adult.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adult Experiment\n",
    "\n",
    "This notebook contains the code to reproduce the Adult experiment. The dataset can be downloaded from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets/adult). \n",
    "\n",
    "**Run the following cells in order to reproduce the experiment from the paper.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-30T01:17:00.615616Z",
     "start_time": "2020-06-30T01:16:56.937676Z"
    }
   },
   "outputs": [],
   "source": [
    "from boexplain import fmin\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the data and prepare it for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# read the data\n",
    "df = pd.read_csv(\"adult.data\",\n",
    "                 names=[\n",
    "                     \"Age\", \"Workclass\", \"fnlwgt\", \"Education\",\n",
    "                     \"Education-Num\", \"Marital Status\", \"Occupation\",\n",
    "                     \"Relationship\", \"Race\", \"Gender\", \"Capital Gain\",\n",
    "                     \"Capital Loss\", \"Hours per week\", \"Country\", \"Income\"\n",
    "                 ],\n",
    "                 na_values=\" ?\")\n",
    "\n",
    "# Mapping binary values to the expected output\n",
    "df['Income'].replace({\" <=50K\": 0, ' >50K': 1}, inplace=True)\n",
    "# Since mode is Prof-specialty, replacing null values with it\n",
    "df['Occupation'] = df['Occupation'].fillna('Prof-specialty')\n",
    "# Since mode is Private, replacing null values with it\n",
    "df['Workclass'] = df['Workclass'].fillna('Private')\n",
    "# Since mode is United-States, replacing null values with it\n",
    "df['Country'] = df['Country'].fillna('United-States')\n",
    "\n",
    "categorical = [\n",
    "    'Workclass', 'Education', 'Marital Status', 'Occupation', 'Relationship',\n",
    "    'Race', 'Gender', 'Country'\n",
    "]\n",
    "for col in df.columns:  # simple label encoding\n",
    "    if col in categorical:\n",
    "        le = LabelEncoder()\n",
    "        df[col] = le.fit_transform(df[col])\n",
    "    df[col] = pd.to_numeric(df[col], downcast='unsigned')\n",
    "\n",
    "# Split the data set into features and label\n",
    "X = df.drop(columns='Income')\n",
    "Y = df['Income']\n",
    "\n",
    "# Split the data into test data and training data\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X.copy(),\n",
    "                                                    Y.copy(),\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=0)\n",
    "\n",
    "# flip training labels satisfying the condition\n",
    "condition = (X_train[\"Education-Num\"] >=\n",
    "             8) & (X_train[\"Education-Num\"] <=\n",
    "                   10) & (X_train[\"Age\"] >= 30) & (X_train[\"Age\"] <= 40)\n",
    "X_train[\"Income\"] = Y_train\n",
    "X_train.loc[condition, \"Income\"] += 1\n",
    "X_train.loc[condition, \"Income\"] %= 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The objective function\n",
    "\n",
    "The objective function trains a random forest classifier, and queries the average predicted value for the group Male. The value 0.259830 is the average predicted value after removing the corrupted tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obj(train_filtered):\n",
    "    # create and train the rf model\n",
    "    rf = RandomForestClassifier(n_estimators=28, random_state=0)\n",
    "    rf.fit(train_filtered.drop(columns='Income'), train_filtered['Income'])\n",
    "    # query the inference data\n",
    "    X_test[\"prediction\"] = rf.predict(X_test)\n",
    "    query = abs(X_test.groupby(\"Gender\")[\"prediction\"].mean()[1] - 0.259830)\n",
    "    X_test.drop(columns='prediction', inplace=True)\n",
    "    return query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOExplain API call\n",
    "\n",
    "The function *fmin* is used to minimize the objective function. The columns Age and Education-Num are searched for an explanation. The runtime is 200 seconds, and the results are averaged over 10 runs. The correct predicate is provided so that F-score, precision and recall can be calculated. Statistics about the run are saved to the file adult_boexplain.json."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rem = fmin(\n",
    "    data=X_train,\n",
    "    f=obj,\n",
    "    num_cols=[\"Age\", \"Education-Num\"],\n",
    "    runtime=200,\n",
    "    runs=10,\n",
    "    random=True,  # perform a random iteration\n",
    "    correct_pred={\n",
    "        'Age_min': 30,\n",
    "        'Age_len': 10,\n",
    "        'Education-Num_min': 8,\n",
    "        'Education-Num_len': 2\n",
    "    },\n",
    "    name=\"adult\",\n",
    "    file=\"adult.json\",\n",
    "    use_seeds_from_paper=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recreate Figure 6\n",
    "\n",
    "From the output of the above call to the BOExplain APi, the following two cells can be used to recreate Figure 6 from the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import altair as alt\n",
    "alt.data_transformers.disable_max_rows()\n",
    "\n",
    "from json import loads\n",
    "experiments = {}\n",
    "\n",
    "fo = open(\"results/adult.json\", \"r\")\n",
    "for i, line in enumerate(fo.readlines()):\n",
    "    experiments[i] = loads(line)\n",
    "fo.close()\n",
    "\n",
    "df = pd.DataFrame({}, columns=[\"Algorithm\", \"Time (seconds)\", \"Value\"])\n",
    "for i in range(len(experiments)):\n",
    "    df_new = pd.DataFrame.from_dict({\"Algorithm\": experiments[i][\"cat_enc\"],\n",
    "                        \"Time (seconds)\": list(range(5, experiments[i][\"runtime\"]+5, 5)),\n",
    "                        \"Value\": experiments[i][\"time_array\"]},\n",
    "                                orient='index').T\n",
    "    df = df.append(df_new)\n",
    "\n",
    "df = df.explode(\"Value\")\n",
    "df = df.set_index(['Algorithm']).apply(pd.Series.explode).reset_index()\n",
    "df[\"Algorithm\"] = df[\"Algorithm\"].replace({\"individual_contribution_warm_start_top1\": \"BOExplain\", np.nan: \"Random\"})\n",
    "\n",
    "line = alt.Chart(df).mark_line().encode(\n",
    "    x='Time (seconds)',\n",
    "    y=alt.Y('mean(Value)', title=['Mean Objective', 'Function Value']),\n",
    "    color=\"Algorithm\"\n",
    ").properties(\n",
    "    title=\" \",\n",
    "    width=225,\n",
    "    height=90\n",
    ")\n",
    "band = alt.Chart(df).mark_errorband(extent='stdev').encode(\n",
    "    x='Time (seconds)',\n",
    "    y=alt.Y('Value', title='Mean Objective Function Value'),\n",
    "    color=alt.Color(\"Algorithm\")\n",
    ")\n",
    "chart = line# + band\n",
    "chart = chart.configure_title(\n",
    "    anchor='start',\n",
    ")\n",
    "chart.configure_legend(\n",
    "    title=None,\n",
    "    orient='none',\n",
    "    legendX=30,\n",
    "    legendY=163,\n",
    "    columns=4,\n",
    "    labelFontSize=15,\n",
    "    symbolSize=700,\n",
    "    labelLimit=275,\n",
    ").configure_axis(\n",
    "    labelFontSize=15,\n",
    "    titleFontSize=15,\n",
    "    titlePadding=2\n",
    ").configure_title(\n",
    "    fontSize=15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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       "        s.src = paths[lib];\n",
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       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from json import loads\n",
    "import altair as alt\n",
    "experiments = {}\n",
    "\n",
    "fo = open(\"results/adult.json\", \"r\")\n",
    "for i, line in enumerate(fo.readlines()):\n",
    "    experiments[i] = loads(line)\n",
    "fo.close()\n",
    "\n",
    "import re\n",
    "df = pd.DataFrame({}, columns=[\"Algorithm\", \"Time (seconds)\", \"Precision\", \"Recall\", \"F-score\", \"Jaccard\"])\n",
    "for i in range(len(experiments)):\n",
    "    df_new = pd.DataFrame.from_dict({\"Algorithm\": experiments[i][\"cat_enc\"],\n",
    "#                         \"Iteration\": tuple(range(experiments[i][\"n_trials\"])),\n",
    "                        \"Time (seconds)\": tuple(range(5, experiments[i][\"runtime\"]+5, 5)),\n",
    "                        \"Precision\": experiments[i][\"precision_time_array\"],\n",
    "                        \"Recall\": experiments[i][\"recall_time_array\"],\n",
    "                        \"F-score\": experiments[i][\"f_score_time_array\"],\n",
    "                        \"Jaccard\": experiments[i][\"jaccard_time_array\"]\n",
    "                        }, orient='index').T\n",
    "    df = df.append(df_new)\n",
    "\n",
    "df = df.set_index(['Algorithm', \"Time (seconds)\"]).apply(pd.Series.explode).reset_index()\n",
    "df = df.set_index(['Algorithm']).apply(pd.Series.explode).reset_index()\n",
    "df[\"Algorithm\"] = df[\"Algorithm\"].replace({\"individual_contribution_warm_start_top1\": \"BOExplain\", np.nan: \"Random\"})\n",
    "\n",
    "\n",
    "num_cols = f\"{len(experiments[0]['num_cols'])} numerical columns: \"\n",
    "for i, col in enumerate(experiments[0][\"num_cols\"]):\n",
    "    num_cols += f\"{col} (range {experiments[0]['num_cols_range'][i][0]} to {experiments[0]['num_cols_range'][i][1]}), \"\n",
    "cat_cols = f\"{experiments[0]['cat_cols']} categorical columns: \"\n",
    "for i, col in enumerate(experiments[0][\"cat_cols\"]):\n",
    "    cat_cols += f\"{col} ({experiments[0]['cat_cols_n_uniq'][i]} unique values), \"\n",
    "\n",
    "out_str = f\"Experiment: {experiments[0]['name']}. Completed {experiments[0]['n_trials']} iterations for {experiments[0]['runs']} runs. Search space includes \"\n",
    "\n",
    "if len(experiments[0]['num_cols']) > 0:\n",
    "    out_str += num_cols\n",
    "    if len(experiments[0]['cat_cols']) > 0:\n",
    "        out_str += \"and \"\n",
    "\n",
    "if len(experiments[0]['cat_cols']) > 0:\n",
    "    out_str += cat_cols\n",
    "\n",
    "out_str = f\"{out_str[:-2]}.\"\n",
    "\n",
    "out_lst = [line.strip() for line in re.findall(r'.{1,140}(?:\\s+|$)', out_str)]\n",
    "# df = pd.melt(df, id_vars=[\"Algorithm\", \"Iteration\"], value_vars=[\"Precision\", \"Recall\", \"F1_score\", \"Jaccard\"], value_name=\"Metric\")\n",
    "# altair\n",
    "metric = \"Metric\"\n",
    "f1_score = alt.Chart(df).mark_line().encode(\n",
    "    x=alt.X('Time (seconds)', axis=alt.Axis()),\n",
    "    y=alt.Y(f'mean(F-score)', scale=alt.Scale(domain=[0, 1]), axis=alt.Axis(values=[0.2, 0.5, 0.8]), title=None),\n",
    "    color=alt.Color(\"Algorithm\")\n",
    ").properties(\n",
    "    title=\"F-score\",\n",
    "    width=225,\n",
    "    height=90\n",
    ")\n",
    "jaccard = alt.Chart(df).mark_line().encode(\n",
    "    x=alt.X('Time (seconds)', axis=alt.Axis(labels=False, title=None,  tickSize=0)),\n",
    "    y=alt.Y(f'mean(Jaccard)', scale=alt.Scale(domain=[0, 1]), axis=alt.Axis(values=[0.2, 0.5, 0.8], labels=False, title=None,  tickSize=0), title=None),\n",
    "    color=alt.Color(\"Algorithm\")\n",
    ").properties(\n",
    "    title=\"Jaccard Similarity\",\n",
    "    width=225,\n",
    "    height=90\n",
    ")\n",
    "prec = alt.Chart(df).mark_line().encode(\n",
    "    x='Time (seconds)',\n",
    "    y=alt.Y(f'mean(Precision)', scale=alt.Scale(domain=[0, 1]), axis=alt.Axis(values=[0.2, 0.5, 0.8], labels=False, title=None,  tickSize=0), title=None),\n",
    "    color=alt.Color(\"Algorithm\")\n",
    ").properties(\n",
    "    title=\"Precision\",\n",
    "    width=225,\n",
    "    height=90\n",
    ")\n",
    "recall = alt.Chart(df).mark_line().encode(\n",
    "    x='Time (seconds)',\n",
    "    y=alt.Y(f'mean(Recall)', scale=alt.Scale(domain=[0, 1]), axis=alt.Axis(values=[0.2, 0.5, 0.8], labels=False, title=None,  tickSize=0), title=None),\n",
    "    color=alt.Color(\"Algorithm\")\n",
    ").properties(\n",
    "    title=\"Recall\",\n",
    "    width=225,\n",
    "    height=90\n",
    ")\n",
    "# first = alt.hconcat(f1_score, jaccard, spacing=0)\n",
    "# second = alt.hconcat(prec, recall, spacing=0)\n",
    "alt.hconcat(f1_score, prec,recall, spacing=0).resolve_scale(x='shared', y='shared').configure_legend(\n",
    "    title=None,\n",
    "    orient='none',\n",
    "    legendX=200,\n",
    "    legendY=135,\n",
    "    labelFontSize=15,\n",
    "    symbolSize=700,\n",
    "    columns=2,\n",
    "    labelLimit=275,\n",
    ").configure_axis(\n",
    "    labelFontSize=15,\n",
    "    titleFontSize=15\n",
    ").configure_title(\n",
    "    fontSize=15\n",
    ")"
   ]
  }
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