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| **/.pyc | ||
| **/.ipynb_checkpoints | ||
| .ipynb_checkpoints | ||
| **/.DS_Store | ||
| **/.DS_Store? | ||
| **/.h5 | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Try CNN with input as 7x7, and then visualize these . filters. Make sure to scale it to 1-255" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.5.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Cross Validation for models on VGG features \n", | ||
| "* Using the folds defined in http://ieeexplore.ieee.org.proxy.lib.uwaterloo.ca/stamp/stamp.jsp?arnumber=7312934\n", | ||
| "* For binary and and 8 class\n", | ||
| "* Using best hyperparameters found in VGG_features_hyperparameter search\n", | ||
| "* Training is done using the full training set, with no validation set.\n", | ||
| "* This was re-run on a newly created dataset compared to the hyperparameter search, because it got overly high accuracy for fold 1." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "The autoreload extension is already loaded. To reload it, use:\n", | ||
| " %reload_ext autoreload\n", | ||
| "/home/rbbidart/breakHis/src\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import os\n", | ||
| "import sys\n", | ||
| "import glob\n", | ||
| "import random\n", | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "from PIL import Image\n", | ||
| "import matplotlib.pyplot as plt \n", | ||
| "from matplotlib.pyplot import imshow\n", | ||
| "from IPython.display import display, HTML\n", | ||
| "from sklearn.metrics import accuracy_score\n", | ||
| "% matplotlib inline\n", | ||
| "\n", | ||
| "\n", | ||
| "# Import modules every time you run code imported using %aimport\n", | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 1\n", | ||
| "\n", | ||
| "# Add the src directory for functions\n", | ||
| "src_dir = os.path.join(os.path.dirname(os.path.dirname(os.getcwd())), 'src')\n", | ||
| "print(src_dir)\n", | ||
| "sys.path.append(src_dir)\n", | ||
| "\n", | ||
| "# import my functions:\n", | ||
| "%aimport models\n", | ||
| "from models import*\n", | ||
| "%aimport functions\n", | ||
| "from functions import*\n", | ||
| "\n", | ||
| "# Base Directory where data is stored\n", | ||
| "base_data_dir = '/home/rbbidart/project/rbbidart/breakHis/'" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Logistic Regression\n", | ||
| "* Binary C=.1\n", | ||
| "* 8-class C=1" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Fold 0\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from sklearn.linear_model import LogisticRegression\n", | ||
| "\n", | ||
| "model_2 = LogisticRegression(C=.1)\n", | ||
| "model_8 = LogisticRegression(C=1)\n", | ||
| "cv_features(model_2, model_8, base_data_dir)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Random Forest\n", | ||
| "* Binary 160, 3\n", | ||
| "* 8-class 160, 3" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Fold 0\n", | ||
| "Fold 1\n", | ||
| "Fold 2\n", | ||
| "Fold 3\n", | ||
| "Fold 4\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from sklearn.ensemble import RandomForestClassifier\n", | ||
| "\n", | ||
| "model_2 = RandomForestClassifier(n_estimators=160, min_samples_split=3)\n", | ||
| "model_8 = RandomForestClassifier(n_estimators=160, min_samples_split=3)\n", | ||
| "cv_features(model_2, model_8, base_data_dir)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## XGBoost\n", | ||
| "* Binary: n_estimators=200\tmax_depth=8\tlearning_rate=0.3\treg_lambda=2\n", | ||
| "* 8-class: n_estimators=250 max_depth=9 learning_rate=0.3\treg_lambda=2 (guess)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from xgboost import XGBClassifier\n", | ||
| "\n", | ||
| "model_2 = XGBClassifier(n_estimators=200, max_depth=8, learning_rate=0.3, reg_lambda=2)\n", | ||
| "model_8 = XGBClassifier(n_estimators=200, max_depth=8, learning_rate=0.3, reg_lambda=2)\n", | ||
| "cv_features(model_2, model_8, base_data_dir)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## KNN\n", | ||
| "* 9 neighbours" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from sklearn.neighbors import KNeighborsClassifier\n", | ||
| "\n", | ||
| "model_2 = KNeighborsClassifier(n_neighbors=9)\n", | ||
| "model_8 = KNeighborsClassifier(n_neighbors=9)\n", | ||
| "cv_features(model_2, model_8, base_data_dir)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.5.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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