y_test2), ...] to compute metrics + List of extra test sets like [(X_test1, y_test1, init_features_df1), (X_test2, y_test2, init_features_df2), ...] + to compute metrics. bootstrap_fraction Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap sensitive_attributes_dct @@ -534,10 +535,10 @@ def compute_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, computation_mode=computation_mode, verbose=verbose) - test_sets_lst = [(dataset.X_test, dataset.y_test)] + extra_test_sets_lst + test_sets_lst = [(dataset.X_test, dataset.y_test, dataset.init_features_df)] + extra_test_sets_lst all_test_sets_metrics_lst = [] - for set_idx, (new_X_test, new_y_test) in enumerate(test_sets_lst): - new_test_protected_groups = create_test_protected_groups(new_X_test, dataset.init_features_df, sensitive_attributes_dct) + for set_idx, (new_X_test, new_y_test, cur_init_features_df) in enumerate(test_sets_lst): + new_test_protected_groups = create_test_protected_groups(new_X_test, cur_init_features_df, sensitive_attributes_dct) if verbose >= 2: print(f'\nProtected groups splits for test set index #{set_idx}:') for g in new_test_protected_groups.keys(): From a32cba9dc9b96e6605900348369fd58f1b694c38 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 01:02:31 +0300 Subject: [PATCH 028/148] Created functions for each separate metric --- .../abstract_overall_variance_analyzer.py | 106 +--------------- virny/analyzers/subgroup_variance_analyzer.py | 10 +- .../analyzers/subgroup_variance_calculator.py | 32 ++--- virny/configs/constants.py | 16 --- virny/metrics/__init__.py | 102 +++++++++++++--- virny/metrics/accuracy_metrics.py | 33 +++++ virny/metrics/stability_metrics.py | 113 ++++-------------- virny/metrics/uncertainty_metrics.py | 38 ++++++ .../metrics_computation_interfaces.py | 5 +- virny/utils/__init__.py | 4 +- virny/utils/common_helpers.py | 2 +- virny/utils/data_viz_utils.py | 68 ----------- virny/utils/stability_utils.py | 95 ++++----------- 13 files changed, 226 insertions(+), 398 deletions(-) create mode 100644 virny/metrics/accuracy_metrics.py create mode 100644 virny/metrics/uncertainty_metrics.py diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index af154605..67edffb5 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -1,6 +1,5 @@ import os import gc -import numpy as np import pandas as pd from copy import deepcopy @@ -8,9 +7,8 @@ from abc import ABCMeta, abstractmethod from virny.custom_classes.custom_logger import get_logger -from virny.utils.data_viz_utils import plot_generic from virny.utils.stability_utils import generate_bootstrap -from virny.utils.stability_utils import count_prediction_stats, compute_std_mean_iqr_metrics +from virny.utils.stability_utils import count_prediction_metrics class AbstractOverallVarianceAnalyzer(metaclass=ABCMeta): @@ -53,6 +51,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.n_estimators = n_estimators self.models_lst = [deepcopy(base_model) for _ in range(n_estimators)] self.models_predictions = None + self.prediction_metrics = None self._verbose = verbose self.__logger = get_logger(verbose) @@ -62,17 +61,6 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.X_test = X_test self.y_test = y_test - # Metrics - self.mean = None - self.std = None - self.iqr = None - self.aleatoric_uncertainty = None - self.overall_uncertainty = None - self.statistical_bias = None - self.jitter = None - self.per_sample_accuracy = None - self.label_stability = None - @abstractmethod def _fit_model(self, classifier, X_train, y_train): pass @@ -85,14 +73,12 @@ def _batch_predict(self, classifier, X_test): def _batch_predict_proba(self, classifier, X_test): pass - def compute_metrics(self, make_plots: bool = False, save_results: bool = True, with_fit: bool = True): + def compute_metrics(self, save_results: bool = True, with_fit: bool = True): """ Measure metrics for the base model. Display plots for analysis if needed. Save results to a .pkl file Parameters ---------- - make_plots - bool, if to display plots for analysis save_results If to save result metrics in a file with_fit @@ -104,41 +90,9 @@ def compute_metrics(self, make_plots: bool = False, save_results: bool = True, w self.models_predictions = self.UQ_by_boostrap(boostrap_size, with_replacement=True, with_fit=with_fit) # Count metrics based on prediction proba results - y_preds, uq_labels, prediction_stats = count_prediction_stats(self.y_test.values, self.models_predictions) + y_preds, self.prediction_metrics = count_prediction_metrics(self.y_test.values, self.models_predictions) self.__logger.info(f'Successfully computed predict proba metrics') - self.__update_metrics(means_lst=prediction_stats.means_lst, - stds_lst=prediction_stats.stds_lst, - iqr_lst=prediction_stats.iqr_lst, - mean_ensemble_entropy_lst=prediction_stats.mean_ensemble_entropy_lst, - overall_entropy_lst=prediction_stats.overall_entropy_lst, - statistical_bias_lst=prediction_stats.statistical_bias_lst, - jitter=prediction_stats.jitter, - per_sample_accuracy_lst=prediction_stats.per_sample_accuracy_lst, - label_stability_lst=prediction_stats.label_stability_lst) - - # Display plots if needed - if make_plots: - self.print_metrics() - - # Count metrics based on label predictions to visualize plots - labels_means_lst, labels_stds_lst, labels_iqr_lst = compute_std_mean_iqr_metrics(uq_labels) - - self.__logger.info(f'Successfully computed predict labels metrics') - per_sample_accuracy_lst = prediction_stats.per_sample_accuracy_lst - label_stability_lst = prediction_stats.label_stability_lst - - plot_generic(labels_means_lst, labels_stds_lst, "Mean of probability", "Standard deviation", x_lim=1.01, - y_lim=0.5, plot_title="Probability mean vs Standard deviation") - plot_generic(labels_stds_lst, label_stability_lst, "Standard deviation", "Label stability", x_lim=0.5, - y_lim=1.01, plot_title="Standard deviation vs Label stability") - plot_generic(labels_means_lst, label_stability_lst, "Mean", "Label stability", x_lim=1.01, y_lim=1.01, - plot_title="Mean vs Label stability") - plot_generic(per_sample_accuracy_lst, labels_stds_lst, "Accuracy", "Standard deviation", x_lim=1.01, - y_lim=0.5, plot_title="Accuracy vs Standard deviation") - plot_generic(per_sample_accuracy_lst, labels_iqr_lst, "Accuracy", "Inter quantile range", x_lim=1.01, - y_lim=1.01, plot_title="Accuracy vs Inter quantile range") - if save_results: self.save_metrics_to_file() else: @@ -189,65 +143,17 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b return models_predictions - def __update_metrics(self, means_lst, stds_lst, iqr_lst, mean_ensemble_entropy_lst, overall_entropy_lst, - statistical_bias_lst, jitter, per_sample_accuracy_lst, label_stability_lst): - self.mean = np.mean(means_lst) - self.std = np.mean(stds_lst) - self.iqr = np.mean(iqr_lst) - self.aleatoric_uncertainty = np.mean(mean_ensemble_entropy_lst) - self.overall_uncertainty = np.mean(overall_entropy_lst) - self.statistical_bias = np.mean(statistical_bias_lst) - self.jitter = jitter - self.per_sample_accuracy = np.mean(per_sample_accuracy_lst) - self.label_stability = np.mean(label_stability_lst) - - def print_metrics(self): - precision = 4 - print('\n') - print("#" * 30, " Stability metrics ", "#" * 30) - print(f'Mean: {np.round(self.mean, precision)}\n' - f'Std: {np.round(self.std, precision)}\n' - f'IQR: {np.round(self.iqr, precision)}\n' - f'Aleatoric uncertainty: {np.round(self.aleatoric_uncertainty, precision)}\n' - f'Overall uncertainty: {np.round(self.overall_uncertainty, precision)}\n' - f'Statistical bias: {np.round(self.statistical_bias, precision)}\n' - f'Jitter: {np.round(self.jitter, precision)}\n' - f'Per sample accuracy: {np.round(self.per_sample_accuracy, precision)}\n' - f'Label stability: {np.round(self.label_stability, precision)}\n\n') - - def get_metrics_dict(self): - return { - 'Mean': self.mean, - 'Std': self.std, - 'IQR': self.iqr, - 'Aleatoric_Uncertainty': self.aleatoric_uncertainty, - 'Overall_Uncertainty': self.overall_uncertainty, - 'Statistical_Bias': self.statistical_bias, - 'Jitter': self.jitter, - 'Per_Sample_Accuracy': self.per_sample_accuracy, - 'Label_Stability': self.label_stability, - } - def save_metrics_to_file(self): metrics_to_report = dict() metrics_to_report['Dataset_Name'] = [self.dataset_name] metrics_to_report['Base_Model_Name'] = [self.base_model_name] metrics_to_report['N_Estimators'] = [self.n_estimators] - metrics_to_report['Mean'] = [self.mean] - metrics_to_report['Std'] = [self.std] - metrics_to_report['IQR'] = [self.iqr] - metrics_to_report['Aleatoric_Uncertainty'] = [self.aleatoric_uncertainty] - metrics_to_report['Overall_Uncertainty'] = [self.overall_uncertainty] - metrics_to_report['Statistical_Bias'] = [self.statistical_bias] - metrics_to_report['Jitter'] = [self.jitter] - metrics_to_report['Per_Sample_Accuracy'] = [self.per_sample_accuracy] - metrics_to_report['Label_Stability'] = [self.label_stability] + for metric in self.prediction_metrics: + metrics_to_report[metric] = self.prediction_metrics[metric] metrics_df = pd.DataFrame(metrics_to_report) - dir_path = os.path.join('..', '..', 'results', 'models_stability_metrics') os.makedirs(dir_path, exist_ok=True) - filename = f"{self.dataset_name}_{self.n_estimators}_estimators_{self.base_model_name}_base_model_stability_metrics.csv" metrics_df.to_csv(f'{dir_path}/{filename}', index=False) diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 84ecd846..9d7b9269 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -92,8 +92,8 @@ def set_test_sets(self, new_X_test, new_y_test): def set_test_protected_groups(self, new_test_protected_groups): self.__subgroup_variance_calculator.test_protected_groups = new_test_protected_groups - def compute_metrics(self, save_results: bool, result_filename: str = None, save_dir_path: str = None, - make_plots: bool = True, with_fit: bool = True): + def compute_metrics(self, save_results: bool, result_filename: str = None, + save_dir_path: str = None, with_fit: bool = True): """ Measure variance metrics for subgroups for the base model. Display variance plots for analysis if needed. Save results to a .csv file if needed. @@ -108,14 +108,12 @@ def compute_metrics(self, save_results: bool, result_filename: str = None, save_ [Optional] Filename for results to save save_dir_path [Optional] Location where to save the results file - make_plots - If to display plots for analysis with_fit If to fit estimators in bootstrap """ - y_preds, y_test_true = self.__overall_variance_analyzer.compute_metrics(make_plots, save_results=False, with_fit=with_fit) - self.overall_variance_metrics_dct = self.__overall_variance_analyzer.get_metrics_dict() + y_preds, y_test_true = self.__overall_variance_analyzer.compute_metrics(save_results=False, with_fit=with_fit) + self.overall_variance_metrics_dct = self.__overall_variance_analyzer.prediction_metrics # Count and display fairness metrics self.__subgroup_variance_calculator.set_overall_variance_metrics(self.overall_variance_metrics_dct) diff --git a/virny/analyzers/subgroup_variance_calculator.py b/virny/analyzers/subgroup_variance_calculator.py index ac37cc59..59a831bd 100644 --- a/virny/analyzers/subgroup_variance_calculator.py +++ b/virny/analyzers/subgroup_variance_calculator.py @@ -1,9 +1,9 @@ -import numpy as np import pandas as pd from colorama import Fore +from virny.metrics import METRIC_TO_FUNCTION from virny.configs.constants import ComputationMode -from virny.utils.stability_utils import count_prediction_stats, combine_bootstrap_predictions +from virny.utils.stability_utils import count_prediction_metrics, combine_bootstrap_predictions from virny.analyzers.abstract_subgroup_analyzer import AbstractSubgroupAnalyzer @@ -83,17 +83,9 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, } if partition_indexes.shape[0] == 0: print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) - metrics_dct = { - 'Jitter': None, - 'Mean': None, - 'Std': None, - 'IQR': None, - 'Aleatoric_Uncertainty': None, - 'Overall_Uncertainty': None, - 'Statistical_Bias': None, - 'Per_Sample_Accuracy': None, - 'Label_Stability': None, - } + metrics_dct = dict() + for metric in METRIC_TO_FUNCTION.keys(): + metrics_dct[metric] = None else: metrics_dct = self._compute_metrics(self.y_test[partition_indexes].reset_index(drop=True), group_models_predictions) @@ -102,18 +94,8 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, return results def _compute_metrics(self, y_test: pd.DataFrame, group_models_predictions): - _, _, prediction_stats = count_prediction_stats(y_test, group_models_predictions) - return { - 'Jitter': prediction_stats.jitter, - 'Mean': np.mean(prediction_stats.means_lst), - 'Std': np.mean(prediction_stats.stds_lst), - 'IQR': np.mean(prediction_stats.iqr_lst), - 'Aleatoric_Uncertainty': np.mean(prediction_stats.mean_ensemble_entropy_lst), - 'Overall_Uncertainty': np.mean(prediction_stats.overall_entropy_lst), - 'Statistical_Bias': np.mean(prediction_stats.statistical_bias_lst), - 'Per_Sample_Accuracy': np.mean(prediction_stats.per_sample_accuracy_lst), - 'Label_Stability': np.mean(prediction_stats.label_stability_lst), - } + _, prediction_metrics = count_prediction_metrics(y_test, group_models_predictions) + return prediction_metrics def compute_subgroup_metrics(self, models_predictions: dict, save_results: bool, result_filename: str = None, save_dir_path: str = None): diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 76b2c8c5..e6228b36 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -1,20 +1,4 @@ -import numpy as np - from enum import Enum -from dataclasses import dataclass - - -@dataclass -class CountPredictionStatsResponse: - jitter: float - means_lst: list - stds_lst: list - iqr_lst: list - mean_ensemble_entropy_lst: list - overall_entropy_lst: np.ndarray - statistical_bias_lst: np.ndarray - per_sample_accuracy_lst: list - label_stability_lst: list class ModelSetting(Enum): diff --git a/virny/metrics/__init__.py b/virny/metrics/__init__.py index 6f165bd1..e08a4ced 100644 --- a/virny/metrics/__init__.py +++ b/virny/metrics/__init__.py @@ -1,23 +1,97 @@ """ This module contains functions for computing subgroup variance and error metrics. """ +from .accuracy_metrics import ( + mean_prediction, + statistical_bias_from_predict_proba, + statistical_bias +) from .stability_metrics import ( - compute_std_mean_iqr_metrics, - compute_churn, - compute_jitter, - compute_entropy_from_predicted_probability, - compute_conf_interval, - compute_std_mean_iqr_metrics, - compute_per_sample_accuracy, + std, + iqr, + churn, + jitter, + per_sample_label_stability, + label_stability +) +from .uncertainty_metrics import ( + entropy_from_predicted_probability, + conf_interval, + aleatoric_uncertainty, + overall_uncertainty, ) +# Accuracy metrics +MEAN_PREDICTION = 'Mean_Prediction' +STATISTICAL_BIAS = 'Statistical_Bias' + +# Stability metrics +STD = 'Std' +IQR = 'IQR' +JITTER = 'Jitter' +LABEL_STABILITY = 'Label_Stability' + +# Uncertainty metrics +ALEATORIC_UNCERTAINTY = 'Aleatoric_Uncertainty' +OVERALL_UNCERTAINTY = 'Overall_Uncertainty' + +# Error disparity metrics +EQUALIZED_ODDS_TPR = 'Equalized_Odds_TPR' +EQUALIZED_ODDS_TNR = 'Equalized_Odds_TNR' +EQUALIZED_ODDS_FPR = 'Equalized_Odds_FPR' +EQUALIZED_ODDS_FNR = 'Equalized_Odds_FNR' +DISPARATE_IMPACT = 'Disparate_Impact' +STATISTICAL_PARITY_DIFFERENCE = 'Statistical_Parity_Difference' +ACCURACY_PARITY = 'Accuracy_Parity' + +# Stability disparity metrics +LABEL_STABILITY_RATIO = 'Label_Stability_Ratio' +IQR_PARITY = 'IQR_Parity' +STD_PARITY = 'Std_Parity' +STD_RATIO = 'Std_Ratio' +JITTER_PARITY = 'Jitter_Parity' + +# Uncertainty disparity metrics +OVERALL_UNCERTAINTY_PARITY = 'Overall_Uncertainty_Parity' +OVERALL_UNCERTAINTY_RATIO = 'Overall_Uncertainty_Ratio' +ALEATORIC_UNCERTAINTY_PARITY = 'Aleatoric_Uncertainty_Parity' +ALEATORIC_UNCERTAINTY_RATIO = 'Aleatoric_Uncertainty_Ratio' + +METRIC_TO_FUNCTION = { + # Accuracy metrics + MEAN_PREDICTION: mean_prediction, + STATISTICAL_BIAS: statistical_bias, + # Stability metrics + STD: std, + IQR: iqr, + JITTER: jitter, + LABEL_STABILITY: label_stability, + # Uncertainty metrics + ALEATORIC_UNCERTAINTY: aleatoric_uncertainty, + OVERALL_UNCERTAINTY: overall_uncertainty, +} + +METRICS_FOR_PREDICT_PROBA = {MEAN_PREDICTION, STATISTICAL_BIAS, + STD, IQR, + ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY} +METRICS_FOR_LABELS = set([metric for metric in METRIC_TO_FUNCTION.keys() if metric not in METRICS_FOR_PREDICT_PROBA]) + __all__ = [ - "compute_std_mean_iqr_metrics", - "compute_churn", - "compute_jitter", - "compute_entropy_from_predicted_probability", - "compute_conf_interval", - "compute_std_mean_iqr_metrics", - "compute_per_sample_accuracy", + "mean_prediction", + "statistical_bias_from_predict_proba", + "statistical_bias", + "std", + "iqr", + "churn", + "jitter", + "per_sample_label_stability", + "label_stability", + "entropy_from_predicted_probability", + "conf_interval", + "aleatoric_uncertainty", + "overall_uncertainty", + "METRIC_TO_FUNCTION", + "METRICS_FOR_PREDICT_PROBA", + "METRICS_FOR_LABELS" ] diff --git a/virny/metrics/accuracy_metrics.py b/virny/metrics/accuracy_metrics.py new file mode 100644 index 00000000..3cbeec6e --- /dev/null +++ b/virny/metrics/accuracy_metrics.py @@ -0,0 +1,33 @@ +import numpy as np +import pandas as pd + + +def mean_prediction(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: + return np.mean(uq_predict_probas.mean().values) + + +def statistical_bias_from_predict_proba(x, y_true): + """ + Compute statistical bias from predicted probability + + Parameters + ---------- + x + Probability of 0 class + y_true + True label + + """ + # If x (main prediction) = 0.4, then expected value = 0 * 0.4 + 1 * (1 - 0.4) = 0.6. + # For true label = 0, we get bias = abs(0 - 0.6) = 0.6. + # For true label = 1, we get bias = abs(1 - 0.6) = 0.4. + expected_val = 0 * x + 1 * (1 - x) + return abs(y_true - expected_val) + + +def statistical_bias(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: + main_predictions = uq_predict_probas.mean().values + statistical_bias_lst = np.array( + [statistical_bias_from_predict_proba(x, y_true) for x, y_true in np.column_stack((main_predictions, y_true))] + ) + return np.mean(statistical_bias_lst) diff --git a/virny/metrics/stability_metrics.py b/virny/metrics/stability_metrics.py index b9c255a6..fcc28da6 100644 --- a/virny/metrics/stability_metrics.py +++ b/virny/metrics/stability_metrics.py @@ -4,24 +4,15 @@ import scipy as sp -def compute_label_stability(predicted_labels: list): - """ - Label stability is defined as the absolute difference between the number of times the sample is classified as 0 and 1. - If the absolute difference is large, the label is more stable. - If the difference is exactly zero, then it's extremely unstable --- equally likely to be classified as 0 or 1. - - Parameters - ---------- - predicted_labels +def std(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: + return np.mean(uq_predict_probas.std().values) - """ - count_pos = sum(predicted_labels) - count_neg = len(predicted_labels) - count_pos - return np.abs(count_pos - count_neg) / len(predicted_labels) +def iqr(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: + return np.mean(sp.stats.iqr(uq_predict_probas, axis=0)) -def compute_churn(predicted_labels_1: list, predicted_labels_2: list): +def churn(predicted_labels_1: list, predicted_labels_2: list): """ Pairwise stability metric for two model predictions. @@ -36,7 +27,7 @@ def compute_churn(predicted_labels_1: list, predicted_labels_2: list): for i in range(len(predicted_labels_1))]) / len(predicted_labels_1) -def compute_jitter(models_prediction_labels): +def jitter(y_true: pd.DataFrame, uq_labels: pd.DataFrame) -> float: """ Jitter is a stability metric that shows how the base model predictions fluctuate. Values closer to 0 -- perfect stability, values closer to 1 -- extremely bad stability. @@ -46,76 +37,34 @@ def compute_jitter(models_prediction_labels): models_prediction_labels """ + models_prediction_labels = uq_labels.values n_models = len(models_prediction_labels) models_idx_lst = [i for i in range(n_models)] churns_sum = 0 for i, j in itertools.combinations(models_idx_lst, 2): - churns_sum += compute_churn(models_prediction_labels[i], models_prediction_labels[j]) + churns_sum += churn(models_prediction_labels[i], models_prediction_labels[j]) return churns_sum / (n_models * (n_models - 1) * 0.5) -def compute_entropy_from_predicted_probability(x): - """ - Compute entropy from predicted probability - - Parameters - ---------- - x - Probability of 0 class - - """ - return sp.stats.entropy([x, 1-x], base=2) - - -def compute_statistical_bias_from_predict_proba(x, y_true): - """ - Compute statistical bias from predicted probability - - Parameters - ---------- - x - Probability of 0 class - y_true - True label - - """ - # If x (main prediction) = 0.4, then expected value = 0 * 0.4 + 1 * (1 - 0.4) = 0.6. - # For true label = 0, we get bias = abs(0 - 0.6) = 0.6. - # For true label = 1, we get bias = abs(1 - 0.6) = 0.4. - expected_val = 0 * x + 1 * (1 - x) - return abs(y_true - expected_val) - - -def compute_conf_interval(labels): +def per_sample_label_stability(predicted_labels: list) -> float: """ - Create 95% confidence interval for population mean weight. - - Parameters - ---------- - labels - - """ - return sp.stats.norm.interval(alpha=0.95, loc=np.mean(labels), scale=sp.stats.sem(labels)) - - -def compute_std_mean_iqr_metrics(results: pd.DataFrame): - """ - Compute mean, standard deviation, and interquartile range metrics. + Label stability is defined as the absolute difference between the number of times the sample is classified as 0 and 1. + If the absolute difference is large, the label is more stable. + If the difference is exactly zero, then it's extremely unstable --- equally likely to be classified as 0 or 1. Parameters ---------- - results + predicted_labels """ - means_lst = results.mean().values - stds_lst = results.std().values - iqr_lst = sp.stats.iqr(results, axis=0) + count_pos = sum(predicted_labels) + count_neg = len(predicted_labels) - count_pos - return means_lst, stds_lst, iqr_lst + return np.abs(count_pos - count_neg) / len(predicted_labels) -def compute_per_sample_accuracy(y_test, results): +def label_stability(y_true: pd.DataFrame, uq_labels: pd.DataFrame) -> float: """ Compute per-sample accuracy for each model predictions. @@ -123,25 +72,15 @@ def compute_per_sample_accuracy(y_test, results): Parameters ---------- - y_test + y_true y test dataset - results - `results` variable from count_prediction_stats() + uq_labels + `uq_labels` variable from count_prediction_metrics() """ - per_sample_predictions = {} - label_stability = [] - per_sample_accuracy = [] - acc = None - for sample in range(len(y_test)): - per_sample_predictions[sample] = [int(x<0.5) for x in results[sample].values] - label_stability.append(compute_label_stability(per_sample_predictions[sample])) - - if y_test[sample] == 1: - acc = np.mean(per_sample_predictions[sample]) - elif y_test[sample] == 0: - acc = 1 - np.mean(per_sample_predictions[sample]) - if acc is not None: - per_sample_accuracy.append(acc) - - return per_sample_accuracy, label_stability + label_stability_lst = [] + for sample in range(len(y_true)): + per_sample_predictions = list(uq_labels[sample].values) + label_stability_lst.append(per_sample_label_stability(per_sample_predictions)) + + return np.mean(label_stability_lst) diff --git a/virny/metrics/uncertainty_metrics.py b/virny/metrics/uncertainty_metrics.py new file mode 100644 index 00000000..6ebfac45 --- /dev/null +++ b/virny/metrics/uncertainty_metrics.py @@ -0,0 +1,38 @@ +import numpy as np +import pandas as pd +import scipy as sp + + +def entropy_from_predicted_probability(x): + """ + Compute entropy from predicted probability + + Parameters + ---------- + x + Probability of 0 class + + """ + return sp.stats.entropy([x, 1-x], base=2) + + +def conf_interval(labels): + """ + Create 95% confidence interval for population mean weight. + + Parameters + ---------- + labels + + """ + return sp.stats.norm.interval(alpha=0.95, loc=np.mean(labels), scale=sp.stats.sem(labels)) + + +def aleatoric_uncertainty(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: + return np.mean(uq_predict_probas.apply(entropy_from_predicted_probability).mean().values) + + +def overall_uncertainty(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: + main_predictions = uq_predict_probas.mean().values + overall_entropy_lst = np.array([entropy_from_predicted_probability(x) for x in main_predictions]) + return np.mean(overall_entropy_lst) diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index f1de523d..cf3c3e48 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -1,5 +1,4 @@ import os -import random import traceback import pandas as pd from river import base @@ -116,8 +115,7 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase verbose=verbose) y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, result_filename=None, - save_dir_path=None, - make_plots=False) + save_dir_path=None) # Compute error metrics for subgroups error_analyzer = SubgroupErrorAnalyzer(X_test=dataset.X_test, @@ -552,7 +550,6 @@ def compute_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, result_filename=None, save_dir_path=None, - make_plots=False, with_fit=True if set_idx == 0 else False) # Compute accuracy metrics for subgroups diff --git a/virny/utils/__init__.py b/virny/utils/__init__.py index 90f5d8a3..d27eeb46 100644 --- a/virny/utils/__init__.py +++ b/virny/utils/__init__.py @@ -2,12 +2,12 @@ Common helpers and utils. """ from .common_helpers import validate_config -from .stability_utils import count_prediction_stats +from .stability_utils import count_prediction_metrics from .protected_groups_partitioning import create_test_protected_groups __all__ = [ "validate_config", "create_test_protected_groups", - "count_prediction_stats", + "count_prediction_metrics", ] diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index dbaac29f..5706fe99 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -94,7 +94,7 @@ def save_metrics_to_file(metrics_df, result_filename, save_dir_path): def confusion_matrix_metrics(y_true, y_preds): - metrics = {} + metrics = dict() TN, FP, FN, TP = confusion_matrix(y_true, y_preds).ravel() metrics['TPR'] = TP/(TP+FN) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index ddf397e0..bb262b3a 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -1,32 +1,5 @@ import os import pandas as pd -import seaborn as sns - -from matplotlib import pyplot as plt - - -def set_size(w,h, ax=None): - """ w, h: width, height in inches """ - if not ax: ax=plt.gca() - l = ax.figure.subplotpars.left - r = ax.figure.subplotpars.right - t = ax.figure.subplotpars.top - b = ax.figure.subplotpars.bottom - figw = float(w)/(r-l) - figh = float(h)/(t-b) - ax.figure.set_size_inches(figw, figh) - - -def plot_generic(x, y, xlabel, ylabel, x_lim, y_lim, plot_title): - sns.set_style("darkgrid") - plt.figure(figsize=(20,10)) - plt.scatter(x, y) - plt.xlim(0, x_lim) - plt.ylim(0, y_lim) - plt.xlabel(xlabel, fontsize=14) - plt.ylabel(ylabel, fontsize=14) - plt.title(plot_title, fontsize=20) - plt.show() def create_average_metrics_df(dataset_name, model_names, metrics_path): @@ -61,44 +34,3 @@ def create_average_metrics_df(dataset_name, model_names, metrics_path): print(f'File with average metrics for {model_name} is created') return models_average_results_dct - - -def visualize_fairness_metrics_for_prediction_metric(models_average_results_dct, x_metric, y_metrics: list): - sns.set_style("darkgrid") - x_lim = 0.5 - y_lim = 0.22 - priv_dis_pairs = [('SEX_RAC1P_priv', 'SEX_RAC1P_dis'), - ('SEX_priv', 'SEX_dis'), - ('RAC1P_priv', 'RAC1P_dis')] - for y_metric in y_metrics: - for fairness_metric_priv, fairness_metric_dis in priv_dis_pairs: - display_fairness_plot(models_average_results_dct, x_metric, y_metric, - fairness_metric_priv, fairness_metric_dis, x_lim, y_lim) - - -def display_fairness_plot(models_average_results_dct, x_metric, y_metric, - fairness_metric_priv, fairness_metric_dis, x_lim, y_lim): - fig, ax = plt.subplots() - set_size(15, 8, ax) - - # List of all markers -- https://matplotlib.org/stable/api/markers_api.html - markers = ['o', '*', '|', '<', '>', '^', 'v', '1', 's', 'x', 'D', 'P', 'H'] - model_names = models_average_results_dct.keys() - shapes = [] - for idx, model_name in enumerate(model_names): - x_val = abs(models_average_results_dct[model_name][fairness_metric_priv].loc[x_metric] - \ - models_average_results_dct[model_name][fairness_metric_dis].loc[x_metric]) - y_val = abs(models_average_results_dct[model_name][fairness_metric_priv].loc[y_metric] - \ - models_average_results_dct[model_name][fairness_metric_dis].loc[y_metric]) - a = ax.scatter(x_val, y_val, marker=markers[idx], s=100) - shapes.append(a) - - plt.axhline(y=0.0, color='r', linestyle='-') - plt.xlabel(f'{x_metric} Difference') - plt.ylabel(f'{y_metric} Difference') - plt.xlim(-0.01, x_lim) - plt.ylim(-0.01, y_lim) - plt.title(f'{fairness_metric_priv}-{fairness_metric_dis} difference for {x_metric} and {y_metric}', fontsize=20) - ax.legend(shapes, model_names, fontsize=12, title='Markers') - - plt.show() diff --git a/virny/utils/stability_utils.py b/virny/utils/stability_utils.py index 24d8db8d..45986087 100644 --- a/virny/utils/stability_utils.py +++ b/virny/utils/stability_utils.py @@ -1,15 +1,7 @@ import numpy as np import pandas as pd -import seaborn as sns -from os import listdir -from os.path import isfile, join -from matplotlib import pyplot as plt - -from virny.configs.constants import CountPredictionStatsResponse -from virny.utils.data_viz_utils import set_size -from virny.metrics.stability_metrics import compute_std_mean_iqr_metrics, compute_entropy_from_predicted_probability,\ - compute_jitter, compute_per_sample_accuracy, compute_statistical_bias_from_predict_proba +from virny.metrics import METRIC_TO_FUNCTION, METRICS_FOR_PREDICT_PROBA, METRICS_FOR_LABELS def combine_bootstrap_predictions(bootstrap_predictions: dict, y_test_indexes: np.ndarray): @@ -37,7 +29,7 @@ def combine_bootstrap_predictions(bootstrap_predictions: dict, y_test_indexes: n return pd.Series(y_preds, index=y_test_indexes) -def count_prediction_stats(y_test, uq_results): +def count_prediction_metrics(y_true, uq_results, with_predict_proba: bool = True): """ Compute means, stds, iqr, entropy, jitter, label stability, and transform predictions to pd.Dataframe. @@ -45,7 +37,7 @@ def count_prediction_stats(y_test, uq_results): Parameters ---------- - y_test + y_true True labels uq_results 2D array of prediction proba for the zero value label by each model @@ -56,35 +48,27 @@ def count_prediction_stats(y_test, uq_results): else: results = pd.DataFrame(uq_results).transpose() - means_lst, stds_lst, iqr_lst = compute_std_mean_iqr_metrics(results) - mean_ensemble_entropy_lst = results.apply(compute_entropy_from_predicted_probability).mean().values + metrics_dct = dict() + # Compute metrics for prediction probabilities + if not with_predict_proba: + uq_labels = results + else: + uq_predict_probas = results + for metric in METRICS_FOR_PREDICT_PROBA: + metrics_dct[metric] = METRIC_TO_FUNCTION[metric](y_true, uq_predict_probas) - # Convert predict proba results of each model to correspondent labels. - # Here we use int(x<0.5) since we use predict_prob()[:, 0] to make predictions. - # Hence, if a value is, for example, 0.3 --> label == 1, 0.6 -- > label == 0 - uq_labels = results.applymap(lambda x: int(x<0.5)) - jitter = compute_jitter(uq_labels.values) + # Convert predict proba results of each model to correspondent labels. + # Here we use int(x<0.5) since we use predict_prob()[:, 0] to make predictions. + # Hence, if a value is, for example, 0.3 --> label == 1, 0.6 -- > label == 0 + uq_labels = results.applymap(lambda x: int(x<0.5)) - main_prediction = results.mean().values - statistical_bias_lst = np.array( - [compute_statistical_bias_from_predict_proba(x, y_true) for x, y_true in np.column_stack((main_prediction, y_test))] - ) - overall_entropy_lst = np.array([compute_entropy_from_predicted_probability(x) for x in main_prediction]) + # Compute metrics for prediction labels + for metric in METRICS_FOR_LABELS: + metrics_dct[metric] = METRIC_TO_FUNCTION[metric](y_true, uq_labels) - y_preds = np.array([int(x<0.5) for x in main_prediction]) + y_preds = np.array([int(x<0.5) for x in results.mean().values]) - per_sample_accuracy_lst, label_stability_lst = compute_per_sample_accuracy(y_test, results) - prediction_stats = CountPredictionStatsResponse(jitter=jitter, - means_lst=means_lst, - stds_lst=stds_lst, - iqr_lst=iqr_lst, - mean_ensemble_entropy_lst=mean_ensemble_entropy_lst, - overall_entropy_lst=overall_entropy_lst, - statistical_bias_lst=statistical_bias_lst, - per_sample_accuracy_lst=per_sample_accuracy_lst, - label_stability_lst=label_stability_lst) - - return y_preds, uq_labels, prediction_stats + return y_preds, metrics_dct def generate_bootstrap(features, labels, boostrap_size, with_replacement=True): @@ -95,42 +79,3 @@ def generate_bootstrap(features, labels, boostrap_size, with_replacement=True): return bootstrap_features, bootstrap_labels else: raise ValueError('Bootstrap samples are not of the size requested') - - -def display_result_plots(results_dir): - sns.set_style("darkgrid") - results = dict() - filenames = [f for f in listdir(results_dir) if isfile(join(results_dir, f))] - - for filename in filenames: - results_df = pd.read_csv(results_dir + filename) - results[f'{results_df.iloc[0]["Base_Model_Name"]}_{results_df.iloc[0]["N_Estimators"]}_estimators'] = results_df - - y_metrics = ['SPD_Race', 'SPD_Sex', 'SPD_Race_Sex', 'EO_Race', 'EO_Sex', 'EO_Race_Sex'] - x_metrics = ['Label_Stability', 'Std'] - for x_metric in x_metrics: - for y_metric in y_metrics: - x_lim = 0.3 if x_metric == 'SD' else 1.0 - display_uncertainty_plot(results, x_metric, y_metric, x_lim) - - -def display_uncertainty_plot(results, x_metric, y_metric, x_lim): - fig, ax = plt.subplots() - set_size(15, 8, ax) - - # List of all markers -- https://matplotlib.org/stable/api/markers_api.html - markers = ['.', 'o', '+', '*', '|', '<', '>', '^', 'v', '1', 's', 'x', 'D', 'P', 'H'] - techniques = results.keys() - shapes = [] - for idx, technique in enumerate(techniques): - a = ax.scatter(results[technique][x_metric], results[technique][y_metric], marker=markers[idx], s=100) - shapes.append(a) - - plt.axhline(y=0.0, color='r', linestyle='-') - plt.xlabel(x_metric) - plt.ylabel(y_metric) - plt.xlim(0, x_lim) - plt.title(f'{x_metric} [{y_metric}]', fontsize=20) - ax.legend(shapes, techniques, fontsize=12, title='Markers') - - plt.show() From 985a483f97b37e39109580f6236c8d3d664f26aa Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 01:36:35 +0300 Subject: [PATCH 029/148] Improved MetricsComposer --- virny/configs/constants.py | 45 +++++++++++++++++++ virny/custom_classes/metrics_composer.py | 57 +++++++++++++++--------- virny/metrics/__init__.py | 38 +--------------- virny/utils/common_helpers.py | 20 ++++----- 4 files changed, 93 insertions(+), 67 deletions(-) diff --git a/virny/configs/constants.py b/virny/configs/constants.py index e6228b36..81d145b2 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -19,3 +19,48 @@ class ReportType(Enum): INTERSECTION_SIGN = '&' MODELS_TUNING_SEED = 42 MODELS_TUNING_TEST_SET_FRACTION = 0.2 + +# Accuracy metrics +MEAN_PREDICTION = 'Mean_Prediction' +STATISTICAL_BIAS = 'Statistical_Bias' +TPR = 'TPR' +TNR = 'TNR' +PPV = 'PPV' +FNR = 'FNR' +FPR = 'FPR' +F1 = 'F1' +ACCURACY = 'Accuracy' +SELECTION_RATE = 'Selection-Rate' +POSITIVE_RATE = 'Positive-Rate' + +# Stability metrics +STD = 'Std' +IQR = 'IQR' +JITTER = 'Jitter' +LABEL_STABILITY = 'Label_Stability' + +# Uncertainty metrics +ALEATORIC_UNCERTAINTY = 'Aleatoric_Uncertainty' +OVERALL_UNCERTAINTY = 'Overall_Uncertainty' + +# Error disparity metrics +EQUALIZED_ODDS_TPR = 'Equalized_Odds_TPR' +EQUALIZED_ODDS_TNR = 'Equalized_Odds_TNR' +EQUALIZED_ODDS_FPR = 'Equalized_Odds_FPR' +EQUALIZED_ODDS_FNR = 'Equalized_Odds_FNR' +DISPARATE_IMPACT = 'Disparate_Impact' +STATISTICAL_PARITY_DIFFERENCE = 'Statistical_Parity_Difference' +ACCURACY_PARITY = 'Accuracy_Parity' + +# Stability disparity metrics +LABEL_STABILITY_RATIO = 'Label_Stability_Ratio' +IQR_PARITY = 'IQR_Parity' +STD_PARITY = 'Std_Parity' +STD_RATIO = 'Std_Ratio' +JITTER_PARITY = 'Jitter_Parity' + +# Uncertainty disparity metrics +OVERALL_UNCERTAINTY_PARITY = 'Overall_Uncertainty_Parity' +OVERALL_UNCERTAINTY_RATIO = 'Overall_Uncertainty_Ratio' +ALEATORIC_UNCERTAINTY_PARITY = 'Aleatoric_Uncertainty_Parity' +ALEATORIC_UNCERTAINTY_RATIO = 'Aleatoric_Uncertainty_Ratio' diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index b18f3866..1b72f1b0 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -1,5 +1,7 @@ import pandas as pd +from virny.configs.constants import * + class MetricsComposer: """ @@ -20,6 +22,34 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict): self.sensitive_attributes_dct = sensitive_attributes_dct self.models_average_metrics_dct = None # will be created in self.compose_metrics() + self.disparity_metric_functions = { + # Error disparity metrics + TPR: [(EQUALIZED_ODDS_TPR, self._difference_operation)], + TNR: [(EQUALIZED_ODDS_TNR, self._difference_operation)], + FPR: [(EQUALIZED_ODDS_FPR, self._difference_operation)], + FNR: [(EQUALIZED_ODDS_FNR, self._difference_operation)], + ACCURACY: [(ACCURACY_PARITY, self._difference_operation)], + POSITIVE_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation), + (DISPARATE_IMPACT, self._ratio_operation)], + # Stability disparity metrics + LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation)], + JITTER: [(JITTER_PARITY, self._difference_operation)], + IQR: [(IQR_PARITY, self._difference_operation)], + STD: [(STD_PARITY, self._difference_operation), + (STD_RATIO, self._ratio_operation)], + # Uncertainty disparity metrics + OVERALL_UNCERTAINTY: [(OVERALL_UNCERTAINTY_PARITY, self._difference_operation, + OVERALL_UNCERTAINTY_RATIO, self._ratio_operation)], + ALEATORIC_UNCERTAINTY: [(ALEATORIC_UNCERTAINTY_PARITY, self._difference_operation), + (ALEATORIC_UNCERTAINTY_RATIO, self._ratio_operation)] + } + + def _difference_operation(self, cfm, metric_name, dis_group, priv_group): + return cfm[dis_group][metric_name] - cfm[priv_group][metric_name] + + def _ratio_operation(self, cfm, metric_name, dis_group, priv_group): + return cfm[dis_group][metric_name] / cfm[priv_group][metric_name] + def compose_metrics(self): """ Compose subgroup metrics from self.model_metrics_df. @@ -39,32 +69,19 @@ def compose_metrics(self): models_composed_metrics_df = pd.DataFrame() for model_name in self.models_average_metrics_dct.keys(): cfm = self.models_average_metrics_dct[model_name] + metric_names = list(cfm['Metric'].unique()) cfm = cfm.set_index('Metric') for sensitive_attr in self.sensitive_attributes_dct.keys(): dis_group = sensitive_attr + '_dis' priv_group = sensitive_attr + '_priv' - groups_metrics_dct[sensitive_attr] = { - # Error disparity metrics - 'Equalized_Odds_TPR': cfm[dis_group]['TPR'] - cfm[priv_group]['TPR'], - 'Equalized_Odds_FPR': cfm[dis_group]['FPR'] - cfm[priv_group]['FPR'], - 'Equalized_Odds_FNR': cfm[dis_group]['FNR'] - cfm[priv_group]['FNR'], - 'Disparate_Impact': cfm[dis_group]['Positive-Rate'] / cfm[priv_group]['Positive-Rate'], - 'Statistical_Parity_Difference': cfm[dis_group]['Positive-Rate'] - cfm[priv_group]['Positive-Rate'], - 'Accuracy_Parity': cfm[dis_group]['Accuracy'] - cfm[priv_group]['Accuracy'], - # Stability disparity metrics - 'Label_Stability_Ratio': cfm[dis_group]['Label_Stability'] / cfm[priv_group]['Label_Stability'], - 'IQR_Parity': cfm[dis_group]['IQR'] - cfm[priv_group]['IQR'], - 'Std_Parity': cfm[dis_group]['Std'] - cfm[priv_group]['Std'], - 'Std_Ratio': cfm[dis_group]['Std'] / cfm[priv_group]['Std'], - 'Jitter_Parity': cfm[dis_group]['Jitter'] - cfm[priv_group]['Jitter'], - # Uncertainty disparity metrics - 'Overall_Uncertainty_Parity': cfm[dis_group]['Overall_Uncertainty'] - cfm[priv_group]['Overall_Uncertainty'], - 'Overall_Uncertainty_Ratio': cfm[dis_group]['Overall_Uncertainty'] / cfm[priv_group]['Overall_Uncertainty'], - 'Aleatoric_Uncertainty_Parity': cfm[dis_group]['Aleatoric_Uncertainty'] - cfm[priv_group]['Aleatoric_Uncertainty'], - 'Aleatoric_Uncertainty_Ratio': cfm[dis_group]['Aleatoric_Uncertainty'] / cfm[priv_group]['Aleatoric_Uncertainty'], - } + groups_metrics_dct[sensitive_attr] = dict() + for metric_name in metric_names: + disparity_metrics = self.disparity_metric_functions[metric_name] + for disparity_metric_name, disparity_metric_func in disparity_metrics: + groups_metrics_dct[sensitive_attr][disparity_metric_name] = ( + disparity_metric_func(cfm, metric_name, dis_group, priv_group)) model_composed_metrics_df = pd.DataFrame(groups_metrics_dct).reset_index() model_composed_metrics_df = model_composed_metrics_df.rename(columns={"index": "Metric"}) diff --git a/virny/metrics/__init__.py b/virny/metrics/__init__.py index e08a4ced..4c8b5044 100644 --- a/virny/metrics/__init__.py +++ b/virny/metrics/__init__.py @@ -1,6 +1,7 @@ """ This module contains functions for computing subgroup variance and error metrics. """ +from virny.configs.constants import * from .accuracy_metrics import ( mean_prediction, statistical_bias_from_predict_proba, @@ -21,43 +22,6 @@ overall_uncertainty, ) - -# Accuracy metrics -MEAN_PREDICTION = 'Mean_Prediction' -STATISTICAL_BIAS = 'Statistical_Bias' - -# Stability metrics -STD = 'Std' -IQR = 'IQR' -JITTER = 'Jitter' -LABEL_STABILITY = 'Label_Stability' - -# Uncertainty metrics -ALEATORIC_UNCERTAINTY = 'Aleatoric_Uncertainty' -OVERALL_UNCERTAINTY = 'Overall_Uncertainty' - -# Error disparity metrics -EQUALIZED_ODDS_TPR = 'Equalized_Odds_TPR' -EQUALIZED_ODDS_TNR = 'Equalized_Odds_TNR' -EQUALIZED_ODDS_FPR = 'Equalized_Odds_FPR' -EQUALIZED_ODDS_FNR = 'Equalized_Odds_FNR' -DISPARATE_IMPACT = 'Disparate_Impact' -STATISTICAL_PARITY_DIFFERENCE = 'Statistical_Parity_Difference' -ACCURACY_PARITY = 'Accuracy_Parity' - -# Stability disparity metrics -LABEL_STABILITY_RATIO = 'Label_Stability_Ratio' -IQR_PARITY = 'IQR_Parity' -STD_PARITY = 'Std_Parity' -STD_RATIO = 'Std_Ratio' -JITTER_PARITY = 'Jitter_Parity' - -# Uncertainty disparity metrics -OVERALL_UNCERTAINTY_PARITY = 'Overall_Uncertainty_Parity' -OVERALL_UNCERTAINTY_RATIO = 'Overall_Uncertainty_Ratio' -ALEATORIC_UNCERTAINTY_PARITY = 'Aleatoric_Uncertainty_Parity' -ALEATORIC_UNCERTAINTY_RATIO = 'Aleatoric_Uncertainty_Ratio' - METRIC_TO_FUNCTION = { # Accuracy metrics MEAN_PREDICTION: mean_prediction, diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index 5706fe99..dc9e81ab 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -4,7 +4,7 @@ from sklearn.metrics import confusion_matrix from river import base -from virny.configs.constants import INTERSECTION_SIGN, ModelSetting, ComputationMode +from virny.configs.constants import * def validate_config(config_obj): @@ -97,14 +97,14 @@ def confusion_matrix_metrics(y_true, y_preds): metrics = dict() TN, FP, FN, TP = confusion_matrix(y_true, y_preds).ravel() - metrics['TPR'] = TP/(TP+FN) - metrics['TNR'] = TN/(TN+FP) - metrics['PPV'] = TP/(TP+FP) - metrics['FNR'] = FN/(FN+TP) - metrics['FPR'] = FP/(FP+TN) - metrics['Accuracy'] = (TP+TN)/(TP+TN+FP+FN) - metrics['F1'] = (2*TP)/(2*TP+FP+FN) - metrics['Selection-Rate'] = (TP+FP)/(TP+FP+TN+FN) - metrics['Positive-Rate'] = (TP+FP)/(TP+FN) + metrics[TPR] = TP/(TP+FN) + metrics[TNR] = TN/(TN+FP) + metrics[PPV] = TP/(TP+FP) + metrics[FNR] = FN/(FN+TP) + metrics[FPR] = FP/(FP+TN) + metrics[ACCURACY] = (TP+TN)/(TP+TN+FP+FN) + metrics[F1] = (2*TP)/(2*TP+FP+FN) + metrics[SELECTION_RATE] = (TP+FP)/(TP+FP+TN+FN) + metrics[POSITIVE_RATE] = (TP+FP)/(TP+FN) return metrics From 8bb2b29e9d367693d736e396918ae363e7a44f29 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 01:58:19 +0300 Subject: [PATCH 030/148] Fixed tests for metrics --- tests/custom_classes/test_metrics_composer.py | 23 ++++++----- tests/utils/test_stability_utils.py | 40 +++++++------------ virny/custom_classes/metrics_composer.py | 4 ++ 3 files changed, 32 insertions(+), 35 deletions(-) diff --git a/tests/custom_classes/test_metrics_composer.py b/tests/custom_classes/test_metrics_composer.py index 6bcdf4d7..c37d21be 100644 --- a/tests/custom_classes/test_metrics_composer.py +++ b/tests/custom_classes/test_metrics_composer.py @@ -4,6 +4,7 @@ from tests import config_params, models_config, ROOT_DIR from virny.utils.custom_initializers import read_model_metric_dfs from virny.custom_classes.metrics_composer import MetricsComposer +from virny.configs.constants import * @pytest.fixture(scope='module') @@ -20,7 +21,7 @@ def test_compose_metrics_true1(models_metrics_dct, config_params): models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape - assert models_composed_metrics_df.shape == (22, 5) + assert models_composed_metrics_df.shape == (24, 5) # Check column names assert sorted(models_composed_metrics_df.columns.tolist()) == sorted(['Metric', 'Model_Name', 'sex', 'race', 'sex&race']) @@ -29,10 +30,11 @@ def test_compose_metrics_true1(models_metrics_dct, config_params): assert sorted(models_composed_metrics_df['Model_Name'].unique().tolist()) == sorted(['DecisionTreeClassifier', 'LogisticRegression']) # Check all metrics presence - assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Equalized_Odds_FNR', - 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity', - 'Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', - 'Std_Ratio', 'Jitter_Parity']) + assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == ( + sorted([EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, + DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, + LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY]) + ) def test_compose_metrics_true2(models_metrics_dct, config_params): @@ -40,7 +42,7 @@ def test_compose_metrics_true2(models_metrics_dct, config_params): models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape - assert models_composed_metrics_df.shape == (22, 4) + assert models_composed_metrics_df.shape == (24, 4) # Check column names assert sorted(models_composed_metrics_df.columns.tolist()) == sorted(['Metric', 'Model_Name', 'sex', 'race']) @@ -49,7 +51,8 @@ def test_compose_metrics_true2(models_metrics_dct, config_params): assert sorted(models_composed_metrics_df['Model_Name'].unique().tolist()) == sorted(['DecisionTreeClassifier', 'LogisticRegression']) # Check all metrics presence - assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Equalized_Odds_FNR', - 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity', - 'Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', - 'Jitter_Parity']) + assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == ( + sorted([EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, + DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, + LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY]) + ) diff --git a/tests/utils/test_stability_utils.py b/tests/utils/test_stability_utils.py index 3296b39d..f0fa66aa 100644 --- a/tests/utils/test_stability_utils.py +++ b/tests/utils/test_stability_utils.py @@ -5,47 +5,37 @@ from sklearn.preprocessing import StandardScaler from tests import config_params, compas_dataset_class, compas_without_sensitive_attrs_dataset_class -from virny.utils.stability_utils import count_prediction_stats, generate_bootstrap +from virny.utils.stability_utils import count_prediction_metrics, generate_bootstrap from virny.preprocessing.basic_preprocessing import preprocess_dataset +from virny.configs.constants import * -# ========================== Test count_prediction_stats ========================== -def test_count_prediction_stats_true1(): +# ========================== Test count_prediction_metrics ========================== +def test_count_prediction_metrics_true1(): y_test = np.array([0, 0, 1, 1, 0, 1, 0, 1, 1, 1]) uq_results = np.array([[0.6, 0.7, 0.3, 0.4, 0.5, 0.3, 0.7, 0.6, 0.4, 0.4], [0.7, 0.6, 0.4, 0.4, 0.5, 0.3, 0.2, 0.6, 0.4, 0.4]]) - y_preds, uq_labels, prediction_stats = count_prediction_stats(y_test, uq_results) - - mean = np.mean(prediction_stats.means_lst) - std = np.mean(prediction_stats.stds_lst) - iqr = np.mean(prediction_stats.iqr_lst) - aleatoric_uncertainty = np.mean(prediction_stats.mean_ensemble_entropy_lst) - overall_uncertainty = np.mean(prediction_stats.overall_entropy_lst) - statistical_bias = np.mean(prediction_stats.statistical_bias_lst) - per_sample_accuracy = np.mean(prediction_stats.per_sample_accuracy_lst) - label_stability = np.mean(prediction_stats.label_stability_lst) + y_preds, prediction_metrics = count_prediction_metrics(y_test, uq_results) assert np.array_equal(y_preds, np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1])) - assert np.array_equal( uq_labels, np.array([[0, 0, 1, 1, 0, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 1, 1, 0, 1, 1]]) ) alpha = 0.000_001 - assert abs(prediction_stats.jitter - 0.1) < alpha - assert abs(mean - 0.47000000000000003) < alpha - assert abs(std - 0.0565685424949238) < alpha - assert abs(iqr - 0.03999999999999998) < alpha - assert abs(aleatoric_uncertainty - 0.9345065014636438) < alpha - assert abs(overall_uncertainty - 0.9560071897163649) < alpha - assert abs(statistical_bias - 0.42000000000000004) < alpha - assert abs(per_sample_accuracy - 0.85) < alpha - assert abs(label_stability - 0.9) < alpha + assert abs(prediction_metrics[MEAN_PREDICTION] - 0.47000000000000003) < alpha + assert abs(prediction_metrics[STATISTICAL_BIAS] - 0.42000000000000004) < alpha + assert abs(prediction_metrics[JITTER] - 0.1) < alpha + assert abs(prediction_metrics[LABEL_STABILITY] - 0.9) < alpha + assert abs(prediction_metrics[STD] - 0.0565685424949238) < alpha + assert abs(prediction_metrics[IQR] - 0.03999999999999998) < alpha + assert abs(prediction_metrics[ALEATORIC_UNCERTAINTY] - 0.9345065014636438) < alpha + assert abs(prediction_metrics[OVERALL_UNCERTAINTY] - 0.9560071897163649) < alpha -def test_count_prediction_stats_true2(): +def test_count_prediction_metrics_true2(): y_test = np.array([0, 0, 1, 1, 0, 1, 0, 1, 1, 1]) uq_results = np.array([[0.6, 0.7, 0.3, 0.4, 0.5, 0.3, 0.7, 0.6, 0.4, 0.4]]) try: - y_preds, uq_labels, prediction_stats = count_prediction_stats(y_test, uq_results) + y_preds, prediction_stats = count_prediction_metrics(y_test, uq_results) actual = True except ZeroDivisionError: actual = False diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index 1b72f1b0..f78b64ed 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -76,8 +76,12 @@ def compose_metrics(self): dis_group = sensitive_attr + '_dis' priv_group = sensitive_attr + '_priv' + # Compute disparity metrics for each metric in cfm groups_metrics_dct[sensitive_attr] = dict() for metric_name in metric_names: + # Skip a metric that does not have correspondent disparity metrics + if metric_name not in self.disparity_metric_functions.keys(): + continue disparity_metrics = self.disparity_metric_functions[metric_name] for disparity_metric_name, disparity_metric_func in disparity_metrics: groups_metrics_dct[sensitive_attr][disparity_metric_name] = ( From f01ac76d1fa593fe417c9303d69d0becafe2dad5 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 02:07:10 +0300 Subject: [PATCH 031/148] Fixed tests for metrics --- docs/examples/experiment_config.yaml | 2 +- ...ssifier_50_Estimators_20231020__225918.csv | 19 +++++++++++++++++++ ...ression_50_Estimators_20231020__225918.csv | 19 +++++++++++++++++++ ...ssifier_50_Estimators_20231020__225918.csv | 19 +++++++++++++++++++ ...ssifier_50_Estimators_20231020__225918.csv | 19 +++++++++++++++++++ ..._Sensitive_Attributes_20231020__225918.csv | 5 +++++ virny/custom_classes/metrics_composer.py | 8 ++++---- 7 files changed, 86 insertions(+), 5 deletions(-) create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 555d36db..44efa1b1 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,5 +1,5 @@ + dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -computation_mode: error_analysis sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv new file mode 100644 index 00000000..e60e46ef --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.41857336172765697,0.41544095906084866,0.41935553564800787,0.4141321622564169,0.42143731278854996,0.4134826288938934,0.42362567393625994,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.0809157217235176,0.07855149605017232,0.08150607866680262,0.0835558343140641,0.07921321921185677,0.08187169522458478,0.0799669631167981,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.8932088655544336,0.899252491044528,0.8916997472367887,0.8852710730922949,0.898327628917869,0.891206327050453,0.8951962905602708,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Std,0.06895833645771993,0.07035364839135345,0.06860992128849308,0.06964657979043051,0.06851451599083176,0.06961900305621098,0.06830265602223631,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.8698426596552785,0.8745972457143654,0.8686554198227732,0.8601037952604418,0.8761228619285847,0.8661276469720228,0.8735296345069627,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.5219352219525485,0.578035221741857,0.5079268196382951,0.5863255907514473,0.48041246076447364,0.5771212289323356,0.4671657131386466,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.8362878787878788,0.8138388625592415,0.8418934911242604,0.8329468599033817,0.838442367601246,0.8321673003802282,0.8403773584905659,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.12457173778602351,0.13906180481671365,0.12095350803043126,0.12282559400571841,0.12569775573780906,0.12488709552261976,0.12425876010781686,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.6539278131634819,0.4666666666666667,0.6893939393939394,0.5170068027210885,0.7160493827160493,0.5425531914893617,0.7279151943462897,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.7282051282051282,0.8014705882352942,0.7060133630289532,0.7827715355805244,0.6823899371069182,0.7840236686390533,0.6518218623481782,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.6595289079229122,0.5645161290322581,0.674074074074074,0.5671641791044776,0.6966966966966966,0.5828571428571429,0.7054794520547946,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.346072186836518,0.5333333333333333,0.3106060606060606,0.48299319727891155,0.2839506172839506,0.4574468085106383,0.27208480565371024,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.2717948717948718,0.19852941176470587,0.29398663697104677,0.21722846441947566,0.31761006289308175,0.21597633136094674,0.3481781376518219,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.6950757575757576,0.6824644549763034,0.6982248520710059,0.6884057971014492,0.6993769470404985,0.6977186311787072,0.6924528301886792,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.6567164179104478,0.5109489051094891,0.6816479400749064,0.5409252669039146,0.7062404870624048,0.5619834710743802,0.7165217391304348,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.4422348484848485,0.2938388625592417,0.47928994082840237,0.32367149758454106,0.5186915887850467,0.33269961977186313,0.5509433962264151,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,0.9915074309978769,0.8266666666666667,1.0227272727272727,0.9115646258503401,1.0277777777777777,0.9308510638297872,1.0318021201413428,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,1056.0,,,,,,,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv new file mode 100644 index 00000000..deffd07d --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.4337924538236714,0.4334044278876343,0.43388934550710784,0.4332653534138835,0.43413235969540376,0.4321642605515456,0.4354083588446866,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.029339925841114185,0.030179801866657946,0.02913020531875947,0.029591627125389396,0.029177613797983448,0.029793187368398956,0.028890085155544767,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.9086195955884198,0.9172087513975933,0.9064748478064842,0.9136998763207738,0.9053435267049392,0.9161672806782127,0.9011288741596818,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.02098879327073452,0.0211390182011289,0.020951281483381604,0.0208418614479099,0.021083543698537315,0.020991752212652995,0.02098585666045317,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.9062620824072268,0.9151648943470244,0.9040390133903071,0.9116550369785078,0.9027843827304198,0.9140803395738709,0.8985028309550482,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.5205499814176662,0.5688705122602331,0.5084841447220666,0.5864075588940767,0.4780810763160556,0.5746925557113814,0.46681603032616764,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9392424242424243,0.9107109004739338,0.9463668639053254,0.9438647342995169,0.9362616822429906,0.9340684410646389,0.944377358490566,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.04616419294990723,0.0661533997485251,0.04117280521676134,0.041646455683722695,0.049077500158942126,0.04895941646620646,0.04339006546014642,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.6263269639065817,0.48,0.6540404040404041,0.4421768707482993,0.7098765432098766,0.48404255319148937,0.7208480565371025,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.7316239316239316,0.8088235294117647,0.7082405345211581,0.8164794007490637,0.660377358490566,0.8106508875739645,0.6234817813765182,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.6526548672566371,0.5806451612903226,0.6641025641025641,0.5701754385964912,0.6804733727810651,0.5870967741935483,0.6868686868686869,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.37367303609341823,0.52,0.34595959595959597,0.5578231292517006,0.29012345679012347,0.5159574468085106,0.2791519434628975,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.26837606837606837,0.19117647058823528,0.29175946547884185,0.18352059925093633,0.33962264150943394,0.1893491124260355,0.3765182186234818,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.6846590909090909,0.6919431279620853,0.6828402366863905,0.6835748792270532,0.6853582554517134,0.6939163498098859,0.6754716981132075,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.6392199349945829,0.5255474452554745,0.6590330788804071,0.49808429118773945,0.6948640483383686,0.5306122448979592,0.7034482758620689,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.42803030303030304,0.2938388625592417,0.46153846153846156,0.2753623188405797,0.5264797507788161,0.2946768060836502,0.560377358490566,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9596602972399151,0.8266666666666667,0.9848484848484849,0.7755102040816326,1.0432098765432098,0.824468085106383,1.0494699646643109,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,,,,,,,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv new file mode 100644 index 00000000..ecdb31df --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.4045419853002584,0.3968153482379772,0.40647135857853217,0.39516480373937674,0.4105889528488644,0.39746748802251475,0.41156309014571724,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.0909045004339339,0.10506143348256215,0.08736945561350722,0.09382085893269923,0.08902385803753385,0.09377805447614175,0.08805263359204463,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.8585954641369864,0.8776478578326691,0.8538380025159342,0.8493126327662205,0.86458158903963,0.8589413874089237,0.858252151606724,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.06813377974355897,0.07491127500458802,0.06644141110441444,0.07034178982046609,0.0667099227780768,0.06980083623796854,0.06647930480759777,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.8341600028048252,0.8464502802805608,0.8310910696126592,0.8223464618313945,0.8417780806288131,0.8325086424925195,0.8357989000204338,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.5216518784554963,0.5761649548269147,0.5080397374917457,0.5945323244703573,0.47465420766086625,0.58373426466956,0.4600380385524821,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.8373484848484849,0.7948815165876778,0.8479526627218935,0.8305314009661836,0.8417445482866042,0.8317110266159696,0.8429433962264151,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.11274737167594309,0.1403772124963732,0.10584808597995415,0.11637188208616779,0.1104100705702843,0.1168945448901996,0.10863149788217191,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.6751592356687898,0.6,0.6893939393939394,0.5714285714285714,0.7222222222222222,0.5851063829787234,0.734982332155477,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.7350427350427351,0.7941176470588235,0.7171492204899778,0.8052434456928839,0.6761006289308176,0.7988165680473372,0.6477732793522267,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.6723044397463002,0.6164383561643836,0.6825,0.6176470588235294,0.6943620178041543,0.6179775280898876,0.7050847457627119,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.3248407643312102,0.4,0.3106060606060606,0.42857142857142855,0.2777777777777778,0.4148936170212766,0.26501766784452296,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.26495726495726496,0.20588235294117646,0.2828507795100223,0.1947565543071161,0.3238993710691824,0.20118343195266272,0.3522267206477733,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.7083333333333334,0.7251184834123223,0.7041420118343196,0.7222222222222222,0.6993769470404985,0.7224334600760456,0.6943396226415094,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.673728813559322,0.6081081081081081,0.6859296482412061,0.5936395759717314,0.708018154311649,0.6010928961748634,0.7197231833910035,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.4479166666666667,0.3459715639810427,0.47337278106508873,0.3285024154589372,0.5249221183800623,0.33840304182509506,0.5566037735849056,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0042462845010616,0.9733333333333334,1.0101010101010102,0.9251700680272109,1.0401234567901234,0.9468085106382979,1.0424028268551238,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,,,,,,,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv new file mode 100644 index 00000000..1c14778c --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.4125518393914469,0.409526539286731,0.4133072693584233,0.407824721529288,0.41560016773246533,0.4080967658656178,0.4169732897208547,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +IQR,0.06044034878582214,0.06001621880237525,0.060546255799440236,0.0592545154661948,0.06120504503866594,0.059346936133197956,0.06152550926748312,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Overall_Uncertainty,0.8780392912187517,0.891988796508956,0.8745560419687717,0.8720906743628122,0.8818753151725819,0.8793201543546371,0.876768094974458,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Std,0.04514831304550171,0.045087143778800964,0.0451635867357254,0.04397762566804886,0.045903243124485016,0.044100865721702576,0.046187859028577805,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Aleatoric_Uncertainty,0.8697774410247803,0.8838504552841187,0.8662634491920471,0.8641999363899231,0.8733742833137512,0.8714115619659424,0.8681557774543762,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Mean_Prediction,0.5249552726745605,0.5797544121742249,0.5112717151641846,0.5921647548675537,0.48161453008651733,0.5824686288833618,0.4678759276866913,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Label_Stability,0.9060227272727273,0.8619905213270144,0.9170177514792901,0.8941062801932368,0.9137071651090342,0.892471482889734,0.9194716981132075,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Jitter,0.0727697897340754,0.10082212979978747,0.06576500422654263,0.07954648526077096,0.06839977112340251,0.08051835182742317,0.06507970735464015,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TPR,0.6624203821656051,0.5333333333333333,0.6868686868686869,0.5578231292517006,0.7098765432098766,0.5691489361702128,0.7243816254416962,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TNR,0.7333333333333333,0.7867647058823529,0.7171492204899778,0.7865168539325843,0.6886792452830188,0.7840236686390533,0.6639676113360324,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +PPV,0.6666666666666666,0.5797101449275363,0.681704260651629,0.5899280575539568,0.6990881458966566,0.5944444444444444,0.7118055555555556,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FNR,0.3375796178343949,0.4666666666666667,0.31313131313131315,0.4421768707482993,0.29012345679012347,0.4308510638297872,0.2756183745583039,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FPR,0.26666666666666666,0.21323529411764705,0.2828507795100223,0.21348314606741572,0.3113207547169811,0.21597633136094674,0.3360323886639676,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Accuracy,0.7017045454545454,0.6966824644549763,0.7029585798816568,0.7053140096618358,0.6993769470404985,0.7072243346007605,0.6962264150943396,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +F1,0.6645367412140575,0.5555555555555556,0.6842767295597484,0.5734265734265734,0.7044410413476263,0.5815217391304348,0.7180385288966725,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Selection-Rate,0.4431818181818182,0.32701421800947866,0.47218934911242605,0.3357487922705314,0.5124610591900312,0.34220532319391633,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Positive-Rate,0.9936305732484076,0.92,1.0075757575757576,0.9455782312925171,1.0154320987654322,0.9574468085106383,1.017667844522968,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Sample_Size,1056.0,,,,,,,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv new file mode 100644 index 00000000..9deda582 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index f78b64ed..cbd58c4c 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -38,8 +38,8 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict): STD: [(STD_PARITY, self._difference_operation), (STD_RATIO, self._ratio_operation)], # Uncertainty disparity metrics - OVERALL_UNCERTAINTY: [(OVERALL_UNCERTAINTY_PARITY, self._difference_operation, - OVERALL_UNCERTAINTY_RATIO, self._ratio_operation)], + OVERALL_UNCERTAINTY: [(OVERALL_UNCERTAINTY_PARITY, self._difference_operation), + (OVERALL_UNCERTAINTY_RATIO, self._ratio_operation)], ALEATORIC_UNCERTAINTY: [(ALEATORIC_UNCERTAINTY_PARITY, self._difference_operation), (ALEATORIC_UNCERTAINTY_RATIO, self._ratio_operation)] } @@ -82,8 +82,8 @@ def compose_metrics(self): # Skip a metric that does not have correspondent disparity metrics if metric_name not in self.disparity_metric_functions.keys(): continue - disparity_metrics = self.disparity_metric_functions[metric_name] - for disparity_metric_name, disparity_metric_func in disparity_metrics: + + for disparity_metric_name, disparity_metric_func in self.disparity_metric_functions[metric_name]: groups_metrics_dct[sensitive_attr][disparity_metric_name] = ( disparity_metric_func(cfm, metric_name, dis_group, priv_group)) From 7a531ed437c1fba8daf06950838e84c299bd5577 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 02:07:49 +0300 Subject: [PATCH 032/148] Fixed tests for metrics --- ...ssifier_50_Estimators_20231020__225918.csv | 19 ------------------- ...ression_50_Estimators_20231020__225918.csv | 19 ------------------- ...ssifier_50_Estimators_20231020__225918.csv | 19 ------------------- ...ssifier_50_Estimators_20231020__225918.csv | 19 ------------------- 4 files changed, 76 deletions(-) delete mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv delete mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv delete mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv delete mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv deleted file mode 100644 index e60e46ef..00000000 --- a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231020__225918.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params -Statistical_Bias,0.41857336172765697,0.41544095906084866,0.41935553564800787,0.4141321622564169,0.42143731278854996,0.4134826288938934,0.42362567393625994,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.0809157217235176,0.07855149605017232,0.08150607866680262,0.0835558343140641,0.07921321921185677,0.08187169522458478,0.0799669631167981,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Overall_Uncertainty,0.8932088655544336,0.899252491044528,0.8916997472367887,0.8852710730922949,0.898327628917869,0.891206327050453,0.8951962905602708,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Std,0.06895833645771993,0.07035364839135345,0.06860992128849308,0.06964657979043051,0.06851451599083176,0.06961900305621098,0.06830265602223631,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.8698426596552785,0.8745972457143654,0.8686554198227732,0.8601037952604418,0.8761228619285847,0.8661276469720228,0.8735296345069627,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.5219352219525485,0.578035221741857,0.5079268196382951,0.5863255907514473,0.48041246076447364,0.5771212289323356,0.4671657131386466,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.8362878787878788,0.8138388625592415,0.8418934911242604,0.8329468599033817,0.838442367601246,0.8321673003802282,0.8403773584905659,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.12457173778602351,0.13906180481671365,0.12095350803043126,0.12282559400571841,0.12569775573780906,0.12488709552261976,0.12425876010781686,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,0.6539278131634819,0.4666666666666667,0.6893939393939394,0.5170068027210885,0.7160493827160493,0.5425531914893617,0.7279151943462897,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.7282051282051282,0.8014705882352942,0.7060133630289532,0.7827715355805244,0.6823899371069182,0.7840236686390533,0.6518218623481782,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.6595289079229122,0.5645161290322581,0.674074074074074,0.5671641791044776,0.6966966966966966,0.5828571428571429,0.7054794520547946,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.346072186836518,0.5333333333333333,0.3106060606060606,0.48299319727891155,0.2839506172839506,0.4574468085106383,0.27208480565371024,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,0.2717948717948718,0.19852941176470587,0.29398663697104677,0.21722846441947566,0.31761006289308175,0.21597633136094674,0.3481781376518219,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.6950757575757576,0.6824644549763034,0.6982248520710059,0.6884057971014492,0.6993769470404985,0.6977186311787072,0.6924528301886792,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.6567164179104478,0.5109489051094891,0.6816479400749064,0.5409252669039146,0.7062404870624048,0.5619834710743802,0.7165217391304348,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,0.4422348484848485,0.2938388625592417,0.47928994082840237,0.32367149758454106,0.5186915887850467,0.33269961977186313,0.5509433962264151,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,0.9915074309978769,0.8266666666666667,1.0227272727272727,0.9115646258503401,1.0277777777777777,0.9308510638297872,1.0318021201413428,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,1056.0,,,,,,,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv deleted file mode 100644 index deffd07d..00000000 --- a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231020__225918.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params -Statistical_Bias,0.4337924538236714,0.4334044278876343,0.43388934550710784,0.4332653534138835,0.43413235969540376,0.4321642605515456,0.4354083588446866,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.029339925841114185,0.030179801866657946,0.02913020531875947,0.029591627125389396,0.029177613797983448,0.029793187368398956,0.028890085155544767,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.9086195955884198,0.9172087513975933,0.9064748478064842,0.9136998763207738,0.9053435267049392,0.9161672806782127,0.9011288741596818,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Std,0.02098879327073452,0.0211390182011289,0.020951281483381604,0.0208418614479099,0.021083543698537315,0.020991752212652995,0.02098585666045317,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.9062620824072268,0.9151648943470244,0.9040390133903071,0.9116550369785078,0.9027843827304198,0.9140803395738709,0.8985028309550482,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.5205499814176662,0.5688705122602331,0.5084841447220666,0.5864075588940767,0.4780810763160556,0.5746925557113814,0.46681603032616764,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9392424242424243,0.9107109004739338,0.9463668639053254,0.9438647342995169,0.9362616822429906,0.9340684410646389,0.944377358490566,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.04616419294990723,0.0661533997485251,0.04117280521676134,0.041646455683722695,0.049077500158942126,0.04895941646620646,0.04339006546014642,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.6263269639065817,0.48,0.6540404040404041,0.4421768707482993,0.7098765432098766,0.48404255319148937,0.7208480565371025,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.7316239316239316,0.8088235294117647,0.7082405345211581,0.8164794007490637,0.660377358490566,0.8106508875739645,0.6234817813765182,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.6526548672566371,0.5806451612903226,0.6641025641025641,0.5701754385964912,0.6804733727810651,0.5870967741935483,0.6868686868686869,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.37367303609341823,0.52,0.34595959595959597,0.5578231292517006,0.29012345679012347,0.5159574468085106,0.2791519434628975,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.26837606837606837,0.19117647058823528,0.29175946547884185,0.18352059925093633,0.33962264150943394,0.1893491124260355,0.3765182186234818,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.6846590909090909,0.6919431279620853,0.6828402366863905,0.6835748792270532,0.6853582554517134,0.6939163498098859,0.6754716981132075,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.6392199349945829,0.5255474452554745,0.6590330788804071,0.49808429118773945,0.6948640483383686,0.5306122448979592,0.7034482758620689,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.42803030303030304,0.2938388625592417,0.46153846153846156,0.2753623188405797,0.5264797507788161,0.2946768060836502,0.560377358490566,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,0.9596602972399151,0.8266666666666667,0.9848484848484849,0.7755102040816326,1.0432098765432098,0.824468085106383,1.0494699646643109,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,1056.0,,,,,,,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv deleted file mode 100644 index ecdb31df..00000000 --- a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231020__225918.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params -Statistical_Bias,0.4045419853002584,0.3968153482379772,0.40647135857853217,0.39516480373937674,0.4105889528488644,0.39746748802251475,0.41156309014571724,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.0909045004339339,0.10506143348256215,0.08736945561350722,0.09382085893269923,0.08902385803753385,0.09377805447614175,0.08805263359204463,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.8585954641369864,0.8776478578326691,0.8538380025159342,0.8493126327662205,0.86458158903963,0.8589413874089237,0.858252151606724,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Std,0.06813377974355897,0.07491127500458802,0.06644141110441444,0.07034178982046609,0.0667099227780768,0.06980083623796854,0.06647930480759777,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.8341600028048252,0.8464502802805608,0.8310910696126592,0.8223464618313945,0.8417780806288131,0.8325086424925195,0.8357989000204338,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.5216518784554963,0.5761649548269147,0.5080397374917457,0.5945323244703573,0.47465420766086625,0.58373426466956,0.4600380385524821,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.8373484848484849,0.7948815165876778,0.8479526627218935,0.8305314009661836,0.8417445482866042,0.8317110266159696,0.8429433962264151,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.11274737167594309,0.1403772124963732,0.10584808597995415,0.11637188208616779,0.1104100705702843,0.1168945448901996,0.10863149788217191,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.6751592356687898,0.6,0.6893939393939394,0.5714285714285714,0.7222222222222222,0.5851063829787234,0.734982332155477,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.7350427350427351,0.7941176470588235,0.7171492204899778,0.8052434456928839,0.6761006289308176,0.7988165680473372,0.6477732793522267,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.6723044397463002,0.6164383561643836,0.6825,0.6176470588235294,0.6943620178041543,0.6179775280898876,0.7050847457627119,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.3248407643312102,0.4,0.3106060606060606,0.42857142857142855,0.2777777777777778,0.4148936170212766,0.26501766784452296,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.26495726495726496,0.20588235294117646,0.2828507795100223,0.1947565543071161,0.3238993710691824,0.20118343195266272,0.3522267206477733,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.7083333333333334,0.7251184834123223,0.7041420118343196,0.7222222222222222,0.6993769470404985,0.7224334600760456,0.6943396226415094,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.673728813559322,0.6081081081081081,0.6859296482412061,0.5936395759717314,0.708018154311649,0.6010928961748634,0.7197231833910035,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.4479166666666667,0.3459715639810427,0.47337278106508873,0.3285024154589372,0.5249221183800623,0.33840304182509506,0.5566037735849056,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0042462845010616,0.9733333333333334,1.0101010101010102,0.9251700680272109,1.0401234567901234,0.9468085106382979,1.0424028268551238,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Sample_Size,1056.0,,,,,,,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv deleted file mode 100644 index 1c14778c..00000000 --- a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231020__225915/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231020__225918.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params -Statistical_Bias,0.4125518393914469,0.409526539286731,0.4133072693584233,0.407824721529288,0.41560016773246533,0.4080967658656178,0.4169732897208547,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -IQR,0.06044034878582214,0.06001621880237525,0.060546255799440236,0.0592545154661948,0.06120504503866594,0.059346936133197956,0.06152550926748312,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Overall_Uncertainty,0.8780392912187517,0.891988796508956,0.8745560419687717,0.8720906743628122,0.8818753151725819,0.8793201543546371,0.876768094974458,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Std,0.04514831304550171,0.045087143778800964,0.0451635867357254,0.04397762566804886,0.045903243124485016,0.044100865721702576,0.046187859028577805,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Aleatoric_Uncertainty,0.8697774410247803,0.8838504552841187,0.8662634491920471,0.8641999363899231,0.8733742833137512,0.8714115619659424,0.8681557774543762,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Mean_Prediction,0.5249552726745605,0.5797544121742249,0.5112717151641846,0.5921647548675537,0.48161453008651733,0.5824686288833618,0.4678759276866913,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Label_Stability,0.9060227272727273,0.8619905213270144,0.9170177514792901,0.8941062801932368,0.9137071651090342,0.892471482889734,0.9194716981132075,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Jitter,0.0727697897340754,0.10082212979978747,0.06576500422654263,0.07954648526077096,0.06839977112340251,0.08051835182742317,0.06507970735464015,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -TPR,0.6624203821656051,0.5333333333333333,0.6868686868686869,0.5578231292517006,0.7098765432098766,0.5691489361702128,0.7243816254416962,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -TNR,0.7333333333333333,0.7867647058823529,0.7171492204899778,0.7865168539325843,0.6886792452830188,0.7840236686390533,0.6639676113360324,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -PPV,0.6666666666666666,0.5797101449275363,0.681704260651629,0.5899280575539568,0.6990881458966566,0.5944444444444444,0.7118055555555556,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -FNR,0.3375796178343949,0.4666666666666667,0.31313131313131315,0.4421768707482993,0.29012345679012347,0.4308510638297872,0.2756183745583039,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -FPR,0.26666666666666666,0.21323529411764705,0.2828507795100223,0.21348314606741572,0.3113207547169811,0.21597633136094674,0.3360323886639676,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Accuracy,0.7017045454545454,0.6966824644549763,0.7029585798816568,0.7053140096618358,0.6993769470404985,0.7072243346007605,0.6962264150943396,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -F1,0.6645367412140575,0.5555555555555556,0.6842767295597484,0.5734265734265734,0.7044410413476263,0.5815217391304348,0.7180385288966725,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Selection-Rate,0.4431818181818182,0.32701421800947866,0.47218934911242605,0.3357487922705314,0.5124610591900312,0.34220532319391633,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Positive-Rate,0.9936305732484076,0.92,1.0075757575757576,0.9455782312925171,1.0154320987654322,0.9574468085106383,1.017667844522968,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Sample_Size,1056.0,,,,,,,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" From 6d44b1e8e8bc0c93283d34be9b555286859ef417 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 13:22:50 +0300 Subject: [PATCH 033/148] Aligned API based on with_predict_proba --- virny/analyzers/abstract_subgroup_analyzer.py | 32 +++++++++++-------- virny/analyzers/subgroup_variance_analyzer.py | 3 +- .../analyzers/subgroup_variance_calculator.py | 19 +++++------ .../metrics_computation_interfaces.py | 3 +- virny/utils/stability_utils.py | 5 ++- 5 files changed, 36 insertions(+), 26 deletions(-) diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 9f28f656..7e114285 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -5,7 +5,8 @@ from datetime import datetime, timezone from abc import ABCMeta, abstractmethod -from virny.configs.constants import ComputationMode +from virny.configs.constants import (ComputationMode, TPR, TNR, PPV, FPR, FNR, ACCURACY, F1, + SELECTION_RATE, POSITIVE_RATE) class AbstractSubgroupAnalyzer(metaclass=ABCMeta): @@ -48,7 +49,7 @@ def _partition_and_compute_metrics(self, y_pred_all, results: dict): return results - def _partition_and_compute_metrics_for_error_analysis(self, y_preds, results: dict): + def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_predictions: dict, results: dict): """ Partition predictions on correct and incorrect and compute subgroup metrics for each of the partitions. Used for the 'error_analysis' mode. @@ -76,15 +77,15 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, results: di if partition_indexes.shape[0] == 0: print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET, flush=True) metrics_dct = { - 'TPR': None, - 'TNR': None, - 'PPV': None, - 'FNR': None, - 'FPR': None, - 'Accuracy': None, - 'F1': None, - 'Selection-Rate': None, - 'Positive-Rate': None, + TPR: None, + TNR: None, + PPV: None, + FNR: None, + FPR: None, + ACCURACY: None, + F1: None, + SELECTION_RATE: None, + POSITIVE_RATE: None, } else: metrics_dct = self._compute_metrics(self.y_test[partition_indexes], y_preds[partition_indexes]) @@ -93,7 +94,7 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, results: di return results - def compute_subgroup_metrics(self, y_preds, save_results: bool, + def compute_subgroup_metrics(self, y_preds, models_predictions: dict, save_results: bool, result_filename: str = None, save_dir_path: str = None): """ Compute metrics for each subgroup in self.test_protected_groups using _compute_metrics method. @@ -103,7 +104,10 @@ def compute_subgroup_metrics(self, y_preds, save_results: bool, Parameters ---------- y_preds - Models predictions + Averaged predictions of the bootstrap + models_predictions + A dictionary of models predictions. Is not used in this function, + but needed for function argument consistency. save_results If to save results in a file result_filename @@ -122,7 +126,7 @@ def compute_subgroup_metrics(self, y_preds, save_results: bool, # Compute metrics for subgroups if self.computation_mode == ComputationMode.ERROR_ANALYSIS.value: - results = self._partition_and_compute_metrics_for_error_analysis(y_pred_all, results) + results = self._partition_and_compute_metrics_for_error_analysis(y_pred_all, models_predictions, results) else: results = self._partition_and_compute_metrics(y_pred_all, results) diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 9d7b9269..5094172c 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -118,7 +118,8 @@ def compute_metrics(self, save_results: bool, result_filename: str = None, # Count and display fairness metrics self.__subgroup_variance_calculator.set_overall_variance_metrics(self.overall_variance_metrics_dct) self.subgroup_variance_metrics_dct = self.__subgroup_variance_calculator.compute_subgroup_metrics( - self.__overall_variance_analyzer.models_predictions, save_results, result_filename, save_dir_path + y_preds, self.__overall_variance_analyzer.models_predictions, + save_results, result_filename, save_dir_path ) return y_preds, pd.DataFrame(self.subgroup_variance_metrics_dct) diff --git a/virny/analyzers/subgroup_variance_calculator.py b/virny/analyzers/subgroup_variance_calculator.py index 59a831bd..8b43b6f8 100644 --- a/virny/analyzers/subgroup_variance_calculator.py +++ b/virny/analyzers/subgroup_variance_calculator.py @@ -1,7 +1,7 @@ import pandas as pd from colorama import Fore -from virny.metrics import METRIC_TO_FUNCTION +from virny.metrics import METRIC_TO_FUNCTION, METRICS_FOR_LABELS from virny.configs.constants import ComputationMode from virny.utils.stability_utils import count_prediction_metrics, combine_bootstrap_predictions from virny.analyzers.abstract_subgroup_analyzer import AbstractSubgroupAnalyzer @@ -28,8 +28,9 @@ class SubgroupVarianceCalculator(AbstractSubgroupAnalyzer): """ def __init__(self, X_test: pd.DataFrame, y_test: pd.DataFrame, sensitive_attributes_dct: dict, - test_protected_groups=None, computation_mode: str = None): + test_protected_groups=None, computation_mode: str = None, with_predict_proba: bool = True): super().__init__(X_test, y_test, sensitive_attributes_dct, test_protected_groups, computation_mode) + self.with_predict_proba = with_predict_proba self.overall_variance_metrics = None self.subgroup_variance_metrics_dict = None @@ -48,7 +49,7 @@ def _partition_and_compute_metrics(self, models_predictions, results: dict): return results - def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, results: dict): + def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_predictions: dict, results: dict): """ Partition predictions on correct and incorrect and compute subgroup metrics for each of the partitions. Used for the 'error_analysis' mode. @@ -56,8 +57,6 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, :param models_predictions: a list of predictions :param results: a dict to add subgroup metrics for each partition """ - # Create a 1D pandas series of predictions for the test set based on bootstrap predictions - y_preds = combine_bootstrap_predictions(models_predictions, self.y_test.index) # Partition and compute subgroup metrics for group_name in self.test_protected_groups.keys(): @@ -84,7 +83,8 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, if partition_indexes.shape[0] == 0: print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) metrics_dct = dict() - for metric in METRIC_TO_FUNCTION.keys(): + metric_names = list(METRIC_TO_FUNCTION.keys()) if self.with_predict_proba else METRICS_FOR_LABELS + for metric in metric_names: metrics_dct[metric] = None else: metrics_dct = self._compute_metrics(self.y_test[partition_indexes].reset_index(drop=True), @@ -94,10 +94,11 @@ def _partition_and_compute_metrics_for_error_analysis(self, models_predictions, return results def _compute_metrics(self, y_test: pd.DataFrame, group_models_predictions): - _, prediction_metrics = count_prediction_metrics(y_test, group_models_predictions) + _, prediction_metrics = count_prediction_metrics(y_test, group_models_predictions, + with_predict_proba=self.with_predict_proba) return prediction_metrics - def compute_subgroup_metrics(self, models_predictions: dict, save_results: bool, + def compute_subgroup_metrics(self, y_preds, models_predictions: dict, save_results: bool, result_filename: str = None, save_dir_path: str = None): """ Compute variance metrics for subgroups. @@ -127,7 +128,7 @@ def compute_subgroup_metrics(self, models_predictions: dict, save_results: bool, # Compute stability metrics for subgroups if self.computation_mode == ComputationMode.ERROR_ANALYSIS.value: - results = self._partition_and_compute_metrics_for_error_analysis(models_predictions, results) + results = self._partition_and_compute_metrics_for_error_analysis(y_preds, models_predictions, results) else: results = self._partition_and_compute_metrics(models_predictions, results) diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index cf3c3e48..929a9882 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -123,7 +123,8 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase sensitive_attributes_dct=sensitive_attributes_dct, test_protected_groups=test_protected_groups, computation_mode=computation_mode) - dtc_res = error_analyzer.compute_subgroup_metrics(y_preds, + dtc_res = error_analyzer.compute_subgroup_metrics(y_preds=y_preds, + models_predictions=dict(), save_results=False, result_filename=None, save_dir_path=None) diff --git a/virny/utils/stability_utils.py b/virny/utils/stability_utils.py index 45986087..1e4780a3 100644 --- a/virny/utils/stability_utils.py +++ b/virny/utils/stability_utils.py @@ -66,7 +66,10 @@ def count_prediction_metrics(y_true, uq_results, with_predict_proba: bool = True for metric in METRICS_FOR_LABELS: metrics_dct[metric] = METRIC_TO_FUNCTION[metric](y_true, uq_labels) - y_preds = np.array([int(x<0.5) for x in results.mean().values]) + if with_predict_proba: + y_preds = np.array([int(x<0.5) for x in results.mean().values]) + else: + y_preds = np.array([int(x>0.5) for x in results.mean().values]) return y_preds, metrics_dct From a61cfa00bcd4e14e4c78205d86b2257d4b712164 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 13:27:01 +0300 Subject: [PATCH 034/148] Removed test files --- ..._COMPAS_Without_Sensitive_Attributes_20231020__225918.csv | 5 ----- 1 file changed, 5 deletions(-) delete mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv deleted file mode 100644 index 9deda582..00000000 --- a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231020__225918.csv +++ /dev/null @@ -1,5 +0,0 @@ -Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params -COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" -COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" -COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" -COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" From 79512a6c03b091ad39cbb769414cdd7204bacd6f Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 21 Oct 2023 21:51:48 +0300 Subject: [PATCH 035/148] Checked error_analysis mode --- virny/analyzers/abstract_subgroup_analyzer.py | 10 ++++------ virny/analyzers/subgroup_variance_analyzer.py | 1 + 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 7e114285..0d813190 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -104,7 +104,7 @@ def compute_subgroup_metrics(self, y_preds, models_predictions: dict, save_resul Parameters ---------- y_preds - Averaged predictions of the bootstrap + Averaged predictions of the bootstrap with y_true indexes models_predictions A dictionary of models predictions. Is not used in this function, but needed for function argument consistency. @@ -116,19 +116,17 @@ def compute_subgroup_metrics(self, y_preds, models_predictions: dict, save_resul [Optional] Location where to save the results file """ - y_pred_all = pd.Series(y_preds, index=self.y_test.index) - # Compute overall metrics results = dict() - metrics_dct = self._compute_metrics(self.y_test, y_pred_all) + metrics_dct = self._compute_metrics(self.y_test, y_preds) metrics_dct['Sample_Size'] = self.y_test.shape[0] results['overall'] = metrics_dct # Compute metrics for subgroups if self.computation_mode == ComputationMode.ERROR_ANALYSIS.value: - results = self._partition_and_compute_metrics_for_error_analysis(y_pred_all, models_predictions, results) + results = self._partition_and_compute_metrics_for_error_analysis(y_preds, models_predictions, results) else: - results = self._partition_and_compute_metrics(y_pred_all, results) + results = self._partition_and_compute_metrics(y_preds, results) self.subgroup_metrics_dict = results if save_results: diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 5094172c..19139089 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -113,6 +113,7 @@ def compute_metrics(self, save_results: bool, result_filename: str = None, """ y_preds, y_test_true = self.__overall_variance_analyzer.compute_metrics(save_results=False, with_fit=with_fit) + y_preds = pd.Series(y_preds, index=y_test_true.index) self.overall_variance_metrics_dct = self.__overall_variance_analyzer.prediction_metrics # Count and display fairness metrics From 3427782aca072b7dd6c3db07f67ccec64730ced6 Mon Sep 17 00:00:00 2001 From: proc1v Date: Sat, 21 Oct 2023 22:33:27 +0300 Subject: [PATCH 036/148] Added functions for postprocessing --- ...verall_variance_analyzer_postprocessing.py | 79 +++++++++++++++++++ virny/analyzers/subgroup_variance_analyzer.py | 48 +++++++---- virny/configs/constants.py | 1 + .../postprocessing_intervention_utils.py | 33 ++++++++ 4 files changed, 147 insertions(+), 14 deletions(-) create mode 100644 virny/analyzers/batch_overall_variance_analyzer_postprocessing.py create mode 100644 virny/utils/postprocessing_intervention_utils.py diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py new file mode 100644 index 00000000..40019417 --- /dev/null +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -0,0 +1,79 @@ +import numpy as np +import pandas as pd + +from tqdm.notebook import tqdm + +from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer +from virny.utils.postprocessing_intervention_utils import contruct_binary_label_dataset, predict_on_binary_label_dataset +from virny.utils.stability_utils import generate_bootstrap + + +class BatchOverallVarianceAnalyzerPostProcessing(BatchOverallVarianceAnalyzer): + def __init__(self, postprocessor, sensitive_attribute: str, + base_model, base_model_name: str, bootstrap_fraction: float, + X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, + target_column: str, dataset_name: str, n_estimators: int, verbose: int = 0): + super().__init__(base_model=base_model, + base_model_name=base_model_name, + bootstrap_fraction=bootstrap_fraction, + X_train=X_train, + y_train=y_train, + X_test=X_test, + y_test=y_test, + target_column=target_column, + dataset_name=dataset_name, + n_estimators=n_estimators, + verbose=verbose) + + self.postprocessor = postprocessor + self.sensitive_attribute = sensitive_attribute + self.test_binary_label_dataset = contruct_binary_label_dataset(X_test, y_test, target_column, sensitive_attribute) + + def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: bool = True) -> dict: + """ + Quantifying uncertainty of the base model by constructing an ensemble from bootstrapped samples + and applying postprocessing intervention. + + Return a dictionary where keys are models indexes, and values are lists of + correspondent model predictions for X_test set. + + Parameters + ---------- + boostrap_size + Number of records in bootstrap splits + with_replacement + Enable replacement or not + with_fit + Whether to fit estimators in bootstrap + + """ + models_predictions = {idx: [] for idx in range(self.n_estimators)} + if self._verbose >= 1: + print('\n', flush=True) + self.__logger.info('Start classifiers testing by bootstrap') + # Remove a progress bar for UQ without estimators fitting + cycle_range = range(self.n_estimators) if with_fit is False else \ + tqdm(range(self.n_estimators), + desc="Classifiers testing by bootstrap", + colour="blue", + mininterval=10) + # Train and test each estimator in models_predictions + for idx in cycle_range: + classifier = self.models_lst[idx] + if with_fit: + X_sample, y_sample = generate_bootstrap(self.X_train, self.y_train, boostrap_size, with_replacement) + classifier = self._fit_model(classifier, X_sample, y_sample) + + train_binary_label_dataset_sample = contruct_binary_label_dataset(X_sample, y_sample, self.target_column, self.sensitive_attribute) + train_binary_label_dataset_sample_pred = predict_on_binary_label_dataset(classifier, train_binary_label_dataset_sample) + test_binary_label_dataset_pred = predict_on_binary_label_dataset(classifier, self.test_binary_label_dataset) + postprocessor_fitted = self.postprocessor.fit(train_binary_label_dataset_sample, train_binary_label_dataset_sample_pred) + models_predictions[idx] = postprocessor_fitted.predict(test_binary_label_dataset_pred).labels.ravel() + self.models_lst[idx] = classifier + + if self._verbose >= 1: + print('\n', flush=True) + self.__logger.info('Successfully tested classifiers by bootstrap') + + return models_predictions + \ No newline at end of file diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 5094172c..935b66c0 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -1,9 +1,10 @@ import pandas as pd -from virny.configs.constants import ModelSetting +from virny.configs.constants import ModelSetting, ComputationMode from virny.custom_classes.base_dataset import BaseFlowDataset from virny.analyzers.subgroup_variance_calculator import SubgroupVarianceCalculator from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer +from virny.analyzers.batch_overall_variance_analyzer_postprocessing import BatchOverallVarianceAnalyzerPostProcessing from virny.analyzers.incremental_overall_variance_analyzer import IncrementalOverallVarianceAnalyzer @@ -42,19 +43,35 @@ class SubgroupVarianceAnalyzer: """ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, base_model_name: str, bootstrap_fraction: float, dataset: BaseFlowDataset, dataset_name: str, - sensitive_attributes_dct: dict, test_protected_groups: dict, computation_mode: str = None, verbose: int = 0): + sensitive_attributes_dct: dict, test_protected_groups: dict, postprocessor=None, + postprocessing_sensitive_attribute : str = None, computation_mode: str = None, verbose: int = 0): if model_setting == ModelSetting.BATCH: - overall_variance_analyzer = BatchOverallVarianceAnalyzer(base_model=base_model, - base_model_name=base_model_name, - bootstrap_fraction=bootstrap_fraction, - X_train=dataset.X_train_val, - y_train=dataset.y_train_val, - X_test=dataset.X_test, - y_test=dataset.y_test, - dataset_name=dataset_name, - target_column=dataset.target, - n_estimators=n_estimators, - verbose=verbose) + if computation_mode == ComputationMode.POSTPROCESSING_INTERVENTION.value: + overall_variance_analyzer = BatchOverallVarianceAnalyzerPostProcessing(postprocessor=postprocessor, + sensitive_attribute=postprocessing_sensitive_attribute, + base_model=base_model, + base_model_name=base_model_name, + bootstrap_fraction=bootstrap_fraction, + X_train=dataset.X_train_val, + y_train=dataset.y_train_val, + X_test=dataset.X_test, + y_test=dataset.y_test, + dataset_name=dataset_name, + target_column=dataset.target, + n_estimators=n_estimators, + verbose=verbose) + else: + overall_variance_analyzer = BatchOverallVarianceAnalyzer(base_model=base_model, + base_model_name=base_model_name, + bootstrap_fraction=bootstrap_fraction, + X_train=dataset.X_train_val, + y_train=dataset.y_train_val, + X_test=dataset.X_test, + y_test=dataset.y_test, + dataset_name=dataset_name, + target_column=dataset.target, + n_estimators=n_estimators, + verbose=verbose) elif model_setting == ModelSetting.INCREMENTAL: overall_variance_analyzer = IncrementalOverallVarianceAnalyzer(base_model=base_model, base_model_name=base_model_name, @@ -75,11 +92,14 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b self.base_model_name = overall_variance_analyzer.base_model_name self.__overall_variance_analyzer = overall_variance_analyzer + + with_predict_proba = False if computation_mode == ComputationMode.POSTPROCESSING_INTERVENTION.value else True self.__subgroup_variance_calculator = SubgroupVarianceCalculator(X_test=dataset.X_test, y_test=dataset.y_test, sensitive_attributes_dct=sensitive_attributes_dct, test_protected_groups=test_protected_groups, - computation_mode=computation_mode) + computation_mode=computation_mode, + with_predict_proba=with_predict_proba) self.overall_variance_metrics_dct = dict() self.subgroup_variance_metrics_dct = dict() diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 81d145b2..b6af0d32 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -8,6 +8,7 @@ class ModelSetting(Enum): class ComputationMode(Enum): ERROR_ANALYSIS = "error_analysis" + POSTPROCESSING_INTERVENTION = "postprocessing_intervention" class ReportType(Enum): diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py new file mode 100644 index 00000000..85628023 --- /dev/null +++ b/virny/utils/postprocessing_intervention_utils.py @@ -0,0 +1,33 @@ +import copy + +import numpy as np +from aif360.datasets import BinaryLabelDataset + + +def contruct_binary_label_dataset(X_sample, y_sample, target_column, sensitive_attribute): + df = X_sample + df[target_column] = y_sample + + binary_label_dataset = BinaryLabelDataset( + df=df, + label_names=[target_column], + protected_attribute_names=[sensitive_attribute], + favorable_label=1, + unfavorable_label=0) + + return binary_label_dataset + + +def predict_on_binary_label_dataset(model, orig_dataset, threshold=0.5): + orig_dataset_pred = copy.deepcopy(orig_dataset) + + fav_idx = np.where(model.classes_ == orig_dataset.favorable_label)[0][0] + y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] + orig_dataset.scores = y_pred_prob.reshape(-1, 1) + + y_pred = np.zeros_like(orig_dataset.labels) + y_pred[y_pred_prob >= threshold] = orig_dataset.favorable_label + y_pred[~(y_pred_prob >= threshold)] = orig_dataset.unfavorable_label + orig_dataset_pred.labels = y_pred + + return orig_dataset_pred From 06edd35a40ceb0b5da33977fbca3b22e77647d72 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sun, 22 Oct 2023 00:16:08 +0300 Subject: [PATCH 037/148] Added tests for MetricsComposer --- .../Multiple_Models_Interface_Use_Case.ipynb | 427 ++++++++++++------ ..._Sensitive_Attributes_20231021__202919.csv | 5 + ..._Sensitive_Attributes_20231021__205710.csv | 5 + ..._Sensitive_Attributes_20231021__205809.csv | 5 + tests/__init__.py | 6 +- tests/custom_classes/test_metrics_composer.py | 93 +++- ...ression_50_Estimators_20231021__205809.csv | 19 + ...ssifier_50_Estimators_20231021__205809.csv | 19 + .../Multiple_Models_Interface_Use_Case.csv | 65 +++ 9 files changed, 504 insertions(+), 140 deletions(-) create mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv create mode 100644 tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv create mode 100644 tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv create mode 100644 tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 7c056ceb..a8bc35c9 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -2,19 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 53, + "execution_count": 1, "id": "248cbed8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:05.208285Z", + "start_time": "2023-10-21T20:58:04.841119Z" } - ], + }, + "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -23,9 +19,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 2, "id": "7ec6cd08", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:05.216534Z", + "start_time": "2023-10-21T20:58:05.208182Z" + } + }, "outputs": [], "source": [ "import os\n", @@ -36,15 +37,20 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 3, "id": "b8cb69f2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:05.226610Z", + "start_time": "2023-10-21T20:58:05.216923Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Current location: /home/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" ] } ], @@ -90,9 +96,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 4, "id": "7a9241de", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.737014Z", + "start_time": "2023-10-21T20:58:05.228621Z" + } + }, "outputs": [], "source": [ "import os\n", @@ -143,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 5, "outputs": [], "source": [ "DATASET_SPLIT_SEED = 42\n", @@ -151,12 +162,17 @@ "TEST_SET_FRACTION = 0.2" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.760654Z", + "start_time": "2023-10-21T20:58:06.738834Z" + } + }, + "id": "ce359a052925eb3a" }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "outputs": [], "source": [ "models_params_for_tuning = {\n", @@ -199,8 +215,13 @@ "}" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.779658Z", + "start_time": "2023-10-21T20:58:06.759908Z" + } + }, + "id": "2ece07ab7e3a9acc" }, { "cell_type": "markdown", @@ -209,7 +230,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "1090a686532d96f5" }, { "cell_type": "markdown", @@ -226,11 +248,12 @@ ], "metadata": { "collapsed": false - } + }, + "id": "d0a03b8f5c5d0ea7" }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 7, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -246,20 +269,30 @@ " f.write(config_yaml_content)" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.800438Z", + "start_time": "2023-10-21T20:58:06.780670Z" + } + }, + "id": "af22ee06f1e3eb1a" }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 8, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)\n", "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name}_Metrics_{datetime.now(timezone.utc).strftime(\"%Y%m%d__%H%M%S\")}')" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.820854Z", + "start_time": "2023-10-21T20:58:06.800568Z" + } + }, + "id": "65181f72484bb92b" }, { "cell_type": "markdown", @@ -285,9 +318,14 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 9, "id": "9e3d7bf3", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.839915Z", + "start_time": "2023-10-21T20:58:06.822383Z" + } + }, "outputs": [], "source": [ "class CompasWithoutSensitiveAttrsDataset(BaseDataLoader):\n", @@ -328,16 +366,21 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 10, "id": "6c55c6a0", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.881602Z", + "start_time": "2023-10-21T20:58:06.842269Z" + } + }, "outputs": [ { "data": { "text/plain": " juv_fel_count juv_misd_count juv_other_count priors_count \\\n0 0.0 -2.340451 1.0 -15.010999 \n1 0.0 0.000000 0.0 0.000000 \n2 0.0 0.000000 0.0 0.000000 \n3 0.0 0.000000 0.0 6.000000 \n4 0.0 0.000000 0.0 7.513697 \n\n age_cat_25 - 45 \n0 1 \n1 1 \n2 0 \n3 1 \n4 1 ", "text/html": "
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" }, - "execution_count": 62, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -349,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 11, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -358,19 +401,29 @@ "])" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.897824Z", + "start_time": "2023-10-21T20:58:06.878077Z" + } + }, + "id": "ebbef5eaf9dc0943" }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 12, "outputs": [], "source": [ "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:06.965760Z", + "start_time": "2023-10-21T20:58:06.898526Z" + } + }, + "id": "97ed4609effbf53f" }, { "cell_type": "markdown", @@ -379,32 +432,32 @@ ], "metadata": { "collapsed": false - } + }, + "id": "d538119a04cb3d80" }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 13, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023/08/13, 01:39:20: Tuning DecisionTreeClassifier...\n", + "2023/10/21, 23:58:06: Tuning DecisionTreeClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:39:22: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6429262328840039, Accuracy = 0.6442550505050505]\n", + "2023/10/21, 23:58:08: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6429262328840039, Accuracy = 0.6442550505050505]\n", "\n", - "2023/08/13, 01:39:22: Tuning LogisticRegression...\n", + "2023/10/21, 23:58:08: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/08/13, 01:39:22: Tuning for LogisticRegression is finished [F1 score = 0.6461022173486363, Accuracy = 0.6505681818181818]\n", + "2023/10/21, 23:58:08: Tuning for LogisticRegression is finished [F1 score = 0.6461022173486363, Accuracy = 0.6505681818181818]\n", "\n", - "2023/08/13, 01:39:22: Tuning RandomForestClassifier...\n", + "2023/10/21, 23:58:08: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:39:23: Tuning for RandomForestClassifier is finished [F1 score = 0.6480756802972086, Accuracy = 0.6518308080808081]\n", + "2023/10/21, 23:58:08: Tuning for RandomForestClassifier is finished [F1 score = 0.6480756802972086, Accuracy = 0.6518308080808081]\n", "\n", - "2023/08/13, 01:39:23: Tuning XGBClassifier...\n", + "2023/10/21, 23:58:08: Tuning XGBClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:39:27: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n", - "\n" + "2023/10/21, 23:58:09: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" ] }, { @@ -412,7 +465,7 @@ "text/plain": " Dataset_Name Model_Name F1_Score \\\n0 COMPAS_Without_Sensitive_Attributes DecisionTreeClassifier 0.642926 \n1 COMPAS_Without_Sensitive_Attributes LogisticRegression 0.646102 \n2 COMPAS_Without_Sensitive_Attributes RandomForestClassifier 0.648076 \n3 COMPAS_Without_Sensitive_Attributes XGBClassifier 0.654881 \n\n Accuracy_Score Model_Best_Params \n0 0.644255 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 0.650568 {'C': 1, 'max_iter': 250, 'penalty': 'l2', 'so... \n2 0.651831 {'max_depth': 10, 'max_features': 0.6, 'min_sa... \n3 0.658775 {'lambda': 100, 'learning_rate': 0.1, 'max_dep... ", "text/html": "
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Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
0COMPAS_Without_Sensitive_AttributesDecisionTreeClassifier0.6429260.644255{'criterion': 'gini', 'max_depth': 20, 'max_fe...
1COMPAS_Without_Sensitive_AttributesLogisticRegression0.6461020.650568{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'so...
2COMPAS_Without_Sensitive_AttributesRandomForestClassifier0.6480760.651831{'max_depth': 10, 'max_features': 0.6, 'min_sa...
3COMPAS_Without_Sensitive_AttributesXGBClassifier0.6548810.658775{'lambda': 100, 'learning_rate': 0.1, 'max_dep...
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" }, - "execution_count": 65, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -422,12 +475,17 @@ "tuned_params_df" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:09.469344Z", + "start_time": "2023-10-21T20:58:06.928166Z" + } + }, + "id": "782741c190a4690b" }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 14, "outputs": [], "source": [ "now = datetime.now(timezone.utc)\n", @@ -436,8 +494,13 @@ "tuned_params_df.to_csv(tuned_df_path, sep=\",\", columns=tuned_params_df.columns, float_format=\"%.4f\", index=False)" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:09.516018Z", + "start_time": "2023-10-21T20:58:09.469540Z" + } + }, + "id": "21ccc879c5c3e215" }, { "cell_type": "markdown", @@ -446,11 +509,12 @@ ], "metadata": { "collapsed": false - } + }, + "id": "2da2057228e94ae5" }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "outputs": [ { "name": "stdout", @@ -479,8 +543,13 @@ "pprint(models_config)" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:09.549780Z", + "start_time": "2023-10-21T20:58:09.496334Z" + } + }, + "id": "3b15f202741fa2ae" }, { "cell_type": "markdown", @@ -500,9 +569,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 16, "id": "197eadaa", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.356124Z", + "start_time": "2023-10-21T20:58:09.523683Z" + } + }, "outputs": [ { "data": { @@ -510,7 +584,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f7e5d56f4d94455e926f7c682a03dfb3" + "model_id": "5ec38df0bd8a4d8e8915ff3e0adade22" } }, "metadata": {}, @@ -520,16 +594,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "############################## [Model 1 / 4] Analyze DecisionTreeClassifier ##############################\n", - "\n", - "\n" + "############################## [Model 1 / 4] Analyze DecisionTreeClassifier ##############################\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:28 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" + "2023-10-21 23:58:09 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" ] }, { @@ -538,7 +610,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c6fea5228c944caca6de5cb97ae230dd" + "model_id": "13887a0c3c7d479f88076e51bac0ede4" } }, "metadata": {}, @@ -547,17 +619,14 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "\n", - "\n" - ] + "text": [] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:28 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", - "2023-08-13 01:39:30 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" + "2023-10-21 23:58:09 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", + "2023-10-21 23:58:10 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" ] }, { @@ -577,7 +646,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:33 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" + "2023-10-21 23:58:11 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" ] }, { @@ -586,7 +655,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1bbb9cf8fb4247e1bafec141c22ed384" + "model_id": "dafb848c3f6c4b56ab74c111acc06b55" } }, "metadata": {}, @@ -604,8 +673,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:35 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", - "2023-08-13 01:39:37 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" + "2023-10-21 23:58:13 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", + "2023-10-21 23:58:13 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" ] }, { @@ -625,7 +694,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:39 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" + "2023-10-21 23:58:15 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" ] }, { @@ -634,7 +703,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "8609a285df254c0bb1cafb0a49bd301a" + "model_id": "cfffd8c5135e4c07838300acae4f167c" } }, "metadata": {}, @@ -652,8 +721,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:53 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", - "2023-08-13 01:39:54 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" + "2023-10-21 23:58:22 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", + "2023-10-21 23:58:23 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" ] }, { @@ -673,7 +742,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:39:57 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" + "2023-10-21 23:58:24 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" ] }, { @@ -682,7 +751,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "18ab96649a2c4d87a111adb502ebb2c7" + "model_id": "f7bfb688c4344f0ea41d252af82b3073" } }, "metadata": {}, @@ -700,8 +769,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-13 01:40:16 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", - "2023-08-13 01:40:17 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" + "2023-10-21 23:58:32 abstract_overall_variance_analyzer.py INFO : Successfully tested classifiers by bootstrap\n", + "2023-10-21 23:58:32 abstract_overall_variance_analyzer.py INFO : Successfully computed predict proba metrics\n" ] }, { @@ -729,16 +798,21 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 17, "id": "bea94683", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.381407Z", + "start_time": "2023-10-21T20:58:34.356989Z" + } + }, "outputs": [ { "data": { - "text/plain": " Metric overall sex_priv sex_dis race_priv \\\n0 Mean 0.521754 0.577841 0.507749 0.588128 \n1 Std 0.070126 0.070049 0.070145 0.070509 \n2 IQR 0.088289 0.085124 0.089079 0.091726 \n3 Aleatoric_Uncertainty 0.867014 0.871857 0.865805 0.859131 \n4 Overall_Uncertainty 0.892403 0.898704 0.890829 0.886515 \n5 Statistical_Bias 0.419045 0.414878 0.420086 0.414936 \n6 Jitter 0.122460 0.126825 0.121370 0.122124 \n7 Per_Sample_Accuracy 0.679205 0.690711 0.676331 0.684203 \n8 Label_Stability 0.833258 0.823886 0.835598 0.827536 \n9 TPR 0.656051 0.493333 0.686869 0.523810 \n10 TNR 0.726496 0.801471 0.703786 0.779026 \n11 PPV 0.658849 0.578125 0.671605 0.566176 \n12 FNR 0.343949 0.506667 0.313131 0.476190 \n13 FPR 0.273504 0.198529 0.296214 0.220974 \n14 Accuracy 0.695076 0.691943 0.695858 0.688406 \n15 F1 0.657447 0.532374 0.679151 0.544170 \n16 Selection-Rate 0.444129 0.303318 0.479290 0.328502 \n17 Positive-Rate 0.995754 0.853333 1.022727 0.925170 \n18 Sample_Size 1056.000000 NaN NaN NaN \n\n race_dis \n0 0.478952 \n1 0.069879 \n2 0.086072 \n3 0.872098 \n4 0.896200 \n5 0.421695 \n6 0.122677 \n7 0.675981 \n8 0.836947 \n9 0.716049 \n10 0.682390 \n11 0.696697 \n12 0.283951 \n13 0.317610 \n14 0.699377 \n15 0.706240 \n16 0.518692 \n17 1.027778 \n18 NaN ", - 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Metricoverallsex_privsex_disrace_privrace_dis
0Mean0.5217540.5778410.5077490.5881280.478952
1Std0.0701260.0700490.0701450.0705090.069879
2IQR0.0882890.0851240.0890790.0917260.086072
3Aleatoric_Uncertainty0.8670140.8718570.8658050.8591310.872098
4Overall_Uncertainty0.8924030.8987040.8908290.8865150.896200
5Statistical_Bias0.4190450.4148780.4200860.4149360.421695
6Jitter0.1224600.1268250.1213700.1221240.122677
7Per_Sample_Accuracy0.6792050.6907110.6763310.6842030.675981
8Label_Stability0.8332580.8238860.8355980.8275360.836947
9TPR0.6560510.4933330.6868690.5238100.716049
10TNR0.7264960.8014710.7037860.7790260.682390
11PPV0.6588490.5781250.6716050.5661760.696697
12FNR0.3439490.5066670.3131310.4761900.283951
13FPR0.2735040.1985290.2962140.2209740.317610
14Accuracy0.6950760.6919430.6958580.6884060.699377
15F10.6574470.5323740.6791510.5441700.706240
16Selection-Rate0.4441290.3033180.4792900.3285020.518692
17Positive-Rate0.9957540.8533331.0227270.9251701.027778
18Sample_Size1056.000000NaNNaNNaNNaN
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" + "text/plain": " Metric overall sex_priv sex_dis race_priv \\\n0 Std 0.069731 0.071849 0.069202 0.069349 \n1 IQR 0.087109 0.084506 0.087760 0.087332 \n2 Overall_Uncertainty 0.892622 0.898820 0.891074 0.884985 \n3 Mean_Prediction 0.519650 0.576085 0.505558 0.584973 \n4 Aleatoric_Uncertainty 0.868202 0.871461 0.867388 0.859540 \n5 Statistical_Bias 0.419017 0.415723 0.419840 0.414650 \n6 Label_Stability 0.822727 0.809668 0.825988 0.824444 \n7 Jitter 0.128776 0.137677 0.126553 0.124616 \n8 TPR 0.653928 0.466667 0.689394 0.517007 \n9 TNR 0.728205 0.801471 0.706013 0.782772 \n10 PPV 0.659529 0.564516 0.674074 0.567164 \n11 FNR 0.346072 0.533333 0.310606 0.482993 \n12 FPR 0.271795 0.198529 0.293987 0.217228 \n13 Accuracy 0.695076 0.682464 0.698225 0.688406 \n14 F1 0.656716 0.510949 0.681648 0.540925 \n15 Selection-Rate 0.442235 0.293839 0.479290 0.323671 \n16 Positive-Rate 0.991507 0.826667 1.022727 0.911565 \n17 Sample_Size 1056.000000 NaN NaN NaN \n\n race_dis \n0 0.069977 \n1 0.086966 \n2 0.897547 \n3 0.477526 \n4 0.873788 \n5 0.421833 \n6 0.821620 \n7 0.131458 \n8 0.716049 \n9 0.682390 \n10 0.696697 \n11 0.283951 \n12 0.317610 \n13 0.699377 \n14 0.706240 \n15 0.518692 \n16 1.027778 \n17 NaN ", + "text/html": "
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Metricoverallsex_privsex_disrace_privrace_dis
0Std0.0697310.0718490.0692020.0693490.069977
1IQR0.0871090.0845060.0877600.0873320.086966
2Overall_Uncertainty0.8926220.8988200.8910740.8849850.897547
3Mean_Prediction0.5196500.5760850.5055580.5849730.477526
4Aleatoric_Uncertainty0.8682020.8714610.8673880.8595400.873788
5Statistical_Bias0.4190170.4157230.4198400.4146500.421833
6Label_Stability0.8227270.8096680.8259880.8244440.821620
7Jitter0.1287760.1376770.1265530.1246160.131458
8TPR0.6539280.4666670.6893940.5170070.716049
9TNR0.7282050.8014710.7060130.7827720.682390
10PPV0.6595290.5645160.6740740.5671640.696697
11FNR0.3460720.5333330.3106060.4829930.283951
12FPR0.2717950.1985290.2939870.2172280.317610
13Accuracy0.6950760.6824640.6982250.6884060.699377
14F10.6567160.5109490.6816480.5409250.706240
15Selection-Rate0.4422350.2938390.4792900.3236710.518692
16Positive-Rate0.9915070.8266671.0227270.9115651.027778
17Sample_Size1056.000000NaNNaNNaNNaN
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" }, - "execution_count": 69, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -766,9 +840,14 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 18, "id": "f94a20dc", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.403007Z", + "start_time": "2023-10-21T20:58:34.380470Z" + } + }, "outputs": [], "source": [ "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=list(models_config.keys()))" @@ -776,9 +855,14 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 19, "id": "b04d06cf", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.440589Z", + "start_time": "2023-10-21T20:58:34.403653Z" + } + }, "outputs": [], "source": [ "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" @@ -794,18 +878,51 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 20, "id": "be6ace22", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.476652Z", + "start_time": "2023-10-21T20:58:34.423407Z" + } + }, "outputs": [], "source": [ "models_composed_metrics_df = metrics_composer.compose_metrics()" ] }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": " Metric sex race sex&race \\\n0 Accuracy_Parity 0.015760 0.010971 -0.005266 \n1 Aleatoric_Uncertainty_Parity -0.004072 0.014248 0.007256 \n2 Aleatoric_Uncertainty_Ratio 0.995327 1.016576 1.008393 \n3 Equalized_Odds_FNR -0.222727 -0.199043 -0.185362 \n4 Equalized_Odds_FPR 0.095457 0.100382 0.132202 \n.. ... ... ... ... \n59 Disparate_Impact 1.159674 1.102332 1.093266 \n60 Std_Parity 0.000692 0.002415 0.002737 \n61 Std_Ratio 1.015140 1.053930 1.060978 \n62 Equalized_Odds_TNR -0.082094 -0.102184 -0.128932 \n63 Equalized_Odds_TPR 0.180202 0.165659 0.165871 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n.. ... \n59 XGBClassifier \n60 XGBClassifier \n61 XGBClassifier \n62 XGBClassifier \n63 XGBClassifier \n\n[64 rows x 5 columns]", + "text/html": "
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Metricsexracesex&raceModel_Name
0Accuracy_Parity0.0157600.010971-0.005266DecisionTreeClassifier
1Aleatoric_Uncertainty_Parity-0.0040720.0142480.007256DecisionTreeClassifier
2Aleatoric_Uncertainty_Ratio0.9953271.0165761.008393DecisionTreeClassifier
3Equalized_Odds_FNR-0.222727-0.199043-0.185362DecisionTreeClassifier
4Equalized_Odds_FPR0.0954570.1003820.132202DecisionTreeClassifier
..................
59Disparate_Impact1.1596741.1023321.093266XGBClassifier
60Std_Parity0.0006920.0024150.002737XGBClassifier
61Std_Ratio1.0151401.0539301.060978XGBClassifier
62Equalized_Odds_TNR-0.082094-0.102184-0.128932XGBClassifier
63Equalized_Odds_TPR0.1802020.1656590.165871XGBClassifier
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" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_composed_metrics_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.488516Z", + "start_time": "2023-10-21T20:58:34.454122Z" + } + }, + "id": "a286da0406c6401d" + }, { "cell_type": "markdown", "id": "deb45226", - "metadata": {}, + "metadata": { + "is_executing": true + }, "source": [ "## Metrics Visualization and Reporting" ] @@ -820,9 +937,14 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 22, "id": "435b9d98", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.516611Z", + "start_time": "2023-10-21T20:58:34.478385Z" + } + }, "outputs": [], "source": [ "visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", @@ -832,16 +954,21 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 23, "id": "5efb1bf2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.601140Z", + "start_time": "2023-10-21T20:58:34.506904Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, - "execution_count": 74, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -855,16 +982,21 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 24, "id": "0eb8528e", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.607261Z", + "start_time": "2023-10-21T20:58:34.557943Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, - "execution_count": 75, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -885,19 +1017,21 @@ "You can use this plot to compare any pair of group fairness and stability metrics for all models." ], "metadata": { - "collapsed": false - } + "collapsed": false, + "is_executing": true + }, + "id": "1f4906acb27ce7dd" }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 25, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.HConcatChart(...)" }, - "execution_count": 76, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -906,19 +1040,29 @@ "visualizer.create_fairness_variance_interactive_bar_chart()" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-21T20:58:34.774635Z", + "start_time": "2023-10-21T20:58:34.606702Z" + } + }, + "id": "b1249b3994b75555" }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 26, "id": "df024aed", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:35.469528Z", + "start_time": "2023-10-21T20:58:34.775075Z" + } + }, "outputs": [ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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" 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\n" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -955,24 +1099,35 @@ { "cell_type": "markdown", "id": "55e6ce42", - "metadata": {}, + "metadata": { + "is_executing": true + }, "source": [ "Create an analysis report. It includes correspondent visualizations and details about your result metrics." ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 27, "id": "5a3811ff", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-21T20:58:36.148703Z", + "start_time": "2023-10-21T20:58:35.395033Z" + } + }, "outputs": [ { - "data": { - "text/plain": "", - "text/markdown": "App saved to ./docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__224023.html" - }, - "metadata": {}, - "output_type": "display_data" + "ename": "AttributeError", + "evalue": "module 'datapane' has no attribute 'Report'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[27], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mvisualizer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_html_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreport_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mReportType\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mMULTIPLE_RUNS_MULTIPLE_MODELS\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43mreport_save_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpath\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43mROOT_DIR\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mresults\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mreports\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_visualizer.py:480\u001B[0m, in \u001B[0;36mMetricsVisualizer.create_html_report\u001B[0;34m(self, report_type, report_save_path)\u001B[0m\n\u001B[1;32m 475\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m report_type \u001B[38;5;241m==\u001B[39m ReportType\u001B[38;5;241m.\u001B[39mMULTIPLE_RUNS_MULTIPLE_MODELS:\n\u001B[1;32m 476\u001B[0m boxes_and_whiskers_plot \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcreate_boxes_and_whiskers_for_models_multiple_runs(\n\u001B[1;32m 477\u001B[0m metrics_lst\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStd\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mIQR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mJitter\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mLabel_Stability\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAccuracy\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTPR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTNR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFPR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFNR\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[1;32m 478\u001B[0m )\n\u001B[0;32m--> 480\u001B[0m \u001B[43mdp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mReport\u001B[49m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m# Fairness and Stability Report\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 481\u001B[0m general_desc,\n\u001B[1;32m 482\u001B[0m \n\u001B[1;32m 483\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Model Composed Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 484\u001B[0m composed_metrics_desc,\n\u001B[1;32m 485\u001B[0m dp\u001B[38;5;241m.\u001B[39mDataTable(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodels_composed_metrics_df),\n\u001B[1;32m 486\u001B[0m \n\u001B[1;32m 487\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Boxes and Whiskers Plot Based On Multiple Models Runs\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 488\u001B[0m boxes_and_whiskers_plot_desc,\n\u001B[1;32m 489\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(boxes_and_whiskers_plot),\n\u001B[1;32m 490\u001B[0m \n\u001B[1;32m 491\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Overall Fairness and Stability Model Metrics Comparison\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 492\u001B[0m overall_metrics_desc,\n\u001B[1;32m 493\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(fairness_overall_metrics_bar_chart, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 494\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(variance_overall_metrics_bar_chart, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 495\u001B[0m \n\u001B[1;32m 496\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Fairness and Stability Interactive Bar Chart\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 497\u001B[0m individual_metrics_interactive_bar_chart_desc,\n\u001B[1;32m 498\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(interactive_bar_chart),\n\u001B[1;32m 499\u001B[0m \n\u001B[1;32m 500\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Model Ranks Based On Group Fairness and Stability Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 501\u001B[0m model_ranked_heatmap_desc,\n\u001B[1;32m 502\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 503\u001B[0m \n\u001B[1;32m 504\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Total Ranks Sum For Group Fairness and Stability Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 505\u001B[0m overall_model_ranked_heatmap_desc,\n\u001B[1;32m 506\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(total_model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 507\u001B[0m )\u001B[38;5;241m.\u001B[39msave(path\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(report_save_path, report_filename))\n\u001B[1;32m 508\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 509\u001B[0m dp\u001B[38;5;241m.\u001B[39mReport(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m# Fairness and Stability Report\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 510\u001B[0m general_desc,\n\u001B[1;32m 511\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 531\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(total_model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 532\u001B[0m )\u001B[38;5;241m.\u001B[39msave(path\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(report_save_path, report_filename))\n", + "\u001B[0;31mAttributeError\u001B[0m: module 'datapane' has no attribute 'Report'" + ] } ], "source": [ @@ -982,9 +1137,13 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "2326c129", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "start_time": "2023-10-21T20:58:36.147687Z" + } + }, "outputs": [], "source": [] } diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv new file mode 100644 index 00000000..9deda582 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv new file mode 100644 index 00000000..9deda582 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv new file mode 100644 index 00000000..9deda582 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/tests/__init__.py b/tests/__init__.py index b8197a1a..a4e6e160 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -5,6 +5,7 @@ from munch import DefaultMunch from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier +from xgboost import XGBClassifier from virny.datasets.base import BaseDataLoader @@ -64,7 +65,10 @@ def models_config(): 'LogisticRegression': LogisticRegression(C=1, max_iter=50, penalty='l2', - solver='newton-cg') + solver='newton-cg'), + 'XGBClassifier': XGBClassifier(learning_rate=0.1, + n_estimators=200, + max_depth=7), } diff --git a/tests/custom_classes/test_metrics_composer.py b/tests/custom_classes/test_metrics_composer.py index c37d21be..1b075669 100644 --- a/tests/custom_classes/test_metrics_composer.py +++ b/tests/custom_classes/test_metrics_composer.py @@ -1,4 +1,6 @@ import os + +import pandas as pd import pytest from tests import config_params, models_config, ROOT_DIR @@ -7,17 +9,41 @@ from virny.configs.constants import * +def compare_composed_metric_dfs(expected_composed_metrics_df, actual_composed_metrics_df, + model_name, composed_metrics_lst, groups, alpha=0.000_001): + for metric_name in composed_metrics_lst: + for group in groups: + expected_metric_val = expected_composed_metrics_df[ + (expected_composed_metrics_df['Model_Name'] == model_name) & + (expected_composed_metrics_df['Metric'] == metric_name) + ][group].values[0] + actual_metric_val = actual_composed_metrics_df[ + (actual_composed_metrics_df['Model_Name'] == model_name) & + (actual_composed_metrics_df['Metric'] == metric_name) + ][group].values[0] + + assert abs(expected_metric_val - actual_metric_val) < alpha, f"Assert for {metric_name} metric and {group} group" + + @pytest.fixture(scope='module') -def models_metrics_dct(models_config): +def models_metrics_dct1(models_config): metrics_dir_path = os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821') models_metrics_dct = read_model_metric_dfs(metrics_dir_path, model_names=list(models_config.keys())) return models_metrics_dct +@pytest.fixture(scope='module') +def models_metrics_dct2(models_config): + metrics_dir_path = os.path.join(ROOT_DIR, 'tests', 'files_for_tests', + 'COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806') + models_metrics_dct = read_model_metric_dfs(metrics_dir_path, model_names=list(models_config.keys())) + return models_metrics_dct + + # ========================== Test compose_metrics ========================== -def test_compose_metrics_true1(models_metrics_dct, config_params): - metrics_composer = MetricsComposer(models_metrics_dct, config_params.sensitive_attributes_dct) +def test_compose_metrics_true1(models_metrics_dct1, config_params): + metrics_composer = MetricsComposer(models_metrics_dct1, config_params.sensitive_attributes_dct) models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape @@ -37,8 +63,8 @@ def test_compose_metrics_true1(models_metrics_dct, config_params): ) -def test_compose_metrics_true2(models_metrics_dct, config_params): - metrics_composer = MetricsComposer(models_metrics_dct, {'sex': 0, 'race': 'Caucasian'}) +def test_compose_metrics_true2(models_metrics_dct1, config_params): + metrics_composer = MetricsComposer(models_metrics_dct1, {'sex': 0, 'race': 'Caucasian'}) models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape @@ -56,3 +82,60 @@ def test_compose_metrics_true2(models_metrics_dct, config_params): DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY]) ) + + +def test_compose_metrics_true3(models_metrics_dct2, config_params): + metrics_composer = MetricsComposer(models_metrics_dct2, config_params.sensitive_attributes_dct) + models_composed_metrics_df = metrics_composer.compose_metrics() + + # Check shape + assert models_composed_metrics_df.shape == (32, 5) + + # Check column names + assert sorted(models_composed_metrics_df.columns.tolist()) == sorted(['Metric', 'Model_Name', 'sex', 'race', 'sex&race']) + + # Check unique Model_Name + assert sorted(models_composed_metrics_df['Model_Name'].unique().tolist()) == sorted(['LogisticRegression', 'XGBClassifier']) + + # Check all metrics presence + assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == ( + sorted([EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, + DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, + LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY, + ALEATORIC_UNCERTAINTY_PARITY, ALEATORIC_UNCERTAINTY_RATIO, + OVERALL_UNCERTAINTY_PARITY, OVERALL_UNCERTAINTY_RATIO]) + ) + + expected_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'composed_metrics', + 'Multiple_Models_Interface_Use_Case.csv'), header=0) + # Check error disparity metrics + compare_composed_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, + actual_composed_metrics_df=models_composed_metrics_df, + model_name='XGBClassifier', + groups=['sex', 'race', 'sex&race'], + composed_metrics_lst=[EQUALIZED_ODDS_TPR, + EQUALIZED_ODDS_TNR, + EQUALIZED_ODDS_FPR, + EQUALIZED_ODDS_FNR, + DISPARATE_IMPACT, + STATISTICAL_PARITY_DIFFERENCE, + ACCURACY_PARITY]) + # Check stability disparity metrics + compare_composed_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, + actual_composed_metrics_df=models_composed_metrics_df, + model_name='XGBClassifier', + groups=['sex', 'race', 'sex&race'], + composed_metrics_lst=[LABEL_STABILITY_RATIO, + IQR_PARITY, + STD_PARITY, + STD_RATIO, + JITTER_PARITY]) + # Check uncertainty disparity metrics + compare_composed_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, + actual_composed_metrics_df=models_composed_metrics_df, + model_name='XGBClassifier', + groups=['sex', 'race', 'sex&race'], + composed_metrics_lst=[OVERALL_UNCERTAINTY_PARITY, + OVERALL_UNCERTAINTY_RATIO, + ALEATORIC_UNCERTAINTY_PARITY, + ALEATORIC_UNCERTAINTY_RATIO]) diff --git a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv new file mode 100644 index 00000000..6c75b817 --- /dev/null +++ b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Std,0.023145077682000158,0.02295679688338056,0.023192092177276767,0.02302529995138481,0.02322231752697641,0.023174469226794232,0.023115907960185658,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.030332964086499062,0.029691096939145224,0.030493240971814644,0.03004027634723248,0.030521706647334536,0.030269718882647973,0.030395731968056942,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.9098202687501268,0.9180360846366616,0.9077687454932526,0.9149079334836466,0.9065394382210347,0.9171805567370475,0.9025155301065038,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.5193473548663423,0.5672318775670018,0.507390391209728,0.584256755568952,0.47748989086185556,0.5727983488084806,0.46629976465206924,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.9069476890999941,0.9155224984956714,0.9048065236769315,0.9124076160022803,0.9034268016583331,0.9146031627382991,0.8993499926212237,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.4342679558492735,0.4335960867937419,0.43443572433532934,0.43407228764765693,0.4343941344091945,0.43288209382904624,0.4356433585334991,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9482575757575756,0.9283412322274881,0.9532307692307692,0.9544927536231883,0.9442367601246107,0.9465399239543726,0.9499622641509433,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.04036796536796538,0.05642325176516105,0.036358893853399354,0.036007098491570515,0.043180113166762076,0.04230154419182134,0.03844897959183675,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.6220806794055201,0.48,0.648989898989899,0.4489795918367347,0.7006172839506173,0.48936170212765956,0.7102473498233216,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.7333333333333333,0.8088235294117647,0.7104677060133631,0.8164794007490637,0.6635220125786163,0.8106508875739645,0.6275303643724697,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.6525612472160356,0.5806451612903226,0.6640826873385013,0.5739130434782609,0.6796407185628742,0.5897435897435898,0.6860068259385665,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.37791932059447986,0.52,0.351010101010101,0.5510204081632653,0.2993827160493827,0.5106382978723404,0.28975265017667845,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.26666666666666666,0.19117647058823528,0.289532293986637,0.18352059925093633,0.33647798742138363,0.1893491124260355,0.3724696356275304,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.6837121212121212,0.6919431279620853,0.6816568047337278,0.6859903381642513,0.6822429906542056,0.6958174904942965,0.6716981132075471,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.6369565217391304,0.5255474452554745,0.6564495530012772,0.5038167938931297,0.6899696048632219,0.5348837209302325,0.6979166666666666,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.4251893939393939,0.2938388625592417,0.45798816568047335,0.2777777777777778,0.5202492211838006,0.2965779467680608,0.5528301886792453,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9532908704883227,0.8266666666666667,0.9772727272727273,0.782312925170068,1.0308641975308641,0.8297872340425532,1.0353356890459364,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,,,,,,,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv new file mode 100644 index 00000000..eec38f52 --- /dev/null +++ b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Std,0.04625367000699043,0.045700035989284515,0.046391911804676056,0.044785287231206894,0.04720057547092438,0.044880133122205734,0.04761683568358421,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +IQR,0.0597414563720425,0.058872319793249195,0.05995848337574118,0.05804315115806561,0.06083662515488741,0.05800687269566177,0.06146294885086563,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Overall_Uncertainty,0.8795207510501655,0.8945882320644919,0.8757583386312036,0.8745047002582029,0.8827554006262907,0.8813648645976837,0.8776905553407417,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Mean_Prediction,0.5246897339820862,0.5801356434822083,0.5108446478843689,0.5917010307312012,0.48147687315940857,0.582183837890625,0.46762949228286743,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Aleatoric_Uncertainty,0.8708176016807556,0.8863023519515991,0.8669508695602417,0.8662495017051697,0.8737633228302002,0.8731518387794495,0.868500828742981,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Statistical_Bias,0.4131621842653575,0.4106973970035241,0.4137776518538153,0.4088316087201598,0.4159547984019804,0.408983829201061,0.4173090045744518,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Label_Stability,0.9079545454545456,0.8591469194312796,0.9201420118343195,0.8995169082125605,0.9133956386292836,0.8946768060836502,0.9211320754716982,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Jitter,0.07335343228200376,0.1062269078247414,0.06514478927665741,0.07706990042393774,0.07095683133066294,0.0811003336695897,0.06566499807470172,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TPR,0.6581740976645435,0.5066666666666667,0.6868686868686869,0.54421768707483,0.7098765432098766,0.5585106382978723,0.7243816254416962,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TNR,0.7384615384615385,0.8014705882352942,0.7193763919821826,0.7940074906367042,0.6918238993710691,0.7928994082840237,0.6639676113360324,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +PPV,0.6695464362850972,0.5846153846153846,0.6834170854271356,0.5925925925925926,0.7012195121951219,0.6,0.7118055555555556,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FNR,0.34182590233545646,0.49333333333333335,0.31313131313131315,0.4557823129251701,0.29012345679012347,0.44148936170212766,0.2756183745583039,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FPR,0.26153846153846155,0.19852941176470587,0.2806236080178174,0.20599250936329588,0.3081761006289308,0.20710059171597633,0.3360323886639676,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Accuracy,0.7026515151515151,0.6966824644549763,0.7041420118343196,0.7053140096618358,0.7009345794392523,0.7091254752851711,0.6962264150943396,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +F1,0.6638115631691649,0.5428571428571428,0.6851385390428212,0.5673758865248227,0.7055214723926381,0.5785123966942148,0.7180385288966725,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Selection-Rate,0.4384469696969697,0.3080568720379147,0.4710059171597633,0.32608695652173914,0.5109034267912772,0.33269961977186313,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Positive-Rate,0.9830148619957537,0.8666666666666667,1.005050505050505,0.9183673469387755,1.0123456790123457,0.9308510638297872,1.017667844522968,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Sample_Size,1056.0,,,,,,,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" diff --git a/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv b/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv new file mode 100644 index 00000000..fdcd3463 --- /dev/null +++ b/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv @@ -0,0 +1,65 @@ +Metric,sex,race,sex&race,Model_Name +Accuracy_Parity,0.015760,0.010971,-0.005266,DecisionTreeClassifier +Aleatoric_Uncertainty_Parity,-0.004072,0.014248,0.007256,DecisionTreeClassifier +Aleatoric_Uncertainty_Ratio,0.995327,1.016576,1.008393,DecisionTreeClassifier +Equalized_Odds_FNR,-0.222727,-0.199043,-0.185362,DecisionTreeClassifier +Equalized_Odds_FPR,0.095457,0.100382,0.132202,DecisionTreeClassifier +IQR_Parity,0.003254,-0.000365,-0.000008,DecisionTreeClassifier +Jitter_Parity,-0.011124,0.006842,0.004612,DecisionTreeClassifier +Label_Stability_Ratio,1.020156,0.996574,0.997767,DecisionTreeClassifier +Overall_Uncertainty_Parity,-0.007746,0.012562,0.003668,DecisionTreeClassifier +Overall_Uncertainty_Ratio,0.991382,1.014195,1.004118,DecisionTreeClassifier +Statistical_Parity_Difference,0.196061,0.116213,0.100951,DecisionTreeClassifier +Disparate_Impact,1.237170,1.127488,1.108450,DecisionTreeClassifier +Std_Parity,-0.002647,0.000628,-0.000949,DecisionTreeClassifier +Std_Ratio,0.963159,1.009053,0.986485,DecisionTreeClassifier +Equalized_Odds_TNR,-0.095457,-0.100382,-0.132202,DecisionTreeClassifier +Equalized_Odds_TPR,0.222727,0.199043,0.185362,DecisionTreeClassifier +Accuracy_Parity,-0.010286,-0.003747,-0.024119,LogisticRegression +Aleatoric_Uncertainty_Parity,-0.010716,-0.008981,-0.015253,LogisticRegression +Aleatoric_Uncertainty_Ratio,0.988295,0.990157,0.983323,LogisticRegression +Equalized_Odds_FNR,-0.168990,-0.251638,-0.220886,LogisticRegression +Equalized_Odds_FPR,0.098356,0.152957,0.183121,LogisticRegression +IQR_Parity,0.000802,0.000481,0.000126,LogisticRegression +Jitter_Parity,-0.020064,0.007173,-0.003853,LogisticRegression +Label_Stability_Ratio,1.026811,0.989255,1.003616,LogisticRegression +Overall_Uncertainty_Parity,-0.010267,-0.008368,-0.014665,LogisticRegression +Overall_Uncertainty_Ratio,0.988816,0.990853,0.984011,LogisticRegression +Statistical_Parity_Difference,0.150606,0.248551,0.205548,LogisticRegression +Disparate_Impact,1.182185,1.317713,1.247712,LogisticRegression +Std_Parity,0.000235,0.000197,-0.000059,LogisticRegression +Std_Ratio,1.010249,1.008557,0.997473,LogisticRegression +Equalized_Odds_TNR,-0.098356,-0.152957,-0.183121,LogisticRegression +Equalized_Odds_TPR,0.168990,0.251638,0.220886,LogisticRegression +Accuracy_Parity,-0.017426,-0.010226,-0.022433,RandomForestClassifier +Aleatoric_Uncertainty_Parity,-0.020202,0.018765,0.001511,RandomForestClassifier +Aleatoric_Uncertainty_Ratio,0.976215,1.022833,1.001816,RandomForestClassifier +Equalized_Odds_FNR,-0.105253,-0.180461,-0.158729,RandomForestClassifier +Equalized_Odds_FPR,0.079867,0.126599,0.145905,RandomForestClassifier +IQR_Parity,-0.011821,-0.003389,-0.002438,RandomForestClassifier +Jitter_Parity,-0.040291,-0.004564,-0.009985,RandomForestClassifier +Label_Stability_Ratio,1.089016,1.017421,1.025583,RandomForestClassifier +Overall_Uncertainty_Parity,-0.025571,0.016989,0.000197,RandomForestClassifier +Overall_Uncertainty_Ratio,0.970915,1.020024,1.000230,RandomForestClassifier +Statistical_Parity_Difference,0.060909,0.145251,0.102699,RandomForestClassifier +Disparate_Impact,1.064341,1.161756,1.109701,RandomForestClassifier +Std_Parity,-0.005710,-0.000903,-0.000312,RandomForestClassifier +Std_Ratio,0.923399,0.987202,0.995557,RandomForestClassifier +Equalized_Odds_TNR,-0.079867,-0.126599,-0.145905,RandomForestClassifier +Equalized_Odds_TPR,0.105253,0.180461,0.158729,RandomForestClassifier +Accuracy_Parity,0.007460,-0.004379,-0.012899,XGBClassifier +Aleatoric_Uncertainty_Parity,-0.019351,0.007514,-0.004651,XGBClassifier +Aleatoric_Uncertainty_Ratio,0.978166,1.008674,0.994673,XGBClassifier +Equalized_Odds_FNR,-0.180202,-0.165659,-0.165871,XGBClassifier +Equalized_Odds_FPR,0.082094,0.102184,0.128932,XGBClassifier +IQR_Parity,0.001086,0.002793,0.003456,XGBClassifier +Jitter_Parity,-0.041082,-0.006113,-0.015435,XGBClassifier +Label_Stability_Ratio,1.070995,1.015429,1.029570,XGBClassifier +Overall_Uncertainty_Parity,-0.018830,0.008251,-0.003674,XGBClassifier +Overall_Uncertainty_Ratio,0.978951,1.009435,0.995831,XGBClassifier +Statistical_Parity_Difference,0.138384,0.093978,0.086817,XGBClassifier +Disparate_Impact,1.159674,1.102332,1.093266,XGBClassifier +Std_Parity,0.000692,0.002415,0.002737,XGBClassifier +Std_Ratio,1.015140,1.053930,1.060978,XGBClassifier +Equalized_Odds_TNR,-0.082094,-0.102184,-0.128932,XGBClassifier +Equalized_Odds_TPR,0.180202,0.165659,0.165871,XGBClassifier From 072085b36e59a9a9f32fc6be369edbeb043e48ad Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sun, 22 Oct 2023 03:25:43 +0300 Subject: [PATCH 038/148] Added tests for all metrics --- ..._Sensitive_Attributes_20231021__202919.csv | 5 - ..._Sensitive_Attributes_20231021__205710.csv | 5 - ..._Sensitive_Attributes_20231021__205809.csv | 5 - tests/__init__.py | 48 + tests/analyzers/__init__.py | 0 .../analyzers/test_subgroup_error_analyzer.py | 41 + tests/custom_classes/test_metrics_composer.py | 74 +- .../COMPAS_RF_expected_metrics.csv | 19 + .../COMPAS_RF_expected_preds.csv | 1057 +++++++++++++++++ .../COMPAS_use_case/COMPAS_RF_predictions.csv | 1057 +++++++++++++++++ .../COMPAS_use_case/COMPAS_y_test.csv | 1057 +++++++++++++++++ tests/user_interfaces/__init__.py | 0 .../test_compute_model_metrics.py | 92 ++ tests/utils/test_stability_utils.py | 25 +- 14 files changed, 3421 insertions(+), 64 deletions(-) delete mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv delete mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv delete mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv create mode 100644 tests/analyzers/__init__.py create mode 100644 tests/analyzers/test_subgroup_error_analyzer.py create mode 100644 tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv create mode 100644 tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_preds.csv create mode 100644 tests/files_for_tests/COMPAS_use_case/COMPAS_RF_predictions.csv create mode 100644 tests/files_for_tests/COMPAS_use_case/COMPAS_y_test.csv create mode 100644 tests/user_interfaces/__init__.py create mode 100644 tests/user_interfaces/test_compute_model_metrics.py diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv deleted file mode 100644 index 9deda582..00000000 --- a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__202919.csv +++ /dev/null @@ -1,5 +0,0 @@ -Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params -COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" -COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" -COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" -COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv deleted file mode 100644 index 9deda582..00000000 --- a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205710.csv +++ /dev/null @@ -1,5 +0,0 @@ -Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params -COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" -COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" -COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" -COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv deleted file mode 100644 index 9deda582..00000000 --- a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231021__205809.csv +++ /dev/null @@ -1,5 +0,0 @@ -Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params -COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" -COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" -COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" -COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/tests/__init__.py b/tests/__init__.py index a4e6e160..04162dfa 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -25,6 +25,22 @@ def get_root_dir(): return root_dir +def compare_metric_dfs(expected_composed_metrics_df, actual_composed_metrics_df, + model_name, metrics_lst, groups, alpha=0.000_001): + for metric_name in metrics_lst: + for group in groups: + expected_metric_val = expected_composed_metrics_df[ + (expected_composed_metrics_df['Model_Name'] == model_name) & + (expected_composed_metrics_df['Metric'] == metric_name) + ][group].values[0] + actual_metric_val = actual_composed_metrics_df[ + (actual_composed_metrics_df['Model_Name'] == model_name) & + (actual_composed_metrics_df['Metric'] == metric_name) + ][group].values[0] + + assert abs(expected_metric_val - actual_metric_val) < alpha, f"Assert for {metric_name} metric and {group} group" + + ROOT_DIR = get_root_dir() @@ -112,3 +128,35 @@ def compas_without_sensitive_attrs_dataset_class(): target=target, numerical_columns=numerical_columns, categorical_columns=categorical_columns) + +@pytest.fixture(scope='package') +def COMPAS_y_test(): + y_test = pd.read_csv(os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'COMPAS_use_case', 'COMPAS_y_test.csv'), header=0) + y_test = y_test.set_index("0") + return y_test + + +@pytest.fixture(scope='package') +def COMPAS_RF_expected_preds(): + expected_preds = pd.read_csv(os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'COMPAS_use_case', + 'COMPAS_RF_expected_preds.csv'), header=0) + expected_preds = expected_preds.set_index("0") + return expected_preds + + +@pytest.fixture(scope='package') +def COMPAS_RF_bootstrap_predictions(): + models_predictions = pd.read_csv(os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'COMPAS_use_case', + 'COMPAS_RF_predictions.csv'), header=0) + models_predictions = models_predictions.reset_index(drop=True) + models_predictions_dct = dict() + for col in models_predictions.columns: + models_predictions_dct[int(col)] = models_predictions[col].to_numpy() + + return models_predictions_dct + + +@pytest.fixture(scope='package') +def COMPAS_RF_expected_metrics(): + return pd.read_csv(os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'COMPAS_use_case', + 'COMPAS_RF_expected_metrics.csv'), header=0) diff --git a/tests/analyzers/__init__.py b/tests/analyzers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/analyzers/test_subgroup_error_analyzer.py b/tests/analyzers/test_subgroup_error_analyzer.py new file mode 100644 index 00000000..d0f612f5 --- /dev/null +++ b/tests/analyzers/test_subgroup_error_analyzer.py @@ -0,0 +1,41 @@ +import pandas as pd +from sklearn.metrics import confusion_matrix + +from virny.configs.constants import * +from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer + + +def test_overall_accuracy_metrics_computation(): + y_test = pd.Series([0, 0, 1, 1, 0, 1, 0, 1, 1, 1]) + y_preds = pd.Series([0, 0, 1, 1, 0, 1, 1, 0, 1, 1]) + + error_analyzer = SubgroupErrorAnalyzer(X_test=pd.DataFrame(), + y_test=pd.DataFrame(), + sensitive_attributes_dct=dict(), + test_protected_groups=dict(), + computation_mode=None) + prediction_metrics = error_analyzer._compute_metrics(y_test, y_preds) + + # Check accuracy metrics + TN, FP, FN, TP = confusion_matrix(y_test, y_preds).ravel() + + alpha = 0.000_001 + expected_TPR = TP/(TP+FN) + expected_TNR = TN/(TN+FP) + expected_PPV = TP/(TP+FP) + expected_FNR = FN/(FN+TP) + expected_FPR = FP/(FP+TN) + expected_ACCURACY = (TP+TN)/(TP+TN+FP+FN) + expected_F1 = (2*TP)/(2*TP+FP+FN) + expected_SELECTION_RATE = (TP+FP)/(TP+FP+TN+FN) + expected_POSITIVE_RATE = (TP+FP)/(TP+FN) + + assert abs(prediction_metrics[TPR] - expected_TPR) < alpha + assert abs(prediction_metrics[TNR] - expected_TNR) < alpha + assert abs(prediction_metrics[PPV] - expected_PPV) < alpha + assert abs(prediction_metrics[FNR] - expected_FNR) < alpha + assert abs(prediction_metrics[FPR] - expected_FPR) < alpha + assert abs(prediction_metrics[ACCURACY] - expected_ACCURACY) < alpha + assert abs(prediction_metrics[F1] - expected_F1) < alpha + assert abs(prediction_metrics[SELECTION_RATE] - expected_SELECTION_RATE) < alpha + assert abs(prediction_metrics[POSITIVE_RATE] - expected_POSITIVE_RATE) < alpha diff --git a/tests/custom_classes/test_metrics_composer.py b/tests/custom_classes/test_metrics_composer.py index 1b075669..fb9cc9b0 100644 --- a/tests/custom_classes/test_metrics_composer.py +++ b/tests/custom_classes/test_metrics_composer.py @@ -3,28 +3,12 @@ import pandas as pd import pytest -from tests import config_params, models_config, ROOT_DIR +from tests import config_params, models_config, ROOT_DIR, compare_metric_dfs from virny.utils.custom_initializers import read_model_metric_dfs from virny.custom_classes.metrics_composer import MetricsComposer from virny.configs.constants import * -def compare_composed_metric_dfs(expected_composed_metrics_df, actual_composed_metrics_df, - model_name, composed_metrics_lst, groups, alpha=0.000_001): - for metric_name in composed_metrics_lst: - for group in groups: - expected_metric_val = expected_composed_metrics_df[ - (expected_composed_metrics_df['Model_Name'] == model_name) & - (expected_composed_metrics_df['Metric'] == metric_name) - ][group].values[0] - actual_metric_val = actual_composed_metrics_df[ - (actual_composed_metrics_df['Model_Name'] == model_name) & - (actual_composed_metrics_df['Metric'] == metric_name) - ][group].values[0] - - assert abs(expected_metric_val - actual_metric_val) < alpha, f"Assert for {metric_name} metric and {group} group" - - @pytest.fixture(scope='module') def models_metrics_dct1(models_config): metrics_dir_path = os.path.join(ROOT_DIR, 'tests', 'files_for_tests', @@ -109,33 +93,33 @@ def test_compose_metrics_true3(models_metrics_dct2, config_params): expected_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'tests', 'files_for_tests', 'composed_metrics', 'Multiple_Models_Interface_Use_Case.csv'), header=0) # Check error disparity metrics - compare_composed_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, - actual_composed_metrics_df=models_composed_metrics_df, - model_name='XGBClassifier', - groups=['sex', 'race', 'sex&race'], - composed_metrics_lst=[EQUALIZED_ODDS_TPR, - EQUALIZED_ODDS_TNR, - EQUALIZED_ODDS_FPR, - EQUALIZED_ODDS_FNR, - DISPARATE_IMPACT, - STATISTICAL_PARITY_DIFFERENCE, - ACCURACY_PARITY]) + compare_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, + actual_composed_metrics_df=models_composed_metrics_df, + model_name='XGBClassifier', + groups=['sex', 'race', 'sex&race'], + metrics_lst=[EQUALIZED_ODDS_TPR, + EQUALIZED_ODDS_TNR, + EQUALIZED_ODDS_FPR, + EQUALIZED_ODDS_FNR, + DISPARATE_IMPACT, + STATISTICAL_PARITY_DIFFERENCE, + ACCURACY_PARITY]) # Check stability disparity metrics - compare_composed_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, - actual_composed_metrics_df=models_composed_metrics_df, - model_name='XGBClassifier', - groups=['sex', 'race', 'sex&race'], - composed_metrics_lst=[LABEL_STABILITY_RATIO, - IQR_PARITY, - STD_PARITY, - STD_RATIO, - JITTER_PARITY]) + compare_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, + actual_composed_metrics_df=models_composed_metrics_df, + model_name='XGBClassifier', + groups=['sex', 'race', 'sex&race'], + metrics_lst=[LABEL_STABILITY_RATIO, + IQR_PARITY, + STD_PARITY, + STD_RATIO, + JITTER_PARITY]) # Check uncertainty disparity metrics - compare_composed_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, - actual_composed_metrics_df=models_composed_metrics_df, - model_name='XGBClassifier', - groups=['sex', 'race', 'sex&race'], - composed_metrics_lst=[OVERALL_UNCERTAINTY_PARITY, - OVERALL_UNCERTAINTY_RATIO, - ALEATORIC_UNCERTAINTY_PARITY, - ALEATORIC_UNCERTAINTY_RATIO]) + compare_metric_dfs(expected_composed_metrics_df=expected_composed_metrics_df, + actual_composed_metrics_df=models_composed_metrics_df, + model_name='XGBClassifier', + groups=['sex', 'race', 'sex&race'], + metrics_lst=[OVERALL_UNCERTAINTY_PARITY, + OVERALL_UNCERTAINTY_RATIO, + ALEATORIC_UNCERTAINTY_PARITY, + ALEATORIC_UNCERTAINTY_RATIO]) diff --git a/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv b/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv new file mode 100644 index 00000000..55f650b9 --- /dev/null +++ b/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Std,0.068128,0.072994,0.066914,0.069108,0.067496,0.068876,0.067387,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Aleatoric_Uncertainty,0.836970,0.850505,0.833591,0.825442,0.844405,0.835656,0.838275,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Mean_Prediction,0.523332,0.579475,0.509313,0.596833,0.475934,0.585774,0.461361,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Statistical_Bias,0.404456,0.395950,0.406580,0.393744,0.411363,0.396692,0.412161,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Overall_Uncertainty,0.860751,0.878872,0.856227,0.850881,0.867116,0.860681,0.860822,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +IQR,0.089400,0.096787,0.087556,0.089845,0.089113,0.090047,0.088758,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Label_Stability,0.840871,0.807204,0.849278,0.833816,0.845421,0.836274,0.845434,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Jitter,0.112213,0.134949,0.106536,0.114909,0.110475,0.115290,0.109160,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +TPR,0.675159,0.613333,0.686869,0.564626,0.725309,0.585106,0.734982,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +TNR,0.738462,0.801471,0.719376,0.812734,0.676101,0.804734,0.647773,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +PPV,0.675159,0.630137,0.683417,0.624060,0.695266,0.625000,0.705085,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +FNR,0.324841,0.386667,0.313131,0.435374,0.274691,0.414894,0.265018,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +FPR,0.261538,0.198529,0.280624,0.187266,0.323899,0.195266,0.352227,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Accuracy,0.710227,0.734597,0.704142,0.724638,0.700935,0.726236,0.694340,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +F1,0.675159,0.621622,0.685139,0.592857,0.709970,0.604396,0.719723,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Selection-Rate,0.446023,0.345972,0.471006,0.321256,0.526480,0.334601,0.556604,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Positive-Rate,1.000000,0.973333,1.005051,0.904762,1.043210,0.936170,1.042403,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." +Sample_Size,1056.000000,NaN,NaN,NaN,NaN,NaN,NaN,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." diff --git a/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_preds.csv b/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_preds.csv new file mode 100644 index 00000000..d684fc51 --- /dev/null +++ b/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_preds.csv @@ -0,0 +1,1057 @@ +0,1 +8,1 +4246,1 +544,0 +1780,1 +3940,0 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sklearn.preprocessing import StandardScaler + +from tests import (COMPAS_y_test, COMPAS_RF_bootstrap_predictions, COMPAS_RF_expected_preds, compare_metric_dfs, + COMPAS_RF_expected_metrics) + +from virny.configs.constants import * +from virny.utils.protected_groups_partitioning import create_test_protected_groups +from virny.analyzers.subgroup_variance_calculator import SubgroupVarianceCalculator +from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer +from virny.utils.stability_utils import count_prediction_metrics +from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset +from virny.preprocessing.basic_preprocessing import preprocess_dataset + + +def test_subgroup_variance_and_error_analyzers(COMPAS_y_test, COMPAS_RF_bootstrap_predictions, COMPAS_RF_expected_preds, + COMPAS_RF_expected_metrics): + dataset_split_seed = 42 + test_set_fraction = 0.2 + + data_loader = CompasWithoutSensitiveAttrsDataset() + sensitive_attributes_dct = {'sex': 1, 'race': 'African-American', 'sex&race': None} + column_transformer = ColumnTransformer(transformers=[ + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('numerical_features', StandardScaler(), data_loader.numerical_columns), + ]) + base_flow_dataset = preprocess_dataset(data_loader, column_transformer, test_set_fraction, dataset_split_seed) + test_protected_groups = create_test_protected_groups(base_flow_dataset.X_test, base_flow_dataset.init_features_df, + sensitive_attributes_dct) + + y_preds, prediction_metrics = count_prediction_metrics(COMPAS_y_test, COMPAS_RF_bootstrap_predictions) + y_preds = pd.Series(y_preds, index=base_flow_dataset.y_test.index) + subgroup_variance_calculator = SubgroupVarianceCalculator(X_test=base_flow_dataset.X_test, + y_test=base_flow_dataset.y_test, + sensitive_attributes_dct=sensitive_attributes_dct, + test_protected_groups=test_protected_groups, + computation_mode=None) + subgroup_variance_calculator.set_overall_variance_metrics(prediction_metrics) + subgroup_variance_metrics_dct = subgroup_variance_calculator.compute_subgroup_metrics( + y_preds, COMPAS_RF_bootstrap_predictions, + save_results=False, result_filename=None, save_dir_path=None + ) + variance_metrics_df = pd.DataFrame(subgroup_variance_metrics_dct) + + # Compute error metrics for subgroups + error_analyzer = SubgroupErrorAnalyzer(X_test=base_flow_dataset.X_test, + y_test=base_flow_dataset.y_test, + sensitive_attributes_dct=sensitive_attributes_dct, + test_protected_groups=test_protected_groups, + computation_mode=None) + dtc_res = error_analyzer.compute_subgroup_metrics(y_preds=y_preds, + models_predictions=dict(), + save_results=False, + result_filename=None, + save_dir_path=None) + error_metrics_df = pd.DataFrame(dtc_res) + + metrics_df = pd.concat([variance_metrics_df, error_metrics_df]) + metrics_df = metrics_df.reset_index() + metrics_df = metrics_df.rename(columns={"index": "Metric"}) + metrics_df['Model_Name'] = 'RandomForestClassifier' + + # Check accuracy metrics + compare_metric_dfs(expected_composed_metrics_df=COMPAS_RF_expected_metrics, + actual_composed_metrics_df=metrics_df, + model_name='RandomForestClassifier', + groups=['overall', 'sex_priv', 'sex_dis', 'race_priv', 'race_dis', 'sex&race_priv', 'sex&race_dis'], + metrics_lst=[MEAN_PREDICTION, + STATISTICAL_BIAS, + TPR, + TNR, + PPV, + FNR, + FPR, + F1, + ACCURACY, + SELECTION_RATE, + POSITIVE_RATE]) + # Check stability metrics + compare_metric_dfs(expected_composed_metrics_df=COMPAS_RF_expected_metrics, + actual_composed_metrics_df=metrics_df, + model_name='RandomForestClassifier', + groups=['overall', 'sex_priv', 'sex_dis', 'race_priv', 'race_dis', 'sex&race_priv', 'sex&race_dis'], + metrics_lst=[STD, IQR, JITTER, LABEL_STABILITY]) + # Check uncertainty metrics + compare_metric_dfs(expected_composed_metrics_df=COMPAS_RF_expected_metrics, + actual_composed_metrics_df=metrics_df, + model_name='RandomForestClassifier', + groups=['overall', 'sex_priv', 'sex_dis', 'race_priv', 'race_dis', 'sex&race_priv', 'sex&race_dis'], + metrics_lst=[ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY]) diff --git a/tests/utils/test_stability_utils.py b/tests/utils/test_stability_utils.py index f0fa66aa..61f16014 100644 --- a/tests/utils/test_stability_utils.py +++ b/tests/utils/test_stability_utils.py @@ -1,10 +1,11 @@ import numpy as np +import pandas as pd from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import OneHotEncoder -from sklearn.preprocessing import StandardScaler +from sklearn.preprocessing import OneHotEncoder, StandardScaler -from tests import config_params, compas_dataset_class, compas_without_sensitive_attrs_dataset_class +from tests import (config_params, compas_dataset_class, compas_without_sensitive_attrs_dataset_class, + COMPAS_y_test, COMPAS_RF_bootstrap_predictions, COMPAS_RF_expected_preds) from virny.utils.stability_utils import count_prediction_metrics, generate_bootstrap from virny.preprocessing.basic_preprocessing import preprocess_dataset from virny.configs.constants import * @@ -19,6 +20,7 @@ def test_count_prediction_metrics_true1(): assert np.array_equal(y_preds, np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1])) + # Check stability and uncertainty metrics alpha = 0.000_001 assert abs(prediction_metrics[MEAN_PREDICTION] - 0.47000000000000003) < alpha assert abs(prediction_metrics[STATISTICAL_BIAS] - 0.42000000000000004) < alpha @@ -30,7 +32,7 @@ def test_count_prediction_metrics_true1(): assert abs(prediction_metrics[OVERALL_UNCERTAINTY] - 0.9560071897163649) < alpha -def test_count_prediction_metrics_true2(): +def test_count_prediction_metrics_false1(): y_test = np.array([0, 0, 1, 1, 0, 1, 0, 1, 1, 1]) uq_results = np.array([[0.6, 0.7, 0.3, 0.4, 0.5, 0.3, 0.7, 0.6, 0.4, 0.4]]) @@ -43,6 +45,21 @@ def test_count_prediction_metrics_true2(): assert actual == False +def test_count_prediction_metrics_true2(COMPAS_y_test, COMPAS_RF_bootstrap_predictions, COMPAS_RF_expected_preds): + y_preds, prediction_metrics = count_prediction_metrics(COMPAS_y_test, COMPAS_RF_bootstrap_predictions) + + alpha = 0.000_001 + assert np.array_equal(y_preds, COMPAS_RF_expected_preds['1'].to_numpy()) + assert abs(prediction_metrics[MEAN_PREDICTION] - 0.5233320457326472) < alpha + assert abs(prediction_metrics[STATISTICAL_BIAS] - 0.4044558084608265) < alpha + assert abs(prediction_metrics[JITTER] - 0.11221320346320351) < alpha + assert abs(prediction_metrics[LABEL_STABILITY] - 0.8408712121212122) < alpha + assert abs(prediction_metrics[STD] - 0.06812843675435999) < alpha + assert abs(prediction_metrics[IQR] - 0.08940024816894414) < alpha + assert abs(prediction_metrics[ALEATORIC_UNCERTAINTY] - 0.8369703514251653) < alpha + assert abs(prediction_metrics[OVERALL_UNCERTAINTY] - 0.8607514359506866) < alpha + + # ========================== Test generate_bootstrap ========================== def test_generate_bootstrap_true1(compas_without_sensitive_attrs_dataset_class, config_params): column_transformer = ColumnTransformer(transformers=[ From aa47d418e5abf5417d278e8152c1d757cccf719a Mon Sep 17 00:00:00 2001 From: proc1v Date: Sun, 22 Oct 2023 17:38:53 +0300 Subject: [PATCH 039/148] Added parameters to computation interfaces for postprocessing --- ..._overall_variance_analyzer_postprocessing.py | 11 ++++++----- .../metrics_computation_interfaces.py | 16 ++++++++++++++++ .../utils/postprocessing_intervention_utils.py | 17 ++++++++++++++++- 3 files changed, 38 insertions(+), 6 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 40019417..c712a91a 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -4,7 +4,7 @@ from tqdm.notebook import tqdm from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer -from virny.utils.postprocessing_intervention_utils import contruct_binary_label_dataset, predict_on_binary_label_dataset +from virny.utils.postprocessing_intervention_utils import contruct_binary_label_dataset_from_df, construct_binary_label_dataset_from_samples, predict_on_binary_label_dataset from virny.utils.stability_utils import generate_bootstrap @@ -27,7 +27,7 @@ def __init__(self, postprocessor, sensitive_attribute: str, self.postprocessor = postprocessor self.sensitive_attribute = sensitive_attribute - self.test_binary_label_dataset = contruct_binary_label_dataset(X_test, y_test, target_column, sensitive_attribute) + self.test_binary_label_dataset = contruct_binary_label_dataset_from_df(X_test, y_test, target_column, sensitive_attribute) def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: bool = True) -> dict: """ @@ -50,7 +50,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: print('\n', flush=True) - self.__logger.info('Start classifiers testing by bootstrap') + self._AbstractOverallVarianceAnalyzer__logger.info('Start classifiers testing by bootstrap') # Remove a progress bar for UQ without estimators fitting cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), @@ -64,16 +64,17 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b X_sample, y_sample = generate_bootstrap(self.X_train, self.y_train, boostrap_size, with_replacement) classifier = self._fit_model(classifier, X_sample, y_sample) - train_binary_label_dataset_sample = contruct_binary_label_dataset(X_sample, y_sample, self.target_column, self.sensitive_attribute) + train_binary_label_dataset_sample = construct_binary_label_dataset_from_samples(X_sample, y_sample, self.X_train.columns, self.target_column, self.sensitive_attribute) train_binary_label_dataset_sample_pred = predict_on_binary_label_dataset(classifier, train_binary_label_dataset_sample) test_binary_label_dataset_pred = predict_on_binary_label_dataset(classifier, self.test_binary_label_dataset) postprocessor_fitted = self.postprocessor.fit(train_binary_label_dataset_sample, train_binary_label_dataset_sample_pred) + models_predictions[idx] = postprocessor_fitted.predict(test_binary_label_dataset_pred).labels.ravel() self.models_lst[idx] = classifier if self._verbose >= 1: print('\n', flush=True) - self.__logger.info('Successfully tested classifiers by bootstrap') + self._AbstractOverallVarianceAnalyzer__logger.info('Successfully tested classifiers by bootstrap') return models_predictions \ No newline at end of file diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index 929a9882..32fe0d9b 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -55,6 +55,7 @@ def compute_model_metrics_with_config(base_model, model_name: str, dataset: Base def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDataset, bootstrap_fraction: float, sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, + postprocessor=None, postprocessing_sensitive_attribute: str = None, model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, save_results: bool = True, save_results_dir_path: str = None, verbose: int = 0): """ @@ -80,6 +81,11 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase Dataset name to name a result file with metrics base_model_name Model name to name a result file with metrics + postprocessor + [Optional] Postprocessor object with fit and predict methods + to apply postprocessing intervention for the base model after training. + postprocessing_sensitive_attribute + [Optional] Sensitive attribute name to apply postprocessing intervention for the base model after training. save_results [Optional] If to save result metrics in a file model_setting @@ -112,6 +118,8 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase sensitive_attributes_dct=sensitive_attributes_dct, test_protected_groups=test_protected_groups, computation_mode=computation_mode, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, verbose=verbose) y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, result_filename=None, @@ -150,6 +158,7 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, models_config: dict, n_estimators: int, sensitive_attributes_dct: dict, model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, + postprocessor=None, postprocessing_sensitive_attribute: str = None, save_results: bool = True, save_results_dir_path: str = None, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. @@ -176,6 +185,11 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, [Optional] Model type: 'batch' or incremental. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + postprocessor + [Optional] Postprocessor object with fit and predict methods + to apply postprocessing intervention for the base model after training. + postprocessing_sensitive_attribute + [Optional] Sensitive attribute name to apply postprocessing intervention for the base model after training. save_results [Optional] If to save result metrics in a file save_results_dir_path @@ -204,6 +218,8 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, computation_mode=computation_mode, dataset_name=dataset_name, base_model_name=model_name, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=save_results, save_results_dir_path=save_results_dir_path, verbose=verbose) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index 85628023..564a346d 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -1,10 +1,25 @@ import copy import numpy as np +import pandas as pd from aif360.datasets import BinaryLabelDataset -def contruct_binary_label_dataset(X_sample, y_sample, target_column, sensitive_attribute): +def construct_binary_label_dataset_from_samples(X_sample, y_sample, column_names, target_column, sensitive_attribute): + df = pd.DataFrame(X_sample, columns=column_names) + df[target_column] = y_sample + + binary_label_dataset = BinaryLabelDataset( + df=df, + label_names=[target_column], + protected_attribute_names=[sensitive_attribute], + favorable_label=1, + unfavorable_label=0) + + return binary_label_dataset + + +def contruct_binary_label_dataset_from_df(X_sample, y_sample, target_column, sensitive_attribute): df = X_sample df[target_column] = y_sample From c2980540ba0b0a7050e8b7dbda43c1cac985a823 Mon Sep 17 00:00:00 2001 From: proc1v Date: Mon, 23 Oct 2023 20:25:53 +0300 Subject: [PATCH 040/148] Updated user_iterfaces for postprocessing --- .../user_interfaces/metrics_computation_interfaces.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index 32fe0d9b..61ec2ab8 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -287,7 +287,9 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, - custom_tbl_fields_dct: dict, db_writer_func, verbose: int = 0) -> dict: + custom_tbl_fields_dct: dict, db_writer_func, + postprocessor=None, postprocessing_sensitive_attribute: str = None, + verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. Save results to a database after each run appending fields and value from custom_tbl_fields_dct and using db_writer_func. @@ -306,6 +308,11 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi Dictionary where keys are column names and values to add to inserted metrics during saving results to a database db_writer_func Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database + postprocessor + [Optional] Postprocessor object with fit and predict methods + to apply postprocessing intervention for the base model after training. + postprocessing_sensitive_attribute + [Optional] Sensitive attribute name to apply postprocessing intervention for the base model after training. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -321,6 +328,8 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi sensitive_attributes_dct=config.sensitive_attributes_dct, model_setting=config.model_setting, computation_mode=config.computation_mode, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=False, verbose=verbose) From 3672df5fc1ec5aad4f6c920b405d9a99ec177907 Mon Sep 17 00:00:00 2001 From: proc1v Date: Mon, 6 Nov 2023 22:13:23 +0200 Subject: [PATCH 041/148] Added CreditCardDefault data loader --- virny/datasets/credit_card_default_clean.csv | 29947 +++++++++++++++++ virny/datasets/data_loaders.py | 30 + 2 files changed, 29977 insertions(+) create mode 100644 virny/datasets/credit_card_default_clean.csv diff --git a/virny/datasets/credit_card_default_clean.csv b/virny/datasets/credit_card_default_clean.csv new file mode 100644 index 00000000..475e1433 --- /dev/null +++ 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'credit_card_default_clean.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath(filename) + + df = pd.read_csv(dataset_path) + target = 'default_payment' + numerical_columns = [ + "limit_bal", "age", + "bill_amt1", "bill_amt2", "bill_amt3", + "bill_amt4", "bill_amt5", "bill_amt6", + "pay_amt1", "pay_amt2", "pay_amt3", + "pay_amt4", "pay_amt5", "pay_amt6" + ] + categorical_columns = [ + "sex", "education", "marriage", + "pay_0", "pay_2", "pay_3", + "pay_4", "pay_5", "pay_6" + ] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) + + class CreditDataset(BaseDataLoader): """ Dataset class for the Credit dataset that contains sensitive attributes among feature columns. From 09155c048fdf68f6310e362aa1ae07f5220d482b Mon Sep 17 00:00:00 2001 From: proc1v Date: Mon, 6 Nov 2023 22:20:29 +0200 Subject: [PATCH 042/148] Updated requirements --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 0b5c8ab7..96d90e01 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ wheel~=0.38.4 twine~=4.0.2 -numpy~=1.24.2 +numpy~=1.23 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 From c6006b23dea7e6e8730937288952e0ea6c24f7df Mon Sep 17 00:00:00 2001 From: proc1v Date: Mon, 6 Nov 2023 23:01:20 +0200 Subject: [PATCH 043/148] Fixed with_predict_proba parameter to variance analyzers --- .../abstract_overall_variance_analyzer.py | 5 +++-- .../batch_overall_variance_analyzer.py | 4 +++- ...verall_variance_analyzer_postprocessing.py | 4 +++- virny/analyzers/subgroup_variance_analyzer.py | 1 + virny/datasets/__init__.py | 3 ++- virny/datasets/data_loaders.py | 2 +- .../metrics_computation_interfaces.py | 22 ++++++++++--------- 7 files changed, 25 insertions(+), 16 deletions(-) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 67edffb5..18edc79b 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -43,7 +43,7 @@ class AbstractOverallVarianceAnalyzer(metaclass=ABCMeta): def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - dataset_name: str, n_estimators: int, verbose: int = 0): + dataset_name: str, n_estimators: int, with_predict_proba: bool = True, verbose: int = 0): self.base_model = base_model self.base_model_name = base_model_name self.bootstrap_fraction = bootstrap_fraction @@ -52,6 +52,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.models_lst = [deepcopy(base_model) for _ in range(n_estimators)] self.models_predictions = None self.prediction_metrics = None + self.with_predict_proba = with_predict_proba self._verbose = verbose self.__logger = get_logger(verbose) @@ -90,7 +91,7 @@ def compute_metrics(self, save_results: bool = True, with_fit: bool = True): self.models_predictions = self.UQ_by_boostrap(boostrap_size, with_replacement=True, with_fit=with_fit) # Count metrics based on prediction proba results - y_preds, self.prediction_metrics = count_prediction_metrics(self.y_test.values, self.models_predictions) + y_preds, self.prediction_metrics = count_prediction_metrics(self.y_test.values, self.models_predictions, self.with_predict_proba) self.__logger.info(f'Successfully computed predict proba metrics') if save_results: diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 3ddcc69e..4326a90b 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -37,7 +37,8 @@ class BatchOverallVarianceAnalyzer(AbstractOverallVarianceAnalyzer): """ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - target_column: str, dataset_name: str, n_estimators: int, verbose: int = 0): + target_column: str, dataset_name: str, n_estimators: int, + with_predict_proba: bool = True, verbose: int = 0): super().__init__(base_model=base_model, base_model_name=base_model_name, bootstrap_fraction=bootstrap_fraction, @@ -47,6 +48,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, y_test=y_test, dataset_name=dataset_name, n_estimators=n_estimators, + with_predict_proba=with_predict_proba, verbose=verbose) self.target_column = target_column diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index c712a91a..8aac6e4b 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -12,7 +12,8 @@ class BatchOverallVarianceAnalyzerPostProcessing(BatchOverallVarianceAnalyzer): def __init__(self, postprocessor, sensitive_attribute: str, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - target_column: str, dataset_name: str, n_estimators: int, verbose: int = 0): + target_column: str, dataset_name: str, n_estimators: int, + with_predict_proba: bool = True, verbose: int = 0): super().__init__(base_model=base_model, base_model_name=base_model_name, bootstrap_fraction=bootstrap_fraction, @@ -23,6 +24,7 @@ def __init__(self, postprocessor, sensitive_attribute: str, target_column=target_column, dataset_name=dataset_name, n_estimators=n_estimators, + with_predict_proba=with_predict_proba, verbose=verbose) self.postprocessor = postprocessor diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 6bf821e7..0625d685 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -59,6 +59,7 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b dataset_name=dataset_name, target_column=dataset.target, n_estimators=n_estimators, + with_predict_proba=False, verbose=verbose) else: overall_variance_analyzer = BatchOverallVarianceAnalyzer(base_model=base_model, diff --git a/virny/datasets/__init__.py b/virny/datasets/__init__.py index 038005a6..2e858c23 100644 --- a/virny/datasets/__init__.py +++ b/virny/datasets/__init__.py @@ -4,12 +4,13 @@ """ from .data_loaders import CompasWithoutSensitiveAttrsDataset, DiabetesDataset, CompasDataset, \ ACSIncomeDataset, ACSEmploymentDataset, ACSMobilityDataset, ACSTravelTimeDataset, ACSPublicCoverageDataset, \ - RicciDataset, LawSchoolDataset + RicciDataset, LawSchoolDataset, CreditCardDefaultDataset __all__ = [ "CompasWithoutSensitiveAttrsDataset", "CompasDataset", + "CreditCardDefaultDataset", "DiabetesDataset", "RicciDataset", "LawSchoolDataset", diff --git a/virny/datasets/data_loaders.py b/virny/datasets/data_loaders.py index e336a796..895bd433 100644 --- a/virny/datasets/data_loaders.py +++ b/virny/datasets/data_loaders.py @@ -36,7 +36,7 @@ def __init__(self, dataset_path: str = None): numerical_columns=numerical_columns, categorical_columns=categorical_columns, ) - + class CreditDataset(BaseDataLoader): """ diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index 61ec2ab8..a7d336e6 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -82,10 +82,9 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase base_model_name Model name to name a result file with metrics postprocessor - [Optional] Postprocessor object with fit and predict methods - to apply postprocessing intervention for the base model after training. + [Optional] Postprocessor object to apply to model predictions before metrics computation postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessing intervention for the base model after training. + [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions save_results [Optional] If to save result metrics in a file model_setting @@ -107,6 +106,8 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase for g in test_protected_groups.keys(): print(g, test_protected_groups[g].shape) + print("postprocessing_sensitive_attribute: ", postprocessing_sensitive_attribute) + # Compute stability metrics for subgroups subgroup_variance_analyzer = SubgroupVarianceAnalyzer(model_setting=model_setting, n_estimators=n_estimators, @@ -186,10 +187,9 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. postprocessor - [Optional] Postprocessor object with fit and predict methods - to apply postprocessing intervention for the base model after training. + [Optional] Postprocessor object to apply to model predictions before metrics computation postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessing intervention for the base model after training. + [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions save_results [Optional] If to save result metrics in a file save_results_dir_path @@ -223,6 +223,7 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, save_results=save_results, save_results_dir_path=save_results_dir_path, verbose=verbose) + print("metrics_computation_interfaces.py: model_metrics_df: ", model_metrics_df) models_metrics_dct[model_name] = model_metrics_df if verbose >= 2: print(f'\n[{model_name}] Metrics matrix:') @@ -287,7 +288,7 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, - custom_tbl_fields_dct: dict, db_writer_func, + custom_tbl_fields_dct: dict, db_writer_func, postprocessor=None, postprocessing_sensitive_attribute: str = None, verbose: int = 0) -> dict: """ @@ -309,10 +310,9 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi db_writer_func Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database postprocessor - [Optional] Postprocessor object with fit and predict methods - to apply postprocessing intervention for the base model after training. + [Optional] Postprocessor object to apply to model predictions before metrics computation postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessing intervention for the base model after training. + [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -332,6 +332,7 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=False, verbose=verbose) + #print(models_metrics_dct) # Concatenate current run metrics with previous results and # create melted_model_metrics_df to save it in a database @@ -360,6 +361,7 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi value_name="Metric_Value") run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) + #print(run_models_metrics_df) # Save results for this run in a database db_writer_func(run_models_metrics_df) From eea74b4ec21c879f427c9e0621a56ba8c11dd31c Mon Sep 17 00:00:00 2001 From: proc1v Date: Wed, 8 Nov 2023 20:08:03 +0200 Subject: [PATCH 044/148] Fixed numpy version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 96d90e01..6f68ab56 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ wheel~=0.38.4 twine~=4.0.2 -numpy~=1.23 +numpy~=1.23.5 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 From 438368857cc5476ad1e325deef5883608e5ffd76 Mon Sep 17 00:00:00 2001 From: proc1v Date: Sun, 12 Nov 2023 17:09:46 +0200 Subject: [PATCH 045/148] Added garbage collector --- .../batch_overall_variance_analyzer_postprocessing.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 8aac6e4b..10d05f2b 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -1,3 +1,5 @@ +import gc + import numpy as np import pandas as pd @@ -65,6 +67,10 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b if with_fit: X_sample, y_sample = generate_bootstrap(self.X_train, self.y_train, boostrap_size, with_replacement) classifier = self._fit_model(classifier, X_sample, y_sample) + + # Force garbage collection to avoid out of memory error + if with_fit and ((idx + 1) % 10 == 0 or (idx + 1) == self.n_estimators): + gc.collect() train_binary_label_dataset_sample = construct_binary_label_dataset_from_samples(X_sample, y_sample, self.X_train.columns, self.target_column, self.sensitive_attribute) train_binary_label_dataset_sample_pred = predict_on_binary_label_dataset(classifier, train_binary_label_dataset_sample) From 1ffc39d3c714d16b107c3d60c4074f9536462732 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 20 Nov 2023 23:15:55 +0200 Subject: [PATCH 046/148] Added Label_Stability_Difference --- virny/custom_classes/metrics_composer.py | 1 + 1 file changed, 1 insertion(+) diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index b18f3866..a7dbb412 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -55,6 +55,7 @@ def compose_metrics(self): 'Accuracy_Parity': cfm[dis_group]['Accuracy'] - cfm[priv_group]['Accuracy'], # Stability disparity metrics 'Label_Stability_Ratio': cfm[dis_group]['Label_Stability'] / cfm[priv_group]['Label_Stability'], + 'Label_Stability_Difference': cfm[dis_group]['Label_Stability'] - cfm[priv_group]['Label_Stability'], 'IQR_Parity': cfm[dis_group]['IQR'] - cfm[priv_group]['IQR'], 'Std_Parity': cfm[dis_group]['Std'] - cfm[priv_group]['Std'], 'Std_Ratio': cfm[dis_group]['Std'] / cfm[priv_group]['Std'], From 3b2a222a241d47163bba51cd95248ef2c4364d61 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 20 Nov 2023 23:20:00 +0200 Subject: [PATCH 047/148] Added Label_Stability_Difference --- virny/configs/constants.py | 1 + virny/custom_classes/metrics_composer.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 81d145b2..fff71dab 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -54,6 +54,7 @@ class ReportType(Enum): # Stability disparity metrics LABEL_STABILITY_RATIO = 'Label_Stability_Ratio' +LABEL_STABILITY_DIFFERENCE = 'Label_Stability_Difference' IQR_PARITY = 'IQR_Parity' STD_PARITY = 'Std_Parity' STD_RATIO = 'Std_Ratio' diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index cbd58c4c..1aae0c97 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -32,7 +32,8 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict): POSITIVE_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation), (DISPARATE_IMPACT, self._ratio_operation)], # Stability disparity metrics - LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation)], + LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation), + (LABEL_STABILITY_DIFFERENCE, self._difference_operation)], JITTER: [(JITTER_PARITY, self._difference_operation)], IQR: [(IQR_PARITY, self._difference_operation)], STD: [(STD_PARITY, self._difference_operation), From 620f36f0ba7ea0e5f497e523f0c3e689ba5792ad Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 28 Nov 2023 02:08:24 +0200 Subject: [PATCH 048/148] Added model performance summary --- ...Multiple_Models_Interface_Vis_Income.ipynb | 102 +++++---- virny/configs/constants.py | 1 + virny/custom_classes/metrics_composer.py | 3 +- .../metrics_interactive_visualizer.py | 205 ++++++++++++++++-- virny/utils/data_viz_utils.py | 26 +++ 5 files changed, 278 insertions(+), 59 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index e19f415f..a37d4449 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-13T20:20:09.765631Z", - "start_time": "2023-10-13T20:20:09.381209Z" + "end_time": "2023-11-27T23:09:00.744106Z", + "start_time": "2023-11-27T23:09:00.258137Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-13T20:20:09.774183Z", - "start_time": "2023-10-13T20:20:09.765873Z" + "end_time": "2023-11-27T23:09:00.753238Z", + "start_time": "2023-11-27T23:09:00.743899Z" } }, "outputs": [], @@ -37,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-13T20:20:09.783681Z", - "start_time": "2023-10-13T20:20:09.774750Z" + "end_time": "2023-11-27T23:09:14.159592Z", + "start_time": "2023-11-27T23:09:14.145555Z" } }, "outputs": [ @@ -72,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-13T20:20:11.549308Z", - "start_time": "2023-10-13T20:20:09.784822Z" + "end_time": "2023-11-27T23:09:16.946143Z", + "start_time": "2023-11-27T23:09:15.322037Z" } }, "outputs": [], @@ -86,12 +86,13 @@ "import pandas as pd\n", "\n", "from virny.datasets import ACSIncomeDataset\n", + "from virny.custom_classes.metrics_composer import MetricsComposer\n", "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "outputs": [], "source": [ "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n", @@ -100,52 +101,67 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:20:12.860282Z", - "start_time": "2023-10-13T20:20:11.551544Z" + "end_time": "2023-11-27T23:09:18.236763Z", + "start_time": "2023-11-27T23:09:16.949112Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_subgroup_metrics.csv'), header=0)\n", - "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_group_metrics.csv'), header=0)" + "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", + " subgroup_metrics_df['Intervention_Param'].astype(str))" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:20:12.888990Z", - "start_time": "2023-10-13T20:20:12.860786Z" + "end_time": "2023-11-27T23:09:18.781790Z", + "start_time": "2023-11-27T23:09:18.747739Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 7, - "outputs": [], + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.047756 0.074977 0.065217 \n1 Aleatoric_Uncertainty_Parity -0.039005 -0.011947 -0.009222 \n2 Aleatoric_Uncertainty_Ratio 0.935159 0.979638 0.984220 \n3 Equalized_Odds_FNR 0.030793 -0.110745 -0.052498 \n4 Equalized_Odds_FPR -0.021317 0.000952 -0.007008 \n\n Model_Name \n0 LGBMClassifier__alpha=0.7 \n1 LGBMClassifier__alpha=0.7 \n2 LGBMClassifier__alpha=0.7 \n3 LGBMClassifier__alpha=0.7 \n4 LGBMClassifier__alpha=0.7 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0477560.0749770.065217LGBMClassifier__alpha=0.7
1Aleatoric_Uncertainty_Parity-0.039005-0.011947-0.009222LGBMClassifier__alpha=0.7
2Aleatoric_Uncertainty_Ratio0.9351590.9796380.984220LGBMClassifier__alpha=0.7
3Equalized_Odds_FNR0.030793-0.110745-0.052498LGBMClassifier__alpha=0.7
4Equalized_Odds_FPR-0.0213170.000952-0.007008LGBMClassifier__alpha=0.7
\n
" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))\n", - "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", - " + models_composed_metrics_df['Intervention_Param'].astype(str))" + "model_names = subgroup_metrics_df['Model_Name'].unique()\n", + "models_metrics_dct = dict()\n", + "for model_name in model_names:\n", + " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", + "\n", + "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", + "models_composed_metrics_df = metrics_composer.compose_metrics()\n", + "models_composed_metrics_df.head()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:20:12.911932Z", - "start_time": "2023-10-13T20:20:12.888583Z" + "end_time": "2023-11-27T23:09:18.905842Z", + "start_time": "2023-11-27T23:09:18.850548Z" } }, - "id": "2d922003e752a4b4" + "id": "44ee5eff6054ce04" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -155,21 +171,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:20:12.937376Z", - "start_time": "2023-10-13T20:20:12.912368Z" + "end_time": "2023-11-27T23:10:14.375071Z", + "start_time": "2023-11-27T23:10:14.339164Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -180,8 +196,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:20:12.963217Z", - "start_time": "2023-10-13T20:20:12.935698Z" + "end_time": "2023-11-27T23:10:15.006243Z", + "start_time": "2023-11-27T23:10:14.979880Z" } }, "id": "15ed7d1ba1f22317" @@ -196,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 58, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-13T20:49:19.030436Z", - "start_time": "2023-10-13T20:49:18.977199Z" + "end_time": "2023-11-27T23:59:15.016940Z", + "start_time": "2023-11-27T23:59:14.968372Z" } }, "outputs": [], @@ -213,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 59, "outputs": [ { "name": "stdout", @@ -232,8 +248,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:50:05.536644Z", - "start_time": "2023-10-13T20:49:19.061199Z" + "end_time": "2023-11-28T00:08:09.433964Z", + "start_time": "2023-11-27T23:59:15.062378Z" } }, "id": "678a9dc8d51243f4" @@ -256,8 +272,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:23:04.989037Z", - "start_time": "2023-10-13T20:23:04.937593Z" + "end_time": "2023-11-27T22:52:53.559673Z", + "start_time": "2023-11-27T22:52:53.432952Z" } }, "id": "277b6d1de837dab7" @@ -270,8 +286,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-13T20:23:04.991061Z", - "start_time": "2023-10-13T20:23:04.988926Z" + "end_time": "2023-11-27T22:52:53.561907Z", + "start_time": "2023-11-27T22:52:53.559309Z" } }, "id": "c207d4345ddca1db" diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 81d145b2..fff71dab 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -54,6 +54,7 @@ class ReportType(Enum): # Stability disparity metrics LABEL_STABILITY_RATIO = 'Label_Stability_Ratio' +LABEL_STABILITY_DIFFERENCE = 'Label_Stability_Difference' IQR_PARITY = 'IQR_Parity' STD_PARITY = 'Std_Parity' STD_RATIO = 'Std_Ratio' diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index cbd58c4c..1aae0c97 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -32,7 +32,8 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict): POSITIVE_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation), (DISPARATE_IMPACT, self._ratio_operation)], # Stability disparity metrics - LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation)], + LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation), + (LABEL_STABILITY_DIFFERENCE, self._difference_operation)], JITTER: [(JITTER_PARITY, self._difference_operation)], IQR: [(IQR_PARITY, self._difference_operation)], STD: [(STD_PARITY, self._difference_operation), diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 54588226..69857337 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -1,12 +1,15 @@ import pandas as pd import gradio as gr import altair as alt +from pprint import pprint +from virny.configs.constants import * from virny.utils.common_helpers import str_to_float from virny.utils.protected_groups_partitioning import create_test_protected_groups from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection, - compute_proportions, compute_base_rates, create_col_facet_bar_chart) + compute_proportions, compute_base_rates, create_col_facet_bar_chart, + create_model_performance_summary_visualization) class MetricsInteractiveVisualizer: @@ -40,6 +43,14 @@ def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics_dct self.demo = None self.max_groups = 8 + # Metric names + self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY, POSITIVE_RATE] + self.all_stability_metrics = [STD, IQR, JITTER, LABEL_STABILITY] + self.all_uncertainty_metrics = [ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY] + self.all_error_disparity_metrics = [EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY] + self.all_stability_disparity_metrics = [LABEL_STABILITY_RATIO, LABEL_STABILITY_DIFFERENCE, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY] + self.all_uncertainty_disparity_metrics = [OVERALL_UNCERTAINTY_PARITY, OVERALL_UNCERTAINTY_RATIO, ALEATORIC_UNCERTAINTY_PARITY, ALEATORIC_UNCERTAINTY_RATIO] + # Create one metrics df with all model_dfs models_metrics_df = pd.DataFrame() for model_name in model_metrics_dct.keys(): @@ -123,7 +134,7 @@ def start_web_app(self): ) with gr.Row(): accuracy_metric = gr.Dropdown( - sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), + sorted(self.all_accuracy_metrics), value='Accuracy', multiselect=False, label="Constraint 1 (C1)", scale=2 ) @@ -131,7 +142,7 @@ def start_web_app(self): acc_max_val = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): fairness_metric = gr.Dropdown( - sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), + sorted(self.all_error_disparity_metrics), value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", scale=2 ) @@ -139,7 +150,7 @@ def start_web_app(self): fairness_max_val = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): subgroup_stability_metric = gr.Dropdown( - sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), + sorted(self.all_stability_metrics), value='Label_Stability', multiselect=False, label="Constraint 3 (C3)", scale=2 ) @@ -147,7 +158,7 @@ def start_web_app(self): subgroup_stab_max_val = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): group_stability_metrics = gr.Dropdown( - sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), + sorted(self.all_stability_disparity_metrics), value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", scale=2 ) @@ -179,15 +190,15 @@ def start_web_app(self): ) subgroup_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") accuracy_metrics = gr.Dropdown( - sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), + sorted(self.all_accuracy_metrics), value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", ) uncertainty_metrics = gr.Dropdown( - sorted(['Aleatoric_Uncertainty', 'Overall_Uncertainty']), + sorted(self.all_uncertainty_metrics), value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:", ) subgroup_stability_metrics = gr.Dropdown( - sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), + sorted(self.all_stability_metrics), value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) subgroup_btn_view2 = gr.Button("Submit") @@ -211,11 +222,11 @@ def start_web_app(self): ) group_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") fairness_metrics = gr.Dropdown( - sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), + sorted(self.all_error_disparity_metrics), value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( - sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), + sorted(self.all_stability_disparity_metrics), value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:", ) group_btn_view2 = gr.Button("Submit") @@ -246,15 +257,15 @@ def start_web_app(self): ### Group Specific Metrics """) accuracy_metrics = gr.Dropdown( - sorted(['Statistical_Bias', 'TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1']), + sorted(self.all_accuracy_metrics), value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", ) uncertainty_metrics = gr.Dropdown( - sorted(['Aleatoric_Uncertainty', 'Overall_Uncertainty']), + sorted(self.all_uncertainty_metrics), value=['Aleatoric_Uncertainty', 'Overall_Uncertainty'], multiselect=True, label="Uncertainty Metrics", info="Select uncertainty metrics to display on the heatmap:", ) subgroup_stability_metrics = gr.Dropdown( - sorted(['Std', 'IQR', 'Jitter', 'Label_Stability']), + sorted(self.all_stability_metrics), value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) btn_view3 = gr.Button("Submit") @@ -264,11 +275,11 @@ def start_web_app(self): ### Disparity Metrics """) fairness_metrics = gr.Dropdown( - sorted(['Equalized_Odds_TPR', 'Equalized_Odds_FPR', 'Disparate_Impact', 'Statistical_Parity_Difference', 'Accuracy_Parity']), + sorted(self.all_error_disparity_metrics), value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:", ) group_stability_metrics = gr.Dropdown( - sorted(['Label_Stability_Ratio', 'IQR_Parity', 'Std_Parity', 'Std_Ratio', 'Jitter_Parity']), + sorted(self.all_stability_disparity_metrics), value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:", ) with gr.Row(): @@ -283,6 +294,90 @@ def start_web_app(self): btn_view3.click(self._create_group_metrics_bar_chart_per_one_model, inputs=[model_name_vw3, fairness_metrics, group_stability_metrics], outputs=[group_metrics_bar_chart]) + # ============================ Model Performance Summary ============================ + with gr.Row(): + # Scale column 1 to a half of a screen + with gr.Column(): + gr.Markdown( + """ + ## Model Performance Summary + """) + model_name_vw4 = gr.Dropdown( + sorted(self.model_names), value=sorted(self.model_names)[0], multiselect=False, scale=1, + label="Model Name", info="Select one model to generate a performance summary:", + ) + with gr.Column(): + pass + with gr.Row(): + with gr.Column(): + gr.Markdown( + """ + ### Group Specific Metrics + """) + with gr.Row(): + accuracy_metric_vw4 = gr.Dropdown( + sorted(self.all_accuracy_metrics), + value=ACCURACY, multiselect=False, label="Accuracy Metric", + scale=3 + ) + acc_threshold_vw4 = gr.Text(value="0.0", label="Threshold", scale=2) + with gr.Row(): + subgroup_stability_metric_vw4 = gr.Dropdown( + sorted(self.all_stability_metrics), + value=LABEL_STABILITY, multiselect=False, label="Stability Metric", + scale=3 + ) + subgroup_stab_threshold_vw4 = gr.Text(value="0.0", label="Threshold", scale=2) + with gr.Row(): + subgroup_uncertainty_metric_vw4 = gr.Dropdown( + sorted(self.all_uncertainty_metrics), + value=ALEATORIC_UNCERTAINTY, multiselect=False, label="Uncertainty Metric", + scale=3 + ) + subgroup_uncertainty_threshold_vw4 = gr.Text(value="0.0", label="Threshold", scale=2) + + btn_view4 = gr.Button("Submit") + with gr.Column(): + gr.Markdown( + """ + ### Disparity Metrics + """) + with gr.Row(): + fairness_metric_vw4 = gr.Dropdown( + sorted(self.all_error_disparity_metrics), + value=EQUALIZED_ODDS_FPR, multiselect=False, label="Error Disparity Metric", + scale=2 + ) + fairness_min_val_vw4 = gr.Text(value="-1.0", label="Min value", scale=1) + fairness_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) + with gr.Row(): + group_stability_metrics_vw4 = gr.Dropdown( + sorted(self.all_stability_disparity_metrics), + value=LABEL_STABILITY_RATIO, multiselect=False, label="Stability Disparity Metric", + scale=2 + ) + group_stab_min_val_vw4 = gr.Text(value="0.7", label="Min value", scale=1) + group_stab_max_val_vw4 = gr.Text(value="1.5", label="Max value", scale=1) + with gr.Row(): + group_uncertainty_metrics_vw4 = gr.Dropdown( + sorted(self.all_uncertainty_disparity_metrics), + value=ALEATORIC_UNCERTAINTY_PARITY, multiselect=False, label="Uncertainty Disparity Metric", + scale=2 + ) + group_uncertainty_min_val_vw4 = gr.Text(value="-1.0", label="Min value", scale=1) + group_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) + with gr.Row(): + model_performance_summary = gr.Plot(label="Model Performance Summary") + + btn_view4.click(self._create_model_performance_summary, + inputs=[model_name_vw4, + accuracy_metric_vw4, acc_threshold_vw4, + subgroup_stability_metric_vw4, subgroup_stab_threshold_vw4, + subgroup_uncertainty_metric_vw4, subgroup_uncertainty_threshold_vw4, + fairness_metric_vw4, fairness_min_val_vw4, fairness_max_val_vw4, + group_stability_metrics_vw4, group_stab_min_val_vw4, group_stab_max_val_vw4, + group_uncertainty_metrics_vw4, group_uncertainty_min_val_vw4, group_uncertainty_max_val_vw4], + outputs=[model_performance_summary]) self.demo = demo self.demo.launch(inline=False, debug=True, show_error=True) @@ -313,6 +408,24 @@ def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, return results + def __check_metric_constraints(self, model_performance_dct, input_constraint_dct): + model_metrics_constraints_check_dct = dict() + for metric_dim in model_performance_dct.keys(): + model_metrics_constraints_check_dct[metric_dim] = dict() + for group in model_performance_dct[metric_dim]: + if group == 'Overall': + constraint_type = 'overall' + threshold = input_constraint_dct[metric_dim][constraint_type][1] + check = 1 if model_performance_dct[metric_dim][group] >= threshold else 0 + model_metrics_constraints_check_dct[metric_dim][group] = check + else: + constraint_type = 'disparity' + min_val, max_val = input_constraint_dct[metric_dim][constraint_type][1] + check = 1 if model_performance_dct[metric_dim][group] >= min_val and model_performance_dct[metric_dim][group] <= max_val else 0 + model_metrics_constraints_check_dct[metric_dim][group] = check + + return model_metrics_constraints_check_dct + def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8, grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8): grp_names = [grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8] @@ -490,6 +603,68 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met return model_rank_heatmap + def _create_model_performance_summary(self, model_name: str, accuracy_metric, acc_threshold, + stability_metric, stability_threshold, + uncertainty_metric, uncertainty_threshold, + fairness_metric, fairness_min_val, fairness_max_val, + group_stability_metrics, group_stab_min_val, group_stab_max_val, + group_uncertainty_metrics, group_uncertainty_min_val, group_uncertainty_max_val): + accuracy_constraint = (accuracy_metric, str_to_float(acc_threshold, 'Accuracy threshold')) + stability_constraint = (stability_metric, str_to_float(stability_threshold, 'Stability threshold')) + uncertainty_constraint = (uncertainty_metric, str_to_float(uncertainty_threshold, 'Uncertainty threshold')) + fairness_constraint = (fairness_metric, [str_to_float(fairness_min_val, 'Error disparity metric min value'), + str_to_float(fairness_max_val, 'Error disparity metric max value')]) + group_stability_constraint = (group_stability_metrics, [str_to_float(group_stab_min_val, 'Stability disparity min value'), + str_to_float(group_stab_max_val, 'Stability disparity max value')]) + group_uncertainty_constraint = (group_uncertainty_metrics, [str_to_float(group_uncertainty_min_val, 'Uncertainty disparity min value'), + str_to_float(group_uncertainty_max_val, 'Uncertainty disparity max value')]) + + input_constraints_dct = { + 'Accuracy': { + 'overall': accuracy_constraint, + 'disparity': fairness_constraint, + }, + 'Stability': { + 'overall': stability_constraint, + 'disparity': group_stability_constraint, + }, + 'Uncertainty': { + 'overall': uncertainty_constraint, + 'disparity': group_uncertainty_constraint, + }, + } + + # Extract overall and disparity metrics from metrics dfs. + # Add the values to a results dict. + model_performance_dct = {} + for metric_dim in input_constraints_dct.keys(): + model_performance_dct[metric_dim] = dict() + subgroup_metric = input_constraints_dct[metric_dim]['overall'][0] + model_performance_dct[metric_dim]['Overall'] = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == subgroup_metric) & + (self.sorted_model_metrics_df.Subgroup == 'overall') & + (self.sorted_model_metrics_df.Model_Name == model_name) + ]['Value'].values[0] + + group_metric = input_constraints_dct[metric_dim]['disparity'][0] + for group_name in self.group_names: + model_performance_dct[metric_dim]['Disparity: ' + group_name] = self.sorted_model_composed_metrics_df[ + (self.sorted_model_composed_metrics_df.Metric == group_metric) & + (self.sorted_model_composed_metrics_df.Subgroup == group_name) & + (self.sorted_model_composed_metrics_df.Model_Name == model_name) + ]['Value'].values[0] + + metric_constraints_check_dct = self.__check_metric_constraints(model_performance_dct, input_constraints_dct) + + model_metrics_matrix = pd.DataFrame(model_performance_dct).T + aligned_column_names = ['Overall'] + [col for col in model_metrics_matrix.columns if col != 'Overall'] + model_metrics_matrix = model_metrics_matrix[aligned_column_names] + metric_constraints_check_matrix = pd.DataFrame(metric_constraints_check_dct).T + metric_constraints_check_matrix = metric_constraints_check_matrix[aligned_column_names] + + model_performance_summary, _ = create_model_performance_summary_visualization(model_metrics_matrix, metric_constraints_check_matrix) + return model_performance_summary + def _create_subgroup_metrics_bar_chart_per_one_model(self, model_name: str, subgroup_accuracy_metrics_lst: list, subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list): metrics_names = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index e8b6aba9..84512ae4 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -266,6 +266,32 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ return fig, ax +def create_model_performance_summary_visualization(main_matrix, matrix_for_colors): + font_increase = 6 + matrix_width = 20 + matrix_height = main_matrix.shape[0] if main_matrix.shape[0] >= 3 else main_matrix.shape[0] * 2.5 + + fig = plt.figure(figsize=(matrix_width, matrix_height)) + ax = sns.heatmap(matrix_for_colors, annot=main_matrix.round(3), + cmap=["#EE8367", "#58D68D"], # [red, green] + fmt='', linewidths=1.0, + vmin=0, vmax=1, + cbar_kws={"ticks":[0, 1]}, + annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 10 + font_increase}) + ax.set(xlabel="", ylabel="") + ax.xaxis.tick_top() + ax.tick_params(axis='y', rotation=0) + ax.tick_params(labelsize=10 + font_increase) + fig.subplots_adjust(left=0.2, top=0.7) + + cbar = ax.collections[0].colorbar + tick_labels = ['Failed', 'Passed'] + cbar.set_ticks([0.25,0.75]) + cbar.set_ticklabels(tick_labels, fontsize=10 + font_increase) + + return fig, ax + + def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, metrics_value_range_dct: dict, group: str): # Compute the number of models that satisfy the conditions From 7d2e55724123e22a9936e062bce0ea4e50804b7a Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 30 Nov 2023 00:00:54 +0200 Subject: [PATCH 049/148] Added Positive-Rate to a model performance summary plot --- ...Multiple_Models_Interface_Vis_Income.ipynb | 77 ++++++++-------- .../metrics_interactive_visualizer.py | 91 ++++++++++++------- virny/utils/data_viz_utils.py | 5 +- 3 files changed, 99 insertions(+), 74 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index a37d4449..ca36b470 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-11-27T23:09:00.744106Z", - "start_time": "2023-11-27T23:09:00.258137Z" + "end_time": "2023-11-29T21:02:04.386021Z", + "start_time": "2023-11-29T21:02:03.727098Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-11-27T23:09:00.753238Z", - "start_time": "2023-11-27T23:09:00.743899Z" + "end_time": "2023-11-29T21:02:04.394975Z", + "start_time": "2023-11-29T21:02:04.386298Z" } }, "outputs": [], @@ -37,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-11-27T23:09:14.159592Z", - "start_time": "2023-11-27T23:09:14.145555Z" + "end_time": "2023-11-29T21:02:04.405571Z", + "start_time": "2023-11-29T21:02:04.395579Z" } }, "outputs": [ @@ -72,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-11-27T23:09:16.946143Z", - "start_time": "2023-11-27T23:09:15.322037Z" + "end_time": "2023-11-29T21:02:08.026244Z", + "start_time": "2023-11-29T21:02:04.404686Z" } }, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "outputs": [], "source": [ "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n", @@ -101,15 +101,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T23:09:18.236763Z", - "start_time": "2023-11-27T23:09:16.949112Z" + "end_time": "2023-11-29T21:02:09.305496Z", + "start_time": "2023-11-29T21:02:08.029615Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -120,22 +120,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T23:09:18.781790Z", - "start_time": "2023-11-27T23:09:18.747739Z" + "end_time": "2023-11-29T21:02:09.332604Z", + "start_time": "2023-11-29T21:02:09.305881Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "outputs": [ { "data": { "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.047756 0.074977 0.065217 \n1 Aleatoric_Uncertainty_Parity -0.039005 -0.011947 -0.009222 \n2 Aleatoric_Uncertainty_Ratio 0.935159 0.979638 0.984220 \n3 Equalized_Odds_FNR 0.030793 -0.110745 -0.052498 \n4 Equalized_Odds_FPR -0.021317 0.000952 -0.007008 \n\n Model_Name \n0 LGBMClassifier__alpha=0.7 \n1 LGBMClassifier__alpha=0.7 \n2 LGBMClassifier__alpha=0.7 \n3 LGBMClassifier__alpha=0.7 \n4 LGBMClassifier__alpha=0.7 ", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0477560.0749770.065217LGBMClassifier__alpha=0.7
1Aleatoric_Uncertainty_Parity-0.039005-0.011947-0.009222LGBMClassifier__alpha=0.7
2Aleatoric_Uncertainty_Ratio0.9351590.9796380.984220LGBMClassifier__alpha=0.7
3Equalized_Odds_FNR0.030793-0.110745-0.052498LGBMClassifier__alpha=0.7
4Equalized_Odds_FPR-0.0213170.000952-0.007008LGBMClassifier__alpha=0.7
\n
" }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -153,15 +153,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T23:09:18.905842Z", - "start_time": "2023-11-27T23:09:18.850548Z" + "end_time": "2023-11-29T21:02:09.385835Z", + "start_time": "2023-11-29T21:02:09.332537Z" } }, "id": "44ee5eff6054ce04" }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "outputs": [], "source": [ "models_metrics_dct = dict()\n", @@ -171,21 +171,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T23:10:14.375071Z", - "start_time": "2023-11-27T23:10:14.339164Z" + "end_time": "2023-11-29T21:02:09.407603Z", + "start_time": "2023-11-29T21:02:09.385768Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -196,8 +196,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T23:10:15.006243Z", - "start_time": "2023-11-27T23:10:14.979880Z" + "end_time": "2023-11-29T21:02:09.456986Z", + "start_time": "2023-11-29T21:02:09.407709Z" } }, "id": "15ed7d1ba1f22317" @@ -212,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 19, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-11-27T23:59:15.016940Z", - "start_time": "2023-11-27T23:59:14.968372Z" + "end_time": "2023-11-29T21:56:28.048524Z", + "start_time": "2023-11-29T21:56:27.922421Z" } }, "outputs": [], @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "outputs": [ { "name": "stdout", @@ -237,8 +237,7 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" + "To create a public link, set `share=True` in `launch()`.\n" ] } ], @@ -247,9 +246,9 @@ ], "metadata": { "collapsed": false, + "is_executing": true, "ExecuteTime": { - "end_time": "2023-11-28T00:08:09.433964Z", - "start_time": "2023-11-27T23:59:15.062378Z" + "start_time": "2023-11-29T21:56:28.049665Z" } }, "id": "678a9dc8d51243f4" @@ -272,8 +271,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T22:52:53.559673Z", - "start_time": "2023-11-27T22:52:53.432952Z" + "end_time": "2023-11-29T21:12:46.494378Z", + "start_time": "2023-11-29T21:12:46.442750Z" } }, "id": "277b6d1de837dab7" @@ -286,8 +285,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-27T22:52:53.561907Z", - "start_time": "2023-11-27T22:52:53.559309Z" + "end_time": "2023-11-29T21:12:46.501645Z", + "start_time": "2023-11-29T21:12:46.483631Z" } }, "id": "c207d4345ddca1db" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 69857337..5333ca08 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -316,25 +316,32 @@ def start_web_app(self): """) with gr.Row(): accuracy_metric_vw4 = gr.Dropdown( - sorted(self.all_accuracy_metrics), + sorted([metric for metric in self.all_accuracy_metrics if metric != POSITIVE_RATE]), value=ACCURACY, multiselect=False, label="Accuracy Metric", - scale=3 + scale=2 ) - acc_threshold_vw4 = gr.Text(value="0.0", label="Threshold", scale=2) + accuracy_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) + accuracy_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): subgroup_stability_metric_vw4 = gr.Dropdown( sorted(self.all_stability_metrics), value=LABEL_STABILITY, multiselect=False, label="Stability Metric", - scale=3 + scale=2 ) - subgroup_stab_threshold_vw4 = gr.Text(value="0.0", label="Threshold", scale=2) + subgroup_stab_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) + subgroup_stab_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): subgroup_uncertainty_metric_vw4 = gr.Dropdown( sorted(self.all_uncertainty_metrics), value=ALEATORIC_UNCERTAINTY, multiselect=False, label="Uncertainty Metric", - scale=3 + scale=2 ) - subgroup_uncertainty_threshold_vw4 = gr.Text(value="0.0", label="Threshold", scale=2) + subgroup_uncertainty_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) + subgroup_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) + with gr.Row(): + positive_rate_metric_vw4 = gr.Text(value=POSITIVE_RATE, label="Positive-Rate Metric", scale=2) + positive_rate_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) + positive_rate_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) btn_view4 = gr.Button("Submit") with gr.Column(): @@ -366,17 +373,27 @@ def start_web_app(self): ) group_uncertainty_min_val_vw4 = gr.Text(value="-1.0", label="Min value", scale=1) group_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) + with gr.Row(): + group_positive_rate_metrics_vw4 = gr.Dropdown( + sorted([DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE]), + value=DISPARATE_IMPACT, multiselect=False, label="Positive-Rate Disparity Metric", + scale=2 + ) + group_positive_rate_min_val_vw4 = gr.Text(value="0.7", label="Min value", scale=1) + group_positive_rate_max_val_vw4 = gr.Text(value="1.5", label="Max value", scale=1) with gr.Row(): model_performance_summary = gr.Plot(label="Model Performance Summary") btn_view4.click(self._create_model_performance_summary, inputs=[model_name_vw4, - accuracy_metric_vw4, acc_threshold_vw4, - subgroup_stability_metric_vw4, subgroup_stab_threshold_vw4, - subgroup_uncertainty_metric_vw4, subgroup_uncertainty_threshold_vw4, + accuracy_metric_vw4, accuracy_min_val_vw4, accuracy_max_val_vw4, + subgroup_stability_metric_vw4, subgroup_stab_min_val_vw4, subgroup_stab_max_val_vw4, + subgroup_uncertainty_metric_vw4, subgroup_uncertainty_min_val_vw4, subgroup_uncertainty_max_val_vw4, + positive_rate_metric_vw4, positive_rate_min_val_vw4, positive_rate_max_val_vw4, fairness_metric_vw4, fairness_min_val_vw4, fairness_max_val_vw4, group_stability_metrics_vw4, group_stab_min_val_vw4, group_stab_max_val_vw4, - group_uncertainty_metrics_vw4, group_uncertainty_min_val_vw4, group_uncertainty_max_val_vw4], + group_uncertainty_metrics_vw4, group_uncertainty_min_val_vw4, group_uncertainty_max_val_vw4, + group_positive_rate_metrics_vw4, group_positive_rate_min_val_vw4, group_positive_rate_max_val_vw4], outputs=[model_performance_summary]) self.demo = demo @@ -413,16 +430,10 @@ def __check_metric_constraints(self, model_performance_dct, input_constraint_dct for metric_dim in model_performance_dct.keys(): model_metrics_constraints_check_dct[metric_dim] = dict() for group in model_performance_dct[metric_dim]: - if group == 'Overall': - constraint_type = 'overall' - threshold = input_constraint_dct[metric_dim][constraint_type][1] - check = 1 if model_performance_dct[metric_dim][group] >= threshold else 0 - model_metrics_constraints_check_dct[metric_dim][group] = check - else: - constraint_type = 'disparity' - min_val, max_val = input_constraint_dct[metric_dim][constraint_type][1] - check = 1 if model_performance_dct[metric_dim][group] >= min_val and model_performance_dct[metric_dim][group] <= max_val else 0 - model_metrics_constraints_check_dct[metric_dim][group] = check + constraint_type = 'overall' if group == 'Overall' else 'disparity' + min_val, max_val = input_constraint_dct[metric_dim][constraint_type][1] + check = 1 if model_performance_dct[metric_dim][group] >= min_val and model_performance_dct[metric_dim][group] <= max_val else 0 + model_metrics_constraints_check_dct[metric_dim][group] = check return model_metrics_constraints_check_dct @@ -603,21 +614,31 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met return model_rank_heatmap - def _create_model_performance_summary(self, model_name: str, accuracy_metric, acc_threshold, - stability_metric, stability_threshold, - uncertainty_metric, uncertainty_threshold, + def _create_model_performance_summary(self, model_name: str, accuracy_metric, accuracy_min_val, accuracy_max_val, + stability_metric, stability_min_val, stability_max_val, + uncertainty_metric, uncertainty_min_val, uncertainty_max_val, + positive_rate_metric, positive_rate_min_val, positive_rate_max_val, fairness_metric, fairness_min_val, fairness_max_val, - group_stability_metrics, group_stab_min_val, group_stab_max_val, - group_uncertainty_metrics, group_uncertainty_min_val, group_uncertainty_max_val): - accuracy_constraint = (accuracy_metric, str_to_float(acc_threshold, 'Accuracy threshold')) - stability_constraint = (stability_metric, str_to_float(stability_threshold, 'Stability threshold')) - uncertainty_constraint = (uncertainty_metric, str_to_float(uncertainty_threshold, 'Uncertainty threshold')) + group_stability_metric, group_stab_min_val, group_stab_max_val, + group_uncertainty_metric, group_uncertainty_min_val, group_uncertainty_max_val, + group_positive_rate_metric, group_positive_rate_min_val, group_positive_rate_max_val): + accuracy_constraint = (accuracy_metric, [str_to_float(accuracy_min_val, 'Accuracy min value'), + str_to_float(accuracy_max_val, 'Accuracy max value')]) + stability_constraint = (stability_metric, [str_to_float(stability_min_val, 'Stability min value'), + str_to_float(stability_max_val, 'Stability max value')]) + uncertainty_constraint = (uncertainty_metric, [str_to_float(uncertainty_min_val, 'Uncertainty min value'), + str_to_float(uncertainty_max_val, 'Uncertainty max value')]) + positive_rate_constraint = (positive_rate_metric, [str_to_float(positive_rate_min_val, 'Positive-Rate min value'), + str_to_float(positive_rate_max_val, 'Positive-Rate max value')]) + fairness_constraint = (fairness_metric, [str_to_float(fairness_min_val, 'Error disparity metric min value'), str_to_float(fairness_max_val, 'Error disparity metric max value')]) - group_stability_constraint = (group_stability_metrics, [str_to_float(group_stab_min_val, 'Stability disparity min value'), - str_to_float(group_stab_max_val, 'Stability disparity max value')]) - group_uncertainty_constraint = (group_uncertainty_metrics, [str_to_float(group_uncertainty_min_val, 'Uncertainty disparity min value'), - str_to_float(group_uncertainty_max_val, 'Uncertainty disparity max value')]) + group_stability_constraint = (group_stability_metric, [str_to_float(group_stab_min_val, 'Stability disparity min value'), + str_to_float(group_stab_max_val, 'Stability disparity max value')]) + group_uncertainty_constraint = (group_uncertainty_metric, [str_to_float(group_uncertainty_min_val, 'Uncertainty disparity min value'), + str_to_float(group_uncertainty_max_val, 'Uncertainty disparity max value')]) + group_positive_rate_constraint = (group_positive_rate_metric, [str_to_float(group_positive_rate_min_val, 'Positive-Rate disparity min value'), + str_to_float(group_positive_rate_max_val, 'Positive-Rate disparity max value')]) input_constraints_dct = { 'Accuracy': { @@ -632,6 +653,10 @@ def _create_model_performance_summary(self, model_name: str, accuracy_metric, ac 'overall': uncertainty_constraint, 'disparity': group_uncertainty_constraint, }, + 'Positive-Rate': { + 'overall': positive_rate_constraint, + 'disparity': group_positive_rate_constraint, + }, } # Extract overall and disparity metrics from metrics dfs. diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 84512ae4..96e1a945 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -77,8 +77,8 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np models_distances_matrix = model_metrics_matrix.copy(deep=True).T metric_names = models_distances_matrix.columns for metric_name in metric_names: - if check_substring_in_list(metric_name, ['TPR', 'TNR', 'PPV', 'Accuracy', 'F1', 'Label_Stability']): - # Cast a metric to a case when the closer value to zero is the better + if check_substring_in_list(metric_name, ['TPR', 'TNR', 'PPV', 'Accuracy', 'F1', 'Label_Stability', 'Positive-Rate']): + # Cast a metric to a case when the closer value to one is the better models_distances_matrix[metric_name] = 1 - models_distances_matrix[metric_name] models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() @@ -308,6 +308,7 @@ def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict 'PPV': 'C1', 'Accuracy': 'C1', 'F1': 'C1', + 'Positive-Rate': 'C1', # C2 'Equalized_Odds_TPR': 'C2', 'Equalized_Odds_FPR': 'C2', From a773815fd7ef365f2f7a83fb2c0876f6066af5cd Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 30 Nov 2023 00:19:00 +0200 Subject: [PATCH 050/148] Improved dataset stats plot --- .../Multiple_Models_Interface_Vis_Income.ipynb | 8 ++++---- .../metrics_interactive_visualizer.py | 16 +++++++++++++--- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index ca36b470..561ddd14 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -212,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T21:56:28.048524Z", - "start_time": "2023-11-29T21:56:27.922421Z" + "end_time": "2023-11-29T22:15:44.175233Z", + "start_time": "2023-11-29T22:15:43.997200Z" } }, "outputs": [], @@ -248,7 +248,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-11-29T21:56:28.049665Z" + "start_time": "2023-11-29T22:15:44.176046Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 5333ca08..f3fcdbac 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -102,11 +102,21 @@ def start_web_app(self): s = gr.Slider(1, self.max_groups, value=default_val, step=1, label="How many groups to show:") grp_names = [] grp_dis_values = [] + sensitive_attr_items = list(self.sensitive_attributes_dct.items()) for i in range(self.max_groups): visibility = True if i + 1 <= default_val else False with gr.Row(): - grp_name = gr.Text(label=f"Group {i + 1}", interactive=True, visible=visibility) - grp_dis_value = gr.Text(label="Disadvantage value", interactive=True, visible=visibility) + if visibility and i + 1 <= len(sensitive_attr_items): + grp, dis_value = sensitive_attr_items[i] + if dis_value is None: + dis_value = '-' + elif isinstance(dis_value, str): + dis_value = f"'{dis_value}'" + grp_name = gr.Text(label=f"Group {i + 1}", value=grp, interactive=True, visible=visibility) + grp_dis_value = gr.Text(label="Disadvantage value", value=dis_value, interactive=True, visible=visibility) + else: + grp_name = gr.Text(label=f"Group {i + 1}", interactive=True, visible=visibility) + grp_dis_value = gr.Text(label="Disadvantage value", interactive=True, visible=visibility) grp_names.append(grp_name) grp_dis_values.append(grp_dis_value) @@ -450,7 +460,7 @@ def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, if '&' in grp_name: input_sensitive_attrs_dct[grp_name] = None else: - converted_grp_dis_val = eval(grp_dis_val) if '[' in grp_dis_val else grp_dis_val + converted_grp_dis_val = eval(grp_dis_val) input_sensitive_attrs_dct[grp_name] = converted_grp_dis_val # Partition on protected groups From 1d5ad3aacfcfad69cad634a81a02f00bdb8b779e Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 30 Nov 2023 01:22:05 +0200 Subject: [PATCH 051/148] Added overall and disparity constraints to a model selection bar chart --- ...Multiple_Models_Interface_Vis_Income.ipynb | 8 +- .../metrics_interactive_visualizer.py | 97 +++++++++++-------- virny/utils/data_viz_utils.py | 53 ++++++++++ 3 files changed, 111 insertions(+), 47 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 561ddd14..b5faed57 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -212,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T22:15:44.175233Z", - "start_time": "2023-11-29T22:15:43.997200Z" + "end_time": "2023-11-29T23:17:11.979632Z", + "start_time": "2023-11-29T23:17:11.761681Z" } }, "outputs": [], @@ -248,7 +248,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-11-29T22:15:44.176046Z" + "start_time": "2023-11-29T23:17:11.980148Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index f3fcdbac..375866a4 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -7,7 +7,7 @@ from virny.utils.common_helpers import str_to_float from virny.utils.protected_groups_partitioning import create_test_protected_groups from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, - create_subgroup_sorted_matrix_by_rank, create_bar_plot_for_model_selection, + create_subgroup_sorted_matrix_by_rank, create_flexible_bar_plot_for_model_selection, compute_proportions, compute_base_rates, create_col_facet_bar_chart, create_model_performance_summary_visualization) @@ -51,6 +51,9 @@ def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics_dct self.all_stability_disparity_metrics = [LABEL_STABILITY_RATIO, LABEL_STABILITY_DIFFERENCE, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY] self.all_uncertainty_disparity_metrics = [OVERALL_UNCERTAINTY_PARITY, OVERALL_UNCERTAINTY_RATIO, ALEATORIC_UNCERTAINTY_PARITY, ALEATORIC_UNCERTAINTY_RATIO] + self.all_overall_metrics = self.all_accuracy_metrics + self.all_stability_metrics + self.all_uncertainty_metrics + self.all_disparity_metrics = self.all_error_disparity_metrics + self.all_stability_disparity_metrics + self.all_uncertainty_disparity_metrics + # Create one metrics df with all model_dfs models_metrics_df = pd.DataFrame() for model_name in model_metrics_dct.keys(): @@ -143,37 +146,38 @@ def start_web_app(self): value=self.group_names[0], multiselect=False, label="Group Name for Disparity Metrics", ) with gr.Row(): - accuracy_metric = gr.Dropdown( - sorted(self.all_accuracy_metrics), - value='Accuracy', multiselect=False, label="Constraint 1 (C1)", + overall_metric1 = gr.Dropdown( + sorted(self.all_overall_metrics), + value='Accuracy', multiselect=False, label="Overall Constraint (C1)", scale=2 ) - acc_min_val = gr.Text(value="0.0", label="Min value", scale=1) - acc_max_val = gr.Text(value="1.0", label="Max value", scale=1) + overall_metric_min_val1 = gr.Text(value="0.0", label="Min value", scale=1) + overall_metric_max_val1 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): - fairness_metric = gr.Dropdown( - sorted(self.all_error_disparity_metrics), - value='Equalized_Odds_FPR', multiselect=False, label="Constraint 2 (C2)", + disparity_metric1 = gr.Dropdown( + sorted(self.all_disparity_metrics), + value='Equalized_Odds_FPR', multiselect=False, label="Disparity Constraint (C2)", scale=2 ) - fairness_min_val = gr.Text(value="-1.0", label="Min value", scale=1) - fairness_max_val = gr.Text(value="1.0", label="Max value", scale=1) + disparity_metric_min_val1 = gr.Text(value="-1.0", label="Min value", scale=1) + disparity_metric_max_val1 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): - subgroup_stability_metric = gr.Dropdown( - sorted(self.all_stability_metrics), - value='Label_Stability', multiselect=False, label="Constraint 3 (C3)", + overall_metric2 = gr.Dropdown( + sorted(self.all_overall_metrics), + value='Label_Stability', multiselect=False, label="Overall Constraint (C3)", scale=2 ) - subgroup_stab_min_val = gr.Text(value="0.0", label="Min value", scale=1) - subgroup_stab_max_val = gr.Text(value="1.0", label="Max value", scale=1) + overall_metric_min_val2 = gr.Text(value="0.0", label="Min value", scale=1) + overall_metric_max_val2 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): - group_stability_metrics = gr.Dropdown( - sorted(self.all_stability_disparity_metrics), - value='Label_Stability_Ratio', multiselect=False, label="Constraint 4 (C4)", + disparity_metric2 = gr.Dropdown( + sorted(self.all_disparity_metrics), + value='Label_Stability_Ratio', multiselect=False, label="Disparity Constraint (C4)", scale=2 ) - group_stab_min_val = gr.Text(value="0.7", label="Min value", scale=1) - group_stab_max_val = gr.Text(value="1.5", label="Max value", scale=1) + disparity_metric_min_val2 = gr.Text(value="0.7", label="Min value", scale=1) + disparity_metric_max_val2 = gr.Text(value="1.5", label="Max value", scale=1) + btn_view1 = gr.Button("Submit") with gr.Column(scale=3): bar_plot_for_model_selection = gr.Plot(label="Bar Chart") @@ -181,10 +185,10 @@ def start_web_app(self): btn_view1.click(self._create_bar_plot_for_model_selection, inputs=[group_name, - accuracy_metric, acc_min_val, acc_max_val, - fairness_metric, fairness_min_val, fairness_max_val, - subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, - group_stability_metrics, group_stab_min_val, group_stab_max_val], + overall_metric1, overall_metric_min_val1, overall_metric_max_val1, + disparity_metric1, disparity_metric_min_val1, disparity_metric_max_val1, + overall_metric2, overall_metric_min_val2, overall_metric_max_val2, + disparity_metric2, disparity_metric_min_val2, disparity_metric_max_val2], outputs=[bar_plot_for_model_selection, df_with_models_satisfied_all_constraints]) # ======================================= Overall Metrics Heatmap ======================================= gr.Markdown( @@ -495,25 +499,31 @@ def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, return col_facet_bar_chart - def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_min_val, acc_max_val, - fairness_metric, fairness_min_val, fairness_max_val, - subgroup_stability_metric, subgroup_stab_min_val, subgroup_stab_max_val, - group_stability_metrics, group_stab_min_val, group_stab_max_val): - accuracy_constraint = (accuracy_metric, str_to_float(acc_min_val, 'C1 min value'), str_to_float(acc_max_val, 'C2 max value')) - fairness_constraint = (fairness_metric, str_to_float(fairness_min_val, 'C2 min value'), str_to_float(fairness_max_val, 'C2 max value')) - subgroup_stability_constraint = (subgroup_stability_metric, str_to_float(subgroup_stab_min_val, 'C3 min value'), str_to_float(subgroup_stab_max_val, 'C3 max value')) - group_stability_constraint = (group_stability_metrics, str_to_float(group_stab_min_val, 'C4 min value'), str_to_float(group_stab_max_val, 'C4 max value')) + def _create_bar_plot_for_model_selection(self, group_name, overall_metric1, overall_metric_min_val1, overall_metric_max_val1, + disparity_metric1, disparity_metric_min_val1, disparity_metric_max_val1, + overall_metric2, overall_metric_min_val2, overall_metric_max_val2, + disparity_metric2, disparity_metric_min_val2, disparity_metric_max_val2): + metric_name_to_alias_dct = { + overall_metric1: 'C1', + disparity_metric1: 'C2', + overall_metric2: 'C3', + disparity_metric2: 'C4', + } + overall_constraint1 = (overall_metric1, str_to_float(overall_metric_min_val1, 'C1 min value'), str_to_float(overall_metric_max_val1, 'C2 max value')) + disparity_constraint1 = (disparity_metric1, str_to_float(disparity_metric_min_val1, 'C2 min value'), str_to_float(disparity_metric_max_val1, 'C2 max value')) + overall_constraint2 = (overall_metric2, str_to_float(overall_metric_min_val2, 'C3 min value'), str_to_float(overall_metric_max_val2, 'C3 max value')) + disparity_constraint2 = (disparity_metric2, str_to_float(disparity_metric_min_val2, 'C4 min value'), str_to_float(disparity_metric_max_val2, 'C4 max value')) # Create individual constraints metrics_value_range_dct = dict() - for constraint in [accuracy_constraint, fairness_constraint, subgroup_stability_constraint, group_stability_constraint]: + for constraint in [overall_constraint1, disparity_constraint1, overall_constraint2, disparity_constraint2]: metrics_value_range_dct[constraint[0]] = [constraint[1], constraint[2]] # Create intersectional constraints - metrics_value_range_dct[f'{accuracy_constraint[0]}&{fairness_constraint[0]}'] = None - metrics_value_range_dct[f'{accuracy_constraint[0]}&{subgroup_stability_constraint[0]}'] = None - metrics_value_range_dct[f'{accuracy_constraint[0]}&{group_stability_constraint[0]}'] = None - metrics_value_range_dct[(f'{accuracy_constraint[0]}&{fairness_constraint[0]}' - f'&{subgroup_stability_constraint[0]}&{group_stability_constraint[0]}')] = None + metrics_value_range_dct[f'{overall_constraint1[0]}&{disparity_constraint1[0]}'] = None + metrics_value_range_dct[f'{overall_constraint1[0]}&{overall_constraint2[0]}'] = None + metrics_value_range_dct[f'{overall_constraint1[0]}&{disparity_constraint2[0]}'] = None + metrics_value_range_dct[(f'{overall_constraint1[0]}&{disparity_constraint1[0]}' + f'&{overall_constraint2[0]}&{disparity_constraint2[0]}')] = None melted_all_subgroup_metrics_per_model_dct = dict() for model_name in self.melted_model_metrics_df['Model_Name'].unique(): @@ -525,10 +535,11 @@ def _create_bar_plot_for_model_selection(self, group_name, accuracy_metric, acc_ melted_all_group_metrics_per_model_dct[model_name] = ( self.melted_model_composed_metrics_df)[self.melted_model_composed_metrics_df.Model_Name == model_name] - return create_bar_plot_for_model_selection(melted_all_subgroup_metrics_per_model_dct, - melted_all_group_metrics_per_model_dct, - metrics_value_range_dct, - group=group_name) + return create_flexible_bar_plot_for_model_selection(melted_all_subgroup_metrics_per_model_dct, + melted_all_group_metrics_per_model_dct, + metrics_value_range_dct, + group=group_name, + metric_name_to_alias_dct=metric_name_to_alias_dct) def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accuracy_metrics_lst: list, subgroup_uncertainty_metrics: list, subgroup_stability_metrics_lst: list, diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 96e1a945..ab4534bd 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -292,6 +292,58 @@ def create_model_performance_summary_visualization(main_matrix, matrix_for_color return fig, ax +def create_flexible_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, + metrics_value_range_dct: dict, group: str, metric_name_to_alias_dct: dict): + # Compute the number of models that satisfy the conditions + models_in_range_df, df_with_models_satisfied_all_constraints = ( + create_models_in_range_dct(all_subgroup_metrics_per_model_dct, all_group_metrics_per_model_dct, + metrics_value_range_dct, group)) + + def get_column_alias(metric_group): + if '&' not in metric_group: + alias = metric_name_to_alias_dct[metric_group] + else: + metrics = metric_group.split('&') + alias = None + for idx, metric in enumerate(metrics): + if idx == 0: + alias = metric_name_to_alias_dct[metric] + else: + alias += ' & ' + metric_name_to_alias_dct[metric] + + return alias + + # Replace metric groups on their aliases + models_in_range_df['Alias'] = models_in_range_df['Metric_Group'].apply(get_column_alias) + models_in_range_df['Title'] = models_in_range_df['Alias'] + + base_font_size = 14 + bar_plot = alt.Chart(models_in_range_df).mark_bar().encode( + x=alt.X("Title", type="nominal", title='Metric Group', axis=alt.Axis(labelAngle=-30), + sort=alt.Sort(order='ascending')), + y=alt.Y("Number_of_Models", title="Number of Models", type="quantitative"), + color=alt.Color('Model_Type', legend=alt.Legend(title='Model Type')) + ).configure(padding={'top': 33} + ).configure_axis( + labelFontSize=base_font_size + 2, + titleFontSize=base_font_size + 4, + labelFontWeight='normal', + titleFontWeight='normal', + labelLimit=300, + tickMinStep=1, + ).configure_title( + fontSize=base_font_size + 2 + ).configure_legend( + titleFontSize=base_font_size + 4, + labelFontSize=base_font_size + 2, + symbolStrokeWidth=4, + labelLimit=300, + titleLimit=220, + ).properties(width=650, height=450) + + return bar_plot, df_with_models_satisfied_all_constraints + + def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict, all_group_metrics_per_model_dct: dict, metrics_value_range_dct: dict, group: str): # Compute the number of models that satisfy the conditions @@ -323,6 +375,7 @@ def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict # C4 'IQR_Parity': 'C4', 'Label_Stability_Ratio': 'C4', + 'Label_Stability_Difference': 'C4', 'Std_Parity': 'C4', 'Std_Ratio': 'C4', 'Jitter_Parity': 'C4', From 2a6e71ee8b4e6986a016c1ca693913c7a016e509 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 30 Nov 2023 01:51:28 +0200 Subject: [PATCH 052/148] Added uncertainty disparity bar charts --- ...Multiple_Models_Interface_Vis_Income.ipynb | 8 ++-- .../metrics_interactive_visualizer.py | 37 +++++++++++++------ 2 files changed, 30 insertions(+), 15 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index b5faed57..18bcc88c 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -212,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T23:17:11.979632Z", - "start_time": "2023-11-29T23:17:11.761681Z" + "end_time": "2023-11-29T23:48:34.526173Z", + "start_time": "2023-11-29T23:48:34.278833Z" } }, "outputs": [], @@ -248,7 +248,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-11-29T23:17:11.980148Z" + "start_time": "2023-11-29T23:48:34.526950Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 375866a4..1b399eb5 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -133,6 +133,7 @@ def start_web_app(self): inputs=[grp_names[0], grp_names[1], grp_names[2], grp_names[3], grp_names[4], grp_names[5], grp_names[6], grp_names[7], grp_dis_values[0], grp_dis_values[1], grp_dis_values[2], grp_dis_values[3], grp_dis_values[4], grp_dis_values[5], grp_dis_values[6], grp_dis_values[7]], outputs=[dataset_proportions_bar_chart]) + # ==================================== Bar Chart for Model Selection ==================================== gr.Markdown( """ @@ -190,6 +191,7 @@ def start_web_app(self): overall_metric2, overall_metric_min_val2, overall_metric_max_val2, disparity_metric2, disparity_metric_min_val2, disparity_metric_max_val2], outputs=[bar_plot_for_model_selection, df_with_models_satisfied_all_constraints]) + # ======================================= Overall Metrics Heatmap ======================================= gr.Markdown( """ @@ -222,6 +224,7 @@ def start_web_app(self): subgroup_btn_view2.click(self._create_subgroup_model_rank_heatmap, inputs=[model_names, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics, subgroup_tolerance], outputs=[subgroup_model_ranking_heatmap]) + # ======================================== Disparity Metrics Heatmap ======================================== gr.Markdown( """ @@ -235,11 +238,15 @@ def start_web_app(self): label="Model Names", info="Select model names to display on the heatmap:", ) group_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") - fairness_metrics = gr.Dropdown( + fairness_metrics_vw2 = gr.Dropdown( sorted(self.all_error_disparity_metrics), value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:", ) - group_stability_metrics = gr.Dropdown( + group_uncertainty_metrics_vw2 = gr.Dropdown( + sorted(self.all_uncertainty_disparity_metrics), + value=['Overall_Uncertainty_Parity'], multiselect=True, label="Uncertainty Disparity Metrics", info="Select uncertainty disparity metrics to display on the heatmap:", + ) + group_stability_metrics_vw2 = gr.Dropdown( sorted(self.all_stability_disparity_metrics), value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:", ) @@ -248,8 +255,9 @@ def start_web_app(self): group_model_ranking_heatmap = gr.Plot(label="Heatmap") group_btn_view2.click(self._create_group_model_rank_heatmap, - inputs=[model_names, fairness_metrics, group_stability_metrics, group_tolerance], + inputs=[model_names, fairness_metrics_vw2, group_uncertainty_metrics_vw2, group_stability_metrics_vw2, group_tolerance], outputs=[group_model_ranking_heatmap]) + # ============================ Group Specific and Disparity Metrics Bar Charts ============================ with gr.Row(): # Scale column 1 to a half of a screen @@ -288,11 +296,15 @@ def start_web_app(self): """ ### Disparity Metrics """) - fairness_metrics = gr.Dropdown( + fairness_metrics_vw3 = gr.Dropdown( sorted(self.all_error_disparity_metrics), value=['Equalized_Odds_FPR', 'Equalized_Odds_TPR'], multiselect=True, label="Error Disparity Metrics", info="Select error disparity metrics to display on the heatmap:", ) - group_stability_metrics = gr.Dropdown( + group_uncertainty_metrics_vw3 = gr.Dropdown( + sorted(self.all_uncertainty_disparity_metrics), + value=['Aleatoric_Uncertainty_Ratio', 'Overall_Uncertainty_Parity'], multiselect=True, label="Uncertainty Disparity Metrics", info="Select uncertainty disparity metrics to display on the heatmap:", + ) + group_stability_metrics_vw3 = gr.Dropdown( sorted(self.all_stability_disparity_metrics), value=['Label_Stability_Ratio', 'Std_Parity'], multiselect=True, label="Stability Disparity Metrics", info="Select stability disparity metrics to display on the heatmap:", ) @@ -306,8 +318,9 @@ def start_web_app(self): inputs=[model_name_vw3, accuracy_metrics, uncertainty_metrics, subgroup_stability_metrics], outputs=[subgroup_metrics_bar_chart]) btn_view3.click(self._create_group_metrics_bar_chart_per_one_model, - inputs=[model_name_vw3, fairness_metrics, group_stability_metrics], + inputs=[model_name_vw3, fairness_metrics_vw3, group_uncertainty_metrics_vw3, group_stability_metrics_vw3], outputs=[group_metrics_bar_chart]) + # ============================ Model Performance Summary ============================ with gr.Row(): # Scale column 1 to a half of a screen @@ -326,7 +339,7 @@ def start_web_app(self): with gr.Column(): gr.Markdown( """ - ### Group Specific Metrics + ### Overall Metrics """) with gr.Row(): accuracy_metric_vw4 = gr.Dropdown( @@ -581,7 +594,7 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura return model_rank_heatmap def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_metrics_lst: list, - group_stability_metrics_lst: list, tolerance: str): + group_uncertainty_metrics: list, group_stability_metrics_lst: list, tolerance: str): """ Create a group model rank heatmap. @@ -591,6 +604,8 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met A list of selected model names to display on the heatmap group_fairness_metrics_lst A list of group fairness metrics to visualize + group_uncertainty_metrics + A list of group uncertainty metrics to visualize group_stability_metrics_lst A list of group stability metrics to visualize tolerance @@ -600,12 +615,11 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met tolerance = str_to_float(tolerance, 'Tolerance') groups_lst = self.sensitive_attributes_dct.keys() - metrics_lst = group_fairness_metrics_lst + group_stability_metrics_lst + metrics_lst = group_fairness_metrics_lst + group_uncertainty_metrics + group_stability_metrics_lst # Find metric values for each model based on metric, group, and model names. # Add the values to a results dict. results = {} - num_models = len(model_names) for metric in metrics_lst: for group in groups_lst: group_metric = metric + '_' + group @@ -717,8 +731,9 @@ def _create_subgroup_metrics_bar_chart_per_one_model(self, model_name: str, subg return self._create_metrics_bar_chart_per_one_model(model_name, metrics_names, metrics_type='subgroup') def _create_group_metrics_bar_chart_per_one_model(self, model_name: str, group_fairness_metrics_lst: list, + group_uncertainty_metrics_lst: list, group_stability_metrics_lst: list): - metrics_names = group_fairness_metrics_lst + group_stability_metrics_lst + metrics_names = group_fairness_metrics_lst + group_uncertainty_metrics_lst + group_stability_metrics_lst return self._create_metrics_bar_chart_per_one_model(model_name, metrics_names, metrics_type='group') def _create_metrics_bar_chart_per_one_model(self, model_name: str, metrics_names: list, metrics_type: str): From ae533db9489def237c44fb8f81714a08a8323f49 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 30 Nov 2023 14:19:38 +0200 Subject: [PATCH 053/148] Set red-green color palette --- ...Multiple_Models_Interface_Vis_Income.ipynb | 197 +++++++++++++++--- .../metrics_interactive_visualizer.py | 16 +- virny/utils/data_viz_utils.py | 3 +- 3 files changed, 184 insertions(+), 32 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index 18bcc88c..a7f8a5b4 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T21:02:04.386021Z", - "start_time": "2023-11-29T21:02:03.727098Z" + "end_time": "2023-11-30T10:14:44.773220Z", + "start_time": "2023-11-30T10:14:44.118473Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T21:02:04.394975Z", - "start_time": "2023-11-29T21:02:04.386298Z" + "end_time": "2023-11-30T10:14:44.781386Z", + "start_time": "2023-11-30T10:14:44.773120Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T21:02:04.405571Z", - "start_time": "2023-11-29T21:02:04.395579Z" + "end_time": "2023-11-30T10:14:44.791947Z", + "start_time": "2023-11-30T10:14:44.781906Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T21:02:08.026244Z", - "start_time": "2023-11-29T21:02:04.404686Z" + "end_time": "2023-11-30T10:14:46.531174Z", + "start_time": "2023-11-30T10:14:44.792591Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:02:09.305496Z", - "start_time": "2023-11-29T21:02:08.029615Z" + "end_time": "2023-11-30T10:14:47.863288Z", + "start_time": "2023-11-30T10:14:46.532997Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:02:09.332604Z", - "start_time": "2023-11-29T21:02:09.305881Z" + "end_time": "2023-11-30T10:14:47.892811Z", + "start_time": "2023-11-30T10:14:47.863607Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:02:09.385835Z", - "start_time": "2023-11-29T21:02:09.332537Z" + "end_time": "2023-11-30T10:14:47.944316Z", + "start_time": "2023-11-30T10:14:47.890948Z" } }, "id": "44ee5eff6054ce04" @@ -171,8 +171,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:02:09.407603Z", - "start_time": "2023-11-29T21:02:09.385768Z" + "end_time": "2023-11-30T10:14:47.966498Z", + "start_time": "2023-11-30T10:14:47.944256Z" } }, "id": "833484748ed512e8" @@ -196,8 +196,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:02:09.456986Z", - "start_time": "2023-11-29T21:02:09.407709Z" + "end_time": "2023-11-30T10:14:48.034503Z", + "start_time": "2023-11-30T10:14:47.966623Z" } }, "id": "15ed7d1ba1f22317" @@ -212,12 +212,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 73, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-11-29T23:48:34.526173Z", - "start_time": "2023-11-29T23:48:34.278833Z" + "end_time": "2023-11-30T12:18:02.265521Z", + "start_time": "2023-11-30T12:18:02.001588Z" } }, "outputs": [], @@ -239,6 +239,54 @@ "\n", "To create a public link, set `share=True` in `launch()`.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 622, in _create_group_model_rank_heatmap\n", + " tolerance = str_to_float(tolerance, 'Tolerance')\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", + " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", + "ValueError: Tolerance must be a float number with a '.' separator.\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 624, in _create_group_model_rank_heatmap\n", + " raise ValueError('Tolerance cannot be smaller than 0.001')\n", + "ValueError: Tolerance cannot be smaller than 0.001\n" + ] } ], "source": [ @@ -248,7 +296,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-11-29T23:48:34.526950Z" + "start_time": "2023-11-30T12:18:02.266315Z" } }, "id": "678a9dc8d51243f4" @@ -271,25 +319,118 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:12:46.494378Z", - "start_time": "2023-11-29T21:12:46.442750Z" + "end_time": "2023-11-30T10:20:01.084744Z", + "start_time": "2023-11-30T10:20:01.041733Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "outputs": [], "source": [], + "metadata": { + "collapsed": false + }, + "id": "21c0ad91536f0af5" + }, + { + "cell_type": "code", + "execution_count": 63, + "outputs": [], + "source": [ + "def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: str = 'dense'):\n", + " \"\"\"\n", + " Rank a pandas series with defined tolerance.\n", + " Ref: https://stackoverflow.com/questions/72956450/pandas-ranking-with-tolerance\n", + "\n", + " Parameters\n", + " ----------\n", + " pd_series\n", + " A pandas series to rank\n", + " tolerance\n", + " A float value for ranking\n", + " method\n", + " Ranking methods for numpy.rank()\n", + "\n", + " Returns\n", + " -------\n", + " A pandas series with dense ranks for the input pd series.\n", + "\n", + " \"\"\"\n", + " tolerance += 1e-10 # Add 0.0000000001 for correct comparison of float numbers\n", + " vals = pd.Series(pd_series.unique()).sort_values()\n", + " vals.index = vals\n", + " print('vals1 -- ', vals)\n", + " vals = vals.mask(vals - vals.shift(1) < tolerance, vals.shift(1))\n", + " print('vals2 -- ', vals)\n", + "\n", + " return pd_series.map(vals).fillna(pd_series).rank(method=method)" + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-29T21:12:46.501645Z", - "start_time": "2023-11-29T21:12:46.483631Z" + "end_time": "2023-11-30T11:49:03.109586Z", + "start_time": "2023-11-30T11:49:03.043461Z" } }, - "id": "c207d4345ddca1db" + "id": "58f9830c22542b19" + }, + { + "cell_type": "code", + "execution_count": 70, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vals1 -- 0.002102 0.002102\n", + "0.002214 0.002214\n", + "0.003088 0.003088\n", + "0.004906 0.004906\n", + "dtype: float64\n", + "vals2 -- 0.002102 0.002102\n", + "0.002214 0.002102\n", + "0.003088 0.002214\n", + "0.004906 0.003088\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": "0 1.0\n1 2.0\n2 1.0\n3 3.0\ndtype: float64" + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# df = pd.Series([0.002, 0.003, 0.002, 0.005])\n", + "df = pd.Series([0.002102,0.003088,0.002214,0.004906])\n", + "rank_with_tolerance(df, tolerance=0.005)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-30T11:58:02.372653Z", + "start_time": "2023-11-30T11:58:02.323106Z" + } + }, + "id": "1a8bdd34f4e1b2a2" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "ec5d1085c5fc393a" } ], "metadata": { diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 1b399eb5..4fc38399 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -339,7 +339,7 @@ def start_web_app(self): with gr.Column(): gr.Markdown( """ - ### Overall Metrics + ### Overall Metric Constraints """) with gr.Row(): accuracy_metric_vw4 = gr.Dropdown( @@ -366,7 +366,11 @@ def start_web_app(self): subgroup_uncertainty_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) subgroup_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): - positive_rate_metric_vw4 = gr.Text(value=POSITIVE_RATE, label="Positive-Rate Metric", scale=2) + positive_rate_metric_vw4 = gr.Dropdown( + [POSITIVE_RATE], + value=POSITIVE_RATE, multiselect=False, label="Positive-Rate Metric", + scale=2 + ) positive_rate_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) positive_rate_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) @@ -374,7 +378,7 @@ def start_web_app(self): with gr.Column(): gr.Markdown( """ - ### Disparity Metrics + ### Disparity Metric Constraints """) with gr.Row(): fairness_metric_vw4 = gr.Dropdown( @@ -575,6 +579,8 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura """ tolerance = str_to_float(tolerance, 'Tolerance') + if tolerance < 0.001: + raise ValueError('Tolerance cannot be smaller than 0.001') metrics_lst = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst # Find metric values for each model based on metric, subgroup, and model names. @@ -588,6 +594,7 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura model_metrics_matrix = pd.DataFrame(results).T model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] + model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) @@ -613,6 +620,8 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met """ tolerance = str_to_float(tolerance, 'Tolerance') + if tolerance < 0.001: + raise ValueError('Tolerance cannot be smaller than 0.001') groups_lst = self.sensitive_attributes_dct.keys() metrics_lst = group_fairness_metrics_lst + group_uncertainty_metrics + group_stability_metrics_lst @@ -644,6 +653,7 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met model_metrics_matrix = pd.DataFrame(results).T model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] + model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index ab4534bd..bbc4b900 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -7,6 +7,7 @@ from altair.utils.schemapi import Undefined from virny.utils.common_helpers import check_substring_in_list +from IPython.display import display def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: str = 'dense'): @@ -243,7 +244,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ num_ranks = int(sorted_matrix_by_rank.values.max()) fig = plt.figure(figsize=(matrix_width, matrix_height)) - rank_colors = sns.color_palette("coolwarm_r", n_colors=num_ranks).as_hex() + rank_colors = sns.diverging_palette(13, 145, s=75, l=70, n=num_ranks).as_hex() # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a coolwarm color scheme reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1 ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix.round(3), cmap=rank_colors, From c185456d4ebf45199bb41bafd2c9c92e8938f3f3 Mon Sep 17 00:00:00 2001 From: dmytro Date: Sun, 3 Dec 2023 11:05:37 +0200 Subject: [PATCH 054/148] Add labels --- virny/utils/common_helpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index dbaac29f..8b3c2a35 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -95,7 +95,7 @@ def save_metrics_to_file(metrics_df, result_filename, save_dir_path): def confusion_matrix_metrics(y_true, y_preds): metrics = {} - TN, FP, FN, TP = confusion_matrix(y_true, y_preds).ravel() + TN, FP, FN, TP = confusion_matrix(y_true, y_preds, labels=[0, 1]).ravel() metrics['TPR'] = TP/(TP+FN) metrics['TNR'] = TN/(TN+FP) From c4c0c8c3d1b6f62bcac4e1147cc8c6b5f502cd07 Mon Sep 17 00:00:00 2001 From: proc1v Date: Wed, 6 Dec 2023 20:18:39 +0200 Subject: [PATCH 055/148] Added saving of eqodss fitted params --- requirements.txt | 2 +- virny/analyzers/abstract_overall_variance_analyzer.py | 2 +- .../batch_overall_variance_analyzer_postprocessing.py | 5 ++++- virny/user_interfaces/metrics_computation_interfaces.py | 9 +++++++++ 4 files changed, 15 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 6f68ab56..867757b7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ wheel~=0.38.4 twine~=4.0.2 -numpy~=1.23.5 +numpy==1.23.5 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 18edc79b..054cf67d 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -3,7 +3,7 @@ import pandas as pd from copy import deepcopy -from tqdm.notebook import tqdm +from tqdm import tqdm from abc import ABCMeta, abstractmethod from virny.custom_classes.custom_logger import get_logger diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 10d05f2b..597ee663 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -3,7 +3,7 @@ import numpy as np import pandas as pd -from tqdm.notebook import tqdm +from tqdm import tqdm from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer from virny.utils.postprocessing_intervention_utils import contruct_binary_label_dataset_from_df, construct_binary_label_dataset_from_samples, predict_on_binary_label_dataset @@ -80,6 +80,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b models_predictions[idx] = postprocessor_fitted.predict(test_binary_label_dataset_pred).labels.ravel() self.models_lst[idx] = classifier + print("Postprocessor fitted params: ", postprocessor_fitted.model_params.x) + postprocessor_fitted.saved_params.append(postprocessor_fitted.model_params.x) + if self._verbose >= 1: print('\n', flush=True) self._AbstractOverallVarianceAnalyzer__logger.info('Successfully tested classifiers by bootstrap') diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index a7d336e6..5fac8338 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -1,5 +1,6 @@ import os import traceback +import numpy as np import pandas as pd from river import base from tqdm.notebook import tqdm @@ -351,6 +352,14 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi # Extend df with technical columns model_metrics_df['Tag'] = 'OK' model_metrics_df['Record_Create_Date_Time'] = datetime.now(timezone.utc) + + if postprocessor: + postprocessor_params = np.array(postprocessor.saved_params) + params_means = np.mean(postprocessor_params, axis=0) + params_stds = np.std(postprocessor_params, axis=0) + model_metrics_df['Postprocessor_coefs_means'] = [params_means.tolist()] * len(model_metrics_df) + model_metrics_df['Postprocessor_coefs_stds'] = [params_stds.tolist()] * len(model_metrics_df) + for column, value in custom_tbl_fields_dct.items(): model_metrics_df[column] = value From 466d81ac079de03277c024121f58a31f216c9a41 Mon Sep 17 00:00:00 2001 From: proc1v Date: Wed, 6 Dec 2023 21:02:24 +0200 Subject: [PATCH 056/148] dubug --- .../batch_overall_variance_analyzer_postprocessing.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 597ee663..6da2edce 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -80,8 +80,10 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b models_predictions[idx] = postprocessor_fitted.predict(test_binary_label_dataset_pred).labels.ravel() self.models_lst[idx] = classifier - print("Postprocessor fitted params: ", postprocessor_fitted.model_params.x) + print("Postprocessor fitted params: ", postprocessor_fitted.model_params.x, flush=True) postprocessor_fitted.saved_params.append(postprocessor_fitted.model_params.x) + + print("Postprocessor fitted params: ", postprocessor_fitted.saved_params, flush=True) if self._verbose >= 1: print('\n', flush=True) From c1feaf941bf116b9ae4e331548ea5fa00b8c1b93 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 7 Dec 2023 20:00:38 +0200 Subject: [PATCH 057/148] Added test metrics for ACS Public Coverage --- ...Multiple_Models_Interface_Vis_Income.ipynb | 218 ++----------- ...iple_Models_Interface_Vis_Law_School.ipynb | 132 ++++---- ...ultiple_Models_Interface_Vis_Pub_Cov.ipynb | 308 ++++++++++++++++++ docs/examples/pub_cov_subgroup_metrics.csv | 153 +++++++++ 4 files changed, 553 insertions(+), 258 deletions(-) create mode 100644 docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb create mode 100644 docs/examples/pub_cov_subgroup_metrics.csv diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index a7f8a5b4..aac2e942 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-11-30T10:14:44.773220Z", - "start_time": "2023-11-30T10:14:44.118473Z" + "end_time": "2023-12-03T22:09:30.506501Z", + "start_time": "2023-12-03T22:09:29.758579Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-11-30T10:14:44.781386Z", - "start_time": "2023-11-30T10:14:44.773120Z" + "end_time": "2023-12-03T22:09:30.515379Z", + "start_time": "2023-12-03T22:09:30.506765Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-11-30T10:14:44.791947Z", - "start_time": "2023-11-30T10:14:44.781906Z" + "end_time": "2023-12-03T22:09:30.525236Z", + "start_time": "2023-12-03T22:09:30.515761Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-11-30T10:14:46.531174Z", - "start_time": "2023-11-30T10:14:44.792591Z" + "end_time": "2023-12-03T22:09:33.037405Z", + "start_time": "2023-12-03T22:09:30.526188Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-30T10:14:47.863288Z", - "start_time": "2023-11-30T10:14:46.532997Z" + "end_time": "2023-12-03T22:09:34.393655Z", + "start_time": "2023-12-03T22:09:33.038803Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-30T10:14:47.892811Z", - "start_time": "2023-11-30T10:14:47.863607Z" + "end_time": "2023-12-03T22:09:34.420850Z", + "start_time": "2023-12-03T22:09:34.393834Z" } }, "id": "2aab7c79ecdee914" @@ -153,30 +153,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-30T10:14:47.944316Z", - "start_time": "2023-11-30T10:14:47.890948Z" + "end_time": "2023-12-03T22:09:34.476159Z", + "start_time": "2023-12-03T22:09:34.421313Z" } }, "id": "44ee5eff6054ce04" }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "models_metrics_dct = dict()\n", - "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-30T10:14:47.966498Z", - "start_time": "2023-11-30T10:14:47.944256Z" - } - }, - "id": "833484748ed512e8" - }, { "cell_type": "code", "execution_count": 9, @@ -196,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-30T10:14:48.034503Z", - "start_time": "2023-11-30T10:14:47.966623Z" + "end_time": "2023-12-03T22:09:34.566417Z", + "start_time": "2023-12-03T22:09:34.499412Z" } }, "id": "15ed7d1ba1f22317" @@ -212,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 10, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-11-30T12:18:02.265521Z", - "start_time": "2023-11-30T12:18:02.001588Z" + "end_time": "2023-12-03T22:09:34.588762Z", + "start_time": "2023-12-03T22:09:34.523515Z" } }, "outputs": [], @@ -229,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "outputs": [ { "name": "stdout", @@ -237,55 +219,8 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 622, in _create_group_model_rank_heatmap\n", - " tolerance = str_to_float(tolerance, 'Tolerance')\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", - " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", - "ValueError: Tolerance must be a float number with a '.' separator.\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 624, in _create_group_model_rank_heatmap\n", - " raise ValueError('Tolerance cannot be smaller than 0.001')\n", - "ValueError: Tolerance cannot be smaller than 0.001\n" + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" ] } ], @@ -294,9 +229,9 @@ ], "metadata": { "collapsed": false, - "is_executing": true, "ExecuteTime": { - "start_time": "2023-11-30T12:18:02.266315Z" + "end_time": "2023-12-03T23:42:27.309199Z", + "start_time": "2023-12-03T22:09:34.550444Z" } }, "id": "678a9dc8d51243f4" @@ -319,118 +254,25 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-30T10:20:01.084744Z", - "start_time": "2023-11-30T10:20:01.041733Z" + "end_time": "2023-12-03T23:42:27.346512Z", + "start_time": "2023-12-03T23:42:27.314034Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "outputs": [], "source": [], - "metadata": { - "collapsed": false - }, - "id": "21c0ad91536f0af5" - }, - { - "cell_type": "code", - "execution_count": 63, - "outputs": [], - "source": [ - "def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: str = 'dense'):\n", - " \"\"\"\n", - " Rank a pandas series with defined tolerance.\n", - " Ref: https://stackoverflow.com/questions/72956450/pandas-ranking-with-tolerance\n", - "\n", - " Parameters\n", - " ----------\n", - " pd_series\n", - " A pandas series to rank\n", - " tolerance\n", - " A float value for ranking\n", - " method\n", - " Ranking methods for numpy.rank()\n", - "\n", - " Returns\n", - " -------\n", - " A pandas series with dense ranks for the input pd series.\n", - "\n", - " \"\"\"\n", - " tolerance += 1e-10 # Add 0.0000000001 for correct comparison of float numbers\n", - " vals = pd.Series(pd_series.unique()).sort_values()\n", - " vals.index = vals\n", - " print('vals1 -- ', vals)\n", - " vals = vals.mask(vals - vals.shift(1) < tolerance, vals.shift(1))\n", - " print('vals2 -- ', vals)\n", - "\n", - " return pd_series.map(vals).fillna(pd_series).rank(method=method)" - ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-30T11:49:03.109586Z", - "start_time": "2023-11-30T11:49:03.043461Z" + "end_time": "2023-12-03T23:42:27.349708Z", + "start_time": "2023-12-03T23:42:27.345872Z" } }, - "id": "58f9830c22542b19" - }, - { - "cell_type": "code", - "execution_count": 70, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "vals1 -- 0.002102 0.002102\n", - "0.002214 0.002214\n", - "0.003088 0.003088\n", - "0.004906 0.004906\n", - "dtype: float64\n", - "vals2 -- 0.002102 0.002102\n", - "0.002214 0.002102\n", - "0.003088 0.002214\n", - "0.004906 0.003088\n", - "dtype: float64\n" - ] - }, - { - "data": { - "text/plain": "0 1.0\n1 2.0\n2 1.0\n3 3.0\ndtype: float64" - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# df = pd.Series([0.002, 0.003, 0.002, 0.005])\n", - "df = pd.Series([0.002102,0.003088,0.002214,0.004906])\n", - "rank_with_tolerance(df, tolerance=0.005)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-30T11:58:02.372653Z", - "start_time": "2023-11-30T11:58:02.323106Z" - } - }, - "id": "1a8bdd34f4e1b2a2" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "ec5d1085c5fc393a" + "id": "21c0ad91536f0af5" } ], "metadata": { diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index a2a5a603..e9af1c88 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -2,24 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 55, + "execution_count": 1, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.385430Z", - "start_time": "2023-10-07T13:37:09.127608Z" + "end_time": "2023-12-06T15:49:13.844713Z", + "start_time": "2023-12-06T15:49:13.202938Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -28,12 +19,12 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 2, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.409539Z", - "start_time": "2023-10-07T13:37:09.385249Z" + "end_time": "2023-12-06T15:49:13.852965Z", + "start_time": "2023-12-06T15:49:13.844443Z" } }, "outputs": [], @@ -46,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 3, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.430322Z", - "start_time": "2023-10-07T13:37:09.408329Z" + "end_time": "2023-12-06T15:49:13.862149Z", + "start_time": "2023-12-06T15:49:13.853366Z" } }, "outputs": [ @@ -81,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 4, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.451279Z", - "start_time": "2023-10-07T13:37:09.431063Z" + "end_time": "2023-12-06T15:49:16.237279Z", + "start_time": "2023-12-06T15:49:13.862719Z" } }, "outputs": [], @@ -94,89 +85,89 @@ "import os\n", "import pandas as pd\n", "\n", + "from virny.datasets import LawSchoolDataset\n", + "from virny.custom_classes.metrics_composer import MetricsComposer\n", "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 5, "outputs": [], "source": [ + "data_loader = LawSchoolDataset()\n", "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.475696Z", - "start_time": "2023-10-07T13:37:09.453496Z" + "end_time": "2023-12-06T15:49:16.300788Z", + "start_time": "2023-12-06T15:49:16.238957Z" } }, "id": "d3c53c7b72ecbcd0" }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 6, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_subgroup_metrics.csv'), header=0)\n", - "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_group_metrics.csv'), header=0)" + "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", + " subgroup_metrics_df['Intervention_Param'].astype(str))" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.500877Z", - "start_time": "2023-10-07T13:37:09.474723Z" + "end_time": "2023-12-06T15:49:16.328190Z", + "start_time": "2023-12-06T15:49:16.301062Z" } }, "id": "2aab7c79ecdee914" }, { "cell_type": "code", - "execution_count": 61, - "outputs": [], - "source": [ - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))\n", - "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", - " + models_composed_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.520270Z", - "start_time": "2023-10-07T13:37:09.500217Z" + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.024413 -0.158856 -0.162998 \n1 Aleatoric_Uncertainty_Parity -0.016769 0.317464 0.274695 \n2 Aleatoric_Uncertainty_Ratio 0.951019 2.126816 1.880052 \n3 Equalized_Odds_FNR 0.006853 0.089260 0.092334 \n4 Equalized_Odds_FPR 0.027311 -0.289259 -0.156572 \n\n Model_Name \n0 LGBMClassifier__alpha=0.6 \n1 LGBMClassifier__alpha=0.6 \n2 LGBMClassifier__alpha=0.6 \n3 LGBMClassifier__alpha=0.6 \n4 LGBMClassifier__alpha=0.6 ", + "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Parity-0.024413-0.158856-0.162998LGBMClassifier__alpha=0.6
1Aleatoric_Uncertainty_Parity-0.0167690.3174640.274695LGBMClassifier__alpha=0.6
2Aleatoric_Uncertainty_Ratio0.9510192.1268161.880052LGBMClassifier__alpha=0.6
3Equalized_Odds_FNR0.0068530.0892600.092334LGBMClassifier__alpha=0.6
4Equalized_Odds_FPR0.027311-0.289259-0.156572LGBMClassifier__alpha=0.6
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } - }, - "id": "2d922003e752a4b4" - }, - { - "cell_type": "code", - "execution_count": 62, - "outputs": [], + ], "source": [ + "model_names = subgroup_metrics_df['Model_Name'].unique()\n", "models_metrics_dct = dict()\n", - "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" + "for model_name in model_names:\n", + " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", + "\n", + "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", + "models_composed_metrics_df = metrics_composer.compose_metrics()\n", + "models_composed_metrics_df.head()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.543689Z", - "start_time": "2023-10-07T13:37:09.521274Z" + "end_time": "2023-12-06T15:49:16.379226Z", + "start_time": "2023-12-06T15:49:16.327124Z" } }, "id": "833484748ed512e8" }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 8, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 63, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -187,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.565841Z", - "start_time": "2023-10-07T13:37:09.543823Z" + "end_time": "2023-12-06T15:49:16.400186Z", + "start_time": "2023-12-06T15:49:16.376928Z" } }, "id": "15ed7d1ba1f22317" @@ -203,23 +194,24 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 9, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:37:09.593512Z", - "start_time": "2023-10-07T13:37:09.565293Z" + "end_time": "2023-12-06T15:49:16.482456Z", + "start_time": "2023-12-06T15:49:16.398934Z" } }, "outputs": [], "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", + "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", + " models_metrics_dct, models_composed_metrics_df,\n", " sensitive_attributes_dct=sensitive_attributes_dct)" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 10, "outputs": [ { "name": "stdout", @@ -238,15 +230,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-07T13:42:17.431036Z", - "start_time": "2023-10-07T13:37:09.593677Z" + "end_time": "2023-12-06T23:49:32.410119Z", + "start_time": "2023-12-06T15:49:16.428590Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 11, "outputs": [ { "name": "stdout", @@ -262,20 +254,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-07T13:42:17.479914Z", - "start_time": "2023-10-07T13:42:17.432456Z" + "end_time": "2023-12-06T23:49:32.447145Z", + "start_time": "2023-12-06T23:49:32.406866Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 11, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-10-07T13:42:17.482254Z", - "start_time": "2023-10-07T13:42:17.478725Z" + "end_time": "2023-12-06T23:49:32.450211Z", + "start_time": "2023-12-06T23:49:32.446290Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb new file mode 100644 index 00000000..6caf5b8b --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-07T00:13:42.978064Z", + "start_time": "2023-12-07T00:13:42.914700Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-07T00:13:42.983725Z", + "start_time": "2023-12-07T00:13:42.954698Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-07T00:13:43.018895Z", + "start_time": "2023-12-07T00:13:42.982387Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": [ + "# Multiple Models Interface Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-07T00:13:43.027909Z", + "start_time": "2023-12-07T00:13:43.006390Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "from virny.datasets import ACSPublicCoverageDataset\n", + "from virny.custom_classes.metrics_composer import MetricsComposer\n", + "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [], + "source": [ + "data_loader = ACSPublicCoverageDataset(state=['CA'], year=2018, with_nulls=False,\n", + " subsample_size=15_000, subsample_seed=42)\n", + "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T00:13:48.771709Z", + "start_time": "2023-12-07T00:13:43.029632Z" + } + }, + "id": "d3c53c7b72ecbcd0" + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'pub_cov_subgroup_metrics.csv'), header=0)\n", + "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", + " subgroup_metrics_df['Intervention_Param'].astype(str))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T00:13:48.805639Z", + "start_time": "2023-12-07T00:13:48.768740Z" + } + }, + "id": "2aab7c79ecdee914" + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "data": { + "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.026847 0.016299 0.040212 \n1 Aleatoric_Uncertainty_Parity -0.013240 0.027276 0.007235 \n2 Aleatoric_Uncertainty_Ratio 0.983584 1.034689 1.009077 \n3 Equalized_Odds_FNR 0.004275 -0.000359 -0.008617 \n4 Equalized_Odds_FPR -0.012072 -0.024172 -0.040481 \n\n Model_Name \n0 LGBMClassifier__alpha=0.6 \n1 LGBMClassifier__alpha=0.6 \n2 LGBMClassifier__alpha=0.6 \n3 LGBMClassifier__alpha=0.6 \n4 LGBMClassifier__alpha=0.6 ", + "text/html": "
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MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0268470.0162990.040212LGBMClassifier__alpha=0.6
1Aleatoric_Uncertainty_Parity-0.0132400.0272760.007235LGBMClassifier__alpha=0.6
2Aleatoric_Uncertainty_Ratio0.9835841.0346891.009077LGBMClassifier__alpha=0.6
3Equalized_Odds_FNR0.004275-0.000359-0.008617LGBMClassifier__alpha=0.6
4Equalized_Odds_FPR-0.012072-0.024172-0.040481LGBMClassifier__alpha=0.6
\n
" + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_names = subgroup_metrics_df['Model_Name'].unique()\n", + "models_metrics_dct = dict()\n", + "for model_name in model_names:\n", + " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", + "\n", + "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", + "models_composed_metrics_df = metrics_composer.compose_metrics()\n", + "models_composed_metrics_df.head()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T00:13:48.849022Z", + "start_time": "2023-12-07T00:13:48.802693Z" + } + }, + "id": "833484748ed512e8" + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "data": { + "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.6', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.6'])" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_metrics_dct.keys()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T00:13:48.873723Z", + "start_time": "2023-12-07T00:13:48.848261Z" + } + }, + "id": "15ed7d1ba1f22317" + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": {}, + "source": [ + "## Metrics Visualization and Reporting" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-07T00:13:48.959344Z", + "start_time": "2023-12-07T00:13:48.871083Z" + } + }, + "outputs": [], + "source": [ + "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", + " models_metrics_dct, models_composed_metrics_df,\n", + " sensitive_attributes_dct=sensitive_attributes_dct)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n", + "Keyboard interruption in main thread... closing server.\n" + ] + } + ], + "source": [ + "visualizer.start_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T00:15:48.056146Z", + "start_time": "2023-12-07T00:13:48.898642Z" + } + }, + "id": "678a9dc8d51243f4" + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], + "source": [ + "visualizer.stop_web_app()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T00:15:48.092702Z", + "start_time": "2023-12-07T00:15:48.056394Z" + } + }, + "id": "277b6d1de837dab7" + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2326c129", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-07T00:15:48.095103Z", + "start_time": "2023-12-07T00:15:48.092153Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/pub_cov_subgroup_metrics.csv b/docs/examples/pub_cov_subgroup_metrics.csv new file mode 100644 index 00000000..788e1430 --- /dev/null +++ b/docs/examples/pub_cov_subgroup_metrics.csv @@ -0,0 +1,153 @@ 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++++++++++++------- .../metrics_interactive_visualizer.py | 12 +-- virny/utils/data_viz_utils.py | 52 +++++++++---- 3 files changed, 96 insertions(+), 46 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index e9af1c88..034d205e 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-06T15:49:13.844713Z", - "start_time": "2023-12-06T15:49:13.202938Z" + "end_time": "2023-12-10T13:46:04.887856Z", + "start_time": "2023-12-10T13:46:04.026304Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-06T15:49:13.852965Z", - "start_time": "2023-12-06T15:49:13.844443Z" + "end_time": "2023-12-10T13:46:04.897038Z", + "start_time": "2023-12-10T13:46:04.888481Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-06T15:49:13.862149Z", - "start_time": "2023-12-06T15:49:13.853366Z" + "end_time": "2023-12-10T13:46:04.906348Z", + "start_time": "2023-12-10T13:46:04.897731Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-06T15:49:16.237279Z", - "start_time": "2023-12-06T15:49:13.862719Z" + "end_time": "2023-12-10T13:46:09.457388Z", + "start_time": "2023-12-10T13:46:04.907162Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-06T15:49:16.300788Z", - "start_time": "2023-12-06T15:49:16.238957Z" + "end_time": "2023-12-10T13:46:09.518413Z", + "start_time": "2023-12-10T13:46:09.456301Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-06T15:49:16.328190Z", - "start_time": "2023-12-06T15:49:16.301062Z" + "end_time": "2023-12-10T13:46:09.544781Z", + "start_time": "2023-12-10T13:46:09.518981Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-06T15:49:16.379226Z", - "start_time": "2023-12-06T15:49:16.327124Z" + "end_time": "2023-12-10T13:46:09.592998Z", + "start_time": "2023-12-10T13:46:09.545292Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-06T15:49:16.400186Z", - "start_time": "2023-12-06T15:49:16.376928Z" + "end_time": "2023-12-10T13:46:09.615874Z", + "start_time": "2023-12-10T13:46:09.592514Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-06T15:49:16.482456Z", - "start_time": "2023-12-06T15:49:16.398934Z" + "end_time": "2023-12-10T16:04:29.715738Z", + "start_time": "2023-12-10T16:04:29.547860Z" } }, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "outputs": [ { "name": "stdout", @@ -219,9 +219,33 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", + "Thanks for being a Gradio user! If you have questions or feedback, please join our Discord server and chat with us: https://discord.gg/feTf9x3ZSB\n", + "\n", "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" + "{1: [0.001, 0.0075], 2: [0.0075, 0.013999999999999999], 3: [0.013999999999999999, 0.020499999999999997], 4: [0.020499999999999997, 0.027]}\n", + "{1: [0.259, 0.3015], 2: [0.3015, 0.344], 3: [0.344, 0.38649999999999995], 4: [0.38649999999999995, 0.429]}\n", + "{1: [0.147, 0.1885], 2: [0.1885, 0.23], 3: [0.23, 0.2715], 4: [0.2715, 0.313]}\n", + "{1: [0.0, 0.0035], 2: [0.0035, 0.007]}\n", + "{1: [0.077, 0.08524999999999999], 2: [0.08524999999999999, 0.0935], 3: [0.0935, 0.10175000000000001], 4: [0.10175000000000001, 0.11]}\n", + "{1: [0.075, 0.081], 2: [0.081, 0.08700000000000001], 3: [0.08700000000000001, 0.09300000000000001], 4: [0.09300000000000001, 0.099]}\n", + "{1: [0.008, 0.0125], 2: [0.0125, 0.017]}\n", + "{1: [0.296, 0.30474999999999997], 2: [0.30474999999999997, 0.3135], 3: [0.3135, 0.32225000000000004], 4: [0.32225000000000004, 0.331]}\n", + "{1: [0.268, 0.27425], 2: [0.27425, 0.28049999999999997], 3: [0.28049999999999997, 0.28674999999999995], 4: [0.28674999999999995, 0.293]}\n", + "{1: [0.0009999999999998899, 0.0040000000000000036]}\n", + "{1: [0.041000000000000036, 0.05275000000000002], 2: [0.05275000000000002, 0.0645], 3: [0.0645, 0.07624999999999998], 4: [0.07624999999999998, 0.08799999999999997]}\n", + "{1: [0.031000000000000028, 0.04300000000000001], 2: [0.04300000000000001, 0.05499999999999999], 3: [0.05499999999999999, 0.06699999999999998], 4: [0.06699999999999998, 0.07899999999999996]}\n", + "{1: [0.0, 0.001]}\n", + "{1: [0.011, 0.015], 2: [0.015, 0.019], 3: [0.019, 0.023]}\n", + "{1: [0.01, 0.014499999999999999], 2: [0.014499999999999999, 0.019]}\n" ] + }, + { + "data": { + "text/plain": " LGBMClassifier__alpha=0.0 \\\nEqualized_Odds_FPR_male 2.0 \nEqualized_Odds_FPR_race 3.0 \nEqualized_Odds_FPR_male&race 2.0 \nEqualized_Odds_TPR_male 2.0 \nEqualized_Odds_TPR_race 4.0 \nEqualized_Odds_TPR_male&race 4.0 \nOverall_Uncertainty_Parity_male 2.0 \nOverall_Uncertainty_Parity_race 3.0 \nOverall_Uncertainty_Parity_male&race 3.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 2.0 \nLabel_Stability_Ratio_male&race 3.0 \nStd_Parity_male 1.0 \nStd_Parity_race 2.0 \nStd_Parity_male&race 2.0 \n\n LGBMClassifier__alpha=0.6 \\\nEqualized_Odds_FPR_male 2.0 \nEqualized_Odds_FPR_race 1.0 \nEqualized_Odds_FPR_male&race 1.0 \nEqualized_Odds_TPR_male 2.0 \nEqualized_Odds_TPR_race 2.0 \nEqualized_Odds_TPR_male&race 3.0 \nOverall_Uncertainty_Parity_male 2.0 \nOverall_Uncertainty_Parity_race 2.0 \nOverall_Uncertainty_Parity_male&race 2.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 2.0 \nLabel_Stability_Ratio_male&race 2.0 \nStd_Parity_male 1.0 \nStd_Parity_race 2.0 \nStd_Parity_male&race 2.0 \n\n LogisticRegression__alpha=0.0 \\\nEqualized_Odds_FPR_male 1.0 \nEqualized_Odds_FPR_race 2.0 \nEqualized_Odds_FPR_male&race 2.0 \nEqualized_Odds_TPR_male 1.0 \nEqualized_Odds_TPR_race 3.0 \nEqualized_Odds_TPR_male&race 1.0 \nOverall_Uncertainty_Parity_male 1.0 \nOverall_Uncertainty_Parity_race 3.0 \nOverall_Uncertainty_Parity_male&race 3.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 1.0 \nLabel_Stability_Ratio_male&race 1.0 \nStd_Parity_male 1.0 \nStd_Parity_race 1.0 \nStd_Parity_male&race 1.0 \n\n LogisticRegression__alpha=0.6 \nEqualized_Odds_FPR_male 1.0 \nEqualized_Odds_FPR_race 1.0 \nEqualized_Odds_FPR_male&race 1.0 \nEqualized_Odds_TPR_male 2.0 \nEqualized_Odds_TPR_race 1.0 \nEqualized_Odds_TPR_male&race 2.0 \nOverall_Uncertainty_Parity_male 1.0 \nOverall_Uncertainty_Parity_race 1.0 \nOverall_Uncertainty_Parity_male&race 1.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 1.0 \nLabel_Stability_Ratio_male&race 1.0 \nStd_Parity_male 1.0 \nStd_Parity_race 1.0 \nStd_Parity_male&race 1.0 ", + "text/html": "
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" + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -229,9 +253,9 @@ ], "metadata": { "collapsed": false, + "is_executing": true, "ExecuteTime": { - "end_time": "2023-12-06T23:49:32.410119Z", - "start_time": "2023-12-06T15:49:16.428590Z" + "start_time": "2023-12-10T16:04:29.716786Z" } }, "id": "678a9dc8d51243f4" @@ -254,8 +278,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-06T23:49:32.447145Z", - "start_time": "2023-12-06T23:49:32.406866Z" + "end_time": "2023-12-10T14:45:32.225285Z", + "start_time": "2023-12-10T14:45:32.184623Z" } }, "id": "277b6d1de837dab7" @@ -266,8 +290,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-06T23:49:32.450211Z", - "start_time": "2023-12-06T23:49:32.446290Z" + "end_time": "2023-12-10T14:45:32.227687Z", + "start_time": "2023-12-10T14:45:32.224834Z" } }, "outputs": [], diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 4fc38399..ebc91aa2 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -215,7 +215,7 @@ def start_web_app(self): ) subgroup_stability_metrics = gr.Dropdown( sorted(self.all_stability_metrics), - value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", + value=['Std', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) subgroup_btn_view2 = gr.Button("Submit") with gr.Column(scale=2): @@ -288,7 +288,7 @@ def start_web_app(self): ) subgroup_stability_metrics = gr.Dropdown( sorted(self.all_stability_metrics), - value=['Jitter', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", + value=['Std', 'Label_Stability'], multiselect=True, label="Stability Metrics", info="Select stability metrics to display on the heatmap:", ) btn_view3 = gr.Button("Submit") with gr.Column(): @@ -579,8 +579,8 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura """ tolerance = str_to_float(tolerance, 'Tolerance') - if tolerance < 0.001: - raise ValueError('Tolerance cannot be smaller than 0.001') + if tolerance < 0.001 or tolerance > 0.2: + raise ValueError('Tolerance should be in the [0.001, 0.2] range') metrics_lst = subgroup_accuracy_metrics_lst + subgroup_uncertainty_metrics + subgroup_stability_metrics_lst # Find metric values for each model based on metric, subgroup, and model names. @@ -620,8 +620,8 @@ def _create_group_model_rank_heatmap(self, model_names: list, group_fairness_met """ tolerance = str_to_float(tolerance, 'Tolerance') - if tolerance < 0.001: - raise ValueError('Tolerance cannot be smaller than 0.001') + if tolerance < 0.001 or tolerance > 0.2: + raise ValueError('Tolerance should be in the [0.001, 0.2] range') groups_lst = self.sensitive_attributes_dct.keys() metrics_lst = group_fairness_metrics_lst + group_uncertainty_metrics + group_stability_metrics_lst diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index bbc4b900..28b6d7cd 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -1,3 +1,4 @@ +import math import numpy as np import pandas as pd import altair as alt @@ -10,7 +11,7 @@ from IPython.display import display -def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: str = 'dense'): +def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): """ Rank a pandas series with defined tolerance. Ref: https://stackoverflow.com/questions/72956450/pandas-ranking-with-tolerance @@ -21,20 +22,41 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.01, method: s A pandas series to rank tolerance A float value for ranking - method - Ranking methods for numpy.rank() Returns ------- A pandas series with dense ranks for the input pd series. """ - tolerance += 1e-10 # Add 0.0000000001 for correct comparison of float numbers - vals = pd.Series(pd_series.unique()).sort_values() - vals.index = vals - vals = vals.mask(vals - vals.shift(1) < tolerance, vals.shift(1)) - - return pd_series.map(vals).fillna(pd_series).rank(method=method) + min_val, max_val = pd_series.min(), pd_series.max() + num_ranks = len(pd_series) + num_bins = math.ceil((max_val - min_val) / tolerance) + # The number of ranks cannot be smaller than 1 and greater than the number of compared models + if num_bins == 0: + num_bins = 1 + elif num_bins > num_ranks: + num_bins = num_ranks + + # Create a dictionary with bin constraints + bin_size = (max_val - min_val) / num_bins + bin_constraints_dct = dict() + min_bin_limit = min_val + for n_bin in range(num_bins): + rank = n_bin + 1 + max_bin_limit = min_bin_limit + bin_size if n_bin + 1 < num_bins else max_val + bin_constraints_dct[rank] = [min_bin_limit, max_bin_limit] + min_bin_limit = max_bin_limit + + print(bin_constraints_dct) + + def get_rank_with_tolerance(val): + for n_bin in range(num_bins): + rank = n_bin + 1 + min_constrain, max_constraint = bin_constraints_dct[rank] + if min_constrain <= val <= max_constraint: + return rank + + return pd_series.apply(get_rank_with_tolerance).rank(method='dense') def compute_proportions(protected_groups, X_data): @@ -68,7 +90,7 @@ def create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np.array: models_distances_matrix = models_distances_matrix.T models_distances_df = pd.DataFrame(models_distances_matrix) sorted_matrix_by_rank = models_distances_df.apply( - lambda row : rank_with_tolerance(row, tolerance, method='dense'), axis = 1 + lambda row : rank_with_tolerance(row, tolerance), axis = 1 ) return sorted_matrix_by_rank @@ -86,7 +108,7 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np models_distances_matrix = models_distances_matrix.T models_distances_df = pd.DataFrame(models_distances_matrix) sorted_matrix_by_rank = models_distances_df.apply( - lambda row : rank_with_tolerance(row, tolerance, method='dense'), axis = 1 + lambda row : rank_with_tolerance(row, tolerance), axis = 1 ) return sorted_matrix_by_rank @@ -244,8 +266,12 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ num_ranks = int(sorted_matrix_by_rank.values.max()) fig = plt.figure(figsize=(matrix_width, matrix_height)) - rank_colors = sns.diverging_palette(13, 145, s=75, l=70, n=num_ranks).as_hex() - # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a coolwarm color scheme + # Set a green color when there is only one rank + if num_ranks == 1: + rank_colors = sns.diverging_palette(145, 13, s=75, l=70, n=num_ranks).as_hex() + else: + rank_colors = sns.diverging_palette(13, 145, s=75, l=70, n=num_ranks).as_hex() + # Convert ranks to minus ranks (1 --> -1; 4 --> -4) to align rank positions with a color scheme reversed_sorted_matrix_by_rank = sorted_matrix_by_rank * -1 ax = sns.heatmap(reversed_sorted_matrix_by_rank, annot=model_metrics_matrix.round(3), cmap=rank_colors, fmt='', annot_kws={'color': 'black', 'alpha': 0.7, 'fontsize': 16 + font_increase}) From 8259f793b34f5deeb8eb5d36f8a64cbd7a41c664 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sun, 10 Dec 2023 18:59:52 +0200 Subject: [PATCH 059/148] Added dynamic tolerance --- ...iple_Models_Interface_Vis_Law_School.ipynb | 48 ++++++++----------- virny/utils/data_viz_utils.py | 45 ++++++++--------- 2 files changed, 42 insertions(+), 51 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 034d205e..b629e5c5 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T16:04:29.715738Z", - "start_time": "2023-12-10T16:04:29.547860Z" + "end_time": "2023-12-10T16:57:44.803014Z", + "start_time": "2023-12-10T16:57:44.583738Z" } }, "outputs": [], @@ -219,33 +219,23 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "Thanks for being a Gradio user! If you have questions or feedback, please join our Discord server and chat with us: https://discord.gg/feTf9x3ZSB\n", - "\n", "To create a public link, set `share=True` in `launch()`.\n", - "{1: [0.001, 0.0075], 2: [0.0075, 0.013999999999999999], 3: [0.013999999999999999, 0.020499999999999997], 4: [0.020499999999999997, 0.027]}\n", - "{1: [0.259, 0.3015], 2: [0.3015, 0.344], 3: [0.344, 0.38649999999999995], 4: [0.38649999999999995, 0.429]}\n", - "{1: [0.147, 0.1885], 2: [0.1885, 0.23], 3: [0.23, 0.2715], 4: [0.2715, 0.313]}\n", - "{1: [0.0, 0.0035], 2: [0.0035, 0.007]}\n", - "{1: [0.077, 0.08524999999999999], 2: [0.08524999999999999, 0.0935], 3: [0.0935, 0.10175000000000001], 4: [0.10175000000000001, 0.11]}\n", - "{1: [0.075, 0.081], 2: [0.081, 0.08700000000000001], 3: [0.08700000000000001, 0.09300000000000001], 4: [0.09300000000000001, 0.099]}\n", - "{1: [0.008, 0.0125], 2: [0.0125, 0.017]}\n", - "{1: [0.296, 0.30474999999999997], 2: [0.30474999999999997, 0.3135], 3: [0.3135, 0.32225000000000004], 4: [0.32225000000000004, 0.331]}\n", - "{1: [0.268, 0.27425], 2: [0.27425, 0.28049999999999997], 3: [0.28049999999999997, 0.28674999999999995], 4: [0.28674999999999995, 0.293]}\n", - "{1: [0.0009999999999998899, 0.0040000000000000036]}\n", - "{1: [0.041000000000000036, 0.05275000000000002], 2: [0.05275000000000002, 0.0645], 3: [0.0645, 0.07624999999999998], 4: [0.07624999999999998, 0.08799999999999997]}\n", - "{1: [0.031000000000000028, 0.04300000000000001], 2: [0.04300000000000001, 0.05499999999999999], 3: [0.05499999999999999, 0.06699999999999998], 4: [0.06699999999999998, 0.07899999999999996]}\n", - "{1: [0.0, 0.001]}\n", - "{1: [0.011, 0.015], 2: [0.015, 0.019], 3: [0.019, 0.023]}\n", - "{1: [0.01, 0.014499999999999999], 2: [0.014499999999999999, 0.019]}\n" + "{'0.001': 1, '0.002': 1, '0.025': 3, '0.027': 3}\n", + "{'0.259': 1, '0.289': 2, '0.377': 3, '0.429': 4}\n", + "{'0.147': 1, '0.157': 2, '0.274': 3, '0.313': 4}\n", + "{'0.0': 1, '0.005': 1, '0.007': 4}\n", + "{'0.077': 1, '0.089': 2, '0.099': 3, '0.11': 4}\n", + "{'0.075': 1, '0.085': 2, '0.092': 3, '0.099': 4}\n", + "{'0.008': 1, '0.011': 1, '0.015': 3, '0.017': 3}\n", + "{'0.296': 1, '0.322': 2, '0.323': 2, '0.331': 4}\n", + "{'0.268': 1, '0.279': 2, '0.287': 3, '0.293': 4}\n", + "{'0.001': 1, '0.002': 1, '0.004': 1}\n", + "{'0.041': 1, '0.045': 1, '0.078': 3, '0.088': 4}\n", + "{'0.031': 1, '0.033': 1, '0.055': 3, '0.079': 4}\n", + "{'0.0': 1, '0.001': 1}\n", + "{'0.011': 1, '0.013': 1, '0.022': 3, '0.023': 3}\n", + "{'0.01': 1, '0.011': 1, '0.019': 3}\n" ] - }, - { - "data": { - "text/plain": " LGBMClassifier__alpha=0.0 \\\nEqualized_Odds_FPR_male 2.0 \nEqualized_Odds_FPR_race 3.0 \nEqualized_Odds_FPR_male&race 2.0 \nEqualized_Odds_TPR_male 2.0 \nEqualized_Odds_TPR_race 4.0 \nEqualized_Odds_TPR_male&race 4.0 \nOverall_Uncertainty_Parity_male 2.0 \nOverall_Uncertainty_Parity_race 3.0 \nOverall_Uncertainty_Parity_male&race 3.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 2.0 \nLabel_Stability_Ratio_male&race 3.0 \nStd_Parity_male 1.0 \nStd_Parity_race 2.0 \nStd_Parity_male&race 2.0 \n\n LGBMClassifier__alpha=0.6 \\\nEqualized_Odds_FPR_male 2.0 \nEqualized_Odds_FPR_race 1.0 \nEqualized_Odds_FPR_male&race 1.0 \nEqualized_Odds_TPR_male 2.0 \nEqualized_Odds_TPR_race 2.0 \nEqualized_Odds_TPR_male&race 3.0 \nOverall_Uncertainty_Parity_male 2.0 \nOverall_Uncertainty_Parity_race 2.0 \nOverall_Uncertainty_Parity_male&race 2.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 2.0 \nLabel_Stability_Ratio_male&race 2.0 \nStd_Parity_male 1.0 \nStd_Parity_race 2.0 \nStd_Parity_male&race 2.0 \n\n LogisticRegression__alpha=0.0 \\\nEqualized_Odds_FPR_male 1.0 \nEqualized_Odds_FPR_race 2.0 \nEqualized_Odds_FPR_male&race 2.0 \nEqualized_Odds_TPR_male 1.0 \nEqualized_Odds_TPR_race 3.0 \nEqualized_Odds_TPR_male&race 1.0 \nOverall_Uncertainty_Parity_male 1.0 \nOverall_Uncertainty_Parity_race 3.0 \nOverall_Uncertainty_Parity_male&race 3.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 1.0 \nLabel_Stability_Ratio_male&race 1.0 \nStd_Parity_male 1.0 \nStd_Parity_race 1.0 \nStd_Parity_male&race 1.0 \n\n LogisticRegression__alpha=0.6 \nEqualized_Odds_FPR_male 1.0 \nEqualized_Odds_FPR_race 1.0 \nEqualized_Odds_FPR_male&race 1.0 \nEqualized_Odds_TPR_male 2.0 \nEqualized_Odds_TPR_race 1.0 \nEqualized_Odds_TPR_male&race 2.0 \nOverall_Uncertainty_Parity_male 1.0 \nOverall_Uncertainty_Parity_race 1.0 \nOverall_Uncertainty_Parity_male&race 1.0 \nLabel_Stability_Ratio_male 1.0 \nLabel_Stability_Ratio_race 1.0 \nLabel_Stability_Ratio_male&race 1.0 \nStd_Parity_male 1.0 \nStd_Parity_race 1.0 \nStd_Parity_male&race 1.0 ", - "text/html": "
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" - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -255,7 +245,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-12-10T16:04:29.716786Z" + "start_time": "2023-12-10T16:57:44.803707Z" } }, "id": "678a9dc8d51243f4" diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 28b6d7cd..41307f44 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -28,33 +28,34 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): A pandas series with dense ranks for the input pd series. """ - min_val, max_val = pd_series.min(), pd_series.max() - num_ranks = len(pd_series) - num_bins = math.ceil((max_val - min_val) / tolerance) - # The number of ranks cannot be smaller than 1 and greater than the number of compared models - if num_bins == 0: - num_bins = 1 - elif num_bins > num_ranks: - num_bins = num_ranks + sorted_vals = sorted(pd_series.tolist()) # Create a dictionary with bin constraints - bin_size = (max_val - min_val) / num_bins bin_constraints_dct = dict() - min_bin_limit = min_val - for n_bin in range(num_bins): - rank = n_bin + 1 - max_bin_limit = min_bin_limit + bin_size if n_bin + 1 < num_bins else max_val - bin_constraints_dct[rank] = [min_bin_limit, max_bin_limit] - min_bin_limit = max_bin_limit - - print(bin_constraints_dct) + for i in range(len(sorted_vals)): + val = sorted_vals[i] + rank = i + 1 + bin_constraints_dct[rank] = [val - tolerance, val + tolerance] + + # Assign ranks for each pandas series value + assigned_ranks_dct = dict() + for i in range(len(sorted_vals)): + val = sorted_vals[i] + max_rank = i + 1 + actual_rank = None + for rank in bin_constraints_dct.keys(): + min_limit, max_limit = bin_constraints_dct[rank] + if min_limit <= val <= max_limit: + actual_rank = rank + break + + assigned_ranks_dct[str(round(val, 3))] = actual_rank + # Dynamically delete constraints from bin_constraints_dct to keep values in the same bin with tolerance + if actual_rank != max_rank: + del bin_constraints_dct[max_rank] def get_rank_with_tolerance(val): - for n_bin in range(num_bins): - rank = n_bin + 1 - min_constrain, max_constraint = bin_constraints_dct[rank] - if min_constrain <= val <= max_constraint: - return rank + return assigned_ranks_dct[str(round(val, 3))] return pd_series.apply(get_rank_with_tolerance).rank(method='dense') From 87318fd3a45cd5d2c08551178ec6bad719d7a361 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sun, 10 Dec 2023 19:24:50 +0200 Subject: [PATCH 060/148] Added tests for tolerance --- ...iple_Models_Interface_Vis_Law_School.ipynb | 89 +++++++++++++++---- tests/utils/test_data_viz_utils.py | 43 +++++++++ virny/utils/data_viz_utils.py | 4 +- 3 files changed, 116 insertions(+), 20 deletions(-) create mode 100644 tests/utils/test_data_viz_utils.py diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index b629e5c5..0533721d 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 56, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T16:57:44.803014Z", - "start_time": "2023-12-10T16:57:44.583738Z" + "end_time": "2023-12-10T17:18:16.810646Z", + "start_time": "2023-12-10T17:18:16.756447Z" } }, "outputs": [], @@ -220,21 +220,72 @@ "Running on local URL: http://127.0.0.1:7860\n", "\n", "To 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+ "{1: [0.008, 0.018], 2: [0.01, 0.02], 3: [0.023, 0.033], 4: [0.027, 0.037]}\n", + "{1: [0.092, 0.102], 2: [0.095, 0.105], 3: [0.095, 0.105], 4: [0.105, 0.115]}\n", + "{1: [0.048, 0.058], 2: [0.05, 0.06], 3: [0.05, 0.06], 4: [0.055, 0.065]}\n", + "{1: [0.048, 0.058], 2: [0.33, 0.34], 3: [0.339, 0.349], 4: [0.339, 0.349]}\n", + "{1: [0.315, 0.325], 2: [0.335, 0.345], 3: [0.34, 0.35], 4: [0.34, 0.35]}\n", + "{1: [0.004, 0.014], 2: [0.004, 0.014], 3: [0.018, 0.028], 4: [0.172, 0.182]}\n", + "{1: [0.008, 0.018], 2: [0.01, 0.02], 3: [0.023, 0.033], 4: [0.183, 0.193]}\n", + "{1: [0.087, 0.107], 2: [0.09, 0.11], 3: [0.09, 0.11], 4: [0.1, 0.12]}\n", + "{1: [0.043, 0.063], 2: [0.045, 0.065], 3: [0.045, 0.065], 4: [0.05, 0.07]}\n", + "{1: [0.043, 0.063], 2: [0.325, 0.345], 3: [0.334, 0.354], 4: [0.334, 0.354]}\n", + "{1: [0.31, 0.33], 2: [0.33, 0.35], 3: [0.335, 0.355], 4: [0.335, 0.355]}\n", + "{1: [-0.001, 0.019], 2: [-0.001, 0.019], 3: [0.013, 0.033], 4: [0.167, 0.187]}\n", + "{1: [0.003, 0.023], 2: [0.005, 0.025], 3: [0.018, 0.038], 4: [0.178, 0.198]}\n", + "{1: [0.09, 0.11], 2: [0.1, 0.12]}\n", + "{1: [0.045, 0.065], 2: [0.05, 0.07]}\n", + "{1: [0.043, 0.063], 2: [0.334, 0.354]}\n", + "{1: [0.31, 0.33], 2: [0.335, 0.355]}\n", + "{1: [-0.001, 0.019], 2: [0.167, 0.187]}\n", + "{1: [0.005, 0.025], 2: [0.178, 0.198]}\n", + "{1: [0.091, 0.101], 2: [0.092, 0.102], 3: [0.095, 0.105], 4: [0.095, 0.105]}\n", + "{1: [0.047, 0.057], 2: [0.048, 0.058], 3: [0.05, 0.06], 4: [0.05, 0.06]}\n", + "{1: [0.328, 0.338], 2: [0.33, 0.34], 3: [0.339, 0.349], 4: [0.339, 0.349]}\n", + "{1: [0.335, 0.345], 2: [0.335, 0.345], 3: [0.34, 0.35], 4: [0.34, 0.35]}\n", + "{1: [0.004, 0.014], 2: [0.004, 0.014], 3: [0.018, 0.028], 4: [0.021, 0.031]}\n", + "{1: [0.008, 0.018], 2: [0.01, 0.02], 3: [0.023, 0.033], 4: [0.027, 0.037]}\n", + "{1: [-0.004, 0.006], 2: [-0.003, 0.007], 3: [0.02, 0.03], 4: [0.022, 0.032]}\n", + "{1: [0.254, 0.264], 2: [0.284, 0.294], 3: [0.372, 0.382], 4: [0.424, 0.434]}\n", + "{1: [0.142, 0.152], 2: [0.152, 0.162], 3: [0.269, 0.279], 4: [0.308, 0.318]}\n", + "{1: [-0.005, 0.005], 2: [0.0, 0.01], 3: [0.0, 0.01], 4: [0.002, 0.012]}\n", + "{1: [0.072, 0.082], 2: [0.084, 0.094], 3: [0.094, 0.104], 4: [0.105, 0.115]}\n", + "{1: [0.07, 0.08], 2: [0.08, 0.09], 3: [0.087, 0.097], 4: [0.094, 0.104]}\n", + "{1: [0.003, 0.013], 2: [0.006, 0.016], 3: [0.01, 0.02], 4: [0.012, 0.022]}\n", + "{1: [0.291, 0.301], 2: [0.317, 0.327], 3: [0.318, 0.328], 4: [0.326, 0.336]}\n", + "{1: [0.263, 0.273], 2: [0.274, 0.284], 3: [0.282, 0.292], 4: [0.288, 0.298]}\n", + "{1: [-0.004, 0.006], 2: [-0.004, 0.006], 3: [-0.003, 0.007], 4: [-0.001, 0.009]}\n", + "{1: [0.036, 0.046], 2: [0.04, 0.05], 3: [0.073, 0.083], 4: [0.083, 0.093]}\n", + "{1: [0.026, 0.036], 2: [0.028, 0.038], 3: [0.05, 0.06], 4: [0.074, 0.084]}\n", + "{1: [-0.005, 0.005], 2: [-0.005, 0.005], 3: [-0.004, 0.006], 4: [-0.004, 0.006]}\n", + "{1: [0.006, 0.016], 2: [0.008, 0.018], 3: [0.017, 0.027], 4: [0.018, 0.028]}\n", + "{1: [0.005, 0.015], 2: [0.006, 0.016], 3: [0.014, 0.024], 4: [0.014, 0.024]}\n" ] } ], @@ -245,7 +296,7 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-12-10T16:57:44.803707Z" + "start_time": "2023-12-10T17:18:16.842929Z" } }, "id": "678a9dc8d51243f4" diff --git a/tests/utils/test_data_viz_utils.py b/tests/utils/test_data_viz_utils.py new file mode 100644 index 00000000..6595c281 --- /dev/null +++ b/tests/utils/test_data_viz_utils.py @@ -0,0 +1,43 @@ +import pandas as pd + +from virny.utils.data_viz_utils import rank_with_tolerance + + +def test_rank_with_tolerance_true1(): + tolerance = 0.005 + pd_series = pd.Series([0.025, 0.027, 0.001, 0.002]) # should be only positive numbers + expected_ranks = [2, 2, 1, 1] + actual_ranks = rank_with_tolerance(pd_series, tolerance) + assert actual_ranks.tolist() == expected_ranks + + +def test_rank_with_tolerance_true2(): + tolerance = 0.005 + pd_series = pd.Series([0.429, 0.289, 0.377, 0.259]) # should be only positive numbers + expected_ranks = [4, 2, 3, 1] + actual_ranks = rank_with_tolerance(pd_series, tolerance) + assert actual_ranks.tolist() == expected_ranks + + +def test_rank_with_tolerance_true3(): + tolerance = 0.005 + pd_series = pd.Series([0.313, 0.157, 0.274, 0.147]) # should be only positive numbers + expected_ranks = [4, 2, 3, 1] + actual_ranks = rank_with_tolerance(pd_series, tolerance) + assert actual_ranks.tolist() == expected_ranks + + +def test_rank_with_tolerance_true4(): + tolerance = 0.005 + pd_series = pd.Series([0.001, 0.001, 0.0, 0.0]) # should be only positive numbers + expected_ranks = [1, 1, 1, 1] + actual_ranks = rank_with_tolerance(pd_series, tolerance) + assert actual_ranks.tolist() == expected_ranks + + +def test_rank_with_tolerance_true5(): + tolerance = 0.01 + pd_series = pd.Series([0.099, 0.092, 0.075, 0.085]) # should be only positive numbers + expected_ranks = [2, 2, 1, 1] + actual_ranks = rank_with_tolerance(pd_series, tolerance) + assert actual_ranks.tolist() == expected_ranks diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 41307f44..c208adfa 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -35,7 +35,9 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): for i in range(len(sorted_vals)): val = sorted_vals[i] rank = i + 1 - bin_constraints_dct[rank] = [val - tolerance, val + tolerance] + bin_constraints_dct[rank] = [round(val - tolerance, 3), round(val + tolerance, 3)] + + print(bin_constraints_dct) # Assign ranks for each pandas series value assigned_ranks_dct = dict() From 52ea84346baca512f20328a95316188e0755a609 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sun, 10 Dec 2023 19:25:51 +0200 Subject: [PATCH 061/148] Added tests for tolerance --- virny/utils/data_viz_utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index c208adfa..41dba3cc 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -37,8 +37,6 @@ def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): rank = i + 1 bin_constraints_dct[rank] = [round(val - tolerance, 3), round(val + tolerance, 3)] - print(bin_constraints_dct) - # Assign ranks for each pandas series value assigned_ranks_dct = dict() for i in range(len(sorted_vals)): From 1d2cc3cc5dfa4957b4a24c45f8bde3f3c33b99bd Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sun, 17 Dec 2023 23:45:17 +0200 Subject: [PATCH 062/148] wip --- ...Multiple_Models_Interface_Vis_Income.ipynb | 60 ++++---- ...iple_Models_Interface_Vis_Law_School.ipynb | 132 ++++-------------- 2 files changed, 56 insertions(+), 136 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index aac2e942..ecd29b0e 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-03T22:09:30.506501Z", - "start_time": "2023-12-03T22:09:29.758579Z" + "end_time": "2023-12-10T22:37:44.370856Z", + "start_time": "2023-12-10T22:37:43.972175Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-03T22:09:30.515379Z", - "start_time": "2023-12-03T22:09:30.506765Z" + "end_time": "2023-12-10T22:37:44.380242Z", + "start_time": "2023-12-10T22:37:44.371542Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-03T22:09:30.525236Z", - "start_time": "2023-12-03T22:09:30.515761Z" + "end_time": "2023-12-10T22:37:44.391659Z", + "start_time": "2023-12-10T22:37:44.380644Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-03T22:09:33.037405Z", - "start_time": "2023-12-03T22:09:30.526188Z" + "end_time": "2023-12-10T22:37:45.918385Z", + "start_time": "2023-12-10T22:37:44.390547Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T22:09:34.393655Z", - "start_time": "2023-12-03T22:09:33.038803Z" + "end_time": "2023-12-10T22:37:47.214487Z", + "start_time": "2023-12-10T22:37:45.921391Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T22:09:34.420850Z", - "start_time": "2023-12-03T22:09:34.393834Z" + "end_time": "2023-12-10T22:37:47.242581Z", + "start_time": "2023-12-10T22:37:47.214727Z" } }, "id": "2aab7c79ecdee914" @@ -153,21 +153,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T22:09:34.476159Z", - "start_time": "2023-12-03T22:09:34.421313Z" + "end_time": "2023-12-10T22:37:47.297089Z", + "start_time": "2023-12-10T22:37:47.240439Z" } }, "id": "44ee5eff6054ce04" }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "outputs": [ { "data": { "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T22:09:34.566417Z", - "start_time": "2023-12-03T22:09:34.499412Z" + "end_time": "2023-12-10T22:37:47.328697Z", + "start_time": "2023-12-10T22:37:47.295950Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-03T22:09:34.588762Z", - "start_time": "2023-12-03T22:09:34.523515Z" + "end_time": "2023-12-10T22:37:47.374721Z", + "start_time": "2023-12-10T22:37:47.317882Z" } }, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "outputs": [ { "name": "stdout", @@ -230,15 +230,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T23:42:27.309199Z", - "start_time": "2023-12-03T22:09:34.550444Z" + "end_time": "2023-12-11T00:26:17.429094Z", + "start_time": "2023-12-10T22:37:47.343749Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "outputs": [ { "name": "stdout", @@ -254,22 +254,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T23:42:27.346512Z", - "start_time": "2023-12-03T23:42:27.314034Z" + "end_time": "2023-12-11T00:26:17.482944Z", + "start_time": "2023-12-11T00:26:17.438287Z" } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-03T23:42:27.349708Z", - "start_time": "2023-12-03T23:42:27.345872Z" + "end_time": "2023-12-11T00:26:17.483195Z", + "start_time": "2023-12-11T00:26:17.479725Z" } }, "id": "21c0ad91536f0af5" diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 0533721d..1826cdfe 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T13:46:04.887856Z", - "start_time": "2023-12-10T13:46:04.026304Z" + "end_time": "2023-12-16T22:10:21.409266Z", + "start_time": "2023-12-16T22:10:20.679843Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T13:46:04.897038Z", - "start_time": "2023-12-10T13:46:04.888481Z" + "end_time": "2023-12-16T22:10:21.418422Z", + "start_time": "2023-12-16T22:10:21.408919Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T13:46:04.906348Z", - "start_time": "2023-12-10T13:46:04.897731Z" + "end_time": "2023-12-16T22:10:21.429731Z", + "start_time": "2023-12-16T22:10:21.418780Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T13:46:09.457388Z", - "start_time": "2023-12-10T13:46:04.907162Z" + "end_time": "2023-12-16T22:10:25.046057Z", + "start_time": "2023-12-16T22:10:21.428148Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-10T13:46:09.518413Z", - "start_time": "2023-12-10T13:46:09.456301Z" + "end_time": "2023-12-16T22:10:25.116829Z", + "start_time": "2023-12-16T22:10:25.048929Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-10T13:46:09.544781Z", - "start_time": "2023-12-10T13:46:09.518981Z" + "end_time": "2023-12-16T22:10:25.144265Z", + "start_time": "2023-12-16T22:10:25.117061Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-10T13:46:09.592998Z", - "start_time": "2023-12-10T13:46:09.545292Z" + "end_time": "2023-12-16T22:10:25.193827Z", + "start_time": "2023-12-16T22:10:25.143225Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-10T13:46:09.615874Z", - "start_time": "2023-12-10T13:46:09.592514Z" + "end_time": "2023-12-16T22:10:25.218033Z", + "start_time": "2023-12-16T22:10:25.193714Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 9, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T17:18:16.810646Z", - "start_time": "2023-12-10T17:18:16.756447Z" + "end_time": "2023-12-16T22:10:25.293585Z", + "start_time": "2023-12-16T22:10:25.217304Z" } }, "outputs": [], @@ -219,73 +219,7 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "{1: [-0.009, 0.011], 2: [-0.008, 0.012], 3: [0.015, 0.035], 4: [0.017, 0.037]}\n", - "{1: [0.249, 0.269], 2: [0.279, 0.299], 3: [0.367, 0.387], 4: [0.419, 0.439]}\n", - "{1: [0.137, 0.157], 2: [0.147, 0.167], 3: [0.264, 0.284], 4: [0.303, 0.323]}\n", - "{1: [-0.01, 0.01], 2: [-0.005, 0.015], 3: [-0.005, 0.015], 4: [-0.003, 0.017]}\n", - "{1: [0.067, 0.087], 2: [0.079, 0.099], 3: [0.089, 0.109], 4: [0.1, 0.12]}\n", - "{1: [0.065, 0.085], 2: [0.075, 0.095], 3: [0.082, 0.102], 4: [0.089, 0.109]}\n", - "{1: [-0.002, 0.018], 2: [0.001, 0.021], 3: [0.005, 0.025], 4: [0.007, 0.027]}\n", - "{1: [0.286, 0.306], 2: [0.312, 0.332], 3: [0.313, 0.333], 4: [0.321, 0.341]}\n", - "{1: [0.258, 0.278], 2: [0.269, 0.289], 3: [0.277, 0.297], 4: [0.283, 0.303]}\n", - "{1: [-0.009, 0.011], 2: [-0.009, 0.011], 3: [-0.008, 0.012], 4: [-0.006, 0.014]}\n", - "{1: [0.031, 0.051], 2: [0.035, 0.055], 3: [0.068, 0.088], 4: [0.078, 0.098]}\n", - "{1: [0.021, 0.041], 2: [0.023, 0.043], 3: [0.045, 0.065], 4: [0.069, 0.089]}\n", - "{1: [-0.01, 0.01], 2: [-0.01, 0.01], 3: [-0.009, 0.011], 4: [-0.009, 0.011]}\n", - "{1: [0.001, 0.021], 2: [0.003, 0.023], 3: [0.012, 0.032], 4: [0.013, 0.033]}\n", - "{1: [0.0, 0.02], 2: [0.001, 0.021], 3: [0.009, 0.029], 4: [0.009, 0.029]}\n", - "{1: [0.086, 0.106], 2: [0.087, 0.107], 3: [0.09, 0.11], 4: [0.09, 0.11]}\n", - "{1: [0.042, 0.062], 2: [0.043, 0.063], 3: [0.045, 0.065], 4: [0.045, 0.065]}\n", - "{1: [0.323, 0.343], 2: [0.325, 0.345], 3: [0.334, 0.354], 4: [0.334, 0.354]}\n", - "{1: [0.33, 0.35], 2: [0.33, 0.35], 3: [0.335, 0.355], 4: [0.335, 0.355]}\n", - "{1: [-0.001, 0.019], 2: [-0.001, 0.019], 3: [0.013, 0.033], 4: [0.016, 0.036]}\n", - "{1: [0.003, 0.023], 2: [0.005, 0.025], 3: [0.018, 0.038], 4: [0.022, 0.042]}\n", - "{1: [0.091, 0.101], 2: [0.092, 0.102], 3: [0.095, 0.105], 4: [0.095, 0.105]}\n", - "{1: [0.047, 0.057], 2: [0.048, 0.058], 3: [0.05, 0.06], 4: [0.05, 0.06]}\n", - "{1: [0.328, 0.338], 2: [0.33, 0.34], 3: [0.339, 0.349], 4: [0.339, 0.349]}\n", - "{1: [0.335, 0.345], 2: [0.335, 0.345], 3: [0.34, 0.35], 4: [0.34, 0.35]}\n", - "{1: [0.004, 0.014], 2: [0.004, 0.014], 3: [0.018, 0.028], 4: [0.021, 0.031]}\n", - "{1: [0.008, 0.018], 2: [0.01, 0.02], 3: [0.023, 0.033], 4: [0.027, 0.037]}\n", - "{1: [0.092, 0.102], 2: [0.095, 0.105], 3: [0.095, 0.105], 4: [0.105, 0.115]}\n", - "{1: [0.048, 0.058], 2: [0.05, 0.06], 3: [0.05, 0.06], 4: [0.055, 0.065]}\n", - "{1: [0.048, 0.058], 2: [0.33, 0.34], 3: [0.339, 0.349], 4: [0.339, 0.349]}\n", - "{1: [0.315, 0.325], 2: [0.335, 0.345], 3: [0.34, 0.35], 4: [0.34, 0.35]}\n", - "{1: [0.004, 0.014], 2: [0.004, 0.014], 3: [0.018, 0.028], 4: [0.172, 0.182]}\n", - "{1: [0.008, 0.018], 2: [0.01, 0.02], 3: [0.023, 0.033], 4: [0.183, 0.193]}\n", - "{1: [0.087, 0.107], 2: [0.09, 0.11], 3: [0.09, 0.11], 4: [0.1, 0.12]}\n", - "{1: [0.043, 0.063], 2: [0.045, 0.065], 3: [0.045, 0.065], 4: [0.05, 0.07]}\n", - "{1: [0.043, 0.063], 2: [0.325, 0.345], 3: [0.334, 0.354], 4: [0.334, 0.354]}\n", - "{1: [0.31, 0.33], 2: [0.33, 0.35], 3: [0.335, 0.355], 4: [0.335, 0.355]}\n", - "{1: [-0.001, 0.019], 2: [-0.001, 0.019], 3: [0.013, 0.033], 4: [0.167, 0.187]}\n", - "{1: [0.003, 0.023], 2: [0.005, 0.025], 3: [0.018, 0.038], 4: [0.178, 0.198]}\n", - "{1: [0.09, 0.11], 2: [0.1, 0.12]}\n", - "{1: [0.045, 0.065], 2: [0.05, 0.07]}\n", - "{1: [0.043, 0.063], 2: [0.334, 0.354]}\n", - "{1: [0.31, 0.33], 2: [0.335, 0.355]}\n", - "{1: [-0.001, 0.019], 2: [0.167, 0.187]}\n", - "{1: [0.005, 0.025], 2: [0.178, 0.198]}\n", - "{1: [0.091, 0.101], 2: [0.092, 0.102], 3: [0.095, 0.105], 4: [0.095, 0.105]}\n", - "{1: [0.047, 0.057], 2: [0.048, 0.058], 3: [0.05, 0.06], 4: [0.05, 0.06]}\n", - "{1: [0.328, 0.338], 2: [0.33, 0.34], 3: [0.339, 0.349], 4: [0.339, 0.349]}\n", - "{1: [0.335, 0.345], 2: [0.335, 0.345], 3: [0.34, 0.35], 4: [0.34, 0.35]}\n", - "{1: [0.004, 0.014], 2: [0.004, 0.014], 3: [0.018, 0.028], 4: [0.021, 0.031]}\n", - "{1: [0.008, 0.018], 2: [0.01, 0.02], 3: [0.023, 0.033], 4: [0.027, 0.037]}\n", - "{1: [-0.004, 0.006], 2: [-0.003, 0.007], 3: [0.02, 0.03], 4: [0.022, 0.032]}\n", - "{1: [0.254, 0.264], 2: [0.284, 0.294], 3: [0.372, 0.382], 4: [0.424, 0.434]}\n", - "{1: [0.142, 0.152], 2: [0.152, 0.162], 3: [0.269, 0.279], 4: [0.308, 0.318]}\n", - "{1: [-0.005, 0.005], 2: [0.0, 0.01], 3: [0.0, 0.01], 4: [0.002, 0.012]}\n", - "{1: [0.072, 0.082], 2: [0.084, 0.094], 3: [0.094, 0.104], 4: [0.105, 0.115]}\n", - "{1: [0.07, 0.08], 2: [0.08, 0.09], 3: [0.087, 0.097], 4: [0.094, 0.104]}\n", - "{1: [0.003, 0.013], 2: [0.006, 0.016], 3: [0.01, 0.02], 4: [0.012, 0.022]}\n", - "{1: [0.291, 0.301], 2: [0.317, 0.327], 3: [0.318, 0.328], 4: [0.326, 0.336]}\n", - "{1: [0.263, 0.273], 2: [0.274, 0.284], 3: [0.282, 0.292], 4: [0.288, 0.298]}\n", - "{1: [-0.004, 0.006], 2: [-0.004, 0.006], 3: [-0.003, 0.007], 4: [-0.001, 0.009]}\n", - "{1: [0.036, 0.046], 2: [0.04, 0.05], 3: [0.073, 0.083], 4: [0.083, 0.093]}\n", - "{1: [0.026, 0.036], 2: [0.028, 0.038], 3: [0.05, 0.06], 4: [0.074, 0.084]}\n", - "{1: [-0.005, 0.005], 2: [-0.005, 0.005], 3: [-0.004, 0.006], 4: [-0.004, 0.006]}\n", - "{1: [0.006, 0.016], 2: [0.008, 0.018], 3: [0.017, 0.027], 4: [0.018, 0.028]}\n", - "{1: [0.005, 0.015], 2: [0.006, 0.016], 3: [0.014, 0.024], 4: [0.014, 0.024]}\n" + "To create a public link, set `share=True` in `launch()`.\n" ] } ], @@ -296,44 +230,30 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-12-10T17:18:16.842929Z" + "start_time": "2023-12-16T22:10:25.247210Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], + "execution_count": null, + "outputs": [], "source": [ "visualizer.stop_web_app()" ], "metadata": { "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T14:45:32.225285Z", - "start_time": "2023-12-10T14:45:32.184623Z" - } + "is_executing": true }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "2326c129", "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T14:45:32.227687Z", - "start_time": "2023-12-10T14:45:32.224834Z" - } + "is_executing": true }, "outputs": [], "source": [] From 06c60fc1f84eceaa709dc8422854fbcd976e2e57 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 18 Dec 2023 01:37:30 +0200 Subject: [PATCH 063/148] Added error handling for a dataset stats screen --- ...iple_Models_Interface_Vis_Law_School.ipynb | 150 +++++++++++++++--- .../metrics_interactive_visualizer.py | 15 +- virny/utils/protected_groups_partitioning.py | 10 +- 3 files changed, 145 insertions(+), 30 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 1826cdfe..83931cbb 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-16T22:10:21.409266Z", - "start_time": "2023-12-16T22:10:20.679843Z" + "end_time": "2023-12-17T21:47:39.813777Z", + "start_time": "2023-12-17T21:47:39.261544Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-16T22:10:21.418422Z", - "start_time": "2023-12-16T22:10:21.408919Z" + "end_time": "2023-12-17T21:47:39.822610Z", + "start_time": "2023-12-17T21:47:39.813658Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-16T22:10:21.429731Z", - "start_time": "2023-12-16T22:10:21.418780Z" + "end_time": "2023-12-17T21:47:39.832179Z", + "start_time": "2023-12-17T21:47:39.823116Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-16T22:10:25.046057Z", - "start_time": "2023-12-16T22:10:21.428148Z" + "end_time": "2023-12-17T21:47:42.380425Z", + "start_time": "2023-12-17T21:47:39.833097Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-16T22:10:25.116829Z", - "start_time": "2023-12-16T22:10:25.048929Z" + "end_time": "2023-12-17T21:47:42.452856Z", + "start_time": "2023-12-17T21:47:42.383371Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-16T22:10:25.144265Z", - "start_time": "2023-12-16T22:10:25.117061Z" + "end_time": "2023-12-17T21:47:42.480240Z", + "start_time": "2023-12-17T21:47:42.453731Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-16T22:10:25.193827Z", - "start_time": "2023-12-16T22:10:25.143225Z" + "end_time": "2023-12-17T21:47:42.525340Z", + "start_time": "2023-12-17T21:47:42.478528Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-16T22:10:25.218033Z", - "start_time": "2023-12-16T22:10:25.193714Z" + "end_time": "2023-12-17T21:47:42.548956Z", + "start_time": "2023-12-17T21:47:42.525477Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-16T22:10:25.293585Z", - "start_time": "2023-12-16T22:10:25.217304Z" + "end_time": "2023-12-17T23:34:25.339529Z", + "start_time": "2023-12-17T23:34:25.210287Z" } }, "outputs": [], @@ -221,6 +221,92 @@ "\n", "To create a public link, set `share=True` in `launch()`.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 489, in _create_dataset_proportions_bar_chart\n", + " converted_grp_dis_val = eval(grp_dis_val)\n", + " File \"\", line 1, in \n", + "NameError: name 'Non' is not defined\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 492, in _create_dataset_proportions_bar_chart\n", + " raise ValueError(f\"Type casting error with the {grp_dis_val} value. Use quotes for string disavantaged values.\")\n", + "ValueError: Type casting error with the Non-White value. Use quotes for string disavantaged values.\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 489, in _create_dataset_proportions_bar_chart\n", + " converted_grp_dis_val = eval(grp_dis_val)\n", + " File \"\", line 1\n", + " 'Non-White\"\n", + " ^\n", + "SyntaxError: EOL while scanning string literal\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 492, in _create_dataset_proportions_bar_chart\n", + " raise ValueError(f\"Type casting error with the {grp_dis_val} value. Use quotes for string disavantaged values.\")\n", + "ValueError: Type casting error with the 'Non-White\" value. Use quotes for string disavantaged values.\n", + "Traceback (most recent call last):\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 495, in _create_dataset_proportions_bar_chart\n", + " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 104, in create_test_protected_groups\n", + " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", + "ValueError: Protected group (race_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n" + ] } ], "source": [ @@ -230,30 +316,44 @@ "collapsed": false, "is_executing": true, "ExecuteTime": { - "start_time": "2023-12-16T22:10:25.247210Z" + "start_time": "2023-12-17T23:34:25.340065Z" } }, "id": "678a9dc8d51243f4" }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closing server running on port: 7860\n" + ] + } + ], "source": [ "visualizer.stop_web_app()" ], "metadata": { "collapsed": false, - "is_executing": true + "ExecuteTime": { + "end_time": "2023-12-17T23:11:31.184662Z", + "start_time": "2023-12-17T23:11:31.135723Z" + } }, "id": "277b6d1de837dab7" }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "2326c129", "metadata": { - "is_executing": true + "ExecuteTime": { + "end_time": "2023-12-17T23:11:31.184935Z", + "start_time": "2023-12-17T23:11:31.183840Z" + } }, "outputs": [], "source": [] diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index ebc91aa2..cbe4908d 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -471,9 +471,13 @@ def __check_metric_constraints(self, model_performance_dct, input_constraint_dct def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8, grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8): grp_names = [grp_name1, grp_name2, grp_name3, grp_name4, grp_name5, grp_name6, grp_name7, grp_name8] - grp_names = [grp for grp in grp_names if grp != '' and grp is not None] + grp_names = [grp.strip() for grp in grp_names if grp != '' and grp is not None] grp_dis_values = [grp_dis_val1, grp_dis_val2, grp_dis_val3, grp_dis_val4, grp_dis_val5, grp_dis_val6, grp_dis_val7, grp_dis_val8] - grp_dis_values = [grp for grp in grp_dis_values if grp != '' and grp is not None] + grp_dis_values = [grp.strip() for grp in grp_dis_values if grp != '' and grp is not None] + + if len(grp_names) != len(grp_dis_values): + raise ValueError("Numbers of sensitive attributes and their disadvantaged groups are different." + "Please, put '-' as a disadvantaged value for intersectional sensitive attributes.") # Create a sensitive attrs dict input_sensitive_attrs_dct = dict() @@ -481,8 +485,11 @@ def _create_dataset_proportions_bar_chart(self, grp_name1, grp_name2, grp_name3, if '&' in grp_name: input_sensitive_attrs_dct[grp_name] = None else: - converted_grp_dis_val = eval(grp_dis_val) - input_sensitive_attrs_dct[grp_name] = converted_grp_dis_val + try: + converted_grp_dis_val = eval(grp_dis_val) + input_sensitive_attrs_dct[grp_name] = converted_grp_dis_val + except Exception as _: + raise ValueError(f"Type casting error with the {grp_dis_val} value. Use quotes for string disavantaged values.") # Partition on protected groups protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct) diff --git a/virny/utils/protected_groups_partitioning.py b/virny/utils/protected_groups_partitioning.py index 658198d7..e13a20b6 100644 --- a/virny/utils/protected_groups_partitioning.py +++ b/virny/utils/protected_groups_partitioning.py @@ -70,8 +70,16 @@ def create_test_protected_groups(X_test: pd.DataFrame, init_features_df: pd.Data """ plain_sensitive_attributes = [attr for attr in sensitive_attributes_dct.keys() if INTERSECTION_SIGN not in attr] - X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index] + # Check spelling of sensitive attributes + attrs_with_errors = [] + for attr in plain_sensitive_attributes: + if attr not in init_features_df.columns: + attrs_with_errors.append(attr) + if len(attrs_with_errors) > 0: + raise ValueError(f"At least one of sensitive attributes is not in dataset columns. Check spelling of {attrs_with_errors} attributes.") + + X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index] groups = dict() for attr in sensitive_attributes_dct.keys(): attr = attr.strip() From f06cc9ff6d1a743e24d62bd5bb888057e7868143 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 18 Dec 2023 18:02:20 +0200 Subject: [PATCH 064/148] Added all error handling --- ...iple_Models_Interface_Vis_Law_School.ipynb | 141 +++++++++++------- .../metrics_interactive_visualizer.py | 11 +- virny/utils/data_viz_utils.py | 4 +- 3 files changed, 97 insertions(+), 59 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 83931cbb..90a3a0df 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-17T21:47:39.813777Z", - "start_time": "2023-12-17T21:47:39.261544Z" + "end_time": "2023-12-18T15:30:30.826849Z", + "start_time": "2023-12-18T15:30:30.355864Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-17T21:47:39.822610Z", - "start_time": "2023-12-17T21:47:39.813658Z" + "end_time": "2023-12-18T15:30:30.836146Z", + "start_time": "2023-12-18T15:30:30.826225Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-17T21:47:39.832179Z", - "start_time": "2023-12-17T21:47:39.823116Z" + "end_time": "2023-12-18T15:30:30.848252Z", + "start_time": "2023-12-18T15:30:30.836766Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-17T21:47:42.380425Z", - "start_time": "2023-12-17T21:47:39.833097Z" + "end_time": "2023-12-18T15:30:32.569645Z", + "start_time": "2023-12-18T15:30:30.847803Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-17T21:47:42.452856Z", - "start_time": "2023-12-17T21:47:42.383371Z" + "end_time": "2023-12-18T15:30:32.635886Z", + "start_time": "2023-12-18T15:30:32.573395Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-17T21:47:42.480240Z", - "start_time": "2023-12-17T21:47:42.453731Z" + "end_time": "2023-12-18T15:30:32.664462Z", + "start_time": "2023-12-18T15:30:32.635793Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-17T21:47:42.525340Z", - "start_time": "2023-12-17T21:47:42.478528Z" + "end_time": "2023-12-18T15:30:32.712298Z", + "start_time": "2023-12-18T15:30:32.663822Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-17T21:47:42.548956Z", - "start_time": "2023-12-17T21:47:42.525477Z" + "end_time": "2023-12-18T15:30:32.759812Z", + "start_time": "2023-12-18T15:30:32.712204Z" } }, "id": "15ed7d1ba1f22317" @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-17T23:34:25.339529Z", - "start_time": "2023-12-17T23:34:25.210287Z" + "end_time": "2023-12-18T15:30:32.808353Z", + "start_time": "2023-12-18T15:30:32.738229Z" } }, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "outputs": [ { "name": "stdout", @@ -227,13 +227,29 @@ "output_type": "stream", "text": [ "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 489, in _create_dataset_proportions_bar_chart\n", - " converted_grp_dis_val = eval(grp_dis_val)\n", - " File \"\", line 1, in \n", - "NameError: name 'Non' is not defined\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 609, in _create_subgroup_model_rank_heatmap\n", + " model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/data_viz_utils.py\", line 267, in create_model_rank_heatmap_visualization\n", + " num_ranks = int(sorted_matrix_by_rank.values.max())\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/numpy/core/_methods.py\", line 41, in _amax\n", + " return umr_maximum(a, axis, None, out, keepdims, initial, where)\n", + "ValueError: zero-size array to reduction operation maximum which has no identity\n", "Traceback (most recent call last):\n", " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", " output = await route_utils.call_process_api(\n", @@ -251,19 +267,29 @@ " result = context.run(func, *args)\n", " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 492, in _create_dataset_proportions_bar_chart\n", - " raise ValueError(f\"Type casting error with the {grp_dis_val} value. Use quotes for string disavantaged values.\")\n", - "ValueError: Type casting error with the Non-White value. Use quotes for string disavantaged values.\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 593, in _create_subgroup_model_rank_heatmap\n", + " raise ValueError('Tolerance should be in the [0.001, 0.2] range')\n", + "ValueError: Tolerance should be in the [0.001, 0.2] range\n", "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 489, in _create_dataset_proportions_bar_chart\n", - " converted_grp_dis_val = eval(grp_dis_val)\n", - " File \"\", line 1\n", - " 'Non-White\"\n", - " ^\n", - "SyntaxError: EOL while scanning string literal\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", + " output = await route_utils.call_process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", + " result = await self.call_function(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", + " result = context.run(func, *args)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", + " response = f(*args, **kwargs)\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 593, in _create_subgroup_model_rank_heatmap\n", + " raise ValueError('Tolerance should be in the [0.001, 0.2] range')\n", + "ValueError: Tolerance should be in the [0.001, 0.2] range\n", "Traceback (most recent call last):\n", " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", " output = await route_utils.call_process_api(\n", @@ -281,9 +307,11 @@ " result = context.run(func, *args)\n", " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 492, in _create_dataset_proportions_bar_chart\n", - " raise ValueError(f\"Type casting error with the {grp_dis_val} value. Use quotes for string disavantaged values.\")\n", - "ValueError: Type casting error with the 'Non-White\" value. Use quotes for string disavantaged values.\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 591, in _create_subgroup_model_rank_heatmap\n", + " tolerance = str_to_float(tolerance, 'Tolerance')\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", + " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", + "ValueError: Tolerance must be a float number with a '.' separator.\n", "Traceback (most recent call last):\n", " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", " output = await route_utils.call_process_api(\n", @@ -301,11 +329,18 @@ " result = context.run(func, *args)\n", " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 495, in _create_dataset_proportions_bar_chart\n", - " protected_groups = create_test_protected_groups(self.X_data, self.X_data, input_sensitive_attrs_dct)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/protected_groups_partitioning.py\", line 104, in create_test_protected_groups\n", - " raise ValueError(f\"Protected group ({dis_grp_name}) from X_test is empty. \"\n", - "ValueError: Protected group (race_dis) from X_test is empty. Please check types of sensitive attributes in config or replace the sensitive attribute\n" + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 591, in _create_subgroup_model_rank_heatmap\n", + " tolerance = str_to_float(tolerance, 'Tolerance')\n", + " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", + " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", + "ValueError: Tolerance must be a float number with a '.' separator.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keyboard interruption in main thread... closing server.\n" ] } ], @@ -314,9 +349,9 @@ ], "metadata": { "collapsed": false, - "is_executing": true, "ExecuteTime": { - "start_time": "2023-12-17T23:34:25.340065Z" + "end_time": "2023-12-18T16:02:02.572226Z", + "start_time": "2023-12-18T15:30:32.768235Z" } }, "id": "678a9dc8d51243f4" @@ -339,8 +374,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-17T23:11:31.184662Z", - "start_time": "2023-12-17T23:11:31.135723Z" + "end_time": "2023-12-18T16:02:02.620717Z", + "start_time": "2023-12-18T16:02:02.578812Z" } }, "id": "277b6d1de837dab7" @@ -351,8 +386,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-17T23:11:31.184935Z", - "start_time": "2023-12-17T23:11:31.183840Z" + "end_time": "2023-12-18T16:02:02.623767Z", + "start_time": "2023-12-18T16:02:02.619001Z" } }, "outputs": [], diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index cbe4908d..18fee7da 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -463,6 +463,9 @@ def __check_metric_constraints(self, model_performance_dct, input_constraint_dct for group in model_performance_dct[metric_dim]: constraint_type = 'overall' if group == 'Overall' else 'disparity' min_val, max_val = input_constraint_dct[metric_dim][constraint_type][1] + if min_val > max_val: + raise ValueError(f'Max value for the {metric_dim} {constraint_type} dimension should be greater than min value.') + check = 1 if model_performance_dct[metric_dim][group] >= min_val and model_performance_dct[metric_dim][group] <= max_val else 0 model_metrics_constraints_check_dct[metric_dim][group] = check @@ -533,10 +536,10 @@ def _create_bar_plot_for_model_selection(self, group_name, overall_metric1, over overall_metric2: 'C3', disparity_metric2: 'C4', } - overall_constraint1 = (overall_metric1, str_to_float(overall_metric_min_val1, 'C1 min value'), str_to_float(overall_metric_max_val1, 'C2 max value')) - disparity_constraint1 = (disparity_metric1, str_to_float(disparity_metric_min_val1, 'C2 min value'), str_to_float(disparity_metric_max_val1, 'C2 max value')) - overall_constraint2 = (overall_metric2, str_to_float(overall_metric_min_val2, 'C3 min value'), str_to_float(overall_metric_max_val2, 'C3 max value')) - disparity_constraint2 = (disparity_metric2, str_to_float(disparity_metric_min_val2, 'C4 min value'), str_to_float(disparity_metric_max_val2, 'C4 max value')) + overall_constraint1 = (overall_metric1, str_to_float(overall_metric_min_val1, 'Overall Constraint (C1) min value'), str_to_float(overall_metric_max_val1, 'Overall Constraint (C1) max value')) + disparity_constraint1 = (disparity_metric1, str_to_float(disparity_metric_min_val1, 'Disparity Constraint (C2) min value'), str_to_float(disparity_metric_max_val1, 'Disparity Constraint (C2) max value')) + overall_constraint2 = (overall_metric2, str_to_float(overall_metric_min_val2, 'Overall Constraint (C3) min value'), str_to_float(overall_metric_max_val2, 'Overall Constraint (C3) max value')) + disparity_constraint2 = (disparity_metric2, str_to_float(disparity_metric_min_val2, 'Disparity Constraint (C4) min value'), str_to_float(disparity_metric_max_val2, 'Disparity Constraint (C4) max value')) # Create individual constraints metrics_value_range_dct = dict() diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 41dba3cc..8cae6f28 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -489,7 +489,7 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro if '&' not in metric_group: min_range_val, max_range_val = value_range if max_range_val < min_range_val: - raise ValueError('The second element in the input range must be greater than the first element, ' + raise ValueError('The second value in the input range must be greater than the first value, ' 'so to be in the following format -- (min_range_val, max_range_val)') metric = metric_group pd_condition = (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val) @@ -498,7 +498,7 @@ def create_models_in_range_dct(all_subgroup_metrics_per_model_dct: dict, all_gro for idx, metric in enumerate(metrics): min_range_val, max_range_val = metrics_value_range_dct[metric] if max_range_val < min_range_val: - raise ValueError('The second element in the input range must be greater than the first element, ' + raise ValueError('The second value in the input range must be greater than the first value, ' 'so to be in the following format -- (min_range_val, max_range_val)') if idx == 0: pd_condition = (pivoted_model_metrics_df[metric] >= min_range_val) & (pivoted_model_metrics_df[metric] <= max_range_val) From b3c88f2a19dae401d50857081b111a7d64fe2d4c Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 18 Dec 2023 22:23:06 +0200 Subject: [PATCH 065/148] wip --- ...iple_Models_Interface_Vis_Law_School.ipynb | 170 +++--------------- 1 file changed, 25 insertions(+), 145 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 90a3a0df..3b630e94 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:30.826849Z", - "start_time": "2023-12-18T15:30:30.355864Z" + "end_time": "2023-12-18T17:11:51.087426Z", + "start_time": "2023-12-18T17:11:50.720930Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:30.836146Z", - "start_time": "2023-12-18T15:30:30.826225Z" + "end_time": "2023-12-18T17:11:51.096433Z", + "start_time": "2023-12-18T17:11:51.087934Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:30.848252Z", - "start_time": "2023-12-18T15:30:30.836766Z" + "end_time": "2023-12-18T17:11:51.105608Z", + "start_time": "2023-12-18T17:11:51.096820Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.569645Z", - "start_time": "2023-12-18T15:30:30.847803Z" + "end_time": "2023-12-18T17:11:52.701377Z", + "start_time": "2023-12-18T17:11:51.106232Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.635886Z", - "start_time": "2023-12-18T15:30:32.573395Z" + "end_time": "2023-12-18T17:11:52.766489Z", + "start_time": "2023-12-18T17:11:52.704609Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.664462Z", - "start_time": "2023-12-18T15:30:32.635793Z" + "end_time": "2023-12-18T17:11:52.791981Z", + "start_time": "2023-12-18T17:11:52.767057Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.712298Z", - "start_time": "2023-12-18T15:30:32.663822Z" + "end_time": "2023-12-18T17:11:52.842306Z", + "start_time": "2023-12-18T17:11:52.792667Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.759812Z", - "start_time": "2023-12-18T15:30:32.712204Z" + "end_time": "2023-12-18T17:11:52.877906Z", + "start_time": "2023-12-18T17:11:52.842425Z" } }, "id": "15ed7d1ba1f22317" @@ -198,8 +198,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.808353Z", - "start_time": "2023-12-18T15:30:32.738229Z" + "end_time": "2023-12-18T17:11:52.959909Z", + "start_time": "2023-12-18T17:11:52.864927Z" } }, "outputs": [], @@ -219,127 +219,7 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 609, in _create_subgroup_model_rank_heatmap\n", - " model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/data_viz_utils.py\", line 267, in create_model_rank_heatmap_visualization\n", - " num_ranks = int(sorted_matrix_by_rank.values.max())\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/numpy/core/_methods.py\", line 41, in _amax\n", - " return umr_maximum(a, axis, None, out, keepdims, initial, where)\n", - "ValueError: zero-size array to reduction operation maximum which has no identity\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 593, in _create_subgroup_model_rank_heatmap\n", - " raise ValueError('Tolerance should be in the [0.001, 0.2] range')\n", - "ValueError: Tolerance should be in the [0.001, 0.2] range\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 593, in _create_subgroup_model_rank_heatmap\n", - " raise ValueError('Tolerance should be in the [0.001, 0.2] range')\n", - "ValueError: Tolerance should be in the [0.001, 0.2] range\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 591, in _create_subgroup_model_rank_heatmap\n", - " tolerance = str_to_float(tolerance, 'Tolerance')\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", - " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", - "ValueError: Tolerance must be a float number with a '.' separator.\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 591, in _create_subgroup_model_rank_heatmap\n", - " tolerance = str_to_float(tolerance, 'Tolerance')\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", - " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", - "ValueError: Tolerance must be a float number with a '.' separator.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "To create a public link, set `share=True` in `launch()`.\n", "Keyboard interruption in main thread... closing server.\n" ] } @@ -350,8 +230,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T16:02:02.572226Z", - "start_time": "2023-12-18T15:30:32.768235Z" + "end_time": "2023-12-18T17:14:45.540473Z", + "start_time": "2023-12-18T17:11:52.892884Z" } }, "id": "678a9dc8d51243f4" @@ -374,8 +254,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T16:02:02.620717Z", - "start_time": "2023-12-18T16:02:02.578812Z" + "end_time": "2023-12-18T17:14:45.583530Z", + "start_time": "2023-12-18T17:14:45.541605Z" } }, "id": "277b6d1de837dab7" @@ -386,8 +266,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T16:02:02.623767Z", - "start_time": "2023-12-18T16:02:02.619001Z" + "end_time": "2023-12-18T17:14:45.584046Z", + "start_time": "2023-12-18T17:14:45.581453Z" } }, "outputs": [], From 901e1a61c64d27554b36236264269b18e85d237c Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Mon, 18 Dec 2023 23:20:30 +0200 Subject: [PATCH 066/148] Fixed tests --- lib_base_packages.txt | 1 + requirements.txt | 1 + tests/custom_classes/test_metrics_composer.py | 12 ++++---- ...verall_variance_analyzer_postprocessing.py | 7 +++-- .../metrics_computation_interfaces.py | 5 ---- .../postprocessing_intervention_utils.py | 28 +++++++++---------- 6 files changed, 26 insertions(+), 28 deletions(-) diff --git a/lib_base_packages.txt b/lib_base_packages.txt index 10cbab0f..d11d6eba 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -6,6 +6,7 @@ scikit-learn~=1.2.0 tqdm~=4.64.1 sklearn-utils seaborn~=0.12.1 +aif360~=0.5.0 folktables~=0.0.11 munch~=2.5.0 PyYAML~=6.0 diff --git a/requirements.txt b/requirements.txt index 867757b7..1f846049 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,6 +10,7 @@ sklearn-utils seaborn~=0.12.1 folktables~=0.0.11 xgboost~=1.7.2 +aif360~=0.5.0 munch~=2.5.0 PyYAML~=6.0 river==0.15.0 diff --git a/tests/custom_classes/test_metrics_composer.py b/tests/custom_classes/test_metrics_composer.py index fb9cc9b0..29dc06b8 100644 --- a/tests/custom_classes/test_metrics_composer.py +++ b/tests/custom_classes/test_metrics_composer.py @@ -31,7 +31,7 @@ def test_compose_metrics_true1(models_metrics_dct1, config_params): models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape - assert models_composed_metrics_df.shape == (24, 5) + assert models_composed_metrics_df.shape == (26, 5) # Check column names assert sorted(models_composed_metrics_df.columns.tolist()) == sorted(['Metric', 'Model_Name', 'sex', 'race', 'sex&race']) @@ -42,7 +42,7 @@ def test_compose_metrics_true1(models_metrics_dct1, config_params): # Check all metrics presence assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == ( sorted([EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, - DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, + DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, LABEL_STABILITY_DIFFERENCE, LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY]) ) @@ -52,7 +52,7 @@ def test_compose_metrics_true2(models_metrics_dct1, config_params): models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape - assert models_composed_metrics_df.shape == (24, 4) + assert models_composed_metrics_df.shape == (26, 4) # Check column names assert sorted(models_composed_metrics_df.columns.tolist()) == sorted(['Metric', 'Model_Name', 'sex', 'race']) @@ -63,7 +63,7 @@ def test_compose_metrics_true2(models_metrics_dct1, config_params): # Check all metrics presence assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == ( sorted([EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, - DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, + DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, LABEL_STABILITY_DIFFERENCE, LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY]) ) @@ -73,7 +73,7 @@ def test_compose_metrics_true3(models_metrics_dct2, config_params): models_composed_metrics_df = metrics_composer.compose_metrics() # Check shape - assert models_composed_metrics_df.shape == (32, 5) + assert models_composed_metrics_df.shape == (34, 5) # Check column names assert sorted(models_composed_metrics_df.columns.tolist()) == sorted(['Metric', 'Model_Name', 'sex', 'race', 'sex&race']) @@ -84,7 +84,7 @@ def test_compose_metrics_true3(models_metrics_dct2, config_params): # Check all metrics presence assert sorted(models_composed_metrics_df['Metric'].unique().tolist()) == ( sorted([EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, - DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, + DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY, LABEL_STABILITY_DIFFERENCE, LABEL_STABILITY_RATIO, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY, ALEATORIC_UNCERTAINTY_PARITY, ALEATORIC_UNCERTAINTY_RATIO, OVERALL_UNCERTAINTY_PARITY, OVERALL_UNCERTAINTY_RATIO]) diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 6da2edce..fa5d50af 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -6,7 +6,9 @@ from tqdm import tqdm from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer -from virny.utils.postprocessing_intervention_utils import contruct_binary_label_dataset_from_df, construct_binary_label_dataset_from_samples, predict_on_binary_label_dataset +from virny.utils.postprocessing_intervention_utils import (contruct_binary_label_dataset_from_df, + construct_binary_label_dataset_from_samples, + predict_on_binary_label_dataset) from virny.utils.stability_utils import generate_bootstrap @@ -55,12 +57,14 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b if self._verbose >= 1: print('\n', flush=True) self._AbstractOverallVarianceAnalyzer__logger.info('Start classifiers testing by bootstrap') + # Remove a progress bar for UQ without estimators fitting cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), desc="Classifiers testing by bootstrap", colour="blue", mininterval=10) + # Train and test each estimator in models_predictions for idx in cycle_range: classifier = self.models_lst[idx] @@ -90,4 +94,3 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b self._AbstractOverallVarianceAnalyzer__logger.info('Successfully tested classifiers by bootstrap') return models_predictions - \ No newline at end of file diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index 5fac8338..9682b3f7 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -107,8 +107,6 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase for g in test_protected_groups.keys(): print(g, test_protected_groups[g].shape) - print("postprocessing_sensitive_attribute: ", postprocessing_sensitive_attribute) - # Compute stability metrics for subgroups subgroup_variance_analyzer = SubgroupVarianceAnalyzer(model_setting=model_setting, n_estimators=n_estimators, @@ -224,7 +222,6 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, save_results=save_results, save_results_dir_path=save_results_dir_path, verbose=verbose) - print("metrics_computation_interfaces.py: model_metrics_df: ", model_metrics_df) models_metrics_dct[model_name] = model_metrics_df if verbose >= 2: print(f'\n[{model_name}] Metrics matrix:') @@ -333,7 +330,6 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=False, verbose=verbose) - #print(models_metrics_dct) # Concatenate current run metrics with previous results and # create melted_model_metrics_df to save it in a database @@ -370,7 +366,6 @@ def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, confi value_name="Metric_Value") run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) - #print(run_models_metrics_df) # Save results for this run in a database db_writer_func(run_models_metrics_df) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index 564a346d..3f899fc1 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -9,13 +9,12 @@ def construct_binary_label_dataset_from_samples(X_sample, y_sample, column_names df = pd.DataFrame(X_sample, columns=column_names) df[target_column] = y_sample - binary_label_dataset = BinaryLabelDataset( - df=df, - label_names=[target_column], - protected_attribute_names=[sensitive_attribute], - favorable_label=1, - unfavorable_label=0) - + binary_label_dataset = BinaryLabelDataset(df=df, + label_names=[target_column], + protected_attribute_names=[sensitive_attribute], + favorable_label=1, + unfavorable_label=0) + return binary_label_dataset @@ -23,19 +22,18 @@ def contruct_binary_label_dataset_from_df(X_sample, y_sample, target_column, sen df = X_sample df[target_column] = y_sample - binary_label_dataset = BinaryLabelDataset( - df=df, - label_names=[target_column], - protected_attribute_names=[sensitive_attribute], - favorable_label=1, - unfavorable_label=0) - + binary_label_dataset = BinaryLabelDataset(df=df, + label_names=[target_column], + protected_attribute_names=[sensitive_attribute], + favorable_label=1, + unfavorable_label=0) + return binary_label_dataset def predict_on_binary_label_dataset(model, orig_dataset, threshold=0.5): orig_dataset_pred = copy.deepcopy(orig_dataset) - + fav_idx = np.where(model.classes_ == orig_dataset.favorable_label)[0][0] y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] orig_dataset.scores = y_pred_prob.reshape(-1, 1) From 6bd58d5d219fd8e65b63071b369d824107738713 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 01:50:43 +0200 Subject: [PATCH 067/148] wip --- ...iple_Models_Interface_Vis_Law_School.ipynb | 48 +++++++++---------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 3b630e94..abf5f339 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T17:11:51.087426Z", - "start_time": "2023-12-18T17:11:50.720930Z" + "end_time": "2023-12-18T21:27:13.678820Z", + "start_time": "2023-12-18T21:27:13.369461Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T17:11:51.096433Z", - "start_time": "2023-12-18T17:11:51.087934Z" + "end_time": "2023-12-18T21:27:13.687293Z", + "start_time": "2023-12-18T21:27:13.679001Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T17:11:51.105608Z", - "start_time": "2023-12-18T17:11:51.096820Z" + "end_time": "2023-12-18T21:27:13.698600Z", + "start_time": "2023-12-18T21:27:13.687813Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.701377Z", - "start_time": "2023-12-18T17:11:51.106232Z" + "end_time": "2023-12-18T21:27:15.048016Z", + "start_time": "2023-12-18T21:27:13.697484Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.766489Z", - "start_time": "2023-12-18T17:11:52.704609Z" + "end_time": "2023-12-18T21:27:15.106638Z", + "start_time": "2023-12-18T21:27:15.051611Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.791981Z", - "start_time": "2023-12-18T17:11:52.767057Z" + "end_time": "2023-12-18T21:27:15.133650Z", + "start_time": "2023-12-18T21:27:15.106939Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.842306Z", - "start_time": "2023-12-18T17:11:52.792667Z" + "end_time": "2023-12-18T21:27:15.178725Z", + "start_time": "2023-12-18T21:27:15.134576Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.877906Z", - "start_time": "2023-12-18T17:11:52.842425Z" + "end_time": "2023-12-18T21:27:15.201295Z", + "start_time": "2023-12-18T21:27:15.179038Z" } }, "id": "15ed7d1ba1f22317" @@ -198,8 +198,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.959909Z", - "start_time": "2023-12-18T17:11:52.864927Z" + "end_time": "2023-12-18T21:27:15.252561Z", + "start_time": "2023-12-18T21:27:15.200692Z" } }, "outputs": [], @@ -230,8 +230,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T17:14:45.540473Z", - "start_time": "2023-12-18T17:11:52.892884Z" + "end_time": "2023-12-18T23:50:34.705984Z", + "start_time": "2023-12-18T21:27:15.229300Z" } }, "id": "678a9dc8d51243f4" @@ -254,8 +254,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T17:14:45.583530Z", - "start_time": "2023-12-18T17:14:45.541605Z" + "end_time": "2023-12-18T23:50:34.802916Z", + "start_time": "2023-12-18T23:50:34.710443Z" } }, "id": "277b6d1de837dab7" @@ -266,8 +266,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T17:14:45.584046Z", - "start_time": "2023-12-18T17:14:45.581453Z" + "end_time": "2023-12-18T23:50:34.805260Z", + "start_time": "2023-12-18T23:50:34.803259Z" } }, "outputs": [], From d5893afba660e8370b9da9a45492cc77a434bdf5 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 14:25:59 +0200 Subject: [PATCH 068/148] Cleaned unnecessary files --- docs/examples/income_group_metrics.csv | 133 ---------- docs/examples/income_subgroup_metrics.csv | 221 ----------------- docs/examples/law_school_group_metrics.csv | 89 ------- docs/examples/law_school_subgroup_metrics.csv | 153 ------------ docs/examples/pub_cov_subgroup_metrics.csv | 153 ------------ docs/examples/ricci_group_metrics.csv | 133 ---------- docs/examples/ricci_subgroup_metrics.csv | 229 ------------------ 7 files changed, 1111 deletions(-) delete mode 100644 docs/examples/income_group_metrics.csv delete mode 100644 docs/examples/income_subgroup_metrics.csv delete mode 100644 docs/examples/law_school_group_metrics.csv delete mode 100644 docs/examples/law_school_subgroup_metrics.csv delete mode 100644 docs/examples/pub_cov_subgroup_metrics.csv delete mode 100644 docs/examples/ricci_group_metrics.csv delete mode 100644 docs/examples/ricci_subgroup_metrics.csv diff --git a/docs/examples/income_group_metrics.csv b/docs/examples/income_group_metrics.csv deleted file mode 100644 index 25b06763..00000000 --- a/docs/examples/income_group_metrics.csv +++ /dev/null @@ -1,133 +0,0 @@ -Metric,SEX,RAC1P,SEX&RAC1P,Model_Name,Intervention_Param -Equalized_Odds_TPR,-0.03079268292682924,0.11074514666563329,0.05249773566501781,LGBMClassifier,0.7 -Equalized_Odds_FPR,-0.02131701139721401,0.0009518370454978109,-0.00700793796337533,LGBMClassifier,0.7 -Equalized_Odds_FNR,0.030792682926829296,-0.11074514666563334,-0.05249773566501781,LGBMClassifier,0.7 -Disparate_Impact,1.0451749734888653,1.3342133960856337,1.248406706539857,LGBMClassifier,0.7 -Statistical_Parity_Difference,0.03246951219512195,0.22563645522532638,0.17651362084581623,LGBMClassifier,0.7 -Accuracy_Parity,0.04775641025641031,0.07497732132443469,0.0652173913043479,LGBMClassifier,0.7 -Label_Stability_Ratio,1.0095819811577007,1.0301209785116932,1.012842085178694,LGBMClassifier,0.7 -IQR_Parity,-0.0026551143311698278,-0.00967660527132716,-0.005313076583927184,LGBMClassifier,0.7 -Std_Parity,-0.002214117425894342,-0.00706110127509476,-0.004207550960833459,LGBMClassifier,0.7 -Std_Ratio,0.9581473862978338,0.8695701641666075,0.9199532195084829,LGBMClassifier,0.7 -Jitter_Parity,-0.007536566378903806,-0.019030010009223178,-0.009410112766584558,LGBMClassifier,0.7 -Equalized_Odds_TPR,-0.01097560975609757,-0.00598674778160968,-0.05549362502612687,LGBMClassifier,0.0 -Equalized_Odds_FPR,-0.025116082735331363,-0.014481520763645256,-0.019877655812003875,LGBMClassifier,0.0 -Equalized_Odds_FNR,0.01097560975609757,0.005986747781609625,0.055493625026126925,LGBMClassifier,0.0 -Disparate_Impact,1.0739042728773152,1.1095233662260287,1.0641057210867602,LGBMClassifier,0.0 -Statistical_Parity_Difference,0.061432926829268264,0.09117681249273446,0.05441371141921547,LGBMClassifier,0.0 -Accuracy_Parity,0.04294871794871791,0.037355018466921575,0.032355915065722995,LGBMClassifier,0.0 -Label_Stability_Ratio,1.013086132198451,1.0203793128013074,1.0132200761605896,LGBMClassifier,0.0 -IQR_Parity,-0.0030377716953829265,-0.007959024854970922,-0.005539567672418727,LGBMClassifier,0.0 -Std_Parity,-0.0021021596178428525,-0.005701112525753914,-0.0038189830524456084,LGBMClassifier,0.0 -Std_Ratio,0.9643024235570372,0.9045579272645509,0.9347582189106831,LGBMClassifier,0.0 -Jitter_Parity,-0.008137675557275542,-0.013465396867056778,-0.009009568683773159,LGBMClassifier,0.0 -Equalized_Odds_TPR,-0.039253048780487854,-0.031483705971248166,-0.11670034139204344,LGBMClassifier,0.4 -Equalized_Odds_FPR,-0.00873786407766991,-0.013488890701911838,-0.010826281732205081,LGBMClassifier,0.4 -Equalized_Odds_FNR,0.039253048780487854,0.03148370597124811,0.1167003413920435,LGBMClassifier,0.4 -Disparate_Impact,1.0594573415613675,1.0609451814521111,0.9916513681265844,LGBMClassifier,0.4 -Statistical_Parity_Difference,0.046265243902439024,0.04779710931142722,-0.00665366125548672,LGBMClassifier,0.4 -Accuracy_Parity,0.02841880341880343,0.034390591589451236,0.015897090214582632,LGBMClassifier,0.4 -Label_Stability_Ratio,1.0214945785801577,1.021203066210909,1.0204956799007319,LGBMClassifier,0.4 -IQR_Parity,-0.004103223054099589,-0.008620361682032895,-0.006324316490461224,LGBMClassifier,0.4 -Std_Parity,-0.0030876402128535846,-0.006194968170589871,-0.004796370058731313,LGBMClassifier,0.4 -Std_Ratio,0.9493557726215703,0.8992698436233884,0.9204716301184785,LGBMClassifier,0.4 -Jitter_Parity,-0.01200876712622645,-0.015122226339372366,-0.012784745357312319,LGBMClassifier,0.4 -Equalized_Odds_TPR,-0.06996951219512193,-0.01604215910411899,-0.10732947815787641,LogisticRegression,0.0 -Equalized_Odds_FPR,-0.023723089911355008,-0.04367572271627097,-0.042787718424982316,LogisticRegression,0.0 -Equalized_Odds_FNR,0.06996951219512193,0.01604215910411899,0.10732947815787636,LogisticRegression,0.0 -Disparate_Impact,0.9908320359799343,0.9996470017208666,0.9084844203751667,LogisticRegression,0.0 -Statistical_Parity_Difference,-0.008079268292682906,-0.00030999341263993063,-0.08123737197798375,LogisticRegression,0.0 -Accuracy_Parity,0.01816239316239321,0.05037905786302077,0.031457139647230625,LogisticRegression,0.0 -Label_Stability_Ratio,1.009100642398287,1.023801267627326,1.0076597165218943,LogisticRegression,0.0 -IQR_Parity,-0.006097632012806539,-0.006310527017404574,-0.0065692439650302525,LogisticRegression,0.0 -Std_Parity,-0.004906109963734577,-0.004854434742104861,-0.005207661750716387,LogisticRegression,0.0 -Std_Ratio,0.9086406912235572,0.9082781978742059,0.9003166552931693,LogisticRegression,0.0 -Jitter_Parity,-0.0055498432332622555,-0.01576926661754173,-0.006298770939673112,LogisticRegression,0.0 -Equalized_Odds_TPR,-0.04298780487804876,0.08555818188863484,0.017661812861422654,LogisticRegression,0.7 -Equalized_Odds_FPR,-0.003925707049387925,-0.030295613390987464,-0.022281313038694295,LogisticRegression,0.7 -Equalized_Odds_FNR,0.04298780487804876,-0.0855581818886349,-0.01766181286142271,LogisticRegression,0.7 -Disparate_Impact,1.0570386018820819,1.15227520571032,1.1070776454221372,LogisticRegression,0.7 -Statistical_Parity_Difference,0.04527439024390245,0.11903747045375279,0.08580087786525459,LogisticRegression,0.7 -Accuracy_Parity,0.02147435897435901,0.07841152076718727,0.05596749428903114,LogisticRegression,0.7 -Label_Stability_Ratio,0.9984503821387735,1.0050781309776278,0.9984788609152078,LogisticRegression,0.7 -IQR_Parity,-0.0030463198184801366,-0.0007317941412861503,-0.0023944641804607703,LogisticRegression,0.7 -Std_Parity,-0.002576978265789877,-0.00016051358574650093,-0.0019729774914916606,LogisticRegression,0.7 -Std_Ratio,0.9398193735885796,0.9961448364837571,0.9529382123765405,LogisticRegression,0.7 -Jitter_Parity,0.0017610005153971056,-0.0049119190685485425,0.0009849212357710413,LogisticRegression,0.7 -Equalized_Odds_TPR,-0.062347560975609784,-0.0033518037741697704,-0.09653034208876188,LogisticRegression,0.4 -Equalized_Odds_FPR,-0.019607429295061207,-0.030417992439694327,-0.030016242697335674,LogisticRegression,0.4 -Equalized_Odds_FNR,0.06234756097560973,0.0033518037741697704,0.09653034208876193,LogisticRegression,0.4 -Disparate_Impact,1.0087170907810161,1.050002258457925,0.961194506547429,LogisticRegression,0.4 -Statistical_Parity_Difference,0.007545731707317094,0.042895338474057354,-0.0338605169650944,LogisticRegression,0.4 -Accuracy_Parity,0.019230769230769273,0.046207801464394516,0.026120660599932566,LogisticRegression,0.4 -Label_Stability_Ratio,1.0072028640298956,1.0153655245856517,1.000428717824364,LogisticRegression,0.4 -IQR_Parity,-0.0068586087713478905,-0.005069227313861113,-0.005323993156258602,LogisticRegression,0.4 -Std_Parity,-0.005429382866877996,-0.004096338358900525,-0.004725493931369902,LogisticRegression,0.4 -Std_Ratio,0.8999072809699203,0.9226669785897548,0.909905157452225,LogisticRegression,0.4 -Jitter_Parity,-0.004078888029894576,-0.01278422827543911,-0.003752072160978573,LogisticRegression,0.4 -Equalized_Odds_TPR,-0.019435975609756184,-0.03632735304374779,-0.07838082630808896,MLPClassifier,0.0 -Equalized_Odds_FPR,-0.0587378640776699,-0.05070571918087624,-0.06589426527992455,MLPClassifier,0.0 -Equalized_Odds_FNR,0.019435975609756073,0.036327353043747845,0.0783808263080889,MLPClassifier,0.0 -Disparate_Impact,0.9938211382113822,0.9835774706003713,0.9036308734717926,MLPClassifier,0.0 -Statistical_Parity_Difference,-0.005792682926829218,-0.01542217227883913,-0.09116560997700829,MLPClassifier,0.0 -Accuracy_Parity,0.05256410256410249,0.04415862113652558,0.05033142343556907,MLPClassifier,0.0 -Label_Stability_Ratio,1.0400222540384076,1.052024430659532,1.0377142721189696,MLPClassifier,0.0 -IQR_Parity,-0.023103483282492315,-0.02199865172956933,-0.021084662231184464,MLPClassifier,0.0 -Std_Parity,-0.017047311682108574,-0.014482498155605067,-0.015528142059770114,MLPClassifier,0.0 -Std_Ratio,0.8378767214807724,0.8575668018548829,0.8441550952374669,MLPClassifier,0.0 -Jitter_Parity,-0.024276623502126712,-0.02999195460552799,-0.02431443345626763,MLPClassifier,0.0 -Equalized_Odds_TPR,-0.018978658536585313,0.05504320533188678,-0.03720476555423957,MLPClassifier,0.7 -Equalized_Odds_FPR,-0.04624314056563952,-0.015542139185771395,-0.04150402137748552,MLPClassifier,0.7 -Equalized_Odds_FNR,0.01897865853658537,-0.05504320533188667,0.037204765554239516,MLPClassifier,0.7 -Disparate_Impact,0.9976159911975059,1.1685710118150436,0.9892333472978635,MLPClassifier,0.7 -Statistical_Parity_Difference,-0.0019817073170732558,0.134343395202852,-0.008952832160523894,MLPClassifier,0.7 -Accuracy_Parity,0.05566239316239319,0.05791971748849867,0.05465678013706321,MLPClassifier,0.7 -Label_Stability_Ratio,1.0116825655056816,1.0147414387548623,1.0142450823394442,MLPClassifier,0.7 -IQR_Parity,-0.015967185873185546,-0.007739632168905608,-0.010375957070957897,MLPClassifier,0.7 -Std_Parity,-0.011280373369031718,-0.0052215839220306065,-0.00846586522301801,MLPClassifier,0.7 -Std_Ratio,0.8633086261228244,0.9337434540716136,0.8922451079710022,MLPClassifier,0.7 -Jitter_Parity,-0.011206411330150795,-0.010024072689631416,-0.009159063489553684,MLPClassifier,0.7 -Equalized_Odds_TPR,-0.018064024390243905,-0.018541480993528814,-0.07190134466662024,MLPClassifier,0.4 -Equalized_Odds_FPR,-0.0478682988602786,-0.03377661744309374,-0.04670430431479396,MLPClassifier,0.4 -Equalized_Odds_FNR,0.018064024390243905,0.01854148099352887,0.07190134466662024,MLPClassifier,0.4 -Disparate_Impact,1.0122367435278448,1.0388483701545854,0.9596397347000302,MLPClassifier,0.4 -Statistical_Parity_Difference,0.01120426829268295,0.03539737280582789,-0.03730927332265033,MLPClassifier,0.4 -Accuracy_Parity,0.04732905982905977,0.03914501393118641,0.039358873534808825,MLPClassifier,0.4 -Label_Stability_Ratio,1.0380533656761675,1.050281055097938,1.039143933855558,MLPClassifier,0.4 -IQR_Parity,-0.021800317105360498,-0.01979599811428892,-0.019109923987836203,MLPClassifier,0.4 -Std_Parity,-0.016632398891789663,-0.013618988897571183,-0.014615438426665558,MLPClassifier,0.4 -Std_Ratio,0.8411837472975571,0.8653910518588449,0.8527524702482466,MLPClassifier,0.4 -Jitter_Parity,-0.023205391272604736,-0.028007441470945263,-0.022731439845363424,MLPClassifier,0.4 -Equalized_Odds_TPR,-0.013109756097560932,0.03212306738481807,-0.022085975057479224,RandomForestClassifier,0.4 -Equalized_Odds_FPR,-0.01192486281131279,-0.004963150308667147,-0.006942443216054084,RandomForestClassifier,0.4 -Equalized_Odds_FNR,0.013109756097560987,-0.03212306738481807,0.02208597505747928,RandomForestClassifier,0.4 -Disparate_Impact,1.0743556828280834,1.1590880271867312,1.1109037589976007,RandomForestClassifier,0.4 -Statistical_Parity_Difference,0.05739329268292681,0.12153679234316273,0.08695046331777334,RandomForestClassifier,0.4 -Accuracy_Parity,0.03856837606837615,0.04690436078533011,0.036587649327790794,RandomForestClassifier,0.4 -Label_Stability_Ratio,1.0010830866898295,1.0245213649857032,1.0016860934867435,RandomForestClassifier,0.4 -IQR_Parity,-0.0024315475130579356,-0.007422421027040563,-0.006348073239051066,RandomForestClassifier,0.4 -Std_Parity,-0.001890817887290966,-0.006005398383778858,-0.005054462180121233,RandomForestClassifier,0.4 -Std_Ratio,0.9693944783088382,0.9044128961451383,0.9181358364959677,RandomForestClassifier,0.4 -Jitter_Parity,-0.0029367081132166684,-0.015830939834926464,-0.004035590076358689,RandomForestClassifier,0.4 -Equalized_Odds_TPR,-0.013262195121951259,0.09049870190258458,0.0275900508604473,RandomForestClassifier,0.7 -Equalized_Odds_FPR,-0.004706627268889818,0.005180713061923803,0.004610830211417033,RandomForestClassifier,0.7 -Equalized_Odds_FNR,0.013262195121951204,-0.09049870190258458,-0.0275900508604473,RandomForestClassifier,0.7 -Disparate_Impact,1.086494036746535,1.2810291207237774,1.2183023097164019,RandomForestClassifier,0.7 -Statistical_Parity_Difference,0.06135670731707321,0.19258340760258852,0.15606493416010592,RandomForestClassifier,0.7 -Accuracy_Parity,0.04049145299145296,0.06377567550055074,0.04785979103471516,RandomForestClassifier,0.7 -Label_Stability_Ratio,1.0042931659613945,1.0158033089241028,0.997701913713356,RandomForestClassifier,0.7 -IQR_Parity,-0.0021576183268891685,-0.0011512403501528212,-0.00012869147334378106,RandomForestClassifier,0.7 -Std_Parity,-0.002107574597948185,-0.0019021823908419097,-0.001451921355860343,RandomForestClassifier,0.7 -Std_Ratio,0.962673667602888,0.9660752793094682,0.9739339258916726,RandomForestClassifier,0.7 -Jitter_Parity,-0.004579489326979741,-0.010039410100458009,-0.0014495632866055874,RandomForestClassifier,0.7 -Equalized_Odds_TPR,-0.006478658536585358,0.034680513039097915,-0.010276597227060535,RandomForestClassifier,0.0 -Equalized_Odds_FPR,-0.018446601941747576,-0.00981751924070598,-0.010793534358544452,RandomForestClassifier,0.0 -Equalized_Odds_FNR,0.006478658536585358,-0.034680513039097915,0.010276597227060535,RandomForestClassifier,0.0 -Disparate_Impact,1.0696725293946165,1.149977548271217,1.1161027349228612,RandomForestClassifier,0.0 -Statistical_Parity_Difference,0.05464939024390236,0.11648002479947306,0.09228035950672331,RandomForestClassifier,0.0 -Accuracy_Parity,0.043910256410256476,0.04994168340568905,0.04126877129910489,RandomForestClassifier,0.0 -Label_Stability_Ratio,1.0018200544605445,1.031331519636685,1.0152022947420831,RandomForestClassifier,0.0 -IQR_Parity,-0.0014266839924084312,-0.005259735864872772,-0.003978617177466615,RandomForestClassifier,0.0 -Std_Parity,-0.0014799865759114808,-0.0045115073360025085,-0.003718279422753004,RandomForestClassifier,0.0 -Std_Ratio,0.9712188827286748,0.913538681205104,0.9275913116942456,RandomForestClassifier,0.0 -Jitter_Parity,-0.0018609822617384336,-0.017820314313740024,-0.008520772043575799,RandomForestClassifier,0.0 diff --git a/docs/examples/income_subgroup_metrics.csv b/docs/examples/income_subgroup_metrics.csv deleted file mode 100644 index f9e45d09..00000000 --- a/docs/examples/income_subgroup_metrics.csv +++ /dev/null @@ -1,221 +0,0 @@ -Metric,Model_Name,Model_Params,Dataset_Name,Intervention_Param,RAC1P_dis,RAC1P_priv,SEX&RAC1P_dis,SEX&RAC1P_priv,SEX_dis,SEX_priv,overall -Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.8299180327868853,0.7549407114624506,0.8333333333333334,0.7681159420289855,0.8041666666666667,0.7564102564102564,0.7793333333333333 -Aleatoric_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.574768407248688,0.5867152963701037,0.5751925521025802,0.5844148021106594,0.5625457885372674,0.6015511473295796,0.5828285751092698 -F1,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.6666666666666666,0.6242424242424243,0.6324786324786325,0.6363636363636364,0.6072423398328691,0.6545454545454545,0.6358635863586358 -FNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.366412213740458,0.47715736040609136,0.4032258064516129,0.4557235421166307,0.4682926829268293,0.4375,0.44952380952380955 -FPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.09803921568627451,0.0970873786407767,0.09183673469387756,0.09884467265725289,0.08737864077669903,0.10869565217391304,0.09743589743589744 -IQR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.06280707944938806,0.07248368472071522,0.06493633506095174,0.07024941164487893,0.06795490302023513,0.07061001735140496,0.06933556247244345 -Jitter,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.05725214185682598,0.07628215186604916,0.062299481905650766,0.07170959467223532,0.0661720407593532,0.07370860713825701,0.07009105527638122 -Label_Stability,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.9190368852459017,0.8921640316205534,0.9104651162790697,0.8989210950080515,0.905375,0.8967820512820512,0.9009066666666667 -Mean,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.7085616306769631,0.6862178585472839,0.7245281799737864,0.6870388713260648,0.7252005764269102,0.6642129917856846,0.6934870324134728 -Overall_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.5874845827200873,0.6027417432630668,0.5884432399246448,0.5997172018108436,0.5773910073017967,0.616596917041452,0.5977780803664176 -PPV,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.7033898305084746,0.7744360902255639,0.6727272727272727,0.7659574468085106,0.7077922077922078,0.782608695652174,0.7526041666666666 -Per_Sample_Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.828125,0.7573962450592885,0.8262015503875969,0.7708937198067634,0.8038958333333333,0.7587243589743589,0.7804066666666668 -Positive-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.9007633587786259,0.6751269035532995,0.8870967741935484,0.7105831533477321,0.751219512195122,0.71875,0.7314285714285714 -Sample_Size,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.24180327868852458,0.2628458498023715,0.2131782945736434,0.2648953301127214,0.21388888888888888,0.2948717948717949,0.256 -Std,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.04707606166754985,0.05413716294264461,0.04835609913224369,0.05256365009307715,0.05068861026634873,0.05290272769224307,0.051839951327813785 -TNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.9019607843137255,0.9029126213592233,0.9081632653061225,0.9011553273427471,0.912621359223301,0.8913043478260869,0.9025641025641026 -TPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 10, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.7,0.6335877862595419,0.5228426395939086,0.5967741935483871,0.5442764578833693,0.5317073170731708,0.5625,0.5504761904761905 -Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.8278688524590164,0.7905138339920948,0.8294573643410853,0.7971014492753623,0.825,0.782051282051282,0.8026666666666666 -Aleatoric_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.5633808779503379,0.6053039361523177,0.5635466006397141,0.5975059813694559,0.5732441010754783,0.6086688449379051,0.5916649678839402 -F1,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6666666666666666,0.7063711911357341,0.6271186440677966,0.705607476635514,0.6752577319587629,0.7098976109215017,0.6960985626283368 -FNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.35877862595419846,0.35279187817258884,0.4032258064516129,0.34773218142548595,0.36097560975609755,0.35,0.35428571428571426 -FPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.10364145658263306,0.11812297734627832,0.09693877551020408,0.11681643132220795,0.10097087378640776,0.12608695652173912,0.11282051282051282 -IQR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.0717782678051346,0.07973729266010553,0.0725611945411923,0.07810076221361102,0.07556831529235587,0.0786060869877388,0.077147956573955 -Jitter,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.06448780789191826,0.07795320475897503,0.06611253944139346,0.07512210812516662,0.06934087102177533,0.07747854657905087,0.07357246231155734 -Label_Stability,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.9095491803278689,0.8913833992094861,0.907093023255814,0.895257648953301,0.9033611111111111,0.8916923076923077,0.8972933333333334 -Mean,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6957755997986098,0.6346221249287431,0.7150553202294089,0.6419418764174092,0.689198775103901,0.6225038013523088,0.6545173887530731 -Overall_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.5802610784913662,0.6241481189584432,0.5806878176395618,0.6159322429458329,0.5913806977913604,0.6269374362563485,0.6098702017931542 -PPV,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6942148760330579,0.7774390243902439,0.6607142857142857,0.7684478371501272,0.7158469945355191,0.7819548872180451,0.755011135857461 -Per_Sample_Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.82547131147541,0.7838833992094861,0.8217635658914728,0.7923550724637681,0.8145833333333333,0.7815641025641025,0.7974133333333333 -Positive-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.9236641221374046,0.8324873096446701,0.9032258064516129,0.8488120950323974,0.8926829268292683,0.83125,0.8552380952380952 -Sample_Size,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.24795081967213115,0.3241106719367589,0.21705426356589147,0.3164251207729469,0.25416666666666665,0.34102564102564104,0.29933333333333334 -Std,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.05403263342459364,0.059733745950347555,0.05471686604121065,0.05853584909365626,0.05678586100735733,0.05888802062520018,0.057878984008635614 -TNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.896358543417367,0.8818770226537217,0.9030612244897959,0.883183568677792,0.8990291262135922,0.8739130434782608,0.8871794871794871 -TPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.0,0.6412213740458015,0.6472081218274112,0.5967741935483871,0.652267818574514,0.6390243902439025,0.65,0.6457142857142857 -Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8278688524590164,0.7934782608695652,0.8178294573643411,0.8019323671497585,0.8194444444444444,0.791025641025641,0.8046666666666666 -Aleatoric_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.5551920500099683,0.6160978769562332,0.5546274648036941,0.6049363010992105,0.5746912318145948,0.6162142115103388,0.5962831812563817 -F1,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.65,0.7027027027027027,0.5765765765765766,0.7043269230769231,0.6524064171122995,0.7135325131810193,0.689289501590668 -FNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.40458015267175573,0.3730964467005076,0.4838709677419355,0.367170626349892,0.40487804878048783,0.365625,0.38095238095238093 -FPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.08683473389355742,0.10032362459546926,0.08673469387755102,0.0975609756097561,0.0912621359223301,0.1,0.09538461538461539 -IQR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.07359398945751415,0.08221435113954705,0.07417332608489044,0.08049764257535166,0.07727618415086056,0.08137940720496015,0.07940986013899233 -Jitter,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.06965174231814622,0.08477396865751859,0.06926843519925442,0.08205318055656674,0.07360964544947027,0.08561841257569672,0.07985420435511108 -Label_Stability,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8993852459016393,0.8807114624505928,0.9017829457364341,0.883671497584541,0.8965972222222222,0.8777307692307692,0.8867866666666667 -Mean,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.7141488205426961,0.6441152206909166,0.7326595827361654,0.6532391750548413,0.6999423689094346,0.6363983617298083,0.6668994851760289 -Overall_Uncertainty,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.5729327590451901,0.6355678274191867,0.5723997447559693,0.624079463458317,0.5933199449151484,0.6353788043889269,0.6151905518415132 -PPV,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.7155963302752294,0.7993527508090615,0.6530612244897959,0.7940379403794038,0.7218934911242604,0.8152610441767069,0.777511961722488 -Per_Sample_Accuracy,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8241188524590164,0.7855237154150199,0.8176744186046511,0.7940096618357488,0.8138402777777777,0.7835320512820513,0.7980799999999999 -Positive-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.8320610687022901,0.7842639593908629,0.7903225806451613,0.796976241900648,0.824390243902439,0.778125,0.7961904761904762 -Sample_Size,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.22336065573770492,0.30533596837944665,0.18992248062015504,0.2971014492753623,0.23472222222222222,0.3192307692307692,0.2786666666666667 -Statistical_Bias,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.2485877124816216,0.2850127848791134,0.2544548274710566,0.2770486284228354,0.25981461969725383,0.28548361000855305,0.2731624946591294 -Std,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.05530566275693515,0.06150063092752502,0.055513806874063584,0.0603101769327949,0.05787962837200925,0.06096726858486284,0.05948520128269312 -TNR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.9131652661064426,0.8996763754045307,0.9132653061224489,0.9024390243902439,0.9087378640776699,0.9,0.9046153846153846 -TPR,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 11, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 351, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 100}",Folktables_GA_2018_Income,0.4,0.5954198473282443,0.6269035532994924,0.5161290322580645,0.6328293736501079,0.5951219512195122,0.634375,0.6190476190476191 -Accuracy,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.860655737704918,0.8102766798418972,0.8527131782945736,0.821256038647343,0.8361111111111111,0.8179487179487179,0.8266666666666667 -Aleatoric_Uncertainty,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': 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False}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.23565573770491804,0.34189723320158105,0.1937984496124031,0.3309178743961353,0.24861111111111112,0.36153846153846153,0.30733333333333335 -Std,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': 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False}",Folktables_GA_2018_Income,0.7,0.5415753343739413,0.6007282744387772,0.5437012248461627,0.5893324161805281,0.5656097473407158,0.5961368702836032,0.5814838512710172 -F1,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7469879518072289,0.7008547008547008,0.7008547008547008,0.7146282973621103,0.6737400530503979,0.7386759581881533,0.7129337539432177 -FNR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.2900763358778626,0.3756345177664975,0.3387096774193548,0.3563714902807775,0.3804878048780488,0.3375,0.35428571428571426 -FPR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.0700280112044818,0.10032362459546926,0.07142857142857142,0.09370988446726572,0.08737864077669903,0.09130434782608696,0.08923076923076922 -IQR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.05486730610239798,0.05559910024368413,0.05337840687496418,0.05577287105542495,0.05377693691077603,0.056823256729256165,0.055361023216385696 -Jitter,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.05049252409589009,0.055404443164438634,0.05462194694401552,0.05363702570824448,0.05472215242881082,0.05296115191341372,0.05380643216080322 -Label_Stability,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9256352459016393,0.9209584980237154,0.9213178294573643,0.9227214170692432,0.9217361111111111,0.9231666666666667,0.9224799999999999 -Mean,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7008671012376522,0.6500786578096867,0.7283286228997712,0.6537793578093689,0.7076286288499484,0.6287309414556592,0.666601831404918 -Overall_Uncertainty,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.5529470303791272,0.6110197586776697,0.5542760168748085,0.5999894800749719,0.5757934710934558,0.6072036505378559,0.5921267644045438 -PPV,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.788135593220339,0.7987012987012987,0.7454545454545455,0.8032345013477089,0.7383720930232558,0.8346456692913385,0.795774647887324 -Per_Sample_Accuracy,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8547438524590164,0.7916353754940711,0.8563953488372092,0.8029790660225442,0.8271319444444443,0.7983525641025639,0.8121666666666667 -Positive-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9007633587786259,0.7817258883248731,0.8870967741935484,0.8012958963282938,0.8390243902439024,0.79375,0.8114285714285714 -Sample_Size,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.24180327868852458,0.30434782608695654,0.2131782945736434,0.29871175523349436,0.2388888888888889,0.32564102564102565,0.284 -Std,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.04147548578761579,0.04163599937336229,0.03995015359051101,0.04192313108200267,0.04024375025525536,0.04282072852104524,0.0415837789534661 -TNR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9299719887955182,0.8996763754045307,0.9285714285714286,0.9062901155327343,0.912621359223301,0.908695652173913,0.9107692307692308 -TPR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7099236641221374,0.6243654822335025,0.6612903225806451,0.6436285097192225,0.6195121951219512,0.6625,0.6457142857142857 -Accuracy,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8545081967213115,0.808300395256917,0.8449612403100775,0.8188405797101449,0.8333333333333334,0.8141025641025641,0.8233333333333334 -Aleatoric_Uncertainty,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': 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False}",Folktables_GA_2018_Income,0.4,0.32061068702290074,0.31725888324873097,0.4032258064516129,0.30669546436285094,0.35609756097560974,0.29375,0.3180952380952381 -FPR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.08123249299719888,0.11165048543689321,0.07653061224489796,0.10654685494223363,0.0912621359223301,0.1108695652173913,0.10051282051282051 -IQR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.0656607376158629,0.07072996492972401,0.06467250997689906,0.06999650313315767,0.06551429974918029,0.07237290852052818,0.06908077631028119 -Jitter,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.060156314358679144,0.07294054263411826,0.06567469128588728,0.06942676344686585,0.06666038525963189,0.07073927328952646,0.06878140703517488 -Label_Stability,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9112704918032787,0.8974802371541502,0.9022868217054263,0.9019001610305957,0.9053333333333333,0.8988589743589742,0.9019666666666666 -Mean,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7086695659799582,0.6312971446946812,0.7346452859100321,0.6402294483610698,0.7007860045694052,0.6155609427426478,0.6564689724194913 -Overall_Uncertainty,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.5366496138386901,0.6070662579052819,0.5393764919119384,0.5934596857006005,0.568778825833585,0.598352961478519,0.5841573763689507 -PPV,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7542372881355932,0.7958579881656804,0.7115384615384616,0.7945544554455446,0.7374301675977654,0.8158844765342961,0.7850877192982456 -Per_Sample_Accuracy,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8464549180327868,0.8029891304347826,0.8463372093023257,0.8110628019323671,0.8285833333333332,0.8065576923076924,0.8171299999999999 -Positive-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9007633587786259,0.8578680203045685,0.8387096774193549,0.8725701943844493,0.8731707317073171,0.865625,0.8685714285714285 -Sample_Size,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.24180327868852458,0.3339920948616601,0.20155038759689922,0.3252818035426731,0.24861111111111112,0.35512820512820514,0.304 -Statistical_Bias,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.2274504031385729,0.266491924606308,0.2318676501443367,0.25834442085021614,0.24554145806397867,0.26140483926556757,0.2537904162888049 -Std,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.048873767867387855,0.05297010622628838,0.0477247218383518,0.052450215769721704,0.04881415172274951,0.05424353458962751,0.051637430813526085 -TNR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9187675070028011,0.8883495145631068,0.923469387755102,0.8934531450577664,0.9087378640776699,0.8891304347826087,0.8994871794871795 -TPR,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 101, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.6793893129770993,0.682741116751269,0.5967741935483871,0.693304535637149,0.6439024390243903,0.70625,0.6819047619047619 -Accuracy,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8524590163934426,0.808300395256917,0.8643410852713178,0.8140096618357487,0.85,0.7974358974358975,0.8226666666666667 -Aleatoric_Uncertainty,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.3816002128122633,0.4663978258721248,0.38038775507957207,0.4509464273948834,0.4122843567010865,0.4632958548848621,0.43881033575664985 -F1,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7142857142857143,0.7460732984293194,0.6956521739130435,0.7436182019977803,0.7272727272727273,0.7451612903225806,0.7381889763779528 -FNR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.31297709923664124,0.2766497461928934,0.3548387096774194,0.27645788336933047,0.2975609756097561,0.278125,0.2857142857142857 -FPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.08683473389355742,0.13754045307443366,0.0663265306122449,0.13222079589216945,0.0912621359223301,0.15,0.11897435897435897 -IQR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.11256027555478235,0.13455892728435168,0.10994393226091104,0.1310285944920955,0.11538822128143578,0.1384917045639281,0.12740203258833177 -Jitter,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.09333336765796346,0.12332532226349145,0.09343558879670463,0.11775002225297226,0.10094409547738649,0.1252207189795132,0.11356793969848812 -Label_Stability,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8686270491803278,0.8256719367588932,0.8656976744186047,0.8342351046698874,0.8567916666666666,0.8238205128205128,0.8396466666666665 -Mean,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7252847058872565,0.6240034562362897,0.7607214461032801,0.6353979879947343,0.7097566779513825,0.6082123411014243,0.6569536227894043 -Overall_Uncertainty,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.4414172929888588,0.533748861417282,0.4365092913234669,0.5176699593972609,0.4698661654226279,0.5349510867032824,0.5037103244885683 -PPV,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.743801652892562,0.7702702702702703,0.7547169811320755,0.7648401826484018,0.7539267015706806,0.77,0.7637474541751528 -Per_Sample_Accuracy,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8301741803278688,0.7933498023715414,0.8309496124031007,0.8000080515297907,0.8233958333333333,0.7886538461538463,0.8053299999999999 -Positive-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9236641221374046,0.9390862944162437,0.8548387096774194,0.9460043196544277,0.9317073170731708,0.9375,0.9352380952380952 -Sample_Size,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.24795081967213115,0.36561264822134387,0.2054263565891473,0.3526570048309179,0.2652777777777778,0.38461538461538464,0.3273333333333333 -Std,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.08719673354183723,0.1016792316974423,0.08411029067199578,0.0996384327317659,0.08810299022278899,0.10515030190489756,0.09696759229748543 -TNR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9131652661064426,0.8624595469255664,0.9336734693877551,0.8677792041078306,0.9087378640776699,0.85,0.8810256410256411 -TPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6870229007633588,0.7233502538071066,0.6451612903225806,0.7235421166306696,0.7024390243902439,0.721875,0.7142857142857143 -Accuracy,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8504098360655737,0.7924901185770751,0.8565891472868217,0.8019323671497585,0.8402777777777778,0.7846153846153846,0.8113333333333334 -Aleatoric_Uncertainty,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.43531743350320395,0.5012712154730118,0.43338857927395535,0.4894582320093163,0.4473101514451836,0.509818036625281,0.47981425173883424 -F1,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7114624505928854,0.7033898305084746,0.672566371681416,0.7099056603773585,0.6933333333333334,0.7133105802047781,0.7055150884495317 -FNR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.31297709923664124,0.3680203045685279,0.3870967741935484,0.34989200863930886,0.36585365853658536,0.346875,0.35428571428571426 -FPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.0896358543417367,0.10517799352750809,0.0663265306122449,0.10783055198973042,0.07766990291262135,0.12391304347826088,0.09948717948717949 -IQR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.0956195011615199,0.10335913333042551,0.09224988054338842,0.10262583761434632,0.09253823634408508,0.10850542221727062,0.10084117299814155 -Jitter,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.08606114589339989,0.0960852185830313,0.08524034903198867,0.09439941252154235,0.08699671970966057,0.09820313103981136,0.0928240536013427 -Label_Stability,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8803483606557377,0.8675592885375495,0.8819767441860465,0.8695893719806763,0.8769861111111112,0.8668589743589745,0.8717199999999999 -Mean,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7102203181759992,0.6607804033163299,0.7506239522883105,0.6615429176615372,0.7249338287368677,0.6324934958147033,0.6768648556173422 -Overall_Uncertainty,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.47514726487681236,0.5407137095576833,0.46988110140488026,0.5296657126971022,0.4835119894611495,0.5524942396413235,0.51938275955484 -PPV,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7377049180327869,0.7929936305732485,0.7450980392156863,0.7818181818181819,0.7647058823529411,0.7857142857142857,0.7775229357798165 -Per_Sample_Accuracy,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8392110655737706,0.7809337944664032,0.8399806201550388,0.7915660225442834,0.8214791666666665,0.7799679487179487,0.7998933333333332 -Positive-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9312977099236641,0.7969543147208121,0.8225806451612904,0.8315334773218143,0.8292682926829268,0.83125,0.8304761904761905 -Sample_Size,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.25,0.3102766798418972,0.19767441860465115,0.30998389694041867,0.2361111111111111,0.34102564102564104,0.2906666666666667 -Std,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.07358699036849117,0.07880857429052178,0.07010008258322892,0.07856594780624693,0.07124402483599133,0.08252439820502305,0.07710981898788782 -TNR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9103641456582633,0.8948220064724919,0.9336734693877551,0.8921694480102695,0.9223300970873787,0.8760869565217392,0.9005128205128206 -TPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.6870229007633588,0.631979695431472,0.6129032258064516,0.6501079913606912,0.6341463414634146,0.653125,0.6457142857142857 -Accuracy,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8504098360655737,0.8112648221343873,0.8565891472868217,0.8172302737520128,0.8486111111111111,0.8012820512820513,0.824 -Aleatoric_Uncertainty,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.38774782756218923,0.4664257591545982,0.3884650538497881,0.45170678278708243,0.4137217228511366,0.46585149700254275,0.4408292054098678 -F1,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7137254901960784,0.7463479415670651,0.6837606837606838,0.745230078563412,0.7240506329113924,0.7471451876019576,0.7380952380952381 -FNR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.3053435114503817,0.2868020304568528,0.3548387096774194,0.28293736501079914,0.3024390243902439,0.284375,0.2914285714285714 -FPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.09243697478991597,0.1262135922330097,0.07653061224489796,0.12323491655969192,0.0912621359223301,0.1391304347826087,0.11384615384615385 -IQR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.1132850208605515,0.13308101897484043,0.11081770385973005,0.12992762784756626,0.11530455602687097,0.13710487313223146,0.12664072092165843 -Jitter,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.0948879644122274,0.12289540588317266,0.09496201939933013,0.11769345924469356,0.10171684812953474,0.12492223940213948,0.11378365159128628 -Label_Stability,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8675819672131148,0.8260474308300394,0.8665891472868218,0.8339452495974236,0.855875,0.8244999999999999,0.8395600000000001 -Mean,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7195732758438664,0.6309284279798154,0.7540229300478088,0.6401879321860268,0.7110902530342773,0.6123927506957696,0.6597675518182533 -Overall_Uncertainty,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.44726996535917407,0.5332731746116136,0.44436157850128466,0.5179508120361502,0.4712478646062651,0.5367201708791272,0.5052934638681533 -PPV,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7338709677419355,0.7827298050139275,0.7272727272727273,0.7757009345794392,0.7526315789473684,0.7815699658703071,0.7701863354037267 -Per_Sample_Accuracy,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8290163934426229,0.7911758893280633,0.8308527131782947,0.7978019323671498,0.8213263888888889,0.7870192307692307,0.8034866666666667 -Positive-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9465648854961832,0.9111675126903553,0.8870967741935484,0.9244060475161987,0.926829268292683,0.915625,0.92 -Sample_Size,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.2540983606557377,0.3547430830039526,0.2131782945736434,0.3446054750402576,0.2638888888888889,0.37564102564102564,0.322 -Statistical_Bias,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.21005937889821227,0.24850722840440323,0.21231194725259486,0.24091933144638822,0.2216758094116745,0.24922014009125384,0.23599886136505574 -Std,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.08755548044966635,0.10117446934723753,0.08464217527528196,0.09925761370194752,0.08809491086883042,0.10472730976062009,0.09674375829256104 -TNR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.907563025210084,0.8737864077669902,0.923469387755102,0.8767650834403081,0.9087378640776699,0.8608695652173913,0.8861538461538462 -TPR,MLPClassifier,"{'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100, 100), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 1000, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 101, 'shuffle': True, 'solver': 'sgd', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.6946564885496184,0.7131979695431472,0.6451612903225806,0.7170626349892009,0.697560975609756,0.715625,0.7085714285714285 -Accuracy,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8463114754098361,0.799407114624506,0.8449612403100775,0.8083735909822867,0.8347222222222223,0.7961538461538461,0.8146666666666667 -Aleatoric_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.578901881115347,0.6427220196011102,0.5776986374569397,0.631153424602836,0.6096726402021011,0.633300642147564,0.6219592012137418 -F1,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.6963562753036437,0.7079136690647482,0.6551724137931034,0.711864406779661,0.6826666666666666,0.7195767195767195,0.7048832271762208 -FNR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.3435114503816794,0.3756345177664975,0.3870967741935484,0.3650107991360691,0.375609756097561,0.3625,0.3676190476190476 -FPR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.08403361344537816,0.0889967637540453,0.08163265306122448,0.08857509627727857,0.08155339805825243,0.09347826086956522,0.08717948717948718 -IQR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.07993080357142858,0.08735322459846914,0.07968225898240433,0.0860303322214554,0.08367405891754848,0.08610560643060641,0.0849384636243386 -Jitter,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.0632354394925448,0.07906637932747126,0.07057457831794871,0.0746101683943074,0.07238895868230105,0.07532566679551772,0.07391604690117412 -Label_Stability,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.911844262295082,0.8900197628458498,0.8983720930232557,0.8968599033816426,0.8976249999999999,0.8966538461538462,0.8971199999999999 -Mean,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.707915635896435,0.645445064778217,0.7366042713332103,0.6510542140652557,0.7011054430941357,0.6331504062118437,0.6657688239153439 -Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.5980926620208493,0.6629771995525174,0.5967855512878455,0.6512330698720273,0.6293954388029462,0.6533813193271806,0.6418680966755481 -PPV,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.7413793103448276,0.8172757475083057,0.7037037037037037,0.8099173553719008,0.7529411764705882,0.8259109311740891,0.7961630695443646 -Per_Sample_Accuracy,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8352459016393443,0.7941007905138341,0.8271317829457364,0.8034057971014492,0.8267013888888889,0.78975,0.8074866666666667 -Positive-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.8854961832061069,0.7639593908629442,0.8709677419354839,0.7840172786177105,0.8292682926829268,0.771875,0.7942857142857143 -Sample_Size,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.23770491803278687,0.2974308300395257,0.20930232558139536,0.2922705314009662,0.2361111111111111,0.31666666666666665,0.278 -Statistical_Bias,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.24958265027322407,0.2865677506822887,0.2556659200043067,0.2784549836253611,0.26328724655533514,0.28491805064611314,0.2745352646825397 -Std,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.056821051436244836,0.0628264498200237,0.05668759886069393,0.061742061040815165,0.059889468244443,0.06178028613173397,0.06087269354583431 -TNR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.9159663865546218,0.9110032362459547,0.9183673469387755,0.9114249037227214,0.9184466019417475,0.9065217391304348,0.9128205128205128 -TPR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.4,0.6564885496183206,0.6243654822335025,0.6129032258064516,0.6349892008639308,0.624390243902439,0.6375,0.6323809523809524 -Accuracy,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8483606557377049,0.7845849802371542,0.8449612403100775,0.7971014492753623,0.8263888888888888,0.7858974358974359,0.8053333333333333 -Aleatoric_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.5809271419254739,0.632326188134487,0.5804399996415808,0.6229090400516942,0.6027107443059488,0.627506168912114,0.6156043651011547 -F1,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.6991869918699187,0.6716867469879518,0.6551724137931034,0.6826196473551638,0.6556473829201102,0.6946983546617916,0.6791208791208792 -FNR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.3435114503816794,0.434010152284264,0.3870967741935484,0.4146868250539957,0.4195121951219512,0.40625,0.4114285714285714 -FPR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.08123249299719888,0.07605177993527508,0.08163265306122448,0.07702182284980745,0.07572815533980583,0.08043478260869565,0.07794871794871795 -IQR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.07655375947339318,0.077704999823546,0.07722390642303431,0.07735259789637809,0.0762085014329806,0.07836611975986976,0.07733046296296296 -Jitter,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.06152195403245778,0.07156136413291579,0.06709497097892671,0.0685445342655323,0.06591387493020666,0.0704933642571864,0.06829520938023337 -Label_Stability,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9143032786885246,0.9000790513833993,0.902984496124031,0.9050644122383253,0.9067222222222221,0.9028461538461539,0.9047066666666667 -Mean,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7055323232175382,0.6729498837168265,0.7335200550633689,0.6731697921459243,0.715874007895172,0.6537125261116199,0.6835500373677248 -Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.5984611689436439,0.6487402421227054,0.5980162595875916,0.6395217234292089,0.6192559619409344,0.6444998498400043,0.6323827836484507 -PPV,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.7478260869565218,0.825925925925926,0.7037037037037037,0.8187311178247734,0.7531645569620253,0.8370044052863436,0.8025974025974026 -Per_Sample_Accuracy,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8360450819672132,0.7759980237154149,0.8300193798449612,0.7883695652173913,0.8178750000000001,0.7749102564102565,0.7955333333333334 -Positive-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.8778625954198473,0.6852791878172588,0.8709677419354839,0.714902807775378,0.7707317073170732,0.709375,0.7333333333333333 -Sample_Size,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.23565573770491804,0.26679841897233203,0.20930232558139536,0.2665056360708535,0.21944444444444444,0.29102564102564105,0.25666666666666665 -Std,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.05416850447476281,0.05607068686560472,0.05424965264513179,0.055701574000992134,0.054355904736851104,0.05646347933479929,0.05545184352778415 -TNR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.9187675070028011,0.9239482200647249,0.9183673469387755,0.9229781771501926,0.9242718446601942,0.9195652173913044,0.9220512820512821 -TPR,RandomForestClassifier,"{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.7,0.6564885496183206,0.565989847715736,0.6129032258064516,0.5853131749460043,0.5804878048780487,0.59375,0.5885714285714285 -Accuracy,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8483606557377049,0.7984189723320159,0.8488372093023255,0.8075684380032206,0.8375,0.7935897435897435,0.8146666666666667 -Aleatoric_Uncertainty,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6034386214355544,0.667663044490748,0.6022916533054496,0.6560078918940269,0.6353339918546749,0.6573238130125916,0.6467686988567917 -F1,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7016129032258065,0.7085714285714285,0.6666666666666666,0.7123947051744886,0.6896551724137931,0.7180385288966725,0.7067510548523207 -FNR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.33587786259541985,0.37055837563451777,0.3709677419354839,0.36069114470842334,0.36585365853658536,0.359375,0.3619047619047619 -FPR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.08403361344537816,0.09385113268608414,0.08163265306122448,0.09242618741976893,0.08155339805825243,0.1,0.09025641025641026 -IQR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.06625906669162206,0.07151880255649483,0.06651334013218053,0.07049195730964715,0.06906575947907051,0.07049244347147894,0.0698076351551229 -Jitter,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.05250175055605957,0.0703220648697996,0.05746932336098636,0.06599009540456216,0.0635568118369625,0.06541779409870094,0.06452452261306409 -Label_Stability,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9285040983606557,0.900296442687747,0.9208914728682169,0.9071014492753622,0.9103333333333334,0.9086794871794872,0.9094733333333334 -Mean,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7044707097166929,0.6383597499240815,0.7315249192761425,0.6449825636808952,0.6940397580188165,0.6283245480658572,0.6598678488432778 -Overall_Uncertainty,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6163162753250673,0.6810105385909122,0.6151568145209929,0.6692709736442993,0.6484095577353082,0.670628366465659,0.6599633382750907 -PPV,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.7435897435897436,0.8104575163398693,0.7090909090909091,0.8043478260869565,0.7558139534883721,0.8167330677290837,0.7919621749408984 -Per_Sample_Accuracy,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8393954918032787,0.7940316205533596,0.8337403100775194,0.8036070853462158,0.8301944444444445,0.7890320512820512,0.80879 -Positive-Rate,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.8931297709923665,0.7766497461928934,0.8870967741935484,0.7948164146868251,0.8390243902439024,0.784375,0.8057142857142857 -Sample_Size,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,488.0,1012.0,258.0,1242.0,720.0,780.0,1500.0 -Selection-Rate,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.23975409836065573,0.30237154150197626,0.2131782945736434,0.2962962962962963,0.2388888888888889,0.3217948717948718,0.282 -Std,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.047667980542325225,0.052179487878327734,0.04763300879630876,0.051351288219061764,0.04994215113887428,0.05142213771478576,0.05071174415834825 -TNR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.9159663865546218,0.9061488673139159,0.9183673469387755,0.9075738125802311,0.9184466019417475,0.9,0.9097435897435897 -TPR,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 90, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 200, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Folktables_GA_2018_Income,0.0,0.6641221374045801,0.6294416243654822,0.6290322580645161,0.6393088552915767,0.6341463414634146,0.640625,0.638095238095238 diff --git a/docs/examples/law_school_group_metrics.csv b/docs/examples/law_school_group_metrics.csv deleted file mode 100644 index f39a023c..00000000 --- a/docs/examples/law_school_group_metrics.csv +++ /dev/null @@ -1,89 +0,0 @@ -Metric,male,race,male&race,Model_Name,Experiment_Iteration,Intervention_Param,Test_Set_Index -Equalized_Odds_TPR,-0.006852677560728049,-0.08926010463166822,-0.09233449477351918,LGBMClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_FPR,0.027310924369747913,-0.2892592592592593,-0.15657230634189157,LGBMClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_FNR,0.006852677560727997,0.08926010463166825,0.09233449477351917,LGBMClassifier,Exp_iter_1,0.6,0 -Disparate_Impact,1.0155706946616037,1.0637883787525366,1.064060803474484,LGBMClassifier,Exp_iter_1,0.6,0 -Statistical_Parity_Difference,0.01661276831014935,0.06794241218413566,0.06852497096399524,LGBMClassifier,Exp_iter_1,0.6,0 -Accuracy_Parity,-0.02441327723235165,-0.15885561838018636,-0.16299790356394128,LGBMClassifier,Exp_iter_1,0.6,0 -Label_Stability_Ratio,1.0022336520605963,0.9215110409144575,0.9448800911879143,LGBMClassifier,Exp_iter_1,0.6,0 -IQR_Parity,-0.0019234170135528535,0.030752485425003136,0.026171410156253943,LGBMClassifier,Exp_iter_1,0.6,0 -Std_Parity,-0.0014947483480208142,0.022654697553924172,0.019420397675297765,LGBMClassifier,Exp_iter_1,0.6,0 -Std_Ratio,0.9362288387603354,2.1935697988767453,1.91757091989168,LGBMClassifier,Exp_iter_1,0.6,0 -Jitter_Parity,-0.0009008483276642908,0.05605017735541579,0.04049042442969653,LGBMClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_TPR,-0.005171382474971176,-0.11004756903064217,-0.0987224157955865,LGBMClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FPR,-0.025282526803824923,-0.4292592592592593,-0.3133640552995392,LGBMClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FNR,0.005171382474971221,0.11004756903064213,0.09872241579558652,LGBMClassifier,Exp_iter_1,0.0,0 -Disparate_Impact,1.0093278423562047,0.9912768659262348,0.9923664122137406,LGBMClassifier,Exp_iter_1,0.0,0 -Statistical_Parity_Difference,0.009888779529904745,-0.009296665118064151,-0.008130081300812941,LGBMClassifier,Exp_iter_1,0.0,0 -Accuracy_Parity,-0.015605423094904092,-0.1335709166202118,-0.11753449368631463,LGBMClassifier,Exp_iter_1,0.0,0 -Label_Stability_Ratio,0.998577907316844,0.911696818570683,0.920586307756427,LGBMClassifier,Exp_iter_1,0.0,0 -IQR_Parity,-0.0018418992707291484,0.03070162807008311,0.02640029144412168,LGBMClassifier,Exp_iter_1,0.0,0 -Std_Parity,-0.001380608364266945,0.022270950149824872,0.01912573180911198,LGBMClassifier,Exp_iter_1,0.0,0 -Std_Ratio,0.9474740574271133,2.0150416987665265,1.7940775240850562,LGBMClassifier,Exp_iter_1,0.0,0 -Jitter_Parity,0.0009153720918688296,0.06001277824710762,0.05223850775990582,LGBMClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_TPR,-0.005365607208478229,-0.07716333043811985,-0.08536585365853655,LogisticRegression,Exp_iter_1,0.6,0 -Equalized_Odds_FPR,0.0021008403361344463,-0.2592592592592593,-0.14691683124862842,LogisticRegression,Exp_iter_1,0.6,0 -Equalized_Odds_FNR,0.005365607208478207,0.07716333043811986,0.08536585365853659,LogisticRegression,Exp_iter_1,0.6,0 -Disparate_Impact,1.014185628316063,1.0865027213593523,1.0769230769230769,LogisticRegression,Exp_iter_1,0.6,0 -Statistical_Parity_Difference,0.015190042348141253,0.09213596057123241,0.0824622531939605,LogisticRegression,Exp_iter_1,0.6,0 -Accuracy_Parity,-0.020523609163160317,-0.15885561838018636,-0.16299790356394128,LogisticRegression,Exp_iter_1,0.6,0 -Label_Stability_Ratio,1.004335707649427,0.9552448804260418,0.9689971045213348,LogisticRegression,Exp_iter_1,0.6,0 -IQR_Parity,-0.0004394323534326547,0.015022317395668628,0.013321803169827343,LogisticRegression,Exp_iter_1,0.6,0 -Std_Parity,-0.00037752191542240673,0.011290698242018816,0.010010789402424683,LogisticRegression,Exp_iter_1,0.6,0 -Std_Ratio,0.9574177968749634,2.658469055430207,2.2721629361081668,LogisticRegression,Exp_iter_1,0.6,0 -Jitter_Parity,-0.002206018404318751,0.03092054469475277,0.022461201723108764,LogisticRegression,Exp_iter_1,0.6,0 -Equalized_Odds_TPR,-0.000257377560966332,-0.098576968913487,-0.07491289198606277,LogisticRegression,Exp_iter_1,0.0,0 -Equalized_Odds_FPR,0.001014198782961384,-0.3766666666666667,-0.2736449418477068,LogisticRegression,Exp_iter_1,0.0,0 -Equalized_Odds_FNR,0.00025737756096630773,0.09857696891348698,0.07491289198606271,LogisticRegression,Exp_iter_1,0.0,0 -Disparate_Impact,1.0178946069357029,1.024124924276844,1.0353452963567156,LogisticRegression,Exp_iter_1,0.0,0 -Statistical_Parity_Difference,0.019005426376910384,0.02574130930979468,0.03774680603948899,LogisticRegression,Exp_iter_1,0.0,0 -Accuracy_Parity,-0.014941561477325838,-0.1421916062753843,-0.11596216664228953,LogisticRegression,Exp_iter_1,0.0,0 -Label_Stability_Ratio,1.000802025127658,0.9591320645489642,0.9674653511862815,LogisticRegression,Exp_iter_1,0.0,0 -IQR_Parity,-0.0004508970638314215,0.01656430213503716,0.014545259937867398,LogisticRegression,Exp_iter_1,0.0,0 -Std_Parity,-0.000250712698052144,0.012597874744057873,0.011208249522494669,LogisticRegression,Exp_iter_1,0.0,0 -Std_Ratio,0.971192962518509,2.943109990424136,2.4624081283525006,LogisticRegression,Exp_iter_1,0.0,0 -Jitter_Parity,-0.0005955631359242288,0.029270523731717988,0.02275483751946553,LogisticRegression,Exp_iter_1,0.0,0 -Equalized_Odds_TPR,-0.00542995159871984,-0.10879522087785565,-0.08885017421602792,MLPClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FPR,0.05143436685018832,-0.3496296296296296,-0.21560236998025017,MLPClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FNR,0.005429951598719784,0.10879522087785565,0.08885017421602788,MLPClassifier,Exp_iter_1,0.0,0 -Disparate_Impact,1.0186228591559474,1.0137681641813756,1.0339168490153174,MLPClassifier,Exp_iter_1,0.0,0 -Statistical_Parity_Difference,0.019652444967672933,0.014621669662876036,0.03600464576074347,MLPClassifier,Exp_iter_1,0.0,0 -Accuracy_Parity,-0.024665731650303835,-0.14909346712325133,-0.13497635415143083,MLPClassifier,Exp_iter_1,0.0,0 -Label_Stability_Ratio,1.001836062082041,0.8553840569742932,0.8648947072020089,MLPClassifier,Exp_iter_1,0.0,0 -IQR_Parity,-0.0020205977060946817,0.09889952827181274,0.08378658970964548,MLPClassifier,Exp_iter_1,0.0,0 -Std_Parity,-0.0033869150678196153,0.10496476391153896,0.09394292815043398,MLPClassifier,Exp_iter_1,0.0,0 -Std_Ratio,0.9706692880546564,2.088873447566327,1.884663033993635,MLPClassifier,Exp_iter_1,0.0,0 -Jitter_Parity,-0.0014995576292047702,0.10470271374925606,0.09636066589101067,MLPClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_TPR,0.004399249792072291,-0.12046528773708765,-0.10859465737514518,MLPClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_FPR,0.03441031585047816,-0.24518518518518517,-0.18981786262892253,MLPClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_FNR,-0.0043992497920722975,0.1204652877370877,0.10859465737514518,MLPClassifier,Exp_iter_1,0.6,0 -Disparate_Impact,1.0262887781579368,1.0295513811087504,1.0238227146814405,MLPClassifier,Exp_iter_1,0.6,0 -Statistical_Parity_Difference,0.027283213025210973,0.03090219564910024,0.024970963995354367,MLPClassifier,Exp_iter_1,0.6,0 -Accuracy_Parity,-0.01372604020570356,-0.17549308486634285,-0.1530398322851153,MLPClassifier,Exp_iter_1,0.6,0 -Label_Stability_Ratio,1.0106622176454783,0.7347549972966689,0.7588685481341745,MLPClassifier,Exp_iter_1,0.6,0 -IQR_Parity,-0.014337453917789456,0.239717291325784,0.20712778100148252,MLPClassifier,Exp_iter_1,0.6,0 -Std_Parity,-0.0043530382426541225,0.14139945806041038,0.12248067260767251,MLPClassifier,Exp_iter_1,0.6,0 -Std_Ratio,0.9757227989415077,1.9199580092412873,1.7324037388625755,MLPClassifier,Exp_iter_1,0.6,0 -Jitter_Parity,-0.005143773566298637,0.14638050323022922,0.12861240469325352,MLPClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_TPR,-0.001291654055960545,-0.09822600844325047,-0.07026713124274109,RandomForestClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_FPR,0.05252100840336138,-0.23222222222222222,-0.11465876673249942,RandomForestClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_FNR,0.0012916540559605519,0.09822600844325045,0.070267131242741,RandomForestClassifier,Exp_iter_1,0.6,0 -Disparate_Impact,1.023725852624935,1.0662711508746496,1.0951859956236325,RandomForestClassifier,Exp_iter_1,0.6,0 -Statistical_Parity_Difference,0.025083588129174883,0.07017189488355191,0.10104529616724744,RandomForestClassifier,Exp_iter_1,0.6,0 -Accuracy_Parity,-0.022010285179990707,-0.1732234344721404,-0.15403929598751886,RandomForestClassifier,Exp_iter_1,0.6,0 -Label_Stability_Ratio,1.001176387259295,0.8802415712253702,0.8891938218753054,RandomForestClassifier,Exp_iter_1,0.6,0 -IQR_Parity,-0.0035096712918958883,0.04988279067634181,0.0462411893925246,RandomForestClassifier,Exp_iter_1,0.6,0 -Std_Parity,-0.0023313579149804586,0.03429320329971825,0.031556199509573314,RandomForestClassifier,Exp_iter_1,0.6,0 -Std_Ratio,0.9518148424379955,1.8242881288324113,1.7054584360968619,RandomForestClassifier,Exp_iter_1,0.6,0 -Jitter_Parity,-4.495690233047994e-05,0.07727229028260862,0.07378751418836879,RandomForestClassifier,Exp_iter_1,0.6,0 -Equalized_Odds_TPR,-0.0020673614272062046,-0.11776112468943789,-0.10162601626016254,RandomForestClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FPR,0.04303100550565053,-0.39185185185185184,-0.2725477287689269,RandomForestClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FNR,0.0020673614272062393,0.11776112468943785,0.1016260162601626,RandomForestClassifier,Exp_iter_1,0.0,0 -Disparate_Impact,1.0209866123993547,0.9959582206649654,1.004931506849315,RandomForestClassifier,Exp_iter_1,0.0,0 -Statistical_Parity_Difference,0.022045103034433744,-0.004287272506918294,0.00522648083623678,RandomForestClassifier,Exp_iter_1,0.0,0 -Accuracy_Parity,-0.020729312762973406,-0.14734809269730031,-0.13025937301935542,RandomForestClassifier,Exp_iter_1,0.0,0 -Label_Stability_Ratio,0.9930186851041846,0.8737383149825507,0.8708797513120977,RandomForestClassifier,Exp_iter_1,0.0,0 -IQR_Parity,-0.001739345488546054,0.055151728119773834,0.04682910389547073,RandomForestClassifier,Exp_iter_1,0.0,0 -Std_Parity,-0.0009731829505101527,0.03857636987638614,0.03338260860983806,RandomForestClassifier,Exp_iter_1,0.0,0 -Std_Ratio,0.9772979817363154,2.072208090053585,1.8414636557441237,RandomForestClassifier,Exp_iter_1,0.0,0 -Jitter_Parity,0.0032763339840793312,0.0841594483260172,0.08485739726863116,RandomForestClassifier,Exp_iter_1,0.0,0 diff --git a/docs/examples/law_school_subgroup_metrics.csv b/docs/examples/law_school_subgroup_metrics.csv deleted file mode 100644 index 3648b11d..00000000 --- a/docs/examples/law_school_subgroup_metrics.csv +++ /dev/null @@ -1,153 +0,0 @@ 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0.6]",0.6,0.5868292198900119,0.4895028089709125,0.8788084526473101,0.31213451114412527,0.2746251249709554,0.7057571058047387,0.32557852040339047,0.27641138563249196,0.7077834171130161,0.34234717313819124,0.29969421995747725,0.7759165127518567,0.334849650521189,0.5991994902774432,0.5141883196970904,0.8794214970052727,0.28173547948471295,0.25202902038499314,0.6539945450780771,0 -F1,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.8365019011406845,1.0,0.0,0.9534231200897868,1.0,0.0,0.9375,1.0,0.0,0.9516658845612389,1.0,0.0,0.9453880324013587,0.8468809073724007,1.0,0.0,0.9611885991510006,1.0,0.0,0 -FNR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.10569105691056911,0.0,1.0,0.013356562137049941,0.0,1.0,0.02334152334152334,0.0,1.0,0.016488845780795344,0.0,1.0,0.01951219512195122,0.0967741935483871,0.0,1.0,0.007514088916718848,0.0,1.0,0 -FPR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.6122448979591837,0.0,1.0,0.7688172043010753,0.0,1.0,0.75,0.0,1.0,0.7226890756302521,0.0,1.0,0.7361702127659574,0.57,0.0,1.0,0.8592592592592593,0.0,1.0,0 -IQR,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.053677237009712865,0.04423043496352387,0.08201764314827989,0.027505826853458922,0.023814295627974335,0.06624466658113452,0.028606573479079064,0.023741376201889028,0.0664265975961035,0.030529990492631918,0.02638713954524289,0.07264227148211065,0.02967000115484146,0.055277359210661375,0.04736805932154612,0.0813487551414487,0.02452487378565824,0.02153550951083108,0.06198534485458611,0 -Jitter,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.05720170620545171,0.028317556776123513,0.1438541544934052,0.016711281775755177,0.008955396961846785,0.09810135012414363,0.019561463230124726,0.008734144021076752,0.10372854840239312,0.020462311557789017,0.011515120972579035,0.1114109381860803,0.02005952841128785,0.06673207993993159,0.03564809064047434,0.16919411874184762,0.010681902584515802,0.006069342974223462,0.06848304020100503,0 -Label_Stability,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.922674418604651,0.9623255813953489,0.8037209302325582,0.9764989517819705,0.9878645235361653,0.8572289156626507,0.973247311827957,0.9883373786407766,0.8559433962264151,0.9710782608695653,0.9843457497612227,0.8362135922330096,0.9720480769230768,0.9076724137931035,0.9531460674157304,0.7577777777777778,0.9849826789838337,0.9915897755610973,0.9021875,0 -Mean,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.25366066560450123,0.19218317287414302,0.43809314379557607,0.09430034646818729,0.07687616230366145,0.27714931523086195,0.10592175970495323,0.08043902837613173,0.30401393531767884,0.10873692088666873,0.08827903023095526,0.3166923725034843,0.10747821901215171,0.2623223387707427,0.2132171520025776,0.4241875840435831,0.07636635199368194,0.0634544416764679,0.2381687281562705,0 -Overall_Uncertainty,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 'random_state': 101, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'min_data_in_leaf': 600}",Run_1,Law_School,200,OK,2023-08-07 01:12:17.630,adaeac61-1e2d-4f1c-ad7e-6a164a02d732,Exp_iter_1,100,100,"[0.0, 0.6]",0.6,0.59599798281351,0.4973836190946347,0.8918410739701361,0.3172760636629066,0.2792196178051038,0.716639489471898,0.3308123858516533,0.2809034869135636,0.7187834492949164,0.3480164031919235,0.3047742004421416,0.7875754932795126,0.34032422236189885,0.6086383785857439,0.5226537972216503,0.892069035674793,0.2864135258457914,0.256243452133116,0.6644822620577554,0 -PPV,101,LGBMClassifier,"{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 20, 'objective': None, 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'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,705.0,486.0,219.0,795.0,551.0,244.0,420.0,303.0,117.0,1080.0,734.0,346.0,842.0,596.0,246.0,658.0,441.0,217.0,1500.0 -Sample_Size,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,705.0,491.0,214.0,795.0,564.0,231.0,420.0,307.0,113.0,1080.0,748.0,332.0,842.0,604.0,238.0,658.0,451.0,207.0,1500.0 -Selection-Rate,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.2127659574468085,0.20781893004115226,0.2237442922374429,0.25157232704402516,0.2395644283121597,0.2786885245901639,0.2119047619047619,0.2079207920792079,0.2222222222222222,0.24166666666666667,0.23160762942779292,0.2630057803468208,0.22921615201900236,0.21308724832214765,0.2682926829268293,0.23860182370820668,0.24036281179138322,0.2350230414746544,0.23333333333333334 -Selection-Rate,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.2453900709219858,0.23421588594704684,0.27102803738317754,0.27044025157232704,0.25886524822695034,0.2987012987012987,0.25,0.23778501628664495,0.2831858407079646,0.262037037037037,0.25133689839572193,0.286144578313253,0.26009501187648454,0.23841059602649006,0.31512605042016806,0.256838905775076,0.25942350332594233,0.25120772946859904,0.25866666666666666 -Statistical_Bias,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.39568703604637867,0.28458620508216764,0.6422395650354499,0.3942061322411184,0.2773123586040772,0.6581752686100107,0.38163450778574454,0.2817164153175404,0.6403967472546834,0.4000617984021977,0.28031053326916255,0.6541006094069601,0.38515223218103145,0.27803639883682396,0.644668641421469,0.4073785046321545,0.28434991282093636,0.6574043525065658,0.39490215702959086 -Statistical_Bias,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.3930658275524083,0.28263691861975415,0.6464330905707877,0.3932696577871171,0.27761160212138475,0.675655559932022,0.3760299855503612,0.2779786792735978,0.6424171627801519,0.3998409189204206,0.280759647194874,0.6681324588321943,0.3819627206936393,0.27447714280776647,0.654741246084678,0.40752002361878664,0.28728043543873466,0.6694913002816052,0.3931738575768039 -Std,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.07138170520710842,0.06883232555967064,0.07703923264388815,0.0685578124754467,0.06568594637599239,0.07504305108528007,0.0713728152178547,0.06906699981411775,0.07734428587881452,0.06930646360878946,0.06637352279495103,0.07552836695375303,0.07010424381613034,0.06735623463028029,0.07676202216884026,0.06960454376262892,0.06689603364064554,0.07510893530085318,0.06988504205932772 -Std,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.08719113374979333,0.08411282120286062,0.09425399104205477,0.08246286338476243,0.08000786245602967,0.08845689162634371,0.08669444508147052,0.08406093977471062,0.09384918958744656,0.08390375810210444,0.08103893198525286,0.09035824585934837,0.08422602181233907,0.0810713362549355,0.09223203053785067,0.08527266765729623,0.08305264420803606,0.09010953034143314,0.08468515045632696 -TNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.8870967741935484,1.0,0.0,0.8603696098562629,1.0,0.0,0.9022556390977443,1.0,0.0,0.8610687022900764,1.0,0.0,0.8766355140186916,1.0,0.0,0.8678756476683938,1.0,0.0,0.8729641693811075 -TNR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.8663594470046083,1.0,0.0,0.8583162217659137,1.0,0.0,0.8796992481203008,1.0,0.0,0.8549618320610687,1.0,0.0,0.8598130841121495,1.0,0.0,0.8652849740932642,1.0,0.0,0.8621064060803475 -TPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:06:10.960,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.0],0.0,0.3726937269372694,1.0,0.0,0.42857142857142855,1.0,0.0,0.4090909090909091,1.0,0.0,0.4,1.0,0.0,0.41368078175895767,1.0,0.0,0.3897058823529412,1.0,0.0,0.40241796200345425 -TPR,101,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 50, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 101, 'verbose': 0, 'warm_start': False}",Run_1,Folktables_CA_2018_Public_Coverage,200,0,OK,2023-08-01 23:13:20.565,c42fae24-694f-408e-98df-ce099940a961,Exp_iter_1,100,100,[0.6],0.6,0.42435424354243545,1.0,0.0,0.474025974025974,1.0,0.0,0.474025974025974,1.0,0.0,0.4423529411764706,1.0,0.0,0.46905537459283386,1.0,0.0,0.43014705882352944,1.0,0.0,0.45077720207253885 diff --git a/docs/examples/ricci_group_metrics.csv b/docs/examples/ricci_group_metrics.csv deleted file mode 100644 index 31cfbb3f..00000000 --- a/docs/examples/ricci_group_metrics.csv +++ /dev/null @@ -1,133 +0,0 @@ -Metric,Race,Model_Name,Experiment_Iteration,Intervention_Param,Test_Set_Index -Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.0,0 -Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.0,0 -Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.0,0 -IQR_Parity,2.7755575615628914e-17,LGBMClassifier,Exp_iter_1,0.0,0 -Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.0,0 -Label_Stability_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.0,0 -Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.0,0 -Std_Parity,-1.3877787807814457e-17,LGBMClassifier,Exp_iter_1,0.0,0 -Std_Ratio,0.9999999999999998,LGBMClassifier,Exp_iter_1,0.0,0 -Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.4,0 -Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.4,0 -Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.4,0 -Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.4,0 -Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.4,0 -IQR_Parity,2.7755575615628914e-17,LGBMClassifier,Exp_iter_1,0.4,0 -Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.4,0 -Label_Stability_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.4,0 -Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.4,0 -Std_Parity,0.0,LGBMClassifier,Exp_iter_1,0.4,0 -Std_Ratio,1.0,LGBMClassifier,Exp_iter_1,0.4,0 -Accuracy_Parity,-0.2299465240641711,LGBMClassifier,Exp_iter_1,0.7,0 -Disparate_Impact,2.2647058823529416,LGBMClassifier,Exp_iter_1,0.7,0 -Equalized_Odds_FNR,0.0,LGBMClassifier,Exp_iter_1,0.7,0 -Equalized_Odds_FPR,0.0,LGBMClassifier,Exp_iter_1,0.7,0 -Equalized_Odds_TPR,0.0,LGBMClassifier,Exp_iter_1,0.7,0 -IQR_Parity,0.0,LGBMClassifier,Exp_iter_1,0.7,0 -Jitter_Parity,0.0,LGBMClassifier,Exp_iter_1,0.7,0 -Label_Stability_Ratio,1.0000000000000002,LGBMClassifier,Exp_iter_1,0.7,0 -Statistical_Parity_Difference,3.0714285714285716,LGBMClassifier,Exp_iter_1,0.7,0 -Std_Parity,-1.3877787807814457e-17,LGBMClassifier,Exp_iter_1,0.7,0 -Std_Ratio,0.9999999999999998,LGBMClassifier,Exp_iter_1,0.7,0 -Accuracy_Parity,0.3529411764705882,LogisticRegression,Exp_iter_1,0.0,0 -Disparate_Impact,0.5384615384615384,LogisticRegression,Exp_iter_1,0.0,0 -Equalized_Odds_FNR,0.0,LogisticRegression,Exp_iter_1,0.0,0 -Equalized_Odds_FPR,-0.6,LogisticRegression,Exp_iter_1,0.0,0 -Equalized_Odds_TPR,0.0,LogisticRegression,Exp_iter_1,0.0,0 -IQR_Parity,-0.004259721871489895,LogisticRegression,Exp_iter_1,0.0,0 -Jitter_Parity,-0.0640968210033926,LogisticRegression,Exp_iter_1,0.0,0 -Label_Stability_Ratio,1.0935446085768203,LogisticRegression,Exp_iter_1,0.0,0 -Statistical_Parity_Difference,-0.8571428571428572,LogisticRegression,Exp_iter_1,0.0,0 -Std_Parity,-0.0011073640847593519,LogisticRegression,Exp_iter_1,0.0,0 -Std_Ratio,0.9758617440249395,LogisticRegression,Exp_iter_1,0.0,0 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...butes_Metrics_Report_20230519__210711.html | 60 -------- ...butes_Metrics_Report_20230519__211200.html | 60 -------- ...butes_Metrics_Report_20230811__222632.html | 60 -------- ...butes_Metrics_Report_20230812__223906.html | 60 -------- ...butes_Metrics_Report_20230812__224023.html | 60 -------- ...butes_Metrics_Report_20230812__224310.html | 60 -------- ...redit_Metrics_Report_20230319__161213.html | 60 -------- ...redit_Metrics_Report_20230319__184958.html | 60 -------- ...redit_Metrics_Report_20230405__194722.html | 60 -------- ...redit_Metrics_Report_20230405__195808.html | 60 -------- ..._2018_Metrics_Report_20230205__165240.html | 60 -------- ..._2018_Metrics_Report_20230319__131915.html | 60 -------- ..._2018_Metrics_Report_20230519__211628.html | 60 -------- lib_base_packages.txt | 1 - virny/custom_classes/metrics_visualizer.py | 143 ------------------ 24 files changed, 49 insertions(+), 1405 deletions(-) delete mode 100644 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docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230811__222632.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__223906.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__224023.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__224310.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230319__161213.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230319__184958.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230405__194722.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230405__195808.html delete mode 100644 docs/examples/results/reports/Folktables_GA_2018_Metrics_Report_20230205__165240.html delete mode 100644 docs/examples/results/reports/Folktables_GA_2018_Metrics_Report_20230319__131915.html delete mode 100644 docs/examples/results/reports/Folktables_GA_2018_Metrics_Report_20230519__211628.html diff --git a/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb b/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb index 2c056f40..4066db1e 100644 --- a/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb +++ b/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb @@ -147,7 +147,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "d8505edef7184a5a" }, { "cell_type": "markdown", @@ -156,7 +157,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "c84bae470652be94" }, { "cell_type": "markdown", @@ -173,7 +175,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8b41b746e152c76f" }, { "cell_type": "code", @@ -195,7 +198,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "a878d125e8bfaf4d" }, { "cell_type": "code", @@ -207,7 +211,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "53d2fcd40c862014" }, { "cell_type": "markdown", @@ -307,7 +312,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8feb498942cc2a8c" }, { "cell_type": "code", @@ -318,7 +324,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "7915190e0847f1a7" }, { "cell_type": "markdown", @@ -581,7 +588,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "ca3fe31f0515a973" }, { "cell_type": "code", @@ -602,7 +610,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "dfc57f1870ed71d1" }, { "cell_type": "code", @@ -647,34 +656,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "55e6ce42", - "metadata": {}, - "source": [ - "Create an analysis report. It includes correspondent visualizations and details about your result metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "5a3811ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "", - "text/markdown": "App saved to ./docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__223906.html" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer.create_html_report(report_type=ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS,\n", - " report_save_path=os.path.join(ROOT_DIR, \"results\", \"reports\"))" - ] - }, { "cell_type": "code", "execution_count": 59, diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index a8bc35c9..1aa89ec1 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -1096,45 +1096,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "55e6ce42", - "metadata": { - "is_executing": true - }, - "source": [ - "Create an analysis report. It includes correspondent visualizations and details about your result metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5a3811ff", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-21T20:58:36.148703Z", - "start_time": "2023-10-21T20:58:35.395033Z" - } - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'datapane' has no attribute 'Report'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[27], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mvisualizer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_html_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreport_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mReportType\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mMULTIPLE_RUNS_MULTIPLE_MODELS\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43mreport_save_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpath\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43mROOT_DIR\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mresults\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mreports\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m~/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_visualizer.py:480\u001B[0m, in \u001B[0;36mMetricsVisualizer.create_html_report\u001B[0;34m(self, report_type, report_save_path)\u001B[0m\n\u001B[1;32m 475\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m report_type \u001B[38;5;241m==\u001B[39m ReportType\u001B[38;5;241m.\u001B[39mMULTIPLE_RUNS_MULTIPLE_MODELS:\n\u001B[1;32m 476\u001B[0m boxes_and_whiskers_plot \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcreate_boxes_and_whiskers_for_models_multiple_runs(\n\u001B[1;32m 477\u001B[0m metrics_lst\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStd\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mIQR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mJitter\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mLabel_Stability\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAccuracy\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTPR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTNR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFPR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFNR\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[1;32m 478\u001B[0m )\n\u001B[0;32m--> 480\u001B[0m \u001B[43mdp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mReport\u001B[49m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m# Fairness and Stability Report\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 481\u001B[0m general_desc,\n\u001B[1;32m 482\u001B[0m \n\u001B[1;32m 483\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Model Composed Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 484\u001B[0m composed_metrics_desc,\n\u001B[1;32m 485\u001B[0m dp\u001B[38;5;241m.\u001B[39mDataTable(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodels_composed_metrics_df),\n\u001B[1;32m 486\u001B[0m \n\u001B[1;32m 487\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Boxes and Whiskers Plot Based On Multiple Models Runs\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 488\u001B[0m boxes_and_whiskers_plot_desc,\n\u001B[1;32m 489\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(boxes_and_whiskers_plot),\n\u001B[1;32m 490\u001B[0m \n\u001B[1;32m 491\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Overall Fairness and Stability Model Metrics Comparison\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 492\u001B[0m overall_metrics_desc,\n\u001B[1;32m 493\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(fairness_overall_metrics_bar_chart, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 494\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(variance_overall_metrics_bar_chart, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 495\u001B[0m \n\u001B[1;32m 496\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Fairness and Stability Interactive Bar Chart\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 497\u001B[0m individual_metrics_interactive_bar_chart_desc,\n\u001B[1;32m 498\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(interactive_bar_chart),\n\u001B[1;32m 499\u001B[0m \n\u001B[1;32m 500\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Model Ranks Based On Group Fairness and Stability Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 501\u001B[0m model_ranked_heatmap_desc,\n\u001B[1;32m 502\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 503\u001B[0m \n\u001B[1;32m 504\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Total Ranks Sum For Group Fairness and Stability Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 505\u001B[0m overall_model_ranked_heatmap_desc,\n\u001B[1;32m 506\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(total_model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 507\u001B[0m )\u001B[38;5;241m.\u001B[39msave(path\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(report_save_path, report_filename))\n\u001B[1;32m 508\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 509\u001B[0m dp\u001B[38;5;241m.\u001B[39mReport(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m# Fairness and Stability Report\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 510\u001B[0m general_desc,\n\u001B[1;32m 511\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 531\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(total_model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 532\u001B[0m )\u001B[38;5;241m.\u001B[39msave(path\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(report_save_path, report_filename))\n", - "\u001B[0;31mAttributeError\u001B[0m: module 'datapane' has no attribute 'Report'" - ] - } - ], - "source": [ - "visualizer.create_html_report(report_type=ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS,\n", - " report_save_path=os.path.join(ROOT_DIR, \"results\", \"reports\"))" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index 9fe2b8cd..b5bf2600 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -154,7 +154,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "76d98eaabfcfc9c0" }, { "cell_type": "code", @@ -202,7 +203,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "ebec7f4488fd3f25" }, { "cell_type": "markdown", @@ -211,7 +213,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "855fb160c6220866" }, { "cell_type": "markdown", @@ -228,7 +231,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "1137cf9bc7be6964" }, { "cell_type": "code", @@ -250,7 +254,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "efc95fa248b9f135" }, { "cell_type": "code", @@ -262,7 +267,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "f3a59ca9319a774d" }, { "cell_type": "markdown", @@ -362,7 +368,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8ee9e8a8c10245bf" }, { "cell_type": "code", @@ -373,7 +380,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "6dba3327ebe01279" }, { "cell_type": "markdown", @@ -382,7 +390,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "c32119a0992e331c" }, { "cell_type": "code", @@ -406,8 +415,7 @@ "\n", "2023/08/13, 01:41:39: Tuning XGBClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:41:42: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n", - "\n" + "2023/08/13, 01:41:42: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" ] }, { @@ -426,7 +434,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "f9f77d878f6a94f8" }, { "cell_type": "code", @@ -440,7 +449,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "cdd137541e77686d" }, { "cell_type": "markdown", @@ -449,7 +459,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "ca709f1927e425b5" }, { "cell_type": "code", @@ -483,7 +494,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8c6061673bb72efa" }, { "cell_type": "markdown", @@ -739,7 +751,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "cdd0a858443ac90e" }, { "cell_type": "code", @@ -760,7 +773,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "5efb9f1d613da1c6" }, { "cell_type": "code", @@ -805,34 +819,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "55e6ce42", - "metadata": {}, - "source": [ - "Create an analysis report. 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- - \ No newline at end of file diff --git a/lib_base_packages.txt b/lib_base_packages.txt index 10cbab0f..b851e9f7 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -10,6 +10,5 @@ folktables~=0.0.11 munch~=2.5.0 PyYAML~=6.0 river==0.15.0 -datapane~=0.16.0 requests-toolbelt==1.0.0 colorama~=0.4.6 \ No newline at end of file diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 377bcdf9..daed2671 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -2,7 +2,6 @@ import altair as alt import numpy as np import pandas as pd -import datapane as dp import seaborn as sns import matplotlib.pyplot as plt from datetime import datetime, timezone @@ -378,145 +377,3 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): total_model_rank_heatmap = self.create_total_model_rank_heatmap(sorted_matrix_by_rank, num_models) if self.__create_report: return model_rank_heatmap, total_model_rank_heatmap - - def create_html_report(self, report_type: ReportType, report_save_path: str): - """ - Create Fairness and Stability Report depending on report type. - It includes visualizations and helpful details to them. - """ - # Create a directory if it does not exist - if not os.path.exists(report_save_path): - os.makedirs(report_save_path, exist_ok=True) - - self.__create_report = True - - # Create plots - fairness_overall_metrics_bar_chart = self.create_overall_metrics_bar_char( - metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'], - metrics_title="Fairness Metrics" - ) - variance_overall_metrics_bar_chart = self.create_overall_metrics_bar_char( - metrics_names=['Label_Stability'], - reversed_metrics_names=['Std', 'IQR', 'Jitter'], - metrics_title="Stability Metrics" - ) - interactive_bar_chart = self.create_fairness_variance_interactive_bar_chart() - model_rank_heatmap, total_model_rank_heatmap = \ - self.create_model_rank_heatmaps(metrics_lst=self.fairness_metrics_lst + self.variance_metrics_lst, - groups_lst=self.sensitive_attributes_dct.keys()) - - # Set descriptions for the report - general_desc = dp.Text( - f"**Date of creation**: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}\n\n\n" - "This report was created based on the following input arguments:\n" - f"* __Dataset name__: {self.dataset_name}\n" - f"* __Model names__: {self.model_names}\n" - f"* __Sensitive attributes__: {list(self.sensitive_attributes_dct.keys())}\n" - ) - composed_metrics_desc = dp.Text( - "Below you can find a dataframe of composed group metrics for all defined models and sensitive attributes.\n" - ) - boxes_and_whiskers_plot_desc = dp.Text( - "The below boxes and whiskers plot is based on _overall_ subgroup error and stability metrics for all defined models and results after all runs.\n" - "This plot can give you the following benefits:\n" - "* You can see combined information on one plot that includes different models, subgroup metrics, and results after multiple runs\n" - "* You can see all quartiles for each model metric based on multiple runs\n" - "* You can compare different models for each metric\n" - "* You can see the stability of each model metric\n" - ) - overall_metrics_desc = dp.Text( - "The below bar chart includes all defined models and all _overall_ subgroup error and stability metrics, which are averaged across multiple runs.\n" - "This plot can give you the following benefits:\n" - "* You can compare all models for each subgroup error or stability metric\n" - "* This comparison also includes reversed metrics, in which values closer to zero are better " - "since straight and reversed metrics in this plot are converted to the same format -- values closer to one are better\n" - ) - individual_metrics_interactive_bar_chart_desc = dp.Text( - "The below interactive bar chart includes all groups, all composed group fairness and stability metrics, " - "and all defined models.\n" - "This plot can give you the following benefits:\n" - "* You can select any pair of group fairness and stability metrics and compare them across all groups and models\n" - "* Since this plot is interactive, it saves a lot of space for other plots. " - "Also, it could be more convenient to compare each group fairness and stability metric using the interactive mode\n" - ) - model_ranked_heatmap_desc = dp.Text( - "The below heatmap includes all group fairness and stability metrics and all defined models.\n" - "On this plot, colors display ranks where 1 is the best model for the metric. " - "These ranks are conditioned on difference or ratio operations used to create these group metrics:\n" - "* If the metric is created based on the difference operation, **closer values to zero** have ranks that are closer to the first rank\n" - "* If the metric is created based on the ratio operation, **closer values to one** have ranks that are closer to the first rank\n\n" - "This plot can give you the following benefits:\n" - "* You can visually compare all models across all group metrics\n" - "* You can visually understand where one model is better or worse than other models\n" - "* You can find the best and worst models for each group metric\n" - ) - overall_model_ranked_heatmap_desc = dp.Text( - "The below heatmap includes all defined models and sums of their fairness and stability ranks.\n" - "On this plot, colors display sums of ranks for one model. If the sum is smaller, the model has better fairness or stability characteristics than other models.\n" - "This plot can give you the following benefits:\n" - "* You can visually compare all models for fairness and stability characteristics\n" - "* You can visually understand where one model is better or worse than other models\n" - "* You can find the best or most balanced model based on fairness or stability metrics\n" - ) - - report_filename = f'{self.dataset_name}_Metrics_Report_{datetime.now(timezone.utc).strftime("%Y%m%d__%H%M%S")}.html' - if report_type == ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS: - boxes_and_whiskers_plot = self.create_boxes_and_whiskers_for_models_multiple_runs( - metrics_lst=['Std', 'IQR', 'Jitter', 'Label_Stability', 'Accuracy', 'TPR', 'TNR', 'FPR', 'FNR'] - ) - - dp.Report("# Fairness and Stability Report", - general_desc, - - "## Model Composed Metrics", - composed_metrics_desc, - dp.DataTable(self.models_composed_metrics_df), - - "## Boxes and Whiskers Plot Based On Multiple Models Runs", - boxes_and_whiskers_plot_desc, - dp.Plot(boxes_and_whiskers_plot), - - "## Overall Fairness and Stability Model Metrics Comparison", - overall_metrics_desc, - dp.Plot(fairness_overall_metrics_bar_chart, responsive=False), - dp.Plot(variance_overall_metrics_bar_chart, responsive=False), - - "## Fairness and Stability Interactive Bar Chart", - individual_metrics_interactive_bar_chart_desc, - dp.Plot(interactive_bar_chart), - - "## Model Ranks Based On Group Fairness and Stability Metrics", - model_ranked_heatmap_desc, - dp.Plot(model_rank_heatmap, responsive=False), - - "## Total Ranks Sum For Group Fairness and Stability Metrics", - overall_model_ranked_heatmap_desc, - dp.Plot(total_model_rank_heatmap, responsive=False), - ).save(path=os.path.join(report_save_path, report_filename)) - else: - dp.Report("# Fairness and Stability Report", - general_desc, - - "## Model Composed Metrics", - composed_metrics_desc, - dp.DataTable(self.models_composed_metrics_df), - - "## Overall Fairness and Stability Model Metrics Comparison", - overall_metrics_desc, - dp.Plot(fairness_overall_metrics_bar_chart, responsive=False), - dp.Plot(variance_overall_metrics_bar_chart, responsive=False), - - "## Fairness and Stability Interactive Bar Chart", - individual_metrics_interactive_bar_chart_desc, - dp.Plot(interactive_bar_chart), - - "## Model Ranks Based On Group Fairness and Stability Metrics", - model_ranked_heatmap_desc, - dp.Plot(model_rank_heatmap, responsive=False), - - "## Total Ranks Sum For Group Fairness and Stability Metrics", - overall_model_ranked_heatmap_desc, - dp.Plot(total_model_rank_heatmap, responsive=False), - ).save(path=os.path.join(report_save_path, report_filename)) - - self.__create_report = False From 8fcd8f6df305ddf56f7becde4ba4fb7c3ca292e0 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 15:02:21 +0200 Subject: [PATCH 070/148] Added gradio to dependencies --- .../Multiple_Models_Interface_Vis.ipynb | 26 +------------------ ...Multiple_Models_Interface_Vis_Income.ipynb | 26 +------------------ ...iple_Models_Interface_Vis_Law_School.ipynb | 26 +------------------ ...ultiple_Models_Interface_Vis_Pub_Cov.ipynb | 26 +------------------ .../Multiple_Models_Interface_Vis_Ricci.ipynb | 26 +------------------ lib_base_packages.txt | 1 + .../metrics_interactive_visualizer.py | 5 +--- 7 files changed, 7 insertions(+), 129 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index 14fb79b7..c241a2f4 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -256,7 +256,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -267,30 +267,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T21:41:49.927075Z", - "start_time": "2023-09-29T21:41:49.639933Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 78, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index ecd29b0e..3a3ab5d9 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -225,7 +225,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -236,30 +236,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-11T00:26:17.482944Z", - "start_time": "2023-12-11T00:26:17.438287Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 11, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 3b630e94..abcaa7bf 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -225,7 +225,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -236,30 +236,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T17:14:45.583530Z", - "start_time": "2023-12-18T17:14:45.541605Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 11, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb index 6caf5b8b..61298c7a 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb @@ -235,7 +235,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -246,30 +246,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:15:48.092702Z", - "start_time": "2023-12-07T00:15:48.056394Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 29, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb index 8e21b6bc..18b24daa 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb @@ -233,7 +233,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -244,30 +244,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:45:45.264959Z", - "start_time": "2023-10-07T13:45:45.221841Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 48, diff --git a/lib_base_packages.txt b/lib_base_packages.txt index b851e9f7..7cbd2201 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -5,6 +5,7 @@ altair~=4.2.0 scikit-learn~=1.2.0 tqdm~=4.64.1 sklearn-utils +gradio==4.10.0 seaborn~=0.12.1 folktables~=0.0.11 munch~=2.5.0 diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 18fee7da..910226ef 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -92,7 +92,7 @@ def __variable_inputs(self, k): k = int(k) return [gr.Textbox(visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) - def start_web_app(self): + def create_web_app(self): with gr.Blocks(theme=gr.themes.Soft()) as demo: # ==================================== Dataset Statistics ==================================== gr.Markdown( @@ -430,9 +430,6 @@ def start_web_app(self): self.demo = demo self.demo.launch(inline=False, debug=True, show_error=True) - def stop_web_app(self): - self.demo.close() - def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, selected_metric: str, selected_subgroup: str, defined_model_names: list): results[subgroup_metric] = dict() From 933100cb0d23003509335c3f18035c3ffa5c8177 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 15:24:47 +0200 Subject: [PATCH 071/148] wip1 --- ..._Models_Interface_For_Incremental_ML.ipynb | 55 ++--- .../Multiple_Models_Interface_Use_Case.ipynb | 39 ---- .../Multiple_Models_Interface_Vis.ipynb | 26 +-- ...Multiple_Models_Interface_Vis_Income.ipynb | 26 +-- ...iple_Models_Interface_Vis_Law_School.ipynb | 192 +++--------------- ...ultiple_Models_Interface_Vis_Pub_Cov.ipynb | 26 +-- .../Multiple_Models_Interface_Vis_Ricci.ipynb | 26 +-- ...Models_Interface_With_Error_Analysis.ipynb | 76 +++---- ...butes_Metrics_Report_20230205__153918.html | 60 ------ ..._2018_Metrics_Report_20230205__154446.html | 60 ------ ...butes_Metrics_Report_20230205__171832.html | 60 ------ ...butes_Metrics_Report_20230317__122752.html | 60 ------ ...butes_Metrics_Report_20230319__110002.html | 60 ------ ...butes_Metrics_Report_20230319__171815.html | 60 ------ ...butes_Metrics_Report_20230519__210711.html | 60 ------ ...butes_Metrics_Report_20230519__211200.html | 60 ------ ...butes_Metrics_Report_20230811__222632.html | 60 ------ ...butes_Metrics_Report_20230812__223906.html | 60 ------ ...butes_Metrics_Report_20230812__224023.html | 60 ------ ...butes_Metrics_Report_20230812__224310.html | 60 ------ ...redit_Metrics_Report_20230319__161213.html | 60 ------ ...redit_Metrics_Report_20230319__184958.html | 60 ------ ...redit_Metrics_Report_20230405__194722.html | 60 ------ ...redit_Metrics_Report_20230405__195808.html | 60 ------ ..._2018_Metrics_Report_20230205__165240.html | 60 ------ ..._2018_Metrics_Report_20230319__131915.html | 60 ------ ..._2018_Metrics_Report_20230519__211628.html | 60 ------ lib_base_packages.txt | 2 +- .../metrics_interactive_visualizer.py | 5 +- virny/custom_classes/metrics_visualizer.py | 143 ------------- virny/utils/data_viz_utils.py | 1 - 31 files changed, 79 insertions(+), 1678 deletions(-) delete mode 100644 docs/examples/results/benchmark/benchmark_reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230205__153918.html delete mode 100644 docs/examples/results/benchmark/benchmark_reports/Folktables_GA_2018_Metrics_Report_20230205__154446.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230205__171832.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230317__122752.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230319__110002.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230319__171815.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230519__210711.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230519__211200.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230811__222632.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__223906.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__224023.html delete mode 100644 docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__224310.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230319__161213.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230319__184958.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230405__194722.html delete mode 100644 docs/examples/results/reports/Credit_Metrics_Report_20230405__195808.html delete mode 100644 docs/examples/results/reports/Folktables_GA_2018_Metrics_Report_20230205__165240.html delete mode 100644 docs/examples/results/reports/Folktables_GA_2018_Metrics_Report_20230319__131915.html delete mode 100644 docs/examples/results/reports/Folktables_GA_2018_Metrics_Report_20230519__211628.html diff --git a/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb b/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb index 2c056f40..4066db1e 100644 --- a/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb +++ b/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb @@ -147,7 +147,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "d8505edef7184a5a" }, { "cell_type": "markdown", @@ -156,7 +157,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "c84bae470652be94" }, { "cell_type": "markdown", @@ -173,7 +175,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8b41b746e152c76f" }, { "cell_type": "code", @@ -195,7 +198,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "a878d125e8bfaf4d" }, { "cell_type": "code", @@ -207,7 +211,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "53d2fcd40c862014" }, { "cell_type": "markdown", @@ -307,7 +312,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8feb498942cc2a8c" }, { "cell_type": "code", @@ -318,7 +324,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "7915190e0847f1a7" }, { "cell_type": "markdown", @@ -581,7 +588,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "ca3fe31f0515a973" }, { "cell_type": "code", @@ -602,7 +610,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "dfc57f1870ed71d1" }, { "cell_type": "code", @@ -647,34 +656,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "55e6ce42", - "metadata": {}, - "source": [ - "Create an analysis report. It includes correspondent visualizations and details about your result metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "5a3811ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "", - "text/markdown": "App saved to ./docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__223906.html" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer.create_html_report(report_type=ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS,\n", - " report_save_path=os.path.join(ROOT_DIR, \"results\", \"reports\"))" - ] - }, { "cell_type": "code", "execution_count": 59, diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index a8bc35c9..1aa89ec1 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -1096,45 +1096,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "55e6ce42", - "metadata": { - "is_executing": true - }, - "source": [ - "Create an analysis report. It includes correspondent visualizations and details about your result metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5a3811ff", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-21T20:58:36.148703Z", - "start_time": "2023-10-21T20:58:35.395033Z" - } - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'datapane' has no attribute 'Report'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[27], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mvisualizer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_html_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreport_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mReportType\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mMULTIPLE_RUNS_MULTIPLE_MODELS\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43mreport_save_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpath\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43mROOT_DIR\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mresults\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mreports\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m~/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_visualizer.py:480\u001B[0m, in \u001B[0;36mMetricsVisualizer.create_html_report\u001B[0;34m(self, report_type, report_save_path)\u001B[0m\n\u001B[1;32m 475\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m report_type \u001B[38;5;241m==\u001B[39m ReportType\u001B[38;5;241m.\u001B[39mMULTIPLE_RUNS_MULTIPLE_MODELS:\n\u001B[1;32m 476\u001B[0m boxes_and_whiskers_plot \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcreate_boxes_and_whiskers_for_models_multiple_runs(\n\u001B[1;32m 477\u001B[0m metrics_lst\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStd\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mIQR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mJitter\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mLabel_Stability\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAccuracy\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTPR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTNR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFPR\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFNR\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[1;32m 478\u001B[0m )\n\u001B[0;32m--> 480\u001B[0m \u001B[43mdp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mReport\u001B[49m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m# Fairness and Stability Report\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 481\u001B[0m general_desc,\n\u001B[1;32m 482\u001B[0m \n\u001B[1;32m 483\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Model Composed Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 484\u001B[0m composed_metrics_desc,\n\u001B[1;32m 485\u001B[0m dp\u001B[38;5;241m.\u001B[39mDataTable(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodels_composed_metrics_df),\n\u001B[1;32m 486\u001B[0m \n\u001B[1;32m 487\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Boxes and Whiskers Plot Based On Multiple Models Runs\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 488\u001B[0m boxes_and_whiskers_plot_desc,\n\u001B[1;32m 489\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(boxes_and_whiskers_plot),\n\u001B[1;32m 490\u001B[0m \n\u001B[1;32m 491\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Overall Fairness and Stability Model Metrics Comparison\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 492\u001B[0m overall_metrics_desc,\n\u001B[1;32m 493\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(fairness_overall_metrics_bar_chart, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 494\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(variance_overall_metrics_bar_chart, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 495\u001B[0m \n\u001B[1;32m 496\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Fairness and Stability Interactive Bar Chart\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 497\u001B[0m individual_metrics_interactive_bar_chart_desc,\n\u001B[1;32m 498\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(interactive_bar_chart),\n\u001B[1;32m 499\u001B[0m \n\u001B[1;32m 500\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Model Ranks Based On Group Fairness and Stability Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 501\u001B[0m model_ranked_heatmap_desc,\n\u001B[1;32m 502\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 503\u001B[0m \n\u001B[1;32m 504\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m## Total Ranks Sum For Group Fairness and Stability Metrics\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 505\u001B[0m overall_model_ranked_heatmap_desc,\n\u001B[1;32m 506\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(total_model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 507\u001B[0m )\u001B[38;5;241m.\u001B[39msave(path\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(report_save_path, report_filename))\n\u001B[1;32m 508\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 509\u001B[0m dp\u001B[38;5;241m.\u001B[39mReport(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m# Fairness and Stability Report\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 510\u001B[0m general_desc,\n\u001B[1;32m 511\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 531\u001B[0m dp\u001B[38;5;241m.\u001B[39mPlot(total_model_rank_heatmap, responsive\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[1;32m 532\u001B[0m )\u001B[38;5;241m.\u001B[39msave(path\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(report_save_path, report_filename))\n", - "\u001B[0;31mAttributeError\u001B[0m: module 'datapane' has no attribute 'Report'" - ] - } - ], - "source": [ - "visualizer.create_html_report(report_type=ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS,\n", - " report_save_path=os.path.join(ROOT_DIR, \"results\", \"reports\"))" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb index 14fb79b7..c241a2f4 100644 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis.ipynb @@ -256,7 +256,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -267,30 +267,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T21:41:49.927075Z", - "start_time": "2023-09-29T21:41:49.639933Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 78, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb index ecd29b0e..3a3ab5d9 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb @@ -225,7 +225,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -236,30 +236,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-11T00:26:17.482944Z", - "start_time": "2023-12-11T00:26:17.438287Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 11, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb index 90a3a0df..abcaa7bf 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:30.826849Z", - "start_time": "2023-12-18T15:30:30.355864Z" + "end_time": "2023-12-18T17:11:51.087426Z", + "start_time": "2023-12-18T17:11:50.720930Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:30.836146Z", - "start_time": "2023-12-18T15:30:30.826225Z" + "end_time": "2023-12-18T17:11:51.096433Z", + "start_time": "2023-12-18T17:11:51.087934Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:30.848252Z", - "start_time": "2023-12-18T15:30:30.836766Z" + "end_time": "2023-12-18T17:11:51.105608Z", + "start_time": "2023-12-18T17:11:51.096820Z" } }, "outputs": [ @@ -76,8 +76,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.569645Z", - "start_time": "2023-12-18T15:30:30.847803Z" + "end_time": "2023-12-18T17:11:52.701377Z", + "start_time": "2023-12-18T17:11:51.106232Z" } }, "outputs": [], @@ -101,8 +101,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.635886Z", - "start_time": "2023-12-18T15:30:32.573395Z" + "end_time": "2023-12-18T17:11:52.766489Z", + "start_time": "2023-12-18T17:11:52.704609Z" } }, "id": "d3c53c7b72ecbcd0" @@ -120,8 +120,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.664462Z", - "start_time": "2023-12-18T15:30:32.635793Z" + "end_time": "2023-12-18T17:11:52.791981Z", + "start_time": "2023-12-18T17:11:52.767057Z" } }, "id": "2aab7c79ecdee914" @@ -153,8 +153,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.712298Z", - "start_time": "2023-12-18T15:30:32.663822Z" + "end_time": "2023-12-18T17:11:52.842306Z", + "start_time": "2023-12-18T17:11:52.792667Z" } }, "id": "833484748ed512e8" @@ -178,8 +178,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.759812Z", - "start_time": "2023-12-18T15:30:32.712204Z" + "end_time": "2023-12-18T17:11:52.877906Z", + "start_time": "2023-12-18T17:11:52.842425Z" } }, "id": "15ed7d1ba1f22317" @@ -198,8 +198,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T15:30:32.808353Z", - "start_time": "2023-12-18T15:30:32.738229Z" + "end_time": "2023-12-18T17:11:52.959909Z", + "start_time": "2023-12-18T17:11:52.864927Z" } }, "outputs": [], @@ -219,175 +219,31 @@ "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", - "To create a public link, set `share=True` in `launch()`.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 609, in _create_subgroup_model_rank_heatmap\n", - " model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/data_viz_utils.py\", line 267, in create_model_rank_heatmap_visualization\n", - " num_ranks = int(sorted_matrix_by_rank.values.max())\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/numpy/core/_methods.py\", line 41, in _amax\n", - " return umr_maximum(a, axis, None, out, keepdims, initial, where)\n", - "ValueError: zero-size array to reduction operation maximum which has no identity\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 593, in _create_subgroup_model_rank_heatmap\n", - " raise ValueError('Tolerance should be in the [0.001, 0.2] range')\n", - "ValueError: Tolerance should be in the [0.001, 0.2] range\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 593, in _create_subgroup_model_rank_heatmap\n", - " raise ValueError('Tolerance should be in the [0.001, 0.2] range')\n", - "ValueError: Tolerance should be in the [0.001, 0.2] range\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 591, in _create_subgroup_model_rank_heatmap\n", - " tolerance = str_to_float(tolerance, 'Tolerance')\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", - " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", - "ValueError: Tolerance must be a float number with a '.' separator.\n", - "Traceback (most recent call last):\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/routes.py\", line 538, in predict\n", - " output = await route_utils.call_process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/route_utils.py\", line 217, in call_process_api\n", - " output = await app.get_blocks().process_api(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1553, in process_api\n", - " result = await self.call_function(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/blocks.py\", line 1191, in call_function\n", - " prediction = await anyio.to_thread.run_sync(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/to_thread.py\", line 33, in run_sync\n", - " return await get_asynclib().run_sync_in_worker_thread(\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n", - " return await future\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 807, in run\n", - " result = context.run(func, *args)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny_env/lib/python3.9/site-packages/gradio/utils.py\", line 659, in wrapper\n", - " response = f(*args, **kwargs)\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/custom_classes/metrics_interactive_visualizer.py\", line 591, in _create_subgroup_model_rank_heatmap\n", - " tolerance = str_to_float(tolerance, 'Tolerance')\n", - " File \"/Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny/virny/utils/common_helpers.py\", line 86, in str_to_float\n", - " raise ValueError(f\"{var_name} must be a float number with a '.' separator.\")\n", - "ValueError: Tolerance must be a float number with a '.' separator.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "To create a public link, set `share=True` in `launch()`.\n", "Keyboard interruption in main thread... closing server.\n" ] } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-18T16:02:02.572226Z", - "start_time": "2023-12-18T15:30:32.768235Z" + "end_time": "2023-12-18T17:14:45.540473Z", + "start_time": "2023-12-18T17:11:52.892884Z" } }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T16:02:02.620717Z", - "start_time": "2023-12-18T16:02:02.578812Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 11, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-18T16:02:02.623767Z", - "start_time": "2023-12-18T16:02:02.619001Z" + "end_time": "2023-12-18T17:14:45.584046Z", + "start_time": "2023-12-18T17:14:45.581453Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb index 6caf5b8b..61298c7a 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb @@ -235,7 +235,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -246,30 +246,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:15:48.092702Z", - "start_time": "2023-12-07T00:15:48.056394Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 29, diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb index 8e21b6bc..18b24daa 100644 --- a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb +++ b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb @@ -233,7 +233,7 @@ } ], "source": [ - "visualizer.start_web_app()" + "visualizer.create_web_app()" ], "metadata": { "collapsed": false, @@ -244,30 +244,6 @@ }, "id": "678a9dc8d51243f4" }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ], - "source": [ - "visualizer.stop_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:45:45.264959Z", - "start_time": "2023-10-07T13:45:45.221841Z" - } - }, - "id": "277b6d1de837dab7" - }, { "cell_type": "code", "execution_count": 48, diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index 9fe2b8cd..b5bf2600 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -154,7 +154,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "76d98eaabfcfc9c0" }, { "cell_type": "code", @@ -202,7 +203,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "ebec7f4488fd3f25" }, { "cell_type": "markdown", @@ -211,7 +213,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "855fb160c6220866" }, { "cell_type": "markdown", @@ -228,7 +231,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "1137cf9bc7be6964" }, { "cell_type": "code", @@ -250,7 +254,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "efc95fa248b9f135" }, { "cell_type": "code", @@ -262,7 +267,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "f3a59ca9319a774d" }, { "cell_type": "markdown", @@ -362,7 +368,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8ee9e8a8c10245bf" }, { "cell_type": "code", @@ -373,7 +380,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "6dba3327ebe01279" }, { "cell_type": "markdown", @@ -382,7 +390,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "c32119a0992e331c" }, { "cell_type": "code", @@ -406,8 +415,7 @@ "\n", "2023/08/13, 01:41:39: Tuning XGBClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:41:42: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n", - "\n" + "2023/08/13, 01:41:42: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" ] }, { @@ -426,7 +434,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "f9f77d878f6a94f8" }, { "cell_type": "code", @@ -440,7 +449,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "cdd137541e77686d" }, { "cell_type": "markdown", @@ -449,7 +459,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "ca709f1927e425b5" }, { "cell_type": "code", @@ -483,7 +494,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "8c6061673bb72efa" }, { "cell_type": "markdown", @@ -739,7 +751,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "cdd0a858443ac90e" }, { "cell_type": "code", @@ -760,7 +773,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "5efb9f1d613da1c6" }, { "cell_type": "code", @@ -805,34 +819,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "55e6ce42", - "metadata": {}, - "source": [ - "Create an analysis report. It includes correspondent visualizations and details about your result metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "5a3811ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "", - "text/markdown": "App saved to ./docs/examples/results/reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230812__224310.html" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer.create_html_report(report_type=ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS,\n", - " report_save_path=os.path.join(ROOT_DIR, \"results\", \"reports\"))" - ] - }, { "cell_type": "code", "execution_count": 79, diff --git a/docs/examples/results/benchmark/benchmark_reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230205__153918.html b/docs/examples/results/benchmark/benchmark_reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230205__153918.html deleted file mode 100644 index 07e98a72..00000000 --- a/docs/examples/results/benchmark/benchmark_reports/COMPAS_Without_Sensitive_Attributes_Metrics_Report_20230205__153918.html +++ /dev/null @@ -1,60 +0,0 @@ - - - - - - - - - - - - - - - - -
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- - \ No newline at end of file diff --git a/lib_base_packages.txt b/lib_base_packages.txt index 10cbab0f..7cbd2201 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -5,11 +5,11 @@ altair~=4.2.0 scikit-learn~=1.2.0 tqdm~=4.64.1 sklearn-utils +gradio==4.10.0 seaborn~=0.12.1 folktables~=0.0.11 munch~=2.5.0 PyYAML~=6.0 river==0.15.0 -datapane~=0.16.0 requests-toolbelt==1.0.0 colorama~=0.4.6 \ No newline at end of file diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 18fee7da..910226ef 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -92,7 +92,7 @@ def __variable_inputs(self, k): k = int(k) return [gr.Textbox(visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) - def start_web_app(self): + def create_web_app(self): with gr.Blocks(theme=gr.themes.Soft()) as demo: # ==================================== Dataset Statistics ==================================== gr.Markdown( @@ -430,9 +430,6 @@ def start_web_app(self): self.demo = demo self.demo.launch(inline=False, debug=True, show_error=True) - def stop_web_app(self): - self.demo.close() - def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, selected_metric: str, selected_subgroup: str, defined_model_names: list): results[subgroup_metric] = dict() diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 377bcdf9..daed2671 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -2,7 +2,6 @@ import altair as alt import numpy as np import pandas as pd -import datapane as dp import seaborn as sns import matplotlib.pyplot as plt from datetime import datetime, timezone @@ -378,145 +377,3 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): total_model_rank_heatmap = self.create_total_model_rank_heatmap(sorted_matrix_by_rank, num_models) if self.__create_report: return model_rank_heatmap, total_model_rank_heatmap - - def create_html_report(self, report_type: ReportType, report_save_path: str): - """ - Create Fairness and Stability Report depending on report type. - It includes visualizations and helpful details to them. - """ - # Create a directory if it does not exist - if not os.path.exists(report_save_path): - os.makedirs(report_save_path, exist_ok=True) - - self.__create_report = True - - # Create plots - fairness_overall_metrics_bar_chart = self.create_overall_metrics_bar_char( - metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'], - metrics_title="Fairness Metrics" - ) - variance_overall_metrics_bar_chart = self.create_overall_metrics_bar_char( - metrics_names=['Label_Stability'], - reversed_metrics_names=['Std', 'IQR', 'Jitter'], - metrics_title="Stability Metrics" - ) - interactive_bar_chart = self.create_fairness_variance_interactive_bar_chart() - model_rank_heatmap, total_model_rank_heatmap = \ - self.create_model_rank_heatmaps(metrics_lst=self.fairness_metrics_lst + self.variance_metrics_lst, - groups_lst=self.sensitive_attributes_dct.keys()) - - # Set descriptions for the report - general_desc = dp.Text( - f"**Date of creation**: {datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}\n\n\n" - "This report was created based on the following input arguments:\n" - f"* __Dataset name__: {self.dataset_name}\n" - f"* __Model names__: {self.model_names}\n" - f"* __Sensitive attributes__: {list(self.sensitive_attributes_dct.keys())}\n" - ) - composed_metrics_desc = dp.Text( - "Below you can find a dataframe of composed group metrics for all defined models and sensitive attributes.\n" - ) - boxes_and_whiskers_plot_desc = dp.Text( - "The below boxes and whiskers plot is based on _overall_ subgroup error and stability metrics for all defined models and results after all runs.\n" - "This plot can give you the following benefits:\n" - "* You can see combined information on one plot that includes different models, subgroup metrics, and results after multiple runs\n" - "* You can see all quartiles for each model metric based on multiple runs\n" - "* You can compare different models for each metric\n" - "* You can see the stability of each model metric\n" - ) - overall_metrics_desc = dp.Text( - "The below bar chart includes all defined models and all _overall_ subgroup error and stability metrics, which are averaged across multiple runs.\n" - "This plot can give you the following benefits:\n" - "* You can compare all models for each subgroup error or stability metric\n" - "* This comparison also includes reversed metrics, in which values closer to zero are better " - "since straight and reversed metrics in this plot are converted to the same format -- values closer to one are better\n" - ) - individual_metrics_interactive_bar_chart_desc = dp.Text( - "The below interactive bar chart includes all groups, all composed group fairness and stability metrics, " - "and all defined models.\n" - "This plot can give you the following benefits:\n" - "* You can select any pair of group fairness and stability metrics and compare them across all groups and models\n" - "* Since this plot is interactive, it saves a lot of space for other plots. " - "Also, it could be more convenient to compare each group fairness and stability metric using the interactive mode\n" - ) - model_ranked_heatmap_desc = dp.Text( - "The below heatmap includes all group fairness and stability metrics and all defined models.\n" - "On this plot, colors display ranks where 1 is the best model for the metric. " - "These ranks are conditioned on difference or ratio operations used to create these group metrics:\n" - "* If the metric is created based on the difference operation, **closer values to zero** have ranks that are closer to the first rank\n" - "* If the metric is created based on the ratio operation, **closer values to one** have ranks that are closer to the first rank\n\n" - "This plot can give you the following benefits:\n" - "* You can visually compare all models across all group metrics\n" - "* You can visually understand where one model is better or worse than other models\n" - "* You can find the best and worst models for each group metric\n" - ) - overall_model_ranked_heatmap_desc = dp.Text( - "The below heatmap includes all defined models and sums of their fairness and stability ranks.\n" - "On this plot, colors display sums of ranks for one model. If the sum is smaller, the model has better fairness or stability characteristics than other models.\n" - "This plot can give you the following benefits:\n" - "* You can visually compare all models for fairness and stability characteristics\n" - "* You can visually understand where one model is better or worse than other models\n" - "* You can find the best or most balanced model based on fairness or stability metrics\n" - ) - - report_filename = f'{self.dataset_name}_Metrics_Report_{datetime.now(timezone.utc).strftime("%Y%m%d__%H%M%S")}.html' - if report_type == ReportType.MULTIPLE_RUNS_MULTIPLE_MODELS: - boxes_and_whiskers_plot = self.create_boxes_and_whiskers_for_models_multiple_runs( - metrics_lst=['Std', 'IQR', 'Jitter', 'Label_Stability', 'Accuracy', 'TPR', 'TNR', 'FPR', 'FNR'] - ) - - dp.Report("# Fairness and Stability Report", - general_desc, - - "## Model Composed Metrics", - composed_metrics_desc, - dp.DataTable(self.models_composed_metrics_df), - - "## Boxes and Whiskers Plot Based On Multiple Models Runs", - boxes_and_whiskers_plot_desc, - dp.Plot(boxes_and_whiskers_plot), - - "## Overall Fairness and Stability Model Metrics Comparison", - overall_metrics_desc, - dp.Plot(fairness_overall_metrics_bar_chart, responsive=False), - dp.Plot(variance_overall_metrics_bar_chart, responsive=False), - - "## Fairness and Stability Interactive Bar Chart", - individual_metrics_interactive_bar_chart_desc, - dp.Plot(interactive_bar_chart), - - "## Model Ranks Based On Group Fairness and Stability Metrics", - model_ranked_heatmap_desc, - dp.Plot(model_rank_heatmap, responsive=False), - - "## Total Ranks Sum For Group Fairness and Stability Metrics", - overall_model_ranked_heatmap_desc, - dp.Plot(total_model_rank_heatmap, responsive=False), - ).save(path=os.path.join(report_save_path, report_filename)) - else: - dp.Report("# Fairness and Stability Report", - general_desc, - - "## Model Composed Metrics", - composed_metrics_desc, - dp.DataTable(self.models_composed_metrics_df), - - "## Overall Fairness and Stability Model Metrics Comparison", - overall_metrics_desc, - dp.Plot(fairness_overall_metrics_bar_chart, responsive=False), - dp.Plot(variance_overall_metrics_bar_chart, responsive=False), - - "## Fairness and Stability Interactive Bar Chart", - individual_metrics_interactive_bar_chart_desc, - dp.Plot(interactive_bar_chart), - - "## Model Ranks Based On Group Fairness and Stability Metrics", - model_ranked_heatmap_desc, - dp.Plot(model_rank_heatmap, responsive=False), - - "## Total Ranks Sum For Group Fairness and Stability Metrics", - overall_model_ranked_heatmap_desc, - dp.Plot(total_model_rank_heatmap, responsive=False), - ).save(path=os.path.join(report_save_path, report_filename)) - - self.__create_report = False diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 8cae6f28..56962143 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -8,7 +8,6 @@ from altair.utils.schemapi import Undefined from virny.utils.common_helpers import check_substring_in_list -from IPython.display import display def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): From 986438d26eb59b42f78be520ffbf8e39266ca8cf Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 15:26:47 +0200 Subject: [PATCH 072/148] Resolved merge conflicts --- .../Multiple_Models_Interface_Vis.ipynb | 300 ------------------ ...Multiple_Models_Interface_Vis_Income.ipynb | 275 ---------------- ...iple_Models_Interface_Vis_Law_School.ipynb | 274 ---------------- ...ultiple_Models_Interface_Vis_Pub_Cov.ipynb | 284 ----------------- .../Multiple_Models_Interface_Vis_Ricci.ipynb | 282 ---------------- virny/utils/data_viz_utils.py | 1 - 6 files changed, 1416 deletions(-) delete mode 100644 docs/examples/Multiple_Models_Interface_Vis.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Income.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb deleted file mode 100644 index c241a2f4..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ /dev/null @@ -1,300 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:16.932083Z", - "start_time": "2023-09-29T20:56:16.278169Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:16.940086Z", - "start_time": "2023-09-29T20:56:16.931485Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:16.951831Z", - "start_time": "2023-09-29T20:56:16.940588Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.072450Z", - "start_time": "2023-09-29T20:56:22.772584Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from virny.utils.custom_initializers import read_model_metric_dfs, create_config_obj\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer\n", - "from virny.custom_classes.metrics_composer import MetricsComposer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", - "config_yaml_content = \"\"\"\n", - "dataset_name: COMPAS_Without_Sensitive_Attributes\n", - "bootstrap_fraction: 0.8\n", - "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", - "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", - "\"\"\"\n", - "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", - " f.write(config_yaml_content)\n", - "\n", - "config = create_config_obj(config_yaml_path=config_yaml_path)\n", - "model_names = ['DecisionTreeClassifier', 'LogisticRegression', 'RandomForestClassifier', 'XGBClassifier']\n", - "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', 'COMPAS_Without_Sensitive_Attributes_Metrics_20230812__224136')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.095448Z", - "start_time": "2023-09-29T20:56:30.073873Z" - } - }, - "id": "d777610462304f63" - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f94a20dc", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.121865Z", - "start_time": "2023-09-29T20:56:30.094816Z" - } - }, - "outputs": [], - "source": [ - "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=model_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b04d06cf", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.139696Z", - "start_time": "2023-09-29T20:56:30.121071Z" - } - }, - "outputs": [], - "source": [ - "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "be6ace22", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.169575Z", - "start_time": "2023-09-29T20:56:30.138633Z" - } - }, - "outputs": [], - "source": [ - "# Compute composed metrics\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "outputs": [ - { - "data": { - "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Mean 0.524270 0.578645 0.600790 \n1 Std 0.067963 0.073618 0.072201 \n2 IQR 0.090596 0.099782 0.098402 \n3 Aleatoric_Uncertainty 0.834874 0.846689 0.826891 \n4 Overall_Uncertainty 0.859083 0.876581 0.856843 \n5 Statistical_Bias 0.405041 0.395811 0.314809 \n6 Jitter 0.106917 0.132090 0.112864 \n7 Per_Sample_Accuracy 0.691061 0.711090 0.918452 \n8 Label_Stability 0.851667 0.807393 0.836903 \n9 TPR 0.679406 0.613333 1.000000 \n10 TNR 0.738462 0.801471 1.000000 \n11 PPV 0.676533 0.630137 1.000000 \n12 FNR 0.320594 0.386667 0.000000 \n13 FPR 0.261538 0.198529 0.000000 \n14 Accuracy 0.712121 0.734597 1.000000 \n15 F1 0.677966 0.621622 1.000000 \n16 Selection-Rate 0.447917 0.345972 0.296774 \n17 Positive-Rate 1.004246 0.973333 1.000000 \n18 Sample_Size 1056.000000 211.000000 155.000000 \n\n sex_priv_incorrect sex_dis sex_dis_correct sex_dis_incorrect \\\n0 0.517352 0.510692 0.514399 0.501767 \n1 0.077539 0.066551 0.064791 0.070788 \n2 0.103600 0.088303 0.085977 0.093900 \n3 0.901488 0.831924 0.817170 0.867440 \n4 0.931213 0.854713 0.839203 0.892051 \n5 0.620012 0.407346 0.301656 0.661771 \n6 0.185306 0.100631 0.091351 0.122972 \n7 0.137143 0.686059 0.936918 0.082177 \n8 0.725714 0.862722 0.873970 0.835645 \n9 0.000000 0.691919 1.000000 0.000000 \n10 0.000000 0.719376 1.000000 0.000000 \n11 0.000000 0.685000 1.000000 0.000000 \n12 1.000000 0.308081 0.000000 1.000000 \n13 1.000000 0.280624 0.000000 1.000000 \n14 0.000000 0.706509 1.000000 0.000000 \n15 0.000000 0.688442 1.000000 0.000000 \n16 0.482143 0.473373 0.458961 0.508065 \n17 0.931034 1.010101 1.000000 1.032787 \n18 56.000000 845.000000 597.000000 248.000000 \n\n race_priv race_priv_correct ... race_dis_correct race_dis_incorrect \\\n0 0.597526 0.618185 ... 0.473863 0.484344 \n1 0.069162 0.066865 ... 0.065947 0.070060 \n2 0.093184 0.089451 ... 0.087919 0.091258 \n3 0.821672 0.807043 ... 0.827404 0.880296 \n4 0.847778 0.832001 ... 0.850193 0.903737 \n5 0.393484 0.296788 ... 0.309510 0.650314 \n6 0.107225 0.097218 ... 0.094812 0.134214 \n7 0.708261 0.930526 ... 0.934866 0.091340 \n8 0.848213 0.861316 ... 0.869732 0.817320 \n9 0.585034 1.000000 ... 1.000000 0.000000 \n10 0.816479 1.000000 ... 1.000000 0.000000 \n11 0.637037 1.000000 ... 1.000000 0.000000 \n12 0.414966 0.000000 ... 0.000000 1.000000 \n13 0.183521 0.000000 ... 0.000000 1.000000 \n14 0.734300 1.000000 ... 1.000000 0.000000 \n15 0.609929 1.000000 ... 1.000000 0.000000 \n16 0.326087 0.282895 ... 0.522321 0.536082 \n17 0.918367 1.000000 ... 1.000000 1.155556 \n18 414.000000 304.000000 ... 448.000000 194.000000 \n\n sex&race_priv sex&race_priv_correct sex&race_priv_incorrect \\\n0 0.586391 0.607290 0.529874 \n1 0.068718 0.066018 0.076019 \n2 0.092020 0.088338 0.101975 \n3 0.832383 0.817398 0.872906 \n4 0.857995 0.841790 0.901818 \n5 0.396398 0.302520 0.650263 \n6 0.108871 0.095304 0.145559 \n7 0.708783 0.933073 0.102254 \n8 0.847224 0.866354 0.795493 \n9 0.595745 1.000000 0.000000 \n10 0.804734 1.000000 0.000000 \n11 0.629213 1.000000 0.000000 \n12 0.404255 0.000000 1.000000 \n13 0.195266 0.000000 1.000000 \n14 0.730038 1.000000 0.000000 \n15 0.612022 1.000000 0.000000 \n16 0.338403 0.291667 0.464789 \n17 0.946809 1.000000 0.868421 \n18 526.000000 384.000000 142.000000 \n\n sex&race_dis sex&race_dis_correct sex&race_dis_incorrect \\\n0 0.462617 0.453857 0.482517 \n1 0.067213 0.066631 0.068536 \n2 0.089184 0.088747 0.090175 \n3 0.837346 0.821026 0.874418 \n4 0.860162 0.843933 0.897027 \n5 0.413620 0.306294 0.657422 \n6 0.104978 0.096287 0.124722 \n7 0.673472 0.933152 0.083580 \n8 0.856075 0.866304 0.832840 \n9 0.734982 1.000000 0.000000 \n10 0.647773 1.000000 0.000000 \n11 0.705085 1.000000 0.000000 \n12 0.265018 0.000000 1.000000 \n13 0.352227 0.000000 1.000000 \n14 0.694340 1.000000 0.000000 \n15 0.719723 1.000000 0.000000 \n16 0.556604 0.565217 0.537037 \n17 1.042403 1.000000 1.160000 \n18 530.000000 368.000000 162.000000 \n\n Model_Name Model_Params \n0 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n1 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n2 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n3 RandomForestClassifier 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RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n17 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n18 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n\n[19 rows x 22 columns]", - "text/html": "
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Metricoverallsex_privsex_priv_correctsex_priv_incorrectsex_dissex_dis_correctsex_dis_incorrectrace_privrace_priv_correct...race_dis_correctrace_dis_incorrectsex&race_privsex&race_priv_correctsex&race_priv_incorrectsex&race_dissex&race_dis_correctsex&race_dis_incorrectModel_NameModel_Params
0Mean0.5242700.5786450.6007900.5173520.5106920.5143990.5017670.5975260.618185...0.4738630.4843440.5863910.6072900.5298740.4626170.4538570.482517RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
1Std0.0679630.0736180.0722010.0775390.0665510.0647910.0707880.0691620.066865...0.0659470.0700600.0687180.0660180.0760190.0672130.0666310.068536RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
2IQR0.0905960.0997820.0984020.1036000.0883030.0859770.0939000.0931840.089451...0.0879190.0912580.0920200.0883380.1019750.0891840.0887470.090175RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
3Aleatoric_Uncertainty0.8348740.8466890.8268910.9014880.8319240.8171700.8674400.8216720.807043...0.8274040.8802960.8323830.8173980.8729060.8373460.8210260.874418RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
4Overall_Uncertainty0.8590830.8765810.8568430.9312130.8547130.8392030.8920510.8477780.832001...0.8501930.9037370.8579950.8417900.9018180.8601620.8439330.897027RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
5Statistical_Bias0.4050410.3958110.3148090.6200120.4073460.3016560.6617710.3934840.296788...0.3095100.6503140.3963980.3025200.6502630.4136200.3062940.657422RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
6Jitter0.1069170.1320900.1128640.1853060.1006310.0913510.1229720.1072250.097218...0.0948120.1342140.1088710.0953040.1455590.1049780.0962870.124722RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
7Per_Sample_Accuracy0.6910610.7110900.9184520.1371430.6860590.9369180.0821770.7082610.930526...0.9348660.0913400.7087830.9330730.1022540.6734720.9331520.083580RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
8Label_Stability0.8516670.8073930.8369030.7257140.8627220.8739700.8356450.8482130.861316...0.8697320.8173200.8472240.8663540.7954930.8560750.8663040.832840RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
9TPR0.6794060.6133331.0000000.0000000.6919191.0000000.0000000.5850341.000000...1.0000000.0000000.5957451.0000000.0000000.7349821.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
10TNR0.7384620.8014711.0000000.0000000.7193761.0000000.0000000.8164791.000000...1.0000000.0000000.8047341.0000000.0000000.6477731.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
11PPV0.6765330.6301371.0000000.0000000.6850001.0000000.0000000.6370371.000000...1.0000000.0000000.6292131.0000000.0000000.7050851.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
12FNR0.3205940.3866670.0000001.0000000.3080810.0000001.0000000.4149660.000000...0.0000001.0000000.4042550.0000001.0000000.2650180.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
13FPR0.2615380.1985290.0000001.0000000.2806240.0000001.0000000.1835210.000000...0.0000001.0000000.1952660.0000001.0000000.3522270.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
14Accuracy0.7121210.7345971.0000000.0000000.7065091.0000000.0000000.7343001.000000...1.0000000.0000000.7300381.0000000.0000000.6943401.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
15F10.6779660.6216221.0000000.0000000.6884421.0000000.0000000.6099291.000000...1.0000000.0000000.6120221.0000000.0000000.7197231.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
16Selection-Rate0.4479170.3459720.2967740.4821430.4733730.4589610.5080650.3260870.282895...0.5223210.5360820.3384030.2916670.4647890.5566040.5652170.537037RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
17Positive-Rate1.0042460.9733331.0000000.9310341.0101011.0000001.0327870.9183671.000000...1.0000001.1555560.9468091.0000000.8684211.0424031.0000001.160000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
18Sample_Size1056.000000211.000000155.00000056.000000845.000000597.000000248.000000414.000000304.000000...448.000000194.000000526.000000384.000000142.000000530.000000368.000000162.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
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" - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct['RandomForestClassifier'].head(100)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-01T20:57:20.233976Z", - "start_time": "2023-10-01T20:57:20.133369Z" - } - }, - "id": "54a73b4d053334b4" - }, - { - "cell_type": "code", - "execution_count": 135, - "outputs": [ - { - "data": { - "text/plain": " Metric sex race sex&race \\\n0 Equalized_Odds_TPR 0.211919 0.195326 0.183576 \n1 Equalized_Odds_FPR 0.098356 0.104728 0.141078 \n2 Equalized_Odds_FNR -0.211919 -0.195326 -0.183576 \n3 Disparate_Impact 1.234115 1.135965 1.125105 \n4 Statistical_Parity_Difference 0.193535 0.123016 0.115123 \n5 Accuracy_Parity 0.009832 0.006840 -0.010984 \n6 Label_Stability_Ratio 1.024740 0.997454 0.995869 \n7 IQR_Parity 0.000768 -0.004804 -0.003282 \n8 Std_Parity -0.005106 -0.000927 -0.001976 \n9 Std_Ratio 0.931699 0.986984 0.972422 \n10 Jitter_Parity -0.013818 0.007192 0.005364 \n11 Equalized_Odds_TPR 0.166465 0.258440 0.226205 \n12 Equalized_Odds_FPR 0.096129 0.156703 0.186079 \n13 Equalized_Odds_FNR -0.166465 -0.258440 -0.226205 \n14 Disparate_Impact 1.176075 1.341036 1.263916 \n15 Statistical_Parity_Difference 0.145556 0.262157 0.216187 \n16 Accuracy_Parity -0.010286 -0.003747 -0.024119 \n17 Label_Stability_Ratio 1.021988 0.988991 1.003152 \n18 IQR_Parity 0.001712 0.001225 0.001058 \n19 Std_Parity 0.000822 0.000278 0.000170 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 LogisticRegression \n12 LogisticRegression \n13 LogisticRegression \n14 LogisticRegression \n15 LogisticRegression \n16 LogisticRegression \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression ", - "text/html": "
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Metricsexracesex&raceModel_Name
0Equalized_Odds_TPR0.2119190.1953260.183576DecisionTreeClassifier
1Equalized_Odds_FPR0.0983560.1047280.141078DecisionTreeClassifier
2Equalized_Odds_FNR-0.211919-0.195326-0.183576DecisionTreeClassifier
3Disparate_Impact1.2341151.1359651.125105DecisionTreeClassifier
4Statistical_Parity_Difference0.1935350.1230160.115123DecisionTreeClassifier
5Accuracy_Parity0.0098320.006840-0.010984DecisionTreeClassifier
6Label_Stability_Ratio1.0247400.9974540.995869DecisionTreeClassifier
7IQR_Parity0.000768-0.004804-0.003282DecisionTreeClassifier
8Std_Parity-0.005106-0.000927-0.001976DecisionTreeClassifier
9Std_Ratio0.9316990.9869840.972422DecisionTreeClassifier
10Jitter_Parity-0.0138180.0071920.005364DecisionTreeClassifier
11Equalized_Odds_TPR0.1664650.2584400.226205LogisticRegression
12Equalized_Odds_FPR0.0961290.1567030.186079LogisticRegression
13Equalized_Odds_FNR-0.166465-0.258440-0.226205LogisticRegression
14Disparate_Impact1.1760751.3410361.263916LogisticRegression
15Statistical_Parity_Difference0.1455560.2621570.216187LogisticRegression
16Accuracy_Parity-0.010286-0.003747-0.024119LogisticRegression
17Label_Stability_Ratio1.0219880.9889911.003152LogisticRegression
18IQR_Parity0.0017120.0012250.001058LogisticRegression
19Std_Parity0.0008220.0002780.000170LogisticRegression
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" - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_composed_metrics_df.head(20)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-01T11:29:37.410638Z", - "start_time": "2023-10-01T11:29:37.382980Z" - } - }, - "id": "5798eb95fbeaea54" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-02T17:55:10.703782Z", - "start_time": "2023-10-02T17:55:06.041613Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=config.sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T17:55:47.535767Z", - "start_time": "2023-10-02T17:55:10.703964Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "2326c129", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb deleted file mode 100644 index 3a3ab5d9..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ /dev/null @@ -1,275 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:44.370856Z", - "start_time": "2023-12-10T22:37:43.972175Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:44.380242Z", - "start_time": "2023-12-10T22:37:44.371542Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:44.391659Z", - "start_time": "2023-12-10T22:37:44.380644Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:45.918385Z", - "start_time": "2023-12-10T22:37:44.390547Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.datasets import ACSIncomeDataset\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n", - "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.214487Z", - "start_time": "2023-12-10T22:37:45.921391Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_subgroup_metrics.csv'), header=0)\n", - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.242581Z", - "start_time": "2023-12-10T22:37:47.214727Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.047756 0.074977 0.065217 \n1 Aleatoric_Uncertainty_Parity -0.039005 -0.011947 -0.009222 \n2 Aleatoric_Uncertainty_Ratio 0.935159 0.979638 0.984220 \n3 Equalized_Odds_FNR 0.030793 -0.110745 -0.052498 \n4 Equalized_Odds_FPR -0.021317 0.000952 -0.007008 \n\n Model_Name \n0 LGBMClassifier__alpha=0.7 \n1 LGBMClassifier__alpha=0.7 \n2 LGBMClassifier__alpha=0.7 \n3 LGBMClassifier__alpha=0.7 \n4 LGBMClassifier__alpha=0.7 ", - 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MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0477560.0749770.065217LGBMClassifier__alpha=0.7
1Aleatoric_Uncertainty_Parity-0.039005-0.011947-0.009222LGBMClassifier__alpha=0.7
2Aleatoric_Uncertainty_Ratio0.9351590.9796380.984220LGBMClassifier__alpha=0.7
3Equalized_Odds_FNR0.030793-0.110745-0.052498LGBMClassifier__alpha=0.7
4Equalized_Odds_FPR-0.0213170.000952-0.007008LGBMClassifier__alpha=0.7
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" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_names = subgroup_metrics_df['Model_Name'].unique()\n", - "models_metrics_dct = dict()\n", - "for model_name in model_names:\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", - "\n", - "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()\n", - "models_composed_metrics_df.head()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.297089Z", - "start_time": "2023-12-10T22:37:47.240439Z" - } - }, - "id": "44ee5eff6054ce04" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.328697Z", - "start_time": "2023-12-10T22:37:47.295950Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.374721Z", - "start_time": "2023-12-10T22:37:47.317882Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", - " models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-11T00:26:17.429094Z", - "start_time": "2023-12-10T22:37:47.343749Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-11T00:26:17.483195Z", - "start_time": "2023-12-11T00:26:17.479725Z" - } - }, - "id": "21c0ad91536f0af5" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb deleted file mode 100644 index ea5db318..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T21:27:13.678820Z", - "start_time": "2023-12-18T21:27:13.369461Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T21:27:13.687293Z", - "start_time": "2023-12-18T21:27:13.679001Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T21:27:13.698600Z", - "start_time": "2023-12-18T21:27:13.687813Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T21:27:15.048016Z", - "start_time": "2023-12-18T21:27:13.697484Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.datasets import LawSchoolDataset\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "data_loader = LawSchoolDataset()\n", - "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T21:27:15.106638Z", - "start_time": "2023-12-18T21:27:15.051611Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_subgroup_metrics.csv'), header=0)\n", - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T21:27:15.133650Z", - "start_time": "2023-12-18T21:27:15.106939Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.024413 -0.158856 -0.162998 \n1 Aleatoric_Uncertainty_Parity -0.016769 0.317464 0.274695 \n2 Aleatoric_Uncertainty_Ratio 0.951019 2.126816 1.880052 \n3 Equalized_Odds_FNR 0.006853 0.089260 0.092334 \n4 Equalized_Odds_FPR 0.027311 -0.289259 -0.156572 \n\n Model_Name \n0 LGBMClassifier__alpha=0.6 \n1 LGBMClassifier__alpha=0.6 \n2 LGBMClassifier__alpha=0.6 \n3 LGBMClassifier__alpha=0.6 \n4 LGBMClassifier__alpha=0.6 ", - "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Parity-0.024413-0.158856-0.162998LGBMClassifier__alpha=0.6
1Aleatoric_Uncertainty_Parity-0.0167690.3174640.274695LGBMClassifier__alpha=0.6
2Aleatoric_Uncertainty_Ratio0.9510192.1268161.880052LGBMClassifier__alpha=0.6
3Equalized_Odds_FNR0.0068530.0892600.092334LGBMClassifier__alpha=0.6
4Equalized_Odds_FPR0.027311-0.289259-0.156572LGBMClassifier__alpha=0.6
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" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_names = subgroup_metrics_df['Model_Name'].unique()\n", - "models_metrics_dct = dict()\n", - "for model_name in model_names:\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", - "\n", - "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()\n", - "models_composed_metrics_df.head()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T21:27:15.178725Z", - "start_time": "2023-12-18T21:27:15.134576Z" - } - }, - "id": "833484748ed512e8" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T21:27:15.201295Z", - "start_time": "2023-12-18T21:27:15.179038Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T21:27:15.252561Z", - "start_time": "2023-12-18T21:27:15.200692Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", - " models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T23:50:34.705984Z", - "start_time": "2023-12-18T21:27:15.229300Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2326c129", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T23:50:34.805260Z", - "start_time": "2023-12-18T23:50:34.803259Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb deleted file mode 100644 index 61298c7a..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 19, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:42.978064Z", - "start_time": "2023-12-07T00:13:42.914700Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:42.983725Z", - "start_time": "2023-12-07T00:13:42.954698Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:43.018895Z", - "start_time": "2023-12-07T00:13:42.982387Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:43.027909Z", - "start_time": "2023-12-07T00:13:43.006390Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.datasets import ACSPublicCoverageDataset\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "data_loader = ACSPublicCoverageDataset(state=['CA'], year=2018, with_nulls=False,\n", - " subsample_size=15_000, subsample_seed=42)\n", - "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.771709Z", - "start_time": "2023-12-07T00:13:43.029632Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'pub_cov_subgroup_metrics.csv'), header=0)\n", - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.805639Z", - "start_time": "2023-12-07T00:13:48.768740Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "data": { - "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.026847 0.016299 0.040212 \n1 Aleatoric_Uncertainty_Parity -0.013240 0.027276 0.007235 \n2 Aleatoric_Uncertainty_Ratio 0.983584 1.034689 1.009077 \n3 Equalized_Odds_FNR 0.004275 -0.000359 -0.008617 \n4 Equalized_Odds_FPR -0.012072 -0.024172 -0.040481 \n\n Model_Name \n0 LGBMClassifier__alpha=0.6 \n1 LGBMClassifier__alpha=0.6 \n2 LGBMClassifier__alpha=0.6 \n3 LGBMClassifier__alpha=0.6 \n4 LGBMClassifier__alpha=0.6 ", - "text/html": "
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MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0268470.0162990.040212LGBMClassifier__alpha=0.6
1Aleatoric_Uncertainty_Parity-0.0132400.0272760.007235LGBMClassifier__alpha=0.6
2Aleatoric_Uncertainty_Ratio0.9835841.0346891.009077LGBMClassifier__alpha=0.6
3Equalized_Odds_FNR0.004275-0.000359-0.008617LGBMClassifier__alpha=0.6
4Equalized_Odds_FPR-0.012072-0.024172-0.040481LGBMClassifier__alpha=0.6
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" - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_names = subgroup_metrics_df['Model_Name'].unique()\n", - "models_metrics_dct = dict()\n", - "for model_name in model_names:\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", - "\n", - "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()\n", - "models_composed_metrics_df.head()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.849022Z", - "start_time": "2023-12-07T00:13:48.802693Z" - } - }, - "id": "833484748ed512e8" - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.6', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.6'])" - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.873723Z", - "start_time": "2023-12-07T00:13:48.848261Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.959344Z", - "start_time": "2023-12-07T00:13:48.871083Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", - " models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:15:48.056146Z", - "start_time": "2023-12-07T00:13:48.898642Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2326c129", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:15:48.095103Z", - "start_time": "2023-12-07T00:15:48.092153Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb deleted file mode 100644 index 18b24daa..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 37, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.642940Z", - "start_time": "2023-10-07T13:42:22.508015Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.677119Z", - "start_time": "2023-10-07T13:42:22.641937Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.689334Z", - "start_time": "2023-10-07T13:42:22.664188Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.711038Z", - "start_time": "2023-10-07T13:42:22.687552Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [], - "source": [ - "sensitive_attributes_dct = {'Race': 'Non-White'}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.732136Z", - "start_time": "2023-10-07T13:42:22.711244Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_subgroup_metrics.csv'), header=0)\n", - "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_group_metrics.csv'), header=0)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.759203Z", - "start_time": "2023-10-07T13:42:22.732607Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [], - "source": [ - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))\n", - "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", - " + models_composed_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.784062Z", - "start_time": "2023-10-07T13:42:22.759791Z" - } - }, - "id": "2d922003e752a4b4" - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [], - "source": [ - "models_metrics_dct = dict()\n", - "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.809161Z", - "start_time": "2023-10-07T13:42:22.782462Z" - } - }, - "id": "833484748ed512e8" - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LGBMClassifier__alpha=0.7', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.4', 'LogisticRegression__alpha=0.7', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7'])" - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.831140Z", - "start_time": "2023-10-07T13:42:22.806994Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.859150Z", - "start_time": "2023-10-07T13:42:22.830292Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:45:45.222662Z", - "start_time": "2023-10-07T13:42:22.859325Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "2326c129", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:45:45.265758Z", - "start_time": "2023-10-07T13:45:45.264074Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 8cae6f28..56962143 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -8,7 +8,6 @@ from altair.utils.schemapi import Undefined from virny.utils.common_helpers import check_substring_in_list -from IPython.display import display def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): From 68795324b068921526510677410f6767df58376f Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 15:29:33 +0200 Subject: [PATCH 073/148] Resolved merge conflicts --- .../Multiple_Models_Interface_Vis.ipynb | 300 ------------------ ...Multiple_Models_Interface_Vis_Income.ipynb | 275 ---------------- ...iple_Models_Interface_Vis_Law_School.ipynb | 274 ---------------- ...ultiple_Models_Interface_Vis_Pub_Cov.ipynb | 284 ----------------- .../Multiple_Models_Interface_Vis_Ricci.ipynb | 282 ---------------- 5 files changed, 1415 deletions(-) delete mode 100644 docs/examples/Multiple_Models_Interface_Vis.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Income.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb delete mode 100644 docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb diff --git a/docs/examples/Multiple_Models_Interface_Vis.ipynb b/docs/examples/Multiple_Models_Interface_Vis.ipynb deleted file mode 100644 index c241a2f4..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis.ipynb +++ /dev/null @@ -1,300 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:16.932083Z", - "start_time": "2023-09-29T20:56:16.278169Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:16.940086Z", - "start_time": "2023-09-29T20:56:16.931485Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:16.951831Z", - "start_time": "2023-09-29T20:56:16.940588Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.072450Z", - "start_time": "2023-09-29T20:56:22.772584Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from virny.utils.custom_initializers import read_model_metric_dfs, create_config_obj\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer\n", - "from virny.custom_classes.metrics_composer import MetricsComposer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", - "config_yaml_content = \"\"\"\n", - "dataset_name: COMPAS_Without_Sensitive_Attributes\n", - "bootstrap_fraction: 0.8\n", - "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", - "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", - "\"\"\"\n", - "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", - " f.write(config_yaml_content)\n", - "\n", - "config = create_config_obj(config_yaml_path=config_yaml_path)\n", - "model_names = ['DecisionTreeClassifier', 'LogisticRegression', 'RandomForestClassifier', 'XGBClassifier']\n", - "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', 'COMPAS_Without_Sensitive_Attributes_Metrics_20230812__224136')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.095448Z", - "start_time": "2023-09-29T20:56:30.073873Z" - } - }, - "id": "d777610462304f63" - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f94a20dc", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.121865Z", - "start_time": "2023-09-29T20:56:30.094816Z" - } - }, - "outputs": [], - "source": [ - "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=model_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b04d06cf", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.139696Z", - "start_time": "2023-09-29T20:56:30.121071Z" - } - }, - "outputs": [], - "source": [ - "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "be6ace22", - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-29T20:56:30.169575Z", - "start_time": "2023-09-29T20:56:30.138633Z" - } - }, - "outputs": [], - "source": [ - "# Compute composed metrics\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "outputs": [ - { - "data": { - "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Mean 0.524270 0.578645 0.600790 \n1 Std 0.067963 0.073618 0.072201 \n2 IQR 0.090596 0.099782 0.098402 \n3 Aleatoric_Uncertainty 0.834874 0.846689 0.826891 \n4 Overall_Uncertainty 0.859083 0.876581 0.856843 \n5 Statistical_Bias 0.405041 0.395811 0.314809 \n6 Jitter 0.106917 0.132090 0.112864 \n7 Per_Sample_Accuracy 0.691061 0.711090 0.918452 \n8 Label_Stability 0.851667 0.807393 0.836903 \n9 TPR 0.679406 0.613333 1.000000 \n10 TNR 0.738462 0.801471 1.000000 \n11 PPV 0.676533 0.630137 1.000000 \n12 FNR 0.320594 0.386667 0.000000 \n13 FPR 0.261538 0.198529 0.000000 \n14 Accuracy 0.712121 0.734597 1.000000 \n15 F1 0.677966 0.621622 1.000000 \n16 Selection-Rate 0.447917 0.345972 0.296774 \n17 Positive-Rate 1.004246 0.973333 1.000000 \n18 Sample_Size 1056.000000 211.000000 155.000000 \n\n sex_priv_incorrect sex_dis sex_dis_correct sex_dis_incorrect \\\n0 0.517352 0.510692 0.514399 0.501767 \n1 0.077539 0.066551 0.064791 0.070788 \n2 0.103600 0.088303 0.085977 0.093900 \n3 0.901488 0.831924 0.817170 0.867440 \n4 0.931213 0.854713 0.839203 0.892051 \n5 0.620012 0.407346 0.301656 0.661771 \n6 0.185306 0.100631 0.091351 0.122972 \n7 0.137143 0.686059 0.936918 0.082177 \n8 0.725714 0.862722 0.873970 0.835645 \n9 0.000000 0.691919 1.000000 0.000000 \n10 0.000000 0.719376 1.000000 0.000000 \n11 0.000000 0.685000 1.000000 0.000000 \n12 1.000000 0.308081 0.000000 1.000000 \n13 1.000000 0.280624 0.000000 1.000000 \n14 0.000000 0.706509 1.000000 0.000000 \n15 0.000000 0.688442 1.000000 0.000000 \n16 0.482143 0.473373 0.458961 0.508065 \n17 0.931034 1.010101 1.000000 1.032787 \n18 56.000000 845.000000 597.000000 248.000000 \n\n race_priv race_priv_correct ... race_dis_correct race_dis_incorrect \\\n0 0.597526 0.618185 ... 0.473863 0.484344 \n1 0.069162 0.066865 ... 0.065947 0.070060 \n2 0.093184 0.089451 ... 0.087919 0.091258 \n3 0.821672 0.807043 ... 0.827404 0.880296 \n4 0.847778 0.832001 ... 0.850193 0.903737 \n5 0.393484 0.296788 ... 0.309510 0.650314 \n6 0.107225 0.097218 ... 0.094812 0.134214 \n7 0.708261 0.930526 ... 0.934866 0.091340 \n8 0.848213 0.861316 ... 0.869732 0.817320 \n9 0.585034 1.000000 ... 1.000000 0.000000 \n10 0.816479 1.000000 ... 1.000000 0.000000 \n11 0.637037 1.000000 ... 1.000000 0.000000 \n12 0.414966 0.000000 ... 0.000000 1.000000 \n13 0.183521 0.000000 ... 0.000000 1.000000 \n14 0.734300 1.000000 ... 1.000000 0.000000 \n15 0.609929 1.000000 ... 1.000000 0.000000 \n16 0.326087 0.282895 ... 0.522321 0.536082 \n17 0.918367 1.000000 ... 1.000000 1.155556 \n18 414.000000 304.000000 ... 448.000000 194.000000 \n\n sex&race_priv sex&race_priv_correct sex&race_priv_incorrect \\\n0 0.586391 0.607290 0.529874 \n1 0.068718 0.066018 0.076019 \n2 0.092020 0.088338 0.101975 \n3 0.832383 0.817398 0.872906 \n4 0.857995 0.841790 0.901818 \n5 0.396398 0.302520 0.650263 \n6 0.108871 0.095304 0.145559 \n7 0.708783 0.933073 0.102254 \n8 0.847224 0.866354 0.795493 \n9 0.595745 1.000000 0.000000 \n10 0.804734 1.000000 0.000000 \n11 0.629213 1.000000 0.000000 \n12 0.404255 0.000000 1.000000 \n13 0.195266 0.000000 1.000000 \n14 0.730038 1.000000 0.000000 \n15 0.612022 1.000000 0.000000 \n16 0.338403 0.291667 0.464789 \n17 0.946809 1.000000 0.868421 \n18 526.000000 384.000000 142.000000 \n\n sex&race_dis sex&race_dis_correct sex&race_dis_incorrect \\\n0 0.462617 0.453857 0.482517 \n1 0.067213 0.066631 0.068536 \n2 0.089184 0.088747 0.090175 \n3 0.837346 0.821026 0.874418 \n4 0.860162 0.843933 0.897027 \n5 0.413620 0.306294 0.657422 \n6 0.104978 0.096287 0.124722 \n7 0.673472 0.933152 0.083580 \n8 0.856075 0.866304 0.832840 \n9 0.734982 1.000000 0.000000 \n10 0.647773 1.000000 0.000000 \n11 0.705085 1.000000 0.000000 \n12 0.265018 0.000000 1.000000 \n13 0.352227 0.000000 1.000000 \n14 0.694340 1.000000 0.000000 \n15 0.719723 1.000000 0.000000 \n16 0.556604 0.565217 0.537037 \n17 1.042403 1.000000 1.160000 \n18 530.000000 368.000000 162.000000 \n\n Model_Name Model_Params \n0 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n1 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n2 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n3 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n4 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n5 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n6 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n7 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n8 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n9 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... \n10 RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 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Metricoverallsex_privsex_priv_correctsex_priv_incorrectsex_dissex_dis_correctsex_dis_incorrectrace_privrace_priv_correct...race_dis_correctrace_dis_incorrectsex&race_privsex&race_priv_correctsex&race_priv_incorrectsex&race_dissex&race_dis_correctsex&race_dis_incorrectModel_NameModel_Params
0Mean0.5242700.5786450.6007900.5173520.5106920.5143990.5017670.5975260.618185...0.4738630.4843440.5863910.6072900.5298740.4626170.4538570.482517RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
1Std0.0679630.0736180.0722010.0775390.0665510.0647910.0707880.0691620.066865...0.0659470.0700600.0687180.0660180.0760190.0672130.0666310.068536RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
2IQR0.0905960.0997820.0984020.1036000.0883030.0859770.0939000.0931840.089451...0.0879190.0912580.0920200.0883380.1019750.0891840.0887470.090175RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
3Aleatoric_Uncertainty0.8348740.8466890.8268910.9014880.8319240.8171700.8674400.8216720.807043...0.8274040.8802960.8323830.8173980.8729060.8373460.8210260.874418RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
4Overall_Uncertainty0.8590830.8765810.8568430.9312130.8547130.8392030.8920510.8477780.832001...0.8501930.9037370.8579950.8417900.9018180.8601620.8439330.897027RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
5Statistical_Bias0.4050410.3958110.3148090.6200120.4073460.3016560.6617710.3934840.296788...0.3095100.6503140.3963980.3025200.6502630.4136200.3062940.657422RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
6Jitter0.1069170.1320900.1128640.1853060.1006310.0913510.1229720.1072250.097218...0.0948120.1342140.1088710.0953040.1455590.1049780.0962870.124722RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
7Per_Sample_Accuracy0.6910610.7110900.9184520.1371430.6860590.9369180.0821770.7082610.930526...0.9348660.0913400.7087830.9330730.1022540.6734720.9331520.083580RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
8Label_Stability0.8516670.8073930.8369030.7257140.8627220.8739700.8356450.8482130.861316...0.8697320.8173200.8472240.8663540.7954930.8560750.8663040.832840RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
9TPR0.6794060.6133331.0000000.0000000.6919191.0000000.0000000.5850341.000000...1.0000000.0000000.5957451.0000000.0000000.7349821.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
10TNR0.7384620.8014711.0000000.0000000.7193761.0000000.0000000.8164791.000000...1.0000000.0000000.8047341.0000000.0000000.6477731.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
11PPV0.6765330.6301371.0000000.0000000.6850001.0000000.0000000.6370371.000000...1.0000000.0000000.6292131.0000000.0000000.7050851.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
12FNR0.3205940.3866670.0000001.0000000.3080810.0000001.0000000.4149660.000000...0.0000001.0000000.4042550.0000001.0000000.2650180.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
13FPR0.2615380.1985290.0000001.0000000.2806240.0000001.0000000.1835210.000000...0.0000001.0000000.1952660.0000001.0000000.3522270.0000001.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
14Accuracy0.7121210.7345971.0000000.0000000.7065091.0000000.0000000.7343001.000000...1.0000000.0000000.7300381.0000000.0000000.6943401.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
15F10.6779660.6216221.0000000.0000000.6884421.0000000.0000000.6099291.000000...1.0000000.0000000.6120221.0000000.0000000.7197231.0000000.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
16Selection-Rate0.4479170.3459720.2967740.4821430.4733730.4589610.5080650.3260870.282895...0.5223210.5360820.3384030.2916670.4647890.5566040.5652170.537037RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
17Positive-Rate1.0042460.9733331.0000000.9310341.0101011.0000001.0327870.9183671.000000...1.0000001.1555560.9468091.0000000.8684211.0424031.0000001.160000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
18Sample_Size1056.000000211.000000155.00000056.000000845.000000597.000000248.000000414.000000304.000000...448.000000194.000000526.000000384.000000142.000000530.000000368.000000162.000000RandomForestClassifier{'bootstrap': True, 'ccp_alpha': 0.0, 'class_w...
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" - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct['RandomForestClassifier'].head(100)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-01T20:57:20.233976Z", - "start_time": "2023-10-01T20:57:20.133369Z" - } - }, - "id": "54a73b4d053334b4" - }, - { - "cell_type": "code", - "execution_count": 135, - "outputs": [ - { - "data": { - "text/plain": " Metric sex race sex&race \\\n0 Equalized_Odds_TPR 0.211919 0.195326 0.183576 \n1 Equalized_Odds_FPR 0.098356 0.104728 0.141078 \n2 Equalized_Odds_FNR -0.211919 -0.195326 -0.183576 \n3 Disparate_Impact 1.234115 1.135965 1.125105 \n4 Statistical_Parity_Difference 0.193535 0.123016 0.115123 \n5 Accuracy_Parity 0.009832 0.006840 -0.010984 \n6 Label_Stability_Ratio 1.024740 0.997454 0.995869 \n7 IQR_Parity 0.000768 -0.004804 -0.003282 \n8 Std_Parity -0.005106 -0.000927 -0.001976 \n9 Std_Ratio 0.931699 0.986984 0.972422 \n10 Jitter_Parity -0.013818 0.007192 0.005364 \n11 Equalized_Odds_TPR 0.166465 0.258440 0.226205 \n12 Equalized_Odds_FPR 0.096129 0.156703 0.186079 \n13 Equalized_Odds_FNR -0.166465 -0.258440 -0.226205 \n14 Disparate_Impact 1.176075 1.341036 1.263916 \n15 Statistical_Parity_Difference 0.145556 0.262157 0.216187 \n16 Accuracy_Parity -0.010286 -0.003747 -0.024119 \n17 Label_Stability_Ratio 1.021988 0.988991 1.003152 \n18 IQR_Parity 0.001712 0.001225 0.001058 \n19 Std_Parity 0.000822 0.000278 0.000170 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 LogisticRegression \n12 LogisticRegression \n13 LogisticRegression \n14 LogisticRegression \n15 LogisticRegression \n16 LogisticRegression \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression ", - "text/html": "
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Metricsexracesex&raceModel_Name
0Equalized_Odds_TPR0.2119190.1953260.183576DecisionTreeClassifier
1Equalized_Odds_FPR0.0983560.1047280.141078DecisionTreeClassifier
2Equalized_Odds_FNR-0.211919-0.195326-0.183576DecisionTreeClassifier
3Disparate_Impact1.2341151.1359651.125105DecisionTreeClassifier
4Statistical_Parity_Difference0.1935350.1230160.115123DecisionTreeClassifier
5Accuracy_Parity0.0098320.006840-0.010984DecisionTreeClassifier
6Label_Stability_Ratio1.0247400.9974540.995869DecisionTreeClassifier
7IQR_Parity0.000768-0.004804-0.003282DecisionTreeClassifier
8Std_Parity-0.005106-0.000927-0.001976DecisionTreeClassifier
9Std_Ratio0.9316990.9869840.972422DecisionTreeClassifier
10Jitter_Parity-0.0138180.0071920.005364DecisionTreeClassifier
11Equalized_Odds_TPR0.1664650.2584400.226205LogisticRegression
12Equalized_Odds_FPR0.0961290.1567030.186079LogisticRegression
13Equalized_Odds_FNR-0.166465-0.258440-0.226205LogisticRegression
14Disparate_Impact1.1760751.3410361.263916LogisticRegression
15Statistical_Parity_Difference0.1455560.2621570.216187LogisticRegression
16Accuracy_Parity-0.010286-0.003747-0.024119LogisticRegression
17Label_Stability_Ratio1.0219880.9889911.003152LogisticRegression
18IQR_Parity0.0017120.0012250.001058LogisticRegression
19Std_Parity0.0008220.0002780.000170LogisticRegression
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" - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_composed_metrics_df.head(20)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-01T11:29:37.410638Z", - "start_time": "2023-10-01T11:29:37.382980Z" - } - }, - "id": "5798eb95fbeaea54" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-02T17:55:10.703782Z", - "start_time": "2023-10-02T17:55:06.041613Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=config.sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T17:55:47.535767Z", - "start_time": "2023-10-02T17:55:10.703964Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "2326c129", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb deleted file mode 100644 index 3a3ab5d9..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Income.ipynb +++ /dev/null @@ -1,275 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:44.370856Z", - "start_time": "2023-12-10T22:37:43.972175Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:44.380242Z", - "start_time": "2023-12-10T22:37:44.371542Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:44.391659Z", - "start_time": "2023-12-10T22:37:44.380644Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:45.918385Z", - "start_time": "2023-12-10T22:37:44.390547Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.datasets import ACSIncomeDataset\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "data_loader = ACSIncomeDataset(state=['GA'], year=2018, with_nulls=False, subsample_size=15_000, subsample_seed=42)\n", - "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.214487Z", - "start_time": "2023-12-10T22:37:45.921391Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'income_subgroup_metrics.csv'), header=0)\n", - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.242581Z", - "start_time": "2023-12-10T22:37:47.214727Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.047756 0.074977 0.065217 \n1 Aleatoric_Uncertainty_Parity -0.039005 -0.011947 -0.009222 \n2 Aleatoric_Uncertainty_Ratio 0.935159 0.979638 0.984220 \n3 Equalized_Odds_FNR 0.030793 -0.110745 -0.052498 \n4 Equalized_Odds_FPR -0.021317 0.000952 -0.007008 \n\n Model_Name \n0 LGBMClassifier__alpha=0.7 \n1 LGBMClassifier__alpha=0.7 \n2 LGBMClassifier__alpha=0.7 \n3 LGBMClassifier__alpha=0.7 \n4 LGBMClassifier__alpha=0.7 ", - 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MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0477560.0749770.065217LGBMClassifier__alpha=0.7
1Aleatoric_Uncertainty_Parity-0.039005-0.011947-0.009222LGBMClassifier__alpha=0.7
2Aleatoric_Uncertainty_Ratio0.9351590.9796380.984220LGBMClassifier__alpha=0.7
3Equalized_Odds_FNR0.030793-0.110745-0.052498LGBMClassifier__alpha=0.7
4Equalized_Odds_FPR-0.0213170.000952-0.007008LGBMClassifier__alpha=0.7
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" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_names = subgroup_metrics_df['Model_Name'].unique()\n", - "models_metrics_dct = dict()\n", - "for model_name in model_names:\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", - "\n", - "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()\n", - "models_composed_metrics_df.head()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.297089Z", - "start_time": "2023-12-10T22:37:47.240439Z" - } - }, - "id": "44ee5eff6054ce04" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.7', 'LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.7', 'LogisticRegression__alpha=0.4', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7', 'RandomForestClassifier__alpha=0.0'])" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.328697Z", - "start_time": "2023-12-10T22:37:47.295950Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-10T22:37:47.374721Z", - "start_time": "2023-12-10T22:37:47.317882Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", - " models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-11T00:26:17.429094Z", - "start_time": "2023-12-10T22:37:47.343749Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-11T00:26:17.483195Z", - "start_time": "2023-12-11T00:26:17.479725Z" - } - }, - "id": "21c0ad91536f0af5" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb deleted file mode 100644 index abcaa7bf..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Law_School.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T17:11:51.087426Z", - "start_time": "2023-12-18T17:11:50.720930Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T17:11:51.096433Z", - "start_time": "2023-12-18T17:11:51.087934Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T17:11:51.105608Z", - "start_time": "2023-12-18T17:11:51.096820Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.701377Z", - "start_time": "2023-12-18T17:11:51.106232Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.datasets import LawSchoolDataset\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "data_loader = LawSchoolDataset()\n", - "sensitive_attributes_dct = {'male': '0.0', 'race': 'Non-White', 'male&race': None}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.766489Z", - "start_time": "2023-12-18T17:11:52.704609Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'law_school_subgroup_metrics.csv'), header=0)\n", - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.791981Z", - "start_time": "2023-12-18T17:11:52.767057Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.024413 -0.158856 -0.162998 \n1 Aleatoric_Uncertainty_Parity -0.016769 0.317464 0.274695 \n2 Aleatoric_Uncertainty_Ratio 0.951019 2.126816 1.880052 \n3 Equalized_Odds_FNR 0.006853 0.089260 0.092334 \n4 Equalized_Odds_FPR 0.027311 -0.289259 -0.156572 \n\n Model_Name \n0 LGBMClassifier__alpha=0.6 \n1 LGBMClassifier__alpha=0.6 \n2 LGBMClassifier__alpha=0.6 \n3 LGBMClassifier__alpha=0.6 \n4 LGBMClassifier__alpha=0.6 ", - "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Parity-0.024413-0.158856-0.162998LGBMClassifier__alpha=0.6
1Aleatoric_Uncertainty_Parity-0.0167690.3174640.274695LGBMClassifier__alpha=0.6
2Aleatoric_Uncertainty_Ratio0.9510192.1268161.880052LGBMClassifier__alpha=0.6
3Equalized_Odds_FNR0.0068530.0892600.092334LGBMClassifier__alpha=0.6
4Equalized_Odds_FPR0.027311-0.289259-0.156572LGBMClassifier__alpha=0.6
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" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_names = subgroup_metrics_df['Model_Name'].unique()\n", - "models_metrics_dct = dict()\n", - "for model_name in model_names:\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", - "\n", - "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()\n", - "models_composed_metrics_df.head()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.842306Z", - "start_time": "2023-12-18T17:11:52.792667Z" - } - }, - "id": "833484748ed512e8" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.6', 'LogisticRegression__alpha=0.0', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0'])" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.877906Z", - "start_time": "2023-12-18T17:11:52.842425Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T17:11:52.959909Z", - "start_time": "2023-12-18T17:11:52.864927Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", - " models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-18T17:14:45.540473Z", - "start_time": "2023-12-18T17:11:52.892884Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2326c129", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-18T17:14:45.584046Z", - "start_time": "2023-12-18T17:14:45.581453Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb deleted file mode 100644 index 61298c7a..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Pub_Cov.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 19, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:42.978064Z", - "start_time": "2023-12-07T00:13:42.914700Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:42.983725Z", - "start_time": "2023-12-07T00:13:42.954698Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:43.018895Z", - "start_time": "2023-12-07T00:13:42.982387Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:43.027909Z", - "start_time": "2023-12-07T00:13:43.006390Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.datasets import ACSPublicCoverageDataset\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "data_loader = ACSPublicCoverageDataset(state=['CA'], year=2018, with_nulls=False,\n", - " subsample_size=15_000, subsample_seed=42)\n", - "sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9'], 'SEX&RAC1P': None}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.771709Z", - "start_time": "2023-12-07T00:13:43.029632Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'pub_cov_subgroup_metrics.csv'), header=0)\n", - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.805639Z", - "start_time": "2023-12-07T00:13:48.768740Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "data": { - "text/plain": " Metric SEX RAC1P SEX&RAC1P \\\n0 Accuracy_Parity 0.026847 0.016299 0.040212 \n1 Aleatoric_Uncertainty_Parity -0.013240 0.027276 0.007235 \n2 Aleatoric_Uncertainty_Ratio 0.983584 1.034689 1.009077 \n3 Equalized_Odds_FNR 0.004275 -0.000359 -0.008617 \n4 Equalized_Odds_FPR -0.012072 -0.024172 -0.040481 \n\n Model_Name \n0 LGBMClassifier__alpha=0.6 \n1 LGBMClassifier__alpha=0.6 \n2 LGBMClassifier__alpha=0.6 \n3 LGBMClassifier__alpha=0.6 \n4 LGBMClassifier__alpha=0.6 ", - "text/html": "
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MetricSEXRAC1PSEX&RAC1PModel_Name
0Accuracy_Parity0.0268470.0162990.040212LGBMClassifier__alpha=0.6
1Aleatoric_Uncertainty_Parity-0.0132400.0272760.007235LGBMClassifier__alpha=0.6
2Aleatoric_Uncertainty_Ratio0.9835841.0346891.009077LGBMClassifier__alpha=0.6
3Equalized_Odds_FNR0.004275-0.000359-0.008617LGBMClassifier__alpha=0.6
4Equalized_Odds_FPR-0.012072-0.024172-0.040481LGBMClassifier__alpha=0.6
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" - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_names = subgroup_metrics_df['Model_Name'].unique()\n", - "models_metrics_dct = dict()\n", - "for model_name in model_names:\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]\n", - "\n", - "metrics_composer = MetricsComposer(models_metrics_dct, sensitive_attributes_dct)\n", - "models_composed_metrics_df = metrics_composer.compose_metrics()\n", - "models_composed_metrics_df.head()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.849022Z", - "start_time": "2023-12-07T00:13:48.802693Z" - } - }, - "id": "833484748ed512e8" - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.6', 'LGBMClassifier__alpha=0.0', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.6', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.6', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.6'])" - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.873723Z", - "start_time": "2023-12-07T00:13:48.848261Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:13:48.959344Z", - "start_time": "2023-12-07T00:13:48.871083Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(data_loader.X_data, data_loader.y_data,\n", - " models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T00:15:48.056146Z", - "start_time": "2023-12-07T00:13:48.898642Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2326c129", - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-07T00:15:48.095103Z", - "start_time": "2023-12-07T00:15:48.092153Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb b/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb deleted file mode 100644 index 18b24daa..00000000 --- a/docs/examples/Multiple_Models_Interface_Vis_Ricci.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 37, - "id": "248cbed8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.642940Z", - "start_time": "2023-10-07T13:42:22.508015Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "7ec6cd08", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.677119Z", - "start_time": "2023-10-07T13:42:22.641937Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b8cb69f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.689334Z", - "start_time": "2023-10-07T13:42:22.664188Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7a9241de", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.711038Z", - "start_time": "2023-10-07T13:42:22.687552Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "from virny.custom_classes.metrics_interactive_visualizer import MetricsInteractiveVisualizer" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [], - "source": [ - "sensitive_attributes_dct = {'Race': 'Non-White'}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.732136Z", - "start_time": "2023-10-07T13:42:22.711244Z" - } - }, - "id": "d3c53c7b72ecbcd0" - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "subgroup_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_subgroup_metrics.csv'), header=0)\n", - "models_composed_metrics_df = pd.read_csv(os.path.join(ROOT_DIR, 'ricci_group_metrics.csv'), header=0)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.759203Z", - "start_time": "2023-10-07T13:42:22.732607Z" - } - }, - "id": "2aab7c79ecdee914" - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [], - "source": [ - "subgroup_metrics_df['Model_Name'] = (subgroup_metrics_df['Model_Name'] + '__alpha=' +\n", - " subgroup_metrics_df['Intervention_Param'].astype(str))\n", - "models_composed_metrics_df['Model_Name'] = (models_composed_metrics_df['Model_Name'] + '__alpha=' \n", - " + models_composed_metrics_df['Intervention_Param'].astype(str))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.784062Z", - "start_time": "2023-10-07T13:42:22.759791Z" - } - }, - "id": "2d922003e752a4b4" - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [], - "source": [ - "models_metrics_dct = dict()\n", - "for model_name in subgroup_metrics_df['Model_Name'].unique():\n", - " models_metrics_dct[model_name] = subgroup_metrics_df[subgroup_metrics_df['Model_Name'] == model_name]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.809161Z", - "start_time": "2023-10-07T13:42:22.782462Z" - } - }, - "id": "833484748ed512e8" - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [ - { - "data": { - "text/plain": "dict_keys(['LGBMClassifier__alpha=0.0', 'LGBMClassifier__alpha=0.4', 'LGBMClassifier__alpha=0.7', 'LogisticRegression__alpha=0.0', 'LogisticRegression__alpha=0.4', 'LogisticRegression__alpha=0.7', 'MLPClassifier__alpha=0.7', 'MLPClassifier__alpha=0.0', 'MLPClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.0', 'RandomForestClassifier__alpha=0.4', 'RandomForestClassifier__alpha=0.7'])" - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models_metrics_dct.keys()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.831140Z", - "start_time": "2023-10-07T13:42:22.806994Z" - } - }, - "id": "15ed7d1ba1f22317" - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "435b9d98", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:42:22.859150Z", - "start_time": "2023-10-07T13:42:22.830292Z" - } - }, - "outputs": [], - "source": [ - "visualizer = MetricsInteractiveVisualizer(models_metrics_dct, models_composed_metrics_df,\n", - " sensitive_attributes_dct=sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on local URL: http://127.0.0.1:7860\n", - "\n", - "To create a public link, set `share=True` in `launch()`.\n", - "Keyboard interruption in main thread... closing server.\n" - ] - } - ], - "source": [ - "visualizer.create_web_app()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-07T13:45:45.222662Z", - "start_time": "2023-10-07T13:42:22.859325Z" - } - }, - "id": "678a9dc8d51243f4" - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "2326c129", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-07T13:45:45.265758Z", - "start_time": "2023-10-07T13:45:45.264074Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From f4d0f316c63093c82949858a9d7b7e3ff45b6b3c Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 16:13:30 +0200 Subject: [PATCH 074/148] Added auto-creation of model_composed_metrics_df --- virny/custom_classes/metrics_interactive_visualizer.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 910226ef..b94bb0b0 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -5,6 +5,7 @@ from virny.configs.constants import * from virny.utils.common_helpers import str_to_float +from virny.custom_classes.metrics_composer import MetricsComposer from virny.utils.protected_groups_partitioning import create_test_protected_groups from virny.utils.data_viz_utils import (create_model_rank_heatmap_visualization, create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank, create_flexible_bar_plot_for_model_selection, @@ -24,21 +25,21 @@ class MetricsInteractiveVisualizer: An original target column pandas series model_metrics_dct Dictionary where keys are model names and values are dataframes of subgroup metrics for each model - model_composed_metrics_df - Dataframe of all model composed metrics sensitive_attributes_dct A dictionary where keys are sensitive attributes names (including attributes intersections), and values are privilege values for these attributes """ def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics_dct: dict, - model_composed_metrics_df: pd.DataFrame, sensitive_attributes_dct: dict): + sensitive_attributes_dct: dict): self.X_data = X_data self.y_data = y_data self.model_names = list(model_metrics_dct.keys()) self.sensitive_attributes_dct = sensitive_attributes_dct self.group_names = list(self.sensitive_attributes_dct.keys()) + model_composed_metrics_df = MetricsComposer(model_metrics_dct, sensitive_attributes_dct).compose_metrics() + # Technical attributes self.demo = None self.max_groups = 8 From dd72b3d3f74e478061106461981707f2599bed4d Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 19 Dec 2023 16:21:55 +0200 Subject: [PATCH 075/148] Simplified input for MetricsInteractiveVisualizer --- .../metrics_interactive_visualizer.py | 23 ++++++++++++++----- 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index b94bb0b0..9218220c 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -23,23 +23,34 @@ class MetricsInteractiveVisualizer: An original features dataframe y_data An original target column pandas series - model_metrics_dct - Dictionary where keys are model names and values are dataframes of subgroup metrics for each model + model_metrics + A dictionary or a dataframe where keys are model names and values are dataframes of subgroup metrics for each model sensitive_attributes_dct A dictionary where keys are sensitive attributes names (including attributes intersections), and values are privilege values for these attributes """ - def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics_dct: dict, - sensitive_attributes_dct: dict): + def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics, sensitive_attributes_dct: dict): + # Preprocessed variables + if isinstance(model_metrics, dict): + model_metrics_dct = model_metrics + elif isinstance(model_metrics, pd.DataFrame): + model_names = model_metrics['Model_Name'].unique() + model_metrics_dct = dict() + for model_name in model_names: + model_metrics_dct[model_name] = model_metrics[model_metrics['Model_Name'] == model_name] + else: + raise ValueError('model_metrics argument must be a dictionary or a pandas dataframe of metrics.') + + model_composed_metrics_df = MetricsComposer(model_metrics_dct, sensitive_attributes_dct).compose_metrics() + + # Attributes from input arguments self.X_data = X_data self.y_data = y_data self.model_names = list(model_metrics_dct.keys()) self.sensitive_attributes_dct = sensitive_attributes_dct self.group_names = list(self.sensitive_attributes_dct.keys()) - model_composed_metrics_df = MetricsComposer(model_metrics_dct, sensitive_attributes_dct).compose_metrics() - # Technical attributes self.demo = None self.max_groups = 8 From 4bfc40fe25607290114bb3a93edadd2bdadbc35c Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 20 Dec 2023 01:09:27 +0200 Subject: [PATCH 076/148] Removed unnecessary dependencies --- docs/api/incremental-ml/.pages | 1 - docs/api/overview.md | 2 - .../user-interfaces/compute-model-metrics.md | 2 +- .../run-metrics-computation.md | 2 +- docs/api/utils/validate-config.md | 2 +- ..._Models_Interface_For_Incremental_ML.ipynb | 689 ------------------ lib_base_packages.txt | 3 +- requirements.txt | 1 - tests/utils/test_common_helpers.py | 3 +- .../incremental_overall_variance_analyzer.py | 106 --- virny/analyzers/subgroup_error_analyzer.py | 2 +- virny/analyzers/subgroup_variance_analyzer.py | 33 +- virny/configs/constants.py | 1 - .../incremental_pandas_dataset.py | 18 - virny/incremental_ml/__init__.py | 1 - virny/incremental_ml/river_utils.py | 70 -- virny/metrics/accuracy_metrics.py | 21 + .../metrics_computation_interfaces.py | 14 +- virny/utils/common_helpers.py | 27 +- 19 files changed, 45 insertions(+), 953 deletions(-) delete mode 100644 docs/api/incremental-ml/.pages delete mode 100644 docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb delete mode 100644 virny/analyzers/incremental_overall_variance_analyzer.py delete mode 100644 virny/custom_classes/incremental_pandas_dataset.py delete mode 100644 virny/incremental_ml/__init__.py delete mode 100644 virny/incremental_ml/river_utils.py diff --git a/docs/api/incremental-ml/.pages b/docs/api/incremental-ml/.pages deleted file mode 100644 index 4d6447c3..00000000 --- a/docs/api/incremental-ml/.pages +++ /dev/null @@ -1 +0,0 @@ -title: incremental_ml \ No newline at end of file diff --git a/docs/api/overview.md b/docs/api/overview.md index 222222e8..7775f764 100644 --- a/docs/api/overview.md +++ b/docs/api/overview.md @@ -53,8 +53,6 @@ The purpose is to provide sample datasets for functionality testing and show exa - [LawSchoolDataset](../datasets/LawSchoolDataset) - [RicciDataset](../datasets/RicciDataset) -## incremental_ml - ## metrics diff --git a/docs/api/user-interfaces/compute-model-metrics.md b/docs/api/user-interfaces/compute-model-metrics.md index 1208b34b..5aa0dc1a 100644 --- a/docs/api/user-interfaces/compute-model-metrics.md +++ b/docs/api/user-interfaces/compute-model-metrics.md @@ -36,7 +36,7 @@ Return a dataframe of model metrics. - **model_setting** (*str*) – defaults to `batch` - [Optional] Model type: 'batch' or 'incremental'. Default: 'batch'. + [Optional] Currently, only batch models are supported. Default: 'batch'. - **computation_mode** (*str*) – defaults to `None` diff --git a/docs/api/user-interfaces/run-metrics-computation.md b/docs/api/user-interfaces/run-metrics-computation.md index 3a46bbff..41a0d489 100644 --- a/docs/api/user-interfaces/run-metrics-computation.md +++ b/docs/api/user-interfaces/run-metrics-computation.md @@ -32,7 +32,7 @@ Return a dictionary where keys are model names, and values are metrics for sensi - **model_setting** (*str*) – defaults to `batch` - [Optional] Model type: 'batch' or incremental. Default: 'batch'. + [Optional] Currently, only batch models are supported. Default: 'batch'. - **computation_mode** (*str*) – defaults to `None` diff --git a/docs/api/utils/validate-config.md b/docs/api/utils/validate-config.md index 39284cb3..1acbe2b7 100644 --- a/docs/api/utils/validate-config.md +++ b/docs/api/utils/validate-config.md @@ -2,7 +2,7 @@ Validate parameters types and values in config yaml file. -Extra details: * config_obj.model_setting is an optional argument that defines a type of models to use to compute fairness and stability metrics. Should be 'batch' or 'incremental'. Default: 'batch'. +Extra details: * config_obj.model_setting is an optional argument that defines a type of models to use to compute fairness and stability metrics. Currently, only batch models are supported. Default: 'batch'. * config_obj.computation_mode is an optional argument that defines a non-default mode for metrics computation. Currently, only 'error_analysis' mode is supported. diff --git a/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb b/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb deleted file mode 100644 index 4066db1e..00000000 --- a/docs/examples/Multiple_Models_Interface_For_Incremental_ML.ipynb +++ /dev/null @@ -1,689 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 37, - "id": "248cbed8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "7ec6cd08", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b8cb69f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current location: /home/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" - ] - } - ], - "source": [ - "cur_folder_name = os.getcwd().split('/')[-1]\n", - "if cur_folder_name != \"Virny\":\n", - " os.chdir(\"../..\")\n", - "\n", - "print('Current location: ', os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "a578f2ab", - "metadata": {}, - "source": [ - "# Multiple Models Interface For Incremental Models" - ] - }, - { - "cell_type": "markdown", - "id": "2251a923", - "metadata": {}, - "source": [ - "In this example, we are going to audit 4 models for stability and fairness, visualize metrics, and create an analysis report. For that, we will use `compute_metrics_with_config` interface that can compute metrics for multiple models. Thus, we will need to do the next steps:\n", - "\n", - "* Initialize input variables\n", - "\n", - "* Compute subgroup metrics\n", - "\n", - "* Make group metrics composition\n", - "\n", - "* Create metrics visualizations and an analysis report" - ] - }, - { - "cell_type": "markdown", - "id": "606df34d", - "metadata": {}, - "source": [ - "## Import dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7a9241de", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "from datetime import datetime, timezone\n", - "\n", - "from river.forest import ARFClassifier\n", - "from river.tree import HoeffdingTreeClassifier\n", - "\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "from virny.user_interfaces.metrics_computation_interfaces import compute_metrics_with_config\n", - "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs\n", - "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", - "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.configs.constants import ReportType\n", - "from virny.datasets.base import BaseDataLoader" - ] - }, - { - "cell_type": "markdown", - "id": "75699f5f", - "metadata": {}, - "source": [ - "## Initialize Input Variables" - ] - }, - { - "cell_type": "markdown", - "id": "e86f6556", - "metadata": {}, - "source": [ - "Based on the library flow, we need to create 3 input objects for a user interface:\n", - "\n", - "* A **dataset class** that is a wrapper above the user’s raw dataset that includes its descriptive attributes like a target column, numerical columns, categorical columns, etc. This class must be inherited from the BaseDataset class, which was created for user convenience.\n", - "\n", - "* A **config yaml** that is a file with configuration parameters for different user interfaces for metrics computation.\n", - "\n", - "* Finally, a **models config** that is a Python dictionary, where keys are model names and values are initialized models for analysis. This dictionary helps conduct audits of multiple models and analyze different types of models." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [], - "source": [ - "TEST_SET_FRACTION = 0.2\n", - "DATASET_SPLIT_SEED = 42" - ], - "metadata": { - "collapsed": false - }, - "id": "d8505edef7184a5a" - }, - { - "cell_type": "markdown", - "source": [ - "### Create a config object" - ], - "metadata": { - "collapsed": false - }, - "id": "c84bae470652be94" - }, - { - "cell_type": "markdown", - "source": [ - "`compute_metrics_with_config` interface requires that your **yaml file** includes the following parameters:\n", - "\n", - "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", - "\n", - "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", - "\n", - "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", - "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including attribute intersections), and values are privileged values for these attributes. Currently, the library supports only intersections among two sensitive attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify privileged values for intersectional groups since they will be derived from privileged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." - ], - "metadata": { - "collapsed": false - }, - "id": "8b41b746e152c76f" - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [], - "source": [ - "ROOT_DIR = os.path.join('docs', 'examples')\n", - "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", - "config_yaml_content = \\\n", - "\"\"\"dataset_name: COMPAS_Without_Sensitive_Attributes\n", - "model_setting: 'incremental'\n", - "bootstrap_fraction: 0.8\n", - "n_estimators: 10 # Better to input the higher number of estimators than 100; this is only for this use case example\n", - "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", - "\"\"\"\n", - "\n", - "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", - " f.write(config_yaml_content)" - ], - "metadata": { - "collapsed": false - }, - "id": "a878d125e8bfaf4d" - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [], - "source": [ - "config = create_config_obj(config_yaml_path=config_yaml_path)\n", - "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name}_Metrics_{datetime.now(timezone.utc).strftime(\"%Y%m%d__%H%M%S\")}')" - ], - "metadata": { - "collapsed": false - }, - "id": "53d2fcd40c862014" - }, - { - "cell_type": "markdown", - "id": "74f57422", - "metadata": {}, - "source": [ - "### Create a Dataset class" - ] - }, - { - "cell_type": "markdown", - "id": "eed149cd", - "metadata": {}, - "source": [ - "Based on the BaseDataset class, your **dataset class** should include the following attributes:\n", - "\n", - "* **Obligatory attributes**: dataset, target, features, numerical_columns, categorical_columns\n", - "\n", - "* **Optional attributes**: X_data, y_data, columns_with_nulls\n", - "\n", - "For more details, please refer to the library documentation." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "9e3d7bf3", - "metadata": {}, - "outputs": [], - "source": [ - "class CompasWithoutSensitiveAttrsDataset(BaseDataLoader):\n", - " \"\"\"\n", - " Dataset class for COMPAS dataset that does not contain sensitive attributes among feature columns\n", - " to test blind classifiers\n", - "\n", - " Parameters\n", - " ----------\n", - " subsample_size\n", - " Subsample size to create based on the input dataset\n", - "\n", - " \"\"\"\n", - " def __init__(self, dataset_path, subsample_size: int = None):\n", - " df = pd.read_csv(dataset_path)\n", - " if subsample_size:\n", - " df = df.sample(subsample_size)\n", - "\n", - " # Initial data types transformation\n", - " int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45',\n", - " 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex']\n", - " int_columns_dct = {col: \"int\" for col in int_columns}\n", - " df = df.astype(int_columns_dct)\n", - "\n", - " # Define params\n", - " target = 'recidivism'\n", - " numerical_columns = ['juv_fel_count', 'juv_misd_count', 'juv_other_count','priors_count']\n", - " categorical_columns = ['age_cat_25 - 45', 'age_cat_Greater than 45','age_cat_Less than 25',\n", - " 'c_charge_degree_F', 'c_charge_degree_M']\n", - "\n", - " super().__init__(\n", - " full_df=df,\n", - " target=target,\n", - " numerical_columns=numerical_columns,\n", - " categorical_columns=categorical_columns\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "6c55c6a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": " juv_fel_count juv_misd_count juv_other_count priors_count \\\n0 0.0 -2.340451 1.0 -15.010999 \n1 0.0 0.000000 0.0 0.000000 \n2 0.0 0.000000 0.0 0.000000 \n3 0.0 0.000000 0.0 6.000000 \n4 0.0 0.000000 0.0 7.513697 \n\n age_cat_25 - 45 \n0 1 \n1 1 \n2 0 \n3 1 \n4 1 ", - "text/html": "
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" - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_loader = CompasWithoutSensitiveAttrsDataset(dataset_path=os.path.join('virny', 'datasets', 'COMPAS.csv'))\n", - "data_loader.X_data[data_loader.X_data.columns[:5]].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "outputs": [], - "source": [ - "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", - " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", - "])" - ], - "metadata": { - "collapsed": false - }, - "id": "8feb498942cc2a8c" - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [], - "source": [ - "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" - ], - "metadata": { - "collapsed": false - }, - "id": "7915190e0847f1a7" - }, - { - "cell_type": "markdown", - "id": "d42b81d1", - "metadata": {}, - "source": [ - "### Create a models config" - ] - }, - { - "cell_type": "markdown", - "id": "3deeecfa", - "metadata": {}, - "source": [ - "**models_config** is a Python dictionary, where keys are model names and values are initialized models for analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "b995b73b", - "metadata": {}, - "outputs": [], - "source": [ - "models_config = {\n", - " 'HoeffdingTreeClassifier': HoeffdingTreeClassifier(grace_period=50, delta=0.01),\n", - " 'AdaptiveRandomForest': ARFClassifier(n_models=20,\n", - " max_depth=4,\n", - " split_criterion='gini'),\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "f445b64a", - "metadata": {}, - "source": [ - "## Subgroup Metrics Computation" - ] - }, - { - "cell_type": "markdown", - "id": "c3530f06", - "metadata": {}, - "source": [ - "After the variables are input to a user interface, the interface uses subgroup analyzers to compute different sets of metrics for each privileged and disprivileged subgroup. As for now, our library supports **Subgroup Variance Analyzer** and **Subgroup Error Analyzer**, but it is easily extensible to any other analyzers. When the variance and error analyzers complete metrics computation, their metrics are combined, returned in a matrix format, and stored in a file if defined." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "197eadaa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "Analyze models in one run: 0%| | 0/2 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Metricoverallsex_privsex_disrace_privrace_dis
0Mean0.5379430.5848640.5262270.5903100.504174
1Std0.1806030.1892170.1784520.1786360.181871
2IQR0.1775520.2025250.1713160.1874400.171175
3Aleatoric_Uncertainty0.7683840.7532170.7721710.7611920.773022
4Overall_Uncertainty0.8977330.8998300.8972100.8903220.902513
5Statistical_Bias0.4285230.4242700.4295850.4240070.431435
6Jitter0.2324280.2300160.2330310.2059580.249498
7Per_Sample_Accuracy0.6499050.6597160.6474560.6615940.642368
8Label_Stability0.7020830.7061610.7010650.7415460.676636
9TPR0.5668790.4533330.5883840.4081630.638889
10TNR0.7350430.7941180.7171490.7865170.691824
11PPV0.6327010.5483870.6472220.5128210.678689
12FNR0.4331210.5466670.4116160.5918370.361111
13FPR0.2649570.2058820.2828510.2134830.308176
14Accuracy0.6600380.6729860.6568050.6521740.665109
15F10.5979840.4963500.6164020.4545450.658188
16Selection-Rate0.3996210.2938390.4260360.2826090.475078
17Positive-Rate0.8959660.8266670.9090910.7959180.941358
18Sample_Size1056.000000NaNNaNNaNNaN
\n" - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_model_metrics_df = metrics_dct[list(models_config.keys())[0]]\n", - "sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20)" - ] - }, - { - "cell_type": "markdown", - "id": "a7ff67e9", - "metadata": {}, - "source": [ - "## Group Metrics Composition" - ] - }, - { - "cell_type": "markdown", - "id": "274c97e2", - "metadata": {}, - "source": [ - "**Metrics Composer** is responsible for this second stage of the model audit. Currently, it computes our custom group fairness and stability metrics, but extending it for new group metrics is very simple. We noticed that more and more group metrics have appeared during the last decade, but most of them are based on the same subgroup metrics. Hence, such a separation of subgroup and group metrics computation allows one to experiment with different combinations of subgroup metrics and avoid subgroup metrics recomputation for a new set of grouped metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "f94a20dc", - "metadata": {}, - "outputs": [], - "source": [ - "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=list(models_config.keys()))" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "b04d06cf", - "metadata": {}, - "outputs": [], - "source": [ - "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" - ] - }, - { - "cell_type": "markdown", - "id": "e1a23ece", - "metadata": {}, - "source": [ - "Compute composed metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "be6ace22", - "metadata": {}, - "outputs": [], - "source": [ - "models_composed_metrics_df = metrics_composer.compose_metrics()" - ] - }, - { - "cell_type": "markdown", - "id": "deb45226", - "metadata": {}, - "source": [ - "## Metrics Visualization and Reporting" - ] - }, - { - "cell_type": "markdown", - "id": "2f5d4cdb", - "metadata": {}, - "source": [ - "**Metrics Visualizer** provides metrics visualization and reporting functionality. It unifies different preprocessing methods for result metrics and creates various data formats required for visualizations. Hence, users can simply call methods of the Metrics Visualizer class and get custom plots for diverse metrics analysis. Additionally, these plots could be collected in an HTML report with comments for user convenience and future reference." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "435b9d98", - "metadata": {}, - "outputs": [], - "source": [ - "visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", - " model_names=list(models_config.keys()),\n", - " sensitive_attributes_dct=config.sensitive_attributes_dct)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "5efb1bf2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": "\n
\n", - "text/plain": "alt.Chart(...)" - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", - " metrics_title=\"Error Metrics\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "0eb8528e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": "\n
\n", - "text/plain": "alt.Chart(...)" - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['Label_Stability'],\n", - " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", - " metrics_title=\"Variance Metrics\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Below is an example of an interactive plot. It requires that you run the below cell in Jupyter in the browser or EDAs, which support JavaScript displaying.\n", - "\n", - "You can use this plot to compare any pair of group fairness and stability metrics for all models." - ], - "metadata": { - "collapsed": false - }, - "id": "ca3fe31f0515a973" - }, - { - "cell_type": "code", - "execution_count": 57, - "outputs": [ - { - "data": { - "text/html": "\n
\n", - "text/plain": "alt.HConcatChart(...)" - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizer.create_fairness_variance_interactive_bar_chart()" - ], - "metadata": { - "collapsed": false - }, - "id": "dfc57f1870ed71d1" - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "df024aed", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "
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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer.create_model_rank_heatmaps(\n", - " metrics_lst=[\n", - " # Group fairness metrics\n", - " 'Equalized_Odds_TPR',\n", - " 'Equalized_Odds_FPR',\n", - " 'Disparate_Impact',\n", - " 'Statistical_Parity_Difference',\n", - " 'Accuracy_Parity',\n", - " # Group stability metrics\n", - " 'Label_Stability_Ratio',\n", - " 'IQR_Parity',\n", - " 'Std_Parity',\n", - " 'Std_Ratio',\n", - " 'Jitter_Parity',\n", - " ],\n", - " groups_lst=config.sensitive_attributes_dct.keys(),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "2326c129", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/lib_base_packages.txt b/lib_base_packages.txt index 6787a9a8..fec2cfe5 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -1,4 +1,4 @@ -numpy~=1.24.2 +numpy==1.23.5 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 @@ -11,6 +11,5 @@ aif360~=0.5.0 folktables~=0.0.11 munch~=2.5.0 PyYAML~=6.0 -river==0.15.0 requests-toolbelt==1.0.0 colorama~=0.4.6 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index cbcecd0b..21a53478 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,7 +13,6 @@ xgboost~=1.7.2 aif360~=0.5.0 munch~=2.5.0 PyYAML~=6.0 -river==0.15.0 python-dotenv~=1.0.0 pytest~=7.2.2 pymongo==4.3.3 diff --git a/tests/utils/test_common_helpers.py b/tests/utils/test_common_helpers.py index 62cc4cd5..624fa239 100644 --- a/tests/utils/test_common_helpers.py +++ b/tests/utils/test_common_helpers.py @@ -3,7 +3,8 @@ from sklearn.model_selection import train_test_split from tests import config_params, compas_dataset_class, compas_without_sensitive_attrs_dataset_class -from virny.utils.common_helpers import validate_config, confusion_matrix_metrics +from virny.metrics.accuracy_metrics import confusion_matrix_metrics +from virny.utils.common_helpers import validate_config def test_validate_config_true1(config_params): diff --git a/virny/analyzers/incremental_overall_variance_analyzer.py b/virny/analyzers/incremental_overall_variance_analyzer.py deleted file mode 100644 index 067b4721..00000000 --- a/virny/analyzers/incremental_overall_variance_analyzer.py +++ /dev/null @@ -1,106 +0,0 @@ -import numpy as np -import pandas as pd - -from virny.custom_classes.incremental_pandas_dataset import IncrementalPandasDataset -from virny.analyzers.abstract_overall_variance_analyzer import AbstractOverallVarianceAnalyzer - - -class IncrementalOverallVarianceAnalyzer(AbstractOverallVarianceAnalyzer): - """ - Analyzer to compute subgroup variance metrics for incremental learning models. - - Parameters - ---------- - base_model - Base model for stability measuring - base_model_name - Model name like 'HoeffdingTreeClassifier' or 'LogisticRegression' - bootstrap_fraction - [0-1], fraction from train_pd_dataset for fitting an ensemble of base models - X_train - Processed features train set - y_train - Targets train set - X_test - Processed features test set - y_test - Targets test set - target_column - Name of the target column - dataset_name - Name of dataset, used for correct results naming - n_estimators - Number of estimators in ensemble to measure base_model stability - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, - X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - target_column: str, dataset_name: str, n_estimators: int, verbose: int = 0): - super().__init__(base_model=base_model, - base_model_name=base_model_name, - bootstrap_fraction=bootstrap_fraction, - X_train=X_train, - y_train=y_train, - X_test=X_test, - y_test=y_test, - dataset_name=dataset_name, - n_estimators=n_estimators, - verbose=verbose) - self.target_column = target_column - self.dataset_reader = IncrementalPandasDataset - - # Create converters for the train set to apply them for train incremental datasets - train_df_for_types = X_train.astype('object') - train_converters = {str(col): type(train_df_for_types.loc[train_df_for_types.index[0], col]) - for col in train_df_for_types} - train_converters[self.target_column] = type(y_train.astype('object')[y_train.index[0]]) - self.train_converters = train_converters - - def _fit_model(self, classifier, X_train: np.ndarray, y_train: np.ndarray): - """ - Fit an incremental classifier that is an instance of self.base_model - """ - train_df = pd.DataFrame(X_train, columns=[key for key in self.train_converters.keys() - if key != self.target_column]) - train_df[self.target_column] = y_train - train_dataset = self.dataset_reader(pd_dataset=train_df, target=self.target_column, converters=self.train_converters) - for x, y_true in train_dataset: - classifier.learn_one(x=x, y=y_true) - - return classifier - - def _batch_predict(self, classifier, X_test: pd.DataFrame): - """ - Predict with the incremental classifier for X_test set. - Return predictions. - """ - predictions = [] - test_df_for_types = X_test.astype('object') - converters = {col: type(test_df_for_types.loc[test_df_for_types.index[0], col]) for col in test_df_for_types} - test_dataset = self.dataset_reader(pd_dataset=X_test, target=None, converters=converters) - for x, _ in test_dataset: - y_pred = classifier.predict_one(x) - predictions.append(y_pred) - - return predictions - - def _batch_predict_proba(self, classifier, X_test: pd.DataFrame): - """ - Predict with the incremental classifier for X_test set. - Return predicted probabilities for each class for each test point. - """ - # Create converters for the test set to apply them for an incremental dataset - test_df_for_types = X_test.astype('object') - converters = {col: type(test_df_for_types.loc[test_df_for_types.index[0], col]) for col in test_df_for_types} - - predictions = [] - test_dataset = self.dataset_reader(pd_dataset=X_test, target=None, converters=converters) - for x, _ in test_dataset: - predict_proba = classifier.predict_proba_one(x) - y_pred = predict_proba[0] - predictions.append(y_pred) - - return predictions diff --git a/virny/analyzers/subgroup_error_analyzer.py b/virny/analyzers/subgroup_error_analyzer.py index c53520b7..0edea0ca 100644 --- a/virny/analyzers/subgroup_error_analyzer.py +++ b/virny/analyzers/subgroup_error_analyzer.py @@ -1,7 +1,7 @@ import pandas as pd from virny.analyzers.abstract_subgroup_analyzer import AbstractSubgroupAnalyzer -from virny.utils.common_helpers import confusion_matrix_metrics +from virny.metrics.accuracy_metrics import confusion_matrix_metrics class SubgroupErrorAnalyzer(AbstractSubgroupAnalyzer): diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 0625d685..819d0fee 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -5,7 +5,6 @@ from virny.analyzers.subgroup_variance_calculator import SubgroupVarianceCalculator from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer from virny.analyzers.batch_overall_variance_analyzer_postprocessing import BatchOverallVarianceAnalyzerPostProcessing -from virny.analyzers.incremental_overall_variance_analyzer import IncrementalOverallVarianceAnalyzer class SubgroupVarianceAnalyzer: @@ -63,28 +62,16 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b verbose=verbose) else: overall_variance_analyzer = BatchOverallVarianceAnalyzer(base_model=base_model, - base_model_name=base_model_name, - bootstrap_fraction=bootstrap_fraction, - X_train=dataset.X_train_val, - y_train=dataset.y_train_val, - X_test=dataset.X_test, - y_test=dataset.y_test, - dataset_name=dataset_name, - target_column=dataset.target, - n_estimators=n_estimators, - verbose=verbose) - elif model_setting == ModelSetting.INCREMENTAL: - overall_variance_analyzer = IncrementalOverallVarianceAnalyzer(base_model=base_model, - base_model_name=base_model_name, - bootstrap_fraction=bootstrap_fraction, - X_train=dataset.X_train_val, - y_train=dataset.y_train_val, - X_test=dataset.X_test, - y_test=dataset.y_test, - dataset_name=dataset_name, - target_column=dataset.target, - n_estimators=n_estimators, - verbose=verbose) + base_model_name=base_model_name, + bootstrap_fraction=bootstrap_fraction, + X_train=dataset.X_train_val, + y_train=dataset.y_train_val, + X_test=dataset.X_test, + y_test=dataset.y_test, + dataset_name=dataset_name, + target_column=dataset.target, + n_estimators=n_estimators, + verbose=verbose) else: raise ValueError('model_setting is incorrect or not supported') diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 30b00d34..739c469f 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -2,7 +2,6 @@ class ModelSetting(Enum): - INCREMENTAL = "incremental" BATCH = "batch" diff --git a/virny/custom_classes/incremental_pandas_dataset.py b/virny/custom_classes/incremental_pandas_dataset.py deleted file mode 100644 index 34017171..00000000 --- a/virny/custom_classes/incremental_pandas_dataset.py +++ /dev/null @@ -1,18 +0,0 @@ -from virny.incremental_ml.river_utils import iter_pd_dataset - - -class IncrementalPandasDataset: - """ - Generic data loader that converts a pandas df to a River dataset for incremental models - """ - def __init__(self, pd_dataset, target, converters: dict): - self.pd_dataset = pd_dataset - self.target = target - self.converters = converters - - def __iter__(self): - return iter_pd_dataset( - self.pd_dataset, - target=self.target, - converters=self.converters - ) diff --git a/virny/incremental_ml/__init__.py b/virny/incremental_ml/__init__.py deleted file mode 100644 index a9a2c5b3..00000000 --- a/virny/incremental_ml/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__all__ = [] diff --git a/virny/incremental_ml/river_utils.py b/virny/incremental_ml/river_utils.py deleted file mode 100644 index 7ae2854e..00000000 --- a/virny/incremental_ml/river_utils.py +++ /dev/null @@ -1,70 +0,0 @@ -import random -import typing -import datetime as dt - -from io import StringIO -from river import base -from river.stream.iter_csv import DictReader - - -def ddict2dict(d): - for k, v in d.items(): - if isinstance(v, dict): - d[k] = ddict2dict(v) - return dict(d) - - -def df_to_stream_buffer(df): - buffer = StringIO() # creating an empty buffer - df.to_csv(buffer, index=False) # filling that buffer - buffer.seek(0) # set to the start of the stream - - return buffer - - -def iter_pd_dataset( - pd_dataset, - target: typing.Union[str, typing.List[str]] = None, - converters: dict = None, - parse_dates: dict = None, - drop: typing.List[str] = None, - drop_nones=False, - fraction=1.0, - seed: int = None, - **kwargs, -) -> base.typing.Stream: - - buffer = df_to_stream_buffer(pd_dataset) - for x in DictReader(fraction=fraction, rng=random.Random(seed), f=buffer, **kwargs): - if drop: - for i in drop: - del x[i] - - # Cast the values to the given types - if converters is not None: - for i, t in converters.items(): - if str(t) == "": - # Fix an issue with converting '1.0' to an int type - x[i] = int(float(x[i])) - else: - x[i] = t(x[i]) - - # Drop Nones - if drop_nones: - for i in list(x): - if x[i] is None: - del x[i] - - # Parse the dates - if parse_dates is not None: - for i, fmt in parse_dates.items(): - x[i] = dt.datetime.strptime(x[i], fmt) - - # Separate the target from the features - y = None - if isinstance(target, list): - y = {name: x.pop(name) for name in target} - elif target is not None: - y = x.pop(target) - - yield x, y diff --git a/virny/metrics/accuracy_metrics.py b/virny/metrics/accuracy_metrics.py index 3cbeec6e..9e0434e6 100644 --- a/virny/metrics/accuracy_metrics.py +++ b/virny/metrics/accuracy_metrics.py @@ -1,6 +1,10 @@ import numpy as np import pandas as pd +from sklearn.metrics import confusion_matrix + +from virny.configs.constants import * + def mean_prediction(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> float: return np.mean(uq_predict_probas.mean().values) @@ -31,3 +35,20 @@ def statistical_bias(y_true: pd.DataFrame, uq_predict_probas: pd.DataFrame) -> f [statistical_bias_from_predict_proba(x, y_true) for x, y_true in np.column_stack((main_predictions, y_true))] ) return np.mean(statistical_bias_lst) + + +def confusion_matrix_metrics(y_true, y_preds): + metrics = {} + TN, FP, FN, TP = confusion_matrix(y_true, y_preds, labels=[0, 1]).ravel() + + metrics[TPR] = TP/(TP+FN) + metrics[TNR] = TN/(TN+FP) + metrics[PPV] = TP/(TP+FP) + metrics[FNR] = FN/(FN+TP) + metrics[FPR] = FP/(FP+TN) + metrics[ACCURACY] = (TP+TN)/(TP+TN+FP+FN) + metrics[F1] = (2*TP)/(2*TP+FP+FN) + metrics[SELECTION_RATE] = (TP+FP)/(TP+FP+TN+FN) + metrics[POSITIVE_RATE] = (TP+FP)/(TP+FN) + + return metrics diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py index 9682b3f7..8d93e41f 100644 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ b/virny/user_interfaces/metrics_computation_interfaces.py @@ -2,7 +2,6 @@ import traceback import numpy as np import pandas as pd -from river import base from tqdm.notebook import tqdm from datetime import datetime, timezone from IPython.display import display @@ -89,7 +88,7 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase save_results [Optional] If to save result metrics in a file model_setting - [Optional] Model type: 'batch' or 'incremental'. Default: 'batch'. + [Optional] Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. save_results_dir_path @@ -142,10 +141,7 @@ def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDatase metrics_df = metrics_df.reset_index() metrics_df = metrics_df.rename(columns={"index": "Metric"}) metrics_df['Model_Name'] = base_model_name - if isinstance(base_model, base.Classifier): # skip for incremental models - metrics_df['Model_Params'] = None - else: - metrics_df['Model_Params'] = str(base_model.get_params()) + metrics_df['Model_Params'] = str(base_model.get_params()) if save_results: # Save metrics @@ -182,7 +178,7 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, A dictionary where keys are sensitive attribute names (including attributes intersections), and values are privilege values for these attributes model_setting - [Optional] Model type: 'batch' or incremental. Default: 'batch'. + [Optional] Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. postprocessor @@ -471,7 +467,7 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo A dictionary where keys are sensitive attribute names (including attributes intersections), and values are privilege values for these attributes model_setting - Model type: 'batch' or incremental. + Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. verbose @@ -544,7 +540,7 @@ def compute_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, base_model_name Model name to name a result file with metrics model_setting - Model type: 'batch' or incremental. + Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. verbose diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index 05b6dd25..87a456a8 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -2,8 +2,6 @@ import re from datetime import datetime, timezone -from sklearn.metrics import confusion_matrix -from river import base from virny.configs.constants import * @@ -14,7 +12,7 @@ def validate_config(config_obj): Extra details: * config_obj.model_setting is an optional argument that defines a type of models to use - to compute fairness and stability metrics. Should be 'batch' or 'incremental'. Default: 'batch'. + to compute fairness and stability metrics. Default: 'batch'. * config_obj.computation_mode is an optional argument that defines a non-default mode for metrics computation. Currently, only 'error_analysis' mode is supported. @@ -87,11 +85,7 @@ def str_to_float(str_var: str, var_name: str): def reset_model_seed(model, new_seed, verbose): - if isinstance(model, base.Classifier): # For incremental models - model.seed = new_seed - if verbose >= 1: - print('Model seed: ', model.seed) - elif 'random_state' in model.get_params(): + if 'random_state' in model.get_params(): model.set_params(random_state=new_seed) if verbose >= 1: print('Model seed: ', model.get_params().get('random_state', None)) @@ -115,20 +109,3 @@ def check_substring_in_list(val_to_check: str, allowed_lst: list): if allowed_val.lower() in val_to_check: return True return False - - -def confusion_matrix_metrics(y_true, y_preds): - metrics = {} - TN, FP, FN, TP = confusion_matrix(y_true, y_preds, labels=[0, 1]).ravel() - - metrics[TPR] = TP/(TP+FN) - metrics[TNR] = TN/(TN+FP) - metrics[PPV] = TP/(TP+FP) - metrics[FNR] = FN/(FN+TP) - metrics[FPR] = FP/(FP+TN) - metrics[ACCURACY] = (TP+TN)/(TP+TN+FP+FN) - metrics[F1] = (2*TP)/(2*TP+FP+FN) - metrics[SELECTION_RATE] = (TP+FP)/(TP+FP+TN+FN) - metrics[POSITIVE_RATE] = (TP+FP)/(TP+FN) - - return metrics From 4a5a0a586b14151b7d0fe11e5ffcd3b02db4f3af Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 20 Dec 2023 01:47:16 +0200 Subject: [PATCH 077/148] Changed structured user interfaces --- .../Multiple_Models_Interface_Use_Case.ipynb | 2 +- ...iple_Models_Interface_With_DB_Writer.ipynb | 18 +- ...Models_Interface_With_Error_Analysis.ipynb | 2 +- ...ls_Interface_With_Multiple_Test_Sets.ipynb | 19 +- virny/user_interfaces/__init__.py | 26 +- .../metrics_computation_interfaces.py | 603 ------------------ virny/user_interfaces/multiple_models_api.py | 277 ++++++++ .../multiple_models_with_db_writer_api.py | 93 +++ ...iple_models_with_multiple_test_sets_api.py | 245 +++++++ 9 files changed, 654 insertions(+), 631 deletions(-) delete mode 100644 virny/user_interfaces/metrics_computation_interfaces.py create mode 100644 virny/user_interfaces/multiple_models_api.py create mode 100644 virny/user_interfaces/multiple_models_with_db_writer_api.py create mode 100644 virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 1aa89ec1..62c98a6e 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -121,7 +121,7 @@ "from sklearn.preprocessing import StandardScaler\n", "\n", "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs, create_models_config_from_tuned_params_df\n", - "from virny.user_interfaces.metrics_computation_interfaces import compute_metrics_with_config\n", + "from virny.user_interfaces.multiple_models_api import compute_metrics_with_config\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb index dea7f371..9bc02030 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb @@ -38,7 +38,7 @@ "id": "8b3e85da", "metadata": {}, "source": [ - "In this example, we are going to audit 4 models for stability and fairness, visualize metrics, and create an analysis report. To get better analysis accuracy, we will use `compute_metrics_multiple_runs_with_db_writer` interface that will compute metrics for multiple models and save results in the user database based on the db_writer function. For that, we will need to do the next steps:\n", + "In this example, we are going to audit 4 models for stability and fairness, visualize metrics, and create an analysis report. To get better analysis accuracy, we will use `compute_metrics_with_db_writer` interface that will compute metrics for multiple models and save results in the user database based on the db_writer function. For that, we will need to do the next steps:\n", "\n", "* Initialize input variables\n", "\n", @@ -76,7 +76,7 @@ "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.preprocessing import StandardScaler\n", "\n", - "from virny.user_interfaces.metrics_computation_interfaces import compute_metrics_multiple_runs_with_db_writer\n", + "from virny.user_interfaces.multiple_models_with_db_writer_api import compute_metrics_with_db_writer\n", "from virny.utils.custom_initializers import create_config_obj, create_models_metrics_dct_from_database_df\n", "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", @@ -117,7 +117,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "5b151f8896bc744e" }, { "cell_type": "markdown", @@ -174,7 +175,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "e249dee6ca87b5fd" }, { "cell_type": "code", @@ -185,7 +187,8 @@ ], "metadata": { "collapsed": false - } + }, + "id": "6cd3c2f8ad510bb2" }, { "cell_type": "markdown", @@ -200,7 +203,7 @@ "id": "2ab8dda3", "metadata": {}, "source": [ - "`compute_metrics_multiple_runs_with_db_writer` interface requires that your **yaml file** includes the following parameters:\n", + "`compute_metrics_with_db_writer` interface requires that your **yaml file** includes the following parameters:\n", "\n", "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", "\n", @@ -417,8 +420,7 @@ } ], "source": [ - "metrics_dct = compute_metrics_multiple_runs_with_db_writer(base_flow_dataset, config, models_config, custom_table_fields_dct,\n", - " db_writer_func)" + "metrics_dct = compute_metrics_with_db_writer(base_flow_dataset, config, models_config, custom_table_fields_dct, db_writer_func)" ] }, { diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index b5bf2600..225e2d76 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -112,7 +112,7 @@ "from sklearn.preprocessing import StandardScaler\n", "\n", "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs, create_models_config_from_tuned_params_df\n", - "from virny.user_interfaces.metrics_computation_interfaces import compute_metrics_with_config\n", + "from virny.user_interfaces.multiple_models_api import compute_metrics_with_config\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", diff --git a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb index 8c54ec59..ccf2f430 100644 --- a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb @@ -48,7 +48,7 @@ "id": "8b3e85da", "metadata": {}, "source": [ - "In this example, we are going to audit 2 models for stability and fairness, visualize metrics, and create an analysis report. To get better analysis accuracy, we will use `compute_metrics_multiple_runs_with_multiple_test_sets` interface that will run metric computation for multiple models and test each model using multiple test sets." + "In this example, we are going to audit 2 models for stability and fairness, visualize metrics, and create an analysis report. To get better analysis accuracy, we will use `compute_metrics_with_multiple_test_sets` interface that will run metric computation for multiple models and test each model using multiple test sets." ] }, { @@ -81,7 +81,7 @@ "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.preprocessing import StandardScaler\n", "\n", - "from virny.user_interfaces.metrics_computation_interfaces import compute_metrics_multiple_runs_with_multiple_test_sets\n", + "from virny.user_interfaces.multiple_models_with_multiple_test_sets_api import compute_metrics_with_multiple_test_sets\n", "from virny.utils.custom_initializers import create_config_obj, create_models_metrics_dct_from_database_df\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset" @@ -123,7 +123,8 @@ "start_time": "2023-04-21T21:00:33.090649Z", "end_time": "2023-04-21T21:00:33.209423Z" } - } + }, + "id": "189c313d70be1af3" }, { "cell_type": "markdown", @@ -189,7 +190,8 @@ "start_time": "2023-04-21T21:00:33.178231Z", "end_time": "2023-04-21T21:00:33.248529Z" } - } + }, + "id": "66d8d02378f28371" }, { "cell_type": "code", @@ -204,7 +206,8 @@ "start_time": "2023-04-21T21:00:33.178397Z", "end_time": "2023-04-21T21:00:33.290936Z" } - } + }, + "id": "3737429ccf1869c" }, { "cell_type": "markdown", @@ -219,7 +222,7 @@ "id": "2ab8dda3", "metadata": {}, "source": [ - "`compute_metrics_multiple_runs_with_multiple_test_sets` interface requires that your **yaml file** includes the following parameters:\n", + "`compute_metrics_with_multiple_test_sets` interface requires that your **yaml file** includes the following parameters:\n", "\n", "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", "\n", @@ -437,8 +440,8 @@ ], "source": [ "extra_test_sets_lst = [(base_flow_dataset.X_test, base_flow_dataset.y_test)]\n", - "compute_metrics_multiple_runs_with_multiple_test_sets(base_flow_dataset, extra_test_sets_lst, config, models_config,\n", - " custom_table_fields_dct, db_writer_func)" + "compute_metrics_with_multiple_test_sets(base_flow_dataset, extra_test_sets_lst, config, models_config,\n", + " custom_table_fields_dct, db_writer_func)" ] }, { diff --git a/virny/user_interfaces/__init__.py b/virny/user_interfaces/__init__.py index b6cf2b77..6e6678f2 100644 --- a/virny/user_interfaces/__init__.py +++ b/virny/user_interfaces/__init__.py @@ -4,21 +4,27 @@ This module contains user interfaces for metrics computation. """ -from .metrics_computation_interfaces import ( - compute_model_metrics, - compute_model_metrics_with_config, - run_metrics_computation, +from .multiple_models_api import ( compute_metrics_with_config, - compute_metrics_multiple_runs_with_multiple_test_sets, - compute_metrics_multiple_runs_with_db_writer + run_metrics_computation, + compute_one_model_metrics_with_config, + compute_one_model_metrics +) +from .multiple_models_with_db_writer_api import compute_metrics_with_db_writer +from .multiple_models_with_multiple_test_sets_api import ( + compute_metrics_with_multiple_test_sets, + run_metrics_computation_with_multiple_test_sets, + compute_one_model_metrics_with_multiple_test_sets ) __all__ = [ "compute_metrics_with_config", - "compute_metrics_multiple_runs_with_multiple_test_sets", - "compute_metrics_multiple_runs_with_db_writer", - "compute_model_metrics", - "compute_model_metrics_with_config", "run_metrics_computation", + "compute_one_model_metrics_with_config", + "compute_one_model_metrics", + "compute_metrics_with_db_writer", + "compute_metrics_with_multiple_test_sets", + "run_metrics_computation_with_multiple_test_sets", + "compute_one_model_metrics_with_multiple_test_sets", ] diff --git a/virny/user_interfaces/metrics_computation_interfaces.py b/virny/user_interfaces/metrics_computation_interfaces.py deleted file mode 100644 index 8d93e41f..00000000 --- a/virny/user_interfaces/metrics_computation_interfaces.py +++ /dev/null @@ -1,603 +0,0 @@ -import os -import traceback -import numpy as np -import pandas as pd -from tqdm.notebook import tqdm -from datetime import datetime, timezone -from IPython.display import display - -from virny.configs.constants import ModelSetting -from virny.utils.protected_groups_partitioning import create_test_protected_groups -from virny.custom_classes.base_dataset import BaseFlowDataset -from virny.analyzers.subgroup_variance_analyzer import SubgroupVarianceAnalyzer -from virny.utils.common_helpers import save_metrics_to_file -from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer - - -def compute_model_metrics_with_config(base_model, model_name: str, dataset: BaseFlowDataset, config, save_results_dir_path: str, - save_results: bool = True, verbose: int = 0) -> pd.DataFrame: - """ - Compute subgroup metrics for the base model. Arguments are defined as an input config object. - Save results in `save_results_dir_path` folder. - - Return a dataframe of model metrics. - - Parameters - ---------- - base_model - Base model for metrics computation - model_name - Model name to name a result file with metrics - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - config - Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes - save_results_dir_path - Location where to save result files with metrics - save_results - [Optional] If to save result metrics in a file - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - return compute_model_metrics(base_model=base_model, - n_estimators=config.n_estimators, - dataset=dataset, - bootstrap_fraction=config.bootstrap_fraction, - sensitive_attributes_dct=config.sensitive_attributes_dct, - dataset_name=config.dataset_name, - base_model_name=model_name, - save_results=save_results, - save_results_dir_path=save_results_dir_path, - verbose=verbose) - - -def compute_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDataset, bootstrap_fraction: float, - sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, - postprocessor=None, postprocessing_sensitive_attribute: str = None, - model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, save_results: bool = True, - save_results_dir_path: str = None, verbose: int = 0): - """ - Compute subgroup metrics for the base model. - Save results in `save_results_dir_path` folder. - - Return a dataframe of model metrics. - - Parameters - ---------- - base_model - Base model for metrics computation - n_estimators - Number of estimators for bootstrap to compute subgroup variance metrics - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - bootstrap_fraction - Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap - sensitive_attributes_dct - A dictionary where keys are sensitive attribute names (including attributes intersections), - and values are privilege values for these attributes - dataset_name - Dataset name to name a result file with metrics - base_model_name - Model name to name a result file with metrics - postprocessor - [Optional] Postprocessor object to apply to model predictions before metrics computation - postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions - save_results - [Optional] If to save result metrics in a file - model_setting - [Optional] Currently, only batch models are supported. Default: 'batch'. - computation_mode - [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. - save_results_dir_path - [Optional] Location where to save result files with metrics - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - model_setting = ModelSetting.BATCH if model_setting is None else ModelSetting[model_setting.upper()] - - test_protected_groups = create_test_protected_groups(dataset.X_test, dataset.init_features_df, sensitive_attributes_dct) - if verbose >= 2: - print('\nProtected groups splits:') - for g in test_protected_groups.keys(): - print(g, test_protected_groups[g].shape) - - # Compute stability metrics for subgroups - subgroup_variance_analyzer = SubgroupVarianceAnalyzer(model_setting=model_setting, - n_estimators=n_estimators, - base_model=base_model, - base_model_name=base_model_name, - bootstrap_fraction=bootstrap_fraction, - dataset=dataset, - dataset_name=dataset_name, - sensitive_attributes_dct=sensitive_attributes_dct, - test_protected_groups=test_protected_groups, - computation_mode=computation_mode, - postprocessor=postprocessor, - postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, - verbose=verbose) - y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, - result_filename=None, - save_dir_path=None) - - # Compute error metrics for subgroups - error_analyzer = SubgroupErrorAnalyzer(X_test=dataset.X_test, - y_test=dataset.y_test, - sensitive_attributes_dct=sensitive_attributes_dct, - test_protected_groups=test_protected_groups, - computation_mode=computation_mode) - dtc_res = error_analyzer.compute_subgroup_metrics(y_preds=y_preds, - models_predictions=dict(), - save_results=False, - result_filename=None, - save_dir_path=None) - error_metrics_df = pd.DataFrame(dtc_res) - - metrics_df = pd.concat([variance_metrics_df, error_metrics_df]) - metrics_df = metrics_df.reset_index() - metrics_df = metrics_df.rename(columns={"index": "Metric"}) - metrics_df['Model_Name'] = base_model_name - metrics_df['Model_Params'] = str(base_model.get_params()) - - if save_results: - # Save metrics - result_filename = f'Metrics_{dataset_name}_{base_model_name}' - save_metrics_to_file(metrics_df, result_filename, save_results_dir_path) - - return metrics_df - - -def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, - models_config: dict, n_estimators: int, sensitive_attributes_dct: dict, - model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, - postprocessor=None, postprocessing_sensitive_attribute: str = None, - save_results: bool = True, save_results_dir_path: str = None, verbose: int = 0) -> dict: - """ - Compute stability and accuracy metrics for each model in models_config. - Save results in `save_results_dir_path` folder. - - Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. - - Parameters - ---------- - dataset - Dataset object that contains all needed attributes like target, features, numerical_columns etc. - bootstrap_fraction - Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap - dataset_name - Dataset name to name a result file with metrics - models_config - Dictionary where keys are model names, and values are initialized models - n_estimators - Number of estimators for bootstrap to compute subgroup stability metrics - sensitive_attributes_dct - A dictionary where keys are sensitive attribute names (including attributes intersections), - and values are privilege values for these attributes - model_setting - [Optional] Currently, only batch models are supported. Default: 'batch'. - computation_mode - [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. - postprocessor - [Optional] Postprocessor object to apply to model predictions before metrics computation - postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions - save_results - [Optional] If to save result metrics in a file - save_results_dir_path - [Optional] Location where to save result files with metrics - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - models_metrics_dct = dict() - num_models = len(models_config) - for model_idx, model_name in tqdm(enumerate(models_config.keys()), - total=num_models, - desc="Analyze models in one run", - colour="red"): - if verbose >= 1: - print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) - try: - base_model = models_config[model_name] - model_metrics_df = compute_model_metrics(base_model=base_model, - n_estimators=n_estimators, - dataset=dataset, - bootstrap_fraction=bootstrap_fraction, - sensitive_attributes_dct=sensitive_attributes_dct, - model_setting=model_setting, - computation_mode=computation_mode, - dataset_name=dataset_name, - base_model_name=model_name, - postprocessor=postprocessor, - postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, - save_results=save_results, - save_results_dir_path=save_results_dir_path, - verbose=verbose) - models_metrics_dct[model_name] = model_metrics_df - if verbose >= 2: - print(f'\n[{model_name}] Metrics matrix:') - display(model_metrics_df) - except Exception as err: - print('#' * 20, f'ERROR with {model_name}', '#' * 20) - traceback.print_exc() - - if verbose >= 1: - print('\n\n\n') - - return models_metrics_dct - - -def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: dict, - save_results_dir_path: str, verbose: int = 0) -> dict: - """ - Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. - Save results in `save_results_dir_path` folder. - - Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. - - Parameters - ---------- - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - config - Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes - models_config - Dictionary where keys are model names, and values are initialized models - save_results_dir_path - Location where to save result files with metrics - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - start_datetime = datetime.now(timezone.utc) - os.makedirs(save_results_dir_path, exist_ok=True) - - model_metrics_dct = dict() - models_metrics_dct = run_metrics_computation(dataset=dataset, - bootstrap_fraction=config.bootstrap_fraction, - dataset_name=config.dataset_name, - models_config=models_config, - n_estimators=config.n_estimators, - sensitive_attributes_dct=config.sensitive_attributes_dct, - model_setting=config.model_setting, - computation_mode=config.computation_mode, - save_results=False, - verbose=verbose) - - # Concatenate with previous results and save them in an overwrite mode each time for backups - for model_name in models_metrics_dct.keys(): - model_metrics_df = models_metrics_dct[model_name] - model_metrics_dct[model_name] = model_metrics_df - - result_filename = f'Metrics_{config.dataset_name}_{model_name}_{config.n_estimators}_Estimators_{start_datetime.strftime("%Y%m%d__%H%M%S")}.csv' - model_metrics_dct[model_name].to_csv(f'{save_results_dir_path}/{result_filename}', index=False, mode='w') - - return model_metrics_dct - - -def compute_metrics_multiple_runs_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, - custom_tbl_fields_dct: dict, db_writer_func, - postprocessor=None, postprocessing_sensitive_attribute: str = None, - verbose: int = 0) -> dict: - """ - Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. - Save results to a database after each run appending fields and value from custom_tbl_fields_dct and using db_writer_func. - - Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. - - Parameters - ---------- - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - config - Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes - models_config - Dictionary where keys are model names, and values are initialized models - custom_tbl_fields_dct - Dictionary where keys are column names and values to add to inserted metrics during saving results to a database - db_writer_func - Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database - postprocessor - [Optional] Postprocessor object to apply to model predictions before metrics computation - postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - multiple_runs_metrics_dct = dict() - run_models_metrics_df = pd.DataFrame() - models_metrics_dct = run_metrics_computation(dataset=dataset, - bootstrap_fraction=config.bootstrap_fraction, - dataset_name=config.dataset_name, - models_config=models_config, - n_estimators=config.n_estimators, - sensitive_attributes_dct=config.sensitive_attributes_dct, - model_setting=config.model_setting, - computation_mode=config.computation_mode, - postprocessor=postprocessor, - postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, - save_results=False, - verbose=verbose) - - # Concatenate current run metrics with previous results and - # create melted_model_metrics_df to save it in a database - for model_name in models_metrics_dct.keys(): - model_metrics_df = models_metrics_dct[model_name] - model_metrics_df['Dataset_Name'] = config.dataset_name - model_metrics_df['Num_Estimators'] = config.n_estimators - - model_metrics_df_copy = model_metrics_df.copy(deep=True) # Version copy for multiple_runs_metrics_dct - # Append current run metrics to multiple_runs_metrics_dct - if multiple_runs_metrics_dct.get(model_name) is None: - multiple_runs_metrics_dct[model_name] = model_metrics_df_copy - else: - multiple_runs_metrics_dct[model_name] = pd.concat([multiple_runs_metrics_dct[model_name], model_metrics_df_copy]) - - # Extend df with technical columns - model_metrics_df['Tag'] = 'OK' - model_metrics_df['Record_Create_Date_Time'] = datetime.now(timezone.utc) - - if postprocessor: - postprocessor_params = np.array(postprocessor.saved_params) - params_means = np.mean(postprocessor_params, axis=0) - params_stds = np.std(postprocessor_params, axis=0) - model_metrics_df['Postprocessor_coefs_means'] = [params_means.tolist()] * len(model_metrics_df) - model_metrics_df['Postprocessor_coefs_stds'] = [params_stds.tolist()] * len(model_metrics_df) - - for column, value in custom_tbl_fields_dct.items(): - model_metrics_df[column] = value - - subgroup_names = [col for col in model_metrics_df.columns if '_priv' in col or '_dis' in col] + ['overall'] - melted_model_metrics_df = model_metrics_df.melt(id_vars=[col for col in model_metrics_df.columns if col not in subgroup_names], - value_vars=subgroup_names, - var_name="Subgroup", - value_name="Metric_Value") - run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) - - # Save results for this run in a database - db_writer_func(run_models_metrics_df) - - return multiple_runs_metrics_dct - - -def compute_metrics_multiple_runs_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test_sets_lst, - config, models_config: dict, custom_tbl_fields_dct: dict, - db_writer_func, verbose: int = 0): - """ - Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set - in extra_test_sets_lst. Arguments are defined as an input config object. Save results to a database after each run - appending fields and value from custom_tbl_fields_dct and using db_writer_func. - Index of each test set is also added as a separate column in out final records in the database - (0 index -- for dataset.X_test, 1 and greater -- for each extra test set in extra_test_sets_lst, keeping the original sequence). - - Parameters - ---------- - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - extra_test_sets_lst - List of extra test sets like [(X_test1, y_test1), (X_test2, y_test2), ...] to compute metrics - that are not equal to original dataset.X_test and dataset.y_test - config - Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes - models_config - Dictionary where keys are model names, and values are initialized models - custom_tbl_fields_dct - Dictionary where keys are column names and values to add to inserted metrics during saving results to a database - db_writer_func - Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - models_metrics_dct = run_metrics_computation_with_multiple_test_sets(dataset=dataset, - bootstrap_fraction=config.bootstrap_fraction, - dataset_name=config.dataset_name, - extra_test_sets_lst=extra_test_sets_lst, - models_config=models_config, - n_estimators=config.n_estimators, - sensitive_attributes_dct=config.sensitive_attributes_dct, - model_setting=config.model_setting, - computation_mode=config.computation_mode, - verbose=verbose) - - # Concatenate current run metrics with previous results and - # create melted_model_metrics_df to save it in a database - run_models_metrics_df = pd.DataFrame() - for model_name in models_metrics_dct.keys(): - model_metrics_dfs_lst = models_metrics_dct[model_name] - for idx, model_metrics_df in enumerate(model_metrics_dfs_lst): - model_metrics_df['Dataset_Name'] = config.dataset_name - model_metrics_df['Num_Estimators'] = config.n_estimators - model_metrics_df['Test_Set_Index'] = idx - - # Extend df with technical columns - model_metrics_df['Tag'] = 'OK' - model_metrics_df['Record_Create_Date_Time'] = datetime.now(timezone.utc) - for column, value in custom_tbl_fields_dct.items(): - model_metrics_df[column] = value - - subgroup_names = [col for col in model_metrics_df.columns if '_priv' in col or '_dis' in col] + ['overall'] - melted_model_metrics_df = model_metrics_df.melt(id_vars=[col for col in model_metrics_df.columns if col not in subgroup_names], - value_vars=subgroup_names, - var_name="Subgroup", - value_name="Metric_Value") - run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) - - # Save results for this run in a database - db_writer_func(run_models_metrics_df) - - if verbose >= 1: - print('Metrics computation interface was successfully executed!') - - -def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, - extra_test_sets_lst: list, models_config: dict, n_estimators: int, - sensitive_attributes_dct: dict, model_setting: str = ModelSetting.BATCH.value, - computation_mode: str = None, verbose: int = 0) -> dict: - """ - Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set - in extra_test_sets_lst. Save results in `save_results_dir_path` folder. - - Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. - - Parameters - ---------- - dataset - Dataset object that contains all needed attributes like target, features, numerical_columns etc. - bootstrap_fraction - Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap - dataset_name - Dataset name to name a result file with metrics - extra_test_sets_lst - List of extra test sets like [(X_test1, y_test1), (X_test2, y_test2), ...] to compute metrics - models_config - Dictionary where keys are model names, and values are initialized models - n_estimators - Number of estimators for bootstrap to compute subgroup stability metrics - sensitive_attributes_dct - A dictionary where keys are sensitive attribute names (including attributes intersections), - and values are privilege values for these attributes - model_setting - Currently, only batch models are supported. Default: 'batch'. - computation_mode - [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - models_metrics_dct = dict() - num_models = len(models_config) - for model_idx, model_name in tqdm(enumerate(models_config.keys()), - total=num_models, - desc="Analyze models in one run", - colour="red"): - if verbose >= 1: - print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) - try: - base_model = models_config[model_name] - model_metrics_dfs_lst = compute_model_metrics_with_multiple_test_sets(base_model=base_model, - n_estimators=n_estimators, - dataset=dataset, - extra_test_sets_lst=extra_test_sets_lst, - bootstrap_fraction=bootstrap_fraction, - sensitive_attributes_dct=sensitive_attributes_dct, - model_setting=model_setting, - computation_mode=computation_mode, - dataset_name=dataset_name, - base_model_name=model_name, - verbose=verbose) - models_metrics_dct[model_name] = model_metrics_dfs_lst - except Exception as err: - print('#' * 20, f'ERROR with {model_name}', '#' * 20) - traceback.print_exc() - - if verbose >= 1: - print('\n\n\n') - - return models_metrics_dct - - -def compute_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, - dataset: BaseFlowDataset, extra_test_sets_lst: list, - bootstrap_fraction: float, sensitive_attributes_dct: dict, - dataset_name: str, base_model_name: str, - model_setting: str = ModelSetting.BATCH.value, - computation_mode: str = None, verbose: int = 0): - """ - Compute subgroup metrics for the base model based on dataset.X_test and each extra test set in extra_test_sets_lst. - Save results in `save_results_dir_path` folder. - - Return a dataframe of model metrics. - - Parameters - ---------- - base_model - Base model for metrics computation - n_estimators - Number of estimators for bootstrap to compute subgroup stability metrics - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - extra_test_sets_lst - List of extra test sets like [(X_test1, y_test1, init_features_df1), (X_test2, y_test2, init_features_df2), ...] - to compute metrics. - bootstrap_fraction - Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap - sensitive_attributes_dct - A dictionary where keys are sensitive attribute names (including attributes intersections), - and values are privilege values for these attributes - dataset_name - Dataset name to name a result file with metrics - base_model_name - Model name to name a result file with metrics - model_setting - Currently, only batch models are supported. Default: 'batch'. - computation_mode - [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - model_setting = ModelSetting.BATCH if model_setting is None else ModelSetting[model_setting.upper()] - subgroup_variance_analyzer = SubgroupVarianceAnalyzer(model_setting=model_setting, - n_estimators=n_estimators, - base_model=base_model, - base_model_name=base_model_name, - bootstrap_fraction=bootstrap_fraction, - dataset=dataset, # will be replaced in the below for-cycle - dataset_name=dataset_name, - sensitive_attributes_dct=sensitive_attributes_dct, - test_protected_groups=dict(), # stub for this attribute - computation_mode=computation_mode, - verbose=verbose) - - test_sets_lst = [(dataset.X_test, dataset.y_test, dataset.init_features_df)] + extra_test_sets_lst - all_test_sets_metrics_lst = [] - for set_idx, (new_X_test, new_y_test, cur_init_features_df) in enumerate(test_sets_lst): - new_test_protected_groups = create_test_protected_groups(new_X_test, cur_init_features_df, sensitive_attributes_dct) - if verbose >= 2: - print(f'\nProtected groups splits for test set index #{set_idx}:') - for g in new_test_protected_groups.keys(): - print(g, new_test_protected_groups[g].shape) - - # Replace test sets and protected groups for each new test set - subgroup_variance_analyzer.set_test_sets(new_X_test, new_y_test) - subgroup_variance_analyzer.set_test_protected_groups(new_test_protected_groups) - - # Compute stability metrics for subgroups - y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, - result_filename=None, - save_dir_path=None, - with_fit=True if set_idx == 0 else False) - - # Compute accuracy metrics for subgroups - error_analyzer = SubgroupErrorAnalyzer(X_test=new_X_test, - y_test=new_y_test, - sensitive_attributes_dct=sensitive_attributes_dct, - test_protected_groups=new_test_protected_groups, - computation_mode=computation_mode) - dtc_res = error_analyzer.compute_subgroup_metrics(y_preds, - save_results=False, - result_filename=None, - save_dir_path=None) - error_metrics_df = pd.DataFrame(dtc_res) - - metrics_df = pd.concat([variance_metrics_df, error_metrics_df]) - metrics_df = metrics_df.reset_index() - metrics_df = metrics_df.rename(columns={"index": "Metric"}) - metrics_df['Model_Name'] = base_model_name - metrics_df['Model_Params'] = str(base_model.get_params()) - - all_test_sets_metrics_lst.append(metrics_df) - - return all_test_sets_metrics_lst diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py new file mode 100644 index 00000000..91222f87 --- /dev/null +++ b/virny/user_interfaces/multiple_models_api.py @@ -0,0 +1,277 @@ +import os +import traceback +import pandas as pd +from tqdm.notebook import tqdm +from datetime import datetime, timezone + +from virny.configs.constants import ModelSetting +from virny.utils.protected_groups_partitioning import create_test_protected_groups +from virny.custom_classes.base_dataset import BaseFlowDataset +from virny.analyzers.subgroup_variance_analyzer import SubgroupVarianceAnalyzer +from virny.utils.common_helpers import save_metrics_to_file +from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer + + +def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: dict, + save_results_dir_path: str, verbose: int = 0) -> dict: + """ + Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. + Save results in `save_results_dir_path` folder. + + Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. + + Parameters + ---------- + dataset + BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. + config + Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes + models_config + Dictionary where keys are model names, and values are initialized models + save_results_dir_path + Location where to save result files with metrics + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + start_datetime = datetime.now(timezone.utc) + os.makedirs(save_results_dir_path, exist_ok=True) + + model_metrics_dct = dict() + models_metrics_dct = run_metrics_computation(dataset=dataset, + bootstrap_fraction=config.bootstrap_fraction, + dataset_name=config.dataset_name, + models_config=models_config, + n_estimators=config.n_estimators, + sensitive_attributes_dct=config.sensitive_attributes_dct, + model_setting=config.model_setting, + computation_mode=config.computation_mode, + save_results=False, + verbose=verbose) + + # Concatenate with previous results and save them in an overwrite mode each time for backups + for model_name in models_metrics_dct.keys(): + model_metrics_df = models_metrics_dct[model_name] + model_metrics_dct[model_name] = model_metrics_df + + result_filename = f'Metrics_{config.dataset_name}_{model_name}_{config.n_estimators}_Estimators_{start_datetime.strftime("%Y%m%d__%H%M%S")}.csv' + model_metrics_dct[model_name].to_csv(f'{save_results_dir_path}/{result_filename}', index=False, mode='w') + + return model_metrics_dct + + +def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, + models_config: dict, n_estimators: int, sensitive_attributes_dct: dict, + model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, + postprocessor=None, postprocessing_sensitive_attribute: str = None, + save_results: bool = True, save_results_dir_path: str = None, verbose: int = 0) -> dict: + """ + Compute stability and accuracy metrics for each model in models_config. + Save results in `save_results_dir_path` folder. + + Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. + + Parameters + ---------- + dataset + Dataset object that contains all needed attributes like target, features, numerical_columns etc. + bootstrap_fraction + Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap + dataset_name + Dataset name to name a result file with metrics + models_config + Dictionary where keys are model names, and values are initialized models + n_estimators + Number of estimators for bootstrap to compute subgroup stability metrics + sensitive_attributes_dct + A dictionary where keys are sensitive attribute names (including attributes intersections), + and values are privilege values for these attributes + model_setting + [Optional] Currently, only batch models are supported. Default: 'batch'. + computation_mode + [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + postprocessor + [Optional] Postprocessor object to apply to model predictions before metrics computation + postprocessing_sensitive_attribute + [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions + save_results + [Optional] If to save result metrics in a file + save_results_dir_path + [Optional] Location where to save result files with metrics + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + models_metrics_dct = dict() + num_models = len(models_config) + for model_idx, model_name in tqdm(enumerate(models_config.keys()), + total=num_models, + desc="Analyze models in one run", + colour="red"): + if verbose >= 1: + print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) + try: + base_model = models_config[model_name] + model_metrics_df = compute_one_model_metrics(base_model=base_model, + n_estimators=n_estimators, + dataset=dataset, + bootstrap_fraction=bootstrap_fraction, + sensitive_attributes_dct=sensitive_attributes_dct, + model_setting=model_setting, + computation_mode=computation_mode, + dataset_name=dataset_name, + base_model_name=model_name, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + save_results=save_results, + save_results_dir_path=save_results_dir_path, + verbose=verbose) + models_metrics_dct[model_name] = model_metrics_df + + except Exception as err: + print('#' * 20, f'ERROR with {model_name}', '#' * 20) + traceback.print_exc() + + if verbose >= 1: + print('\n\n\n') + + return models_metrics_dct + + +def compute_one_model_metrics_with_config(base_model, model_name: str, dataset: BaseFlowDataset, config, save_results_dir_path: str, + save_results: bool = True, verbose: int = 0) -> pd.DataFrame: + """ + Compute subgroup metrics for the base model. Arguments are defined as an input config object. + Save results in `save_results_dir_path` folder. + + Return a dataframe of model metrics. + + Parameters + ---------- + base_model + Base model for metrics computation + model_name + Model name to name a result file with metrics + dataset + BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. + config + Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes + save_results_dir_path + Location where to save result files with metrics + save_results + [Optional] If to save result metrics in a file + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + return compute_one_model_metrics(base_model=base_model, + n_estimators=config.n_estimators, + dataset=dataset, + bootstrap_fraction=config.bootstrap_fraction, + sensitive_attributes_dct=config.sensitive_attributes_dct, + dataset_name=config.dataset_name, + base_model_name=model_name, + save_results=save_results, + save_results_dir_path=save_results_dir_path, + verbose=verbose) + + +def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDataset, bootstrap_fraction: float, + sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, + postprocessor=None, postprocessing_sensitive_attribute: str = None, + model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, save_results: bool = True, + save_results_dir_path: str = None, verbose: int = 0): + """ + Compute subgroup metrics for the base model. + Save results in `save_results_dir_path` folder. + + Return a dataframe of model metrics. + + Parameters + ---------- + base_model + Base model for metrics computation + n_estimators + Number of estimators for bootstrap to compute subgroup variance metrics + dataset + BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. + bootstrap_fraction + Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap + sensitive_attributes_dct + A dictionary where keys are sensitive attribute names (including attributes intersections), + and values are privilege values for these attributes + dataset_name + Dataset name to name a result file with metrics + base_model_name + Model name to name a result file with metrics + postprocessor + [Optional] Postprocessor object to apply to model predictions before metrics computation + postprocessing_sensitive_attribute + [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions + save_results + [Optional] If to save result metrics in a file + model_setting + [Optional] Currently, only batch models are supported. Default: 'batch'. + computation_mode + [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + save_results_dir_path + [Optional] Location where to save result files with metrics + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + model_setting = ModelSetting.BATCH if model_setting is None else ModelSetting[model_setting.upper()] + + test_protected_groups = create_test_protected_groups(dataset.X_test, dataset.init_features_df, sensitive_attributes_dct) + if verbose >= 2: + print('\nProtected groups splits:') + for g in test_protected_groups.keys(): + print(g, test_protected_groups[g].shape) + + # Compute stability metrics for subgroups + subgroup_variance_analyzer = SubgroupVarianceAnalyzer(model_setting=model_setting, + n_estimators=n_estimators, + base_model=base_model, + base_model_name=base_model_name, + bootstrap_fraction=bootstrap_fraction, + dataset=dataset, + dataset_name=dataset_name, + sensitive_attributes_dct=sensitive_attributes_dct, + test_protected_groups=test_protected_groups, + computation_mode=computation_mode, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + verbose=verbose) + y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, + result_filename=None, + save_dir_path=None) + + # Compute error metrics for subgroups + error_analyzer = SubgroupErrorAnalyzer(X_test=dataset.X_test, + y_test=dataset.y_test, + sensitive_attributes_dct=sensitive_attributes_dct, + test_protected_groups=test_protected_groups, + computation_mode=computation_mode) + dtc_res = error_analyzer.compute_subgroup_metrics(y_preds=y_preds, + models_predictions=dict(), + save_results=False, + result_filename=None, + save_dir_path=None) + error_metrics_df = pd.DataFrame(dtc_res) + + metrics_df = pd.concat([variance_metrics_df, error_metrics_df]) + metrics_df = metrics_df.reset_index() + metrics_df = metrics_df.rename(columns={"index": "Metric"}) + metrics_df['Model_Name'] = base_model_name + metrics_df['Model_Params'] = str(base_model.get_params()) + + if save_results: + # Save metrics + result_filename = f'Metrics_{dataset_name}_{base_model_name}' + save_metrics_to_file(metrics_df, result_filename, save_results_dir_path) + + return metrics_df diff --git a/virny/user_interfaces/multiple_models_with_db_writer_api.py b/virny/user_interfaces/multiple_models_with_db_writer_api.py new file mode 100644 index 00000000..3e52a9ba --- /dev/null +++ b/virny/user_interfaces/multiple_models_with_db_writer_api.py @@ -0,0 +1,93 @@ +import numpy as np +import pandas as pd +from datetime import datetime, timezone + +from virny.custom_classes.base_dataset import BaseFlowDataset +from virny.user_interfaces.multiple_models_api import run_metrics_computation + + +def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, + custom_tbl_fields_dct: dict, db_writer_func, + postprocessor=None, postprocessing_sensitive_attribute: str = None, + verbose: int = 0) -> dict: + """ + Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. + Save results to a database after each run appending fields and value from custom_tbl_fields_dct and using db_writer_func. + + Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. + + Parameters + ---------- + dataset + BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. + config + Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes + models_config + Dictionary where keys are model names, and values are initialized models + custom_tbl_fields_dct + Dictionary where keys are column names and values to add to inserted metrics during saving results to a database + db_writer_func + Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database + postprocessor + [Optional] Postprocessor object to apply to model predictions before metrics computation + postprocessing_sensitive_attribute + [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + multiple_runs_metrics_dct = dict() + run_models_metrics_df = pd.DataFrame() + models_metrics_dct = run_metrics_computation(dataset=dataset, + bootstrap_fraction=config.bootstrap_fraction, + dataset_name=config.dataset_name, + models_config=models_config, + n_estimators=config.n_estimators, + sensitive_attributes_dct=config.sensitive_attributes_dct, + model_setting=config.model_setting, + computation_mode=config.computation_mode, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + save_results=False, + verbose=verbose) + + # Concatenate current run metrics with previous results and + # create melted_model_metrics_df to save it in a database + for model_name in models_metrics_dct.keys(): + model_metrics_df = models_metrics_dct[model_name] + model_metrics_df['Dataset_Name'] = config.dataset_name + model_metrics_df['Num_Estimators'] = config.n_estimators + + model_metrics_df_copy = model_metrics_df.copy(deep=True) # Version copy for multiple_runs_metrics_dct + # Append current run metrics to multiple_runs_metrics_dct + if multiple_runs_metrics_dct.get(model_name) is None: + multiple_runs_metrics_dct[model_name] = model_metrics_df_copy + else: + multiple_runs_metrics_dct[model_name] = pd.concat([multiple_runs_metrics_dct[model_name], model_metrics_df_copy]) + + # Extend df with technical columns + model_metrics_df['Tag'] = 'OK' + model_metrics_df['Record_Create_Date_Time'] = datetime.now(timezone.utc) + + if postprocessor: + postprocessor_params = np.array(postprocessor.saved_params) + params_means = np.mean(postprocessor_params, axis=0) + params_stds = np.std(postprocessor_params, axis=0) + model_metrics_df['Postprocessor_coefs_means'] = [params_means.tolist()] * len(model_metrics_df) + model_metrics_df['Postprocessor_coefs_stds'] = [params_stds.tolist()] * len(model_metrics_df) + + for column, value in custom_tbl_fields_dct.items(): + model_metrics_df[column] = value + + subgroup_names = [col for col in model_metrics_df.columns if '_priv' in col or '_dis' in col] + ['overall'] + melted_model_metrics_df = model_metrics_df.melt(id_vars=[col for col in model_metrics_df.columns if col not in subgroup_names], + value_vars=subgroup_names, + var_name="Subgroup", + value_name="Metric_Value") + run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) + + # Save results for this run in a database + db_writer_func(run_models_metrics_df) + + return multiple_runs_metrics_dct diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py new file mode 100644 index 00000000..57c5664f --- /dev/null +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -0,0 +1,245 @@ +import traceback +import pandas as pd +from tqdm.notebook import tqdm +from datetime import datetime, timezone + +from virny.configs.constants import ModelSetting +from virny.utils.protected_groups_partitioning import create_test_protected_groups +from virny.custom_classes.base_dataset import BaseFlowDataset +from virny.analyzers.subgroup_variance_analyzer import SubgroupVarianceAnalyzer +from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer + + +def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test_sets_lst, + config, models_config: dict, custom_tbl_fields_dct: dict, + db_writer_func, verbose: int = 0): + """ + Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set + in extra_test_sets_lst. Arguments are defined as an input config object. Save results to a database after each run + appending fields and value from custom_tbl_fields_dct and using db_writer_func. + Index of each test set is also added as a separate column in out final records in the database + (0 index -- for dataset.X_test, 1 and greater -- for each extra test set in extra_test_sets_lst, keeping the original sequence). + + Parameters + ---------- + dataset + BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. + extra_test_sets_lst + List of extra test sets like [(X_test1, y_test1), (X_test2, y_test2), ...] to compute metrics + that are not equal to original dataset.X_test and dataset.y_test + config + Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes + models_config + Dictionary where keys are model names, and values are initialized models + custom_tbl_fields_dct + Dictionary where keys are column names and values to add to inserted metrics during saving results to a database + db_writer_func + Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + models_metrics_dct = run_metrics_computation_with_multiple_test_sets(dataset=dataset, + bootstrap_fraction=config.bootstrap_fraction, + dataset_name=config.dataset_name, + extra_test_sets_lst=extra_test_sets_lst, + models_config=models_config, + n_estimators=config.n_estimators, + sensitive_attributes_dct=config.sensitive_attributes_dct, + model_setting=config.model_setting, + computation_mode=config.computation_mode, + verbose=verbose) + + # Concatenate current run metrics with previous results and + # create melted_model_metrics_df to save it in a database + run_models_metrics_df = pd.DataFrame() + for model_name in models_metrics_dct.keys(): + model_metrics_dfs_lst = models_metrics_dct[model_name] + for idx, model_metrics_df in enumerate(model_metrics_dfs_lst): + model_metrics_df['Dataset_Name'] = config.dataset_name + model_metrics_df['Num_Estimators'] = config.n_estimators + model_metrics_df['Test_Set_Index'] = idx + + # Extend df with technical columns + model_metrics_df['Tag'] = 'OK' + model_metrics_df['Record_Create_Date_Time'] = datetime.now(timezone.utc) + for column, value in custom_tbl_fields_dct.items(): + model_metrics_df[column] = value + + subgroup_names = [col for col in model_metrics_df.columns if '_priv' in col or '_dis' in col] + ['overall'] + melted_model_metrics_df = model_metrics_df.melt(id_vars=[col for col in model_metrics_df.columns if col not in subgroup_names], + value_vars=subgroup_names, + var_name="Subgroup", + value_name="Metric_Value") + run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) + + # Save results for this run in a database + db_writer_func(run_models_metrics_df) + + if verbose >= 1: + print('Metrics computation interface was successfully executed!') + + +def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, + extra_test_sets_lst: list, models_config: dict, n_estimators: int, + sensitive_attributes_dct: dict, model_setting: str = ModelSetting.BATCH.value, + computation_mode: str = None, verbose: int = 0) -> dict: + """ + Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set + in extra_test_sets_lst. Save results in `save_results_dir_path` folder. + + Return a dictionary where keys are model names, and values are metrics for sensitive attributes defined in config. + + Parameters + ---------- + dataset + Dataset object that contains all needed attributes like target, features, numerical_columns etc. + bootstrap_fraction + Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap + dataset_name + Dataset name to name a result file with metrics + extra_test_sets_lst + List of extra test sets like [(X_test1, y_test1), (X_test2, y_test2), ...] to compute metrics + models_config + Dictionary where keys are model names, and values are initialized models + n_estimators + Number of estimators for bootstrap to compute subgroup stability metrics + sensitive_attributes_dct + A dictionary where keys are sensitive attribute names (including attributes intersections), + and values are privilege values for these attributes + model_setting + Currently, only batch models are supported. Default: 'batch'. + computation_mode + [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + models_metrics_dct = dict() + num_models = len(models_config) + for model_idx, model_name in tqdm(enumerate(models_config.keys()), + total=num_models, + desc="Analyze models in one run", + colour="red"): + if verbose >= 1: + print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) + try: + base_model = models_config[model_name] + model_metrics_dfs_lst = compute_one_model_metrics_with_multiple_test_sets(base_model=base_model, + n_estimators=n_estimators, + dataset=dataset, + extra_test_sets_lst=extra_test_sets_lst, + bootstrap_fraction=bootstrap_fraction, + sensitive_attributes_dct=sensitive_attributes_dct, + model_setting=model_setting, + computation_mode=computation_mode, + dataset_name=dataset_name, + base_model_name=model_name, + verbose=verbose) + models_metrics_dct[model_name] = model_metrics_dfs_lst + except Exception as err: + print('#' * 20, f'ERROR with {model_name}', '#' * 20) + traceback.print_exc() + + if verbose >= 1: + print('\n\n\n') + + return models_metrics_dct + + +def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, + dataset: BaseFlowDataset, extra_test_sets_lst: list, + bootstrap_fraction: float, sensitive_attributes_dct: dict, + dataset_name: str, base_model_name: str, + model_setting: str = ModelSetting.BATCH.value, + computation_mode: str = None, verbose: int = 0): + """ + Compute subgroup metrics for the base model based on dataset.X_test and each extra test set in extra_test_sets_lst. + Save results in `save_results_dir_path` folder. + + Return a dataframe of model metrics. + + Parameters + ---------- + base_model + Base model for metrics computation + n_estimators + Number of estimators for bootstrap to compute subgroup stability metrics + dataset + BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. + extra_test_sets_lst + List of extra test sets like [(X_test1, y_test1, init_features_df1), (X_test2, y_test2, init_features_df2), ...] + to compute metrics. + bootstrap_fraction + Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap + sensitive_attributes_dct + A dictionary where keys are sensitive attribute names (including attributes intersections), + and values are privilege values for these attributes + dataset_name + Dataset name to name a result file with metrics + base_model_name + Model name to name a result file with metrics + model_setting + Currently, only batch models are supported. Default: 'batch'. + computation_mode + [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + verbose + [Optional] Level of logs printing. The greater level provides more logs. + As for now, 0, 1, 2 levels are supported. + + """ + model_setting = ModelSetting.BATCH if model_setting is None else ModelSetting[model_setting.upper()] + subgroup_variance_analyzer = SubgroupVarianceAnalyzer(model_setting=model_setting, + n_estimators=n_estimators, + base_model=base_model, + base_model_name=base_model_name, + bootstrap_fraction=bootstrap_fraction, + dataset=dataset, # will be replaced in the below for-cycle + dataset_name=dataset_name, + sensitive_attributes_dct=sensitive_attributes_dct, + test_protected_groups=dict(), # stub for this attribute + computation_mode=computation_mode, + verbose=verbose) + + test_sets_lst = [(dataset.X_test, dataset.y_test, dataset.init_features_df)] + extra_test_sets_lst + all_test_sets_metrics_lst = [] + for set_idx, (new_X_test, new_y_test, cur_init_features_df) in enumerate(test_sets_lst): + new_test_protected_groups = create_test_protected_groups(new_X_test, cur_init_features_df, sensitive_attributes_dct) + if verbose >= 2: + print(f'\nProtected groups splits for test set index #{set_idx}:') + for g in new_test_protected_groups.keys(): + print(g, new_test_protected_groups[g].shape) + + # Replace test sets and protected groups for each new test set + subgroup_variance_analyzer.set_test_sets(new_X_test, new_y_test) + subgroup_variance_analyzer.set_test_protected_groups(new_test_protected_groups) + + # Compute stability metrics for subgroups + y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, + result_filename=None, + save_dir_path=None, + with_fit=True if set_idx == 0 else False) + + # Compute accuracy metrics for subgroups + error_analyzer = SubgroupErrorAnalyzer(X_test=new_X_test, + y_test=new_y_test, + sensitive_attributes_dct=sensitive_attributes_dct, + test_protected_groups=new_test_protected_groups, + computation_mode=computation_mode) + dtc_res = error_analyzer.compute_subgroup_metrics(y_preds, + save_results=False, + result_filename=None, + save_dir_path=None) + error_metrics_df = pd.DataFrame(dtc_res) + + metrics_df = pd.concat([variance_metrics_df, error_metrics_df]) + metrics_df = metrics_df.reset_index() + metrics_df = metrics_df.rename(columns={"index": "Metric"}) + metrics_df['Model_Name'] = base_model_name + metrics_df['Model_Params'] = str(base_model.get_params()) + + all_test_sets_metrics_lst.append(metrics_df) + + return all_test_sets_metrics_lst From d784771e821f8b2c8a10d2a5189df2098d4155ff Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 20 Dec 2023 02:05:05 +0200 Subject: [PATCH 078/148] wip --- ...odels_Interface_For_Incremental_ML_41_0.png | Bin 195385 -> 0 bytes ...odels_Interface_For_Incremental_ML_41_1.png | Bin 24156 -> 0 bytes ...tiple_Models_Interface_With_DB_Writer.ipynb | 2 +- ..._Runs_Interface_For_Incremental_ML_37_0.png | Bin 30274 -> 0 bytes ..._Runs_Interface_For_Incremental_ML_42_0.png | Bin 132542 -> 0 bytes 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zp@VpAHiH?7!AuKu9^5CPp)padzf4kTuMl@TUUUe3UOiNJP_w219VXi{e(01Ea2j$} z3(nV~1yt1qYdelTR!gb`@klf_1;u_1G%f4ULcKcuS-g=l=F+;{AY;$~)(6a{i0khR5=s zU-`N%j0Iqe%3MD9Gm?l7rL$6ofkJ~NI2sUk zCAd~d%1`YxHzKiJK)I|D%u<|1VicjoB3BuL>H9bz7n#9D;CU?6)nnv0fkW%&R70gRD)+ckdvm8gP%NC`wcYAy8Y_Lr=!auzLgqmp{d{(%dM v41Y<)`UmqtI@jMBN%Ubn|G%e2Q)$mqIhRQ?#L(mHddg8L#Usg*=WhHT_}^2? diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 739c469f..e4e2c218 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -10,12 +10,6 @@ class ComputationMode(Enum): POSTPROCESSING_INTERVENTION = "postprocessing_intervention" -class ReportType(Enum): - MULTIPLE_RUNS_MULTIPLE_MODELS = "multiple_runs_multiple_models" - ONE_RUN_MULTIPLE_MODELS = "one_run_multiple_models" - ONE_RUN_ONE_MODEL = "one_run_one_model" - - INTERSECTION_SIGN = '&' MODELS_TUNING_SEED = 42 MODELS_TUNING_TEST_SET_FRACTION = 0.2 diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index daed2671..08e53ab9 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -4,9 +4,7 @@ import pandas as pd import seaborn as sns import matplotlib.pyplot as plt -from datetime import datetime, timezone -from virny.configs.constants import ReportType from virny.utils.data_viz_utils import create_sorted_matrix_by_rank From 2a7c1df35aa4ea792b30f1a51d9f811f73de4394 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 20 Dec 2023 23:03:12 +0200 Subject: [PATCH 079/148] Added postprocessing in compute_metrics_with_config() --- .../abstract_overall_variance_analyzer.py | 8 ++-- ...verall_variance_analyzer_postprocessing.py | 18 +++----- virny/user_interfaces/multiple_models_api.py | 11 ++++- .../multiple_models_with_db_writer_api.py | 42 +++++++++---------- .../postprocessing_intervention_utils.py | 2 +- 5 files changed, 39 insertions(+), 42 deletions(-) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 054cf67d..ced454b4 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -55,7 +55,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.with_predict_proba = with_predict_proba self._verbose = verbose - self.__logger = get_logger(verbose) + self._logger = get_logger(verbose) self.X_train = X_train self.y_train = y_train @@ -92,7 +92,7 @@ def compute_metrics(self, save_results: bool = True, with_fit: bool = True): # Count metrics based on prediction proba results y_preds, self.prediction_metrics = count_prediction_metrics(self.y_test.values, self.models_predictions, self.with_predict_proba) - self.__logger.info(f'Successfully computed predict proba metrics') + self._logger.info(f'Successfully computed predict proba metrics') if save_results: self.save_metrics_to_file() @@ -119,7 +119,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: print('\n', flush=True) - self.__logger.info('Start classifiers testing by bootstrap') + self._logger.info('Start classifiers testing by bootstrap') # Remove a progress bar for UQ without estimators fitting cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), @@ -140,7 +140,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b if self._verbose >= 1: print('\n', flush=True) - self.__logger.info('Successfully tested classifiers by bootstrap') + self._logger.info('Successfully tested classifiers by bootstrap') return models_predictions diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index fa5d50af..028d5157 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -1,15 +1,12 @@ import gc - -import numpy as np import pandas as pd - from tqdm import tqdm +from virny.utils.stability_utils import generate_bootstrap from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer -from virny.utils.postprocessing_intervention_utils import (contruct_binary_label_dataset_from_df, +from virny.utils.postprocessing_intervention_utils import (construct_binary_label_dataset_from_df, construct_binary_label_dataset_from_samples, predict_on_binary_label_dataset) -from virny.utils.stability_utils import generate_bootstrap class BatchOverallVarianceAnalyzerPostProcessing(BatchOverallVarianceAnalyzer): @@ -33,7 +30,7 @@ def __init__(self, postprocessor, sensitive_attribute: str, self.postprocessor = postprocessor self.sensitive_attribute = sensitive_attribute - self.test_binary_label_dataset = contruct_binary_label_dataset_from_df(X_test, y_test, target_column, sensitive_attribute) + self.test_binary_label_dataset = construct_binary_label_dataset_from_df(X_test, y_test, target_column, sensitive_attribute) def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: bool = True) -> dict: """ @@ -56,7 +53,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: print('\n', flush=True) - self._AbstractOverallVarianceAnalyzer__logger.info('Start classifiers testing by bootstrap') + self._logger.info('Start classifiers testing by bootstrap') # Remove a progress bar for UQ without estimators fitting cycle_range = range(self.n_estimators) if with_fit is False else \ @@ -83,14 +80,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b models_predictions[idx] = postprocessor_fitted.predict(test_binary_label_dataset_pred).labels.ravel() self.models_lst[idx] = classifier - - print("Postprocessor fitted params: ", postprocessor_fitted.model_params.x, flush=True) - postprocessor_fitted.saved_params.append(postprocessor_fitted.model_params.x) - - print("Postprocessor fitted params: ", postprocessor_fitted.saved_params, flush=True) if self._verbose >= 1: print('\n', flush=True) - self._AbstractOverallVarianceAnalyzer__logger.info('Successfully tested classifiers by bootstrap') + self._logger.info('Successfully tested classifiers by bootstrap') return models_predictions diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 91222f87..19f0674e 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -13,7 +13,7 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: dict, - save_results_dir_path: str, verbose: int = 0) -> dict: + save_results_dir_path: str, postprocessor=None, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. Save results in `save_results_dir_path` folder. @@ -30,6 +30,8 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: Dictionary where keys are model names, and values are initialized models save_results_dir_path Location where to save result files with metrics + postprocessor + [Optional] Postprocessor object to apply to model predictions before metrics computation verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -38,6 +40,11 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: start_datetime = datetime.now(timezone.utc) os.makedirs(save_results_dir_path, exist_ok=True) + # Check if a type of postprocessing_sensitive_attribute is not NoneType. + # In other words, check if postprocessing_sensitive_attribute is defined in a config yaml. + postprocessing_sensitive_attribute = config.postprocessing_sensitive_attribute \ + if type(config.postprocessing_sensitive_attribute) != type(None) else None + model_metrics_dct = dict() models_metrics_dct = run_metrics_computation(dataset=dataset, bootstrap_fraction=config.bootstrap_fraction, @@ -47,6 +54,8 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: sensitive_attributes_dct=config.sensitive_attributes_dct, model_setting=config.model_setting, computation_mode=config.computation_mode, + postprocessor=postprocessor, + postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=False, verbose=verbose) diff --git a/virny/user_interfaces/multiple_models_with_db_writer_api.py b/virny/user_interfaces/multiple_models_with_db_writer_api.py index 3e52a9ba..15eb6cab 100644 --- a/virny/user_interfaces/multiple_models_with_db_writer_api.py +++ b/virny/user_interfaces/multiple_models_with_db_writer_api.py @@ -1,4 +1,3 @@ -import numpy as np import pandas as pd from datetime import datetime, timezone @@ -8,8 +7,7 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, custom_tbl_fields_dct: dict, db_writer_func, - postprocessor=None, postprocessing_sensitive_attribute: str = None, - verbose: int = 0) -> dict: + postprocessor=None, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. Save results to a database after each run appending fields and value from custom_tbl_fields_dct and using db_writer_func. @@ -30,14 +28,17 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database postprocessor [Optional] Postprocessor object to apply to model predictions before metrics computation - postprocessing_sensitive_attribute - [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. """ - multiple_runs_metrics_dct = dict() + # Check if a type of postprocessing_sensitive_attribute is not NoneType. + # In other words, check if postprocessing_sensitive_attribute is defined in a config yaml. + postprocessing_sensitive_attribute = config.postprocessing_sensitive_attribute \ + if type(config.postprocessing_sensitive_attribute) != type(None) else None + + multiple_models_metrics_dct = dict() run_models_metrics_df = pd.DataFrame() models_metrics_dct = run_metrics_computation(dataset=dataset, bootstrap_fraction=config.bootstrap_fraction, @@ -59,35 +60,30 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf model_metrics_df['Dataset_Name'] = config.dataset_name model_metrics_df['Num_Estimators'] = config.n_estimators - model_metrics_df_copy = model_metrics_df.copy(deep=True) # Version copy for multiple_runs_metrics_dct - # Append current run metrics to multiple_runs_metrics_dct - if multiple_runs_metrics_dct.get(model_name) is None: - multiple_runs_metrics_dct[model_name] = model_metrics_df_copy + model_metrics_df_copy = model_metrics_df.copy(deep=True) # Version copy for multiple_models_metrics_dct + # Append current run metrics to multiple_models_metrics_dct + if multiple_models_metrics_dct.get(model_name) is None: + multiple_models_metrics_dct[model_name] = model_metrics_df_copy else: - multiple_runs_metrics_dct[model_name] = pd.concat([multiple_runs_metrics_dct[model_name], model_metrics_df_copy]) + multiple_models_metrics_dct[model_name] = pd.concat( + [multiple_models_metrics_dct[model_name], model_metrics_df_copy]) # Extend df with technical columns model_metrics_df['Tag'] = 'OK' model_metrics_df['Record_Create_Date_Time'] = datetime.now(timezone.utc) - if postprocessor: - postprocessor_params = np.array(postprocessor.saved_params) - params_means = np.mean(postprocessor_params, axis=0) - params_stds = np.std(postprocessor_params, axis=0) - model_metrics_df['Postprocessor_coefs_means'] = [params_means.tolist()] * len(model_metrics_df) - model_metrics_df['Postprocessor_coefs_stds'] = [params_stds.tolist()] * len(model_metrics_df) - for column, value in custom_tbl_fields_dct.items(): model_metrics_df[column] = value subgroup_names = [col for col in model_metrics_df.columns if '_priv' in col or '_dis' in col] + ['overall'] - melted_model_metrics_df = model_metrics_df.melt(id_vars=[col for col in model_metrics_df.columns if col not in subgroup_names], - value_vars=subgroup_names, - var_name="Subgroup", - value_name="Metric_Value") + melted_model_metrics_df = model_metrics_df.melt( + id_vars=[col for col in model_metrics_df.columns if col not in subgroup_names], + value_vars=subgroup_names, + var_name="Subgroup", + value_name="Metric_Value") run_models_metrics_df = pd.concat([run_models_metrics_df, melted_model_metrics_df]) # Save results for this run in a database db_writer_func(run_models_metrics_df) - return multiple_runs_metrics_dct + return multiple_models_metrics_dct diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index 3f899fc1..2cc54232 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -18,7 +18,7 @@ def construct_binary_label_dataset_from_samples(X_sample, y_sample, column_names return binary_label_dataset -def contruct_binary_label_dataset_from_df(X_sample, y_sample, target_column, sensitive_attribute): +def construct_binary_label_dataset_from_df(X_sample, y_sample, target_column, sensitive_attribute): df = X_sample df[target_column] = y_sample From ac258829d73df5a92d92ad8da3aae6499fb198ab Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Wed, 20 Dec 2023 23:04:59 +0200 Subject: [PATCH 080/148] Added postprocessing in compute_metrics_with_config() --- docs/examples/Multiple_Models_Interface_Use_Case.ipynb | 3 +-- docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb | 3 +-- .../Multiple_Models_Interface_With_Error_Analysis.ipynb | 3 +-- 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 62c98a6e..733f8b4a 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -126,8 +126,7 @@ "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", "from virny.utils.model_tuning_utils import tune_ML_models\n", - "from virny.datasets.base import BaseDataLoader\n", - "from virny.configs.constants import ReportType" + "from virny.datasets.base import BaseDataLoader" ] }, { diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb index 72b944a3..9dcb187a 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb @@ -81,8 +81,7 @@ "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", - "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset\n", - "from virny.configs.constants import ReportType" + "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset" ] }, { diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index 225e2d76..bfb12049 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -117,8 +117,7 @@ "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", "from virny.utils.model_tuning_utils import tune_ML_models\n", - "from virny.datasets.base import BaseDataLoader\n", - "from virny.configs.constants import ReportType" + "from virny.datasets.base import BaseDataLoader" ] }, { From b925dab4aa3e70766196ad06ec4762e1ba0a11ee Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 00:44:34 +0200 Subject: [PATCH 081/148] Added a notebook_logs_stdout argument for all interfaces --- ..._Models_Interface_With_Postprocessor.ipynb | 1002 +++++++++++++++++ docs/examples/experiment_config.yaml | 5 +- ...ssifier_50_Estimators_20231220__221838.csv | 19 + ...ression_50_Estimators_20231220__221838.csv | 19 + ...ssifier_50_Estimators_20231220__221838.csv | 19 + ...ng_results_Law_School_20231220__213427.csv | 3 + ...ng_results_Law_School_20231220__214029.csv | 3 + ...ng_results_Law_School_20231220__214637.csv | 3 + ...ng_results_Law_School_20231220__215134.csv | 4 + ...ng_results_Law_School_20231220__220900.csv | 4 + ...ng_results_Law_School_20231220__221838.csv | 4 + .../abstract_overall_variance_analyzer.py | 12 +- virny/analyzers/abstract_subgroup_analyzer.py | 4 +- .../batch_overall_variance_analyzer.py | 3 +- ...verall_variance_analyzer_postprocessing.py | 9 +- virny/analyzers/subgroup_variance_analyzer.py | 5 +- virny/custom_classes/metrics_visualizer.py | 2 +- virny/user_interfaces/multiple_models_api.py | 27 +- .../multiple_models_with_db_writer_api.py | 6 +- ...iple_models_with_multiple_test_sets_api.py | 27 +- 20 files changed, 1161 insertions(+), 19 deletions(-) create mode 100644 docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb create mode 100644 docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__215134.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__220900.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__221838.csv diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb new file mode 100644 index 00000000..e46ca573 --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -0,0 +1,1002 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 126, + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.311059Z", + "start_time": "2023-12-20T22:18:34.154529Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.313317Z", + "start_time": "2023-12-20T22:18:34.250530Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.313466Z", + "start_time": "2023-12-20T22:18:34.272596Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": [ + "# Multiple Models Interface With Postprocessor" + ] + }, + { + "cell_type": "markdown", + "id": "2251a923", + "metadata": {}, + "source": [ + "In this example, we are going to audit 4 models for stability and fairness, visualize metrics, and create an analysis report. For that, we will use `compute_metrics_with_config` interface that can compute metrics for multiple models. Thus, we will need to do the next steps:\n", + "\n", + "* Initialize input variables\n", + "\n", + "* Compute subgroup metrics\n", + "\n", + "* Make group metrics composition\n", + "\n", + "* Create metrics visualizations and an analysis report" + ] + }, + { + "cell_type": "markdown", + "id": "606df34d", + "metadata": {}, + "source": [ + "## Import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.314083Z", + "start_time": "2023-12-20T22:18:34.294167Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "from pprint import pprint\n", + "from datetime import datetime, timezone\n", + "\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.preprocessing import StandardScaler\n", + "from aif360.algorithms.postprocessing import EqOddsPostprocessing\n", + "\n", + "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs, create_models_config_from_tuned_params_df\n", + "from virny.user_interfaces.multiple_models_api import compute_metrics_with_config\n", + "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", + "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", + "from virny.custom_classes.metrics_composer import MetricsComposer\n", + "from virny.utils.model_tuning_utils import tune_ML_models" + ] + }, + { + "cell_type": "markdown", + "id": "75699f5f", + "metadata": {}, + "source": [ + "## Initialize Input Variables" + ] + }, + { + "cell_type": "markdown", + "id": "e86f6556", + "metadata": {}, + "source": [ + "Based on the library flow, we need to create 3 input objects for a user interface:\n", + "\n", + "* A **config yaml** that is a file with configuration parameters for different user interfaces for metrics computation.\n", + "\n", + "* A **dataset class** that is a wrapper above the user’s raw dataset that includes its descriptive attributes like a target column, numerical columns, categorical columns, etc. This class must be inherited from the BaseDataset class, which was created for user convenience.\n", + "\n", + "* Finally, a **models config** that is a Python dictionary, where keys are model names and values are initialized models for analysis. This dictionary helps conduct audits for different analysis modes and analyze different types of models." + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "outputs": [], + "source": [ + "DATASET_SPLIT_SEED = 42\n", + "MODELS_TUNING_SEED = 42\n", + "TEST_SET_FRACTION = 0.2" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.355072Z", + "start_time": "2023-12-20T22:18:34.314571Z" + } + }, + "id": "ce359a052925eb3a" + }, + { + "cell_type": "code", + "execution_count": 131, + "outputs": [], + "source": [ + "models_params_for_tuning = {\n", + " 'DecisionTreeClassifier': {\n", + " 'model': DecisionTreeClassifier(random_state=MODELS_TUNING_SEED),\n", + " 'params': {\n", + " \"max_depth\": [20, 30],\n", + " \"min_samples_split\" : [0.1],\n", + " \"max_features\": ['sqrt'],\n", + " \"criterion\": [\"gini\", \"entropy\"]\n", + " }\n", + " },\n", + " 'LogisticRegression': {\n", + " 'model': LogisticRegression(random_state=MODELS_TUNING_SEED),\n", + " 'params': {\n", + " 'penalty': ['l2'],\n", + " 'C' : [0.0001, 0.1, 1, 100],\n", + " 'solver': ['newton-cg', 'lbfgs'],\n", + " 'max_iter': [250],\n", + " }\n", + " },\n", + " 'RandomForestClassifier': {\n", + " 'model': RandomForestClassifier(random_state=MODELS_TUNING_SEED),\n", + " 'params': {\n", + " \"max_depth\": [6, 10],\n", + " \"min_samples_leaf\": [1],\n", + " \"n_estimators\": [50, 100],\n", + " \"max_features\": [0.6]\n", + " }\n", + " },\n", + "}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.356643Z", + "start_time": "2023-12-20T22:18:34.335696Z" + } + }, + "id": "2ece07ab7e3a9acc" + }, + { + "cell_type": "markdown", + "source": [ + "### Create a config object" + ], + "metadata": { + "collapsed": false + }, + "id": "1090a686532d96f5" + }, + { + "cell_type": "markdown", + "source": [ + "`compute_metrics_with_config` interface requires that your **yaml file** includes the following parameters:\n", + "\n", + "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", + "\n", + "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", + "\n", + "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", + "\n", + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including attribute intersections), and values are privileged values for these attributes. Currently, the library supports only intersections among two sensitive attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify privileged values for intersectional groups since they will be derived from privileged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." + ], + "metadata": { + "collapsed": false + }, + "id": "d0a03b8f5c5d0ea7" + }, + { + "cell_type": "code", + "execution_count": 132, + "outputs": [], + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", + "config_yaml_content = \"\"\"\n", + "dataset_name: Law_School\n", + "bootstrap_fraction: 0.8\n", + "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", + "sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None}\n", + "postprocessing_sensitive_attribute: 'race_binary'\n", + "\"\"\"\n", + "\n", + "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", + " f.write(config_yaml_content)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.378951Z", + "start_time": "2023-12-20T22:18:34.358265Z" + } + }, + "id": "af22ee06f1e3eb1a" + }, + { + "cell_type": "code", + "execution_count": 133, + "outputs": [], + "source": [ + "config = create_config_obj(config_yaml_path=config_yaml_path)\n", + "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name}_Metrics_{datetime.now(timezone.utc).strftime(\"%Y%m%d__%H%M%S\")}')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.398318Z", + "start_time": "2023-12-20T22:18:34.378384Z" + } + }, + "id": "65181f72484bb92b" + }, + { + "cell_type": "markdown", + "id": "74f57422", + "metadata": {}, + "source": [ + "### Preprocess the dataset, create a BaseFlowDataset class, and define a postprocessor" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "6c55c6a0", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.463427Z", + "start_time": "2023-12-20T22:18:34.398638Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": " decile1b decile3 lsat ugpa zfygpa\n0 10.0 10.0 44.0 3.5 1.33\n1 5.0 4.0 29.0 3.5 -0.11\n2 8.0 7.0 37.0 3.4 0.63\n3 8.0 7.0 43.0 3.3 0.67\n4 3.0 2.0 41.0 3.3 -0.67", + "text/html": "

    " + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from virny.datasets.data_loaders import LawSchoolDataset\n", + "\n", + "data_loader = LawSchoolDataset()\n", + "data_loader.X_data[data_loader.X_data.columns[:5]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "outputs": [], + "source": [ + "column_transformer = ColumnTransformer(transformers=[\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", + "])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.479660Z", + "start_time": "2023-12-20T22:18:34.461227Z" + } + }, + "id": "ebbef5eaf9dc0943" + }, + { + "cell_type": "code", + "execution_count": 136, + "outputs": [], + "source": [ + "# Create a binary race column for postprocessing since aif360 postprocessors can postprocess a dataset only based on binary columns.\n", + "data_loader.X_data['race_binary'] = data_loader.X_data['race'].apply(lambda x: 1 if x == 'White' else 0)\n", + "\n", + "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)\n", + "base_flow_dataset.X_train_val['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_train_val.index, 'race_binary']\n", + "base_flow_dataset.X_test['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_test.index, 'race_binary']" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.548777Z", + "start_time": "2023-12-20T22:18:34.480182Z" + } + }, + "id": "97ed4609effbf53f" + }, + { + "cell_type": "code", + "execution_count": 137, + "outputs": [], + "source": [ + "# Define a postprocessor\n", + "privileged_groups = [{'race_binary': 1}]\n", + "unprivileged_groups = [{'race_binary': 0}]\n", + "postprocessor = EqOddsPostprocessing(privileged_groups=privileged_groups,\n", + " unprivileged_groups=unprivileged_groups,\n", + " seed=None)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:34.567197Z", + "start_time": "2023-12-20T22:18:34.548002Z" + } + }, + "id": "4535191384245578" + }, + { + "cell_type": "markdown", + "source": [ + "### Tune models and create a models config for metrics computation" + ], + "metadata": { + "collapsed": false + }, + "id": "d538119a04cb3d80" + }, + { + "cell_type": "code", + "execution_count": 138, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023/12/21, 00:18:34: Tuning DecisionTreeClassifier...\n", + "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", + "2023/12/21, 00:18:35: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", + "\n", + "2023/12/21, 00:18:35: Tuning LogisticRegression...\n", + "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", + "2023/12/21, 00:18:36: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", + "\n", + "2023/12/21, 00:18:36: Tuning RandomForestClassifier...\n", + "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", + "2023/12/21, 00:18:38: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" + ] + }, + { + "data": { + "text/plain": " Dataset_Name Model_Name F1_Score Accuracy_Score \\\n0 Law_School DecisionTreeClassifier 0.524303 0.887660 \n1 Law_School LogisticRegression 0.660552 0.899359 \n2 Law_School RandomForestClassifier 0.653102 0.895272 \n\n Model_Best_Params \n0 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 {'C': 100, 'max_iter': 250, 'penalty': 'l2', '... \n2 {'max_depth': 10, 'max_features': 0.6, 'min_sa... ", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0Law_SchoolDecisionTreeClassifier0.5243030.887660{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1Law_SchoolLogisticRegression0.6605520.899359{'C': 100, 'max_iter': 250, 'penalty': 'l2', '...
    2Law_SchoolRandomForestClassifier0.6531020.895272{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    \n
    " + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tuned_params_df, models_config = tune_ML_models(models_params_for_tuning, base_flow_dataset, config.dataset_name, n_folds=3)\n", + "tuned_params_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:38.640025Z", + "start_time": "2023-12-20T22:18:34.567356Z" + } + }, + "id": "782741c190a4690b" + }, + { + "cell_type": "code", + "execution_count": 139, + "outputs": [], + "source": [ + "now = datetime.now(timezone.utc)\n", + "date_time_str = now.strftime(\"%Y%m%d__%H%M%S\")\n", + "tuned_df_path = os.path.join(ROOT_DIR, 'results', 'models_tuning', f'tuning_results_{config.dataset_name}_{date_time_str}.csv')\n", + "tuned_params_df.to_csv(tuned_df_path, sep=\",\", columns=tuned_params_df.columns, float_format=\"%.4f\", index=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:38.683671Z", + "start_time": "2023-12-20T22:18:38.640683Z" + } + }, + "id": "21ccc879c5c3e215" + }, + { + "cell_type": "markdown", + "source": [ + "Create models_config from the saved tuned_params_df for higher reliability" + ], + "metadata": { + "collapsed": false + }, + "id": "2da2057228e94ae5" + }, + { + "cell_type": "code", + "execution_count": 140, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'DecisionTreeClassifier': DecisionTreeClassifier(max_depth=20, max_features='sqrt', min_samples_split=0.1,\n", + " random_state=42),\n", + " 'LogisticRegression': LogisticRegression(C=100, max_iter=250, random_state=42, solver='newton-cg'),\n", + " 'RandomForestClassifier': RandomForestClassifier(max_depth=10, max_features=0.6, n_estimators=50,\n", + " random_state=42)}\n" + ] + } + ], + "source": [ + "models_config = create_models_config_from_tuned_params_df(models_params_for_tuning, tuned_df_path)\n", + "pprint(models_config)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:18:38.751348Z", + "start_time": "2023-12-20T22:18:38.662797Z" + } + }, + "id": "3b15f202741fa2ae" + }, + { + "cell_type": "markdown", + "id": "f445b64a", + "metadata": {}, + "source": [ + "## Subgroup Metrics Computation" + ] + }, + { + "cell_type": "markdown", + "id": "c3530f06", + "metadata": {}, + "source": [ + "After the variables are input to a user interface, the interface uses subgroup analyzers to compute different sets of metrics for each privileged and disadvantaged subgroup. As for now, our library supports **Subgroup Variance Analyzer** and **Subgroup Error Analyzer**, but it is easily extensible to any other analyzers. When the variance and error analyzers complete metrics computation, their metrics are combined, returned in a matrix format, and stored in a file if defined." + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "197eadaa", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.494397Z", + "start_time": "2023-12-20T22:18:38.683106Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "Analyze models in one run: 0%| | 0/3 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Aleatoric_Uncertainty0.3893170.3736970.4099590.3275070.733076
    1Statistical_Bias0.1584680.1463280.1745100.1271700.332535
    2Overall_Uncertainty0.4059880.3895350.4277290.3438390.751630
    3Std0.0383510.0365940.0406720.0364560.048889
    4IQR0.0488710.0475340.0506370.0451890.069345
    5Mean_Prediction0.1084660.1018000.1172750.0780050.277877
    6Label_Stability0.9506350.9598990.9383930.9994210.679306
    7Jitter0.0289250.0235930.0359710.0005770.186585
    8TPR0.9772000.9823260.9702151.0000000.817204
    9TNR0.2175930.1926610.2429910.0000000.556213
    10PPV0.9150970.9230770.9043120.9254110.835165
    11FNR0.0228000.0176740.0297850.0000000.182796
    12FPR0.7824070.8073390.7570091.0000000.443787
    13Accuracy0.8983170.9096280.8833710.9254110.747634
    14F10.9451290.9517800.9361050.9612610.826087
    15Selection-Rate0.9569710.9662160.9447541.0000000.717666
    16Positive-Rate1.0678651.0641861.0728771.0806010.978495
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n" + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_model_metrics_df = metrics_dct[list(models_config.keys())[0]]\n", + "sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20)" + ] + }, + { + "cell_type": "markdown", + "id": "a7ff67e9", + "metadata": {}, + "source": [ + "## Group Metrics Composition" + ] + }, + { + "cell_type": "markdown", + "id": "274c97e2", + "metadata": {}, + "source": [ + "**Metrics Composer** is responsible for this second stage of the model audit. Currently, it computes our custom group fairness and stability metrics, but extending it for new group metrics is very simple. We noticed that more and more group metrics have appeared during the last decade, but most of them are based on the same subgroup metrics. Hence, such a separation of subgroup and group metrics computation allows one to experiment with different combinations of subgroup metrics and avoid subgroup metrics recomputation for a new set of grouped metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "f94a20dc", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.545930Z", + "start_time": "2023-12-20T22:19:34.520902Z" + } + }, + "outputs": [], + "source": [ + "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=list(models_config.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "b04d06cf", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.589123Z", + "start_time": "2023-12-20T22:19:34.543201Z" + } + }, + "outputs": [], + "source": [ + "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" + ] + }, + { + "cell_type": "markdown", + "id": "e1a23ece", + "metadata": {}, + "source": [ + "Compute composed metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "be6ace22", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.593643Z", + "start_time": "2023-12-20T22:19:34.560989Z" + } + }, + "outputs": [], + "source": [ + "models_composed_metrics_df = metrics_composer.compose_metrics()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "outputs": [ + { + "data": { + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.026258 -0.177777 -0.156160 \n1 Aleatoric_Uncertainty_Parity 0.036262 0.405569 0.379883 \n2 Aleatoric_Uncertainty_Ratio 1.097037 2.238354 2.057633 \n3 Equalized_Odds_FNR 0.012110 0.182796 0.181716 \n4 Equalized_Odds_FPR -0.050330 -0.556213 -0.483264 \n5 IQR_Parity 0.003103 0.024156 0.024353 \n6 Jitter_Parity 0.012378 0.186008 0.177862 \n7 Label_Stability_Ratio 0.977596 0.679699 0.685269 \n8 Label_Stability_Difference -0.021506 -0.320115 -0.306855 \n9 Overall_Uncertainty_Parity 0.038195 0.407791 0.382356 \n10 Overall_Uncertainty_Ratio 1.098052 2.185994 2.017833 \n11 Statistical_Parity_Difference 0.008691 -0.102106 -0.129628 \n12 Disparate_Impact 1.008167 0.905510 0.879567 \n13 Std_Parity 0.004077 0.012433 0.012021 \n14 Std_Ratio 1.111423 1.341053 1.321444 \n15 Equalized_Odds_TNR 0.050330 0.556213 0.483264 \n16 Equalized_Odds_TPR -0.012110 -0.182796 -0.181716 \n17 Accuracy_Parity -0.018219 -0.142315 -0.116971 \n18 Aleatoric_Uncertainty_Parity 0.045657 0.323508 0.333794 \n19 Aleatoric_Uncertainty_Ratio 1.143547 2.121618 2.072413 \n20 Equalized_Odds_FNR 0.010849 0.078355 0.097660 \n21 Equalized_Odds_FPR -0.110992 -0.310280 -0.364363 \n22 IQR_Parity 0.001995 0.018493 0.018210 \n23 Jitter_Parity 0.002784 0.026328 0.027789 \n24 Label_Stability_Ratio 0.996221 0.961584 0.957938 \n25 Label_Stability_Difference -0.003735 -0.038128 -0.041642 \n26 Overall_Uncertainty_Parity 0.045810 0.325162 0.335366 \n27 Overall_Uncertainty_Ratio 1.143586 2.124282 2.074306 \n28 Statistical_Parity_Difference 0.001883 0.057303 -0.016102 \n29 Disparate_Impact 1.001757 1.053792 0.985000 \n30 Std_Parity 0.001491 0.013507 0.013329 \n31 Std_Ratio 1.164048 2.760372 2.536684 \n32 Equalized_Odds_TNR 0.110992 0.310280 0.364363 \n33 Equalized_Odds_TPR -0.010849 -0.078355 -0.097660 \n34 Accuracy_Parity -0.015836 -0.143893 -0.113419 \n35 Aleatoric_Uncertainty_Parity 0.034493 0.330836 0.330217 \n36 Aleatoric_Uncertainty_Ratio 1.109040 2.178305 2.082683 \n37 Equalized_Odds_FNR 0.009075 0.070978 0.086800 \n38 Equalized_Odds_FPR -0.124496 -0.295903 -0.361339 \n39 IQR_Parity 0.004176 0.050280 0.048795 \n40 Jitter_Parity 0.005902 0.083418 0.078802 \n41 Label_Stability_Ratio 0.991906 0.881949 0.889259 \n42 Label_Stability_Difference -0.007796 -0.115383 -0.107233 \n43 Overall_Uncertainty_Parity 0.035521 0.342422 0.341721 \n44 Overall_Uncertainty_Ratio 1.108588 2.179807 2.083746 \n45 Statistical_Parity_Difference 0.002767 0.074206 0.000946 \n46 Disparate_Impact 1.002572 1.069499 1.000878 \n47 Std_Parity 0.003397 0.036488 0.036150 \n48 Std_Ratio 1.109524 2.355694 2.220968 \n49 Equalized_Odds_TNR 0.124496 0.295903 0.361339 \n50 Equalized_Odds_TPR -0.009075 -0.070978 -0.086800 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", + "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.026258-0.177777-0.156160DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0362620.4055690.379883DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0970372.2383542.057633DecisionTreeClassifier
    3Equalized_Odds_FNR0.0121100.1827960.181716DecisionTreeClassifier
    4Equalized_Odds_FPR-0.050330-0.556213-0.483264DecisionTreeClassifier
    5IQR_Parity0.0031030.0241560.024353DecisionTreeClassifier
    6Jitter_Parity0.0123780.1860080.177862DecisionTreeClassifier
    7Label_Stability_Ratio0.9775960.6796990.685269DecisionTreeClassifier
    8Label_Stability_Difference-0.021506-0.320115-0.306855DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0381950.4077910.382356DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0980522.1859942.017833DecisionTreeClassifier
    11Statistical_Parity_Difference0.008691-0.102106-0.129628DecisionTreeClassifier
    12Disparate_Impact1.0081670.9055100.879567DecisionTreeClassifier
    13Std_Parity0.0040770.0124330.012021DecisionTreeClassifier
    14Std_Ratio1.1114231.3410531.321444DecisionTreeClassifier
    15Equalized_Odds_TNR0.0503300.5562130.483264DecisionTreeClassifier
    16Equalized_Odds_TPR-0.012110-0.182796-0.181716DecisionTreeClassifier
    17Accuracy_Parity-0.018219-0.142315-0.116971LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0456570.3235080.333794LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1435472.1216182.072413LogisticRegression
    20Equalized_Odds_FNR0.0108490.0783550.097660LogisticRegression
    21Equalized_Odds_FPR-0.110992-0.310280-0.364363LogisticRegression
    22IQR_Parity0.0019950.0184930.018210LogisticRegression
    23Jitter_Parity0.0027840.0263280.027789LogisticRegression
    24Label_Stability_Ratio0.9962210.9615840.957938LogisticRegression
    25Label_Stability_Difference-0.003735-0.038128-0.041642LogisticRegression
    26Overall_Uncertainty_Parity0.0458100.3251620.335366LogisticRegression
    27Overall_Uncertainty_Ratio1.1435862.1242822.074306LogisticRegression
    28Statistical_Parity_Difference0.0018830.057303-0.016102LogisticRegression
    29Disparate_Impact1.0017571.0537920.985000LogisticRegression
    30Std_Parity0.0014910.0135070.013329LogisticRegression
    31Std_Ratio1.1640482.7603722.536684LogisticRegression
    32Equalized_Odds_TNR0.1109920.3102800.364363LogisticRegression
    33Equalized_Odds_TPR-0.010849-0.078355-0.097660LogisticRegression
    34Accuracy_Parity-0.015836-0.143893-0.113419RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0344930.3308360.330217RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1090402.1783052.082683RandomForestClassifier
    37Equalized_Odds_FNR0.0090750.0709780.086800RandomForestClassifier
    38Equalized_Odds_FPR-0.124496-0.295903-0.361339RandomForestClassifier
    39IQR_Parity0.0041760.0502800.048795RandomForestClassifier
    40Jitter_Parity0.0059020.0834180.078802RandomForestClassifier
    41Label_Stability_Ratio0.9919060.8819490.889259RandomForestClassifier
    42Label_Stability_Difference-0.007796-0.115383-0.107233RandomForestClassifier
    43Overall_Uncertainty_Parity0.0355210.3424220.341721RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1085882.1798072.083746RandomForestClassifier
    45Statistical_Parity_Difference0.0027670.0742060.000946RandomForestClassifier
    46Disparate_Impact1.0025721.0694991.000878RandomForestClassifier
    47Std_Parity0.0033970.0364880.036150RandomForestClassifier
    48Std_Ratio1.1095242.3556942.220968RandomForestClassifier
    49Equalized_Odds_TNR0.1244960.2959030.361339RandomForestClassifier
    50Equalized_Odds_TPR-0.009075-0.070978-0.086800RandomForestClassifier
    \n
    " + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models_composed_metrics_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.617229Z", + "start_time": "2023-12-20T22:19:34.587591Z" + } + }, + "id": "a286da0406c6401d" + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": { + "is_executing": true + }, + "source": [ + "## Metrics Visualization and Reporting" + ] + }, + { + "cell_type": "markdown", + "id": "2f5d4cdb", + "metadata": {}, + "source": [ + "**Metrics Visualizer** provides metrics visualization and reporting functionality. It unifies different preprocessing methods for result metrics and creates various data formats required for visualizations. Hence, users can simply call methods of the Metrics Visualizer class and get custom plots for diverse metrics analysis. Additionally, these plots could be collected in an HTML report with comments for user convenience and future reference." + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.663736Z", + "start_time": "2023-12-20T22:19:34.611112Z" + } + }, + "outputs": [], + "source": [ + "visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", + " model_names=list(models_config.keys()),\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "5efb1bf2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.684283Z", + "start_time": "2023-12-20T22:19:34.637595Z" + } + }, + "outputs": [ + { + "data": { + "text/html": "\n
    \n", + "text/plain": "alt.Chart(...)" + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualizer.create_overall_metrics_bar_char(\n", + " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", + " metrics_title=\"Error Metrics\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "0eb8528e", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.734849Z", + "start_time": "2023-12-20T22:19:34.681314Z" + } + }, + "outputs": [ + { + "data": { + "text/html": "\n
    \n", + "text/plain": "alt.Chart(...)" + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualizer.create_overall_metrics_bar_char(\n", + " metrics_names=['Label_Stability'],\n", + " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", + " metrics_title=\"Variance Metrics\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Below is an example of an interactive plot. It requires that you run the below cell in Jupyter in the browser or EDAs, which support JavaScript displaying.\n", + "\n", + "You can use this plot to compare any pair of group fairness and stability metrics for all models." + ], + "metadata": { + "collapsed": false, + "is_executing": true + }, + "id": "1f4906acb27ce7dd" + }, + { + "cell_type": "code", + "execution_count": 150, + "outputs": [ + { + "data": { + "text/html": "\n
    \n", + "text/plain": "alt.HConcatChart(...)" + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualizer.create_fairness_variance_interactive_bar_chart()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-20T22:19:34.876618Z", + "start_time": "2023-12-20T22:19:34.731537Z" + } + }, + "id": "b1249b3994b75555" + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "df024aed", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:35.451280Z", + "start_time": "2023-12-20T22:19:34.877287Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
    ", + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer.create_model_rank_heatmaps(\n", + " metrics_lst=[\n", + " # Group fairness metrics\n", + " 'Equalized_Odds_TPR',\n", + " 'Equalized_Odds_FPR',\n", + " 'Disparate_Impact',\n", + " 'Statistical_Parity_Difference',\n", + " 'Accuracy_Parity',\n", + " # Group stability metrics\n", + " 'Label_Stability_Ratio',\n", + " 'IQR_Parity',\n", + " 'Std_Parity',\n", + " 'Std_Ratio',\n", + " 'Jitter_Parity',\n", + " ],\n", + " groups_lst=config.sensitive_attributes_dct.keys(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "2326c129", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-20T22:19:35.451464Z", + "start_time": "2023-12-20T22:19:35.426378Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 44efa1b1..1205bf0f 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,5 +1,6 @@ -dataset_name: COMPAS_Without_Sensitive_Attributes +dataset_name: Law_School bootstrap_fraction: 0.8 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} +sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} +postprocessing_sensitive_attribute: 'race_binary' diff --git a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv new file mode 100644 index 00000000..715e8048 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Aleatoric_Uncertainty,0.3893172497885363,0.3736965000305002,0.4099589548259412,0.327506902860199,0.7330763716644314,0.359182293248589,0.7390653817521671,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Statistical_Bias,0.1584682105386421,0.14632828533128414,0.17451025456265082,0.12716969681195228,0.3325353389302957,0.14321139172980774,0.33553977428965914,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.40598780108322047,0.3895346408955927,0.4277294770454429,0.3438388882086417,0.7516298622752781,0.375656695677424,0.7580124486717071,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Std,0.03835055869809331,0.03659412720362341,0.04067155745864283,0.03645567315290321,0.04888899155667421,0.037396967694245026,0.04941799368215066,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.048870501641202935,0.04753369945696757,0.05063699024179967,0.045189017097965405,0.06934513018924005,0.0469386310159418,0.07129190920105191,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.10846602714729843,0.10179985517370609,0.11727489725525977,0.07800479837887082,0.2778765833578281,0.09342782226195807,0.28300034445291544,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.9506346153846155,0.9598986486486486,0.9383928571428571,0.9994214407260352,0.6793059936908518,0.974976501305483,0.6681212121212122,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.02892503924646794,0.023592801985659228,0.035971209912536606,0.0005767071434359316,0.18658469065859876,0.01481579368039641,0.19267779839208504,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.9771995708154506,0.9823255813953489,0.9702154626108999,1.0,0.8172043010752689,0.9890929965556832,0.8073770491803278,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.2175925925925926,0.1926605504587156,0.24299065420560748,0.0,0.5562130177514792,0.12138728323699421,0.6046511627906976,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.9150967093695052,0.9230769230769231,0.9043118724158299,0.9254112308564946,0.8351648351648352,0.9189333333333334,0.8528138528138528,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.022800429184549355,0.017674418604651163,0.029784537389100127,0.0,0.1827956989247312,0.010907003444316877,0.19262295081967212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.7824074074074074,0.8073394495412844,0.7570093457943925,1.0,0.4437869822485207,0.8786127167630058,0.3953488372093023,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.8983173076923077,0.9096283783783784,0.8833705357142857,0.9254112308564946,0.7476340694006309,0.9107049608355091,0.7545454545454545,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9451290699182774,0.9517800811176206,0.9361051666157139,0.9612608631609957,0.8260869565217391,0.952723251313243,0.8294736842105264,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.9569711538461538,0.9662162162162162,0.9447544642857143,1.0,0.7176656151419558,0.97911227154047,0.7,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.0678648068669527,1.064186046511628,1.0728770595690749,1.0806006742261722,0.978494623655914,1.076349024110218,0.9467213114754098,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv new file mode 100644 index 00000000..e24a6bb1 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Aleatoric_Uncertainty,0.3377334115071367,0.31806567984367007,0.363722914062432,0.28842958613839725,0.6119373361919563,0.31125459579626136,0.6450481514242666,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.13794915375351804,0.1277101394591294,0.15147927978538872,0.11221451494719288,0.28107271279311186,0.12533924340068053,0.28430053754554113,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.33877340223799474,0.31903994855665746,0.36484975174547607,0.28921745012016437,0.6143795334169693,0.31216985252623297,0.6475358125290487,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.009730985772521347,0.009088713899186371,0.01057970217657113,0.007672539657578457,0.021179063061620124,0.008673662823564944,0.022002339998288056,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.01289369301485991,0.012034289957932147,0.014029332768657312,0.010075228003716451,0.028568626184089944,0.011449152489704012,0.02965911789772986,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10391238183569104,0.0931586776220085,0.11812263383234299,0.07516073288182125,0.2638150856390741,0.08781407715497667,0.29075028161489114,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9867019230769232,0.9883108108108107,0.984575892857143,0.9925127623369255,0.954384858044164,0.9900052219321148,0.9483636363636365,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.00937853218210366,0.008179123000551566,0.010963465743440213,0.0053660851748527005,0.03169381317195647,0.007174082165503277,0.03496351267779825,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9847103004291845,0.9893023255813953,0.9784537389100126,0.9944836040453571,0.9161290322580645,0.9911021814006888,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.24305555555555555,0.18807339449541285,0.29906542056074764,0.12167300380228137,0.4319526627218935,0.17052023121387283,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9182091045522761,0.9231770833333334,0.911452184179457,0.9335443037974683,0.8160919540229885,0.9232620320855615,0.8449612403100775,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01528969957081545,0.010697674418604652,0.021546261089987327,0.0055163959546429666,0.08387096774193549,0.008897818599311137,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7569444444444444,0.8119266055045872,0.7009345794392523,0.8783269961977186,0.5680473372781065,0.8294797687861272,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9076923076923077,0.9155405405405406,0.8973214285714286,0.9293817356778219,0.7870662460567823,0.9169712793733682,0.8,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9502976960911209,0.9550965424337674,0.9437652811735942,0.9630508977593115,0.8632218844984803,0.9559800664451827,0.8685258964143426,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9610576923076923,0.972972972972973,0.9453125,0.9858196256381169,0.8233438485804416,0.9765013054830287,0.7818181818181819,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0724248927038627,1.0716279069767443,1.073510773130545,1.0652773521299417,1.1225806451612903,1.0734787600459241,1.0573770491803278,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv new file mode 100644 index 00000000..85daca2e --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Aleatoric_Uncertainty,0.3311936909247063,0.31633513567743937,0.3508282103585948,0.280772979211635,0.6116091948683804,0.30499860106656673,0.6352154913994775,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.1409061534787769,0.13182434926033107,0.15290710905315183,0.11395438327212519,0.2907988060791775,0.1277691308109008,0.2933752347453393,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3424221745189872,0.32712067741232775,0.3626420099813586,0.29023573719904144,0.6326577864907992,0.3153145042755336,0.6570354382536155,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.03247523048330209,0.031012095376433062,0.034408659017378995,0.026914372205238443,0.06340202273638157,0.029607570058186112,0.0657574711747996,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.04087022310275561,0.03907128821735308,0.043247387058466126,0.03320729406396774,0.08348771173172417,0.03699949292660661,0.08579415211684856,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10484149566164079,0.09588367347861963,0.11667861783206161,0.07669949912823015,0.2613536088742684,0.08965967295777133,0.2810426500732169,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9598173076923077,0.9631756756756757,0.9553794642857143,0.9774021554169029,0.8620189274447949,0.9683237597911228,0.861090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.029084183673469245,0.026541643684500845,0.03244396865889211,0.016370981744938503,0.09978883667031473,0.022833058027388466,0.10163512677798393,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9868562231759657,0.9906976744186047,0.9816223067173637,0.9957094698130555,0.9247311827956989,0.9925373134328358,0.9057377049180327,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.2222222222222222,0.16055045871559634,0.2850467289719626,0.10646387832699619,0.40236686390532544,0.15028901734104047,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9163138231631383,0.920881971465629,0.9101057579318449,0.932548794489093,0.8097928436911488,0.9216417910447762,0.8403041825095057,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.013143776824034335,0.009302325581395349,0.018377693282636248,0.004290530186944529,0.07526881720430108,0.007462686567164179,0.0942622950819672,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7777777777777778,0.8394495412844036,0.7149532710280374,0.8935361216730038,0.5976331360946746,0.8497109826589595,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9074519230769231,0.9142736486486487,0.8984375,0.9293817356778219,0.7854889589905363,0.9164490861618799,0.803030303030303,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.9502776701536872,0.9545149002912839,0.9445121951219512,0.9630947087594487,0.8634538152610441,0.9557766721945826,0.8717948717948718,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9651442307692307,0.9767736486486487,0.9497767857142857,0.9880884855360181,0.8375394321766562,0.9796344647519583,0.796969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0769849785407726,1.0758139534883722,1.0785804816223068,1.0677290836653386,1.1419354838709677,1.0769230769230769,1.0778688524590163,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv new file mode 100644 index 00000000..1ff1eed2 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv new file mode 100644 index 00000000..1ff1eed2 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv new file mode 100644 index 00000000..1ff1eed2 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__215134.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__215134.csv new file mode 100644 index 00000000..daa33103 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__215134.csv @@ -0,0 +1,4 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__220900.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__220900.csv new file mode 100644 index 00000000..daa33103 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__220900.csv @@ -0,0 +1,4 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__221838.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__221838.csv new file mode 100644 index 00000000..daa33103 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__221838.csv @@ -0,0 +1,4 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index ced454b4..dfd94224 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -3,7 +3,6 @@ import pandas as pd from copy import deepcopy -from tqdm import tqdm from abc import ABCMeta, abstractmethod from virny.custom_classes.custom_logger import get_logger @@ -43,7 +42,8 @@ class AbstractOverallVarianceAnalyzer(metaclass=ABCMeta): def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - dataset_name: str, n_estimators: int, with_predict_proba: bool = True, verbose: int = 0): + dataset_name: str, n_estimators: int, with_predict_proba: bool = True, + notebook_logs_stdout: bool = False, verbose: int = 0): self.base_model = base_model self.base_model_name = base_model_name self.bootstrap_fraction = bootstrap_fraction @@ -54,6 +54,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.prediction_metrics = None self.with_predict_proba = with_predict_proba + self._notebook_logs_stdout = notebook_logs_stdout self._verbose = verbose self._logger = get_logger(verbose) @@ -120,12 +121,19 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b if self._verbose >= 1: print('\n', flush=True) self._logger.info('Start classifiers testing by bootstrap') + # Remove a progress bar for UQ without estimators fitting + if self._notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm + cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), desc="Classifiers testing by bootstrap", colour="blue", mininterval=10) + # Train and test each estimator in models_predictions for idx in cycle_range: classifier = self.models_lst[idx] diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 0d813190..5a116f9f 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -45,7 +45,9 @@ def _compute_metrics(self, y_test, y_preds): def _partition_and_compute_metrics(self, y_pred_all, results: dict): for group_name in self.test_protected_groups.keys(): X_test_group = self.test_protected_groups[group_name] - results[group_name] = self._compute_metrics(self.y_test[X_test_group.index], y_pred_all[X_test_group.index]) + metrics_dct = self._compute_metrics(self.y_test[X_test_group.index], y_pred_all[X_test_group.index]) + metrics_dct['Sample_Size'] = X_test_group.shape[0] + results[group_name] = metrics_dct return results diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 4326a90b..d70efd8d 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -38,7 +38,7 @@ class BatchOverallVarianceAnalyzer(AbstractOverallVarianceAnalyzer): def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, target_column: str, dataset_name: str, n_estimators: int, - with_predict_proba: bool = True, verbose: int = 0): + with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0): super().__init__(base_model=base_model, base_model_name=base_model_name, bootstrap_fraction=bootstrap_fraction, @@ -49,6 +49,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, dataset_name=dataset_name, n_estimators=n_estimators, with_predict_proba=with_predict_proba, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) self.target_column = target_column diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 028d5157..1f53687d 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -1,6 +1,5 @@ import gc import pandas as pd -from tqdm import tqdm from virny.utils.stability_utils import generate_bootstrap from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer @@ -14,7 +13,7 @@ def __init__(self, postprocessor, sensitive_attribute: str, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, target_column: str, dataset_name: str, n_estimators: int, - with_predict_proba: bool = True, verbose: int = 0): + with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0): super().__init__(base_model=base_model, base_model_name=base_model_name, bootstrap_fraction=bootstrap_fraction, @@ -26,6 +25,7 @@ def __init__(self, postprocessor, sensitive_attribute: str, dataset_name=dataset_name, n_estimators=n_estimators, with_predict_proba=with_predict_proba, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) self.postprocessor = postprocessor @@ -56,6 +56,11 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b self._logger.info('Start classifiers testing by bootstrap') # Remove a progress bar for UQ without estimators fitting + if self._notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm + cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), desc="Classifiers testing by bootstrap", diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 819d0fee..1217fa7d 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -43,7 +43,8 @@ class SubgroupVarianceAnalyzer: def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, base_model_name: str, bootstrap_fraction: float, dataset: BaseFlowDataset, dataset_name: str, sensitive_attributes_dct: dict, test_protected_groups: dict, postprocessor=None, - postprocessing_sensitive_attribute : str = None, computation_mode: str = None, verbose: int = 0): + postprocessing_sensitive_attribute : str = None, computation_mode: str = None, + notebook_logs_stdout: bool = False, verbose: int = 0): if model_setting == ModelSetting.BATCH: if computation_mode == ComputationMode.POSTPROCESSING_INTERVENTION.value: overall_variance_analyzer = BatchOverallVarianceAnalyzerPostProcessing(postprocessor=postprocessor, @@ -59,6 +60,7 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b target_column=dataset.target, n_estimators=n_estimators, with_predict_proba=False, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) else: overall_variance_analyzer = BatchOverallVarianceAnalyzer(base_model=base_model, @@ -71,6 +73,7 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b dataset_name=dataset_name, target_column=dataset.target, n_estimators=n_estimators, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) else: raise ValueError('model_setting is incorrect or not supported') diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 08e53ab9..f483ce65 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -370,7 +370,7 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): results[group_metric][model_name] = metric_value model_metrics_matrix = pd.DataFrame(results).T - sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix) + sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance=0.001) model_rank_heatmap = self.create_model_rank_heatmap(model_metrics_matrix, sorted_matrix_by_rank, num_models) total_model_rank_heatmap = self.create_total_model_rank_heatmap(sorted_matrix_by_rank, num_models) if self.__create_report: diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 19f0674e..e506fb0a 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -1,7 +1,6 @@ import os import traceback import pandas as pd -from tqdm.notebook import tqdm from datetime import datetime, timezone from virny.configs.constants import ModelSetting @@ -13,7 +12,8 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: dict, - save_results_dir_path: str, postprocessor=None, verbose: int = 0) -> dict: + save_results_dir_path: str, postprocessor=None, + notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. Save results in `save_results_dir_path` folder. @@ -32,6 +32,9 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: Location where to save result files with metrics postprocessor [Optional] Postprocessor object to apply to model predictions before metrics computation + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -57,6 +60,7 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: postprocessor=postprocessor, postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=False, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) # Concatenate with previous results and save them in an overwrite mode each time for backups @@ -74,7 +78,8 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, models_config: dict, n_estimators: int, sensitive_attributes_dct: dict, model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, postprocessor=None, postprocessing_sensitive_attribute: str = None, - save_results: bool = True, save_results_dir_path: str = None, verbose: int = 0) -> dict: + save_results: bool = True, save_results_dir_path: str = None, + notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Save results in `save_results_dir_path` folder. @@ -108,11 +113,20 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, [Optional] If to save result metrics in a file save_results_dir_path [Optional] Location where to save result files with metrics + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. """ + # Set a specific tqdm type for Jupyter notebooks and python modules + if notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm + models_metrics_dct = dict() num_models = len(models_config) for model_idx, model_name in tqdm(enumerate(models_config.keys()), @@ -136,6 +150,7 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=save_results, save_results_dir_path=save_results_dir_path, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) models_metrics_dct[model_name] = model_metrics_df @@ -192,7 +207,7 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, postprocessor=None, postprocessing_sensitive_attribute: str = None, model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, save_results: bool = True, - save_results_dir_path: str = None, verbose: int = 0): + save_results_dir_path: str = None, notebook_logs_stdout: bool = False, verbose: int = 0): """ Compute subgroup metrics for the base model. Save results in `save_results_dir_path` folder. @@ -228,6 +243,9 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. save_results_dir_path [Optional] Location where to save result files with metrics + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -254,6 +272,7 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa computation_mode=computation_mode, postprocessor=postprocessor, postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, result_filename=None, diff --git a/virny/user_interfaces/multiple_models_with_db_writer_api.py b/virny/user_interfaces/multiple_models_with_db_writer_api.py index 15eb6cab..4f37a2af 100644 --- a/virny/user_interfaces/multiple_models_with_db_writer_api.py +++ b/virny/user_interfaces/multiple_models_with_db_writer_api.py @@ -7,7 +7,7 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, custom_tbl_fields_dct: dict, db_writer_func, - postprocessor=None, verbose: int = 0) -> dict: + postprocessor=None, notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. Save results to a database after each run appending fields and value from custom_tbl_fields_dct and using db_writer_func. @@ -28,6 +28,9 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database postprocessor [Optional] Postprocessor object to apply to model predictions before metrics computation + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -51,6 +54,7 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf postprocessor=postprocessor, postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=False, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) # Concatenate current run metrics with previous results and diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py index 57c5664f..0182ec53 100644 --- a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -1,6 +1,5 @@ import traceback import pandas as pd -from tqdm.notebook import tqdm from datetime import datetime, timezone from virny.configs.constants import ModelSetting @@ -12,7 +11,7 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test_sets_lst, config, models_config: dict, custom_tbl_fields_dct: dict, - db_writer_func, verbose: int = 0): + db_writer_func, notebook_logs_stdout: bool = False, verbose: int = 0): """ Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set in extra_test_sets_lst. Arguments are defined as an input config object. Save results to a database after each run @@ -35,6 +34,9 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test Dictionary where keys are column names and values to add to inserted metrics during saving results to a database db_writer_func Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -49,6 +51,7 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test sensitive_attributes_dct=config.sensitive_attributes_dct, model_setting=config.model_setting, computation_mode=config.computation_mode, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) # Concatenate current run metrics with previous results and @@ -84,7 +87,8 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, extra_test_sets_lst: list, models_config: dict, n_estimators: int, sensitive_attributes_dct: dict, model_setting: str = ModelSetting.BATCH.value, - computation_mode: str = None, verbose: int = 0) -> dict: + computation_mode: str = None, notebook_logs_stdout: bool = False, + verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set in extra_test_sets_lst. Save results in `save_results_dir_path` folder. @@ -112,11 +116,20 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. """ + # Set a specific tqdm type for Jupyter notebooks and python modules + if notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm + models_metrics_dct = dict() num_models = len(models_config) for model_idx, model_name in tqdm(enumerate(models_config.keys()), @@ -137,6 +150,7 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo computation_mode=computation_mode, dataset_name=dataset_name, base_model_name=model_name, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) models_metrics_dct[model_name] = model_metrics_dfs_lst except Exception as err: @@ -154,7 +168,8 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: bootstrap_fraction: float, sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, model_setting: str = ModelSetting.BATCH.value, - computation_mode: str = None, verbose: int = 0): + computation_mode: str = None, notebook_logs_stdout: bool = False, + verbose: int = 0): """ Compute subgroup metrics for the base model based on dataset.X_test and each extra test set in extra_test_sets_lst. Save results in `save_results_dir_path` folder. @@ -185,6 +200,9 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + notebook_logs_stdout + [Optional] True, if this interface was execute in a Jupyter notebook, + False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. As for now, 0, 1, 2 levels are supported. @@ -201,6 +219,7 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: sensitive_attributes_dct=sensitive_attributes_dct, test_protected_groups=dict(), # stub for this attribute computation_mode=computation_mode, + notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) test_sets_lst = [(dataset.X_test, dataset.y_test, dataset.init_features_df)] + extra_test_sets_lst From 11dcd2bc0ed5e87894b9b1a87e42c837b129ff6f Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 01:14:53 +0200 Subject: [PATCH 082/148] wip --- ..._Models_Interface_With_Postprocessor.ipynb | 342 +++++++++++------- docs/examples/experiment_config.yaml | 2 +- ...ssifier_50_Estimators_20231220__221838.csv | 19 - ...ression_50_Estimators_20231220__221838.csv | 19 - ...ssifier_50_Estimators_20231220__225223.csv | 19 + ...ression_50_Estimators_20231220__225223.csv | 19 + ...ssifier_50_Estimators_20231220__225223.csv | 19 + ...assifier_3_Estimators_20231220__230412.csv | 19 + ...assifier_3_Estimators_20231220__230503.csv | 19 + ...assifier_3_Estimators_20231220__231230.csv | 19 + ...gression_3_Estimators_20231220__230412.csv | 19 + ...gression_3_Estimators_20231220__230503.csv | 19 + ...gression_3_Estimators_20231220__231230.csv | 19 + ...assifier_3_Estimators_20231220__230412.csv | 19 + ...assifier_3_Estimators_20231220__230503.csv | 19 + ...assifier_3_Estimators_20231220__231230.csv | 19 + ...assifier_3_Estimators_20231220__231414.csv | 19 + ...gression_3_Estimators_20231220__231414.csv | 19 + ...ssifier_3_Estimators_20231220__231414.csv} | 34 +- ...g_results_Law_School_20231220__224840.csv} | 0 ...g_results_Law_School_20231220__225046.csv} | 0 ...g_results_Law_School_20231220__225120.csv} | 0 ...g_results_Law_School_20231220__230100.csv} | 1 + ...g_results_Law_School_20231220__230118.csv} | 1 + ...g_results_Law_School_20231220__230412.csv} | 1 + ...ng_results_Law_School_20231220__231414.csv | 4 + .../abstract_overall_variance_analyzer.py | 17 +- virny/analyzers/abstract_subgroup_analyzer.py | 3 +- ...verall_variance_analyzer_postprocessing.py | 17 +- .../analyzers/subgroup_variance_calculator.py | 3 +- virny/user_interfaces/__init__.py | 2 - virny/user_interfaces/multiple_models_api.py | 52 +-- ...iple_models_with_multiple_test_sets_api.py | 11 +- 33 files changed, 531 insertions(+), 263 deletions(-) delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv rename docs/examples/results/{Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv => Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv} (52%) rename docs/examples/results/models_tuning/{tuning_results_Law_School_20231220__215134.csv => tuning_results_Law_School_20231220__224840.csv} (100%) rename docs/examples/results/models_tuning/{tuning_results_Law_School_20231220__220900.csv => tuning_results_Law_School_20231220__225046.csv} (100%) rename docs/examples/results/models_tuning/{tuning_results_Law_School_20231220__221838.csv => tuning_results_Law_School_20231220__225120.csv} (100%) rename docs/examples/results/models_tuning/{tuning_results_Law_School_20231220__214029.csv => tuning_results_Law_School_20231220__230100.csv} (69%) rename docs/examples/results/models_tuning/{tuning_results_Law_School_20231220__214637.csv => tuning_results_Law_School_20231220__230118.csv} (69%) rename docs/examples/results/models_tuning/{tuning_results_Law_School_20231220__213427.csv => tuning_results_Law_School_20231220__230412.csv} (69%) create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__231414.csv diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index e46ca573..c10d61ad 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -2,24 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 126, + "execution_count": 1, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.311059Z", - "start_time": "2023-12-20T22:18:34.154529Z" + "end_time": "2023-12-20T23:14:08.800440Z", + "start_time": "2023-12-20T23:14:08.484828Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -28,12 +19,12 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 2, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.313317Z", - "start_time": "2023-12-20T22:18:34.250530Z" + "end_time": "2023-12-20T23:14:08.800835Z", + "start_time": "2023-12-20T23:14:08.777524Z" } }, "outputs": [], @@ -46,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 3, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.313466Z", - "start_time": "2023-12-20T22:18:34.272596Z" + "end_time": "2023-12-20T23:14:08.800975Z", + "start_time": "2023-12-20T23:14:08.787723Z" } }, "outputs": [ @@ -105,15 +96,34 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 4, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.314083Z", - "start_time": "2023-12-20T22:18:34.294167Z" + "end_time": "2023-12-20T23:14:09.853327Z", + "start_time": "2023-12-20T23:14:08.797652Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:No module named 'tempeh': LawSchoolGPADataset will be unavailable. To install, run:\n", + "pip install 'aif360[LawSchoolGPA]'\n", + "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n", + "pip install 'aif360[AdversarialDebiasing]'\n", + "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n", + "pip install 'aif360[AdversarialDebiasing]'\n", + "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n", + "pip install 'aif360[Reductions]'\n", + "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n", + "pip install 'aif360[Reductions]'\n", + "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n", + "pip install 'aif360[Reductions]'\n" + ] + } + ], "source": [ "import os\n", "from pprint import pprint\n", @@ -160,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 5, "outputs": [], "source": [ "DATASET_SPLIT_SEED = 42\n", @@ -170,15 +180,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.355072Z", - "start_time": "2023-12-20T22:18:34.314571Z" + "end_time": "2023-12-20T23:14:09.873259Z", + "start_time": "2023-12-20T23:14:09.854883Z" } }, "id": "ce359a052925eb3a" }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 6, "outputs": [], "source": [ "models_params_for_tuning = {\n", @@ -214,8 +224,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.356643Z", - "start_time": "2023-12-20T22:18:34.335696Z" + "end_time": "2023-12-20T23:14:09.892785Z", + "start_time": "2023-12-20T23:14:09.875280Z" } }, "id": "2ece07ab7e3a9acc" @@ -250,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 7, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -269,15 +279,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.378951Z", - "start_time": "2023-12-20T22:18:34.358265Z" + "end_time": "2023-12-20T23:14:09.912042Z", + "start_time": "2023-12-20T23:14:09.893873Z" } }, "id": "af22ee06f1e3eb1a" }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 8, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)\n", @@ -286,8 +296,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.398318Z", - "start_time": "2023-12-20T22:18:34.378384Z" + "end_time": "2023-12-20T23:14:09.929669Z", + "start_time": "2023-12-20T23:14:09.912484Z" } }, "id": "65181f72484bb92b" @@ -302,12 +312,12 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 9, "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.463427Z", - "start_time": "2023-12-20T22:18:34.398638Z" + "end_time": "2023-12-20T23:14:09.989067Z", + "start_time": "2023-12-20T23:14:09.930522Z" } }, "outputs": [ @@ -316,7 +326,7 @@ "text/plain": " decile1b decile3 lsat ugpa zfygpa\n0 10.0 10.0 44.0 3.5 1.33\n1 5.0 4.0 29.0 3.5 -0.11\n2 8.0 7.0 37.0 3.4 0.63\n3 8.0 7.0 43.0 3.3 0.67\n4 3.0 2.0 41.0 3.3 -0.67", "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    decile1bdecile3lsatugpazfygpa
    010.010.044.03.51.33
    15.04.029.03.5-0.11
    28.07.037.03.40.63
    38.07.043.03.30.67
    43.02.041.03.3-0.67
    \n
    " }, - "execution_count": 134, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 10, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -341,15 +351,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.479660Z", - "start_time": "2023-12-20T22:18:34.461227Z" + "end_time": "2023-12-20T23:14:10.008914Z", + "start_time": "2023-12-20T23:14:09.989188Z" } }, "id": "ebbef5eaf9dc0943" }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 11, "outputs": [], "source": [ "# Create a binary race column for postprocessing since aif360 postprocessors can postprocess a dataset only based on binary columns.\n", @@ -362,15 +372,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.548777Z", - "start_time": "2023-12-20T22:18:34.480182Z" + "end_time": "2023-12-20T23:14:10.079900Z", + "start_time": "2023-12-20T23:14:10.008637Z" } }, "id": "97ed4609effbf53f" }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 12, "outputs": [], "source": [ "# Define a postprocessor\n", @@ -383,8 +393,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:34.567197Z", - "start_time": "2023-12-20T22:18:34.548002Z" + "end_time": "2023-12-20T23:14:10.089856Z", + "start_time": "2023-12-20T23:14:10.070945Z" } }, "id": "4535191384245578" @@ -401,23 +411,23 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 13, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023/12/21, 00:18:34: Tuning DecisionTreeClassifier...\n", + "2023/12/21, 01:14:10: Tuning DecisionTreeClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 00:18:35: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", + "2023/12/21, 01:14:11: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", "\n", - "2023/12/21, 00:18:35: Tuning LogisticRegression...\n", + "2023/12/21, 01:14:11: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/12/21, 00:18:36: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", + "2023/12/21, 01:14:11: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", "\n", - "2023/12/21, 00:18:36: Tuning RandomForestClassifier...\n", + "2023/12/21, 01:14:11: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 00:18:38: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" + "2023/12/21, 01:14:13: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" ] }, { @@ -425,7 +435,7 @@ "text/plain": " Dataset_Name Model_Name F1_Score Accuracy_Score \\\n0 Law_School DecisionTreeClassifier 0.524303 0.887660 \n1 Law_School LogisticRegression 0.660552 0.899359 \n2 Law_School RandomForestClassifier 0.653102 0.895272 \n\n Model_Best_Params \n0 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 {'C': 100, 'max_iter': 250, 'penalty': 'l2', '... \n2 {'max_depth': 10, 'max_features': 0.6, 'min_sa... ", "text/html": "
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    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0Law_SchoolDecisionTreeClassifier0.5243030.887660{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1Law_SchoolLogisticRegression0.6605520.899359{'C': 100, 'max_iter': 250, 'penalty': 'l2', '...
    2Law_SchoolRandomForestClassifier0.6531020.895272{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    \n
    " }, - "execution_count": 138, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -437,15 +447,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:38.640025Z", - "start_time": "2023-12-20T22:18:34.567356Z" + "end_time": "2023-12-20T23:14:13.999088Z", + "start_time": "2023-12-20T23:14:10.090566Z" } }, "id": "782741c190a4690b" }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 14, "outputs": [], "source": [ "now = datetime.now(timezone.utc)\n", @@ -456,8 +466,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:38.683671Z", - "start_time": "2023-12-20T22:18:38.640683Z" + "end_time": "2023-12-20T23:14:14.057530Z", + "start_time": "2023-12-20T23:14:13.998563Z" } }, "id": "21ccc879c5c3e215" @@ -474,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 15, "outputs": [ { "name": "stdout", @@ -495,8 +505,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:18:38.751348Z", - "start_time": "2023-12-20T22:18:38.662797Z" + "end_time": "2023-12-20T23:14:14.061017Z", + "start_time": "2023-12-20T23:14:14.025272Z" } }, "id": "3b15f202741fa2ae" @@ -519,12 +529,12 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 16, "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.494397Z", - "start_time": "2023-12-20T22:18:38.683106Z" + "end_time": "2023-12-20T23:14:21.937185Z", + "start_time": "2023-12-20T23:14:14.047512Z" } }, "outputs": [ @@ -534,40 +544,59 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "065c2fa7e6524a5e8acf1831c3c58837" + "model_id": "07343a63429c40fa8ae944f660fce21b" } }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-21 01:14:14 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\n", + "\n", "############################## [Model 1 / 3] Analyze DecisionTreeClassifier ##############################\n" ] }, + { + "data": { + "text/plain": "Classifiers testing by bootstrap: 0%| | 0/3 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Aleatoric_Uncertainty0.3893170.3736970.4099590.3275070.733076
    1Statistical_Bias0.1584680.1463280.1745100.1271700.332535
    2Overall_Uncertainty0.4059880.3895350.4277290.3438390.751630
    3Std0.0383510.0365940.0406720.0364560.048889
    4IQR0.0488710.0475340.0506370.0451890.069345
    5Mean_Prediction0.1084660.1018000.1172750.0780050.277877
    6Label_Stability0.9506350.9598990.9383930.9994210.679306
    7Jitter0.0289250.0235930.0359710.0005770.186585
    8TPR0.9772000.9823260.9702151.0000000.817204
    9TNR0.2175930.1926610.2429910.0000000.556213
    10PPV0.9150970.9230770.9043120.9254110.835165
    11FNR0.0228000.0176740.0297850.0000000.182796
    12FPR0.7824070.8073390.7570091.0000000.443787
    13Accuracy0.8983170.9096280.8833710.9254110.747634
    14F10.9451290.9517800.9361050.9612610.826087
    15Selection-Rate0.9569710.9662160.9447541.0000000.717666
    16Positive-Rate1.0678651.0641861.0728771.0806010.978495
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n" + "text/plain": " Metric overall male_priv male_dis race_priv \\\n0 Statistical_Bias 0.157629 0.146737 0.172023 0.125782 \n1 Aleatoric_Uncertainty 0.386951 0.377734 0.399130 0.323657 \n2 Mean_Prediction 0.106570 0.101639 0.113087 0.075560 \n3 Overall_Uncertainty 0.397430 0.387947 0.409962 0.332854 \n4 IQR 0.028789 0.028104 0.029694 0.024105 \n5 Std 0.031122 0.030284 0.032229 0.025542 \n6 Label_Stability 0.968269 0.974381 0.960193 1.000000 \n7 Jitter 0.031731 0.025619 0.039807 0.000000 \n8 TPR 1.000000 1.000000 1.000000 1.000000 \n9 TNR 0.000000 0.000000 0.000000 0.000000 \n10 PPV 0.896154 0.907939 0.880580 0.925411 \n11 FNR 0.000000 0.000000 0.000000 0.000000 \n12 FPR 1.000000 1.000000 1.000000 1.000000 \n13 Accuracy 0.896154 0.907939 0.880580 0.925411 \n14 F1 0.945233 0.951749 0.936499 0.961261 \n15 Selection-Rate 1.000000 1.000000 1.000000 1.000000 \n16 Positive-Rate 1.115880 1.101395 1.135615 1.080601 \n17 Sample_Size 4160.000000 2368.000000 1792.000000 3526.000000 \n\n race_dis \n0 0.334751 \n1 0.738963 \n2 0.279034 \n3 0.756573 \n4 0.054838 \n5 0.062153 \n6 0.791798 \n7 0.208202 \n8 1.000000 \n9 0.000000 \n10 0.733438 \n11 0.000000 \n12 1.000000 \n13 0.733438 \n14 0.846224 \n15 1.000000 \n16 1.363441 \n17 634.000000 ", + "text/html": "
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    Metricoverallmale_privmale_disrace_privrace_dis
    0Statistical_Bias0.1576290.1467370.1720230.1257820.334751
    1Aleatoric_Uncertainty0.3869510.3777340.3991300.3236570.738963
    2Mean_Prediction0.1065700.1016390.1130870.0755600.279034
    3Overall_Uncertainty0.3974300.3879470.4099620.3328540.756573
    4IQR0.0287890.0281040.0296940.0241050.054838
    5Std0.0311220.0302840.0322290.0255420.062153
    6Label_Stability0.9682690.9743810.9601931.0000000.791798
    7Jitter0.0317310.0256190.0398070.0000000.208202
    8TPR1.0000001.0000001.0000001.0000001.000000
    9TNR0.0000000.0000000.0000000.0000000.000000
    10PPV0.8961540.9079390.8805800.9254110.733438
    11FNR0.0000000.0000000.0000000.0000000.000000
    12FPR1.0000001.0000001.0000001.0000001.000000
    13Accuracy0.8961540.9079390.8805800.9254110.733438
    14F10.9452330.9517490.9364990.9612610.846224
    15Selection-Rate1.0000001.0000001.0000001.0000001.000000
    16Positive-Rate1.1158801.1013951.1356151.0806011.363441
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n
    " }, - "execution_count": 142, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -708,12 +773,12 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 18, "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.545930Z", - "start_time": "2023-12-20T22:19:34.520902Z" + "end_time": "2023-12-20T23:14:21.973942Z", + "start_time": "2023-12-20T23:14:21.955161Z" } }, "outputs": [], @@ -723,12 +788,12 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 19, "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.589123Z", - "start_time": "2023-12-20T22:19:34.543201Z" + "end_time": "2023-12-20T23:14:21.999232Z", + "start_time": "2023-12-20T23:14:21.974989Z" } }, "outputs": [], @@ -746,12 +811,12 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 20, "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.593643Z", - "start_time": "2023-12-20T22:19:34.560989Z" + "end_time": "2023-12-20T23:14:22.050819Z", + "start_time": "2023-12-20T23:14:21.990464Z" } }, "outputs": [], @@ -761,14 +826,14 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 21, "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.026258 -0.177777 -0.156160 \n1 Aleatoric_Uncertainty_Parity 0.036262 0.405569 0.379883 \n2 Aleatoric_Uncertainty_Ratio 1.097037 2.238354 2.057633 \n3 Equalized_Odds_FNR 0.012110 0.182796 0.181716 \n4 Equalized_Odds_FPR -0.050330 -0.556213 -0.483264 \n5 IQR_Parity 0.003103 0.024156 0.024353 \n6 Jitter_Parity 0.012378 0.186008 0.177862 \n7 Label_Stability_Ratio 0.977596 0.679699 0.685269 \n8 Label_Stability_Difference -0.021506 -0.320115 -0.306855 \n9 Overall_Uncertainty_Parity 0.038195 0.407791 0.382356 \n10 Overall_Uncertainty_Ratio 1.098052 2.185994 2.017833 \n11 Statistical_Parity_Difference 0.008691 -0.102106 -0.129628 \n12 Disparate_Impact 1.008167 0.905510 0.879567 \n13 Std_Parity 0.004077 0.012433 0.012021 \n14 Std_Ratio 1.111423 1.341053 1.321444 \n15 Equalized_Odds_TNR 0.050330 0.556213 0.483264 \n16 Equalized_Odds_TPR -0.012110 -0.182796 -0.181716 \n17 Accuracy_Parity -0.018219 -0.142315 -0.116971 \n18 Aleatoric_Uncertainty_Parity 0.045657 0.323508 0.333794 \n19 Aleatoric_Uncertainty_Ratio 1.143547 2.121618 2.072413 \n20 Equalized_Odds_FNR 0.010849 0.078355 0.097660 \n21 Equalized_Odds_FPR -0.110992 -0.310280 -0.364363 \n22 IQR_Parity 0.001995 0.018493 0.018210 \n23 Jitter_Parity 0.002784 0.026328 0.027789 \n24 Label_Stability_Ratio 0.996221 0.961584 0.957938 \n25 Label_Stability_Difference -0.003735 -0.038128 -0.041642 \n26 Overall_Uncertainty_Parity 0.045810 0.325162 0.335366 \n27 Overall_Uncertainty_Ratio 1.143586 2.124282 2.074306 \n28 Statistical_Parity_Difference 0.001883 0.057303 -0.016102 \n29 Disparate_Impact 1.001757 1.053792 0.985000 \n30 Std_Parity 0.001491 0.013507 0.013329 \n31 Std_Ratio 1.164048 2.760372 2.536684 \n32 Equalized_Odds_TNR 0.110992 0.310280 0.364363 \n33 Equalized_Odds_TPR -0.010849 -0.078355 -0.097660 \n34 Accuracy_Parity -0.015836 -0.143893 -0.113419 \n35 Aleatoric_Uncertainty_Parity 0.034493 0.330836 0.330217 \n36 Aleatoric_Uncertainty_Ratio 1.109040 2.178305 2.082683 \n37 Equalized_Odds_FNR 0.009075 0.070978 0.086800 \n38 Equalized_Odds_FPR -0.124496 -0.295903 -0.361339 \n39 IQR_Parity 0.004176 0.050280 0.048795 \n40 Jitter_Parity 0.005902 0.083418 0.078802 \n41 Label_Stability_Ratio 0.991906 0.881949 0.889259 \n42 Label_Stability_Difference -0.007796 -0.115383 -0.107233 \n43 Overall_Uncertainty_Parity 0.035521 0.342422 0.341721 \n44 Overall_Uncertainty_Ratio 1.108588 2.179807 2.083746 \n45 Statistical_Parity_Difference 0.002767 0.074206 0.000946 \n46 Disparate_Impact 1.002572 1.069499 1.000878 \n47 Std_Parity 0.003397 0.036488 0.036150 \n48 Std_Ratio 1.109524 2.355694 2.220968 \n49 Equalized_Odds_TNR 0.124496 0.295903 0.361339 \n50 Equalized_Odds_TPR -0.009075 -0.070978 -0.086800 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", - 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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.026258-0.177777-0.156160DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0362620.4055690.379883DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0970372.2383542.057633DecisionTreeClassifier
    3Equalized_Odds_FNR0.0121100.1827960.181716DecisionTreeClassifier
    4Equalized_Odds_FPR-0.050330-0.556213-0.483264DecisionTreeClassifier
    5IQR_Parity0.0031030.0241560.024353DecisionTreeClassifier
    6Jitter_Parity0.0123780.1860080.177862DecisionTreeClassifier
    7Label_Stability_Ratio0.9775960.6796990.685269DecisionTreeClassifier
    8Label_Stability_Difference-0.021506-0.320115-0.306855DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0381950.4077910.382356DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0980522.1859942.017833DecisionTreeClassifier
    11Statistical_Parity_Difference0.008691-0.102106-0.129628DecisionTreeClassifier
    12Disparate_Impact1.0081670.9055100.879567DecisionTreeClassifier
    13Std_Parity0.0040770.0124330.012021DecisionTreeClassifier
    14Std_Ratio1.1114231.3410531.321444DecisionTreeClassifier
    15Equalized_Odds_TNR0.0503300.5562130.483264DecisionTreeClassifier
    16Equalized_Odds_TPR-0.012110-0.182796-0.181716DecisionTreeClassifier
    17Accuracy_Parity-0.018219-0.142315-0.116971LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0456570.3235080.333794LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1435472.1216182.072413LogisticRegression
    20Equalized_Odds_FNR0.0108490.0783550.097660LogisticRegression
    21Equalized_Odds_FPR-0.110992-0.310280-0.364363LogisticRegression
    22IQR_Parity0.0019950.0184930.018210LogisticRegression
    23Jitter_Parity0.0027840.0263280.027789LogisticRegression
    24Label_Stability_Ratio0.9962210.9615840.957938LogisticRegression
    25Label_Stability_Difference-0.003735-0.038128-0.041642LogisticRegression
    26Overall_Uncertainty_Parity0.0458100.3251620.335366LogisticRegression
    27Overall_Uncertainty_Ratio1.1435862.1242822.074306LogisticRegression
    28Statistical_Parity_Difference0.0018830.057303-0.016102LogisticRegression
    29Disparate_Impact1.0017571.0537920.985000LogisticRegression
    30Std_Parity0.0014910.0135070.013329LogisticRegression
    31Std_Ratio1.1640482.7603722.536684LogisticRegression
    32Equalized_Odds_TNR0.1109920.3102800.364363LogisticRegression
    33Equalized_Odds_TPR-0.010849-0.078355-0.097660LogisticRegression
    34Accuracy_Parity-0.015836-0.143893-0.113419RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0344930.3308360.330217RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1090402.1783052.082683RandomForestClassifier
    37Equalized_Odds_FNR0.0090750.0709780.086800RandomForestClassifier
    38Equalized_Odds_FPR-0.124496-0.295903-0.361339RandomForestClassifier
    39IQR_Parity0.0041760.0502800.048795RandomForestClassifier
    40Jitter_Parity0.0059020.0834180.078802RandomForestClassifier
    41Label_Stability_Ratio0.9919060.8819490.889259RandomForestClassifier
    42Label_Stability_Difference-0.007796-0.115383-0.107233RandomForestClassifier
    43Overall_Uncertainty_Parity0.0355210.3424220.341721RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1085882.1798072.083746RandomForestClassifier
    45Statistical_Parity_Difference0.0027670.0742060.000946RandomForestClassifier
    46Disparate_Impact1.0025721.0694991.000878RandomForestClassifier
    47Std_Parity0.0033970.0364880.036150RandomForestClassifier
    48Std_Ratio1.1095242.3556942.220968RandomForestClassifier
    49Equalized_Odds_TNR0.1244960.2959030.361339RandomForestClassifier
    50Equalized_Odds_TPR-0.009075-0.070978-0.086800RandomForestClassifier
    \n
    " + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.027359 -0.191973 -0.170267 \n1 Aleatoric_Uncertainty_Parity 0.021396 0.415307 0.389406 \n2 Aleatoric_Uncertainty_Ratio 1.056643 2.283171 2.093650 \n3 Equalized_Odds_FNR 0.000000 0.000000 0.000000 \n4 Equalized_Odds_FPR 0.000000 0.000000 0.000000 \n5 IQR_Parity 0.001590 0.030733 0.030393 \n6 Jitter_Parity 0.014187 0.208202 0.200322 \n7 Label_Stability_Ratio 0.985440 0.791798 0.796454 \n8 Label_Stability_Difference -0.014187 -0.208202 -0.200322 \n9 Overall_Uncertainty_Parity 0.022015 0.423720 0.398081 \n10 Overall_Uncertainty_Ratio 1.056748 2.272991 2.088096 \n11 Statistical_Parity_Difference 0.034219 0.282840 0.253148 \n12 Disparate_Impact 1.031069 1.261743 1.230279 \n13 Std_Parity 0.001945 0.036610 0.036016 \n14 Std_Ratio 1.064230 2.433322 2.274228 \n15 Equalized_Odds_TNR 0.000000 0.000000 0.000000 \n16 Equalized_Odds_TPR 0.000000 0.000000 0.000000 \n17 Accuracy_Parity -0.020044 -0.144744 -0.120785 \n18 Aleatoric_Uncertainty_Parity 0.049487 0.319597 0.332387 \n19 Aleatoric_Uncertainty_Ratio 1.154018 2.087376 2.051006 \n20 Equalized_Odds_FNR 0.011482 0.080505 0.101758 \n21 Equalized_Odds_FPR -0.097231 -0.298873 -0.355693 \n22 IQR_Parity 0.001905 0.014927 0.014466 \n23 Jitter_Parity 0.004193 0.036200 0.038110 \n24 Label_Stability_Ratio 0.995767 0.963587 0.961569 \n25 Label_Stability_Difference -0.004193 -0.036200 -0.038110 \n26 Overall_Uncertainty_Parity 0.049592 0.321545 0.334056 \n27 Overall_Uncertainty_Ratio 1.153847 2.091073 2.053104 \n28 Statistical_Parity_Difference 0.002645 0.056072 -0.019339 \n29 Disparate_Impact 1.002471 1.052682 0.981970 \n30 Std_Parity 0.001987 0.015588 0.015155 \n31 Std_Ratio 1.204301 2.899581 2.615769 \n32 Equalized_Odds_TNR 0.097231 0.298873 0.355693 \n33 Equalized_Odds_TPR -0.011482 -0.080505 -0.101758 \n34 Accuracy_Parity -0.015550 -0.141181 -0.112635 \n35 Aleatoric_Uncertainty_Parity 0.035296 0.330150 0.326744 \n36 Aleatoric_Uncertainty_Ratio 1.110779 2.164538 2.061203 \n37 Equalized_Odds_FNR 0.006581 0.070059 0.085939 \n38 Equalized_Odds_FPR -0.105976 -0.305622 -0.361339 \n39 IQR_Parity 0.002381 0.030084 0.028413 \n40 Jitter_Parity 0.004253 0.070426 0.064483 \n41 Label_Stability_Ratio 0.995644 0.928520 0.934177 \n42 Label_Stability_Difference -0.004253 -0.070426 -0.064483 \n43 Overall_Uncertainty_Parity 0.035971 0.338240 0.334685 \n44 Overall_Uncertainty_Ratio 1.110478 2.168190 2.064039 \n45 Statistical_Parity_Difference 0.007458 0.072669 0.001807 \n46 Disparate_Impact 1.006951 1.068098 1.001679 \n47 Std_Parity 0.002622 0.031563 0.029989 \n48 Std_Ratio 1.098870 2.382150 2.186846 \n49 Equalized_Odds_TNR 0.105976 0.305622 0.361339 \n50 Equalized_Odds_TPR -0.006581 -0.070059 -0.085939 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", + "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.027359-0.191973-0.170267DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0213960.4153070.389406DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0566432.2831712.093650DecisionTreeClassifier
    3Equalized_Odds_FNR0.0000000.0000000.000000DecisionTreeClassifier
    4Equalized_Odds_FPR0.0000000.0000000.000000DecisionTreeClassifier
    5IQR_Parity0.0015900.0307330.030393DecisionTreeClassifier
    6Jitter_Parity0.0141870.2082020.200322DecisionTreeClassifier
    7Label_Stability_Ratio0.9854400.7917980.796454DecisionTreeClassifier
    8Label_Stability_Difference-0.014187-0.208202-0.200322DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0220150.4237200.398081DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0567482.2729912.088096DecisionTreeClassifier
    11Statistical_Parity_Difference0.0342190.2828400.253148DecisionTreeClassifier
    12Disparate_Impact1.0310691.2617431.230279DecisionTreeClassifier
    13Std_Parity0.0019450.0366100.036016DecisionTreeClassifier
    14Std_Ratio1.0642302.4333222.274228DecisionTreeClassifier
    15Equalized_Odds_TNR0.0000000.0000000.000000DecisionTreeClassifier
    16Equalized_Odds_TPR0.0000000.0000000.000000DecisionTreeClassifier
    17Accuracy_Parity-0.020044-0.144744-0.120785LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0494870.3195970.332387LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1540182.0873762.051006LogisticRegression
    20Equalized_Odds_FNR0.0114820.0805050.101758LogisticRegression
    21Equalized_Odds_FPR-0.097231-0.298873-0.355693LogisticRegression
    22IQR_Parity0.0019050.0149270.014466LogisticRegression
    23Jitter_Parity0.0041930.0362000.038110LogisticRegression
    24Label_Stability_Ratio0.9957670.9635870.961569LogisticRegression
    25Label_Stability_Difference-0.004193-0.036200-0.038110LogisticRegression
    26Overall_Uncertainty_Parity0.0495920.3215450.334056LogisticRegression
    27Overall_Uncertainty_Ratio1.1538472.0910732.053104LogisticRegression
    28Statistical_Parity_Difference0.0026450.056072-0.019339LogisticRegression
    29Disparate_Impact1.0024711.0526820.981970LogisticRegression
    30Std_Parity0.0019870.0155880.015155LogisticRegression
    31Std_Ratio1.2043012.8995812.615769LogisticRegression
    32Equalized_Odds_TNR0.0972310.2988730.355693LogisticRegression
    33Equalized_Odds_TPR-0.011482-0.080505-0.101758LogisticRegression
    34Accuracy_Parity-0.015550-0.141181-0.112635RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0352960.3301500.326744RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1107792.1645382.061203RandomForestClassifier
    37Equalized_Odds_FNR0.0065810.0700590.085939RandomForestClassifier
    38Equalized_Odds_FPR-0.105976-0.305622-0.361339RandomForestClassifier
    39IQR_Parity0.0023810.0300840.028413RandomForestClassifier
    40Jitter_Parity0.0042530.0704260.064483RandomForestClassifier
    41Label_Stability_Ratio0.9956440.9285200.934177RandomForestClassifier
    42Label_Stability_Difference-0.004253-0.070426-0.064483RandomForestClassifier
    43Overall_Uncertainty_Parity0.0359710.3382400.334685RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1104782.1681902.064039RandomForestClassifier
    45Statistical_Parity_Difference0.0074580.0726690.001807RandomForestClassifier
    46Disparate_Impact1.0069511.0680981.001679RandomForestClassifier
    47Std_Parity0.0026220.0315630.029989RandomForestClassifier
    48Std_Ratio1.0988702.3821502.186846RandomForestClassifier
    49Equalized_Odds_TNR0.1059760.3056220.361339RandomForestClassifier
    50Equalized_Odds_TPR-0.006581-0.070059-0.085939RandomForestClassifier
    \n
    " }, - "execution_count": 146, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -779,8 +844,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.617229Z", - "start_time": "2023-12-20T22:19:34.587591Z" + "end_time": "2023-12-20T23:14:22.057783Z", + "start_time": "2023-12-20T23:14:22.014609Z" } }, "id": "a286da0406c6401d" @@ -788,9 +853,7 @@ { "cell_type": "markdown", "id": "deb45226", - "metadata": { - "is_executing": true - }, + "metadata": {}, "source": [ "## Metrics Visualization and Reporting" ] @@ -805,12 +868,12 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 22, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.663736Z", - "start_time": "2023-12-20T22:19:34.611112Z" + "end_time": "2023-12-20T23:14:22.080227Z", + "start_time": "2023-12-20T23:14:22.039175Z" } }, "outputs": [], @@ -822,21 +885,21 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 23, "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.684283Z", - "start_time": "2023-12-20T22:19:34.637595Z" + "end_time": "2023-12-20T23:14:22.132409Z", + "start_time": "2023-12-20T23:14:22.061356Z" } }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 148, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -850,21 +913,21 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 24, "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.734849Z", - "start_time": "2023-12-20T22:19:34.681314Z" + "end_time": "2023-12-20T23:14:22.158869Z", + "start_time": "2023-12-20T23:14:22.109131Z" } }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 149, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -885,21 +948,20 @@ "You can use this plot to compare any pair of group fairness and stability metrics for all models." ], "metadata": { - "collapsed": false, - "is_executing": true + "collapsed": false }, "id": "1f4906acb27ce7dd" }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 25, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.HConcatChart(...)" }, - "execution_count": 150, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -910,27 +972,27 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T22:19:34.876618Z", - "start_time": "2023-12-20T22:19:34.731537Z" + "end_time": "2023-12-20T23:14:22.313571Z", + "start_time": "2023-12-20T23:14:22.154675Z" } }, "id": "b1249b3994b75555" }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 26, "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:35.451280Z", - "start_time": "2023-12-20T22:19:34.877287Z" + "end_time": "2023-12-20T23:14:22.784314Z", + "start_time": "2023-12-20T23:14:22.313906Z" } }, "outputs": [ { "data": { "text/plain": "
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" 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" 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WXC6XL126VC6TyeRTpkyR0pycnORWVlbyqKgoKS01NVXeo0cPuUwmk1+7dk0ul8vlf/75p1wmk8kXLlyokOeVK1eUzrnM5OZ1Z9++fdJ5lfGzGBAQIK9QoYLKa7wqMTEx8q5du0r7lslk8urVq8t79+4tX79+vdIxTafqmrJ+/Xq5TCaTL168WGHdlJQUeadOneQymUweEBCglEeTJk3kHz58kNLv3Lkjt7KyklerVk3+8eNHhePTuXNnaT1V55WqcmVV1lWrVimU9fz583KZTCafNWvWZ4+dTCaT161bVy6Xy+VDhgyRy2QypeM1evRouY2NjTwhIUE+cuRIuUwmU/gsDRo0SC6TyeRbt25V2C4oKEhuZWUlt7W1lcfGxmZZl/TvIGtra3l4eLhCPp9+P8nlcvmkSZPkMplMPnv2bHlqaqqUfuzYMblMJpMPGDBALpfL5bNnz5bLZDL5+fPnFfJcvny5XCaTyVeuXPnZY5SRaGH8D9mzZw8Aw4cPx9TUVErX1NRk/PjxnDhxAl9fXyZOnJhpi0VKSgpTp04lX758Sr9ybW1tgbRunm+xbNkyli1bpnKZlpYWnTp1krpPIO0X49ChQ0lOTqZMmTIK65crVw5jY2PCwsKU8tLX11fqsmjUqBHLly/PdKyKh4cHW7ZsoXnz5sybNw9NzX8+IundT0+ePOH9+/dS17mpqSmHDx+mUKFC2ag9/Pbbb9jY2LBp0yb+/PNPIiMjuXv3Lnfv3mXjxo1Sd3KVKlWylV9m8uXLh7Ozs/R3wYIFKVOmDHfv3sXV1VXhjsnq1asDZHpcVLl8+bLUvfKpQYMGSeMI/fz8SExMVLrLVk1NjcGDB3P48GHOnTtHSEiIwnmbVRfQp8LDwwEUupnTnT9/XqmLFaBYsWL88ssvCmmq9unt7Y26urp0Z3s6Q0NDJkyYgIuLCzt37qR169bZKqsqZcqUUThXNTU1GTt2LEePHmXv3r0MGDAgW/lk9z0pVKgQHh4eVKxYEQ0NDYX1atWqxdq1a7P9OU/vzlOlb9++aGlpSX/b2tqir6+vcJ75+voSFxfHiBEjFN5/gP79+7N161Z8fX0ZP348WlpayOVy4uPjCQwMpHbt2kDauTR//nzgnyEt6Z/X+/fvExsbK713NWrU4OjRo5+9Wze3rzv79u0DwM3NTeGzWK9ePRo2bMixY8eyLF86XV1dNm/ejK+vLz4+Ply/fp2oqChOnz7N6dOnmTNnDh06dGDChAkK568qdnZ2uLu706xZM4V0dXV1bGxsuHHjhvRZy2jo0KEUKFBA+rty5cp06tSJDRs2cOzYsW+6OTMzbdu2ZcGCBezbt49+/fpJ6enTxH3pPlu0aMGRI0c4fPgwffv2BdLuQD9+/DjNmjVTeI/SvXv3jmPHjlGpUiWFay1A+fLl6dixIxs3buTEiRO0atXqs2WwtbWVzt/MJCYmcvDgQQoWLMiIESMUxq02atSIQYMGSTNAyP+eTunatWvY2tpK6/bo0YP27dt/8UwRImD8D7l79y6AygH/+fPnx9zcnCtXrvD48eNMu/d0dHSki8XLly95/PgxoaGhPH36lBs3bgBpQeW3yDj1R1xcHCdOnODZs2dYW1uzePFipZNYTU1N6kIPCwsjKCiIkJAQnj17RmBgoHQBS0lJUfjyK1mypELAB0h3Xau6O3zv3r3S3FgODg5K25qbm1OjRg2uXr2Kg4MD1atXp06dOtStW/eL72KrVKkSHh4epKSkcO/ePS5fvsz58+e5ePEiDx8+pGfPnvj4+HxT97CpqalSMJAvXz4ApXzTg6SEhIRs5z9o0KBs3fV47949QPV5qampibW1Nc+fP+f+/fsKAcOnwUNWDAwMCAsLIzIyEmNjY4Vl58+fZ+3atUrbVKtWTSlg/HSfMTExPHv2jNKlS6u8uFarVg0NDQ2pjl+rZs2aCl2IAEWKFKFo0aI8f/6c6OhohWELmcnue1KkSBHatWtHamoqjx494smTJ4SGhvL48WMp4Mzu5zyr9+nTQAvSPoNRUVHS5zUwMBBIm5t06dKlSuvnzZuX9+/f8+zZM8qXL4+zszMTJkygR48elClThjp16mBvb4+tra1CsG9nZ0epUqUICAigTp061KxZEzs7OxwcHChduvRn65Xb15379++jq6tL+fLllfZdvXr1bAeMkBbQOTk54eTkRFRUFFevXuXy5csEBATw5MkT9uzZw7t371i9enWW+VSoUIEKFSqQkJDA7du3efbsGSEhITx8+JCLFy9K9f2UqilfrK2t2bBhA/fu3cuVgNHIyIh69epx/PhxAgMDqVKlCjExMRw7dgwLC4vPjmX+VL169dDV1eXIkSNSwHjq1Cmio6MzDfbu3r2LXC4nOTlZ5bkbEhIirZedgDE717yQkBCio6NxcHBQ+DGWLuPn38nJiR07drB06VK2b9+OnZ0dderUwcHB4bM/mFQRAeN/SPpNIplNRZP+hZfx5hdVbty4wZw5c6QB/2pqapiZmVGrVi3u3LmjchLYL/Hp1B8jRoxg3Lhx+Pn5MXz4cNavX0/evHkVtgkODsbDw4PTp09L+zcxMaFGjRo8evSIyMhIpXKpakVN/4Wlqg5v376lXr16XLx4kdmzZ1O3bl2lVsN169axYcMGDhw4wMWLF7l48SLz58+nVKlSjB8//rNjoj6loaFBlSpVqFKlCr169SI0NJShQ4dy584dNm/eLN3h+DWyak3IjTFxmUm/EeFz52VsbKxC+qfnQFZMTU0JCwvj2bNnSgGjm5ubwsTyoaGhNGzYUGU+n+7zc58pTU1NDA0NVba6fInMLt6FChUiNDSUqKiobAWMX2Lv3r0sX75c+lLT0tLC3NycypUrExoamu3PeVatwNn5DH78+BFIa4nOSmRkJAC//PILxsbGbNmyhUuXLrFlyxa2bNmCrq4unTt3ZuTIkWhqaqKjo8POnTtZs2aNNJ4sICCAmTNnUrlyZaZMmZLlOGbI3evOx48fFVrlMlLVUp5d+vr6NGjQgAYNGjBmzBgOHz7MmDFjCAgI4O7du1mOBU5MTGTZsmVs27ZN+tzq6elRpUoVypUrp7KlXk1NTWXvSvqP09y8catDhw4cP36cffv2UaVKFY4cOUJsbOxXBag6Ojo4Ojpy4MABXrx4QcmSJTl48CDGxsaZzm2afk4GBQURFBSUad7p62WnDJ8TEREBZH5Nykgmk7F7927Wrl0rzcfp5+eHhoYGjo6OTJkyRel6mRURMP6HpH+hvH37FjMzM6Xl6SdtZhcpgNevX+Pq6opcLmfMmDHSr3QdHR0SEhLYuXNnjpdbU1OTGTNm8PjxY65du8akSZOYN2+etDw2NpYePXoQHh5Ov379aNiwIWXKlJHqm9kNDF/KwcGBlStXsm7dOhYsWMDUqVOVus7z5s3LwIEDGThwIG/evOHixYscP36cY8eO8fvvv3PkyBGlgczpUlJSpG7LzO6qLlGiBBMmTKBLly48efJEaXnGuzLTfe4HwI+W8bwsW7as0vL081LVNEPZ1aRJE65fv86RI0dydILb9C+9zJ7KkJqaSnR0tMrP1KfvVVbvU2bL0r+0v+XYqHL8+HHGjh2LiYkJ8+fPp3LlypiamqKpqcnp06elm4i+h/RjvGvXrmxPLF6/fn3q169PXFwc169f58yZM/j6+uLp6Un+/PmlLvwCBQowevRoRo8ezfPnzzl//jz+/v5cuHCB3r17c/LkyUwD8dy+7hQoUIDo6GjkcrnSdDif/njKzPHjx5k+fTq//PKL0gMe0jVv3pzz58+za9cunjx5kmXAOHfuXDZv3oytrS2urq5UqFBB+kE3b948lQGjXC4nLi5O6Qdq+mcmq++bb+Xg4EChQoU4dOgQ48eP58CBA2hra2erNU+V5s2bc+DAAY4cOUK3bt04deoUv/zyi1JPTbr0c7dz585MnTr1q+vxJdL3+emMAOkyDr+AtK7xOXPmkJKSwt27dzl//jz79u3j2LFjxMTEsGHDhmzvW0yr8x+SPj5M1RimhIQEbt26ha6urhTQqJq0+OjRo8TGxjJgwADpgpH+qyd9XsBvbWFURVtbm7lz56Ktrc3+/fsVAqrz58/z119/8csvvzB8+HAsLS2li/b79++lsVbfWq5KlSqhoaFBr169MDc359ixYwrluHnzJrNnz+bmzZsAFC1alHbt2rFs2TKcnJxISkqSuu1V0dDQQC6XExwcLOWhSvr7krELNH38zKe/1t+/f5/pheNnkdV5mTFdVddcdrVp04b8+fOzc+fOTKcXSfcl54menh5mZmb89ddfKqehuH37NnFxcQrdX+ndRJ++V1lNY3H79m2ltPRu2IyfwZySPs5r/vz5tGrVitKlS0vdqI8fPwZy53OuSvpwjlu3bqlcvmDBAlavXk1sbCzR0dEsX75c+pLLmzcvderUYezYsVKX4JUrV4C0hwBMnz5dmorKzMyMLl26sHHjRmrVqkVkZGSWrUK5fd2xsLAgLi6OO3fuKC1TdT6oUqhQIV6/fs2hQ4dU/phMl35NyThVlyq+vr7o6uqydu1a6tWrp3ANyuq8SB8OlVH6bBffOhY7K5qamrRt25bw8HACAgK4cuUKDRs2/OoWWgcHB/T09PD39ycgIIC4uDhatmyZ6frp5276sIpPHTt2jIULF2b7/cyOMmXKoK2tzd27d1UOD+jWrRs1atQgJiaGHTt24O7ujlwuR0NDA0tLS/r374+3tze6urrSZyW7RMD4H9KhQwcg7aaS9G4mSJtHasaMGXz8+JHmzZtLXz7pXxAZn62bvuzT52pGRkYyffp0Kb/cUK5cOX7//XcApk+fLjW9p5fp0znAEhISmDRpknShzKlyaWpqMn36dNTV1XF3d5e+GKKjo9mwYQNLly5VuDjL5XLpeH1uDIqrqyuQ1g2vai7G9Pm8AIXxdeljwf7880+F9ZcvX/7dvti/Vps2bdDS0mLTpk1KY/1WrFjBkydPsLW1pXjx4l+9DyMjI2bNmkViYiKurq6cOHFC5XoXLlyQWmI+HTOYmQ4dOiCXy5k2bZpCy8+HDx+kZzNnfLRmeitqxvcqOTmZVatWZbqPCxcuKIxZS0pKYsaMGSQnJ9OxY8dslfNLZPaZCgoKYs2aNVKZv4e2bduipaXF8uXLlVrVvby8WL16Nf7+/ujq6qKrq8vOnTtZsmQJT58+VVg3/ZqX/oP41atXbNmyRWn8akJCAmFhYairq2NiYpJpuXL7upP+vnp4eCjMOXvt2rVsz+tatWpVqlevTnBwMOPGjVOauxbSAmg/Pz/KlCkj3dwG//ywyXj9z5MnDwkJCUo3PB04cICAgACl9dMtXLhQYfzz1atX8fPzw9jYONPhH19CVVnTpX/vTZ8+naSkpG8aL5k+rVP6kCATExOsra0zXb948eLUqVOHu3fv4unpqbAsJCSEyZMns2rVKoVhClnVJbtlbNGiBeHh4SxfvlxhWUBAAPfu3cPKyop8+fJx5coVvLy82L9/v8J67969IyEhIdPesMyILun/EGtrawYOHMiKFSto164djo6O0jyMQUFBVKhQgXHjxknrp4+bOn36NB4eHjRs2FCau3Hnzp28efMGc3Nz3r17x8mTJ4mLi0NPT4+oqCiSk5OVBnbnhN69e3P48GEePHjA7NmzmT17NtWrV6dUqVKcOXOGrl27Ym1tzcePHzl16hRhYWEULFiQDx8+EBER8UXj3rJiaWlJt27d2Lx5M9OnT2fBggXY2dlRv359AgICaN26NXZ2dmhoaHDx4kXu379P06ZNPzuH4q+//kpwcDAbNmzAycmJ6tWrU6lSJXR0dAgNDeX06dPExMQwatQohYt7p06d2L59OytWrODx48eUKFGCK1eu8Pz5c2QyWZYtJT+aiYkJkydP5o8//qBjx47SPIw3b97k5s2bmJiYfPM8hpB2h+DKlSsZP348AwcOpGTJktSsWRMjIyPev3/P5cuXpdYmR0dHJkyYkK18XV1duXDhAufOnaNVq1Y4ODiQnJxMQEAAYWFhODk5Kcw15+zszIkTJ5g8eTKXL1+mYMGCnD59mvj4+EyDYj09PQYPHkzDhg0xMTHhwoULBAUFUa9evc9OdPw12rdvz8GDBxk/fjynT5+mcOHCPH36lFOnTpE/f37gn7FSua1EiRLS+dGuXTsaNWpEsWLFuH//PufOncPAwEA6P9TV1RkzZgwjRozAycmJpk2bUqhQIZ4/f86JEycwNDSU5tFr06YNO3fuZPfu3Tx8+JAaNWqQlJTE6dOnef78Oa6urlneJZrb1x1HR0c6dOiAt7c3bdu2pX79+nz48IGjR49SvHhxlZP0q7J48WJ69uyJr68vf/75J3Xq1KFEiRIkJSVx9+5drly5gpGREUuXLlX4kZR+/Z82bZr0rPAOHTqwatUqfvnlF5o1a4aWlha3b9/m6tWrGBsb8+7dO5XnRXh4OG3atMHR0ZG//voLf39/NDQ0mDt3bo6Ml1ZV1nRlypTB2tqaGzduUKRIkW8eKtCiRQt8fX25ceOG0pyMqri7u9O1a1c8PDw4evQoVlZWfPz4EX9/f6KjoxkyZIjCgxWyqkt2jRkzhhs3brB8+XLOnz+PtbU1r1+/5ujRoxgYGDBlyhQAfv/9d06fPi2NZS1btqw0T6ZcLlcY250dooXxP2bo0KGsWLECCwsLTp48ya5du4C0Fq1du3YpDJQtVqwYI0eOJG/evHh5eXH+/HkKFy7M5s2badCgAXfu3GHLli1cv34dBwcHfHx8aNasGUlJSSonE80J6eMZNTQ02Lt3L+fOnSNv3rx4enrSqlUrQkJC2Lx5M+fPn6dKlSps3bpVmsLi09a3bzVs2DCKFy/OwYMHOX78OOrq6ixatAg3NzepfDt27EBNTY1x48ZJ03p8ztixY9m+fTvt27cnLCyMPXv24OnpyY0bN3B0dGTnzp307t1bYZty5crh6elJjRo1OHXqFLt27aJw4cLs3Lnzi+4m/lF+/fVXtmzZgr29PRcuXGDbtm1ERETQr18/9u7dm2VLz5dwdHTkyJEj/PHHHxQvXpxz587h6enJ0aNH0dfXx9XVlQMHDrBy5cps/7rW0tJi7dq1jBs3jvz587N3716OHDlCqVKlmD9/PrNmzVJY397eXnoyyqFDh9i7dy9VqlRhx44dUjD2qfr16+Pu7k5wcDA7duwgJSWF0aNHs2LFimy3hH4Je3t7li9fToUKFThx4gTbt2/nxYsX0lOLSpYsyfXr16UbUnJb+vlRp04dzp07x+bNmwkJCaFjx454e3srTCDesmVLPD09qVatGufOnWPDhg3cunWL9u3b4+3tLX0e0q8b/fv3JzY2lh07duDt7Y2hoSGzZ89WmnD7U9/jujNjxgz++OMPdHV12b17Nzdu3GDw4MGZPtNYlUKFCrF3716mTJlClSpVuHr1Khs3bmTXrl1ERUUxcOBADh8+TLly5RS269+/vxRobdmyhejoaAYPHsyoUaPInz8/u3fvxs/Pj5SUFCZNmiSNX09vacxo8+bNmJubs3PnTk6ePImdnZ10V25OUFXWjNLHhrdr1y7T8YbZZWdnJ3VpZ9Udnc7ExAQfHx969uxJeHg4Xl5eBAQEYGFhwcqVK6Ves+zWJTsMDQ3ZtWsXrq6uhIWFsWXLFi5evEjz5s3ZvXu3dG0rU6YMO3bsoE2bNgQFBbFp0yaOHTtG9erV8fLykmYByC41+c/enyUIgvAfdenSJbp3707r1q0VbvQSBCH7JkyYgLe3N/7+/ipv+BRyhmhhFARBEAThX+nx48ccPHiQOnXqiGAxl4kxjIIgCIIg/KssXLiQs2fP8vjxY5KSkhg6dOiPLtJ/nmhhFARBEAThX6Vo0aI8ffoUY2Nj5s+f/9lJ2IVvJ8YwCoIgCIIgCFkSLYyCIAiCIAhClkTAKAiCIAiCIGRJBIyCIAiCIAhClkTAKAhfwcXFBXNzc5UvDw+Pz25/6dIlzM3NCQ0N/Q6lzT3pE1RnfFWqVAlbW1sGDhyo8IjKnDB27FhcXFxyNM9P3b59m379+lGzZk2qVKlC06ZNmT9//ldNsPtv5uLikuVj1iZOnEjTpk2/OF8fHx+FJ1/8G4SGhqr8rFtbW9OuXTsOHjyYo/v7HteHjx8/Mnv2bBwdHbGwsMDW1pZBgwYpPb5TENKJaXUE4Ss1b95c5ePlsvOYMGtra86ePYuhoWFuFO27Klq0KHv27JH+TkpK4v79+7i7u9O/f38OHDiAmpraDyxh9j169AgXFxe6devGiBEj0NXV5f79+8yaNYtbt26xefPmH13E7+aXX35h9OjRBAcHS8/HTpeQkMCRI0fo16/fF+fbokUL6tatm1PF/K6WLl0qPVtYLpcTFhbG6tWrcXNzw8TE5LOPBv2ZDBgwgOTkZGbOnImpqSnh4eGsXbuWrl27smfPHqX3XBBEwCgIX0lHR4dChQp91bba2tpfve3PRkNDQ6kuxYsXJyoqijFjxvDw4UOFR7v9zHx8fDAzM2PUqFFSmqmpKTo6OvTp04cHDx78a+ryrZo2bYq7uzv79+9n2LBhCsuOHz9OXFycwjO0s0tHRwcdHZ2cKeR3ZmBgoHCuFy5cmHnz5mFjY8Phw4f/NQFjUFAQV69eZe/evVSqVAlIe8TdggULaNSoEbt27WLcuHE/uJTCz0Z0SQtCLoiMjGTixInUrVuXypUrU7t2bSZOnEhcXByg3OXk6OiIh4cHLVq0oFatWly+fBkXFxfmzZvH+PHjqVGjBtWqVWPkyJEKXaPBwcH06dMHa2tr7O3tGTlyJGFhYdLyZ8+e0atXL6pXr461tTW9evXi4cOH0vJTp07h5ORE1apVqV27NmPHjiUyMjJHjoG2tjaQ9ixmgMTERDw8PKQusJo1azJ06FDev38P/NPt5+/vz6+//oqFhYX0bO3MzJgxAxsbG27fvp0j9VFTU+Ply5c8fvxYId3Ozo6DBw9SunRpQHXXeMa09LocPHiQdu3aUaVKFZycnAgODmb58uXY2dlRs2ZNpk6dys86s5mOjg4tW7bkwIEDSsv27t1LvXr1+PDhA/369cPGxgYLCwsaNmyIp6entN7SpUvp1q0bw4cPp1q1ari7uyt1SQcFBX02jx49erBmzRocHByoUqUK3bp1Izg4WFonJiYGd3d37O3tsba2plu3bty5c0dafv36dbp27YqlpSX169dn6tSpOTbEQF1dHU1NTTQ1/2l/OX78OL/++itWVlbSe3/mzBlpeXY+2xldvXoVa2trFi5cCEB4eDhDhgyhVq1aWFpa0rlzZy5fvvxFZYa0z0vG809LSwsvLy/69u0LqO4a/zTNxcUFDw8P3NzcpOvQ9u3buXbtGm3btqVq1ap07tyZZ8+eZbt8ws9JBIyCkAvGjh3LvXv3WLZsGf7+/owbNw5fX98sgx8vLy8mTpzIunXrpJaKjRs3YmxszJ49e5g7dy4nTpxg48aNALx9+xZnZ2fMzMzYs2cPq1atIjo6mk6dOhEbGwvAiBEjKFKkCN7e3uzevRt1dXUGDRoEwPv37xk0aBAdOnTg0KFDLFu2jCtXrjBnzpxvrv/Dhw9ZsWIFVapUkYKsOXPmcPToUWbPno2/vz+zZ8/m4sWLrFy5UmHbWbNm0b9/fw4fPkz9+vWZMmWKyrGQc+bMYd++fWzYsAFLS8scqU+nTp3Q1NSkVatWdO7cmQULFnDmzBlSUlIoV64cefLk+aLjsHDhQsaPH8/u3bv5+PEjXbp04dmzZ2zZsoXhw4ezbds2/vzzzy/K83vq0KEDISEh3LhxQ0oLCwvj/PnzdOjQAVdXVwoUKMCOHTs4cOAAzZo1w8PDg/v370vrX7lyBWNjY/bt26cUZMfFxWUrj6tXr3Lt2jXWrFnDtm3bCA8PZ+rUqdLyYcOGcfr0aWbNmoWvry+mpqa4uroSGRnJgwcP6NmzJ3Xr1sXPz4958+Zx9+5dXF1dvzlYj4yMZPbs2cTFxdGqVSsA7ty5w+DBg2nZsiX79+9n165dGBoaMnr0aBITE6Vts/psZ3Tz5k369u1Lz549GT58OABTpkwhISEBLy8v9u/fT+nSpRk4cKD0uf+ccuXK4ejoyKJFi2jQoAHjx4/Hx8eHt2/fYmpqipGR0Rcdhy1btlCxYkX8/Pxo2LAh06dPZ8qUKYwfPx4vLy/++usv5s+f/0V5Cj8f0SUtCF9p//79+Pv7K6RVr16ddevWUadOHWxsbKSWlBIlSuDl5UVQUFCm+dWrVw87OzuFtHLlyjFixAgASpUqRZ06daQv7+3bt1O0aFEmTpworb9o0SJsbW05cuQITk5OvHjxAjs7O0xMTNDS0mLmzJk8efKE1NRU3r59S2JiIsWLF8fExAQTExNWrVpFSkrKFx2HV69eSeO6IK0lUU9PD0dHR0aNGiW1ZlSpUoVmzZpRo0YNIK0LzM7OTumY9OjRg4YNGwIwfPhwtm7dyq1btzA1NZXWWbhwId7e3mzcuFHqUsuJ+piZmeHr64unpycnTpxg9erVrF69mvz58zNq1Cg6duz4RcfG1dWVmjVrAtC4cWO2bNnCtGnTyJs3L2XLlmXp0qU8evQIR0fHL8r3e7G0tEQmk7F//37pPfbz88PIyIiqVavSvXt3unbtSr58+QAYMmQI69at4+HDh1SsWFHKZ8iQIejr6wNprX3p4uLispVHcnIyc+bMwcDAAIDOnTszd+5cAJ48ecLp06dZv3499vb2QFpAlT9/fj58+MD69eupU6cO/fv3B9I+R/Pnz6dRo0ZcvnyZWrVqZft49OnTBw0NDQBSU1NJTk7G0tIST09PqawaGhpMmjQJZ2dnabvu3bvTp08fwsPDKVasGJD1ZzvdnTt3mDhxIr169eL333+X0l+8eIFMJpOGS0yYMIHWrVtLZcuOZcuWsXPnTvbv38++ffvw9vZGTU2N5s2b4+7ujp6eXrbzqlixIr169QKgW7du7NixAxcXF+nYNm/enOPHj2c7P+HnJAJGQfhKjo6OuLm5KaSlj81ydnbm5MmT7N27l2fPnvH48WNCQ0MpU6ZMpvmZmZkppX26vr6+Ph8/fgTg3r17PHr0SCFYg7QbEtK764YPH87MmTPZtm0bNWvWpG7durRq1Qp1dXUqVqxIq1at6N+/P4UKFaJOnTrUr1+fxo0bf9FxKFy4MFu2bAHSgsfZs2ejq6vLiBEjFG7qadu2LefPn2fevHk8e/aMJ0+e8PTpUymATJdxsH16kJGUlCSl3bx5kytXrmBoaCh9+QI5Vp9ixYoxYcIEJkyYQEhICOfPn2fbtm1MmjSJIkWKUK9evWznlfE91dXVxdjYWOGmKB0dHYVWp59Rhw4dWL16NePHj0dTUxNfX1/at2+PsbExzs7OHDhwgHv37vHixQsePHgApAVT6YyMjKT38VOGhobZysPY2FgKFiHtvEg/J9J/cGQcP5gnTx5pDN69e/d4/vy50ucE0oZ0fEnAOH36dKpWrUp8fDw7d+7k4MGD9OrVC1tbW2mdihUrYmBgwJo1a3jy5AnPnz+X6pTxx0tWn+10o0aNIikpCRMTE4X0QYMGMWrUKPz9/alevTr29va0atXqi1rANTQ0cHZ2xtnZmejoaK5evcrhw4fZt28fcrmcRYsWZTuvkiVLSv9PP78z/sDT0dFR+AwL/06iS1oQvlK+fPkwMzNTeBUpUoTU1FT69evH9OnT0dTUpEWLFqxevZpq1aplmZ+qGwHSxwGqkpqaiq2tLb6+vgovf39/6dd+165dOX36NBMnTkRfX58lS5bQsmVL3r17B8D8+fM5fPgwvXv35sOHD4waNUraNrs0NTWl+teuXZv169cTEhJC3759FYKhP/74g+HDh5OUlISjoyPz58+nZcuW2apzxq5DXV1dtm/fjo6ODtOnT1dY71vrM2fOHC5cuCD9bWpqSqdOndi9ezdFixbl1KlTmW6bnJyslJZxXBv8M3bs36RNmzZERUVx7tw56UdKhw4dCAsLo02bNuzevZsiRYrg7OzM3r17lbbP6gaX7OaR1efg02P8qdTUVFq3bq30OTl69CitW7fOcttPFSlSBDMzM8zNzfnjjz9wdHRk2LBhXLt2TVrn8uXLNG3alMDAQCpUqMCgQYOk1tDs1ind77//Ts+ePZk1a5bC2OTGjRtz5swZZs+ejYmJCRs2bKBZs2Y8evQoW/U4evQoK1askP7W09Ojfv36eHh40LNnzyzPc1Ut9unjlDP6N57rQtbEOyoIOez+/fucPn2axYsX4+bmRps2bShZsiQvXrzI0RscypcvT3BwMMWKFZMCNgMDA2bOnElQUBDh4eFMmzaNpKQknJycmDt3Ln5+foSFhXH58mVu3brFzJkzKVOmjHRTwcyZM7l48SLh4eFfXS5jY2NmzJjBvXv3WLJkCQAfPnxg586dTJ48mXHjxuHk5ETFihV58uTJFx8TmUyGtbU1U6ZM4cCBA1JXV07U58KFCwo3XKTT1tZGR0dHGtulpaWldIPC8+fPv6ge/xaGhoY4Ojpy6NAhDh48iI2NDWZmZhw4cICIiAi2b9/OwIEDady4sXSDUXbf05zII71FOjAwUEpLTk7G0dGRI0eOUL58eR4/fqzwwy45OZlZs2bx+vXrLzkUSiZOnEiRIkUYPXq0dEObp6cntWrVkm7WqVOnjrSfLz3XW7VqxZAhQ9DT02PKlClA2pCPWbNmERISQosWLZg+fTrHjx9HXV2dgICAbOX75s0bVqxYobL++fPnVzjPAYVzXdy88v9LBIyCkMOMjY3R1NTk8OHDhISEEBgYyLBhwwgLC8vR7kdnZ2eioqJwc3PjwYMHPHjwgOHDhxMYGIhMJsPAwICAgAAmTpzI/fv3CQkJYceOHWhpaWFhYYGenh7btm1j7ty5PH/+nKCgIA4dOkSpUqUoWLDgN5WtXr16tGnThg0bNnDv3j309PTQ19fnxIkTPH/+nIcPHzJp0iTu3r371cfEwcGBVq1aMWXKFCIjI3OkPsOHD+f8+fMMHTqUK1eu8PLlSy5fvszo0aOJiYmhU6dOQFr354MHD/Dz8yMkJITly5dnOT713+6XX37hzz//xN/fn19++QVIm38zLi6OI0eO8OrVK86ePSuNycvue5oTeZQuXZomTZowdepULl68yNOnT5k0aRIJCQnUrFkTV1dX7t27x9SpUwkODubGjRuMHDmSZ8+eUapUqS8/GBnky5cPd3d3QkNDWbx4MZA2pOHhw4dcvXqV0NBQvL29pWVfc67nzZuXqVOncvz4cQ4cOIC2tjaBgYFMmjSJmzdvEhoaio+PD7GxsSq73VVxcnKiZMmSuLi4SOfwgwcP2Lp1K2vWrJHGS8pkMnR1dVmzZg0vXrzgzJkzbNiw4YvrIPw3iIBREHJYkSJFmD17NidPnqRFixYMHTqUIkWK0KNHD4WpPr6VqakpXl5exMTE0KVLF7p164aWlhabN2/G0NAQTU1N1q5di7q6Oj169KBly5acP3+eNWvWULJkSemmi4sXL9KuXTu6dOmChoaGtM23Gj9+PPnz52fixImoq6uzePFigoKCaN26Nb179yYuLo4RI0bw+PFjqXXmS02YMIGkpCSmT5+eI/VxcHBgy5YtJCYmMnToUJo2bcrw4cNRU1Njx44dGBsbA2ndtF27dmX69Om0bduWV69e8dtvv31VHf4N7O3t0dXVJSIiQnq6S7NmzejVqxezZ8+mefPmzJw5k19++QUbGxuF1r6s5EQeADNnzsTGxoahQ4fi5OTE69evWb9+PYaGhlhZWbFu3Tru379P+/btGTBgAKVLl2bjxo3Z6hb+HDs7O5ycnNi8eTOBgYEMGTIEKysr+vfvT7t27di9ezczZ85ER0fni+qUkb29PW3btsXd3Z3w8HAWLlyIqakpAwYMoFmzZuzYsYN58+YpjQfOTPqPq4YNG7J8+XJatmxJly5dOHjwIHPnzqV9+/bSenPnzuXevXu0aNGCxYsXM2bMmK+qg/Dvpyb/WScBEwRBEARBEH4KooVREARBEARByJKYVkcQBCVr165VuItSlfHjx/Prr79+pxJ9m/9afYSc8fbtW5o1a5blOlWqVPnXPEP8v1Yf4eciuqQFQVASGRlJRERElusYGRl90eS+P9J/rT5CzkhJSVF47J0qefLkoWjRot+pRN/mv1Yf4eciAkZBEARBEAQhS2IMoyAIgiAIgpAlETAKgiAIgiAIWRIBoyAIgiAIgpAlETAKgpArGjZsSMOGDX90MQQhV4nzXPh/IQJGQRAEQRAEIUsiYBQEQRAEQRCyJAJGQRAEQRAEIUsiYBQEQRAEQRCyJAJGQRAEQRAEIUsiYBQEQRAEQRCyJAJGQRAEQRAEIUviWdLC/w371qd+dBEEIddtWmz6o4sgCLmubJkyuZp/3bZnpP+f2Vc3V/f1b6H5owsgCIIgCILwM9HQEuHRp8QREQRBEARByEArj/aPLsJPRwSMgiAIgiAIGWhqaf3oIvx0RMAoCIIgCIKQgeiSViaOiCAIgiAIQgZaeUQL46dEwCgIwmdpa6lxdJc9mpqKM3HFxqXQpONZAHw22FLYOI/Sti27niPyY3KmeTdyKMRvncwoXkSH13/F47UnhCMn3+ZsBQThC013d+fx48ds3LRJSnMbOZJ79+4prbto8WJkMlmmeV2/do1Nmzbx4sULChQoQKtWrXDq0AE1NbVcKbvw7TRFC6OSXD0ijo6OvHz5UvpbS0sLY2Nj6tWrx9ChQzE0NMyx/bRv357Bgwd/dl0XFxdMTEyYPXt2juzb3Nw8y+Xt27fPsX1l5urVq2zYsIGbN28SExNDiRIlaNeuHd27d0dbO23g7tixY3n58iVbtmzJ1bIAhIaG0rBhQzZv3kytWrWIiIhg2LBhXLt2DXNzc+rVq8fevXs5efJkrpdFyBmlzfKhqanO1Hn3efUmTkpPSU371yC/JoWN87DcM5jb9yIVto2OzjxYrGdnzB8jK7J7/0suXXtPXVtjJg6vQFJSKifOhOVKXQThc06ePMn58+cpXLiwlCaXy3n69CntnZyoa2+vsH7JkiUzzevB/ftMmTKFug4OuHTvzt27d/H09CQlNZWOHTvmWh2Eb6OppfGji/DTyfUQ2tXVFVdXVwDi4+MJCgpi7ty5dOvWjZ07d6Kvr//N+9izZw958ii3bKiydOlSNDRy7kQ4e/as9P9Dhw4xc+ZMhTQdHZ0c25cqW7ZsYfbs2XTv3p2BAweSP39+rl+/joeHB1euXGHlypWoq3/f+dmLFSvG2bNnMTAwAMDPz4+rV6+ybds2ihQpgp6eHl27dv2uZRK+TfkyeiQnpxJwLoykZOWpW8uX1gPg1IV3vHoTn+18+7mU5s9zYSxdFwzA5RsfyK+vSe9upUTAKPwQ4eHhrFq5EmNjY4X0169fExcXh02NGlSoWDHb+Xl5eVGmbFlGjRoFQI0aNUhJTmbnjh20bds2299dwvcluqSV5XrAqKurS6FChaS/TU1NqVixIi1btmTdunUMHz78m/fxJS2VBQoU+Ob9ZZSxbunBb8a03PTgwQNmz57N6NGj+e2336R0U1NTihcvTrdu3Th06BCtWrX6LuVJp6GhoXAMPn78SKFChbC0tJTS8uXL913LJHyb8qX1eB4aqzJYhLSAMiY2+YuCxaKF81CyhC7rtz1TSA84F0bDuoUpUSwvoa/jVG8sCLlk8aJFVKtWDW1tbW7fvi2lPwlO+1FTpmzZbOeVlJjI7du36ebiopBub2/Pnj17uHv3LtWqVcuZggs5SlNTtDB+6oc8GrB48eI0btyYgwcPAhAVFcWkSZOwtbWlevXqdO/encDAQIVtzpw5Q6dOnahatSoODg4sXLiQlJQUIK1LeunSpQDExcUxYcIE6tSpQ5UqVWjXrh1Hjx6V8nFxcWHs2LHS3zdu3KB79+5Ur16dWrVqMW7cOD58+CAtd3R0ZP369QwePBhra2tq1arF9OnTSU7OvJvtUy4uLkyaNIlff/2VGjVq4OfnB4C3tzfNmzfH0tKS5s2bs2nTJlJTU6Xt3r59y/Dhw6lRowa1atWif//+PHv2TFq+e/du9PX1VbbW2djYsHHjRhwcHFSW6erVq3Tv3p1q1aphYWFB8+bN2bdvn7Q8PDycIUOGUKtWLSwtLencuTOXL1+Wlt++fRtnZ2esra2xsbFh8ODBvHr1CkjrkjY3N+fSpUuMHTuWpUuX8urVK8zNzfHx8WHp0qU4Ojpmu55jx45lyJAhuLq6Uq1aNdauXZvtYy/kjPJl9EhJkbNgWhWO7bbn0DY7Rv1enrx50y6q5Urr8TEqmenjKnFkRx2O7rJn6uiKGBXMfC6zUqZpPxpCXikGhaGv04LOkiXy5lJtBEG1I0eO8PjxYwYMHKi0LPjJE/Lmzcu6devo3KkTbdu04Y9JkwgNDc00v9dv3pCcnIyJiYlCerHixQF4mcW2wo+loaUhvYQ0P+xZ0jKZjJCQEKKjo+nTpw8hISGsXr2aXbt2YWVlRZcuXaTBxTdu3KBv375Ur14dHx8fpk+fzo4dO1ixYoVSvosXL+bhw4esWbOGQ4cO4eDgwPDhw1V+qG/fvo2Liwvly5dn165dLF68mFu3btGrVy8pGE3P08bGBj8/P0aPHo2XlxcHDhz4ovru3r2b7t27s23bNurWrcvOnTuZM2cOgwYN4uDBgwwbNoy1a9cyb948AGJjY3H5+1epl5cXW7ZsoWDBgnTs2JG3b9NuCLhz5w6WlpZoaqpuKK5duzb58+dXSn/79i29evWiSpUq7N27F19fXywtLZkwYQLv3r0DYMqUKSQkJODl5cX+/fspXbo0AwcOJDY2lpSUFPr16ycdk40bN/Lq1SvGjx+vtK8JEybg6upK0aJFOXv2LC1atFBYnp16Avj7+2NnZ4e3t/d3bzEVoGypfJQolpezF8NxmxLI5l0vaORQmHmTq6CmlhZQFjLS5uHjaEZPu8Oy9cFYWRRg2ayq6ORRfZnJp5t2IY6JVfzxFRuX/PdyMehc+H7evn3L2jVrGPj779JwmoyePHlCXFwcenp6TJw0iaHDhvHq1StGubkRHh6uMs/YmBggracto/S/Y2Njc7gWQk7R1taQXkKaH3ZFTg9kTp48yc2bN7l48aLUXTxixAiuX7/O5s2bmT17Nlu2bKFq1aqMHj0agLJlyzJt2jSVH9IXL16QL18+TE1NyZ8/P0OHDsXGxkblBcDT0xNzc3MmTZok5btgwQLatm3L2bNnqVevHpDWfdC9e3cgrbt3y5YtXL9+nXbt2mW7vhUrVqR169bS3ytWrGDAgAG0bNlSyjc6OpqpU6cydOhQDh48yMePH5k7d64UEM6YMYNLly6xa9cuBg8eTEREBKamX/7c2ISEBAYPHkyvXr2ku/T69u2Lr68vz549w9jYmBcvXiCTyTA1NUVHR4cJEybQunVrNDQ0iI6O5sOHDxQuXBgTExNMTU1ZtGiRyvdDX18fXV1dpW7qdNmpJ4CBgQG9e/f+4roK305NDca63yHiYxJPX6R9wd26G0n4h0Qmu1WkVjVDPJYGkZIq58GjKABu34vk6YsYVs6xppljEXwPv1bKV1096ztEU1PFY+6F70Mul7No4UJsbGyw/+SGlnS//fYbv/zyC1WqVJHSKlasSL++fdnn64trr15K26TKsz6H1b7z+HIh+8RNL8p+WMAYFZX2xRISEoJcLqdBgwYKyxMTE0lISAAgKCiIOnXqKCxv2rSpynz79OlD//79qV27NpaWltSpU4fWrVurvLlGVb4VKlRAX1+fhw8fSgFj2U/GrOjr65OUlPQFtQUzMzPp/+/fv+fNmzcsWLCAxYsXS+mpqakkJCQQGhrKvXv3iIyMxMbGRiGfhIQEgv8eS2NoaEhERMQXlQPS7uhzcnJi8+bNBAUF8eLFCx48eAAgtawOGjSIUaNG4e/vT/Xq1bG3t6dVq1bkyZOHPHny0Lt3b9zd3VmyZAm2trbUq1eP5s2bf3FZslNPUDx+wvcll8ONO5FK6Reupv1AKFc6H157QpSWB97/SFR0MuX+viHmU9ExaS2JunkVL0PpLYsxsSlK2whCbjiwfz9Pnz5lxcqV0jVQ/newl5KSgpqaGmXKlFHarlixYpQsWZKnT5+qzDd9rHbcJy2J6S2L+T5peRR+HppaIpj/1A8LGO/evUupUqXQ0tJCT08PHx8fpXXSp4TJrMtVFWtra06dOsW5c+e4cOECvr6+rFy5knXr1lG7dm2FdeWZ/PqTy+VoZXgsUHo5srNtZjLeLZ0+TnHcuHHY2dkprVusWDFSU1MpXbo0K1euVFqe3p1hbW3Nnj17SElJUXnnt5ubG9WqVcPZ2Vkh/fHjxzg7O1O5cmXs7Oxo0qQJBQsW5Ndff5XWady4MWfOnOHMmTOcP3+eDRs2sGzZMnbt2kX58uVxc3PD2dmZU6dOceHCBdzd3Vm3bh2+vr5fdFyyU0/I/bvNhcwZGWpjV8OQyzc+8DYsQUrP83dXTUJCKi0bFeVe0EepBRLSWia1tNSIiFT94+rFy7R1SxTT4dGTaCm9RLG0sYvPQmJyvC6CoMrZs2f5+PEj3VSMB2/dqhWdOnfGpHhxTEqUoOInd0gnJCSQX0UPFqRdy9XV1Xn1WrGFPX28t2kW0/EIP5ZWDrUwvn37VuW9BLNmzcLJyYn79+8zY8YM7ty5g6GhIT169JB6NH82PySEfvPmDSdOnKB169bIZDKio6NJSkrCzMxMeq1du5YTJ04AaS18n94Es2nTJoUAJ92SJUu4du0aDRs2ZOLEifj7+2Nqaoq/v7/Suubm5ly7dk0h7cGDB0RHRyu1KuYkIyMjDA0NCQkJUajz3bt3WbRoEZA2xvPVq1fo6+tLy4sXL878+fO5cuUKAB06dCAmJgYvLy+lfVy6dIn9+/ejp6fcurNjxw6MjIzYsGEDffr0oV69etLYRblcTmJiIrNmzSIkJIQWLVowffp0jh8/jrq6OgEBATx58oTJkydjZGREly5dWLJkCevWrSM4OFhqqcyu7NRT+LE0NdQYM9icts2KKaQ71i1Ecoqc64ERDO9fDpdfFb/87GsZoZNHg+uBESrzffk6nldv4qhfR3GoQj07Y0JexvLmrwSV2wlCThs8ZAiLFi9WeNWsWRNDQ0MWLV5Mq1at2LZtG+vXrVPY7vHjx7x+/ZqqGWaAyEhbWxuLKlU4f+6cQiPDubNnyZcvX5aTfQs/lqaWuvT6Fg8ePCBPnjycOXOGs2fPSq8WLVrw4cMHevbsScmSJfH29ub3339n3rx5eHt751AtclautzDGxsYSFpY2n1p8fDwPHz5k0aJFlChRgp49e6Kjo0PFihUZPnw4EyZMoFixYmzbtg0fHx/Wr18PQO/evenQoQOLFy+mbdu2PH/+nBUrVqiMwkNCQvDz88Pd3Z2SJUty69YtXr16hbW1tdK6PXv2xNnZGXd3d5ydnXn37h3u7u5UqlRJqTUyJ6mpqdGnTx8WLlxI8eLFcXBw4OHDh0yZMoWGDRuira1NmzZtWLNmDUOGDGHUqFHo6emxYsUKTp8+zdChQ4G0QHro0KHMnj2bt2/f0qZNG/LkycOFCxdYtGgRjRs3lsZIZlS0aFHevHnDqVOnKFeuHHfv3mX69OlA2lAAbW1tAgMDuXr1KpMmTcLY2JjTp08TGxuLtbU1BQsW5ODBg8THx9O3b1/U1dXZu3cvBgYGlClT5ou6ybNTT+HHehuWwMFjr+nS3pSEhFTuPPiIZSUDXDqWxOfAS4KfxeC1J4TeXUvxPiKJi1fDKVMqH65dSnH64juu344AQDevBqVL6vLydTwRH9NaHTfseM6EYRX4GJXE2Uvh2NcypmHdwvzhofw0DUHILSVKlFBKy58/P5qamlJQ17VrV+bPn8+8efNwdHTkr7/+wmvLFsqUKUPDRo2AtGl0goODMTY2xvjvMdudO3dmwvjxzJo5k8ZNmnD//n28vb3p8ff3n/Bz0tLMmafwBAUFUapUKYVJ4NNt2rQJLS0tpk2bhqamJmXLluX58+esWbOGDh065Mj+c1KuB4yenp54enoCaU96KVasGC1atMDV1VUa3+Hp6cncuXMZNmwYcXFxlC1blmXLlklBW8WKFVm+fDlLlixh7dq1FC5cmO7duzNgwACl/U2ePBkPDw9GjRpFREQEJiYmuLm50bZtW6V1q1atyrp161i0aBHt2rVDT0+PRo0aMXLkSIUu6dzg6upKnjx5pIm3jY2N6dixI0OGDAHSxkl6eXkxZ84c6a7typUr4+npqdD62bdvX8qUKcOWLVvw8fEhPj4eU1NTBg4ciLOzs8qu6u7du/PkyRNGjx5NYmIipUqVYsSIESxZsoTAwEBp2qJZs2YxYMAAoqKiKFOmDPPmzaNGjRoArF27lvnz59OxY0dSUlKwsrJiw4YN6OnpfVHAmN16Cj/WvBWPePUmnqYNitC9kxlh7xJYv/UZ23zSxi5u2vmciMhEnFqa0L55MSKjktl3+BXrtz+X8jAvq8fSWVbMWPSAwyfS7oA/fOIt2lrqdG5fghaNivHqTRzuC+5z8qyYtFv4uTRs1AgtbW327NmD+7Rp6OjoYGdnR4+ePaXr7PsPHxgxYgTOXbvSrVs3AKysrJgwYQJeXl64T5uGsbExvXr1wuknDAiEf2jl0BjGhw8fZvpddvXqVWrWrKkw7M7W1pbVq1fz7t07pcnjfzQ1+ZcOxhOEfyn71qd+dBEEIddtWvzlMycIwr9NWRU3IeWkOd7/zInsv6JxluumD59TpU2bNhQsWJDk5GSePn2KmZkZAwYMwMHBgdatW+Pg4CA9BQjShjm0bNmSPXv2KNyR/zMQtwEJgiAIgiBkoKmpJr2+VnJyMk+ePCEyMpLBgwezZs0arKys6Nu3LxcuXCA+Pl7pptr0R0WmzxLzMxEz4wqCIAiCIGSgnWFUWlYtiFnR1NTk0qVLaGhoSONVLSwsePToEevXr0dHR4fExESFbdIDxU8ne/8ZiBZGQRAEQRCEDDQ11KTXt8iXL5/SzU3ly5fn7du3FC1alL/++kthWfrfRYoU+ab95gYRMAqCIAiCIGSgqfnP62s9evSIatWqcenSJYX0O3fuUK5cOWxsbLh27ZrCo4gvXrxI6dKlMTIy+vod5xIRMAqCIAiCIGSgrfnP62uVLVuWMmXKMG3aNK5evUpwcDCzZs3i5s2bDBgwgA4dOhAdHc2ECRN4/PgxPj4+bNy4kX79+uVcRXKQGMMoCIIgCIKQgaZGxglkvq5bWl1dnVWrVjF//nyGDRvGx48fqVSpEhs2bJDm91y3bh0zZsygffv2FCpUiNGjR9O+ffscqEHOEwGjIAiCIAhCBjkRMAIYGxsza9asTJdbWlqyc+fOr87/exIBo/B/w7/NsR9dBEHIdbc6Kj8GVRD+a8pezd1Hx2ppiCmqPyUCRkEQBEEQhAw0RcCoRASMgiAIgiAIGWiqi4DxUyJgFARBEARByEBLI/XzK/2fEQGjIAiCIAhCBprqImD8lAgYBUEQBEEQMhABo7J/7cTdjo6OLF26NNfyHzt2LC4uLtlaVy6Xs3fvXsLDwwHw8fHB3Nw8W9uGhoZibm6u9LK2tqZdu3YcPHjwq+vws0iv46ez3Qv/TiP3naflGsXz8sWHaIb6nMVhqS+Oy/cx89g1ohOSPpvXvTfv6bMjgDqLfWiycj9LzwSSlCIu1MIPoqZG8W7dsPbxptbZM1TdthXjZs0UVslrZkaFBfOpGfAnNieOYz53DnlMTD6btUGtWlhu2kSts2eots+X4t265VYthBygqZ4svYQ0ooUxExMmTFB4XE9Wrly5wtixY6UHlLdo0YK6det+0f6WLl2KtbU1kBaAhoWFsXr1atzc3DAxMcHKyuqL8vuZFCtWjLNnz2JgYPCjiyJ8o4P3nvPno5cUy68rpUXFJ9J/VwBG+XSY1tyG97EJLD51m5eRMSz/xSHTvEIjoum/+zSWxYzwaF2bp+EfWX72Dh/jE5nQuPr3qI4gKDDt3w+T7t0JWbWa6Hv3KFinDrLp7iBP5Z3/UbSLFMFi/Trinr/g0YSJqOvoUHLgACotW8qtzl1ITUhQma+ehQUVFy3k3bFjvFi1Cn2rqpgNGYyahgYvN236zrUUskNTTfxw/ZQIGDOhr6+f7XXlcsW7qXR0dJQeNv45BgYGFCpUSPq7cOHCzJs3DxsbGw4fPvyvDhg1NDQU6ib8O4VFxzH35A2K6OdVSN99K5iIuES2ujSmoG4eAIro5WWwz1luvnyHlYmxyvw2Xn5IPm1NFravg5aGOvZliqGjpYnHieu41qqoEJQKQm5Tz5OH4l268Hr7DimIi7xyhXwVK1CsUyfe+R/FtG8fUqJjuDdwoBQcxr96RcUF88lXsSJRN2+qzNu0X19iHj7k8R+TAYi4cAF1TU1Mevbg9Y4dmQaawo+joZ69BqP/J//aLunP8fX1pU2bNlhaWuLo6MiKFSsUWgxfvHhBnz59sLa2pm7dumzYsIHGjRvj4+MDKHdJr1+/nkaNGmFhYYGjoyPLly9HLpdz6dIlunfvDkDDhg3x8fFR6pKOiYnB3d0de3t7rK2t6datG3fu3PlsHdTV1dHU1EQzw9PPg4ODpXLb29szcuRIwsLCpOUpKSksXLgQe3t7rKysGDJkCDNmzJDqcunSJSpVqsSaNWuoVasWTk5OpKam8vbtW4YPH06NGjWoVasW/fv359mzZ1K+4eHhDBkyhFq1amFpaUnnzp25fPmytPz27ds4OztjbW2NjY0NgwcP5tWrV4Byl3RKSgobN26kadOmVKlShaZNm7J9+3Ypr/Qynjp1ilatWmFhYUGzZs04fvz4Z4+ZkHum+V/F1qwoNUsWVki/8PQt1iWMpWARwLZUUfJpa3L2yetM87vw7A32ZYqhpfHPZaiRrASp8rRlgvA9pSYlEejai1dbtyqky5OSUMuTdm4bOTry134/hQAv5v59rjZvkWmwqKalhUH16rz/M0AhPfzESTT19NC3qpqj9RByhpZasvQS0vwnA8aNGzcyadIkOnXqhJ+fH0OHDmX9+vXMnj0bgLi4OHr06EFqairbt29n4cKF+Pj4EBISojK/kydPsnr1aqZOncrRo0dxc3Nj5cqV+Pn5YW1tLY2l3L17Ny1atFDaftiwYZw+fZpZs2bh6+uLqakprq6uREZGZlqHyMhIZs+eTVxcHK1atQLg7du3ODs7Y2Zmxp49e1i1ahXR0dF06tSJ2NhYAObNm8fOnTuZPHky3t7eFCpUiC1btijknZKSwqlTp9i5cyczZswgPj5eCii9vLzYsmULBQsWpGPHjrx9+xaAKVOmkJCQgJeXF/v376d06dIMHDiQ2NhYUlJS6NevHzY2Nvj5+bFx40ZevXrF+PHjVdZt9uzZrFixgkGDBrF//366du3KjBkz2Lhxo0IZ586dy4QJEzhw4AAymYwxY8YQExOT6TETcs/e20+4//YDYxpaKy17+v4jZgUVW+Q11NUobpCP5++jVOYXn5TC64+xStsV1M2DnrZmptsJQq5JTSX28WOS/h6LrmVoiMlvv2FQsyZvdu8hT/HiaOrrk/D6NaVHj8bmxHFsz52lwvx5aBcunGm2OiYmqGtrE/fihUJ63N/fN3nNzHKvTsJX01BLkV5Cmv9cl7RcLmft2rV069aNrl27AlCqVCkiIiKYO3cuQ4YM4ejRo7x//x4fHx8KFCgAwNy5c2nbtq3KPF+8eIG2tjYmJiYUL16c4sWLU7hwYYoXL462trY0Ns/Q0FCpK/rJkyecPn2a9evXY29vD6QFX/nz5+fDhw9S62GfPn3Q0NAAIDU1leTkZCwtLfH09KRixYoAbN++naJFizJx4kQp/0WLFmFra8uRI0do3rw527ZtY9y4cTRu3BiAiRMncuPGDaU6ubq6UqpUKSAt0P348SNz586VyjNjxgwuXbrErl27GDx4MC9evEAmk2FqaoqOjg4TJkygdevWaGhoEB0dzYcPHyhcuDAmJiaYmpqyaNEi6SagjKKjo9m+fTtjx46ldevW0vsTGhrKmjVr+O2336R1hw0bRu3atQEYOHAg/v7+BAUFSWM9he/jVWQMCwJuMaWZjUIrYrrohCTy5dFSSs+nrUlMoupf59GJSdI6n9LV1pKWC8KPYNy0CbIZMwB4f+YM7w4fRrdcWQDMBg8m6u5dgsZPQMvQELPff6fyqpXccu5Kany8Ul4aenoApHzyYzfl7x/5Gvny5WZVhK+kgQgUP/WfCxjfv3/Pu3fvqF5dcdB8zZo1SUpK4smTJ9y7d4/SpUtLwSJAhQoVMh232KZNG7y9vWnatCnlypXDzs6Opk2bUrx48c+WJygoCEBhDGKePHkYN24ckNZdCzB9+nSqVq1KfHw8O3fu5ODBg/Tq1QtbW1tpu3v37vHo0SOlgCkhIYHg4GCCg4OJj49X2JeamhrVq1fnwYMHCtukB4vp+UZGRmJjY6MyX4BBgwYxatQo/P39qV69Ovb29rRq1Yo8efKQJ08eevfujbu7O0uWLMHW1pZ69erRvHlzpePx5MkTkpKSVL4/mzZtUggyy5QpI/1f7++LblKSCCS+J7lczlT/q9QpXYyGshIq10mVZ/5EBDW1zPPNinpmGwrCdxB15y53+vRFt3w5SvbvT8UlS3ixciUAie/f83DUaPj7HI4PCcFy4wYKNW/O2717lfJSU/9MR16qeKLIz0hDdEUr+c8FjJl9EaWmpt3xpKmpiYaGhvR3dhgaGrJv3z5u3LjBuXPnOHv2LJs3b2bw4MEMGjQoy20zjj/MSpEiRTD7u2vijz/+IC4ujmHDhrFp0yYpuEpNTcXW1pbJkycrba+vr89ff/0FfP7LGNKC1nSpqamULl2alX9fEDPS1U278aBx48acOXOGM2fOcP78eTZs2MCyZcvYtWsX5cuXx83NDWdnZ06dOsWFCxdwd3dn3bp1+Pr6KuSXnfcnnba2ttJ62ambkHN23gjmUVgEu3o0Jfnv9yj9LUhOTUVdTQ29PFrEqmgRjE5IppBeXqV0gHzaaS2SqlogYxKT0NNWbrEUhO8l4eVLEl6+5OONG6TExFB+6lQp8Is4d/6fDwEQfecOyVFR5DOXqcwrOToaAA1dxZu4NP9uWUxfLvxcNFNF48Sn/nNjGI2NjTE2NubatWsK6VevXkVLS4uSJUtSoUIFnj9/TkREhLQ8ODiYqCjV46b8/PzYvn071atXZ8iQIezatYtff/2VQ4cOAWmteJkpWzatGyMwMFBKS05OxtHRkSNHjmS63cSJEylSpAijR48mLi4OgPLlyxMcHEyxYsUwMzPDzMwMAwMDZs6cSVBQEGZmZujo6HDzk8HXt27dynQ/ADKZjFevXqGvry/lW7x4cebPn8+VK1dITExk1qxZhISE0KJFC6ZPn87x48dRV1cnICCAJ0+eMHnyZIyMjOjSpQtLlixh3bp1BAcHK7Vsli1bFi0tLZXvT6FChcTUOz+ZE0GhRMQl0mTlfmou8KbmAm8O3HvO64+x1FzgzZrz9yhlqE/IB8UvvZRUOa8iYyhtmF9lvrramhTWy0tIhOJ272PiiUlMprSR6u0EIbdoFihAoZYt0CpYUCE95sFDALSLFEGemoq6ih+yahoamd7pHB8aijw5GR1TxRb69L/jnj3NieILOUwjNUl6CWn+1QHj8+fPOX36tMLr8uXL9OrVCy8vL7Zt28bz58/Zv38/y5Yto1OnTujr69OqVSsKFiyIm5sbDx484ObNm4waNQpQHfwlJCTg4eGBr68voaGhXL16lStXrkhdw+mtcA8ePFC6KaN06dI0adKEqVOncvHiRZ4+fcqkSZNISEigZs2amdYtX758uLu7ExoayuLFiwFwdnYmKipKKveDBw8YPnw4gYGByGQy8ubNi4uLC0uWLOH48eM8ffoUDw+PzwaMbdq0wcDAgCFDhnDr1i2Cg4MZO3Ysp0+fxtzcHG1tbQIDA5k0aRI3b94kNDQUHx8fYmNjsba2pmDBghw8eJA//viD4OBgnj59yt69ezEwMFDoVoa0ruVOnTqxZMkSDhw4wPPnz9m6dSvbtm3D1dU1y+Bb+P4mNKmOV7eGCq+6ZYphnE8Hr24N6VC1DLZmRbkWGsaH2H++MC8+e0NsUjK1SxXJNG/bUkU4E/yaxOR/xgqdePQSDTU1bEpmfhOBIOQG9Tx5KD91KoU/GctuYFsLgJgHD/h44waGDeqjpvVPC7iBjQ0aurp8vHFTZb7yxEQ+3riBUYMGCulGjo4kR0URfedujtZDyBkaqcnSS0jzr+6S3r9/P/v371dIMzEx4eTJk2hra7Np0yZmzpxJ0aJF6dOnD7169QLSujrXrVvHtGnT6NixIwYGBvTv35+7d++ipaXcFfbrr78SERHBihUreP36NQYGBjRt2hQ3NzcgrYWuXr16DBs2jBEjRiiMjQSYOXMmc+bMYejQoSQmJlK1alXWr1+PoaGhdHezKnZ2djg5ObF582ZatmxJlSpV8PLyYv78+XTp0gUNDQ2qVavG5s2bMTQ0BGDo0KEkJSUxceJE4uLiaNCgAQ0bNiQhi3m+9PX18fLyYs6cOfTq1YuUlBQqV66Mp6en1EK6cOFCZs2axYABA4iKiqJMmTLMmzePGjVqALB27Vrmz59Px44dSUlJwcrKig0bNqCnp6fQkgswbtw4ChYsyLx583j37h2lSpXijz/+oGPHjpmWUfgxShkqj+stkFcbLQ11KhVNO+d+tSrLjhuPGLD7FH3tKhMZl8Di07epU7ooVTPMwXj7VTgFdfNgWiBtPOpvNub4P3jBIO8zdKsh48WHaJadCcTJsoyYg1H47hLfvuXtvn2U6N0LeXIyMQ8fkt/aCpPffuOtry9xT5/yfNlyLFavouLixbzy8kq76WXwIKICA3l/+jSQNo1OPnNzEv/6i8S/hwmFrPek8orlyGbP4i+//ehbWlLcxYXny5aJORh/UpqpiT+6CD8dNfn/4aCw0NBQnj17Jt21DGlT1jg4OLB161YpCPo3OnbsGNWrV5cCSEi7I7po0aLMnDnzB5bsx4tZO/HzKwmfNfnwZa6GhHGwb0sp7XFYJPP+vMntV+HoamtSv5wJw+tbSmMVAarN203rymZMbf5Py/r10DAWnbpN0F8RFMibh5aVzOhfp7LC3IzCl7m12v9HF+FfS01Tk+LdXSjcsiV5ihUj4e1b3u7dy6stXtK4RX1LS0oOHICehQWp8fG8Dwjg2aLFpPw9FjFPsWJU3+9HyJo1hKxZK+VtWL8+pv36ktfMjMS/wnize7fSnI9C9tldvZKr+YffOS/938jCLlf39W/xfxkwBgcH07p1a0aOHEmTJk2Iiopi0aJFPH/+nAMHDqhsZfy36Ny5M3nz5mX06NHo6elx4sQJ5syZg6enp8Id1/+PRMAo/D8QAaPw/yC3A8YPt05J/y9YtV6u7uvf4v/yZ3zZsmVZsGAB+/fvp1WrVvTs2RNdXV02bNjwrw4WIW3i7nz58tGjRw9atWrF/v37Wbx48f99sCgIgiAI2aWRkii9hDT/6jGM36JZs2Y0a9bsRxcjx5UoUYJly5b96GIIgiAIwr+WWoq4O/pT/7cBoyAIgiAIgipqKeLu6E+JgFEQBEEQBCED9SRx9/qnRMAoCIIgCIKQgWhhVCYCRkEQBEEQhAzEGEZlImAU/m8EDNz9o4sgCLlO7+aNH10EQfj3SxJ3R39KBIyCIAiCIAgZJYsWxk/9X87DKAiCIAiCkKnkpH9eOeTp06dYW1vj4+Mjpd2/f59u3bphZWWFo6MjmzdvzrH95TQRMAqCIAiCIGSUmPjPKwckJSXh5uZGbGyslPbhwwd69uxJyZIl8fb25vfff2fevHl4e3vnyD5zmuiSFgRBEARByECew13SS5cuRU9PTyFt165daGlpMW3aNDQ1NSlbtizPnz9nzZo1dOjQIUf3nxNEC6MgCIIgCEIG8qQk6fWtrly5ws6dO5k9e7ZC+tWrV6lZsyaamv+03dna2vLs2TPevXv3zfvNaaKFURCEz1NTo8ywnpTs0wmdEkWJefSM4HnreLV9v7SK49NT5C1RVGnTo0VtSQr/kGnWxTu1pNy4AeiWMSXu2Usez13Dyy2+uVELQci2lR4jefHkPrNWHwKgr5N1puvKLGrgNm1tpsufPb7Lnk0LeR58D528+bBr0IbWnfqjqaWV4+UWcoY8Q1d0w4YNs1z3xIkTmS77+PEjo0ePZuLEiRQrVkxh2Zs3b5DJZApphQsXBuD169cYGxt/abFz1RcFjC4uLly+fFkhTUtLC2NjYxwdHRk1ahR58+bN0QJm5OjoSPv27Rk8eHCu7cPc3DzTZatWraJBgwa5tu/PefToES9fvqR+/fpSWnJyMlu3bmXfvn08ffqUPHnyUKlSJfr27Yutra20nrm5ObNmzcLJySnXy7l06VL27t3LyZMnATh16hRTp04lLCyMUaNGsXHjxlx/H4WcJZsylLJuvQiasoSIq4EUbl4P683zIDWVVzsPomVUkLwlinJ/tAfvz19T2DY54mOm+RZt3wSrzfN4unQzYf5nKNq2EVaeHqQmJPJ616HcrpYgqHTx1EFuXDqJUaF/vuDHztqktN71Syc56ruJek1+yTSvsDehLJzSnzLmlvQd6cHr0Kf4bltOTHQk3fpPzJXyC99OnpQzE3dPmTIFa2trWrdurbQsPj4ebW1thbQ8efIAkJDw8z1p5otbGJs3b86ECROkv2NjYzl79iyzZs0iNTWVKVOm5GT5fojx48fTokULpXQDA4MfUJp/9OvXj/bt20sBY0JCAj179uT169cMGTIEa2tr4uPj8fb2pmfPnsyZM0flSZrbXF1d6dq1q/T3okWLKF26NJs3b6ZAgQK0atVK+lAIPz/1vDqUHtKdp0u3EDw3rRUl/M+LGFSrTKlBLrzaeZD8VSsA8GbfMWKfhGQ7b3P3Ebzec4T7brMAeHfsLFoFDTCfMlQEjMIPEfH+L3asn0NBoyIK6WXMLRX+fv/uDWeP+VC/eSds7Jtmmt+RvRvRyZuP38cuQlNLiyrV66KdR4ft6zxo3qGXQlAq/DwydkVn1YKYFV9fX65evcr+/ftVLtfR0SHxk5tq0gNFXV3dr9pnbvrigFFHR4dChQoppJmZmXHnzh0OHTr0nwgY9fX1ler4M1q8eDEPHz7kwIEDCk3dEyZMIDo6munTp+Po6Ei+fPm+a7ny5cunsM/IyEgaNGhAiRIlvms5hJyRmpDIeYcuJP4VrpiemISmgT4ABlUrkvQx+ouCxbxmJuiZlyZo2hKF9Nc+/hTv2ALdcmbEPn7+7RUQhC+wecU0KlW1RUs7D0F3rma63u6NC9DSzkP7roOyzO/ezfNUqW6v0P1cvXYjtq2Zxb2bF6jbOPd7fYQvl5oDd0d7e3sTHh6u0CsIMHnyZA4dOkTRokX566+/FJal/12kiOIPlp9Bjt30kidPHmng5qtXrxg+fDi1a9emcuXKODg4MHfuXFJTUwHw8fGhcePG0r8WFhY4OTlx7do/XVlRUVGMGTOGGjVqYGtry4YNG5T2eePGDbp370716tWpVasW48aN48OHf8ZKOTo6smbNGvr27UvVqlVxdHTk+PHjHD9+nKZNm2JlZUWvXr0IDw9XyjsrERERTJ06lXr16mFpaUnnzp25dOmStHzp0qV069aN4cOHU61aNdzd3QG4fv06Xbt2xdLSkvr16zN16lSio6Ol7W7fvo2zszPW1tbY2NgwePBgXr16JdXl5cuXLFu2DBcXF5KSkvD29sbJyUlpXATAsGHDWLt2LTo6OkrLUlNTWb16NU2bNsXCwoJq1arRu3dvXrx4Ia1z6tQpnJycqFq1KrVr12bs2LFERkZKy9evX0+jRo2wsLDA0dGR5cuXI5fLpfo7OjoCaV3hL1++ZPny5VJ3v6OjI0uXLpXy+vPPP3FycsLS0pLGjRuzaNEihV9d5ubmLFmyhAYNGmBvb8+zZ8+y/2YJ3y41lajAhyS8TRuErV3YiLKj+mDc0I7nq7YBkN+qIknvI6i2cwlN3l2l6YfrWG9dSJ6imf/w0qtQFoCYR88U0mOD04JEPVnpXKiMIGTuzDEfngffp0ufsVmu9+Thba6dP0b7roPJq6uX6XqJCfGEh72mSHEzhXR9A0N0dPV48/JZThRbyAWpicnS62vNmzePQ4cO4evrK70AhgwZwowZM7CxseHatWukpKRI21y8eJHSpUtjZGT0rVXIcd8cMCYnJxMQEMC+ffto27YtAAMGDCAqKooNGzZw5MgRXF1dWbdunTSmDdIGdO7YsYO5c+eyd+9e8ubNy9ixY6WgY9iwYdy+fZtVq1axYcMGAgICePnypbT97du3cXFxoXz58uzatYvFixdz69YtevXqpXDwV6xYQYsWLdi/fz8VKlRg9OjRrFq1irlz57Jq1SoCAwNZuzbzwcqfSklJwdXVlatXrzJ37lx8fHyQyWT06tWL27dvS+tduXIFY2Nj9u3bh4uLCw8ePKBnz57UrVsXPz8/5s2bx927d3F1dUUul5OSkkK/fv2wsbHBz8+PjRs38urVK8aPHw/Anj17KFq0KK6urixdupSQkBAiIiKoVq2aynIWKVIES0tLNDQ0lJZt3ryZ9evXM3bsWPz9/Vm+fDnPnj2T7uB6//49gwYNokOHDhw6dIhly5Zx5coV5syZA8DJkydZvXo1U6dO5ejRo7i5ubFy5Ur8/PyU9nX27Fmp3GfPnlVafvr0aYYNG0bHjh05cOAAkydP5vDhw4waNUphvW3btrFkyRKWLVtGqVKlsvdmCTmueKeWNH55ngoz3fjr8Clebk17z/NbVkDHpAiR1+9ypW0/7o2ajaGDDbYntqChq3pcs6ZB2hdt8sdohfTkqJi05fkz/yIWhJwW/tcrdm9cgHPfcejnL5jluv6+mzAqXJxa9ZSHLmUUF5t2buvkVe7l0dHRJT4u5usLLOSq1KQk6fW1ihQpgpmZmcILwMjIiCJFitChQweio6OZMGECjx8/xsfHh40bN9KvX7+cqkaO+uIu6f379+Pv7y/9HR8fT/HixenVqxf9+/cnPj6etm3b0rx5c6nlq0ePHqxdu5aHDx/SqFEjIG0Sy6lTp1KxYkUAevbsye+//05YWBjR0dGcPXuWjRs3UqNGDQDmz5+vcMOJp6cn5ubmTJo0CYCyZcuyYMEC2rZty9mzZ6lXrx4A9evXp127dgB07NiREydOMHz4cCwt08aj2NnZ8ejRI4U6Tp48WWoVTNevXz/69+/P2bNnuXv3Lvv375fubpo6dSqBgYGsX7+exYsXS9sMGTIEff20LrtRo0ZRp04d+vfvD0CpUqWYP38+jRo14vLly1SoUIEPHz5QuHBhTExMMDU1ZdGiRVLrp6GhIRoaGujq6lKgQAGePn0KfN24ypIlS+Lh4SEdTxMTE5o1a8aRI0cAePv2LYmJiRQvXhwTExNMTExYtWqVFIi/ePECbW1tTExMKF68OMWLF6dw4cIUL15caV+FChWSyq2qm3/VqlV07NiRzp07S2WbOnUqv/32G6GhoVI3dtu2balSpcoX11XIWRFXbnOhQVf0Lc0xnzKUmgfXcbGhC7cHTEKenELk1UAAPpy7RvS9x9id2o6JSzterN6ulJeaeta/V+V/90gIQm6Ty+VsWj4Vi2p1qF67UZbrfnj3lptXAujYYyQaGll/hcrlWZ/DampqX1xW4ftIScj9Z0kbGRmxbt06ZsyYQfv27SlUqBCjR4+mffv2ub7vr/HFAaOjoyNubm7I5XJu377NjBkzsLOzo3///mhqaqKpqUm3bt04cuQIt2/f5vnz5zx8+JB3795JXdLpypYtK/0/PbBKSkoiKCgIQCFAMDY2xtTUVPo7KCiIOnXqKORXoUIF9PX1efjwoRQwpkf0gHQHd8mSJaU0HR0dpS7pIUOG0KRJE4W09MAsKCgIfX19hVvh1dTUqFGjhkILmpGRkVQngHv37vH8+XOsrZWnZggODqZWrVr07t0bd3d3lixZgq2tLfXq1aN58+ZK60NaAAlp3eNfytHRkVu3brF48WKePn3K06dPefz4sTRmomLFirRq1Yr+/ftTqFAh6tSpQ/369WncuDEAbdq0wdvbm6ZNm1KuXDns7Oxo2rSpyoDxc+7du8ft27fZs2ePlJbeyhwcHCwFjBnfR+HHiX0SQuyTEN6fvUryx2isNszB0L4G788qj/X6cP46SREfyW9ZQWVeyZFRAGjqK7a+pLcsJkdGK20jCLnhz8M7CX0exOSFu0lJ+bsL8u/rUEpKMmpq6qj//QPn+qUTqKGW5Y0u6XTypp3L8fGxSsvi42LIq6uvlC78HFK+oSs6Kw8fPlT429LSkp07d+bKvnLaFweM+fLlk768S5UqReHChenZsycaGhpMmTKF2NhYunXrRnx8PM2aNaN9+/ZYWloq3DWb7tPbySEtWEj/1fVpgJlxcsv0oELV9loZBhdn3Cbd537VGRkZZRqgZLXfjPv6dOxgamoqrVu3lloYM0oP/tzc3HB2dubUqVNcuHABd3d31q1bh6+vr9KxMjU1xdjYmOvXr6u8ozs4OJgZM2Ywbtw4ypcvr7BszZo1LF++nPbt21O7dm169OjBiRMnOHjwoLTO/Pnz+f333zl9+jTnz59n1KhRVK9enU2bNmFoaMi+ffu4ceMG586d4+zZs2zevJnBgwczaFDWA8A/lZqaSu/evVX+osrYIqlqLKbwfWgbF6RQMwfC/M+QGPZeSo+8cQ+AvKVLUKKcGRFXbhN9N0NrvZoa6tpaCttkFB2U1kquW9aMjzfvS+n5yqZ99qIfBOd0VQRBpesXjhP9MYJRvRorLRvwqw2tOvajTee0a/ftq2coX6ka+Qt8foyZTl5dChgWJuy14s1gHyPeEx8XQ7ESYpzuzyq3AsZ/s28ew2hra0vPnj3Zvn07p0+flrpsN2/ezJAhQ2jRogV6enqEh4dnGmx9Kr2b+vr161Lax48fFW7KMDc3V7hJBuDBgwdER0crtFzmNHNzc6KioqRWUEgLFq9du0a5cuUy3a58+fI8fvxYYSxDcnIys2bN4vXr1zx58oTJkydjZGREly5dWLJkCevWrSM4OJgHDx4o5aeurs4vv/yCj48Pr1+/Vlq+bt06AgMDMTExUVq2atUqfv/9d6ZMmUKnTp2wsrLi2bNn0vtz69YtZs6cSZkyZejRowdr1qxh5syZXLx4kfDwcPz8/Ni+fTvVq1dnyJAh7Nq1i19//ZVDh758GpTy5cvz9OlThePy5s0b5syZQ0yMGN/zM1DPq4PVhjmY9lSca65Q47QW/sirgVgsnkS5MYrjboq0dkRDNy/hAZdQJTb4BbFPQijmpNhSU7R9E6KDnhL3/KXK7QQhp3XrP5Hxc7wUXpY1HDAoaMz4OV44NEm7k1kul/Ps0R3KVaia7bwrWdXm9tXTJCX908V5/eJx1NU1MK9ik+N1EXJGSkKi9BLS5MiTXoYOHcqJEyeYMmUKHh4eAPj5+dG0aVNev37NggULSEpKUppvKDMlS5akWbNmTJs2DW1tbYyNjVmwYIHC9j179sTZ2Rl3d3ecnZ159+4d7u7uVKpUidq1a+dEtVSyt7enYsWKjBw5kkmTJmFkZISXlxdBQUFMnjw50+3S5yacOnUq3bp14+PHj0ydOpX4+HhKlSpFTEwMBw8eJD4+nr59+6Kurs7evXsxMDCgTJkyQFrrbvojg4yNjenfvz9nzpzB2dmZoUOHUq1aNSIiIti+fTu+vr4sXLhQ5VxOxYoV49y5czg6OqKurs6+ffs4evSoNKu8np4e27ZtQ0tLi44dO5KQkMChQ4coVaoUBQsWJCEhAQ8PD/Lly0eNGjV48+YNV65ckcabfok+ffowbNgwli1bRsuWLXnz5g0TJkygRIkS/4qpjf4fxIe8JmTDHspP/B15UjKRN+9haF+DsqP78sJzN9H3g3k8Zy3mU4aQ8PYdfx0+Rf4qMspPGsybfccJD7gIpHU961UqR2zwCxLfpc1m8GjGcqqun03S+wje7j9JkTYNKd6xBdedh/3AGgv/b4qalFJKy6dvgKamFqXKVZbS3oe9Ji42mmKmZTLN68nD2+gZFKRw0bQhVE3b/caVs0dY4j6Ixm268fbVc/ZuXUbdxk5iDsafmGhhVJYjAWOePHlwd3ene/fu+Pv7M27cODZu3MiiRYsoUqQILVq0oFixYgQGBmY7Tw8PDzw8PBg+fDipqal06tSJ9+//6dqqWrUq69atY9GiRbRr1w49PT0aNWrEyJEjFbqkc5qGhgaenp54eHgwaNAgEhMTsbCwYOPGjVhZWWW6nZWVFevWrWPx4sW0b98eXV1dateuzZgxY9DW1kZbW5u1a9cyf/58OnbsSEpKClZWVmzYsEF6YLmLiwseHh48evQIPz8/8ubNi5eXF56enqxdu5ZXr16ho6NDpUqV2LJlS6YB3Jw5c5g2bRodOnQgX758VK1alalTpzJlyhRevXpF2bJlWbp0KcuWLWPbtm2oq6tja2vL2rVrUVdX59dffyUiIoIVK1bw+vVrDAwMaNq0KW5ubl98PJs1a8bChQtZvXo1q1atokCBAtI4WeHnEfj7FGKfhGDauyMyMxPiQ14TNGUJTxasB+DxzBUkvntPqf7OmPXrQmJ4BC/W7CBo2j/TJ+W3rkztE1u41WssoZv3AhC6eS/qebQpM9yVEj06EPskhJs9RvN69+EfUk9ByMrHyLTvIN18+TNdZ/a436jdoDU9B08DoFiJ0gz7YwV7Ni1i1dxR6OkXoFHrrrTpPOC7lFn4OskJImD8lJo8u/3EgvAvd1Ar88c+CsJ/hd7NGz+6CIKQ6+pVzt0nodxr/8/zoyvt/bonvfzX5EgLoyAIgiAIwn9FckLK51f6PyMCRkEQBEEQhAxEl7QyETAKgiAIgiBkkBT79U94+a8SAaMgCIIgCEIGyfGihfFTImAUBEEQBEH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" }, "metadata": {}, "output_type": "display_data" @@ -966,12 +1028,12 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 26, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T22:19:35.451464Z", - "start_time": "2023-12-20T22:19:35.426378Z" + "end_time": "2023-12-20T23:14:22.784450Z", + "start_time": "2023-12-20T23:14:22.759937Z" } }, "outputs": [], diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 1205bf0f..ec44ee61 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,6 +1,6 @@ dataset_name: Law_School bootstrap_fraction: 0.8 -n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example +n_estimators: 3 # Better to input the higher number of estimators than 100; this is only for this use case example sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} postprocessing_sensitive_attribute: 'race_binary' diff --git a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv deleted file mode 100644 index 715e8048..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__221838.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Aleatoric_Uncertainty,0.3893172497885363,0.3736965000305002,0.4099589548259412,0.327506902860199,0.7330763716644314,0.359182293248589,0.7390653817521671,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Statistical_Bias,0.1584682105386421,0.14632828533128414,0.17451025456265082,0.12716969681195228,0.3325353389302957,0.14321139172980774,0.33553977428965914,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Overall_Uncertainty,0.40598780108322047,0.3895346408955927,0.4277294770454429,0.3438388882086417,0.7516298622752781,0.375656695677424,0.7580124486717071,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Std,0.03835055869809331,0.03659412720362341,0.04067155745864283,0.03645567315290321,0.04888899155667421,0.037396967694245026,0.04941799368215066,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.048870501641202935,0.04753369945696757,0.05063699024179967,0.045189017097965405,0.06934513018924005,0.0469386310159418,0.07129190920105191,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.10846602714729843,0.10179985517370609,0.11727489725525977,0.07800479837887082,0.2778765833578281,0.09342782226195807,0.28300034445291544,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.9506346153846155,0.9598986486486486,0.9383928571428571,0.9994214407260352,0.6793059936908518,0.974976501305483,0.6681212121212122,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.02892503924646794,0.023592801985659228,0.035971209912536606,0.0005767071434359316,0.18658469065859876,0.01481579368039641,0.19267779839208504,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,0.9771995708154506,0.9823255813953489,0.9702154626108999,1.0,0.8172043010752689,0.9890929965556832,0.8073770491803278,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.2175925925925926,0.1926605504587156,0.24299065420560748,0.0,0.5562130177514792,0.12138728323699421,0.6046511627906976,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.9150967093695052,0.9230769230769231,0.9043118724158299,0.9254112308564946,0.8351648351648352,0.9189333333333334,0.8528138528138528,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.022800429184549355,0.017674418604651163,0.029784537389100127,0.0,0.1827956989247312,0.010907003444316877,0.19262295081967212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,0.7824074074074074,0.8073394495412844,0.7570093457943925,1.0,0.4437869822485207,0.8786127167630058,0.3953488372093023,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.8983173076923077,0.9096283783783784,0.8833705357142857,0.9254112308564946,0.7476340694006309,0.9107049608355091,0.7545454545454545,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.9451290699182774,0.9517800811176206,0.9361051666157139,0.9612608631609957,0.8260869565217391,0.952723251313243,0.8294736842105264,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,0.9569711538461538,0.9662162162162162,0.9447544642857143,1.0,0.7176656151419558,0.97911227154047,0.7,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,1.0678648068669527,1.064186046511628,1.0728770595690749,1.0806006742261722,0.978494623655914,1.076349024110218,0.9467213114754098,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv b/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv deleted file mode 100644 index e24a6bb1..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__221838.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Aleatoric_Uncertainty,0.3377334115071367,0.31806567984367007,0.363722914062432,0.28842958613839725,0.6119373361919563,0.31125459579626136,0.6450481514242666,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.13794915375351804,0.1277101394591294,0.15147927978538872,0.11221451494719288,0.28107271279311186,0.12533924340068053,0.28430053754554113,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.33877340223799474,0.31903994855665746,0.36484975174547607,0.28921745012016437,0.6143795334169693,0.31216985252623297,0.6475358125290487,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Std,0.009730985772521347,0.009088713899186371,0.01057970217657113,0.007672539657578457,0.021179063061620124,0.008673662823564944,0.022002339998288056,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.01289369301485991,0.012034289957932147,0.014029332768657312,0.010075228003716451,0.028568626184089944,0.011449152489704012,0.02965911789772986,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10391238183569104,0.0931586776220085,0.11812263383234299,0.07516073288182125,0.2638150856390741,0.08781407715497667,0.29075028161489114,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9867019230769232,0.9883108108108107,0.984575892857143,0.9925127623369255,0.954384858044164,0.9900052219321148,0.9483636363636365,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.00937853218210366,0.008179123000551566,0.010963465743440213,0.0053660851748527005,0.03169381317195647,0.007174082165503277,0.03496351267779825,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9847103004291845,0.9893023255813953,0.9784537389100126,0.9944836040453571,0.9161290322580645,0.9911021814006888,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.24305555555555555,0.18807339449541285,0.29906542056074764,0.12167300380228137,0.4319526627218935,0.17052023121387283,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.9182091045522761,0.9231770833333334,0.911452184179457,0.9335443037974683,0.8160919540229885,0.9232620320855615,0.8449612403100775,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.01528969957081545,0.010697674418604652,0.021546261089987327,0.0055163959546429666,0.08387096774193549,0.008897818599311137,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.7569444444444444,0.8119266055045872,0.7009345794392523,0.8783269961977186,0.5680473372781065,0.8294797687861272,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9076923076923077,0.9155405405405406,0.8973214285714286,0.9293817356778219,0.7870662460567823,0.9169712793733682,0.8,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.9502976960911209,0.9550965424337674,0.9437652811735942,0.9630508977593115,0.8632218844984803,0.9559800664451827,0.8685258964143426,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9610576923076923,0.972972972972973,0.9453125,0.9858196256381169,0.8233438485804416,0.9765013054830287,0.7818181818181819,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0724248927038627,1.0716279069767443,1.073510773130545,1.0652773521299417,1.1225806451612903,1.0734787600459241,1.0573770491803278,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv new file mode 100644 index 00000000..c781bb21 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Overall_Uncertainty,0.40548677779775943,0.38795631205377373,0.42865203610231184,0.3435620568563354,0.7498819923710415,0.3752852290804241,0.7560077826080445,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Std,0.03959730437660162,0.03751554919350445,0.04234819515426574,0.03782226695330002,0.0494692002039856,0.038696877331316555,0.05004771523551618,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.38800472750643894,0.3716927405362876,0.4095598531455674,0.32636198960405727,0.7308316894051734,0.3579700915919998,0.7365885322103841,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.05085689080983673,0.04813448829089488,0.05445435128129559,0.04717748683665128,0.07131994823799424,0.048938786197364095,0.0731185291909584,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Statistical_Bias,0.1581828042428406,0.1460526321053724,0.17421196028163785,0.12696135029842903,0.33182136356144487,0.14304701492180225,0.3338496924233768,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.10803563244845835,0.10098575350775986,0.1173515439058099,0.0782186902842951,0.27386297956334726,0.09330979902126024,0.2789445476792725,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.9522980769230769,0.9616891891891891,0.9398883928571429,0.9993079977311402,0.6908517350157729,0.9759060052219322,0.6783030303030303,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.027038657770800754,0.021833287369001655,0.03391718294460644,0.0006822785835831734,0.17362003476469545,0.013898651888953978,0.17954236239950647,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.9796137339055794,0.9841860465116279,0.973384030418251,1.0,0.8365591397849462,0.9902411021814007,0.8278688524590164,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.20833333333333334,0.1834862385321101,0.2336448598130841,0.0,0.5325443786982249,0.11560693641618497,0.5813953488372093,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.9143715573360041,0.9224062772449869,0.9035294117647059,0.9254112308564946,0.8311965811965812,0.9185303514376997,0.8487394957983193,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.0203862660944206,0.01581395348837209,0.026615969581749048,0.0,0.16344086021505377,0.00975889781859931,0.1721311475409836,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.7916666666666666,0.8165137614678899,0.7663551401869159,1.0,0.46745562130177515,0.884393063583815,0.4186046511627907,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.8995192307692308,0.910472972972973,0.8850446428571429,0.9254112308564946,0.7555205047318612,0.9112271540469974,0.7636363636363637,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9458689458689459,0.9522952295229523,0.937156802928615,0.9612608631609957,0.8338692390139335,0.9530386740331491,0.8381742738589212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.9600961538461539,0.96875,0.9486607142857143,1.0,0.7381703470031545,0.9806788511749347,0.7212121212121212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.071351931330472,1.0669767441860465,1.0773130544993663,1.0806006742261722,1.0064516129032257,1.0780711825487945,0.9754098360655737,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv new file mode 100644 index 00000000..01bc66f6 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Overall_Uncertainty,0.3400075911752074,0.32004199157416013,0.36639070493373416,0.29063676702172864,0.6145841305524411,0.3134555249670144,0.6481724808036297,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.010103878160329828,0.00935231042471382,0.0110970212395367,0.007823807874810204,0.022784521420175573,0.008943623875954035,0.023569859703236794,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.33888584189513504,0.319007703031287,0.3651533825366486,0.28981575320333874,0.6117898367331064,0.3124791294001381,0.6453637475188876,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.013502950167524836,0.012498306939440759,0.014830514433207364,0.01052416618107057,0.030069499593767316,0.012036267683870904,0.03052535596265982,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.13831382318307825,0.12802644174473343,0.151907862940891,0.11258537896030874,0.2814029309582915,0.12569762072738785,0.2847382335021514,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10438330911775877,0.09355351708337806,0.11869410573461898,0.07579635563611556,0.2633700567144055,0.08833236873037274,0.2906714960380269,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9866538461538462,0.9879729729729729,0.9849107142857143,0.9927283040272262,0.9528706624605678,0.990046997389034,0.9472727272727274,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.009536106750392487,0.00851282404853833,0.010888301749271127,0.0051806406056466945,0.03375909354277992,0.007189428251718451,0.03677179962894243,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9849785407725322,0.9897674418604652,0.9784537389100126,0.9947900704872816,0.9161290322580645,0.9913892078071183,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.25,0.2018348623853211,0.29906542056074764,0.13307984790874525,0.4319526627218935,0.1791907514450867,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.918918918918919,0.9244135534317984,0.911452184179457,0.9343696027633851,0.8160919540229885,0.9240235420010701,0.8449612403100775,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.015021459227467811,0.010232558139534883,0.021546261089987327,0.005209929512718358,0.08387096774193549,0.008610792192881744,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.75,0.7981651376146789,0.7009345794392523,0.8669201520912547,0.5680473372781065,0.8208092485549133,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9086538461538461,0.9172297297297297,0.8973214285714286,0.9305161656267725,0.7870662460567823,0.9180156657963446,0.8,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9508026929052305,0.9559748427672956,0.9437652811735942,0.9636336648359805,0.8632218844984803,0.9565217391304348,0.8685258964143426,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9605769230769231,0.9721283783783784,0.9453125,0.9852524106636416,0.8233438485804416,0.9759791122715404,0.7818181818181819,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0718884120171674,1.0706976744186048,1.073510773130545,1.0646644192460926,1.1225806451612903,1.0729047072330655,1.0573770491803278,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv new file mode 100644 index 00000000..f9fdc731 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Overall_Uncertainty,0.34201637972980087,0.32680237773966914,0.36212059664533214,0.2897690184868528,0.6325908209642407,0.3149930260476729,0.6556510603435897,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.03234031410876854,0.030926760529202145,0.03420822419605269,0.026747316125816958,0.06344585178682416,0.029488892520486015,0.06543408587580507,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.3308789883371455,0.3160747377401646,0.3504417480545845,0.28039220092815453,0.6116619732016598,0.3047506225064036,0.6341263857060583,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.040657672162544324,0.038563060999410155,0.04342555119954304,0.03306069051713258,0.08290839342708976,0.0367250077498452,0.08630041367962807,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.14076712510064224,0.13157874351515925,0.15290891505288765,0.11379018271041676,0.2907997731573222,0.12763896035201963,0.29313340081950445,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10487583662696706,0.09585243705047859,0.11679961463875536,0.0764053553082597,0.26321482263605556,0.0894833738419283,0.2835216925866591,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.95975,0.962972972972973,0.9554910714285715,0.9765967101531481,0.8660567823343849,0.9678851174934726,0.8653333333333335,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.029047095761381325,0.026747793712079457,0.032085459183673504,0.016884716450391888,0.09668834095152255,0.023215431342249654,0.09672974644403205,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9876609442060086,0.9911627906976744,0.9828897338403042,0.9963224026969046,0.9268817204301075,0.9931113662456946,0.9098360655737705,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.23148148148148148,0.1834862385321101,0.2803738317757009,0.10646387832699619,0.4260355029585799,0.16184971098265896,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9172894867962132,0.9229103508012126,0.9096774193548387,0.9325874928284567,0.8162878787878788,0.9226666666666666,0.8409090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.012339055793991416,0.008837209302325582,0.017110266159695818,0.003677597303095311,0.07311827956989247,0.006888633754305396,0.09016393442622951,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7685185185185185,0.8165137614678899,0.719626168224299,0.8935361216730038,0.5739644970414202,0.838150289017341,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9091346153846154,0.9168074324324325,0.8989955357142857,0.9299489506522972,0.7933753943217665,0.9180156657963446,0.806060606060606,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.9511754068716094,0.9558196905135681,0.9448674992385013,0.9634019854793303,0.8680765357502518,0.9565938623168372,0.8740157480314961,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9649038461538462,0.9750844594594594,0.9514508928571429,0.9886557005104935,0.832807570977918,0.97911227154047,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.076716738197425,1.073953488372093,1.0804816223067173,1.068342016549188,1.135483870967742,1.076349024110218,1.0819672131147542,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv new file mode 100644 index 00000000..8fe5eb43 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.03288923190596696,0.031199547876634367,0.035122028659013586,0.031537609797356245,0.040406297450070086,0.03226215216216488,0.04016715741736682,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.4064422861784247,0.3897836557091545,0.42845547644138904,0.3442293676346454,0.7524403158083394,0.3762888666346476,0.7564047008834742,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.11063761354885507,0.10258287628184667,0.12128137350883042,0.07833984961667391,0.29026208614328836,0.0948548932431735,0.2938128219450987,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.03124058080255428,0.029644695862137638,0.0333494287595334,0.030167069421535284,0.03721092958721196,0.030745903277452007,0.0369818381393473,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Statistical_Bias,0.1597862222670465,0.14696841375919223,0.17672404065242533,0.12853165427244964,0.3336089458458297,0.14448998479387562,0.3373152814253632,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.3956233449539585,0.38012604272230577,0.4161019229029282,0.33384421950219023,0.739208828144708,0.3656739212633842,0.743218171423351,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.008173076923076924,0.007038288288288288,0.009672619047619048,0.0,0.05362776025236593,0.004351610095735422,0.052525252525252523,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.9918269230769231,0.9929617117117115,0.9903273809523808,1.0,0.9463722397476341,0.9956483899042645,0.9474747474747476,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.9707618025751072,0.978139534883721,0.9607097591888466,1.0,0.7655913978494624,0.9865097588978186,0.7459016393442623,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.2523148148148148,0.24311926605504589,0.2616822429906542,0.0,0.6449704142011834,0.1531791907514451,0.6511627906976745,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.9180618975139523,0.9272486772486772,0.9056152927120669,0.9254112308564946,0.8557692307692307,0.9214477211796247,0.8584905660377359,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.029238197424892705,0.02186046511627907,0.03929024081115336,0.0,0.23440860215053763,0.0134902411021814,0.2540983606557377,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.7476851851851852,0.7568807339449541,0.7383177570093458,1.0,0.35502958579881655,0.846820809248555,0.3488372093023256,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.8961538461538462,0.910472972972973,0.8772321428571429,0.9254112308564946,0.7334384858044164,0.9112271540469974,0.7212121212121212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9436766623207301,0.9520144861928475,0.932349323493235,0.9612608631609957,0.8081725312145289,0.9528694205711117,0.7982456140350878,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.9475961538461538,0.9577702702702703,0.9341517857142857,1.0,0.6561514195583596,0.9738903394255874,0.6424242424242425,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.0574034334763949,1.0548837209302326,1.0608365019011408,1.0806006742261722,0.8946236559139785,1.0706084959816302,0.8688524590163934,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv new file mode 100644 index 00000000..2d619b5e --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.031770934397012174,0.03179588407276876,0.03173796518261954,0.03021693850570537,0.04041350460639352,0.03095858612070298,0.04119909772508553,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.3983894745523038,0.37953898808229,0.42329904595910783,0.3343008855637001,0.754819071987346,0.3672408267409202,0.7599025688480595,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.10919085836888948,0.09977103289182301,0.12163848489215587,0.07825493034734682,0.2812414612142513,0.09401753098365194,0.2852934155975552,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.029399891123827526,0.029524613293780425,0.02923507968496118,0.028113070894052253,0.036556560098886845,0.028723274560670305,0.037252743962894644,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Statistical_Bias,0.1576461741629812,0.1448501636355344,0.1745551880742502,0.12585049281415528,0.3344783073427291,0.14211527986946687,0.3378986745998295,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.3874506713996536,0.36915934374048587,0.4116213543778398,0.3236753328689938,0.7421381219660677,0.35648278707105796,0.7468658137588103,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.03317307692307692,0.02702702702702703,0.041294642857142856,0.0,0.21766561514195584,0.01671018276762402,0.22424242424242424,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.9668269230769231,0.972972972972973,0.9587053571428571,1.0,0.7823343848580442,0.983289817232376,0.7757575757575756,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.9726394849785408,0.9786046511627907,0.9645120405576679,1.0,0.7806451612903226,0.986796785304248,0.7704918032786885,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.24305555555555555,0.22935779816513763,0.2570093457943925,0.0,0.621301775147929,0.14450867052023122,0.6395348837209303,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.9172780166961801,0.926056338028169,0.9054134443783463,0.9254112308564946,0.8501170960187353,0.920728441349759,0.8584474885844748,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.02736051502145923,0.021395348837209303,0.035487959442332066,0.0,0.21935483870967742,0.01320321469575201,0.22950819672131148,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.7569444444444444,0.7706422018348624,0.7429906542056075,1.0,0.378698224852071,0.8554913294797688,0.36046511627906974,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.896875,0.9096283783783784,0.8800223214285714,0.9254112308564946,0.7381703470031545,0.9107049608355091,0.7363636363636363,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9441478974091915,0.9516056083220262,0.9340288432034366,0.9612608631609957,0.8139013452914798,0.9526184538653366,0.8120950323974082,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.9502403846153846,0.9594594594594594,0.9380580357142857,1.0,0.6735015772870663,0.974934725848564,0.6636363636363637,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.060354077253219,1.0567441860465117,1.0652724968314322,1.0806006742261722,0.9182795698924732,1.0717566016073479,0.8975409836065574,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv new file mode 100644 index 00000000..18d3d46d --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.03252910468942265,0.03252902524267529,0.03252920967262452,0.030693039438224382,0.04274040764797955,0.031555425926226406,0.043829679425912385,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.4037381628750886,0.3923425606544777,0.41879663723803884,0.33849872997500174,0.766568195060745,0.3718477732674574,0.7738599574121419,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.10993265890368518,0.10422415679301861,0.11747603669278028,0.07764457815121702,0.2895032783566864,0.09398128679796605,0.29506525031248587,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.030819876820101517,0.03089316049137479,0.03072303768306183,0.029335641796964584,0.039074470970859906,0.030024239668031603,0.040054089827458374,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Statistical_Bias,0.15912684455902867,0.1474117950989567,0.1746074456312667,0.1267741995898805,0.3390565388196225,0.1430736551226199,0.3454411340785611,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.39359992330357896,0.38201221280895464,0.4089122550286182,0.3284308605961883,0.75603859066361,0.36177256196527935,0.762990207926875,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.04294871794871794,0.03490990990990991,0.05357142857142857,0.0007562866326337683,0.277602523659306,0.02193211488250653,0.2868686868686869,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.957051282051282,0.96509009009009,0.9464285714285714,0.9992437133673663,0.722397476340694,0.9780678851174934,0.7131313131313131,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9452332657200812,0.9517485613103143,0.9364985163204748,0.9612608631609957,0.8462238398544131,0.9526934645884605,0.8501742160278746,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.1158798283261802,1.1013953488372092,1.1356147021546261,1.0806006742261722,1.3634408602150538,1.0993111366245694,1.3524590163934427,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv new file mode 100644 index 00000000..e1b79040 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.009462385929412266,0.009012175653533759,0.010057306651108868,0.007180899249844511,0.02215090648486323,0.008435748571833603,0.021377601321916156,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.33549114826103926,0.31836021170681267,0.3581284572791243,0.285181081566371,0.6152912983641939,0.30876545484727985,0.6456711657601252,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10368731898989081,0.09421840135990592,0.11619981728665656,0.0743386380596007,0.2669104246053528,0.0875476151393161,0.2910057000435306,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.009041797085257739,0.00862125024111327,0.009597519700734358,0.006863431768917732,0.021156806715249644,0.008063663837118703,0.02039407084396233,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.1373039332079171,0.12778934038053086,0.14987678801553456,0.11150324023694122,0.28079485342189336,0.12475828626977171,0.28290947191427085,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.3346258082488791,0.31753205972953247,0.35721397593515863,0.28458446323586517,0.612931458904853,0.30800866599900106,0.6435459743611006,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.010576923076923076,0.009290540540540541,0.012276785714285714,0.005861221402911704,0.03680336487907466,0.008181026979982594,0.03838383838383839,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.989423076923077,0.9907094594594594,0.9877232142857143,0.9941387785970883,0.9631966351209255,0.9918189730200173,0.9616161616161616,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9847103004291845,0.9883720930232558,0.9797211660329531,0.9944836040453571,0.9161290322580645,0.9908151549942594,0.8975409836065574,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.2569444444444444,0.21559633027522937,0.29906542056074764,0.12927756653992395,0.4556213017751479,0.18497109826589594,0.5465116279069767,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9195891783567134,0.9255226480836237,0.9115566037735849,0.9340817501439264,0.8223938223938224,0.9244777718264595,0.8488372093023255,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01528969957081545,0.011627906976744186,0.020278833967046894,0.0055163959546429666,0.08387096774193549,0.009184845005740528,0.10245901639344263,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7430555555555556,0.7844036697247706,0.7009345794392523,0.870722433460076,0.5443786982248521,0.815028901734104,0.45348837209302323,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9091346153846154,0.9172297297297297,0.8984375,0.9299489506522972,0.7933753943217665,0.9180156657963446,0.806060606060606,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9510362694300518,0.9559154295996402,0.9444105070250458,0.9633367967938251,0.866734486266531,0.9564976447769465,0.8725099601593626,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9596153846153846,0.9695945945945946,0.9464285714285714,0.9852524106636416,0.8170347003154574,0.974934725848564,0.7818181818181819,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0708154506437768,1.067906976744186,1.0747782002534854,1.0646644192460926,1.113978494623656,1.0717566016073479,1.0573770491803278,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv new file mode 100644 index 00000000..d0c40ea3 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.009158053769605738,0.008212514239325449,0.010407516720333266,0.006961527846575738,0.021374063871504454,0.007914826128421525,0.023587029120319496,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3405731063979544,0.31839992701142616,0.3698733791587238,0.2914926154073046,0.6135349537686661,0.3139366328601028,0.6497176326099899,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10348223300075418,0.09177920427944804,0.11894694952533731,0.07569101828523213,0.25804346815364165,0.08765151711350165,0.2872144810255336,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.008760780941090826,0.00786667055488113,0.009942283951439354,0.006660075235377984,0.020443885544156257,0.007576159832381081,0.022509565323994844,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.1386288789089302,0.12788058574650565,0.1528319805878484,0.11296577176985978,0.28135461356565317,0.12603925146210496,0.2847448580645082,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.3397863513294368,0.3177008670684129,0.36897074124578977,0.29094203476185165,0.6114347112936402,0.31328202754094536,0.6473971395413217,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.010576923076923076,0.008445945945945946,0.013392857142857144,0.006239364719228588,0.03470031545741325,0.007832898172323759,0.04242424242424242,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.989423076923077,0.9915540540540541,0.9866071428571429,0.9937606352807714,0.9652996845425867,0.9921671018276762,0.9575757575757575,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9852467811158798,0.9911627906976744,0.9771863117870723,0.9938706711615078,0.9247311827956989,0.9916762342135477,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.23842592592592593,0.1743119266055046,0.3037383177570093,0.11406844106463879,0.4319526627218935,0.16184971098265896,0.5465116279069767,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9177911044477761,0.9221116399826915,0.911886457717327,0.9329689298043728,0.8174904942965779,0.9225634178905207,0.8482490272373541,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.014753218884120171,0.008837209302325582,0.022813688212927757,0.006129328838492185,0.07526881720430108,0.008323765786452353,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7615740740740741,0.8256880733944955,0.6962616822429907,0.8859315589353612,0.5680473372781065,0.838150289017341,0.45348837209302323,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9076923076923077,0.9159628378378378,0.8967633928571429,0.9282473057288713,0.7933753943217665,0.9167101827676241,0.803030303030303,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9503234152652005,0.9553911678995741,0.9434077699602325,0.962457337883959,0.8678102926337034,0.9558721814912159,0.8702594810379242,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9620192307692308,0.9759290540540541,0.9436383928571429,0.9858196256381169,0.8296529968454258,0.9778067885117493,0.7787878787878788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0734978540772533,1.0748837209302327,1.0716096324461344,1.0652773521299417,1.1311827956989247,1.0749138920780712,1.0532786885245902,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv new file mode 100644 index 00000000..b020be4b --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.008988904602918851,0.008328296086880599,0.009861851570540831,0.007081741892447545,0.019595617090492713,0.008046087869020623,0.019931292756949822,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3362012212622353,0.31866174345248494,0.35937838836797675,0.2873796619777732,0.6077230162732972,0.31019950178123873,0.6379787534204678,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10435870549531724,0.09454120231275444,0.11733183470084667,0.07588751371437953,0.26270164275018537,0.08856826968010649,0.2876234605627632,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.008540626404047149,0.007912095966912237,0.00937118448168971,0.0067239326019294015,0.018644194773553734,0.0076361836959867885,0.01903764328850527,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.13772743781485963,0.12801423590309075,0.15056274034112568,0.11213124888977462,0.2800810058745596,0.12527179394444676,0.2822883954623788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.3353985952973466,0.31795390058455897,0.35845051331067296,0.2867634594847761,0.6058835935230934,0.30948497636317873,0.6361536271696577,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.010256410256410256,0.008727477477477479,0.012276785714285714,0.005672149744753261,0.035751840168243953,0.007832898172323759,0.03838383838383838,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9897435897435899,0.9912725225225226,0.9877232142857143,0.9943278502552467,0.9642481598317559,0.9921671018276762,0.9616161616161616,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9841738197424893,0.987906976744186,0.9790874524714829,0.9938706711615078,0.9161290322580645,0.9902411021814007,0.8975409836065574,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.25925925925925924,0.22018348623853212,0.29906542056074764,0.14068441064638784,0.4437869822485207,0.1907514450867052,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.919779393331662,0.925893635571055,0.911504424778761,0.9348515422311905,0.8192307692307692,0.9249329758713136,0.8455598455598455,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01582618025751073,0.012093023255813953,0.02091254752851711,0.006129328838492185,0.08387096774193549,0.00975889781859931,0.10245901639344263,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7407407407407407,0.7798165137614679,0.7009345794392523,0.8593155893536122,0.5562130177514792,0.8092485549132948,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9088942307692308,0.9172297297297297,0.8978794642857143,0.9302325581395349,0.7902208201892744,0.9180156657963446,0.803030303030303,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.950887650641441,0.9558955895589559,0.9440879926672777,0.9634581105169341,0.86497461928934,0.956473523703909,0.8707753479125249,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9588942307692307,0.96875,0.9458705357142857,0.9838343732274532,0.8201892744479495,0.9738903394255874,0.7848484848484848,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.070010729613734,1.0669767441860465,1.0741444866920151,1.0631320870364696,1.118279569892473,1.0706084959816302,1.0614754098360655,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv new file mode 100644 index 00000000..ba09b4f1 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.028931583448394384,0.027887440060036513,0.03031134435443872,0.023990508544688052,0.056411441666799005,0.026523966251049417,0.05687453455697389,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3407296076505943,0.32351157675879033,0.3634820056147639,0.2889025793842711,0.6289663610686632,0.3137768789694428,0.6535446102227468,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10449644700581025,0.09454884299671718,0.11764149516068323,0.07709571085209536,0.2568860300941363,0.08925642579476645,0.28137305681883384,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.02750846834515793,0.026513245234631513,0.028823584598353554,0.022807488757621504,0.05365303305439051,0.0252261103756494,0.05399765326399938,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.14130656744276707,0.13179506249596598,0.15387534183675428,0.11453587752255483,0.2901921394595943,0.1280896522383968,0.29470288632985225,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.332699552133727,0.31566221667372774,0.35521317399158303,0.2821091907817342,0.6140585649525385,0.3063264215158082,0.6387877044568451,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.027564102564102563,0.025056306306306304,0.030877976190476192,0.01588201928530913,0.09253417455310199,0.022106179286335945,0.0909090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9724358974358973,0.9749436936936936,0.9691220238095237,0.9841179807146909,0.9074658254468979,0.977893820713664,0.9090909090909091,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9860515021459227,0.9902325581395349,0.9803548795944234,0.9947900704872816,0.9247311827956989,0.9919632606199771,0.9016393442622951,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.22916666666666666,0.16055045871559634,0.29906542056074764,0.12547528517110265,0.3905325443786982,0.15895953757225434,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9169368919930158,0.9208477508650519,0.9116087212728344,0.9338319907940161,0.8067542213883677,0.922337870296237,0.8396946564885496,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.013948497854077254,0.009767441860465116,0.01964512040557668,0.005209929512718358,0.07526881720430108,0.008036739380022962,0.09836065573770492,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7708333333333334,0.8394495412844036,0.7009345794392523,0.8745247148288974,0.6094674556213018,0.8410404624277457,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9074519230769231,0.9138513513513513,0.8989955357142857,0.9299489506522972,0.7823343848580442,0.9167101827676241,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.950239110766447,0.9542805916629314,0.944732824427481,0.9633476776969877,0.8617234468937875,0.9558843866685106,0.8695652173913043,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.963701923076923,0.9763513513513513,0.9469866071428571,0.9858196256381169,0.8406940063091483,0.9783289817232376,0.793939393939394,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0753755364806867,1.0753488372093023,1.0754119138149556,1.0652773521299417,1.146236559139785,1.07548794489093,1.0737704918032787,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv new file mode 100644 index 00000000..427d2847 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.02731277021398364,0.026098285030024305,0.028917625635644182,0.022816685936188654,0.05231780674948068,0.025009235155152944,0.05404773771495805,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3355373115096594,0.32007249335487853,0.35597296407133416,0.2835616671135385,0.624600595643291,0.30847561224805253,0.6496170332428544,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.1044820373831202,0.0952400387637054,0.11669467841591832,0.0751686456789891,0.26750888146634777,0.08848177855052348,0.2901820111068944,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.025993141221894018,0.02484872556064418,0.027505404774259886,0.021677693068287117,0.04999356738848382,0.02379752103182506,0.0514756422157247,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.14006175423450606,0.13093058869046512,0.15212793727484586,0.1130408437373988,0.290338931541762,0.1269055665965932,0.29275326530482826,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.3282891285396943,0.31304866325567177,0.3484283148078669,0.2773056053326578,0.6118347165964936,0.3017389153451399,0.6364325119795219,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.029006410256410254,0.026182432432432432,0.03273809523809524,0.01588201928530913,0.10199789695057833,0.022976501305483028,0.098989898989899,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9709935897435896,0.9738175675675675,0.9672619047619049,0.9841179807146909,0.8980021030494217,0.977023498694517,0.9010101010101009,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.983637339055794,0.9897674418604652,0.9752851711026616,0.9944836040453571,0.9075268817204301,0.9913892078071183,0.8729508196721312,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.2152777777777778,0.16055045871559634,0.27102803738317754,0.09505703422053231,0.40236686390532544,0.13872832369942195,0.5232558139534884,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9153769345981029,0.9208135006490696,0.9079646017699115,0.9316681022107379,0.8068833652007649,0.9205756929637526,0.8385826771653543,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.01636266094420601,0.010232558139534883,0.024714828897338403,0.0055163959546429666,0.09247311827956989,0.008610792192881744,0.12704918032786885,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7847222222222222,0.8394495412844036,0.7289719626168224,0.9049429657794676,0.5976331360946746,0.861271676300578,0.47674418604651164,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9038461538461539,0.9134290540540541,0.8911830357142857,0.9273964832671583,0.7728706624605678,0.9143603133159269,0.7818181818181819,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.9482803206620118,0.9540461779869984,0.9404216315307058,0.9620515861251112,0.854251012145749,0.9546710889994472,0.8554216867469879,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9629807692307693,0.9759290540540541,0.9458705357142857,0.9878048780487805,0.8249211356466877,0.9796344647519583,0.7696969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0745708154506437,1.0748837209302327,1.0741444866920151,1.067422617223414,1.124731182795699,1.0769230769230769,1.040983606557377,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv new file mode 100644 index 00000000..57a0d632 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Std,0.02910723994297415,0.026928841960414063,0.031985837277071404,0.02406843700085443,0.05713061403432136,0.02625843319768955,0.06217066368370148,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.339549700014784,0.3223706841105062,0.3622505424597227,0.2860013335129002,0.637359700465324,0.31158688287314923,0.6640878504767885,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10585760054889846,0.09587305366283486,0.11905146607691108,0.07655124883957513,0.2688452916010658,0.09003632089781252,0.28948033104483517,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.02766986928860808,0.02561402859426212,0.0303865159204224,0.022821771127225433,0.05463263603472044,0.024933084815688063,0.059433155747043484,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.14127003364930704,0.1315025307892209,0.15417709100013516,0.11386115794551417,0.29370488496093744,0.12792228047458631,0.29618486594985377,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.33170366680118574,0.31509850080823687,0.3536462075775823,0.27938316425520743,0.6226848844307115,0.30447423779212607,0.6477300701487562,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.03092948717948718,0.025900900900900903,0.03757440476190477,0.01777273586689355,0.10410094637223975,0.023672758920800698,0.11515151515151516,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9690705128205128,0.9740990990990991,0.9624255952380952,0.9822272641331065,0.8958990536277602,0.9763272410791993,0.8848484848484849,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9844420600858369,0.9883720930232558,0.9790874524714829,0.9954030033711309,0.9075268817204301,0.9908151549942594,0.8934426229508197,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.2337962962962963,0.17889908256880735,0.2897196261682243,0.10266159695817491,0.4378698224852071,0.15895953757225434,0.5348837209302325,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9172706823294177,0.9223090277777778,0.9104301708898055,0.9322617680826636,0.816247582205029,0.9222548757681005,0.8449612403100775,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.01555793991416309,0.011627906976744186,0.02091254752851711,0.004596996628869139,0.09247311827956989,0.009184845005740528,0.10655737704918032,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7662037037037037,0.8211009174311926,0.7102803738317757,0.8973384030418251,0.5621301775147929,0.8410404624277457,0.46511627906976744,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9064903846153847,0.9138513513513513,0.8967633928571429,0.9288145207033466,0.7823343848580442,0.9156657963446475,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.949670073748221,0.9541984732824428,0.9435114503816794,0.9627982807173558,0.8594704684317719,0.9553064895530649,0.8685258964143426,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9617788461538461,0.972972972972973,0.9469866071428571,0.9880884855360181,0.8154574132492114,0.9772845953002611,0.7818181818181819,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0732296137339057,1.0716279069767443,1.0754119138149556,1.0677290836653386,1.1118279569892473,1.0743398392652124,1.0573770491803278,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv new file mode 100644 index 00000000..311b6f9d --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Statistical_Bias,0.15762928227594142,0.14673659171544048,0.1720231948023177,0.12578150198603236,0.3347511644560981,0.14208153240951818,0.33807740951352044,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.386950875806316,0.37773416084195277,0.399130106294939,0.3236565528406809,0.7389631514795489,0.35606052126946247,0.745466202703738,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.10657036051415189,0.10163912610901985,0.11308663454950496,0.07556025173941279,0.2790335206714548,0.09124926320216717,0.2843879444683988,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.3974300124357602,0.38794661663771,0.4099616425974693,0.3328535067228989,0.7565731656590235,0.36585143418564836,0.7639329054597851,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.028788818348307276,0.02810393092336335,0.029693848159840313,0.024104967435758084,0.0548381216884468,0.02637784143766344,0.05677076249305239,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Std,0.03112197497218865,0.03028406320740209,0.03222921551851375,0.025542396096752382,0.0621528820933058,0.02826493870222408,0.06428091107511076,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.9682692307692308,0.9743806306306305,0.9601934523809523,1.0,0.7917981072555205,0.984160139251523,0.7838383838383838,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.03173076923076923,0.02561936936936937,0.03980654761904762,0.0,0.2082018927444795,0.015839860748476937,0.21616161616161614,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9452332657200812,0.9517485613103143,0.9364985163204748,0.9612608631609957,0.8462238398544131,0.9526934645884605,0.8501742160278746,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.1158798283261802,1.1013953488372092,1.1356147021546261,1.0806006742261722,1.3634408602150538,1.0993111366245694,1.3524590163934427,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv new file mode 100644 index 00000000..caa85bb2 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Statistical_Bias,0.13911835183160592,0.12850643408600126,0.15314124313829783,0.11342056111362148,0.28203697970481295,0.126487995588439,0.2857070318659372,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.34262372638915,0.32130628488352536,0.3707932026644397,0.29391588633995114,0.6135130702589842,0.3162564489931639,0.6486439458637764,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.1055255194388021,0.09409588622030891,0.12062896333466813,0.07704961102346518,0.26389468832283697,0.0894641848481739,0.29193434211185076,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.34371032781994726,0.3223475783519096,0.37193967533128297,0.294705592099116,0.6162508611821733,0.31721070380431815,0.6512665701831585,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.01009711856213468,0.00927632992422369,0.011181732119374205,0.007822120130984433,0.02274955463190719,0.00894954949629287,0.023415874689935095,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.010581545614139397,0.009725625054281333,0.011712583496808981,0.008205907190389791,0.02379369243139667,0.009379356305060225,0.02453422759527039,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9886217948717947,0.9904279279279278,0.986235119047619,0.9941387785970882,0.9579390115667717,0.991644908616188,0.9535353535353536,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.01137820512820513,0.009572072072072073,0.013764880952380952,0.005861221402911704,0.042060988433228176,0.00835509138381201,0.04646464646464646,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9844420600858369,0.9893023255813953,0.9778200253485425,0.9944836040453571,0.9139784946236559,0.9911021814006888,0.889344262295082,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.25,0.2018348623853211,0.29906542056074764,0.13307984790874525,0.4319526627218935,0.1791907514450867,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9188783174762143,0.924380704041721,0.9113998818665091,0.934350705441981,0.8157389635316699,0.924003211131924,0.8443579766536965,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01555793991416309,0.010697674418604652,0.022179974651457542,0.0055163959546429666,0.08602150537634409,0.008897818599311137,0.11065573770491803,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.75,0.7981651376146789,0.7009345794392523,0.8669201520912547,0.5680473372781065,0.8208092485549133,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9081730769230769,0.9168074324324325,0.8967633928571429,0.9302325581395349,0.7854889589905363,0.9177545691906005,0.796969696969697,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9505309505309505,0.9557402830824534,0.9434423723631917,0.963479809976247,0.8620689655172413,0.9563772330702118,0.8662674650698603,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9600961538461539,0.971706081081081,0.9447544642857143,0.9849688031764039,0.8217665615141956,0.9757180156657963,0.7787878787878788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.071351931330472,1.0702325581395349,1.0728770595690749,1.064357952804168,1.1204301075268817,1.072617680826636,1.0532786885245902,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv similarity index 52% rename from docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv rename to docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv index 85daca2e..a8cb2f00 100644 --- a/docs/examples/results/Law_School_Metrics_20231220__221834/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__221838.csv +++ b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv @@ -1,19 +1,19 @@ Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Aleatoric_Uncertainty,0.3311936909247063,0.31633513567743937,0.3508282103585948,0.280772979211635,0.6116091948683804,0.30499860106656673,0.6352154913994775,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.1409061534787769,0.13182434926033107,0.15290710905315183,0.11395438327212519,0.2907988060791775,0.1277691308109008,0.2933752347453393,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3424221745189872,0.32712067741232775,0.3626420099813586,0.29023573719904144,0.6326577864907992,0.3153145042755336,0.6570354382536155,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Std,0.03247523048330209,0.031012095376433062,0.034408659017378995,0.026914372205238443,0.06340202273638157,0.029607570058186112,0.0657574711747996,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.04087022310275561,0.03907128821735308,0.043247387058466126,0.03320729406396774,0.08348771173172417,0.03699949292660661,0.08579415211684856,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10484149566164079,0.09588367347861963,0.11667861783206161,0.07669949912823015,0.2613536088742684,0.08965967295777133,0.2810426500732169,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9598173076923077,0.9631756756756757,0.9553794642857143,0.9774021554169029,0.8620189274447949,0.9683237597911228,0.861090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.029084183673469245,0.026541643684500845,0.03244396865889211,0.016370981744938503,0.09978883667031473,0.022833058027388466,0.10163512677798393,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.9868562231759657,0.9906976744186047,0.9816223067173637,0.9957094698130555,0.9247311827956989,0.9925373134328358,0.9057377049180327,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.2222222222222222,0.16055045871559634,0.2850467289719626,0.10646387832699619,0.40236686390532544,0.15028901734104047,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.9163138231631383,0.920881971465629,0.9101057579318449,0.932548794489093,0.8097928436911488,0.9216417910447762,0.8403041825095057,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.013143776824034335,0.009302325581395349,0.018377693282636248,0.004290530186944529,0.07526881720430108,0.007462686567164179,0.0942622950819672,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.7777777777777778,0.8394495412844036,0.7149532710280374,0.8935361216730038,0.5976331360946746,0.8497109826589595,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9074519230769231,0.9142736486486487,0.8984375,0.9293817356778219,0.7854889589905363,0.9164490861618799,0.803030303030303,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.9502776701536872,0.9545149002912839,0.9445121951219512,0.9630947087594487,0.8634538152610441,0.9557766721945826,0.8717948717948718,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9651442307692307,0.9767736486486487,0.9497767857142857,0.9880884855360181,0.8375394321766562,0.9796344647519583,0.796969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0769849785407726,1.0758139534883722,1.0785804816223068,1.0677290836653386,1.1419354838709677,1.0769230769230769,1.0778688524590163,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.14160192322656046,0.13250634279432627,0.1536210830834413,0.11480265114659247,0.29064645533060945,0.12862223562285258,0.2922449642029273,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.3338194028061136,0.31861506241835186,0.35391085260422744,0.28350324017729134,0.6136534555336017,0.3078998030904282,0.6346438479911906,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10550488873417896,0.09621410994261566,0.11778198928017335,0.07742909080243023,0.26164883748393614,0.09009781379592052,0.28432033422972386,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3410912822145201,0.3255959318793119,0.3615672808717596,0.2895421496952407,0.6277825144905123,0.3145417793817695,0.6492264211522013,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.026286222351811234,0.025260415737814762,0.02764175252030657,0.021701250259225893,0.05178560973107924,0.02403232502568423,0.05244509131867918,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.027646336340995586,0.026516976756715504,0.029138704363079988,0.02283603925213981,0.054398871885641395,0.025267436164910118,0.05525599596041179,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9745192307692307,0.9763513513513513,0.9720982142857143,0.9852524106636416,0.9148264984227129,0.9796344647519583,0.9151515151515152,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.025480769230769234,0.023648648648648646,0.027901785714285716,0.01474758933635848,0.08517350157728705,0.020365535248041775,0.08484848484848485,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9860515021459227,0.9888372093023255,0.982256020278834,0.9947900704872816,0.9247311827956989,0.9916762342135477,0.9057377049180327,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.2222222222222222,0.16972477064220184,0.2757009345794392,0.10266159695817491,0.40828402366863903,0.15028901734104047,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9162512462612163,0.9215431296055483,0.9090909090909091,0.9322228604250431,0.8113207547169812,0.9215790877567351,0.8403041825095057,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.013948497854077254,0.011162790697674419,0.017743979721166033,0.005209929512718358,0.07526881720430108,0.008323765786452353,0.0942622950819672,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7777777777777778,0.8302752293577982,0.7242990654205608,0.8973384030418251,0.591715976331361,0.8497109826589595,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9067307692307692,0.9134290540540541,0.8978794642857143,0.9282473057288713,0.7870662460567823,0.9156657963446475,0.803030303030303,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.9498708010335918,0.9540049360556428,0.9442583003350594,0.9624907338769458,0.864321608040201,0.9553435642195492,0.8717948717948718,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9644230769230769,0.9742398648648649,0.9514508928571429,0.9875212705615428,0.8359621451104101,0.9788511749347258,0.796969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0761802575107295,1.0730232558139534,1.0804816223067173,1.0671161507814895,1.1397849462365592,1.0760619977037889,1.0778688524590163,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__215134.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__224840.csv similarity index 100% rename from docs/examples/results/models_tuning/tuning_results_Law_School_20231220__215134.csv rename to docs/examples/results/models_tuning/tuning_results_Law_School_20231220__224840.csv diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__220900.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__225046.csv similarity index 100% rename from docs/examples/results/models_tuning/tuning_results_Law_School_20231220__220900.csv rename to docs/examples/results/models_tuning/tuning_results_Law_School_20231220__225046.csv diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__221838.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__225120.csv similarity index 100% rename from docs/examples/results/models_tuning/tuning_results_Law_School_20231220__221838.csv rename to docs/examples/results/models_tuning/tuning_results_Law_School_20231220__225120.csv diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230100.csv similarity index 69% rename from docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv rename to docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230100.csv index 1ff1eed2..daa33103 100644 --- a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214029.csv +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230100.csv @@ -1,3 +1,4 @@ Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230118.csv similarity index 69% rename from docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv rename to docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230118.csv index 1ff1eed2..daa33103 100644 --- a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__214637.csv +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230118.csv @@ -1,3 +1,4 @@ Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230412.csv similarity index 69% rename from docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv rename to docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230412.csv index 1ff1eed2..daa33103 100644 --- a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__213427.csv +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__230412.csv @@ -1,3 +1,4 @@ Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__231414.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__231414.csv new file mode 100644 index 00000000..daa33103 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__231414.csv @@ -0,0 +1,4 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index dfd94224..988b9a03 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -1,6 +1,7 @@ import os import gc import pandas as pd +from tqdm.notebook import tqdm from copy import deepcopy from abc import ABCMeta, abstractmethod @@ -119,14 +120,15 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b """ models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: - print('\n', flush=True) + # print('\n', flush=True) + print('\n') self._logger.info('Start classifiers testing by bootstrap') - # Remove a progress bar for UQ without estimators fitting - if self._notebook_logs_stdout: - from tqdm.notebook import tqdm - else: - from tqdm import tqdm + # # Remove a progress bar for UQ without estimators fitting + # if self._notebook_logs_stdout: + # from tqdm.notebook import tqdm + # else: + # from tqdm import tqdm cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), @@ -147,7 +149,8 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b gc.collect() if self._verbose >= 1: - print('\n', flush=True) + # print('\n', flush=True) + print('\n') self._logger.info('Successfully tested classifiers by bootstrap') return models_predictions diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 5a116f9f..33b74fc2 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -77,7 +77,8 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_pred # Compute metrics for each group partition for group_partition_name, partition_indexes in partition_indexes_dct.items(): if partition_indexes.shape[0] == 0: - print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET, flush=True) + # print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET, flush=True) + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET) metrics_dct = { TPR: None, TNR: None, diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 1f53687d..5afa3218 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -1,5 +1,6 @@ import gc import pandas as pd +from tqdm.notebook import tqdm from virny.utils.stability_utils import generate_bootstrap from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer @@ -52,14 +53,15 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b """ models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: - print('\n', flush=True) + # print('\n', flush=True) + print('\n') self._logger.info('Start classifiers testing by bootstrap') - # Remove a progress bar for UQ without estimators fitting - if self._notebook_logs_stdout: - from tqdm.notebook import tqdm - else: - from tqdm import tqdm + # # Remove a progress bar for UQ without estimators fitting + # if self._notebook_logs_stdout: + # from tqdm.notebook import tqdm + # else: + # from tqdm import tqdm cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), @@ -87,7 +89,8 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b self.models_lst[idx] = classifier if self._verbose >= 1: - print('\n', flush=True) + # print('\n', flush=True) + print('\n') self._logger.info('Successfully tested classifiers by bootstrap') return models_predictions diff --git a/virny/analyzers/subgroup_variance_calculator.py b/virny/analyzers/subgroup_variance_calculator.py index 8b43b6f8..659d1950 100644 --- a/virny/analyzers/subgroup_variance_calculator.py +++ b/virny/analyzers/subgroup_variance_calculator.py @@ -81,7 +81,8 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_pred for model_idx in models_predictions.keys() } if partition_indexes.shape[0] == 0: - print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) + # print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET) metrics_dct = dict() metric_names = list(METRIC_TO_FUNCTION.keys()) if self.with_predict_proba else METRICS_FOR_LABELS for metric in metric_names: diff --git a/virny/user_interfaces/__init__.py b/virny/user_interfaces/__init__.py index 6e6678f2..4ed6fbfc 100644 --- a/virny/user_interfaces/__init__.py +++ b/virny/user_interfaces/__init__.py @@ -7,7 +7,6 @@ from .multiple_models_api import ( compute_metrics_with_config, run_metrics_computation, - compute_one_model_metrics_with_config, compute_one_model_metrics ) from .multiple_models_with_db_writer_api import compute_metrics_with_db_writer @@ -21,7 +20,6 @@ __all__ = [ "compute_metrics_with_config", "run_metrics_computation", - "compute_one_model_metrics_with_config", "compute_one_model_metrics", "compute_metrics_with_db_writer", "compute_metrics_with_multiple_test_sets", diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index e506fb0a..61955396 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -1,6 +1,7 @@ import os import traceback import pandas as pd +from tqdm.notebook import tqdm from datetime import datetime, timezone from virny.configs.constants import ModelSetting @@ -121,11 +122,11 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, As for now, 0, 1, 2 levels are supported. """ - # Set a specific tqdm type for Jupyter notebooks and python modules - if notebook_logs_stdout: - from tqdm.notebook import tqdm - else: - from tqdm import tqdm + # # Set a specific tqdm type for Jupyter notebooks and python modules + # if notebook_logs_stdout: + # from tqdm.notebook import tqdm + # else: + # from tqdm import tqdm models_metrics_dct = dict() num_models = len(models_config) @@ -134,6 +135,8 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, desc="Analyze models in one run", colour="red"): if verbose >= 1: + # print('\n\n', flush=True) + print('\n\n') print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] @@ -164,45 +167,6 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, return models_metrics_dct -def compute_one_model_metrics_with_config(base_model, model_name: str, dataset: BaseFlowDataset, config, save_results_dir_path: str, - save_results: bool = True, verbose: int = 0) -> pd.DataFrame: - """ - Compute subgroup metrics for the base model. Arguments are defined as an input config object. - Save results in `save_results_dir_path` folder. - - Return a dataframe of model metrics. - - Parameters - ---------- - base_model - Base model for metrics computation - model_name - Model name to name a result file with metrics - dataset - BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. - config - Object that contains bootstrap_fraction, dataset_name, n_estimators, sensitive_attributes_dct attributes - save_results_dir_path - Location where to save result files with metrics - save_results - [Optional] If to save result metrics in a file - verbose - [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. - - """ - return compute_one_model_metrics(base_model=base_model, - n_estimators=config.n_estimators, - dataset=dataset, - bootstrap_fraction=config.bootstrap_fraction, - sensitive_attributes_dct=config.sensitive_attributes_dct, - dataset_name=config.dataset_name, - base_model_name=model_name, - save_results=save_results, - save_results_dir_path=save_results_dir_path, - verbose=verbose) - - def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDataset, bootstrap_fraction: float, sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, postprocessor=None, postprocessing_sensitive_attribute: str = None, diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py index 0182ec53..b03db260 100644 --- a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -1,5 +1,6 @@ import traceback import pandas as pd +from tqdm.notebook import tqdm from datetime import datetime, timezone from virny.configs.constants import ModelSetting @@ -124,11 +125,11 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo As for now, 0, 1, 2 levels are supported. """ - # Set a specific tqdm type for Jupyter notebooks and python modules - if notebook_logs_stdout: - from tqdm.notebook import tqdm - else: - from tqdm import tqdm + # # Set a specific tqdm type for Jupyter notebooks and python modules + # if notebook_logs_stdout: + # from tqdm.notebook import tqdm + # else: + # from tqdm import tqdm models_metrics_dct = dict() num_models = len(models_config) From 0ede2ab955971943ff28a1fcf1513690dd265b06 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 01:38:14 +0200 Subject: [PATCH 083/148] Added a notebook_logs_stdout argument for all interfaces --- ..._Models_Interface_With_Postprocessor.ipynb | 376 ++++++------------ docs/examples/experiment_config.yaml | 2 +- ...ssifier_50_Estimators_20231220__225223.csv | 19 - ...ression_50_Estimators_20231220__225223.csv | 19 - ...ssifier_50_Estimators_20231220__225223.csv | 19 - ...assifier_3_Estimators_20231220__230412.csv | 19 - ...assifier_3_Estimators_20231220__230503.csv | 19 - ...assifier_3_Estimators_20231220__231230.csv | 19 - ...gression_3_Estimators_20231220__230412.csv | 19 - ...gression_3_Estimators_20231220__230503.csv | 19 - ...gression_3_Estimators_20231220__231230.csv | 19 - ...assifier_3_Estimators_20231220__230412.csv | 19 - ...assifier_3_Estimators_20231220__230503.csv | 19 - ...assifier_3_Estimators_20231220__231230.csv | 19 - ...assifier_3_Estimators_20231220__231414.csv | 19 - ...gression_3_Estimators_20231220__231414.csv | 19 - ...assifier_3_Estimators_20231220__231414.csv | 19 - .../abstract_overall_variance_analyzer.py | 17 +- virny/analyzers/abstract_subgroup_analyzer.py | 3 +- ...verall_variance_analyzer_postprocessing.py | 17 +- .../analyzers/subgroup_variance_calculator.py | 3 +- virny/user_interfaces/multiple_models_api.py | 22 +- .../multiple_models_with_db_writer_api.py | 6 +- ...iple_models_with_multiple_test_sets_api.py | 19 +- 24 files changed, 166 insertions(+), 584 deletions(-) delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv delete mode 100644 docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index c10d61ad..0426d106 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -2,15 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 27, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:08.800440Z", - "start_time": "2023-12-20T23:14:08.484828Z" + "end_time": "2023-12-20T23:36:28.607041Z", + "start_time": "2023-12-20T23:36:28.185272Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -19,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:08.800835Z", - "start_time": "2023-12-20T23:14:08.777524Z" + "end_time": "2023-12-20T23:36:28.623127Z", + "start_time": "2023-12-20T23:36:28.605439Z" } }, "outputs": [], @@ -37,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:08.800975Z", - "start_time": "2023-12-20T23:14:08.787723Z" + "end_time": "2023-12-20T23:36:28.643424Z", + "start_time": "2023-12-20T23:36:28.623829Z" } }, "outputs": [ @@ -96,34 +105,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:09.853327Z", - "start_time": "2023-12-20T23:14:08.797652Z" + "end_time": "2023-12-20T23:36:28.676678Z", + "start_time": "2023-12-20T23:36:28.642513Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:No module named 'tempeh': LawSchoolGPADataset will be unavailable. To install, run:\n", - "pip install 'aif360[LawSchoolGPA]'\n", - "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n", - "pip install 'aif360[AdversarialDebiasing]'\n", - "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n", - "pip install 'aif360[AdversarialDebiasing]'\n", - "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n", - "pip install 'aif360[Reductions]'\n", - "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n", - "pip install 'aif360[Reductions]'\n", - "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n", - "pip install 'aif360[Reductions]'\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "from pprint import pprint\n", @@ -170,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 31, "outputs": [], "source": [ "DATASET_SPLIT_SEED = 42\n", @@ -180,15 +170,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:09.873259Z", - "start_time": "2023-12-20T23:14:09.854883Z" + "end_time": "2023-12-20T23:36:28.681093Z", + "start_time": "2023-12-20T23:36:28.663087Z" } }, "id": "ce359a052925eb3a" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 32, "outputs": [], "source": [ "models_params_for_tuning = {\n", @@ -224,8 +214,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:09.892785Z", - "start_time": "2023-12-20T23:14:09.875280Z" + "end_time": "2023-12-20T23:36:28.700653Z", + "start_time": "2023-12-20T23:36:28.680939Z" } }, "id": "2ece07ab7e3a9acc" @@ -260,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 33, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -279,15 +269,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:09.912042Z", - "start_time": "2023-12-20T23:14:09.893873Z" + "end_time": "2023-12-20T23:36:28.715739Z", + "start_time": "2023-12-20T23:36:28.698015Z" } }, "id": "af22ee06f1e3eb1a" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 34, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)\n", @@ -296,8 +286,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:09.929669Z", - "start_time": "2023-12-20T23:14:09.912484Z" + "end_time": "2023-12-20T23:36:28.734477Z", + "start_time": "2023-12-20T23:36:28.716316Z" } }, "id": "65181f72484bb92b" @@ -312,12 +302,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:09.989067Z", - "start_time": "2023-12-20T23:14:09.930522Z" + "end_time": "2023-12-20T23:36:28.793207Z", + "start_time": "2023-12-20T23:36:28.735018Z" } }, "outputs": [ @@ -326,7 +316,7 @@ "text/plain": " decile1b decile3 lsat ugpa zfygpa\n0 10.0 10.0 44.0 3.5 1.33\n1 5.0 4.0 29.0 3.5 -0.11\n2 8.0 7.0 37.0 3.4 0.63\n3 8.0 7.0 43.0 3.3 0.67\n4 3.0 2.0 41.0 3.3 -0.67", "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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    " }, - "execution_count": 9, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 36, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -351,15 +341,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:10.008914Z", - "start_time": "2023-12-20T23:14:09.989188Z" + "end_time": "2023-12-20T23:36:28.813482Z", + "start_time": "2023-12-20T23:36:28.793131Z" } }, "id": "ebbef5eaf9dc0943" }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 37, "outputs": [], "source": [ "# Create a binary race column for postprocessing since aif360 postprocessors can postprocess a dataset only based on binary columns.\n", @@ -372,15 +362,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:10.079900Z", - "start_time": "2023-12-20T23:14:10.008637Z" + "end_time": "2023-12-20T23:36:28.878211Z", + "start_time": "2023-12-20T23:36:28.811771Z" } }, "id": "97ed4609effbf53f" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 38, "outputs": [], "source": [ "# Define a postprocessor\n", @@ -393,8 +383,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:10.089856Z", - "start_time": "2023-12-20T23:14:10.070945Z" + "end_time": "2023-12-20T23:36:28.897222Z", + "start_time": "2023-12-20T23:36:28.877762Z" } }, "id": "4535191384245578" @@ -411,23 +401,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 39, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023/12/21, 01:14:10: Tuning DecisionTreeClassifier...\n", + "2023/12/21, 01:36:28: Tuning DecisionTreeClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 01:14:11: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", + "2023/12/21, 01:36:30: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", "\n", - "2023/12/21, 01:14:11: Tuning LogisticRegression...\n", + "2023/12/21, 01:36:30: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/12/21, 01:14:11: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", + "2023/12/21, 01:36:30: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", "\n", - "2023/12/21, 01:14:11: Tuning RandomForestClassifier...\n", + "2023/12/21, 01:36:30: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 01:14:13: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" + "2023/12/21, 01:36:32: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" ] }, { @@ -435,7 +425,7 @@ "text/plain": " Dataset_Name Model_Name F1_Score Accuracy_Score \\\n0 Law_School DecisionTreeClassifier 0.524303 0.887660 \n1 Law_School LogisticRegression 0.660552 0.899359 \n2 Law_School RandomForestClassifier 0.653102 0.895272 \n\n Model_Best_Params \n0 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 {'C': 100, 'max_iter': 250, 'penalty': 'l2', '... \n2 {'max_depth': 10, 'max_features': 0.6, 'min_sa... ", "text/html": "
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    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0Law_SchoolDecisionTreeClassifier0.5243030.887660{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1Law_SchoolLogisticRegression0.6605520.899359{'C': 100, 'max_iter': 250, 'penalty': 'l2', '...
    2Law_SchoolRandomForestClassifier0.6531020.895272{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    \n
    " }, - "execution_count": 13, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -447,15 +437,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:13.999088Z", - "start_time": "2023-12-20T23:14:10.090566Z" + "end_time": "2023-12-20T23:36:32.973701Z", + "start_time": "2023-12-20T23:36:28.895912Z" } }, "id": "782741c190a4690b" }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 40, "outputs": [], "source": [ "now = datetime.now(timezone.utc)\n", @@ -466,8 +456,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:14.057530Z", - "start_time": "2023-12-20T23:14:13.998563Z" + "end_time": "2023-12-20T23:36:33.015070Z", + "start_time": "2023-12-20T23:36:32.973263Z" } }, "id": "21ccc879c5c3e215" @@ -484,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 41, "outputs": [ { "name": "stdout", @@ -505,8 +495,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:14.061017Z", - "start_time": "2023-12-20T23:14:14.025272Z" + "end_time": "2023-12-20T23:36:33.049631Z", + "start_time": "2023-12-20T23:36:32.996820Z" } }, "id": "3b15f202741fa2ae" @@ -529,12 +519,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 42, "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:21.937185Z", - "start_time": "2023-12-20T23:14:14.047512Z" + "end_time": "2023-12-20T23:37:28.230538Z", + "start_time": "2023-12-20T23:36:33.017100Z" } }, "outputs": [ @@ -544,171 +534,47 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "07343a63429c40fa8ae944f660fce21b" + "model_id": "46d9562a6ce145e2b75fcf7a4a7583e2" } }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-21 01:14:14 abstract_overall_variance_analyzer.py INFO : Start classifiers testing by bootstrap\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "############################## [Model 1 / 3] Analyze DecisionTreeClassifier ##############################\n" - ] - }, { "data": { - "text/plain": "Classifiers testing by bootstrap: 0%| | 0/3 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Statistical_Bias0.1576290.1467370.1720230.1257820.334751
    1Aleatoric_Uncertainty0.3869510.3777340.3991300.3236570.738963
    2Mean_Prediction0.1065700.1016390.1130870.0755600.279034
    3Overall_Uncertainty0.3974300.3879470.4099620.3328540.756573
    4IQR0.0287890.0281040.0296940.0241050.054838
    5Std0.0311220.0302840.0322290.0255420.062153
    6Label_Stability0.9682690.9743810.9601931.0000000.791798
    7Jitter0.0317310.0256190.0398070.0000000.208202
    8TPR1.0000001.0000001.0000001.0000001.000000
    9TNR0.0000000.0000000.0000000.0000000.000000
    10PPV0.8961540.9079390.8805800.9254110.733438
    11FNR0.0000000.0000000.0000000.0000000.000000
    12FPR1.0000001.0000001.0000001.0000001.000000
    13Accuracy0.8961540.9079390.8805800.9254110.733438
    14F10.9452330.9517490.9364990.9612610.846224
    15Selection-Rate1.0000001.0000001.0000001.0000001.000000
    16Positive-Rate1.1158801.1013951.1356151.0806011.363441
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n" + "text/plain": " Metric overall male_priv male_dis race_priv \\\n0 Statistical_Bias 0.158320 0.146128 0.174431 0.126948 \n1 Aleatoric_Uncertainty 0.387875 0.372405 0.408317 0.325687 \n2 Mean_Prediction 0.107724 0.101227 0.116309 0.077584 \n3 Overall_Uncertainty 0.404555 0.388055 0.426359 0.342643 \n4 IQR 0.047685 0.046265 0.049561 0.046204 \n5 Std 0.038076 0.036202 0.040552 0.036683 \n6 Label_Stability 0.960500 0.967905 0.950714 0.999648 \n7 Jitter 0.026088 0.021304 0.032411 0.000349 \n8 TPR 1.000000 1.000000 1.000000 1.000000 \n9 TNR 0.000000 0.000000 0.000000 0.000000 \n10 PPV 0.896154 0.907939 0.880580 0.925411 \n11 FNR 0.000000 0.000000 0.000000 0.000000 \n12 FPR 1.000000 1.000000 1.000000 1.000000 \n13 Accuracy 0.896154 0.907939 0.880580 0.925411 \n14 F1 0.945233 0.951749 0.936499 0.961261 \n15 Selection-Rate 1.000000 1.000000 1.000000 1.000000 \n16 Positive-Rate 1.115880 1.101395 1.135615 1.080601 \n17 Sample_Size 4160.000000 2368.000000 1792.000000 3526.000000 \n\n race_dis \n0 0.332794 \n1 0.733735 \n2 0.275348 \n3 0.748878 \n4 0.055917 \n5 0.045819 \n6 0.742776 \n7 0.169238 \n8 1.000000 \n9 0.000000 \n10 0.733438 \n11 0.000000 \n12 1.000000 \n13 0.733438 \n14 0.846224 \n15 1.000000 \n16 1.363441 \n17 634.000000 ", + "text/html": "
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    Metricoverallmale_privmale_disrace_privrace_dis
    0Statistical_Bias0.1583200.1461280.1744310.1269480.332794
    1Aleatoric_Uncertainty0.3878750.3724050.4083170.3256870.733735
    2Mean_Prediction0.1077240.1012270.1163090.0775840.275348
    3Overall_Uncertainty0.4045550.3880550.4263590.3426430.748878
    4IQR0.0476850.0462650.0495610.0462040.055917
    5Std0.0380760.0362020.0405520.0366830.045819
    6Label_Stability0.9605000.9679050.9507140.9996480.742776
    7Jitter0.0260880.0213040.0324110.0003490.169238
    8TPR1.0000001.0000001.0000001.0000001.000000
    9TNR0.0000000.0000000.0000000.0000000.000000
    10PPV0.8961540.9079390.8805800.9254110.733438
    11FNR0.0000000.0000000.0000000.0000000.000000
    12FPR1.0000001.0000001.0000001.0000001.000000
    13Accuracy0.8961540.9079390.8805800.9254110.733438
    14F10.9452330.9517490.9364990.9612610.846224
    15Selection-Rate1.0000001.0000001.0000001.0000001.000000
    16Positive-Rate1.1158801.1013951.1356151.0806011.363441
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n
    " }, - "execution_count": 17, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -773,12 +639,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 44, "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:21.973942Z", - "start_time": "2023-12-20T23:14:21.955161Z" + "end_time": "2023-12-20T23:37:28.299558Z", + "start_time": "2023-12-20T23:37:28.252898Z" } }, "outputs": [], @@ -788,12 +654,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 45, "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:21.999232Z", - "start_time": "2023-12-20T23:14:21.974989Z" + "end_time": "2023-12-20T23:37:28.300848Z", + "start_time": "2023-12-20T23:37:28.274195Z" } }, "outputs": [], @@ -811,12 +677,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 46, "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.050819Z", - "start_time": "2023-12-20T23:14:21.990464Z" + "end_time": "2023-12-20T23:37:28.320080Z", + "start_time": "2023-12-20T23:37:28.295027Z" } }, "outputs": [], @@ -826,14 +692,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 47, "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.027359 -0.191973 -0.170267 \n1 Aleatoric_Uncertainty_Parity 0.021396 0.415307 0.389406 \n2 Aleatoric_Uncertainty_Ratio 1.056643 2.283171 2.093650 \n3 Equalized_Odds_FNR 0.000000 0.000000 0.000000 \n4 Equalized_Odds_FPR 0.000000 0.000000 0.000000 \n5 IQR_Parity 0.001590 0.030733 0.030393 \n6 Jitter_Parity 0.014187 0.208202 0.200322 \n7 Label_Stability_Ratio 0.985440 0.791798 0.796454 \n8 Label_Stability_Difference -0.014187 -0.208202 -0.200322 \n9 Overall_Uncertainty_Parity 0.022015 0.423720 0.398081 \n10 Overall_Uncertainty_Ratio 1.056748 2.272991 2.088096 \n11 Statistical_Parity_Difference 0.034219 0.282840 0.253148 \n12 Disparate_Impact 1.031069 1.261743 1.230279 \n13 Std_Parity 0.001945 0.036610 0.036016 \n14 Std_Ratio 1.064230 2.433322 2.274228 \n15 Equalized_Odds_TNR 0.000000 0.000000 0.000000 \n16 Equalized_Odds_TPR 0.000000 0.000000 0.000000 \n17 Accuracy_Parity -0.020044 -0.144744 -0.120785 \n18 Aleatoric_Uncertainty_Parity 0.049487 0.319597 0.332387 \n19 Aleatoric_Uncertainty_Ratio 1.154018 2.087376 2.051006 \n20 Equalized_Odds_FNR 0.011482 0.080505 0.101758 \n21 Equalized_Odds_FPR -0.097231 -0.298873 -0.355693 \n22 IQR_Parity 0.001905 0.014927 0.014466 \n23 Jitter_Parity 0.004193 0.036200 0.038110 \n24 Label_Stability_Ratio 0.995767 0.963587 0.961569 \n25 Label_Stability_Difference -0.004193 -0.036200 -0.038110 \n26 Overall_Uncertainty_Parity 0.049592 0.321545 0.334056 \n27 Overall_Uncertainty_Ratio 1.153847 2.091073 2.053104 \n28 Statistical_Parity_Difference 0.002645 0.056072 -0.019339 \n29 Disparate_Impact 1.002471 1.052682 0.981970 \n30 Std_Parity 0.001987 0.015588 0.015155 \n31 Std_Ratio 1.204301 2.899581 2.615769 \n32 Equalized_Odds_TNR 0.097231 0.298873 0.355693 \n33 Equalized_Odds_TPR -0.011482 -0.080505 -0.101758 \n34 Accuracy_Parity -0.015550 -0.141181 -0.112635 \n35 Aleatoric_Uncertainty_Parity 0.035296 0.330150 0.326744 \n36 Aleatoric_Uncertainty_Ratio 1.110779 2.164538 2.061203 \n37 Equalized_Odds_FNR 0.006581 0.070059 0.085939 \n38 Equalized_Odds_FPR -0.105976 -0.305622 -0.361339 \n39 IQR_Parity 0.002381 0.030084 0.028413 \n40 Jitter_Parity 0.004253 0.070426 0.064483 \n41 Label_Stability_Ratio 0.995644 0.928520 0.934177 \n42 Label_Stability_Difference -0.004253 -0.070426 -0.064483 \n43 Overall_Uncertainty_Parity 0.035971 0.338240 0.334685 \n44 Overall_Uncertainty_Ratio 1.110478 2.168190 2.064039 \n45 Statistical_Parity_Difference 0.007458 0.072669 0.001807 \n46 Disparate_Impact 1.006951 1.068098 1.001679 \n47 Std_Parity 0.002622 0.031563 0.029989 \n48 Std_Ratio 1.098870 2.382150 2.186846 \n49 Equalized_Odds_TNR 0.105976 0.305622 0.361339 \n50 Equalized_Odds_TPR -0.006581 -0.070059 -0.085939 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", - 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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.027359-0.191973-0.170267DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0213960.4153070.389406DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0566432.2831712.093650DecisionTreeClassifier
    3Equalized_Odds_FNR0.0000000.0000000.000000DecisionTreeClassifier
    4Equalized_Odds_FPR0.0000000.0000000.000000DecisionTreeClassifier
    5IQR_Parity0.0015900.0307330.030393DecisionTreeClassifier
    6Jitter_Parity0.0141870.2082020.200322DecisionTreeClassifier
    7Label_Stability_Ratio0.9854400.7917980.796454DecisionTreeClassifier
    8Label_Stability_Difference-0.014187-0.208202-0.200322DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0220150.4237200.398081DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0567482.2729912.088096DecisionTreeClassifier
    11Statistical_Parity_Difference0.0342190.2828400.253148DecisionTreeClassifier
    12Disparate_Impact1.0310691.2617431.230279DecisionTreeClassifier
    13Std_Parity0.0019450.0366100.036016DecisionTreeClassifier
    14Std_Ratio1.0642302.4333222.274228DecisionTreeClassifier
    15Equalized_Odds_TNR0.0000000.0000000.000000DecisionTreeClassifier
    16Equalized_Odds_TPR0.0000000.0000000.000000DecisionTreeClassifier
    17Accuracy_Parity-0.020044-0.144744-0.120785LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0494870.3195970.332387LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1540182.0873762.051006LogisticRegression
    20Equalized_Odds_FNR0.0114820.0805050.101758LogisticRegression
    21Equalized_Odds_FPR-0.097231-0.298873-0.355693LogisticRegression
    22IQR_Parity0.0019050.0149270.014466LogisticRegression
    23Jitter_Parity0.0041930.0362000.038110LogisticRegression
    24Label_Stability_Ratio0.9957670.9635870.961569LogisticRegression
    25Label_Stability_Difference-0.004193-0.036200-0.038110LogisticRegression
    26Overall_Uncertainty_Parity0.0495920.3215450.334056LogisticRegression
    27Overall_Uncertainty_Ratio1.1538472.0910732.053104LogisticRegression
    28Statistical_Parity_Difference0.0026450.056072-0.019339LogisticRegression
    29Disparate_Impact1.0024711.0526820.981970LogisticRegression
    30Std_Parity0.0019870.0155880.015155LogisticRegression
    31Std_Ratio1.2043012.8995812.615769LogisticRegression
    32Equalized_Odds_TNR0.0972310.2988730.355693LogisticRegression
    33Equalized_Odds_TPR-0.011482-0.080505-0.101758LogisticRegression
    34Accuracy_Parity-0.015550-0.141181-0.112635RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0352960.3301500.326744RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1107792.1645382.061203RandomForestClassifier
    37Equalized_Odds_FNR0.0065810.0700590.085939RandomForestClassifier
    38Equalized_Odds_FPR-0.105976-0.305622-0.361339RandomForestClassifier
    39IQR_Parity0.0023810.0300840.028413RandomForestClassifier
    40Jitter_Parity0.0042530.0704260.064483RandomForestClassifier
    41Label_Stability_Ratio0.9956440.9285200.934177RandomForestClassifier
    42Label_Stability_Difference-0.004253-0.070426-0.064483RandomForestClassifier
    43Overall_Uncertainty_Parity0.0359710.3382400.334685RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1104782.1681902.064039RandomForestClassifier
    45Statistical_Parity_Difference0.0074580.0726690.001807RandomForestClassifier
    46Disparate_Impact1.0069511.0680981.001679RandomForestClassifier
    47Std_Parity0.0026220.0315630.029989RandomForestClassifier
    48Std_Ratio1.0988702.3821502.186846RandomForestClassifier
    49Equalized_Odds_TNR0.1059760.3056220.361339RandomForestClassifier
    50Equalized_Odds_TPR-0.006581-0.070059-0.085939RandomForestClassifier
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    " + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.027359 -0.191973 -0.170267 \n1 Aleatoric_Uncertainty_Parity 0.035912 0.408048 0.382995 \n2 Aleatoric_Uncertainty_Ratio 1.096432 2.252882 2.071335 \n3 Equalized_Odds_FNR 0.000000 0.000000 0.000000 \n4 Equalized_Odds_FPR 0.000000 0.000000 0.000000 \n5 IQR_Parity 0.003295 0.009713 0.010086 \n6 Jitter_Parity 0.011107 0.168890 0.161775 \n7 Label_Stability_Ratio 0.982239 0.743037 0.748244 \n8 Label_Stability_Difference -0.017191 -0.256872 -0.246740 \n9 Overall_Uncertainty_Parity 0.038304 0.406235 0.381743 \n10 Overall_Uncertainty_Ratio 1.098707 2.185592 2.019959 \n11 Statistical_Parity_Difference 0.034219 0.282840 0.253148 \n12 Disparate_Impact 1.031069 1.261743 1.230279 \n13 Std_Parity 0.004350 0.009136 0.009471 \n14 Std_Ratio 1.120150 1.249055 1.253741 \n15 Equalized_Odds_TNR 0.000000 0.000000 0.000000 \n16 Equalized_Odds_TPR 0.000000 0.000000 0.000000 \n17 Accuracy_Parity -0.016967 -0.139728 -0.108141 \n18 Aleatoric_Uncertainty_Parity 0.046101 0.327392 0.337331 \n19 Aleatoric_Uncertainty_Ratio 1.145380 2.140452 2.087435 \n20 Equalized_Odds_FNR 0.010849 0.080812 0.097660 \n21 Equalized_Odds_FPR -0.120424 -0.324229 -0.396357 \n22 IQR_Parity 0.002085 0.018222 0.017440 \n23 Jitter_Parity 0.003113 0.026663 0.028340 \n24 Label_Stability_Ratio 0.995603 0.962031 0.957595 \n25 Label_Stability_Difference -0.004348 -0.037695 -0.041997 \n26 Overall_Uncertainty_Parity 0.046287 0.329109 0.338870 \n27 Overall_Uncertainty_Ratio 1.145524 2.143333 2.089168 \n28 Statistical_Parity_Difference 0.000447 0.048701 -0.028110 \n29 Disparate_Impact 1.000417 1.045717 0.973807 \n30 Std_Parity 0.001636 0.013754 0.012985 \n31 Std_Ratio 1.181103 2.799539 2.490934 \n32 Equalized_Odds_TNR 0.120424 0.324229 0.396357 \n33 Equalized_Odds_TPR -0.010849 -0.080812 -0.097660 \n34 Accuracy_Parity -0.015142 -0.142883 -0.110911 \n35 Aleatoric_Uncertainty_Parity 0.034370 0.331481 0.330334 \n36 Aleatoric_Uncertainty_Ratio 1.109409 2.190531 2.091127 \n37 Equalized_Odds_FNR 0.007511 0.070672 0.082127 \n38 Equalized_Odds_FPR -0.115408 -0.290413 -0.349778 \n39 IQR_Parity 0.004738 0.051576 0.052061 \n40 Jitter_Parity 0.006689 0.081155 0.079320 \n41 Label_Stability_Ratio 0.991500 0.886445 0.889799 \n42 Label_Stability_Difference -0.008190 -0.110927 -0.106721 \n43 Overall_Uncertainty_Parity 0.035448 0.343738 0.342564 \n44 Overall_Uncertainty_Ratio 1.109036 2.193591 2.093758 \n45 Statistical_Parity_Difference 0.005092 0.073282 0.006766 \n46 Disparate_Impact 1.004744 1.068712 1.006293 \n47 Std_Parity 0.003489 0.037351 0.037079 \n48 Std_Ratio 1.112086 2.386766 2.249068 \n49 Equalized_Odds_TNR 0.115408 0.290413 0.349778 \n50 Equalized_Odds_TPR -0.007511 -0.070672 -0.082127 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", + "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.027359-0.191973-0.170267DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0359120.4080480.382995DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0964322.2528822.071335DecisionTreeClassifier
    3Equalized_Odds_FNR0.0000000.0000000.000000DecisionTreeClassifier
    4Equalized_Odds_FPR0.0000000.0000000.000000DecisionTreeClassifier
    5IQR_Parity0.0032950.0097130.010086DecisionTreeClassifier
    6Jitter_Parity0.0111070.1688900.161775DecisionTreeClassifier
    7Label_Stability_Ratio0.9822390.7430370.748244DecisionTreeClassifier
    8Label_Stability_Difference-0.017191-0.256872-0.246740DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0383040.4062350.381743DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0987072.1855922.019959DecisionTreeClassifier
    11Statistical_Parity_Difference0.0342190.2828400.253148DecisionTreeClassifier
    12Disparate_Impact1.0310691.2617431.230279DecisionTreeClassifier
    13Std_Parity0.0043500.0091360.009471DecisionTreeClassifier
    14Std_Ratio1.1201501.2490551.253741DecisionTreeClassifier
    15Equalized_Odds_TNR0.0000000.0000000.000000DecisionTreeClassifier
    16Equalized_Odds_TPR0.0000000.0000000.000000DecisionTreeClassifier
    17Accuracy_Parity-0.016967-0.139728-0.108141LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0461010.3273920.337331LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1453802.1404522.087435LogisticRegression
    20Equalized_Odds_FNR0.0108490.0808120.097660LogisticRegression
    21Equalized_Odds_FPR-0.120424-0.324229-0.396357LogisticRegression
    22IQR_Parity0.0020850.0182220.017440LogisticRegression
    23Jitter_Parity0.0031130.0266630.028340LogisticRegression
    24Label_Stability_Ratio0.9956030.9620310.957595LogisticRegression
    25Label_Stability_Difference-0.004348-0.037695-0.041997LogisticRegression
    26Overall_Uncertainty_Parity0.0462870.3291090.338870LogisticRegression
    27Overall_Uncertainty_Ratio1.1455242.1433332.089168LogisticRegression
    28Statistical_Parity_Difference0.0004470.048701-0.028110LogisticRegression
    29Disparate_Impact1.0004171.0457170.973807LogisticRegression
    30Std_Parity0.0016360.0137540.012985LogisticRegression
    31Std_Ratio1.1811032.7995392.490934LogisticRegression
    32Equalized_Odds_TNR0.1204240.3242290.396357LogisticRegression
    33Equalized_Odds_TPR-0.010849-0.080812-0.097660LogisticRegression
    34Accuracy_Parity-0.015142-0.142883-0.110911RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0343700.3314810.330334RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1094092.1905312.091127RandomForestClassifier
    37Equalized_Odds_FNR0.0075110.0706720.082127RandomForestClassifier
    38Equalized_Odds_FPR-0.115408-0.290413-0.349778RandomForestClassifier
    39IQR_Parity0.0047380.0515760.052061RandomForestClassifier
    40Jitter_Parity0.0066890.0811550.079320RandomForestClassifier
    41Label_Stability_Ratio0.9915000.8864450.889799RandomForestClassifier
    42Label_Stability_Difference-0.008190-0.110927-0.106721RandomForestClassifier
    43Overall_Uncertainty_Parity0.0354480.3437380.342564RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1090362.1935912.093758RandomForestClassifier
    45Statistical_Parity_Difference0.0050920.0732820.006766RandomForestClassifier
    46Disparate_Impact1.0047441.0687121.006293RandomForestClassifier
    47Std_Parity0.0034890.0373510.037079RandomForestClassifier
    48Std_Ratio1.1120862.3867662.249068RandomForestClassifier
    49Equalized_Odds_TNR0.1154080.2904130.349778RandomForestClassifier
    50Equalized_Odds_TPR-0.007511-0.070672-0.082127RandomForestClassifier
    \n
    " }, - "execution_count": 21, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -844,8 +710,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.057783Z", - "start_time": "2023-12-20T23:14:22.014609Z" + "end_time": "2023-12-20T23:37:28.344905Z", + "start_time": "2023-12-20T23:37:28.320359Z" } }, "id": "a286da0406c6401d" @@ -868,12 +734,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 48, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.080227Z", - "start_time": "2023-12-20T23:14:22.039175Z" + "end_time": "2023-12-20T23:37:28.366716Z", + "start_time": "2023-12-20T23:37:28.341494Z" } }, "outputs": [], @@ -885,21 +751,21 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 49, "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.132409Z", - "start_time": "2023-12-20T23:14:22.061356Z" + "end_time": "2023-12-20T23:37:28.416249Z", + "start_time": "2023-12-20T23:37:28.364856Z" } }, "outputs": [ { "data": { - "text/html": "\n
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    \n", "text/plain": "alt.HConcatChart(...)" }, - "execution_count": 25, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -972,27 +838,27 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.313571Z", - "start_time": "2023-12-20T23:14:22.154675Z" + "end_time": "2023-12-20T23:37:28.615450Z", + "start_time": "2023-12-20T23:37:28.451027Z" } }, "id": "b1249b3994b75555" }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 52, "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.784314Z", - "start_time": "2023-12-20T23:14:22.313906Z" + "end_time": "2023-12-20T23:37:29.081363Z", + "start_time": "2023-12-20T23:37:28.612113Z" } }, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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" 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" 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -1028,12 +894,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 52, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:14:22.784450Z", - "start_time": "2023-12-20T23:14:22.759937Z" + "end_time": "2023-12-20T23:37:29.081466Z", + "start_time": "2023-12-20T23:37:29.069427Z" } }, "outputs": [], diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index ec44ee61..1205bf0f 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,6 +1,6 @@ dataset_name: Law_School bootstrap_fraction: 0.8 -n_estimators: 3 # Better to input the higher number of estimators than 100; this is only for this use case example +n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} postprocessing_sensitive_attribute: 'race_binary' diff --git a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv deleted file mode 100644 index c781bb21..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231220__225223.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Overall_Uncertainty,0.40548677779775943,0.38795631205377373,0.42865203610231184,0.3435620568563354,0.7498819923710415,0.3752852290804241,0.7560077826080445,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Std,0.03959730437660162,0.03751554919350445,0.04234819515426574,0.03782226695330002,0.0494692002039856,0.038696877331316555,0.05004771523551618,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.38800472750643894,0.3716927405362876,0.4095598531455674,0.32636198960405727,0.7308316894051734,0.3579700915919998,0.7365885322103841,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.05085689080983673,0.04813448829089488,0.05445435128129559,0.04717748683665128,0.07131994823799424,0.048938786197364095,0.0731185291909584,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Statistical_Bias,0.1581828042428406,0.1460526321053724,0.17421196028163785,0.12696135029842903,0.33182136356144487,0.14304701492180225,0.3338496924233768,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.10803563244845835,0.10098575350775986,0.1173515439058099,0.0782186902842951,0.27386297956334726,0.09330979902126024,0.2789445476792725,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.9522980769230769,0.9616891891891891,0.9398883928571429,0.9993079977311402,0.6908517350157729,0.9759060052219322,0.6783030303030303,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.027038657770800754,0.021833287369001655,0.03391718294460644,0.0006822785835831734,0.17362003476469545,0.013898651888953978,0.17954236239950647,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,0.9796137339055794,0.9841860465116279,0.973384030418251,1.0,0.8365591397849462,0.9902411021814007,0.8278688524590164,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.20833333333333334,0.1834862385321101,0.2336448598130841,0.0,0.5325443786982249,0.11560693641618497,0.5813953488372093,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.9143715573360041,0.9224062772449869,0.9035294117647059,0.9254112308564946,0.8311965811965812,0.9185303514376997,0.8487394957983193,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.0203862660944206,0.01581395348837209,0.026615969581749048,0.0,0.16344086021505377,0.00975889781859931,0.1721311475409836,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,0.7916666666666666,0.8165137614678899,0.7663551401869159,1.0,0.46745562130177515,0.884393063583815,0.4186046511627907,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.8995192307692308,0.910472972972973,0.8850446428571429,0.9254112308564946,0.7555205047318612,0.9112271540469974,0.7636363636363637,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.9458689458689459,0.9522952295229523,0.937156802928615,0.9612608631609957,0.8338692390139335,0.9530386740331491,0.8381742738589212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,0.9600961538461539,0.96875,0.9486607142857143,1.0,0.7381703470031545,0.9806788511749347,0.7212121212121212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,1.071351931330472,1.0669767441860465,1.0773130544993663,1.0806006742261722,1.0064516129032257,1.0780711825487945,0.9754098360655737,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv deleted file mode 100644 index 01bc66f6..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_LogisticRegression_50_Estimators_20231220__225223.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Overall_Uncertainty,0.3400075911752074,0.32004199157416013,0.36639070493373416,0.29063676702172864,0.6145841305524411,0.3134555249670144,0.6481724808036297,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Std,0.010103878160329828,0.00935231042471382,0.0110970212395367,0.007823807874810204,0.022784521420175573,0.008943623875954035,0.023569859703236794,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.33888584189513504,0.319007703031287,0.3651533825366486,0.28981575320333874,0.6117898367331064,0.3124791294001381,0.6453637475188876,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.013502950167524836,0.012498306939440759,0.014830514433207364,0.01052416618107057,0.030069499593767316,0.012036267683870904,0.03052535596265982,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.13831382318307825,0.12802644174473343,0.151907862940891,0.11258537896030874,0.2814029309582915,0.12569762072738785,0.2847382335021514,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10438330911775877,0.09355351708337806,0.11869410573461898,0.07579635563611556,0.2633700567144055,0.08833236873037274,0.2906714960380269,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9866538461538462,0.9879729729729729,0.9849107142857143,0.9927283040272262,0.9528706624605678,0.990046997389034,0.9472727272727274,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.009536106750392487,0.00851282404853833,0.010888301749271127,0.0051806406056466945,0.03375909354277992,0.007189428251718451,0.03677179962894243,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9849785407725322,0.9897674418604652,0.9784537389100126,0.9947900704872816,0.9161290322580645,0.9913892078071183,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.25,0.2018348623853211,0.29906542056074764,0.13307984790874525,0.4319526627218935,0.1791907514450867,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.918918918918919,0.9244135534317984,0.911452184179457,0.9343696027633851,0.8160919540229885,0.9240235420010701,0.8449612403100775,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.015021459227467811,0.010232558139534883,0.021546261089987327,0.005209929512718358,0.08387096774193549,0.008610792192881744,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.75,0.7981651376146789,0.7009345794392523,0.8669201520912547,0.5680473372781065,0.8208092485549133,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9086538461538461,0.9172297297297297,0.8973214285714286,0.9305161656267725,0.7870662460567823,0.9180156657963446,0.8,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.9508026929052305,0.9559748427672956,0.9437652811735942,0.9636336648359805,0.8632218844984803,0.9565217391304348,0.8685258964143426,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9605769230769231,0.9721283783783784,0.9453125,0.9852524106636416,0.8233438485804416,0.9759791122715404,0.7818181818181819,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0718884120171674,1.0706976744186048,1.073510773130545,1.0646644192460926,1.1225806451612903,1.0729047072330655,1.0573770491803278,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv b/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv deleted file mode 100644 index f9fdc731..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__225116/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231220__225223.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Overall_Uncertainty,0.34201637972980087,0.32680237773966914,0.36212059664533214,0.2897690184868528,0.6325908209642407,0.3149930260476729,0.6556510603435897,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Std,0.03234031410876854,0.030926760529202145,0.03420822419605269,0.026747316125816958,0.06344585178682416,0.029488892520486015,0.06543408587580507,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.3308789883371455,0.3160747377401646,0.3504417480545845,0.28039220092815453,0.6116619732016598,0.3047506225064036,0.6341263857060583,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.040657672162544324,0.038563060999410155,0.04342555119954304,0.03306069051713258,0.08290839342708976,0.0367250077498452,0.08630041367962807,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.14076712510064224,0.13157874351515925,0.15290891505288765,0.11379018271041676,0.2907997731573222,0.12763896035201963,0.29313340081950445,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10487583662696706,0.09585243705047859,0.11679961463875536,0.0764053553082597,0.26321482263605556,0.0894833738419283,0.2835216925866591,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.95975,0.962972972972973,0.9554910714285715,0.9765967101531481,0.8660567823343849,0.9678851174934726,0.8653333333333335,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.029047095761381325,0.026747793712079457,0.032085459183673504,0.016884716450391888,0.09668834095152255,0.023215431342249654,0.09672974644403205,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.9876609442060086,0.9911627906976744,0.9828897338403042,0.9963224026969046,0.9268817204301075,0.9931113662456946,0.9098360655737705,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.23148148148148148,0.1834862385321101,0.2803738317757009,0.10646387832699619,0.4260355029585799,0.16184971098265896,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.9172894867962132,0.9229103508012126,0.9096774193548387,0.9325874928284567,0.8162878787878788,0.9226666666666666,0.8409090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.012339055793991416,0.008837209302325582,0.017110266159695818,0.003677597303095311,0.07311827956989247,0.006888633754305396,0.09016393442622951,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.7685185185185185,0.8165137614678899,0.719626168224299,0.8935361216730038,0.5739644970414202,0.838150289017341,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9091346153846154,0.9168074324324325,0.8989955357142857,0.9299489506522972,0.7933753943217665,0.9180156657963446,0.806060606060606,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.9511754068716094,0.9558196905135681,0.9448674992385013,0.9634019854793303,0.8680765357502518,0.9565938623168372,0.8740157480314961,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9649038461538462,0.9750844594594594,0.9514508928571429,0.9886557005104935,0.832807570977918,0.97911227154047,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.076716738197425,1.073953488372093,1.0804816223067173,1.068342016549188,1.135483870967742,1.076349024110218,1.0819672131147542,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv deleted file mode 100644 index 8fe5eb43..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230412.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.03288923190596696,0.031199547876634367,0.035122028659013586,0.031537609797356245,0.040406297450070086,0.03226215216216488,0.04016715741736682,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Overall_Uncertainty,0.4064422861784247,0.3897836557091545,0.42845547644138904,0.3442293676346454,0.7524403158083394,0.3762888666346476,0.7564047008834742,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.11063761354885507,0.10258287628184667,0.12128137350883042,0.07833984961667391,0.29026208614328836,0.0948548932431735,0.2938128219450987,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.03124058080255428,0.029644695862137638,0.0333494287595334,0.030167069421535284,0.03721092958721196,0.030745903277452007,0.0369818381393473,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Statistical_Bias,0.1597862222670465,0.14696841375919223,0.17672404065242533,0.12853165427244964,0.3336089458458297,0.14448998479387562,0.3373152814253632,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.3956233449539585,0.38012604272230577,0.4161019229029282,0.33384421950219023,0.739208828144708,0.3656739212633842,0.743218171423351,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.008173076923076924,0.007038288288288288,0.009672619047619048,0.0,0.05362776025236593,0.004351610095735422,0.052525252525252523,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.9918269230769231,0.9929617117117115,0.9903273809523808,1.0,0.9463722397476341,0.9956483899042645,0.9474747474747476,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,0.9707618025751072,0.978139534883721,0.9607097591888466,1.0,0.7655913978494624,0.9865097588978186,0.7459016393442623,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.2523148148148148,0.24311926605504589,0.2616822429906542,0.0,0.6449704142011834,0.1531791907514451,0.6511627906976745,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.9180618975139523,0.9272486772486772,0.9056152927120669,0.9254112308564946,0.8557692307692307,0.9214477211796247,0.8584905660377359,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.029238197424892705,0.02186046511627907,0.03929024081115336,0.0,0.23440860215053763,0.0134902411021814,0.2540983606557377,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,0.7476851851851852,0.7568807339449541,0.7383177570093458,1.0,0.35502958579881655,0.846820809248555,0.3488372093023256,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.8961538461538462,0.910472972972973,0.8772321428571429,0.9254112308564946,0.7334384858044164,0.9112271540469974,0.7212121212121212,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.9436766623207301,0.9520144861928475,0.932349323493235,0.9612608631609957,0.8081725312145289,0.9528694205711117,0.7982456140350878,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,0.9475961538461538,0.9577702702702703,0.9341517857142857,1.0,0.6561514195583596,0.9738903394255874,0.6424242424242425,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,1.0574034334763949,1.0548837209302326,1.0608365019011408,1.0806006742261722,0.8946236559139785,1.0706084959816302,0.8688524590163934,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv deleted file mode 100644 index 2d619b5e..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__230503.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.031770934397012174,0.03179588407276876,0.03173796518261954,0.03021693850570537,0.04041350460639352,0.03095858612070298,0.04119909772508553,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Overall_Uncertainty,0.3983894745523038,0.37953898808229,0.42329904595910783,0.3343008855637001,0.754819071987346,0.3672408267409202,0.7599025688480595,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.10919085836888948,0.09977103289182301,0.12163848489215587,0.07825493034734682,0.2812414612142513,0.09401753098365194,0.2852934155975552,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.029399891123827526,0.029524613293780425,0.02923507968496118,0.028113070894052253,0.036556560098886845,0.028723274560670305,0.037252743962894644,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Statistical_Bias,0.1576461741629812,0.1448501636355344,0.1745551880742502,0.12585049281415528,0.3344783073427291,0.14211527986946687,0.3378986745998295,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.3874506713996536,0.36915934374048587,0.4116213543778398,0.3236753328689938,0.7421381219660677,0.35648278707105796,0.7468658137588103,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.03317307692307692,0.02702702702702703,0.041294642857142856,0.0,0.21766561514195584,0.01671018276762402,0.22424242424242424,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.9668269230769231,0.972972972972973,0.9587053571428571,1.0,0.7823343848580442,0.983289817232376,0.7757575757575756,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,0.9726394849785408,0.9786046511627907,0.9645120405576679,1.0,0.7806451612903226,0.986796785304248,0.7704918032786885,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.24305555555555555,0.22935779816513763,0.2570093457943925,0.0,0.621301775147929,0.14450867052023122,0.6395348837209303,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.9172780166961801,0.926056338028169,0.9054134443783463,0.9254112308564946,0.8501170960187353,0.920728441349759,0.8584474885844748,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.02736051502145923,0.021395348837209303,0.035487959442332066,0.0,0.21935483870967742,0.01320321469575201,0.22950819672131148,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,0.7569444444444444,0.7706422018348624,0.7429906542056075,1.0,0.378698224852071,0.8554913294797688,0.36046511627906974,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.896875,0.9096283783783784,0.8800223214285714,0.9254112308564946,0.7381703470031545,0.9107049608355091,0.7363636363636363,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.9441478974091915,0.9516056083220262,0.9340288432034366,0.9612608631609957,0.8139013452914798,0.9526184538653366,0.8120950323974082,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,0.9502403846153846,0.9594594594594594,0.9380580357142857,1.0,0.6735015772870663,0.974934725848564,0.6636363636363637,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,1.060354077253219,1.0567441860465117,1.0652724968314322,1.0806006742261722,0.9182795698924732,1.0717566016073479,0.8975409836065574,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv deleted file mode 100644 index 18d3d46d..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231230.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.03252910468942265,0.03252902524267529,0.03252920967262452,0.030693039438224382,0.04274040764797955,0.031555425926226406,0.043829679425912385,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Overall_Uncertainty,0.4037381628750886,0.3923425606544777,0.41879663723803884,0.33849872997500174,0.766568195060745,0.3718477732674574,0.7738599574121419,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.10993265890368518,0.10422415679301861,0.11747603669278028,0.07764457815121702,0.2895032783566864,0.09398128679796605,0.29506525031248587,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.030819876820101517,0.03089316049137479,0.03072303768306183,0.029335641796964584,0.039074470970859906,0.030024239668031603,0.040054089827458374,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Statistical_Bias,0.15912684455902867,0.1474117950989567,0.1746074456312667,0.1267741995898805,0.3390565388196225,0.1430736551226199,0.3454411340785611,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.39359992330357896,0.38201221280895464,0.4089122550286182,0.3284308605961883,0.75603859066361,0.36177256196527935,0.762990207926875,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.04294871794871794,0.03490990990990991,0.05357142857142857,0.0007562866326337683,0.277602523659306,0.02193211488250653,0.2868686868686869,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.957051282051282,0.96509009009009,0.9464285714285714,0.9992437133673663,0.722397476340694,0.9780678851174934,0.7131313131313131,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.9452332657200812,0.9517485613103143,0.9364985163204748,0.9612608631609957,0.8462238398544131,0.9526934645884605,0.8501742160278746,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,1.1158798283261802,1.1013953488372092,1.1356147021546261,1.0806006742261722,1.3634408602150538,1.0993111366245694,1.3524590163934427,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv deleted file mode 100644 index e1b79040..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230412.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.009462385929412266,0.009012175653533759,0.010057306651108868,0.007180899249844511,0.02215090648486323,0.008435748571833603,0.021377601321916156,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.33549114826103926,0.31836021170681267,0.3581284572791243,0.285181081566371,0.6152912983641939,0.30876545484727985,0.6456711657601252,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10368731898989081,0.09421840135990592,0.11619981728665656,0.0743386380596007,0.2669104246053528,0.0875476151393161,0.2910057000435306,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.009041797085257739,0.00862125024111327,0.009597519700734358,0.006863431768917732,0.021156806715249644,0.008063663837118703,0.02039407084396233,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.1373039332079171,0.12778934038053086,0.14987678801553456,0.11150324023694122,0.28079485342189336,0.12475828626977171,0.28290947191427085,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.3346258082488791,0.31753205972953247,0.35721397593515863,0.28458446323586517,0.612931458904853,0.30800866599900106,0.6435459743611006,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.010576923076923076,0.009290540540540541,0.012276785714285714,0.005861221402911704,0.03680336487907466,0.008181026979982594,0.03838383838383839,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.989423076923077,0.9907094594594594,0.9877232142857143,0.9941387785970883,0.9631966351209255,0.9918189730200173,0.9616161616161616,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9847103004291845,0.9883720930232558,0.9797211660329531,0.9944836040453571,0.9161290322580645,0.9908151549942594,0.8975409836065574,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.2569444444444444,0.21559633027522937,0.29906542056074764,0.12927756653992395,0.4556213017751479,0.18497109826589594,0.5465116279069767,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.9195891783567134,0.9255226480836237,0.9115566037735849,0.9340817501439264,0.8223938223938224,0.9244777718264595,0.8488372093023255,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.01528969957081545,0.011627906976744186,0.020278833967046894,0.0055163959546429666,0.08387096774193549,0.009184845005740528,0.10245901639344263,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.7430555555555556,0.7844036697247706,0.7009345794392523,0.870722433460076,0.5443786982248521,0.815028901734104,0.45348837209302323,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9091346153846154,0.9172297297297297,0.8984375,0.9299489506522972,0.7933753943217665,0.9180156657963446,0.806060606060606,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.9510362694300518,0.9559154295996402,0.9444105070250458,0.9633367967938251,0.866734486266531,0.9564976447769465,0.8725099601593626,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9596153846153846,0.9695945945945946,0.9464285714285714,0.9852524106636416,0.8170347003154574,0.974934725848564,0.7818181818181819,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0708154506437768,1.067906976744186,1.0747782002534854,1.0646644192460926,1.113978494623656,1.0717566016073479,1.0573770491803278,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv deleted file mode 100644 index d0c40ea3..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__230503.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.009158053769605738,0.008212514239325449,0.010407516720333266,0.006961527846575738,0.021374063871504454,0.007914826128421525,0.023587029120319496,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3405731063979544,0.31839992701142616,0.3698733791587238,0.2914926154073046,0.6135349537686661,0.3139366328601028,0.6497176326099899,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10348223300075418,0.09177920427944804,0.11894694952533731,0.07569101828523213,0.25804346815364165,0.08765151711350165,0.2872144810255336,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.008760780941090826,0.00786667055488113,0.009942283951439354,0.006660075235377984,0.020443885544156257,0.007576159832381081,0.022509565323994844,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.1386288789089302,0.12788058574650565,0.1528319805878484,0.11296577176985978,0.28135461356565317,0.12603925146210496,0.2847448580645082,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.3397863513294368,0.3177008670684129,0.36897074124578977,0.29094203476185165,0.6114347112936402,0.31328202754094536,0.6473971395413217,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.010576923076923076,0.008445945945945946,0.013392857142857144,0.006239364719228588,0.03470031545741325,0.007832898172323759,0.04242424242424242,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.989423076923077,0.9915540540540541,0.9866071428571429,0.9937606352807714,0.9652996845425867,0.9921671018276762,0.9575757575757575,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9852467811158798,0.9911627906976744,0.9771863117870723,0.9938706711615078,0.9247311827956989,0.9916762342135477,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.23842592592592593,0.1743119266055046,0.3037383177570093,0.11406844106463879,0.4319526627218935,0.16184971098265896,0.5465116279069767,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.9177911044477761,0.9221116399826915,0.911886457717327,0.9329689298043728,0.8174904942965779,0.9225634178905207,0.8482490272373541,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.014753218884120171,0.008837209302325582,0.022813688212927757,0.006129328838492185,0.07526881720430108,0.008323765786452353,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.7615740740740741,0.8256880733944955,0.6962616822429907,0.8859315589353612,0.5680473372781065,0.838150289017341,0.45348837209302323,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9076923076923077,0.9159628378378378,0.8967633928571429,0.9282473057288713,0.7933753943217665,0.9167101827676241,0.803030303030303,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.9503234152652005,0.9553911678995741,0.9434077699602325,0.962457337883959,0.8678102926337034,0.9558721814912159,0.8702594810379242,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9620192307692308,0.9759290540540541,0.9436383928571429,0.9858196256381169,0.8296529968454258,0.9778067885117493,0.7787878787878788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0734978540772533,1.0748837209302327,1.0716096324461344,1.0652773521299417,1.1311827956989247,1.0749138920780712,1.0532786885245902,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv deleted file mode 100644 index b020be4b..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231230.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.008988904602918851,0.008328296086880599,0.009861851570540831,0.007081741892447545,0.019595617090492713,0.008046087869020623,0.019931292756949822,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3362012212622353,0.31866174345248494,0.35937838836797675,0.2873796619777732,0.6077230162732972,0.31019950178123873,0.6379787534204678,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10435870549531724,0.09454120231275444,0.11733183470084667,0.07588751371437953,0.26270164275018537,0.08856826968010649,0.2876234605627632,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.008540626404047149,0.007912095966912237,0.00937118448168971,0.0067239326019294015,0.018644194773553734,0.0076361836959867885,0.01903764328850527,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.13772743781485963,0.12801423590309075,0.15056274034112568,0.11213124888977462,0.2800810058745596,0.12527179394444676,0.2822883954623788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.3353985952973466,0.31795390058455897,0.35845051331067296,0.2867634594847761,0.6058835935230934,0.30948497636317873,0.6361536271696577,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.010256410256410256,0.008727477477477479,0.012276785714285714,0.005672149744753261,0.035751840168243953,0.007832898172323759,0.03838383838383838,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9897435897435899,0.9912725225225226,0.9877232142857143,0.9943278502552467,0.9642481598317559,0.9921671018276762,0.9616161616161616,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9841738197424893,0.987906976744186,0.9790874524714829,0.9938706711615078,0.9161290322580645,0.9902411021814007,0.8975409836065574,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.25925925925925924,0.22018348623853212,0.29906542056074764,0.14068441064638784,0.4437869822485207,0.1907514450867052,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.919779393331662,0.925893635571055,0.911504424778761,0.9348515422311905,0.8192307692307692,0.9249329758713136,0.8455598455598455,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.01582618025751073,0.012093023255813953,0.02091254752851711,0.006129328838492185,0.08387096774193549,0.00975889781859931,0.10245901639344263,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.7407407407407407,0.7798165137614679,0.7009345794392523,0.8593155893536122,0.5562130177514792,0.8092485549132948,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9088942307692308,0.9172297297297297,0.8978794642857143,0.9302325581395349,0.7902208201892744,0.9180156657963446,0.803030303030303,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.950887650641441,0.9558955895589559,0.9440879926672777,0.9634581105169341,0.86497461928934,0.956473523703909,0.8707753479125249,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9588942307692307,0.96875,0.9458705357142857,0.9838343732274532,0.8201892744479495,0.9738903394255874,0.7848484848484848,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.070010729613734,1.0669767441860465,1.0741444866920151,1.0631320870364696,1.118279569892473,1.0706084959816302,1.0614754098360655,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv deleted file mode 100644 index ba09b4f1..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230412.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.028931583448394384,0.027887440060036513,0.03031134435443872,0.023990508544688052,0.056411441666799005,0.026523966251049417,0.05687453455697389,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3407296076505943,0.32351157675879033,0.3634820056147639,0.2889025793842711,0.6289663610686632,0.3137768789694428,0.6535446102227468,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10449644700581025,0.09454884299671718,0.11764149516068323,0.07709571085209536,0.2568860300941363,0.08925642579476645,0.28137305681883384,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.02750846834515793,0.026513245234631513,0.028823584598353554,0.022807488757621504,0.05365303305439051,0.0252261103756494,0.05399765326399938,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.14130656744276707,0.13179506249596598,0.15387534183675428,0.11453587752255483,0.2901921394595943,0.1280896522383968,0.29470288632985225,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.332699552133727,0.31566221667372774,0.35521317399158303,0.2821091907817342,0.6140585649525385,0.3063264215158082,0.6387877044568451,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.027564102564102563,0.025056306306306304,0.030877976190476192,0.01588201928530913,0.09253417455310199,0.022106179286335945,0.0909090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9724358974358973,0.9749436936936936,0.9691220238095237,0.9841179807146909,0.9074658254468979,0.977893820713664,0.9090909090909091,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.9860515021459227,0.9902325581395349,0.9803548795944234,0.9947900704872816,0.9247311827956989,0.9919632606199771,0.9016393442622951,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.22916666666666666,0.16055045871559634,0.29906542056074764,0.12547528517110265,0.3905325443786982,0.15895953757225434,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.9169368919930158,0.9208477508650519,0.9116087212728344,0.9338319907940161,0.8067542213883677,0.922337870296237,0.8396946564885496,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.013948497854077254,0.009767441860465116,0.01964512040557668,0.005209929512718358,0.07526881720430108,0.008036739380022962,0.09836065573770492,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.7708333333333334,0.8394495412844036,0.7009345794392523,0.8745247148288974,0.6094674556213018,0.8410404624277457,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9074519230769231,0.9138513513513513,0.8989955357142857,0.9299489506522972,0.7823343848580442,0.9167101827676241,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.950239110766447,0.9542805916629314,0.944732824427481,0.9633476776969877,0.8617234468937875,0.9558843866685106,0.8695652173913043,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.963701923076923,0.9763513513513513,0.9469866071428571,0.9858196256381169,0.8406940063091483,0.9783289817232376,0.793939393939394,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0753755364806867,1.0753488372093023,1.0754119138149556,1.0652773521299417,1.146236559139785,1.07548794489093,1.0737704918032787,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv deleted file mode 100644 index 427d2847..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__230503.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.02731277021398364,0.026098285030024305,0.028917625635644182,0.022816685936188654,0.05231780674948068,0.025009235155152944,0.05404773771495805,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3355373115096594,0.32007249335487853,0.35597296407133416,0.2835616671135385,0.624600595643291,0.30847561224805253,0.6496170332428544,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.1044820373831202,0.0952400387637054,0.11669467841591832,0.0751686456789891,0.26750888146634777,0.08848177855052348,0.2901820111068944,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.025993141221894018,0.02484872556064418,0.027505404774259886,0.021677693068287117,0.04999356738848382,0.02379752103182506,0.0514756422157247,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.14006175423450606,0.13093058869046512,0.15212793727484586,0.1130408437373988,0.290338931541762,0.1269055665965932,0.29275326530482826,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.3282891285396943,0.31304866325567177,0.3484283148078669,0.2773056053326578,0.6118347165964936,0.3017389153451399,0.6364325119795219,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.029006410256410254,0.026182432432432432,0.03273809523809524,0.01588201928530913,0.10199789695057833,0.022976501305483028,0.098989898989899,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9709935897435896,0.9738175675675675,0.9672619047619049,0.9841179807146909,0.8980021030494217,0.977023498694517,0.9010101010101009,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.983637339055794,0.9897674418604652,0.9752851711026616,0.9944836040453571,0.9075268817204301,0.9913892078071183,0.8729508196721312,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.2152777777777778,0.16055045871559634,0.27102803738317754,0.09505703422053231,0.40236686390532544,0.13872832369942195,0.5232558139534884,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.9153769345981029,0.9208135006490696,0.9079646017699115,0.9316681022107379,0.8068833652007649,0.9205756929637526,0.8385826771653543,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.01636266094420601,0.010232558139534883,0.024714828897338403,0.0055163959546429666,0.09247311827956989,0.008610792192881744,0.12704918032786885,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.7847222222222222,0.8394495412844036,0.7289719626168224,0.9049429657794676,0.5976331360946746,0.861271676300578,0.47674418604651164,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9038461538461539,0.9134290540540541,0.8911830357142857,0.9273964832671583,0.7728706624605678,0.9143603133159269,0.7818181818181819,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.9482803206620118,0.9540461779869984,0.9404216315307058,0.9620515861251112,0.854251012145749,0.9546710889994472,0.8554216867469879,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9629807692307693,0.9759290540540541,0.9458705357142857,0.9878048780487805,0.8249211356466877,0.9796344647519583,0.7696969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0745708154506437,1.0748837209302327,1.0741444866920151,1.067422617223414,1.124731182795699,1.0769230769230769,1.040983606557377,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv b/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv deleted file mode 100644 index 57a0d632..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__230408/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231230.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Std,0.02910723994297415,0.026928841960414063,0.031985837277071404,0.02406843700085443,0.05713061403432136,0.02625843319768955,0.06217066368370148,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.339549700014784,0.3223706841105062,0.3622505424597227,0.2860013335129002,0.637359700465324,0.31158688287314923,0.6640878504767885,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10585760054889846,0.09587305366283486,0.11905146607691108,0.07655124883957513,0.2688452916010658,0.09003632089781252,0.28948033104483517,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.02766986928860808,0.02561402859426212,0.0303865159204224,0.022821771127225433,0.05463263603472044,0.024933084815688063,0.059433155747043484,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.14127003364930704,0.1315025307892209,0.15417709100013516,0.11386115794551417,0.29370488496093744,0.12792228047458631,0.29618486594985377,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.33170366680118574,0.31509850080823687,0.3536462075775823,0.27938316425520743,0.6226848844307115,0.30447423779212607,0.6477300701487562,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.03092948717948718,0.025900900900900903,0.03757440476190477,0.01777273586689355,0.10410094637223975,0.023672758920800698,0.11515151515151516,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9690705128205128,0.9740990990990991,0.9624255952380952,0.9822272641331065,0.8958990536277602,0.9763272410791993,0.8848484848484849,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.9844420600858369,0.9883720930232558,0.9790874524714829,0.9954030033711309,0.9075268817204301,0.9908151549942594,0.8934426229508197,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.2337962962962963,0.17889908256880735,0.2897196261682243,0.10266159695817491,0.4378698224852071,0.15895953757225434,0.5348837209302325,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.9172706823294177,0.9223090277777778,0.9104301708898055,0.9322617680826636,0.816247582205029,0.9222548757681005,0.8449612403100775,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.01555793991416309,0.011627906976744186,0.02091254752851711,0.004596996628869139,0.09247311827956989,0.009184845005740528,0.10655737704918032,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.7662037037037037,0.8211009174311926,0.7102803738317757,0.8973384030418251,0.5621301775147929,0.8410404624277457,0.46511627906976744,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9064903846153847,0.9138513513513513,0.8967633928571429,0.9288145207033466,0.7823343848580442,0.9156657963446475,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.949670073748221,0.9541984732824428,0.9435114503816794,0.9627982807173558,0.8594704684317719,0.9553064895530649,0.8685258964143426,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9617788461538461,0.972972972972973,0.9469866071428571,0.9880884855360181,0.8154574132492114,0.9772845953002611,0.7818181818181819,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0732296137339057,1.0716279069767443,1.0754119138149556,1.0677290836653386,1.1118279569892473,1.0743398392652124,1.0573770491803278,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv deleted file mode 100644 index 311b6f9d..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_DecisionTreeClassifier_3_Estimators_20231220__231414.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Statistical_Bias,0.15762928227594142,0.14673659171544048,0.1720231948023177,0.12578150198603236,0.3347511644560981,0.14208153240951818,0.33807740951352044,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.386950875806316,0.37773416084195277,0.399130106294939,0.3236565528406809,0.7389631514795489,0.35606052126946247,0.745466202703738,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Mean_Prediction,0.10657036051415189,0.10163912610901985,0.11308663454950496,0.07556025173941279,0.2790335206714548,0.09124926320216717,0.2843879444683988,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Overall_Uncertainty,0.3974300124357602,0.38794661663771,0.4099616425974693,0.3328535067228989,0.7565731656590235,0.36585143418564836,0.7639329054597851,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -IQR,0.028788818348307276,0.02810393092336335,0.029693848159840313,0.024104967435758084,0.0548381216884468,0.02637784143766344,0.05677076249305239,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Std,0.03112197497218865,0.03028406320740209,0.03222921551851375,0.025542396096752382,0.0621528820933058,0.02826493870222408,0.06428091107511076,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Label_Stability,0.9682692307692308,0.9743806306306305,0.9601934523809523,1.0,0.7917981072555205,0.984160139251523,0.7838383838383838,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Jitter,0.03173076923076923,0.02561936936936937,0.03980654761904762,0.0,0.2082018927444795,0.015839860748476937,0.21616161616161614,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -TNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -PPV,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FNR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -FPR,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Accuracy,0.8961538461538462,0.9079391891891891,0.8805803571428571,0.9254112308564946,0.7334384858044164,0.9096605744125327,0.7393939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -F1,0.9452332657200812,0.9517485613103143,0.9364985163204748,0.9612608631609957,0.8462238398544131,0.9526934645884605,0.8501742160278746,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Selection-Rate,1.0,1.0,1.0,1.0,1.0,1.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Positive-Rate,1.1158798283261802,1.1013953488372092,1.1356147021546261,1.0806006742261722,1.3634408602150538,1.0993111366245694,1.3524590163934427,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv deleted file mode 100644 index caa85bb2..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_LogisticRegression_3_Estimators_20231220__231414.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Statistical_Bias,0.13911835183160592,0.12850643408600126,0.15314124313829783,0.11342056111362148,0.28203697970481295,0.126487995588439,0.2857070318659372,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.34262372638915,0.32130628488352536,0.3707932026644397,0.29391588633995114,0.6135130702589842,0.3162564489931639,0.6486439458637764,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.1055255194388021,0.09409588622030891,0.12062896333466813,0.07704961102346518,0.26389468832283697,0.0894641848481739,0.29193434211185076,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.34371032781994726,0.3223475783519096,0.37193967533128297,0.294705592099116,0.6162508611821733,0.31721070380431815,0.6512665701831585,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.01009711856213468,0.00927632992422369,0.011181732119374205,0.007822120130984433,0.02274955463190719,0.00894954949629287,0.023415874689935095,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Std,0.010581545614139397,0.009725625054281333,0.011712583496808981,0.008205907190389791,0.02379369243139667,0.009379356305060225,0.02453422759527039,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9886217948717947,0.9904279279279278,0.986235119047619,0.9941387785970882,0.9579390115667717,0.991644908616188,0.9535353535353536,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.01137820512820513,0.009572072072072073,0.013764880952380952,0.005861221402911704,0.042060988433228176,0.00835509138381201,0.04646464646464646,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9844420600858369,0.9893023255813953,0.9778200253485425,0.9944836040453571,0.9139784946236559,0.9911021814006888,0.889344262295082,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.25,0.2018348623853211,0.29906542056074764,0.13307984790874525,0.4319526627218935,0.1791907514450867,0.5348837209302325,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.9188783174762143,0.924380704041721,0.9113998818665091,0.934350705441981,0.8157389635316699,0.924003211131924,0.8443579766536965,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.01555793991416309,0.010697674418604652,0.022179974651457542,0.0055163959546429666,0.08602150537634409,0.008897818599311137,0.11065573770491803,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.75,0.7981651376146789,0.7009345794392523,0.8669201520912547,0.5680473372781065,0.8208092485549133,0.46511627906976744,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9081730769230769,0.9168074324324325,0.8967633928571429,0.9302325581395349,0.7854889589905363,0.9177545691906005,0.796969696969697,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.9505309505309505,0.9557402830824534,0.9434423723631917,0.963479809976247,0.8620689655172413,0.9563772330702118,0.8662674650698603,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9600961538461539,0.971706081081081,0.9447544642857143,0.9849688031764039,0.8217665615141956,0.9757180156657963,0.7787878787878788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.071351931330472,1.0702325581395349,1.0728770595690749,1.064357952804168,1.1204301075268817,1.072617680826636,1.0532786885245902,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv b/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv deleted file mode 100644 index a8cb2f00..00000000 --- a/docs/examples/results/Law_School_Metrics_20231220__231409/Metrics_Law_School_RandomForestClassifier_3_Estimators_20231220__231414.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params -Statistical_Bias,0.14160192322656046,0.13250634279432627,0.1536210830834413,0.11480265114659247,0.29064645533060945,0.12862223562285258,0.2922449642029273,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.3338194028061136,0.31861506241835186,0.35391085260422744,0.28350324017729134,0.6136534555336017,0.3078998030904282,0.6346438479911906,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10550488873417896,0.09621410994261566,0.11778198928017335,0.07742909080243023,0.26164883748393614,0.09009781379592052,0.28432033422972386,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3410912822145201,0.3255959318793119,0.3615672808717596,0.2895421496952407,0.6277825144905123,0.3145417793817695,0.6492264211522013,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -IQR,0.026286222351811234,0.025260415737814762,0.02764175252030657,0.021701250259225893,0.05178560973107924,0.02403232502568423,0.05244509131867918,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Std,0.027646336340995586,0.026516976756715504,0.029138704363079988,0.02283603925213981,0.054398871885641395,0.025267436164910118,0.05525599596041179,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9745192307692307,0.9763513513513513,0.9720982142857143,0.9852524106636416,0.9148264984227129,0.9796344647519583,0.9151515151515152,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Jitter,0.025480769230769234,0.023648648648648646,0.027901785714285716,0.01474758933635848,0.08517350157728705,0.020365535248041775,0.08484848484848485,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TPR,0.9860515021459227,0.9888372093023255,0.982256020278834,0.9947900704872816,0.9247311827956989,0.9916762342135477,0.9057377049180327,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -TNR,0.2222222222222222,0.16972477064220184,0.2757009345794392,0.10266159695817491,0.40828402366863903,0.15028901734104047,0.5116279069767442,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -PPV,0.9162512462612163,0.9215431296055483,0.9090909090909091,0.9322228604250431,0.8113207547169812,0.9215790877567351,0.8403041825095057,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FNR,0.013948497854077254,0.011162790697674419,0.017743979721166033,0.005209929512718358,0.07526881720430108,0.008323765786452353,0.0942622950819672,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -FPR,0.7777777777777778,0.8302752293577982,0.7242990654205608,0.8973384030418251,0.591715976331361,0.8497109826589595,0.4883720930232558,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9067307692307692,0.9134290540540541,0.8978794642857143,0.9282473057288713,0.7870662460567823,0.9156657963446475,0.803030303030303,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -F1,0.9498708010335918,0.9540049360556428,0.9442583003350594,0.9624907338769458,0.864321608040201,0.9553435642195492,0.8717948717948718,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9644230769230769,0.9742398648648649,0.9514508928571429,0.9875212705615428,0.8359621451104101,0.9788511749347258,0.796969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Positive-Rate,1.0761802575107295,1.0730232558139534,1.0804816223067173,1.0671161507814895,1.1397849462365592,1.0760619977037889,1.0778688524590163,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 988b9a03..dfd94224 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -1,7 +1,6 @@ import os import gc import pandas as pd -from tqdm.notebook import tqdm from copy import deepcopy from abc import ABCMeta, abstractmethod @@ -120,15 +119,14 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b """ models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: - # print('\n', flush=True) - print('\n') + print('\n', flush=True) self._logger.info('Start classifiers testing by bootstrap') - # # Remove a progress bar for UQ without estimators fitting - # if self._notebook_logs_stdout: - # from tqdm.notebook import tqdm - # else: - # from tqdm import tqdm + # Remove a progress bar for UQ without estimators fitting + if self._notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), @@ -149,8 +147,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b gc.collect() if self._verbose >= 1: - # print('\n', flush=True) - print('\n') + print('\n', flush=True) self._logger.info('Successfully tested classifiers by bootstrap') return models_predictions diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 33b74fc2..5a116f9f 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -77,8 +77,7 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_pred # Compute metrics for each group partition for group_partition_name, partition_indexes in partition_indexes_dct.items(): if partition_indexes.shape[0] == 0: - # print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET, flush=True) - print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET) + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Error metrics are set to None.' + Fore.RESET, flush=True) metrics_dct = { TPR: None, TNR: None, diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 5afa3218..1f53687d 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -1,6 +1,5 @@ import gc import pandas as pd -from tqdm.notebook import tqdm from virny.utils.stability_utils import generate_bootstrap from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer @@ -53,15 +52,14 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b """ models_predictions = {idx: [] for idx in range(self.n_estimators)} if self._verbose >= 1: - # print('\n', flush=True) - print('\n') + print('\n', flush=True) self._logger.info('Start classifiers testing by bootstrap') - # # Remove a progress bar for UQ without estimators fitting - # if self._notebook_logs_stdout: - # from tqdm.notebook import tqdm - # else: - # from tqdm import tqdm + # Remove a progress bar for UQ without estimators fitting + if self._notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm cycle_range = range(self.n_estimators) if with_fit is False else \ tqdm(range(self.n_estimators), @@ -89,8 +87,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b self.models_lst[idx] = classifier if self._verbose >= 1: - # print('\n', flush=True) - print('\n') + print('\n', flush=True) self._logger.info('Successfully tested classifiers by bootstrap') return models_predictions diff --git a/virny/analyzers/subgroup_variance_calculator.py b/virny/analyzers/subgroup_variance_calculator.py index 659d1950..8b43b6f8 100644 --- a/virny/analyzers/subgroup_variance_calculator.py +++ b/virny/analyzers/subgroup_variance_calculator.py @@ -81,8 +81,7 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_pred for model_idx in models_predictions.keys() } if partition_indexes.shape[0] == 0: - # print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) - print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET) + print(Fore.YELLOW + f'WARNING: "{group_partition_name}" group is empty. Stability metrics are set to None.' + Fore.RESET, flush=True) metrics_dct = dict() metric_names = list(METRIC_TO_FUNCTION.keys()) if self.with_predict_proba else METRICS_FOR_LABELS for metric in metric_names: diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 61955396..67122ce4 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -1,7 +1,6 @@ import os import traceback import pandas as pd -from tqdm.notebook import tqdm from datetime import datetime, timezone from virny.configs.constants import ModelSetting @@ -38,9 +37,13 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. + As for now, 0, 1, 2 levels are supported. Currently, verbose works only with notebook_logs_stdout = False. """ + # Currently, verbose works only with notebook_logs_stdout = False + if notebook_logs_stdout: + verbose = 0 + start_datetime = datetime.now(timezone.utc) os.makedirs(save_results_dir_path, exist_ok=True) @@ -122,21 +125,20 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, As for now, 0, 1, 2 levels are supported. """ - # # Set a specific tqdm type for Jupyter notebooks and python modules - # if notebook_logs_stdout: - # from tqdm.notebook import tqdm - # else: - # from tqdm import tqdm + # Set a specific tqdm type for Jupyter notebooks and python modules + if notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm models_metrics_dct = dict() num_models = len(models_config) for model_idx, model_name in tqdm(enumerate(models_config.keys()), total=num_models, - desc="Analyze models in one run", + desc="Analyze multiple models", colour="red"): if verbose >= 1: - # print('\n\n', flush=True) - print('\n\n') + print('\n\n', flush=True) print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] diff --git a/virny/user_interfaces/multiple_models_with_db_writer_api.py b/virny/user_interfaces/multiple_models_with_db_writer_api.py index 4f37a2af..d3f50ba2 100644 --- a/virny/user_interfaces/multiple_models_with_db_writer_api.py +++ b/virny/user_interfaces/multiple_models_with_db_writer_api.py @@ -33,9 +33,13 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. + As for now, 0, 1, 2 levels are supported. Currently, verbose works only with notebook_logs_stdout = False. """ + # Currently, verbose works only with notebook_logs_stdout = False + if notebook_logs_stdout: + verbose = 0 + # Check if a type of postprocessing_sensitive_attribute is not NoneType. # In other words, check if postprocessing_sensitive_attribute is defined in a config yaml. postprocessing_sensitive_attribute = config.postprocessing_sensitive_attribute \ diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py index b03db260..f1327778 100644 --- a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -1,6 +1,5 @@ import traceback import pandas as pd -from tqdm.notebook import tqdm from datetime import datetime, timezone from virny.configs.constants import ModelSetting @@ -40,9 +39,13 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test False, otherwise. verbose [Optional] Level of logs printing. The greater level provides more logs. - As for now, 0, 1, 2 levels are supported. + As for now, 0, 1, 2 levels are supported. Currently, verbose works only with notebook_logs_stdout = False. """ + # Currently, verbose works only with notebook_logs_stdout = False + if notebook_logs_stdout: + verbose = 0 + models_metrics_dct = run_metrics_computation_with_multiple_test_sets(dataset=dataset, bootstrap_fraction=config.bootstrap_fraction, dataset_name=config.dataset_name, @@ -125,17 +128,17 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo As for now, 0, 1, 2 levels are supported. """ - # # Set a specific tqdm type for Jupyter notebooks and python modules - # if notebook_logs_stdout: - # from tqdm.notebook import tqdm - # else: - # from tqdm import tqdm + # Set a specific tqdm type for Jupyter notebooks and python modules + if notebook_logs_stdout: + from tqdm.notebook import tqdm + else: + from tqdm import tqdm models_metrics_dct = dict() num_models = len(models_config) for model_idx, model_name in tqdm(enumerate(models_config.keys()), total=num_models, - desc="Analyze models in one run", + desc="Analyze multiple models", colour="red"): if verbose >= 1: print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) From 330a412bd86d8ee4314daeceb08f9b30bd6bbf01 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 01:47:45 +0200 Subject: [PATCH 084/148] Added a notebook_logs_stdout argument for all interfaces --- ..._Models_Interface_With_Postprocessor.ipynb | 248 +++++++++--------- ...ng_results_Law_School_20231220__234456.csv | 4 + .../abstract_overall_variance_analyzer.py | 4 +- ...verall_variance_analyzer_postprocessing.py | 4 +- virny/user_interfaces/multiple_models_api.py | 4 +- ...iple_models_with_multiple_test_sets_api.py | 4 +- 6 files changed, 145 insertions(+), 123 deletions(-) create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231220__234456.csv diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index 0426d106..a060810d 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -2,24 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 27, + "execution_count": 1, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.607041Z", - "start_time": "2023-12-20T23:36:28.185272Z" + "end_time": "2023-12-20T23:44:51.569353Z", + "start_time": "2023-12-20T23:44:51.216259Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -28,12 +19,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.623127Z", - "start_time": "2023-12-20T23:36:28.605439Z" + "end_time": "2023-12-20T23:44:51.577729Z", + "start_time": "2023-12-20T23:44:51.569503Z" } }, "outputs": [], @@ -46,12 +37,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.643424Z", - "start_time": "2023-12-20T23:36:28.623829Z" + "end_time": "2023-12-20T23:44:51.590135Z", + "start_time": "2023-12-20T23:44:51.578520Z" } }, "outputs": [ @@ -105,15 +96,34 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.676678Z", - "start_time": "2023-12-20T23:36:28.642513Z" + "end_time": "2023-12-20T23:44:52.557910Z", + "start_time": "2023-12-20T23:44:51.588012Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:No module named 'tempeh': LawSchoolGPADataset will be unavailable. To install, run:\n", + "pip install 'aif360[LawSchoolGPA]'\n", + "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n", + "pip install 'aif360[AdversarialDebiasing]'\n", + "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n", + "pip install 'aif360[AdversarialDebiasing]'\n", + "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n", + "pip install 'aif360[Reductions]'\n", + "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n", + "pip install 'aif360[Reductions]'\n", + "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n", + "pip install 'aif360[Reductions]'\n" + ] + } + ], "source": [ "import os\n", "from pprint import pprint\n", @@ -160,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "outputs": [], "source": [ "DATASET_SPLIT_SEED = 42\n", @@ -170,15 +180,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.681093Z", - "start_time": "2023-12-20T23:36:28.663087Z" + "end_time": "2023-12-20T23:44:52.576414Z", + "start_time": "2023-12-20T23:44:52.559175Z" } }, "id": "ce359a052925eb3a" }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "outputs": [], "source": [ "models_params_for_tuning = {\n", @@ -214,8 +224,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.700653Z", - "start_time": "2023-12-20T23:36:28.680939Z" + "end_time": "2023-12-20T23:44:52.594883Z", + "start_time": "2023-12-20T23:44:52.577786Z" } }, "id": "2ece07ab7e3a9acc" @@ -250,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -269,15 +279,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.715739Z", - "start_time": "2023-12-20T23:36:28.698015Z" + "end_time": "2023-12-20T23:44:52.611819Z", + "start_time": "2023-12-20T23:44:52.595583Z" } }, "id": "af22ee06f1e3eb1a" }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)\n", @@ -286,8 +296,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.734477Z", - "start_time": "2023-12-20T23:36:28.716316Z" + "end_time": "2023-12-20T23:44:52.631115Z", + "start_time": "2023-12-20T23:44:52.612178Z" } }, "id": "65181f72484bb92b" @@ -302,12 +312,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 9, "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.793207Z", - "start_time": "2023-12-20T23:36:28.735018Z" + "end_time": "2023-12-20T23:44:52.690961Z", + "start_time": "2023-12-20T23:44:52.631728Z" } }, "outputs": [ @@ -316,7 +326,7 @@ "text/plain": " decile1b decile3 lsat ugpa zfygpa\n0 10.0 10.0 44.0 3.5 1.33\n1 5.0 4.0 29.0 3.5 -0.11\n2 8.0 7.0 37.0 3.4 0.63\n3 8.0 7.0 43.0 3.3 0.67\n4 3.0 2.0 41.0 3.3 -0.67", "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    decile1bdecile3lsatugpazfygpa
    010.010.044.03.51.33
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    28.07.037.03.40.63
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    43.02.041.03.3-0.67
    \n
    " }, - "execution_count": 35, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 10, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -341,15 +351,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.813482Z", - "start_time": "2023-12-20T23:36:28.793131Z" + "end_time": "2023-12-20T23:44:52.704333Z", + "start_time": "2023-12-20T23:44:52.688388Z" } }, "id": "ebbef5eaf9dc0943" }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 11, "outputs": [], "source": [ "# Create a binary race column for postprocessing since aif360 postprocessors can postprocess a dataset only based on binary columns.\n", @@ -362,15 +372,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.878211Z", - "start_time": "2023-12-20T23:36:28.811771Z" + "end_time": "2023-12-20T23:44:52.765598Z", + "start_time": "2023-12-20T23:44:52.705152Z" } }, "id": "97ed4609effbf53f" }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 12, "outputs": [], "source": [ "# Define a postprocessor\n", @@ -383,8 +393,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:28.897222Z", - "start_time": "2023-12-20T23:36:28.877762Z" + "end_time": "2023-12-20T23:44:52.779451Z", + "start_time": "2023-12-20T23:44:52.761173Z" } }, "id": "4535191384245578" @@ -401,23 +411,23 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 13, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023/12/21, 01:36:28: Tuning DecisionTreeClassifier...\n", + "2023/12/21, 01:44:52: Tuning DecisionTreeClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 01:36:30: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", + "2023/12/21, 01:44:54: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", "\n", - "2023/12/21, 01:36:30: Tuning LogisticRegression...\n", + "2023/12/21, 01:44:54: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/12/21, 01:36:30: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", + "2023/12/21, 01:44:54: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", "\n", - "2023/12/21, 01:36:30: Tuning RandomForestClassifier...\n", + "2023/12/21, 01:44:54: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 01:36:32: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" + "2023/12/21, 01:44:56: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" ] }, { @@ -425,7 +435,7 @@ "text/plain": " Dataset_Name Model_Name F1_Score Accuracy_Score \\\n0 Law_School DecisionTreeClassifier 0.524303 0.887660 \n1 Law_School LogisticRegression 0.660552 0.899359 \n2 Law_School RandomForestClassifier 0.653102 0.895272 \n\n Model_Best_Params \n0 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 {'C': 100, 'max_iter': 250, 'penalty': 'l2', '... \n2 {'max_depth': 10, 'max_features': 0.6, 'min_sa... ", "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0Law_SchoolDecisionTreeClassifier0.5243030.887660{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1Law_SchoolLogisticRegression0.6605520.899359{'C': 100, 'max_iter': 250, 'penalty': 'l2', '...
    2Law_SchoolRandomForestClassifier0.6531020.895272{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    \n
    " }, - "execution_count": 39, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -437,15 +447,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:32.973701Z", - "start_time": "2023-12-20T23:36:28.895912Z" + "end_time": "2023-12-20T23:44:56.739306Z", + "start_time": "2023-12-20T23:44:52.779255Z" } }, "id": "782741c190a4690b" }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 14, "outputs": [], "source": [ "now = datetime.now(timezone.utc)\n", @@ -456,8 +466,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:33.015070Z", - "start_time": "2023-12-20T23:36:32.973263Z" + "end_time": "2023-12-20T23:44:56.777854Z", + "start_time": "2023-12-20T23:44:56.740473Z" } }, "id": "21ccc879c5c3e215" @@ -474,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 15, "outputs": [ { "name": "stdout", @@ -495,8 +505,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:36:33.049631Z", - "start_time": "2023-12-20T23:36:32.996820Z" + "end_time": "2023-12-20T23:44:56.807127Z", + "start_time": "2023-12-20T23:44:56.763886Z" } }, "id": "3b15f202741fa2ae" @@ -519,22 +529,22 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 16, "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.230538Z", - "start_time": "2023-12-20T23:36:33.017100Z" + "end_time": "2023-12-20T23:45:51.332050Z", + "start_time": "2023-12-20T23:44:56.785652Z" } }, "outputs": [ { "data": { - "text/plain": "Analyze models in one run: 0%| | 0/3 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Statistical_Bias0.1583200.1461280.1744310.1269480.332794
    1Aleatoric_Uncertainty0.3878750.3724050.4083170.3256870.733735
    2Mean_Prediction0.1077240.1012270.1163090.0775840.275348
    3Overall_Uncertainty0.4045550.3880550.4263590.3426430.748878
    4IQR0.0476850.0462650.0495610.0462040.055917
    5Std0.0380760.0362020.0405520.0366830.045819
    6Label_Stability0.9605000.9679050.9507140.9996480.742776
    7Jitter0.0260880.0213040.0324110.0003490.169238
    8TPR1.0000001.0000001.0000001.0000001.000000
    9TNR0.0000000.0000000.0000000.0000000.000000
    10PPV0.8961540.9079390.8805800.9254110.733438
    11FNR0.0000000.0000000.0000000.0000000.000000
    12FPR1.0000001.0000001.0000001.0000001.000000
    13Accuracy0.8961540.9079390.8805800.9254110.733438
    14F10.9452330.9517490.9364990.9612610.846224
    15Selection-Rate1.0000001.0000001.0000001.0000001.000000
    16Positive-Rate1.1158801.1013951.1356151.0806011.363441
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n" + "text/plain": " Metric overall male_priv male_dis race_priv \\\n0 Std 0.039277 0.036842 0.042494 0.036063 \n1 Overall_Uncertainty 0.405922 0.388361 0.429128 0.342869 \n2 Aleatoric_Uncertainty 0.389055 0.372690 0.410681 0.326654 \n3 IQR 0.048580 0.046042 0.051933 0.044797 \n4 Statistical_Bias 0.158352 0.145985 0.174693 0.126872 \n5 Mean_Prediction 0.108119 0.101100 0.117394 0.077675 \n6 Jitter 0.029905 0.024434 0.037134 0.001234 \n7 Label_Stability 0.948577 0.958311 0.935714 0.998752 \n8 TPR 0.978541 0.983256 0.972117 1.000000 \n9 TNR 0.212963 0.188073 0.238318 0.000000 \n10 PPV 0.914744 0.922741 0.903948 0.925411 \n11 FNR 0.021459 0.016744 0.027883 0.000000 \n12 FPR 0.787037 0.811927 0.761682 1.000000 \n13 Accuracy 0.899038 0.910051 0.884487 0.925411 \n14 F1 0.945568 0.952038 0.936794 0.961261 \n15 Selection-Rate 0.958654 0.967483 0.946987 1.000000 \n16 Positive-Rate 1.069742 1.065581 1.075412 1.080601 \n17 Sample_Size 4160.000000 2368.000000 1792.000000 3526.000000 \n\n race_dis \n0 0.057147 \n1 0.756592 \n2 0.736101 \n3 0.069616 \n4 0.333428 \n5 0.277430 \n6 0.189357 \n7 0.669527 \n8 0.827957 \n9 0.544379 \n10 0.833333 \n11 0.172043 \n12 0.455621 \n13 0.752366 \n14 0.830636 \n15 0.728707 \n16 0.993548 \n17 634.000000 ", + "text/html": "
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    Metricoverallmale_privmale_disrace_privrace_dis
    0Std0.0392770.0368420.0424940.0360630.057147
    1Overall_Uncertainty0.4059220.3883610.4291280.3428690.756592
    2Aleatoric_Uncertainty0.3890550.3726900.4106810.3266540.736101
    3IQR0.0485800.0460420.0519330.0447970.069616
    4Statistical_Bias0.1583520.1459850.1746930.1268720.333428
    5Mean_Prediction0.1081190.1011000.1173940.0776750.277430
    6Jitter0.0299050.0244340.0371340.0012340.189357
    7Label_Stability0.9485770.9583110.9357140.9987520.669527
    8TPR0.9785410.9832560.9721171.0000000.827957
    9TNR0.2129630.1880730.2383180.0000000.544379
    10PPV0.9147440.9227410.9039480.9254110.833333
    11FNR0.0214590.0167440.0278830.0000000.172043
    12FPR0.7870370.8119270.7616821.0000000.455621
    13Accuracy0.8990380.9100510.8844870.9254110.752366
    14F10.9455680.9520380.9367940.9612610.830636
    15Selection-Rate0.9586540.9674830.9469871.0000000.728707
    16Positive-Rate1.0697421.0655811.0754121.0806010.993548
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n
    " }, - "execution_count": 43, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -639,12 +649,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 18, "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.299558Z", - "start_time": "2023-12-20T23:37:28.252898Z" + "end_time": "2023-12-20T23:45:51.385855Z", + "start_time": "2023-12-20T23:45:51.355767Z" } }, "outputs": [], @@ -654,12 +664,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 19, "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.300848Z", - "start_time": "2023-12-20T23:37:28.274195Z" + "end_time": "2023-12-20T23:45:51.453333Z", + "start_time": "2023-12-20T23:45:51.379688Z" } }, "outputs": [], @@ -677,12 +687,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 20, "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.320080Z", - "start_time": "2023-12-20T23:37:28.295027Z" + "end_time": "2023-12-20T23:45:51.469788Z", + "start_time": "2023-12-20T23:45:51.397935Z" } }, "outputs": [], @@ -692,14 +702,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 21, "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.027359 -0.191973 -0.170267 \n1 Aleatoric_Uncertainty_Parity 0.035912 0.408048 0.382995 \n2 Aleatoric_Uncertainty_Ratio 1.096432 2.252882 2.071335 \n3 Equalized_Odds_FNR 0.000000 0.000000 0.000000 \n4 Equalized_Odds_FPR 0.000000 0.000000 0.000000 \n5 IQR_Parity 0.003295 0.009713 0.010086 \n6 Jitter_Parity 0.011107 0.168890 0.161775 \n7 Label_Stability_Ratio 0.982239 0.743037 0.748244 \n8 Label_Stability_Difference -0.017191 -0.256872 -0.246740 \n9 Overall_Uncertainty_Parity 0.038304 0.406235 0.381743 \n10 Overall_Uncertainty_Ratio 1.098707 2.185592 2.019959 \n11 Statistical_Parity_Difference 0.034219 0.282840 0.253148 \n12 Disparate_Impact 1.031069 1.261743 1.230279 \n13 Std_Parity 0.004350 0.009136 0.009471 \n14 Std_Ratio 1.120150 1.249055 1.253741 \n15 Equalized_Odds_TNR 0.000000 0.000000 0.000000 \n16 Equalized_Odds_TPR 0.000000 0.000000 0.000000 \n17 Accuracy_Parity -0.016967 -0.139728 -0.108141 \n18 Aleatoric_Uncertainty_Parity 0.046101 0.327392 0.337331 \n19 Aleatoric_Uncertainty_Ratio 1.145380 2.140452 2.087435 \n20 Equalized_Odds_FNR 0.010849 0.080812 0.097660 \n21 Equalized_Odds_FPR -0.120424 -0.324229 -0.396357 \n22 IQR_Parity 0.002085 0.018222 0.017440 \n23 Jitter_Parity 0.003113 0.026663 0.028340 \n24 Label_Stability_Ratio 0.995603 0.962031 0.957595 \n25 Label_Stability_Difference -0.004348 -0.037695 -0.041997 \n26 Overall_Uncertainty_Parity 0.046287 0.329109 0.338870 \n27 Overall_Uncertainty_Ratio 1.145524 2.143333 2.089168 \n28 Statistical_Parity_Difference 0.000447 0.048701 -0.028110 \n29 Disparate_Impact 1.000417 1.045717 0.973807 \n30 Std_Parity 0.001636 0.013754 0.012985 \n31 Std_Ratio 1.181103 2.799539 2.490934 \n32 Equalized_Odds_TNR 0.120424 0.324229 0.396357 \n33 Equalized_Odds_TPR -0.010849 -0.080812 -0.097660 \n34 Accuracy_Parity -0.015142 -0.142883 -0.110911 \n35 Aleatoric_Uncertainty_Parity 0.034370 0.331481 0.330334 \n36 Aleatoric_Uncertainty_Ratio 1.109409 2.190531 2.091127 \n37 Equalized_Odds_FNR 0.007511 0.070672 0.082127 \n38 Equalized_Odds_FPR -0.115408 -0.290413 -0.349778 \n39 IQR_Parity 0.004738 0.051576 0.052061 \n40 Jitter_Parity 0.006689 0.081155 0.079320 \n41 Label_Stability_Ratio 0.991500 0.886445 0.889799 \n42 Label_Stability_Difference -0.008190 -0.110927 -0.106721 \n43 Overall_Uncertainty_Parity 0.035448 0.343738 0.342564 \n44 Overall_Uncertainty_Ratio 1.109036 2.193591 2.093758 \n45 Statistical_Parity_Difference 0.005092 0.073282 0.006766 \n46 Disparate_Impact 1.004744 1.068712 1.006293 \n47 Std_Parity 0.003489 0.037351 0.037079 \n48 Std_Ratio 1.112086 2.386766 2.249068 \n49 Equalized_Odds_TNR 0.115408 0.290413 0.349778 \n50 Equalized_Odds_TPR -0.007511 -0.070672 -0.082127 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", - 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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.027359-0.191973-0.170267DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0359120.4080480.382995DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0964322.2528822.071335DecisionTreeClassifier
    3Equalized_Odds_FNR0.0000000.0000000.000000DecisionTreeClassifier
    4Equalized_Odds_FPR0.0000000.0000000.000000DecisionTreeClassifier
    5IQR_Parity0.0032950.0097130.010086DecisionTreeClassifier
    6Jitter_Parity0.0111070.1688900.161775DecisionTreeClassifier
    7Label_Stability_Ratio0.9822390.7430370.748244DecisionTreeClassifier
    8Label_Stability_Difference-0.017191-0.256872-0.246740DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0383040.4062350.381743DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0987072.1855922.019959DecisionTreeClassifier
    11Statistical_Parity_Difference0.0342190.2828400.253148DecisionTreeClassifier
    12Disparate_Impact1.0310691.2617431.230279DecisionTreeClassifier
    13Std_Parity0.0043500.0091360.009471DecisionTreeClassifier
    14Std_Ratio1.1201501.2490551.253741DecisionTreeClassifier
    15Equalized_Odds_TNR0.0000000.0000000.000000DecisionTreeClassifier
    16Equalized_Odds_TPR0.0000000.0000000.000000DecisionTreeClassifier
    17Accuracy_Parity-0.016967-0.139728-0.108141LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0461010.3273920.337331LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1453802.1404522.087435LogisticRegression
    20Equalized_Odds_FNR0.0108490.0808120.097660LogisticRegression
    21Equalized_Odds_FPR-0.120424-0.324229-0.396357LogisticRegression
    22IQR_Parity0.0020850.0182220.017440LogisticRegression
    23Jitter_Parity0.0031130.0266630.028340LogisticRegression
    24Label_Stability_Ratio0.9956030.9620310.957595LogisticRegression
    25Label_Stability_Difference-0.004348-0.037695-0.041997LogisticRegression
    26Overall_Uncertainty_Parity0.0462870.3291090.338870LogisticRegression
    27Overall_Uncertainty_Ratio1.1455242.1433332.089168LogisticRegression
    28Statistical_Parity_Difference0.0004470.048701-0.028110LogisticRegression
    29Disparate_Impact1.0004171.0457170.973807LogisticRegression
    30Std_Parity0.0016360.0137540.012985LogisticRegression
    31Std_Ratio1.1811032.7995392.490934LogisticRegression
    32Equalized_Odds_TNR0.1204240.3242290.396357LogisticRegression
    33Equalized_Odds_TPR-0.010849-0.080812-0.097660LogisticRegression
    34Accuracy_Parity-0.015142-0.142883-0.110911RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0343700.3314810.330334RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1094092.1905312.091127RandomForestClassifier
    37Equalized_Odds_FNR0.0075110.0706720.082127RandomForestClassifier
    38Equalized_Odds_FPR-0.115408-0.290413-0.349778RandomForestClassifier
    39IQR_Parity0.0047380.0515760.052061RandomForestClassifier
    40Jitter_Parity0.0066890.0811550.079320RandomForestClassifier
    41Label_Stability_Ratio0.9915000.8864450.889799RandomForestClassifier
    42Label_Stability_Difference-0.008190-0.110927-0.106721RandomForestClassifier
    43Overall_Uncertainty_Parity0.0354480.3437380.342564RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1090362.1935912.093758RandomForestClassifier
    45Statistical_Parity_Difference0.0050920.0732820.006766RandomForestClassifier
    46Disparate_Impact1.0047441.0687121.006293RandomForestClassifier
    47Std_Parity0.0034890.0373510.037079RandomForestClassifier
    48Std_Ratio1.1120862.3867662.249068RandomForestClassifier
    49Equalized_Odds_TNR0.1154080.2904130.349778RandomForestClassifier
    50Equalized_Odds_TPR-0.007511-0.070672-0.082127RandomForestClassifier
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    " + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.025564 -0.173045 -0.150360 \n1 Aleatoric_Uncertainty_Parity 0.037992 0.409447 0.383894 \n2 Aleatoric_Uncertainty_Ratio 1.101939 2.253460 2.070529 \n3 Equalized_Odds_FNR 0.011139 0.172043 0.169995 \n4 Equalized_Odds_FPR -0.050244 -0.544379 -0.474526 \n5 IQR_Parity 0.005891 0.024819 0.024473 \n6 Jitter_Parity 0.012700 0.188123 0.180307 \n7 Label_Stability_Ratio 0.976420 0.670363 0.674450 \n8 Label_Stability_Difference -0.022597 -0.329225 -0.316996 \n9 Overall_Uncertainty_Parity 0.040767 0.413723 0.388156 \n10 Overall_Uncertainty_Ratio 1.104972 2.206651 2.034723 \n11 Statistical_Parity_Difference 0.009831 -0.087052 -0.114095 \n12 Disparate_Impact 1.009225 0.919441 0.894083 \n13 Std_Parity 0.005651 0.021084 0.020138 \n14 Std_Ratio 1.153395 1.584622 1.534453 \n15 Equalized_Odds_TNR 0.050244 0.544379 0.474526 \n16 Equalized_Odds_TPR -0.011139 -0.172043 -0.169995 \n17 Accuracy_Parity -0.018083 -0.141022 -0.114202 \n18 Aleatoric_Uncertainty_Parity 0.045674 0.324489 0.334561 \n19 Aleatoric_Uncertainty_Ratio 1.143410 2.123939 2.073617 \n20 Equalized_Odds_FNR 0.010383 0.078048 0.097373 \n21 Equalized_Odds_FPR -0.106491 -0.308592 -0.370211 \n22 IQR_Parity 0.002433 0.018288 0.018191 \n23 Jitter_Parity 0.002983 0.028429 0.029001 \n24 Label_Stability_Ratio 0.996168 0.960424 0.957890 \n25 Label_Stability_Difference -0.003788 -0.039298 -0.041702 \n26 Overall_Uncertainty_Parity 0.045877 0.326364 0.336291 \n27 Overall_Uncertainty_Ratio 1.143620 2.127507 2.076070 \n28 Statistical_Parity_Difference 0.002645 0.056072 -0.019339 \n29 Disparate_Impact 1.002471 1.052682 0.981970 \n30 Std_Parity 0.001735 0.014278 0.013799 \n31 Std_Ratio 1.194368 2.904041 2.608315 \n32 Equalized_Odds_TNR 0.106491 0.308592 0.370211 \n33 Equalized_Odds_TPR -0.010383 -0.078048 -0.097373 \n34 Accuracy_Parity -0.016108 -0.142758 -0.115666 \n35 Aleatoric_Uncertainty_Parity 0.035359 0.333828 0.332150 \n36 Aleatoric_Uncertainty_Ratio 1.112633 2.199608 2.096895 \n37 Equalized_Odds_FNR 0.009581 0.076511 0.093848 \n38 Equalized_Odds_FPR -0.124582 -0.317457 -0.370077 \n39 IQR_Parity 0.005105 0.051159 0.050195 \n40 Jitter_Parity 0.005513 0.083227 0.077578 \n41 Label_Stability_Ratio 0.992528 0.884698 0.892591 \n42 Label_Stability_Difference -0.007186 -0.112511 -0.103852 \n43 Overall_Uncertainty_Parity 0.036434 0.346205 0.344033 \n44 Overall_Uncertainty_Ratio 1.112218 2.203762 2.098884 \n45 Statistical_Parity_Difference 0.002092 0.061916 -0.009914 \n46 Disparate_Impact 1.001948 1.058022 0.990782 \n47 Std_Parity 0.003578 0.037686 0.036663 \n48 Std_Ratio 1.116316 2.418831 2.247181 \n49 Equalized_Odds_TNR 0.124582 0.317457 0.370077 \n50 Equalized_Odds_TPR -0.009581 -0.076511 -0.093848 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", + "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.025564-0.173045-0.150360DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0379920.4094470.383894DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.1019392.2534602.070529DecisionTreeClassifier
    3Equalized_Odds_FNR0.0111390.1720430.169995DecisionTreeClassifier
    4Equalized_Odds_FPR-0.050244-0.544379-0.474526DecisionTreeClassifier
    5IQR_Parity0.0058910.0248190.024473DecisionTreeClassifier
    6Jitter_Parity0.0127000.1881230.180307DecisionTreeClassifier
    7Label_Stability_Ratio0.9764200.6703630.674450DecisionTreeClassifier
    8Label_Stability_Difference-0.022597-0.329225-0.316996DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0407670.4137230.388156DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.1049722.2066512.034723DecisionTreeClassifier
    11Statistical_Parity_Difference0.009831-0.087052-0.114095DecisionTreeClassifier
    12Disparate_Impact1.0092250.9194410.894083DecisionTreeClassifier
    13Std_Parity0.0056510.0210840.020138DecisionTreeClassifier
    14Std_Ratio1.1533951.5846221.534453DecisionTreeClassifier
    15Equalized_Odds_TNR0.0502440.5443790.474526DecisionTreeClassifier
    16Equalized_Odds_TPR-0.011139-0.172043-0.169995DecisionTreeClassifier
    17Accuracy_Parity-0.018083-0.141022-0.114202LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0456740.3244890.334561LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1434102.1239392.073617LogisticRegression
    20Equalized_Odds_FNR0.0103830.0780480.097373LogisticRegression
    21Equalized_Odds_FPR-0.106491-0.308592-0.370211LogisticRegression
    22IQR_Parity0.0024330.0182880.018191LogisticRegression
    23Jitter_Parity0.0029830.0284290.029001LogisticRegression
    24Label_Stability_Ratio0.9961680.9604240.957890LogisticRegression
    25Label_Stability_Difference-0.003788-0.039298-0.041702LogisticRegression
    26Overall_Uncertainty_Parity0.0458770.3263640.336291LogisticRegression
    27Overall_Uncertainty_Ratio1.1436202.1275072.076070LogisticRegression
    28Statistical_Parity_Difference0.0026450.056072-0.019339LogisticRegression
    29Disparate_Impact1.0024711.0526820.981970LogisticRegression
    30Std_Parity0.0017350.0142780.013799LogisticRegression
    31Std_Ratio1.1943682.9040412.608315LogisticRegression
    32Equalized_Odds_TNR0.1064910.3085920.370211LogisticRegression
    33Equalized_Odds_TPR-0.010383-0.078048-0.097373LogisticRegression
    34Accuracy_Parity-0.016108-0.142758-0.115666RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0353590.3338280.332150RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1126332.1996082.096895RandomForestClassifier
    37Equalized_Odds_FNR0.0095810.0765110.093848RandomForestClassifier
    38Equalized_Odds_FPR-0.124582-0.317457-0.370077RandomForestClassifier
    39IQR_Parity0.0051050.0511590.050195RandomForestClassifier
    40Jitter_Parity0.0055130.0832270.077578RandomForestClassifier
    41Label_Stability_Ratio0.9925280.8846980.892591RandomForestClassifier
    42Label_Stability_Difference-0.007186-0.112511-0.103852RandomForestClassifier
    43Overall_Uncertainty_Parity0.0364340.3462050.344033RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1122182.2037622.098884RandomForestClassifier
    45Statistical_Parity_Difference0.0020920.061916-0.009914RandomForestClassifier
    46Disparate_Impact1.0019481.0580220.990782RandomForestClassifier
    47Std_Parity0.0035780.0376860.036663RandomForestClassifier
    48Std_Ratio1.1163162.4188312.247181RandomForestClassifier
    49Equalized_Odds_TNR0.1245820.3174570.370077RandomForestClassifier
    50Equalized_Odds_TPR-0.009581-0.076511-0.093848RandomForestClassifier
    \n
    " }, - "execution_count": 47, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -710,8 +720,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.344905Z", - "start_time": "2023-12-20T23:37:28.320359Z" + "end_time": "2023-12-20T23:45:51.491890Z", + "start_time": "2023-12-20T23:45:51.425043Z" } }, "id": "a286da0406c6401d" @@ -734,12 +744,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 22, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.366716Z", - "start_time": "2023-12-20T23:37:28.341494Z" + "end_time": "2023-12-20T23:45:51.514444Z", + "start_time": "2023-12-20T23:45:51.448954Z" } }, "outputs": [], @@ -751,21 +761,21 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 23, "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.416249Z", - "start_time": "2023-12-20T23:37:28.364856Z" + "end_time": "2023-12-20T23:45:51.561059Z", + "start_time": "2023-12-20T23:45:51.474363Z" } }, "outputs": [ { "data": { - "text/html": "\n
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    \n", "text/plain": "alt.HConcatChart(...)" }, - "execution_count": 51, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -838,27 +848,27 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:37:28.615450Z", - "start_time": "2023-12-20T23:37:28.451027Z" + "end_time": "2023-12-20T23:45:51.759552Z", + "start_time": "2023-12-20T23:45:51.566308Z" } }, "id": "b1249b3994b75555" }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 26, "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:29.081363Z", - "start_time": "2023-12-20T23:37:28.612113Z" + "end_time": "2023-12-20T23:45:52.277604Z", + "start_time": "2023-12-20T23:45:51.759242Z" } }, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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" + "image/png": 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" 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" 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Xy1euXCmXyWTymTNnSss6d+4st7GxkUdHR0vL0tLS5H379pXLZDL5zZs35XK5XP7nn3/KZTKZfOnSpQppXr9+Pcs1l538vO8cOnRIuq4yfxZ9fX3lFStWVHqPVyY2Nlbeq1cv6dgymUxevXp1+YABA+SbN2/Ock4zKLunbN68WS6TyeTLly9X2DY1NVXerVs3uUwmk/v6+mZJo3nz5vKIiAhp+f379+U2NjbyatWqyT9+/Khwfrp37y5tp+y6UpavnPK6bt06hbxevnxZLpPJ5PPmzfvsuZPJZPL69evL5XK5fOTIkXKZTJblfE2YMEFes2ZNeWJionzcuHFymUym8FkaPny4XCaTyXfu3KmwX0BAgNzGxkZuZ2cnj4uLy7EsGd9Btra28vDwcIV0Pv1+ksvl8unTp8tlMpnczc1NnpaWJi0/deqUXCaTyYcOHSqXy+VyNzc3uUwmk1++fFkhzdWrV8tlMpl87dq1nz1HmYkaxn+RAwcOADBmzBjMzc2l5erq6kyZMoUzZ87g5eXFtGnTsq2xSE1NxcXFBV1d3Sy/cu3s7ID0Zp5vsWrVKlatWqV0nYaGBt26dZOaTyD9F+OoUaNISUmhbNmyCtuXL18eY2NjwsLCsqSlr6+fpcmiadOmrF69Otu+KvPnz2f79u20atWKRYsWoa7+90cko/np+fPnfPjwQWo6Nzc35/jx45iYmOSi9PB///d/1KxZkz/++IM///yTqKgoHjx4wIMHD9i6davUnGxlZZWr9LKjq6tLz549pb8LFy5M2bJlefDgAU5OTgojJqtXrw6Q7XlR5tq1a1LzyqeGDx8u9SP09vYmKSkpyyhbFRUVRowYwfHjx7l06RLBwcEK121OTUCfCg8PB1BoZs5w+fLlLE2sAMWKFeOXX35RWKbsmAcPHkRVVVUa2Z7B0NCQqVOn4ujoyN69e2nXrl2u8qpM2bJlFa5VdXV1Jk2axMmTJ/H09GTo0KG5Sie374mJiQnz58+nUqVKqKmpKWxXu3ZtNm7cmOvPeUZznjKDBg1CQ0ND+tvOzg59fX2F68zLy4v4+HjGjh2r8P4DDBkyhJ07d+Ll5cWUKVPQ0NBALpeTkJCAv78/derUAdKvpcWLFwN/d2nJ+Lw+evSIuLg46b2rUaMGJ0+e/Oxo3fy+7xw6dAgAZ2dnhc9iw4YNadKkCadOncoxfxl0dHTYtm0bXl5eeHh4cOvWLaKjozl//jznz59nwYIFdOnShalTpypcv8rY29vj6upKy5YtFZarqqpSs2ZNbt++LX3WMhs1ahSFChWS/q5SpQrdunVjy5YtnDp16psGZ2anQ4cOLFmyhEOHDjF48GBpecY0cV96zNatW3PixAmOHz/OoEGDgPQR6KdPn6Zly5YK71GG9+/fc+rUKSpXrqxwrwWoUKECXbt2ZevWrZw5c4a2bdt+Ng92dnbS9ZudpKQkjh49SuHChRk7dqxCv9WmTZsyfPhwaQYI+f+mU7p58yZ2dnbStn379qVTp05fPFOECBj/RR48eACgtMN/wYIFsbCw4Pr16zx79izb5j1tbW3pZhEaGsqzZ88ICQnhxYsX3L59G0gPKr9F5qk/4uPjOXPmDEFBQdja2rJ8+fIsF7GKiorUhB4WFkZAQADBwcEEBQXh7+8v3cBSU1MVvvxKliypEPAB0qhrZaPDPT09pbmxGjRokGVfCwsLatSowY0bN2jQoAHVq1enbt261K9f/4tHsVWuXJn58+eTmprKw4cPuXbtGpcvX8bPz48nT57Qr18/PDw8vql52NzcPEswoKurC5Al3YwgKTExMdfpDx8+PFejHh8+fAgovy7V1dWxtbXl5cuXPHr0SCFg+DR4yImBgQFhYWFERUVhbGyssO7y5cts3Lgxyz7VqlXLEjB+eszY2FiCgoIoU6aM0ptrtWrVUFNTk8r4tWrVqqXQhAhgampK0aJFefnyJTExMQrdFrKT2/fE1NSUjh07kpaWxtOnT3n+/DkhISE8e/ZMCjhz+znP6X36NNCC9M9gdHS09Hn19/cH0ucmXblyZZbtCxQowIcPHwgKCqJChQr07NmTqVOn0rdvX8qWLUvdunWpV68ednZ2CsG+vb09pUuXxtfXl7p161KrVi3s7e1p0KABZcqU+Wy58vu+8+jRI3R0dKhQoUKWY1evXj3XASOkB3SdO3emc+fOREdHc+PGDa5du4avry/Pnz/nwIEDvH//nvXr1+eYTsWKFalYsSKJiYncu3ePoKAggoODefLkCX5+flJ5P6VsyhdbW1u2bNnCw4cP8yVgNDIyomHDhpw+fRp/f3+srKyIjY3l1KlTWFpafrYv86caNmyIjo4OJ06ckALGc+fOERMTk22w9+DBA+RyOSkpKUqv3eDgYGm73ASMubnnBQcHExMTQ4MGDRR+jGXI/Pnv3Lkze/bsYeXKlezevRt7e3vq1q1LgwYNPvuDSRkRMP6LZAwSyW4qmowvvMyDX5S5ffs2CxYskDr8q6ioUKpUKWrXrs39+/eVTgL7JT6d+mPs2LFMnjwZb29vxowZw+bNmylQoIDCPoGBgcyfP5/z589LxzczM6NGjRo8ffqUqKioLPlSVoua8QtLWRnevXtHw4YN8fPzw83Njfr162epNdy0aRNbtmzhyJEj+Pn54efnx+LFiyldujRTpkz5bJ+oT6mpqWFlZYWVlRX9+/cnJCSEUaNGcf/+fbZt2yaNcPwaOdUm5EefuOxkDET43HUZFxensPzTayAn5ubmhIWFERQUlCVgdHZ2VphYPiQkhCZNmihN59Njfu4zpa6ujqGhodJaly+R3c3bxMSEkJAQoqOjcxUwfglPT09Wr14tfalpaGhgYWFBlSpVCAkJyfXnPKda4Nx8Bj9+/Aik10TnJCoqCoBffvkFY2Njtm/fztWrV9m+fTvbt29HR0eH7t27M27cONTV1dHW1mbv3r1s2LBB6k/m6+vL3LlzqVKlCjNnzsyxHzPk733n48ePCrVymSmrKc8tfX19GjduTOPGjZk4cSLHjx9n4sSJ+Pr68uDBgxz7AiclJbFq1Sp27dolfW719PSwsrKifPnySmvqVVRUlLauZPw4zc+BW126dOH06dMcOnQIKysrTpw4QVxc3FcFqNra2jg4OHDkyBFevXpFyZIlOXr0KMbGxtnObZpxTQYEBBAQEJBt2hnb5SYPnxMZGQlkf0/KTCaTsX//fjZu3CjNx+nt7Y2amhoODg7MnDkzy/0yJyJg/BfJ+EJ59+4dpUqVyrI+46LN7iYF8ObNG5ycnJDL5UycOFH6la6trU1iYiJ79+7N83yrq6szZ84cnj17xs2bN5k+fTqLFi2S1sfFxdG3b1/Cw8MZPHgwTZo0oWzZslJ5sxvA8KUaNGjA2rVr2bRpE0uWLMHFxSVL03mBAgUYNmwYw4YN4+3bt/j5+XH69GlOnTrFb7/9xokTJ7J0ZM6QmpoqNVtmN6q6RIkSTJ06lR49evD8+fMs6zOPyszwuR8AP1rm67JcuXJZ1mdcl8qmGcqt5s2bc+vWLU6cOJGnE9xmfOll91SGtLQ0YmJilH6mPn2vcnqfsluX8aX9LedGmdOnTzNp0iTMzMxYvHgxVapUwdzcHHV1dc6fPy8NIvoeMs7xvn37cj2xeKNGjWjUqBHx8fHcunWLCxcu4OXlhbu7OwULFpSa8AsVKsSECROYMGECL1++5PLly/j4+HDlyhUGDBjA2bNnsw3E8/u+U6hQIWJiYpDL5Vmmw/n0x1N2Tp8+zezZs/nll1+yPOAhQ6tWrbh8+TL79u3j+fPnOQaMCxcuZNu2bdjZ2eHk5ETFihWlH3SLFi1SGjDK5XLi4+Oz/EDN+Mzk9H3zrRo0aICJiQnHjh1jypQpHDlyBE1NzVzV5inTqlUrjhw5wokTJ+jduzfnzp3jl19+ydJSkyHj2u3evTsuLi5fXY4vkXHMT2cEyJC5+wWkN40vWLCA1NRUHjx4wOXLlzl06BCnTp0iNjaWLVu25PrYYlqdf5GM/mHK+jAlJiZy9+5ddHR0pIBG2aTFJ0+eJC4ujqFDh0o3jIxfPRnzAn5rDaMympqaLFy4EE1NTQ4fPqwQUF2+fJm//vqLX375hTFjxmBtbS3dtD98+CD1tfrWfFWuXBk1NTX69++PhYUFp06dUsjHnTt3cHNz486dOwAULVqUjh07smrVKjp37kxycrLUbK+MmpoacrmcwMBAKQ1lMt6XzE2gGf1nPv21/uHDh2xvHD+LnK7LzMuVNc3lVvv27SlYsCB79+7NdnqRDF9ynejp6VGqVCn++usvpdNQ3Lt3j/j4eIXmr4xmok/fq5ymsbh3716WZRnNsJk/g3klo5/X4sWLadu2LWXKlJGaUZ89ewbkz+dcmYzuHHfv3lW6fsmSJaxfv564uDhiYmJYvXq19CVXoEAB6taty6RJk6QmwevXrwPpDwGYPXu2NBVVqVKl6NGjB1u3bqV27dpERUXlWCuU3/cdS0tL4uPjuX//fpZ1yq4HZUxMTHjz5g3Hjh1T+mMyQ8Y9JfNUXcp4eXmho6PDxo0badiwocI9KKfrIqM7VGYZs118a1/snKirq9OhQwfCw8Px9fXl+vXrNGnS5KtraBs0aICenh4+Pj74+voSHx9PmzZtst0+49rN6FbxqVOnTrF06dJcv5+5UbZsWTQ1NXnw4IHS7gG9e/emRo0axMbGsmfPHlxdXZHL5aipqWFtbc2QIUM4ePAgOjo60mclt0TA+C/SpUsXIH1QSUYzE6TPIzVnzhw+fvxIq1atpC+fjC+IzM/WzVj36XM1o6KimD17tpRefihfvjy//fYbALNnz5aq3jPy9OkcYImJiUyfPl26UeZVvtTV1Zk9ezaqqqq4urpKXwwxMTFs2bKFlStXKtyc5XK5dL4+1wfFyckJSG+GVzYXY8Z8XoBC/7qMvmB//vmnwvarV6/+bl/sX6t9+/ZoaGjwxx9/ZOnrt2bNGp4/f46dnR3Fixf/6mMYGRkxb948kpKScHJy4syZM0q3u3LlilQT82mfwex06dIFuVzOrFmzFGp+IiIipGczZ360ZkYtaub3KiUlhXXr1mV7jCtXrij0WUtOTmbOnDmkpKTQtWvXXOXzS2T3mQoICGDDhg1Snr+HDh06oKGhwerVq7PUqu/YsYP169fj4+ODjo4OOjo67N27lxUrVvDixQuFbTPueRk/iF+/fs327duz9F9NTEwkLCwMVVVVzMzMss1Xft93Mt7X+fPnK8w5e/PmzVzP61q1alWqV69OYGAgkydPzjJ3LaQH0N7e3pQtW1Ya3AZ//7DJfP/X0tIiMTExy4CnI0eO4Ovrm2X7DEuXLlXo/3zjxg28vb0xNjbOtvvHl1CW1wwZ33uzZ88mOTn5m/pLZkzrlNElyMzMDFtb22y3L168OHXr1uXBgwe4u7srrAsODmbGjBmsW7dOoZtCTmXJbR5bt25NeHg4q1evVljn6+vLw4cPsbGxQVdXl+vXr7Njxw4OHz6ssN379+9JTEzMtjUsO6JJ+l/E1taWYcOGsWbNGjp27IiDg4M0D2NAQAAVK1Zk8uTJ0vYZ/abOnz/P/PnzadKkiTR34969e3n79i0WFha8f/+es2fPEh8fj56eHtHR0aSkpGTp2J0XBgwYwPHjx3n8+DFubm64ublRvXp1SpcuzYULF+jVqxe2trZ8/PiRc+fOERYWRuHChYmIiCAyMvKL+r3lxNramt69e7Nt2zZmz57NkiVLsLe3p1GjRvj6+tKuXTvs7e1RU1PDz8+PR48e0aJFi8/Oofjrr78SGBjIli1b6Ny5M9WrV6dy5cpoa2sTEhLC+fPniY2NZfz48Qo3927durF7927WrFnDs2fPKFGiBNevX+fly5fIZLIca0p+NDMzM2bMmMHvv/9O165dpXkY79y5w507dzAzM/vmeQwhfYTg2rVrmTJlCsOGDaNkyZLUqlULIyMjPnz4wLVr16TaJgcHB6ZOnZqrdJ2cnLhy5QqXLl2ibdu2NGjQgJSUFHx9fQkLC6Nz584Kc8317NmTM2fOMGPGDK5du0bhwoU5f/48CQkJ2QbFenp6jBgxgiZNmmBmZsaVK1cICAigYcOGn53o+Gt06tSJo0ePMmXKFM6fP0+RIkV48eIF586do2DBgsDffaXyW4kSJaTro2PHjjRt2pRixYrx6NEjLl26hIGBgXR9qKqqMnHiRMaOHUvnzp1p0aIFJiYmvHz5kjNnzmBoaCjNo9e+fXv27t3L/v37efLkCTVq1CA5OZnz58/z8uVLnJycchwlmt/3HQcHB7p06cLBgwfp0KEDjRo1IiIigpMnT1K8eHGlk/Qrs3z5cvr164eXlxd//vkndevWpUSJEiQnJ/PgwQOuX7+OkZERK1euVPiRlHH/nzVrlvSs8C5durBu3Tp++eUXWrZsiYaGBvfu3ePGjRsYGxvz/v17pddFeHg47du3x8HBgb/++gsfHx/U1NRYuHBhnvSXVpbXDGXLlsXW1pbbt29jamr6zV0FWrdujZeXF7dv384yJ6Myrq6u9OrVi/nz53Py5ElsbGz4+PEjPj4+xMTEMHLkSIUHK+RUltyaOHEit2/fZvXq1Vy+fBlbW1vevHnDyZMnMTAwYObMmQD89ttvnD9/XurLWq5cOWmeTLlcrtC3OzdEDeO/zKhRo1izZg2WlpacPXuWffv2Aek1Wvv27VPoKFusWDHGjRtHgQIF2LFjB5cvX6ZIkSJs27aNxo0bc//+fbZv386tW7do0KABHh4etGzZkuTkZKWTieaFjP6MampqeHp6cunSJQoUKIC7uztt27YlODiYbdu2cfnyZaysrNi5c6c0hcWntW/favTo0RQvXpyjR49y+vRpVFVVWbZsGc7OzlL+9uzZg4qKCpMnT5am9ficSZMmsXv3bjp16kRYWBgHDhzA3d2d27dv4+DgwN69exkwYIDCPuXLl8fd3Z0aNWpw7tw59u3bR5EiRdi7d+8XjSb+UX799Ve2b99OvXr1uHLlCrt27SIyMpLBgwfj6emZY03Pl3BwcODEiRP8/vvvFC9enEuXLuHu7s7JkyfR19fHycmJI0eOsHbt2lz/utbQ0GDjxo1MnjyZggUL4unpyYkTJyhdujSLFy9m3rx5CtvXq1dPejLKsWPH8PT0xMrKij179kjB2KcaNWqEq6srgYGB7Nmzh9TUVCZMmMCaNWtyXRP6JerVq8fq1aupWLEiZ86cYffu3bx69Up6alHJkiW5deuWNCAlv2VcH3Xr1uXSpUts27aN4OBgunbtysGDBxUmEG/Tpg3u7u5Uq1aNS5cusWXLFu7evUunTp04ePCg9HnIuG8MGTKEuLg49uzZw8GDBzE0NMTNzS3LhNuf+h73nTlz5vD777+jo6PD/v37uX37NiNGjMj2mcbKmJiY4OnpycyZM7GysuLGjRts3bqVffv2ER0dzbBhwzh+/Djly5dX2G/IkCFSoLV9+3ZiYmIYMWIE48ePp2DBguzfvx9vb29SU1OZPn261H89o6Yxs23btmFhYcHevXs5e/Ys9vb20qjcvKAsr5ll9A3v2LFjtv0Nc8ve3l5q0s6pOTqDmZkZHh4e9OvXj/DwcHbs2IGvry+WlpasXbtWajXLbVlyw9DQkH379uHk5ERYWBjbt2/Hz8+PVq1asX//funeVrZsWfbs2UP79u0JCAjgjz/+4NSpU1SvXp0dO3ZIswDklor8Z2/PEgRB+Je6evUqffr0oV27dgoDvQRByL2pU6dy8OBBfHx8lA74FPKGqGEUBEEQBOEf6dmzZxw9epS6deuKYDGfiT6MgiAIgiD8oyxdupSLFy/y7NkzkpOTGTVq1I/O0r+eqGEUBEEQBOEfpWjRorx48QJjY2MWL1782UnYhW8n+jAKgiAIgiAIORI1jIIgCIIgCEKORMAoCIIgCIIg5EgEjIIgCIIgCEKORMAoCF/B0dERCwsLpa/58+d/dv+rV69iYWFBSEjId8ht/smYoDrzq3LlytjZ2TFs2DCFR1TmhUmTJuHo6JinaX7q3r17DB48mFq1amFlZUWLFi1YvHjxV02w+0/m6OiY42PWpk2bRosWLb44XQ8PD4UnX/wThISEKP2s29ra0rFjR44ePZqnx/se94ePHz/i5uaGg4MDlpaW2NnZMXz48CyP7xSEDGJaHUH4Sq1atVL6eLncPCbM1taWixcvYmhomB9Z+66KFi3KgQMHpL+Tk5N59OgRrq6uDBkyhCNHjqCiovIDc5h7T58+xdHRkd69ezN27Fh0dHR49OgR8+bN4+7du2zbtu1HZ/G7+eWXX5gwYQKBgYHS87EzJCYmcuLECQYPHvzF6bZu3Zr69evnVTa/q5UrV0rPFpbL5YSFhbF+/XqcnZ0xMzP77KNBfyZDhw4lJSWFuXPnYm5uTnh4OBs3bqRXr14cOHAgy3suCCJgFISvpK2tjYmJyVftq6mp+dX7/mzU1NSylKV48eJER0czceJEnjx5ovBot5+Zh4cHpUqVYvz48dIyc3NztLW1GThwII8fP/7HlOVbtWjRAldXVw4fPszo0aMV1p0+fZr4+HiFZ2jnlra2Ntra2nmTye/MwMBA4VovUqQIixYtombNmhw/fvwfEzAGBARw48YNPD09qVy5MpD+iLslS5bQtGlT9u3bx+TJk39wLoWfjWiSFoR8EBUVxbRp06hfvz5VqlShTp06TJs2jfj4eCBrk5ODgwPz58+ndevW1K5dm2vXruHo6MiiRYuYMmUKNWrUoFq1aowbN06haTQwMJCBAwdia2tLvXr1GDduHGFhYdL6oKAg+vfvT/Xq1bG1taV///48efJEWn/u3Dk6d+5M1apVqVOnDpMmTSIqKipPzoGmpiaQ/ixmgKSkJObPny81gdWqVYtRo0bx4cMH4O9mPx8fH3799VcsLS2lZ2tnZ86cOdSsWZN79+7lSXlUVFQIDQ3l2bNnCsvt7e05evQoZcqUAZQ3jWdellGWo0eP0rFjR6ysrOjcuTOBgYGsXr0ae3t7atWqhYuLCz/rzGba2tq0adOGI0eOZFnn6elJw4YNiYiIYPDgwdSsWRNLS0uaNGmCu7u7tN3KlSvp3bs3Y8aMoVq1ari6umZpkg4ICPhsGn379mXDhg00aNAAKysrevfuTWBgoLRNbGwsrq6u1KtXD1tbW3r37s39+/el9bdu3aJXr15YW1vTqFEjXFxc8qyLgaqqKurq6qir/13/cvr0aX799VdsbGyk9/7ChQvS+tx8tjO7ceMGtra2LF26FIDw8HBGjhxJ7dq1sba2pnv37ly7du2L8gzpn5fM15+GhgY7duxg0KBBgPKm8U+XOTo6Mn/+fJydnaX70O7du7l58yYdOnSgatWqdO/enaCgoFznT/g5iYBREPLBpEmTePjwIatWrcLHx4fJkyfj5eWVY/CzY8cOpk2bxqZNm6Saiq1bt2JsbMyBAwdYuHAhZ86cYevWrQC8e/eOnj17UqpUKQ4cOMC6deuIiYmhW7duxMXFATB27FhMTU05ePAg+/fvR1VVleHDhwPw4cMHhg8fTpcuXTh27BirVq3i+vXrLFiw4JvL/+TJE9asWYOVlZUUZC1YsICTJ0/i5uaGj48Pbm5u+Pn5sXbtWoV9582bx5AhQzh+/DiNGjVi5syZSvtCLliwgEOHDrFlyxasra3zpDzdunVDXV2dtm3b0r17d5YsWcKFCxdITU2lfPnyaGlpfdF5WLp0KVOmTGH//v18/PiRHj16EBQUxPbt2xkzZgy7du3izz///KI0v6cuXboQHBzM7du3pWVhYWFcvnyZLl264OTkRKFChdizZw9HjhyhZcuWzJ8/n0ePHknbX79+HWNjYw4dOpQlyI6Pj89VGjdu3ODmzZts2LCBXbt2ER4ejouLi7R+9OjRnD9/nnnz5uHl5YW5uTlOTk5ERUXx+PFj+vXrR/369fH29mbRokU8ePAAJyenbw7Wo6KicHNzIz4+nrZt2wJw//59RowYQZs2bTh8+DD79u3D0NCQCRMmkJSUJO2b02c7szt37jBo0CD69evHmDFjAJg5cyaJiYns2LGDw4cPU6ZMGYYNGyZ97j+nfPnyODg4sGzZMho3bsyUKVPw8PDg3bt3mJubY2Rk9EXnYfv27VSqVAlvb2+aNGnC7NmzmTlzJlOmTGHHjh389ddfLF68+IvSFH4+oklaEL7S4cOH8fHxUVhWvXp1Nm3aRN26dalZs6ZUk1KiRAl27NhBQEBAtuk1bNgQe3t7hWXly5dn7NixAJQuXZq6detKX967d++maNGiTJs2Tdp+2bJl2NnZceLECTp37syrV6+wt7fHzMwMDQ0N5s6dy/Pnz0lLS+Pdu3ckJSVRvHhxzMzMMDMzY926daSmpn7ReXj9+rXUrwvSaxL19PRwcHBg/PjxUm2GlZUVLVu2pEaNGkB6E5i9vX2Wc9K3b1+aNGkCwJgxY9i5cyd3797F3Nxc2mbp0qUcPHiQrVu3Sk1qeVGeUqVK4eXlhbu7O2fOnGH9+vWsX7+eggULMn78eLp27fpF58bJyYlatWoB0KxZM7Zv386sWbMoUKAA5cqVY+XKlTx9+hQHB4cvSvd7sba2RiaTcfjwYek99vb2xsjIiKpVq9KnTx969eqFrq4uACNHjmTTpk08efKESpUqSemMHDkSfX19IL22L0N8fHyu0khJSWHBggUYGBgA0L17dxYuXAjA8+fPOX/+PJs3b6ZevXpAekBVsGBBIiIi2Lx5M3Xr1mXIkCFA+udo8eLFNG3alGvXrlG7du1cn4+BAweipqYGQFpaGikpKVhbW+Pu7i7lVU1NjenTp9OzZ09pvz59+jBw4EDCw8MpVqwYkPNnO8P9+/eZNm0a/fv357fffpOWv3r1CplMJnWXmDp1Ku3atZPylhurVq1i7969HD58mEOHDnHw4EFUVFRo1aoVrq6u6Onp5TqtSpUq0b9/fwB69+7Nnj17cHR0lM5tq1atOH36dK7TE35OImAUhK/k4OCAs7OzwrKMvlk9e/bk7NmzeHp6EhQUxLNnzwgJCaFs2bLZpleqVKksyz7dXl9fn48fPwLw8OFDnj59qhCsQfqAhIzmujFjxjB37lx27dpFrVq1qF+/Pm3btkVVVZVKlSrRtm1bhgwZgomJCXXr1qVRo0Y0a9bsi85DkSJF2L59O5AePLq5uaGjo8PYsWMVBvV06NCBy5cvs2jRIoKCgnj+/DkvXryQAsgMmTvbZwQZycnJ0rI7d+5w/fp1DA0NpS9fIM/KU6xYMaZOncrUqVMJDg7m8uXL7Nq1i+nTp2NqakrDhg1znVbm91RHRwdjY2OFQVHa2toKtU4/oy5durB+/XqmTJmCuro6Xl5edOrUCWNjY3r27MmRI0d4+PAhr1694vHjx0B6MJXByMhIeh8/ZWhomKs0jI2NpWAR0q+LjGsi4wdH5v6DWlpaUh+8hw8f8vLlyyyfE0jv0vElAePs2bOpWrUqCQkJ7N27l6NHj9K/f3/s7OykbSpVqoSBgQEbNmzg+fPnvHz5UipT5h8vOX22M4wfP57k5GTMzMwUlg8fPpzx48fj4+ND9erVqVevHm3btv2iGnA1NTV69uxJz549iYmJ4caNGxw/fpxDhw4hl8tZtmxZrtMqWbKk9P+M6zvzDzxtbW2Fz7DwzySapAXhK+nq6lKqVCmFl6mpKWlpaQwePJjZs2ejrq5O69atWb9+PdWqVcsxPWUDATL6ASqTlpaGnZ0dXl5eCi8fHx/p136vXr04f/4806ZNQ19fnxUrVtCmTRvev38PwOLFizl+/DgDBgwgIiKC8ePHS/vmlrq6ulT+OnXqsHnzZoKDgxk0aJBCMPT7778zZswYkpOTcXBwYPHixbRp0yZXZc7cdKijo8Pu3bvR1tZm9uzZCtt9a3kWLFjAlStXpL/Nzc3p1q0b+/fvp2jRopw7dy7bfVNSUrIsy9yvDf7uO/ZP0r59e6Kjo7l06ZL0I6VLly6EhYXRvn179u/fj6mpKT179sTT0zPL/jkNcMltGjl9Dj49x59KS0ujXbt2WT4nJ0+epF27djnu+ylTU1NKlSqFhYUFv//+Ow4ODowePZqbN29K21y7do0WLVrg7+9PxYoVGT58uFQbmtsyZfjtt9/o168f8+bNU+ib3KxZMy5cuICbmxtmZmZs2bKFli1b8vTp01yV4+TJk6xZs0b6W09Pj0aNGjF//nz69euX43WurMY+o59yZv/Ea13ImXhHBSGPPXr0iPPnz7N8+XKcnZ1p3749JUuW5NWrV3k6wKFChQoEBgZSrFgxKWAzMDBg7ty5BAQEEB4ezqxZs0hOTqZz584sXLgQb29vwsLCuHbtGnfv3mXu3LmULVtWGlQwd+5c/Pz8CA8P/+p8GRsbM2fOHB4+fMiKFSsAiIiIYO/evcyYMYPJkyfTuXNnKlWqxPPnz7/4nMhkMmxtbZk5cyZHjhyRmrryojxXrlxRGHCRQVNTE21tbalvl4aGRpYBCi9fvvyicvxTGBoa4uDgwLFjxzh69Cg1a9akVKlSHDlyhMjISHbv3s2wYcNo1qyZNMAot+9pXqSRUSPt7+8vLUtJScHBwYETJ05QoUIFnj17pvDDLiUlhXnz5vHmzZsvORVZTJs2DVNTUyZMmCANaHN3d6d27drSYJ26detKx/nSa71t27aMHDkSPT09Zs6cCaR3+Zg3bx7BwcG0bt2a2bNnc/r0aVRVVfH19c1Vum/fvmXNmjVKy1+wYEGF6xxQuNbF4JX/LhEwCkIeMzY2Rl1dnePHjxMcHIy/vz+jR48mLCwsT5sfe/bsSXR0NM7Ozjx+/JjHjx8zZswY/P39kclkGBgY4Ovry7Rp03j06BHBwcHs2bMHDQ0NLC0t0dPTY9euXSxcuJCXL18SEBDAsWPHKF26NIULF/6mvDVs2JD27duzZcsWHj58iJ6eHvr6+pw5c4aXL1/y5MkTpk+fzoMHD776nDRo0IC2bdsyc+ZMoqKi8qQ8Y8aM4fLly4waNYrr168TGhrKtWvXmDBhArGxsXTr1g1Ib/58/Pgx3t7eBAcHs3r16hz7p/7T/fLLL/z555/4+Pjwyy+/AOnzb8bHx3PixAlev37NxYsXpT55uX1P8yKNMmXK0Lx5c1xcXPDz8+PFixdMnz6dxMREatWqhZOTEw8fPsTFxYXAwEBu377NuHHjCAoKonTp0l9+MjLR1dXF1dWVkJAQli9fDqR3aXjy5Ak3btwgJCSEgwcPSuu+5lovUKAALi4unD59miNHjqCpqYm/vz/Tp0/nzp07hISE4OHhQVxcnNJmd2U6d+5MyZIlcXR0lK7hx48fs3PnTjZs2CD1l5TJZOjo6LBhwwZevXrFhQsX2LJlyxeXQfh3EAGjIOQxU1NT3NzcOHv2LK1bt2bUqFGYmprSt29fhak+vpW5uTk7duwgNjaWHj160Lt3bzQ0NNi2bRuGhoaoq6uzceNGVFVV6du3L23atOHy5cts2LCBkiVLSoMu/Pz86NixIz169EBNTU3a51tNmTKFggULMm3aNFRVVVm+fDkBAQG0a9eOAQMGEB8fz9ixY3n27JlUO/Olpk6dSnJyMrNnz86T8jRo0IDt27eTlJTEqFGjaNGiBWPGjEFFRYU9e/ZgbGwMpDfT9urVi9mzZ9OhQwdev37N//3f/31VGf4J6tWrh46ODpGRkdLTXVq2bEn//v1xc3OjVatWzJ07l19++YWaNWsq1PblJC/SAJg7dy41a9Zk1KhRdO7cmTdv3rB582YMDQ2xsbFh06ZNPHr0iE6dOjF06FDKlCnD1q1bc9Us/Dn29vZ07tyZbdu24e/vz8iRI7GxsWHIkCF07NiR/fv3M3fuXLS1tb+oTJnVq1ePDh064OrqSnh4OEuXLsXc3JyhQ4fSsmVL9uzZw6JFi7L0B85Oxo+rJk2asHr1atq0aUOPHj04evQoCxcupFOnTtJ2Cxcu5OHDh7Ru3Zrly5czceLEryqD8M+nIv9ZJwETBEEQBEEQfgqihlEQBEEQBEHIkZhWRxCELDZu3KgwilKZKVOm8Ouvv36nHH2bf1t5hLzx7t07WrZsmeM2VlZW/5hniP/byiP8XESTtCAIWURFRREZGZnjNkZGRl80ue+P9G8rj5A3UlNTFR57p4yWlhZFixb9Tjn6Nv+28gg/FxEwCoIgCIIgCDkSfRgFQRAEQRCEHImAURAEQRAEQciRCBgFQRAEQRCEHImAURCEfNGkSROaNGnyo7MhCPlKXOfCf4UIGAVBEARBEIQciYBREARBEARByJEIGAVBEARBEIQciYBREARBEARByJEIGAVBEARBEIQciYBREARBEARByJEIGAVBEARBEIQciWdJC/8Z9dqd+9FZEIR8V756xR+dBUHId1tnmuZr+vU7XJD+f+FQ/Xw91j+F+o/OgCAIgiAIws9ETUOER58SZ0QQBEEQBCETDS3NH52Fn44IGAVBEARBEDJR19D40Vn46YhBL4IgCIIgCJmoaahLr2/x7t07LCwssrw8PDwAePToEb1798bGxgYHBwe2bduWF9nPF6KGURAEQRAEIRMNrbypYXz8+DFaWlqcPn0aFRUVabm+vj4RERH069cPBwcHXFxcuHPnDi4uLujq6tKlS5c8OX5eEgGjIAifpamhwsl99VBXV2yUiItPpXnXiwA0rmtMzy7mlDLTITo2hRt3I1n3x3MiIpNzTLumbWEGOZamTEldPkQm4Xn0Nbs9Q/KtLIKQHQ11WDu5COpqKgrLE5LSGDI3DIDSxdXp3lyf0sXViU+Uc/FOAl6+MaSm5px2lXKadHHQw6yIOh9j0jhzPY4Tl+PyqyjCN1LPo0EvAQEBlC5dmiJFimRZ98cff6ChocGsWbNQV1enXLlyvHz5kg0bNvz3AkYHBwdCQ0OlvzU0NDA2NqZhw4aMGjUKQ0PDPDtOp06dGDFixGe3dXR0xMzMDDc3tzw5toWFRY7rO3XqlGfHys6NGzfYsmULd+7cITY2lhIlStCxY0f69OmDpmZ6x91JkyYRGhrK9u3b8zUvACEhITRp0oRt27ZRu3ZtIiMjGT16NDdv3sTCwoKGDRvi6enJ2bNn8z0vQt4oU0oXdXVVXBY94vXbeGl5alr6v03qm+AyoTJex1+zYXsQRoU0GdC7NCvmVKX/6JskJSufvauKhT4Lplty5mIYm3YEYV3ZgKF9y6KmpsKOA8Hfo2iCIDEroo66mgrrD0bxV8TfEWBaWvr1a1JYjQl9CvMsOJk1+6MobqxOlyZ66BVQ4Y8j0dmmW66EBmN6FuLa/QQ8/4yhQkkNujbTQ00Vjl4UQePPSF1DLU/SefLkCeXKlVO67saNG9SqVQt19b9DMTs7O9avX8/79+8xNjbOkzzklXyvYXRycsLJyQmAhIQEAgICWLhwIb1792bv3r3o6+t/8zEOHDiAlpZWrrZduXIlamp5cyEAXLx4Ufr/sWPHmDt3rsIybW3tPDuWMtu3b8fNzY0+ffowbNgwChYsyK1bt5g/fz7Xr19n7dq1qKp+366qxYoV4+LFixgYGADg7e3NjRs32LVrF6ampujp6dGrV6/vmifh21Qoq0dKShq+l8JITska/Dn+WpLL18NZtOaptOxVaBwbFlfDvqYRvpffK03XqWdpnj6PYfaSxwBcvRWBupoKjr+WZJ93KElJaflTIEFQomRRDVJS5Vx/mECKkhrD1nV1iE+Us3xPJKmpcO9pEonJchxb63P4QiwfopRfrx0b6fLyTQobPD8C4P8sCTVVFdrW1+WkXxzJKflZKuFrZG6SbtKkSY7bnjlzJtt1AQEBFC5cmF69evHixQtKlSrF0KFDadCgAW/fvkUmkylsn1ET+ebNm58uYMz3SEJHRwcTExNMTEwwNzenSZMmuLu78+bNGzZt2pQnxzA0NERXVzdX2xYqVChPgtQMGWUzMTGR0lW2LD88fvwYNzc3JkyYwMSJE6lSpQrm5uZ06NCB5cuX4+vry7Fjx/Lt+NlRU1PDxMREqt38+PEjJiYmWFtbY2pqiq6ubp7VLgvfR4UyerwMiVMaLKqowI07EXj7vFFY/jIkvebErFgBpWlqqKtga1WI836KweSfl9+jq6OOdWWDPMq9IOROyaLqvHmfojRYBLAqr8W9p4kKzc83HiagqqqCVTnllRbqalCxtCa3HicoLL/xMIECWqrISorpW35G6upq0utrpaSk8Pz5c6KiohgxYgQbNmzAxsaGQYMGceXKFRISEqTvyQwZlV+JiYnflP/88EP6MBYvXpxmzZpx9OhRxowZQ3R0NAsWLODUqVMkJydTpUoVxo8fj5WVlbTPhQsXWLVqFY8fP8bAwIBOnToxcuRI1NTUFJqk4+PjmT17Nr6+vnz8+JFy5coxbNgwmjdvDmRtkr59+zZLly7lwYMHqKur4+DgwIQJEyhcuDCQ3tzdq1cv7ty5w8WLF9HU1KRdu3ZMmjRJoRo5J46OjpQuXZrHjx/z4sULfv/9d9q3b8/BgwfZtGkToaGhmJmZ0b17dxwdHaUawXfv3uHm5saFCxdQU1PD1taWSZMmUbp0aQD279+Pvr6+0tq6mjVrsnXrVqpUqaI0Tzdu3GDFihXcv3+fpKQkzM3NGTJkCB06dAAgPDwcFxcXrl69Snx8PJUrV2bs2LHUqlULgHv37uHm5sajR49QV1fHzs6OyZMnU7x4cYUmaU9PTzw9PYH05vt58+YRGhqq0CT9uXJOmjSJuLg4YmJiuHPnDkOHDmXgwIG5OvdC3qhQVo/UVDlLZllhVcmA5OQ0/rwUxir358THp7LK/XmWferbpf86fvEqVmmaxYsWQFNDlVeh8QrLQ1+n/13SrAA37kTkcUkEIXsli6qTlgbOjoWoYK5Jcqqc6w8S2HsyhtQ0OcaF1HgbrhhNRsfJiUtIo6ix8sDCpLAaGuoqWfZ79yH976LGajzI+vERfjC1TE3SOdUg5kRdXZ2rV6+ipqYmtTZaWlry9OlTNm/ejLa2NklJSQr7ZASKOjo6X5nz/PPDptWRyWQEBwcTExPDwIEDCQ4OZv369ezbtw8bGxt69OjBw4cPgfSgbtCgQVSvXh0PDw9mz57Nnj17WLNmTZZ0ly9fzpMnT9iwYQPHjh2jQYMGjBkzhpCQrJ3o7927h6OjIxUqVGDfvn0sX76cu3fv0r9/f1Iz/YRcvnw5NWvWxNvbmwkTJrBjxw6OHDnyReXdv38/ffr0YdeuXdSvX5+9e/eyYMEChg8fztGjRxk9ejQbN25k0aJFAMTFxeHo6AjAjh072L59O4ULF6Zr1668e/cOgPv372NtbZ1t4FqnTh0KFiyYZfm7d+/o378/VlZWeHp64uXlhbW1NVOnTuX9+/TanpkzZ5KYmMiOHTs4fPgwZcqUYdiwYcTFxZGamsrgwYOlc7J161Zev37NlClTshxr6tSpODk5UbRoUS5evEjr1q0V1uemnAA+Pj7Y29tz8OBB2rZt+0XnXvh25UrrUqJYAS76heM8059t+17RtEERFs2wQkUl6/bFi2rzm1NZAgKjuXLjg9I09XTTb8hxcYrtcXHx6X/r6ogxecL3ZW6qThFDNW4/TmTxzgiOnI/FzkqbMb0KoVsg/esyPjFrLXtCkpwCWko+CICOtvL9EpLS/85uP+HH0tRUk17fQldXN0vXtAoVKvDu3TuKFi3KX3/9pbAu429T0/x99OHX+GF35IxA5uzZs9y5cwc/Pz8KFSoEwNixY7l16xbbtm3Dzc2N7du3U7VqVSZMmABAuXLlmDVrFuHh4VnSffXqFbq6upibm1OwYEFGjRpFzZo1pf50mbm7u2NhYcH06dOldJcsWUKHDh24ePEiDRs2BKBevXr06dMHAHNzc7Zv386tW7fo2LFjrstbqVIl2rVrJ/29Zs0ahg4dSps2baR0Y2JicHFxYdSoURw9epSPHz+ycOFCKSCcM2cOV69eZd++fYwYMYLIyEjMzc1znYcMiYmJjBgxgv79+0vD/AcNGoSXlxdBQUEYGxvz6tUrZDIZ5ubmaGtrM3XqVNq1a4eamhoxMTFERERQpEgRzMzMMDc3Z9myZUrfD319fXR0dKRm6k/lppwABgYGDBgw4IvLKnw7FRWY5HqfyI/JvHiV3sx890EU4RFJzHCuRO1qhvjd/DsoLFmiAEtnWZOaKmea20Oye1q9irJIMxPxmHvhe1JRgeW7I/kYm8brsPQKg4CXyUTFpDG4iwGVSufcdJz9dZ7zccVl/nPKi0EvT58+pVu3bqxdu5batWtLy+/fv0/58uWpVKkSe/bsITU1VRpb4efnR5kyZTAyMvrm4+e1HxYwRkenjygLDg5GLpfTuHFjhfVJSUlS1WxAQAB169ZVWN+iRQul6Q4cOJAhQ4ZQp04drK2tqVu3Lu3atVPal1BZuhUrVkRfX58nT55IAeOnI5z09fVJTs55qpBPlSpVSvr/hw8fePv2LUuWLGH58uXS8rS0NBITEwkJCeHhw4dERUVRs2ZNhXQSExMJDAwE0vtuRkZGflE+AEqWLEnnzp3Ztm0bAQEBvHr1iseP0wcdZNSsDh8+nPHjx+Pj40P16tWpV68ebdu2RUtLCy0tLQYMGICrqysrVqzAzs6Ohg0b0qpVqy/OS27KCYrnT/i+5HK4fT8qy/IrN9J/IJQvoysFjLaWBsyZUoX4hFRGTr3L67cJWfbLEPu/mkWdAoo3Zp3/1SzGxIqRAML3I5fD46Cs9/W7T9O/h4wLp1+n2ppZI8ACWirEJSiP/OIS0pTul1GzmN1+wo+lrvHtDbDlypWjbNmyzJo1CxcXFwoXLsy+ffu4c+cOBw8exMjIiE2bNjF16lQGDBjAvXv32Lp1Ky4uLnlQgrz3wwLGBw8eULp0aTQ0NNDT05NmPc8sozNobvsKAtja2nLu3DkuXbrElStX8PLyYu3atWzatIk6deoobJtdDYZcLkcj02OBPu2UmtO+2clcJZ2Wln4DmTx5Mvb29lm2LVasGGlpaZQpU4a1a9dmWZ/Rt8HW1pYDBw4o/DrJzNnZmWrVqtGzZ0+F5c+ePaNnz55UqVIFe3t7mjdvTuHChfn111+lbZo1a8aFCxe4cOECly9fZsuWLaxatYp9+/ZRoUIFnJ2d6dmzJ+fOnePKlSu4urqyadMmvLy8vui85KackP+jzYXsGRlqYl/DkGu3I3gX9ndHbK3/NdVERKV/yTZtYMLU0RV5GRKH80x/3n9IUppehtA38aSkyrMMiinxv79fBovpRoTvp5C+KlUraOEfmKgw2llDPT2wi4pO5cPHVEwNFe+1+roqFNBS5c175T9wwiJSSU2TZ9mvyP/+zm4/4cfSyIMaRlVVVdatW8fixYsZPXo0Hz9+pHLlymzZskUaHb1p0ybmzJlDp06dMDExYcKECXTq1Ombj50ffkgfxrdv33LmzBnatWuHTCYjJiaG5ORkSpUqJb02btwodTQtV64c/v7+Cmn88ccfCgFOhhUrVnDz5k2aNGnCtGnT8PHxwdzcHB8fnyzbWlhYcPPmTYVljx8/JiYmJtt5k/KCkZERhoaGBAcHK5T5wYMHLFu2DEjv4/n69Wv09fWl9cWLF2fx4sVcv34dgC5duhAbG8uOHTuyHOPq1ascPnwYPT29LOv27NmDkZERW7ZsYeDAgTRs2FDquyiXy0lKSmLevHkEBwfTunVrZs+ezenTp1FVVcXX15fnz58zY8YMjIyM6NGjBytWrGDTpk0EBgZKNZW5lZtyCj+WupoKE0dY0KFlMYXlDvVNSEmVc+9BFHbVDZk2thL3H39k2MQ7nw0WAZKS5dy9H0lDe8WuCo3sjYmOSeHh0+zntROEvKaqCv3aF6RxdcUfMLUttUlNk/PkVTL3A5OoKtMi88DZGpXS1z98ofyaT06BJy+TqV5JcRR1jUraxCWk8Tz0y1qrhO9DXUNVen0LY2Nj5s2bx8WLF7l37x579uyhRo0a0npra2v27t2Lv78/Z8+epXfv3t+a9XyT7wFjXFwcYWFhhIWFERwczOnTpxkwYAAlSpSgX79+1K9fn0qVKjFmzBj8/Px4+fIl8+bNw8PDQwraBgwYwJ07d1i+fDlBQUGcO3eONWvW0KhRoyzHCw4OZsaMGVy5coXQ0FB8fHx4/fo1tra2Wbbt168fT548wdXVlcDAQK5evYqzszOVK1fOUhuZl1RUVBg4cCDbt29nx44dvHr1ilOnTjFz5ky0tbXR1NSkffv2GBgYMHLkSO7evUtgYCCTJk3i/Pnz0mTh5cqVY9SoUbi5ubFgwQJpFPauXbsYMWIEzZo1k/pIZla0aFHevn3LuXPnCA0N5eTJk8ycORNI7wqgqamJv78/06dP586dO4SEhODh4UFcXBy2trYULlyYo0eP8vvvvxMYGMiLFy/w9PTEwMCAsmXLftG5yE05hR/rXVgiR0+9oUcnc/6va0mqWxeiX/dSDO1bFo8jobwLS2DSCBnx8Sn8se8VZUrqUMVCX3qZGKXX0Guoqyj8DfDHvldUlunjOrEydtUNGdCrND06m7N9/ysSE8UcjML38yEqjfO342lVV5d2DXSpVEaTDg11+bWpHmeuxfEuPJXjl2IpqKvK2N6FqSrTpEUdHXq21Mf3ZrxUK6mulj5Rd+GCf3+9Hj4fQ9kSGvz2qwFW5TXp3FiXVnV1OHwhliQRL/6UNNRVpJeQLt+bpN3d3XF3dwfSn/RSrFgxWrdujZOTkzR3oru7OwsXLmT06NHEx8dTrlw5Vq1aJQVtlSpVYvXq1axYsYKNGzdSpEgR+vTpw9ChQ7Mcb8aMGcyfP5/x48cTGRmJmZkZzs7O0nQxmVWtWpVNmzaxbNkyOnbsiJ6eHk2bNmXcuHEKTdL5wcnJCS0tLWnibWNjY7p27crIkSOB9H6SO3bsYMGCBdKo7SpVquDu7q5Q+zlo0CDKli3L9u3b8fDwICEhAXNzc4YNG0bPnj2VNlX36dOH58+fM2HCBJKSkihdujRjx45lxYoV+Pv706BBA5YuXcq8efMYOnQo0dHRlC1blkWLFkm/jDZu3MjixYvp2rUrqamp2NjYsGXLFvT09L6oX2Vuyyn8WIvWPOX12wRaNDalT7dShL1PZPPOIHZ5BGNrVQhjo/Tak2Wu1ln2dd8VhPvulxgZarJ+UTXpb4Bb9yKZNu8BTj1LM3dqFd6HJ7Jmy3P2eIlHAwrf37YjHwmLSMXeWpv2DXT58DEVzz9jOP6/R/i9eZ/Kou0RdGuux/CuhYiOS8PnShyef8ZIaRTSV2X6AEO8fGPw8k2fUurRi2RW7Y2iU2NdRnYvRER0KvtOxnDiiuh28bPSyIM+jP82KnIxFFH4j6jX7tyPzoIg5Lvy1Sv+6CwIQr7bOjN/p51ZcPDvFo4JXUTwCD9w0IsgCIIgCMLPSF00RWchAkZBEARBEIRMNPO3V9o/kggYBUEQBEEQMlFXEzWMnxIBoyAIgiAIQiZfMP3zf4Y4JYIgCIIgCJloiugoC3FKBEEQBEEQMlFXyzyBjGieBhEwCoIgCIIgKBABY1YiYBT+MyafGPSjsyAI+a5IROEfnQVByH8z/fI1eQ01MUX1p0TAKAiCIAiCkIm6CBizEAGjIAiCIAhCJuqqImD8lAgYBUEQBEEQMtFQS/v8Rv8xImAUBEEQBEHIRF1VBIyfEgGjIAiCIAhCJiJgzEr1R2fgazk4OLBy5cp8S3/SpEk4Ojrmalu5XI6npyfh4eEAeHh4YGFhkat9Q0JCsLCwyPKytbWlY8eOHD169KvL8LPIKOPVq1d/dFaEr6WiQtkxTjR66EPLj3epf/MQxXu0U9jE4cU52iQ/yfLSMMp51G7xbm1ocOcILT/epeG9Y5g5dszHgghCDlRUKNqjJ1a791P9jC9Vtm7HsFkLhU20S5aivNtCqvmcwfaYD+XnuqFVvPhnky5YsxaVN7pT7bQv1vs8KNqjZ36VQsgD6qop0ktIJ2oYszF16lRSU1Nzte3169eZNGkSZ86cAaB169bUr1//i463cuVKbG1tgfQANCwsjPXr1+Ps7IyZmRk2NjZflN7PpFixYly8eBEDA4MfnRXhK8lmjqKcc38CZq4g8oY/RVo1xHbbIkhL4/Xeo2gYFaZAiaI8mjCfD5dvKuybEvkx23SLdmqOzbZFvFi5jTCfCxTt0BQb9/mkJSbxZt+x/C6WICgwGzCIoj17E7p5A7GPHlGojj3lZriAPI0Pp0+hWaQIldZuIOHVSwJnTkdVWxuzgYORLVnO/T69kSclKk1Xt0oVKixYzIczpwnZtAF966qUGDoc1NR4u2P7dy6lkBvqKqKG8VMiYMyGvr5+rreVyxVHU2lra6Otrf1FxzMwMMDExET6u0iRIixatIiaNWty/Pjxf3TAqKamplA24Z9FtYA2ZUb24cXK7QQu3AhA+J9+GFSrQunhjrzee5SCVSsC8PbQKeKeB+c6bQvXsbw5cIJHzvMAeH/qIhqFDbCYOUoEjMJ3paqlhemv3Xi3f68UxEXfvIGORUVMf+nKh9OnKO40gJTYGJ6MHkFaYnpwmPj6NRXmL0S3YkVi7t1VmraZ00DingbwYrYLAB+v+qGirk5xx76827cv20BT+HHUVHNXYfRf8o9tkv4cLy8v2rdvj7W1NQ4ODqxZs0ahxvDVq1cMHDgQW1tb6tevz5YtW2jWrBkeHh5A1ibpzZs307RpUywtLXFwcGD16tXI5XKuXr1Knz59AGjSpAkeHh5ZmqRjY2NxdXWlXr162Nra0rt3b+7fv//ZMqiqqqKuro56pqegBwYGSvmuV68e48aNIywsTFqfmprK0qVLqVevHjY2NowcOZI5c+ZIZbl69SqVK1dmw4YN1K5dm86dO5OWlsa7d+8YM2YMNWrUoHbt2gwZMoSgoCAp3fDwcEaOHEnt2rWxtrame/fuXLt2TVp/7949evbsia2tLTVr1mTEiBG8fv0ayNoknZqaytatW2nRogVWVla0aNGC3bt3S2ll5PHcuXO0bdsWS0tLWrZsyenTpz97zoS8l5aYxOUGPXix1F1xeVIyqtpaABhUrUTyx5gvChYLlDJDz6IMbw+dUlj+xsMH3Qql0Slf6tszLwi5lJaczKOhg3i7Z5fCcnlyMiqamgAUbtiY90ePSMEiQNyTx9zt2C7bYFFFQwN922pEnPdVWB7x51nUdHXRt66atwUR8oSGSor0EtL9KwPGrVu3Mn36dLp164a3tzejRo1i8+bNuLm5ARAfH0/fvn1JS0tj9+7dLF26FA8PD4KDlX/ZnT17lvXr1+Pi4sLJkydxdnZm7dq1eHt7Y2trK/Wl3L9/P61bt86y/+jRozl//jzz5s3Dy8sLc3NznJyciIqKyrYMUVFRuLm5ER8fT9u2bQF49+4dPXv2pFSpUhw4cIB169YRExNDt27diIuLA2DRokXs3buXGTNmcPDgQUxMTNi+XbHJIzU1lXPnzrF3717mzJlDQkKCFFDu2LGD7du3U7hwYbp27cq7d+8AmDlzJomJiezYsYPDhw9TpkwZhg0bRlxcHKmpqQwePJiaNWvi7e3N1q1bef36NVOmTFFaNjc3N9asWcPw4cM5fPgwvXr1Ys6cOWzdulUhjwsXLmTq1KkcOXIEmUzGxIkTiY2NzfacCfkkLY1o/yckvnsPgGYRI8qNH4hxE3terkv/ci1oU4nkD5FU27uC5u9v0CLiFrY7l6JVNPuaZb2K5QCIfRqksDwu8GX6elmZfCiMIGQjLY34wGekfPgAgHphQ4r2dqRgjZr85emBZrFiqOvrk/T2DSXHOmN7zIfqZ85Rft4CNHJoQdEqboaqpiaJrxS/XxJCQwDQLlky/8okfDU1lVTpJaT71zVJy+VyNm7cSO/evenVqxcApUuXJjIykoULFzJy5EhOnjzJhw8f8PDwoFChQgAsXLiQDh06KE3z1atXaGpqYmZmRvHixSlevDhFihShePHiaGpqSn3zDA0NszRFP3/+nPPnz7N582bq1asHpAdfBQsWJCIiQqo9HDhwIGpqagCkpaWRkpKCtbU17u7uVKpUCYDdu3dTtGhRpk2bJqW/bNky7OzsOHHiBK1atWLXrl1MnjyZZs2aATBt2jRu376dpUxOTk6ULl0aSA90P378yMKFC6X8zJkzh6tXr7Jv3z5GjBjBq1evkMlkmJubo62tzdSpU2nXrh1qamrExMQQERFBkSJFMDMzw9zcnGXLlkmDgDKLiYlh9+7dTJo0iXbt2knvT0hICBs2bOD//u//pG1Hjx5NnTp1ABg2bBg+Pj4EBARIfT2F7694tzbY7lgCwLujfxK60xuAgtYV0TYz5dWmfbxY8Qd6lcohmzESuzPbuVizE6lx8VnSUjfQAyDlY4zC8pTo9B8F6gX18rMogpAtw6bNKDfTFYDISxcJ9zmBTrn0Hzglhv5G7MOHBM6YjkZhQ0oMHkrFFWt40M+RtISELGmp6ekCkBqn+GM39X8/8tV0dfOzKMJXUkMEip/61wWMHz584P3791SvXl1hea1atUhOTub58+c8fPiQMmXKSMEiQMWKFbPtt9i+fXsOHjxIixYtKF++PPb29rRo0YLiuRgZFxAQAKDQB1FLS4vJkycD6c21ALNnz6Zq1aokJCSwd+9ejh49Sv/+/bGzs5P2e/jwIU+fPs0SMCUmJhIYGEhgYCAJCQkKx1JRUaF69eo8fvxYYZ+MYDEj3aioKGrWrKk0XYDhw4czfvx4fHx8qF69OvXq1aNt27ZoaWmhpaXFgAEDcHV1ZcWKFdjZ2dGwYUNatWqV5Xw8f/6c5ORkpe/PH3/8oRBkli1bVvq/nl568JCcnJwlTeH7ibx+jyuNe6FvbYHFzFHUOroJvyaO3Bs6HXlKKlE3/AGIuHSTmIfPsD+3GzPHjrxavztLWiqqOTdwyNNEp3Phx4h9+JBHvw1Bp1x5zAYOQrZ4KaEb1wOQ/OEDz6ZOgv/1XU8IDaby+s0YNW9BmPehLGmpqHzmOpeL6/xnpI74rvnUvy5g/HQASoa0/335qKuro6amJv2dG4aGhhw6dIjbt29z6dIlLl68yLZt2xgxYgTDhw/Pcd/M/Q9zYmpqSqlS6X22fv/9d+Lj4xk9ejR//PGHFFylpaVhZ2fHjBkzsuyvr6/PX3/9BWR/DjLT0tKS/p+WlkaZMmVYu3Ztlu10dHQAaNasGRcuXODChQtcvnyZLVu2sGrVKvbt20eFChVwdnamZ8+enDt3jitXruDq6sqmTZvw8vJSSC83708Gzf/1G8rN/sL3Efc8mLjnwXy4eIOUjzHYbFmAYb0afLh4I8u2EZdvkRz5kYLWFZWmlRIVDYC6vmINS0bNYkpUTJZ9BOF7SHwdSuLrUGLu3iE1Lpay02bw+n8/cKL8rkjBIkDsgwekREejU0H5VGopsenXser/7qUZ1HT+V/MYI7rZ/IzU5KLv4qf+dX0YjY2NMTY25uZNxak9bty4gYaGBiVLlqRixYq8fPmSyMhIaX1gYCDR0dFK0/T29mb37t1Ur16dkSNHsm/fPn799VeOHUsfxamiopJtfsr9rxnD399fWpaSkoKDgwMnTpzIdr9p06ZhamrKhAkTiI9Pb86rUKECgYGBFCtWjFKlSlGqVCkMDAyYO3cuAQEBlCpVCm1tbe7cuaOQ1t27yjtjZ5DJZLx+/Rp9fX0p3eLFi7N48WKuX79OUlIS8+bNIzg4mNatWzN79mxOnz6Nqqoqvr6+PH/+nBkzZmBkZESPHj1YsWIFmzZtIjAwMEvNZrly5dDQ0FD6/piYmIipd35CmsaFMevdAU0TQ4XlUbcfAlCgTAlK9O2CXpUKijuqqKCqqUFS2Ael6cYEvABAp5zi4Bbd//0d8zgwL7IvCLmiXqgQRi1boV5Icd7QuCdPANAsYoo8LQ1VJT9kVdTUFAbCZJYYGoo8JQVtsxIKy7VLpP+d8DIoD3Iv5DW1tGTpJaT7RweML1++5Pz58wqva9eu0b9/f3bs2MGuXbt4+fIlhw8fZtWqVXTr1g19fX3atm1L4cKFcXZ25vHjx9y5c4fx48cDyoO/xMRE5s+fj5eXFyEhIdy4cYPr169LTcMZtXCPHz/OMiijTJkyNG/eHBcXF/z8/Hjx4gXTp08nMTGRWrVqZVs2XV1dXF1dCQkJYfny5QD07NmT6OhoKd+PHz9mzJgx+Pv7I5PJKFCgAI6OjqxYsYLTp0/z4sUL5s+f/9mAsX379hgYGDBy5Eju3r1LYGAgkyZN4vz581hYWKCpqYm/vz/Tp0/nzp07hISE4OHhQVxcHLa2thQuXJijR4/y+++/ExgYyIsXL/D09MTAwEChWRnSm5a7devGihUrOHLkCC9fvmTnzp3s2rULJyenHINv4cdQLaCNzZYFmPf7RWG5SbO6AETd8Mdy+XTKTxyssN60nQNqOgUI91U+YXtc4CvingdTrLPixMhFOzUnJuAF8S9D87AUgpAzVS0tyk6bgUlbxQnpC9aqDUBswGOi796hcINGqGhoSOv1q9dATUeH6Ht3lKYrT0pK369hI4XlhRs1JiU6mtiHD/K0HELeUEtLkV5Cun90k/Thw4c5fPiwwjIzMzPOnj2LpqYmf/zxB3PnzqVo0aIMHDiQ/v37A+lNnZs2bWLWrFl07doVAwMDhgwZwoMHD9DIdCPI8OuvvxIZGcmaNWt48+YNBgYGtGjRAmdnZyC9hq5hw4aMHj2asWPHKvSNBJg7dy4LFixg1KhRJCUlUbVqVTZv3oyhoaE0ulkZe3t7OnfuzLZt22jTpg1WVlbs2LGDxYsX06NHD9TU1KhWrRrbtm3D0DC99mfUqFEkJyczbdo04uPjady4MU2aNCExm1+/kN6cvWPHDhYsWED//v1JTU2lSpUquLu7SzWkS5cuZd68eQwdOpTo6GjKli3LokWLqFGjBgAbN25k8eLFdO3aldTUVGxsbNiyZQt6enoKNbkAkydPpnDhwixatIj3799TunRpfv/9d7p27ZptHoUfJyH4DcFbDlBh2m/Ik1OIuvMQw3o1KDdhEK/c9xPzKJBnCzZiMXMkie/e89fxcxS0klFh+gjeHjpNuK8fkN70rFe5PHGBr0h6HwHA0zmrqbrZjeQPkbw7fBbT9k0o3rU1t3qO/oElFv6Lkt69I+yIN8X7OSFPTSE2IAD9qjYU6+VI2GFvEoKCCFm3hoor1yBbuIS3e3ahXtgQ86G/EfPgPpEXLwDp0+joyGQk/fUXyf+b8uz1H1uwWLaScq5zeH/0CHqWVhTt0YuQdWuyrZkUfiz1tKQfnYWfjor8P9gpLCQkhKCgIGnUMqRPWdOgQQN27twpBUH/RKdOnaJ69epSAAnpI6KLFi3K3Llzf2DOfryjGrl7XKOQlYqGBuXG9cfMsSMFSpmREPyGV5v28XzJ5vT+XCoqlBzUndJDeqJTriRJ4ZG83n2YgFkrSUtI/0I0bFCLOme2c7f/JEK2eUpplxzYjbJjnNA2L0bc82ACF2wgdGfWwQNC7hSpnfOjGIXsqairU7Rnb4xbtUbTtChJf70jzPsQb3fvlPot6llaYTZoCLqVq5CWkEDkhfMEr15Bakx6X0XNosWoesCTUPdNvHbfJKVdqEFDzJwGol2yJEnvw/jL4yDvPpnzUci9mhf98jX98PuXpf8bWdrn67H+Kf6TAWNgYCDt2rVj3LhxNG/enOjoaJYtW8bLly85cuSI0lrGf4ru3btToEABJkyYgJ6eHmfOnGHBggW4u7srjLj+LxIBo/BfIAJG4b8gvwPGiLvnpP8XrtowX4/1T/GfDBgBTpw4wbp163jx4gXa2trUqVOHCRMm5GqqnJ9ZSEgIbm5uXL9+nYSEBMqXL8+QIUOkeRn/y0TAKPwXiIBR+C/I74Dx462/n0BVsJr4/oT/cMAo/PeIgFH4LxABo/BfkN8BY/T1v59lr18z6xPc/ov+0YNeBEEQBEEQ8ppKqhgd/SkRMAqCIAiCIGSimixGr39KBIyCIAiCIAiZiBrGrP7RE3cLgiAIgiDkNZXUZOmVV168eIGtrS0eHh7SskePHtG7d29sbGxwcHBg27ZteXa8vCZqGIX/jIo9yv/oLAhCvjvQ/vDnNxKEf7ia+X2A5LyduDs5ORlnZ2eFh3VERETQr18/HBwccHFx4c6dO7i4uKCrq0uXLl3y9Ph5QQSMgiAIgiAImaXk7TOkV65ciZ6ensKyffv2oaGhwaxZs1BXV6dcuXK8fPmSDRs2iIBREARBEAThp5cpYGzSpEmOm545cybH9devX2fv3r14eXnRqFEjafmNGzeoVasW6up/h2J2dnasX7+e9+/fY2xs/HV5zyeiD6MgCIIgCEJmSUl/v77Bx48fmTBhAtOmTaNYsWIK696+fUvRokUVlhUpUgSAN2/efNNx84OoYRQEQRAEQchEnqmG8XM1iDmZOXMmtra2tGvXLsu6hIQENDU1FZZpaWkBkJj4803rIwJGQRAEQRCETOTJ396H0cvLixs3bnD4sPKBaNra2iR9UoOZESjq6Oh88/HzmggYBUH4PBUVDFp2omDjlqgbGpP8NpTIoweJueKLunERSi3Zku2uH8+fImzTsmzXF7C0xfCXPmialST1YyRRp48Sddwj2+0F4Xs4s3ME4a8f0nX837VLb4NucPPkMj68fYymdkFKVW5K9Waj0NDSzTGt9yH3uXZiAe9DH6ChpUuFap2wdfgNNXXNHPcTfhz5NzZFAxw8eJDw8HCFfosAM2bM4NixYxQtWpS//vpLYV3G36ampt98/Lz2RQGjo6Mj165dU1imoaGBsbExDg4OjB8/ngIFCuRpBjNzcHCgU6dOjBgxIt+OYWGR/fOG161bR+PGjfPt2J/z9OlTQkNDFS6+lJQUdu7cyaFDh3jx4gVaWlpUrlyZQYMGYWdnJ21nYWHBvHnz6Ny5c77nc+XKlXh6enL27FkAzp07h4uLC2FhYYwfP56tW7fm+/so5C3Dzr0p1KYLHzx2kvg8AJ2qNTAdOh7kcmJuXCLEZWyWfQyatkWvdn2iz5/MNl2tchYUGzuDmKsX+HBwBwVklTHq1g8VNTUij+zPzyIJQrae3fHm5cPT6BUqLi2LePcUny39KVKqGo27LyXu4zuun1hE9IdgmvVZm21aHz8Ec2KLEybmNjTuvoTIsOfcOrWMxLgo6nac+R1KI3wNefK3T9y9aNEiEhISFJY1b96ckSNH0r59ew4dOsSePXtITU1FTU0NAD8/P8qUKYORkdE3Hz+vfXENY6tWrZg6dar0d1xcHBcvXmTevHmkpaUxc+bMvMzfDzFlyhRat876sHEDA4MfkJu/DR48mE6dOkkBY2JiIv369ePNmzeMHDkSW1tbEhISOHjwIP369WPBggVK+03kNycnJ3r16iX9vWzZMsqUKcO2bdsoVKgQbdu2lfppCD8/FU0tDFp0IOqktxTExT+8i1bp8hg0b0eM3zkSA58o7KNZujx6tesTvn8bCQEPs03bsHMvEl8+56/1i9PT9b8JauoUbteVKJ9DyPN4LjRB+Jy4j3/hd2QuugaKgxEC7x4BVGjaa5VUo5iWlsrlQzOJiQhFr7CZ0vT8z29CQ0uXpr1XoaauiblFQ9Q1tPE7PJuqjQYpBKXCzyMvmqSzqyU0MjLC1NSULl26sGnTJqZOncqAAQO4d+8eW7duxcXF5ZuPnR++OGDU1tbGxMREYVmpUqW4f/8+x44d+1cEjPr6+lnK+DNavnw5T5484ciRIwqjr6ZOnUpMTAyzZ8/GwcEBXd2cm0vymq6ursIxo6KiaNy4MSVKlPiu+RDyhjw5mVBXZ1I/RikuT0lBVUf5tWXSZyhJocFEnfDKPmF1dQpUtOaD506FxbHXL1K47S9oyyoT/+DON+ZeEL7MRc9pmJW3R01di7cv/m5RS01JRFVNHXWNv1vRtHQKAZAQF5ltwBj69CIlZA0Vmp9LW7bgivcsQp9exKJm1/wpiPBN0vKgSfpzjIyM2LRpE3PmzKFTp06YmJgwYcIEOnXqlO/H/hp5Nq2OlpaWNJfQ69evGTNmDHXq1KFKlSo0aNCAhQsXkpaWBoCHhwfNmjWT/rW0tKRz587cvHlTSi86OpqJEydSo0YN7Ozs2LIlax+p27dv06dPH6pXr07t2rWZPHkyERER0noHBwc2bNjAoEGDqFq1Kg4ODpw+fZrTp0/TokULbGxs6N+/P+Hh4V9U1sjISFxcXGjYsCHW1tZ0796dq1evSutXrlxJ7969GTNmDNWqVcPV1RWAW7du0atXL6ytrWnUqBEuLi7ExMRI+927d4+ePXtia2tLzZo1GTFiBK9fv5bKEhoayqpVq3B0dCQ5OZmDBw/SuXPnLEP1AUaPHs3GjRvR1tbOsi4tLY3169fTokULLC0tqVatGgMGDODVq1fSNufOnaNz585UrVqVOnXqMGnSJKKi/g4YNm/eTNOmTbG0tMTBwYHVq1cjl8ul8js4OADpTeGhoaGsXr1aau53cHBg5cqVUlp//vknnTt3xtrammbNmrFs2TKFjsAWFhasWLGCxo0bU69ePYKCgnL/ZgnfTp5GUnAQqVHpny21goUo1PZXClSxIerM0Syb69VugHb5irzfuQHkadkmq1GkGCoaGiS/DVVYnvwufToJjWLiB4bwfT25vp/3oQ+p0256lnWy6undea4edyMhLoKId0+5c3Y1hU1lGBarqDS9lOQEYiJfY2BcWmF5AV1DNLT0iHoflNdFEPJIWlKK9MpLT548UegaZm1tzd69e/H39+fs2bP07t07T4+Xl745YExJScHX15dDhw7RoUMHAIYOHUp0dDRbtmzhxIkTODk5sWnTJqlPG6TPMbRnzx4WLlyIp6cnBQoUYNKkSVLQMXr0aO7du8e6devYsmULvr6+hIb+/cVy7949HB0dqVChAvv27WP58uXcvXuX/v37k5qaKm23Zs0aWrduzeHDh6lYsSITJkxg3bp1LFy4kHXr1uHv78/GjRtzXd7U1FScnJy4ceMGCxcuxMPDA5lMRv/+/bl375603fXr1zE2NubQoUM4Ojry+PFj+vXrR/369fH29mbRokU8ePAAJycn5HI5qampDB48mJo1a+Lt7c3WrVt5/fo1U6ZMAeDAgQMULVoUJycnVq5cSXBwMJGRkVSrVk1pPk1NTbG2tpb6RWS2bds2Nm/ezKRJk/Dx8WH16tUEBQXh5uYGwIcPHxg+fDhdunTh2LFjrFq1iuvXr7NgwQIAzp49y/r163FxceHkyZM4Ozuzdu1avL29sxzr4sWLUr4vXryYZf358+cZPXo0Xbt25ciRI8yYMYPjx48zfvx4he127drFihUrWLVqFaVLl87dmyXkOT27hpRetROjrn2Ju3eDmEt/ZtmmUOsuxAc8IOGxf45pqRZIHwWYFh+nsDwtIU5hvSB8DzERoVw7Ph/79r+jrVs4y/rCpjJqtnDm0ZWd7Jpjj+eK9iQnxtKszzpUVbPeZwGSEqIBlA6K0dDSJSkhJsty4eeQlpwsvYR0X9wkffjwYXx8fKS/ExISKF68OP3792fIkCEkJCTQoUMHWrVqJdV89e3bl40bN/LkyROaNm0KpD9X0cXFhUqVKgHQr18/fvvtN8LCwoiJieHixYts3bqVGjVqALB48WKFASfu7u5YWFgwfXr6L8Fy5cqxZMkSOnTowMWLF2nYsCEAjRo1omPHjgB07dqVM2fOMGbMGKytrQGwt7fn6dOnCmWcMWOGVCuYYfDgwQwZMoSLFy/y4MEDDh8+jEwmA8DFxQV/f382b97M8uXLpX1GjhyJvr4+AOPHj6du3boMGTIEgNKlS7N48WKaNm3KtWvXqFixIhERERQpUgQzMzPMzc1ZtmyZVPtpaGiImpoaOjo6FCpUiBcvXgBf16+yZMmSzJ8/XzqfZmZmtGzZkhMnTgDw7t07kpKSKF68OGZmZpiZmbFu3TopEH/16hWampqYmZlRvHhxihcvTpEiRShePGtfHBMTEynfypr5161bR9euXenevbuUNxcXF/7v//6PkJAQqRm7Q4cOWFlZfXFZhbyV8PwJoXMmoGleBsMujhQbP4vXcydJ67XKV0KrTHneLHPNIZX/UfnM79X//XgUhPwml8u54DGNErIGlLZsrnSbu+c2cvPkEirV7kmpKs1IiIvg7p9rOeHejzaDdlBAT8lTOT5zDat87jMg/DCpiaL/9Ke+OGB0cHDA2dkZuVzOvXv3mDNnDvb29gwZMgR1dXXU1dXp3bs3J06c4N69e7x8+ZInT57w/v17qUk6Q7ly5aT/ZwRWycnJBAQEACgECMbGxpibm0t/BwQEULduXYX0KlasiL6+Pk+ePJECxlKlSknrM0ZwlyxZUlqmra2dpUl65MiRNG+ueNPICMwCAgLQ19eXgkUAFRUVatSooVCDZmRkJJUJ4OHDh7x8+RJbW1s+FRgYSO3atRkwYACurq6sWLECOzs7GjZsSKtWrbJsD+kBJKQ3j38pBwcH7t69y/Lly3nx4gUvXrzg2bNnUgfdSpUq0bZtW4YMGYKJiQl169alUaNGNGvWDID27dtz8OBBWrRoQfny5bG3t6dFixZKA8bPefjwIffu3ePAgQPSsoxa5sDAQClgzPw+Cj9Oyl9vSfnrLQlPHpAWH4fp4HFoW1Qh4ckDAPRq1SU1Jpq4u9c/m1ZafCwAqtqKMytINY9xsXmce0FQ7pHfLiLePqHjyEOkpWY0Qabfh9JSU5DL07j751rKVW1HnfZ/N1cXK1OL/Yub43/BnVqtJmRJN6NmMTkp67WcnBiDprZeluXCzyE1j5ui/w2+OGDU1dWVvrxLly5NkSJF6NevH2pqasycOZO4uDh69+5NQkICLVu2pFOnTlhbWyuMms3w6QznkB4sqKioAGQJMDM/b1GezS83uVyOhoaG0n0yZKSfHSMjo2wDlJyOm/lYn/YdTEtLo127dlINY2YZwZ+zszM9e/bk3LlzXLlyBVdXVzZt2oSXl1eWc2Vubo6xsTG3bt1SOqI7MDCQOXPmMHnyZCpUqKCwbsOGDaxevZpOnTpRp04d+vbty5kzZzh69O/+aIsXL+a3337j/PnzXL58mfHjx1O9enX++OMPDA0NOXToELdv3+bSpUtcvHiRbdu2MWLECIYPH670/GQnLS2NAQMGKO3km7lGUllfTOH7UNUviI51DeLv3SQ1+u9+rElBgQCoF/p7+gcdm1rE3roCmbqFZCflrzfIU1PRMFXsg6thmv7DI+l1cF5kXxA+K+iBDwlxEexxa5Bl3dbfrbCo2ZWU5HiKlFL8wV9AzwgD4zJEvHumNF0NLV10CpryMfyVwvL4mHCSE2MpVKSc0v2EH08EjFl9c324nZ0d/fr1Y/fu3Zw/f15qst22bRsjR46kdevW6OnpER4enm2w9amMZupbt25Jyz5+/KgwKMPCwkJhkAzA48ePiYmJUai5zGsWFhZER0dLtaCQHizevHmT8uXLZ7tfhQoVePbsGaVKlZJeKSkpzJs3jzdv3vD8+XNmzJiBkZERPXr0YMWKFWzatInAwEAeP36cJT1VVVV++eUXPDw8lD5zctOmTfj7+2NmlnXk3rp16/jtt9+YOXMm3bp1w8bGhqCgIOn9uXv3LnPnzqVs2bL07duXDRs2MHfuXPz8/AgPD8fb25vdu3dTvXp1Ro4cyb59+/j11185duzYF5/PChUq8OLFC4Xz8vbtWxYsWEBsrKhh+hmoamphOngc+g0Va90LWKV/eSYGp3ePUNXVQ7OoGQkBj3KVrjw5mYQn99GtYa+wXLdGXVJjY0h8HpDNnoKQt+p2cKH90P0KL3OLRhTQN6H90P3YNB6GVgED3gUpfuckxEbwMTwIfcPsB2iZla9L8BNfUlP+buIMenASFVU1ipWtnW9lEr5NamKS9BLS5cmTXkaNGsWZM2eYOXMm8+fPB8Db25sWLVrw5s0blixZQnJycpZH4GSnZMmStGzZklmzZqGpqYmxsTFLlixR2L9fv3707NkTV1dXevbsyfv373F1daVy5crUqVMnL4qlVL169ahUqRLjxo1j+vTpGBkZsWPHDgICApgxY0a2+2XMTeji4kLv3r35+PEjLi4uJCQkULp0aWJjYzl69CgJCQkMGjQIVVVVPD09MTAwoGzZskB67W5QUBDv37/H2NiYIUOGcOHCBXr27MmoUaOoVq0akZGR7N69Gy8vL5YuXar08ULFihXj0qVLODg4oKqqyqFDhzh58iTGxul9cPT09Ni1axcaGhp07dqVxMREjh07RunSpSlcuDCJiYnMnz8fXV1datSowdu3b7l+/brU3/RLDBw4kNGjR7Nq1SratGnD27dvmTp1KiVKlPhHTG30X5ASHsbHcycp3LEH8tRUkl4Gom1RhUJtfuWjrw/J/6sJ1CxRGoCk16+UJ6SujlapcqR8eE9qRHo3kIhDeyg2cQ6mwycTff4kWhUqUah1Zz7s24o86ed7lqrw72RgUibLMi2dQqipaWBcwhIA2yYj8DsyGw0tPcpYtSAhNoJ75zaioqKGZb1+0n5/vbqDtq4hBY3Suz5ZNejP83tHOfnHIKrU/T8+vn/JzVNLsajxq5iD8ScmahizypOAUUtLC1dXV/r06YOPjw+TJ09m69atLFu2DFNTU1q3bk2xYsXw98951GRm8+fPZ/78+YwZM4a0tDS6devGhw8fpPVVq1Zl06ZNLFu2jI4dO6Knp0fTpk0ZN26cQpN0XlNTU8Pd3Z358+czfPhwkpKSsLS0ZOvWrdjY2GS7n42NDZs2bWL58uV06tQJHR0d6tSpw8SJE9HU1ERTU5ONGzeyePFiunbtSmpqKjY2NmzZsgU9vfR+Lo6OjsyfP5+nT5/i7e1NgQIF2LFjB+7u7mzcuJHXr1+jra1N5cqV2b59e7YB3IIFC5g1axZdunRBV1eXqlWr4uLiwsyZM3n9+jXlypVj5cqVrFq1il27dqGqqoqdnR0bN25EVVWVX3/9lcjISNasWcObN28wMDCgRYsWODs7f/H5bNmyJUuXLmX9+vWsW7eOQoUKSf1khZ9H2NbVJP/1loKNW6JhVISUD2FEeOwgMtMj/NQMCgGQFqt85Kd6IUNKzFjCB8+dRHjuAiD+0T3erZxL4U69KDpqOikR4YTvcSfqhGe+l0kQvkTlOr3QLKDP/YtbeXrLA22dwpiWrk6TXisVahiPrO9BeduONPhlHgCFTMrSot8mrp9YyJ+7R6OlU5gq9v9HtabiSVc/s5REETB+SkWe23ZiQfiHC+zT5kdnQRDy3YH2h390FgQh3038JX9HmD/s1ET6f2XPMzls+d+RJzWMgiAIgiAI/xYpiZ8fuPdfIwJGQRAEQRCETESTdFYiYBQEQRAEQcgkOU484eVTImAUBEEQBEHIJCVB1DB+SgSMgiAIgiAImaQkiD6MnxIBoyAIgiAIQiZJH0QN46dEwCgIgiAIgpBJcrQIGD8l5mEUBEEQBEEQcpS/M18KgiAIgiAI/3giYBQEQRAEQRByJAJGQRAEQRAEIUciYBQEQRAEQRByJAJGQRAEQRAEIUciYBQEQRAEQRByJAJGQRAEQRAEIUciYBQEQRAEQRByJAJGQRAEQRAEIUf/D3xqtMA0+DkrAAAAAElFTkSuQmCC" }, "metadata": {}, "output_type": "display_data" @@ -894,12 +904,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 26, "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:37:29.081466Z", - "start_time": "2023-12-20T23:37:29.069427Z" + "end_time": "2023-12-20T23:45:52.277701Z", + "start_time": "2023-12-20T23:45:52.273314Z" } }, "outputs": [], diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__234456.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__234456.csv new file mode 100644 index 00000000..daa33103 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231220__234456.csv @@ -0,0 +1,4 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index dfd94224..f2a8b16b 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -1,5 +1,6 @@ import os import gc +import sys import pandas as pd from copy import deepcopy @@ -132,7 +133,8 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b tqdm(range(self.n_estimators), desc="Classifiers testing by bootstrap", colour="blue", - mininterval=10) + mininterval=10, + file=sys.stdout) # Train and test each estimator in models_predictions for idx in cycle_range: diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 1f53687d..a753e520 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -1,4 +1,5 @@ import gc +import sys import pandas as pd from virny.utils.stability_utils import generate_bootstrap @@ -65,7 +66,8 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b tqdm(range(self.n_estimators), desc="Classifiers testing by bootstrap", colour="blue", - mininterval=10) + mininterval=10, + file=sys.stdout) # Train and test each estimator in models_predictions for idx in cycle_range: diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 67122ce4..604ec3a0 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -1,4 +1,5 @@ import os +import sys import traceback import pandas as pd from datetime import datetime, timezone @@ -136,7 +137,8 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, for model_idx, model_name in tqdm(enumerate(models_config.keys()), total=num_models, desc="Analyze multiple models", - colour="red"): + colour="red", + file=sys.stdout): if verbose >= 1: print('\n\n', flush=True) print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py index f1327778..74a5491f 100644 --- a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -1,3 +1,4 @@ +import sys import traceback import pandas as pd from datetime import datetime, timezone @@ -139,7 +140,8 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo for model_idx, model_name in tqdm(enumerate(models_config.keys()), total=num_models, desc="Analyze multiple models", - colour="red"): + colour="red", + file=sys.stdout): if verbose >= 1: print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: From ed55c1f5fa7864b7644a4da797285285cf0d0643 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 14:24:28 +0200 Subject: [PATCH 085/148] Completed a use case for a postprocessor --- ..._Models_Interface_With_Postprocessor.ipynb | 149 +++++++++--------- ...ssifier_50_Estimators_20231221__122242.csv | 19 +++ ...ression_50_Estimators_20231221__122242.csv | 19 +++ ...ssifier_50_Estimators_20231221__122242.csv | 19 +++ ...ng_results_Law_School_20231221__122242.csv | 4 + 5 files changed, 135 insertions(+), 75 deletions(-) create mode 100644 docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231221__122242.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__122242.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__122242.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231221__122242.csv diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index a060810d..4091c67a 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:44:51.569353Z", - "start_time": "2023-12-20T23:44:51.216259Z" + "end_time": "2023-12-21T12:22:33.321331Z", + "start_time": "2023-12-21T12:22:32.400819Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:44:51.577729Z", - "start_time": "2023-12-20T23:44:51.569503Z" + "end_time": "2023-12-21T12:22:33.330344Z", + "start_time": "2023-12-21T12:22:33.320778Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:44:51.590135Z", - "start_time": "2023-12-20T23:44:51.578520Z" + "end_time": "2023-12-21T12:22:33.340224Z", + "start_time": "2023-12-21T12:22:33.330669Z" } }, "outputs": [ @@ -100,8 +100,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.557910Z", - "start_time": "2023-12-20T23:44:51.588012Z" + "end_time": "2023-12-21T12:22:38.226990Z", + "start_time": "2023-12-21T12:22:33.341502Z" } }, "outputs": [ @@ -180,8 +180,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.576414Z", - "start_time": "2023-12-20T23:44:52.559175Z" + "end_time": "2023-12-21T12:22:38.244903Z", + "start_time": "2023-12-21T12:22:38.227234Z" } }, "id": "ce359a052925eb3a" @@ -224,8 +224,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.594883Z", - "start_time": "2023-12-20T23:44:52.577786Z" + "end_time": "2023-12-21T12:22:38.265352Z", + "start_time": "2023-12-21T12:22:38.246137Z" } }, "id": "2ece07ab7e3a9acc" @@ -279,8 +279,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.611819Z", - "start_time": "2023-12-20T23:44:52.595583Z" + "end_time": "2023-12-21T12:22:38.284235Z", + "start_time": "2023-12-21T12:22:38.265046Z" } }, "id": "af22ee06f1e3eb1a" @@ -296,8 +296,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.631115Z", - "start_time": "2023-12-20T23:44:52.612178Z" + "end_time": "2023-12-21T12:22:38.302605Z", + "start_time": "2023-12-21T12:22:38.284404Z" } }, "id": "65181f72484bb92b" @@ -316,8 +316,8 @@ "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.690961Z", - "start_time": "2023-12-20T23:44:52.631728Z" + "end_time": "2023-12-21T12:22:38.367706Z", + "start_time": "2023-12-21T12:22:38.302834Z" } }, "outputs": [ @@ -351,8 +351,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.704333Z", - "start_time": "2023-12-20T23:44:52.688388Z" + "end_time": "2023-12-21T12:22:38.386761Z", + "start_time": "2023-12-21T12:22:38.367870Z" } }, "id": "ebbef5eaf9dc0943" @@ -372,8 +372,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.765598Z", - "start_time": "2023-12-20T23:44:52.705152Z" + "end_time": "2023-12-21T12:22:38.460362Z", + "start_time": "2023-12-21T12:22:38.387441Z" } }, "id": "97ed4609effbf53f" @@ -393,8 +393,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:52.779451Z", - "start_time": "2023-12-20T23:44:52.761173Z" + "end_time": "2023-12-21T12:22:38.489420Z", + "start_time": "2023-12-21T12:22:38.444854Z" } }, "id": "4535191384245578" @@ -417,17 +417,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023/12/21, 01:44:52: Tuning DecisionTreeClassifier...\n", + "2023/12/21, 14:22:38: Tuning DecisionTreeClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 01:44:54: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", + "2023/12/21, 14:22:40: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", "\n", - "2023/12/21, 01:44:54: Tuning LogisticRegression...\n", + "2023/12/21, 14:22:40: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/12/21, 01:44:54: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", + "2023/12/21, 14:22:40: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", "\n", - "2023/12/21, 01:44:54: Tuning RandomForestClassifier...\n", + "2023/12/21, 14:22:40: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 01:44:56: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" + "2023/12/21, 14:22:42: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" ] }, { @@ -447,8 +447,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:56.739306Z", - "start_time": "2023-12-20T23:44:52.779255Z" + "end_time": "2023-12-21T12:22:42.846255Z", + "start_time": "2023-12-21T12:22:38.464922Z" } }, "id": "782741c190a4690b" @@ -466,8 +466,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:56.777854Z", - "start_time": "2023-12-20T23:44:56.740473Z" + "end_time": "2023-12-21T12:22:42.902571Z", + "start_time": "2023-12-21T12:22:42.845818Z" } }, "id": "21ccc879c5c3e215" @@ -505,8 +505,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:44:56.807127Z", - "start_time": "2023-12-20T23:44:56.763886Z" + "end_time": "2023-12-21T12:22:42.939840Z", + "start_time": "2023-12-21T12:22:42.876922Z" } }, "id": "3b15f202741fa2ae" @@ -533,8 +533,8 @@ "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.332050Z", - "start_time": "2023-12-20T23:44:56.785652Z" + "end_time": "2023-12-21T12:23:38.657769Z", + "start_time": "2023-12-21T12:22:42.902946Z" } }, "outputs": [ @@ -544,7 +544,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "5d7d6c26b64346daa891596f5c031093" + "model_id": "90ed20abc2054b7491beb01e89d58a79" } }, "metadata": {}, @@ -556,7 +556,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "51bad26c686b4fb8b8b0e4c26b537913" + "model_id": "4449db779e584f99bb03f5145a7f85b5" } }, "metadata": {}, @@ -568,7 +568,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "12e2a561af5c49a69668d2aa80aa2296" + "model_id": "47ce5b825f0e45168307bec17fe9867d" } }, "metadata": {}, @@ -580,7 +580,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "3d341a95faa84ba098af3b5166f2160e" + "model_id": "5705bfcf5ccb4da390303805d7adb0d6" } }, "metadata": {}, @@ -593,8 +593,7 @@ " models_config=models_config,\n", " save_results_dir_path=SAVE_RESULTS_DIR_PATH,\n", " postprocessor=postprocessor,\n", - " notebook_logs_stdout=True,\n", - " verbose=1)" + " notebook_logs_stdout=True)" ] }, { @@ -611,15 +610,15 @@ "id": "bea94683", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.358477Z", - "start_time": "2023-12-20T23:45:51.332840Z" + "end_time": "2023-12-21T12:23:38.686827Z", + "start_time": "2023-12-21T12:23:38.657448Z" } }, "outputs": [ { "data": { - "text/plain": " Metric overall male_priv male_dis race_priv \\\n0 Std 0.039277 0.036842 0.042494 0.036063 \n1 Overall_Uncertainty 0.405922 0.388361 0.429128 0.342869 \n2 Aleatoric_Uncertainty 0.389055 0.372690 0.410681 0.326654 \n3 IQR 0.048580 0.046042 0.051933 0.044797 \n4 Statistical_Bias 0.158352 0.145985 0.174693 0.126872 \n5 Mean_Prediction 0.108119 0.101100 0.117394 0.077675 \n6 Jitter 0.029905 0.024434 0.037134 0.001234 \n7 Label_Stability 0.948577 0.958311 0.935714 0.998752 \n8 TPR 0.978541 0.983256 0.972117 1.000000 \n9 TNR 0.212963 0.188073 0.238318 0.000000 \n10 PPV 0.914744 0.922741 0.903948 0.925411 \n11 FNR 0.021459 0.016744 0.027883 0.000000 \n12 FPR 0.787037 0.811927 0.761682 1.000000 \n13 Accuracy 0.899038 0.910051 0.884487 0.925411 \n14 F1 0.945568 0.952038 0.936794 0.961261 \n15 Selection-Rate 0.958654 0.967483 0.946987 1.000000 \n16 Positive-Rate 1.069742 1.065581 1.075412 1.080601 \n17 Sample_Size 4160.000000 2368.000000 1792.000000 3526.000000 \n\n race_dis \n0 0.057147 \n1 0.756592 \n2 0.736101 \n3 0.069616 \n4 0.333428 \n5 0.277430 \n6 0.189357 \n7 0.669527 \n8 0.827957 \n9 0.544379 \n10 0.833333 \n11 0.172043 \n12 0.455621 \n13 0.752366 \n14 0.830636 \n15 0.728707 \n16 0.993548 \n17 634.000000 ", - "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Std0.0392770.0368420.0424940.0360630.057147
    1Overall_Uncertainty0.4059220.3883610.4291280.3428690.756592
    2Aleatoric_Uncertainty0.3890550.3726900.4106810.3266540.736101
    3IQR0.0485800.0460420.0519330.0447970.069616
    4Statistical_Bias0.1583520.1459850.1746930.1268720.333428
    5Mean_Prediction0.1081190.1011000.1173940.0776750.277430
    6Jitter0.0299050.0244340.0371340.0012340.189357
    7Label_Stability0.9485770.9583110.9357140.9987520.669527
    8TPR0.9785410.9832560.9721171.0000000.827957
    9TNR0.2129630.1880730.2383180.0000000.544379
    10PPV0.9147440.9227410.9039480.9254110.833333
    11FNR0.0214590.0167440.0278830.0000000.172043
    12FPR0.7870370.8119270.7616821.0000000.455621
    13Accuracy0.8990380.9100510.8844870.9254110.752366
    14F10.9455680.9520380.9367940.9612610.830636
    15Selection-Rate0.9586540.9674830.9469871.0000000.728707
    16Positive-Rate1.0697421.0655811.0754121.0806010.993548
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n
    " + "text/plain": " Metric overall male_priv male_dis race_priv \\\n0 Aleatoric_Uncertainty 0.390402 0.375005 0.410747 0.327560 \n1 Mean_Prediction 0.108335 0.101489 0.117382 0.077704 \n2 Overall_Uncertainty 0.406805 0.390247 0.428684 0.343977 \n3 IQR 0.048624 0.046831 0.050993 0.045589 \n4 Statistical_Bias 0.158779 0.146627 0.174837 0.127163 \n5 Std 0.038083 0.035925 0.040934 0.036594 \n6 Label_Stability 0.947317 0.957162 0.934308 0.999592 \n7 Jitter 0.029109 0.023845 0.036064 0.000406 \n8 TPR 0.978541 0.983256 0.972117 1.000000 \n9 TNR 0.212963 0.188073 0.238318 0.000000 \n10 PPV 0.914744 0.922741 0.903948 0.925411 \n11 FNR 0.021459 0.016744 0.027883 0.000000 \n12 FPR 0.787037 0.811927 0.761682 1.000000 \n13 Accuracy 0.899038 0.910051 0.884487 0.925411 \n14 F1 0.945568 0.952038 0.936794 0.961261 \n15 Selection-Rate 0.958654 0.967483 0.946987 1.000000 \n16 Positive-Rate 1.069742 1.065581 1.075412 1.080601 \n17 Sample_Size 4160.000000 2368.000000 1792.000000 3526.000000 \n\n race_dis \n0 0.739895 \n1 0.278689 \n2 0.756219 \n3 0.065505 \n4 0.334613 \n5 0.046364 \n6 0.656593 \n7 0.188740 \n8 0.827957 \n9 0.544379 \n10 0.833333 \n11 0.172043 \n12 0.455621 \n13 0.752366 \n14 0.830636 \n15 0.728707 \n16 0.993548 \n17 634.000000 ", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Aleatoric_Uncertainty0.3904020.3750050.4107470.3275600.739895
    1Mean_Prediction0.1083350.1014890.1173820.0777040.278689
    2Overall_Uncertainty0.4068050.3902470.4286840.3439770.756219
    3IQR0.0486240.0468310.0509930.0455890.065505
    4Statistical_Bias0.1587790.1466270.1748370.1271630.334613
    5Std0.0380830.0359250.0409340.0365940.046364
    6Label_Stability0.9473170.9571620.9343080.9995920.656593
    7Jitter0.0291090.0238450.0360640.0004060.188740
    8TPR0.9785410.9832560.9721171.0000000.827957
    9TNR0.2129630.1880730.2383180.0000000.544379
    10PPV0.9147440.9227410.9039480.9254110.833333
    11FNR0.0214590.0167440.0278830.0000000.172043
    12FPR0.7870370.8119270.7616821.0000000.455621
    13Accuracy0.8990380.9100510.8844870.9254110.752366
    14F10.9455680.9520380.9367940.9612610.830636
    15Selection-Rate0.9586540.9674830.9469871.0000000.728707
    16Positive-Rate1.0697421.0655811.0754121.0806010.993548
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n
    " }, "execution_count": 17, "metadata": {}, @@ -653,8 +652,8 @@ "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.385855Z", - "start_time": "2023-12-20T23:45:51.355767Z" + "end_time": "2023-12-21T12:23:38.754548Z", + "start_time": "2023-12-21T12:23:38.684882Z" } }, "outputs": [], @@ -668,8 +667,8 @@ "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.453333Z", - "start_time": "2023-12-20T23:45:51.379688Z" + "end_time": "2023-12-21T12:23:38.755147Z", + "start_time": "2023-12-21T12:23:38.723143Z" } }, "outputs": [], @@ -691,8 +690,8 @@ "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.469788Z", - "start_time": "2023-12-20T23:45:51.397935Z" + "end_time": "2023-12-21T12:23:38.868859Z", + "start_time": "2023-12-21T12:23:38.738044Z" } }, "outputs": [], @@ -706,8 +705,8 @@ "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.025564 -0.173045 -0.150360 \n1 Aleatoric_Uncertainty_Parity 0.037992 0.409447 0.383894 \n2 Aleatoric_Uncertainty_Ratio 1.101939 2.253460 2.070529 \n3 Equalized_Odds_FNR 0.011139 0.172043 0.169995 \n4 Equalized_Odds_FPR -0.050244 -0.544379 -0.474526 \n5 IQR_Parity 0.005891 0.024819 0.024473 \n6 Jitter_Parity 0.012700 0.188123 0.180307 \n7 Label_Stability_Ratio 0.976420 0.670363 0.674450 \n8 Label_Stability_Difference -0.022597 -0.329225 -0.316996 \n9 Overall_Uncertainty_Parity 0.040767 0.413723 0.388156 \n10 Overall_Uncertainty_Ratio 1.104972 2.206651 2.034723 \n11 Statistical_Parity_Difference 0.009831 -0.087052 -0.114095 \n12 Disparate_Impact 1.009225 0.919441 0.894083 \n13 Std_Parity 0.005651 0.021084 0.020138 \n14 Std_Ratio 1.153395 1.584622 1.534453 \n15 Equalized_Odds_TNR 0.050244 0.544379 0.474526 \n16 Equalized_Odds_TPR -0.011139 -0.172043 -0.169995 \n17 Accuracy_Parity -0.018083 -0.141022 -0.114202 \n18 Aleatoric_Uncertainty_Parity 0.045674 0.324489 0.334561 \n19 Aleatoric_Uncertainty_Ratio 1.143410 2.123939 2.073617 \n20 Equalized_Odds_FNR 0.010383 0.078048 0.097373 \n21 Equalized_Odds_FPR -0.106491 -0.308592 -0.370211 \n22 IQR_Parity 0.002433 0.018288 0.018191 \n23 Jitter_Parity 0.002983 0.028429 0.029001 \n24 Label_Stability_Ratio 0.996168 0.960424 0.957890 \n25 Label_Stability_Difference -0.003788 -0.039298 -0.041702 \n26 Overall_Uncertainty_Parity 0.045877 0.326364 0.336291 \n27 Overall_Uncertainty_Ratio 1.143620 2.127507 2.076070 \n28 Statistical_Parity_Difference 0.002645 0.056072 -0.019339 \n29 Disparate_Impact 1.002471 1.052682 0.981970 \n30 Std_Parity 0.001735 0.014278 0.013799 \n31 Std_Ratio 1.194368 2.904041 2.608315 \n32 Equalized_Odds_TNR 0.106491 0.308592 0.370211 \n33 Equalized_Odds_TPR -0.010383 -0.078048 -0.097373 \n34 Accuracy_Parity -0.016108 -0.142758 -0.115666 \n35 Aleatoric_Uncertainty_Parity 0.035359 0.333828 0.332150 \n36 Aleatoric_Uncertainty_Ratio 1.112633 2.199608 2.096895 \n37 Equalized_Odds_FNR 0.009581 0.076511 0.093848 \n38 Equalized_Odds_FPR -0.124582 -0.317457 -0.370077 \n39 IQR_Parity 0.005105 0.051159 0.050195 \n40 Jitter_Parity 0.005513 0.083227 0.077578 \n41 Label_Stability_Ratio 0.992528 0.884698 0.892591 \n42 Label_Stability_Difference -0.007186 -0.112511 -0.103852 \n43 Overall_Uncertainty_Parity 0.036434 0.346205 0.344033 \n44 Overall_Uncertainty_Ratio 1.112218 2.203762 2.098884 \n45 Statistical_Parity_Difference 0.002092 0.061916 -0.009914 \n46 Disparate_Impact 1.001948 1.058022 0.990782 \n47 Std_Parity 0.003578 0.037686 0.036663 \n48 Std_Ratio 1.116316 2.418831 2.247181 \n49 Equalized_Odds_TNR 0.124582 0.317457 0.370077 \n50 Equalized_Odds_TPR -0.009581 -0.076511 -0.093848 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", - "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.025564-0.173045-0.150360DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0379920.4094470.383894DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.1019392.2534602.070529DecisionTreeClassifier
    3Equalized_Odds_FNR0.0111390.1720430.169995DecisionTreeClassifier
    4Equalized_Odds_FPR-0.050244-0.544379-0.474526DecisionTreeClassifier
    5IQR_Parity0.0058910.0248190.024473DecisionTreeClassifier
    6Jitter_Parity0.0127000.1881230.180307DecisionTreeClassifier
    7Label_Stability_Ratio0.9764200.6703630.674450DecisionTreeClassifier
    8Label_Stability_Difference-0.022597-0.329225-0.316996DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0407670.4137230.388156DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.1049722.2066512.034723DecisionTreeClassifier
    11Statistical_Parity_Difference0.009831-0.087052-0.114095DecisionTreeClassifier
    12Disparate_Impact1.0092250.9194410.894083DecisionTreeClassifier
    13Std_Parity0.0056510.0210840.020138DecisionTreeClassifier
    14Std_Ratio1.1533951.5846221.534453DecisionTreeClassifier
    15Equalized_Odds_TNR0.0502440.5443790.474526DecisionTreeClassifier
    16Equalized_Odds_TPR-0.011139-0.172043-0.169995DecisionTreeClassifier
    17Accuracy_Parity-0.018083-0.141022-0.114202LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0456740.3244890.334561LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1434102.1239392.073617LogisticRegression
    20Equalized_Odds_FNR0.0103830.0780480.097373LogisticRegression
    21Equalized_Odds_FPR-0.106491-0.308592-0.370211LogisticRegression
    22IQR_Parity0.0024330.0182880.018191LogisticRegression
    23Jitter_Parity0.0029830.0284290.029001LogisticRegression
    24Label_Stability_Ratio0.9961680.9604240.957890LogisticRegression
    25Label_Stability_Difference-0.003788-0.039298-0.041702LogisticRegression
    26Overall_Uncertainty_Parity0.0458770.3263640.336291LogisticRegression
    27Overall_Uncertainty_Ratio1.1436202.1275072.076070LogisticRegression
    28Statistical_Parity_Difference0.0026450.056072-0.019339LogisticRegression
    29Disparate_Impact1.0024711.0526820.981970LogisticRegression
    30Std_Parity0.0017350.0142780.013799LogisticRegression
    31Std_Ratio1.1943682.9040412.608315LogisticRegression
    32Equalized_Odds_TNR0.1064910.3085920.370211LogisticRegression
    33Equalized_Odds_TPR-0.010383-0.078048-0.097373LogisticRegression
    34Accuracy_Parity-0.016108-0.142758-0.115666RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0353590.3338280.332150RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1126332.1996082.096895RandomForestClassifier
    37Equalized_Odds_FNR0.0095810.0765110.093848RandomForestClassifier
    38Equalized_Odds_FPR-0.124582-0.317457-0.370077RandomForestClassifier
    39IQR_Parity0.0051050.0511590.050195RandomForestClassifier
    40Jitter_Parity0.0055130.0832270.077578RandomForestClassifier
    41Label_Stability_Ratio0.9925280.8846980.892591RandomForestClassifier
    42Label_Stability_Difference-0.007186-0.112511-0.103852RandomForestClassifier
    43Overall_Uncertainty_Parity0.0364340.3462050.344033RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1122182.2037622.098884RandomForestClassifier
    45Statistical_Parity_Difference0.0020920.061916-0.009914RandomForestClassifier
    46Disparate_Impact1.0019481.0580220.990782RandomForestClassifier
    47Std_Parity0.0035780.0376860.036663RandomForestClassifier
    48Std_Ratio1.1163162.4188312.247181RandomForestClassifier
    49Equalized_Odds_TNR0.1245820.3174570.370077RandomForestClassifier
    50Equalized_Odds_TPR-0.009581-0.076511-0.093848RandomForestClassifier
    \n
    " + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.025564 -0.173045 -0.150360 \n1 Aleatoric_Uncertainty_Parity 0.035741 0.412335 0.385770 \n2 Aleatoric_Uncertainty_Ratio 1.095309 2.258806 2.072181 \n3 Equalized_Odds_FNR 0.011139 0.172043 0.169995 \n4 Equalized_Odds_FPR -0.050244 -0.544379 -0.474526 \n5 IQR_Parity 0.004162 0.019916 0.019940 \n6 Jitter_Parity 0.012219 0.188334 0.179519 \n7 Label_Stability_Ratio 0.976123 0.656861 0.662353 \n8 Label_Stability_Difference -0.022854 -0.342999 -0.328662 \n9 Overall_Uncertainty_Parity 0.038438 0.412241 0.386115 \n10 Overall_Uncertainty_Ratio 1.098495 2.198454 2.026424 \n11 Statistical_Parity_Difference 0.009831 -0.087052 -0.114095 \n12 Disparate_Impact 1.009225 0.919441 0.894083 \n13 Std_Parity 0.005009 0.009770 0.009791 \n14 Std_Ratio 1.139419 1.266986 1.262459 \n15 Equalized_Odds_TNR 0.050244 0.544379 0.474526 \n16 Equalized_Odds_TPR -0.011139 -0.172043 -0.169995 \n17 Accuracy_Parity -0.019486 -0.143166 -0.114463 \n18 Aleatoric_Uncertainty_Parity 0.046563 0.321422 0.332505 \n19 Aleatoric_Uncertainty_Ratio 1.147821 2.123594 2.077199 \n20 Equalized_Odds_FNR 0.011482 0.080505 0.097373 \n21 Equalized_Odds_FPR -0.101903 -0.304790 -0.367321 \n22 IQR_Parity 0.001609 0.018242 0.017678 \n23 Jitter_Parity 0.002724 0.027312 0.029017 \n24 Label_Stability_Ratio 0.996701 0.962152 0.958633 \n25 Label_Stability_Difference -0.003261 -0.037570 -0.040960 \n26 Overall_Uncertainty_Parity 0.046695 0.323242 0.334164 \n27 Overall_Uncertainty_Ratio 1.147771 2.126884 2.079338 \n28 Statistical_Parity_Difference 0.002011 0.053922 -0.019052 \n29 Disparate_Impact 1.001879 1.050661 0.982233 \n30 Std_Parity 0.001352 0.014139 0.013621 \n31 Std_Ratio 1.147642 2.863905 2.572841 \n32 Equalized_Odds_TNR 0.101903 0.304790 0.367321 \n33 Equalized_Odds_TPR -0.011482 -0.080505 -0.097373 \n34 Accuracy_Parity -0.016530 -0.141181 -0.112635 \n35 Aleatoric_Uncertainty_Parity 0.033235 0.333090 0.331327 \n36 Aleatoric_Uncertainty_Ratio 1.105448 2.195036 2.092734 \n37 Equalized_Odds_FNR 0.008482 0.074053 0.089463 \n38 Equalized_Odds_FPR -0.110735 -0.313654 -0.367186 \n39 IQR_Parity 0.004679 0.051838 0.052185 \n40 Jitter_Parity 0.006285 0.085427 0.081421 \n41 Label_Stability_Ratio 0.990591 0.878776 0.883924 \n42 Label_Stability_Difference -0.009064 -0.118486 -0.112393 \n43 Overall_Uncertainty_Parity 0.034337 0.345531 0.343992 \n44 Overall_Uncertainty_Ratio 1.105292 2.198752 2.096863 \n45 Statistical_Parity_Difference 0.004755 0.064680 -0.005242 \n46 Disparate_Impact 1.004433 1.060646 0.995124 \n47 Std_Parity 0.003429 0.037755 0.037590 \n48 Std_Ratio 1.110082 2.405060 2.268096 \n49 Equalized_Odds_TNR 0.110735 0.313654 0.367186 \n50 Equalized_Odds_TPR -0.008482 -0.074053 -0.089463 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", + "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.025564-0.173045-0.150360DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0357410.4123350.385770DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0953092.2588062.072181DecisionTreeClassifier
    3Equalized_Odds_FNR0.0111390.1720430.169995DecisionTreeClassifier
    4Equalized_Odds_FPR-0.050244-0.544379-0.474526DecisionTreeClassifier
    5IQR_Parity0.0041620.0199160.019940DecisionTreeClassifier
    6Jitter_Parity0.0122190.1883340.179519DecisionTreeClassifier
    7Label_Stability_Ratio0.9761230.6568610.662353DecisionTreeClassifier
    8Label_Stability_Difference-0.022854-0.342999-0.328662DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0384380.4122410.386115DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0984952.1984542.026424DecisionTreeClassifier
    11Statistical_Parity_Difference0.009831-0.087052-0.114095DecisionTreeClassifier
    12Disparate_Impact1.0092250.9194410.894083DecisionTreeClassifier
    13Std_Parity0.0050090.0097700.009791DecisionTreeClassifier
    14Std_Ratio1.1394191.2669861.262459DecisionTreeClassifier
    15Equalized_Odds_TNR0.0502440.5443790.474526DecisionTreeClassifier
    16Equalized_Odds_TPR-0.011139-0.172043-0.169995DecisionTreeClassifier
    17Accuracy_Parity-0.019486-0.143166-0.114463LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0465630.3214220.332505LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1478212.1235942.077199LogisticRegression
    20Equalized_Odds_FNR0.0114820.0805050.097373LogisticRegression
    21Equalized_Odds_FPR-0.101903-0.304790-0.367321LogisticRegression
    22IQR_Parity0.0016090.0182420.017678LogisticRegression
    23Jitter_Parity0.0027240.0273120.029017LogisticRegression
    24Label_Stability_Ratio0.9967010.9621520.958633LogisticRegression
    25Label_Stability_Difference-0.003261-0.037570-0.040960LogisticRegression
    26Overall_Uncertainty_Parity0.0466950.3232420.334164LogisticRegression
    27Overall_Uncertainty_Ratio1.1477712.1268842.079338LogisticRegression
    28Statistical_Parity_Difference0.0020110.053922-0.019052LogisticRegression
    29Disparate_Impact1.0018791.0506610.982233LogisticRegression
    30Std_Parity0.0013520.0141390.013621LogisticRegression
    31Std_Ratio1.1476422.8639052.572841LogisticRegression
    32Equalized_Odds_TNR0.1019030.3047900.367321LogisticRegression
    33Equalized_Odds_TPR-0.011482-0.080505-0.097373LogisticRegression
    34Accuracy_Parity-0.016530-0.141181-0.112635RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0332350.3330900.331327RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1054482.1950362.092734RandomForestClassifier
    37Equalized_Odds_FNR0.0084820.0740530.089463RandomForestClassifier
    38Equalized_Odds_FPR-0.110735-0.313654-0.367186RandomForestClassifier
    39IQR_Parity0.0046790.0518380.052185RandomForestClassifier
    40Jitter_Parity0.0062850.0854270.081421RandomForestClassifier
    41Label_Stability_Ratio0.9905910.8787760.883924RandomForestClassifier
    42Label_Stability_Difference-0.009064-0.118486-0.112393RandomForestClassifier
    43Overall_Uncertainty_Parity0.0343370.3455310.343992RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1052922.1987522.096863RandomForestClassifier
    45Statistical_Parity_Difference0.0047550.064680-0.005242RandomForestClassifier
    46Disparate_Impact1.0044331.0606460.995124RandomForestClassifier
    47Std_Parity0.0034290.0377550.037590RandomForestClassifier
    48Std_Ratio1.1100822.4050602.268096RandomForestClassifier
    49Equalized_Odds_TNR0.1107350.3136540.367186RandomForestClassifier
    50Equalized_Odds_TPR-0.008482-0.074053-0.089463RandomForestClassifier
    \n
    " }, "execution_count": 21, "metadata": {}, @@ -720,8 +719,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.491890Z", - "start_time": "2023-12-20T23:45:51.425043Z" + "end_time": "2023-12-21T12:23:38.880650Z", + "start_time": "2023-12-21T12:23:38.770811Z" } }, "id": "a286da0406c6401d" @@ -748,8 +747,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.514444Z", - "start_time": "2023-12-20T23:45:51.448954Z" + "end_time": "2023-12-21T12:23:38.882568Z", + "start_time": "2023-12-21T12:23:38.797331Z" } }, "outputs": [], @@ -765,14 +764,14 @@ "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.561059Z", - "start_time": "2023-12-20T23:45:51.474363Z" + "end_time": "2023-12-21T12:23:38.982013Z", + "start_time": "2023-12-21T12:23:38.828027Z" } }, "outputs": [ { "data": { - "text/html": "\n
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    \n", "text/plain": "alt.HConcatChart(...)" }, "execution_count": 25, @@ -848,8 +847,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-20T23:45:51.759552Z", - "start_time": "2023-12-20T23:45:51.566308Z" + "end_time": "2023-12-21T12:23:39.135512Z", + "start_time": "2023-12-21T12:23:38.941813Z" } }, "id": "b1249b3994b75555" @@ -860,15 +859,15 @@ "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:52.277604Z", - "start_time": "2023-12-20T23:45:51.759242Z" + "end_time": "2023-12-21T12:23:39.656969Z", + "start_time": "2023-12-21T12:23:39.116888Z" } }, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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" 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" 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -908,8 +907,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-20T23:45:52.277701Z", - "start_time": "2023-12-20T23:45:52.273314Z" + "end_time": "2023-12-21T12:23:39.657152Z", + "start_time": "2023-12-21T12:23:39.619030Z" } }, "outputs": [], diff --git a/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231221__122242.csv b/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231221__122242.csv new file mode 100644 index 00000000..4c806066 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_DecisionTreeClassifier_50_Estimators_20231221__122242.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Aleatoric_Uncertainty,0.3904015786565288,0.37500536711664856,0.4107465724770849,0.3275601954662841,0.739894823339183,0.35979960223771895,0.7455699716384744,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.10833525935562162,0.10148919990567276,0.11738183791448262,0.07770435810067403,0.278689451508532,0.09324165601001617,0.2835125345485577,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.40680453141613165,0.39024683535026217,0.4286843440746021,0.3439774011206043,0.756218508422487,0.3761751993981025,0.7622904151405308,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.048623839366874894,0.046830781755678064,0.050993236924527854,0.045588528712084346,0.06550476266149871,0.04704205764703207,0.06698209387292954,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Statistical_Bias,0.15877920016243754,0.1466274642565268,0.1748368511809624,0.1271630632067788,0.33461279465084875,0.14337657195328,0.3375430366505388,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Std,0.038082747546925824,0.03592517350958205,0.040933827524844375,0.03659375858419384,0.04636378080022699,0.03730603467950348,0.04709732415973668,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.9473173076923077,0.9571621621621623,0.9343080357142857,0.9995916052183779,0.6565930599369085,0.9733890339425587,0.6447272727272727,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.02910851648351642,0.023845146166574672,0.036063684402332444,0.00040561658582888516,0.18874010171892053,0.014867799861458947,0.19438713667285035,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.9785407725321889,0.9832558139534884,0.9721166032953105,1.0,0.8279569892473119,0.9896670493685419,0.819672131147541,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.21296296296296297,0.18807339449541285,0.2383177570093458,0.0,0.5443786982248521,0.11849710982658959,0.5930232558139535,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.9147442326980942,0.9227411610650371,0.9039481437831467,0.9254112308564946,0.8333333333333334,0.9187316813216094,0.851063829787234,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.02145922746781116,0.01674418604651163,0.02788339670468948,0.0,0.17204301075268819,0.010332950631458095,0.18032786885245902,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.7870370370370371,0.8119266055045872,0.7616822429906542,1.0,0.4556213017751479,0.8815028901734104,0.4069767441860465,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.8990384615384616,0.9100506756756757,0.8844866071428571,0.9254112308564946,0.7523659305993691,0.9109660574412533,0.7606060606060606,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.9455676516329704,0.9520378293177212,0.936793893129771,0.9612608631609957,0.8306364617044228,0.9528810280502971,0.8350730688935282,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.9586538461538462,0.9674831081081081,0.9469866071428571,1.0,0.7287066246056783,0.9798955613577024,0.7121212121212122,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,1.0697424892703862,1.0655813953488371,1.0754119138149556,1.0806006742261722,0.9935483870967742,1.0772101033295063,0.9631147540983607,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__122242.csv b/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__122242.csv new file mode 100644 index 00000000..64edb8d5 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__122242.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Aleatoric_Uncertainty,0.3350521505568387,0.31499437253669144,0.36155707151203315,0.28606614957152676,0.6074884904215229,0.3086755590540871,0.6411804701190769,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10366639983556378,0.09273345824475004,0.11811350122342476,0.07504224386927637,0.2628600495786701,0.08757539038540739,0.29041963072677274,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3361088681844626,0.31599424554949923,0.3626889052378071,0.28684558485395767,0.610087317748123,0.3096006943696695,0.6437643400349411,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.012890570946112812,0.012197639643006929,0.013806230168074156,0.010110443726723099,0.028352287942277052,0.011488206206177189,0.029166501109608063,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.1375252212471927,0.12723509864144286,0.1511228832619336,0.11185609757286755,0.28028441695014306,0.12495367156906041,0.28343138872369766,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.009740640786988941,0.009158183719396435,0.010510316197736177,0.00758577728995229,0.02172494471530318,0.008660128162631783,0.022281135790891686,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.986923076923077,0.9883277027027028,0.9850669642857144,0.9926488939307998,0.9550788643533122,0.990172323759791,0.9492121212121213,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.009299843014128768,0.00812637892995036,0.010850491982507288,0.0051373470545336775,0.03244962338247605,0.0069980284542015166,0.036014842300556474,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9844420600858369,0.9893023255813953,0.9778200253485425,0.9944836040453571,0.9139784946236559,0.9908151549942594,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.2523148148148148,0.2018348623853211,0.3037383177570093,0.13307984790874525,0.4378698224852071,0.1791907514450867,0.5465116279069767,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9191084397695968,0.924380704041721,0.9119385342789598,0.934350705441981,0.8173076923076923,0.923982869379015,0.8482490272373541,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01555793991416309,0.010697674418604652,0.022179974651457542,0.0055163959546429666,0.08602150537634409,0.009184845005740528,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7476851851851852,0.7981651376146789,0.6962616822429907,0.8669201520912547,0.5621301775147929,0.8208092485549133,0.45348837209302323,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9084134615384616,0.9168074324324325,0.8973214285714286,0.9302325581395349,0.7870662460567823,0.9174934725848564,0.803030303030303,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9506540603548763,0.9557402830824534,0.9437308868501529,0.963479809976247,0.8629441624365483,0.9562326869806094,0.8702594810379242,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9598557692307692,0.971706081081081,0.9441964285714286,0.9849688031764039,0.8201892744479495,0.9754569190600523,0.7787878787878788,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0710836909871244,1.0702325581395349,1.0722433460076046,1.064357952804168,1.118279569892473,1.0723306544202067,1.0532786885245902,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__122242.csv b/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__122242.csv new file mode 100644 index 00000000..64e49a8a --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231221__122238/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__122242.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Aleatoric_Uncertainty,0.32949237833068323,0.31517586556370025,0.3484106273441966,0.2787281420898376,0.6118183988121058,0.30320921973092263,0.6345363099582084,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10462842659957715,0.09601239833579621,0.11601389251957339,0.07621634526392657,0.26264262027387364,0.08936113382319123,0.2818215518527833,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3409022096221413,0.32611094064321744,0.3604478150585763,0.28824201863903565,0.6337726093168266,0.3136143777939593,0.6576064396280111,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.04134972204143557,0.039334362711794996,0.044012875441317746,0.0334493362972768,0.0852878295081608,0.037210085589494186,0.08939459358972489,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.14057601358524738,0.13152726321016808,0.15253329086660214,0.11360954077117458,0.29055043494553234,0.12744468092612535,0.2929790562653606,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.032624941579868776,0.031147912146687984,0.034576730473714824,0.02687090054321785,0.06462612248717346,0.029643025746880238,0.06723323745970544,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9593557692307693,0.9632601351351351,0.9541964285714286,0.9774134997163926,0.8589274447949526,0.9682715404699739,0.8558787878787879,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.029465659340659315,0.026758480419194753,0.0330430029154519,0.016446224547675015,0.10187343076031673,0.023006767197740714,0.10442795299938173,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9852467811158798,0.9888372093023255,0.9803548795944234,0.9944836040453571,0.9204301075268817,0.9911021814006888,0.9016393442622951,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.22916666666666666,0.1743119266055046,0.2850467289719626,0.10646387832699619,0.42011834319526625,0.15606936416184972,0.5232558139534884,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9168746879680479,0.9219427580225499,0.91,0.9324712643678161,0.8136882129277566,0.9220293724966622,0.842911877394636,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.014753218884120171,0.011162790697674419,0.01964512040557668,0.0055163959546429666,0.07956989247311828,0.008897818599311137,0.09836065573770492,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7708333333333334,0.8256880733944955,0.7149532710280374,0.8935361216730038,0.5798816568047337,0.8439306358381503,0.47674418604651164,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9067307692307692,0.9138513513513513,0.8973214285714286,0.9282473057288713,0.7870662460567823,0.9156657963446475,0.803030303030303,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.9498319110421516,0.9542190305206463,0.9438682123245882,0.9624796084828712,0.863773965691221,0.9553188546133629,0.8712871287128713,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9629807692307693,0.9738175675675675,0.9486607142857143,0.9869540555870675,0.8296529968454258,0.9778067885117493,0.7909090909090909,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0745708154506437,1.0725581395348838,1.0773130544993663,1.0665032178976401,1.1311827956989247,1.0749138920780712,1.069672131147541,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231221__122242.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231221__122242.csv new file mode 100644 index 00000000..daa33103 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231221__122242.csv @@ -0,0 +1,4 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,DecisionTreeClassifier,0.5243,0.8877,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" From e46ccf11293d9eb4da390988d166072d2c534cd6 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 15:46:12 +0200 Subject: [PATCH 086/148] Fixed visualizations --- ..._Models_Interface_With_Postprocessor.ipynb | 100 +++--- .../metrics_interactive_visualizer.py | 1 - virny/custom_classes/metrics_visualizer.py | 313 ++++++------------ 3 files changed, 150 insertions(+), 264 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index 4091c67a..ce757c6d 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -743,12 +743,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 44, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.882568Z", - "start_time": "2023-12-21T12:23:38.797331Z" + "end_time": "2023-12-21T13:44:14.079970Z", + "start_time": "2023-12-21T13:44:13.793765Z" } }, "outputs": [], @@ -760,144 +760,124 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 45, "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.982013Z", - "start_time": "2023-12-21T12:23:38.828027Z" + "end_time": "2023-12-21T13:44:15.426120Z", + "start_time": "2023-12-21T13:44:15.287725Z" } }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 23, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", - " metrics_title=\"Error Metrics\"\n", + " metric_names=['Accuracy', 'F1', 'TPR', 'TNR', 'PPV', 'Selection-Rate'],\n", + " plot_title=\"Accuracy Metrics\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 35, "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.982605Z", - "start_time": "2023-12-21T12:23:38.927358Z" + "end_time": "2023-12-21T13:13:52.893596Z", + "start_time": "2023-12-21T13:13:52.842914Z" } }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 24, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['Label_Stability'],\n", - " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", - " metrics_title=\"Variance Metrics\"\n", + " metric_names=['Aleatoric_Uncertainty', 'Overall_Uncertainty', 'Label_Stability', 'Std', 'IQR', 'Jitter'],\n", + " plot_title=\"Stability and Uncertainty Metrics\"\n", ")" ] }, - { - "cell_type": "markdown", - "source": [ - "Below is an example of an interactive plot. It requires that you run the below cell in Jupyter in the browser or EDAs, which support JavaScript displaying.\n", - "\n", - "You can use this plot to compare any pair of group fairness and stability metrics for all models." - ], - "metadata": { - "collapsed": false - }, - "id": "1f4906acb27ce7dd" - }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "outputs": [ { "data": { - "text/html": "\n
    \n", - "text/plain": "alt.HConcatChart(...)" + "text/plain": "
    ", + "image/png": 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ObXM58utvuP5M2cOHmz+7XC6tX7dOknTZZZfpmokTW51rsVj0jbvvkuQNWA8fOnP++nXrmpcK/tZ3vq2IiIhW50+YMEGDBg+WJH34/gd+XxMAILA0lFWrcn+RJO+Mk9MPTk8zmUzqM2OMZJKcDU2qyD3qV71lu/Oblynse+sEBYdZWpVJHp6t2OxUSWp+iHxayYb98rjcMluC1XPS8FbnhsVHqcdV3nHs5M4jctlbLpFfV1QmSUoa7H0Y/UXp4wacVbbcr2sCAASeQB0HfclfskWNZbWS2aQBX7m61TUDANBdCEhxUXrvvffk8XgUGhqqiRMnasKECUpOTpYkzZ8/v8PzrVar3nrrLd1555264oorNHToUE2ZMkVPPPGEDp8VCnxRZWWl/vKXv2jWrFkaO3ashg0bpqlTp+qXv/yljh8/3qr8gAEDNGDAAL388svt1nn33XdrwIABuvvuu1t8//LLL2vAgAH62te+psrKSj366KO6/PLLNXLkSM2aNavF7C673a53331XDzzwgK655hoNHz5cI0aM0OTJk/Xoo4/qs88+65LfR2Njoy6//HINGDBAP/nJT3zW+dOf/lQDBgzQLbfc4rMcgJZ27tghyXvjO3LUyDbLJCYmKjs7W5KaXxbxh8nkHdZd7exrGhwUdKbsWXvOHD9+XA0NDZKkcePHt1v/4MGDZbF4b7R3nLoOScrL8y7DlJCQoH79+rV7/vDh3hvt3NzcdvdeBQAEtqqDxd4PJilxYM82y4TGRioqI1GSVLG30K96rcUVkiRLbIRieiW3Wy6uX4YkqTrvuNwuV/P3lQe8D6vjctIUHNr2g9vEgb0kSW67S9VHSloeNJu8x9rZS84UdNatt8nk40oAAIEsYMfBdtQWlun4Ju/zrR5XDlZ0jyS/zgMA4MtAQIqLjtvt1vvvvy9Jmjx5siIiIhQUFKSbbrpJkrR8+XJVVFS0e/6BAwc0c+ZMzZ07V9u3b1dlZaUcDoeKi4v13nvv6bbbbtPChQtbnbdp0ybddNNNev7555Wbm6uamhrZ7XYVFBTozTff1I033qgNGzZ0+fXa7XZ961vf0tKlS9XQ0KD6+noVFhaqd+/ekryzaW+++Wb97//+r1avXq0TJ06oqalJjY2NKikp0dKlS3X//fe3G9J25vcRHh6uqVO9+x2uWLFCdru93T4vX75ckghIgU4qyC+QJCUlJSkmJqbdcllZWZKk/Lw8v+vOyekjSdq6davq6upaHV+9ek3z5wH9z8xkqa+vb/6cnNz+DavZbFZkpHevmcLCMzfq9VarJCkpuf0bcUnN1+twOFRSXOyzLAAgMFlLKiVJoXGRCokMa7dcZHqCJKmu2L/Zls7GJklSWFyUz3Kn2/Q43Wos8y4t73a51FhWLUmKymh/HIxIjWsOOuuKW96PnH7gW7n/mBwNTa3OPbHlUPPnmN4pPvsIAAhcgToOtufI4o3NS9D3mjLCr3MAAPiyEJDiorNu3TqVlHjfRJs1a1bz97ed2jvP4XA070/6RTU1Nbr//vtVWFioiIgI/ehHP9Ly5cv1+eef65VXXlHv3r3lcDhazZw8duyYvv3tb6uiokKJiYmaM2eOVq9erU8++US//vWvlZSUpPr6ej366KM+w9lzsWfPHuXm5urRRx/Vp59+qg8++EBz585VWFiYXC6XHnnkERUUFCgiIkJPPPGElixZog0bNmjRokV64oknFBcXJ0n64x//qGPHjp337+PWW2+VJNXV1Wnt2rVt9nnt2rWqra2V2WzWzTff3KW/DyDQlZV7b3BTUlN9ljsdNlZWVsp11pu9vnzlzq/KYrGopqZGc+c8rW1bt6mqskpFRUWaP+/feveddyR5Xz7pcypMlaTws5b5bfCx56nH42neE7XyrP8XhoV7lxK0dbBfan29tflzZWWVX9cEAAgsTdXesSAsIdpnubB47wNee21Du7MyzxZ0arZLR3u1ORvPvADYVOtdPcFe0yCPy9Oi3baYTCaFxnlfFGqqavkiUu/rRsocEiSH1aZdf1+qiv3H1FTboIaT1cr/eKuOrtwuSUod3Y/ZMwBgYIE6DralfE+BrMe897+9Jl/W7sxUAAC6S3B3dwD4otPhZ3Jysq666qrm7wcOHKiBAwdq//79evvtt/Xtb39bpi8sT/Xyyy+rrKxMwcHBevXVVzVy5JnlK6+77joNHDhQt956q6xWq1577TU9++yzkqRnn31WNptNUVFR+ve//908e1PyBrM5OTn6yle+ourqas2fP18PP/xwl17zzTffrIceekiSlJKSooEDB0qSPvvsM+3fv1+SNGfOnBazNePj49WvXz9lZmbq4Ycfltvt1rp16/TVr371vH4f48aNU2pqqk6cOKHFixfr+uvP7Fl42ukZp2PHjlVqByEPgJZqa2olqXkmZnvCI7yho8fjUX19vc/Zpqf169dPP/v5/+r11/6h/Px8/d+vf93ieFRUtGbdPlPTT+3rfFpaerpCQkLkcDi0e9dujRs3rs36Dx8+LJvNJqllkNqzZ09t3rRJRUVFqqqsUnxCfJvn783d2/y5obGhw+sBAAQee713HAkOD/VZ7vSDXnkkl80us49ZNpIUkRqvitxCNZysVlNtg0JjWu+HLUk1eaXNn10270Pks2d8+tuvsx8wS1JMr2QN++YNOrxwo+pLKrX3jZUtjgdHhKrX5OHKmDDYZ/0AgMAWqONgW4o+3SPJO3s0bXT7W7EAANBdmEGKi0pNTY1WrFghyRsaBp21X54kzZw5U5JUVFSkTz/9tMUxt9utJUuWSPIu+3p2GHhaZmamvvKVr2jEiBHNMy9ra2ub67rvvvtahKOnDRs2TNOnT9eoUaMUFub7H6XnYvr06W1+HxkZqXvuuUc33nijZnwh0Djt7CCjsrKy+fO5/j7OnhW6Zs2a5n0JT7Narc0zS1leF+g8p9N7E2qx+H57NtRy5sbU4fD9FvDZGuobmmd0tjrWUK/Dh4+orKysxfcWi0UjR42SJK1ds0aHDh1qda7T6dS8f81r/vPZ+5yOHz9OJpNJbrdbr732mjweT6vzd+3cqZ07d7Z5PgDAODxO76oI5uAgn+WCQs68y+t2drySQvLQ3pJJktujIws3tDkWVR0qVtWhM0u8n56R43acGZPMIf71y+1o3SenzdHu7Binza66Y+VqqrK2eRwAYAyBPA6erfboCdWdmj2aeeWQDq8XAIDuwAxSXFQWLVrUvO/l6SV1z3bzzTfrN7/5jZxOp+bPn69rrrmm+djBgwdVfmrpykmTJrXbxo9//OMWf968eXNz+ODrvOeff97Pq+i8wYPbfpN89OjRGj16dLvn1dTUaMuWLc1/PnsZznP9fUjeZXb//ve/q7GxUStXrmyxjO6KFStks9kUFhbWvF8pAP+ZzBfu3aRFCxfqrTff0v/P3n2HR1Xlfxz/THrvoYTQS0LvVZEqHSkrdrB33F1Wd5X9KTYsuxZUcC2sDZSqsErvvYQSauglENJ7b5PM748hQ0I6BIPM+/U8PE7uPefccydjztz7ved7JKlX794aO3asGgQ2UFZWlg4dPKj58+Zr544dOn7smF57fZoCAgIsde+59x4dOnhQOTk5euft6bp7wt3q0bOnnJ2ddeHCBS1etFgnT5yQj4+PkpKSZGt35StEo8aN1b9/f23atEl79+zR9Lena/yfxqtRo0bKysrS7l279cvPP8vb29vyIEfx+gAAK2JjqLzMNXCt56O6XVsqdt9pJYZd1JFv16jRgI5yrecjY06eEo6c18WNh+Tg4aK8VPMDgDaX11Ez1ECfLm07qvOrzN/L/do3UcN+7eVSx0vGnDwln4pU+Nr9ij98XqnhMWr/+DC5+Hte9zEBAH9At+g4eLVLO8zZg2yd7VWvZ1CNtw8AQE3g7iRuKkXpdQMDA2UwGHTq1KlSZdq1a6eDBw9q8+bNio2NtaR4jY2NtZRp0qRJlY95rfVqko+PT4X7CwsLdeDAAR05ckTh4eGKiIjQ+fPnFRUVVeKpwOKvr+e8WrVqpdatW+v48eNasWJFiQBpUXrdgQMHys2t/LUpAJTNydE8MzQvr+JZobl5V9IcOTg4VNpuVFSUZYbn4Dvv1BNPPmHZ5+npqTv69VPbdu30f1P/qeTkZH3z32/02rTXLGUaNGigKX+bok9mfKLs7Gz9OPdHS7BVMq83c+999yk6Kkpbt24tsW6pJD36+GNKT0/Xvn37FHb0qMKOHi2xv35AgB5/4nFNf+vty+9Dzc/GBwDc/GwvZ1CobDZMQYnZLFW7bG0+uqfyM3OUdDxCqWdjdORsTIn9zv4eanFXbx35Zo25XQe7y/+9Muuzqv0qPsMmKz5V59fslyTV6xmklmN6W/Y5uDmrbpcW8mpeXwf+s1x5adk689sudXh8WJXOCQBwa7kVx8GrGXPzlXQiQpLk17YJa48CAG5aBEhx0zhx4oTCwsIkmVPoFg/KlaWgoECLFi3SCy+8IMk8m7KIcznpJctyrfVqkqNj+Ws87N69W9OmTdOFCxdK7QsMDNTtt9+uBQsWlNp3vec1ZswYHT9+XNu3b1dqaqo8PT2VmJio3bt3SyK9LnCtXC6vPZqdVfEanFmZl5/qtbGp0sMImzdtUmFhoRwcHPTAgw+UWcbX11djx43T9999p7CjRxUdHa369etb9nfs1EkffPShfv3frzp44ICSk5Pl7u6uoOBgjRw1Ui1bttT7774nSZa03EUcHBz04t9f0o7t27Vhw0ZdCA9XYWGh6tatqz633aZhw4cp4mKEpby3d9nrlAIAbm12TuaHfgpyKl67zLLfxiA758ofFJLMaf/aPDRQ8YfOKWbvKWVEJ8lUaJKzr7v8OzRVQO82yopNtpR3dHcp0afq9MvO5cr399j9p6VCk2zsbdV0aNcy6zl6uqpR/w46uyxEqWdjlJ2QJme/ytcXBwDcWm7FcfBqSSciZDKa0/fW6disSn0HAKA2ECDFTePnn3+udp3Fixf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}, - "execution_count": 25, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "visualizer.create_fairness_variance_interactive_bar_chart()" + "visualizer.create_overall_metric_heatmap(\n", + " model_names=list(models_params_for_tuning.keys()),\n", + " metrics_lst=visualizer.all_accuracy_metrics + visualizer.all_uncertainty_metrics,\n", + " tolerance=0.005,\n", + ")" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:23:39.135512Z", - "start_time": "2023-12-21T12:23:38.941813Z" + "end_time": "2023-12-21T13:44:40.071955Z", + "start_time": "2023-12-21T13:44:39.509081Z" } }, - "id": "b1249b3994b75555" + "id": "eeb7c1e88b43163b" }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 48, "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:39.656969Z", - "start_time": "2023-12-21T12:23:39.116888Z" + "end_time": "2023-12-21T13:44:51.171978Z", + "start_time": "2023-12-21T13:44:50.683129Z" } }, "outputs": [ { "data": { - "text/plain": "
    ", - "image/png": 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AwED69++Pv7+/0rrY2FhCQkI4deqUUoFBasnJyYwZM4Zjx45Jy+Li4qRnfYsWLVi0aFGaBVE/io6ODs2aNePQoUOcO3eOhIQElbMiJCQkSF0Q2rZt+03Hyu7vSYDNmzfj7OysMF1ffHw83t7eeHt7s3v3btatW0eJEiW+Kc6bNm1SeMYEBwdnqntYaGgoQ4cOVSpUfvPmDSdOnODkyZOMGTOG/v37S+u8vb1xdHRUGosgODiY4OBg3N3d6devH+PGjfumc/lfJpr9C0IW1K5dG0NDw3SbaoWHh3P37l1y586dYROtuXPnShn/mjVrSqW1Z8+eZfTo0ejq6hISEkKfPn0ICwtTCPvq1SuGDBlCTEwMZmZmLF26lBs3bnDu3DlGjhxJYmKiwkP6aytWrJAy/k2bNmXXrl3cunWLc+fOMWHCBPT19Xn8+DGDBg3KscF//P39OXz4MJBS2vt1yfCWLVukjH+rVq3Yu3cv169f58KFC6xbt076mPT19WXLli1pHufvv//GyMiIBQsWcOXKFa5cucLUqVPR0dFBJpMxa9YsoqOjM4zv58+fGTBgAL6+vujo6LB27VqqVq0qrY+JiWH8+PEkJCRga2vLpk2buHLlClevXmX16tVYWVkhk8mk0vbskpCQwPv37zl//jwODg58+vQJKysrhg8frrTtqVOncHFxQSaTUbt2bbZu3Spdk61bt9KgQQMg5WW+YsUKhbDHjh2TPtZ79+7N0aNHuXnzJqdPn2bUqFHkypWL8PBwZs6cqXTcSZMmSRn/Ll264Obmxu3btzlx4gRDhgxBU1OT69evM2rUKKVCCTc3NynjX6VKFXbs2MHNmzc5fPgwHTp04MWLFyozXJkhH9k/X758FC1aNEthU9eiqRptH1Ka53/48IERI0Zw5swZPDw8WLNmjZRp27dvn8rZBdJz/fp1APLkyUOlSpWyFNbKyirDzNSePXto1qyZlGGZPn26dK9t3rwZFxcXkpOTsbGxYe3atdy4cYNLly4xffp0TExM+PTpE4MGDeLJkydZiltmLVy4kDdv3tC9e3dOnDjBjRs3WLx4Mfnz5ycuLo5hw4bx8uXLH3Ls9Dg7O0vpsGfPnhw6dEh63i9btkz6IL9586ZSpjcz0vtd5JYvX87z58/p27cvx44d4+bNmwqZ3KtXryodOykpib/++otbt26RK1cuBgwYwLFjx7h16xZHjhyhZ8+eqKmpcfToUWbPnq0Q1svLS8r4t2nThgMHDuDh4cG5c+eYNm0aurq6REdHM3HiRIXCou95lqTn4cOHTJkyhcTERMqVK4erqysXL17k+vXr7Nmzh/bt2wMpBQHp1fp+y3378eNH+vXrx/v37zEyMmLWrFlcuXKFy5cvM3XqVLS1tb+5/726urpUuPj27VtatWrFP//8w8WLFzP17gIwNzfn3r17rFu3Tlp2/Phx7t27x6BBg4CUtDB06FCpVdvEiRM5efIkN2/e5NixY0ycOFFqEbZ69WqptvZroaGhHDt2jIoVK0qt4A4cOCA9M0+ePMmSJUu+6VpkZP369dy7dw8zMzMABg0axL1796T3j/w6RkVFcfnyZZX7uHz5MpGRkairq6dbyZKe7P6e3L9/P/PnzycpKYlq1aqxadMmPDw8uHTpErNnzyZ//vwEBwfTr18/ha4eqc8dUgY0vXfvnsp7a8+ePVSrVo0jR45w5coVZs6cmWG8EhMTGThwII8ePSJXrlw4OTlx4sQJPDw82Lx5MzY2NshkMpydnblx4waQ0ipk3LhxREZGYmVlxapVq7h48SI3btxgy5Yt2NraArBx40bu3buX7vEFZaLmXxCyIHfu3DRu3Bg3N7c0m2qdOHECmUxG/fr10x2R9tmzZ1K/2hYtWrBkyRLU1VPK40xMTBg4cCBVq1bFwcGBjx8/Mm/ePIWazYULF5KYmIiRkRG7d++WSo3z5s3L4MGDsba25q+//lJ5bH9/f6kfsoODA5MnT5bWGRkZ0bdvXypXrsyff/7JkydP2LVrF3379s3axcpAXFycNAp5arGxsbx9+xYPDw/Wr18vfbiMHz9eoQQ+OTmZTZs2ASkv0cWLFys0CTc3N6datWo0a9aM8PBwrl27luaUPpqamuzZs0ehFrtHjx6oqakxY8YMoqKiuH79Os2aNUvzfOLj43FycuLBgwdoa2uzZs0aqW+93O3bt4mIiADAxcVFoaS/UaNGlCxZkqZNm5KYmMjZs2e/6ZofOnSIQ4cOpbtNo0aNmD17tsomhBs2bADA2tqatWvXKlxzU1NTqlWrxh9//IGPj4/Ux1TuzJkzQEpB1j///CMtNzY2xsnJiS9fvuDi4sKNGzf4+PGj1PTbw8NDajY5YcIEhfPOkycPI0aMoEyZMgwbNowrV65w9uxZafCzuLg4aaChypUrs3nzZinOxsbGzJ8/H0NDw2/6qE5OTpZqt6ytrbMc3srKCjU1NWQyWZqZzdjYWNauXatQE21vb0+RIkWkpsOnTp1SKERKT0JCAq9fvwagRIkSWa49zgxzc3OWLFki1crJWxe8e/dOekbJfwstLS0pXLdu3ahVqxadO3fm06dPTJ06lf3792d7/GJiYhg2bJjC/d66dWtsbW3p0KED0dHRLFq0iNWrV3/T/tN6dqWmqampcO9ERUVJBZWdO3dmypQp0joTExOKFClClSpVaNy4MXFxcVy9elXKiGZWWr9LarGxsUyZMkWhZVGNGjXYuHEjjRo1IiYmhlOnTikc+9ChQ1JroaVLlyoMPGhkZMSUKVMoXLgw8+fPZ+/evXTt2hUbGxvg32eCpaUlzs7O0jPaxMSE7t27o6Ojw4QJE3j16hVPnjxRCpfVZ0lGNm7ciEwmw8TEhE2bNpEnTx5pXb58+bCzsyM6Oppz585x584d4uLiVA7W+S337apVq4iIiEBTU5MtW7YodO/q0aMH5cuXp3v37t88qvuYMWN4+PAhPj4+xMbGcvDgQQ4ePIiGhgalS5emSpUq1KxZkxo1aqjs8qWmpoaenp7C+X49uOW1a9d4+vQpADNmzFCo9TY2Nsba2prChQvz119/kZyczI0bN+jatavK+NrZ2bF161bpGWFsbMyqVasYMWIEp0+fZuvWrfTu3TvbWx7Kz0+eFjU1NRXOsXr16piamhIeHs7x48dVZm7lBVPVqlX75vhl5/dkVFQU8+bNA1Ja9Lm4uEjfk5DyzKlZsybt2rUjLCyM1atXS999Gf3mqenq6rJy5Urpvknrt01t586d+Pr6AikFoKnHn6lVqxZbtmyhffv2BAcH4+rqSq1atXjx4oX0zpwzZ45C68maNWuyceNGGjduzKdPnzhx4kSWC7n/14maf0HIIvmD6/79+0pz6QJSCWpGfXX379+PTCYjd+7cTJ06VeFBLWdnZyc1Azxz5ozUVDD1nK99+/ZV2VyscePGaTbr27t3L8nJyejo6DBq1CiV29ja2krnqqpZ/fdq1aoVlSpVUvqrXbs27du3Z8GCBXz48AF9fX3mzJmj1GT68+fPdO7cmdatWzNo0CCVfcF1dHQoX748QLqDGrVr107ltIOpP+zSq4lPTExk2LBh3L59Gy0tLVavXq2ySXPqpv6qRuy1sLBg3bp17N+/nz/++CPN430vDw8P1q1bpxAfSMnsNmjQgPbt2zNkyBCVzR3V1dWlF/HHjx8V1sn3FxERwZcvX5TCdu/enXXr1nH8+HGFvrq7d+8GUjIvvXv3Vhnnpk2bSi/41Onx5s2b0lgQY8aMURnnv//+O90BsNISGRkp1UZ+S/hcuXJJH0nyQp+vlSpVSmUT9BIlSkhdFbLSCuTTp09Sk898+fKluZ1MJuPz58/p/qVuOppakyZNVDbHPXbsGLGxsQBMnz5dIeMvZ2lpKRVKPnz4UKkZaHYoUqQIgwcPVrlc/oF95coVoqKivmn/AwcOVPnsSv3n7OysECYqKoo+ffrQokWLNAv18ufPL7Uu+ZZB2NL6XVIzMjJSOY+3iYmJVJv2dXqT359Vq1ZNc8aBXr16Sc3MU9+f8oxsTEyMygKT5s2bs3r1atzd3RUK2L71WZKRSpUq8ccff/DXX38pZPxTk/cjTk5O5tOnTyq3yep9K5PJpObbHTp0UDmui62tbab6sadFX1+fXbt20b9/f4XMXFJSEj4+PmzduhUnJydq1KjBP//8802jxuvp6dGrVy9atWqV5iCi1atXl/6dXjqePHmy0jNCTU2N8ePHo6amxpcvXxS6BfwsqWvzL126pNCtEiA6OlpqEfCtTf7lsut78ujRo9L9NWHCBJXfk4ULF5YK/dzc3FTeVxmpXbt2mvdNWlIXlKhKM4aGhvTq1QsbGxsKFSqETCZT+DZRNdaToaEhq1atkmaBErJGZP4FIYtq1qyJkZERMpmM06dPK6wLCAjA29sbXV3dDPuUypsEVqtWLd1BXOQPy+TkZGngsTt37kgP7nr16qUZNq0+3fJanGLFigGk+fEvH+ju5cuXShm9H0lTU5NmzZoxbdo0zp8/rzIjbGBgwMiRI1m8eLHCx4bcly9f8Pb2lgpM0nvRpTWgX968eaV/yzM1X0tOTmbs2LHSx8A///xD7dq1VW5bsWJFaSCvvn37smjRIu7evauQyapbty62trbfPJBVmzZtuHfvnsLfnTt3uHDhAq6urjRr1oyYmBg2b96Mk5OTQi2Turo6Q4cOZcGCBSpf0snJyfj6+koftV9fU3kt15MnT+jSpQu7du1S+AA2MTGhfv36FC9eXCGTIr8XypYtS2xsbJrpUd7tw8vLS2r6f/PmTSAlPaRV+q+trf1N/VtTd3dRVaiQGfLz/Lqrglx6g0nK019aaU+V1E2n0xMeHp5hBtbT01Nl2LQGpJQ/V4oWLUrJkiXTPHaLFi2kf2e1S0NmNGvWLM0WD/LncmJiosKgXz+amZkZY8aMYdmyZRQvXlxpfUJCAp6entJv/S0f5pkZKNTGxibNAgJV6S06OprHjx8DKfdnWvdmXFycVNCauhmuvKDw7du3dOrUiU2bNuHn5yet19HRkVo9pb7HvvVZkpHevXszZ86cNMdU8ff3V4hfWr9DVu9bX19f6V30Le/szNLW1mbs2LFcu3aNhQsX0rp1a6U+2XFxcRw8eJDmzZvj4eGRpf1XqVKFSZMmKbQwSe3Tp08K91VaBYjm5uaUK1cuzXXygqAf8XzIDHllQ2xsLOfPn1dYd+7cOalFSHqtATMju74n5d3KjI2NMTExSfM+lRfwff78WWrBkRVZHYw4KipKmlUovXPo06cPbm5uzJ8/HzU1NUqUKCF1Hxk3bhwzZ87kxo0bCoUCVatWxc7OLlODIAqKRLN/QciiXLly0axZM/bu3cvJkycVmmrJS2kbN26c4bze8j78qj4EU0u9PiQkBEChhDi9kavT2rf8I8rHxyfTzaXCwsIy3bQyM1KPWJ2cnMzbt285dOgQK1euJDExkffv39OwYUOVI8p/LTg4mFu3bvHy5UsCAgJ4/fo1r169yvRYBWm9PFJ/jKaVebt7967CukOHDtGlSxeVJe8FChRg9OjRzJ8/n6ioKNavX8/69esxNDSkZs2a1K9fn0aNGmXqnNOSK1culU32DA0NMTc3p0GDBsyePZvt27dz/fp13NzcVDbde/fuHR4eHrx48YLAwEBev37Ny5cvlWpBUuvRowenT5/mwYMH0mBekJIZrFOnDo0aNaJ69eoK1yY6OlqqHTp79mym5nGOjo4mKioKQ0ND6V7IaAT3jO4zVVIXwHw96FBmyWsO0/pN0/twkae/tNKeKnny5JG6GqQ1O8b3SivOmX2mFShQAENDQyIjI6VnWnZKr+Ah9XgGX4+jklnbtm1TWeCYWX5+fnh6evLq1SvpeeXv7/9NGf7UMvMRnN4zXFV6Cw4OlgqUtm7dmqnuM6nfT02bNqVx48acO3cOf39/FixYwIIFCzAzM6NOnTo0bNiQOnXqKBWufcuzJCuioqK4ceMGvr6+BAQEEBgYiJ+fn9J9nta9l9X7NnVa+5Z3dlYZGBjQrl07KRPr7+/P7du3uXLlCpcuXSIxMZHo6GiGDBnCsWPH0h0cUJXk5GS8vLzw9vbG39+fwMBAXr16RUhIiMJ5p3X9Mho/xdLSEl9f3x/yfMiMkiVLUqZMGZ48ecLx48cV+vXLa7Lt7e3TbYqfGdn1PSn/pvv48WOWvunSKoBJS1Yz2m/evJHSQFYG5tTS0mLatGmMGTOG+Ph4du7cyc6dO9HV1aVq1arUr1+fJk2aKA0CLWSOyPwLwjdo0aIFe/fu5cGDB4SGhkpNxlOPypoReV/29KbxA8Xp2OQZr9TNVdObri2t2uPMDgD0vWEyS11dHVNTU5ycnChdujRDhgzB09OTnj17snv37jQf8BEREUydOpUzZ84ofWTo6elRo0YN3r59K9VcpeV7RhSWyWTkypWL5s2bS9N37dixQ5ou52t9+/alTJkybNy4EQ8PDxITE4mMjOT06dOcPn0aTU1NHBwcGDNmzA/prw0wbNgw9u/fT1xcHPv371fI/MfHxzN37lz279+vVGujpaVF9erVSU5OVlkjo62tzY4dO9i+fTsHDhyQ+uy9evWKV69esX37dszNzZk+fbpU+5VR3+m0REdHY2hoKN0LGX0cfctHmq6uLubm5gQHB3/TAHEvX76UWlbIW9l8LbtHs9bW1sbMzIzg4OB0uwsULFhQacR3UJw6Li2qmvND5p9pkPLcioyMTLcw6Vul10UjdTr5kc80VYKDg/nnn3+k1iqpGRkZUb16dR4/fpzmIGkZSet3SS2rU7d+77tCXV0dFxcXaXYQeSY+JCSEffv2sW/fPkxMTBg/frzCOAPf8izJjOTkZFxcXNi4caNS4bCmpiZ2dnYYGhqmOdCbXFbv29SFCt/yzv5e8oE8u3TpQlhYGFOmTOHKlStSK7DU4/5k5ObNm0ydOlUaWyS1woULU6dOnXQHG4b0r0Hq9Tk12DCk1P4/efKEa9eu8enTJ/LkycP79++l+/d7m/zLZef3ZFZ8S5jMPGNSS93dLaPf/GstW7bE0tKSdevWcenSJeLi4oiJieHy5ctcvnxZ6g46ZcqUTL1zhH+JzL8gfIPq1auTP39+3r59y6lTp+jbty8vXrzA19cXIyMjatWqleE+dHV1M/XxmzpzJH/Ape5zFRsbm2bG5us+3XLa2tpER0fTsmVLli5dmmFcf6YGDRowdOhQli9fTlBQEH/99Re7d+9W+thKTEykX79+Up/hatWqUbNmTUqWLEmxYsWwsrJCXV2dMWPGZJj5/x6amposWbKExo0bExAQwMOHD1m6dCmNGzeWRhP+Wo0aNahRowbR0dHcuHEDDw8Prl27RkBAAImJiWzatAmZTMaECRN+SJzz5MlD0aJFefLkidJ0TaNGjZKaOdrY2FC/fn2sra0pUaIExYoVI1euXCxdujTN5pi5c+emX79+9OvXD39/f65fvy6d4+fPnwkODmbIkCHs3bsXGxsbhczYgAEDGDNmTJbPBTJuGp/WvZCRmjVrcuDAAQIDAwkICMjSHPGpm9R+T01xVsnjHB4ezsOHD6Wmnj+a/PmUmQy9/LmW1Q/CzMxhnl6GIXXcsrMlU0YiIyNxcHAgODgYdXV16tWrR5UqVbC2tqZ48eJSP/Fu3bp9c+b/R0j9+0yfPj3LU0dCSgFA586d6dy5M2FhYVy9ehUPDw+uX79OREQEHz58YPz48RgaGmJvby+Fy+qzJDPmzZvHtm3bgJQCOXt7e0qVKkXx4sWxtrYmd+7c7N+/P8PMf1alfmend39863Nq7969vHr1CgsLC3r06JHutgULFmTVqlU0adKEsLAwHj58mOnjPHz4kP79+5OYmIienh5NmjShfPnylChRgpIlS2JiYsKXL18yzPxndB/Lnw8/qjAkM1q3bo2zszOJiYmcOXOGzp07c+rUKb58+YKxsTF169bNluNkx/ek/D1aoUKFHzJG07dKnSnPSvc1ORsbG5YvXy5Noyl/bvj6+pKUlISbmxvR0dG4uLhkZ7R/e6LPvyB8A3V1damvl3yKFnkpbbNmzTJVuyLPGKbuX6hK6nnu5WFSZyrTq5FM6yNSHj44ODjdY2eluXF2cnJykvp3P3z4UOWD/dSpU1LGf8KECWzfvp0hQ4bQuHFjihUrJjUH/dFjFdjZ2dG0aVPU1dWZOXMmGhoaxMTEMG3atAzD6uvr07RpU6ZNm8bZs2fZv3+/1Pxy165d390MOD3y65N6oMR79+5JGX8HBwfc3NwYMWIELVu2pGTJklIBTGavqZWVFT169GDVqlXcvHmTf/75BzU1NRITE6VBxAwNDaXCq29Jj/K07O/vn25/92+dOrFDhw7Sv7MyY0BSUhI7d+4EUlqhpJ7270dLXYMqn0r0Z8jsMy08PFyqdUrd3FieJtPLAKU1cGJq6WWeX716Jf07q02dv8euXbuk9L18+XJcXV0ZMGAADRo0kDL+8OOfV1mVejDZ7HhfFCxYkM6dO7NkyRJpCkZ5M/nt27enGS4zz5KMhIaGSjPsNGnShGPHjjF27Fjatm2LjY2NFI8f8RukTmvf8s7OyL59+9i8ebNUsJGR3LlzS+MWZKZATW7ZsmUkJiZiYGDAkSNHWLBgAT179qRGjRpSk/DMXL+M0pL8GllaWmY6btktf/780sC98vfiuXPngJRa6exqtZWd35O/2jdd6pkQAgIC0twuJCSExYsXs2/fPpXpR1tbm/r16zNhwgTc3d05ceKEwswg4eHh2R/535jI/AvCN5IPWvXgwQPCwsI4efIkkLkmWvDvQEi3b99Od0Rc+ctATU1NyhBXq1ZNqpGRv4xUkc8IkNaxfXx80u33OnXqVKpXr06nTp1+ahNZdXV15syZI730NmzYoNRE2cvLS/p3WtPNxMbGcv/+fSDzA6F9jzJlyuDg4ACkXHv59HVyrq6utG7dOs3aM1tbW6nJdXx8fJojTX+v2NhY6eMqdd/LzFzT5ORkhTnr5dc1NjaWfv36Ua9ePSnTm1ru3Lnp3bu31B9b/rJWU1OjcuXKANy4cSPd2oH+/ftTq1Yt+vTpI33EyJv8xsTESHMEq4rztWvX0txveuTTY0HKqOepzz09a9askTLBDg4OP7UGq2rVqlJLgwMHDnDhwoVMh/2eAif5c+XVq1fS1E6qyJ+VgMI89PJaotQzFnwtdRpNS1rPPfj3I15PTy/T0ydmB3m8jY2N0xwxPywsTGqJ8zOeV5lhYmJCiRIlALhw4UKamYfk5GRatWpF3bp1GTt2rLR8xIgRNGrUSGn2AwANDQ1at24tDcYpfyZ867MkIw8ePJCu6x9//JFmt6rULXayK7NUvHhxqQDg6wHkUksv7aZH/gz19/dXmoI1LfLM2NfTmKqaOUdOno5r1aqlUGiVWurrl1Y6lo8PoMrz58+lQjr5eeUU+ZgJN2/e5P3791KLt+xq8i+XXd+T796948GDB2lu5+rqSpUqVWjbtq1CZjy93/x7mJiYSN8Y6aXLixcvsm7dOqZMmcKXL184cOAA7du3x97eXuU9WLx4cYUZXUTmP2tE5l8QvlHlypUpWLAgMpmM1atX8/LlS0xNTRXmI01P586dgZRarpkzZ6p8ST58+FCq1ahfv77U911HR0d6WWzfvp3nz58rhb1//740YMzXunTpAqR86M+YMUPlh/aDBw84dOgQERERGBkZfffANllVokQJBgwYIMVz6tSpCtco9Ydb6tYRcsnJycycOVMqtPjWuZOzavjw4VJt2dy5cxUKdnLlysXz58/x8vJSGBE7tSdPngAprQJ+1Ci2GzZskDLZqUf1z+iaAqxcuVKhq4D8uuro6BAeHk54eDh79+5V2fT606dP0gdf6ubz8vQYERGhMpMAKYMBXrt2jffv31OkSBHpY6Vq1apS7dCCBQtUFlJt2bIlwxqR9MycORNDQ0OSkpIYPHhwuh/oycnJrF27lpUrVwIpH9aqpp370WbNmiU1Nf77778zbIYrH216/Pjx33zM9u3bS31Cp0+frjINBAYGsmbNGiBlUK3Uo6bLf8eEhASVBRZ3797NVCHOrVu3VBaK+vn5SbXLrVq1+uYZHL6F/N769OmTyinW4uPjmTRpkvSh+7OeV5khf1f5+fmxceNGldts27YNPz8/3rx5IxUWQEqtclBQEEePHlVZyJ2QkCAVksmfCd/zLElP6pratJ5vBw8eVChE/NZm+KrIp/E7ceKEylH2AwMDM11z/7Vu3bpJheXjxo2TxlZIy+7du6V3zddTP6Z+D3ydDuXrXr16pfKbJTQ0lEWLFqUZXk4mkzF37lyljJ38ewhSCgNTD7SX3eTpIb17rUmTJujq6hIbG8uiRYtITEzE0tJSqojJLt/7Pdm+fXvpeTZr1iyVhegBAQFs3ryZqKgoEhISFApvUt8b2f3skaf7a9euqXx3RkdHS8+VatWqkT9/fvT19Xny5AnBwcFpfsfK06+6uro0eLSQOSLzLwjfSE1NjebNmwP/zmvcsmXLTI8+XLp0aamW+OTJk/Tr149bt27x8eNHAgMD2bhxI3369CExMZE8efIwffp0hfBjx47FyMiImJgYevbsyb59+3jz5g3h4eHs2LGDfv36pVlrUaZMGbp37w6k1Ob06tWLa9eu8eHDBwICAti+fTsDBgwgMTERLS0thZqcn2nw4MHSCLH3799n165d0rrUU7eNHj2a8+fP8+bNG0JDQzl79iw9e/bEzc1N2uZbB5bLKj09PWnwpI8fPzJ37lxpXadOnaRpfYYMGcKOHTt4+fIlHz584OnTp8yaNYvDhw8DKR9z31Ia/+XLF5VT/Hz8+JGHDx8ybdo0KWNauHBhhQ+/2rVrS8ecNWsWR48eJSwsjPDwcK5evYqTkxOrVq1SOF7q69qvXz8Anj17Rt++fbl69ar0EX/58mUcHR2JiopCQ0NDoWVBo0aNaNCgAQA7d+6UBnz8+PEjL1++ZNWqVYwePRpIqTmVzxMPKS/+GTNmoKamhq+vL926dePy5ct8/PgRPz8/5s2bx8KFC79r8MQiRYqwdu1aDAwM+Pz5MwMGDOCvv/7i3LlzBAcH8+nTJ16+fMm+ffvo1KkTS5cuRSaTYWVlhaura4aDEf4IlpaWrF+/HhMTE2JjY5k2bRodOnRgy5YteHt78/btW8LDw3nw4AFr1qyhdevWDB8+XMqYNmvWjFKlSmXpmCYmJowaNQpIyah369aNS5cu8eHDB8LCwti3bx9//vknERERaGpqsmDBAoU03rBhQ+kjdNKkSRw9epS3b98SEBDA+vXr6d+/f6ZbUIwaNYp169YREhLCu3fvcHNzw8HBgZiYGIyMjKR4/izy51VycjKDBg3Cw8OD9+/fExgYyJEjR/jjjz8UCjZ+1vMqM7p3707ZsmUBcHZ25p9//uHRo0dERETw7Nkz5s2bx/z584GU5vny9xr8+0x48+YNvXv35syZMwQHB/Pu3Ttu376Nk5OTNHBc6hZR3/osSU/lypWle3HlypXs3LmToKAg3r17x507dxg/fjz//POPQpjs/B0GDBiAlZUVycnJDB48mA0bNkjp8/Dhw3Tr1u2b+kVDSgsu+XPww4cPdO7cmXHjxnH+/HkCAwP59OkTQUFBnD59msGDB0vfE926dVNqAZN6ZpLjx48TGRkpFarK07Gvry9jxozhyZMn0rN2w4YNtG/fXqEWNq3rp6GhwdmzZxk8eDAPHz7k48ePeHp60rdvX2mqwPHjx//QFlPy85SnLVWFUzo6OlJLnUOHDgHZX+sP3/89mS9fPkaMGAGAt7c3Xbp04cyZM7x7946QkBAOHTqEg4MDERERqKmpMWnSJIVnb+oxKU6dOkVERES2tTx0cHCQZrEYNmwYrq6uBAYG8vbtWy5fvkyPHj0IDg5GQ0NDGvOnUaNG0rff5MmTWb16Nc+fP+fjx4+8ePECFxcXXF1dAWjevLmY7i+LxIB/gvAdWrZsyZYtW6RMdqtWrbIUfsKECSQkJLB3715u3LihstmyhYUFy5cvl0aAlTMxMWHLli0MGDCAt2/fMmXKFIX1WlpajBw5ksWLF6s89j///ENCQgIHDhzA09NT+thKTU9PjyVLllC6dOksnVd2yZ07N9OmTaNv374ALF26lCZNmmBqakr9+vVp1aoVx48fJyAggCFDhiiFL1CgAPb29uzZs4fY2FjCw8MV+qD9KE2aNKFhw4ZcvHgRd3d32rZtS7169TAyMmLZsmUMGTKEjx8/MmvWLJXhGzZsyPDhw7/p2O7u7tJUROmxtLRUyphaW1szYMAA1q1bx/v371UW+hgYGNC5c2c2bdoEpDQzlb94O3TowIMHD9i9ezd3796lf//+SuE1NTWZNWuWwnRsampqLF68mNGjR3Pp0iXOnz+vsmlsvnz5WLNmjdJvWLNmTebPn8/kyZPx9fVl4MCBCuvNzc1p0qQJW7ZsyfC6pKVy5cq4ubkxefJkqWY5rS436urqtG/fngkTJih8VP1sFSpU4PDhwyxYsICTJ0/y+PHjDAe/rFKlCk5OTt88mFXfvn35/PkzK1euxMfHh0GDBiltY2JiwuLFi6UMpVzhwoUZMWIEixcv5tOnT0rpL3/+/MybN09lukqtYcOGeHp6snjxYqXnX8GCBXF1df3pH4t//PEHJ06c4M6dO/j4+ChM6SVXrFgxypYty7FjxwgODiYxMTHLo/P/CLlz52bdunUMGTKEhw8fcvDgQQ4ePKi0nZWVFevXr1cY5KtatWqMHj2aJUuW4Ovry7Bhw5TCqaurM2LECOrXry8t+9ZnSXqMjY2ZMGECM2bMIDY2Vqph/vpcHR0dWbt2LQCvX7/OtgEztbS02LhxIwMGDODly5c4OzsrtHRSV1dn/PjxzJs375v236lTJ3R0dJg7dy5v377lyJEjHDlyROW2mpqaODo6MnLkSKV1lpaWFCpUiNDQUFxcXHBxcaFDhw7Mnz+fMWPG4Onpydu3bzl+/LjKWtmGDRvy7t07vL29Vc4IAEiD2F28eJGLFy8qrR86dKhSi4TsVr16dR48eMDjx4+pV68empqaeHl5Kd1z7dq14/Dhw9J33o/I/MP3f0/269ePz58/s2bNmjTvNU1NTaZNm6b0fNfW1qZixYrcv3+f/fv3s3//fqpVq5buOByZpaOjw7p16xg4cCB+fn4sWbKEJUuWKGyjpaXFnDlzpJZgmpqarFixAkdHR969e8fy5ctZvny50r4rVKig8j4W0icy/4LwHSpUqCBNBWZpaUn58uWzFD5XrlzMnDmTNm3asGfPHu7evcu7d+8wNDTEysqKVq1a0b59e5XztkNKDf7Ro0fZsWMHZ8+eJTAwEB0dHapWrcpff/2Vbi2CpqYmc+bMoX379uzZswcvLy/evn2Luro6FhYW1K1bl969eysM+JQTatWqRZs2bXB3dyc6OpqZM2dKtc+LFy+mevXqHDp0CF9fX+Lj49HX16do0aLY29vTtWtXPn/+zL59+0hOTpZaBPwMU6dO5datW9Lgf8eOHUNPT4+aNWty/Phxtm7dyo0bNwgKCiIxMRFjY2PKlStH+/btpcF/souamhra2tqYmJhQsmRJGjVqRNu2bVVO2zN69GhsbGzYvXs3jx8/5vPnz+jq6lKkSBHq1q1L9+7dMTQ0ZO/evXz+/JmzZ88qzCs8ffp0GjZsyIEDB3j48CHv379HU1MTU1NTateujYODg8r5fvX19XF1deXcuXMcPnyYBw8e8PHjRzQ1NaXf08HBIc3MdPv27SlfvjwbN27k9u3bhIeHky9fPho1asTQoUOzZQTkIkWKsG3bNjw9PXF3d+fevXsEBwcTFxeHgYEBRYsWpUaNGnTo0CFHB6pKzdTUlCVLljBy5EiOHTvGrVu38PPzIyIiAg0NDUxMTLC0tKRq1ao0atQoWwr6hg4dSuPGjdm2bRu3b9/mzZs36OjoYGFhQdOmTfnjjz/SzHwPHDgQGxsbtm/fzv379/n8+TOFChWiUaNGDBgwIFPNsG1sbJg8eTIuLi5cvXqVmJgYihQpQvPmzenRo0eOFMjkzp2bTZs2sXXrVk6cOMGrV69ITEzE0NCQEiVK0Lx5czp16sSjR484duwYsbGxXLt2jYYNG/70uKqSP39+9uzZg7u7O8eOHePx48d8+vQJbW1trK2tadasGd26dVPZymXgwIFUrVqVXbt2ce/ePd68eYOamhoFChSgWrVqdO/eXeV849/6LElPt27dsLKyYsuWLTx48IDIyEi0tbUxNzenevXq9OzZEysrK06ePMnr1685e/ZstjY9L1y4MAcOHGDfvn24u7vj7++PhoYGFSpUwMnJCUtLy2/O/ENKBrJ+/fqcPHmSa9eu8fTpUz5+/Mjnz58xNDTEzMyMunXr0rZtW4XxXlLLlSsXa9euZe7cuXh7ewP/1uAXLlyYQ4cO4erqyuXLlwkNDUVNTY28efNStmxZOnbsSOPGjVm9ejXe3t54enry4cMHpftdQ0ODVatWsWXLFtzc3AgMDERfX59KlSrh6Oj4U/r6Dx06lNjYWKmm28TEhLCwMKWxDGrUqIGpqSnh4eFUrFgxSzO+ZMX3fk+qqakxYsQImjVrxo4dO6T3YHJyMmZmZtSoUYNevXpJtfBfW7p0KbNmzeLOnTskJCRk6zSshQsX5vDhw+zZs4cTJ07g5+dHbGws+fPnp1atWjg6OirFq1SpUhw7doxt27Zx+fJl/P39iY+PJ0+ePJQuXZqWLVvSoUOHHzYl8u9MTZZTw3kLgiAIgiBkE3k3haFDh6qs9RIEQcgqmUxGgwYNCAsLY+rUqRlOpSgIvzrR518QBEEQBEEQBOErt2/fJiwsDE1NTYUBcgXhv0pk/gVBEARBEARBEFJJSkpi/fr1ADRt2hRjY+McjpEgfD/R518QBEEQBEEQhP95Hz58YNOmTRgbG3Pp0iVu376Nuro6jo6OOR01QcgWIvMvCIIgCIIgCML/PG1tbam2X65Xr14qB6UUhP8ikfkXBEEQBEEQBOF/nq6uLnZ2dvj4+JAvXz66du2qcspSQfivEqP9C4IgCIIgCIIgCMJvTgz4JwiCIAiCIAiCIAi/OZH5FwRBSMXe3h4XF5cftv8JEybg4OCQqW1lMhmHDh3i/fv3ALi5uUlzmWckKCiIUqVKKf3Z2dnRvn17jh8//s3n8KuQn+OtW7dyOio5zt7eXuF3LleuHA0aNGDatGl8+PAhW4+T2fvDwcGBCRMmZNuxVaXn1H/Zeay0eHp68tdff1G7dm0qVqxI69at2bBhAwkJCdI2WbnHv9fX90BERAR9+vShfPny/PHHH7i4uGBvb/9T4pKdHBwclH5feZqeOXMmsbGxP/T4P/o9AOmn54sXL/7QY2fk+fPnXLp0SWHZly9f2Lp1Kx07dsTOzo4aNWrg6OjIzZs3FbYrVaoUbm5uPyWeX6fvy5cvY29vT/ny5dm2bdtP+R0F4b9G9PkXBEH4iSZNmkRSUlKmtr1z5w4TJkzg/PnzALRs2ZK6detm6XguLi7Y2dkBKYUJb9++xdXVlTFjxmBubk7FihWztL9fSaFChbh27Rp58uTJ6aj8EhwdHaURqePi4vD19cXZ2ZmePXuyd+9eDAwMvvsYBw4cQEtLK1Pburi4oKGh8d3HlLt27Zr07xMnTjB37lyFZdra2tl2LFW2b9/O/Pnz6dWrF0OGDMHQ0JB79+6xYMEC7ty5w5o1a1BX/7l1Kl/fA0ePHsXT05Ndu3ZhamqKvr4+PXr0+Klxyi4tWrRg0qRJ0v/HxMRw7do15s2bR3JyMtOnT8+5yGWTf/75R+Xc8Tn9TBs0aBAdOnSgQYMGAMTHx9O3b19CQ0MZPnw4dnZ2xMXFcfDgQfr27cvChQtp06bNT4+no6OjQvpetmwZRYsWZdu2bRgZGdG6detMP68E4X+FyPwLgiD8RFnJgH09JIu2tnaWMzh58uQhf/780v8XKFCARYsWUbVqVU6ePPmfzvxraGgonNv/Ol1dXYXrYWFhQZkyZWjVqhUbNmxg1KhR330MExOTTG9rZGT03cdLLfW5ye+jn/X7P336lPnz5zNu3Dh69+4tLbewsMDMzIyePXty4sQJWrdu/VPiI/f1PRAZGUn+/PmxtbWVlunp6f3UOGUXbW1tpd/X0tKSR48eceLEid8i829gYPCfeIYtX76cZ8+ecezYMQoVKiQtnzRpEtHR0cyePRt7e/ufntb09PQUjvnp0ycaNmxI4cKFf2o8BOG/RDT7FwRByILDhw/Ttm1bbG1tsbe3Z/Xq1Qo1+QEBAQwYMAA7Ozvq1q3L5s2badKkidQM8usmwRs3bqRx48aUK1cOe3t7Vq1ahUwm49atW/Tq1QuARo0a4ebmptTs//Pnz8yaNYs6depgZ2dHz549efToUYbnoK6uTq5cuciV69/yXz8/PynederUYfTo0bx9+1Zan5SUxNKlS6lTpw4VK1Zk+PDhzJkzRzqXW7duUbZsWdatW0f16tXp2LEjycnJhIeHM2rUKKpUqUL16tVxcnLC399f2u/79+8ZPnw41atXx9bWlj///JPbt29L6x8+fEj37t2xs7OjatWqDBs2jJCQEEC5yXNSUhJbtmyhWbNmlC9fnmbNmrF7925pX/I4Xr58mdatW1OuXDmaN2/OuXPnMrxm/1VmZmY0adJE6uYRFRXFlClTqFGjBpUrV6ZXr154e3srhLl69Spdu3alQoUK1KtXj6VLl0ppPHUz2tjYWCZNmkTt2rUpX7487du358yZM9J+vm727+XlRa9evahcuTLVq1dn4sSJfPz4UVpvb2/Pxo0bGTZsGHZ2dlSvXp3Zs2fz5cuXTJ+vg4MDU6ZMoXPnzlSpUoWjR48CcPDgQVq0aIGtrS0tWrRg69atJCcnS+EySqf79+/HwMBAZS161apV2bJlC/Xq1VMZJ09PT3r16kWlSpUoV64cLVq04MiRI9L67LoHJkyYgIuLCyEhIVLT66+bRWd0nhMmTGD48OE4OjpSqVIlpSnPfgVaWlrSsyskJIRRo0ZRs2ZNbGxsqFevHs7OztJv6+bmJj1/mzRpQrly5ejYsSN3796V9hcVFcX48eOpUqUKNWrUYPPmzUrHzEzaXbduHQMHDqRChQrY29tz7tw5zp07R7NmzahYsSL9+vWTunBlVkREBDNmzKB+/fpS2kjdzcnFxYWePXsyatQoKlWqxKxZswC4d+8ePXr0wNbWlgYNGjBjxgyio6OlcOmlKXt7e4KDg1m5ciUODg4kJiZy8OBBOnbsqJDxlxs5ciTr169XWTCdnJyMq6srzZo1o1y5clSqVIn+/fsTEBAgbXP58mU6duxIhQoVqFmzJhMmTODTp0/S+rTekfLzl6fvUqVKERwczKpVq6T35NfN/i9evEjHjh2xtbWlSZMmLFu2TKHLTqlSpVixYgUNGzakTp06CveGIPwuROZfEAQhk7Zs2cKUKVPo2rUrR48eZcSIEWzcuJH58+cDKZmhPn36kJyczO7du1m6dClubm4EBgaq3N+FCxdwdXVlxowZnDlzhjFjxrBmzRqOHj2KnZ2d9NGyf/9+lU1DR44cyZUrV5g3bx6HDx/GwsICR0dHhQ+nr3369In58+cTGxsr1VKGh4fTvXt3LC0tOXDgAGvXriU6OpquXbsSExMDwKJFi9i7dy/Tpk3j4MGD5M+fn+3btyvsOykpicuXL7N3717mzJlDXFycVDiwY8cOtm/fjrGxMV26dCE8PByA6dOnEx8fz44dO3B3d6do0aIMGTKEmJgYkpKSGDRoEFWrVuXo0aNs2bKFkJAQ/vnnH5XnNn/+fFavXs3QoUNxd3enR48ezJkzhy1btijE0dnZmUmTJnHs2DFKlizJ+PHj+fz5c5rX7L+uZMmSBAYGEh0dzYABAwgMDMTV1ZV9+/ZRsWJFunXrxuPHj4GUTM7AgQOpXLkybm5uzJ49mz179rB69Wql/cprA9etW8eJEyeoV68eo0aNIigoSGnbhw8f4uDggLW1Nfv27WP58uU8ePCAfv36KRSeLV++XPq9x40bx44dOzh27FiWznf//v306tWLXbt2UbduXfbu3cvChQsZOnQox48flzIrixYtAlKak2eUTh89eoStra1CgVlqNWvWxNDQUGl5eHg4/fr1o3z58hw6dIjDhw9ja2vLpEmTePfuHZB998CkSZNwdHSkYMGCXLt2TemZkZnzBDh9+jS1atXi4MGDP70lQ3q+fPnCpUuXOHLkCO3atQNg8ODBREVFsXnzZk6dOoWjoyMbNmzgwoULUrjQ0FD27NmDs7Mzhw4dQkdHhwkTJkgZyJEjR/Lw4UPWrl3L5s2buXTpEsHBwVL4zKbd1atX07JlS9zd3SldujTjxo1j7dq1ODs7s3btWry9vbNUmJKUlISjoyOenp44Ozvj5uZGyZIl6devHw8fPpS2u3PnDvny5ePIkSM4ODjw9OlT+vbtS926dTl69CiLFi3Cx8cHR0dHZDJZhmnqwIEDFCxYEEdHR1xcXAgMDCQiIoJKlSqpjKepqSm2trYqu/hs27aNjRs3MmHCBE6fPs2qVavw9/eX3pkfPnxg6NChdOrUiRMnTrBy5Uru3LnDwoULgfTfkV+7du2aFO/U3YHkrly5wsiRI+nSpQvHjh1j2rRpnDx5krFjxypst2vXLlasWMHKlSuxsrLK3I8lCP8hotm/IAhCJshkMtavX0/Pnj2l2j8rKysiIiJwdnZm+PDhnDlzhg8fPuDm5iY1eXZ2dpY+VL8WEBBA7ty5MTc3x8zMDDMzMwoUKICZmRm5c+eW+n2amJgo1aq8fPmSK1eusHHjRurUqQOkZCIMDQ35+PGjlEkZMGCA9FGWnJzMly9fsLW1ZdOmTZQpUwaA3bt3U7BgQSZPniztf9myZdSoUYNTp07RokULdu3axcSJE2nSpAkAkydPxsvLS+mcHB0dpQ+m/fv3ExkZibOzsxSfOXPmcOvWLfbt28ewYcMICAigZMmSWFhYoK2tzaRJk2jTpg0aGhpER0fz8eNHChQogLm5ORYWFixbtkxl7Vl0dDS7d+9mwoQJUt9TKysrgoKCWLdunUJT7ZEjR1KzZk0AhgwZwunTp/H19ZXGRvjdyDOlFy5c4P79+9y8eVNKn3///Tf37t1j27ZtzJ8/n+3bt1OhQgXGjRsHQPHixZk5c6bKax4QEICenh4WFhYYGhoyYsQIqlatqrK/8qZNmyhVqhRTpkyR9rtkyRLatWvHtWvXqF+/PgB16tSRWrxYWFiwfft27t27R/v27TN9vmXKlFHof7x69WoGDx5Mq1atpP1GR0czY8YMRowYwfHjxzNMpxEREVhYWGQ6DnLx8fEMGzaMfv36oaamBsDAgQM5fPgw/v7+5MuXL9vuAQMDA3R1ddPsDpOZ84SUrkL9+/fP8rlmN3d3d06fPi39f1xcHGZmZvTr1w8nJyfi4uJo164dLVq0kGqk+/Tpw/r163n27BmNGzcGIDExkRkzZkjPu759+/LXX3/x9u1boqOjuXbtGlu2bKFKlSoALF68mIYNG0rHzWzabdCggZROu3Tpwvnz5xk1apTUBaNWrVo8f/5c4RynTZsm1dbLDRo0CCcnJ65du4aPjw/u7u6ULFkSgBkzZuDt7c3GjRtZvny5FGb48OFSV5ixY8dSu3ZtnJycgJTn4OLFi2ncuDG3b9+mdOnS6aYpExMTNDQ00NXVxcjIiFevXgHfNg5BkSJFWLBggXQ9zc3Nad68OadOnQJSCscSEhIwMzPD3Nwcc3Nz1q5dKxWqpPeO/Fr+/PmleKtK/2vXrqVLly78+eefUtxmzJhB7969CQoKkroKtGvXjvLly2f5XAXhv0Jk/gVBEDLhw4cPvHv3jsqVKyssr1atGomJibx8+ZLHjx9TtGhRhb7OpUuXTrOff9u2bTl48CDNmjWjRIkS1KpVi2bNmqn8sPmar68vgEKffS0tLSZOnAgg1b7Onj2bChUqEBcXx969ezl+/Dj9+vWjRo0aUrjHjx/z/PlzpcxvfHw8fn5++Pn5ERcXp3AsNTU1KleuzNOnTxXCpK4pefz4MZ8+faJq1aoq9wswdOhQxo4dy+nTp6lcuTJ16tSRBmnS0tKif//+zJo1ixUrVlCjRg3q169PixYtlK7Hy5cvSUxMVPn7bN26VSGzVKxYMenf+vr6QEoG4XcVFRUFQGBgIDKZTCFjA5CQkEB8fDyQkq5q166tsL5Zs2Yq9ztgwACcnJyoWbMmtra21K5dmzZt2qhM76r2K783nj17JmWgihcvrrCNgYFBln8bS0tL6d8fPnwgLCyMJUuWKGSWkpOTiY+PJygoKFPp1MTEhIiIiCzFA1IyGB07dmTbtm34+voSEBAg3TPyDE523QMZycx5guL1y0n29vaMGTMGmUzGw4cPmTNnDrVq1cLJyUnqttSzZ09OnTrFw4cPef36Nc+ePePdu3cKXTpAMV3J02diYqL0HE2d2cuXL59CQU9m027q66ajowOk/P5y2traSoU2w4cPp2nTpgrL5JlsX19fDAwMpIw/pDx3q1SpolCznTdvXoV77vHjx7x+/VplYaafnx/Vq1fPUpqSj/PxLenf3t6eBw8esHz5cl69esWrV6948eIFpqamQEpBXevWrXFyciJ//vzUrl2bBg0aSIXM3/OO/Nrjx495+PAhBw4ckJbJW3/4+flJmf9fJf0Lwo8iMv+CIAiZ8PXge3Lyj8xcuXKhoaGh9NGZHhMTE44cOYKXlxfXr1/n2rVrbNu2jWHDhjF06NB0w6bV/Phrpqam0sfM1KlTiY2NZeTIkWzdulXKKCcnJ1OjRg2mTZumFN7AwIA3b94AaV+D1FKPrJycnEzRokVZs2aN0na6uroANGnShKtXr3L16lVu3LjB5s2bWblyJfv27cPa2poxY8bQvXt3Ll++jIeHB7NmzWLDhg0cPnxYYX+Z+X3kcufOrbRdZs7tv8rHxwcrKys0NTXR19dXOQ2X/JpkNl0B2NnZcfnyZa5fv46HhweHDx9mzZo1bNiwQWpZIZfW9ZXJZGhqairFIzNh05K6lYz89584cSK1atVS2rZQoUKZSqd2dnYcOHCApKQklc2bx4wZQ6VKlejevbvC8hcvXtC9e3dsbGyoVasWTZs2xdjYmM6dO0vbZNc9kJHMnCf8+FkTMktPT096dllZWVGgQAH69u2LhoYG06dPJyYmhp49exIXF0fz5s3p0KEDtra2KsdlSCtdyVtjfP3cTn0fZDbtqrp35PtPS968edPMbKZ33NTH+vr3Sk5Opk2bNlLNf2ryjHx6aerra2VhYUG+fPm4d++eyu5nfn5+zJkzh4kTJ2Jtba2wbt26daxatYoOHTpQs2ZN+vTpw/nz5xWmml28eDF//fUXV65c4caNG4wdO5bKlSuzdevW73pHfi05OZn+/fvToUMHpXWpWwr8KulfEH4U0edfEAQhE/Lly0e+fPkUBoqClMG8NDU1KVKkCKVLl+b169cKNSR+fn5SzevXjh49yu7du6lcuTLDhw9n3759dO7cmRMnTgDpfzjKa7JSD9b25csX7O3tpSaVqkyePBlTU1PGjRsnzZVtbW2Nn58fhQoVwtLSEktLS/LkycPcuXPx9fXF0tISbW1t7t+/r7CvBw8epHkcSOlrHhISgoGBgbRfMzMzFi9ezJ07d0hISGDevHkEBgbSsmVLZs+ezblz51BXV+fSpUu8fPmSadOmkTdvXrp168aKFSvYsGEDfn5+Si0OihcvjqampsrfJ3/+/Dk+dVZOCQsL4/z587Rp04aSJUsSHR1NYmKi9HtYWlqyfv16aTrJ4sWLKw0AuHXrVoXMqtyKFSu4e/cujRo1YvLkyZw+fRoLCwuFptpypUqVUvptnj59SnR0tFJtf3bKmzcvJiYmBAYGKpyzj48Py5YtAzJOpwCdOnXi8+fP7NixQ+kYt27dwt3dXWpFktqePXvImzcvmzdvZsCAAdSvX1/q6y+TybL1HshIZs7zV1ajRg369u3L7t27uXLlitQsftu2bQwfPpyWLVuir6/P+/fvM11gJO8KcO/ePWlZZGSkwoB0OZV2S5UqRVRUlNQ6AVLSzN27dylRokSa4aytrXnx4oVCev/y5Qvz5s0jNDQ0y2lKXV2dP/74Azc3N0JDQ5XWb9iwAW9vb8zNzZXWrV27lr/++ovp06fTtWtXKlasiL+/v/T7PHjwgLlz51KsWDH69OnDunXrmDt3Ljdv3uT9+/cZviOzwtramlevXilcl7CwMBYuXPhbj/kiCF8TmX9BEISvvH79mitXrij83b59m379+rFjxw527drF69evcXd3Z+XKlXTt2hUDAwNat26NsbExY8aM4enTp9y/f18aTEhVRj4+Pp4FCxZw+PBhgoKC8PT05M6dO1JzTXlt3NOnT5U+TooWLUrTpk2ZMWMGN2/e5NWrV0yZMoX4+HiqVauW5rnp6ekxa9YsgoKCpGbQ3bt3JyoqSor306dPGTVqFN7e3pQsWRIdHR0cHBxYsWIF586d49WrVyxYsCDDzH/btm3JkycPw4cP58GDB/j5+TFhwgSuXLlCqVKlyJ07N97e3kyZMoX79+8TFBSEm5sbMTEx2NnZYWxszPHjx5k6dSp+fn68evWKQ4cOkSdPHoWm+5DSfL9r166sWLGCY8eO8fr1a3bu3MmuXbtwdHTMsAbudxATE8Pbt295+/YtgYGBnDt3jv79+1O4cGFpALAyZcowatQobt68yevXr5k3bx5ubm5SJqZ///7cv3+f5cuX4+/vz+XLl1m9erU033dqgYGBTJs2DQ8PD4KDgzl9+jQhISEqmxv37duXZ8+eMWvWLPz8/Lh16xZjxoyhbNmySq0EspOamhoDBgxg+/bt7Nixg4CAAM6ePcv06dPR1tYmd+7cGaZTSCkUGTFiBPPnz2fhwoU8ffqUV69esWvXLoYNG0aTJk2kMQVSK1iwIGFhYVy+fJng4GDOnDkjTVGXkJCQrfdARjJznr+6ESNGYGVlxfTp0zE2NgZSClGDg4Px9PRkyJAhJCYmKozgnp4iRYrQvHlzZs6cyY0bN/D19WXcuHEK4XMq7dapU4cyZcowevRobt++jZ+fHzNnzsTX11dhDJOvOTo68vjxY2bMmIGfnx9eXl6MHj0af39/rKysMpWm9PT08Pf3lwqqnJycsLKyonv37hw+fJiAgAAePnzIxIkTOXz4MLNmzVJoPSJXqFAhrl+/zosXL3j58iVLly7lzJkz0vXV19dn165dODs78/r1a3x9fTlx4oQUz4zekVkxYMAATp8+zcqVK3n16hUeHh5MnDiRqKio/8R0i4KQXUSzf0EQhK+4u7vj7u6usMzc3JwLFy6QO3dutm7dyty5cylYsCADBgygX79+QErT0g0bNjBz5ky6dOlCnjx5cHJywsfHR6F5qFznzp2JiIhg9erVhIaGkidPHpo1a8aYMWOAlJq6+vXrM3LkSP7++2+ledPnzp3LwoULGTFiBAkJCVSoUIGNGzdiYmIijdKvSq1ataR+yK1ataJ8+fLs2LGDxYsX061bNzQ0NKhUqRLbtm2TmomOGDGCxMREJk+eTGxsLA0bNqRRo0ZSX3FVDAwM2LFjBwsXLpRGxraxsWHTpk1SZnPp0qXMmzdPGrW7WLFiLFq0SBp8a/369SxevJguXbqQlJRExYoV2bx5M/r6+kp9UCdOnIixsTGLFi3i3bt3WFlZMXXqVLp06ZJmHH8nmzZtYtOmTQBoampSqFAhWrZsiaOjozQX9qZNm3B2dmbkyJHExsZSvHhxVq5cKWViypQpw6pVq1ixYgXr16+nQIEC9OrVi8GDBysdb9q0aSxYsICxY8cSERGBubk5Y8aMUTnAZYUKFdiwYQPLli2jffv26Ovr07hxY0aPHq3y3shOjo6OaGlpsX37dubPn0++fPno0qULw4cPBzKXTiFloL5ixYqxfft23NzciIuLw8LCgiFDhtC9e3eV3QF69erFy5cvpQyllZUVf//9NytWrMDb21uaSjG77oH0ZPY8f2VaWlrMmjWLXr16cfr0aSZOnMiWLVtYtmwZpqamtGzZkkKFCim1XknPggULWLBgAaNGjSI5OZmuXbvy4cMHaX1OpV0NDQ02bdrEggULGDp0KAkJCZQrV44tW7YojL/ytYoVK7JhwwaWL19Ohw4d0NXVpWbNmowfP57cuXOTO3fudNMUpEyZuWDBAp4/f87Ro0fR0dFhx44dbNq0ifXr1xMSEoK2tjZly5Zl+/btUlr92sKFC5k5cyadOnVCT0+PChUqMGPGDKZPn05ISAjFixfHxcWFlStXsmvXLtTV1alRowbr169HXV09w3dkVjRv3pylS5fi6urK2rVrMTIyksaVEIT/JWqy37mjoyAIwk8UFBSEv7+/NPo+pIxmXK9ePXbu3JnmB9J/wdmzZ6lcubJUGABI04rNnTs3B2MmCIIgCIIgZIZo9i8IgpBN4uPjGThwIBs3biQwMJDHjx8zZcoUrKysqFChQk5H77ts3LiR0aNH8+TJEwIDA9myZQs3b96kbdu2OR01QRAEQRAEIRNEzb8gCEI2OnXqFGvXruXVq1doa2tTs2ZNxo0b901TE/1KgoKCmD9/Pnfu3CEuLo4SJUrg5OQkTckkCIIgCIIg/NpE5l8QBEEQBEEQBEEQfnOi2b8gCIIgCIIgCIIg/OZE5l8QBEEQBEEQBEEQfnMi8y8IgiAIgiAIgiAIvzmR+RcEQRAEQRAEQRCE35zI/AuCIAiCIAiCIAjCb05k/gVBEAQaNWpEo0aNcjoagvBDiXQu/C8Q6VwQhLSIzL8gCIIgCIIgCIIg/OZE5l8QBEEQBEEQBEEQfnMi8y8IgiAIgiAIgiAIvzmR+RcEQRAEQRAEQRCE35zI/AuCIAiCIAiCIAjCb05k/gVBEARBEARBEAThNycy/4IgCIIgCIIgCILwm1OTyWSynI6EIAjC1176+eV0FARBEIRsYHpuU05HQRB+OL1Bc3I6CpIlR37N7N3f7dRyOgr/80TNvyAIgiAIgiAIgiD85kTmXxAEQRAEQRAEQRB+cyLzLwiCIAiCIAiCIAi/OZH5FwRBEARBEARBEITfXK6cjoAgCIKQPeLj4zlw4AD37t0jPj4ea2trunbtSsGCBdMNFxkZyd59+/Dx8SEpKYny5cvTpXNnjIyMVG5//vx5zp49y/z5879pvSB8D5HOhd9dXOIXtt94wK2XwcQlfqFMoXz0rWOHmbFBuuEiYuLYeu0+9wPDSEqWUcmyEL1qV8BET0faJik5mf13HnPpqT+RcfEUz2+MQ60KlCyYV2Gbo17POP/kFR8/x1LIyIAOlUpT27rIDztnQRB+DlHz/5M4ODhQqlSpNP8+fPjwU+Lh5uZGqVKlpP+3t7fHxcXlpx0vs8LDw5kxYwb29vaUK1eOevXq8ffff+Pj45NhWBcXF+zt7dPdplSpUri5uWU5XqrY29un+9s6ODio3K506dJUqlSJnj17cufOHWl/EyZMUNqHjY0NderUYezYsT8traQlKCiIUqVKcevWrRyNh6Bs3fr13L17l46dOtHX0ZGPEREsWryYz58/pxkmKSmJZcuX8+rVK3r26EHPHj148eIFy5Yt48uXL0rb3759m33796e5v4zWC8L3Eulc+N0tO3OTm35B9KhZnqGNqvHhcyzTj1wiOi4hzTBJycnMcb/C8zcfGFC/MgPqV+Jp6DtmH73Cl6Rkabut1x/gfv8Zbe1K8XfTmmioqzPz6GXCPkVL2+y77cPuW4+oV8qSCa3qULpQPilOgiD8t4ma/5+oRYsWTJo0SeU6Y2PjnxybFAcOHEBLSytHjp2WJ0+e0LdvX4oXL86MGTMoVqwY4eHh7Ny5k65duzJv3jzatGmT09GUHDhwgKSkJAC8vLwYNmwY+/fvp1ChQgBoampK2zo6OuLo6AiATCYjIiKCJUuW0L9/f06ePImZmRkAdnZ2CoUycXFxeHl5MXPmTCIiIli/fv3POj3hP8LPz4+HDx4wfMQIypcrB0BJa2smTpzIpUuXaNWqlcpwd+/eJTAggBkzZkjpz8LCgunTp+Pp6UmNGjUAiIqK4vDhw1y5cgU9fX2l/WS0XhCyg0jnwu/uWdg77vqH8E/ruthZpnxHlDHLx1/bT3D60Qs6VSmrMpyHXxD+7yJY2q05hU0MASiaz5i/95zmxotA6pWy5F10DGce+dG3TkWalS8BQIUipgzfeZJD954wuGFVAC4+9aduySJ0qWoDQPnCprx885FT3i+oUbzwj74EgiD8QKLm/yfS1tYmf/78Kv/U1HJm3ksTExP09PRy5NiqfPnyhREjRlC+fHm2bdtG3bp1MTc3p1KlSixevBgHBwemTJlCYGBgTkdVYmJiIv2OefLkUVqWukmprq6utLxAgQKULFmSGTNmEBcXx9mzZ6XtNDU1FdKHhYUFbdu2pU+fPly5coWoqKiffZrCL87HxwctLS1syv77YWhgYEDJkiXxfvQo3XCmBQtKGSIAMzMzChYsqBDu+PHj+Pj44DR4MBVsbZX2k9F6QcgOIp0Lv7sHAeFoaeaigsW/3Vjy6GhT1iw/Xq/D0gkXRiEjAynjD1DYxBAzIwO8XocC4B0UTlJyMtWKmUvbaGpoUMmykMK+E74koa2pWD9ooJObqLj47z4/QRBylsj8/0KioqKYOHEiVapUoWbNmqxZswYHBwepBlhVE/qvl4WEhDBq1Chq1qyJjY0N9erVw9nZmeTkZFRJ3ew/rWbrK1euBCAhIQFnZ2fq1q2LnZ0dXbp04dq1awr7O3v2LG3atKF8+fJ0796dkJCQLF2Dy5cv8/r1a0aOHImGhobS+hEjRqCmpsbevXulZXv37qVJkybY2tri5OTEp0+fFMKEhYUxePBg7OzsqFevHu7u7grrY2NjmTRpErVr16Z8+fK0b9+eM2fOZCne3yNXrpQXbO7cuTPcVktLCzU1NZXXRpVbt25RtmxZzp49S7NmzbC1taVXr16EhoYye/ZshbQml5CQwIIFC6QuF9WqVWPEiBHpdjc4ePAgLVq0wNbWlhYtWrB169Y005zwY4SGhpIvf37U1RUf6/kLFCAsLO0PxtDQUExNTZWWFzA1JTxVuPr16zN79mwqV6qkcj8ZrReE7CDSufC7C/oYiamhHurqipVCBfPoExwRmW44MyPlMQEKGekTEpFSYRD8IQptzVwYpxoDAKBQHgM+fo4lLjGlC0yrCtZcfvYar9ehxCQkcuXZa+6/DqNeKcvvPT1BEHKYaPb/Cxk+fDghISGsWbMGXV1dZs+ezaNHj6hWrVqm9zF48GDy58/P5s2b0dPT4/z588ybNw87OzsaN26cbtivM/Jz587F09OTzp07AzBx4kT8/PxYtGgRpqamXLx4EScnJ1auXEmDBg24d+8ew4YNY+jQobRq1QpPT09mzZqVpWvg5eWFrq4uZcuqbtamra1NpUqVuHv3LgDHjh1j5syZ/PPPP9SqVYuzZ8+ydOlSqcn9ly9f6N+/P/r6+uzYsYOEhARmzJihsM/ly5fz7Nkz1q1bh6GhIfv372fUqFGcPn2awoV/bPO28PBw5s6di66uLvXr109zO5lMhpeXF1u3bqVp06bo6upm+hhJSUmsWbOGRYsW8eXLFwYNGkS7du3o1KkT+/fv5+jRoyxbtkwak2DhwoVcvHiR+fPnY25uzrNnz5g4cSJr1qxR2W1l7969LFmyhKlTp2Jra8vjx4+ZNWsW4eHhjBs37puui5B1sbGxaGtrKy3X1tIiLi4uzXAxsbEUKFBAZbjYVOHk91RaMlovCNlBpHPhdxeTkIhOqu6CcjqauYhNUB6fQu5zfCKF8ihn/nU0NYlJTCk0+JyQgG5uFfvOnUs6trZmLlpXKIlv2HvmHrsqbdOwTFHa2ZXO8vkIgvBrEZn/n8jd3Z3Tp08rLW/cuDGDBg3ixo0bbN68mapVU/pcLVu2jEaNGmV6/3FxcbRr144WLVpIHyh9+vRh/fr1PHv2LMPMf/78+aV/b9myhYsXL7Jz505MTU15/fo1x44d4/Dhw5QpUwaAvn378vTpUzZu3EiDBg3YsWMHlSpVYujQoQAULVoUX19ftm3blulz+PTpE4aGhul2gzAyMiIoKGXQme3bt9OyZUt69OgBwMCBA7l//z5Pnz4FwMPDg+fPn3P27FmKFEkZpXbevHm0b99e2l9AQAB6enpYWFhgaGjIiBEjqFq1qtSEPzu5urqyadMmIKVgIiEhgeLFi7Ns2TKF5qienp7Y2dlJ/x8fH4+JiQktW7Zk5MiRWT6uvCsFQI0aNXjw4AHjxo1DTU2NQYMGsXr1ap4/f06pUqUoX748zZs3p0qVKgCYm5tTq1YtfH19Ve579erVDB48WOpra2FhQXR0NDNmzGDEiBG/3JgSvwOZTKbUsiJZJktz+/TuJ9k3hhOEH02kc+F3J5PJlNJ0+mk8nX2RTjjU/v946cdHDUhMSmLKoYt8/BzLwAaVMTMy4FnYew56PkZbMxeOde3S34kgCL80kfn/iezt7RkzZozScl1dXTw9PQGwTdWH0NTUFCsrq0zvX1tbm549e3Lq1CkePnzI69evefbsGe/evctSE+wLFy7g7OzM0qVLsbFJGezl8ePHAHTv3l1h28TERAwNU/qX+fr6Urt2bYX1dnZ2Wcr8GxsbEx0dne42kZGRmJiYSMf8eoAnOzs7KfPv6+tLnjx5pIw/QJkyZRRqjgYMGICTkxM1a9bE1taW2rVr06ZNGwwM0p9S51v8+eef0uj/6urqGBkZqTxOuXLlWLRoEZAywNWsWbMoXbo0I0aMyFKtv5yl5b9N9XR1dSlcuLD0wSu/FgkJKaMIt2vXjhs3brBo0SL8/f15+fIlr169kgoDUvvw4QNhYWEsWbKE5cuXS8uTk5OJj48nKCiI4sWLZzm+Qvrc3d2Vuq9UrlyZyEjlJqFx8fHoqKgpldPV0SEuXrkfZ2xcHLo6OipCCMLPIdK58Lvbf+cx++8ozmJUo3hhPsUot2KJTfyistZeTi93bmITE5WWxyQkoqeVEk43tyaxicqtB2ISUsLpaeXmpl8Qr99FMKVtfWwtUrrK2JgXQC+3Jhuu3KNx2WIUyZv9lSOCIPwcIvP/E+np6SlkwlT5OpOeUT9w+SjzADExMfTs2ZO4uDiaN29Ohw4dsLW1lWrFM+PJkyeMHj2a4cOH07RpU2m5vNZk586dSgMEyvteqqmpKcVfU0XTtfRUrlwZV1dXnjx5IrUwSC0+Pp4HDx5IXRFA+ZqlPqaqOMG//ewhpbDg8uXLXL9+HQ8PDw4fPsyaNWvYsGEDNWvWzFL8M5InT54M0wCkZMjl21laWlKkSBE6d+7M33//zdq1a7NcU5X6fAGl/rKpTZ06ldOnT9O+fXvs7e3566+/2LhxI+Hh4Urbyq/txIkTqVWrltJ60UT2x6hXrx7lvxps7L6XFz4+PshkMoX08SY8PN3fwdTUlICAAKXlb9+8oWjRotkXaUHIIpHOhd9dY5tiVLJUTLd3XgVzPyBMKY2HRkRR2Njw611IzIwMePn2o9LysE/RWJumVJiYGxsQm5DIp9g48uhoK2yT30CP3Lk0eBsVA0DpQvkU9lPGLKV1aOCHTyLzLwj/YWLAv1+EvI/7vXv3pGXR0dG8evVK+n95pjZ1zbi/v7/072vXruHj48O2bdsYPnw4LVu2RF9fn/fv36fb5FEuPDycQYMG0bRpUwYNGqSwztraGoC3b99iaWkp/bm5ueHm5gZA6dKl8fLyUgj3KJ3Rl1WpU6cOJUuWZNGiRQoFG3Kurq7Ex8fTpUsXIKUWP/U1A/D29pb+XaZMGaKionj+/Lm0zN/fX+Earlixgrt379KoUSMmT57M6dOnsbCwUNlFI6eUKFGCMWPGcOnSJfbs2fPDjvPx40f27t3LtGnTmDhxIh07dqRMmTK8fPlSZRrKmzcvJiYmBAYGKqQLHx8fli1b9sPi+b/OyMiIolZWCn9lbWyIi4vjkc+/tUjytJ/WGBoAZW1sCAsLUxicMyQkhNDQ0HTDCcKPJtK58Lsz0dOhhKmJwp+thSlxiV+4H/DvQJSfYuN4EvpOqolXxdbClJCIKII+/NsyJuhDJMEfI6Vw5Qun/PfmiyBpm8SkJO76h0rbmBuntEZ8EvJWYf/Pwt4BYGooprgUhP8yUfP/E8XFxfH27VuV6woVKkTLli2ZOXMmmpqaFChQgKVLlxITEyNtU7FiRdTU1HBxccHBwQFvb28OHTokrS9YMGVamKNHj9KsWTNCQ0NZsmQJiYmJUpPutMTExODk5ISZmRmjR4/m3bt3UmZPU1MTa2trGjZsyLRp05g6dSrW1tacOnUKV1dX5s2bB6TMYd+5c2cWLFhAly5d8Pb2ZseOHVm6RhoaGixbtgxHR0d69+6Nk5MTxYsX5+3bt+zbt4/Dhw8zZ84cqVZ84MCBDB48mA0bNtC4cWOuXr3K6dOnpYGdqlevToUKFRg3bhzTpk1DQ0ODWbNmKdR8BwYGcvToUWbNmkWRIkV48OABISEhCn3ufwXdu3fnxIkTLFq0CHt7e5UjV38vfX19DAwMOH/+PDb//5G9Y8cOfHx8qFChgtL2ampqDBgwgKVLl2JmZka9evV49uwZ06dPp1GjRpmawUDIHqVKlqRUqVJs2LCBPzp1Qk9Pj6Pu7ujo6tKgQQNpu5CQEL58+SJ1halapQonTpxg+YoVdOrYEYCDbm6YFy6ssquHIOQkkc6F352NeQFszAuw/OwtHGrZoq+dm323fdDLrUmzciWk7YI+RJKYlETR/MYA1La2wO3uE+Ycu0KPmiktZnZ6PKRI3jzUKmEBQAFDPRqUtmLL9fskJCVhZmSA+31fPscn0L5SymB+Va3MsTbNy4pzt+hS1QZzY0Oeh7/n4N0nVClqRon/b0UgCMJ/k8j8/0QnT57k5MmTKtctX76c+fPn4+zszKhRo5DJZHTt2pUnT55I21hYWDBjxgxcXV3ZtWsXlStXZty4cYwfPx5IGS9g4sSJbNmyhWXLlmFqakrLli0pVKiQQm24Kt7e3lK//rp16yqsq1atGtu3b2fp0qUsXbqUqVOn8unTJ4oUKcKcOXPo0KEDkFLLvn79epydndmxYwfW1tY4OTlJfdczq3jx4hw6dIgNGzYwa9YsQkNDMTQ0pHr16uzdu1cahwCgQYMGLF68GBcXF5YvX07FihVxdHTk2LFjQErzdldXV2bPno2joyPa2toMGjSI4OBgaR/Tpk1jwYIFjB07loiICMzNzRkzZgzt2rXLUrx/NDU1NWbPnk27du2YPn26wvR82UVTU1NKi23atCFPnjxUr16dv//+G1dXV2JjY5XCODo6oqWlxfbt25k/fz758uWjS5cuDB8+PNvjJ6Rv8ODB7Nu3j/0HDiCTyShRvDhOgwYpdNXZuXMn79+/Z/78+UDKb/73qFHs2bOHbdu2oZErF2XLlqVrly6ZnlJSEH4mkc6F392Y5rXYev0+2248QCaDUgXzMrpZLfS1/y1QX3/5Lm+jYljdK2XcI00NDaa2q8/mq16svehJLnV1KliY0rtORTRSVXgMbFAZPa3cHL73lLjELxQvYMKUtvUpmCelRl9dXY3Jbeux+6Y3B+8+ISouHlNDfTpVLkObiorTTQuC8N+jJstMe3Ahx9jb29OhQweGDRuW01ERhJ/qpZ9fTkdBEARByAam5zbldBQE4YfTGzQnp6MgWXLk18ze/d1OzK6S00Sff0EQBEEQBEEQBEH4zYlm/8JPER4eTvPmzdPdpnz58lmaFvBnqFKlisqBB+Xy5s3LuXPnfmKM/vUrx00QBEEQBEEQhF+LyPz/4i5cuJDTUcgW+fLl4/Dhw+luo6Wl9XMikwVubm7pzpSQk31Ff+W4CYIgCIIgCILwaxGZf+Gn0NDQyNT89r8a+UjRv6JfOW6CIAiCIAiCIPxaRJ9/QRAEQRAEQRAEQfjNicy/IAiCIAiCIAiCIPzmROZfEARBEARBEARBEH5zos+/IAiCIOSQw4+K5XQUBOGHG5TTERAEQRAAUfMvCIIgCIIgCIIgCL89kfkXBEEQBEEQBEEQhN+cyPwLgiAIgiAIgiAIwm9OZP4FQRAEQRAEQRAE4TcnMv+CIAiCIAiCIAiC8JsTo/3nEAcHB27fvp3meg8PD0xMTH54PNzc3Jg4cSLPnj0DwN7eng4dOjBs2LCfcrzMCg8PZ+3atVy+fJk3b95gYmJClSpV6NevHzY2NumGdXFx4dChQ1y4cCHNbUqVKsW8efPo2LFjluKlir29PcHBwWmur1atGtu3b1faTk1NDV1dXcqWLcuIESOoWrUqABMmTODQoUMK+8iVKxfGxsbUrFmTiRMn/pS0Ivza4uPjOXDgAPfu3SM+Ph5ra2u6du1KwYIF0w0XGRnJ3n378PHxISkpifLly9Olc2eMjIykbaKionA7dAhvb28SEhKwtLSk8x9/UKRIEZX7DAgIYM7cucyZM4d8efNm52kK/+MSE+K4c34z/k89SEyMo6BFWWo0HUCevOaZ3sf5A/PQzK1DvbYjpWVXji7j+cO03xFdhm7AwKgASV8S8b55iBfeF/gc+R49w7wUL9cA21p/oKEhPqmE7BGX+IXtNx5w62UwcYlfKFMoH33r2GFmbJBuuIiYOLZeu8/9wDCSkmVUsixEr9oVMNHTAWDV+dtceuqfZvhVDq0oYKinsCwpOZlJBy+glUuDGR0afve5CYKQs8SbKge1aNGCSZMmqVxnbGz8k2OT4sCBA2hpaeXIsdPy5MkT+vbtS/HixZkxYwbFihUjPDycnTt30rVrV+bNm0ebNm1yOpqSAwcOkJSUBICXlxfDhg1j//79FCpUCABNTU1pW0dHRxwdHQGQyWRERESwZMkS+vfvz8mTJzEzMwPAzs4OFxcXKVxcXBxeXl7MnDmTiIgI1q9f/7NOT/hFrVu/nlcvX9Lpjz/Q1tbG3d2dRYsXM2P6dPT09FSGSUpKYtny5cTFxdGzRw+SkpI46ObGsmXLmDx5Mrly5UImk7FmzRpCw8Lo1LEjeYyMOH36NM7OzkybNo18+fIp7DM4OJgVLi4k//89IAjZ6dIhZ96E+FLVvg+aWjp4XdnNiR2T6DhwJVo6+umGlclk3Dq7Af+nHljb2iusq1inK6Urt1BYFh8bxYWDCyhoWQ79PPkB8DizDj/vS1Ss25X8hax5G/ocryt7iP70hrqth2fruQr/u5aducnz8Pf0rGWLjqYm++/4MP3IJZb82Qx97dwqwyQlJzPH/QqxiV8YUL8yScnJ7PTwZvbRKyzs0oRcGup0qlKWJjbFFcJFxyew+NQNbMwLkN9AV2m/h+49xe/NB8qa5f8h5yoIws8lMv85SFtbm/z5f62H6a9Wg/zlyxdGjBhB+fLlWbt2LRoaGgCYm5tTqVIlChQowJQpU6hYsSIWFhY5HNsUqa9hnjx5pGWqfmtdXV2F5QUKFGDGjBnUq1ePs2fP0rt3byClwODr8BYWFgQEBODi4kJUVBQGBunXCAi/Lz8/Px4+eMDwESMoX64cACWtrZk4cSKXLl2iVatWKsPdvXuXwIAAZsyYIRU0WVhYMH36dDw9PalRowbh4eE8f/6c3r17U6dOHQBKFC/OqL//5oaHB23/v+Dty5cvnL9wgaNHjqCZW/XHqSB8j/CgpwQ8v0PTP6dhUaIyAAUtbNi3agBP7p6gYp0uaYb9EO6PxxlX3gY/RyOXcvo0NCmEIYUUlp0/MA8tHQMatB+DmpoacbFRPPM6Q1X73tjWTGklZla0AgCeF7ZRpWFvdPTyZNfpCv+jnoW9465/CP+0roudZUqaLGOWj7+2n+D0oxd0qlJWZTgPvyD830WwtFtzCpsYAlA0nzF/7znNjReB1CtlScE8+hTMo1hItujUDQy0tRjRpDpqamoK6/zfReB29wlGuto/4EwFQcgJos//LywqKoqJEydSpUoVatasyZo1a3BwcJBqgN3c3ChVqpRCmK+XhYSEMGrUKGrWrImNjQ316tXD2dmZ5ORklce0t7eX9l+qVCmVfytXrgQgISEBZ2dn6tati52dHV26dOHatWsK+zt79ixt2rShfPnydO/enZCQkCxdg8uXL/P69WtGjhwpZfxTGzFiBGpqauzdu1datnfvXpo0aYKtrS1OTk58+vRJIUxYWBiDBw/Gzs6OevXq4e7urrA+NjaWSZMmUbt2bcqXL0/79u05c+ZMluL9PXLlSimTy52JDJSWlhZqamoqr40qt27domzZsqxbt47q1avTsWNHkpOT8fT0pFevXlSqVIly5crRokULjhw5ohD26NGjtG3bFltbWxo1asTWrVuldVFRUUyZMoUaNWpQuXJlevXqhbe3dxbOWvgePj4+aGlpYVP2349CAwMDSpYsifejR+mGMy1YUMr4A5iZmVGwYEEp3JcvX4CUwko5bW1tNDU1+fz5s7TM29ubY+7utGzZkk7Z0H1GEL4W7HePXLm1MS9mJy3T0ctDwSI2BL7wTDfs5aNLSE5Kom3fRZnKoAe+8MT/qQfVm/RDSzul5UxifAxlKjXHsmR1hW2N8qUUPEdFhGX1lARByYOAcLQ0c1HB4t8uW3l0tClrlh+v12mnsQcBYRQyMpAy/gCFTQwxMzLA63WoyjD3Xodyyy+I3rUroKel+M3xJSkZl3O3aGVrjZmRqFwQhN+FqPn/hQ0fPpyQkBDWrFmDrq4us2fP5tGjR1SrVi3T+xg8eDD58+dn8+bN6Onpcf78eebNm4ednR2NGzdON+zXGfm5c+fi6elJ586dAZg4cSJ+fn4sWrQIU1NTLl68iJOTEytXrqRBgwbcu3ePYcOGMXToUFq1aoWnpyezZs3K0jXw8vKS+sGroq2tTaVKlbh79y4Ax44dY+bMmfzzzz/UqlWLs2fPsnTpUqnJ/ZcvX+jfvz/6+vrs2LGDhIQEZsyYobDP5cuX8+zZM9atW4ehoSH79+9n1KhRnD59msKFC2cp/lkVHh7O3Llz0dXVpX79+mluJ5PJ8PLyYuvWrTRt2hRdXeWmemlJSkri8uXL7N27l9jYWN6+fUu/fv3o2bMns2bNIjExkfXr10sFIPny5ePEiROMHz+eMWPG0KhRI3x8fJgwYQIGBgZ06NCBAQMGoK2tjaurK/r6+hw5coRu3bqxb9++NH87IfuEhoaSL39+1NUVy3PzFyjArVu30g1namqqtLyAqSnhYSkfmYULF6ZU6dIcO3aMQoUKkSdPHo4fP05CQgJVq1SRwlhZWTFv3jz09fW5fuNGNp2ZIPwr4n0gBkamSunc0LgQfo8upxu2ftu/MTG1ytRxZDIZt89toqClDUXL1JaWGxiZUqvFYKXtXz/1QF0jF3lMMj/ugCCkJehjJKaGeqirK9bCF8yjz1Xf1+mGU5VJL2SkT0hElNJymUzGtusPKGuWn5ollFtO7r/jQ1KyjC7VbJh99Mo3nIkgCL8ikfnPQe7u7pw+fVppeePGjRk0aBA3btxg8+bN0sBvy5Yto1GjRpnef1xcHO3ataNFixZS5rdPnz6sX7+eZ8+eZZj5T93MfMuWLVy8eJGdO3diamrK69evOXbsGIcPH6ZMmTIA9O3bl6dPn7Jx40YaNGjAjh07qFSpEkOHDgWgaNGi+Pr6sm3btkyfw6dPnzA0NFRqipaakZERQUFBAGzfvp2WLVvSo0cPAAYOHMj9+/d5+vQpkDKQ4vPnzzl79qw0WNm8efNo3769tL+AgAD09PSwsLDA0NBQGnxP3oQ/O7m6urJp0yYgpWAiISGB4sWLs2zZMoXaWE9PT+zs/q3tio+Px8TEhJYtWzJy5MgsH9fR0RErKysg5XyHDRtGv379pOs8cOBADh8+jL+/P/ny5WPr1q20bNmSfv36ASkZvc+fP6Otrc3Nmze5f/8+N2/elAaJ+/vvv7l37x7btm1j/vz533BlhKyIjY1VqJmX09bSIi4uLs1wMbGxFChQQGW42FThevbowfLly5k+fTqQMjhl7z59KFGihLRNTo1TIvzvSIiPQTO3jtJyzdw6JCbEphs2sxl/gIDnt4l4F0TzpgMz3Nb/qQfPvS9StkqrDMccEITMiElIRCfV2EByOpq5iE34kma4z/GJFMqjnPnX0dQkJjFSabmnfwjBHyNxrKtc0fAi/ANH7z9jZoeGaGayZaEgCP8NIvOfg+zt7RkzZozScl1dXTw9U5ow2traSstNTU2lDFtmaGtr07NnT06dOsXDhw95/fo1z5494927d2k2+1flwoULODs7s3TpUmlk/cePHwPQvXt3hW0TExMxNExpcubr60vt2rUV1tvZ2WUp829sbEx0dHS620RGRkr97H19fZX6N9vZ2UmZf19fX/LkyaMwSnmZMmUUMk4DBgzAycmJmjVrYmtrS+3atWnTps0P6VP/559/4uDgAIC6ujpGRkYqj1OuXDkWLVoEpPTvnjVrFqVLl2bEiBFZqvWXS52OihQpQseOHdm2bRu+vr4EBARI10s+cKGq69qlS0r/2g0bNiCTyWjYUHEU4ISEBOLj47McNyF9MplM6f5NlsnS3D69gjNZJsKFhISwYOFC8ubNi9Pgwehoa+Pp6cm2bdvQyp2bKqlq/wUhu8hkMmRfpfOv/z+19NJ5Vj2+c4y8BYtiXqxiutu9enqDy4cXY2pRhqr2fbLt+ML/DplMpvT8Tv95ns6+SCccygFPer/AKp8RthaKrb8SviSx8vxtWlcoibWpmK1FEH43IvOfg/T09LC0tEx3m68/8jPqB56UaoTtmJgYevbsSVxcHM2bN6dDhw7Y2tpKteKZ8eTJE0aPHs3w4cNp2rSptFyeadi5c6fSSOLyJplqampK8ddUUZqdnsqVK+Pq6sqTJ0+kFgapxcfH8+DBA6krAihfs9THVBUn+LefPaQUFly+fJnr16/j4eHB4cOHWbNmDRs2bKBmzZpZin9G8uTJk2EagJSCHPl2lpaWFClShM6dO/P333+zdu3aLH/4pp7R4cWLF3Tv3h0bGxtq1apF06ZNMTY2Vrimqa/P15KTk9HX18fNzU1pXWbGLRCyxt3dXWmcisqVKxMZqVyzExcfj46KFgFyujo6xKkooImNi0NXJ6WG9dy5cyQnJ/P3qFHo66fUbJYtW5bY2Fh27dpF5cqVszXjJQgAXld243V1j8IyqzK1iP0cobRtYkIsmlpZLwRVJS42itDXj6hq3yvd7R7dOsKtc5soZFmOJp0nk0tTPOuErNt/5zH77/goLKtRvDCfYpRbbMUmfkE3d9rfUHq5cxObmKi0PCYhET0txXBRcfE8Dn5L9xrllbbfc+sRyTIZnaqUJemr76Wk5GTU1dTEM18Q/sNE5v8XJe8nfe/ePRo0aABAdHQ0r169kmpY5Zna6Oho6aPc399f2se1a9fw8fHh+vXr0nRcERERvH//Pt0aP7nw8HAGDRpE06ZNGTRokMI6a2trAN6+favQp3vp0qWoq6szYsQISpcujZeXl0K4R+kMPqZKnTp1KFmyJIsWLWLdunVKA9u5uroSHx8v1UKXKVOGe/fu0adPH2mb1APPlSlThqioKJ4/fy6dg7+/v0LrghUrVlC5cmUaNWpEo0aNmDhxIq1ateL06dPZnvn/ViVKlGDMmDHMnDmTPXv20K1bt2/e1549e8ibNy+bN2+Wll24kDLftTydFC9eXGkAv3nz5hEaGkrHjh2Jjo4mMTFRoRn45MmTKV26ND179vzmuAnK6tWrR/lULYIA7nt54ePjg0wmU/goexMeLnX5UcXU1JSAgACl5W/fvKFo0aIAvP/wgUIFC0rPGDlra2s8PT2JioqSWvsIQnYpXak5FtZVFZa9fnaTYD8vpXQe+SEEo3zZMx5LsN89ZMlJWJWurXK9TCbj5pn1PL5zjGI2danXdhQaGuJTSvg2jW2KUclS8Rl951Uw9wPClNJ5aEQUhY3TftaaGRnw8u1HpeVhn6KxNlWcyel+QBhJycnUKK5839z0C+Jt1Gcc1ikX6P+55gBDGlWlYemiGZ6bIAi/JjHafw6Ki4vj7du3Kv8KFSpEy5YtmTlzJtevX+f58+eMGzeOmJgYKXzFihVRU1PDxcWFoKAgTp48yaFDh6T1BQumjBR79OhRgoOD8fT0ZMiQISQmJpKQkJBu3GJiYnBycsLMzIzRo0fz7t07KW4RERFYW1vTsGFDpk2bxoULFwgMDGT9+vW4urpKTeodHR15+vQpCxYs4NWrVxw9epQdO3Zk6RppaGiwbNkyXrx4Qe/evbl27RqhoaE8fPiQyZMns27dOmbOnCnVig8cOJCzZ8+yYcMG/P392b59u8K4CtWrV6dChQqMGzeO+/fv4+3tzbhx4xQGkAoMDGTatGl4eHgQHBzM6dOnCQkJUehz/yvo3r07VapUYdGiRYSHh3/zfgoWLEhYWBiXL18mODiYM2fOSH275elk4MCBnDhxgu3btxMQEIC7uzu7d+/G3t6eunXrUqZMGUaNGsXNmzd5/fo18+bNw83NjeLFi6dzZOFbGBkZUdTKSuGvrI0NcXFxPPL5twZJXsiV3oCLZW1sCAsLU5iFIyQkhNDQUClcwYIFCQkJUep+88LPDx1dXaWWP4KQHXQNTMhvZq3wZ17MjsSEWIL87knbxX7+RFjgY8yLZs/z+U3wM/QM82FgpDwWBoDnxW08vnOMctXb0aD9GJHxF76LiZ4OJUxNFP5sLUyJS/zC/YB/R/b/FBvHk9B3Sk30U7O1MCUkIoqgD/+2Agv6EEnwx0ilcM/DP5BXX5cChsrP7/EtazPvj8YKf0XzG1M0vzHz/mhMFSszpTCCIPx3iLdWDjp58iQnT55UuW758uXMnz8fZ2dnRo0ahUwmo2vXrjx58kTaxsLCghkzZuDq6io1vx03bhzjx48HUsYLmDhxIlu2bGHZsmWYmprSsmVLChUqlOE0bN7e3lK//rp16yqsq1atGtu3b2fp0qUsXbqUqVOn8unTJ4oUKcKcOXPo0KEDkFLLvn79epydndmxYwfW1tY4OTlJfdczq3jx4hw6dIgNGzYwa9YsQkNDMTQ0pHr16uzdu1cahwCgQYMGLF68GBcXF5YvX07FihVxdHTk2LFjQEqXBFdXV2bPno2joyPa2toMGjSI4OBgaR/Tpk1jwYIFjB07loiICMzNzRkzZgzt2rXLUrx/NDU1NWbPnk27du2YPn06a9as+ab99OrVi5cvXzJu3DgSEhKwsrLi77//ZsWKFXh7e1OvXj3s7e2ZOXMm69evZ8GCBZibmzNx4kRpoMRNmzbh7OzMyJEjiY2NpXjx4qxcufKXaSnxuytVsiSlSpViw4YN/NGpE3p6ehx1d0dHV1dqOQQpGfsvX75IBXRVq1ThxIkTLF+xQpqe76CbG+aFC0t9+Zs0acKtW7dYsnQpLVu0QEdHh3v37nHn9m26dOmS6WkmBeF7FbIsRyHLclw6sphq9n3Q0jHg3pVd5NbSo0zlltJ2H98GkpyUSN6CxbJ8jI9v/NNsRfA+7CUPPdzIZ1aComXr8DbEV2G9UT4LcmdT9wPhf5eNeQFszAuw/OwtHGrZoq+dm323fdDLrUmzcv+2rgv6EEliUhJF86cMtlrb2gK3u0+Yc+wKPWqmtA7b6fGQInnzUOur0fwD3n/C3Fj1OEaW+YyUlulopmQXSnzVgkAQhP8eNVlm2n8Lvwx7e3s6dOjAsGHDcjoqgvBDvfTzy+ko/Kd8/vyZffv24XX/PjKZjBLFi9O1a1epBRCAs7Mz79+/V5iB4cOHD+zZs4fHjx+jkSsXZcuWpWuXLtLMDQBhYWG4ubnx9NkzZMnJFDIzo1mzZlSuVEllXK7fuMGWzZuZN38++fKKAaPSc/hR1jOo/8viY6O5dXYDr31vIZPJMC1cmhpNB5An77/T7B3fPpHoiDd0HbZR5T72uvSjkGV56rUdqbTu4NohGBewwr7jOKV1dy/v5P7VvWnGrWXPORSyUu5DLcCgsMk5HYX/lOi4BLZev8/tV8HIZFCqYF761rHDLFWGfdqhi7yNimF1r38H430XHcPmq148CAwnl7o6FSxM6V2nIiZ6irNkjNx1Csu8eRjVLHMF9NMOXQRgRoeGGWz5v01v0JycjoJkyZFfM3v3dzsxXkROE5n//xiR+Rf+V4jMv/C/QGT+hf8FIvMv/C8Qmf+Micx/zhPN/oUcER4eTvPmzdPdpnz58lmaFvBnqFKlisKMCl/Lmzcv586d+4kx+tevHDdBEARBEARBEHKWyPz/x8hHYf+vy5cvH4cPH053m9TT0f0q3Nzc0p0pISf7P//KcRMEQRAEQRAEIWeJzL+QIzQ0NDI1v/2vRj5Q2q/oV46bIAiCIAiCIAg5S0z1JwiCIAiCIAiCIAi/OZH5FwRBEARBEARBEITfnMj8C4IgCIIgCIIgCMJvTmT+BUEQBEEQBEEQBOE3JzL/giAIgiAIgiAIgvCbE5l/QRAEQRAEQRAEQfjNicy/IAiCIAiCIAiCIPzmROZfEARBEARBEARBEH5zIvMvCIIgCIIgCIIgCL+5XDkdAUEQBOH7xcfHc+DAAe7du0d8fDzW1tZ07dqVggULphsuMjKSvfv24ePjQ1JSEuXLl6dL584YGRlJ23z8+JH9Bw7g4+NDclISxYoXp3379hS1sgLg2bNnLFq0KM1jtGnblrZt2mTHaQqCksSEOO6c34z/Uw8SE+MoaFGWGk0HkCeveab3cf7APDRz61Cv7Uhp2ZWjy3j+8EKaYboM3YCBUYHvibogqBSX+IXtNx5w62UwcYlfKFMoH33r2GFmbJBuuIiYOLZeu8/9wDCSkmVUsixEr9oVMNHTkbaJjI1n582HeL0OI/7LF4rlN6ZXrQoUzW8sbZOYlMS+2z5c9Q0gMi4ecyMD2lUqTR3rIj/snAVB+DnUZDKZLKcjkR0cHBy4fft2mus9PDwwMTH54fFwc3Nj4sSJPHv2DAB7e3s6dOjAsGHDfsrxMis8PJy1a9dy+fJl3rx5g4mJCVWqVKFfv37Y2NikG9bFxYVDhw5x4ULaH0WlSpVi3rx5dOzYMUvxUsXe3p7g4OA011erVo3t27crbaempoauri5ly5ZlxIgRVK1aFYAJEyZw6NAhhX3kypULY2NjatasycSJE39KWklLUFAQjRo1Ytu2bVSvXj3T4Z4/f86MGTPw8fHB0tKSadOmYWdn9wNj+mO99PPL6Sj8p7isXMmrly/p9McfaGtr4+7uTnR0NDOmT0dPT09lmKSkJObMnUtcXBwdO3QgKSmJg25u6OroMHnyZHLlykVMTAwzZ84kISGBNm3bYlqgAHfv3sXj5k3GjhlD0aJFiY2NJTQ0VGn/hw4fxt/fn0n//JNhIcT/qsOPiuV0FP7zzu6dxZsQX6ra90FTSwevK7uJj42i48CVaOnopxtWJpNx6+wGfG67Y21rr5D5j/wQSlxspML28bFRXDi4gIKW5WjadSpqamo/4pR+O4PCJud0FP5T5h+/xvPw9/SsZYuOpib77/gQFZfAkj+boa+dW2WYpORkJuw/R2ziF7rXKE9ScjI7PbzRza3Jwi5NyKWhjkwmY9rhSwR9iKRnTVuM9bQ54vUMvzcfWPRnU0wNU+6XhSeuc+91KG0qlqR8YVP83nzggOdjetS0paWt9c+8FP8peoPm5HQUJEuO/JrZu7/biWdmTvutav5btGjBpEmTVK4zNjZWufxHO3DgAFpaWjly7LQ8efKEvn37Urx4cWbMmEGxYsUIDw9n586ddO3alXnz5tHmF6qlO3DgAElJSQB4eXkxbNgw9u/fT6FChQDQ1NSUtnV0dMTR0RFI+aiLiIhgyZIl9O/fn5MnT2JmZgaAnZ0dLi4uUri4uDi8vLyYOXMmERERrF+//medXraQyWQ4OTlRpUoV5s6di4uLC0OHDuXq1auoq4vePb87Pz8/Hj54wPARIyhf7v/Yu/O4nu8HgOOvdN/plFSIQgeRoxBytxG2YcZGM7K5mS1MzmEZGUPuI5vwy7mR5r6vcoUi0h1Jl+76/v5offnq3pDl/Xw8vo9HvT/v9/v7fn/69P1+3ufHBgDLxo3x9PTkxIkTfPDBB6Wmu3r1KtFRUcyZM0f6v2Fqasrs2bO5cuUK7dq14+zZszx9+pTvvvuORo0aAdCsWTMyMjLw37mT77/7DlVVVRo2lG3EXrt2jbt37jDaw0M0/IU3JjHmLlH3LtNjsBemjVoBUMfUmp2/fsWdq3/SosPAMtMmJ0Zy/ogvT2LvIa9QskGlpWuMFsYyYUd3L0RZVZPO/aaKhr/wRoQlJHE1Mo7pH3bE3rzo+mtaV59vtv1J4K37fOTQrNR05yNiiExKYdmnvainqwVAA/3aTN4RyLn70ThbmROfksGduCeM6dIal2YNALAy1sd9wz5Ohj1iYGtrHj55xuWHsQxuayN9LztTI1QUFdh+4SadrMxRVy69A0IQhHdfjWoVqKioYGBgUOqrur6kdXV1yxx1qw75+flMmDABW1tbtm7dSseOHTExMaFly5b8/PPPDBs2jB9++IHo6OjqLqqUrq6u9O+ora1dIuzl6clqamrScENDQywtLZkzZw7Z2dkEBQVJ4ykqKspcH6ampvTt25fhw4dz6tQp0tPT33Y1/5W0tDRiYmJwcXHBzMwMV1dXkpKSSEtLqzix8J8XGhqKsrIy1s1e3BRqampiaWnJzVu3yk1nVKeOtOEPULduXerUqSNNFx8fj5q6urThX6xJkyZE3L/P8+fPS+Sbm5vL7zt2YGdnh0OrVv+2eoJQptiIYBSUVDBp+GKWk6q6NnXMrIm+f6XctCf3L6WwoIC+I5agqq5d4XtF379C5N3ztO3+Jcoq7873ulCzXI9KRFlRgeamLzpNtVVVaFbXgJBHCeWkS8BYR1Pa8Aeop6tFXR1NQh4VzczK+3sgRUXpxdifqqICSgryZGTnAhDzrOi+waH+i+8FAJt6huTk5RMa++Rf1lAQhOpUoxr/FUlPT8fT0xMHBwccHR1ZvXo1w4YNk44ABwQEYGVlJZPm1bC4uDgmTZqEo6Mj1tbWODs74+3tTWFhYanv6eLiIs3fysqq1NfKlSuBohtmb29vOnbsiL29PQMHDuTMmTMy+QUFBdGnTx9sbW0ZMmQIcXFxVToHJ0+e5NGjR0ycOBF5efkSxydMmICcnBz+/v7SMH9/f7p3746dnR0eHh6kpqbKpElISGDMmDHY29vj7OzMgQMHZI5nZWUxY8YM2rdvj62tLf369ePIkSNVKve/oaBQ9CWnpFRxT7WysjJycnKlnpvSXLx4kWbNmhEUFETPnj2xs7Pj888/Jz4+nvnz58tca8Vyc3NZvHgxLi4u2NjY0KZNGyZMmEBycnKZ7/O///2P3r17Y2dnR+/evdmyZYvMNaetrY2NjQ0rV64kKioKX19fOnXqJNMxAkXX4C+//EKXLl3o0KEDkZGRlbqmb9y4wfDhw7G3t8fJyQkvLy+ysrKAolkH69ato2vXrjRv3hw3Nzf2799fqfMnvB7x8fHoGxiUmOVhYGhIQkLZN4vx8fEYGRmVCDc0MiLx73QamppkZ2eXaOQ/fvwYgKSkpBLpjx49SsqzZwwaNKjKdRGEqkh5Go2mjlGJa1+rtjGpT8teLgbQqe9k+gz/CV2j+hW+j0Qi4dJfG6ljbk2Dpu3/TZEFoVwxz9Iw0lKnVi3ZQas62hrEppTdoR/zLI26OiX3BDDW0SAupWhAw1xfB2sTQ3Zfvk3U01TSs3PYcvY6ufkFODUyBUBLpWi26pP0TJl8ElIzAEhMy/jnlRMEodq9V43/8ePHExwczOrVq1m/fj2nTp3i2rVrVcpjzJgxpKens2nTJg4fPoy7uzvr168vd/17sTNnzsi8XF1dMTQ05JNPPgHA09OTs2fPsmTJEvbs2UPv3r3x8PDgxIkTAAQHBzNu3Dh69uzJ/v376d+/P2vXrq1S+UNCQqTr4EujoqJCy5YtuXr1KgAHDx5k7ty5DB8+nH379tGyZUu2b98ujZ+fn8/IkSN59uwZfn5+LF++nA0bNsjkuXz5csLCwli7di1//vknzs7OTJo0iZiYmCqV/Z9ITExk7ty5qKmp0alTpzLjSSQSgoOD2bJlCz169EBNTa3S71FQUMDq1atZsmQJW7Zs4e7du7i5uaGoqMiuXbsYPHgwPj4+0n0ZfvrpJ44cOcKiRYsIDAxk0aJFXLhwQaaD4GX+/v789NNPjB07lj/++IOJEyeybt26EhuszZ07l/v379OjRw9MTU1ZtmxZqfn99ttv/PLLL6xcuZL69etXeE1HR0fzxRdfYGhoiL+/PytWrODs2bPMmTMHgGXLlvH777/zww8/cODAAT7//HNmz54tc50Ib1ZWVhYqKiolwlWUlcnOzi4zXWZWFqplpMv6O127tm2Rk5Nj9Zo1xMXFkZmZyYULFzh77hxQtNHgy/Lz8/nr6FFat26NoaHYDE14s3JzMlFUUi0RrqikSl5uVrlpK9PoLxZ17xIpSTG0aC86tIQ3KzM3D9WXljMWU1VUICs3v8x0z3PyUFMqLZ0imXl50t9HdWpFTn4+U3YE4r5hH3/euMfoLq1oYqwPgLWJIUZaGmw8HcLNmEQyc/O4HfcEv/M3kJOTIzuv7DIIgvDuq1Fr/g8cOEBgYGCJ8G7dujF69GjOnTvHpk2bpBu/+fj40LVr10rnn52djZubG71795auNx8+fDjr1q0jLCyMbt26lZvewMBA+vPmzZs5fvw427dvx8jIiEePHnHw4EH27t1L06ZNARgxYgR3795lw4YNdO7cGT8/P1q2bMnYsWMBaNCgAeHh4WzdurXSdUhNTUVLS6vcZRA6OjrShvm2bdtwdXXls88+A2DUqFFFa3nv3gWKNlK8d+8eQUFBmJkV7QK7cOFC+vXrJ80vKioKdXV1TE1N0dLSkm6+VzyF/3Xy9fVl48aNQFEjJDc3FwsLC3x8fGSmNl+5ckVmM7ycnBx0dXVxdXVl4sSJVX7f4qUUAO3ateP69etMmzYNOTk5Ro8ezapVq7h37x5WVlbY2trSq1cvHBwcADAxMcHJyYnw8PBS8161ahVjxoyRrts2NTUt2shtzhwmTJiAsrIywcHBTJkyBQsLCyIiIjA0NERdXZ3MzExUVVVl/t5ubm7Sslbmmt65cyc6Ojr8+OOP0lkU8+fPJyQkhMzMTDZv3szSpUvp3LkzAGZmZsTGxrJhwwbpdSO8PhKJpMRMo8Jy9m0t73+9vP1ei9PVrVuXcWPHsm3bNry8vAAwNzfHrW9fduzYUWJGzdWrV0lLTaVnz54V1kUQqkIikSB55dp/9feXvc7lfrcvH0SvTgNMGrZ4bXkKgkQiKfH5Xf7neTl5UU46ihLGJKcxM+AYBppqTOnlhJqSIufuR+N7/CrKCgo4NTJFQb4WM/s6s+rYZebuOwlAbXVVRnRowbIjF1BWqFFNB0F479So/2AXFxemTp1aIlxNTY0rV4rW/tnZ2UnDjYyMqP/3o6oqQ0VFhaFDh3L48GFu3LjBo0ePCAsLIykpqcxp/6U5duwY3t7eLFu2TLqz/u3btwEYMmSITNy8vDy0tIrWb4WHh9O+vex0Q3t7+yo1/mvXrk1GRvlTttLS0qS73YeHh5fYLMze3l7a+A8PD0dbW1va8Ado2rSpzCjkV199hYeHB46OjtjZ2dG+fXv69OmDpmb5j6z5JwYPHsywYcMAqFWrFjo6OqW+j42NjXTkPCIignnz5tGkSRMmTJhQpVH/Yubm5tKf1dTUqFevnvTGs/hc5OYWradzc3Pj3LlzLFmyhMjISB48eMDDhw+lnQEvS05OJiEhgaVLl7J8+XJpeGFhITk5OcTExKChocGXX35J//79mTFjBhs2bODnn3/G2tqaP/74g+TkZH7//fdSy1qZazo8PBxra2tpwx+KOjjatWvHjRs3yMnJYcqUKTLTbos7XrKzs0sdkRb+uQMHDpRYWtOqVatS93fIzskpdWS/mJqqKtmvjNwDZGVno6b6YjTV2tqahQsX8vTpUwD09fWlS5I0NGR3U7969Sp169bF1NS08pUShEoIOfU7Iad3yITVb+pE1vOUEnHzcrNQVK76Z3lpsrPSiX90i9Yun7+W/ASh2K7Lt9l1OVQmrJ1FPVIzS87YysrLL3Vkv5i6khJZL43wF8vMzUNduSjdwevhFEokzHLrhObf0/vtTI3IzM1jw6lgHC2K7l3qaGswt38XUrOySc/KxVhHg6T0TCQSCZplPG1AEIT/hhrV+FdXV5dp2JTm1UZ6RevAi3eZB8jMzGTo0KFkZ2fTq1cv+vfvj52dXZVGN+/cucOUKVMYP348PXr0kIYXj8Bt3769xAaBxY0qOTm5EuVXLGVqWHlatWqFr68vd+7ckc4weFlOTg7Xr1+XLkWAkufs5fcsrUyATEPR3t6ekydPcvbsWc6fP8/evXulSy8cHR2rVP6KaGtrV3gNQFGjtzieubk5ZmZmfPLJJ0yePJk1a9ZUecRI4ZWe8PJ22J81axaBgYH069cPFxcXvvnmGzZs2EBiYmKJuMXn1tPTEycnpxLHjY2N2bFjB7Vq1cLT0xN5eXlGjRpFeHg406dPJz8/n0mTJpWoe7HKXNOv1u1lxdetj49Pid3eoXL7LAhV4+zsjO1LnZgA10JCCA0NRSKRyFy7jxMTpTM6SmNkZERUVFSJ8CePH9OgQdFO0E+fPuXOnTu0a9cOfX19aZyoqCjUNTTQ09OThuXn5xMaGkqv3r3/cf0EoSxNWvbCtHFrmbBHYReIjQgpce2nJceho1/vtbxvbEQwksIC6jcRa/2F16ubdUNamst+Rl9+GMu1qIQS13R8Sjr1amu9moVUXR1NHjx5ViI8ITWDxkZFAzpP0p9jUltT2vAv1tRYn/P3o0nNykFNSZHzEdE0MdbHSEsDbdWie4bivBsYVM/TswRBeD3emzX/xWvcg4ODpWEZGRk8fPhQ+ntxo/blkfHIyEjpz2fOnCE0NJStW7cyfvx4XF1d0dDQ4OnTp+VOny2WmJjI6NGj6dGjB6NHj5Y51rhx0XNTnzx5grm5ufQVEBBAQEAAULS7dkhIiEy6W+Xs5F2aDh06YGlpyZIlS2Q6Nor5+vqSk5PDwIFFj0dq2rSpzDkDuHnzpvTnpk2bkp6ezr1796RhkZGRMufwl19+4erVq3Tt2pWZM2cSGBiIqalpqUs0qkujRo2YOnUqJ06cYMeOHRUn+IeePXuGv78/Xl5eeHp6MmDAAJo2bcqDBw9KvYb09PTQ1dUlOjpa5roIDQ3Fx8cHAFVVVXJycmTO+fz581FWViYvL086Hb80lbmmGzVqxO3bt2Wul6CgIFxcXGjYsCEKCgrExcXJlO/kyZNs2LBBPGbwDdDR0aFB/foyr2bW1mRnZ3Mr9MUIUvH/ZVn7ewA0s7YmISFBZuPQuLg44uPjpenS0tOle1kUS01N5dLlyzS3s5O5OY2NjSU3N5dGFhavs8qCAICapi4GdRvLvEwa2pOXm0VMxIvvqaznqSRE38akgX05uVXe49gw1LX00dQRe1gIr5euuiqNjHRlXnamRmTn5XMt6sVmralZ2dyJT8LOtOQGrcXsTI2IS0knJvnFLLCY5DRin6VJ05nU1iI6OY30bNkZX2EJT1FTVkRTRQmFWrXYeDqEv0IfSI8XFBZy+OZ9jLQ1MNd7/Us2BUF4e2rUnXl2djZPnjwp9WVsbIyrqytz587l7Nmz3Lt3j2nTppGZ+WI30xYtWiAnJ8eKFSuIiYnh0KFD7NmzR3q8+FnV+/fvJzY2litXrvD111+Tl5cnndJdlszMTDw8PKhbty5TpkwhKSlJWraUlBQaN25Mly5d8PLy4tixY0RHR7Nu3Tp8fX2lU+rd3d25e/cuixcv5uHDh+zfvx8/P78qnSN5eXl8fHy4f/8+X3zxBWfOnCE+Pp4bN24wc+ZM1q5dy9y5c6Wj4qNGjSIoKIj169cTGRnJtm3bZBrtbdu2pXnz5kybNo1r165x8+ZNpk2bJtPoi46OxsvLi/PnzxMbG0tgYCBxcXEya+7fBUOGDMHBwYElS5aUOgr/OmhoaKCpqcnRo0elU+x/+OEHQkNDS72G5OTk+Oqrr9i2bRt+fn5ERUURFBTE7NmzUVFRQUlJiV69elG7dm0mTZrEnTt3uH37NtOmTQOKZjV88803ZT66sTLX9JAhQ3j27BleXl5ERERw+fJlfvrpJ9q1a4empiaDBw9m+fLl7Nu3j+joaHbv3o23t7fY7O0tsrK0xMrKivXr13P69GmCg4P5eelSVNXUZDp/4uLiZEb6Wzs4YGhkxPJffuHSpUtcunSJ5b/8gkm9etJlKPXNzbFo1Ijt27dzNTiYa9eusXTpUmrJydG3b1+ZcsTEFu2uXt5sA0F4nYzNbTA2t+HEvp8JCzlC5N3zHNo+EyVldZq2cpXGe/YkmqcJD8rJqWzPHke+tlkEglARaxNDrE0MWR50kaO3H3DxQQxz951EXUmRnjYvHrkak5zGw5dG+ts3NqWOtgYLDp7izL0oztyLYsHBU5jpaUt38v+wuSWK8rWYu+8k5+5Hcz0qAd8TVzh7L4pPHKyRr1WLWrXk6GnTiD9u3OPwzfvciE7k58PnuRufxIgOLart0dmCILweNWra/6FDhzh06FCpx5YvX86iRYvw9vZm0qRJSCQSBg0axJ07d6RxTE1NmTNnDr6+vvz222+0atWKadOm8d133wFF+wV4enqyefNmfHx8MDIywtXVFWNjY5nR8NLcvHlTuq6/Y8eOMsfatGnDtm3bWLZsGcuWLWPWrFmkpqZiZmbGggUL6N+/P1A0yr5u3Tq8vb3x8/OjcePGeHh4lNj1vSIWFhbs2bOH9evXM2/ePOLj49HS0qJt27b4+/tL9yEA6Ny5Mz///DMrVqxg+fLltGjRAnd3dw4ePAgUTW/39fVl/vz5uLu7o6KiwujRo4mNffGIJS8vLxYvXsy3335LSkoKJiYmTJ06FTc3tyqV+02Tk5Nj/vz5uLm5MXv27DJ33/83FBUVpddinz590NbWpm3btkyePBlfX1/p4/Ne5u7ujrKyMtu2bWPRokXo6+szcOBAxo8fDxQ9z33z5s0sWrSITz/9FHl5eZycnNi1axdKSkp8/fXXRERElLoGuzLXtJGRERs3bsTb25t+/fqhra2Nq6srkydPBoqWJNSuXZvly5fz+PFjjI2NGT9+PCNHjnzt508o25gxY9i5cye7du9GIpHQyMICj9GjZZYRbd++nadPn7Jo0SKg6HqcPGkSO3bsYOvWrcgrKNCsWTMGDRwofdylnJwcX48Zww5/f7Zt2wYUdTaMGTNGZso/QPrf+w78k30zBOGf6vrxdC4GrefS0U1IJBKM6jWh60ffo6z6Yj+Kc4dXkZHymEHjNpSTU+mynqdQ27D+ayyxIJRvai8ntpy9xtZz15FIwKqOHlN6OqHx0nr7dSev8iQ9k1WfF+3LpCgvzyy3Tmw6HcKa41dQqFWL5qZGfNGhBfJ/D8gYaqmzYEBXtl+4ge+JKxQWSjDV1WZKLyfaWbzo4BrY2ho5YG/wXTJycqmvp8P0DzvS3KzOWz0PgiC8fnKSysxXr8FcXFzo378/48aNq+6iCMIb8eq6wf+KBxER1V0EQXjj9t4quVeGINQ0oxNmVncRBOGNUx+9oLqLILV037vZvJvs9t+7H61patS0f0EQSvovNvwFQRAEQRAEQXi9atS0//dZYmIivXr1KjeOra1tlR4L+DY4ODiUuvFgMT09Pf7666+3WKIX3uWyCYIgCIIgCIIgVMV73/g/duxYdRfhtdDX12fv3r3lxlFWVi73eHUICAgo90kJxeuOq8O7XDZBEARBEARBEISqeO8b/zWFvLx8pZ5v/64pfpLBu+hdLpsgCIIgCIIgCEJViDX/giAIgiAIgiAIglDDica/IAiCIAiCIAiCINRwovEvCIIgCIIgCIIgCDWcaPwLgiAIgiAIgiAIQg0nNvwTBEEQhGoyOmFmdRdBEN443zrzq7sIgvDGTa7uAghCJYiRf0EQBEEQBEEQBEGo4UTjXxAEQRAEQRAEQRBqONH4FwRBEARBEARBEIQaTjT+BUEQBEEQBEEQBKGGExv+CYIg1AA5OTns3r2b4OBgcnJyaNy4MYMGDaJOnTqVzmP16tWoqKgwYsQImfCCggIOHDjAufPnycjIwNzcnE8+/piGDRtK4+Tl5XHkyBHOX7jAs+Rkauvq0rZtW3r36oWCgviqEd6c7Lx8tp27zsUHsWTn5dPUWJ8RHeypW1uz3HQpmdlsOXONa9EJFBRKaGluzOftm6OrriqNc/T2A9Ycv1IibU/bRox0bvna6yIIxfJys7l8dBORd8+Tl5dNHdNmtOvxFdp6JpXO4+juhSgqqeLcd+IbOS4Iwn+PGPl/S4YNG4aVlVWZr+Tk5LdSjoCAAKysrKS/u7i4sGLFirf2fpWVmJjInDlzcHFxwcbGBmdnZyZPnkxoaGiFaVesWIGLi0u5caysrAgICKhyucrLr6zX8ePHgaJz/XJ4kyZNaNmyJUOHDuXy5cvSvL7//vsSeVhbW9OhQwe+/fbbt3atlCUmJgYrKysuXrxYreUQZK1dt46rV68y4KOPGOHuzrOUFJb8/DPPnz+vMK1EImHHjh0EBweXenznrl0EBQXRs0cPRo8ahXytWixdupTHjx9L4+zYsYM///yT9k5OjB07lvZOThw6dAi/7dtfWx0FoTQ+Ry5wISKGzxxtGdu1DcnPs5i97wQZ2bllpikoLGTBgVPce5zMV51a8VWnltyNT2L+/lPkFxRK4z1MSsFYR5MFH3WVefVtUfXvNUGoihN7vHl49xwOLl/g3Hciz9Of8qffDHKyMipMK5FIuHBkHZF3z7+R44Ig/HeJ4Zi3qHfv3syYMaPUY7Vr137LpSmye/dulJWVq+W9y3Lnzh1GjBiBhYUFc+bMoWHDhiQmJrJ9+3YGDRrEwoUL6dOnT3UXs4Tp06fj6upaIlxbW1v6s7u7O+7u7kDRl2tKSgpLly5l5MiRHDp0iLp16wJgb28v0ymTnZ1NSEgIc+fOJSUlhXXr1r3h2gj/JREREdy4fp3xEyZga2MDgGXjxnh6enLixAk++OCDMtPGxMTw+++/8zAyEkUlpRLHk5OTOXHiBIMHD6ZL584ANGvWjJk//MChw4f54vPPycjI4PTp03z00Uf07NkTgKZNmwJFHYAfDRiApmb5o7CC8E+EJSRxNTKO6R92xN7cGICmdfX5ZtufBN66z0cOzUpNdz4ihsikFJZ92ot6uloANNCvzeQdgZy7H42zlTkAkU9SaGyki2UdvbdTIUEAEmPuEnXvMj0Ge2HaqBUAdUyt2fnrV9y5+ictOgwsM21yYiTnj/jyJPYe8gqlfKb/y+OCIPy3iZH/t0hFRQUDA4NSX3JyctVSJl1dXdTV1avlvUuTn5/PhAkTsLW1ZevWrXTs2BETExNatmzJzz//zLBhw/jhhx+Ijo6u7qKWoKmpWerfVumlBpWampo03NDQEEtLS+bMmUN2djZBQUHSeIqKijJ5mJqa0rdvX4YPH86pU6dIT0+vjioK76jQ0FCUlZWxbvaioaOpqYmlpSU3b90qN+2GjRspKCxkuqcnWqU00O/cvUthQQEt7e2lYYqKitjZ2nLz5k2gqHOqU6dONG/eXCatsXFRY+zJkyf/uG6CUJ7rUYkoKyrQ3PTF8hZtVRWa1TUg5FFCOekSMNbRlDb8AerpalFXR5OQR/FAUQfto6cpmOvpvLHyC0JpYiOCUVBSwaThi89dVXVt6phZE32/5DKUl53cv5TCggL6jliCqrr2az8uCMJ/m2j8v0PS09Px9PTEwcEBR0dHVq9ezbBhw6QjwKVNoX81LC4ujkmTJuHo6Ii1tTXOzs54e3tTWFhIaV6e9l/WtPWVK1cCkJubi7e3Nx07dsTe3p6BAwdy5swZmfyCgoLo06cPtra2DBkyhLi4uCqdg5MnT/Lo0SMmTpyIvLx8ieMTJkxATk4Of39/aZi/vz/du3fHzs4ODw8PUlNTZdIkJCQwZswY7O3tcXZ25sCBAzLHs7KymDFjBu3bt8fW1pZ+/fpx5MiRKpX73yheD61Uyqjrq5SVlZGTkyv13JTm4sWLNGvWrGjKds+e2NnZ8fnnnxMfH8/8+fNlrrViubm5LF68WLrkok2bNkyYMKHc5Qb/+9//6N27N3Z2dvTu3ZstW7aUec0Jr198fDz6BgbUqiX7kW5gaEhCQtkNIIAv3d35/rvvqFevXpl5q6ioyMxgATA0NCQ1JYWcnBz09fX57LPPSuwvEBISgryCAkZGRv+gVoJQsZhnaRhpqVOrlmwHeh1tDWJT0spNV1enZGeXsY4GcSlFnauJac/Jzssn4nEy47cfYvDq3YzffogTdyNfax0E4VUpT6PR1DEq8ZmuVduY1Kex5abt1HcyfYb/hK5R/TdyXBCE/zbR+H+HjB8/nuDgYFavXs369es5deoU165dq1IeY8aMIT09nU2bNnH48GHc3d1Zv349x44dqzDtmTNnZF6urq4YGhryySefAODp6cnZs2dZsmQJe/bsoXfv3nh4eHDixAkAgoODGTduHD179mT//v3079+ftWvXVqn8ISEhqKmp0axZ6VM1VVRUaNmyJVevXgXg4MGDzJ07l+HDh7Nv3z5atmzJ9pfWGOfn5zNy5EiePXuGn58fy5cvZ8OGDTJ5Ll++nLCwMNauXcuff/6Js7MzkyZNIiYmpkpl/ycSExOZO3cuampqdOrUqcx4EomE4OBgtmzZQo8ePVBTU6v0exQUFLB69WqWLFnCli1buHv3Lm5ubigqKrJr1y4GDx6Mj48PYWFhAPz0008cOXKERYsWERgYyKJFi7hw4YJMB8HL/P39+emnnxg7dix//PEHEydOZN26dSxZsqRqJ0P4x7KyslBRUSkRrqKsTHZ2drlpy2r0S/POzERFVbVEePH7ZWVllZouODiY8+fP06lTp3dqdpFQs2Tm5qGqqFgiXFVRgazc/DLTPc/JQ02ptHSKZOblAfDwyTMAHqc/54v2zfn+gw5YGNTm16OX+Cv0wWuqgSCUlJuTiaJSyc9dRSVV8nJL/8wtVlGj/d8eFwThv02s+X+LDhw4QGBgYInwbt26MXr0aM6dO8emTZto3bo1AD4+PnTt2rXS+WdnZ+Pm5kbv3r2l022HDx/OunXrCAsLo1u3buWmNzAwkP68efNmjh8/zvbt2zEyMuLRo0ccPHiQvXv3Stfyjhgxgrt377JhwwY6d+6Mn58fLVu2ZOzYsQA0aNCA8PBwtm7dWuk6pKamoqWlVe4yCB0dHWnDfNu2bbi6uvLZZ58BMGrUKK5du8bdu3cBOH/+PPfu3SMoKAgzMzMAFi5cSL9+/aT5RUVFoa6ujqmpKVpaWkyYMIHWrVuXGOmsiJeXF/PmzZMJGz16NB4eHtLffX192bhxI1DUMZGbm4uFhQU+Pj7S9f4AV65cwf6ladY5OTno6uri6urKxIkTq1QuQLqUAqBdu3Zcv36dadOmIScnx+jRo1m1ahX37t3DysoKW1tbevXqhYODAwAmJiY4OTkRHh5eat6rVq1izJgx0nXlpqamZGRkMGfOHCZMmPDO7SnxXyeRSErMqiiUSMqM/2+XFEnKybus/K8GB7N+/XoaNWrERwMG/Kv3F4RiEomkxLVe/rVfTl6Uk46ihM1MDPj+gw5Ymxiiolh0u9TCrA6p2Tn4X7pF12YNqm3JnlBzSCQSJK98pr/6+8vENScIwr8hGv9vkYuLC1OnTi0RrqamxpUrRWu47OzspOFGRkbUr1+/0vmrqKgwdOhQDh8+zI0bN3j06BFhYWEkJSVVaQr2sWPH8Pb2ZtmyZVhbWwNw+/ZtAIYMGSITNy8vDy2tojWT4eHhtG/fXua4vb19lRr/tWvXJiOj/J1s09LS0NXVlb7nq5uZ2dvbSxv/4eHhaGtrSxv+ULQR2cujpF999RUeHh44OjpiZ2dH+/bt6dOnT5U3KBs/fjw9evSQCXu1A2Hw4MEMGzYMgFq1aqGjo1Pq+9jY2EhHziMiIpg3bx5NmjRhwoQJVRr1L2Zubi79WU1NjXr16klvIIrPRW5u0c7Ybm5unDt3jiVLlhAZGcmDBw94+PChtDPgZcnJySQkJLB06VKWL18uDS8sLCQnJ4eYmBgsLCyqXF6hbAcOHCixdKVVq1akpZWc4pydk4NqKTMCqkJVVbXU2QNZf4epvjIrICgoiF27dmFpZcXYb76p1HIWQaiMXZdvs+uy7BNf2lnUIzWzlOszL7/Ukf1i6kpKZP09wv+yzNw81JWL0mmrqtCqft0ScVqaGXMzOpGUzGxqq5ccnRWEqgg59Tshp3fIhNVv6kTW85QScfNys1BUrvo9gCAIQjHR+H+L1NXVZRphpXm1kV7RjXNBQYH058zMTIYOHUp2dja9evWif//+2NnZSUfFK+POnTtMmTKlREO2ePRv+/btJabwFq9Jk5OTK1F+xVKmY5anVatW+Pr6cufOHekMg5fl5ORw/fp16VIEKHnOXn7P0soEyDx33N7enpMnT3L27FnOnz/P3r17pUsvHB0dK112PT29Cv++2traFcaBogZ5cTxzc3PMzMz45JNPmDx5MmvWrKlyz/+rz1l/dR3hy2bNmkVgYCD9+vXDxcWFb775hg0bNpCYmFgibvG59fT0xMnJqcTx4hkowuvj7OyM7UudhADXQkIIDQ1FIpHIXBuPExP/9d+gTp06ZGdlkZ6eLtNR9eTxY/T09KSfURKJhB3+/hw7epTWbdrgPmJEietOEP6NbtYNaWkuez1ffhjLtaiEEtd+fEo69WprvZqFVF0dTR78Pa3/ZQmpGTQ2Kupcvh33hMS0DLo0aSATJ7eggFpycmiqiFlNwr/XpGUvTBu3lgl7FHaB2IiQEtd1WnIcOvrlL9USBEEoj1jz/44oXuP+8nO2MzIyePjwofT34kbtyyPjkZGR0p/PnDlDaGgoW7duZfz48bi6uqKhocHTp08rnLoLRevPR48eTY8ePRg9erTMscaNGwNFu3abm5tLXwEBAQQEBADQpEkTQkJCZNLdqmCn8Vd16NABS0tLlixZItOxUczX15ecnBwGDix6zE3Tpk1LPJu8eAfy4uPp6encu3dPGhYZGSlzDn/55ReuXr1K165dmTlzJoGBgZiampa6RKO6NGrUiKlTp3LixAl27NhRcYJ/6NmzZ/j7++Pl5YWnpycDBgygadOmPHjwoNRrSE9PD11dXaKjo2Wui9DQUHx8fN5YOd9nOjo6NKhfX+bVzNqa7OxsboW+GBUtvu7L2j+jspr+nf7K3/tsQNGMn+s3bsjkHbBnD8eOHqV79+58NXKkaPgLr52uuiqNjHRlXnamRmTn5XMt6sXGlqlZ2dyJT8LOtOyNJu1MjYhLSScm+cWMmZjkNGKfpUnThcY+ZtXRy8Q9e/F0FYlEwoWIGKyM9VGQF7dQwr+npqmLQd3GMi+Thvbk5WYRE/Hi/ibreSoJ0bcxaWBfTm6CIAjlE3dnb1F2dnaZj7wyNjbG1dWVuXPnoqioiKGhIcuWLSMzM1Map0WLFsjJybFixQqGDRvGzZs32bNnj/R48U7b+/fvp2fPnsTHx7N06VLy8vKkU7rLkpmZiYeHB3Xr1mXKlCkkJSVJG3uKioo0btyYLl264OXlxaxZs2jcuDGHDx/G19eXhQsXAkXPsP/kk09YvHgxAwcO5ObNm/j5+VXpHMnLy+Pj44O7uztffPEFHh4eWFhY8OTJE3bu3MnevXtZsGCBdFR81KhRjBkzhvXr19OtWzdOnz5NYGAghoaGALRt25bmzZszbdo0vLy8kJeXZ968eTIj39HR0ezfv5958+ZhZmbG9evXiYuLk1lz/y4YMmQIf/75J0uWLMHFxeWN7KCuoaGBpqYmR48exfrvBqWfnx+hoaElHuMGRTMrvvrqK5YtW0bdunVxdnYmLCyM2bNn07VrVzHl+y2xsrTEysqK9evX8/FHH6Gurs7+AwdQVVOjc+fO0nhxcXHk5+fLLIOpiL6eHk5OTuzcuZO8vDzqGBlxJCiIrMxMevXqBRTtmxF4+DD169fHwcFBptMSij7fXl0eIAivg7WJIdYmhiwPusgwJzs0VJTYeSkUdSVFeto0ksaLSU4jr6CABga1AWjf2JSAq3dYcPAUnzkWzaTZfv4GZnraODUyBaC7tQVHbkWw6M8zDGpjjYqiAodv3ifqaSpz+3d5+5UV3hvG5jYYm9twYt/PtHEZjrKqJsGnfkNJWZ2mrVyl8Z49iaawIA+9Og2rsbSCIPyXiMb/W3To0CEOHTpU6rHly5ezaNEivL29mTRpEhKJhEGDBnHnzh1pHFNTU+bMmYOvry+//fYbrVq1Ytq0aXz33XdA0X4Bnp6ebN68GR8fH4yMjHB1dcXY2FhmNLw0N2/elK7r79ixo8yxNm3asG3bNpYtW8ayZcuYNWsWqampmJmZsWDBAvr37w8UjbKvW7cOb29v/Pz8aNy4MR4eHlXe9d3CwoI9e/awfv165s2bR3x8PFpaWrRt2xZ/f3/pPgQAnTt35ueff2bFihUsX76cFi1a4O7uzsGDB4Gi6e2+vr7Mnz8fd3d3VFRUGD16NLGxLx6V4+XlxeLFi/n2229JSUnBxMSEqVOn4ubmVqVyv2lycnLMnz8fNzc3Zs+eXebu+/+GoqKi9Frs06cP2tratG3blsmTJ+Pr61vqzu7u7u4oKyuzbds2Fi1ahL6+PgMHDmT8+PGvvXxC2caMGcPOnTvZtXs3EomERhYWeIweLbNMZ/v27Tx9+pRFixZVKe+hQ4eipqbG4UOHyMnJwdzcnEmTJkk72UJCiqanRkZGSjsDXzZ16tQSjykVhNdlai8ntpy9xtZz15FIwKqOHlN6OqGh8qLzcd3JqzxJz2TV50V7xCjKyzPLrRObToew5vgVFGrVormpEV90aIH8353DOmoqzBvgwvbzN9h4OoSsvHwaG+ri5dYJyzp61VJX4f3R9ePpXAxaz6Wjm5BIJBjVa0LXj75HWVVDGufc4VVkpDxm0LgN5eQkCILwgpykMvPBhWrj4uJC//79GTduXHUXRRDeqgcREdVdBEF444z+2ljdRRCEN863zvzqLoIgvHGT3d6dJzEs3fduNu/epXP0vhIL1gRBEARBEARBEAShhhPT/oW3IjExUbo+uCy2trZVeizg2+Dg4FDqxoPF9PT0+Ouvv95iiV54l8smCIIgCIIgCMK7RTT+33HHjh2r7iK8Fvr6+uzdu7fcOMrK795jkwICAsp9UoK8vPxbLI2sd7lsgiAIgiAIgiC8W0TjX3gr5OXlK/V8+3dNVXZFf9ve5bIJgiAIgiAIgvBuEWv+BUEQBEEQBEEQBKGGE41/QRAEQRAEQRAEQajhRONfEARBEARBEARBEGo4seZfEIR3knj+ufA+EM8/F94H/WweVHcRBOEtsKjuAghChcTIvyAIgiAIgiAIgiDUcKLxLwiCIAiCIAiCIAg1nGj8C4IgCIIgCIIgCEINJxr/giAIgiAIgiAIglDDica/IAiCIAiCIAiCINRwYrf/ajJs2DAuXbpU5vHz58+jq6v7xssREBCAp6cnYWFhALi4uNC/f3/GjRv3Vt6vshITE1mzZg0nT57k8ePH6Orq4uDgwJdffom1tXW5aVesWMGePXs4duxYmXGsrKxYuHAhAwYMqFK5ysuvLGvWrKFLly64uLgQGxsrDZeTk0NNTY1mzZoxYcIEWrduDcD333/Pnj17ZPJQUFCgdu3aODo64unp+VauFeHdlp2Xz7Zz17n4IJbsvHyaGuszooM9dWtrlpsuLCGJ387fJOLJM1QVFXBsZMqnbW1QVVKsUt43ohOZt/9kifxb1jfG84OOr6+iwnstLzeby0c3EXn3PHl52dQxbUa7Hl+hrWdSbroTe38m4lbJ69Plo+9o0LR9ifCk+Pvs3/QtHT4Yi2XzrqWXJSeLgLVjMTa3xbnvxH9UH0EoTU5ODrt37yY4OJicnBwaN27MoEGDqFOnTrnpIiIiCAgI4NGjRyirqODg4ED/fv1QUVEpNf7OnTt59OgR3377bZl5ZmdnM3v2bKysrBgxYsS/qpcgCNVPNP6rUe/evZkxY0apx2rXrv2WS1Nk9+7dKCsrV8t7l+XOnTuMGDECCwsL5syZQ8OGDUlMTGT79u0MGjSIhQsX0qdPn+ouZgnTp0/H1dW1RLi2trb0Z3d3d9zd3QGQSCSkpKSwdOlSRo4cyaFDh6hbty4A9vb2rFixQpouOzubkJAQ5s6dS0pKCuvWrXvDtRHedT5HLnAv8SlDnexQVVRk1+VQZu87wdLBPdFQUSo1zaOkFObsO4ltPUO+7eVEcmYW28/fJPZZGj/07VSlvCOTUlBVUmRmH2eZ99BQLv29BeGfOLHHm8dx4bR2GY6isiohp37nT78ZDBi1EmVVjTLTPU18QEPrjli36SsTrqVbt0Tcgvw8Tu5fhqSwoNyyXAhaR0bqk39WEUEox9p163j44AEfffwxKioqHDhwgCU//8yc2bNRV1cvNU1MTAw/L11K0yZNGDNmDCmpqQT8738kJCQwaeLEEvEDjxwhKCgIS0vLcsuyw9+fp0+fvo5qCYLwDhCN/2qkoqKCgYFBdRdDxrs2gpyfn8+ECROwtbVlzZo1yMvLA2BiYkLLli0xNDTkhx9+oEWLFpiamlZzaWVpampW+PdVU1OTiWNoaMicOXNwdnYmKCiIL774AgBFRcUSeZmamhIVFcWKFStIT09HU7P8EV6h5gpLSOJqZBzTP+yIvbkxAE3r6vPNtj8JvHWfjxyalZru4PVwNJWV+LZXexTkX6wCW3X0MnHP0qlbW7PSeUcmpWCup41lHb03XFvhfZUYc5eoe5fpMdgL00atAKhjas3OX7/iztU/adFhYKnp8vNySX0ah03bfhialD0rq9jVk37k5WSVGyf6/hUe3j6LkkrpDTFB+KciIiK4cf064ydMwNbGBgDLxo3x9PTkxIkTfPDBB6WmCwoKQl1dnTFjxqCg8OL2fvOmTSQkJEhnDSQlJbFz506uX7+OqppauWW5cfMmV69cqTCeIAj/HWLN/zssPT0dT09PHBwccHR0ZPXq1QwbNkw6AhwQEFBievmrYXFxcUyaNAlHR0esra1xdnbG29ubwsLCUt/TxcVFmr+VlVWpr5UrVwKQm5uLt7c3HTt2xN7enoEDB3LmzBmZ/IKCgujTpw+2trYMGTKEuLi4Kp2DkydP8ujRIyZOnCht+L9swoQJyMnJ4e/vLw3z9/ene/fu2NnZ4eHhQWpqqkyahIQExowZg729Pc7Ozhw4cEDmeFZWFjNmzKB9+/bY2trSr18/jhw5UqVy/xvFX9pKShWPmCorKyMnJ1fquSnNxYsXadasGWvXrqVt27YMGDCAwsJCrly5wueff07Lli2xsbGhd+/e7Nu3Tybt/v376du3L3Z2dnTt2pUtW7ZIj6Wnp/PDDz/Qrl07WrVqxeeff87NmzerUGvh37gelYiyogLNTV9MCdVWVaFZXQNCHiWUmW5wWxumf9hRpuGvUKvo59yCgirl/TDpGfX1dV5XlQShhNiIYBSUVDBpaC8NU1XXpo6ZNdH3r5SZ7tmTR0gKC9AzalDheyRG3+H25T9w7DW6zDg5WRmc+WMlrbsOR0lZNIqE1ys0NBRlZWWsm73otNXU1MTS0pKbt26Vma5fv35MGD9epuGv8Pe9QV5enjRsh78/jx8/ZvKUKZjWq1dmfs+fP2fr1q189PHHqKmq/psqCYLwDhEj/++w8ePHExcXx+rVq1FTU2P+/PncunWLNm3aVDqPMWPGYGBgwKZNm1BXV+fo0aMsXLgQe3t7unXrVm7aVxvyP/74I1euXOGTTz4BwNPTk4iICJYsWYKRkRHHjx/Hw8ODlStX0rlzZ4KDgxk3bhxjx47lgw8+4MqVK8ybN69K5yAkJES6Dr40KioqtGzZkqtXrwJw8OBB5s6dy/Tp03FyciIoKIhly5ZhbFw0Ypmfn8/IkSPR0NDAz8+P3Nxc5syZI5Pn8uXLCQsLY+3atWhpabFr1y4mTZpEYGAg9cr5onwdEhMT+fHHH1FTU6NTp05lxpNIJISEhLBlyxZ69OiBWhV65QsKCjh58iT+/v5kZWXx5MkTvvzyS4YOHcq8efPIy8tj3bp10g4QfX19/vzzT7777jumTp1K165dCQ0N5fvvv0dTU5P+/fvz1VdfoaKigq+vLxoaGuzbt49PP/2UnTt3lvm3E16fmGdpGGmpU6uWnEx4HW0NToc/KjOdnoYaehpF1052Xj7hCU/5/cItrOroSxvylck7N7+A+JQMDLXUmep/hJjkNGqrq9DbtjF9WlgiJyebVhD+iZSn0WjqGFGrluy4hVZt41LX8xdLTnwIQFjIEY74zyMnKx2Duo1p081dZiZAfl4Opw740Lz9x+galt1RcD7QFx39ejRp2Ysb53b/y1oJgqz4+Hj0DQxKXOcGhoZcvHixzHS1a9eWLhnNyckhIiKCPXv2YNGokczMyP79+lG3bt0KP5d///13jI2N6eTszOFDh/5FjQRBeJeIxn81OnDgAIGBgSXCu3XrxujRozl37hybNm2Sbvzm4+ND166lbzxUmuzsbNzc3Ojdu7e08Tt8+HDWrVtHWFhYhY3/l6eZb968mePHj7N9+3aMjIx49OgRBw8eZO/evTRt2hSAESNGcPfuXTZs2EDnzp3x8/OjZcuWjB07FoAGDRoQHh7O1q1bK12H1NRUtLS0yv2S0tHRISYmBoBt27bh6urKZ599BsCoUaO4du0ad+/eBYo2Urx37x5BQUGYmZkBsHDhQvr16yfNLyoqCnV1dUxNTdHS0pJuvvfyWv3K8PLyKtHZMXr0aDw8PKS/+/r6snHjRqCoYyI3NxcLCwt8fHyk6/0Brly5gr39i9GunJwcdHV1cXV1ZWIpa/kq4u7uTv369YGi+o4bN44vv/xSep5HjRrF3r17iYyMRF9fny1btuDq6sqXX34JQP369Xn+/DkqKipcuHCBa9euceHCBXR0dACYPHkywcHBbN26lUWLFlW5fELVZObmoaqoWCJcVVGBrNz8CtNLJBLcN+4jL78ADRUlvnR2qlLeUU9TKSgsJD4lg0/b2qChosTlh3H4nb/B85xcPm1n+y9qJwhFcnMyUVQqOQKpqKRKXm7Z0/SfJj4AID8vmy79ppKdnc6Nc7v5c9sM+o5Ygq5RfQAuH9uCopIKzZ0+4Xl66WucI++e51H4RQaMWik6tYQ3Iisrq9QN+lSUlcnOzq4wvUQiYdLkyeTl5qKuocGQTz+VOW5iUv7mmADBwcFcu3aN2XPmiOtcEGoY0fivRi4uLkydOrVEuJqaGleuFE1htLOzk4YbGRlJG2yVoaKiwtChQzl8+DA3btzg0aNHhIWFkZSUVOa0/9IcO3YMb29vli1bJt1Z//bt2wAMGTJEJm5eXh5aWloAhIeH07697C7K9vb2VWr8165dm4yMjHLjpKWlSfcqCA8PL7Eezt7eXtr4Dw8PR1tbW9rwB2jatKnMF+1XX32Fh4cHjo6O2NnZ0b59e/r06VPlNfXjx4+nR48eMmGvdiAMHjyYYcOGAVCrVi10dHRKfR8bGxuWLFkCFK0HnDdvHk2aNGHChAlVGvUv9vJ1ZGZmxoABA9i6dSvh4eFERUVJz1fB31O/SzuvAwcWra9dv349EomELl26yBzPzc0lJyenymUTyieRSCiUSGTCXv39ZZW5bysolPC9awdy8wvYE3yHH/YcZ/4AF+rr61Qqb2MdDaZ/2JGGhrXRVi36X7KtZ0ROfj77QsLoa2+Futj4T6gCiUSC5JXvqVd/f1l5DRTr1n0wa9yGehYtpWF16zdn16rRXDvrj8uA74iPvElYSCB9R/xMrTKWUWU9T+XsoVW06ToCTR3DKtZIEEqSSCQl7sfK/8yt+AO9oKCAsd98Q15eHn8eOsRPP/3Ed999V+l9kdLT0/Hz8+PjTz5BX0/s4SIINY1o/FcjdXV1zM3Ny43z6pdCRevAixtrAJmZmQwdOpTs7Gx69epF//79sbOzk46KV8adO3eYMmVKiYas5O8vp+3bt5fYebZ4qpqcnFyJ8iuWMoJYnlatWuHr68udO3ekMwxelpOTw/Xr16VLEaDkOXv5PUsrEyCzRs7e3p6TJ09y9uxZzp8/z969e1m9ejXr16/H0dGx0mXX09Or8O+rra1dYRwo6sgpjmdubo6ZmRmffPIJkydPZs2aNVXumX/5iQ73799nyJAhWFtb4+TkRI8ePahdu7bMOX35/LyqsLAQDQ0NAgICShyrzL4FQtXsunybXZdDZcLaWdQjNbPkiFBWXj5qShX/zynI18LO1Ago2szv621/8Mf1cL7p2gZ1JcUK81ZXVpJuBviyVuZ1OXb7IbHP0sVGgEKVhJz6nZDTO2TC6jd1Iut5Som4eblZKJaz9l5bz6TEowCVVdQxqteE5MSH5OVmc+qAD7aOA9DRN6WwoEC6079EUkhhQQG15OU5d2gVtfVNsWzRncKXvmslSCgsKECuVi0xSipUyYEDB0rsO9SqVSvS0tJKxM3OyUG1jEf2vUxBQUG63K5x48Z87+nJX0ePMmL48EqVyc/Pj7p169KhfXuZe0qJREJBQQG1xHUuCP9povH/jir+4A4ODqZz584AZGRk8PDhQ+kIa3GjNiMjAw2NokccRUZGSvM4c+YMoaGhnD17Fn19fQBSUlJ4+vSptPFensTEREaPHk2PHj0YPVp286PGjRsD8OTJE5k13cuWLaNWrVpMmDCBJk2aEBISIpPuVjmb1ZSmQ4cOWFpasmTJEtauXVtiYztfX19ycnKko9BNmzYlODiY4S99yb288VzTpk1JT0/n3r170jpERkbKzC745ZdfaNWqFV27dqVr1654enrywQcfEBgYWKXG/5vUqFEjpk6dyty5c9mxYwefvjKtryp27NiBnp4emzZtkoYdO3YMeNHJY2FhUWIDv4ULFxIfH8+AAQPIyMggLy+PRo0aSY/PnDmTJk2aMHTo0H9cNqGkbtYNaflKQ/vyw1iuRSUgkUhkbsriU9KpV1urzLyuPIxDVUkBa5MXo5jqykrU0dLg2d8N/ro6mhXm/fDJM8ITntLDxkImTvGmgVqq79bjQ4V3X5OWvTBt3Fom7FHYBWIjQkpci2nJcejol70fy4PQ0yipalDvpY0CAQryc1FR0yYp7h4ZqU+4dtqfa6f9ZeKcObiCMwdX8OXM/UTePQ/A5oUDZOLcv3Gc+zeO4zp0Acb1xRIXofKcnZ2xfWmGJ8C1kBBCQ0NLXOePExOlSzhLc/36dVRUVbF66dF9ampqGBoYkJqSUukyBQcHA0V7Rr3s/PnznD9/nqlTp5bYbFoQhP8O0fivRtnZ2Tx5Uvozgo2NjXF1dWXu3LkoKipiaGjIsmXLyMzMlMZp0aIFcnJyrFixgmHDhnHz5k327NkjPV78WJf9+/fTs2dP4uPjWbp0KXl5eeTm5pZbtszMTDw8PKhbty5TpkwhKSlJ2hBUVFSkcePGdOnSBS8vL2bNmkXjxo05fPgwvr6+LFy4EChaV/7JJ5+wePFiBg4cyM2bN/Hz86vSOZKXl8fHxwd3d3e++OILPDw8sLCw4MmTJ+zcuZO9e/eyYMEC6aj4qFGjGDNmDOvXr6dbt26cPn2awMBADA2LGjdt27alefPmTJs2DS8vL+Tl5Zk3b57MxjrR0dHs37+fefPmYWZmxvXr14mLi5NZc/8uGDJkCH/++SdLlizBxcUFIyOjf5RPnTp1SEhI4OTJkzRq1IjQ0FDmz58PIL1ORo0axbhx47Czs6NTp05cv36d33//nblz59KxY0eaNm3KpEmTmDFjBsbGxvz2228EBASwYcOG11ZfoYiuuiq66rLrnnPy8wm4eodrUQnSEfjUrGzuxCfRv2WTMvM6eD2clMxslg7uKd3QLykjk+hnafSuV9SRY2dqVGHej56msv5UMMY6mtIZBABn70VhoKmOkZZ4HJpQNWqauqhpyj56Nj8vh+tndxETESx91F/W81QSom/T3OnjMvO6G3KY9JREPh6zBnn5otue52lPSYy5i01bN/SNG9HX/WeZNFkZzwjaOR/7joOlnRCvxgH4a9cC9Oo0xL7jp+jovdkNYYWaR0dHR7pXTrHc3Fz+/PNPboWGSh/1Vzxo0bt37zLzCvrrL9JSU5k9e7b0niY5OZm4+HhcXlmWV57pM2aUCPv1118xNzPjwz59MK5Tp5RUgiD8V4jGfzU6dOgQh8rYQXX58uUsWrQIb29vJk2ahEQiYdCgQdy5c0cax9TUlDlz5uDr68tvv/1Gq1atmDZtGt999x1QtF+Ap6cnmzdvxsfHByMjI1xdXTE2Nq7wMWw3b96Uruvv2LGjzLE2bdqwbds2li1bxrJly5g1axapqamYmZmxYMEC+vfvDxSNsq9btw5vb2/8/Pxo3LgxHh4e0rXrlWVhYcGePXtYv3498+bNIz4+Hi0tLdq2bYu/v790HwKAzp078/PPP7NixQqWL19OixYtcHd35+DBg0DRkgRfX1/mz5+Pu7s7KioqjB49mtjYWGkeXl5eLF68mG+//ZaUlBRMTEyYOnUqbm5uVSr3myYnJ8f8+fNxc3Nj9uzZrF69+h/l8/nnn/PgwQOmTZtGbm4u9evXZ/Lkyfzyyy/cvHkTZ2dnXFxcmDt3LuvWrWPx4sWYmJjg6ekp3Shx48aNeHt7M3HiRLKysrCwsGDlypXvzEyJms7axBBrE0OWB11kmJMdGipK7LwUirqSIj1tXszGiElOI6+ggAYGRTtCf+zQjHn7T7L0yHm6N2tIalYOu6/cRkNZiT4trCqdt6NFPfaF3GXFXxf5tK0ttdVVOHMviquR8Uzu6SimiAqvhbG5DcbmNpzY9zNtXIajrKpJ8KnfUFJWp2krV2m8Z0+iKSzIQ69OQwBadBjEoe0/8NfO+TRr3Yfc7AxCTv+OsqoGNm37oaisikHdxjLvlZ7yGAANHSPpsVfjANSqJY+KqlapxwThn7CytMTKyor169fz8Ucfoa6uzv4DB1BVU5POBIWiRznn5+dL9zD68IMPWLZsGb6+vjg7O5Oens7BP/5ATU2txP5D5WlQyt5SCvLyaGholHpMEIT/FjlJZeZ/C+8MFxcX+vfvz7hx46q7KILwRj33LTn6IJQtIzuXLWevcelhLBIJWNXRY0QHe+rWfrGBpNee4zxJz2TV5y82b7wV8xj/S7eITEpBvlYtWpjVYaiTHfoaalXK+9nzLH6/eIvrUQmkZedgpqvNx62b0bpBxTtLv89868yv7iL8p+RkZXAxaD2Pwi8ikUgwqteEdj2+klnT/8c2TzJSHjNo3IuZR3EPrxN86jeSHz9CTk6OehYtae0yHA1tg9LehvSUx+xcOZKOfSZg2bzsp+z4r/gSY3NbnPtOfG11rIn62Tyo7iL8pzx//pydO3cScu0aEomERhYWDBo0SDqjE8Db25unT5/KPFHn7t277Nu/n5joaGrJy2Ntbc3HH30k3RT5Vd7e3gB8++235Zbn+++/x8rKihEjRryG2tVcDS0sqrsIUkv3vZvNu8luYjCguonG/3+MaPwL7wvR+BfeB6LxL7wPRONfeB+Ixn/FROO/+olp/0K1SExMpFevXuXGsbW1rdJjAd8GBwcHmd1vX6Wnp8dff/31Fkv0wrtcNkEQBEEQBEEQqpdo/P/HFO/C/l+nr6/P3r17y43z8uPo3hUBAQHlPinh1acRvE3vctkEQRAEQRAEQaheovEvVAt5eflKPd/+XVO8sc676F0umyAIgiAIgiAI1atWxVEEQRAEQRAEQRAEQfgvE41/QRAEQRAEQRAEQajhRONfEARBEARBEARBEGo40fgXBEEQBEEQBEEQhBpObPgnCIIgCNVEPP9ceB8Y/bWxuosgCG+exYLqLoEgVEiM/AuCIAiCIAiCIAhCDSca/4IgCIIgCIIgCIJQw4nGvyAIgiAIgiAIgiDUcKLxLwiCIAiCIAiCIAg1nNjwTxAEoQbIzstn27nrXHwQS3ZePk2N9RnRwZ66tTXLTReWkMRv528S8eQZqooKODYy5dO2NqgqKUrj5BUUsOvybU6FPSItOwdjbQ0+cmiGUyNTAHZeCmXX5dAy32N2v85Ymxi+nooK77WcnBx2795NcHAwOTk5NG7cmEGDBlGnTp1K53Ht2jV+/fVXpk6dipWVFQCbNm3i3LlzZaZZuGgR+np6PH78mBkzZpQ4XrduXebMmVP1CglCKcTnuSAIb0qNafwPGzaMS5culXn8/Pnz6OrqvvFyBAQE4OnpSVhYGAAuLi7079+fcePGvZX3q6zExETWrFnDyZMnefz4Mbq6ujg4OPDll19ibW1dbtoVK1awZ88ejh07VmYcKysrFi5cyIABA6pUrvLyK8uaNWvo0qULLi4uxMbGSsPl5ORQU1OjWbNmTJgwgdatWwPw/fffs2fPHpk8FBQUqF27No6Ojnh6er6Va6UsMTExdO3ala1bt9K2bdtKp7t37x5z5swhNDQUc3NzvLy8sLe3f4MlFd4lPkcucC/xKUOd7FBVVGTX5VBm7zvB0sE90VBRKjXNo6QU5uw7iW09Q77t5URyZhbbz98k9lkaP/TtJI33S9BFrkcn8lk7W4x1NDkV9gifIxdQVVTA3twYl2YNaGEm2/jKKyjA58gFdNRUaGyk90brLrw/1q5bx8MHD/jo449RUVHhwIEDLPn5Z+bMno26unqF6TMyMti2bVuJ8A8++IBOnTrJxn3+HN81a7CyskLv7++EqOhoAKZMmYKS0ov/q5d/FoR/S3yeC4LwptSYxj9A7969S+2RB6hdu/ZbLk2R3bt3o6ysXC3vXZY7d+4wYsQILCwsmDNnDg0bNiQxMZHt27czaNAgFi5cSJ8+faq7mCVMnz4dV1fXEuHa2trSn93d3XF3dwdAIpGQkpLC0qVLGTlyJIcOHaJu3boA2Nvbs2LFCmm67OxsQkJCmDt3LikpKaxbt+4N1+b1kkgkeHh44ODgwI8//siKFSsYO3Ysp0+fplYtsbqnpgtLSOJqZBzTP+yIvbkxAE3r6vPNtj8JvHWfjxyalZru4PVwNJWV+LZXexTkX1wnq45eJu5ZOnVra3I77gkXImJk8ratZ0hCagYhUQnYmxujr6GGvoaaTN6bz1wjKy+fBb3bo6Qg/4ZqLrxPIiIiuHH9OuMnTMDWxgYAy8aN8fT05MSJE3zwwQcV5rF9+3bkFUre+hgaGmJoKDuauXr1atQ1NBg5ciRycnIAREdFUVtXlyZNmryGGglCSeLzXBCEN6lGNf5VVFQwMDCo7mLIqM4R5NLk5+czYcIEbG1tWbNmDfLyRR/iJiYmtGzZEkNDQ3744QdatGiBqalpNZdWlqamZoV/XzU1NZk4hoaGzJkzB2dnZ4KCgvjiiy8AUFRULJGXqakpUVFRrFixgvT0dDQ1y59e9y5JS0sjJiaGadOmYWZmhqurK/v37yctLQ0dHZ3qLp7whl2PSkRZUYHmpi9Ga7RVVWhW14CQRwll3iwObmvDh80tZW4UFf7uLMotKADgQkQMRtoa0htFKJpVM/8jlzLL8ygphUM37vGZox2GWhWPxgpCZYSGhqKsrIx1sxfXs6amJpaWlty8davCxv/ly5e5fecOnw0ZUmEH742bNwkODma0hwdqai8aQjExMZjWq/fvKiII5RCf54IgvEnv1ZBgeno6np6eODg44OjoyOrVqxk2bJh0BDggIKDE9PJXw+Li4pg0aRKOjo5YW1vj7OyMt7c3hYWFpb6ni4uLNH8rK6tSXytXrgQgNzcXb29vOnbsiL29PQMHDuTMmTMy+QUFBdGnTx9sbW0ZMmQIcXFxVToHJ0+e5NGjR0ycOFHa8H/ZhAkTkJOTw9/fXxrm7+9P9+7dsbOzw8PDg9TUVJk0CQkJjBkzBnt7e5ydnTlw4IDM8aysLGbMmEH79u2xtbWlX79+HDlypErl/jcU/h7lqcy0TGVlZeTk5Eo9N6W5ePEizZo1IygoiJ49e2JnZ8fnn39OfHw88+fPl7nWiuXm5rJ48WJcXFywsbGhTZs2TJgwgeTk5DLf53//+x+9e/fGzs6O3r17s2XLFplrTltbGxsbG1auXElUVBS+vr506tSpRMPfysqKX375hS5dutChQwciIyMrdU3fuHGD4cOHY29vj5OTE15eXmRlZQFFsw7WrVtH165dad68OW5ubuzfv79S5094PWKepWGkpU6tWnIy4XW0NYhNSSsznZ6GGub6OkDRGtMb0Yn8fuEWVnX0qf93+KOkFEx1tTgd/ogJvx1i0KpdTPjtEJcexJaZ77bzNzDQUueD5o3/dd0EoVh8fDz6BgYlZjMZGBqSkJBQbtq0tDS2//YbgwcNkpktVhqJRMLuXbuwtLTEoVUrmWNR0dFkZ2ezcNEixnz9NVOmTOF/AQHk5+f/s0oJwivE57kgCG9SjRr5r8j48eOJi4tj9erVqKmpMX/+fG7dukWbNm0qnceYMWMwMDBg06ZNqKurc/ToURYuXIi9vT3dunUrN+2rDfkff/yRK1eu8MknnwDg6elJREQES5YswcjIiOPHj+Ph4cHKlSvp3LkzwcHBjBs3jrFjx/LBBx9w5coV5s2bV6VzEBISIl0HXxoVFRVatmzJ1atXATh48CBz585l+vTpODk5ERQUxLJlyzA2Luo1zs/PZ+TIkWhoaODn50dubm6JTY+WL19OWFgYa9euRUtLi127djFp0iQCAwOp94ZHUBITE/nxxx9RU1MrsZ7zZRKJhJCQELZs2UKPHj1kRnoqUlBQwOrVq1myZAn5+fmMHj0aNzc3PvroI3bt2sX+/fvx8fHBxcUFKysrfvrpJ44fP86iRYswMTEhLCwMT09PVq9eXeqyFX9/f5YuXcqsWbOws7Pj9u3bzJs3j8TERKZNmyaNN3fuXD7++GN69OhBnz59mD17dqnl/e2331i3bh0FBQXUr18fNze3cq/p6OhovvjiC7p3746/vz/p6el89913zJkzh0WLFrFs2TIOHjzIrFmzaNiwIZcvX2b27Nmkp6fz2WefVfo8Cv9cZm4eqoqKJcJVFRXIyq24USKRSHDfuI+8/AI0VJT40tlJeiw1K4f41AwePklhcFsbdNVVOXzzPksOn8Pzgw4yI0hQdHN5PSoBjy4OyIslJ8JrlJWVhYqKSolwFWVlsrOzy027dds2LBo2xNHRscI9cq5fv058fDyDBw+WCU9PTyfl2TMKCwr46OOP0dPT4+6dOxw6fJjk5GS+Gjmy6pUShFeIz3NBEN6kGtX4P3DgAIGBgSXCu3XrxujRozl37hybNm2Sbvzm4+ND165dK51/dnY2bm5u9O7dW9r4HT58OOvWrSMsLKzCxv/L08w3b97M8ePH2b59O0ZGRjx69IiDBw+yd+9emjZtCsCIESO4e/cuGzZsoHPnzvj5+dGyZUvGjh0LQIMGDQgPD2fr1q2VrkNqaipaWlrS9Yul0dHRISYmBoBt27bh6uoqbcSNGjWKa9eucffuXaBoI8V79+4RFBSEmZkZAAsXLqRfv37S/KKiolBXV8fU1BQtLS3p5nsVjb68ysvLq0Rnx+jRo/Hw8JD+7uvry8aNG4Gijonc3FwsLCzw8fGRrvcHuHLlisxmeDk5Oejq6uLq6srEiROrVC5AupQCoF27dly/fp1p06YhJyfH6NGjWbVqFffu3cPKygpbW1t69eqFg4MDULTkwsnJifDw8FLzXrVqFWPGjJFOaTU1NSUjI4M5c+YwYcIElJWVCQ4OZsqUKVhYWBAREYGhoSHq6upkZmaiqqoq8/d2c3OTlrUy1/TOnTvR0dHhxx9/lM6imD9/PiEhIWRmZrJ582aWLl1K586dATAzMyM2NpYNGzaIxv8bIJFIKJRIZMJe/f1l5fyrSxUUSvjetQO5+QXsCb7DD3uOM3+AC/X1dcgvLOTZ8ywWD+xOQ4OivVNsTAyZ4h/I7iu3S9wsHrp5Hy1VZTpZ1a9y3QShmEQiKTGjrvzrvOwL/ey5c0UbopbRIfqqY8ePY2pmVqKTXFlZmUmTJmFoZIS+XtGmZ1aWligoKLB3714+cHWV+Z4RhIqIz3NBEN62GtX4d3FxYerUqSXC1dTUuHLlCgB2dnbScCMjI+rXr1/p/FVUVBg6dCiHDx/mxo0bPHr0iLCwMJKSksqc9l+aY8eO4e3tzbJly6Q769++fRuAIUOGyMTNy8tDS0sLgPDwcNq3by9z3N7evkqN/9q1a5ORkVFunLS0NOleBeHh4SXWUdrb20sb/+Hh4Whra0sb/gBNmzaVGZ356quv8PDwwNHRETs7O9q3b0+fPn2qvKZ+/Pjx9OjRQybs1Q6EwYMHM2zYMABq1aqFjo5Oqe9jY2PDkiVLgKJNpObNm0eTJk2YMGFClUb9i5mbm0t/VlNTo169etKb0eJzkZubCxQ1vs+dO8eSJUuIjIzkwYMHPHz4UNoZ8LLk5GQSEhJYunQpy5cvl4YXFhaSk5NDTEwMGhoafPnll/Tv358ZM2awYcMGfv75Z6ytrfnjjz9ITk7m999/L7Wslbmmw8PDsba2ljb8oaiDo127dty4cYOcnBymTJkiMxW3uOMlOzu71JE64Z/bdfl2iccwtbOoR2pmyZHPrLx81JRKjiC9SkG+FnamRkDRxlJfb/uDP66H803XNqgqKlBbXVV6owhQq5YcdvWMCLr9QCafwkIJlx7E4tTYVGbdqSBU1YEDB0osIWvVqhVpaSWnPWfn5KBaxudMcnIy/v7+DPzkEzQ1NSkoKJB+thUWFlJYWCjz2ZWRkUFYWFipT6pRUlIqddacra0te/fuJSYmRjT+hSoRn+eCILxtNarxr66uLtOwKc2rjfSK1oEX/L1JCkBmZiZDhw4lOzubXr160b9/f+zs7Ko0unnnzh2mTJlSoiEr+bund/v27SUeV1R8YyInJ1ei/IqlTA0rT6tWrfD19eXOnTvSGQYvy8nJ4fr169KlCFDynL38nqWVCZBpKNrb23Py5EnOnj3L+fPn2bt3L6tXr2b9+vU4OjpWuux6enoV/n21tbUrjANFjd7ieObm5piZmfHJJ58wefJk1qxZU+4oUmkUXtk9urwd9mfNmkVgYCD9+vXDxcWFb775hg0bNpCYmFgibvG59fT0xMnJqcRxY2NjduzYQa1atfD09EReXp5Ro0YRHh7O9OnTyc/PZ9KkSSXqXqwy1/SrdXtZ8XXr4+NDw4YNSxwXj796/bpZN6TlK6Mzlx/Gci0qAYlEInPtxqekU6+2Vpl5XXkYh6qSgswzm9WVlaijpcGzv28+jXU0ScrILJF3fmEhSq/sjRGe+JT07BwcLcSGaMK/4+zsjO1LnfUA10JCCA0NLXEtPk5MlM5cetWdu3fJysxky5YtbNmyRebY0qVL0dPTY9GiRdKw0NBQCgsKaPXKWn8oWkZ29+5dWrduLdNJnJeXB/Cf2iRWeDeIz3NBEN6296Yrr7i3Pjg4WBqWkZHBw4cPpb8XN2pfHhmPjIyU/nzmzBlCQ0PZunUr48ePx9XVFQ0NDZ4+fSptBJUnMTGR0aNH06NHD0aPHi1zrHHjoo1Unjx5grm5ufQVEBBAQEAAAE2aNCEkJEQm3a1btypTfakOHTpgaWnJkiVLZDo2ivn6+pKTk8PAgQOBolH8l88ZwM2bN6U/N23alPT0dO7duycNi4yMlDmHv/zyC1evXqVr167MnDmTwMBATE1NS12iUV0aNWrE1KlTOXHiBDt27Hhj7/Ps2TP8/f3x8vLC09OTAQMG0LRpUx48eFDqNaSnp4euri7R0dEy10VoaCg+Pj4AqKqqkpOTI3PO58+fj7KyMnl5edLp+KWpzDXdqFEjbt++LXO9BAUF4eLiQsOGDVFQUCAuLk6mfCdPnmTDhg3iMYNvgK66Ko2MdGVedqZGZOflcy3qxaZnqVnZ3IlPko4Alebg9XDWnQymsPDFtZeUkUn0szTM9Ypm1dib1yEjO5cb0S86p/ILCrkelUjTuvoy+d1LfIp8rVriOdDCv6ajo0OD+vVlXs2srcnOzuZW6IuR0uLvn7L2sWluZ8f0GTNkXkOHDgVg6NCh0mV0xR48fEhtXV3ptP6XpaSm4ufnJ51JWOzy5cuoqKpWquNZEF4mPs8FQXjbatSdeXZ2Nk+ePCn1ZWxsjKurK3PnzuXs2bPcu3ePadOmkZmZKU3fokUL5OTkWLFiBTExMRw6dIg9e/ZIj9epU/TYlf379xMbG8uVK1f4+uuvycvLk07pLktmZiYeHh7UrVuXKVOmkJSUJC1bSkoKjRs3pkuXLnh5eXHs2DGio6NZt24dvr6+0in17u7u3L17l8WLF/Pw4UP279+Pn59flc6RvLw8Pj4+3L9/ny+++IIzZ84QHx/PjRs3mDlzJmvXrmXu3LnSm5hRo0YRFBTE+vXriYyMZNu2bTKN9rZt29K8eXOmTZvGtWvXuHnzJtOmTZNp9EVHR+Pl5cX58+eJjY0lMDCQuLg4mTX374IhQ4bg4ODAkiVLSh2Ffx00NDTQ1NTk6NGj0in2P/zwA6GhoaVeQ3Jycnz11Vds27YNPz8/oqKiCAoKYvbs2aioqKCkpESvXr2oXbs2kyZN4s6dO9y+fVu6EaC5uTnffPMN0dHRpZanMtf0kCFDePbsGV5eXkRERHD58mV++ukn2rVrh6amJoMHD2b58uXs27eP6Ohodu/ejbe3d4lnZgtvjrWJIdYmhiwPusjR2w+4+CCGuftOoq6kSE+bRtJ4MclpPHzyTPr7xw7NiE9JZ+mR81yPSuBU2CPm7juJhrISfVoUPeWkY2NzGhjUluYd8iiexX+eISkjk09aW8uUI+ppKoZa6uI50MIbYWVpiZWVFevXr+f06dMEBwfz89KlqKqpyXRyxsXFERUVBRR95r7aiVD8uVenTp0Sm87GxsSUOYvAsnFjmjRtyq5duzh69Ci3b9/G39+fY8eO0bdPn3+0ZEwQXiU+zwVBeJNq1LT/Q4cOcejQoVKPLV++nEWLFuHt7c2kSZOQSCQMGjSIO3fuSOOYmpoyZ84cfH19+e2332jVqhXTpk3ju+++A4r2C/D09GTz5s34+PhgZGSEq6srxsbGMqPhpbl586Z0XX/Hjh1ljrVp04Zt27axbNkyli1bxqxZs0hNTcXMzIwFCxbQv39/oGiUfd26dXh7e+Pn50fjxo3x8PCQrl2vLAsLC/bs2cP69euZN28e8fHxaGlp0bZtW/z9/aX7EAB07tyZn3/+mRUrVrB8+XJatGiBu7s7Bw8eBIqmt/v6+jJ//nzc3d1RUVFh9OjRxMa+eGyMl5cXixcv5ttvvyUlJQUTExOmTp2Km5tblcr9psnJyTF//nzc3NyYPXu2zOP5XhdFRUXptdinTx+0tbVp27YtkydPxtfXV/r4vJe5u7ujrKzMtm3bWLRoEfr6+gwcOJDx48cDRVNNN2/ezKJFi/j000+Rl5fHycmJXbt2oaSkxNdff01ERASmpqYl8q7MNW1kZMTGjRvx9vamX79+aGtr4+rqyuTJk4GiJQm1a9dm+fLlPH78GGNjY8aPH89IsfP1WzW1lxNbzl5j67nrSCRgVUePKT2d0FB5sfRi3cmrPEnPZNXnRft42NQz5Ie+nfC/dIslh88hX6sWLczqMNTJDh21ouUhCvK1mNW3E79duMlvF26SlZdPQ/3azHLrJLNuFCAlMxsNZbHUQ3hzxowZw86dO9m1ezcSiYRGFhZ4jB4ts1xu+/btPH36VGY6f2WlpaVhUsZTaOTk5Ph6zBgOHDhAUFAQqWlpGOjrM2zYsBLf64Lwb4jPc0EQ3hQ5SWXmq9dgLi4u9O/fn3HjxlV3UQThjXh1bd9/xXPfko89FISaJrGbe3UXQRDeOKO/NlZ3EQThjVMfvaC6iyC1dN+72byb7Pbfux+taWrUtH9BEEr6Lzb8BUEQBEEQBEF4vWrUtP/3WWJiIr169So3jq2tbZUeC/g2ODg4lLrxYDE9PT3++uuvt1iiF97lsgmCIAiCIAiCIFTFe9/4P3bsWHUX4bXQ19dn79695cZRVlZ+O4WpgoCAgHKflCAvX30bzbzLZRMEQRAEQRAEQaiK977xX1PIy8v/Jx8zVPwkg3fRu1w2QRAEQRAEQRCEqhBr/gVBEARBEARBEAShhhONf0EQBEEQBEEQBEGo4UTjXxAEQRAEQRAEQRBqONH4FwRBEARBEARBEIQaTmz4JwiCIAjVZO+thtVdBEF44/p1c6/uIgjCGyc+zYX/AjHyLwiCIAiCIAiCIAg1nGj8C4IgCIIgCIIgCEINJxr/giAIgiAIgiAIglDDica/IAiCIAiCIAiCINRwYsM/QRCEGiIpI5MpOwL5tnd7bEwMy4175l4Uuy/f5nH6cww01ejXsgldmjSQiXM/MZlt564T8eQZakqKdG5Sn4GtrVGQf9Fv/ODJM36/cJOIx8+QIKGBQW2GOtrR0KD2G6mjIGSkJbFn7Ti6fTwd4/q25caNCD3FtdM7SE95jIaOAXaOH2PZvKtMnCdx97h0dCNJ8REoKavR2M4Fe+chyMu/uEVKjLnL1eNbeRwbjqKyKqaNHGjd5QtUNXTeRBUFgeTkZGbPmcM3X3+NlZVVuXEvXbrEwYMHSXr6FD09PXr16kV7JyeZOA8jI9m9axePHj1CRVUVJycn+vbpg4JC6U2Ba9eu8euvvzJ16tQK318QhP8OMfL/nnBxccHKykr6srGxoXPnznh5eZGcnCyNZ2VlRUBAQDWWtHKuXr3KlStXXmuew4YN4/vvv3+teb5umZmZbN++vbqL8Z+5Tt4nSRmZzN9/isycvArjXoiI4Zegi7Qwq8O03u2xrmvIqqOXOXMvShonMTWDeQdOoqQgz+SejnzY3JID18LYcDpYGichNYNZe46Tm1/AGBcHvnFpQ0FBITMDjhH3LP2N1FN4v2WkJRH42yxys59XGPfh3XOc2PszJhYt6fbJdIzNbDl9YDkRoaekcdKeJXD4t1koKCjjMmAaNm3duHVxH+cDfaVxnsSG8+e26eRkZ+DcdyIdPxxPekoC+zdPJacS5RCEqkpOTsbHx4eszMwK414NDmb9+vVYW1vz9ZgxWFlasnnTJi5duiSN8+TJE5YtW4aSkhKjRo+me/fuBAUF8fvvv5eaZ0ZGBtu2bXtt9REE4d0hRv7fI+7u7ri7Fz1uJzs7m/DwcLy9vRk6dCj+/v5oampy5swZNDU1q7mkFRsyZAgLFy7EwcGhuovyVm3cuJGAgAA+++yz6i6K8I6QSCScCItk69nrlU7z24WbtLOox/AOLQBoYVaHjJxcdly8RYfGZgDsDbmLqqIi37l2QEG+Fi3NjVFWkGfD6RAGtGqKgaY6f964h7KCPJ4fdkRFsejrxKaeIV9v/YM/b95jpHPL115f4f0kkUi4d+MYl/7aWOk0V49vpUFTJ9p1HwlAPYuW5GSnE3zCDwtrZwBunP8fikqqdBs4E3l5BUwbOaCgoMy5QF9atB+IhrYB187uRElFHdehP6KsqgFA3fp27F49hpvnA3DoMuz1V1h4L0kkEs6dP8+uXbsqnWbPnj20atWKQYMGAWBjY8Pz58/Zu28fbdq0AeDw4cOoqKjwzTffoKCggJ2tLUpKSvz+22+4urqip6cnk+f27duRL2NGgCAI/21i5P89oqamhoGBAQYGBpiamtK1a1c2btxIfHw869evB8DAwAAVFZVqLqlQFolEUt1FEN4xkUkprD1xlU5W9RnXrW2F8R+nPSc+JZ02DU1kwttZ1CMxNYP4lKIR++tRidib15GZ4t+uUT0kEgnXoxIBMNHRpK+9lbThD6CiqICehiqJqRmvo3qCAEBy4kPOHVpFI7sudHKbVGH89JTHpD6Nw9zKUSa8fpP2pD1LIDU5DoDYiGDqNWolM8W/ftP2IJEQ86BolktKUjRG9ZpKG/4ACorKGJhYEn3/8uuoniAAEB0djZ+fH47t2kkHa8qT9PQpiQkJ2Nvby4S3atWKJ48fk5hY9FkdGhqKrY2NzBR/h1atkEgkhIaGyqS9fPkyt+/c4eOPPnoNNRIE4V0jGv/vubp169K9e3f++OMPQHY699OnTxk/fjxt27bFzs6OwYMHy0wjc3FxYdWqVXz55ZfY2dnRvXv3Er3Vu3btok+fPtjZ2dGiRQuGDBnCzZs3ZfJYvHgxrq6utG3blkuXLpGamsrMmTPp2LEj1tbWODo6MnPmTLKysqRlBPD09JRO009MTGTSpEk4ODjQtm1bPDw8iIyM/FfnxsrKCn9/f4YMGYKtrS29e/cmODgYf39/OnfuTMuWLZk4cSLZ2dkABAQE4OzszM6dO+nQoQP29vZ888030i9fgLi4OCZNmoSjoyPW1tY4Ozvj7e1NYWGhNM6NGzcYPnw49vb2ODk54eXlRVZWFitWrGDlypXExsZiZWVFTExMhXWIiYnBysqKP/74g379+mFra8uAAQOIiIjg119/xcnJiTZt2jBnzhxpx0JhYSG+vr707NkTGxsbWrZsyciRI4mKiirzfY4fP86AAQOk14GPjw+5ubn/9NQLVaCvqcaKoa4M79ACJQX5CuPHPksDoK6O7AwfY+2ihk1cSjq5+QU8SX9eIo62qgqqSorE/d1B0NO2EW72TWTiJKRmEJ2chqmu9j+ukyC8Sl3bgI+/9qVd95EoKChXGD8lKRoAbV3ZTi5t3boApD6NJT8vl4zUJyXiqKpro6isRurTWABU1LTISH1c4j3Sn8WT/iyxRLgg/FO6urosmD+fQYMGoaykVGH8+Ph4AIyMjGTCDQ2L9nxJTEwkNzeXp0+fYlSnjkwcTU1NVFRVZe5R0tLS2P7bbwweNAhtbfEZLgg1kWj8C1haWhIdHc3z57JrF2fPnk1OTg5+fn4cOHCABg0a8PXXX5P50hq0VatWYW9vz969e/nss8+YNWsWf/75JwBBQUHMnTuXkSNHcujQITZv3kxOTg4zZ86UeR8/Pz9mzpzJ+vXradGiBd9//z23b99m5cqVBAYG4unpyd69e/H39wfgzJkzAEyfPp0ZM2aQmZnJsGHDpHlt27aN2rVrM3DgQJkvtX9i2bJljBw5kn379qGpqYmHhweBgYGsXbuWhQsX8tdff8l0eCQnJ7NlyxZ8fHzYsmUL8fHxjBw5kvz8fADGjBlDeno6mzZt4vDhw7i7u7N+/XqOHTsGFPX6f/HFFxgaGuLv78+KFSs4e/Ysc+bMkS7bqFOnDmfOnMHY2LhK9Zg+fTq7du0iLS2NTz/9lMjISLZt28akSZP47bffOH78OABbt25lw4YNfP/99wQGBvLrr78SGRnJokWLSs371KlTTJw4kYEDB3Lw4EG8vLw4dOgQ33777T897UIVaKooo6+hVun4z3OL9gRQU1KUCVf9+/fM3Dye5+SWGgdAVVGBzNzS9xXIzS9g5V+XUJCvRS/bRpUukyBUREVVEw0t/UrHz80p+j5TUpb931BUUgUgLyezzDjF8fJyir7rLJt352nCAy4cWUdmejKZGc+4dHQzKUkx5OVl/6P6CEJpNDQ00NXVrXT84j0BVFVVZcKLZ3BmZWVJB05US5nVqaKiIj0OsHXbNiwaNsTR0bFEXEEQagbR+BfQ0tICijZ4eVlUVBRaWlqYmppibm7OjBkz+OWXX5CXfzG62KFDB8aOHUvDhg0ZPnw4vXr1YsuWLQDo6OiwYMEC3NzcMDExoUWLFnz88ceEh4fLvE+nTp1wcnLC9u81aO3bt2fhwoU0b96cevXq0bdvX5o1ayZNZ2BgABT1WmtqavLHH3+QlpaGt7c3TZo0wdLSkgULFqChocHOnTv/1bn56KOPcHFxoWHDhri5uZGamsqsWbOwtLSkZ8+eNG3alHv37knj5+XlsXjxYhwcHLCzs8Pb25vw8HDOnz9PdnY2bm5uzJs3jyZNmmBqasrw4cPR19cnLCwMgJ07d6Kjo8OPP/6IpaUlrVq1Yv78+Zibm6Ouro6amhry8vIYGBjI/B0q4u7uTps2bWjSpAndu3cnMzOTuXPnYmFhwaeffoqenp60HmZmZixevJguXbpgYmKCo6MjvXr1KvF3K7ZmzRoGDhzI4MGDMTMzo0OHDsyZM4fDhw9XanaC8HZVtHRETk6OihaXyMmVDMvKzePHg6e5/ziZCd3bYqil/s8LKQj/VkVLpOTkKhcHsLLvQZtu7oRdC+L35cP5fflwMlITsbLviYJixbMQBOFNqdTneSXiAJw9d4579+5JB1MEQaiZxG4eAunpRVN4NTQ0ZMLHjh3Lt99+S2BgIK1ataJDhw58+OGHKCu/uNlp21Z2jbG9vT0nTpwAoHXr1tLp5Q8ePODRo0eEhYXJTHEHMDc3l/l9yJAhHDt2jD179hAZGcn9+/eJiYmhYcOGpZb/9u3bpKam0rp1a5nwnJwcIiIiKn8iSvFy2Yp71s3MzKRhKioqMtPb1dXVsbGxkf5uYWGBtrY24eHhdOzYkaFDh3L48GFu3LghPR9JSUnScxIeHo61tbXMurx27drRrl2711YPNTU19PX1ZUYKXq6Hi4sL169fZ/ny5Tx8+JCHDx9y//79EtMKi92+fZsbN26we/duaVjxzUZERAT16tX7V2UXXq/i0fysV0bvi0fz1ZUUX4qTXyJ9Zm4e6q9MR03KyGThwdPEpaQzsUc7WjcwKZFOEN4mxb9H8/Nys2TCc/8ezVdSVi8zDhTNDFBSftGBZduuH81af0j6swSUVTVRVdfm5L6lMvsACMLbVvw9Xrz8sJh0tF9Nrcw4ANlZWaiqqZGcnIy/vz8DP/kETU1NCgoKpPclhYWFFBYWUquWGC8UhJpANP4FQkNDqV+/PurqsiN13bt35/Tp05w+fZpz586xadMmVq5cyc6dO2ncuDFAiefDvvwFceDAAb7//nv69OlDy5YtGTx4MOHh4cydO1cmzcsbDBYWFjJ69Gju3bvHhx9+iKurK9bW1vzwww9llr+wsJAGDRqwevXqEsfU1Co/Hbo0pT3/trwvQEXFktOkCwoKkJeXJzMzk6FDh5KdnU2vXr3o378/dnZ2Mjv3l/W83X/r1XzLq8PatWv59ddf6d+/P46OjgwfPpyjR49K94V4VWFhISNHjqR///4ljhXP0hDeHSa1i2b6xKdm0MCgtjQ84e8N+kxqa6GiqICuuirxqbKP60vNyiY7Lx8T3Rd7ATxKSmHBwdPk5Oczs48z1iaGb6EWglA+Hb2iTsfU5Dj06rzoOE57VrRGWkffFEUlFdQ09Uj7e/O/YlnPU8nLzUJH3xSAJ3H3yEh7QoMmTujov+jMTEqIQL+OxZuuiiCUqc7f6/gfP34sMzDx5MkTAIyNjVFWVkandm0eP5bdtyI9PZ3s7GyMjY25c/cuWZmZbNmyRTp7s9jSpUvR09Mrc+mfIAj/LaLx/55LSEjg6NGjfPXVVzLhubm5/Pzzz7i5ueHq6oqrqyvZ2dm0b9+eEydOSBv/L2/eBxAcHEyzZs2Aokbkxx9/zJw5c6THjx49ChSNDMuVMnf4zp07nDp1ip07d9K8eXOgaCp9VFQUpqampdbB0tJSuia/eK1cXl4eU6ZMoVevXri6uv6TU/OPpKSkEB0dLS3rvXv3yMjIoFmzZpw5c4bQ0FDOnj2Lvr6+NP7Tp0+lI+WNGjXiwIED0g4DKNo7YeHChRw6dKjUc/a6rVmzhm+++YZRo0ZJwzZs2FDm1MHGjRvz8OFDmdkFFy9eZOvWrcyePftfd8AIr1cdbQ0MtdS5EBGDU6MX/1MXImKoo6Mhna5vZ2pE8KN48goKUPz7WrxwP4ZacnLY/N3AT8rIZO7+k8jXqsWCAV2pp6v19iskCKXQ0jVGQ8eQyLtnadisgzQ88u5ZtHSN0dQpuoZNGrYg6v5l2uaPRF6hqPM28s5Z5GrVom59OwASom5x9cR26k5sjrJK0f9H7INrpDyJxrbdgLdcM0F4wdDQEH19fa5evSrz6OOrV69iaGSE/t+P8GvWrBk3bt5kYF6edJDiytWr1KpViyZWVigrKzN9xgyZvKMePcLPz4+hQ4diYSE6uQShphBzeN4jmZmZPHnyhCdPnhAdHc1ff/3FyJEjqVevHiNGjJCJq6SkxM2bN/nhhx+4du0aMTExBAQEkJmZKfNImT/++IPt27cTGRnJ+vXrCQoKYuTIomcqGxsbExwcTGhoKFFRUWzevBk/Pz+AMneC19fXR0FBgUOHDhEdHc3NmzeZOHEiT548kUmjpqZGREQEz549o2/fvmhrazN+/HiuX79OREQE33//PadOnZI+GeBt+vbbb7l16xbXrl1j2rRp2Nvb07p1a2kP/f79+4mNjeXKlSt8/fXX5OXlSes2ZMgQnj17hpeXFxEREVy+fJmffvqJdu3aoaysjJqaGqmpqTx8+JC8vNI3Xfu3jI2NOXv2LPfv3+fBgwcsW7aMI0eOlPk3++qrrwgMDGTlypU8fPiQ8+fP4+npSXp6uhj5fwdk5uYRnvCU1KwXUz4/bt2M8/ejWXfyKiGP4ll74irn70czuM2LJSv9WjYhNSuHBQdOczUyjgPXwth89hrdrBtioFnUANp4KoS0rBw+ad1M+j7Fr5jktLdeV+H9lZuTyePYMLKep0rD7Dt+ysPbZzl3aDXR969y9s9VPLx9lladhkrj2Dl+RPbzNAJ3zCbq3mVuXtjLxb820MS+JxraRZ9fFjadUVBU5tj/FhH74Bph14I4+r9FGNZrQiPbLm+9rsL7KysriwcPHkiXawJ82KcPV65cYfv27dy8dQs/Pz+uXLlCPzc3aZxePXuSnp7O8l9+4caNGxwJCmLnzp04Ozujp6eHhoYGDerXl3kV37PUqVNHLN8ThBpEjPy/RzZu3MjGjRuBounpxsbGuLq64u7uXmLKPxTtEL9w4ULpDvUNGzZkyZIlMr3L/fv3JygoiEWLFlG/fn18fHzo1KkTAD/88AOzZs1i6NChKCkp0aRJE3766ScmTZrEzZs3ZfIpZmRkxKJFi1ixYgXbt2/HwMCAzp07M3z4cOmO+IB0l/yIiAjWrFmDn58fP/30E19++SUFBQVYW1uzcePGaumt7tOnD6NGjSI3NxcXFxdmzJiBnJwcdnZ2eHp6snnzZnx8fDAyMsLV1RVjY2PpDAojIyM2btyIt7c3/fr1Q1tbG1dXVyZPngxAjx492LlzJ3379sXPz086O+J1+umnn5g7dy4fffQR6urqNG/enDlz5jB79mzi4uKoW7euTPxevXqxbNkyfH19WbNmDTo6Ori4uDB16tTXXjah6h48ecacvSf4umtrujRpAECXJg3ILyhkX0gYx+48xEhLg7Hd2tC+8Ytpoya1tZjZxxm/czdYcvgcWirKfNjckkF/dxDkFxQS/KhoCvXaE1dLvG+zugbM6S8aRsLb8TQ+gj/9ZtCxzwQsm3cFwLJ5VwoL8rh5PoDw63+hqWNEJ7dJNLTuKE2no1+PXkPmcOnoJo79bxHKqlpYt+lLy04vlmOpadSm15A5XPxrA3/t/hFlFQ0sm3elVaehYh208FZFRUWxZMkSho8YQXsnJwDaOzmRn5dH4JEjnDl7FgN9fdzd3WX2QTI2NmbihAns/t//WL1mDRoaGnTr1g23vn2rqyqCIFQTOUlF24AKQhlcXFzo378/48aNq+6ivBMCAgLw9PSU7twv/DvPfWdUHEkQ/uN868yv7iIIwhvXz+ZBdRdBEN64hu/Q8ogH/3LD6zflXTpH7yvRZS0IgiAIgiAIgiAINZyY9i/UeH379iU6OrrcOBcvXkTplceXvetqar0EQRAEQRAEQXj9xLR/ocaLi4urcHM8MzOzt7KT/utUU+tVTEz7F94HYtq/8D4Q0/6F98G7NKVdTPsXyiJG/oUa79UN6mqKmlovQRAEQRAEQRBeP7HmXxAEQRAEQRAEQRBqONH4FwRBEARBEARBEIQaTjT+BUEQBEEQBEEQBKGGE41/QRAEQRAEQRAEQajhRONfEARBEARBEARBEGo40fgXBEEQBEEQBEEQhBpONP4FQRAEQRAEQRAEoYYTjX9BEARBEARBEARBqOFE418QBEEQBEEQBEEQajiF6i6A8Pa5uLgQGxsr/V1RURF9fX06derEhAkT0NXVBcDKyoqFCxcyYMCA6ipqpVy9ehWJRIKDg8Nry3PYsGGYmJiwaNGi15bn65aZmcmePXv47LPPqrsowjsiKSOTKTsC+bZ3e2xMDMuNe+ZeFLsv3+Zx+nMMNNXo17IJXZo0kIkzevMBkp9nlUi7wd0NLVVlmbCCwkJ+CDhOC7M6DGxj/e8rIwiviHkQwtUT23j2OApVdW2atf4Qm7b9kJOTKzV+QX4ewad+I+LWSbIz09DRN8HW8SMsrJ1LjV9YUMDBrd9Rr2FLWnYaInPs2ZNoLh/bRPyjW9SSV6CuuS0OLl+grVv3tddTEACSk5OZPWcO33z9NVZWP1zVswABAABJREFUVuXGvXTpEgcPHiTp6VP09PTo1asX7Z2cZOLEx8ez+3//IzwsjFry8lhaWjLwk08wMDAoNc/Vq1ejoqLCiBEjXludBEGofqLx/55yd3fH3d0dgOzsbMLDw/H29mbo0KH4+/ujqanJmTNn0NTUrOaSVmzIkCEsXLjwtTb+/ws2btxIQECAaPwLQFHDf/7+U2Tm5FUY90JEDL8EXcTVrjEtzOpw6UEsq45eRlFeng6NzQBIy8oh+XkWw5ya08RYXya9urKizO+5+QWsPHqJe4lPaWFW5/VVShD+9jg2jCD/eTRs1oGWzp+RGH2bS0c3U1hYQHOnj0tNc3zPT0Tdu4Jtu37Urd+cpIT7nDm4guznqVi36SMTNz8vl1MHlvEkNpx6DVvKHEtPSeTg1u9QUlbDsedoVNV1CL92hAObvsXty2Vo6pTf0SYIVZWcnIyPjw9ZmZkVxr0aHMz69evp2rUr1tbWXLt2jc2bNqGooECbNm2k+S3+6SeMjIz46quvyM3NZe++fSzz8WG2lxdKSkrS/CQSCf7+/gQHB+P0SgeCIAj/faLx/55SU1OT6e01NTWladOmfPDBB6xfv55JkyaV2RssvBskEkl1F0F4B0gkEk6ERbL17PVKp/ntwk3aWdRjeIcWALQwq0NGTi47Lt6SNv4jk1IAaNPQhDraGmXmdTvuCRtOBZc6Q0AQXpfgk9vRM2pAJ7fJAJg2akVhYQHXz+zCunVfFBSVZOI/TXjAo7CLtOo8lBYdBgJg0rAFiooqXD6+lUZ2LiirqAOQEBXKucNryExPLvW9b13cR0FeDr3dl6JVu6hzq55FS/ZvmsKVE1vp0m/qm6q28J6RSCScO3+eXbt2VTrNnj17aNWqFYMGDQLAxsaG58+fs3ffPmnjf/+BA6iqqDB50iSUlYtmbenp6/Prr78SGRmJpaUlADExMfz+++88jIxEUUmp9DcUBOE/Taz5F6Tq1q1L9+7d+eOPP4Ciaf8BAQEAPH36lPHjx9O2bVvs7OwYPHgwly5dkqZ1cXFh1apVfPnll9jZ2dG9e/cSX167du2iT58+2NnZ0aJFC4YMGcLNmzdl8li8eDGurq60bduWS5cukZqaysyZM+nYsSPW1tY4Ojoyc+ZMsrKypGUE8PT05PvvvwcgMTGRSZMm4eDgQNu2bfHw8CAyMvJfnRsrKyv8/f0ZMmQItra29O7dm+DgYPz9/encuTMtW7Zk4sSJZGdnAxAQEICzszM7d+6kQ4cO2Nvb880335CYmCjNMy4ujkmTJuHo6Ii1tTXOzs54e3tTWFgojXPjxg2GDx+Ovb09Tk5OeHl5kZWVxYoVK1i5ciWxsbFYWVkRExNTYR1iYmKwsrLC19eX9u3b07VrVzIyMggPD2f06NG0bt0aGxsbunbtysaNG2XSnj59mkGDBtG8eXOcnZ1ZtmwZBQUFAOTm5uLt7U3Hjh2xt7dn4MCBnDlz5l+db6HyIpNSWHviKp2s6jOuW9sK4z9Oe058SjptGprIhLezqEdiagbxKenSfFUUFTDSUi83v8V/nkFfU43Fn3T/55UQhHIU5OcR/+gW5k0cZcIbNG1PXm4WidG3S6RJSYoGwKxxG5lw4/p25Odmk/DoxXdP0M75aGgb4PblslLfPyUpGh39etKGP4CcnBx1zGyIuX/1H9dLEF4VHR2Nn58fju3aSWdnlifp6VMSExKwt7eXCW/VqhVPHj8mMTERiURCcHAw7Tt0kDb8ARrUr88Sb29pwx9gw8aNFBQWMt3TE63/wMxPQRCqToz8CzIsLS3Zt28fz58/lwmfPXs2ubm5+Pn5oaSkxJo1a/j66685deoUampqAKxatQoPDw9mzJjBqVOnmDVrFurq6ri6uhIUFMTcuXOZP38+Dg4OPHnyhHnz5jFz5kz27dsnfR8/Pz98fX3R1NTEysqKCRMmkJiYyMqVK9HT0yM4OJjp06fTqFEjhg8fzpkzZ+jQoQPTp09nwIABZGZmMmzYMKytrfHz86NWrVps2rSJgQMHcuDAAYyMjP7xuVm2bBk//vgj9evX5/vvv8fDwwMbGxvWrl3Lw4cPmTJlCrt27WLYsGFA0TS7LVu24OPjg5KSErNnz2bkyJHs2bMHBQUFxowZg4GBAZs2bUJdXZ2jR4+ycOFC7O3t6datG9HR0XzxxRd0794df39/0tPT+e6775gzZw4//PADmZmZ/Pnnn+zevVu6T0Nl7Nmzhy1btpCVlYW8vDzu7u60b9+eHTt2IC8vz65du1i8eDGOjo40bdqUkJAQRo0axYgRI/jxxx+JjY3l22+/RUFBgXHjxuHp6UlERARLlizByMiI48eP4+HhwcqVK+ncufM/Pt9C5ehrqrFiqCv6Gmrcin1cYfzYZ2kA1NWRvbEz/nt0Py4lHWMdTSKTUtBUUebnwPPciE6kUCKhpbkxwzu0QFddVZpubr8umOvrvL4KCcIr0p4lUFiQj9Yr6+u1ahsDkPo0BpOGLWSOKatpAZCR+hhdo/ov5RUvzbPYB8MWysR5lYqaFs+eRFFQkI+8/IvbpvRnCeRmPyc7Kx0VVdFQEv49XV1dFsyfj66uLmFhYRXGj48vup5fvbcxNCxaipKYmIi8vDxZmZno6eqyfft2Ll2+TG5uLtbW1gz59FOZ+4cv3d2pV6/ea6yRIAjvGjHyL8jQ0vr7hikjQyY8KioKLS0tTE1NMTc3Z8aMGfzyyy/Iy8tL43To0IGxY8fSsGFDhg8fTq9evdiyZQsAOjo6LFiwADc3N0xMTGjRogUff/wx4eHhMu/TqVMnnJycsLW1RUlJifbt27Nw4UKaN29OvXr16Nu3L82aNZOmK16aoKmpiaamJn/88QdpaWl4e3vTpEkTLC0tWbBgARoaGuzcufNfnZuPPvoIFxcXGjZsiJubG6mpqcyaNQtLS0t69uxJ06ZNuXfvnjR+Xl4eixcvxsHBATs7O7y9vQkPD+f8+fNkZ2fj5ubGvHnzaNKkCaampgwfPhx9fX3pF/7OnTvR0dHhxx9/xNLSklatWjF//nzMzc1RV1dHTU0NeXl5DAwMZP4OFRkyZAiNGjXC1taWrKwsPv/8c2bNmoWFhQX169dn/PjxANJybNu2jebNmzNt2jQsLCxwdnZm7ty56Onp8ejRIw4ePMjChQtp27Yt9evXZ8SIEXzwwQds2LDhX51voXI0VZTR11CrdPznuUV7Aqgpya7bV/3798y/jz9Mekby8ywa6Ovw/Qcd+KJ9c27HPcFr73Gy8/Kl6UTDX3jT8nKL1j0rKcle54pKRZ1QuTkl10Ubm9uiWduI84G+xD28Tm5OJglRoVw+thnk5MjPy5bGLa/hD2DZvDtZGc84tX8Zac8SyM5K59bFfcREBAOQn5fzL2onCC9oaGhUqTO/eE8AVVVVmXAVFZWi41lZpKcXzeb6X0AAz1JSGPXVV3w+bBhRUVEs+flncnJeXL+i4S8INZ8Y+RdkFH9JaGjIrvEdO3Ys3377LYGBgbRq1YoOHTrw4Ycfykwha9tWdsqxvb09J06cAKB169ZERETw66+/8uDBAx49ekRYWJjMFHcAc3Nzmd+HDBnCsWPH2LNnD5GRkdy/f5+YmBgaNmxYavlv375NamoqrVu3lgnPyckhIiKi8ieiFC+XrfiL1szMTBqmoqJCbm6u9Hd1dXVsbGykv1tYWKCtrU14eDgdO3Zk6NChHD58mBs3bkjPR1JSkvSchIeHY21tjYLCi3/Tdu3a0a5du9dWD11dXYYMGcLBgwe5ffs2UVFR3L17F0CmHO3bt5fJo2fPngAcOnQIKPo7vSwvL0/akSS8WyraK6J453SPLg7Iy9WikVHRjWizugaY6mrzQ8AxTt6NpKdtozdeVkEAkLzyPfEquVolxzHk5RXo9elcTh1czqHtPwCgpqlLux5fcSzgJxQUlUukKYtJwxZ0cpvMhaD1PAg9DUDdBs1p3v5jgk/+VqW8BOF1qszneX5+UWetlpYW33z9tfQz3sjIiIULF3LhwgU6der0xssqCMK7QTT+BRmhoaHUr18fdXXZdb7du3fn9OnTnD59mnPnzrFp0yZW/p+9+46q4ngbOP6ld0GkYwELKCCCWII9oBERsUVjbDGILRqjaWLUn+Jr1EgUiSUWNBY0tliw9x4Fe0EFQZGOiEjv8P5xZeUKKrYYzXzO4ejdndmdWZZ77+w8M7NwIZs2baJBgwYAco1UkDUeFR9/Kdu5cyc+Pj5069aNpk2b0q9fPyIiIpg+fbpcnrKn1WX5R4wYwe3bt/Hw8MDd3R1bW1umTJnyzPKXlJRgaWnJ77//XmFf2fCEV/V0/QCpfpVRUVGpsK24uBglJSVycnIYOHAgeXl5uLm50bNnT+zt7eVm7q/sfG9C+WuckpLCZ599hr6+Pi4uLrRp04bGjRvLfRF4XjnKvnisW7euwj3zvGsjvDtlPf65BfKrApT1+Gs93m9tIj/DP0BDUwM01VSITn30dgspCOWoPp6Yr7BAflLJgmdEBJSppm+Kx+DZ5Gank5eTga6+GVkZKVBaitpLhunXb9yBurbtyHyUhLKyGlrVanDh+DpQUEBV7fnzYgjC21LWEVE231CZsnmRNDQ1pc98Ozs7uWUx69ati4amJrGxsf9QaQVB+DcQjX9BkpSUxOHDhxk2bJjc9oKCAubOnUv37t1xd3fH3d2dvLw8WrduzbFjx6TGf/nJ+wAuXryIjY0NAMuWLePTTz/F19dX2n/48GFA1oCsbJ3mmzdvcuLECTZt2kSTJk0AWY9yTEwMtWrVqrQOZXMW6OjoSKFzhYWFfPfdd7i5ueHu7v4ql+aVPHr0iNjYWKmst2/fJisrCxsbG06dOkVYWBinT5/GwMBASp+amio1qOvXr8/OnTulBwYABw8eZNasWezdu/eZa1u/jF27dvHo0SP2798vPawoC/cvK0e9evUq/G5Xr17Nrl27mDlzJiB7iFD2uwbZ/AiKiop88803r11G4c0yry6LyEhMz8LSsLq0PSk9S9qfnV9ASFQ89Y31qV1DV0pTWlpKYXEJuhrqCMI/RUfPBAVFRWm8fpnMh7LXegYVPw+KCguIvnUa41o26OgZo6Elu49Tk2QRYDVM6lX5/GkpsTxIvE0Dexd0y807kJoURQ1jC/GgU3hnTExkk1Dev39fLhIxJSUFAFNTU3S0tWURAIUVl4EtKS6utKNCEIQPl/jE+o/KyckhJSWFlJQUYmNjOXToEN7e3tSsWZMvv/xSLq2qqirXrl1jypQpXL58mbi4OLZu3UpOTo7cDLO7d+9m3bp1REdHExgYyMGDB/H29gZkH0AXL14kLCyMmJgYVq1aRVBQEIBcqHx5BgYGKCsrs3fvXmJjY7l27Rrjxo0jJSVFLo+mpiZRUVGkpaXh6emJrq4uY8eO5cqVK0RFReHj48OJEyeklQH+ST/88APXr1/n8uXL/Pjjjzg6OtK8eXPpAzs4OJj4+HjOnz/PV199RWFhoVS3/v37k5aWxtSpU4mKiuLcuXPMmTOHjz76CDU1NTQ1NUlPT+fu3bsUVvKhXhUmJibk5uayb98+EhISOHXqFN9+K1tKq6wc3t7eXL58mYCAAKKjozl+/DiLFy+mQ4cONGjQgI8//pipU6dy5MgRYmNjWb58OUuXLpX7IiL8e5joamNUTYuzUfIrRJyNisNET7ZPRUmJwJMX2Xbhplyac3cTKCwqxs5crGsu/HOUVVQxqW1L9K2/5cKc7976G1V1LQzNrSrkUVRS5sz+Zdy6uE/aVlJczI3zu6hW3QR9I4sqn//RgxhOBM8nPTVe2paWEktc1CVqW73eMCxBeB1GRkYYGBhw4YL8qhMXLlzAyNgYgxo1UFNTo0GDBly8eFHuu8LNmzfJz8+XOnAEQfhvED3//1ErV66UlnNTUVHB1NQUd3d3vLy8KoRvg6wnd9asWYwaNYrMzEzq1q3Lr7/+SrNmzaQ0PXv25ODBg8yePRsLCwvmz58vhY9PmTKF//3vfwwcOBBVVVUaNmzInDlzGD9+PNeuXZM7ThljY2Nmz57NggULWLduHYaGhnTo0IEhQ4Zw5MgRKZ2XlxeBgYFERUWxZMkSgoKCmDNnDkOHDqW4uBhbW1tWrlxJvXpV7+l5U7p168bw4cMpKCjAxcWFSZMmoaCggL29PRMnTmTVqlXMnz8fY2Nj3N3dMTU1lXrZjY2NWblyJX5+fvTo0QNdXV3c3d2lxvknn3zCpk2b8PT0JCgoSIqOeBlubm6EhYUxe/ZssrKyMDc3p0+fPhw+fJhr167x+eef06hRIxYtWsRvv/3G8uXLMTIyYvDgwYwaNQqQ3Rv+/v7873//Iz09ndq1a/Pzzz/Ts2fPN3chhVeWU1BI3MMMjHW1pB77T5vbsPjwOZarq9LMwoxzdxM4ExnLuE9kDRlVZSV6Nm3IptAwdDXVaVrHlJjUdDadC6OZpRl2NUXjX/hnObT5jL3rpnBk6y9YNenE/bibXDu7jeYfD0ZZRY2C/BwePYhFR88EDS1dFBUVaeTUheshwWhVM0BX35ybF3aTHHuTjn0mvVTkVK36zahW3YSj2/xw6jCQgvwczh1eRbXqxti17P4Way0I8nJzc0lMTMTQ0BCdx0vxeXTrxqo//kBbWxv7Jk24cvky58+fZ/jw4VK+Xr164ffrrwT89hudO3cmIyODv7ZswbJuXRwcHN5RbQRBeBcUSl80W4ggVIGLiws9e/bk66+/ftdF+VfYunUrEydOrNJSPULlspdOetdFeO9cj7+P7/ZjTO3RQeqdL9v2lWtzPm5oKaU9GBbFjkvhpGblYFxNm55ODWlvbSHtLy0t5cD1KPZdjyQ5IxsdNVXaWtWhbwtbVJUrX12iz6JN9GluS98Wtm+1nh+SpSYz3nUR3hvRt85w8cR60lPj0dTRx6ZZVxp/JHvImBh9jT1Bk2jb7RusmrgCUFxcxKUTfxJ57Sj5eVnoG1ng2O5zatZ1fOY5VszwxLFtP5q2l5/ENONhImcPLic59iZKSirUrNeUZh8PRlOn6jOz/5f1sLvzrovw3gkPD+fXX3/l+++/lyIXy7YN+fJLWrdqJaU9fvw4+w8cIC0tDUMDA7p06YKzs7Pc8SIjI9m+fTt37t5FVVUVBwcH+vbp88z5kHx8fLC2tq4QDSo8W9130Mn0LHdec5Lrt+XfdI3+q0TjX3gjRONfnmj8vz7R+Bf+C0TjX/gvEI1/4b/g39SwFY1/4VlE2L/wn+Lp6fnCmW1DQkJQVVX9h0r0Znyo9RIEQRAEQRAE4c0QPf/Cf0pCQsILJ8erXbv2G5lJ/5/0IdZL9PwL/wWi51/4LxA9/8J/wb+pV1v0/AvPInr+hf8UMzOzFyd6D32o9RIEQRAEQRAE4c0QS/0JgiAIgiAIgiAIwgdONP4FQRAEQRAEQRAE4QMnGv+CIAiCIAiCIAiC8IETjX9BEARBEARBEARB+MCJCf8EQfhXSu7o9a6LIAhv3/V3XQBBePuMD61810UQhLev3s/vugSC8EKi518QBEEQBEEQBEEQPnCi8S8IgiAIgiAIgiAIHzjR+BcEQRAEQRAEQRCED5xo/AuCIAiCIAiCIAjCB05M+CcIgvCeCwsLY/v27cQnJFBNR4ePXVz4pFMnFBQUKk1fWFhI8M6dhISEkJWVhYmJCW6dO9OiRQu5dBEREWzbvp242Fg0NDVxdHSkZ48eqKurS2nu37/Ptm3buB0ZSX5+Pubm5nT39KRRo0Zvtc7Cf0/cnUtcOLaWtPsxaGjpYtPcA7uWPZ55n5cUF3MtZBsRlw6Qk5VGNX1TmrTqQ13btnLpIq4c5trZrWSmJaGhXR2rJh1p0qoPikpKlR73QWIkwX/8QJuuY7Bq4vrG6yn8t12JSeLPkOvEPExHT0Mdt8b16eZg9ez38+JiNoWGcTIihoy8fMz1dOjetCFtGtSWS3chOoHN525wL/UR1dTVcK5fi34t7VBXedIUOHzjDkuOnq9wjs6N6+PdrumbraggCO+EaPy/IS4uLsTHx0uvVVRUMDAwoH379nzzzTfo6+sDYG1tzaxZs+jVq9e7KmqVXLhwgdLSUpo1a/bGjjlo0CDMzc2ZPXv2Gzvmm5aTk8O2bdsYMGDAOy3Hq9wnSUlJTJs2jZCQEIyMjJgwYQIuLi5vsZTCv8GdO3dYsHAhzZs1w7N7dyJv3+avLVsoKS6mS5culeZZtnw5V69e5ZNPPqFRw4bcu3eP1atXk5mZiaurrDGTkJCA//z51K9fn+EjRpD+6BFb/vqLBykpfP311wBkZWUxx88PbW1t+n32GWrq6pw6eRJ/f3+++/57rK2s/rHrIHzY7seHc3Dj/1HXpg1N2w0gOfYGoYdXUVJSTJNWn1aa5+KJ9Vz5ewuObfthUsuG6PAzHN3mh4KSEpYNWwFwPTSYkAOBWDRqRQtXL/Jy0rl4fB0Pk+/i+unECscsLirkeLA/pSXFb7W+wn9TRFIqs3afonWDWnzWwpZbiQ8IOnOV4pISejpV/kDVf/9ZLt5LpJuDFY1rGhN1/yG/HzlHRm4+7vYNAAi5E8fcfWewMTPk287OFBWXsOX8DcKTHjCjlwtKirJA4LsPHmGqp8MYV/kHwXqa6hXOKwjC+0k0/t8gLy8vvLxky5Pl5eURERGBn58fAwcOZOPGjejo6HDq1Cl0dHTecUlfrH///syaNeuNNv7fBytXrmTr1q3vvPH/Kn744QdUVVXZtm0bW7ZsYfz48Rw9elR68CR8mHYEB1O7Vi2GDh0KQGM7O4pLStizZw+urq6oqqrKpY+JieHypUv06NGDrl27AmBjY4Oamhpbt27F2dkZTU1NzoaEoKCgwJjRo1FTUwOguLiYoKAgHqSmYlCjBn+fOUNWVhaTfvqJ6tWrA2BrY4Ovry8H9u8XjX/hjbl4fB01jC1p3/1bAGrVd6KkpJgrpzZj29wTZRXVCnluXz1Mfbv2NG33OQBmlk14kBjJzXO7sGzYipKSEi6f3IiZZRNce/tI+WqY1GPbsq+Jv3MZ87oOcse8cDyIwvzct1dR4T9tY+h1LA31+LpjSwAc65hSXFLK1gs36drEClVl+WiUuylpnLsbT7+WdvRuZgOAfS1j1FWUWXf2Gu2t66Clpsqm0DDM9HSY3K0dykqyhn4jM0PGBO3h6M1oOtrWBSA65RENjPWxMqnxD9ZaEIR/khjz/wZpampiaGiIoaEhtWrVwtXVlZUrV5KYmEhgYCAAhoaGciGzwr9LaWnpuy7CK7t58yatW7fGwsICT09P8vLySExMfNfFEt6iwsJCwsPDcXR0lNvu5OREXl4ekZGRFfKU3RNNmjSR296wYUPy8/MJDw+XHbugACVFRbmHB1ra2gBkZ2UBoF+9Op06dZIa/gCKiooYGRtzPyXlDdRQEGS97Yn3rlOnobPcdstGrSksyCU59kal+YqKClBR1ZDbpq5ZjbzcTADysh+Rn5tJ7QbyvZz6RnVQ06xGbOQ5ue3JsTe5cW43zm4jXrdKglBBYXExYfEptLA0l9v+Ub2a5BUWcSvxQYU8cWkZADSzMJPbblfTiPzCIsLiZe/D8WmZONQ2kRr+IOvNr1m9GhfuJQCy7z/3Uh9Rp4bem6yWIAj/MqLx/5aZmZnRqVMndu/eDcjCubdu3QpAamoqY8eOpWXLltjb29OvXz9CQ0OlvC4uLixevJihQ4dib29Pp06d2Lx5s9zxN2/eTLdu3bC3t8fBwYH+/ftz7do1uWP88ssvuLu707JlS0JDQ0lPT2fy5Mm0bdsWW1tbnJ2dmTx5Mrm5uVIZASZOnIiPj6w3JDk5mfHjx9OsWTNatmzJyJEjiY6Ofq1rY21tzcaNG+nfvz+NGzemS5cuXLx4kY0bN9KhQweaNm3KuHHjyMvLA2Dr1q20a9eOTZs20aZNGxwdHRk9ejTJycnSMRMSEhg/fjzOzs7Y2trSrl07/Pz8KCkpkdJcvXqVIUOG4OjoSKtWrZg6dSq5ubksWLCAhQsXEh8fj7W1NXFxcS+sQ1xcHNbW1uzevZsePXrQuHFjevXqRVRUFIsWLaJVq1a0aNECX19f6cFCSUkJS5cupXPnztjZ2dG0aVO8vb2JiYl55nmOHj1Kr169pPtg/vz5FBQUyKVp1aoVf/zxB3fv3mX+/PlYW1tLv8syr3I/ANy7d49Ro0bh5OREy5Yt+fbbb0lNTZX2//XXX3Tp0gV7e3u6dOnC6tWr5a658HakpKRQXFSEkbGx3HYjQ0NANhTkadqPG/Dlf38gG7sPkPJA9gWzTZs2AGzcuJGsrCwSEhLYtXMn5jVrUqtWLQCaNWtG76eGpmRnZxMREYGZqenrVk8QAMhIS6KkuIhq+vINnGrVZfdYemrl79V2LTy5fe0osZEXKMjPIfLaMeKiLlK/8ccAqKproaCoRFb6fbl8+blZFORlkZn25O+nqDCfEzvn06T1p+gbWb7B2gmCTHJ6NsUlJZjqyUeHmujK3rPjHzf0y6umLovKSsnMkduelC57QJucIfu3moYaD7Lk0xQVl5CSmc39jOzHabPJKywi6v5Dxq7bS7/ftzB23V6O3Yp+/coJgvCvIRr//wArKytiY2PJzs6W2z5t2jTy8/MJCgpi586dWFpa8tVXX5GT8+QNevHixTg6OrJ9+3YGDBjA//73P/bs2QPAwYMHmT59Ot7e3uzdu5dVq1aRn5/P5MmT5c4TFBTE5MmTCQwMxMHBAR8fH27cuMHChQvZv38/EydOZPv27WzcuBGAU6dOAfDTTz8xadIkcnJyGDRokHSstWvXUr16dfr27SvX8H4V/v7+eHt7s2PHDnR0dBg5ciT79+9n2bJlzJo1i0OHDsk98Hj48CGrV69m/vz5rF69msTERLy9vSkqKgJg1KhRZGZm8scff7Bv3z68vLwIDAzkyJEjAMTGxvLFF19gZGTExo0bWbBgAadPn8bX11catmFiYsKpU6cwfYnGi7+/Pz/99BObN28mIyODzz//nOjoaNauXcv48eNZv349R48eBWDNmjWsWLECHx8f9u/fz6JFi4iOjn7mXAgnTpxg3Lhx9O3bl127djF16lT27t3LDz/8IJdu0qRJZGVl0aVLF/Lz81m5ciXKyhVH9rzs/ZCRkcGAAQMoKChg9erV/PHHH8TExDBu3DhA1jicM2cOY8aMYffu3YwbN47ly5fz66+/Vvn6Ca+m7MGYxlPRRGXRReUf4JSxtrbGwNCQP//8k5s3b5Kbm0tERAR/bd2KgoIC+fn5AJibm9P70085cuQI48ePZ+rUqeTl5TH2669RVKz8o6O0tJQ1a9aQl5uLm5vbm6yq8B9WWCD7TFRV1ZTbXtarX5CfUyEPgF2L7pjUsuHABl/W+vXj+I55NLB3wd5Z9sBKWUWNujZtuHF+N+GXD5Kfm0V6ajxHt/uhqKhEYWGedKxzR1ajoqpOk1Z93kYVBYGcgkIANFVV5LZrqCrL7S/P1twI42rarDx5iWtxyeQUFHIjIYWgM1dRUFAgr1D23ejjhhaERMWx7cJN0nPzSMnM5vej58gpKCS/UDZ/xd2UNADuZ2bzResm+HRtQz3D6iw6HMqhsDtvrd6CIPyzxJj/f0C1atUA2eRY5cXExGBlZUWtWrVQV1dn0qRJdOvWDaVyMwy3adOGMWPGAFC3bl2uXLnC6tWrcXd3R09Pj59//hlPT09A9mX9008/Zfr06XLnad++Pa1atZJet27dmubNm0u9wjVr1iQoKIiIiAhANjQBQEdHBx0dHalB6+fnJzUmf/75Z0JCQti0aZM0+der6N27tzQpXffu3Zk+fTr/+9//sLCwwMrKisDAQG7fvi2lLyws5JdffsHOzg4APz8/3N3dOXPmDM2bN6d79+506dJFargPGTKE5cuXEx4eTseOHdm0aRN6enrMnDlTqsuMGTO4dOkSWlpaaGpqoqSkJF2DqvLy8pJmSu/UqRNr165l+vTpaGhoUK9ePRYsWMDt27dxcXGhdu3a/PLLL3z8saz3ydzcHDc3N/bt21fpsZcsWULfvn3p168fALVr18bX15cvvviCuLg4atasyZ07d/jmm28wNDQkOTmZatWqYWBgQF5eHioqKnL31MveD3v27CE7O5t58+ahq6srXbPdu3dTUFDA4sWLGTVqlDR+vFatWmRlZeHr68s333wjjRcX3rwXRVdU1khXVlZm/LhxrFq1innz5gGgq6dHv379WLZ0qRTmv3fvXrZu3crHH39M06ZNycrKYteuXcydN48JP/4ova+VKSoq4o9Vq7h48SL9+/fH0lL0jgpvRukL7nOFSu7z4qJCdq2ZQE5WGq3dv0K3Rk3ux93k8qlNKKuo49x5OACt3UejpKzCqd0LObVrAUrKqjRp/SmF+bkoq8jeuxKjrxF+aT+eX8595goAgvC6Sl4w7FCxktn+lZUUmezZjsVHzjF9x3EAqmtp8GUbB/wPnEXt8fecvi1sKSktZWNoGOvPXkNJURFXG0uaW5oT91AWUWBjbohP1zbYmhtJKwA41DYhPS+fjaHXcbWxfOaKA4IgvD9E4/8fkJkpG19YFm5bZsyYMfzwww/s378fJycn2rRpg4eHh1xjqWXLlnJ5HB0dOXbsGADNmzeXwsvv3LnDvXv3CA8Pr9AgqFOnjtzr/v37c+TIEbZt20Z0dDSRkZHExcVRt27dSst/48YN0tPTad68udz2/Px8oqKiqn4hKlG+bBoasl6c2rWfLE+jrq4uF96upaUlNfwB6tWrh66uLhEREbRt25aBAweyb98+rl69Kl2PBw8eSNckIiICW1tbuR7xjz76iI8++uiN1UNTUxMDAwOpPk/Xw8XFhStXrhAQEMDdu3e5e/cukZGRGD8Vul3mxo0bXL16lS1btkjbyoYQREVFoa+vz5AhQ2jcuDHz5s3j4MGDfPfdd9jY2JCSksKBAwc4evSo9KH9svdDREQEFhYWUsMfZOPDGzZsyMOHD0lKSmLevHkEBARI+0tKSsjPzycuLo569eq90jUVXkxTU9YTWhYBUKasx19dQ6NCHgAjIyN+/PFHMjMzyczMxNjYmIcPH1JaWoq2lhbFxcXs2rWLli1b0r9/fymftbU1EydOZP/+/fTp86QHNCcnh8W//05EeDiff/659GBLEN4EVXUtAAoL5CNZCp4REQAQfetvHiZH49Z/ujRpn2kdO1TVtPh73xKsHTujb1QHFVV12nqM5aNPhpOVfh9tXSNUVNUJv3SAavqmFBbkcWLnfBo790LPoBYlxcXSTP+lpSWUFBeLBwLCG6GlJuvxzy2U7+F/VkRAGRNdbab3/Jj03Dwycwsw1dPmQWYOpaWl6KjLHuYqKSoywNmePs1tuZ+RTXUtdbTUVPnftqNSGl0NdZyemjsAoGltU67FJvMoJ4/qWpV/pgiC8P4Qjf9/QFhYGBYWFmhpaclt79SpEydPnuTkyZP8/fff/PHHHyxcuJBNmzbRoIFseZanw7ZLSkqk3rydO3fi4+NDt27daNq0Kf369SMiIqJCz3/5CQZLSkoYMWIEt2/fxsPDA3d3d2xtbZkyZcozy19SUoKlpSW///57hX1ljY9XVVlY+rNCikG2hOLTiouLUVJSIicnh4EDB5KXl4ebmxs9e/bE3t5ebub+ys73Jjx93OfVYdmyZSxatIiePXvi7OzMkCFDOHz4sDQvxNNKSkrw9vamZ8+eFfYZGhpy5swZkpOT2bZtG2pqanh4eHD79m38/f3R0NCge/fuck/rX/Z+eN41K3uoMnHiRLlogjIvM3RCeHkGBgYoKiqS8tTkemWvKxt3X1BQwIULF2jQoAEGBgbS6iP3Hs85Ubt2bTIzMykoKKBe/fpyeXV0dDAxMSEhIUHa9vDhQ/znz+fBgwcMHz78P7dCiPD26eiZoKCoSEaa/ASmmQ9lr/UMalXIUzaO37iWjdx249q2ADxKiUHfqA4xt8+hpq6Nca1GVDeUPXjOzU4nOzOVGib1eJBwm6z0FC6f3MjlkxvljnVq1wJO7VrA0MnBb6aiwn+acTVtFBUUpPH6Zcpem+tXq5CnoKiYM1GxNDQ1wLiaNroass/3O49D+C0NZZOxXo+/T1FxCQ61Taj5+DjFJSXcS33Exw1lUVo3ElJIzsiSXkvnKC5GUUEBHXURxScIHwIx5v8tS0pK4vDhw3Tr1k1ue0FBAbNmzSI2NhZ3d3dmzJjBoUOHUFRUlHr2AbnJ+wAuXryIjY3sy8yyZcv49NNPmT17NgMGDKB58+bExsYCz561/ubNm5w4cYKAgAC+//57PD09qV27NjExMc/MY2VlRUJCAjo6OtSpU4c6depgZmbG3LlzOXfuXKV53pZHjx5JdQS4ffs2WVlZ2NjYcOrUKcLCwlizZg1jx47F3d0dbW1tUlNTpbrVr1+fGzduUFz8ZI3mgwcP4uLiQn5+/j8S0rZkyRJGjx7NtGnT+Oyzz3BwcCA6OvqZ179BgwbcvXtXuvZ16tQhKSmJOXPmkJ2dLT2AKd8AHDduHJaWlmRlZdGhQ4dnlqUq90P9+vWJjo6WIlhA9kDL2dmZwsJC9PX1iY2NlStfWFgY8+fPf/2LJTyXqqoqDaysuHDxotz9c+HiRTQ0NbGwsKiQR1lZmT83bOD4iRPStuLiYo4eOYKhkRE1a9akWrVqaGlryw25AVkUU3JyMgYGBoAswmCevz+PHj1i/LhxouEvvBXKKqqY1LYl+tbfcvf53Vt/o6quhaF5xSUldWvUBCApNkxu+/24mwDoVDcB4NbFvYQcWiGXJix0BwoKitSu3xwD0/p4es2V++nUVzavjmPbfnh6zX1zFRX+01SVlWhkZkhIVLzcfR4SFY+mmgoNjCou26usqMjKk5fkxuQXl5Sw71okxrra1Kkhi9g7GxXHkqPnKSp+Ehl65OZdcvILaW4p6+0Pi7/P4sPnSEh78llfWlrK2ag4rE0N5FYKEATh/SX+kt+gnJwcUlJSSElJITY2lkOHDuHt7U3NmjX58ssv5dKqqqpy7do1pkyZwuXLl4mLi2Pr1q3k5OTILdu1e/du1q1bR3R0NIGBgRw8eBBvb29A1qt68eJFwsLCiImJYdWqVQQFBQFUmAm+jIGBAcrKyuzdu5fY2FiuXbvGuHHjSElJkcujqalJVFQUaWlpeHp6oqury9ixY7ly5QpRUVH4+Phw4sSJCrPJ/xN++OEHrl+/zuXLl/nxxx9xdHSkefPmmJjIvswFBwcTHx/P+fPn+eqrrygsLJTq1r9/f9LS0pg6dSpRUVGcO3eOOXPm8NFHH6Gmpoampibp6encvXuXwsKKk+u8Caamppw+fZrIyEju3LmDv78/Bw4ceObvbNiwYezfv5+FCxdy9+5dzpw5w8SJE8nMzMTQ0JDmzZvTsGFDJkyYwKVLl4iMjMTX15e4uDgaNmzITz/9RFhYWKXHrsr90K1bN3R1dfnhhx+4desW169fZ+rUqVhZWWFqasqwYcNYu3YtQUFBxMTEcPDgQaZNm4a6unqFNeaFN8+ja1ei795l6dKlXLt+ne3bt3Ng/37cu3RBTU2N3Nxc7ty5Iz28UVRUpEOHDhw+fJijR49y48YNlixdSmRkJP0++wwFBQUUFRXx7NaNc6GhrF27lps3b3Lu3Dnm+fujoKjIJ598Asj+1pKTkujUqRPKysrcuXNH+nne6hWC8LIc2nxGSsJtjmz9hdjIC1w4FsS1s9to0upTlFXUKMjP4X58OLnZ6QDUtmqJobkVx3fM4+b5PSREX+XK31sIObSS2lYtMDSTRdfZNO9GSnwEZw8GkhB9lfNH13Ll9BYaf9SDavqmqKhpYGjWQO6nupEFANp6xtJxBOFN+LSZDZH3HzJv/xku3UtkQ8h1gi+H07NpI9RUlMkpKCQiKZX0XNlQL0VFBTrb1Wf31dvsuxbJ1dhk5u47w63EB3zZxkHq0PjEth6PcvJYdDiUa3HJ7LwczooTl2hVvxa25kYAdLKth56mOrP3nOL07RguRCcwc9dJYlLTGehs/86uiSAIb5YI+3+DVq5cycqVKwFZeLqpqSnu7u54eXlVCPkH2Qzxs2bNkmaor1u3Lr/++qtc71nPnj05ePAgs2fPxsLCgvnz59O+fXsApkyZwv/+9z8GDhyIqqoqDRs2ZM6cOYwfP55r165V2gtnbGzM7NmzWbBgAevWrcPQ0JAOHTowZMgQaUZ8QJolPyoqiiVLlhAUFMScOXMYOnQoxcXF2NrasnLlyncynrtbt24MHz6cgoICXFxcmDRpEgoKCtjb2zNx4kRWrVrF/PnzMTY2xt3dHVNTUymCwtjYmJUrV+Ln50ePHj3Q1dXF3d2db7/9FoBPPvmETZs24enpSVBQUIW10N+EOXPmMH36dHr37o2WlhZNmjTB19eXadOmkZCQgJmZ/Jg7Nzc3/P39Wbp0KUuWLEFPTw8XFxe+//57QNaTu3z5cmbOnIm3tzclJSU4Ojqyfv16ateuzejRo7l27Rq2trYVylKV+0FDQ4MVK1Ywa9Ys+vXrh7q6Oh06dGDChAmA7F5RU1Nj7dq1zJ49GwMDA/r27cvYsWPf+LUTKmrYsCEjR44keOdOFi1aRHU9PXp/+imdHzfQY2Ji+PXXXxny5Ze0fjw0w7NbNxSAvfv2kZOdTc1atRg7dqzcPeLi4oKGhgYHDx3i9N9/o62tTYMGDRj91VdSz/+FixcB2BkczM5g+dDnGjVqPHMFC0F4WWYW9rj29uHiifUc2vwzmjr6tHAdQuOPZMOhUhOj2BM0ibbdvsGqiSuKioq49Z/OhWNruXx6E/m5mejoGePQpi92LXtIx61Z15EOPb/n8skN3LqwD209Qz76ZBi2Lbo9oySC8PbY1TTiOzdnNoWG8cue09TQ1mCgsz2ejrKOljspafhuP8ZXrs2l8Py+zW1RALZfvEVWfgEWNfT4yaMtTWqbSMetXUOXnzzasu7MVWbtPkV1TXV6NWtEr6aNpDR6mur8Xy8X1p25ysqTl8gtLKKBkT5Tu7fHyqTGP3odBEF4exRKnxVrLLxzLi4u9OzZ87Vm0/+QbN26lYkTJxIeHv6ui/JeKS0tfS9n6L3zmpNJCsL7YPv1yidaFYQPyYikyS9OJAjvOa0RP7/rIkj+rd+h6opJoN85EfYvCB+497HhLwiCIAiCIAjCmyXC/oXX5unpKTcJX2VCQkLeu/HfH2q9BEEQBEEQBEH47xFh/8JrS0hIeOHkeLVr137veqA/1Hq9L/6tIWuC8CaJsH/hv0CE/Qv/BSLs/8VE2P+7J3r+hdf29AR1H4oPtV6CIAiCIAiCIPz3iDH/giAIgiAIgiAIgvCBE41/QRAEQRAEQRAEQfjAica/IAiCIAiCIAiCIHzgRONfEARBEARBEARBED5wYsI/QRD+lYwPrXzXRRCEt89kxrsugSC8dckdvd51EQThrRNrtwjvA9HzLwiCIAiCIAiCIAgfONH4FwRBEARBEARBEIQPnGj8C4IgCIIgCIIgCMIHTjT+BUEQBEEQBEEQBOEDJyb8EwRBeM9diUniz5DrxDxMR09DHbfG9enmYIWCgsIz85y6HcOWcze4n5mNoY4mPZo25OOGlnJpIpMfsvbvK0SlpKGpqkKHhhb0bW6LstKT58Z3UtL48+w1ou6nUUoplobVGehsT13D6m+tvoJQJu7OJS4cW0va/Rg0tHSxae6BXcsez733o8JOcPnkBjIf3UdbzxB750+xauJaadrC/Fy2LhuDY7v+z0wjCG9SWFgY27dvJz4hgWo6Onzs4sInnTo9954ODQ1l165dPEhNpUaNGri5udG6VSu5NHejo9myeTP37t1DXUODVq1a4dmtG8rKT5oCgYGBhISEVDj+iJEjaebk9OYqKQjCO/NB9vwHBwfTt29fHBwccHR0pHfv3mzYsEHan5aWxubNm1/qmBcuXOD8+fMAxMXFYW1tXekbZGUSEhLYvXu39NrFxYUFCxa81PmfZevWrVhbW1c5fUhICNbW1nI/tra2tG3blkmTJpGenv5a5fHx8WHQoEHS6/LX7U1wcXGRK7udnR0dOnRg6tSpPHz4UC6ttbU1W7duBaCgoICxY8dib29PmzZtKrwuKSl5Y2X8ryh/fYV3JyIplVm7T2FeXYcf3FrR1qo2QWeusv3irWfmORsVx28HQ3CobcKPXVpja2bE4sPnOHU7RkqTnJ7F/+08jqqyEt92dsajiRU7L4ez4uRFKU1Sehb/23aUgqJiRrk0Y7RLC4qLS5i89QgJaZlvtd6CcD8+nIMb/w+9GjVx/XQi9ew6EHp4FVfP/PXMPHdv/c2x7XMxr9eUjn1+wrR2Y07uDCAq7ESFtPm5Wezf6EtWesrbrIYgSO7cucOChQsxMTFh1KhRtGzZkr+2bGHfvn3PzHPh4kUCAwOxtbXlq1GjsLayYtUffxAaGiqlSUlJwd/fH1VVVYaPGEGnTp04ePAgf/75p9yxYmNjad6iBRMnTpT7afgS3zMFQfh3++B6/rds2cLPP//MpEmTcHJyorS0lNOnTzNjxgwePHjAmDFjmDNnDnFxcfTp06fKx+3fvz+zZs2iWbNmmJqacurUKXR1dauUd8KECZibm9O1a1epjGpqaq9Uvzdl8+bNmJqaAlBcXEx4eDg+Pj48ePCApUuXvvJxJ02aRHFxsfS6/HV7U7y8vPDyki0blJeXR0REBH5+fgwcOJCNGzeio6MDwKlTp6T/nzx5kv3797N06VKsra0rvFZU/CCfgwn/ARtDr2NpqMfXHVsC4FjHlOKSUrZeuEnXJlaoKitVyLP+7DU+qleTIW0cAHCobUJWfgEbQq7TpkFtALZfuoWGigoT3NugrKRI0zqmqCkrseLkJXo5NcJQR4s9V2+jpqzERI+2qKvIPk7sahrx1Zrd7Ll2G+92Tf+ZiyD8J108vo4axpa07/4tALXqO1FSUsyVU5uxbe6JsopqhTwXjq7BslErPurkDUDNek3Jz8vk4rEg6tm2k9Ldiwjh7P5lFBbk/TOVEQRgR3AwtWvVYujQoQA0trOjuKSEPXv24OrqiqpqxXt627ZtODk58dlnnwFgZ2dHdnY223fsoEWLFgDs27cPdXV1Ro8ejbKyMvaNG6Oqqsqf69fj7u5OjRo1KCgoICk5mU6dOlG3rli0ThA+VB9ci2f9+vX07t2bTz/9FEtLS+rWrcugQYMYMmQIa9asAaC0tPS1zqGkpIShoWGlb8JVoa+vj5aW1muV4XXp6+tjaGiIoaEhJiYmtG/fni+++ILjx4+TkZHxysfV0dFBT0/vzRW0EpqamlLZa9WqhaurKytXriQxMZHAwEApnaGhIerq6gBSndq3b4+pqWmF14LwPiosLiYsPoUWluZy2z+qV5O8wiJuJT6okOd+RjaJjzJpUbdinuT0LBIfyXrsr8Qk41jHRC7E/6P6NSktLeVKTDIA5no6eDpaSw1/AHUVZWpoa5CcnvXG6ikITysuKiTx3nXqNHSW227ZqDWFBbkkx96okCfz0X3SUxOoYy2fx6JhazLSkkh/mABAfl42hzbPxKSOHZ0/n/bW6iAI5RUWFhIeHo6jo6PcdicnJ/Ly8oiMjKyQ50FqKslJSZXmSbl/n+Rk2Xt1WFgYje3s5EL8mz3uIAsLCwMgPiGBkuJiatas+aarJgjCv8gH1/hXVFTk0qVLFcLXhw8fzsaNG/Hx8WHbtm2EhoZK4fLp6elMnjyZtm3bYmtri7OzM5MnTyY3NxdASjdx4kR8fHwqhP1HR0czdOhQnJyccHR0ZOjQoYSHhwMwaNAgQkND2bZtGy4uLkDFsP+TJ0/y2Wef0aRJE9q1a4e/v7/Ue56QkMD48eNxdnbG1taWdu3a4efn91bC1JWUlFBQUEBFRQWAQ4cO0adPHxwcHGjcuDG9evXi5MmTUvpBgwYxZcoU+vTpQ7NmzQgODpYL+3/6uo0ePZrBgwfLnfPOnTtYW1tz+/bt1yq7mZkZnTp1khteURaWvmDBAnx8fABo2LBhpa8BLl68yIABA7C3t6dDhw74+vqSlfWkAePi4sIvv/yCu7s7LVu2JDQ0lNLSUpYvX46rqytNmjShe/fuBAcHS3lCQkKwsbHh+PHjeHh4YGdnh5ubG4cOHZLSlJaWsnr1ajp37oy9vT1du3Zl165d0v7k5GTGjx9Ps2bNaNmyJSNHjiQ6OrrK16bsft29ezc9evSQfpdRUVEsWrSIVq1a0aJFC3x9faUHYyUlJSxdupTOnTtjZ2dH06ZN8fb2JiYm5pnnOXr0KL169cLe3p5OnToxf/58CgoKqlxO4eUlp2dTXFKCqZ6O3HYTXW0A4tMqPsgr22b2VB7Tx3kSHmVSUFRMSmZ2hTS6GupoqKqQ8PgBQefG9enu2FAuTVJ6FrEPM6ilX7XIKEF4FRlpSZQUF1FN30xue7Xqsoe56alxFfI8ehALgK6+/IMv3cfHSE+NB0BZRY1PRy6mved41DXFfSz8M1JSUiguKsLI2Fhuu5GhIQBJSUkV8iQmJgJg/HQeIyNA9v2hoKCA1NRUjE1M5NLo6OigrqEhPSCIi5X9fZw6dYrvvv+ekaNG8cucOdy5c+cN1E4QhH+LD67x7+3tzY0bN2jXrh3Dhw9n2bJlXL16FR0dHSwtLZk0aRJdunTB0dGRU6dOAbJx6jdu3GDhwoXs37+fiRMnsn37djZu3Aggpfvpp5+YNGlShXN+++23GBsb89dff7F582YUFRUZM2YMAAsWLMDR0ZEuXbqwZcuWCnkvXbrE8OHDcXJyYuvWrcyYMYMNGzawePFiAEaNGkVmZiZ//PEH+/btw8vLi8DAQI4cOfLGrllRURHnz59nzZo1tG/fHg0NDa5fv87XX39N165d2blzJ5s2bUJfX58ff/xRrkG3efNmBg8ezPr162nbtq3ccZ++br169SI0NFT6sALYvn07jRs3pkGDBq9dDysrK2JjY8nOzpbb7uXlxU8//SSVqbLXt27d4ssvv6Rt27YEBwfz66+/EhYWhpeXl1ykSFBQEJMnTyYwMBAHBwf8/f35888/mTJlCjt37mTw4MFMmzaNdevWSXmKi4vx8/Nj0qRJ7Nq1CysrKyZMmCCVMzAwEH9/f7y9vdm1axf9+vXjxx9/5OzZs+Tk5EgPU4KCgli7di3Vq1enb9++0gd2Vfn7+/PTTz+xefNmMjIy+Pzzz4mOjmbt2rWMHz+e9evXc/ToUQDWrFnDihUr8PHxYf/+/SxatIjo6Ghmz55d6bFPnDjBuHHj6Nu3L7t27WLq1Kns3buXH3744aXKKLycnIJCADRVVeS2a6gqy+0vL/uZeVSkPNn5BZWmAdBQUa70uAAFRcUsPBSKspIibo3rv0xVBOGlFBbkAKCqqim3XUVVA4CC/JwKeQryZe+5qmqV5yl8nEdJSRndGvIPCAThbcvLkw0x0XgcsVimLIKxrEOqvNwc2T2roaHxzDxl+Z4+blm6sv2xjxv/+fn5DB82jGHDhlFUWMivc+cSF1fxYZogCO+nD27Mv5ubGyYmJqxZs4bTp09z/PhxACwsLJg5cyZOTk6oq6ujoqKC4eOnqa1bt6Z58+ZST3XNmjUJCgoiIiICQEqno6ODjo5OhaiCmJgYWrVqhbm5OSoqKsycOZM7d+5QUlKCnp4eKioqqKuro6+vX6G8a9eupUmTJvz4448A1KtXj+nTp5OamkpeXh7du3enS5cuUmj6kCFDWL58OeHh4XTs2PGVr5OHh4c0c2xeXh5KSkq0b9+e6dOnA7IogClTptC/f38pz+DBgxk2bBipqalSeRo1akS3bt0qPcfT1619+/YYGBgQHBzMiBEjKCkpYceOHQwfPvyV61FetWrVAMjKypIbVqGlpSWN/S9fpvKvV6xYQevWrRk5ciQgu1/mzp1Lx44dCQ0NpWVL2Xjq9u3b0+rxDLo5OTmsWrWKefPm0aFDBwBq165NfHw8K1asYMCAAVIZxo0bh7OzLNT0q6++Yv/+/URERODg4MDq1asZPHiwNAfFoEGDyMvLo6ioiN27d5ORkYGfn58Urvfzzz8TEhLCpk2b+Prrr6t8fby8vKTxf506dWLt2rVMnz4dDQ0N6tWrx4IFC7h9+zYuLi7Url2bX375hY8//hgAc3Nz3Nzcnjnp0JIlS+jbty/9+vWTroOvry9ffPEFcXFxIozwLSl5wRAmxUpmh37RsCcFBQVeNDCqskmncwsK+WXPaSLvP+Q7N2eMqr3boU3Ch630BdFvCpXN4/KiIX/PmU1dEN62F0V0VjY3UZXez6uQBmTRjfb29tjZ2Un7GjVsyKTJk9m9ezcjRox47nEEQXg/fHCNfwAHBwccHBwoKSnh1q1bHD9+nKCgIIYNG8bBgwcrpO/fvz9Hjhxh27ZtREdHExkZSVxcXJUnPBk/fjwzZ85k/fr1tGjRgrZt2+Lh4VGlSeQiIiJo3bq13LbOnTtL/x84cCD79u3j6tWr3Lt3j/DwcB48ePDaYf/Lli2TwsRUVVWpUaOG3BwGjRo1QldXl2XLlnHnzh3u3bvHrVuy2cPLT+hXp06dKp9TWVkZT09PduzYwYgRIzh79iwPHz7Ew8PjtepSJjNTFoqsra390nlv3LjBvXv3KoybA4iKipIa/+XrGxkZSX5+Pt99953c77qoqIiCggLpKT4gdy+Vla+wsJC0tDRSUlJo0qSJ3DmHDRsGgK+vL+np6TRv3lxuf35+PlFRUS9Vx/Jl19TUxMDAQK63QF1dXYrqcHFx4cqVKwQEBHD37l3u3r1LZGRkhdDCMjdu3ODq1aty0S1lXziioqJE4/8t0VKT9cznFsr3xD8rIqD8ttyCyvNoqaqUS1NUIX9OQSFaT8138iArh1m7TpLwKJNxn3xEc0vRayq8XarqsodLhQXyvaEFz4gIAFB53ONfIc/jHn9VNfHASnh3NDVl92f57w7wpMdf/anefXjS4/+sPBqams9MA5CXm4vG4/OamJhg8tTQAE1NTerVq0es6PkXhA/GB9X4T0pKYunSpYwYMQITExMUFRWxsbHBxsaGjh074uHhwblz5+TylJSUMGLECG7fvo2Hhwfu7u7Y2toyZcqUKp93wIABuLm5cfz4cc6cOcNvv/3G77//zvbt2zEwMHhu3vKTrzwtJyeHgQMHkpeXh5ubGz179sTe3l6uR/lVmZmZPbdBFhoaytChQ+nQoQNOTk5069aN3NxcRo8eLZdOvZIwsufp3bs3K1as4Pr16wQHB+Pq6lrlVRNeJCwsDAsLi1eaTLGkpIRu3bpJPf/llY/YKF/fssbt/PnzK31QVP5hSmWTQ5aWlkrzKzyvXJaWlvz+++8V9pV9Uaiqp++15z2cWrZsGYsWLaJnz544OzszZMgQDh8+LDenwtPl9Pb2pmfPnhX2lUVXCG+ecTVtFBUUSHpqcr2y1+b61SrkMa8u25aYnoWlYfWKeapXQ11FGX0tDRLT5ZfrS8/NI6+wCHP9J3MB3HvwiJ93nSS/qIjJ3dpha270ZionCM+ho2eCgqIiGWmJctszH8pe6xnUqpBHr4bsMy/9YQI1TJ68Z5cdo7I8gvBPMTAwQFFRkZQU+aUly16bVTI5cVlj/f79+9SuXbtCHlNTU9TU1NCrXp379+/L5c3MzCQvL0+K5Dx37hyamprY2trKpSssLJSiJQVBeP99UGP+VVVV2bx5s9yEa2XKQsINDAykECeAmzdvcuLECQICAvj+++/x9PSkdu3axMTEVGlVgNTUVKZPn05hYSG9evXCz8+P4OBgUlJS5NZYfZZ69epx7do1uW2rV6+mT58+nDp1irCwMNasWcPYsWNxd3dHW1ub1NTU116x4EVWrlxJy5YtWbBgAUOGDKF169bSWP3XOXe9evVwdHRk7969HD58mF69er2R8iYlJXH48OFnDkF4kQYNGhAZGUmdOnWkn6KiImbNmiU3R0F5devWRVlZmYSEBLl8x48fZ8WKFVWK/NDR0cHIyKjCPTB27FhmzZqFlZUVCQkJ6OjoSMc3MzNj7ty5FR5kvUlLlixh9OjRTJs2jc8++wwHBweio6Of+btv0KABd+/elbsOSUlJzJkzp8IcDMKbo6qsRCMzQ0Ki4uV+NyFR8WiqqdDAqOJQIxNdbYyqaXE2Sr4n52xUHCZ62lK4vn0tYy7eS6SwXKTP2cg4FBUUsHvcwH+QlcP0YNnQqp97uYqGv/CPUVZRxaS2LdG3/pa79+/e+htVdS0Mza0q5Kmmb4q2nhHRt07LbY++dZpq+qbo6In7V3h3VFVVaWBlxYWLF+Xu6QsXL6KhqYmFhUWFPEZGRhgYGHDhwgW57RcuXMDI2BiDGjUAsLGx4eq1axSWixI7f+ECioqKNHw85PX4iRMEBQVRVPQk4istLY2oyEgpjSAI778PqvGvr6+Pt7c3AQEB+Pv7c/PmTWJjYzl69ChjxoyhZcuWNGvWDE1NTe7fv09sbCwGBgYoKyuzd+9eYmNjuXbtGuPGjSMlJUVuYjtNTU2ioqJIS0uTO6euri7Hjh1j8uTJ0vk2bNiAioqKNG5KS0uL+Pj4Smdq9fb25vLlywQEBBAdHc3x48dZvHgxHTp0kJ7oBgcHEx8fz/nz5/nqq68oLCx867Oom5qaEh4ezvnz54mLi+Ovv/4iICAA4KXOXdl16927N0FBQairq1cY8lAVOTk5pKSkkJKSQmxsLIcOHcLb25uaNWvy5ZdfvvTxQDYe/saNG/j6+hIVFcWlS5f47rvviI6OrvQDF2QN9379+hEQEMCOHTuIjY1ly5Yt+Pn5STPtVsXw4cNZvXo1O3bsICYmhjVr1nD48GFcXV3x9PREV1eXsWPHcuXKFaKiovDx8eHEiRPSHBVvg6mpKadPnyYyMpI7d+7g7+/PgQMHnvm7HzZsGPv372fhwoXcvXuXM2fOMHHiRDIzM0XP/1v2aTMbIu8/ZN7+M1y6l8iGkOsEXw6nZ9NGqD2enC8iKZX03Cchn582t+FMZCzLj1/g0r1Elh27wJnIWPq1eDLWs0fThqTn5vPzzpNciE5g5+VwVp2+TEfbuhjqyB4QrDxxiYzcfPo0t5HOU/YT9/DVlwwVhKpwaPMZKQm3ObL1F2IjL3DhWBDXzm6jSatPUVZRoyA/h/vx4eRmP5mnx7Ht59y9cZq/9/5ObOQFTu9ZzN0bp3FqP/Ad1kQQZDy6diX67l2WLl3KtevX2b59Owf278e9SxfU1NTIzc3lzp070jBHAI9u3Th//jzr1q3j2vXrBAUFcf78eXp07y6lcevcmczMTAJ++42rV69y4OBBNm3aRLt27ajx+AGBR9eupKamsmjRIq5dv05ISAhz581DS1ubTp06/ePXQhCEt+ODCvsH2cRqFhYWbNq0iXXr1pGXl4eZmRldunSRJivp0aMHBw8exMPDgwMHDjB79mwWLFjAunXrMDQ0pEOHDgwZMkRuRv2yWfajoqKYPHmytF1ZWZnly5fzyy+/MGTIEHJzc2nUqBHLli2TQrD69evHhAkT8PT05MyZM3LlbdSoEYsWLeK3335j+fLlGBkZMXjwYEaNGoWioiITJ05k1apVzJ8/H2NjY9zd3TE1Na3QU/ymjR07lgcPHkhh8PXr12fmzJn88MMPXLt2jXr16lXpOOWv25IlSwDo0qUL//d//0ePHj1QUlJ66bKtXLmSlStXAqCiooKpqSnu7u54eXm9Usg/yOaJCAwMJCAggJ49e6KpqYmzszMTJkyoNGS/zMSJE6levToBAQHcv38fU1NTxo4di7e3d5XPXTa0IyAggJSUFCwsLPD395cm5wsKCmLOnDkMHTqU4uJibG1tWblyZZV/B69izpw5TJ8+nd69e6OlpUWTJk3w9fVl2rRpJCQkYGYmv7yWm5sb/v7+LF26lCVLlqCnp4eLiwvff//9WyujIGNX04jv3JzZFBrGL3tOU0Nbg4HO9ng6yh4O3UlJw3f7Mb5ybc7HDS0B+LihJUXFJey4FM6Rm3cxrqbNmI4taN3gSdioefVqTO7WjqC/r/Lrvr+ppq6GRxMrPnv8gKCouISL92RRMcuOXeBpNmaG+Pb8+G1XX/gPM7Owx7W3DxdPrOfQ5p/R1NGnhesQGn8kG36UmhjFnqBJtO32DVZNXAGwauJKSXEh185sJeLKIXT0jGnffTx1bds+71SC8I9o2LAhI0eOJHjnThYtWkR1PT16f/opnT/5BJBNMP3rr78y5Msvaf148uHWrVpRVFjI/gMHOHX6NIYGBnh5ecnNFWRqasq4b75hy19/8fuSJWhra9OxY0e6e3rKnXv8+PEE79zJsmXLUFBQwM7Ojt69er30MENBEP69FErfdvy4IDwlNjaWTz75hL179z6zV10QspdWXFZTED40S01mvOsiCMJb18NOrBUvfPjqvsVOmZd15yUnhf6n/Juu0X/VB9fzL/x7JSYmcvXqVdavX0/btm1Fw18QBEEQBEEQBOEfIhr/H4jk5GTc3Nyem6Zx48asWbPmHypRRWlpafj4+GBhYcHChQvl9o0cOZKQkJDn5t+6dSuWlpZvs4jvHU9PT2JjY5+bJiQk5LlDFwRBEARBEARB+PCJxv8HwsDAgO3btz83jZqa2j9TmGewsbHh0qVLle7z9fWtdA3a8p4eZy7IZuUvfGqN96e9aDlBQRAEQRAEQfg3S01NZfbs2Zw8eZL8/HyaN2/OhAkT3uocWG9aYWEh69atY8iQIe+sDKLx/4FQUlKiTp0677oYr8zY2PhdF+G9JB6ICIIgCIIgCB+60aNHU1JSwrJly9DS0iIgIIAhQ4Zw4MABNDQ03nXxqmTXrl3MmjXrnTb+P6il/gRBEARBEARBEIQPR3p6Oubm5syYMQN7e3vq1avHV199xf3797l9+/a7Ll6V/Rvm2ReNf0EQBEEQBEEQBOFfSVdXl7lz52JlZQXAw4cPWbVqFSYmJtSvX79Kx+jVqxczZjxZYefQoUNYW1uzb98+advs2bOlXvlHjx7h6+tL+/btsbe3p1+/fnLzky1YsICBAwcyfvx4mjZtyv/93/9RXFyMn58f7du3x87ODjc3N/78809ANnfZxIkTAbC2tn7hXGdviwj7FwRBEARBEARBEN4qV1fX5+4/fPjwC48xZcoUNm3ahKqqKr///juamppVOvfHH3/Mnj17pNd///03CgoKhISESJOmHzt2jAEDBlBcXIyXlxeFhYX4+fmhr6/PmjVrGDp0KOvXr8fe3h6Ac+fOMXjwYHbs2EFxcTHr169n3759+Pv7Y2xszNGjR5k2bRoNGjTA3d2dzMxMZs6cyalTp9DV1a1Sud800fMvCIIgCIIgCIIg/Ot98cUX/PXXX3h4eDB69GjCwsKqlM/FxYU7d+6QmJgIwOnTp3F1dZV64GNiYrh79y4uLi6cOnWKsLAw5s6dS4sWLahfvz6+vr40aNCAFStWyB137Nix1KpVCwsLC2JiYtDU1KRmzZqYm5szcOBA/vjjDywtLVFXV0dHRwcAQ0PDd7YSl+j5FwRBEARBEARBEN6qqvTsv0hZmP/PP//MlStXCAoKYtasWS/MZ2tri7GxMadPn6ZVq1bExcXh5+dHnz59SElJ4dixYzRq1Ahzc3P27NmDjo6ONMwAQEFBgWbNmnHq1ClpW40aNaQGPcCAAQM4dOgQ7du3p1GjRrRu3ZquXbtSo0aN1673myJ6/gVBEARBEARBEIR/pYcPH7J7926KioqkbYqKitSvX5/79+9X+Tgff/wxp0+f5u+//6Zx48bY29tjbGxMSEgIx48fl4YlPGtivtLSUpSVn/Sdq6ury+23sLDgwIEDBAYG8tFHH3Hs2DF69OjBtm3bXqa6b5Vo/AuCIAiCIAiCIAj/Sg8ePODbb7/lzJkz0rbCwkJu3LhBvXr1qnwcFxcXzpw5w5kzZ3B2dgbA2dmZI0eOEBISIjX+ra2tyczMJCIiQspbWlrKhQsXnjvB4Jo1azhw4ACtW7fmxx9/ZOfOnTg7O0tzDSgoKLxUvd8G0fgXBEEQBEEQBEEQ/pWsrKxo164dM2bM4Ny5c0RERODj40NGRoY0O39xcTEpKSnk5eU98zjOzs7k5+dz4MABucb/3r17MTQ0xMbGBoA2bdrQqFEjvvvuO0JDQ4mKimL69OlERETwxRdfPPP4Dx8+ZPr06Rw+fJj4+HhOnjzJzZs3cXR0BJAmJ7x+/fpzy/k2ica/IAiCIAiCIAiC8K81b948nJ2dGT9+PH369OHRo0esW7cOMzMzABITE2nTpo3cjP5PU1VVpVWrVigqKuLg4ADIGv8lJSW4uLhI6ZSUlFi5ciU2NjaMGTOG3r17c/v2bVatWiXlq8yYMWP49NNPmTFjBp07d+Z///sfn3/+OSNGjADgo48+okmTJvTr14+jR4++/kV5BQqlzxrU8IEIDg4mKCiIiIgIFBQUqFu3Ln369KFfv34ApKWlcejQIfr06VPlY164cIHS0lKaNWtGXFwcrq6urFmzhpYtW74wb0JCApcuXaJr166ALPykZ8+efP31169WwXLK1o8MDw+vUvqQkBAGDx4st01ZWRl9fX3atWvHjz/++FrLUPj4+BAfH8/atWsB+ev2Jri4uBAfHy+9VlFRwcDAgPbt2/PNN9+gr68v7bO2tmbWrFn06tWLgoICvv/+e44dO0a1atU4cuSI3OsTJ06gqCiei71r2UsnvesivDeuxCTxZ8h1Yh6mo6ehjlvj+nRzsHpueNmp2zFsOXeD+5nZGOpo0qNpQz5uaCmXZsSqnTzMzq2Qd4VXd6ppqFXYvvtKBKtOXWbRoK4YVdN6/Yr9Byw1mfHiRIKksCCPc4f/IPrWGQoL8zCpZcNHnwxDt4b5c/PlZj3i7KFA4qMuUVJSTK36TrTsOBRNnSefE3k5GZw7spq4qAsUFeZjYFqPFq5e1DCpC0DElcOc3BnwzHO07fYNVk2ev4zVf1UPuzvvugjvjbCwMLZv3058QgLVdHT42MWFTzp1eu77eWhoKLt27eJBaio1atTAzc2N1q1ayaVJTExky19/EREejqKSElZWVvTt0wdDQ0MpTWBgYKVrj48YOZJmTk5vrpIfqLovEX7+tt2JinrXRajU275GgYGBmJmZ4e7u/lbP8z77oGf737JlCz///DOTJk3CycmJ0tJSTp8+zYwZM3jw4AFjxoxhzpw5xMXFvVTjv3///syaNYtmzZphamr6Ums1TpgwAXNzc6nxv2XLFtTUKn6J/idt3rwZU1NTQBYyEx4ejo+PDw8ePGDp0qWvfNxJkyZRXFwsvS5/3d4ULy8vvLy8AMjLyyMiIgI/Pz8GDhzIxo0bpRk4T506Jf3/5MmT7N+/n6VLl2JtbV3htWj4C++TiKRUZu0+ResGtfishS23Eh8QdOYqxSUl9HRqVGmes1Fx/HYwBHf7BjjUNiH0TjyLD59DRUmJNg1qA5CRm8/D7FwGtWpCQ1MDufxaaioVjpmQlsm6s9fefAUFoZxj2/y4nxBBc5chqKhpcOnEn+wJmkSv4QtR09CuNE9JcTH7N0ylMD+XVl1GUVpSzLkjq9n35//oPnQ+SkrKlJaWcmjLTB49iKO5yxdoautz7exWdq+ZSM/hv6GjZ0yt+s3o9qVfheOf3PUbhfm51Kr/5j7bhP+mO3fusGDhQpo3a4Zn9+5E3r7NX1u2UFJcTJcuXSrNc+HiRQIDA3F1dcXW1pbLly+z6o8/UFFWpkWLFoAsFPmXOXMwNjZm2LBhFBQUsH3HDvznz2fa1KnSkmOxsbE0b9GCjk+txW5kZPR2Ky4Ib0BWVhZ79uypsBSfIO+DbvyvX7+e3r178+mnn0rb6tatS3JyMmvWrGHMmDHPnM2xqpSUlOSemr6s8r3T74q+vr5cHUxMTPjiiy+YP38+GRkZVKtW7ZWOW37pi7dFU1NTruy1atWiUaNGdO3alcDAQMaPHw8glyYjIwOA9u3bo6CgwNmzZ+VeC8L7ZGPodSwN9fi6oyzyyLGOKcUlpWy9cJOuTaxQVVaqkGf92Wt8VK8mQ9o4AOBQ24Ss/AI2hFyXGv/RDx4B0KKuOSa6lTeqypSUlLLoSCjV1NVIzcp5c5UThHKS424Rc/scn/SbSq36sl5Ik1q2bFo0jJsX9uDQpm+l+e7eOk1q0l16jVhEdcNaAOgb12XrsjHcvXGK+o07kPEwgeSYG7Tx+Bprh04AGNdqxLp5A7l99QhN232OhpYuGlryD/rDQnfy6EEc3YbMqbBPEF7WjuBgateqxdChQwFobGdHcUkJe/bswdXVtdJ1wbdt24aTkxOfffYZAHZ2dmRnZ7N9xw6p8R+8cyca6up8O3681OFUw8CARYsWER0djZWVFQUFBSQlJ9OpUyfq1q37D9VYEN4cbW1tNmzYUOnfifDEB93FqaioyKVLl0hPT5fbPnz4cDZu3IiPjw/btm0jNDQUa2trANLT05k8eTJt27bF1tYWZ2dnJk+eTG6uLPS1LN3EiRPx8fEhLi4Oa2trKUwqOjqaoUOH4uTkhKOjI0OHDpXC8AcNGkRoaCjbtm2TxpW4uLiwYMECqWwnT57ks88+o0mTJrRr1w5/f3+p9zwhIYHx48fj7OyMra0t7dq1w8/Pj5KSkjd+7ZSUlFBQUEBFRdbDVzY0wsHBgcaNG9OrVy9OnjwppR80aBBTpkyhT58+NGvWjODgYHx8fBg0aFCl12306NEVhhzcuXMHa2trbt++/VplNzMzo1OnTuzevVvaZm1tzdatW1mwYAE+Pj4ANGzYsNLXABcvXmTAgAHY29vToUMHfH19ycrKko7n4uLCL7/8gru7Oy1btiQ0NJTS0lKWL1+Oq6srTZo0oXv37gQHB0t5QkJCsLGx4fjx43h4eGBnZ4ebmxuHDh2S0pSWlrJ69Wo6d+6Mvb09Xbt2ZdeuXdL+5ORkxo8fT7NmzWjZsiUjR44kOjq6ytem7H5dunQprVu3xtXVlaysLCIiIhgxYgTNmzfHzs4OV1dXVq5cKZf3efdmQUEBfn5+tG3bFkdHR/r27Su3DqrwdhQWFxMWn0ILS/mQ54/q1SSvsIhbiQ8q5LmfkU3io0xa1K2YJzk9i8RHmYCs8a+uooxxFcL3d1y6RXpOPj2bNnyN2gjC88VHXURZVR3zuo7SNg0tXUxq2xIbef65+XRrmEkNf4DqhrXQq2FObJQsX3FRIQAqqhpSGhVVDZSUVcnPzaz0uLlZj7hwLIhGTl0wMrd+rboJQmFhIeHh4dLEYGWcnJzIy8sjMjKyQp4HqakkJyVVmifl/n2Sk5MpLS3l4sWLtG7TRi7S1NLCgl/9/KR1zOMTEigpLqZmzZpvoXaC8M8QDf8X+6Ab/97e3ty4cYN27doxfPhwli1bxtWrV9HR0cHS0pJJkybRpUsXHB0dpYaKj48PN27cYOHChezfv5+JEyeyfft2Nm7cCCCl++mnn5g0qeKY5G+//RZjY2P++usvNm/ejKKiImPGjAFgwYIFODo60qVLF7Zs2VIh76VLlxg+fDhOTk5s3bqVGTNmsGHDBhYvXgzAqFGjyMzM5I8//mDfvn14eXkRGBjIkSNH3tg1Kyoq4vz586xZs4b27dujoaHB9evX+frrr+natSs7d+5k06ZN6Ovr8+OPP1JQUCDl3bx5M4MHD2b9+vW0bdtW7rhPX7devXoRGhpKYmKilGb79u00btyYBg0avHY9rKysiI2NJTs7W267l5cXP/30k1Smyl7funWLL7/8krZt2xIcHMyvv/5KWFgYXl5ecpEiQUFBTJ48mcDAQBwcHPD39+fPP/9kypQp7Ny5k8GDBzNt2jTWrVsn5SkuLsbPz49Jkyaxa9curKysmDBhglTOwMBA/P398fb2ZteuXfTr148ff/yRs2fPkpOTIz1MCQoKYu3atVSvXp2+ffuSnJz8Utdn27ZtrF69mvnz56OkpISXlxd6enps2LCBXbt24ebmxi+//MLNmzeBF9+bEydO5PTp0/z6669s27aNLl26MHLkSI4dO/ZS5RJeTnJ6NsUlJZjqyUfZlPXUx6dlVMhTts3sqTymj/MklGv866irMXf/GQYv38bAZVuZt/9MhTkAYh+ms/n8DUa5NENVpWKUgSC8KY9SY9HRM64wNKtadVPSU+OfkQsePYhFV7/inADV9M3IeJxP39gCU4vGXD61gYf375GXm0nIoRUUFeZT16ZthbwAF06sQ0FREacOg16jVoIgk5KSQnFREUbGxnLbjR5HLiYlJVXIU/YdyvjpPI/D9JOTk0lNTSU3J4ca+vqsW7eOb8aNY9RXX7Fw0SIePnwo5YmLjQVk34W++/57Ro4axS9z5nDnjpivQRA+JB902L+bmxsmJiasWbOG06dPc/z4cQAsLCyYOXMmTk5OqKuro6KiIoWFt27dmubNm0s91TVr1pQmDIQn4eM6Ojro6OhUiCqIiYmhVatWmJubo6KiwsyZM7lz5w4lJSXo6emhoqKCurp6peH+a9eupUmTJvz4448A1KtXj+nTp5OamkpeXh7du3enS5cu0vj8IUOGsHz5csLDw+nYseMrXycPDw8p3D0vLw8lJSXat2/P9OnTAVkUwJQpU+jfv7+UZ/DgwQwbNozU1FSpPI0aNaJbt26VnuPp69a+fXsMDAwIDg5mxIgRlJSUsGPHDoYPH/7K9SivbKhCVlYWWlpPei61tLSk4Qjly1T+9YoVK2jdujUjR44EZPfL3Llz6dixI6GhodLEju3bt6fV4wl1cnJyWLVqFfPmzaNDhw4A1K5dm/j4eFasWMGAAQOkMowbN05aXuSrr75i//79RERE4ODgwOrVqxk8eLA0B8WgQYPIy8ujqKiI3bt3k5GRgZ+fH8rKsj/dn3/+mZCQEDZt2vRSk0b2799fWqf04cOHDB48mAEDBkjXauzYsQQGBhIeHk6jRo2ee2/eu3ePXbt2sX37dho1ko0x//LLL7l16xYrVqyQrofw5uUUyHorNVXlx+BrqCrL7S8v+5l5VOTy3H2QxsPsXFwNLHG3b0B8WgYbQ8OYuv0ofn0/QV1FmeKSEhYcCsW1kSW25kbcv3X3zVZQEMopyM+R65kvo6KqQWFBxYkpn+TLppq+WaX5CvKfDFNp3eUr9v05lW3LHr+XKijQ1mMsxrUqzp2Rm51O5NWj2LXsjpq6mNxSeH1ly35pqKvLbVd//LosArW83BzZ/auhofHMPJmZsge6f23dioWFBcOHDSMjI4Nt27fz69y5TP3f/1BTUyP2ceM/Pz+f4cOGkZWdzb69e/l17lx+mjhRRAQIwgfig278Azg4OODg4EBJSQm3bt3i+PHjBAUFMWzYMA4ePFghff/+/Tly5Ajbtm0jOjqayMhI4uLiqjz+afz48cycOZP169fTokUL2rZti4eHR5UmkYuIiKB169Zy2zp37iz9f+DAgezbt4+rV69y7949wsPDefDgwWuH/S9btkx6aqyqqkqNGjXkwmYaNWqErq4uy5Yt486dO9y7d49bt24ByE3oV6dOnSqfU1lZGU9PT3bs2MGIESM4e/YsDx8+xMPD47XqUqbsw05b+/ljlStz48YN7t27VyGMDiAqKkpq/Jevb2RkJPn5+Xz33Xdyv+uioiIKCgrk1vIsfy+Vla+wsJC0tDRSUlJo0qSJ3DmHDRsGgK+vL+np6TRv3lxuf35+PlEvOatr+bLr6+vTv39/du3axY0bN4iJiZF+v2X31vPuzb179wLIPRwqq9OrzhchVE3JC+YsUaxkDosXzXNS9iBw5MfNUFJQpL6x7EGljZkhtfR1mbL1CMdvRdO5cX3+unCTnPxCBjjbv2INBKFypaWllD712fb06/KeN1/L8+75snxpKbHsWjMBHV1DWnzqg6qqJndvnuLU7oUoq6hR16aNXL7wS/spLS3BtoVnVaojCC/0ou9ylX2PrMr7eVFRESDrFBn91VfSPW9sbMysWbM4e/Ys7du3x8XFBXt7e+zs7KT8jRo2ZNLkyezevVtaqkwQhPfbB9v4T0pKYunSpYwYMQITExMUFRWxsbHBxsaGjh074uHhwblz5+TylJSUMGLECG7fvo2Hhwfu7u7Y2toyZcqUKp93wIABuLm5cfz4cc6cOcNvv/3G77//zvbt2zEwMHhu3rLe3Mrk5OQwcOBA8vLycHNzo2fPntjb28v1KL8qMzOz5z7RDQ0NZejQoXTo0AEnJye6detGbm4uo0ePlkun/tTT6hfp3bs3K1as4Pr16wQHB+Pq6vpaSwuWFxYWhoWFhVyvf1WVlJTQrVs3qee/vPIRG+XrW/YBPH/+/EofFJV/mFLZeKTS0lJpfoXnlcvS0pLff/+9wj5NTc3n5n1a+bKnpKTw2Wefoa+vj4uLC23atKFx48a0b99eSvO8e7Os7uvWratwvcXKCW9X2az7uYXyPfzPiggovy23oPI8Wo/3W5tUfL9qaGqAppoK0amPuJuSxtbzN5nUrS0qSooUl5RQ+vi7a0lpKSUlpSgqigk0hVdz6cSfXDq5QW6bRaNW5GY/qpC2sCAXFbVnvweqqmtVGhlQmJ+DqprsPSssdAelJSW4DZiBuoYsGsy8rgMF+dmc2b8Uy0at5R4wRN86jXldBzHJn/DGlH2Ol+8sgCc9/uoaFaNeynr8n5VHQ1NT+ry3s7OTu4fr1q2Lhqam1ONvYmKCiYlJhTLVq1eP2Li4V66XIAj/Lh9s419VVVVawu7pUPKy3kgDAwO5N8KbN29y4sQJNm3aJPW+FhYWEhMTQ61atXiR1NRUFi1axPDhw+nVqxe9evUiOTmZdu3aERoa+sI1J+vVq8e1a/JLZa1evZpdu3YxbNgwwsLCOH36tPQQ4dGjR6Smpr72igUvsnLlSlq2bCk3MeHatWuBFz91fp569erh6OjI3r17OXz4MHPnzn3tsoLswc/hw4elHvOX1aBBAyIjI+V6x6OiovDz8+Pbb7+tdBWDunXroqysTEJCAh9//LG0fc2aNURGRkpDKJ5HR0cHIyMjrl27hmu5ZXbGjh2LqakpVlZW7NixAx0dHekhRGFhId999x1ubm6vvKbprl27ePToEfv375ceQJRNUln2+33evTlz5kxA9hDBxsZG2u/v74+ioiLffPPNK5VLeDHjatooKiiQlJ4lt73stbl+xcgL8+qybYnpWVgaVq+Yp3o1svMLCImKp76xPrVrPGnclJaWUlhcgq6GOufuJlBcUsL0HccrnOProD3YmBni2/PjCvsEoSoaNnWjVgP5KKd74WeJj7pEaWmp3Gd3xsME9Aye/QBbV9+c1KSK0VEZaYkYmskmO8tKv49eDXOp4V/GpJYtd2+cJi87HQ1tPQCyM1JJTbqLbYvur1o9QajAwMAARUVFUlJS5LaXvTZ7PMSyvLLG+v3796ldu3aFPKampuhoa8siAAorDgMrKS6WPvfPnTuHpqYmtra2cmkKCwv/kdWbBEH4Z3yw3XL6+vp4e3sTEBCAv78/N2/eJDY2lqNHjzJmzBhatmxJs2bN0NTU5P79+8TGxmJgYICysjJ79+4lNjaWa9euMW7cOFJSUuQmttPU1CQqKoq0tDS5c+rq6nLs2DEmT54snW/Dhg2oqKhIYVRaWlrEx8dXOnGLt7c3ly9fJiAggOjoaI4fP87ixYvp0KGD9AYfHBxMfHw858+f56uvvqKwsFCubG+Dqakp4eHhnD9/nri4OP766y8CAgIAXurclV233r17ExQUhLq6eoWw8qrIyckhJSWFlJQUYmNjOXToEN7e3tSsWZMvv/zypY8HskkBb9y4ga+vL1FRUVy6dInvvvuO6OhoLCwsKs2jo6NDv379CAgIYMeOHcTGxrJlyxb8/Pxean3c4cOHs3r1anbs2EFMTAxr1qzh8OHDuLq64unpia6uLmPHjuXKlStERUXh4+PDiRMnpDkqXoWJiQm5ubns27ePhIQETp06xbfffgs8+f0+795s0KABH3/8MVOnTuXIkSPExsayfPlyli5dKvdlRHjzVJWVaGRmSEhUvNyDuJCoeDTVVGhgVHFuERNdbYyqaXE2Sr4n52xUHCZ6sn0qSkoEnrzItgs35dKcu5tAYVExduZGdLSty6xPO8r99Gku+9I4wb0NIzqINc+FV6epo4+hWQO5H/O6jhQW5BIXdVFKl5udTlLsDcwtKw7TKmNe15FHqfGkpcRK29JSYnn0IE5aOUC3Rk3SHsSS99TM/slxN1FV10Kt3EOB+wmyh6PGNSvOBSAIr0pVVZUGVlZcuHhR7v38wsWLaGhqVvr9w8jICAMDAy5cuCC3/cKFCxgZG2NQowZqamo0aNCAixcvUljuAcDNmzfJz8+XJlk+fuIEQUFB0jABgLS0NKIiI2n4Gt8xBEH4d/lge/5BNrGahYUFmzZtYt26deTl5WFmZkaXLl2ksUs9evTg4MGDeHh4cODAAWbPns2CBQtYt24dhoaGdOjQgSFDhsjNqF82y35UVBSTJ0+WtisrK7N8+XJ++eUXhgwZQm5uLo0aNWLZsmVSI6hfv35MmDABT09Pzpw5I1feRo0asWjRIn777TeWL1+OkZERgwcPZtSoUSgqKjJx4kRWrVrF/PnzMTY2xt3dHVNT0wo9sm/a2LFjefDggRQGX79+fWbOnMkPP/zAtWvXqFevXpWOU/66LVmyBIAuXbrwf//3f/To0QMlpZefKXzlypXSknQqKiqYmpri7u6Ol5fXK4X8g2yeiMDAQAICAujZsyeampo4OzszYcKE5y4hMnHiRKpXr05AQAD379/H1NSUsWPH4u3tXeVzlw3tCAgIICUlBQsLC/z9/aW1eoOCgpgzZw5Dhw6luLgYW1tbVq5cWeXfQWXc3NwICwtj9uzZZGVlYW5uTp8+fTh8+DDXrl3j888/f+69CbJefn9/f/73v/+Rnp5O7dq1+fnnn+nZs+crl0uomk+b2TA9+Djz9p/BpZEl4UmpBF8Op/9HjVFTUSanoJC4hxkY62qhqyEL//y0uQ2LD59juboqzSzMOHc3gTORsYz75CNA9lChZ9OGbAoNQ1dTnaZ1TIlJTWfTuTCaWZphV1P2QEtfSz4MNTZNNgFq7Rq6GFVhiUBBeBmmdewwrWPHsR1zaeEyBDUNHS6eWI+qmhaNnJ5EPqWlxFJSXEgNE9kQrLo2bblyehP7N0ylucsQAM4dWUV1ozpYNpKN5bdr2YOo68fZt24y9q36oKquRfStv7kTdpKWnYaiWO7zKe3+PZSUVaimX7EnVhBeh0fXrsybN0+2HG+bNkRFRnJg/3569eqFmpoaubm5JCYmYmhoKPXGe3Trxqo//kBbWxv7Jk24cvky58+fl4t67dWrF36//krAb7/RuXNnMjIy+GvLFizr1sXBwUHu3IsWLcLF1ZWc7Gx27tqFlrY2nTp1eheXQxCEt0Ch9G3HjAvCc8TGxvLJJ5+wd+/eZ/aqC/9N2UsrLqUpVC7kThybQsOIT8ukhrYGne3q4+ko66m5Hn8f3+3H+Mq1OR83tJTyHAyLYselcFKzcjCupk1Pp4a0t7aQ9peWlnLgehT7rkeSnJGNjpoqba3q0LeFLarKlT+oO3rrLosPn2PRoK6i8V9FS01mvOsivFfyc7MIORjIvYgQSktLMa7ZkI8+GYZujSdL+e1eO5GsR/f57OsV0rasjAec3b+M+LuXUVRSxtzSgY86eaOp8yQ6Jj01nnNHVpN47xqlJSXoGdaisXMvLBu2kivD33t/J/rWGfqPX/P2K/yB6GEnlourqosXLxK8cydJSUlU19Ojw8cf0/mTTwDZkLxff/2VIV9+SetWT+7L48ePs//AAdLS0jA0MKBLly7SqkJlIiMj2b59O3fu3kVVVRUHBwf69ukjN2fQzZs3Cd65k7i4OBQUFLCzs6N3r17UqFHjn6n8e67ua3TEvGl3XnIi6H/Kv+ka/VeJxr/wTiQmJnL16lXWr1+Pmpoay5Yte9dFEv5lRONf+C8QjX/hv0A0/oX/gn9Tw1Y0/oVn+aDD/v+rkpOTcXNze26axo0bs2bNu+u1SEtLw8fHBwsLCxYuXCi3b+TIkYSEhDw3/9atW7G0tHxumv8aT09PadbeZwkJCXnu0AVBEARBEARBED5MovH/ATIwMGD79u3PTaOmpvbPFOYZbGxsuHTpUqX7fH19Kyxb8zQzM7O3Uaz32pIlS+Qm86nMi5YTFARBEARBEAThwyQa/x8gJSUluWXq3jfGxsbvugjvJfFARBAEQRAEQRCEZ/lgl/oTBEEQBEEQBEEQBEFGNP4FQRAEQRAEQRAE4QMnGv+CIAiCIAiCIAiC8IETjX9BEARBEARBEARB+MCJCf8EQfhXSu7o9a6LIAhv3/V3XQBBePuMD61810UQhLev3s/vugSC8EKi518QBEEQBEEQBEEQPnCi8S8IgiAIgiAIgiAIHzjR+BcEQRAEQRAEQRCED5xo/AuCIAiCIAiCIAjCB05M+CcIgvAByM/PZ8uWLVy8eJH8/HwaNGjAZ599homJyXPzZWRksHHTJsLCwiguLqZx48b07dMHPT29StMfPnyYgwcPMnv2bLnthYWFBO/cSUhICFlZWZiYmODWuTMtWrR4U1UUBAAKC/I4d/gPom+dobAwD5NaNnz0yTB0a5hX+RiHt8xCRVWDdp7jXmk/QFbGA7YuHYNdC0+atu//krUQhGfLKyxi7d9XCLkTT15hEY1MDfiyjSNm1XWem+9RTh6rT13mcmwSxSWlNK1jyuDWTdDX0pDSFJeUsPncDY7diiYjL596htUZ1KoJViY1pDRJ6Vl8HbSnwvFr6lfD/3O3N1dRQRD+ca/U8x8cHEzfvn1xcHDA0dGR3r17s2HDBml/WloamzdvfqljXrhwgfPnzwMQFxeHtbU1ISEhVcqbkJDA7t27pdcuLi4sWLDgpc7/LFu3bsXa2rrK6UNCQrC2tpb7sbW1pW3btkyaNIn09PTXKo+Pjw+DBg2SXpe/bm+Ci4uLXNnt7Ozo0KEDU6dO5eHDh3Jpra2t2bp1KwAFBQWMHTsWe3t72rRpU+F1SUnJGyvjf0X561tVSUlJjBw5EkdHRzp37syRI0feUumEf5tly5dz4cIFevXuzZdeXqQ9esSvc+eSnZ39zDzFxcXMDwjg7t27DBwwgIEDBhAZGcn8+fMpKiqqkD40NJRNz3hvX7Z8OQcOHKBly5aMGT2a5s2asXr1ag4fPvzG6igIAMe2+XH31t80c/mCdp7jyM5MZU/QJPJzs16Yt7S0lLMHlhN968wr7S+f7uTOAArzc16pDoLwPPMPnOVsVBwDnBszxrUFD7NzmbbjGFl5Bc/MU1xSws87T3D7/kOGtXdiWPum3Ep8wIzgExQVP/kOtvr0FXZeDsfT0ZpvP3FGSVGR6cHHSUp/8vdzNyUNgKndO/Bzb1fpZ1ynj95epQVB+Ee8dM//li1b+Pnnn5k0aRJOTk6UlpZy+vRpZsyYwYMHDxgzZgxz5swhLi6OPn36VPm4/fv3Z9asWTRr1gxTU1NOnTqFrq5ulfJOmDABc3NzunbtKpVRTU3tZav2Rm3evBlTU1NA9gU7PDwcHx8fHjx4wNKlS1/5uJMmTaK4uFh6Xf66vSleXl54ecmWWcvLyyMiIgI/Pz8GDhzIxo0b0dGRPXk+deqU9P+TJ0+yf/9+li5dirW1dYXXiopihMk/4YcffkBVVZVt27axZcsWxo8fz9GjR9HX13/XRRPeoqioKK5eucLYb76hsZ0dAFYNGjBx4kSOHTsmvTc+7cKFC8TGxODr64uZmRkAtWrVYtq0aZw/f56PPpJ90cvMzGT79u2cOHECLW3tCseJiYnh8qVL9OjRQzqXjY0NampqbN26FWdnZzQ1Nd9G1YX/mOS4W8TcPscn/aZSq74TACa1bNm0aBg3L+zBoU3fZ+Z9mBzNmQNLSYm/jZKy6kvvL+/Whb2kp8a/XmUEoRLhSQ+4EJ3ATx5tcawj+x7ZyMyA0Wv3sP96JL2b2VSa70xUHNEPHuH/uRs19asBYGlQnW837OfvyFjaWdfhQVYOB65H8WUbBzo3rg9Ak9rGjF23l20XbzLq4+YARD94RA1tTexqGv0DNRYE4Z/00i2y9evX07t3bz799FMsLS2pW7cugwYNYsiQIaxZswaQPRF/HUpKShgaGqKq+vwP32fR19dHS0vrtcrwuvT19TE0NMTQ0BATExPat2/PF198wfHjx8nIyHjl4+ro6DwzHPdN0dTUlMpeq1YtXF1dWblyJYmJiQQGBkrpDA0NUVdXB5Dq1L59e0xNTSu8Fv4ZN2/epHXr1lhYWODp6UleXh6JiYnvuljCWxYWFoaamhq2Nk++FOro6GBlZcW1689eSD4sLAxjExOp4Q9gZmaGiYmJXL7du3cTFhbGyFGjaGJvX+E4ZfdYkyZN5LY3bNiQ/Px8wsPDX7luglBefNRFlFXVMa/rKG3T0NLFpLYtsZHPj4I7HjyPkuJiPL/8FQ2tip0LL9pfJiMtidAjq2jtPvrVKyIIz3AlJhk1FWWa1HoyZEtXQx0bM0Mu3Ut6Tr4kTPV0pIY/yML0zfR0uHRP9h59LS6Z4pISWtR9MkRGRUmJpnVM5Y4dnfqIOgZV64ATBOH98tKNf0VFRS5dulQhfH348OFs3LgRHx8ftm3bRmhoqBQun56ezuTJk2nbti22trY4OzszefJkcnNzAaR0EydOxMfHp0LYf3R0NEOHDsXJyQlHR0eGDh0qfZkcNGgQoaGhbNu2DRcXF6Bi2P/Jkyf57LPPaNKkCe3atcPf31/qPU9ISGD8+PE4Oztja2tLu3bt8PPzeyth6kpKSigoKKCiogLAoUOH6NOnDw4ODjRu3JhevXpx8uRJKf2gQYOYMmUKffr0oVmzZgQHB8uF/T993UaPHs3gwYPlznnnzh2sra25ffv2a5XdzMyMTp06yQ2vKAtLX7BgAT4+PoDsy35lrwEuXrzIgAEDsLe3p0OHDvj6+pKV9STMzMXFhV9++QV3d3datmxJaGgopaWlLF++HFdXV5o0aUL37t0JDg6W8oSEhGBjY8Px48fx8PDAzs4ONzc3Dh06JKUpLS1l9erVdO7cGXt7e7p27cquXbuk/cnJyYwfP55mzZrRsmVLRo4cSXR0dJWvTdn9unv3bnr06CH9LqOioli0aBGtWrWiRYsW+Pr6Sg/GSkpKWLp0KZ07d8bOzo6mTZvi7e1NTEzMM89z9OhRevXqhb29PZ06dWL+/PkUFMiHALZq1Yo//viDu3fvMn/+fGn4RnmVXecX/Y0C3Lt3j1GjRuHk5ETLli359ttvSU1Nlfb/9ddfdOnSBXt7e7p06cLq1avFcI9/SGJiIgaGhhUibAyNjEhKevaXxcTERIyNjStsNzI2Jrlcvvbt2zNjxgycmjat9Djaj6MByt8PAPfv3wcg5cGDqlVEEF7gUWosOnrGFe71atVNX9gT397zW7oNmYO+scUr7QfZ58mJnfOpa9NGijwQhDcpLi0D42paKCoqyG030dUm/tGzO4/i0jIw06s4J4CpnjYJjzIBiH+YibqKMtXLzQEAYKqrQ1p2LnmFsuFe0SmPyCsoYtJfh/l8yRa8/whm3ZmrcsMHBEF4P71049/b25sbN27Qrl07hg8fzrJly7h69So6OjpYWloyadIkunTpgqOjI6dOnQJk49Rv3LjBwoUL2b9/PxMnTmT79u1s3LgRQEr3008/MWnSpArn/PbbbzE2Nuavv/5i8+bNKCoqMmbMGAAWLFiAo6MjXbp0YcuWLRXyXrp0ieHDh+Pk5MTWrVuZMWMGGzZsYPHixQCMGjWKzMxM/vjjD/bt24eXlxeBgYFvdKx0UVER58+fZ82aNbRv3x4NDQ2uX7/O119/TdeuXdm5cyebNm1CX1+fH3/8Ua5Bt3nzZgYPHsz69etp27at3HGfvm69evUiNDRUrqd3+/btNG7cmAYNGrx2PaysrIiNja0whtjLy4uffvpJKlNlr2/dusWXX35J27ZtCQ4O5tdffyUsLAwvLy+5SJGgoCAmT55MYGAgDg4O+Pv78+effzJlyhR27tzJ4MGDmTZtGuvWrZPyFBcX4+fnx6RJk9i1axdWVlZMmDBBKmdgYCD+/v54e3uza9cu+vXrx48//sjZs2fJycmRHqYEBQWxdu1aqlevTt++fUlOTn6p6+Pv789PP/3E5s2bycjI4PPPPyc6Opq1a9cyfvx41q9fz9GjRwFYs2YNK1aswMfHh/3797No0SKio6MrTKJW5sSJE4wbN46+ffuya9cupk6dyt69e/nhhx/k0k2aNImsrCy6dOlCfn4+K1euRFm54uiep6/zi/5GMzIyGDBgAAUFBaxevZo//viDmJgYxo0bB8DGjRuZM2cOY8aMYffu3YwbN47ly5fz66+/vtQ1FF5Nbm6uFIVTnrqaGnl5ec/Ml5Obi8Yz8uWWy2dqalrpfVTG2toaA0ND/vzzT27evElubi4RERH8tXUrCgoK5Ofnv2SNBKFyBfk5qKhqVNiuoqpBYUFuJTmeeF6jvir7Aa6H7CDr0X1adBz6wrSC8CpyCgrReNxJVJ6GijK5BRXnYimTnV+Ipmpl+VTIKSyUpSkoqDyNqrJ07ozcfB5m5xL/KJNOtvWY3K0dnWzqsvNyBIsOh75qtQRB+Jd46TH/bm5umJiYsGbNGk6fPs3x48cBsLCwYObMmTg5OaGuro6KigqGhoYAtG7dmubNm0s9kDVr1iQoKIiIiAgAKZ2Ojg46OjoVogpiYmJo1aoV5ubmqKioMHPmTO7cuUNJSQl6enqoqKigrq5e6bjmtWvX0qRJE3788UcA6tWrx/Tp00lNTSUvL4/u3bvTpUsXKTR9yJAhLF++nPDwcDp27Piyl0fi4eGBgoLsqW1eXh5KSkq0b9+e6dOnA7IogClTptC//5MZggcPHsywYcNITU2VytOoUSO6detW6Tmevm7t27fHwMCA4OBgRowYQUlJCTt27GD48OGvXI/yqlWThZJlZWXJDavQ0tKSxv6XL1P51ytWrKB169aMHDkSkN0vc+fOpWPHjoSGhtKyZUtA1sPYqlUrAHJycli1ahXz5s2jQ4cOANSuXZv4+HhWrFjBgAEDpDKMGzcOZ2dnAL766iv2799PREQEDg4OrF69msGDB0tzUAwaNIi8vDyKiorYvXs3GRkZ+Pn5SY2bn3/+mZCQEDZt2sTXX39d5evj5eUlzWzeqVMn1q5dy/Tp09HQ0KBevXosWLCA27dv4+LiQu3atfnll1/4+OOPATA3N8fNzY19+/ZVeuwlS5bQt29f+vXrJ10HX19fvvjiC+Li4qhZsyZ37tzhm2++wdDQkOTkZKpVq4aBgQF5eXmoqKigpKQkHa/8dYYX/43u2bOH7Oxs5s2bJ83FMWPGDHbv3k1BQQGLFy9m1KhR0njvWrVqkZWVha+vL9988807n4PjQ1JaWlohoqLkOUOtyt6HnnWsV8n3NGVlZcaPGyf9vQLo6unRr18/li1d+spDuIT/ttLSUkqfuteffl3ey9yzr+LRgzguHAvC9dOJqKm/26GFwoehtLS0wvv389/Pn3MsnpMPhcfne355FAB1FWWmeLbHRFcbo2qy+9zW3AhlJUU2hFyndzMbuaEFgiC8X15pqT8HBwccHBwoKSnh1q1bHD9+nKCgIIYNG8bBgwcrpO/fvz9Hjhxh27ZtREdHExkZSVxcHHXr1q3S+caPH8/MmTNZv349LVq0oG3btnh4eFRpErmIiAhat24tt61z587S/wcOHMi+ffu4evUq9+7dIzw8nAcPHrx2uPKyZcukcFpVVVVq1Kgh9wW4UaNG6OrqsmzZMu7cucO9e/e4desWgNyEfnXq1KnyOZWVlfH09GTHjh2MGDGCs2fP8vDhQzw8PF6rLmUyM2VhY9qVTPj1Ijdu3ODevXs4OjpW2BcVFSU1/svXNzIykvz8fL777ju533VRUREFBQVyPZrl76Wy8hUWFpKWlkZKSkqFscjDhg0DwNfXl/T0dJo3by63Pz8/n6ioqJeqY/mya2pqYmBggIbGkx4qdXV1KarDxcWFK1euEPB4pvW7d+8SGRlZaQg2yK7f1atX5aJbyhpuUVFR6OvrM2TIEBo3bsy8efM4ePAg3333HTY2NqSkpHDgwAGOHj0qfTl++r560d9oREQEFhYWcpNwNmzYkIYNG/Lw4UOSkpKYN28eAQEB0v6SkhLy8/OJi4ujXr16L3UthWfbuXMnO3fulNvm5ORU6Vwiefn5lfbsl9HU0CCvkl753Lw8NDUq9q4+j5GRET/++COZmZlkZmZibGzMw4cPKS0tRfsdz8EivJ8unfiTSyc3yG2zaNSK3OxHFdIWFuSiovb2JpUsKSnhxM75WNq0xtzSgZJyn9OllFJSXIxiuQesglAVm8/dYPO5MLltH9WrSXpOxYit3MKiSnvty2ipqpL7uIe/vJyCQrTUZPk0VVXILawYPZBTIMunpaaKqrIS9rUqfhdpWseUDSHXiU59JBr/gvAee6nGf1JSEkuXLmXEiBGYmJigqKiIjY0NNjY2dOzYEQ8PD86dOyeXp6SkhBEjRnD79m08PDxwd3fH1taWKVOmVPm8AwYMwM3NjePHj3PmzBl+++03fv/9d7Zv346BgcHzK/icUNWcnBwGDhxIXl4ebm5u9OzZE3t7e7ke5VdlZmZGzZo1n7k/NDSUoUOH0qFDB5ycnOjWrRu5ubmMHi0/gVBlobzP07t3b1asWMH169cJDg7G1dW1yqsmvEhYWBgWFhavNJliSUkJ3bp1k3r+yysfsVG+vmWN2/nz51f6oKj8w5TKehZLS0ul+RWeVy5LS0t+//33Cvtednbyp++15z2cWrZsGYsWLaJnz544OzszZMgQDh8+LDenwtPl9Pb2pmfPnhX2GRoacubMGZKTk9m2bRtqamp4eHhw+/Zt/P390dDQoHv37nK9YuWvc1X+Rp/3d1T2oGzixIly0QRlxISPb1a7du1o/NSke5cvXSIsLIzS0lK53/P95OTnXn9jY+NK55lIuX8fS0vLKpepoKCACxcu0KBBAwwMDKTIn3uPj127du0qH0sQyjRs6katBvIPZu+FnyU+6lKFez3jYQJ6Bs/+zH1d2RkPSImPICU+gsirR+X2XT65kcsnN9J3TCA6emJ2dKHqOtrWpWkd+ffoc3fjuRyTVOEeT3yUSc3qz250m+npcOfxEn3lJaVn0cBY9j3LvLoOuQWFpOfmoauhLpfGUEcLVWUlEh9lcj3+Pq3q10JL7cl3q4LHD7x0NUQknyC8z15qzL+qqiqbN2+Wm3CtTFlIuIGBgdyb1c2bNzlx4gQBAQF8//33eHp6Urt2bWJiYqq0KkBqairTp0+nsLCQXr164efnR3BwMCkpKYSGvnjsUb169bh27ZrcttWrV9OnTx9OnTpFWFgYa9asYezYsbi7u6OtrU1qauprr1jwIitXrqRly5YsWLCAIUOG0Lp1a2ms/uucu169ejg6OrJ3714OHz5Mr1693kh5k5KSOHz48DOHILxIgwYNiIyMpE6dOtJPUVERs2bNeuZs9HXr1kVZWZmEhAS5fMePH2fFihVVivzQ0dHByMiowj0wduxYZs2ahZWVFQkJCejo6EjHNzMzY+7cuRUeZL1JS5YsYfTo0UybNo3PPvsMBwcHoqOjn/m7b9CgAXfv3pW7DklJScyZM4fs7GzpQUVKSoqUZ9y4cVhaWpKVlSUNm6hMVf5G69evT3R0tBT9AbKHQc7OzhQWFqKvr09sbKxc+cLCwpg/f/7rXyxBjp6eHpYWFnI/Nra25OXlcT3sSQ9SZmYmt2/fxsam8mWhAGxsbUlKSiIhIUHalpCQQGJi4nPzPU1ZWZk/N2zg+IkT0rbi4mKOHjmCoZHRcx+ECsKzaOroY2jWQO7HvK4jhQW5xEVdlNLlZqeTFHsDc8uKkWVvsiyeXnMr/ABYO36Cp9dcNHXEkqrCy9HX0qC+sb7cj30tY/IKi7gc82TS1fTcPG4mPqi0R76MfS1jEh5lEvfwSRRY3MMM4tMypHyNa8r+PRsZJ6UpLC7mQnSilOZhdi7Ljl3gTLk0AH/fjkVDVYW6htVfv+KCILwzL9Xzr6+vj7e3NwEBAWRnZ+Pm5oa2tjaRkZEsXryYli1b0qxZM/bu3cv9+/eJjY3FwMAAZWVl9u7di76+Po8ePWLJkiWkpKTITWynqalJVFQUaWnyTy11dXU5duwYMTExfPfdd2hra7N161ZUVFSwe7yetZaWFvHx8SQlJWFiYiKX39vbm969exMQEED37t25d+8eixcvZvDgwVLa4OBgOnfuTGJiIvPmzaOwsLDCLOpvmqmpKYcOHeL8+fOYmJgQEhIihUy/zLnLX7fq1WVvyL1792bGjBlUq1atwpCHqsjJyZEakXl5eYSHhzN//nxq1qzJl19++dLHA9l4+AEDBuDr68vAgQPJyMjA19eXvLw8LCwsKs2jo6NDv379CAgIQFtbm6ZNmxISEoKfnx8jRoyo8rmHDx/OvHnzsLS0xNHRkWPHjnH48GH++OMPGjVqxLJlyxg7diw//PAD2traLF68mBMnTvDNN9+8Ul2rwtTUlNOnT+Pi4oKioiI7duzgwIEDz4xkGTZsGOPGjWPhwoV07dqVpKQkJk2aRM2aNTE0NKR69eo0bNiQCRMmMG3aNHR0dAgKCiIuLo6GDRvy008/sXTpUmxtbSscuyp/o926dWPx4sX88MMPjBs3jqKiIqZNm4aVlRWmpqYMGzYMf39/zMzMaNeuHeHh4UybNg1XV1cx3vsfYG1lhbW1NYGBgXzauzdaWloE79yJhqam3IOfhIQEioqKpJ745s2asWfPHgJ++43ejx8U/rV1K+Y1a9KsWbMqn19RUZEOHTpw6NAh9KtXx9jYmKPHjhEZGcno0aPf+lhs4b/DtI4dpnXsOLZjLi1chqCmocPFE+tRVdOikZO7lC4tJZaS4kJqmFRteOGLKCkpY2hW+cS5mtr6z9wnCC/L1twIW3MjAg6GMKiVPdrqqmwKDUNLVYXOdvWldHEPMygsLsbycWO8dYNabL1wk593nWCAsyw6bN2Zq9SuoUur+rUAMKqmRYeGFqw6fZmC4mLM9HTYeTmC7PwCejRtCICNmSF2NY1Y/ThNzerVuHgvkb3XIhncqolcNIAgCO+flx7zP27cOCwsLNi0aRPr1q0jLy8PMzMzunTpIjXIevTowcGDB/Hw8ODAgQPMnj2bBQsWsG7dOgwNDenQoQNDhgyRm1G/bJb9qKgoJk+e/KSAysosX76cX375hSFDhpCbmys12Mq+wPbr148JEybg6enJmTNn5MrbqFEjFi1axG+//cby5csxMjJi8ODBjBo1CkVFRSZOnMiqVauYP38+xsbGuLu7Y2pqWqGn+E0bO3YsDx48kMLg69evz8yZM/nhhx+4du1alcdIl79uS5YsAaBLly783//9Hz169JCb5K2qVq5cycqVKwFQUVHB1NQUd3d3vLy8XinkH2TzRAQGBhIQEEDPnj3R1NTE2dmZCRMmPLdxOHHiRKpXr05AQAD379/H1NSUsWPH4u3tXeVzlw3tCAgIICUlBQsLC/z9/aXJ+YKCgpgzZw5Dhw6luLgYW1tbVq5c+VbHqc+ZM4fp06fT+3FDrUmTJvj6+jJt2jQSEhLk1l0H2USb/v7+LF26lCVLlqCnp4eLiwvff/898OTvZObMmXh7e1NSUoKjoyPr16+ndu3ajB49mmvXrlXa+Dc2Nn7h36iGhgYrVqxg1qxZ9OvXD3V1dTp06MCECRMA2X2opqbG2rVrmT17NgYGBvTt25exY8e+tWsoyBs1ahSbNm1i85YtlJaWUr9ePUaOGCH3N7tu3TpSU1OlVSVUVFT4dvx4NmzYwJo1a1BSVsbGxobP+vZ96fcOz27dUAD27ttHTnY2NWvVYuzYsZXec4LwOlw//YmQg4GEHv6D0tJSjGs2xLW3D2oaT+aj+XvfYrIe3eezr1e8w5IKwqv53q0Vq09fZs3fVygtBWuTGnzXuRXa6k++Ly0/foGUzBwWD5ZNtKuipMT/urfnj5OXWHL0PMqKijSpZcwXbRxQKhcpObyDE1pqqmy/eIu8wiLqGelLE/yBbOLMH7q0ZvO5MHZdjiAtJxfjatqM6OCEq82beZgmCMK7o1D6tuPbhX9cbGwsn3zyCXv37n1mr7rw3/L02MH3wZ2XnHBREN5H26+LL9PCh29E0uQXJxKE95zWiJ/fdREk/9bvUHXFBNDv3CvN9i/8OyUmJnL16lXWr19P27ZtRcNfkLxvDX9BEARBEARBEN4s0fivouTkZNzc3J6bpnHjxqxZs+YfKlFFaWlp+Pj4YGFhwcKFC+X2jRw5kpCQkOfm37p160vN8P1f4OnpSWxs7HPThISEiHHtgiAIgiAIgiD8q4nGfxUZGBiwffv256ZRU3u3y5/Y2Nhw6dKlSveVTa73PE+PMxdks/IXVrJubnkvWk5QEARBEARBEAThXRON/ypSUlKiTp0677oYr8zY+NnLwwjPJh6ICIIgCIIgCILwIXjxQumCIAiCIAiCIAiCILzXRONfEARBEARBEARBED5wovEvCIIgCIIgCIIgCB840fgXBEEQBEEQBEEQhA+caPwLgiAIgiAIgiAIwgdONP4FQRAEQRAEQRAE4QMnGv+CIAiCIAiCIAiC8IETjX9BEARBEARBEARB+MCJxr8gCIIgCIIgCIIgfOCU33UBBEEQhNeXn5/Pli1buHjxIvn/z959x1Vd/Q8cf7H3EERA3CCggKg4UVHBDDEXZJorNWemqVmOtPyVJeXKkabmQDQ3KGDubbhxgQoiCIKILNnjAvf3B3H1egEB8avleT4ePJLP53PO55zLp3vv+8z8fJo2bcqgQYMwMzOrMF1GRgY7d+0iLCyMoqIiHBwc+GjgQAwNDcu8/vjx4xw9ehRvb2+54xKJhIDAQC5evEhWVhZmZma4v/8+7dq1q6kqCgKSgjwuH9/Eg7vnkUjyMKvfnA49x2JgbFFhutysp1w49gfx969RXFxEfSsn2vf4FG09I9k1OVlpXD21lfjoa+TnZmFgVJcWHb1oYtdFLq/Ye5e5dnY7aU9i0dDSo5FtR9p0H4GauuZrqbPw7smTFOIbfIOLUfHkSQppZl6bUZ1bUbeWXoXpnubk4XPuOtcfPqaoWErrhuaM6OSIkY5Wmdf/dfMeQdcjWD2it+zYybvRrD5+udx7fObWlu62jatXMUEQ3jjR8y+UKSsrC0dHR5ydnZFIJG+6OG/crFmzsLGxkftxdHSkT58+7Nq165Xzt7Gxwc/PDygJojZv3vzKeb5OFy9exMbGhri4uDddFOEf69av5+rVq3h6eTFq9GjSnj5l8ZIlZGdnl5umqKiIX5cvJzo6mmFDhzJs6FAiIyP59ddfKSwsVLj+0qVL7Nq9u9z7HzlyhPbt2/P5pEm0bdMGHx8fjh8/XmN1FIRT/ouIvhtMG9dPcOk7lezMFP7a+g35uVnlpikuKuLwju9Ijo/AuddEOvWaSOLDOxza/i1FRSXPeVGhhMPbv+NR9HVauwylx4dzqF23KSf9FxF566Qsr9iISxzdtYBatRvQc/C3ODp7ce/Gcc4dWPna6y68O349coEL9+MY2tGBz93akZqdy/z9p8jKKyg3TVFxMT8GnuHek1TGdnVibNfW3E1IZkHAGQqLihWuP3cvli1/31A43rqhOT96uSn81DPSx1hXm9YNzWu0roIg/G+Jnn+hTAcOHMDY2JikpCSOHj2Kh4fHmy7SG9eqVStWrnz2BS8vL4+9e/cyb9489PX1cXd3r3be586dQ0+vpEU/KCiIhQsXMnLkyFctsvCOuH//Pjdv3GDKF1/gYG8PgHXTpsyePZtTp07Ru3fvMtNdvXqVh7Gx/N///R9169YFoH79+syfP58rV67QoUMHADIzM9m3bx9nzpxBR1dXIZ/Y2FiuX7tG//79Zfdq3rw5Ghoa+Pn50bFjR7S1tV9H1YV3SGLcXWLvXabn4O+ob+UEgFl9O3b9NpY7V/+iZeePykwXffdvUh5H4zn+N2qZ1AfAyLQJfus+J/r2OawcuhEbeZnUxAf0Hb0Ek7pNAbBo0pLs9CRuBO/ByqE7ABeP/kHjZs649J0KQN1GLSiWFnP7ciCFknxU1TRe86sg/NeFP07m6oNHzPmgC63+CbSb1a3NJN+/OBwaiVeb5mWmO38/jgfJT1n2sTv1jPQBaFy7FtN3HCY48iEuNg0BSM/NY8fFUI6FRaGrqa6Qj4GWJgZa8qNY/rp5j/i0TBZ4uiqcEwTh30X0/Atl2rt3L126dKFDhw7s2LHjTRfnraCmpoaJiYnsp379+kydOpVGjRoRGBj4SnmbmJigqVnygSqVSmuiuMI7JCwsDA0NDeyaP/tSqKenh7W1NbdCQytMZ2pmJgv8AerWrYuZmZlcugMHDhAWFsaEiRNxbNFCIZ+EhAQAHB0d5Y7b2tqSn59PeHh4tesmCKXi74egqq6JRZNWsmNaOgaYNbDjYeSVCtMZGNeVBf4AtUzqY2hswcP7JenU1bWxaf0+tc2t5NIa1K5HZloiACmPo8hIe0zztn3krrFv15ePJq0Xgb9QI27EJqKhpopj/WdTtgy0NGle14RrMY8rSPcYc0M9WeAPUM9In7qGelyLSZAd87tyhxuxiXzp7kybRnXLykrO05w8tl+4RU97S6zNjKtZK0EQ3hYi+BcU3L9/nxs3btCpUyd69uzJxYsXiY6Olp2XSCQsX76c7t274+joiKenJ3///bfsfExMDBMnTsTJyYn27dszffp0UlJSgJLh88OHD5e73/PH4uLisLGxYe3atXTq1Ak3NzeysrKIiIhg/PjxtG3bFnt7e9zc3Ni4caNcPmfPnmXQoEE4Ojri4uLCsmXLKCoqwsfHh1atWpGbmyu7tri4GBcXF7Zt2/bKr5eKigrq6iWt5wUFBfz888+4urpib29Pu3bt+OKLL0hNTa2wfqXD/v38/Jg9ezZQMhXg4MGD2Nvbs2/fPrl7LlmyBC8vr0qVz8/Pj/fee48dO3bQrVs3HB0dmTJlComJicyYMYNWrVrh4uLCnj17ZGnS09OZO3cuXbp0wc7Ojo4dOzJ37ly51/B5UqmU9evX4+bmhqOjI/369SMgIKCqL6VQTQkJCdQ2MUFZWf4t3aROHR4/Lv/LYkJCAqampgrH65iakvhcuq5du7JgwQKcWrcuMx/df0YDlP5/XurJkycAJCUnV64iglCBpykP0TM0VXjO9WuZk54SX3665IcYGCmuCaBvVJeMf9JZNGlJZ49JKCkpyc4XFRXy8N5lWaNBSmIUACoqahzZ+T2bvT/Ed8kQLhxZT1GhmB4n1Iy4tAxM9XVQVlaSO25moEv804wK09U1VFwTwNxQl0dPM2W/97S3ZMXQXnSwrFep8uy8FIqyshIft7evZA0EQXibieBfULBnzx60tbVxcXHhvffeQ01NTa73/8cff2THjh3MnDmTwMBAunTpwoQJE4iKiiIjI4OhQ4dSUFCAj48PmzZtIjY2lqlTp1apDP7+/vj4+PDrr7+ioqLC6NGjMTQ0ZMeOHQQFBeHu7s7PP//MnTt3ALh27Rrjxo3DyckJPz8/FixYwI4dO1i9ejV9+vRBIpFw5MgRWf7BwcGkpaXxwQcfVPt1ysrKYt26ddy/f59evXoB8Msvv3DkyBG8vb05fPgw3t7eXLhwgTVr1pRbP93nhlF7eHgwZ84coGQqgJubG926dZML/ouLiwkICMDT07PSZX306BGHDh1i3bp1rFixguPHj9OnTx/s7OzYu3cvLi4uzJ8/n7S0NKCkQeb27dusWrWKw4cPM3v2bPbt28fOnTvLzH/ZsmVs376defPmERgYyIgRI5g/f36NNK4IL5ebmysbOfI8TQ0N8vLyyk2Xk5uLVjnpcp9LZ25ujqpq+bPEbGxsqG1iwvbt27lz5w65ublERESw188PJSUl8vPzq1gjQVBUkJ+DmrriwmVq6lpICspumCxJl42ahuK0EzV1LQryc8pNd/nEZjJSE2jRaSAAednpABzfsxDD0jn/Hb24G3KYM4G/VrE2glC2nAIJWmpqCse11FTJLVBci6VUdr4EbfWy0qmR89zaTRa19FFVqdzX//TcPE6Hx+Bub4WOhuIUAUEQ/n3EnH9BTmFhIQEBAbi6uqKpqYmmpiadO3dm3759TJ8+HYlEwp49e5g3b55sjvu0adOQSqVkZWVx6dIlsrOzWbp0KQYGBgAsWLCAAwcOUFBQ/kI1LxoyZAhWViXDL1NTUxkxYgRDhw5FR0cHgClTpvDHH38QHh5Os2bN8PX1xdHRka+//hoAS0tLvv/+e1JSUjAyMsLV1ZWAgAD69esHlATfrq6usjJWxpUrV2jVqmS4qVQqJTc3F2NjY2bMmEHPnj0BcHBwwN3dnTZt2gBgYWGBs7MzERER5dbveZqamrK5/yYmJgB4eXnx2WefkZiYiKmpKefPnyc1NbVKDReFhYXMmzcPS0tLrK2tsbW1RU1NjVGjRgEwatQodu/ezYMHD6hVqxadOnWibdu22NjYAFCvXj22bt2qUA+AnJwcNm/ezNKlS+nWrRsADRo0ID4+ng0bNjB06NBKl1N4OalUSnGx/OJNxRVMFXm+J7OsvKqT7kWqqqpMmzpV9hwAGBgaMnjwYNatXSsbGSMIlSWVSpG+8Jy/+PvzavI5l0qlXD6xmbCLATh0HEBjW2cAiopLAq+GNh1o5zYSKJnzL0XKlRNbaO0y5KW7DgjC86RSqcL7d8Xv5xXkRQXpqPz7+fOOhUVRXCzFw7FptdILgvD2EcG/IOf06dMkJyfLLRDWu3dvTp48ycGDB7G0tEQikSjM7Z0+fToA+/bto1GjRnJBta2tLba2tlUqR8OGDWX/NjIyYsiQIQQFBXH79m1iY2O5e/cugCwIioiIoFOnTnJ5vP/++7J/e3l5MXHiRJ48eYK2tjbHjh1jxYoVVSqTvb09ixcvBkBZWRltbW2MjeXnv/Xr14/g4GAWL17MgwcPiIqKIjo6WtYYUFb9XsbFxQVjY2P279/PuHHj8Pf3x83NrUoNF1ASkJfS1tbG3PzZir0aGiVzVUsbaIYMGcKJEyfw9/fnwYMHREZGEhcXR5MmTRTyjYyMJD8/ny+//FJuOG5hYSEFBQXk5eWV2SstVE9gYKDCGhNOTk5kZCgOB83Lzy+zZ7+UtpYWeWX0yufm5aGtVfbWUOWpU6cOX3/9NZmZmWRmZmJqakpqaipSqRTdfxrtBKGyrp3ZzrWz8uvNNGrmTG72U4VrJQW5Zfbsl1LX1ClzZIAkPwd1Dflns6hQwpnAX4kKO4tDxwG0cxslO1c66qB+07Zyaeo1ac2VE1tIeRwlgn+hSnZfvs3uy2FyxzpY1iM9R3HEVq6ksMye/VI66urklrE7U06BBB2N8tNV5ML9OBwbmIpF/gThP0QE/4Kc0u3mPv/8c4VzO3bsYP78+RWmr2hocHnK2lLs+WAxKSmJQYMGyXrwO3fujIODA127dq30fTt37kzt2rUJCgrC0NAQfX19OnfuXKVyampqvjRo//bbbzl8+DD9+/fH1dWVSZMmsWHDBhITE8ut38uoqKjQv39/AgMDGTZsGMeOHWP58uVVKjuULFj4vBfnzZYqLi5m/Pjx3Lt3jw8++AAPDw/s7OyYN29emdeX9qr9+uuvZTYOiF7fmuXi4oLDC4vuXb92jbCwMKRSqVxP5pPERLlGnheZmpoSGxurcDzpyRMaN678Ps4FBQVcvXqVpk2bUrt2bdnolZh/8n6+4UkQKsO2tbtCkB0TfoH4+9cUnvOM1EcY1i5//rKBkQUpj+8rHM9IS8CkrrXs9/y8bI7s+D+exIfTvucY7Nv1fSGfksXRil+Y319cXASAipp4rxOqpoddE4Wt8y5Hx3M99rHCc57wNJN6tfRfzEKmrqEeUUlpCscfp2fR1NSoymVLycrhQfJTejtav/xiQRD+NUTwL8ikpKRw+vRpPD09ZcPBS23evJm9e/cCJUHkrVu35HrzP/roIzw8PLCysmL37t1kZmbKAoCwsDDGjBmDv78/ampqZGXJ78ccExNTYTAcFBTE06dPOXz4sCyALV09vDTwtLS05NatW3LpfHx8CAoKYvfu3bIA+ujRo+jr69OvXz9UVFSq8zKVKy0tjZ07d7Js2TK5rRGjoqKqtM1ZWcNQvby8WL9+Pb6+vujp6VW54aIq7ty5w5kzZ9i1a5dshIdEIiE2Npb69esrXN+kSRNUVVV59OgR3bt3lx3fsmULkZGRfP/996+trO8iQ0NDDA0N5Y4VFBTw119/ERoWJtvqLzMzk3v37snWoyhLczs7Ll26xKNHj2Qr/j969IiEhIRytwcsi6qqKtt37KBr1654/bMWRVFRESdPnMCkTh3q1avcwlKCUEpbzwhtPfmApVCSz42/dxN3P0S21V9udjqPH97G0fnDcvOyaNKK+2FnSEt6KFu8Ly3pIU+T42jZeRAAxUVFHN31A8kJkbh6fk3jZp0U8jFrYI+quib3w07TwLqd7HhsxEWUlFWoY1G1EW6CYKSjhZGO/Cir/MJC/K7e4XrsY9lWf+m5edxJSGZA6/KfsRb1TTl3L5a41AzZiv9xqRnEp2Xg1aZZlct2L7FkoWJb89pVTisIwttLBP+CTEBAAIWFhYwdO1ahB3fChAn4+/uza9cuhg0bxvLlyzEyMqJp06bs2bOHiIgIvL29MTc3Z/Xq1Xz11VdMnTqVwsJC5s+fj7W1NWZmZrRs2ZI9e/YQEBBAq1atCAgIICIighZlbB9WyszMjNzcXA4dOoSTkxNRUVEsXLgQeDZMfcyYMXh5ebF8+XL69etHTEwMq1evZsSIEbJ8PD09+eOPP1BRUZGtDVCTdHV10dPT4/jx49jZ2ZGXl8fWrVsJCwtTmCZRkdKGgtDQUKysrNDU1KRx48a0bt2a1atXM3z48BpvuHhe7dq1UVVV5eDBgxgZGfH06VN+//13kpKSyly3QU9Pj8GDB7N8+XJ0dXVp3bo1Fy9eZNGiRYwfP/61lVN4xsbaGhsbG/744w8+9PJCR0eHgMBAtLS1ZeswQElgX1hYKOuJb9umDX/99RfLV6yQBe17/fywqFdPYapKRZSVlenWrRvHjh3DqFYtTE1NOXnqFJGRkUyaNKlK6wcIQnnMG9pj3tCeU/uX0M51JBpaeoSc+RN1DR2aOT1rcE1LekhxkQRjs5LPsSbNu3Dj710c3vEdbV1HAiWL+dWq05DGzUoaUm9fPUBi7G1sWr+Pjn5tnsTLb09Zx8IGNXVNWrsM4dKxjWho6tLI1pnEuDvcPO+HXbs+aOlUbSqWIJTFzqIOdhZ1WH70IsOdW6Crqc6uS2HoqKvxvv2ztYLiUjOQFBXR2KQWAJ2a1sfv6h1+DDrD0I4l36m2nb9JA2MDnK0UG+5fJjY1HVUVZcwMdF9+sSAI/xoi+Bdk/Pz8cHZ2LnPodoMGDejRowcBAQGcPHkSFRUVvvvuOzIzM7G1tWXdunWydBs2bGDhwoUMHjwYTU1NunXrxsyZMwHo27cvd+7cYcGCBRQWFtKrVy8++eQTrl27Vm653N3dCQsLw9vbm6ysLCwsLBg4cCDHjx/n1q1bfPzxxzRr1ozffvuNFStWsH79eurUqcOIESOYOHGiLJ9GjRrh6OhIcXExlpaWNfzqlYyIWL58Od7e3vTp0wcDAwPZVodr164td5u8F3Xo0AFHR0cGDx7MokWLZD23np6ehISEMGDAgBov+/NMTU3x9vZm5cqVbNu2DRMTE7p168bIkSM5ceJEmWlmz55NrVq1WL58OU+ePMHc3JwpU6YwZsyY11pW4ZmJEyeya9cudu/Zg1QqxcrSkgnjx8sWyQTYtm0bKSkpeHt7AyXP7PRp09ixYwdbtmxBRVWV5s2bM+ijj6rcwNS3Tx+UgIOHDpGTnU29+vWZMmUKdnZ2NVlN4R3n9uEcLh79g0vHNyGVSjGtZ4ub1yw0tJ4FKMGHVpP19AmDJm8AQEVVDfehC7hweB3nDqxCWUUVi8Yt6fDeGJT/ec4f3A0GIDzkMOEhhxXu++nckq1LHTr0R0NLl1sX/Am/fhRt3Vq0dvmYFhWMPBCEqprh7ozP39fZEnwDqRRszIz58n1ndDWfTS1Zf/oqSZk5rB5RMkpLTUWFb/t1ZdPZa/x+8gqqyso41jflk84tUSlnml9F0nPyxAr/gvAfpCStaBlcQfgPkUql9OjRgwkTJjBw4MA3XZwqW7lyJcHBwWzfvv1NF+V/Iuq+4hxdQfiv2Req2NgqCP814x/PfdNFEITXTmf8j2+6CDJv63eoJq+h802oGtHzL/znSSQSTpw4wYULF8jJyanSXOa3wdWrV4mOjmbLli1i/rwgCIIgCIIgCNUign/hP09NTY0FCxYAsGjRIrnF99avX8/q1asrTD9nzpw3OlLg5MmTbN26FS8vL7nF2xITE3F3d68wrYODA1u2bHndRRQEQRAEQRAE4S0nhv0L77T09HSePn1a4TXGxsbo6r59C94UFRURFxdX4TUaGhqYmZn9j0pUs97WIWuCUJPEsH/hXSCG/QvvAjHs/+XEsP83T/T8C+80AwMDDAz+nSs0q6io0LBhwzddDEEQBEEQBEEQ/gWqvvynIAiCIAiCIAiCIAj/KiL4FwRBEARBEARBEIT/OBH8C4IgCIIgCIIgCMJ/nAj+BUEQBEEQBEEQBOE/TgT/giAIgiAIgiAIgvAfJ4J/QRAEQRAEQRAEQfiPE8G/IAiCIAiCIAiCIPzHieBfEARBEARBEARBEN5ioaGhHDlyhIyMjGrnIYJ/QRAEQRAEQRAEQXhLPHnyhOHDh7N69WoAtm7dysCBA5kyZQo9e/bk3r171cpXtSYLKfw3ZWVl0alTJ3R0dDh9+jRqampvukhv1KxZs/D395c7pqmpSYMGDRg+fDgfffTRK+VvY2PDwoUL8fT0RCKRsG3bNkaOHPlKeQr/ffn5+ezZs4eQkBDy8/Np2rQpgwYNwszMrNJ5rFmzBk1NTUaNGlXuNampqXw3fz7v9ehB3759ZccLCgoIOnCAixcvkpmZSb169ejbpw/29vavVC9BeJ6kII/Lxzfx4O55JJI8zOo3p0PPsRgYW1Q6j+N7FqKmroVL36myY2cCfuXezRPlpvno8z/QM6xDoaSA6+d2EBl6irzsDIxMG9HaZQj1LFu/SrUEQU6epBDf4BtcjIonT1JIM/PajOrcirq19Cqdx+JDwWipqTLJrV251yRn5TB9+2E+cLTmo3Z2ZV4TnZTG7D3HWTG0F3X0dapcF0EQqmfRokVER0czbtw4iouL+f3333F2duarr75iwYIFLFmyhN9//73K+Yqef+GlDhw4gLGxMZmZmRw9evRNF+et0KpVK86dOyf7CQoKws3NjXnz5nHo0KFXyvvcuXN4eHgAEBQUxMKFC2uiyMJ/3Lr167l69SqeXl6MGj2atKdPWbxkCdnZ2S9NK5VK2bFjByEhIS+9brOPD3m5uQrntmzZwsmTJ3F3d+fzSZMwrVOHlStXEhERUe06CcKLTvkvIvpuMG1cP8Gl71SyM1P4a+s35OdmvTStVCrlwpH1PLh7XuFcy86D6DNqkdxPz8HfoqqmQT0rJ3QNTAA4d2Alt6/8hWPHD3nvo7kYGNXlyM7veRwbVuN1Fd5dvx65wIX7cQzt6MDnbu1Izc5l/v5TZOUVvDStVCpl09lrXLwf99LrVp+4TG6BpNxrYlPSWXjgHEXFxVWugyAIr+bcuXPMnDmTLl26EBISQnJyMiNGjMDW1pYxY8Zw5cqVauUrev6Fl9q7dy9dunTh0aNH7NixQxaYvsvU1NQwMTGROzZ16lQOHjxIYGAg7u7u1c77+XylUmm18xHeHffv3+fmjRtM+eILHP7pabdu2pTZs2dz6tQpevfuXW7auLg4tm/fTvSDB6ipq1d4n1OnT/P48WOF48kpKVy8eJEhQ4bQvVs3AJo1a0ZkZCSnTp/G2tq6+pUThH8kxt0l9t5leg7+jvpWTgCY1bdj129juXP1L1p2Ln/UVWriA84fWUtS/D1UVBWfc30jc/Qxlzt2fM9CNLT06NZ/BkpKSmQ+fcL90NN0dB9PszYln4N1Gzvy+OFt7lz9C7MGZfecCkJVhD9O5uqDR8z5oAutGpY8k83q1maS718cDo3Eq03zctPGJD9l49lr3HuSipqqSoX3ORJ6n0dpmWWeKywq5q+b99h5KRT1l+QjCMLrkZOTIxu9eebMGdTV1enQoQMA6urq1Y4RRPAvVOj+/fvcuHGDMWPGkJ6ezty5c4mOjqZx48YASCQSVq9ezb59+0hNTcXS0pIvv/ySTp06ARATE4O3tzeXLl1CVVWVTp068c0332BsbMysWbOIj4/H19dXdr/nj8XFxeHm5sb06dPZsmULmpqa7N+/n0ePHrFkyRJCQkLIzc3F1NSUoUOHMnr0aFk+Z8+eZdWqVdy9excDAwMGDBjAlClT2Lp1K7/++ivBwcFoaWkBUFxcTLdu3Rg/fjxDhw59pddLRUUF9X8CqIKCApYtW8bhw4d58uQJ2tradOzYke+++w4jI6Ny6+fk5CTr7Z89ezZQMhXg119/lQ316d+/v+yeS5YsITg4mL179760fH5+fqxZs4auXbvi7+9P+/btWb16NceOHWPt2rXcu3ePoqIimjZtyrRp0+jSpQtQ0gixZcsW/vzzTxISEqhfvz4TJ07kgw8+ACAxMRFvb2/Onj2LiooKrVq1YtasWTRq1OiVXk+hcsLCwtDQ0MCu+bMvhXp6elhbW3MrNLTC4H/Dxo1oaGgwZ/ZsVq1aVe51SUlJ7N2zh/ETJrBi+XK5cwb6+sz55htM69SRHVNSUkJZRQWJpPxeJUGoivj7Iaiqa2LRpJXsmJaOAWYN7HgYeaXC4P90wFJU1TTpO2oxR3f98NJ7PYy8woO753H1momGZslQZ23dWvQdvQR9o7qy65SUlFBWVqGo8OU9soJQGTdiE9FQU8Wx/rMpWwZamjSva8K1mMcVBv8rj19CU1WVhV5u/PzX3+Vel5iehe/5m3z5fkd+CjqrcD4kJoHdl8PwdGqGobYmv5+sXg+j8OaYHtv4potQNssf33QJ/jUaNWrElStXaNmyJYcPH6Zdu3ZoaGgAEBAQUO3v2GLYv1ChPXv2oK2tjYuLC++99x5qamrs2LFDdv7HH39kx44dzJw5k8DAQLp06cKECROIiooiIyODoUOHUlBQgI+PD5s2bSI2NpapU6dWqQz+/v74+Pjw66+/oqKiwujRozE0NGTHjh0EBQXh7u7Ozz//zJ07dwC4du0a48aNw8nJCT8/PxYsWMCOHTtYvXo1ffr0QSKRcOTIEVn+wcHBpKWlyQLZ6sjKymLdunXcv3+fXr16AfDLL79w5MgRvL29OXz4MN7e3ly4cIE1a9aUWz9dXV3ZcQ8PD+bMmQOUDP1xc3OjW7du7Nu3T3ZNcXExAQEBeHp6VrqssbGxPHnyhH379jFt2jRCQ0OZPHkyvXv3JjAwkF27dmFkZMTXX39NQUHJF9o//viDZcuWMWbMGIKCghg8eDBff/01Fy5cICcnh+HDhwMli5H4+vpSq1YtPvroIxITE6v1egpVk5CQQG0TE5SV5d/STerUKbOn/nmfjh7NrJkzqVevXrnXSKVSNm3aRJu2bWUjC56npqZG40aN0NbWRiqVkpqayo4dO0h68oSuLi7Vq5QgvOBpykP0DE0VnnP9Wuakp8RXmLZr3+n0GfkLRqaNXnofqVTKpWMbMWtoR+NmnWTHVVTVMKnbFA1NHaRSKVkZyVw4sp6MtMfYtu5VrToJwovi0jIw1ddBWVlJ7riZgS7xTyte4XuyWzsWeLnSsLZhuddIpVJ+O3EZZ6v6spEFL7KsU4vVI3rj1aa5QjkEQfjfGDt2LKtWraJjx448fPhQth7Thx9+SEBAAJ9++mm18hU9/0K5CgsLCQgIwNXVFU1NTTQ1NencuTP79u1j+vTpSCQS9uzZw7x582TD3KdNm1bypSgri0uXLpGdnc3SpUsxMDAAYMGCBRw4cEAWVFbGkCFDsLKyAkoWGxsxYgRDhw5FR6ekN2bKlCn88ccfhIeH06xZM3x9fXF0dOTrr78GwNLSku+//56UlBSMjIxwdXUlICCAfv36ASXBt6urq6yMlXHlyhVatSrpfZJKpeTm5mJsbMyMGTPo2bMnAA4ODri7u9OmTRsALCwscHZ2VpgD/Xz9nqepqYmeXsniPqVTAby8vPjss89ITEzE1NSU8+fPk5qaWuWGi88++4z69esDcOfOHebNm8eQIUNk50eMGMHYsWNJSUnBzMwMHx8fRowYwcCBAwEYPnw4eXl5FBYWcuDAATIyMli0aBGqqiVvKT/++CMXL15k165dTJ48uUplE6ouNzcXTU1NheOaGhrk5eVVmLaioL/U0WPHSE5J4fPPP3/ptQcPHpQtiNmlSxeaNy+/l0oQqqIgPwc1dS2F42rqWkgKFNeheF5lgv5Ssfcu8TQ5Dvee48q95mbwHq6cLBm1ZtOqJ3Ubt6x0/oJQkZwCCVplLKyspaZKbkFhhWkrCvpLBd2I4ElGNjM9OpV7jbGu9kvzEQTh9frggw8wNzfn6tWrtGvXjpYtWwLQtm1bpkyZgks1O1dE8C+U6/Tp0yQnJ8sNGe7duzcnT57k4MGDWFpaIpFIcHR0lEs3ffp0APbt20ejRo3kgmpbW1tsbW2rVI6GDRvK/m1kZMSQIUMICgri9u3bxMbGcvfuXaCkFxwgIiJCNu2g1Pvvvy/7t5eXFxMnTpQNxT927BgrVqyoUpns7e1ZvHgxAMrKymhra2NsbCx3Tb9+/QgODmbx4sU8ePCAqKgooqOjZY0BZdXvZVxcXDA2Nmb//v2MGzcOf39/3NzcqtRwAcgNFWrWrBkGBgasW7eOqKgoYmJiZK9pUVERaWlpJCUlKfydx44dC8D//d//kZ6eTtu2beXO5+fnc//+/SqVS3g5qVQqe9ZLFVcw70tJ6dV6bRISEti3bx8TJ05EW/vlXwgdHR2xtLIi8t49gg4cQCKRVLt1Wnh3SaVSpC885y/+/rxXfc6fd/tyEMZmjbFo0rLca+o3bUedes1IfHib6+d2UlRYQNd+02usDMK7QSqVKrx/V/x+/mr3i0/LYPvFUL5yd0ZHo+I1XgRBeLOysrJwcnLCyclJ7vjMmTOBkqH/z++6VFki+BfK5efnB1Bmb9+OHTuYP39+helLe4GrorBQsVX7+R7NpKQkBg0aJOvB79y5Mw4ODnTt2rXS9+3cuTO1a9cmKCgIQ0ND9PX16dy5c5XKqamp+dKg/dtvv+Xw4cP0798fV1dXJk2axIYNGxSGwpfVY1seFRUV+vfvT2BgIMOGDePYsWMsf2H+dWXLX+rSpUt8+umndOvWDScnJ/r06UNubi6TJk0CeOnWjsXFxTRu3FhhOgNQqWBRqJrAwEACAwPljjk5OZGRoTgcNC8/H60qPF8vKi4uZtPmzbRxcqJ5s2YUFRXJzkmlUoqKilBRkV8MysKiZMs1G2triqVSAvbvp3///gqNY4JQkWtntnPt7A65Y42aOZOb/VThWklBLmoaNfNek5ebSUJMKG1dR1R4nVGdkvd/84b2SKXFhJz+E6duw2W7AghCZey+fJvdl+V3iuhgWY/0HMURW7mSQrTVq7/VcnGxlN+OX8bZsj4t6pvKreAvlUopKi5GRVnMBhaEt8Unn3yCj4+P3JRggEePHvHdd99x7tw5EfwLNSclJYXTp0/j6empsOf35s2bZYvLqampcevWLbne/I8++ggPDw+srKzYvXs3mZmZsuHrYWFhjBkzBn9/f9TU1MjKkt+eKSYmpsJgOCgoiKdPn3L48GFZUBoeHg48Wxnf0tKSW7duyaXz8fEhKCiI3bt3ywLoo0ePoq+vT79+/RQCmFeVlpbGzp07WbZsmdzuCFFRUVUKiMvqzfLy8mL9+vX4+vqip6dX5YaLF23cuJH27duzcuVK2bHSRRilUil6enrUqVOHW7du4ebmJrtmypQpmJubY21tzf79+9HT08PIyAgoWQjyyy+/xN3dXewOUcNcXFxwaNFC7tj1a9cICwtDKpXKPTNPEhMxNy97TmdlpKalER0VRXRUFOfPy2+PFhQUVLIVpbc3AHfv3KFdu3ayBS8BGjZoAEB6eroI/oUqsW3tTv2m8qOJYsIvEH//msJznpH6CMPaL5++Uhnx90OQFhfRyFZxSHTm0yc8enADS7uuqKo9e86NzSwByMlKFcG/UCU97JrQ+oV595ej47ke+1jhOU94mkm9WvrVvldyVg73ElO4l5jC6fAHcuf2XLnNniu3+W14b+ro61T7HoIg1JyUlBRGjBjBpk2bZCN8fXx8WL58OcrKysydO7da+YrgXyhTQEAAhYWFjB07liZNmsidmzBhAv7+/uzatYthw4axfPlyjIyMaNq0KXv27CEiIgJvb2/Mzc1ZvXo1X331FVOnTqWwsJD58+djbW2NmZkZLVu2ZM+ePQQEBNCqVSsCAgKIiIigxQuBzfPMzMzIzc3l0KFDODk5ERUVJVsZv3QdgTFjxuDl5cXy5cvp168fMTExrF69mhEjnvXkeHp68scff6CioiJbG6Am6erqoqenx/Hjx7GzsyMvL4+tW7cSFhamMHy+IqUNBaGhoVhZWaGpqUnjxo1p3bo1q1evZvjw4a/ccGFubs6xY8e4cuUKZmZmXLx4UTaaoPQ1HTduHEuXLqVx48a0atWKU6dOcfz4cTZt2kSzZs1Yt24dU6ZM4auvvkJXV5fVq1dz5swZvvjii1cqm6DI0NAQQ0NDuWMFBQX89ddfhIaFyRbky8zM5N69e7IFKKt1LwMD5nzzjcLxn378kS5dutDFxQVDAwPuR0Xh4+ODuro67dq1k10Xdvs2KqqqmJqaVrsMwrtJW88IbT0juWOFknxu/L2buPshsq3+crPTefzwNo7OH9bIfZ/Eh6OjXxs9wzoK57LSn3AuaCWqahpY2j2baxkfdQ1lFVUMjCxqpAzCu8NIRwsjHfl1LPILC/G7eofrsY9lC/Kl5+ZxJyGZAa2rNm3yxXst/LCHwvHZe47h1rwJPZo3USiLIAhvzp9//snIkSMZOXIks2fPZvHixdy8eZNevXoxZ84chS3HK0sE/0KZ/Pz8cHZ2Vgj8ARo0aECPHj0ICAjg5MmTqKio8N1335GZmYmtrS3r1q2TpduwYQMLFy5k8ODBaGpq0q1bN9lclb59+3Lnzh0WLFhAYWEhvXr14pNPPuHatWvllsvd3Z2wsDC8vb3JysrCwsKCgQMHcvz4cW7dusXHH39Ms2bN+O2331ixYgXr16+nTp06jBgxgokTJ8ryadSoEY6OjhQXF2NpaVnDr17JiIjly5fj7e1Nnz59MDAwoH379kyfPp21a9eSm1vx4lSlOnTogKOjI4MHD2bRokWyQM7T05OQkBAGDBjwymWdMmUKycnJTJgwAQArKyt++uknvvrqK27duoWlpSXDhg0jLy+P5cuXk5SURKNGjVi2bJks0Nu6dSu//PILn376KUVFRdjZ2bFx48bX8toKimysrbGxseGPP/7gQy8vdHR0CAgMREtbm27dusmue/ToEYWFhTT4p0f+ZVRVVWlczlYyhoaGsnPWTZvSrHlz/ty+ndzcXExMTLh58yYnT5ygb79+ssU5BeFVmDe0x7yhPaf2L6Gd60g0tPQIOfMn6ho6NHN6NsIoLekhxUUSjM0UP79eJu3Jg3JHEZg1sKNuY0fOH1qLJD8HPUMzHkZe5vaVA7TuOgQNLd0y0wlCVdhZ1MHOog7Lj15kuHMLdDXV2XUpDB11Nd63f7Y4cFxqBpKiIhqb1KpUvqoqyliZGpV5zkhHq9xzgiC8GXXr1mXbtm2MGjWKTz75hHr16rF+/XrZNtzVpSSVVrCyiCD8R0mlUnr06MGECRNkK9j/m6xcuZLg4GC2b9/+povy2kSJxQKrJDs7m127dnHt+nWkUilWlpYMGjQIM7Nne0UvWrSIlJQUvP8Zqv+iWbNmYWNjozDV50Vjx46lT58+cnPN8vLyCAwM5OrVqzxNT8e0Th3ee++9V56W8l+3L7TqAeq7LD83i4tH/yAm4iJSqRTTerZ06DkWA+Nnve4HfGeT9fQJgyZvKDOPnSs/xbyhAy59pyqc2/v7Z9Sq0whXz7JHhEnycwk5u50Hd/4mJysNfSNz7Nv3x6blezVSv/+q8Y+rNzz1XZWVV4DP39e5FB2PVAo2ZsaM6tyKurX0ZNd853+SpMwcVo/oXWYen205gJ2FCZPc2pV5vtTA33YxsK0dH7WzK/P8ybvRrD5+WUwJqASd8W/PHvbZaxVH7b0N3qbX6N/i6dOnjBkzhry8PLZs2SKbYltdIvgX3ikSiYQTJ05w4cIFDh06xPHjx/9Vi9JdvXqV6Ohofv75Z77//vtXGtL9thPBv/AuEMG/8C4Qwb/wLnibAlsR/P872dralrneV2m4/vw5JSUlbt++XeV7iGH/wjtFTU2NBQsWACW9oM8H/uvXr2f16tUVpp8zZ84bHSlw8uRJtm7dipeXl1zgn5iYiLu7e4VpHRwc2LJly+suoiAIgiAIgiAIVTRp0qQa3bq2LKLnXxD+kZ6eztOnTyu8xtjYWGHLjbdBUVERcXFxFV6joaEhNwT8bSd6/oV3gej5F94FoudfeBe8Tb3aoudfKI/o+ReEfxgYGMi20vi3UVFRoWHDhm+6GIIgCIIgCIIg1ACpVMqdO3fIycmhrP76tm3blpGqYiL4FwRBEARBEARBEIS3xM2bN/niiy94/PgxID/vXyqVoqSkxJ07d6qcrwj+BUEQBEEQBEEQBOEtsXDhQlRVVVm4cCFmZmYoKyvXSL4i+BcEQRAEQRAEQRCEt0RYWBhLly6lR48eNZpvzTQhCIIgCIIgCIIgCILwyoyNjVFRUanxfEXPvyAIbyXTYxvfdBEE4bXr32P0my6CILx+j990AQRBEP5dhgwZwtq1a2nfvr3c1uSvSgT/giAIgiAIgiAIgvCWiImJ4f79+3Tq1ImmTZuiqakpd15JSQkfH58q5yuCf0EQBEEQBEEQBEF4S8TExGBrayv7/cWt/sra+q8yRPAvCIIgCIIgCIIgCG8JX1/f15KvWPBPEARBEARBEARBEP4FcnJyOHPmTLXSip5/QRCE/4g8SSG+wTe4GBVPnqSQZua1GdW5FXVr6VU6j8WHgtFSU2WSW7tqnQdIzsph+vbDfOBozUft7KpcD0Goivz8fPbs2UNISAj5+fk0bdqUQYMGYWZmVuk81qxZg6amJqNGjZI7fvbsWbZs2aJwfffu3RkyZMgrl10QyvMm38+Ts3LYGnyTmw8TkRQV4VDPlOHOLTA3rPy9BUF4NfHx8cyfP59Lly5RUFBQ5jV37typcr6i5/8NycrKwtHREWdnZyQSyZsuzhs3a9YsbGxs5H4cHR3p06cPu3bteuX8bWxs8PPzA0AikbB58+ZXzvN1unjxIjY2NsTFxVUp3ZUrV/D09KRVq1YMGTKEqKio11RC4W3065ELXLgfx9CODnzu1o7U7Fzm7z9FVl7ZHxrPk0qlbDp7jYv3y37mXnb++etWn7hMboF4XxP+N9atX8/Vq1fx9PJi1OjRpD19yuIlS8jOzn5pWqlUyo4dOwgJCSnz/MOHDzE1M2P27NlyPz3ff7+mqyEIct7U+3mepJDv958mOjmNsV1b88V7HUjNzuVb/5Nk5uW/cr0EQaichQsXEhISwsCBA2nWrBmtW7dm9OjR2NjYoKSkxKpVq6qVrwj+35ADBw5gbGxMZmYmR48efdPFeSu0atWKc+fOyX6CgoJwc3Nj3rx5HDp06JXyPnfuHB4eHgAEBQWxcOHCmijyWyUzM5MJEybQuXNn/P39UVdX5+uvv37TxRL+R8IfJ3P1wSM+d2tHd9vGdLCsx7f9upJTIOFwaGSFaWOSnzJ/3ymO3o5CTVVxT9mXnX/ekdD7PErLfKW6CEJl3b9/n5s3bjBq9Gg6OTvj1Lo1X06fTl5uLqdOnaowbVxcHIsXL+bM2bOoqauXeU3sw4c0btSIJk2ayP3UNjZ+DbURhBJv8v38/P2HJDzNZIa7Mx2t6tOmcV2mv9+Rpzl5XHhJ468gCDXn8uXLTJs2jblz5+Lp6YmGhgZfffUVe/fupW3bthw/frxa+Yrg/w3Zu3cvXbp0oUOHDuzYseNNF+etoKamhomJieynfv36TJ06lUaNGhEYGPhKeZuYmMi2yKju6phvu7i4ODIzM+nZsyeNGjWiR48eouf/HXIjNhENNVUc6z8b6mygpUnzuiZci6l4k+2Vxy9RVCxloZcbhlqaVT5fKjE9C9/zNxnfzan6FRGEKggLC0NDQwO75s1lx/T09LC2tuZWaGiFaTds3EhRcTFzZs9GX09xOLNUKiU+Lo569evXeLkFoSJv8v28XWMLfvB0pb6RgeyYqkpJuFBQWFTdKgmCUEXZ2dnY2NgA0KRJE27fvg2AiooKQ4YM4cKFC9XKVwT/b8D9+/e5ceMGnTp1omfPnly8eJHo6GjZeYlEwvLly+nevTuOjo54enry999/y87HxMQwceJEnJycaN++PdOnTyclJQUoGT4/fPhwufs9fywuLg4bGxvWrl1Lp06dcHNzIysri4iICMaPH0/btm2xt7fHzc2NjRs3yuVz9uxZBg0ahKOjIy4uLixbtoyioiJ8fHxo1aoVubm5smuLi4txcXFh27Ztr/x6qaiooP5Pr0xBQQE///wzrq6u2Nvb065dO7744gtSU1MrrF/psH8/Pz9mz54NlEwFOHjwIPb29uzbt0/unkuWLMHLy6tS5fPz8+O9995jx44ddOvWDUdHR6ZMmUJiYiIzZsygVatWuLi4sGfPHlma9PR05s6dS5cuXbCzs6Njx47MnTtX7jV8nlQqZf369bi5ueHo6Ei/fv0ICAiQu8bS0hJTU1OWLFlCdHQ0vr6+9OvXT+6a1/H3L/27LFq0iC5dutCqVSs++ugjzp07V6nXT6gZcWkZmOrroKysJHfczECX+KcZFaad7NaOBV6uNKxtWK3zUPKM/nbiMs5W9WnV0LyqxReEaklISKC2iQnKyvJfZ0zq1OHx44qDpE9Hj2bWzJnUq1evzPNJSUnk5eXx4MED5s6bx/gJE5g7bx7B58/XWPkFoSxv8v1cR0MdW/PaABQWFROT/JRVxy+hp6lBRyvRECYI/yt16tQhOTkZgIYNG5Kenk5SUhIAhoaGstivqkTw/wbs2bMHbW1tXFxceO+991BTU5Pr/f/xxx/ZsWMHM2fOJDAwkC5dujBhwgSioqLIyMhg6NChFBQU4OPjw6ZNm4iNjWXq1KlVKoO/vz8+Pj78+uuvqKioMHr0aAwNDdmxYwdBQUG4u7vz888/yxaSuHbtGuPGjcPJyQk/Pz8WLFjAjh07WL16NX369EEikXDkyBFZ/sHBwaSlpfHBBx9U+3XKyspi3bp13L9/n169egHwyy+/cOTIEby9vTl8+DDe3t5cuHCBNWvWlFs/XV1d2XEPDw/mzJkDlEwFcHNzo1u3bnLBf3FxMQEBAXh6ela6rI8ePeLQoUOsW7eOFStWcPz4cfr06YOdnR179+7FxcWF+fPnk5aWBpQ0yNy+fZtVq1Zx+PBhZs+ezb59+9i5c2eZ+S9btozt27czb948AgMDGTFiBPPnz5drXFFXV+fbb78lODgYDw8PXF1d+eabb8rMryb//gCzZ8/m77//ZvHixfj7+9OrVy8mTJjw0mG3Qs3JKZCgpaamcFxLTZXcgsIK01YU1FfmPEDQjQieZGTzSSfHl14rCDUlNzdXNqrreZoaGuTl5VWYtrygv1Tsw4cApCQn89HAgUz+/HMaNmzIpo0bq73KsiBUxpt+Py+18MBZZuw8QmjcE4Z3aoGRjlal0wqC8Gq6du3Kr7/+yrVr17CwsMDMzIyNGzeSlZXF3r17MTU1rVa+YrX//7HCwkICAgJwdXVFU1MTTU1NOnfuzL59+5g+fToSiYQ9e/Ywb9483N3dAZg2bRpSqZSsrCwuXbpEdnY2S5cuxcCgZEjWggULOHDgQLkrQZZlyJAhWFlZAZCamsqIESMYOnQoOjo6AEyZMoU//viD8PBwmjVrhq+vL46OjrI55JaWlnz//fekpKRgZGSEq6srAQEBsp5mf39/XF1dZWWsjCtXrtCqVSugpBcxNzcXY2NjZsyYQc+ePQFwcHDA3d2dNm3aAGBhYYGzszMRERHl1u95mpqa6P0zvNPExAQALy8vPvvsMxITEzE1NeX8+fOkpqZWqeGisLCQefPmYWlpibW1Nba2tqipqclWjh41ahS7d+/mwYMH1KpVi06dOtG2bVvZcJ569eqxdetWhXpAyXYemzdvZunSpXTr1g2ABg0aEB8fz4YNGxg6dCgAx44dY+7cudjY2BAeHk79+vVRVVUlKytLrgHkxdfnVf/+MTExBAUFsW/fPpo1ayar7927d9mwYYOszELNkUqlFL8wfeXF35+npFTuqRoRn5bB9ouhfOXujI5G2XOnBeFVSaVSiouL5Y5V/Ny/2oNvY23N5MmTsbGxQUNDAwB7e3syMzPZHxBAly5dXvkegvC2vZ8/78M2zenf2pYz4TGsPn4ZaTG4Nm/8vyuAILzDpkyZQmhoKMuXL2fz5s1MmzaNWbNmyRYt//bbb6uVrwj+/8dOnz5NcnIyvXv3lh3r3bs3J0+e5ODBg1haWiKRSHB0lO89mz59OgD79u2jUaNGckG1ra0ttra2VSpHw4YNZf82MjJiyJAhBAUFcfv2bWJjY7l79y6A7ItWREQEnTp1ksvj/edWO/by8mLixIk8efIEbW1tjh07xooVK6pUJnt7exYvXgyAsrIy2traGL+wqFK/fv0IDg5m8eLFPHjwgKioKKKjo2WNAWXV72VcXFwwNjZm//79jBs3Dn9/f9zc3KrUcAElAXkpbW1tzM2fDX0u/eJY2kAzZMgQTpw4gb+/Pw8ePCAyMpK4uDiaNGmikG9kZCT5+fl8+eWXckNbCwsLKSgoIC8vj8jISKZMmcLUqVMZN24c3377LT/99BPW1tYsWLAAKysr2WsLNfv3P3jwoKxOz5NIJOjr61f25ROqYPfl2+y+HCZ3rINlPdJzFHs6cyWFaKsr9iDVlOJiKb8dv4yzZX1a1Del6LngTCqVUlRcjIqyGGQmvLrAwECF9V+cnJzIyFAcBp2Xn49WGSMCqkJPT48WLVooHHewt+fO7dtkZGRU+XNCEF70Nr2fv6hZ3ZJOEod6piRl5rD36m0R/AvC/0itWrXYvXs3T548AaBv377UrVuX69ev06JFC9q1K3/L5YqI4P9/rHS7uc8//1zh3I4dO5g/f36F6VVVq/4nKyxUHCL2/DDJpKQkBg0aJOvB79y5Mw4ODnTt2rXS9+3cuTO1a9cmKCgIQ0ND9PX16dy5c5XKqamp+dKg/dtvv+Xw4cP0798fV1dXJk2axIYNG0hMTCy3fi+joqJC//79CQwMZNiwYRw7dozly5dXqexQsmDh816cg1qquLiY8ePHc+/ePT744AM8PDyws7Nj3rx5ZV5fukDhr7/+WmbjgLq6OoGBgTRq1Ihx48YBMG/ePKKjo/nss89IT09nwoQJcmlq8u9fWr5t27bJRg687DUQXk0Puya0fmFe/eXoeK7HPkYqlcr1RiY8zaRerdfXCJOclcO9xBTuJaZwOvyB3Lk9V26z58ptfhvemzr6OmVnIAiV5OLigsMLwfj1a9cICwtTeO6fJCbKNcBWR0REBEnJyXRydpY7LpFIUFZWVni/E4TqeJvezwEiE1N5kpmN8wvz+5uY1CL8cfJrvbcgCIrq1Kkj+3ebNm1o06YNUqmUbdu2yUb/VoUI/v+HUlJSOH36NJ6enrLh4KU2b97M3r17gZIg8tatW3K9+R999BEeHh5YWVmxe/duMjMzZcPXw8LCGDNmDP7+/qipqZGVlSWXd0xMTIXBcFBQEE+fPuXw4cOyADY8PBx4FthZWlpy69YtuXQ+Pj4EBQWxe/duWQB99OhR9PX16devHyoqFW8JVlVpaWns3LmTZcuWybbtA4iKikJbW7vS+ZQ1TNPLy4v169fj6+uLnp5elRsuquLOnTucOXOGXbt2yUZ4SCQSYmNjqV/GqtJNmjRBVVWVR48e0b17d9nxLVu2EBkZyffff4+WlhYZGRkUFBSgrq6Ompoaixcvlk0v6dixY7nledW//08//QSUNCI0f27F7WXLlqGsrMwXX3xRnZdJqICRjpbC3Mv8wkL8rt7heuxj2YJ76bl53ElIZkDrqo0MqmpZFn7YQ+H47D3HcGvehB7Nm4h5okKNMDQ0xNDQUO5YQUEBf/31F6FhYTjY2wMl257eu3dPtlZMdYVHRBCwfz+WTZpgZlay6rpUKuVqSAiWlpbVaowXhBe9Te/nANdiE9h75Q7WZsbU1i35blVcLOVWXCINjQ1f670FQYAzZ87g7++PkpIS/fr1k+uMg5Jp0gsWLCA8PLxawb/olvsfCggIoLCwkLFjx2JtbS33M2HCBJSVldm1axfDhg1j+fLlHD9+nNjYWJYuXUpERAQuLi706dMHAwMDvvrqK+7evUtoaCjfffcd1tbWmJmZ0bJlS+7evUtAQAAPHz7kt99+K3Me+fPMzMzIzc3l0KFDPHr0iHPnzsmmGZQOUx8zZgzXr19n+fLlPHjwgNOnT7N69Wq5+dyenp7cuHGD4OBgBgwYUOOvn66uLnp6ehw/fpyYmBjCw8OZN28eYWFhVVrvoLShIDQ0VLYgVOPGjWndujWrV69+LQ0Xz6tduzaqqqocPHiQhw8fcuvWLaZOnUpSUlKZ9dDT02Pw4MEsX76c/fv38/DhQ/bs2cOiRYtkrYEffvgh2dnZzJ49m8jISEJCQpgxYwZmZmbo6Ogwfvx42Y4IL3rVv3/Tpk3p3r073333HSdOnODhw4esX7+etWvXyk2FEF4vO4s62FnUYfnRixy/HcXFqDi+338aHXU13rd/tv5FXGoG0UlpNXZfVRVlrEyNFH6g5EutlamRbJsoQahpNtbW2NjY8Mcff3D27FlCQkJYsnQpWtracp9Pjx49IjY2tkp5d3VxQd/AgFW//cbly5e5efMmK1asID4+ng8//LCGayIIz7yp93OA9+ws0dfSYGHQWS7cj+Pqg0csPHCWh6kZDOuoOA1GEISaExAQwLhx4zh27BinT59mwoQJHD16FICnT58yY8YMhg8fTmRkpEJHcmWJZuv/IT8/P5ydncscut2gQQN69OhBQEAAJ0+eREVFhe+++47MzExsbW1Zt26dLN2GDRtYuHAhgwcPRlNTk27dujFz5kygZD7InTt3WLBgAYWFhfTq1YtPPvmEa9eulVsud3d3wsLC8Pb2JisrCwsLCwYOHMjx48e5desWH3/8Mc2aNeO3335jxYoVrF+/njp16jBixAgmTpwoy6dRo0Y4OjpSXFyMpaVlDb96JSMili9fjre3t6wRpHSrw7Vr15a7Td6LOnTogKOjI4MHD2bRokWy3iFPT09CQkJeS8PF80xNTfH29mblypVs27YNExMTunXrxsiRIzlx4kSZaWbPnk2tWrVYvnw5T548wdzcnClTpjBmzBigZMHATZs28csvvzBgwAC0tbVxdXVl2bJlpKamMn36dB49eqTQawY18/dftmwZy5Yt49tvvyU9PZ0GDRrw448/vvbXUpA3w90Zn7+vsyX4BlIp2JgZ8+X7zuhqPluEb/3pqyRl5rB6RO8KchKEf4+JEyeya9cudu/Zg1QqxcrSkgnjx8sNy9+2bRspKSl4e3tXOl99fX1mfv01fn5+bN++nbz8fBo3asSX06eX+TkuCDXpTb2fG2pr8sOA7mw9f5P1p6+SKynE2tSY7/p1la0BIAjC6+Hj44OjoyMbNmxAXV2d2bNn89tvv9G0aVNGjRpFQkICXbp0Yc6cOTRuXL31N5Sk0gqWFBWEKpBKpfTo0YMJEyYwcODAN12cKlu5ciXBwcFs3779TRelxr04b/DfIHtt2dsUCsJ/SWKP0W+6CILw2pke2/imiyAIr53O+B/fdBFk3tbvUG/Ta/Q2cnJy4ocffpBNb46OjsbDw4PGjRuTnp7Ot99+K7fgenWInn/hlUkkEk6cOMGFCxfIycmR28ng3+Dq1atER0ezZcsWvv/++zddnNfi3xb4C4IgCIIgCMK7JCcnR26xWgsLC6RSKaqqqgQEBCjsglYdIvgXXpmamhoLFiwAYNGiRXKL761fv57Vq1dXmH7OnDlvdKTAyZMn2bp1K15eXnILRCUmJuLu7l5hWgcHB7Zs2fK6iygIgiAIgiAIwn+YVCqVW3es9N/Tpk2rkcAfRPAv1JCzZ8+Wefyjjz6iZ8+eFaatqYe5umbMmMGMGTMUjteuXZt9+/ZVmFZDQ+M1lUoQBEEQBEEQhHfd89v9vSoR/AuvlYGBAQYGBm+6GNWioqJCw4YN33QxBEEQBEEQBEF4R9Xk9F0R/AuCIAiCIAiCIAjCGzZ//nx0dXWBkmkAAPPmzZPbwQZKGgR8fHyqnL8I/gVBEARBEARBEAThDWrbti3wLOgv71hZv1eWCP4FQRAEQRAEQRAE4Q3y9fV97fdQfu13EARBEARBEARBEAThjRI9/4IgCILwhuwLbfKmiyAIr13/HqPfdBEE4bUT7+bCv4Ho+RcEQRAEQRAEQRCE/zgR/AuCIAiCIAiCIAjCf5wI/gVBEARBEARBEAThP07M+RcEQRAEQRAEQRCEN+jy5ctVur50G8CqEMG/IAjCv9yN2MdsvxhKbGo6hlqauDtY0aelNUpKSmVeLykqYtelMM5GxJKRl4+FoR79WtvSuWkDueuuPnjE7su3iUl5ir6mBh2t6jO4vT2aaiUfHb8dv8Spuw/KLddvw3tTR1+nxuopvNvioq5x9ZQvaU9i0dIxoHnbD7Bv37/c57yoUELImT+5H3qavJwMDGtb4NDRC0s7F9k1Gxb0Lfd+5g3t8Rj+kyyva2e3E3nrFHk5GegbmdOy8yCaNO9co3UUhLCwMPbt20f8o0fo6+nR3dWVnu+9V/77uURCQGAgFy9eJCsrCzMzM9zff5927drJXXfz5k0CAwOJi49HV1eXNk5O9O/fHw0NjTLzPX78OEePHsXb27vG6ygIQtmGDx9e7v/rz5NKpSgpKXHnzp0q30ME/28JV1dXBgwYwOTJk6ucNi4uDjc3N7Zs2UL79u2rdf/q5CGVSvH19WXv3r1ER0ejpqaGra0tw4cPx93dXXbdo0ePuHbtGr17965Uvn5+fsyePZvw8PByr3n+9Vq5ciX+/v6cOHECABsbGxYuXIinpycSiYRt27YxcuTISt37ZUpfpxdpa2vTsGFDxo4dW+l6AuTk5ODv78/QoUMBmDVrFvHx8f+TfT6rqyaeN6HmRDxOYeGBc3RqWp9B7ey4m5DM1vM3KSouZoBTszLTLDt8gZCYBPq0tMahnin3n6Sy5sRlMnLz8WjRFICLUXEsOXSe5nVNmP5+RwqLitlz5Tbhj5NZ4OmKirIyXm2a856dpVzeWfkFLDkUjJ1FHUz0tF97/YV3w5P4cI7u/IEmzTvT2mUoiQ9vc+n4ZoqLi3B0/rDMNCf9fyH23hUcOvSnbiNHkh9Hci5oJXnZ6di16wNAn1GLFNI9uBvMrfP+2LbuJTt2av8S4qOu07b7CPSN6hIZepKT/otQU9eivpXT66m08M6Jiopi5apVtG3Thr79+hF57x579+yhuKiIXr16lZlm3fr13Lx5k549e9LM1paYmBh8fHzIzMyUfV8JCQnh999/x9rGhvHjxlFYVERQUBD3lyxh5syZqKioyOV56dIldu3eTS1Dw9ddZUEQnrNly5bXfg8R/AvVtmLFCnbv3s2cOXNwcHAgLy+PgwcPMnXqVLy9venfvz8AM2fOxMLCokpB8cvs2bOn3Nbqc+fOoaenB0BQUBALFy6sseC/1MqVK2nVqhVQ0giSlJTE2rVrmTFjBhYWFrRs2bJS+WzcuBE/Pz9Z8P/NN99QVFRUo2UV/tt2XgqlsYkhk3uUNMS0amhOUbEUv6t36O1ojbqq/Je66KQ0LkfHM7i9PV5tmgPQor4pmmqqbLtwi642DdHRUGfXpTDqGuoxt48Lqioly8M0q2vC51v/4uSdB/Swa4KZgS5mBrpy+S8+FIyepgZfvNe+Uq3XglAZIae3YWzamK79pgNQ38qJ4uIibpzbjV3bvqiqqctdn/I4ipjwizh1G0bLzh8BYNGkJWpqmlw+uQWrFq5oaOpQx8JGLl1WRjLh147QrI0HTey6APA4NowHd4LpOfg7WaBft7EjGakJxN2/KoJ/ocbsDwigQf36fPrppwA42NtTVFzMX3/9hZubG+rq8s95bGws169do3///rLvWM2bN0dDQwM/Pz86duyItrY2AYGBmJmZMfWLL1BVLfnqb920KXPmzOHvv//GxaVkNExmZib79u3jzJkz6OjKv7cLgvD6vThi53n5+fmoq6u/8ncrseCfUG1//vknY8aMwcPDg/r169O0aVOmTJlCr1698PHxea33NjIyQken7OHEJiYmaGpqAiWB+etgYGCAiYkJJiYm1KlTBzs7OxYvXoy6ujoHDx6sdD4vlk9PTw9D0dIuVJKkqIiw+CTaNbaQO97Bsh55kkLuJiQrpIlLywCgTaO6csft69UhX1JIWHwSAPFpmbRsYCYL/AEMtTWpV0ufqzGPyixPSEwCF+/H8UknR3Q01Mu8RhCqqqhQQkJMKA1tO8odb9ysE5KCXBIf3lZI8zT5IQANmsp/kTJv1ILCgjwex9wq816Xjm1AVU2DNt1HyI5F3/kb/VpmckG+kpISfUb+Qsf3x1W7XoLwPIlEQnh4uKxjoZSTkxN5eXlERkYqpElISADA0dFR7ritrS35+fmyEZSPHz/Gzs5OFvgD6OvrY25uzs1bz/5fOHDgAGFhYUyYOBHHFi1qrG6CIFRPVFQUU6dOpV27drRq1Yrbt2/zf//3f680QlgE//8CBQUF/Pzzz7i6umJvb0+7du344osvSE1Nlbvu2rVr9OnTB3t7ezw9Pblw4YLc+b1799KrVy9atGghC9CLi4urXS5lZWUuXLhAXl6e3PG5c+eycuVKoGTuyqVLl/D398fV1RUomQYwbdo0OnbsiJ2dHS4uLixatEihLLt27aJLly44OjoyYcIE4uPjZedcXV1l93iRjY0Nfn5+sukDpccOHjyIvb09+/btk7t+yZIleHl5Vft1KKWsrIyqqqrch+uxY8cYOHAgLVu2xMHBAU9PT86ePQuUjB5YtWoV8fHx2NjYEBcXx6xZsxg+fLgs/f3795kwYQLt27fHycmJKVOmyL0OL3Px4kWaN2/O0aNHef/992nRogUjRowgISGBBQsW0KZNGzp27MiaNWtkaSr7vD2vpp8toXIS07MpKi7G3FBP7nhpb3z8P4H+8/Q1S0bMJGXmyB1/nJ5VkmdGyX/1tTRIzpK/prComKTMbJ5kZCvkK5VK2fL3DZrXNaGjVf1q1kgQFGWkPaa4qBB9I/kGK/1a5gCkp8QppNHQ1gcgK/3JC3klyPJ80ZP4cKJv/41Tt+GoazybspL6JBpDkwbcDz3Nnt8nsvGn/uz5fSIx4RcU8hCE6kpKSqKosJA6pqZyx+uYmAAlAfyLdP/pnU9JSZE7/uRJyXOflJwsu+7Fz/DCwkJSUlJITn7WSNy1a1cWLFiAU+vWr1gbQRBe1Z07d/jwww8JCwujT58+sg5DFRUVfvrpJ/z9/auVrwj+/wV++eUXjhw5gre3N4cPH8bb25sLFy7IBWwAGzZsYOLEiezfv5/mzZszfvx4EhMTAdi5cye//PILn3/+OQcOHGDq1KmsX7+exYsXV7tc48eP5+TJk3Tq1InJkyfj4+NDeHg4xsbG1KtXD3g2PL5Xr17s2bMHgIkTJ5KZmcmmTZs4dOgQo0eP5o8//pDN2S/l6+vL8uXL2bZtG2lpaUyaNKlKPfkeHh7MmTMHKJkK4ObmRrdu3eSC/+LiYgICAvD09Kz26wCQnp6Ot7c3ubm5fPDBBwCEhoYyefJkevfuTWBgILt27cLIyIivv/6agoICRo8ezejRozEzM+PcuXOYm5vL5RkfH8+gQYNQV1fHx8eHjRs3kpSUxLBhw8jKyqp02YqKilizZg2LFy/Gx8eHu3fv0q9fP9TU1Ni9ezeDBw/m119/lfUQVPZ5K/U6ni2hcnIKJABoq6vJHddSV5U7/zw7izqY6uuy8ew1bsUlklMg4fajJLaev4mSkhJ5kkIAuts24uL9OPyv3iE9N4+kzGzWnLxMToGEfIni1JQrDx4Rn5Yhm0ogCDVFUlDSCKWuLr+GhJq6FgAF+TkKacwbOqBXy5Tzh9fyKPoGBfk5PI4N4/KJzaCkRKEkTyHNzeC96BqYYGXfTe54XnY6yQmRXD7hg6PzQN4fPB9D43oc27OQh5FXa6aSwjuvtCNF65+Ri6VKRzLm5uYqpLGxsaG2iQnbt2/nzp075ObmEhERwV4/P5SUlMjPzwegk7MzISEhHDx4kMzMTFJSUvDx8SE3L092DYC5ublcB4YgCG/Ozz//jL29PQcPHmT27NmyGGju3Ll8+OGH1V4fQPwf/i/g4OCAu7s7bdq0AcDCwgJnZ2ciIiLkrps8eTIeHh4AzJ8/n+DgYP7880+mTZvG6tWrmThxomxOWP369cnKyuL//u//+OKLL6pVrpEjR9KkSRO2b9/OuXPnOHLkiKy83t7eWFlZYWhoiJqaGpqamhgZGZGXl0e/fv3o1auXLNgdOXIk69evJzw8nB49esjyX7RoEba2tkDJ/wDvv/8+58+fx9nZuVLl09TUlM39N/mn5dzLy4vPPvuMxMRETE1NOX/+PKmpqbKAvbLGjh0rWyCnuLiYwsJCWrRowcaNG2nWrGSRNRUVFebNm8eQIUNk6UaMGMHYsWNJSUnB3NwcbW1tVFRUZOV73p9//om2trZsOgGUrLPg5ubG/v37ZesEVMYXX3yBg4MDAB06dODGjRt8/fXXKCkpMX78eFavXs29e/ewsbGp9PNW6mXPVnlrMwivrvgljWHKZcwLU1VRZm5fF1afuMz3+08DUEtHi1GdW7LsyAU0/vni91E7O4qlUnZeCuPPC7dQUVbGrXlj2ja2IC5VcUTBwVuRNKptSIv6pgrnBOFVSF8yikhJWbEfQ0VFFfePv+dM0HIObpsHgLaeER16juWE3y+oqsm/L2VlJBMTcZH2732K8guLnxUXFZKTmUq/T5dR27xkgUvzRi3wXz+Z6+d2iDn/Qo142Wg55TKec1VVVaZNncrmzZtZunQpAAaGhgwePJh1a9fKvjv07duXYqmU/QEB+Pn5oaKqSpfOnWnp6CibOiAIwtvl+vXrLF26FFVVVYX1wDw8PAgKCqpWviL4/xfo168fwcHBLF68mAcPHhAVFUV0dLQsOCvl5PTsC4iqqirNmzfn3r17pKam8vjxY5YuXcry5ctl1xQXF5Ofn09cXFy1AzQXFxdcXFyQSCTcunWLkydPsm3bNsaMGcORI0cUFqfR1NRk2LBhHDp0iJs3bxITE0N4eDjJyclyH3w6OjqywB+gUaNGGBgYEBERUengv7zyGhsbs3//fsaNG4e/vz9ubm4YGBhUKZ8FCxbg6OhIXl4eO3fu5MCBA3z66ad06NBBdk2zZs0wMDBg3bp1REVFERMTw927dwEqtahfREQE9vb2cq+hiYkJjRs3LjcQL0/Dhg1l/9bW1qZevXqyBUNKexUKCgqAyj9vQKWeLUtLS4V0Qs3Q0Sjp8c+VyPfwlzcioJSZgS7fD+hOem4embkFmBvqkpyZg1QqRU+z5HlTUVZmaMcWDGxrx5OMbGrpaKKjoc63/idl15TKzMvndnwSQzo41HQVBQF1zZL1XSQF8j2fBeWMCCilb2TOByO8yc1OJy8nAwOjumRlJIFUioaW/FSZmLvnUVJSwrK5i0I+ahpaaOsZyQJ/KAnELBq35G7I4VeqmyCU0tYueY5fnEpZ2uOvqaVVZro6derw9ddfk5mZSWZmJqampqSmpiKVStH9Z20kFRUVvDw96fPBByQnJ2NoaIi2tja//PJLuesnCYLwZmloaCi8H5R6+vSpQoxVWSL4/xf49ttvOXz4MP3798fV1ZVJkyaxYcMG2ZD+Ui9u1VJUVISGhoYsqJ49e3aZgbO5ublsflhl3b17lz///JNvvvkGDQ0N1NTUaN26Na1bt8bJyYnx48cTHh4u620ulZOTw7Bhw8jLy8Pd3Z0BAwbQokULhV7sF+sCJQFldR/05/Pt378/gYGBDBs2jGPHjskFrZVlamoqC6i//fZbcnNzmTp1Kj4+PrJGmEuXLvHpp5/SrVs3nJyc6NOnD7m5uUyaNKlS9yhvikNxcTFqamUHdeV5cRhfWT0IpSr7vJWWBSp+toTXx1RfF2UlJdl8/VKlv1sY6SukKSgs4vz9h9ia18ZUXxcDrZLGn6ikNAAam9QCIDT+CYVFxbRsYEa9f/IpKi4mJuUp3W0by+V5PfYxRcXFdLCsV7MVFARAz9AMJWVl2Xz9UpmpJb8b1lZcY6JQUsCDu39jWr85eoamaOmUNPCmPL4PgLGZfKNk7L1LmDWwQ0vXUCEvfaO6ZKUny/ZVLlVcVIiKmljYUqgZtWvXRllZmaSkJLnjpb/XLePztKCggKtXr9K0aVNq164tG+0YExsLQIMGDQAIDw9HIpFgb29P3bola2cUFRURFx9Pp1foUBEE4fXp1KkTK1asoHXr1rIRwkpKSmRnZ7Nx48Zqd4aKOf9vubS0NHbu3Ml3333H7Nmz8fT0pFmzZkRFRSkEh6GhobJ/FxQUEBoaStOmTTE2NsbIyIiHDx/SsGFD2U9YWBi//vprtcu2c+dOjh8/rnBcT08PJSUljI2NFc6dO3eOsLAwtmzZwpQpU/Dw8EBXV5eUlBS5+mRkZBD7z4cXlHxwZWZmYm1tXaUylrUdhpeXFxEREfj6+qKnp0fnzp2rlGdZ5s6di6mpKV9//bWslX7jxo20b9+elStXMnLkSDp16iQbXlda14q267CxseHWrVuyHnmA5ORkYmJiXltvelWeN+C1PVtC5airqtCsrgkX78fL/X0u3o9HW0ONpnWMFNKoKiuz8ew1joVFyY4VFRdz6FYkpga6NDQuCZIu3I/j95NXKCx6NiLnxJ1ocvIltG0sv/DavcRUjHW1qaMvepCEmqeqpo5ZAzse3A2We86j7wajrqmDiYXi54KyiirnD6/jbsgh2bHioiJuXwlCv5YZRnUayY5LpVKSEyIxrdeszPvXs2xDfm4m8dHXZceKigqJiwrBrL5Y40KoGerq6jS1tuZqSIjcc341JAQtbW0aNWqkkEZVVZXtO3Zw+swZ2bGioiJOnjiBSZ06svWXrl69yhZfXwoLC2XXnTt3jtycHFq+sLuAIAhvh6+++oqcnBzc3d0ZOnQoSkpKeHt74+7uTkJCAtOnT69WviL4f4vExMRw5swZuZ979+6hp6fH8ePHZUPk582bR1hYmFxQCCWr1h87dozIyEhmzZpFQUGB7GEZO3Ysvr6+bN26ldjYWI4ePcr8+fPR1NSsVm+6ra0tffv25ZtvvmH9+vVERkby4MEDDh06xJw5cxgwYICsdVlHR4f4+HgeP36MmZkZAAEBAcTHx3PlyhU+++wzJBKJXH2UlZWZOnUq169f5/r163z99de0a9euzKHnFSkdRhcaGiobOtO4cWNat27N6tWr6devX5mjDKpKR0eHH374gbi4ONlIAnNzc8LDw7ly5QpxcXHs3btXdq60rtra2qSnpxMdHY3khaHbH3/8MdnZ2Xz11VfcvXuXmzdv8sUXX1CrVi3Z/PqapqurW+nnDXgtz5ZQNR+2aU7kk1SWHj7PtZgEdlwMJeB6OANaN0NDTZWcAgkRj1NIzy15/pWVlXjf3ooDN+9x6FYkNx8msuTQee4mJDOqc0tZg1RPO0ue5uTx2/FL3IpLJPB6OBvOXMPZqj52FnXkyhCbko5FLT2FsglCTWnZeRBJj+5xwu9nHkZe5eqprdy64I+j84eoqmlQkJ/Dk/hwcrPTgZLPkGZOvQi7FMjtKweIj7rOCT9vEh/eoX3PsXINr1npSRTkZWNo0qDMe1vad8XYrAmn9i0h/NoRHkZe5diuBWRnpNCqy8f/k/oL74YPevfmQXQ0a9eu5VZoKPv27ePI4cN49OqFhoYGubm5REVFkZmZCZQ85926deP48eOcPHmS27dv8/vatURGRjJ40CDZc961a1cyMjLYtHkzd+7c4cjRo2zfsYM2bdtiU8VOFUEQ/jfMzc3Zv38/n3zyCVKplAYNGpCTk8MHH3yAn58f9etXb2clMez/LRIYGEhgYKDcMQsLC5YvX463tzd9+vTBwMCA9u3bM336dNauXSu3+uvkyZNZvHgxcXFxtGjRgk2bNsn2jB89ejQaGhr4+vri7e1N7dq1+eijj5gyZUq1y7tw4ULs7e3Zv38/a9asQSKR0LBhQwYOHMgnn3wiu27w4MHMnDmTvn37cv78eWbPns3mzZv59ddfMTU1xcPDA3Nzc249t9eskZER/fr147PPPiM3N5fu3bszd+7cKpexQ4cOODo6MnjwYBYtWkSvXr0A8PT0JCQkhAEDBlS7/i9ydnbG09OTLVu20Lt3b6ZMmUJycjITJkwAwMrKip9++omvvvqKW7duYWlpSc+ePdm1axd9+/Zl69atcvnVq1ePrVu3smjRItmq/506dWLRokXo6ysO564JampqlX7eSr2OZ0uoPPt6dfjSvSO7LoXx819/Y6yrxbCOLejbygYoGc7/f/tO8ZlbW9lw/Y/a2qEE7Au5S1Z+AY2MDZnzQRccG5jJ8m1gbMCcD7qw7fxNFh44Ry1tTTzbNMOztWLv6NOcPNmIAUF4Heo2aoGb1yxCzvzJsd0/oq1nRDu3kTh0KHkPT0m4z19bv6FLny+wdnQDoJXLEECJm8F7yc/LwqhOI3oO/o56TeR7OvOynwKgrlH2yBUVFVV6DV3AlZNbuHLKF0l+LsZmTeg19Ae5dQAE4VXZ2toyYcIEAgID+e2336hlaIjXhx/yfs+eAMTGxrJ48WJGjholG67ft08flICDhw6Rk51Nvfr1mTJlCnZ2drJ8LSwsmDJ5Mn5+fqxctQoDfX08PDzw+Oc7kSAIb6datWoxbdq0Gs1TSVqVvdME4T9i5cqVBAcHs3379jddFKEc2Wu/edNFEITXbq3ZgjddBEF47frbR738IkH4l2vyFi1w/LZ+h9IZ/+ObLsJb7fLly1W6vm3btlW+h+j5F94pV69eJTo6mi1btvD999+/6eIIgiAIgiAIgiAwfPjwMtcDe76v/vnzd+7cqfI9RPAvKOjbty8PHz6s8JqLFy/+K+dznzx5kq1bt+Ll5SWbAgCQmJiIu7t7hWkdHBzYsmXL6y5ipbVp06bCLQONjY05duzY/7BEgiAIgiAIgiBUx/NxxqNHj5g3b54sZjExMeHp06ecOHGCHTt2VLsTUwz7FxQ8evRIYfG5FzVo0KDCler/bYqKioiLi6vwGg0NDdmChW+D2NjYcrcDhJJtDUtX+v03eluHrAlCTRLD/oV3gRj2L7wLxLD/lxPD/itv+PDhtGzZki+//FLh3KpVqzh9+jS7d++ucr6i519QULpK/7tERUWFhg0bvuliVEnp/r2CIAiCIAiCIPx33Lx5k4kTJ5Z5rlWrVqxfv75a+Yqt/gRBEARBEARBEAThLWFmZsbZs2fLPHfo0KFqdwKKnn9BEARBEARBEARBeEuMGjWK+fPn8+TJE7p3706tWrVITk7m0KFDnDp1iqVLl1YrXxH8C4IgCIIgCIIgCMJbYvDgwRQWFrJmzRoOHDggO25ubs7ixYvlFi6vChH8C4IgCIIgCIIgCMJbZNiwYQwbNoyoqCjS09OpVasWjRo1eqU8RfAvCIIgCIIgCIIgCG+Z+/fvc+nSJTIzM6lVqxbFxcU0adKk2vmJ4F8QBEEQBEEQBEEQ3hJSqZTvvvuO3bt3y23traSkxIABA/jpp5+qla8I/gVBEARBEARBEAThLfHHH3+wd+9epkyZQt++fTExMeHJkyfs37+fNWvWYG1tzciRI6ucrwj+BUEQBEEQBEEQBOEtsWfPHsaMGcPEiRNlx+rVq8ekSZOQSCTs2rWrWsG/cg2WURAEQRAEQRAEQRCEV5CQkECHDh3KPNe+fXvi4uKqla/o+X9Lubq6MmDAACZPnlzltHFxcbi5ubFlyxbat29frftXJw+pVIqvry979+4lOjoaNTU1bG1tGT58OO7u7rLrHj16xLVr1+jdu3el8vXz82P27NmEh4eXe83zr9fKlSvx9/fnxIkTANjY2LBw4UI8PT2RSCRs27atWi1lZSl9nV6kra1Nw4YNGTt2bKXrCZCTk4O/vz9Dhw4FYNasWcTHx+Pr61sj5RX+m27EPmb7xVBiU9Mx1NLE3cGKPi2tUVJSKvN6SVERuy6FcTYiloy8fCwM9ejX2pbOTRvIXZeSlcPW4Jtcf/iYwqJirEyNGOHsSGOTWrJrHqdn8eeFW9xNSCZPUkgDIwMGtbfDoZ7pa62z8O6Ji7rG1VO+pD2JRUvHgOZtP8C+ff9yn3OA2HuXuXZ2O2lPYtHQ0qORbUfadB+Bmrqm7Jrty0eRk5mikHbo9K1oauuzYUHfcvM3b2iPx/DqzbsUhLKEhYWxb98+4h89Ql9Pj+6urvR8771yn/OioiKOHDnC2XPnSH/6lDqmpnj06kXbtm0BSE5JYfasWeXez9nZmVGjRgElC4v5+/sTFR2NpoYGDi1a4OXpib6+fs1XVBCECllYWBAeHk7Hjh0Vzt29excjI6Nq5SuCf6HGrFixgt27dzNnzhwcHBzIy8vj4MGDTJ06FW9vb/r37w/AzJkzsbCwqFJQ/DJ79uxBQ0OjzHPnzp1DT08PgKCgIBYuXFhjwX+plStX0qpVK6CkESQpKYm1a9cyY8YMLCwsaNmyZaXy2bhxI35+frLg/5tvvqGoqKhGyyr8t0Q8TmHhgXN0alqfQe3suJuQzNbzNykqLmaAU7My0yw7fIGQmAT6tLTGoZ4p95+ksubEZTJy8/Fo0RSA3AIJ3/mfQlVFmXFdnVBXVWHPldt8H3CaJYPfx0hHi8y8fL71P4mepjojO7dES02V43ei+SHgDN/164qdRZ3/5Ush/Ic9iQ/n6M4faNK8M61dhpL48DaXjm+muLgIR+cPy0wTG3GJo7t/pKlDd9q6juRpUixXTvqSl5NO9wFfAZCXk0FOZgrteozCtH5zufTqGjoA9Bm1SCHvB3eDuXXeH9vW1dtnWRDKEhUVxcpVq2jbpg19+/Uj8t499u7ZQ3FRUbl7egcEBHDw4EE+6NOHplZWXLt2jXXr1qGsooJT69YY6Osze/ZshXQnT57k8pUrdO7cGYDo6GgWLV6Mubk5o0eNQl1dncOHD7Nw4ULmzZuHtrb2a627IAjyPvjgA1auXImpqSnu7u4oKSkhlUo5ePAgq1atYtCgQdXKVwT/Qo35888/mThxIh4eHrJjTZs2JTo6Gh8fH1nw/zpU1PplYmIi+/fzq2XWJAMDA7n71KlTh8WLF9O2bVsOHjxY6eD/xfKVNloIQnl2XgqlsYkhk3uUjNBp1dCcomIpflfv0NvRGnVVFbnro5PSuBwdz+D29ni1KQl2WtQ3RVNNlW0XbtHVpiE6GuoE3YggMz+fZR+7Y6SjBUATk1rM3H2M24+S6Ny0AafuPiAjN5+FH7phrFvyxdCxvhlf7jxMwPVwEfwLNSbk9DaMTRvTtd90AOpbOVFcXMSNc7uxa9sXVTV1hTQXj/5B42bOuPSdCkDdRi0olhZz+3IghZJ8VNU0SHkcBUBD6w7oG5mXee86FjZyv2dlJBN+7QjN2njQxK5LDdZSeNftDwigQf36fPrppwA42NtTVFzMX3/9hZubG+rqis/538HBtG/fnr59+gDQrFkzHsTEcPLECZxat0ZNTU1hW7AHDx5w+coVBgwYQNOmJQ2+B/76C20tLWZ8+SU6OiUNXzY2Nsz79lsOHz7MgAEDXmfVBUF4wdixY7ly5QrTpk3jq6++olatWqSlpVFUVES7du344osvqpWvmPP/L1RQUMDPP/+Mq6sr9vb2sgcgNTVV7rpr167Rp08f7O3t8fT05MKFC3Ln9+7dS69evWjRogW9evXCx8eH4uLiapdLWVmZCxcukJeXJ3d87ty5rFy5EoDhw4dz6dIl/P39cXV1BUqmAUybNo2OHTtiZ2eHi4sLixYtUijLrl276NKlC46OjkyYMIH4+HjZOVdXV9k9XmRjY4Ofn59s+kDpsYMHD2Jvb8++ffvkrl+yZAleXl7Vfh1KKSsro6qqiqrqsza2Y8eOMXDgQFq2bImDgwOenp6cPXsWKBk9sGrVKuLj47GxsSEuLo5Zs2YxfPhwWfr79+8zYcIE2rdvj5OTE1OmTJF7HV7m4sWLNG/enHXr1tG+fXs8PT0pLi7mypUrjBgxgtatW2Nvb0+vXr3Yv3+/XNqAgAD69u1LixYtcHNzw8fHR3YuMzOTefPm0aFDB5ycnBgxYgS3bt2q7ksnVJKkqIiw+CTaNbaQO97Bsh55kkLuJiQrpIlLywCgTaO6csft69UhX1JIWHwSABfux9G+ST1Z4A9QS0eLdSP7yKYHGOtq06eltSzwB1BWVsLcQI/H6Vk1U0nhnVdUKCEhJpSGtvJDHxs364SkIJfEh7cV0qQ8jiIj7THN2/aRO27fri8fTVqPqlrJSLHUJ9GoqWuhV8us0uW5dGwDqmoatOk+ohq1EYSySSQSwsPDZaMISzk5OZGXl0dkZGS56TQ1NeWO6enqkpWdXeb1UqmUP7dvx9zcnPd69JAdT0hIwMrKShb4A2hoaNCkcWNuis9zQfifU1dXZ9OmTaxdu5ZPPvmEbt26MXLkSH7//Xd8fHzKHfH8MiL4/xf65ZdfOHLkCN7e3hw+fBhvb28uXLjAmjVr5K7bsGEDEydOZP/+/TRv3pzx48eTmJgIwM6dO/nll1/4/PPPOXDgAFOnTmX9+vUsXry42uUaP348J0+epFOnTkyePBkfHx/Cw8MxNjamXr16wLPh8b169WLPnj0ATJw4kczMTDZt2sShQ4cYPXo0f/zxh2zOfilfX1+WL1/Otm3bSEtLY9KkSVXqyffw8GDOnDlAyVQANzc3unXrJhf8FxcXExAQgKenZ7VfB4D09HS8vb3Jzc3lgw8+ACA0NJTJkyfTu3dvAgMD2bVrF0ZGRnz99dcUFBQwevRoRo8ejZmZGefOncPcXL4XKj4+nkGDBqGuro6Pjw8bN24kKSmJYcOGkZVV+UCrqKiI06dPs3PnTn788UeSkpL49NNPcXBwwN/fn3379tGiRQu++eYbkpNLgse//vqLmTNn0q9fPwICApg+fTqLFy/Gz88PqVTK2LFjefjwIWvXrmXXrl20bNmSjz/+mNu3Fb+UCzUnMT2bouJizA3lR4iYGegCEP9PoP88fc2SD4ukzBy546XBemJGFoVFxcSnZWJRS48dF0MZuymAwWv2MH/fKR6mpsvSOFvVZ2jHFnL5ZOUVcPtREvWNDF69goIAZKQ9prioEH0j+QYr/Vol75HpKYqLHqUklvToq6iocWTn92z2/hDfJUO4cGQ9RYWSZ9c9jkJDS5cTe73ZsmgwPr98xAm/n8nJTFXIE0qmH0Tf/hunbsNR1xDDoIWak5SURFFhIXVM5ddLqfPPqMLHjx+Xma5Hjx6cP3+eW6Gh5ObmcuHCBUJDQ+lYzkJhly9fJjoqisGDBqGs/CwM0NXVJSVV8bl/kpREclJSdaslCMIr6tq1K1999RU//PADM2bMoGvXrq+Unxj2/y/k4OCAu7s7bdq0AUoWhHB2diYiIkLuusmTJ8uG4M+fP5/g4GD+/PNPpk2bxurVq5k4caJs3n39+vXJysri//7v/6o9jGTkyJE0adKE7du3c+7cOY4cOSIrr7e3N1ZWVhgaGqKmpoampiZGRkbk5eXRr18/evXqJQt2R44cyfr16wkPD6fHc63SixYtwtbWFoCff/6Z999/n/Pnz+Ps7Fyp8mlqasqG0ZcO0ffy8uKzzz4jMTERU1NTzp8/T2pqqixgr6yxY8eiolIyvLq4uJjCwkJatGjBxo0badasZN61iooK8+bNY8iQIbJ0I0aMYOzYsaSkpGBubo62tjYqKipyUwhK/fnnn2hra7N48WLZ0L8VK1bg5ubG/v37ZesEVMbo0aNp1KgRALGxsUyePJlPP/1UtqDQuHHj2LdvHw8ePKB27dr4+Pjg4eEhG4rYqFEjsrOz0dTU5MKFC1y/fp0LFy5gaGgIwPTp0wkJCWHLli14e3tX6bUUKi+noCSI0VZXkzuupa4qd/55dhZ1MNXXZePZa2ioqWBZx4gHyU/Zev4mSkpK5EkKyS4ooKi4mAM37lFHT4cJ3dsgKSpm56VQvvU/KZvz/yKpVMrvp66QWyChXysbhfOCUB2SgpKGKnV1+WBbTb3kGSzIz1FIk5dd0kh1fM9Cmti5YN++P8mP7hFyZju52U9lc/5TEqPIzkzFppUldu368jT5ISGn/+SA72z6j1kutzAgwM3gvegamGBl362mqym840pHTWq90Itf2qufm5tbZrr3evQg6v59VixfLjvWqXNn3n///TKvP3zkCJZWVtjYyL9Hd+7UiS1btrBjxw7Z/OKjx46RkJBAUWFhteslCELllbU+R3mUlJT46aeqLzgrgv9/oX79+hEcHMzixYt58OABUVFRREdHyxoDSjk5Ocn+raqqSvPmzbl37x6pqak8fvyYpUuXsvy5D4vi4mLy8/OJi4ur9lASFxcXXFxckEgk3Lp1i5MnT7Jt2zbGjBnDkSNHFOaraWpqMmzYMA4dOsTNmzeJiYkhPDyc5ORkuWH/Ojo6ssAfSoJPAwMDIiIiKh38l1deY2Nj9u/fz7hx4/D398fNzQ0Dg6r1Wi5YsABHR0fy8vLYuXMnBw4c4NNPP5XboqNZs2YYGBiwbt06oqKiiImJ4e7duwCVWtQvIiICe3t7udfQxMSExo0bKzT8vExp4A/QoEEDPD092bJlCxEREcTGxiqUKyIiQmGBxo8++giAP/74A6lUSvfu3eXOFxQUkJ+fX6VyCVVT/JKRL8plrA6tqqLM3L4urD5xme/3nwZKhvOP6tySZUcuoKGqiqTo2f97c/u6oKlW8lFhWacWk7ce5NCtSIZ0cJDLt7ComN+OX+Li/Tg+dWlNU1PjV62eIAAgfcl0NCVlxUGMRcUlwUpDmw60cxsJlMz5lyLlyokttHYZgoGxBQk52Q8AAQAASURBVJ17T0ZZWQWTuiXzns0a2FHLpCFBPjOJvHmCZm2erWGTlZFMTMRF2r/3KcoqKgr3FIRX8bJpl8plPOcSiYSff/mF9KdPGTZsGGbm5tyPjOTAgQNoaGjw8eDBctdHRkYSGxPDpEmTFPLq0qULuXl5BOzfz/Hjx1FSUsLJyQkXFxf+Pnfu1SonCEKl+Pv7o6SkhKmpaZn/zz+vop1uKiKC/3+hb/9ZfKV///64uroyadIkNmzYIBvSX0rlhS8nRUVFaGhoyD5gZs+eXWbgbG5uzpMnT6pUprt37/Lnn3/yzTffoKGhgZqaGq1bt6Z169Y4OTkxfvx4wsPDcXCQDxhycnIYNmwYeXl5uLu7M2DAAFq0aKHQi/1iXaDkg7KsxW+qQkVFhf79+xMYGMiwYcM4duyYXINIZZmamtKwYUOg5O+Tm5vL1KlT8fHxkTXCXLp0iU8//ZRu3brh5OREnz59yM3NLfNDuCzlTXEoLi5GTU2tzHPleb5xJzIykiFDhmBnZ4ezszM9e/akVq1aDBw4UHbN8+sWlHV/XV1d/Pz8FM696t9HqJiORsnfPVci38Nf3oiAUmYGunw/oDvpuXlk5hZgbqhLcmYOUqkUPU11tP4J9pvXNZEF/gAmejrUM9InOilNLr/s/AIWHQzm9qMkRndphbuDVY3VURDUNUvmIEsK5Hs+C8oZEQDPRgXUb9pW7ni9Jq25cmILKY+jMDC2wLSerUJa0/rNUNfUIeVJtNzxmLvnUVJSwrK5S/UrIwjlKF1N/8V1k0p7/DW1FEdbhYSEEPfwIdOmTaN585IFXG2srdHS1ubPbdtw6dIFC4tna8JcvXoVbR0d7O3tyyxDz/few7V7d5KSktDV1UVPT48NGzago6tbI3UUBKFivXr14tSpUxQUFODu7k7v3r3lOnNrggj+/2XS0tLYuXMny5Ytk1tVPyoqSmEbltDQUFlveUFBAaGhoQwePBhjY2OMjIx4+PChLGCFknndR48e5eeff65W2Xbu3EmHDh3kygUlK9YrKSlhbKzYE3ju3DnCwsL4+++/qV27NgBPnz4lJSVFLtjNyMggNjaWBg1KFhoLDw8nMzMTa2vrKpWxrFYyLy8v1q9fj6+vL3p6erJtb17F3LlzuXTpEl9//TVBQUFoaWmxceNG2rdvL7cwoa+vL/AssK+oFc/GxoaAgAAKCgpkQXVycjIxMTFyUwmqaseOHRgbG7Np0ybZsdL1FkrLZWlpqbCA38KFC0lISMDT05OsrCwkEglWVs+Cvrlz52Jra8uwYcOqXTahYqb6uigrKSksrlf6u4WR4t7MBYVFnL//EFvz2pjq62KgVTKkNOqfgL6xSS10NNTR19KgsIyeqMKiYrkdBJKzcvgh4DRPMrKZ2rMDzlb1a6x+ggCgZ2iGkrIyGWkJcsczU0t+N6yt+MwZ/LM+QHGhfMNYcXHJaCYVNXXy87J5cDcYk7rWGNV59lkolUopKpSgpS0/Aiz23iXMGtihpWv4ynUShBfVrl0bZWVlkl6YX1/6e11zxd0oUlJSAOQ+ewGs/1nB/9GjR3LB/81bt2jZsmWZDfrRDx6QmpqKU+vWcmsOPf/dSxCE12vZsmXk5uZy8uRJ/vrrL0aNGkXt2rXx8PCgd+/esqnEr0Is+PcWi4mJ4cyZM3I/9+7dQ09Pj+PHj8uGyM+bN4+wsDAKCgrk0i9ZsoRjx44RGRnJrFmzKCgoYOjQoSgpKTF27Fh8fX3ZunUrsbGxHD16lPnz56OpqVmt3lpbW1v69u3LN998w/r164mMjOTBgwccOnSIOXPmMGDAAOrWLfkypqOjQ3x8PI8fP8bMrGSF5YCAAOLj47ly5QqfffYZEolErj7KyspMnTqV69evc/36db7++mvatWunMNXhZUobSEJDQ2Wt640bN6Z169asXr2afv36lTnKoKp0dHT44YcfiIuLk40kMDc3Jzw8nCtXrhAXF8fevXtl50rrqq2tTXp6OtHR0Uhe6M39+OOPyc7O5quvvuLu3bvcvHmTL774glq1aikMya8KMzMzHj9+zOnTp4mPj+fIkSPMnz9frlzjxo3jr7/+wtfXl9jYWAIDA9m+fTuurq506dKFZs2aMW3aNC5cuEBMTAwLFy7Ez88PS0vLapdLeDl1VRWa1TXh4v14ucayi/fj0dZQo2kdxS0wVZWV2Xj2GsfComTHioqLOXQrElMDXRoalwQ8rRuac/NhIhm5z6ZuPErLJOFpJs3MS9akyCmQ8P3+06Rl5zGvb1cR+AuvhaqaOmYN7HhwN1juOY++G4y6pg4mFoqNwGYN7FFV1+R+2Gm547ERF1FSVqGOhS0qKmqcP7SWG8G7Fa4pKizAvNGzxSylUinJCZGY1nv1L16CUBZ1dXWaWltzNSRE7jm/GhKClra23HS9UqXfoe7duyd3vHRngNrPrR+UlZXFk8REmlqVPTIrIiKCDRs2kJPzbA2N27dv8+jRo0pvVywIwqvT0tLCw8ODVatWERwczOTJkwkPD2fgwIG4u7uzatUqoqOjX55ROUTP/1ssMDCQwMBAuWMWFhYsX74cb29v+vTpg4GBAe3bt2f69OmsXbtWbkGYyZMns3jxYuLi4mjRogWbNm2SLcg2evRoNDQ08PX1xdvbm9q1a/PRRx8xZcqUapd34cKF2Nvbs3//ftasWYNEIqFhw4YMHDiQTz75RHbd4MGDmTlzJn379uX8+fPMnj2bzZs38+uvv2JqaoqHhwfm5uZyPc1GRkb069ePzz77jNzcXLp3787cuXOrXMYOHTrg6OjI4MGDWbRoEb169QLA09OTkJCQGt3H1tnZWTaXvnfv3kyZMoXk5GQmTJgAlLTU//TTT3z11VfcunULS0tLevbsya5du+jbty9bt26Vy69evXps3bqVRYsWyVb979SpE4sWLUJfX7GHt7JGjBhBVFSUbNeBRo0aMX36dFasWMGtW7dwcXHB1dWV77//nvXr1/Pzzz9jYWHB7Nmz6d+/PwAbN25k0aJFTJ06ldzcXCwtLVm1ahUdO3as+ObCK/uwTXO+DzjN0sPncW3WmPDHKQRcD2dIBwc01FTJKZAQl5qBqYEOBlqaKCsr8b69FUE3IjDW1aauoR6HbkVyNyGZrz06yUaffNimOZei4/kh4DQD29pRWFzMnxduYayrjVvzxgDsuhRGwtNMBrazQ1VZmYjHKbJyqako09ik1ht5TYT/npadB3Fw2zxO+P2MteN7PIm7w60L/rTtPgJVNQ0K8nN4mvwQPUMztHQMUFPXpLXLEC4d24iGpi6NbJ1JjLvDzfN+2LXrg5ZOSSOXY6cPCTn9J1o6htS3bENaUgwhZ7bTwLoddZ8L/rPSkyjIy8bQRPSACq/PB717s3TpUtauXUunzp25HxnJkcOH8fT0RENDg9zcXBISEjAxMUFPT4+WLVvSuEkTNmzYQJ++fTE3MyM6OpoDBw7g2LIljZ9rMIh/9AhAYSehUh3at+fgwYP8vnYt7u+/T2pqKjt37cLSyqrcnQMEQXi9dHV1GTBgAAMGDODp06ccPXq05P/T33/H2tq6zCm3L6MkrcpeaYLwH7Vy5UqCg4PZvn37my6K8I/std+86SL8a1yMimPXpTDi0zIx1tXifXsr+v6z2n5o/BP+b98pPnNrS3fbkqC9sKiY3ZfDOB0eQ1Z+AY2MDRnYtjmODeT3Oo9LzcD3/A3C4pNQUVaiRT1TPuncktq6JSNoJvgEkZKluNI6lKwPsHpE9UekvCvWmi1400X413hw9zwhZ/4kPSUebT0jmrfpjUOHkgbbhAe3+GvrN3Tp8wXWjm6yNBE3jnHrgj8ZqQlo69bCtrU7LZw/lDVySaVS7l49yO2rB8hMS0RDSw9L+660dhmCqtqzUXBJ8REEbJpBz8HfUd+qZudfvgv620e9/CIBKJnHHxAYyOPHj6llaEi37t15v2dPoGTK4+LFixk5ahSd/lmzKTc3F/99+wgJCSE7OxuT2rXp2LEj7733ntzw/itXrrB27Vq+//77chsAHjx4wO7du4mJiUFbR4fWrVrRv39/2Y4DQsWavEWjHd/W71A6439800X410pMTOTw4cMcOnSIa9euoa+vz8WLF6ucjwj+hXfa1atXiY6O5ueff+b777+XjQQQ3ry39YNLEGqSCP6Fd4EI/oV3gQj+X04E/1WTmJjIoUOHOHToEDdu3EBbW5sePXrQq1cvOnXqVOGC3OURw/6Fl+rbty8PHz6s8JqLFy/+K1d2P3nyJFu3bsXLy0su8E9MTMTd3b3CtA4ODmzZsuV1F7HS2rRpU+GWgcbGxhw7dux/WCJBEARBEARBECrr+YD/+vXraGlp0b17d8aMGUOXLl1eOd4Swb/wUr///rvC4nMvqupWc2+LGTNmMGPGDIXjtWvXZt++fRWmfX67vLeBn59fudsBQtnbJQqCIAiCIAiC8OZ9/PHH3LhxAw0NDbp27cry5cvp2rVrjcYcIvgXXqp0lf53iYqKitw2iP8GYiseQRAEQRAEQfh3unbtGioqKlhZWZGamsrWrVsVFgAvpaSkhI+PT5XvIYJ/QRAEQRAEQRAEQXiD2rZtK/v3y5blq+6yfSL4FwRBEARBEARBEIQ3yNfX97XfQ/m130EQBEEQBEEQBEEQhDdKBP+CIAiCIAiCIAiC8B8ngn9BEARBEARBEARB+I8Twb8gCIIgCIIgCIIg/MeJ4F8QBEEQBEEQBEEQ/uNE8C8IgiAIgiAIgiAI/3Ei+BcEQRAEQRAEQRCE/zjVN10AQRAE4dXciH3M9ouhxKamY6ilibuDFX1aWqOkpFTm9ZKiInZdCuNsRCwZeflYGOrRr7UtnZs2kLtu7t4ThD9OVki/8MMeWJkaARD+OJntF0KJSExBS02V1g3NGdqxBYbamjVfUeGdFhd1jaunfEl7EouWjgHN236Affv+5T7nALH3LnPt7HbSnsSioaVHI9uOtOk+AjX1Z8/n0+Q4Lh/fREJMKEoqKpg3sKNdj0/Rr2UmuyY54T5XT/mSlBAJUinGZk1o6zqS2uaWr7XOwrsnLCyMffv2Ef/oEfp6enR3daXne++V+5wXFRVx5MgRzp47R/rTp9QxNcWjVy/atm0rd921a9cIOnCAxMeP0TcwoEOHDnj06oWqatmhwPXr1/ntt9+YMWMGNjY2NV5PQRDejHem59/V1ZWVK1dWK21cXBw2NjZcvHix2vevTh5SqZQtW7bQr18/WrRogZOTE0OHDuXQoUNy1z169IgDBw5UOl8/P7+XvpE//3qtXLkSV1dX2TkbGxv8/PwAkEgkbN68udL3fpnS1+nFn1atWtG/f/8q1RMgJyeHbdu2yX6fNWsWw4cPr7Hyvg7Vfd7u3bvHsGHDZK/VtWvXXlMJhbdJxOMUFh44h0UtPb5yd6aLdQO2nr/JvpC75aZZdvgCgdcj6GLdgFkenXG2qs+aE5f56+Y92TVSqZSYlKd80NKaH73c5H7qGekDcC8xhe/8T5GVX8Dnbu34zLUtTzKy+WbvcbLzC1573YV3x5P4cI7u/AFD43q4fTgbS/tuXDq+mZvn95abJjbiEkd3LaBW7Qb0HPwtjs5e3LtxnHMHnn0XyMpIJshnJnm5GXQbMINOvT7jafJDDv/5LYWSkmc4IzWBA76zKSzMp8sHk3Hp8wXFxYUE+cwkPSX+tdddeHdERUWxctUqzMzMmDhxIu3bt2fvnj0K3/ueFxAQgL+/Px06dODzzz+nqZUV69at42pIiOya27dvs2bNGkxNTfnss8/o3q0bhw4dYtfu3WXmmZWVha+vb43XTxCEN0/0/L/FVqxYwe7du5kzZw4ODg7k5eVx8OBBpk6dire3N/379wf4f/buO67K6g/g+Ie993YBLpShKE7coIUDF1pm6s9VWuZomWaWWo7UNLOlZTlQc6Qg5jZzCwoagoEKLpAlICBDxuX3x5WrV3AGub7v14vX73fPPd/nOef2CPc855zvw0cffUT16tXp3r17pZ1748aN6OnpVfjeoUOHMDExAWDr1q3Mnj2boUOHVtq5QXnDoUmTJoByEJKWlsaSJUv44IMPqF69Op6eng91nF9++YVNmzbx+uuvAzBlyhRKSkoqta1Pg9LSUkaPHk2zZs2YNWsWixcv5p133uHgwYNoar4w9/heSOvConC2MWds55YANHF0oERRyqbwf+jeuD662lpq9S+kZXL8QiIDWroT0MwVgEY17dDX0Wb1sdN0cHHESE+XlOxcCoqKaeroQH17qwrPvSn8H4z0dJjWqyPG+roAuFe3ZfyaHWw5GctrrTyqsOfiRRKxfzVWds506PUeADXreqFQlPD3oQ24Ne+Jto5uuZjQ3T/j3NCb9j0nAFDNqRGKUgVnjodQXHQTbR09Th5Yg46eAV1f/wJtHeXfPBNzO/ZsmMm1pHPY13Ij+ngI2jp6vPTqZ6oVAw5OjVj37Uiij4fg7Tf6v/kQxHMveMsWatWsyYgRIwDwcHenRKFg27Zt+Pr6oqtb/jo/fOQILVu2pKe/PwANGzbk4qVL7PvzT7yaNgXg0OHDWFpaMnLECDQ1NXF1dSU7J4fdu3fzSv/+5Wb/V69ejdY9VgQIIZ5tMip4iq1Zs4aRI0fSrVs3atasSb169Rg3bhxdu3ZlxYoVVXpuS0tLjIyMKnzPxsYGfX3lF6DS0tIqOb+ZmRk2NjbY2Nhga2uLm5sb8+fPR1dXl+3btz/0ce5un4mJCebm5pXc2icvOzubhIQEfHx8qFWrFt26dePatWtkZ2c/6aaJKlRUUkJ0YhotnKurlbeqU4OComJiksov2U/IVF4TzZyqqZW717DlZlEx0YlpgPImAYCTtfk9z5+QmU0DB2vVwB9AT0ebenaWhF9Keqw+CXG3kuIiki5F4digtVq5c8M2FBXmk3LlTLmY9OR4sjOTcW3ur1bu3qInr4z5CW0dPUpLS7kYc5T6nl1UA38Am2r1eG38cuxruQFgblUDj1Z91LYK6OjqY2xqRU5mcmV2VbzAioqKiI2NVU18lPHy8qKgoIDz58/fM67sO1kZE2NjbuTmql4XFxWhq6urNhlgbGxMSXExBQUFarHHjx/nzD//0C8g4N92SQjxFJLBP1BYWMiXX36Jj48P7u7utGjRgvHjx5ORkaFW7+TJk/j7++Pu7k7fvn05duyY2vu///47Xbt2pVGjRqoBukKheOx2aWpqcuzYsXK/mD/55BPVkvzBgwcTFhbG5s2bVUvzr169yrvvvkvr1q1xc3Ojffv2zJs3r1xb1q9fT7t27WjcuDGjR48mMfH28sX7bZMoW/a/adMmJk+erCrbvn077u7uBAUFqdX/6quvCKiEPyKamppoa2ur3aHes2cP/fv3x9PTEw8PD/r27cvBgwcB5eqBb7/9lsTERFxcXEhISCi37D8uLo7Ro0fTsmVLvLy8GDdunNrn8CChoaG4urqye/duXn75ZRo1asSQIUNISkriiy++oFmzZrRu3ZoffvhBFfOw19udHnRtmZmZ4e7uzrfffsvly5dZsmQJHTp0KHejw8XFhW+++YZOnTrRtm1bLl68+FDXS2RkJEOHDqVJkyZ4e3vz2WefkZ+fDyhvsPz000/4+vrSuHFjevXqxZYtWx76MxSPLyUrlxKFAgdzE7VyezNjABIzy9/8MdVXDnLScvLUypOzbiiPma3834vXrqOno83Kw38zbFkQr/24kZkhB7iamaN2rLuPU3aslFvHE+Lfys5MRlFSjKml+g0rUwsHALLSE8rFpKfEA6ClpcOudTNYPqcfq74ayLFdP1FSXATAjaxUCgtyMTGz5cj2H1j11UCWzwlg9/ovuJF9+8ZZw2bdaNS6r3qbMpLISL2MuY16ngwhHldaWholxcXY2tmpldva2ACQnFzxjabOnTtz9OhRTkdFkZ+fz7Fjx4iKiqJ1q1aqOp06dSI1LY2du3aRl5dHfHw8e/bswcPDA2NjY1W97OxsVq9Zw4BXX8XMzKwKeimEeNJk8A/MnTuXXbt2MWfOHHbu3MmcOXM4duyY2oANYNmyZbz11lsEBwfj6urKqFGjSElJAWDdunXMnTuXd955hz/++IMJEybw008/MX/+/Mdu16hRo9i3bx9t2rRh7NixrFixgtjYWKysrKhRowZwe3l8165d2bhxIwBvvfUWOTk5/Prrr+zYsYPhw4fz888/8+eff6odf9WqVSxatIjVq1eTmZnJmDFjHmkmv1u3bnz88ceAciuAr68vHTt2VBv8KxQKtmzZQt++fe9xlIeTlZXFnDlzyM/Pp0ePHgBERUUxduxYunfvTkhICOvXr8fS0pKJEydSWFjI8OHDGT58OPb29hw6dAgHBwe1YyYmJvLqq6+iq6vLihUr+OWXX0hLS2PQoEHcuPHwA5eSkhJ++OEH5s+fz4oVK4iJiaFXr17o6OiwYcMGBgwYwNdff01sbCzw8NdbmYe9tmbMmMH58+d56aWXqFmzJgsXLqzweGvWrOGbb77h22+/xcnJ6YHXy5UrV/jf//6Hra0t69atY/HixRw+fJjp06cDsHDhQtauXcvUqVMJCQlhyJAhTJs2TS3XgqgaeYXKQYyhro5auYGuttr7d3KrboudqTG/HDzJ6YQU8gqLOHM1jcCjkWhoaFBQVAzAxfTr3CwqxkhPlw+7tmF0p2YkZd1g6uY/ychV3vjxaejMhbRMfj14kozcfDJz8wk8EklCZjY3i5+/7TXiySgqVN5g0tU1VCvX0TUAoPBm+RtQBblZAOzdOBvzsj3/rQOIidjJgZCv1eoc/3MFuTnpdOrzIW27v0N6cjzbV31MUWFBueMCFBcVsn/LQrS0dXBt1qNS+ihE2USPwV2z+GWz+mU33O/WpXNn6tWrxzeLFjFu3DiWLVtGa29vXn75ZVWdBg0a8PJLL7FxwwbGjx/P7NmzMTExYeTIkWrHWrlqFXVq16Z169Z3n0YI8ZyQDT2Ah4cHfn5+NGvWDIDq1avj7e3N2bNn1eqNHTuWbt26ATBt2jSOHDnCmjVrePfdd/n+++956623VPvua9asyY0bN5g+fTrjx49/rHYNHTqU2rVrs3btWg4dOsSuXbtU7Z0zZw5169bF3NwcHR0d9PX1sbS0pKCggF69etG1a1fVYHfo0KH89NNPxMbG0rlzZ9Xx582bR4MGDQD48ssvefnllzl69Cje3t4P1T59fX3V3n+bW3emAwICePvtt0lJScHOzo6jR4+SkZGhGrA/rDfeeAMtLeVeZYVCQXFxMY0aNeKXX36hYcOGAGhpaTF16lQGDhyoihsyZAhvvPEG6enpODg4YGhoiJaWlqp9d1qzZg2Ghoaq7QSgzLPg6+tLcHCwKk/Awxg/fjweHsr9za1ateLvv/9m4sSJaGhoMGrUKL7//nvOnTuHi4vLQ19vZR50benp6REREcH7779PnTp1iIuLw9bWFiMjI/Ly8jAwMFDLEtyrVy9VWx/melm/fj3m5ubMmjVLteriiy++4OTJk+Tl5bF8+XIWLFhAx44dAahVqxaJiYksW7bskT5D8egUD7hZp1lBdmhtLU0+6dme7/88zozg/QBYGBkwrK0nC3cdQ+/Wf+PXWnrQ09MFt+q2tyJtaGBvzfg1O9j29zkGeTfC17U2eYVFrAuLZlvkOTQ0NGhVpwZd3Orw5z8XKrWv4sVV+oAVdBoV5DUpUShvYjm6tKKF71BAuee/lFJO/LmSpu0HquoYGJnRuf8U1e9JU8tqhPz6IXFRf9GgqZ/acYtu5rN7wxekXT2Hb79JmJjbIkRleNBK0Yry9xQVFfHl3LlkXb/OoEGDsHdwIO78ef744w/09PR4bcAAAAIDAzl85Ajdu3enYcOGXEtPJ2TLFr5etIj333sPPT09Dh85wrlz55g+bVpVdE8I8ZSQwT/KwdCRI0eYP38+Fy9eJD4+ngsXLqgGZ2W8vLxU/19bWxtXV1fOnTtHRkYGycnJLFiwgEWLFqnqKBQKbt68SUJCwj2T5z1I+/btad++PUVFRZw+fZp9+/axevVqRo4cya5du8olf9HX12fQoEHs2LGDyMhILl26RGxsLNeuXVP7w2JkZKQa+AM4OTlhZmbG2bNnH3rwf6/2WllZERwczJtvvsnmzZvx9fV95OVjX3zxBY0bN6agoIB169bxxx9/MGLECFrdsYytYcOGmJmZsXTpUuLj47l06RIxMcoM5w+T1O/s2bO4u7urfYY2NjY4OzvfcyB+L46Ojqr/b2hoSI0aNVRfJMvu2hcWKjNHP+z1BjzUtWVsbMyIESPo06cPU6ZMYdmyZXz11Ve4ubnxxx9/kJGRwdq1ayts68NcL2fPnsXNzU1tu0WrVq1o1aoVkZGR3Lx5k/fff1/ti0lxcTGFhYUUFBSU24soKo+RnnLGP79IfYb/XisCytibGTOjTyey8gvIyS/EwdyYazl5lJaWYnJr/35Fe/3tzIypYWnKxfTrqjJ/Txe6etQjJfsGxvq6mBnos3hPqOo4QvxbuvrK/DNFheozn4X3WBEAt1cF1Kyn/rizGrWbcuLPlaQnx2NmrVxBV6OOl9oNUtvqLujqG5GeHK8WeyP7Grt+m052xlU69f0Qx/ot/2XPhLjN0FB5Hd+91bNsxl/fwKBcTEREBAlXrvDuu+/i6qpM4OpSvz4GhoasWb2a9u3aYWhoyMGDB+natasqUbQL4OzkxGeffcbhw4fx9PRk3bp1vNK/PyYmJpSUlKi+AygUChQKhSQPFuI5IYN/4NNPP2Xnzp307t0bHx8fxowZw7Jly1RL+suUzUSXKSkpQU9PT/ULcvLkyRUOnB0cHEhNTX2kNsXExLBmzRqmTJmCnp4eOjo6NG3alKZNm+Ll5cWoUaOIjY1VzeCWycvLY9CgQRQUFODn50efPn1o1KhRuRnYu/sCyl/wFWWSfRRaWlr07t2bkJAQBg0axJ49e9QGrQ/Lzs5ONUj99NNPyc/PZ8KECaxYsUJ1EyYsLIwRI0bQsWNHvLy88Pf3Jz8/nzFjxjzUOe61xUGhUKCjU/Gg6V7uzpR7vz+SD3u9lbUF7n9t/fbbb2hqajJ58mS0tLR48803OXv2LB9//DHFxcW8++67ajF3DsYf5nq51zOA4fZn+PXXX1O7du1y7//b60ncn52pMZoaGqr9+mXKXle/9Ui+OxUWl3A07goNHKyxMzXGzEB5PcTfSvDnbGNBiULBgbOXqGZugou9dbl4MwPlzczzKRlcu5FHqzo1qG5x+1zxaZk4W1tUXkfFC83E3B4NTU2yM9WTSOZkKF+bW9csF2N2Kz+Aolj9xphCobwxrKWjq8wZoKGhygFwdz2tO54gkJFykZ2/TaO46CYvvzYdB0f3f9cpIe5ibW2NpqYmaWlpauVlr6vdtXURID09HYC6deuqldevVw9Q5oCytLSktLSUurfKylSrVg0jY2OuXr2Knr4++Xl5rFixolxC6QULFmBlZcWcOXP+XQeFEE+FF/42XmZmJuvWreOzzz5j8uTJ9O3bl4YNGxIfH19ucBgVFaX6/4WFhURFRVGvXj2srKywtLTkypUrODo6qn6io6P5+uuvH7tt69atY+/eveXKTUxM0NDQwMqq/OO3Dh06RHR0NCtXrmTcuHF069YNY2Nj0tPT1fqTnZ3N5cuXVa9jY2PJycmhfv36j9RGjQqWFQcEBHD27FlWrVqFiYkJbdu2faRjVuSTTz7Bzs6OiRMnqu6C//LLL7Rs2ZLFixczdOhQ2rRpQ1KS8stgWV8ral8ZFxcXTp8+rZqRB7h27RqXLl2iTp06/7rNFXmU6w14qGvLwMCAmzdvquUp+OKLL9DT06OoqEi1HL8iD3O91K1blzNnzqitpti9ezc+Pj7Url0bbW1trl69qta+/fv3s2zZMpkpqGK62lo0rGZDaFyi2vUTGpeIoZ4O9Wwty8Voa2ryy8GT7Im+PatZolCw4/R57MyMcbQyQ0tTk43Hz7DqcKRabHxaJslZN3CrplzqfOZqGt/sCSX35u1/Q5FXUkjIyKZ5bfXkbEI8Lm0dXexruXEx5ojadX4h5gi6+kbYVC//d8u+ljvauvrERe9XK798NhQNTS1sqzdAR1cf+1quXIw5onYD4OqFvykuLMC+pjLb/43sa2xfMxWAHv+bKwN/USV0dXWpV78+4RERatd5eEQEBoaGODk5lYuxt7cH4Ny5c2rlZU8GsL71xCRNTc1ydZKTk8m9cQMbGxsaN2rEx1OmqP0MGjQIgEGDBvHOO+9UZleFEE/QC/XN/NKlSxw4cEDt59y5c5iYmLB3717VkuepU6cSHR2tNigEZdb6PXv2cP78eSZNmkRhYSGvv/46GhoavPHGG6xatYrAwEAuX77M7t27mTZtGvr6+o81+9mgQQN69uzJlClT+Omnnzh//jwXL15kx44dfPzxx/Tp04dq1ZRfro2MjEhMTCQ5OVn1h2DLli0kJiZy4sQJ3n77bYqKitT6o6mpyYQJEzh16hSnTp1i4sSJtGjRosKl5/dTtkwtKipKtVTN2dmZpk2b8v3339OrV68KVxk8KiMjIz7//HMSEhJUKwkcHByIjY3lxIkTJCQk8Pvvv6veK+uroaEhWVlZXLhwgaK7lka/9tpr5Obm8uGHHxITE0NkZCTjx4/HwsJCtb++shkbGz/09QY81LXl5+eHhYUF7777Lv/88w9nzpxh4sSJgHKJ/5gxY7hy5UqF7XmY62XgwIFkZmby2WefERcXx/Hjx5k7dy6tWrXCxMSEAQMGsGjRIoKDg7ly5QobN25k3rx52NrKXtj/Qr9mrpxPzWDBzqOcvJTEb6FRbDkVS5+mDdHT0SavsIizyelk5Sv/fWpqavCye13+iDzHjtPnibySwlc7jhKTdI1hbT1VN8z6N3cjNvkai/eEEnklhb1n4pnzxyGcrM3p2MAJgHb1a6GnrcVXO48SeSWFP89cYP6OI7jYW9OhvtMT+kTE88iz7aukXT3Hn5u+5Mr5cML/CuT0sc009u6Hto4ehTfzSE2MJf9WEj8dXX2ath9IfPRBjmz/gasX/ubkwd+IPLoJtxb+GBgpt6I16/Q/8m5ksvO3aVw5H87Zv/eyL2g+NtXrU+vWsv5jO5dQkJtFk3YDKCpUnqfsJzOt4t+tQjyOHt27c/HCBZYsWcLpqCiCgoLYtXMn3bp2RU9Pj/z8fOLj48nJUT51xdPTE+fatVm2bBn7/vqLmJgYtm/fzoYNG2js6YmzkxMmJiZ07tyZnTt3smnzZmJiYjh85Ahff/01VlZWtGvXDmNjY5ydnNR+yr4f2Nvbq5JMCyGefS/Usv+QkBBCQkLUyqpXr86iRYuYM2cO/v7+mJmZ0bJlS9577z2WLFmill117NixzJ8/n4SEBBo1asSvv/6qepTa8OHD0dPTY9WqVcyZMwdra2teeeUVxo0b99jtnT17Nu7u7gQHB/PDDz9QVFSEo6Mj/fv353//+5+q3oABA/joo4/o2bMnR48eZfLkySxfvpyvv/4aOzs7unXrhoODA6dPn1bFWFpa0qtXL95++23y8/Pp1KkTn3zyySO3sVWrVjRu3JgBAwYwb948unbtCkDfvn2JiIigT58+j93/u3l7e9O3b19WrlxJ9+7dGTduHNeuXWP06NGAcoZ61qxZfPjhh5w+fZo6derw0ksvsX79enr27ElgYKDa8WrUqEFgYCDz5s1TZf1v06YN8+bNw9S0/HLpyqCjo/PQ11uZB11bJiYmLF++nDlz5vDaa6+hpaWFt7c3GzZsQFdXl7fffpu4uDhq1iy/NLZRo0YPvF7s7Oz45ZdfmDdvHr1798bMzIxu3brx3nvvAcotCRYWFixatIjU1FQcHBwYN25cuSzComq417Dlfb/WrA+L5stth7EyNmBQ60b0bOICKGfrpwf9xdu+zenUwBmAV5q7oQEERcRw42YhTlbmfNyjHY1r2auO27GBE7raWgRHxPDltkPo62jTonZ1BrbyQFNTeYPAwsiAT/zbs/Lw38zdfhhjPV06NXBiQEt3VR0hKkM1p0b4Bkwi4sAa9myYiaGJJS18h+LRSvk3Jj0pjm2BU2jnP576jX0B8GjVGz0DY04f20zsqd0YGlvQtP1rNPLupzquXY0GdBs0k/C/VrF342y0dfRwdGlJi84j0NTUpKSkmMvnTgBweNv35dpl7+hG98Gz/4NPQLwIGjRowOjRo9kSEsJ3332Hhbk5Af368fJLLwFw+fJl5s+fz9Bhw2jj7Y2mpibvTpjA5qAg/vjjD3Jzc7GxtqZ79+506dJFddx+/fphbm7O/gMH2LVrF+ZmZri6utKnTx/VJI4Q4sWgUfooz3YT4iEtXryYI0eOqCWaE09GaWnpfbc/PK1yl0x50k0Qosotsf/iSTdBiCrX2z3+wZWEeMbVrqIto4/jaf0OZTRq5pNuwgvvhZr5F1UvPDycCxcusHLlSmbMmPGkmyO4f94DIYQQQgghxItBBv9PQM+ePe+5B7tMaGjoM5kpfd++fQQGBhIQEKDaAgCQkpKCn5/ffSLBw8ODlStXVnUTH1qzZs3u+8hAKysr9uzZ8x+2SAghhBBCCCEejwz+n4Aff/yxXPK5uz3qo+aeFh988AEffPBBuXJra2uCgoLuG6unp1dFrXo8mzZtuufjAKHixyUKIYQQQgghxNNIBv9PQFmW/heJlpYWjo6OT7oZj6RWrVpPuglCCCGEEEIIUSleqEf9CSGEEEIIIYQQLyIZ/AshhBBCCCGEEM85GfwLIYQQQgghhBDPORn8CyGEEEIIIYQQzzlJ+CeEEEI8Ib3d4590E4QQQgjxgpCZfyGEEEIIIYQQ4jkng38hhBBCCCGEEOI5J4N/IYQQQgghhBDiOSeDfyGEEEIIIYQQ4jknCf+EEOIZ9/flZNaGRnE5IwtzA338POri71kfDQ2Ne8YcOneZjcfPkJqTi42JIb2bNqBTA2e1OudTMlh15G/i0jIx1NWhYwMnXmnuhrbW7fvG8WmZrD12mrjUTEopxdnGgkGtG1HbxqLK+iteTNHR0QQFBZF49SqmJiZ08vHhpS5d7nudh4WFsXXrVq6lp2NlZYWfnx9tvL3V6ly4eJGNGzZw6dIl9A0M8Pb2pqe/P9raFX9FOnXqFN999x0ffPABLi4uldpHIZ7kdf7zzz8TGhpa7vijRo+mmZdX5XVSCPHEyOBfPLUmTZpEYmIiq1atAkChULBx40Y2bdrE+fPnAahbty79+vUjICBA7Q/j4MGDCQsLUzuejo4O1tbW+Pj48OGHH2JgYPBQ7QgNDWXIkCFqZdra2lhaWtK+fXsmTpyImZlZpfUzPDyc0tJSmjVr9tjHrGo+Pj706dOHsWPHPummvPDOJqcz+49DtKlXk1dbuBGTdI3Ao5GUKBT08WpYYcyxuAS+2R1Kt0b18KxlT1h8It/vPY6OlhZt69UCICXrBp+H7Ke+nRXvvdyahIxs1oaeJqfgJqM6Kq/N5KwbfLp5H3VsLHjLpxkaaBByKpZPNv3J/FdeopqFyX/2OYjnW3x8PIu//ZbmzZrRs1cvzp87x+8bN6IoKaFr164VxoRHRPDzzz/j6+uLm5sbp06dYvmvv6KjrU2LFi0ASEtLY+HChdSpXZs3R40iKSmJoKAgcm/cYPDgweWOeePGDdXvaiEq25O+zq9cuULzFi3o7Ourdg5bW9uq67QQ4j8lg3/xTCguLmbMmDFERkYyduxYvL29KSkp4eDBg8yZM4c///yTxYsXo6WlpYrp2rUrU6ZMUb3Oy8vj0KFDzJ49G4VCwbRp0x6pDRs2bMDBwQGAkpISYmNjmTRpEteuXWPJkiWP3bcpU6ZQUlKiej1w4EBmz579VA/+xdNjXVgUzjbmjO3cEoAmjg6UKErZFP4P3RvXR1dbq1zMmmOnaVWnBkPbegLgWcueGzcL+S00SjX4DzoZg4GODh91a4u2liZNHR3Q09Zi2cGT9PVqiI2JEdsiz6GnrcXkHu3Q11H+OXGvYcvbK/9g2+lzjGzf9L/5EMRzL3jLFmrVrMmIESMA8HB3p0ShYNu2bfj6+qKrq1suZvPmzXh5efHqq68C4O7uTm5uLkHBwapB0Y4dO9DX12fMmDFoa2vTyMMDXV1d1q5ZQ7du3bCyslI75urVq9G6x4oAIf6tJ3mdFxYWkpySQpcuXahdu/Z/12khxH9K9vyLZ8KyZcsICwtj9erVDBw4ECcnJ+rUqcPQoUNZuXIlBw4cYNmyZWox+vr62NjYqH4cHR15/fXX8ff3Z9u2bY/cBktLS9Wx7O3t6dChA//73//Yv38/2dnZj903ExMTzM3NHztevLiKSkqITkyjhXN1tfJWdWpQUFRMTNK1cjGp2bkkXc+hRe3yMSlZN0i6ngPA35dTaOJor7bEv1XdGpSWlvL35RQAqpub0LOJi2rgD6Cvo42VsQEpWTcqrZ/ixVZUVERsbCxNmjRRK/fy8qKgoEC1EuxO19LTSUlOrjAmLTWVlBTlNRwdHY2Hu7va0udmXl6UlpYSHR2tFnv8+HHO/PMP/QICKqtrQqg86es88epVFCUl1KhRo7K7JoR4isjgXzz1FAoFgYGB9OvXr8K70a6urvTq1YtVq1ahUCgeeDw9Pb177uV8VFpaWmhoaKCjowPAnj176N+/P56ennh4eNC3b18OHjyoqj948GCmTp1K//79adasGVu2bGHSpEmqZXdl+0cnT57MpEmTGDNmTLktB/Hx8bi4uHDu3LmHaqOLiwvr1q1j4MCBeHh40LVrVyIiIli3bh0dO3akadOmTJgwgYKCAlXMhg0b8Pf3p1GjRnh6ejJw4EBOnz59z3NERETw+uuv06hRIzp27Mj06dO5cUMGf1UtJSuXEoUCB3P15fX2ZsYAJGaWvylVVlbtrhiHWzFXr+dQWFxCWk5uuTpmBvoY6Opw9dYNgpc96tKrSQO1OslZN7iSkU1Ny8ffCiPEndLS0igpLsbWzk6t3NbGBoDk5ORyMUlJSQDY3R1za/lySkoKhYWFpKenY2dvr1bHxMQEfQMD1cAJIDs7m9Vr1jDg1Vf/1TYvIe7lSV/nCVeuAHDo0CHe/+ADRr/1Fl/OnUt8fHwl9E4I8bSQwb946l2+fJnU1FS87pNspnXr1qSmppKQkHDPOsXFxfz1118EBwfTq1evf9Wm4uJiTpw4wcqVK+nQoQMGBgZERUUxduxYunfvTkhICOvXr8fS0pKJEydSWFioit2wYQNDhgxhzZo1tGvXTu24hw4dAuDjjz9mypQp9O3bl7CwMNUfeICgoCA8PDyoV6/eQ7d34cKFjBw5kuDgYExMTBg9ejQ7d+5k6dKlzJ49mz179rBhwwYAdu/ezYwZMxg5ciTbt29n+fLl3Lx5k08++aTCY8fExDBs2DDatWvHli1bmD9/PtHR0QwfPpzS0tKHbqN4dHmFRQAY6uqolRvoaqu9f6fce8boqGJybxZWWAfAQEe7wuMCFBaX8O2eMLS1NPHzqPsoXRHinspuTBro66uV6996nZ+fXy4mPy9PGXNXbpc7Y8ri7j5uWb07j7ty1Srq1K5N69atH7cbQtzXk77Or9wa/N+8eZM333iDN954g+KiIuZ/9dV9v1sJIZ4tsnFNPPUyMzMBMDU1vWcdCwtlZvH09HRq1VLuWQ4JCWHnzp2qOgUFBVSrVo0RI0YwevToR25Hjx49VEkFCwoK0NLSokOHDsyYMQNQrgKYOnUqAwcOVMUMGTKEN954g/T0dFW+gIYNG+Lv71/hOWxu3eE3MTHBxMSEDh06YG1tzZYtWxg1ahQKhYLg4GDefPPNR2p7QEAAPj4+APTq1YsZM2bw6aef4uTkRP369fn5559VKwnMzc2ZOXMmPXv2BKB69er069dP1c+7LVu2jDZt2qg+UycnJ7766is6d+5MWFgYLVu2fKS2ioeneMDNFc0KskM/6IaMhoYGD7plU1HS6fzCIr7cdpjzqRm879caW1OjBxxFiIfzoBVdmprl5zEe6jp/iDoAh48c4dy5c0x/xDwxQjyKJ32d+/j40KhRI9zd3VXvNWzQgCmffMIff/zBqFGj7nscIcSzQQb/4qlnaWkJwPXr1+9ZJysrC1C/QeDj48MHH3xAaWkpkZGRzJw5E29vb0aPHv1Yy/6XLl2qWlqnq6uLlZWVWvKdhg0bYmZmxtKlS4mPj+fSpUvExMQAqCX0c3R0fOhzamtr07NnT4KDgxk1ahTHjh0jIyODHj16PFLb7zxn2QxB2U0SUN79L1ud0Lx5c+Li4vjuu+9U/YiNjb3nF5MzZ85w6dKlcnsOAeLi4mTwX4WM9JQz8/lF6jPx91oRcGdZfmHFMUa6OnfUKS4Xn1dYhNFdSaeu3chj9taDXL2ew4SXWtH8rhwEQvwbhoaGAGpbk+D2TKh+BU9uKfs9d68YA0PDe9YBKMjPx8DQkIyMDNatW8cr/ftjYmJCSUmJ6nehQqFAoVBUOCgT4lE9yescwN7eHvu7tgYYGhpSp04drsjMvxDPDRn8i6dezZo1sbOz4/jx43Tr1q3COqGhoZiamuLk5KQqMzIyUg16nZycsLW1ZdiwYWhpaT1ypn+AatWq3TcRTlhYGCNGjKBjx454eXnh7+9Pfn4+Y8aMUaunX8HSu/sJCAhg2bJlREVFsWXLFnx9fR95z2lFNzvu9YU1JCSESZMm4e/vT9OmTRkwYABnz56958y/QqHA39+/wtUUZTduRNWwMzVGU0OD5LuS65W9rm5ZfrVMdQtlWVLWDZxtLMrHWJiir6ONpZEBSVk5arFZ+QUUFBVT3fJ2LoBL164zc+tBbhYX84l/e9yqyyOhROWytrZGU1OTtLQ0tfKy19Vuraq6U9kgJjU1Ve1GZ1mMg4MDenp6mFtYkJqaqhabk5NDQUEBDg4O/BMTQ35eHitWrGDFihVq9RYsWICVlRVz5sz5950UL7wneZ2DMqGloaEhbm5uavWKioowMZHHtgrxvJDb1eKpp6mpyeDBg9m8ebNakruy5HmhoaEEBQUxcOBAtUf93a1Vq1YMGzaMtWvXcuDAgUpv5y+//ELLli1ZvHgxQ4cOpU2bNqq9+v9m73udOnVo0qQJ27dvZ+/evfTt27eymlyhpUuX0q9fP+bMmcPrr79O8+bNVXsBK+pHvXr1OH/+PI6Ojqqf4uJiZs+erZarQFQ+XW0tGlazITQuUe2/TWhcIoZ6OtSzLX/zxd7MGFtTI47Fqc/kHItLwN7cWLVcv1FNOyIuJVF0x6qVY+cT0NTQwP3WAP/ajTxmbNkPwMy+vjLwF1VCV1eXevXrEx4RoXadh0dEYGBoqHbTt4ytrS3W1taEh4erlYeHh2NrZ4f1rUf4ubq6Enn6NEV3rJ45ER6OpqYmDVxcaNyoER9PmaL2M2jQIAAGDRrEO++8UwU9Fi+iJ3mdA+w/cIDAwECKi2+v+MrMzCTu/HlVHSHEs08G/+KZMGLECNq1a8egQYNYvXo1Fy9eZOTIkezbt48hQ4ZgY2NTboa9IuPHj8fJyYlp06aRm5tbqW10cHAgNjaWEydOkJCQwO+//86iRYsA1BL+PYihoSFxcXGqXAegnP0PDAxEX1+fNm3aVGq77+bg4EBERATR0dFcvnyZ5cuXExgYCFTcj+HDh3PmzBmmT59OXFwcJ0+e5P333+fixYsVflkRlatfM1fOp2awYOdRTl5K4rfQKLaciqVP04bo3UrOdzY5naz820s++zV35ej5K/y0P5yTl5JY+lc4R89fYUCL23s9ezdtQFb+TWaGHCT84lVCTsWy/PApOrvVxsZEeYPglwMnyc6/Sf/mrqrzlP0kZDz+4y+FuFuP7t25eOECS5Ys4XRUFEFBQezauZNuXbuip6dHfn4+8fHx5OTcXq3Sw9+fEydOsHr1ak5HRREYGMiJEyfofUfCV7+XXyYnJ4dF33xDZGQku3bvZv369bRv3x4rKyuMjY1xdnJS+ymbbbW3t5fHoolK9aSu87Jzp6en891333E6KorQ0FC+WrAAI2NjunTp8p9/FkKIqiGDf/FM0NTU5JtvvmHixImEhIQQEBDAu+++i52dHaNHj6a4uJgRI0YQFxd33+Po6enx+eefc/XqVRYuXFipbRw3bhyenp6MHj2a3r17s2HDBmbNmoW+vv59H5N3t+HDhxMYGMjkyZNVZV27dqW0tJTevXvfd3VDZZg6dSrW1tYMGjSI/v37s2/fPubOnQtQYT88PT35+eef+eeff+jTpw9vvfUWzs7OLF++XC0ngqga7jVsed+vNVev5/DltsMcPHuJQa0b0bup8hF88WmZTPl9LxGXbq/C6NTAmTc7evH3lRTmbj/MmatpvNO5BW3q3V42Wt3ClE/821NYXML8HUfYeuosPRrXZ1hbZW6H4hKF6phL/wpnyu971X5+2q8+EyXEv9GgQQNGjx5NckoK3333HaGhoQT064efnx+gfCrM7Nmzibzjd1Qbb28GDRpE9JkzfP/995w9e5bhw4fTvHlzVR0HBwcmjB9PYWEhP/z4I7t376Zz5868+uqr/3kfhXiS13mDBg149913Kbh5k6VLl7J6zRpq1arFxA8/VOUjEEI8+zRK5Vlc4jmQl5fHxo0b6dKli2r/2vPkypUrvPTSS2zfvv2FmU3PXTLlSTdBiCqX0nn4k26CEEKISlC7Tp0n3QSVp/U7lNGomU+6CS88SfgnnguGhoYMGTLkSTej0iUlJREZGcmaNWto167dCzPwF0IIIYQQQlQuGfyLF1ZKSopqKd29eHh4sHLlyv+oReVlZmYyadIknJyc+Pbbb9XeGz16NKGhofeN37RpE87OzlXZRCGEEEIIIcQzQAb/4oVlbW1NUFDQfevo6en9N425B1dXV06ePFnhe9OnT6/wub13qlatWlU0SwghhBBCCPGMkcG/eGFpaWnh6Oj4pJvx2Ozs7J50E4QQQgghhBDPCMn2L4QQQgghhBBCPOdk8C+EEEIIIYQQQjznZPAvhBBCCCGEEEI852TPvxBCCCGEqDJBUbWfdBOEqHLv1XnSLRDiwWTmXwghhBBCCCGEeM7J4F8IIYQQQgghhHjOyeBfCCGEEEIIIYR4zsngXwghhBBCCCGEeM7J4F8IIYQQQgghhHjOSbZ/8cyYNGkSiYmJrFq1CgCFQsHGjRvZtGkT58+fB6Bu3br069ePgIAANDQ0VLGDBw8mLCxM7Xg6OjpYW1vj4+PDhx9+iIGBwUO1IzQ0lCFDhqiVaWtrY2lpSfv27Zk4cSJmZmaV1s/w8HBKS0tp1qzZYx9TPN/+vpzM2tAoLmdkYW6gj59HXfw966v9G7jboXOX2Xj8DKk5udiYGNK7aQM6NXCusG5+YRHv/7aL/i1cy9W5dO06gUcjOZeSgY6WJo1r2jPIuxHmhvqV2kchKhIdHU1QUBCJV69iamJCJx8fXurS5b7XflhYGFu3buVaejpWVlb4+fnRxttbrc6JEyfYuXMnSUlJGBoZ0bBBAwICAjA1Na3qLokXXEL8ScL/WkVm6mUMjMxwbd4D95a973tNx0Uf4NTB38i5noqxuQ2NWvejfmNftTprFw0jLye9XOzr7wWib6i8rq9ejOTkwbVkpFxES0sHu5oNae4zFFNLh8rtpBDiiZHBv3gmFRcXM2bMGCIjIxk7dize3t6UlJRw8OBB5syZw59//snixYvR0tJSxXTt2pUpU6aoXufl5XHo0CFmz56NQqFg2rRpj9SGDRs24OCg/INYUlJCbGwskyZN4tq1ayxZsuSx+zZlyhRKSkpUrwcOHMjs2bNl8C8qdDY5ndl/HKJNvZq82sKNmKRrBB6NpEShoI9XwwpjjsUl8M3uULo1qodnLXvC4hP5fu9xdLS0aFuvllrdGwWFzN1+mLSc3HLHuZ5XwPTg/VgZGzDGtzmFxSWsPnqamSEHmN2vM9pasrhMVJ34+HgWf/stzZs1o2evXpw/d47fN25EUVJC165dK4wJj4jg559/xtfXFzc3N06dOsXyX39FR1ubFi1aAMqbAz/99BPt27end+/eZGVnExwczPz58/nkk0/Q1dX9L7spXiCpibHsXvc5tV3b0rT966RcOUPY3uUoFCU09u5XYcyFmCP8FfQVbi38qVG7KZdij3EwZBFa2jrUcWsPQEFeNnk56bToPAy7mq5q8bp6RgCkXPmHHWs+xbF+Szr2fp+iwgJOHVrH1hUf0XfUt6obBEKIZ5sM/sUzadmyZYSFhfH7779Tu/bt5wfXqVOHFi1a8Morr7Bs2TLefPNN1Xv6+vrY2NioHcfR0ZGoqCi2bdv2yIN/S0tLtePZ29vzv//9j6+//prs7OzHniEyMTF5rDjxYloXFoWzjTljO7cEoImjAyWKUjaF/0P3xvXR1dYqF7Pm2Gla1anB0LaeAHjWsufGzUJ+C41SG/wfv5DIrwdPkV9UVOG5j19IJKfgJrP6+WJvZgyAkZ4uM0MOEJt8DbfqtpXcWyFuC96yhVo1azJixAgAPNzdKVEo2LZtG76+vhUO0jdv3oyXlxevvvoqAO7u7uTm5hIUHKwa/G/bvh0PDw8GDx6sirO3s2P27NlEnj5NMy+v/6B34kUUsX81VnbOdOj1HgA163qhUJTw96ENuDXvibZO+Ws6fN9KnBt606rLSABq1GnKzYIcIv4KVA3+05PjAXCs3+qes/iRRzZibl0Tn4BJqlUG9jVdWfvNMM5F7sWjVZ9K768Q4r8n0zLimaNQKAgMDKRfv35qA/8yrq6u9OrVi1WrVqFQKB54PD09PbS1K+c+mJaWFhoaGujo6ACwZ88e+vfvj6enJx4eHvTt25eDBw+q6g8ePJipU6fSv39/mjVrxpYtW5g0aZLqS6eLiwsAkydPZtKkSYwZM6bcloP4+HhcXFw4d+7cQ7XRxcWFb775hk6dOtG2bVsuXrzI1atXeffdd2ndujVubm60b9+eefPmqX1+kZGRDB06lCZNmuDt7c1nn31Gfn4+AKWlpfz000/4+vrSuHFjevXqxZYtWx7/gxQPpaikhOjENFo4V1crb1WnBgVFxcQkXSsXk5qdS9L1HFrULh+TknWDpOs5AOTeLGTe9iO4VrNhSo/29zi/8vow0L3978dYT/nlNKeg8PE7JsQDFBUVERsbS5MmTdTKvby8KCgoUG0Fu9O19HRSkpMrjElLTSUlJYXS0lJcGzakfXv1a97e3h6AtNTUSu6JEEolxUUkXYrCsUFrtXLnhm0oKswn5cqZcjE511PJSr+Ko4t6jFODNmRnJpOVcRWAjNQL6OgaYGJhf8/z21R3wa1FT7XtBYYmlujqG5GdkfRvuiaEeIrI4F88cy5fvkxqaipe95l9ad26NampqSQkJNyzTnFxMX/99RfBwcH06tXrX7WpuLiYEydOsHLlSjp06ICBgQFRUVGMHTuW7t27ExISwvr167G0tGTixIkUFt4eGG3YsIEhQ4awZs0a2rVrp3bcQ4cOAfDxxx8zZcoU+vbtS1hYGElJt/8QBwUF4eHhQb169R66vWvWrOGbb77h22+/xcnJibfeeoucnBx+/fVXduzYwfDhw/n555/5888/Abhy5Qr/+9//sLW1Zd26dSxevJjDhw8zffp0ABYuXMjatWuZOnUqISEhDBkyhGnTprF69erH/kzFg6Vk5VKiUOBgrr5apGwWPjEzu1xMWVm1u2IcbsVcvTX419PW5uvX/HincwtMDfQqPH/rOjWwMDJg2YGTZOTmk5J9g8CjkVgYGdCopt2/65wQ95GWlkZJcTG2durXme2t1VjJycnlYsp+b9rdHWOrXKGSkpKChoYGr7zyCp6enmp1Tp46BUD16uo3zYSoLNmZyShKijG1rKZWbmqhnKnPSi//feb6tSsAmFmqX5dmt46RlZ4IKGf+9QyM+fP3OaycN4AVc1/hz01fkpeToYrxbPsKLp5d1I6TdCmKwvwbWNiobwcTQjy7ZNm/eOZkZmYC3HdZvYWFBQDp6enUqqX8oxUSEsLOnTtVdQoKCqhWrRojRoxg9OjRj9yOHj16qO6QFxQUoKWlRYcOHZgxYwagXAUwdepUBg4cqIoZMmQIb7zxBunp6ap8AQ0bNsTf37/Cc5RtKzAxMcHExIQOHTpgbW3Nli1bGDVqFAqFguDgYLXtDQ+jV69eeHh4qNreq1cvunbtqmrT0KFD+emnn4iNjaVz586sX78ec3NzZs2apVol8cUXX3Dy5Eny8vJYvnw5CxYsoGPHjgDUqlWLxMREli1bxuuvv/5IbRMPL69QuRzfUFdHrbxsJr7s/Tvl3jNGRy1GW0uTahb334JiYWTAGx2asnDXMY6eV34JNdLTZVrvjuWOL0RlKigoAMBAXz2xpP6t12Wrku6Un5enjLkruev9YgBSU1PZuHEjNWvVUv3eFKKyFRUqr09dXUO1ch1d5fVaeDOvXEzhTWUuFl29imOKbsWkp8STm5OBS5M6uLXoyfVrV4jYv4Y/Vk2m98hF6OiWT9BakJfNoT8WY2hiSb1GvuXeF0I8m2TwL545lpaWAFy/fv2edbKysgD1GwQ+Pj588MEHlJaWEhkZycyZM/H29mb06NGPtex/6dKlqhkkXV1drKys1PaYNmzYEDMzM5YuXUp8fDyXLl0iJiYGQC2hn6Oj40OfU1tbm549exIcHMyoUaM4duwYGRkZ9OjR45Hafuc59fX1GTRoEDt27CAyMpJLly4RGxvLtWvXVMv+z549i5ubm9rn1KpVK1q1akVkZCQ3b97k/fffR1Pz9mKi4uJiCgsLKSgoUH25FpVLUVp63/c1K8gOXfqAmPtllL7bwbOXWLwnjNZ1auDT0JnCkhJCTp3l8y37mdGnE9UtJEGUqBoP2tJ15++iMo9z7SclJbHw66/R1NRk9KhRj/TvQ4hHUfqAa1qjgmuaB1zT3Lpe23Yfi6amFjbVlCsE7Wu5YWHjyNYVH3E+8k8aNuumFpaXk8GOtZ+RdyOTrq9/jo7ewz0NSQjx9JPBv3jm1KxZEzs7O44fP063bt0qrBMaGoqpqSlOTk6qMiMjI9Wg18nJCVtbW4YNG4aWltYjJ/sDqFatGjVq1Ljn+2FhYYwYMYKOHTvi5eWFv78/+fn5jBkzRq3eow6MAwICWLZsGVFRUWzZsgVfX99HfrTgnefMy8tj0KBBFBQU4OfnR58+fWjUqJHajP39bo6UfaH++uuvK8zBIJmxq46RnnJ2/e6EfPdaEXBnWX5hxTFGjzBjv/54NC72Vrz78u39po1r2jNhzQ7WhkbxgZ/3faKFeHyGhsqZzrIVAGXKZu/1K3h0a9mM/71iDAzVZ09jY2P5/ocf0NPT44P331dtDxCiKujqK7PuFxWqr0ApvMeKAACdWzP+5WJuzfiXZfK3q9GgXKxdzYbo6huRnnpBrTwj5SK71s+gqLAAv9emY1vd5XG6I4R4Ssmef/HM0dTUZPDgwWzevFktyV1Z8rzQ0FCCgoIYOHCg2qP+7taqVSuGDRvG2rVrOXDgQKW385dffqFly5YsXryYoUOH0qZNG9We0wfNQN1PnTp1aNKkCdu3b2fv3r307dv3X7Xz0KFDREdHs3LlSsaNG0e3bt0wNjYmPT1d1c66dety5swZtRULu3fvxsfHh9q1a6Otrc3Vq1dxdHRU/ezfv59ly5ZVOAMnKoedqTGaGhokZ91QKy97Xd2y/Mx72Wx80r1iHmG2Pi07Dxd7a7UyXW0tattacCUj66GPI8Sjsra2RlNTk7S0NLXystfVHMpnNC9L2pd6V9K+shiHO2LCwsJY+PXXWFhYMHnSJLX3hKgKJub2aGhqkp2pnlwv51ayPXPrmuVizK2UExBlif3KlB3D3LomNwtyiT21m4zUS2p1SktLKSkuwsDw9uTB1YuRbF05CUqhx5A52NWs+HGxQohnl3wrF8+kESNG0K5dOwYNGsTq1au5ePEiI0eOZN++fQwZMgQbG5tyM+wVGT9+PE5OTkybNo3c3PLPMf83HBwciI2N5cSJEyQkJPD777+zaNEiALWEfw9iaGhIXFycKtcBKGf/AwMD0dfXp02bNv+qnWVfiLds2UJiYiInTpzg7bffpqioSNXOgQMHkpmZyWeffUZcXBzHjx9n7ty5tGrVChMTEwYMGMCiRYsIDg7mypUrbNy4kXnz5slMWRXT1daiYTUbQuMS1W4ohcYlYqinQz1by3Ix9mbG2JoacSxOPXnUsbgE7M2V7z2s6hYmxCRdUzt3YXEJF9IysTM1foweCfFwdHV1qVe/PuEREWrXX3hEBAaGhmqrvsrY2tpibW1NeHi4Wnl4eDi2dnZYW1kBEHn6NMuWLaNO7dp8NHGiKoeMEFVJW0cX+1puXIw5onZNX4g5gq6+ETbV65eLMbV0wNjclosxh9XKL8YcxtTSARNzW7S0dDi6Ywl/H9mgVufy2VBKigtxcGoEKJMC7l73OUamVvQcNl+S/AnxnJJl/+KZpKmpyTfffMOmTZvYsGEDCxYsoLS0FGdnZwICAggODmbEiBFMmzaNOnXq3PM4enp6fP755wwZMoSFCxfyySefVFobx40bx7Vr11TJBOvWrcusWbP48MMPOX369H3bdaeyzPtxcXH8+OOPAHTt2pXPP/+c3r1733d1w8No1KgRkydPZvny5Xz99dfY2dnRrVs3HBwcOH36NKDMjv3LL78wb948evfujZmZGd26deO995TPIp48eTIWFhYsWrSI1NRUHBwcGDduHCNHjvxXbRMP1q+ZKzO27GfBzqP4NHQmNjmdLadiGdjKAz0dbfIKi0jIyMbOzAgzA+V2j37NXfl+73F+0telmVM1jl+4ytHzV5jwUqtHOveAlu7M235Ede5ihYKtp86SfiOf8V0e7VhCPKoe3buzYMEClixZQpu2bYk7f55dO3fSt29f9PT0yM/PJykpCRsbG0xMlMkre/j7s/zXXzE2NqZR48b8feoUJ06cUCVNLSwsZOXKlejp69O9e3e1J6sAmJubq/LOCFHZPNu+yvbVU/lz05fUb9yF1IR/OH1sM807DUFbR4/Cm3lcv3YFE3N7DIyUM/ZN2r3GwZBFHDH4gZr1WnD5bCgXzhymU58PAeVNhcZt+hGxfw0GRubUrNOMzLRLRBxYS636Lah2a/B/cOsiFIoSmnZ4nRvZadzIvr2qRt/AFFNLWf0ixPNAo/TfrD8W4imVl5fHxo0b6dKly3O5XPPKlSu89NJLbN++vcIZrudB7pIpT7oJz4zQ+ATWh0WTmJmDlbEBL7vXpWcT5T7NqMRUpgf9xdu+zenUwFkVszs6juCTsaTfyMPO1Jg+Xg3o4OJU4fFTs3MZs+qPcscAOHkpid9P/EP8tUwMdLSpa2fJwJYeOFqbV1V3nyspnYc/6SY80yIiItgSEkJycjIW5uZ07NSJl196CVDu2Z8/fz5Dhw2jjfft/BP79+9n565dZGZmYmNtTdeuXWndWpm3IiYmhq+++uqe5/P396dnz55V26nnUFBU+XwwomIXY44ScWANWemJGJpY4tqsOx6t+gCQdPE02wKn0M5/PPUb387AHxOxg9NHN5Gbk46JuR2N2/Snrkcn1fulpaXEhG/nTPgf5GSmoGdgQh33DjRtPxBtHV2yM5PZ8N29nxpUr5EP7XtOqLI+Py/e6/X0JAR9Wr9DGY2a+aSb8MKTwb8Qz5CkpCQiIyNZs2YNenp6LF269Ek3qco8rX+4hKhMMvgXLwIZ/IsXgQz+H0wG/0+eLPsX4paUlBT8/PzuW8fDw4OVK1f+Ry0qLzMzk0mTJuHk5MS3336r9t7o0aMJDQ29b/ymTZtwdna+bx0hhBBCCCHE80cG/0LcYm1tTVBQ0H3r6Onp/TeNuQdXV1dOnjxZ4XvTp08v9wiru1WrVq0qmiWEEEIIIUSVuX79OgsWLOCvv/7ixo0buLi48P7779OsWbMn3bRnigz+hbhFS0sLR0fHJ92Mx2ZnZ/ekmyCEEEIIIUSle++990hLS2PBggVYWVmxatUqRowYwebNm6ldW7YWPSx51J8QQgghhBBCiKfSpUuXOHz4MNOmTaNZs2Y4OzszdepUbG1tCQkJedLNe6bI4F8IIYQQQgghxFPJwsKCpUuX4uHhoSrT0NBAQ0OD7OzsJ9iyZ48M/oUQQgghhBBCPJVMTU3p0KEDurq6qrKdO3dy6dIl2rVr9wRb9uyRPf9CCCGEEEIIIaqUr6/vfd/fu3fvQx0nIiKCyZMn89JLL9GxY8dKaNmLQwb/QgghhBBCCPGcMBo180k3oWLr7z/4fxh79uzhgw8+oGnTpsyfP78SGvVikcG/EEIIIYQQQogq9bAz+/cSGBjIzJkz8fPz48svv1TbBiAejuz5F0IIIYQQQgjx1FqzZg2ff/45r7/+OgsWLJCB/2OSmX8hhBBCCCGEEE+lCxcuMGvWLLp06cKoUaO4du2a6j19fX1MTEyeYOueLTL4F0IIIYQQQgjxVNq5cydFRUXs3r2b3bt3q73Xp08f5syZ84Ra9uyRwb8QQjzj/r6czNrQKC5nZGFuoI+fR138PeujoaFxz5hD5y6z8fgZUnNysTExpHfTBnRq4Fxh3fzCIt7/bRf9W7iWq3Pp2nUCj0ZyLiUDHS1NGte0Z5B3I8wN9Su1j0JUJDo6mqCgIBKvXsXUxIROPj681KXLfa/9sLAwtm7dyrX0dKysrPDz86ONt7danRMnTrBz506SkpIwNDKiYYMGBAQEYGpqWtVdEi+4hPiThP+1iszUyxgYmeHavAfuLXvf95qOiz7AqYO/kXM9FWNzGxq17kf9xuqJ1dYuGkZeTnq52NffC0TfUHldX70YycmDa8lIuYiWlg52NRvS3GcoppYOldtJIR7R6NGjGT169JNuxnNBBv8vsEmTJpGYmMiqVasAUCgUbNy4kU2bNnH+/HkA6tatS79+/QgICFD7wzN48GDCwsLUjqejo4O1tTU+Pj58+OGHGBgYPFQ7QkNDGTJkiFqZtrY2lpaWtG/fnokTJ2JmZlZp/QwPD6e0tJRmzZo99jGrmo+PD3369GHs2LEPHZOdnc2MGTPYv38/RkZGjBkzhv79+1dhK8XT4GxyOrP/OESbejV5tYUbMUnXCDwaSYlCQR+vhhXGHItL4JvdoXRrVA/PWvaExSfy/d7j6Ghp0bZeLbW6NwoKmbv9MGk5ueWOcz2vgOnB+7EyNmCMb3MKi0tYffQ0M0MOMLtfZ7S1JK2MqDrx8fEs/vZbmjdrRs9evTh/7hy/b9yIoqSErl27VhgTHhHBzz//jK+vL25ubpw6dYrlv/6KjrY2LVq0AJQ3B3766Sfat29P7969ycrOJjg4mPnz5/PJJ5/IPlNRZVITY9m97nNqu7alafvXSblyhrC9y1EoSmjs3a/CmAsxR/gr6CvcWvhTo3ZTLsUe42DIIrS0dajj1h6Agrxs8nLSadF5GHY1XdXidfWMAEi58g871nyKY/2WdOz9PkWFBZw6tI6tKz6i76hvVTcIhBDPNhn8CwCKi4sZM2YMkZGRjB07Fm9vb0pKSjh48CBz5szhzz//ZPHixWhpaaliunbtypQpU1Sv8/LyOHToELNnz0ahUDBt2rRHasOGDRtwcFDeXS4pKSE2NpZJkyZx7do1lixZ8th9mzJlCiUlJarXAwcOZPbs2U/14P9xzJ49m/Pnz7N27VqOHj3Kp59+SuPGjalfv/6TbpqoQuvConC2MWds55YANHF0oERRyqbwf+jeuD662lrlYtYcO02rOjUY2tYTAM9a9ty4WchvoVFqg//jFxL59eAp8ouKKjz38QuJ5BTcZFY/X+zNjAEw0tNlZsgBYpOv4VbdtpJ7K8RtwVu2UKtmTUaMGAGAh7s7JQoF27Ztw9fXt8JB+ubNm/Hy8uLVV18FwN3dndzcXIKCg1WD/23bt+Ph4cHgwYNVcfZ2dsyePZvI06dp5uX1H/ROvIgi9q/Gys6ZDr3eA6BmXS8UihL+PrQBt+Y90dYpf02H71uJc0NvWnUZCUCNOk25WZBDxF+BqsF/enI8AI71W91zFj/yyEbMrWviEzBJNdljX9OVtd8M41zkXjxa9an0/goh/nsyLSMAWLZsGWFhYaxevZqBAwfi5OREnTp1GDp0KCtXruTAgQMsW7ZMLUZfXx8bGxvVj6OjI6+//jr+/v5s27btkdtgaWmpOpa9vT0dOnTgf//7H/v37yc7O/ux+2ZiYoK5ufljxz8rzpw5g5eXF3Xr1qVv374oFAouXrz4pJslqlBRSQnRiWm0cK6uVt6qTg0KioqJSbpWLiY1O5ek6zm0qF0+JiXrBknXcwDIvVnIvO1HcK1mw5Qe7e9xfgUABrq37yMb6ym/nOYUFD5+x4R4gKKiImJjY2nSpIlauZeXFwUFBarVa3e6lp5OSnJyhTFpqamkpKRQWlqKa8OGtG+vfs3b29sDkJaaWsk9EUKppLiIpEtRODZorVbu3LANRYX5pFw5Uy4m53oqWelXcXRRj3Fq0IbszGSyMq4CkJF6AR1dA0ws7O95fpvqLri16Km2ytPQxBJdfSOyM5L+TdeEEE8RGfwLFAoFgYGB9OvXj9q1a5d739XVlV69erFq1SoUCsUDj6enp4e2duUsKtHS0kJDQwMdHR0A9uzZQ//+/fH09MTDw4O+ffty8OBBVf3BgwczdepU+vfvT7NmzdiyZQuTJk1SzeC4uLgAMHnyZCZNmsSYMWPKbTmIj4/HxcWFc+fOPVQbXVxcWLduHQMHDsTDw4OuXbsSERHBunXr6NixI02bNmXChAkUFBSoYjZs2IC/vz+NGjXC09OTgQMHcvr06XueIyIigtdff51GjRrRsWNHpk+fzo0bN9TqeHt7s2nTJs6cOcPcuXOxtbWldWv1LwQVfT6FhYV8+eWX+Pj44O7uTosWLRg/fjwZGRmquGvXrjFx4kRatmyJl5cXo0aN4tKlS6r39+3bR9++fWnUqBFdunTh66+/prBQBn9VLSUrlxKFAgdz9Sy3ZbPwiZnlb5qVlVW7K8bhVszVW4N/PW1tvn7Nj3c6t8DUQK/C87euUwMLIwOWHThJRm4+Kdk3CDwaiYWRAY1q2v27zglxH2lpaZQUF2Nrp36d2drYAJCcnFwuJilJOYCxuzvGVrlCJSUlBQ0NDV555RU8PT3V6pw8dQqA6tXVb5oJUVmyM5NRlBRjallNrdzUQjlTn5WeUC7m+rUrAJhZql+XZreOkZWeCChn/vUMjPnz9zmsnDeAFXNf4c9NX5KXc/vvvGfbV3Dx7KJ2nKRLURTm38DCRn07mBDi2SWDf8Hly5dJTU3F6z5LGVu3bk1qaioJCeX/+JQpLi7mr7/+Ijg4mF69ev2rNhUXF3PixAlWrlxJhw4dMDAwICoqirFjx9K9e3dCQkJYv349lpaWTJw4UW2guWHDBoYMGcKaNWto166d2nEPHToEwMcff8yUKVPo27cvYWFhqi+FAEFBQXh4eFCvXr2Hbu/ChQsZOXIkwcHBmJiYMHr0aHbu3MnSpUuZPXs2e/bsYcOGDQDs3r2bGTNmMHLkSLZv387y5cu5efMmn3zySYXHjomJYdiwYbRr144tW7Ywf/58oqOjGT58OKWlpap6Y8eOxcTEhL59+xIdHc2qVasqfPTJ3Z/P3Llz2bVrF3PmzGHnzp3MmTOHY8eO8cMPP6j+WwwfPpzz58/z/fffs379ehQKBSNHjqSkpIQDBw4wYcIEXnnlFbZu3cpnn33G9u3b+fDDDx/68xOPJ69QuRzfUFdHrbxsJr7s/Tvl3jNGRy1GW0uTahb3f3SOhZEBb3RoyomLVxm1PIR3Vm3j4rXrfNyjXbnjC1GZym6mGuirJ5bUv/U6Pz+/XEx+Xp4y5q58NPeLAUhNTWXjxo3UrFULDw+Pf9dwIe6hqFB5ferqGqqV6+gqr9fCm3nlYgpvKnOx6OpVHFN0KyY9JZ7cnAys7Ovw0qtTadl5OMmXovlj1WSKCguoSEFeNof+WIyhiSX1GvlWWEcI8eyRPf+CzMxMgPtmMbawsAAgPT2dWrWUd4BDQkLYuXOnqk5BQQHVqlVjxIgRj5WRs0ePHqrlZgUFBWhpadGhQwdmzJgBKFcBTJ06lYEDB6pihgwZwhtvvEF6eroqX0DDhg3x9/ev8Bw2t2aFTExMMDExoUOHDlhbW7NlyxZGjRqFQqEgODiYN99885HaHhAQgI+PDwC9evVixowZfPrppzg5OVG/fn1+/vln1UoCc3NzZs6cSc+ePQHlTFK/fv1U/bzbsmXLaNOmjeozdXJy4quvvqJz586EhYXRsmVL0tLSmDBhAgqFAnNzc3R1dalevbrqpside1/v/nw8PDzw8/NT5UCoXr063t7enD17FoCjR48SGxvLjh07cHZWZnr/4osvWL58OVlZWfz444+88sorDBgwAIBatWoxffp0/ve//5GQkECNGjUe6bMUD09xx82fimhWkB269AEx98sofbeDZy+xeE8YrevUwKehM4UlJYScOsvnW/Yzo08nqltIgihRNR60Ck1Ts/zcxuNc+0lJSSz8+ms0NTUZPWrUI/37EOJRlD7gmtao4JrmAdc0t67Xtt3HoqmphU015aSGfS03LGwc2briI85H/knDZt3UwvJyMtix9jPybmTS9fXP0dF7uATOQoinnwz+BZaWlgBcv379nnWysrIA9RsEPj4+fPDBB5SWlhIZGcnMmTPx9vZm9OjRj7Xsf+nSparlmLq6ulhZWZUbtJqZmbF06VLi4+O5dOkSMTExAGoJ/RwdHR/6nNra2vTs2ZPg4GBGjRrFsWPHyMjIoEePHo/U9jvPWTarVHaTBJQzS2UD8ebNmxMXF8d3332n6kdsbOw9v8yeOXOGS5culdunChAXF0fz5s0ZNWoUurq6/PHHH5w9e5Zhw4Yxc+ZMnJ2dWbhwIYcOHcLY2LhcW0F5s+LIkSPMnz+fixcvEh8fz4ULF1Q3A86ePYuZmZlq4A/KZbMfffSRqn2RkZFs3LhR9X7Zl+y4uDgZ/FchIz3l7PrdCfnutSLgzrL8wopjjB5hxn798Whc7K149+Xb20sa17RnwpodrA2N4gM/7/tEC/H4DA2VM513bqeC27P3+hU8babsd/O9YgwM1WdPY2Nj+f6HH9DT0+OD999XbQ8Qoiro6iuz7hcVqq9AKbzHigAAnVsz/uVibs34l2Xyt6vRoFysXc2G6OobkZ56Qa08I+Uiu9bPoKiwAL/XpmNb3eVxuiOEeErJ4F9Qs2ZN7OzsOH78ON26dauwTmhoKKampjg5OanKjIyMVANJJycnbG1tGTZsGFpaWo+c6R+gWrVq9x0ohoWFMWLECDp27IiXlxf+/v7k5+czZswYtXr6+o/2fPGAgACWLVtGVFQUW7ZswdfX95EfLVjRzY6KZp5AuWJi0qRJ+Pv707RpUwYMGMDZs2fvOfOvUCjw9/evcDWFpaUlZ8+eJTo6mg0bNmBmZkbz5s357LPP+OSTTzAzM6Nly5aqgT+U/3w+/fRTdu7cSe/evfHx8WHMmDEsW7aMlJSUe/bt7vaNHDmSPn3KZwIuW2khqoadqTGaGhokZ6nnfyh7Xd2y/Mx72Wx8UtYNnG0sysc8wmx9WnYeLZ3V/83qamtR29aCKxlZD30cIR6VtbU1mpqapKWlqZWXva7mUD6jeVnSvtTUVLWbs2UxDnfEhIWF8cuvv2Jvb8/4ceNUq9+EqCom5vZoaGqSnameXC/nVrI9c+ua5WLMrZS/f7MyrmJlfztnU9kxzK1rcrMgl4sxR7CpVh9L29s3/0tLSykpLsLA8Pb3nasXI9mzYRa6eob0GDJH9voL8RySPf8CTU1NBg8ezObNm9WS3JUlhwsNDSUoKIiBAweqPervbq1atWLYsGGsXbuWAwcOVHo7f/nlF1q2bMnixYsZOnQobdq0Ue3Vf9ByzvupU6cOTZo0Yfv27ezdu5e+fftWVpMrtHTpUvr168ecOXN4/fXXad68OVeuKJP2VNSPevXqcf78eRwdHVU/xcXFzJ49m6SkJNUM2J1fgvv370/79u3JysqiY8eO92xLZmYm69at47PPPmPy5Mn07duXhg0bEh8fr2pL3bp1ycrKUkvwl5GRQcuWLTl16hT16tXjwoULau1LTk5m7ty55OaWfza8qDy62lo0rGZDaFyi2rUTGpeIoZ4O9Wwty8XYmxlja2rEsTj1/B3H4hKwN1e+97CqW5gQk3RN7dyFxSVcSMvEztT4PpFC/Du6urrUq1+f8IgItesvPCICA0NDtRvVZWxtbbG2tiY8PFytPDw8HFs7O6ytrACIPH2aZcuWUad2bT6aOFEG/uI/oa2ji30tNy7GHFG7pi/EHEFX3wib6uUf22tq6YCxuS0XYw6rlV+MOYyppQMm5rZoaelwdMcS/j6yQa3O5bOhlBQX4uDUCFAmBdy97nOMTK3oOWy+DPyFeE7J4F8AMGLECNq1a8egQYNYvXo1Fy9eZOTIkezbt48hQ4ZgY2NTboa9IuPHj8fJyYlp06ZV+sDPwcGB2NhYTpw4QUJCAr///juLFi0CeKTM8oaGhsTFxalyHYBy9j8wMBB9fX3atGlTqe2+m4ODAxEREURHR3P58mWWL19OYGAgUHE/hg8fzpkzZ5g+fTpxcXGcPHmS999/n4sXL+Lk5EStWrXo0KEDM2fO5MiRI1y8eJFFixZx9OhRPDw8mDt3rirR4d2MjY0xMTFh7969qu0HU6dOJTo6WtWW1q1b4+7uzkcffURkZCTnzp3jo48+wtLSEjc3N9544w127tzJt99+y4ULFzh69CiTJ08mJydHZv7/A/2auXI+NYMFO49y8lISv4VGseVULH2aNkRPR5u8wiLOJqeTlX97qXO/5q4cPX+Fn/aHc/JSEkv/Cufo+SsMaOH+SOce0NKdsynpqnMfv5DIzJADpN/Ip18z18ruqhBqenTvzsULF1iyZAmno6IICgpi186ddOvaFT09PfLz84mPjycnJ+d2jL8/J06cYPXq1ZyOiiIwMJATJ07Q+1aS2sLCQlauXImevj7du3cnKSmJ+Ph41c+dT0ERorJ5tn2VtKvn+HPTl1w5H074X4GcPraZxt790NbRo/BmHqmJseTn3l5Z1aTda1w4c5gj23/gyvlwDm/7ngtnDuPVYRCgvKnQuE0/4qMOcGz3zyTGnyIqNJj9W76mVv0WVLs1+D+4dREKRQlNO7zOjew0UhNjVT/yqD8hnh+y7F8Aytn/b775hk2bNrFhwwYWLFhAaWkpzs7OBAQEEBwczIgRI5g2bRp16tS553H09PT4/PPPGTJkCAsXLrxnBvvHMW7cOK5du6Za/l63bl1mzZrFhx9+yOnTp+/brjsNHz6cn3/+mbi4OH788UcAunbtyueff07v3r3vu7qhMkydOpVPP/2UQYMGoaurS4MGDZg7dy7vvvsup0+fVu21L+Pp6cnPP//MokWL6NOnD4aGhrRu3ZqPPvpIlRNhwYIFfPnll7z77rvk5+fj6urK0qVLadasGZMmTSIsLIy2bduWa4uOjg6LFi1izpw5+Pv7q7YJvPfeeyxZsoT8/HwMDAz4/vvvmT17NsOGDUNDQ4NWrVrx888/o6Ojg5+fHwsXLmTJkiX8+OOPmJubq/JBiKrnXsOW9/1asz4smi+3HcbK2IBBrRvRs4lyn2Z8WibTg/7ibd/mdGqgzNvQqYEzxSUKgk/G8uc/F7AzNeadzi1oU+/RZnqaO1dncve2/H7iH+btOIKBjjZ17Sz5sn9nHK3NK7urQqhp0KABo0ePZktICN999x0W5uYE9OvHyy+9BCifZDN//nyGDhtGG29l/ok23t4UFxWxc9cuDh0+jI21NcOHD6d58+aA8lGvWbfy3yxcuLDcOf39/VXJWoWobNWcGuEbMImIA2vYs2EmhiaWtPAdikcr5ba69KQ4tgVOoZ3/eOo3Vmbgr9/YF0VJEaePbuLs33swMbejQ693qe12+2lHnm1fRd/AlDPhfxATvgM9AxMaNPWjaXtlAuXszGTSk5V7///cOKdcu+o18qF9zwlV3HshxH9Bo/TfrJcWL4y8vDw2btxIly5d1PZFPi+uXLnCSy+9xPbt2ytcLir+e7lLpjzpJghR5VI6D3/STRCiygVF1X5wJSGece/1kqeBiKefzPyLh2JoaMiQIUOedDMqXVJSEpGRkapn3svAXwghhBBCCPE8ksG/qDIpKSn4+fndt46HhwcrV678j1pUXmZmJpMmTcLJyYlvv/1W7b3Ro0cTGhp63/hNmzapPQJPCCGEEEIIIZ5GMvgXVcba2pqgoKD71tHT0/tvGnMPrq6unDx5ssL3pk+fXu550HerVq1aVTRLCCGEEEIIISqVDP5FldHS0sLR0fHBFZ9SdnZ2T7oJQgghhBBCCFEp5FF/QgghhBBCCCHEc04G/0IIIYQQQgghxHNOBv9CCCGEEEIIIcRzTgb/QgghhBBCCCHEc04S/gkhhBBPSFBU7SfdBCGqXG/3+CfdBCH+A3WedAOEeCCZ+RdCCCGEEEIIIZ5zMvgXQgghhBBCCCGeczL4F0IIIYQQQgghnnMy+BdCCCGEEEIIIZ5zkvBPCCGecX9fTmZtaBSXM7IwN9DHz6Mu/p710dDQuGfMoXOX2Xj8DKk5udiYGNK7aQM6NXBWq3M+JYNVR/4mLi0TQ10dOjZw4pXmbmhrVXzfeP6OIxjoaDPGt0Wl9k8IgIT4k4T/tYrM1MsYGJnh2rwH7i173/c6j4s+wKmDv5FzPRVjcxsate5H/ca+anXSrp4jbO8vXEuKQ1fPkHqNfGjSfiBaWre/Iv0V9BVxUfvLHd8n4COcG7apvE6KF150dDRBQUEkXr2KqYkJnXx8eKlLl/te52FhYWzdupVr6elYWVnh5+dHG29vtToXLl5k44YNXLp0CX0DA7y9venp74+29u3rPC4ujk2bNnHp0iX09PVp1qwZfXr3Rl9fv8r6K4T4b8nMv3jmbNmyhVdeeQVPT0+aNGlCQEAAv/32m+r9zMxMNmzYcN9jDB48mEmTJj30OSdNmoSLi4vaT+PGjfH392f9+vWP3ZcyLi4ubNq0CYCioiKWL1/+r49ZlUJDQ3FxcSEhIeFJN+WFdzY5ndl/HKK6hQkf+nnTrn4tAo9GEhQRc8+YY3EJfLM7FM9a9kzs2ga3arZ8v/c4h85dVtVJybrB5yH70dXW4r2XW9OjcX1CTsWy7GBEueOVlpby68GThMbJ9SCqRmpiLLvXfY65VQ18+02mjntHwvYuJ/Lo7/eMuRBzhL+CvqJ6naZ07v8xDrU8OBiyiLjoA6o62ZnJ7FjzKdraevj0nYh7y15EhQZzdOcStWOlp8RT260d/sPmqf04ODWqsj6LF098fDyLv/0We3t73nrrLVq2bMnvGzeyY8eOe8aER0Tw888/4+bmxttvvYVL/fos//VXwsLCVHXS0tJYuHAhurq6vDlqFF26dGH37t2sXbtWVSchIYGvFixAX1+ft956i759+3Li+HF++PHHKu2zEOK/JTP/4pmyceNGZs6cyZQpU/Dy8qK0tJTDhw/zxRdfcO3aNd555x3mzp1LQkIC/fv3r9RzN2nShMWLF6teFxQU8PvvvzN16lRMTU3x8/N77GMfOnQIExMTALZu3crs2bMZOnTov22yeAGsC4vC2cacsZ1bAtDE0YESRSmbwv+he+P66GprlYtZc+w0rerUYGhbTwA8a9lz42Yhv4VG0bZeLQCCTsZgoKPDR93aoq2lSVNHB/S0tVh28CR9vRpiY2IEwKVr1/nl4EnOpWagU8G5hKgMEftXY2XnTIde7wFQs64XCkUJfx/agFvznmjr6JaLCd+3EueG3rTqMhKAGnWacrMgh4i/Aqnj1h6AyKO/o6NrQOdXPkFLS5uadZuhra3HkZ1L8GzzCsZmNhQXFZKVfhX3lr2xre7y33VavHCCt2yhVs2ajBgxAgAPd3dKFAq2bduGr68vurrlr/PNmzfj5eXFq6++CoC7uzu5ubkEBQfTooVyFdaOHTvQ19dnzJgxaGtr08jDA11dXdauWUO3bt2wsrJi9+7dGBkZ8dZbb6mtBlj+668kJydjb2//H3wCQoiqJjP/4pmyZs0aAgIC6NevH87OztSuXZvBgwczdOhQVq5cCShnIauCjo4ONjY2qp+aNWsyYcIEnJycCAkJ+VfHtrGxUS2rq6r2i+dPUUkJ0YlptHCurlbeqk4NCoqKiUm6Vi4mNTuXpOs5tKhdPiYl6wZJ13MA+PtyCk0c7dWW+LeqW4PS0lL+vpyiKlu8N4wSRSmzA3wxN5CloaLylRQXkXQpCscGrdXKnRu2oagwn5QrZ8rF5FxPJSv9Ko4u6jFODdqQnZlMVsZVABLjIqhR10ttib9TwzZQWkpCvHKVS2baJUoVJVjZqW+LEaIyFRUVERsbS5MmTdTKvby8KCgo4Pz58+VirqWnk5KcXGFMWmoqKSnK39XR0dF4uLurDeqb3ZpAiY6OBqB3796MHzdOrY62lpaqbUKI54MM/sUzRVNTk5MnT5KVlaVW/uabb7Ju3TomTZrE5s2bCQsLw8VFOUNTWFjIrFmzaN26NV5eXsybNw+FQlFpbdLS0lLdjS8sLOTLL7/Ex8cHd3d3WrRowfjx48nIyACUy+pcXFxYsmQJbdq0wdfXlxs3bqiW/W/atInJkycDyq0A27dvx93dnaCgILVzfvXVVwQEBDxU+zZt2kSXLl347bff6NixI40bN2bcuHGkpKTwwQcf0KRJE9q3b8/GjRtVMVlZWXzyySe0a9cONzc3WrduzSeffEJ+fn6F5ygtLeWnn37C19eXxo0b06tXL7Zs2fKoH6V4RClZuZQoFDiYm6iV25sZA5CYmV0upqys2l0xDrdirl7PobC4hLSc3HJ1zAz0MdDV4eqtGwQAY31b8EWAD47W5v+6P0JUJDszGUVJMaaW1dTKTS0cAMhKL7/d5Pq1KwCYWarf5DK7dYys9ESKiwq5kZVWro6BkRk6eoZkpScCkJFyAYDYk7tY8/X/+HV2X7au+IjUxNhK6J0QSmlpaZQUF2NrZ6dWbmtjA0BycnK5mKSkJADs7o6xtQUgJSWFwsJC0tPTsbtr5t7ExAR9AwPVDQILCwtq1KgBwM2bNzlz5gybN2+mTt261KxZsxJ6KIR4Gsiyf/FMGTlyJO+++y7t27enZcuWNGvWjFatWuHh4YGpqSlTpkyhoKCA5ORk1RL9L774gj///JM5c+ZQrVo1fvzxR06cOPGv/5jduHGDNWvWEBcXx4QJEwCYO3cu+/btY86cOVSvXp3Y2FgmT57MDz/8wJQpU1SxmzdvZsWKFeTn52NsbKwq79atGzk5OcyaNYtDhw5hZmZGx44dCQoKonfv3gAoFAq2bNnCm2+++dBtvXr1Kjt27GDp0qUkJSXx9ttvc+zYMd566y3efvttfvnlF6ZNm4avry8WFhZMmjSJlJQUvv32W6ysrIiIiODjjz+mbt26FW5HWLhwIVu3buXTTz+ldu3aHD9+nGnTppGTk8Prr7/+WJ+veLC8QuVsjKGujlq5ga622vt3yr1njI4qJvdmYYV1AAx0tNWOK4N+UdWKCvMA0NU1VCvX0TUAoPBmXrmYwpu5yhi9imOKbubds05ZvaJbx01PiQeguKiATr0/oKAgh8gjG9m2ago9h83H0s7pcbsmhEpBQQEABncl1ytbFVjRzff8POU1amBgcM+Ysri7j1tW7+7jlpaW8u5771FUWIiRsTEDX3vtcbojhHhKyeBfPFP8/Pywt7dn5cqVHD58mP37ldmXnZycmDVrFl5eXujr66uW6N+4cYNNmzbx2Wef0aFDBwBmzZrFsWPHHvncJ06cUC2tKy0tJT8/HysrKz744ANeeuklADw8PPDz86NZs2YAVK9eHW9vb86ePat2rIEDB1K3bt1y59DX11ft/be5dbc/ICCAt99+m5SUFOzs7Dh69CgZGRn06NHjodteXFzM1KlTqVOnDvXr16dBgwbo6OgwbNgwAIYNG8aGDRu4ePEiFhYWtGnThubNm6tWT9SoUYPAwMBy/QDIy8tj+fLlLFiwgI4dOwJQq1YtEhMTWbZsmQz+q5DiAVtENCvIDv2gbSUaGho8aOPJfZJOC1HpSh+wUktDs4JFjA/aPqWh8XB1ALfm/tSq14IadZqq3qrm1JgN34/i1OF1+PT96P7HEeIhPGhFomYF1/lD/T5/iDp3Kikp4Z0xYygqKmLb9u3MnTuXjz76SGb/hXhOyOBfPHM8PT3x9PREoVAQExPD/v37CQwM5I033mD37t1qdS9cuEBRUREeHh6qMj09PVxdXR/5vO7u7syfPx9Q/hE2NDTEyspKrU6vXr04cuQI8+fP5+LFi8THx3PhwgXVzYAyjo6OD33e9u3bY2VlRXBwMG+++SabN2/G19cXMzOzR2p/rVq1VP/f0NAQBwcH1Ws9PT1AuW0BlDcn/vzzTzZv3szFixc5f/48CQkJ1K5du9xxz58/z82bN3n//ffVvpwUFxdTWFhIQUGBPCaoihjpKWfm8+/aj3mvFQF3luUXVhxjpKtzR53icvF5hUUYVZB0SoiqoquvTC5ZVKg+Q1l4jxUBADq3ZvPLxdyazdfVM7pnHVCuDNDVU57XzKo6ZlbqWwP09I2wq9FAtSVAiH/L0FB5PZatAChTNjOvf9fsPtye8b9XjIGh4T3rABTk52NgqP7vR1tbW/UdqV69ekyaPJk9e/cyTJIQC/FckMG/eGYkJyezZMkSRo0ahb29PZqamri6uuLq6krnzp3p0aMHx48fV4spu6N9953vOxPaPCx9ff0HDto//fRTdu7cSe/evfHx8WHMmDEsW7ZMtafuzmM9LC0tLXr37k1ISAiDBg1iz549LFq06JHbr6OjPhCsaBYBlLMPo0aN4ty5c/To0YNu3brh5ubG1KlTK6xf9tl+/fXXFd4cqCg7sagcdqbGaGpokJx1Q6287HV1S9NyMdUtlGVJWTdwtrEoH2Nhir6ONpZGBiRl5ajFZuUXUFBUTHVL9VwAQlQlE3N7NDQ1yc5MUivPyVC+NrcuPyNpbqXcu5yVcRUr+9u/l8qOYW5dEx1dfQxNrMi+lfyvTH5uFkWF+arjxkcfRNfAmBq11ZOqlRQXom/4aDdhhbgXa2trNDU1SUtLUysve13tjhv2Zcoy8Kempqrd4C+LcXBwQE9PD3MLC1JTU9Vic3JyKCgoUE0E/P333+gbGOBSv76qjqGhIbY2NmRdv/7vOyiEeCpIwj/xzNDV1WXDhg0VJpIzNVUOaKytrdWWsDk7O6Onp0dExO1nkxcXFxMTc+9noD+uzMxM1q1bx2effcbkyZPp27cvDRs2JD4+/pEy+N+9BA+US//Pnj3LqlWrMDExoW3btpXZdDX//PMPBw4cYNGiRXzwwQf07NmTWrVqcfny5Qr7Ubt2bbS1tbl69SqOjo6qn/3797Ns2bJ73mQQ/56uthYNq9kQGpeo9t8mNC4RQz0d6tlalouxNzPG1tSIY3HqSdKOxSVgb658D6BRTTsiLiVRVFJyu875BDQ1NHCvbltFPRKiPG0dXexruXEx5ojadX4h5gi6+kbYVK9fLsbU0gFjc1suxhxWK78YcxhTSwdMzJXXcPXanlw+f5yS4tsrYS7+cxgNTU2qOTUCIObkDg5v+46SktsrYXKz00lJiMHhVh0h/i1dXV3q1a9PeESE2nUeHhGBgaEhTk5O5WJsbW2xtrYmPDxcrTw8PBxbOzusb61OdHV1JfL0abWs/SfCw9HU1KTBre19u/fsYXVgoNr2g4yMDK4mJakSAQohnn3yrVw8MywtLRk5ciSLFi1i4cKF/PPPP1y5coV9+/bxzjvvqBIAGhoakpqaypUrVzAyMmLQoEF888037Nq1i7i4OD777LNyM/GVwdjYGBMTE/bu3culS5eIjY1l6tSpREdHq5bTP4yypX9RUVGqZXrOzs40bdqU77//nl69eqGlVXXPU7e2tkZbW5vt27dz5coVTp8+zYQJE0hLS6uwHyYmJgwYMIBFixYRHBzMlStX2LhxI/PmzVNlHBZVp18zV86nZrBg51FOXkrit9AotpyKpU/ThujdSs53NjmdrPzbSz77NXfl6Pkr/LQ/nJOXklj6VzhHz19hQAt3VZ3eTRuQlX+TmSEHCb94lZBTsSw/fIrObrWxMTF6El0VLzDPtq+SdvUcf276kivnwwn/K5DTxzbT2Lsf2jp6FN7MIzUxlvzc20+CadLuNS6cOcyR7T9w5Xw4h7d9z4Uzh/HqMEhVp1HrAApys9n52zQunzvO6WNBhO5ZRoMmL2NsZqM6942sNPas/4Ir58OJi9rP9tVT0DMwxr1l7//6oxDPsR7du3PxwgWWLFnC6agogoKC2LVzJ926dkVPT4/8/Hzi4+PJybm9KquHvz8nTpxg9erVnI6KIjAwkBMnTtC7Vy9VHb+XXyYnJ4dF33xDZGQku3bvZv369apthWXnTklJYcmSJURHR3Ps2DEWLFyIoaGhKq+REOLZJ8v+xTNlwoQJODk5sX79elavXk1BQQHVqlWja9eujBo1ClA+q3b37t306NGDXbt28f7776Onp8eMGTPIzc2la9eu+Pj4VHrbdHR0WLRoEXPmzMHf3x8zMzNatmzJe++9x5IlS+75mLy7tWrVisaNGzNgwADmzZtH165dAejbty8RERH06dOn0tt+Jzs7O+bMmcPixYtZvXo1NjY2dOzYkaFDh/Lnn39WGDN58mQsLCxYtGgRqampODg4MG7cOEaOHFmlbRXgXsOW9/1asz4smi+3HcbK2IBBrRvRs4lyNic+LZPpQX/xtm9zOjVQPqe8UwNniksUBJ+M5c9/LmBnasw7nVvQpt7tZaPVLUz5xL89gUcimb/jCKb6evRoXJ9X77hBIMR/pZpTI3wDJhFxYA17NszE0MSSFr5D8Wil/H2YnhTHtsAptPMfT/3GvgDUb+yLoqSI00c3cfbvPZiY29Gh17vUdmunOq65dQ38Bk4nbO+v/Pn7HPQMTHFr0ZOmHV5XO3fXgTOIOLCGfZvnoaGhQY06TWnuMxQ9fbkRJipPgwYNGD16NFtCQvjuu++wMDcnoF8/Xr41+L58+TLz589n6LBhtPH2BqCNtzfFRUXs3LWLQ4cPY2NtzfDhw2nevLnquA4ODkwYP56Nv//ODz/+iLGxMZ07d6ZXz55q53733XcJ3rKFH3/8EU0tLdzc3OgXEKBaXSmEePZplD7KemQhxBOzePFijhw5wtq1a590U/4TuUumPLiSEM+4JfZfPOkmCFHlervHP+kmCFHlatep86SbIMQDycy/EE+58PBwLly4wMqVK5kxY8aTbo4QQgghhBDiGSSDf/FC++mnn/j+++/vW+fjjz+mf//+/1GLytu3bx+BgYEEBASotgAApKSk4Ofnd99YDw8PVq5cWdVNFEIIIYQQQjzlZNm/eKFlZWVx/QGPsLGyssLY2Pi/adAjKCkpISEh4b519PT0VI8CetbIsn/xIpBl/+JFIMv+xYtAlv2LZ4HM/IsXmpmZGWZmz+ZzmrW0tHB0dHzSzRBCCCGEEEI8A+RRf0IIIYQQQgghxHNOBv9CCCGEEEIIIcRzTgb/QgghhBBCCCHEc04S/gkhnkrxcXFPuglCVDm7Pb886SYIUeUksaV4EbzXS+NJN0GIB5KZfyGEEEIIIYQQ4jkng38hhBBCCCGEEOI5J4N/IYQQQgghhBDiOSeDfyGEEEIIIYQQ4jkng38hhBBCCCGEEOI5p/2kGyDEv7FlyxYCAwM5e/YsGhoa1K5dm/79+zNgwAAAMjMz2bNnD/3797/nMQYPHkz16tWZM2fOQ51z0qRJbN68Wa1MX1+fWrVqMXjwYF555ZXH7xDg4uLC7Nmz6du3L0VFRaxevZqhQ4f+q2OK51t0dDRBQUEkXr2KqYkJnXx8eKlLFzQ07p15OCwsjK1bt3ItPR0rKyv8/Pxo4+1dYd2CggKmTZuGf8+e96wDcOrUKb777js++OADXFxc/nW/hHiQvy8nszY0issZWZgb6OPnURd/z/r3vfYPnbvMxuNnSM3JxcbEkN5NG9CpgXOFdfMLi3j/t130b+F6zzpCVKaE+JOE/7WKzNTLGBiZ4dq8B+4te9/3mo6LPsCpg7+Rcz0VY3MbGrXuR/3Gvmp1zv69l9PHNpGTmYKxmTUNvbrj2ryH2nGTL0dzYt9KMlIuoqtvhKNLK5p1HIyOnkGV9VcI8d+Swb94Zm3cuJGZM2cyZcoUvLy8KC0t5fDhw3zxxRdcu3aNd955h7lz55KQkHDfwf/jaNKkCYsXL1a9Ligo4Pfff2fq1KmYmpri5+f32Mc+dOgQJiYmAGzdupXZs2fL4F/cU3x8PIu//ZbmzZrRs1cvzp87x+8bN6IoKaFr164VxoRHRPDzzz/j6+uLm5sbp06dYvmvv6KjrU2LFi3U6ubm5vLdd9+Rnp5+33bcuHGDVatWVVq/hHiQs8npzP7jEG3q1eTVFm7EJF0j8GgkJQoFfbwaVhhzLC6Bb3aH0q1RPTxr2RMWn8j3e4+jo6VF23q11OreKChk7vbDpOXk/hfdEYLUxFh2r/uc2q5tadr+dVKunCFs73IUihIae/erMOZCzBH+CvoKtxb+1KjdlEuxxzgYsggtbR3quLUHIPbkLg798S0erftSo3YTUhNjCd2zjKLCfDzbKicsMtMus2PNp9jVbEinvhPJu5HB8b3LybmezEuvfvqffQZCiKolg3/xzFqzZg0BAQH063f7D2Lt2rVJSUlh5cqVvPPOO5SWllbJuXV0dLCxsVErmzBhAtu3byckJORfDf7vPG5VtV88P4K3bKFWzZqMGDECAA93d0oUCrZt24avry+6urrlYjZv3oyXlxevvvoqAO7u7uTm5hIUHKw2+D916hS//fYbBTdvPrAdq1evRktb/qSI/866sCicbcwZ27klAE0cHShRlLIp/B+6N66PrrZWuZg1x07Tqk4Nhrb1BMCzlj03bhbyW2iU2uD/+IVEfj14ivyiov+kL0IAROxfjZWdMx16vQdAzbpeKBQl/H1oA27Ne6KtU/73efi+lTg39KZVl5EA1KjTlJsFOUT8Faga/J86vB6nht608B0KQDXnxmRnXOXMiT9Ug/+4qL8ADTr3/wQdXX0AShUlHN72PTnXUzExt63azgsh/hOy5188szQ1NTl58iRZWVlq5W+++Sbr1q1TLc8PCwtTLUEuLCxk1qxZtG7dGi8vL+bNm4dCoai0NmlpaakGW4WFhXz55Zf4+Pjg7u5OixYtGD9+PBkZGQAkJCTg4uLCkiVLaNOmDb6+vty4cQMXFxc2bdrEpk2bmDx5MqDcCrB9+3bc3d0JCgpSO+dXX31FQEDAQ7Vv06ZNdOnShS+++AIvLy/efvttANXWCE9PTzw8POjbty8HDx5UxZWWlrJixQpefvllGjVqRPfu3dm6davq/ZSUFN59912aNWtGy5YtGT16NBcvXnzcj1E8pKKiImJjY2nSpIlauZeXFwUFBZw/f75czLX0dFKSkyuMSUtNJSUlBYC8vDy+//576tevz/jx4+/bjuPHj3Pmn3/o95DXoRD/VlFJCdGJabRwrq5W3qpODQqKiolJulYuJjU7l6TrObSoXT4mJesGSddzAMi9Wci87UdwrWbDlB7tq64TQtyhpLiIpEtRODZorVbu3LANRYX5pFw5Uy4m53oqWelXcXRRj3Fq0IbszGSyMq4C8NKrn9HCd7haHU0tbUqKC1Wvi4sL0dTUQltHT1WmZ6BchXgzP+ffdU4I8dSQwb94Zo0cOZIzZ87Qvn173nzzTZYuXUpkZCQmJiY4OzszZcoUunbtSpMmTTh06BAAX3zxBdu2bWPOnDn89ttvJCcnc+LEiX/dlhs3brB06VLi4uJUS63nzp3Lrl27mDNnDjt37mTOnDkcO3aMH374QS128+bNrFixgq+//hpjY2NVebdu3fj4448B5VYAX19fOnbsqDb4VygUbNmyhb59+z50Wy9fvkxqaipBQUG8++67REVFMXbsWLp3705ISAjr16/H0tKSiRMnUlio/GLw888/s3DhQkaOHMnWrVsZMGAAEydO5NixY+Tl5TF48GAAAgMDWbVqFRYWFrzyyiuqgaSoGmlpaZQUF2NrZ6dWbntr9UhycnK5mKSkJADs7o6xVc7qlP0309XVZcaMGQwfPly1DaUi2dnZrF6zhgGvvoqZmdnjd0aIR5CSlUuJQoGDufq1aW+m/B2amJldLqasrNpdMQ63Yq7eGvzraWvz9Wt+vNO5BaYGegjxX8jOTEZRUoypZTW1clMLBwCy0hPKxVy/dgUAM0v1G1pmt46RlZ4IgIVNTUzMbSktLaUgP4fYk7s4f3ofDb26qWLqN+4CQOjunynIzyEz7TInD67FwtYRSzvJdyHE80LWaIpnlp+fH/b29qxcuZLDhw+zf/9+AJycnJg1axZeXl7o6+urlujfuHGDTZs28dlnn9GhQwcAZs2axbFjxx753CdOnFDNnJaWlpKfn4+VlRUffPABL730EgAeHh74+fnRrFkzAKpXr463tzdnz55VO9bAgQOpW7duuXPo6+urBl1lWwECAgJ4++23SUlJwc7OjqNHj5KRkUGPHj0eqf1vv/02NWvWBOCff/5h6tSpDBw4UPX+kCFDeOONN0hPT8fe3p4VK1YwZMgQVe6EwYMHU1BQQHFxMX/88QfZ2dnMmzcP7VvLvmfOnEloaCjr169n7Nixj9Q28fAKCgoAMNDXVyvXv/U6Pz+/XEx+Xp4yxsDgvjHa2trY29s/sA0rV62iTu3atG7dmtjY2EfsgRCPJ69QuRzfUFdHrdxAV1vt/Tvl3jNGRy1GW0uTahb3vuElRFUoKlT+btbVNVQr19FV/q4uvJlXLqbwpjIfha5exTFFd8WkJsaydflEAKyr1cW9VW/Ve5a2jjT3HcqRHT8SHRYCgLGZDd3/9yWamjJXKMTzQgb/4pnm6emJp6cnCoWCmJgY9u/fT2BgIG+88Qa7d+9Wq3vhwgWKiorw8PBQlenp6eHq6vrI53V3d2f+/PmAcvuBoaEhVlZWanV69erFkSNHmD9/PhcvXiQ+Pp4LFy6obgaUcXR0fOjztm/fHisrK4KDg3nzzTfZvHkzvr6+jzzj6uTkpPr/DRs2xMzMjKVLlxIfH8+lS5eIiYkBoKSkhMzMTNLS0mjcuLHaMd544w0Apk+fTlZWFs2bN1d7/+bNm8TFxT1Su8SjedCWlYq+sD0oj8T9Mkrf7fCRI5w7d47p06Y9dIwQlUHxgOtYs4LruDKvfSEqW+kDfp9rVDQAf1BeoLuuaRMzW7oNnkXO9RTC/wpk6/IP6T1yEdo6evx9ZCMn/lxJw2bdcGrgTUFeNqcO/cb2wCn0GPIlBsbmj9gjIcTTSAb/4pmUnJzMkiVLGDVqFPb29mhqauLq6oqrqyudO3emR48eHD9+XC2m7Ivd3V8AtR8jSZm+vv4DB+2ffvopO3fupHfv3vj4+DBmzBiWLVtWbim8/l2ztvejpaVF7969CQkJYdCgQezZs4dFixY9VvvLhIWFMWLECDp27IiXlxf+/v7k5+czZswYQJnc8H4UCgXOzs7ltjMAGBoaVhAhKkvZ51u2AqBM2ey9vkH5xzOVzfjfK8bgIf+bZWRksG7dOl7p3x8TExNKSkpUNyMUCgUKhUJmi0SVMdJT/l66OyHfvVYE3FmWX1hxjFEFMUL8V3T1jQAoKlRfsVV4jxUBADq3ZvzLxdya8dfVM1IrNzSxxNDEEgdHd0zM7di26mMu/HOYOm4dOHVwHXXcO+DtN1pV38HRg/XfvUHksU207KyeM0AI8WySwb94Junq6rJhwwYcHBx488031d4zNTUFwNraWm0mx9nZGT09PSIiImjYUPkYqOLiYmJiYmjZsmWlti8zM5N169axcOFCunW7vacuPj7+kQbEFc1EBQQE8NNPP7Fq1SpMTExo27btv2rrL7/8QsuWLdUeXVj2yLbS0lJMTEywtbXl9OnT+Prefm7wuHHjcHBwoH79+gQHB2NiYoKlpSWgTET3/vvv4+fnp9Z/Ubmsra3R1NQkLS1NrbzsdTUHh3IxZUv5U1NTqVWrVrkYhwpiKvJPTAz5eXmsWLGCFStWqL23YMECrKysmDNnzsN3RohHYGdqjKaGBslZN9TKy15XtzQtF1PdQlmWlHUDZxuL8jEW5WOE+K+YmNujoalJdmaSWnlOhvK1uXXNcjHmVjUAyMq4ipV9bVV52THMrWtSVFjA5bOh2FSrj6nl7d/v1g7K7YZ5NzIoyMuiuOgmdjXUH5FpYGSGuVV1rqddroQeCiGeBjItI55JlpaWjBw5kkWLFrFw4UL++ecfrly5wr59+3jnnXdo2bIlzZo1w9DQkNTUVK5cuYKRkRGDBg3im2++YdeuXcTFxfHZZ59VSVI6Y2NjTExM2Lt3L5cuXSI2NpapU6cSHR2tSqL3MMpuFERFRalmap2dnWnatCnff/89vXr1Qkur/OOsHoWDgwOxsbGcOHGChIQEfv/9d9VqgrK2vvnmm6xYsYLg4GAuX77MypUr2bt3L76+vvTs2RMzMzPGjRvH33//TVxcHJMmTeLAgQOqpyyIqqGrq0u9+vUJj4hQW9ESHhGBgaGh2vaOMra2tlhbWxMeHq5WHh4ejq2dHdZ3bV+5l8aNGvHxlClqP4MGDQJg0KBBvPPOO4/fMSEeQFdbi4bVbAiNS1S79kPjEjHU06GerWW5GHszY2xNjTgWp5447VhcAvbmyveEeFK0dXSxr+XGxZgjatf0hZgj6OobYVO9frkYU0sHjM1tuRhzWK38YsxhTC0dMDG3RUNDk4NbFxN5bJNanYT4CAAsbZ3QNzJHz8CE5CvRanUK8rLJSr+Kibl6glghxLNLZv7FM2vChAk4OTmxfv16Vq9eTUFBAdWqVaNr166MGjUKgN69e7N792569OjBrl27eP/999HT02PGjBnk5ubStWtXfHx8Kr1tOjo6LFq0iDlz5uDv74+ZmRktW7bkvffeY8mSJRUmYqtIq1ataNy4MQMGDGDevHmqJwn07duXiIgI+vTp86/bOm7cOK5du8bo0cqlfnXr1mXWrFl8+OGHnD59mjp16jBo0CAKCgpYtGgRaWlpODk5sXDhQtUz4QMDA5k7dy4jRoygpKQENzc3fvnlF+rUqfOv2yfur0f37ixYsED5yMi2bYk7f55dO3fSt29f9PT0yM/PJykpCRsbG1UCyR7+/iz/9VeMjY1p1Lgxf586xYkTJ8qtorkfY2NjtadTABTevAkoVxfUqFGj8jopRAX6NXNlxpb9LNh5FJ+GzsQmp7PlVCwDW3mgp6NNXmERCRnZ2JkZYWag3OrUr7kr3+89zk/6ujRzqsbxC1c5ev4KE15q9YR7IwR4tn2V7aun8uemL6nfuAupCf9w+thmmncagraOHoU387h+7Qom5vYYGClz/TRp9xoHQxZxxOAHatZrweWzoVw4c5hOfT4ElDcVGrfpR8SBtRgYmuHg1IiMlAucPPgb1ZwbU6OOFxoaGjRt/xpHdy5FR8+Q2g3bUpCXzd9HNqChqYl7q3//XUMI8XTQKH1QBhwhxFNn8eLFHDlyhLVr1z7pplSZeEkW+NAiIiLYEhJCcnIyFubmdOzUiZdvPXUiNjaW+fPnM3TYMNp4e6ti9u/fz85du8jMzMTG2pquXbvSunXrCo9/LT2dyZMmlTvG3crO9cEHH8iqj4dkt+eXJ92EZ1pofALrw6JJzMzBytiAl93r0rOJ8tqLSkxletBfvO3bnE4Nbj+qbHd0HMEnY0m/kYedqTF9vBrQwcWpwuOnZucyZtUf5Y4hHs0S+y+edBOeGRdjjhJxYA1Z6YkYmlji2qw7HrcG30kXT7MtcArt/MdTv/HtbXgxETs4fXQTuTnpmJjb0bhNf+p6dFK9X1paSkzEDs6c2EpOZgr6hqbUce9Ak3avoa2jq6p3/vQ+okKDyEy7gr6hKXY1XWnu8z+Z+X9I7/WSpKHi6SeDfyGeIeHh4Vy4cIEvv/ySGTNmqFYCPI9k8C9eBDL4Fy8CGfyLF4EM/sWzQJb9C3HLTz/9xPfff3/fOh9//LHqWfdPwr59+wgMDCQgIEBt4J+SkoKfn999Yz08PFi5cmVVN1EIIYQQQgjxFJKZfyFuycrK4vr16/etY2VlVW6f89OgpKSEhISE+9bR09NTZXp/FsjMv3gRyMy/eBHIzL94EcjMv3gWyMy/ELeYmZlhZmb2pJvxWLS0tHB0dHzSzRBCCCGEEEI8peRRf0IIIf7P3n1GRXm8DRi/pHeQXmxYQBEExN4FNdh7NJa8do1J1JhmiZpiwZIY1Ji/NbHGCqjE3isWsBcUAaVIRwQBaft+QFZXULHFaO7fOZyTnZ2ZZ2az7u7M3DOPEEIIIYR4z8ngXwghhBBCCCGEeM/J4F8IIYQQQgghhHjPyeBfCCGEEEIIIYR4z8ngXwghhBBCCCGEeM/J4F8IIYQQQgghhHjPyeBfCCGEEEIIIYR4z8ngXwghhBBCCCGEeM/J4F8IIYQQQgghhHjPabztBgghhHg1ly9fJiAggJjYWIwMDWnp6Umb1q0pU6bMU8ucOnWKwMBAkpKTMTMzw9vbm8aNGpWYNzs7m++//56OnTo9NQ/AuXPn+O233/jqq69wdHR85X4J8Tznb8fx18lL3E5Jw0RXB2+XqnR0c3jme//ojdtsOn2FhPT7WBjq0aV2dVpWty8xb1ZOLl+u203Pek5PzSPE6xQdfpbgg6tITbiNrr4xTnU74Fy/yzPf0zcvH+bckXWk303AwMSCWg174ODqpZLn+vl9XAzyIz01HgNjc2p4tMepbgeVeuNuX+bMgZWkxEeipaNPRccG1GnRH01t3TfWXyHEP0tW/kUxW7du5cMPP8TNzQ13d3e6d+/OunXrlM+npqaycePGZ9bRv39/xo0bV+prjhs3DkdHR5U/V1dXOnbsyIYNG166L0UcHR3x8/MDIDc3lz///POV63yTTp48iaOjI9HR0S9U7syZM3Tr1g13d3f69OlDeHj4G2qh+LcIDw9n/oIFWFtb88knn1C/fn02b9rEzp07n1omOCSEpUuXUrNmTUZ+8gmODg78+ccfnDp1qlje+/fvM2/ePJKTk5/ZjoyMDFatWvXK/RGitK7HJTPj76PYlTXka+9GNHWowOoTFwgIufbUMkE3o5m35yRuFaz5pm1jatpasnDfaY7euF0sb0Z2DjP+Pkpi+v032Q0hlBJiQtmz/idMzMrh1WM8VZxbcGrfn1w4sfmpZSKuHedgwM/YValNq54TsKngwpFtvty8fFiZJ/Tsbo5s86V81bq06TWJqi6enNy7jPPHHv2WS028zc61k1HX0KRlt29wb/YRNy8d4kDA7DfaZyHEP0tW/oWKTZs2MW3aNCZOnIiHhwcKhYJjx44xdepUkpKS+Oyzz5g1axbR0dH07NnztV7b3d2d+fPnKx9nZ2ezefNmJk2ahJGREd7e3i9d99GjRzE0NAQgMDCQGTNmMGDAgFdt8r9Keno6I0aMoE+fPvzyyy98//33fPPNN2zatOltN028QVu2bqVC+fIMHjwYABdnZ/ILCti+fTteXl5oaWkVK+Pv74+Hhwe9evUCwNnZmfv37xOwZQv16tVT5jt37hzr1q0j+8GD57ZjzZo1qGvIV4r456w/dQl7CxM+b1UfAPeKNuQXKPALvkp7Vwe0NNSLlVkbdJEGVcoxoIkbAG4VrMl4kMO6k5doUq2CMt/piBj+OHKOrNzcf6QvQgCEHFqDmZU9zTuPBaB8VQ8KCvI5f3QjNet2QkOz+Od58IGV2NdoRIPWQwAoV6U2D7LTCTm4mio1mwFw7tgGKtVoRD2vAQDY2rtyLyWWK2f+xq3JhwDcvHQQKEOrnt+hqaUDgKIgn2PbF5J+NwFDE8s323khxD9CVv6FirVr19K9e3d69OiBvb09lStXpn///gwYMICVK1cCoFAo3si1NTU1sbCwUP6VL1+eMWPGUKlSJbZt2/ZKdVtYWKCj8/DL7A21/22Ljo4mPT2dNm3aUKlSJVq1aiUr/++53NxcQkNDcXd3V0n38PAgOzubsLCwYmWSkpOJj4srsUxiQgLx8fEAZGZmsnDhQhwcHBg9evQz23H69GmuXL1Kj+7dX7FHQpRObn4+l2MSqWdvp5LeoEo5snPzuHYnqViZhHv3uXM3nXqVi5eJT8vgzt10AO4/yGH2juM42VowsUOzN9cJIR6Tn5fLnVuXqFi9oUq6fY3G5OZkER91pViZ9LsJpCXHUtFRtUyl6o25lxpHWkosAG16TaGe1yCVPGrqGuTn5Sgf5+XloKamjoamtjJNW7dw0eRBVvqrdU4I8a8hg3+hQk1NjbNnz5KWlqaSPmzYMNavX8+4cePw9/fn1KlTyj29OTk5TJ8+nYYNG+Lh4cHs2bMpKCh4bW1SV1dXrl7m5OQwc+ZMPD09cXZ2pl69eowePZqUlBSgcADs6OjIokWLaNy4MV5eXmRkZCjD/v38/Bg/fjxQuBVgx44dODs7ExAQoHLNn3/+me6lHMj4+fnRunVr1q1bR4sWLXB1dWXUqFHEx8fz1Vdf4e7uTrNmzVRW4NPS0vjuu+9o2rQpNWvWpGHDhnz33XdkZWWVeA2FQsGSJUvw8vLC1dWVzp07s3XrVpU8VapUwcrKip9//pmIiAhWrVpF586dVfI87fW5fv06w4cPp27dujg7O+Pl5cXy5ctVyh45coRevXrh6upKs2bNmDt3Lvn5+cr/L7Nnz6Zp06a4u7vz4YcfcvTo0VK9fuLlJSYmkp+Xh6WVlUq6pYUFAHFxccXK3LlzBwCrJ8tYFq7qFA3+tbS0+PHHHxk0aJAyaqYk9+7dY83atfTu1QtjY+OX74wQLyA+7T75BQXYmKi+N62NDQCISb1XrExRmu0TZWwelol9OPjX1tDg14+8+axVPYx0tRHin3AvNY6C/DyMTG1V0o3K2gCQllx8G+DdpCgAjE1VJ7SMH9aRlhwDQFmL8hiaWKJQKMjOSif07G7CLh6ghkc7ZRkH19YAnNyzlOysdFITb3P2yF+UtayIqZWcdyHE+0JiNIWKIUOG8MUXX9CsWTPq169PnTp1aNCgAS4uLhgZGTFx4kSys7OJi4tThuhPnTqV/fv34+Pjg62tLf/73/84c+YM5cuXf6W2ZGRksHbtWm7evMmYMWMAmDVrFgcOHMDHxwc7OztCQ0MZP348v//+OxMnTlSW9ff3Z8WKFWRlZWFgYKBMb9euHenp6UyfPp2jR49ibGxMixYtCAgIoEuXLgAUFBSwdetWhg0bVuq2xsbGsnPnThYvXsydO3cYOXIkQUFBfPLJJ4wcOZLly5fz/fff4+XlRdmyZRk3bhzx8fEsWLAAMzMzQkJCmDBhAlWrVi1xO8LcuXMJDAxk8uTJVK5cmdOnT/P999+Tnp5O3759gcLB2uTJk/n0009p164dAwYM4MsvvyyxvY+/Purq6gwaNIjGjRuzbt061NXV2bhxIzNnzqRhw4bUqFGDs2fPMmzYMAYOHMj06dOJiYnh66+/RkNDg88//5zx48dz8+ZN5syZg5WVFQcOHGDEiBEsWLCAFi1alPp1FC8mOzsbAN2HUS1FiqJcSppMysrMLCyjq/vMMhoaGlhbWz+3DStXraJK5co0bNiQ0NDQF+yBEC8nM6cwHF9PS1MlXVdLQ+X5x91/ahlNlTIa6mrYln36hJcQb0JuTuFns5aWnkq6plbhZ3XOg8xiZXIeFJ5HoaVdcpncJ8okxIQS+Oc3AJjbVsW5QRflc6aWFanrNYDjO//H5VOF0ZYGxha0/7+ZqKnJWqEQ7wsZ/AsV3t7eWFtbs3LlSo4dO8ahQ4cAqFSpEtOnT8fDwwMdHR1liH5GRgZ+fn5MmTKF5s2bAzB9+nSCgoJe+NpnzpxRhiIrFAqysrIwMzPjq6++ok2bNgC4uLjg7e1NnTp1ALCzs6NRo0Zcv35dpa4+ffpQtWrVYtfQ0dFRrmJaPFwd7d69OyNHjiQ+Ph4rKytOnDhBSkoKHTp0KHXb8/LymDRpElWqVMHBwYHq1aujqanJwIEDARg4cCAbN24kMjKSsmXL0rhxY+rWrauMnihXrhyrV68u1g8oDL/+888/+eWXX5QD6QoVKhATE8OyZcuUg/+9e/fy3Xff4ejoSGhoKOXLl0dDQ4OMjAyVCZAnX5+UlBQ+/vhj+vbti76+PgCjRo1i6dKlhIaGUqNGDVatWoWrqyvffFP4o6FKlSr8+OOPJCcnc+vWLQIDAwkICKBGjRrK/l67do1ly5bJ4P8Nel6ETUk/2J637eVZJ0o/6djx49y4cYMfvv++1GWEeB0KnvM+Vivhffw63/tCvG6K53yelylpAP68bYxPvKcNjS1p13866XfjCT64msA/v6bLEF80NLU5f3wTZ/avpEaddlSq3ojszHucO7qOHasn0uHjmegamLxgj4QQ/0Yy+BfFuLm54ebmRkFBAdeuXePQoUOsXr2aoUOHsmfPHpW8ERER5Obm4uLiokzT1tbGycnpha/r7OzMnDlzgMJBi56eHmZmZip5OnfuzPHjx5kzZw6RkZGEh4cTERGhnAwoUrFixVJft1mzZpiZmbFlyxaGDRuGv78/Xl5eLxzCXKHCo8Oi9PT0sLGxUT7W1i4MHc3JKdxf16dPH/bv34+/vz+RkZGEhYURHR1N5cqVi9UbFhbGgwcP+PLLL1UGc3l5eeTk5Cj3do8aNYoxY8YwbNgwJk+ezPTp03FwcGDq1KlUrVpV+dqC6utjampKnz59CAwM5MqVK9y+fZtr1wpPyy4aXF6/fp3GjRurtOuDDz4AYMeOHco+PS43NxcjI6PSvnziJejpFa72FEUAFClavdfRLX57pqIV/6eV0dXTK1amJCkpKaxfv54Pe/bE0NCQ/Px85fuloKCAgoICWS0Sb4y+duFq/ZMH8j0tIuDxtKycksvol1BGiH+Klk7h5HtujmrEVs5TIgIANB+u+Bcr83DFX0tbXyVdz9AUPUNTbCo6Y2hixfZVE4i4eowqNZtz7sh6qjg3p5H3CGV+m4oubPhtKBeC/KjfSvXMACHEu0kG/0IpLi6ORYsWMXz4cKytrVFTU8PJyQknJydatWpFhw4dOH36tEqZopWSJ1dUNF7i1G8dHZ3nDtonT57Mrl276NKlC56ennz66acsW7ZMuU/58bpKS11dnS5durBt2zb69evH3r178fX1feH2a2qq/nB82sCnoKCA4cOHc+PGDTp06EC7du2oWbMmkyZNKjF/0Wv766+/ljg5oKWlxbZt26hUqZJyq8KkSZOIiIhg5MiRpKWlMWLECJUyj78+iYmJ9OrVC1NTUzw9PWnSpAkuLi7KSA549v/PovatWbNGGTnwvNdAvB7m5uaoqamRmJiokl702PaxCagiRaH8CQkJKhNWRWVsSihTkqvXrpGVmcmKFStYsWKFynO//PILZmZm+Pj4lL4zQrwAKyMD1MqUIS4tQyW96LGdafGJR7uyhWl30jKwtyhbvExZmawUb4+hiTVl1NS4l3pHJT09pfCxiXnxrZQmZuUASEuJxcz60e+DojpMzMuTm5PN7esnsbB1wMj00ee7uU1h9F9mRgrZmWnk5T7AqlwNlfp19Y0xMbPjbmLxW2EKId5NMvgXSlpaWmzcuBEbG5ti+92LVnDNzc1VQiPt7e3R1tYmJCREGfKdl5fHtWvXqF+//mttX2pqKuvXr2fu3Lm0a/fokJrw8HDlCmhplBTa2b17d5YsWcKqVaswNDSkSZMmr6XNJbl69SqHDx9mw4YNuLq6AoWr5Ldv3y7xnITKlSujoaFBbGwsLVu2VKavXLmSsLAwfvzxR3R1dbl37x45OTloaWmhqanJnDlz8PT0REdHh4YNGxart0hgYCB3795l165dygmMor3bRQP7KlWqcPHiRZVyK1asIDAwkOnTpwOFg8fHIz7mzp2Lmprac0+KFy9PS0uLag4OBIeE0KZNG+V7OzgkBF09PSpVqlSsjKWlJebm5gQHB6tEzAQHB2NpZYX5E9E2T+NaqxYTHjtnA+D2rVusXr2afv36UaVKlZfvmBDPoaWhTg1bC07ejKGTm6PyvX/yZgx62ppUszQtVsba2ABLI32CbkbTqOqjz9qgm9FYmxQ+J8TboqGphXWFmkReO45Lg67K93TEteNo6ehjYedQrIyRqQ0GJpZEXjtGZadHv1sirx3DyNQGQxNL8nJzOBI4n6q1WtKk3afKPNHhIQCYWlZCR98EbV1D4qIuU6POo99X2Zn3SEuOxcK2+LWFEO8mWZYTSqampgwZMgRfX1/mzp3L1atXiYqK4sCBA3z22WfKAwD19PRISEggKioKfX19+vXrx7x589i9ezc3b95kypQpxVbiXwcDAwMMDQ3Zt28ft27dIjQ0lEmTJnH58mVlOH1pFE0UXLp0SRn6bG9vT+3atVm4cCGdO3dGXb34/aFfF3NzczQ0NNixYwdRUVFcvHiRMWPGkJiYWGI/DA0N6d27N76+vmzZsoWoqCg2bdrE7NmzlSe09+jRg/v37zN+/HjCwsIICQnhq6++wtraGn19fYYPH668I8KTrK2tycrKYufOncTGxnL06FHGji28x3BRe4YMGcK5c+fw9fUlMjKSQ4cOsXDhQlq0aEG1atVo2bIlU6ZMYf/+/URFRbFkyRIWLVqksrIs3owO7dsTGRHBokWLuHjpEgEBAezetYt2bduira1NVlYW4eHhpKc/ulVTh44dOXPmDGvWrOHipUusXr2aM2fO0OWJu0M8i4GBAfaVKqn8FUUVWFtbU65cudfeVyEe16OOE2EJKfyy6wRnb91h3clLbD0XStfaNdDW1CAzJ5frccmkZT3a4tKjrhMnwqJYciiYs7fusPhgMCfCouhdz/kt9kSIQm5NepEYe4P9fjOJCgsm+OBqLgb549qoBxqa2uQ8yCQhJpSs+4/uyOTe9CMirhzj+I7fiQoL5tj2hURcOYZH835A4aSCa+MehJ7dTfDB1cRGXuDSyS0cDZyPrb0r5ap4oKamRu1mHxF++QhHt/9GbMR5wi8fYcea7yijpoZzg65v6yURQrxmsvIvVIwZM4ZKlSqxYcMG1qxZQ3Z2Nra2trRt25bhw4cD0KVLF/bs2UOHDh3YvXs3X375Jdra2vz444/cv3+ftm3b4unp+drbpqmpia+vLz4+PnTs2BFjY2Pq16/P2LFjWbRo0VNvk/ekBg0a4OrqSu/evZk9ezZt27YFoFu3boSEhNC165v9krOyssLHx4f58+ezZs0aLCwsaNGiBQMGDGD//v0llhk/fjxly5bF19eXhIQEbGxsGDVqFEOGDAEKDwz8448/mDVrFl27dkVPTw9PT0/mzp1LSkoKY8eOJTY2FhMTk2J1e3t7c/nyZXx8fMjIyMDOzo6ePXuyb98+Ll68yEcffUSNGjX47bffmDdvHkuWLMHS0pKPP/6YTz75BChc5Z87dy6TJ08mLS2NChUqMG3atDf+WgqoXr06I0aMYOu2bfz222+UNTGhe48efPDwkMzbt28zZ84cBgwcSONGjQBo3KgRebm57Nq9m6PHjmFhbs6gQYOoW7fu2+yKEC/EuZwlX3o3ZMOpy8zcfgwzA136NaxFJ/fCg1TDE1P5IeAgI73q0rJ64a3KWla3Jy+/gC1nQ9l/NQIrIwM+a1WPxtVkolK8fbaVauHVfRwhh9eyd+M09AxNqec1AJeHg+/kOzfZvnoiTTuOxsHVCwAHVy8K8nO5eMKP6+f3YmhiRfPOX1C5ZlNlvW5NeqGjZ8yVM4FcDApAR8+I6rW9cW/6kTLCwKluB7R09Ll0MoAb5/eho2eEVXknWvWciKGJVfHGCiHeSWUUzzv+Voj/iPnz53P8+HH++uuvt92U106hULxzJ1mH37z5tpsgxBtntXf5226CEG/cIuupb7sJQrxxYzu/W7+zxH+TrPyL/7zg4GAiIiJYuXIlP/7449tuzhvxrg38hRBCCCGEEK+XDP7FG7VkyRIWLlz4zDwTJkygZ8+e/1CLijtw4ACrV6+me/fuyi0AAPHx8Xh7ez+zrIuLCytXrnzTTRRCCCGEEEKIVyJh/+KNSktL4+7du8/MY2ZmhoGBwT/ToBeQn59PdHT0M/Noa2srDzkTr5eE/Yv/Agn7F/8FEvYv/gsk7F+8C2TlX7xRxsbGGBsbv+1mvBR1dXUqVqz4tpshhBBCCCGEEK9MbvUnhBBCCCGEEEK852TwL4QQQgghhBBCvOdk8C+EEEIIIYQQQrznZPAvhBBCCCGEEEK85+S0fyHEv9IvW+SjSbz/ujiHv+0mCPHGyV0txH+B/vBpb7sJQjyXrPwLIYQQQgghhBDvORn8CyGEEEIIIYQQ7zkZ/AshhBBCCCGEEO85GfwLIYQQQgghhBDvORn8CyHEeyLjXhKr5nzEnciLpS6THBfO8uldSb+b8Mx8QXuWsmxqp2LpKfGR7Prre1b93Ie1v/4fh7bOJSvj7os2XYhSS0lJYdTo0YSGhpa6zO3btxk+YgRJycnFnjt2/DhTpkxh5Kef8t2kSezbt48nz0K+cOEC06ZN45ORI/n6m29Yv349Dx48eOW+CPEikjIy+b+l/lyKefbn9eMiElPp/fsmEu7df6nnhRDvFxn8i3fa1q1b+fDDD3Fzc8Pd3Z3u3buzbt065fOpqals3LjxmXX079+fcePGlfqa8+fPx9HRUeXPycmJBg0aMHLkSKKiol6oDzdu3ODgwYPKx46Ojvj5+b1QHf+0+fPn4+np+babIR6TcS+JXWsnk5Nd+h9wKQm32L3+RxQF+c/Md+fWJS6f2lYsPSvjLtvXfEfW/VSadRxNg9ZDiLt1iV3rppCfn/fCfRDieVJSUvj111/JyswsdZmYmBjmzZ9PQX7x9/mRI0f4848/cKlVi88/+4yGDRqwYcMGtm/frswTEhLCggUL0NbRYfiwYfTq1Yur167x888/k19CnUK8CUkZmUzdepjMB7mlLnM7OY0Zfx8lv6DgpZ4XQrx/NN52A4R4WZs2bWLatGlMnDgRDw8PFAoFx44dY+rUqSQlJfHZZ58xa9YsoqOj6dmz52u9trW1NZs2bVI+zs3N5erVq/z000+MGDGCwMBAypQpU6q6hg8fTteuXWnRogUAR48exdDQ8LW2V7y/FAoFNy7s59QL3EorPz+PK6e3EXJoLeoaWs/Mm5uTzZFAX/QNzbh/L0nluVvXg3iQeY9OA2ZjZGoDgJaOPrv++p6E6GvYVHR+8Q4JUQKFQsHxEyeeO5n7uLy8PPbt38/WLVvQ1Cr5fb59+3Y8PDzo0b07ADVq1CAhIYH9Bw7Qvn17ALZu24a1tTVjRo9GQ6PwZ5NDtWpMmDCBY8eO0axZs1fsnRBPp1AoOBgaycpj50tdJi+/gO0XbrD+1CW0NNRf+HkhxPtLVv7FO2vt2rV0796dHj16YG9vT+XKlenfvz8DBgxg5cqVAMVCN18XdXV1LCwslH+2trZ4eXkxZswYwsLCXigc9UkWFhbo6Oi8xtaK91lKfATHdyykaq2WNO/8RanKRIed4ezhdbg27kldz/97Zt5Te5ejq1+Waq5exZ7LzytcgdLU1lOmaesWTlxlZ90rbReEeK6oqChWr15NwwYNGDRoUKnKXLx4kcBt22jXrh3du3UrMc/nn39Ojycmh9XV1cnNfbS6GhcXR82aNZUDfwAjIyNsbGy4cLH0W2yEeBmRSXdZfDCY5o6V+LxV/VKVCbl1h42nL9PNowb9GtZ64eeFEO8vGfyLd5aamhpnz54lLS1NJX3YsGGsX7+ecePG4e/vz6lTp3B0dAQgJyeH6dOn07BhQzw8PJg9ezYFrzHcTevh6pKmpqbyejNnzsTT0xNnZ2fq1avH6NGjSUlJAcDT05OYmBgWLFhA//79geJh/wEBAXTq1IlatWrh6enJwoULXyjUdNy4cXzzzTdMnTqVOnXqUK9ePebNm8fNmzfp06cPtWrVomPHjpw//2hV4fr16wwfPpy6devi7OyMl5cXy5c/fWU5PT2dSZMm0aBBAzw8PPj444+5KD+K/xH6xhb0GLmIBq2HoKGhXaoy5jbV+PDzpbg1+ZAyak9f9YkJP0fYxQM06ziaMhSPZLF3aoKeoSkndv2PzPQU0u/Gc3rfH+gZmmJn7/ayXRKiGFNTU6ZNnUqvXr3Qfsoq/pMqVarEjBkzaN++PWrqJb/PbW1tMTczQ6FQkJGRwZEjRzgRFETLh5FYAAYGBsrP7CJ5eXkkJyeTlJSEEG+SuaEe8/u1Y0ATt1Kv0lexLMvCj9vTvY4TamrFP7uf97wQ4v0lg3/xzhoyZAhXrlyhWbNmDBs2jMWLF3PhwgUMDQ2xt7dn4sSJtG3bFnd3d44ePQrA1KlT2b59Oz4+Pqxbt464uDjOnDnzWtoTGhrKwoULcXFxwd7eHoBZs2axe/dufHx82LVrFz4+PgQFBfH7778DhVsXrK2tGTRoEPPnzy9W559//smkSZPo1asXW7duZfTo0SxbtgwfH58Xatv27dtRV1fHz8+PAQMG8NtvvzFixAgGDx7Mxo0b0dbW5ocffgAgKyuLQYMGYWJiwrp16wgMDMTb25uZM2dy9erVYnUrFAqGDh1KVFQUixYtYsOGDbi5ufHRRx9x5cqVF30ZxQvS0TXEwMj8hcroG5mho/vsrSUPsu9zJHAetZv3wdjMrsQ8egZladT2E25fP81fvgPYsGAoyQmRtOk9Ba3HogGEeFUGBgaYmpq+UJmyZctiYGBQqrzh4eF88cUXrFy5knJ2drRu3Vr5XONGjQgJCWHHjh2kp6eTnJzMihUryMrOlkP/xBtnqKONucGLfZ6aGehhqPP0yeDnPS+EeH/Jnn/xzvL29sba2pqVK1dy7NgxDh06BBSu9kyfPh0PDw90dHTQ1NTEwsKCjIwM/Pz8mDJlCs2bNwdg+vTpBAUFvfC1Y2NjcXd3Vz7OycnBwMAAT09Pvv76a9TUCufVXFxc8Pb2pk6dOgDY2dnRqFEjrl+/DhSuZqmrq6Onp4eJiYnKNRQKBUuWLKFfv3707dtX2be7d+8ye/ZsRo0aVeqzAUxMTPj2229RU1NjwIAB+Pr60q5dO7y8CkO5u3XrxvTp04HCwf/HH39M37590dfXB2DUqFEsXbqU0NBQatSooVJ3UFAQ586dIygoSNmHsWPHEhISwsqVK194okL8O5zcvQR9I3Oc63d5ap6blw5xcMsv2NdojINba/LzcrgY5M/OtZNp338GJubl/rkGC/EKzMzM+Orrr0lKSiIgIACfmTOZ9N13aGtr06lTJwoUCrZs3Yqfnx/qGho0bdIEN1dX7ty587abLoQQQpSaDP7FO83NzQ03NzcKCgq4du0ahw4dYvXq1QwdOpQ9e/ao5I2IiCA3NxcXFxdlmra2Nk5OTi98XUtLS1atWgUUTgT4+Pigp6fH2LFjVVanOnfuzPHjx5kzZw6RkZGEh4cTERGhnAx4lpSUFJKSkvDw8FBJr1evHrm5uYSHh+Pq6lqq9pYrV045IaGnV7iCUL58eeXzOjo6yj2upqam9OnTh8DAQK5cucLt27e5du0aQIlbJC5fvoxCoaBly5Yq6Tk5ObIq9o66feM04VeO0nnwLygKClAACgrPzyjIz6eMmhplypQh5PBarMpVx7PbN8qydvbubP7fJwQfXIVXj/FvqQdCvBgTExNMTExwdHDA3NycObNnExwSQqOGDVFXV6d7t2507NCBpKQkTExM0NPTY9asWcoJUiGEEOJdIIN/8U6Ki4tj0aJFDB8+HGtra9TU1HBycsLJyYlWrVrRoUMHTp8+rVKm6PT9Jw8BfPwQp9LS0NCgYsWKAFSsWJFly5bRpUsX5XkDRXv/J0+ezK5du+jSpQuenp58+umnLFu2jPj4+Ode42mHFRYNwF+k3UVnEDyuaDLgSYmJifTq1QtTU1M8PT1p0qQJLi4uymiJktpjYGBQ4u0JtUq5N1f8u0RePUZ+Xg5+iz4r9twfM7pSrZYnzTqNISMtkYqODVWe19DUwtymKqlJt/+p5grxUh48eMC5c+ewt7fH0tJSmV7p4Wd72t27QOGWrtzcXJydnbG1tQUgPz+f6JgYGjdq9I+3WwghhHhZMvgX7yQtLS02btyIjY0Nw4YNU3nOyMgIAHNzc5Xb7dnb26OtrU1ISIgydD0vL49r165Rv37pTtB9GnNzc6ZNm8awYcOYN28eX331Fampqaxfv565c+fSrl07Zd7w8HDl6vvz6jQ3Nyc4OJhWrVop08+cOYOmpiYVKlR4pTY/TWBgIHfv3mXXrl3KSYOiuxeUNCHh4OBARkYGubm5VK1aVZn+3XffUb16dfr16/dG2ineHPdmfahRp71KWujZXYSe3U2nQT+jo2cMgLGZHfFRV1AoFMp/a3m5OSTF3cTEvHyxeoX4NylTpgwrVq6kYYMGygNXAS5dvgyAXbnCbSvBwcGcO3+e6dOmKSddjx49SlZmJm6Pbf8SQggh/u1k8C/eSaampgwZMgRfX1/u37+Pt7c3BgYGhIWFsXDhQurXr0+dOnXYsWMHCQkJREVFUb58efr168e8efOwsLCgSpUqLF++vFSr8KXRvHlzOnXqxB9//EG7du2oVq0ahoaG7Nu3j5o1a5Kdnc3q1au5fPmySri+vr4+kZGRJCUlYW6uenDb4MGDmTt3LuXLl6dx48ZcuHCBBQsW0KtXr1Lv939R1tbWZGVlsXPnTjw8PAgPD2fGjBlAYSj/k5o2bUqNGjX44osvmDhxIjY2NqxduxY/Pz+WLVv2RtooSi/nQSZ3k6IwNLFGV9+4VGUMTSwxNLFUSYu6URhJY2FbTZnm0aIfezdOZ7/fTBxcW1NQkMelkwFkpqfQsuvXr68TQjxHVlYWd+7cwcLCotSfjVpaWrRt25ZtW7diaGhI9erViYqKYltgIDWcnHBxdgYKP9sPHznCH3/+SZPGjYmKjsbPz486devi6ODwJrslxHNl5uQSnXIPK2N9jHXlNsFCiGeTwb94Z40ZM4ZKlSqxYcMG1qxZQ3Z2Nra2trRt25bhw4cD0KVLF/bs2UOHDh3YvXs3X375Jdra2vz444/cv3+ftm3b4unp+draNGHCBI4ePcp3333Hxo0b8fX1xcfHh44dO2JsbEz9+vUZO3YsixYtIisrC11dXfr378/MmTO5ceMGW7duValv0KBBaGlpsWLFCqZPn461tTVDhw5l8ODBr63NT/L29uby5cv4+PiQkZGBnZ0dPXv2ZN++fVy8eJGPPvpIJb+6ujrLly9n9uzZjBkzhqysLKpUqcKCBQto2LDhU64i/inJd26yffVEmnYcjYOr12utu6JDfdr0msy5o+vZt2kGmtq6WNhWo/OgXzC1qvRaryXEs9y+fZs5c+YwYODAFwrF79C+PYaGhhzYv5/de/ZgYGBA8+bN6dihgzKaxc7OjlGff46fnx/zFyzA2MiIdu3a0a5t2zfVHSFKLTwxlR8CDjLSqy4tq9u/7eYIIf7lyiietrFYCCHeol+2yEeTeP91cQ5/200Q4o2z2rv8bTdBiDdOf/i0t90EIZ6r5BO/hBBCCCGEEEII8d6QsH8hHlqyZAkLFy58Zp4JEybQs2fPf6hFz7Z9+3YmTpz4zDwDBw5k1KhR/1CLhBBCCCGEEP9WMvgX4qEPP/yQNm3aPDOPmZnZP9Sa52vevDkBAQHPzFN05wMhhBBCCCHEf5sM/oV4yNjYGGPj0p2G/m+gr6+Pvr7+226GEEIIIYQQ4h0ge/6FEEIIIYQQQoj3nAz+hRBCCCGEEEKI95wM/oUQQgghhBBCiPec7PkXQggh3hK5/7n4L4hvNehtN0GIN67y226AEKUgK/9CCCGEEEIIIcR7Tgb/QgghhBBCCCHEe04G/0IIIYQQQgghxHtOBv9CCCGEEEIIIcR7Tgb/QgghhBBCCCHEe05O+xfvla1bt7J69WquX79OmTJlqFy5Mj179qR3794ApKamsnfvXnr27PnUOvr374+dnR0+Pj6luub8+fNZsGCBSpq6ujpGRkbUrl2b8ePHU758+VL34caNG8TExNCiRQsAHB0dmTFjBt26dSt1HeK/Q6FQcC1kJ1eDt5N+Nx4dPSMqOjagdrM+aGnrlVimID+fiyf9uX52N5kZqRiZ2uDaqCeVazZVyXc3KZrT+/7gzq1LlFFXx6ZCTeq1GoxRWesS6711/SR7N0yjXb9p2FRyee19FQIgKSOTL9ft4uu2jXG2s3xqvtz8fLaeDeVQ6C2SMzIxM9CjqUMFutaugYb6o7WPmNR7rDp+gcsxCairqeFka8H/NXbFytgAgJEr/yYx/X6J17Aw1Gfhx+1fbwfFf5ZCoeDQ4cMcPHiQpMREDA0NcXNzo1OnTujq6j63fH5+Pj4+PmhpafH111+rPBcRGcmmjRu5desWOrq6NGrUiE4dO6Kh8WgosHTpUk6ePFms3uEjRlDHw+PVOyiEeOtk8C/eG5s2bWLatGlMnDgRDw8PFAoFx44dY+rUqSQlJfHZZ58xa9YsoqOjnzn4fxnW1tZs2rRJ+Tg3N5erV6/y008/MWLECAIDAylTpkyp6ho+fDhdu3ZVDv6PHj2KoaHha22veH9cOLGZ4IOrcWnQFdtKrtxLiSX40BpSE27h3efHEt93IYfXcv74Jtyb9sa6vBORoSc44D+bMurq2FdvBEDGvSQCV3yLsZktLbp+RV7uA0IOrWbX2sl0HbYADU0tlTqzs9I59vdv/0ifxX9XUkYmU7ceJvNB7nPzLj9ylsOht+hRx4mqVqaExaew8cwVEtMzGelZV1nfd377sTUxZEybBjzIy2fdyUv8tO0wv/T+AC0Ndb5u24jc/AKVuq/HJbPi2Dla15Sbe4nXZ+fOnQQEBNDmgw+oUb068QkJbNmyhZjYWL4YM+a5vyN27NxJZGQkDg4OKumJiYnMnTuXKpUrM2z4cO7cuUNAQAD3MzLo37+/Ml9UVBR169WjlZeXSnlLy6dPsgkh3i0y+BfvjbVr19K9e3d69OihTKtcuTLx8fGsXLmSzz77DIVC8Uaura6ujoWFhUqara0t6enpfPvtt4SGhlK9evWXqvvJeoUoolAouHB8M9XdP6Cu5/8BYFfZDW1dQw74zybpThgWttWKlbtxYR9VnZtTu9lHANjau5J0J4yrpwOVg/+zh9eiqa1L275T0dDUBsDQxIq9G6eRdOcG1hVqqtR5fMdC1NTlK0W8GQqFgoOhkaw8dr5U+dOzH7DvSgR9G7rQ2b3ws9elnBUAa4Mu0rehC8a6Omw4dRk9LU0md2qOtmbh+9fSUJ9ZO44RlpCCk60F9hZlVerOzMnl191B1K5kQ1ePGq+xl+K/TKFQsGPnTpo1a0b3h5F+Tk5OGOjrs3jxYiJv3cK+UqWnlo+KimL79u0YGRsXe27nzp3o6Ojw6aefoqGhQS0XF7S0tPhr7VratWuHmZkZOTk5xMXH07p1aypXlkktId5XsudfvDfU1NQ4e/YsaWlpKunDhg1j/fr1jBs3Dn9/f06dOoWjoyMAOTk5TJ8+nYYNG+Lh4cHs2bMpKCgoqfqXoqVVuDqqqampvN7MmTPx9PTE2dmZevXqMXr0aFJSUgDw9PQkJiaGBQsWKGfjHR0d8fPzU9YZEBBAp06dqFWrFp6enixcuJD8/PxSt2ncuHGMGjWKQYMGUbt2bZYsWUJBQQGLFi3igw8+wNnZmdq1azNkyBBu376tLHf//n1++uknmjRpgru7O/369ePSpUvK50NCQujbty+1atWiRYsW/PDDD2RkZLz8iyeeK+dBJlVdWlLFuYVKurF5OQDSU+NKLJeXl4OmlmoIqY6eEdlZ6UDhj9DIaydwcGutHPgDWNhW46PRfxYb+IdfPkJMxHnqeg14xR4JUbLIpLssPhhMc8dKfN6q/nPzZ+bk0rpmZepWslNJL2dqBEB82n0UCgUnw6NpWcNeOfAHqGplyuIBHXGyLXnidfOZK6RlZTO4ae1X6JEQqrKysmjYsCH166u+v62tC7dZJSUmPrVsXl4ey5Yvx8vLC2srq2LPX758GRdnZ5UQ/zoPIyQvX74MQExsLAX5+ZQrV+51dEcI8S8lg3/x3hgyZAhXrlyhWbNmDBs2jMWLF3PhwgUMDQ2xt7dn4sSJtG3bFnd3d44ePQrA1KlT2b59Oz4+Pqxbt464uDjOnDnzWtoTGhrKwoULcXFxwd7eHoBZs2axe/dufHx82LVrFz4+PgQFBfH7778DhVsXrK2tGTRoEPPnzy9W559//smkSZPo1asXW7duZfTo0SxbtqzU5xMU2bVrF40aNWLz5s106NCBlStXsmzZMsaNG8euXbv47bffiIyMVKl3zJgxHD58mBkzZhAQEED58uUZNGgQaWlpXLt2jYEDB9K0aVO2bt3KnDlzuHz5MoMGDXpj0RYCtHX0afjBMKzKq64+3goNAsDEokKJ5ZzrdeLGxQNEhQWT8yCTsIsHib4ZQlWXlgBkpCWQk30fQ2NLju/4nVU/9+FPn+7s2TCVjHtJKnVlZdzl+M7/0bDNUPT0y5ZwNSFenbmhHvP7tWNAEze0NNSfm9/KyIChzT2wLau6ZepUeAzqamrYmhiSkH6fzAe5WBjqseRQMAOWBvDR/zYxc/tRkjIyS6w3Mf0+f5+/QWf36lga6b+WvgkBoKenx0e9e1O1alWV9LPnzgGF0YRPs23bNgry8+nUsWOx53JyckhOTsbKWvWsFkNDQ3R0dYmPjwcgOioKKNxq+OVXXzHik0+YOWsW4eHhr9ItIcS/jMRoiveGt7c31tbWrFy5kmPHjnHo0CEAKlWqxPTp0/Hw8EBHRwdNTU0sLCzIyMjAz8+PKVOm0Lx5cwCmT59OUFDQC187NjYWd3d35eOcnBwMDAzw9PTk66+/Rk2tcJ7NxcUFb29v6tSpA4CdnR2NGjXi+vXrAJiamqKuro6enh4mJiYq11AoFCxZsoR+/frRt29fZd/u3r3L7NmzGTVqVKnPBjA2NmbIkCHKxxUqVGDmzJm0bNlS2S5vb2927twJQHh4OIcPH2bZsmU0adIEgO+//x4jIyNSU1NZtmwZjRs3ZsSIEcp2/fzzz7Rq1YpTp04VW8kQb05CTCgXjm+mQrW6mFpWLDGPc73OJERfY/e6H5RpDm6tqNWwMNQ0+35h9Mzp/Sswt6lKy65fk33/LmcOrGLHqgl0GToPTS0dAI5uX4BlOUequrTkTuTFN9w78V9lqKPNq558cjI8mkOht/B2qYqBjhZ30gojXdacuEgVy7J80aYBd7Oy+SvoEt8HHGROrzboaKr+TPr7/A001dVoV6v4dhohXrfw8HB27txJLVdX7OzsSswTERnJ7j17+Obrr5VRho/LysoCQFdHp9hzOjo6yuejHg7+Hzx4wLChQ8m4f5+dO3Yw5+efmTB+vEQECPGekMG/eK+4ubnh5uZGQUEB165d49ChQ6xevZqhQ4eyZ88elbwRERHk5ubi4vLoVHJtbW2cnJxe+LqWlpasWrUKKJwI8PHxQU9Pj7Fjx2JqaqrM17lzZ44fP86cOXOIjIwkPDyciIgI5WTAs6SkpJCUlITHEyfu1qtXj9zcXMLDw3F1dS1VeytWVB0Uenp6cv78eXx9fYmIiCAiIoKwsDCsHoYPFk1OuLm5Kctoa2szfvx4AK5cucKtW7dUJkCK3Lx5Uwb//5D4qKvsXv8jhiaWNO00psQ8+Xm5BK78lsyMVBq3G4mxWTkSoq9y7ugGNDR1aPjBMPIL8gDQ1TemVc+JykOmjExt2fbH19y8dJDqtb25fn4fcVFX6D5sQYnXEuLfIuhmNL57gqhuY06/hrUAyHt4iJ+xrjbftG2sfJ/bGBsycfM+jly/ReuaVZR15OTls/9qBF5OlTHQ0Sp+ESFeo7CwMObNn4+5mRkDBwwoMU9OTg5/LF9Oq1atlBGGT3pe9F3R+97T05NatWrh7OysfK5G9epM/O47/v77b4YPH/5yHRFC/KvI4F+8F+Li4li0aBHDhw/H2toaNTU1nJyccHJyolWrVnTo0IHTp0+rlCn6wnvyi/HxPXGlpaGhoRxQV6xYkWXLltGlSxfleQNFe/8nT57Mrl276NKlC56ennz66acsW7ZMGXb3LE/7Ai86o+BF2q3zxArA4sWL+e233+jatSsNGzZkwIAB7Nu3j7///rtUdRcUFNCxY0flyv/jHp/8EG9O+OUjHN7mi5GpDd4f/YCObsnrpJHXjpMSH4l3nx+xq+wGgE1FZ7S09Tm+8384un+gPA+gXBUPldOlLe0c0dLRJzkunIx7SZzcs5R6rQaho2dMQX4+CkXhe1GhKKCgoEAZ8SLE2xR47jorj5/HydaCb9s1Vm4b0NUqXCV1r2ij8j53sDZDT1uTiMS7KvWcj4ojKyeXJtVK3k4jxOty+vRp/vjzTywtLRkzejQGBgYl5tuyZQsFCgUd2rcvdvZPfn4+ampqylsEZmdnFyufnZWFrl7hLWGtra2V5wsU0dPTo0qVKkRFR7+Obgkh/gVk8C/eC1paWmzcuBEbGxuGDRum8pyRUeEBT+bm5io/8Ozt7dHW1iYkJIQaNQr3TOfl5XHt2rVXXqk2Nzdn2rRpDBs2jHnz5vHVV1+RmprK+vXrmTt3Lu3atVPmDQ8PR0+v5PuxP1mnubk5wcHBtGrVSpl+5swZNDU1qVDh5X+Q/u9//+PTTz9Vee2WLVumnHCoUqVw9evixYs0bNgQKHyt2rRpwzfffEO1atUICwtTiSi4efMms2fPZuzYsXKrwjfsYpA/p/b9iU1FZ7x6TEBb5+l7kTPSEgCwKq8a4WL18BC/u4m3KV+tLpQpQ35e8dupFRTko66pRWzEeXKy73M0cD5HA1XPp9ixZhIGxhb0+nzZq3ZNiJemUCj44+g5dly4QeNqFfjMqx4a6o8mpKyNDShTpgy5JRyYml+gKHa2QHDkHSyN9KlqJROa4s3ZtXs3mzdtwsHRkZGffPLM3wfBwcEkJyfz2WefFXtuxIgRDBg4kMaNGmFStiwJCQkqz6enp5OdnY2NjQ1QOOGgp6dHzZqqB7rm5ubKd7gQ7xEZ/Iv3gqmpKUOGDMHX15f79+/j7e2NgYEBYWFhLFy4kPr161OnTh127NhBQkICUVFRlC9fnn79+jFv3jwsLCyoUqUKy5cvL9UqfGk0b96cTp068ccff9CuXTuqVauGoaEh+/bto2bNmmRnZ7N69WouX76sEq6vr69PZGQkSUlJmJubq9Q5ePBg5s6dS/ny5WncuDEXLlxgwYIF9OrV65W+nG1sbDh27Bienp6oqamxZcsWdu/erby+vb09bdq04YcffuD777/HysqKxYsX8+DBA+rVq4e1tTV9+/blhx9+oF+/fty7d48ffviB7OxsKj3j1kTi1V0L2cmpvX9g79SE5p3Hov6c2+0ZmxXu24yLuky5yo+2aSREXwXAsKw1mlo6WFdwIvLaceq0/Bh1jcIV0tiI8+TlZGNdvibWFZ3pNOhnlbqT425ybPtCGrcbiaXdy93aUojXZW3QRXZcuEEHNwc+buRa7B7pOpoa1LAx5+TNGPo0cEFTvXCwfzE6nge5edSwVf38vRGfTHUb1TQhXqdDhw6xaeNG6tSty+BBg54bdffZZ5+Rm5enkrZ69WoA+vXrh8XD73AnJycuXLzIh7m5ynMBzgQHo6amRvWHdz86dPgwyUlJ/PTTT8rrpqamcjMsjNatW7/Wfgoh3h4Z/Iv3xpgxY6hUqRIbNmxgzZo1ZGdnY2trS9u2bZV71bp06cKePXvo0KEDu3fv5ssvv0RbW5sff/yR+/fv07ZtWzw9PV9bmyZMmMDRo0f57rvv2LhxI76+vvj4+NCxY0eMjY2pX78+Y8eOZdGiRWRlZaGrq0v//v2ZOXMmN27cYOvWrSr1DRo0CC0tLVasWMH06dOxtrZm6NChDB48+JXaOWvWLH788Ue6d++Ovr4+rq6uyoF+bGwstra2TJ8+nVmzZjF69GhycnJwdXVl2bJlmJqaYmpqytKlS/H19aVr167o6enRsGFDvv32W+WWB/H6ZWakErR7KQbGFjjV7UBy3E2V5w1NrFHX0ORuUhSGJtbo6htTwaE+FnYOHNryC7WbfoSxeTkSY69z7ugGKjjUw8K28CCzOi3/j+2rJrBr3fe4NOhG1v27nN7/JxZ2DlRwqI+amlqxrQV5OYVhpcamdphaVfpHXgMhoPDWftEp97Ay1sdYV4eIxFS2nA2liqUpDauU50Z8ikr+cqZG6Glp0qehC1P8DzJt2xE6uztyNyub1ccvUM3KTOU2gQUFCmJS02ksIf/iDUlLS2P9hg2YmZnh2bKlyq12ASwsLNDQ0ODOnTtYWFhgaGhY4iF8OtqFt2e1f2zi3fuDDzh9+jS+8+bRpnVr4uLj8ff3p1mzZpiZmQHQoX17fvnlF3777Tc8vbzIvH+fbYGB6BsYyOBfiPdIGYXch0sI8S/0yxb5aHqe0HN7ioXcP65px9EYGluyffVEmnYcjYOrFwA5DzIJPriKyGsneJCVjqGJFVVreeJcv4tK5EB81FWCD64iIeY6GpraVHSsT71Wg5+6reBO5EW2r55Iu37TsKnkUmIeoWp43HdvuwnvnEsxCfwQcJApXVrgbGepkjbSqy4tq9uz/uQlNp258tQ6Hi977U4S605e4np8Mtoa6tS1t+P/Gruir/1o4jItK5shy7cytLkHbZyrPK1a8RTxrQa97Sb86x09epQVK1Y89fkBAwdibmbGnDlzlOH8JZk9ezYAX3/9tUr69evX2bR5M1FRURgYGNCgQQM6d+qkEl1w9epVtm7bRnR0NGXKlMHZ2Znu3bopJwjEs1WuIp8N4t9PBv9CiH8lGfyL/wIZ/Iv/Ahn8i/8CGfyLd4GE/QvxFEuWLGHhwoXPzDNhwgR69uz5D7Xo2bZv387EiROfmWfgwIGMGjXqH2qREEIIIYQQ4t9CBv9CPMWHH35ImzZtnpnn3xQK17x5cwICAp6Zp+jOB0IIIYQQQoj/Fhn8C/EUxsbGGBsbv+1mlJq+vj76+k+/xZsQQgghhBDiv0vt+VmEEEIIIYQQQgjxLpPBvxBCCCGEEEII8Z6Twb8QQgghhBBCCPGek8G/EEIIIYQQQgjxnpPBvxBCCCGEEEII8Z6Twb8QQgghhBBCCPGek8G/EEIIIYQQQgjxnpPBvxBCCCGEEEII8Z6Twb8QQgghhBBCCPGe03jbDRBCCPHyFAoF10J2cjV4O+l349HRM6KiYwNqN+uDlrZeiWXupdxh48LhxdJNLMrTffhv3Im8yPbVE596TfdmH1G72UcA3L5xmrNH/uJuYhTaeoZUq+WFW5NeqKvL14t4M5IyMvly3S6+btsYZzvLp+bLzc9n69lQDoXeIjkjEzMDPZo6VKBr7RpoqD9a+4hJvceq4xe4HJOAupoaTrYW/F9jV6yMDZR5whNT+SvoIjcTUlGgwN6iLP0a1qKyRdk32lfx36JQKDh0+DAHDx4kKTERQ0ND3Nzc6NSpE7q6uiWWyc3NZffu3ZwICiI1JYWypqbUr1+ftt7eaGg8+hy+c+cOmzZv5npoKGrq6jg4OPBhz55YWFgo80RHR7N582bCIyLQ0NCgppMTPXr0wMjI6I33XQjxz5BfZ+KZtm7dyurVq7l+/TplypShcuXK9OzZk969ewOQmprK3r176dmz51Pr6N+/P3Z2dvj4+JTqmvPnz2fBggUqaerq6hgZGVG7dm3Gjx9P+fLlS92HGzduEBMTQ4sWLQBwdHRkxowZdOvWrdR1/NPmz5+Pv78/+/fvf6Fy69evZ9GiRaSlpdGiRQt+/PFH9PX131Arxb/BhRObCT64GpcGXbGt5Mq9lFiCD60hNeEW3n1+pEyZMsXKJMeHA9C231Q0NLWV6Roahf9tZlOFjgNnFysXfHAVibFhVKnZDIDo8LPs2TCVarU8qdPiY9KSozl9YCVZGak0af/Zm+iu+I9Lyshk6tbDZD7IfW7e5UfOcjj0Fj3qOFHVypSw+BQ2nrlCYnomIz3rKuv7zm8/tiaGjGnTgAd5+aw7eYmfth3ml94foKWhTlxaBpP9D1DFoiyfeNahDGXYdi6U7/z2M+fDNtiWNXzT3Rb/ETt37iQgIIA2H3xAjerViU9IYMuWLcTExvLFmDElfp6vW7eOoKAgOnToQKVKlYiMjGRbYCDJyckM+L//AyAlJYWZs2ZhZWXF0KFDycnJIWDLFub++ivfT5mClpYW9+7d4+dffqFs2bIMHDCA3NxcNm/ezK++vkwYP15lIkEI8e6Sf8niqTZt2sS0adOYOHEiHh4eKBQKjh07xtSpU0lKSuKzzz5j1qxZREdHP3Pw/zKsra3ZtGmT8nFubi5Xr17lp59+YsSIEQQGBpb4JViS4cOH07VrV+Xg/+jRoxgavn8/1k6dOsUPP/zAr7/+iq2tLZ988gm//fYb33zzzdtumnhDFAoFF45vprr7B9T1LPyRZ1fZDW1dQw74zybpThgWttWKlUuOC0ffyBzbSrVKrFdLWw9LO0eVtFvXTxIbcQHP7t9ibGYHwIVjGzG3qUKzjqOV187OvMe5Yxuo33oImlo6r7O74j9MoVBwMDSSlcfOlyp/evYD9l2JoG9DFzq7VwfApZwVAGuDLtK3oQvGujpsOHUZPS1NJndqjrZm4U8iS0N9Zu04RlhCCk62Fmy/cANtDXXGd2iKzsM8zuUsGbnyb7ZfvMGQZrXfQI/Ff41CoWDHzp00a9aM7g8XJ5ycnDDQ12fx4sVE3rqFfaVKKmUyMjI4cuQI3bt354MPPgCgRo0aAPj5+dG9WzcMDQ3Zum0bujo6jP3iC7S1H07ympvz22+/ERkZiYODA2fPniUjPZ3x48ZhaVkYUaOrq4uvry83w8NxdHD4h14JIcSbJIN/8VRr166le/fu9OjRQ5lWuXJl4uPjWblyJZ999hkKheKNXFtdXV0lFA3A1taW9PR0vv32W0JDQ6levfpL1f1kve+LK1euoKenh5eXF+rq6ri7uxMeHv62myXeoJwHmVR1aUllp6Yq6cbm5QBIT40rcfCfkhCBqVWlUl8nLzeHE7sWUb5aHexrNFamN+kwCkVBvkpeNXUNFAoFBU+kC/EqIpPusvhgMB84V6VWeStmBB55Zv7MnFxa16xM3Up2KunlTAvDl+PT7mOko83J8Gg6ujkqB/4AVa1MWTygo/KxnYkhndwdlQN/AB1NDcwMdIlPy3gd3ROCrKwsGjZsSN06dVTSra2tAUhKTCw2+M/OzqZ58+a4urqqpNvY2ACQmJiIgYEBISEhtGnTRjnwB7CvVIk5sx9FeOXl5QGobC/QNyjc+pKRIe9zId4XMvgXT6WmpsbZs2dJS0vD2NhYmT5s2DC6d+/OuHHj8Pf3BwpD6UNDQ8nJyWHOnDls27aNnJwcevfuTUFBwWtrk5aWFgCampoA5OTkMHfuXHbt2kVCQgJ6eno0bNiQKVOmYGpqiqenJzExMSxYsIBTp06xatWqYmH/AQEBLF++nMjISMzNzenRowfDhw9HXV29VG0aN24cBQUFGBkZERAQgJqaGv369aN9+/ZMmjSJS5cuUbFiRaZOnar8gr5+/To///wzISEhZGVlYWVlRd++fRk0aFCJ10hPT2fWrFns2bOH3Nxcatasyddff42Li4syT8OGDZk5cya///47rq6uHD58mBkzZqjU4+fnx++//07z5s3x9/enfv36LFy4kL1797Jo0SJu3LhBfn4+1apV44svvqBp08JBpUKhYOXKlaxdu5Y7d+5Qvnx5PvnkEzp06ABAfHw8Pj4+HDlyRDnxMG7cOCo98UNFvF7aOvo0/GBYsfRboUEAmFhUKLFcclwERqbWbPvza5LjwtHS1qeaqxe1m/ctca/+5dNbyUxPoV3faSrpRmWtlf+d8yCTmIhzXDwZQOWaTdHWke0m4vUxN9Rjfr92mBvocSkm4bn5rYwMGNrco1j6qfAY1NXUsDUxJCH9PpkPcrEw1GPJoWCO3YjiQV4ebhWsGdysNuYGhWdmfOBStVg9cWkZRKXcU0YTCPGq9PT0+OjhlsrHnT13DihcAHmSubk5ffv2LV7m7FnUNTSwsrIiOTmZrMxMzExNWbNmDadOnyYnJ4eaNWvS56OPMDU1BaBOnTrs2LmTtWvX0qtXL/Ly8ti8aRPGJiY4PYwmEEK8++S0f/FUQ4YM4cqVKzRr1oxhw4axePFiLly4gKGhIfb29kycOJG2bdvi7u7O0aNHAZg6dSrbt2/Hx8eHdevWERcXx5kzZ15Le0JDQ1m4cCEuLi7Y29sDMGvWLHbv3o2Pjw+7du3Cx8eHoKAgfv/9d6Bw64K1tTWDBg1i/vz5xer8888/mTRpEr169WLr1q2MHj2aZcuWlfp8giLbt29HXV0dPz8/BgwYwG+//caIESMYPHgwGzduRFtbmx9++AEonN0fNGgQJiYmrFu3jsDAQLy9vZk5cyZXr14tVrdCoWDo0KFERUWxaNEiNmzYgJubGx999BFXrlxR5nN0dKRfv37Mnz+fL774gu+//562bdsWq+/27dskJCQQEBDAF198waVLl/j8889p374927ZtY8OGDZiamvLNN9+Qk5MDwNKlS5k7dy5DhgwhMDCQ3r1788033xAUFERmZib9+/cHYPXq1axatYqyZcvy4YcfEh8f/0Kvo3h1CTGhXDi+mQrV6mJqWbHY89mZ98hMTyYtKYbqtdvywUc/4Fj7Ay6d3MLhbb8Wy5+fn8flU9uo7NQUI1ObEq+ZmZ7Cqtm92b/JB20dfeq0+Ph1d0v8xxnqaCsH4y/rZHg0h0Jv0ca5CgY6WtzLegDAmhMXSbmfxRdtGjCiZR0iEu/yfcBBsnPzSqwnJy+fBXtPoaGuhncJEwNCvC7h4eHs3LmTWq6u2NnZPb8AEBISwokTJ2jevDn6+vqkp6cDsNnPj9S7dxk2dCgf9+/P7du3mfPzzzx4UPjvwNjYmH59+3L+wgW+/vprxo8fT1R0NKNHjXrqYYNCiHePrPyLp/L29sba2pqVK1dy7NgxDh06BEClSpWYPn06Hh4e6OjooKmpiYWFBRkZGfj5+TFlyhSaN28OwPTp0wkKCnrha8fGxuLu7q58nJOTg4GBAZ6ennz99deoqRXOW7m4uODt7U2dh2FydnZ2NGrUiOvXrwNgamqKuro6enp6mJiYqFxDoVCwZMkS+vXrp5w5r1SpEnfv3mX27NmMGjWq1GcDmJiY8O2336KmpsaAAQPw9fWlXbt2eHl5AdCtWzemT58OFA7+P/74Y/r27as8jG/UqFEsXbqU0NBQ5X69IkFBQZw7d46goCBlH8aOHUtISAgrV65UTlQsWLCA9evXU61aNaKioqhWrTDcOyMjAwMDA5U6R44cqTw08erVq0yaNIk+ffoon//4448ZOnQoycnJWFtbs2LFCj7++GPl2Q79+/cnOzubvLw8/v77b+7du8fs2bOVBwJNmzaNkydPsmHDBj7//PNSvYbi1cVHXWX3+h8xNLGkaacxJebR0NTBu8+PGJnaYmhSuK/TpqIz6uqaBB9cjVvjXpS1eHSgZuTVY2RlpOLS8OkHZKpratO231SyM+9x9vBatv3xFZ0Hz0XfyOy19k+IlxV0MxrfPUFUtzGnX8PCsy7y8guj0ox1tfmmbWPlOTI2xoZM3LyPI9dv0bpmFZV6snJymbm98DyAL70bYmkkES7izQgLC2Pe/PmYm5kxcMCAUpUJDglh6dKlVK1aVXluQFE4v5GREZ+OHKl8n1tZWTFjxgyCgoJo3rw5J0+eZNmyZXjUqUOTxo2VdxGYO3cuX3/9tXIrgRDi3SaDf/FMbm5uuLm5UVBQwLVr1zh06BCrV69m6NCh7NmzRyVvREQEubm5KqHo2traODk5vfB1LS0tWbVqFVA4EeDj44Oenh5jx45VhqgBdO7cmePHjzNnzhwiIyMJDw8nIiJCORnwLCkpKSQlJeHhoRoaWq9ePXJzcwkPDy+2j+5pypUrp5yQ0NMrXJ16/I4EOjo65OYWnk5tampKnz59CAwM5MqVK9y+fZtr164BlLhF4vLlyygUClq2bKmSnpOTo5yxX7FiBUuXLmXx4sW4urrSt29fPvvsM5YuXUrnzp2ZMmWKyqGMj4fj16hRA2NjYxYvXkx4eDi3bt1Stic/P5/U1FQSExOLvRZDhw4F4IcffiAtLY26deuqPP/gwQNu3rxZqtdPvLrwy0c4vM0XI1MbvD/6AR3dkieuNDS1sKvsViy9fNU6BB9cTUpChMrgP+LqMUwsymNmZf/Ua2vr6CsPD7SwdWDDb0O5fn4P7k2Lh7AK8U8LPHedlcfP42RrwbftGqOlUbilS1ercPuYe0UblQNkHazN0NPWJCLxrko9SRmZzAg8QuzddMa0aUBd+9KtxArxok6fPs0ff/6JpaUlY0aPLjaBX5I9e/awceNGHBwd+ezTT5XbJHV0Cg9edXZ2VnmfV65cGV09PaKiogDYum0bVapWZfiwR1vJnJycmDx5MgEBAXzyySevs4tCiLdEBv+iRHFxcSxatIjhw4djbW2NmpoaTk5OODk50apVKzp06MDp06dVyhR9qTx5CODL3B5GQ0ODihULQ5YrVqzIsmXL6NKlC8OGDWP9+vXKL7XJkyeza9cuunTpgqenJ59++inLli0rVbj50w4rLBqAv0i7i84geFzRZMCTEhMT6dWrl/JMgiZNmuDi4qKMliipPQYGBvj5+RV7ruh1CAgIoEuXLjRo0ACAhQsX0qNHDz76qPBe7M2aNVMpV/RjAArvEjB48GBatGiBh4cHHTt2JCsri08//fSpfXuyffb29sqtFo8rmggRb9bFIH9O7fsTm4rOePWY8Mz99mkpsdyJvIC9k+q+/Ly8wi0eunqPzvfIz88jJvwstRp1L1ZPQUEBkdeOY2xqi5l1ZWW6oYkl2joG3E9Pfh1dE+KlKRQK/jh6jh0XbtC4WgU+86qHhvqjz2VrYwPKlClDbn7xwynzCxTKSQKAW0l3mRZ4hAd5eXzXsRk17Sz/kT6I/55du3ezedMmHBwdGfnJJ8/9HlUoFKxbv579+/ZRt149Bg0cqPL7xdLSkjJlypCXW/z2mAX5+crv+OTkZJWISyj8jVGxYkVi79x5DT0TQvwbyJ5/USItLS02btzI1q1biz1nZFR4WrK5ubnKLLK9vT3a2tqEhIQo0/Ly8pSryK/C3NycadOmceXKFebNmwdAamoq69evZ8qUKYwfP55u3bpRo0YNwsPDS3UXAnNzc8zNzQkODlZJP3PmDJqamlSoUPJhaa8qMDCQu3fv8tdffzFy5Ehat25NWloaUPKEhIODAxkZGeTm5lKxYkXl35IlS9i3bx9QeDpvUlKSsoy1tTUTJkwgLS0NZ2dnrKyefijV8uXLqV+/PvPnz2fAgAE0btyYOw+/6BUKBYaGhlhaWnLx4kWVcqNGjWLGjBk4ODgQGxuLoaGhsm22trb8/PPPxSaIxOt3LWQnp/b+gX2Nxnzw0Q/PPWgvMz2FY9sXEnH1qEp6xJUjaGrrYWbzaA9zasIt8nIfYFWu+GFPampqnN7/J6f3/6mSnnTnJg+y0jGzfHqkgBD/hLVBF9lx4QYd3BwY3bq+ysAfCk/sr2FjzsmbMSoTABej43mQm0cNW3OgcMX/x62F296mdfOSgb94Yw4dOsSmjRvxqFOHMaNHl2oC3c/fn/379tG6dWuGDhlSbOFCW1ubatWqERISooxAhMItfw8ePFBuEbS2tiYsLEzld0hOTg63b9/Gwtz8NfVQCPG2yeBflMjU1JQhQ4bg6+vL3LlzuXr1KlFRURw4cIDPPvuM+vXrU6dOHfT09EhISCAqKgp9fX369evHvHnz2L17Nzdv3mTKlCmv7dC35s2b06lTJ/744w+uXLmCgYEBhoaG7Nu3j1u3bhEaGsqkSZO4fPmy8qA6AH19fSIjI1UGx0UGDx7M6tWrWbt2Lbdu3WLbtm0sWLCAXr16lXq//4uytrYmKyuLnTt3Ehsby9GjRxk7diyASruLNG3alBo1avDFF18QFBTErVu3mDFjBn5+flSpUrgftX///uzdu5clS5Zw+/Zt9uzZw5w5c3BxceHChQt8//33yn1/T7KxsSE0NJQzZ84QHR3N5s2b8fX1VWnPsGHDWLFiBVu2bOH27dusXLmSffv24eXlRadOnTA2NmbUqFGcP3+emzdvMm7cOA4fPoyjo2OJ1xSvR2ZGKkG7l2JgbIFT3Q4kx90kISZU+Zd1P42cB5nK/wawrlATW/tanNqznMunthETfo6gPUu5fDqQ2s0+Upk8SEmIBMDEvHxJl6d2sz7EhJ/j2PaFxEac59rZXezZ8BNlLStQzbXVG++/EEUyc3K5HpdMWlY2ABGJqWw5G0oVS1MaVinPjfgUrsclK/8ycwoHQX0aupByP4tp245w9tYdDlyL4NfdQVSzMlPeJnD54bPcy3pAz7pOyusU/UWn3HtrfRbvl7S0NNZv2ICZmRmeLVty+/ZtwsPDlX/p6elkZWUp/xsKD/DdtXMnlSpVok6dOkRERKiUycrKAgrPHbqblobvvHlcvHSJY8ePs2TJEuwrV8bNzQ2ALp07E37zJosWLeLipUucO3cOX19fUu/eVd7ZRwjx7pOwf/FUY8aMoVKlSmzYsIE1a9aQnZ2Nra0tbdu2Zfjw4QB06dKFPXv20KFDB3bv3s2XX36JtrY2P/74I/fv36dt27Z4enq+tjZNmDCBo0eP8t1337Fx40Z8fX3x8fGhY8eOGBsbU79+fcaOHcuiRYvIyspCV1eX/v37M3PmTG7cuFEskmHQoEFoaWmxYsUKpk+fjrW1NUOHDmXw4MGvrc1P8vb25vLly/j4+JCRkYGdnR09e/Zk3759XLx4URmqX0RdXZ3ly5cze/ZsxowZQ1ZWFlWqVGHBggU0bNgQgLZt25KVlcXixYvx9fXF0tKSbt26MXz4cPbs2cPSpUu5f/9+ie0ZNWoUSUlJjBgxAoCqVasyffp0vv76ay5evEiVKlXo168f2dnZ+Pr6kpiYSKVKlZg7dy716tUDCk/5nzVrFoMHDyY/P5+aNWuyfPly5eSEeDOiws6Qn5dDRloif68YV+z5ph1HY2hsyfbVE2nacTQOrl6UKVMGrx4TOHv4Ly6dDCDr/l0MTaxo0u5THN3bqJTPvn8XAC2dkvebVqvliYamNheObyLs4gE0tHSo6NiAui3/Dw1NrdfeXyGeJjwxlR8CDjLSqy4tq9tzKjwGhULBzYQUJm7eVyz/lC4tcLazxNHanO+7tGDdyUvM3nkcbQ116trb8X+NXVFTK0NefgEhtwojoRYfDC5Wj5OtBT90bVksXYgXdfHiRXJzckhOTmbWrFnFnh8wcCDmZmbMmTOHAQMH0rhRI86ePYtCoSAyMrLYrX0BvvrqKxwdHalSpQpfffklAQEB/P7772hpaeHm5saHPXsqtyi6ubnx+ahR/P333/z+++/oaGtjb2/PdxMnUq5cuTfefyHEP6OMojTx0UKId5pCoVDZovEu+GWLfDSJ99/wuO/edhOEeOPiWw16200Q4o2rLAse4h0gYf9C/Ae8awN/IYQQQgghxOslYf/iH7NkyRIWLlz4zDwTJkxQuSXd27R9+3YmTpz4zDwDBw5k1KhR/1CLhBBCCCGEEOLlyOBf/GM+/PBD2rRp88w8ZmZm/1Brnq958+YEBAQ8M0/RnQ+EEEIIIYQQ4t9MBv/iH2NsbIyxsfHzM/5L6Ovro6//7NumCSGEEEIIIcS7QPb8CyGEEEIIIYQQ7zkZ/AshhBBCCCGEEO85GfwLIYQQQgghhBDvORn8CyGEEEIIIYQQ7zk58E8I8a80PO67t90EId64RdZT33YThHjjuhD+tpsghBACWfkXQgghhBBCCCHeezL4F0IIIYQQQggh3nMy+BdCCCGEEEIIId5zMvgXQgghhBBCCCHec3LgnxBCvOPO347jr5OXuJ2ShomuDt4uVeno5kCZMmWeWubojdtsOn2FhPT7WBjq0aV2dVpWt1fJExafwqrj57mZmIqeliYtqlfiw7o10VB/NG8cGpfE2hMXuZmYiq6mBg2rluej+s7oamm+sf6K/6bo8LMEH1xFasJtdPWNcarbAef6XZ75Pr95+TDnjqwj/W4CBiYW1GrYAwdXrxLz5j7Iwm/xZ7g361MsT9KdmwQfXEXinTBQKDCzrkxdzwGY21R5rX0U4vLlywQEBBATG4uRoSEtPT1p07r1M9/np06dIjAwkKTkZMzMzPD29qZxo0YqeSIiI9m0cSO3bt1CR1eXRo0a0aljRzQ0Hg0FMjMz8ff3JyQkhAcPHmBrZ0e3rl2pXr36G+uvEOKfJSv/4l/J0dERPz8/5s+fj6enpzL9xo0bHDx4UPk4NjaWv//++423Z9y4cTg6Oqr8ubq60rFjRzZs2PDK9Rf1FyA3N5c///zzlet8k06ePImjoyPR0dFvuyn/edfjkpnx91HsyhrytXcjmjpUYPWJCwSEXHtqmaCb0czbcxK3CtZ807YxNW0tWbjvNEdv3FbmiU/L4Kdth9DSUGfsBw3p4OrAtnOhLDsSosxzK+kuP2w5hI6WBl97N6JPQxeOh0UxZ+fxN9pn8d+TEBPKnvU/YWJWDq8e46ni3IJT+/7kwonNTy0Tce04BwN+xq5KbVr1nIBNBReObPPl5uXDxfI+yMpg1/ofyEhLLPbcvZQ7/L1qPHl5D2ja4XOadRxNQUEegSu+JS055rX2U/y3hYeHM3/BAqytrfnkk0+oX78+mzdtYufOnU8tExwSwtKlS6lZsyYjP/kERwcH/vzjD06dOqXMk5iYyNy5c9HS0mLY8OG0bt2aPXv28NdffynzFBQU8KuvL2fPnaN7jx6MGDECfT09fOfNk+96Id4jsvIv/tUGDRpE3759lY+HDx9O165dadGiBQDffvstdnZ2tG/f/o23xd3dnfnz5ysfZ2dns3nzZiZNmoSRkRHe3t4vXffRo0cxNDQEIDAwkBkzZjBgwIBXbbL4D1h/6hL2FiZ83qo+AO4VbcgvUOAXfJX2rg5oaagXK7M26CINqpRjQBM3ANwqWJPxIId1Jy/RpFoFAALOXkNXU5Nv2zVBQ12N2hVt0NZQZ9mRs3TzqIGFoT6B569jqK3F196NVaIBFu47TWxqOrZlDd/8CyD+E0IOrcHMyp7mnccCUL6qBwUF+Zw/upGadTuhoalVrEzwgZXY12hEg9ZDAChXpTYPstMJObiaKjWbKfPdun6SoF2Lyc3JLvHal09vQ0NTmza9pqCppQOATaVarF8whMunt9HIe8Tr7q74j9qydSsVypdn8ODBALg4O5NfUMD27dvx8vJCS6v4+9zf3x8PDw969eoFgLOzM/fv3ydgyxbq1asHwM6dO9HR0eHTTz9FQ0ODWi4uaGlp8dfatbRr1w4zMzNOnjzJrVu3mPTdd5QrVw4ABwcHfvjxRy5fuaJME0K822TlX/yr6evrY2pq+rabAYCmpiYWFhbKv/LlyzNmzBgqVarEtm3bXqluCwsLdHQKf1QqFIrX0VzxH5Cbn8/lmETq2duppDeoUo7s3Dyu3UkqVibh3n3u3E2nXuXiZeLTMrhzNx2A87fjca9orTKob1C1HAqFgvO34wHoXd+ZCR2aquTRUCv875z8/NfTSfGfl5+Xy51bl6hYvaFKun2NxuTmZBEfdaVYmfS7CaQlx1LRUbVMpeqNuZcaR1pKLAAPsu+zd+N0rCs688FH35d4fROzcrg06Koc+ANoaulgYGRGemrcK/ZOiEK5ubmEhobi7u6uku7h4UF2djZhYWHFyiQlJxMfF1dimcSEBOLjCz+rL1++jIuzs0qIfx0PDxQKBZcvXwYKIwgcHBxUBvlaWlpMmzqVD9q0eW39FEK8XTL4F/9qj4f9e3p6EhMTw4IFC+jfvz/9+/fn1KlT+Pv7K/Pk5OQwe/ZsmjZtiru7Ox9++CFHjx5V1ufn50fr1q2ZOnUqHh4ejBw58pXbqK6urpyNz8nJYebMmXh6euLs7Ey9evUYPXo0KSkpAERHR+Po6MiiRYto3LgxXl5eZGRkKMP+/fz8GD9+PFC4FWDHjh04OzsTEBCgcs2ff/6Z7t27l6p9RX1et24dLVq0wNXVlVGjRhEfH89XX32Fu7s7zZo1Y9OmTcoyaWlpfPfddzRt2pSaNWvSsGFDvvvuO7Kyskq8hkKhYMmSJXh5eeHq6krnzp3ZunXri76U4gXFp90nv6AAGxPVFXZrYwMAYlLvFStTlGb7RBmbh2Vi76aTk5dPYvr9YnmMdXXQ1dIk9uEEgZmBHhXNTQDIzs3jQlQ8fwVdwtHanEoP04V4VfdS4yjIz8PI1FYl3aisDQBpycVDku8mRQFgbKo6yWX8sI6icH0NTW16jFhI805foKNnXOL1a9RpR62G3VTblHKHlITbmFhUeIkeCVFcYmIi+Xl5WFpZqaRbWlgAEBdXfKLpzp07AFg9WcbSEoD4+HhycnJITk7GytpaJY+hoSE6urrKCYKoqChsbW3Zs2cP48aNY/iIEUydOpXr16+/ng4KIf4VJOxfvDM2bdpE165dadeuHcOHDwdgxIgRWFtbM3nyZADGjx/PzZs3mTNnDlZWVhw4cIARI0awYMEC5VaB27dvk5CQQEBAANnZJYd5lkZGRgZr167l5s2bjBkzBoBZs2Zx4MABfHx8sLOzIzQ0lPHjx/P7778zceJEZVl/f39WrFhBVlYWBgYGyvR27dqRnp7O9OnTOXr0KMbGxrRo0YKAgAC6dOkCFO7L27p1K8OGDSt1W2NjY9m5cyeLFy/mzp07jBw5kqCgID755BNGjhzJ8uXL+f777/Hy8qJs2bKMGzeO+Ph4FixYgJmZGSEhIUyYMIGqVauWuB1h7ty5BAYGMnnyZCpXrszp06f5/vvvSU9PV9m2IV6vzJxcAPSeOFxPV0tD5fnH3X9qGU1lmfsPckrMA6CrqVGsXoVCwaDlW8jNy8dAR4vBzRoVKyfEy8rNyQRAS0tPJV1TSxeAnAeZxcrkPLhfWEa75DK5D8uoq2tgbKY6QfA8ebk5HNo6F3UNTZzqdHihskI8TdHvEV0dHZX0oqjAkibfszIL38e6urpPLVNU7sl6i/IVPZ+enk5wcDB6urr06NkTLS0tdu7Ywa++vkwYP17C/oV4T8jgX7wzTE1NUVdXR09PDxMTE6AwFF9HRwdTU1Nu3bpFYGAgAQEB1KhRA4CBAwdy7do1li1bphz8A4wcOZLy5cu/0PXPnDmjDK1TKBRkZWVhZmbGV199RZuHIXEuLi54e3tTp04dAOzs7GjUqFGxmfM+ffpQtWrVYtfQ0dFR7v23eDjb3717d0aOHEl8fDxWVlacOHGClJQUOnQo/Y/OvLw8Jk2aRJUqVXBwcKB69epoamoycOBAoPB12rhxI5GRkZQtW5bGjRtTt25dHB0dAShXrhyrV68ucQUgMzOTP//8k19++UX5GleoUIGYmBiWLVsmg/83qOA5W0TUSjgd+nnbSsqUKcPzNp48WW1+gYJx7ZqQk5ePf8hVJvkfYGo3T1n9F6+FoqDgmc+XUSshiPF526eecXL6s+Q+yGLPxqkkxt7Aq8c4DE0sX6oeIZ5U8Jz3uVoJ7/NSfZ6XIg9Afl4emZmZTBg/Xrnd0qFaNSZMmMD2HTsYNnToM+sRQrwbZPAv3htXrhTu++zTp49Kem5uLkZGRipplSpVeuH6nZ2dmTNnDlD4Jaynp4eZmZlKns6dO3P8+HHmzJlDZGQk4eHhREREKCcDilSsWLHU123WrBlmZmZs2bKFYcOG4e/vj5eXF8bGJYeoPk2FCo/CU/X09LCxsVE+1tbWBgq3LUDha7h//378/f2JjIwkLCyM6OhoKleuXKzesLAwHjx4wJdffqny4yQvL4+cnByys7OVqxDi9dLXLlyZz8pVXYl/WkTA42lZOSWX0dfSfCxPXrHymTm56D9x6JSGuhq1yheGndawNWfkqr/5+/x1PvWq98J9EuJJWjr6AOTmqK585jwlIgBA8+GKf7EyD1f8tbT1X7gdGfeS2L3uB+6lxNKy29dUdKj/wnUI8TR6eoXv2ScjEotW5nWeWN2HRyv+Tyujq6f31DwA2VlZ6D68rraODjY2NirnLOno6FC5ShWioqJeqk9CiH8fGfyLty4pKYnk5GTlKnPRLLW6evFTyp+lqNyaNWvQ11f9YffkjPnLDEZ1dHSeO2ifPHkyu3btokuXLnh6evLpp5+ybNky5Z66l7m+uro6Xbp0Ydu2bfTr14+9e/fi6+v7wu3X1FQdCJa0igCFqw/Dhw/nxo0bdOjQgXbt2lGzZk0mTZpUYv6i1/3XX38tcXKgpNOJxethZWSAWpkyxKVlqKQXPbYzNSpWxq5sYdqdtAzsLcoWL1PWCB1NDUz1dbmTlq5SNi0rm+zcPOxMC6NTzkTEoqulQU27R6uf+tpaWBsZkJr58ltqhHicoYk1ZdTUuJd6RyU9PaXwsYl58SguE7PCEOW0lFjMrB99LhXVUVKZZ0mJj2TXuu/Jy33ABx/9gE1F5xcqL8TzmJubo6amRmKi6u0mix7bPjZhX8T64T7+hIQElQn+ojI2NjZoa2tjUrYsCQkJKmXT09PJzs5WLgRYWVqSl1d8wrcgP7/Y7wchxLtLDvwTb92yZcsYO3as8nFaWhrAC5/yX61aNaDwS69ixYrKv6KD9N601NRU1q9fz5QpUxg/fjzdunWjRo0ahIeHv9AJ/mVKCEft3r07169fZ9WqVRgaGtKkSZPX2XQVV69e5fDhw/j6+vLVV1/RqVMnKlSowO3bt0vsR+XKldHQ0CA2NlbldT906BDLli176iSDeHVaGurUsLXg5M0Ylf83J2/GoKetSTXL4v+GrI0NsDTSJ+im6iFpQTejsTYpfA6gVnkrQm7dIfexU/uDwqJRK1MG54eD/cDz11lyKISCgkfXTsrIJCr1HhXNXiwyRYin0dDUwrpCTSKvHVd5n0dcO46Wjj4Wdg7FyhiZ2mBgYknktWMq6ZHXjmFkavNC4foZ95LYsbZw8rPD/82Sgb94I7S0tKjm4EBwSIjK+zw4JARdPb0SIxYtLS0xNzcnODhYJT04OBhLKyvMH0YnOjk5ceHiRXIfixI7ExyMmpoa1R8uvLi4uBB1+zaxsbHKPBkZGYTdvKn8fSWEePfJr3Lx1jVq1IiwsDD8/f2Jiopi3rx5GBkZFbt1DRTe+i8yMpKkpCTl45iYGOLi4qhWrRotW7ZkypQp7N+/n6ioKJYsWcKiRYtUZsTfFAMDAwwNDdm3bx+3bt0iNDSUSZMmcfnyZWU4fWkUhf5dunRJGaZnb29P7dq1WbhwIZ07d37hqIgXYW5ujoaGBjt27CAqKoqLFy8yZswYEhMTS+yHoaEhvXv3xtfXly1bthAVFcWmTZuYPXu28sRh8eb0qONEWEIKv+w6wdlbd1h38hJbz4XStXYNtB8eznc9Lpm0rEcr8T3qOnEiLIolh4I5e+sOiw8GcyIsit71Hg1qutSuTlrWA6ZtO0JwZCzbzoXy57FztKpZGQtDfeW179xN55fdJzh/O47Dobf4ccshDLS16Ojm+I+/FuL95dakF4mxN9jvN5OosGCCD67mYpA/ro16oKGpTc6DTBJiQsm6n6Ys4970IyKuHOP4jt+JCgvm2PaFRFw5hkfzfi907aBdi8i+n4Z7097k5hRep+gvNVHCocXr06F9eyIjIli0aBEXL10iICCA3bt20a5tW7S1tcnKyiI8PJz09EdRWR06duTMmTOsWbOGi5cusXr1as6cOUOXzp2Vebw/+ID09HR8583jwoUL7N6zhw0bNii3FQKFB/6amjJ//nxOnTrFuXPnlFGGcqs/Id4fEvYv3rqmTZsybtw45s+fT1JSEg4ODvz+++8qp+AX6d+/PzNnzuTGjRts3bqV3r178+2339KpUydOnDjB3LlzmTt3LpMnTyYtLY0KFSowbdo0unbt+sb7oampia+vLz4+PnTs2BFjY2Pq16/P2LFjWbRo0VNvk/ekBg0a4OrqSu/evZk9ezZt27YFoFu3boSEhLzxvlhZWeHj48P8+fNZs2YNFhYWtGjRggEDBrB///4Sy4wfP56yZcvi6+tLQkICNjY2jBo1iiFDhrzRtgpwLmfJl94N2XDqMjO3H8PMQJd+DWvRyb1w8B2emMoPAQcZ6VWXltXtAWhZ3Z68/AK2nA1l/9UIrIwM+KxVPRpXezRJZlfWiO86NmP18QvM2XkcIx1tOrg60OuxCQLncpZM6tSc9acuMWfncdTV1HCrYE2/RrUw0ZNzHsTrY1upFl7dxxFyeC17N05Dz9CUel4DcGlQ+HmYfOcm21dPpGnH0Ti4egHg4OpFQX4uF0/4cf38XgxNrGje+Qsq12xa6uvm5+dx+8YZAI5tX1jseeuKNWnff8Zr6KEQUL16dUaMGMHWbdv47bffKGtiQvcePZSD79u3bzNnzhwGDBxI40aFd1Vp3KgRebm57Nq9m6PHjmFhbs6gQYOoW7eusl4bGxvGjB7Nps2b+f1//8PAwIBWrVrRuVMnZR59fX2+/eYbNvv5sWbtWvLz8qhatSrjvv32hSMxhRD/XmUULxKPLIR4a+bPn8/x48f566+/3nZT/hH3F018fiYh3nGLrKe+7SYI8cZ1cQ5/200Q4o2rXKXK226CEM8lK/9C/MsFBwcTERHBypUr+fHHH992c4QQQgghhBDvIBn8i/+0JUuWsHBh8VDOx02YMIGePXv+Qy0q7sCBA6xevZru3bsrtwAAxMfH4+3t/cyyLi4urFy58k03UQghhBBCCPEvJ2H/4j8tLS2Nu3fvPjOPmZlZiecPvG35+flER0c/M4+2trbyVkDvGgn7F/8FEvYv/gsk7F/8F0jYv3gXyMq/+E8zNjbG2PjdvCWZuro6FStWfNvNEEIIIYQQQrwD5FZ/QgghhBBCCCHEe04G/0IIIYQQQgghxHtOBv9CCCGEEEIIIcR7Tgb/QgghhBBCCCHEe04G/0IIIYQQQgghxHtOBv9CCCGEEEIIIcR7Tgb/QgghhBBCCCHEe04G/0IIIYQQQgghxHtOBv9CCCGEEEIIIcR7TuNtN0CI53F0dGTGjBnExMTg7+/P/v37Abhx4wYxMTG0aNECgNjYWM6ePUv79u3faHvGjRuHv7+/SpqOjg4VKlSgf//+fPjhh69Uf1F/u3XrRm5uLmvWrGHAgAGvVKd4v52/HcdfJy9xOyUNE10dvF2q0tHNgTJlyjy1zNEbt9l0+goJ6fexMNSjS+3qtKxur5LneFgUW8+GEp16DwNtLVzKWdK3YS1M9HSfgabRAAAvg0lEQVQAGLnybxLT75dYv4WhPgs/frP/FsV/S3T4WYIPriI14Ta6+sY41e2Ac/0uz3yfF0mOC2fL8i/pOXIRhiaWKs/dCg3i7JF1pKXEoqdvQhWXlrg27om6+qOfSCnxkZze/ycJsddRV9fErrIb9TwHomtg8rq7Kf7jLl++TEBAADGxsRgZGtLS05M2rVs/831+6tQpAgMDSUpOxszMDG9vbxo3aqSS5+zZswT+/TfxcXEYGRvToEED2rVti4bGo/f50qVLOXnyZLH6h48YQR0Pj9fXSSHEWyODf/HOGDRoEH379lU+Hj58OF27dlUO/r/99lvs7Oze+OAfwN3dnfnz5ysfZ2dns3nzZiZNmoSRkRHe3t4vXffRo0cxNDQEIDAwkBkzZsjgXzzV9bhkZvx9lMbVytOrXk2u3Uli9YkL5BcU0NWjRollgm5GM2/PSdrVqoZbBWtOhcewcN9pNNXVaVKtAlA4OeC7O4hWNSvzUX1nUjOzWX/qEt8HHGTWh63R0lDn67aNyM0vKNaeFcfO0bpm5Tfed/HfkRATyp71P1HZqQm1m/UlPuoKp/b9SUFBPq6NejyzbErCLXav/xFFQX6x52LCz7F30wwqOzWhrucAUhNvcebAKh5k3aPhB8MByMq4y/Y132FgZEazjqPJz83h9P4/2bVuCh0H/qwySSDEqwgPD2f+ggXUrVOHTp07E3bjBps3baIgP5+2bduWWCY4JISlS5fi5eVFzZo1OXfuHH/+8QeaGhrUq1cPgCtXrvD7779Tp25dunfrVriYEhBARkYGfT76SFlXVFQUdevVo5WXl8o1LC1VJ8yEEO8u+cYS7wx9fX309fXfdjMA0NTUxMLCQiVtzJgx7Nixg23btr3S4P/xehUKxUvXI/4b1p+6hL2FCZ+3qg+Ae0Ub8gsU+AVfpb2rA1oa6sXKrA26SIMq5RjQxA0AtwrWZDzIYd3JS8rBv3/wVdwr2jC8RR1lOVsTQyZu3kdwZCwNq5bH3qKsSr2ZObn8ujuI2pVsnjrxIMTLCDm0BjMre5p3HgtA+aoeFBTkc/7oRmrW7YSGplaxMvn5eVw5vY2QQ2tR1yj+PMD183swMDKneecvUVNTw66yG1n373Lp5BbqtRqMuroGt64H8SDzHp0GzMbI1AYALR19dv31PQnR17Cp6PzmOi7+U7Zs3UqF8uUZPHgwAC7OzuQXFLB9+3a8vLzQ0ir+Pvb398fDw4NevXoB4OzszP379wnYskU5+D967BimpqYMGTwYNTU1nJycuJeezp49e/iwZ080NDTIyckhLj6e1q1bU7myTN4K8b6SPf/inTF//nw8PT0B8PT0JCYmhgULFtC/f3/69+/PqVOn8Pf3V+bJyclh9uzZNG3aFHd3dz788EOOHj2qrM/Pz4/WrVszdepUPDw8GDly5Cu3UV1dXfnlnJOTw8yZM/H09MTZ2Zl69eoxevRoUlJSAIiOjsbR0ZFFixbRuHFjvLy8yMjIwNHRET8/P/z8/Bg/fjxQuBVgx44dODs7ExAQoHLNn3/+me7du5eqfU/r8969e+nZsydubm64uLjQrVs3jhw5oiynUChYsWIFH3zwAbVq1aJ9+/YEBgYqn4+Pj+eLL76gTp061K9fnxEjRhAZGfmyL6Mopdz8fC7HJFLP3k4lvUGVcmTn5nHtTlKxMgn37nPnbjr1KhcvE5+WwZ276SgUCmqVtyq2em9XtjAiJS4to8T2bD5zhbSsbAY3rf0q3RJCRX5eLnduXaJi9YYq6fY1GpObk0V81JUSy0WHneHs4XW4Nu5JXc//e2rdGpraqKk9+jmko2dEQX4euTlZyjwAmtp6yjzauoX/FrKz7r18x4R4TG5uLqGhobi7u6uke3h4kJ2dTVhYWLEyScnJxMfFlVgmMSGB+Ph4APJyc9HS0lJ5nxsYGJCfl0d2djYAMbGxFOTnU65cudfdNSHEv4gM/sU7adOmTVhbWzNo0CDmz5/P/PnzcXd3p23btmzatAmA8ePHc+zYMebMmYO/vz9t27ZlxIgRHDx4UFnP7du3SUhIICAggC+++OKl25ORkcHixYu5efOmMjRv1qxZ7N69Gx8fH3bt2oWPjw9BQUH8/vvvKmX9/f1ZsWIFv/76KwYGBsr0du3aMWHCBKBwK4CXlxctWrRQGfwXFBSwdetWunXrVuq2PtnnS5cu8fnnn9O+fXu2bdvGhg0bMDU15ZtvviEnJwco3Ac4d+5chgwZQmBgIL179+abb74hKCiIzMxM+vfvD8Dq1atZtWoVZcuW5cMPP1T+8BBvRnzaffILCrAxMVRJtzYufB/FpBYfmBSl2T5RxuZhmdi76ZQpU4b/a+xG3ScmFU5FxABQwcy4WL2J6ff5+/wNOrtXx9Lo3xGhI94P91LjKMjPw8jUViXdqGzhKnxacnSJ5cxtqvHh50txa/IhZdSKR8AAONVpz73UOC4G+fMg+z4JMaFcOrmVclU90Hk4wLd3aoKeoSkndv2PzPQU0u/Gc3rfH+gZmmJn7/b6Oir+0xITE8nPy8PSykol3fJhNGBcXFyxMnfu3AHA6skyD8P0i76DW7ZsSUJiIrt27yYzM5Pw8HD27t2Li4uL8ndHdFQUUPh748uvvmLEJ58wc9YswsPDX2MvhRBvm4T9i3eSqakp6urq6OnpYWJiAhSG4uvo6GBqasqtW7cIDAwkICCAGjUKw48HDhzItWvXWLZsmfKcAICRI0dSvnz5F7r+mTNnlDPtCoWCrKwszMzM+Oqrr2jTpg0ALi4ueHt7U6dOYdi0nZ0djRo14vr16yp19enTh6pVqxa7ho6OjnLvf9FWgO7duzNy5Eji4+OxsrLixIkTpKSk0KFDhxdq/+N9vnr1KpMmTaJPnz7K5z/++GOGDh1KcnIy1tbWrFixgo8//piePXsC0L9/f7Kzs8nLy+Pvv//m3r17zJ49W3lw0LRp0zh58iQbNmzg888/f6G2idLLzClckdTT0lRJ19XSUHn+cfefWkbzqWWgcLV/1bELVDI3oXZFm2LP/33+BprqarSrVe0FeyHEs+XmZAKgpaWnkq6ppQtAzoPMEsvpG5k9t26bSrVwadCVU3v/4NTePwAws7anRZevlHn0DMrSqO0nHPCbTcSVY4Vt0TWgXb9paGnrlVivEC+qaAVeV0dHJV3n4eOsrKxiZbIyC9/7urq6zyxTvXp1PmjThk0bN7Jp40YAyleowJAhQ5Rloh4O/h88eMCwoUPJuH+fnTt2MOfnn5kwfrxEBAjxnpDBv3gvXblSGAb6+IAWCsPqjIyMVNIqVar0wvU7OzszZ84cANTU1NDT08PMTPWHZufOnTl+/Dhz5swhMjKS8PBwIiIilJMBRSpWrFjq6zZr1gwzMzO2bNnCsGHD8Pf3x8vLC2Pj4iuxz/J4n2vUqIGxsTGLFy8mPDycW7duce3aNQDy8/NJTU0lMTERV1dXlTqGDh0KwA8//EBaWhp169ZVef7BgwfcvHnzhdolXkzBc86EUCvhdOjnnSNR0onSMan3+GnrYdTVyvCld6NieXLy8tl/NQIvp8oY6JS8t1qIl6UoKHjm82XUXj6I8diOhdw4vw+3Jh9iW8mV9LQEzh5ey66/ptCu3zQ0NLW5eekQB7f8gn2Nxji4tSY/L4eLQf7sXDuZ9v1nYGIugyLx6gqe8z5XK+F9XtrP89WrV3Ps+HHat29PjRo1SEpOZtvWrfzq68uXY8eira2Np6cntWrVwtn50RkWNapXZ+J33/H3338zfPjwl+iVEOLfRgb/4l8lKSmJ5ORkHB0dgUdfbOrqJYdsPk1RuTVr1hQ7JPDJL1CdJ2bZS0NHR+e5g/bJkyeza9cuunTpgqenJ59++inLli0rFgr/ItdXV1enS5cubNu2jX79+rF37158fX1fqv1FTp06xeDBg2nRogUeHh507NiRrKwsPv30U6AwouJZCgoKsLe3L7adAUBPT1bF3iR97cL/N1m5qqv1T4sIeDwtK6fkMvpPlLkUk8CcHcfR0dTg+y4tlFsKHnc+Ko6snFzlYYFCvE5aOoWf4UV78IvkPCUioLTu30sm9OxuXBv1wKNFPwBsAAtbB/wWfcr1c3twqtuBkMNrsSpXHc9u3yjL2tm7s/l/nxB8cBVePca/1PWFeFzR92VRBECRotV7nSdW9+HRiv/Tyujq6ZGamsqRI0do27YtXbp0AcARsK9UiSlTpnDs2DE8PT2xtrbG2tq6WJuqVKlCVHTJW2uEEO8eGfyLf5Vly5Zx+PBh/v77bwDS0tKAwjD/27dvl7qeatUKQ48TExNxcnJSps+dOxc1NTVGjx79GltdXGpqKuvXr2fu3Lm0a9dOmR4eHv5CA+KSVmG7d+/OkiVLWLVqFYaGhjRp0uSV2rp8+XLq16+vcuvCVatWAYWTKIaGhlhaWnLx4kW8Hrv9z6hRo7CxscHBwYEtW7ZgaGiIqakpUBhh8eWXX+Lt7a3Sf/F6WRkZoFamTLED+Ioe25kaFStjV7Yw7U5ahspp/coyZR+VOXrjNgv2nsKurCETOjTFzKDk925w5B0sjfSpamX6ah0SogSGJtaUUVPjXuodlfT0lMLHJuYvtm2rSMa9RFAosCrvpJJe1qI82rqGpCYWfudkpCVS0VH1sEENTS3MbaqSmlT67yUhnsXc3Bw1NTUSExNV0ose29oU325VNFhPSEigQoUKxcrY2NiQkpKCQqGgajXVLVm2trboGxgQGxsLwOnTp9HT06NmzZoq+XJzc5VbEIUQ7z458E/8qzRq1IiwsDD8/f2Jiopi3rx5GBkZFTvJFgpv/RcZGUlSUpLycUxMDHFxcVSrVo2WLVsyZcoU9u/fT1RUFEuWLGHRokUqX5BvioGBAYaGhuzbt49bt24RGhrKpEmTuHz5svIQvdIomii4dOmScmbf3t6e2rVrs3DhQjp37vzCURFPsrGxITQ0lDNnzhAdHc3mzZuV0QRFbR02bBgrVqxgy5Yt3L59m5UrV7Jv3z68vLzo1KkTxsbGjBo1ivPnz3Pz5k3GjRvH4cOHlREc4s3Q0lCnhq0FJ2/GqIR/nrwZg562JtUsiw/GrY0NsDTSJ+im6kpO0M1orE0MlIf1hdy6w/w9J3G0NuOnbp5PHfgD3IhPprqN+WvqlRCqNDS1sK5Qk8hrx1Xe5xHXjqOlo4+FncNL1WtU1oYyamrER11WSU9LjuFBVjqGZQsHVsZmdsRHXVG5dl5uDklxNzE0UV0pFeJlaWlpUc3BgeCQEJX3WnBICLp6eiVuUbS0tMTc3Jzg4GCV9ODgYCytrDA3M8PS0hI1NTVu3LihkicuLo77GRnKM4UOHT7M6tWrycvLU+ZJTU3lZlgY1eW7XIj3hgz+xb9K06ZNGTduHPPnz6d9+/ZcuHCB33//XeUU/CL9+/fn4MGDDBo0CIDevXtz/fp1OnXqRH5+PnPnzqVNmzZMnjyZdu3aERAQwLRp0+jatesb74empia+vr5cv36djh07MmTIELKyshg7dixhYWElHtxTkgYNGuDq6krv3r05cOCAMr1bt25kZ2e/lr6MGjUKNzc3RowYQZcuXdi4cSPTp09HR0eHixcvAtCvXz9GjhyJr68v7du3Z+PGjcydO5d69ephaGjI6tWrKVu2LIMHD6ZHjx7Ex8ezfPlyqlSp8srtE8/Wo44TYQkp/LLrBGdv3WHdyUtsPRdK19o10NbUIDMnl+txyaRlPQoL7VHXiRNhUSw5FMzZW3dYfDCYE2FR9K5XuNczJy+f/x04g46WBt3rOBGdco/rccnKv6SMRwesFRQoiElNV4kYEOJ1c2vSi8TYG+z3m0lUWDDBB1dzMcgf10Y90NDUJudBJgkxoWTdTyt1nbr6xtSs14kLJ/w5c2AlsZEXuH5+HzvXTsbA2AJH9w8A8GjRj4SYUOW1b10/ya51U8hMT8G9ae831WXxH9ShfXsiIyJYtGgRFy9dIiAggN27dtGubVu0tbXJysoiPDyc9PT0R2U6duTMmTOsWbOGi5cusXr1as6cOUOXzp0BMDQ0pFWrVuzatQs/f3+uXbvGsePH+fXXXzEzM6Np06bKaycnJ/Pbb79x8dIlTp48yc+//IK+gQGtW7d+K6+HEOL1K6N43mkhQoh/nfnz53P8+HH++uuvt92UN+b+oolvuwnvjJPh0Ww4dZmY1HTMDHT5wLkqndwLV2ouxSTwQ8BBRnrVpWV1e2WZPZdvsuVsKMkZmVgZGdDVozrNHSsVlolO4IctB596vZ51a/JhvcLQ0LSsbIYs38rQ5h60cZbJnhe1yHrq227COyPy2glCDq8lLTkGPUNTnOq0x6VB4QTonciLbF89kaYdR+Pg6lWs7PXz+ziyzZcPP1uKoYmlMl2hUHD51Fauhewg/W4CegZlsavsjkeL/ujqPzpINSosmHNH15McF46m9v+3d+dhVVXrA8e/zPMMgqKIoiAKkTkkooWQBY4IakaTWteBul5/DiWaw+2WU5gapJlZDjh0NUEkp1DTUNEEy5lBcABkEJBJFAR+fyCnjuAsF4T38zznedx7r7XPu45bz3n3GrYOFi3a09X9HUwtbeu83Y2Bj5M8Lu5hxcXFEbF9OxkZGZgYG+Pepw+v3XmKUHx8PEFBQYwcNQq3nj0VdQ4cOMDuPXvIy8vDwtwcb29vXF3/mqpSWVlJVFQUBw4e5Nq1axgbGdGxY0eGDBmiNKT/3LlzRGzfTmpqKioqKjg5OeHn61tjQWNRu7bS4SGeAZL8C/EMiY2NJSUlhQULFvDpp5/i7e1d3yHVGUn+RVMgyb9oCiT5F02BJP/iWSAL/glxx8qVK1m2bNl9y0yfPl3xrPv6sH//fkJDQ/Hz81NK/DMzM/Hy8rpvXWdnZ9auXVvXIQohhBBCCCEaIOn5F+KO/Px8rl+/ft8yZmZmta4/UN/Ky8tJfcCjeLS0tGo8xqchk55/0RRIz79oCqTnXzQF0vMvngXS8y/EHUZGRhgZGT24YAOkpqZG69at6zsMIYQQQgghRAMlq/0LIYQQQgghhBCNnCT/QgghhBBCCCFEIyfJvxBCCCGEEEII0chJ8i+EEEIIIYQQQjRykvwLIYQQQgghhBCNnCT/QgghhBBCCCFEIyfJvxBCCCGEEEII0chJ8i+EEEIIIYQQQjRykvwLIYQQQgghhBCNnHp9ByCEEOLJ/Hk5g41HT3M5Nx9jHW28nNsx8Hl7VFRU7lknOvEyW34/S1ZhMRYGuvi80IE+HdoolTmcdIWIE/Gk5hWgr6WJc8tmvOn6HMa62ooy8RnX2BhzmoTMHHQ01HmhdfMaZYSoK6nJJ4j9dR15WZfR0TOiY7cBOL3oc99rv1pORjLbvp/MsIAVGBg3Uzp2KT6GE79tIj83HV09Y+yc++DiNgw1NfnZJOrWmTNnCA8PJy09HUMDA/p4ePBq3773vaaPHTtGZGQk13JyMDMzw8vLC7eePZXKnDhxgsiffyYzIwNDIyN69OhBP29v1NVrv6aXL1+OtrY2o0aNeqrtE0LUL+n5b6IcHBzYunUrwcHBeHh4KPYnJiby66+/KrbT09P5+eef6zyeadOm4eDgoPRycXFh4MCB/Pe//33i81e3F6CsrIzVq1c/8Tnr0tGjR3FwcCA1NfWR6h0/fhxfX186d+6Mv78/ycnJdRShaCgSMnKY93M01iYGTPXqSW97G0KPnCQ87vw968RcSOWrX47yvI0VH3m70alFM5bt/Z3oxMuKMtGJl1m8+whtLIyZ6tWTES86cTotiznhv1J6uxyAxMwcZof9StGtUj707E6ARzeyCoqZ8dNeim+V1nnbRdOWlRbPLz/+B2OzlngODcTOyZ1je1dz8shPD6ybm3WJPT9+SmVFeY1jacl/ELVlHkZm1rwydDqOXftz8vBPHItaVRfNEEIhOTmZ4JAQrKysGD9+PC+++CI/bdnCrl277lknNi6O7777jk6dOhEwfjwO9vas/uEHjh07pihz9uxZli9fjqWlJQEBAfRxd2fXrl38d/PmGuerrKxk06ZNxMXF1UkbhRD1S25hN3GjR4/mzTffVGyPHTuWIUOG4O7uDsDHH3+MtbU1/fv3r/NYOnfuTHBwsGL75s2b/PTTT8ycORNDQ0O8vLwe+9zR0dEYGBgAEBkZybx58xg5cuSThtygFBYWMm7cOPz9/fnyyy+ZM2cOH330EVu2bKnv0EQd+vHYadpYGPPPV14EoHPr5pRXVLI19hz9XezRVFerUWdDzCl62LVkZK/nAXjexoqiW6VsOnqaXu1tAAiLPUfn1s0Z695VUa+FsQEzftpL7MV0XNu1YmvsOfS0NJgz2B19bU0AnKyb8a8Nu4g4Ec8bPZzruPWiKYs7sB4zyza8PHgSAK3adaGiopw/ozfTqdsg1DU0a9QpL7/N2d+3E3dgA2rqNY8DJPz5C/qG5rw8eDKqqqpYt32ekuLrnD66je6vvCe9/6LObIuIwKZVK9577z0AnJ2cKK+oYMeOHXh6eqKpWfOaDQsLo0uXLrz++usAODk5UVxcTPi2bXTv3h2A6EOHMDU15f333kNVVZWOHTtSUFjIL7/8wvBhwxS9/6mpqWzcuJGUixfRqOW9hBDPPun5b+L09PQwNTWt7zAA0NDQwMLCQvFq1aoVEydOxNbWlu3btz/RuS0sLNDWrhqGXFlZ+TTCbXBSU1MpLCzk1VdfxdbWlldeeUV6/hu5svJyzqRl072NtdL+HnYtuVl2m/NXr9Wok1VQzNXrhXRvW7NOZn4RV68XUllZyXOtLOnbqa1SGWuTqhtoGflFAKTmFdChubki8QfQ0lCnvaUpsZeuPpU2ClGb8ttlXL10mtYdXJX2t3F0o6y0hMwrZ2utl5p0nBMHN+HiNoxuHu/e89zqGlqoqv71E0lb15CK8tuUlZY8vUYI8TdlZWXEx8fTuXNnpf1dunTh5s2bJCUl1ahzLSeHzIyMWutkZ2WRmZkJwO2yMjQ1NZWuaX19fcpv3+bmzZuKfau+/57yigqmBwZieKfDRAjRuEjy38T9fdi/h4cHaWlphISE8Pbbb/P2229z7NgxwsLCFGVKS0v54osv6N27N507d2b48OFER0crzrd161b69u3LZ599RpcuXQgICHjiGNXU1BR3u0tLS1mwYAEeHh44OTnRvXt3/vWvf5GbmwtUJcAODg6sWLECNzc3PD09KSoqUgz737p1K4GBgUDVVICdO3fi5OREeHi40nsuWrQIPz+/h4qvus2bNm3C3d0dFxcXJkyYQGZmJlOmTKFz58689NJLSj3w+fn5fPLJJ/Tu3ZtOnTrh6urKJ598QklJ7T8sKysrWblyJZ6enri4uDB48GAiIiKUytjZ2WFpacmiRYtISUlh3bp1DB48WKnMvT6fhIQExo4dS7du3XBycsLT05Pvv/9eqe5vv/3G66+/jouLCy+99BKLFy+mvLxc8fdyv+tC1I3M/GLKKypobqz8I83KSB+AtLyCGnWq97W4q07zO3XSrxeioqLCu27P0+2umwrHUtIAsDEzAsBQW4vswhs13iMjv4jMOzcIhKgLBXkZVJTfxtC0hdJ+Q5PmAOTn1D5lyrx5e4b/8zue7zUcFdWao2IAOnbtT0FeBqdiwrh1s5istHhOH42gZbsuaOtIQiTqRnZ2NuW3b9PM0lJpfzMLCwAyMjJq1Ll6teomq+XddZpVrWFRnfz36dOHrOxsdu/Zw40bN0hOTiYqKgpnZ2f09fUV9d4bPZppH39My5Ytn17DhBANioxdEwpbtmxhyJAh9OvXj7FjxwIwbtw4rKysmDVrFgCBgYFcuHCBoKAgLC0t2b9/P+PGjSMkJEQxVeDy5ctkZWURHh6udEf5URUVFbFhwwYuXLjAxIkTAVi4cCH79+9n/vz5WFtbEx8fT2BgIMuXL2fGjBmKumFhYaxZs4aSkhKlL7Z+/fpRWFjI3LlziY6OxsjICHd3d8LDw/Hx8QGgoqKCiIgIxowZ89Cxpqens2vXLr799luuXr1KQEAAMTExjB8/noCAAL7//nvmzJmDp6cnJiYmTJs2jczMTEJCQjAzMyMuLo7p06fTrl27WqcjLF68mMjISGbNmkXbtm35/fffmTNnDoWFhYppG5qamsyaNYsPPviAfv36MXLkSCZPnlxrvH//fNTU1Bg9ejRubm5s2rQJNTU1Nm/ezIIFC3B1dcXR0ZETJ04wZswYRo0axdy5c0lLS2Pq1Kmoq6vzz3/+86GuC/H03SgtA0BXU0Npv46mutLxvyu+Zx2Ne9aBqoR+3aGT2Job80LrqgTLw7EN3+w/zg+/nWDwCx1QAX7+M5HUvALKKxrnCBvRMJSVVt100tTUVdqvoakDQOmtmjelAPQMzR547ua2z+HcYwjHon7gWNQPAJhZtcHdZ8qThCzEfVX/XtLRVl4stXrUYm2dAyU3qq5zHR2d+9bp0KEDr736Kls2b2bLnXn+rWxseP/995XqSdIvROMnyb9QMDU1RU1NDV1dXYyNjYGqofja2tqYmppy6dIlIiMjCQ8Px9HREYBRo0Zx/vx5Vq1apZTkBQQE0KpVq0d6/+PHjyuGrlVWVlJSUoKZmRlTpkzh1VdfBcDZ2RkvLy+6dq2ah2xtbU3Pnj1JSEhQOpe/vz/t2rWr8R7a2tqKuf8Wd+6m+/n5ERAQQGZmJpaWlhw5coTc3FwGDBjw0LHfvn2bmTNnYmdnh729PR06dEBDQ0OxSu6oUaPYvHkzFy9exMTEBDc3N7p164aDgwNQ9YUbGhpaox0AN27cYPXq1Xz55ZeKz9jGxoa0tDRWrVqlSP6joqL45JNPcHBwID4+nlatWqGurk5RUZHSDZC7P5/c3Fzeeecd3nzzTfT09ACYMGEC3333HfHx8Tg6OrJu3TpcXFz46KOPgKpRBp9++ik5OTmPdF2Ip6viAVNYVGtZHfpB015qW1E6La+A/0QcRE1VhclePRVlPDu25UZpGT8eO8OOk4moqKjQw64lfTvZse9cyiO0RIhHU1lRcd/jKqqPP7Dx0M5lJP65l+d7DaeFrQuF+VmcOLiB3Rtn0++tz1HX0HrscwtxLxUPuKZVa7mmH/b/89DQUA4dPkz//v1xdHTkWk4O2yMiWLJ0KZMnTUJLS65pIZoKSf7FQzt7tmoOpb+/v9L+srIyDA0NlfbZ2to+8vmdnJwICgoCqr7kdHV1MTNT7qUZPHgwhw8fJigoiIsXL5KcnExKSoriZkC11q1bP/T7vvTSS5iZmbFt2zbGjBlDWFgYnp6eGBkZPVL8NjY2ij/r6urSvHlzxXb1F2tpadUK6P7+/uzbt4+wsDAuXrxIUlISqamptG2rPMcaICkpiVu3bjF58mSlL//bt29TWlqqmAs4YcIEJk6cyJgxY5g1axZz587F3t6ezz77jHbt2ik+W1D+fExNTfH39ycyMpKzZ89y+fJlzp+vWim++sdIQkICbm5uSnG99tprAOzcuVPRpr+r7boQT5eeVlVvfUmZcm/9vUYE/H1fSWntdfTuqnM6LYugnYfR1lBnjo+7YkpBtYHPO+Dt3J7MgiL0tTUx0tEmOOooBtqyWJSoO5raVTcq756DX3qPEQEPq7ggh/gTe3DpOZQu7m8B0BywaGHP1hUfkPDHL3Ts9vA3hoV4WLq6Vdfs3SMmq3vvte/q3Ye/evzvVUdHV5e8vDx+++03vL29FSMcHYA2trbMnj2bQ4cOKT31SQjRuEny3wRcu3aNnJwcRS9z9Z1iNbXa5zveS3W99evXK3qIq919R1pb+9Gf8a2trf3ApH3WrFns3r0bHx8fPDw8+OCDD1i1apViXtvjvL+amho+Pj5s376dt956i6ioKJYuXfrI8WtoKCdNtd2lh6qEeuzYsSQmJjJgwAD69etHp06dmDlzZq3lqz/3JUuW1HpzQFNTk+3bt2Nra6uYqjBz5kxSUlIICAggPz+fcePGKdX5++eTnZ3N66+/jqmpKR4eHvTq1QtnZ2defvllRZl7PQf47/E9zHUhni5LQ31UVVQUC/BVq962Nq1588XapGrf1fwi2liY1Kxj8led6MTLhEQdw9rEgOkDemOmr5xQJWXmcq3oBj3sWirVS87Oo425CULUFQNjK1RUVSnIU15YsjC3atvY/NFGnlUrKsiGykosW3VU2m9i0QotHQPysi/fo6YQT8bc3BxVVVWys7OV9ldvt/hbh0I1KysrALKyspQ6IKrrNG/enNzcXCorK2nXvr1S3RYtWqCnr096evpTbYcQomGTX+ZNwKpVq5g0aZJiOz8/H+CRV/lvf+eLIzs7m9atWyte1Qvp1bW8vDx+/PFHZs+eTWBgIL6+vjg6OpKcnPxIK/jXNqzZz8+PhIQE1q1bh4GBAb169XqaoSs5d+4cBw8eZOnSpUyZMoVBgwZhY2PD5cuXa21H27ZtUVdXJz09XelzP3DgAKtWrUJVVRUdHR0KCgoUIws0NDQICgqiuLgYbW1tXF1da5y3WmRkJNevX2fjxo0EBATQt29fxTVSHY+dnR2nTp1SqrdmzRqGDRtW79dFU6aproZjCwuOXkhTunaOXkhDV0uD9s1q/hu3MtKnmaEeMReUF0SLuZCKlXHVMYC4S1cJ/uUoDlZm/MfXo0biD3A2PZuvoo5SfKtUse/klUxScwvo1rZFjfJCPC3qGppY2XTi4vnDStd+yvnDaGrrYWFt/1jnNTRpjoqqKplXzijtz89J41ZJIQYmVk8UtxD3oqmpSXt7e2Lj4pSu6di4OHR0dWsdUdmsWTPMzc2JjY1V2h8bG0szS0vMzcxo1qwZqqqqJCYmKpXJyMiguKhIMQVSCNE0SPLfBPTs2ZOkpCTCwsK4cuUKX331FYaGhjUeDQNVj/67ePEi165dU2ynpaWRkZFB+/bt6dOnD7Nnz2bfvn1cuXKFlStXsmLFCqU7znVFX18fAwMD9u7dy6VLl4iPj2fmzJmcOXNGkfQ+jOqhdadPn1YMlWvTpg0vvPACy5YtY/DgwY88KuJRmJubo66uzs6dO7ly5QqnTp1i4sSJZGdn19oOAwMDRowYwdKlS9m2bRtXrlxhy5YtfPHFF4oVfYcOHUpxcTGBgYEkJSURFxfHlClTsLKyQk9Pj7FjxyqeiHA3KysrSkpK2LVrF+np6URHRytuFlXH8/777/PHH3+wdOlSLl68yIEDB1i2bBnu7u71fl00dUO7diQpK5cvdx/hxKWrbDp6mog/4hnygiNaGurcKC0jISOH/JK/hoUO7daRI0lXWHkglhOXrvLtr7EcSbrCiO5OAJTeLueb/cfR1lTHr2tHUnMLSMjIUbyuFVUNre5tb4OWuhqLdh/h5JVM9p1NIWjXYRyszHnZ3rY+Pg7RhDzf63Wy0xPZt3UBV5Jiif01lFMxYbj0HIq6hhalt26QlRZPSXH+Q59TR8+ITt0HcfJIGMf3ryX94kkS/tzLrg2z0DeywKHza3XYItHUDejfn4spKaxYsYJTp08THh7Ont276eftjZaWFiUlJSQnJ1NYWPhXnYEDOX78OOvXr+fU6dOEhoZy/PhxfO487cfAwIBXXnmF3bt3szUsjPPnz3Po8GGWLFmCmZkZvXv3rq/mCiHqgQz7bwJ69+7NtGnTCA4O5tq1a9jb27N8+fIai8ABvP322yxYsIDExEQiIiIYMWIEH3/8MYMGDeLIkSMsXryYxYsXM2vWLPLz87GxseHzzz9nyJAhdd4ODQ0Nli5dyvz58xk4cCBGRka8+OKLTJo0iRUrVtzzMXl369GjBy4uLowYMYIvvvgCb29vAHx9fYmLi6vztlhaWjJ//nyCg4NZv349FhYWuLu7M3LkSPbt21drncDAQExMTFi6dClZWVk0b96cCRMmKFbqbdmyJT/88AMLFy5kyJAh6Orq4uHhweLFi8nNzWXSpEmkp6crFnL8Oy8vL86cOcP8+fMpKirC2tqaYcOGsXfvXk6dOsUbb7yBo6MjX3/9NV999RUrV66kWbNmvPPOO4wfPx6gXq+Lps6pZTMme7ny32NnWLDjEGb6Orzl+hyDOldN80nOzuPf4b8S4NmNPh3aANCnQxtul1ew7UQ8+86lYGmoz4evdMetfdXNmoSMHPKKq/49/SfiQI33HNatE8O7d8JET4dPBr7E2kN/snDnIfS1NOnTwZYRLzqhqlpzhI0QT1ML2+fw9JtG3MENRG3+HF0DU7p7jsS5R9X/OzlXL7AjdAa9B/4LexfPhz5vd89R6BmYcT5uJ6diwtHVN8G6bWe6uL+Nlrbeg08gxGPq0KED48aNI2L7dr7++mtMjI3xGzqU1+4senz58mWCgoIYOWoUbj17AuDWsye3y8rYvWcP0YcOYWFuzujRo+nWrZvivEOHDsXY2JgDBw+yZ88ejI2M6Nixo+L3ghCi6VCpfJTx0kI0YsHBwRw+fJiNGzfWdyhPXWVlZa3THRqy4hUzHlxIiGfcCqvP6jsEIeqcj1NyfYcgRJ1ra2dX3yEI8UDS8y+avNjYWFJSUli7di2ffvppfYdTJ561xF8IIYQQQgjxdEnyL+rUypUrWbZs2X3LTJ8+nWHDhv2PIqpp//79hIaG4ufnp5gCAJCZmYmXl9d96zo7O7N27dq6DlEIIYQQQgghnogM+xd1Kj8/n+vXr9+3jJmZWa3rD9S38vJyUlNT71tGS0tL8agd8XTJsH/RFMiwf9EUyLB/0RTIsP//nRUrVhAdHc26deseqZ6DgwPz5s3D19e3jiKrGlFcWVlJ165d6+w9noT0/Is6ZWRkhJGRUX2H8VjU1NRo3bp1fYchhBBCCCGEANavX8+SJUsabHLt7+/PvHnzGmx8kvwLIYQQQgghhGiwMjMzmT17NkePHsXW1ra+w3lmqdZ3AEIIIYQQQgghxL2cOXMGDQ0NIiIicHFxeezzJCcnM2LECJycnPD29mbnzp1Kx/fv34+vry/PPfccffv2ZcmSJZSWliqOHzhwAF9fX1xcXHB1dWXatGnk5+cDVdMKoOoR3dOmTXvsGOuSJP9CCCGEEEIIIRosDw8PgoODadWq1ROdZ82aNfj4+LB9+3Zee+01/u///o/Tp08DcPDgQSZOnMjw4cOJjIxk9uzZ7Ny5k6lTpwKQm5vLhx9+iJ+fHzt27CAkJITff/+dhQsXAhAdHQ1ULWY+Y0bDXLtKhv0LIYQQQgghhKhTnp6e9z2+d+/eOo/B39+fESNGADBx4kRiYmJYvXo1QUFBfPPNNwwfPlxx3MbGhn//+9+8++67pKamUlhYSGlpKS1atMDa2hpra2u++eYbysvLAbCwsADAwMAAAwODOm/L45DkXwjRIOmN/by+QxCizk2q7wCE+J+QVdCFEA1Dly5dlLZdXFyIiYkB4OzZs5w8eZItW7Yojlc/GO/ChQu8/PLLDBgwgHHjxmFhYYGbmxvu7u707dv3f9eAJyTJvxBCCCGEEEKIOvW/6Nl/EFVV5Vnv5eXlaGpqAlBRUcH777/PkCFDatSr7tVftGgRH3zwAQcPHuTw4cNMnTqVLl26sGbNmroP/imQOf9CCCGEEEIIIRq9M2fOKG3HxcXRvn17ANq3b09KSgqtW7dWvDIyMli4cCHFxcX8+eefzJ07l7Zt2zJy5Ei+/fZb5s6dS0xMDDk5OfXRnEcmPf9CCCGEEEIIIZ5Z5eXl5ObmYmBggLa29j3LrV69GhsbG1xcXNi0aRMJCQksWrQIgH/84x9MnDiRkJAQ+vfvT0ZGBjNmzKBly5ZYWFhQUFDAhg0b0NDQYPjw4dy6dYsdO3Zga2uLiYkJALq6uly4cIG8vDzFvoZEev6FEEIIIYQQQjyzrl69Sq9evdixY8d9ywUEBLBu3ToGDRrEsWPH+Pbbb2nTpg0AXl5eLF68mKioKAYOHMjUqVPp1asXISEhANjZ2REcHExMTAw+Pj688cYbqKmpsXLlSsV0gtGjRxMaGkpgYGDdNvgxqVRWr2IghBBCCCGEEEI8g7777jtatGhBv3796juUBkt6/oUQQgghhBBCPLOKiorYsWMHrq6u9R1KgyY9/0IIIYQQQgghnmmlpaWKlftF7ST5F0IIIYQQQgghGjkZ9i+EEEIIIYQQQjRykvwLIYQQQgghhBCNnCT/QgghhBBCCCFEIyfJvxBCCCGEEEII0chJ8i+EEEIIIYQQQjRykvwLIYQQQgghhBCNnCT/QgghhBBCCCFEIyfJvxBCCCGEEEII0chJ8i+EEEIIIYQQQjRy/w9eu39MpOfQdAAAAABJRU5ErkJggg==" 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" 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AvfltwwZLOuh//t8/Va9+fUlS1apV9chjj8q3enUt+Oor7d2zR6dOnbIsFVMee/fuVfCpU5KkFyZNUvsO7S37Bt13nwICAvXev/+t06dPa9fOnerZq5dlf15enn5Zu1aS1LZtW/3jlamWe5mqPj565Z+vauaMmTr5559a/fPPuu/++0odGH3y5El9NOtDqxnTAIDyi9l7SjmJ6TI4GtR29G3yrOVr3uHlroZ3dJJrVQ+d+WmvEo6FKy08Vt71y7/8S8LxcKWFx0mSWjzST77NAi37Avu0kbt/VZ1YtFEZEQmKPxIq/1saWfbbSkddXOb5ZIWuOyBJ8u/QSLW7Xvoadnb1XuUmZZS7/wAAVLbKWxwOgF375JNPdPz4cUnSP/7xD40ZM6bMdGy333675s6dK0nKzMzU9OnT/5Z+wtq4ceMsry9njefiunXrpo4dO0qSfv/9d+Xn51+iBoCbjclk0tqj5pRptzZpYgkSF3dbq1aq7VNVkrTpz5MVPkZSZqY+3bxFM382p7b2dndTda8rG8SWX1CgjzdtljG/QL5ennqyV0+r/Vl5eToWFSVJur11K6sgcRFnR0cN6dhBkpSRk6vg2Lgr6hMA3IxMJpPWrF4jSerZs6clSFzc7Xfcrtp16kiSNv22sULtr/nZnAWnZatWVkHiIh07dVTbtm0lSRs3Wn/3jYqMUk5OjiRpwMCgEvcyDg4OCgoaIEnKzs5WVGRUifaTEpP06cefaMbr/1JycrK8vavKz8+vQucAAPbOZDIpaqf5+VKNdo0uBImLqd2thdxreEuSYvYHV6j9qO3HJElVG9a0ChIXqd6irnya1JYkna9A24UFBTq1fJsKjQVyqeqhxvf1uGSdhOPhij14Ws5ebqrWPKDcxwIAoDIxoxh/u7CwMH399dfavXu3YmJi5OvrqwEDBmjChAkKCQnRqFGjJEmn/hoJLplHhhdtX7Rokbp162az7aLR5hMmTNDEiRNL7A8JCdGyZcu0f/9+xcTEKDMzU15eXqpXr5769OmjkSNHysfHp9znMmDAAEVFRWnw4MF69913JUlz5syxBEDLw9b5JCcna+HChdq8ebMiIiJUUFCg2rVrq3fv3nryySdVu3btUtvLyMjQsmXLtHr1aoWHh8vJyUnt2rXT008/rYCAq/OlMyMjQ//73/8kSa1atdITTzxRrnq9e/fWoEGD9PPPP+vAgQPat2+funbtWqJcSEiIvvrqKx04cECxsbHy8/PTgAEDyrUObl5enlatWqUVK1bo7NmzKigoUMuWLfX444/rtttuK7PumTNntGjRIu3Zs0fR0dFycnKSv7+/unTposcee0wtW7Yss/6NoHbt2vLx8VFKSoqioko+7KqomjXNo3gLCgqUlJQkf3//K25Tkl555RWtXLlSgwYN0gsvvKDXX39dBw8elIuLi5o0aaLZs2erRo0aksy/j99//722bdum4OBgpaSkyNnZWX5+furYsaMee+wxtWvXrtRjJSUlafny5fr1118VGRmp7Oxs1alTR71799ZTTz1V6ucvOjpaX3/9tbZv366YmBgZDAbVrVtX/fv31xNPPKFq1UqugwfcbMITEy0zfDvZePgvmbMUdKxXT2tSjurQuXMymUzlWuexyLJ9+7UtOESS1Dqgjp7t20efbt6ixIzMy+732qPHFJ6YJEl6qlevEmsnx6eny93FRRk5uWpco/S/azW9vS2vU7LKN9MZAOxJeFi4ZZZtpy6dbZYxGAzq1KmjVkdH6+DBQ+W+TqSnp+v06dOSpM6dO5VarlPnzjp69Kj+PHFCGRkZ8vLyshy3SEEpAx4di2WncHAo2adlS5dq69atkqTWbdpo7Lix+uTjT5SQkHDJ/gMAzDJjkizrEldvWddmGYPBIN8WdRUVf1xJJyPLfa0wZuUqPdL8N9m3Zb1Sy1VvWU8pp2OUGnpexuxcObuXXKbrYlE7TygzxnyNa3J/j0uunZyXnqWQVeZlb5oOvlUJx8MveQwAAK4GAsX4W/3222966aWXLCO1JfMarP/73/+0YcMGTZ5csTWoKmLu3LmaO3euTBetJZKSkqKUlBQdOXJEP/zwg7799tsyA7GVzd3deq2SPXv26Pnnn1dqqvV6i6GhoQoNDdV3332n9957T3fccUeJtiIiIjRmzBiFhYVZbd++fbt27Nih0aNHV3r/JWnr1q1KS0uTJJupi8vy2GOP6eeff5Yk/fjjjyUCxT/88IOmT59uNTs1KipKixcv1rp169SjR+kjNJOSkjR27Fj98ccfVtv379+v/fv3a8yYMaXW3bJliyZOnKi8vDzLtry8PIWFhSksLEzff/+9pk2bphEjRlTofK9HRTdTDg5XnmSi6OGcs7NzhQZdlFdqaqoef/xxS1A7JydHycnJliDx0aNHNXbs2BIP44xGo86dO6dz587pxx9/1JtvvqmhQ4eWaH/fvn2aNGmSEhMTrbYX/buvWLHC5praa9as0auvvqrc3Fyr7adOndKpU6e0dOlSffzxx+rc2fYDUeBmEVbss9OoRukzqBr4mWcaZ+TkKi493SrAWh61qnrroc6ddWuTK1+yIDUrSysOHZIkta9XVx3rl3xgVL96dX3++Cjl5efLoYwHULF/XQslycPGcgIAYO+K36c0atSo1HINGjSQJGVkpCsuLs4yGLEs4eHhlnu9hmW03bBRQ0nmGWthYWFq06aNJKl2ndry8PBUVlamtm7Zqp69elkFHUwmk7ZtMQeBq1TxVq1S7hlr1a6thx5+SD179rS5HwBQtoyYJMtrr4DS7ym8aptnGudn5SonOUPuvlUu2XZmTJJkKmq7ZPYjS9t1/prFbJIyo5Pk07js54R5Gdk6t8n87Kla8wBVb2E7wF1c8Iqdys/MlX/Hxqresh6BYgDANUOgGH+b06dPa9KkSTIajapTp46mTp2qrl27Kjk5Wd9++60WL16sN99886oce926dZozZ44kc4qzZ555Rg0bmh8QhIaGasGCBdqyZYtiYmI0e/ZsvfPOO5d9rGeffVZPPvlkqft37NihF154QSaTSQ8//LDVzMbg4GA9++yzysnJUWBgoJ5//nl1795dzs7OOnr0qGbPnq1jx47pxRdf1KJFi9Sp04WR8nl5eZYgsZubmyZOnKi77rpLrq6u2rlzpz744AN99dVXl31eZdm3b5/ldUUDYe3bt5efn58SEhK096+1IYvs3btX//znPyVJzZo105QpU9S2bVslJyfr+++/14IFC/TTTz+V2vYLL7ygP/74Qw4ODnr66ac1ZMgQeXt76/Dhw/rggw/0xRdf2KyXlZWlqVOnKi8vT+3atdOkSZPUpEkTGQwGHT16VO+9957CwsL07rvvql+/fgoMLJmq6EYRERFhmdVxpWtEr127VsHB5rRMffr0sbnm8pXatm2bnJ2dNXPmTAUFBSkqKkopKSmSzDOJx40bp4SEBPn5+Wny5Mnq0qWLvL29FRsbq99++01ffPGFsrOz9dZbb+nuu++Wh4eHpe2IiAg9/fTTysnJUfXq1fX888+rT58+cnR01O7du/X+++8rISFBL7zwgtauXavqf6XU3blzp6ZMmaLCwkK1aNFCEydOVIcOHVRQUKADBw7oo48+UlhYmJ555hmtWLHC8uATuBnFp6VLkhwdDKpextrmfl4XHuLEVzBQPLhjB9X09q7QLOSyrDz8u3KN+TIYpMdKyVhS5FLrHP924k9J5vNvWrNyMioAwM0kLs6clt/R0dHyXcoWv78GARbVKU+gOCH+wkDBsrLaFE8FHR8Xb3nt6uqqoQ89qEULF+no0aP6z/sf6P7BD8jf31/xcfFatWqVfv/9dxkMBo0cNcLmd93BDw5RzZo1K+0aBQD2KCfZvF6vwdEg16oepZZz9fEqVie9XIHinJQLawG7VfMqtdzFbUtlB4ojthxRYV6+ZJAa3nnp52LRe04q+VSUXKt5qvG9Zd+DAABwtREoRoWYTCZlZpYvtaODg4PVbNl///vfMhqN8vHx0bfffqtatWpJknx9fTVt2jT5+/vrP//5z1Xpd1FAsGnTpvrss8+sbupr1qyprl27aujQoTp+/Li2b99+RcdycXEpNUB29uxZTZs2TSaTSR07dtRrr71mtX/GjBmWIPH3339vlaq2b9++6t69u0aMGKEjR45oxowZVkHSJUuWWEboz549W3379rXsu//++9WpUycNHjzYMvO3Mp09e1aS5OTkVObMAFsMBoPq16+vhIQERUVFKS8vz/L+vfXWW5LMMwqWLFmiKlXMX/p9fX01depU1apVS2+//bbNdjds2GAJYE+bNk2PPfaYZV9QUJA6deqkoUOHKiIiokTdffv2WYKPc+bMsfyuFtVt1qyZbr/9dhmNRm3YsOGqzdT+O3z00UeW17ZmqUulf+6LtkdERGj9+vVasmSJJMnDw0MvvfTS1emwpKeeekrDhg2TZP2g78cff1R8vPlh3+zZs60GUlSrVk0tWrRQlSpV9PbbbysrK0uHDh1Sr169LGXefvtt5eTkyMvLS99++63qF0ub+8ADD6hx48Z6+OGHlZKSoqVLl+q5555TQUGBXnvtNRUWFqpdu3b65ptv5Op6ISXV3XffrVtvvVVDhgxRVFSU3n33XX322WdX7b0BrrX0vzKGuLu4lJmlwMPlQhq2jItm4l9KrapVL69zNqTn5GjzSfNSF53q11eg7+WniN9z5qwOhZ+TZF6f2dP10unpAMDepKebBxS5u3uUfZ0odh9Z3vvP9PQL9zmeZQxWKj5QMDMzw2rf3ffcI3d3D323bJkOHDigAwcOWO0PCAjUiJEj1OGvNekvVvy+AQBwefIzzfcUjq4uMpRxrXByu3BPkZ+dV2o567Yv3Hs4lZFO2tHtwnO9S7VtzMrV+QPmQfPVW9aTZ82y7ymy4lMVum6/ZJCaPdhLTm5kIgIAXFsEilEh0dHR6tixY7nKBgQEaNOmTZLMa+7u3LlTkjR69GibN9BjxozRqlWrdObMmcrrsKTCwkL169dPjRs3Vt++fW0GcR0cHNS5c2cdP37cMruysqWlpWncuHFKS0tTrVq1NGfOHKu+hISEWB5EjB8/3uZ6pq6urpo8ebJGjx6tU6dO6Y8//tAtt9wiSZb0zT179rQKEhcJDAzU008/fVWC8UVBVS8vr8saPV8U7CssLFRqaqpq1KihkJAQyzrVEyZMsASJixs1apSWLl1qCVQXV/R+NGjQwCpIXMTHx0eTJ0/Wiy++WGJf8XTT8fHxJX5f69atq/nz56tq1aqWmenXo7y8vBIP1kwmk9LT03Xy5El988032rFjhyTzzO7SAsUV+dzXq1dPH3zwwRXPTi7LXXfdZXN77dq19dhjj6mwsNAqSFxc8fXAk5IupLNKS0uzDBIZPXq0VZC4SNu2bXXXXXfp/PnzcnNzk2RO616UBvull16yChIX8fHx0bhx4zRt2jRt2bJF8fHxllTZwM3GWFggSXJxdCyzXPGZucb8gqvap7L8duJP5f21tMEDHWw/9C+PU+fP69OidKRubnqka5dK6R8A3GzyjUZJkotL2es2uhT7TmXMK9/D/7w844X6ZWS2Kb6veB3JfD+Sk5sj17++610sKSlRf/55Qq1at7L5vQ8AcOUKCwolSQ7OZd9TODhfuKcoNJbvnqKw2LJmDk6lt198X+El7ldi9p1UYZ65TN1+7cosW1hQqFPfb1dhXoHq9Gwln0Z/39J3AACUhkAx/hYHDhxQQYH5S1OfPn1slnFwcNCdd96pjz/+uFKP7eDgoAkTJpS6v7CwUKdPn1ZkZKQkWa2FW1kKCgo0adIkhYWFydXVVXPmzLGaCSlZp29u1qxZqSPnW7RoIUdHRxUUFOjgwYO65ZZblJ6eruPHj0sq/f2VzLNhr0aguGhd1st9WOJYLKBQtK7Ynj17LNtKOyeDwaCgoCCbgeKiNNa9e/cu9bgDBgyQg4ODCgsLrba3b99ezs7OMhqNGj16tIYPH67+/furffv2lr6W1e714vXXX9frr79+yXKtW7fW7NmzL3uNYl9fX/Xr1099+/ZVUFCQnJ3LfvB3JZydndW0aVOb+wYMGKABAwaUWjchIUGHDx+2/Fz0N0kyr1tt/OvBZb9+/Upt47///a/Vz8XTpZf1uS1a+85kMunQoUOlBuWBG52D4crXOv+75BcUaP1f1842AXXU2P/yBnCciI7RB7/+qrz8fDk5OmhiUH9VK2MmGwDYs7Jmhl2py/0uW6SgoEAfzvpQ+/ftk6Ojo4Y8+KD69e8nX19fJScna9vWbVq5YoV++vEnBQeH6JVXX7EMHgQAVJ6rmr7foXLbLiwoUPTuk5Ikn8a1VSWw9DWVJenc5j+UEZEgd/+qanB7+QbkAwBwtREoRoUUnyVcEbGxsZbX9erVK7Vcs2bNLqtf5ZWQkKDdu3fr9OnTioiIUHh4uM6ePausrKyretx33nnHMqN65syZVusSFymeAnno0KHlajcmJkaS+f0tCrCW9f42bNjQEmSuTN5/rS15uWmtU1NTJZlvBqr+lVK06Nx8fHws22yxNXM1OzvbMsvZ1szQIu7u7qpdu7ZlRmgRf39/vfTSS3r33XeVnp6uzz//XJ9//rm8vb3Vo0cPS0DUx8enIqd5XTAYDPL09FT16tXVqlUr3X777br99tvlVMa6mxd/7o1Go8LDwzV//nz9+OOPSk5OlrOzs/r3739Vg8SSVLVqVauBBbYYjUbt379fx48f17lz53Tu3DmdPXvWsiZekaLPjGT9N6oiawgXDTCRpB49epSrTtHvNnCjMZlMyi1jMJWzo6Nc//pbkneJ60xesXYute7v1XI8OlopWdmSpL7NL+/7x96zofp482YZ8wvk6GDQhAED1PYGXrceAK6EyWSyDCC1xdnZ2RJYvXgm78XyirXjUs7BqK5uxWYhG42lfmcsnj2o+MzmrVu2av9fg3efmzhBt956q2VfjRo19ODQB9W0aRO98/Y7Ovnnn1qzeo0eHPpgufoGADAzmUwqNJZ+T2FwdJSDi/n+4FKzhIu343iJ2ccXyl34u28qKJAcbQ8yKj6LuKyZxylnzsuYbr6nqNmpSZnHTjsXr4gtR2RwNKj5Q73l6MxjeQDA9YErEv4WRWtRSbJat/hiRQHHypabm6u3335by5cvLxEkdXV1Vbdu3VRYWKj9+/dX+rG/++47LV68WJI5pe0DDzxgs1xGRobN7WUpqlM8QFvW++vg4CAPDw+rf4/K0KRJEx09elTZ2dmKiYlR7doVS50THGxeyyUgIMAyK7moj5capW8rJXXx9+Ny6kvmf6uWLVvqyy+/1O7du2U0GpWWlqZff/1Vv/76q5ydnTVy5EhNmTLlkoHLa+Wdd97RkCFDKr1dZ2dnNWnSRO+9955q1qyp+fPna9myZYqLi9PcuXPLDDpfqUvNWv/ll1/01ltvWdYqLmIwGNSoUSPdcsstWrlyZYl6RYMVpLI/Qxe7ks8tcKNJyMjQ80uWlrp/bL++8nQ1p/PMMRplMplKnQ2QWewhfZVrNBtrf2iYJMnZyVGdKzBApMhPv/+hpfv2yWQytzFp4EB1rF/6YC0AuNklJCRo4nOlZ3IaN368ZX3gnJzssq8TxQbylvZ9/WKeHheyOWRlZpV6H5Bl1faF+89NGzdKkpo1b24VJC6u3S23qEvXrtq3d69+27CBQDEAVFBuSqb2v/99qfubDb2wZm9Bbtn3FMXXDnbyLN89hZO79drDjqUshVCQc6Ft5zLaTjwRLsmcJrt6q9LvBQryjDq1fJtUaFLdoPaqElD2zGMAAP5OBIrxt/Dy8rK8zs7Otvq5uLxyrj9lS05OTqn7Jk+erI1/3fi3bt1affv2VdOmTdWkSRM1atRITk5OmjVrVqUHivfv36+ZM2dKkm699Va9/PLLpZYt/iDjyJEjFUrjXHzG7aVmR1/Je1yaHj16WIJvO3bs0EMPPVTuuqdPn7YE9YqvH1t0TtnZ2WXWt3U+xWf6Xk79It27d1f37t2VkZGhXbt2affu3dqxY4fOnTsno9Gor776SiaTSa+88kqZx7iZvfjiizp27Jh27dqlzZs36/3339err756Tfqyfv16TZ48WSaTSb6+vrrtttvUpk0bNWrUSM2aNZO3t7fCw8NtBoqLB4fL+ht1saLPrZ+fnyVrAGDPalf1kSTlFxQqJSur1BTMicUGTPiV8/NWmUwmkw6EmR/qdKpXT24VyIZQWFior3bs1MY/zSnmqri56eU7b1fTmjWvSl8B4GZSu455QGl+fr5SklNUzbeazXKJCQmW1xcv2XOptiUpPiFevtV9bZZLKNZ2db/qltfR0easL80vkWWiVatW2rd3r5KTk5WdnV2hQYYAgEtz9zMP4jEVFCovPVuu3h42y+WmXlj6ya1q+ZZ+KWpbknJTMuRaSr3clAv3K6WVMZlMSjxxTpLk26JuqUFnSUqPTFBOonlCxLmNv+vcxt9LLZubnKnt//xaklQv6BbVD+pQalkAACoDgWL8LYqn/z179qzN1MuSdO7cOZvbi8/YLFpH9GJFqYYvdujQIUuQeOTIkZo2bZrNcsnJyTa3X67IyEhNnDhRRqNRdevW1axZs8qceVqnTh2rurZSKhe5eERlrVq1LGvtnj17VkFBQTbrxcXFlZkO7nINHDhQVapUUXp6uhYvXqwHH3yw3GuELVq0yPL6vvvus7wuej9SU1OVmJio6tWrl6grWafsLuLq6qrq1asrMTHR5vrFRQoKCsqVBtjLy8uSolkyB/InTZqkqKgoLVmyRFOmTLmqs2ivZwaDQe+++67uuecepaena+HCherTp4969uz5t/flP//5j0wmkwIDA/X999+rWrWSDx5L+5wXnwUfERGhli1b2iy3e/duHTx4UHXr1tX9999v+T1NTk5WVlaWZZYMcDOqUaWKvn326TLLRCQlWV6HJiSWGigOTUiUJHm4uqhGlb8/UByakKDUvwYSdWnYsNz18gsK9OFvG3XwryBzraremnrXnapVxhIJAGAvatSooaXfLSuzTPH7vdDQ0FIDxaGhoZIkDw9P1ahRvjXkAwMDZTAYZDKZFBYapubNm9ssV3R/YDAYrO5TCwrMKUyNZaREvZjRaCRQDAAV4FbNS73ffqLMMpnnL9y3Z0Qnlhoozog231M4ujvLtVr57ik8/H0kgySTlBGTJO/6tgd7pkeZ25ZB8qxte+BRRnSijBnmSStlzSYGAOB6V75IDnCFOnbsKBcXc3qX3377rdRy27Zts7m9+M13UrGH0MUdOnTI5vbDhw9bXg8bNsxmmcLCQu3du9fq5yuRmZmpcePGKTk5WR4eHvrkk08uuZ5t586dLa+LAtu2HDp0SLfccovuuOMO/fLLL5IkT09PS/2y6pb2/l4pT09PPfnkk5KkU6dO6dNPPy1Xvd27d2v58uWSpA4dOqh79+6WfX369LG8vpzfmaL6W7ZsKXVN5n379tmccTxv3jzde++9euSRR2zWa9eunUaNGiXJnNa8eNpie1SzZk1NnTpVknkQw/Tp0y85k7uyJSUlKSwsTJJ0++232wwSS+bfuSLFP+cdOnSwDL7Yvn17qcf59ttvNWfOHMvveNHnrqCgQFu2bCm13s8//6wOHTronnvu0YEDB8p1TsCNKLBaNfn9Ffg9FB5us4zJZNLhvwIFt/z1UP/vdur8hXXJG/uXLwBhMpn08abNliBxk5r+mnH//QSJAaAC6tatawn8Hjx40GYZk8mkQ4fM93C3tL+l3NcJDw8PNW/Rwtx2Gd+3Dh0wH7dJkyZWaa2LBg4eO3qszOOcPGnOKOHtXbXcabEBAOXnUdNHrtXMA06TTpacHCCZrxVJpyIlSdWaBpT7WuHk5mIJDif+abvt4setEugnZw/bGf/SwuIsr6vULfueomqDmrr1X4+V+V+N9o0kSa4+npZtdfvZnmgDAEBlIlCMv4WXl5cGDRokyTyD9PTp0yXKbNmypdQATd26dS0zVIuCo8Xl5ORo/vz5NusWn8Vr67iSNHfuXEuQSSp91nJ5mEwmTZkyRcHBwXJwcND777+vZs3KTl8mmYOPRbMYP//8c6v+FMnJydG7776r3NxcRUVFWc3MfvBB8/pYhw8f1ooVK0rUTUlJ0SeffHKZZ3VpY8aMUZs2bSRJc+bM0WeffSaTyVRq+a1bt+q5555TYWGhPDw89Oabb1rtDwwMtKSinjNnjuLi4kq0sW7dulKDbkXvR0xMjM3zzs3N1QcffGCzrpOTk0JCQnT48OFSByD8+eefksy/276+tkeX2pOhQ4eqS5cukswz4ufMmfO3Hr/4jO4zZ87YLHPy5EmrvxPFP+f+/v7q1auXJOmrr75SbGxsifpHjx7Vpk2bJEn33HOPJCkoKMiSDvGDDz6wOZAlKSlJs2fPVlZWlhISEkqdrQzcDAwGg3o3bSpJ2hocrLBi6T2LbDhxQjEp5gE2d7dr+7f2r8jZv5Y88HJzVU1v70uUNvvl6DHtOWue4dasVk393z13y9v92qyvDAA3KoPBoF69e0uStm7ZorC/1osvbv2v6xUTHS3pwneu8urbt68kcwagQwdLfo8/dPCQjh49Kkm6+17rtm/taV6XODIyQut+WWez/WPHjmnvnj2W8tdisBMA3OwMBoP825uz7MUePG2ZOVxczN6Tyo5PkyQF9mxdofZrdjS3nRISrUQbgejEkxFKOW3OPhfQq/S2M6LN9zpOHq5y9y174JDBwUGOLs5l/mdw+OuaYpBlm0MZmQkBAKgsBIpRISaTSZmZmRX6r8hLL70kPz8/ZWdna8SIEVq+fLni4uIUExOjL774Qs8//3ypx/X29rbMNt20aZNmzJih0NBQJSQkaNOmTRo+fLj+/PNPedt42NuzZ0/LDfwbb7yhn376SefPn1dsbKy2b9+usWPH6uOPP7aqU7zfFTVr1ixLMOmll17SwIEDlZeXp6ysLJvvT/G1ladPny4nJyelpaVp2LBh+uabbxQZGanExETt2LFDTzzxhP744w9J0lNPPaWAgABL3fvvv98SqJs2bZr++9//Kjw8XElJSfrtt980fPhwxcTEXLWHGS4uLvrss8/UrFkzmUwmzZo1S0OGDNHy5csVGhqqlJQURUZGasOGDXruuef0zDPPKDMzUx4eHpo7d66aNGlSos3XX39dLi4uio+P1/Dhw7V27VolJSUpIiJCn3zyiaZMmVJqOu8uXbro/vvvl2QeCDB9+nSFhIQoOTlZu3fv1ogRI3Ts2DGb9R988EH5+PjIZDJp/Pjx+uabb3T27FklJSXp5MmTeuONN7Rq1SpJ0iOPPMIDIplv5GbMmCHnv9b5XLhwoSWY/nfw9va2DJzYunWr3nzzTZ05c0bJyck6efKkPvzwQw0fPtxqDe+LP+dTp06Vm5ubkpOTNXz4cP3000+Kj49XZGSkli9frmeeeUZGo1E1a9bUE088Icn8e/9///d/kqSoqCgNHTpUq1atUmxsrGJjY7V+/XqNHDnSkmbxpZdekmcpqXiBm8WgW9rJ19ND+QWFemvNWm0+eUopWVmKTUvTsn37tXCneWZ/t0YN1cTfv0T9jzdt1ovLvtOLy767an2M+mu5ivLOBk7Lztay/eaBSV5urhrbzxyIyDEaS/2v4AqzkwDAzeq++++Tr6+v8vPz9eYbb2rzps1KSU5RbGysln67VAu//lqS1K17dzVpWvIe4eO5czV50mRNnjS5xL6+/fqqQYMGkqQPZ83S6p9/VmJiohITE7X655/14axZkqSmTZtaZTOSpDvvuksBAYGSpK8XLND8efN15vQZpaenKzo6Wit+WKH33v23TCaTqlevriEPDqnEdwUAUFxgn7ZyqeohU0Ghjn61XucPBCsvPUvZSekKW39IZ1bvkyT5talvczbvqeXbdGDWCh2YVXIiRc2OTeRZxzzg/+S3WxS5/ZhyUzOVm5qpyO3HdPLbLZKkKnX95NemQal9zIozD351r052CQDAjc0+F9XEZYuOjlbHjh0rVGf//v3y9vZW9erV9eWXX+rpp59WXFxcibWCq1Spol69epWaOvmf//ynRowYoZSUFC1ZskRLliyx7DMYDPrHP/6h7du3W6WWlcwPAZ5++mnNnz9fiYmJevnll0u0XaVKFT300EP66quvJElhYWGXPUt03rx5lteffvqpZs2apfz80te56tq1qxYvXizJnKJ79uzZmjJlilJSUvTGG2/ojTfeKFHnoYceKhFYNxgMmjt3rsaOHavDhw9r3rx5Vn2RpClTpmj27NnKy8u7rHO7lBo1amjp0qX697//re+//14nTpwodU1oSerUqZNmzpxpM0gsSY0bN9Znn32miRMnKioqSpMnWz8M8vHx0YgRIzR37lyb9WfOnKmsrCxt2LBBy5Yt07Jl1mumPfLII9qzZ49lDbTi7X744YcaP368kpOTbf4bSFL//v3LHOBgbxo3bqwxY8bo008/VX5+vl577TV999135V6v+kpNnz5do0aNUlZWlhYvXmz5XBU3dOhQ7d69W1FRUQq/KC1u06ZN9cknn+j5559XdHS0zb8VNWvW1Oeff26VZvDuu+9WWlqa3nzzTUVFRVnScBdnMBj03HPP6eGHH66EMwWub+4uLnr5zjv09ppflJ6To/lbSy4R0LxWTY3v389m/cSMDMuM46slIT1DkuTx17IYl/Lbn38q769reUZOrl5ceukg9th+fdW3+aUzigCAvXF3d9c/pk7VW2++pfT0NM377LMSZZq3aKHnJjxns35CQqJlxvHFHBwc9NLLU/TmzDcUGxurbxZ/o28Wf2NVpnadOpryj5dLDPZ0cXHRP6f9Ux+8975CQ0O1aeNGbbJxb1qrdm29/I+XbQ5SBgBUDidXZ7UeGaSjC9YrPzNXISt2lSjj3cBfzR7qbbN+bkqmZcbxxQwODmr12AAd/XKdcpIyFPrLAYX+Yp2tzr2Gt1qNDCpzYkBOivmewsm9fPcUAABcrwgU42/VokULrVmzRl9++aV+/fVXRUdHq0qVKurTp48mTJigFStWlBoobtq0qX7++WfNnz9fW7du1fnz5+Xl5aUOHTroySefVOfOnUtNXf3SSy+pdevW+vbbb3XixAnLTNZ69eqpd+/eevTRR+Xt7a1ly5YpMzNTGzZsqHBA3JaMjIwK1wkKCtL69eu1ePFibdu2TREREcrNzVW1atXUoUMHDRs2TD179rRZ18fHR4sWLdKPP/6oH374QWfPnlV+fr5atmyp0aNHa+DAgZo9e/aVnlaZPD09NXPmTD311FNauXKlJRCbnp4uV1dX1a1bVx07dtS9995rtS5zaXr27Kk1a9ZowYIF2rZtm6Kjo+Xt7a3evXtr4sSJVmtQX8zNzU2zZ8/Wr7/+qqVLlyo4OFjZ2dlq3LixHnnkEQ0dOlR33nmnzbo9evTQmjVrtHDhQu3atUuRkZEyGo2qVq2a2rRpowceeEB33HHHZb9PN6tx48Zp7dq1Cg8P19GjR7V48WI9/vjjf8ux27Ztq5UrV2revHnavXu34uPj5eTkpBo1aqhdu3YaNmyYunXrpv/7v//T999/r82bN8toNFpmQUvm37dff/1VCxYs0NatWxUVFaWCggLVq1dPAwcO1BNPPGFzvfHhw4erZ8+eWrhwoXbv3q3o6GgZjUb5+/urc+fOGjFihFWqeOBm18DPTx88/JB+/uMPHQwPV0JGhgwyKLCaj3o2aaLbW7eS0zVMo5aVlytJ8ixnoPh0bMnlDwAAl69Bwwb6z6z/6OefftKBAweVmJAgg8GgwMBA9ezVU7ffcYfV0iIVUaNGDf37/fe0ZvUa7d2zR3FxcSosLFTNmrXUrXs33TvoXrm52V46oHr16nrjrTe1fdt27dq5U2Fh4crKypSbm7vq1qurbt27acCAAXJ1tb1eJQCg8njVqa7OkwYrYttRJZ2MUG5KpmSQPPx95H9LI9Xu3uKyUzO7VfNSx+fvV9SO44o/Fq6cpHTJZJKbbxX5tWmgwN6t5ejiXGYbBTnmSRiObgSKAQA3NoOprEVEgb/ZnDlzLLNDT506dY17AwDXn+Q5H13rLgAArnOGv5ZIAADAlrEH5l26EADAri3t/49r3YUbwvDN713rLtw0+J27dlijGAAAAAAAAAAAAADsDIFiAAAAAAAAAAAAALAzrFEM2DGTyaSsrKwrasPT07OSenPj4n0sXUFBgXJyci67voODg9zd3SuxRwAAAAAAAAAAQCJQDNi1qKgoBQUFXVEbrCXN+1iWAwcOaNSoUZddPyAgQJs2barEHgEAAAAAAAAAAInU0wAAAAAAAAAAAABgd5hRjOvKxIkTNXHixGvdDbsRGBh4085k/TvxPpauW7duvDcAAAAAAAAAAFyHmFEMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAAAAAAAAHaGQDEAAAAAAAAAAAAA2BkCxQAAAAAAAAAAAABgZwgUAwAAAAAAAAAAAICdIVAMAAAAAAA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KCitWrGDPnj2Eh4eTnp6Ou7s7AQEBdOnShSFDhlxV1nt2djbffPMNmzZtIjw8nNzcXAICAujevTvDhg0rs8+mTZtaHv/6669Wf3MnTpgv0ho3boy3t7fdPoo/p6ZNm0atWrX48MMPOXv2LFWrVqVbt27897//tewbExNjKQ0fHR1NRkYGnp6e1K5dm3/84x8MHTqUmmVkKoaFhbFixQr27t1LbGwsBoOBRo0acc899zB48GC76+UdOXKEb775hqCgIJKTk/Hw8KB58+bcf//9DBgw4G+/jIFIWSKTky0Zvh3tfH4ZDAY61K/P+rTjHD53DpPJVOZkrcutPHCQnSGhALSuU5sXe/fi823bSc7Muupxbzh+gsjkFACev/3267J2soiIo4qMiLRMIOzYuZPNfQwGAx07dmBdbCyHDh0u93dBRkYGYWFhAHTq1NHufh07deL48eOcPnXKqprN2bPmMqLt2t9mNzvY09OTZs2acfz4cX777Sj/fOihK44rKyuLRQu/BqBZs2bce++9V2wjIuLIsuJSLOsS+7W0PbHGYDDg26IeMYknSTkTXe7vCmN2HhnRSQD4tqxvdz+/lvVJC4vjQvh5jDl5uHpcXN6sxCFMhbYnujs5X8rLMjhdapARk2x57N+mod1j+zarTdTWoxTmGEk/l4hPo6ursiQiIlJeChRfpc2bN/Paa6+Rm5treS4uLs4SRBk/vmJrKVXE7NmzmT17NqbL1tJLS0sjLS2NY8eO8d1337F8+fIyA7HX2+WBk3379jF27FguXLBeGzA8PJzw8HC+/fZbPvzwQ+6+++5SfUVFRTF8+HAiIiKsnt+1axe7d+9m2LBh1338ADt27CA9PR3AZunisjzzzDOsXbsWgB9//LFUoPi7775j8uTJVtmpMTExLFmyhF9++YXu3bvb7TslJYWRI0dy9OhRq+cPHjzIwYMHGT58uN2227dvZ8yYMeTn51uey8/PJyIigoiICFavXs2kSZMYNGhQhc73z6j4osDJ6dqLJRTfZHJ1da3QpIuKeP/991m0yHots5MnTzJ58mSioqJ44oknGDJkCHFxcZbtycnJrFu3jt27d7NmzZpSkyZ27NjBuHHjyM62LrdqNBrJzMzk7NmzrFmzhs8++4xevXqVe6zBwcGMHDmS2NhYq+ejo6NZtWoVa9as4c0332Tw4ME22zdv3pxu3bqxb98+vvzyS/r168ett97Kr7/+yo8//gjAa6+9Vq6x/Pbbb0yZMgWj0QhAUlKSVem+VatWWW0vlp6eTnp6OmfOnGH16tUsWrSIVq1alep/8eLF/Oc//ymVSX7y5ElOnjzJ999/z9dff22ZTABQVFTEhx9+yMKFC63a5OfnW96n3377LXPnzrVqJ+JIIpIv3RhpXN3f7n4N/c2Zxpm5eSRkZFCjjAkkttSs6s3jnTrxj6bXvgzEhexs1hw+DEC7+vXo0MD+zSQREbmyktd3xVVlbGnYsCEAmZkZJCQklOv3U2RkpOUauVEZfTdq3AgwZ6xFRETQpk0bALKyMgHw969e5nG8q5orX5yLPHfFMQH88P33pKWlYTAYGPqs7UxlERG5JDMuxfLYq4796wavWuZM44LsPHJTM/HwrXLFvrPiUsBU3HfpCkeWvmtfzGI2QVZsCj5NzPdXPfyr4uzhSmGOkfjDYVS/rXGp9e7jD5vvJ7lUdqeS36VrmYLcPMtjdx/7VY9cK1+6v5oVl6JAsYiI3HAqPX0VwsLCGDduHLm5udSuXZtPP/2UwMBANmzYwODBg0lISGDatGk35Ni//PILs2bNwmQy0aNHDxYtWsTOnTvZuXMnixYtsqylGhcXx8yZM6/pWC+++CKHDx+2+7+ZM2dafgw98cQTtG3b1tI2JCSEF198kQsXLlC3bl0+/PBDdu7cSWBgIPPnz6dNmzbk5uby6quvcujQIavj5ufnW4LElSpV4vXXX2fr1q3s2bOHDz/8kOrVq7NgwYJrOjd7Dhw4YHncqZPtGe72tGvXDn9/8w/Y/RfXMSy2f/9+3nrrLQoKCmjevDnz58+3/M0899xzJCUl8dNPP9nt+5VXXuHo0aM4OTnx4osv8uuvvxIYGMicOXNo3LgxX375pc122dnZTJw4kfz8fNq2bcuCBQvYuXMnu3btYs6cOTRs2BCTycQHH3xgWT/2ryoqKsqSnXCta0Rv2LCBkBBzeaFevXrZXHP5Wh09epRFixbRtWtXvvnmG/bu3ctXX31lmdyxcOFCnnvuObKyspgyZQo7d+5kx44djBkzBicnJ9LS0pg7d65Vn7GxsbzyyitkZ2fTsGFDZsyYwebNmwkMDOS7775j8ODBuLi4kJ+fb1nXuTwSEhIYNmwYsbGx+Pr68s4777B161b27dvH4sWL6dGjB4WFhUybNo3169fb7WfatGlUrVoVo9HIqFGjmDx5Mq+88gomk4nJkyfTv3//co1n9erV+Pn5sWDBAvbu3cucOXMsAepjx47x9ttvYzQaadOmDfPmzWPbtm3s2bOHFStW8PDDDwPmoHFx9YSS1q1bx3vvvUdBQQEtW7bk888/Z+/evWzcuJGxY8fi4uLC2bNnS00GmjlzpiVIfNddd7Fs2TL279/P5s2b+de//oWXlxenTp3ixRdfJC8vr9RxRRxBYnoGAM5OBvzKWJfR3+vSDZ7EjIwKHeORDu3535NPXJcgMcD3R34jz1iAwQDP2KkCIyIi5ZeQYF4T0tnZGT8bSxAU869+KVhb3OZKkhKTLI/LqgZUfM0GkJiQaHlcqZL5xnzJyeC2ZGWaA8qZmRlWk3FtSU1J5ZeffwGga7duNG3WtMz9RUQEclPNn7MGZwPuVT3t7ufu41WiTfmuG3LTMi2PK1Xzsrufvb6d3VxocHHd4LSwOE4t3Ur6uUTyM3PIiErk9DfbSA2JAQM0vrczzq4uJdpeqjBWmGd/ibOCnEv3DPLTs+3uJyIicr04bEaxyWQiK6t8ZQidnJyssmX/85//YDQa8fHxYfny5Zbypb6+vkyaNImAgACrEqjXU3FAsFmzZsydO9cqgFWjRg26dOnCgAEDOHnyJLt27bqmY7m5udkNkJ09e5ZJkyZhMpno0KEDb7/9ttX2KVOmkJubS926dVm9ejXVql1ad6N3795069aNQYMGcezYMaZMmWIVJF22bJllpvnMmTPp3bu3ZdtDDz1Ex44deeSRRyyZv9fT2bNnAXBxcSlzhrstBoOBBg0akJSURExMDPn5+ZbX77333gPMM+OXLVtGlSrmm+C+vr5MnDiRmjVr8v7779vsd9OmTZYA9qRJk3jmmWcs2/r160fHjh0ZMGAAUVFRpdoeOHCAtLQ0wFzyuGSp3X79+tG8eXPuuusujEYjmzZtumGZ2n+ETz/91PLYVpY62H/fFz8fFRXFxo0bWbZsGWAuLVfeLNeKysvLo23btnz11VeWcsS33347EydOZNy4cRQUFBAdHc3y5ctp3769pd3o0aMJCwvj559/Zu/evVZ9Ll26lJycHFxdXfnyyy+t1j7z9fWlTZs2ODk5sWjRIqKioggPD6dRo0ZXHOvHH39McnIyVatWZeXKldSvfymjrmvXrnTu3JnRo0ezZcsW3nvvPfr374+7u3upfurVq8fLL7/M+++/T0JCAitXrqRZs2ZMmzaNdu3aVej1mz59Ov/4xz8A899ysa+++gqTyYSvry8LFiyg6sWMDzDfFGzfvj2ZmZls3ryZgwcPkpubayktmJeXZ3kftmnThqVLl1o++/38/Hj55ZepXLky06dP59ChQwQFBdGpUyciIiKYN28eAIMHD2bSpEmWY/r4+DBs2DA6duzIU089xenTp1m2bNlf+r0mcrUyLt5493BzK7Pyg2eJGyiZFZxYUbPEe/5aZeTmsu2MefmQjg0aUNe39BpiIiJSMRkXJwB5eHiW/V1Q4vq7vNftGRmXrg8rlzEhydPzUtChOIsYoH6D+pw+dYrjx47bLWGan59PaGiY5d852dllTir95eefLVVuHnnk4XKdh4iIoyvIMl83OLu7YSjju8Kl0qXrhoKcsifuXOr70vWFi0fp+xbFnCtd+my/vO86PVrj7O5K5OYjpJyOIuW09f04j4CqNL63M763WK9/XHJN4rSwWKq3tX0/Ju3spapyBXlGm/uIiIhcTw4bKI6NjaVDhw7l2rdOnTps3boVMK+5u2fPHgCGDRtmc43L4cOH88MPP/D7779fvwFjLm16xx130KRJE3r37m3zgtTJyYlOnTpx8uRJS3bl9Zaens5LL71Eeno6NWvWZNasWVZjCQ0NJSgoCIBRo0ZZBYmLubu7M378eIYNG0ZwcDBHjx7ltttuA7CUb+7Ro4dVkLhY3bp1GTFixA0JxhcHVb28vK6qJFjx7PSioiIuXLhA9erVCQ0NtaxTPXr0aEuQuKQhQ4awYsUKS6C6pOLXo2HDhlZB4mI+Pj6MHz+eV199tdS2kjPcExMTS/291qtXj/nz51O1atVyBQxvlvz8/FI3iEwmExkZGZw5c4alS5eye/duwJzZbS9QXJH3ff369fn444+vOTu5LMOGDSu1Zm3JTPZOnTpZBYmLtWvXjp9//pn4+Hir55s3b86TTz6Jn5+fVZC4pC5duljKXaekpFzxv/uFCxfYsGEDAIMGDbIKEhdzcnJi4sSJbNmyheTkZLZs2cJ9991ntU9aWhr/+c9/+P77762ed3Nzo0WLFmWO4XI+Pj52S7V36NABLy8vWrZsaRUkLqlLly5s3rzZ8j4tDhQHBgaSfLE07sSJE22uQzxw4EC+++47AgICLH+TK1eupKioCA8PD7vLDrRt25b77ruPtWvX8u233ypQLA7JWFQIgJuzc5n7ublc+nlqLCi8oWMqy+ZTp8m/WIL+YRufxSIiUnEFF4Ombm6uZe7nVmLSofEKWbvF8vMv3UwvK3hbclvJNt26deP0qVPExETz84YN3Hf//aXarvlujVVwuaDQ/vdUbm4umzZtBqB9+/Y0uFhOW0REylZ0ce1fJ9eyrxucSmTrFhnLd91QVGKJKScX+/2X3FZ02TWJqaiIwvwCnNxs31bPv5DFhfDzVG1UE+cS+3g3DMDN24P89BwiNh7Cp2ltXD2tg9V5F7KI2XPq0rHsrIMsIiJyPTlsoPhqBQUFUXjxYtDe+p5OTk7cc889fPbZZ9f12E5OTowePdru9qKiIsLCwiwlhC9fX/N6KCwsZNy4cURERODu7s6sWbOsSneBdfnm5s2b250B3qJFC5ydnSksLOTQoUPcdtttZGRkcPLkScD+6wvmDMIbESguLglrKxuyPJxL3PwuXh9r3759lufsnZPBYKBfv342A8XFZax79uxp97h9+/bFycmJoiLrH5Dt2rXD1dUVo9HIsGHDeOqpp+jTpw/t2rWzjLWsfv8s3nnnHd55550r7te6dWtmzpx51WsU+/r6cscdd9C7d2/69etXKoh7vRVPjiipZAk+W+vngnkiA1BqDd6HH37YUlrZlri4OE6dunTBUVjGja1iR44csRynRYsWdt/P/v7+VK9encTERA4dOmQVKI6MjOS5554jOjoaJycnBg8eTGpqKmvXruXkyZP83//9n+X9nJmZSXBwMK1bt7YEcC/XokULuxM5hg4dWub5REREWE3iKfk5GRgYCJhf386dO9ts7+7ubpm8Uaz4M6+4CoG91+i2225j7dq1nD17ltTUVJuTaET+zpwMf50VTwoKC9l48fdImzq1aRJQ9nqVIiJSPmVlhl2rq70GKNanbx82bdxEdHQUixctJi7uPHfe2R9fPz+Sk5L45edf2LZtG76+vqSkmNfPdClj8tP2bdvJzjb/LnxI2cQiIuV2Q9dyd7q2vosKizizYjvJJ89hcDZQr+9t1OzQFLeqnuSn55BwJIxz244RvfME6ecSafNsf0vJaSdnZxre3YmQVbvITcnkt8/X0fDODlRtXBNMJtLOnifi10MU5hdY1kE2OP91rqFEROSvy2EDxSWzhCuiZAafrcy6Ys2bN7+qcZVXUlISgYGBhIWFERUVRWRkJGfPniU7+8auXTF9+nRLRvXUqVOt1iUuVrIE8oABA8rVb1ycuaxKfHy8JcBa1uvbqFEjS5D5evL29ga46rLWFy5cAMw/aouzGYvPzcfHx26GI9heVzcnJ8eS5dygQQO7bT08PKhVqxYxMTFWzwcEBPDaa6/xwQcfkJGRwRdffMEXX3yBt7c33bt3twREfXx8KnKafwoGg4HKlSvj5+dHq1atuOuuu7jrrrtwcbH/sXb5+95oNBIZGcn8+fP58ccfSU1NxdXVlT59+tzwIDFgM1BY8gaXrezzy/exJTc3l8DAQM6cOcO5c+eIiooiLCysVJWB4vdaWUq+n8eMGXPF/eHS3zyYg6bDhw8nOjoaT09PZs6cSc+ePcnPzyc6OpojR46wbt06WrZsyfDhw9m6dSuvv/46Li4uLFiwgK421gT19fW94hgyMjLYu3cvISEhltfg999/L/XeLvkaFH++N2jQoEIXpsWTc06ePFnujPXz588rUCx/KyaTibwyJqi5OjvjfvHzOf8K3935JfpxK+Mz/UY6GRtLWnYOAL1vubG/6URE/i5MJpNl4q0trq6ulomAJTN5bckv0Y9bOSfxulcqkYVsNFpN4rXqu0SGcsnMZjc3N9741xtMf386cbGxbNq4kU0bN1q17dSpE7e1a8dXF5eEcrczsRFgz8WKRzVr1apwBR0Rkb8rk8lEkdH+dYPB2dmSqXulLOGS/ThfIfv40n6XPvdNhYVgJxBbMou4ZHZxwuEwkk+eA+CWx3tZlY+uVM2L+n3bUaVedU58vYn0iHhidp+k/sU1jQFqtG9C3oVMIjcdITc5gzMrdliPz8OVlk/fQdgPgRTmGK0ykkVERG4UfdtUUPGaSoDNsqTFigOO11vxGpqrVq0qFSR1d3ena9euFBUVcfDgwet+7G+//ZYlS5YA5pK59jIXMzMzbT5fluI2JYM4Zb2+Tk5OeHp6Wv33uB6aNm3K8ePHycnJIS4ujlq1alWofUhICGAOSBZnJReP0V52ZDFbQcGSr8fVtAfzf6uWLVvy1VdfERgYiNFoJD09nV9//ZVff/0VV1dXBg8ezIQJE+zeTLnZpk+fzqOPPnrd+3V1daVp06Z8+OGH1KhRg/nz57Ny5UoSEhKYPXt2mUHn66Hk+mjXy9KlS/nkk09KvTecnJxo2bIlDRs25Oeffy53f9fyfi4ez7lz5ouoqVOnWjLY3dzcmD17No8//jixsbH873//45ZbbmH9+vWA+fPM3rrFZWX8FxUVMWvWLL766qtSNypdXV1p37493t7e7Nixo1Tb4okeV3qvXe5aXyORv4OkzEzGLlthd/vIO3pT2d1c6jPXaLS79iNAVokb+FUq+H68Xg6GRwDg6uJMJ5UKFREpl6SkJMa8bL8C1kujRll+/+bm5pT9XVBiArS965zLVfa8tC5xdla23d902VZ9W1+3BwQEMP2D6fy84WcC9+7l/PnzuLm506BhA/r260uPHj346ccfAfP1qr1jpKakEhZmXsv4H//4R7nGLyLiCPLSsjj40Wq725sPuB2Xi+sDF+aVfd1Qcu1gl8rlu25w8bBee9jZzlIIhbmX+nYt0XdckPm+n3eDALtrDFdrVge/1g1IPhFJ3IFgq0AxQP07bsOncS2id58kPSKegtx83L098W1Rj7o92+Dm7Ykx23w/w63K9b9vJCIicjkFiiuouOQrmLM9S/67pPxyrqNkS25urt1t48ePZ8uWLYC5zG7v3r1p1qwZTZs2pXHjxri4uDBjxozrHig+ePAgU6dOBcwXuq+//rrdfUteLB87dqxCZZxLZtxeKTv6Wl5je7p3725ZQ3X37t08/vjj5W4bFhZGYmIigFUWZPE55eTklNne1vmUzPS9mvbFunXrRrdu3cjMzGTv3r0EBgaye/duzp07h9FoZMGCBZhMJv71r3+VeYy/s1dffZUTJ06wd+9etm3bxkcffcSbb755s4dVIV9//TXTp08HoHbt2vTv35+WLVvSpEkTmjVrhqenJ3v37q1QoLjkhI0NGzZUeM3mbdu2AVCjRg0eeOABq23+/v58/vnnDBw4kOzsbMaPH28JoN59991XVQJ++vTpLF68GDCXgu7bty+33HKL5TVwc3Nj1apVNgPFxeda1mewLZUqVSIzM5P77ruPGTNmVHjMIo6iVlUfAAoKi0jLzqZa5co290suMZHC387vrBvJZDIRFBEJQMf69an0B1SYEBFxFLVqmyfiFhQUkJaaRjVf2xVWkpOSLI8vX+roSn0DJCYl4utnuwpNUom+/fz9Sm2vVKkSjzz6CI88+ojN9pGR5kmQtWvXtjuWAwcOWCrX/KOHAsUiIhXh4W+exGMqLCI/Iwd3b9vB0rwLl5Z9qlTV9rWFvb4B8tIycbfTLi/t0jVJyX1ykswTzKvUL3tpGp9GNUk+EUl+eg4FeUZc3K2vKbzrB9Dq6QCbbXOS0inKLyg1XhERkRtFgeIKKln+9+zZszZLLwOWDLrLlczYvHx90WLFpYYvd/jwYUuQePDgwUyaNMnmfpeXl71W0dHRjBkzBqPRSL169ZgxY0aZmaclL5ijo6PLDCxdPjOwZs2alrV2z549S79+/Wy2S0hIKLOs2dXq378/VapUISMjgyVLlvDYY4+Ve62r4uAUwD//+U/L4+LX48KFCyQnJ1utQVtSyRK/xdzd3fHz8yM5Odnm+sXFCgsLrcr92uPl5WUp0QzmQP64ceOIiYlh2bJlTJgw4YZn0f5ZGQwGPvjgA+6//34yMjJYtGgRvXr1okePHjd7aOWSm5vL7NmzAWjbti1LliyxmeFQ0c+Hkln1MTExFXo/lzyer6+vzVnALVq04KOPPmL06NGWLGhXV1dGjRpVoXGCueT10qVLAbjzzjv59NNPbX5W2XsNis/V1nuxpJUrV5Kenk6bNm3o3r07tWvXJiQkpFTp98uVNRNa5K+uepUqLH9xRJn7RF1czxEgPCnZbqA4PCkZAE93N6pX+eMDxeFJSVy4ODmrcyPbWQIiIlJa9erVWfHtyjL3KXmdHB4ebjdQHB4eDoCnZ2WqVy/fOvF169bFYDBgMpmICI/glltusblf8XWVwWAotbxPYWFhmRPCTSYTx48dB6Bps2Z2x3L0t98AqFW7NnXr1i3X+EVEHEGlal70fP/ZMvfJOn/pmj0zNtluoDgz1nzd4Ozhinu18l03eAb4gAEwQWZcCt4NatjcLyPG3DcGqFzr0sQjU2GR1f+Xh6mgEEoEigty8nFyc8bJzr3V1LAYy7Gr1C3fZCkREZFrUb4ImFh06NABNzdzmZLNmzfb3W/nzp02ny+ZnZdS4oZpSYcPH7b5/JEjRyyPn3zySZv7FBUVsX//fqt/X4usrCxeeuklUlNT8fT0ZM6cOVdcz7ZTp06Wx8WBbVsOHz7Mbbfdxt13323JcKxcubKlfVlt7b2+16py5co899xzAAQHB/P555+Xq11gYCCrVq0CoH379nTr1s2yrVevXpbHV/M3U9x++/btdtdkPnDggM2M43nz5vHAAw8wcOBAm+3atm3LkCFDAHNZ8+LSu46qRo0aTJw4ETDfBJo8efIVM7n/LEJDQy2B1ocffthuGbzAwEDL4/J8PnTs2NEyWaKs92RMTAzt27enf//+VpMmiidKhIWF2f3M69+/v9Xkijp16pT7hmBJR48etZzTgAED7E5oKfkalFyjuHh94fT0dH67eHPvciaTiU8//ZSPP/7Y8rlV/Jl18uRJzp8/b3d8kydPpmvXrjz22GMqPS0OqW61avhfDPwejoy0uY/JZOLIxSDCbRdv+P/Rgs/HWx43Caj4Z5GIiNhXr149y++8Q4cO2dzHZDJx+LD52ve2dreV+7vA09OTWy6uBXwoKMjufoeDzMdt2rSpVVnrFctXMOjpZ3hjwutWvxFLOhR0iPR08zVT5y6d7Y4/ODjk4jEqVo1HRETAs4YP7tXMk0pTztieyG0ymUgJjgbMpZ7L+13hUsnNEhxOPm1/knjxcavU9cfV81K1Mw9/c9XAtN/LTta4EGG+pnD1qoTLxfaF+Ub2/HsJge8uI/5QmN2254NCAfBuUAM3L/vL8omIiFwvChRXkJeXFw8++CBgziAtXneopO3bt7Nr1y6b7evVq2cJutgq/5qbm8v8+fNtti0Z9LB1XIDZs2cTERFh+be9rOXyMJlMTJgwgZCQEJycnPjoo49o3rz5Fdu1bduWli1bAvDFF19YjadYbm4uH3zwAXl5ecTExFhlZj/22GOAOTC+Zs2aUm3T0tKYM2fOVZ7VlQ0fPpw2bdoAMGvWLObOnWv3RgHAjh07ePnllykqKsLT05Np06ZZba9bt66lFPWsWbNISEgo1ccvv/xCkJ2bGcWvR1xcnM3zzsvL4+OPP7bZ1sXFhdDQUI4cOWJ3AsLp06cB89+2r6/t8myOZMCAAXTubL7pEx0dzaxZs27yiMqnZCa4vc+HPXv2WL2nyvP54O/vT58+fQD47rvvbN7QKyoqYvr06eTk5BAVFWV5/wDcc889lmP997//tXmMzZs3s2HDBsu/IyIiGDduHAUFBVccX0nleQ2+++479u7da/l3yZLt/fr1s0yE+fjjj22+PkuWLCE52Tyz+P777wfgiSeeAMwlFKdMmWJzQsfRo0f5/vvvSUtLw8fHx26WisjfmcFgoOfF7KsdISFElCj9WWzTqVPEpZlvwN/X9tY/dHzFzl5cRsKrkjs1vFXqTUTkejIYDNzesycAO7ZvJ+LimvAlbfx1I3GxscCl31vl1bt3b8BcOenwodLXP4cPHeb4cXNG8H0PWPfdvHlzc+AhJYUDBw6Uapuens6SixMiGzVqZPWbt6SEhASyssyTAps0aVqh8YuIiPm7IqCdeaJN/KEwS+ZwSXH7z5CTmA5A3R6tK9R/jQ7mvtNCY0m2EYhOPhNFWpg5EFznduu+i9clzo5PIzbwtM3+036PI+lEhGX/4iC2s5urOaMZiDsQTJGNewcxe06SFWueZF+3Z8XOS0RE5Go5bKDYZDKRlZVVof8Ve+211/D39ycnJ4dBgwaxatUqEhISiIuL48svv2Ts2LF2j+vt7W3JNt26dStTpkwhPDycpKQktm7dylNPPcXp06fxtnFjskePHpYfF++++y4//fQT58+fJz4+nl27djFy5Eg+++wzqzYlx11RM2bMYOvWrZZz7t+/P/n5+WRnZ9t8fUqu6zl58mRcXFxIT0/nySefZOnSpURHR5OcnMzu3bt59tlnOXr0KADPP/88derUsbR96KGHLIG6SZMm8b///Y/IyEhSUlLYvHkzTz31FHFxcTcsy8jNzY25c+dabhTMmDGDRx99lFWrVhEeHk5aWhrR0dFs2rSJl19+mRdeeIGsrCw8PT2ZPXs2TZuWvhnwzjvv4ObmRmJiIk899RQbNmwgJSWFqKgo5syZw4QJE+xmP3bu3JmHHnoIME8EmDx5MqGhoaSmphIYGMigQYM4ceKEzfaPPfYYPj4+mEwmRo0axdKlSzl79iwpKSmcOXOGd999lx9++AGAgQMHqiwu5guSKVOm4HpxTcpFixZZgul/Zs2bNycgwLy+zYoVK5gzZ47lfXPs2DGmTZvGCy+8YBXELO/nw8SJE/Hy8sJoNPL888/z+eefExERQUpKCkFBQYwcOZJNmzYB8MADD1gycwEeffRRbr3VHOxZvXo1kyZNIiQkxNJ2woQJvPzyyxiNRtq3b0/fvn0B89rGI0aMsFuK35aOHTtaMqlnz57NN998Q3R0NElJSRw8eJCJEyfy1ltvWbUp+RpUqlSJN954AzCvyz506FACAwNJTU0lLCyMGTNm8OGHHwLQp08fywSQli1b8vTTTwPmz/UhQ4awe/duUlJSOHfuHEuWLGHEiBEYjUbc3d3LXONd5O/uwdva4lvZk4LCIt5bv4FtZ4JJy84mPj2dlQcOsmiPOeO/a+NGNA0ovWbXZ1u38erKb3l15bc3bIwxFz93alatesOOISLiyP750D/x9fWloKCAae9OY9vWbaSlphEfH8+K5StY9PXXAHTt1o2mzUpfW302ezbjx41n/Ljxpbb1vqM3DRs2BOCTGTNYt3YtycnJJCcns27tWj6ZMQOAZs2aWVWBAmjXvh3169cHYN7n89i8aTOJiYmkpqSyZ88e/u/Nt4iPj8fNzY0RL7xg9/xioi8tR1KzZs0KvTYiImJWt9etuFX1xFRYxPEFGzkfFEJ+RjY5KRlEbDzM7+vME3r82zSgSr3SVYCCV+0kaMYagmaUTkCp0aEplWubEyXOLN9O9K4T5F3IIu9CFtG7TnBm+XYAqtTzx79NQ6u2tbu3xCPAfJ3w+9r9hH6/l4zoJIzZeWQnXuDctqOcXLwZTOBW1ZP6fW6zPq+e5klGWbEpnP5mG+nnEjFm5ZIZm0zoD3s5u/6g+bxubYhfy/pX/wKKiIhUgGMuRgrExsZaBTPK4+DBg3h7e+Pn58dXX33FiBEjSEhIKLVWcJUqVbj99tvtlml96623GDRoEGlpaSxbtoxly5ZZthkMBt544w127dplVR4VzBezI0aMYP78+SQnJ9sMNlSpUoXHH3+cBQsWAObMvKvNEp03b57l8eeff86MGTPKzPDr0qULS5YsAcwlXGfOnMmECRNIS0vj3Xff5d133y3V5vHHHy8VWDcYDMyePZuRI0dy5MgR5s2bZzUWgAkTJjBz5kyrbMDrqXr16qxYsYL//Oc/rF69mlOnTtldExrMAaqpU6faDBIDNGnShLlz5zJmzBhiYmIYP976poaPjw+DBg2yrDF7ualTp5Kdnc2mTZtYuXIlK1dar/01cOBA9u3bZ1nLq2S/n3zyCaNGjSI1NdXmfwMwB73KmuDgaJo0acLw4cP5/PPPKSgo4O233+bbb78t93rVN4OzszNTp05l9OjRFBQU8Omnn/Lpp59a7ePk5MSLL77IwoULyc/PJ9JO6dfLNWjQgC+//JLRo0eTlJTEJ598wieffFJqvz59+pTKqHd1dWXevHm88MILnDhxglWrVlnKtJd09913M336dFxdXRkxYgT79u1j7969LFu2rNzrFVerVo1//etfTJkyhZycHKZOnVpqHzc3N5577jnmzp0LQGRkZKmKBklJScyYMYNDhw7x7LPPluqjY8eOpbL433rrLfLz81m9ejVBQUE8//zzpdpVrlyZ//3vf7S4WBJRxBF5uLnx+j138/76n8nIzWX+jtLLLtxSswaj+txhs31yZqYl4/hGScowZ4F5XlxqREREri8PDw/emDiR96a9R0ZGOvMu/i4r6ZYWLXh59Ms22yclJVsyji/n5OTEa69PYNrUd4mPj2fpkqUsXbLUap9atWsz4Y3XS02SdXJyYtyr43l3ylRSU1P58osvSvXv6VmZceNfoXGTxnbPL/FiZQqAyl6V7e4nIiL2ubi70npwP44v3EhBVh6ha/aW2se7YQDNH+9ps31eWpYl4/hyBicnWj3Tl+Nf/UJuSibhPwcR/rN1lT+P6t60Gtyv1HeFs6sLtw67i5NLtpAVm8L5gyGcPxhS6hiV/KvQenA/XCtbLwtW/dZGZEQnEbPrJClnokk5E12qrV+bBjQfcLvNsYuIiNwIDhsovlYtWrRg/fr1fPXVV/z666/ExsZSpUoVevXqxejRo1mzZo3dQHGzZs1Yu3Yt8+fPZ8eOHZw/fx4vLy/at2/Pc889R6dOneyWrn7ttddo3bo1y5cv59SpU5ZM1vr169OzZ0+efvppvL29WblyJVlZWWzatKnCAXFbrmY9zX79+rFx40aWLFnCzp07iYqKIi8vj2rVqtG+fXuefPJJevToYbOtj48Pixcv5scff+S7777j7NmzFBQU0LJlS4YNG0b//v2ZOXPmtZ5WmSpXrszUqVN5/vnn+f777y2B2IyMDNzd3alXrx4dOnTggQcesFqX2Z4ePXqwfv16Fi5cyM6dO4mNjcXb25uePXsyZswYqzWoL1epUiVmzpzJr7/+yooVKwgJCSEnJ4cmTZowcOBABgwYYCnxe7nu3buzfv16Fi1axN69e4mOjsZoNFKtWjXatGnDww8/zN13333Vr9Pf1UsvvcSGDRuIjIzk+PHjLFmyhKFDh97sYZWpT58+rFy5ki+//JKgoCBSU1Nxc3OjZs2adOjQgWeeeYZWrVpx7NgxAgMD2bhxIyNHjixX3+3bt+eXX35h2bJlbN26lfDwcLKysvD29ubWW2/lkUce4d5777XZ1s/Pj5UrV7JmzRrWrl1LSEgIWVlZ+Pv70759ex577DFuv/3SRdD8+fN55513yM7OLvf4ig0cOJCGDRvy9ddfc/ToUdLT06lUqRJ16tSha9euDBo0iIYNG/Lzzz8TGRnJpk2bLMsJFHvxxRfp1asXixcvZv/+/SQmJuLq6sott9zCQw89xOOPP14qg9/V1ZX33nuPhx9+mBUrVnDkyBESExNxcnKiXr169OzZk6FDhyqrRARo6O/Px088ztqjRzkUGUlSZiYGDNSt5kOPpk25q3UrXOxU2fgjZOfnAVBZgWIRkRumYaOG/HfGf1n7008EBR0iOSkJg8FA3bp16XF7D+66+26rZUUqonr16vznow9Zv249+/ftIyEhgaKiImrUqEnXbl154MEHLFVoLle7dm0+/Pgj1v20lqCgQyQmmpcNqlGjJh06tOe+B+6n6hUqTmRnZ1see3p6XtU5iIgIeNX2o9O4R4jaeZyUM1HkpWWBATwDfAi4rTG1urXA6SqvGypV86LD2IeI2X2SxBOR5KZkgMlEJd8q+LdpSN2erXF2c7XZ1r1qZdq9dD8JR34n8Vg4mXEpFObmm0tL16iGf5sG1OzUHGc3299jje/tjE+TWsQGniYjKomC3HxcPd2pUtefmp2bK5NYRET+cAZTWYuvylWbNWuWJTs0ODj4Jo9GROSvp6Cg4KpvEP6dpc769Mo7iYiIQzPYqIghIiJSbGTQvCvvJCIiDm1Fnzdu9hD+Ep7a9uHNHsLfhv7mbp4/bx1VERFxaAoSi4iIiIiIiIiIiIjcOAoUi4iIiIiIiIiIiIiIiIg4GKVryd+CyWSyWgvqalSuXPk6jeavS6+jfYWFheTm5l51eycnJzw8PK7jiERERERERERERERERK6eAsXytxATE0O/fv2uqQ+tJa3XsSxBQUEMGTLkqtvXqVOHrVu3XscRiYiIiIiIiIiIiIiIXD2VnhYRERERERERERERERERcTDKKL5BxowZw5gxY272MBxG3bp1/7aZrH8kvY72de3aVa+NiIiIiIiIiIiIiIj8bSijWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDcbnZAxAREZHye6lN3s0egoiI/MnNvdkDEBGRP7XPT7jf7CGIiMifXZ+bPQAR+aMoo1hERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg3G52QOQP5fBgwdz4MCBCrd75JFH+OCDD27AiP44+/fvZ8iQIQAsXryYrl27Wrb17duXmJiYv8x5RkdH069fP6D0uVxvv//+O+vWrSMwMJDo6GjS0tJwc3OjTp06dOjQgQcffJBOnTpd83FmzZrF7NmzqVOnDlu3bq1w+1tuuQWA6dOn8+ijj17zeK5V8XgqouTYS/692uLk5ISbmxtVq1alcePG9O7dmyeeeILKlSuX2rc873tnZ2c8PDyoWbMmt956K08//TRt27at8Dn8lRX/Nxs9ejRjxoy5yaMR+WvLz8whetcJUs5EkZuaibOrCx7VqxLQrgm1ujTH4HT1cxkL843E7D5J4olIcpPTMTgZqOTnTfVbG1L7H61wdrX/8/fkki2knI664jFaDemHX4t6Vz1GERGBCxcusPannzh06DBJiYm4ublTu05tevbsSf87++N0Dd8Fl/s97HfenjSJW1q04J1/v3PF/c+cOcP6desJPhNMdnYW3t7e3NKiBffedy/Nmzcvs21iYiK//Pwzx44eJzExgYKCAnx8fLilRQvuvufuK7YXEZHS0nNy+Om3oxw+d46kjEzcXJyp7eNDz2bN6NeyxfX9zkhIZPIPP3JLzRpM/ueD5W738a8bORQRydsP3k+r2rVv6LFERESuJwWKReSqZGZm8t577/Hjjz9SWFhotc1oNBISEkJISAgrVqygS5cuvPvuuzRs2PDmDNYBFRUVkZubS25uLvHx8QQGBrJw4UK++uormjVrVuH+CgsLyczMJCwsjLCwMH744QfGjh3LqFGjbsDoReTvLCclg2PzN5CfnmN5rqAgn4xziWScSyTx6FlaP3snLu6uFe7bmJ3H0fkbyEm4YPV8VmwKWbEpxB/+nVufvxt3b0+b7TNjkyt8TBERqbj4+Hj+PfkdUlNTLc8ZjUZCQ0IIDQlh9+7dvPnWm3h4eFzzsdLT05k9ezZFRUXl2n/jrxtZuGABJpPJ8lxKSgqBe/eyLzCQZwY9wwMP2r6Zv3//fj7/bA65ublWzyclJZG0ezd7du/m4Uce4amBT139CYmIOJj49HSm/LSW1Kxsy3PGwkJC4xMIjU9gd1gY/7r3Hjzc3K75WOk5uXy2bRtFJb4DyuOX4yc4FBH5hxxLRETkelOgWGyqXbs269atK/f+rq4Vv5n7V1KnTh2cnZ3x8/O72UP5Uzh//jwjRowgJCQEgBYtWvD000/TqVMnfH19SUlJITQ0lBUrVhAYGMiBAwd49NFHmTNnDt26dbvJo/9zefDBB5kyZUq59nV3d7f5/JQpU3jQxs0qo9FIQkICS5cuZeXKlcTHxzNq1CjWrVtns6+y3vdGo5H4+Hi2bdvGvHnzyM7O5tNPP6VVq1bccccd5Rq/iEhhvpETCzeSn56DaxUPGt/XGZ8mtSjMM3I+KJToXSdIj0wg9LvdtHy6T4X6NplMnFqyhZyECzi7u9Dw7k74taqPqaiIpGMRRG45Qk7iBU59s5V2I+/HYDBYtc/PzCH/gvnmU+uh/anaqIbdYzm5OFf85EVEBIDc3Fymv/c+qamp+Pj4MHjoENq0aUNOTg7btmxl7dq1hAQHM/fzuYx/dfw1HevChQtMf+994mJjy7X/kcNHLEHidu3aMeDxx6lRswbR0dGsXLGSM6dP883Sb6hduw4dOnawaht+NpxZn86koKAAf39/nnzqSVq3boMJE+Fnw/l25UrOnTvHD99/j5+fH3fedec1nZuIiCPINRqZvuFnUrOy8fH0YHD37rSpU5vs/Hy2nQlm3dFjhJyPZ96OnYy7s/81HSs9J4f3N/xMXNqFK+9cwuZTp1kcGPiHHEtERORGUKBYbDIYDDZL1DqqJUuW3Owh/Gnk5+czatQoQkJCcHZ25tVXX+X555+3uuFerVo1mjRpwj333MPGjRt5/fXXycrKYtSoUaxatYomTZrcxDP4c3Fxcbnm95qbm5vdPnx8fJg6dSoAKy/enPr+++956qnSWQxXet/7+Phwyy230L59e4YOHYrJZGL+/PkKFItIucXtDyY3OQODs4Fbh91J5Zq+5g1eHjS6uyPuVT35/af9JJ2IJD0yHu8G9oO1l0s6GUl6ZAIALQbegW/zupZtdXu1wSOgKqcWbyEzKonEY+EE3NbYqr0lm9gA3vUDcHb7e0+CExG5WTZv2sT58+dxdnbmrf97i/oNGgBQtWpVBj7zNL5+fixcsID9+/YRHBx8VUu2gLl89KczPrHKWi6LyWRi2TfLMJlMNL/lFl6f+AbOzuaJQS1btmTS25OYOmUqIcHBLFmyhPYd2ltdA61csYKCggK8vavy7rRpVPOtZtnm5+fHbe1uY+q/pxAaGsrKFSvp07cPLi66JSMiUpbNp04TfyEdZycDb953L/UvJnB4e3gwsGsX/Lwqs3D3XvafDSf4/HluqVnzqo5zJu48M7dsscpavpL8ggIW7Q1k6+kzN/xYIiIiN9L1W8BBRBzCnDlzOHnyJABvvPEGw4cPL5WVVdJdd93F7NmzAcjKymLy5Ml/yDjF2ksvvWR5fDVrPJfUtWtXOnQwZ1D89ttvFBQUXFN/IuIYTCYTMXvM3x/V2za+FCQuoVbXFnhU9wYg7mBIhfqP2XUCgKqNalgFiYv5taiHT9NaAJy30XdxoNjD3xsXj2svWyciIqWZTCbWr1sPQI8ePSxB4pLuuvsual1c23Hr5i0VPkZKcgqffzaHKe/8m9TUVLy9q+Lv73/FdseOHiUq6hwATzz5hCVIXMzFxYVnBj0DQFxsLKdPn7Zsy87O5vjx45bxlwwSF3N1deXRxx4DIDMzw1KdSUREbDOZTGy4+Nn6j6ZNLUHiku5s1YpaPlUBKhywBUjJyuLzbduZutZc2trboxJ+XmVP5jeZTOwN+53Xvl1lOWaj6lf+nrmaY4mIiPwRNH1VbpiIiAi+/vprAgMDiYuLw9fXl759+zJ69GhCQ0MZMmQIAMHBwZY2+/fvtzy/ePFiunbtarPv4lnlo0ePZsyYMaW2h4aGsnLlSg4ePEhcXBxZWVl4eXlRv359evXqxeDBg/Hx8Sn3ufTt25eYmBgeeeQRPvjgAwBmzZplCYCWh63zSU1NZdGiRWzbto2oqCgKCwupVasWPXv25LnnnqNWrVp2+8vMzGTlypWsW7eOyMhIXFxcaNu2LSNGjKBOnTrlHldFZGZm8s033wDQqlUrnn322XK169mzJw8++CBr164lKCiIAwcO0KVLl1L7hYaGsmDBAoKCgoiPj8ff35++ffuWax3c/Px8fvjhB9asWcPZs2cpLCykZcuWDB06lDvvLLus2++//87ixYvZt28fsbGxuLi4EBAQQOfOnXnmmWdo2bJluc7zz6xWrVr4+PiQlpZGTEzMNfdXo4Y5y6+wsJCUlBQCAgKuuU+49L7q0qULS5YsYfPmzSxevJgzZ85gNBpp1KgRTz75JE8++SQAaWlpzJs3j82bN3P+/HmqVKlC9+7dGTt2LA1s3HgE8xpzK1asYM+ePYSHh5Oeno67uzsBAQF06dKFIUOGXFXWe3Z2Nt988w2bNm0iPDyc3NxcAgIC6N69O8OGDVMmvTi8rLgUy7rEfi3r2dzHYDDg26IeMYknSTkTjclkKnMyUjFjdh4Z0UkA+Lasb3c/v5b1SQuL40L4eYw5ebh6XCrDnxmbAkCVule+ySMiIlcnMiLSkuHbsXMnm/sYDAY6duzAuthYDh06XO7vgmIrV6xgx44dALRu04aRL41kzmdzSEpKKrPdkSO/AeDpWZlWrVrZ3Kd58+ZUqeJNRkY6QQeDLPslJiTg4eFJZmYGTZs2tXuMGjUvVcoob6aziIijikxOtmTddrRzfW8wGOhQvz7r045z+Ny5in9nHDjIzpBQAFrXqc2LvXvx+bbtJGdm2W2TlJnJrC3mCfjuri4807Urdar58O7a9df9WCIiIn8EBYrlhti8eTOvvfYaubm5lufi4uIsQZTx469tramyzJ49m9mzZ2MymayeT0tLIy0tjWPHjvHdd9+xfPnyMgOx15uHh4fVv/ft28fYsWO5cMF6PZLw8HDCw8P59ttv+fDDD7n77rtL9RUVFcXw4cOJiIiwen7Xrl3s3r2bYcOGXffxA+zYsYP09HQAm6WLy/LMM8+wdu1aAH788cdSgeLvvvuOyZMnW2WnxsTEsGTJEn755Re6d+9ut++UlBRGjhzJ0aNHrZ4/ePAgBw8eZPjw4Xbbbt++nTFjxpCfn295Lj8/n4iICCIiIli9ejWTJk1i0KBBFTrfP6PiiyUnp2svJhEWFgaYMyMqMumiIt5//30WLVpk9dzJkyeZPHkyUVFRPPHEEwwZMoS4uDjL9uTkZNatW8fu3btZs2ZNqUkTO3bsYNy4cWRnW5d4MhqNZGZmcvbsWdasWcNnn31Gr169yj3W4OBgRo4cSexl699FR0ezatUq1qxZw5tvvsngwYPL3afI301mXIrlsVcd+8FYr1rmTOOC7DxyUzPx8K1yxb6z4lLAVNx36UwDS9+1L2YxmyArNgWfJpd+B2TGmAMIngE+RO86QeLxcLIT0gDw8K+Kf5sG1PlHK5WkFhG5BiWvXxo3bmx3v4YNGwLmzNuEhATLJMXyqlmrFo8/8Tg9evQod5vIyEjzsRs1tPt72WAw0LBhA44fP87Zs2ctzzdo2JAvF3xJfn5+mb+148/HWx5X9lQWmYhIWSKSky2PG5eRsdvQ3/z7PzM3j4SMDGp4e1foODWrevN4p078o2n5J3c7Oxno2bwZj3fqhG/lypy67F7A9TyWiIjIjaZAsVx3YWFhjBs3DqPRSO3atZk4cSJdunQhNTWV5cuXs2TJEqZNm3ZDjv3LL78wa9YswFzK7IUXXqBRo0aAOQC7cOFCtm/fTlxcHDNnzmT69OlXfawXX3yR5557zu723bt388orr2AymXjiiSdo27atZVtISAgvvvgiubm51K1bl7Fjx9KtWzdcXV05fvw4M2fO5MSJE7z66qssXryYjh07Wtrm5+dbgsSVKlVizJgx3Hvvvbi7u7Nnzx4+/vhjFixYcNXnVZYDBw5YHnfqZDsDwJ527drh7+9PUlIS+/fvt9q2f/9+3nrrLcA8S3/ChAnceuutpKamsnr1ahYuXMhPP/1kt+9XXnmFo0eP4uTkxIgRI3j00Ufx9vbmyJEjfPzxx3z55Zc222VnZzNx4kTy8/Np27Yt48aNo2nTphgMBo4fP86HH35IREQEH3zwAXfccQd165YuZfpXERUVZclauNbM1g0bNlhK5fXq1Qs3t+tfovXo0aMcOHCArl27MnbsWBo1asTp06eZNGkScXFxLFy4kF9++YWsrCymTJlCnz59MJlMrF69ms8++4y0tDTmzp3Lu+++a+kzNjaWV155hZycHBo2bMgrr7zCrbfeSuXKlYmNjeWHH35g+fLl5OfnM3XqVDZv3lyusSYkJDBs2DCSk5Px9fVlzJgx9O7dG09PT0JCQpg3bx579uxh2rRp+Pr6cv/991/310vkryA3NRMAg7MB96qedvdz9/Eq0SajXIHi3LRMy+NK1bzs7nd532AOFBuz88hLNc/kj9z8G6bCIqt2WbEpZMWmEB8USuuhd+JZveoVxyQiIqUlJJjXknd2dsbPRgnRYv7Vq1u1qUig+JHHHqVGjRoVyigDc1YwQECJY9sc28Uy1sX7l3Sl38WbN20CzOffrHmzCo1PRMTRJKZnAOagrF9l+5Nr/L0uXS8kVjBQ/EiH9tTw9q7Qd4Z3JQ9mPj0Q3zLGdL2OJSIi8kdQoFhsMplMZGWVr/SJk5OTVbbsf/7zH4xGIz4+PixfvpyaNWsC4Ovry6RJkwgICOC///3vDRl3cUCwWbNmzJ071+pCvUaNGnTp0oUBAwZw8uRJdu3adU3HcnNzs3sj4OzZs0yaNAmTyUSHDh14++23rbZPmTLFEiRevXo11apdWsOqd+/edOvWjUGDBnHs2DGmTJliFSRdtmyZZSb+zJkz6d27t2XbQw89RMeOHXnkkUcsmb/XU/GseRcXlzIzAGwxGAw0aNCApKQkYmJiyM/Pt7x+7733HmDOHFi2bBlVqph/5Pv6+jJx4kRq1qzJ+++/b7PfTZs2WQLYkyZN4plnnrFs69evHx07dmTAgAFERUWVanvgwAHS0tIAc8nj4r/V4rbNmzfnrrvuwmg0smnTphuSqV1QUHDF95rBYMDT035QpTw+/fRTy2NbWepg/31f/HxUVBQbN25k2bJlAHh6evLaa69d07jsycvLo23btnz11Ve4upqz926//XYmTpzIuHHjKCgoIDo6muXLl9O+fXtLu9GjRxMWFsbPP//M3r17rfpcunQpOTk5uLq68uWXX1Kv3qXSt76+vrRp0wYnJycWLVpEVFQU4eHhlokmZfn4449JTk6matWqrFy5kvr1L5W97dq1K507d2b06NFs2bKF9957j/79++Pu7l5GjyJ/TwVZ5iojzu5uGMrItnKpdCljtyAn3+5+1n3nXWrvYf/95Vzp0vd2yb6L1ycuVrf3rQS0a4xbFU9yUzM4fzCE8wdCyE3J5MSiTbR/+UGrstUiIlI+GRnmm/4eHp5lZt56lri+LO91abGSv+mvZmyVr7BepGdlz6saV2BgIIcOHQLMk5orVzDAICLiaDIuVin0cHMr+zujRMWfzLw8u/vZUrNqxSeAuru64O5a8VvqV3MsERGRP4ICxWJTbGwsHTp0KNe+derUYetW89ocqamp7NmzB4Bhw4bZvEgfPnw4P/zwA7///vv1GzBQVFTEHXfcQZMmTejdu7fNIK6TkxOdOnXi5MmTN2xNqPT0dF566SXS09OpWbMms2bNshpLaGgoQUFBAIwaNcoqSFzM3d2d8ePHM2zYMIKDgzl69Ci33XYbgKV8c48ePayCxMXq1q3LiBEjbkgwvjio6uXldVUzIItn3xcVFXHhwgWqV69OaGioZZ3q0aNHW4LEJQ0ZMoQVK1ZYlXcrVvx6NGzY0CpIXMzHx4fx48fz6quvltpWstx0YmJiqb/XevXqMX/+fKpWrVqugOHVWLt2reUc7KlSpYrlb8aW/Pz8UjeqTCYTGRkZnDlzhqVLl7J7927AnNltL1Bckfd9/fr1+fjjj2/ourvDhg2zBImLlcxk79Spk1WQuFi7du34+eefiY+Pt3q+efPmPPnkk/j5+VkFiUvq0qWLpdx1SkrKFf+7X7hwgQ0bNgAwaNAgqyBxMScnJyZOnMiWLVtITk5my5Yt3HfffWX2K/J3VHQxS9fJ1bnM/ZxK3HQpMhaWr+8SyxY4udjvv+S2ooJLfRszc3D1qkRBTj6tn+1PtSa1LdtcPd2pUscfz+pVObv+IHkpmURtP0bjezuXa2wiInJJgdEIgNsVyvi7lZhUZ8wv36Sha1V8beDqWnZWcPH2/AqMKzg4mLlzPgegShVvBj799FWOUkTEcRiLzL/X3ZzLvn5wc7l0/WAsKN/1g4iIiFyiQLFcV0FBQRQWmn+U2Vvf08nJiXvuuYfPPvvsuh7bycmJ0aNH291eVFREWFgY0dHRAFZr4V4vhYWFjBs3joiICNzd3Zk1a5YlOFqsZPnm5s2b252J3qJFC5ydnSksLOTQoUPcdtttZGRkcPLkScD+6wvmbNgbESjOuzgz82qzIZ1L/LgvXkN63759lufsnZPBYKBfv342A8XFZax79uxp97h9+/bFycmJoiLrUqLt2rXD1dUVo9HIsGHDeOqpp+jTpw/t2rWzjLWsfv8s3nnnHd55550r7te6dWtmzpx51WsU+/r6cscdd9C7d2/69etXKoh7vRVPjiipZInCVq1a2Wzn5WUuLWu8eCOy2MMPP8zDDz9s93hxcXGcOnXK8u/iz7KyHDlyxHKcFi1a2H0/+/v7U716dRITEzl06JACxeKQbmiJNadr6zugXRMC2jWhqLAQJzs3our0aM35Q2Fkn08l/lAYje7ppLJxIiIVVFZFiZvN2dn5hlwjnjp1io8//Ii8vDxcXFwY+8oYqvmWniwsIiLWnAx/3u8MERGRvxMFisWmklnCFVEyg89WZl2x5s2bX9W4yispKYnAwEDCwsKIiooiMjKSs2fPkp2dfUOPO336dEtG9dSpU63WJS5WsgTygAEDytVvXFwcYH59iwOsZb2+jRo1sgSZryfvi+u8XG1Z6wsXLgDmYEHViyV3is/Nx8fH8pwttjJXc3JyLFnODRo0sNvWw8ODWrVqERMTY/V8QEAAr732Gh988AEZGRl88cUXfPHFF3h7e9O9e3dLQNTHx6cip1khjzzyCB988MF179dgMFC5cmX8/Pxo1aoVd911F3fddRcuLvY/9i9/3xuNRiIjI5k/fz4//vgjqampuLq60qdPnxseJAZsZtuXDHLbyj6/fB9bcnNzCQwM5MyZM5w7d46oqCjCwsJKVRkofq+VpeT7ecyYMVfcHy79zYv8nZhMJoqM9m+uG5ydcXIzf/5cKUu4ZD/OV8g+vrTfpc8kU2EhONv+HCiZRWwr89hekLiYX4t6ZJ9PpSA7j9zkDDz8y7/+mYjI353JZLJMLLXF1dWVSpUqAZCfb7S7H0B+iX7c/qAlO9zc3CkoKKCgoOyxGY35F/cvO/MYzJNaP5s1m/z8fJydnRnzylhutXGNKCLiaEwmE3llTM5xdXbG/eL9i/wr3NvKL9GPWxn3PERERMQ2fXvKdVW8rhNgtW7x5YoDjtdbXl4e77//PqtWrSoVJHV3d6dr164UFRVx8ODB637sb7/9liVLlgDmkrn2MhczMzMr3Hdxm5IB2rJeXycnJzw9Pa3+e1wPTZs25fjx4+Tk5BAXF0etWrUq1D4kJAQwBySLs5KLx1h808geW0HBkq/H1bQH83+rli1b8tVXXxEYGIjRaCQ9PZ1ff/2VX3/9FVdXVwYPHsyECROsMqL/TKZPn86jjz563ft1dXWladOmfPjhh9SoUYP58+ezcuVKEhISmD17dplB5+vhWtdltmXp0qV88sknpd4bTk5OtGzZkoYNG/Lzzz+Xu79reT+L/J3kpWVx8KPVdrc3H3A7LhfXBy7MM2Iymexm45ZcO9ilctmf7Zb9PKzXHna2U9K0MPdS367l7Lskd59L60kas3IUKBYRKSEpKYkxL9uv8PTSqFGW33e5uTllfhdklZjga+93/PVWubIn2dlZZGeVPbm4ePuVxvXTjz+yfNlyTCYTbm5ujBs/ng4dy7fMi4jI311SZiZjl62wu33kHb2p7G7+jZ9rLPv6IavEUgBVrnBvSEREREpToFiuq+KSr2DO9iz575Iqsp7T5XJzc+1uGz9+PFu2bAHMZXZ79+5Ns2bNaNq0KY0bN8bFxYUZM2Zc90DxwYMHmTp1KgD/+Mc/eP311+3uWzKgeezYsQqVcS6ZcXul7OhreY3t6d69O99//z0Au3fv5vHHHy9327CwMBITEwHo2rWr5fnic8rJySmzva3zKZnpezXti3Xr1o1u3bqRmZnJ3r17CQwMZPfu3Zw7dw6j0ciCBQswmUz861//KvMYf2evvvoqJ06cYO/evWzbto2PPvqIN99882YPq0K+/vprpk+fDkDt2rXp378/LVu2pEmTJjRr1gxPT0/27t1boUBxyQkbGzZsuKFrNov81RUHVU2FReRn5ODubXsySN6FSyXcK1WtbHMfe30D5KVl4m6nXV7apYkatvYp6wYUmMdezOkK62uKiEhptWqbJ5oWFBSQlppmtwRzclKS5fHlS/ncsLHVqkViYqLlmsWepItj869e3eb2oqIivvryK7Zs3gyY1yR+419v0KxZs+s7YBGRv7laVX0AKCgsIi07m2qVbf/GTy4xGdvfzn1IERERsU+BYrmuSpb/PXv2rM3SywDnzp2z+XzJjM3L1xctVlxq+HKHDx+2BIkHDx7MpEmTbO53eXnZaxUdHc2YMWMwGo3Uq1ePGTNmlJl5Wrt2bau2ZQWWLr9hXbNmTctau2fPnqVfv3422yUkJJRZ9u1q9e/fnypVqpCRkcGSJUt47LHHyr3e7eLFiy2P//nPf1oeF78eFy5cIDk52WoN2pJKlvgt5u7ujp+fH8nJyTbXLy5WWFhYrnK/Xl5elhLNYA7kjxs3jpiYGJYtW8aECRNueBbtn5XBYOCDDz7g/vvvJyMjg0WLFtGrVy969Ohxs4dWLrm5ucyePRuAtm3bsmTJEptZ6BX9fCiZVR8TE1Oh97PI30mlal70fP/ZMvfJOn/p/ZUZm2w3UJwZmwyAs4cr7tXKd6PHM8AHDIAJMuNS8G5Qw+Z+GTHmvjFA5Vq+luePffEzmedT8KrlR9vh99g9TnZCmvmBkwEPvz8mw01E5K+ievXqrPh2ZZn7lLwODA8PtxsoDg8PB8DTszLV7QRkr7f6Depz7NgxIiPP2f3dZjKZiIiIBKBhw4althcUFPDJ/2YQFBQEQM1atfjXm/+iZs2aN3TsIiJ/NdWrVGH5iyPK3CcqJcXyODwp2W6gODzJ/Bvf092N6lUUKBYREamo8kV4RMqpQ4cOlrWaNl+cQW3Lzp07bT5fMjsvpcQPwpIOHz5s8/kjR45YHj/55JM29ykqKmL//v1W/74WWVlZvPTSS6SmpuLp6cmcOXOuuJ5tp06dLI+LA9u2HD58mNtuu427777bkuFYuXJlS/uy2tp7fa9V5cqVee655wAIDg7m888/L1e7wMBAVq1aBUD79u3p1q2bZVuvXr0sj6/mb6a4/fbt2+2uyXzgwAGbGcfz5s3jgQceYODAgTbbtW3bliFDhgDmsubFayw7qho1ajBx4kTAfJNs8uTJV8zk/rMIDQ21lJt++OGH7ZYqDwwMtDwuz+dDx44dLZMlynpPxsTE0L59e/r37281aULEkXjW8MG9mvnmTsqZ0pN/wPzZkhIcDUC1ZnXKPbnCpZKbJTicfNp23yWPW6WuP66elyp6OLm5UJhjJD0ywar0dUmFxgKSTpqDA1UbBNgtby0iIvbVq1fPEvg9dOiQzX1MJhOHD5uv7W5rd9sfNtGuXfv2AKSnXyA0NNTmPiEhIWRkmJe/ue2226y2mUwmZs+abQkSN2vWjKnvTlWQWETkKtWtVg3/i4Hfw5GRNvcxmUwcuTgJ6ba6dTU5W0RE5CooUCzXlZeXFw8++CBgziANCwsrtc/27dvZtWuXzfb16tWzBF1slX/Nzc1l/vz5NtuWzOK1dVyA2bNnExERYfm3vazl8jCZTEyYMIGQkBCcnJz46KOPaN68+RXbtW3blpYtWwLwxRdfWI2nWG5uLh988AF5eXnExMRYZWY/9thjgDkwvmbNmlJt09LSmDNnzlWe1ZUNHz6cNm3aADBr1izmzp2LyWSyu/+OHTt4+eWXKSoqwtPTk2nTplltr1u3rqUU9axZs0hISCjVxy+//GK54XK54tcjLi7O5nnn5eXx8ccf22zr4uJCaGgoR44csTsB4fTp04D5b9vX19fmPo5kwIABdO7cGTBnxM+aNesmj6h8SmaC2/t82LNnj9V7qjyfD/7+/vTp0weA7777zuYNz6KiIqZPn05OTg5RUVGW94+IozEYDAS0M2fdxx8Ks2QOlxS3/ww5ieYb8HV7tK5Q/zU6mPtOC40l2UYgOvlMFGlh5uoSdW637rt4XKbCIs7+bHt5irPrD2DMNC9/Uaen3sciIlfDYDBwe8+eAOzYvp2I8IhS+2z8dSNxsbEA3H///X/Y2Fq3bm0JYi9dspSCggKr7QUFBXyz9BsA6tatx23trAPFP2/YwL6Lkw6b33IL//f2JLy9tZa9iMjVMhgM9LxYtn9HSAgRJZYlKLbp1Cni0syT+u9re+sfOj4REZG/CwWKxSaTyURWVlaF/lfstddew9/fn5ycHAYNGsSqVatISEggLi6OL7/8krFjx9o9rre3tyXbdOvWrUyZMoXw8HCSkpLYunUrTz31FKdPn7Z5wd2jRw/LzMF3332Xn376ifPnzxMfH8+uXbsYOXIkn332mVWbkuOuqBkzZrB161bLOffv35/8/Hyys7Ntvj4l11aePHkyLi4upKen8+STT7J06VKio6NJTk5m9+7dPPvssxw9ehSA559/njp16ljaPvTQQ5ZA3aRJk/jf//5HZGQkKSkpbN68maeeeoq4uLgbNovSzc2NuXPn0rx5c0wmEzNmzODRRx9l1apVhIeHk5aWRnR0NJs2beLll1/mhRdeICsrC09PT2bPnk3Tpk1L9fnOO+/g5uZGYmIiTz31FBs2bCAlJYWoqCjmzJnDhAkT7Jbz7ty5Mw899BBgnggwefJkQkNDSU1NJTAwkEGDBnHixAmb7R977DF8fHwwmUyMGjWKpUuXcvbsWVJSUjhz5gzvvvsuP/zwAwADBw7UzFTMF2pTpkzB1dWcSbdo0SJLMP3PrHnz5gQEBACwYsUK5syZY3nfHDt2jGnTpvHCCy9YZaWX9/Nh4sSJeHl5YTQaef755/n888+JiIggJSWFoKAgRo4cyaZNmwB44IEH6NChw/U/QZG/iLq9bsWtqiemwiKOL9jI+aAQ8jOyyUnJIGLjYX5fdwAA/zYNqFKvdKnR4FU7CZqxhqAZpSdK1ejQlMq1zRN6zizfTvSuE+RdyCLvQhbRu05wZvl2AKrU88e/TUOrttXbNsK7gfkzIj4olNPLt5N+LhFjVi7p5xI59c1Wzh8IASCgfRP8WtS7Xi+JiIjD+edD/8TX15eCggKmvTuNbVu3kZaaRnx8PCuWr2DR118D0LVbN5o2K33t8Nns2YwfN57x48Zf13EZDAYGX6wmFBIczHvT3iM4OJiMjAyCL/47JDgYg8HAwKetrw0uXLjAyhXmstteXlV4adRLgHkCsL3/2auGJCIilzx4W1t8K3tSUFjEe+s3sO1MMGnZ2cSnp7PywEEW7TFP0OnauBFNL17zl/TZ1m28uvJbXl357R89dBERkb8Mx1xsU64oNja2wsGMgwcP4u3tjZ+fH1999RUjRowgISGh1FrBVapU4fbbb7dbpvWtt95i0KBBpKWlsWzZMpYtW2bZZjAYeOONN9i1a5dViVgwl/YaMWIE8+fPJzk5mddff71U31WqVOHxxx9nwYIFAERERFx1lui8efMsjz///HNmzJhRatZ5SV26dGHJkiWAuUT3zJkzmTBhAmlpabz77ru8++67pdo8/vjjpQLrBoOB2bNnM3LkSI4cOcK8efOsxgIwYcIEZs6cSX6+7fKZ16p69eqsWLGC//znP6xevZpTp07ZXRMazOV5p06dajNIDNCkSRPmzp3LmDFjiImJYfx465s+Pj4+DBo0yLLG7OWmTp1KdnY2mzZtYuXKlaxcab022sCBA9m3b59lrbOS/X7yySeMGjWK1NRUm/8NAPr06VPmBAdH06RJE4YPH87nn39OQUEBb7/9Nt9++22516u+GZydnZk6dSqjR4+moKCATz/9lE8//dRqHycnJ1588UUWLlxIfn4+kXZKW12uQYMGfPnll4wePZqkpCQ++eQTPvnkk1L79enTp1RGvYijcXF3pfXgfhxfuJGCrDxC1+wttY93wwCaP97TZvu8tCxLxvHlDE5OtHqmL8e/+oXclEzCfw4i/GfrahQe1b1pNbhfqYk/BoOBVoP6cnLxZjKikkg6HkHS8YhSx6jerjHNHv1rrM0uIvJn5eHhwRsTJ/LetPfIyEhn3ty5pfa5pUULXh79ss32SUnJlozj661L1y48/sQTrPr2W06fOsU7b0+22m4wGBjy7FA6dupo9fyWzVvIy8sDIDMzg/GvjLvisV4aNYred/S+bmMXEfk78nBz4/V77ub99T+TkZvL/B2llyW7pWYNRvW5w2b75MxMS8axiIiI2KZAsdwQLVq0YP369Xz11Vf8+uuvxMbGUqVKFXr16sXo0aNZs2aN3UBxs2bNWLt2LfPnz2fHjh2cP38eLy8v2rdvz3PPPUenTp3slq5+7bXXaN26NcuXL+fUqVOWTNb69evTs2dPnn76aby9vVm5ciVZWVls2rTpumT3ZWZmVrhNv3792LhxI0uWLGHnzp1ERUWRl5dHtWrVaN++PU8++SQ9eti+Ge3j48PixYv58ccf+e677zh79iwFBQW0bNmSYcOG0b9/f2bOnHmtp1WmypUrM3XqVJ5//nm+//57SyA2IyMDd3d36tWrR4cOHXjggQes1mW2p0ePHqxfv56FCxeyc+dOYmNj8fb2pmfPnowZM8ZqDerLVapUiZkzZ/Lrr7+yYsUKQkJCyMnJoUmTJgwcOJABAwZwzz332GzbvXt31q9fz6JFi9i7dy/R0dEYjUaqVatGmzZtePjhh7n77ruv+nX6u3rppZfYsGEDkZGRHD9+nCVLljB06NCbPawy9enTh5UrV/Lll18SFBREamoqbm5u1KxZkw4dOvDMM8/QqlUrjh07RmBgIBs3bmTkyJHl6rt9+/b88ssvLFu2jK1btxIeHk5WVhbe3t7ceuutPPLII9x77703+AxF/hq8avvRadwjRO08TsqZKPLSssAAngE+BNzWmFrdWuBkp4rElVSq5kWHsQ8Rs/skiSciyU3JAJOJSr5V8G/TkLo9W9tdW9i1ciXavnAfCUfCSDh6lqy4VArzjLhWdqdK/QBqdmqGb/O613LqIiJyUcNGDfnvjP+y9qefCAo6RHJSEgaDgbp169Lj9h7cdffdVkuH/JEeG/AYrdu0ZsP6DQSfOUNmZiaVK3vRvHkz7nvgflq1alWqTZidNY1FROTaNfT35+MnHmft0aMciowkKTMTAwbqVvOhR9Om3NW6FS5Xef0gIiIiYDCVtbioyA0ya9YsS3ZocHDwTR6NiMhfx1PbPrzZQxARkT+5uZ1evNlDEBGRPzHTxRLvIiIi9lQb88rNHsJfgu7TXT8r+rxxs4fgsP68dUJFREREREREREREREREROSGUKBYRERERERERERERERERMTBaI1iEQdgMpnIzs6+pj4qV658nUbz16XX0b7CwkJyc3Ovur2TkxMeHh7XcUQiIiIiIiIiIiIiIlIWBYpFHEBMTAz9+vW7pj60lrRex7IEBQUxZMiQq25fp04dtm7deh1HJCIiIiIiIiIiIiIiZVHpaRERERERERERERERERERB6OMYrkpxowZw5gxY272MBxG3bp1/7aZrH8kvY72de3aVa+NiIiIiIiIiIiIiMhfiDKKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYl5s9ABERESm/z0+43+whiIjIn9xI5t3sIYiIyJ/Y3GdfvNlDEBEREZE/CWUUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYl5s9APlrGDx4MAcOHKhwu0ceeYQPPvjgBozoj7N//36GDBkCwOLFi+natatlW9++fYmJifnLnGd0dDT9+vUDSp/L9fb777+zbt06AgMDiY6OJi0tDTc3N+rUqUOHDh148MEH6dSp0zUfZ9asWcyePZs6deqwdevWCre/5ZZbAJg+fTqPPvroNY/nWhWPpyJKjr3k36stTk5OuLm5UbVqVRo3bkzv3r154oknqFy5cql9y/O+d3Z2xsPDg5o1a3Lrrbfy9NNP07Zt2wqfg4j8/aTn5PDTb0c5fO4cSRmZuLk4U9vHh57NmtGvZQucnK5uvmJEUhJvfvf9FfdrVN2f9x99pFx9pmZl8cbq78jMzWPkHb3pfUvzCo1p5YGD/HDkN/yreDHr6YEVaisi4qjyM3OI3nWClDNR5KZm4uzqgkf1qgS0a0KtLs0xXOX3BEBOUjrRu0+QFhZLXno2zm6ueFaviv+tDanZuTnOrmXfCrkQEU/MnpOkRyZQkJOPq1clvBsEUOcfrfCuH2C3XWZsMkdmr73i+Lzq+NH+5QcrfF4iIo7mwoULrP3pJw4dOkxSYiJubu7UrlObnj170v/O/ld9TQEQFxfHurXrOHH8OCkpKbi7V6J2ndp0/0d3+vXrh5ubW4X6+/jDjwgKCuLtdybTunXrCo9nxfIV/PD991SvXp1Zn82ucHsREZGrpUCxiFxXmZmZvPfee/z4448UFhZabTMajYSEhBASEsKKFSvo0qUL7777Lg0bNrw5g3VARUVF5ObmkpubS3x8PIGBgSxcuJCvvvqKZs2aVbi/wsJCMjMzCQsLIywsjB9++IGxY8cyatSoGzB6EfmriE9PZ8pPa0nNyrY8ZywsJDQ+gdD4BHaHhfGve+/Bo4I3XwDOJiZdz6ECMG/HTjJz866qbfD58/z029HrPCIRkb+3nJQMjs3fQH56juW5goJ8Ms4lknEukcSjZ2n97J24uLtWuO/EY+GEfLebIuOla5GCgjzSIxNIj0wgbv8ZWg+9Ew/fKjbbx+47w+9r94Hp0nP5F7JJOhZB0vEIGt3Tibo929hsmxmTXOHxioiIbfHx8fx78jukpqZanjMajYSGhBAaEsLu3bt586038fDwqHDfe/fuZe6cz8nPz7fqOyQ4mJDgYDb+upF/vfkvatSoUa7+fv75Z4KCgio8jmLBwcH89OOPV91eRETkWihQLBVSu3Zt1q1bV+79XV0rfmH/V1KnTh2cnZ3x8/O72UP5Uzh//jwjRowgJCQEgBYtWvD000/TqVMnfH19SUlJITQ0lBUrVhAYGMiBAwd49NFHmTNnDt26dbvJo/9zefDBB5kyZUq59nV3d7f5/JQpU3jwwdKZCkajkYSEBJYuXcrKlSuJj49n1KhRrFu3zmZfZb3vjUYj8fHxbNu2jXnz5pGdnc2nn35Kq1atuOOOO8o1fhH5e8k1Gpm+4WdSs7Lx8fRgcPfutKlTm+z8fLadCWbd0WOEnI9n3o6djLuzf4X7j0gyB4qb16zBm/fda3c/J4OhXP1tPHmSo1HRFR4HQE5+Pp9t3U6RyXTlnUVEBIDCfCMnFm4kPz0H1yoeNL6vMz5NalGYZ+R8UCjRu06QHplA6He7afl0nwr1nRmbTPCqXZgKi6jkV4WGd3XAu34ARcZCks9EcW7LEXIS0zm5eDMdxjyEk7N1JlpKcLQlSFyteR0a9GtHJT9vsuNTidh0hPSIeMJ/CcKjelX8WtSzeXwA7wYBtBl2p/2BlvM7SkTEUeXm5jL9vfdJTU3Fx8eHwUOH0KZNG3Jycti2ZStr164lJDiYuZ/PZfyr4yvUd0R4BHNmf0ZBQQE1a9bkyYFP0bx5c/Lz8zl86BCrV60mLjaWDz/4kA8//hBnZ+cy+9u8aTOLv1501eeak5PDZ7NmU1RUdNV9iIiIXAsFiqVCDAaDzRK1jmrJkiU3ewh/Gvn5+YwaNYqQkBCcnZ159dVXef755zGUuAlSrVo1mjRpwj333MPGjRt5/fXXycrKYtSoUaxatYomTZrcxDP4c3Fxcbnm95qbm5vdPnx8fJg6dSoAK1eu5Ny5c3z//fc89dRTpfa90vvex8eHW265hfbt2zN06FBMJhPz589XoFjEQW0+dZr4C+k4Oxl48757qX9xMpW3hwcDu3bBz6syC3fvZf/ZcILPn+eWmjUr1P/Zi4HipgEBVLrGCWkxqWks3bf/qtsv2htIYkbGNY1BRMTRxO0PJjc5A4OzgVuH3Unlmr7mDV4eNLq7I+5VPfn9p/0knYgkPTIe7wbly+YCiNh0GFNhES6V3Wk7/B7cq176DVv39tZU8qnM6WXbyUm4QNKJCAJua2zZbjKZCP8lCEzmQG+rwf0sgeSqjWpy6/N3cfzLX0mPTCD854P43lLX6loHIONioLhKPX+c3f7ek6ZFRG6kzZs2cf78eZydnXnr/96ifoMGAFStWpWBzzyNr58fCxcsYP++fQQHB1doGa+VK1ZQUFBAlSreTH7nHXz9fC3b7n/gAfyrV2fGf/9HTEw0+/bto0ePHjb7yc/P5+uFX7N1y5ZrOtdFC78mISHhmvoQERG5Fle/kIOISAlz5szh5MmTALzxxhsMHz681I2Tku666y5mzzavuZKVlcXkyZP/kHGKtZdeesny+GrWeC6pa9eudOjQAYDffvuNgoKCa+pPRP56TCYTG44fB+AfTZtagsQl3dmqFbV8qgKw9fSZCvVfVFTEuZQUAJpUr35NYy0sKmLOtm0YCwrp1bzipfcPhIezIzgEb49KtKtfOqtMRERKM5lMxOwxXzNUb9v4UpC4hFpdW+BR3RuAuIMh5e67MN9IWlgcADU7NrMKEhfza90AJzfzfPmMaOulDFJDY8mOTwOgQf/2pbKNnZydaXRPRwByEtO5EBFvfW5FRWSfN5dH9arjX+5xi4iINZPJxPp16wHo0aOHJUhc0l1330Wt2rUB2Lq5/IHa3Nxcjl+8XunT9w6rIHGxLl26UKlSJQB+D/vd5vj27NnDa+NftQSJGzduXGq/8jiw/wDbt2/H27sq7du3v6o+RERErpUyiuUPFxERwddff01gYCBxcXH4+vrSt29fRo8eTWhoKEOGDAHM63MU279/v+X5xYsX07VrV5t9F88gHD16NGPGjCm1PTQ0lJUrV3Lw4EHi4uLIysrCy8uL+vXr06tXLwYPHoyPj0+5z6Vv377ExMTwyCOP8MEHHwAwa9YsSwC0PGydT2pqKosWLWLbtm1ERUVRWFhIrVq16NmzJ8899xy1atWy219mZiYrV65k3bp1REZG4uLiQtu2bRkxYgR16tQp97gqIjMzk2+++QaAVq1a8eyzz5arXc+ePXnwwQdZu3YtQUFBHDhwgC5dupTaLzQ0lAULFhAUFER8fDz+/v707du3XOvg5ufn88MPP7BmzRrOnj1LYWEhLVu2ZOjQodx5Zxnl4IDff/+dxYsXs2/fPmJjY3FxcSEgIIDOnTvzzDPP0LJly3Kd559ZrVq18PHxIS0tjZiYmGvur3j9nsLCQlJSUggICLjmPgH+9a9/8f333/Pggw/yyiuv8M4773Do0CHc3Nxo2rQpM2fOpPrFoFFmZiarV69m586dhISEkJaWhqurK/7+/nTo0IFnnnmGtm3b2j1WSkoKq1at4tdffyU6OpqcnBxq165Nz549ef755+2+/2JjY/n666/ZtWsXcXFxGAwG6tWrR58+fXj22WepVq3adXktRP7MIpOTLesSd7RxQwfMVQo61K/P+rTjHD53DpPJVObEopKiU1MxFpjXnGwScG2B4u8OHeZsYhKNq/vzSIf27AwJLXfbtOxsvty5G4ARvXpyMDzimsYiIuIosuJSLOsS+7W0PcnGYDDg26IeMYknSTkTXe7vCWc3V7pNGkh2fBruVT3t7lfclcHJus/UEPMyBM4erlRtZDuLuUr9AFwqu1OQlUfyqXP4NLpUFSM7Ic2yLnKVetf2HSUi4sgiIyIt6xJ37NzJ5j4Gg4GOHTuwLjaWQ4cOl/u7olKlSnzx1ZdER0fj52t/GbnivpydS+dYJSUlMevTmZb+nn7mGerUrcO7U6Ze8fglpaWm8cX8LwB44cUXOHjgQIXai4iIXC8KFMsfavPmzbz22mvk5uZanouLi+Obb75h06ZNjB9fsXVFKmL27NnMnj0b02XrCKalpZGWlsaxY8f47rvvWL58eZmB2OvNw8PD6t/79u1j7NixXLhwwer58PBwwsPD+fbbb/nwww+5++67S/UVFRXF8OHDiYiIsHp+165d7N69m2HDhl338QPs2LGD9PR0AJuli8vyzDPPsHbtWgB+/PHHUoHi7777jsmTJ1tlp8bExLBkyRJ++eUXunfvbrfvlJQURo4cydGjR62eP3jwIAcPHmT48OF2227fvp0xY8aQn59veS4/P5+IiAgiIiJYvXo1kyZNYtCgQRU63z+j4gsgJ6drLzIRFhYGmNcnr8iki/K6cOECQ4cOtQS1c3NzSU1NtQSJjx8/zsiRI0lKss4QMRqNnDt3jnPnzvHjjz8ybdo0BgwYUKr/AwcOMG7cOJKTk62eL/7vvmbNGptraq9fv54333yTvLw8q+eDg4MJDg5mxYoVfPbZZ3TqZPsiV+TvIqLEe6dxdfvZVA39zTdlMnPzSMjIoIa3d7n6D08y9+9VyZ30nBxWBR3idGws6bm5VHZzp0Wtmtx7a5srlrMOiY/nxyO/4erizKg+fXCu4OffvB07ycjNpVfzZnRq2FCBYhGRcsqMS7E8Livr1quWOcOrIDuP3NRMPHyrlKt/F3dXvOvbD9KeDwqhMM98XVGtmfUk2qyLY/Oq5YfBzveCwWDAq5YvaWFxZMZY/17MjDW3d/F0x5iZS+TmI1wIP48xMxcXDzeqNqxBnR6tKlRKW0TEEZW8p1VWpm7Dhg0ByMzMICEhwTJx/Uo8PDxo1sx+RaGtW7aSk2Oe1GRvkrmzszO9evXi8SeewNfP11JhryLmzZ1LRkY6vXv3plPnTgoUi4jITaNAsfxhwsLCGDduHEajkdq1azNx4kS6dOlCamoqy5cvZ8mSJUybNu2GHPuXX35h1qxZgLlszQsvvECjRo0AcwB24cKFbN++nbi4OGbOnMn06dOv+lgvvvgizz33nN3tu3fv5pVXXsFkMvHEE09Y/egMCQnhxRdfJDc3l7p16zJ27Fi6deuGq6srx48fZ+bMmZw4cYJXX32VxYsX07FjR0vb/Px8S5C4UqVKjBkzhnvvvRd3d3f27NnDxx9/zIIFC676vMpyoMSP2YoGwtq1a4e/vz9JSUns32+9TuT+/ft56623AGjevDkTJkzg1ltvJTU1ldWrV7Nw4UJ++uknu32/8sorHD16FCcnJ0aMGMGjjz6Kt7c3R44c4eOPP+bLL7+02S47O5uJEyeSn59P27ZtGTduHE2bNsVgMHD8+HE+/PBDIiIi+OCDD7jjjjuoW7duhc75zyQqKsoyU/da14jesGEDISHm8oC9evXCzc3tmsd3uZ07d+Lq6srUqVPp168fMTExpKWlAeZM4pdeeomkpCT8/f0ZP348nTt3xtvbm/j4eDZv3syXX35JTk4O7733Hvfddx+enpeyTaKiohgxYgS5ubn4+fkxduxYevXqhbOzM4GBgXz00UckJSXxyiuvsGHDBvwultTds2cPEyZMoKioiBYtWjBmzBjat29PYWEhQUFBfPrpp0RERPDCCy+wZs0ay8WsyN9RYrp5vV5nJwN+Zaxt7u916YZ/YoUCxeZJILlGI5N/sP78v5CTw/6z4ew/G87D7dvxZJfONvvINRqZs207RSYTg7p0oU41nwqtM7zp5Cl+OxeFfxUvhvb4R7nbiYgI5KZmAmBwNpSZ9evu41WiTUa5A8WXKyosxJiVR07iBeIPh5Lw21kAAto3plrT2jbHVqmaV6l+bI0tN9X6uyPz4vrEhXlGjs5db7XNmJlL0olIkk5EUu+OtjS8q8NVnY+IiCMoXq/X2dnZct1ti3+JpWgqEii+XEFBARnpGcTExrBj+3Z27zJXDurZqye32ggUe3t7M2v2bJtlq8tr468bOXLkCNWrV2fosGevuh8REZHrQYFiqRCTyURWVla59nVycrLKlv3Pf/6D0WjEx8eH5cuXU/Nito+vry+TJk0iICCA//73vzdk3MUBwWbNmjF37lyrAFaNGjXo0qULAwYM4OTJk+zateuajuXm5mY3QHb27FkmTZqEyWSiQ4cOvP3221bbp0yZYgkSr1692qpUbe/evenWrRuDBg3i2LFjTJkyxSpIumzZMsusy5kzZ9K7d2/LtoceeoiOHTvyyCOPWDJ/r6ezZ803XFxcXCq8LovBYKBBgwYkJSURExNDfn6+5fV77733APMs0WXLllGlivkGka+vLxMnTqRmzZq8//77NvvdtGmTJYA9adIknnnmGcu2fv360bFjRwYMGEBUVFSptgcOHLAEH2fNmmX5Wy1u27x5c+666y6MRiObNm26IZnaBQUFV3yvGQwGq0Dn1fj0008tj21lqYP9933x81FRUWzcuJFly5YB4OnpyWuvvXZN4yrL888/z5NPPgmAv/+lTJQff/yRxMREwPweKDmRolq1arRo0YIqVarw/vvvk52dzeHDh7n99tst+7z//vvk5ubi5eXF8uXLaVCibO7DDz9MkyZNeOKJJ0hLS2PFihW8/PLLFBYW8vbbb1NUVETbtm1ZunQp7u7/z959hzdVvn0A/ybdexdo2dBSoBQKyN4gU2UIAgoICMpUEJRXRfwxFFQUpQUZypY9Ze/dMkqZZbSle9C9Z5rk/SPkkJBBFzPfz3VxXeGc85w85zTJSc793PdjJrTr27cv2rVrh0GDBiE+Ph6LFi3CihUrntu5IXrZch5XDLEwNdVbpcDS1ER4nPtUJr4+ykBxiVSG2s5OGNS8Oeq7ukAOICQ+ATuCriElJwd7r9+Ajbk5+vo00djH+oAAJGVlo7G7G3p7Ny71cwNAQmYmNl26DJEImNilMyyfw4AYIqI3WUme4jphZGaqM2sXAIzNn1wnSgqKdW73LMnXHyJsd8CTBSKgdq/mqN7RW2NbSb6ib8YW+j/blX17ul/KDGO5VAYrN0fU7NYUNtVdALkcmRGPEH3yOorScxF75hZMrMzg3r5s1yAiIkOR83gQp4WFpf7fFCr3HEt7r1Kb8+fOY6XK73SRSIThH36Id997V+v2ZmZmar/7yyohIQH/btoEkUiECZMmVvi+DhERUUUxUExlkpCQgObNSzf62d3dHadOnQKgmHP34sWLAIAxY8aoBd6Uxo0bh7179+Lhw4eV12EAMpkMXbp0Qb169dC5c2etQVyxWIyWLVsiJCREyK6sbNnZ2Zg4cSKys7NRtWpV+Pn5qfUlLCwMQUFBAIBJkyZpnc/UzMwM06dPx5gxY/DgwQPcvHkTTZs2BQChfHP79u3VgsRK1atXx/jx459LMF4ZVLW2ti71PJOqlME+mUyGrKwsuLi4ICwsTJinesqUKUKQWNWoUaOwdetWIVCtSnk+ateurRYkVrK3t8f06dPx5ZdfaqxTLTedkpKi8XqtUaMGVq1aBTs7OyEzvbLt379fOAZdbGxshNeMNsXFxRo/luRyOXJycnD//n1s2rQJFy4oRso2a9ZMZ6C4LO/7mjVrYvHixRXOTtanT58+WpdXq1YNH330EWQymVqQWJXqfODp6U9KH2ZnZwuDRMaMGaMWJFZq0qQJ+vTpg0ePHsHc3ByAoqy7sgz2jBkztP5YtLe3x8SJEzF79mycOXMGKSkpQqlsojeNRKaYm9HUyEjvdqbGT76CKuccLo0SqRQmxkZoVK0aZvbqCWOV5+no6YGmNWrg+717kZydg21Xg9DBoz5sVW4gBUVF4cz9UFiamWJC585lumZJZTIsP30GxSUl6NPEG43c3J7diIiI1MikMgCA2ET/dUJs8uQ6oZz3tzwKM58KHMiBuPMhkMvkqNHFR+06oHwesbH+2yTK9bKnrl8yqRRiEyPY1amKRiO7QaxyjariWw+Onu648dcBFKbnIup4MFya1oWptfo0REREBJRIJAAAU5XBpdqYqvz+lhSXf1CRcsC5klwux/7/9kMqlWLgoIHlus+li1QqxXL/ZSgqKkLffv3QuDEHDRER0cvHQDG9EEFBQZBKFT+kO3XqpHUbsViM3r17Y9myZZX63GKxGFOmTNG5XiaTITw8HHFxcQCgNhduZZFKpZg2bRqioqJgZmYGPz8/tUxIQL18s6enp87RkF5eXjAyMoJUKsW1a9fQtGlT5OTkCPOh6Dq/gCIb9nkEipXzspZ3RKWRyk0U5RzSly5dEpbpOiaRSITu3btrDRQry1h37NhR5/N269YNYrEYMplMbXmzZs1gYmICiUSCMWPGYNiwYejatSuaNWsm9FXffl8VP/zwA3744Ydnbte4cWMsXbq03HMUOzo6okuXLujcuTO6d+8OExP9P+YqwsTEROdcQt26dUO3bt10tk1NTcX169eF/ys/kwDFvNWSxz9Gu3TponMfv//+u9r/Vcul63vfensrslbkcjmCg4N1BuWJXndiUcXnOtfnx0EDIZfLIZPLtc4rbGthjg9bt8Yfx0+guKQEAeEP0buJ4v2XlZ+P1ecUA0JGtW0LZxv9pUWftvtaMB4mp8DdwR7DdJS1JiIi/SrzZntpVHvLE9XbN4bIWIzcuNTH8wYnIfr4dUjyClHvnSeDCEViMeRSmZ696ec76V3I5XLIZXKIjTSvUSZW5qjTuyXubT4DWbEUKbci4d6uUbmfj4joTaWv4sTz0L1Hd/R7px9MTEzw8OFDbN+2Hffu3sX2bduQnZ2N0ZVYGnrXzl0IDw+Hu3t1DBs+rNL2S0REVBEMFFOZqGYJl0VSUpLwuGbNmjq38/T0LFe/Sis1NRWBgYEIDw9HbGwsoqOjERERgfz8/Of6vAsXLhQyqufNm6c2L7GSagnkwYMHl2q/iYmJABTnVxlg1Xd+69SpIwSZK5Pt47kly1vWOisrC4DixpGdnR2AJ8dmb28vLNNGW+ZqQUGBkOWsLTNUycLCAtWqVRMyQpVcXV0xY8YMLFq0CDk5OVi9ejVWr14NW1tbtG3bVgiI2tvbl+Uwy2TgwIFYtGhRpe9XJBLBysoKTk5OaNSoEXr27ImePXvCWE/mxNPve4lEgujoaKxatQr79u1DRkYGTExM0LVr1+caJAYAOzs7tYEF2kgkEly9ehUhISGIiYlBTEwMIiIihHmOlJTvGUD9M6oscwgrB5gAQNu2bUvVRvnaJnrdyOVyFOkZTGViZASzx58lxc+4zhSr7Mf0GZlbTxOJRDDSE2jwrVkDRmIRpDI5wpKT0fvx8pVnzyG7oBAta9dC5wZl+74RlpSEvddvwEgswqSuXcrcZyIiQyCXyyGT6L5OiIyMIDZ9nI37jCxh1f0YPSP7WB8zOyvhsV2dqvAe2wt31h5DVsQjJATeQ7XWDWDpYg8AEJsaQVogg+wZ1zDZ42uY2FizXyKRCCIj3dcohwbVITISQS6VIyc2Red2RERvKrlcLiQbaGNiYiJU8CoulujdV7HKfkwrUApadR7khg0bYvb3s/Hjgh9xNyQER48cwds934a7u3u5968UFhaGvXv2wMjICJOnTNY5bR0REdGLxrtc9EIo5xcBoDZv8dOUAcfKVlRUhJ9++gk7duzQCJKamZmhdevWkMlkuHr1aqU/9/bt27Fx40YAipK2AwYM0Lpdbm5umfetbKMaoNV3fsViMSwtLdX+HpWhfv36uH37NgoKCpCYmIhq1aqVqX1oaCgARUBSmZWs7KPyB4Iu2kpSq56P8rQHFH+rhg0b4p9//kFgYCAkEgmys7Nx9OhRHD16FCYmJhg5ciRmzpz5zMDly7Jw4UIMGjSo0vdrYmKC+vXr45dffkGVKlWwatUqbNu2DcnJyfD399cbdK6oZ2WtHz58GD/++KNG6SiRSIS6deuiadOm2LNnj0Y75WAFQP976GkVed8SvW5Sc3Px+eatOtdP6NIZVmaKmx2FEgnkcrnOzLE8ldJwNs/4nC4rU2Nj2FpYICMvH9mP50w+cfcersfEwtbCHOM7la0iRKFEgmWnz0Aml2Nwi+aoy9LxRERaFWXm4eqvO3Wu9xzcAcbmiuuEtEj/dUJ1/l9jq8q7ToiNxKj9ti9urjwMyIH0+3FCoNjY3BTSAskz50QuKVQELkysyh6UMDIxhomVOYqzCyB5PF8zEZEhSU1NxdTJuqv+TZw0SZizt7CwQP9vCpWkD133dsrDyMgIQ4cNxQ/fz4FcLsf14OAKB4oLCwuxzM8fMpkMg4cMQd16dSupt0RERBXHQDG9ENbWT8o7FhQUqP1fVXEF5hQpLNT9Q3v69Ok4efIkAEWZ3c6dO8PDwwP169dH3bp1YWxsjCVLllR6oPjq1auYN28eAKBdu3b46quvdG6rGtC8detWmco4q2bcPis7uiLnWJe2bdsKwbcLFy5gyJAhpW4bHh4uBPVU549VHlNBQYHe9tqORzXTtzztldq0aYM2bdogNzcXAQEBCAwMxIULFxATEwOJRII1a9ZALpfj//7v//Q+x5vsyy+/xJ07dxAQEIDTp0/j119/xTfffPNS+nLs2DFMnz4dcrkcjo6OePvtt+Ht7Y26devC09MTtra2iI6O1hooVg0O6/uMepryfevs7CxUDSAyZNXs7AEAJVIZMvPz4WBlpXW7NJUBE86lfL+VheTxoDDzxwNXAsLDAQDZBYX4bMMmvW1XnDmLFWfOAgC2fDYeESkpSMpSDEDaGRSMnUHBOtum5uRi+MrVAID3WzTH4Jba50snIjJUFs6KgcFyqQzFOQUws7XUul1R1pPpPMzttF9LysvK7UnmWGG6yoBmZzsUZeShKFP/oD7lenP78l2/ZCWK8tZGz5h7k4jIUFVzUyQflJSUIDMjEw6ODlq3S0tNFR4/Pb1bRdWpU0d4nJSUrGfL0nn48CEePXoEANi5Ywd27tihc9uUlBQM+2AoAOD9wYMx5IPS32MjIiIqDwaK6YVQLf8bERGhtfQyAMTExGhdrpqxqZxH9GnKUsNPCw4OFoLEI0eOxOzZs7Vul5GRoXV5ecXFxWHq1KmQSCSoUaMGlixZojfz1M3NTa2ttpLKSk+PqKxataow125ERAS6d++utV1ycrLeEj/l1aNHD9jY2CAnJwcbN27E+++/X+r5bjds2CA8fu+994THyvORlZWFtLQ0tVJAqlRLdiuZmZnByckJaWlpWucvVpJKpaUqA2xtbS2UaAYUgfxp06YhPj4emzdvxsyZM59rFu2rTCQSYdGiRejXrx9ycnKwfv16dOrUCe3bt3/hffntt98gl8tRvXp17Ny5Ew4Omj8mdb3PVbPgY2Nj0bBhQ63bBQYG4tq1a6hRowb69+8vvE4zMjKQn58vjHwmehO52Nhgy2fj9W4Tm54uPI5MTdMZKI5MTQMAWJqZwqWUcwVfjojEhoAAZBcW4pu+fdBI5bqpKrugALmFimtdNXvdUxcQEVHlMnewRsefRuvdJu/Rk+9iuQlpOgPFuQmK64SRhQnMHEp3nciOSUHMqRsozMhB41E9YOGkvVqVrORJhSllKWwAsKrqgMywBOQ9ytCZwSaXy5GbqLjWWVVzFJan3onCw4NXIMkthPeYt2FfV3uFpeLcApTkK65RyqA5EZEhcXFxwdbt2/Ruo3pvMDIyUmegODIyEgBgaWkFl1JW/QkLC8OuHTuRlJyMWf83C1WrVtW6nWpSgZkZS0QTEdGbzTAjG/TCNW/eHKampiguLsaJEyd0BorPnTundblqtl+6yk1oVcHB2jN8rl+/LjweOnSo1m1kMhkuX76s9v/SBjq1ycvLw8SJE5GRkQFLS0ssX778mfPZtmzZUnh88uRJnYHi4OBgjB49GtWqVcO0adPQp08fWFlZoWXLlrhy5QpOnjyJ8eO138jXdX4rysrKCmPHjsWff/6JBw8e4K+//sLkyZOf2S4wMBA7Ho+i9PX1RZs2bYR1nTp1Eh6fOHFC599O1zF16tQJe/bswZkzZyCVSrUG6a9cuaI143jlypXYv38/bGxssGXLFo31Pj4+GDVqFBYuXIiioiJkZWXpDGQbgipVqmDWrFmYPXs25HI55syZgwMHDpSphHNFpaenIyoqCgDQs2dPrUFiQPGaU5LJZMJjX19fiEQiyOVynD9/XmegeMuWLTh69Cjq1KmD/v37o2XLlti0aROkUinOnDmDvn37am23f/9+zJkzB25ubpg7d67a+53oTVLdwQHONtZIzclFcHQ0mteqqbGNXC7H9cc3f5pWr66zlNzTnK2tkZ6nqJpxPSZWZ6D4Qli48LhZjRoAgP/r2wcylXnJn5aam4uvtivKpX7SsQM6eNQX1nlVrYq1Y0fr7dvf5y/gYlg4nKytsPjxiH/jCnyPICJ6U1lWsYeZgxWKMvKQfj8WTl41NLaRy+VIfxAHAHDwcC/1dUJsLEZGaDwAIDUkGjU6NdG6nXIbALBWyS529KyO+PMhkOQWIic2BbY1XTXa5sQkoyRPEeh18HxShtTM3hrFWYprVPqDOJ2B4pSbTwaxqrYnIqInatSoARcXF6SkpODatWto3qK5xjZyuRzBwYr7fU2bNS31tcLE2AQ3btwAAFy5fBnv9e+vdbubN28Kj+vUrXiZ6IYNG2LdhvV6t/l79WpcOH8Bzs7OWPz7bwBgsEkJRET0YvEOFr0Q1tbWePfddwEoMkjDw8M1tjlz5gzOnz+vtX2NGjWEwO3hw4c11hcWFmLVqlVa26oGCLU9LwD4+/sLQSZAd9ZyacjlcsycOROhoaEQi8X49ddf4enp+cx2Pj4+QnBq9erVav1RKiwsxKJFi1BUVIT4+Hi1gPv7778PQBEY3717t0bbzMxMLF++vJxH9Wzjxo2Dt7c3AMDPzw8rVqyAXM9N+bNnz2Ly5MmQyWSwtLTEggUL1NZXr15dKEXt5+eH5GTNUj9HjhxBUFCQ1v0rz0diYqLW4y4qKsLixYu1tjU2NkZYWBiuX7+ucwDCvXv3AChe246Ojlq3MSSDBw/GW2+9BUCREe/n5/dCn1/1x9PDhw+1bnP//n21zwnV97mrqys6dOgAAFizZg2SkpI02t++fRunTp0CAPTr1w8A0L17d6HE1eLFi7UOZElPT8fSpUuRn5+P1NRUnUFoojeBSCRCRw8PAMDZ0FBEqZSDUzp+9y4SMxXzgvf10X4TX5t6ri5ChvDRkBAkaKkkEp+RiV2PP7frujgLwWRTY2OYm5jo/Gem8hliYmQkLAcAsVist625iQmMHt+YEolEwjLjV3T+eiKil0kkEsG1mWJAbNK1cCFzWFXi5fsoSFGU/K/evnGp923t5gQLV8V1Iv5CCIpzNKfkKc4tQNSxawAAExsLODV8Eqi2q1sVZg6KShiRh4Mgk0rV2sqkUkQeUbS1rGIPB48ngV6b6s6wcFFkCCdeuo/8lCyN585PyUT0qRuKvlZ30hlMJiIydCKRCB06dgQAnD1zBlGRURrbHDt6DIkJCQCe/D4vjdp1asPdvToA4MD+g8jMyNTYJiszE1s3K5IG7O3t0aJFxaeTEYvFMDc31/vPSKz4/SASiYRlDBQTEdGLwEAxlYlcLkdeXl6Z/inNmDEDzs7OKCgowIgRI7Bjxw4kJycjMTERf//9Nz7//HOdz2traytkm546dQpz585FZGQkUlNTcerUKQwbNgz37t2Dra1m+a727dsLIwvnz5+P//77D48ePUJSUhLOnz+PCRMmYNmyZWptVPtdVkuWLBGCSTNmzECPHj1QXFyM/Px8redHdW7lOXPmwNjYGNnZ2Rg6dCg2bdqEuLg4pKWl4cKFCxg9erQwqvGTTz6Bu/uTmxP9+/cXAnWzZ8/G77//jujoaKSnp+PEiRMYNmwYEhMTSz3KsqxMTU2xYsUKeHp6Qi6XY8mSJRg0aBB27NiByMhIZGZmIi4uDsePH8fkyZPx6aefIi8vD5aWlvD390f9+vU19vnDDz/A1NRUMT/LsGE4dOgQ0tPTERsbi+XLl2PmzJk6y3m/9dZb6P94ZKi/vz/mzJmDsLAwZGRkIDAwECNGjMCdO3e0tn///fdhb28PuVyOSZMmYdOmTYiIiEB6ejru37+P+fPnY+/evQCA4cOHP7dz+joRiUSYO3cuTB4HVtavXy8E018EW1tbYeDE2bNnsWDBAjx8+BAZGRm4f/8+/vjjDwwbNkxtDu+n3+ezZs2Cubk5MjIyMGzYMPz3339ISUlBXFwcduzYgU8//RQSiQRVqlTB6NGjAShe99999x0AID4+HoMHD8bevXuRlJSEpKQkHDt2DCNHjhRKZ82YMQNWOkrxEr0p3m3qA0crS5RIZfjx4CGcvv8Amfn5SMrOxrYrV7H+oiKzv3XdOqjvqpmttezUaXy5bTu+3LZdY92otm0hEgGSEinm/rcfp+8/QEpODtJyc3H87l3M/W8/8ouKYWZijM86d9JoT0REL1/1Tk1gamcJuVSG22uO4VFQKIpz8lGQnoOoY8F4eOAKAMDZuxZsamiWEn2w4xyCluxG0BLNwbH13mkNiABJbiFu/HUQyTceojAzF0XZ+UgKDseN5QdQlJEHiID6/duozRMsEolQt28rAEB2dDLurDmG7OgkSPKLkB2d9Pj/yYAIqN2zhcZvgLr9WgEiQCaR4tbqw3gUFIrCjFwUZeUh8fJ93Fx1GNICCcSmxvAc9OKnaSEiep281/89ODo6oqSkBAvmL8DpU6eRmZGJpKQkbN2yFevXrQMAtG7TBvU9NO8nLfP3x/Rp0zF92nSNdaPHfAyRSITs7CzM/u47XDh/HqmpqchIz8C5s2fx3bffISUlBSKRCJ+MGwdzc/PnfbhEREQvFYclUZkkJCSgeXPNki/6XL16Fba2tnBycsI///yD8ePHIzk5WWOuYBsbG3To0EGYT/hp3377LUaMGIHMzExs3rwZmzdvFtaJRCJ8/fXXOH/+vFppWQDw8PDA+PHjsWrVKqSlpeGrr77S2LeNjQ2GDBmCNWvWAACioqLKnSW6cuVK4fFff/2FJUuWoKSkROf2rVq1wsaNGwEoSnQvXboUM2fORGZmJubPn4/58+drtBkyZIhGYF0kEsHf3x8TJkzA9evXsXLlSrW+AMDMmTOxdOlStblWKpOLiwu2bt2Kn3/+GTt37sTdu3d1zgkNAC1atMC8efO0BokBoF69elixYgWmTp2K+Ph4TJ+u/gXf3t4eI0aMgL+/v9b28+bNQ35+Po4fP45t27Zh2zb1eXCGDx+OS5cuCfPaqO73jz/+wKRJk5CRkaH1bwAAXbt21TvAwdDUq1cP48aNw19//YWSkhJ8//332L59e4XKuJfFnDlzMGrUKOTn52Pjxo3C+0rV4MGDERgYiPj4eERHR6ut8/DwwPLly/H5558jISFB62dFlSpVsHr1atjY2AjL+vbti+zsbCxYsADx8fGYNWuWRjuRSITJkyfjgw8+qIQjJXq1WZia4qvevfDTwcPIKSzEqrOaUwQ0qFoFk7p20do+LTdXyDh+WrOaNTC+UyesuXAB2QXa921rYY4vevRATQOeEoCI6FVmbGaCxiO74/baYyjJK0LY7gCNbWxru8JzSEet7Ysy84SM46c51HeD5+COCN8bgKLMPDzYrlmxSmxqBI+B7eHcqJbGOufGtVCrRzNEn7iBrMgk3Fz5VDUrkSIgrJqJrOToWR0eA9shfN8lSHILtR6XibU5vIZ3gVVVViQiItLHwsICX8+ahR8X/IicnGysXLFCY5sGXl6YPEX7tGepqWlCxvHTmvj4YNLkSVi9ajVSU1Ph76d5T8nMzAyffvYp3mr1VsUOhIiI6DXAQDG9UF5eXjh48CD++ecfHD16FAkJCbCxsUGnTp0wZcoU7N69W2eg2MPDA/v378eqVatw9uxZPHr0CNbW1vD19cXYsWPRsmVLnaWrZ8yYgcaNG2PLli24e/eukMlas2ZNdOzYER9++CFsbW2xbds25OXl4fjx42UOiGuTm5tb5jbdu3fHsWPHsHHjRpw7dw6xsbEoKiqCg4MDfH19MXToULRvr30Eur29PTZs2IB9+/Zh165diIiIQElJCRo2bIgxY8agR48eWLp0aUUPSy8rKyvMmzcPn3zyCfbs2SMEYnNycmBmZoYaNWqgefPmeOedd0o1T2v79u1x8OBBrF27FufOnUNCQgJsbW3RsWNHTJ06VW0O6qeZm5tj6dKlOHr0KLZu3YrQ0FAUFBSgXr16GD58OAYPHozevXtrbdu2bVscPHgQ69evR0BAAOLi4iCRSODg4ABvb28MGDAAvXr1Kvd5elNNnDgRhw4dQnR0NG7fvo2NGzfi448/fiHP3aRJE+zZswcrV65EYGAgUlJSYGxsDBcXF/j4+GDo0KFo3bo1vvvuO+zcuROnT5+GRCIRsqABxevt6NGjWLt2Lc6ePYv4+HhIpVLUrFkTPXr0wOjRo7XONz5s2DC0b98e69evR2BgIBISEiCRSODq6oqWLVtixIgROudmJ3oT1XZ2xuIPhmD/zZu4Fh2N1NxciCBCdQd7tK9fHz0bNyp3aeauXg3QoGoVHL59B7fj45GelwcjkRhVbG3QolYt9G7iDRuO+icieqVZuzmh5bSBiD13G+n3Y1GUqcjytXS1h2vTuqjWxgvicl4nqvjWg21NF8RfvIuM8HgUZeZBJBbD3MEaDp7ucG/XCGZ2uiu81OzWDHZ1qyE+4C6yo5JQUlAEYwsz2NRwgXuHxrCvU1Vn26otPWFbqwriA+4iMzwBxdn5EIlFMHe0gWPDmnBv1wgmlmblOi4iIkNTu05t/LbkN+z/7z8EBV1DWmoqRCIRqlevjvYd2qNnr17lLs3csVMneHh64tDBQ7h16xbSUlNhZGQEFxdXNG3mgz59+8KJA0+JiMhAiOT6JhElesH8/PyE7NAHDx685N4QEb16Mvz+fNldICKiV9xE76KX3QUiInqFrWj52cvuAhERveLsbexedhdeC8NO//Kyu/DG2Nr165fdBYPFOYqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGcxQTGTC5XI78/PwK7cPKSvf8XoaC51E3qVSKwsLCcrcXi8WwsLCoxB4RERERERERERERERHAQDGRQYuPj0f37t0rtA/OJc3zqE9QUBBGjRpV7vbu7u44depUJfaIiIiIiIiIiIiIiIgAlp4mIiIiIiIiIiIiIiIiIjI4IrlcLn/ZnSAiIqLSyfD782V3gYiIXnETvYtedheIiOgVtqLlZy+7C0RE9Iqzt7F72V14LQw7/cvL7sIbY2vXr192FwwWM4qJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRjjl90BIiIiKj3R6NEvuwtERPSqC1r5sntARESvsAm8ThAR0TNs7fr1y+4CEb0gzCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGCMX3YHXrSRI0fiypUrZW43cOBALFq06Dn06MW5fPkyRo0aBQDYsGEDWrduLazr1q0b4uPjX5vjjIuLQ/fu3QFoHktle/jwIQ4cOIDAwEDExcUhMzMTpqamcHd3R/PmzfHuu++iZcuWFX4ePz8/+Pv7w93dHadOnSpz+wYNGgAAFi5ciEGDBlW4PxWl7E9ZqPZd9fWqjVgshqmpKezs7FC3bl107twZH3zwAaysrDS2Lc373sjICBYWFqhatSqaNGmCDz/8ED4+PmU+hteZ8m82ZcoUTJ069bk8R1xcHP766y+cP38e6enpsLe3R6dOnTBlyhS4ubk9l+ckopcjKysL+//7D9euBSM1JQWmpmZwc3dDx44d0ePtHhCLK3e84tYtW7F3zx64uLjAb5m/xvq5/5uLe3fvlmmffsv84eLiIvw/KjIK/zdr1jPb1a1bFz8tWlim5yIiMjTFuQWIO38H6fdjUZiRCyMTY1i42MG1WT1Ua+UJUQWuEwWp2Yi7cAeZ4Qkoys6HkakJLF3s4NykNqq+5QkjE/23QopzCxB/8a7QN8jlMHe0gVPjWqjevjGMLUzVtk8KDkfozgtl6mPN7k1Rq7tvmY+NiMiQ8FrBawURET1/BhcoJiqt3Nxc/Pjjj9i3bx+kUqnaOolEgtDQUISGhmLr1q1o1aoV5s+fj9q1a7+czhogmUyGwsJCFBYWIikpCYGBgVi7di3++ecfeHh4lHl/UqkUubm5CA8PR3h4OPbu3YvPP/8ckyZNeg69N0y3bt3C2LFjkZOTIyxLSUnBrl27cOrUKWzevBl169Z9iT0kosqSlJSE/835ARkZGcIyiUSCsNBQhIWG4sKFC/jm229gYWFRKc/34MED/LdvX6XsS8nIyAimJiZqyyIiIir1OYiIDFVBeg5urTqE4uwCYVlJSTFyYlKQE5OClJsRaDz6bRibmejZi3YptyIRuusCZJInv+FKSoqQHZ2M7OhkJF6+j8Yfvw0LRxut7TPCE3Bvy2lICyRqy/OTMpGflInk6+Fo8klvne1Ly8jM9NkbEREZMF4reK0gIqIXw2ADxW5ubjhw4ECptzcxKfuXjteJu7s7jIyM4OTk9LK78kp49OgRxo8fj9DQUACAl5cXPvzwQ7Rs2RKOjo5IT09HWFgYtm7disDAQFy5cgWDBg3C8uXL0aZNm5fc+1fLu+++i7lz55ZqWzMzM63L586di3fffVdjuUQiQXJyMjZt2oRt27YhKSkJkyZNwoEDB7TuS9/7XiKRICkpCadPn8bKlSuRn5+PP//8E40aNUKXLl1K1X/STSKRYPr06cjJyYGbmxsWLFgAT09PnD17Fv/73/+QkZGB//3vf9iwYcPL7ioRVVBhYSEW/vgTMjIyYG9vj5Efj4K3tzcKCgpw+uQp7N+/H6EPHmDFXysw/cvpFX6+goICLPPzh0wm07vdN99+88xtzpw+g3Vr1wIAxowdCzt7e7X1kZGRAADPBg3w7Xff6txPZWdLExG9SaTFEtxZewzF2QUwsbFA3b5vwb5eNUiLJHgUFIa483eQHZ2MsF0X0PDDrmXad25CGh7sOA+5VAZzJxvU7tkctjVdIZNIkXY/FjEnr6MgJRshG06g+dT+EBupf17nPUrH3Y0nIZNIYWpnido9W8C+blWUFBYj5VYk4s7dRlFGHu5tPg3fSe8ImWwuTevC2buW3r4VZxfgxsqDKMkrgl2dKnBr61W2E0dEZEB4reC1goiIXhyDDRSLRCKtJWoN1caNG192F14ZxcXFmDRpEkJDQ2FkZIQvv/wSn3zyCUQikbCNg4MD6tWrh969e+PYsWP46quvkJeXh0mTJmHHjh2oV6/eSzyCV4uxsXGF32umpqY692Fvb4958+YBALZt24aYmBjs2bMHw4YN09j2We97e3t7NGjQAL6+vvj4448hl8uxatUqBoorQVBQEOLi4gAA3333Hdq3bw8AGDx4MO7du4dNmzbhypUryM3NhbW19cvsKhFV0Injx/Ho0SMYGRnh2+++Rc1aipshdnZ2GP7Rh3B0csLaNWtw+dIlPHjwoFxTFahav3YdkpOTn7mdqan+0fgxMTHY/O+/AICOnRTlsZ8W+Tij2MOjPszNzcvRWyIiSrz8AIVpORAZidBkzNuwquqoWGFtgTq9WsDMzhIP/7uM1DvRyI5Ogm2tKqXed9TxYMilMhhbmcFnXG+Y2T357l+9Q2OY21vh3uYzKEjOQuqdKLg2Va9mE7Y3UHHj39YCPuP7CJlgZnZWsHrbAaY2Fnj432XkJaQjPTQeTl41AEARRDDSPUhILpcjdPdFlOQVwcTaHF7DOkNsZFTq4yIiMjS8VvBaQURELw7THYiesnz5coSEhAAAvv76a4wbN04tSPy0nj17wt9fMRdiXl4e5syZ80L6SeomTpwoPC7PHM+qWrdujebNmwMAbty4gZKSkgrtj6BWftbd3V1tnbLctFwuf2a2HxG92uRyOQ4eOAgAaN++vRAkVtWzV09Uezwn+akTJyv0fFcuX8GZM2dga2sHX9/yz91VUlKCZX7+KC4uhpOTE8Z+8onGNjKZDDExMQCAuhwQRkRULnK5HPEXFb+1XHzqPrnxr6Jaay9YuNgCABKvhpZ639JiCTLDEwEAVVt4qN34V3JqXAtiU8V4+Zy4VLV1OXGpyIlJAQDU6fOW1nKhVVt6wsjcBCJjMfIS0krdt/jzIciOSgIAeAxsB1Mby1K3JSIyNLxW8FpBREQvlsFmFFeGqKgorFu3DoGBgUhMTISjoyO6deuGKVOmICwsDKNGjQKgmDdP6fLly8LyDRs2oHXr1lr3rcyumTJlCqZOnaqxPiwsDNu2bcPVq1eRmJiIvLw8WFtbo2bNmujUqRNGjhwJ+6fKJerTrVs3xMfHY+DAgVi0aBEAwM/PTwiAloa248nIyMD69etx+vRpxMbGQiqVolq1aujYsSPGjh2LatWq6dxfbm4utm3bhgMHDiA6OhrGxsbw8fHB+PHjNQJNlSU3Nxf/Ps4matSoEUaPHl2qdh07dsS7776L/fv3IygoCFeuXEGrVq00tgsLC8OaNWsQFBSEpKQkODs7o1u3bqWaB7e4uBh79+7F7t27ERERAalUioYNG+Ljjz/G22+/rbftw4cPsWHDBly6dAkJCQkwNjaGq6sr3nrrLXz00Udo2LBhqY7zVVatWjXY29sjMzMT8fHxFd5flSqK0ahSqRTp6elwdXWt8D6BJ++rVq1aYePGjThx4gQ2bNiA+/fvQyKRoE6dOhg6dCiGDh0KAMjMzMTKlStx4sQJPHr0CDY2Nmjbti0+//xz1NISgAGA9PR0bN26FRcvXkRkZCSys7NhZmYGV1dXtGrVCqNGjSpX1nt+fj7+/fdfHD9+HJGRkSgsLISrqyvatm2LMWPG6N1n/fr1hcdHjx5Ve83duXMHgCJgbGtrq3Mfys+pBQsWoFq1avjll18QEREBOzs7tGnTBr/99puwbXx8vFAaPi4uDjk5ObC0tISbmxvatWuHjz/+GFWrVtX5XOHh4di6dSsCAgKQkJAAkUiEOnXqoHfv3hg5cqTOeVWvX7+Of//9F0FBQUhLS4OFhQU8PT3Rr18/DB48+I2fxoAoOipaGBjS4q2WWrcRiURo0aI5DiQk4Nq1YMjlcr0DsnTJzMjE6lWrAQCffvYprl65Uu5+Hzp4ENHR0QCAT8aN0/oej4uNRXFxMQD1zzQiIiq9vMR0Ya5Jp4Y1tG4jEong6FUD8SkhSL8fV+rrhJGpCdrMHo78pEyY2em+ua7clUisvs+U24rpBcwcrDSyx5TExkZo/c1QGJmU/lZKYUYuok9dBwA4e9eCU8OapW5LRGSIeK3gtYKIiF4sBorL6cSJE5gxYwYKCwuFZYmJiUIQZfr0is+5p4u/vz/8/f0hl8vVlmdmZiIzMxO3bt3Crl27sGXLFr2B2Mr29E3VS5cu4fPPP0dWVpba8sjISERGRmL79u345Zdf0KtXL419xcbGYty4cYiKilJbfv78eVy4cAFjxoyp9P4DwNmzZ5GdnQ0AWksX6/PRRx9h//79AIB9+/ZpBIp37dqFOXPmqGWnxsfHY+PGjThy5Ajatm2rc9/p6emYMGECbt68qbb86tWruHr1KsaNG6ez7ZkzZzB16lTh5jagCDpHRUUhKioKO3fuxOzZszFixIgyHe+rSPmjoDLmhgwPDwegmJ+8LIMuyuKnn37C+vXr1ZaFhIRgzpw5iI2NxQcffIBRo0YhMTFRWJ+WloYDBw7gwoUL2L17t8agibNnz2LatGnIz89XWy6RSJCbm4uIiAjs3r0by5YtQ6dOnUrd1wcPHmDChAlISEhQWx4XF4cdO3Zg9+7d+OabbzBy5Eit7T09PdGmTRtcunQJf//9N7p3744mTZrg6NGj2LdvHwBgxowZperLjRs3MHfuXEgkEgBAamqqWknxHTt2qK1Xys7ORnZ2Nu7fv4+dO3di/fr1aNSokcb+N2zYgJ9//lkjkzwkJAQhISHYs2cP1q1bJwwmABSZhr/88gvWPp7bVKm4uFh4n27fvh0rVqxQa0f0plG9biurBWhTu3ZtAEBubg6Sk5PL9b5YuWIFcnKy0blzZ7R8q2W5A8VZmZnYvWs3AMDX1xfNWzTXul1kZBQAwNraBtnZ2dixfTvuhtxFdnY2rKys4NWwIfr261vhUtpERG+y3MR04bG1u7PO7ayrKbLHSvKLUJiRqzVjSxtjMxPY1nTRuf5RUCikRYrveA4e6t+jcx9njdnV0RxMKJPKhDkqy3LjHwAijwRBViyF2MQIdfq+Vaa2RESGiNcKXiuIiOjFYqC4HMLDwzFt2jRIJBK4ublh1qxZaNWqFTIyMrBlyxZs3LgRCxYseC7PfeTIEfj5+QFQlHT89NNPUadOHQCKAOzatWtx5swZJCYmYunSpVi4cGG5n+uzzz7D2LFjda6/cOECvvjiC8jlcnzwwQfw8fER1oWGhuKzzz5DYWEhqlevjs8//xxt2rSBiYkJbt++jaVLl+LOnTv48ssvsWHDBrRo0UJoW1xcLASJzc3NMXXqVPTp0wdmZma4ePEiFi9ejDVr1pT7uPS5onKTuWVL7ZlQujRr1gzOzs5ITU3F5cuX1dZdvnwZ3377LQBFwGzmzJlo0qQJMjIysHPnTqxduxb//fefzn1/8cUXuHnzJsRiMcaPH49BgwbB1tYW169fx+LFi/H3339rbZefn49Zs2ahuLgYPj4+mDZtGurXrw+RSITbt2/jl19+QVRUFBYtWoQuXbqgevXqZTrmV0lsbKyQxVbROaIPHTqE0FBF6aJOnTo9c17L8rh58yauXLmC1q1b4/PPP0edOnVw7949zJ49G4mJiVi7di2OHDmCvLw8zJ07F127doVcLsfOnTuxbNkyZGZmYsWKFZg/f76wz4SEBHzxxRcoKChA7dq18cUXX6BJkyawsrJCQkIC9u7diy1btqC4uBjz5s3DiRMnStXX5ORkjBkzBmlpaXB0dMTUqVPRuXNnWFpaIjQ0FCtXrsTFixexYMECODo6ol+/flr3s2DBArz//vvIysrCpEmT0LVrV2zfvh0ikQhz5sxBjx6a84Fqs3PnTlStWhU//fQTvLy8cOPGDdSsqRhte+vWLXz//feQy+Xw9vbG1KlT4enpCVNTU8TGxmLr1q3Yu3cvsrOzsWjRImzYsEFt3wcOHMCPP/4IAGjYsCE+//xzNG3aFLm5uThw4ACWL1+OiIgITJ8+HZs3bxbaLV26VAgS9+zZE6NHj0a9evWQk5ODEydOwN/fH3fv3sVnn32Gbdu2wczMrFTHSvS6Uc4VbGRkBCcnJ53bObs8uTFTnkDxsaPHcP36dbi4uODjMaPL1VelPXv2orCwECKRCB+O+EjndpGRiuyBwsICfP/dbLV1WVlZuHzpEi5fuoQBAwdi2PCyDTYjIjIUhRm5AACRkUhvJpeZvbVKm5xS3/x/mkwqhSSvCAUpWUgKDkPyDcVc866+deFQ301t27ykTACAhbOiwk1ScDgeXQ1FbkIaZCVSmNlbwcWnLmp0agJji9L9PsiJT0Xq7SgAQLU2XjBXOS4iItKO1wpeK4iI6MUy2ECxXC5HXl5eqbYVi8Vq2bI///wzJBIJ7O3tsWXLFqF8qaOjI2bPng1XV1e1EqiVSRkQ9PDwwIoVK9QCWFWqVEGrVq0wePBghISE4Pz58xV6LlNTU50BsoiICMyePRtyuRzNmzfH999/r7Z+7ty5QpB4586dcHBwENZ17twZbdq0wYgRI3Dr1i3MnTtXLUi6efNmISNp6dKl6Ny5s7Cuf//+aNGiBQYOHChk/lamiAjFl0FjY2O9mVDaiEQi1KpVC6mpqYiPj0dxcbFw/pSBp9q1a2Pz5s2wsVF8eXV0dMSsWbOEoJc2x48fFwLYs2fPxkcfPbmJ3b17d7Ro0QKDBw9GbGysRtsrV64gMzMTgKLksWqp3e7du8PT0xM9e/aERCLB8ePHn0umdklJyTPfayKRCJaWFZt75c8//xQea8tSB3S/75XLY2NjcezYMSEAaGlpWeos17IqKiqCj48P/vnnH6EccYcOHTBr1ixMmzYNJSUliIuLw5YtW9Tm3ZwyZQrCw8Nx+PBhBAQEqO1z06ZNKCgogImJCf7++2/UqPGkRJOjoyO8vb0hFouxfv16xMbGIjIyUhhoos/ixYuRlpYGOzs7bNu2TQjKAor5nN966y1MmTIFJ0+exI8//ogePXpoDYTWqFEDkydPxk8//YTk5GRs27YNHh4eWLBgAZo1a1am87dw4UK0a9cOgOK1rPTPP/9ALpfD0dERa9asgZ2dnbDO2dkZvr6+yM3NxYkTJ3D16lUUFhbC3NwcgOJvonwfent7Y9OmTcJnv5OTEyZPngwrKyssXLgQ165dQ1BQEFq2bImoqCisXLkSADBy5EjMnv0kgGRvb48xY8agRYsWGDZsGO7du4fNmzc/t6oIRC9bTk4OAMDCwlJvdQdLle9Vpf0+ppSQkIB/N22CSCTChEkTK3T9yMnJwenH89q3aNlS7XPzacrvCCUlJahTpw4Gvf8+6tevD7lcjpCQO9ixfQeSk5Oxd88e2NraoK+OQTNERIasJE9RkcvIzBQiPdcJY/Mn03WUFBTr3O5Zkq8/RNhule/MIqB2r+ao3tFbbTuZVIqS/CJF30xNELLhBNLvx6ltU5SRh7izt5FyKwLeo3vC0sUOzxJ3XjHFitjESOM5iYhIO14riIiIXiyDDRQnJCSgeXPtpQWf5u7ujlOPbyJmZGTg4sWLAIAxY8ZoneNy3Lhx2Lt3Lx4+fFh5HYaitGmXLl1Qr149dO7cWWsQVywWo2XLlggJCRGyKytbdnY2Jk6ciOzsbFStWhV+fn5qfQkLC0NQUBAAYNKkSWpBYiUzMzNMnz4dY8aMwYMHD3Dz5k00bdoUAITyze3bt1cLEitVr14d48ePfy7BeGVQ1draulzzJTo7K0riyGQyZGVlwcXFBWFhYcI81VOmTBGCxKpGjRqFrVu3CjehVSnPR+3atdWCxEr29vaYPn06vvzyS411quWmU1JSNF6vNWrUwKpVq2BnZ1eqgGF57N+/XzgGXWxsbITXjDbFxcUagQS5XI6cnBzcv38fmzZtwoULFwAoMrt1BYrL8r6vWbMmFi9eXOHsZH3GjBmjMWetaiZ7y5Yt1YLESs2aNcPhw4eRlJSkttzT0xNDhw6Fk5OTzmBHq1athHLX6enpz/y7Z2Vl4dChQwCAESNGqAWJlcRiMWbNmoWTJ08iLS0NJ0+eRN++fdW2yczMxM8//4w9e/aoLTc1NYWXl5fePjzN3t5eZ6n25s2bw9raGg0bNlQLEqtq1aoVTpw4IbxPlYHiwMBApKWlAQBmzZqldY7S4cOHY9euXXB1dRVek9u2bYNMJoOFhYXOaQd8fHzQt29f7N+/H9u3b2egmN5YJY9Lvpua6p+P21RlMImkuPQ3daRSKZb7L0NRURH69uuHxo0bl6+jj504fgJFRYqbPQMHDtT/3CUlMDU1RaNGjTDz669gbPzka3THTp3QtFkzfP/dbCQlJWHb1m1o36GDzs8hIiJDJZPKAChuhusjVinZKZNIy/18hZlPDUaSA3HnQyCXyVGji4/wm09ZYhQA4s7dRnF2ARwb1kCt7s1g6WoPSV4hkoLDEXPqBooy8nB300n4Tn4PRqa6b6kUZuQi9U40AKBK8/owtdb8bklERJp4rSAiInqxDDZQXF5BQUGQShVfPnTN7ykWi9G7d28sW7asUp9bLBZjypQpOtfLZDKEh4cjLk4xmu3p+TUrg1QqxbRp0xAVFQUzMzP4+fkJwVEl1fLNnp6eOjOFvLy8YGRkBKlUimvXrqFp06bIyclBSEgIAN3nF1BkED6PQLHyZnF5y8IaGT35EqucQ/rSpUvCMl3HJBKJ0L17d62BYmUZ644dO+p83m7dukEsFkMmk6ktb9asGUxMTCCRSDBmzBgMGzYMXbt2RbNmzYS+6tvvq+KHH37ADz/88MztGjdujKVLl5Z7jmJHR0d06dIFnTt3Rvfu3TWCuJVNOThClWqpVm3z5wKKgQwANObgHTBgAAYMGKDz+RITE3H37l3h/8rPMn2uX78uPI+Xl5fO97OzszNcXFyQkpKCa9euqQWKo6OjMXbsWMTFxUEsFmPkyJHIyMjA/v37ERISgu+++054P+fm5uLBgwdo3LixEMB9mpeXl86BHB9//LHe44mKilIbxKP6ORkYGAhAcX7fekv7nEBmZmYaAx+Un3nKKgS6zlHTpk2xf/9+REREICMjQ+sgGqLXnb4R/5Vh185dCA8Ph7t79QqXdy4pKcHRI0cAAN7eTVCvvv6BQT8u/AlyuRwymUzteq9ka2uLD0d8hCW//Y6ioiIEBASgT58+FeojEdGbpjyDcSui2lueqN6+MUTGYuTGpSL6xHVkRSYh+vh1SPIKUe+d1gAAmeTJd8Li7AK4+tZFgyFPfruZ2VmhZtemMLO3RuiO8yhIycajqw/g3l73gKWES/cAmRwQi1C9U5Pnd5BERG8YXiuIiIheLIMNFKtmCZeFagaftsw6JU9Pz3L1q7RSU1MRGBiI8PBwxMbGIjo6GhEREcjPz3+uz7tw4UIho3revHlq8xIrqZZAHjx4cKn2m5iYCEBxfpUBVn3nt06dOkKQuTLZ2irmGClvWeusrCwAii+1yiwi5bHZ29vrzSzSlrlaUFAgZDnXqlVLZ1sLCwtUq1YN8fHxastdXV0xY8YMLFq0CDk5OVi9ejVWr14NW1tbtG3bVgiI2tvbl+Uwy2TgwIFYtGhRpe9XJBLBysoKTk5OaNSoEXr27ImePXuqZXg97en3vUQiQXR0NFatWoV9+/YhIyMDJiYm6Nq163MPEgPQGihUDXJryz5/ehttCgsLERgYiPv37yMmJgaxsbEIDw/XqDKgfK/po/p+njp16jO3B5685gFF0HTcuHGIi4uDpaUlli5dio4dO6K4uBhxcXG4fv06Dhw4gIYNG2LcuHE4deoUvvpKkam3Zs0atG7dWmP/jo6Oz+xDTk4OAgICEBoaKpyDhw8fary3Vc+B8vO9Vq1aZfphqhycExISUuqM9UePHjFQTK8duVwuDKjSxsTERBjgUVws0bkdABSr7Me0lIOzwsLCsHfPHhgZGWHylMkVnj8+5M4d4RrbpatmBRNtRCKR1iCxkq+vr/D9JDwsHGCcmIgMiFwuV7uJ/jSRkRHEj7OqnpX5pbofo2dklOljZmclPLarUxXeY3vhztpjyIp4hITAe6jWugEsXezVstJERiLU6aN90GAV33qIO38H+Y8ykHonWufNf7lcjpSbikHA9nWrwtyB800SEQG8VqjitYKIiF4VBhsoLi/l3HsAtJYlVVIGHCubcg7NHTt2aARJzczM0Lp1a8hkMly9erXSn3v79u3YuHEjAEXJXF2Zi7m5uWXet7KNahBH3/kVi8WwtLRU+3tUhvr16+P27dsoKChAYmIiqlWrVqb2oaGhABQBSWVWsrKPurIjlbQFBVXPR3naA4q/VcOGDfHPP/8gMDAQEokE2dnZOHr0KI4ePQoTExOMHDkSM2fO1Hvz+2VauHAhBg0aVOn7NTExQf369fHLL7+gSpUqWLVqFbZt24bk5GT4+/vrDTpXhorOy6zNpk2b8Mcff2i8N8RiMRo2bIjatWvj8OHDpd5fRd7Pyv7ExMQAUAwuUWawm5qawt/fH0OGDEFCQgJ+//13NGjQAAcPHgSg+DzTNW+xvox/mUwGPz8//PPPPxoBLRMTE/j6+sLW1hZnz57VaKsc6PGs99rTKnqOiF4XqampmDpZd2WTiZMmCZ9rhYUFkMvlOgdd5KkMbNN1/VJVWFiIZX7+kMlkGDxkCOrWq1vG3mu6ckXxXcnU1BQtdVQRKCtTU1PY2toiIyMD2Y8/U4iIDEVRZh6u/rpT53rPwR1gbK4Y5CMtkui9TqjONWlsVbbvZvqIjcSo/bYvbq48DMiB9PtxsHSxh5HZk+/9lq72ekt/2tWpgvxHGchPztS5TU5sCoqzCwAALj7PZ4ofIqLXEa8VT/BaQURErwoGistIWfIVUGR7qv5fVXEZ5tt7WmFhoc5106dPx8mTJwEoyux27twZHh4eqF+/PurWrQtjY2MsWbKk0gPFV69exbx58wAA7dq1w1dffaVzW9Ugy61bt8pUxlk14/ZZ2dEVOce6tG3bVphD9cKFCxgyZEip24aHhyMlJQUA1LIglcdUUFCgt72241HN9C1Pe6U2bdqgTZs2yM3NRUBAAAIDA3HhwgXExMRAIpFgzZo1kMvl+L//+z+9z/Em+/LLL3Hnzh0EBATg9OnT+PXXX/HNN9+87G6Vybp167Bw4UIAgJubG3r06IGGDRuiXr168PDwgKWlJQICAsoUKFYdsHHo0KEyz9l8+vRpAECVKlXwzjvvqK1zdnbGX3/9heHDhyM/Px/Tp08XAqi9evUqVwn4hQsXYsOGDQAUpaC7deuGBg0aCOfA1NQUO3bs0BooVh6rvs9gbczNzZGbm4u+fftiyZIlZe4z0ZukmptigFVJSQkyMzLh4Kg9cz4tNVV4/PQUFto8fPgQjx49AgDs3LEDO3fs0LltSkoKhn0wFADw/uDBGPKB5rVcLpcj6PF3peYtWpR5gIg+kseZDWaVuE8iojeFhbNiQLVcKkNxTgHMbLUPnCzKejKVh7lKpldlsHJ7MtVLYbpicKXYyAimthYozi6A2Fj/4FljM0UAQ1aiO9MtNUQx36TISAznxrUr2GMiIsPCawUREdGLxUBxGamW/42IiNBaehmAkEH3NNWMzafnF1VSlkF8WnBwsBAkHjlyJGbPnq11u6fLy1ZUXFwcpk6dColEgho1amDJkiV6M0/d3NzU2uoLLD09MrBq1arCXLsRERHo3r271nbJycl6y1+WV48ePWBjY4OcnBxs3LgR77//fqnnu1UGpwDgvffeEx4rz0dWVhbS0tLU5qBVpVriV8nMzAxOTk5IS0vTOn+xklQqVSv3q4u1tbVQohlQBPKnTZuG+Ph4bN68GTNnznzuWbSvKpFIhEWLFqFfv37IycnB+vXr0alTJ7Rv3/5ld61UCgsL4e/vDwDw8fHBxo0btQY+yvr5oJpVHx8fX6b3s+rzOTo6ah0F7OXlhV9//RVTpkwRsqBNTEwwadKkMvUTUJS83rRpEwDg7bffxp9//qn1s0rXOVAeq7b3oqpt27YhOzsb3t7eaNu2Ldzc3BAaGqpR+v1p+kZCE70OXFxcsHX7Nr3bqH7/iYyM1BkojoyMBABYWlrBxcWl8jpZSpERkUIVgbdaPTub+PLly1i/dh2ys7PxzXffonFj7eXjsrKykJur+CxzcytbVRIioteduYM1Ov40Wu82eY+efA/LTUjTefM/NyENAGBkYQKzUpbizI5JQcypGyjMyEHjUT1g4aS9ypfqTXtleVMAsKrmiOLseBRm5Or93lacqxjAa6qj7wCQEar4XmhXtyqMLSo2VQIR0ZuE14oneK0gIqJXRekiYCRo3ry5MCfeiRMndG537tw5rctVs/PS09O1bhMcHKx1+fXr14XHQ4cO1bqNTCbD5cuX1f5fEXl5eZg4cSIyMjJgaWmJ5cuXP3M+25YtWwqPlYFtbYKDg9G0aVP06tVLyHC0srIS2utrq+v8VpSVlRXGjh0LAHjw4AH++uuvUrULDAzEjsfZTb6+vmjTpo2wrlOnTsLj8rxmlO3PnDmjc07mK1euaM04XrlyJd555x0MHz5cazsfHx+MGjUKgKKseZaBl8msUqUKZs2aBUAR1JszZ84zM7lfFWFhYUKgdcCAATqz4wIDA4XHpfl8aNGihTBYQt97Mj4+Hr6+vujRo4faoAnlQInw8HCdn3k9evRQG1zh7u5ersDRzZs3hWMaPHiwzgEtqudAdY5i5fzC2dnZuHHjhta2crkcf/75JxYvXix8bik/s0JCQoSMR23mzJmD1q1b4/3332fpaXpj1ahRQ3j/Xrt2Tes2crkcwcGK7zRNmzUt1QCKhg0bYt2G9Xr/dejYAYAiQ1m5bOCggVr3d//BfeFx/fr1n/n8zk7OSE9PR0lJCW6ofB972sULF4THTXWUzyciMmSWVexh5qDI+kq/r31wnlwuR/qDOACAg4d7qQfaiY3FyAiNR0FKtpClpY3yxjwAWKtkjDk2qAEAkOQWIisqSWffMh8mAABsa2r/viopKBJKjdrUePGDoYiIXne8VhAREb1YDBSXkbW1Nd59910AigzS8PBwjW3OnDmD8+fPa21fo0YNIeiirfxrYWEhVq1apbWtatBD2/MCgL+/P6KiooT/68paLg25XI6ZM2ciNDQUYrEYv/76Kzw9PZ/ZzsfHBw0bNgQArF69Wq0/SoWFhVi0aBGKiooQHx+vlpn9/vvvA1AExnfv3q3RNjMzE8uXLy/nUT3buHHj4O3tDQDw8/PDihUr1IJJTzt79iwmT54MmUwGS0tLLFiwQG199erVhVLUfn5+SE5O1tjHkSNHEBQUpHX/yvORmJio9biLioqwePFirW2NjY0RFhaG69ev6xyAcO/ePQCK17ajo6OOozQcgwcPxluP56qMi4uDn5/fS+5R6ahmguv6fLh48aLae6o0nw/Ozs7o2rUrAGDXrl1aAz8ymQwLFy5EQUEBYmNjhfcPAPTu3Vt4rt9++03rc5w4cQKHDh0S/h8VFYVp06ahpKTkmf1TVZpzsGvXLgQEBAj/Vy3Z3r17d2EgzOLFi7Wen40bNyItTTFquV+/fgCADz74AICi1O7cuXO1Dui4efMm9uzZg8zMTNjb2+uctoDodScSidDh8VzkZ8+cQVRklMY2x44eQ2KC4saJ8n30LGKxGObm5nr/GYmNhD4ol+mqkhH5uEqHtbUNqlSp8sznr1e/Hqo9Hvhy9MhRJDzuv6r4+Hjs3LFLsX29ejqzjomIDJlIJIJrM0WFmqRr4UI2mKrEy/dRkJINAKjevvSfpdZuTrBwVUz7E38hBMU5mlMZFecWIOqY4vusiY0FnBrWENa5+NSBkbkJACDi4BVIizW/C8ZfCEFRhqLUqWtz7QON8hLSgcc/H22qP3t6BSIiUsdrBRER0YtlsIFiuVyOvLy8Mv1TmjFjBpydnVFQUIARI0Zgx44dSE5ORmJiIv7++298/vnnOp/X1tZWyDY9deoU5s6di8jISKSmpuLUqVMYNmwY7t27B1tbzdIn7du3F0bIzZ8/H//99x8ePXqEpKQknD9/HhMmTMCyZcvU2qj2u6yWLFmCU6dOCcfco0cPFBcXIz8/X+v5UZ3Xc86cOTA2NkZ2djaGDh2KTZs2IS4uDmlpabhw4QJGjx6NmzdvAgA++eQTuLu7C2379+8vBOpmz56N33//HdHR0UhPT8eJEycwbNgwJCYmPrcSrqamplixYgU8PT0hl8uxZMkSDBo0CDt27EBkZCQyMzMRFxeH48ePY/Lkyfj000+Rl5cHS0tL+Pv7a81M+uGHH2BqaqqYN3HYMBw6dAjp6emIjY3F8uXLMXPmTJ3Zj2+99Rb69+8PQDEQYM6cOQgLC0NGRgYCAwMxYsQI3LlzR2v7999/H/b29pDL5Zg0aRI2bdqEiIgIpKen4/79+5g/fz727t0LABg+fDjL4kLxg2Tu3LkwMVF88V+/fr0QTH+VeXp6wtXVFQCwdetWLF++XHjf3Lp1CwsWLMCnn36qFsQs7efDrFmzYG1tDYlEgk8++QR//fUXoqKikJ6ejqCgIEyYMAHHjx8HALzzzjtCZi4ADBo0CE2aNAEA7Ny5E7Nnz0ZoaKjQdubMmZg8eTIkEgl8fX3RrVs3AIq5jcePH6+zFL82LVTmGfX398e///6LuLg4pKam4urVq5g1axa+/fZbtTaq58Dc3Bxff/01AMW87B9//DECAwORkZGB8PBwLFmyBL/88gsAoGvXrsIAkIYNG+LDDz8EoPhcHzVqFC5cuID09HTExMRg48aNGD9+PCQSCczMzPTO8U70Jniv/3twdHRESUkJFsxfgNOnTiMzIxNJSUnYumUr1q9bBwBo3aYN6ntoXjOX+ftj+rTpmD5t+nPrY3ycIkOgWrWqpW7z8ccfQyQSobi4GP+b8z+cPnUaKSkpSEtLw/Fjx/G/Of9Dfn4ezM3N8dmEz55X14mIXnvVOzWBqZ0l5FIZbq85hkdBoSjOyUdBeg6ijgXj4YErAABn71pas6we7DiHoCW7EbREc1BxvXdaAyJFpteNvw4i+cZDFGbmoig7H0nB4bix/IDi5r0IqN+/DYxMTYS2JpZmqNtX8Ts0LyEdN1YcQurdaBTnFiA/JQsRh68i8ohicK9zk9pwqOem8fwAhAwxALBwsin3eSIiMmS8VhAREb04hjkZKYCEhAS1YEZpXL16Fba2tnBycsI///yD8ePHIzk5WWOuYBsbG3To0EFnmdZvv/0WI0aMQGZmJjZv3ozNmzcL60QiEb7++mucP39erTwqAHh4eGD8+PFYtWoV0tLStAYbbGxsMGTIEKxZswaAIjOvvFmiK1euFB7/9ddfWLJkid4Mv1atWmHjxo0AFCVcly5dipkzZyIzMxPz58/H/PnzNdoMGTJEI7AuEong7++PCRMm4Pr161i5cqVaXwBg5syZWLp0qVo2YGVycXHB1q1b8fPPP2Pnzp24e/euzjmhAUWAat68eTrLV9arVw8rVqzA1KlTER8fj+nT1W9+29vbY8SIEcIcs0+bN28e8vPzcfz4cWzbtg3btqnPETl8+HBcunRJmPNRdb9//PEHJk2ahIyMDK1/A0AR9NI3wMHQ1KtXD+PGjcNff/2FkpISfP/999i+fXup56t+GYyMjDBv3jxMmTIFJSUl+PPPP/Hnn3+qbSMWi/HZZ59h7dq1KC4uRnS07jJLqmrVqoW///4bU6ZMQWpqKv744w/88ccfGtt17dpVI6PexMQEK1euxKeffoo7d+5gx44dQpl2Vb169cLChQthYmKC8ePH49KlSwgICMDmzZtLPV+xg4MD/u///g9z585FQUEB5s2bp7GNqakpxo4dixUrVgAAoqOjNSoapKamYsmSJbh27RpGjx6tsY8WLVpoZPF/++23KC4uxs6dOxEUFIRPPvlEo52VlRV+//13eHl5lep4iF5XFhYW+HrWLPy44Efk5GRj5eP3m6oGXl6YPGWy1vapqWlCxvHzkpKSCgCwtNQ9Z9jTmvk2w6effYZ//v4b2dlZWo/L1tYO076chpq1alVaX4mI3jTGZiZoPLI7bq89hpK8IoTtDtDYxra2KzyHdNTavigzT8gie5pDfTd4Du6I8L0BKMrMw4PtmpW+xKZG8BjYHs6NND+rq7b0RElBMaKOXUP+owzc23RaYxt7Dzd4DGin8/gKM59MMWJszjkniYjKg9cKIiKiF8dgA8UV5eXlhYMHD+Kff/7B0aOKEoQ2Njbo1KkTpkyZgt27d+sMFHt4eGD//v1YtWoVzp49i0ePHsHa2hq+vr4YO3YsWrZsqbN09YwZM9C4cWNs2bIFd+/eFTJZa9asiY4dO+LDDz+Era0ttm3bhry8PBw/frzMAXFtyjOfZvfu3XHs2DFs3LgR586dQ2xsLIqKiuDg4ABfX18MHToU7du319rW3t4eGzZswL59+7Br1y5ERESgpKQEDRs2xJgxY9CjRw8sXbq0ooell5WVFebNm4dPPvkEe/bsEQKxOTk5MDMzQ40aNdC8eXO88847avMy69K+fXscPHgQa9euxblz55CQkABbW1t07NgRU6dOVZuD+mnm5uZYunQpjh49iq1btyI0NBQFBQWoV68ehg8fjsGDBwslfp/Wtm1bHDx4EOvXr0dAQADi4uIgkUjg4OAAb29vDBgwAL169Sr3eXpTTZw4EYcOHUJ0dDRu376NjRs34uOPP37Z3dKra9eu2LZtG/7++28EBQUhIyMDpqamqFq1Kpo3b46PPvoIjRo1wq1btxAYGIhjx45hwoQJpdq3r68vjhw5gs2bN+PUqVOIjIxEXl4ebG1t0aRJEwwcOBB9+vTR2tbJyQnbtm3D7t27sX//foSGhiIvLw/Ozs7w9fXF+++/jw4dOgjbr1q1Cj/88APy8/NL3T+l4cOHo3bt2li3bh1u3ryJ7OxsmJubw93dHa1bt8aIESNQu3ZtHD58GNHR0Th+/LgwnYDSZ599hk6dOmHDhg24fPkyUlJSYGJiggYNGqB///4YMmSIRga/iYkJfvzxRwwYMABbt27F9evXkZKSArFYjBo1aqBjx474+OOPUbVq6bMXiV5ntevUxm9LfsP+//5DUNA1pKWmQiQSoXr16mjfoT169uqlsyz0i5Cfr6gmYGllVaZ2Xbt1RQOvBjh08BDu3L6N9PR0GBkZwdXVFS1atkSfvn1gY8OMACKiZ7F2c0LLaQMRe+420u/HoihTkbll6WoP16Z1Ua2NF8Q6Ki49SxXferCt6YL4i3eRER6Posw8iMRimDtYw8HTHe7tGsHMTvfnf/WO3nDwcEd8wF1kRiSiODsfxuamsHS1R5UWHnBtWgciPQNISwqeDGY24s1/IqJy47WCiIjoxRDJ9U2+SuXm5+cnZIc+ePDgJfeGiOj1U1JS8lIDSa+qzJysl90FIiJ6xU0IWvnsjYiIiIiIiHTY2vXrl92F18Kw07+87C68Mfiae3le3TqqRERk0BgkJiIiIiIiIiIiIiJ6fhgoJiIiIiIiIiIiIiIiIiIyMEzXojeCXC5Hfn5+hfZhVcZ5Ct9EPI+6SaVSFBYWlru9WCyGhYVFJfaIiIiIiIiIiIiIiIio/BgopjdCfHw8unfvXqF9cC5pnkd9goKCMGrUqHK3d3d3x6lTpyqxR0REREREREREREREROXH0tNERERERERERERERERERAZGJJfL5S+7E0RERFQ6mTlZL7sLRET0ipsQtPJld4GIiIiIiF5jW7t+/bK78FoYdvqXl92FNwZfcy8PM4qJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRjjl90BIiIiIiIiIiIiIiIiotfJipafvewuEFUYM4qJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGionohZNKpS+7C1QJ+HckIiIiIiIiIiIiInp9Gb/sDhC9aN26dUN8fLzWdWKxGGZmZnBwcICHhwd69uyJvn37wtLSUuv2DRo0AABMmTIFU6dOfW59fpMcPHgQJ0+exO+///6yu6JG+bpo1aoVNm7c+LK788q7c+cO5syZg927d7/srrwQly9fxqhRowAAGzZsQOvWrV9yj4hebevWrsORw4cxcdIkdO7SuUL7SklJwb69+3Dr5k2kp6fDwsISNWvVRLfu3dC+fXu9beVyOc6fO4fTp88gOioKJSUlcHR0hG/z5njn3Xfg5OSkt31ISAiOHjmK0AcPkJubC0tLK3g28ETPnm/Dp2nTCh0XEZGhe3jgMhIC7sFzcAdUaV6/QvsqzMhF7LnbyAiLR3FWPozMTWBV1QFVW3rCtWldvW3lcjmSrz9E0rUw5CamQy6VwdTWEo4NqqN6R2+Y2VnpbZ8dk4zEy/eRFZWE4pwCiMRimNlbwcHDHe7tGsHcwbpCx0ZEZMjelGuFtv3dWn0E2VFJcG1eDw0Gd6zIoREREVUIA8VEKmQyGQoKClBQUICEhAScPXsWy5cvx6JFi9CqVauX3b3X3pIlS7BixQqey9fcuXPnMGHCBGYUE5FWQVeDcPTIkUrZV3hYOH5csAAFBQXCspycbITcuYOQO3dw+dJlfDHtCxgZGWm0lcvl8Fvqh4CLF9WWP3r0CIcPHcLZM2fx5cwv4e3trbXturXrNI4jOzsLQVevIujqVXTv0QOfjPsEYjEL9BARlVXavRgkBN6rlH3lxKbg9pqjkBaVCMtK8oqQ9fARsh4+QmpINLyGdobYSPPzWi6X48G2c0i5Fam2vDAtBwkB95B0PRyNPuwG+3rVtD535JEgxJ27o75PyFCQnIWC5Cw8uhoKr6Gd4NSwZiUcKRGRYXlTrhXaxJ27jeyopPIfEBERUSVioJgMVosWLbB69Wq1ZVKpFPn5+YiJicGJEyewefNmxMfHY/z48Vi3bh18fX3Vtq9ZU/GD387O7oX1+3WWlMQvwW+C1NRUBomJSKtrQdfwx5IlkMvlFd5XWloafl70MwoKClC1WjWMGjUK9T3qIysrC4cPHcapkydx5fJlbNm8GSNGjtRov3XzFiFI3O+dfujeowesrKxw9+5dbNqwEWlpafh98e/49bdfNTKLd2zfIQSJ3d2rY/iHw+Hh6YGCggJcOH8Be3bvxskTJyCTSvHZxAkVPlYiIkOSdi8W9zafASp+qUBRVh7ubDgBaVEJzJ1tULdvK9jWcEFxbgESAu7h0dVQpN2JRpTDNdTt85ZG+6hjwcKNf/cOjVD1rQYwtjBFVuQjRBy6iuKsfNzdfAotPh+gkS2WEHhPCBLb1q6Cmt2awrqaIyT5RciKfISoY8EoyS/CvS1n0GxCP1i76a9iQURET7wp1wptchPSEH3iRsUPjIiIqJIwBYIMlpGREaysrNT+2draomrVqmjVqhW+/fZbbNiwAZaWligsLMTEiRORk5Ojto/jx4/j+PHjQklaIiIiQySXy7F923Ys/vVXlJSUPLtBKezbuw85OdmwtLTCDz/8gOYtmsPW1hY1atTAp599infefQcAcOTwEaSkpKi1TU9Lx8GDBwEA/fv3x8hRo+Dm5gY7Ozu0bdsW/5s3F9bWNsjPz8OunbvU2qakpOC/ffsAALVq1cL8H+ej5VstYWdnh6pVq2LwkMGYPHUKAOD06dO4e/dupRwvEdGbTi6XI+rEddzddBJyqaxS9hl79jZK8opgZGECn3F94ORVAyZW5rCq4gCPge3g3rExACAh4B4KM3LV2hZl5SH+QggAoHrnJqjbtxUsXexgam0BlyZ10PTTvjC2NIO0QIKYUzfV2spKpIg+cR0AYFenCpp80gsO9d1gYmUOSxc7VGvVAL6T34WRhQnkJTJhWyIi0u9NulZoI5WU4P72c5V2bERERJWBgWIiPZo3b47vv/8eAJCRkYE1a9a85B4RERG9Wm7euIFZX32N3bt2QS6Xo25d/fN7lUZeXh7OnD4NAOjdpzccHB00thk8ZAgsLa1QUlKCs2fOqq07euQISkpKYG5ujgGDBmq0dXFxQb93+gEALl64gKKiImFdYECAEOwe9+l4WFpaarRv164dGjZqBAD4b+++ch4lEZHhSA+NR7Dff4g9dROQA9buFc+uLSkoRtK1MACAW9tGMLPV/Lyu1b2ZIlgrlSEpOFxtXcKl+5BLZRCbGqNGFx+NtuYO1nDvoPisT775ENLiJwOhMh8moqSgGABQs4ev1lKl5g7WqNrSEwCQEZYAGSvyEBHp9aZdK7SJOnoNBclZsKtXFWb2ZZvXmIiI6HlhoJjoGQYOHIg6deoAADZv3gyZ7MmovwYNGqBBgwbw8/PTaPfw4UP88MMP6NWrF5o0aQJfX1/06tULs2fPxr172udY6datGxo0aIAtW7YgNzcXP//8M7p164YmTZqge/fumDFjhs62Sunp6Vi+fDk++ugjtGvXDt7e3mjRogX69OmDH374AQ8fPtTaTnksAQEB+O+//9CrVy94e3ujU6dO+Pnnn9W2DQsLw4IFC9C/f3+0atUKjRs3RuvWrTFkyBD4+fkhMzNTbXs/Pz80aNAAe/bsAQBcuXJFeL64uDi1bSUSCbZs2YKRI0eidevW8Pb2RufOnTFjxgzcuHFD77E/L8q/y+7du5GXl4elS5eiT58+8PHxQfv27TFlyhS1jLLg4GBMmjQJbdu2RZMmTdCnTx/89ddfkEgkGvseOXIkGjRogMWLF0MikWD58uXo3bs3fHx80KVLF0yaNAlXr17V27/c3FysW7cOY8eORYcOHeDt7Q1fX1+8/fbbmDVrFm7duqW3fXp6OlauXIlBgwahVatWaNKkCXr16oUFCxYgMTFR2O7y5cto0KABvvnmG2GZ8u+4e/fu0p5OvZTnw8/PDxKJBGvXrkX//v3RrFkztG7dGmPHjsWlS5eE7cPCwjBz5kzhuLt3746ff/4ZeXl5Op+jrK/f0kpISMBPP/2EPn36oFmzZvD19cV7772HJUuWICMjo1z7JHodLPxpIWJiYmBsbIzBQ4bg82lfVHifIXdCUFysuAH/1lua5d8AwNzcHN5NFPMLBz31OXn9uiJzq3HjxrCwsNDavkXLFgCAoqIi3L51W1geEaEoK+fo6AgPDw+dffTxUdwoCgkJqbQsaiKiN1XIuuPIf5QBkZEYNbs3g9ewzhXeZ2ZEImQSRfDVWcccwEamJrCv5wZAMdelqvQHit8h9vWqwtjMRGt7Jy/FfmXFUmQ+TBCWF2XlQWyqmMnLprqzzj5aONkCAORSGSR5RTq3IyKiN+9a8bSM8AQkBN6DkYUJPN/vAIjKdixERETPC+coJnoGkUiEPn36YPny5cjMzMTdu3fh7e2tt82ZM2cwdepU4SY3ABQXFyMqKgpRUVHYuXMnZs+ejREjRmhtn5OTg6FDhyI8/MlIxri4OMTFxeHQoUOYO3cuPvjgA412Z8+exbRp05Cfn6+2XCKRIDc3FxEREdi9ezeWLVuGTp06aX3uo0ePYuvWrcL/k5KSYG9vL/zf398f/v7+GvNPZmZmIjMzE7du3cKuXbuwZcsWVKtWTfdJ0iIxMRGffvopQkND1ZY/evQIBw4cwIEDBzB+/HjMmDEDItGL/0admpqK999/H5GRkcKyoqIiHD9+HBcuXMCmTZtw69YtLFiwQG0O34iICPzxxx+4d+8eli5dqnXfEokEY8eOxZUrV4RliYmJSExMxMmTJzFp0iR88YVm8OX27duYMGECUlNTNfYXExODmJgY7Nu3DwsWLMDgwYM12l+5cgXTpk1DWlqa2nLla3X37t1Yvnw52rRpU7qTVEkKCgowatQoBAcHqy27ePEiLl26hGXLlkEqlWLGjBkoLCwUtomLi8OaNWsQHByMTZs2wcRE/Yfc83r9Hjx4EN98841aViIAPHjwAA8ePMDWrVuxbNkytGzZsiyngei1IBKJ8FarVhg6bCjc3d01ykCXR1RUFADFNBG1atfSuV2dOnVw5fJlxMTEoKSkBMbGxigpKUF8fLxivZ7s5ho1agjbR0REoOVbivdnXq6i3Jyzi4vePtraKm7+SyQSJMTHo2Yt3f0kIjJ4IsCpcS3UftsXli72GqU9yyM3MV2xayMRrKppVp5QsnZzRNqdaOQ9yoBMKoXYyAgyqRQFKZmP1+sO9FpWsYfISAy5VIac+DQ4PQ4yVGvVANVaNUBJYTGMTHTfVilIyxYeG1uYluXwiIgMzxt2rVAlKShC6K4LgByo1681zO2tK3ZgRERElYgZxUSl0LhxY+GxMktJl/z8fMyaNQvFxcXw8fHBmjVrcO7cOZw/fx7Lly9H7dq1IZfLsWjRIo1sWqW//voL4eHh6N27N/bu3YvAwECsXLkSderUgUwmw/fff4/AwEC1NgkJCfjiiy+Qn5+P2rVrY8mSJThx4gQCAwOxa9cujBw5EsbGxiguLsa8efN09n/r1q3w9PTE1q1bceHCBSxevBgDByrKdh45cgR+fn6Qy+Vo37491q9fj3PnzuHcuXNYv349unTpAkAR4FQNiH722WcIDg7Gu+++CwBo0aIFgoODERwcDHd3d+G8jR07FqGhobC0tMSMGTNw9OhRXL58Gdu3b0e/fooSoatXr8bq1av1/g2eF39/f8TExGDixIk4duwYTp8+jS+//BIikQgFBQX48ssvMX/+fPj4+GDDhg24dOkSdu3ahVatWgFQBOFVA5+qtm/fjitXrqBNmzbYunUrLl26hI0bN6Jp06YAgOXLl2Pnzp1qbXJzczFx4kSkpqbC2dkZP/74I44dO4ZLly5h3759mDp1KiwsLCCXy/Hjjz9qDCCIjY3F+PHjkZaWBicnJ8ydOxenT5/GuXPn8PPPP8PZ2Rl5eXn44osvkJaWhpYtWyI4OBhz584V9qH8O7733nuVeaqxadMmBAcHY9iwYThw4ADOnTuH+fPnw9TUFFKpFHPnzsXMmTNRvXp1rFixAoGBgTh48CB69+4NALhx4waOHDmits/yvn6f5eLFi5g5cyaKiorg5eWFZcuWISAgAOfPn8eSJUtQu3ZtZGZm4tNPPxWCX0Rvkt+W/I4vZ3wpfJ5XhtTHwWYnJyeIxbq/rjo7K8rRyWQyYcBMenq6MFjH1VV3sFckEsHZWXHDJyUlWVhu/jgDubCgQG8f8/Ke3LhKT2fVACIifVpMG4hGH3aFpYt9pe2z6HEAwdTOCiI91wozu8c342VyFGUqqs4UZ+VDLlUMHDR30H2zXiQSCaVBizJyNNYbm+sO/kqLS5B8IwKAonyqvoAyERG9udcKAAjfG4jirHw4NaqJKs3rl/k4iIiInif+UiEqBdWb309nbj7typUrQulaPz8/VK1aVVjXvXt3eHp6omfPnpBIJDh+/DjGjBmjsY/8/HwMGjQICxcuFJZ16dIFTZs2xaBBg5CQkIBFixZh374n8yJu2rQJBQUFMDExwd9//40aNWoI6xwdHeHt7Q2xWIz169cjNjYWkZGRQkltVWKxGEuXLhXWKYO7APD3338DADw8PLBixQqYmj65MVKlShW0atUKgwcPRkhICM6fPy+sMzU1hampKYyNFR85RkZGsLJSn4tl9erViIiIgImJCdatWycESAHA3t4ev//+OxwdHbFx40YsXboUAwcOhMszsr0qW1FREb777juMGjVKWPbZZ5/h2rVrOHv2LKKjo+Hl5YUNGzYI58bBwQH+/v7o2LEjioqKEBAQgObNm2vsOz8/H+3bt8eqVauE89SqVSts2LABH330Ee7cuYM//vgD7777LszMzAAA+/btEzL3li5dihYtWgj7c3BwgJeXF2xsbPDTTz8hPz8fwcHB6NChg7DNTz/9hMLCQlhbW2PLli2opZINN2DAANSrVw8ffPABMjMzsXXrVkyePBlWVlZqf/en/46VpaioCKNHj1Yrc/3BBx/g3r172Lx5MxITE+Hq6op///1XyHh3dHTE4sWLcf36dSQlJSEgIKBSXr/6SKVSfP/995DJZPDx8cGmTZuEvw8A9O3bF+3atcOgQYMQHx+PRYsWYcWKFRU5NUSvHDc3t0rfZ06O4gbLsz5jLC2frM/LVdzQycl+cnPGykr/SH1lWWplW0CRaXz1yhXExcUhIz1D6/zIAHA35MmUA/kF+Vq3ISIiBUsXu0rfpyRfUVVGX7D26fXKeYUl+U+qwBhbmGm0UWX0uNSosm1pRR6+CkmOYtBRtTZeZWpLRGSI3tRrRdL1h0i9HQUTa3PUH9C2dB0nIiJ6gZhRTFQKlpaWwuNnzV+qWm5aW/nNGjVqYNWqVdixY4fWUsCA4sb4d999p7HcwcEBU6dOBQDcv39frTS1p6cnhg4divHjx6sFiVUpM1sBRcaVNl5eXloDyDKZDF26dMGAAQMwadIktSCbklgsFkrrlmVOVrlcjm3btgEA+vXrpxYkVjVt2jSYm5tDIpEI8x2/SFZWVvjwww81lquWEx41apTGubGzsxPOaXJyMrQRiUSYO3euECRWMjc3x9dffw1A8XpSnZ+3WrVq+OijjzB8+HC1ILGq1q1bC49V/+bZ2dlCMHTMmDFqQWIl5fzKLVq0gLm5udb9P0/jx4/XWKZ6rocMGaJWFh0ATExMhNLwquf6eb1+z58/L5S4nTFjhlqQWMne3h4TJ04EoChLXxlleYnedMWP53Q30fJeVWVq+qS8vKRE8rhtsdb12ij3X1z8ZA75Nm1aQyQSQSaTYc2aNRql6gHg1s2buHnzpvB/KecoJiJ64WQliuoRz8rUFZsYabSRSUq0rtdGuX/lHJelEX8xBImXHwAAbGtXYfYYEdFL8rKvFYWZuXh4QHEfx2NgO5haW5Sy50RERC8OM4qJSkE1+PusuXGbNWsGExMTSCQSjBkzBsOGDUPXrl3RrFkzGBkpvlh27NhR7z46d+4Ma2vtWVBdu3YVHgcEBKB+fcVNhwEDBmDAgAE695mYmIi7d59kP6nOoauqYcOGWpeLxWJMmTJF5/5lMhnCw8OFctolZbhpHh4eLsyR27BhQ+Tl5WndTiQSoUGDBrh586bOEs7PU+PGjTUCuYAik1V1G22Uf0/V15IqHx8fvQF+a2tr5ObmIiAgAJ07dwYAdOvWDd26ddPZ39TUVLVS6ap/86tXr0LyOBCjLLmsze+//65z3fPk7u4ulIRVpXquGzVqpLWttnP9vF6/ly9fFh57enrqfO0qg9dyuRzBwcHo1atXqfZPZKjEFZiHXl+p6tKoWasWunTpgtOnT+PqlStYMH8BBr0/CDVr1kR+fj4uBV7Crp074eDgIAzAMdJybSAioufrWb/L9LYVl7/ts8RfDEHEwasAAFM7S3gN61yhvhIRUfm9zGuFXC5H6M4LkBZI4Nq8nta5i4mIiF4FvKtFVArKEpgAYGtrq3dbV1dXzJgxA4sWLUJOTo4wp66trS3atm2Lzp07o3v37hqZkKo8PT11rnNwcICdnR2ysrLw6NEjjfWFhYUIDAzE/fv3ERMTg9jYWISHh2tkSGrLkALUA3G6pKamIjAwEOHh4YiNjUV0dDQiIiI05sAtrdjYWOHxwoUL1Upu65KYmFiu56oIBwft5UdVgxK6AvzPClzo+5uLRCLUrFkTd+/e1fo3l0gkuHr1KkJCQhATE4OYmBhERERoZC+r/s2TkpKEx7Vr19bbt5ehNOfaxsbmmdtoU5mvX9V5xtu2LV0JqZfx2iV63Zg9rmJQIpHo3U41E9jURJEdbK6S2S+R6B/0IXk8oOTpzOMxn4xFTk4OgoKCEHLnDkLu3FFbX83NDZ+M+wQL5s1//JwvvuoCEZGhM3r82a3M/NJFNbtLmREmVvncf1Z76eNrybOyyeRyOaKOBSPu7G0AgKmtBZqM7QkzW0u97YiI6Pl5mdeK+AshyIp4BDMHK9R7p7WupkRERC8dA8VEpaAayNSV9alqzJgxaNiwIf755x8EBgZCIpEgOzsbR48exdGjR2FiYoKRI0di5syZQpaxqmcFo83NzZGVlYXc3Fy15Zs2bcIff/yhFtgGFIGzhg0bonbt2jh8+LDefWsrnatUVFSEn376CTt27NDISDYzM0Pr1q0hk8lw9epVvc/xtKeP43m1qSjVEuSVTVfQU0lZ+vnp4z58+DB+/PFHjXLGIpEIdevWRdOmTbWW6c7KyhIeK+fofJU8j3P9PF6/r8trl+h1Y/X4M+BZAzjy859k8dvYKj5HLVXmNVZdr729Yv9PfwabmppixlczcfHCBZw8eQrRUVGQyWSoUqUK2rVvj959eiM25sl3A12DW4iI6PkxMn88H2Sh/rmDVdebWCq+U6vORSl9RnvlemNL3b+TpJIShO44j9Q70QAAM0drNBnTExZO+n/XERHR8/WyrhV5j9IRdTwYEAGegzo8c45kIiKil4mBYqJSUJ2HsEmTJqVq06ZNG7Rp00YoFxwYGIgLFy4gJiYGEolEmPfw//7v/zTaFhUV6d238sa26o3pdevWCZm4bm5u6NGjBxo2bIh69erBw8MDlpaWCAgIeGagWJ/p06fj5MmTABQlljt37gwPDw/Ur18fdevWhbGxMZYsWVLmQLFqoHL16tXo1KlTufv4utJVklpJ+TdXzUQ/duwYpk+fDrlcDkdHR7z99tvw9vZG3bp14enpCVtbW0RHR2sNFKue84KCAp2Z0G+S5/H6VQbwnZ2dcfHixefSbyJDVM3NDQCQlpYGuVyus2Rcaqpi2gIjIyPhmujs7AxTU1MUFxcjJSVV53PI5XJh2gMnLaXuRSIROnTsiA46pouIjo4WtqvmVq2UR0ZERJXF0tkOAFCUlaf3WlGUpRikJzISwdRW8R3YzN4KYhMjyCRSFGbqHsQnl8tRlJX/uI3278vFuQW4u/EkcmIV1xxrdyc0/rgH56EkInoFvKxrRWpINOQlMgDA7X+O6u1jcvBDJAc/BAA0GdcL9nX524KIiF4sBoqJnkEikeDYsWMAgOrVq6NBgwZlam9tbY2ePXuiZ8+eAIBbt25h2rRpiI+Px+bNmzFz5kyNeW9VM5iflpqaKmQMu7u7A1CUm/b39wegmOt248aNQgBL1dPlp8siODhYCLKNHDkSs2fP1rpdeZ6jWrUnX4Lj4+P1bqvvi/3rTN/fXCaTISYmBoDiNaj022+/QS6Xo3r16tj5eL7Mp+n6e6ie89jYWJ1zUwcGBuLatWuoUaMG+vfvX6pjeRU9r9ev2+NgVkZGBvLz859r1jmRIalZUzF/l0QiQVxcnM5qHpGRkQAUn43Ka6lIJEL16tURERGBqKgonc8RGxMjzEdep04dtXVyuRw5OTl6K3zcunXr8XPX0HrNJSKi58uyquK7r7xEhvzkTFhV0V7dITdBMZ+8pas9xI+rOYlEIli62iM3Pg15j9drk5+UAblUcaPf2k1zip6i7HzcWn0YhWmK32eOXtXhNayzUOqUiIherlfhWkFERPSq0z+RIxFhx44dQlnfIUOGPDNIuXLlSrzzzjsYPny41vU+Pj4YNWoUAEXmsGoJYKXz58/rnENYGewSiUTo3LkzACAsLEwIHg8YMEDnDevAwEDhsUwm03scT7t+/brweOjQoVq3kclkuHz5ss7n0HXuvLy8hIxW5fFpk5eXh/bt26Nr165YvHhxqfv+Orh69arOEqsBAQHCOuXfPD09XQiA9OzZU2fZU11/c19fX+Hvcf78eZ392rJlC/z8/PDXX38Jy17HQH1lvH61admyJQBAKpXizJkzOrfbv38/fH190a9fPwQFBZWy10SGq1HjRsJUCNeCrmndprCwEHduK+YObtasmdq6Zr6+AIA7t++gsLBQa/tr14IBACYmJmjUuJGw/OaNGxjx4Uf4dNx4nXOKZ2Zk4nqwov1brd4q5VEREVFlsq9bFWJTxc389HvaB11KiyXIfJgAAHDwrK62zqGB4v+ZDxMhVZnzXlXafcV+RcZi2NdRz/CS5Bfh9j9HhSBx1VaeaDSiG4PERESvkJd1rajRxQft/veR3n9m9oopc1ya1RWW2dWuUsEjJiIiKjsGion0uHbtGn755RcAQNWqVfHxxx8/s42xsTHCwsJw/fp1BD++ify0e/fuAVBkGzs6ao42jI2NxcaNGzWWp6amCpnD7dq1Q5UqVYTnVAoPD9f6nBcvXsTu3buF/0sk2r/g6qI6l7Ku5/D391fL3nr6OZT7eHq5sbExBg0aBEARtNRVHnvJkiVIS0tDQkICvLy8ytT/V11+fj7+/PNPrct//fVXAIqMN2UwRPVv/vDhQ637vH//PlatWiX8X/W8u7q6okOHDgCANWvWICkpSaP97du3cerUKQBAv379hOWqr4Vnlcx+VVTG61eb7t27w/lxydrFixcjPV1zlHF6ejqWLl2K/Px8pKam6szeJqInzM3N0ap1KwDAgf0HkJqqWUJ6544dyM/Pg7GxMXr27qW2rkPHDhCLxcjLy8WunTs12qampuLggYMAgC5du8JKZV7juvXqCQNijh7RLBMnl8vxzz//oLi4GObm5nj77bfLf6BERFRuRqYmcG5cCwAQdyFEa1nQ6JM3IC2QQGQkhlsb9d8Prk3rAmIRSgqKEX3yhkbbwsxcxF+4CwCo2sIDxhbq80uG7rqAghTFoF+3dg3hMaAdRGLeYiEiepW8rGuF2MgIRqYmev/h8Rh8kVgkLON1hIiIXgaWniaDJZVKkZeXp7ZMIpEgJycHDx8+xPHjx7Fv3z5IJBKYm5tj6dKlavO66vL+++9j1apVyMzMxKRJkzBlyhS0a9cO9vb2SE5Oxo4dO7B3714AwPDhw3VmZy5cuBBJSUkYMmQIbG1tce3aNfz8889ITk6GqampWulcT09PuLq6Ijk5GVu3boWLiwv69esHGxsbxMXF4b///sOWLVsglUqFNk8f+7O0b98eIpEIcrkc8+fPh0QiQatWrSASiRAaGop///0Xp0+fVmuTl5cnZIQBT+bXffDgAUJCQuDm5gZra2uYmJhg0qRJOH78OBITEzFjxgzcunULAwcOhIuLC+Li4rBp0ybhvLVo0QJ9+/YtU/9fB+vWrUN2djbGjBkDFxcX3Lt3D4sXL8b9+/chEonwv//9D+LHPxpsbW3h4+ODW7du4ezZs1iwYAGGDx8OR0dHJCUl4ciRI1i3bh0KCgqE/T/9N581axauXr2KjIwMDBs2DNOnT0fbtm1RVFSEwMBA/P7775BIJKhSpQpGjx4ttFOdJ3n//v14++23YWRkpBZoedVUxutXG1NTU3z33XeYPn064uPjMXjwYHz++edo27YtAMX85n/++adQOnzGjBmv9HkietGmT5sOAKhfvx4mT5mitm7Y8OG4euUqcnNz8L85P2Dkx6Pg5eWFnJwcHD50GCdPnAAA9O7TG05OTmpt3dzc0LNXLxw5fBj7/9uP/PwC9OnbBzY2Nrh//z42rt+A3NwcWFvboP8A9bL6NjY26NqtG44fO4ajR47AxMQYnbt0gY2NDWJjY7F7127cDQkBAHz40UdwcNRe0YGIiCpH0BLFYFeb6s5oMKST2rraPVsgNSQGJflFuLXqMOr2fQu2taugJL8I8QF38ehKKABFINfMTv07mKWLHdzaeCEh4B7iz4dAWiSBe7tGMLY0Q3ZUEiIOXUVJfhGMLc1QvXMTtbZp92OFzDTbWq6o1d1XZ6aZktjE+LWszENE9Dp4Fa8VRERErwsGislgXbt2Dc2bN3/mdtWrV8dvv/2Gpk2blmq/9vb2+OOPPzBp0iRkZGRg/vz5Wrfr2rUrPv/8c63rWrVqhZiYGPz999/4+++/1dbZ2tpi6dKlqFu3rrDMyMgI8+bNw5QpU1BSUoI///xTIztVLBbjs88+w9q1a1FcXIzo6OhSHY+Sh4cHxo8fj1WrViEtLQ1fffWVxjY2NjYYMmQI1qxZAwCIiopSy5hu3bo1Vq9ejfz8fCGDeMOGDWjdujUcHBywZs0aTJw4EVFRUVizZo2wH1U+Pj7w9/cXAqZvigYNGkAul2P37t1qmd+AIhi5cOFCtGnTRm35nDlzMGrUKOTn52Pjxo1as9AHDx6MwMBAxMfHa/zNPTw8sHz5cnz++edISEjQ+jetUqUKVq9eDRsbG2GZt7c3LC0tkZ+fj2+//RbffvstpkyZgqlTp1bkFDxXlfH61aVv377Izs7GggULEB8fj1mzZmlsIxKJMHnyZHzwwQcVPxiiN0higqLEm+oAFCUnJydM/3I6fv/td6SmpmLJb79rbNOmbVt8NGKE1n1/+NGHSE5KUsxRfuKEEFhWMjc3x9f/97VQFUDViJEjEBcXh3t372L/f/ux/7/9auvFYjGGDhuGnr16lvZQiYionApSsgEAptaag3bN7KzQ8MOuuLf5FIoy83Bv8xmNbZyb1Ead3i217rt2rxYoTM9B+v04PLoSKgQLlMSmxmg8qjvM7a3VlidcvCs8zo5ORuD8zc88jre+GgxzB+tnbkdERGX3Kl4riIiIXhcMFBOpEIvFsLCwQJUqVdCgQQN069YNvXv3hqmp6bMbq2jbti0OHjyI9evXIyAgAHFxcZBIJHBwcIC3tzcGDBiAXr166Wzv7u6OP//8E8uWLcPx48eRmZkJd3d3dO3aFR9//LFQclpV165dsW3bNvz9998ICgpCRkYGTE1NUbVqVTRv3hwfffQRGjVqhFu3biEwMBDHjh3DhAkTynRcM2bMQOPGjbFlyxbcvXsXeXl5sLS0RM2aNdGxY0d8+OGHsLW1xbZt25CXl4fjx4+rBeM7duyIOXPmYMOGDYiPj4eNjY1aOdG6deviv//+w44dO3D06FGEhoYiNzcX1tbWaNCgAd59910MGjRIrYzwm8LOzg4rV67EypUrcfDgQSQlJaFq1apo27Ytxo4di9q1a2u0adKkCfbs2YOVK1ciMDAQKSkpMDY2houLC3x8fDB06FC0bt0a3333HXbu3InTp09DIpHAxOTJvGnt27fH0aNHsXbtWpw9exbx8fGQSqWoWbMmevTogdGjR2sEcBwdHbFixQr89ttvePDgAYyNjbXOtf2qqejrV59hw4ahffv2WL9+PQIDA5GQkACJRAJXV1e0bNkSI0aMgI+Pz3M+QqI3T9NmzbD499+wb+8+3Lp5E+np6TAxMUHNWrXQtWtXdO7SWWd2lqmpKb6a9TXOnT2HM2fOIDoqGsXFRXBwcIBP06Z4r/97Wq+nAGBmZobZ38/GyRMncf7cOcTGxqKkpN6tp8UAAQAASURBVAQODg74f/buOzyqKv/j+GfSCekkhN4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}, "metadata": {}, "output_type": "display_data" } ], "source": [ - "visualizer.create_model_rank_heatmaps(\n", + "visualizer.create_disparity_metric_heatmap(\n", + " model_names=list(models_params_for_tuning.keys()),\n", " metrics_lst=[\n", - " # Group fairness metrics\n", + " # Error disparity metrics\n", " 'Equalized_Odds_TPR',\n", " 'Equalized_Odds_FPR',\n", " 'Disparate_Impact',\n", - " 'Statistical_Parity_Difference',\n", - " 'Accuracy_Parity',\n", - " # Group stability metrics\n", - " 'Label_Stability_Ratio',\n", + " # Stability disparity metrics\n", + " 'Label_Stability_Difference',\n", " 'IQR_Parity',\n", - " 'Std_Parity',\n", " 'Std_Ratio',\n", - " 'Jitter_Parity',\n", " ],\n", " groups_lst=config.sensitive_attributes_dct.keys(),\n", + " tolerance=0.005,\n", ")" ] }, diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 9218220c..9dbc6a00 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -1,7 +1,6 @@ import pandas as pd import gradio as gr import altair as alt -from pprint import pprint from virny.configs.constants import * from virny.utils.common_helpers import str_to_float diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index f483ce65..55bbb2c8 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -1,11 +1,11 @@ -import os import altair as alt -import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt -from virny.utils.data_viz_utils import create_sorted_matrix_by_rank +from virny.configs.constants import * +from virny.utils.data_viz_utils import (create_sorted_matrix_by_rank, create_subgroup_sorted_matrix_by_rank, + create_model_rank_heatmap_visualization) class MetricsVisualizer: @@ -34,21 +34,17 @@ def __init__(self, models_metrics_dct: dict, models_composed_metrics_df: pd.Data self.dataset_name = dataset_name self.model_names = model_names self.sensitive_attributes_dct = sensitive_attributes_dct - self.__create_report = False - self.fairness_metrics_lst = [ - 'Accuracy_Parity', - 'Equalized_Odds_TPR', - 'Equalized_Odds_FPR', - 'Disparate_Impact', - 'Statistical_Parity_Difference', - ] - self.variance_metrics_lst = [ - 'IQR_Parity', - 'Label_Stability_Ratio', - 'Std_Parity', - 'Std_Ratio', - 'Jitter_Parity', - ] + + # Metric names + self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY, POSITIVE_RATE] + self.all_stability_metrics = [STD, IQR, JITTER, LABEL_STABILITY] + self.all_uncertainty_metrics = [ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY] + self.all_error_disparity_metrics = [EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_PARITY] + self.all_stability_disparity_metrics = [LABEL_STABILITY_RATIO, LABEL_STABILITY_DIFFERENCE, IQR_PARITY, STD_PARITY, STD_RATIO, JITTER_PARITY] + self.all_uncertainty_disparity_metrics = [OVERALL_UNCERTAINTY_PARITY, OVERALL_UNCERTAINTY_RATIO, ALEATORIC_UNCERTAINTY_PARITY, ALEATORIC_UNCERTAINTY_RATIO] + + self.all_overall_metrics = self.all_accuracy_metrics + self.all_stability_metrics + self.all_uncertainty_metrics + self.all_disparity_metrics = self.all_error_disparity_metrics + self.all_stability_disparity_metrics + self.all_uncertainty_disparity_metrics # Create models_average_metrics_dct models_average_metrics_dct = dict() @@ -78,13 +74,53 @@ def __init__(self, models_metrics_dct: dict, models_composed_metrics_df: pd.Data self.all_models_metrics_df = all_models_metrics_df self.models_average_metrics_df = models_average_metrics_df self.models_composed_metrics_df = models_composed_metrics_df + + self.models_metrics_df = self._align_input_metric_df(all_models_metrics_df, allowed_cols=["Metric", "Model_Name", "overall"], + sensitive_attrs=list(self.sensitive_attributes_dct.keys())) + self.melted_model_metrics_df = self.models_metrics_df.melt(id_vars=["Metric", "Model_Name"], + var_name="Subgroup", + value_name="Value") + self.sorted_model_metrics_df = self.melted_model_metrics_df.sort_values(by=['Value']) self.melted_models_composed_metrics_df = self.models_composed_metrics_df.melt(id_vars=["Metric", "Model_Name"], var_name="Subgroup", value_name="Value") self.sorted_models_composed_metrics_df = self.melted_models_composed_metrics_df.sort_values(by=['Value']) - def create_overall_metrics_bar_char(self, metrics_names: list, reversed_metrics_names: list = None, - metrics_title: str = "Overall Metrics"): + def _align_input_metric_df(self, model_metrics_df: pd.DataFrame, allowed_cols: list, sensitive_attrs: list): + # Filter columns in the input dataframe based on allowed_cols and sensitive_attrs + filtered_cols = allowed_cols + for col in model_metrics_df.columns: + for sensitive_attr in sensitive_attrs: + if sensitive_attr in col: + filtered_cols.append(col) + break + + return model_metrics_df[filtered_cols] + + def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, + selected_metric: str, selected_subgroup: str, defined_model_names: list): + results[subgroup_metric] = dict() + + # Get distinct sorted model names + sorted_model_names_arr = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == selected_metric) & + (self.sorted_model_metrics_df.Subgroup == selected_subgroup) + ]['Model_Name'].values + sorted_model_names_arr = [model for model in sorted_model_names_arr if model in defined_model_names] + + # Add values to a results dict + for idx, model_name in enumerate(sorted_model_names_arr): + metric_value = self.sorted_model_metrics_df[ + (self.sorted_model_metrics_df.Metric == selected_metric) & + (self.sorted_model_metrics_df.Subgroup == selected_subgroup) & + (self.sorted_model_metrics_df.Model_Name == model_name) + ]['Value'].values[0] + metric_value = metric_value + results[subgroup_metric][model_name] = metric_value + + return results + + def create_overall_metrics_bar_char(self, metric_names: list, plot_title: str = "Overall Metrics"): """ This bar chart includes all defined models and all overall subgroup error and stability metrics, which are averaged across multiple runs. Using it, you can compare all models for each subgroup error or stability metric. @@ -93,22 +129,16 @@ def create_overall_metrics_bar_char(self, metrics_names: list, reversed_metrics_ Parameters ---------- - metrics_names + metric_names List of subgroup metric names to visualize that have a scale from 0 to 1 where closer to 1 is better - reversed_metrics_names - List of subgroup metric names to visualize that have a scale from 0 to 1 where closer to 0 is better - metrics_title - Title to input metrics (both metrics_names and reversed_metrics_names) to display on the plot + plot_title + Title for input metrics to display on the plot """ - if reversed_metrics_names is None: - reversed_metrics_names = [] - metrics_names = set(metrics_names + reversed_metrics_names) - overall_metrics_df = pd.DataFrame() for model_name in self.models_average_metrics_dct.keys(): model_average_results_df = self.models_average_metrics_dct[model_name].copy(deep=True) - model_average_results_df = model_average_results_df.loc[model_average_results_df['Metric'].isin(metrics_names)] + model_average_results_df = model_average_results_df.loc[model_average_results_df['Metric'].isin(metric_names)] overall_model_metrics_df = pd.DataFrame() overall_model_metrics_df['overall'] = model_average_results_df['overall'] @@ -116,19 +146,19 @@ def create_overall_metrics_bar_char(self, metrics_names: list, reversed_metrics_ overall_model_metrics_df['model_name'] = model_name overall_metrics_df = pd.concat([overall_metrics_df, overall_model_metrics_df]) - overall_metrics_df.loc[overall_metrics_df['metric'].isin(reversed_metrics_names), 'overall'] = \ - 1 - overall_metrics_df.loc[overall_metrics_df['metric'].isin(reversed_metrics_names), 'overall'] - + font_increase = 2 models_metrics_chart = ( alt.Chart(overall_metrics_df).mark_bar().encode( - alt.Row('metric:N', title=metrics_title), + alt.Row('metric:N', title=plot_title, sort=metric_names), alt.Y('model_name:N', axis=None), alt.X('overall:Q', axis=alt.Axis(grid=True), title=''), alt.Color('model_name:N', scale=alt.Scale(scheme="tableau20"), legend=alt.Legend(title='Model Name', - labelFontSize=13, - titleFontSize=13) + labelFontSize=13 + font_increase, + titleFontSize=13 + font_increase, + labelLimit=300, + titleLimit=300) ) ) ).properties( @@ -137,10 +167,11 @@ def create_overall_metrics_bar_char(self, metrics_names: list, reversed_metrics_ labelAngle=0, labelPadding=10, labelAlign='left', - labelFontSize=14, - titleFontSize=18 + labelFontSize=14 + font_increase, + titleFontSize=18 + font_increase, ).configure_axis( - labelFontSize=14, titleFontSize=18 + labelFontSize=14 + font_increase, + titleFontSize=18 + font_increase, ) return models_metrics_chart @@ -173,184 +204,59 @@ def create_boxes_and_whiskers_for_models_multiple_runs(self, metrics_lst: list): fig = ax.get_figure() fig.tight_layout() - if self.__create_report: - plt.close() - return fig - - def create_models_metrics_bar_chart(self, metrics_lst: list, metrics_group_name: str, default_plot_metric: str = None): - if default_plot_metric is None: - default_plot_metric = metrics_lst[0] - - df_for_model_metrics_chart = self.melted_models_composed_metrics_df.loc[self.melted_models_composed_metrics_df['Metric'].isin(metrics_lst)] - df_for_model_metrics_chart = df_for_model_metrics_chart.reset_index(drop=True) - - radio_select = alt.selection_single(fields=['Metric'], init={'Metric': default_plot_metric}, empty="none") - color_condition = alt.condition(radio_select, - alt.Color('Metric:N', legend=None, scale=alt.Scale(scheme="tableau20")), - alt.value('lightgray')) - - models_metrics_chart = ( - alt.Chart(df_for_model_metrics_chart) - .mark_bar() - .transform_filter(radio_select) - .encode( - x='Value:Q', - y=alt.Y('Model_Name:N', axis=None), - color=alt.Color( - 'Model_Name:N', - scale=alt.Scale(scheme="tableau20") - ), - row=alt.Row('Subgroup:N', title='Group'), - ) - ) - - select_metric_legend = ( - alt.Chart(df_for_model_metrics_chart) - .mark_circle(size=200) - .encode( - y=alt.Y("Metric:N", axis=alt.Axis(title=f"Select {metrics_group_name} Metric", titleFontSize=15)), - color=color_condition, - ) - .add_selection(radio_select) - ) - - color_legend = ( - alt.Chart(df_for_model_metrics_chart) - .mark_circle(size=200) - .encode( - y=alt.Y("Model_Name:N", axis=alt.Axis(title="Model Name", titleFontSize=15)), - color=alt.Color("Model_Name:N", scale=alt.Scale(scheme="tableau20")), - ) - ) - - return models_metrics_chart, select_metric_legend, color_legend - - def create_fairness_variance_interactive_bar_chart(self): - """ - This interactive bar chart includes all groups, all composed group fairness and stability metrics, - and all defined models. Using it, you can select any pair of group fairness and stability metrics and - compare them across all groups and models. Since this plot is interactive, it saves a lot of space for other plots. - Also, it could be more convenient to compare each group fairness and stability metric using the interactive mode. - """ - models_fairness_metrics_chart, select_fairness_metric_legend, fairness_color_legend = \ - self.create_models_metrics_bar_chart(self.fairness_metrics_lst, metrics_group_name="Fairness") - - models_variance_metrics_chart, select_variance_metric_legend, variance_color_legend = \ - self.create_models_metrics_bar_chart(self.variance_metrics_lst, metrics_group_name="Variance") - - return ( - alt.hconcat( - alt.vconcat( - select_fairness_metric_legend.properties(height=200, width=50), - select_variance_metric_legend.properties(height=200, width=50), - fairness_color_legend.properties(height=200, width=50), - ), - models_fairness_metrics_chart.properties(height=200, width=300, title="Fairness Metric Plot"), - models_variance_metrics_chart.properties(height=200, width=300, title="Variance Metric Plot"), - ) - ) - - def create_model_rank_heatmap(self, model_metrics_matrix, sorted_matrix_by_rank, num_models: int): + def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, tolerance: float = 0.001): """ - This heatmap includes all group fairness and stability metrics and all defined models. - Using it, you can visually compare all models across all group metrics. On this plot, - colors display ranks where 1 is the best model for the metric. These ranks are conditioned - on difference or ratio operations used to create these group metrics: - - 1) if the metric is created based on the difference operation, closer values to zero have ranks that are closer to the first rank - - 2) if the metric is created based on the ratio operation, closer values to one have ranks that are closer to the first rank + Create a heatmap for overall metrics. Parameters ---------- - model_metrics_matrix - Matrix of model metrics values where indexes are group metric names and columns are model names - sorted_matrix_by_rank - Matrix of model ranks per metric where indexes are group metric names and columns are model names - num_models - Number of models to visualize + model_names + A list of selected model names to display on the heatmap + metrics_lst + List of group metric names to visualize + tolerance + An acceptable value difference for metrics dense ranking """ - matrix_width = num_models * 3 - matrix_height = model_metrics_matrix.shape[0] // 3 - plt.figure(figsize=(matrix_width, matrix_height)) - rank_colors = sns.color_palette("coolwarm", n_colors=num_models).as_hex()[::-1] - ax = sns.heatmap(sorted_matrix_by_rank, annot=model_metrics_matrix, cmap=rank_colors, - fmt = '', annot_kws={'color': 'black', 'alpha': 0.7}) - ax.set(xlabel="", ylabel="") - ax.xaxis.tick_top() - - cbar = ax.collections[0].colorbar - model_ranks = [idx for idx in range(num_models)] - cbar.set_ticks([float(idx) for idx in model_ranks]) - tick_labels = [str(idx + 1) for idx in model_ranks] - tick_labels[0] = tick_labels[0] + ', best' - tick_labels[-1] = tick_labels[-1] + ', worst' - cbar.set_ticklabels(tick_labels) - cbar.set_label('Model Ranks') - - if self.__create_report: - plt.close() - return ax - - ax.set_title('Model Ranks Based On Group Fairness and Stability Metrics', fontsize=20) - - def create_total_model_rank_heatmap(self, sorted_matrix_by_rank, num_models): - """ - This heatmap includes all defined models and sums of their fairness and stability ranks. - On this plot, colors display sums of ranks for one model. If the sum is smaller, - the model has better fairness or stability characteristics than other models. - Using this plot, you can visually compare all models for fairness and stability characteristics. + if tolerance < 0.001 or tolerance > 0.2: + raise ValueError('Tolerance should be in the [0.001, 0.2] range') - Parameters - ---------- - sorted_matrix_by_rank - Matrix of model ranks per metric where indexes are group metric names and columns are model names - num_models - Number of models to visualize + # Find metric values for each model based on metric, subgroup, and model names. + # Add the values to a results dict. + results = {} + for metric in metrics_lst: + # Add an overall metric + subgroup_metric = metric + results = self.__filter_subgroup_metrics_df(results, subgroup_metric, metric, + selected_subgroup='overall', defined_model_names=model_names) + model_metrics_matrix = pd.DataFrame(results).T + model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] + model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise + sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) + + def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, + tolerance: float = 0.001): """ - total_model_ranks = dict() - matrix_fairness_metrics = [metric_name for metric_name in sorted_matrix_by_rank[self.model_names[0]].index - if metric_name[:metric_name.rfind('_')] in self.fairness_metrics_lst] - matrix_variance_metrics = [metric_name for metric_name in sorted_matrix_by_rank[self.model_names[0]].index - if metric_name[:metric_name.rfind('_')] in self.variance_metrics_lst] - - for model_name in self.model_names: - model_ranks = dict() - model_ranks['Fairness_Ranks_Sum'] = np.sum(sorted_matrix_by_rank[model_name][matrix_fairness_metrics] + 1) - model_ranks['Variance_Ranks_Sum'] = np.sum(sorted_matrix_by_rank[model_name][matrix_variance_metrics] + 1) - total_model_ranks[model_name] = model_ranks - - total_model_ranks_df = pd.DataFrame(total_model_ranks).T - - matrix_width = 6 - matrix_height = num_models // 2 - plt.figure(figsize=(matrix_width, matrix_height)) - ax = sns.heatmap(total_model_ranks_df, annot=True, cmap="coolwarm_r", fmt = '') - ax.set(xlabel="", ylabel="") - ax.xaxis.tick_top() - - if self.__create_report: - plt.close() - return ax - - ax.set_title('Total Ranks Sum For Group Fairness and Stability Metrics', fontsize=15) - - def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): - """ - Create model rank and total model rank heatmaps. + Create a heatmap for disparity metrics. Parameters ---------- + model_names + A list of selected model names to display on the heatmap metrics_lst List of group metric names to visualize groups_lst List of sensitive attributes + tolerance + An acceptable value difference for metrics dense ranking """ + if tolerance < 0.001 or tolerance > 0.2: + raise ValueError('Tolerance should be in the [0.001, 0.2] range') + results = {} - num_models = len(self.model_names) for metric in metrics_lst: for group in groups_lst: group_metric = metric + '_' + group @@ -359,6 +265,8 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): (self.sorted_models_composed_metrics_df.Metric == metric) & (self.sorted_models_composed_metrics_df.Subgroup == group) ]['Model_Name'].values + sorted_model_names_arr = [model for model in sorted_model_names_arr if model in model_names] + # Add values to results dict for idx, model_name in enumerate(sorted_model_names_arr): metric_value = self.sorted_models_composed_metrics_df[ @@ -370,8 +278,7 @@ def create_model_rank_heatmaps(self, metrics_lst: list, groups_lst): results[group_metric][model_name] = metric_value model_metrics_matrix = pd.DataFrame(results).T - sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance=0.001) - model_rank_heatmap = self.create_model_rank_heatmap(model_metrics_matrix, sorted_matrix_by_rank, num_models) - total_model_rank_heatmap = self.create_total_model_rank_heatmap(sorted_matrix_by_rank, num_models) - if self.__create_report: - return model_rank_heatmap, total_model_rank_heatmap + model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] + model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise + sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) + model_rank_heatmap = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) From a4e904a56504d54de97ad40334df23e9333b8f3a Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 15:53:05 +0200 Subject: [PATCH 087/148] Fixed visualizations --- .../Multiple_Models_Interface_Use_Case.ipynb | 457 ++++++------------ docs/examples/experiment_config.yaml | 5 +- ...ssifier_50_Estimators_20231221__135051.csv | 19 + ...ression_50_Estimators_20231221__135051.csv | 19 + ...ssifier_50_Estimators_20231221__135051.csv | 19 + ...ssifier_50_Estimators_20231221__135051.csv | 19 + ..._Sensitive_Attributes_20231221__135051.csv | 5 + 7 files changed, 234 insertions(+), 309 deletions(-) create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__135051.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__135051.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__135051.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__135051.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__135051.csv diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 733f8b4a..347997ab 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -2,15 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:05.208285Z", - "start_time": "2023-10-21T20:58:04.841119Z" + "end_time": "2023-12-21T13:50:48.357216Z", + "start_time": "2023-12-21T13:50:48.244178Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -19,12 +28,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:05.216534Z", - "start_time": "2023-10-21T20:58:05.208182Z" + "end_time": "2023-12-21T13:50:48.384153Z", + "start_time": "2023-12-21T13:50:48.272299Z" } }, "outputs": [], @@ -37,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:05.226610Z", - "start_time": "2023-10-21T20:58:05.216923Z" + "end_time": "2023-12-21T13:50:48.399319Z", + "start_time": "2023-12-21T13:50:48.293841Z" } }, "outputs": [ @@ -96,12 +105,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.737014Z", - "start_time": "2023-10-21T20:58:05.228621Z" + "end_time": "2023-12-21T13:50:48.399842Z", + "start_time": "2023-12-21T13:50:48.313165Z" } }, "outputs": [], @@ -153,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "outputs": [], "source": [ "DATASET_SPLIT_SEED = 42\n", @@ -163,15 +172,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.760654Z", - "start_time": "2023-10-21T20:58:06.738834Z" + "end_time": "2023-12-21T13:50:48.400602Z", + "start_time": "2023-12-21T13:50:48.332826Z" } }, "id": "ce359a052925eb3a" }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "outputs": [], "source": [ "models_params_for_tuning = {\n", @@ -216,8 +225,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.779658Z", - "start_time": "2023-10-21T20:58:06.759908Z" + "end_time": "2023-12-21T13:50:48.401256Z", + "start_time": "2023-12-21T13:50:48.351269Z" } }, "id": "2ece07ab7e3a9acc" @@ -252,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -270,15 +279,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.800438Z", - "start_time": "2023-10-21T20:58:06.780670Z" + "end_time": "2023-12-21T13:50:48.421191Z", + "start_time": "2023-12-21T13:50:48.369137Z" } }, "id": "af22ee06f1e3eb1a" }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)\n", @@ -287,8 +296,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.820854Z", - "start_time": "2023-10-21T20:58:06.800568Z" + "end_time": "2023-12-21T13:50:48.445927Z", + "start_time": "2023-12-21T13:50:48.386863Z" } }, "id": "65181f72484bb92b" @@ -317,12 +326,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 22, "id": "9e3d7bf3", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.839915Z", - "start_time": "2023-10-21T20:58:06.822383Z" + "end_time": "2023-12-21T13:50:48.451224Z", + "start_time": "2023-12-21T13:50:48.406474Z" } }, "outputs": [], @@ -365,12 +374,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.881602Z", - "start_time": "2023-10-21T20:58:06.842269Z" + "end_time": "2023-12-21T13:50:48.460968Z", + "start_time": "2023-12-21T13:50:48.424468Z" } }, "outputs": [ @@ -379,7 +388,7 @@ "text/plain": " juv_fel_count juv_misd_count juv_other_count priors_count \\\n0 0.0 -2.340451 1.0 -15.010999 \n1 0.0 0.000000 0.0 0.000000 \n2 0.0 0.000000 0.0 0.000000 \n3 0.0 0.000000 0.0 6.000000 \n4 0.0 0.000000 0.0 7.513697 \n\n age_cat_25 - 45 \n0 1 \n1 1 \n2 0 \n3 1 \n4 1 ", "text/html": "
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    juv_fel_countjuv_misd_countjuv_other_countpriors_countage_cat_25 - 45
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    40.00.0000000.07.5136971
    \n
    " }, - "execution_count": 10, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -391,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -402,15 +411,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.897824Z", - "start_time": "2023-10-21T20:58:06.878077Z" + "end_time": "2023-12-21T13:50:48.469253Z", + "start_time": "2023-12-21T13:50:48.450810Z" } }, "id": "ebbef5eaf9dc0943" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "outputs": [], "source": [ "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" @@ -418,8 +427,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:06.965760Z", - "start_time": "2023-10-21T20:58:06.898526Z" + "end_time": "2023-12-21T13:50:48.517963Z", + "start_time": "2023-12-21T13:50:48.469754Z" } }, "id": "97ed4609effbf53f" @@ -436,27 +445,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023/10/21, 23:58:06: Tuning DecisionTreeClassifier...\n", + "2023/12/21, 15:50:48: Tuning DecisionTreeClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/10/21, 23:58:08: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6429262328840039, Accuracy = 0.6442550505050505]\n", + "2023/12/21, 15:50:49: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6429262328840039, Accuracy = 0.6442550505050505]\n", "\n", - "2023/10/21, 23:58:08: Tuning LogisticRegression...\n", + "2023/12/21, 15:50:49: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/10/21, 23:58:08: Tuning for LogisticRegression is finished [F1 score = 0.6461022173486363, Accuracy = 0.6505681818181818]\n", + "2023/12/21, 15:50:49: Tuning for LogisticRegression is finished [F1 score = 0.6461022173486363, Accuracy = 0.6505681818181818]\n", "\n", - "2023/10/21, 23:58:08: Tuning RandomForestClassifier...\n", + "2023/12/21, 15:50:49: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/10/21, 23:58:08: Tuning for RandomForestClassifier is finished [F1 score = 0.6480756802972086, Accuracy = 0.6518308080808081]\n", + "2023/12/21, 15:50:50: Tuning for RandomForestClassifier is finished [F1 score = 0.6480756802972086, Accuracy = 0.6518308080808081]\n", "\n", - "2023/10/21, 23:58:08: Tuning XGBClassifier...\n", + "2023/12/21, 15:50:50: Tuning XGBClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/10/21, 23:58:09: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" + "2023/12/21, 15:50:51: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" ] }, { @@ -464,7 +473,7 @@ "text/plain": " Dataset_Name Model_Name F1_Score \\\n0 COMPAS_Without_Sensitive_Attributes DecisionTreeClassifier 0.642926 \n1 COMPAS_Without_Sensitive_Attributes LogisticRegression 0.646102 \n2 COMPAS_Without_Sensitive_Attributes RandomForestClassifier 0.648076 \n3 COMPAS_Without_Sensitive_Attributes XGBClassifier 0.654881 \n\n Accuracy_Score Model_Best_Params \n0 0.644255 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 0.650568 {'C': 1, 'max_iter': 250, 'penalty': 'l2', 'so... \n2 0.651831 {'max_depth': 10, 'max_features': 0.6, 'min_sa... \n3 0.658775 {'lambda': 100, 'learning_rate': 0.1, 'max_dep... ", "text/html": "
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    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0COMPAS_Without_Sensitive_AttributesDecisionTreeClassifier0.6429260.644255{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1COMPAS_Without_Sensitive_AttributesLogisticRegression0.6461020.650568{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'so...
    2COMPAS_Without_Sensitive_AttributesRandomForestClassifier0.6480760.651831{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    3COMPAS_Without_Sensitive_AttributesXGBClassifier0.6548810.658775{'lambda': 100, 'learning_rate': 0.1, 'max_dep...
    \n
    " }, - "execution_count": 13, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -476,15 +485,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:09.469344Z", - "start_time": "2023-10-21T20:58:06.928166Z" + "end_time": "2023-12-21T13:50:51.107392Z", + "start_time": "2023-12-21T13:50:48.495826Z" } }, "id": "782741c190a4690b" }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "outputs": [], "source": [ "now = datetime.now(timezone.utc)\n", @@ -495,8 +504,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:09.516018Z", - "start_time": "2023-10-21T20:58:09.469540Z" + "end_time": "2023-12-21T13:50:51.170619Z", + "start_time": "2023-12-21T13:50:51.110452Z" } }, "id": "21ccc879c5c3e215" @@ -513,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "outputs": [ { "name": "stdout", @@ -544,8 +553,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:09.549780Z", - "start_time": "2023-10-21T20:58:09.496334Z" + "end_time": "2023-12-21T13:50:51.186336Z", + "start_time": "2023-12-21T13:50:51.152495Z" } }, "id": "3b15f202741fa2ae" @@ -568,223 +577,79 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.356124Z", - "start_time": "2023-10-21T20:58:09.523683Z" + "end_time": "2023-12-21T13:51:16.130436Z", + "start_time": "2023-12-21T13:50:51.176259Z" } }, "outputs": [ { "data": { - "text/plain": "Analyze models in one run: 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallsex_privsex_disrace_privrace_dis
    0Std0.0697310.0718490.0692020.0693490.069977
    1IQR0.0871090.0845060.0877600.0873320.086966
    2Overall_Uncertainty0.8926220.8988200.8910740.8849850.897547
    3Mean_Prediction0.5196500.5760850.5055580.5849730.477526
    4Aleatoric_Uncertainty0.8682020.8714610.8673880.8595400.873788
    5Statistical_Bias0.4190170.4157230.4198400.4146500.421833
    6Label_Stability0.8227270.8096680.8259880.8244440.821620
    7Jitter0.1287760.1376770.1265530.1246160.131458
    8TPR0.6539280.4666670.6893940.5170070.716049
    9TNR0.7282050.8014710.7060130.7827720.682390
    10PPV0.6595290.5645160.6740740.5671640.696697
    11FNR0.3460720.5333330.3106060.4829930.283951
    12FPR0.2717950.1985290.2939870.2172280.317610
    13Accuracy0.6950760.6824640.6982250.6884060.699377
    14F10.6567160.5109490.6816480.5409250.706240
    15Selection-Rate0.4422350.2938390.4792900.3236710.518692
    16Positive-Rate0.9915070.8266671.0227270.9115651.027778
    17Sample_Size1056.000000NaNNaNNaNNaN
    \n" + "text/plain": " Metric overall sex_priv sex_dis race_priv \\\n0 Statistical_Bias 0.418919 0.415082 0.419877 0.415231 \n1 Aleatoric_Uncertainty 0.869658 0.875145 0.868287 0.863469 \n2 IQR 0.083137 0.080341 0.083835 0.085395 \n3 Mean_Prediction 0.519149 0.573816 0.505498 0.584583 \n4 Overall_Uncertainty 0.893219 0.901567 0.891134 0.888872 \n5 Std 0.068139 0.071017 0.067420 0.068772 \n6 Label_Stability 0.823636 0.806825 0.827834 0.821643 \n7 Jitter 0.124932 0.136899 0.121944 0.123261 \n8 TPR 0.656051 0.480000 0.689394 0.523810 \n9 TNR 0.731624 0.801471 0.710468 0.786517 \n10 PPV 0.663090 0.571429 0.677419 0.574627 \n11 FNR 0.343949 0.520000 0.310606 0.476190 \n12 FPR 0.268376 0.198529 0.289532 0.213483 \n13 Accuracy 0.697917 0.687204 0.700592 0.693237 \n14 F1 0.659552 0.521739 0.683354 0.548043 \n15 Selection-Rate 0.441288 0.298578 0.476923 0.323671 \n16 Positive-Rate 0.989384 0.840000 1.017677 0.911565 \n17 Sample_Size 1056.000000 211.000000 845.000000 414.000000 \n\n race_dis \n0 0.421298 \n1 0.873649 \n2 0.081680 \n3 0.476953 \n4 0.896022 \n5 0.067730 \n6 0.824922 \n7 0.126009 \n8 0.716049 \n9 0.685535 \n10 0.698795 \n11 0.283951 \n12 0.314465 \n13 0.700935 \n14 0.707317 \n15 0.517134 \n16 1.024691 \n17 642.000000 ", + "text/html": "
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    Metricoverallsex_privsex_disrace_privrace_dis
    0Statistical_Bias0.4189190.4150820.4198770.4152310.421298
    1Aleatoric_Uncertainty0.8696580.8751450.8682870.8634690.873649
    2IQR0.0831370.0803410.0838350.0853950.081680
    3Mean_Prediction0.5191490.5738160.5054980.5845830.476953
    4Overall_Uncertainty0.8932190.9015670.8911340.8888720.896022
    5Std0.0681390.0710170.0674200.0687720.067730
    6Label_Stability0.8236360.8068250.8278340.8216430.824922
    7Jitter0.1249320.1368990.1219440.1232610.126009
    8TPR0.6560510.4800000.6893940.5238100.716049
    9TNR0.7316240.8014710.7104680.7865170.685535
    10PPV0.6630900.5714290.6774190.5746270.698795
    11FNR0.3439490.5200000.3106060.4761900.283951
    12FPR0.2683760.1985290.2895320.2134830.314465
    13Accuracy0.6979170.6872040.7005920.6932370.700935
    14F10.6595520.5217390.6833540.5480430.707317
    15Selection-Rate0.4412880.2985780.4769230.3236710.517134
    16Positive-Rate0.9893840.8400001.0176770.9115651.024691
    17Sample_Size1056.000000211.000000845.000000414.000000642.000000
    \n
    " }, - "execution_count": 17, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -839,12 +704,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.403007Z", - "start_time": "2023-10-21T20:58:34.380470Z" + "end_time": "2023-12-21T13:51:16.182871Z", + "start_time": "2023-12-21T13:51:16.158081Z" } }, "outputs": [], @@ -854,12 +719,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.440589Z", - "start_time": "2023-10-21T20:58:34.403653Z" + "end_time": "2023-12-21T13:51:16.215947Z", + "start_time": "2023-12-21T13:51:16.178854Z" } }, "outputs": [], @@ -877,12 +742,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.476652Z", - "start_time": "2023-10-21T20:58:34.423407Z" + "end_time": "2023-12-21T13:51:16.268629Z", + "start_time": "2023-12-21T13:51:16.197532Z" } }, "outputs": [], @@ -892,14 +757,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "outputs": [ { "data": { - "text/plain": " Metric sex race sex&race \\\n0 Accuracy_Parity 0.015760 0.010971 -0.005266 \n1 Aleatoric_Uncertainty_Parity -0.004072 0.014248 0.007256 \n2 Aleatoric_Uncertainty_Ratio 0.995327 1.016576 1.008393 \n3 Equalized_Odds_FNR -0.222727 -0.199043 -0.185362 \n4 Equalized_Odds_FPR 0.095457 0.100382 0.132202 \n.. ... ... ... ... \n59 Disparate_Impact 1.159674 1.102332 1.093266 \n60 Std_Parity 0.000692 0.002415 0.002737 \n61 Std_Ratio 1.015140 1.053930 1.060978 \n62 Equalized_Odds_TNR -0.082094 -0.102184 -0.128932 \n63 Equalized_Odds_TPR 0.180202 0.165659 0.165871 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n.. ... \n59 XGBClassifier \n60 XGBClassifier \n61 XGBClassifier \n62 XGBClassifier \n63 XGBClassifier \n\n[64 rows x 5 columns]", - "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricsexracesex&raceModel_Name
    0Accuracy_Parity0.0157600.010971-0.005266DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity-0.0040720.0142480.007256DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio0.9953271.0165761.008393DecisionTreeClassifier
    3Equalized_Odds_FNR-0.222727-0.199043-0.185362DecisionTreeClassifier
    4Equalized_Odds_FPR0.0954570.1003820.132202DecisionTreeClassifier
    ..................
    59Disparate_Impact1.1596741.1023321.093266XGBClassifier
    60Std_Parity0.0006920.0024150.002737XGBClassifier
    61Std_Ratio1.0151401.0539301.060978XGBClassifier
    62Equalized_Odds_TNR-0.082094-0.102184-0.128932XGBClassifier
    63Equalized_Odds_TPR0.1802020.1656590.165871XGBClassifier
    \n

    64 rows × 5 columns

    \n
    " + "text/plain": " Metric sex race sex&race \\\n0 Accuracy_Parity 0.013388 0.007698 -0.007181 \n1 Aleatoric_Uncertainty_Parity -0.006858 0.010180 0.002563 \n2 Aleatoric_Uncertainty_Ratio 0.992164 1.011790 1.002951 \n3 Equalized_Odds_FNR -0.209394 -0.192240 -0.180043 \n4 Equalized_Odds_FPR 0.091003 0.100982 0.131112 \n.. ... ... ... ... \n63 Disparate_Impact 1.130710 1.070610 1.068836 \n64 Std_Parity -0.000697 0.002292 0.001908 \n65 Std_Ratio 0.985011 1.051406 1.042383 \n66 Equalized_Odds_TNR -0.086549 -0.094693 -0.123015 \n67 Equalized_Odds_TPR 0.153535 0.152053 0.155233 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n.. ... \n63 XGBClassifier \n64 XGBClassifier \n65 XGBClassifier \n66 XGBClassifier \n67 XGBClassifier \n\n[68 rows x 5 columns]", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricsexracesex&raceModel_Name
    0Accuracy_Parity0.0133880.007698-0.007181DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity-0.0068580.0101800.002563DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio0.9921641.0117901.002951DecisionTreeClassifier
    3Equalized_Odds_FNR-0.209394-0.192240-0.180043DecisionTreeClassifier
    4Equalized_Odds_FPR0.0910030.1009820.131112DecisionTreeClassifier
    ..................
    63Disparate_Impact1.1307101.0706101.068836XGBClassifier
    64Std_Parity-0.0006970.0022920.001908XGBClassifier
    65Std_Ratio0.9850111.0514061.042383XGBClassifier
    66Equalized_Odds_TNR-0.086549-0.094693-0.123015XGBClassifier
    67Equalized_Odds_TPR0.1535350.1520530.155233XGBClassifier
    \n

    68 rows × 5 columns

    \n
    " }, - "execution_count": 21, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -910,8 +775,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.488516Z", - "start_time": "2023-10-21T20:58:34.454122Z" + "end_time": "2023-12-21T13:51:16.271947Z", + "start_time": "2023-12-21T13:51:16.224275Z" } }, "id": "a286da0406c6401d" @@ -936,12 +801,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 35, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.516611Z", - "start_time": "2023-10-21T20:58:34.478385Z" + "end_time": "2023-12-21T13:51:16.274597Z", + "start_time": "2023-12-21T13:51:16.246062Z" } }, "outputs": [], @@ -953,155 +818,135 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 36, "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.601140Z", - "start_time": "2023-10-21T20:58:34.506904Z" + "end_time": "2023-12-21T13:51:16.357907Z", + "start_time": "2023-12-21T13:51:16.272378Z" } }, "outputs": [ { "data": { - "text/html": "\n
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    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 23, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", - " metrics_title=\"Error Metrics\"\n", + " metric_names=['Accuracy', 'F1', 'TPR', 'TNR', 'PPV', 'Selection-Rate'],\n", + " plot_title=\"Accuracy Metrics\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 37, "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.607261Z", - "start_time": "2023-10-21T20:58:34.557943Z" + "end_time": "2023-12-21T13:51:16.376273Z", + "start_time": "2023-12-21T13:51:16.319687Z" } }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 24, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['Label_Stability'],\n", - " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", - " metrics_title=\"Variance Metrics\"\n", + " metric_names=['Aleatoric_Uncertainty', 'Overall_Uncertainty', 'Label_Stability', 'Std', 'IQR', 'Jitter'],\n", + " plot_title=\"Stability and Uncertainty Metrics\"\n", ")" ] }, { - "cell_type": "markdown", - "source": [ - "Below is an example of an interactive plot. It requires that you run the below cell in Jupyter in the browser or EDAs, which support JavaScript displaying.\n", - "\n", - "You can use this plot to compare any pair of group fairness and stability metrics for all models." - ], + "cell_type": "code", + "execution_count": 38, + "id": "df024aed", "metadata": { - "collapsed": false, - "is_executing": true + "ExecuteTime": { + "end_time": "2023-12-21T13:51:16.713805Z", + "start_time": "2023-12-21T13:51:16.363906Z" + } }, - "id": "1f4906acb27ce7dd" - }, - { - "cell_type": "code", - "execution_count": 25, "outputs": [ { "data": { - "text/html": "\n
    \n", - "text/plain": "alt.HConcatChart(...)" + "text/plain": "
    ", + "image/png": 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}, - "execution_count": 25, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "visualizer.create_fairness_variance_interactive_bar_chart()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-21T20:58:34.774635Z", - "start_time": "2023-10-21T20:58:34.606702Z" - } - }, - "id": "b1249b3994b75555" + "visualizer.create_overall_metric_heatmap(\n", + " model_names=list(models_params_for_tuning.keys()),\n", + " metrics_lst=visualizer.all_accuracy_metrics + visualizer.all_uncertainty_metrics,\n", + " tolerance=0.005,\n", + ")" + ] }, { "cell_type": "code", - "execution_count": 26, - "id": "df024aed", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-21T20:58:35.469528Z", - "start_time": "2023-10-21T20:58:34.775075Z" - } - }, + "execution_count": 39, "outputs": [ { "data": { - "text/plain": "
    ", - "image/png": 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" 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "visualizer.create_model_rank_heatmaps(\n", + "visualizer.create_disparity_metric_heatmap(\n", + " model_names=list(models_params_for_tuning.keys()),\n", " metrics_lst=[\n", - " # Group fairness metrics\n", + " # Error disparity metrics\n", " 'Equalized_Odds_TPR',\n", " 'Equalized_Odds_FPR',\n", " 'Disparate_Impact',\n", - " 'Statistical_Parity_Difference',\n", - " 'Accuracy_Parity',\n", - " # Group stability metrics\n", - " 'Label_Stability_Ratio',\n", + " # Stability disparity metrics\n", + " 'Label_Stability_Difference',\n", " 'IQR_Parity',\n", - " 'Std_Parity',\n", " 'Std_Ratio',\n", - " 'Jitter_Parity',\n", " ],\n", " groups_lst=config.sensitive_attributes_dct.keys(),\n", + " tolerance=0.005,\n", ")" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T13:51:17.292738Z", + "start_time": "2023-12-21T13:51:16.712859Z" + } + }, + "id": "4bfca852f6ef428a" }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "2326c129", "metadata": { "ExecuteTime": { - "start_time": "2023-10-21T20:58:36.147687Z" + "end_time": "2023-12-21T13:51:17.292882Z", + "start_time": "2023-12-21T13:51:17.249454Z" } }, "outputs": [], diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 1205bf0f..44efa1b1 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,6 +1,5 @@ -dataset_name: Law_School +dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} -postprocessing_sensitive_attribute: 'race_binary' +sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__135051.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__135051.csv new file mode 100644 index 00000000..a44b3560 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__135051.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.41891910031265095,0.4150815529059087,0.41987735179528124,0.4152307092520529,0.42129759548256923,0.41398299774668107,0.4238179492743493,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.8696577240228027,0.8751451505450195,0.8682874908912197,0.8634686167515803,0.8736488305808806,0.8683713888735639,0.8709343509822359,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +IQR,0.08313659075623443,0.08034106995809731,0.08383464387860949,0.08539543244702731,0.08167995452572314,0.08374608712347983,0.08253169436157201,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Mean_Prediction,0.5191487180764912,0.5738164867333877,0.5054979498083192,0.5845827296378737,0.4769529536116743,0.5748282270618159,0.4638894318004895,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.8932189415818417,0.9015666679518202,0.8911344797308767,0.8888722411435764,0.896021954013994,0.8938622395481105,0.8925804986945635,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Std,0.0681388129658159,0.07101718238035581,0.0674200722007651,0.0687720369148963,0.06773047228837153,0.06899564101696645,0.06728845154146647,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Label_Stability,0.8236363636363636,0.806824644549763,0.8278343195266272,0.8216425120772947,0.8249221183800624,0.8225095057034221,0.8247547169811321,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Jitter,0.12493197278911591,0.13689911983750863,0.12194372660306727,0.1232613625160207,0.12600928221755975,0.12442616590362393,0.12543396226415116,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.6560509554140127,0.48,0.6893939393939394,0.5238095238095238,0.7160493827160493,0.5478723404255319,0.7279151943462897,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.7316239316239316,0.8014705882352942,0.7104677060133631,0.7865168539325843,0.6855345911949685,0.7869822485207101,0.6558704453441295,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.6630901287553648,0.5714285714285714,0.6774193548387096,0.5746268656716418,0.6987951807228916,0.5885714285714285,0.7079037800687286,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.34394904458598724,0.52,0.3106060606060606,0.47619047619047616,0.2839506172839506,0.4521276595744681,0.27208480565371024,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.26837606837606837,0.19852941176470587,0.289532293986637,0.21348314606741572,0.31446540880503143,0.21301775147928995,0.3441295546558704,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.6979166666666666,0.6872037914691943,0.7005917159763314,0.6932367149758454,0.7009345794392523,0.7015209125475285,0.6943396226415094,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.6595517609391676,0.5217391304347826,0.6833541927409261,0.5480427046263345,0.7073170731707317,0.5674931129476584,0.7177700348432056,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.4412878787878788,0.2985781990521327,0.47692307692307695,0.32367149758454106,0.5171339563862928,0.33269961977186313,0.5490566037735849,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,0.9893842887473461,0.84,1.0176767676767677,0.9115646258503401,1.0246913580246915,0.9308510638297872,1.028268551236749,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__135051.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__135051.csv new file mode 100644 index 00000000..66acfdff --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__135051.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.433597771372966,0.43267830053955414,0.43382736704852803,0.4331229956770561,0.4339039351394874,0.43193136776857183,0.4352515983463836,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.9060551858025602,0.9142531588730223,0.9040081179707643,0.911204396355948,0.9027346668475719,0.9134704734610974,0.8986958625791817,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.028872561973586907,0.02854548269272321,0.028954235024784832,0.02818344922868752,0.029316943089456603,0.02861417053383365,0.029129003289266556,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.522170752595099,0.5704694384039461,0.5101103706949017,0.5875456467638047,0.48001311056107376,0.5760572157518616,0.4686909797263119,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.908833744526394,0.9166264214181747,0.9068878808291564,0.913543414518137,0.9057966676158309,0.9158997674184807,0.9018210501089647,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.022056638947103583,0.02184602110150353,0.022109231095531534,0.02176009024767625,0.022247871285986633,0.02198729412067529,0.02212546041635129,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9373484848484848,0.9127962085308058,0.9434792899408284,0.9451207729468598,0.9323364485981308,0.935361216730038,0.9393207547169811,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.04743815708101424,0.0654763516781121,0.04293394517570347,0.04172335600907024,0.05112340263208101,0.048979591836734775,0.04590835579514834,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.6220806794055201,0.48,0.648989898989899,0.4489795918367347,0.7006172839506173,0.48936170212765956,0.7102473498233216,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.7333333333333333,0.8088235294117647,0.7104677060133631,0.8164794007490637,0.6635220125786163,0.8106508875739645,0.6275303643724697,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.6525612472160356,0.5806451612903226,0.6640826873385013,0.5739130434782609,0.6796407185628742,0.5897435897435898,0.6860068259385665,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.37791932059447986,0.52,0.351010101010101,0.5510204081632653,0.2993827160493827,0.5106382978723404,0.28975265017667845,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.26666666666666666,0.19117647058823528,0.289532293986637,0.18352059925093633,0.33647798742138363,0.1893491124260355,0.3724696356275304,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.6837121212121212,0.6919431279620853,0.6816568047337278,0.6859903381642513,0.6822429906542056,0.6958174904942965,0.6716981132075471,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.6369565217391304,0.5255474452554745,0.6564495530012772,0.5038167938931297,0.6899696048632219,0.5348837209302325,0.6979166666666666,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.4251893939393939,0.2938388625592417,0.45798816568047335,0.2777777777777778,0.5202492211838006,0.2965779467680608,0.5528301886792453,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9532908704883227,0.8266666666666667,0.9772727272727273,0.782312925170068,1.0308641975308641,0.8297872340425532,1.0353356890459364,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__135051.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__135051.csv new file mode 100644 index 00000000..910d8844 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__135051.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.4048890030650768,0.3980995154472693,0.4065843662453813,0.3950437620978315,0.41123780331498255,0.3977688499067287,0.4119554192184562,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.835374162138633,0.8465667224609636,0.8325793334664296,0.8223539803957951,0.8437703541036405,0.8329267581340214,0.8378030951696249,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.09001986313375351,0.10033631056614047,0.08744380347903914,0.0935534563071117,0.08774119090046648,0.0921066358167481,0.08794883967855512,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.523707671401242,0.5803827613326719,0.5095556666964708,0.5967568523198149,0.47660119024814346,0.5859709024932104,0.46191435148732596,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.8597869152341028,0.8779062198750585,0.855262449814882,0.8489034571948296,0.8668052199510174,0.8590197727335408,0.8605482679799437,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.06762120278007556,0.07407632513994103,0.06600933198962394,0.06923158973846101,0.06658272894709802,0.06881039196804807,0.06644098860484247,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.8510606060606061,0.8098578199052132,0.8613491124260355,0.8457971014492754,0.8544548286604361,0.8459315589353613,0.8561509433962264,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.10787414965986396,0.13157558758100404,0.1019558024393189,0.10929705215419501,0.10695657702333253,0.10972297664312888,0.106039276087794,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.6751592356687898,0.5733333333333334,0.6944444444444444,0.564625850340136,0.7253086419753086,0.5797872340425532,0.7385159010600707,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.7384615384615385,0.8088235294117647,0.7171492204899778,0.8127340823970037,0.6761006289308176,0.8047337278106509,0.6477732793522267,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.6751592356687898,0.6231884057971014,0.6840796019900498,0.6240601503759399,0.6952662721893491,0.6228571428571429,0.706081081081081,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.3248407643312102,0.4266666666666667,0.3055555555555556,0.43537414965986393,0.27469135802469136,0.42021276595744683,0.26148409893992935,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.26153846153846155,0.19117647058823528,0.2828507795100223,0.18726591760299627,0.3238993710691824,0.1952662721893491,0.3522267206477733,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.7102272727272727,0.7251184834123223,0.7065088757396449,0.7246376811594203,0.7009345794392523,0.7243346007604563,0.6962264150943396,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.6751592356687898,0.5972222222222222,0.6892230576441103,0.5928571428571429,0.7099697885196374,0.6005509641873278,0.7219343696027634,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.4460227272727273,0.32701421800947866,0.4757396449704142,0.321256038647343,0.5264797507788161,0.33269961977186313,0.5584905660377358,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0,0.92,1.0151515151515151,0.9047619047619048,1.0432098765432098,0.9308510638297872,1.0459363957597174,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__135051.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__135051.csv new file mode 100644 index 00000000..23d49838 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__135048/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__135051.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params +Statistical_Bias,0.4131684930092006,0.4099694751379614,0.4139673010220189,0.40873872452743965,0.41602507268435485,0.4086465600580544,0.4176562981644891,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Aleatoric_Uncertainty,0.8707223534584045,0.8852691650390625,0.8670899271965027,0.8657431602478027,0.8739331364631653,0.8727299571037292,0.8687297105789185,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +IQR,0.06076557358557528,0.06208778557619212,0.06043541177490054,0.05884081341218257,0.06200677407122104,0.05945907708809856,0.062062209732127636,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Mean_Prediction,0.5237338542938232,0.5788225531578064,0.5099779963493347,0.5907356142997742,0.4805271327495575,0.5812770128250122,0.46662503480911255,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Overall_Uncertainty,0.8791543129810355,0.8938904882534106,0.8754746289781109,0.8737721061587747,0.8826250818664185,0.8808825205062522,0.8774391485314805,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Std,0.04597282409667969,0.046530935913324356,0.045833464711904526,0.044579602777957916,0.046871256083250046,0.04501526802778244,0.04692315682768822,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Label_Stability,0.910530303030303,0.8676777251184833,0.9212307692307693,0.9039613526570048,0.9147663551401869,0.8996197718631178,0.9213584905660377,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Jitter,0.07056122448979589,0.09975432827159318,0.06327158555729992,0.07386374839791,0.06843155953970381,0.07676883681229159,0.06440046207162112,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TPR,0.6624203821656051,0.5333333333333333,0.6868686868686869,0.5578231292517006,0.7098765432098766,0.5691489361702128,0.7243816254416962,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TNR,0.7350427350427351,0.8014705882352942,0.7149220489977728,0.7865168539325843,0.6918238993710691,0.7869822485207101,0.6639676113360324,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +PPV,0.6680942184154176,0.5970149253731343,0.68,0.5899280575539568,0.7012195121951219,0.5977653631284916,0.7118055555555556,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FNR,0.3375796178343949,0.4666666666666667,0.31313131313131315,0.4421768707482993,0.29012345679012347,0.4308510638297872,0.2756183745583039,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FPR,0.26495726495726496,0.19852941176470587,0.28507795100222716,0.21348314606741572,0.3081761006289308,0.21301775147928995,0.3360323886639676,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Accuracy,0.7026515151515151,0.7061611374407583,0.7017751479289941,0.7053140096618358,0.7009345794392523,0.7091254752851711,0.6962264150943396,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +F1,0.6652452025586354,0.5633802816901409,0.6834170854271356,0.5734265734265734,0.7055214723926381,0.5831062670299727,0.7180385288966725,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Selection-Rate,0.4422348484848485,0.3175355450236967,0.47337278106508873,0.3357487922705314,0.5109034267912772,0.3403041825095057,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Positive-Rate,0.9915074309978769,0.8933333333333333,1.0101010101010102,0.9455782312925171,1.0123456790123457,0.9521276595744681,1.017667844522968,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__135051.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__135051.csv new file mode 100644 index 00000000..9deda582 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__135051.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" From 953fbaf99b3f2e1569ec15cbe75c0f094e70aad1 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 18:36:38 +0200 Subject: [PATCH 088/148] Updated all visualizations in use case notebooks --- ...iple_Models_Interface_With_DB_Writer.ipynb | 339 ++++++++---- ...Models_Interface_With_Error_Analysis.ipynb | 501 ++++++++---------- ..._Models_Interface_With_Postprocessor.ipynb | 187 +++---- docs/examples/experiment_config.yaml | 1 + ...ssifier_50_Estimators_20231221__161031.csv | 19 + ...ression_50_Estimators_20231221__161031.csv | 19 + ...ssifier_50_Estimators_20231221__161031.csv | 19 + ...ssifier_50_Estimators_20231221__161031.csv | 19 + ...ssifier_50_Estimators_20231221__163532.csv | 19 + ...ression_50_Estimators_20231221__163532.csv | 19 + ...ssifier_50_Estimators_20231221__163532.csv | 19 + ...ssifier_50_Estimators_20231221__163532.csv | 19 + ...ression_50_Estimators_20231221__162628.csv | 19 + ...ssifier_50_Estimators_20231221__162628.csv | 19 + ..._Sensitive_Attributes_20231221__161031.csv | 5 + ...ng_results_Law_School_20231221__162628.csv | 3 + virny/utils/data_viz_utils.py | 1 + 17 files changed, 715 insertions(+), 512 deletions(-) create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__161031.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__161031.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__161031.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__161031.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__163532.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__163532.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__163532.csv create mode 100644 docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__163532.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__162628.csv create mode 100644 docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__162628.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__161031.csv create mode 100644 docs/examples/results/models_tuning/tuning_results_Law_School_20231221__162628.csv diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb index 9dcb187a..caea38fe 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb @@ -2,10 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "id": "68ae1475", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.579235Z", + "start_time": "2023-12-21T16:28:55.465243Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", @@ -14,9 +28,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 27, "id": "9a1a7163", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.633647Z", + "start_time": "2023-12-21T16:28:55.495625Z" + } + }, "outputs": [], "source": [ "import os\n", @@ -59,9 +78,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "id": "dec1f3f0", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.635772Z", + "start_time": "2023-12-21T16:28:55.518300Z" + } + }, "outputs": [], "source": [ "import os\n", @@ -108,14 +132,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, "outputs": [], "source": [ "TEST_SET_FRACTION = 0.2\n", "DATASET_SPLIT_SEED = 42" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.635910Z", + "start_time": "2023-12-21T16:28:55.539588Z" + } }, "id": "5b151f8896bc744e" }, @@ -143,16 +171,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "id": "30a74059", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.637465Z", + "start_time": "2023-12-21T16:28:55.559514Z" + } + }, "outputs": [ { "data": { "text/plain": " juv_fel_count juv_misd_count juv_other_count priors_count \\\n0 0.0 -2.340451 1.0 -15.010999 \n1 0.0 0.000000 0.0 0.000000 \n2 0.0 0.000000 0.0 0.000000 \n3 0.0 0.000000 0.0 6.000000 \n4 0.0 0.000000 0.0 7.513697 \n\n age_cat_25 - 45 \n0 1 \n1 1 \n2 0 \n3 1 \n4 1 ", "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    juv_fel_countjuv_misd_countjuv_other_countpriors_countage_cat_25 - 45
    00.0-2.3404511.0-15.0109991
    10.00.0000000.00.0000001
    20.00.0000000.00.0000000
    30.00.0000000.06.0000001
    40.00.0000000.07.5136971
    \n
    " }, - "execution_count": 5, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -173,19 +206,27 @@ "])" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.666695Z", + "start_time": "2023-12-21T16:28:55.588141Z" + } }, "id": "e249dee6ca87b5fd" }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 32, "outputs": [], "source": [ "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.672789Z", + "start_time": "2023-12-21T16:28:55.607559Z" + } }, "id": "6cd3c2f8ad510bb2" }, @@ -215,9 +256,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "id": "79dcac74", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.692731Z", + "start_time": "2023-12-21T16:28:55.636185Z" + } + }, "outputs": [], "source": [ "ROOT_DIR = os.getcwd()\n", @@ -235,9 +281,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "id": "abc8bd6f", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.706258Z", + "start_time": "2023-12-21T16:28:55.655769Z" + } + }, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)" @@ -261,9 +312,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "id": "a711e1af", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.722882Z", + "start_time": "2023-12-21T16:28:55.675721Z" + } + }, "outputs": [], "source": [ "models_config = {\n", @@ -303,9 +359,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "id": "899e6ab4", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.924472Z", + "start_time": "2023-12-21T16:28:55.695436Z" + } + }, "outputs": [], "source": [ "import os\n", @@ -329,15 +390,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 37, "id": "db8df420", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:28:55.944986Z", + "start_time": "2023-12-21T16:28:55.925471Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Current session uuid: 7013282e-2bf0-42df-ac7a-acf338c1cf58\n" + "Current session uuid: 670d69fb-5066-4e88-a2e0-bdfa35c0b1db\n" ] } ], @@ -353,17 +419,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 38, "id": "46961cf7", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:09.489475Z", + "start_time": "2023-12-21T16:28:55.945278Z" + } + }, "outputs": [ { "data": { - "text/plain": "Analyze models in one run: 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallsex_privsex_disrace_privrace_dis
    0Mean0.5200750.5754810.5062400.5844220.478580
    1Std0.0732160.0760310.0725130.0734520.073063
    2IQR0.0883070.0874920.0885100.0886130.088109
    3Aleatoric_Uncertainty0.8614990.8685710.8597330.8540250.866318
    4Overall_Uncertainty0.8885120.8974060.8862910.8823790.892467
    5Statistical_Bias0.4161860.4116540.4173180.4114420.419246
    6Jitter0.1130700.1358740.1073750.1113620.114171
    7Per_Sample_Accuracy0.6875190.6887200.6872190.6922220.684486
    8Label_Stability0.8582950.8229380.8671240.8567150.859315
    9TPR0.6560510.4666670.6919190.5170070.719136
    10TNR0.7333330.8088240.7104680.7865170.688679
    11PPV0.6645160.5737700.6782180.5714290.701807
    12FNR0.3439490.5333330.3080810.4829930.280864
    13FPR0.2666670.1911760.2895320.2134830.311321
    14Accuracy0.6988640.6872040.7017750.6908210.704050
    15F10.6602560.5147060.6850000.5428570.710366
    16Selection-Rate0.4403410.2891000.4781070.3212560.517134
    17Positive-Rate0.9872610.8133331.0202020.9047621.024691
    18Sample_Size1056.000000NaNNaNNaNNaN
    \n" + "text/plain": " Metric overall sex_priv sex_dis race_priv \\\n0 Mean_Prediction 0.520278 0.576229 0.506307 0.584245 \n1 Statistical_Bias 0.416429 0.412431 0.417428 0.411377 \n2 Overall_Uncertainty 0.888363 0.899384 0.885611 0.881777 \n3 IQR 0.090021 0.091652 0.089614 0.091259 \n4 Std 0.073992 0.076675 0.073322 0.074674 \n5 Aleatoric_Uncertainty 0.859969 0.868305 0.857887 0.851974 \n6 Label_Stability 0.825720 0.792986 0.833893 0.832464 \n7 Jitter 0.132337 0.152425 0.127321 0.125576 \n8 TPR 0.660297 0.493333 0.691919 0.530612 \n9 TNR 0.731624 0.808824 0.708241 0.786517 \n10 PPV 0.664530 0.587302 0.676543 0.577778 \n11 FNR 0.339703 0.506667 0.308081 0.469388 \n12 FPR 0.268376 0.191176 0.291759 0.213483 \n13 Accuracy 0.699811 0.696682 0.700592 0.695652 \n14 F1 0.662407 0.536232 0.684145 0.553191 \n15 Selection-Rate 0.443182 0.298578 0.479290 0.326087 \n16 Positive-Rate 0.993631 0.840000 1.022727 0.918367 \n17 Sample_Size 1056.000000 211.000000 845.000000 414.000000 \n\n race_dis \n0 0.479029 \n1 0.419687 \n2 0.892610 \n3 0.089223 \n4 0.073552 \n5 0.865124 \n6 0.821371 \n7 0.136697 \n8 0.719136 \n9 0.685535 \n10 0.699700 \n11 0.280864 \n12 0.314465 \n13 0.702492 \n14 0.709285 \n15 0.518692 \n16 1.027778 \n17 642.000000 ", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallsex_privsex_disrace_privrace_dis
    0Mean_Prediction0.5202780.5762290.5063070.5842450.479029
    1Statistical_Bias0.4164290.4124310.4174280.4113770.419687
    2Overall_Uncertainty0.8883630.8993840.8856110.8817770.892610
    3IQR0.0900210.0916520.0896140.0912590.089223
    4Std0.0739920.0766750.0733220.0746740.073552
    5Aleatoric_Uncertainty0.8599690.8683050.8578870.8519740.865124
    6Label_Stability0.8257200.7929860.8338930.8324640.821371
    7Jitter0.1323370.1524250.1273210.1255760.136697
    8TPR0.6602970.4933330.6919190.5306120.719136
    9TNR0.7316240.8088240.7082410.7865170.685535
    10PPV0.6645300.5873020.6765430.5777780.699700
    11FNR0.3397030.5066670.3080810.4693880.280864
    12FPR0.2683760.1911760.2917590.2134830.314465
    13Accuracy0.6998110.6966820.7005920.6956520.702492
    14F10.6624070.5362320.6841450.5531910.709285
    15Selection-Rate0.4431820.2985780.4792900.3260870.518692
    16Positive-Rate0.9936310.8400001.0227270.9183671.027778
    17Sample_Size1056.000000211.000000845.000000414.000000642.000000
    \n
    " }, - "execution_count": 14, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -469,9 +546,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 40, "id": "180f429c", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:09.549380Z", + "start_time": "2023-12-21T16:29:09.528185Z" + } + }, "outputs": [], "source": [ "def read_model_metric_dfs_from_db(collection, session_uuid):\n", @@ -495,9 +577,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 41, "id": "64a38bb0", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:09.881359Z", + "start_time": "2023-12-21T16:29:09.549462Z" + } + }, "outputs": [], "source": [ "model_metric_dfs = read_model_metric_dfs_from_db(collection, custom_table_fields_dct['session_uuid'])\n", @@ -506,9 +593,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "id": "b30c703c", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:09.907843Z", + "start_time": "2023-12-21T16:29:09.881472Z" + } + }, "outputs": [], "source": [ "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" @@ -524,9 +616,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 43, "id": "896f7906", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:09.940990Z", + "start_time": "2023-12-21T16:29:09.906131Z" + } + }, "outputs": [], "source": [ "models_composed_metrics_df = metrics_composer.compose_metrics()" @@ -550,9 +647,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 44, "id": "de09882f", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:09.973135Z", + "start_time": "2023-12-21T16:29:09.938835Z" + } + }, "outputs": [], "source": [ "visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", @@ -562,137 +664,137 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "id": "0d29adad", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:10.017912Z", + "start_time": "2023-12-21T16:29:09.970111Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 21, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", - " metrics_title=\"Error Metrics\"\n", + " metric_names=['Accuracy', 'F1', 'TPR', 'TNR', 'PPV', 'Selection-Rate'],\n", + " plot_title=\"Accuracy Metrics\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 46, "id": "ed6a0671", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:10.066293Z", + "start_time": "2023-12-21T16:29:10.018199Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 22, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['Label_Stability'],\n", - " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", - " metrics_title=\"Variance Metrics\"\n", + " metric_names=['Aleatoric_Uncertainty', 'Overall_Uncertainty', 'Label_Stability', 'Std', 'IQR', 'Jitter'],\n", + " plot_title=\"Stability and Uncertainty Metrics\"\n", ")" ] }, - { - "cell_type": "markdown", - "id": "9cba9334", - "metadata": {}, - "source": [ - "Below is an example of an interactive plot. It requires that you run the below cell in Jupyter in the browser or EDAs, which support JavaScript displaying.\n", - "\n", - "You can use this plot to compare any pair of group fairness and stability metrics for all models." - ] - }, { "cell_type": "code", - "execution_count": 23, - "id": "9c595f71", - "metadata": {}, + "execution_count": 47, + "id": "dee49825", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:10.440918Z", + "start_time": "2023-12-21T16:29:10.065694Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
    \n", - "text/plain": "alt.HConcatChart(...)" + "text/plain": "
    ", + "image/png": 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}, - "execution_count": 23, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "visualizer.create_fairness_variance_interactive_bar_chart()" + "visualizer.create_overall_metric_heatmap(\n", + " model_names=list(models_config.keys()),\n", + " metrics_lst=visualizer.all_accuracy_metrics + visualizer.all_uncertainty_metrics,\n", + " tolerance=0.005,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "dee49825", - "metadata": {}, + "execution_count": 48, "outputs": [ { "data": { - "text/plain": "
    ", - "image/png": 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\n" 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\n" 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ - "visualizer.create_model_rank_heatmaps(\n", + "visualizer.create_disparity_metric_heatmap(\n", + " model_names=list(models_config.keys()),\n", " metrics_lst=[\n", - " # Group fairness metrics\n", + " # Error disparity metrics\n", " 'Equalized_Odds_TPR',\n", " 'Equalized_Odds_FPR',\n", " 'Disparate_Impact',\n", - " 'Statistical_Parity_Difference',\n", - " 'Accuracy_Parity',\n", - " # Group variance metrics\n", - " 'Label_Stability_Ratio',\n", + " # Stability disparity metrics\n", + " 'Label_Stability_Difference',\n", " 'IQR_Parity',\n", - " 'Std_Parity',\n", " 'Std_Ratio',\n", - " 'Jitter_Parity',\n", " ],\n", " groups_lst=config.sensitive_attributes_dct.keys(),\n", + " tolerance=0.005,\n", ")" - ] - }, - { - "cell_type": "markdown", - "id": "41669cf9", - "metadata": {}, - "source": [ - "Create an analysis report. It includes correspondent visualizations and details about your result metrics." - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:29:10.930750Z", + "start_time": "2023-12-21T16:29:10.440394Z" + } + }, + "id": "ec2f9dad494526f0" }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 49, "id": "9e73354b", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:11.003751Z", + "start_time": "2023-12-21T16:29:10.926144Z" + } + }, "outputs": [], "source": [ "client.close()" @@ -700,9 +802,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 49, "id": "a94b000c", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:29:11.003965Z", + "start_time": "2023-12-21T16:29:10.989072Z" + } + }, "outputs": [], "source": [] } diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index bfb12049..8fe51f1a 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -2,9 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 54, + "execution_count": 65, "id": "248cbed8", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.138777Z", + "start_time": "2023-12-21T16:35:32.027637Z" + } + }, "outputs": [ { "name": "stdout", @@ -23,9 +28,14 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 66, "id": "7ec6cd08", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.182456Z", + "start_time": "2023-12-21T16:35:32.059370Z" + } + }, "outputs": [], "source": [ "import os\n", @@ -36,15 +46,20 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 67, "id": "b8cb69f2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.182787Z", + "start_time": "2023-12-21T16:35:32.082016Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Current location: /home/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" ] } ], @@ -92,14 +107,17 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 68, "id": "7a9241de", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.182902Z", + "start_time": "2023-12-21T16:35:32.104678Z" + } + }, "outputs": [], "source": [ "import os\n", - "import pandas as pd\n", - "from pprint import pprint\n", "from datetime import datetime, timezone\n", "\n", "from xgboost import XGBClassifier\n", @@ -113,11 +131,11 @@ "\n", "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs, create_models_config_from_tuned_params_df\n", "from virny.user_interfaces.multiple_models_api import compute_metrics_with_config\n", + "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.utils.model_tuning_utils import tune_ML_models\n", - "from virny.datasets.base import BaseDataLoader" + "from virny.utils.model_tuning_utils import tune_ML_models" ] }, { @@ -144,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 69, "outputs": [], "source": [ "DATASET_SPLIT_SEED = 42\n", @@ -152,59 +170,14 @@ "TEST_SET_FRACTION = 0.2" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.184141Z", + "start_time": "2023-12-21T16:35:32.122775Z" + } }, "id": "76d98eaabfcfc9c0" }, - { - "cell_type": "code", - "execution_count": 59, - "outputs": [], - "source": [ - "models_params_for_tuning = {\n", - " 'DecisionTreeClassifier': {\n", - " 'model': DecisionTreeClassifier(random_state=MODELS_TUNING_SEED),\n", - " 'params': {\n", - " \"max_depth\": [20, 30],\n", - " \"min_samples_split\" : [0.1],\n", - " \"max_features\": ['sqrt'],\n", - " \"criterion\": [\"gini\", \"entropy\"]\n", - " }\n", - " },\n", - " 'LogisticRegression': {\n", - " 'model': LogisticRegression(random_state=MODELS_TUNING_SEED),\n", - " 'params': {\n", - " 'penalty': ['l2'],\n", - " 'C' : [0.0001, 0.1, 1, 100],\n", - " 'solver': ['newton-cg', 'lbfgs'],\n", - " 'max_iter': [250],\n", - " }\n", - " },\n", - " 'RandomForestClassifier': {\n", - " 'model': RandomForestClassifier(random_state=MODELS_TUNING_SEED),\n", - " 'params': {\n", - " \"max_depth\": [6, 10],\n", - " \"min_samples_leaf\": [1],\n", - " \"n_estimators\": [50, 100],\n", - " \"max_features\": [0.6]\n", - " }\n", - " },\n", - " 'XGBClassifier': {\n", - " 'model': XGBClassifier(random_state=MODELS_TUNING_SEED, verbosity=0),\n", - " 'params': {\n", - " 'learning_rate': [0.1],\n", - " 'n_estimators': [200],\n", - " 'max_depth': [5, 7],\n", - " 'lambda': [10, 100]\n", - " }\n", - " }\n", - "}" - ], - "metadata": { - "collapsed": false - }, - "id": "ebec7f4488fd3f25" - }, { "cell_type": "markdown", "source": [ @@ -235,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 70, "outputs": [], "source": [ "ROOT_DIR = os.path.join('docs', 'examples')\n", @@ -252,20 +225,28 @@ " f.write(config_yaml_content)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.210052Z", + "start_time": "2023-12-21T16:35:32.139953Z" + } }, "id": "efc95fa248b9f135" }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 71, "outputs": [], "source": [ "config = create_config_obj(config_yaml_path=config_yaml_path)\n", "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name}_Metrics_{datetime.now(timezone.utc).strftime(\"%Y%m%d__%H%M%S\")}')" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.213237Z", + "start_time": "2023-12-21T16:35:32.163029Z" + } }, "id": "f3a59ca9319a774d" }, @@ -293,71 +274,33 @@ }, { "cell_type": "code", - "execution_count": 62, - "id": "9e3d7bf3", - "metadata": {}, - "outputs": [], - "source": [ - "class CompasWithoutSensitiveAttrsDataset(BaseDataLoader):\n", - " \"\"\"\n", - " Dataset class for COMPAS dataset that does not contain sensitive attributes among feature columns\n", - " to test blind classifiers\n", - "\n", - " Parameters\n", - " ----------\n", - " subsample_size\n", - " Subsample size to create based on the input dataset\n", - "\n", - " \"\"\"\n", - " def __init__(self, dataset_path, subsample_size: int = None):\n", - " df = pd.read_csv(dataset_path)\n", - " if subsample_size:\n", - " df = df.sample(subsample_size)\n", - "\n", - " # Initial data types transformation\n", - " int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45',\n", - " 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex']\n", - " int_columns_dct = {col: \"int\" for col in int_columns}\n", - " df = df.astype(int_columns_dct)\n", - "\n", - " # Define params\n", - " target = 'recidivism'\n", - " numerical_columns = ['juv_fel_count', 'juv_misd_count', 'juv_other_count','priors_count']\n", - " categorical_columns = ['age_cat_25 - 45', 'age_cat_Greater than 45','age_cat_Less than 25',\n", - " 'c_charge_degree_F', 'c_charge_degree_M']\n", - "\n", - " super().__init__(\n", - " full_df=df,\n", - " target=target,\n", - " numerical_columns=numerical_columns,\n", - " categorical_columns=categorical_columns\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 63, + "execution_count": 72, "id": "6c55c6a0", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.216529Z", + "start_time": "2023-12-21T16:35:32.181628Z" + } + }, "outputs": [ { "data": { "text/plain": " juv_fel_count juv_misd_count juv_other_count priors_count \\\n0 0.0 -2.340451 1.0 -15.010999 \n1 0.0 0.000000 0.0 0.000000 \n2 0.0 0.000000 0.0 0.000000 \n3 0.0 0.000000 0.0 6.000000 \n4 0.0 0.000000 0.0 7.513697 \n\n age_cat_25 - 45 \n0 1 \n1 1 \n2 0 \n3 1 \n4 1 ", "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    juv_fel_countjuv_misd_countjuv_other_countpriors_countage_cat_25 - 45
    00.0-2.3404511.0-15.0109991
    10.00.0000000.00.0000001
    20.00.0000000.00.0000000
    30.00.0000000.06.0000001
    40.00.0000000.07.5136971
    \n
    " }, - "execution_count": 63, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data_loader = CompasWithoutSensitiveAttrsDataset(dataset_path=os.path.join('virny', 'datasets', 'COMPAS.csv'))\n", + "data_loader = CompasWithoutSensitiveAttrsDataset()\n", "data_loader.X_data[data_loader.X_data.columns[:5]].head()" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 73, "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", @@ -366,26 +309,34 @@ "])" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.225359Z", + "start_time": "2023-12-21T16:35:32.208465Z" + } }, "id": "8ee9e8a8c10245bf" }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 74, "outputs": [], "source": [ "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.265530Z", + "start_time": "2023-12-21T16:35:32.225727Z" + } }, "id": "6dba3327ebe01279" }, { "cell_type": "markdown", "source": [ - "### Tune models and create a models config for metrics computation" + "### Create a models config for metrics computation" ], "metadata": { "collapsed": false @@ -393,106 +344,44 @@ "id": "c32119a0992e331c" }, { - "cell_type": "code", - "execution_count": 66, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023/08/13, 01:41:37: Tuning DecisionTreeClassifier...\n", - "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:41:38: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6429262328840039, Accuracy = 0.6442550505050505]\n", - "\n", - "2023/08/13, 01:41:38: Tuning LogisticRegression...\n", - "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/08/13, 01:41:38: Tuning for LogisticRegression is finished [F1 score = 0.6461022173486363, Accuracy = 0.6505681818181818]\n", - "\n", - "2023/08/13, 01:41:38: Tuning RandomForestClassifier...\n", - "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:41:39: Tuning for RandomForestClassifier is finished [F1 score = 0.6480756802972086, Accuracy = 0.6518308080808081]\n", - "\n", - "2023/08/13, 01:41:39: Tuning XGBClassifier...\n", - "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/08/13, 01:41:42: Tuning for XGBClassifier is finished [F1 score = 0.6548814644409034, Accuracy = 0.6587752525252525]\n" - ] - }, - { - "data": { - "text/plain": " Dataset_Name Model_Name F1_Score \\\n0 COMPAS_Without_Sensitive_Attributes DecisionTreeClassifier 0.642926 \n1 COMPAS_Without_Sensitive_Attributes LogisticRegression 0.646102 \n2 COMPAS_Without_Sensitive_Attributes RandomForestClassifier 0.648076 \n3 COMPAS_Without_Sensitive_Attributes XGBClassifier 0.654881 \n\n Accuracy_Score Model_Best_Params \n0 0.644255 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 0.650568 {'C': 1, 'max_iter': 250, 'penalty': 'l2', 'so... \n2 0.651831 {'max_depth': 10, 'max_features': 0.6, 'min_sa... \n3 0.658775 {'lambda': 100, 'learning_rate': 0.1, 'max_dep... ", - "text/html": "
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    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0COMPAS_Without_Sensitive_AttributesDecisionTreeClassifier0.6429260.644255{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1COMPAS_Without_Sensitive_AttributesLogisticRegression0.6461020.650568{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'so...
    2COMPAS_Without_Sensitive_AttributesRandomForestClassifier0.6480760.651831{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    3COMPAS_Without_Sensitive_AttributesXGBClassifier0.6548810.658775{'lambda': 100, 'learning_rate': 0.1, 'max_dep...
    \n
    " - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", "source": [ - "tuned_params_df, models_config = tune_ML_models(models_params_for_tuning, base_flow_dataset, config.dataset_name, n_folds=3)\n", - "tuned_params_df" + "**models_config** is a Python dictionary, where keys are model names and values are initialized models for analysis" ], "metadata": { "collapsed": false }, - "id": "f9f77d878f6a94f8" + "id": "b37d875602beb206" }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 75, "outputs": [], "source": [ - "now = datetime.now(timezone.utc)\n", - "date_time_str = now.strftime(\"%Y%m%d__%H%M%S\")\n", - "tuned_df_path = os.path.join(ROOT_DIR, 'results', 'models_tuning', f'tuning_results_{config.dataset_name}_{date_time_str}.csv')\n", - "tuned_params_df.to_csv(tuned_df_path, sep=\",\", columns=tuned_params_df.columns, float_format=\"%.4f\", index=False)" - ], - "metadata": { - "collapsed": false - }, - "id": "cdd137541e77686d" - }, - { - "cell_type": "markdown", - "source": [ - "Create models_config from the saved tuned_params_df for higher reliability" + "models_config = {\n", + " 'DecisionTreeClassifier': DecisionTreeClassifier(criterion='gini',\n", + " max_depth=20,\n", + " max_features=0.6,\n", + " min_samples_split=0.1),\n", + " 'LogisticRegression': LogisticRegression(penalty='l2',\n", + " C=0.1,\n", + " max_iter=250),\n", + " 'RandomForestClassifier': RandomForestClassifier(max_depth=4,\n", + " max_features=0.6,\n", + " min_samples_leaf=1,\n", + " n_estimators=50),\n", + " 'XGBClassifier': XGBClassifier(learning_rate=0.1,\n", + " n_estimators=200,\n", + " max_depth=5,\n", + " verbosity=0)\n", + "}" ], "metadata": { - "collapsed": false - }, - "id": "ca709f1927e425b5" - }, - { - "cell_type": "code", - "execution_count": 68, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'DecisionTreeClassifier': DecisionTreeClassifier(max_depth=20, max_features='sqrt', min_samples_split=0.1,\n", - " random_state=42),\n", - " 'LogisticRegression': LogisticRegression(C=1, max_iter=250, random_state=42, solver='newton-cg'),\n", - " 'RandomForestClassifier': RandomForestClassifier(max_depth=10, max_features=0.6, random_state=42),\n", - " 'XGBClassifier': XGBClassifier(base_score=None, booster=None, callbacks=None,\n", - " colsample_bylevel=None, colsample_bynode=None,\n", - " colsample_bytree=None, early_stopping_rounds=None,\n", - " enable_categorical=False, eval_metric=None, feature_types=None,\n", - " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", - " interaction_constraints=None, lambda=100, learning_rate=0.1,\n", - " max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n", - " max_delta_step=None, max_depth=5, max_leaves=None,\n", - " min_child_weight=None, missing=nan, monotone_constraints=None,\n", - " n_estimators=200, n_jobs=None, num_parallel_tree=None,\n", - " predictor=None, ...)}\n" - ] + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:32.278518Z", + "start_time": "2023-12-21T16:35:32.250118Z" } - ], - "source": [ - "models_config = create_models_config_from_tuned_params_df(models_params_for_tuning, tuned_df_path)\n", - "pprint(models_config)" - ], - "metadata": { - "collapsed": false }, "id": "8c6061673bb72efa" }, @@ -514,17 +403,22 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 76, "id": "197eadaa", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.343781Z", + "start_time": "2023-12-21T16:35:32.270247Z" + } + }, "outputs": [ { "data": { - "text/plain": "Analyze models in one run: 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallsex_privsex_priv_correctsex_priv_incorrect
    0Mean0.5203710.5754230.5974960.525844
    1Std0.0706730.0747590.0711420.082884
    2IQR0.0849660.0843510.0815600.090620
    3Aleatoric_Uncertainty0.8688700.8701610.8532010.908254
    4Overall_Uncertainty0.8939370.8988220.8801480.940765
    5Statistical_Bias0.4191890.4148320.3267130.612761
    6Jitter0.1342730.1453290.1139390.215837
    7Per_Sample_Accuracy0.6785420.6873930.9235620.156923
    8Label_Stability0.8156440.7998100.8476710.692308
    9TPR0.6581740.4800001.0000000.000000
    10TNR0.7333330.8088241.0000000.000000
    11PPV0.6652360.5806451.0000000.000000
    12FNR0.3418260.5200000.0000001.000000
    13FPR0.2666670.1911760.0000001.000000
    14Accuracy0.6998110.6919431.0000000.000000
    15F10.6616860.5255471.0000000.000000
    16Selection-Rate0.4412880.2938390.2465750.400000
    17Positive-Rate0.9893840.8266671.0000000.666667
    18Sample_Size1056.000000211.000000146.00000065.000000
    \n" + "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Statistical_Bias 0.416854 0.411786 0.322057 \n1 IQR 0.083967 0.081475 0.073670 \n2 Aleatoric_Uncertainty 0.862966 0.868639 0.854205 \n3 Std 0.071922 0.074882 0.070336 \n4 Overall_Uncertainty 0.888640 0.897111 0.879981 \n5 Mean_Prediction 0.522485 0.580255 0.601939 \n6 Jitter 0.110890 0.125428 0.095310 \n7 Label_Stability 0.858674 0.834692 0.877823 \n8 TPR 0.656051 0.493333 1.000000 \n9 TNR 0.733333 0.808824 1.000000 \n10 PPV 0.664516 0.587302 1.000000 \n11 FNR 0.343949 0.506667 0.000000 \n12 FPR 0.266667 0.191176 0.000000 \n13 Accuracy 0.698864 0.696682 1.000000 \n14 F1 0.660256 0.536232 1.000000 \n15 Selection-Rate 0.440341 0.298578 0.251701 \n16 Positive-Rate 0.987261 0.840000 1.000000 \n17 Sample_Size 1056.000000 211.000000 147.000000 \n\n sex_priv_incorrect \n0 0.617881 \n1 0.099402 \n2 0.901792 \n3 0.085323 \n4 0.936456 \n5 0.530447 \n6 0.194605 \n7 0.735625 \n8 0.000000 \n9 0.000000 \n10 0.000000 \n11 1.000000 \n12 1.000000 \n13 0.000000 \n14 0.000000 \n15 0.406250 \n16 0.684211 \n17 64.000000 ", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallsex_privsex_priv_correctsex_priv_incorrect
    0Statistical_Bias0.4168540.4117860.3220570.617881
    1IQR0.0839670.0814750.0736700.099402
    2Aleatoric_Uncertainty0.8629660.8686390.8542050.901792
    3Std0.0719220.0748820.0703360.085323
    4Overall_Uncertainty0.8886400.8971110.8799810.936456
    5Mean_Prediction0.5224850.5802550.6019390.530447
    6Jitter0.1108900.1254280.0953100.194605
    7Label_Stability0.8586740.8346920.8778230.735625
    8TPR0.6560510.4933331.0000000.000000
    9TNR0.7333330.8088241.0000000.000000
    10PPV0.6645160.5873021.0000000.000000
    11FNR0.3439490.5066670.0000001.000000
    12FPR0.2666670.1911760.0000001.000000
    13Accuracy0.6988640.6966821.0000000.000000
    14F10.6602560.5362321.0000000.000000
    15Selection-Rate0.4403410.2985780.2517010.406250
    16Positive-Rate0.9872610.8400001.0000000.684211
    17Sample_Size1056.000000211.000000147.00000064.000000
    \n
    " }, - "execution_count": 70, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -630,9 +529,14 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 78, "id": "f94a20dc", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.399593Z", + "start_time": "2023-12-21T16:35:56.369447Z" + } + }, "outputs": [], "source": [ "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=list(models_config.keys()))" @@ -640,9 +544,14 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 79, "id": "b04d06cf", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.419402Z", + "start_time": "2023-12-21T16:35:56.390290Z" + } + }, "outputs": [], "source": [ "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" @@ -658,9 +567,14 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 80, "id": "be6ace22", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.437820Z", + "start_time": "2023-12-21T16:35:56.408780Z" + } + }, "outputs": [], "source": [ "models_composed_metrics_df = metrics_composer.compose_metrics()" @@ -684,9 +598,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 81, "id": "435b9d98", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.466890Z", + "start_time": "2023-12-21T16:35:56.437258Z" + } + }, "outputs": [], "source": [ "visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", @@ -696,135 +615,139 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 82, "id": "5efb1bf2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.508905Z", + "start_time": "2023-12-21T16:35:56.463627Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 75, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['TPR', 'PPV', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'],\n", - " metrics_title=\"Error Metrics\"\n", + " metric_names=['Accuracy', 'F1', 'TPR', 'TNR', 'PPV', 'Selection-Rate'],\n", + " plot_title=\"Accuracy Metrics\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 83, "id": "0eb8528e", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.558973Z", + "start_time": "2023-12-21T16:35:56.505201Z" + } + }, "outputs": [ { "data": { - "text/html": "\n
    \n", + "text/html": "\n
    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 76, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "visualizer.create_overall_metrics_bar_char(\n", - " metrics_names=['Label_Stability', 'Aleatoric_Uncertainty', 'Overall_Uncertainty'],\n", - " reversed_metrics_names=['Std', 'IQR', 'Jitter'],\n", - " metrics_title=\"Variance Metrics\"\n", + " metric_names=['Aleatoric_Uncertainty', 'Overall_Uncertainty', 'Label_Stability', 'Std', 'IQR', 'Jitter'],\n", + " plot_title=\"Stability and Uncertainty Metrics\"\n", ")" ] }, { - "cell_type": "markdown", - "source": [ - "Below is an example of an interactive plot. It requires that you run the below cell in Jupyter in the browser or EDAs, which support JavaScript displaying.\n", - "\n", - "You can use this plot to compare any pair of group fairness and stability metrics for all models." - ], + "cell_type": "code", + "execution_count": 84, + "id": "df024aed", "metadata": { - "collapsed": false + "ExecuteTime": { + "end_time": "2023-12-21T16:35:56.939166Z", + "start_time": "2023-12-21T16:35:56.550634Z" + } }, - "id": "cdd0a858443ac90e" - }, - { - "cell_type": "code", - "execution_count": 77, "outputs": [ { "data": { - "text/html": "\n
    \n", - "text/plain": "alt.HConcatChart(...)" + "text/plain": "
    ", + "image/png": 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UqCSp8lRJk4DnmUqP5do/m6ubBlF73jxOsWP6yODmpsz1+7Xr719r6x8+U/L7q1SZW6zQpFgNuH+m/Du2Phu0QebGA5Ksq0U7DuHLCYDLX5mtD+Pv59dqOT/bIJDFYmky0NOShOhoSVJmbq59MMiRg2lp9s9VtgGmkrIy+7Ga2lo9+ac/6fOlS5Wdlyez2Syz2az0zEy989lnevmf/1Q1/RsAlyFjmfX96uvf+ioeb19bENFiUXVl2+/XqNiOkqRT2bmtBjyPHzq9I0pNVesD+If3pygvM1uSNHrqBLnbvuM767tlayRJAcFBGjyGPHcA2p8r93EXrFihtOPHFduxo262LVYBAKCxyzYwajabHQbcioqKtG/fPs2bN08PPvigJMnT01O/+MUvJEkHDx7UgQPWgfdHHnlEcXFxzdr29fXVCy+8IEmqqqqyr2aUZN9etqWtch9++GG98847+uKLL84qp97ZalgFWlVVpTVr1jQ737CNbt++fdW5c2f78YYVsEOHDtWUKVMctn3HHXfYV6w2rNQ8VzNnztSgQYOa/YwePVpz5syxB54DAgL00ksvNQniSpLRaNT111+vq6++Wvfff7/D36mvr6/69u0rSU2C2G0pLS3V0qVLJUm33XabEhISmpVxc3OzB4oLCwu1evVqp9sHLhSLyToj0s3DvdVy7p6nB2DqTW3P1OxwRSfJIKneoqOLtjTZdqZBcVqWitNO52c+cyWqucYkg8EgN0/Hgz+Vp0pUeiKvzXuRpLITeSq3rRaNG92nzecFgMtBnS3nkVcbg+CNJ37V1bW+Ul+Shg8YIIPBYJ0k9vnnDt/Re1NStDclxf7fDfmXGg8C/fPDD3WqoECzr7pKrz33nP43b57+8vTTGj1kiCRp/6FDeuuTT9q8HwC41BreaR5tbIfo6XX6vMmJ92ufIQMkg0GW+not/vBLh+/XIwdSlXYg1f7fbeUY3bx8rSTJPzBAQ8aObPMeGjuRdlyZx05IkkZNGd/m8wLApeCqfdyjGRn6atkyubu76+HbbyetBADAobOb5ngJ7dy5U4MGtb01o6+vr1555RX16tVLkuyrJSVp6tSW83b07NlTnTt31vHjx7Vt2zbdfffdkqwBxcOHD2vt2rW64447NGfOHI0ZM0YdOnSQJMXFxTkMtl5o8fHxGjRokHbu3KklS5bo6quvtp8zGo1au9b6xaxxoLGiokIHbcnTe/fuLaPR2GL7ffv2VVZWVpNtZy80T09PTZw4USNGjNCMGTMUEhLSrExgYKAef/zxFtswmUxKSUmxB6pNbXxhbWz37t32TlvPnj1b/H1ERESoQ4cOys/P186dOzVjxgynrwFcEG4XZ5KFf8cwRQ3urrwdaSpMztD+d5crYUJ/+XcMk6m6VgX7jytjzV55BfmpttS6otTN/fR8mZqySu1/Z7mq8kvlGeCj7teOUljPeHn4eKnyVImyNiXr1O5jOrLge1WeKlXXmcNavZ/MTdb3k7uvpzoOT7oozwwAF5rbRZoI1yk2VuOHD9faLVu0fe9evfj66/rJtGlKiIlRZVWVvt+9W198+61Cg4NVVFIiSfZdSGoa5YkvLi3V/bfcookjTw/Ux0dH69E775S3p6fWfP+9vtuxQzMnTlSX+PiL8iwAcC7c3C7OPO2O8TEadOUw7dq4VSm79um9P/9TE66Zoqi4GFVXVunA9j1a+81yBYYEqbzYupqptRWguSezdfTgYUnSiMlj5eXt1WJZRzavsH539/Hz1bAJo87xqQDgwnLFPm5tXZ1enz9fJrNZ18+Yoa4OFkgAACBdxoHRlnh7eysoKEhdu3bVsGHDdP311ysyMtJ+PjfXuiVkUFCQPZjZkq5du+r48ePKycmxH3v44Ye1ceNGZWRkaOvWrdq6dasMBoOSkpI0duxYTZo0qVmOzItlzpw52rlzpzZu3KiysjIFBQVJklavXq2qqip5eHho5syZ9vJZWVn27X3ff/99vf/++21eo/GztxZI9fT0bDJLrMHq1avtgeL6+nrl5+drwYIFev3111VXV6fCwkJNmDDBYVD0TFlZWdq6dauOHTumjIwMnThxQsePHz/n7W1Pnjxp//zzn//cqTqNfx/ApeJumwXf1ipQc93piQEtreA8U9dZw1VnrFZRykmVHs3V/qO5Tc77dghSt2tGav87y63tep1u9/iyHarKL5W7j6f63Ttdfh2C7ecCYsKVdP1YeQcH6OS6fcredFARvRMU3Lmjw/sw1dSpKNX6dzKiTyK5RQH8YPh4W7c5r21jclZto4EcR30mR+6+4QaVG43asX+/kg8fVvLhw03OR0dG6p4bb9SLr70mSfK2tevdqP2EmJgmA0aN3TRrltZt3ar6+npt27OHwCiAy4qnLcDY1irQutrT5z2dfL9efet1qiw3KnXPAR1PTdPx1LQm58M7Ruqa26/Xe39+Q5JaDXbu27rT+sFg0MDRQ526foOa6mod2mudHNhncH9yiwK4bLhiH/eDBQuUnZenrgkJmtvCLnoAAEiXcWB02LBh+uCDD866XkMuS7829siXrKtNJTXJ4RkeHq4FCxborbfe0jfffKPs7GxZLBalpqYqNTVVb775pnr06KGXXnpJ/fr1O+v7OxvTpk3Tiy++qNraWq1YsULXXXedpNPb6I4ZM0ZhYafz+jU8+9loXKe1FbqPPPJIm8FFNzc3RUVF6YEHHlDPnj310EMPaceOHbrtttv08ccfNwlgN1ZSUqJnnnlGK1asaLbFhr+/v0aMGKH8/Hz7alhnne/vA7hUPHysnf+28obaz7sZ5OHr3BcSd08P9b5tovL3HlPu9sOqyCmSpd4i3/BAdejXWTEje6syr9he3jvQ+u4019Ypf99xSVLMiF5NgqKNJUzqr7xdaaotq1LOtkMtBkaLUk/KYrJO3Ijs38WpeweAy4Gfrb9Y1UZOpYacS25ubgpwoh8qSV6ennri3nv13Y4dWr15s9IzM1VvsahjRIRGDR6s6ePG6WR2tr18aLD1XdwwkCVJfXr0aLH94MBAxUZF6WROjjLznNv2HAAuFR8/6/u1rfyeDXlFDW5u8vV37v3q6eWpW37+M+3bslM7NnyvnAzrJOLwyAj1HTZIIyaP1ams05NiA0OCWmzr4K79kqRO3TsrOCzUqes3OLT3oH2b3n4jBp9VXQC4mFytj7snJUUrNm6Up6enHr7jDrm7k7oHANCyyzYweq4aAqKNg50taVgh2RAgbRAQEKBf/OIX+sUvfqHU1FRt2rRJmzdv1vbt21VTU6PDhw/r7rvv1uLFi9Wxo+MgwIUQFBSkCRMmaPny5Vq6dKmuu+46FRcXa/PmzZKseUgba/wczz33nG6++eaLdm9tGT9+vB555BH9/e9/V2Zmph5++GF9/PHH9u0xGtTV1elnP/uZPS/ssGHDNHLkSPXo0UNdunRRYmKi3Nzc9MQTT5x1YLTx72Pp0qXq2rXr+T8YcBH4RgSp9FiuqktaD8xXl1jfWd5BfmeV49hgMChyQFdFDnD8d6Ai15a712BdQSpJVYXlUr11okJQJ8eTGiTJzd1dgQmRKjxwQpX5pS2WK0y25lXyDPRVcJeL994EgAstJjJSB9PSlN9GnvPCYuskk7Dg4LN+R48ZOlRjhjpehZSelWUvF2ObZBYZHm4/31bepIb+kDM5oQDgUoqIilR66hGVFBa3Wq7Udj4o5Ozfr/1HDlH/kUMcns85mdVQUBFRjvu7p7JyVJh7SpLUd1jbqX7OdHDnXklSQHCQOvfsdtb1AeBicbU+7uadO+3//cvf/77Vul98+62++PZbSdLrzz2nDo2uCwD4cXC5wGhMTIwkqaysTPn5+a1up3v06NEmdRzp2bOnevbsqZ/97GcyGo3617/+pbfeekvl5eVasGCBHnzwwQv7AGeYM2eOli9frq1bt6q0tFQrVqxQXV2dAgICNGnSpCZlGwdps2wdjJZYLJZmHZpDhw5duBuX9MADD2j9+vXas2eP9u3bp9dee02/+MUvmpRZtmyZPSj6m9/8RnfddZfDtoqLW/+y7Eh0dLT9c1ZWVquBUUe/D+BS8Y+yzjyvLqqQqbrWvoL0TBXZ1ly7/tFhDs87YrFYZKqskad/y9t2laRZZ2r6RYbYt/W1mOvt59va4td+rUZ1zryH4qPWa4T3iufvGoAflHhbf+JUYaEqq6rss+vPdDwzU5KUeBa56C0Wi8qNRgUFBLRYZl9qqiQprmNH+yz6jh06yNvbWzU1NcorKGj1GqXl5ZKk0KCWV0MBQHuIirN+fy3KL1R1VZV8Wni/ZmdY36/RCbFOt22xWFRZYZR/YMvv16PJ1u+/kTEd5eXj7bDM4f0p9s+9BvV1+voN99CQm7TngCvoAwO4rNDHBQD8mLm19w1caEOGnJ4Nunz58hbLpaSk6MQJ6wqmhi1ks7KydPvtt2vUqFFav359szr+/v564oknFGD7hz3PyS3JzucL0JgxYxQaGiqTyaT169frW9uMpqlTp8rbu+mXt7CwMHXrZp2FumbNmmbb0jaor6/XzJkzNWbMGP36178+53tri5ubm1566SV52mZ5vf32282Cr7t377Z/vvHGGx22U1VVpT179kiSPYeqMwYPHiw3N+sf8dWrV7dYLisrSwMHDtTkyZM1f/58p9sHLpTQJNsXjHqLig5lOixTU2qUMcc6kzOsh3ODQkWHs7Tpmfna8tInqiooc1imtrzSfs3wPp3sx33CAyU367ur5Ei2w7qSVG+uV3mGdRZ9S9vtGnOLZa6yzuIMjItw6t4B4HIxsE8fSdY+yO4Wdq8oLC5Wum3QaEDv3k61uyclRbf+4he697e/Vc6pUw7LFJeVaVdysiRpWP/+9uMGg0EDbdfZm5Ki6hbysefm5ysvP1+S1KML25gDuLz06Gt9j1nq63V4X4rDMqVFxcrJsE767d63l1Ptpu1P0XP3/f4m5v4AAMIJSURBVEp/fOz/VJCX77BMeWmZPfdn71YCnifSrKklgsJCFBTiuK/bktyT2fZtgOO6JJxVXQC42Fytj3vvjTfq/b/8pdWfBnOmTLEfiwhzfuI5AMB1uFxg9IorrlAf2z/ur7/+usOVk9XV1Xr++eclSd7e3vYtaTt06KBDhw6psLBQH3zwgcPA4smTJ+1b8CYkOPflpmFf+3PZwszT01MzZ86UJH399dfavn27JGn27NkOy19//fWSrKth33nnHYdl5s+fr6NHj+rUqVP2QOrF0q1bN917772SJJPJpGeeeaZJcLPxnv9HjhxpVr++vl4vvPCCPfeno99hw/a8Z56LiIjQhAkTJElffvmldtq21Tiz/T/84Q+qqqrSyZMndcUVV5ztIwLnzTcsUEGJ1q1jTqzeLVNV01yjFotFx5ZulyySh7+3Igc6ty20NQhpDW5mb2k+2GSxWHTkmy2qrzPLzctD0cOS7Oc8fb0V2s26mj53Z5rKsxzP1sxcv0+1ZdYBnw4t5A6taFQ3MK7lVfwAcDmKiohQkm3A5bMlS2Q8I12DxWLR/AULZLFYFBgQ0OJ2YWfqmpCghqlzyzZsaHbeYrHonc8+U11dnby9vTX5yiubnJ80apQkqaq6WvO/+sph/Q8WLJBk7e8ObzToBACXg7DICCV0t75f1yz8VlWVTfPcWSwWffvp15LFIr8Afw1oYUvcM8V2TrBPTt66emOz8xaLRYs++EKmujp5entp6ITRLbaVfeKkJCmu89kHNhvqWut3aqUkAFx6rtbH9fT0lI+3d6s/DTzc3e3HWM0PAD9OLhcYlaRnn31Wnp6eKi4u1o033qjPPvtMOTk5Kioq0rp163TLLbfYVyo+9dRT9i1ovby8dMcdd0iSNm7cqAcffFDbt29XQUGBsrKytGzZMt1zzz2yWCzy8/PTnDlznLqfkJAQSdatapOTk1VcXHxWQdKGIOh3330nk8mkmJgYDRs2zGHZW265Rb1ts6v+/Oc/6+mnn9aBAwdUUlKiQ4cO6Q9/+IP++Mc/SpISExN1++23O30f5+rBBx9UYmKiJGnPnj366KOP7OeubNQB+tWvfqXVq1fr1KlTysnJ0cqVK3Xbbbfpq0YdoYagdGMNv98dO3boxIkTKmqUH+Gpp55SQECAPZfpv/71L6Wnp6uoqEg7duzQAw88oJUrV0qSrr76avvqYeBS6zJjmGSQqgvKtfetb1WclqU6Y7UqsgqV8tFaFexPlyR1mjTAvt1tgx3zvtKOeV/p0OdNv3R4+nmr45DukqTs71N07NvtqjxVotqKKpUczdH+d5arMDlDktR52hB5B/k1qd95+hC5eXnIYqrXvreW6eS6farML1VdZY3KMwt06IuNOrFqjyQptEesIvo4HvBpnHvUJzzwnH9HANBe7rz2WhkMBuXm5+u5v/9de1NSVFZRoWMnT+qv77yjLbZ+5fXTpzcZdJGkx198UY+/+KJeP2NXikB/f02wDfws37BBHy5cqMzcXJWWl+vA4cN64bXXtH2vNTfdbbNnKyy46Uqlfj172geoVm/erD+/+aYOHTumcqNRR9LT9cd//1s79u+31/f3a/qOB4DLwfSb5kgGgwrz8vXOH1/TkQOpMpZXKPvESX38xntK3r5HkjRx9rRm292++vTLevXpl/XFWx82Oe4X4K/BY0ZIkras3qhln32jU9m5MpaV61hKmt798xtK2bVPkjT1+mtaXAlaW12jsqISSVJ41NlP7svPPr3DVFgku6YAuPzQxwUA/Fi5XI5RSerfv79ef/11/fKXv1R+fr5+97vfNSvj4eGhX/7yl7r11lubHL///vt18OBBrV69WmvXrtXatWub1fX399drr72mMCe3Wxg+fLjeeustVVZW6tprr5VkXbU5fPhwp+r369dPnTt31vHj1m18Zs2a1eKMJi8vL7355pt66KGHtG/fPn355Zf68ssvm5VLTEzUW2+9Jb9L0IHw8vLSs88+a88fOm/ePF111VWKiorSuHHjNHPmTC1ZskQZGRl66KGHmtWPjIzUxIkT9cknn6iqqkp5eXmKioqynx8+fLiWLl2q3NxcTZkyRZJ169y4uDh16tRJb7/9th555BEVFBTo1Vdf1auvvtrsGhMmTNDv20jODlxMgXER6vGTK5W2YJMqc4t14L2VzcrEXtlbMSOabyFWlW/dJtcroHlOkM7Th6ryVIlKj+cpa2OysjYmNy3gZlDiVQMVM6Jns7r+UaHqc8ckpX6yXnUV1UpfsUvpK3Y1KxeaFKueN41v8dlqiq0rvg0ebnL3dMl/dgC4uK6dOumBW2/Vmx9/rIzsbL38z382KzNzwgRNHTu22fGGLcRCHOQ/un3OHGXm5CjlyBEtWr1ai87Y+t/NzU03Xn21powZ4/C+HrjlFpnMZn2/a5d27N9vHyRq7JrJk3XVGTPxAeByEdc5QXPvullfv/+J8jKz9f7f/t2szKgp4zV8UvP3YGGu9f0aGNx84t3UG2brVHau0g8d1aZla7Rp2Zom5w1ubpo8d4aGT2z5/VhSeHrCbUv5T1vTUN/dw0OeZ0xsBIDLAX1cAMCPlcuOUI8fP14rVqzQ+++/r/Xr1+vkSes2NjExMRo1apRuuukmde3afDtKT09PvfHGG1qyZIm+/vprHTx4UCUlJfLx8VFMTIzGjh2rO++8U5GRkU7fy5gxY/TMM89o/vz5ysrKUmBgoAraSCJ+ptmzZ9sDeg1b/7akQ4cO+uSTT7Ro0SItXrxYBw8eVGlpqXx8fNS9e3dNnTpVN998s3x8fM7qHs7HqFGjNGvWLC1atEgVFRV64YUX9MYbb0iS/vrXv2r48OFasGCBDh8+rJqaGgUEBKhz586aOHGibrzxRhmNRn322Weqr6+3ryRtcMMNN6igoEBffvml8vPzFRISotzcXMXZEsMPHDhQy5Yt00cffaQ1a9bo+PHjMhqNCgoKUt++fTV37lxNnz79kv0ugJZEDeqmgJgwZW48oJJjuaqrqJa7l4cCYsMVM7KXwnud/RZe7l4euuLuqcrdflindh+RMa9EFnO9vIJ8FdIlWjGjeisguuVJHiFdojX48bnK2ZKiwtSTqiosU32tWR5+3gqMi1DUwK4K79Op1e1nTNXWrYE9fLzO+v4B4HIxfvhwdY6L06LVq3UwLU2l5eXy9vZWl/h4TRs7VkP69TvrNr29vPS7Rx7R6s2btX7bNp3MyZHZZFJIcLCu6NFD08eNU6KtP+OIh4eHHr/rLo0ZOlRrvv9eR9LTVWE0KjgwUN0SEzVt7Fj17t79fB4bAC66QVcOU0ynOH23bI2OHzoiY1m5PL29FdspTsMnjVGvgS3nAG2Jl7eXfvrEQ9qx/nvt2bxdeVk5qjebFRgcrM69umnk5HGKTohttY2qymr7Zx+/sw+MNuQXPZe6AHCp0McFAPwYGSyOEmkCOCc3rX2lvW8BAH6U3gyf1t63AAA/WttCTO19CwDwozSsxGXXOwDAZS/oHCYOwHll+/a19y1cEvw5ah8umWMUAAAAAAAAAAAAABojMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFyeR3vfAAAAAAAAAAAAAPBjMmnSpFbPr169+hLdyY8LK0YBAAAAAAAAAAAAuDxWjAIAAAAAAAAAAACXECtC2wcrRgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACX59HeNwC4kjfDp7X3LQDAj9J9hcva+xYA4EfrnpDJ7X0LAAAAAAA4hRWjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIwCAAAAAAAAAAAAcHkERgEAAAAAAAAAAAC4PAKjAAAAAAAAAAAAAFwegVEAAAAAAAAAAAAALo/AKAAAAAAAAAAAAACXR2AUAAAAAAAAAAAAgMsjMAoAAAAAAAAAAADA5REYBQAAAAAAAAAAAODyCIzCZZjN5va+BQAAAAAAAAAAAFymPNr7BgCTyaSlS5dqzZo12r9/v4qKiiRJoaGh6tatm8aMGaO5c+cqICDAYf38/Hy9/PLLuvnmmzVs2LCzvv7tt9+ubdu2ae7cufrjH/94Xs8CnK8TWVlatHq1ktPSVFZergB/f3WJj9eUsWM1sHfvc243v7BQC1et0t6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}, - "execution_count": 77, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "visualizer.create_fairness_variance_interactive_bar_chart()" - ], - "metadata": { - "collapsed": false - }, - "id": "5efb9f1d613da1c6" + "visualizer.create_overall_metric_heatmap(\n", + " model_names=list(models_metrics_dct.keys()),\n", + " metrics_lst=visualizer.all_accuracy_metrics + visualizer.all_stability_metrics,\n", + " tolerance=0.005,\n", + ")" + ] }, { "cell_type": "code", - "execution_count": 78, - "id": "df024aed", - "metadata": {}, + "execution_count": 85, + "id": "2326c129", + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-21T16:35:57.471811Z", + "start_time": "2023-12-21T16:35:56.925576Z" + } + }, "outputs": [ { "data": { - "text/plain": "
    ", - "image/png": 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\n" 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\n" 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58uXccccd3HfffeL3RUVF/Pjjj9jZ2fHwww8zadIksQlmzJgxREREsGrVKoKDg0W/YOjNLA4LCyMtLU1szlOCzp6envj6+lJVVUVWVpZVcBggOjqaw4cPU1lZSXNzM15eXkRGRuLh4UFFRQXh4eGMHj3a6lz09PSgUqkwmUx89dVXvPjiiyQlJfHmm2/i7OyMj48PHh4eQG8mNPy27GlJkiRJkqT/bzI4LEmSJEmSJP2p9e1z1z9zdqAAiUqloqamhvfee4/vvvuOhoYGcZurqyvPPvssDz/8MFdddZUoK21nZ4efnx96vR69Xk9LS4vo+6tWqwkICKCwsJDTp0+fFRyOi4ujpKQEo9FIXV0dOp0OvV6PnZ0d0NvP+Nlnn6Wrq+us96ZWqwkPDyckJIQxY8aInsRKlgxAZmameF+SJP1v6OnpQaPRiM95V1eX1VjVnzIO9g/qDDQGWiwW9u3bx/PPP4+bmxtff/01H374IStXrqSzsxPoHXsqKyv54YcfSE1NZenSpTLwIUn/jyorKzl+/DiHDh3i5MmT6PV6bGxsCAsLIygoiKSkJC6//HJcXV2Bn+YEkZGRODs7U1dXR1FR0Tmfv+/n22w2c+bMGSoqKlCpVKSkpIgNbX0dO3aMO++8k5aWFqKjoxkxYgSRkZF0d3fzxRdfUFFRwbvvvktkZCTTpk3DxsaGzMxMdDodLS0tJCYmiuNU/t/X15c777wTe3t7q9dUqVRER0eTlpZGZWUlLS0t4r3qdDr8/f1JS0sjMzOTyy+/3Oo4o6OjAaiurhYb/CIiIoiNjaWiooJdu3Zxww034OXlJYLjyvkwm8189dVXAHR0dIgx2cPDA29vbzFWKpnMkiRJkiRJfxYyOCxJkiRJkiT9x/T09JCdnc3hw4cJCwvjwgsv/N3LH/cPWNTX19Pc3ExQUNCAwYzKykoee+wxjh8/jkajITY2lqioKBwdHdm+fTttbW0sXrwYd3d3Lr74YvG42NhY9Ho9NTU1NDY2inKrLi4uBAcHU1hYKLJH+kpMTGTr1q0YjUaMRiPh4eHY29uLYzMajUDvgmhsbCyDBg0iISGB2NhYEWjuz9XVVSxQKuUMZeBGkv53KJ/noUOHotFoCA4OFhtKBtoI0vfz39nZiV6vx2QyERkZedZ9lVL3Z86cobW1lXfeeYc333yToUOHctlllxEdHU1OTg4fffQRlZWVfPPNN1x44YVceOGF/6F3K0nSQJTNHenp6Xz44Yds375d9OhVq9W4urpSUFBAVlYW27dvZ/PmzSxatIjhw4eL5/D398fNzY2amhrKysrEY/vLzc1l79697N27l1OnTmEymQAIDAzEzc2Ne+65h/PPPx+1Wi02v61cuZKWlhZGjBjBokWLiIuLE8/3t7/9jWeeeYbc3FwsFgtdXV2inLNOp6OxsZHnn3+eMWPGkJycjL+/P11dXZhMJlGtpb+YmBgA9Ho9DQ0NBAQEAODk5IS/vz8AWVlZgPU4qWQgGwwGamtrgd5exNOnT2fXrl2Ul5ezdOlSXn31VavXa2xs5MMPPxRVXG666Sarv0tgYCBarZaqqirKysqIjIz8VZUdJEmSJEmS/ghkcFiSJEmSJEn6jzlz5gwPPfQQJSUljBs37lcFF/pmAitZcD8nJyeH3bt38+OPP1JUVERHRwchISHExMTwt7/9jQkTJmBraysWM7/88kuOHj2Kt7c3jz/+OBdffDFqtZqenh7mzp3LsmXLOHHiBNnZ2YwbNw4nJyegN8i7d+9eamtrqa2tJTAwEAAHBwdCQ0MByM/PF8elHHdiYiLQG7SuqakBeksgKuUIg4KC2LFjxznfX1dXF/v27cNgMBAZGcmoUaNwdHTEx8cHjUZDTU0NbW1tsuyrJP0PUYI3SUlJnDx5UvQlP5cTJ06wdetWDhw4QGlpKSqViqioKCIiIpgzZw5jx45FrVaLwEVQUBABAQFUVlby+uuvM3LkSJ555hkRTB4yZAiRkZG88MILZGVlsX//fkaMGHFWyX1Jkv512dnZqFQq4uLizlnpZPXq1Tz77LMADB48mClTpjB69Gh8fHxoaWkhPT2dU6dOsXr1arKysrjtttv47LPPiI+PB8Dd3R0fHx/y8/Opqqqio6MDe3t7q9c5fPgwK1eu5MCBA+J3AQEB2NraUlpaSkVFBQ8//DBvvfUWI0aMQK1Wo9frRaD1iiuuEIHh7u5u1Go1/v7+PPPMM/T09BASEiKed+jQoYwfP57PPvuMHTt2cPDgQbRaLU1NTXh4eBAbG4urqyseHh5ceeWVIiAMPwV5q6urxcY66J2HKcFhZZNe302IQUFBoi2IwWDAbDZjY2PD9OnT2bhxI3v37uX777/HaDQydepUhgwZQnV1NT/++CPr16+ns7PTaoOMUtnBy8sLZ2dnvLy8aG9vF38zSZIkSZKkPwMZHJYkSZIkSZJ+k6qqKgoKCigrK6Orq4vo6GiGDRuGnZ0dNjY2Vguc3t7exMTEUFpaSl1dHXB26ef+fksG7PHjx3n99dc5duyY+J1Op6O0tJTc3Fx27NjBs88+y6xZs1Cr1bS3t4uskquuuopp06ZhNpsxmUyo1WqSk5N55plnqK2tFe9JyXRWgrx1dXUYDAbxera2tiI4XFhYSFdXF1qt1qrXHUBDQ4N4nKenJ1FRUfz4449UVlZSUVFBQECAyNRRguJqtZrMzEwWLFgAYJXV4ufnh06no7a2lqKiIgYNGmTVY0+SpD82i8Ui/neujTBqtRqtVovBYKC1tZWQkBCrMdRsNrNz504++OADTp06BfT2Rvfx8aGgoICcnBy2b9/OwoULufbaa8VmFyVgVFlZiVarZf78+SLrDXoDHIMHD2bUqFFkZWVRVFREbW2tDA5L0u9Emb9MmTKFuLi4AT//GzZsYMmSJajVaiZOnMhtt93GkCFDxPe8r68vkZGRzJkzh+DgYN555x1aWlpYunQpr7/+utiEFhISwsGDB6mtrUWv1xMWFibmCzk5OTzyyCMYDAZGjBjBDTfcQFJSEs7OzpSWlvLtt9+yZcsWqqur+eGHHxg+fDhqtZqmpiYxHhw4cIChQ4fi6OgoXhMQm+jgp1Yb7u7u3HnnnahUKk6ePEl2djZtbW2o1WoaGxutAtTbtm3jxRdf5PzzzwcQ1VRqa2ut5mE2Njb4+vri5OREa2srtbW1eHp6ivmoh4cHwcHBFBcXo9fr6ejoEBvqlixZwhtvvMHatWs5duwYaWlpdHd3i+d2dnbm+uuv55ZbbsHZ2RmLxSLKXc+ZM4errrrq3/hXIEmSJEmS9P9HBoclSZIkSZKkX1RcXMw333zDjh07ziqdrNPpUKlU3HzzzcyePRsfHx9xm6OjI97e3tjY2FBRUUFTU5PoEafoG9Csq6sjNzeX3NxcSkpK6OnpYcyYMQwfPlz0+FVUVFTwzDPPUFhYSEpKCtdddx2JiYnY2dmxd+9e1q5dK0oxRkREMHjwYGpqakQGXmlpKdCb5aKUawWsslQsFosIxCi/b2hooLq6WtxHrVaLBdCysjLq6+vx9fW1CpA7ODjQ3t5OdXW1CB6PGTOGDRs20NjYyOrVq7n99ttFqWqz2Swev3r1aqC3fHXfhVZfX1+8vLyora0lNzeXQYMGicCOJEl/XErAom9v4J+73/z58zlw4ABjx47lhRdewM/PT4ybR44cYdGiRTQ1NTF8+HDuvPNOkpOT6ejo4Mcff+S7777jxx9/ZPny5ZhMJu666y7gp/EkLS2NqKgoq7FFOSatVis2xVRVVVFXVycy9yRJ+tf19PTwxBNPUFZWxqhRowa8T3V1Ne+99x4mk4no6Gj+/ve/4+Lictb9lPnC/PnzycjIYN++ffj7+1sFOENDQ9FoNLS0tFBaWkpYWBg9PT2o1WpWr16NwWBg0KBBVqWhe3p6iIuLIygoiNbWVtasWUN2djb19fV4enoSFBTEsGHDOHjwIBs3bmTPnj2MHTsWW1tburu7CQwMJC4ujsDAQHx9ffHz8xPH4+npyaJFiygsLKSurg6VSkVpaSmlpaXU19fT0tLC9u3bMRqNvPbaayQnJ+Pi4oKnp6eoeKDX6+ns7BTzNy8vL7y9vWltbSU/Px9PT0+RIazVaomKiqK4uBiDwSCqrZjNZry9vXniiSeYOHEiaWlpHD16lObmZry8vBgyZAgjR45k6NCh4tz3HbP7zh0lSZIkSZL+bGRwWJIkSZIkSTons9nMtm3bWL16NSdOnMBkMuHj40NUVBS+vr40Njaye/duAJYvX87+/ft5/vnnCQsLE4GNgIAA7O3taWxspKSkhCFDhlgFhNVqNUVFRXz88cf88MMPNDQ0iNdXq9WsXbuW2NhYHn/8cUaPHi2Cq/v376ewsJC4uDgeffRRBg8eDPQGVC6//HISEhL45JNPmDJlighoeHt7k5iYyO7du9m5cyfTp09n8uTJ+Pr6YjabcXBwIDIykpiYGFQqlVWp5uDgYGxsbGhubqa6utqqd7Kvry/u7u7U19dTUVEhAtnKfaKiojh9+jQGg4Gmpia8vLyYMGECKSkp7Ny5k5UrV+Lm5sbMmTPx9PRErVbT0dHBypUr+f777wGYO3euVXawp6cntra2QG/Wzty5c2VwWJL+BJTgQkVFhajC0N3dzXnnnUdERITYwGIymURlggMHDtDT00NjYyN+fn6o1Wrq6+v54IMPRGD4+eefF2Ods7Mzs2fPZvjw4bz88sts376d77//ntGjRzNs2DAcHR1FQLilpUVkAPYPVitlpo1Go1WmniRJ/xpl05mvry9lZWVUVVVRX1+Pu7u7uF2lUrFmzRpKSkoAePzxx3FxcaG7u1t87yuUHsAqlYqnnnoKJycnEbQ0mUxoNBrCw8Oxt7envb1dbIyztbXlzJkzlJeXAzBo0CDi4uLEHEOZ3zg6OorAbmlpKQ0NDXh6euLo6MjVV19Neno6+/bto7m5mc2bN4vj0mg0ohrKqFGjWLBgASkpKVZjTGRkpBhjUlJSxO9NJhNLlizhm2++EWNkYmIijo6OREREUFlZSVVVFa2treK9uru74+fnR0lJCTk5OYwePdpqTqSMjRkZGdTW1uLl5SXmoQ4ODkyePJmJEyfS0dEhKixIkiRJkiT9L5PBYUmSJEmSJOmcfvjhBxYvXkxDQwOJiYnccsstjB8/Xiyc1dbW0tHRwfLly9m1axfHjh3jxRdf5JFHHhELfoGBgTg5OdHS0kJhYSFDhgyxWrA7ePAgixcv5syZMzg6OpKSkkJsbCx2dnakpqaSmppKbm4uK1asYPjw4Wi1WkwmE83NzcBP5U8VfXv9PvXUUzg7O4vfOTg4cPnll7Nnzx4yMjIoLCy0yoR2dXVFo9HQ1NTEtGnTuP3220WpVZVKRUREBPn5+RgMBpqbm3FzcwN6FyUDAwOpr6+nqKiIYcOGAYj3GR8fz+nTpzEajdTX1+Pl5QXAwoULaWpq4tixYyxbtoyvv/6aCRMm0NraSnZ2Nvn5+XR3dzN27FiuuOIKsQgMvVkyU6ZMYdiwYaIPnlLqUJKk/zyLxSI+j/3LQg/UP1Tx/fff8/HHH4sy0ND72X311VcZO3YsDz74INHR0WL8GDJkCF988QUGgwGDwUBsbCzQW8b+wIEDODk5MW3atLOyei0WC8HBwdxzzz1s376dM2fOsHv3boYNG2bVo7OyspKurq4Bj9XX1xdXV1eampqorq4WwSZJkv41yqaPmJgYjh07RmNjo+gBrARmu7q6yMzMBGDEiBGidUX/wLBCCXIqJZ2VYLES4A0ODsbFxYWWlhYRcFbuf/HFFzNu3Dgxj1Keq7W1lcLCQnbs2CGCvjU1NRgMBjG/8/T05I033uDkyZPs2rWL+vp61Go1tbW1ZGdn09HRAcCRI0eora3lzTffJCwsjI6ODgoKCmhsbCQuLk6UgAbo6urCzs6OhIQEVq9eLXorQ+/8Ljo6mh9//BG9Xk9zc7N4zzqdjoCAAACOHTvGjTfeaDXXTEpKIjExkUsuueSsSjQKGxsbGRiWpL8Qs9n8s609JEmS/tfJqzpJkiRJkiRpQCdPnuTZZ5+loaGBKVOm8PDDDxMUFIRKpRKBD09PTwCWLl3KK6+8wqeffsrevXsZN26cWDwMCAjA1dWV6upq8vLygJ+CpnV1dbzxxhuUlpYSFRXF888/z9ChQ8V9Kisr+eyzz/joo48oLi7m+PHjjBkzBrVajbe3NwBFRUU8++yzjBo1ipiYGAIDA2lsbLS6T1++vr68/vrrHDhwgJ07d1JUVISzszMmk4mioiKR7btx40ZKS0t54YUXiIqKAiA2Npb8/Hxqamqor68XwWEnJydCQkLIyMigoKDgrNccPHgwa9asoa6uDqPRSHR0NGazmbi4OJYuXcrKlStZs2YNer2ejz/+WDxOp9NxzTXXcOutt+Lh4SEWMKB3Ufe22277d//MkiT9i/oGXwA6OzsxGAw4OjqKsbEvs9nMl19+ySuvvEJnZyc+Pj7ExsYSGBhIaWkphw4dYs+ePdTX17Nq1SqRERcfHw9AfX29KGlvsVhExp9Go+GSSy45KyCt/BwdHU1CQgJZWVlkZGSIUqze3t4i8KvX661KSyt0Oh2hoaGcPn2aqqoq2tvbByxtK0nSTxtGlO9q5fu6LyXAq8xP0tPTueuuuygrK2PmzJnce++95OTkiM+3m5sbgYGBv2ljRv/X9ff3x8PDg8rKSvG80JsVfPnll4v/rqurIy0tjWPHjnHkyBGysrKA3k1+jo6OtLW1UVFRIcaanp4e7O3tOe+88zjvvPMAaGtro7GxEWdnZ7q7u1m+fDlbt26loKCAY8eOERYWxpYtW3juuefQaDQ888wzzJgxA+gds+zs7KiqquLHH38EevslK6WuAbE5pqKigtraWhE4d3BwEOOuMnb2DaZfcMEFXHDBBb/q/EmS9L/HbDZjNputxtGBxmhJkqS/EhkcliRJkiRJks7S1dXFJ598QkNDA2FhYdxxxx0EBweL2/sGIHp6erCzs+Oqq66io6OD4OBgxo0bJ25XSi4D5OfnWz0+IyODtLQ0tFotixcvZujQoVgsFkwmk+jlO3v2bFatWkVdXR1nzpwRi65Tpkzhvffeo7i4mNWrV7Njxw7MZjNNTU2EhIQQFRWFo6MjISEhXHPNNSKQazKZCAwMZO7cucydOxcbGxuMRiPl5eX09PQA8Nlnn7F9+3ZOnTrFvn37RHB40KBBfPfdd9TV1VFbW0t4eDjQ25szLCwMQATA4adFByW409jYKII7yjkIDg7mqaee4s4772T79u00NDTg6+tLVFQU0dHRokfzz2UiSpL07/u5TOCBtLS0sHfvXvbs2UNaWhpGoxFHR0cSEhIYNGgQl156KcHBwSIbsLS0lDfffJP29nZmzJjBQw89JDLYDAYDW7Zs4aWXXqKlpYWcnBySkpIAxDjT2NiIXq8HED06NRoNHR0d2NnZDXi8ymsnJiaSlZUlyrGGhYXh5eWFj48PTU1NFBUVMXz48LMe37csfmVlJS0tLTI4LEnn0H/DSF/KxrOcnBwWLFjAmTNnsLGxERUBALG5zMHBgcLCQtRqtQhknOt5fw2lUkBGRgbV1dU0NjaKUvLQu7Hlxx9/5JtvviE1NRWj0Yi9vT3x8fFMmTKFwYMHs2LFCk6dOkV5eTnd3d1otdqzNsfY2dnh6Oho1ZLj+uuvJysri6ysLIqLi4HeMS0iIoKsrCzee+89SktLufDCC7GxsaGoqIjNmzfzww8/AHD55Zej0+msWpVA72aZzs5Oq/e4cOFCHnzwwX/5PEmS9P+jq6uLM2fOEBAQwIYNG1i7di0+Pj4sXrz4nJn+v1X/DTvt7e0UFBRQWFhIT08P48aN+91eS5Ik6c9CBoclSZIkSZL+YiwWi1UGav/bVCoV6enp7NixA4DRo0eTkJBg1WO3L+V3kZGRPP/882fd7uHhITJkysrKrB5TUVFBQkICWq1WBFBVKpVVtofRaMTNzY3a2lrOnDlDR0cHDg4OODo68vLLL/Pxxx+TmZkpSiXa2NhQXFxMUVGReI7jx49z7733kpSUNOBCq5eXlyj1DL3ZcrW1tRw7dkwEtAESEhKA3uwaJcgLvdkpISEhAJSXl9Pa2oqTk5M4x0rg2GAwUFlZKd5n38f7+/szb968s86fQgaGJek/61yBnYE2ZjQ3N/P++++zdu1a0SddGbf279/P/v37OXnyJCtXrhQ9hIuKiqivr8fDw4MXXngBrVaLxWKhp6cHHx8fbrjhBoKDg4mPjxcBEIvFglarxd/fn6qqKlHSXgnQqtVqnJ2dqaiosCqB3/fYAZEVbDKZqKmpISwsDJ1Oh6+vLwUFBVbjXH8xMTEAVFVV0djYKMpRS9Jfwa/dmGWxWKiqqiIrK0tk2tvb25OSkkJKSor4TOt0OsxmM+7u7jQ3N6PRaJg+fTrz588Xn1NlUxggSk7/1jmActzKBpHg4GDRq7yiogKdTieykffu3ctjjz1GW1sbAQEB3H777Zx33nmMGDFClLkOCgri1KlTlJSU0NHRgVarpaKigvfee4/U1FTmzp3LjTfeCPQGwi0WCxqNhoaGBoqLi7G1tRVzoaSkJB588EEefvhhCgoKeOutt/jwww9pb28Xxx8fH89tt93G1KlTrd5/cnIy27dvt9qwqFDGWkmS/vgqKipYs2YNO3fupLy8nM7OTj755BPKy8vJysqis7OTmpoafH19BxyHf02lhr73zczMZPXq1YSFhTFv3jxuvPFG0d7D09OTiy666D/6fiVJkv6IZHBYkiRJkiTpL0BZHITeBTblArvv75XbAFFe2cXFhUmTJgG/Pmulp6cHlUolntfW1hY/Pz+0Wi0GgwG9Xo+fnx8A48aNIzExEScnJxwdHbFYLDQ0NFBSUkJqaiq7du0iPT1dZPOdOXOG1tZWHBwcsFgsDB48mGXLlpGVlUVTUxPd3d2UlJRQWlpKW1sblZWVHD16lIMHD+Lj40NSUhKNjY1s3bqVEydOkJyczDXXXCOOu6enB61Wi06no6KiAsCqPKySQdzc3ExNTY3VeVN2m5eXl6PX60VZbQBnZ2eWL19OaGioKIkoSdJ/hxKoGGjxUFlw7OrqorS0lKysLEpKSlCr1aSkpDBs2DDs7OysFiYtFgtfffUVK1euxN7enoULF3L++ecTGBhIZWUl69atY/Xq1Rw5coRPPvmE+fPno1ar0ev1ODs7U1dXR2VlJUFBQVaZgSqVismTJ1sdn9lsxsbGhpiYGKqqqkTfchcXFzw8PFCr1XR0dFBeXj5gcFihjGNarZbu7m6gNwClBKwGKoevUHoZGwwGamtr/5U/gST9aSjBBmXO0/dz/3MB2pUrV/L5559bbRwDWLNmDT4+Prz++usMHToUf39/NmzYgE6nY/r06RQWFhIQECA2YQB0d3fj5eWF0WjEbDbT0tKCs7Pzb3of/Y81PDwcrVZLa2srZWVlJCQkoNFoKCkp4e2336atrY2JEyfyxBNP4OPjg729vZh7NTQ00NbWBkBxcTGNjY24urqiUqkoKioiPz+fNWvWEB8fT2xsrKjUcvToUV5++WXa29tJTEwUgV61Ws3YsWNZsWIFW7duJSMjg5KSEpydnYmKimLUqFGcd955og9yX1qtdsDAsCRJfyxtbW2kp6djMBgYMWIE/v7+YhxtaWnhgw8+YM2aNeLay8vLCxsbG5KTk4He68fm5mZg4M0xA23o6+joEBtq+o7ZJpOJXbt28fXXXxMQEEBhYSGnTp0iPj6e4OBgbG1tZVUUSZL+kmRwWJIkSZIk6b/sv1keWHktJSDS0tJCWVkZNTU1+Pj4iGzd/lJTU1GpVDQ3Nw+4OPdz+l6oK8HnwMBAHBwcaGxspLi4GD8/P8xmM8HBwWKRr6mpiS1btvDjjz+KEq0Aw4cPR6vVcujQIVEOsW+Wr1qtZtCgQeK/J0yYIH4uLi7mtddeY9u2bWJ3eE9PD9999x3Hjh3j1KlTTJw4ES8vL1Eisbq6mldffZXKykrc3Ny45JJLxPN5eHig1Wppa2sjPz9flFEECAoK4rrrrsPT09Pq+JS/w7Rp037TeZQk6V/Tf9NL/8XDvlUQVCoVmZmZvP766+zbt8/qfg4ODgQEBPDMM88wcuRI8XuLxcLKlSsBuP3225k3bx5OTk5AbxB28ODBODg44OrqypQpU8R4n5iYiKurKy0tLVx33XUMGzaM5ORk7OzsMJvN+Pr6kpSUhJOTE05OTqKnp42NDYMGDWLv3r0YjUaMRiMhISFERkaKPqLKWHauDT8mk0m8d6XKgbOzs8gCLioqEhUP+gsNDUWtVtPY2Gi1KUaS/qz6B4D76vv5aWlpobKyErPZbNX3tr/HH3+cb775Bnt7e8aOHUtycjL+/v6kp6ezadMmDAYDd999N9988w0+Pj4iCBEUFERhYSFlZWXU1dXh4eEB9GYLBwYGYjQaqampwWg04uzs/Kvnj+3t7VRWVoqAi4ODA6GhoTg4ONDc3CzKO0NvRYCcnBzc3NyYPHmyGB/6novGxkaOHDkCQGVlJQaDgeDgYPz9/bnzzjuZP38+RUVF3HjjjUyaNAmz2UxZWRmlpaXi3N1zzz1WwReLxcLw4cMZPnw41dXVODk5/eYAuCRJfzzKPGTNmjW89NJL2NnZsWzZMvz9/TGZTNja2pKamsqXX36JWq3miiuu4OGHHxYVl2pra/nss8+IiIgQY+JAamtrSU1N5ciRI2RkZNDa2kpkZCTjx48nJSXFahOJra0tUVFRODg4UFdXx4YNG7j88stZtGgR9vb2GI1GWaFJkqS/JBkcliRJkiRJ+i+orKwkIyMDFxcXxowZc84Szb/kt/bEVEoKrl69mm+//VYESKE3C9bX15eHH35YLHoqF/SVlZVYLBYcHR1pbW3Fw8Pj3wpqBwYG4uzsTGNjI3l5eYwZM0aUO7VYLGzatInXXnuNyspK7OzsCAsL4+KLL2by5MkMHz6cbdu2cejQIQwGA/X19eK9dXR0UFhYSENDA2PHjhUlEpX3ER4eTlBQEIAo/erh4cE111zDsWPHKC0t5dJLL2XWrFl4eHhQXl5Oeno6RUVF2Nract1114lzozz3+eefT21tLePGjbM6H8HBwTz55JPn/DtIkvT7UsaQ/p+vvpthnJ2d2bt3L9u2bRM9PF944QWRDfv999/zwgsvYDQaCQwMJDk5mfDwcOrr6/nmm28oLCzkpptu4p133mHcuHGo1WqKiopwc3OjsbGRESNGDBhQXbhwITY2NmLzCPSWQ73qqqtYsWIFRqORH374QfTVhN7y9o2NjQQFBTF//nwuueQSkQHTt6S9snEmPDyc2NhYKisr2bNnD3ffffdZ3ytqtRqz2czXX38N9Aa8lTHRzs4OPz8/VCoVer0eo9E44Hvx8vKyyq7u6uqS5Vul/6i+G6/+XSaT6awMs58rP3r69Gk2bdrEvn37RLuKsLAwIiIimDVrltWGD4CMjAz279+PxWLh1ltvZd68eSLIedlllxEbG8vatWuxsbER2bfKHCUmJkZs+lBKzkNvVn9UVBSnTp2ioqKCkpISwsLCftU8rL6+nhdffJFNmzZx0UUX8Y9//APonaO4urrS0NBAaWmpuH9PTw/Qe86Vc9T3dUpKSnj33Xdpa2sTGX9FRUUMHTpUZAC/+eabvPLKKzQ0NLBr1y7x3C4uLlx44YXMmzfvrM2Ifd+H7PMpSf97lHmLvb292FimjL3K+O7p6cnUqVOtNo54enqKaif9N7wp6urqeP3111m3bp0Yw6C3Csr3339PfHw8jz/+OCkpKeLxXl5e6HQ69Ho97u7u3HTTTdjb29PT03PWpl5JkqS/ChkcliRJkiRJ+h3V1dVRWFhIdnY2WVlZZGZmUlxcLLK2AgIC2LVrlwhq/Fbn6ol5LvX19bz77rt8++23NDQ04ODgQFhYGC4uLpw4cYKCggJOnjzJyy+/zEUXXSQW65R+d+7u7iJD5NdSAth9g9cBAQGiVLPS21I5B+np6SxbtoyamhpCQ0OZN28eI0eOJCIiQrxXZdGgpqYGg8EgHv/++++zatUqLBYL27dvFwurykJAbm6uyAacOHGiKDc2depUsbDQ0NDAJ598YvUeEhISuOWWW6yyfZXSr2+99davPheSJP37+m6IUfQNXvQtIwiwfv16XnzxRby8vFi8eDFLliyhvLxc3O7g4AD0loD/4IMPMBqNpKSk8Oijj4pKCZ2dndx44408+eSTHDlyhOXLl+Pm5saQIUPQaDT4+/tTWlrKihUrGD9+PElJSURGRqJSqWhtbcXX11eMGcp7UKvV3HDDDSQnJ7Nz504OHjyI2WwWC6fK2FZcXMyzzz5LdXU1d999N4AoR9/Q0CDu5+vry9/+9jd2795NYWEhL774Ik899ZTVuWtpaeHzzz8XPdgXLlxodbufnx9eXl7U1NRQWFhIaGjoWeff2dmZv//97zg6OjJy5EgZGJZ+N/X19RQWFpKVlSV69TY0NHDrrbdy4403njMw8Fv0/RxC7zwtJydH9MGdM2eO6Bd+8OBB3nnnHY4dOwb8VD2grq6OXbt2sWvXLu69916uv/56EQA+evQoRqOR+Ph4ZsyYgbOzMyaTCbPZjFarZc6cOYwdOxYPDw8xt1LGrujoaKA3A85oNBIZGSkeN2LECNatW0dNTQ0nTpxg4sSJv2ruqNVqOXXqFBaLhdzcXPHefH198fLyorS0lIqKCnFu/f39cXBwoL29nX/+8584OjoyevRoVCoVqampfPHFF+zbt48RI0ZQWVlJRUUFW7ZsYfTo0QQHB2OxWLjwwgsZN24cR44c4cyZM3h6ehIREUFYWJgcLyTpL0YZs6Oiopg+fTphYWEMGzYM+KmSi9J/vL6+XgRmq6urcXV1xcHBgeeee44dO3aI9kGOjo7i+VtbW7n11lvJzMwkKCiImTNnMnLkSBwdHdm1axc7d+4kOzubBx98kOXLlzNixAigd3Own58fer2euLg4MTb+K5u1JUmS/lfI4LAkSZIkSdK/oLOzk+LiYnJycsjMzCQrK4u8vDzRG6m/gIAAwsPDGTVqFHD2YqXSE9PGxuasrBBlAa+jo4Pi4mIyMjIoLCzEZDKRkpLC8OHD8fb2tnqMEjhZu3atCHzeeeedXH/99Xh4eNDU1ERmZiYfffQR+/bt49VXX0Wr1YqSzMpFu9lstgrG/poM2IEC2N7e3mIXeGFhoTgHZrOZL7/8kpqaGvz8/Fi7dq1YPIWfsnWVRVylVKKSuRYdHY2NjQ2NjY08+OCDzJgxg6FDh2I2m8nKymLNmjXk5+fj5OTENddcI3roqdVqrrnmGs4//3wOHz5Meno69vb2REREkJiYSGRkpFj4/W+WAZekP5P/VgZp3+BQZWUl7e3tREZGkpOTw7333kt5eTm7d+8W2Wc9PT10d3djNpt5+OGH6enp4b777mPIkCEYjUaxILhu3ToyMzOJiIjg6aefJiYmRvTjtbW1JTg4mNtuu42GhgZycnLYuXMnQ4YMISwsjEmTJnH48GGOHDnCyZMn8fDwoLGxEa1WS3x8PD4+Pjg4OHDRRRdx/vnno1arsVgs2NnZMXLkSJKTk3n88ccxm83k5+fT3NyMVqslPz+fl19+maamJr744gsRHA4KCkKj0dDS0oLBYBDVJ2bPns2GDRs4cuQIn3/+OTU1NUydOpVBgwZRU1PDzp07+fjjj+np6eHqq69m/PjxwE/fKy4uLri5uVFTU0NXV9c5/wZ9y+tL0m+lzJlyc3PJyMggKyuL/Px8mpqazrqvSqUSG+oGCgwrG9D6z5nO9V29YcMGdu7cyVVXXYWtrS1PP/20yJwdO3YskydPxtPTU2zKKCsrY+jQoSxYsIDhw4cDcOrUKb755hu++eYbXn/9dRwcHLjxxhsBxFxBr9ezf/9+Lr74YhwcHMQmFEdHRzGn6t/qQwkO19XVibmWYsyYMbi5udHQ0MCmTZu49957fzaIoXymCwsL0ev1AMydO1fcrlarCQoK4uTJkxgMBgwGA35+fkRGRnLBBRewefNmCgoKWLJkCfb29lRVVYmMvDFjxvDcc8/x/fff895774ny9sHBweKc29vbW7X1kCTpr83Dw0NULuirubmZgoICXF1daWpq4o477qC+vp62tjZeeeUVLrnkEkpLSzEYDNTV1VFbW4ujo6O4Jnz11VfJzMwkICCABx54gIsvvliMqUOGDGHixIksW7aMEydO8OWXXxISEoKvry8uLi4EBgaSlpaGWq22ut6UJEn6q5LBYUmSJEmSpN+goaGBe+65h6NHjw54u1qtRqPRMHLkSP72t78RExNDSEgI7u7uVvfrv4jZd8Gvp6cHs9mMra2tuBDOzc3lrbfesipBCvDZZ5/h6+vLfffdx9SpU62CnxkZGXz++ecA3H333SxcuBCz2UxPTw/Ozs6MGTMGR0dHmpubSU1NZcOGDWJhLyoqCujNOlOCub+WXq9n3bp1uLq6MmbMGKKionBzc8Pb2xu1Ws2ZM2dEUEmtVnP48GEAhg0bJoLmJpMJGxsb8d+bNm0SQZvi4mJaWlrw8PBg6tSp5OTk8Mknn3Do0CGRLdPe3i6OZ+TIkdx9990MHjzYKgtJpVIREhJCSEgIV1xxxTnfjwwMS391ShnRrKwscnNzKSsro6uri6ioKMaOHcuIESPQ6XS/+Dx9y+L3DZD8kq1bt7Jp0yYOHTpEW1sbAQEBnH/++cTHx4vS+eXl5SI4HBQUhK+vrwgA3XHHHdxxxx1Wz9nW1iayaYcPH05MTAyACBwrQkJCiIiIIDc3l+PHj4ss5blz56JWq9m4cSNFRUVUV1cDveO30psTYMeOHVx33XXcddddVmOJElRXqVQiKxh6Fzarq6tZuXIltbW1GAwGfHx8gN4y0vn5+RgMBpqamsT3yt///neWLVvG5s2b+eGHH9i/f7/VGOjj48PVV1/NddddJ15XOfdxcXF8/fXXv1jCV/nbyQwb6bdoaGhg4cKFIhO3P51OR0REBPHx8cTHx59zztRX/w1oPT09dHZ2iswyZX6lzJ+++uor0tLScHV1JScnh9LSUsaOHYutrS3jx48Xj/vggw8oKytj2LBhLF68WARuTSYTo0aNIj4+HkdHRz7//HO+//57kpOTSU5OJiEhgejoaPLz81m6dCnr1q0jJiYGk8mEVqslKiqK6OhoPDw8RJlVZSwIDg5Gp9PR1NQkArrKZ9PX15dLL72UVatWodfree+998RmkYFakyiPe+211+js7MTJyUnM6ZRzERISgkajobW1lYqKCvz8/AB46qmn8Pf3Z9++fRQVFYng/JAhQxg/fjzTp08nODiYm266idtuu+2X//CSJP3P6rtBR61W/+xcrry8nMOHD9PW1sbMmTOB3k0rlZWVQO9cqKKiArDeOD1s2DAOHDhAQ0MDer2e4OBgNBoNhYWFHD9+HIBLL72UadOmYTabMZlM4liSk5NZsGABN910EydPnuTgwYPMmTMHR0dH/P39gd5e63I+I0mSJIPDkiRJkiRJv4lOp6O+vh6NRiP6PSqLmt3d3Tz88MM0NTUxadIkLr/8cqvH9g2KKAuD9fX1uLq6curUKb744gsKCgpoaWnh6aefZvz48Wg0Gg4ePMiTTz5JZWUlQUFBpKSkEBMTQ1NTE9u3b6ewsJCnn34avV7PnXfeKV4vKyuL6upqoqOjmT17NnB2Fk5SUhIzZswgNTWV06dPU1hYSGRkJElJSQC0t7eTmpo64GPP5eDBg6xYsQKAlStXikBzQEAAdnZ21NXVUV5eTmRkJNC7AKrX6ykrK6OiooLo6GixQFBQUMAbb7xBVlYWTk5OtLa2UlpaSnV1tSghfe+99xIbG8vu3bvJzMxEr9fj5eVFXFwc5513Hueff75Y5P13y1NK0v8yk8lESUkJubm5osxrbm4udXV1A97/2LFjfPnll1x44YU899xz4jN5Lr+1LH5PTw9fffUVK1eupKqqCugdR5SAj5OTkxhLc3JySElJAXr71fn4+FBaWkpoaKgIkPTdHKLX6ykuLgZ+CtQWFhaKihDZ2dnk5+dTVlYmjufEiRMUFRWRkJCAs7Mz119/PRdffDEFBQWo1WqqqqooLS2lqqoKk8nE/v37qaur44033mDu3Ln4+vqSmprK1q1baWlp4YEHHsDT0xOLxSI2BWm1WpqammhvbycsLEz0KAWIj48nPz+furo6GhoacHd3p6enBz8/PxYvXsyECRM4ceIEJ0+epK6uDh8fH4YOHcr48eNFycX+lGC4xWIRC72/x99OkqB3ztTd3Y1KpcLOzo7JkyeTmJhITEwMERER+Pv7/+YNWMXFxRw9epQjR45QXFyMxWIhISGBESNGMHbsWHx8fKzKL48bN460tDR2795Nc3Mz9913H3fccQcdHR10dXXh4OBATk6O2Kg2btw4oqOjxXihzEdcXV2ZP38+n3/+OYWFhRw+fJjk5GTi4uJ49NFHWbhwIT09PWRmZpKZmSle38bGhp6eHhwcHJgzZw4PPvig6O3t7OxMYGCgmK8pvZaVAPett97Kjz/+SF5eHitWrMDd3Z3LLrtswIoNxcXFfPTRRyJwcsstt4iNJ8o5Dg8Px2QyYTQayc7OZvjw4ZhMJtzc3HjooYe48sorKSsrw9fXl5CQkLNep//mGUmS/noGmg/0bwGgbGD56KOP+OKLL/Dw8CA5OZnExEQGDx6Mg4MDNjY25OXlMXbsWJ555hlCQkLE45Vrx+bmZioqKkRpaL1eT05ODgEBAYwcORLAKkBdV1eHXq8nMzMTe3t7GhsbSUtLY86cOTg4OIjgcEVFBa2traLygyRJ0l+VDA5LkiRJkiT9BiqVig8++ACdTmfV4xLAYDDg7+9PU1MThYWF1NfXW2W/KGVFVSoV27Zt46GHHiI0NJQbb7yRtWvXcurUKaB3AbKlpQXovQh+5513qKysJC4ujieffFIEQACuuOIK/vnPf/LZZ5+xevVq4uPjmThxIq2treTl5QG9C5NKz+D29nbOnDlDQUEB2dnZojcy9F4op6enExkZSVhYGJGRkRQVFXHs2DGqq6tFVt65KAsDp06dEiWjlQVQgMDAQJycnGhvb6eoqEgEh4cPH86pU6fIyMhg0aJFzJkzB29vb7Kysjhw4ACnTp0iOjqaUaNGsXr1ao4fP85XX33Fs88+KxYfpk2bxpQpU6iqqsLNzU30KJYk6dfp6uriggsuwGg0nnWbo6OjyO6LiorCw8OD/Px8Nm7cSH19PTt27MDLy4v777//rAzivlUSqqurycrKIjs7W4yXkyZNEn0plTFEeczhw4d55ZVXaGtrY9KkSTzyyCOEh4dTWlrKtm3b+PLLL0XQWBnv4KfgMPSO2YGBgYD15hA3NzcMBgNqtZpNmzaxbt06Ojo6znrvSoWB6Oho4uLiRKadwtvb+6yy/opVq1bx4YcfUl1dzalTp5gyZQppaWls2LCBpqYmEhMTmTVrFk5OTiIAtXnzZrZt2wbA5MmTCQsLo7u7G1tbWxISEti4cSP5+flUVlYSHh4u3pOzszMzZ85k6tSpNDc3/2KgfqD3KaskSL8n5XMcHh5ORkYGZrOZJ554QrSYUCgb5+DcG7iUseHw4cO88847Vtn5arWanJwc1q9fz8iRI5k/fz4TJkwQzxsXFwf0bsZLTEzkjjvuwGKxYG9vL+ZxXV1dVFRU4OXlxbhx48TzKnOm/Px8cnNzyczMxNbWlpaWFo4ePcodd9yBRqNh3Lhx7Nixgy1btpCWloZKpaK7u5vy8nIKCgqwt7enq6uLL774Ak9PT2666SaxWSM8PFwEhxsaGsRcy2w24+7uzkMPPcQrr7xCXl4ezz33HIcPHxaZzIGBgbS2tpKens6WLVvYt28fALNnz2b+/PniHCmf7bCwMFJSUvD39yc+Ph6wztYLDg4W80VJkv46lPHylzbR9vT0UFJSQlpaGqdPn6a8vBy1Ws2IESMYOXIkCQkJYj5nY2NDcnIyX3zxhShVn5SUxLJly9BqtWzcuJFHHnmE1tZWMR4q853IyEhsbGxElQOFUhVFabHx/fffk52dTU5ODnl5eaICQ1+nT58WFat8fX1xdnampaWFqqqqX7y2lSRJ+l8ng8OSJEmSJEm/kXIhqWRaKX3v7OzsCA0NJTc3F71eT1tb2znLSbu4uGBjY0NnZyeffPIJBQUFPPDAAyQlJdHR0SF2TKempnLs2DE8PT158cUXiY+PFxlmGo2GgIAA7r//fvbt20dFRQVffvklEydOxMHBgYqKClQqFeXl5TzwwAPk5+dTVFQkesj1pdVq8fb2xsnJSVxAT5s2jVWrVtHY2Mi3337LddddN2DmmfK+1Go1NTU15OTkYDKZGD9+PDExMeI9BwYG4uzsjNFoJC8vj4suugiAyy+/nJKSEnbt2kVGRgbp6elWzz1x4kQeffRR/P39cXNzo6OjQ/TN7LtzXaPRyEVNSfoXWCwWtFotQUFB1NbWotPpuOKKKxg1ahTBwcEiW7e/uXPnsmjRIo4fP86mTZu44IILGD9+vAjk9P3/zz77TARKFWq1mldffZUZM2Zw5513EhERITZ8tLS0sHbtWtra2hg+fDgvvvgiOp0Os9lMaGgot912G7Gxsdx+++0AVuXvXVxcRHC4urp6wMwQJXhqNptF31MfHx9iYmJITEwkMTGR2NhYQkJCzhk0raqqIi8vj9jYWPz8/MT7VcbQoUOH4uLiQnV1tci+njRpEseOHWPXrl28/PLLbN26lXHjxtHU1ERubi7p6ek0NjaSnJwsSjAqrz948GB8fX2ZNm2aGOv6HpvFYsHW1tbqvSkZlDLrV/pvU777Q0NDxXzn5MmTXHTRRSIAAL8cjFA+V3v37mXBggV0d3eTnJzMrFmzSExMpKGhga1bt7J7926OHj0qxrDk5GQAkT37cz0mlc9nbW0tX3/9NR9//DF5eXkUFxcPOGdSq9VotVqam5txcXHBZDLh6enJ9ddfz/XXXw+A0WjEZDLh4eHBiRMn+Mc//kFGRgb79+9n8uTJImgdGxvL5s2bMRqN1NXV4evra/W5Hj9+PA4ODrz66qukpaXxww8/8MMPP+Dt7U1ra6tVdYHo6GiuueYarrzySqvzqvyckJDAp59++rPnW5Kk/339q4X8mspKFouFdevW8e6774qy0Ir9+/fj4ODAwoULufnmm8UYpmwEbmpqory8HPhpQ4pyPV1eXk5tbS1eXl7ieyE4OBh3d3fq6uqsXqulpQWNRkNTUxMPPfTQWceo0WiIiooiPj6e6Oho4uPjRcAaftrQp7RLUb4nJEmS/qpkcFiSJEmSJKmfnp4esaj5cwvq/TOtHBwcCAsLA3ozfhsbG0XGWt/HAISGhlr1xJw7dy633HKL1cV5d3c3O3fuBHovkpUsDxsbm7P67SUmJlJdXc3Jkyepra0VpUotFgutra1s2bJF3D8wMJC4uDgSEhIYNGgQUVFRZx0nwKxZs0hLS2P//v189tlnBAYGMn36dKtMQCXwoPz3p59+yqlTp7CxsWHq1Kk4OzuLYI+/vz9eXl6UlJSIQE5PTw/h4eEsXryY888/nx07dqDX6/H29iYpKYlhw4YxaNAgkWm0YMGCc/49JEn61ygZHlFRUaSlpaHVapkxY4Yox67cR6Fk94aGhjJ79mzS0tJobW3l6NGjVsFhtVpNW1sbL7zwAuvWrcPBwYHRo0eTlJSEu7s7J0+eZOfOnWzatIni4mLeeOMNAgICAGhsbGTr1q04ODgwffp03NzcAOtg6IQJE5g+fTpbtmyhoqJC9NVUskMcHBxob2+nqanJamOLMiaFhoaSnp5OTEwMr7zyiug73F97ezsZGRmkpqaSnJzMyJEjOXHiBMuWLSMtLY0FCxawYMECMX5rtVq6urrYsWMHBQUF+Pn5ie+GsLAwHnzwQQB27drFyZMnrXrYOzg4cO2113Lbbbfh6+srNgJBb5WFvXv3nvPv2D+ILcvoS/9pffuIq9XqATdShIWFYWdnR2dnJwUFBVx00UW/6d+mWq2mtraWpUuX0t3dzeDBg3niiScYMmSIuM/48ePZsWMHDz/8MIWFhbz66qt88sknAKJXpclkwt3d3Sowrejo6MDFxYXm5mbWrFljdVv/OVNERMRZG9GUz2jf+aOXl5e4fcyYMVx22WVkZGTQ3t4uqsNAb3BYrVaTn5/P7t27iYyMFK03lLFqxIgRrFy5kq+//pojR45QV1dHWVkZ3d3deHt7ExMTw6hRoxg9ejRxcXHysy9Jf3I5OTmsWrWK3bt389VXXxESEmJ1/fXvUJ6nb1WXoqIiqqur8fLyYujQoVaVnxSffvopS5cuxc7OjgsvvJDhw4cTFhZGUVERa9eupaSkhJdffpmoqCixiTc0NBSdTkdra6sI8irjkzI219bWYjAYxEYei8WCnZ0dQUFBGI1GampqRDUujUaDs7MzDQ0NeHp6ir7vSrsCZQ7ZnzIv1el0+Pv7U1xcTH5+/r99LiVJkv7sZHBYkiRJkqS/tO7ubmxsbKwW0s4VEP6li/K+mavV1dXU1taedR/l8T4+Pnh4eFBaWopKpeLqq6+2OgYlA0wp+RweHk5tbS0NDQ3k5uZa9cTsW0Krs7OTtLQ0Jk+eLHZk29vbc8MNN3D55Zfj6emJg4PDgMdfVlZGV1cX4eHh2NjYEBQUxNVXXy1KwC5fvhy9Xs/8+fPFgqXyfgoLC/nggw/YsGED0Bvsnjp1qtX59PDwEPdXSmgrt3l7e3P11VczZ86cs8p1S5J0tra2NtauXcuOHTu46qqrmDp16r8dEFCCo0qZ0ujoaBFw7f/cyngYExODvb09LS0tlJSUANZByi1btrBu3TpsbW25+eabufnmm8Wi4xVXXEFGRgbz588nMzOTV155heXLlwNw5swZNBoN7e3tXHjhhec85kmTJnHgwAEMBgMVFRWEhoYCvWOsm5sb7e3tFBcXW2X2Kptahg8fTnp6OnV1dVRVVRETE0NHRwcajQaVSoXZbMbW1pbU1FQWLFhAW1sbt99+OyNHjsTHx4fQ0FDS0tJYv349DQ0NXHLJJXh6elJdXc3OnTtFkOn8889n9OjR4vUjIyP5+9//Tk5ODrt27aKlpQVfX1+R4aL0xOvfww9+Cj71HX8l6f/Lz22i67sZztHRkaampnMuxiub2ZTH9f+3ffz4cUpLS9FoNDzwwANWgWHo/axceOGFzJ07l3Xr1nHkyBFyc3NFsCEsLIyCggIsFotV2XVlHHNwcMDV1ZXm5mZGjhzJlVdeSXR0NKGhodjZ2Q14zAaDAa1Wi5ubG4cOHeLll1/G29ubJ554grCwMEwmEyqVSgTNa2pqgN4S1kOGDBGf75iYGKKjo8nNzeWjjz7ijTfewNHRkZMnT1qdWycnJ2644QZuuOEGioqKgN65k2yjIUl/Psp1VH/KmFRcXMzOnTtpbGwkJyeHkJAQsZGvP6VKyK+dG6hUKpqbm/noo4/YvHmz2KgMvW05PDw8uOaaa7j66qvF8dTX1/Pmm29ia2vLpZdeyr333is27U2aNImZM2dyww03EBUVZbUZz9nZmYCAALKzs6murqa1tRUnJycsFguenp4EBQVRUlJCZWWlGBN7enrQaDRERESQlpYmgsfu7u54e3vj6+tLQ0MD8+bNE9Vj+lLmrUePHuWtt94iICCAK6+8kuTkZFxcXMQcSwaHJUmSZHBYkiRJkqQ/gfT0dP75z39y5MgRXnrpJSZMmHDOi+rfqn/2SG1tLenp6aSmplJSUoJOp2PixImMHDnyFxfg1Go1QUFBQG+JQoPBMOD9lMBvQEAAp0+fxmQyibKn/QPQShBl165dbNy4ccDyhra2toSFhREXF0dISIgI8CQnJ7N+/Xo6OjoICgoiKChIZPko/9NqtSIjZ/PmzUyYMIH33ntPnN+JEyfy9NNP89RTT1FRUcGKFSv4+uuvGTNmDHFxcaK3cVpaGmVlZTg6OjJu3Djuueees4K8Tk5O3HLLLcyfP18s2PYnA8OSdLY9e/YQHh5OaGioWDxraWnh66+/Jj8/n0GDBnHRRReJsnkDUT7zAwVzlDFHyRTu7u6mrKwMOHf2qfIYrVYryvwpZfSV529ra+P9998HYM6cOSxcuFA8v0qlwtHRkZEjR3LnnXfywQcfsGXLFu6++24iIyMpKCgQASWlbGr/qgUqlYqwsDCxUFhUVCSCw76+vri7u1NVVUVmZiZjxoyxymgGGD16NFu2bKG6upotW7YwYcIE7O3txSKr8v1w+vRp2tra0Ol0TJgwAejNeLnjjjvIzs4mLy+Pzz77jE2bNtHY2CjOkdJbVOn92fdcuri4MGLECEaMGHHOv9lA516Wh5b+W3p6ekRwU9H/M3jmzBkyMjIoLCzE0dGRKVOmiHmQcr+AgADc3NzQ6/UUFxeLx/Z9rv4B4f4bI3bs2AH0ZvEOHjz4rGNR7jtjxgyOHDlCfn4+R48eJSIiAltbW+Li4igoKKCpqYnGxkY8PDxEMBrA39+f4OBgKioqiIiIYPr06VbvWWnnodVq2blzJ3fffTcJCQk88sgjjB49mp6eHrKzsykqKuLtt99m4cKF+Pn5odFoaGho4Ntvv+Wtt94CYPr06VZjtb+/P/fffz/vvPMOp06dQq1WEx0dTUVFxYBVXSwWCxEREb/hLylJ0n9b3/HJaDSSm5tLamoq+fn5NDc34+npyYgRI4iMjGTo0KFWG9egd44RFRXFiRMnyMzMZMqUKVa399V/rmAymWhubsbNzQ2VSnXWtWV9fT1PPvkkhw4doq2tDS8vLyIjI3FycuLAgQPU1tbywgsvEBAQIOY8VVVVtLW1YTKZuPvuu0Vg2GQyYWNjg7e3N++99x5ubm7ielm5lgwPDyc7O5u6ujqMRqMIDtvZ2REZGUlJSQllZWWYTCarsVG5nm1sbESv1xMbG0tgYCBRUVHk5uZy6NAhLrzwQiIjI+ns7ESr1aJSqUQlhy1btnDkyBGCg4O57777gN5rUSU4XFRURFtb2zlbJkmSJP0VyOCwJEmSJEl/WMrioNFo5MiRI9TX11NYWCguVM/1mIECIANl/XZ0dPD6669jNBq5++67cXR05Omnn2bPnj3iPmq1mrVr1zJmzBhee+01dDrdzx6z0suopqaGqqoqOjs7z8o66duHT61Wo9Fo0Ov1hIWFWZX66urqEgFkJeAQFBRETEwMgwYNIjExkejo6HOW0EpISCAqKors7Gw2btzInDlzREZc34WEhoYGtm3bhkqlEtnGyrlTq9VMmTIFFxcXPvnkE/bs2UNxcbFY4O3/epdffjlXX331Oc/Pz/3tJEk6W05ODo899hi33XYbN998Mz09PajVapydnUlOTiY/Px+j0Sj63J5L36Ao/DQO9e05FxERgYODA11dXSI43L+nrXJ/5ffr169HpVJhMpmYNWuW1Wsqxwa9WcKK/ptywsLC8PT0pK2tjUOHDhEZGYmdnR1msxlHR0fOnDljNT725erqipeXF7m5ueTn5zNp0iQAvLy8RFnXjIyMs84FwNChQ5k5cyYrV65k586dLF68mHvvvRdnZ2eRtbx27VpeffVVoDcDeNiwYeJ5wsPDee+991i1ahU5OTnk5ubi7OxMYGAgKSkpjBs3jhEjRpyzWoOi7/fWuUrzSpLy799oNGI0GgkICDhnD91/9bn7/tx3DqVUQlFaPAC8/fbbrFy5ko6ODqB3o8jKlSt59tln+dvf/ibup9Pp8PHxIS8vT1Q6URbvu7q6qKiooLCwUHyGjh49ymOPPcacOXPEPFCZAw0aNIiOjg6cnZ0H/JyEhIQQHh5Ofn4+2dnZdHR0YGtry6BBg/juu+9EcCI8PBywDmAPGjSIw4cPc+TIEfbt28f48eNFcMPGxkYc86ZNm4DeTYBK3+CUlBRmzJjBpk2b2LhxI6mpqQwZMoTa2loKCgowGo24uLgwfvx40ZO4r4kTJ4oy0oGBgWeNkX3J8UGS/tiUMbSrq4uvvvqKNWvWnJWlqlar2bRpE46OjsyePZurr77aqlqLp6en2HCnzGEG+ux3dHRQVFTEiRMnRPC5tbWV4OBgkpOTmTJlComJiVaPWb9+PTt37sTOzo6lS5dy0UUX4eLiQnt7O1lZWdx///0YDAa2bdvG8OHDcXZ2pr29HX9/f8rLy/nqq68YP368aBcEvYHgvuX2+wayY2Ji2LJlC/X19VRVVREaGipuj4mJYefOnRQXF9Pe3i4CvMpt0NtnuKKiAujdIHTBBRewefNmMjMzWbt2LY899hh2dnZWr3ns2DG2b98O9Jbu71tNSxlj9Xo9RqORkJCQ3/T3lSRJ+l8ig8OSJEmSJP1h9S1JGBoaSnNzM3V1dcC5s9n6B0CUhUEbG5uzMlHs7e356KOPALjgggt47bXXqKio4LzzzmP48OHY2dnx1VdfUVVVxaFDh/jkk0+45ZZbfnaHsU6nIygoSASHW1tbz1mSUCnfDIg+TP3fi7KAGR4ezvPPP09KSsqAz9Xd3c3+/fspLCxEp9NxxRVXEBISwvTp08nOziYjI4OlS5fy9NNPWy0CFxYWsnTpUsxmM/b29sybN++s57ZYLIwZM4aEhASys7PJzc0lMzOT9vZ20esuISFB7DpXHiMXMCXp39PS0sLHH39MQ0PDWQuL9vb2zJ07l9GjR5OcnCyqDwykvr6e7OxsTp48SV5eHj09PSQlJTFp0iSrvsK+vr54e3tTVlZGZWUl7e3tVoHNvtl9ZWVlrF69mlWrVgG9mcHKAqQy1qampgJgZ2dHR0cHLS0tnD59WpTGz8nJobi4mM7OTvEae/bs4brrrsPb2xsbGxs6OjooLi5m3LhxVgt/ynEoARuA3Nxc8bO7u7tYtFR+3/97w9XVlRtuuIEjR46Qnp7Ol19+yb59+5g8eTKdnZ0iK9jW1pbY2Fjuvffes86tv78/jz76KNXV1ahUKnx8fM75dziX/t9bktSf8p367bff8uijj+Ll5cWyZcsYM2bMr/q+VTYfnOt+yu+bmppwdXXFZDLx7bffsmXLFtLT01Gr1SQlJTFlyhQuu+wynnvuOb788kv8/PwYNGgQjo6O/PDDD9TX17No0SISEhIIDg4WwdXg4GDUajUNDQ38/e9/p7m5mYyMjLM+/wqlRL3yufDz8wPOXYpV4eDgIDKXy8vLxftSxialPGl/zs7OXHLJJXzwwQeUl5fz3nvvMWzYMDGuKoGedevWsX//fqC3lKqSPWdvb8+SJUuwtbXlxx9/xGg0snnzZvH80dHRzJgxg5tuumnAwK/FYjmrj7EkSX9OKpWKnJwcnn/+eU6cOIGDgwNDhw4lJSWFyMhIVCoVp0+f5uDBgxQVFfHFF1+QnZ3N0qVLxXWfTqfD29sbgLy8PODsOYzZbOajjz5izZo1VFVVWb1+ZWUlR44cYfPmzXz44YdWlWe+/fZbAG677TYuueQStFqtCEoPHz6cl156ScwTlTEwKSmJ6OhoysvLWbFiBevWrSM6Opquri5sbGyIiooiNjYWNzc3Bg0ahLe3tyiDrcwzW1pazrrejYqKAnrH65aWFnQ6nRi3w8PDsbe3p729XTxOpVIxYcIEkpOTSUtLY9WqVWi1WiZOnEhiYiI9PT0cOnSIV155hdraWry8vLj//vuBn75HXVxc6O7uBqCiokIGhyVJ+kuTwWFJkiRJkv4jysvLyc3Nxdvbm6SkpAF7J/4S5eIwODiYJ598Ep1OJxbPBlrgbGtrIzMzk8OHD5OWlobBYMDDw4ORI0eSlJTEuHHjxH379nrLy8vj1VdfpaysjLvvvpv58+eLAPDMmTN5+OGHOXLkCDt37mTSpEkMHjz4nO/H0dGR0NBQUlNTqaqqEr3tBlq8DQ0NxcnJifr6eqt+TwqNRkNycjLQG9wpKCggJSWFzs5O0VNKyTRrbW1lwYIFmM1mrrjiCq644gocHByYN28eP/zwA+np6axZs4asrCxmzZpFYGAgRUVF7Nmzh8OHDwOwcOFCcZHe/+9gsVjQ6XSMHj2aUaNG/ap+VpL0V/avjHn9qdVqEWxU+o/3vS05OVmMEedSWFjIihUr2Lp1q9Xvd+7cyWuvvcaiRYusep4HBwdTVlZGQ0MDHR0dIpO4urqawsJCsrKySE9PJzs7m5qaGjEuLFy4UASSlSCuRqOhra0NW1tbbr/9dlpbWwc8Rh8fH6KjowkLCxPVBUJCQvD39ycnJ4djx46dlW2njKlubm4i+Nu3ooGzs7M4d/0DTX15eXmxYsUK3nzzTb777jtqa2tFwFt5ntmzZ3PPPfeIPqX9WSwWkZUCP1/CW5L+FX3LqENv9n3fEub9mc1m4Kd/8780FuXn53PzzTdTU1PDwYMHWb9+PStWrBCBW61Wy759+9i3bx+ZmZmsW7eOiy66iMcee4yAgADMZjMpKSl88MEHlJaWsm/fPq699lqrTX4ajQaTySQ25Sl8fHyIjY0lISGBwYMHExkZKQIkyntRgsN6vX7AYHJfyjhUUVEhAhuRkZHAT+VJ4ex5SlxcHDfeeCMff/wxJ06cYN68eUyfPp2RI0fS2trK4cOHWbNmDa2trURGRnLXXXdZPV7JwistLeXYsWOYTCaCgoKIiooSx38ucs4kSX9M5eXllJWVYTAY8Pb2/sXNeNC7eW7x4sWkpqbi4ODAPffcw8yZM60qL8yaNYuuri6WLFnCmjVrSEtL4/HHH2f16tVA7/Wkj48PGo2Gmpoaq816yhzrlltu4eDBg7i5uTF79mxGjRqFn58fdXV1rF+/noyMDM6cOcPatWu5/fbbcXFxoaWlBS8vL/Ly8kSLD7DeaHfeeeeJn/vO5xYuXEhXVxeHDx+msrLSKtD7448/ip8nTZrE/PnzxYbmyMhIMR88c+YM8NN3kvKdVllZSV1dHYGBgeI2JTP5zJkzVFVViQC2s7Mzy5Yt4/bbb6eoqIj333+f7777DicnJyorK8Vcc8iQIdx2221i/Ffea1JSEp9++im+vr4yMCxJ0l+eDA5LkiRJkvS7y8/P54orrqC9vZ1LL72UpKSkX3yMcvE50AKZVqtl0KBBP/v4np4ePvvsMz799FNqamrE7zUaDUeOHMHW1pZ58+Zx88034+npKfoaRUVFkZeXR1lZGXPnzhU9MZVeez4+Plx88cUcOXKEyspKCgsLRb+7gWi1WnGhW1VVRX19PaGhoVbvS7noDQoKws3NDaPRKILD/RdwR4wYgYuLC42NjXz++efMmjVLLA70DT6tX78eOzs72tvbmTJlirhdq9Xy8ssv88477/Dtt9+SlpZGWlqa1WuEh4dz5513MnPmzHO+r77HLxcxJWlgWVlZ3Hvvveh0Ou68804mT578b2XRK5tNoLc3GvQGhZQFMqPRyBtvvMHx48d55JFHmDhxotXjT548yT333CPKqF5wwQXExsZiMBhYs2YNZWVlPP/88zg6OjJr1izUajVRUVEcOHAAo9HIY489RmNjoyhT2JeNjQ3Dhg3jiiuuOGvsUN6v0i/TbDbT2tqKi4sLkZGRJCQkMGjQIOLi4ggPDx+w7HJAQAApKSnk5ORw+PBhamtrrYKzymtkZGTQ1NQE9I65LS0tODs7o1ar8fX1RafT0djYSGlpqShl2L+3qa+vL8888wx33HEHBw8epKqqCi8vL2JiYoiKihLZgef6W/b/ncwEln5vyr+n0NBQXFxcqKurE5li/f/99S0VD1BdXU1ZWRkdHR0kJyeLfpB92draiion//znP1m1ahWxsbHccccdxMTEsGnTJtauXUtVVRVffvkliYmJPPDAAwQEBNDd3Y2trS2zZs0iNTWV0tJS0tPTmT17tqgmomSAdXR0kJCQwO23346/vz9RUVG/2O9RrVaLHrtlZWWUlZWdM9hqb29Pfn6+eP/KPMnDwwNXV1eampqorq4esOUHwGOPPQbA2rVrycrKIi8vD5PJZHUsM2fOZOHChSIzrv9nXal2I0nSn4dSAj47O5vMzExycnIoLCwUG22gt6y+s7MzV111FTfddNOAz9Pd3c0777xDamoqKpWKlStXWlV96juP0Gg0PPfcc5SVlXH48GEMBgOFhYUimOnn54dOp6O2tpaioiISExPF/G/VqlUcO3YMjUbD7bffzrXXXmvVWuSCCy7gqaee4rvvviMjI4OKigri4uJwcnIiNjaWgwcP8tVXX3H8+HHGjx8vrl1VKhURERFERkaK11I2u8XHx/Pmm2+SmprKgQMH6OnpoaOjg/LycoqKijCbzVRXV7N7924qKirYuHEj0DsX9PT0xGAwnPW95e/vj7OzMy0tLej1enGNrYytoaGhnDlzhtraWurq6vDx8cFsNhMcHMwXX3zB22+/TUZGBqWlpSJYHRAQwPnnn8/s2bMZOnToWX8jpQWUJEmSJIPDkiRJkiT9B/j7+xMeHk52djb19fXA2UFPi8UiLvz6lzocaAF+x44dvPPOO+Tn57NmzRri4uKsAspPPvkkGzZsQKvVMmfOHCZPnoy/vz8nT55ky5YtpKWl8eGHH2IymVi4cKHY9T148GC2bNkiegADYqFTWRBITEzEw8ODxsZGCgsLf/a929rail3INTU1ot/mQDw8PPDx8aGgoICqqqqzeoZaLBacnJy45ppr+PTTT8nPz+e5557j+uuvFz05TSYTGzZs4K233qK9vZ2JEyeKYLxarcZsNhMWFsaiRYuYOXMmJ0+e5MSJE0BvZl5ycjJJSUlWWTqSJP02ypil0WgoLy+nrq5O9OgcKJiojH9KEOfnAoldXV04OjrS1tbGvHnzyMnJ4aqrrmLBggW0t7fzww8/0NDQQElJiVXJ1dbWVt5//32MRiNRUVE8+uijnH/++eJ5x40bxz/+8Q/279/P0aNHGTVqFAEBAaJ6QGtrK3v37hX3d3d3JyoqCo1Gw6lTp2hraxNZfEpAtu+YDj+VC7SxseHSSy/l+eefP+f5O3PmDAcOHKCqqor58+fj6urKzJkz2bBhA01NTSxevJhly5ZZBXQKCwtZvny5KGvY3NxMWVkZCQkJQO/CqrOzM42NjaSnp4uyin2zedVqNRaLBY1GQ2BgIJdffvk5/xZyY4z0/6HvWOHm5oafnx/5+fno9foBe40rJUvXrl3Lrl27xDzE3d0dHx8f0fe2bwl0nU4nFuE/+OAD4uLieOGFF0TPx7vuugs7OzuWLVsGQHx8PGFhYWK+BL2b44YMGcL69espLi6mpaVFBIdDQkJwcXGhoaGBgIAAq57EfcfDc/XdTk5ORqVSUVdXx969exk5cuSA56q9vZ2CggLMZjPnnXcenZ2d2Nvbo1KpiIqK4uTJk9TU1NDU1HTO4MBjjz3GhAkTOH78OMeOHcNgMODm5kZcXByjRo0iJSVFPFZuApGkP76f26RXUVHBpZdees5KDAEBAYSEhODo6MihQ4eora1l2bJltLe3n1U9ACAzM1OUbb7vvvsYNmyY1TEMtNn20UcfpaamRvTGVe7r6+uLu7s7tbW1ZGVlkZiYiMViwWKxkJaWRnd3N3/729+47rrrsLW1FZnAarUaBwcHUlJS+O6779Dr9RgMBuLi4lCpVMyePZsjR46QlZXF8ePHOX78uDgmLy8vHBwc6Ojo4MILL+TWW28lICBAvAd7e3vGjBnDmDFjgN65Yk9PD66urhgMBpYsWcLevXvJy8ujuLiY8PBwbG1tCQoKorq6GoPBQF1dnaiqpdPpCAsLE1nOyjy2b4UvZW5YVVVl9b3l5ubGE088QUNDAwUFBTg7OxMYGDjgBihJkiRpYDI4LEmSJEnS787Z2ZmxY8cSFxfHyJEjz+oRp1z0Kr/r6urizJkzGI1G3NzcxGIk/NRfTim93NXVRUlJCXFxcZhMJmxtbfn666/ZsGEDtra2XHfdddx+++3odDqgN7A7ffp03njjDVavXs3mzZsJCQnh2muvFbdDb+9JZYFVKa3Vt8yqp6cndXV158zw7cvPzw+tVktLSwvV1dUDZpYo5yAoKAiVSkVtbS3V1dUEBweftYBwxx13cObMGTZv3syGDRvYt28f5513HjY2NuTl5VFYWEhPTw+JiYncddddVhfFSuDD1dWVsWPHMnr0aFnmVJJ+Z8pnNSIiAo1Gg1arFf3MznX/X/ocGgwGbr75ZgoKCoDewMvRo0cBqKuro6urCzc3N4YOHcru3bspLy+nu7tbPO/BgwfZu3cvtra23HLLLZx//vliUVGtVhMXF8ftt99OYGAgU6dOxdXVFYDY2FgRnElJSeGpp57Cy8tLZM82NjZy6NAh3nzzTQoKCnjqqafYuXMnzz77rMjm69sSICAggMrKSvLz8+nq6hILmH2D4mq1mnfffZd169aJDT6urq4MGTKEK6+8klWrVvHDDz9gNBqZNWsW8fHxFBcX8/XXX5OWlkZiYiJNTU2Ul5dTUlIigsOenp6EhITQ3d0tspMHOu/9F437B6ok6f9T/3+D4eHh5OfnYzAYaGpqEr21FXv37uXNN9/k9OnTQG/Z5qCgIAwGA7m5ueTm5pKRkcEDDzwgsrQcHBwIDQ3lwIEDAFx44YXExMRYjRkjRozA3d2d+vp6kU3b//OkZLxVVFTQ0NAgyq37+fnh7u5OeXk5+fn5Vs/7S+OhxWLBz8+PMWPGiGy3SZMmiWw8ZY7V09PDRx99REVFhXgPSll8pQLNyZMnMRqN1NbW4u3tfc6gkRL8UDa+SJL051NfX4+tre3Pfoa9vb2xtbXFxsYGFxcXLrnkEhISEoiOjiYkJERcTyqbzx577DHy8/N58803ufjii0VVA2Uc+vTTTzGbzTg5OXHeeeeJ67CfqzoSHx9PfHz8Wbd7eXmJiimZmZlcfvnl2Nra0tTUxJAhQ2hsbCQlJUVs0OnbzzwvL4/U1FQAcY2piI2N5dVXX2Xr1q3s3r2b/Px8dDodarValH0G+PLLLzl+/Diff/45rq6uVr3pHRwcsLGxERuAoPe75rLLLiM9PR29Xk9tba3YfBwZGcmJEyeor6+npqZGBIdtbGyIiYkR2b9dXV04ODiIDeBKD3k3Nzd6enoA6+9Ei8WCm5ubVXa2JEmS9OvJ4LAkSZIkSf8RDz744DlvU6lU1NTU8O2337Jt2zYyMjKwWCzY2toSFhZGeHg48+bNIyUlRVwcRkRE4OfnR0FBAbm5uVx88cVoNBoaGxs5ePAg0JtZ8tBDD5110ejh4cHdd9/N0aNHKSoqYv369SI4rGS2dXR0iD52/S/glWyb/Px8qqqq6OjowN7e/pzvz8vLC39/f0pLS6mqqqK9vd3q4hkQ2WthYWFWGW99g8PK/RwcHHj22WeJjY3l448/pquri02bNonncnR05JJLLuHWW28lLCzsrEWIvj/LwLAk/edoNBo+//xz/Pz8rHrQ9tXT00NZWRmnTp0Spf40Gg2jR49m+PDhREZGYmtra5VpYW9vT01NDdOnT+fpp59Gq9WKzA6lD3tJSQltbW1ibNLr9VgsFqKjo7ngggvOyuoFSElJOWtBTSlb29zcjE6ns+pD3tPTg06n4+KLL2bIkCHccccdFBQUsGfPHh544AGWL18uAsTK640fP16UtD906BATJkxAq9Va9UQ1GAxkZ2cDMHnyZKvMkIULF+Lk5MS7777LyZMnSU9PtyrzescddzBq1CiWLl0K9Jadhd6xf/DgwVb9TX9tiW8ZEJb+P/XdUGYwGMjPz6ekpARXV1dmzJhBQkICP/zwg8jA8vLyEo/Jy8vjxRdfpKSkhMjISB5//HHOO+88VCoVubm5bN++nffff59Dhw7x8ssv8+677+Lk5IRGoxFlRd3d3cXPYF3S2tfXl/r6elHKvf9nJTAwEI1GQ21tLQaDgdjYWKC3H6+/vz+ZmZnU1NRQU1Nj9Tn/Ocrn9oYbbqCmpob8/HyWLl3KnDlzmDBhAiEhIRiNRtasWcPbb7+NyWTi2muvZfz48SL4AD/1tqyoqBBVbc41HiivKQPDkvTHpYx7HR0dlJaWkpaWRmpqKoWFhTQ1NeHq6kpUVBSBgYGMGzeO5ORkq8dbLBa0Wi2hoaEYjUa8vb15+OGHzyo5b7FYcHFxITExkWuvvZZ33nmH6upq0tPTiYiIEMfR2toqMpCHDRsmxrhfM+/oOz9R/t/Dw0NUKVDmSGazGRcXF+bOncuVV16Jg4MD3d3dYvPPiRMn2Lt3LwUFBWJ8VjYr9xUaGsqtt97K1VdfjbOzsyjZ39bWho2NDZ999hlpaWnk5+ezb98+LrnkEo4ePcrKlSs5deoUK1asYNSoUeKYoPf74PTp0xgMBjw9Pa2+H5TvgtbWVqqqqoiNjRXX+Mq8MS8vT/RWVjZqX3bZZVxxxRVWge++ZEUXSZKkf48MDkuSJEmS9Jso5f+AAUv/KTo7O0lLSyMvL4/k5GQGDx4ssoCrq6v5xz/+wfbt22lvb8fGxobg4GBsbW3Jz88XGTFPPvmk6DXs6+srMtvy8/OB3gvCzs5Odu7ciVarZdiwYSJ7RFkM7HuBPW3aNN58800yMzNFSSsPDw8RCFGy8fqWaFQu+IODg0WGr16vHzAIq3BxcSEoKEgEh1tbW88KDiuPCw0NFZktpaWljB07VlwsK+dYWZS47bbbuOaaa9i/fz8VFRV4eHgQGRlJZGSkWMD8d/qbSpL06ykZDH03XPT09Iiy7mazmZ6eHqsFrZ6eHr744gs++OADq4U6lUrF9u3b8fT05MEHH+TSSy/FxcVFlIt/7bXX+Oyzz+ju7kan04msZGVRE6C8vJzm5mY8PDzo6OgQvddaW1txdXW1Glf6MplMoqyySqVCp9Ph6+tLY2Mj5eXlNDY2iswZ5b2aTCYCAgJYuXIlCxcu5PTp05w8eZJFixbx7LPPEhwcLMbOq666iszMTE6fPs3zzz9PfX09EydOFJnI+fn5vPTSS2RlZeHo6MjMmTNFD1KLxYKDgwPz588nOTmZAwcOcPDgQUwmE+Hh4YwfP55p06bR2tqKnZ0darWa5uZmq/N6rr+VJP239C0L/UstNJTPTUtLC2+//TZr1qyhpaVF3L5hwwbs7e1Rq9UiyBoTE4NKpcJkMrFmzRpKSkoIDQ1l+fLl4jaz2UxcXBxxcXF4eXmxePFicnJy+Pzzz7ntttuwsbERLTFaWlpEpn3f41PGhpycHPR6PW1tbWf1C/b09CQoKIiSkhIqKipEr0qVSkVISAg2Nja0tbVRXFwsekf+0mYM5fYJEyZQX1/P8uXLycrKIisri6+++oru7m7KyspEidJZs2Zxyy23WFUmAJg6dSoREREkJib+YtlROY+SpD8+tVpNdXU1H3/8MZs2bTqrlY+jo6OooPDll19y1VVXMW/ePDGnUTbqhoeHi6zW06dPk5KSIvrtQu94oFxbRkdH4+HhQXV1tciy7enpEVm3dXV1QG8Wr6+v768a45TX6M/V1VUEh5UqMspzKWNYZ2cnmzdvZtu2baSnp4uNL9HR0dx444289tpr1NTUYDAYzqqEoFarxbV1WFiY1aag8PBwFi9ezLFjx8R1NyA2B3300Ueij71Op6OpqYnNmzfz2WefYTabmTJlCsOGDROtB5TnLi0tpbi4mIkTJ4r3PHfuXEaOHGk1Niu3DdQbXpIkSfr9yOCwJEmSJP2P+G8FBfuX/zOZTJhMJpGtplw8Hzt2jFtuuQWABQsWiNKFAGvXrmXjxo3odDoWLVrE+PHj8fHxoaKigu3bt/PSSy+RlpbG+vXrRXDYy8tLlE4sKSkRz2U2m+nq6sJisRAfH2+VJdKXjY0NCQkJeHp6UltbS15eHqNHjwZ6S12lpaVRU1NDY2OjVR86JaASFhaGRqOhpaWFsrKynw0OOzg4EBYWxoEDB6isrKSpqQkfH58Bd4WHhoZiZ2dHc3OzWMDo/5x9/9vZ2ZmpU6f+7N9HkqTfz0CBnf59axU2NjZs3bqVBx54gPj4eB577DFGjBghAiTvvfceb7zxBg4ODkyePJnk5GRCQ0PJyspi48aNVFZWsnTpUsLCwkSfOhsbG9zd3QHIyMgQv4PehT2l5F5VVRVGo5HQ0FDs7e1pbm5GrVZjY2NDZ2fnORfYlMVP5X2p1WrCw8PJy8ujsbERvV6PTqezGr80Gg09PT34+vqyaNEili1bxvHjxzlw4ABvvPEG9913H4GBgZhMJuLi4rjpppt46aWXOHPmDI8//jjJycnExcVRUVFBVlYWtbW16HQ6rr/+eiZMmCCOR3k9e3t7xo4dy4gRI7jvvvvO6rFqa2uL0WhEpVKdtbDY93xJ0n9D/2BA35+7urrEv3dHR8ez5hFqtZra2loWLVrEnj17cHR0ZNSoUcTFxWE0Gtm9ezdtbW1Ab4l3g8EA9P57b2xs5JtvvsHW1pbzzjtPZGr1PQaLxcLVV1/Np59+SlFREXv27OHGG29Eq9Xi4+ODTqejsbGR5ubms6qYKBvl1Go19fX1VFVVERkZaXU/Ozs7IiMjKSkpoaysjO7ubjH2KL0nu7q6KCgoYNSoUefctHIus2fPJi4ujrfffpuKigoKCgro7OxEo9EwatQoLrroIi655BKrAIhybO7u7mLeJ0nSH1tnZyf79+/n8OHDREZGMnfu3LO++9evX8/y5csxGo3odDouuugiRo4cSXx8PC4uLlRVVXHs2DGOHDlCRkYGb731FhaLhTvuuENUMFHKGkNvFany8nJSUlLO2sijcHBwEP1tlTL6yhzD3t5ebPw7Vw/j30Kr1eLr64u9vT2tra3U1tbi6ekpbq+urubee+8lLS0NgKFDh3LttdcyefJkUaZ6165d7Ny5E4PBQH19Pc7OzrS0tJCamsqWLVu48MILmTx5stj83dPTg1arxd7eXmwGVyprjRw5krlz55KRkcGePXs4duwYgwYNoqmpidLSUlG9ZubMmdx0003AT6WuBw0axAMPPEBcXBxDhw4FfvpeCgwMJDAw8N8+X5IkSdJvJ4PDkiRJkvQnVVdXR1lZGTU1Neh0OoYPH/4vLYArF7y/NqhYWlrK4cOHOXr0KDk5OahUKpKTkxk7dizjx48XGbKhoaGEhYVRUVEhFi9tbGyoq6vjiy++wM7OjptvvplZs2Zha2uL2WwmMDCQG2+8EXt7e1xdXa0W8ezt7fH19UWj0aDX60Xmb2NjIz4+PlRXV58zk1lZuPT09BTB4fLycvH8CQkJIjhcV1dn1YdOeb6wsDDs7e1pb28XfYfPtahpa2srsvlqa2sHLF+o/Ozn50dERARhYWFnXSxLkvT/r+/nsb29nfb2djw8PMjMzOTmm29m6NChvPDCC2LBTllwrK+vF599tVpNeXk577//PnZ2dlx11VXcfffdIoAxZcoUZsyYwc0338ywYcOsxnIHBwcCAgJQq9VUVlaeVd3A19cXNzc3GhoaqKqqEht0HB0dUalUdHR0UF1dTUhIyIAbWtra2qiqqsJkMuHn54dOpyM6Oppt27bR2tpKaWmpKP/XP+BqsVhISkrivvvuY/HixeTn5/Pdd9/R09PDK6+8IgLP06ZNIyAggGXLlpGdnU1GRobohQcwZMgQrrnmGmbPnn3W+e/o6CAvL4+uri6GDh0qXtdkMqFSqdBoNBQVFdHa2kpPT8+Affskqba2lsLCQtEewmKxMGnSJGJiYkTm1K+h9MrtOz/oTxkzlE0ZGRkZbNq0iQMHDlBeXo6npycpKSmMGzeOmTNnnvX4devWsWfPHjQaDQsXLmTevHlik0dJSQkPP/wweXl5tLS0oNfrxeNqamro6OjAZDKdcxOZcuwTJkzgzJkz5ObmUl5eTmRkJDqdjoCAABF07unpEZ/hc22U6x8cBoiJiWHnzp2UlpbS0dEhgsNKpZTW1lZyc3N/9TnvLy4ujjfeeIOioiJRVjsoKMhqo4skSX9uW7Zs4fHHHwdg+fLlaLVaq7Hm+++/5/XXX8doNBISEsJNN93E3/72N9GjF3pLGU+cOJHKykpWrFjBhg0bWLduHV5eXlxzzTViXIuOjgZ6N+8UFxcDP2UDK2O9jY0NZ86c4f3336e7u5vhw4czZcoU4Kcx39fXl5aWFlQqFV1dXVa3/RoDZRkrczy9Xk9+fj6enp4iq3nZsmWkpaXh6+vL9ddfz8SJEwkKChKBXbVaLTY8K9nDwcHB1NXV8fXXX7Nt2zaqqqpEf2UbGxtsbGxobGzks88+48SJE3h5eTF27FhxPJdddhk6nY7XX3+d9vZ2jhw5Im5LSEjg4osv5tJLLxUbuhU6nY7bbrvtV58LSZIk6b9Dzp4lSZIk6Q+uvb2d4uJicnJyyMjIICsri4KCAqsyg4GBgWi1Wi655BKuvPJKvLy8fjaTuG+Zv773+aXSV+np6bz99tvs2bNH/M7GxoaCggK+/vprZsyYwZIlS7CzsxMXs0r2iMLJyYmmpiZMJhO+vr5iR3Hf17388svFc8NPi5mBgYE4ODjQ3NxMSUmJWABQgsMVFRU/ey41Go3IcK6qqhK/V7KT6+rqqKmpscq2Uc5PcHAwLi4utLS0iIWDc51fGxsbAgICAEQPpxEjRgx4X2dnZ1atWvWzxy1Jf3W/V2UEJTNCpVL96gW7PXv2sHXrVk6cOEFDQwPR0dHMnTsXZ2dnGhsbRZBGCQ4nJiYC0NDQYBW40ev1dHR0oNFouO+++0TARFnki4yM5OOPP8bb2/usPpc+Pj54enpSU1NDSUkJMTExYrx2d3cnICCAhoYGysvL6e7uxsbGRnwvKEGxkJCQATOes7OzWbZsGfb29lx//fVMnjxZ9BlWglHnovxNUlJSeOaZZ1i4cCH19fXs3r2bRx99lBdeeEEEspOTk/nkk08oKCjg6NGjqFQqgoODiY6O/tmMkZqaGl544QUqKyt56KGHmDFjBmq1Wnx3FBQU8NJLL9Hc3ExsbCxxcXG/+DeV/ncNNGfKz8+ntbXV6n5arZYPP/yQkSNHcv/994vNWf0pY0bfIMHPjUX19fUsWbKEXbt2cdtttzF58mSefvppsrKygN75gcFgYOPGjWzbto329nauvPJK8fimpiY+/fRToHcRXsm+MpvN2NnZERsbyz/+8Q9uvPFGamtrqa6uFqVCy8vLcXNzw2g0irnOucbO6OhonJ2dqauro6ioiMjISBwdHQkJCSE7O5vi4mKRkQtYbZRzcHAQpaEnTZp01kY5ZfwoKysTvcsBgoKC0Ol0GI1GMa78q5vhzGYzERERRERE/EuPlyTpv6u9vZ38/HxMJhOJiYk/Wy64vb2db775BoAZM2aIzS7KOFReXi76/kZGRvLGG2+ILN7+lDYYd911F7m5ueI6Gn6qnBIRESFa/CjzNmXu0tHRQWFhIWlpaezbt4+DBw+SnJzMkiVLrOZTFosFe3t73N3d0ev1NDU1UVNTY1WN6pco46HSOsTW1hYfHx88PDzQ6/VkZWUxevRoNBoN6enpYpOdUkq//3MZDAYKCwuB3rmUcu3r7+/PBRdcwLZt2zh58iTz589nzpw5+Pv7U1FRwbFjx0hLS0OlUjFp0iTOP/988by2trZMmzaNKVOmcODAAbq6uvDz8yMsLOwXy/VLkiRJfzwyOCxJkiRJf1BHjx5l4cKF5yxL5ebmRnBwMPb29mRmZtLW1saKFSs4ePAgjz32GIMHDz5nsFf5XVdXF+Xl5dTW1uLj42PVa6i/7OxsbrrpJlpbW0lKSmLmzJkMHjyY2tpaPv/8c44dO8amTZvw8vLi3nvvxd7eHn9/f5GRazQa8fLyws7OjpiYGLKysnj77bdJS0tj+PDhREdH093djcViITQ0VCwmAiJ7JTAwEGdnZ5qbmykoKGDYsGG4uroSFBTE6dOnycjIoKOjQyyKKpTFUY1GQ2FhIRqNRpTKAkSmWX19vSgH1r/8s7+/P56enhgMBtHL8+cWNUNDQ5k2bRohISGMHDnynPeTJGlgu3bt4umnnyYwMJCnn36axMTEfztI3L8s/s8xm82sWbOGTz/9VCyu2dvbk5eXx6JFixg0aJBYfFPKOQOiBGBbWxvV1dWi31pHRwfu7u7U19fz9ddfM2LECHx8fETvXYvFQnh4+IDH4unpia+vLzU1NeTk5FgFh52cnAgLCyMrK4vS0lK6urqwt7cnLi6OgIAACgsLOXr0KJMmTbIKDisZxvX19aSlpeHh4SEWMaOjo9FoNJhMJlEp4ZfOe0pKCs8//zwLFiygvb2d7du3c8kll1iVidZoNKLvaX/n+tsGBwej0+k4deoUL730Eunp6UyaNAm1Wk1WVha7d+/mxIkT2Nracuedd/6mHn/S/45fmjO5u7sTFRVFeHg4KpWKw4cPU1paytGjR1myZAlLliwhPj7+rH87fceMrq4uysrKKC8vB3oz3vuW+ITeTP+WlhY6OjooLi7mwQcfpLi4mLvvvptJkyZhZ2fH999/z5dffklDQwOvvvoq06ZNE4vqeXl5dHR0AD9tlOt7TCaTiejoaMaPH8+GDRvQ6/U0NDTg7OyMSqUSAZczZ84wZMiQc5bA9/HxEQFvZWOdvb29mAeWlZXR1tYmqsH03Sjn7OxMU1PTOQO8ynPo9Xrq6+tF6XulbDVAamqqGBv/FfLzLUl/Hlu3bmXJkiUYjUYcHBx44oknxPjWlzIv2bZtG0eOHMHFxYWpU6eiUqms5gg7duwgLy8PnU7HXXfdRWRkpFWP4L6U3wUEBLBkyRKgN8MVrDN+fXx8KC0tJT09nVdffZXc3Fxyc3OtNhMrMjIyeOSRR5g3bx4TJkzAwcEBk8mEra0tcXFx6PV6DAYDRUVFeHt7/+o5yeeff86qVauYOnUqc+fOJTg4GE9PT7EZOjMzU9y3q6uLiooKXFxcxIbCrq4ukf0LcPjwYY4dOwb0lrlWxmxbW1tmzZpFdXU1y5cvp7y8nDfeeMPqWEJDQ5k3bx7XXnvtgMeq0Wis5neSJEnSn5MMDkuSJEnSH5S7uzuNjY3Y2Njg6+vLuHHjiIiIIC4ujvDwcNzd3UVvuvLycnbu3MlHH33EiRMneOihh/jwww/Fglxf3d3drF27ls2bN3Py5EksFotYsA8ODubhhx8WWa99LVq0iNbWVhISEnj44YdJSUkRt02aNIl77rmH3bt3c+rUKWpqaggODhYlqhoaGqioqBAlpq666ipee+01ysrKqKysZMOGDaKfUXR0NE5OTqhUKi677DKmT58ugr0BAQH8H3v3HV5FtfVx/HvSe0IKqYRO6L1KL6KICHgtiIIFFBtee7l21Ndy9aqAvYCdooAKiNJ77zUJkIT03ns57x/hjAkphA7m93keH8OZPTN7DpydmbP2WtvNzY34+HjCwsKM96lDhw788ccfHDp0iD179tC7d+9KXxJYHsgjIyPJzc3FwcGhUiavJSCTmZlpBIdP5eTkhLu7OyUlJRw4cICMjAwjqFOdli1b8r///a/G7SJSO0dHRyMDLjEx8ayCwxXb5+Xlcfz4cY4ePUpSUhL+/v4MHjzYGG9OtXXrVl555RUARo4cyZ133knjxo0JDw/nt99+Y968eUB54CY2NpauXbsaaxM3a9aMQ4cOkZSURFZWFl5eXnTt2pXGjRuTnp7Oa6+9ZmS9Wb5QbNasGSEhITRo0ID27dvj6upqfFFqKfd64MABDhw4wA033GBk69nZ2RmB6cjISPLz83Fzc6NFixZ069aNY8eOsW7dOkaPHm0EZS0Bo9zcXP7880+gvMR9x44dgfIAkLe3N6mpqUYWzene97KyMoYNG8Yvv/yCv79/pdKO1f29nFrBorrjW67/oYceoqioiC1btvDDDz/www8/VGrXvXt3Hn74YXr37q3AcD11unsmX19fo21RURExMTHMmzeP2bNnc+zYMWbPns3bb79d5bjHjh1j5cqVrFy5kn379hmfO29vb9zd3bn55pu58847K62P3aRJEzZu3MjSpUspKyvj+eef55ZbbjGW0HjkkUcwmUx89913ZGRksHv3bgYMGACUT8QrKSnB3d2d0tJSoPolKa666iqWLFlCYmIiaWlpBAUF4eHhgZOTEwChoaFcd911VbJ6Lft7enqSmpqKs7OzcR5bW1uCg4OB8uByVlaWMWGk4lIYXl5exMbGGkHlUz+7/v7+ODk5kZ2dTXR0NB06dDDG4jZt2mBjY0P79u0pKio66+CwiFw5rr32WsrKynj77bdJTEzkgw8+IDc3l7vuuqtSO8szo+V3fOfOnRkyZEil3+tZWVn88ssvQPlYM3LkSIDTlpW3trY2gsIVj2f5OSgoiKioKCIjI/nss8+M/WxtbWnatCnNmzfHz8+P1NRU1q9fz65du9i1axdTpkxh0qRJxvIEPXv2ZM2aNeTn57Nr1y569epVp/coJyeH/fv3ExUVxfLlyxk3bhxQ/rvNMg4fOXLEaG+577Pcx40YMYJGjRoBGGWj//e//+Hk5ISzszPp6ens3r3bmERtNpu57777uOqqq1i/fj0HDhzAysqKpk2b0rFjR9q1a4e/v3+d+i4iIlcuBYdFREQuU/7+/saD7tChQ3n++eerbefr64uvry8dO3bE29ub//73v8TGxvLKK6/w5ZdfVmqblpbGF198waJFi0hPT8fGxsbIArFk3m7bto3p06fTrVs3Y7+NGzca6wZPmTKlUmDY8oXfAw88wDXXXEPjxo2Nh9hmzZphZ2dHXl4ekZGRdOrUCbPZzC233IKPjw9Lly5l69atJCUl4ezsjI2NDeHh4cax9+/fz6ZNm3jvvfeA8qyTBg0aAFTK5Bs6dCjvvPMO0dHRzJ49m+7du1f5kiA2NpZZs2YB5VktlmsoKyvDwcGBwMBAY31kS4lGC8sXB/369cPHx4cePXrUWg5NRM6dpTRpXl7eaUvG18Sy7tunn37KwoULK2WAuLm58corr3D//fdz2223GZ95y5j2xhtvAOWTX/79738bQZPu3bvTvXt3XFxc+Pnnn8nKyiI6OpqSkhIjW6N169YcOnSIlJQUMjIy8PLywtnZmccff5z33nuP/fv3c/z4cY4fP16pr5ZAzg033MDdd99tVDVwcXHBz88PKA8eWdpD5YBObGwsWVlZ+Pr64uXlxfjx45k3bx4RERG88MILPP/880Y52ZSUFGbPns3vv/+OlZWVUZLQbDZjZ2eHu7s7CQkJHDx4kMTExErBtepYWVlhNpuNstoV38vq/l7qksFtadOpUyfefPNN1q9fz+bNm4mIiDCyo3v27EmHDh2ML0UVGK6f6nLPZKkYYmdnR7NmzZg6dSpLliwhOTmZtWvXApX//ezYsYMvv/ySdevWUVZWZlQwcXZ25vDhw6SkpPDWW2/RpEkTBg0aVKkvTk5OZGVlMXDgQG666SZsbW0rfR4GDBjAhg0b2Lt3L/v27TOCw9bW1uTn5+Pn50deXl6Va7D0r3nz5nh5eZGSkkJycjJQPoEuKCiI8PBwtm3bVuV6KiopKcFsNlNYWGgspWFjY2NMDkxMTCQpKalKqVZ7e3uCgoKM609LS6syEcTNzY1WrVoRHR2Nq6trpet+8cUXq+2PiPyzWSokPPnkk6SmpvLJJ58AcNddd1UaI9auXcv+/ftxcHAwyupXHMcsz5QAffr0qdOkwYrVoqysrCodz/KM17JlSzZu3IidnR3Dhw9n1KhR+Pv7ExwcXKkiVUFBAaGhoXzyySesWbOGn376CXd3d+655x6g/B6xRYsWHD16lA0bNjBu3Djj2bU6lv4XFxezfv16TCYTbm5uxj2fm5sbDRs2BDCWNQLw8fGhf//+bNq0iYMHDxrP52lpaRw+fJj4+HicnZ2ZMmUKGRkZfP3112zcuJHdu3fTp08f4z1r3749bdu21b2TiEg9peCwiIjIZcrZ2dko3Wd5GKyYaVWR5cv8SZMmsXHjRrZt28aGDRvYvXt3pXX0fvzxR2bNmoXJZOKhhx5i3Lhx+Pj4kJyczM6dO/n666/Zt28fb731Fi+88AKdOnUCymcqp6Sk0LJlSyMbueIafFBemtkSyLBo3LgxTk5OZGRkGA/yloy1wYMH06dPHxITE7G3t+fEiRNERUWRlZVFfn4+v//+O1FRUaxevZodO3bQvXv3SmVPK65j3LhxY8aPH8/8+fNZs2YNL7/8MiNGjKBz5844Ojpy8OBBPvjgA/bs2YObmxtPP/20Edy1XIenpyexsbEkJiaSm5tbKThseWA+dYa7iFw4li/TcnNzjeDwmX55lZiYyCuvvMLGjRspKyujVatWtG3bFgcHB1asWEFKSgrvvfcex48f54knnsDb2xuTycTatWvJzs4G4F//+pcRfIW/x4wJEyYQHR3N8uXLOXHiBIWFhUYJ1vbt27NgwQJSU1MrBVh69uzJl19+ydatW9myZQtms5mCggJOnDhhjPMpKSn89ttvJCUlMXv2bKA8i9oStLFUTbAETk0mEwEBAZhMJpKTk0lOTqZly5ZAeZD6xRdf5N133+XAgQNMnDiRvn37UlxcTHh4OElJSbi5uTF69Giuvvpq4O8y/iNGjKBTp0707t3byIg5nVN/N52PdaIt/P39ueWWWxg1ahSOjo7n7bjyz1CXe6ZTAwLOzs60a9eO9evXk5GRQXx8PP7+/pjNZpKSknj++eeJioqic+fO3HPPPfTs2RNHR0eSkpJYuXIl8+bN4/jx4yxevJjWrVsbX+YHBQXh6upKVlYWrVq1wt7evkoJZT8/P4KCgti7d6/xmQaMSQ6FhYXVlsi2XIulfH1CQoJR8cTb25suXbqwevVqDh8+bNw7Vbf/rFmzsLKywsnJqdK9m4+PD76+viQmJpKQkFAp8FJxnfPi4mKOHTtGbGwsnp6eldrZ2NgwZ86cM/47FJF/tv79+/PWW2/x5JNPkp2dzbvvvouHhwdjxowByqs6WCY2d+/end69e1c5xtatW3FzcyMtLY327dvX6T7j1HtHSzn+oKAg43mv4iSZPn36VCqZbDabjcowDg4OdOrUiYceeoitW7eSlZXF0qVLjeBwixYtuPrqqzl69CihoaHMnz+f++67zzhOTfdJCxYsIDMzE7PZzIQJEyr13dfXF1dXV6Mag+X3xGuvvcbjjz9e7YTDjh07MmHCBEaNGkVGRgZDhw6lYcOGxr61vT8iIlJ/KDgsIiJymTKZTLRu3ZotW7aQnJxcaxljk8lkfGl3xx13EBsbS1RUFEuWLKFx48Z4enqyc+dOfvrpJwAeffRRpkyZgtlsprS0FG9vb6699lqcnJx499132b9/P7///rsRHLY8uLq4uBiloat7kCwtLTUeni0lujw8PEhNTTWCuRWzxRwcHIyyWH5+fpXW5u3Vqxf33XcfeXl5nDhxgi5dumBtbY2fnx/29vakpKQQGxtLYGAgAP/5z39ISkpi3bp1/PLLL6xcuZKAgABjPVAof/C/55576NOnT6X3DsofsM1mM61atarzmqQicuHY2Njg5uZGVlYWiYmJFBUVYWdnd0bHmDVrFqtXrwbgkUce4e6778bR0ZHi4mKmTJnCJ598woIFC1i4cCF+fn48+OCD2NraEhMTQ2JiIq1btzbGGMuXepaxz9/fnwEDBhjB4by8PONLRkvpwrS0NKPqgiUr2NXVlWHDhjFs2DCgvERiWVkZbm5uREVFMW3aNLZs2cKWLVuMrDwbGxv8/PxwdHQkMzOT9PT0Spko3t7e+Pr6kpCQQGxsbKWSibfffju+vr588cUXxMXFsWbNGmO/Zs2aMW7cOCZMmFApsANw//33n9F7fbEoMCzVOZN7JijPnLWzs8NkMhn3Qampqfj7+2MymZg1axZRUVEEBwfzyCOPcNVVVwHl9zmNGjXirrvuIiMjg08//ZTIyEji4+ON4LC/vz9ubm5GNRKoes/k6upqTLY7evSoUerTUjEhPT3duG+qLvjh5eVFQUEB+fn5JCYmUlZWhp2dHSNHjmT27NmkpaXx6quv8tlnn1VaKqSoqIg5c+awefNmysrKmDRpUqX3ycPDg4CAABITEzl8+DAjR46sMu6OHj2abt26ERISYkx8OZ8TQUTkn6msrIzBgwfzf//3f7zyyiukp6fz7LPPYm1tzahRo1i8eDFRUVEA3HvvvVhZWRn3M5b/p6enk5+fD2Csz25ZguJUWVlZxMTEEBYWxuHDhzl8+DDHjx8nJSUFHx8fPvroI2M5jRYtWhjVZiwVWizHrTjJyHIv2KJFC3r27MnatWs5evQo+fn5ODo64uTkxPjx45k7dy6ZmZl88skndOvWja5du1YaJy3HKSoqYufOnXz33XeUlJTQs2dP4znVcs2WpTqys7M5cOAAjRo1ori4GD8/P77++mtjwqGTkxMtW7akdevWBAcHG793PDw8KlUEExERsVBwWERE5DLWq1cvtmzZQmZmJvHx8Xh4eNRYPsvyANipUyfatGlDVFQUBw4cICoqCk9PT7Zt20ZqaiodOnTghhtuAKqW9hwwYACHDx8mLCyMHTt2EBcXR0BAgFFOy7L+Z01OfTD39vbGx8fHyC6xBHcKCgo4fPgwa9as4c477zRKEpaVlVFaWoqtrS1+fn74+Phw4sQJHBwcjGMHBQXh6OhoZCMHBgYa6wtPnz6dH3/8kb/++ovY2FgOHToElJejHjBgAGPGjKmSRWNtbY3ZbDbW4hSRy0f//v1ZsmQJKSkpRuCmrnbt2sVvv/0GwAMPPMCDDz4IlI8ztra2+Pv78+ijj2JnZ8f333/P6tWr6d69O3379jXGWGtra7y8vIDqgx+WL9uio6PJyMgwSv9ZAiYV1zGvuH9ubi6Ojo6YzeZKWblNmzbllltu4dChQ2RmZpKRkWGMjwEBAfj6+hIZGckPP/zADTfcQHFxMYGBgXh5eREcHExCQgIRERFG4Mvy+2LYsGEMHTqUvXv3EhkZibe3N02bNjUC35b35dQAVklJSZ1LQItcatXdM9W0BrWdnR1RUVHG57Nt27Y0adIEKF/70VI5oGfPnlx11VXGZ6niZ8ES3E1JSTGOA+VZvZaAq6WU/al9cHBwMEq1W8YPPz8//P39adasGcePH2fHjh3ceeedVYKzZWVlZGdnG6VSk5KSyM7Oxt3dncDAQCZNmsTHH39MeHg4d999N8OGDaNfv37k5uayZcsWfv/9dzIzMxkwYAA33XQT1tbWxvvk4OBAy5YtiYiIoHHjxpXGLcs1dOzY0QioiIjUlZWVFSUlJVxzzTWUlpby4YcfEhUVxWuvvUZWVhbr16+nqKiI66+/3qh8ZRl3LBPs3NzcyM/Px8rKyph8U529e/dy66231rjdbDaTlZVl/Llx48a4ubmRnZ1NTEwMUPW5Fv6+l3N0dMTNzQ0bGxsKCgrIysrC0dGR0tJSfHx8eOaZZ3j99dfJzs5m8uTJPPTQQwwYMIAGDRrg5eWFlZUViYmJLFmyhJkzZ5KXl0fz5s155plnaNCgQaXnfXd3d+P3QFpaGvD3RD4HBwcGDhxYKdNZRESkrhQcFhERuYxZMnct6yu1adPmtGsreXl50blzZ5YtW0ZcXJxREtGSgeLs7GwEWEpKSoiLi+P48eOEhoZy8OBB9u/fD0BoaCiHDh0iICAAJycnoHw9y8zMTNzd3as9d35+Pvv27aOoqIj27dvToEEDAgMDMZlMpKamkpiYSKNGjYiIiODtt99mz549lJSU8NBDD+Hk5GRkHGdmZvLFF19w4sQJGjRoUCnrJSgoCHd3dzIyMti3bx99+/Y1tlkyp8ePH8/evXuN2daWgE1NlPEicnnq3bs3S5YsITMzk4SEBKPka22fWUuQ4/Dhw6SlpdG0aVOGDx8OYFQ2sLTz9PTkpptu4vvvv+fEiRPs2LGDvn37GmXnExMTq6xfXlFAQABWVlbGup+tWrUCyrMCGzRoQHp6OklJSUZGydq1a/n2228JDQ1l9uzZRpZgaWmpkZW8b98+MjMz8ff3p6SkxDhXy5YtadKkCZGRkXzzzTfMnDkTX19f3nvvPbp3724EmVNSUiguLjayIi1MJhOdO3emc+fOla7h1Izoimq7dpHLTcV7poiICNq0aUNJSQm2trZVxoyDBw8yY8YMDh06hKOjI1dffbWR+W9tbc3w4cNp3ry58Zm27J+dnU14eDhr167lr7/+AsqzfBMSEoxjV1wCIyYmptoxy8rKCj8/P9zd3cnMzCQ2NtbIPB42bBg//vgja9euZc+ePUZVldLSUqN/8+bNM4IEiYmJpKenG/dmkyZNwtXVlU8//ZSoqChmz57NV199ZZzb3d2d+++/n3vuuQc3N7dK46KLiwvTpk1j2rRpZ/8XISJSA8t9xXXXXYfJZOK9994jJiaGd999l+LiYlxdXbn66qsrTXCDv8fgZs2aAeXjoCWIW909oWXyW/PmzWndujVt27alXbt2ODo6cuutt1aZ1OPm5oa/v79xv5mVlVXjkhqWZQLy8vIoKSmhYcOGZGVl4evri5WVFWazmdGjR1NWVsaHH35IQkIC7777Lh999BHdu3fH3t6euLg4IiIijPvDwYMHM2nSJNq1a1flmtq1a8e3335rVIypyzrLIiIidaGnfRERkcuYJYulsLDQWLO3LizrJlnKaZWVlREXFweUly98+eWXOXjwIMePHycvL6/aY/j4+BgPnpZyVmlpaYSGhlZaf7OiyMhIPv30UzZv3swnn3zC4MGDCQ4OxtramqysLGOdpBYtWtCqVSv27NnDTz/9xLFjx7j++utp2LAhUVFRrF+/nrVr1wJw880307lzZ+NB3NvbGzs7u0rZxKcGMEwmU6W1lkXkytShQwegPJMvLi6OLl26nPZLMSsrKwoLC42MPZPJRJs2bapkEFp+btasGSEhIYSFhRnVBhwdHbGxsSElJYWkpCQje/dUpaWlBAQEEBMTQ1xcnJHJZ2VlRYsWLdi+fTtJSUmkp6fj6OhISUkJ8fHxpKSk8MUXX3DzzTfTpk0bnJ2dycrKYuHChcyfPx+AG264gVatWhnrpjo6OnLfffdha2vL2rVrcXBwoE2bNsY4+Pzzz/Pqq68amc41sfTRUiZRXzDKP4XlnqmoqMi457FkWxUUFBATE8OhQ4fYtWsXW7duJSIiAltbW8aNG8fNN99sHMfR0bFSJlZmZia7du1i27ZtbN261RgnmjVrho2NDYWFhZVK31smpjk4OBhLYAQFBVUZuyzVVTIzMzl+/LhRieDmm29m9+7dbN++nTfeeIOpU6cyYMAA41qWLFnCN998g42NDTY2NiQnJ5OWlkaTJk2Mce6WW26ha9eu7Ny5k3Xr1pGcnIynpycdOnSgR48etGvXzlgjXWOAiFxMlrFwxIgRuLu78+STTxqTXYKDg7nmmmuqjJcV79mgfIKzpfxzdZPbvL29OXz4cJXxraSkhICAAOLi4oiPj6ewsNCYENikSROOHDlCZmYmcXFxxuQZk8lkrDsMYGtrS1RUlDEpqEuXLsYySRXPN3bsWNq0acMPP/zAkiVLcHBwYP369ZX60759e4YNG8b1119vVKM4lb29vdHHU88hIiJyLhQcFhERuYx5e3tja2tLaWlprWvPncrLywtra2sKCgpIT083SidDeVbZ3LlzK7Vt1aoV7dq1o23btoSEhBAcHIytra3RpkmTJjRu3Ji0tDQ2bdpEt27d8PT0NL6EtDw4Hz16lK1btxpltaC8TKqDgwP5+flERUVx1VVXYWtry+TJk8nNzWXJkiWsWbOGtWvXGn2E8gzhSZMmcdttt2E2m43+tGzZknnz5tW67qQemkX+GSyZH7m5ucTGxgJ1+3zb2NiQmpoK/J1tV92Xh1AePGrcuDGhoaEkJiaSmZlJ06ZN8fHxIT4+nv3799OyZctK5QUtY15MTIxRWcFSztkyWaV9+/Zs376dlJQU0tLSCAgIYMiQIRw6dIiPPvqIX3/9lY0bN9KmTRsyMzOJiIggOzsbR0dHxo4da5RDrNjvrl270qJFC/Lz842StBZ1Lbld0/sgcqWz3DOVlJSwefNmHBwc2L17N2FhYcbn08LV1ZWRI0dy0003Ges7nqq0tJQ1a9awaNEidu3aRWpqKo6OjnTo0IGBAwfyr3/9i4ceeohDhw6RnJxMdna2MTkjMDAQR0dH0tPTOX78eKXgsOX/Hh4e+Pr6cvToUcLDw4HyyRuNGjVi0qRJJCUlERoaypNPPknfvn1p1qwZR48eNdaWvPXWW1m6dCnh4eGcOHGiypqWLVq0oEWLFowdO/aM12sXEblQKo5TvXr14r777uOtt97CZDJx/PhxZs2axd13313tvm5ubrRp04YjR45w6NAhEhMTq9wPVTxPWVmZMSkOyu+BmjVrRlxcHAkJCeTm5hqBV0s1F0vFrtatW1NaWoqNjU2lyXTh4eG8++67HDx4EHt7e4YOHVrjGNu6dWteeeUVnnnmGbZs2UJSUhIeHh4EBwfTpEkTo2KFiIjIpaDgsIiIyGXMzs4Ob29vEhISaixNWNN+DRs2JD4+3giMWMoVOjo6MnHiRK677jqjpGF1kpKSKCwsNNa57NOnD7t372bVqlV07NiRsWPHYmVlZQSIc3Jy2LZtG2VlZbRs2ZL+/fsD5YFld3d3kpOTjQB3aWkpwcHBvPrqq/Tr148tW7YQGhqKlZUVTZs2pVu3bnTt2tWYHX7qzPHaAsMi8s9h+dIsNzfXyASsyxhobW2Nra2tEQhNSkqqtry8ZfyquK5wSkoKbdq0oVWrVsTHx7N+/Xp69epFcHCw0d7ShyNHjnD06FGgPDhcUFBglCG0lAZMT083AtUmk4nJkyfj6enJF198QVFRUaUskrZt23L99dczduxYo3zgqdzc3IxzVMxUFqnvKt4zbdy4kY0bN1ba7u3tTXFxMZmZmZSWltKwYUOjOkF1tm3bxiuvvEJycjINGzZk8uTJ9OvXj+7duxuTQEJCQozgcFpaWqXgsKurK+np6YSHhzNgwABjApxl/HB1dTUmwFjGEcu2QYMG4e3tzTvvvMOePXtYuXIlK1euNK7jqaeeYvTo0UB5Gevu3btX2v/U90VE5HJjNpuxtrbml19+MV4rLi7m7bffxsXFhTFjxmBra2s8/1ruwQYPHkxcXByZmZn89ttv3HHHHTg6OtZYwt/KyqrS8h3Nmzdnw4YNREVFkZ6eblSHad26NYBRDcKyf3JyMpGRkYSGhrJv3z727NljTA688cYbGTVqVK3XaW1tjYuLC8OGDTufb5+IiMg5U3BYRETkMte1a1eWLFlCamoqSUlJNc6OrigvLw83Nzfi4+ONjNvWrVuzePFiiouL6dy5s1F62mw2U1ZWRmlpKXZ2diQmJvLmm2+ybNkyxowZw1tvvQXA8OHD2bJlC7t27eKTTz4hLy+PgQMHEhAQwIkTJ/j222+ZP38+JpOJiRMnGrOwLWuEFhUVsW/fPgAjA8/FxYWxY8cyfPhwHB0dFeAQkUpMJpOxzm5SUhI5OTmnzbKwfHno5+eHjY0N6enpnDhxgoYNG1YpLW35syXY6uTkZGQXDhs2jLVr17J69WqaNm3KY489VmnfY8eO8fPPPxsB2piYGGPsBWjTpg0ACQkJREdHA+XjraOjI7fffjtjxoxh/fr1lJaWEhgYSJMmTfDw8KjSt9pozBSprEuXLixduhRHR0dGjhxJnz59aN68OY0aNcJsNrNq1Srmz5/P9u3b+eabb9i3bx9PPfVUlbW48/Ly+Pjjj0lOTqZv374899xzBAcHG4HW4uJizGazUTkgOTmZxMREWrZsCVBp8l1YWFi1fXV2djaCw5GRkVXGt/bt2/Pll19y6NAhNmzYgJ2dHU2bNqVt27YEBgZiNpt59tlnz+v7JyJyMVgCuT/99BPR0dHY2Ngwbtw4tmzZwtGjR3nxxRcpKChg/PjxWFtbG0tsQPl6xbt27WLLli38/PPPNG/enCFDhmAymSpVcKnI8uy5bNkyFi1aBJSvIV+xXHOzZs2wt7enrKyMhQsXsm7dOkJDQ0lJSalyvO7du3PrrbeeNjAsIiJyOVNwWERE5DLXvXt3lixZYqzZ6+vrW2PQwPKgnZ2dTVZWlvEalGekNW7cmIiICBYtWsTgwYON9tbW1sZDc1JSEn/++WelgAmUB5eff/55HnjgAU6cOMFrr73GnDlzyM/PJyYmBij/MvTmm2/mmmuuMfZzdXXlrrvuwsbGxlhP71SWde9ERE41cOBAIiMjSUtLIzk5GRcXlzpVUQgJCcHb25u4uDh27dpF9+7dK5Wuh/Ly03l5eSQnJwPlAVnLxJlBgwYxbtw45syZw08//URsbCw333wzjRo1Ijw8nNmzZ7N9+3YGDBjAunXriI+PJzMz06jSYFmb3cPDA1dXV6ByVp+zszPXXnttlX5bvgBV4FfkzPXo0YOlS5fi4uLC3XffTfPmzY1tpaWl3HDDDfTr14///ve/LFy4kJ07d/Liiy/y6KOPMnToUIqLi7G1tWXfvn0cOnQIa2tro5x7Rba2tsTGxrJq1SqgfMkOyyQQgIYNGxrZaFFRUQCVStNDeUavJRgcFxdHampqlckvdnZ2dO7cuUrwGrSEhohcuUwmEzk5OaxYsYKCggL69u3L008/zYoVK/jggw84ceIE77//PpmZmTz00EPGMkZQXv75lltuYdeuXURFRTF9+nTs7Ozo169ftYFhs9nM8ePHWbx4MQsWLCAzM5MGDRowevRo454Nypc0cnBwIDMzkyNHjhivOzo60qJFC9q0aUOnTp1o164dTZo0wcHB4cK/USIiIheQgsMiIiKXOUtpUsuavdUFOCwsAZPCwkLi4+NxdHQ0HnrbtGnDsGHD+OKLL9iyZQszZsxg6tSplYIs4eHhvP/++5jNZlxcXLjjjjuq9GX+/Pn873//IyoqisOHD1NYWGgEfm+44Qb+9a9/VenXxIkTz+dbIiL1SI8ePfjmm2/IysoiISGBpk2b1hoctrweEhJC+/btiYuLY9WqVYwaNcpYl7diedfY2Fh+//13oLxSg4WPjw9PPvkk8fHxrF27lsWLF7NmzRpycnKMNrfffjvDhg1j+/bt5OTkGJNyACPAVFtJV7PZbPTFEgxWUFjk7FnumfLy8ti1axfNmzc3Ar7W1taUlJTg6enJc889h7u7O7NnzyY8PJw333wTe3t7+vXrB5SXas7NzcXR0ZGMjAygPLhsCfBGR0fz2WefER8fD5SXvj927JjRD1dXV6PSy6FDh8jLyzOyjCsaMGAA33zzDcHBwXVeN1xE5EpmmeS8Zs0aNm7ciLW1NQMGDMDOzo7rrrsOOzs7XnzxRdLT0/n2228pKSnh0UcfrXTvNnToUJ588kn+7//+jyNHjjB16lTGjRvHgAEDcHNzIyAgADs7O1JTU9m6dSvLli0zlhpo0qQJzz33HAMHDjT6ZDabsbOzo3fv3kaVrfbt29OiRYtqlyURERH5J1BwWERE5DIXFBQElJcwjIiIAKrPFjGbzVhZWVFSUsKmTZuMdiNHjgTA3d2de+65h7/++ouoqCi+/vpr9uzZw9ixY/Hz8yMsLIyVK1ca+/773/82Mt8q8vX15e233yY6Oprk5GS8vb0JCgpSQENELohWrVoB5eX/YmNja21bceJMYGAgY8aM4a+//uLw4cM8//zzfPjhh9jb2xsB2z179vDiiy9SXFxM48aNq0xkcXZ2ZsaMGfz444/s3LmT3bt3G+vVDR06lHHjxpGVlYWfnx8RERHk5uZW2t/Ozq7WdYFNJpOy/0TOo4r3TNVl7Fqyytzc3Lj//vvJyMhg0aJFxMfH89JLL/Hpp5/SqlUrGjdujJubGzk5OSxevJjGjRszbNgw7Ozs2LdvH7/88gvLly8nODgYd3d39u/fz+LFi5kwYQKNGzcGyieYBAYGEhQURG5ubrXB4UaNGtGoUaML/baIiFw2rKysyMnJYc6cOUB5OefbbrvNmIAzbNgwXFxceOCBB8jKyuLzzz+nrKyMxx9/3DiGvb09EydOxMPDg7feeouMjAxmzZrFrFmzaNKkCfb29iQlJZGenm7s07ZtW0aNGsW//vUv3NzcKlXistyLffjhhxfxnRAREbm0FBwWERG5zLm5ueHk5ERhYSEnTpwAag4yAERERBgP24MHD66UidKgQQP+97//8eGHH7Ju3To2btzIpk2bKgVUmjdvzgMPPMD1119fY5/MZrO+0BSRi8KSsZGXl2cEh2uqnlAx0GoymRgyZAjjx4/nxx9/ZNOmTVxzzTWMHj2ahg0bEh4ezs6dO4mKisLV1ZXnnnuuypruJpMJOzs77rrrLsaMGUNpaSleXl6V2sTExBhBn7S0NKByhqEmzohcPHW9Z4Lyku/Tpk0jMjKSPXv2EBcXxxNPPMGXX36Jr68vEydO5LPPPiM9PZ333nuPzz//nKSkJGP/nj178uyzzxIZGcmMGTPIzc0lLS3NCA4/8MADTJ069cJftIjIFWbjxo3s2LEDa2trbrnllipVVnr37s3777/Ps88+S3Z2Np9//jkODg5MnjzZaGs2m7nhhhvo3bs3ixcv5s8//6SoqIjk5GQiIyOB8t8JHTt2pGfPnvTu3ZuQkBBjnWHdn4mISH1nMtf0zYqIiIhcNkaPHk1oaCgdO3Zk1qxZODs7V1l3OCsri9WrVzNjxgxiYmLw8fHh66+/pmXLlkYbyz5JSUns3LmTXbt2sWfPHkwmE40aNaJr165069aNli1bVlkbT0TkUmnTpg0AY8aM4c0336y0rbi4mPz8fLKzs8nIyCA5ORkvLy9at26Nra0thYWF/PDDD3zyySdkZ2dXOfbQoUN55JFHjLWGKyoqKiI2NhYbGxv8/f2NrMPS0lLKysqwtbVl7dq1TJkyhQYNGvDaa68xbNiwC/AOiEhdVbxn+u6774xAwKks5ekTEhKYOnUqBw8epKysjMGDB/Of//yHRo0a8dlnn7F27VrCwsLIycnB2tqa9u3b07dvX6655hpCQkLqtAa6iIiUKy4uZvz48ezfvx9/f39+/fVX3NzcKrWxjKu//fYb7777rjEx55lnnuGmm27C1dW1UjuLuLg4YmJicHR0xN/fH29v74t3YSIiIlcYZQ6LiIhcAXr27EloaCgZGRkUFBTg7OyMlZUV2dnZREREcODAAbZt28aOHTtISUnBzc2NF198sVJgGMpnSJvNZho2bMiIESMYOnQotra2+lJTRC5r3bt3Z/v27SQkJHDkyBHs7OxITEwkJSWFhIQEEhMTiY+PJykpiePHjzNo0CCefvppfH19sbe356677uK6665j+/bt7NixAzs7O1q0aEH79u1p1qwZjo6Olb5gtPy8b98+3njjDby9vbn55psZPny4kRVsbW3NsWPH+PHHHwHw9/dnyJAhl/JtEhH+vmdKS0sjPj6eJk2aVBvANZlMlJWV4efnx7PPPssbb7zBoUOHWL16Nfb29rz00ktMmTKFG2+8kRMnTuDj41PtMhq6hxIRqbtly5YRHh4OwL333oubm1uVMdoyPt9www3Y2Njw5ptvkpyczNtvv43JZOKuu+4y2lmYzWYCAgIICAi4qNcjIiJypVJwWERE5ArQpUsXvvvuOzIzM3nnnXcoLCzk4MGDREdHV2rn5ubGTTfdxKRJk2jatGmNX4ZanFrCS0TkctSvXz+2b99OREQE7733HgUFBcTFxZGamkpBQUGV9rGxseTl5QF/r8fu5+fHqFGjGDVqVLXnqC7AExgYCMD69euJjo4mPj6e/v37U1JSwt69e1m2bBkbN27ExsaGp556SiUKRS4DlnumgoICYmJiagwOw9+T5rp168Yrr7zC/v37adeuHa1atcLJyQmz2YyPjw8+Pj6X4EpERP45zGYzZWVlLFq0iIKCAtq0aUPfvn2B6u/BLPdU1113HU2bNqWgoICWLVvi4uJS7fE1UUdEROTMqKy0iIjIFeDo0aM1rgHcrFkz2rZtS9euXenUqRNNmzbFycmpStlpEZEr1bZt25g4cWK12zw8PGjZsiVt27alffv2hISE0LRpU2xtbattb/lyEsq/SDzdOLl27VpeeOEFkpOTsbGxwcrKiqKiImN7jx49eOCBB7jqqqvO8upE5Hw6duwYI0eOBOCxxx5jypQpldYBFxGRS+OXX37h+eefB2DKlCk89thjemYVERG5RJQ5LCIicgXw8/PD29ubFi1a0KFDBzp06ECrVq0IDg6u8WFaD9ki8k/RuHFjoHzt4ZCQENq3b0/btm1p3rw57u7uZ3Qsk8lU5yCR2Wxm4MCBvP3222zcuJHdu3cTGRmJg4MDbdu2pUePHvTp04dWrVqd8TWJyIXh6+uLt7c3ISEhNG/eHECBYRGRS6yoqIjt27fTsWNH+vXrx5gxYwA9s4qIiFwqyhwWEREREZErWmlpqVE21srK6ryWFqyY0ZKSkoKzszOOjo7n7fgiIiIiIiIiIheTgsMiIiIiInJFqBgErktJ6POlpvVKRURERERERESuNAoOi4iIiIiIiIiIiIiIiIjUA1rYQURERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERETkIistLb3o57S56GcUERERERERERERERERETlLISEhNW6ztrbG1dWVhg0b0rNnT26++WZat259EXt3ekVFRXz22WfY29tz3333XdRzK3NYRERE5Ao1dOhQhg4deqm7ISJSK41VInIl0FglIlcKjVciIqdXWlpKRkYGYWFhfP/999x44418/fXXl7pblUycOJGZM2dSWFh40c+tzGERERERERERERERERERueKMGjWKV199tdJrxcXFZGVlceDAAWbOnMmxY8d45513aNWqFf369btEPa0sKSnpkp1bwWERERERERERERERERERueLY2Njg7Oxc5XUPDw+Cg4Pp0aMHV199Nfn5+Xz++eeXTXD4UlJZaRERERERERERERERERH5x/Hx8aF3794AHDx48BL35vKgzGERERERERERERERERER+UeysSkPhzo6OtbYJi8vjx9++IHly5cTERFBQUEBDRs2pE+fPtx99900b9682v1KSkpYsGABS5Ys4ciRI+Tm5uLq6krLli25+uqrueWWW7C3tzfaT5gwgW3bthl/njlzJjNnziQwMJBVq1adpyuunYLDIiIiIiIiIiIiIiIiIvKPk5WVZQRjhw0bVm2b0NBQ7r//fuLi4iq9HhMTw/z581mwYAHPPfccEyZMqLS9qKiIyZMns3Xr1kqvp6WlsXXrVrZu3crcuXP55ptv8PLyOo9XdW5UVlpERERERERERERERERE/hGKiopITU1l5cqVTJgwgczMTJo0acIjjzxSpW1SUhJ33303cXFxeHp68vLLL7Nq1Sq2bNnCt99+S9++fSktLeX1119nyZIllfadNWsWW7duxdramkcffZSlS5eyZcsWlixZwl133QVAeHg4H3zwgbHPF198wa5duwgICABgypQp7Nq1q8qxLyRlDouIiIiIiIiIiIiIiIjIRTV06NBat69cufK0x1i4cCELFy487Xlef/11PD09q2x79913SU1Nxd3dnblz5xIcHGxs69WrFz169ODhhx9m5cqVvPHGGwwbNswoE/3XX38BMHbsWB544AFjvwYNGvDcc8+RlZXFggULWLZsGa+++ipWVlY4ODgAYDKZALC1tcXZ2fm013k+mcxms/minlFERKSeytq371J3QUTktLZ5lFzqLoiI1Em3X9df6i6IiJzWA+0LL3UXRETqZM7gpy91F64I41a/c6m78I+S/PqftW6vLTgcEhJS5/M4OTlx66238vjjj2NnZ2e8npmZSd++fSkuLuahhx6qNrMYICoqiuHDhwPw/vvvc9111wEwatQowsLCGDx4MJ9++mmV/WJiYoiMjKRRo0YEBwcbAWGAIUOGEBsby8MPP8zUqVPrfC3ngzKHRUREREREREREREREROSiqktm8OmMGjWKV199tdJrpaWlZGdnEx4ezoIFC/jzzz+ZNWsWYWFhfPbZZ9ja2gKwe/duiouLAWjdujW5ubnVnsPb2xsfHx+Sk5PZuXOnERzu0aMHYWFhrF69mokTJzJmzBj69++Pj48PAEFBQQQFBZ3zNZ5vCg6LiIiIiIiIiIiIiIiIyBXHxsam2rLMbm5uBAYGMmjQIF5//XW+++47Nm7cyIIFC7j11lsBiI6ONtrXNXs3Pj7e+Pmhhx5i/fr1nDhxgq1bt7J161ZMJhMhISEMGDCAoUOH0rlz53O7wAvA6lJ3QERERERERERERERERETkQpg6daqx1u/8+fON13Nycs74WBX38fLyYuHChdx///0EBAQAYDabOXLkCJ9//jm33noro0aNYt9lttygModFRERERERERERERERE5B/J3d2dpk2bcvjwYSIjI43XHR0djZ+XLl1K8+bNz/jYLi4uPPbYYzz22GMcOXKEjRs3smnTJrZv305hYSFhYWHcc889LF68GD8/v/NxOedMmcMiIiIiIiIiIiIiIiIi8o9lZVUeEjWZTMZr/v7+xs+xsbG17m82m097jtatWzNp0iS++uorNm/ezL333gtAdnY2CxcuPJtuXxAKDouIiIiIiIiIiIiIiIjIP1J+fj7Hjx8HoGnTpsbr3bp1M4LGK1eurHH/2NhYunTpwrBhw/j222+N1yZMmMBVV13F2rVrq+zj7OzMk08+iYuLCwCJiYnn7XrOlYLDIiIiIiIiIiIiIiIiIvKP9OWXX5Kfnw/AddddZ7zu7e3N4MGDAfjll1/YuXNnlX3Lysp48803yc/PJzo6mvbt2wPg4+NDaGgoqampfPfdd9VmFkdHR5ObmwtAcHBwpW02NuUr/xYXF5+HKzwzCg6LiIiIiIiIiIiIiIiIyBWnpKSE3NzcKv+lp6ezb98+Xn75ZWbOnAlAUFAQ48aNq7T/M888g4uLC8XFxUyaNIlPPvmEyMhI0tLS2LFjB/fffz/Lly8H4Prrr6dr164A2NnZMXHiRADWr1/PAw88wPbt20lJSSE2NpZly5YxefJkzGYzTk5OjBkzptJ5PTw8jH0TExNJS0u7gO9SZSZzXYpki4iIyDnL2rfvUndBROS0tnmUXOouiIjUSbdf11/qLoiInNYD7QsvdRdEROpkzuCnL3UXrgjjVr9zqbvwj3Iu/+5CQkLOqH3jxo357LPPKpWVtti9ezcPP/wwKSkpNe4/ePBg3n//fRwdHY3XiouL+fe//11rSWpnZ2dmzJhB3759K73+3nvv8fnnnxt/trW1Zffu3dja2p7JZZ0Vmwt+BhERERERERERERERERGRi8BkMuHg4ICnpyetWrVi6NCh3HDDDdjb21fbvkuXLixbtowff/yRVatWERERQW5uLm5ubnTo0IGxY8cyYsSIKvvZ2try0UcfsWTJEn799VcOHTpERkYGDg4OBAQEMGDAAO68804aNmxYZd+HH36Y/Px8li1bRkZGBp6eniQkJNCoUaPz/n6cSpnDIiIiF4kyh0XkSqDMYRG5UihzWESuBMocFpErhTKH60aZw+eX/t1dGlpzWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHrC51B2Qy8uECRPYtm3bGe83duxY3nrrrQvQo4tn69atTJw4EYBvv/2WXr16GduGDBlCbGzsFXOdMTExDB06FKh6LefbsWPHWLx4MZs3byYmJoaMjAzs7OwIDAyka9eujBo1iu7du5/zeWbMmMHMmTMJDAxk1apVZ7x/SEgIAG+++SY33njjOffnbD377LMsXLjwrPdfuXIlQUFBlf69VsfKygo7Ozvc3d1p1qwZAwcO5JZbbsHZ2blK27p87q2trXF0dMTPz48OHTowfvx4OnbseNbXIfJPExUby+8rV3IwPJys7GxcnJ1p1qgRwwcMoEvbtuftPEtWr+bbBQu4acQIbr7uulrb5ubl8cfatWzft4/45GTKysrw9/GhR8eOjBg0CNdqxoPamM1mXvnwQ44cO8bAXr148I47zuVSROQSSIiOY8OyVRw/Ek5edg6Ozs4ENAmi15D+tOrQ5rydZ9Nfa/hjziIG33ANQ8aMqLbNi/c8ekbH9PDy5In/vnTadmazma/enkFU2HG69O3JjZPGn9F5ROTycCI1ld/37uNQXBxZBQW42NvT1Nub4e3a0Tm40VkfNywxkeUHD3EkIYHMvHysrEx4u7jQMSiIER3a4+PqekbH2xcdw1t//IHZDNPHj6t1/0Nxcfx54CBhiYnkFBbiZGdHK19frm7blo6Ngs76mkTk0slNSCNm/QEyjidQnFOAjZM9roFe+PdujWer8/e5jt14kONLthM8tBONh3Y54/3Tw2M5MHs5mKHHUzfh0MClxrZlpaXEbw0lZX8kecmZlBYWY+fmiEfzABoN6ICjt9u5XIqIiFwmFBwWkbOSk5PDG2+8wa+//kppaWmlbcXFxYSFhREWFsacOXPo2bMnr732Gk2aNLk0na2HysrKKCgooKCggMTERDZv3sysWbP46quvaNmy5Rkfr7S0lJycHI4ePcrRo0dZtGgRjzzyCA8++OAF6L3IlWXHvn28//XXlFQYCzOysth18CC7Dh7k2oEDufumm875POGRkcxZvLhObSNiYnjns89Iy8io9PqJuDhOxMWxYuNGnrn/fpoHB9f5/L+uWMGRY8fOpMsichk5vHs/cz+ZTWnJ32NVTmYWYXsPEbb3EL2HDWDk+HOfQBd9LJIVC5ae83FOZe/oUKd265euJCrs+Hk/v4hcPDsiI/lwxUpKSsuM1zLy8tl9IprdJ6K5pn077up71Rkf98etW/l9z77KL5ZCbHoGsekZrDpyhIeHDKZ7HZ9bswsK+HTtWszm2tuZzWa+2bSJPw8cqvR6Vn4BOyKj2BEZxdA2rbmnX1+srFTgT+RKkXr4BId/XIO5wlhVnJ1P2pEY0o7EEHBVG5pff+7JGlknkolcvuus9y/OKyTslw1wmrEKoCAjhwOzlpOfnFnp9cL0XBJ3hJO89zitxw3Cq83ZT9IREZHLg4LDUq2AgAAW1/ELaABbW9sL2JtLLzAwEGtra7y8vC51Vy4LCQkJ3HvvvYSFhQHQunVrxo8fT/fu3fH09CQtLY3w8HDmzJnD5s2b2bZtGzfeeCMff/wxvXv3vsS9v7SmTZvGiy++WOX1+Ph4Ro4cCcCUKVOYMmVKtfs7OTlVee3VV19l1KhRVV4vLi4mKSmJ77//nrlz55KYmMiDDz7I4sWLsbe3r9K+ts99cXExiYmJrF69ms8++4y8vDw+/PBD2rZty6BBg2q7ZJF/tIiYGD6YPZuS0lKaBwdzx5gxNAoIICklhQV//cWOfftYtnYtAQ0bcs2AAWd9nqORkfzfxx9TVFR02rZpmZm8+fHHZGZnYzKZGDl4MIP79MHd1ZWI6Gh++v13jp84wavTp/PmU08R6Otbp+ucv2TJWfdfRC6t+BMxzPv0W0pLSglsEsw1t96Ab6A/acmprF28nCO797NlxTq8fX3oNbT/WZ8n5ngU377/GcV1GKte/Pjt07ZZMOsnDm7fg62dHTdOuu207eNPxLDq1z/q1FcRuTxFpqQwfeUqSkrLaObjze29e9PIswFJWdks2r2bHZFR/HngIAEe7gxv167Ox/3zwEEjMNza348bu3ahsZc3OYUFHIqLZ+727eQUFDJ9xSqmjbmBJt7epz3mV+s3kJ6bd9p2P+/YaQSGAxt4MK5nD1r5+pJXVMTGo0dZuGs3Kw8fodRcxpSBA+t8TSJy6eTEpXJkzlrMpWW4BHnRdEQPnH0bUJCWTfSafaQeOkHcpsM4ersR0Pvsq7NkRydz4Ju/KCsqPX3jGhxdtImirPzTtistKmH/V39SkJqNydqK4CGd8OnYFJOVFRnH44n4YwcleYUcmbuWbo+OwcGj5uxjERG5/Ck4LNUymUzVlp+tr7777rtL3YXLRlFREQ8++CBhYWFYW1vz+OOPM2nSJEwmk9GmQYMGNG/enGuvvZa//vqLp556itzcXB588EHmz59P8+bNL+EVXFp2dnbY2dlVed3B4e9sGFtb2zP6/NnZ2dXY3sPDg2nTpgEwd+5cTpw4wcKFCxk3blyVtqf73Ht4eBASEkKXLl248847MZvNfP755woOS702b/FiiouL8fXx4aVHHsHh5MQLV2dnnpw8mQ9mzWLL7t3MW7qUAT174uhQt8y3iv5av55vFiygpKSkTu1/+eMPMrOzAXjg9tsZWGFpgY6tW9O6eXNeev99IqKj+WrePF6aOrXW4xUVFzPjm28qZUaLyJVlxcKllBQX49nQm3uefgg7h/KxysnFmfEP38PcT7/h4PY9rFz0B5379sD+LMaqras28MecRZTWcayy9KEmO9Zt5uD2PQCMvP1GAhrXnqFSXFTM/M++q5QZLSJXnnnbd1BcUoqvuxsvjroeh5MT0V0dHHh8+NVMX7GSLccjmL9jJ/1btsSxmmerUxWXljJ/xw4A2vj78fz1I7E+maHr5uhAgIcHnRoF8ewvC8grLGLe9h08PeLaWo+5LiyMrccjTnvu5Oxsftu7F4DGXp68dMMonE722c3RkX9160aAhwfTV6xizZEw+rdsRdsA/9MeV0QuragVuykrLsXBy5WOk6/F2q58rLJ1sqfN7YM5MmctKfsjiVqxh4ZdWmBjf+ZJNXFbjnB86TbMJWWnb1yDxF1HSTkQVae2J9bspSA1G0zQ9o4heIb8XRbbr1tLXAK82PPx75QVlRC3+TDNRvQ4636JiMilp3o1InJGPv74Yw4ePAjA008/zeTJkysFhk81fPhwZs6cCUBubi4vvXT6teLk/HvggQeMn89mzeaKevXqRdeuXQHYs2dPnQNWIv80sYmJ7Do5Ho4dPtwIDFuYTCYmjh2LyWQiJzeXrSe/GKyro5GRvPzBB3w1bx4lJSU0q0MJ6NLSUjbu3AlApzZtKgWGLexsbbljzBgADoaFcTQystZj/vDrr8QmJNCuVSu8GzQ4o2sQkUsvOT6RsL3lGWsDr7+6SlDWZDIx4tbRYDKRn5vHwR37qjtMjWKOR/HlW9NZ/P3PlJaUENDk3MsMpiensvSnhQC07tKBbv1PX3nmr59/Jzk+kaatW+LuqbFK5EoUm57B7hPRAIzp0tkIDFuYTCbu6NMbkwlyCgrZFhFZp+MeiI0lt7C8osFN3bsbgeGKfFxdGdK6NQD7Y2NrnRSXnJ3N7I2bMJlgQKval+zZfOy4UR57Uv9+RmC4oj7Nm9PG3w+A3/bsqdM1icilk5ecQdqRGAAaDepoBIYtTCYTza7rASYoySsk9WDdgrMW2dHJ7P38D479tgVzSRkugWdXxbAgPYdji7eCCRp2rT1Jo6y0lIStoQD49WhVKTBs4eLviXszf7AykRufdlZ9EhGRy4eCw3LBREZG8sorr3DNNdfQsWNHBg0axLRp00hLS2Pr1q2EhIQQEhJSaZ+Kr2/durXGY1vazJgxo9rt4eHhvP7664wePZqePXvSrl07evXqxc0338yMGTPIOGUNxtMZMmQIISEhPPvss8ZrM2bMMPpRl/+qu5709HQ++OADRo8eTdeuXenUqRPXXnstb7zxBvHx8bX2KScnh6+++oqxY8fStWtXevbsyeTJk2t9385VTk4OP/zwAwBt27blrrvuqtN+/fv3N8oe79ixg23btlXbLjw8nOeee46rr76ajh07MmTIEF5//XXS0k5/01lUVMS8efMYN24cPXv2pFu3btxxxx0sX778tPseO3aMl19+mWuuuYYOHTrQpUsXrrnmGl544QUOHz5cp2u83Pn7++Ph4QFAbGzsOR/P92QZ2tLS0jr9/ZyJjIwMZsyYwY033kiXLl3o0KEDAwcOZOrUqaxYsaLWffPy8vjiiy+45ZZb6NGjBx06dGDo0KG88MILHKtmrdR9+/bRtm1bQkJCGD16dLWB7hMnTtClSxdCQkK48cYbKS4uPm/XKle2PYfKgy0mk4lu7dtX28arQQOaNioPlGzfd2YBl/dnzeLIsWOYTCaG9+/Pq48+etp94pOTyS8oAKBX5841tmvbooWxJMSeWsa5fUeO8Oe6dTg5OvLg7bdDLZOBROTyFL7/5GfcZCKkU/UlWN09GxDQuPxLwMO795/R8ed+8k35Gr8mEz2H9GPys4+cU38Bfv/+Z4oLi7B3dGDUhNOv2X70YChbVq7HwcmRGyeNr3XioohcvvZGlweGTSboGty42jZeLi5Gyecdp5ngZpGak4u9bXnhvBYNfWps5+vmBkBJaRnZJ++nTmU2m/lk9Rryi4q5tn172gYE1HruiORkADydnWhZy1IeHYLKx+BDcfGq1iJymUsPO/mdigm8Wlc/Kc7e3RmXgPKgbuqhE2d0/MNz1pAVmQgm8O/dmo73jTjjPprNZsJ+Xk9pQTEBV7XFo1ntFQnSw+MoyS+fRNNoQIca27WbMJR+r02kwz3XnHGfRETk8qKy0nJBrFixgieeeIKCCg9U8fHx/PDDDyxfvpzHHnvsgp175syZzJw5E7PZXOn1jIwMMjIy2LdvH7/88gs//fQT/v4Xr1yTo6NjpT9v2bKFRx55hMzMzEqvR0REEBERwbx583jnnXe45pqqN1zR0dFMnjyZyFMehtevX8+GDRu4++67z3v/AdauXUtWVhZAtWWJa3P77bfz+++/A/Drr7/Ss2fPStt/+eUXXnrppUrBudjYWL777juWLVtGnz59ajx2Wloa999/P3tPycrbvn0727dvZ/LkyTXuu2bNGqZOnVppHc+ioiIiIyOJjIzk559/5oUXXuCOO+44o+u9HFm+KLWqZqb8mTp69ChQXgLbEnQ+H6Kjo5kwYUKVyREJCQkkJCTw119/cd111/Hee+9VuY7Q0FDuv/9+4uLiKr0eExPD/PnzWbBgAc899xwTJkwwtnXs2JF7772XTz/9lCNHjjBr1izuvfdeY3tZWRnPPvsseXl5ODo68u677/7j11iXuouMKZ8t7tWgAW4uNa+31CQwkOMnThBx4sy+FABo16oV40eNokWTJnVqn5v397p3Pp6eNbazsrLCxcmJ9MxMok75zFjk5OXxyQ8/YDabufNf/8K7luOJyOUr/kT5F5geng1wdq15rPJvFEhcZDRxUdFnfI6mrVsy/KbrCWpWfTDnTITuO2QEtAdePxw3D/da2+fn5rHg6x/BbGbEuLF4eClrWORKFZmaCpQHgN0cay5v38TLi4jkFI6npNTpuMPatmFY2zbkFRVhZ1Pz12CJJ591AZztqy99//vefRyOTzDWDt587Hit5845+Zzp7epaazvL9RaXlhKXkUGw19llCorIhZcTVz5B3t7DGVvnmscqZ39PcmJTyY6t21hVkXtzP5oO74Zro5ontNQmZv0BMiMScWzoTpPhXUnZH1lr++yY8j7aN3DGwbPyeFVWWoqVtTUAVjbWZ9UfERG5/Cg4LOfd0aNHefTRRykuLiYgIIBnnnmGnj17kp6ezk8//cR3333H66+/fkHOvWzZMiObuG/fvtx33300bdoUKA+6zpo1izVr1hAfH8/06dN58803z/pcU6ZM4Z577qlx+4YNG/j3v/+N2WzmlltuoWPHjsa2sLAwpkyZQkFBAUFBQTzyyCP07t0bW1tb9u/fz/Tp0zlw4ACPP/443377Ld26dTP2LSoqMgLDDg4OTJ06lREjRmBvb8/GjRt59913+frrr8/6umpTMeO3e/fuZ7Rv586d8fb2JiUlpUp289atW/nPf/4DQKtWrXjyySfp0KED6enp/Pzzz8yaNYvffvutxmP/+9//Zu/evVhZWXHvvfdy44034ubmxu7du3n33Xf58ssvq90vLy+PZ555hqKiIjp27Mijjz5KixYtMJlM7N+/n3feeYfIyEjeeustBg0aRFBQ1bI6V4ro6GjS09MBznnN56VLlxIWFgbAgAEDql1D+Wy98sorxMfH4+3tzdNPP03Xrl1xdnYmKiqKmTNnsmHDBpYuXcqQIUOMbHSApKQk7r77blJTU/H09GTq1KkMHDgQJycnwsLC+Oyzz9i4cSOvv/46np6ejBw50tj3oYceYvXq1YSGhvLRRx9x7bXX0uhkpufXX3/NzpMlep9++mmaNWt23q5VrnzJJ7PmfU9mr9TEElRNy8yktLQUa+u6PVA//+CDBNSSYVKdiqWt82vIeIHymeR5J7ennRwbTvXFnDmkZWTQvWNHBlVTnlpErgwZqeVjVQOf2gMNHt7lY1VW+pmNVXc+cT/efg3PrZMVLP95MQBunh5cdfWA07b/9dt5ZKdn0rpLB7r263na9iJy+UrJzgb+zuCtiffJiS7pubmUlpVVWya6OtWVdLYoLC5hQ3g4AE19vKsNIkelpjJ/xw6srUw8OHhQrYFmC8eTE0sLTlN9yFL2GiAtN0/BYZHLWGFGDkCVIOqpHBqUj1VFWXmUlZZhZV23sar9XcNx8ql9clxtcuLTiFq+G5O1iZCb+2Nte/qxKi8pAwBHr/LxNzMigdiNh8iIiKc0vxgbJ3s8QwJpNKjTOfVNREQuHwoOS7XMZjO5ubl1amtlZVUpK/btt9+muLgYDw8PfvrpJ/z8ytfO8fT05IUXXqBhw4a89957F6TfliBgy5Yt+fTTTysFrXx9fenZsyc33XQTBw8eZP369ed0Ljs7uxqDYsePH+eFF17AbDbTtWtXXnzxxUrbX331VSMw/PPPP9OgwhqOAwcOpHfv3txxxx3s27ePV199tVJg9McffzQyhqdPn87AgQONbaNHj6Zbt26MHTvWyPA9n44fL58VbWNjc8ZBMpPJROPGjUlJSSE2NpaioiLj/XvjjTcAaNKkCT/++COuJ2dVe3p68swzz+Dn58f//d//VXvc5cuXG0HrF154gdtvv93YNnToULp168ZNN91EdHTVLJht27YZJcZnzJhh/Fu17NuqVSuGDx9OcXExy5cvv2AZ2RfDhx9+aPxcXTY61Py5t7weHR3NX3/9xY8//giAk5MTTzzxxHnrY05ODhs3bgTKA7GjR482tnl6evLJJ59www03EBERwZIlSyoFh999911SU1Nxd3dn7ty5BFdYm7VXr1706NGDhx9+mJUrV/LGG28wbNgw7E8G0ezs7HjnnXe46aabyM/P55VXXuGrr77i6NGjxvs2aNAgxo8ff96uVf4ZsnLKvxRwdnKqtZ2TQ/lscrPZTG5+fq1ZxhWdaWAYwL9hQ2xsbCgpKWFfaGiNpaWPRkVRWFgIYASJK1q3bRtbdu/GzcWF+86wUoSIXF5ys8rHKkdnx1rb2Vuy9MxmCvLya80yruh8BobD9h8mMaa8mkHfawZjfZrAy55N2zm4fQ/Ori6MufOW89YPEbk0sk7ekzifZvKpk235drO5PKhaW5ZxXf2wZQsZefkADG/Xtsr24tJSZq5cTUlpGTd170ozn7pl8zVq0IDtEZHEpKWTnptLA2fnatsdqlDJJb+4qNo2InJ5KMotH6tsHKuvMGBhbX+y6pgZSguKsKoly7iicwm+lpWUEjpvHebSMoKHdsY1sPaJzBZFWeUVqGydHDixZi9Ry3dDhYKMJXmFJO0+TsqBKFqPG4hXm+AajiQiIlcKBYelWnFxcXTt2rVObQMDA1m1ahVQvoauJbhz9913Vwq2WUyePJlFixZVu/7nuSgrK2PQoEE0b96cgQMHVhu4tbKyonv37hw8eNDIojzfsrKyeOCBB8jKysLPz48ZM2ZU6kt4eDg7duwA4MEHH6wUGLawt7fnscce4+677yY0NJS9e/fSqVMnAKM0c9++fSsFhi2CgoK49957L0gA3hJIdXFxOau13LxPZteVlZWRmZmJj48P4eHhhIaGAvDwww8bgeGKJk6cyJw5c4zgdEWW96NJkyaVAsMWHh4ePPbYYzz++ONVtlUsJZ2cnFzl32ujRo34/PPPcXd3NzLQL0dFRUVVgrpms5ns7GyOHDnC999/z4YNG4DyDO6agsNn8rkPDg7m3XffPecs5IpKSkqMcvAp1ZSIswRxi4qKKgV/MzMzWbp0KQB33HFHpW0WVlZWPPPMM6xcuZLU1FRWrlzJddddZ2xv3bo1Dz74IB9++KGRnTxr1iyKiorw9PQ0JjCIVFR8sgz+6bJGKv4OuNBrVtvZ2tKtfXu27tnDmi1bGNSrFy1PKUldUlLCD7/++vefT1nXLiUtjVk//wzAlNtuw/00ZRBF5PJmWbLD5jTLItja/b295AKPVTXZ9OdqAJxdXeg+oOYlRQAyUtNZ8uMCAEbfdSvObhqrRK50xSfvSWxPd29VYXtxaUktLetm6b79LD9UXs6+tb8fA1u1qtLmp63biElPp5mPN2O6dKnzsXs1a8rC3bspM5uZtXETj109rMqz9L7oGPadXK4Eytc8FpHLl7mkfKw6XYnlihm7ZSUXZy3xiD93kpeYgUuQF40GdTz9DieVFpbf+2VEJJC8LwInXw+ajuiOexM/zKVlpB6JJuKP7RTnFHBkzlo6PzASZz8tOyQiciVTcFjOqx07dlB68oFuwIDqy8BZWVlx7bXX8tFHH53Xc1tZWfHwww/XuL2srIyjR48Sc/Khq+LatudLaWkpjz76KJGRkdjb2zNjxgwjIGpRsTRzq1ataszQbt26NdbW1pSWlrJz5046depEdnY2Bw8eBGp+f6E86/VCBIctWWb2Nay/dDoVyxNagoBbtmwxXqvpmkwmE0OHDq02OGwpUd2/f/8azztkyBCsrKwoK6v8kN25c2dsbW0pLi7m7rvvZty4cQwePJjOnTsbfa3tuJeLl19+mZdffvm07dq1a8f06dPPes1hT09PBg0axMCBAxk6dOh5X3vXw8ODli1bEh4eznvvvUdYWBjXXHMNvXv3xulkZmbF8uwWu3fvNgJurVu3rvEz5e3tjY+PD8nJyezcubNScBjgvvvuY+XKlRw4cICnn37aOOYbb7xR5XMsAmB1FpNkLoZbr7+ePYcPU1hYyGszZ3LziBH06twZRwcHomJjmbdkCaHHj+Pp4UFaRgY2p4zNH33/PXn5+Qzs1Yvu1XzmROTKcra/9y+2hOg4jh0qX7ai97AB2NnXnDloNpv55asfKMjLp0vfnrTp0uFidVNELqBLcW+1dN9+vttc/kzq6ezEI0OHVAneHoyNY9mBA9jaWPPg4MF1LmMNEOzlxcCQVqw5Esb2iEjeWLKUG7t2oZGnJ3lFRWw5dpxfdu2igZMTabnlmXs2dSw9KyKXiNXl+RyYcSyeuE2HsLK1JuTm/nUuYw1QVlz+HWlxdj6ODd3pdP9IbOz//s7Ht0tzXIO82f3R75QVlRC5fDftJgw979cgIiIXj4LDUq2K2cBnIjEx0fi5uuw9i1bVzMQ9n1JSUti8eTNHjx4lOjqaqKgojh8/Tl5e3gU975tvvmlkTk+bNq3aQFbF8sY33XRTnY4bHx8PlL+/lqBqbe9v06ZNjcDy+eR2cu2nsy1ZnZmZCZQHe93dy8vkWK7Nw8PDeK061WWo5ufnG9nMjRs3rnFfR0dH/P39iY2NrfR6w4YNeeKJJ3jrrbfIzs7miy++4IsvvsDNzY0+ffoYQVAPD48zuczLgslkwtnZGS8vL9q2bcvw4cMZPnw4NrXMwj/1c19cXExUVBSff/45v/76K+np6dja2jJ48ODzHhi2eOWVV5g8eTL5+fksWrSIRYsWYWtrS9euXRk4cCBXX311lX/7FT9TU6dOrdN5LP/uKrKxseHtt99m7NixRlb5rbfeypAhQ87hiuSfzLK+b9FpJhtVrFJwPtforkmgry+PT5rE+19/TUFBAd8vWsT3ixYZ200mE7defz3xSUms27YNxwoTfhavWsWh8HC8PT2561//uuB9FZELz/ZkkPV02cDFRX9vt70IY9Wp9m3dWf6DyUSXvj1qbbvxz9VEHjmKh5cn140fexF6JyIXg/3JZ4zi091bVdhel3V/q2M2m5mzbTu/7dkLQANnJ/4zcmSVss+5hYV8vGYNZjPc1rMngQ08zvhcd/ftS3ZBITsjozgYG8fB2LhK2/093JnUrx+vL14CgL3NhXnWEpHzw/pktZXTZQOXFv89VlnVYd3fc1GSX0Toz+vBDE2Gd8PJx+OM9q/YvyZXd60UGLZw8nHHr3tL4jYdJj0sltKiYuO9EBGRK4+Cw3JeZWdnGz9XXIf4VJYg4/lWWFjI//3f/zF//vwqgVF7e3t69epFWVkZ27dvP+/nnjdvHt999x1QXlJ7zJgx1bbLOblG5Zmw7FMxKFvb+2tlZYWTk1Olv4/zoUWLFuzfv5/8/Hzi4+Px9/c/o/3DwsqzQQIDA43sY0sfHRxqX3ulunLTFd+Ps9kfyv+u2rRpw1dffcXmzZspLi4mKyuLP//8kz///BNbW1smTJjAk08+WSnz+XLy5ptvcuONN57349ra2tKiRQveeecdfH19+fzzz5k7dy5JSUnMnDmz1kDz2erevTu//fYbn3zyCcuXLyc7O5vi4mK2bt3K1q1beeeddxgyZAivvfaakc17Lp+pUwUHB+Pv709UVBRQPoFApCZOJ8fh/Pz8WtvlntxuZWWFy2nWJz5fOrdpw3vPPceiFSvYffAgGZmZuLq4ENKsGdcPGULLJk1485NPAPA4+Ts5KjaWOYsXYzKZeOD2243rE5Erm4NT+We5ML/q+uIVFZxca9NkZYWj88UZqyo6tGs/AI1bNsXds+qyKxYJ0XGsWLAUTCbG3nMbDhqrRP4xLGsN551mMkvuyYl3ViYTLmdR1aqopISPV69h6/EIAHxcXfnPyBH4VTNZ+asNG0nLyaVdYADXtm93xueC8gD2E8OvZuPRo6w6fITI1FTKzGb83Nzo07w517ZvT3R6mtG+wUW6XxSRs2PjUD5WlRbUvj64sd3KhI3jhZ14d/TXzRRl5uHe3I+Aq9qc8f7WFYLBHs1q/q7PvYkvcZsOYy4toyAtW6WlRUSuYAoOy3nl4uJi/Jyfn1/pzxVVzKI6UwUFNX+x9dhjj7Fy5UqgvITuwIEDadmyJS1atKBZs2bY2Njw/vvvn/fg8Pbt25k2bRoAV111FU899VSNbSsGMfft23dGJZorZtaeLgv6XN7jmvTp04eFCxcCsGHDBm6++eY673v06FGSk5MB6NWrl/G65ZpOF1yp7noqZvSezf4WvXv3pnfv3uTk5LBp0yY2b97Mhg0bOHHiBMXFxXz99deYzWaeffbZWs/xT/b4449z4MABNm3axOrVq/nvf//Lc889d0HOFRwczJtvvsm0adPYtWsXmzZtYuPGjRw4cACz2cyqVatISkri559/xmQyVZoosXTp0nNaB3n69OlGYBjg008/ZejQobRpc+YPV/LPF9CwIYfCw0lOS6u1XerJNe493d3Par32s+Xt6cnkW26pcXvUyWoK/icnQWzbu9dYcuG1GTNqPfbarVtZe7Ks/0uPPEK7li3PR5dF5ALw9m1I5JGjZKSm19ou8+R2N4+LO1YBJMXGk5qQBECHnl1rbXto515KT45Vs/5b+zI1uzduY/fG8iVd7nn6IZq21lglcjnzd3fnUFw8KaeZ5Jx6cqJnA2enMx6vsvLz+e+ff3E0sXzMaerjzTPXXoN7DQHZzUePAeWlpcd//mWtx37kxzkAtPH346UbRlXaZjKZ6NeyJf1quGeKSk092a48k1hELl+O3m5kHk+gIKP2ieoFGeVLXtm7nflYdaaS95VPdsk8lsCG57+pte32//4MgHtTXzreOwIAhwYuZJ8o/87OZFNzOWprh7+D3KVFF2cdZRERuTC0kImcVxVL+1a3PqzFiRMnqn29YmZmcQ2zhS1lhE+1a9cuIzA8YcIEFixYwL///W+uu+46WrVqZWQ5pqfX/sXYmYqJiWHq1KkUFxfTqFEj3n///VozTAMCAirtWxtLCWkLPz8/Y9242t7fpKQkY33g82nYsGFGBu53331XZQ3f2nz77bfGzzfccIPxs+X9yMzMJPXkA3F1KpYOtrC3t8fLywuo/f0oLS2ttozwqVxcXBg+fDgvv/wyy5cvZ/78+QQGBgLw448/XpB1qq8UJpOJt956y/j7/+abb4wS6heKra0tvXr14rHHHuPnn39m9erV9OvXD4ADBw6wa9cugEoZ7KeWDj/VqZ+pivbs2cPXX38NwPjx4wkICKC4uJhnnnnmgky2kCtfo5P/9pJSU8mrZYJKxMmxvklQ0EXpF0BRcTEFtfweiI6PJ/1kqf9WTZterG6JyCXgG+QHQFpyKgW1jFVxJ8rHKv/gwIvSr4rC9h82fm7TVesHi9RXjTzLM9CSsrLJq+X+OyIlBYAmJysJ1VV6bi4v/fqbERjuEtyIl0ZdX2Ng+Hwxm81knaZ6w/6Y8ueYoAYNcLhAS/iIyPnh7Fte4aQgLYeSWrKHc+LKv+Ny9r/8s2td/L2MnwvTaw56F2X/fS9p764qByIiVzJlDst51bVrV+zs7CgqKmLFihXVrrkLsG7dumpfr5gBmFZDJpYlIHSq3bt3Gz/feuut1bYpKytj68lMJ8ufLcHWs5Gbm8sDDzxAeno6Tk5OfPzxx6ddn7Z79+7GzytXrqwxy3HXrl3cdddd+Pv78+ijjzJixAicnZ3p3r0727ZtY+XKldx7773V7lvT+3uunJ2dueeee/jwww8JDQ3lk08+4aGHHjrtfps3b2b+/PkAdOnShd69exvbBgwYYPy8YsWKGv/uarqmAQMGsHDhQtasWUNpaWm1gflt27ZVm1n82Wef8fvvv+Pq6spPP/1UZXvHjh2ZOHEib775JoWFhWRmZhrB6PrI19eXZ555hhdeeAGz2cxLL73E4sWLay1xfibWrFnDzJkzOXHiBCtXrqxSCtzf358nnniCDRs2AH+vcd6tWzesrKwoKytj5cqVlf5NVRQbG8vIkSPx9vZm4sSJTJw40dhWUFDAM888Q2lpKY0aNeKZZ55hwIAB3H///YSGhvLRRx/x2GOPnZfrlH+OLu3aMevnnykrK2P3oUP07datSpvU9HQiTwaHO7dte1H69fIHH3Dk2DGu6taNf991V7VtVm3eDJRPsunYujUAY4cPZ9TQobUe+4k33iAlPZ1+3btz77hxANjpC0yRy1qrDm1ZwgLMZWWE7TtMx15VM3Mz09KJP1EemGjZ4eJXy4gKL892cfP0wO00GXMDr7+aftcOqbXN9BfeIjMtnY69uzF6YnkFBRutiSdy2esc3Ag2QpnZzJ4T0VzVouqzcmpOjpFl26lR3SfeZRcU8PriJSRmli9NNLRNa+7p1/e03wfMuueuWrdvCD/KV+vLn0/+e8tNeLu4YFUhQ3BvdDT/XfYnpWVm/jfuFvyrKV2dkZfHrpMT6Hs0aVLnaxKRS6NBSBD8vhXKzKSFxtCwU7MqbQozc8mNL/9e07PVhZ94d9Urt9e6PWnPcY4uKn8G7PboGOw9nMtLFZzk2TqIiGU7AEg5EEnwkM7VHifjaPn9op27E3ZuCg6LiFzJlDks55WLiwujRpWXT/r22285evRolTZr1qxh/fr11e7fqFEj4+Hsjz/+qLK9oKCAzz//vNp9KwYFqzsvwMyZM4mMjDT+XFN2cl2YzWaefPJJwsLCsLKy4r///S+tWrU67X4dO3Y0StR+8cUXlfpjUVBQwFtvvUVhYSGxsbGVguz/+te/gPJg+IIFC6rsm5GRwccff3yWV3V6kydPpn379gDMmDGDTz/9tNZszLVr1/LQQw9RVlaGk5MTr7/+eqXtQUFBRpnpGTNmkJSUVOUYy5YtY8eOHdUe3/J+xMfHV3vdhYWFvPvuu9Xua2NjQ3h4OLt3765x0sHhw+WZLC4uLnh6Xv6zPS+0m266iR49egDlme8zTlN69kx4eXmxf/9+MjMz+fHHH6ttY/n7gPLy0wDe3t4MHjwYgF9++YWdO3dW2a+srIw333yT/Px8oqOjjX/DFv/73/+Mz+K0adNwcHBg8ODBXHPNNQB8+eWX7N+//5yvUf5ZfL29CWlW/kXAvCVLyD2l3L/ZbObbhQsxm824urjQ/+Rn50JrcbKKx84DB0iupiJDeGQkf538PTz0qqtwPLncgY2NDQ729rX+Z/kCwdra2njtXCZZiciF59nQm+CW5WPVqkV/kJ9XecKc2Wzmj7m/gtmMk4sznft0r+4wF1RcVHmFmKCmwadta21jg52Dfa3/mSqMVZbXNFaJXP583dwI8fMFYP6OHeSeUgXFbDbz/eYtmM3g6uBA/zNY1uKzteuIyyivmnJth/ZMHtC/TuOCg61trf/ZVvgewt7GBgdbW+xs/s7DaObjY4xJfx44WOX4ZrOZrzdspLikFHtbG4a11XI2Ipc7R09X3JqUL80TtXI3JfmVs4fNZjPHl24HM9g429Owy9kvfVVX1na2tf5nZfP3WGVla1P+uu3fY5VTQw/cGpdfU/S6A+QlZ1Y5R9aJZJL3RQLg27XFRV+GREREzi89IUu1zGYzubm5Z/SfxRNPPIG3tzf5+fnccccdzJ8/n6SkJOLj4/nyyy955JFHajyvm5ubkVW6atUqXn31VSIiIkhJSWHVqlWMGzeOw4cP4+bmVmXfvn37Gjcmr732Gr/99hsJCQkkJiayfv167r//fj76qPK6ZBX7fabef/99Vq1aZVzzsGHDKCoqIi8vr9r3p+JayS+99BI2NjZkZWVx66238v333xMTE0NqaiobNmzgrrvuYu/evQBMmjTJKG0MMHr0aCM498ILL/C///2PqKgo0tLSWLFiBePGjSM+Pv6C3aTZ2dnx6aef0qpVK8xmM++//z433ngj8+fPJyIigoyMDGJiYli+fDkPPfQQ9913H7m5uTg5OTFz5kxatGhR5Zgvv/wydnZ2JCcnM27cOJYuXUpaWhrR0dF8/PHHPPnkkzWW6u7RowejR48GyoP/L730EuHh4aSnp7N582buuOMODhw4UO3+//rXv/Dw8MBsNvPggw/y/fffc/z4cdLS0jhy5AivvfYaixYtAuC2227TjS/l5aVfffVVbE9mCn7zzTeVArbnokOHDvTs2ROADz/8kLfffpvDhw+TlpZGREQEs2fP5o033gCgU6dOlQK8zzzzDC4uLhQXFzNp0iQ++eQTIiMjSUtLY8eOHdx///0sX74cgOuvv56uXf/Omtq+fbtR9nzs2LFcddVVxrYXXngBV1dXSkpKeO6551ReWqq488YbMZlMJCQn88qHH7L38GGycnI4Hh3Ne199xZaTVS1uHjGiPLhawaOvvcajr73GzApl98+HawcMwM7OjsLCQt789FN2HjhARlYWCcnJ/LpiBdNmzKCkpARfHx9uHjHivJ5bRC5PI8aNAZOJ1MRkvnprBkcPHCE3O4e4qGh++mgWB7fvAWDI6Guxc6g8Vn3wn//jg//8Hz9/8f0F6VtRQSFZaRkAePn6XJBziMiVY0KfPphMkJCZxbTfF7MvOoas/AIiklN4f/kKthwvrzRwU/euVcovPz53Ho/PncdHq1ZXen1X1Al2RkYB0MrPl5u6daWguLjW/2qbAH0mXB0cGBwSAsBfBw/yw5atxKSnk5Wfz8HYOF5fvITtEZEA3N6rFw2cnc/LeUXkwmp2XU8wQUFKNnu/+IP08FiKcwvIiU3l8I+rSdkfCUDjoZ2xPqV6yY73F7Dj/QWEzr8wVf/OVovRfbCytaasqIS9ny0lbssRCjJyKMzKI27LEQ7M/gtzaRn2ni40GqhlQERErnQqKy3ViouLqxQ8qYvt27fj5uaGl5cXX331Fffeey9JSUm88MILldq5urrSr18/Y33gU/3nP//hjjvuICMjgx9//LFSBqHJZOLpp59m/fr1bD5ZEtOiZcuW3HvvvXz++eekpqby1FNPVTm2q6srN998s7GuaGRk5Flng3722WfGz5988gnvv/9+rWvS9uzZk++++w4oL789ffp0nnzySTIyMnjttdd47bXXquxz8803Vwmmm0wmZs6cyf3338/u3bv57LPPKvUF4Mknn2T69OkXLJDl4+PDnDlzePvtt/n55585dOhQlb/nirp168a0adOqDQwDNG/enE8//ZSpU6cSGxtbpXyvh4cHd9xxBzNnzqx2/2nTppGXl8fy5cuZO3cuc+fOrbT9tttuY8uWLURERFQ57gcffMCDDz5Ienp6tX8HAIMHD651UkN907x5cyZPnswnn3xCSUkJL774IvPmzTsvGTn//e9/ufPOO4mMjOTrr782PqsVNWnShA8//LDSa40bN+bLL7/k4YcfJiUlhQ8++IAPPvigyr6DBw+ulL2el5fHc889h9lsxsvLi2effbZS+4YNG/Lkk0/y8ssvEx4ezocffljt2CL1V/PGjbn/9tv5/KefOBEXx/9VU8Fg5ODBXFNNufP4k5USPKqZ8HQufLy8eHjCBKZ/8w2xCQm8c8rvCIAgf3+emTIFp/NUFl5ELm9BTYMZe/dt/PrNHBJj4vjmf59WaXPV8EH0Gtq/yuupCeVjlau7a5Vt50NG6t9LyThoTBKp95o39GHKwIF8sW4dJ1LTeHNp1Ypi13XswPB27aq8Hn8yM9jjlLHkjwoVgMISEpk8+/QT86aPH4eP6/kZ927v3ZuY9HQOxyeweO8+Fu/dV2m7lcnELT26c3W7i7MEiYicO9cgb1r9qx/hCzeSl5DOgVnLq7QJ7NeWgN5VqwHkJ5eXt7dzubzue5z9GtBu4jAO/7iakrxCjv22hWO/VW5j7+lCuwlDqwS8RUTkyqPgsFwQrVu3ZsmSJXz11Vf8+eefxMXF4erqyoABA3j44YdZsGBBjcHhli1b8vvvv/P555+zdu1aEhIScHFxoUuXLtxzzz107969xrLUTzzxBO3ateOnn37i0KFDRsZqcHAw/fv3Z/z48bi5uTF37lxyc3NZvnz5GQfBq5OTk3PG+wwdOpS//vqL7777jnXr1hEdHU1hYSENGjSgS5cu3HrrrfTt27fafT08PPj222/59ddf+eWXXzh+/DglJSW0adOGu+++m2HDhjF9+vRzvaxaOTs7M23aNCZNmsTChQuN4Gt2djb29vY0atSIrl27cv3111daZ7kmffv2ZcmSJcyaNYt169YRFxeHm5sb/fv3Z+rUqZXWlD6Vg4MD06dP588//2TOnDmEhYWRn59P8+bNue2227jpppu49tprq923T58+LFmyhG+++YZNmzYRExNDcXExDRo0oH379owZM8YoLSx/e+CBB1i6dClRUVHs37+f7777jjvvvPOcj+vn58fChQv54YcfWLlyJceOHSMvLw9XV1eaN2/O1VdfzW233Yb9KRmYUL6e9bJly/jxxx9ZtWoVERER5Obm4ubmRocOHRg7diwjTsmSfOedd4iOLi9l+Z///KfaNcNvvfVWfvvtN3bu3MmsWbO4+uqr6dy58zlfq/xzDOrVi6ZBQfy+ciWHwsPJPDkONmvUiGsHDKB7haUBLpZenTvztp8fv61YwYHQUDKys7G1tSU4IIC+XbsyrG9fbGx0GyhSn3Tt15OAxkFsWLaKiNCj5GZlY2tvT2DjIHoN7U+bLpcmAyQ/7+/qOg5Ol9eXpCJyaQwMaUVTby9+37uPQ/HxZOXnY29jQ1MfH65p15buZ7gub3g1SxddTPa2Njx//UhWHT7CuvBwotPSKC0rw8PJiXYBAYzo0J7GXl6XtI8icuZ8u7bAJcCTmPUHyDieQHFOAdZ2NrgEehHQpw1ebU6/XMblxqO5P90fv5HYjYdIPRJNQVo2JisTDp6u+LRvgn+v1tg42l3qboqIyHlgMp+vWjkiZ2DGjBlGFmhoaOgl7o2IyMWRtW/f6RuJiFxi2zxqroQiInI56fZr9ZOGRUQuJw+0Lzx9IxGRy8CcwU9f6i5cEcatfudSd+EfRf/uLg2tOSwiIiIiIiIiIiIiIiIiUg8oOCwiIiIiIiIiIiIiIiIiUg9osTmResBsNpOXl3dOx3B2dj5Pvbly6X2sWWlpKQUFBadvWAMrKyscHbXOoIiIiIiIiIiIiIjIhaTgsEg9EBsby9ChQ8/pGFobWu9jbXbs2MHEiRPPev/AwEBWrVp1HnskIiIiIiIiIiIiIiKnUllpEREREREREREREREREZF6QJnDcklMnTqVqVOnXupu1BtBQUH/2IzVi0nvY8169eql90ZERERERERERERE5DKnzGERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXpAwWERERERERERERERERERkXrA5lJ3QEREpL7Y5lFyqbsgInJaPTP0iCAiV4Zto/tf6i6IiJzesRWXugciIiIilShzWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHlBwWERERERERERERERERESkHrC51B2QC2/ChAls27btjPcbO3Ysb7311gXo0cWzdetWJk6cCMC3335Lr169jG1DhgwhNjb2irnOmJgYhg4dClS9lvPt2LFjLF68mM2bNxMTE0NGRgZ2dnYEBgbStWtXRo0aRffu3c/5PDNmzGDmzJkEBgayatWqM94/JCQEgDfffJMbb7zxnPtztp599lkWLlx41vuvXLmSoKCgSv9eq2NlZYWdnR3u7u40a9aMgQMHcsstt+Ds7FylbV0+99bW1jg6OuLn50eHDh0YP348HTt2POvrEKnPEqLj2LBsFcePhJOXnYOjszMBTYLoNaQ/rTq0OevjnjgawbbVG4kKO052ZhZW1lZ4eDagRfvW9Ll6IA28PWvc12w2s2fTdnZt2Er8iVhKS0pxa+BOSKe29L1mMO6eDS7YuUXk8hQVG8vvK1dyMDycrOxsXJydadaoEcMHDKBL27ZnfdywiAj+Wr+ew8eOkZGVhbW1Nd4NGtCpTRuuGzgQHy+vWvePiIlh2dq1HAgLIyMrC1sbG4IDAhjYqxdD+vTBZDLVuO/+0FCWrVvH0chIcnJzcXBwoHFgIAN79mRAz5617isil69/4r3V8cPhbFm5nuhjkeTn5uLk4kxA40Z07deLtt30HCZyJcpNSCNm/QEyjidQnFOAjZM9roFe+PdujWeroPN2ntiNBzm+ZDvBQzvReGiX07bPiUslbvNhMo7HU5Sdj5WNNc5+DfDt0gLf7i1rvT9KORBJwo5wsmNSKC0sxtbFAbfGDfHvEYJHc//zdk0iInJpKTgsIoacnBzeeOMNfv31V0pLSyttKy4uJiwsjLCwMObMmUPPnj157bXXaNKkyaXpbD1UVlZGQUEBBQUFJCYmsnnzZmbNmsVXX31Fy5Ytz/h4paWl5OTkcPToUY4ePcqiRYt45JFHePDBBy9A70X+uQ7v3s/cT2ZTWvL3uJmTmUXY3kOE7T1E72EDGDn+zCew/Dn/Nzb8UXniTGkJJMcnkhyfyI51m7n5vgm06dKhyr5ms5n5n33H/m27Kr2elpTC5uXr2L1xO7c9dA/N2lQ/dpzLuUXk8rRj3z7e//prSirc42VkZbHr4EF2HTzItQMHcvdNN53xcX/49Vd+W7Gi0mslJSXEJiQQm5DAyk2beGTiRLrXMAFt0fLlzF28mLKyskr7hx4/Tujx42zetYunp0zBzta2yr7fLVzI4lMmGObk5nIwLIyDYWFs2LGDp+67r9p9ReTy9U+8t1r600I2L19b6bXsjCxCMw4SuvcgIZ3aMe7Bu7DReCVyxUg9fILDP67BXPr3PUxxdj5pR2JIOxJDwFVtaH79uSd2ZJ1IJnL5rtM3PCl67T4il++GMrPxWmlJGVmRSWRFJpG8P4K2E4ZibVs5LFBWWkrovPWk7I+s9HpRZh4p+yJJ2ReJX89WtBhd+8Q9ERG5Mig4XI8EBASwePHiOre3/Yc/lAQGBmJtbY3XaTIZ6ouEhATuvfdewsLCAGjdujXjx4+ne/fueHp6kpaWRnh4OHPmzGHz5s1s27aNG2+8kY8//pjevXtf4t5fWtOmTePFF1+s8np8fDwjR44EYMqUKUyZMqXa/Z2cnKq89uqrrzJq1KgqrxcXF5OUlMT333/P3LlzSUxM5MEHH2Tx4sXY29tXaV/b5764uJjExERWr17NZ599Rl5eHh9++CFt27Zl0KBBtV2yiJwUfyKGeZ9+S2lJKYFNgrnm1hvwDfQnLTmVtYuXc2T3frasWIe3rw+9hvav83G3rFxnfHnZuFUzBo26Bv/gQPJz84g4cpTlvywmPzePuZ98w5QXHsU/uPKs9OW/LDa+vLxq+CB6DLoKRydHIkKP8sfcX8lKy+Cnj77m4WlPV8lyOddzi8jlJyImhg9mz6aktJTmwcHcMWYMjQICSEpJYcFff7Fj3z6WrV1LQMOGXDNgQJ2Pu2ztWiMw3Lp5c/517bU0CQoiJzeXQ0eP8tPvv5OTm8v7s2bx+hNP0DSo8nixYuNGfvrtNwBaNGnCrSNHEhwQQEpaGr+uXMm2PXvYHxrKD7/+WiVwvWrzZiMw3KJJE8Zdfz2N/P1JzchgyerVbNyxg31HjvD1vHncf/vt5/L2ichF9E+8t9q8Yp0RGG7WphWDbhhOQ39fsjKy2LF2E9tWbyR070EWf/8LY+4edy5vn4hcJDlxqRyZsxZzaRkuQV40HdEDZ98GFKRlE71mH6mHThC36TCO3m4E9D77agfZ0ckc+OYvyopKT98YiN8eSuSf5WOVayNvGl/dFWe/BhSm5xC9/gCpB6LIOBpP5J87qwSuI//caQSGvTs0IbBvOxw8XShMzyFmw0FS9keSsC0Mew9nggd1OutrEhGRy4PWHK5HTCYTzs7Odf7Pzs7uUnf5gvruu+9Yvnw5Tz311KXuyiVXVFTEgw8+SFhYGNbW1jz11FMsWrSIW2+9lebNm9OgQQOaN2/Otddey+zZs5kxYwYODg7k5uby4IMPcuzYsUt9CZeUnZ1dtZ8hBwcHo42trW2Nn7XqZlzWdEwPDw9atWrFtGnTuPXWWwE4ceJEjWWta/vce3h4EBISwv3338+nn35q9OPzzz+/AO+SyD/TioVLKSkuxrOhN/c8/RBNQ1rg5OJMUNNgxj98D+16dAZg5aI/KCwoqNMxS4qLWbloGQBNQppz91MP0aJdCM6uLnj7NaTHoKt44OUncXBypLSkhBULl1baPys9g01/rQGg/3VDGTFuDN5+DXF2c6V9jy5MfvYRHJ2dKMjLZ/Vvf53Xc4vI5Wne4sUUFxfj6+PDS488QtuWLXF1dqZ548Y8OXkyvbuUlyect3Qp+XUcq4qLi5m3tHwMaNOiBS9NnUrH1q1xc3EhwNeXYX378tZTT+Hk6EhJSQnzTpmslpmdzQ+//gpAu1atePmRR+jYujUebm60aNKEJyZNMvq1YuNGsnNzK+2/aPlyABr5+/PyI4/QISQEDzc3mgcH88idd9L35BIoa7ZuJS0j4+zeOBG56P5p91bFRcWs/rX83MEtm3HnE/fTNKQFzm6u+AcHMmrCzfQeVj4pZ9fGbWSmpZ/tWyciF1HUit2UFZfi4OVKx8nX4tHUD1sne1yDvGlz+2C8OzQ52W4PJYXFZ3WOuC1H2PvFH5Tm123/opx8IpbtAMC9uR8dJl9LgxYB2Lk44trIh7bj/+5X/LZQivMKjX0Ls/KI23wEAJ+OTWlz2yDcgn2MfdvcNgjPNo0AiFl/gNLikrO6JhERuXwoOCwifPzxxxw8eBCAp59+msmTJ9daImb48OHMnDkTgNzcXF566aWL0k+p7IEHHjB+Pps1myvq1asXXbt2BWDPnj2UlOhGX+R0kuMTCdt7CICB11+NnUPl7H2TycSIW0eDyUR+bh4Hd+yr03GPHQ6nIDcPgCFjRmBtbV2lTQNvT7oN6FPe/mAopRU+s1tWrqe0pBRbezsGXn91tfv2vWYQAPu27KSosOi8nVtELj+xiYnsOnmfN3b4cBzsq45VE8eOxWQykZOby9a9e+t03P1hYeTmlY8Xt1x3XbXjhY+XF0OvugqAfUeOVLq/WLdtG3n5+dja2nL/+PHVln4eOXgwANbW1kRERxuvZ+fmkpicDED/nj2r3Xd4v35AeSnYo1FRdbomEbm0/on3VpGhR8k/ee6B11+NlVXVr+E69SmfzGIuKyMuKqZO1yQil05ecgZpR8o/q40GdcTarvJ9iMlkotl1PcAEJXmFpB48s/uQ7Ohk9n7+B8d+24K5pAyXwLpVPEzafYzS/GKsbK1pNbZvlbLRAIF925b30cqKnLhU4/W0I9FGeezgIdVnBTfs3AyA0vxi8lOyzuiaRETk8qOy0nJGIiMjmT17Nps3byY+Ph5PT0+GDBnCww8/THh4OBMnTgQgNDTU2Gfr1q3G699++y29elW/3kZISAgADz/8MFOnTq2yPTw8nLlz57J9+3bi4+PJzc3FxcWF4OBgBgwYwIQJE/Dw8KjztQwZMoTY2FjGjh3LW2+9BcCMGTOMoGddVHc96enpfPPNN6xevZro6GhKS0vx9/enf//+3HPPPfj7+9d4vJycHObOncvixYuJiorCxsaGjh07cu+99xIYGFjnfp2JnJwcfvjhBwDatm3LXXfdVaf9+vfvz6hRo/j999/ZsWMH27Zto2fPnlXahYeH8/XXX7Njxw4SExPx9vZmyJAhdVrXtqioiEWLFrFgwQKOHz9OaWkpbdq04c477+Tqq6s+lFd07Ngxvv32W7Zs2UJcXBw2NjY0bNiQHj16cPvtt9OmzdmX9blc+Pv74+HhQUZGBrGxsed8PF9fX6B8LeK0tDQaNmx4zscEz0Fk2QABAABJREFUePbZZ1m4cCGjRo3i3//+Ny+//DI7d+7Ezs6OFi1aMH36dHx8fIDyf48///wz69atIywsjIyMDGxtbfH29qZr167cfvvtdKxhzUKAtLQ05s+fz59//klMTAz5+fkEBATQv39/Jk2aVOPnLy4ujtmzZ7N+/Xri4+MxmUw0atSIwYMHc9ddd9GgQYNq95P6LXz/4fIfTCZCOrWrto27ZwMCGgcRFxnN4d376dqv6jh5qqy0DGzt7SguLCKoaeMa23k19AagtKSU3Jxc3DzcAQjbV96vZv/P3n2HN1W2YQC/0703LZ3ssgstW2S1yBJEEFkCirIFREFRPz4UREHED6SACAIyZO8lUCibUih7lbZ0772bJk3z/ZEmJDRJW1qkkPt3XVxXe855z3lPSE5PzvM+z9usCYyVKhgoa9a2NU7vPw6xSIQnDx8r5tar7rGJqPa5/VAWaBEIBGjXqpXabextbdHA3R2RsbG4fvcuemq4X1eWmZ0NY2NjFBcXo3H9+hq3q1v2N75EIkFuQQHsrGXXi6CbspKHXby94ahhmhfPBg2w9X//Kxf81VMaxCiRqC+zqBz80eO8eESvhNfx3qpJ6+aYu/wHpCYmw6Nx/Qr7qqcmcE1EtUtWWNnzFwFg38xd7TbG1uawcLFHfkIGMh7GwsmncaX3/2jnORRnFQACwLlTMzTo3x5XvttWYbu0e1EAAIdW9WFiZ6l2GysPR7yxYEy5wLFzx6awa+qGovRcmDnaVHgsPX3mmxERveoYHKZKO336NGbPng2hUummpKQk/P333wgICMDnn3/+wo69atUqrFq1ClKpVGV5dnY2srOzcffuXezbtw87duzQGnytaaampiq/X716FTNnzkROTo7K8qioKERFRWH37t1YunQp+vbtW25fcXFxmDBhAqKjo1WWX7x4EZcuXcL48eNrvP8AcP78eeTmykb8jRxZtfmNPvjgAxw5cgQAcOjQoXLB4X379mH+/PkqWSIJCQnYunUrTpw4gS5dumjcd2ZmJqZMmYI7z2SvXL9+HdevX8eECRM0tj137hxmzJgBkejpiG2RSITo6GhER0dj7969mDdvHsaMGVOl862N5Bne6kagV1VERAQAWQnsqgy0qKycnBx8+OGHikC2UChEVlaWIjB87949TJkyBenp6SrtxGIxYmNjERsbi0OHDmHRokUY9sy8gwBw7do1zJo1CxkZGSrL5f/v+/fvVztH9rFjx/DNN9+guLhYZfnjx4/x+PFj7Ny5E6tXr0b7svKURHJJsbL3so2dLcwtLTRu5+zuisToOCTGxGncRlmHnm+gQ883ICwqgqFR+Ww4uYzUp58V07K5yyUlJUhLSgYAuNRX/6ACABxd60LfQB+SEgkSo+MUDzCrc2wiqp2i42WZLfa2trCy0Hytqu/qisjYWETFxlZqv727dkXvrl1RWFSkNnNXLrkswxcALMquFyUlJYgpux9o6empsn1paSkEAoHiHkfdvs3NzFC3Th0kp6Xh8o0bGOTrC8Nntjt39SoAwEBfHw3raQ4GEVHt8TreWwGAhbUlLKzVB2qkUimunr4AADAyMYZHo/qVOiciennyEzMBAMY25jA0Vz9gBADMne2Qn5CBvIR0jdtoYt2oLhr0aQdL9zqV2r5UIkFBkqwsvU0j1eei0tJSQOneSl1GMSALaBtbm2vcf1KwLBHI2NYcJvZWleoXERHVXgwOU6VERERg1qxZEIvFcHFxwdy5c9GxY0dkZWVhx44d2Lp1KxYtWvRCjn3ixAn4+/sDALp27YpJkyahQYMGAGRB102bNuHcuXNISkrCypUrsXjx4uc+1uTJk/Hxxx9rXH/p0iV89tlnkEqlGD58uEoGY1hYGCZPngyhUAg3NzfMnDkTnTt3hqGhIe7du4eVK1fi/v37+OKLL7Blyxa0a9dO0VYkEikCwyYmJpgxYwb69+8PY2NjXL58GcuWLcPGjRuf+7y0uXbtmuLnqga/2rZtCwcHB6SnpyM4OFhlXXBwML799lsAgKenJ+bMmYPWrVsjKysLe/fuxaZNm3D48GGN+/7ss89w584d6OnpYeLEiRg6dCisrKxw69YtLFu2DH/++afadoWFhZg7dy5EIhG8vLwwa9YsNG7cGAKBAPfu3cPSpUsRHR2NJUuWoGfPnnBzc6vSOdcmcXFxyMqS3fw3atSoWvs6fvw4wsLCAADdu3d/IXOOX7hwAYaGhli4cCH8/PyQkJCA7LI5APPz8zF16lSkp6fDwcEBn3/+OTp06AArKyukpKTg9OnT+PPPP1FUVIQff/wRAwYMgJlSQCouLg4TJ06EUCiEvb09Zs6cie7du0NfXx9BQUH45ZdfkJ6ejs8++wzHjx+HfVmG0uXLlzFnzhyUlpaiWbNmmDFjBry9vSGRSBASEoLffvsN0dHRmDRpEvbv34/6WjKjSPdkZ8geCtjW0V7my8bBDgCQm5UDiUSitpShOibPDEBSJioW4U6QbD4pl/ruigeduVk5KC0rB2Zbdlx1BAIBrO1skZmajqz0zBo5NhHVTmmZss+4k4OD1u0c7GTXjMycql2rzLRcL4pFIly8fh0A0NDDQxHoTUhJQUlZxq9znToQFhfjaGAgLt+4gZSyQWLuzs7we+MN9O7aVe0guNHvvIPlGzciPikJi1avxvsDBsDd2RlZOTk4dfEizly5AgAY0revIluZiGq31/neSplYJEZ+Ti4SomMRdPoiYsMjAQADRg2BqTkH3RHVdsXZ+QCgMTtXzsRWNshFlFuIUklppbNtW33UB2Z1qnbvUpSWoygLbWpvCYlIjIRLD5B6JwrCTFlCiJmTLeq294RzR08IKpFgIBGJIcotQm5sKuIvPUBhchYE+npoPLgLM4eJiF4DDA7rEKlUioKCgkptq6enp5IV+/PPP0MsFsPGxgY7duxA3bp1AQB2dnaYN28eHB0d8euvv76QfsuDgE2aNMHatWtVglZOTk7o2LEjhg0bhgcPHuDixYvVOpaRkZHGoFhkZCTmzZsHqVQKHx8f/Pe//1VZv2DBAkVgeO/evSplaHv06IHOnTtjzJgxuHv3LhYsWKASGN2+fbsiY3jlypXo0aOHYt3gwYPRrl07DBkyRJHhW5MiI2VfRA0MDNCwYcMqtRUIBKhXrx7S09ORkJAAkUikeP1+/PFHAED9+vWxfft2WFrKbprt7Owwd+5c1K1bFz/99JPa/QYEBCiC1vPmzcMHH3ygWOfn54d27dph2LBhiIsrP1L82rVrioCjv7+/4r0qb+vp6Yk+ffpALBYjICDghWVk/xt+++03xc/qstEBzZ97+fK4uDicOnUK27dvBwCYmZlh9uzZL6bDAD755BOMGDECAOCg9JD60KFDSCvLLFq5cqXK4AlbW1s0a9YMlpaW+Omnn1BYWIibN2/izbK5BAHgp59+glAohIWFBXbs2IF6ShlC7777Lho1aoThw4cjOzsbO3fuxKeffgqJRIL//ve/KC0thZeXF7Zt2wZjpXkYBwwYgDfeeANDhw5FQkIClixZgrVr176w14ZePQW5socCpuaaHzQCgLFp2WhyqRTCwiKtmTCVdXL3IeTnyP4mdPJ9+lkoyH/6ea/o4aK8X0Vlc4ZW99hEVDvl5suuVeYVZPmblZVKlUqlKCgq0pplXFlbDx5Edtn9a59u3RTLs5XuaYtFInz188+KOYTlouPjsWH3bly7cwdzJk4sN1dyp7ZtMWfCBGw9eBChT57gh7LBpHL2NjYYOWgQuquZ9oSIaiddubc6tHmXIhANACZmphg2cYzGUtpEVLuICmQVFQ1MjbVup29cNohWCkiEIuhpyTJWVtXAMACI8ooUP0vEEtz0PwxhRp7KNgWJmXhy+CoyHsagxRjfcnMlP+v+X6eRG52i+N3I2gzNR/WClUflspmJiKh2Y3BYhyQmJsLHx6dS27q6uiIwMBCAbA7dy5cvAwDGjx+vEmyTmzBhAg4ePIgnT57UXIchKyvXs2dPNGrUCD169FAbuNXT00P79u3x4MEDRRZlTcvNzcXUqVORm5uLunXrwt/fX6Uv4eHhCAmRfbmbNm2a2vlJjY2N8fnnn2P8+PF4/Pgx7ty5gzZt2gCAojRz165dVQLDcm5ubpg4ceILCcDLA6kWFhaKEjNVIQ/wlZaWIicnB3Xq1EF4eLhi3unp06crAsPKxo0bh507dyqC08rkr0f9+vVVAsNyNjY2+Pzzz/HFF1+UW6dcSjotLa3c+9Xd3R3r1q2DtbW1IgO9NhKJROWCulKpFHl5eQgNDcW2bdtw6dIlALIMbk3B4ap87j08PLBs2bJqZyFr079/f7XLnZ2d8cEHH6C0tFQlMKxMeX7vzMyno/Fzc3MVA0PGjx+vEhiWa926Nfr374/k5GSYlD38vnjxoqLE9ezZs1UCw3I2NjaYOnUq5s2bh3PnziEtLU1RBptIXjLfQEs5VQAqmbUlYnG1j3vl1DlcOyv7u1zPsyG8uz4NfCjvv8J+la0vEZVo3a6yxyai2klcdq0yMtD+1U/53lZcA9eqY2fPIqDs73OzRo1U5jEWKk3lsGbbNmTn5mLwW2+hd9eusLO2RlJaGg6cOoXLISG49/gx1u/ciRkffljuGIVCYbmgsVxOfj4eR0aiTfPmsFZzL0pEtY+u3FvlZKo+txAWFuGfnQchkZSihU9rDa2IqLaQlsiqn+gZaK9aoFy+ubSszYsiKX56rQrbexGivCK49WgN5w6eMLI2Q1F6LuLO3UXanShkRyQh/GAQmg3vrnWf8gxpOVFOISIOB6HRwE6wru/0Qs6DiIj+PQwOU4VCQkIgKSv71r27+hsHPT099OvXD6tXr67RY+vp6WH69Oka15eWliIiIgLxZXOpKc9tW1MkEglmzZqF6OhoGBsbw9/fXyXjEVAtzezp6akxQ7tZs2bQ19eHRCLBjRs30KZNG+Tl5eHBgwcANL++gCzr9UUEh+XzrKoLjFWGcgkv+ZzQV8vmeAM0n5NAIICfn5/a4LC8RHU3pQyTZ/n6+kJPTw+lpaUqy9u2bQtDQ0OIxWKMHz8eI0eORK9evdC2bVtFX7Xtt7b47rvv8N1331W4XcuWLbFy5crnnnPYzs4OPXv2RI8ePeDn51duvr6aZGhoiCZNmqhd5+vrC19fX41t09PTcevWLcXv8msSIJuHWv4Qu2fPnhr38b///U/ld+VS6No+t61atQIge3/fvHlTYyCedE9NzPVdVVdOncM/Ow8CACxtrTF8yocqA3sEghfXp4qOTUS1k95L+JweO3sWW/bvBwDY2djgs/HjVa4XxUqD+bJycjB59Gj4dumiWObu7IyZH34IY0NDBAYF4VJICN729UVD96fzfW7auxcnzp8HALzVrRv69+gBJ3t75BYUIPjWLew6dgynL1/GoydPMH/GDNhYcW48otpOV+6tBn80Eta2NigpKcGTB6E4ufsIMlLSsGP1RoyY8iFadWhb48ckohqkV/u+A0nET5+RiHKL0GToG6jb3lOxzNzJFs1G9ICeoQFSQsKRdjsSbl1bwsJVcxn/VuP7wMTWAiVCETJD4xB14gYKEjNxf9MptP64D6zqMUBMRPQqY3BYhyhnA1dFSsrTEiIeHh4at/P09NS4riakp6cjKCgIERERiIuLQ0xMDCIjI1FYxXKYVbV48WJF5vTChQtV5hmWUy5vPGzYsErtNykpCYDs9ZUHVbW9vg0aNFAElmuSVdmDsuctWZ2TkwOgbI6lsvnc5OdmY2OjWKaOugzVoqIiRTazugxQOVNTUzg7OysyP+UcHR0xe/ZsLFmyBHl5eVi/fj3Wr18PKysrdOnSRREEtbGxqcpp1goCgQDm5uawt7dHixYt0KdPH/Tp0wcGWjKBnv3ci8VixMTEYN26dTh06BCysrJgaGiIXr16vdDAMABYW1tXOB+YWCzG9evX8eDBA8TGxiI2NhaRkZFITU1V2U7+mQFUr1FVmRNYPqgEALooPZDWRv7eJgIAQ2NZll1FGSti0dP1hs85n7dUKkXAvqO4ePwMAMDSxgrj50yDlY3qNdbI+On+JRUMmJIPqjAw0n47WNljE1HtJM+sFVVwTVCuvqJpmpWKSKVS7DhyBIcCAgAAttbWmDd9erk5f42V9u/h4qISGFY2ctAgnAsORmlpKa7dvq0IDt8NDVUEhke98w7efestRRs7a2v079kTzRs3xvwVK5CQnIydR45gippqNERUu+jKvZWDk6wSkaGRIVp18IZHk4ZY890vKMjLx8ndh9HcuxX0K6j2QEQvj7wcc0XZwBLx02uGnuGL/UzrGz591mJW11YlMKysfh8fpNyMAEqlSH8QozU4LC9vbWRhirrtPWHpVge3fz+KUrEEUSduoM3kATV7EkRE9K/i3SZVKC/v6RwVyvMQP8vqBY3GLy4uxk8//YQ9e/aUC4waGxujU6dOKC0txfXr12v82Lt378bWrVsByMrVvvvuu2q3y8/PV7tcG3kb5aCsttdXT08PZmZmKv8fNaFx48a4d+8eioqKkJSUBGdn5yq1DwsLAyALQsqzj+V9lJfv1URduWnl1+N52gOy/6vmzZtjw4YNCAoKglgsRm5uLk6ePImTJ0/C0NAQY8eOxZw5cyoMVr4sixcvxtChQ2t8v4aGhmjcuDGWLl0KJycnrFu3Drt27UJqaipWrVqlNdBcXRVlp//zzz/48ccfFXMPywkEAjRs2BBt2rTBgQMHyrWTD1AAtH+GnlWdzy0RIJsfDgCKi4RatxMWyuZ/EujpVThXnTpikRj7/tyGByF3AAA2Dnb4aPZU2DuVL3Eu75PycSvql7mWeUWrcmwiqp3Myv42FhVpvyYUlK3X09ODRQXzE6sjEouxassWBN++DQCoY2+PeZ9+irpqpmNQLgXdUssAU2tLS7g6OSEuKQnxSoPBzly5AkA2r/A7fn5q29Z3c8NbXbviaGAgLly/jvHvv68SlCai2kcX7q3UsbKxRpe3uuP0/uPIzshEUlwi3BpoHjhORC+XgYnsfkIiFGndTrFeTwAD0xd7D6KY3xiATcPy0wHKGVmYwqyONQpTslGYll2lY5jXtUWdNg2REhKO3JhUiAuEMKzkPMpERFT7MDhMFbJQ+mJTVFSk8rsy5WyDqhIKNX/5+/zzz3HmjGw0b8uWLdGjRw80adIEjRs3RsOGDWFgYIDly5fXeHD4+vXrWLhwIQDgjTfewJdffqlxW+Ug5t27d6tUolk5s7aiLOjqvMaadOnSRRFwu3TpEt5///1Kt42IiFAE8pTng5WfU0UPIdWdj3JG7/O0l+vcuTM6d+6M/Px8XLlyBUFBQbh06RJiY2MhFouxceNGSKVSfP3111qP8Tr74osvcP/+fVy5cgVnz57FL7/8gm+++eal9OXUqVP4/PPPIZVKYWdnh7feegutWrVCw4YN4enpCSsrK8TExKgNDisHhLVdo54l/9w6ODgoqgMQVYWDkyOiQyOQnaF9vvucsvVWNtZVLsNckJuHbSv/RHxkDADApb47xn42CRbW6gfH2NjbwsDQECViMbIzMtVuA8iyZXKzsgEA1nY2NXJsIqqdXBwd8TA8HGmZmq8JAJCRJbtW2VlX/VqVk5eHpevWISI6GgDQ0MMDcydP1ljK2dH+aZaKUQWVS+R/55XnQU4uu/9s0qCB1jK0LZo0wdHAQEgkEiSnpaGeq2ulzoeIXo7X/d5KG+d6T8vmZ6dnMDhMVIuZOlghJzIZwmztg8eF2bKpq4ytzF74dDwmtk+vYRVlKcuD26XiqlcmtHS1R0pIOABAmJXP4DAR0SuMwWGqkHJp38jISLVllQEgNjZW7XLlzEyxhvJQ8jLCz7p586YiMDx27FjMmzdP7XZZWdq/PFZVfHw8ZsyYAbFYDHd3dyxfvlxrhqmLi4tKW3XlkuWkUqnKTWHdunUVc+dGRkbCT0P2Q2pqqmJ+4JrUu3dvWFpaIi8vD1u3bsV7771X6bmetmzZovj5nXfeUfwsfz1ycnKQkZEBe3v1ZWqUy3HLGRsbw97eHhkZGWrnI5aTSCSVKvFrYWGhKL8MyIL3s2bNQkJCArZv3445c+a80GzZ2kwgEGDJkiV4++23kZeXh82bN6N79+7o2rXrv96XX3/9FVKpFG5ubti7dy9sbW3LbaPpc66c7R4XF4fmzZur3S4oKAg3btyAu7s7Bg8erHifZmVlobCwEGbPkSVFus3JTTYiOzMtA8KiIphoyFxPjJWVMHf2qFpQIjc7BxuW+CMzNR0A4NmmBUZM/hBGJpoHIAkEAji61kVidBySYhM0bpcSnwRJWRk05YeR1Tk2EdVO7mV/J1MzMlBYVKTIJH5WVNl0C/Xd3Kq0/8ycHHz/229IKQvY+rRsic/Gj1fJDn5W3Tp1YGxsjOLiYqSkp2vdf05ZRRpbpUCzuKy0a0kFJV6VVWVbIno5Xsd7qxsXr+J2UAjExSJM+e8XGtuX1ECpbCL6d5g7yZ5XCDPzUSIUKYKtz8pPzJBt72z3wvtkYm8JPSMDlIpKIMzUXnFQlC9LxDCyevoMJO78XWQ+joehuQlafOCrsa1yKW09g9pZiY+IiCqnchEg0mk+Pj6KecdOnz6tcbsLFy6oXa6c1ZepIWPh5s2bapffunVL8fOIESPUblNaWorg4GCV36ujoKAAU6dORVZWFszMzLBmzZoK56dt37694md5MFudmzdvok2bNujbty/++ecfAIC5ubmivba2ml7f6jI3N8fHH38MAHj8+DF+//33SrULCgrCnj17AADe3t7o3LmzYl337t0VPz/Pe0be/ty5cxrnWL527ZrazOI//vgDAwcOxKhRo9S28/Lywrhx4wDISpYrlyTWRU5OTpg7dy4A2cCF+fPnV5ixXdMyMzMRXZZp1KdPH7WBYUD2npNT/px7e3srBlxcvHhR43F27NgBf39/xXtc/rmTSCQ4d+6cxnZHjhyBt7c33n77bYSEhFTqnEg3eLZuAQCQlpYi7O4jtdvkZGYpHiQ2aa1+4II6hfkF2PTLasXDy/Y93sAHMyZUKjjrWXacJ4/CIRKqH1T0+M59AIC+gQEaNGtcY8cmotrHu2VLALK/nbcePlS7TUZWFqLLgsNtW7So9L7zCgrwg7+/IjDcu2tXfDlpktbAMCALtniXHefOo0cQahgAmZyWpti3Z8OGiuUuTk4AgNDISI2DTwEg9MkTALLBqurKWxNR7fI63lsVFwkRHRqBhKhYRbayOuH3Q2U/CARwrle1QTpE9O+ybVr2GS2VIvNxvNptinMKUJAkewZq5/niK5cIBALYlfUrKzwBEpH6+6OijFwIM2TBYyuPp/dGorwi5EanIjM0DsW5mqsaZobJrr/6xgYwrfNiphckIqJ/B4PDVCELCwsMGjQIgCxTNCIiotw2586d0xiUcXd3V2SiygOiyoRCIdatW6e2rXK2rrrjAsCqVasUgSVAc3ZyZUilUsyZMwdhYWHQ09PDL7/8Ak8t86DJeXl5KbIV169fr9IfOaFQiCVLlqC4uBgJCQkqGdjvvfceAFkwfP/+/eXaZmdnY82aNc95VhWbMGECWrVqBQDw9/fH2rVrIZVKNW5//vx5fPrppygtLYWZmRkWLVqkst7NzU1RZtrf3x+pqanl9nHixAmNgTb565GUlKT2vIuLi7Fs2TK1bQ0MDBAeHo5bt25pHHTw6JHsQYOFhQXs7F78CM7abtiwYejQoQMAWea7v7//v3p85cztJ2UPcZ8VGhqqcp1Q/pw7OjrizTffBABs3LgRKUpzEsrdu3cPgYGBAIC3334bAODn5wcHBwcAwLJly9QOXsnMzMTKlStRWFiI9PR0jVnJpJvsHB3g0UQWrAg8+A+KnpmHTiqV4p9dhwCpFGYW5mjbpb263ah1YOMOpCfJrp1d3uqOwR8Or3RVhzZd2kOgpwdhQSECD58stz47IwuXT54DALTr1gmmZqpZOdU5NhHVPk4ODmhaFljdfewYCp6ZxkQqlWLLgQOQSqWwtLBAt7J7gspY+/ffSCz7u9u/Z09MHDmy0tcLvzfeAAAUCYXYoub+VyqVYmvZdBLGxsbo1KaNYl1XHx8AQH5BAXYePap2//HJyThZ9v3Eu0ULmLNCCFGt9zreW7Vs3wb6Zdl1p/YeVTuYPTrsCW5dlg149/RqDisb63LbEFHtYWpnCav6jgCAmDO3UFKkOuWZVCpF5PHrgBQwMDeGo7fm6oI1qW4H2fNLiVCMyGPlp95T9AuAnpEBHFrWV6xzbCO79kolUkSfVP+sLvVOJLLDE2Xb+zSGnpYKi0REVPvxSZ8OkUqlKCgoqNI/udmzZ8PBwQFFRUUYM2YM9uzZg9TUVCQlJeHPP//EzJkzNR7XyspKkVUaGBiIBQsWICoqCunp6QgMDMTIkSPx6NEjWKmZk6xr166KjMAffvgBhw8fRnJyMlJSUnDx4kVMmTIFq1evVmmj3O+qWr58uSKANHv2bPTu3RsikQiFhYVqXx/luZLnz58PAwMD5ObmYsSIEdi2bRvi4+ORkZGBS5cu4aOPPsKdO3cAAJ988glcleY8Gzx4sCI4N2/ePPzvf/9DTEwMMjMzcfr0aYwcORJJSUkvbI4SIyMjrF27Fp6enpBKpVi+fDmGDh2KPXv2ICoqCtnZ2YiPj0dAQAA+/fRTTJo0CQUFBTAzM8OqVavQuHHjcvv87rvvYGRkhLS0NIwcORLHjx9HZmYm4uLisGbNGsyZM0djqe4OHTpg8ODBAGTB//nz5yM8PBxZWVkICgrCmDFjcP/+fbXt33vvPdjY2EAqlWLatGnYtm0bIiMjkZmZidDQUPzwww84ePAgAGDUqFEvfN6XV4FAIMCCBQtgWDbn3+bNmxUB9H+DlZWVYrDE+fPnsWjRIjx58gRZWVkIDQ3FihUrMHLkSJU5uZ/9nM+dOxcmJibIysrCyJEjcfjwYaSlpSE+Ph579uzBpEmTIBaL4eTkhI8++giA7H3/n//8BwCQkJCAYcOG4eDBg0hJSUFKSgpOnTqFsWPHKkrmz549G+bm5v/CK0Kvkv4j3wUEAmSkpGHDEn9E3A9FQV4+EmPisGP1Jjy4fhsA4Du4X7nMlBXf/oQV3/6Eveu3qSx/fOcBQm/Lsk88GjdAr8H9IRIWa/2nPKDHoa4jOvnKBkxcPhGIQ5t3IzUxGQW5eXgQcgd/Ll6JooJCmJqboduA3jV6bCKqnT4cOhQCgQDJaWn4/rffcOfRI+Tm5yMyLg6/btiAq2XVet7v379c1u+sH37ArB9+wCql6UQA4Mb9+wi5dw+ALKv3/f79ISwu1vpP+Xrh1ayZIhB95soV/LJuHR5HRiKvoAAR0dFYsnatYv9jBg9WCe529vZGy7IBnEcDA/Hrhg14GB6O3Px8pGVk4J9z5zB/+XIUFxfDzNQUH5TdVxJR7fe63VtZ29miW3/Z1FFRoeGyc3rwGAW5echIScP5o6ew+de1kJRIYGZhjrdHDa3Jl5OIXpCGAzoCAkCYnoc76/9BVngCxAVC5Cdk4NH2s0i/Fw0AqOfXFvpGhiptQ5bvR8jy/Xi8p2YrBNo2doGjtyzIm3w9DA+2nkFuTArEhcXIi0vDg82nkflINr1bg37tYWD6tBy2pXsdRRA79VYkHmw9LWtbIERBShYi/7mOx3tkg+5M7C1Rz8+7RvtORET/PoGUT/Ree2PHjsW1a9eeq+3169cVQdvQ0FBMnDhRbRaopaUlOnbsqCiL/PjxY5X14eHhGDNmjNq5hQUCAb788ktcvHgRQUFBmD59OmbMmKFY/+uvv2rMLJYf+/3338fGjRsByErH+pRlEwQHBytKCG/ZskWRzQoAvr6+SEhIwJAhQ7BkyRIAQNOmTRXrLSwsIBQKtc5P1rFjR2zdulXx+5kzZzBnzhyVINaz3n//fSxYsKBcYDM7OxtTpkxRKaWtbM6cOVi5ciVEIlG5c6kpBQUF+Pnnn7F3716N5Zzl2rVrh4ULF6oNDMtdvnwZM2bMUBuwt7GxwZgxY7Bq1Sq4uroqgvJyQqEQc+bMQUBAgNp9jxo1ClevXkVUVBQWL16MoUOffokOCgrCtGnTtP4/9OrVCytXrlSUTH8R4uPjFXNIP/u+Vkf5/frsOVWV/HOv7rXVZMWKFYqSy61bt8bu3btrJFvw66+/xoEDB7T25d69exg3bpzW/7Nhw4YhKCgICQkJGD58OH744QeV9ZcvX8bMmTORn5+vtr2TkxPWr1+v8jkHgJ07d2LRokUaqw4IBAJ8+umnFf7/VcbpWPXZ7PRqu3npGg5t3olSifppDd7o01P2oPMZ//14FgCgftNG+GTu0/fXpl/WIPJRWJX68MXS+bB1eFoJQSwSY+fvmxB2R30JWUNjI3w0eyo8GjdQWV4Tx6ZXX8dsg4o3olfOueBgrNuxQ+M93tu9emGcmnuPEWV//5o3bozvP/tMsfyHVatw/5l7/oqs+v571LG3V/xeUlKCVVu3IkhDtRcAeKd3b4x+551yA/oKCguxfONG3NPSB2tLS8yeMEGROU2vn2s2nEv6dfQ63VsBsoH6x7bvR/AZzVPg2NjbYfSMj+HswZLSr6M/n2ieboteXSk3IxB+4DKkEvWP1l3fbCELIj/j4rd/AQCsGzjBa2L/Sh1L3sbDr43WwGypRILHey4i/W60xm3curdC/b7tyt1blZZIELr7AjLuay6Bb+5ihxYf+MLE1qJS/aZXz85eX73sLrwSRp5d+rK78Frh++7l4JMfqrRmzZrh2LFj2LBhA06ePInExERYWlqie/fumD59Ovbv369xztwmTZrgyJEjWLduHc6fP4/k5GRYWFjA29sbH3/8Mdq3b6+xLPXs2bPRsmVL7NixAw8fPlRkrHp4eKBbt24YPXo0rKyssGvXLhQUFCAgIEARHK4OTQEmbfz8/HDq1Cls3boVFy5cQFxcHIqLi2Frawtvb2+MGDECXbt2VdvWxsYGW7ZswaFDh7Bv3z5ERkaipKQEzZs3x/jx49G7d2+sXLmyuqellbm5ORYuXIhPPvkEBw4cUARf8/LyYGxsDHd3d/j4+GDgwIEq8yxr0rVrVxw7dgybNm3ChQsXkJiYCCsrK3Tr1g0zZszQGAgHABMTE6xcuRInT57Ezp07ERYWhqKiIjRq1AijRo3CsGHD0K9fP7Vtu3TpgmPHjmHz5s24cuUK4uPjIRaLYWtri1atWuHdd99F3759n/t1el1NnToVx48fR0xMDO7du4etW7fiww8//FeO3bp1axw4cAB//PEHgoKCkJaWBgMDA9SpUwdeXl4YMWIEOnXqhP/85z/Yu3cvzp49C7FYrMh2BmTvt5MnT2LTpk04f/48EhISIJFI4OHhgd69e+Ojjz5SO3/4yJEj0bVrV2zevBlBQUFITEyEWCyGo6Mj2rdvjzFjxqiUgSd6ls+bHeFSzw2XTgQi6nEECnLzYGhsDNd6bujk1w3NvVtXaX9xkdHV7pOhkSHGzJyIW5ev49blYCTHJUIsEsHS2hqNWzVDt/6+sHN0eCHHJqLaqWenTmjg5oYjZ87gYXg4csru7xq6u6Nf9+5oX8W/deFqplGpKgMDA8waPx7dOnRAYFAQIqKjkV9QAGtLSzSuXx/9undHiyZN1LY1NzPDfz79FMG3b+PCtWt4EhuL/IICGBkZwblOHbRr3Rp9u3eHBctJE71yXqd7K0A22HTgB++hZfs2CA68hNjwSBTmF8DQyAiOLnXRop0XOvR4o1LzHxNR7eHk0xgWLnaIv3gf2ZHJEOcLoW9kAAtXe7h0aQ775h7/ep/09PXRfGRPZLSNQ0pIOHLj0lBSWAxDCxNYudeBc5fmsGlQV31bA320GN0L6Q9jkBISjrz4dJQUFUPf2AgWznao49UAjj6NWE6aiOg1wcxhqjH+/v5YtWoVgPKZw0RExMxhIno1MHOYiF4VzBwmolcBM4eJ6FXBDM7KYeZwzeL77uXgnMNERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3AmnFEryipVIrCwsJq7cPc3LyGevPq4uuomUQigVAofO72enp6MDU1rcEeERERERERERERERFRdTA4TPSKSkhIgJ+fX7X2wbmh+TpqExISgnHjxj13e1dXVwQGBtZgj4iIiIiIiIiIiIiIqDpYVpqIiIiIiIiIiIiIiIiISAcIpFKp9GV3goiISBecjr35srtARFShjtksLkREr4ZrNiUvuwtERBX688npl90FIqJK2dnrq5fdhVfCyLNLX3YXXit8370czBwmIiIiIiIiIiIiIiIiItIBDA4TEREREREREREREREREekABoeJiIiIiIiIiIiIiIiIiHQAg8NERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMYHCYiIiIiIiIiIiIiIiIi0gEMDhMRERERERERERERERER6QAGh4mIiIiIiIiIiIiIiIiIdACDw0REREREREREREREREREOoDBYSIiIiIiIiIiIiIiIiIiHcDgMBERERERERERERERERGRDmBwmIiIiIiIiIiIiIiIiIhIBzA4TERERERERERERERERESkAxgcJiIiIiIiIiIiIiIiIiLSAQwOExERERERERERERERERHpAAaHiYiIiIiIiIiIiIiIiIh0AIPDREREREREREREREREREQ6gMFhIiIiIiIiIiIiIiIiIiIdwOAwEREREREREREREREREZEOYHCYiIiIiIiIiIiIiIiIiEgHMDhMRERERERERERERERERKQDGBwmIiIiIiIiIiIiIiIiItIBDA4TEREREREREREREREREekABoeJiIiIiIiIiIiIiIiIiHQAg8NERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMMXnYHiIiIiIio9pCcP/uyu0BEVCkde/R62V0gIqrQny+7A0RERETPYOYwEREREREREREREREREZEOYHCYiIiIiIiIiIiIiIiIiEgHMDhMRERERERERERERERERKQDGBwmIiIiIiIiIiIiIiIiItIBDA4TEREREREREREREREREekABoeJiIiIiIiIiIiIiIiIiHQAg8NERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMYHCYiIiIiIiIiIiIiIiIi0gEMDhMRERERERERERERERER6QAGh4mIiIiIiIiIiIiIiIiIdACDw0REREREREREREREREREOoDBYSIiIiIiIiIiIiIiIiIiHcDgMBERERERERERERERERGRDmBwmIiIiIiIiIiIiIiIiIhIBzA4TERERERERERERERERESkAxgcJiIiIiIiIiIiIiIiIiLSAQwOExERERERERERERERERHpAAaHiYiIiIiIiIiIiIiIiIh0AIPDREREREREREREREREREQ6gMFhIiIiIiIiIiIiIiIiIiIdwOAwEREREREREREREREREZEOYHCYiIiIiIiIiIiIiIiIiEgHMDhMRERERERERERERERERKQDGBwmIiIiIiIiIiIiIiIiItIBDA4TEREREREREREREREREekABoeJiIiIiIiIiIiIiIiIiHQAg8NERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ4weNkd+LeNHTsW165dq3K7IUOGYMmSJS+gR/+e4OBgjBs3DgCwZcsWdOrUSbHO19cXCQkJr8x5xsfHw8/PD0D5c6lpT548wdGjRxEUFIT4+HhkZ2fDyMgIrq6u8PHxwaBBg9C+fftqH8ff3x+rVq2Cq6srAgMDq9y+adOmAIDFixdj6NCh1e7P8/r6669x4MCB525/5swZuLm5qbxf1dHT04ORkRGsra3RsGFD9OjRA8OHD4e5uXm5bSvzudfX14epqSnq1q2L1q1bY/To0fDy8nru83gdhYaG4o8//kBwcDByc3Ph6OgIPz8/fPrpp7CxsXnZ3SOq1ZLjEnHpRCAiQ8NRmJcPU3NzuNR3QyffbvBs3bzGjnPl1Dn8s/Mger3TF77v9te6rVgkRnDgRTwIuYO0pBSUiMWwtLZGg2aN0eWtHnD2cK2xfhFR7RCbkYEjd+7iYWIicoVCWBgbo4GDA/q0bIm2Hu7Pvd+wlBQEPHiI0ORk5BQWQU9PAAcLC3i5uaF/61aoY2mpsW1BcTFO3L+P61HRSMrJhUAAOFpawqeeB/q2bAlbNfd2e0NuYN+Nm1Xq45SePdCjqWeVz42IXo6YhAQcOXMGD8LDkZuXBwtzczR0d0ef7t3h3aJFjR3n2Nmz2LJ/P4b174/3BwzQum1BYSH+OX8e1+/eRVJaGkpLS+Fcpw46eHmhf8+esFRzvVJWUlKCgEuXcOXWLSSmpEAoFMLG2hpeTZvind694ezoWGPnRUT/joLkTMRfvI/syGSI84UwMDOGpas9nDs3g52nW40dJ+HyA0Qeuw4Pvzao5+etcTupVIqgRdshKRJXuM83vv8A+kaG5ZZnP0lC4tVHyI1NQ0lhMQzNjWHhag8nnyZwaFmvWudBRES1h84Fh4kqKz8/Hz/++CMOHToEiUSisk4sFiMsLAxhYWHYuXMnOnbsiB9++AH169d/OZ3VQaWlpRAKhRAKhUhJSUFQUBA2bdqEDRs2oEmTJlXen0QiQX5+PiIiIhAREYGDBw9i5syZmDZt2gvo/asnMDAQn332GUQikWJZQkICtmzZgnPnzmHnzp2wt7d/iT0kqr0e3bqHXb//BUnJ078l+Tm5CLvzEGF3HqJz7+54e3T1B/XEPYnG6f3HK7VtXk4uNv+6FinxiSrLszMycevyNdy5GoKBHwxDh55vVLtfRFQ7hERH47fTZ1AiKVUsyy4swq3YONyKjUPfVi3xUdeqf+a3BwfjyO27qgslQEJWNhKyshEYGorpvr3QXs19cnxmFhb/8w8y8wtUlsdlZiEuMwsBDx9hum8veHt4VLlfzzIxLP/wk4hqp5C7d7F840aUKH0Pz87Nxc0HD3DzwQP069ED44cNq/ZxwqOjsfPo0UptGxUfj6V//IHM7GyV5bGJiYhNTMTpy5cxd8oUNNJwvUrPzMSPa9YgMSWl3PLAoCBcDAnB5+PHo13r1s91LkT078t4FItH289BqnRvJc4rQmZoPDJD4+HyRnM0Glj9ZJbc2DREB1RuUJwwM69SgWFNnhy7hsTLD1WWiXKLkJkbj8xH8bBr7obmo3pBz0D/uY9BRES1g84Gh11cXHC0kl8CAMDwNX+Y4OrqCn19fQZ3yiQnJ2PixIkICwsDADRr1gyjR49G+/btYWdnh8zMTISHh2Pnzp0ICgrCtWvXMHToUKxZswadO3d+yb1/uRYuXIj//ve/5ZYnJSXh7bffBgBMnjwZkydPVtvezMys3LIFCxZg0KBB5ZaLxWKkpqZi27Zt2LVrF1JSUjBt2jQcPXoUxsbG5bbX9rkXi8VISUnB2bNn8ccff6CwsBC//fYbWrRogZ49e2o75ddeVlYWvvrqK4hEInh6emLhwoVwcXHBoUOH8OuvvyI2NhbLli3D4sWLX3ZXiWqdpNh47F67BZISCVzre6DviHfg5OqMzLQMnD8agNBb93D19AU4ONVBJ79uz32c+MgYbFn+B8RKAzi02fX7X0iJT4RATw9deneHz5udYGphhvjIWJzacxgZKWk4vHUPHJwd0aBp4+fuFxHVDtHp6Vh5JhAlklI0rOOADzp3hrudLVJz83Dw1i2ERMfg5P0HcLGxRp+WLSu935P3HygCw82c62Kojzfq2Tsgv1iIh4lJ2HX9OvKFxVh5OhAL330H9R0cFG2LRCL8fOIEMvMLYGpkiPfa+cDbwwMmhoYIS0nBzmvXkZKTi99On8FPQ4fARalKybvebTGwjfYKL3GZmfjh6DGISyTo0rgROjVsULUXjYheiqj4eKz46y+USCRo5OGBMe++C3cXF6Smp2P/qVMIuXsXJ86fh4ujI/p27/7cx4mIjsZPa9aoDH7VJDMnB4vXrEFOXh4EAgHe7tULvbp0gbWlJaLi4rDjyBFExsZiwcqVWPzll3B1clJpXywSYeGqVUhJS4OBvj6G9uuHru3aQV9PD/fDw7Ht4EHkFxTgt82b8b9vv4WDnd1znxcR/TvyEzMQuvM8pJJSWLjZo0H/DjB3soUwMw9x5+4i42EsEq88gqmDFVw6P3+lqLy4NNzffAqlIknFG5f1CwAE+nro9M0I6BlonlHy2azhhCsPFYFhm0bOcPdtA7M61hDlFSH52mMkBT9G5qN4RBy+Cs+hXZ/zjIiIqLbQ2eCwQCBQW35WV23duvVld6HWEIlEmDZtGsLCwqCvr48vvvgCn3zyCQQCgWIbW1tbNGrUCP369cOpU6fw5ZdfoqCgANOmTcOePXvQqFGjl3gGL5eRkRGMjIzKLTcxMVH8bGhoWKXPn5GRkcbtbWxssHDhQgDArl27EBsbiwMHDmDkyJHltq3oc29jY4OmTZvC29sbH374IaRSKdatW6fzweEzZ84gLy8PALB06VI0by77YjNp0iTcvHkTZ8+efa5S6ES64PSB4ygRi2Hn6ICPv/oURiaygStmFuYYPf1j7Fq7GQ+u38aZg/+gbdcOMFa6VlZWcOAl/LPzICQlJZXaPiYsEjFhkQAA38H90HNQH8W6Fj6t4dbQA6vnL0VhfgHOHznF4DDRa2D39RCISyRwsrbCfwcNVGTRWpqY4Is+b2Hl6TO4GhmFPSE30K1JE5iquZd7llgiwZ6QEABAc+e6+M/At6GvJ3sAaWVqAhcbG7Rxd8PX+/ajsFiE3ddD8FX/for2px89QnpePgQCYKafn0pZ684NG6JRnTr4as8+CMViHL97DxO6Px1AY6CvDwN9zRkrQrEYv587D3GJBC421pjU/fkH3xDRv2v30aMQi8VwqlMH82fOhEnZoF9Lc3PMmTABKzZtwtVbt7D7+HF079gRps9x73Tq4kVs3r8fJZW8d9r3zz/IKfs+NPWDD9BDaVorr2bN0KxRI8xfvhxRcXHYsHs35s+YodJ+/8mTSElLg0AgwOyJE+GjNAinl709Gri54dtly1BcXIx/zp/H2CFDqnxORPTvijl9C6ViCUzsLeE1oZ8i0GpoZozmH/RC6M7zSL8XjZjTt+Ho3RgGxlVPOkq8GorI49cgLSmteOMy+Qmy4LCZkw0MzconTWgiEZcgNvAOAMCqviNajX8LgrL7OiMLUzQe3AUCfT0kXnmElJsRqOfXFsbWfK5ORPQq0zx8iEhHrVmzBg8ePAAAfPXVV5gwYYJKYPhZffr0wapVqwAABQUFmD9//r/ST1I1depUxc/VDVR26tQJPj4+AIDbt29X+qHB6yozM1Pxs6ur6hykDRs2BCAr801EqtKSUhB2RzbyusfAtxSBYTmBQID+IwYDAgGKCgrxIOSuut1oFB8Zgz+XrMTRbXshKSmBS/3KzRcaHxWj+LlDjy7l1lvZWKOZd6uybWOr1Cciqn0SsrJxKzYOgCzj9tnyygKBAGO6dIZAAOQLi3EtKrpS+72fkICCYlnG3bD27RWBYWV1LC3h26wZAOBeQoJKiVj5cRrWqaN2vuM6lpZo5lwXABCRllapPsltvxqMpOwc6AkE+NS3F0tKE70iElJScLPsu/iQPn0UgWE5gUCAcUOGQCAQIL+gAMF37lRp/xHR0fhuxQps2L0bJSUlaFiJkvUSiQSXb9wAALRp3lwlMCxnZGiIMe++CwB4EBaGiOhoxTr5PMMA4PfGGyqBYbn6bm5o2aQJ9PT0EJOQUKVzIqJ/X2FaNjJD4wEA7j29ymXgCgQCNBzQARAAJYXFyHgQo243GuXFpeHOun/w5PBVSEtKYeFa+SqP8sxhSzeHCrZUlROVgpLCYgCycxKoua9zbFuWCFMqVRyHiIheXTqbOVwToqOj8ddffyEoKAhJSUmws7ODr68vpk+fjvDwcIwbNw4A8PjxY0Wb4OBgxfItW7agk5ovFgDQtGlTAMD06dMx45lRpwAQHh6OXbt24fr160hKSkJBQQEsLCzg4eGB7t27Y+zYsbBRKr1WEV9fXyQkJGDIkCFYsmQJAMDf318R9KwMdeeTlZWFzZs34+zZs4iLi4NEIoGzszO6deuGjz/+GM7Ozhr3l5+fj127duHo0aOIiYmBgYEBvLy8MHHixHIBqpqSn5+Pv//+GwDQokULfPTRR5Vq161bNwwaNAhHjhxBSEgIrl27ho4dO5bbLjw8HBs3bkRISAhSUlLg4OAAX1/fSs1rKxKJcPDgQezfvx+RkZGQSCRo3rw5PvzwQ7z11lta2z558gRbtmzB1atXkZiYCAMDAzg6OqJDhw744IMPFJmgrzJnZ2fY2NggOzsbCTXwhdqprBSYRCJBZmYmHB0dq71PuezsbGzduhVnz55FVFQUSkpKYGdnBy8vLwwePBi9e/fW2LawsBB///03AgICEBUVBaFQCEdHR3Tp0gXjx48vl7V+9+5djBw5EhKJBM2aNcO+fftgYKB66Y+NjcXgwYNRWFiIli1bYteuXSql9JXncD558iTef/99xe/37t0DALRp00brOcuvaZs2bUJ6ejpWr16NhIQE2NnZ4e2338bcuXMV21b3+nbnzh3s3LkTN2/eRFJSEgwNDeHp6YlBgwZh+PDh5c5f7vz589i7dy9u3bqF7OxsWFhYoGXLlhgyZAjefvttrYNEiNQJv/dI9oNAgKZt1JdptbazhUs9NyRGx+HRrXvwebP83w5Ndv2+GdkZmYBAgI69uqLf8MFYOOXLCtspv5clEvXlyfTLMvLUPRQgolfLnThZYFggAHw86qndxt7CAvUdHBCVlo6Q6Gj0aOpZ4X4z8gtgbGiAYnEJGjvW0bidk5UVAKBEUoo8oRC2ZVVcvhs0EEk5OZBKpRUeS78Kf4PDU1Jw+pHs+tu/dSs0rKO5b0RUu9x+KBtUJxAI0K5VK7Xb2NvaooG7OyJjY3H97l301PBMRZ3lmzYhPTMTAoEAb735JsYOGYKxX3yhtU1SWhqKhEIAQKe2bTVu16JxYxgaGkIsFuP2o0doXDbP+p3QUBQUFgIABmv53v7VpEkwMDDgdw6iV0BWWNkzJwFg30z9AF1ja3NYuNgjPyEDGQ9j4eRT+WpMj3aeQ3FWASAAnDs1Q4P+7XHlu22VapufKBvcX9XgsJ2nKzp9OwKFKdmwqlfx8y9+TyQievUxOPycTp8+jdmzZ0NY9iUBkM2pKg/afP755y/s2KtWrcKqVavKPUjJzs5GdnY27t69i3379mHHjh1ag681zdTUVOX3q1evYubMmcjJyVFZHhUVhaioKOzevRtLly5F3759y+0rLi4OEyZMQLTSiFsAuHjxIi5duoTx48fXeP8BWXAoNzcXANSWJdbmgw8+wJEjRwAAhw4dKhcc3rdvH+bPn6+ShZqQkICtW7fixIkT6NKlfPaWXGZmJqZMmYI7z4yMvn79Oq5fv44JEyZobHvu3DnMmDFDZS4lkUiE6OhoREdHY+/evZg3bx7GjBlTpfOtjeRfpPVq4CY1IiICgKwEdlUGWlQkLi4OY8eORVJSksry5ORkJCcn49SpUxgwYAB+/fXXcufx+PFjTJkyBYmJiSrL4+PjsWfPHuzfvx/ffPMNxo4dq1gnH1Cxdu1ahIaGYtOmTZg4caJifWlpKb7++msUFhbC1NQUy5YtKzfH+ptvvomGDRsiMjISy5YtQ5cuXeDm5obNmzfj2rVrMDAwwGeffVap8z958iR27typ+D0lJUXl9a3O9a20tBTLly/HunXrVJYXFxfj5s2buHnzJo4cOYL169fDwsJCsV4kEuHrr7/GsWPHVNplZWXh0qVLuHTpEvbv34+VK1eqtCOqSFKs7KGBjZ0tzC01v3ec3V2RGB2HxJi4Kh+jQbMm6DNsINwaqg/4qOPa4Om2Ny9dUykrDQCF+QUIvX0fAODRuH6V+0REtUt0hiyzw97CAlammsuv1re3R1RaOiLT0yu1394tmqN3i+YoFIlgpGHgFQCklN1bA4C5Uhaggb4+3LXMqxmXmYkHCbJ7Hi83t0r1CQA2XwmCVArYmJnivXY+lW5HRC9fdLwsE8/e1hZWWu6767u6IjI2FlGxVa9w0tLTE6MHDVIEbysiD+wCQB0t1yw9PT1YmJkhKycHMUrf157EyDIGHezs4Givmv1XUlKiGLj67HcwIqq95AFYYxtzGJprvrcyd7ZDfkIG8hIqd2+lzLpRXTTo0w6W7pUf5CbMyldk/xpZmiHy+DVkPo6HMDMfekb6sHCxR912TVCnTUO1A1GMLExhZGFabjkASKVSJAbJBvDoGxvA0oOD74iIXnUMDj+HiIgIzJo1C2KxGC4uLpg7dy46duyIrKws7NixA1u3bsWiRYteyLFPnDgBf39/AEDXrl0xadIkNGjQAIAs6Lpp0yacO3cOSUlJWLlyJRYvXvzcx5o8eTI+/vhjjesvXbqEzz77DFKpFMOHD4eXl5diXVhYGCZPngyhUAg3NzfMnDkTnTt3hqGhIe7du4eVK1fi/v37+OKLL7Blyxa0a9dO0VYkEikCwyYmJpgxYwb69+8PY2NjXL58GcuWLcPGjRuf+7y0uXbtmuLn9u3bV6lt27Zt4eDggPT0dAQHB6usCw4OxrfffgsA8PT0xJw5c9C6dWtkZWVh79692LRpEw4fPqxx35999hnu3LkDPT09TJw4EUOHDoWVlRVu3bqFZcuW4c8//1TbrrCwEHPnzoVIJIKXlxdmzZqFxo0bQyAQ4N69e1i6dCmio6OxZMkS9OzZE25VePBW28TFxSErKwsAqj3n8/HjxxEWFgYA6N69u9o5lJ/X999/j6SkJDg4OOCrr76Cj48PzM3NERMTg1WrVuHSpUs4fvw4fH19MWjQIEW71NRUjB8/HhkZGbCzs8OMGTPQo0cPmJmZISwsDH/88QcuX76MRYsWKbJx5T799FOcPXsWjx8/xurVq9GvXz+4u8tGt27cuBE3ysqkffXVV4oy0coMDQ2xePFijBkzBtnZ2Zg6dSoaNWqEf/75B0ZGRli+fDlat25dqfPfuXMnPD09sXDhQri5ueHq1auKigPVvb6tX79eERju1KkTpk2bBk9PT5Vr882bN7FgwQL88ssvinb/+c9/FIHh4cOHY+TIkXBzc0N6ejqOHj2K9evX4/Lly/j888+xbt06juanSsvOkD00sK2jvQyYjYPsQWNuVg4kEokia7ciH86eAoe6Va9qUK9JAzTzbo3QW/dw9vAJiIqL0bZLe5hamCMxJh4Be48iLzsXJmameGvowCrvn4hql/SyeTLlGbyaOJQNYskqKICktFRtmWh1zLTcJxWLS3ApPBwA0KCOg9YgslQqRZ6wGOn5eQiOjELAw4cQSyTwsLfDwDZeGtspC46MwpNUWQnqoT4+lZo7mYhqj7Sy6WycHLRnvDmUBWkzc6p27/SfadPgUlYhqrKUS1sXKSUHPEsqlaKwbH1m2fdSAIhLTgYAOJdVMXgUEYGjZ8/iYXg4CouKYGFuDu8WLTC0b98q942IXo7i7HwAgImdpdbtTGxl91ai3EKUSkqhp1+5e6tWH/WBWR3rKvcrXykI/XBbIKSSp9N/SYpKkfMkGTlPkpF6OxLNR/csVw77WRJxCcR5RchLSEdi0CPkRqcCABq+3RGGppWfz5iIiGonnQ0OS6VSFBQUVGpbPT09lazYn3/+GWKxGDY2NtixYwfq1pXNhWVnZ4d58+bB0dERv/766wvptzwI2KRJE6xdu1YlaOXk5ISOHTti2LBhePDgAS5evFitYxkZGWkMikVGRmLevHmQSqXw8fHBf//7X5X1CxYsUASG9+7dC1tbW8W6Hj16oHPnzhgzZgzu3r2LBQsWqARGt2/frsgYXrlyJXr06KFYN3jwYLRr1w5DhgxRZPjWpMjISACAgYGB2iCZNgKBAPXq1UN6ejoSEhIgEokUr9+PP/4IAKhfvz62b98OS0vZDaSdnR3mzp2LunXr4qefflK734CAAEXQet68efjggw8U6/z8/NCuXTsMGzYMcXHlM86uXbuG7OxsALIy4fL3qrytp6cn+vTpA7FYjICAgBeWkf1v+O233xQ/q8tGBzR/7uXL4+LicOrUKWzfvh0AYGZmhtmzZ9dYH/Pz83H58mUAskDs4MGDFevs7Ozw+++/45133kFUVBSOHTumEhxetmwZMjIyYG1tjV27dsFDaX6sTp06oUOHDpg+fTrOnDmDH3/8Eb1794Zx2YMMIyMjLF26FMOGDUNRURG+//57bNiwAREREYrXrWfPnhg9erTGvrdt2xajR4/G5s2bERYWhrCwMPj4+GDRokVVCsbr6elh5cqViqCv8jlW5/qWkpKC1atXAwB69eqF1atXKx4Sya/NALB161YcPXoUs2bNgqurK4KCghTXn6+//lrlM2BtbY3PPvsMzZs3x4wZM3DhwgUEBASgTx/VLEsiTQpyZQ8NTM3Vj76WM5Zn8kmlEBYWac0yVvY8gWG5kVM/RMC+YwgOvISLx8/g4vEzKus9vVqg7/B34OhSV8MeiOhVkVsWrDCvIFBqZihbL5UCBcUirVnGlfX31avILiwCAPRp2ULrtql5eZi1Y5fKss4NG+CT7t1UMo61OXpXNne7jZkpejZr+hw9JqKXKTdfdu9kbmamdTszE9n1SSqVoqCoSGuWsbLnCb46OzrCwMAAJSUluPv4scbS0hExMSgulmXsFSoFkbPKKqlZWlhg/8mT2H3smEqVpPyCAly8fh1Xb9/GrI8+Qnuvyg2GIaKXR1Qg+4wbVBAg1TcuC75KAYlQBD0tWcbKnicwDDzNaJb1zQgevm1h5+kKPSMDFCRnIe7cXeREJiMrLAGhuy6g5Vg/rfuLOHgFqbciFb/rmxqi6fvdNZbSJiKiV4vOBocTExPh41O5MmOurq4IDAwEICszKg/ujB8/XiXYJjdhwgQcPHgQT548qbkOQ1YytWfPnmjUqBF69OihNnCrp6eH9u3b48GDB4osypqWm5uLqVOnIjc3F3Xr1oW/v79KX8LDwxESEgIAmDZtmkpgWM7Y2Biff/45xo8fj8ePH+POnTuKOUvlpZm7du2qEhiWc3Nzw8SJE19IAF4eSLWwsHiu7ECHshHOpaWlyMnJQZ06dRAeHq6Yd3r69OmKwLCycePGYefOnYrgtDL561G/fn2VwLCcjY0NPv/8c3yhZq4k5VLSaWlp5d6v7u7uWLduHaytrRXButpIJBKVC+pKpVLk5eUhNDQU27Ztw6VLlwDIgpiagsNV+dx7eHhg2bJl1c5CVlZSUqJ4EJCupmSjPIgrEolUgr85OTk4fvw4AGDMmDEq6+T09PQwd+5cnDlzBhkZGThz5gwGDBigWN+sWTNMmzYNv/32myI7edOmTRCJRLCzs1MMYFAnKSkJCxYswNmzZ1WWW1paVvl906xZM7Vtqnt9O336NIqLiyEQCPCf//xHbfbAxIkTcfHiRdSvXx8ZGRlwdXXFjh07AMiu8x9++KHaPvfp0wc+Pj64efMmdu/ezeAwVZp8GgGDCsoEGiqN2C4Ri19on+SKhcUQ6AlgaGSo9pipCcmICY9kcJjoNSAum1vcUEvWLgCVrF6xpETLlpVz/O49BDyUzf3bzLkuenhqn8c4PS+/3LKQmBgYXNbHx292rTAL+HFyMiJSZBktA7xaw7CSmYREVHuIy+6dtFUZAKDyXUH8gu+djAwN0a5VKwTfvo1zV6+iZ6dOaPJMSeqSkhL8fejQ09/LrrsAFFORPQwPx5UbN+Du7IwP3n0XLRo3RklJCW7cv49tBw8iJy8PK/76Cz/Ono16rq4v9JyIqHqkJbLPuJ6B9nsNfcOn17LSEomWLWuGRCSGvqkh9I0M0XbK2zC2NlesM2psCpuGdfFoxzlkPIhF5qM4ZITGaQ30FmerPoeTFIkRefwapKWlcGhR+WmNiIiodtLZ4PDzCgkJgaTsRr979+5qt9HT00O/fv0UWWw1RU9PD9OnT9e4vrS0FBEREYgvm6dHeW7bmiKRSDBr1ixER0fD2NgY/v7+ioConHJpZk9PT40Z2s2aNYO+vj4kEglu3LiBNm3aIC8vDw8ePACg+fUFZFmvLyI4LB/pa1zJ7IRnKQek5EHAq1evKpZpOieBQAA/Pz+1wWF5iepu3bppPK6vry/09PRQWlqqsrxt27YwNDSEWCzG+PHjMXLkSPTq1Qtt27ZV9FXbfmuL7777Dt99912F27Vs2RIrV6587jmH7ezs0LNnT/To0QN+fn41Pu+TjY0NmjRpgvDwcPz6668ICwtD37590blzZ5iVjY73UjNS/NatW4qHHs2aNdP4mXJwcECdOnWQlpaGGzduqASHAWDSpEk4c+YM7t+/j6+++kqxzx9//LHc51juzp07mDx5MrKysmBkZISpU6ciODgYV69exfnz57FixQrFwIS0tDQkJyejWbNmGl+75s2bq11e3etbUFAQAFnWsbxk9rOcnJxw8uRJlWXXr18HALRo0QJFRUUaj9+2bVvcvHkTt27dglQqZWlpqpSamP/8RcjNzsGmX1YjPSkV5pYWeHf8SDRr0xLGpiZIS0rB5ZPncCcoBIc370ZaYgoGjBrysrtMRNWg9xL+Zh2/ew9bg2T3wHbmZpjp51vh304PezusGTMaliYmSM7NxYl793HmUSguhUcgMTsbCwa/AwMtAd/jd+8BAMyMjdBbw/0GEdVuL+N6VRkjBg7E7UePUFxcjB9WrcL7/fujU9u2MDUxQUxCAnYfO4bHkZGws7FBZna2yrWquOw7V3ZuLlzr1sUPX3wB07LMZ2MjI3Tv2BGN69XD17/8guLiYuw+dgxfTpr0Us6TiCpJr3ZeqxoN7IRGAzuhVCKBnpp7JoGeHhoN6ozM0HhIJaVIuRGuNTjcZEhXGFmbQVpSiqyIBESdCIEwPQ+P/j6LZqN6ok6r+i/wbIiI6EXT2eCwcjZwVaSkpCh+Vpe9J+dZwcj46kpPT0dQUBAiIiIQFxeHmJgYREZGorCw8IUed/HixYrM6YULF6oNZCmXNx42bFil9puUlARA9vrKg6raXt8GDRooAss1yapsLrbnLVmdU1YySiAQwNpaVgZGfm42NjaKZeqoy1AtKipSZDPXq6d5VJ6pqSmcnZ2RkJCgstzR0RGzZ8/GkiVLkJeXh/Xr12P9+vWwsrJCly5dFEFQGxubqpxmrSAQCGBubg57e3u0aNECffr0QZ8+fWCgZZT5s597sViMmJgYrFu3DocOHUJWVhYMDQ3Rq1evGg8My33//feYMGECioqKcPDgQRw8eBCGhobw8fFBjx498NZbb5V77yt/pmbMmFGp48jfd8oMDAzw888/Y8iQIYqs8hEjRsDX11ftPlJSUjBp0iRkZ2ejTp06+OOPP9CyZUuMHj0aw4cPR0xMDP744w80a9YMAwYMwN69e7FixQoYGRnh2LFjaj/DdmXzg2nzPNc3+bW5/jOj+LXJz89HZtm8ZgEBAQgICKhUm7y8PMW1gkgbQ2NZVktF2cBi0dP1hv/C/Jgndx9GelIqjE1NMOGbmSrlqZ093DBs4hhY29viwtEABAWcR4t2XqjvWXNVFIjo32Vcdk8jrmDgqEhpfUVZe5pIpVLsvHYdh2/fAQDYmpvh27ffhq25eQUtAUuTp6UW3WxtMaF7N1iZmuLAzVuITEvH+bAw+GkI+haJRLgVK7tf6tSgAecaJnpFyef3FVV0vVKqkKVpKqya5OrkhC8++QTLN26EUCjEtoMHse3gQcV6gUCAEQMHIik1FReuXYOp0mBzY6XvlSMHDlQEhpW5ODmhV+fOOHH+PG4/fAhhcbHKXMdEVLvI5+qtKBtYIn56LdMz/PcewasLDMsZW5nB0s0BuTGpyItL07ofU4ey5x6GQJ3WDWBVzwm3Vh2GOF+IqH+uw765u9ZjERFR7aazweHnlZeXp/hZeR7iZ72owEFxcTF++ukn7Nmzp1xg1NjYGJ06dUJpaakiG64m7d69G1u3bgUgK6n97rvvqt0uP798SbiKyNsoB2W1vb56enowMzNT+f+oCY0bN8a9e/dQVFSEpKQkODs7V6l9WFgYAFkQUp59LO+jiZovgcrUlZtWfj2epz0g+79q3rw5NmzYgKCgIIjFYuTm5uLkyZM4efIkDA0NMXbsWMyZM0dtKd7aYPHixRg6dGiN79fQ0BCNGzfG0qVL4eTkhHXr1mHXrl1ITU3FqlWrtAaan1f79u1x+PBh/P777wgICEBeXh7EYjGCg4MRHByMpUuXwtfXFz/88IMim7c6n6lneXh4wNnZGTExMQBkAwg0Wb16tWJwwm+//YaWLVsCkA10+P333zFixAjk5eXh22+/RcOGDRWlr11cXDQO7tCWlV+d65t8YEZFnxNllZ13/ln5+fkMDlOlmJjJ/o4VFwm1bicsm49ToKcHU3Ptc+xVl0hYjHvXbgEAOvl10zhvse87fXHrUjDysnNx/exlBoeJXmHyuYYLKxioUlAWbNETCGDxHEEJUUkJ1pw9h+DIKABAHUtLfPt2f9TVMjiyIu96t8U/9+5DKBbjRnSMxuDwrdg4Rfnsro0bP/fxiOjlMit7BqCtog8AFJSt19PTg0UF8xPXlLbNm+PXb77BwdOncevBA2Tn5MDSwgJNGzbEQF9fNKlfH4t//x0AYKP0XUH5+0nLJk007r9F48Y4cf48SiQSpKSns7Q0US1mYCK7t5IIRVq3U6zXE8DAtPYMXJOXmxYXFFetnZUZXN5ojphTt1CcVYCCpCxYuqmvQkdERLUfg8NVZGFhofi5qKhI5XdlyiNZq0o+J406n3/+Oc6cOQNAVkK3R48eaNKkCRo3boyGDRvCwMAAy5cvr/Hg8PXr17Fw4UIAwBtvvIEvv/xS47bKX37u3r1bpRLNypm1FWVBV+c11qRLly44cOAAAODSpUt4//33K902IiICaWmyUXedOnVSLJefU0VfcNWdj3JG7/O0l+vcuTM6d+6M/Px8XLlyBUFBQbh06RJiY2MhFouxceNGSKVSfP3111qP8Tr74osvcP/+fVy5cgVnz57FL7/8gm+++eaFHMvDwwOLFy/GwoULcfPmTVy5cgWXL1/G/fv3IZVKERgYiNTUVOzduxcCgUBloMTx48erNQ/yypUrFYFhAFi7di38/PzUlnuWZ1l7e3ujXbt2KusaNWqE5cuXY/LkySgqKsK4ceMUAdpBgwY9V9+qc32Tv0barp/PUr5WTZw4EXPmzHmufhNp4uDkiOjQCGRnZGndLqdsvZWN9QsvWZ6Rmg5p2RQEHo01zxmub2AA90b18fDGXaQlp77QPhHRi+VsbY2HiUlIr2BQZUbZwDJbc7MqX4tyi4rwy8lTijl/G9RxwNx+fWFdzaCNkYEB3OxsEZGSilQt/b8WJQtI25iZooVL1QZ3ElHt4eLoiIfh4Ugrq+6jSUaW7N7JzvrF3zspc7Czw4ThwzWujymr5OWsNADX0d4e4WXXKG3VqZQzikUveB5lIqoeUwcr5EQmQ5itfSC/sGzOXmOrqt9bVUdFU2HJp6TTM6p6goiFi73iZ2FWHoPDRESvsNo5GV4tplzaV938sHKxsbFqlytnZoo13PDLM/WedfPmTUXgZOzYsdi/fz8+++wzDBgwAJ6enoosx6ws7Q+hqyo+Ph4zZsyAWCyGu7s7li9frjXD1MXFRaWtNvIS0nJ169ZVzNGo7fVNTU1VzA9ck3r37q3IwN26dWu5OXy12bJli+Lnd955R/Gz/PXIyclBRkaGxvbKpYPljI2NYW8vu/HS9npIJBK1ZYSfZWFhgT59+uC7775DQEAA9uzZA9eyEcnbt29/IfNUvyoEAgGWLFmi+P/fvHmzooT6i2JoaIhOnTrh888/x969e3H27Fm8+eabAID79+/j5s2bAKCSwf5s6fBnPfuZUnb79m1s3LgRADB69Gi4uLhALBZj7ty5agcXyK9F8vfgs7p166YYUCAPDNva2uKjjz7S2kd1qnt9k79Gmq69cuvXr8emTZtw9+5dWFlZKQb4VOd1JdLEya0uACAzLQNCLQN8EmNlfyudPV58hohyVr5ymTNtSiq5HRHVTu5lUzqk5uahUMtgwqj0dABAfYeqPeTLKijA/EOHFYFhbw93zB80UGtguKC4GP87FYCv9+7DxbBwrfuXl5fVVOpaKpXiXtnf8Xb16v2rD1+JqGa5l93Tp2ZkoFDLvVNU2XOG+m5u/0q/AFnAVqjlGURcUhKyyr4TeTZ4OgCvvlIGcKqW5wHZSlXD7KpRcYGIXjxzJ1sAgDAzHyVasofzE2WfeXPniqfXqq7inAJc+2UPLn+/FbGBt7VuW5iaDQAwdXh6rUkOCcPdP0/g1u9HtbYtFT/9PvlvlsomIqKax+BwFfn4+CjmtDl9+rTG7S5cuKB2uXIGYKaG0bDygNCzbt26pfh5xIgRarcpLS1FcHCwyu/VUVBQgKlTpyIrKwtmZmZYs2ZNhfPTtm/fXvGzPNijzs2bN9GmTRv07dsX//zzDwDA3Nxc0V5bW02vb3WZm5vj448/BgA8fvwYv5eVhapIUFAQ9uzZA0CWZdm5c2fFuu7duyt+fp73jLz9uXPnNM6xfO3aNbWZxX/88QcGDhyIUaNGqW3n5eWFcePGAZCV9JUH+HSVk5MT5s6dC0D2oHH+/PkVZmxXxblz5zBs2DB07NhRbUl0Z2dnzJ49W/G7fB7ddu3aKQZNaPtcJCQkwNvbG71791YZrADIMmrnzp0LiUQCd3d3zJ07F/Pnzwcge6+vXr263P7kAxvu3LmjMTN93LhxKp/5Jk2awOw5soSqe33z8fEBICvtrjw3vLK8vDysWLECS5YswdWrVyEQCBQZ0VeuXNH6fz1hwgS88cYb+OijjxgopkrzbN0CACAtLUXY3Udqt8nJzEJSrCyo0aS1+nKpNcne0QGCsuvJk4ePNW4nkUgQ9yQaAODo4vTC+0VEL05bD3cAQKlUitux5QcjArKs4ZiyoEUb98oHW/KEQiw6egwpObKghl/zZpjTtw9MtGTHAYCZkREeJCYiJiMTlyMiNG6XkZ+P+EzZwLCGGoLWsZmZKCyW3ac00jJdBhHVft5l09iUlpbi1sOHarfJyMpCdFlwuG2LFv9Kv75bsQJjv/gCf+zYoXGbwKAgALIB3l7NmimW+7Rqpfj5qtJ3nmfdDQ0FANjb2MCugmcuRPRy2TYtu1cqlSLzsfqkmOKcAhQkyZ772nm++EHARpamKCkSoVQkQWaY5sHv+YkZKErNKdcvSbEYOZHJyI9L1zoXcVZ42b4FqlnERET06mFwuIosLCwUJVO3bNmCCDUPM86dO4eLFy+qbe/u7q4I8sgDosqEQiHWrVuntq1ytq664wLAqlWrEB0drfhdU3ZyZUilUsyZMwdhYWHQ09PDL7/8Ak9PzwrbeXl5KUrUrl+/XqU/ckKhEEuWLEFxcTESEhLg5eWlWPfee+8BkAWL9u/fX65tdnY21qxZ85xnVbEJEyagVdkXOH9/f6xdu1ZrMOj8+fP49NNPUVpaCjMzMyxatEhlvZubm6LMtL+/P1JTy5fnPHHiBEJCQtTuX/56JCUlqT3v4uJiLFu2TG1bAwMDhIeH49atWxoHHTx6JAtYWFhYwM7uxY9mrO2GDRuGDh06AJBlvvv7+9fYvu3t7XHv3j3k5ORg+/btareR/38AUMzb6+DggF69egEA9u3bhxs3bpRrV1paisWLF6OoqAhxcXGK97Dc//73P8VnceHChTAxMUGvXr3Qt29fAMCff/6Je/fuqbTp168fACAtLQ1//vmn2v7+/fffKu/da9euYcGCBRpfA02qe30bPHgwDAwMUFpaiqVLl6r9zK5atQolJSXQ09ND//79AQDDy8rCZWdn45dfflF73ICAAFy6dAkZGRnw8PBgRhJVmp2jAzyaNAQABB78B0WFqgMQpFIp/tl1CJBKYWZhjrZd2qvbTY0yNTdD45ZNAQA3LgYjIVp9oOjCsdPIy5YFe7w6tVO7DRG9GpysrNC0rmyQx56QEBQ8k/kmlUqxLegqpFLA0sQE3bTMifmsP85fQGK27AFjv9atMKF7N8V3HW0EAgG6lk2TcScuHvfVVPCQlJZiw8VLKJVKIRAAvZSCLcoi09IVPzd2rFPpvhNR7ePk4ICmDWX3TruPHUPBM1NNSaVSbDlwAFKpFJYWFuhW9r3tRWtcVkHuxv37SFOT/RseHY1TZc+A/N54Q6VEtFvduvAsO6fDZ84gUc1A1vDoaASVfV/v0akTv28Q1XKmdpawqi8bkBZz5hZKilQH00ulUkQevw5IAQNzYzh6P//UYJUl0NNDHS9Z1YL8uHSk3HpSbhuJSIzwA1cAAPrGBnDu2FSxzqFVfQj0ZfdwUaduKKYiUpYTlYyUG7LnNXZN3WBs9e/M+U5ERC+GzgaHpVIpCgoKqvRPbvbs2XBwcEBRURHGjBmDPXv2IDU1FUlJSfjzzz8xc+ZMjce1srJSZJUGBgZiwYIFiIqKQnp6OgIDAzFy5Eg8evQIVlZW5dp27dpV8SXhhx9+wOHDh5GcnIyUlBRcvHgRU6ZMKZf9p9zvqlq+fLliztHZs2ejd+/eEIlEKCwsVPv6KM/1OX/+fBgYGCA3NxcjRozAtm3bEB8fj4yMDFy6dAkfffQR7ty5AwD45JNPFKWNAVmQRx6cmzdvHv73v/8hJiYGmZmZOH36NEaOHImkpKQX9oXJyMgIa9euhaenJ6RSKZYvX46hQ4diz549iIqKQnZ2NuLj4xEQEIBPP/0UkyZNQkFBAczMzLBq1So0bty43D6/++47GBkZIS0tDSNHjsTx48eRmZmJuLg4rFmzBnPmzNFYqrtDhw4YPHgwAFlwa/78+QgPD0dWVhaCgoIwZswY3L9/X2379957DzY2NpBKpZg2bRq2bduGyMhIZGZmIjQ0FD/88AMOHjwIABg1ahS/hEL2sHLBggWK+aA2b96sErCtjtatW6Njx44AgN9++w0///wzHj16hMzMTERFReGvv/7Cjz/+CABo06aNSoB37ty5sLCwgFgsxieffILff/8d0dHRyMzMREhICKZMmYKAgAAAwMCBAxWZtIBsznB5JvGQIUPwxhtvKNbNmzcPlpaWKCkpwTfffKOSITxhwgTFZ9Pf318RYM7IyMDly5cxadIkxVzkvXv3Rps2bQAAO3fuxJw5c6pU+r261zcnJydMnjwZAHD06FFMmzYNt27dQlZWFh49eoT58+dj8+bNAGQltd3dZVlUfn5+6NmzJwBZoHvatGkICQlBVlYWIiMjsXr1akU2t62tLT799NNKnxMRAPQf+S4gECAjJQ0blvgj4n4oCvLykRgThx2rN+HB9dsAAN/B/WBkYqzSdsW3P2HFtz9h7/ptNdqnfsPfgaGxESQlJdjwsz/OHwtAenIqCvMLEB8Vi/0btiPwoGwAW5PWzdGinVcFeySi2m5sly4QCIDknFwsPHIUd+PikVskRFRaOpYHnMbVSNl8mMPa+5TL+v1i1258sWs3VgeeVVl+MyYWN6JjAACedZ0wrJ0PhGKx1n/Kg7fea+cDK1NZAOWXE6dw4OYtJGRlI7dIiHvx8fjhyFHcKst07t+6NRppCPwmKE054aTmOxQRvVo+HDoUAoEAyWlp+P6333Dn0SPk5ucjMi4Ov27YoMi+fb9/f5gYq947zfrhB8z64QeseqaKUnX1694dRkZGKC4uxuK1a3Hj/n1k5+YiOS0Nh06fxkJ/f5SUlMCpTh28XzYIVdnEESNgaGiI4uJi/Hf5cpy6eBHpmZnIzMnBqYsX8ePq1SiRSFDH3h6D33qrRvtORC9GwwEdAQEgTM/DnfX/ICs8AeICIfITMvBo+1mk34sGANTzawt9I9V7q5Dl+xGyfD8e76nZqogevm1hYCa7Lobvv4zo07dQkJIFUX4RMh7F4fba48hPkA1wafh2RxhZPg3uGlubw61HawBAzpNk3F1/AlkRiRDlF6EoIxex5+7g/l8BkEpKYWBujIYDO9Vo34mI6N8nkOpYfcyxY8fi2rVrz9X2+vXriqBtaGgoJk6cqDYL1NLSEh07dlSUf338WLVsY3h4OMaMGaN2bmGBQIAvv/wSFy9eRFBQEKZPn44ZM2Yo1v/6668aM4vlx37//fcV84ru2LFDESQKDg5WlBDesmWLIpsVAHx9fZGQkIAhQ4ZgyZIlAICmTZ+OILOwsIBQKNQ6J23Hjh2xdetWxe9nzpzBnDlzUPjMaF9l77//PhYsWFAusJmdnY0pU6aolJpVNmfOHKxcuRIikajcudSUgoIC/Pzzz9i7d6/Gcs5y7dq1w8KFC9UGhuUuX76MGTNmqA3Y29jYYMyYMVi1ahVcXV0VQXk5oVCIOXPmKIJ/zxo1ahSuXr2KqKgoLF68GEOHDlWsCwoKwrRp07T+P/Tq1QsrV65UlEx/EeLj4+Hn5wcA5d7X6ii/X589p6qSf+7VvbaarFixQlFWvHXr1ti9e3elMmEqkpycjA8//FBtRr1c/fr18ddff6nMNQzIsumnT5+O9PR0DS1l/5fLly9XlLAvLCzEO++8g7i4ONjb2+P48ePlSsPv3LkT3333HQBZQPjLL79UrIuJicGECRM0zuUrEAgwatQo/Oc//0F+fj7Gjh2LsLAwAMDSpUsVAxvk1xNt//fVub4BsuzpRYsW4e+//9a4j759++LXX39VBP8BID8/H7Nnz8a5c+c0tnNwcMDvv/+uUuXgeZyOVZ/BT6+3m5eu4dDmnSiVqJ/q4Y0+PWVB5Gf89+NZAID6TRvhk7nar5nPtun1Tl/4vlv+4aRcVGg4dv2+GQV5+Rq38fRqgeFTxsFYKfuFdEO7Q+or4NCr7fzjMKy/cAGSUvVf/wZ4tcbYLp3LLR/1x3oAQHPnupj/ziDF8h+PHsP9hMQq9WHl6JGoY2mp+D06PR2/nDyFzHzNA1r7t26FsV06axzEuCLgNIIjo2Cor48tEz6uUn/o1affo9fL7gK9AOeCg7Fuxw6N38Pf7tUL49R8PxxR9j2jeePG+P6zzyp1LHmbYf374/0BAzRuF3z7NlZu3qzxmYibszPmTp4MR3v1ZVbvh4Xhfxs2lMuGlqtjb4+5kycr5l2m18ukjBMvuwv0AqTcjED4gcuQStTfW7m+2UIWRH7GxW//AgBYN3CC10TN39nUtfHwa4N6ft4at8uLT8fDbWcgylU/bZZAXw8N+rWHa9fyZfmlUimeHA1GUlCoxv0b25qjxQe+LCn9GtvZ66uX3YVXwsizS192F14rfN+9HJw5/jk1a9YMx44dw4YNG3Dy5EkkJibC0tIS3bt3x/Tp07F//36Nc4M2adIER44cwbp163D+/HkkJyfDwsIC3t7e+Pjjj9G+fXuNZalnz56Nli1bYseOHXj48KEiY9XDwwPdunXD6NGjYWVlhV27dqGgoAABAQEqwZPnlZ+v+eGxJn5+fjh16hS2bt2KCxcuIC4uDsXFxbC1tYW3tzdGjBiBrl27qm1rY2ODLVu24NChQ9i3bx8iIyNRUlKC5s2bY/z48ejduzdWrlxZ3dPSytzcHAsXLsQnn3yCAwcOKIKveXl5MDY2hru7O3x8fDBw4ECVOVc16dq1K44dO4ZNmzbhwoULSExMhJWVFbp164YZM2ZoDIQDgImJCVauXImTJ09i586dCAsLQ1FRERo1aoRRo0Zh2LBhihLAz+rSpQuOHTuGzZs348qVK4iPj4dYLIatrS1atWqFd999V1FamJ6aOnUqjh8/jpiYGNy7dw9bt27Fhx9+WO391q1bFwcOHMDff/+NM2fO4MmTJygsLISlpSUaNWqEt956C6NGjYLxM6PgAdl81idOnMD27dsRGBiIqKgoFBQUwMrKCq1bt8aQIUMU5ZLlli5dirg4WdbNt99+q3bO8BEjRuDw4cO4ceMGNm3ahLfeegtt27YFANSrVw9HjhzB9u3bcfLkSTx58gTFxcVwdHREx44dMXLkSEXGsI2NDbZt24bZs2ejUaNGisBwZVX3+qanp4f58+ejX79+2L59O27cuIGsrCyYmpqiZcuWGD58OAaoeeBjYWGBP/74A6dPn8bBgwdx584dZGVlwdDQEA0aNICvry/Gjh0La2vrKp0PkZzPmx3hUs8Nl04EIupxBApy82BobAzXem7o5NcNzb1b/+t9atCsCWb++A2CAy/h8e0HyEhNg1gkgqm5OdwaeKDtGx3Qop0XK0oQvUZ6NPVEAwd7HLlzFw+TkpBbVARjAwM0qFMHfVu2QPv69au0v3A1g2Srqr6DA5YOew+nHjzEtagoJOXkoLRUCmszUzR3dkafli3QuIJ5hAvKqp6YvcBBjkT07+rZqRMauLnhyJkzeBgejpyy7+AN3d3Rr3t3tK/mgM3n0altW/xcty4Onz6N+48fIzsvD4aGhvBwcUFXHx/07toVBgaaH7G18vTEiv/+F8fPnsWN+/eRkpEBPYEATg4O6OztjT5vvglzM5ZoJXqVOPk0hoWLHeIv3kd2ZDLE+ULoGxnAwtUeLl2aw765x7/eJ0s3B/jMfBdJVx8h/WEsitJzAakURlZmsGnkDJcuzWHuZKu2rUAgQONBneHQqj6SroYiNyYF4oJi6Bnpw9zRFvYt68G5o2e5TGgiIno16Vzm8L/F398fq1atAlA+c5iI6HVWWloKgUDAoJIazBwmolcBM4eJ6FXBzGEiehUwc5iIXhXM4KwcZg7XLL7vXg5mDhMRUY2qifLbRERERERERERERERU8/gEn4iIiIiIiIiIiIiIiIhIBzBzmF4LUqkUhYWF1dqHubl5DfXm1cXXUTOJRAKhUPjc7fX09GBqalqDPSIiIiIiIiIiIiIiIqoaBofptZCQkAA/P79q7YNzQ/N11CYkJATjxo177vaurq4IDAyswR4RERERERERERERERFVDctKExERERERERERERERERHpAGYOvyAzZszAjBkzXnY3dIabm9trm7H6b+LrqFmnTp342hARERERERERERER0SuNmcNERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMYHCYiIiIiIiIiIiIiIiIi0gEMDhMRERERERERERERERER6QAGh4mIiIiIiIiIiIiIiIiIdACDw0REREREREREREREREREOoDBYSIiIiIiIiIiIiIiIiIiHcDgMBERERERERERERERERGRDmBwmIiIiIiIiIiIiIiIiIhIBzA4TERERERERERERERERESkAxgcJiIiIiIiIiIiIiIiIiLSAQwOExERERERERERERERERHpAAaHiYiIiIiIiIiIiIiIiIh0AIPDREREREREREREREREREQ6gMFhIiIiIiIiIiIiIiIiIiIdwOAwEREREREREREREREREZEOYHCYiIiIiIiIiIiIiIiIiEgHMDhMRERERERERERERERERKQDGBwmIiIiIiIiIiIiIiIiItIBDA4TEREREREREREREREREekABoeJiIiIiIiIiIiIiIiIiHQAg8NERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMYHCYiIiIiIiIiIiIiIiIi0gEMDhMRERERERERERERERER6QAGh4mIiIiIiIiIiIiIiIiIdIDBy+4AERERERHVHvo9er3sLhARVco1m5KX3QUiooplvOwOEBEREali5jARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMYHCYiIiIiIiIiIiIiIiIi0gEMDhMRERERERERERERERER6QAGh4mIiIiIiIiIiIiIiIiIdACDw0REREREREREREREREREOoDBYSIiIiIiIiIiIiIiIiIiHcDgMBERERERERERERERERGRDmBwmIiIiIiIiIiIiIiIiIhIBzA4TERERERERERERERERESkAxgcJiIiIiIiIiIiIiIiIiLSAQwOExERERERERERERERERHpAAaHiYiIiIiIiIiIiIiIiIh0AIPDREREREREREREREREREQ6gMFhIiIiIiIiIiIiIiIiIiIdwOAwEREREREREREREREREZEOYHCYiIiIiIiIiIiIiIiIiEgHMDhMRERERERERERERERERKQDGBwmIiIiIiIiIiIiIiIiItIBDA4TEREREREREREREREREekABoeJiIiIiIiIiIiIiIiIiHQAg8NERERERERERERERERERDqAwWEiIiIiIiIiIiIiIiIiIh3A4DARERERERERERERERERkQ5gcJiIiIiIiIiIiIiIiIiISAcwOExEREREREREREREREREpAMYHCYiIiIiIiIiIiIiIiIi0gEMDhMRERERERERERERERER6QAGh4mIiIiIiIiIiIiIiIiIdACDw0REREREREREREREREREOoDBYSIiIiIiIiIiIiIiIiIiHWDwsjtAtcvYsWNx7dq1KrcbMmQIlixZ8gJ69O8JDg7GuHHjAABbtmxBp06dFOt8fX2RkJDwypxnfHw8/Pz8AJQ/l5r25MkTHD16FEFBQYiPj0d2djaMjIzg6uoKHx8fDBo0CO3bt6/2cfz9/bFq1Sq4uroiMDCwyu2bNm0KAFi8eDGGDh1a7f5Ul7w/VaHcd+X3qzp6enowMjKCtbU1GjZsiB49emD48OEwNzcvt21lPvf6+vowNTVF3bp10bp1a4wePRpeXl5VPgei11VyXCIunQhEZGg4CvPyYWpuDpf6bujk2w2erZs/936z0jNx8fhphN8PRV52DoxNTODk5oL2PbrAq5OP1rZFhUW4evoCHt68i4yUNEhLpbB3qoPm3q3Q5a0eMLMofz1QFh8Vi6BT5xAdHomC3DwYGhnB0aUuWnX0RoceXWBgaPjc50VEL0dMQgKOnDmDB+HhyM3Lg4W5ORq6u6NP9+7wbtGixo5z7OxZbNm/H8P698f7AwZo3VYqleLCtWs4e/UqouPjUSKRwM7GBj4tW2KQry/sbW1f2LGJqPZ6He+tniWVSrHhZ3/EhEXCu2tHDP1k9HOfFxG9HAXJmYi/eB/ZkckQ5wthYGYMS1d7OHduBjtPt+ferzArH3EX7iErPAGinELomxjCvK4t6rb3hGObhmrbXPz2ryodw9jWHB2/fL/C7aRSKe6uP4Hc6BQ4+jRC02HdqnQcIiKqnRgcJqLnkp+fjx9//BGHDh2CRCJRWScWixEWFoawsDDs3LkTHTt2xA8//ID69eu/nM7qoNLSUgiFQgiFQqSkpCAoKAibNm3Chg0b0KRJkyrvTyKRID8/HxEREYiIiMDBgwcxc+ZMTJs27QX0nujV8ujWPez6/S9ISp5eC/NzchF25yHC7jxE597d8fboqg9KiY+MwaZlayASFiuWFeYXICo0HFGh4XgQchvDp3wIfX39cm2TYuOx9bf1yMvKUVmeEp+IlPhEXD8fhLGzJsG1vrvaYwedvoB/dh6EtLRUsUxSUoTYiCjERkTh1uVr+PCLKTC3tKjyeRHRyxFy9y6Wb9yIEqX7tuzcXNx88AA3HzxAvx49MH7YsGofJzw6GjuPHq3UtlKpFCs3b8aVGzdUlqekpeGfc+dwPjgYsydMQCtPzxo/NhHVXq/jvZU6F4+fQUxYZJXPg4hqh4xHsXi0/RykkqffmcR5RcgMjUdmaDxc3miORgOrnqyRF5eGextPQlJcolhWUlCMnCfJyHmSjPQHMWg2ogf09KtXEFTf2KhS28VfuIfc6JRqHYuIiGofBodJLRcXFxytwoMVw9c8e8jV1RX6+vqwt7d/2V2pFZKTkzFx4kSEhYUBAJo1a4bRo0ejffv2sLOzQ2ZmJsLDw7Fz504EBQXh2rVrGDp0KNasWYPOnTu/5N7XLoMGDcKCBQsqta2xsbHa5QsWLMCgQYPKLReLxUhNTcW2bduwa9cupKSkYNq0aTh69KjafWn73IvFYqSkpODs2bP4448/UFhYiN9++w0tWrRAz549K9V/otdRUmw8dq/dAkmJBK71PdB3xDtwcnVGZloGzh8NQOite7h6+gIcnOqgk1/lR1jnZGZh64p1EAmLYe9UB/1Hvgu3hvVQkJuHKwEXcONCEB7euIuAvUfRb8Rglba52TnY/L8/UJCbBwgE6NqnJ3y6dYKFlSUSY+IRsO8oEqPjsOFnf0ydPxt1nJ1U2kc8eIzjOw4AUikcnB3RZ9gguDbwQGFeAW5cvIqrZy4iKSYeu37fjI+/+rRGXkcierGi4uOx4q+/UCKRoJGHB8a8+y7cXVyQmp6O/adOIeTuXZw4fx4ujo7o2737cx8nIjoaP61ZA5FIVKntdxw5oggMv92rF3p37QpzMzM8DA/H1gMHkJGdjV///BPLvvmmwgziqh6biGqn1/HeStN5Bh76p8qvDxHVDvmJGQjdeR5SSSks3OzRoH8HmDvZQpiZh7hzd5HxMBaJVx7B1MEKLp0rX+2gOKcA97echqS4BCYOlmg4oCOs3OtAlF+ExCuPkHw9DBn3YxBtewMN+3dQafvG9x9UuP+wfZeRfi8aekb6aPrem5U6z5jTtyvdfyIienUwOExqCQQCteVnddXWrVtfdhdqDZFIhGnTpiEsLAz6+vr44osv8Mknn0AgECi2sbW1RaNGjdCvXz+cOnUKX375JQoKCjBt2jTs2bMHjRo1eolnULsYGBhU+7NmZGSkcR82NjZYuHAhAGDXrl2IjY3FgQMHMHLkyHLbVvS5t7GxQdOmTeHt7Y0PP/wQUqkU69atY3CYdNrpA8dRIhbDztEBH3/1KYxMZAMvzCzMMXr6x9i1djMeXL+NMwf/QduuHWBsYlKp/V44fgaF+QUwMTPFx3Onw8rGGgBgbmmBdz8aARNTE1w+eRZXz1xAJ79usHWwU7Q9e+ik7OElgKEfj4J3146KdY1bNkW9Jg2xfvFvSIqJx5Gte8sFeC8ePwNIpbCwtsKEuTNgbmUJALCyscbbo4fCyMQYF44GICo0HDHhUajXpMHzv4BE9K/YffQoxGIxnOrUwfyZM2FSNkjM0twccyZMwIpNm3D11i3sPn4c3Tt2hGklr1XKTl28iM3796OkpKTijQFkZmfjWNlUIYPfeguj33lHsa6Ljw8a16uHr3/5BfkFBdh34gQmjRpVY8cmotrrdby3epZYJMaeP7aqZEYT0asl5vQtlIolMLG3hNeEftA3kiXNGJoZo/kHvRC68zzS70Uj5vRtOHo3hoFx5ZJq4s7fQ0lBMfRNDeE1oT+Mrcxk+zU3QZMhb0DfxBAJFx8g8cojuHRuDhPbp5Wc5H3QJPl6GNLvRQMAGg3sBAtX7QkwEnEJQndfUMmMJiKi10f16k8Qkc5Zs2YNHjx4AAD46quvMGHCBJXA8LP69OmDVatWAQAKCgowf/78f6WfpGrq1KmKn59nzmZlnTp1go+PbD6u27dv80Es6ay0pBSE3XkIAOgx8C3Fw0s5gUCA/iMGAwIBigoK8SDkbqX2W1RYhJsXgwEAnf26KR5eKvMd3A8mZqaQlEhw6/LTOcMlEgnuXbsJAGjcqpnKw0s5QyND9Bsuy4iJCg1HfGSMyvr4KNnvLXxaKwLDyjr2fEPxc0JUTLn1RFS7JKSk4GbZvduQPn0UgWE5gUCAcUOGQCAQIL+gAMF37lRp/xHR0fhuxQps2L0bJSUlaOjhUal2Jy5cQIlEAmNjYwzp06fc+jr29ni7Vy8AwMWQEBSryQh+3mMTUe30ut5bPevU3iNIS0pBg2ZNYG1XtXnViejlK0zLRmZoPADAvadXuaCsQCBAwwEdAAFQUliMjAeV+85UUiRCyo1wAIBLlxaKwLCyen5toW9qCKmkFCk3IyrdZ2FmHp4ck13b7Ft4oG77iqfsiD55A0WpObBuVBfGNkwgIiJ63TA4TC9MdHQ0vv/+e/Tt2xdeXl7o2bMnFi5ciMzMTAQHB6Np06Zo2rSpShvl5cHBwRr3Ld/G399f7frw8HAsWrQIgwcPRseOHdGyZUt06tQJ77//Pvz9/ZGdnV2lc/H19UXTpk3x9ddfK5b5+/sr+lGZf+rOJysrCytWrMDgwYPh4+ODNm3aoF+/fvjxxx+RlJSktU/5+fnYsGEDhgwZAh8fH3Ts2BETJkzQ+rpVV35+Pv7++28AQIsWLfDRRx9Vql23bt0UZY9DQkJw7do1tduFh4fjm2++wVtvvQUvLy/4+vpi0aJFyMzMrPAYIpEIu3fvxsiRI9GxY0e0a9cOY8aMQUBAQIVtnzx5gu+++w59+/ZF69at4e3tjb59+2LevHl49OhRpc6xtnN2doaNjQ0AICEhodr7c3KSlUqTSCSV+v+piuzsbPj7+2Po0KHw9vZG69at0aNHD8yYMQOnT5/W2rawsBDr16/H8OHD0aFDB7Ru3Rp+fn6YN28enjx5Um77u3fvokWLFmjatCkGDx6sNtAdGxsLb29vNG3aFEOHDoVYLK6xc6VXW/i9suuDQICmbVqq3cbazhYu9dwAyObPq4yoR+EoKXufNfdprXYbIxNjNGzepNx+M1LSUFwkBAC0bN9G4zHqN20Eg7IpIcLuqV7n9PRkt4easln09Z8WnhHo8VaSqLa7/VAWaBEIBGjXqpXabextbdHAXTZP5vW7lQu2yC3ftAmhT55AIBCgT7duWDBrVqXa3SoLWLdq0kRjpnL71rJroEgkwr3Q0Bo7NhHVTq/rvZWyiAePcfXMRZiYmWLoJ6O1DrQmotopK6zsmYoAsG+mfp5xY2tzWLjIMnMzHsZWar/ZkUkoFcu+gzk0Vz/gTd/IEDaNXGT7fVS5/QJAxOGrKBWVQN/EEI3eqXi6t6yIRCQGPYK+qSE833sT4KWKiOi1w7LS9EKcPn0as2fPhlAoVCxLSkrC33//jYCAAHz++ecv7NirVq3CqlWrIJVKVZZnZ2cjOzsbd+/exb59+7Bjxw44Ozu/sH48y9TUVOX3q1evYubMmcjJyVFZHhUVhaioKOzevRtLly5F3759y+0rLi4OEyZMQHR0tMryixcv4tKlSxg/fnyN9x8Azp8/j9zcXABQW5ZYmw8++ABHjhwBABw6dAgdO6qOuN63bx/mz5+vEpxLSEjA1q1bceLECXTp0kXjvjMzMzFlyhTceSbT5fr167h+/TomTJigse25c+cwY8YMlfnpRCIRoqOjER0djb1792LevHkYM2ZMlc63NpI/eNCrgWBORIRshKqhoaEi6FwT4uLiMHbs2HKDI5KTk5GcnIxTp05hwIAB+PXXX8udx+PHjzFlyhQkJiaqLI+Pj8eePXuwf/9+fPPNNxg7dqxinZeXFyZOnIi1a9ciNDQUmzZtwsSJExXrS0tL8fXXX6OwsBCmpqZYtmzZaz/HOlVeUqzsoYCNnS3MLS00bufs7orE6DgkxsRVcr+yUeh6+nqo6+6qcTuXem54eOMuUuKTICkpgb6BAYoKihTrbeztNLbV09ODqbkp8rLFSIlX/cy41nfHk4dhCL3zAIX5BTCzUB0lfuPSVcXPHo1ZUpqotouOl11T7G1tYWWh+VpV39UVkbGxiIqt/INGuZaenhg9aBAa169fqe1LSkqQkJICAFqzfd2dnWGgr48SiQSRcXFo7+VV7WMTUe31ut5byRUVFGL/xu2AVIr+I4fAxp5Zw0SvovxE2QB5YxtzGJprLm1v7myH/IQM5CWkV26/SbL9CvQFMHfWfH2wcLFDxv0YFCRnoVQigZ6+vtb9Zj6OVwS0PXq2UZuRrExcVIywfZcAKdDo7U4wsdF8PSYiolcX0z2oxkVERGDWrFkQCoVwcXHBb7/9hqCgIBw/fhxjx45FamoqFi1a9EKOfeLECfj7+0MqlaJr167YvHkzLly4gAsXLmDz5s2KuVGTkpKwcuXKah1r8uTJuHnzpsZ/K1euVATjhg8fDi+lh1lhYWGYPHkycnJy4ObmhqVLl+LChQsICgrCunXr0KpVKwiFQnzxxRe4ceOGynFFIpEiMGxiYoIvv/wSgYGBuHz5MpYuXYo6depg48aN1To3TZQzftu3b1+ltm3btoWDgwMAlMtuDg4OxrfffouSkhJ4enpi3bp1ivfMxx9/jPT0dBw+fFjjvj/77DPcuXMHenp6mDx5Mk6ePImgoCCsWbMGDRs2xJ9//qm2XWFhIebOnQuRSAQvLy9s3LgRFy5cwMWLF7FmzRrUr18fUqkUS5YsQXzZg9VXVVxcHLKysgCg2nM+Hz9+HGFhYQCA7t27w8jIqNr9k/v++++RlJQEBwcHLF26FKdPn0ZQUBB27tyJN998U3H8Y8eOqbRLTU3F+PHjkZiYCDs7O3z33XcIDAzE1atXsWXLFnTt2hUSiQSLFi0q1/bTTz9VVDFYvXo14uKePmTauHGj4jP41VdfoWHDhjV2rvTqy86QfXm3raN9riabsjnrcrNyIJFUPLdcdobss2pla6N1MIe8DKG0tFTRxlip/KI8y0UdqVQKYdn6nMxslXV+QwbAwNAQBbl52Lh0NR7feYDc7BykJiYjYN9RBB78BwDg060TXOurHylPRLVHWlmFD6ey+zBNHOxk16rMnMpdq+T+M20a5s+YUaXgrPIx6thpDrYIBALYl61PzciokWMTUe31ut5byR3asht5WTlo5t0aPm+WL09NRK+G4ux8AICJXfkpeJTJ5wMW5RaitBLz9hZnyfZrZG2utUKTsXVZsLZUiuLsggr3G3XyRtl+zeDStXmF20ccDIIopxD2Lf7P3n2HR1GubQC/d9N7JT0htNAkQOgghCbtiAiiIAKKgkcRRARFz8cRwQIqHjQgKghIkS5FmpRQAyEQEiCEkoT03ns2W7LfH5tddsnupiKBvX/XxXUlM/POvLPsTmbneZ/n9YFrQNtatycioicTM4dJK7lcjrKy2m8wgOoRsmpZsd988w0kEgns7e2xY8cOuLm5AQAcHR2xePFiuLi44Pvvv38k/VYGAdu1a4dffvlFI2jl6uqK3r17Y+LEiYiOjsaFCxcadSxTU1OdQbH4+HgsXrwYcrkcAQEB+O9//6uxfunSpRCJRPDy8sLevXvh4PBgRGBgYCD69u2LqVOn4ubNm1i6dKlGYHT79u2qjOGgoCAEBgaq1o0bNw49evTA+PHjVRm+TSk+Ph4AYGxsXO8gmUAgQMuWLZGbm4u0tDSIxWLV6/fVV18BAHx9fbF9+3bY2ChusB0dHbFo0SK4ubnh66+/1rrfkydPqoLWixcvxmuvvaZaN2zYMPTo0QMTJ07UCPgpXblyRVVifPXq1ar3qrKtn58fRowYAYlEgpMnTz6SjGypVFrrZ00gEMDSUv/Iztr8+OOPqp+1ZaMDuj/3yuUpKSk4ceIEtm/fDgCwtLTEggULGtUvdaWlpbh48SIARSB23LhxqnWOjo74+eef8cILLyAhIQFHjhxRlSoHgJUrVyIvLw92dnbYtWsXfNSykPr06YNevXphzpw5CA4OxldffYXhw4fDrHrORVNTU3z77beYOHEiKioq8Pnnn2PDhg2Ii4tTvW6DBw/GlClTmuxc6elQVqz48m5hZaF3OzOL6tHkcjlE5RV6M2EAoLy0rHq/+j/35pYPjltRrshqcXJtASNjY8ikUty/fU9n+cPU+CRIKhUVEypFmg86vdv44o2Fs3Fk+5/ISErFth/Xa6y3sLLE4LEj0O+5QBBR81dcqrhWWdVyL2FZXdpZLpejrKJCb5axOo/qqSbqo6S6TwBgXcd+lZWXN8mxiaj5elrvrQDg+qWriL56HVY21njx9Vf09oOImjdxmeIzbmxhpnc7I7PqqmNyQCYSQ6gnyxgAJOXV+zXXPwBffb20QqxnSyA/JhXlmYrBLl7PPlNrlnFW5H3kRiXCxNocbV/UXcGPiIiefAwOk1bp6ekICAio07aenp44ffo0AMUcusrgzowZMzSCbUozZ87EgQMHtM7/2RhVVVUYPHgw2rRpg8DAQK2BW6FQiJ49eyI6OlqVRdnUiouL8e6776K4uBhubm5YvXq1Rl9iY2MRHh4OAJg9e7ZGYFjJzMwM8+fPx4wZM3Dv3j3cuHEDXbsqvoQqSzMPGDBAIzCs5OXlhVmzZj2SALwykGptbd2guZGUmcNVVVUoKipCixYtEBsbi3v37gEA5syZowoMq5s+fTp27typCk6rU74evr6+GoFhJXt7e8yfPx8ffvhhjXXqpaRzcnJqvF+9vb2xbt062NnZoVWrR1M69dChQ6pz0MXGxkb1ntFGLBbXCOrK5XKUlJTg7t272LZtG0JCQgAoMrh1BYfr87n38fHBypUrG52FrE4qlarKwefm1iy7pAziisVijeBvUVERjh49CgCYOnWqxjoloVCIRYsWITg4GHl5eQgODsaYMWNU6zt06IDZs2fjxx9/REhICI4ePYpNmzZBLBbD0dFRNYCBSJ2yDL5xLaXGTUwfrJfWYc5qiVixTW0lzNWPK5NIVcfq0K0zosNvICLkCroP6A3vNr4a7WRSKU7sPaT2e82MG1FFhUamjOY6EVLjk1GYm19rZg8RPX6S6muVqbH+r37q96uSOlyrGkOstv/arnXK9WK1qUeI6On0tN5bFeYV4Mj2fQCAcW9MgpWt/mxDImre5NWfcaGx/kCrkcmDe68qLd+5HqbcRr2dNkKTB8etbb9pIdEAABNrc7j18tO7raiwFPcPK6YQaje+P0yt9Q/UISKiJxuDw9SkwsPDVWWdBg0apHUboVCIUaNG4aeffmrSYwuFQsyZM0fn+qqqKsTFxanKA0sfwQMmmUyGDz74AImJiTAzM8Pq1atVAVEl9dLMfn5+OrNGO3ToACMjI8hkMly7dg1du3ZFSUkJoqMVN3a6Xl9AkfX6KILDlZWVAKDKuKwvI7URisog4OXLD+au1HVOAoEAw4YN0xocVpaoHjhwoM7jDh06FEKhEFVVmmV8unXrBhMTE0gkEsyYMQOTJ0/GkCFD0K1bN1Vf9e23uViyZAmWLFlS63adO3dGUFBQg+ccdnR0xODBgxEYGIhhw4Y1+dy79vb2aNeuHWJjY/H9998jJiYGI0eORN++fVWZ0/5a5hqMjIxUPcTu0KGDzs+Us7MzWrRogZycHFy7dk0jOAwAb7/9NoKDg3Hr1i18/PHHqn1+9dVXNT7HREDTzN+tfb/1H3yjbtj4MYiJugNJpRibVq7F0HGj0LlHV5hZmCMzJR3BB44hOTYeNg52KCkogtFDDzVC/j6N47sVFSs69+qGwH8NRwt3V1RWiBATdQcn/zyMqCsRSIyJw5sfz4Gzm0uj+ktEj5awAQP6HrVHdf0koifb03hvJZfL8eeGPyAqr0D3Ab3RsXuXxp4OET1ujbym6NKQJAx9yjLzURiXAQDw6NcRRqa6wwByuRwxe0Mgq5DAJaANnDrWHHRPRERPFwaHSSv1bOD6yMrKUv2sLXtPyc9P/2i1xsrNzUVoaCji4uKQkpKCpKQkxMfHo1xLObqmtHz5clXm9LJly7QGstTLG0+cOLFO+83IUNzMZWVlqYKq+l7fVq1aqQLLTcnW1hYAGlyyuqioCIDihtfOzg7Ag3Ozt7dXLdNGW4ZqRUWFKpu5ZcuWOttaWFjA3d0daWlpGstdXFywYMECrFixAiUlJVi/fj3Wr18PW1tb9OvXTxUEtbe3r89p1sv48eOxYsWKJt+vQCCAlZUVnJyc0KlTJ4wYMQIjRoyAsZ6soYc/9xKJBElJSVi3bh0OHjyIgoICmJiYYMiQIU0eGFb6/PPPMXPmTFRUVODAgQM4cOAATExMEBAQgMDAQDz33HM13vvqn6m5c+fW6TjK9506Y2NjfPPNNxg/frwqq3zSpEkYOnRoI86InmYmZoosu9oyVpTZKgBgUoc5uk2qB+DUNohJ/bjGahk0Ldxd8ersGdj58+8QiypxfPdfqmAvAEAgwPAJY5CbmYPrl67CzOxBebPczGyc2HsYANBrcH+8MP1B2UNjExN0H9AbrTu2wy9frEJJYTH+2rIHb378Xq3nRESPj3n1NaW2zFv1iiq6pk5pKmbqWcq19Es5WKu2zGcievI9jfdWF4+fQeLdONg7OWLMlPG19pWImj+j6utDbVm7ygoEACCsJRu4PvutkjxYr55F/LDsGwmKHwSode7gtJBoFMVnwszBCm2e71NrX4mI6MnHb9jUpEpKSlQ/q89D/DBlkLGpVVZW4uuvv8aePXtqBEbNzMzQp08fVFVV4erVq01+7N27d2Pr1q0AFCW1X3zxRa3blarNsVZXyjbqQVl9r69QKISlpaXG/0dTaNu2LaKiolBRUYGMjAy4u7vXq31MTAwARRBSmX2s7KO5uf65V7SVm1Z/PRrSHlD8X3Xs2BEbNmxAaGgoJBIJiouLcfz4cRw/fhwmJiaYNm0aFi5cqJH53JwsX74cEyZMaPL9mpiYoG3btvj222/h6uqKdevWYdeuXcjOzsaaNWv0BpobqmfPnvjrr7/w888/4+TJkygpKYFEIkFYWBjCwsLw7bffYujQofjiiy9U2byN+Uw9zMfHB+7u7khKSgKgGEBApItyXrrKiprzyqkTVc9ZJxAKa53rDgAsqverbFfbfgHA0tpKY127Lh0x94tPcOHoKcTcvIOSoiJYWlvBp11rDBgxGN5tfLFl1a8AAGu7B9fHaxcuQ15VBWMTE4x4eSy0sXN0QODzw3Hkj31IuBuL3KwcOLu2qPW8iOjxsKy+Z6yo0H9NKateLxQKa50HuLGs1O5jy+vYL5s6zoFMRE+up+3eKjMlHaf2HQUEAox/81WY6/kOT0RPDuWcvzKR/vl+VeuFAhhb1D6QxchcERyW1rJf9fUmlrqfheXdVjzXsG3pCjM7K53blWXmI/FkBCAA/CY8W+ucx0RE9HRgcJialLXaQ5uKigqN39WpZybUl0ik+4vi/PnzERwcDEBRQjcwMBDt2rVD27Zt0bp1axgbG2PVqlVNHhy+evUqli1bBgDo378/PvroI53bqgcxb968Wa8SzeqZtbVlQTfmNdalX79+2L9/PwAgJCQEL7/8cp3bxsXFIScnBwDQp8+DUYjKc6rtgaW281HP6G1Ie6W+ffuib9++KC0txaVLlxAaGoqQkBAkJydDIpFg48aNkMvl+OSTT/Qe42n24Ycf4tatW7h06RLOnDmD7777Dp9++ukjOZaPjw+WL1+OZcuWISIiApcuXcLFixdx69YtyOVynD59GtnZ2di7dy8EAoHGQImjR482ah7koKAgVWAYAH755RcMGzYMHTt2bNQ50dPJ2dUFiXfjUJinfw77our1tvZ2dSoV5uSmCLQW5RdCLpfrbFOUr9iv0EgIW/uag67snRwwdpru63RmiqKagnpZ6NxMxXXa1dNd7wPMVu3bPWiTkcXgMFEz5uHigtuxscjJz9e7XV6B4priaFe3a1VjODs6qqb20NcvuVyO/Op+OTs4PNI+EdHj97TdW92+dgOy6mzlTd/pn1Yr8uIVRF5UTEH15sfvoVWHdnq3J6LHx8LZFkXxmRAV6h+oLipUTHllZmtZp2uVpbPi+VhlUZnea1VlkeK4AiMBTG21f2cryypARY4ioaKFfyu9x82NToJcqpiGLWrDcb3bZkfcR3bEfQBAl5kjYd+6fkkjRETUfHCyJ2pS6qV9tc0Pq5ScnKx1uXpmpkRHKSllGeGHRUREqALD06ZNw759+zBv3jyMGTMGfn5+qizHggL9XzTrKzU1FXPnzoVEIoG3tzdWrVqlN8PUw8NDo60+yhLSSm5ubqp5mPS9vtnZ2ar5gZvS8OHDVRm4W7durTGHrz5btmxR/fzCCy+ofla+HkVFRcjLy9PZXr10sJKZmRmcnJwA6H89ZDKZ1jLCD7O2tsaIESOwZMkSnDx5Env27IGnpycAYPv27Y9knuonhUAgwIoVK1T//5s3b1aVUH9UTExM0KdPH8yfPx979+7FmTNn8OyzzwIAbt26hYiICADQyGB/uHT4wx7+TKm7fv06Nm7cCACYMmUKPDw8IJFIsGjRokcy2IKefK5ebgCA/Jw8iPQMUElPVlzr3X0867RfNy/FdVEmlSInPVP3fpMU+3XxcIPRQ5n8ErEEYpHuvwPZaRkoKVQ8LPBu46taXlVddaM+1zupxHCvjURPAu/qv5PZeXl6s3QTqu9Lfb28HnmfBAKBql+Jeu6Hk9PTIa2+LrXy9n7k/SKix+tpvLcioqePlatiwJoov1Rvlm9puuIZl5W7Y532a+mm2K9cWoXy7EI9+1UMrLN0sYdQx/PHgpgHz0acOnH+YCIiqonBYWpSAQEBqjnKTp06pXO78+fPa12ungGYryOLQBkQelhkZKTq50mTJmndpqqqCmFhYRq/N0ZZWRneffddFBQUwNLSEmvXrq11ftqePXuqflYGs7WJiIhA165dMXLkSBw7dgwAYGVlpWqvr62u17exrKys8OabbwIA7t27h59//rlO7UJDQ7Fnzx4AQPfu3dG3b1/VukGDBql+bsh7Rtn+7NmzOudYvnLlitbM4l9//RXPP/88Xn31Va3t/P39MX36dACKkuXKOZMNlaurKxYtWgRAEWT97LPPas3Yro+zZ89i4sSJ6N27t9aS6O7u7liwYIHqd+Uc5z169FANmtD3uUhLS0P37t0xfPhwjcEKgKIiwaJFiyCTyeDt7Y1Fixbhs88+A6B4r//0k/6R/mSY/Lp0AgDIq6oQc/OO1m2K8guQkaz4Yt6uS90y0Fu1b6uaP+/O9VtatxGLKhF/J1brfn9bEYRl73yEA7/v0nmM8POXASjm9mv7THvVcqfqDOCcjEwUF+q+5iXG3Ff97OLhqu90iOgx6965MwDFfW/k7dtat8krKFAFabt16vTP9Kv6OLdiYiDSMajx2i3FNdDY2Bid2uqfK4+InnxP271V4PPP4b9rv9H7z85REQzy79tDtaylX8MrIRHRo+fQvnogXZUc+fe0D3KrLCpDWYbiuaajX90Gsti3doPQVBHszb9TM0ECAGRiCQrvpyv64ad7QF9xUjYAwNTOEma2+svvew/2R//PX9P7z8xeUZa6RbfWqmV2vvweSET0JGNwmJqUtbU1xo5VzFG4ZcsWxMXF1djm7NmzuHDhgtb23t7eqiCPMiCqTiQSYd26dVrbqmfrajsuAKxZswaJiYmq33VlJ9eFXC7HwoULERMTA6FQiO+++w5+fn61tvP391eVqF2/fr1Gf5REIhFWrFiByspKpKWlwd/fX7XupZdeAqAIhu/bt69G28LCQqxdu7aBZ1W7mTNn4plnngEArF69Gr/88ovebMxz587hvffeQ1VVFSwtLfHll19qrPfy8lKVmV69ejWys7Nr7OPvv/9GeHi41v0rX4+MjAyt511ZWYmVK1dqbWtsbIzY2FhERkbqHHRw547ioYS1tTUcHes22vNpNnHiRPTq1QuAIvN99erVTbZvJycnREVFoaioCNu3b9e6jfL/A1CUnwYAZ2dnDBkyBADw559/4tq1azXaVVVVYfny5aioqEBKSorqPaz0v//9T/VZXLZsGczNzTFkyBCMHDkSAPDbb78hKiqq0edITxdHF2f4tGsNADh94BgqHprHTi6X49iug4BcDktrK3Tr11PbbmowNTdDpx6K6/7Fv89oLa14+uDfEJVXwMjYCH2GPquxzquVoorH3Ru3UJBbc6BVyv1EXDmjyPzvOagfzNSmO/DvEwAAqJJV4eiO/Vqv78WFRTh3+AQAwNXLAy6eLCVG1Jy5OjujfWvFtWr3kSMoe2hqErlcji37FZ93G2trDKz+O/+oPduzJ4RCIcrKy7FXy31/bn4+jpw5AwAY0q8frB7xPMhE9Pg9bfdWRsbGMDU30/tPWTbWyMhItUz5TISImicLRxvY+irKxycFR0JaoZk9LJfLEX/0KiAHjK3M4NK9bgM+jExN4NxZcb1JDYnWWrY6Kfg6ZBUSCIyE8OjbQee+StJyAQA23rVP/yM0MoKRqYnef6iucC0QClTLBLxWERE90XgVJ63kcjnKysrq9U9pwYIFcHZ2RkVFBaZOnYo9e/YgOzsbGRkZ+O233/D+++/rPK6tra0qq/T06dNYunQpEhISkJubi9OnT2Py5Mm4c+cObG1rzv8zYMAA1RerL774An/99RcyMzORlZWFCxcu4J133qmR/afe7/patWoVTp8+rTrn4cOHQywWo7y8XOvroz5X8meffQZjY2MUFxdj0qRJ2LZtG1JTU5GXl4eQkBC88cYbuHHjBgDgrbfeUpU2BoBx48apgnOLFy/G//73PyQlJSE/Px+nTp3C5MmTkZGR8cjmijM1NcUvv/wCPz8/yOVyrFq1ChMmTMCePXuQkJCAwsJCpKam4uTJk3jvvffw9ttvo6ysDJaWllizZg3aasn6WLJkCUxNTZGTk4PJkyfj6NGjyM/PR0pKCtauXYuFCxfqLNXdq1cvjBs3DoAi+P/ZZ58hNjYWBQUFCA0NxdSpU3Hr1i2t7V966SXY29tDLpdj9uzZ2LZtG+Lj45Gfn4+7d+/iiy++wIEDBwAAr7766iOff+9JIBAIsHTpUpiYmABQlJdWD9g2RpcuXdC7d28AwI8//ohvvvkGd+7cQX5+PhISEvD777/jq6++AgB07dpVI8C7aNEiWFtbQyKR4K233sLPP/+MxMRE5OfnIzw8HO+88w5OnjwJAHj++ecREBCganv16lVVJvH48ePRv39/1brFixfDxsYGUqkUn376KctLUw2jJ78ICATIy8rBhhWrEXfrLspKSpGelIIdP21C9NXrAICh40bB1Fxzjvkf/vM1fvjP19i7fluN/T730r9gYmaKirJy/LY8CNHhN1BWXILs9Ewc3LwbF48rAiZ9hw1SZZwo9Rk2ECamppBUirF11a+4dyMapUUlyMvKwYWjwdj03VrIpFI4ujhj6IujNNp6tW6J7s8qPofRV69j03drERt1B2XFJSjKL0BEyBX8+uUqlBQWw8jYCM9PfamJXkkiepRenzABAoEAmTk5+PzHH3Hjzh0Ul5YiPiUF32/YgMvVFXheHj0a5maa16oPvvgCH3zxBdY8VHWjsTxcXTFi4EAAwKHgYKzfuROpmZkoKilB2PXrWPLDDygtK4O1lRVeHD68SY9NRM3X03ZvRURPp9ZjegMCQJRbghvrj6EgNg2SMhFK0/JwZ/sZ5EYlAgBaDuumCK6qCV+1D+Gr9uHenpoV8nxH9IDQ1BjS8krcXHcMubcSIS6tQHl2IWIPXELahWgAgEf/jjCzs9LaN5lYAnGRYjCghVPN56dEREQAIJDrS/kjgzNt2jRcuXKlQW2vXr2qCtrevXsXs2bN0poFamNjg969e6vKv967d09jfWxsLKZOnap1bmGBQICPPvoIFy5cQGhoKObMmYO5c+eq1n///fc6M4uVx3755ZdV84ru2LFDFSQKCwtTlRDesmWLKpsVAIYOHYq0tDSMHz8eK1asAAC0b/+gDKe1tTVEIpHeORp79+6NrVu3qn4PDg7GwoULUf5Q9oa6l19+GUuXLq0R2CwsLMQ777yjUUpb3cKFCxEUFASxWFzjXJpKWVkZvvnmG+zdu1dnOWelHj16YNmyZVoDw0oXL17E3LlztQbs7e3tMXXqVKxZswaenp6qoLySSCTCwoULVcG/h7366qu4fPkyEhISsHz5ckyYMEG1LjQ0FLNnz9b7/zBkyBAEBQWpSqY3BeX7R/09VR/q79eHz6m+lJ97ba+tLj/88IOqrHiXLl2we/fuJhnhnpmZiddff11rRr2Sr68vfv/9d425hgFFNv2cOXOQm5urs+2QIUOwatUqVQn78vJyvPDCC0hJSYGTkxOOHj1aozT8zp07sWTJEgCKzPmPPvqoYScH4FSy9gx1erJFhFzBwc07USXTPlVB/xGDFQ86H/LfNz8AAPi2b4O3Fs2tsT426g52/LQJEh2DEjr36oZJ77yudeBKdPgN7Fm3FTIdf5daeLhh2rxZcGjhVGOdTCrFvo07cPNyzSx8JVNzM7w0cyo6BXTRuQ09uXoXGte+ET1xzoaFYd2OHTrv2/41ZAima7mfmFR9r92xbVt8Pm9enY6lbDNx9Gi8PGaMzu3EEglWbdiAiOhorevNzMyw+L334NeqVZ2OW59j09Phij3nvX8aPW33Vvp8/9EyFOblo/uA3pjw1pR6taUnx2/3dU+hRU+urIg4xO6/CLlM+6N1z2c7KYLID7nwn98BAHatXOE/a3SN9fkxabiz/TSqxNrv2Zy7+KLD5ECdCQxlWQWI+PEgAMB3ZAC8A/21blcfV77bg8qCMrgEtEH7iQMbvT9qvnYO+fhxd+GJMPnMt4+7C08Vvu8eDz75oUeiQ4cOOHLkCDZs2IDjx48jPT0dNjY2GDRoEObMmYN9+/bpnBu0Xbt2OHToENatW4dz584hMzMT1tbW6N69O95880307NlTZ1nqBQsWoHPnztixYwdu376tylj18fHBwIEDMWXKFNja2mLXrl0oKyvDyZMnNTIIG6q0tGapl9oMGzYMJ06cwNatW3H+/HmkpKSgsrISDg4O6N69OyZNmoQBAwZobWtvb48tW7bg4MGD+PPPPxEfHw+pVIqOHTtixowZGD58OIKCghp7WnpZWVlh2bJleOutt7B//35V8LWkpARmZmbw9vZGQEAAnn/+eY15lnUZMGAAjhw5gk2bNuH8+fNIT0+Hra0tBg4ciLlz5+oMhAOAubk5goKCcPz4cezcuRMxMTGoqKhAmzZt8Oqrr2LixIkYNUr7CO5+/frhyJEj2Lx5My5duoTU1FRIJBI4ODjgmWeewYsvvqgqLUwPvPvuuzh69CiSkpIQFRWFrVu34vXXX2/0ft3c3LB//3788ccfCA4Oxv3791FeXg4bGxu0adMGzz33HF599VWYPZTVBCjms/7777+xfft2nD59GgkJCSgrK4OtrS26dOmC8ePHY/RozS9e3377LVJSFHP5/Oc//9E6Z/ikSZPw119/4dq1a9i0aROee+45dOvWrdHnSk+PgGd7w6OlF0L+Po2Ee3EoKy6BiZkZPFt6oc+wgejYvWEB1HZdOmLul5/gwtFTiL11FyWFRTAyNoabtyd6DOyD7gN663wg0LlnV7TwcEXIsdO4fycGpUXFMDYxgZuXB7r06Y5egf1hZKz9NtDI2Bgvvz0N3Qf0Rvj5UKTcT0RZcQmERkZwbOEMP/+O6DtsIGwd7Bt0XkT0eAzu0wetvLxwKDgYt2NjUVR9z9ba2xujBg1CT//GPzisL1MTE3z873/j3JUrOHv5MpLS0iAWi2FvZ4duHTviheHD4ers/I/3i4ger6ft3oqInk6uAW1h7eGI1Au3UBifCUmpCEamxrD2dIJHv45w6ujToP06+nmix7zxSDkfhYLYNIiLyiE0FsLK3RGuPdrBNaCt3sp2MtGDATDG5k2X5EBERE8XZg7TY7F69WqsWbMGQM3MYSKipxUzh4noScDMYSJ6UjBzmIieBMwcJqInBTM464aZw02L77vHg3MOExEREREREREREREREREZAAaHiYiIiIiIiIiIiIiIiIgMAGvGERkAuVyO8vLyRu3DysqqiXrz5OLrqJtMJoNIJGpwe6FQCAsLiybsERERERERERERERERPYzBYSIDkJaWhmHDhjVqH5wbmq+jPuHh4Zg+fXqD23t6euL06dNN2CMiIiIiIiIiIiIiInoYy0oTERERERERERERERERERkAgVwulz/uThARERmCU8kRj7sLRES16l3I4kJE9GS4Yi993F0gIqrVb/dPPe4uEBHVyc4hHz/uLjwRJp/59nF34anC993jwcxhIiIiIiIiIiIiIiIiIiIDwOAwEREREREREREREREREZEBYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAaAwWEiIiIiIiIiIiIiIiIiIgPA4DARERERERERERERERERkQFgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGh4mIiIiIiIiIiIiIiIiIDACDw0REREREREREREREREREBoDBYSIiIiIiIiIiIiIiIiIiA8DgMBERERERERERERERERGRAWBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAaHiYiIiIiIiIiIiIiIiIgMAIPDREREREREREREREREREQGgMFhIiIiIiIiIiIiIiIiIiIDwOAwEREREREREREREREREZEBYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAbA+HF3gIiIyFD8dv/U4+4CEVGtetwye9xdICKqm3EDH3cPiIhqtc5p1OPuAhEREZEGZg4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAaAwWEiIiIiIiIiIiIiIiIiIgPA4DARERERERERERERERERkQFgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGh4mIiIiIiIiIiIiIiIiIDACDw0REREREREREREREREREBoDBYSIiIiIiIiIiIiIiIiIiA8DgMBERERERERERERERERGRAWBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAaHiYiIiIiIiIiIiIiIiIgMAIPDREREREREREREREREREQGgMFhIiIiIiIiIiIiIiIiIiIDwOAwEREREREREREREREREZEBYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAaAwWEiIiIiIiIiIiIiIiIiIgPA4DARERERERERERERERERkQFgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQDjx90BevSmTZuGK1eu1Lvd+PHjsWLFikfQo39OWFgYpk+fDgDYsmUL+vTpo1o3dOhQpKWlPTHnmZqaimHDhgGoeS5N7f79+zh8+DBCQ0ORmpqKwsJCmJqawtPTEwEBARg7dix69uzZ6OOsXr0aa9asgaenJ06fPl3v9u3btwcALF++HBMmTGh0fxpL2Z/6UO+7+vtVG6FQCFNTU9jZ2aF169YIDAzEK6+8Aisrqxrb1uVzb2RkBAsLC7i5uaFLly6YMmUK/P39630ORASUZeYj9cItFMZnQlIqgrGlGWw8neDetwMc/bwavF9RQSlSzkehIDYN4qJyGJmbwMrNAW49/eDStbXettIKMdJDbyP3djIqcosBuRzmTrZw6uQDz/6dYGJpprd9SWou0i5GoygxC5JSEYSmRrBycYBzF1+4924PobFRg8+LiB6P5Lw8HLpxE7fT01EsEsHazAytnJ0xonNndPPxbrLjHL0Zha2hl/FSjwBM7NlD53ZyuRwzN29BeaW41n1uevMNmJuY1Fgulclw6vYdhMbHI72wECKJBPaWluji6Ymx3brC3c6uUedCRI9HZko6Qv4+jfi7sSgvKYWFlRU8fL3QZ+hA+HXp2GTHuXTiLI7tPIAhL4zE0BdH691WLpfj+qWriAgJQ0ZyGmRSGWwd7NC+aycMGDkEdo4OtR6nNgNGDsGoSePqexpE9JgkpaXhUHAwomNjUVxSAmsrK7T29saIQYPQvVOnJjvOkTNnsGXfPkwcPRovjxmjd9uy8nIcO3cOV27cQEZODgQCAVwcHdGjSxeMHDQIjrXcG0XHxuLv8+cREx+P0rIyWFpYwK91a4x49ll07dh0118iInq8GBwmIpXS0lJ89dVXOHjwIGQymcY6iUSCmJgYxMTEYOfOnejduze++OIL+Pr6Pp7OGqCqqiqIRCKIRCJkZWUhNDQUmzZtwoYNG9CuXbt6708mk6G0tBRxcXGIi4vDgQMH8P7772P27NmPoPdET6+8O8m4s/0s5LIq1TJJSQXy76Yi/24qPPp3RJvn6z+gpyQlB1Ebj0NWKVUtk5ZVouh+JoruZyI3OgkdJgVCaFSzEExpeh6itwZDXFSusbw8swDlmQXIvHIPnV8fDhtPZ63HTrt0G/FHrwJVctUyWUUVipOyUZyUjayIOHSZMQImVub1Pi8iejzCExPx46lgSNWuVYXlFYhMTkFkcgpGPtMZbwzo3+jjxGZlYdfV8Dptm1VcXKfAsC65JaVYfvQo0guLaiw/c/ceQuLiMG/4MPRo2bLBxyCif96dyCjs+vl3yKQPvpOWFhUj5sZtxNy4jb7DB+FfUxo/ODjlfiJO7Ttap23lcjn2/LoVUVciNJbnZ+ci9OR5RF68ilffexOtO2r/XpaWmNLo/hJR8xJ+8yZWbdwIqdrzs8LiYkRERyMiOhqjAgMxY+LERh8nNjEROw8frtO2KRkZWL52LfIKC2ssT8nIwIkLFzD39dcR0LlzjbZyuRyb9u7F8fPnNZYXl5Yi/OZNhN+8ieEDBuCtV16BUMhipERETzoGhw2Ih4cHDtfxZgIATLSMzH+aeHp6wsjICE5OTo+7K81CZmYmZs2ahZiYGABAhw4dMGXKFPTs2ROOjo7Iz89HbGwsdu7cidDQUFy5cgUTJkzA2rVr0bdv38fc++Zl7NixWLp0aZ22NTPTnrm3dOlSjB07tsZyiUSC7OxsbNu2Dbt27UJWVhZmz56Nw4cPa92Xvs+9RCJBVlYWzpw5g19//RXl5eX48ccf0alTJwwePLhO/ScydKXpebi78xzksipYezmh1ehesHJ1gCi/BClnbyLvdjLSL92BhbMtPPrWfZR1ZVEZbm05BVmlFObONmg9pjdsvVtAXFqB9Et3kHk1Bnm3kpDocA2tR/fSbFtcjlu/n4SkVAQIAM9nO8OtRzuYWFugND0PicevoTQtDzfX/43u7z0Pyxb2Gu0L4tIRf+QKIAcsWtih1agesPZ0hrRchMzwWKSH3kFZej7u7DwL/7dGNcXLSESPWGJuLoKCT0Mqq0LrFs54rW9feDs6ILu4BAciIxGemITjt6LhYW+HEVoeFtZVXHY2Vhz7G2KptPaNASTm5gEAjI2EWDv1NZgY6a5I8HDWcKVEii+PHEFWUTGMjYQYH9Ad/du0gZFQiOj0dPxxOQylokqsDj6NlS+/DGcb6wafFxH9czKSU7H7ly2QSWXw9PXByEkvwNXTHfk5eTh3+CTuRkbh8qnzcHZtgT7DBjb4OKnxSdiy6ldIxHUboHLyz8OqwHD/EYPRa3B/WFhaIOFeHI7tOoji/ELs+Gkj5iz7WGsGcUZSKgBg4JhhGPz8CJ3HMWJlFqInQkJqKn74/XdIZTK08fHB1BdfhLeHB7Jzc7HvxAmE37yJv8+dg4eLC0YOGtTg48QlJuLrtWshrsO1qkIkwopffkFeYSEszM0xcfRoBHTuDHMzM8QkJGD7oUPIysnBD5s2YcVHH8HD1VWj/Z6jR1WBYU83N0wZOxbtWrVChUiEkPBw7Dt+HKcuXoRMJsM7r73W4HMiIqLmgcFhAyIQCLSWnzVUW7dufdxdaDbEYjFmz56NmJgYGBkZ4cMPP8Rbb70FgUCg2sbBwQFt2rTBqFGjcOLECXz00UcoKyvD7NmzsWfPHrRp0+YxnkHzYmxs3OjPmqmpqc592NvbY9myZQCAXbt2ITk5Gfv378fkyZNrbFvb597e3h7t27dH9+7d8frrr0Mul2PdunUMDhPVUdKpSFRJZDB3soH/zFEwMlUEL0wszdDxtSG4u/MccqMSkXTqOly6t4WxWd0GXqWci4K0rBJGFibwnzkaZraWiv1amaPd+P4wMjdB2oVopF+6A4++HWHu8CDokXz6uiIwDMDvpWfhGtBWtc6hrQdsW7rgxq9HUZaej7i/LtcI8KaeiwLkgImNBfxnjYKptQUAwMzWEm2e7wMjUxOknL2JovuZKE7Kgm1LzYcKRNT87L4aDolUBlc7W/x37POqQKuNuTk+HPEcgk4F43J8AvaEX8PAdu1gYWpa72OcjL6NraGXIXmo+ow+8bk5AAAvBwfYmNevEsH+yAhkFRVDIAA+HPEcuvv4qNYNbt8erZyd8X/79qNSIsXft25haj8OZiR6EpzafxRSiQSOLs548+P3YGquGABraW2FKXPexK5fNiP66nUEHziGbgN6waye1w4ACDsdgmM7D0BWx4EsxQWFuHTiLABFcHfExAeDeJ/p1R2erVri56UrUVFWjjN/ncCLb0zSaC8WVSInMxsA4NO2leqciOjJtfvwYUgkEri2aIHP3n8f5tWD9W2srLBw5kz8sGkTLkdGYvfRoxjUuzcsGnCtOnHhAjbv2wdpHa9VJ0NCkJufD4FAgHkzZmiUte7bvTva+Phg4YoVEIlEOHLmDGapPUPKycvDwZMnAQAtPT3x+bx5sLRQfA+0s7HBxNGj4eHqih83bcKZy5cxqHdvdGpABTsiImo+WAOCiLB27VpER0cDAD7++GPMnDlTIzD8sBEjRmDNmjUAgLKyMnz22Wf/SD9J07vvvqv6uSFzNqvr06cPAgICAADXr1+v85cPIkNWnlOI/LuKLBDvwf6qwLCSQCBA6zG9AAEgLa9EXnRSnfYrrRAj61osAMCjXydVYFhdy2HdYGRhArmsClkRcarlVbIq5NxMAAA4+HloBIaVjEyMVdnGRfczUZKSo7G+JFXxu3MnH1VgWJ17n/Zq2+bW6ZyI6PFJKyhEZLKinOmL3bvVyMAVCASY2q8vBAKgVFSJKwmJ9dp/XHY2lv51CBtDLkIik6FVC+3l6rVJqM4cbtOiRb2OqZxnGACGduigERhWaunkhE4eHhAKBEjKz6/X/ono8cjJyELMjdsAgMDnn6sRRBUIBBg9aRwgEKCirBzR4Tfrtf/U+CT8tiIIh7fthUwqhYdv3eZavxx8ATKpDCZmpgh8/rka6x2cHTFg5GAAwM3L1yB+qFx+Rko6IFdM1eHZqub1ioieLGlZWYiofoY2fsQIVWBYSSAQYPr48RAIBCgtK0PYjRv12n9cYiKW/PADNuzeDalUitZa7nO0UR6njY+P1vmOWzg5oWN1YkdckuZ300uRkary2LMmT1YFhtX1DwhAx7aK75cHT52q+wkREVGzxMxhqpfExET8/vvvCA0NRUZGBhwdHTF06FDMmTMHsbGxmD59OgDg3r17qjZhYWGq5Vu2bEGfPtrnXWzfXvGwec6cOZg7d26N9bGxsdi1axeuXr2KjIwMlJWVwdraGj4+Phg0aBCmTZsGe3v7Op/L0KFDkZaWhvHjx2PFihUAgNWrV6uCnnWh7XwKCgqwefNmnDlzBikpKZDJZHB3d8fAgQPx5ptvwt3dXef+SktLsWvXLhw+fBhJSUkwNjaGv78/Zs2aBU9Pzzr3qz5KS0vxxx9/AAA6deqEN954o07tBg4ciLFjx+LQoUMIDw/HlStX0Lt37xrbxcbGYuPGjQgPD0dWVhacnZ0xdOjQOs1rKxaLceDAAezbtw/x8fGQyWTo2LEjXn/9dTz3XM0v5eru37+PLVu24PLly0hPT4exsTFcXFzQq1cvvPbaa+jYse7lXZsrd3d32Nvbo7CwEGlpaY3en2t1SSGZTIb8/Hy4uLg0ep8A8Mknn2D//v0YO3Ys5s2bhyVLluDatWswNTVF27ZtERQUhBbVD4VLS0uxd+9enD9/HjExMSgsLISJiQmcnZ0REBCA1157Df7+/jqPlZ+fjz179uD48eNITU1FRUUFPDw8MHDgQLz11ls6P3/p6en4/fffceHCBWRkZEAgEMDb2xtDhgzBG2+8AQeHmqXhiApiqj93AsCpg/aHi2Z2VrD2cEJpWh7ybidrDdY+rDA+A1USxRdz547aHwQYmZrAvo0H8m4lIe9OMloO6wYAEOUVQyaSKNp29tV5DLtWrhCaGKFKIkN+TBpsvNUCM0LF4KAqtXlJ1QnU5zjWM5CIiJqHGymKwLBAAAT4aJ9718naGr7OzkjIyUV4YiIC2/vVef8/ngpGbkkpBALguU6d8FrfPnh9w6Y6tU3MVQwwaVPPe46bqWkoqw6+vNCtm87tPho1EsZCod5Bj0TUfMRGKQZ9QCBA+67aS9zbOTrAo6UX0hNTcCcyCgHP1vwOqsuunzejMC8fEAjQe8gAjHplHJa981Gt7WJuKvrVukM7nZnKHbp1wal9RyERi3H/9j107N5FtS49SXEdtnW0h42dbZ37S0TN0/XbikEsAoEAPZ55Rus2Tg4OaOXtjfjkZFy9eRODdTwL1WbVpk2qDODnnn0W08aPx7QPP6y13dJ585CenQ159WAUfYwemjM4PjkZAOBob492vr462/l36IA7cXG4FRMDqVQKY2OGFoiInlS8glOdnTp1CgsWLIBIJFIty8jIwB9//IGTJ09i/vz5j+zYa9aswZo1a2rc4BQWFqKwsBA3b97En3/+iR07dugNvjY1i4dG0l2+fBnvv/8+ioqKNJYnJCQgISEBu3fvxrfffouRI0fW2FdKSgpmzpyJxMREjeUXLlxASEgIZsyY0eT9B4Bz586huLgYALSWJdbntddew6FDhwAABw8erBEc/vPPP/HZZ59pZKGmpaVh69at+Pvvv9GvXz+d+87Pz8c777yDGw+NsLx69SquXr2KmTNn6mx79uxZzJ07V2NOFrFYjMTERCQmJmLv3r1YvHgxpk6dWq/zbY6UDzuFwsYXgoiLU2QfmpiY1GugRV0VFRXh9ddfVwWyRSIRCgoKVIHhqKgovPPOO8jN1cxElEgkSE5ORnJyMg4ePIgvv/wSEydOrLH/K1eu4IMPPkBeXp7GcuX/+759+7TOkX3kyBF8+umnqKys1Fh+79493Lt3Dzt37sRPP/2Enj17Nvo1oKdLaboiE83M3gomVrrLhFm5O6I0LQ8laXXLsi3NUOxXYCSAlbvugQnWHo7Iu5WEsswCVMlkEBoZQVrx4H1s5qB7fk2BUAhjC1OIJRUoyyrQWGfj6YzCuAzk302BpLwSJpaaI+GzwmNVP9u2bJpBJET06CRW/110sraGrYXua5WvkxMScnIRn1v/igCdPT0wuXcvtK1HkDenpASlIsU1y97SAltDLyMyORk5JSUwNTZGK2dnDG7vhwFt29YI7t7PVpRndbaxhoutjcY6qUwG4+q5i/XNYUxEzU9GsuJ7gr2jA6z0zBPu7u2J9MQUVdC1Plp1aIcRE5+HV2vtg2UeJpNKkZORCQB6M41dPN1gZGwEmVSG9MQUjeCwcr5hT18fRF2JRERIGFITkiGprIStgz3ademIgaOHwd6JA1KJngSJqYrPtJODA2ytdV+rfD09EZ+cjITqwGt9dPbzw5SxY9FWT6D2YcbGxvDx8NC5Pjk9HVHVyTz+HTporCsrLwcAODs66j2G8nylUinSsrLQ8hElshAR0aPH4DDVSVxcHD744ANIJBJ4eHhg0aJF6N27NwoKCrBjxw5s3boVX3755SM59t9//43Vq1cDAAYMGIC3334brVq1AqAIum7atAlnz55FRkYGgoKCsHz58gYf69///jfefPNNnetDQkIwb948yOVyvPLKKxoZjDExMfj3v/8NkUgELy8vvP/+++jbty9MTEwQFRWFoKAg3Lp1Cx9++CG2bNmCHj16qNqKxWJVYNjc3Bxz587F6NGjYWZmhosXL2LlypXYuHFjg89LnytXrqh+rm/wq1u3bnB2dkZubi7CwsI01oWFheE///kPAMDPzw8LFy5Ely5dUFBQgL1792LTpk3466+/dO573rx5uHHjBoRCIWbNmoUJEybA1tYWkZGRWLlyJX777Tet7crLy7Fo0SKIxWL4+/vjgw8+QNvqh4pRUVH49ttvkZiYiBUrVmDw4MHw8vKq1zk3JykpKSgoUAR1Gjvn89GjRxETEwMAGDRoEEwbMNdgbc6fPw8TExMsW7YMw4YNQ1paGgoLCwEoMobfffdd5ObmwtnZGfPnz0evXr1ga2uLrKwsnDp1Cr/99hsqKirw1VdfYcyYMbC0fFBqNyUlBbNmzYJIJIKTkxPef/99DBo0CEZGRggNDcV3332H3NxczJs3D0ePHoWTkxMA4OLFi1i4cCGqqqrQoUMHzJ07F927d4dMJkN4eDh+/PFHJCYm4u2338a+ffvgW48vZvT0qywsBQCYO9ro3U45H7C4uBxVsioIjfQP5qgsUOzX1M4KAj0DP8zsqh9EVMlRWVgGCydbGKnNaSwTiXW0BORyOaTVGcaVRWUa61oOD0Bx0t+QlIpw87e/4TsiANYeTpCJxMi6fh9pF24BAFx7toONZ93LxxLR45FbUgIAcLXVn63mXB2IKSgrg6yqqkZGiS6fjhkNjwYMKovPeRCE/t+Jk5CqVSuQysSITktHdFo6QmLj8MFzwzXKYadW3/+42dkBAO5kZODozSjczshAeaUY1uZm6ObtjfEB3RvUNyJ6PArzFAPkHFo46d3O3lkRvCguKIJMJoNRHQeCvL7gHTi71W9gW3FBkaqaioOz7qCJQCCAnaMD8rNzUZCrWco+vTo4HHMzGnciNEthF+Tk4crpEESGXMHL/56mEVQmouYpp3q6Cldn/d+FlIHW/KL6Xav+b/ZseFRXdmsMuVyOkrIy5OTnI+z6dZy4cAFSqRQ+Hh4YO2yYxrbm1VUR1BOCtCmrqFD9nF9UxOAwEdETjMFhAyKXy1FWVlb7hlBkIapnxX7zzTeQSCSwt7fHjh074ObmBgBwdHTE4sWL4eLigu+///6R9FsZBGzXrh1++eUXjaCVq6srevfujYkTJyI6OhoXLlxo1LFMTU11BsXi4+OxePFiyOVyBAQE4L///a/G+qVLl6oCw3v37tUoQxsYGIi+ffti6tSpuHnzJpYuXaoRGN2+fbsqYzgoKAiBgYGqdePGjUOPHj0wfvx4VYZvU4qPjwegGGHYunXrerUVCARo2bIlcnNzkZaWBrFYrHr9vvrqKwCAr68vtm/fDhsbRfDE0dERixYtgpubG77++mut+z158qQqaL148WK89tprqnXDhg1Djx49MHHiRKSk1BwpfuXKFVXAcfXq1ar3qrKtn58fRowYAYlEgpMnTz6SjGypVFrrZ00gEGgENxvixx9/VP2sLRsd0P25Vy5PSUnBiRMnsH37dgCApaUlFixY0Kh+6fPWW29h0qRJAABntS9SBw8eRE6OYp7ToKAgjcETDg4O6NChA2xsbPD111+jvLwcERERePbZZ1XbfP311xCJRLC2tsaOHTvQsuWDTIAXX3wRbdq0wSuvvILCwkLs3LkT7733HmQyGf773/+iqqoK/v7+2LZtG8zU5goaM2YM+vfvjwkTJiAtLQ0rVqzAL7/88sheG3ryiMsUX5yNLcz0bqcK2MoVAVuhnixjAJCUV+/XXP8gDfX10gpFINjcyRYCYyHk0ioU3M+A8zO+WtuWpOSiSqyo6qAsQ61k69MCXd4cgbhDYShLz8ftLcGax7U0g88Qf3j0rzmPFRE1P8XVD/msahn4ZWmiWC+XA2WVYr1ZxuoaGnxNVMtQtjI1w0s9AtDV2xvmJsZIysvDgcjruJ2egRspqVhz+gwWjhyh2r6gOrvFxtwc+yMisSc8HOoFhkpFlQiJjUNYQgLeHzYUPTm4i+iJUFasGCBnYVVzrkt1Zsrrk1wOUXmF3ixjdfUNDANAWemD71IWVvq/vyn7VVF9jQIAiViiyjyWSWXo3Ksb+g0PhLNbC1SUV+B2+A2cPXwCkkoxdv38O95aNBfebXzr3U8i+ucUlyquVVa1PNOxrA64yuVylFVU6M0yVtcUgWEAyM7Lw/tLl2os69u9O2ZNnlyj7z7u7rh64wZSMzORX1QEx+oBeA+7HfugilRFLYFkIiJq3hgcNiDp6ekICAio07aenp44ffo0AMUcuhcvXgQAzJgxQyPYpjRz5kwcOHAA9+/fb7oOA6iqqsLgwYPRpk0bBAYGag3cCoVC9OzZE9HR0aosyqZWXFyMd999F8XFxXBzc8Pq1as1+hIbG4vw8HAAwOzZs7XOT2pmZob58+djxowZuHfvHm7cuIGuXbsCgKo084ABAzQCw0peXl6YNWvWIwnAKwOp1tbWDZqPTRngq6qqQlFREVq0aIHY2FjVvNNz5sxRBYbVTZ8+HTt37lQFp9UpXw9fX1+NwLCSvb095s+fjw+1zLmiXko6JyenxvvV29sb69atg52dnSoDvakdOnRIdQ662NjYqN4z2ojF4hpBXblcjpKSEty9exfbtm1DSEgIAEUGt67gcH0+9z4+Pli5cmWjs5D1GT16tNbl7u7ueO2111BVVaURGFanPr93fv6D0fjFxcWqgSEzZszQCAwrdenSBaNHj0ZmZqZqROyFCxdUJa4XLFigERhWsre3x7vvvovFixfj7NmzyMnJUZXBJpJLFfMCC431jwA3Mnlwu1VV3UYf5Tbq7bQRmjw4rnobpw7eyL2VhKxrsXDt3ha2Pprv2SqZDInHH1x/5LKafZKKJDBWy0LWXCdGSUouKgtKa82aJqLHT1L9GTepZU44U7X1EplUz5ZNo0IigaWZKcxNTPDFi+PgaGWlWtfFywudPTzww6lgXE1IxLXEJEQkJSOgpWIedpFEMajlTno6QuPuw9vRAVP69EFHd3dIq2SISErGH5fDUFRRgaDg0/jyxXHwcdKfiUhEj59yOiJjE+33IEompg/WSyUSPVs2QZ/U9l9rv6rXS8UPrqFF+QWwtbdHUUEhhrwwEkNeePC9zcrGGoP+NRytOrTFhm9WQyaV4cgf+/DOZ7XPLUpEj4+k+lplWtu9ldpzQ8kjvlZpk5OfX2NZ+M2bMDE2xluvvAILtTnU+3Trhn3Hj6Oqqgob9+zBgrfeqvGM8MadO7hx547qd/Up5IiI6MnD4DDVKjw8HLLqh0qDBg3Suo1QKMSoUaPw008/NemxhUIh5syZo3N9VVUV4uLikFo938ejuDGRyWT44IMPkJiYCDMzM6xevVoj4xHQLM3s5+enM2u0Q4cOMDIygkwmw7Vr19C1a1eUlJQgOjoagO7XF1BkvT6K4LBynlVtgbG6UC+Lo5wT+vLly6plus5JIBBg2LBhWoPDyhLVAwcO1HncoUOHQigUoqqqSmN5t27dYGJiAolEghkzZmDy5MkYMmQIunXrpuqrvv02F0uWLMGSJUtq3a5z584ICgpq8JzDjo6OGDx4MAIDAzFs2DDVA41HwcTEBO3atdO6bujQoRg6dKjOtrm5uYiMjFT9LlMLZl29elX1RWvw4ME69/G///1P43f1Uuj6PrfPPPMMAMX7OyIiQmcgngyQsP4DauqiIQN11LV8rjvyY9JQJZYiauNxtBzWDc6dW8LI3BRlGflICo5EcWI2TO0sIS4qh+Ch8mapF24h4ZgieOzcxRfegV1g6WIPqUiMgpg0JJ64hpybCShKzESXt0bBsoX2UeVE1DwIG3lNeVTeGNAfbwzorzFHsDqhUIgZA/ojMjkZUlkVzt67pwoOV1bf8xeWV8DTwR5Lx70Ai+oHsGYwxkC/dmjj0gL/2bcflRIpdodf08g8JqLmqaHfaR4lgaBxfXJ2c8GC7z6DTCqFkY5AkncbX/QM7I+w4AtIS0xGZko63Lx1zxtKRI9Xc723elhLT0/8/OWXsLWyQmZuLo6dPYtTFy/iwtWrSM/KwrL582FcfV1q6emJwX364Mzly7h64wa+WLMGL40aBR8PD5RXVCA0MhJ7jx2Dg50d8quTTIxrCY4TEVHzxqu4AVHPBq6PrKws1c8+Pj46t/Pz82tQv+oqNzcXoaGhiIuLQ0pKCpKSkhAfH49ytZJNj8Ly5ctVmdPLli3TmGdYSb288cSJE+u034yMDACK11cZVNX3+rZq1UoVWG5KttXzzzW0ZHVRURGA6jmWqsvOKM/N3t5etUwbbRmqFRUVqmxmbRmgShYWFnB3d1dlfiq5uLhgwYIFWLFiBUpKSrB+/XqsX78etra26NevnyoIav8I558bP348VqxY0eT7FQgEsLKygpOTEzp16oQRI0ZgxIgRem/IH/7cSyQSJCUlYd26dTh48CAKCgpgYmKCIUOGPNLAMADY2dnVOseORCLB1atXER0djeTkZCQnJyM+Ph7Z2dka28nVakeqX6PqMyewclAJAPTr169ObZTvbSIAMKrOWqktG1gmeTBwSVhLNnB99lslebBePYvYsoU9Ok4Zgrs7zkBWKUXCsXBVsBcAIABajuiOipxiZEfeh5HZgz6V5xQh4fg1AIBbn/ZoN+7BZ8PU2gKuAW1h38YdkWsPQ1xcgbi/QuH/1qhaz4mIHh+z6r/vkloGUYrV1teWCdOUtAWGlRysrNC6RQvEZGYhTu1ewEytf6/06qkKDKvzsLfH4PbtcfxWNG6kpEAkkWjMW0xEzY+JmeKzXFs2sET8YL1JLSXzG8vU7MH+ZbVcR5UDVo1Na15DdQWGlTp0ewZhwYpqSCnxiQwOEzVj5tXJFeLa7q3UKtvpmsLuUbJRq8ri5eaGWZMnw9baGvuOH8f95GScDQvD8AEDVNu8+corKCkrQ3hUFKJjYhAdE6OxP3cXF8ycNAlfrF4NADB7DOdERERNh8FhqlVJSYnqZ/V5iB+mDDI2tcrKSnz99dfYs2dPjcComZkZ+vTpg6qqKly9erXJj717925s3boVgKJc7Ysvvqh1u9Lq+UbqQ9lGPSir7/UVCoWwtLTU+P9oCm3btkVUVBQqKiqQkZEBd3f3erWPqb5Z9PT0VGUfK/tobq5/rjpt5abVX4+GtAcU/1cdO3bEhg0bEBoaColEguLiYhw/fhzHjx+HiYkJpk2bhoULF9YarHxcli9fjgkTJjT5fk1MTNC2bVt8++23cHV1xbp167Br1y5kZ2djzZo1j3TkZ23Z6ceOHcNXX32lmntYSSAQoHXr1ujatSv2799fo51ygAKg/zP0sMZ8bomAB3P+ykRivdup1gsFMLao/Qu0kXl1ScJa9qu+3sRS83rp6OeJgHkvIvVcFPJjUiEuroCJlRlsW7rCc0Bn2Pq0wK3fTwIATG0efG6yrsUCVXIITYzQaqT2Eu9mdlbwGeyP+4fCUHQ/ExW5xbBwfjT3AETUeMq5hstrCbaUVT/AFAoEsG5gRZlHwcnaGkAWStTmtVMP8nb20B1A6ejujuO3oiGVVSG7uJilpYmaOXNLxT1JZYX+eSxF5RUAAIFQWOs8wE3VJ/Xj1tYvqzrOK6rO3unB1FTlJfzOQdScWVY/d6io0H9NKKteLxQKYV3L/MT/lPEjR+LouXMQiUS4FhWlERw2NTHBwlmzEBIejuBLl5CYmooquRxuzs7o36MHRgcGIiU9XbW9g55kEKKn3cw2wx93F4gajcFhqpW12hebiooKjd/VqY+Iqy+RSPeXv/nz5yM4OBiAooRuYGAg2rVrh7Zt26J169YwNjbGqlWrmjw4fPXqVSxbtgwA0L9/f3z00Uc6t1UPYt68ebNeJZrVM2try4JuzGusS79+/VQBt5CQELz88st1bhsXF6cK5KnPB6s8p9pulLWdj3pGb0PaK/Xt2xd9+/ZFaWkpLl26hNDQUISEhCA5ORkSiQQbN26EXC7HJ598ovcYT7MPP/wQt27dwqVLl3DmzBl89913+PTTTx9LX06cOIH58+dDLpfD0dERzz33HJ555hm0bt0afn5+sLW1RVJSktbgsHpAWN816mHKz62zs7OqOgBRfVg426IoPhOiQv0P8ESFipLlZraWdSoZbemsuIZWFpVBLpfrbFNZpDiuwEgAU9uaAyPM7a3RdpzurPiyzPzq83jwd6giVzFAx9LVXhX81sau1YP53MtzihgcJmrG3O3scDs9A7m1DDDMqx4A5WBVt2tVU9F3nQMeTCWhns3cwsYGsVmKTGITPQP9LNXmJa3kvHhEzZ6zqwsS78ahMK9A73ZF1ett7e0e+fXK3skBxiYmkEokKMyrOX+nklwuR3FBIQDAztFe63q91zq1ijEmzWiADhHV5OHigtuxsVrn9FWXV6C4VjnaPfprVV2ZmpjAy80NcYmJyMrLq7FeIBBgYK9eGNirl9b2idXV+wQCATxcXB5pX4mI6NFqfhO6ULOjXtpX2/ywSsnJyVqXq2dmSnRkLCjLCD8sIiJCFRieNm0a9u3bh3nz5mHMmDHw8/NTZTkWFOj/8lhfqampmDt3LiQSCby9vbFq1Sq9GaYeahkL6qVqtVEvhwsAbm5uqrmV9L2+2dnZqvmBm9Lw4cNVGbhbt26tMYevPlu2bFH9/MILL6h+Vr4eRUVFyNNys6mkXo5byczMDE7VWR36Xg+ZTFanEr/W1tYYMWIElixZgpMnT2LPnj3w9PQEAGzfvv2RzFP9pBAIBFixYoXq/3/z5s2PLUj6/fffQy6Xw8vLC0ePHsWyZcvwyiuvoGfPnqqqBLo+5+rZ7treU0qhoaFYs2YNDh48CODB+7SgoOCRl6enp5OVqyLDQ5RfqjfLtzRdcR20cnes034t3RT7lUurUJ5dqGe/iocRli72ED70N0omkUIm1p0lWJZVAHGxYgCOrXcL1fKq6iBMlbTufwuqmni6AyJqWt6OimtPdnEJyvUMrEvIzQUA+Do7P/I+5ZWWYu72HXh9wyb8eS1C77Zp1d8T3NUGVPo6P8gAztYT9C5Uy/JzVCutSETNk6uXYvBZfk4eRHoGCqcnK75zu/t4PvI+CQQCuHgq+pWRnKZzu6zUDFWA172lt2r58T1/YcW8xfhi9iKNctgPy8nIVP3s7MaAC1Fz5l39DCI7Lw/leq5VCdXPB329vB55n8rKy7Hyt9/w8YoVOH/lit5tlYkWD5eFlsvlKK6lWtrNu3cBKMpUm3MgCxHRE43BYapVQECAam6MU6dO6dzu/PnzWperZ/Xl6xhVFxGh/aFQZGSk6udJkyZp3aaqqgphYWEavzdGWVkZ3n33XRQUFMDS0hJr166tdX7anj17qn5WBrO1iYiIQNeuXTFy5EgcO3YMAGBlZaVqr6+trte3saysrPDmm28CAO7du4eff/65Tu1CQ0OxZ88eAED37t3Rt29f1bpBgwapfm7Ie0bZ/uzZszrnWL5y5YrWzOJff/0Vzz//PF599VWt7fz9/TF9+nQAipLl6iWJDZGrqysWLVoEQPFF4LPPPqs1Y7up5efnIzExEQAwYsQIODg4aN0uNDRU9bP657x79+6qUbgXLlzQeZwdO3Zg9erVqve48nMnk8lw9uxZne0OHTqE7t2741//+hfCw8N1bkeGx6F99Zf8Kjny72kfGFRZVIayDMXfPke/uj3AtG/tBqGpItibf0f7gAeZWILC+4qSXg5+mg8bbqw7hktLtiF23yWdx8gMjwUACE2NYd/uQb+UWcQVOYWoLNY9aKIo8cFc35YtWE6MqDnr5qMIUlTJ5bierP2akldaiqTqAX1dvR/9A0wHS0uUVVZCLJXiup6BXYm5uUirzsTr5v0g2NLdx0f1c1h8gs72N6sfyjpaWzE4TPQE8OvSCQAgr6pCzM07Wrcpyi9QBWnbden4D/VLcZz7d2IhFmkfsH3vxi0AirmFW3Voq1puaW2NspJSSCrFiL8bq/MYN0KvAVDMu9yyXaum6joRPQLdO3cGoHguEXn7ttZt8goKkFh9H9KtU6dH3idLCwtEx8QgKS0NF69d07ldXkEBUjMVg1Faq91bXb9zB6/Nn49Zn36KjOxsrW0LiosRER0NAOjdtWsT9p6IiB4HBoepVtbW1hg7diwARaZoXFxcjW3Onj2rMyjj7e2tyoxVBkTViUQirFu3Tmtb9WxdbccFgDVr1qgCS4Du7OS6kMvlWLhwIWJiYiAUCvHdd9/Bz8+v1nb+/v7o2FHxhXH9+vUa/VESiURYsWIFKisrkZaWBn9/f9W6l156CYAiGL5v374abQsLC7F27doGnlXtZs6ciWeeeQYAsHr1avzyyy81MpzVnTt3Du+99x6qqqpgaWmJL7/8UmO9l5eXqsz06tWrka3lxvLvv//WGWhTvh4ZGRlaz7uyshIrV67U2tbY2BixsbGIjIzUOejgzh3FgwZra2s4OtYtk+9pNnHiRPSqLhmUmpqK1atX/6PHV5/n+P79+1q3uXv3rsZ1Qv1z7uLigmeffRYAsHHjRmRlZdVoHxUVhdOnTwMA/vWvfwEAhg0bBufq7KiVK1dqHbySn5+PoKAglJeXIzc3V/U5JwIAC0cb2PoqMjuSgiMhrdDMyJPL5Yg/ehWQA8ZWZnDp3qZO+zUyNYFzZ0XVjtSQaK1lq5OCr0NWIYHASAiPvh001tl4K97XeXdTICqo2bY4OQcZYYoR3269/GBs9qDsqou/4mGkXCZH/JErWv8WVBaXI+XsDQCKLGdlBjURNU+utrZo7+YKANgTHo6yhyrRyOVybAu9DLkcsDE3x8B27R55n4RCIfq3UVwT72fn4EJMzYCJSCLBuvOK7xfmJiYY3unB32AvBwf4VZ/ToRs3kK6lClFsVhYuV1ehCfTzazblHIlIN0cXZ/i0aw0AOH3gGCoemuNXLpfj2K6DgFwOS2srdOvXU9tumlzXfj0hEAohKivH6b+O11hfmFeAi8fPAgB6DOwDC7V5ip/p1Q1GxornGsd3HYRUy/OKm2ERuHtdEVzuPXgAzNSmrSKi5sfV2RntWyuuVbuPHEHZQ5XI5HI5tuzfD7lcDhtra50lmpuSQCDAgOoB8Ndv30bUvXs1tpHJZPht1y5UVVVBIBBgaL8HUxC18fGB8k7pby2JHHK5HBt274ZEIoGZmRmGVz+DISKiJxeDwwZELpejrKysXv+UFixYAGdnZ1RUVGDq1KnYs2cPsrOzkZGRgd9++w3vv/++zuPa2tqqskpPnz6NpUuXIiEhAbm5uTh9+jQmT56MO3fuqErHqhswYIDqQc4XX3yBv/76C5mZmcjKysKFCxfwzjvv4KefftJoo97v+lq1apUqgLRgwQIMHz4cYrEY5eXlWl8f9bmSP/vsMxgbG6O4uBiTJk3Ctm3bkJqairy8PISEhOCNN97AjRuKh+lvvfWWqrQxAIwbN04VnFu8eDH+97//ISkpCfn5+Th16hQmT56MjIyMR/ZQy9TUFL/88gv8/Pwgl8uxatUqTJgwAXv27EFCQgIKCwuRmpqKkydP4r333sPbb7+NsrIyWFpaYs2aNWjbtm2NfS5ZsgSmpqbIycnB5MmTcfToUeTn5yMlJQVr167FwoULdZbq7tWrF8aNGwdAEfz/7LPPEBsbi4KCAoSGhmLq1Km4deuW1vYvvfQS7O3tIZfLMXv2bGzbtg3x8fHIz8/H3bt38cUXX+DAgQMAgFdffZUPCqH4ErF06VKYmCgCRJs3b1YF0P8Jtra2qsES586dw5dffon79++joKAAd+/exQ8//IDJkydrlH5++HO+aNEimJubo6CgAJMnT8Zff/2FnJwcpKamYs+ePXj77bchkUjg6uqKN954A4Diff9///d/AIC0tDRMnDgRBw4cQFZWFrKysnDixAlMmzZNVTJ/wYIFsGLWET2k9ZjegAAQ5ZbgxvpjKIhNg6RMhNK0PNzZfga5UYkAgJbDusFIbe5LAAhftQ/hq/bh3p6aX759R/SA0NQY0vJK3Fx3DLm3EiEurUB5diFiD1xC2gXFiG2P/h1hZqf5vvTo2xFCUyNUiaW4tfkk8u6mQFxagYq8YqScj0LUxr8hl1bB3MkGLYd102hr490Crj0U1/TcqEREbTyO/Jg0iEsrUFlUhqyIOFz/+TDExRUQGAnRdmwfEFHzN61fPwgEQGZRMZYdOoybKakorhAhIScXq06ewuXq7NuJPQNgbqJ5rfpw1258uGs3fjp9pkn79FKPAFibK8oRrjt/HnuuhiM1vwDFFRW4lpSEJQf/QkKOotT19P79YG9pqdF+5sBnYWJshEqJFEsO/oWT0beRW1KKgrIynIy+jeVHj0Eqq0ILGxu80I3ZLURPitGTXwQEAuRl5WDDitWIu3UXZSWlSE9KwY6fNiH66nUAwNBxo2BqrlnS9If/fI0f/vM19q7f1qR9cnZzQZ+hikDIxb9P4+Dm3chOz0RZcQmiw2/gt+VBqCgrh4WVJQaOGa7R1sHZEQNGDgEA5GRk4devfkBM1B2UFpUgJyMLJ/Yewp+/Kfrbwt0VQ8eNatK+E9Gj8fqECRAIBMjMycHnP/6IG3fuoLi0FPEpKfh+wwZcrq6E+PLo0TXKL3/wxRf44IsvsEZtqramMHHUKNhaWwMAvl23DvuOH0daVhaKS0tx8+5dLA0KUmX+jhk8GG3UphG0sbLCkP79AQDHz5/HtgMHkJqZiaKSEtyKicGy1atxtfqZ5tRx4+Box+pRRERPOoFcX3ogPRWmTZuGK7XMN6HL1atXVUHbu3fvYtasWVqzQG1sbNC7d29VWeR7D41Qi42NxdSpU7XOLSwQCPDRRx/hwoULCA0NxZw5czB37lzV+u+//15nZrHy2C+//DI2btwIQFE6NiAgAAAQFhamKiG8ZcsWVTYrAAwdOhRpaWkYP348VqxYAQBo3769ar21tTVEIpHeOWl79+6NrVu3qn4PDg7GwoUL9c5f+vLLL2Pp0qU1ApuFhYV45513NEppq1u4cCGCgoIgFotrnEtTKSsrwzfffIO9e/fqLOes1KNHDyxbtkxrYFjp4sWLmDt3rtaAvb29PaZOnYo1a9bA09NTFZRXEolEWLhwIU6ePKl136+++iouX76MhIQELF++HBMmTFCtCw0NxezZs/X+PwwZMgRBQUGqkulNQfn+UX9P1Yf6+/Xhc6ov5ede22uryw8//KAqudylSxfs3r1blfXfGJ988gn279+vty9RUVGYPn263v+ziRMnIjQ0FGlpaXjllVfwxRdfaKy/ePEi3n//fZTqmCPH1dUV69ev1/icA8DOnTvx5Zdf6qw6IBAI8N5772lclxpq8plvG70Pan6yIuIQu/8i5DLtt1Sez3ZSBJEfcuE/vwMA7Fq5wn/W6Brr82PScGf7aVSJtV+Pnbv4osPkQK2DXHJvJeLu7vOQ65g72NLVHp2nDYO5o02NdVUyGWL+vIic67rnfTcyM4bfywPh3Kmlzm3oyfXzLc4f9jQ6dy8G68+fh6xK+7VqjH8XTOvXt8byV39dDwDo6O6Gz14YW6djKdu81CMAE3v20Lnd/ewcfH/iBArKtP/9NzYSYkqfPhjd5Rmt66PT0rHq5EmUVWqfS7mFjQ0+HjUSXo6scPC0ujZu4OPuAj0CESFXcHDzTlTJtN/H9B8xWBFEfsh/3/wAAODbvg3eWlS3e3dlmyEvjMTQF2vejylJxBLs/HkTYm5oLyFrYmaKNxa8C5+2NUtCy+Vy/LVlD8LP6Z7yw83HE9M+eBu29gy4PI16FxrXvhE9cc6GhWHdjh06n5/9a8gQTNfyXGdS9bOFjm3b4vN58+p0LGWbiaNH4+UxY3Rul5Caiu9+/RV5Wp6/Ko0ZMgTTx4+v8T2yUizG8p9/xh0dlRuFQiEmPf88XnzuuTr1mZ5MtmqVLkm3U8naq1VSwwz3CXjcXTBIvDuhOuvQoQOOHDmCDRs24Pjx40hPT4eNjQ0GDRqEOXPmYN++fTrnzG3Xrh0OHTqEdevW4dy5c8jMzIS1tTW6d++ON998Ez179tRZlnrBggXo3LkzduzYgdu3b6syVn18fDBw4EBMmTIFtra22LVrF8rKynDy5ElVcLgxdAWY9Bk2bBhOnDiBrVu34vz580hJSUFlZSUcHBzQvXt3TJo0CQMGDNDa1t7eHlu2bMHBgwfx559/Ij4+HlKpFB07dsSMGTMwfPhwBAUFNfa09LKyssKyZcvw1ltvYf/+/arga0lJCczMzODt7Y2AgAA8//zzGvMs6zJgwAAcOXIEmzZtwvnz55Geng5bW1sMHDgQc+fO1RkIBwBzc3MEBQXh+PHj2LlzJ2JiYlBRUYE2bdrg1VdfxcSJEzFqlPZR1f369cORI0ewefNmXLp0CampqZBIJHBwcMAzzzyDF198ESNHjmzw6/S0evfdd3H06FEkJSUhKioKW7duxeuvv/6PHLtLly7Yv38/fv31V4SGhiInJwfGxsZo0aIF/P39MWnSJPTp0wf/93//h7179+LMmTOQSCSqbGdA8X47fvw4Nm3ahHPnziEtLQ0ymQw+Pj4YPnw43njjDa3zh0+ePBkDBgzA5s2bERoaivT0dEgkEri4uKBnz56YOnWqRhl4ooe5BrSFtYcjUi/cQmF8JiSlIhiZGsPa0wke/TrCqaNP7TvRwtHPEz3mjUfK+SgUxKZBXFQOobEQVu6OcO3RDq4BbXVWP3B+xhcBLvZIOR+FwvsZkJRWQGhsBCs3R7TwbwW33n4Q6qjeIDQyQodXBsE1oC0yr8agODkbklIRBEZCmDvawLG9Fzz6dqiRsUxEzVtgez+0cnbCoRs3cTsjA8UVFTAzNkarFi0wsnMn9PT1/cf71MalBb6Z+BJORN9GeGIiMoqKUCWXw9HKCs94eGDUM8/oDex29vTA/yZNwrGoKEQkJyOruBhCgQAutrbo27o1nuvUEVZmHOxA9KQJeLY3PFp6IeTv00i4F4ey4hKYmJnBs6UX+gwbiI7du/zjfTIxNcHU92ch8uJVRF4MQ2ZKOiRiMWzs7ND2mQ4YOHooHF2ctbYVCAQY9/oreKZXN1w5E4KU+4koLy2DqZkZXL3c0aV3AHoM6quzshYRNU+D+/RBKy8vHAoOxu3YWBRVPztr7e2NUYMGoedjeI7QyssL3336KY5fuIArN24gPTsbMpkM9ra26NS2LUYOHIi2Ou75zExN8d85cxB86RLOXbmClIwMyKRS2NvZ4Rk/P4wODISvl9c/e0JERPTIMHOYmszq1auxZs0aADUzh4mIiJnDRPRkYOYwET0pmDlMRE8CZg4T0ZOCmcN1w8zhpsXM4ceDcw4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwA65oQPaHkcjnKy8sbtQ8rK84XyddRN5lMBpFI1OD2QqEQFhYWTdgjIiIiIiIiIiIiIiJqDAaHiZ5QaWlpGDZsWKP2wbmh+TrqEx4ejunTpze4vaenJ06fPt2EPSIiIiIiIiIiIiIiosZgWWkiIiIiIiIiIiIiIiIiIgMgkMvl8sfdCSIiIkMw+cy3j7sLRES1+vmW2ePuAhFRnVwbN/Bxd4GIqFa9C1m4kYieDLb+/o+7C0+EU8kRj7sLT5XhPgGPuwsGiZnDREREREREREREREREREQGgMFhIiIiIiIiIiIiIiIiIiIDwOAwEREREREREREREREREZEBYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAaAwWEiIiIiIiIiIiIiIiIiIgPA4DARERERERERERERERERkQFgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGh4mIiIiIiIiIiIiIiIiIDACDw0REREREREREREREREREBoDBYSIiIiIiIiIiIiIiIiIiA8DgMBERERERERERERERERGRAWBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAaHiYiIiIiIiIiIiIiIiIgMAIPDREREREREREREREREREQGgMFhIiIiIiIiIiIiIiIiIiIDwOAwEREREREREREREREREZEBYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAaAwWEiIiIiIiIiIiIiIiIiIgPA4DARERERERERERERERERkQFgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGh4mIiIiIiIiIiIiIiIiIDACDw0REREREREREREREREREBoDBYSIiIiIiIiIiIiIiIiIiA8DgMBERERERERERERERERGRAWBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAaHiYiIiIiIiIiIiIiIiIgMAIPDREREREREREREREREREQGgMFhIiIiIiIiIiIiIiIiIiIDwOAwEREREREREREREREREZEBYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGBwmIiIiIiIiIiIiIiIiIjIADA4TERERERERERERERERERkABoeJiIiIiIiIiIiIiIiIiAwAg8NERERERERERERERERERAaAwWEiIiIiIiIiIiIiIiIiIgPA4DARERERERERERERERERkQFgcJiIiIiIiIiIiIiIiIiIyAAwOExEREREREREREREREREZAAYHCYiIiIiIiIiIiIiIiIiMgAMDhMRERERERERERERERERGQAGh4mIiIiIiIiIiIiIiIiIDACDw0REREREREREREREREREBsD4cXfgnzZt2jRcuXKl3u3Gjx+PFStWPIIe/XPCwsIwffp0AMCWLVvQp08f1bqhQ4ciLS3tiTnP1NRUDBs2DEDNc2lq9+/fx+HDhxEaGorU1FQUFhbC1NQUnp6eCAgIwNixY9GzZ89GH2f16tVYs2YNPD09cfr06Xq3b9++PQBg+fLlmDBhQqP701jK/tSHet/V36/aCIVCmJqaws7ODq1bt0ZgYCBeeeUVWFlZ1di2Lp97IyMjWFhYwM3NDV26dMGUKVPg7+9f73N4mt29exe//vorwsLCUFxcDBcXFwwbNgzvvfce7O3tH3f3iB6rssx8pF64hcL4TEhKRTC2NIONpxPc+3aAo59Xkx0n7WI04o9chc+wrmg5rLvebaUVYqRduo2820moyCsGIIC5gzUcO3rDo29HmNla1uvYcrkcN9f/jeLELLgEtEH7iQMbcSZE9Dgk5+Xh0I2buJ2ejmKRCNZmZmjl7IwRnTujm493kx3n6M0obA29jJd6BGBizx56txVLpTgRfRthCQlIKyiARCaDvaUlOrm7Y4x/F7R0cqrRZm/4Nfx5LaJefXpncCAC2/vVqw0RNU+ZKekI+fs04u/GorykFBZWVvDw9UKfoQPh16Vjkx3n0omzOLbzAIa8MBJDXxxd7/Zxt+5i86pfAbkcH377GRycHZusb0TUPCSlpeFQcDCiY2NRXFICaysrtPb2xohBg9C9U6cmO86RM2ewZd8+TBw9Gi+PGVPv9jfu3MHyn3+GXC7Hms8/Rwst91dKUqkUJ0NCcCkyEulZWRCJRLC3s4N/+/Z4YfhwuLu4NOZUiIiomTC44DBRXZWWluKrr77CwYMHIZPJNNZJJBLExMQgJiYGO3fuRO/evfHFF1/A19f38XTWAFVVVUEkEkEkEiErKwuhoaHYtGkTNmzYgHbt2tV7fzKZDKWlpYiLi0NcXBwOHDiA999/H7Nnz34EvX/ynD59GvPmzYNYLFYtS0tLw5YtW3D27Fns3LkTTnq+XBA9zfLuJOPO9rOQy6pUyyQlFci/m4r8u6nw6N8RbZ5v/CCm4uQcJJ6sWzCkLKsAt34/CXFRucby8qxClGcVIiPsLjq8EgjH9nUPXKeej0JxYla9+kxEzUd4YiJ+PBUMqdq1qrC8ApHJKYhMTsHIZzrjjQH9G32c2Kws7LoaXqdtC8vLsfzoMSTn5Wsszy0pxfmSWFyMi8MbAwZgeKfGB3vMTUwavQ8ievzuREZh18+/QyZ98B29tKgYMTduI+bGbfQdPgj/mtL4wdIp9xNxat/RBrcvLy3Dvo3bAbm80X0houYp/OZNrNq4EVK1Z4aFxcWIiI5GRHQ0RgUGYsbEiY0+TmxiInYePtzg9iVlZfj5jz8gr8P1KDc/H1+tXYv0rKway0+HhuJCeDjmz5iBHl26NLg/RETUPBhscNjDwwOH6/GH1eQpf5jg6ekJIyMjBneqZWZmYtasWYiJiQEAdOjQAVOmTEHPnj3h6OiI/Px8xMbGYufOnQgNDcWVK1cwYcIErF27Fn379n3MvW9exo4di6VLl9ZpWzMzM63Lly5dirFjx9ZYLpFIkJ2djW3btmHXrl3IysrC7NmzcfjwYa370ve5l0gkyMrKwpkzZ/Drr7+ivLwcP/74Izp16oTBgwfXqf9Pq4KCAnz88ccQi8Xw8/PDsmXL4OHhgYMHD+L7779HcnIyVq5cieXLlz/urhL940rT83B35znIZVWw9nJCq9G9YOXqAFF+CVLO3kTe7WSkX7oDC2dbePRteHCjJCUHtzafQJVYVuu20koJorecgrioHEbmJvAZ2g2O7b1gZGqM4pQcJB6/BlFeCe7sOIvu742FZQu7Op1n0qnrDe4/ET1eibm5CAo+DamsCq1bOOO1vn3h7eiA7OISHIiMRHhiEo7fioaHvR1GdO7c4OPEZWdjxbG/IZZK67T9j6eCkZyXD6FAgFFdnsHg9n6wNjNDXHYOtoeFIbOoGBtDQuBhb49OHu6qdi9274bnu+qv8JKSn48vDh+BRCpDv7Zt0Kd1qwafFxE1DxnJqdj9yxbIpDJ4+vpg5KQX4OrpjvycPJw7fBJ3I6Nw+dR5OLu2QJ9hDa9wkhqfhC2rfoVEbWBsff21ZTdKCosb3J6ImreE1FT88PvvkMpkaOPjg6kvvghvDw9k5+Zi34kTCL95E3+fOwcPFxeMHDSowceJS0zE12vXagzUr6/1O3eioKio1u0qxWIsW7MGWTk5MDYywoRRozCgRw8YCYW4FRuLbQcOoLSsDD9u3oz//ec/cHZkNQQioieZwQaHBQKB1vKzhmrr1q2PuwvNhlgsxuzZsxETEwMjIyN8+OGHeOuttyAQCFTbODg4oE2bNhg1ahROnDiBjz76CGVlZZg9ezb27NmDNm3aPMYzaF6MjY0b/VkzNTXVuQ97e3ssW7YMALBr1y4kJydj//79mDx5co1ta/vc29vbo3379ujevTtef/11yOVyrFu3zuCDw8HBwSgpKQEAfPvtt+jYURHgevvttxEREYEzZ840qBQ60dMg6VQkqiQymDvZwH/mKBiZKgaTmViaoeNrQ3B35znkRiUi6dR1uHRvC2Oz+g82S798F/FHr0Aurap9YwCZYfdQWVAGCIAOkwM1ylq3sLOCjaczIoIOQFYpRdrFaLR7UX+moEwixd3d5zUyo4noybL7ajgkUhlc7Wzx37HPq7JobczN8eGI5xB0KhiX4xOwJ/waBrZrBwtT03of42T0bWwNvQyJrPZBLABwNyMTdzMyAQAv9QzAhIAA1bperazQ1qUFFu3dhxKRCPsjIzWCw8ZGRjA2MtK5b5FEgp/PnoNEKoOHvR3eHsQy+ERPg1P7j0IqkcDRxRlvfvweTM0VA4Itra0wZc6b2PXLZkRfvY7gA8fQbUAvmJmb1/sYYadDcGznAcjqOMhFm8iLVxAdfqPB7Ymo+dt9+DAkEglcW7TAZ++/D/PqBAUbKyssnDkTP2zahMuRkdh99CgG9e4NiwZcj05cuIDN+/ZB2ojr0bmwMIRdv16nbfcdP46snBwIBAIsmDULAWoDBoc4OaGVlxf+s3IlKisrcezcOUwbP77B/SIiosdP+Lg7QNTcrF27FtHR0QCAjz/+GDNnztQIDD9sxIgRWLNmDQCgrKwMn3322T/ST9L07rvvqn5ubKCyT58+CKh+QHn9+vVG3Yg/DfLzH5R69PT01FjXunVrAIoy30SGpjynEPl3UwEA3oP9VYFhJYFAgNZjegECQFpeibzopHrtvyQlBzfWHcP9vy5DLq2CtWfdqnvkRicCAKy9nLXOd2zuYA1bX1fFMVJza91f4vFrqMgugl0bN5jZc2Ad0ZMmraAQkckpABQZtw+XVxYIBJjary8EAqBUVIkrCYn12n9cdjaW/nUIG0MuQiKToVUL5zq1u5+Trfp5eMealRUcrKzQw9dHsW12do31+my/HIaMwiIIBQK8N3QIS0oTPQVyMrIQc+M2ACDw+edUgWElgUCA0ZPGAQIBKsrKER1+s177T41Pwm8rgnB4217IpFJ4+DZsHvaC3Hwc2b4PEAjQfUDvBu2DiJq3tKwsRFQ/Nxw/YoQqMKwkEAgwffx4CAQClJaVIexG/QaLxCUmYskPP2DD7t2QSqVo7ePToH7m5OVh0969EAgECOyjf5oj5TzDADCsf3+NwLCSr5cXOrdrB6FQiKS0tAb1iYiImg+DzRxuComJifj9998RGhqKjIwMODo6YujQoZgzZw5iY2Mxffp0AMC9e/dUbcLCwlTLt2zZgj46/ji3b98eADBnzhzMnTu3xvrY2Fjs2rULV69eRUZGBsrKymBtbQ0fHx8MGjQI06ZNg729fZ3PZejQoUhLS8P48eOxYsUKAMDq1atVQc+60HY+BQUF2Lx5M86cOYOUlBTIZDK4u7tj4MCBePPNN+Hu7q5jb4o5f3ft2oXDhw8jKSkJxsbG8Pf3x6xZs2oEqJpKaWkp/vjjDwBAp06d8MYbb9Sp3cCBAzF27FgcOnQI4eHhuHLlCnr3rvlFMDY2Fhs3bkR4eDiysrLg7OyMoUOH1mleW7FYjAMHDmDfvn2Ij4+HTCZDx44d8frrr+O5557T2/b+/fvYsmULLl++jPT0dBgbG8PFxQW9evXCa6+9psoEfZK5u7vD3t4ehYWFSGuCm1RXV0XgRCaTIT8/Hy4uLo3ep1JhYSG2bt2KM2fOICEhAVKpFI6OjvD398e4ceMwfPhwnW3Ly8vxxx9/4OTJk0hISIBIJIKLiwv69euHGTNm1Mhav3nzJiZPngyZTIYOHTrgzz//hLGx5qU/OTkZ48aNQ3l5OTp37oxdu3ZplNJXn8P5+PHjePnll1W/R0VFAQC6du2q95yV17RNmzYhNzcXP/30E9LS0uDo6Ih//etfWLRokWrbxl7fbty4gZ07dyIiIgIZGRkwMTGBn58fxo4di1deeaXG+SudO3cOe/fuRWRkJAoLC2FtbY3OnTtj/Pjx+Ne//qV3kAgZpoKY6muNAHDqoP0BopmdFaw9nFCaloe828lwDWhb5/3f2XlWlQHs3qcDWo3uiUtLttXazv/t0ajIKQZQ+5xSAqH+93VBXDrSQ+/AyMIEfi89i5vrj9W1+0TUTNxIUQSGBQIgwKel1m2crK3h6+yMhJxchCcmIrC9X533/+OpYOSWlEIgAJ7r1Amv9e2D1zdsqrWdAA+uP1Idg8yMhYrsYGE9/gbHZmXh1J07AIDRXZ5B6xYt6tyWiJqv2CjF5xoCAdp31V7+3s7RAR4tvZCemII7kVEIeLbuwdldP29GYV4+IBCg95ABGPXKOCx756N69VEul2Pfhj9QWSFCv+cC4ebticiLV+q1DyJq/q7fVgxUEQgE6PHMM1q3cXJwQCtvb8QnJ+PqzZsYXEtwVt2qTZuQm58PgUCA5559FtPGj8e0Dz+sVx/lcjl+2rYNFSIRxgwZgpaenjgXFqZz+xt376KsvBwAME7PM8aP334bxsbGfD5CRPQUYHC4gU6dOoUFCxZAJBKplmVkZKiCNvPnz39kx16zZg3WrFkDuVzzoW9hYSEKCwtx8+ZN/Pnnn9ixY4fe4GtTs7Cw0Pj98uXLeP/991H00LwWCQkJSEhIwO7du/Htt99i5MiRNfaVkpKCmTNnIjExUWP5hQsXEBISghkzZjR5/wFFcKi4WDEvkLayxPq89tprOHToEADg4MGDNYLDf/75Jz777DONLNS0tDRs3boVf//9N/r166dz3/n5+XjnnXdw46HRhlevXsXVq1cxc+ZMnW3Pnj2LuXPnasxPIhaLkZiYiMTEROzduxeLFy/G1KlT63W+zZHy5lQobHxRhLi4OACK+cbrM9CiNikpKZg2bRoyMjI0lmdmZiIzMxMnTpzAmDFj8P3339c4j3v37uGdd95Benq6xvLU1FTs2bMH+/btw6effopp06ap1ikHVPzyyy+4e/cuNm3ahFmzZqnWV1VV4ZNPPkF5eTksLCywcuXKGnOsP/vss2jdujXi4+OxcuVK9OvXD15eXti8eTOuXLkCY2NjzJs3r07nf/z4cezcuVP1e1ZWlsbr25jrW1VVFVatWoV169ZpLK+srERERAQiIiJw6NAhrF+/HtbW1qr1YrEYn3zyCY4cOaLRrqCgACEhIQgJCcG+ffsQFBSk0Y6oNF2RVW9mbwUTK91lwqzcHVGaloeStNqzdB9m18YNrUb0gI133YMbQiMjWLk56FxfllmAwvuKa5BDW92DrSQVlYj5MwSQA23+1Qfm9nz/Ez2JEvPyACgCwLYWuq9Vvk5OSMjJRXxu/a9VnT09MLl3L7Stx2C6Ni4Prmtn793TKCsNACUiEa4lKSou+FUP2quLzZdCIZcD9pYWeKlHQO0NiOiJkJGsGJRn7+gAKxvd9yTu3p5IT0xBelJKvY/RqkM7jJj4PLxaax9IU5uQY6eReO8+Wri74rmXnkfUlcgG7YeImrfEVEX1KCcHB9jqeUbg6+mJ+ORkJCQn1/sYnf38MGXsWLT19W1QH/8KDsaduDh4urnh1bFjcSkiQu/296vvuZwdHeHipFmxSiqVqgbZP/y8iIiInlwMDjdAXFwcPvjgA0gkEnh4eGDRokXo3bs3CgoKsGPHDmzduhVffvnlIzn233//jdWrVwMABgwYgLfffhutWrUCoAi6btq0CWfPnkVGRgaCgoKwfPnyBh/r3//+N958802d60NCQjBv3jzI5XK88sor8Pf3V62LiYnBv//9b4hEInh5eeH9999H3759YWJigqioKAQFBeHWrVv48MMPsWXLFvTo0UPVViwWqwLD5ubmmDt3LkaPHg0zMzNcvHgRK1euxMaNGxt8XvpcufJgVG/Pnj3r1bZbt25wdnZGbm4uwh4ajRcWFob//Oc/AAA/Pz8sXLgQXbp0QUFBAfbu3YtNmzbhr7/+0rnvefPm4caNGxAKhZg1axYmTJgAW1tbREZGYuXKlfjtt9+0tisvL8eiRYsgFovh7++PDz74AG3btoVAIEBUVBS+/fZbJCYmYsWKFRg8eDC8vGqWH31SpKSkoKCgAAAaPefz0aNHERMTAwAYNGgQTBsw754un3/+OTIyMuDs7IyPP/4YAQEBsLKyQlJSEtasWYOQkBAcPXoUQ4cOxdixY1XtsrOzMWPGDOTl5cHR0RFz585FYGAgLC0tERMTg19//RUXL17El19+qcrGVXrvvfdw5swZ3Lt3Dz/99BNGjRoFb29FluPGjRtx7do1AIoy6soy0epMTEywfPlyTJ06FYWFhXj33XfRpk0bHDt2DKampli1ahW6dOlSp/PfuXMn/Pz8sGzZMnh5eeHy5cuqigONvb6tX79eFRju06cPZs+eDT8/P41rc0REBJYuXYrvvvtO1e7//u//VIHhV155BZMnT4aXlxdyc3Nx+PBhrF+/HhcvXsT8+fOxbt06jpAllcrCUgCAuaON3u3MHRQPDMTF5aiSVUFoVLcBLM+8MQKWLewa10koRo1LyyshKixF7q0kZITdhVxaBUs3B3gN1D7SHQDiDoRCXFQOp04+9cp4JqLmJbekBADgamurdzvn6mBLQVkZZFVVMKrjYLtPx4yGRwMG0rV3c0NP35YIT0zCvmsRqJRIMbBdO1ibmyEhNxc7r1xFQVk5LM1MMbl3rzrtMyw+AfezcwAAEwICGjR3MhE1T4V5ikF5Di30T7Nh7+wIACguKIJMJoORnvnJ1b2+4B04uzW8WlRGchqCDxyF0EiIl2ZNhYkpAyhET6uc6qm3XJ31T6Xh7Ki4HuUX1e969H+zZ8OjHgPjHpaYmordhw/DyMgI702bBtM6BHRTMjMBAO7VFVfuxMXh8JkzuB0bi/KKClhbWaF7p06YMHJko/pGRETNh8EGh+VyOcrKyuq0rVAo1MiK/eabbyCRSGBvb48dO3bAzc0NAODo6IjFixfDxcUF33///SPptzII2K5dO/zyyy8aQStXV1f07t0bEydORHR0NC5cuNCoY5mamuoMisXHx2Px4sWQy+UICAjAf//7X431S5cuVQWG9+7dCweHBxlMgYGB6Nu3L6ZOnYqbN29i6dKlGoHR7du3qzKGg4KCEBgYqFo3btw49OjRA+PHj1dl+Dal+Ph4AICxsbHWIJk+AoEALVu2RG5uLtLS0iAWi1Wv31dffQUA8PX1xfbt22FjowgkODo6YtGiRXBzc8PXX3+tdb8nT55UBa0XL16M1157TbVu2LBh6NGjByZOnIiUlJojo69cuYLCwkIAijLhyveqsq2fnx9GjBgBiUSCkydPPpKMbKlUWutnTSAQwNLSslHH+fHHH1U/a8tGB3R/7pXLU1JScOLECWzfvh0AYGlpie0ytZUAAQAASURBVAULFjSqX+pKS0tx8eJFAIpA7Lhx41TrHB0d8fPPP+OFF15AQkICjhw5ohEcXrlyJfLy8mBnZ4ddu3bBR23OmT59+qBXr16YM2cOgoOD8dVXX2H48OEwq573xtTUFN9++y0mTpyIiooKfP7559iwYQPi4uJUr9vgwYMxZcoUnX3v1q0bpkyZgs2bNyMmJgYxMTEICAjAl19+Wa9gvFAoRFBQkCroq36Ojbm+ZWVl4aeffgIADBkyBD/99JPqi5fy2gwAW7duxeHDh/HBBx/A09MToaGhquvPJ598ovEZsLOzw7x589CxY0fMnTsX58+fx8mTJzFixIg6ny893cRliuohxhZmerczMqv+Mi4HZCIxhHqyjNU1RWAYAEQFpQhf+afGMucuvmj7Yj8YW2j/O58VeR+5UYkwsTZH2xd1V7YgouavuLrSkVUtgVJLE8V6uRwoqxTrzTJW15DAsNK84cOw88pVnLh9G39dv4G/rmtWyenm443X+vaBl4PuagjqDt9UzDFqb2mBwR3aN7hfRNT8lBUrBuVZWFno3c5Mee2SyyEqr9CbZayuMYFhqUSCPeu2QiaVYci4UfBs4HzFRPRkKC5VXI+sanmOZWmuuB7J5XKUVVTozTJW15jgq0QiweotWyCVyfDymDFoU8f5iguqqz7aWFtj3/Hj2H3kiEZFt9KyMly4ehWXr1/HB2+8gZ5qCUJERPRkMtjgcHp6OgIC6lZmzNPTE6dPnwagKDOqDO7MmDFDI9imNHPmTBw4cAD3799vug5DUTJ18ODBaNOmDQIDA7UGboVCIXr27Ino6GhVFmVTKy4uxrvvvovi4mK4ublh9erVGn2JjY1FeHg4AGD27NkagWElMzMzzJ8/HzNmzMC9e/dw48YN1ZylytLMAwYM0AgMK3l5eWHWrFmPJACvDKRaW1s3KDvQuXrUYFVVFYqKitCiRQvExsaq5p2eM2eOKjCsbvr06di5c6cqOK1O+Xr4+vpqBIaV7O3tMX/+fHyoZf4R9VLSOTk5Nd6v3t7eWLduHezs7FTBuqZ26NAh1TnoYmNjo3rPaCMWi2sEdeVyOUpKSnD37l1s27YNISEhABRBTF3B4fp87n18fLBy5cpGZyGrk0qlqpvrXC0lG5VBXLFYrBH8LSoqwtGjRwEAU6dO1VinJBQKsWjRIgQHByMvLw/BwcEYM2aMan2HDh0we/Zs/Pjjj6rs5E2bNkEsFsPR0VE1gEGbjIwMLF26FGfOnNFYbmNjU+/3TYcOHbS2aez17dSpU6isrIRAIMD//d//aR2RO2vWLFy4cAG+vr7Iy8uDp6cnduzYAUBxnX/99de19nnEiBEICAhAREQEdu/ezeAwqcilMgCA0Fj/CHAjkwe3W1XVbf5JlQWlNZbl3U6G0FiINi/0g7GZ5khyUWEp7h++DABoN74/TK31P4QlouZNIlNcd0yM9X/1M1VbL5FJ9WzZdCokEgiFApgZG0Oi5fqYWlCAe5mZdQoO38vMRFxWNgBgjH8XmNQxO4eIngzK6ZmMa8mAU8/YlUokj7RPSif2HkZOeiY8fX0Q+LzuuTqJ6Okgqb4emdZ2b6X2XEPyD12Pth86hNSMDLTx8cH4ejy7UE6beDs2FpeuXYO3uztee/FFdGrbFlKpFNdu3cK2AwdQVFKCH37/HV8tWICWnrqnKCIioubPYIPDDRUeHg5Z9QOWQYMGad1GKBRi1KhRqiy2piIUCjFnzhyd66uqqhAXF4fU6rkv1Oe2bSoymQwffPABEhMTYWZmhtWrV6sCokrqpZn9/Px0Zo126NABRkZGkMlkuHbtGrp27YqSkhJER0cD0P36Aoqs10cRHK6srAQAVcZlfakHpJRBwMuXL6uW6TongUCAYcOGaQ0OK0tUDxw4UOdxhw4dCqFQiKqqKo3l3bp1g4mJCSQSCWbMmIHJkydjyJAh6Natm6qv+vbbXCxZsgRLliypdbvOnTsjKCiowXMOOzo6YvDgwQgMDMSwYcOafC4Ve3t7tGvXDrGxsfj+++8RExODkSNHom/fvqrMaX8toy8jIyNVXyQ6dOig8zPl7OyMFi1aICcnB9euXdMIDgPA22+/jeDgYNy6dQsff/yxap9fffVVjc+x0o0bN/Dvf/8bBQUFMDU1xbvvvouwsDBcvnwZ586dww8//KAamJCTk4PMzEx06NBB52vXsWNHrcsbe30LDQ0FoMg6VpbMfpirqyuOHz+usezq1asAgE6dOqGiokLn8bt164aIiAhERkZCLpeztDQpCJ+M94GVuyN6f/IKTKzMIMorQdql28i8EoPsyHiU5xSh67/HQFj9N0EulyNmbwhkFRK4BLSBU8e6jTQnouZL2Ez/ZhWUleHLw0eQXlgEWwtzvB04CD1a+sDC1BRpBQX/z959h0dRtX0c/246aST03gkgvTfpSLUAooAKigoCgsKD74PdRyxgpYoKSJciiCBFIHQwoSMdAoT0EAhJSE82yb5/JFkTsilAEDC/z3VxXcvMnDNnZncns3Ofcx82nTzF/ouXmL93P8GRUQxrl3cWg80nTwHgaG9H91zuN0Tk4XWnv/HuNd9zF/HevhcbW1ueHvF8gdPGisjD60G9tzrt48Mfu3dja2vL68OG3db1KCnj+VBUdDQVy5Xjk//8h2IZI5/t7ezo2KoVtapW5e2vviIpKYlfNm3i/0aOvCfHISIi/4wiGxzOOhr4doSFhZlfWxq9l8nDw+OO2lVQ4eHheHt7c+nSJQIDA/H398fX15f4+Ph7ut8pU6aYR05PnjzZYiAra3rjgQMHFqje0NBQIP38ZgZV8zq/1atXNweWC5Nrxlxsd5qy+mZGGhaDwUDx4unpQDOPzc3NzbzMEksjVBMSEsyjmatWrZpr2WLFilG+fHmCg4OzLS9TpgwTJ05k6tSpxMTEMG/ePObNm4erqytt27Y1B0Hd7iIdYH769+/P1KlTC71eg8GAk5MTJUuW5JFHHqFHjx706NEDmzx6bt76vTcajfj7+zN37lzWr19PZGQktra2dOnSpdADw5n+97//8eqrr5KQkMC6detYt24dtra2NGvWjE6dOvHYY4/l+Oxn/U6NGzeuQPvJ/NxlZWNjwxdffEH//v3No8oHDRpE165dLdYRFhbGyJEjiYqKonTp0vz444/Ur1+f5557jmeffRZ/f39+/PFH6tatS58+fVizZg3Tp0/Hzs6OTZs2WfwOl8iYcycvd3J9y7w2V6tWLd/6M8XGxhKRMVeQp6cnnp6eBSoTExNjvlZI0WadMTIlv9HAqca/OzNY2f7zt162jn93eHIs40btfu2wdXIgcNdJYoNuEHbsEuVbpqdfDd5/hpu+V7F3d6Lm463/8baKSOGzz7inMebTcTQ5y/r8RsIUhp8PHiIk6ibF7Gz56MknsqWnrlaqFK937UIpZ2fWHf+LP06dpmX1atQrX95iXQnJyRwPSL9fal29uuYaFvkXsrVP/17nNxrYmPz3ett7fC1IiE/g159+BpOJxwY+TunymodTpChwyBhQkpzfvVWWbH65TdtXWOLi45mzdCkmk4nnnnySireZmto+yzOwwY8/bg4MZ1WhbFm6tGnDlj17+OvsWRKTksznQkREHj5FNjh8p2JiYsyvs85DfKt7FThISkri888/Z/Xq1TkCo/b29rRu3Zq0tDTzaLjC9Msvv7B06VIgPaV2v379LG4XG5szfWV+MstkDcrmdX6trKxwdHTM9n4Uhlq1anHq1CkSEhIIDQ2lfC4PoHLj4+MDpAchM0cfZ7bRwcKNVVaW0k1nPR93Uh7S36t69erx008/4e3tjdFoJDo6mq1bt7J161ZsbW0ZOnQob7311gPby3nKlCkMGDCg0Ou1tbWlVq1afPnll5QtW5a5c+eyatUqrl27xuzZs/MMNN+pFi1a8Pvvv/P999/j6elJTEwMRqORgwcPcvDgQb788ku6du3KJ598Yh7NezffqVtVqVKF8uXL4+/vD6R3IMjNd999Z+6cMGPGDOrXrw+kd3T4/vvvGTRoEDExMbz77rvUqFHDnPq6QoUKuXbuyGtU/t1c3zI7ZuT3PcmqoPPO3yo2NlbBYQHAxiH9B35qYnKe25nXWxlyneP3n1a5cyNCvM6SmpRCxLlAyresQ9zVCPw8j4EBPAY8aj4+EXm4Zc41HJ9PQCUu4wGmlcGA8z1+0JdoNOJ9KX0Knp716+c6b/HTzZuxx8eHyLh4tp89l2tw+HhAoDl9dvtate5Jm0Xk/nJwTH8+kJSQmOd2ifHp2YAMVlYUc8p7PtC7tWHJaqIjoqhetzZtu+ee+UxE/l0cM55X5pV9DCAuY72VlRXO+cxPfLfm//ILN6KiqO/hQW8LU/TlJ+uzlPq1a+e63SO1arFlzx5SUlMJCw9XamkRkYeYgsO3ydnZ2fw6ISEh2/+zyto77HZlzvNgyYQJE9ixYweQnkK3U6dO1K5dm1q1alGjRg1sbGyYNm1aoQeHDx8+zOTJkwFo164d//d//5frtllvKE6ePHlbKZqzjqzNbxT03Zzj3LRt25bffvsNgP379/PMM88UuOylS5e4fv06AK1b/z3aKvOY8rtptHQ8WUf03kn5TG3atKFNmzbExsbi5eWFt7c3+/fvJyAgAKPRyIIFCzCZTLz99tt57uPf7D//+Q+nT5/Gy8uLXbt28dVXX/HOO+/ck31VqVKFKVOmMHnyZI4dO4aXlxd//vknp0+fxmQysXPnTq5du8aaNWswGAzZOkps3rz5ruZBnjlzpjkwDPDDDz/QrVs3i+meM0dZN23alObNm2dbV7NmTaZNm8Zrr71GQkICw4YNMwdon3jiiTtq291c3zLPUV7Xz1tlvVaNGDGCt956647aLUVXsVKu3PS9SmJU3h04EqPSOyLYuzo+MCnJrW1tcCzjRkxgOAkR6Z2Yws/4Y0pJn57g1E9b8yrOtWOXuXYsPbDT8NWeuNW4vc5UIvLPKV+8OGdDQgnPp1PljYyOZe5O9/5aFRYdTVpGtiCPcrmPbLGxtqZ2mTIcuuJHSEaHNUsOXbkCgJtjMR6poOuRyL9RqbJl8Dt/iagbkXludzNjvatb8Xt+LTt16BgAV85f5MNXJuS57bf/TX+eUq1OTV6ZVLBsUCLyYKpQpgxnL17kekYmstzciEy/HpUofu+vR15HjwJwxseHwW+8kee2Y//3PwDq1arF/958E4AyJUtyMeN+Kq9MellHFCf/Q/Moi4jIvfFgTtryAMua2tfS/LCZAgICLC7POjLTmMsf0ahcHnwcO3bMHDgZOnQoa9eu5c0336RPnz54eHiYRzlGRub9Y+l2BQUFMW7cOIxGI5UrV2batGl5jjCtUKFCtrJ5yUwhnalcuXLmuYTyOr/Xrl0zzw9cmLp3724egbt06dIcc/jmZcmSJebXTz75pPl15vm4efMmN27cyLV81tTBmezt7SlZsiSQ9/lITU21mEb4Vs7OzvTo0YOPPvoIT09PVq9eTcWMXn7Lly+/J/NUPywMBgNTp041v/+LFy82p1C/V2xtbWndujUTJkxgzZo17Nq1i0cffRSA06dPc+xY+sOGrCPYb00dfqtbv1NZ/fXXXyxYsACA5557jgoVKmA0Gpk0aZLFzgWZ16LMz+CtOnToYO5QkBkYdnd356WXXsqzjZbc7fUt8xzldu3NNG/ePBYuXMjJkydxdXU1d/C5m/MqRZdTWXcAEiNiSclj9HBsSPq136l8/mnV71ZKQjJnf97JsVnrCTt+Oc9tU43po+ys70OqaxH551TOmNLhWnQM8Xl0JrwSHg6kp3S+11JS/77HNhZwmpisZbIymUycyvg73rxq1QemE46IFK6ylcoBEHH9Bol5dJwOCUh/BlG+ikazici9UTnj+cO1GzeIz+N6dCXjmWi1SpX+kXbdjWpZRgBfy+PZZVSWDIcl8pg6T0REHnx6GnibmjVrhp2dHcnJyWzfvt3inLsAe/futbg86wjAiFx6mGUGhG51/Phx8+tBgwZZ3CYtLY2DBw9m+39msPVOxMXFMXr0aCIjI3F0dGTOnDn5zk/bokUL8+sdO3bkOsrx2LFjvPTSS5QvX57x48fTu3dvnJycaNGiBYcOHWLHjh2MGDHCYtnczu/dcnJy4uWXX2bGjBlcuHCB77//ntdffz3fct7e3qxevRpIH2XZpk0b87qOHf9OL7V9+/Zc37vcjqljx4789ttv7N69m9TUVIuB+UOHDlkcWfzjjz+yYcMGXFxcWLFiRY71jRo1YtiwYUyZMoWkpCRu3ryZayCwKChbtiyTJk3i/fffx2Qy8eGHH7Jx48Y8U5zfjt27dzN79mwCAgLYsWNHjlTg5cuXZ+LEiezfvx/4ex7d5s2bY2VlRVpaGjt27Mj2mcoqODiYvn37UqpUKYYNG8awYcPM6xITE5k0aRKpqalUrlyZSZMm0bFjR0aNGsWFCxf47rvvmDAhe2/3ChUq4O/vz4kTJ0hOTrY4R86wYcPYunUrR44cAaB27do43kG6pLu9vjVr1oxdu3bh4+NDWFgYZS3MrxMTE8P06dNJSUlh4sSJNGrUiObNm7Nnzx68vLxISEjI9b1+9dVXOXfuHB4eHixcuFAPngUA9zqVYMNBSDMRcSGIMo1r5Ngm6WYccaHpf+9LeNz7h5TWDrZE+YaSmmDk+glfyja1/Dc46WYc8deiAHCumH7dr9y5EZU6NMiz/qPT15EUFUfpJjWo3a8tAFY2D+aUBCKSrkmVyvAnpJlM/BUQSLtaOa8LN2Jj8c94ENi48r1/gFm2uCtWBgNpJhOng4JpVb26xe1S09K4eO0aABXd3SxuExARQXxSetC7Zh7TZYjIw82j4SNsYi2mtDR8Tp6jUetmOba5GRFJaEB6Z5HaDXNmRipsH8z5Is/1Jw4c5fclvwAw7tO3cSvhjuEuns+IyIOhaf36LFyzhrS0NI6fPUv7WzKtQfqoYb+M4HCTRx65521a/PXXea7ff+QI81auBOCb996jlLt7tufFzRo04Of16wE4cPw4A3v3tljPyfPnASjp5kaJfJ4Pi4jIg013pbfJ2dnZnDJ1yZIlXLp0Kcc2u3fvZt++fRbLV65c2fzH948//sixPjExkblz51osmzUoaGm/ALNnz8bPz8/8/9xGJxeEyWTirbfewsfHBysrK7766is8PDzyLdeoUSNzitp58+Zla0+mxMREpk6dSlJSEsHBwdmC7E8//TSQHixau3ZtjrJRUVHMmTPnDo8qf6+++ioNGqQ/HJ81axY//PBDnqMG9+zZw+uvv05aWhqOjo58+umn2dZXqlTJnGZ61qxZXMt4wJXVli1bzMG1W2Wej9DQUIvHnZSUxNe53ATa2Nhw8eJFjh8/nmung3PnzgHpn+0SJe79qLYH3cCBA2nZsiWQPvJ91qxZhVZ3yZIlOXXqFDdv3mT58uUWt8l8PwDzvL2lSpWiS5cuAPz6668czUgXlFVaWhpTpkwhISGBwMBA82c407fffmv+Lk6ePBkHBwe6dOlCz549AZg/fz6nTp3KVqZXr14AXL9+nfnz51ts788//5zts3vo0CE+/vjjXM9Bbu72+vbUU09hY2NDWloaX375pcXv7OzZs0lJScHKyoreGT90nn32WSD9uvLVV19Z3K+npyf79+/nxo0bVKlSRYFhMStWwgXXaumBCP8dx0lJyD4iz2Qy4bv5MJjAxsmeMrkEaguTwWCgTKP0IHWkTzCRl0NybJOWmsal9d6QZgIDlGuR/rfdytoaazvbPP+R8fE3WBnMy/SQU+TBVtbVlToZqZtXHzlC3C3Zd0wmE8u8D2AygYuDAx3ymGeusDjb29OwUnqHmd0XfPDNmJrlVuuO/0VkXPpUM5aC2gC+18PNr2uVKV3ILRWRB0WJMqWoUjv9Hmfnuj9IiM/eOdpkMvHHqvVgMuHo7ESTti0sVVOo7Bzs8/xnbfP3eAxbOzvsHOyxtcs9XauIPBzKlipFnRrp16NfNm0i7pZp8UwmE0t++w2TyYSLszMdMp4x3UsO9vZ5/rPJcj2yt7XFwd4euyzpoyuVK4dHxjH9vmMHIRmDFbK66OeHd8azxU6tW+vZiIjIQ67IPs0zmUzExcXd1r9MEydOpFSpUiQkJPDCCy+wevVqrl27RmhoKPPnz+eNPOZ2cHV1NY8q3blzJx9//DFXrlwhPDycnTt3MnjwYM6dO4erq2uOsu3btzf/4f3kk0/4/fffuXr1KmFhYezbt49Ro0bx3XffZSuTtd23a9q0aeY5RydOnEj37t1JTk4mPj7e4vnJOtfnhx9+iI2NDdHR0QwaNIhly5YRFBTEjRs32L9/Py+99BInTpwA4JVXXjGnNob0IE9mcO7999/n22+/xd/fn4iICLZv387gwYMJDQ29ZzchdnZ2/PDDD3h4eGAymZg2bRoDBgxg9erVXLlyhaioKIKCgvD09OT1119n5MiRxMXF4ejoyOzZs6lVq1aOOj/66CPs7Oy4fv06gwcPZvPmzURERBAYGMicOXN46623ck3V3bJlS5566ikgPbj14YcfcvHiRSIjI/H29uaFF17g9OnTFss//fTTuLm5YTKZGDNmDMuWLcPX15eIiAjOnz/PJ598wrp16wAYMmSIbuxID6x8/PHH5jlWFi9enC1gezcaNmxIq1atAJgxYwZffPEF586dIyIigitXrrBo0SI+++wzABo3bpwtwDtp0iScnZ0xGo288sorfP/99/j5+REREcGRI0cYNWoUnp6eADz++OM0a/Z3T/rDhw+b057379+fdu3amde9//77uLi4kJKSwjvvvJMtvfSrr75q/m7OmjXLHGC+ceMGf/75JyNHjjTPRd69e3caN24MwMqVK3nrrbduK/X73V7fypYty2uvvQbAxo0bGTNmDMePHycyMpJz587x4YcfsnjxYiA9pXblypUB6NatG507dwbSA91jxozhyJEjREZG4uvry3fffcfEiROB9JTZBckkIEVLjT6twACJ4TGcmPcHkReDMcYlEht8g3PLdxF+yg+Aqt2apAdXszgybS1Hpq3lwurCzYZRpVsTbJ3T54I6u3QHAbtPEH89CmNcIpGXQjj10xYizqf3Yq/Y/hFcKt37FLIicn8NbdsWgwGu3oxm8oaNnAwMIjohkSvXw5nmuZ0DvulzzA1s0QyHW+aZ+8+qX/jPql/4bueuQm3TC23aYG9rgzE1lckbNrLu+F+EREURk5jI5WvX+X7XbtYcSe8Q17hypVxHFwdnmXKirIXfUCLy79F7cD8wGLgRdp2fps7i0unzxMXEEuIfyIrvFnLm8F8AdH2qF3YO9tnKTn/3c6a/+zlr5i375xsuIv86Lw4YgMFg4Or16/xvxgxOnDtHdGwsvoGBfPPTTxzIyI72TO/eONhnvx6N/+QTxn/yCbOzTE/3IBgxaBC2trYkJSXxwbRpbNu3j/CICCJu3mTbvn189t13pKSmUrpkSZ567LH73VwREblLRTatdEhISLbgSUEcPnwYV1dXSpYsyU8//cSIESO4du0a77//frbtXFxcePTRR83zZ97q3Xff5YUXXiAqKorly5dnG0FoMBj473//y759+/D29s5Wrnbt2owYMYK5c+dy48YN/u///i9H3S4uLjzzzDPmeUX9/PzueDTojz/+aH79/fffM23atDznpG3VqhVLly4F0lO8zpw5k7feeouoqCg++eQTPvnkkxxlnnnmmRzBdIPBwOzZsxk1ahTHjx/nxx9/zNYWgLfeeouZM2danCe1MJQuXZqVK1fyxRdfsGbNGs6ePZvjfc6qefPmTJ482WJgGKBmzZr88MMPjBs3juDg4Bzpe93c3HjhhReYPXu2xfKTJ08mPj4eT09PVq1axapVq7KtHzJkCAcOHODKlSs56p0+fTpjxowhMjLS4nsA0KVLlzw7NRQ1NWvW5NVXX+X7778nJSWFDz74gF9++eWuUrRn+uqrr3jxxRfx8/NjwYIF5u9qVtWqVWPGjBnZllWtWpX58+czduxYwsPDmT59OtOnT89RtkuXLtlGr8fHx/POO+9gMpkoWbKkeY7gTGXKlOGtt97io48+4uLFi8yYMcN8bXF1dWXhwoW8+uqrBAQEWPwuGgwGhgwZwnvvvUdsbCxDhw7Fx8eHDRs20KFDB3PHhvwUxvVt7NixREVF8fPPP7Nz505z55asevbsme0cGAwGvvnmGyZOnMju3bvZsWOHxWt3qVKl+P777y2mq5aizaVSKTyefpSLv/1J/NVITi/0zLFNxUcfoUKbnKkNE66nz9dk51w4qesz2TkXo8FLj3Fm6Q6Sb8bjv+04/tuO59iuQvtHqN773vdiF5H7r2aZ0rzWqRPz9u4l4EYEUzbnzGDUp1FDetSvn2N5aNRNANwKaZqNTJVKuPPfXj2ZsX0H0QmJrDp0mFWHDufYrkmVyrzRrWuu9YTHxgJga22NnU2R/XkrUiRUql6F/sOHsH7xSsKCQlj87Q85tmnXozOtu3XIsfzG1fQMXi7FXXKsExG5XTWrVmXU888zd8UKAkJC+NxCpr++XbrQ08K0YKEZGQXdHrBObVUqVODtUaP49qefiI2L46dffuGnW7YpXbIkk157LUfAW0REHj769XyH6taty6ZNm/jpp5/YunUrISEhuLi40LFjR8aOHcvatWtzDQ7Xrl2bDRs2MHfuXPbs2cPVq1dxdnamadOmvPzyy7Ro0SLXtNQTJ06kfv36rFixgrNnz5pHrFapUoUOHTrw3HPP4erqyqpVq4iLi8PT0/O2g+CWxGY8dLkd3bp1Y9u2bSxdupS9e/cSGBhIUlIS7u7uNG3alEGDBtG+fXuLZd3c3FiyZAnr16/n119/xdfXl5SUFOrVq8fw4cPp3r07M2fOvNvDypOTkxOTJ0/mlVde4bfffjMHX2NiYrC3t6dy5co0a9aMxx9/PNs8y7lp3749mzZtYuHChezdu5eQkBBcXV3p0KED48aNyzbn6q0cHByYOXMmW7duZeXKlfj4+JCQkEDNmjUZMmQIAwcONKcAvlXbtm3ZtGkTixcvxsvLi6CgIIxGI+7u7jRo0IB+/fqZUwvL30aPHs3mzZvx9/fn1KlTLF26lBdffPGu6y1Xrhy//fYbP//8Mzt27ODy5cvEx8fj4uJCzZo1eeyxxxgyZAj2Fm60mzZtypYtW1i+fDk7d+7kypUrxMXF4erqSsOGDenfv785XXKmL7/8ksDAQCC9Y4qlOcMHDRrE77//ztGjR1m4cCGPPfYYTZo0AdKD0hs2bGD58uVs3bqVy5cvk5SURJkyZWjVqhWDBw82jxh2c3Nj2bJlTJw4kZo1axY4MJzpbq9vVlZWfPjhh/Tq1Yvly5dz9OhRIiMjKVasGPXr1+fZZ5+lT58+Ofbr7OzMjz/+yPbt21m3bh0nTpwgMjISW1tbqlevTteuXRk6dCjFixe/reORoqNss1o4VyhB0L7TRPlexRibiLWdDc4VS1KhbT1K1qvyj7fJuUJJmr/Rj5CD5wg/7U9CeDSmtDTsXIpRvFo5KrSpi0tlpV8VKUo61fGgeqmSbDhxkrOhoUQnJGBvY0P10qXpWf8RWlSr9o+36ZEKFfj62WfYduYsx/z9uRodTXJKCs729tQsU4YOtWvTqnq1PLPbxGV0FnW0s/unmi0i91GzR1tRoWol9m/ZyZULl4iLjsHW3p6KVSvRulsH6jVteL+bKCJFROfWraleqRIbduzg7MWL3Mx4XlijcmV6dexIiyxT6D0sGnh4MP2DD9i8axdHT58m7MYNrAwGypYqRZumTenx6KM4OTre72aKiEghMJjymkxV7tisWbPMo0AvXLhwn1sjIvLPSUtLw2AwKE25BYN3fXm/myAikq/vT2skgIg8HI4+lXOEqIjIg6ZVlMbmiMjDwfUh7NRwP2wPOHa/m/Cv0r3K3Q9ulNunuxMRESlUhZF+W0RERERERERERERECp+e4IuIiIiIiIiIiIiIiIiIFAEaOSz/CiaTifj4+Luqw8nJqZBa8/DSecxdamoqiYmJd1zeysqKYsWKFWKLREREREREREREREREbo+Cw/KvEBwcTLdu3e6qDs0NrfOYlyNHjjBs2LA7Ll+xYkV27txZiC0SERERERERERERERG5PUorLSIiIiIiIiIiIiIiIiJSBBhMJpPpfjdCRESkKBi868v73QQRkXx9f9r+fjdBRKRAjj7V4X43QUQkX62ilLhRRB4Oro0a3e8mPBS2Bxy73034V+lepdn9bkKRpJHDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgILDIiIiIiIiIiIiIiIiIiJFgM39boCIiIiIiIiIiIiIiIiIFC3dunXLc/2OHTv+oZYULQoOi4iI/EPmlux1v5sgIpKvQ0+l3O8miIgUSPP1++53E0RE8jWyQdL9boKISIGspNH9boKI/EMUHBYRERERERERERERERGRf5RGBt8fmnNYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRERERERERERERERERKQIUHBYRP5xqamp97sJUgj0PoqIiIiIiIiIiIiIPFxs7ncDRP5pXbt2JTg42OI6Kysr7O3tcXd3p3bt2vTo0YM+ffrg6Ohocfs6deoAMHbsWMaNG3fP2vxvsmnTJnbs2MG33357v5uSTebnolWrVixduvR+N+eBd/r0aT788EPWrl17v5si8kBauGYNW/bsYfQLL9C5deu7qsvnyhW27dvHucuXiYqOxtramlLu7jSuV48+nTpRumTJXMuaTCb2HjrErgMH8AsKIiU1lRJubjSrX58nunalpLt7nvs+c/EiW/buxcfXl9i4OByLFcOjRg16PPoojevVu6vjEpH7b9PytRzYvpf+Lz9Hs0dbFWrdXtt288fKdXR5sidd+/XOc9uE+AQObN/L2WMnuRF2HVOaiZJlS1OvaQPaPtYJR2ene7ZvEXk4LPrTi62nzzCqcyc61fG4q7p8wsLwPHOW81evcjM+ASsrA6WcnWlUqRK9GzagtItLrmWTU1LYduYsB69cITgyEmNqKm6OjjxSvjx9GjWkqoX7sjVHjvLr0WO31cbCOE4RuT8ubzxIiNc5PAY+StlmtQq17uA/z+C76TBVujWmaremBSqTkpjM0RnrSL4ZT4fPX8p3+6jLoYQcOEd0wHVS4pOwdbLHuWJJyjarTan6Ve/yCERE5EGh4LBIFmlpaSQkJJCQkEBISAh79uxhzpw5TJ06lVatCveBWVE0bdo0fvjhB53Lh9zevXsZNWqURg6L5OLIyZNs3bu3UOr6ef16ft++PduylJQUgq9eJfjqVXZ4efHGsGG0aNQoR1mTycTMxYvxOno02/Kw69f5Y/du9hw8yMRXX6WBR84HjyaTiYVr1uQ4jujYWI6cPMmRkyfp3r49rzz7LFZWSkQj8jA6d/wUB3bsuyd1B172Y/vazQXaNjQgiKUz5hETeTPb8rCgEMKCQji8x5uh40dSsVrlQt+3iDwcjvj5se3MmUKpa/nBg2z462T2hakQHBlFcGQUO8+fZ2zXLrSoVi1H2aj4eKZs/oOAGxHZlofHxLI35iJ/XrrES+3b0/2Ru+9A52Bre9d1iMg/78a5AEK8z92TuqMDruPneXsdTUwmExd/8yL5ZnyBtr+86RAhf57Ntiw5OoGI6CAizgVRol4l6g3pgpWN9W21Q0REHjwKDkuR1bx5c+bNm5dtWWpqKvHx8QQEBLB9+3aWL19OcHAwI0aMYNGiRTRtmr1XXpUqVQAoXrz4P9buh1lYWNj9boIUgvDwcAWGRXJx9NQppi1YgMlkuuu6tuzZYw4M161Zk6d79aJapUrExsVx9tIlVmzYQGxcHNMWLuTTiROpXqlStvIrNmwwB4b7dulC9/btcXJ05OzFiyz97TduREXxzfz5fP3OOzlGEK/evNkcGK5YrhzPPfEEtatXJyExkf1HjrB261a2//knqampjHr++bs+VhH5Z53/6zSrvl8EhXCtulWQrz9Lpv2IMTk5322jo26y+NsfiYuOAYOB9j0606xDa5xdXQjxD8Lz142E+AXy0xezGP3hREqXL1to+xaRh8NRf39mbN9RKJerrafPmAPDdcuXY0CzplQtWYrYpETOhoSy6vBhYhOTmLl9J5P7PUm1UqWylZ+xfQcBNyKwMhjo1bABnet44Gxvz6Vr11l+8CBXb0azYP9+Kri58UiF8uZy/Zo24fHGOTvyZRUYEcEnGzdhTEmlba2atK5R/e4PWET+UTfOBXJu+W4o/NsrYgKvc3rxNtKSC/4sJi01lYtrvQg/5Veg7YO9zpoDw241y1O5a2McSxcnOSaBq4cuEHrwAhHngrj0+wE8BrS/k8MQEZEHiILDUmRZW1vj5JQzRZ2rqyvlypWjVatW9OrVi1deeYX4+HhGjx6Np6cnLllSTHl6ev6TTRYRkQeUyWRi9ebNrN26tVACw0ajkV82p498q1erFh+MHYu1dXrvbFdnZyqULUvjunX57xdfEJ+QwC8bNzJp1Chz+YioKDbt3AnAU489xnNPPmle17ZZM2pVrcrbX31FbFwcv27ZwsghQ8zrr9+4wfqMv29VK1bkf2++iWOxYgAUd3FhYO/eVChblhkLF7LrwAE6tmrFI7Vr3/Uxi8i9ZzKZ2LnuD3Zv9LwngeGDO/fzx8p1pKakFGj7Xeu3pgeGgQEvD6Fp+7+zy9SqX4eqtWswb8oMQv2D2LB0DS//9/VC27eIPNhMJhNrjhzlt+PHC+VyZUxNZfWRIwDUK1+O9x7vi3VG9hPXYg5UcHOjceVKvP3rWuKTkvnl8BH+27uXufz50KucD70KwNMtmjGgWTPzupbVnahVpjST1qwlJjGR344fzxYctrG2xsY691F2iUYj3+/egzEllQpuxRnZscPdH7CI/GNMJhP+O/4icNeJexIYDjlwHt/NhzClpBW4TNLNOM6v3EO0/7UCbZ9qTCFg5wkAXKuVocHwxzBkXCPtnItR66m2GKytCPE6R9ixS1Tt1gT74rc37YeIiDxYlAdQJA/NmjXjgw8+ACAyMpIFCxbc5xaJiMiD5q9z5/jv1Kn8umULJpOJGhlZJe7GKR8f4uLTU38926ePOTCcVemSJenWrh0AJ8+fJyVLQGTL3r2kpKZib29P/x49LJbt26ULAPuOHCEpyyg7r+PHScnIDjBi8GBzYDirds2aUa9W+vxZ629Jey0iD6aLp87x3UdfsXvDNjCZqFDANM0FEeTrz/ypM9m4bA2pKSkFqjs1NZVTh9JTI9ZqUDdbYDiTrZ0tvZ59CoAr5y8S5OtfKPsWkQfbicBA3v51LWuPpQeGq5culX+hfJwODiYuKf1+Z2CLFubAcFalXVzoWrcuAKeCg833QwCXr/8dYOleL2faaHcnJ5pXS78HvHytYMGYTMsPHCQ06iZWBgOvd+2ilNIiD5EIn2COzfqdwJ3pgWHnijnnHb9TMYHXOTH3Dy7/fgBTSlqB6k41phCw6wRHpv1GtP81DNYGnMq751vu5pUwUuKTAKjcuZE5MJxVmSY101+kmYgNuXF7ByMiIg8cBYdF8tG/f3+qV09P6bR8+XLS0v7uqVenTh3q1KnDrFmzcpS7fPkyH330ET179qRhw4Y0bdqUnj178v7773PunOX5R7p27UqdOnVYsWIFsbGxfPHFF3Tt2pWGDRvSrVs3Jk6cmGvZTBEREcyZM4fnn3+edu3a0aBBA5o3b07v3r356KOPuHz5ssVymcfi5eXF77//Ts+ePWnQoAEdO3bkiy++yLbtxYsX+fTTT3nqqado1aoV9evXp3Xr1jzzzDPMmjWLqKiobNvPmjWLOnXq8NtvvwFw6NAh8/6CgoKybWs0GlmxYgVDhw6ldevWNGjQgE6dOjFx4kT++uuvPI/9Xsl8X9auXUtcXBwzZ86kd+/eNGrUiPbt2zN27FjOnv17TpZjx44xZswY2rZtS8OGDenduzfff/89RqMxR91Dhw6lTp06fP311xiNRubMmUOvXr1o1KgRnTt3ZsyYMRw+fDjP9sXGxrJo0SJefvllHn30URo0aEDTpk157LHHmDRpEidPnsyzfEREBD/++CMDBgygVatWNGzYkJ49e/Lpp58SGhpq3u7gwYPUqVOHd955x7ws831cu3ZtQU9nvu7ku5MpJCSEzz//nN69e9OkSROaNm3Kk08+ybRp04iMjMyx/aJFi8zHMHnyZIt1bt682bzNp59+WijHKP8uU+bMISAkBBtra57p04c3X3rpruuMiIrC3t4egFoW5rzLVK50aQBSUlOJjoszLz+eMS9fg9q1KebgYLFsi4YNAUhOTubU+fPm5b4BAQCUcHOjdh77bpTx8PS0j0+2wLSIPJiWTPuRsKAQrG2s6fJUL559bVih1b3q+8X4+/iCwUCrro/y6ttv5FvmRth1khISAajfonGu21WrUxObjECJz6mc9wF3sm8RebBN3byFgBsR2FhbMbBFM97o1vWu67wRG4e9bXryvFplSue6XVlXVwBSUtOISUw0LzdgML9OSbM8es/GKr0zn5XBYHG9JRfDwtie8Rund8MG1Cide9tE5MFzZpEn8VcjMVhbUaVbE+oO7lRodZ9buZtovzAwQPk2dWk0sne+ZcJP+eHveZy05BQcSjjT4OWelHwk/87LJTwq0vrdQTR8pSduNcrnu72l4LGIiDxclFZaJB8Gg4HevXszZ84coqKiOHv2LA0aNMizzO7duxk3bhzJWUZiJScn4+fnh5+fH2vWrOH999/nhRdesFg+JiaGQYMGcenSJfOyoKAggoKC2Lx5Mx9//DHPPvtsjnJ79uxh/PjxxGeMNstkNBqJjY3F19eXtWvX8t1339GxY0eL+966dSsrV640/z8sLAw3Nzfz/2fPns3s2bNzpE2NiooiKiqKkydP8uuvv7JixQrKl8//hjKr0NBQRo4ciY+PT7blV69eZePGjWzcuJERI0YwceJEDLfxg7uwhIeH8/TTT3PlyhXzsqSkJDw9Pdm/fz/Lli3j5MmTfPrpp9nm5PX19WX69OmcO3eOmTNnWqzbaDTy8ssvc+jQIfOy0NBQQkND2bFjB2PGjOHNN9/MUe7UqVOMGjWK8PDwHPUFBAQQEBDA+vXr+fTTTxk4cGCO8ocOHWL8+PHcuJG912fmZ3Xt2rXMmTOHNm3aFOwk3aW7+e5s2rSJd955h6SkpGzLL1y4wIULF1i5ciXfffcdLVq0MK8bNmwYnp6eHDlyhBUrVvDkk0/SpEkT8/pr167x8ccfA1C7dm3+7//+r5CPWP4NDAYDLRs3ZvDjj1OxbFmu37j7XtTd27ene/v2xCckYJfH6JGr16+bXzs7OgKQkpJCcMYc73mNYq5cvjw21takpKbiGxhIi0bpc+FljlguVaJEnm10dXbOtr+qFSsW4MhE5L4xGHikWUO6D+hL6fJliQyPKNTqq9etTY+Bj1OpRtUCbZ8Ql2B+7VYy9+uNlZUVxZyKERNlJCwopFD2LSIPNoMBWlarxrMtW1LR3Y3rMTF3XWf3R+rR/ZF6xCcnY2eT+6OwsOho82unjI56ADWzBJR3X7iQLa00QExiIkf907MbeJTNe370rBZ7eWMygZtjMZ5u3iz/AiLyYDFAyfpVqfZYUxxLu5EYGVuo1RevWY7qPZrjUrngHUesi9lSqUMDKrarj7WdDTd9Q/MvRHr6aDvnnFmjID11doh3+qAIa3sbXKqoI4uIyMNOwWGRAqhfv7759fHjx/MMDsfHxzNp0iSSk5Np1KgR48ePp1atWhgMBk6dOsWXX36Jn58fU6dOpXPnzlSqVClHHd9//z3x8fH06tWLUaNGUbZsWU6ePMnUqVO5cuUKH3zwAZUrV6Zt27bmMiEhIbz55pskJCRQrVo13nzzTRo2bIiTkxMhISGsW7eOFStWkJyczOTJk9meSxrQlStX4uHhweTJk6lUqRIHDhygdevWAGzZssU8Srp9+/aMHDnSPKr6ypUrLFy4kN27dxMaGsrMmTOZMmUKAK+99hovv/wyH330ERs2bKB58+bMmzcPAMeMYEZ8fDwvv/wyvr6+ODo6Mnr0aHr06IGbmxv+/v4sXryYTZs2MW/ePFxdXRk5cmSB37/CMnv2bFJSUhg9ejT9+/fH1taWDRs2MG3aNBISEvjPf/5DYGAgjRs3ZsKECXh4eBAcHMwXX3zBoUOH2Lp1K8eOHaNZs5w/+n/55Rfi4+Np06YN48ePp1q1aly8eJGvv/6aEydOMGfOHCpWrJgtwBsbG8vo0aMJDw+nVKlSTJgwgZYtW+Lq6kpYWBjbt29n/vz5JCQk8Nlnn9GnTx/z+QYIDAxkxIgRJCYmUrJkSd544w06duyItbU13t7efPXVV4SHh/Pmm2+yefNmWrRowbFjx9iwYQMfffQRkD5KGjCPcLwbd/Pd+fPPP3nrrbdIS0ujbt26jBs3jqZNm5KamsqRI0eYMWMGfn5+jBw5krVr11ItYzSklZUVU6ZM4amnniI+Pp4PPviA3377DZuMB0bvv/8+UVFR2Nra8vXXXxfKccq/z7fvvUeF23gIeDsspXTOlJSczL6MzAI1qlQxB5Ejbt40d1ApnUeA12AwULJECcKuX+daloC2Q8ZI48Qso2UsiUv4O7ATcfOmgsMiD7g3P3uHUuXK3JO6X5w46rbrtnf4+29q5ghiS0wmE4kZ629GRBXKvkXkwfb1s89QIUsH5cLkaGeX67okYwr7L14E0lNZZw0i1ylXjhbVqnLEz5+1R4+RZEyhQ+3aODvYcyU8nJWHDhMZF4+jvR2DW7UsUFsO+l7h8rX0jn4DmjWjWB5tE5EHU/Px/XEsXfye1N3gpR63XbdbrQq0nvQs1nZ3n54+1ZiCMSaBmOBwQrzPEe2XnjK/Rt9W2BbTsxERkYedgsMiBVAxywPvW0do3urQoUPmtMqzZs2iXLly5nXdunXDw8ODHj16YDQa8fT0ZPjw4TnqiI+PZ8CAAebgKkDnzp1p3LgxAwYMICQkhKlTp7J+/Xrz+mXLlpGQkICtrS3z58+ncuW/51srUaIEDRo0wMrKisWLFxMYGMiVK1fMgd2srKysmDlzpnndE088YV43f/58IH0E5Q8//IBdlh+vZcuWpVWrVgwcOJAzZ86wb98+8zo7Ozvs7OzMATdra2ucnJyy7XfevHn4+vpia2vLokWLaNz47/SCbm5ufPvtt5QoUYKlS5cyc+ZM+vfvT+l/OOVWUlIS7733HsOG/Z2G8bXXXuPo0aPs2bMHf39/6taty5IlS8znxt3dndmzZ9OhQweSkpLw8vKyGByOj4+nffv2zJ0713yeWrVqxZIlS3j++ec5ffo006dP54knnjAHKNevX8/1jFGDM2fOpHnz5ub63N3dqVu3Li4uLnz++efEx8dz7NgxHn30UfM2n3/+OYmJiTg7O7NixQqqVv17tE2/fv2oWbMmzz77LFFRUaxcuZLXX38dJyenbO/7re/j3bjT705qaioffPABaWlpNGrUiGXLlmUL4vbp04d27doxYMAAgoODmTp1Kj/88IN5fZUqVXjrrbeYPHkyPj4+LFiwgJEjR7J69Wr27NkDwPjx46mbkUJX5Fb3KjCcn6Xr1hGVMbqlR4cO5uUxsX/3VnfO0iHEEseMQHBclowTVcqX5/CJEwRdvUrEzZuUKG75gcTZjIenAAn5BJJF5P67lwHUO6m7ZNnSWNvYkJqSwuWzF3JNLR3k648xY57QJAvXGgWGRf597lVgOD8/HzhAVHx657ce9R/Jsf7N7t1Yeegw286e5fe/TvD7XyeyrW9SpTLPt2lNJff85/cE2Jgx/Y+bYzE6161zl60XkfvhXgWG77Rue9e8f//djkvrvLh23Nf8f+tittR5piMl61bOo5SIiDwsNEGASAFkHW1563y6t8qaDvd6lnSfmSpXrszcuXNZvXq1xTS/kB5we++993Isd3d3Z9y4cQCcP38+W9ppDw8PBg0axIgRI7IFhrNq1aqV+XVEhOVUgnXr1rUYNE5LS6Nz587069ePMWPGZAsQZrKysjKn7LU0v2tuTCYTq1atAqBv377ZAsNZjR8/HgcHB4xGo3n+4n+Sk5MTzz33XI7lt6YpvvXcFC9e3HxOr127ZrFug8HAxx9/bA4MZ3JwcOC///0vkP55OnDggHld+fLlef755xkyZEi2wHBWmaO+Ift7Hh0dbQ7gDx8+PFtgOFPmfMnNmzc3jyS8l+70u7Nv3z6Cg4MBmDhxosXRvW5ubowePRpIT119a/3PPfcc7dq1A2DOnDkcP36cqVOnAunfm5dffvkuj06kcG3atQvPjO9w3Zo16Zzlu56cZX5z2zxSUmddn5xlzuDWTZpgMBhIS0tjwerVOaYRADhx7hwnsswBrjmHReR22drZUrdJenaeY/sPEXjZL8c2qSkpbFuzIcv/U3NsIyJSGDafPIXn2fR7m7rly9HJwyPHNglGI1ZWBuxzSUsdFBnJhatXC7S/C1evciks/bdhn0YNsbW2vsOWi4jcG0lRcdn+n5pgxHfzIcLP+t+nFomISGHSyGGRAsgatMpvrtsmTZpga2uL0Whk+PDhDB48mC5dutCkSROsM37wdcgywsuSTp064Zwxl+OtunTpYn7t5eVFrVq1gPSRnv369cu1ztDQUM6ePWv+f9Y5cbOqV6+exeVWVlaMHTs21/rT0tK4dOkSQUFBwO0FCi5dumSe87ZevXrExcVZ3M5gMFCnTh1OnDhhTmf8T6pfv36O4C2kj8zOuo0lme9n1s9SVo0aNcozqO/s7ExsbCxeXl506tQJgK5du9K1a9dc2xseHs7x48fN/8/6nh8+fBhjRgCpc+fOudbx7bff5rqusN3pd+fgwYPm1x4eHrl+fjLTwZtMJo4dO0bPnj3N6wwGA5999hlPPPEEsbGxDB06FKPRiKurK19++SVWVupLJQ+OTbt2sWTtWgBKuLnx5vDh2f423e3ntWrFinRu3ZpdBw5w+MQJPpk9m6d79aJKhQrEJyTgffw4a/74A/fixYnI6DBl6dooIpKfbv374HPqHMakZBZ+PYeuT/WifvPG2Bdz4GpgCDvW/UHARV9c3IsTE3kTaxsFT0Sk8G0+eYql3umdcEs4OfJGt645fvdHxsXx6cZNhETdxLWYAyM7daR51SoUs7MjODKSTSdPsf/iJebv3U9wZBTD2rW1tKts+wRwtLejey6/wUVE7qfa/dtjV9wRU0oakZeCubLlCInhMZz7eRd1h3SmdINq97uJIiJyF/QkT6QAYmJizK9dXV3z3LZMmTJMnDiRqVOnEhMTw7x588zz5LZt25ZOnTrRrVs33PJIleVhoZdyJnd3d4oXL87Nmze5aqFXcmJiIt7e3pw/f56AgAACAwO5dOlSjpG8lkaCQfZAZ27Cw8Px9vbm0qVLBAYG4u/vj6+vL/FZ0pLejsDAQPPrKVOmZEunnZvQ0NA72tfdcM8lPVjWQExuQf38gjV5vecGg4EqVapw9uxZi++50Wjk8OHDnDlzhoCAAAICAvD19c0xSjnrex4WFmZ+nTn/7v12p9+dzA4JQLZ5uPNi6fNToUIF3nnnHd577z1z4Pyjjz6ifPnyd35QIoXIZDKxYsMG1nt6AuBevDjvjx2bI+2zfZbsBcZ8Oupkftbtbgnuvvzss8TExXHk1CnO+Phwxscn2/ryZcrw6qBBfJIxD7295sgTkTtQunxZhowZzsrvF5GcmMTWX35n6y+//72BwUD3AX0Iv3qdv7wOY29/7zOZiEjRYTKZWHnosDk9tLuTI+/27Yu7halzfj54iJComxSzs+WjJ5/Ilvq6WqlSvN61C6WcnVl3/C/+OHWaltWrUS+X3xEJyckcD0j/Ddy6enXNNSwiD6RipTKef9pC6YbVca1aluOzf8cYm8iVPw5Tsl5lrJT1QETkoaXgsEgBZA1e5ja6M6vhw4dTr149fvrpJ7y9vTEajURHR7N161a2bt2Kra0tQ4cO5a233jKPiMwqvwC0g4MDN2/eJDbLvJKQPu/w9OnTswWzIT0wWa9ePapVq8Yff/yRZ92WUvJmSkpK4vPPP2f16tU5Rh7b29vTunVr0tLSOHz4cJ77uNWtx3Gvytwtx3zm7rwbLi4uea7PTOt863H/8ccffPbZZznSJBsMBmrUqEHjxo0tpuC+efOm+XWxYsXutNmF7k6+O4X5+WnRogXW1tbmz/c/Pa+1SG6SjUZmL1nCwb/+AqB0yZK8//rrlLPwGXXK8p2OT0jIs964jPUut3RssbO15a0RI9h/5Ag7vLzwCwoizWSiXKlStGvenN6dOhEYEmLe3j2XeYlFRPJTu2E9xn3yNvs2b8fn5Dlibt7E0dmJKrVr0L5HZyrXrMaSaT8C4Fw87/slEZGCSk5JYc6u3Rz0vQJAaRcX3u3bm3IW7mkSjUa8L10GoGf9+rnOifx082bs8fEhMi6e7WfP5RocPh4QiDHj90b7jExgIiIPOntXRyq0q4f/tuMkRcYRFxqJS6VS97tZIiJyhxQcFimAEydOmF83bNiwQGXatGlDmzZtzKmAvb292b9/PwEBARiNRhYsWIDJZOLtt9/OUTYpKSnPujNH6GYdybpo0SLziNsKFSrQvXt36tWrR82aNalduzaOjo54eXnlGxzOy4QJE9ixYweQnj65U6dO1K5dm1q1alGjRg1sbGyYNm3abQeHswYn582bR8eOHe+4jQ+r3NJNZ8p8z7OOmt22bRsTJkzAZDJRokQJHnvsMRo0aECNGjXw8PDA1dUVf39/i8HhrOc8ISEh1xHP98PtfncyA+elSpXizz//vOP9pqWl8fbbb2fr+PDuu+/y+++/42Rh9IDIP+VmTAxfzp3LJT8/AGpUqcKk117DLZeORKVKlDCnaL+ey/zykD5aJiIjq0QpC5kRDAYDHVq2pEPLlhbL+2XM9W0wGKhQpsztHJKISDZuJd15Yugzua6/Gph+vSlVTtcaEbl70QkJfLV1m3nO3+qlSzGpV0+K59IZOCw6mrSMLEwe5crmWq+NtTW1y5Th0BU/QjKm3rDk0JX0gLSbYzEeqaAsRSLy8HCuUNL8OjEyRsFhEZGHmILDIvkwGo1s27YNgEqVKlGnTp3bKu/s7EyPHj3o0aMHACdPnmT8+PEEBwezfPly3nrrrRxzNWYdqXyr8PBw88jgihUrAumppGfPng2kz127dOlSc8Asq1tTS9+OY8eOmQPDQ4cO5f3337e43Z3sI2va3uCMYENuTCZTvvM+P4zyes/T0tIICAgA0j+Dmb755htMJhOVKlVizZo1FtNe5/Z+ZD3ngYGBuc417e3tzdGjR6lcuTJPPfVUgY6lsBT0u1OhQgUg/Vjj4+PveIT3ggULzHM0T5gwgZkzZxIUFMSXX37Jxx9/XDgHJXKbIm7e5H8zZhCWkR2gWf36vDl8OA55ZHkwGAxULl8e34AA/LKkXb9VQEgIKRmdIarfkhXDZDIRExeHax4dR06ePw9ApXLl8myPiEhejMlGTGlp2DlYvo5cCw4lJioagMo1q/2DLRORf6PIuDg+3rCRsJvp15WmVSrzRvduONja5lomJTXN/Np4SwatgpTJymQycSrjN2/zqlX/lb9tReThc/WID9f+8iXVmELT0Y/nul2a8e9roJWtwgoiIg+zvCfBFBFWr15tTtn7zDPP5Pvj7ccff+Txxx9nyJAhFtc3atSIYcOGAekjhLOm9820b9++XOcEzgzQGgwGOnXqBMDFixfNAeN+/fpZDAxDeqAvU1qa5R+ruckMmgEMGjTI4jZpaWkcPHgw133kdu7q1q1rHrmaeXyWxMXF0b59e7p06cLXX39d4LY/DA4fPpzrnM1eXl7mdZnveUREBH4Zowh79OiR63zIub3nTZs2Nb8f+/bty7VdK1asYNasWXz//ffmZffqAcadfndatGgBQGpqKrt37861/g0bNtC0aVP69u3LkSNHsq27dOkSM2bMAKBv376MGjWKF198EYCVK1fi5eV1V8cmcidi4uL4ZNYsc2C4e/v2/N/IkQUKxDZ95BEATvv4kJhLNoqjp08DYGNjwyNZUhr+de4cz0+YwIh33iH0lrnLM0VGR3PszBkAWjVuXPCDEhHJYv7UmUwe9X+sW7Qq122O7D0AgK29HbUa3F4nTRGRrGISE/l04yZzYLhbvbq81bNHnoFhgLLFXbHK+A10Oij3zsypaWlczLh3qujuZnGbgIgI4pPSs0bVVOYVEXlApCYZuel7ldjAcGICr+e6XeTFjGugIfsoYhERefgoOCySh6NHj/Lll18CUK5cOXOwKC82NjZcvHiR48ePc+zYMYvbnDt3DkgfGVmiRIkc6wMDA1m6dGmO5eHh4eYRwu3ataNs2bLmfWa6dOmSxX3++eefrF271vx/o9GY77FklXVu5Nz2MXv2bHPA0tI+Muu4dbmNjQ0DBgwA0gOVuaW+njZtGjdu3CAkJIS6deveVvsfdPHx8ebg5K3Lv/rqKwCqV69OkyZNgOzv+eXLly3Wef78eebOnWv+f9bzXqZMGR599FEgfcRsWFhYjvKnTp1i586dQHrANFPWz0J+6bBvx51+d7p160apUumpjL7++msiLKTRjYiIYObMmcTHxxMeHp5tpHRKSgqTJk0iOTkZNzc33nvvPQDGjRtnHp3/3nvv3Zd5rqVo++HnnwnJ+G727tyZEYMHY2VVsFu3R1u0wMrKirj4eNZYuKaGR0SwadcuALq0bYtTlhH3NatUIbMLyJa9e3OUNZlM/PTLLxiNRuzt7emecS0REbldlapXBeD8idNEhuf8+x142Y9Du9KnjGjRsS32uXSAFBEpiB/37CUkKr2Daa+GDXi1Y4cC3Vs529vTsFL674LdF3zwvW45cLLu+F9ExqV36m1Xq6bFbXyvh5tf1ypT+rbaLyJyr5RqUA2Ddfr18Mq2o5gsDCi5eeUqYUfTnweWqFMJe9c7y9omIiIPBuV/kCIrNTWVuLi4bMuMRiMxMTFcvnwZT09P1q9fj9FoxMHBgZkzZ2abpzU3Tz/9NHPnziUqKooxY8YwduxY2rVrh5ubG9euXWP16tWsW7cOgCFDhuQ6CnPKlCmEhYXxzDPP4OrqytGjR/niiy+4du0adnZ22dI6e3h4UKZMGa5du8bKlSspXbo0ffv2xcXFhaCgIH7//XdWrFiRbS7VW489P+3bt8dgMGAymfjkk08wGo20atUKg8GAj48PP//8M7syAg1Z92GfZYRb5ny5Fy5c4MyZM1SoUAFnZ2dsbW0ZM2YMnp6ehIaGMnHiRE6ePEn//v0pXbo0QUFBLFu2zHzemjdvTp8+fW6r/Q+DRYsWER0dzfDhwyldujTnzp3j66+/5vz58xgMBv73v/+ZH164urrSqFEjTp48yZ49e/j0008ZMmQIJUqUICwsjC1btrBo0SISEhLM9d/6nk+aNInDhw8TGRnJ4MGDmTBhAm3btiUpKQlvb2++/fZbjEYjZcuW5aWXXjKXyzrv8YYNG3jsscewtra+63l57/S7Y2dnx3vvvceECRMIDg5m4MCBvPHGG7Rt2xZInzN8xowZ5tTcEydOzNbWuXPncjpjBOWkSZMoWTK996ujoyMfffQRI0eOJCQkhClTpvDZZ5/d1TGK3Gr8J58AUKtqVcZmjIyH9FG9R06dAsCjRg2e6d071xHAmezt7Mzfiwply9KjQwe27NnDhh07SEhMpHfnzrg4OXH+8mWWrF1LbFwczk5O9OvePVs9Lk5OdGnXDs99+9i6dy+2NjZ0btMGFycnAkND+XXLFs5evAjAC089RYnixQvtfIjIg2n6u58DUKl6FQaOeKHQ6m3drQOHdv2JMSmZpdN+pOezT1KxWhWSEhM5e/Qku37fSmpKCiXKlKJrv16Ftl8R+ff6z6pfAKhZujSvd+1iXn7MP4Cjfv5A+rzBA5s3IzGfDtP2Njbme6sX2rTh/NV1JBlTmLxhI/2aNqVV9Wq4ODhwLTqGbWfOsNcn/f6oceVKtKpe3WKdwVmm/Snr6nrnByoiD70j09IHcLhUKkWdZzre17bYF3eiUqeGBO48wc3LVzk5bwtVujXBqZw7qUlGrp+6QuCuk5hS07BxsqfG463va3tFROTuKTgsRdbRo0dp1qxZvttVqlSJb775hsYFTJvp5ubG9OnTGTNmDJGRkXyS8eD/Vl26dOGNN96wuK5Vq1YEBAQwf/585s+fn22dq6srM2fOpEaNGuZl1tbWTJ48mbFjx5KSksKMGTNyjEK1srLitddeY+HChSQnJ+Pv71+g48lUu3ZtRowYwdy5c7lx4wb/93//l2MbFxcXnnnmGRYsWACAn59ftpHRrVu3Zt68ecTHx5tHCi9ZsoTWrVvj7u7OggULGD16NH5+fixYsMBcT1aNGjVi9uzZBR4997CoU6cOJpOJtWvXZhvhDenBzylTptCmTZtsyz/88EOGDRtGfHw8S5cutTjafODAgXh7exMcHJzjPa9duzZz5szhjTfeICQkxOJ7WrZsWebNm4eLi4t5WYMGDXB0dCQ+Pp53332Xd999l7FjxzJu3Li7OQV39d3p06cP0dHRfPrppwQHBzNp0qQc5QwGA6+//jrPPvusedn58+eZM2cOkD4aP/NzmalTp0707duXTZs2sWbNGnr06GFO7S1SGDLTNrvd8nBwc5YU6T6+vrxs4TN9q9n/+x+lS/6d2uv5p57iWng4x86cYfuff7L9zz+zbW9vb8+k116jlIUMFkP79SMoNJRzly6xYccONtyS8t/KyopBjz9Ojw4d8m2XiDz8blxNv1a5FHfJZ8vb416qBE+/+jyr5y7lemgYy2bMy7FN6QrlGPrmCBwK0ElTRCQ0Y2Sw2y3XjD8yOt0B+FwN49VFS/Kta+Zzgymd8TuoUgl3/turJzO27yA6IZFVhw6z6tDhHGWaVKnMG9265lpneEY2Iltra+xs9EhOpChLuJ6e4t7O+cG4x6narQkpCUmEep8n2v8apxdsy7GNvbsTjzzflWIlCveeUERE/nm6ExXJwsrKimLFilG2bFnq1KlD165d6dWrF3Z2drdVT9u2bdm0aROLFy/Gy8uLoKAgjEYj7u7uNGjQgH79+tGzZ89cy1esWJEZM2bw3Xff4enpSVRUFBUrVqRLly68+OKL5nTSWXXp0oVVq1Yxf/58jhw5QmRkJHZ2dpQrV45mzZrx/PPP88gjj3Dy5Em8vb3Ztm0bo0aNuq3jmjhxIvXr12fFihWcPXuWuLg4HB0dqVKlCh06dOC5557D1dWVVatWERcXh6enZ7YAfIcOHfjwww9ZsmQJwcHBuLi4EB7+d1qtGjVq8Pvvv7N69Wq2bt2Kj48PsbGxODs7U6dOHZ544gkGDBiQLa3xv0Xx4sX58ccf+fHHH9m0aRNhYWGUK1eOtm3b8vLLL1OtWrUcZRo2bMhvv/3Gjz/+iLe3N9evX8fGxobSpUvTqFEjBg0aROvWrXnvvfdYs2YNu3btwmg0YptlTq327duzdetWFi5cyJ49ewgODiY1NZUqVarQvXt3XnrppWwjhQFKlCjBDz/8wDfffMOFCxewsbGxOHf2nbib787gwYNp3749ixcvxtvbm5CQEIxGI2XKlKFFixa88MILNGrUyLy90Whk0qRJ5uwAH3/8scV633vvPfbv38/Nmzf54IMP2LhxI67q5S/32MUsKfrvlJ2tLf997TX2HDrE7gMH8A8OTk+fXrw4TerV48nu3SmbkZL9VvZ2dnwwdiw7vLzYc+gQgaGhpKak4Fa8OA08POjdqRPVKlW66zaKiNRv0ZjSFcqy/4+dXD7nQ+zNaGxsbSlXqQINWzelZad2WCuAIiJ3KXMu4LvxSIUKfP3sM2w7c5Zj/v5cjY4mOSUFZ3t7apYpQ4fatWlVvVqu2cEA4jKm5XG8zWcMIiL3msFgoNYTbSjVoBqhB84T7R+GMS4JKztrnMq4U7J+Vcq38sDaLu952kVE5OFgMJlMpvvdCBFJ17VrV4KDg+nfvz9Tp069382Rf8DQoUM5dOgQrVq1sjjyV/5dok+evN9NEBHJ1yG3lPvdBBGRAmm+ft/9boKISL5GN8h7ehgRkQfFyi7/vd9NeChsDzh2v5vwr9K9Sv7ZXaXw/bvysoqIiIiIiIiIiIiIiIiIiEUKDouIiIiIiIiIiIiIiIiIFAGavElEHlopKSkkJd15eiZra2scHBwKsUVFV3JyMkaj8Y7L29ra3vbc3iIiIiIiIiIiIiIicnsUHBaRh9bvv//OO++8c8flNc9v4fnxxx+ZPXv2HZfXPNsiIiIiIiIiIiIiIvee0kqLiIiIiIiIiIiIiIiIiBQBBpPJZLrfjRARESkKok+evN9NEBHJ1yG3lPvdBBGRAmm+ft/9boKISL5GN7jz6bBERP5JK7v893434aGwPeDY/W7Cv0r3Ks3udxOKJI0cFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAhQcFhEREREREREREREREREpAmzudwNERESKipE3ttzvJoiI5Ov7Pfb3uwkiIgVy9KkO97sJIiL5mhulx68iIiLyYNHIYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYRERERERERERERERERGRIkDBYREpFKmpqfe7CVII9D6KiIiIiIiIiIiIiPx72dzvBogUhq5duxIcHGxxnZWVFfb29ri7u1O7dm169OhBnz59cHR0tLh9nTp1ABg7dizjxo27Z23+N9m0aRM7duzg22+/vd9NySbzc9GqVSuWLl16v5vzwDt9+jQffvgha9euvd9NEXnoXd54kBCvc3gMfJSyzWoVat3Bf57Bd9NhqnRrTNVuTfPcNiUhmWCvs9w460/CjWjAgIO7MyXqVaZCm3rYu1r+W5gp6nIoIQfOER1wnZT4JGyd7HGuWJKyzWpTqn7VQjwqEbkfFv3pxdbTZxjVuROd6njcVV1ngkPYeuYMF8PCiE1KwsXBgeqlStGpjgetqle/7fpOBgYx9Y8/MJlg5nODKe3iYnG7zSdPsdT7QL719W3UkBfatrntdojIg2HT8rUc2L6X/i8/R7NHWxVq3V7bdvPHynV0ebInXfv1znU7k8nE5+PeJTE+Id86P5jzBXYO9jmWB1y6wqFdf+Lv40vMzWisrK1wK+FOrQZ1aftYJ9xLlbirYxGR+2vhmjVs2bOH0S+8QOfWrQu17k27drFk7VoG9u7NM3365LltstHI1r17OfDXXwRfvYrRaMSteHHq165Nn86dqVap0j3bt4iIPBwUHJZ/vbS0NBISEkhISCAkJIQ9e/YwZ84cpk6dSqtWhfujsiiaNm0aP/zwg87lQ27v3r2MGjVKI4dFCsGNcwGEeJ+7J3VHB1zHz/NYgbaNC4vk9CJPkm/GZ1seHxZFfFgUoQfPU/fZTpSoY/nBwOVNhwj582y2ZcnRCUREBxFxLogS9SpRb0gXrGys7+xgROS+OuLnx7YzZwqlriVe3vxx6nS2ZZFx8UTGBXDMP4BmVasw/rHu2FoX7HoRk5jID3v2YDLlv61vePidNFlEHiLnjp/iwI5996TuwMt+bF+7uUDbRlwLL1BgODdbV//O/j92ZluWmgLXQ8O4HhrGkb3ePDNyKPWaNrzjfYjI/XPk5Em27t17T+q+6OfHyo0bC7RtZHQ0n3/3HQEhIdmWh0dEsOfgQfYfOcLLzzxD9/btC33fIiLy8FBwWP5Vmjdvzrx587ItS01NJT4+noCAALZv387y5csJDg5mxIgRLFq0iKZNs4+6qlKlCgDFixf/x9r9MAsLC7vfTZBCEB4ersCwSCG4cS6Qc8t3QwECGrcrJvA6pxdvIy05/+9qSpKRM0u2k3wzHmsHW6p0bUKJOpWwtrMhOvA6fluPkngjhnMrdtP09SdwLJ39b16w11lzYNitZnkqd22MY+niJMckcPXQBUIPXiDiXBCXfj+Ax4CCPVQQkQfHUX9/ZmzfUaDga362nDptDgw3qFiB/s2aUcndjcj4eHacPYfn2XMc8w9g4f4/GdmpY4Hq/GnffiLj4vPfEPDLCA4/2aQx/Zvlnk3BxkozKok8jM7/dZpV3y+iUC5Ytwjy9WfJtB8xJicXaPvQgCAArG2s+e+3k7Gxyf2R2q2jhg/s2GsODFf1qEHnJ3pSvkpFEuLiuXL+Ep6/biQhLp5V3y/mtffHU77K7Y3qE5H76+ipU0xbsADTPbhWXfLz4/M5c0gu4LVq+oIFBISEYGVlRe9Onejcpg3OTk5c9vdn2bp1XL1+nfmrVlGhTBkeqV27UPctIiIPDwWH5V/F2toaJyenHMtdXV0pV64crVq1olevXrzyyivEx8czevRoPD09ccmSps7T0/OfbLKIiPwLmEwm/Hf8ReCuE/ckMBxy4Dy+mw9hSkkr0PZXD14gKTIODFB3cCdKePz9gLF0cSdcKpbi2Mx1pCalEPznGWr3a2den2pMIWDnCQBcq5WhwfDHMGQEVeyci1HrqbYYrK0I8TpH2LFLVO3WBPviOf/2isiDx2QysebIUX47frxQ4izJKSn8eiw9m0GdcmV5p09vrDKuF67FivFyh0exsrJi6+kz7Lngw9PNm1HS2TnPOvf6+HDQ90qB9p9oNBISFQWAR7myONja3vnBiMgDxWQysXPdH+ze6HlPAsMHd+7nj5XrSE1JKXCZYL9AAMpULI+jc8HvfVKMRnas2wJAtTo1eemtMVhnZFJwcnGmVLky1GpQlzn/+4rE+AS2/7aZoW+OvI2jEZH7xWQysXrzZtZu3XpPAsPb9u1j8dq1pBTwWnX+8mXOX74MwMDevXm6Vy/zuhKNGlGzalX+O3UqMbGxrN26Nc/g8O3uW0REHi7qPi1FTrNmzfjggw8AiIyMZMGCBfe5RSIi8jCL8Anm2KzfCdyZHhh2rliy0OqOCbzOibl/cPn3A5hS0gpcd/gZPwCcK5XKFhjO5ODujGu1sun7CMqekvXmlTBS4pMAqNy5kTkwnFWZJjXTX6SZiA25UdDDEZH76ERgIG//upa1x9IDw9VLl7rrOs+FhhKbmH696Ne0qTkwnFWHjIeOaSYTV/JJAX09JoZFf3phMEBHj7xHsgD437hhjhnVLF36NlsvIg+qi6fO8d1HX7F7wzYwmahQrXKh1R3k68/8qTPZuGwNqSkpt1V3qH/6yOGK1arc1j4vn7tIYkY2hK79epsDw1m5lypB845t07c/c+G2gtYicn/8de4c/506lV+3bMFkMlGjyu1dG/Jyyc+Pj6ZP56dffiElJaXAdV/y9ze/tpQ2ukTx4rRo0CDHtoWxbxERebho5LAUSf3792fu3LlcuXKF5cuXM27cOPPDrDp16gAwduxYxo0bl63c5cuXWbJkCQcOHCAkJAQbGxvKlClDy5Ytef7556lXr16OfXXt2pXg4GD+97//8cQTT/Ddd9+xdetWrl+/TpkyZWjSpAmvvvqqxbKZIiIiWLlyJX/++SdXrlwhOjoae3t7ypQpQ6tWrRg2bBg1a9bMUS7zWBYuXEh4eDjfffcdwcHBlChRgr59+zJp0iTzthcvXmTVqlUcPnyY0NBQ4uLicHZ2pkqVKnTs2JGhQ4fi5uZm3n7WrFnMnj3b/P9Dhw6Z97djxw4qVfo7GGE0GlmzZg2bN2/Gx8eHuLg4SpYsSYsWLRg6dChNmjTJ9djvlcz3ZcqUKfTs2ZOffvqJP/74g+DgYFxcXGjatCljxozhkUceAeDYsWPMnz+f48ePExsbS6VKlXjyySd59dVXsb1llMrQoUM5dOgQI0aM4M0332TevHn8/vvvhISEUKJECR555BGGDx9Oy5Ytc21fbGwsa9asYe/evfj4+BAVFYWtrS2lSpWiWbNmPP/88zRq1CjX8hEREaxevZqtW7cSFBREQkICFSpUoEOHDrzyyiuUL18egIMHDzJs2LBsZTPfxylTpjBgwIA7Or9ZZZ7rTz/9lPLly/Pll1/i6+tL8eLFadOmDd9884152+DgYFauXIm3tzdBQUHExMTg6OhIhQoVaNeuHS+++CLlypXLdV+XLl1i5cqVeHl5ERISgsFgoHr16vTq1YuhQ4dSrFgxi+WOHz/Ozz//zJEjR7hx4wbFihXDw8ODvn37MnDgwBzvsUhWZxalZ5wwWFtRuXMjyjSpwZFv1hZK3edW7jaPAC7fui7Ve7fA66Nl+ZZrNLI3CdejKcgwZoOVIdv/S3hUpPW7g4gPi8K1apkClFdfQ5GHwdTN6aPWbKyt6Ne0Ce1r1WLCyl/uqs7GlSvzw9DnCYqMwqNc2Xy3t87jemEymfh+124Sko30btiAqiVLstfnYp71XbmeHmwu4eyEm6Pj7TVeRB5YS6b9CKSnb+7Y9zEat2nO9Hc+K5S6V32/mKgbEWAw0KpLe3o9+xSTR/1fgcqGZASHK9W4vUBJdEQUtvZ2GJOSqVS9aq7blSyT3mknNSWVuNg4XN001ZXIg2zKnDkA2Fhb079nTx5t0YI3J08ulLqnLVxIeEQEBoOBxx59lKH9+zP0P//Jt5zB8Pdvu9ymDrPOSIlvqVPf3exbREQeLgoOS5FkMBjo3bs3c+bMISoqirNnz9Igo+dcbnbv3s24ceOyzbORnJyMn58ffn5+rFmzhvfff58XXnjBYvmYmBgGDRrEpUuXzMuCgoIICgpi8+bNfPzxxzz77LM5yu3Zs4fx48cTH5993jWj0UhsbCy+vr6sXbuW7777jo4dLc/jtnXrVlauXGn+f1hYWLZA7+zZs5k9e3aOFDhRUVFERUVx8uRJfv31V1asWGEOKhZUaGgoI0eOxMfHJ9vyq1evsnHjRjZu3MiIESOYOHFitpvYf0p4eDhPP/00V678nb4wKSkJT09P9u/fz7Jlyzh58iSffvppthtrX19fpk+fzrlz55g5c6bFuo1GIy+//DKHDh0yLwsNDSU0NJQdO3YwZswY3nzzzRzlTp06xahRowi/ZXSN0WgkICCAgIAA1q9fz6effsrAgQNzlD906BDjx4/nxo3so/kyP6tr165lzpw5tGnTpmAnqZD89ddffPzxxxiNRiD93GdNA7969eps6zNFR0cTHR3N+fPnWbNmDYsXLzYH7bNasmQJX3zxRY6UR2fOnOHMmTP89ttvLFq0iLJl/354nZaWxpdffsnChQuzlUlOTubw4cMcPnyYX375hR9++CFbOZFsDFCyflWqPdYUx9JuJEbGFmr1xWuWo3qP5rhULvioOCtra5zKuee6Pu5qJFGXQwFwr1Uxx3o752LYOVvuTGEymQjxTp+P2NreBpcqGq0n8jAwGKBltWo827IlFd3duB4TUyj1Fnd0pHgugVmTycSW0+nzETvY2lI7j7+lG06c5FzoVSq6uzG4VUu8L/vmu+/Mkcg1S5fG+/Jldl/w4fK1aySlpFDCyYkmlSvzROPGlHLJO5W1iDxgDAYeadaQ7gP6Urp8WSLDIwq1+up1a9Nj4ONUqpF7oPZWkeERJGSM/nUu7sofK9dx4eRZosJvYGtnR/kqlWjeoTWN2jTP8bu2Zed2tOzcjsSEBGztcu90euPa37//iqnDi8gDz2Aw0LJxYwY//jgVy5bl+o3CzahU38OD5554glrVqhW4TK2qf1/Xdh04kC2tNEBMXBxHT50CoE716oW6bxERebgoOCxFVv369c2vjx8/nmdwOD4+nkmTJpGcnEyjRo0YP348tWrVwmAwcOrUKb788kv8/PyYOnUqnTt3zjZqNtP3339PfHw8vXr1YtSoUZQtW5aTJ08ydepUrly5wgcffEDlypVp27atuUxISAhvvvkmCQkJVKtWjTfffJOGDRvi5ORESEgI69atY8WKFSQnJzN58mS2b99usf0rV67Ew8ODyZMnU6lSJQ4cOEDr1q0B2LJlC7NmzQKgffv2jBw5kuoZN4hXrlxh4cKF7N69m9DQUGbOnMmUKVMAeO2113j55Zf56KOP2LBhA82bN2fevHkAOGb8kI2Pj+fll1/G19cXR0dHRo8eTY8ePXBzc8Pf35/FixezadMm5s2bh6urKyNH/vPzKs2ePZuUlBRGjx5N//79sbW1ZcOGDUybNo2EhAT+85//EBgYSOPGjZkwYQIeHh4EBwfzxRdfcOjQIbZu3cqxY8do1qxZjrp/+eUX4uPjadOmDePHj6datWpcvHiRr7/+mhMnTjBnzhwqVqyYLcAbGxvL6NGjCQ8Pp1SpUkyYMIGWLVvi6upKWFgY27dvZ/78+SQkJPDZZ5/Rp08f8/kGCAwMZMSIESQmJlKyZEneeOMNOnbsiLW1Nd7e3nz11VeEh4fz5ptvsnnzZlq0aMGxY8fYsGEDH330EZA+ShrA3t6+UM/1mjVrKFeuHJ9//jl169blr7/+okpGeqKTJ0/ywQcfYDKZaNCgAePGjcPDwwM7OzsCAwNZuXIl69atIzo6mqlTp7JkyZJsdW/cuJHPPksfTVCvXj3eeOMNGjduTGxsLBs3bmTOnDn4+voyYcIEli9fbi43c+ZMc2C4R48evPTSS9SsWZOYmBi2b9/O7NmzOXv2LK+99hqrVq0q9HMi/w7Nx/fHsfS9GdnR4KUehVK3yWQiJT6JxKhYwk/7E3rwPKaUNBzLuVOpQ96doyB9HmJjTAIxweGEeJ8j2u8aADX6tsK2mL4XIg+Dr599hgpZOgfeK8kpKUTFJ+B7/TpbTp/mwtUwAIa1a4tzLn9H/W/cYPWRI1hbGRjTpTN2NgX7meqX8RD2eEAAh6/4ZVt3LTqGbWfOsvuCD+O6daGFHm6KPDTe/OwdSpXLP3vJnXhx4qg7qjskY75hgBWzfyI15e+Ow6kpCVw5f5Er5y9y4sBRBo9+CTuHnNc7h1yyGAEkJyVzwvsIABWqVc4ziCwiD4Zv33uPCveoE/l7Y8bcUd11atSgRaNGHDl5kl+3bCEpOZkOLVvi7OTElcBAVvz+O5E3b+JYrBiDn3iiUPctIiIPFwWHpciqWPHvkVK3jtC81aFDh4iKigLS0ylnTWvbrVs3PDw86NGjB0ajEU9PT4YPH56jjvj4eAYMGGAOrgJ07tyZxo0bM2DAAEJCQpg6dSrr1683r1+2bBkJCQnY2toyf/58Klf+ez6kEiVK0KBBA6ysrFi8eDGBgYFcuXLFHNjNysrKipkzZ5rXPZHlBnD+/PkA1K5dmx9++AE7OzvzurJly9KqVSsGDhzImTNn2Ldvn3mdnZ0ddnZ22GQ8vLO2ts42ChRg3rx5+Pr6Ymtry6JFi2jcuLF5nZubG99++y0lSpRg6dKlzJw5k/79+1P6H54vLikpiffeey9bauXXXnuNo0ePsmfPHvz9/albty5Lliwxnxt3d3dmz55Nhw4dSEpKwsvLy2JwOD4+nvbt2zN37lzzeWrVqhVLlizh+eef5/Tp00yfPp0nnnjCHHRcv349169fB9IDl82bNzfX5+7uTt26dXFxceHzzz8nPj6eY8eO8eijj5q3+fzzz0lMTMTZ2ZkVK1ZQNUuv0X79+lGzZk2effZZoqKiWLlyJa+//jpOTk7Z3vdb38fCNGXKFNq1awekf3cy/fTTT5hMJkqUKMGCBQsoXvzvYFipUqVo2rQpsbGxbN++ncOHD5OYmIiDgwOQ/h5+/vnnADRo0IBly5aZ00eXLFnSfIxTpkzh6NGjHDlyhBYtWuDn58ePP6anrBs6dCjvv/++eZ9ubm4MHz6c5s2bM3jwYM6dO8fy5cstfrdF7lVguDDrToyM5cjXv2ZbVqphNWr1a4tNMbtcSv3t0jovrh3/exSfdTFb6jzTkZJ1C28OQBG5t/6JwDDAvL372H/x70w5jvZ2vN6lC82qWk7DakxNZfaOXaSkpjGwRTNqFPBeMDklheDISABSUtNoU6M6vRo2oHxxN+KSkzjke4Xfjh8nyZjCjO07+PCJx/McuSwiD457FRi+m7pDAoLMrx0cHenyZE88GtbDzt6O0MAQ9mzyxO/8JS6eOsfquUt5/o1Xb6v+rb+sJ/ZmNACtuz6az9Yi8iC4lwHUu6l7wvDhrNiwga379rHe05P1np7Z1jetX58X+vWjUi5TdikwLCJSNGiSOCmyso62zAz85iZrKunMwF1WlStXZu7cuaxevdpiml9ID7i99957OZa7u7ub5zY+f/58trTTHh4eDBo0iBEjRmQLDGfVqlUr8+uICMvpturWrWsxaJyWlkbnzp3p168fY8aMyRYgzGRlZUWLFi0AiMx4AFcQJpOJVatWAdC3b99sgeGsxo8fj4ODA0ajkd9++63A9RcWJycnnnvuuRzLM48ZYNiwYTnOTfHixc3n9Nq1axbrNhgMfPzxx+bAcCYHBwf++9//AumfpwMHDpjXlS9fnueff54hQ4ZkCwxnlTnqG7K/59HR0eYA/vDhw7MFhjM1bNiQ3r1707x5c3Nw9Z/i5uaWbWR8Vs2aNWPgwIG8/vrr2QLDWWV+1tPS0rh586Z5ube3tzmF9qRJkyzOKzxkyBA8PDx49NFHiYuLA2DVqlWkpaVRrFgxJkyYYHGfjRo1ok+fPkD6SHCRh1WShVTXN84G4LvxIClJRgslbikfFZft/6kJRnw3HyL8rH+htVFE/h1uxGa/3sQnJbPU2zvHyN5MKw4eIigykhqlS9GvadMC7yc8NhZ3JyesDAYGtmjOm491p065crgWc6B88eI81bQJ7/Xtg421FSmpaSz60+tuDktEirjkxCQcHIvhWsKNMR9NpHXXR3EvXRInVxdq1a/D8LfGUK9ZIwDO/3WaCyfOFLhur227ObTrTwCqetSgaftW+ZQQEcldQlISVlZW2Ft4xgcQGBrK+cuX/+FWiYjIg0Yjh6XIyhrwzW+u2yZNmmBra4vRaGT48OEMHjyYLl260KRJE6ytrQHo0KFDnnV06tQJZ2fL85116dLF/NrLy4tatWoB6SM9+/Xrl2udoaGhnD171vz/rHPiZlWvXj2Ly62srBg7dmyu9aelpXHp0iWCgtJ7Sd86l2teLl26ZA7Y1atXzxyQu5XBYKBOnTqcOHHCnM74n1S/fv0cwVtIH5mddRtLMt/PrJ+lrBo1apRnUN/Z2ZnY2Fi8vLzo1KkTAF27dqVr1665tjc8PJzjx4+b/5/1PT98+LB5vt7OnTvnWse3336b67p7qW7durl+11588cU8y/r5+XE5y4+XrJ9Fb29vIP39aNmypcXy9vb2bNiwIduyzLmga9SoAZDrZ7Rx48Zs2LABX19fIiMjcXfPfR5XkQeVU/kStHr7WWyd7Em8EUOw11muHvLh2nFf4q/fpPFrfbDK+HtmSe3+7bEr7ogpJY3IS8Fc2XKExPAYzv28i7pDOlO6QbV/7mBE5IE2olNHSjo5YUxN5VRQMD8fPMjVm9FM8/Tkje7daJPxdxfgTHAIW06fxtbGmjFdumBtVfC+yxXc3Jj13BBSUlOxyeX6VbtsWbrVq8vW02fxvR5OwI0bVClZ8q6PUUSKnr7PDaDvcwNITUnB2sLvRysrKx5/4Wl8Tp4hNSWVo/sOUqex5d+RWXlt280fK9cB4OJenGdHvZjv8wkRkdxE3LzJJ7NmERIWhquzM6899xzNGzTA0cGBoLAwNu3cyb7Dh5m3ciXBYWG8OGDA/W6yiIjcJwoOS5EVExNjfu3q6prntmXKlGHixIlMnTqVmJgY5s2bZ54nt23btnTq1Ilu3brhlke6Pg8Pj1zXubu7U7x4cW7evMnVq1dzrE9MTMTb25vz588TEBBAYGAgly5dyjGS12QyWaw/a6AzN+Hh4Xh7e3Pp0iUCAwPx9/fH19eX+Pj4fMtaEhj495xMU6ZMyZZOOzehoaF3tK+7kVugzyrLw8ncgvpW+TzAzOs9NxgMVKlShbNnz1p8z41GI4cPH+bMmTMEBAQQEBCAr69vjlHKWd/zsLAw8+tqD+C8egX5HMbExODl5YWPj4/5s3758mWio6OzbWfpuKtWrXpbD1IyOz2cOXPGYlpwS65evargsDyUbB3/nvfOsYwbtfu1w9bJgcBdJ4kNukHYsUuUb1kn1/LFSmX8nbSF0g2r41q1LMdn/44xNpErfxymZL3KeQaXRaToKJ+RAcTOxoY2NWtQp1xZ3v51LdEJifx84CAtqlbFxtqauKQk5uzejckEQ1q1oqK72x3tL7fAcKbmVauy9XR6Z8qL164pOCwid8VSYDiTq1txKlarQsClKwT5+uVZj8lkwvPXjezbvAMAFzdXhr81Ble3ezddiYj8+/28bh0hYWEUc3Dg4/Hjs6WIrl6pEmOHDaOUuzu/bdvG5l27aNWoEfUyBqiIiEjRouCwFFlZg5e5je7Mavjw4dSrV4+ffvoJb29vjEYj0dHRbN26la1bt2Jra8vQoUN56623zKOJs8ovAO3g4MDNmzeJvSUV37Jly5g+fXq2YDakBybr1atHtWrV+OOPP/KsO3M+W0sy52tdvXp1jpHH9vb2tG7dmrS0NA4fPpznPm5163HcqzJ3K2t68cLm4uKS5/rMtM63Hvcff/zBZ599liOFucFgoEaNGjRu3NhiCu6sqZYtpVa+3/L6HKalpTFr1ix++uknkpKSsq2ztbWladOmuLq6smfPnhxlM4/7dtNkPyyfUZF7pXLnRoR4nSU1KYWIc4F5BodvZe/qSIV29fDfdpykyDjiQiNxqVTqHrZWRB5W7k5O9GrQgF8OHyE8Jhb/GxHULFOan/b/SURsHPUrVqBXg/xH192pklk6+UUnJN6z/YiIABQv4Q5cIT7WclYiAGOykV/nL+PMkRMAuJUqwUsTR1OybMHmXBcRsSQxKQmvjIx8vTp2zHXu4IG9e7P74EEib97E888/FRwWESmiFByWIuvEiRPm1w0bNixQmTZt2tCmTRtzKmBvb2/2799PQEAARqORBQsWYDKZePvtt3OUvTXgdavMEbpZRyUuWrTIPOK2QoUKdO/enXr16lGzZk1q166No6MjXl5e+QaH8zJhwgR27EjvrVy/fn06depE7dq1qVWrFjVq1MDGxoZp06bddnA4a3By3rx5dOzY8Y7b+LDKLd10psz3POuI823btjFhwgRMJhMlSpTgscceo0GDBtSoUQMPDw9cXV3x9/e3GBzOes4TEhJyHfH8IJoyZQpLliwB0tM8d+3alTp16pg/63Z2dqxevdpicDjzuBMTb++Br4ODA7GxsfTp04dp06bd/UGIPGSsbW1wLONGTGA4CREx+Re4hXOFv0ffJUbGKDgsIrmqXurv68P1mBhqlimN96X06SLOBIfw3Nz5eZZ/Y/lKAOqVL8eHTz6RbZ3JZMozc0hqWpr5tYOt7W23XUQkq3yvOanp09/Y5jLXZ1x0DMtmzifI1x+ACtUqM/TNkTgXz7tjsYhIfq5ev05axn2PR5ZpPG5lY2ND7erVOfTXX4RkyUAnIiJFi4LDUiQZjUa2bdsGQKVKlahTp+CjpSA9zXCPHj3o0aMHACdPnmT8+PEEBwezfPly3nrrrRzz2GYdqXyr8PBw88jgihUrAumBrtmzZwPpc9cuXbrU4sjIW1NL345jx46ZA8NDhw7l/ffft7jdneyjfPny5tfBwcF5bpvfD+yHVV7veVpaGgEBAUD6ZzDTN998g8lkolKlSqxZs8ZiCuPc3o+s5zwwMDDXuaa9vb05evQolStX5qmnnirQsdxLoaGhLFu2DIDHHnuMGTNmWBx9n99x53W+AVatWkV0dDQNGjSgbdu2VKhQAR8fnyL7+ZR/r5SEZHzW7icxIoaKjzagbNOauW6bakzPGGFt+/ffrKtHfLj2ly+pxhSajn4817Jpxr+zTVjZ6pZSpCjadf4C+y9eJCklhU/798t1u+SMYAmkp5suDMsPHmTPBR+SUlKYO2xorvUGR0aZX2emvBYRuR03IyKZP2UWcTGxPNqrC1379c512+sh6YEWS6OAo6Nu8tPUWURcCwfAo/EjDHrtRewccs+wJCJSUFmzAaakpOSx5d+MBdxORET+ffQkT4qk1atXm1P2PvPMM/kGfn788Uc2bNiAi4sLK1asyLG+UaNGDBs2jClTppCUlMTNmzcpect8Zvv27cs1yJQZoDUYDHTq1AmAixcvmgPG/fr1yzVlrre3t/l1WpaREQVx/Phx8+tBgwZZ3CYtLY2DBw9m+3/WuXZzO3d169bF2dmZ2NhYduzYwZAhQyxuFxcXx2OPPYa9vT19+/blrbfeuq1jeJAdPnyY+Ph4i6mrvby8zCOHM9/ziIgI/Pz8AOjRo0euc9vm9p43bdoUg8GAyWRi3759uQaHV6xYwdatW6levbo5OHw/g58nTpwwH8fAgQMtBoYh+3FnnXO4WbNmLFu2jOjoaP766y+aNGmSo6zJZGLGjBncuHGDQYMG0bZtW1q0aIGPjw9nzpzh6tWrlCtXzuJ+P/zwQ7Zt20alSpVYvHjxQzUiW4omawdbonxDSU0wcv2Eb67B4aSbccRfiwLAueLff7NSk4zc9E2fCz0m8DoulS2nOIy8mNGxwpB9FLGIFB0JycmcDQkF4NK1a9QqU8bidicDgwAwGKB6qfTrxcKXX8qz7v0XL/HTvv0AfPXsQEo5O2OV5X7FxcHBnCb6TEgITatUsVzPpUsA2NvaUKec5fSKIiJ5cXErTkJ8PMbkZHxOncs1OBwaEMT10PTgsEfD7L/F4mPjWPjVd+bAcItO7Xhi6MBsv61FRO5G2dKlsbKyIi0tjZMXLtCqcWOL26WmpnLxyhUAKuaSelpERP79dBcqRc7Ro0f58ssvAShXrhwvvvhivmVsbGy4ePEix48f51jG/B23OnfuHJA+qrhEiRI51gcGBrJ06dIcy8PDw80jhNu1a0fZjBuzrCOPL2U81LrVn3/+ydq1a83/NxqN+R5LVlmDcLntY/bs2eaApaV9ZNZx63IbGxsGDBgApAfGc0t9PW3aNG7cuEFISAh169a9rfY/6OLj45kxY4bF5V999RUA1atXNwczs77nly9ftljn+fPnmTt3rvn/Wc97mTJlePTRRwFYsGABYRbSA506dYqdO3cC0LdvX/PyrJ+F/NJhF7aCfNZ//fVXvLy8zP/P2sZu3bqZU3N//fXXFr8HS5cu5caNG8Dfx/3ss88C6T1qP/744xxzbkN64Pq3334jKioKNzc3BYbloWAwGCjTKD2NWKRPMJGXQ3Jsk5aaxqX13pBmAgOUa+FhXleqQTUM1um3iFe2HcVkoePRzStXCTua/n0tUacS9q73bv52EXlwta5RHZuM68XKQ4ctdlQ8FxrKHh8fAJpUroy7kxOQnuI5r3+2We5N7G1scLC1zTY6uE2NGuZ9/3zgIEYLf8e9Ll3mqF966tbu9epRLJc0ryIiebGysqJhq2YABF8J4C+vnFMuJScmsW7RKgDsHOxp2aV9tvW/LVhBeOg1ANo+1pGnXnxWgWERKVTOjo40yniutsvbm8sZ2epu9du2bUTevAnAoy1a/GPtExGRB4tGDsu/SmpqKnFxcdmWGY1GYmJiuHz5Mp6enqxfvx6j0YiDgwMzZ87MNk9rbp5++mnmzp1LVFQUY8aMYezYsbRr1w43NzeuXbvG6tWrWbduHQBDhgzJdRTmlClTCAsL45lnnsHV1ZWjR4/yxRdfcO3aNezs7LKldfbw8KBMmTJcu3aNlStXUrp0afr27YuLiwtBQUH8/vvvrFixIltA69Zjz0/79u3NI00/+eQTjEYjrVq1wmAw4OPjw88//8yuXbuylYmLi8Pe/u+0V5lBuQsXLnDmzBkqVKiAs7Mztra2jBkzBk9PT0JDQ5k4cSInT56kf//+lC5dmqCgIJYtW2Y+b82bN6dPnz631f6HwaJFi4iOjmb48OGULl2ac+fO8fXXX3P+/HkMBgP/+9//zA8FXF1dadSoESdPnmTPnj18+umnDBkyhBIlShAWFsaWLVtYtGgRCQkJ5vpvfc8nTZrE4cOHiYyMZPDgwUyYMIG2bduSlJSEt7c33377LUajkbJly/LSSy+Zy2Wd93jDhg089thjWFtb45TxAPdeat68OQ4ODuZU6sWKFaNTp044ODhw5coV1qxZY/6cZMp63A4ODvz3v//l3Xff5fDhw7z44ouMGzeOunXrcuPGDTZs2MBPP/0EQJcuXWjdujUA9erV47nnnmP58uXs3LmTYcOGMXr0aB555BFiY2PZs2cPs2bNwmg0Ym9vz//93//d83MhRc+RaekdfFwqlaLOM4U3N3uVbk0IP+OPMTaRs0t3ULlzI0rVr4qtowOxoREE7PyLaL/0B5QV2z+Sbb5g++JOVOrUkMCdJ7h5+Son522hSrcmOJVzJzX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}, "metadata": {}, "output_type": "display_data" } ], "source": [ - "visualizer.create_model_rank_heatmaps(\n", + "visualizer.create_disparity_metric_heatmap(\n", + " model_names=list(models_metrics_dct.keys()),\n", " metrics_lst=[\n", - " # Group fairness metrics\n", + " # Error disparity metrics\n", " 'Equalized_Odds_TPR',\n", " 'Equalized_Odds_FPR',\n", " 'Disparate_Impact',\n", - " 'Statistical_Parity_Difference',\n", - " 'Accuracy_Parity',\n", - " # Group stability metrics\n", - " 'Label_Stability_Ratio',\n", + " # Stability disparity metrics\n", + " 'Label_Stability_Difference',\n", " 'IQR_Parity',\n", - " 'Std_Parity',\n", " 'Std_Ratio',\n", - " 'Jitter_Parity',\n", " ],\n", " groups_lst=config.sensitive_attributes_dct.keys(),\n", + " tolerance=0.005,\n", ")" ] }, { "cell_type": "code", - "execution_count": 79, - "id": "2326c129", - "metadata": {}, + "execution_count": 85, "outputs": [], - "source": [] + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-21T16:35:57.473340Z", + "start_time": "2023-12-21T16:35:57.471719Z" + } + }, + "id": "a6ebe5c2fae387cb" } ], "metadata": { diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index ce757c6d..ec255437 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:22:33.321331Z", - "start_time": "2023-12-21T12:22:32.400819Z" + "end_time": "2023-12-21T16:26:23.346807Z", + "start_time": "2023-12-21T16:26:22.952137Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:22:33.330344Z", - "start_time": "2023-12-21T12:22:33.320778Z" + "end_time": "2023-12-21T16:26:23.354069Z", + "start_time": "2023-12-21T16:26:23.308502Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:22:33.340224Z", - "start_time": "2023-12-21T12:22:33.330669Z" + "end_time": "2023-12-21T16:26:23.354407Z", + "start_time": "2023-12-21T16:26:23.319975Z" } }, "outputs": [ @@ -100,8 +100,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.226990Z", - "start_time": "2023-12-21T12:22:33.341502Z" + "end_time": "2023-12-21T16:26:24.533814Z", + "start_time": "2023-12-21T16:26:23.331919Z" } }, "outputs": [ @@ -180,8 +180,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.244903Z", - "start_time": "2023-12-21T12:22:38.227234Z" + "end_time": "2023-12-21T16:26:24.553691Z", + "start_time": "2023-12-21T16:26:24.535190Z" } }, "id": "ce359a052925eb3a" @@ -192,15 +192,6 @@ "outputs": [], "source": [ "models_params_for_tuning = {\n", - " 'DecisionTreeClassifier': {\n", - " 'model': DecisionTreeClassifier(random_state=MODELS_TUNING_SEED),\n", - " 'params': {\n", - " \"max_depth\": [20, 30],\n", - " \"min_samples_split\" : [0.1],\n", - " \"max_features\": ['sqrt'],\n", - " \"criterion\": [\"gini\", \"entropy\"]\n", - " }\n", - " },\n", " 'LogisticRegression': {\n", " 'model': LogisticRegression(random_state=MODELS_TUNING_SEED),\n", " 'params': {\n", @@ -224,8 +215,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.265352Z", - "start_time": "2023-12-21T12:22:38.246137Z" + "end_time": "2023-12-21T16:26:24.573125Z", + "start_time": "2023-12-21T16:26:24.554343Z" } }, "id": "2ece07ab7e3a9acc" @@ -279,8 +270,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.284235Z", - "start_time": "2023-12-21T12:22:38.265046Z" + "end_time": "2023-12-21T16:26:24.593114Z", + "start_time": "2023-12-21T16:26:24.573989Z" } }, "id": "af22ee06f1e3eb1a" @@ -296,8 +287,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.302605Z", - "start_time": "2023-12-21T12:22:38.284404Z" + "end_time": "2023-12-21T16:26:24.612107Z", + "start_time": "2023-12-21T16:26:24.593673Z" } }, "id": "65181f72484bb92b" @@ -316,8 +307,8 @@ "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.367706Z", - "start_time": "2023-12-21T12:22:38.302834Z" + "end_time": "2023-12-21T16:26:24.671534Z", + "start_time": "2023-12-21T16:26:24.612701Z" } }, "outputs": [ @@ -351,8 +342,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.386761Z", - "start_time": "2023-12-21T12:22:38.367870Z" + "end_time": "2023-12-21T16:26:24.688534Z", + "start_time": "2023-12-21T16:26:24.670376Z" } }, "id": "ebbef5eaf9dc0943" @@ -372,8 +363,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.460362Z", - "start_time": "2023-12-21T12:22:38.387441Z" + "end_time": "2023-12-21T16:26:24.750378Z", + "start_time": "2023-12-21T16:26:24.689319Z" } }, "id": "97ed4609effbf53f" @@ -393,8 +384,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:38.489420Z", - "start_time": "2023-12-21T12:22:38.444854Z" + "end_time": "2023-12-21T16:26:24.767288Z", + "start_time": "2023-12-21T16:26:24.747396Z" } }, "id": "4535191384245578" @@ -417,23 +408,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023/12/21, 14:22:38: Tuning DecisionTreeClassifier...\n", - "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 14:22:40: Tuning for DecisionTreeClassifier is finished [F1 score = 0.5243029506705218, Accuracy = 0.8876602564102564]\n", - "\n", - "2023/12/21, 14:22:40: Tuning LogisticRegression...\n", + "2023/12/21, 18:26:24: Tuning LogisticRegression...\n", "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n", - "2023/12/21, 14:22:40: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", + "2023/12/21, 18:26:26: Tuning for LogisticRegression is finished [F1 score = 0.6605519139439457, Accuracy = 0.8993589743589743]\n", "\n", - "2023/12/21, 14:22:40: Tuning RandomForestClassifier...\n", + "2023/12/21, 18:26:26: Tuning RandomForestClassifier...\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", - "2023/12/21, 14:22:42: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" + "2023/12/21, 18:26:28: Tuning for RandomForestClassifier is finished [F1 score = 0.6531017911447438, Accuracy = 0.8952724358974359]\n" ] }, { "data": { - "text/plain": " Dataset_Name Model_Name F1_Score Accuracy_Score \\\n0 Law_School DecisionTreeClassifier 0.524303 0.887660 \n1 Law_School LogisticRegression 0.660552 0.899359 \n2 Law_School RandomForestClassifier 0.653102 0.895272 \n\n Model_Best_Params \n0 {'criterion': 'gini', 'max_depth': 20, 'max_fe... \n1 {'C': 100, 'max_iter': 250, 'penalty': 'l2', '... \n2 {'max_depth': 10, 'max_features': 0.6, 'min_sa... ", - "text/html": "
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    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0Law_SchoolDecisionTreeClassifier0.5243030.887660{'criterion': 'gini', 'max_depth': 20, 'max_fe...
    1Law_SchoolLogisticRegression0.6605520.899359{'C': 100, 'max_iter': 250, 'penalty': 'l2', '...
    2Law_SchoolRandomForestClassifier0.6531020.895272{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    \n
    " + "text/plain": " Dataset_Name Model_Name F1_Score Accuracy_Score \\\n0 Law_School LogisticRegression 0.660552 0.899359 \n1 Law_School RandomForestClassifier 0.653102 0.895272 \n\n Model_Best_Params \n0 {'C': 100, 'max_iter': 250, 'penalty': 'l2', '... \n1 {'max_depth': 10, 'max_features': 0.6, 'min_sa... ", + "text/html": "
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    Dataset_NameModel_NameF1_ScoreAccuracy_ScoreModel_Best_Params
    0Law_SchoolLogisticRegression0.6605520.899359{'C': 100, 'max_iter': 250, 'penalty': 'l2', '...
    1Law_SchoolRandomForestClassifier0.6531020.895272{'max_depth': 10, 'max_features': 0.6, 'min_sa...
    \n
    " }, "execution_count": 13, "metadata": {}, @@ -447,8 +434,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:42.846255Z", - "start_time": "2023-12-21T12:22:38.464922Z" + "end_time": "2023-12-21T16:26:28.708794Z", + "start_time": "2023-12-21T16:26:24.766862Z" } }, "id": "782741c190a4690b" @@ -466,8 +453,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:42.902571Z", - "start_time": "2023-12-21T12:22:42.845818Z" + "end_time": "2023-12-21T16:26:28.753897Z", + "start_time": "2023-12-21T16:26:28.711938Z" } }, "id": "21ccc879c5c3e215" @@ -490,9 +477,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'DecisionTreeClassifier': DecisionTreeClassifier(max_depth=20, max_features='sqrt', min_samples_split=0.1,\n", - " random_state=42),\n", - " 'LogisticRegression': LogisticRegression(C=100, max_iter=250, random_state=42, solver='newton-cg'),\n", + "{'LogisticRegression': LogisticRegression(C=100, max_iter=250, random_state=42, solver='newton-cg'),\n", " 'RandomForestClassifier': RandomForestClassifier(max_depth=10, max_features=0.6, n_estimators=50,\n", " random_state=42)}\n" ] @@ -505,8 +490,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:22:42.939840Z", - "start_time": "2023-12-21T12:22:42.876922Z" + "end_time": "2023-12-21T16:26:28.779244Z", + "start_time": "2023-12-21T16:26:28.734481Z" } }, "id": "3b15f202741fa2ae" @@ -533,18 +518,18 @@ "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.657769Z", - "start_time": "2023-12-21T12:22:42.902946Z" + "end_time": "2023-12-21T16:27:18.526691Z", + "start_time": "2023-12-21T16:26:28.757510Z" } }, "outputs": [ { "data": { - "text/plain": "Analyze multiple models: 0%| | 0/3 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Aleatoric_Uncertainty0.3904020.3750050.4107470.3275600.739895
    1Mean_Prediction0.1083350.1014890.1173820.0777040.278689
    2Overall_Uncertainty0.4068050.3902470.4286840.3439770.756219
    3IQR0.0486240.0468310.0509930.0455890.065505
    4Statistical_Bias0.1587790.1466270.1748370.1271630.334613
    5Std0.0380830.0359250.0409340.0365940.046364
    6Label_Stability0.9473170.9571620.9343080.9995920.656593
    7Jitter0.0291090.0238450.0360640.0004060.188740
    8TPR0.9785410.9832560.9721171.0000000.827957
    9TNR0.2129630.1880730.2383180.0000000.544379
    10PPV0.9147440.9227410.9039480.9254110.833333
    11FNR0.0214590.0167440.0278830.0000000.172043
    12FPR0.7870370.8119270.7616821.0000000.455621
    13Accuracy0.8990380.9100510.8844870.9254110.752366
    14F10.9455680.9520380.9367940.9612610.830636
    15Selection-Rate0.9586540.9674830.9469871.0000000.728707
    16Positive-Rate1.0697421.0655811.0754121.0806010.993548
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n" + "text/plain": " Metric overall male_priv male_dis race_priv \\\n0 Overall_Uncertainty 0.337915 0.316983 0.365574 0.288437 \n1 Mean_Prediction 0.103832 0.092501 0.118805 0.075184 \n2 Aleatoric_Uncertainty 0.336957 0.316096 0.364524 0.287719 \n3 IQR 0.012106 0.011306 0.013164 0.009428 \n4 Std 0.009250 0.008588 0.010123 0.007255 \n5 Statistical_Bias 0.137826 0.127310 0.151721 0.112085 \n6 Jitter 0.008652 0.007309 0.010425 0.004766 \n7 Label_Stability 0.987702 0.989409 0.985446 0.993114 \n8 TPR 0.984442 0.988837 0.978454 0.994177 \n9 TNR 0.252315 0.197248 0.308411 0.129278 \n10 PPV 0.919108 0.923946 0.912530 0.934063 \n11 FNR 0.015558 0.011163 0.021546 0.005823 \n12 FPR 0.747685 0.802752 0.691589 0.870722 \n13 Accuracy 0.908413 0.915963 0.898438 0.929665 \n14 F1 0.950654 0.955291 0.944343 0.963183 \n15 Selection-Rate 0.959856 0.971706 0.944196 0.984969 \n16 Positive-Rate 1.071084 1.070233 1.072243 1.064358 \n17 Sample_Size 4160.000000 2368.000000 1792.000000 3526.000000 \n\n race_dis \n0 0.613087 \n1 0.263155 \n2 0.610794 \n3 0.027002 \n4 0.020341 \n5 0.280986 \n6 0.030261 \n7 0.957603 \n8 0.916129 \n9 0.443787 \n10 0.819231 \n11 0.083871 \n12 0.556213 \n13 0.790221 \n14 0.864975 \n15 0.820189 \n16 1.118280 \n17 634.000000 ", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Metricoverallmale_privmale_disrace_privrace_dis
    0Overall_Uncertainty0.3379150.3169830.3655740.2884370.613087
    1Mean_Prediction0.1038320.0925010.1188050.0751840.263155
    2Aleatoric_Uncertainty0.3369570.3160960.3645240.2877190.610794
    3IQR0.0121060.0113060.0131640.0094280.027002
    4Std0.0092500.0085880.0101230.0072550.020341
    5Statistical_Bias0.1378260.1273100.1517210.1120850.280986
    6Jitter0.0086520.0073090.0104250.0047660.030261
    7Label_Stability0.9877020.9894090.9854460.9931140.957603
    8TPR0.9844420.9888370.9784540.9941770.916129
    9TNR0.2523150.1972480.3084110.1292780.443787
    10PPV0.9191080.9239460.9125300.9340630.819231
    11FNR0.0155580.0111630.0215460.0058230.083871
    12FPR0.7476850.8027520.6915890.8707220.556213
    13Accuracy0.9084130.9159630.8984380.9296650.790221
    14F10.9506540.9552910.9443430.9631830.864975
    15Selection-Rate0.9598560.9717060.9441960.9849690.820189
    16Positive-Rate1.0710841.0702331.0722431.0643581.118280
    17Sample_Size4160.0000002368.0000001792.0000003526.000000634.000000
    \n
    " }, "execution_count": 17, "metadata": {}, @@ -652,8 +625,8 @@ "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.754548Z", - "start_time": "2023-12-21T12:23:38.684882Z" + "end_time": "2023-12-21T16:27:18.581429Z", + "start_time": "2023-12-21T16:27:18.556276Z" } }, "outputs": [], @@ -667,8 +640,8 @@ "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.755147Z", - "start_time": "2023-12-21T12:23:38.723143Z" + "end_time": "2023-12-21T16:27:18.611090Z", + "start_time": "2023-12-21T16:27:18.577456Z" } }, "outputs": [], @@ -690,8 +663,8 @@ "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.868859Z", - "start_time": "2023-12-21T12:23:38.738044Z" + "end_time": "2023-12-21T16:27:18.633323Z", + "start_time": "2023-12-21T16:27:18.596278Z" } }, "outputs": [], @@ -705,8 +678,8 @@ "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.025564 -0.173045 -0.150360 \n1 Aleatoric_Uncertainty_Parity 0.035741 0.412335 0.385770 \n2 Aleatoric_Uncertainty_Ratio 1.095309 2.258806 2.072181 \n3 Equalized_Odds_FNR 0.011139 0.172043 0.169995 \n4 Equalized_Odds_FPR -0.050244 -0.544379 -0.474526 \n5 IQR_Parity 0.004162 0.019916 0.019940 \n6 Jitter_Parity 0.012219 0.188334 0.179519 \n7 Label_Stability_Ratio 0.976123 0.656861 0.662353 \n8 Label_Stability_Difference -0.022854 -0.342999 -0.328662 \n9 Overall_Uncertainty_Parity 0.038438 0.412241 0.386115 \n10 Overall_Uncertainty_Ratio 1.098495 2.198454 2.026424 \n11 Statistical_Parity_Difference 0.009831 -0.087052 -0.114095 \n12 Disparate_Impact 1.009225 0.919441 0.894083 \n13 Std_Parity 0.005009 0.009770 0.009791 \n14 Std_Ratio 1.139419 1.266986 1.262459 \n15 Equalized_Odds_TNR 0.050244 0.544379 0.474526 \n16 Equalized_Odds_TPR -0.011139 -0.172043 -0.169995 \n17 Accuracy_Parity -0.019486 -0.143166 -0.114463 \n18 Aleatoric_Uncertainty_Parity 0.046563 0.321422 0.332505 \n19 Aleatoric_Uncertainty_Ratio 1.147821 2.123594 2.077199 \n20 Equalized_Odds_FNR 0.011482 0.080505 0.097373 \n21 Equalized_Odds_FPR -0.101903 -0.304790 -0.367321 \n22 IQR_Parity 0.001609 0.018242 0.017678 \n23 Jitter_Parity 0.002724 0.027312 0.029017 \n24 Label_Stability_Ratio 0.996701 0.962152 0.958633 \n25 Label_Stability_Difference -0.003261 -0.037570 -0.040960 \n26 Overall_Uncertainty_Parity 0.046695 0.323242 0.334164 \n27 Overall_Uncertainty_Ratio 1.147771 2.126884 2.079338 \n28 Statistical_Parity_Difference 0.002011 0.053922 -0.019052 \n29 Disparate_Impact 1.001879 1.050661 0.982233 \n30 Std_Parity 0.001352 0.014139 0.013621 \n31 Std_Ratio 1.147642 2.863905 2.572841 \n32 Equalized_Odds_TNR 0.101903 0.304790 0.367321 \n33 Equalized_Odds_TPR -0.011482 -0.080505 -0.097373 \n34 Accuracy_Parity -0.016530 -0.141181 -0.112635 \n35 Aleatoric_Uncertainty_Parity 0.033235 0.333090 0.331327 \n36 Aleatoric_Uncertainty_Ratio 1.105448 2.195036 2.092734 \n37 Equalized_Odds_FNR 0.008482 0.074053 0.089463 \n38 Equalized_Odds_FPR -0.110735 -0.313654 -0.367186 \n39 IQR_Parity 0.004679 0.051838 0.052185 \n40 Jitter_Parity 0.006285 0.085427 0.081421 \n41 Label_Stability_Ratio 0.990591 0.878776 0.883924 \n42 Label_Stability_Difference -0.009064 -0.118486 -0.112393 \n43 Overall_Uncertainty_Parity 0.034337 0.345531 0.343992 \n44 Overall_Uncertainty_Ratio 1.105292 2.198752 2.096863 \n45 Statistical_Parity_Difference 0.004755 0.064680 -0.005242 \n46 Disparate_Impact 1.004433 1.060646 0.995124 \n47 Std_Parity 0.003429 0.037755 0.037590 \n48 Std_Ratio 1.110082 2.405060 2.268096 \n49 Equalized_Odds_TNR 0.110735 0.313654 0.367186 \n50 Equalized_Odds_TPR -0.008482 -0.074053 -0.089463 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n5 DecisionTreeClassifier \n6 DecisionTreeClassifier \n7 DecisionTreeClassifier \n8 DecisionTreeClassifier \n9 DecisionTreeClassifier \n10 DecisionTreeClassifier \n11 DecisionTreeClassifier \n12 DecisionTreeClassifier \n13 DecisionTreeClassifier \n14 DecisionTreeClassifier \n15 DecisionTreeClassifier \n16 DecisionTreeClassifier \n17 LogisticRegression \n18 LogisticRegression \n19 LogisticRegression \n20 LogisticRegression \n21 LogisticRegression \n22 LogisticRegression \n23 LogisticRegression \n24 LogisticRegression \n25 LogisticRegression \n26 LogisticRegression \n27 LogisticRegression \n28 LogisticRegression \n29 LogisticRegression \n30 LogisticRegression \n31 LogisticRegression \n32 LogisticRegression \n33 LogisticRegression \n34 RandomForestClassifier \n35 RandomForestClassifier \n36 RandomForestClassifier \n37 RandomForestClassifier \n38 RandomForestClassifier \n39 RandomForestClassifier \n40 RandomForestClassifier \n41 RandomForestClassifier \n42 RandomForestClassifier \n43 RandomForestClassifier \n44 RandomForestClassifier \n45 RandomForestClassifier \n46 RandomForestClassifier \n47 RandomForestClassifier \n48 RandomForestClassifier \n49 RandomForestClassifier \n50 RandomForestClassifier ", - "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.025564-0.173045-0.150360DecisionTreeClassifier
    1Aleatoric_Uncertainty_Parity0.0357410.4123350.385770DecisionTreeClassifier
    2Aleatoric_Uncertainty_Ratio1.0953092.2588062.072181DecisionTreeClassifier
    3Equalized_Odds_FNR0.0111390.1720430.169995DecisionTreeClassifier
    4Equalized_Odds_FPR-0.050244-0.544379-0.474526DecisionTreeClassifier
    5IQR_Parity0.0041620.0199160.019940DecisionTreeClassifier
    6Jitter_Parity0.0122190.1883340.179519DecisionTreeClassifier
    7Label_Stability_Ratio0.9761230.6568610.662353DecisionTreeClassifier
    8Label_Stability_Difference-0.022854-0.342999-0.328662DecisionTreeClassifier
    9Overall_Uncertainty_Parity0.0384380.4122410.386115DecisionTreeClassifier
    10Overall_Uncertainty_Ratio1.0984952.1984542.026424DecisionTreeClassifier
    11Statistical_Parity_Difference0.009831-0.087052-0.114095DecisionTreeClassifier
    12Disparate_Impact1.0092250.9194410.894083DecisionTreeClassifier
    13Std_Parity0.0050090.0097700.009791DecisionTreeClassifier
    14Std_Ratio1.1394191.2669861.262459DecisionTreeClassifier
    15Equalized_Odds_TNR0.0502440.5443790.474526DecisionTreeClassifier
    16Equalized_Odds_TPR-0.011139-0.172043-0.169995DecisionTreeClassifier
    17Accuracy_Parity-0.019486-0.143166-0.114463LogisticRegression
    18Aleatoric_Uncertainty_Parity0.0465630.3214220.332505LogisticRegression
    19Aleatoric_Uncertainty_Ratio1.1478212.1235942.077199LogisticRegression
    20Equalized_Odds_FNR0.0114820.0805050.097373LogisticRegression
    21Equalized_Odds_FPR-0.101903-0.304790-0.367321LogisticRegression
    22IQR_Parity0.0016090.0182420.017678LogisticRegression
    23Jitter_Parity0.0027240.0273120.029017LogisticRegression
    24Label_Stability_Ratio0.9967010.9621520.958633LogisticRegression
    25Label_Stability_Difference-0.003261-0.037570-0.040960LogisticRegression
    26Overall_Uncertainty_Parity0.0466950.3232420.334164LogisticRegression
    27Overall_Uncertainty_Ratio1.1477712.1268842.079338LogisticRegression
    28Statistical_Parity_Difference0.0020110.053922-0.019052LogisticRegression
    29Disparate_Impact1.0018791.0506610.982233LogisticRegression
    30Std_Parity0.0013520.0141390.013621LogisticRegression
    31Std_Ratio1.1476422.8639052.572841LogisticRegression
    32Equalized_Odds_TNR0.1019030.3047900.367321LogisticRegression
    33Equalized_Odds_TPR-0.011482-0.080505-0.097373LogisticRegression
    34Accuracy_Parity-0.016530-0.141181-0.112635RandomForestClassifier
    35Aleatoric_Uncertainty_Parity0.0332350.3330900.331327RandomForestClassifier
    36Aleatoric_Uncertainty_Ratio1.1054482.1950362.092734RandomForestClassifier
    37Equalized_Odds_FNR0.0084820.0740530.089463RandomForestClassifier
    38Equalized_Odds_FPR-0.110735-0.313654-0.367186RandomForestClassifier
    39IQR_Parity0.0046790.0518380.052185RandomForestClassifier
    40Jitter_Parity0.0062850.0854270.081421RandomForestClassifier
    41Label_Stability_Ratio0.9905910.8787760.883924RandomForestClassifier
    42Label_Stability_Difference-0.009064-0.118486-0.112393RandomForestClassifier
    43Overall_Uncertainty_Parity0.0343370.3455310.343992RandomForestClassifier
    44Overall_Uncertainty_Ratio1.1052922.1987522.096863RandomForestClassifier
    45Statistical_Parity_Difference0.0047550.064680-0.005242RandomForestClassifier
    46Disparate_Impact1.0044331.0606460.995124RandomForestClassifier
    47Std_Parity0.0034290.0377550.037590RandomForestClassifier
    48Std_Ratio1.1100822.4050602.268096RandomForestClassifier
    49Equalized_Odds_TNR0.1107350.3136540.367186RandomForestClassifier
    50Equalized_Odds_TPR-0.008482-0.074053-0.089463RandomForestClassifier
    \n
    " + "text/plain": " Metric male race male&race \\\n0 Accuracy_Parity -0.017525 -0.139445 -0.111172 \n1 Aleatoric_Uncertainty_Parity 0.048429 0.323075 0.334918 \n2 Aleatoric_Uncertainty_Ratio 1.153208 2.122882 2.079025 \n3 Equalized_Odds_FNR 0.010383 0.078048 0.097373 \n4 Equalized_Odds_FPR -0.111164 -0.314509 -0.381839 \n5 IQR_Parity 0.001859 0.017574 0.017040 \n6 Jitter_Parity 0.003116 0.025495 0.028660 \n7 Label_Stability_Ratio 0.995995 0.964242 0.958477 \n8 Label_Stability_Difference -0.003962 -0.035511 -0.041148 \n9 Overall_Uncertainty_Parity 0.048591 0.324650 0.336400 \n10 Overall_Uncertainty_Ratio 1.153293 2.125552 2.080875 \n11 Statistical_Parity_Difference 0.002011 0.053922 -0.023437 \n12 Disparate_Impact 1.001879 1.050661 0.978149 \n13 Std_Parity 0.001535 0.013086 0.012857 \n14 Std_Ratio 1.178673 2.803597 2.562347 \n15 Equalized_Odds_TNR 0.111164 0.314509 0.381839 \n16 Equalized_Odds_TPR -0.010383 -0.078048 -0.097373 \n17 Accuracy_Parity -0.015007 -0.139728 -0.104850 \n18 Aleatoric_Uncertainty_Parity 0.033388 0.330824 0.329516 \n19 Aleatoric_Uncertainty_Ratio 1.105807 2.183549 2.084654 \n20 Equalized_Odds_FNR 0.007174 0.069441 0.078890 \n21 Equalized_Odds_FPR -0.115322 -0.307737 -0.370077 \n22 IQR_Parity 0.004622 0.051304 0.050187 \n23 Jitter_Parity 0.006612 0.080553 0.078087 \n24 Label_Stability_Ratio 0.990545 0.886772 0.889406 \n25 Label_Stability_Difference -0.009117 -0.110648 -0.107149 \n26 Overall_Uncertainty_Parity 0.034449 0.343123 0.342014 \n27 Overall_Uncertainty_Ratio 1.105555 2.187655 2.088974 \n28 Statistical_Parity_Difference 0.005598 0.071443 0.005044 \n29 Disparate_Impact 1.005208 1.066873 1.004684 \n30 Std_Parity 0.003473 0.037506 0.037557 \n31 Std_Ratio 1.112416 2.406057 2.276967 \n32 Equalized_Odds_TNR 0.115322 0.307737 0.370077 \n33 Equalized_Odds_TPR -0.007174 -0.069441 -0.078890 \n\n Model_Name \n0 LogisticRegression \n1 LogisticRegression \n2 LogisticRegression \n3 LogisticRegression \n4 LogisticRegression \n5 LogisticRegression \n6 LogisticRegression \n7 LogisticRegression \n8 LogisticRegression \n9 LogisticRegression \n10 LogisticRegression \n11 LogisticRegression \n12 LogisticRegression \n13 LogisticRegression \n14 LogisticRegression \n15 LogisticRegression \n16 LogisticRegression \n17 RandomForestClassifier \n18 RandomForestClassifier \n19 RandomForestClassifier \n20 RandomForestClassifier \n21 RandomForestClassifier \n22 RandomForestClassifier \n23 RandomForestClassifier \n24 RandomForestClassifier \n25 RandomForestClassifier \n26 RandomForestClassifier \n27 RandomForestClassifier \n28 RandomForestClassifier \n29 RandomForestClassifier \n30 RandomForestClassifier \n31 RandomForestClassifier \n32 RandomForestClassifier \n33 RandomForestClassifier ", + "text/html": "
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    Metricmaleracemale&raceModel_Name
    0Accuracy_Parity-0.017525-0.139445-0.111172LogisticRegression
    1Aleatoric_Uncertainty_Parity0.0484290.3230750.334918LogisticRegression
    2Aleatoric_Uncertainty_Ratio1.1532082.1228822.079025LogisticRegression
    3Equalized_Odds_FNR0.0103830.0780480.097373LogisticRegression
    4Equalized_Odds_FPR-0.111164-0.314509-0.381839LogisticRegression
    5IQR_Parity0.0018590.0175740.017040LogisticRegression
    6Jitter_Parity0.0031160.0254950.028660LogisticRegression
    7Label_Stability_Ratio0.9959950.9642420.958477LogisticRegression
    8Label_Stability_Difference-0.003962-0.035511-0.041148LogisticRegression
    9Overall_Uncertainty_Parity0.0485910.3246500.336400LogisticRegression
    10Overall_Uncertainty_Ratio1.1532932.1255522.080875LogisticRegression
    11Statistical_Parity_Difference0.0020110.053922-0.023437LogisticRegression
    12Disparate_Impact1.0018791.0506610.978149LogisticRegression
    13Std_Parity0.0015350.0130860.012857LogisticRegression
    14Std_Ratio1.1786732.8035972.562347LogisticRegression
    15Equalized_Odds_TNR0.1111640.3145090.381839LogisticRegression
    16Equalized_Odds_TPR-0.010383-0.078048-0.097373LogisticRegression
    17Accuracy_Parity-0.015007-0.139728-0.104850RandomForestClassifier
    18Aleatoric_Uncertainty_Parity0.0333880.3308240.329516RandomForestClassifier
    19Aleatoric_Uncertainty_Ratio1.1058072.1835492.084654RandomForestClassifier
    20Equalized_Odds_FNR0.0071740.0694410.078890RandomForestClassifier
    21Equalized_Odds_FPR-0.115322-0.307737-0.370077RandomForestClassifier
    22IQR_Parity0.0046220.0513040.050187RandomForestClassifier
    23Jitter_Parity0.0066120.0805530.078087RandomForestClassifier
    24Label_Stability_Ratio0.9905450.8867720.889406RandomForestClassifier
    25Label_Stability_Difference-0.009117-0.110648-0.107149RandomForestClassifier
    26Overall_Uncertainty_Parity0.0344490.3431230.342014RandomForestClassifier
    27Overall_Uncertainty_Ratio1.1055552.1876552.088974RandomForestClassifier
    28Statistical_Parity_Difference0.0055980.0714430.005044RandomForestClassifier
    29Disparate_Impact1.0052081.0668731.004684RandomForestClassifier
    30Std_Parity0.0034730.0375060.037557RandomForestClassifier
    31Std_Ratio1.1124162.4060572.276967RandomForestClassifier
    32Equalized_Odds_TNR0.1153220.3077370.370077RandomForestClassifier
    33Equalized_Odds_TPR-0.007174-0.069441-0.078890RandomForestClassifier
    \n
    " }, "execution_count": 21, "metadata": {}, @@ -719,8 +692,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T12:23:38.880650Z", - "start_time": "2023-12-21T12:23:38.770811Z" + "end_time": "2023-12-21T16:27:18.651459Z", + "start_time": "2023-12-21T16:27:18.619472Z" } }, "id": "a286da0406c6401d" @@ -743,12 +716,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 22, "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T13:44:14.079970Z", - "start_time": "2023-12-21T13:44:13.793765Z" + "end_time": "2023-12-21T16:27:18.693988Z", + "start_time": "2023-12-21T16:27:18.642843Z" } }, "outputs": [], @@ -760,21 +733,21 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 23, "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T13:44:15.426120Z", - "start_time": "2023-12-21T13:44:15.287725Z" + "end_time": "2023-12-21T16:27:18.718138Z", + "start_time": "2023-12-21T16:27:18.668162Z" } }, "outputs": [ { "data": { - "text/html": "\n
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    \n", "text/plain": "alt.Chart(...)" }, - "execution_count": 35, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -816,12 +789,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 25, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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Oe1ZxcXEqLi5WVlaWvvv2O738r5fl7f3rylLb07wwDADAhcBsBLQDU6dO1YoVK7R27VqtWLFCK1eu1LgzSJcybdo0vfDCC5afy8vLtXfvXr388svas2ePMjIyFBoaqgEDBpyP4QNWxdHe/KZw1WnemK085fxv9wptKZPJpC+2x+j73eY3db1dXfTYpEmtmkq3KcGenpLMY78isrOiggL16DeLVVRWrs+2btOA8HDZ2Ta/eggA0P7Ynlwxc7pVoTVVv86DNme5X6fJZFLiqlilrt8nSXLwcNZld42Vo0fT+7PZNDM3OXq4yD3ET0VJ2SpOyWn1vgEA7Z/TyT0+K5vYz7pORWWF5fNv9wptKZPJpC8Wfq7vvvtOkuTt7a1/PPF4kylwfys1NVUvPv+CcnLMc97ESZN0w/QbGi1756y7VFxcrB07dihu/37F7d9f73xwhw6adfcsPfvMPEmSkyNZyQAAbY8VpEA7MXfuXMueFHPnzlVeXt5pajTNyclJgwYN0v/+9z8NGjRIRqNR8+bN0/vvv99awwWsVt3eo8bTpEkqrayUZF5x6ubY/D5tjamsrtYbP6+2BEf93d315JTJ6ujtdcZttQZvV1eN791bknSiuERJuWf/bxQA4NJl52SeB2vKK5stZzlvY5Cd85k/GK6pqtbBz9dZApSOPm7qc88Eufh7nXFbp6pbXVpVWtFkmfPVNwDg0udy8mXVMmPze3AaS83nbWxs5Obmdsb9VFZW6vXXXrcERwMCAvTU3KfVsWPHFtXft3evnnz8SUtw9PobbtDM/5vZZHkHBwc9+PBDuu/++9SjZ0+5uLjIyclJ4eHhuuXWW/XCiy/UC4qeupoUAIC2wgpSoJ0IDAzUY489pkcffVS5ubl65pln9Prrr59Tmw4ODnrjjTd07bXXKicnR6+88ooiIyM1evTo1hk0YIWCPT0Vn56hE8XFzZar28PT29XljFPxFZWV6V8rV+loVrYkKcLfT38fP06eLm27aiXilPTcOcXFigzwb8PRAADagrOfhwqPZ6q8oKTZcuUFpZLMqzbPdB6sLClT/KerVZxyQpLk1tFXvf5vjBzcnE9b12QyNdtfbW2tJMnGofGVpufSNwCg/QsODlZ8XJxyTpxotlzuyfM+Pj5nPA8WFhbq5Zf+pSNHzOngO3furL8/+nd5enm1qP66tev0nw8+UHV1tWxsbHTXrFkac82Y09YzGAwadtVVGnbVVY2eT0pKspQL7hDcsosBAOA8YgUp0I5MmzZNw4cPlyT9+OOPWrVq1Tm36ePjo+eee87y82OPPWZ5gxDAmQv18ZEkZRcVy1jZ9OqZhJM3xJ3OcM/f/NJSPfnd95bg6OVhoXpyyuTzGhxde/CQ5i39QY8v+bbZcpU15542GABwaXMNNK8YKc8rUXUzq0hL0nPN5YN9zqj9iiKj9ry33BKg9Okeoj73jG82QFlRWKrt/1qkTU9/quQ1u5tt35hdIEly9vNslb4BANYlNDRUkpSdlSVjM6tIExISJEmdOnU6o/bz8/L11BNPWoKj0dHRevLpp1ocHP3+u+/07jvvqLq6Wk5OTnro4YdbFBw1mUwqKipqtszevXslSSEhoXJyIsUuAKDtESAF2pl58+ZZUu0+/fTTKi0tPec2hw8frmuvvVaSVFBQUC9gCuDM9Asz3xDXmkzanZzSaJnckhIl5ZofDPcNDWlx28Xl5Xr2h2XKKjTfmI7u0V0PjRsrp5P7np4vZZWVik/P0LHsHB3Nzm6y3N6UVEmSwSBF+Pme1zEBAC5O3lEn57Vak/IOpTZapqKwVKUZ5lTsPt1algpQkqqMFdr34UqV55qzNAQN6qaet4+y7HvaFAd3Z1WXVaq2skZ5h9OaLFeSnquy7MJGx3W2fQMArMvl0ZdLMmck2LVrV6NlcnNzlZiYKEnq269fi9suLi7WvHnzlJmZKUkaPWaMHnrk4RYHI1etXKWFny2UJHl4eOrJp55SdP/o09bbs3u3br/1Nv3+7nuUkZHRaJmC/ALtio2VJA0cNLBF4wEA4HwjQAq0M4GBgZozZ44k8y/VBQUFrdLunDlz5HXyjcMff/xR69evb5V2AWsT6OGhqKBASdKiHTtUWlF/DzOTyaT/bdkqk0lyd3LSVV27trjt99ZvUHqB+cHt+Mt66+6rr5KNzfmf6gd3jpCdrbmfL7bHWNIPnupARobWHz4sSeoXGirvk3vvAACsi7OPuzw6BUiSklbvUnVZ/VWkJpNJx5fHSCbJztVRAZdHtrjtw9/8orIc8zzYYWgPdZ06VIYWzIMGGxv594mQJJWknFDWrmMNytRUVunIks2SJFtHOwUPimqVvgEA1iUwMFBR3btLkhZ9tajBS+0mk0mfLvhUJpNJ7u4euurqxtPVNua9d95Vepr5RZ8JEyfqnt/f0+L7waNHjmrBJ59IMgdHn37maXWO7Nyiup0jIy1pgFeuWNngvMlk0ocffqjKyko5OTnpmmuuaVG7AACcb9yxAe3Q9ddfb0m121p8fHz08MMPW36eO3dus+lgADRtxpAhMhikzMIiPbP0B+1NSVVRWbkSck7otZ9+1tbj5nRK0wdEN1j9+bcvv9LfvvxKb69ZW+94bFKydiaa93TpFhSo6f2jVV5V1ewfk8nUKtfj6+ama/v2lSTFpaVr7tIftC81VUVlZcosLNSS2F16fvmPqq6plbuTk/7vyqGt0i8A4NLUeeIgySCVnyjWng9+VP6RNFWVlqskLVcHFq7ViX2JkqTw0f0arMDc8dpi7XhtsQ4t2lDveO7BFOUdMGdm8AgPUPjoy1VTWdXsn1PnwbBR/WTn4ihJOrJ4kxJ/3qXSrHxVlpQp90CKdr+7XCVp5uwOnScNkoO7S6v1DQCwLjNnzpTBYFBmRobmPvW09u7Zo6KiIiUcT9Crr7yqrVu2SJKm3zi9werPB/76gB746wN6+6236h2P3RmrHTt2SJK6RUVp+o3TVV5e3uyfU+ei/370kaqrq2UwGPT7P/xePj4+zdatqqqy1HV3d9fIUaMkSStXrNBn//ufUlNTVVhYqP3792veM/MUs327JOnW226Tt49363+pAACcBTYAA9qpefPmadKkSSouLm61Nm+44QYtWbJEO3bsUFpamt588009+uijrdY+YC0iA/z1h+HD9cGGDUrOzdPzy39sUGZin8s0tlevBsczTq4Q9XKuv5/Zj/v2WT4fzszS3R8vOO043rz1Zvm7u5/p8Bs1fUB/lVZWaOX+eB3OzNI/lzW8Jj93Nz049hoFnkwDDgCwTu4hfup2wzAdWbJJxsx87f/vTw3KdBzWUx2u6NHgeFmOOY38b/f1TN8Ub/lclJStLfMWnnYcAx+eLidvN0mSo4eLet9xjeL/t1qVRWVKWbNHKWv21CtvsLVRxPgBChrQrVX7BgBYl8gukbp39my9/957Sk5O1j+f+2eDMpMmT9K48eMaHM9IT5ckS4avOsuXLbd8PnzokGbdeddpxzH/7bfk7++vgwcP6tgxc/YEk8mkl//1r9PWHT58uGb/6Y+Wn2+fcbtSU1N1ID5eS79fqqXfL61X3sbGRr+7+WaNHTf2tG0DAHChECAF2qm6VLuPPfZYq7VpMBg0d+5cTZ06VVVVVVqwYIGmTJmiXo0EcQA0b3hUN0X4+Wrpnr2Kz8hQUVmZHO3sFOHvr3G9empAp05n1N6RZvb+vBAMBoPuuPJKDYrorJ/i4nQoK0vF5eVysLNTiLe3BkV00ugePc77fqgAgEtDYHQXuXXwUerG/So4nqmqknLZOtjJraOvOgzpId8eYWfUXlFKzjmPyT3ET9F/nqqMrQd0Ij5ZZSeKJJNJDh4u8ooMVochPeQa2HDVS2v0DQCwLsNHDFdERCctXbpU8XHxKiwslKOjkzp3jtC48eM1YOCAM2rv6NEjZz2Wo0fOvm4dR0dHPf7E41r982pt3LBBKSkpqq6ulre3t3r16qUJEyco/AzvcQEAON8MJnL7AGgD+fPfaOshAADQZmb3rjh9IQAA2ql3B/yhrYcAAECb8XL3bOshtCs3r32prYdwTr4Y+UhbD8FqsQcpAAAAAAAAAAAAAKtBgBQAAAAAAAAAAACA1SBACgAAAAAAAAAAAMBqECAFAAAAAAAAAAAAYDUIkAIAAAAAAAAAAACwGgRIAQAAAAAAAAAAAFgNAqQAAAAAAAAAAAAArAYBUgAAAAAAAAAAAABWgwApAAAAAAAAAAAAAKtBgBQAAAAAAAAAAACA1SBACgAAAAAAAAAAAMBqECAFAAAAAAAAAAAAYDUIkAIAAAAAAAAAAACwGgRIAQAAAAAAAAAAAFgNAqQAAAAAAAAAAAAArAYBUgAAAAAAAAAAAABWgwApAAAAAAAAAAAAAKtBgBQAAAAAAAAAAACA1SBACgAAAAAAAAAAAMBqECAFAAAAAAAAAAAAYDUIkAIAAAAAAAAAAACwGgRIAQAAAAAAAAAAAFgNAqQAAAAAAAAAAAAArAYBUgAAAAAAAAAAAABWgwApAAAAAAAAAAAAAKtBgBQAAAAAAAAAAACA1SBACgAAAAAAAAAAAMBqECAFAAAAAAAAAAAAYDUIkAIAAAAAAAAAAACwGgRIAQAAAAAAAAAAAFgNAqQAAAAAAAAAAAAArAYBUgAAAAAAAAAAAABWgwApAAAAAAAAAPw/e3ceH1V1+P//PZNksu97CJAQ9k1kFxcWFxZXUFrbqj+tXdw+X2sXW9tPq0i19tNWq9buWquWoraiAgKyu7DvEHaSEJIQyJ5Mksmsvz8GAjHJZALBwNzX8/HI4zHMPfecc/MQD/e+7zkHAAAYBgEpAAAAAAAAAAAAAMMgIAUAAAAAAAAAAABgGMHd3QEAxvTg0Kbu7gIAAAAAoBs8sOUv3d0FAAC6zfzJj3d3FwCIGaQAAAAAAAAAAAAADISAFAAAAAAAAAAAAIBhEJACAAAAAAAAAAAAMAwCUgAAAAAAAAAAAACGQUAKAAAAAAAAAAA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}, "metadata": {}, "output_type": "display_data" @@ -837,27 +810,27 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-12-21T13:44:40.071955Z", - "start_time": "2023-12-21T13:44:39.509081Z" + "end_time": "2023-12-21T16:27:19.054718Z", + "start_time": "2023-12-21T16:27:18.765592Z" } }, "id": "eeb7c1e88b43163b" }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 26, "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T13:44:51.171978Z", - "start_time": "2023-12-21T13:44:50.683129Z" + "end_time": "2023-12-21T16:27:19.500752Z", + "start_time": "2023-12-21T16:27:19.055714Z" } }, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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z7QLFAAkJCezbt4+qqipaW1vx9/cnLi4OX19fKisriYmJYfz48XbXoq+vD51Oh8Vi4eOPP+bJJ58kNTWVV199FXd3dwIDA/H19QX6M6Thp2VVCyHEz5UEioUQQgghhPiFG9gf7NSM2sEm7HU6HXV1dbz55pv84x//oKmpSb3m6enJsmXLuPvuu7nssstU6WknJyeCg4OpqamhpqaGtrY21SdYr9cTGhpKUVERx48fPy1QnJycTGlpKWazmYaGBry8vKipqcHJyQno73+8bNkyenp6Tjs3vV5PTEwMkZGRTJgwQfUw1rIIAE6cOKHOSwghxL+mr68Pg8Ggxtienh67+8SptHvQqUGGwe4/NpuNnTt38uijj+Lt7c0nn3zCW2+9xfLly+nu7gb6x/2qqiq++eYbjh49yhNPPCET8UKIX6WqqioOHTrE3r17OXLkCDU1NTg4OBAdHU14eDipqalcfPHFeHp6At/9Fo6Li8Pd3Z2GhgaKi4vPuP+BY6vVauXkyZNUVlai0+lIT09XizoHOnjwIIsWLaKtrY2EhATGjBlDXFwcvb29fPjhh1RWVvLGG28QFxfH7NmzcXBw4MSJE3h5edHW1sbQoUPVcWr/NygoiEWLFuHs7Gz3mTqdjoSEBDIyMqiqqqKtrU2dq5eXFyEhIWRkZHDixAkuvvhiu+NMSEgAoLa2Vi1yjY2NJSkpicrKSrZu3co111yDv7+/CpRr18NqtfLxxx8D0NXVpe6Hvr6+BAQEqPuUluEshBC/dBIoFkIIIYQQ4j+or6+PnJwc9u3bR3R0NOecc86/vUTyqRPojY2NtLa2Eh4ePujkelVVFffeey+HDh3CYDCQlJREfHw8rq6ubNq0iY6ODh5++GF8fHw477zz1PuSkpKoqamhrq6O5uZmVRbUw8ODiIgIioqK1Or6gYYOHcpXX32F2WzGbDYTExODs7OzOjaz2Qz0TxIlJSUxbNgwUlJSSEpKUkHnU3l6eqpJG630mwQShBDiX6eNpSNHjsRgMBAREaEW9gy2IGfg2Nvd3U1NTQ0Wi4W4uLjTttVaEZw8eZL29nZef/11Xn31VUaOHMlFF11EQkICubm5vP3221RVVfH5559zzjnncM455/yHzlYIIX5etEU2mZmZvPXWW2zatEn19NXr9Xh6elJYWEh2djabNm1iw4YNLF26lNGjR6t9hISE4O3tTV1dHeXl5eq9p8rLy2PHjh3s2LGDY8eOYbFYAAgLC8Pb25tbb72Vs846C71erxaALl++nLa2NsaMGcPSpUtJTk5W+/vNb37DQw89RF5eHjabjZ6eHlXy2cvLi+bmZh599FEmTJhAWloaISEh9PT0YLFYVPWiUyUmJgJQU1NDU1MToaGhALi5uRESEgJAdnY2YH+P0jKTTSYT9fX1QH/v4jlz5rB161YqKip44okneOGFF+w+r7m5mbfeektVNbruuuvsvpewsDCMRiPV1dWUl5cTFxf3o6ptCCHEz5kEioUQQgghhPgPOnnyJHfddRelpaVMnjz5R012D8wQ1jK0vk9ubi7btm3j22+/pbi4mK6uLiIjI0lMTOQ3v/kNU6dOxdHRUU3wfPTRRxw4cICAgADuu+8+zjvvPPR6PX19fSxYsIBnnnmGw4cPk5OTw+TJk3FzcwP6A747duygvr6e+vp6wsLCAHBxcSEqKgqAgoICdVzacQ8dOhToD2DX1dUB/eXitNJt4eHhbN68+Yzn19PTw86dOzGZTMTFxTFu3DhcXV0JDAzEYDBQV1dHR0eHlCcVQoh/Ay2YkJqaypEjR1QP+TM5fPgwX331Fbt376asrAydTkd8fDyxsbFceOGFTJo0Cb1erybSw8PDCQ0NpaqqipdeeomxY8fy0EMPqcDyiBEjiIuL4/HHHyc7O5tdu3YxZsyY01oiCCHEL1FOTg46nY7k5OQzVv5ZtWoVy5YtA2D48OHMnDmT8ePHExgYSFtbG5mZmRw7doxVq1aRnZ3NjTfeyPvvv8+QIUMA8PHxITAwkIKCAqqrq+nq6sLZ2dnuc/bt28fy5cvZvXu3+ltoaCiOjo6UlZVRWVnJ3XffzV//+lfGjBmDXq+npqZGBV0vueQSFSTu7e1Fr9cTEhLCQw89RF9fH5GRkWq/I0eOZMqUKbz//vts3ryZPXv2YDQaaWlpwdfXl6SkJDw9PfH19eXSSy9VwWH4LuBbW1urFpdC//OHFijWFqoOXIgbHh6uWuaYTCasVisODg7MmTOHdevWsWPHDr788kvMZjOzZs1ixIgR1NbW8u233/LZZ5/R3d1tt1BJq7bh7++Pu7s7/v7+dHZ2qu9MCCF+ySRQLIQQQgghxE9UXV1NYWEh5eXl9PT0kJCQwKhRo3BycsLBwcFu0icgIIDExETKyspoaGgATi8Pfaqfkhl76NAhXnrpJQ4ePKj+5uXlRVlZGXl5eWzevJlly5Yxf/589Ho9nZ2datX9ZZddxuzZs7FarVgsFvR6PWlpaTz00EPU19erc9IyoLWAb0NDAyaTSX2eo6OjChQXFRXR09OD0Wi06xEG0NTUpN7n5+dHfHw83377LVVVVVRWVhIaGqoyGbQAuV6v58SJEyxevBjAbtV/cHAwXl5e1NfXU1xczLBhw+x6kwkhhBiczWZT/zvTgiS9Xo/RaMRkMtHe3k5kZKTd/ctqtbJlyxZWrFjBsWPHgP4+9oGBgRQWFpKbm8umTZtYsmQJv/vd79SiIy2AUVVVhdFoZOHChSojC/on3IcPH864cePIzs6muLiY+vp6CRQLIX7xtN/tM2fOJDk5edCxd+3atTz22GPo9XqmTZvGjTfeyIgRI9Tv26CgIOLi4rjwwguJiIjg9ddfp62tjSeeeIKXXnpJLcSMjIxkz5491NfXU1NTQ3R0tPqdnJuby5/+9CdMJhNjxozhmmuuITU1FXd3d8rKyvjiiy/YuHEjtbW1fPPNN4wePRq9Xk9LS4sai3fv3s3IkSNxdXVVnwmohaTwXRsaHx8fFi1ahE6n48iRI+Tk5NDR0YFer6e5udkuWP3111/z5JNPctZZZwGo6kL19fV2zx8ODg4EBQXh5uZGe3s79fX1+Pn5qecwX19fIiIiKCkpoaamhq6uLrWo9LHHHuPll19mzZo1HDx4kIyMDHp7e9W+3d3dueqqq7jhhhtwd3fHZrOpktgXXnghl1122b/w/wVCCPHzI4FiIYQQQgghfoSSkhI+//xzNm/efFp5ZS8vL3Q6Hddffz0XXHABgYGB6jVXV1cCAgJwcHCgsrKSlpYW1VtLMzC42dDQQF5eHnl5eZSWltLX18eECRMYPXq06gmsqays5KGHHqKoqIj09HSuvPJKhg4dipOTEzt27GDNmjWqbF1sbCzDhw+nrq5OZYeVlZUB/VkAWllRwG4Vv81mU4EB7e9NTU3U1taqbfR6vZoUKi8vp7GxkaCgILtguYuLC52dndTW1qpA8oQJE1i7di3Nzc2sWrWKm266SZWztlqt6v2rVq0C+ktcD5x8CgoKwt/fn/r6evLy8hg2bJgKNAghhDidNoE+sJfw9223cOFCdu/ezaRJk3j88ccJDg5W96z9+/ezdOlSWlpaGD16NIsWLSItLY2uri6+/fZb/vGPf/Dtt9/y/PPPY7FY+MMf/gB8N5ZnZGQQHx9vN65rx2Q0GtXipOrqahoaGlRWmRBC/BL19fXx5z//mfLycsaNGzfoNrW1tbz55ptYLBYSEhL4y1/+goeHx2nbab+TFy5cSFZWFjt37iQkJMQu2BkVFYXBYKCtrY2ysjKio6Pp6+tDr9ezatUqTCYTw4YNsysf3dfXR3JyMuHh4bS3t7N69WpycnJobGzEz8+P8PBwRo0axZ49e1i3bh3bt29n0qRJODo60tvbS1hYGMnJyYSFhREUFERwcLA6Hj8/P5YuXUpRURENDQ3odDrKysooKyujsbGRtrY2Nm3ahNls5sUXXyQtLQ0PDw/8/PxUFYqamhq6u7vVc4u/vz8BAQG0t7dTUFCAn5+fyhw2Go3Ex8dTUlKCyWRS1YesVisBAQH8+c9/Ztq0aWRkZHDgwAFaW1vx9/dnxIgRjB07lpEjR6prP/B+OfCZSQgh/ldIoFgIIYQQQojvYbVa+frrr1m1ahWHDx/GYrEQGBhIfHw8QUFBNDc3s23bNgCef/55du3axaOPPkp0dLSaaA8NDcXZ2Znm5mZKS0sZMWKEXXBYr9dTXFzMypUr+eabb2hqalKfr9frWbNmDUlJSdx3332MHz9eBVp37dpFUVERycnJ3HPPPQwfPhzon+C/+OKLSUlJ4Z133mHmzJlqgj0gIIChQ4eybds2tmzZwpw5c5gxYwZBQUFYrVZcXFyIi4sjMTERnU5nV845IiICBwcHWltbqa2tteu1HBQUhI+PD42NjVRWVqqgtrZNfHw8x48fx2Qy0dLSgr+/P1OnTiU9PZ0tW7awfPlyvL29mTdvHn5+fuj1erq6uli+fDlffvklAAsWLLDLGvbz88PR0RHoz2pYsGCBBIqFEOJ7aJPdlZWVqjJGb28vEydOJDY2Vi0kslgsqlrE7t276evro7m5meDgYPR6PY2NjaxYsUIFiR999FF1n3F3d+eCCy5g9OjRPP3002zatIkvv/yS8ePHM2rUKFxdXVVwuK2tTWWnnRq41kpRm81muywyIYT4pdEWXgYFBVFeXk51dTWNjY34+Pio13U6HatXr6a0tBSA++67Dw8PD3p7e9XvXY3WM1in0/HAAw/g5uamApgWiwWDwUBMTAzOzs50dnaqxaGOjo6cPHmSiooKAIYNG0ZycrL6ba39rnd1dVVB3rKyMpqamvDz88PV1ZXLL7+czMxMdu7cSWtrKxs2bFDHZTAYVHWgcePGsXjxYtLT0+3G97i4ODW+p6enq79bLBYee+wxPv/8c3V/Gjp0KK6ursTGxlJVVUV1dTXt7e3qXH18fAgODqa0tJTc3FzGjx9v9yyg3ZeysrKor6/H399fPX+5uLgwY8YMpk2bRldXl6p6IYQQv0YSKBZCCCGEEOJ7fPPNNzz88MM0NTUxdOhQbrjhBqZMmaImE+rr6+nq6uL5559n69atHDx4kCeffJI//elPahIkLCwMNzc32traKCoqYsSIEXaTGHv27OHhhx/m5MmTuLq6kp6eTlJSEk5OThw9epSjR4+Sl5fHK6+8wujRozEajVgsFlpbW4HvynRqBvYGfuCBB3B3d1d/c3Fx4eKLL2b79u1kZWVRVFRklyHt6emJwWCgpaWF2bNnc9NNN6mSoDqdjtjYWAoKCjCZTLS2tuLt7Q30T9SEhYXR2NhIcXExo0aNAlDnOWTIEI4fP47ZbKaxsRF/f38AlixZQktLCwcPHuSZZ57hk08+YerUqbS3t5OTk0NBQQG9vb1MmjSJSy65RE2MQX8WwcyZMxk1apTqH6aVhRNCiP9lNptNjYWnlo4erOel5ssvv2TlypWqVDT0j5svvPACkyZN4s477yQhIUGN3SNGjODDDz/EZDJhMplISkoC+tsM7N69Gzc3N2bPnn1atq/NZiMiIoJbb72VTZs2cfLkSbZt28aoUaPs+kpWVVXR09Mz6LEGBQXh6elJS0sLtbW1KvghhBC/NNrim8TERA4ePEhzc7PqGawFaXt6ejhx4gQAY8aMUW1dTg0Sa7SAp1b2WQsca8HeiIgIPDw8aGtrU8FnbfvzzjuPyZMnq+cHbV/t7e0UFRWxefNmFQCuq6vDZDKp5xo/Pz9efvlljhw5wtatW2lsbESv11NfX09OTg5dXV0A7N+/n/r6el599VWio6Pp6uqisLCQ5uZmkpOTVZlogJ6eHpycnEhJSWHVqlWqFzP0P9ckJCTw7bffUlNTQ2trqzpnLy8vQkNDATh48CDXXnut3TNWamoqQ4cO5fzzzz+tMpPGwcFBgsRCiF89+YUthBBCCCHEGRw5coRly5bR1NTEzJkzufvuuwkPD0en06mJeD8/PwCeeOIJnn32Wd577z127NjB5MmT1YRKaGgonp6e1NbWkp+fD3wXQG1oaODll1+mrKyM+Ph4Hn30UUaOHKm2qaqq4v333+ftt9+mpKSEQ4cOMWHCBPR6PQEBAQAUFxezbNkyxo0bR2JiImFhYTQ3N9ttM1BQUBAvvfQSu3fvZsuWLRQXF+Pu7o7FYqG4uFhlAa9bt46ysjIef/xx4uPjAUhKSqKgoIC6ujoaGxtVoNjNzY3IyEiysrIoLCw87TOHDx/O6tWraWhowGw2k5CQgNVqJTk5mSeeeILly5ezevVqampqWLlypXqfl5cXV1xxBb///e/x9fVV/TShf6Lrxhtv/Fe/ZiGE+MUZGAwA6O7uxmQy4erqqu5LA1mtVj766COeffZZuru7CQwMJCkpibCwMMrKyti7dy/bt2+nsbGRd999V2VrDRkyBIDGxkbVcsBms6lsNIPBwPnnn39acFr754SEBFJSUsjOziYrK0uVDA0ICFBB4JqaGrvy0xovLy+ioqI4fvw41dXVdHZ2DlqCVQgh/j9pC3e036ja79SBtGCv9rs8MzOTP/zhD5SXlzNv3jxuu+02cnNz1djq7e1NWFjYT1ogc+rnhoSE4OvrS1VVldov9GcLX3zxxerfGxoayMjI4ODBg+zfv5/s7Gygf6Grq6srHR0dVFZWqnG+r68PZ2dnJk6cyMSJEwHo6OigubkZd3d3ent7ef755/nqq68oLCzk4MGDREdHs3HjRh555BEMBgMPPfQQc+fOBfrvF05OTlRXV/Ptt98C/f2VtXLYgFqkVFlZSX19vQqiu7i4qHuedt8aGFg/++yzOfvss3/U9RNCiF8zCRQLIYQQQggxiJ6eHt555x2ampqIjo7m5ptvJiIiQr0+cEK8r68PJycnLrvsMrq6uoiIiGDy5Mnqda0sM0BBQYHd+7OyssjIyMBoNPLwww8zcuRIbDYbFotF9f694IILePfdd2loaODkyZNqImrmzJm8+eablJSUsGrVKjZv3ozVaqWlpYXIyEji4+NxdXUlMjKSK664QgV1LRYLYWFhLFiwgAULFuDg4IDZbKaiooK+vj4A3n//fTZt2sSxY8fYuXOnChQPGzaMf/zjHzQ0NFBfX09MTAzQ308yOjoaQAXD4btJKy3Y0NzcrIIN2jWIiIjggQceYNGiRWzatImmpiaCgoKIj48nISFB9XT+viw5IYT4Jfu+DOHBtLW1sWPHDrZv305GRgZmsxlXV1dSUlIYNmwYv/3tb4mIiFCZamVlZbz66qt0dnYyd+5c7rrrLpVdZTKZ2LhxI0899RRtbW3k5uaSmpoKoMb45uZmampqAFRfSYPBQFdXF05OToMer/bZQ4cOJTs7W5UNjY6Oxt/fn8DAQFpaWiguLmb06NGnvX9g24Kqqira2tokUCyE+Nk5deHOQNriy9zcXBYvXszJkydxcHBQVRoAtcDSxcWFoqIi9Hq9Cg6fab8/hla9ISsri9raWpqbm1Wpf+hfYPTtt9/y+eefc/ToUcxmM87OzgwZMoSZM2cyfPhwXnnlFY4dO0ZFRQW9vb0YjcbTFik5OTnh6upq167mqquuIjs7m+zsbEpKSoD++0lsbCzZ2dm8+eablJWVcc455+Dg4EBxcTEbNmzgm2++AeDiiy/Gy8vLro0P9C9a6u7utjvHJUuWcOedd/7T10kIIYQEioUQQgghxK+QzWazy0w99TWdTkdmZiabN28GYPz48aSkpNj15B1I+1tcXByPPvroaa/7+vqqDILy8nK791RWVpKSkoLRaFTBVJ1OZ7ca3mw24+3tTX19PSdPnqSrqwsXFxdcXV15+umnWblyJSdOnFBl5RwcHCgpKaG4uFjt49ChQ9x2222kpqYOOvnk7++vykFDfyZXfX09Bw8eVMFtgJSUFKA/+0AL+EL/6v3IyEgAKioqaG9vx83NTV1jLYhsMpmoqqpS5znw/SEhIVx99dWnXT+NBImFEP+rzhRoGGyBTGtrK3/7299Ys2aN6mmv3TN27drFrl27OHLkCMuXL1c9h4uLi2lsbMTX15fHH38co9GIzWajr6+PwMBArrnmGiIiIhgyZIiakLfZbBiNRkJCQqiurlYtB7RgrV6vx93dncrKSrsWBQOPHVDZwhaLhbq6OqKjo/Hy8iIoKIjCwkK7e8ypEhMTAaiurqa5uVmVrBZCiP+kH7s40WazUV1dTXZ2tqp+4OzsTHp6Ounp6Wo89fLywmq14uPjQ2trKwaDgTlz5rBw4UI1RmoLIwFVlvqn/vbVjltbqBMREaH6yldWVuLl5aWylHfs2MG9995LR0cHoaGh3HTTTUycOJExY8aoUtjh4eEcO3aM0tJSurq6MBqNVFZW8uabb3L06FEWLFjAtddeC/QHxW02GwaDgaamJkpKSnB0dFTPAKmpqdx5553cfffdFBYW8te//pW33nqLzs5OdfxDhgzhxhtvZNasWXbnn5aWxqZNm+wW7Wq0+5wQQoh/ngSKhRBCCCHEr4I2YQL9kw7axMPAv2uvAaoEs4eHB9OnTwd+/Kr+vr4+dDqd2q+joyPBwcEYjUZMJhM1NTUEBwcDMHnyZIYOHYqbmxuurq7YbDaampooLS3l6NGjbN26lczMTJVpdvLkSdrb23FxccFmszF8+HCeeeYZsrOzaWlpobe3l9LSUsrKyujo6KCqqooDBw6wZ88eAgMDSU1Npbm5ma+++orDhw+TlpbGFVdcoY67r68Po9GIl5cXlZWVAHZlTLXM4tbWVurq6uyum5adVlFRQU1NjSq9DeDu7s7zzz9PVFSUKh8nhBD/67SJ88HKkWoT+j09PZSVlZGdnU1paSl6vZ709HRGjRqFk5OTXcDCZrPx8ccfs3z5cpydnVmyZAlnnXUWYWFhVFVV8emnn7Jq1Sr279/PO++8w8KFC9Hr9dTU1ODu7k5DQwNVVVWEh4fbZa3pdDpmzJhhd3xWqxUHBwcSExOprq5WPeY9PDzw9fVFr9fT1dVFRUXFoIFijXYPMRqN9Pb2Av0BES2AMli7Ao3W+9hkMlFfX//PfAVCCPGDtNLR2m/9gWPu9wVrly9fzgcffGC3eBJg9erVBAYG8tJLLzFy5EhCQkJYu3YtXl5ezJkzh6KiIkJDQ9ViGIDe3l78/f0xm81YrVba2tpwd3f/Sedx6rHGxMRgNBppb2+nvLyclJQUDAYDpaWlvPbaa3R0dDBt2jT+/Oc/ExgYiLOzs3rmaGpqoqOjA4CSkhKam5vx9PREp9NRXFxMQUEBq1evZsiQISQlJanKRQcOHODpp5+ms7OToUOHqqCvXq9n0qRJvPLKK3z11VdkZWVRWlqKu7s78fHxjBs3jokTJ6q+yQMZjcZBg8RCCCH+PSRQLIQQQggh/uv+myWEtc/SJujb2tooLy+nrq6OwMBAlcV7qqNHj6LT6WhtbR10wuL7DAwoa4HosLAwXFxcaG5upqSkhODgYKxWKxEREWrio6WlhY0bN/Ltt9+qUqIAo0ePxmg0snfvXlU6bmD2r16vZ9iwYerfp06dqv65pKSEF198ka+//ppjx44B/YGLf/zjHxw8eJBjx44xbdo0/P39VTm52tpaXnjhBaqqqvD29ub8889X+/P19cVoNNLR0UFBQYEqOQcQHh7OlVdeiZ+fn93xad/D7Nmzf9J1FEKIX5pTFx+dusBoYGUKnU7HiRMneOmll9i5c6fddi4uLoSGhvLQQw8xduxY9Xebzcby5csBuOmmm7j66qtxc3MD+gOyw4cPx8XFBU9PT2bOnKnutUOHDsXT05O2tjauvPJKRo0aRVpaGk5OTlitVoKCgkhNTcXNzQ03NzfVh9LBwYFhw4axY8cOzGYzZrOZyMhI4uLiVO9L7T5ypoVXFotFnbtWecLd3V1lBxcXF6sqFKeKiopCr9fT3NxstzhJCCF+qlODwQMNHLva2tqoqqrCarXa9ck91X333cfnn3+Os7MzkyZNIi0tjZCQEDIzM1m/fj0mk4lbbrmFzz//nMDAQFWNITw8nKKiIsrLy2loaMDX1xfozyIOCwvDbDZTV1eH2WzG3d39Rz83dXZ2UlVVhdFoxN/fHxcXF6KionBxcaG1tVWVgIb+Kg25ubl4e3szY8YMNTYPvBbNzc3s378fgKqqKkwmExEREYSEhLBo0SIWLlxIcXEx1157LdOnT8dqtVJeXk5ZWZm6drfeeqtdywCbzcbo0aMZPXo0tbW1uLm5/eRguBBCiH8/CRQLIYQQQoj/iqqqKrKysvDw8GDChAlnLOP8Q35qH0et/NqqVav44osvVLAU+rNjg4KCuPvuu9VEkDbRXVVVhc1mw9XVlfb2dnx9ff+lAHdYWBju7u40NzeTn5/PhAkTVFlOm83G+vXrefHFF6mqqsLJyYno6GjOO+88ZsyYwejRo/n666/Zu3cvJpOJxsZGdW5dXV0UFRXR1NTEpEmTVDk57TxiYmIIDw8HUCVKfX19ueKKKzh48CBlZWX89re/Zf78+fj6+lJRUUFmZibFxcU4Ojpy5ZVXqmuj7fuss86ivr6eyZMn212PiIgI7r///jN+D0II8b9CG79PHdsGLkpyd3dnx44dfP3116rv5OOPP66yZL/88ksef/xxzGYzYWFhpKWlERMTQ2NjI59//jlFRUVcd911vP7660yePBm9Xk9xcTHe3t40NzczZsyYQYOrS5YswcHBQS3igf6ynZdddhmvvPIKZrOZb775RvWChP72A83NzYSHh7Nw4ULOP/98Vfp0YMsBbQFTTEwMSUlJVFVVsX37dm655ZbT7ul6vR6r1conn3wC9Ae/tfuRk5MTwcHB6HQ6ampqMJvNg56Lv7+/XdZ1T0+PlBkV4n/UwMWH/yqLxXJaSf/BWs5ojh8/zvr169m5c6dq5RIdHU1sbCzz58+3W3gDkJWVxa5du7DZbPz+97/n6quvVgHPiy66iKSkJNasWYODg4PKytV+mycmJqrFN1pLAOivtBAfH8+xY8eorKyktLSU6OjoH/X80djYyJNPPsn69es599xzee6554D+3+aenp40NTVRVlamtu/r6wP6r7l2jQZ+TmlpKW+88QYdHR3odDra2tooLi5m5MiRKjP41Vdf5dlnn6WpqYmtW7eqfXt4eHDOOedw9dVXn7Ygd+B5aJWIhBBC/P+TQLEQQgghhPi3amhooKioiJycHLKzszlx4gQlJSUqoyg0NJStW7eqSfaf6kx9HM+ksbGRN954gy+++IKmpiZcXFyIjo7Gw8ODw4cPU1hYyJEjR3j66ac599xz1QSG1ifMx8dHraD/sbRg9sBAdmhoqCrnrPVj1K5BZmYmzzzzDHV1dURFRXH11VczduxYYmNj1blqq/Hr6uowmUzq/X/729949913sdlsbNq0SU02aZNheXl5KlNt2rRpdHV14ezszKxZs2hoaOCll16iqamJd955x+4cUlJSuOGGG+yygLUSpX/9619/9LUQQohfsoELkzQDJ9O1MVXz2Wef8eSTT+Lv78/DDz/MY489RkVFhXrdxcUF6C/Rv2LFCsxmM+np6dxzzz2qekV3dzfXXnst999/P/v37+f555/H29ubESNGYDAYCAkJoaysjFdeeYUpU6aQmppKXFwcOp2O9vZ2goKC1HitnYNer+eaa64hLS2NLVu2sGfPHqxWK87Oznb3lZKSEpYtW0ZtbS233HILgGoX0NTUpLYLCgriN7/5Ddu2baOoqIgnn3ySBx54wO7atbW18cEHH1BcXAz0B7AHCg4Oxt/fn7q6OoqKioiKijrt+ru7u/OXv/wFV1dXxo4dK0FiIf4HNDY2UlRURHZ2turt29TUxO9//3uuvfba06oT/DMGjoHQ/3ySm5ur+uZeeOGFqrf7nj17eP311zl48CDwXUWHhoYGtm7dytatW7ntttu46qqrVDD4wIEDmM1mhgwZwty5c3F3d8disWC1WjEajVx44YVMmjQJX19f9Uyh3TcSEhIAqK+vx2w2ExcXp943ZswYPv30U+rq6jh8+DDTpk37Uc9MRqORY8eOYbPZyMvLU+cWFBSEv78/ZWVlVFZWqmsbEhKCi4sLnZ2d/P3vf8fV1ZXx48ej0+k4evQoH374ITt37mTMmDFUVVVRWVnJxo0bGT9+PBEREdhsNs455xwmT57M/v37OXnyJH5+fsTGxhIdHS1jtRBC/MJIoFgIIYQQQvxTuru7KSkpITc3lxMnTpCdnU1+fj6tra2Dbh8aGkpMTAzjxo0DTp/A0fo4Ojg4nLZqXpvU6OrqoqSkhKysLIqKirBYLKSnpzN69GgCAgLs3qNN5K9Zs0YFQRctWsRVV12Fr68vLS0tnDhxgrfffpudO3fywgsvYDQaVdnm6Oho9dkDA7M/JjN2sGB2QECA6tNYVFSkroHVauWjjz6irq6O4OBg1qxZoyaU4LssXm1iSysrp2VVJSQk4ODgQHNzM3feeSdz585l5MiRWK1WsrOzWb16NQUFBbi5uXHFFVeo3mN6vZ4rrriCs846i3379pGZmYmzszOxsbEMHTqUuLg4NRn23ywVLoQQP8Z/K7N0YLCiqqqKzs5O4uLiyM3N5bbbbqOiooJt27apzKi+vj56e3uxWq3cfffd9PX1cfvttzNixAjMZrOavP/00085ceIEsbGxPPjggyQmJqr+vY6OjkRERHDjjTfS1NREbm4uW7ZsYcSIEURHRzN9+nT27dvH/v37OXLkCL6+vjQ3N2M0GhkyZAiBgYG4uLhw7rnnctZZZ6HX67HZbDg5OTF27FjS0tK47777sFqtFBQU0NraitFopKCggKeffpqWlhY+/PBDFSgODw/HYDDQ1taGyWRSFUEuuOAC1q5dy/79+/nggw+oq6tj1qxZDBs2jLq6OrZs2cLKlSvp6+vj8ssvZ8qUKcB393QPDw+8vb2pq6ujp6fnjN/BwPYHQohfDu1ZIS8vj6ysLLKzsykoKKClpeW0bXU6nVpUOliQWFuEeeqzwpl+o65du5YtW7Zw2WWX4ejoyIMPPqgyaidNmsSMGTPw8/NTi2PKy8sZOXIkixcvZvTo0QAcO3aMzz//nM8//5yXXnoJFxcXrr32WgD1G7mmpoZdu3Zx3nnn4eLiohYDubq6qmeJU9vgaIHihoYG9YyhmTBhAt7e3jQ1NbF+/Xpuu+22710gq42nRUVF1NTUALBgwQL1ul6vJzw8nCNHjmAymTCZTAQHBxMXF8fZZ5/Nhg0bKCws5LHHHsPZ2Znq6mqVbTxhwgQeeeQRvvzyS958803VfiAiIkJdc2dnZ7uWN0IIIX6ZJFAshBBCCCF+kqamJm699VYOHDgw6Ot6vR6DwcDYsWP5zW9+Q2JiIpGRkfj4+Nhtd+rEzsBJkL6+PqxWK46OjipQmpeXx1//+le7UpkA77//PkFBQdx+++3MmjXLLhCalZXFBx98AMAtt9zCkiVLsFqt9PX14e7uzoQJE3B1daW1tZWjR4+ydu1aNdkRHx8P9GdEaYHdH6umpoZPP/0UT09PJkyYQHx8PN7e3gQEBKDX6zl58qQKcuj1evbt2wfAqFGjVADdYrHg4OCg/n39+vUqiFBSUkJbWxu+vr7MmjWL3Nxc3nnnHfbu3auyCTo7O9XxjB07lltuuYXhw4fbZWnodDoiIyOJjIzkkksuOeP5SJBYCPH/RSt3mZ2dTV5eHuXl5fT09BAfH8+kSZMYM2YMXl5eP7ifgW0LBk7Y/5CvvvqK9evXs3fvXjo6OggNDeWss85iyJAhqrVBRUWFChSHh4cTFBSkAhI333wzN998s90+Ozo6VJbt6NGjSUxMBFBBZE1kZCSxsbHk5eVx6NAhlb28YMEC9Ho969ato7i4mNraWqD/3qn1kwTYvHkzV155JX/4wx/sxnEtwK7T6VS2MMCIESOora1l+fLl1NfXYzKZCAwMBPpLTRcUFGAymWhpaVH39L/85S8888wzbNiwgW+++YZdu3bZ3X8CAwO5/PLLufLKK9Xnatc+OTmZTz755AdLzWrf3T/TrkII8d/X1NTEkiVLVIbuqby8vIiNjWXIkCEMGTLkjM8KA526CLOvr4/u7m5cXV2B754rtOeGjz/+mIyMDDw9PcnNzaWsrIxJkybh6OjIlClT1PtWrFhBeXk5o0aN4uGHH1ZBXIvFwrhx4xgyZAiurq588MEHfPnll6SlpZGWlkZKSgoJCQkUFBTwxBNP8Omnn5KYmIjFYsFoNBIfH09CQgK+vr6qfL82DkdERODl5UVLS4sK7mrjYlBQEL/97W959913qamp4c0331SLdgZr26O978UXX6S7uxs3Nzf1LKNdi8jISAwGA+3t7VRWVhIcHAzAAw88QEhICDt37qS4uFgF6keMGMGUKVOYM2cOERERXHfdddx4440//MULIYT4xZJAsRBCCCGE+Em8vLxobGzEYDCoHoXaRE9vby933303LS0tTJ8+nYsvvtjuvQMn6bXJksbGRjw9PTl27BgffvghhYWFtLW18eCDDzJlyhQMBgN79uzh/vvvp6qqivDwcNLT00lMTKSlpYVNmzZRVFTEgw8+SE1NDYsWLVKfl52dTW1tLQkJCVxwwQXA6VkKqampzJ07l6NHj3L8+HGKioqIi4sjNTUVgM7OTo4ePTroe89kz549vPLKKwAsX75cBZ1DQ0NxcnKioaGBiooK4uLigP5JoZqaGsrLy6msrCQhIUEFiAsLC3n55ZfJzs7Gzc2N9vZ2ysrKqK2tVWWmb7vtNpKSkti2bRsnTpygpqYGf39/kpOTmThxImeddZaa+PpXS/kJIcR/gsViobS0lLy8PFWONC8vj4aGhkG3P3jwIB999BHnnHMOjzzyiBoPz+Snti3o6+vj448/Zvny5VRXVwP9Y7gWgHBzc1P3sdzcXNLT0wHw8/MjMDCQsrIyoqKi1IT9wEU6NTU1lJSUAN8FbYuKilSVjpycHAoKCigvL1fHc/jwYYqLi0lJScHd3Z2rrrqK8847j8LCQvR6PdXV1ZSVlVFdXY3FYmHXrl00NDTw8ssvs2DBAoKCgjh69ChfffUVbW1t3HHHHfj5+WGz2dTiLKPRSEtLC52dnURHR6u+mgBDhgyhoKCAhoYGmpqa8PHxoa+vj+DgYB5++GGmTp3K4cOHOXLkCA0NDQQGBjJy5EimTJnC2LFjVVBmIC0wbrPZsNlsZ7w//dTvTgjx/8vLy4ve3l50Oh1OTk7MmDGDoUOHkpiYSGxsLCEhIT95EWJJSQkHDhxg//79lJSUYLPZSElJYcyYMUyaNInAwEC7Es2TJ08mIyODbdu20drayu23387NN99MV1cXPT09uLi4kJubqxZrTp48mYSEBDVWa7/DPT09WbhwIR988AFFRUXs27ePtLQ0kpOTueeee1iyZAl9fX2cOHGCEydOqM93cHCgr68PFxcXLrzwQu68807Vh93d3Z2wsDD1nKL1ZtaC3b///e/59ttvyc/P55VXXsHHx4eLLrpo0CoaJSUlvP322xw6dAiAG264QS0A0q5xTEwMFosFs9lMTk4Oo0ePxmKx4O3tzV133cWll15KeXk5QUFBREZGnvY5py5iEkII8b9HAsVCCCGEEOIn0el0rFixAi8vL7u+jAAmk4mQkBBaWlooKiqisbHRLjtAK3+p0+n4+uuvueuuu4iKiuLaa69lzZo1HDt2DOiflGlrawP6J9Rff/11qqqqSE5O5v7771cT8gCXXHIJf//733n//fdZtWoVQ4YMYdq0abS3t5Ofnw/0T9ZoPYY7Ozs5efIkhYWF5OTkqF7KAJWVlWRmZhIXF0d0dDRxcXEUFxdz8OBBamtrVcbYmWiTS8eOHVNlpbVJIYCwsDDc3Nzo7OykuLhYBYpHjx7NsWPHyMrKYunSpVx44YUEBASQnZ3N7t27OXbsGAkJCYwbN45Vq1Zx6NAhPv74Y5YtW6ayC2bPns3MmTOprq7G29tb9TQWQoifu56eHs4++2zMZvNpr7m6uqrMs/j4eHx9fSkoKGDdunU0NjayefNm/P39+eMf/3haZvHAyhW1tbVkZ2eTk5Oj7lXTp09XvRS18Vt7z759+3j22Wfp6Ohg+vTp/OlPfyImJoaysjK+/vprPvroIxVA1u418F2gGPrvl2FhYYD9Ih1vb29MJhN6vZ7169fz6aef0tXVddq5a1UfEhISSE5OVllgmoCAgNPaLmjeffdd3nrrLWprazl27BgzZ84kIyODtWvX0tLSwtChQ5k/fz5ubm4qILJhwwa+/vprAGbMmEF0dDS9vb04OjqSkpLCunXrKCgooKqqipiYGHVO7u7uzJs3j1mzZtHa2vqDQfvBzlMqVwjxv0EbQ2NiYsjKysJqtfLnP/9ZtV/RaItH4cyLGLVxed++fbz++ut2FRP0ej25ubl89tlnjB07loULFzJ16lS13+TkZKB/QerQoUO5+eabsdlsODs7q+eXnp4eKisr8ff3Z/LkyWq/2rNCQUEBeXl5nDhxAkdHR9ra2jhw4AA333wzBoOByZMns3nzZjZu3EhGRgY6nY7e3l4qKiooLCzE2dmZnp4ePvzwQ/z8/LjuuuvUopmYmBgVKG5qalLPGFarFR8fH+666y6effZZ8vPzeeSRR9i3b5/KcA4LC6O9vZ3MzEw2btzIzp07AbjgggtYuHChukbauBodHU16ejohISEMGTIEsG8BFBERoZ6ThBBC/DpJoFgIIYQQQvxk2mSGlgWk9QtzcnIiKiqKvLw8ampq6OjoOGPJaQ8PDxwcHOju7uadd96hsLCQO+64g9TUVLq6ulQW7tGjRzl48CB+fn48+eSTDBkyRGU/GQwGQkND+eMf/8jOnTuprKzko48+Ytq0abi4uFBZWYlOp6OiooI77riDgoICiouLVe+tgYxGIwEBAbi5uamy0LNnz+bdd9+lubmZL774giuvvHLQrCjtvPR6PXV1deTm5mKxWJgyZQqJiYnqnMPCwnB3d8dsNpOfn8+5554LwMUXX0xpaSlbt24lKyuLzMxMu31PmzaNe+65h5CQELy9venq6lK9HgdmWRkMBpnoEUL8othsNoxGI+Hh4dTX1+Pl5cUll1zCuHHjiIiIUFm8p1qwYAFLly7l0KFDrF+/nrPPPpspU6aowMLA//v++++roKlGr9fzwgsvMHfuXBYtWkRsbKxaeNPW1saaNWvo6Ohg9OjRPPnkk3h5eWG1WomKiuLGG28kKSmJm266CcCuPYGHh4cKFNfW1qo+lgNpgVSr1ap6dQYGBpKYmMjQoUMZOnQoSUlJREZGnjGAWl1dTX5+PklJSQQHB6vz1e5fI0eOxMPDg9raWpWVPX36dA4ePMjWrVt5+umn+eqrr5g8eTItLS3k5eWRmZlJc3MzaWlpzJs3D/gu0DB8+HCCgoKYPXu2us8MPDabzYajo6PduWnZfZINLMSvh/abNyoqSv3OP3LkCOeee65aeAI/XOFGG9N27NjB4sWL6e3tJS0tjfnz5zN06FCampr46quv2LZtGwcOHFD3j7S0NACVVavX6/H09Bz0M7Sxsb6+nk8++YSVK1eSn59PSUnJoM8Ker0eo9FIa2srHh4eWCwW/Pz8uOqqq7jqqqsAMJvNWCwWfH19OXz4MM899xxZWVns2rWLGTNmqAB2UlISGzZswGw209DQQFBQkN2YOmXKFFxcXHjhhRfIyMjgm2++4ZtvviEgIID29na7ig8JCQlcccUVXHrppXbXVfvnlJQU3nvvve+93kIIIX7dJFAshBBCCCFO09fXpyZ6vm+C99QsIBcXF6Kjo4H+TODm5maVTTXwPQBRUVF2fRwXLFjADTfcYDfB0dvby5YtW4D+1e7aKngHB4fT+pQNHTqU2tpajhw5Qn19vSqpabPZaG9vZ+PGjWr7sLAwkpOTSUlJYdiwYcTHx592nADz588nIyODXbt28f777xMWFsacOXPsstS0iXDt39977z2OHTuGg4MDs2bNwt3dXQUfQkJC8Pf3p7S0VAUW+vr6iImJ4eGHH+ass85i8+bN1NTUEBAQQGpqKqNGjWLYsGEqE2Px4sVn/D6EEOKXRus9Gx8fT0ZGBkajkblz56py+do2Gi3rNyoqigsuuICMjAza29s5cOCAXaBYr9fT0dHB448/zqeffoqLiwvjx48nNTUVHx8fjhw5wpYtW1i/fj0lJSW8/PLLhIaGAtDc3MxXX32Fi4sLc+bMwdvbG7APjE6dOpU5c+awceNGKisrVS9Io9FIUFAQLi4udHZ20tLSYrfASLsfREVFkZmZSWJiIs8++6zqU3yqzs5OsrKyOHr0KGlpaYwdO5bDhw/zzDPPkJGRweLFi1m8eLG6dxqNRnp6eti8eTOFhYUEBwer+3J0dDR33nknAFu3buXIkSMcOHBAfZaLiwu/+93vuPHGGwkKClILsqC/8sWOHTvO+D2eGtCWNgdC/O8a2PNdr9cPuqAlOjoaJycnuru7KSws5Nxzz/1J44Jer6e+vp4nnniC3t5ehg8fzp///GdGjBihtpkyZQqbN2/m7rvvpqioiBdeeIF33nkH6H9uMBgMWCwWfHx87ILUmq6uLjw8PGhtbWX16tV2r536rBAbG3vaYkxtfBz43OTv769enzBhAhdddBFZWVl0dnaqaknQHyjW6/UUFBSwbds24uLiVFsa7T4xZswYli9fzieffML+/ftpaGigvLyc3t5eAgICSExMZNy4cYwfP57k5GQZd4UQQvzTJFAshBBCCPEr19vbi4ODg93kwpmCwwMDpIMZmNFaW1tLfX39adto7w8MDMTX15eysjJ0Oh2XX3653TFo2UlaWeiYmBjq6+tpamoiLy/Pro9jTU2Nel93dzcZGRnMmDFDZT47OztzzTXXcPHFF+Pn54eLi8ugx19eXk5PTw8xMTE4ODgQHh7O5ZdfrkqVPv/889TU1LBw4UI1iaOdT1FREStWrGDt2rVAf+B71qxZdtfT19dXba+V2dZeCwgI4PLLL+fCCy88raS3EEL8nHR0dLBmzRo2b97MZZddxqxZs/7lCWotUKqV00xISFDB11P3rd2LEhMTcXZ2pq2tjdLSUsA+YLlx40Y+/fRTHB0duf7667n++utVO4BLLrmErKwsFi5cyIkTJ3j22Wd5/vnnATh58iQGg4HOzk7OOeecMx7z9OnT2b17NyaTicrKSqKiooD++5u3tzednZ2UlJTYZfxqi4tGjx5NZmYmDQ0NVFdXk5iYSFdXFwaDAZ1Oh9VqxdHRkaNHj7J48WI6Ojq46aabGDt2LIGBgURFRZGRkcFnn31GU1MT559/Pn5+ftTW1rJlyxYV9DjrrLMYP368+vy4uDj+8pe/kJuby9atW2lrayMoKIghQ4aQkpJCSEgIYN9TWaMFQwbe+4QQvz7ft5B04IJQV1dXWlpaKCgoGHRbbUGn9r5Tx5VDhw5RVlaGwWDgjjvusAsSQ/84dc4557BgwQI+/fRT9u/fT15ensomjo6OprCwEJvNZlcWX7uHuLi44OnpSWtrK2PHjuXSSy8lISGBqKgonJycBj1mk8mE0WjE29ubvXv38vTTTxMQEMCf//xnoqOjsVgs6HQ6FUCvq6sD+stcjxgxQo2tiYmJJCQkkJeXx9tvv83LL7+Mq6srR44csbu2bm5uXHPNNVxzzTUUFxcD/c8M0mJGCCHEv5MEioUQQgghfgEyMzP5+9//zv79+3nqqaeYOnWqClT+q05dXV9fX09mZiZHjx6ltLQULy8vpk2bxtixY39wUkKv1xMeHg70l3MzmUyDbqcFgUNDQzl+/DgWi0WV5zw1GK1N6m/dupV169YNWgrO0dGR6OhokpOTiYyMVAGHtLQ0PvvsM7q6uggPDyc8PFxlQWj/MxqNKmNhw4YNTJ06lTfffFNd32nTpvHggw/ywAMPUFlZySuvvMInn3zChAkTSE5OVr2QMzIyKC8vx9XVlcmTJ3PrrbeeFvB1c3PjhhtuYOHChWoS61QSJBZC/Jxs376dmJgYoqKi1AR3W1sbn3zyCQUFBQwbNoxzzz0Xo9F4xn1o4+1gwQVtvNcyiHt7eykvLwfOnJWqvcdoNNLW1obBYFBtDrT9d3R08Le//Q2ACy+8kCVLlqj963Q6XF1dGTt2LIsWLWLFihVs3LiRW265hbi4OAoLC1WAQyvveWolCZ1OR3R0NEFBQTQ1NVFcXKwCxUFBQfj4+FBdXc2JEyeYMGGCXaYzwPjx49m4cSO1tbVs3LiRqVOn4uzsrAIn2r35+PHjdHR04OXlxdSpU4H+TLmbb76ZnJwc8vPzef/991m/fj3Nzc3qGmn9MLV+lQOvpYeHB2PGjGHMmDFn/M4Gu/ZSQlqI/319fX0q0Kk5dfw7efIkWVlZFBUV4erqysyZM9Xvf2270NBQvL29qampoaSkRL134L5ODQ6fukBl8+bNQH927/Dhw087Fm3buXPnsn//fgoKCjhw4ACxsbE4OjqSnJxMYWEhLS0tNDc34+vrqwLTACEhIURERFBZWUlsbCxz5syxO2et1Y3RaGTLli3ccsstpKSk8Kc//Ynx48fT19dHTk4OxcXFvPbaayxZsoTg4GAMBgNNTU188cUX/PWvfwVgzpw5dvfJkJAQ/vjHP/L6669z7Ngx9Ho9CQkJVFZWDlrlyGazERsb+xO+SSGEEOLHk0CxEEIIIcTPmDZhYjab2b9/P42NjRQVFanJ4jO9Z7AJ+cGygbu6unjppZcwm83ccsstuLq68uCDD7J9+3a1jV6vZ82aNUyYMIEXX3wRLy+v7z3mgIAAAgICqKuro7q6mu7u7tNW5Q/sX6bX6zEYDNTU1BAdHa1e0+l09PT0qGCyNgEeHh5OYmIiw4YNY+jQoSQkJKhyoadKSUkhPj6enJwc1q1bx4UXXqiytQZORDU1NfH111+j0+lUFrJ27fR6PTNnzsTDw4N33nmH7du3U1JSoia9Tv28iy++mMsvv/yM1+f7vjshhPg5yc3N5d577+XGG2/k+uuvp6+vD71ej7u7O2lpaRQUFGA2m1Vf3DMZGCCF7+4BWm93gNjYWFxcXOjp6VGB4lN74Grba3//7LPP0Ol0WCwW5s+fb/eZ2rFBf/aw5tTFUdHR0fj5+dHR0cHevXuJi4vDyckJq9WKq6srJ0+etLs3DeTp6Ym/vz95eXkUFBQwffp0APz9/VX50aysrNOuBcDIkSOZN28ey5cvZ8uWLTz88MPcdtttuLu7q2zmNWvW8MILLwD9mcGjRo1S+4mJieHNN9/k3XffJTc3l7y8PNzd3QkLCyM9PZ3JkyczZsyYM1bQ0Az8zXCmErJCiP8/2thjNpsxm82EhoaesefuP7vvgf888NlBqwyktT8BeO2111i+fDldXV1A/4Kd5cuXs2zZMn7zm9+o7by8vAgMDCQ/P19V/tFKNff09FBZWUlRUZEavw4cOMC9997LhRdeqJ5/tN/+w4YNo6urC3d390HHqMjISGJiYigoKCAnJ4euri4cHR0ZNmwY//jHP2hoaMBsNhMTEwPYB7OHDRvGvn372L9/Pzt37mTKlClqsaiDg4M65vXr1wP9C2G1PsPp6enMnTuX9evXs27dOo4ePcqIESOor6+nsLAQs9mMh4cHU6ZMUT2MB5o2bZoqNR0WFnba/WkgGZuFEEL8J0mgWAghhBDiZ2xg+baoqChaW1tpaGgAzpxpdeqEvDZZ4uDgcNpKfWdnZ95++20Azj77bF588UUqKyuZOHEio0ePxsnJiY8//pjq6mr27t3LO++8ww033GDXb/FUXl5ehIeHq0Bxe3v7Gcu3aSWeAaqqqgY9F21SJyYmhkcffZT09PRB99Xb28uuXbsoKirCy8uLSy65hMjISObMmUNOTg5ZWVk88cQTPPjgg3YTY0VFRTzxxBNYrVacnZ25+uqrT9u3zWZjwoQJpKSkkJOTQ15eHidOnKCzs1P1CEtJSSEuLk5lQP9QmW4hhPg5a2trY+XKlTQ1NZ1WNtTZ2ZkFCxYwfvx40tLSVEWIwTQ2NpKTk8ORI0fIz8+nr6+P1NRUpk+fbteHOCgoiICAAMrLy6mqqqKzs9MuyDkw86y8vJxVq1bx7rvvAv0Zw0OHDgW+W2B19OhRAJycnOjq6qKtrY3jx4+r1gW5ubmUlJTQ3d2tPmP79u1ceeWVBAQE4ODgQFdXFyUlJUyePNkuC007Di2AAJCXl6f+2cfHRwWKtb+fes/29PTkmmuuYf/+/WRmZvLRRx+xc+dOZsyYQXd3t8oWdnR0JCkpidtuu+20axsSEsI999xDbW0tOp2OwMDAM34PZ3LqbwYhxM+H9lvyiy++4J577sHf359nnnmGCRMm/KjfmdoikDNtp/29paUFT09PLBYLX3zxBRs3biQzMxO9Xk9qaiozZ87koosu4pFHHuGjjz4iODiYYcOG4erqyjfffENjYyNLly4lJSWFiIgIFWiNiIhAr9fT1NTEX/7yF1pbW8nKyjpt7NVoLQS0MSk4OBjgB6soubi4qIzmiooKdV7afaG+vn7QKkfu7u6cf/75rFixgoqKCt58801GjRql7mnaotVPP/2UXbt2Af0tB7S+9c7Ozjz22GM4Ojry7bffYjab2bBhg9p/QkICc+fO5brrrhs0CGyz2U7reyyEEEL8f5BAsRBCCCHEf0hFRQV5eXkEBASQmpo6aL+/H6JNdERERHD//ffj5eWlJhQGm/Tp6OjgxIkT7Nu3j4yMDEwmE76+vowdO5bU1FQmT56sth3YIys/P58XXniB8vJybrnlFhYuXKiCwfPmzePuu+9m//79bNmyhenTpzN8+PAzno+rqytRUVEcPXqU6upq1RNssAmtqKgo3NzcaGxspKys7LR9GQwG0tLSgP5gQ2FhIenp6XR3d6seiVoWVHt7O4sXL8ZqtXLJJZdwySWX4OLiwtVXX80333xDZmYmq1evJjs7m/nz5xMWFkZxcTHbt29n3759ACxZsoT4+PhBvwebzYaXlxfjx49n3LhxPzg5J0FiIcT/l3/mfnMqvV6vAo9ar/iBr6Wlpanx+UyKiop45ZVX+Oqrr+z+vmXLFl588UWWLl1q158+IiKC8vJympqa6OrqUhnGtbW1FBUVkZ2dTWZmJjk5OdTV1akxecmSJSqorAV0DQYDHR0dODo6ctNNN9He3j7oMQYGBpKQkEB0dLSq+BAZGUlISAi5ubkcPHjwtEww7X7m7e2tAsEDq0y4u7ura3dq4GMgf39/XnnlFV599VX+8Y9/UF9fr4Lf2n4uuOACbr31VtVb81Q2m01VwoDvL/MthPhlGVjmHvorIgwsMX8qq9UKfDfe/NB9oKCggOuvv566ujr27NnDZ599xiuvvKKCuEajkZ07d7Jz505OnDjBp59+yrnnnsu9995LaGgoVquV9PR0VqxYQVlZGTt37uR3v/ud3UJXg8GAxWJRC1M1gYGBJCUlkZKSwvDhw4mLi1OLQ7Vz0QLFNTU1gwaWB9LuAZWVlSrQGxcXB0Bzc7PKaj7193lycjLXXnstK1eu5PDhw1x99dXMmTOHsWPH0t7ezr59+1i9ejXt7e3ExcXxhz/8we79Tk5OPPHEE5SVlXHw4EEsFgvh4eHEx8er4z8TeVYQQgjxcyGBYiGEEEKI/4CCggIuueQSOjs7+e1vf0tqauoPvkeb3B5s0sBoNDJs2LDvfX9fXx/vv/8+7733HnV1dervBoOB/fv34+joyNVXX83111+Pn58fFosFo9FIfHw8+fn5lJeXs2DBAtXHUetRFhgYyHnnncf+/fupqqqiqKhI9QkbjNFoVBNa1dXVNDY2EhUVZXde2sRVeHg43t7emM1mFSg+dVJrzJgxeHh40NzczAcffMD8+fPVZNDAYMhnn32Gk5MTnZ2dzJw5U71uNBp5+umnef311/niiy/IyMggIyPD7jNiYmJYtGgR8+bNO+N5DTx+mdgRQvzcZGdnc9ttt+Hl5cWiRYuYMWPGv1TZQFv0A1BcXAz0ByksFgsGgwGz2czLL7/MoUOH+NOf/sS0adPs3n/kyBFuvfVWVe7z7LPPJikpCZPJxOrVqykvL+fRRx/F1dWV+fPno9friY+PZ/fu3ZjNZu69916am5spKCg4Lcjr4ODAqFGjuOSSS04bt7Xz1Xo8Wq1W2tvb8fDwIC4ujpSUFIYNG0ZycjIxMTGDlmYODQ0lPT2d3Nxc9u3bR319vV2gVvuMrKwsWlpagP77XVtbG+7u7uj1eoKCgvDy8qK5uZmysjKioqJO+z6sVitBQUE89NBD3HzzzezZs4fq6mr8/f1JTEwkPj5eZa6d6bs89W+SISzE/w7tv+WoqCg8PDxoaGiguroaOP2//YGl/AFqa2spLy+nq6uLtLQ0PDw8Ttu/o6Ojqvrz97//nXfffZekpCRuvvlmEhMTWb9+PWvWrKG6upqPPvqIoUOHcscddxAaGkpvby+Ojo7Mnz+fo0ePUlZWRmZmJhdccIGqrhMTE4OzszNdXV2kpKRw0003ERISQnx8/PdWJ9LOXevJW15eTnl5+RkDr87OzhQUFKjz154PfH198fT0pKWlhdra2kHb4QDce++9AKxZs4bs7Gzy8/OxWCx2xzJv3jyWLFlCQEDAoIuxtOpPQgghxC+RBIqFEEIIIf4DQkJCiImJIScnh8bGRuD0AKjNZlMTDaeWhRtsQnjz5s28/vrrFBQUsHr1apKTk+2Cy/fffz9r167FaDRy4YUXMmPGDEJCQjhy5AgbN24kIyODt956C4vFwpIlS9Rq++HDh7Nx40bVMxhQkz9aZsLQoUPx9fWlubmZoqKi7z13R0dHIiMjAairq1M9Igfj6+tLYGAghYWFVFdXn9bn0maz4ebmxhVXXMF7771HQUEBjzzyCFdddZXqI2mxWFi7di1//etf6ezsZNq0aSowr9frsVqtREdHs3TpUubNm8eRI0c4fPgw0J81lpaWRmpqql0WgxBC/FJo9wuDwUBFRQUNDQ2qr+RggUXt3qMFFb4vqNjT04OrqysdHR1cffXV5Obmctlll7F48WI6Ozv55ptvaGpqorS01K40aHt7O3/7298wm83Ex8dzzz33cNZZZ6n9Tp48meeee45du3Zx4MABxo0bR2hoqKro0N7ezo4dO9T2Pj4+xMfHYzAYOHbsGB0dHSrDTAvODryfAmpfDg4O/Pa3v+XRRx894/U7efIku3fvprq6moULF+Lp6cm8efNYu3YtLS0tPPzwwzzzzDN2AYaioiKef/55enp6cHBwoLW1lfLyclJSUoD+kqnu7u40NzeTmZlJVFQUVqvVLstXr9djs9kwGAyEhYVx8cUXn/G7kAVKQvy6DBynvb29CQ4OpqCggJqamkH7wut0Oo4fP86aNWvYunWr+v3t4+NDYGCg6pM7sES9l5cXUVFRnDx5khUrVpCcnMzjjz9OYmIiAH/4wx9wcnLimWeeAWDIkCFER0er5wToXyA6YsQIPvvsM0pKSmhra1OB4sjISDw8PGhqaiI0NNSuh/HAe9GZeqSnpaWh0+loaGhgx44djB07dtBr1dnZSWFhIVarlYkTJ9Ld3Y2zszM6nY74+HiOHDlCXV0dLS0tBAQEDLqPe++9l6lTp3Lo0CEOHjyIyWTC29ub5ORkxo0bR3p6unqvLMYRQgjxv0YCxUIIIYQQ/wHu7u5MmjSJ5ORkxo4de1pvLW1SRPtbT08PJ0+exGw24+3trSZo4Lu+XFp55p6eHkpLS0lOTsZiseDo6Mgnn3zC2rVrcXR05Morr+Smm27Cy8sL6A/yzpkzh5dffplVq1axYcMGIiMj+d3vfqdeh/5+idqkk9Z3cWA5UD8/PxoaGs6Y+TtQcHAwRqORtrY2amtrB115r12D8PBwdDod9fX11NbWEhERoV7TJoxuvvlmTp48yYYNG1i7di07d+5k4sSJODg4kJ+fT1FREX19fQwdOpQ//OEPdlkT2kS8p6cnkyZNYvz48VKOUwjxP0MbJ2NjYzEYDBiNRnp7e793+x8aA00mE9dffz2FhYVAfyDgwIEDADQ0NNDT04O3tzcjR45k27ZtVFRU0Nvbq/a7Z88eduzYgaOjIzfccANnnXUWNptNBT2Sk5O56aabCAsLY9asWXh6egKQlJSkggXp6ek88MAD+Pv7q6za5uZm9u7dy6uvvkphYSEPPPAAW7ZsYdmyZSrTbGDLhtDQUKqqqigoKKCnpwdHR0d6e3vtAuR6vZ433niDTz/9VC208vT0ZMSIEVx66aW8++67fPPNN5jNZubPn8+QIUMoKSnhk08+ISMjg6FDh9LS0kJFRQWlpaUqUOzn50dkZCS9vb0qa3mw635qYOTUwIkQ4tfp1P/+Y2JiKCgowGQy0dLSovqga3bs2MGrr77K8ePHgf7SzuHh4ZhMJvLy8sjLyyMrK4s77rhDVQZycXEhKiqK3bt3A3DOOeeQmJhoN16PGTMGHx8fGhsbVZbtqWOZVuK5srKSpqYmVQ4/ODgYHx8fKioqKCgosNvvD92LbDYbwcHBTJgwgT179vDxxx8zffp00tPTge+yhvv6+nj77beprKxU56C1LdAqMh05cgSz2Ux9fT0BAQFnrNAwYcIEJkyYoBYgCSGEEL8WEigWQgghhPgPufPOO8/4mk6no66uji+++IKvv/6arKwsbDYbjo6OREdHExMTw9VXX016errKGo6NjSU4OJjCwkLy8vI477zzMBgMNDc3s2fPHqB/5f1dd91lN7lks9nw9fXllltu4cCBAxQXF/PZZ5+pQLGWddXV1aX6f506eaJlIxQUFFBdXU1XVxfOzs5nPD9/f39CQkIoKyujurqazs5OlV2g0TKroqOj7bKxBgaKte1cXFxYtmwZSUlJrFy5kp6eHtavX6/25erqyvnnn8/vf/97oqOjT5sAGvjPEiQWQvwvMhgMfPDBBwQHB9v1rB2or6+P8vJyjh07RlZWFpWVlRgMBsaPH8/o0aOJi4vD0dERR0dHwsPDqa2txdnZmbq6OubMmcODDz6I0WjExcWFrq4uIiIigP4+vB0dHeq+UFNTg81mIyEhgbPPPvu0bF+A9PR0NeGv0cqrtra24uXlZdczvq+vDy8vL8477zxGjBjBzTffTGFhIdu3b+eOO+7g+eefV8Fi7fOmTJmiWg7s3buXqVOnYjQa7fp4mkwmcnJyAJgxY4Zdtt2SJUtwc3PjjTfe4MiRI2RmZtqVI7355psZN24cTzzxBNBfHhX677vDhw+368n5Y8uAS3BYiF+ngYsqTSYTBQUFlJaW4unpydy5c0lJSeGbb77BZDLR0NCAv7+/ek9+fj5PPvkkpaWlxMXFcd999zFx4kR0Oh15eXls2rSJv/3tb+zdu5enn36aN954Azc3NwwGg2oX4+Pjo/4Z7MteBwUF0djYqErtnzpOhYWFYTAYqK+vx2QykZSUBPT37w0JCeHEiRPU1dVRV1dnN8Z+H23MvOaaa6irq6OgoIAnnniCCy+8kKlTpxIZGYnZbGb16tW89tprWCwWfve73zFlyhRsNpv6va+dU2VlparydKaxWPtMCRILIYT4tZFAsRBCCCHET6SVSgMGLZOm6e7uJiMjg/z8fNLS0hg+fLjKDq6treW5555j06ZNdHZ24uDgQEREBI6OjhQUFKiMgfvvv1/1Jg4KClJZVwUFBUD/REd3dzdbtmzBaDQyatQotbpemyDRjs/X15fZs2fz6quvcuLECRoaGvD19cXX11dNzGuZYgPL2WmTUBERESrzt6amZtCArMbDw4Pw8HAVKG5vbz8tUKy9LyoqSq38LysrY9KkSSo4rl1jm82Gh4cHN954I1dccQW7du2isrISX19f4uLiiIuLU5M6/0pPTiGE+CXo6+sD7Be+9PX1qbL7VquVvr4+VRpUe/3DDz9kxYoV1NbWqr/rdDo2bdqEn58fd955J7/97W/x8PBQ5fxffPFF3n//fXp7e/Hy8lLZykajUbUrqKiooLW1FV9fX7q6uqiqqgL6S0h7enrajekDWSwWVXpZp9Ph5eVFUFAQzc3NVFRU0NzcrKpjaOdqsVgIDQ1l+fLlLFmyhOPHj3PkyBGWLl3KsmXLiIiIUPetyy67jBMnTnD8+HEeffRRGhsbmTZtmspQLigo4KmnniI7OxtXV1fmzZun+mbabDZcXFxYuHAhaWlp7N69mz179mCxWIiJiWHKlCnMnj2b9vZ2nJyc0Ov1tLa22l3XM31XQoj/fQNLR/9QexltzGpra+O1115j9erVtLW1qdfXrl2Ls7Mzer1eBVwTExPR6XRYLBZWr15NaWkpUVFRPP/88+o1q9VKcnIyycnJ+Pv78/DDD5Obm8sHH3zAjTfeiIODg2oX09bWpqofDDw+bVzOzc2lpqaGjo6O0/oL+/n5ER4eTmlpKZWVlVitVlUZKDIyEgcHBzo6OigpKSEwMHDQSkOn0l6fOnUqjY2NPP/882RnZ5Odnc3HH39Mb28v5eXl2Gw2vLy8mD9/PjfccINdtQiAWbNmERsby9ChQwft0zyQPD8IIYT4tZJAsRBCCCH+Z/y3AoSnlkqzWCxYLBaVSaUFaQ8ePMgNN9wAwOLFi1WZN4A1a9awbt06vLy8WLp0KVOmTCEwMJDKyko2bdrEU089RUZGBp999pkKFPv7+6syc6WlpWpfVquVnp4ebDYbQ4YMsVtFP5CDgwMpKSn4+flRX19Pfn4+48ePB/pLxmVkZFBXV0dzc7Nd/y5tgj86OhqDwUBbWxvl5eXfGyh2cXEhOjqa3bt3U1VVRUtLC4GBgXbbDwwUOzk50draqsrlnbrPgf/u7u7OrFmzvvf7EUKI/wWDBRpO7XOrcXBw4KuvvuKOO+5gyJAh3HvvvYwZM0ZN2L/55pu8/PLLuLi4MGPGDNLS0oiKiiI7O5t169ZRVVXFE088QXR0NKNGjVL79PHxASArK0v9Dfon4cPDwwGorq7GbDYTFRWFs7Mzra2t6PV6HBwc6O7utuvtO5DW5kA7L71eT0xMDPn5+TQ3N1NTU4OXl5fdvcNgMNDX10dQUBBLly7lmWee4dChQ+zevZuXX36Z22+/nbCwMCwWC8nJyVx33XU89dRTnDx5kvvuu4+0tDSSk5OprKwkOzub+vp6vLy8uOqqq5g6dao6Hu3znJ2dmTRpEmPGjOH2228/rS+oo6MjZrMZnU6nghBSxUKIX59Tg58D/7mnp0eNNa6urqf9ftbr9dTX17N06VK2b9+Oq6sr48aNIzk5GbPZzLZt2+jo6AD6S/CbTCagf6xpbm7m888/x9HRkYkTJ6ps3oHHYLPZuPzyy3nvvfcoLi5m+/btXHvttRiNRgIDA/Hy8qK5uZnW1tbTqvpoi0X1ej2NjY1UV1cTFxdnt52TkxNxcXGUlpZSXl5Ob2+vGvdjYmJwdHSkp6eHwsJCxo0bd8bFQ2dywQUXkJyczGuvvUZlZSWFhYV0d3djMBgYN24c5557Lueff75dJrB2bD4+Pup5RwghhBCDk0CxEEIIIX6xGhoaKC8vp66uDi8vL0aPHv1PTchqkxU/NsBYVlbGvn37OHDgALm5ueh0OtLS0pg0aRJTpkxRmbNRUVFER0dTWVmpJnQcHBxoaGjgww8/xMnJieuvv5758+fj6OiI1WolLCyMa6+9FmdnZzw9Pe0mNpydnQkKCsJgMFBTU6MygpubmwkMDKS2tvaMGc7aZI6fn58KFFdUVKj9p6SkqEBxQ0ODXf8ubX/R0dE4OzvT2dmp+hSfaaLH0dFRZZrV19cPWupN++fg4GBiY2OJjo5m5MiRgJTeFEIIsB8LOzs76ezsxNfXlxMnTnD99dczcuRIHn/8cfz8/ABUWeXGxkY17ur1eioqKvjb3/6Gk5MTl112GbfccouaUJ85cyZz587l+uuvZ9SoUXb3URcXF0JDQ9Hr9VRVVZ1WcSIoKAhvb2+ampqorq5WC6VcXV3R6XR0dXVRW1tLZGTkoAuLOjo6qK6uxmKxEBwcjJeXFwkJCXz99de0t7dTVlZGUlLSae91cHDAZrORmprK7bffzsMPP0xBQQH/+Mc/6Ovr49lnn1VB6NmzZxMaGsozzzxDTk4OWVlZHD16VO1rxIgRXHHFFVxwwQWnXf+uri7y8/Pp6elh5MiR6nMtFgs6nQ6DwUBxcTHt7e309fUxZMiQf/arFkL8B9TX11NUVKRap9hsNqZPn05iYqKq0vNjaL11B/4uPpU2XmuLY7Kysli/fj27d++moqICPz8/0tPTmTx5MvPmzTvt/Z9++inbt2/HYDCwZMkSrr76arXYprS0lLvvvpv8/Hza2tqoqalR76urq6OrqwuLxXLGhZTasU+dOpWTJ0+Sl5dHRUUFcXFxeHl5ERoaqgLQfX19avw802LRUwPFAImJiWzZsoWysjK6urpUoFirHNTe3k5eXt6PvuanSk5O5uWXX6a4uFiV3g4PD7dbcCSEEEKIf47cTYUQQgjxs9fZ2UlJSQm5ublkZWWRnZ1NYWGhXUm2sLAwjEYj559/Ppdeein+/v7fm2E8sCTawG1+qBRaZmYmr732Gtu3b1d/c3BwoLCwkE8++YS5c+fy2GOP4eTkpCbQtdX1Gjc3N1paWrBYLAQFBanSoAM/9+KLL1b7hu8meMLCwnBxcaG1tZXS0lJ8fX0BVKC4srLye6+lwWBQmc/V1dXq71rWckNDA3V1dXbZCNr1iYiIwMPDg7a2NkpKSuxeO5WDgwOhoaFAf8/G8vJyxowZM+i27u7uvPvuu9973EII8f/l31WtQmtboNPpfvRimO3bt/PVV19x+PBhmpqaSEhIYMGCBbi7u9Pc3KyCBlqgeOjQoQA0NTXZBRJqamro6urCYDBw++23qwl8i8WCwWAgLi6OlStXEhAQcFpvxsDAQPz8/Kirq6O0tJTExER1r/Tx8SE0NJSmpiYqKiro7e3FwcFB3ZO1IE1kZOSgmdA5OTk888wzODs7c9VVVzFjxgzVl1gLjpyJ9p2kp6fz0EMPsWTJEhobG9m2bRv33HMPjz/+uApqp6Wl8c4771BYWMiBAwfQ6XRERESQkJBAWFjYGT+jrq6Oxx9/nKqqKu666y7mzp2LXq9X9+3CwkKeeuopWltbSUpKIjk5+Qe/UyHEv99gzwoFBQW0t7fbbWc0GnnrrbcYO3Ysf/zjH9UCxVNp47W2APP7AsQAjY2NPPbYY2zdupUbb7yRGTNm8OCDD5KdnQ30/y42mUysW7eOr7/+ms7OTi699FL1/paWFt577z0ALrroIq677jqg/7nEycmJpKQknnvuOa699lrq6+upra2lra0Nd3d3Kioq8Pb2xmw2q9/4Z7pvJSQk4O7uTkNDA8XFxcTFxeHq6kpkZCQ5OTmUlJSoTF3AbrGoi4uLKh89ffr00xaLamN3eXm56jMPEB4ejpeXF2azWY3p/+yCUKvVSmxsLLGxsf/U+4UQQggxOAkUCyGEEOJn68CBAyxZsoTm5uZBX/f29iYiIgJnZ2dOnDhBR0cHr7zyCnv27OHee+9l+PDhZwz8an/r6emhoqKC+vp6AgMDiY6OPuPx5OTkcN1119He3k5qairz5s1j+PDh1NfX88EHH3Dw4EHWr1+Pv78/t912G87OzoSEhKhMXbPZjL+/P05OTiQmJpKdnc1rr71GRkYGo0ePJiEhgd7eXmw2G1FRUWqCBVCr+8PCwnB3d6e1tZXCwkJGjRqFp6cn4eHhHD9+nKysLLq6utREkUabMDIYDBQVFWEwGFSfZUBlQTU2NqrelaeWiA4JCcHPzw+TyaT6T37fRE9UVBSzZ88mMjKSsWPHnnE7IYT4udm6dSsPPvggYWFhPPjggwwdOvRfDhif2rbg+1itVlavXs17771HUVER0F9VIj8/n6VLlzJs2DD0ej0mk0mVfIb+DF9nZ2c6Ojqora2lt7cXR0dHurq68PHxobGxkU8++YQxY8YQGBioevXabDZiYmIGPRY/Pz+CgoKoq6sjNzfXLlDs5uZGdHQ02dnZlJWV0dPTg7OzM8nJyYSGhlJUVMSBAweYPn26XaBYyzxubGwkIyMDX19f1fIgISEBg8GAxWJR1St+6Lqnp6fz6KOPsnjxYjo7O9m0aRPnn3++XSlpg8GgenWe6kzfbUREBF5eXhw7doynnnqKzMxMpk+fjl6vJzs7m23btnH48GEcHR1ZtGgRQUFBP6r3phDi3+OHnhV8fHyIj48nJiYGnU7Hvn37KCsr48CBAzz22GM89thjDBky5LT/bgeO1z09PZSXl1NRUQH0VyHQFudoXFxcaGtro6uri5KSEu68805KSkq45ZZbmD59Ok5OTnz55Zd89NFHNDU18cILLzB79mxVrj4/P5+uri7gu8WiA4/JYrGQkJDAlClTWLt2LTU1NTQ1NeHu7o5Op1OLf06ePMmIESPO2KIgMDBQBb+1xaXOzs7q+ae8vJyOjg5VHWngYlF3d3daWlrOGOzV9lFTU0NjY6NqTaCVtgY4evSoui/9M2RsFUIIIf4zJFAshBBCiJ8tHx8fmpubcXBwICgoiMmTJxMbG0tycjIxMTH4+Pionl4VFRVs2bKFt99+m8OHD3PXXXfx1ltvqUmKgXp7e1mzZg0bNmzgyJEj2Gw2NYEcERHB3XffrbJhB1q6dCnt7e2kpKRw9913k56erl6bPn06t956K9u2bePYsWPU1dURERFBZGQkDg4ONDU1UVlZqXoMX3bZZbz44ouUl5dTVVXF2rVr6e7uBvonyd3c3NDpdFx00UXMmTNHBX5DQ0Px9PSkurqa/Px8dZ2GDx/Ol19+SXZ2NhkZGYwfP15lisF3EyulpaW0t7fj7Oxsl+GrBQiam5tVoPhUrq6ueHl5YbFYyMrKoqmpSQUZBpOQkMDzzz9/xteFEOLnysXFRWVn1dbW/lOB4oHbd3R0UFxcTGFhISaTiZCQEKZPn67G+lPt37+fZcuWATBnzhyuueYaoqKiKCgoYN26daxevRroDyRUVlYyatQo1cs4NjaW7OxsTCYTLS0t+Pn5MWrUKKKiomhsbOTRRx9VGVkWiwVHR0diY2NJSkrCx8eHYcOG4eHhoYK5WlnSrKwssrKymDdvnsokMxqNKkhdWlpKZ2cnnp6exMfHM3r0aIqKiti5cyfz589XAVotgNHe3s7XX38N9LcgGDFiBNAfkPD396e+vl5lRf/QdbdarZxzzjl8+umnhISEqGobZ/peTq0qMtj+tfO/5ZZb6OnpYd++fXzwwQd88MEHdtulp6ezePFixo8fL0FiIf7LfuhZISgoSG3b09PDyZMnWb16NStXrqSoqIiVK1fyl7/85bT9FhUVsWXLFrZs2UJmZqYa8/z9/fHy8uLiiy/mmmuusetlHh0dze7du9m4cSNWq5WlS5dyySWXqPYyt956Kzqdjvfee4+mpiaOHj3KlClTgP7FqBaLBS8vL/r6+oDB27VMnDiRDRs2UFtbS0NDA+Hh4Xh7e+Pq6gpAXl4es2fPPi3bV3u/r68v9fX1uLm5qc9xdHQkMjIS6A80t7S0qIU7A9vE+Pn5UVlZqQLMp46bISEhuLq60traSkVFBcOHD1f3wSFDhmAwGBg2bBg9PT3/dKBYCCGEEP8ZEigWQgghxM9WSEiICnTOmDGDpUuXDrpdUFAQQUFBjBgxAn9/f5555hkqKytZtmwZK1assNu2oaGB5cuX8/nnn9PY2IjBYFCr5LWM3AMHDvDyyy8zevRo9b7du3erPsM33XSTXZBYmwRZtGgRv/nNb4iKilITLLGxsRiNRjo6OigtLSU1NRWbzcYll1xCQEAAGzduZP/+/ZhMJtzc3DAYDBQUFKh9Hz9+nD179vDcc88B/avyfXx8AOyyzGbMmMHTTz9NRUUFK1euJD09/bSeXZWVlbz99ttA/6p/7RysVivOzs6EhYWpfspaOTuNNvk9efJkAgICGDNmjMpeEEKI/zVaCc2Ojo4fLOl/Jjqdjp6eHt544w3Wrl1rV+7f09OTZcuWcfPNN3P55Zer8Va7nzz++ONA/yKk2267TU3ip6enk56ejru7O5988gktLS1UVFRgsVhU9lhycjLZ2dmYzWaamprw8/PDzc2NO+64g+eee47jx49TXFxMcXGx3bFqgYV58+Zx3XXXqUoT7u7uBAcHA/3BDG17sA8wVFZW0tLSQlBQEH5+flxxxRWsXr2akpIS7r//fpYuXarKnprNZlauXMn69evR6/XccMMN6vyNRiNeXl7U1NRw4sQJamtr7YI9g9Hr9dhsNlV6e+C1HOx7+TGZ3do2qampPPnkk+zatYu9e/dSUlKisqbHjh3L8OHDiYiIUMchhPjv+THPCloFHaPRSGxsLEuWLGHDhg3U1dWxY8cOwP6/3UOHDrFixQp27tyJ1WpVFX3c3NzIycnBbDbz1FNPER0dzbRp0+yOxdXVlZaWFqZOncpFF12Eo6Oj3Vg0ZcoUvv32W44dO0ZmZqYKFDs4ONDZ2UlwcDAdHR2nnYN2fHFxcfj5+WE2m6mrqwP6F5GGh4dTUFDAgQMHTjufgSwWCzabje7ubtVmxmAwqAWytbW1mEwm4uLi7N7n5OREeHi4Ov+GhobTFuR4enqSmJhIRUUFHh4eduf9wAMPDHo8QgghhPh5kECxEEIIIX623NzcVJkzrSfuwCyggbTJ5YULF7J7924OHDjAt99+y9GjR+36j3344Ye8/fbb6HQ6brnlFi677DICAgKoq6vj8OHD/P3vfyczM5OnnnqK+++/n9TUVAByc3Mxm80kJCSoLOWBvcugv3yzNrGuiYqKwtXVlaamJlWqTcummj59OhMmTKC2thYnJyfKy8spKyujpaWFzs5O1q9fT1lZGdu2bePQoUOkp6fblecc2Pc4KiqKK664gjVr1rB9+3YeeughZs2aRVpaGi4uLpw4cYIXX3yRjIwMPD09+dOf/qQCvdp5+Pr6UllZSW1tLe3t7XaBYm3C6dprr/2XvlMhhPgl0BbktLe3q0DxTw0C1tbWsmzZMnbv3o3VaiUxMZGUlBScnZ3ZvHkzZrOZ5557juLiYu688078/f3R6XTs2LGD1tZWABYsWKACsfDdeH3VVVdRUVHBpk2bKC8vp7u7W5UKHTZsGJ999hn19fV2E/5jx45lxYoV7N+/n3379mGz2ejq6qK8vFzdY81mM+vWrcNkMrFy5UqgP7taCyJolSy0IKpOpyM0NBSdTkddXR11dXUkJCQA/QHrBx54gGeffZasrCyuvvpqJk2aRG9vLwUFBZhMJjw9PZk/fz7nnnsu8F2bhVmzZpGamsr48ePx9PT8Udf71N8F/46+0pqQkBAuueQS5s6di4uLy79tv0KIf82PeVYYOHZbrVbc3NwYOnQou3btoqmpierqakJCQrDZbJhMJpYuXUpZWRlpaWlcf/31jB07FhcXF0wmE1u2bGH16tUUFxfzj3/8g+TkZLWQJjw8HA8PD1paWkhMTMTJyem0MsvBwcGEh4dz7NgxNZ4CarFJd3f3oGW0tXPR2gvU1NSoCkD+/v6MHDmSbdu2kZOTo54ZBnv/22+/jV6vx9XV1e6ZJSAggKCgIGpra6mpqbEL8g7sSd/b20tRURGVlZX4+vrabWcwGFi1atVP/g6FEEII8f9PAsVCCCGE+NnS6XQkJyezb98+6urqvrfUsU6nUxMZV155JZWVlZSVlbFhwwaioqLw9fXl8OHDfPTRRwDcfvvt3HTTTdhsNvr6+vD39+e8887D1dWVZ599luPHj7N+/XoVKNYmQdzd3VX56MGCBn19far8p16vVyXh6uvrVWB3YCaTs7OzKtsZHBxs18t33Lhx3HjjjXR0dFBeXs7IkSNxcHAgODgYJycnzGYzlZWVhIWFAfDnP/8Zk8nEzp07+fTTT9myZQuhoaGqhyVAUlIS119/PRMmTLC7dgCPPvooNpuNxMTEH91HUwgh/hcZDAY8PT1paWmhtraWnp4ejEbjT9rH22+/zbZt2wC49dZbue6663BxcaG3t5ebbrqJ119/nc8++4y1a9cSHBzMH/7wBxwdHTl58iS1tbUkJyer8V2bjNfuOyEhIUyZMkUFijs6OtTinpSUFKC/goZWCUPLFvbw8OCcc87hnHPOAaClpQWr1YqnpydlZWU88sgj7Nu3j3379qmMMYPBQHBwMC4uLjQ3N9PY2KgC6dAfpAgKCqKmpobKykq78su/+93vCAoKYvny5VRVVbF9+3b1vtjYWC677DKuuuoqu0ADwM033/yTrvV/iwSJhfh5+SnPCtCfUWs0GtHpdOr3f319PSEhIeh0Ot5++23KysqIjIzk1ltvZeLEiUD/7/uIiAiuvfZampqaeOONNygtLaW6uloFikNCQvD09FTVeeD0ZwUPDw+14LSwsJCuri6cnZ1VFYvGxkb1vDDYYhc/Pz+6urro7OyktrYWq9WK0Whkzpw5rFy5koaGBh5++GHefPNNuzY6PT09rFq1ir1792K1Wlm4cKHddfL29iY0NJTa2lpycnKYM2fOafe8+fPnM3r0aJKSktQCpH/nghwhhBBC/P+RukhCCCGE+FkbN24c0N87VyvbeWrfLY02GZOamqpWyWdlZVFWVgbAgQMHqK+vZ/jw4cybNw/4rgTlwJJwc+bMAfpLz1VVVQGoHsFaz8ozcXBwwGAwqGPx9/dXGcCVlZX09PQA0NXVxdGjR3nhhRdoaGhQ77darfT29gL9gWPtvc7Ozip4Gx4ejouLC93d3SpLWetH/PLLL/OnP/2JsWPH4ubmpsqPBgYGctFFF/HAAw8wf/58u4kdBwcHbDYbycnJDBkyRILEQggBnHXWWUD/uF9fX/+T3nvkyBHWrVsHwKJFi/jDH/6Ai4sLVqsVR0dHQkJCuP3227nssssA2LZtmyoZqo3PDg4O+Pn52f1tIK09QkVFBU1NTerv2gT+wJ7zA7Pr2tvbsVqt9PX14enpibe3N3q9npiYGC655BKVwTtwn6Ghoar88wcffEB5eTlFRUV0dXXh5+ensp5LSkqwWCzAd/fqc845h1WrVvHKK6/w1FNPsWLFCrZs2cLGjRu5+uqr1UKvU1ksFtVDUwghzmSwZ4XBxhToLz9dVlamxsaUlBSio6MBaGtrU9Ucxo4dy8SJE9U4NvC3sRboNZvNaj/Qn+2rBV+14zg1UOzs7KzG0oFjd0hICLGxsXR2dnLo0CH1vDCQ1WqlublZnZvJZFLHGxYWxsKFC3Fzc6OgoIDrrruOZ555hr1797J582aefvpp/vrXv9LU1MSUKVO46KKLcHBwUPtydnYmISEBb29voqKi7O452jmMGDGC2bNnn1aWWgghhBC/fJJRLIQQQoifNS2jV+vxO2TIkDP2HdT4+fmRlpbGV199RVVVlSofp63Qd3NzIyQkBOifiK6qqqK4uJi8vDxOnDjB8ePHAcjLyyM7O5vQ0FBcXV2B/mBvc3MzXl5eg352Z2cnmZmZ9PT0MGzYMHx8fAgLC0On01FfX09tbS0RERGUlJTwl7/8hYyMDCwWC7fccguurq4qE7m5uZnly5dTXl6Oj4+PXVZAeHg4Xl5eNDU1kZmZyaRJk9RrWkb1FVdcwbFjx9Dr9YSEhBAYGPi911kyAoQQwt748ePZsGEDzc3N1NTUqNKk3zdeatm0OTk5NDQ0EBMTw8yZMwFUtQltO19fXy666CLef/99ysvLOXToEJMmTVJtAWpra0/rNT9QaGgoer1e9apMTEwE+jPWfHx8aGxsxGQy0dnZiYuLCzt27ODdd98lLy+PlStXqgy2vr4+la2cmZlJc3MzISEhKuALkJCQQHR0NKWlpbzzzju8+uqrBAUF8dxzz5Genq6Cy2azmd7eXpWxp9HpdKSlpZGWlmZ3DqdmSg/0fecuhBCagc8KJSUlDBkyBIvFgqOj42nj9YkTJ3jllVfIzs7GxcWFc889V1VjcHBwYObMmcTFxanxVHt/a2srBQUF7Nixg2+++Qboz/6tqalR+x7YHubkyZOD3i/0ej3BwcF4eXnR3NxMZWWlykg+55xz+PDDD9mxYwcZGRmqypC2YMbBwYHVq1erBaa1tbU0NjaqZ5KFCxfi4eHBG2+8QVlZGStXruStt95Sn+3l5cXNN9/M9ddfj6enp909yd3dnUceeYRHHnnkn/8ihBBCCPGLJU9eQgghhPhZ01b5D8ye/TGSkpKA/rKaJ0+exGq1quzgwsJCHnroIU6cOEFxcTEdHR2D7iMgIEBN8ISEhODr60tDQwN5eXl2PSMHKi0t5Y033mDv3r28/vrrTJ8+ncjISBwcHGhpaaGiooKIiAji4+NJTEwkIyODjz76iKKiIs4//3wCAwMpKytj165d7NixA4CLL76YtLQ01efM398fo9Fol2V86oS6Tqez680shBDipxk+fDjQn2VWVVXFyJEjfzBQrNfr6e7uVtlkOp2OIUOG2JVj1raD/vLLSUlJ5Ofnk52dDfSXNzYYDJjNZkwmE76+voN+Vl9fH6GhoZw8eZKqqiqVGabX64mPj+fgwYOYTCYaGxtxcXHBYrFQXV2N2Wxm+fLlXHzxxQwZMgQ3NzdaWlpYu3Yta9asAWDevHkkJiaqXp8uLi7ceOONODo6smPHDpydne0qUCxdupSHH35YZUCfiXaMWoazLFISQvyrtGeFnp4e9VtfK5vc1dXFyZMnyc7O5siRI+zfv5+SkhIcHR257LLLuPjii9V+XFxcmDp1KlOnTgX6M5SPHDnCgQMH/o+9+46Oqlr7OP6b9EYIIYSS0HuVXqQT7KKCKKiAoqiAoKB40fsiXrBe9V4UsIAFAS+CKKACIkjvXWlCAiQhjfReJ8m8f4wZMmQSEghS5vtZy+XknL332WfI5Mw5z97P1t69ey1/oxs1aiQnJyfl5uZaLU1QNDjTzc3NsjxMYGBgietGUbah1NRUnT171pId4qGHHtLhw4e1f/9+vfXWW5o4caL69OljOZc1a9Zo4cKFcnJykpOTk+Lj45WUlKQGDRpYrjEPP/ywOnbsqIMHD2rbtm2Kj4+Xr6+v2rZtqy5duqh169aW9ez5+wsAAIoQKAYAANc1Pz8/OTs7q6CgoMw1uy5WvXp1OTo6KicnR8nJyZb0ypJ5xtOyZcusyjZr1kytW7dWq1at1Lx5c9WrV0/Ozs6WMg0aNFD9+vWVlJSkXbt2qVOnTvL19bU8mCl6CHT69Gnt3btX7u7ulhkADRs2lJubm7KzsxUeHq5bb71Vzs7OGjNmjDIzM7VmzRpt2bJFW7dutUqrHRgYqKeeekqPPPKITCaTpT9NmzbVd999V+ZaiTz8AYArU7Q+cGZmpqKioiSV72+rk5OTJVV10XXA1oxZyRzMqF+/vk6dOqXY2FilpqaqYcOGqlGjhmJiYnT06FE1bdrUKu1p0fUmMjLSku2iKOVz0aChNm3aaP/+/UpISFBSUpLq1KmjAQMG6MSJE/r444/1448/aufOnWrZsqVSU1MVGhqq9PR0ubu7a/DgwRo2bFiJfnfs2FFNmjRRdna2JXVqkaIsHZdS2vsAAJer6F4hPz9fu3fvlpubmw4fPqzg4GCrdPiSOePCPffco6FDh6pHjx422ysoKNCWLVu0atUqHTp0SImJiXJ3d1fbtm3Vt29fPfjgg3ruued04sQJxcfHKz093TJIJiAgQO7u7kpOTtbZs2etAsVF//fx8VHNmjV1+vRphYSESDIPoqlbt66eeuopxcXF6dSpU5oyZYp69uypRo0a6fTp09qzZ488PDw0bNgwrV27ViEhITp37pw6duxodW1q0qSJmjRposGDB5dYZxgAAMAWAsUAAOC65uLiIj8/P50/f77UNG6l1fP391dMTIzlQX1Rajd3d3eNGjVKd999tyX9my1xcXHKzc21rM3Yo0cPHT58WJs2bVK7du00ePBgOTg4WILFGRkZ2rdvnwoLC9W0aVPL+pYNGjRQ1apVFR8fbwl2FxQUqF69epoxY4Z69eqlPXv26NSpU5Z1Ijt16qSOHTuqUaNGklRirbCygsQAgCtXlI40MzPTMkutPNc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KCitWrGDPnj2Eh4eTnp6Ou7s7AQEBdOnShSFDhlxV1nt2djbffPMNmzZtIjw8nNzcXAICAujevTvDhg0rs8+mTZtaHv/6669Wf3MnTpgv0ho3boy3t7fdPoo/p6ZNm0atWrX48MMPOXv2LFWrVqVbt27897//tewbExNjKQ0fHR1NRkYGnp6e1K5dm3/84x8MHTqUmmVkKoaFhbFixQr27t1LbGwsBoOBRo0acc899zB48GC76+UdOXKEb775hqCgIJKTk/Hw8KB58+bcf//9DBgw4G+/jIFIWSKTky0Zvh3tfH4ZDAY61K/P+rTjHD53DpPJVOZkrcutPHCQnSGhALSuU5sXe/fi823bSc7Muupxbzh+gsjkFACev/3267J2soiIo4qMiLRMIOzYuZPNfQwGAx07dmBdbCyHDh0u93dBRkYGYWFhAHTq1NHufh07deL48eOcPnXKqprN2bPmMqLt2t9mNzvY09OTZs2acfz4cX777Sj/fOihK44rKyuLRQu/BqBZs2bce++9V2wjIuLIsuJSLOsS+7W0PbHGYDDg26IeMYknSTkTXe7vCmN2HhnRSQD4tqxvdz+/lvVJC4vjQvh5jDl5uHpcXN6sxCFMhbYnujs5X8rLMjhdapARk2x57N+mod1j+zarTdTWoxTmGEk/l4hPo6ursiQiIlJeChRfpc2bN/Paa6+Rm5treS4uLs4SRBk/vmJrKVXE7NmzmT17NqbL1tJLS0sjLS2NY8eO8d1337F8+fIyA7HX2+WBk3379jF27FguXLBeGzA8PJzw8HC+/fZbPvzwQ+6+++5SfUVFRTF8+HAiIiKsnt+1axe7d+9m2LBh1338ADt27CA9PR3AZunisjzzzDOsXbsWgB9//LFUoPi7775j8uTJVtmpMTExLFmyhF9++YXu3bvb7TslJYWRI0dy9OhRq+cPHjzIwYMHGT58uN2227dvZ8yYMeTn51uey8/PJyIigoiICFavXs2kSZMYNGhQhc73z6j4osDJ6dqLJRTfZHJ1da3QpIuKeP/991m0yHots5MnTzJ58mSioqJ44oknGDJkCHFxcZbtycnJrFu3jt27d7NmzZpSkyZ27NjBuHHjyM62LrdqNBrJzMzk7NmzrFmzhs8++4xevXqVe6zBwcGMHDmS2NhYq+ejo6NZtWoVa9as4c0332Tw4ME22zdv3pxu3bqxb98+vvzyS/r168ett97Kr7/+yo8//gjAa6+9Vq6x/Pbbb0yZMgWj0QhAUlKSVem+VatWWW0vlp6eTnp6OmfOnGH16tUsWrSIVq1alep/8eLF/Oc//ymVSX7y5ElOnjzJ999/z9dff22ZTABQVFTEhx9+yMKFC63a5OfnW96n3377LXPnzrVqJ+JIIpIv3RhpXN3f7n4N/c2Zxpm5eSRkZFCjjAkkttSs6s3jnTrxj6bXvgzEhexs1hw+DEC7+vXo0MD+zSQREbmyktd3xVVlbGnYsCEAmZkZJCQklOv3U2RkpOUauVEZfTdq3AgwZ6xFRETQpk0bALKyMgHw969e5nG8q5orX5yLPHfFMQH88P33pKWlYTAYGPqs7UxlERG5JDMuxfLYq4796wavWuZM44LsPHJTM/HwrXLFvrPiUsBU3HfpCkeWvmtfzGI2QVZsCj5NzPdXPfyr4uzhSmGOkfjDYVS/rXGp9e7jD5vvJ7lUdqeS36VrmYLcPMtjdx/7VY9cK1+6v5oVl6JAsYiI3HAqPX0VwsLCGDduHLm5udSuXZtPP/2UwMBANmzYwODBg0lISGDatGk35Ni//PILs2bNwmQy0aNHDxYtWsTOnTvZuXMnixYtsqylGhcXx8yZM6/pWC+++CKHDx+2+7+ZM2dafgw98cQTtG3b1tI2JCSEF198kQsXLlC3bl0+/PBDdu7cSWBgIPPnz6dNmzbk5uby6quvcujQIavj5ufnW4LElSpV4vXXX2fr1q3s2bOHDz/8kOrVq7NgwYJrOjd7Dhw4YHncqZPtGe72tGvXDn9/8w/Y/RfXMSy2f/9+3nrrLQoKCmjevDnz58+3/M0899xzJCUl8dNPP9nt+5VXXuHo0aM4OTnx4osv8uuvvxIYGMicOXNo3LgxX375pc122dnZTJw4kfz8fNq2bcuCBQvYuXMnu3btYs6cOTRs2BCTycQHH3xgWT/2ryoqKsqSnXCta0Rv2LCBkBBzeaFevXrZXHP5Wh09epRFixbRtWtXvvnmG/bu3ctXX31lmdyxcOFCnnvuObKyspgyZQo7d+5kx44djBkzBicnJ9LS0pg7d65Vn7GxsbzyyitkZ2fTsGFDZsyYwebNmwkMDOS7775j8ODBuLi4kJ+fb1nXuTwSEhIYNmwYsbGx+Pr68s4777B161b27dvH4sWL6dGjB4WFhUybNo3169fb7WfatGlUrVoVo9HIqFGjmDx5Mq+88gomk4nJkyfTv3//co1n9erV+Pn5sWDBAvbu3cucOXMsAepjx47x9ttvYzQaadOmDfPmzWPbtm3s2bOHFStW8PDDDwPmoHFx9YSS1q1bx3vvvUdBQQEtW7bk888/Z+/evWzcuJGxY8fi4uLC2bNnS00GmjlzpiVIfNddd7Fs2TL279/P5s2b+de//oWXlxenTp3ixRdfJC8vr9RxRRxBYnoGAM5OBvzKWJfR3+vSDZ7EjIwKHeORDu3535NPXJcgMcD3R34jz1iAwQDP2KkCIyIi5ZeQYF4T0tnZGT8bSxAU869+KVhb3OZKkhKTLI/LqgZUfM0GkJiQaHlcqZL5xnzJyeC2ZGWaA8qZmRlWk3FtSU1J5ZeffwGga7duNG3WtMz9RUQEclPNn7MGZwPuVT3t7ufu41WiTfmuG3LTMi2PK1Xzsrufvb6d3VxocHHd4LSwOE4t3Ur6uUTyM3PIiErk9DfbSA2JAQM0vrczzq4uJdpeqjBWmGd/ibOCnEv3DPLTs+3uJyIicr04bEaxyWQiK6t8ZQidnJyssmX/85//YDQa8fHxYfny5Zbypb6+vkyaNImAgACrEqjXU3FAsFmzZsydO9cqgFWjRg26dOnCgAEDOHnyJLt27bqmY7m5udkNkJ09e5ZJkyZhMpno0KEDb7/9ttX2KVOmkJubS926dVm9ejXVql1ad6N3795069aNQYMGcezYMaZMmWIVJF22bJllpvnMmTPp3bu3ZdtDDz1Ex44deeSRRyyZv9fT2bNnAXBxcSlzhrstBoOBBg0akJSURExMDPn5+ZbX77333gPMM+OXLVtGlSrmm+C+vr5MnDiRmjVr8v7779vsd9OmTZYA9qRJk3jmmWcs2/r160fHjh0ZMGAAUVFRpdoeOHCAtLQ0wFzyuGSp3X79+tG8eXPuuusujEYjmzZtumGZ2n+ETz/91PLYVpY62H/fFz8fFRXFxo0bWbZsGWAuLVfeLNeKysvLo23btnz11VeWcsS33347EydOZNy4cRQUFBAdHc3y5ctp3769pd3o0aMJCwvj559/Zu/evVZ9Ll26lJycHFxdXfnyyy+t1j7z9fWlTZs2ODk5sWjRIqKioggPD6dRo0ZXHOvHH39McnIyVatWZeXKldSvfymjrmvXrnTu3JnRo0ezZcsW3nvvPfr374+7u3upfurVq8fLL7/M+++/T0JCAitXrqRZs2ZMmzaNdu3aVej1mz59Ov/4xz8A899ysa+++gqTyYSvry8LFiyg6sWMDzDfFGzfvj2ZmZls3ryZgwcPkpubayktmJeXZ3kftmnThqVLl1o++/38/Hj55ZepXLky06dP59ChQwQFBdGpUyciIiKYN28eAIMHD2bSpEmWY/r4+DBs2DA6duzIU089xenTp1m2bNlf+r0mcrUyLt5493BzK7Pyg2eJGyiZFZxYUbPEe/5aZeTmsu2MefmQjg0aUNe39BpiIiJSMRkXJwB5eHiW/V1Q4vq7vNftGRmXrg8rlzEhydPzUtChOIsYoH6D+pw+dYrjx47bLWGan59PaGiY5d852dllTir95eefLVVuHnnk4XKdh4iIoyvIMl83OLu7YSjju8Kl0qXrhoKcsifuXOr70vWFi0fp+xbFnCtd+my/vO86PVrj7O5K5OYjpJyOIuW09f04j4CqNL63M763WK9/XHJN4rSwWKq3tX0/Ju3spapyBXlGm/uIiIhcTw4bKI6NjaVDhw7l2rdOnTps3boVMK+5u2fPHgCGDRtmc43L4cOH88MPP/D7779fvwFjLm16xx130KRJE3r37m3zgtTJyYlOnTpx8uRJS3bl9Zaens5LL71Eeno6NWvWZNasWVZjCQ0NJSgoCIBRo0ZZBYmLubu7M378eIYNG0ZwcDBHjx7ltttuA7CUb+7Ro4dVkLhY3bp1GTFixA0JxhcHVb28vK6qJFjx7PSioiIuXLhA9erVCQ0NtaxTPXr0aEuQuKQhQ4awYsUKS6C6pOLXo2HDhlZB4mI+Pj6MHz+eV199tdS2kjPcExMTS/291qtXj/nz51O1atVyBQxvlvz8/FI3iEwmExkZGZw5c4alS5eye/duwJzZbS9QXJH3ff369fn444+vOTu5LMOGDSu1Zm3JTPZOnTpZBYmLtWvXjp9//pn4+Hir55s3b86TTz6Jn5+fVZC4pC5duljKXaekpFzxv/uFCxfYsGEDAIMGDbIKEhdzcnJi4sSJbNmyheTkZLZs2cJ9991ntU9aWhr/+c9/+P77762ed3Nzo0WLFmWO4XI+Pj52S7V36NABLy8vWrZsaRUkLqlLly5s3rzZ8j4tDhQHBgaSfLE07sSJE22uQzxw4EC+++47AgICLH+TK1eupKioCA8PD7vLDrRt25b77ruPtWvX8u233ypQLA7JWFQIgJuzc5n7ublc+nlqLCi8oWMqy+ZTp8m/WIL+YRufxSIiUnEFF4Ombm6uZe7nVmLSofEKWbvF8vMv3UwvK3hbclvJNt26deP0qVPExETz84YN3Hf//aXarvlujVVwuaDQ/vdUbm4umzZtBqB9+/Y0uFhOW0REylZ0ce1fJ9eyrxucSmTrFhnLd91QVGKJKScX+/2X3FZ02TWJqaiIwvwCnNxs31bPv5DFhfDzVG1UE+cS+3g3DMDN24P89BwiNh7Cp2ltXD2tg9V5F7KI2XPq0rHsrIMsIiJyPTlsoPhqBQUFUXjxYtDe+p5OTk7cc889fPbZZ9f12E5OTowePdru9qKiIsLCwiwlhC9fX/N6KCwsZNy4cURERODu7s6sWbOsSneBdfnm5s2b250B3qJFC5ydnSksLOTQoUPcdtttZGRkcPLkScD+6wvmDMIbESguLglrKxuyPJxL3PwuXh9r3759lufsnZPBYKBfv342A8XFZax79uxp97h9+/bFycmJoiLrH5Dt2rXD1dUVo9HIsGHDeOqpp+jTpw/t2rWzjLWsfv8s3nnnHd55550r7te6dWtmzpx51WsU+/r6cscdd9C7d2/69etXKoh7vRVPjiipZAk+W+vngnkiA1BqDd6HH37YUlrZlri4OE6dunTBUVjGja1iR44csRynRYsWdt/P/v7+VK9encTERA4dOmQVKI6MjOS5554jOjoaJycnBg8eTGpqKmvXruXkyZP83//9n+X9nJmZSXBwMK1bt7YEcC/XokULuxM5hg4dWub5REREWE3iKfk5GRgYCJhf386dO9ts7+7ubpm8Uaz4M6+4CoG91+i2225j7dq1nD17ltTUVJuTaET+zpwMf50VTwoKC9l48fdImzq1aRJQ9nqVIiJSPmVlhl2rq70GKNanbx82bdxEdHQUixctJi7uPHfe2R9fPz+Sk5L45edf2LZtG76+vqSkmNfPdClj8tP2bdvJzjb/LnxI2cQiIuV2Q9dyd7q2vosKizizYjvJJ89hcDZQr+9t1OzQFLeqnuSn55BwJIxz244RvfME6ecSafNsf0vJaSdnZxre3YmQVbvITcnkt8/X0fDODlRtXBNMJtLOnifi10MU5hdY1kE2OP91rqFEROSvy2EDxSWzhCuiZAafrcy6Ys2bN7+qcZVXUlISgYGBhIWFERUVRWRkJGfPniU7+8auXTF9+nRLRvXUqVOt1iUuVrIE8oABA8rVb1ycuaxKfHy8JcBa1uvbqFEjS5D5evL29ga46rLWFy5cAMw/aouzGYvPzcfHx26GI9heVzcnJ8eS5dygQQO7bT08PKhVqxYxMTFWzwcEBPDaa6/xwQcfkJGRwRdffMEXX3yBt7c33bt3twREfXx8KnKafwoGg4HKlSvj5+dHq1atuOuuu7jrrrtwcbH/sXb5+95oNBIZGcn8+fP58ccfSU1NxdXVlT59+tzwIDFgM1BY8gaXrezzy/exJTc3l8DAQM6cOcO5c+eIiooiLCysVJWB4vdaWUq+n8eMGXPF/eHS3zyYg6bDhw8nOjoaT09PZs6cSc+ePcnPzyc6OpojR46wbt06WrZsyfDhw9m6dSuvv/46Li4uLFiwgK421gT19fW94hgyMjLYu3cvISEhltfg999/L/XeLvkaFH++N2jQoEIXpsWTc06ePFnujPXz588rUCx/KyaTibwyJqi5OjvjfvHzOf8K3935JfpxK+Mz/UY6GRtLWnYOAL1vubG/6URE/i5MJpNl4q0trq6ulomAJTN5bckv0Y9bOSfxulcqkYVsNFpN4rXqu0SGcsnMZjc3N9741xtMf386cbGxbNq4kU0bN1q17dSpE7e1a8dXF5eEcrczsRFgz8WKRzVr1apwBR0Rkb8rk8lEkdH+dYPB2dmSqXulLOGS/ThfIfv40n6XPvdNhYVgJxBbMou4ZHZxwuEwkk+eA+CWx3tZlY+uVM2L+n3bUaVedU58vYn0iHhidp+k/sU1jQFqtG9C3oVMIjcdITc5gzMrdliPz8OVlk/fQdgPgRTmGK0ykkVERG4UfdtUUPGaSoDNsqTFigOO11vxGpqrVq0qFSR1d3ena9euFBUVcfDgwet+7G+//ZYlS5YA5pK59jIXMzMzbT5fluI2JYM4Zb2+Tk5OeHp6Wv33uB6aNm3K8ePHycnJIS4ujlq1alWofUhICGAOSBZnJReP0V52ZDFbQcGSr8fVtAfzf6uWLVvy1VdfERgYiNFoJD09nV9//ZVff/0VV1dXBg8ezIQJE+zeTLnZpk+fzqOPPnrd+3V1daVp06Z8+OGH1KhRg/nz57Ny5UoSEhKYPXt2mUHn66Hk+mjXy9KlS/nkk09KvTecnJxo2bIlDRs25Oeffy53f9fyfi4ez7lz5ouoqVOnWjLY3dzcmD17No8//jixsbH873//45ZbbmH9+vWA+fPM3rrFZWX8FxUVMWvWLL766qtSNypdXV1p37493t7e7Nixo1Tb4okeV3qvXe5aXyORv4OkzEzGLlthd/vIO3pT2d1c6jPXaLS79iNAVokb+FUq+H68Xg6GRwDg6uJMJ5UKFREpl6SkJMa8bL8C1kujRll+/+bm5pT9XVBiArS965zLVfa8tC5xdla23d902VZ9W1+3BwQEMP2D6fy84WcC9+7l/PnzuLm506BhA/r260uPHj346ccfAfP1qr1jpKakEhZmXsv4H//4R7nGLyLiCPLSsjj40Wq725sPuB2Xi+sDF+aVfd1Qcu1gl8rlu25w8bBee9jZzlIIhbmX+nYt0XdckPm+n3eDALtrDFdrVge/1g1IPhFJ3IFgq0AxQP07bsOncS2id58kPSKegtx83L098W1Rj7o92+Dm7Ykx23w/w63K9b9vJCIicjkFiiuouOQrmLM9S/67pPxyrqNkS25urt1t48ePZ8uWLYC5zG7v3r1p1qwZTZs2pXHjxri4uDBjxozrHig+ePAgU6dOBcwXuq+//rrdfUteLB87dqxCZZxLZtxeKTv6Wl5je7p3725ZQ3X37t08/vjj5W4bFhZGYmIigFUWZPE55eTklNne1vmUzPS9mvbFunXrRrdu3cjMzGTv3r0EBgaye/duzp07h9FoZMGCBZhMJv71r3+VeYy/s1dffZUTJ06wd+9etm3bxkcffcSbb755s4dVIV9//TXTp08HoHbt2vTv35+WLVvSpEkTmjVrhqenJ3v37q1QoLjkhI0NGzZUeM3mbdu2AVCjRg0eeOABq23+/v58/vnnDBw4kOzsbMaPH28JoN59991XVQJ++vTpLF68GDCXgu7bty+33HKL5TVwc3Nj1apVNgPFxeda1mewLZUqVSIzM5P77ruPGTNmVHjMIo6iVlUfAAoKi0jLzqZa5co290suMZHC387vrBvJZDIRFBEJQMf69an0B1SYEBFxFLVqmyfiFhQUkJaaRjVf2xVWkpOSLI8vX+roSn0DJCYl4utnuwpNUom+/fz9Sm2vVKkSjzz6CI88+ojN9pGR5kmQtWvXtjuWAwcOWCrX/KOHAsUiIhXh4W+exGMqLCI/Iwd3b9vB0rwLl5Z9qlTV9rWFvb4B8tIycbfTLi/t0jVJyX1ykswTzKvUL3tpGp9GNUk+EUl+eg4FeUZc3K2vKbzrB9Dq6QCbbXOS0inKLyg1XhERkRtFgeIKKln+9+zZszZLLwOWDLrLlczYvHx90WLFpYYvd/jwYUuQePDgwUyaNMnmfpeXl71W0dHRjBkzBqPRSL169ZgxY0aZmaclL5ijo6PLDCxdPjOwZs2alrV2z549S79+/Wy2S0hIKLOs2dXq378/VapUISMjgyVLlvDYY4+Ve62r4uAUwD//+U/L4+LX48KFCyQnJ1utQVtSyRK/xdzd3fHz8yM5Odnm+sXFCgsLrcr92uPl5WUp0QzmQP64ceOIiYlh2bJlTJgw4YZn0f5ZGQwGPvjgA+6//34yMjJYtGgRvXr1okePHjd7aOWSm5vL7NmzAWjbti1LliyxmeFQ0c+Hkln1MTExFXo/lzyer6+vzVnALVq04KOPPmL06NGWLGhXV1dGjRpVoXGCueT10qVLAbjzzjv59NNPbX5W2XsNis/V1nuxpJUrV5Kenk6bNm3o3r07tWvXJiQkpFTp98uVNRNa5K+uepUqLH9xRJn7RF1czxEgPCnZbqA4PCkZAE93N6pX+eMDxeFJSVy4ODmrcyPbWQIiIlJa9erVWfHtyjL3KXmdHB4ebjdQHB4eDoCnZ2WqVy/fOvF169bFYDBgMpmICI/glltusblf8XWVwWAotbxPYWFhmRPCTSYTx48dB6Bps2Z2x3L0t98AqFW7NnXr1i3X+EVEHEGlal70fP/ZMvfJOn/pmj0zNtluoDgz1nzd4Ozhinu18l03eAb4gAEwQWZcCt4NatjcLyPG3DcGqFzr0sQjU2GR1f+Xh6mgEEoEigty8nFyc8bJzr3V1LAYy7Gr1C3fZCkREZFrUb4ImFh06NABNzdzmZLNmzfb3W/nzp02ny+ZnZdS4oZpSYcPH7b5/JEjRyyPn3zySZv7FBUVsX//fqt/X4usrCxeeuklUlNT8fT0ZM6cOVdcz7ZTp06Wx8WBbVsOHz7Mbbfdxt13323JcKxcubKlfVlt7b2+16py5co899xzAAQHB/P555+Xq11gYCCrVq0CoH379nTr1s2yrVevXpbHV/M3U9x++/btdtdkPnDggM2M43nz5vHAAw8wcOBAm+3atm3LkCFDAHNZ8+LSu46qRo0aTJw4ETDfBJo8efIVM7n/LEJDQy2B1ocffthuGbzAwEDL4/J8PnTs2NEyWaKs92RMTAzt27enf//+VpMmiidKhIWF2f3M69+/v9Xkijp16pT7hmBJR48etZzTgAED7E5oKfkalFyjuHh94fT0dH67eHPvciaTiU8//ZSPP/7Y8rlV/Jl18uRJzp8/b3d8kydPpmvXrjz22GMqPS0OqW61avhfDPwejoy0uY/JZOLIxSDCbRdv+P/Rgs/HWx43Caj4Z5GIiNhXr149y++8Q4cO2dzHZDJx+LD52ve2dreV+7vA09OTWy6uBXwoKMjufoeDzMdt2rSpVVnrFctXMOjpZ3hjwutWvxFLOhR0iPR08zVT5y6d7Y4/ODjk4jEqVo1HRETAs4YP7tXMk0pTztieyG0ymUgJjgbMpZ7L+13hUsnNEhxOPm1/knjxcavU9cfV81K1Mw9/c9XAtN/LTta4EGG+pnD1qoTLxfaF+Ub2/HsJge8uI/5QmN2254NCAfBuUAM3L/vL8omIiFwvChRXkJeXFw8++CBgziAtXneopO3bt7Nr1y6b7evVq2cJutgq/5qbm8v8+fNtti0Z9LB1XIDZs2cTERFh+be9rOXyMJlMTJgwgZCQEJycnPjoo49o3rz5Fdu1bduWli1bAvDFF19YjadYbm4uH3zwAXl5ecTExFhlZj/22GOAOTC+Zs2aUm3T0tKYM2fOVZ7VlQ0fPpw2bdoAMGvWLObOnWv3RgHAjh07ePnllykqKsLT05Np06ZZba9bt66lFPWsWbNISEgo1ccvv/xCkJ2bGcWvR1xcnM3zzsvL4+OPP7bZ1sXFhdDQUI4cOWJ3AsLp06cB89+2r6/t8myOZMCAAXTubL7pEx0dzaxZs27yiMqnZCa4vc+HPXv2WL2nyvP54O/vT58+fQD47rvvbN7QKyoqYvr06eTk5BAVFWV5/wDcc889lmP997//tXmMzZs3s2HDBsu/IyIiGDduHAUFBVccX0nleQ2+++479u7da/l3yZLt/fr1s0yE+fjjj22+PkuWLCE52Tyz+P777wfgiSeeAMwlFKdMmWJzQsfRo0f5/vvvSUtLw8fHx26WisjfmcFgoOfF7KsdISFElCj9WWzTqVPEpZlvwN/X9tY/dHzFzl5cRsKrkjs1vFXqTUTkejIYDNzesycAO7ZvJ+LimvAlbfx1I3GxscCl31vl1bt3b8BcOenwodLXP4cPHeb4cXNG8H0PWPfdvHlzc+AhJYUDBw6Uapuens6SixMiGzVqZPWbt6SEhASyssyTAps0aVqh8YuIiPm7IqCdeaJN/KEwS+ZwSXH7z5CTmA5A3R6tK9R/jQ7mvtNCY0m2EYhOPhNFWpg5EFznduu+i9clzo5PIzbwtM3+036PI+lEhGX/4iC2s5urOaMZiDsQTJGNewcxe06SFWueZF+3Z8XOS0RE5Go5bKDYZDKRlZVVof8Ve+211/D39ycnJ4dBgwaxatUqEhISiIuL48svv2Ts2LF2j+vt7W3JNt26dStTpkwhPDycpKQktm7dylNPPcXp06fxtnFjskePHpYfF++++y4//fQT58+fJz4+nl27djFy5Eg+++wzqzYlx11RM2bMYOvWrZZz7t+/P/n5+WRnZ9t8fUqu6zl58mRcXFxIT0/nySefZOnSpURHR5OcnMzu3bt59tlnOXr0KADPP/88derUsbR96KGHLIG6SZMm8b///Y/IyEhSUlLYvHkzTz31FHFxcTcsy8jNzY25c+dabhTMmDGDRx99lFWrVhEeHk5aWhrR0dFs2rSJl19+mRdeeIGsrCw8PT2ZPXs2TZuWvhnwzjvv4ObmRmJiIk899RQbNmwgJSWFqKgo5syZw4QJE+xmP3bu3JmHHnoIME8EmDx5MqGhoaSmphIYGMigQYM4ceKEzfaPPfYYPj4+mEwmRo0axdKlSzl79iwpKSmcOXOGd999lx9++AGAgQMHqiwu5guSKVOm4HpxTcpFixZZgul/Zs2bNycgwLy+zYoVK5gzZ47lfXPs2DGmTZvGCy+8YBXELO/nw8SJE/Hy8sJoNPL888/z+eefExERQUpKCkFBQYwcOZJNmzYB8MADD1gycwEeffRRbr3VHOxZvXo1kyZNIiQkxNJ2woQJvPzyyxiNRtq3b0/fvn0B89rGI0aMsFuK35aOHTtaMqlnz57NN998Q3R0NElJSRw8eJCJEyfy1ltvWbUp+RpUqlSJN954AzCvyz506FACAwNJTU0lLCyMGTNm8OGHHwLQp08fywSQli1b8vTTTwPmz/UhQ4awe/duUlJSOHfuHEuWLGHEiBEYjUbc3d3LXONd5O/uwdva4lvZk4LCIt5bv4FtZ4JJy84mPj2dlQcOsmiPOeO/a+NGNA0ovWbXZ1u38erKb3l15bc3bIwxFz93alatesOOISLiyP750D/x9fWloKCAae9OY9vWbaSlphEfH8+K5StY9PXXAHTt1o2mzUpfW302ezbjx41n/Ljxpbb1vqM3DRs2BOCTGTNYt3YtycnJJCcns27tWj6ZMQOAZs2aWVWBAmjXvh3169cHYN7n89i8aTOJiYmkpqSyZ88e/u/Nt4iPj8fNzY0RL7xg9/xioi8tR1KzZs0KvTYiImJWt9etuFX1xFRYxPEFGzkfFEJ+RjY5KRlEbDzM7+vME3r82zSgSr3SVYCCV+0kaMYagmaUTkCp0aEplWubEyXOLN9O9K4T5F3IIu9CFtG7TnBm+XYAqtTzx79NQ6u2tbu3xCPAfJ3w+9r9hH6/l4zoJIzZeWQnXuDctqOcXLwZTOBW1ZP6fW6zPq+e5klGWbEpnP5mG+nnEjFm5ZIZm0zoD3s5u/6g+bxubYhfy/pX/wKKiIhUgGMuRgrExsZaBTPK4+DBg3h7e+Pn58dXX33FiBEjSEhIKLVWcJUqVbj99tvtlml96623GDRoEGlpaSxbtoxly5ZZthkMBt544w127dplVR4VzBezI0aMYP78+SQnJ9sMNlSpUoXHH3+cBQsWAObMvKvNEp03b57l8eeff86MGTPKzPDr0qULS5YsAcwlXGfOnMmECRNIS0vj3Xff5d133y3V5vHHHy8VWDcYDMyePZuRI0dy5MgR5s2bZzUWgAkTJjBz5kyrbMDrqXr16qxYsYL//Oc/rF69mlOnTtldExrMAaqpU6faDBIDNGnShLlz5zJmzBhiYmIYP976poaPjw+DBg2yrDF7ualTp5Kdnc2mTZtYuXIlK1dar/01cOBA9u3bZ1nLq2S/n3zyCaNGjSI1NdXmfwMwB73KmuDgaJo0acLw4cP5/PPPKSgo4O233+bbb78t93rVN4OzszNTp05l9OjRFBQU8Omnn/Lpp59a7ePk5MSLL77IwoULyc/PJ9JO6dfLNWjQgC+//JLRo0eTlJTEJ598wieffFJqvz59+pTKqHd1dWXevHm88MILnDhxglWrVlnKtJd09913M336dFxdXRkxYgT79u1j7969LFu2rNzrFVerVo1//etfTJkyhZycHKZOnVpqHzc3N5577jnmzp0LQGRkZKmKBklJScyYMYNDhw7x7LPPluqjY8eOpbL433rrLfLz81m9ejVBQUE8//zzpdpVrlyZ//3vf7S4WBJRxBF5uLnx+j138/76n8nIzWX+jtLLLtxSswaj+txhs31yZqYl4/hGScowZ4F5XlxqREREri8PDw/emDiR96a9R0ZGOvMu/i4r6ZYWLXh59Ms22yclJVsyji/n5OTEa69PYNrUd4mPj2fpkqUsXbLUap9atWsz4Y3XS02SdXJyYtyr43l3ylRSU1P58osvSvXv6VmZceNfoXGTxnbPL/FiZQqAyl6V7e4nIiL2ubi70npwP44v3EhBVh6ha/aW2se7YQDNH+9ps31eWpYl4/hyBicnWj3Tl+Nf/UJuSibhPwcR/rN1lT+P6t60Gtyv1HeFs6sLtw67i5NLtpAVm8L5gyGcPxhS6hiV/KvQenA/XCtbLwtW/dZGZEQnEbPrJClnokk5E12qrV+bBjQfcLvNsYuIiNwIDhsovlYtWrRg/fr1fPXVV/z666/ExsZSpUoVevXqxejRo1mzZo3dQHGzZs1Yu3Yt8+fPZ8eOHZw/fx4vLy/at2/Pc889R6dOneyWrn7ttddo3bo1y5cv59SpU5ZM1vr169OzZ0+efvppvL29WblyJVlZWWzatKnCAXFbrmY9zX79+rFx40aWLFnCzp07iYqKIi8vj2rVqtG+fXuefPJJevToYbOtj48Pixcv5scff+S7777j7NmzFBQU0LJlS4YNG0b//v2ZOXPmtZ5WmSpXrszUqVN5/vnn+f777y2B2IyMDNzd3alXrx4dOnTggQcesFqX2Z4ePXqwfv16Fi5cyM6dO4mNjcXb25uePXsyZswYqzWoL1epUiVmzpzJr7/+yooVKwgJCSEnJ4cmTZowcOBABgwYYCnxe7nu3buzfv16Fi1axN69e4mOjsZoNFKtWjXatGnDww8/zN13333Vr9Pf1UsvvcSGDRuIjIzk+PHjLFmyhKFDh97sYZWpT58+rFy5ki+//JKgoCBSU1Nxc3OjZs2adOjQgWeeeYZWrVpx7NgxAgMD2bhxIyNHjixX3+3bt+eXX35h2bJlbN26lfDwcLKysvD29ubWW2/lkUce4d5777XZ1s/Pj5UrV7JmzRrWrl1LSEgIWVlZ+Pv70759ex577DFuv/3SRdD8+fN55513yM7OLvf4ig0cOJCGDRvy9ddfc/ToUdLT06lUqRJ16tSha9euDBo0iIYNG/Lzzz8TGRnJpk2bLMsJFHvxxRfp1asXixcvZv/+/SQmJuLq6sott9zCQw89xOOPP14qg9/V1ZX33nuPhx9+mBUrVnDkyBESExNxcnKiXr169OzZk6FDhyqrRARo6O/Px088ztqjRzkUGUlSZiYGDNSt5kOPpk25q3UrXOxU2fgjZOfnAVBZgWIRkRumYaOG/HfGf1n7008EBR0iOSkJg8FA3bp16XF7D+66+26rZUUqonr16vznow9Zv249+/ftIyEhgaKiImrUqEnXbl154MEHLFVoLle7dm0+/Pgj1v20lqCgQyQmmpcNqlGjJh06tOe+B+6n6hUqTmRnZ1see3p6XtU5iIgIeNX2o9O4R4jaeZyUM1HkpWWBATwDfAi4rTG1urXA6SqvGypV86LD2IeI2X2SxBOR5KZkgMlEJd8q+LdpSN2erXF2c7XZ1r1qZdq9dD8JR34n8Vg4mXEpFObmm0tL16iGf5sG1OzUHGc3299jje/tjE+TWsQGniYjKomC3HxcPd2pUtefmp2bK5NYRET+cAZTWYuvylWbNWuWJTs0ODj4Jo9GROSvp6Cg4KpvEP6dpc769Mo7iYiIQzPYqIghIiJSbGTQvCvvJCIiDm1Fnzdu9hD+Ep7a9uHNHsLfhv7mbp4/bx1VERFxaAoSi4iIiIiIiIiIiIjcOAoUi4iIiIiIiIiIiIiIiIg4GKVryd+CyWSyWgvqalSuXPk6jeavS6+jfYWFheTm5l51eycnJzw8PK7jiERERERERERERERERK6eAsXytxATE0O/fv2uqQ+tJa3XsSxBQUEMGTLkqtvXqVOHrVu3XscRiYiIiIiIiIiIiIiIXD2VnhYRERERERERERERERERcTDKKL5BxowZw5gxY272MBxG3bp1/7aZrH8kvY72de3aVa+NiIiIiIiIiIiIiIj8bSijWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDcbnZAxAREZHye6lN3s0egoiI/MnNvdkDEBGRP7XPT7jf7CGIiMifXZ+bPQAR+aMoo1hERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg3G52QOQP5fBgwdz4MCBCrd75JFH+OCDD27AiP44+/fvZ8iQIQAsXryYrl27Wrb17duXmJiYv8x5RkdH069fP6D0uVxvv//+O+vWrSMwMJDo6GjS0tJwc3OjTp06dOjQgQcffJBOnTpd83FmzZrF7NmzqVOnDlu3bq1w+1tuuQWA6dOn8+ijj17zeK5V8XgqouTYS/692uLk5ISbmxtVq1alcePG9O7dmyeeeILKlSuX2rc873tnZ2c8PDyoWbMmt956K08//TRt27at8Dn8lRX/Nxs9ejRjxoy5yaMR+WvLz8whetcJUs5EkZuaibOrCx7VqxLQrgm1ujTH4HT1cxkL843E7D5J4olIcpPTMTgZqOTnTfVbG1L7H61wdrX/8/fkki2knI664jFaDemHX4t6Vz1GERGBCxcusPannzh06DBJiYm4ublTu05tevbsSf87++N0Dd8Fl/s97HfenjSJW1q04J1/v3PF/c+cOcP6desJPhNMdnYW3t7e3NKiBffedy/Nmzcvs21iYiK//Pwzx44eJzExgYKCAnx8fLilRQvuvufuK7YXEZHS0nNy+Om3oxw+d46kjEzcXJyp7eNDz2bN6NeyxfX9zkhIZPIPP3JLzRpM/ueD5W738a8bORQRydsP3k+r2rVv6LFERESuJwWKReSqZGZm8t577/Hjjz9SWFhotc1oNBISEkJISAgrVqygS5cuvPvuuzRs2PDmDNYBFRUVkZubS25uLvHx8QQGBrJw4UK++uormjVrVuH+CgsLyczMJCwsjLCwMH744QfGjh3LqFGjbsDoReTvLCclg2PzN5CfnmN5rqAgn4xziWScSyTx6FlaP3snLu6uFe7bmJ3H0fkbyEm4YPV8VmwKWbEpxB/+nVufvxt3b0+b7TNjkyt8TBERqbj4+Hj+PfkdUlNTLc8ZjUZCQ0IIDQlh9+7dvPnWm3h4eFzzsdLT05k9ezZFRUXl2n/jrxtZuGABJpPJ8lxKSgqBe/eyLzCQZwY9wwMP2r6Zv3//fj7/bA65ublWzyclJZG0ezd7du/m4Uce4amBT139CYmIOJj49HSm/LSW1Kxsy3PGwkJC4xMIjU9gd1gY/7r3Hjzc3K75WOk5uXy2bRtFJb4DyuOX4yc4FBH5hxxLRETkelOgWGyqXbs269atK/f+rq4Vv5n7V1KnTh2cnZ3x8/O72UP5Uzh//jwjRowgJCQEgBYtWvD000/TqVMnfH19SUlJITQ0lBUrVhAYGMiBAwd49NFHmTNnDt26dbvJo/9zefDBB5kyZUq59nV3d7f5/JQpU3jQxs0qo9FIQkICS5cuZeXKlcTHxzNq1CjWrVtns6+y3vdGo5H4+Hi2bdvGvHnzyM7O5tNPP6VVq1bccccd5Rq/iEhhvpETCzeSn56DaxUPGt/XGZ8mtSjMM3I+KJToXSdIj0wg9LvdtHy6T4X6NplMnFqyhZyECzi7u9Dw7k74taqPqaiIpGMRRG45Qk7iBU59s5V2I+/HYDBYtc/PzCH/gvnmU+uh/anaqIbdYzm5OFf85EVEBIDc3Fymv/c+qamp+Pj4MHjoENq0aUNOTg7btmxl7dq1hAQHM/fzuYx/dfw1HevChQtMf+994mJjy7X/kcNHLEHidu3aMeDxx6lRswbR0dGsXLGSM6dP883Sb6hduw4dOnawaht+NpxZn86koKAAf39/nnzqSVq3boMJE+Fnw/l25UrOnTvHD99/j5+fH3fedec1nZuIiCPINRqZvuFnUrOy8fH0YHD37rSpU5vs/Hy2nQlm3dFjhJyPZ96OnYy7s/81HSs9J4f3N/xMXNqFK+9cwuZTp1kcGPiHHEtERORGUKBYbDIYDDZL1DqqJUuW3Owh/Gnk5+czatQoQkJCcHZ25tVXX+X555+3uuFerVo1mjRpwj333MPGjRt5/fXXycrKYtSoUaxatYomTZrcxDP4c3Fxcbnm95qbm5vdPnx8fJg6dSoAKy/enPr+++956qnSWQxXet/7+Phwyy230L59e4YOHYrJZGL+/PkKFItIucXtDyY3OQODs4Fbh91J5Zq+5g1eHjS6uyPuVT35/af9JJ2IJD0yHu8G9oO1l0s6GUl6ZAIALQbegW/zupZtdXu1wSOgKqcWbyEzKonEY+EE3NbYqr0lm9gA3vUDcHb7e0+CExG5WTZv2sT58+dxdnbmrf97i/oNGgBQtWpVBj7zNL5+fixcsID9+/YRHBx8VUu2gLl89KczPrHKWi6LyWRi2TfLMJlMNL/lFl6f+AbOzuaJQS1btmTS25OYOmUqIcHBLFmyhPYd2ltdA61csYKCggK8vavy7rRpVPOtZtnm5+fHbe1uY+q/pxAaGsrKFSvp07cPLi66JSMiUpbNp04TfyEdZycDb953L/UvJnB4e3gwsGsX/Lwqs3D3XvafDSf4/HluqVnzqo5zJu48M7dsscpavpL8ggIW7Q1k6+kzN/xYIiIiN9L1W8BBRBzCnDlzOHnyJABvvPEGw4cPL5WVVdJdd93F7NmzAcjKymLy5Ml/yDjF2ksvvWR5fDVrPJfUtWtXOnQwZ1D89ttvFBQUXFN/IuIYTCYTMXvM3x/V2za+FCQuoVbXFnhU9wYg7mBIhfqP2XUCgKqNalgFiYv5taiHT9NaAJy30XdxoNjD3xsXj2svWyciIqWZTCbWr1sPQI8ePSxB4pLuuvsual1c23Hr5i0VPkZKcgqffzaHKe/8m9TUVLy9q+Lv73/FdseOHiUq6hwATzz5hCVIXMzFxYVnBj0DQFxsLKdPn7Zsy87O5vjx45bxlwwSF3N1deXRxx4DIDMzw1KdSUREbDOZTGy4+Nn6j6ZNLUHiku5s1YpaPlUBKhywBUjJyuLzbduZutZc2trboxJ+XmVP5jeZTOwN+53Xvl1lOWaj6lf+nrmaY4mIiPwRNH1VbpiIiAi+/vprAgMDiYuLw9fXl759+zJ69GhCQ0MZMmQIAMHBwZY2+/fvtzy/ePFiunbtarPv4lnlo0ePZsyYMaW2h4aGsnLlSg4ePEhcXBxZWVl4eXlRv359evXqxeDBg/Hx8Sn3ufTt25eYmBgeeeQRPvjgAwBmzZplCYCWh63zSU1NZdGiRWzbto2oqCgKCwupVasWPXv25LnnnqNWrVp2+8vMzGTlypWsW7eOyMhIXFxcaNu2LSNGjKBOnTrlHldFZGZm8s033wDQqlUrnn322XK169mzJw8++CBr164lKCiIAwcO0KVLl1L7hYaGsmDBAoKCgoiPj8ff35++ffuWax3c/Px8fvjhB9asWcPZs2cpLCykZcuWDB06lDvvLLus2++//87ixYvZt28fsbGxuLi4EBAQQOfOnXnmmWdo2bJluc7zz6xWrVr4+PiQlpZGTEzMNfdXo4Y5y6+wsJCUlBQCAgKuuU+49L7q0qULS5YsYfPmzSxevJgzZ85gNBpp1KgRTz75JE8++SQAaWlpzJs3j82bN3P+/HmqVKlC9+7dGTt2LA1s3HgE8xpzK1asYM+ePYSHh5Oeno67uzsBAQF06dKFIUOGXFXWe3Z2Nt988w2bNm0iPDyc3NxcAgIC6N69O8OGDVMmvTi8rLgUy7rEfi3r2dzHYDDg26IeMYknSTkTjclkKnMyUjFjdh4Z0UkA+Lasb3c/v5b1SQuL40L4eYw5ebh6XCrDnxmbAkCVule+ySMiIlcnMiLSkuHbsXMnm/sYDAY6duzAuthYDh06XO7vgmIrV6xgx44dALRu04aRL41kzmdzSEpKKrPdkSO/AeDpWZlWrVrZ3Kd58+ZUqeJNRkY6QQeDLPslJiTg4eFJZmYGTZs2tXuMGjUvVcoob6aziIijikxOtmTddrRzfW8wGOhQvz7r045z+Ny5in9nHDjIzpBQAFrXqc2LvXvx+bbtJGdm2W2TlJnJrC3mCfjuri4807Urdar58O7a9df9WCIiIn8EBYrlhti8eTOvvfYaubm5lufi4uIsQZTx469tramyzJ49m9mzZ2MymayeT0tLIy0tjWPHjvHdd9+xfPnyMgOx15uHh4fVv/ft28fYsWO5cMF6PZLw8HDCw8P59ttv+fDDD7n77rtL9RUVFcXw4cOJiIiwen7Xrl3s3r2bYcOGXffxA+zYsYP09HQAm6WLy/LMM8+wdu1aAH788cdSgeLvvvuOyZMnW2WnxsTEsGTJEn755Re6d+9ut++UlBRGjhzJ0aNHrZ4/ePAgBw8eZPjw4Xbbbt++nTFjxpCfn295Lj8/n4iICCIiIli9ejWTJk1i0KBBFTrfP6PiiyUnp2svJhEWFgaYMyMqMumiIt5//30WLVpk9dzJkyeZPHkyUVFRPPHEEwwZMoS4uDjL9uTkZNatW8fu3btZs2ZNqUkTO3bsYNy4cWRnW5d4MhqNZGZmcvbsWdasWcNnn31Gr169yj3W4OBgRo4cSexl699FR0ezatUq1qxZw5tvvsngwYPL3afI301mXIrlsVcd+8FYr1rmTOOC7DxyUzPx8K1yxb6z4lLAVNx36UwDS9+1L2YxmyArNgWfJpd+B2TGmAMIngE+RO86QeLxcLIT0gDw8K+Kf5sG1PlHK5WkFhG5BiWvXxo3bmx3v4YNGwLmzNuEhATLJMXyqlmrFo8/8Tg9evQod5vIyEjzsRs1tPt72WAw0LBhA44fP87Zs2ctzzdo2JAvF3xJfn5+mb+148/HWx5X9lQWmYhIWSKSky2PG5eRsdvQ3/z7PzM3j4SMDGp4e1foODWrevN4p078o2n5J3c7Oxno2bwZj3fqhG/lypy67F7A9TyWiIjIjaZAsVx3YWFhjBs3DqPRSO3atZk4cSJdunQhNTWV5cuXs2TJEqZNm3ZDjv3LL78wa9YswFzK7IUXXqBRo0aAOQC7cOFCtm/fTlxcHDNnzmT69OlXfawXX3yR5557zu723bt388orr2AymXjiiSdo27atZVtISAgvvvgiubm51K1bl7Fjx9KtWzdcXV05fvw4M2fO5MSJE7z66qssXryYjh07Wtrm5+dbgsSVKlVizJgx3Hvvvbi7u7Nnzx4+/vhjFixYcNXnVZYDBw5YHnfqZDsDwJ527drh7+9PUlIS+/fvt9q2f/9+3nrrLcA8S3/ChAnceuutpKamsnr1ahYuXMhPP/1kt+9XXnmFo0eP4uTkxIgRI3j00Ufx9vbmyJEjfPzxx3z55Zc222VnZzNx4kTy8/Np27Yt48aNo2nTphgMBo4fP86HH35IREQEH3zwAXfccQd165YuZfpXERUVZclauNbM1g0bNlhK5fXq1Qs3t+tfovXo0aMcOHCArl27MnbsWBo1asTp06eZNGkScXFxLFy4kF9++YWsrCymTJlCnz59MJlMrF69ms8++4y0tDTmzp3Lu+++a+kzNjaWV155hZycHBo2bMgrr7zCrbfeSuXKlYmNjeWHH35g+fLl5OfnM3XqVDZv3lyusSYkJDBs2DCSk5Px9fVlzJgx9O7dG09PT0JCQpg3bx579uxh2rRp+Pr6cv/991/310vkryA3NRMAg7MB96qedvdz9/Eq0SajXIHi3LRMy+NK1bzs7nd532AOFBuz88hLNc/kj9z8G6bCIqt2WbEpZMWmEB8USuuhd+JZveoVxyQiIqUlJJjXknd2dsbPRgnRYv7Vq1u1qUig+JHHHqVGjRoVyigDc1YwQECJY9sc28Uy1sX7l3Sl38WbN20CzOffrHmzCo1PRMTRJKZnAOagrF9l+5Nr/L0uXS8kVjBQ/EiH9tTw9q7Qd4Z3JQ9mPj0Q3zLGdL2OJSIi8kdQoFhsMplMZGWVr/SJk5OTVbbsf/7zH4xGIz4+PixfvpyaNWsC4Ovry6RJkwgICOC///3vDRl3cUCwWbNmzJ071+pCvUaNGnTp0oUBAwZw8uRJdu3adU3HcnNzs3sj4OzZs0yaNAmTyUSHDh14++23rbZPmTLFEiRevXo11apdWsOqd+/edOvWjUGDBnHs2DGmTJliFSRdtmyZZSb+zJkz6d27t2XbQw89RMeOHXnkkUcsmb/XU/GseRcXlzIzAGwxGAw0aNCApKQkYmJiyM/Pt7x+7733HmDOHFi2bBlVqph/5Pv6+jJx4kRq1qzJ+++/b7PfTZs2WQLYkyZN4plnnrFs69evHx07dmTAgAFERUWVanvgwAHS0tIAc8nj4r/V4rbNmzfnrrvuwmg0smnTphuSqV1QUHDF95rBYMDT035QpTw+/fRTy2NbWepg/31f/HxUVBQbN25k2bJlAHh6evLaa69d07jsycvLo23btnz11Ve4upqz926//XYmTpzIuHHjKCgoIDo6muXLl9O+fXtLu9GjRxMWFsbPP//M3r17rfpcunQpOTk5uLq68uWXX1Kv3qXSt76+vrRp0wYnJycWLVpEVFQU4eHhlokmZfn4449JTk6matWqrFy5kvr1L5W97dq1K507d2b06NFs2bKF9957j/79++Pu7l5GjyJ/TwVZ5iojzu5uGMrItnKpdCljtyAn3+5+1n3nXWrvYf/95Vzp0vd2yb6L1ycuVrf3rQS0a4xbFU9yUzM4fzCE8wdCyE3J5MSiTbR/+UGrstUiIlI+GRnmm/4eHp5lZt56lri+LO91abGSv+mvZmyVr7BepGdlz6saV2BgIIcOHQLMk5orVzDAICLiaDIuVin0cHMr+zujRMWfzLw8u/vZUrNqxSeAuru64O5a8VvqV3MsERGRP4ICxWJTbGwsHTp0KNe+derUYetW89ocqamp7NmzB4Bhw4bZvEgfPnw4P/zwA7///vv1GzBQVFTEHXfcQZMmTejdu7fNIK6TkxOdOnXi5MmTN2xNqPT0dF566SXS09OpWbMms2bNshpLaGgoQUFBAIwaNcoqSFzM3d2d8ePHM2zYMIKDgzl69Ci33XYbgKV8c48ePayCxMXq1q3LiBEjbkgwvjio6uXldVUzIItn3xcVFXHhwgWqV69OaGioZZ3q0aNHW4LEJQ0ZMoQVK1ZYlXcrVvx6NGzY0CpIXMzHx4fx48fz6quvltpWstx0YmJiqb/XevXqMX/+fKpWrVqugOHVWLt2reUc7KlSpYrlb8aW/Pz8UjeqTCYTGRkZnDlzhqVLl7J7927AnNltL1Bckfd9/fr1+fjjj2/ourvDhg2zBImLlcxk79Spk1WQuFi7du34+eefiY+Pt3q+efPmPPnkk/j5+VkFiUvq0qWLpdx1SkrKFf+7X7hwgQ0bNgAwaNAgqyBxMScnJyZOnMiWLVtITk5my5Yt3HfffWX2K/J3VHQxS9fJ1bnM/ZxK3HQpMhaWr+8SyxY4udjvv+S2ooJLfRszc3D1qkRBTj6tn+1PtSa1LdtcPd2pUscfz+pVObv+IHkpmURtP0bjezuXa2wiInJJgdEIgNsVyvi7lZhUZ8wv36Sha1V8beDqWnZWcPH2/AqMKzg4mLlzPgegShVvBj799FWOUkTEcRiLzL/X3ZzLvn5wc7l0/WAsKN/1g4iIiFyiQLFcV0FBQRQWmn+U2Vvf08nJiXvuuYfPPvvsuh7bycmJ0aNH291eVFREWFgY0dHRAFZr4V4vhYWFjBs3joiICNzd3Zk1a5YlOFqsZPnm5s2b252J3qJFC5ydnSksLOTQoUPcdtttZGRkcPLkScD+6wvmbNgbESjOuzgz82qzIZ1L/LgvXkN63759lufsnZPBYKBfv342A8XFZax79uxp97h9+/bFycmJoiLrUqLt2rXD1dUVo9HIsGHDeOqpp+jTpw/t2rWzjLWsfv8s3nnnHd55550r7te6dWtmzpx51WsU+/r6cscdd9C7d2/69etXKoh7vRVPjiipZInCVq1a2Wzn5WUuLWu8eCOy2MMPP8zDDz9s93hxcXGcOnXK8u/iz7KyHDlyxHKcFi1a2H0/+/v7U716dRITEzl06JACxeKQbmiJNadr6zugXRMC2jWhqLAQJzs3our0aM35Q2Fkn08l/lAYje7ppLJxIiIVVFZFiZvN2dn5hlwjnjp1io8//Ii8vDxcXFwY+8oYqvmWniwsIiLWnAx/3u8MERGRvxMFisWmklnCFVEyg89WZl2x5s2bX9W4yispKYnAwEDCwsKIiooiMjKSs2fPkp2dfUOPO336dEtG9dSpU63WJS5WsgTygAEDytVvXFwcYH59iwOsZb2+jRo1sgSZryfvi+u8XG1Z6wsXLgDmYEHViyV3is/Nx8fH8pwttjJXc3JyLFnODRo0sNvWw8ODWrVqERMTY/V8QEAAr732Gh988AEZGRl88cUXfPHFF3h7e9O9e3dLQNTHx6cip1khjzzyCB988MF179dgMFC5cmX8/Pxo1aoVd911F3fddRcuLvY/9i9/3xuNRiIjI5k/fz4//vgjqampuLq60qdPnxseJAZsZtuXDHLbyj6/fB9bcnNzCQwM5MyZM5w7d46oqCjCwsJKVRkofq+VpeT7ecyYMVfcHy79zYv8nZhMJoqM9m+uG5ydcXIzf/5cKUu4ZD/OV8g+vrTfpc8kU2EhONv+HCiZRWwr89hekLiYX4t6ZJ9PpSA7j9zkDDz8y7/+mYjI353JZLJMLLXF1dWVSpUqAZCfb7S7H0B+iX7c/qAlO9zc3CkoKKCgoOyxGY35F/cvO/MYzJNaP5s1m/z8fJydnRnzylhutXGNKCLiaEwmE3llTM5xdXbG/eL9i/wr3NvKL9GPWxn3PERERMQ2fXvKdVW8rhNgtW7x5YoDjtdbXl4e77//PqtWrSoVJHV3d6dr164UFRVx8ODB637sb7/9liVLlgDmkrn2MhczMzMr3Hdxm5IB2rJeXycnJzw9Pa3+e1wPTZs25fjx4+Tk5BAXF0etWrUq1D4kJAQwBySLs5KLx1h808geW0HBkq/H1bQH83+rli1b8tVXXxEYGIjRaCQ9PZ1ff/2VX3/9FVdXVwYPHsyECROsMqL/TKZPn86jjz563ft1dXWladOmfPjhh9SoUYP58+ezcuVKEhISmD17dplB5+vhWtdltmXp0qV88sknpd4bTk5OtGzZkoYNG/Lzzz+Xu79reT+L/J3kpWVx8KPVdrc3H3A7LhfXBy7MM2Iymexm45ZcO9ilctmf7Zb9PKzXHna2U9K0MPdS367l7Lskd59L60kas3IUKBYRKSEpKYkxL9uv8PTSqFGW33e5uTllfhdklZjga+93/PVWubIn2dlZZGeVPbm4ePuVxvXTjz+yfNlyTCYTbm5ujBs/ng4dy7fMi4jI311SZiZjl62wu33kHb2p7G7+jZ9rLPv6IavEUgBVrnBvSEREREpToFiuq+KSr2DO9iz575Iqsp7T5XJzc+1uGz9+PFu2bAHMZXZ79+5Ns2bNaNq0KY0bN8bFxYUZM2Zc90DxwYMHmTp1KgD/+Mc/eP311+3uWzKgeezYsQqVcS6ZcXul7OhreY3t6d69O99//z0Au3fv5vHHHy9327CwMBITEwHo2rWr5fnic8rJySmzva3zKZnpezXti3Xr1o1u3bqRmZnJ3r17CQwMZPfu3Zw7dw6j0ciCBQswmUz861//KvMYf2evvvoqJ06cYO/evWzbto2PPvqIN99882YPq0K+/vprpk+fDkDt2rXp378/LVu2pEmTJjRr1gxPT0/27t1boUBxyQkbGzZsuKFrNov81RUHVU2FReRn5ODubXsySN6FSyXcK1WtbHMfe30D5KVl4m6nXV7apYkatvYp6wYUmMdezOkK62uKiEhptWqbJ5oWFBSQlppmtwRzclKS5fHlS/ncsLHVqkViYqLlmsWepItj869e3eb2oqIivvryK7Zs3gyY1yR+419v0KxZs+s7YBGRv7laVX0AKCgsIi07m2qVbf/GTy4xGdvfzn1IERERsU+BYrmuSpb/PXv2rM3SywDnzp2z+XzJjM3L1xctVlxq+HKHDx+2BIkHDx7MpEmTbO53eXnZaxUdHc2YMWMwGo3Uq1ePGTNmlJl5Wrt2bau2ZQWWLr9hXbNmTctau2fPnqVfv3422yUkJJRZ9u1q9e/fnypVqpCRkcGSJUt47LHHyr3e7eLFiy2P//nPf1oeF78eFy5cIDk52WoN2pJKlvgt5u7ujp+fH8nJyTbXLy5WWFhYrnK/Xl5elhLNYA7kjxs3jpiYGJYtW8aECRNueBbtn5XBYOCDDz7g/vvvJyMjg0WLFtGrVy969Ohxs4dWLrm5ucyePRuAtm3bsmTJEptZ6BX9fCiZVR8TE1Oh97PI30mlal70fP/ZMvfJOn/p/ZUZm2w3UJwZmwyAs4cr7tXKd6PHM8AHDIAJMuNS8G5Qw+Z+GTHmvjFA5Vq+luePffEzmedT8KrlR9vh99g9TnZCmvmBkwEPvz8mw01E5K+ievXqrPh2ZZn7lLwODA8PtxsoDg8PB8DTszLV7QRkr7f6Depz7NgxIiPP2f3dZjKZiIiIBKBhw4althcUFPDJ/2YQFBQEQM1atfjXm/+iZs2aN3TsIiJ/NdWrVGH5iyPK3CcqJcXyODwp2W6gODzJ/Bvf092N6lUUKBYREamo8kV4RMqpQ4cOlrWaNl+cQW3Lzp07bT5fMjsvpcQPwpIOHz5s8/kjR45YHj/55JM29ykqKmL//v1W/74WWVlZvPTSS6SmpuLp6cmcOXOuuJ5tp06dLI+LA9u2HD58mNtuu427777bkuFYuXJlS/uy2tp7fa9V5cqVee655wAIDg7m888/L1e7wMBAVq1aBUD79u3p1q2bZVuvXr0sj6/mb6a4/fbt2+2uyXzgwAGbGcfz5s3jgQceYODAgTbbtW3bliFDhgDmsubFayw7qho1ajBx4kTAfJNs8uTJV8zk/rMIDQ21lJt++OGH7ZYqDwwMtDwuz+dDx44dLZMlynpPxsTE0L59e/r37281aULEkXjW8MG9mvnmTsqZ0pN/wPzZkhIcDUC1ZnXKPbnCpZKbJTicfNp23yWPW6WuP66elyp6OLm5UJhjJD0ywar0dUmFxgKSTpqDA1UbBNgtby0iIvbVq1fPEvg9dOiQzX1MJhOHD5uv7W5rd9sfNtGuXfv2AKSnXyA0NNTmPiEhIWRkmJe/ue2226y2mUwmZs+abQkSN2vWjKnvTlWQWETkKtWtVg3/i4Hfw5GRNvcxmUwcuTgJ6ba6dTU5W0RE5CooUCzXlZeXFw8++CBgziANCwsrtc/27dvZtWuXzfb16tWzBF1slX/Nzc1l/vz5NtuWzOK1dVyA2bNnExERYfm3vazl8jCZTEyYMIGQkBCcnJz46KOPaN68+RXbtW3blpYtWwLwxRdfWI2nWG5uLh988AF5eXnExMRYZWY/9thjgDkwvmbNmlJt09LSmDNnzlWe1ZUNHz6cNm3aADBr1izmzp2LyWSyu/+OHTt4+eWXKSoqwtPTk2nTplltr1u3rqUU9axZs0hISCjVxy+//GK54XK54tcjLi7O5nnn5eXx8ccf22zr4uJCaGgoR44csTsB4fTp04D5b9vX19fmPo5kwIABdO7cGTBnxM+aNesmj6h8SmaC2/t82LNnj9V7qjyfD/7+/vTp0weA7777zuYNz6KiIqZPn05OTg5RUVGW94+IozEYDAS0M2fdxx8Ks2QOlxS3/ww5ieYb8HV7tK5Q/zU6mPtOC40l2UYgOvlMFGlh5uoSdW637rt4XKbCIs7+bHt5irPrD2DMNC9/Uaen3sciIlfDYDBwe8+eAOzYvp2I8IhS+2z8dSNxsbEA3H///X/Y2Fq3bm0JYi9dspSCggKr7QUFBXyz9BsA6tatx23trAPFP2/YwL6Lkw6b33IL//f2JLy9tZa9iMjVMhgM9LxYtn9HSAgRJZYlKLbp1Cni0syT+u9re+sfOj4REZG/CwWKxSaTyURWVlaF/lfstddew9/fn5ycHAYNGsSqVatISEggLi6OL7/8krFjx9o9rre3tyXbdOvWrUyZMoXw8HCSkpLYunUrTz31FKdPn7Z5wd2jRw/LzMF3332Xn376ifPnzxMfH8+uXbsYOXIkn332mVWbkuOuqBkzZrB161bLOffv35/8/Hyys7Ntvj4l11aePHkyLi4upKen8+STT7J06VKio6NJTk5m9+7dPPvssxw9ehSA559/njp16ljaPvTQQ5ZA3aRJk/jf//5HZGQkKSkpbN68maeeeoq4uLgbNovSzc2NuXPn0rx5c0wmEzNmzODRRx9l1apVhIeHk5aWRnR0NJs2beLll1/mhRdeICsrC09PT2bPnk3Tpk1L9fnOO+/g5uZGYmIiTz31FBs2bCAlJYWoqCjmzJnDhAkT7Jbz7ty5Mw899BBgnggwefJkQkNDSU1NJTAwkEGDBnHixAmb7R977DF8fHwwmUyMGjWKpUuXcvbsWVJSUjhz5gzvvvsuP/zwAwADBw7UzFTMF2pTpkzB1dWcSbdo0SJLMP3PrHnz5gQEBACwYsUK5syZY3nfHDt2jGnTpvHCCy9YZaWX9/Nh4sSJeHl5YTQaef755/n888+JiIggJSWFoKAgRo4cyaZNmwB44IEH6NChw/U/QZG/iLq9bsWtqiemwiKOL9jI+aAQ8jOyyUnJIGLjYX5fdwAA/zYNqFKvdKnR4FU7CZqxhqAZpSdK1ejQlMq1zRN6zizfTvSuE+RdyCLvQhbRu05wZvl2AKrU88e/TUOrttXbNsK7gfkzIj4olNPLt5N+LhFjVi7p5xI59c1Wzh8IASCgfRP8WtS7Xi+JiIjD+edD/8TX15eCggKmvTuNbVu3kZaaRnx8PCuWr2DR118D0LVbN5o2K33t8Nns2YwfN57x48Zf13EZDAYGX6wmFBIczHvT3iM4OJiMjAyCL/47JDgYg8HAwKetrw0uXLjAyhXmstteXlV4adRLgHkCsL3/2auGJCIilzx4W1t8K3tSUFjEe+s3sO1MMGnZ2cSnp7PywEEW7TFP0OnauBFNL17zl/TZ1m28uvJbXl357R89dBERkb8Mx1xsU64oNja2wsGMgwcP4u3tjZ+fH1999RUjRowgISGh1FrBVapU4fbbb7dbpvWtt95i0KBBpKWlsWzZMpYtW2bZZjAYeOONN9i1a5dViVgwl/YaMWIE8+fPJzk5mddff71U31WqVOHxxx9nwYIFAERERFx1lui8efMsjz///HNmzJhRatZ5SV26dGHJkiWAuUT3zJkzmTBhAmlpabz77ru8++67pdo8/vjjpQLrBoOB2bNnM3LkSI4cOcK8efOsxgIwYcIEZs6cSX6+7fKZ16p69eqsWLGC//znP6xevZpTp07ZXRMazOV5p06dajNIDNCkSRPmzp3LmDFjiImJYfx465s+Pj4+DBo0yLLG7OWmTp1KdnY2mzZtYuXKlaxcab022sCBA9m3b59lrbOS/X7yySeMGjWK1NRUm/8NAPr06VPmBAdH06RJE4YPH87nn39OQUEBb7/9Nt9++22516u+GZydnZk6dSqjR4+moKCATz/9lE8//dRqHycnJ1588UUWLlxIfn4+kXZKW12uQYMGfPnll4wePZqkpCQ++eQTPvnkk1L79enTp1RGvYijcXF3pfXgfhxfuJGCrDxC1+wttY93wwCaP97TZvu8tCxLxvHlDE5OtHqmL8e/+oXclEzCfw4i/GfrahQe1b1pNbhfqYk/BoOBVoP6cnLxZjKikkg6HkHS8YhSx6jerjHNHv1rrM0uIvJn5eHhwRsTJ/LetPfIyEhn3ty5pfa5pUULXh79ss32SUnJlozj661L1y48/sQTrPr2W06fOsU7b0+22m4wGBjy7FA6dupo9fyWzVvIy8sDIDMzg/GvjLvisV4aNYred/S+bmMXEfk78nBz4/V77ub99T+TkZvL/B2llyW7pWYNRvW5w2b75MxMS8axiIiI2KZAsdwQLVq0YP369Xz11Vf8+uuvxMbGUqVKFXr16sXo0aNZs2aN3UBxs2bNWLt2LfPnz2fHjh2cP38eLy8v2rdvz3PPPUenTp3slq5+7bXXaN26NcuXL+fUqVOWTNb69evTs2dPnn76aby9vVm5ciVZWVls2rTpumT3ZWZmVrhNv3792LhxI0uWLGHnzp1ERUWRl5dHtWrVaN++PU8++SQ9eti+Ge3j48PixYv58ccf+e677zh79iwFBQW0bNmSYcOG0b9/f2bOnHmtp1WmypUrM3XqVJ5//nm+//57SyA2IyMDd3d36tWrR4cOHXjggQes1mW2p0ePHqxfv56FCxeyc+dOYmNj8fb2pmfPnowZM8ZqDerLVapUiZkzZ/Lrr7+yYsUKQkJCyMnJoUmTJgwcOJABAwZwzz332GzbvXt31q9fz6JFi9i7dy/R0dEYjUaqVatGmzZtePjhh7n77ruv+nX6u3rppZfYsGEDkZGRHD9+nCVLljB06NCbPawy9enTh5UrV/Lll18SFBREamoqbm5u1KxZkw4dOvDMM8/QqlUrjh07RmBgIBs3bmTkyJHl6rt9+/b88ssvLFu2jK1btxIeHk5WVhbe3t7ceuutPPLII9x77703+AxF/hq8avvRadwjRO08TsqZKPLSssAAngE+BNzWmFrdWuBkp4rElVSq5kWHsQ8Rs/skiSciyU3JAJOJSr5V8G/TkLo9W9tdW9i1ciXavnAfCUfCSDh6lqy4VArzjLhWdqdK/QBqdmqGb/O613LqIiJyUcNGDfnvjP+y9qefCAo6RHJSEgaDgbp169Lj9h7cdffdVkuH/JEeG/AYrdu0ZsP6DQSfOUNmZiaVK3vRvHkz7nvgflq1alWqTZidNY1FROTaNfT35+MnHmft0aMciowkKTMTAwbqVvOhR9Om3NW6FS5Xef0gIiIiYDCVtbioyA0ya9YsS3ZocHDwTR6NiMhfx1PbPrzZQxARkT+5uZ1evNlDEBGRPzHTxRLvIiIi9lQb88rNHsJfgu7TXT8r+rxxs4fgsP68dUJFREREREREREREREREROSGUKBYRERERERERERERERERMTBaI1iEQdgMpnIzs6+pj4qV658nUbz16XX0b7CwkJyc3Ovur2TkxMeHh7XcUQiIiIiIiIiIiIiIlIWBYpFHEBMTAz9+vW7pj60lrRex7IEBQUxZMiQq25fp04dtm7deh1HJCIiIiIiIiIiIiIiZVHpaRERERERERERERERERERB6OMYrkpxowZw5gxY272MBxG3bp1/7aZrH8kvY72de3aVa+NiIiIiIiIiIiIiMhfiDKKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYl5s9ABERESm/z0+43+whiIjIn9xI5t3sIYiIyJ/Y3GdfvNlDEBEREZE/CWUUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYBYpFRERERERERERERERERByMAsUiIiIiIiIiIiIiIiIiIg5GgWIREREREREREREREREREQejQLGIiIiIiIiIiIiIiIiIiINRoFhERERERERERERERERExMEoUCwiIiIiIiIiIiIiIiIi4mAUKBYRERERERERERERERERcTAKFIuIiIiIiIiIiIiIiIiIOBgFikVEREREREREREREREREHIwCxSIiIiIiIiIiIiIiIiIiDkaBYhERERERERERERERERERB6NAsYiIiIiIiIiIiIiIiIiIg1GgWERERERERERERERERETEwShQLCIiIiIiIiIiIiIiIiLiYBQoFhERERERERERERERERFxMAoUi4iIiIiIiIiIiIiIiIg4GAWKRUREREREREREREREREQcjALFIiIiIiIiIiIiIiIiIiIORoFiEREREREREREREREREREHo0CxiIiIiIiIiIiIiIiIiIiDUaBYRERERERERERERERERMTBKFAsIiIiIiIiIiIiIiIiIuJgFCgWEREREREREREREREREXEwChSLiIiIiIiIiIiIiIiIiDgYl5s9APlrGDx4MAcOHKhwu0ceeYQPPvjgBozoj7N//36GDBkCwOLFi+natatlW9++fYmJifnLnGd0dDT9+vUDSp/L9fb777+zbt06AgMDiY6OJi0tDTc3N+rUqUOHDh148MEH6dSp0zUfZ9asWcyePZs6deqwdevWCre/5ZZbAJg+fTqPPvroNY/nWhWPpyJKjr3k36stTk5OuLm5UbVqVRo3bkzv3r154oknqFy5cql9y/O+d3Z2xsPDg5o1a3Lrrbfy9NNP07Zt2wqfg4j8/aTn5PDTb0c5fO4cSRmZuLk4U9vHh57NmtGvZQucnK5uvmJEUhJvfvf9FfdrVN2f9x99pFx9pmZl8cbq78jMzWPkHb3pfUvzCo1p5YGD/HDkN/yreDHr6YEVaisi4qjyM3OI3nWClDNR5KZm4uzqgkf1qgS0a0KtLs0xXOX3BEBOUjrRu0+QFhZLXno2zm6ueFaviv+tDanZuTnOrmXfCrkQEU/MnpOkRyZQkJOPq1clvBsEUOcfrfCuH2C3XWZsMkdmr73i+Lzq+NH+5QcrfF4iIo7mwoULrP3pJw4dOkxSYiJubu7UrlObnj170v/O/ld9TQEQFxfHurXrOHH8OCkpKbi7V6J2ndp0/0d3+vXrh5ubW4X6+/jDjwgKCuLtdybTunXrCo9nxfIV/PD991SvXp1Zn82ucHsREZGrpUCxiFxXmZmZvPfee/z4448UFhZabTMajYSEhBASEsKKFSvo0qUL7777Lg0bNrw5g3VARUVF5ObmkpubS3x8PIGBgSxcuJCvvvqKZs2aVbi/wsJCMjMzCQsLIywsjB9++IGxY8cyatSoGzB6EfmriE9PZ8pPa0nNyrY8ZywsJDQ+gdD4BHaHhfGve+/Bo4I3XwDOJiZdz6ECMG/HTjJz866qbfD58/z029HrPCIRkb+3nJQMjs3fQH56juW5goJ8Ms4lknEukcSjZ2n97J24uLtWuO/EY+GEfLebIuOla5GCgjzSIxNIj0wgbv8ZWg+9Ew/fKjbbx+47w+9r94Hp0nP5F7JJOhZB0vEIGt3Tibo929hsmxmTXOHxioiIbfHx8fx78jukpqZanjMajYSGhBAaEsLu3bt586038fDwqHDfe/fuZe6cz8nPz7fqOyQ4mJDgYDb+upF/vfkvatSoUa7+fv75Z4KCgio8jmLBwcH89OOPV91eRETkWihQLBVSu3Zt1q1bV+79XV0rfmH/V1KnTh2cnZ3x8/O72UP5Uzh//jwjRowgJCQEgBYtWvD000/TqVMnfH19SUlJITQ0lBUrVhAYGMiBAwd49NFHmTNnDt26dbvJo/9zefDBB5kyZUq59nV3d7f5/JQpU3jwwdKZCkajkYSEBJYuXcrKlSuJj49n1KhRrFu3zmZfZb3vjUYj8fHxbNu2jXnz5pGdnc2nn35Kq1atuOOOO8o1fhH5e8k1Gpm+4WdSs7Lx8fRgcPfutKlTm+z8fLadCWbd0WOEnI9n3o6djLuzf4X7j0gyB4qb16zBm/fda3c/J4OhXP1tPHmSo1HRFR4HQE5+Pp9t3U6RyXTlnUVEBIDCfCMnFm4kPz0H1yoeNL6vMz5NalGYZ+R8UCjRu06QHplA6He7afl0nwr1nRmbTPCqXZgKi6jkV4WGd3XAu34ARcZCks9EcW7LEXIS0zm5eDMdxjyEk7N1JlpKcLQlSFyteR0a9GtHJT9vsuNTidh0hPSIeMJ/CcKjelX8WtSzeXwA7wYBtBl2p/2BlvM7SkTEUeXm5jL9vfdJTU3Fx8eHwUOH0KZNG3Jycti2ZStr164lJDiYuZ/PZfyr4yvUd0R4BHNmf0ZBQQE1a9bkyYFP0bx5c/Lz8zl86BCrV60mLjaWDz/4kA8//hBnZ+cy+9u8aTOLv1501eeak5PDZ7NmU1RUdNV9iIiIXAsFiqVCDAaDzRK1jmrJkiU3ewh/Gvn5+YwaNYqQkBCcnZ159dVXef755zGUuAlSrVo1mjRpwj333MPGjRt5/fXXycrKYtSoUaxatYomTZrcxDP4c3Fxcbnm95qbm5vdPnx8fJg6dSoAK1eu5Ny5c3z//fc89dRTpfa90vvex8eHW265hfbt2zN06FBMJhPz589XoFjEQW0+dZr4C+k4Oxl48757qX9xMpW3hwcDu3bBz6syC3fvZf/ZcILPn+eWmjUr1P/Zi4HipgEBVLrGCWkxqWks3bf/qtsv2htIYkbGNY1BRMTRxO0PJjc5A4OzgVuH3Unlmr7mDV4eNLq7I+5VPfn9p/0knYgkPTIe7wbly+YCiNh0GFNhES6V3Wk7/B7cq176DVv39tZU8qnM6WXbyUm4QNKJCAJua2zZbjKZCP8lCEzmQG+rwf0sgeSqjWpy6/N3cfzLX0mPTCD854P43lLX6loHIONioLhKPX+c3f7ek6ZFRG6kzZs2cf78eZydnXnr/96ifoMGAFStWpWBzzyNr58fCxcsYP++fQQHB1doGa+VK1ZQUFBAlSreTH7nHXz9fC3b7n/gAfyrV2fGf/9HTEw0+/bto0ePHjb7yc/P5+uFX7N1y5ZrOtdFC78mISHhmvoQERG5Fle/kIOISAlz5szh5MmTALzxxhsMHz681I2Tku666y5mzzavuZKVlcXkyZP/kHGKtZdeesny+GrWeC6pa9eudOjQAYDffvuNgoKCa+pPRP56TCYTG44fB+AfTZtagsQl3dmqFbV8qgKw9fSZCvVfVFTEuZQUAJpUr35NYy0sKmLOtm0YCwrp1bzipfcPhIezIzgEb49KtKtfOqtMRERKM5lMxOwxXzNUb9v4UpC4hFpdW+BR3RuAuIMh5e67MN9IWlgcADU7NrMKEhfza90AJzfzfPmMaOulDFJDY8mOTwOgQf/2pbKNnZydaXRPRwByEtO5EBFvfW5FRWSfN5dH9arjX+5xi4iINZPJxPp16wHo0aOHJUhc0l1330Wt2rUB2Lq5/IHa3Nxcjl+8XunT9w6rIHGxLl26UKlSJQB+D/vd5vj27NnDa+NftQSJGzduXGq/8jiw/wDbt2/H27sq7du3v6o+RERErpUyiuUPFxERwddff01gYCBxcXH4+vrSt29fRo8eTWhoKEOGDAHM63MU279/v+X5xYsX07VrV5t9F88gHD16NGPGjCm1PTQ0lJUrV3Lw4EHi4uLIysrCy8uL+vXr06tXLwYPHoyPj0+5z6Vv377ExMTwyCOP8MEHHwAwa9YsSwC0PGydT2pqKosWLWLbtm1ERUVRWFhIrVq16NmzJ8899xy1atWy219mZiYrV65k3bp1REZG4uLiQtu2bRkxYgR16tQp97gqIjMzk2+++QaAVq1a8eyzz5arXc+ePXnwwQdZu3YtQUFBHDhwgC5dupTaLzQ0lAULFhAUFER8fDz+/v707du3XOvg5ufn88MPP7BmzRrOnj1LYWEhLVu2ZOjQodx5Zxnl4IDff/+dxYsXs2/fPmJjY3FxcSEgIIDOnTvzzDPP0LJly3Kd559ZrVq18PHxIS0tjZiYmGvur3j9nsLCQlJSUggICLjmPgH+9a9/8f333/Pggw/yyiuv8M4773Do0CHc3Nxo2rQpM2fOpPrFoFFmZiarV69m586dhISEkJaWhqurK/7+/nTo0IFnnnmGtm3b2j1WSkoKq1at4tdffyU6OpqcnBxq165Nz549ef755+2+/2JjY/n666/ZtWsXcXFxGAwG6tWrR58+fXj22WepVq3adXktRP7MIpOTLesSd7RxQwfMVQo61K/P+rTjHD53DpPJVObEopKiU1MxFpjXnGwScG2B4u8OHeZsYhKNq/vzSIf27AwJLXfbtOxsvty5G4ARvXpyMDzimsYiIuIosuJSLOsS+7W0PcnGYDDg26IeMYknSTkTXe7vCWc3V7pNGkh2fBruVT3t7lfclcHJus/UEPMyBM4erlRtZDuLuUr9AFwqu1OQlUfyqXP4NLpUFSM7Ic2yLnKVetf2HSUi4sgiIyIt6xJ37NzJ5j4Gg4GOHTuwLjaWQ4cOl/u7olKlSnzx1ZdER0fj52t/GbnivpydS+dYJSUlMevTmZb+nn7mGerUrcO7U6Ze8fglpaWm8cX8LwB44cUXOHjgQIXai4iIXC8KFMsfavPmzbz22mvk5uZanouLi+Obb75h06ZNjB9fsXVFKmL27NnMnj0b02XrCKalpZGWlsaxY8f47rvvWL58eZmB2OvNw8PD6t/79u1j7NixXLhwwer58PBwwsPD+fbbb/nwww+5++67S/UVFRXF8OHDiYiIsHp+165d7N69m2HDhl338QPs2LGD9PR0AJuli8vyzDPPsHbtWgB+/PHHUoHi7777jsmTJ1tlp8bExLBkyRJ++eUXunfvbrfvlJQURo4cydGjR62eP3jwIAcPHmT48OF2227fvp0xY8aQn59veS4/P5+IiAgiIiJYvXo1kyZNYtCgQRU63z+j4gsgJ6drLzIRFhYGmNcnr8iki/K6cOECQ4cOtQS1c3NzSU1NtQSJjx8/zsiRI0lKss4QMRqNnDt3jnPnzvHjjz8ybdo0BgwYUKr/AwcOMG7cOJKTk62eL/7vvmbNGptraq9fv54333yTvLw8q+eDg4MJDg5mxYoVfPbZZ3TqZPsiV+TvIqLEe6dxdfvZVA39zTdlMnPzSMjIoIa3d7n6D08y9+9VyZ30nBxWBR3idGws6bm5VHZzp0Wtmtx7a5srlrMOiY/nxyO/4erizKg+fXCu4OffvB07ycjNpVfzZnRq2FCBYhGRcsqMS7E8Livr1quWOcOrIDuP3NRMPHyrlKt/F3dXvOvbD9KeDwqhMM98XVGtmfUk2qyLY/Oq5YfBzveCwWDAq5YvaWFxZMZY/17MjDW3d/F0x5iZS+TmI1wIP48xMxcXDzeqNqxBnR6tKlRKW0TEEZW8p1VWpm7Dhg0ByMzMICEhwTJx/Uo8PDxo1sx+RaGtW7aSk2Oe1GRvkrmzszO9evXi8SeewNfP11JhryLmzZ1LRkY6vXv3plPnTgoUi4jITaNAsfxhwsLCGDduHEajkdq1azNx4kS6dOlCamoqy5cvZ8mSJUybNu2GHPuXX35h1qxZgLlszQsvvECjRo0AcwB24cKFbN++nbi4OGbOnMn06dOv+lgvvvgizz33nN3tu3fv5pVXXsFkMvHEE09Y/egMCQnhxRdfJDc3l7p16zJ27Fi6deuGq6srx48fZ+bMmZw4cYJXX32VxYsX07FjR0vb/Px8S5C4UqVKjBkzhnvvvRd3d3f27NnDxx9/zIIFC676vMpyoMSP2YoGwtq1a4e/vz9JSUns32+9TuT+/ft56623AGjevDkTJkzg1ltvJTU1ldWrV7Nw4UJ++uknu32/8sorHD16FCcnJ0aMGMGjjz6Kt7c3R44c4eOPP+bLL7+02S47O5uJEyeSn59P27ZtGTduHE2bNsVgMHD8+HE+/PBDIiIi+OCDD7jjjjuoW7duhc75zyQqKsoyU/da14jesGEDISHm8oC9evXCzc3tmsd3uZ07d+Lq6srUqVPp168fMTExpKWlAeZM4pdeeomkpCT8/f0ZP348nTt3xtvbm/j4eDZv3syXX35JTk4O7733Hvfddx+enpeyTaKiohgxYgS5ubn4+fkxduxYevXqhbOzM4GBgXz00UckJSXxyiuvsGHDBvwultTds2cPEyZMoKioiBYtWjBmzBjat29PYWEhQUFBfPrpp0RERPDCCy+wZs0ay8WsyN9RYrp5vV5nJwN+Zaxt7u916YZ/YoUCxeZJILlGI5N/sP78v5CTw/6z4ew/G87D7dvxZJfONvvINRqZs207RSYTg7p0oU41nwqtM7zp5Cl+OxeFfxUvhvb4R7nbiYgI5KZmAmBwNpSZ9evu41WiTUa5A8WXKyosxJiVR07iBeIPh5Lw21kAAto3plrT2jbHVqmaV6l+bI0tN9X6uyPz4vrEhXlGjs5db7XNmJlL0olIkk5EUu+OtjS8q8NVnY+IiCMoXq/X2dnZct1ti3+JpWgqEii+XEFBARnpGcTExrBj+3Z27zJXDurZqye32ggUe3t7M2v2bJtlq8tr468bOXLkCNWrV2fosGevuh8REZHrQYFiqRCTyURWVla59nVycrLKlv3Pf/6D0WjEx8eH5cuXU/Nito+vry+TJk0iICCA//73vzdk3MUBwWbNmjF37lyrAFaNGjXo0qULAwYM4OTJk+zateuajuXm5mY3QHb27FkmTZqEyWSiQ4cOvP3221bbp0yZYgkSr1692qpUbe/evenWrRuDBg3i2LFjTJkyxSpIumzZMsusy5kzZ9K7d2/LtoceeoiOHTvyyCOPWDJ/r6ezZ803XFxcXCq8LovBYKBBgwYkJSURExNDfn6+5fV77733APMs0WXLllGlivkGka+vLxMnTqRmzZq8//77NvvdtGmTJYA9adIknnnmGcu2fv360bFjRwYMGEBUVFSptgcOHLAEH2fNmmX5Wy1u27x5c+666y6MRiObNm26IZnaBQUFV3yvGQwGq0Dn1fj0008tj21lqYP9933x81FRUWzcuJFly5YB4OnpyWuvvXZN4yrL888/z5NPPgmAv/+lTJQff/yRxMREwPweKDmRolq1arRo0YIqVarw/vvvk52dzeHDh7n99tst+7z//vvk5ubi5eXF8uXLaVCibO7DDz9MkyZNeOKJJ0hLS2PFihW8/PLLFBYW8vbbb1NUVETbtm1ZunQp7u7/z959hzdVvn0A/ybdexdo2dBSoBQKyN4gU2UIAgoICMpUEJRXRfwxFFQUpQUZypY9Ze/dMkqZZbSle9C9Z5rk/SPkkJBBFzPfz3VxXeGc85w85zTJSc793PdjJrTr27cv2rVrh0GDBiE+Ph6LFi3CihUrntu5IXrZch5XDLEwNdVbpcDS1ER4nPtUJr4+ykBxiVSG2s5OGNS8Oeq7ukAOICQ+ATuCriElJwd7r9+Ajbk5+vo00djH+oAAJGVlo7G7G3p7Ny71cwNAQmYmNl26DJEImNilMyyfw4AYIqI3WUme4jphZGaqM2sXAIzNn1wnSgqKdW73LMnXHyJsd8CTBSKgdq/mqN7RW2NbSb6ib8YW+j/blX17ul/KDGO5VAYrN0fU7NYUNtVdALkcmRGPEH3yOorScxF75hZMrMzg3r5s1yAiIkOR83gQp4WFpf7fFCr3HEt7r1Kb8+fOY6XK73SRSIThH36Id997V+v2ZmZmar/7yyohIQH/btoEkUiECZMmVvi+DhERUUUxUExlkpCQgObNSzf62d3dHadOnQKgmHP34sWLAIAxY8aoBd6Uxo0bh7179+Lhw4eV12EAMpkMXbp0Qb169dC5c2etQVyxWIyWLVsiJCREyK6sbNnZ2Zg4cSKys7NRtWpV+Pn5qfUlLCwMQUFBAIBJkyZpnc/UzMwM06dPx5gxY/DgwQPcvHkTTZs2BQChfHP79u3VgsRK1atXx/jx459LMF4ZVLW2ti71PJOqlME+mUyGrKwsuLi4ICwsTJinesqUKUKQWNWoUaOwdetWIVCtSnk+ateurRYkVrK3t8f06dPx5ZdfaqxTLTedkpKi8XqtUaMGVq1aBTs7OyEzvbLt379fOAZdbGxshNeMNsXFxRo/luRyOXJycnD//n1s2rQJFy4oRso2a9ZMZ6C4LO/7mjVrYvHixRXOTtanT58+WpdXq1YNH330EWQymVqQWJXqfODp6U9KH2ZnZwuDRMaMGaMWJFZq0qQJ+vTpg0ePHsHc3ByAoqy7sgz2jBkztP5YtLe3x8SJEzF79mycOXMGKSkpQqlsojeNRKaYm9HUyEjvdqbGT76CKuccLo0SqRQmxkZoVK0aZvbqCWOV5+no6YGmNWrg+717kZydg21Xg9DBoz5sVW4gBUVF4cz9UFiamWJC585lumZJZTIsP30GxSUl6NPEG43c3J7diIiI1MikMgCA2ET/dUJs8uQ6oZz3tzwKM58KHMiBuPMhkMvkqNHFR+06oHwesbH+2yTK9bKnrl8yqRRiEyPY1amKRiO7QaxyjariWw+Onu648dcBFKbnIup4MFya1oWptfo0REREBJRIJAAAU5XBpdqYqvz+lhSXf1CRcsC5klwux/7/9kMqlWLgoIHlus+li1QqxXL/ZSgqKkLffv3QuDEHDRER0cvHQDG9EEFBQZBKFT+kO3XqpHUbsViM3r17Y9myZZX63GKxGFOmTNG5XiaTITw8HHFxcQCgNhduZZFKpZg2bRqioqJgZmYGPz8/tUxIQL18s6enp87RkF5eXjAyMoJUKsW1a9fQtGlT5OTkCPOh6Dq/gCIb9nkEipXzspZ3RKWRyk0U5RzSly5dEpbpOiaRSITu3btrDRQry1h37NhR5/N269YNYrEYMplMbXmzZs1gYmICiUSCMWPGYNiwYejatSuaNWsm9FXffl8VP/zwA3744Ydnbte4cWMsXbq03HMUOzo6okuXLujcuTO6d+8OExP9P+YqwsTEROdcQt26dUO3bt10tk1NTcX169eF/ys/kwDFvNWSxz9Gu3TponMfv//+u9r/Vcul63vfensrslbkcjmCg4N1BuWJXndiUcXnOtfnx0EDIZfLIZPLtc4rbGthjg9bt8Yfx0+guKQEAeEP0buJ4v2XlZ+P1ecUA0JGtW0LZxv9pUWftvtaMB4mp8DdwR7DdJS1JiIi/SrzZntpVHvLE9XbN4bIWIzcuNTH8wYnIfr4dUjyClHvnSeDCEViMeRSmZ696ec76V3I5XLIZXKIjTSvUSZW5qjTuyXubT4DWbEUKbci4d6uUbmfj4joTaWv4sTz0L1Hd/R7px9MTEzw8OFDbN+2Hffu3sX2bduQnZ2N0ZVYGnrXzl0IDw+Hu3t1DBs+rNL2S0REVBEMFFOZqGYJl0VSUpLwuGbNmjq38/T0LFe/Sis1NRWBgYEIDw9HbGwsoqOjERERgfz8/Of6vAsXLhQyqufNm6c2L7GSagnkwYMHl2q/iYmJABTnVxlg1Xd+69SpIwSZK5Pt47kly1vWOisrC4DixpGdnR2AJ8dmb28vLNNGW+ZqQUGBkOWsLTNUycLCAtWqVRMyQpVcXV0xY8YMLFq0CDk5OVi9ejVWr14NW1tbtG3bVgiI2tvbl+Uwy2TgwIFYtGhRpe9XJBLBysoKTk5OaNSoEXr27ImePXvCWE/mxNPve4lEgujoaKxatQr79u1DRkYGTExM0LVr1+caJAYAOzs7tYEF2kgkEly9ehUhISGIiYlBTEwMIiIihHmOlJTvGUD9M6oscwgrB5gAQNu2bUvVRvnaJnrdyOVyFOkZTGViZASzx58lxc+4zhSr7Mf0GZlbTxOJRDDSE2jwrVkDRmIRpDI5wpKT0fvx8pVnzyG7oBAta9dC5wZl+74RlpSEvddvwEgswqSuXcrcZyIiQyCXyyGT6L5OiIyMIDZ9nI37jCxh1f0YPSP7WB8zOyvhsV2dqvAe2wt31h5DVsQjJATeQ7XWDWDpYg8AEJsaQVogg+wZ1zDZ42uY2FizXyKRCCIj3dcohwbVITISQS6VIyc2Red2RERvKrlcLiQbaGNiYiJU8CoulujdV7HKfkwrUApadR7khg0bYvb3s/Hjgh9xNyQER48cwds934a7u3u5968UFhaGvXv2wMjICJOnTNY5bR0REdGLxrtc9EIo5xcBoDZv8dOUAcfKVlRUhJ9++gk7duzQCJKamZmhdevWkMlkuHr1aqU/9/bt27Fx40YAipK2AwYM0Lpdbm5umfetbKMaoNV3fsViMSwtLdX+HpWhfv36uH37NgoKCpCYmIhq1aqVqX1oaCgARUBSmZWs7KPyB4Iu2kpSq56P8rQHFH+rhg0b4p9//kFgYCAkEgmys7Nx9OhRHD16FCYmJhg5ciRmzpz5zMDly7Jw4UIMGjSo0vdrYmKC+vXr45dffkGVKlWwatUqbNu2DcnJyfD399cbdK6oZ2WtHz58GD/++KNG6SiRSIS6deuiadOm2LNnj0Y75WAFQP976GkVed8SvW5Sc3Px+eatOtdP6NIZVmaKmx2FEgnkcrnOzLE8ldJwNs/4nC4rU2Nj2FpYICMvH9mP50w+cfcersfEwtbCHOM7la0iRKFEgmWnz0Aml2Nwi+aoy9LxRERaFWXm4eqvO3Wu9xzcAcbmiuuEtEj/dUJ1/l9jq8q7ToiNxKj9ti9urjwMyIH0+3FCoNjY3BTSAskz50QuKVQELkysyh6UMDIxhomVOYqzCyB5PF8zEZEhSU1NxdTJuqv+TZw0SZizt7CwQP9vCpWkD133dsrDyMgIQ4cNxQ/fz4FcLsf14OAKB4oLCwuxzM8fMpkMg4cMQd16dSupt0RERBXHQDG9ENbWT8o7FhQUqP1fVXEF5hQpLNT9Q3v69Ok4efIkAEWZ3c6dO8PDwwP169dH3bp1YWxsjCVLllR6oPjq1auYN28eAKBdu3b46quvdG6rGtC8detWmco4q2bcPis7uiLnWJe2bdsKwbcLFy5gyJAhpW4bHh4uBPVU549VHlNBQYHe9tqORzXTtzztldq0aYM2bdogNzcXAQEBCAwMxIULFxATEwOJRII1a9ZALpfj//7v//Q+x5vsyy+/xJ07dxAQEIDTp0/j119/xTfffPNS+nLs2DFMnz4dcrkcjo6OePvtt+Ht7Y26devC09MTtra2iI6O1hooVg0O6/uMepryfevs7CxUDSAyZNXs7AEAJVIZMvPz4WBlpXW7NJUBE86lfL+VheTxoDDzxwNXAsLDAQDZBYX4bMMmvW1XnDmLFWfOAgC2fDYeESkpSMpSDEDaGRSMnUHBOtum5uRi+MrVAID3WzTH4Jba50snIjJUFs6KgcFyqQzFOQUws7XUul1R1pPpPMzttF9LysvK7UnmWGG6yoBmZzsUZeShKFP/oD7lenP78l2/ZCWK8tZGz5h7k4jIUFVzUyQflJSUIDMjEw6ODlq3S0tNFR4/Pb1bRdWpU0d4nJSUrGfL0nn48CEePXoEANi5Ywd27tihc9uUlBQM+2AoAOD9wYMx5IPS32MjIiIqDwaK6YVQLf8bERGhtfQyAMTExGhdrpqxqZxH9GnKUsNPCw4OFoLEI0eOxOzZs7Vul5GRoXV5ecXFxWHq1KmQSCSoUaMGlixZojfz1M3NTa2ttpLKSk+PqKxataow125ERAS6d++utV1ycrLeEj/l1aNHD9jY2CAnJwcbN27E+++/X+r5bjds2CA8fu+994THyvORlZWFtLQ0tVJAqlRLdiuZmZnByckJaWlpWucvVpJKpaUqA2xtbS2UaAYUgfxp06YhPj4emzdvxsyZM59rFu2rTCQSYdGiRejXrx9ycnKwfv16dOrUCe3bt3/hffntt98gl8tRvXp17Ny5Ew4Omj8mdb3PVbPgY2Nj0bBhQ63bBQYG4tq1a6hRowb69+8vvE4zMjKQn58vjHwmehO52Nhgy2fj9W4Tm54uPI5MTdMZKI5MTQMAWJqZwqWUcwVfjojEhoAAZBcW4pu+fdBI5bqpKrugALmFimtdNXvdUxcQEVHlMnewRsefRuvdJu/Rk+9iuQlpOgPFuQmK64SRhQnMHEp3nciOSUHMqRsozMhB41E9YOGkvVqVrORJhSllKWwAsKrqgMywBOQ9ytCZwSaXy5GbqLjWWVVzFJan3onCw4NXIMkthPeYt2FfV3uFpeLcApTkK65RyqA5EZEhcXFxwdbt2/Ruo3pvMDIyUmegODIyEgBgaWkFl1JW/QkLC8OuHTuRlJyMWf83C1WrVtW6nWpSgZkZS0QTEdGbzTAjG/TCNW/eHKampiguLsaJEyd0BorPnTundblqtl+6yk1oVcHB2jN8rl+/LjweOnSo1m1kMhkuX76s9v/SBjq1ycvLw8SJE5GRkQFLS0ssX778mfPZtmzZUnh88uRJnYHi4OBgjB49GtWqVcO0adPQp08fWFlZoWXLlrhy5QpOnjyJ8eO138jXdX4rysrKCmPHjsWff/6JBw8e4K+//sLkyZOf2S4wMBA7Ho+i9PX1RZs2bYR1nTp1Eh6fOHFC599O1zF16tQJe/bswZkzZyCVSrUG6a9cuaI143jlypXYv38/bGxssGXLFo31Pj4+GDVqFBYuXIiioiJkZWXpDGQbgipVqmDWrFmYPXs25HI55syZgwMHDpSphHNFpaenIyoqCgDQs2dPrUFiQPGaU5LJZMJjX19fiEQiyOVynD9/XmegeMuWLTh69Cjq1KmD/v37o2XLlti0aROkUinOnDmDvn37am23f/9+zJkzB25ubpg7d67a+53oTVLdwQHONtZIzclFcHQ0mteqqbGNXC7H9cc3f5pWr66zlNzTnK2tkZ6nqJpxPSZWZ6D4Qli48LhZjRoAgP/r2wcylXnJn5aam4uvtivKpX7SsQM6eNQX1nlVrYq1Y0fr7dvf5y/gYlg4nKytsPjxiH/jCnyPICJ6U1lWsYeZgxWKMvKQfj8WTl41NLaRy+VIfxAHAHDwcC/1dUJsLEZGaDwAIDUkGjU6NdG6nXIbALBWyS529KyO+PMhkOQWIic2BbY1XTXa5sQkoyRPEeh18HxShtTM3hrFWYprVPqDOJ2B4pSbTwaxqrYnIqInatSoARcXF6SkpODatWto3qK5xjZyuRzBwYr7fU2bNS31tcLE2AQ3btwAAFy5fBnv9e+vdbubN28Kj+vUrXiZ6IYNG2LdhvV6t/l79WpcOH8Bzs7OWPz7bwBgsEkJRET0YvEOFr0Q1tbWePfddwEoMkjDw8M1tjlz5gzOnz+vtX2NGjWEwO3hw4c11hcWFmLVqlVa26oGCLU9LwD4+/sLQSZAd9ZyacjlcsycOROhoaEQi8X49ddf4enp+cx2Pj4+QnBq9erVav1RKiwsxKJFi1BUVIT4+Hi1gPv7778PQBEY3717t0bbzMxMLF++vJxH9Wzjxo2Dt7c3AMDPzw8rVqyAXM9N+bNnz2Ly5MmQyWSwtLTEggUL1NZXr15dKEXt5+eH5GTNUj9HjhxBUFCQ1v0rz0diYqLW4y4qKsLixYu1tjU2NkZYWBiuX7+ucwDCvXv3AChe246Ojlq3MSSDBw/GW2+9BUCREe/n5/dCn1/1x9PDhw+1bnP//n21zwnV97mrqys6dOgAAFizZg2SkpI02t++fRunTp0CAPTr1w8A0L17d6HE1eLFi7UOZElPT8fSpUuRn5+P1NRUnUFoojeBSCRCRw8PAMDZ0FBEqZSDUzp+9y4SMxXzgvf10X4TX5t6ri5ChvDRkBAkaKkkEp+RiV2PP7frujgLwWRTY2OYm5jo/Gem8hliYmQkLAcAsVist625iQmMHt+YEolEwjLjV3T+eiKil0kkEsG1mWJAbNK1cCFzWFXi5fsoSFGU/K/evnGp923t5gQLV8V1Iv5CCIpzNKfkKc4tQNSxawAAExsLODV8Eqi2q1sVZg6KShiRh4Mgk0rV2sqkUkQeUbS1rGIPB48ngV6b6s6wcFFkCCdeuo/8lCyN585PyUT0qRuKvlZ30hlMJiIydCKRCB06dgQAnD1zBlGRURrbHDt6DIkJCQCe/D4vjdp1asPdvToA4MD+g8jMyNTYJiszE1s3K5IG7O3t0aJFxaeTEYvFMDc31/vPSKz4/SASiYRlDBQTEdGLwEAxlYlcLkdeXl6Z/inNmDEDzs7OKCgowIgRI7Bjxw4kJycjMTERf//9Nz7//HOdz2traytkm546dQpz585FZGQkUlNTcerUKQwbNgz37t2Dra1m+a727dsLIwvnz5+P//77D48ePUJSUhLOnz+PCRMmYNmyZWptVPtdVkuWLBGCSTNmzECPHj1QXFyM/Px8redHdW7lOXPmwNjYGNnZ2Rg6dCg2bdqEuLg4pKWl4cKFCxg9erQwqvGTTz6Bu/uTmxP9+/cXAnWzZ8/G77//jujoaKSnp+PEiRMYNmwYEhMTSz3KsqxMTU2xYsUKeHp6Qi6XY8mSJRg0aBB27NiByMhIZGZmIi4uDsePH8fkyZPx6aefIi8vD5aWlvD390f9+vU19vnDDz/A1NRUMT/LsGE4dOgQ0tPTERsbi+XLl2PmzJk6y3m/9dZb6P94ZKi/vz/mzJmDsLAwZGRkIDAwECNGjMCdO3e0tn///fdhb28PuVyOSZMmYdOmTYiIiEB6ejru37+P+fPnY+/evQCA4cOHP7dz+joRiUSYO3cuTB4HVtavXy8E018EW1tbYeDE2bNnsWDBAjx8+BAZGRm4f/8+/vjjDwwbNkxtDu+n3+ezZs2Cubk5MjIyMGzYMPz3339ISUlBXFwcduzYgU8//RQSiQRVqlTB6NGjAShe99999x0AID4+HoMHD8bevXuRlJSEpKQkHDt2DCNHjhRKZ82YMQNWOkrxEr0p3m3qA0crS5RIZfjx4CGcvv8Amfn5SMrOxrYrV7H+oiKzv3XdOqjvqpmttezUaXy5bTu+3LZdY92otm0hEgGSEinm/rcfp+8/QEpODtJyc3H87l3M/W8/8ouKYWZijM86d9JoT0REL1/1Tk1gamcJuVSG22uO4VFQKIpz8lGQnoOoY8F4eOAKAMDZuxZsamiWEn2w4xyCluxG0BLNwbH13mkNiABJbiFu/HUQyTceojAzF0XZ+UgKDseN5QdQlJEHiID6/duozRMsEolQt28rAEB2dDLurDmG7OgkSPKLkB2d9Pj/yYAIqN2zhcZvgLr9WgEiQCaR4tbqw3gUFIrCjFwUZeUh8fJ93Fx1GNICCcSmxvAc9OKnaSEiep281/89ODo6oqSkBAvmL8DpU6eRmZGJpKQkbN2yFevXrQMAtG7TBvU9NO8nLfP3x/Rp0zF92nSNdaPHfAyRSITs7CzM/u47XDh/HqmpqchIz8C5s2fx3bffISUlBSKRCJ+MGwdzc/PnfbhEREQvFYclUZkkJCSgeXPNki/6XL16Fba2tnBycsI///yD8ePHIzk5WWOuYBsbG3To0EGYT/hp3377LUaMGIHMzExs3rwZmzdvFtaJRCJ8/fXXOH/+vFppWQDw8PDA+PHjsWrVKqSlpeGrr77S2LeNjQ2GDBmCNWvWAACioqLKnSW6cuVK4fFff/2FJUuWoKSkROf2rVq1wsaNGwEoSnQvXboUM2fORGZmJubPn4/58+drtBkyZIhGYF0kEsHf3x8TJkzA9evXsXLlSrW+AMDMmTOxdOlStblWKpOLiwu2bt2Kn3/+GTt37sTdu3d1zgkNAC1atMC8efO0BokBoF69elixYgWmTp2K+Ph4TJ+u/gXf3t4eI0aMgL+/v9b28+bNQ35+Po4fP45t27Zh2zb1eXCGDx+OS5cuCfPaqO73jz/+wKRJk5CRkaH1bwAAXbt21TvAwdDUq1cP48aNw19//YWSkhJ8//332L59e4XKuJfFnDlzMGrUKOTn52Pjxo3C+0rV4MGDERgYiPj4eERHR6ut8/DwwPLly/H5558jISFB62dFlSpVsHr1atjY2AjL+vbti+zsbCxYsADx8fGYNWuWRjuRSITJkyfjgw8+qIQjJXq1WZia4qvevfDTwcPIKSzEqrOaUwQ0qFoFk7p20do+LTdXyDh+WrOaNTC+UyesuXAB2QXa921rYY4vevRATQOeEoCI6FVmbGaCxiO74/baYyjJK0LY7gCNbWxru8JzSEet7Ysy84SM46c51HeD5+COCN8bgKLMPDzYrlmxSmxqBI+B7eHcqJbGOufGtVCrRzNEn7iBrMgk3Fz5VDUrkSIgrJqJrOToWR0eA9shfN8lSHILtR6XibU5vIZ3gVVVViQiItLHwsICX8+ahR8X/IicnGysXLFCY5sGXl6YPEX7tGepqWlCxvHTmvj4YNLkSVi9ajVSU1Ph76d5T8nMzAyffvYp3mr1VsUOhIiI6DXAQDG9UF5eXjh48CD++ecfHD16FAkJCbCxsUGnTp0wZcoU7N69W2eg2MPDA/v378eqVatw9uxZPHr0CNbW1vD19cXYsWPRsmVLnaWrZ8yYgcaNG2PLli24e/eukMlas2ZNdOzYER9++CFsbW2xbds25OXl4fjx42UOiGuTm5tb5jbdu3fHsWPHsHHjRpw7dw6xsbEoKiqCg4MDfH19MXToULRvr30Eur29PTZs2IB9+/Zh165diIiIQElJCRo2bIgxY8agR48eWLp0aUUPSy8rKyvMmzcPn3zyCfbs2SMEYnNycmBmZoYaNWqgefPmeOedd0o1T2v79u1x8OBBrF27FufOnUNCQgJsbW3RsWNHTJ06VW0O6qeZm5tj6dKlOHr0KLZu3YrQ0FAUFBSgXr16GD58OAYPHozevXtrbdu2bVscPHgQ69evR0BAAOLi4iCRSODg4ABvb28MGDAAvXr1Kvd5elNNnDgRhw4dQnR0NG7fvo2NGzfi448/fiHP3aRJE+zZswcrV65EYGAgUlJSYGxsDBcXF/j4+GDo0KFo3bo1vvvuO+zcuROnT5+GRCIRsqABxevt6NGjWLt2Lc6ePYv4+HhIpVLUrFkTPXr0wOjRo7XONz5s2DC0b98e69evR2BgIBISEiCRSODq6oqWLVtixIgROudmJ3oT1XZ2xuIPhmD/zZu4Fh2N1NxciCBCdQd7tK9fHz0bNyp3aeauXg3QoGoVHL59B7fj45GelwcjkRhVbG3QolYt9G7iDRuO+icieqVZuzmh5bSBiD13G+n3Y1GUqcjytXS1h2vTuqjWxgvicl4nqvjWg21NF8RfvIuM8HgUZeZBJBbD3MEaDp7ucG/XCGZ2uiu81OzWDHZ1qyE+4C6yo5JQUlAEYwsz2NRwgXuHxrCvU1Vn26otPWFbqwriA+4iMzwBxdn5EIlFMHe0gWPDmnBv1wgmlmblOi4iIkNTu05t/LbkN+z/7z8EBV1DWmoqRCIRqlevjvYd2qNnr17lLs3csVMneHh64tDBQ7h16xbSUlNhZGQEFxdXNG3mgz59+8KJA0+JiMhAiOT6JhElesH8/PyE7NAHDx685N4QEb16Mvz+fNldICKiV9xE76KX3QUiInqFrWj52cvuAhERveLsbexedhdeC8NO//Kyu/DG2Nr165fdBYPFOYqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGcxQTGTC5XI78/PwK7cPKSvf8XoaC51E3qVSKwsLCcrcXi8WwsLCoxB4RERERERERERERERHAQDGRQYuPj0f37t0rtA/OJc3zqE9QUBBGjRpV7vbu7u44depUJfaIiIiIiIiIiIiIiIgAlp4mIiIiIiIiIiIiIiIiIjI4IrlcLn/ZnSAiIqLSyfD782V3gYiIXnETvYtedheIiOgVtqLlZy+7C0RE9Iqzt7F72V14LQw7/cvL7sIbY2vXr192FwwWM4qJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRjjl90BIiIiKj3R6NEvuwtERPSqC1r5sntARESvsAm8ThAR0TNs7fr1y+4CEb0gzCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGCMX3YHXrSRI0fiypUrZW43cOBALFq06Dn06MW5fPkyRo0aBQDYsGEDWrduLazr1q0b4uPjX5vjjIuLQ/fu3QFoHktle/jwIQ4cOIDAwEDExcUhMzMTpqamcHd3R/PmzfHuu++iZcuWFX4ePz8/+Pv7w93dHadOnSpz+wYNGgAAFi5ciEGDBlW4PxWl7E9ZqPZd9fWqjVgshqmpKezs7FC3bl107twZH3zwAaysrDS2Lc373sjICBYWFqhatSqaNGmCDz/8ED4+PmU+hteZ8m82ZcoUTJ069bk8R1xcHP766y+cP38e6enpsLe3R6dOnTBlyhS4ubk9l+ckopcjKysL+//7D9euBSM1JQWmpmZwc3dDx44d0ePtHhCLK3e84tYtW7F3zx64uLjAb5m/xvq5/5uLe3fvlmmffsv84eLiIvw/KjIK/zdr1jPb1a1bFz8tWlim5yIiMjTFuQWIO38H6fdjUZiRCyMTY1i42MG1WT1Ua+UJUQWuEwWp2Yi7cAeZ4Qkoys6HkakJLF3s4NykNqq+5QkjE/23QopzCxB/8a7QN8jlMHe0gVPjWqjevjGMLUzVtk8KDkfozgtl6mPN7k1Rq7tvmY+NiMiQ8FrBawURET1/BhcoJiqt3Nxc/Pjjj9i3bx+kUqnaOolEgtDQUISGhmLr1q1o1aoV5s+fj9q1a7+czhogmUyGwsJCFBYWIikpCYGBgVi7di3++ecfeHh4lHl/UqkUubm5CA8PR3h4OPbu3YvPP/8ckyZNeg69N0y3bt3C2LFjkZOTIyxLSUnBrl27cOrUKWzevBl169Z9iT0kosqSlJSE/835ARkZGcIyiUSCsNBQhIWG4sKFC/jm229gYWFRKc/34MED/LdvX6XsS8nIyAimJiZqyyIiIir1OYiIDFVBeg5urTqE4uwCYVlJSTFyYlKQE5OClJsRaDz6bRibmejZi3YptyIRuusCZJInv+FKSoqQHZ2M7OhkJF6+j8Yfvw0LRxut7TPCE3Bvy2lICyRqy/OTMpGflInk6+Fo8klvne1Ly8jM9NkbEREZMF4reK0gIqIXw2ADxW5ubjhw4ECptzcxKfuXjteJu7s7jIyM4OTk9LK78kp49OgRxo8fj9DQUACAl5cXPvzwQ7Rs2RKOjo5IT09HWFgYtm7disDAQFy5cgWDBg3C8uXL0aZNm5fc+1fLu+++i7lz55ZqWzMzM63L586di3fffVdjuUQiQXJyMjZt2oRt27YhKSkJkyZNwoEDB7TuS9/7XiKRICkpCadPn8bKlSuRn5+PP//8E40aNUKXLl1K1X/STSKRYPr06cjJyYGbmxsWLFgAT09PnD17Fv/73/+QkZGB//3vf9iwYcPL7ioRVVBhYSEW/vgTMjIyYG9vj5Efj4K3tzcKCgpw+uQp7N+/H6EPHmDFXysw/cvpFX6+goICLPPzh0wm07vdN99+88xtzpw+g3Vr1wIAxowdCzt7e7X1kZGRAADPBg3w7Xff6txPZWdLExG9SaTFEtxZewzF2QUwsbFA3b5vwb5eNUiLJHgUFIa483eQHZ2MsF0X0PDDrmXad25CGh7sOA+5VAZzJxvU7tkctjVdIZNIkXY/FjEnr6MgJRshG06g+dT+EBupf17nPUrH3Y0nIZNIYWpnido9W8C+blWUFBYj5VYk4s7dRlFGHu5tPg3fSe8ImWwuTevC2buW3r4VZxfgxsqDKMkrgl2dKnBr61W2E0dEZEB4reC1goiIXhyDDRSLRCKtJWoN1caNG192F14ZxcXFmDRpEkJDQ2FkZIQvv/wSn3zyCUQikbCNg4MD6tWrh969e+PYsWP46quvkJeXh0mTJmHHjh2oV6/eSzyCV4uxsXGF32umpqY692Fvb4958+YBALZt24aYmBjs2bMHw4YN09j2We97e3t7NGjQAL6+vvj4448hl8uxatUqBoorQVBQEOLi4gAA3333Hdq3bw8AGDx4MO7du4dNmzbhypUryM3NhbW19cvsKhFV0Injx/Ho0SMYGRnh2+++Rc1aipshdnZ2GP7Rh3B0csLaNWtw+dIlPHjwoFxTFahav3YdkpOTn7mdqan+0fgxMTHY/O+/AICOnRTlsZ8W+Tij2MOjPszNzcvRWyIiSrz8AIVpORAZidBkzNuwquqoWGFtgTq9WsDMzhIP/7uM1DvRyI5Ogm2tKqXed9TxYMilMhhbmcFnXG+Y2T357l+9Q2OY21vh3uYzKEjOQuqdKLg2Va9mE7Y3UHHj39YCPuP7CJlgZnZWsHrbAaY2Fnj432XkJaQjPTQeTl41AEARRDDSPUhILpcjdPdFlOQVwcTaHF7DOkNsZFTq4yIiMjS8VvBaQURELw7THYiesnz5coSEhAAAvv76a4wbN04tSPy0nj17wt9fMRdiXl4e5syZ80L6SeomTpwoPC7PHM+qWrdujebNmwMAbty4gZKSkgrtj6BWftbd3V1tnbLctFwuf2a2HxG92uRyOQ4eOAgAaN++vRAkVtWzV09Uezwn+akTJyv0fFcuX8GZM2dga2sHX9/yz91VUlKCZX7+KC4uhpOTE8Z+8onGNjKZDDExMQCAuhwQRkRULnK5HPEXFb+1XHzqPrnxr6Jaay9YuNgCABKvhpZ639JiCTLDEwEAVVt4qN34V3JqXAtiU8V4+Zy4VLV1OXGpyIlJAQDU6fOW1nKhVVt6wsjcBCJjMfIS0krdt/jzIciOSgIAeAxsB1Mby1K3JSIyNLxW8FpBREQvlsFmFFeGqKgorFu3DoGBgUhMTISjoyO6deuGKVOmICwsDKNGjQKgmDdP6fLly8LyDRs2oHXr1lr3rcyumTJlCqZOnaqxPiwsDNu2bcPVq1eRmJiIvLw8WFtbo2bNmujUqRNGjhwJ+6fKJerTrVs3xMfHY+DAgVi0aBEAwM/PTwiAloa248nIyMD69etx+vRpxMbGQiqVolq1aujYsSPGjh2LatWq6dxfbm4utm3bhgMHDiA6OhrGxsbw8fHB+PHjNQJNlSU3Nxf/Ps4matSoEUaPHl2qdh07dsS7776L/fv3IygoCFeuXEGrVq00tgsLC8OaNWsQFBSEpKQkODs7o1u3bqWaB7e4uBh79+7F7t27ERERAalUioYNG+Ljjz/G22+/rbftw4cPsWHDBly6dAkJCQkwNjaGq6sr3nrrLXz00Udo2LBhqY7zVVatWjXY29sjMzMT8fHxFd5flSqK0ahSqRTp6elwdXWt8D6BJ++rVq1aYePGjThx4gQ2bNiA+/fvQyKRoE6dOhg6dCiGDh0KAMjMzMTKlStx4sQJPHr0CDY2Nmjbti0+//xz1NISgAGA9PR0bN26FRcvXkRkZCSys7NhZmYGV1dXtGrVCqNGjSpX1nt+fj7+/fdfHD9+HJGRkSgsLISrqyvatm2LMWPG6N1n/fr1hcdHjx5Ve83duXMHgCJgbGtrq3Mfys+pBQsWoFq1avjll18QEREBOzs7tGnTBr/99puwbXx8vFAaPi4uDjk5ObC0tISbmxvatWuHjz/+GFWrVtX5XOHh4di6dSsCAgKQkJAAkUiEOnXqoHfv3hg5cqTOeVWvX7+Of//9F0FBQUhLS4OFhQU8PT3Rr18/DB48+I2fxoAoOipaGBjS4q2WWrcRiURo0aI5DiQk4Nq1YMjlcr0DsnTJzMjE6lWrAQCffvYprl65Uu5+Hzp4ENHR0QCAT8aN0/oej4uNRXFxMQD1zzQiIiq9vMR0Ya5Jp4Y1tG4jEong6FUD8SkhSL8fV+rrhJGpCdrMHo78pEyY2em+ua7clUisvs+U24rpBcwcrDSyx5TExkZo/c1QGJmU/lZKYUYuok9dBwA4e9eCU8OapW5LRGSIeK3gtYKIiF4sBorL6cSJE5gxYwYKCwuFZYmJiUIQZfr0is+5p4u/vz/8/f0hl8vVlmdmZiIzMxO3bt3Crl27sGXLFr2B2Mr29E3VS5cu4fPPP0dWVpba8sjISERGRmL79u345Zdf0KtXL419xcbGYty4cYiKilJbfv78eVy4cAFjxoyp9P4DwNmzZ5GdnQ0AWksX6/PRRx9h//79AIB9+/ZpBIp37dqFOXPmqGWnxsfHY+PGjThy5Ajatm2rc9/p6emYMGECbt68qbb86tWruHr1KsaNG6ez7ZkzZzB16lTh5jagCDpHRUUhKioKO3fuxOzZszFixIgyHe+rSPmjoDLmhgwPDwegmJ+8LIMuyuKnn37C+vXr1ZaFhIRgzpw5iI2NxQcffIBRo0YhMTFRWJ+WloYDBw7gwoUL2L17t8agibNnz2LatGnIz89XWy6RSJCbm4uIiAjs3r0by5YtQ6dOnUrd1wcPHmDChAlISEhQWx4XF4cdO3Zg9+7d+OabbzBy5Eit7T09PdGmTRtcunQJf//9N7p3744mTZrg6NGj2LdvHwBgxowZperLjRs3MHfuXEgkEgBAamqqWknxHTt2qK1Xys7ORnZ2Nu7fv4+dO3di/fr1aNSokcb+N2zYgJ9//lkjkzwkJAQhISHYs2cP1q1bJwwmABSZhr/88gvWPp7bVKm4uFh4n27fvh0rVqxQa0f0plG9biurBWhTu3ZtAEBubg6Sk5PL9b5YuWIFcnKy0blzZ7R8q2W5A8VZmZnYvWs3AMDX1xfNWzTXul1kZBQAwNraBtnZ2dixfTvuhtxFdnY2rKys4NWwIfr261vhUtpERG+y3MR04bG1u7PO7ayrKbLHSvKLUJiRqzVjSxtjMxPY1nTRuf5RUCikRYrveA4e6t+jcx9njdnV0RxMKJPKhDkqy3LjHwAijwRBViyF2MQIdfq+Vaa2RESGiNcKXiuIiOjFYqC4HMLDwzFt2jRIJBK4ublh1qxZaNWqFTIyMrBlyxZs3LgRCxYseC7PfeTIEfj5+QFQlHT89NNPUadOHQCKAOzatWtx5swZJCYmYunSpVi4cGG5n+uzzz7D2LFjda6/cOECvvjiC8jlcnzwwQfw8fER1oWGhuKzzz5DYWEhqlevjs8//xxt2rSBiYkJbt++jaVLl+LOnTv48ssvsWHDBrRo0UJoW1xcLASJzc3NMXXqVPTp0wdmZma4ePEiFi9ejDVr1pT7uPS5onKTuWVL7ZlQujRr1gzOzs5ITU3F5cuX1dZdvnwZ3377LQBFwGzmzJlo0qQJMjIysHPnTqxduxb//fefzn1/8cUXuHnzJsRiMcaPH49BgwbB1tYW169fx+LFi/H3339rbZefn49Zs2ahuLgYPj4+mDZtGurXrw+RSITbt2/jl19+QVRUFBYtWoQuXbqgevXqZTrmV0lsbKyQxVbROaIPHTqE0FBF6aJOnTo9c17L8rh58yauXLmC1q1b4/PPP0edOnVw7949zJ49G4mJiVi7di2OHDmCvLw8zJ07F127doVcLsfOnTuxbNkyZGZmYsWKFZg/f76wz4SEBHzxxRcoKChA7dq18cUXX6BJkyawsrJCQkIC9u7diy1btqC4uBjz5s3DiRMnStXX5ORkjBkzBmlpaXB0dMTUqVPRuXNnWFpaIjQ0FCtXrsTFixexYMECODo6ol+/flr3s2DBArz//vvIysrCpEmT0LVrV2zfvh0ikQhz5sxBjx6a84Fqs3PnTlStWhU//fQTvLy8cOPGDdSsqRhte+vWLXz//feQy+Xw9vbG1KlT4enpCVNTU8TGxmLr1q3Yu3cvsrOzsWjRImzYsEFt3wcOHMCPP/4IAGjYsCE+//xzNG3aFLm5uThw4ACWL1+OiIgITJ8+HZs3bxbaLV26VAgS9+zZE6NHj0a9evWQk5ODEydOwN/fH3fv3sVnn32Gbdu2wczMrFTHSvS6Uc4VbGRkBCcnJ53bObs8uTFTnkDxsaPHcP36dbi4uODjMaPL1VelPXv2orCwECKRCB+O+EjndpGRiuyBwsICfP/dbLV1WVlZuHzpEi5fuoQBAwdi2PCyDTYjIjIUhRm5AACRkUhvJpeZvbVKm5xS3/x/mkwqhSSvCAUpWUgKDkPyDcVc866+deFQ301t27ykTACAhbOiwk1ScDgeXQ1FbkIaZCVSmNlbwcWnLmp0agJji9L9PsiJT0Xq7SgAQLU2XjBXOS4iItKO1wpeK4iI6MUy2ECxXC5HXl5eqbYVi8Vq2bI///wzJBIJ7O3tsWXLFqF8qaOjI2bPng1XV1e1EqiVSRkQ9PDwwIoVK9QCWFWqVEGrVq0wePBghISE4Pz58xV6LlNTU50BsoiICMyePRtyuRzNmzfH999/r7Z+7ty5QpB4586dcHBwENZ17twZbdq0wYgRI3Dr1i3MnTtXLUi6efNmISNp6dKl6Ny5s7Cuf//+aNGiBQYOHChk/lamiAjFl0FjY2O9mVDaiEQi1KpVC6mpqYiPj0dxcbFw/pSBp9q1a2Pz5s2wsVF8eXV0dMSsWbOEoJc2x48fFwLYs2fPxkcfPbmJ3b17d7Ro0QKDBw9GbGysRtsrV64gMzMTgKLksWqp3e7du8PT0xM9e/aERCLB8ePHn0umdklJyTPfayKRCJaWFZt75c8//xQea8tSB3S/75XLY2NjcezYMSEAaGlpWeos17IqKiqCj48P/vnnH6EccYcOHTBr1ixMmzYNJSUliIuLw5YtW9Tm3ZwyZQrCw8Nx+PBhBAQEqO1z06ZNKCgogImJCf7++2/UqPGkRJOjoyO8vb0hFouxfv16xMbGIjIyUhhoos/ixYuRlpYGOzs7bNu2TQjKAor5nN966y1MmTIFJ0+exI8//ogePXpoDYTWqFEDkydPxk8//YTk5GRs27YNHh4eWLBgAZo1a1am87dw4UK0a9cOgOK1rPTPP/9ALpfD0dERa9asgZ2dnbDO2dkZvr6+yM3NxYkTJ3D16lUUFhbC3NwcgOJvonwfent7Y9OmTcJnv5OTEyZPngwrKyssXLgQ165dQ1BQEFq2bImoqCisXLkSADBy5EjMnv0kgGRvb48xY8agRYsWGDZsGO7du4fNmzc/t6oIRC9bTk4OAMDCwlJvdQdLle9Vpf0+ppSQkIB/N22CSCTChEkTK3T9yMnJwenH89q3aNlS7XPzacrvCCUlJahTpw4Gvf8+6tevD7lcjpCQO9ixfQeSk5Oxd88e2NraoK+OQTNERIasJE9RkcvIzBQiPdcJY/Mn03WUFBTr3O5Zkq8/RNhule/MIqB2r+ao3tFbbTuZVIqS/CJF30xNELLhBNLvx6ltU5SRh7izt5FyKwLeo3vC0sUOzxJ3XjHFitjESOM5iYhIO14riIiIXiyDDRQnJCSgeXPtpQWf5u7ujlOPbyJmZGTg4sWLAIAxY8ZoneNy3Lhx2Lt3Lx4+fFh5HYaitGmXLl1Qr149dO7cWWsQVywWo2XLlggJCRGyKytbdnY2Jk6ciOzsbFStWhV+fn5qfQkLC0NQUBAAYNKkSWpBYiUzMzNMnz4dY8aMwYMHD3Dz5k00bdoUAITyze3bt1cLEitVr14d48ePfy7BeGVQ1draulzzJTo7K0riyGQyZGVlwcXFBWFhYcI81VOmTBGCxKpGjRqFrVu3CjehVSnPR+3atdWCxEr29vaYPn06vvzyS411quWmU1JSNF6vNWrUwKpVq2BnZ1eqgGF57N+/XzgGXWxsbITXjDbFxcUagQS5XI6cnBzcv38fmzZtwoULFwAoMrt1BYrL8r6vWbMmFi9eXOHsZH3GjBmjMWetaiZ7y5Yt1YLESs2aNcPhw4eRlJSkttzT0xNDhw6Fk5OTzmBHq1athHLX6enpz/y7Z2Vl4dChQwCAESNGqAWJlcRiMWbNmoWTJ08iLS0NJ0+eRN++fdW2yczMxM8//4w9e/aoLTc1NYWXl5fePjzN3t5eZ6n25s2bw9raGg0bNlQLEqtq1aoVTpw4IbxPlYHiwMBApKWlAQBmzZqldY7S4cOHY9euXXB1dRVek9u2bYNMJoOFhYXOaQd8fHzQt29f7N+/H9u3b2egmN5YJY9Lvpua6p+P21RlMImkuPQ3daRSKZb7L0NRURH69uuHxo0bl6+jj504fgJFRYqbPQMHDtT/3CUlMDU1RaNGjTDz669gbPzka3THTp3QtFkzfP/dbCQlJWHb1m1o36GDzs8hIiJDJZPKAChuhusjVinZKZNIy/18hZlPDUaSA3HnQyCXyVGji4/wm09ZYhQA4s7dRnF2ARwb1kCt7s1g6WoPSV4hkoLDEXPqBooy8nB300n4Tn4PRqa6b6kUZuQi9U40AKBK8/owtdb8bklERJp4rSAiInqxDDZQXF5BQUGQShVfPnTN7ykWi9G7d28sW7asUp9bLBZjypQpOtfLZDKEh4cjLk4xmu3p+TUrg1QqxbRp0xAVFQUzMzP4+fkJwVEl1fLNnp6eOjOFvLy8YGRkBKlUimvXrqFp06bIyclBSEgIAN3nF1BkED6PQLHyZnF5y8IaGT35EqucQ/rSpUvCMl3HJBKJ0L17d62BYmUZ644dO+p83m7dukEsFkMmk6ktb9asGUxMTCCRSDBmzBgMGzYMXbt2RbNmzYS+6tvvq+KHH37ADz/88MztGjdujKVLl5Z7jmJHR0d06dIFnTt3Rvfu3TWCuJVNOThClWqpVm3z5wKKgQwANObgHTBgAAYMGKDz+RITE3H37l3h/8rPMn2uX78uPI+Xl5fO97OzszNcXFyQkpKCa9euqQWKo6OjMXbsWMTFxUEsFmPkyJHIyMjA/v37ERISgu+++054P+fm5uLBgwdo3LixEMB9mpeXl86BHB9//LHe44mKilIbxKP6ORkYGAhAcX7fekv7nEBmZmYaAx+Un3nKKgS6zlHTpk2xf/9+REREICMjQ+sgGqLXnb4R/5Vh185dCA8Ph7t79QqXdy4pKcHRI0cAAN7eTVCvvv6BQT8u/AlyuRwymUzteq9ka2uLD0d8hCW//Y6ioiIEBASgT58+FeojEdGbpjyDcSui2lueqN6+MUTGYuTGpSL6xHVkRSYh+vh1SPIKUe+d1gAAmeTJd8Li7AK4+tZFgyFPfruZ2VmhZtemMLO3RuiO8yhIycajqw/g3l73gKWES/cAmRwQi1C9U5Pnd5BERG8YXiuIiIheLIMNFKtmCZeFagaftsw6JU9Pz3L1q7RSU1MRGBiI8PBwxMbGIjo6GhEREcjPz3+uz7tw4UIho3revHlq8xIrqZZAHjx4cKn2m5iYCEBxfpUBVn3nt06dOkKQuTLZ2irmGClvWeusrCwAii+1yiwi5bHZ29vrzSzSlrlaUFAgZDnXqlVLZ1sLCwtUq1YN8fHxastdXV0xY8YMLFq0CDk5OVi9ejVWr14NW1tbtG3bVgiI2tvbl+Uwy2TgwIFYtGhRpe9XJBLBysoKTk5OaNSoEXr27ImePXuqZXg97en3vUQiQXR0NFatWoV9+/YhIyMDJiYm6Nq163MPEgPQGihUDXJryz5/ehttCgsLERgYiPv37yMmJgaxsbEIDw/XqDKgfK/po/p+njp16jO3B5685gFF0HTcuHGIi4uDpaUlli5dio4dO6K4uBhxcXG4fv06Dhw4gIYNG2LcuHE4deoUvvpKkam3Zs0atG7dWmP/jo6Oz+xDTk4OAgICEBoaKpyDhw8fary3Vc+B8vO9Vq1aZfphqhycExISUuqM9UePHjFQTK8duVwuDKjSxsTERBjgUVws0bkdABSr7Me0lIOzwsLCsHfPHhgZGWHylMkVnj8+5M4d4RrbpatmBRNtRCKR1iCxkq+vr/D9JDwsHGCcmIgMiFwuV7uJ/jSRkRHEj7OqnpX5pbofo2dklOljZmclPLarUxXeY3vhztpjyIp4hITAe6jWugEsXezVstJERiLU6aN90GAV33qIO38H+Y8ykHonWufNf7lcjpSbikHA9nWrwtyB800SEQG8VqjitYKIiF4VBhsoLi/l3HsAtJYlVVIGHCubcg7NHTt2aARJzczM0Lp1a8hkMly9erXSn3v79u3YuHEjAEXJXF2Zi7m5uWXet7KNahBH3/kVi8WwtLRU+3tUhvr16+P27dsoKChAYmIiqlWrVqb2oaGhABQBSWVWsrKPurIjlbQFBVXPR3naA4q/VcOGDfHPP/8gMDAQEokE2dnZOHr0KI4ePQoTExOMHDkSM2fO1Hvz+2VauHAhBg0aVOn7NTExQf369fHLL7+gSpUqWLVqFbZt24bk5GT4+/vrDTpXhorOy6zNpk2b8Mcff2i8N8RiMRo2bIjatWvj8OHDpd5fRd7Pyv7ExMQAUAwuUWawm5qawt/fH0OGDEFCQgJ+//13NGjQAAcPHgSg+DzTNW+xvox/mUwGPz8//PPPPxoBLRMTE/j6+sLW1hZnz57VaKsc6PGs99rTKnqOiF4XqampmDpZd2WTiZMmCZ9rhYUFkMvlOgdd5KkMbNN1/VJVWFiIZX7+kMlkGDxkCOrWq1vG3mu6ckXxXcnU1BQtdVQRKCtTU1PY2toiIyMD2Y8/U4iIDEVRZh6u/rpT53rPwR1gbK4Y5CMtkui9TqjONWlsVbbvZvqIjcSo/bYvbq48DMiB9PtxsHSxh5HZk+/9lq72ekt/2tWpgvxHGchPztS5TU5sCoqzCwAALj7PZ4ofIqLXEa8VT/BaQURErwoGistIWfIVUGR7qv5fVXEZ5tt7WmFhoc5106dPx8mTJwEoyux27twZHh4eqF+/PurWrQtjY2MsWbKk0gPFV69exbx58wAA7dq1w1dffaVzW9Ugy61bt8pUxlk14/ZZ2dEVOce6tG3bVphD9cKFCxgyZEip24aHhyMlJQUA1LIglcdUUFCgt72241HN9C1Pe6U2bdqgTZs2yM3NRUBAAAIDA3HhwgXExMRAIpFgzZo1kMvl+L//+z+9z/Em+/LLL3Hnzh0EBATg9OnT+PXXX/HNN9+87G6Vybp167Bw4UIAgJubG3r06IGGDRuiXr168PDwgKWlJQICAsoUKFYdsHHo0KEyz9l8+vRpAECVKlXwzjvvqK1zdnbGX3/9heHDhyM/Px/Tp08XAqi9evUqVwn4hQsXYsOGDQAUpaC7deuGBg0aCOfA1NQUO3bs0BooVh6rvs9gbczNzZGbm4u+fftiyZIlZe4z0ZukmptigFVJSQkyMzLh4Kg9cz4tNVV4/PQUFto8fPgQjx49AgDs3LEDO3fs0LltSkoKhn0wFADw/uDBGPKB5rVcLpcj6PF3peYtWpR5gIg+kseZDWaVuE8iojeFhbNiQLVcKkNxTgHMbLUPnCzKejKVh7lKpldlsHJ7MtVLYbpicKXYyAimthYozi6A2Fj/4FljM0UAQ1aiO9MtNUQx36TISAznxrUr2GMiIsPCawUREdGLxUBxGamW/42IiNBaehmAkEH3NNWMzafnF1VSlkF8WnBwsBAkHjlyJGbPnq11u6fLy1ZUXFwcpk6dColEgho1amDJkiV6M0/d3NzU2uoLLD09MrBq1arCXLsRERHo3r271nbJycl6y1+WV48ePWBjY4OcnBxs3LgR77//fqnnu1UGpwDgvffeEx4rz0dWVhbS0tLU5qBVpVriV8nMzAxOTk5IS0vTOn+xklQqVSv3q4u1tbVQohlQBPKnTZuG+Ph4bN68GTNnznzuWbSvKpFIhEWLFqFfv37IycnB+vXr0alTJ7Rv3/5ld61UCgsL4e/vDwDw8fHBxo0btQY+yvr5oJpVHx8fX6b3s+rzOTo6ah0F7OXlhV9//RVTpkwRsqBNTEwwadKkMvUTUJS83rRpEwDg7bffxp9//qn1s0rXOVAeq7b3oqpt27YhOzsb3t7eaNu2Ldzc3BAaGqpR+v1p+kZCE70OXFxcsHX7Nr3bqH7/iYyM1BkojoyMBABYWlrBxcWl8jpZSpERkUIVgbdaPTub+PLly1i/dh2ys7PxzXffonFj7eXjsrKykJur+CxzcytbVRIioteduYM1Ov40Wu82eY+efA/LTUjTefM/NyENAGBkYQKzUpbizI5JQcypGyjMyEHjUT1g4aS9ypfqTXtleVMAsKrmiOLseBRm5Or93lacqxjAa6qj7wCQEar4XmhXtyqMLSo2VQIR0ZuE14oneK0gIqJXRekiYCRo3ry5MCfeiRMndG537tw5rctVs/PS09O1bhMcHKx1+fXr14XHQ4cO1bqNTCbD5cuX1f5fEXl5eZg4cSIyMjJgaWmJ5cuXP3M+25YtWwqPlYFtbYKDg9G0aVP06tVLyHC0srIS2utrq+v8VpSVlRXGjh0LAHjw4AH++uuvUrULDAzEjsfZTb6+vmjTpo2wrlOnTsLj8rxmlO3PnDmjc07mK1euaM04XrlyJd555x0MHz5cazsfHx+MGjUKgKKseZaBl8msUqUKZs2aBUAR1JszZ84zM7lfFWFhYUKgdcCAATqz4wIDA4XHpfl8aNGihTBYQt97Mj4+Hr6+vujRo4faoAnlQInw8HCdn3k9evRQG1zh7u5ersDRzZs3hWMaPHiwzgEtqudAdY5i5fzC2dnZuHHjhta2crkcf/75JxYvXix8bik/s0JCQoSMR23mzJmD1q1b4/3332fpaXpj1ahRQ3j/Xrt2Tes2crkcwcGK7zRNmzUt1QCKhg0bYt2G9Xr/dejYAYAiQ1m5bOCggVr3d//BfeFx/fr1n/n8zk7OSE9PR0lJCW6ofB972sULF4THTXWUzyciMmSWVexh5qDI+kq/r31wnlwuR/qDOACAg4d7qQfaiY3FyAiNR0FKtpClpY3yxjwAWKtkjDk2qAEAkOQWIisqSWffMh8mAABsa2r/viopKBJKjdrUePGDoYiIXne8VhAREb1YDBSXkbW1Nd59910AigzS8PBwjW3OnDmD8+fPa21fo0YNIeiirfxrYWEhVq1apbWtatBD2/MCgL+/P6KiooT/68paLg25XI6ZM2ciNDQUYrEYv/76Kzw9PZ/ZzsfHBw0bNgQArF69Wq0/SoWFhVi0aBGKiooQHx+vlpn9/vvvA1AExnfv3q3RNjMzE8uXLy/nUT3buHHj4O3tDQDw8/PDihUr1IJJTzt79iwmT54MmUwGS0tLLFiwQG199erVhVLUfn5+SE5O1tjHkSNHEBQUpHX/yvORmJio9biLioqwePFirW2NjY0RFhaG69ev6xyAcO/ePQCK17ajo6OOozQcgwcPxluP56qMi4uDn5/fS+5R6ahmguv6fLh48aLae6o0nw/Ozs7o2rUrAGDXrl1aAz8ymQwLFy5EQUEBYmNjhfcPAPTu3Vt4rt9++03rc5w4cQKHDh0S/h8VFYVp06ahpKTkmf1TVZpzsGvXLgQEBAj/Vy3Z3r17d2EgzOLFi7Wen40bNyItTTFquV+/fgCADz74AICi1O7cuXO1Dui4efMm9uzZg8zMTNjb2+uctoDodScSidDh8VzkZ8+cQVRklMY2x44eQ2KC4saJ8n30LGKxGObm5nr/GYmNhD4ol+mqkhH5uEqHtbUNqlSp8sznr1e/Hqo9Hvhy9MhRJDzuv6r4+Hjs3LFLsX29ejqzjomIDJlIJIJrM0WFmqRr4UI2mKrEy/dRkJINAKjevvSfpdZuTrBwVUz7E38hBMU5mlMZFecWIOqY4vusiY0FnBrWENa5+NSBkbkJACDi4BVIizW/C8ZfCEFRhqLUqWtz7QON8hLSgcc/H22qP3t6BSIiUsdrBRER0YtlsIFiuVyOvLy8Mv1TmjFjBpydnVFQUIARI0Zgx44dSE5ORmJiIv7++298/vnnOp/X1tZWyDY9deoU5s6di8jISKSmpuLUqVMYNmwY7t27B1tbzdIn7du3F0bIzZ8/H//99x8ePXqEpKQknD9/HhMmTMCyZcvU2qj2u6yWLFmCU6dOCcfco0cPFBcXIz8/X+v5UZ3Xc86cOTA2NkZ2djaGDh2KTZs2IS4uDmlpabhw4QJGjx6NmzdvAgA++eQTuLu7C2379+8vBOpmz56N33//HdHR0UhPT8eJEycwbNgwJCYmPrcSrqamplixYgU8PT0hl8uxZMkSDBo0CDt27EBkZCQyMzMRFxeH48ePY/Lkyfj000+Rl5cHS0tL+Pv7a81M+uGHH2BqaqqYN3HYMBw6dAjp6emIjY3F8uXLMXPmTJ3Zj2+99Rb69+8PQDEQYM6cOQgLC0NGRgYCAwMxYsQI3LlzR2v7999/H/b29pDL5Zg0aRI2bdqEiIgIpKen4/79+5g/fz727t0LABg+fDjL4kLxg2Tu3LkwMVF88V+/fr0QTH+VeXp6wtXVFQCwdetWLF++XHjf3Lp1CwsWLMCnn36qFsQs7efDrFmzYG1tDYlEgk8++QR//fUXoqKikJ6ejqCgIEyYMAHHjx8HALzzzjtCZi4ADBo0CE2aNAEA7Ny5E7Nnz0ZoaKjQdubMmZg8eTIkEgl8fX3RrVs3AIq5jcePH6+zFL82LVTmGfX398e///6LuLg4pKam4urVq5g1axa+/fZbtTaq58Dc3Bxff/01AMW87B9//DECAwORkZGB8PBwLFmyBL/88gsAoGvXrsIAkIYNG+LDDz8EoPhcHzVqFC5cuID09HTExMRg48aNGD9+PCQSCczMzPTO8U70Jniv/3twdHRESUkJFsxfgNOnTiMzIxNJSUnYumUr1q9bBwBo3aYN6ntoXjOX+ftj+rTpmD5t+nPrY3ycIkOgWrWqpW7z8ccfQyQSobi4GP+b8z+cPnUaKSkpSEtLw/Fjx/G/Of9Dfn4ezM3N8dmEz55X14mIXnvVOzWBqZ0l5FIZbq85hkdBoSjOyUdBeg6ijgXj4YErAABn71pas6we7DiHoCW7EbREc1BxvXdaAyJFpteNvw4i+cZDFGbmoig7H0nB4bix/IDi5r0IqN+/DYxMTYS2JpZmqNtX8Ts0LyEdN1YcQurdaBTnFiA/JQsRh68i8ohicK9zk9pwqOem8fwAhAwxALBwsin3eSIiMmS8VhAREb04hjkZKYCEhAS1YEZpXL16Fba2tnBycsI///yD8ePHIzk5WWOuYBsbG3To0EFnmdZvv/0WI0aMQGZmJjZv3ozNmzcL60QiEb7++mucP39erTwqAHh4eGD8+PFYtWoV0tLStAYbbGxsMGTIEKxZswaAIjOvvFmiK1euFB7/9ddfWLJkid4Mv1atWmHjxo0AFCVcly5dipkzZyIzMxPz58/H/PnzNdoMGTJEI7AuEong7++PCRMm4Pr161i5cqVaXwBg5syZWLp0qVo2YGVycXHB1q1b8fPPP2Pnzp24e/euzjmhAUWAat68eTrLV9arVw8rVqzA1KlTER8fj+nT1W9+29vbY8SIEcIcs0+bN28e8vPzcfz4cWzbtg3btqnPETl8+HBcunRJmPNRdb9//PEHJk2ahIyMDK1/A0AR9NI3wMHQ1KtXD+PGjcNff/2FkpISfP/999i+fXup56t+GYyMjDBv3jxMmTIFJSUl+PPPP/Hnn3+qbSMWi/HZZ59h7dq1KC4uRnS07jJLqmrVqoW///4bU6ZMQWpqKv744w/88ccfGtt17dpVI6PexMQEK1euxKeffoo7d+5gx44dQpl2Vb169cLChQthYmKC8ePH49KlSwgICMDmzZtLPV+xg4MD/u///g9z585FQUEB5s2bp7GNqakpxo4dixUrVgAAoqOjNSoapKamYsmSJbh27RpGjx6tsY8WLVpoZPF/++23KC4uxs6dOxEUFIRPPvlEo52VlRV+//13eHl5lep4iF5XFhYW+HrWLPy44Efk5GRj5eP3m6oGXl6YPGWy1vapqWlCxvHzkpKSCgCwtNQ9Z9jTmvk2w6effYZ//v4b2dlZWo/L1tYO076chpq1alVaX4mI3jTGZiZoPLI7bq89hpK8IoTtDtDYxra2KzyHdNTavigzT8gie5pDfTd4Du6I8L0BKMrMw4PtmpW+xKZG8BjYHs6NND+rq7b0RElBMaKOXUP+owzc23RaYxt7Dzd4DGin8/gKM59MMWJszjkniYjKg9cKIiKiF8dgA8UV5eXlhYMHD+Kff/7B0aOKEoQ2Njbo1KkTpkyZgt27d+sMFHt4eGD//v1YtWoVzp49i0ePHsHa2hq+vr4YO3YsWrZsqbN09YwZM9C4cWNs2bIFd+/eFTJZa9asiY4dO+LDDz+Era0ttm3bhry8PBw/frzMAXFtyjOfZvfu3XHs2DFs3LgR586dQ2xsLIqKiuDg4ABfX18MHToU7du319rW3t4eGzZswL59+7Br1y5ERESgpKQEDRs2xJgxY9CjRw8sXbq0ooell5WVFebNm4dPPvkEe/bsEQKxOTk5MDMzQ40aNdC8eXO88847avMy69K+fXscPHgQa9euxblz55CQkABbW1t07NgRU6dOVZuD+mnm5uZYunQpjh49iq1btyI0NBQFBQWoV68ehg8fjsGDBwslfp/Wtm1bHDx4EOvXr0dAQADi4uIgkUjg4OAAb29vDBgwAL169Sr3eXpTTZw4EYcOHUJ0dDRu376NjRs34uOPP37Z3dKra9eu2LZtG/7++28EBQUhIyMDpqamqFq1Kpo3b46PPvoIjRo1wq1btxAYGIhjx45hwoQJpdq3r68vjhw5gs2bN+PUqVOIjIxEXl4ebG1t0aRJEwwcOBB9+vTR2tbJyQnbtm3D7t27sX//foSGhiIvLw/Ozs7w9fXF+++/jw4dOgjbr1q1Cj/88APy8/NL3T+l4cOHo3bt2li3bh1u3ryJ7OxsmJubw93dHa1bt8aIESNQu3ZtHD58GNHR0Th+/LgwnYDSZ599hk6dOmHDhg24fPkyUlJSYGJiggYNGqB///4YMmSIRga/iYkJfvzxRwwYMABbt27F9evXkZKSArFYjBo1aqBjx474+OOPUbVq6bMXiV5ntevUxm9LfsP+//5DUNA1pKWmQiQSoXr16mjfoT169uqlsyz0i5Cfr6gmYGllVaZ2Xbt1RQOvBjh08BDu3L6N9PR0GBkZwdXVFS1atkSfvn1gY8OMACKiZ7F2c0LLaQMRe+420u/HoihTkbll6WoP16Z1Ua2NF8Q6Ki49SxXferCt6YL4i3eRER6Posw8iMRimDtYw8HTHe7tGsHMTvfnf/WO3nDwcEd8wF1kRiSiODsfxuamsHS1R5UWHnBtWgciPQNISwqeDGY24s1/IqJy47WCiIjoxRDJ9U2+SuXm5+cnZIc+ePDgJfeGiOj1U1JS8lIDSa+qzJysl90FIiJ6xU0IWvnsjYiIiIiIiHTY2vXrl92F18Kw07+87C68Mfiae3le3TqqRERk0BgkJiIiIiIiIiIiIiJ6fhgoJiIiIiIiIiIiIiIiIiIyMEzXojeCXC5Hfn5+hfZhVcZ5Ct9EPI+6SaVSFBYWlru9WCyGhYVFJfaIiIiIiIiIiIiIiIio/BgopjdCfHw8unfvXqF9cC5pnkd9goKCMGrUqHK3d3d3x6lTpyqxR0REREREREREREREROXH0tNERERERERERERERERERAZGJJfL5S+7E0RERFQ6mTlZL7sLRET0ipsQtPJld4GIiIiIiF5jW7t+/bK78FoYdvqXl92FNwZfcy8PM4qJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRjjl90BIiIiIiIiIiIiIiIiotfJipafvewuEFUYM4qJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGiomIiIiIiIiIiIiIiIiIDAwDxUREREREREREREREREREBoaBYiIiIiIiIiIiIiIiIiIiA8NAMRERERERERERERERERGRgWGgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBgoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAaKiYiIiIiIiIiIiIiIiIgMDAPFREREREREREREREREREQGhoFiIiIiIiIiIiIiIiIiIiIDw0AxEREREREREREREREREZGBYaCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGCgmIiIiIiIiIiIiIiIiIjIwDBQTERERERERERERERERERkYBoqJiIiIiIiIiIiIiIiIiAwMA8VERERERERERERERERERAaGgWIiIiIiIiIiIiIiIiIiIgPDQDERERERERERERERERERkYFhoJiIiIiIiIiIiIiIiIiIyMAwUExEREREREREREREREREZGAYKCYiIiIiIiIiIiIiIiIiMjAMFBMRERERERERERERERERGRgGionohZNKpS+7C1QJ+HckIiIiIiIiIiIiInp9Gb/sDhC9aN26dUN8fLzWdWKxGGZmZnBwcICHhwd69uyJvn37wtLSUuv2DRo0AABMmTIFU6dOfW59fpMcPHgQJ0+exO+///6yu6JG+bpo1aoVNm7c+LK788q7c+cO5syZg927d7/srrwQly9fxqhRowAAGzZsQOvWrV9yj4hebevWrsORw4cxcdIkdO7SuUL7SklJwb69+3Dr5k2kp6fDwsISNWvVRLfu3dC+fXu9beVyOc6fO4fTp88gOioKJSUlcHR0hG/z5njn3Xfg5OSkt31ISAiOHjmK0AcPkJubC0tLK3g28ETPnm/Dp2nTCh0XEZGhe3jgMhIC7sFzcAdUaV6/QvsqzMhF7LnbyAiLR3FWPozMTWBV1QFVW3rCtWldvW3lcjmSrz9E0rUw5CamQy6VwdTWEo4NqqN6R2+Y2VnpbZ8dk4zEy/eRFZWE4pwCiMRimNlbwcHDHe7tGsHcwbpCx0ZEZMjelGuFtv3dWn0E2VFJcG1eDw0Gd6zIoREREVUIA8VEKmQyGQoKClBQUICEhAScPXsWy5cvx6JFi9CqVauX3b3X3pIlS7BixQqey9fcuXPnMGHCBGYUE5FWQVeDcPTIkUrZV3hYOH5csAAFBQXCspycbITcuYOQO3dw+dJlfDHtCxgZGWm0lcvl8Fvqh4CLF9WWP3r0CIcPHcLZM2fx5cwv4e3trbXturXrNI4jOzsLQVevIujqVXTv0QOfjPsEYjEL9BARlVXavRgkBN6rlH3lxKbg9pqjkBaVCMtK8oqQ9fARsh4+QmpINLyGdobYSPPzWi6X48G2c0i5Fam2vDAtBwkB95B0PRyNPuwG+3rVtD535JEgxJ27o75PyFCQnIWC5Cw8uhoKr6Gd4NSwZiUcKRGRYXlTrhXaxJ27jeyopPIfEBERUSVioJgMVosWLbB69Wq1ZVKpFPn5+YiJicGJEyewefNmxMfHY/z48Vi3bh18fX3Vtq9ZU/GD387O7oX1+3WWlMQvwW+C1NRUBomJSKtrQdfwx5IlkMvlFd5XWloafl70MwoKClC1WjWMGjUK9T3qIysrC4cPHcapkydx5fJlbNm8GSNGjtRov3XzFiFI3O+dfujeowesrKxw9+5dbNqwEWlpafh98e/49bdfNTKLd2zfIQSJ3d2rY/iHw+Hh6YGCggJcOH8Be3bvxskTJyCTSvHZxAkVPlYiIkOSdi8W9zafASp+qUBRVh7ubDgBaVEJzJ1tULdvK9jWcEFxbgESAu7h0dVQpN2JRpTDNdTt85ZG+6hjwcKNf/cOjVD1rQYwtjBFVuQjRBy6iuKsfNzdfAotPh+gkS2WEHhPCBLb1q6Cmt2awrqaIyT5RciKfISoY8EoyS/CvS1n0GxCP1i76a9iQURET7wp1wptchPSEH3iRsUPjIiIqJIwBYIMlpGREaysrNT+2draomrVqmjVqhW+/fZbbNiwAZaWligsLMTEiRORk5Ojto/jx4/j+PHjQklaIiIiQySXy7F923Ys/vVXlJSUPLtBKezbuw85OdmwtLTCDz/8gOYtmsPW1hY1atTAp599infefQcAcOTwEaSkpKi1TU9Lx8GDBwEA/fv3x8hRo+Dm5gY7Ozu0bdsW/5s3F9bWNsjPz8OunbvU2qakpOC/ffsAALVq1cL8H+ej5VstYWdnh6pVq2LwkMGYPHUKAOD06dO4e/dupRwvEdGbTi6XI+rEddzddBJyqaxS9hl79jZK8opgZGECn3F94ORVAyZW5rCq4gCPge3g3rExACAh4B4KM3LV2hZl5SH+QggAoHrnJqjbtxUsXexgam0BlyZ10PTTvjC2NIO0QIKYUzfV2spKpIg+cR0AYFenCpp80gsO9d1gYmUOSxc7VGvVAL6T34WRhQnkJTJhWyIi0u9NulZoI5WU4P72c5V2bERERJWBgWIiPZo3b47vv/8eAJCRkYE1a9a85B4RERG9Wm7euIFZX32N3bt2QS6Xo25d/fN7lUZeXh7OnD4NAOjdpzccHB00thk8ZAgsLa1QUlKCs2fOqq07euQISkpKYG5ujgGDBmq0dXFxQb93+gEALl64gKKiImFdYECAEOwe9+l4WFpaarRv164dGjZqBAD4b+++ch4lEZHhSA+NR7Dff4g9dROQA9buFc+uLSkoRtK1MACAW9tGMLPV/Lyu1b2ZIlgrlSEpOFxtXcKl+5BLZRCbGqNGFx+NtuYO1nDvoPisT775ENLiJwOhMh8moqSgGABQs4ev1lKl5g7WqNrSEwCQEZYAGSvyEBHp9aZdK7SJOnoNBclZsKtXFWb2ZZvXmIiI6HlhoJjoGQYOHIg6deoAADZv3gyZ7MmovwYNGqBBgwbw8/PTaPfw4UP88MMP6NWrF5o0aQJfX1/06tULs2fPxr172udY6datGxo0aIAtW7YgNzcXP//8M7p164YmTZqge/fumDFjhs62Sunp6Vi+fDk++ugjtGvXDt7e3mjRogX69OmDH374AQ8fPtTaTnksAQEB+O+//9CrVy94e3ujU6dO+Pnnn9W2DQsLw4IFC9C/f3+0atUKjRs3RuvWrTFkyBD4+fkhMzNTbXs/Pz80aNAAe/bsAQBcuXJFeL64uDi1bSUSCbZs2YKRI0eidevW8Pb2RufOnTFjxgzcuHFD77E/L8q/y+7du5GXl4elS5eiT58+8PHxQfv27TFlyhS1jLLg4GBMmjQJbdu2RZMmTdCnTx/89ddfkEgkGvseOXIkGjRogMWLF0MikWD58uXo3bs3fHx80KVLF0yaNAlXr17V27/c3FysW7cOY8eORYcOHeDt7Q1fX1+8/fbbmDVrFm7duqW3fXp6OlauXIlBgwahVatWaNKkCXr16oUFCxYgMTFR2O7y5cto0KABvvnmG2GZ8u+4e/fu0p5OvZTnw8/PDxKJBGvXrkX//v3RrFkztG7dGmPHjsWlS5eE7cPCwjBz5kzhuLt3746ff/4ZeXl5Op+jrK/f0kpISMBPP/2EPn36oFmzZvD19cV7772HJUuWICMjo1z7JHodLPxpIWJiYmBsbIzBQ4bg82lfVHifIXdCUFysuAH/1lua5d8AwNzcHN5NFPMLBz31OXn9uiJzq3HjxrCwsNDavkXLFgCAoqIi3L51W1geEaEoK+fo6AgPDw+dffTxUdwoCgkJqbQsaiKiN1XIuuPIf5QBkZEYNbs3g9ewzhXeZ2ZEImQSRfDVWcccwEamJrCv5wZAMdelqvQHit8h9vWqwtjMRGt7Jy/FfmXFUmQ+TBCWF2XlQWyqmMnLprqzzj5aONkCAORSGSR5RTq3IyKiN+9a8bSM8AQkBN6DkYUJPN/vAIjKdixERETPC+coJnoGkUiEPn36YPny5cjMzMTdu3fh7e2tt82ZM2cwdepU4SY3ABQXFyMqKgpRUVHYuXMnZs+ejREjRmhtn5OTg6FDhyI8/MlIxri4OMTFxeHQoUOYO3cuPvjgA412Z8+exbRp05Cfn6+2XCKRIDc3FxEREdi9ezeWLVuGTp06aX3uo0ePYuvWrcL/k5KSYG9vL/zf398f/v7+GvNPZmZmIjMzE7du3cKuXbuwZcsWVKtWTfdJ0iIxMRGffvopQkND1ZY/evQIBw4cwIEDBzB+/HjMmDEDItGL/0admpqK999/H5GRkcKyoqIiHD9+HBcuXMCmTZtw69YtLFiwQG0O34iICPzxxx+4d+8eli5dqnXfEokEY8eOxZUrV4RliYmJSExMxMmTJzFp0iR88YVm8OX27duYMGECUlNTNfYXExODmJgY7Nu3DwsWLMDgwYM12l+5cgXTpk1DWlqa2nLla3X37t1Yvnw52rRpU7qTVEkKCgowatQoBAcHqy27ePEiLl26hGXLlkEqlWLGjBkoLCwUtomLi8OaNWsQHByMTZs2wcRE/Yfc83r9Hjx4EN98841aViIAPHjwAA8ePMDWrVuxbNkytGzZsiyngei1IBKJ8FarVhg6bCjc3d01ykCXR1RUFADFNBG1atfSuV2dOnVw5fJlxMTEoKSkBMbGxigpKUF8fLxivZ7s5ho1agjbR0REoOVbivdnXq6i3Jyzi4vePtraKm7+SyQSJMTHo2Yt3f0kIjJ4IsCpcS3UftsXli72GqU9yyM3MV2xayMRrKppVp5QsnZzRNqdaOQ9yoBMKoXYyAgyqRQFKZmP1+sO9FpWsYfISAy5VIac+DQ4PQ4yVGvVANVaNUBJYTGMTHTfVilIyxYeG1uYluXwiIgMzxt2rVAlKShC6K4LgByo1681zO2tK3ZgRERElYgZxUSl0LhxY+GxMktJl/z8fMyaNQvFxcXw8fHBmjVrcO7cOZw/fx7Lly9H7dq1IZfLsWjRIo1sWqW//voL4eHh6N27N/bu3YvAwECsXLkSderUgUwmw/fff4/AwEC1NgkJCfjiiy+Qn5+P2rVrY8mSJThx4gQCAwOxa9cujBw5EsbGxiguLsa8efN09n/r1q3w9PTE1q1bceHCBSxevBgDByrKdh45cgR+fn6Qy+Vo37491q9fj3PnzuHcuXNYv349unTpAkAR4FQNiH722WcIDg7Gu+++CwBo0aIFgoODERwcDHd3d+G8jR07FqGhobC0tMSMGTNw9OhRXL58Gdu3b0e/fooSoatXr8bq1av1/g2eF39/f8TExGDixIk4duwYTp8+jS+//BIikQgFBQX48ssvMX/+fPj4+GDDhg24dOkSdu3ahVatWgFQBOFVA5+qtm/fjitXrqBNmzbYunUrLl26hI0bN6Jp06YAgOXLl2Pnzp1qbXJzczFx4kSkpqbC2dkZP/74I44dO4ZLly5h3759mDp1KiwsLCCXy/Hjjz9qDCCIjY3F+PHjkZaWBicnJ8ydOxenT5/GuXPn8PPPP8PZ2Rl5eXn44osvkJaWhpYtWyI4OBhz584V9qH8O7733nuVeaqxadMmBAcHY9iwYThw4ADOnTuH+fPnw9TUFFKpFHPnzsXMmTNRvXp1rFixAoGBgTh48CB69+4NALhx4waOHDmits/yvn6f5eLFi5g5cyaKiorg5eWFZcuWISAgAOfPn8eSJUtQu3ZtZGZm4tNPPxWCX0Rvkt+W/I4vZ3wpfJ5XhtTHwWYnJyeIxbq/rjo7K8rRyWQyYcBMenq6MFjH1VV3sFckEsHZWXHDJyUlWVhu/jgDubCgQG8f8/Ke3LhKT2fVACIifVpMG4hGH3aFpYt9pe2z6HEAwdTOCiI91wozu8c342VyFGUqqs4UZ+VDLlUMHDR30H2zXiQSCaVBizJyNNYbm+sO/kqLS5B8IwKAonyqvoAyERG9udcKAAjfG4jirHw4NaqJKs3rl/k4iIiInif+UiEqBdWb309nbj7typUrQulaPz8/VK1aVVjXvXt3eHp6omfPnpBIJDh+/DjGjBmjsY/8/HwMGjQICxcuFJZ16dIFTZs2xaBBg5CQkIBFixZh374n8yJu2rQJBQUFMDExwd9//40aNWoI6xwdHeHt7Q2xWIz169cjNjYWkZGRQkltVWKxGEuXLhXWKYO7APD3338DADw8PLBixQqYmj65MVKlShW0atUKgwcPRkhICM6fPy+sMzU1hampKYyNFR85RkZGsLJSn4tl9erViIiIgImJCdatWycESAHA3t4ev//+OxwdHbFx40YsXboUAwcOhMszsr0qW1FREb777juMGjVKWPbZZ5/h2rVrOHv2LKKjo+Hl5YUNGzYI58bBwQH+/v7o2LEjioqKEBAQgObNm2vsOz8/H+3bt8eqVauE89SqVSts2LABH330Ee7cuYM//vgD7777LszMzAAA+/btEzL3li5dihYtWgj7c3BwgJeXF2xsbPDTTz8hPz8fwcHB6NChg7DNTz/9hMLCQlhbW2PLli2opZINN2DAANSrVw8ffPABMjMzsXXrVkyePBlWVlZqf/en/46VpaioCKNHj1Yrc/3BBx/g3r172Lx5MxITE+Hq6op///1XyHh3dHTE4sWLcf36dSQlJSEgIKBSXr/6SKVSfP/995DJZPDx8cGmTZuEvw8A9O3bF+3atcOgQYMQHx+PRYsWYcWKFRU5NUSvHDc3t0rfZ06O4gbLsz5jLC2frM/LVdzQycl+cnPGykr/SH1lWWplW0CRaXz1yhXExcUhIz1D6/zIAHA35MmUA/kF+Vq3ISIiBUsXu0rfpyRfUVVGX7D26fXKeYUl+U+qwBhbmGm0UWX0uNSosm1pRR6+CkmOYtBRtTZeZWpLRGSI3tRrRdL1h0i9HQUTa3PUH9C2dB0nIiJ6gZhRTFQKlpaWwuNnzV+qWm5aW/nNGjVqYNWqVdixY4fWUsCA4sb4d999p7HcwcEBU6dOBQDcv39frTS1p6cnhg4divHjx6sFiVUpM1sBRcaVNl5eXloDyDKZDF26dMGAAQMwadIktSCbklgsFkrrlmVOVrlcjm3btgEA+vXrpxYkVjVt2jSYm5tDIpEI8x2/SFZWVvjwww81lquWEx41apTGubGzsxPOaXJyMrQRiUSYO3euECRWMjc3x9dffw1A8XpSnZ+3WrVq+OijjzB8+HC1ILGq1q1bC49V/+bZ2dlCMHTMmDFqQWIl5fzKLVq0gLm5udb9P0/jx4/XWKZ6rocMGaJWFh0ATExMhNLwquf6eb1+z58/L5S4nTFjhlqQWMne3h4TJ04EoChLXxlleYnedMWP53Q30fJeVWVq+qS8vKRE8rhtsdb12ij3X1z8ZA75Nm1aQyQSQSaTYc2aNRql6gHg1s2buHnzpvB/KecoJiJ64WQliuoRz8rUFZsYabSRSUq0rtdGuX/lHJelEX8xBImXHwAAbGtXYfYYEdFL8rKvFYWZuXh4QHEfx2NgO5haW5Sy50RERC8OM4qJSkE1+PusuXGbNWsGExMTSCQSjBkzBsOGDUPXrl3RrFkzGBkpvlh27NhR7z46d+4Ma2vtWVBdu3YVHgcEBKB+fcVNhwEDBmDAgAE695mYmIi7d59kP6nOoauqYcOGWpeLxWJMmTJF5/5lMhnCw8OFctolZbhpHh4eLsyR27BhQ+Tl5WndTiQSoUGDBrh586bOEs7PU+PGjTUCuYAik1V1G22Uf0/V15IqHx8fvQF+a2tr5ObmIiAgAJ07dwYAdOvWDd26ddPZ39TUVLVS6ap/86tXr0LyOBCjLLmsze+//65z3fPk7u4ulIRVpXquGzVqpLWttnP9vF6/ly9fFh57enrqfO0qg9dyuRzBwcHo1atXqfZPZKjEFZiHXl+p6tKoWasWunTpgtOnT+PqlStYMH8BBr0/CDVr1kR+fj4uBV7Crp074eDgIAzAMdJybSAioufrWb/L9LYVl7/ts8RfDEHEwasAAFM7S3gN61yhvhIRUfm9zGuFXC5H6M4LkBZI4Nq8nta5i4mIiF4FvKtFVArKEpgAYGtrq3dbV1dXzJgxA4sWLUJOTo4wp66trS3atm2Lzp07o3v37hqZkKo8PT11rnNwcICdnR2ysrLw6NEjjfWFhYUIDAzE/fv3ERMTg9jYWISHh2tkSGrLkALUA3G6pKamIjAwEOHh4YiNjUV0dDQiIiI05sAtrdjYWOHxwoUL1Upu65KYmFiu56oIBwft5UdVgxK6AvzPClzo+5uLRCLUrFkTd+/e1fo3l0gkuHr1KkJCQhATE4OYmBhERERoZC+r/s2TkpKEx7Vr19bbt5ehNOfaxsbmmdtoU5mvX9V5xtu2LV0JqZfx2iV63Zg9rmJQIpHo3U41E9jURJEdbK6S2S+R6B/0IXk8oOTpzOMxn4xFTk4OgoKCEHLnDkLu3FFbX83NDZ+M+wQL5s1//JwvvuoCEZGhM3r82a3M/NJFNbtLmREmVvncf1Z76eNrybOyyeRyOaKOBSPu7G0AgKmtBZqM7QkzW0u97YiI6Pl5mdeK+AshyIp4BDMHK9R7p7WupkRERC8dA8VEpaAayNSV9alqzJgxaNiwIf755x8EBgZCIpEgOzsbR48exdGjR2FiYoKRI0di5syZQpaxqmcFo83NzZGVlYXc3Fy15Zs2bcIff/yhFtgGFIGzhg0bonbt2jh8+LDefWsrnatUVFSEn376CTt27NDISDYzM0Pr1q0hk8lw9epVvc/xtKeP43m1qSjVEuSVTVfQU0lZ+vnp4z58+DB+/PFHjXLGIpEIdevWRdOmTbWW6c7KyhIeK+fofJU8j3P9PF6/r8trl+h1Y/X4M+BZAzjy859k8dvYKj5HLVXmNVZdr729Yv9PfwabmppixlczcfHCBZw8eQrRUVGQyWSoUqUK2rVvj959eiM25sl3A12DW4iI6PkxMn88H2Sh/rmDVdebWCq+U6vORSl9RnvlemNL3b+TpJIShO44j9Q70QAAM0drNBnTExZO+n/XERHR8/WyrhV5j9IRdTwYEAGegzo8c45kIiKil4mBYqJSUJ2HsEmTJqVq06ZNG7Rp00YoFxwYGIgLFy4gJiYGEolEmPfw//7v/zTaFhUV6d238sa26o3pdevWCZm4bm5u6NGjBxo2bIh69erBw8MDlpaWCAgIeGagWJ/p06fj5MmTABQlljt37gwPDw/Ur18fdevWhbGxMZYsWVLmQLFqoHL16tXo1KlTufv4utJVklpJ+TdXzUQ/duwYpk+fDrlcDkdHR7z99tvw9vZG3bp14enpCVtbW0RHR2sNFKue84KCAp2Z0G+S5/H6VQbwnZ2dcfHixefSbyJDVM3NDQCQlpYGuVyus2Rcaqpi2gIjIyPhmujs7AxTU1MUFxcjJSVV53PI5XJh2gMnLaXuRSIROnTsiA46pouIjo4WtqvmVq2UR0ZERJXF0tkOAFCUlaf3WlGUpRikJzISwdRW8R3YzN4KYhMjyCRSFGbqHsQnl8tRlJX/uI3278vFuQW4u/EkcmIV1xxrdyc0/rgH56EkInoFvKxrRWpINOQlMgDA7X+O6u1jcvBDJAc/BAA0GdcL9nX524KIiF4sBoqJnkEikeDYsWMAgOrVq6NBgwZlam9tbY2ePXuiZ8+eAIBbt25h2rRpiI+Px+bNmzFz5kyNeW9VM5iflpqaKmQMu7u7A1CUm/b39wegmOt248aNQgBL1dPlp8siODhYCLKNHDkSs2fP1rpdeZ6jWrUnX4Lj4+P1bqvvi/3rTN/fXCaTISYmBoDiNaj022+/QS6Xo3r16tj5eL7Mp+n6e6ie89jYWJ1zUwcGBuLatWuoUaMG+vfvX6pjeRU9r9ev2+NgVkZGBvLz859r1jmRIalZUzF/l0QiQVxcnM5qHpGRkQAUn43Ka6lIJEL16tURERGBqKgonc8RGxMjzEdep04dtXVyuRw5OTl6K3zcunXr8XPX0HrNJSKi58uyquK7r7xEhvzkTFhV0V7dITdBMZ+8pas9xI+rOYlEIli62iM3Pg15j9drk5+UAblUcaPf2k1zip6i7HzcWn0YhWmK32eOXtXhNayzUOqUiIherlfhWkFERPSq0z+RIxFhx44dQlnfIUOGPDNIuXLlSrzzzjsYPny41vU+Pj4YNWoUAEXmsGoJYKXz58/rnENYGewSiUTo3LkzACAsLEwIHg8YMEDnDevAwEDhsUwm03scT7t+/brweOjQoVq3kclkuHz5ss7n0HXuvLy8hIxW5fFpk5eXh/bt26Nr165YvHhxqfv+Orh69arOEqsBAQHCOuXfPD09XQiA9OzZU2fZU11/c19fX+Hvcf78eZ392rJlC/z8/PDXX38Jy17HQH1lvH61admyJQBAKpXizJkzOrfbv38/fH190a9fPwQFBZWy10SGq1HjRsJUCNeCrmndprCwEHduK+YObtasmdq6Zr6+AIA7t++gsLBQa/tr14IBACYmJmjUuJGw/OaNGxjx4Uf4dNx4nXOKZ2Zk4nqwov1brd4q5VEREVFlsq9bFWJTxc389HvaB11KiyXIfJgAAHDwrK62zqGB4v+ZDxMhVZnzXlXafcV+RcZi2NdRz/CS5Bfh9j9HhSBx1VaeaDSiG4PERESvkJd1rajRxQft/veR3n9m9oopc1ya1RWW2dWuUsEjJiIiKjsGion0uHbtGn755RcAQNWqVfHxxx8/s42xsTHCwsJw/fp1BD++ify0e/fuAVBkGzs6ao42jI2NxcaNGzWWp6amCpnD7dq1Q5UqVYTnVAoPD9f6nBcvXsTu3buF/0sk2r/g6qI6l7Ku5/D391fL3nr6OZT7eHq5sbExBg0aBEARtNRVHnvJkiVIS0tDQkICvLy8ytT/V11+fj7+/PNPrct//fVXAIqMN2UwRPVv/vDhQ637vH//PlatWiX8X/W8u7q6okOHDgCANWvWICkpSaP97du3cerUKQBAv379hOWqr4Vnlcx+VVTG61eb7t27w/lxydrFixcjPV1zlHF6ejqWLl2K/Px8pKam6szeJqInzM3N0ap1KwDAgf0HkJqqWUJ6544dyM/Pg7GxMXr27qW2rkPHDhCLxcjLy8WunTs12qampuLggYMAgC5du8JKZV7juvXqCQNijh7RLBMnl8vxzz//oLi4GObm5nj77bfLf6BERFRuRqYmcG5cCwAQdyFEa1nQ6JM3IC2QQGQkhlsb9d8Prk3rAmIRSgqKEX3yhkbbwsxcxF+4CwCo2sIDxhbq80uG7rqAghTFoF+3dg3hMaAdRGLeYiEiepW8rGuF2MgIRqYmev/h8Rh8kVgkLON1hIiIXgaWniaDJZVKkZeXp7ZMIpEgJycHDx8+xPHjx7Fv3z5IJBKYm5tj6dKlavO66vL+++9j1apVyMzMxKRJkzBlyhS0a9cO9vb2SE5Oxo4dO7B3714AwPDhw3VmZy5cuBBJSUkYMmQIbG1tce3aNfz8889ITk6GqampWulcT09PuLq6Ijk5GVu3boWLiwv69esHGxsbxMXF4b///sOWLVsglUqFNk8f+7O0b98eIpEIcrkc8+fPh0QiQatWrSASiRAaGop///0Xp0+fVmuTl5cnZIQBT+bXffDgAUJCQuDm5gZra2uYmJhg0qRJOH78OBITEzFjxgzcunULAwcOhIuLC+Li4rBp0ybhvLVo0QJ9+/YtU/9fB+vWrUN2djbGjBkDFxcX3Lt3D4sXL8b9+/chEonwv//9D+LHPxpsbW3h4+ODW7du4ezZs1iwYAGGDx8OR0dHJCUl4ciRI1i3bh0KCgqE/T/9N581axauXr2KjIwMDBs2DNOnT0fbtm1RVFSEwMBA/P7775BIJKhSpQpGjx4ttFOdJ3n//v14++23YWRkpBZoedVUxutXG1NTU3z33XeYPn064uPjMXjwYHz++edo27YtAMX85n/++adQOnzGjBmv9HkietGmT5sOAKhfvx4mT5mitm7Y8OG4euUqcnNz8L85P2Dkx6Pg5eWFnJwcHD50GCdPnAAA9O7TG05OTmpt3dzc0LNXLxw5fBj7/9uP/PwC9OnbBzY2Nrh//z42rt+A3NwcWFvboP8A9bL6NjY26NqtG44fO4ajR47AxMQYnbt0gY2NDWJjY7F7127cDQkBAHz40UdwcNRe0YGIiCpH0BLFYFeb6s5oMKST2rraPVsgNSQGJflFuLXqMOr2fQu2taugJL8I8QF38ehKKABFINfMTv07mKWLHdzaeCEh4B7iz4dAWiSBe7tGMLY0Q3ZUEiIOXUVJfhGMLc1QvXMTtbZp92OFzDTbWq6o1d1XZ6aZktjE+LWszENE9Dp4Fa8VRERErwsGislgXbt2Dc2bN3/mdtWrV8dvv/2Gpk2blmq/9vb2+OOPPzBp0iRkZGRg/vz5Wrfr2rUrPv/8c63rWrVqhZiYGPz999/4+++/1dbZ2tpi6dKlqFu3rrDMyMgI8+bNw5QpU1BSUoI///xTIztVLBbjs88+w9q1a1FcXIzo6OhSHY+Sh4cHxo8fj1WrViEtLQ1fffWVxjY2NjYYMmQI1qxZAwCIiopSy5hu3bo1Vq9ejfz8fCGDeMOGDWjdujUcHBywZs0aTJw4EVFRUVizZo2wH1U+Pj7w9/cXAqZvigYNGkAul2P37t1qmd+AIhi5cOFCtGnTRm35nDlzMGrUKOTn52Pjxo1as9AHDx6MwMBAxMfHa/zNPTw8sHz5cnz++edISEjQ+jetUqUKVq9eDRsbG2GZt7c3LC0tkZ+fj2+//RbffvstpkyZgqlTp1bkFDxXlfH61aVv377Izs7GggULEB8fj1mzZmlsIxKJMHnyZHzwwQcVPxiiN0higqLEm+oAFCUnJydM/3I6fv/td6SmpmLJb79rbNOmbVt8NGKE1n1/+NGHSE5KUsxRfuKEEFhWMjc3x9f/97VQFUDViJEjEBcXh3t372L/f/ux/7/9auvFYjGGDhuGnr16lvZQiYionApSsgEAptaag3bN7KzQ8MOuuLf5FIoy83Bv8xmNbZyb1Ead3i217rt2rxYoTM9B+v04PLoSKgQLlMSmxmg8qjvM7a3VlidcvCs8zo5ORuD8zc88jre+GgxzB+tnbkdERGX3Kl4riIiIXhcMFBOpEIvFsLCwQJUqVdCgQQN069YNvXv3hqmp6bMbq2jbti0OHjyI9evXIyAgAHFxcZBIJHBwcIC3tzcGDBiAXr166Wzv7u6OP//8E8uWLcPx48eRmZkJd3d3dO3aFR9//LFQclpV165dsW3bNvz9998ICgpCRkYGTE1NUbVqVTRv3hwfffQRGjVqhFu3biEwMBDHjh3DhAkTynRcM2bMQOPGjbFlyxbcvXsXeXl5sLS0RM2aNdGxY0d8+OGHsLW1xbZt25CXl4fjx4+rBeM7duyIOXPmYMOGDYiPj4eNjY1aOdG6deviv//+w44dO3D06FGEhoYiNzcX1tbWaNCgAd59910MGjRIrYzwm8LOzg4rV67EypUrcfDgQSQlJaFq1apo27Ytxo4di9q1a2u0adKkCfbs2YOVK1ciMDAQKSkpMDY2houLC3x8fDB06FC0bt0a3333HXbu3InTp09DIpHAxOTJvGnt27fH0aNHsXbtWpw9exbx8fGQSqWoWbMmevTogdGjR2sEcBwdHbFixQr89ttvePDgAYyNjbXOtf2qqejrV59hw4ahffv2WL9+PQIDA5GQkACJRAJXV1e0bNkSI0aMgI+Pz3M+QqI3T9NmzbD499+wb+8+3Lp5E+np6TAxMUHNWrXQtWtXdO7SWWd2lqmpKb6a9TXOnT2HM2fOIDoqGsXFRXBwcIBP06Z4r/97Wq+nAGBmZobZ38/GyRMncf7cOcTGxqKkpN6tp8UAAQAASURBVAQODg74f/buOzyqKv/j+GfSCekkhN4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lqlatWon7u379ek2aNCnf6+rcuXM6d+6cli5dqm+//Vbt2rUrzWUA/hGsrKzUvkMHPTryUVWvXt08IKisNG3WTI899pi865d8cIqtra1q1apV6P4rV67o1KlTkqTmLVrk2ZeclCRJ8qhcuchjuLi4SDKtO381JES17rmnxP0DAEtjZWWaeftI+/aq7uaqyMTEMm2/aXUvjezQXt5/G3BZUmuPn9CZ0DBVd3PVyA7ttf/ipWLr1K1cWaeCQ+QbeEWJaWmq4OCQZ7/PufPmxw2qet5QvwAAf2MlVWp6j2rf11pOlV2VFptUps1XrFdVdfq1VYWaRf8WyM3G0U417m2m6l2aysbeVvGXQsu0TwAA3GoEigHcVsOGDdMXX3yhlJQUPfLII+rbt6/69++vTp06lWjGpa+vr0JDQzVo0CBJ0ty5c9WuXbs8AcHvvvvOHCTu27evnnvuOdWsWVOBgYH6/vvvtXPnzltybjdiw4YNOn/edBOpe/fusre3z7N/0qRJOn78uGxsbDR+/Hj1799flStXVmxsrHx9fTV79myFhoZq1apVevjhh9W+fXtJpsClwWAwp7WeOnWqBg8enG/mXEGOHj2ql19+WRkZGfLw8NArr7yie++9Vw4ODjp27Ji+/vprnT17VjNmzJCTk5Mee+yxMr4q0qJFi5Senq6RI0dq9OjRcnFx0a5du/TRRx8pIyNDU6dOVVxcnGrUqKE333xTLVu2VExMjGbNmqVNmzbp2LFj2rRpkwYPHmxuc9OmTZo1a5YkqWvXrnr22WdVp04dSdLly5f1888/a+fOnQoNDdU333yjTz/9tER93bt3r958800ZDAY1atRIL7/8slq3bq3s7GwdOXJEX3/9tQICAvTss89q5cqVql27dplfL+BO+nLmV0UuJXAzJr37Tpm0bTQalZiYqKjIKB08cEBbt25VZmamatWqpcEPDs5T1uFa+tC01NQi20xOvn5TKiYmlkAxABThi0dGyOsGMraUxKSBA26q7cDoaK04ckQ21lZ6oVdP2ZcwQ8Qj7drqXFiY4lNT9fG69Xq0fXvV8aik5IwM/XnhgtYdPyFJ6tmogeoWM/gIAFAybV8bJqfKFW9J282e7Ffqtl29vdRx4iOysbcrvjAAAHcpAsUAbqunnnpKhw4d0u7du5WZmamNGzdq48aNsrKyUv369dW2bVt17NhR3bp1U4UKFfLVd3Z2lkOu0foODg55UgyHh4frf//7nyRp4MCB+uqrr8wpmd3c3PT999/r1Vdf1ZYtW27peRqNRiUnJxe6PSgoSFu2bNHixYslSU5OTpowYUKesv7+/uag9ssvv6zx48eb97m5ualu3bpq1qyZhgwZIknas2ePOVDs+Ld10uzt7QtNxfx3H3/8sTIyMuTm5qZly5apRo0a5n29e/dW586d9cQTT+j48eP67LPP1L9/f7m7u5eo7ZJKT0/Xk08+qUmTJpm3PfLIIzpz5owWL16s0NBQValSRb/++qt5Fra7u7u++OILHT16VOHh4dq3b1+eQHHO66J+/fqaM2dOnqC8p6enOnTooOHDh8vPz0979uwpUT+zs7P1/vvvy2AwqEWLFlq0aJHKlStn3j9w4EB16dJFDz30kEJCQjR9+nTNmTPnZi4NcNe5VUHismw7IiJCr778Sp5tnTp31n/G/Sffe2PNmjV1+NAhBQcHKzYmVm7uBQ9iOu132vw4JTWlTPoJAP9WtypIfLNtZ2Zna/Z2H2VlGzS8XZtSBXTre3rq3UED9cvefQqIitbnmzbn2V/eoZyGtW6tAc2b3XD/AAB53aog8Y22Xc7F6Rb0BACA28u6+CIAUHZsbW01Z84cvfnmm+a0nZIpgHr+/HktWbJEr732mjp37qxXXnlFgYGBpWp/27ZtSktLk7W1tSZOnJhn3V5Jsra21rvvvitr61v79nf16lW1adMm31/btm3VvXt3Pf7445o/f755Rtsvv/yievXq5WkjOztbTz/9tPr371/orN1GjRqZr2NMTMxN9/v06dPmdKwvvfRSniBxDkdHR3344YeSTOmgV69efdPHLci4cePybcudunnEiBH5UnXb2dmpWTPTzbiIiAjzdoPBoJ49e2ro0KF64YUX8s3clkyvjZz2Y2NLtp7Qnj17zGtBT5gwIU+QOIerq6s5yL9z584yT8sLoHgF/b87cviw5v/yi1L/NnO4U6eOsrKyksFg0Lx58/KlqpekE8eP51kSIDsrq+w7DQC45ZYcPKTg2FjVreyhoa1bl7p+SkaGHO0KnkWWkp4h/8hIRSaWbVpUAAAAAChLzCgGcNvZ2Nho3LhxGjNmjHbv3q2dO3fqwIED5oCbZFrzcfPmzfLx8dG0adPyzAwtyoEDBySZZo1WrVq1wDJVq1ZV8+bNC1z393Zxd3dXz5491aNHD/Xp0yffWrqS1LBhQ02cOLHQNpKTk3Xs2DFz0Ds7O/um+3Xo0CHz4/79+xdarlGjRqpTp44uX76sQ4cO6emnn77pY+dWvXp1eXh45Nuee+ZyYetbly9fXpKUkZFh3mZtba2XXnqp0OMZDAb5+/srODhYkpRVwqDPwYMHzY8bNGhQ4CxySebgtdFolK+vb5HXFkDZu+eee/T9nDmq4FJBYWFh2rhho7Zv26Y9u/foashVTf3oQ9leSzVa65571LNnT/n4+OjwoUP6+KOP9dDDD6lWrVpKSUnRgf0H9Ptvv8nNzc08QMemhGlKAQB3D7+Qq9p06pTsbG30Qq9esinlQNJ1x0/o1wOm74Kd6tbRkNatVN3NTakZGToWFKSlhw5rv/9FnQ0N1XsPDLqls6oBAAAA4EZxVwvAHePg4KB+/fqpX79+kqTQ0FAdPHhQe/bs0Y4dO5SSkqKMjAz997//Va1atdSyZcti2wwNDZVkCgoUpV69erc0UFy9enXt2LHD/DwzM1OBgYGaO3eu/vjjD8XGxsrOzk69evUqMEj8d35+fjp69KgCAgIUFBSkgIAAXblyRQaDwVymoFlvpRUWFiZJcnFxUeViUu/Vq1dPly9fNl/zslTYetW5Z4IXlJr872UKEhUVpf3798vf319BQUEKDAzUpUuXlJJS+tSxOYFlSercuXOJ6tyK6wWgaLnfL2rUqKFxz46Ti4uLVq1cqYsXL2rXzl3q07ePucxTzzytxMREHTlyRH6nTsnvWqaFHNW8vPTMf57Rxx9+JElyKOcgAMA/R3J6ur7buVNGo/RYhw6q7uZaqvpX4+K05KBpgGXfJo31zL3dzPvsHB3VvUEDNfXy0nurVis2OUXz/tyr9x4YVJanAAAAAABlgkAxgLtGtWrVNHToUA0dOlRxcXH67LPPtHLlSmVlZWnOnDn6/vvvi20jMTFRkvKsY1yQnFmnt4udnZ28vb01Y8YMeXp6au7cuVq2bJkiIiI0e/Zs80y2vzt79qzeeecd+fn55dtXuXJlde3aVT4+PoqPjy+TfiYlmVLjOTkVv85OzjrINxJgLU5Jjl9a6enpmjZtmlasWJFv9nW5cuXUsWNHGQwGHT58uMRt5lyv0riROgDK3rCHhmnTxo1KTU3VX0eO5AkU29vba8Jbb2rvn39q+/YdCgwIkMFgkKenp7p07ar7B9yvoCtB5vKFDW4BANydfvpzr2KSktW0upfub9a01PV3njsng9EoO1sbPdaxQ4FlKpUvr6GtW+uXvfvkF3JVofHxqlbx1q2tCQAAAAA3gkAxgNtmw4YNOnXqlJycnIpMAyyZ1nX99NNPdeHCBZ08eVInTpwo0TEqXrv58vc1J/8ud1ri2+2NN97QqVOntG/fPvn4+Ojzzz/XpEmT8pULDg7W6NGjlZiYKDs7O/Xt21etWrWSt7e36tevL09PT0lS9+7dyyxQnBOgLUnwNyfNck7A+G73+uuva/v27ZKkpk2bqkePHqpfv768vb1Vt25d2draaubMmaUKFOcMSPDw8NDevXtvSb8B3Br29vaqUaOGLly4oPDwiHz7rays1O3ee9Xt3nsLrB8YGGguV82r2i3tKwCgbO33vyjJlH561Nz/FVn2lcVLJUmNq1XV5AdNy+GExpm+e9d0c5OTvX2hdZvk+ny4GhdHoBgAAADAXYdAMYDbZtOmTdq8ebOcnJz07LPPyr6Imyo52rdvr5MnTyotLa1Ex/Dy8tLx48d16dKlIsvlThl8u1lZWWn69OkaNGiQEhMTNX/+fHXv3l1du3bNU+6HH35QYmKibGxstHjxYrVo0SJfW0ajscyCxJLp+klSQkKCIiMji0w/ffHixTx17ma+vr7mIPGYMWP03nvvFVguNja2VO3mnHtsbKxSUlJuyUxoAKWTnJysOd/PUUR4uB4Y/IDu7d690LLp6emSpHLl8n4eGY1GJSYmysXFpdC6OQOYatSoWWwWCwDAv0vWteVfsnItA1OczL9ltAEAAACAu0HRCzkCQBlq27atJNNs1ZUrV5aozpUrVyRJ9evXN2+zsrIqtHz3awGBixcvFhosTkpKkq+vb4mOf6t4enpq4sSJkkwBicmTJ+ebBX306FFJUuPGjQsMEkumAGhOEN1QihtVhWnXrp358ebNmwstd+bMGfNsujZt2tz0cW+1nGspSY8++miBZQwGgw4ePJjneXFyrld2drZ27txZaLm1a9eqdevWGjRokI4cOVLCXgO4EU5OTvI75afAwEDt/bPwmf7R0dHmQUN16tY1bz9+7JhGj3pcz/5nXKFrisfFxunotc+R9h3al2HvAQC3w89PP1nkX+41hz9/ZLh+fvpJ/XfgAPO2nJnBIbGxir2WZacgZ0PDzI9ruLJMAQAAAIC7D4FiALfNkCFDzKmhp0+frj///LPI8rt27dKOHTskSSNHjjRvz72eb2ZmZp46ffv2NR/j448/zrdfkmbOnHlL1tUtreHDh6t9e1OAITg4WLNmzcqz38bGRpIUEhJS4Izq+Ph4ffjhh+bnBZ1rzrUqaF9BmjVrpqZNTeu0zZ49WyEhIfnKpKWlaer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eXuVquyLeffdd9ezZU5I0YMAAy/Yvv/xSJpNJXl5e+uqrr1S9+q8ffL29vdWhQwelpaVp27ZtOnLkiLKysizvmezsbM2aNUuS1Lp1ay1ZssRyrWrUqKEXXnhBrq6uevfdd/XLL7/o6NGj6ty5s0JDQ7VgwQJJ0tixY/X6669bjunh4aGnn35anTp10iOPPKKzZ89q6dKlevrppyv9mgBAWUwmk77Ys1e5eUZJ0qCAgHLXbebjo9eGDtE3+/YrNC5e/9m0uch+NydHjezQQfe2aV1ke2qhpSRcb/CAjPP1h7rSS5jVY2/wBR28dFnuzk4a1+eucvcbAADgVphMJoWs2a/8XPP9U91uLSpUPzrwouJOhcrg5iS/ET3KVSc349d7IXvnsu+f7K5PS10w1bRknk5bkgwuTrqy64TCtgZKpl/r5GVkKybwkuJOh6nFI30tI5EBAID1IigG8Lthb2+vzz//XF999ZUWLlxomdrYZDIpODhYwcHB+u6772QwGNS/f39NmTJFDRs2LHf727ZtU1ZWlmxtbTV16tRia/HY2trqtdde07Zt25RfyjqMleHq1auW0bw30qBBA73//vtq2rRpke1Go1HPPPOMIiMjSx2126JFC7m7uyslJUUJCQm33O8zZ87o9OnTkqRJkyYVCYkLODs7a+bMmRo+fLgyMzO1Zs0aPfPMM7d87MI8PDzUo0fJH7I7duwoNzc3tWzZskhIXFjXrl0t/42Tk5MtQfGBAwcUHx8vSZo6dWqJ6xA/+uij+uGHH1SrVi1L2L98+XLl5+fL2dlZL730UonHbNu2rYYMGaJ169ZpxYoVBMUAbrvFBw4q8Ip5NExPv6YKqFe3QvUzcnIsYW6xfdk5ComNVWxqmmq5//ogV47x1yn8HW4wa4WDvfljSa7RWGR7XGqavtm/X5I0rs9dci9ljXgAAIDKdmnjESWcM68XXLNdY3k0rVPuullJabq4/qAkqdnInnJwK989TH7ur/dPtoay75/sDPbX6/x6/2TMNi/BlHT5mmJPXpaLj4ca39tZ1RvVlsmYr/hz4br80xHlpmXp3LLdaj9xqFxrV/7D3QAA4I+DoBjA74qdnZ3GjRunsWPHas+ePdq1a5cOHjyoyMhIS5nc3Fxt3rxZO3fu1KxZszRs2LBytX3w4PUPac2aqXbt2iWWqV27ttq0aVPiur+3i5eXl/r166e+fftqwIABRabOLuDv76+pU6eW2kZ6erqOHz8uW1tbSeZg+VYdPnzY8nrw4MGllmvRooUaN26sy5cv6/Dhw5UeFLdo0aJYyF/gySefLLNuaGioLl68aPm58DrEBw4ckCS5ublZRl//lqOjo9atW1dkW8F1adKkiSSVOFpcktq1a6d169bp0qVLSkxMLNd06gBwq0wmkxYfOKifTpkf9GlQw6vCo3LXnzip/x08JEnq3qSxhndor3qensrMydHx8HAtO3xEB0Iu6lxUlF6/b6jqenhIkmxL+V1dkb5/tmuXMrJz1Kd5sxLXLgYAAKhsJpNJlzYe0dV9ZyRJLrU91WxkzwrVD/7+Zxkzc1WrY9MKjdq1sb21+6eCoDk3NVPOtaqr3YShsnf89TsFnw5NVc3XW4GfrFN+Tp5CtwYqYOyA0poDAABWgKAYwO+Sk5OTBg0apEGDBkmSoqKidOjQIe3du1c7duxQRkaGcnJy9M9//lMNGjRQu3btbthmVFSUJN1wFHLTpk2rNCiuV6+eduzYYfk5NzdXYWFhWrhwoX788UclJibKYDDo7rvvLjEk/q2goCAFBgYqNDRU4eHhCg0N1ZUrV4qMijaZTGW0UD7Xrl2TJLm7u6tmoTUoS9K0aVNdvnzZcs0rU3mmsk5NTdX+/fsVHBysK1euKDw8XBcvXrSMUi9Q+LpER0dLMr8/SguiSxIRYX7CPCgoqNwjxa9du0ZQDKDK5RmNWrB7j36+ECJJqufpoWlD7pVTOf62FLialKTvDpkfiBnYqqWevau3ZZ/B2Vl9mjdXQN26en31GiWmZ+irn/fp9fuGSlKR4+Qay56pI+f6gzuGQiOPN5w8pTNXo+RdzU1P9ir/l7MAAAA3K99o1IVV+xQTeEmS5Fyruto8fY/sHMp//xT5c5CSL12To6ermt7XrULHty10nPy8sh/4Nl4PhQuPPLY1/PpVb6N7OhYJiQu41Kyu2p2b6er+s0oMjpQxJ7dC5wcAAP5cCIoB/CHUqVNHI0aM0IgRI5SUlKT33ntPq1atUl5enj7//HN99tlnN2wjNTVVkoqsY1wSNze3SulzeRkMBvn5+WnOnDny8fHRwoULtXz5csXExGj+/Pmyty/5V/W5c+f0r3/9S0FBQcX21axZU7169dLOnTuVnJxcKf1MS0uTZF4z+UYKpm3OyMiolGMX5ljGOpf5+fmaN2+evvzyS2X/Zp1Lg8GgDh06yN3dXbt37y5Wt+A63ej98VsF16Wq6wBARaRmZWnulq06G2V+yKdxTW/9896/VHjq5l3nzyvfZJLB3k6PdutaYpkabm4a0aGDvtm3X0GRVxWVnKw61avLxcHBUiYjp/jaw4Vl5JjX1qt2/Xfwlfh4rThyVDY20oS+fYu0BQAAUBVyM7J19n87lHzZ/BCxW70aCnhyYLmnjZak9GsJCt16TLKRmj/QW/ZOFbuHKVzemJVTRslf99u7/PoZ2a5QMOzRpPSpsqs38tHV/WdlMuYrKyGV6acBALBiBMUAfhc2btyo06dPy8XFRZMmTSqzrIeHh959911duHBBp06d0smTJ8t1jII1azMzM8ssl5NT9oexqvTyyy/r9OnT2r9/v3bu3Kn//Oc/mjZtWrFyERERevzxx5WamiqDwaCBAweqffv28vPzU7NmzeTj4yNJ6tOnT6UFxQUBcXnC34Lpl0ta57cqvfvuu/r2228lmaeC7t+/v/z9/dW0aVM1a9ZMDg4OWrlyZYlBcUFfs7KyKnRMJycnpaWlaciQIZo7d+6tnwQA3KJrycmas2mzopLMv//b1ffV3+8ZWKGRxAUK2qjv6VlmWNuq7q9fRF5NSlKd6tXl7eYmg72dcvOMik0t/QEZk8mkhOt/N7yvP6x1+HKoZb3it9dvKLOPe4IvaE/wBUnSG8OGqlXdiq2/DAAAkBmfoqBvtykz1jwLlWfzemo5pl+FR9rGBYXJlGeeSeXUl5vLLBtz7KJijpmXRmrz18HyaFJHjh6usjXYKT/XqKyksu+fspPNn80dPX592N3J002pV2IlSTb2tqXWtyscSOfc+lJVAADgj4ugGMDvwqZNm7R582a5uLjoueeek0M5Rg516dJFp06dKnewV7duXZ04cUKXLl0qs1zBVMJ3go2NjWbPnq2hQ4cqNTVVixYtUp8+fdSrV68i5RYsWKDU1FTZ2dlp6dKlatu2bbG2TCZTpYXEkvn6SVJKSopiY2PLnH66YB3gurfxy/qoqCgtWbJEknTPPffoo48+kl2hKUwLJCYmlli/Th1zyBEeHl7mcZYvX66UlBS1bt1aPXr0UN26dRUcHFxkHe2SmEymCk1pDQA3IyIhUW+tX6+UTPPfxv4tW+iZ3r1kZ1v6F4Vlybu+jEFeftlTRxdWEPDa2NjI19NTl2PjFBYfX2r58IQE5V2fmrqRt/dN9RMAAOBmpUcn6tSXm5WbZr5/qt2luZre3122djd3/3QrbGxs5FLLQ2mR8Uq/mlBquYzoRJmu3z+51f11NLBbnRqKPXFZkpSdmCaXWh4l1s9J/fUBesfqN541DAAA/HkRFAP4XejUqZM2b96sjIwMrVq1So888sgN61y5ckWS1KxZM8u2soK4Pn366KefftLFixd16dIlNWnSpFiZtLQ0HTt27CbOoPL4+Pho6tSpev3112UymTR9+nStX7++yOjcwMBASVLLli1LDIkl6dixY5YQPb8CX/CXpnPnzpbXmzdv1uOPP15iubNnzyosLEySyr1mb2U4ceKE5TxHjRpVYkgsSQcOHLC8LrxGcceOHbVkyRKlpKTo+PHjat++fbG6JpNJH330keLj4/Xwww+rR48e6ty5s4KDgxUUFKRr166pdu3aJR53+vTp2rJli3x9fbVo0aLbPsU5gD+/6JQUvbNhgyUkHt2lkx64xd/DdapX13GFKzIxUYnp6fJ0dS2x3LnrU1xLkq/Hr2uwd6hfX5dj43Q6MlJZubkljmr+Jcz899xgZ2cZmTyiQ3vd167kv28FXlmxUvFp6erVzE9/vb52skMpv/sBAABKkpmQqlNfbbGExA3v6aAGd7e76fbq92sr37tal1nml/9bo+ykdNVs30TNRvSQJNna/3oP4+nvq7TIeCVdjCp1/eD4c+YHnG3sbeXR+NeZXbxa+OrypqOSpLjToWrQv32JfUgKMT/o7FDdRQ7uBMUAAFiz2/9oHACUYPjw4ZapoWfPnq2ff/65zPK7d+/Wjh07JKlIqFx4Pd/c3NwidQYOHGg5xttvv11svyTNnTu3StbVrahRo0apS5cukswjnOfNm1dkf0EIGhkZWeKI6uTkZM2cOdPyc0nnWnCtStpXktatWysgIECSNH/+/BJH0GZlZWnGjBmSzGsJ33///eVquzIU/m8fEhJSYpkffvhB+/fvt/xceJrxAQMGyMPDQ5L0/vvvl3hdFi9erPjro+KGDh0qSXrooYckSXl5eZoxY4aMxuLTdp04cUKrV69WUlKSPDw8CIkBVLo8o1Efb9uupAzz6JCxPbrfckgsST39mkqSjPkmfbv/QJEHbAokpqdr9fUHmBrU8JKv169Bca9mfrK1sVF6do5++KX4g1hxqWnaeOqUJKmff3O5Xl+H3t7OTk4GQ5n/Ch4Os7OxsWyzvcmR0wAAwPrkG406t2yXcq+Prm0ytMsthcSSZGtnJzsHQ5n/dP35dhtbG8s2m0L3MLXaNZFsbZSXmaOw7ceLHSMrKU2RP5+RJNXu1Ez2zr/OyOZSy0PuDWtJksL3nFZGbPFZxlKuxCr2ZKgkyaejHzN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}, "metadata": {}, "output_type": "display_data" @@ -887,8 +860,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2023-12-21T12:23:39.657152Z", - "start_time": "2023-12-21T12:23:39.619030Z" + "end_time": "2023-12-21T16:27:19.500905Z", + "start_time": "2023-12-21T16:27:19.490641Z" } }, "outputs": [], diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 44efa1b1..f6442b99 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -2,4 +2,5 @@ dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example +computation_mode: error_analysis sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__161031.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__161031.csv new file mode 100644 index 00000000..00df3f25 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__161031.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params 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+Std,0.06824957398882589,0.07201899756082523,0.06382518763622798,0.0896295741151835,0.0673083333099006,0.06524419157131524,0.07211111979613662,0.07035439088382889,0.06450066127987136,0.08314253863401301,0.06689226215933795,0.0652593269063873,0.07074804121211677,0.07041532636246477,0.06487318126922148,0.0832075732129068,0.06610016691612014,0.06505893128385831,0.06846544292027044,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Overall_Uncertainty,0.8935853314967799,0.8999675962760768,0.8810366375271829,0.9406550300050431,0.8919916535459731,0.8809632023194847,0.917652341242251,0.8892187038213423,0.8734206513167277,0.9237313723698848,0.8964011942033705,0.885736282485098,0.921583786794684,0.8941306193016607,0.8794490120269823,0.9280183543318931,0.8930441590715585,0.882502013480509,0.9169917490561647,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" 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+Label_Stability,0.8188257575757576,0.8015165876777252,0.8538888888888889,0.6889552238805972,0.8231479289940827,0.8433164128595602,0.7762204724409447,0.8192270531400966,0.8592957746478874,0.7316923076923078,0.8185669781931464,0.836629711751663,0.775916230366492,0.8172623574144486,0.8539509536784741,0.7325786163522012,0.8203773584905659,0.8368478260869566,0.782962962962963,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TPR,0.6539278131634819,0.4533333333333333,1.0,0.0,0.6919191919191919,1.0,0.0,0.5102040816326531,1.0,0.0,0.7191358024691358,1.0,0.0,0.5372340425531915,1.0,0.0,0.7314487632508834,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +TNR,0.7299145299145299,0.8088235294117647,1.0,0.0,0.7060133630289532,1.0,0.0,0.7827715355805244,1.0,0.0,0.6855345911949685,1.0,0.0,0.7869822485207101,1.0,0.0,0.6518218623481782,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +PPV,0.6609442060085837,0.5666666666666667,1.0,0.0,0.6748768472906403,1.0,0.0,0.5639097744360902,1.0,0.0,0.6996996996996997,1.0,0.0,0.5838150289017341,1.0,0.0,0.7064846416382252,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FNR,0.346072186836518,0.5466666666666666,0.0,1.0,0.30808080808080807,0.0,1.0,0.4897959183673469,0.0,1.0,0.2808641975308642,0.0,1.0,0.4627659574468085,0.0,1.0,0.26855123674911663,0.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +FPR,0.27008547008547007,0.19117647058823528,0.0,1.0,0.29398663697104677,0.0,1.0,0.21722846441947566,0.0,1.0,0.31446540880503143,0.0,1.0,0.21301775147928995,0.0,1.0,0.3481781376518219,0.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Accuracy,0.6960227272727273,0.6824644549763034,1.0,0.0,0.6994082840236686,1.0,0.0,0.6859903381642513,1.0,0.0,0.7024922118380063,1.0,0.0,0.6977186311787072,1.0,0.0,0.6943396226415094,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +F1,0.6574172892209178,0.5037037037037037,1.0,0.0,0.683291770573566,1.0,0.0,0.5357142857142857,1.0,0.0,0.7092846270928462,1.0,0.0,0.5595567867036011,1.0,0.0,0.71875,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Selection-Rate,0.4412878787878788,0.2843601895734597,0.2361111111111111,0.3880597014925373,0.4804733727810651,0.46362098138747887,0.5196850393700787,0.321256038647343,0.2640845070422535,0.4461538461538462,0.5186915887850467,0.516629711751663,0.5235602094240838,0.3288973384030418,0.27520435967302453,0.4528301886792453,0.5528301886792453,0.5625,0.5308641975308642,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Positive-Rate,0.9893842887473461,0.8,1.0,0.6341463414634146,1.0252525252525253,1.0,1.0819672131147542,0.9047619047619048,1.0,0.8055555555555556,1.0277777777777777,1.0,1.098901098901099,0.9202127659574468,1.0,0.8275862068965517,1.0353356890459364,1.0,1.131578947368421,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" +Sample_Size,1056.0,211.0,144.0,67.0,845.0,591.0,254.0,414.0,284.0,130.0,642.0,451.0,191.0,526.0,367.0,159.0,530.0,368.0,162.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__161031.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__161031.csv new file mode 100644 index 00000000..350d70ce --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__161031.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.43458841412389493,0.4345690505308617,0.35839085914833946,0.6056769880977578,0.4345932492932796,0.34933589240446233,0.6181510661770389,0.43432213528014024,0.3492048065838192,0.6202707610474878,0.43476012664930686,0.3524321418752339,0.6127994631804301,0.43321261694918295,0.3539502320044722,0.6145253225102089,0.4359538279237034,0.3483083765809398,0.6168175627755333,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.028446188975494127,0.028503341916602486,0.027785172031601378,0.030116461965989584,0.028431917649371214,0.028154882316455413,0.029028370586283225,0.02874090574096246,0.02746576876778079,0.03152658959006704,0.02825613797720147,0.02847773094113629,0.027776929547805477,0.02886574400001693,0.02795751767346172,0.030943311722011962,0.02802980040398659,0.028206024495018443,0.0276661472219151,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.907651785494834,0.9165441672154672,0.904382636532744,0.9438608361335845,0.9054313209468416,0.8958440681661287,0.9260725330903921,0.913299958308673,0.9053874983366781,0.9305856400936471,0.9040095058298352,0.8925098923417318,0.9288781281021374,0.9155187557282067,0.9069127367415412,0.935205024160204,0.8998441886217136,0.8879883210594753,0.924309765036274,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.022060657884588324,0.022373260532284785,0.021760325618544164,0.023750006646225237,0.021982599708654654,0.0217336409153433,0.02251860427485111,0.022236916032643616,0.02114743740936959,0.02461700779425764,0.02194699608817884,0.022121745157629998,0.021569090465080036,0.022416228189165267,0.021653852887767347,0.024160161691113,0.02170777112947989,0.021826353477696594,0.02146306651495179,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.9102366040828535,0.9190351751016758,0.90669793217075,0.9467465207619096,0.9080395644556682,0.8984663928212351,0.928650460101444,0.9156846380223627,0.9074197336693979,0.9337402752242242,0.9067233859349458,0.8954118505527862,0.9311852777219802,0.9179957630180802,0.9091561314761026,0.938216420170354,0.9025360048377038,0.890873564802601,0.9266024273378872,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.518659147842532,0.5663410604596762,0.5795809152462557,0.5366023097082823,0.5067527767629847,0.5060643060308699,0.5082350439735454,0.5838815040641153,0.6001647028349902,0.5483089775185118,0.4765998714005763,0.46963813804243293,0.491655048465724,0.5722020210738759,0.5861033905801047,0.5404026383283773,0.46552037176765104,0.45407304552786254,0.48914288892143415,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.04153679653679644,0.05971177096430981,0.05611406206318067,0.06779277864992254,0.036998430141287265,0.030097973331446884,0.051855010660980916,0.03910480134082611,0.033569991376832434,0.051196232339089986,0.0431050925042916,0.036504114174142954,0.057380114607419416,0.04501745945526517,0.03974127355860372,0.05708673469387757,0.03808240277242977,0.030851197621906138,0.053004600684204366,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9434848484848485,0.9181042654028436,0.9221917808219177,0.908923076923077,0.9498224852071007,0.958266897746967,0.9316417910447762,0.9470531400966185,0.9545070422535212,0.9307692307692308,0.941183800623053,0.9487015945330296,0.9249261083743842,0.9387832699619773,0.9453551912568307,0.9237500000000001,0.9481509433962264,0.956750700280112,0.9304046242774565,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.6284501061571125,0.48,1.0,0.0,0.6565656565656566,1.0,0.0,0.4489795918367347,1.0,0.0,0.7098765432098766,1.0,0.0,0.48936170212765956,1.0,0.0,0.7208480565371025,1.0,0.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.7299145299145299,0.8088235294117647,1.0,0.0,0.7060133630289532,1.0,0.0,0.8164794007490637,1.0,0.0,0.6572327044025157,1.0,0.0,0.8106508875739645,1.0,0.0,0.6194331983805668,1.0,0.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.6519823788546255,0.5806451612903226,1.0,0.0,0.6632653061224489,1.0,0.0,0.5739130434782609,1.0,0.0,0.6784660766961652,1.0,0.0,0.5897435897435898,1.0,0.0,0.6845637583892618,1.0,0.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.37154989384288745,0.52,0.0,1.0,0.3434343434343434,0.0,1.0,0.5510204081632653,0.0,1.0,0.29012345679012347,0.0,1.0,0.5106382978723404,0.0,1.0,0.2791519434628975,0.0,1.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.27008547008547007,0.19117647058823528,0.0,1.0,0.29398663697104677,0.0,1.0,0.18352059925093633,0.0,1.0,0.34276729559748426,0.0,1.0,0.1893491124260355,0.0,1.0,0.3805668016194332,0.0,1.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.6846590909090909,0.6919431279620853,1.0,0.0,0.6828402366863905,1.0,0.0,0.6859903381642513,1.0,0.0,0.6838006230529595,1.0,0.0,0.6958174904942965,1.0,0.0,0.6735849056603773,1.0,0.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.64,0.5255474452554745,1.0,0.0,0.6598984771573604,1.0,0.0,0.5038167938931297,1.0,0.0,0.693815987933635,1.0,0.0,0.5348837209302325,1.0,0.0,0.7022375215146299,1.0,0.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.42992424242424243,0.2938388625592417,0.2465753424657534,0.4,0.463905325443787,0.4506065857885615,0.4925373134328358,0.2777777777777778,0.2323943661971831,0.3769230769230769,0.5280373831775701,0.5239179954441914,0.5369458128078818,0.2965779467680608,0.25136612021857924,0.4,0.5622641509433962,0.5714285714285714,0.5433526011560693,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9639065817409767,0.8266666666666667,1.0,0.6666666666666666,0.98989898989899,1.0,0.9705882352941176,0.782312925170068,1.0,0.6049382716049383,1.0462962962962963,1.0,1.1595744680851063,0.8297872340425532,1.0,0.6666666666666666,1.0530035335689045,1.0,1.1898734177215189,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,211.0,146.0,65.0,845.0,577.0,268.0,414.0,284.0,130.0,642.0,439.0,203.0,526.0,366.0,160.0,530.0,357.0,173.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__161031.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__161031.csv new file mode 100644 index 00000000..7d91dfc7 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__161031.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.4057529795538864,0.4001343172873597,0.3169331725707535,0.6144830290996335,0.4071559827944037,0.3004531970056693,0.6554290001217344,0.3957012901929103,0.2936802516746284,0.6456528345627011,0.41223491007638496,0.31046701039813884,0.6489902829029784,0.3986028317620757,0.30260901909319593,0.6414865054276307,0.4128491639661362,0.30507672530866176,0.6533657038968413,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.09550958855507456,0.10525364068192744,0.0972231068101904,0.12594247370741946,0.09307645837902019,0.08881125000457703,0.10300062432113002,0.09975033019444675,0.09052450314489616,0.12235360646584571,0.09277490469417099,0.09053709813642428,0.09798099352540564,0.09845549125747542,0.09091318926272436,0.11753903388848982,0.09258591908061635,0.09013958643663081,0.09804541754219381,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.8341547489465653,0.8473944293526388,0.8294688111585099,0.8935756830053099,0.8308487459102559,0.814779553611577,0.8682380870461588,0.8234975846111919,0.8060655316299036,0.8662061144153482,0.8410271259478811,0.8254581496243737,0.8772471796745898,0.8335962763406038,0.8181656290428339,0.8726389208456995,0.8347090066649346,0.8173921675720958,0.8733551231770024,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.07146374471551348,0.07717838563711223,0.07145848963222383,0.0919143889039433,0.07003677520727995,0.06695926138030117,0.07719744714328175,0.07326600026127167,0.06717631519060815,0.08818572868439725,0.07030154254114604,0.06834026109981563,0.07486431646424117,0.07280812205187451,0.06737321173991735,0.08655953941837012,0.07012951362320047,0.06840140184127642,0.07398615333164069,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.8605559997706556,0.879359174256425,0.8575772776072988,0.9354752469795972,0.8558607692185878,0.8382976996759444,0.8967260215796203,0.8522349778271725,0.8310009672568217,0.9042583037245322,0.865921892425799,0.8496022323636682,0.9038883554718957,0.8617643502266639,0.8429623188961622,0.9093372751367249,0.8593567689407304,0.8414997062320748,0.8992085064490716,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.5235114544231775,0.5787398201976752,0.6026386729026747,0.5171698945847953,0.5097207027327408,0.5134076310039558,0.5011420625426304,0.5971463876467995,0.6225649757841047,0.5348708467104017,0.47602724514813155,0.47214005640093004,0.4850704977258178,0.5857281613231033,0.6089157210203067,0.5270589666529982,0.4617643075753266,0.4520867797264721,0.4833617172867945,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.12160636982065523,0.15213076699874298,0.12115467239527392,0.23193358699411962,0.11398430141287298,0.09840533167581765,0.15023300658846223,0.12169575076407403,0.09570179092044981,0.18538095238095223,0.12154873164218973,0.10787691468569596,0.15335518663423814,0.1245146271436333,0.1000811995885888,0.18633611833995284,0.11872006160954966,0.10612690978030544,0.14682429069188635,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.8271969696969697,0.7793364928909953,0.8276315789473685,0.6549152542372881,0.8391479289940827,0.8621319796954314,0.7856692913385827,0.8231884057971015,0.8648979591836735,0.721,0.8297819314641744,0.848641425389755,0.7859067357512953,0.82212927756654,0.8595225464190981,0.72751677852349,0.8322264150943396,0.8504918032786885,0.7914634146341464,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.6602972399150743,0.56,1.0,0.0,0.6792929292929293,1.0,0.0,0.5374149659863946,1.0,0.0,0.7160493827160493,1.0,0.0,0.5638297872340425,1.0,0.0,0.7243816254416962,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.7384615384615385,0.8088235294117647,1.0,0.0,0.7171492204899778,1.0,0.0,0.8052434456928839,1.0,0.0,0.6823899371069182,1.0,0.0,0.8017751479289941,1.0,0.0,0.6518218623481782,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.6702586206896551,0.6176470588235294,1.0,0.0,0.6792929292929293,1.0,0.0,0.6030534351145038,1.0,0.0,0.6966966966966966,1.0,0.0,0.6127167630057804,1.0,0.0,0.7044673539518901,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.33970276008492567,0.44,0.0,1.0,0.3207070707070707,0.0,1.0,0.46258503401360546,0.0,1.0,0.2839506172839506,0.0,1.0,0.43617021276595747,0.0,1.0,0.2756183745583039,0.0,1.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.26153846153846155,0.19117647058823528,0.0,1.0,0.2828507795100223,0.0,1.0,0.1947565543071161,0.0,1.0,0.31761006289308175,0.0,1.0,0.19822485207100593,0.0,1.0,0.3481781376518219,0.0,1.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.7035984848484849,0.7203791469194313,1.0,0.0,0.6994082840236686,1.0,0.0,0.7101449275362319,1.0,0.0,0.6993769470404985,1.0,0.0,0.7167300380228137,1.0,0.0,0.690566037735849,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.66524064171123,0.5874125874125874,1.0,0.0,0.6792929292929293,1.0,0.0,0.5683453237410072,1.0,0.0,0.7062404870624048,1.0,0.0,0.5872576177285319,1.0,0.0,0.7142857142857143,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.4393939393939394,0.3222748815165877,0.27631578947368424,0.4406779661016949,0.4686390532544379,0.45516074450084604,0.5,0.3164251207729469,0.2687074829931973,0.43333333333333335,0.5186915887850467,0.5167037861915368,0.5233160621761658,0.3288973384030418,0.28116710875331563,0.44966442953020136,0.5490566037735849,0.5601092896174863,0.524390243902439,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9851380042462845,0.9066666666666666,1.0,0.7878787878787878,1.0,1.0,1.0,0.891156462585034,1.0,0.7647058823529411,1.0277777777777777,1.0,1.0978260869565217,0.9202127659574468,1.0,0.8170731707317073,1.028268551236749,1.0,1.1025641025641026,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,211.0,152.0,59.0,845.0,591.0,254.0,414.0,294.0,120.0,642.0,449.0,193.0,526.0,377.0,149.0,530.0,366.0,164.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__161031.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__161031.csv new file mode 100644 index 00000000..b4bae67b --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__161029/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__161031.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.41177298562516546,0.40794664222341015,0.3187830113127929,0.6127443569712341,0.4127284394213434,0.3087687818741156,0.6587524895530298,0.4067863095641712,0.3045701889244661,0.6514347294559244,0.41498869261776916,0.31477787066873303,0.6481216410898791,0.40696909328207315,0.3097912938564375,0.6417102711154269,0.41654062217698906,0.31172736526181705,0.656764794858346,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +IQR,0.0632287031745143,0.0628391144823689,0.058613368443080356,0.07254512491635978,0.06332598508462398,0.061602180202802025,0.06740542771331817,0.061397889638004674,0.05755460736890362,0.07059656523290228,0.06440932124945976,0.06325593507767785,0.06709258752474513,0.06168696828214841,0.05809203046624378,0.0703708440452427,0.06475880233184346,0.0639502028947277,0.06661205197343174,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Aleatoric_Uncertainty,0.8666431307792664,0.8814342617988586,0.8611461520195007,0.9280335307121277,0.8629497289657593,0.8511209487915039,0.8909429907798767,0.8610659837722778,0.8464793562889099,0.8959784507751465,0.8702396154403687,0.8574217557907104,0.9000594615936279,0.868338942527771,0.8534196615219116,0.9043776988983154,0.8649601340293884,0.852797269821167,0.8928363919258118,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Std,0.046930842101573944,0.04674633592367172,0.043640051037073135,0.053881071507930756,0.046976909041404724,0.0457439050078392,0.04989485815167427,0.04533577337861061,0.04239189997315407,0.05238176882266998,0.047959428280591965,0.04723503813147545,0.049644678831100464,0.04556935280561447,0.04284577816724777,0.05214837193489075,0.04828204587101936,0.04782746359705925,0.049323905259370804,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Overall_Uncertainty,0.8755009783093672,0.8901655175033827,0.8691204870746968,0.9385033217692705,0.8718391821319268,0.8597118969474544,0.900538813205937,0.869447253930658,0.8539626988179938,0.9065086481347393,0.8794047818806845,0.8665311143249758,0.9093544022564005,0.8767628677474288,0.8610811693498033,0.9146433340066286,0.8742486125651778,0.8620796297795164,0.902139014104986,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Mean_Prediction,0.5249260663986206,0.5810354948043823,0.6066330671310425,0.5222412347793579,0.5109152793884277,0.5136960744857788,0.5043344497680664,0.592885434627533,0.6172518134117126,0.5345658659934998,0.4811018109321594,0.4767773151397705,0.4911624789237976,0.5833811163902283,0.6067781448364258,0.5268635153770447,0.4669122099876404,0.45688092708587646,0.48990318179130554,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Jitter,0.07367733457019156,0.10463294322468333,0.07520755240871863,0.17221938775510204,0.06594759087066782,0.059200164914451535,0.08191560289454451,0.07658286503007013,0.0626083310036339,0.11003011040481758,0.07180367474092432,0.06222444434343924,0.09408903457756068,0.07957942112206116,0.06258942286592066,0.12062019613040002,0.0678197920677706,0.06216027874564418,0.08079097477500309,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Label_Stability,0.9041287878787879,0.8580094786729857,0.9001360544217687,0.76125,0.9156449704142012,0.9238383838383838,0.8962549800796813,0.8990338164251209,0.9173972602739726,0.8550819672131148,0.9074143302180685,0.9202672605790647,0.8775129533678757,0.8943726235741445,0.9174193548387097,0.8387012987012987,0.9138113207547169,0.9208672086720866,0.8976397515527951,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TPR,0.6624203821656051,0.5333333333333333,1.0,0.0,0.6868686868686869,1.0,0.0,0.5578231292517006,1.0,0.0,0.7098765432098766,1.0,0.0,0.5691489361702128,1.0,0.0,0.7243816254416962,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +TNR,0.7333333333333333,0.7867647058823529,1.0,0.0,0.7171492204899778,1.0,0.0,0.7865168539325843,1.0,0.0,0.6886792452830188,1.0,0.0,0.7840236686390533,1.0,0.0,0.6639676113360324,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +PPV,0.6666666666666666,0.5797101449275363,1.0,0.0,0.681704260651629,1.0,0.0,0.5899280575539568,1.0,0.0,0.6990881458966566,1.0,0.0,0.5944444444444444,1.0,0.0,0.7118055555555556,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FNR,0.3375796178343949,0.4666666666666667,0.0,1.0,0.31313131313131315,0.0,1.0,0.4421768707482993,0.0,1.0,0.29012345679012347,0.0,1.0,0.4308510638297872,0.0,1.0,0.2756183745583039,0.0,1.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +FPR,0.26666666666666666,0.21323529411764705,0.0,1.0,0.2828507795100223,0.0,1.0,0.21348314606741572,0.0,1.0,0.3113207547169811,0.0,1.0,0.21597633136094674,0.0,1.0,0.3360323886639676,0.0,1.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Accuracy,0.7017045454545454,0.6966824644549763,1.0,0.0,0.7029585798816568,1.0,0.0,0.7053140096618358,1.0,0.0,0.6993769470404985,1.0,0.0,0.7072243346007605,1.0,0.0,0.6962264150943396,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +F1,0.6645367412140575,0.5555555555555556,1.0,0.0,0.6842767295597484,1.0,0.0,0.5734265734265734,1.0,0.0,0.7044410413476263,1.0,0.0,0.5815217391304348,1.0,0.0,0.7180385288966725,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Selection-Rate,0.4431818181818182,0.32701421800947866,0.272108843537415,0.453125,0.47218934911242605,0.45791245791245794,0.5059760956175299,0.3357487922705314,0.2808219178082192,0.4672131147540984,0.5124610591900312,0.512249443207127,0.5129533678756477,0.34220532319391633,0.28763440860215056,0.474025974025974,0.5433962264150943,0.5555555555555556,0.515527950310559,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Positive-Rate,0.9936305732484076,0.92,1.0,0.8285714285714286,1.0075757575757576,1.0,1.0241935483870968,0.9455782312925171,1.0,0.8769230769230769,1.0154320987654322,1.0,1.053191489361702,0.9574468085106383,1.0,0.9012345679012346,1.017667844522968,1.0,1.064102564102564,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" +Sample_Size,1056.0,211.0,147.0,64.0,845.0,594.0,251.0,414.0,292.0,122.0,642.0,449.0,193.0,526.0,372.0,154.0,530.0,369.0,161.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__163532.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__163532.csv new file mode 100644 index 00000000..81bd53d3 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20231221__163532.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.41685442432123365,0.41178555216277213,0.3220569231111469,0.6178809970157236,0.41812014269452996,0.31806123398593944,0.6509343751621559,0.41208369107072995,0.3090793228911572,0.6422340762219628,0.4199308784734277,0.32504396158442006,0.6456618597041194,0.410591607463566,0.3150110887966134,0.6372889914813383,0.4230699746365793,0.32272406013120075,0.6510162495623774,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +IQR,0.08396684432202904,0.08147534663006231,0.07367035586778797,0.09940243478716118,0.08458898161552605,0.08078043876712794,0.0934505911564839,0.08695634044467553,0.07780476732691835,0.10740438662966426,0.08203903841116354,0.08035092515141325,0.08605497100804316,0.08505779090535287,0.07747194875220406,0.10304985242243656,0.08288413129782465,0.08126674072179874,0.08655820371743904,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.8629658790313663,0.8686388569173913,0.8542046273815229,0.9017924778825888,0.8615493129556845,0.8540145682457733,0.8790809433633912,0.8549955809903783,0.8432975472587794,0.8811333751094194,0.8681056039363028,0.8608575034122234,0.8853484536041124,0.8604508086903533,0.8496299344222673,0.8861157027877373,0.8654619677094284,0.85849895196222,0.8812791886660498,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Std,0.07192234383480468,0.07488169252907513,0.07033597230648443,0.08532264366533812,0.07118338220818804,0.06834802493587579,0.07778061113707209,0.07291865103615726,0.06718112567037626,0.08573843427532417,0.07127986535916611,0.06973289540802695,0.07496002545345504,0.07300522460616495,0.0682025611136163,0.08439615724874826,0.07084763574851129,0.06928837786445044,0.07438965365798285,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Overall_Uncertainty,0.8886398485723692,0.8971107775428077,0.8799809748808415,0.9364557930320114,0.8865246225217626,0.8764431469353451,0.9099819141421275,0.8820549255515885,0.8663795499310125,0.9170795929538129,0.8928862008007229,0.8839613979336353,0.9141178370950583,0.8877952647941868,0.8735615307816268,0.9215547621316693,0.8894780581333577,0.8807536324920402,0.9092965065037576,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Mean_Prediction,0.522485491855023,0.5802545033971829,0.6019393673033503,0.530447081612705,0.5080603303930161,0.5113069656658656,0.5005061514707559,0.5862214685669483,0.6093238372070368,0.5346021136367509,0.48138472182583747,0.4787630227011209,0.4876216060593737,0.5778482947639998,0.5980533370541774,0.529926079075758,0.46754052142083086,0.4602928505219389,0.4840043664257214,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Jitter,0.11088976499690788,0.12542799110165392,0.09531028738025832,0.1946045918367347,0.10725950972104825,0.09156393521875768,0.14377952755905526,0.11237306516809605,0.08298844013129718,0.17802933673469387,0.1099332443257677,0.09820841610980692,0.13782599355531713,0.1128113602855593,0.08706232763375645,0.17388278388278391,0.10898267231420887,0.09758651286601601,0.1348702443940541,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Label_Stability,0.8586742424242424,0.8346919431279621,0.8778231292517008,0.735625,0.8646627218934912,0.8871065989847715,0.8124409448818898,0.8533333333333333,0.8966433566433566,0.7565625,0.8621183800623053,0.8780530973451327,0.8242105263157896,0.8535361216730037,0.8911351351351352,0.7643589743589743,0.8637735849056603,0.8793478260869566,0.8283950617283949,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +TPR,0.6560509554140127,0.49333333333333335,1.0,0.0,0.6868686868686869,1.0,0.0,0.5170068027210885,1.0,0.0,0.7191358024691358,1.0,0.0,0.5478723404255319,1.0,0.0,0.7279151943462897,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +TNR,0.7333333333333333,0.8088235294117647,1.0,0.0,0.7104677060133631,1.0,0.0,0.7865168539325843,1.0,0.0,0.6886792452830188,1.0,0.0,0.7899408284023669,1.0,0.0,0.6558704453441295,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +PPV,0.6645161290322581,0.5873015873015873,1.0,0.0,0.6766169154228856,1.0,0.0,0.5714285714285714,1.0,0.0,0.7018072289156626,1.0,0.0,0.5919540229885057,1.0,0.0,0.7079037800687286,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +FNR,0.34394904458598724,0.5066666666666667,0.0,1.0,0.31313131313131315,0.0,1.0,0.48299319727891155,0.0,1.0,0.2808641975308642,0.0,1.0,0.4521276595744681,0.0,1.0,0.27208480565371024,0.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +FPR,0.26666666666666666,0.19117647058823528,0.0,1.0,0.289532293986637,0.0,1.0,0.21348314606741572,0.0,1.0,0.3113207547169811,0.0,1.0,0.21005917159763313,0.0,1.0,0.3441295546558704,0.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Accuracy,0.6988636363636364,0.6966824644549763,1.0,0.0,0.6994082840236686,1.0,0.0,0.6908212560386473,1.0,0.0,0.7040498442367601,1.0,0.0,0.7034220532319392,1.0,0.0,0.6943396226415094,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +F1,0.6602564102564102,0.5362318840579711,1.0,0.0,0.681704260651629,1.0,0.0,0.5428571428571428,1.0,0.0,0.7103658536585366,1.0,0.0,0.569060773480663,1.0,0.0,0.7177700348432056,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Selection-Rate,0.4403409090909091,0.2985781990521327,0.25170068027210885,0.40625,0.4757396449704142,0.4602368866328257,0.5118110236220472,0.321256038647343,0.26573426573426573,0.4453125,0.5171339563862928,0.5154867256637168,0.5210526315789473,0.33079847908745247,0.27837837837837837,0.4551282051282051,0.5490566037735849,0.5597826086956522,0.5246913580246914,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Positive-Rate,0.9872611464968153,0.84,1.0,0.6842105263157895,1.0151515151515151,1.0,1.0483870967741935,0.9047619047619048,1.0,0.8028169014084507,1.0246913580246915,1.0,1.0879120879120878,0.925531914893617,1.0,0.8352941176470589,1.028268551236749,1.0,1.103896103896104,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Sample_Size,1056.0,211.0,147.0,64.0,845.0,591.0,254.0,414.0,286.0,128.0,642.0,452.0,190.0,526.0,370.0,156.0,530.0,368.0,162.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__163532.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__163532.csv new file mode 100644 index 00000000..c4bd7a47 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231221__163532.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.4355243749845668,0.4349119132225668,0.3603465544435503,0.6023971806338961,0.43567730922336206,0.35269062530031936,0.6143464011024503,0.4350670430808048,0.3511645927342516,0.6163219548981679,0.4358192899505441,0.3562125181892032,0.6092201789356432,0.4338594435746669,0.3558991537028685,0.6106017156442718,0.437176740874392,0.35254161074160784,0.6133359070810009,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.026953868622667815,0.026007547865099807,0.024503788190296274,0.02938522344235082,0.027190168835504323,0.027254948111558177,0.02705070002101524,0.02725258505719998,0.025650314970478358,0.030713977687293264,0.026761238398530243,0.02737413408978852,0.025426218080937976,0.026924920481797926,0.025635444391783963,0.029848266897046977,0.026982598287002835,0.027784136684779828,0.02531427999395537,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.9109396587602655,0.9193408645684022,0.9072616307713284,0.9464726820202909,0.9088418428720798,0.8995421442166115,0.9288639552758307,0.9161942174281421,0.9081285644311542,0.9336184906964435,0.9075512050398592,0.896580980844505,0.9314467428911258,0.9183617038358751,0.9096752949150466,0.9380544942464487,0.9035736291191889,0.8923590297810244,0.9269156440207168,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.021861451152448613,0.021587981175959524,0.020896610494291132,0.023140906091706978,0.021929737738293816,0.02185839400035,0.02208333974125494,0.02201321902674074,0.020932890186312475,0.024347059193467444,0.02176358214940042,0.02213452397191369,0.020955590060757635,0.021972773725921588,0.021175246798854682,0.023780831666166437,0.02175096874934147,0.022162663097168987,0.020894070048630704,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.9133659606805408,0.9214066897192073,0.9091149129225626,0.949015911447056,0.9113581573347909,0.9021259970334263,0.931234860670191,0.9183627751022434,0.9099455239614196,0.9365466077194426,0.9101437158665454,0.8994156688497712,0.9335117390714004,0.9205372482628517,0.9115836078691709,0.9408358740000782,0.906248796023379,0.8953336891137811,0.9289674487770775,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.5210562326759071,0.5681094947672082,0.5807469839223097,0.5397237498957493,0.5093068382365408,0.5083471276644546,0.511373080774204,0.5847252827320665,0.6008180352221054,0.5499600235360284,0.47999862095744916,0.4728951098799811,0.4954716153836171,0.5735043129653769,0.5870758180123848,0.5427366151879982,0.4690039869546598,0.45760524787856716,0.4927292694502478,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.043890692640692626,0.05716607021955708,0.05078557450377346,0.07149764521193179,0.04057577587247918,0.03152406889965691,0.06006396588486135,0.040467317361727276,0.03235883752794424,0.057984109674403775,0.04609828978320311,0.037378478664192874,0.06509193776520483,0.0456522076511214,0.03715739446463513,0.06491063506147807,0.04214247208317299,0.03363584539961231,0.059848125296629945,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9448863636363636,0.9306161137440759,0.938904109589041,0.912,0.9484497041420118,0.9589601386481801,0.9258208955223881,0.9490821256038646,0.9595759717314486,0.926412213740458,0.9421806853582555,0.9519090909090908,0.9209900990099011,0.9431178707224335,0.9541917808219179,0.9180124223602485,0.9466415094339623,0.9556424581005587,0.927906976744186,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.6263269639065817,0.48,1.0,0.0,0.6540404040404041,1.0,0.0,0.4421768707482993,1.0,0.0,0.7098765432098766,1.0,0.0,0.48404255319148937,1.0,0.0,0.7208480565371025,1.0,0.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.7316239316239316,0.8088235294117647,1.0,0.0,0.7082405345211581,1.0,0.0,0.8164794007490637,1.0,0.0,0.660377358490566,1.0,0.0,0.8106508875739645,1.0,0.0,0.6234817813765182,1.0,0.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.6526548672566371,0.5806451612903226,1.0,0.0,0.6641025641025641,1.0,0.0,0.5701754385964912,1.0,0.0,0.6804733727810651,1.0,0.0,0.5870967741935483,1.0,0.0,0.6868686868686869,1.0,0.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.37367303609341823,0.52,0.0,1.0,0.34595959595959597,0.0,1.0,0.5578231292517006,0.0,1.0,0.29012345679012347,0.0,1.0,0.5159574468085106,0.0,1.0,0.2791519434628975,0.0,1.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.26837606837606837,0.19117647058823528,0.0,1.0,0.29175946547884185,0.0,1.0,0.18352059925093633,0.0,1.0,0.33962264150943394,0.0,1.0,0.1893491124260355,0.0,1.0,0.3765182186234818,0.0,1.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.6846590909090909,0.6919431279620853,1.0,0.0,0.6828402366863905,1.0,0.0,0.6835748792270532,1.0,0.0,0.6853582554517134,1.0,0.0,0.6939163498098859,1.0,0.0,0.6754716981132075,1.0,0.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.6392199349945829,0.5255474452554745,1.0,0.0,0.6590330788804071,1.0,0.0,0.49808429118773945,1.0,0.0,0.6948640483383686,1.0,0.0,0.5306122448979592,1.0,0.0,0.7034482758620689,1.0,0.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.42803030303030304,0.2938388625592417,0.2465753424657534,0.4,0.46153846153846156,0.4488734835355286,0.48880597014925375,0.2753623188405797,0.22968197879858657,0.37404580152671757,0.5264797507788161,0.5227272727272727,0.5346534653465347,0.2946768060836502,0.2493150684931507,0.39751552795031053,0.560377358490566,0.5698324022346368,0.5406976744186046,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9596602972399151,0.8266666666666667,1.0,0.6666666666666666,0.9848484848484849,1.0,0.9562043795620438,0.7755102040816326,1.0,0.5975609756097561,1.0432098765432098,1.0,1.148936170212766,0.824468085106383,1.0,0.6597938144329897,1.0494699646643109,1.0,1.1772151898734178,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,211.0,146.0,65.0,845.0,577.0,268.0,414.0,283.0,131.0,642.0,440.0,202.0,526.0,365.0,161.0,530.0,358.0,172.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__163532.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__163532.csv new file mode 100644 index 00000000..f54038c9 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20231221__163532.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.42600923482040987,0.4252238761319109,0.3400644379772749,0.6043091651923955,0.4262053421378931,0.33854230545793346,0.6336628871095905,0.42387448883547585,0.3325472990448744,0.6302595555276223,0.42738584671723656,0.34284948721529734,0.6255179392999066,0.42273236090099636,0.33729576204612893,0.6235355773178504,0.42926137761590344,0.3403837170590793,0.6311563102388124,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +IQR,0.0501986777951334,0.048435667814831454,0.04607361813654257,0.05340291934417427,0.050638908689622994,0.050213466072436076,0.05164573305061517,0.05090252290556761,0.048830472594250975,0.05558503030200757,0.04974479636877863,0.049779956902227966,0.049662388868506734,0.05007338518130903,0.04829648527893938,0.054249665843566344,0.05032302480432515,0.050526965142999945,0.04985975292387864,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.9062386611453274,0.9134150743825525,0.8972762145697877,0.9473541472241026,0.9044466810352036,0.8960926201557275,0.9242168490129281,0.9023888598344135,0.8917837272644427,0.9263547893429303,0.9087212432990941,0.8992168562913038,0.9309971503486031,0.9068212503500915,0.8961466545925445,0.9319099499331157,0.9056604688402219,0.8964983682373174,0.9264731418147207,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Std,0.03785318650084872,0.03657190803478883,0.03423018137202376,0.04149642145795656,0.03817312704089445,0.03779273175415542,0.03907334536887445,0.03780743878755588,0.0359566866489919,0.041989847163680795,0.037882687362691764,0.0378316211775712,0.03800237373406808,0.03732905843981729,0.03569297234348377,0.04117438181272853,0.038373358878400675,0.03851383642234294,0.03805424939586514,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.9114620672912461,0.9183168006888655,0.9017339768060497,0.9531895038541978,0.9097504119694737,0.9013560976786655,0.9296158410082783,0.9076628826836916,0.8966243316023866,0.9326082697729396,0.9139120087671456,0.9044939945211281,0.9359854796562499,0.9119451932746626,0.9009272189442857,0.9378409418600712,0.9109825875416665,0.9019329807444324,0.9315397190316799,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.5221609058473612,0.5748101511733728,0.5937159365084486,0.5350523967187284,0.5090141712156588,0.5102175362821871,0.5061663670343131,0.5813311780813659,0.60051313655764,0.5379829727058488,0.48400437515440486,0.4791629450672992,0.4953514769210589,0.5732940174881811,0.5902493139682722,0.5334436709840185,0.4714137044830756,0.4624146701577035,0.4918559552962667,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Jitter,0.0723113790970933,0.08794274107747366,0.0627772227772224,0.14086434573829532,0.0684081632653061,0.06031333745619558,0.08756484267013599,0.06778467908902688,0.051269288203086114,0.10510686164229545,0.07523046601818308,0.06686439909297061,0.09483843537414949,0.07280204857608463,0.056315469277141234,0.11155076043156126,0.07182441278398165,0.06527950310559008,0.08669186192995727,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9074242424242425,0.8860663507109003,0.9197202797202796,0.815294117647059,0.9127573964497041,0.9243771043771043,0.8852589641434262,0.9139130434782609,0.9360278745644599,0.8639370078740156,0.9032398753894081,0.9154666666666668,0.8745833333333334,0.9060076045627377,0.9282384823848238,0.8537579617834392,0.9088301886792453,0.9186956521739131,0.8864197530864198,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +TPR,0.6496815286624203,0.44,1.0,0.0,0.6893939393939394,1.0,0.0,0.5102040816326531,1.0,0.0,0.7129629629629629,1.0,0.0,0.5319148936170213,1.0,0.0,0.7279151943462897,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +TNR,0.7367521367521368,0.8088235294117647,1.0,0.0,0.7149220489977728,1.0,0.0,0.7940074906367042,1.0,0.0,0.6886792452830188,1.0,0.0,0.7958579881656804,1.0,0.0,0.6558704453441295,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +PPV,0.6652173913043479,0.559322033898305,1.0,0.0,0.6807980049875312,1.0,0.0,0.5769230769230769,1.0,0.0,0.7,1.0,0.0,0.591715976331361,1.0,0.0,0.7079037800687286,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +FNR,0.3503184713375796,0.56,0.0,1.0,0.3106060606060606,0.0,1.0,0.4897959183673469,0.0,1.0,0.28703703703703703,0.0,1.0,0.46808510638297873,0.0,1.0,0.27208480565371024,0.0,1.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +FPR,0.26324786324786326,0.19117647058823528,0.0,1.0,0.28507795100222716,0.0,1.0,0.20599250936329588,0.0,1.0,0.3113207547169811,0.0,1.0,0.20414201183431951,0.0,1.0,0.3441295546558704,0.0,1.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Accuracy,0.6979166666666666,0.6777251184834123,1.0,0.0,0.7029585798816568,1.0,0.0,0.6932367149758454,1.0,0.0,0.7009345794392523,1.0,0.0,0.7015209125475285,1.0,0.0,0.6943396226415094,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +F1,0.6573576799140709,0.4925373134328358,1.0,0.0,0.685069008782936,1.0,0.0,0.5415162454873647,1.0,0.0,0.7064220183486238,1.0,0.0,0.5602240896358543,1.0,0.0,0.7177700348432056,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.4356060606060606,0.2796208530805687,0.23076923076923078,0.38235294117647056,0.47455621301775147,0.4595959595959596,0.5099601593625498,0.3140096618357488,0.2613240418118467,0.4330708661417323,0.514018691588785,0.5133333333333333,0.515625,0.32129277566539927,0.27100271002710025,0.4394904458598726,0.5490566037735849,0.5597826086956522,0.5246913580246914,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Positive-Rate,0.9766454352441614,0.7866666666666666,1.0,0.6190476190476191,1.0126262626262625,1.0,1.0406504065040652,0.8843537414965986,1.0,0.7638888888888888,1.0185185185185186,1.0,1.064516129032258,0.898936170212766,1.0,0.7840909090909091,1.028268551236749,1.0,1.103896103896104,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" +Sample_Size,1056.0,211.0,143.0,68.0,845.0,594.0,251.0,414.0,287.0,127.0,642.0,450.0,192.0,526.0,369.0,157.0,530.0,368.0,162.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__163532.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__163532.csv new file mode 100644 index 00000000..63a59a64 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20231221__163532/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231221__163532.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params +Statistical_Bias,0.40720891379786545,0.40487960908756154,0.3190305304899812,0.6159839007209559,0.40779055083203597,0.3022665962303222,0.6647370906955585,0.3994737880277461,0.3015477910291317,0.6603209127762676,0.4121969855561667,0.30836246789216865,0.6519797892338529,0.4021352715329922,0.3075129626920139,0.6507635451082525,0.4122442644230037,0.3036680215483774,0.658886593915982,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +IQR,0.09345036378186761,0.10203963080288675,0.09448990613222122,0.12060452753403147,0.09130558822987347,0.08903205720630035,0.09684154385231375,0.09420147365418033,0.08917634941612763,0.1075869815803207,0.09296600321000238,0.0907625156521265,0.09805446932303537,0.09532086273563226,0.0897586586203162,0.10993603354897992,0.09159398180133892,0.09050445448931145,0.0940689574237223,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Aleatoric_Uncertainty,0.8281413912773132,0.8500380516052246,0.8310413360595703,0.8967512845993042,0.8226737380027771,0.8084244728088379,0.857370138168335,0.8271499276161194,0.8159921765327454,0.8568710684776306,0.8287807703018188,0.810912549495697,0.8700435757637024,0.834785521030426,0.8225513100624084,0.8669319152832031,0.8215473890304565,0.8030173182487488,0.863640308380127,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Std,0.06786706298589706,0.07241317629814148,0.0679454356431961,0.08339941501617432,0.06673187762498856,0.06500115245580673,0.07094614952802658,0.06702424585819244,0.06328020244836807,0.07699735462665558,0.06841056793928146,0.06714322417974472,0.07133719325065613,0.06775374710559845,0.06403175741434097,0.07753360271453857,0.06797952950000763,0.06720490753650665,0.06973915547132492,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Overall_Uncertainty,0.8561069679863097,0.880608091725886,0.8595942893341809,0.9322813762956531,0.8499889359045929,0.835289079239358,0.8857824893292909,0.8546313981627752,0.8415825903948471,0.8893897268189381,0.8570585036669067,0.8391985315974412,0.8983021505077341,0.8628398768821438,0.8489010326865079,0.8994653916306768,0.8494248734972367,0.8311032837253889,0.8910442872999527,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Mean_Prediction,0.5221894979476929,0.580057680606842,0.604785144329071,0.5192525386810303,0.5077396035194397,0.5096397399902344,0.5031129121780396,0.595963180065155,0.6166356801986694,0.5408974289894104,0.47461584210395813,0.46960851550102234,0.48617932200431824,0.5840541124343872,0.6052732467651367,0.5282988548278809,0.4607919156551361,0.4494099020957947,0.48664724826812744,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Jitter,0.10840058750773027,0.14140245671728466,0.11820952380952396,0.19843425894948116,0.10015988407197202,0.0930952948792203,0.11736187157789972,0.10765256827368626,0.09544782697132026,0.14016254289326383,0.10888295505117931,0.09992346938775512,0.12957290132547894,0.11274773027081562,0.09835770528683961,0.15055876143560906,0.10408625336927235,0.09788376220053234,0.1181758629377677,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Label_Stability,0.8510227272727273,0.793175355450237,0.8274666666666667,0.7088524590163936,0.8654674556213017,0.872921535893155,0.8473170731707318,0.8473429951690821,0.8623255813953489,0.807433628318584,0.8533956386292835,0.8648214285714284,0.8270103092783505,0.8402281368821294,0.8591076115485565,0.7906206896551724,0.8617358490566038,0.8686956521739131,0.8459259259259259,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 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'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +PPV,0.6806167400881057,0.6060606060606061,1.0,0.0,0.6932989690721649,1.0,0.0,0.6349206349206349,1.0,0.0,0.698170731707317,1.0,0.0,0.6272189349112426,1.0,0.0,0.712280701754386,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +FNR,0.34394904458598724,0.4666666666666667,0.0,1.0,0.3207070707070707,0.0,1.0,0.4557823129251701,0.0,1.0,0.2932098765432099,0.0,1.0,0.43617021276595747,0.0,1.0,0.2826855123674912,0.0,1.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +FPR,0.24786324786324787,0.19117647058823528,0.0,1.0,0.2650334075723831,0.0,1.0,0.17228464419475656,0.0,1.0,0.3113207547169811,0.0,1.0,0.1863905325443787,0.0,1.0,0.3319838056680162,0.0,1.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Accuracy,0.709280303030303,0.7109004739336493,1.0,0.0,0.7088757396449704,1.0,0.0,0.7270531400966184,1.0,0.0,0.6978193146417445,1.0,0.0,0.7243346007604563,1.0,0.0,0.6943396226415094,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +F1,0.6681081081081081,0.5673758865248227,1.0,0.0,0.6862244897959183,1.0,0.0,0.5860805860805861,1.0,0.0,0.7024539877300614,1.0,0.0,0.5938375350140056,1.0,0.0,0.7147887323943662,1.0,0.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Selection-Rate,0.42992424242424243,0.3127962085308057,0.26666666666666666,0.4262295081967213,0.4591715976331361,0.44908180300500833,0.483739837398374,0.30434782608695654,0.26578073089701,0.40707964601769914,0.5109034267912772,0.5111607142857143,0.5103092783505154,0.32129277566539927,0.2782152230971129,0.43448275862068964,0.5377358490566038,0.5516304347826086,0.5061728395061729,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Positive-Rate,0.9639065817409767,0.88,1.0,0.7428571428571429,0.9797979797979798,1.0,0.937007874015748,0.8571428571428571,1.0,0.6865671641791045,1.0123456790123457,1.0,1.0421052631578946,0.898936170212766,1.0,0.7682926829268293,1.0070671378091873,1.0,1.025,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" +Sample_Size,1056.0,211.0,150.0,61.0,845.0,599.0,246.0,414.0,301.0,113.0,642.0,448.0,194.0,526.0,381.0,145.0,530.0,368.0,162.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0}" diff --git a/docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__162628.csv b/docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__162628.csv new file mode 100644 index 00000000..6c6baeea --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_LogisticRegression_50_Estimators_20231221__162628.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Overall_Uncertainty,0.3379146525015478,0.31698301368143045,0.36557431808527424,0.28843668679351653,0.6130870611553619,0.3112290928339746,0.6476288753100494,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10383184082162612,0.092500662696882,0.11880518334360946,0.0751844571285563,0.263154671896964,0.08765811711052117,0.2915450584383896,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.33695731699097037,0.31609578616676326,0.3645243398658155,0.28771943715908277,0.6107944846364527,0.3103893117085918,0.6453071964803948,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.012106196433930455,0.011305571346017763,0.013164165300100799,0.00942781771750627,0.027002037686472538,0.01075449443020784,0.02779413181046871,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.009249518380562094,0.00858848921876339,0.010123021201510382,0.007255235166834066,0.020340752783724592,0.00822957606677199,0.021087030689095745,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.13782584169655065,0.12731023392037352,0.15172146625792757,0.1120846242964449,0.2809859876788422,0.1251755206969637,0.28464623390387794,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.008651687598116185,0.0073093629343629445,0.010425473760932932,0.004766226399805538,0.030260735208910056,0.0063782170831779015,0.035037724180581306,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9877019230769231,0.9894087837837838,0.9854464285714286,0.9931140102098696,0.957602523659306,0.9909660574412532,0.949818181818182,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9844420600858369,0.9888372093023255,0.9784537389100126,0.9941771376034324,0.9161290322580645,0.9908151549942594,0.8934426229508197,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.2523148148148148,0.19724770642201836,0.308411214953271,0.12927756653992395,0.4437869822485207,0.17630057803468208,0.5581395348837209,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9191084397695968,0.9239461103867883,0.9125295508274232,0.9340627699395335,0.8192307692307692,0.9237356168049238,0.8515625,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01555793991416309,0.011162790697674419,0.021546261089987327,0.0058228623965675755,0.08387096774193549,0.009184845005740528,0.10655737704918032,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7476851851851852,0.8027522935779816,0.6915887850467289,0.870722433460076,0.5562130177514792,0.8236994219653179,0.4418604651162791,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9084134615384616,0.9159628378378378,0.8984375,0.9296653431650596,0.7902208201892744,0.9172323759791122,0.806060606060606,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9506540603548763,0.9552909458548641,0.94434250764526,0.9631828978622328,0.86497461928934,0.9561002631214514,0.872,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9598557692307692,0.971706081081081,0.9441964285714286,0.9849688031764039,0.8201892744479495,0.9757180156657963,0.7757575757575758,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0710836909871244,1.0702325581395349,1.0722433460076046,1.064357952804168,1.118279569892473,1.072617680826636,1.0491803278688525,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__162628.csv b/docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__162628.csv new file mode 100644 index 00000000..0a1860a8 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20231221__162624/Metrics_Law_School_RandomForestClassifier_50_Estimators_20231221__162628.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params +Overall_Uncertainty,0.3412010627157903,0.32636142854525696,0.3608105792982808,0.2889078274947532,0.63203063273058,0.31407012745684443,0.6560843416302223,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10459042506727127,0.09564780796836321,0.11640745480511407,0.07614890013353995,0.26276836973026285,0.08918998818029855,0.28332882893728795,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.32993762075283867,0.31555513129652635,0.3489430532486799,0.27951875496078704,0.6103428585805581,0.30379813340987455,0.6333140950666344,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +IQR,0.04062829313471337,0.038637065069480434,0.04325955879234259,0.03280937527171772,0.08411331582386579,0.036647105128822376,0.08683420241520576,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Std,0.03239077536618118,0.03089469107856324,0.034367743889104865,0.02667469431063594,0.06418084129970246,0.029411458561025405,0.06696890677147388,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.1403046664894489,0.131352067515171,0.15213488656260177,0.11346032853651143,0.28959983308575404,0.12728304765242746,0.29143436390093996,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Jitter,0.028329670329670154,0.025481591285162752,0.03209320335276976,0.016053109842916184,0.09660593574969421,0.02213523738477112,0.10022263450834872,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9603461538461538,0.9642736486486485,0.9551562499999999,0.9772093023255815,0.8665615141955836,0.968845953002611,0.8616969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TPR,0.9876609442060086,0.9906976744186047,0.9835234474017744,0.9963224026969046,0.9268817204301075,0.9928243398392652,0.9139344262295082,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +TNR,0.22685185185185186,0.16972477064220184,0.2850467289719626,0.10646387832699619,0.41420118343195267,0.1531791907514451,0.5232558139534884,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +PPV,0.9168326693227091,0.9216789268714842,0.9102639296187683,0.9325874928284567,0.8132075471698114,0.9219083155650319,0.8446969696969697,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FNR,0.012339055793991416,0.009302325581395349,0.016476552598225603,0.003677597303095311,0.07311827956989247,0.007175660160734788,0.0860655737704918,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +FPR,0.7731481481481481,0.8302752293577982,0.7149532710280374,0.8935361216730038,0.5857988165680473,0.846820809248555,0.47674418604651164,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9086538461538461,0.9151182432432432,0.9001116071428571,0.9299489506522972,0.7902208201892744,0.9169712793733682,0.8121212121212121,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +F1,0.9509297520661157,0.9549428379287155,0.9454766981419434,0.9634019854793303,0.8663316582914573,0.9560530679933665,0.8779527559055118,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9653846153846154,0.9759290540540541,0.9514508928571429,0.9886557005104935,0.8359621451104101,0.9796344647519583,0.8,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Positive-Rate,1.0772532188841202,1.0748837209302327,1.0804816223067173,1.068342016549188,1.1397849462365592,1.0769230769230769,1.0819672131147542,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__161031.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__161031.csv new file mode 100644 index 00000000..9deda582 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20231221__161031.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6429,0.6443,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6461,0.6506,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6481,0.6518,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6549,0.6588,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20231221__162628.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20231221__162628.csv new file mode 100644 index 00000000..1ff1eed2 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20231221__162628.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6606,0.8994,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6531,0.8953,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 56962143..ad73e07a 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -279,6 +279,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ ax.xaxis.tick_top() ax.tick_params(axis='x', rotation=10) ax.tick_params(labelsize=16 + font_increase) + ax.set_yticklabels(ax.get_yticklabels(), rotation = 0) fig.tight_layout() cbar = ax.collections[0].colorbar From 68e2304ed7e894d06cdbde62467fb36804792aa6 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 22:43:29 +0200 Subject: [PATCH 089/148] Improved models tuning --- ...Models_Interface_With_Error_Analysis.ipynb | 3 +- virny/utils/model_tuning_utils.py | 47 ++++++------------- 2 files changed, 16 insertions(+), 34 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index 8fe51f1a..7b4d3cc5 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -134,8 +134,7 @@ "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", - "from virny.custom_classes.metrics_composer import MetricsComposer\n", - "from virny.utils.model_tuning_utils import tune_ML_models" + "from virny.custom_classes.metrics_composer import MetricsComposer" ] }, { diff --git a/virny/utils/model_tuning_utils.py b/virny/utils/model_tuning_utils.py index c0d8e4e7..309e3f78 100644 --- a/virny/utils/model_tuning_utils.py +++ b/virny/utils/model_tuning_utils.py @@ -14,19 +14,7 @@ from virny.custom_classes.base_dataset import BaseFlowDataset -def folds_iterator(n_folds, samples_per_fold, size): - """ - Iterator for GridSearch based on Cross-Validation - - :param n_folds: number of folds for Cross-Validation - :param samples_per_fold: number of samples per fold - """ - for i in range(n_folds): - yield np.arange(0, size - samples_per_fold * (i + 1)), \ - np.arange(size - samples_per_fold * (i + 1), size - samples_per_fold * i) - - -def validate_model(model, x, y, params, n_folds, samples_per_fold): +def validate_model(model, x, y, params, n_folds): """ Use GridSearchCV for a special model to find the best hyperparameters based on validation set """ @@ -38,15 +26,15 @@ def validate_model(model, x, y, params, n_folds, samples_per_fold): }, refit="F1_Score", n_jobs=-1, - cv=folds_iterator(n_folds, samples_per_fold, x.shape[0]), - verbose=10) + cv=n_folds, + verbose=0) grid_search.fit(x, y.values.ravel()) best_index = grid_search.best_index_ return grid_search.best_estimator_, \ - grid_search.cv_results_["mean_test_F1_Score"][best_index], \ - grid_search.cv_results_["mean_test_Accuracy_Score"][best_index], \ - grid_search.best_params_ + grid_search.cv_results_["mean_test_F1_Score"][best_index], \ + grid_search.cv_results_["mean_test_Accuracy_Score"][best_index], \ + grid_search.best_params_ def test_evaluation(cur_best_model, model_name, cur_best_params, @@ -57,7 +45,7 @@ def test_evaluation(cur_best_model, model_name, cur_best_params, :return: F1 score, accuracy and predicted values, which we use to visualisations for model comparison later. """ - cur_best_model.fit(cur_x_train, cur_y_train.values.ravel()) # refit model on the whole train set + cur_best_model.fit(cur_x_train, cur_y_train.values.ravel()) # refit model on the whole train set cur_model_pred = cur_best_model.predict(cur_x_test) test_f1_score = f1_score(cur_y_test, cur_model_pred, average='macro') test_accuracy = accuracy_score(cur_y_test, cur_model_pred) @@ -84,7 +72,7 @@ def test_evaluation(cur_best_model, model_name, cur_best_params, def tune_ML_models(models_params_for_tuning: dict, base_flow_dataset: BaseFlowDataset, - dataset_name: str, n_folds: int = 3, samples_per_fold: int = None): + dataset_name: str, n_folds: int = 3): """ Tune each model on a validation set with GridSearchCV. @@ -92,22 +80,19 @@ def tune_ML_models(models_params_for_tuning: dict, base_flow_dataset: BaseFlowDa results_df is a dataframe with metrics and tuned parameters; models_config is a dict with model tuned params for the metrics computation stage """ - if samples_per_fold is None: - samples_per_fold = len(base_flow_dataset.y_test) - models_config = dict() tuned_params_df = pd.DataFrame(columns=('Dataset_Name', 'Model_Name', 'F1_Score', 'Accuracy_Score', 'Model_Best_Params')) # Find the most optimal hyperparameters based on accuracy and F1-score for each model in models_config for model_idx, (model_name, model_params) in enumerate(models_params_for_tuning.items()): try: - print(f"{datetime.now().strftime('%Y/%m/%d, %H:%M:%S')}: Tuning {model_name}...") + print(f"{datetime.now().strftime('%Y/%m/%d, %H:%M:%S')}: Tuning {model_name}...", flush=True) cur_model, cur_f1_score, cur_accuracy, cur_params = validate_model(deepcopy(model_params['model']), base_flow_dataset.X_train_val, base_flow_dataset.y_train_val, model_params['params'], - n_folds, samples_per_fold) + n_folds) print(f'{datetime.now().strftime("%Y/%m/%d, %H:%M:%S")}: Tuning for {model_name} is finished ' - f'[F1 score = {cur_f1_score}, Accuracy = {cur_accuracy}]\n') + f'[F1 score = {cur_f1_score}, Accuracy = {cur_accuracy}]\n', flush=True) except Exception as err: print(f"ERROR with {model_name}: ", err) @@ -127,10 +112,7 @@ def test_ML_models(best_results_df, models_config, n_folds, X_train, y_train, X_ Tune each model on a validation set with GridSearchCV and return best_model with its hyperparameters, which has the highest F1 score """ - results_df = pd.DataFrame(columns=('Dataset_Name', 'Model_Name', 'F1_Score', - 'Accuracy_Score', - 'Model_Best_Params')) - samples_per_fold = len(y_test) + results_df = pd.DataFrame(columns=('Dataset_Name', 'Model_Name', 'F1_Score', 'Accuracy_Score', 'Model_Best_Params')) best_f1_score = -np.Inf best_accuracy = -np.Inf best_model_pred = [] @@ -143,11 +125,12 @@ def test_ML_models(best_results_df, models_config, n_folds, X_train, y_train, X_ print(f"{datetime.now().strftime('%Y/%m/%d, %H:%M:%S')}: Tuning {model_config['model_name']}...") cur_model, cur_f1_score, cur_accuracy, cur_params = validate_model(deepcopy(model_config['model']), X_train, y_train, model_config['params'], - n_folds, samples_per_fold) + n_folds) print(f'{datetime.now().strftime("%Y/%m/%d, %H:%M:%S")}: Tuning for {model_config["model_name"]} is finished') test_f1_score, test_accuracy, cur_model_pred = test_evaluation(cur_model, model_config['model_name'], cur_params, - X_train, y_train, X_test, y_test, dataset_title, show_plots, debug_mode) + X_train, y_train, X_test, y_test, + dataset_title, show_plots, debug_mode) except Exception as err: print(f"ERROR with {model_config['model_name']}: ", err) continue From a473faa62fc29855419c8e1d3b9d110edb1ee947 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 22:53:55 +0200 Subject: [PATCH 090/148] Improved documentation --- docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb | 2 +- .../Multiple_Models_Interface_With_Error_Analysis.ipynb | 2 +- .../Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb | 2 +- .../examples/Multiple_Models_Interface_With_Postprocessor.ipynb | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb index caea38fe..a6495b02 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb @@ -251,7 +251,7 @@ "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including attribute intersections), and values are privileged values for these attributes. Currently, the library supports only intersections among two sensitive attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify privileged values for intersectional groups since they will be derived from privileged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n" + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n" ] }, { diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index 7b4d3cc5..9d898a85 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -198,7 +198,7 @@ "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including attribute intersections), and values are privileged values for these attributes. Currently, the library supports only intersections among two sensitive attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify privileged values for intersectional groups since they will be derived from privileged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." ], "metadata": { "collapsed": false diff --git a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb index ccf2f430..17cf86fb 100644 --- a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb @@ -230,7 +230,7 @@ "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup variance metrics.\n", "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including attribute intersections), and values are privileged values for these attributes. Currently, the library supports only intersections among two sensitive attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify privileged values for intersectional groups since they will be derived from privileged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n" + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n" ] }, { diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index ec255437..94227b4f 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -242,7 +242,7 @@ "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including attribute intersections), and values are privileged values for these attributes. Currently, the library supports only intersections among two sensitive attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify privileged values for intersectional groups since they will be derived from privileged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." ], "metadata": { "collapsed": false From 73bc45c6efd95cd1627bd2f20c97245a379dbd7a Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Thu, 21 Dec 2023 23:15:12 +0200 Subject: [PATCH 091/148] Added started_app argument in the interactive visualizer --- virny/custom_classes/metrics_interactive_visualizer.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 9dbc6a00..8eba1c5f 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -103,7 +103,7 @@ def __variable_inputs(self, k): k = int(k) return [gr.Textbox(visible=True)] * k + [gr.Textbox(value='', visible=False)] * (self.max_groups - k) - def create_web_app(self): + def create_web_app(self, start_app=True): with gr.Blocks(theme=gr.themes.Soft()) as demo: # ==================================== Dataset Statistics ==================================== gr.Markdown( @@ -439,7 +439,10 @@ def create_web_app(self): outputs=[model_performance_summary]) self.demo = demo - self.demo.launch(inline=False, debug=True, show_error=True) + if start_app: + self.demo.launch(inline=False, debug=True, show_error=True) + else: + return self.demo def __filter_subgroup_metrics_df(self, results: dict, subgroup_metric: str, selected_metric: str, selected_subgroup: str, defined_model_names: list): From c37559f60aa4373984a4518ce13c3eba9060bd0e Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 00:48:32 +0200 Subject: [PATCH 092/148] Added figsize_scale --- virny/custom_classes/metrics_visualizer.py | 19 +++++++++++++------ virny/utils/data_viz_utils.py | 6 ++++-- 2 files changed, 17 insertions(+), 8 deletions(-) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 55bbb2c8..04ec31a0 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -204,7 +204,8 @@ def create_boxes_and_whiskers_for_models_multiple_runs(self, metrics_lst: list): fig = ax.get_figure() fig.tight_layout() - def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, tolerance: float = 0.001): + def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, + tolerance: float = 0.001, figsize_scale: float = 1.0): """ Create a heatmap for overall metrics. @@ -215,7 +216,9 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, to metrics_lst List of group metric names to visualize tolerance - An acceptable value difference for metrics dense ranking + [Optional] An acceptable value difference for metrics dense ranking + figsize_scale + [Optional] A scale factor for a heatmap size. """ if tolerance < 0.001 or tolerance > 0.2: @@ -234,10 +237,11 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, to model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) - model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) + model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, + figsize_scale=figsize_scale) def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, - tolerance: float = 0.001): + tolerance: float = 0.001, figsize_scale: float = 1.0): """ Create a heatmap for disparity metrics. @@ -250,7 +254,9 @@ def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst List of sensitive attributes tolerance - An acceptable value difference for metrics dense ranking + [Optional] An acceptable value difference for metrics dense ranking + figsize_scale + [Optional] A scale factor for a heatmap size. """ if tolerance < 0.001 or tolerance > 0.2: @@ -281,4 +287,5 @@ def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, model_metrics_matrix = model_metrics_matrix[sorted(model_metrics_matrix.columns)] model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) - model_rank_heatmap = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank) + model_rank_heatmap = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, + figsize_scale=figsize_scale) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index ad73e07a..bbcfd156 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -240,7 +240,7 @@ def create_row_facet_bar_chart(df, x_col, y_col, facet_column_name, y_sort_by_ls return final_chart -def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank): +def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, figsize_scale: float = 1.0): """ This heatmap includes group fairness and stability metrics and defined models. Using it, you can visually compare the models across defined group metrics. On this plot, @@ -257,6 +257,8 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ Matrix of model metrics values where indexes are group metric names and columns are model names sorted_matrix_by_rank Matrix of model ranks per metric where indexes are group metric names and columns are model names + figsize_scale + [Optional] A scale factor for a heatmap size. """ font_increase = 4 @@ -265,7 +267,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ else model_metrics_matrix.shape[0] * 2.5 num_ranks = int(sorted_matrix_by_rank.values.max()) - fig = plt.figure(figsize=(matrix_width, matrix_height)) + fig = plt.figure(figsize=(matrix_width * figsize_scale, matrix_height * figsize_scale)) # Set a green color when there is only one rank if num_ranks == 1: rank_colors = sns.diverging_palette(145, 13, s=75, l=70, n=num_ranks).as_hex() From 2fa1aea868fe5eef36c785e3b35073ca7507d85a Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 00:50:21 +0200 Subject: [PATCH 093/148] Added figsize_scale --- lib_base_packages.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/lib_base_packages.txt b/lib_base_packages.txt index fec2cfe5..a62ec5c4 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -4,7 +4,6 @@ pandas~=1.5.2 altair~=4.2.0 scikit-learn~=1.2.0 tqdm~=4.64.1 -sklearn-utils gradio==4.10.0 seaborn~=0.12.1 aif360~=0.5.0 From 486724960e1536931b8a9e4859a64be69c2476fe Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 00:55:24 +0200 Subject: [PATCH 094/148] Added figsize_scale --- virny/custom_classes/metrics_visualizer.py | 10 ++++++++-- virny/utils/data_viz_utils.py | 6 ++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 04ec31a0..b3115244 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -219,6 +219,8 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, [Optional] An acceptable value difference for metrics dense ranking figsize_scale [Optional] A scale factor for a heatmap size. + font_increase + [Optional] An integer to increase or decrease the plot font. """ if tolerance < 0.001 or tolerance > 0.2: @@ -238,7 +240,8 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise sorted_matrix_by_rank = create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) model_rank_heatmap, _ = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, - figsize_scale=figsize_scale) + figsize_scale=figsize_scale, + font_increase=font_increase) def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, tolerance: float = 0.001, figsize_scale: float = 1.0): @@ -257,6 +260,8 @@ def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, [Optional] An acceptable value difference for metrics dense ranking figsize_scale [Optional] A scale factor for a heatmap size. + font_increase + [Optional] An integer to increase or decrease the plot font. """ if tolerance < 0.001 or tolerance > 0.2: @@ -288,4 +293,5 @@ def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, model_metrics_matrix = model_metrics_matrix.round(3) # round to make tolerance more precise sorted_matrix_by_rank = create_sorted_matrix_by_rank(model_metrics_matrix, tolerance) model_rank_heatmap = create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, - figsize_scale=figsize_scale) + figsize_scale=figsize_scale, + font_increase=font_increase) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index bbcfd156..85e61859 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -240,7 +240,8 @@ def create_row_facet_bar_chart(df, x_col, y_col, facet_column_name, y_sort_by_ls return final_chart -def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, figsize_scale: float = 1.0): +def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, + figsize_scale: float = 1.0, font_increase: int = 4): """ This heatmap includes group fairness and stability metrics and defined models. Using it, you can visually compare the models across defined group metrics. On this plot, @@ -259,9 +260,10 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ Matrix of model ranks per metric where indexes are group metric names and columns are model names figsize_scale [Optional] A scale factor for a heatmap size. + font_increase + [Optional] An integer to increase or decrease the plot font. """ - font_increase = 4 matrix_width = 20 matrix_height = model_metrics_matrix.shape[0] if model_metrics_matrix.shape[0] >= 3 \ else model_metrics_matrix.shape[0] * 2.5 From 4e867e23f516d84aa6ec1b13b1d2023b2ed6b35e Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 00:58:02 +0200 Subject: [PATCH 095/148] Added font_increase --- virny/custom_classes/metrics_visualizer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index b3115244..2e82b835 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -205,7 +205,7 @@ def create_boxes_and_whiskers_for_models_multiple_runs(self, metrics_lst: list): fig.tight_layout() def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, - tolerance: float = 0.001, figsize_scale: float = 1.0): + tolerance: float = 0.001, figsize_scale: float = 1.0, font_increase: int = 4): """ Create a heatmap for overall metrics. @@ -244,7 +244,7 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, font_increase=font_increase) def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, - tolerance: float = 0.001, figsize_scale: float = 1.0): + tolerance: float = 0.001, figsize_scale: float = 1.0, font_increase: int = 4): """ Create a heatmap for disparity metrics. From e204f79142c7907aa4d78acd9f3c0367f0828d3b Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 01:01:40 +0200 Subject: [PATCH 096/148] Added figsize_scale --- virny/custom_classes/metrics_visualizer.py | 8 ++++---- virny/utils/data_viz_utils.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 2e82b835..ddc74a68 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -205,7 +205,7 @@ def create_boxes_and_whiskers_for_models_multiple_runs(self, metrics_lst: list): fig.tight_layout() def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, - tolerance: float = 0.001, figsize_scale: float = 1.0, font_increase: int = 4): + tolerance: float = 0.001, figsize_scale: tuple = (1.0, 1.0), font_increase: int = 4): """ Create a heatmap for overall metrics. @@ -218,7 +218,7 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, tolerance [Optional] An acceptable value difference for metrics dense ranking figsize_scale - [Optional] A scale factor for a heatmap size. + [Optional] Scale factors for a heatmap size. The first element is a scale factor for a plot width, the second one is for height. font_increase [Optional] An integer to increase or decrease the plot font. @@ -244,7 +244,7 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, font_increase=font_increase) def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, - tolerance: float = 0.001, figsize_scale: float = 1.0, font_increase: int = 4): + tolerance: float = 0.001, figsize_scale: tuple = (1.0, 1.0), font_increase: int = 4): """ Create a heatmap for disparity metrics. @@ -259,7 +259,7 @@ def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, tolerance [Optional] An acceptable value difference for metrics dense ranking figsize_scale - [Optional] A scale factor for a heatmap size. + [Optional] Scale factors for a heatmap size. The first element is a scale factor for a plot width, the second one is for height. font_increase [Optional] An integer to increase or decrease the plot font. diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index 85e61859..c2d98a4f 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -241,7 +241,7 @@ def create_row_facet_bar_chart(df, x_col, y_col, facet_column_name, y_sort_by_ls def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_by_rank, - figsize_scale: float = 1.0, font_increase: int = 4): + figsize_scale: tuple = (1.0, 1.0), font_increase: int = 4): """ This heatmap includes group fairness and stability metrics and defined models. Using it, you can visually compare the models across defined group metrics. On this plot, @@ -259,7 +259,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ sorted_matrix_by_rank Matrix of model ranks per metric where indexes are group metric names and columns are model names figsize_scale - [Optional] A scale factor for a heatmap size. + [Optional] Scale factors for a heatmap size. The first element is a scale factor for a plot width, the second one is for height. font_increase [Optional] An integer to increase or decrease the plot font. @@ -269,7 +269,7 @@ def create_model_rank_heatmap_visualization(model_metrics_matrix, sorted_matrix_ else model_metrics_matrix.shape[0] * 2.5 num_ranks = int(sorted_matrix_by_rank.values.max()) - fig = plt.figure(figsize=(matrix_width * figsize_scale, matrix_height * figsize_scale)) + fig = plt.figure(figsize=(matrix_width * figsize_scale[0], matrix_height * figsize_scale[1])) # Set a green color when there is only one rank if num_ranks == 1: rank_colors = sns.diverging_palette(145, 13, s=75, l=70, n=num_ranks).as_hex() From 9e0b2d56dd68c30b90228f39b5b5cb97ca5c2a11 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 01:05:15 +0200 Subject: [PATCH 097/148] Added figsize_scale --- requirements.txt | 1 - virny/custom_classes/metrics_visualizer.py | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 21a53478..26efba9b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,6 @@ pandas~=1.5.2 altair~=4.2.0 scikit-learn~=1.2.0 tqdm~=4.64.1 -sklearn-utils seaborn~=0.12.1 folktables~=0.0.11 xgboost~=1.7.2 diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index ddc74a68..b9a7f1d9 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -205,7 +205,7 @@ def create_boxes_and_whiskers_for_models_multiple_runs(self, metrics_lst: list): fig.tight_layout() def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, - tolerance: float = 0.001, figsize_scale: tuple = (1.0, 1.0), font_increase: int = 4): + tolerance: float = 0.001, figsize_scale: tuple = (0.75, 0.6), font_increase: int = -2): """ Create a heatmap for overall metrics. @@ -244,7 +244,7 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, font_increase=font_increase) def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, - tolerance: float = 0.001, figsize_scale: tuple = (1.0, 1.0), font_increase: int = 4): + tolerance: float = 0.001, figsize_scale: tuple = (0.75, 0.6), font_increase: int = -2): """ Create a heatmap for disparity metrics. From 3f6c4a201d009ab54fa2340cbde2b566b255d92b Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Fri, 22 Dec 2023 01:24:09 +0200 Subject: [PATCH 098/148] Improved visualizations --- virny/custom_classes/metrics_visualizer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index b9a7f1d9..95d1a3b7 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -205,7 +205,7 @@ def create_boxes_and_whiskers_for_models_multiple_runs(self, metrics_lst: list): fig.tight_layout() def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, - tolerance: float = 0.001, figsize_scale: tuple = (0.75, 0.6), font_increase: int = -2): + tolerance: float = 0.001, figsize_scale: tuple = (0.7, 0.5), font_increase: int = -3): """ Create a heatmap for overall metrics. @@ -244,7 +244,7 @@ def create_overall_metric_heatmap(self, model_names: list, metrics_lst: list, font_increase=font_increase) def create_disparity_metric_heatmap(self, model_names: list, metrics_lst: list, groups_lst: list, - tolerance: float = 0.001, figsize_scale: tuple = (0.75, 0.6), font_increase: int = -2): + tolerance: float = 0.001, figsize_scale: tuple = (0.7, 0.5), font_increase: int = -3): """ Create a heatmap for disparity metrics. From f38e37aaf8ee94c7273d0ff80823285438b3d898 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 23 Dec 2023 00:43:23 +0200 Subject: [PATCH 099/148] Aligned namings for dimensions in model performance summary --- virny/custom_classes/metrics_composer.py | 4 +-- .../metrics_interactive_visualizer.py | 26 +++++++++---------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index 1aae0c97..93d22fd2 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -29,8 +29,8 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict): FPR: [(EQUALIZED_ODDS_FPR, self._difference_operation)], FNR: [(EQUALIZED_ODDS_FNR, self._difference_operation)], ACCURACY: [(ACCURACY_PARITY, self._difference_operation)], - POSITIVE_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation), - (DISPARATE_IMPACT, self._ratio_operation)], + POSITIVE_RATE: [(DISPARATE_IMPACT, self._ratio_operation)], + SELECTION_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation)], # Stability disparity metrics LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation), (LABEL_STABILITY_DIFFERENCE, self._difference_operation)], diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index 8eba1c5f..e2fcd220 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -354,8 +354,8 @@ def create_web_app(self, start_app=True): """) with gr.Row(): accuracy_metric_vw4 = gr.Dropdown( - sorted([metric for metric in self.all_accuracy_metrics if metric != POSITIVE_RATE]), - value=ACCURACY, multiselect=False, label="Accuracy Metric", + sorted([metric for metric in self.all_accuracy_metrics if metric not in (POSITIVE_RATE, SELECTION_RATE)]), + value=ACCURACY, multiselect=False, label="Correctness Metric", scale=2 ) accuracy_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) @@ -378,8 +378,8 @@ def create_web_app(self, start_app=True): subgroup_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): positive_rate_metric_vw4 = gr.Dropdown( - [POSITIVE_RATE], - value=POSITIVE_RATE, multiselect=False, label="Positive-Rate Metric", + [POSITIVE_RATE, SELECTION_RATE], + value=POSITIVE_RATE, multiselect=False, label="Representation Metric", scale=2 ) positive_rate_min_val_vw4 = gr.Text(value="0.0", label="Min value", scale=1) @@ -418,7 +418,7 @@ def create_web_app(self, start_app=True): with gr.Row(): group_positive_rate_metrics_vw4 = gr.Dropdown( sorted([DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE]), - value=DISPARATE_IMPACT, multiselect=False, label="Positive-Rate Disparity Metric", + value=DISPARATE_IMPACT, multiselect=False, label="Representation Disparity Metric", scale=2 ) group_positive_rate_min_val_vw4 = gr.Text(value="0.7", label="Min value", scale=1) @@ -688,14 +688,14 @@ def _create_model_performance_summary(self, model_name: str, accuracy_metric, ac group_stability_metric, group_stab_min_val, group_stab_max_val, group_uncertainty_metric, group_uncertainty_min_val, group_uncertainty_max_val, group_positive_rate_metric, group_positive_rate_min_val, group_positive_rate_max_val): - accuracy_constraint = (accuracy_metric, [str_to_float(accuracy_min_val, 'Accuracy min value'), - str_to_float(accuracy_max_val, 'Accuracy max value')]) + accuracy_constraint = (accuracy_metric, [str_to_float(accuracy_min_val, 'Correctness min value'), + str_to_float(accuracy_max_val, 'Correctness max value')]) stability_constraint = (stability_metric, [str_to_float(stability_min_val, 'Stability min value'), str_to_float(stability_max_val, 'Stability max value')]) uncertainty_constraint = (uncertainty_metric, [str_to_float(uncertainty_min_val, 'Uncertainty min value'), str_to_float(uncertainty_max_val, 'Uncertainty max value')]) - positive_rate_constraint = (positive_rate_metric, [str_to_float(positive_rate_min_val, 'Positive-Rate min value'), - str_to_float(positive_rate_max_val, 'Positive-Rate max value')]) + positive_rate_constraint = (positive_rate_metric, [str_to_float(positive_rate_min_val, 'Representation min value'), + str_to_float(positive_rate_max_val, 'Representation max value')]) fairness_constraint = (fairness_metric, [str_to_float(fairness_min_val, 'Error disparity metric min value'), str_to_float(fairness_max_val, 'Error disparity metric max value')]) @@ -703,11 +703,11 @@ def _create_model_performance_summary(self, model_name: str, accuracy_metric, ac str_to_float(group_stab_max_val, 'Stability disparity max value')]) group_uncertainty_constraint = (group_uncertainty_metric, [str_to_float(group_uncertainty_min_val, 'Uncertainty disparity min value'), str_to_float(group_uncertainty_max_val, 'Uncertainty disparity max value')]) - group_positive_rate_constraint = (group_positive_rate_metric, [str_to_float(group_positive_rate_min_val, 'Positive-Rate disparity min value'), - str_to_float(group_positive_rate_max_val, 'Positive-Rate disparity max value')]) + group_positive_rate_constraint = (group_positive_rate_metric, [str_to_float(group_positive_rate_min_val, 'Representation disparity min value'), + str_to_float(group_positive_rate_max_val, 'Representation disparity max value')]) input_constraints_dct = { - 'Accuracy': { + 'Correctness': { 'overall': accuracy_constraint, 'disparity': fairness_constraint, }, @@ -719,7 +719,7 @@ def _create_model_performance_summary(self, model_name: str, accuracy_metric, ac 'overall': uncertainty_constraint, 'disparity': group_uncertainty_constraint, }, - 'Positive-Rate': { + 'Representation': { 'overall': positive_rate_constraint, 'disparity': group_positive_rate_constraint, }, From 698c62ce808eefb09a05e5625f0633095c126bd7 Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Sat, 23 Dec 2023 01:21:28 +0200 Subject: [PATCH 100/148] Aligned namings for dimensions in model performance summary --- virny/custom_classes/metrics_interactive_visualizer.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index e2fcd220..e8759259 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -218,7 +218,7 @@ def create_web_app(self, start_app=True): subgroup_tolerance = gr.Text(value="0.005", label="Tolerance", info="Define an acceptable tolerance for metric dense ranking.") accuracy_metrics = gr.Dropdown( sorted(self.all_accuracy_metrics), - value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", + value=['Accuracy', 'F1'], multiselect=True, label="Correctness Metrics", info="Select correctness metrics to display on the heatmap:", ) uncertainty_metrics = gr.Dropdown( sorted(self.all_uncertainty_metrics), @@ -291,7 +291,7 @@ def create_web_app(self, start_app=True): """) accuracy_metrics = gr.Dropdown( sorted(self.all_accuracy_metrics), - value=['Accuracy', 'F1'], multiselect=True, label="Accuracy Metrics", info="Select accuracy metrics to display on the heatmap:", + value=['Accuracy', 'F1'], multiselect=True, label="Correctness Metrics", info="Select correctness metrics to display on the heatmap:", ) uncertainty_metrics = gr.Dropdown( sorted(self.all_uncertainty_metrics), @@ -590,7 +590,7 @@ def _create_subgroup_model_rank_heatmap(self, model_names: list, subgroup_accura model_names A list of selected model names to display on the heatmap subgroup_accuracy_metrics_lst - A list of subgroup accuracy metrics to visualize + A list of subgroup correctness metrics to visualize subgroup_uncertainty_metrics A list of subgroup uncertainty metrics to visualize subgroup_stability_metrics_lst From d20a68b89b7b8d47383d289e5b57937d28afc2ee Mon Sep 17 00:00:00 2001 From: denysgerasymuk799 Date: Tue, 26 Dec 2023 00:21:16 +0200 Subject: [PATCH 101/148] Added interactive web app demonstration --- docs/diagrams/RAI_Toolkit_Architecture_v7.png | Bin 0 -> 156791 bytes .../RAI_Toolkit_Architecture_v7.drawio | 100 ++++++++++++++++++ docs/examples/Interactive_Web_App_Demo.md | 91 ++++++++++++++++ .../Step1-4.png | Bin 0 -> 489545 bytes .../Step5-6.png | Bin 0 -> 316474 bytes .../UI_View.png | Bin 0 -> 403134 bytes 6 files changed, 191 insertions(+) create mode 100644 docs/diagrams/RAI_Toolkit_Architecture_v7.png create mode 100644 docs/diagrams/drawio_files/RAI_Toolkit_Architecture_v7.drawio create mode 100644 docs/examples/Interactive_Web_App_Demo.md create mode 100644 docs/examples/Interactive_Web_App_Demo_files/Step1-4.png create mode 100644 docs/examples/Interactive_Web_App_Demo_files/Step5-6.png create mode 100644 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/examples/Interactive_Web_App_Demo.md b/docs/examples/Interactive_Web_App_Demo.md new file mode 100644 index 00000000..cde97fc5 --- /dev/null +++ b/docs/examples/Interactive_Web_App_Demo.md @@ -0,0 +1,91 @@ +# Interactive Web App Demonstration + +* [Hosted interactive app for three fair-ML benchmark datasets](https://huggingface.co/spaces/denys-herasymuk/virny-demo) +* [Demonstrative Jupyter notebooks for the Virny capabilities](https://huggingface.co/spaces/denys-herasymuk/virny-demo/tree/main/notebooks) + + +## Application overview + +This interactive app serves as a visualization component within the Virny model profiling library, empowering data scientists +_to engage in responsible model selection_ and _to generate a nutritional label for their model_. Users can declaratively +define various configurations for model profiling, supplying a dataset and models for scrutiny to Virny. This library +computes overall and disparity metrics, profiling model accuracy, stability, uncertainty, and fairness. +Subsequently, the tool utilizes the output from Virny to construct an interactive web application for metric analytics. +This application allows users to scrutinize dataset properties related to protected groups, compare models +across diverse performance dimensions, and generate a comprehensive nutritional label for the most optimal model. + +![UI view of the second screen in the interactive web app](./Interactive_Web_App_Demo_files/UI_View.png) +*Figure 1. A sample UI view of the second screen in the interactive web app.* + +The application comprises six screens, each featuring user input options and a corresponding visualization +based on the provided inputs. To facilitate analysis, users have the flexibility to interactively choose a specific combination of models, +overall metrics, and disparity metrics across various model dimensions. This selection dynamically alters the visualization perspective. +Refer to Figure 1 for a visual representation of the sample web interface showcasing these screens. + + +## User flow description + +Our tool is developed with a user-friendly flow design specifically tailored to guide users through a responsible model selection process. +This streamlined flow reduces the complexity associated with numerous model types and pre-, in-, and post-processing techniques. +It facilitates a comprehensive comparison of selected models based on various performance dimensions using intuitive visualizations +such as colors and tolerance. Additionally, it allows users to break down the performance of a specific model +concerning multiple protected groups and performance dimensions. + +The user flow comprises six steps, as illustrated in Figures 2 and 3, and can be outlined as follows. + +![Steps 1-4 in the user flow for responsible model selection](./Interactive_Web_App_Demo_files/Step1-4.png) +*Figure 2. Steps 1-4 in the user flow for responsible model selection.* + +_**Step 1**: Analyze demographic composition of the dataset._ + +The application is structured from a high-level overview to a detailed examination. The upper screens provide general insights +into the dataset and models, while the lower screens delve into the performance of individual models, dissected by protected groups. +Thus, prior to delving into metric analysis, it is crucial to establish a comprehensive understanding of the proportions and +base rates of the protected groups within the dataset. This information serves to elucidate potential disparities, for example, +such as significant variations in overall accuracy and stability among different racial groups. Additionally, it can indicate +whether the model has learned something by comparing its accuracy to the base rate in the dataset. + +_**Step 2**: Reduce the number of models to compare based on overall and disparity metric constraints._ + +Creating an accurate, robust, and fair model requires thorough validation of various model types, pre-processing techniques, +and fairness interventions. However, the complexity arises when attempting to directly compare all models on a single plot or +visualize every possible combination of the models. As a result, in the second step, we allow the user to define overall and +disparity metric constraints to effectively narrow down the selection of models that meet these criteria. This strategic reduction allows +for a more detailed comparison of diverse metrics, focusing on a more manageable number of models in the subsequent steps. + +_**Steps 3-4**: Compare the selected models that satisfy all constraints using overall and disparity metric heatmaps._ + +In the third and fourth steps, we select a set of models that satisfy all constraints from the second step, +and compare their overall and disparity metrics side-by-side. To enhance clarity, we added a color scheme, where green signifies +the most favorable model metric and red denotes the least favorable compared to other models. + +It's crucial to acknowledge that the color scheme accommodates variations in optimal values for different metrics. +For instance, a score of 1.0 is optimal for Disparate Impact, while 0.0 is optimal for Equalized Odds FNR. Furthermore, +users have the option to introduce a tolerance parameter to the comparison process. This means that if the discrepancy +between metrics of different models falls below the tolerance threshold, these models are grouped together. This proves beneficial +in cases where minor differences, such as 0.001%, can be considered negligible in comparison to the differences of other model metrics. +Steps 3 and 4 collectively provide users with a better understanding of model performance across diverse dimensions relative to other models. +Subsequently, users can choose one or two models for a detailed breakdown of performance across protected groups +and generate a comprehensive nutritional label in the later steps. + + +![Steps 5-6 in the user flow for responsible model selection](./Interactive_Web_App_Demo_files/Step5-6.png) +*Figure 3. Steps 5-6 in the user flow for responsible model selection.* + +_**Step 5**: Generate a nutritional label for the selected model._ + +In the fifth step, users choose a particular model and a combination of overall and disparity metrics to generate a nutritional label, +segmented by multiple protected groups and performance dimensions. The nutritional label includes bar charts for +the overall and disparity metrics presented side-by-side that helps to find interesting insights between them. +This graphical representation proves particularly effective in identifying performance gaps among binary or intersectional groups. + +Moreover, this visualization can be added to a model card, contributing to a responsible reporting mechanism that transparently +communicates the model's performance across diverse dimensions. + +_**Step 6**: Summarize the performance of a particular model across different dimensions and protected groups._ + +Finally, in the sixth step, users select a particular model and specify a desirable range for overall and disparity metrics +encompassing accuracy, stability, uncertainty, and positive rate dimensions. This allows for a clear indication of whether a specific model +meets these defined constraints. The matrix adopts a color-coded scheme, where green signifies compliance with the constraints, +and red signals non-compliance. This final colored matrix serves as a model performance summary and can be incorporated into a model card, +similarly to the visualization generated in the fifth step. diff --git a/docs/examples/Interactive_Web_App_Demo_files/Step1-4.png b/docs/examples/Interactive_Web_App_Demo_files/Step1-4.png new file mode 100644 index 0000000000000000000000000000000000000000..d4bf0a2d002eb97814104d1336ee93dc1093242c GIT binary patch literal 489545 zcmdRV^;=X?yS9o*!vN9^(hbrjFo1M-iF9|DG>CM!fPi!lNJ^(5T>{eG%@E(lde3#v z`wx6Sh!?}!YpPu5i#HhmXZ^!kCp-)mg#Y?b12RJLe|ZV8RyX*m%zxc1lu7gn z)qmX#oJ96G?*E<`Ser8t{Z8_~i(~xy>x=(-f=#ynFU5KO>_Io5BFx7uI|ftEmZ~>d zP32Z{-x`R1{%i)HxNI6@Fa9ajiTIe_nB