Homework1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v1jmdmhAjkHq"
   },
   "source": [
    "\n",
    "\n",
    "# Developing Fair Classifiers: A case study using COMPAS data\n",
    "Sean Steinle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l1fhIA_YjkHw"
   },
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8RKUzjX6jkHw"
   },
   "source": [
    "##### There are many different ways to measure fairness in AI. As such, there are many ways to adjust your AI models so that they better fit these fairness metrics. Here, you will examine this firsthand with the COMPAS dataset, which is a dataset that has been widely used to predict inmates' likelihood of recidivism (reoffense). First, you will run a simple classifier on this dataset and examine the results. Then, you will be asked how this classifier could be made more fair and, for extra credit, you can try implementing these changes yourself.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VUSqhNCvjkHw"
   },
   "source": [
    "## Dataset exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "043ta_lx65N7"
   },
   "source": [
    "##### First, you will run the cells and answer the questions below in order to explore the contents of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1_BVlQfdjkHw"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "57JEWrz1jkHx"
   },
   "outputs": [],
   "source": [
    "#uncomment these lines if you need to install either of these two packages\n",
    "\n",
    "#!pip install plotly\n",
    "#!pip install aif360[LFR]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "JbNBFnJvjkHy"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No module named 'tensorflow': AdversarialDebiasing will be unavailable. To install, run:\n",
      "pip install 'aif360[AdversarialDebiasing]'\n",
      "WARNING:root:No module named 'numba.decorators': LFR will be unavailable. To install, run:\n",
      "pip install 'aif360[LFR]'\n"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "import urllib.request\n",
    "import os,sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from functools import reduce\n",
    "pd.set_option('display.max_columns',None)\n",
    "from pprint import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.metrics as performance\n",
    "from sklearn.preprocessing import scale\n",
    "from random import seed, shuffle\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "try:\n",
    "    import plotly.express as px\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.io as pio\n",
    "    import plotly.graph_objects as go\n",
    "    pio.renderers.default = \"notebook_connected\" #change to \"colab\" if using Google Colab, or \"notebook\" if offline\n",
    "except ImportError as e:\n",
    "#     !conda install --yes --prefix {sys.prefix} -c plotly plotly-orca==1.2.1 psutil requests\n",
    "    print(\"plotly is not installed !! please install the package to be able to render plotly visualizations\")\n",
    "\n",
    "\n",
    "try:\n",
    "    from aif360.metrics import BinaryLabelDatasetMetric\n",
    "    from aif360.metrics import ClassificationMetric\n",
    "    from aif360.metrics.utils import compute_boolean_conditioning_vector\n",
    "    from aif360.algorithms.postprocessing.calibrated_eq_odds_postprocessing import CalibratedEqOddsPostprocessing\n",
    "\n",
    "    from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions\\\n",
    "                    import load_preproc_data_compas\n",
    "    \n",
    "except ImportError as e:\n",
    "    print('please install aif360 package to proceed with the fairness section')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vM-K4B-FjkHy"
   },
   "source": [
    "### Setting up visualization properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "G1DOUO0gjkHy",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SMALL_SIZE = 8\n",
    "MEDIUM_SIZE = 10\n",
    "BIGGER_SIZE = 12\n",
    "fig=plt.figure(figsize=(10,6))\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=14)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dfjK9V_tjkH1"
   },
   "source": [
    "### UTILS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3nX5ZHNHjkH2"
   },
   "outputs": [],
   "source": [
    "def hist(column_name, x):\n",
    "    '''\n",
    "    helper function to plot historgram for a dataframe \n",
    "    '''\n",
    "    return px.histogram(column_name, x = x,\n",
    "                       title = x,\n",
    "                       opacity = 0.8,\n",
    "                       color_discrete_sequence = ['indianred'])\n",
    "def plot_roc(fpr,tpr,thresholds=None,label=None):\n",
    "    '''\n",
    "    plot roc curve for multiple instances.\n",
    "    '''\n",
    "    \n",
    "    colors = ['red','blue','aqua', 'darkorange', 'cornflowerblue', 'burlywood', 'lightsalmon', 'olive']\n",
    "\n",
    "    plt.figure(figsize=(12,8))\n",
    "    lw = 2\n",
    "    for i in range(len(fpr)):\n",
    "        plt.plot(fpr[i], tpr[i], color=colors[i],label=label[i],\n",
    "             lw=lw)\n",
    "        \n",
    "     \n",
    "    plt.plot([0, 1], [0, 1], color='black', lw=lw, linestyle='--')\n",
    "      \n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curves of attributes ')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "#for each race/decile pair, calculate stats for recidivism\n",
    "def recid_count_fn(df_recid_column):\n",
    "    \"\"\" Computes the total number of examples in which recidivism occurred.\n",
    "        Parameters:\n",
    "            df_recid_column: DataFrame column where each row takes value 1 if\n",
    "            recidivism occured, and 0 if recidivism did not occur.\n",
    "        Returns:\n",
    "            The total number of examples in which recidivism occurred.\n",
    "    \"\"\"\n",
    "    recid_count = df_recid_column.sum()\n",
    "    return recid_count\n",
    "\n",
    "def non_recid_count_fn(df_recid_column):\n",
    "    \"\"\"Computes the total number of examples in which recidivism did not occur.\n",
    "    Parameters:\n",
    "        df_recid_column: dataframe column where each row takes value 1 if\n",
    "        recidivism occured, and 0 if recidivism did not occur.\n",
    "    Returns:\n",
    "        The total number of examples in which recidivism did not occur.\n",
    "    \"\"\"\n",
    "    non_recid_count = sum(df_recid_column == 0) \n",
    "    return non_recid_count\n",
    "\n",
    "def total_count_fn(df_recid_column):\n",
    "    \"\"\"Computes the total number of examples in the dataset.\n",
    "    Parameters:\n",
    "        df_recid_column: dataframe column where each row takes value 1 if\n",
    "        recidivism occured, and 0 if recidivism did not occur.\n",
    "    Returns:\n",
    "        The total number of examples in the dataset.\n",
    "    \"\"\"\n",
    "    total_count = df_recid_column.count()\n",
    "    return total_count\n",
    "\n",
    "\n",
    "#compute the classifier outcomes for different decision thresholds (using summary DataFrame above)\n",
    "#often in statistics, we can express the probabilities in the introduction in terms of true positive rate, \n",
    "#true negative rate, false positive rate, and false negative rate\n",
    "def TP(compas_stats_df):\n",
    "    \"\"\"\n",
    "        Args:\n",
    "            compas_stats_df: DataFrame found above\n",
    "        Returns:\n",
    "            An array of the number of true positives for each decile_score. We classify an example as 1 if \n",
    "            that example's score is equal to or above the decision threshold. Take recid_count. \n",
    "    \"\"\"\n",
    "    TPs = []\n",
    "    for threshold in compas_stats_df['decile_score']:\n",
    "        x = compas_stats_df[compas_stats_df['decile_score'] >= threshold]['recid_count'].sum()\n",
    "        true_positives = x\n",
    "        TPs.append(true_positives)\n",
    "    return np.array(TPs, dtype=np.int32)\n",
    "\n",
    "def FP(compas_stats_df):\n",
    "    \"\"\"\n",
    "        Args:\n",
    "            compas_stats_df: DataFrame found above\n",
    "        Returns:\n",
    "            An array of the number of false positives for each decile_score. We classify an example as 1 if \n",
    "            that example's score is equal to or above the decision threshold. Take non_recid_count. \n",
    "    \"\"\"\n",
    "    FPs = []\n",
    "    for threshold in compas_stats_df['decile_score']:\n",
    "        x = compas_stats_df[compas_stats_df['decile_score'] >= threshold]['non_recid_count'].sum()\n",
    "        false_positives = x\n",
    "        FPs.append(false_positives)\n",
    "    return np.array(FPs, dtype=np.int32)\n",
    "\n",
    "def TN(compas_stats_df):\n",
    "    \"\"\"\n",
    "        Args:\n",
    "            compas_stats_df: DataFrame found above\n",
    "        Returns:\n",
    "            An array of the number of true negatives for each decile_score. We classify an example as 1 if \n",
    "            that example's score less than the decision threshold. Take non_recid_count. \n",
    "    \"\"\"\n",
    "    TNs = []\n",
    "    for threshold in compas_stats_df['decile_score']:\n",
    "        x = compas_stats_df[compas_stats_df['decile_score'] < threshold]['non_recid_count'].sum()\n",
    "        true_negatives = x\n",
    "        TNs.append(true_negatives)\n",
    "    return np.array(TNs, dtype=np.int32)\n",
    "\n",
    "def FN(compas_stats_df):\n",
    "    \"\"\"\n",
    "        Args:\n",
    "            compas_stats_df: DataFrame found above\n",
    "        Returns:\n",
    "            An array of the number of true positives for each decile_score. We classify an example as 1 if \n",
    "            that example's score is equal to or above the decision threshold. Take recid_count. \n",
    "    \"\"\"\n",
    "    FNs = []\n",
    "    for threshold in compas_stats_df['decile_score']:\n",
    "        x = compas_stats_df[compas_stats_df['decile_score'] < threshold]['recid_count'].sum()\n",
    "        false_negatives = x\n",
    "        FNs.append(false_negatives)\n",
    "    return np.array(FNs, dtype=np.int32)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r3wdcXNYjkH2"
   },
   "source": [
    "### Download data file and dependency files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "0iONiuLnjkH2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for file compas-scores-two-years.csv in the current directory...\n",
      "File found in current directory..\n"
     ]
    }
   ],
   "source": [
    "SEED = 1234\n",
    "seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "\n",
    "def check_data_file(fname):\n",
    "    files = os.listdir(\".\") # get the current directory listing\n",
    "    print(\"Looking for file %s in the current directory...\" % (fname))\n",
    "\n",
    "    if fname not in files:\n",
    "        print(\" %s not found! Downloading from GitHub...\" % fname)\n",
    "        addr = \"https://raw.githubusercontent.com/propublica/compas-analysis/master/compas-scores-two-years.csv\"\n",
    "        response = urllib.request.urlopen(addr)\n",
    "        data = response.read()\n",
    "        fileOut = open(fname, \"wb\")\n",
    "        fileOut.write(data)\n",
    "        fileOut.close()\n",
    "        print(\"%s download and saved locally..\" % fname)\n",
    "    else:\n",
    "        print(\"File found in current directory..\")\n",
    "    \n",
    "COMPAS_INPUT_FILE = \"compas-scores-two-years.csv\"\n",
    "check_data_file(COMPAS_INPUT_FILE)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "J1AOWe3EjkH3"
   },
   "source": [
    "### Load and clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true,
    "id": "DwTsFy-DjkH3"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(COMPAS_INPUT_FILE)\n",
    "\n",
    "dataset = dataset.dropna(subset=[\"days_b_screening_arrest\"]) # dropping missing vals\n",
    "\n",
    "dataset = dataset[ (dataset.days_b_screening_arrest <= 30) &\n",
    "(dataset.days_b_screening_arrest >= -30) &\n",
    "(dataset.is_recid != -1) & (dataset.c_charge_degree != 'O') & (dataset.score_text != 'N/A') ]\n",
    "\n",
    "dataset.reset_index(inplace=True, drop=True) # renumber the rows from 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true,
    "id": "GRkkfYKAjkH3",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6172\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>compas_screening_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>dob</th>\n",
       "      <th>age</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>race</th>\n",
       "      <th>juv_fel_count</th>\n",
       "      <th>decile_score</th>\n",
       "      <th>juv_misd_count</th>\n",
       "      <th>juv_other_count</th>\n",
       "      <th>priors_count</th>\n",
       "      <th>days_b_screening_arrest</th>\n",
       "      <th>c_jail_in</th>\n",
       "      <th>c_jail_out</th>\n",
       "      <th>c_case_number</th>\n",
       "      <th>c_offense_date</th>\n",
       "      <th>c_arrest_date</th>\n",
       "      <th>c_days_from_compas</th>\n",
       "      <th>c_charge_degree</th>\n",
       "      <th>c_charge_desc</th>\n",
       "      <th>is_recid</th>\n",
       "      <th>r_case_number</th>\n",
       "      <th>r_charge_degree</th>\n",
       "      <th>r_days_from_arrest</th>\n",
       "      <th>r_offense_date</th>\n",
       "      <th>r_charge_desc</th>\n",
       "      <th>r_jail_in</th>\n",
       "      <th>r_jail_out</th>\n",
       "      <th>violent_recid</th>\n",
       "      <th>is_violent_recid</th>\n",
       "      <th>vr_case_number</th>\n",
       "      <th>vr_charge_degree</th>\n",
       "      <th>vr_offense_date</th>\n",
       "      <th>vr_charge_desc</th>\n",
       "      <th>type_of_assessment</th>\n",
       "      <th>decile_score.1</th>\n",
       "      <th>score_text</th>\n",
       "      <th>screening_date</th>\n",
       "      <th>v_type_of_assessment</th>\n",
       "      <th>v_decile_score</th>\n",
       "      <th>v_score_text</th>\n",
       "      <th>v_screening_date</th>\n",
       "      <th>in_custody</th>\n",
       "      <th>out_custody</th>\n",
       "      <th>priors_count.1</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>event</th>\n",
       "      <th>two_year_recid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>miguel hernandez</td>\n",
       "      <td>miguel</td>\n",
       "      <td>hernandez</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>1947-04-18</td>\n",
       "      <td>69</td>\n",
       "      <td>Greater than 45</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2013-08-13 06:03:42</td>\n",
       "      <td>2013-08-14 05:41:20</td>\n",
       "      <td>13011352CF10A</td>\n",
       "      <td>2013-08-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Aggravated Assault w/Firearm</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>2014-07-14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>kevon dixon</td>\n",
       "      <td>kevon</td>\n",
       "      <td>dixon</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>Male</td>\n",
       "      <td>1982-01-22</td>\n",
       "      <td>34</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2013-01-26 03:45:27</td>\n",
       "      <td>2013-02-05 05:36:53</td>\n",
       "      <td>13001275CF10A</td>\n",
       "      <td>2013-01-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Felony Battery w/Prior Convict</td>\n",
       "      <td>1</td>\n",
       "      <td>13009779CF10A</td>\n",
       "      <td>(F3)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-07-05</td>\n",
       "      <td>Felony Battery (Dom Strang)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13009779CF10A</td>\n",
       "      <td>(F3)</td>\n",
       "      <td>2013-07-05</td>\n",
       "      <td>Felony Battery (Dom Strang)</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>3</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>2013-01-26</td>\n",
       "      <td>2013-02-05</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>ed philo</td>\n",
       "      <td>ed</td>\n",
       "      <td>philo</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>1991-05-14</td>\n",
       "      <td>24</td>\n",
       "      <td>Less than 25</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2013-04-13 04:58:34</td>\n",
       "      <td>2013-04-14 07:02:04</td>\n",
       "      <td>13005330CF10A</td>\n",
       "      <td>2013-04-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Possession of Cocaine</td>\n",
       "      <td>1</td>\n",
       "      <td>13011511MM10A</td>\n",
       "      <td>(M1)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>Driving Under The Influence</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>3</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>marsha miles</td>\n",
       "      <td>marsha</td>\n",
       "      <td>miles</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>1971-08-22</td>\n",
       "      <td>44</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-11-30 04:50:18</td>\n",
       "      <td>2013-12-01 12:28:56</td>\n",
       "      <td>13022355MM10A</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Battery</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>edward riddle</td>\n",
       "      <td>edward</td>\n",
       "      <td>riddle</td>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>Male</td>\n",
       "      <td>1974-07-23</td>\n",
       "      <td>41</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2014-02-18 05:08:24</td>\n",
       "      <td>2014-02-24 12:18:30</td>\n",
       "      <td>14002304CF10A</td>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Possession Burglary Tools</td>\n",
       "      <td>1</td>\n",
       "      <td>14004485CF10A</td>\n",
       "      <td>(F2)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>Poss of Firearm by Convic Felo</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>2</td>\n",
       "      <td>Low</td>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>steven stewart</td>\n",
       "      <td>steven</td>\n",
       "      <td>stewart</td>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>1973-02-25</td>\n",
       "      <td>43</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2013-08-29 08:55:23</td>\n",
       "      <td>2013-08-30 08:42:13</td>\n",
       "      <td>13012216CF10A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>arrest case no charge</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>3</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>2014-05-22</td>\n",
       "      <td>2014-06-03</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>elizabeth thieme</td>\n",
       "      <td>elizabeth</td>\n",
       "      <td>thieme</td>\n",
       "      <td>2014-03-16</td>\n",
       "      <td>Female</td>\n",
       "      <td>1976-06-03</td>\n",
       "      <td>39</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2014-03-15 05:35:34</td>\n",
       "      <td>2014-03-18 04:28:46</td>\n",
       "      <td>14004524MM10A</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Battery</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2014-03-16</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2014-03-16</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>2014-03-18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>747</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>benjamin franc</td>\n",
       "      <td>benjamin</td>\n",
       "      <td>franc</td>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988-06-01</td>\n",
       "      <td>27</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2013-11-25 06:31:06</td>\n",
       "      <td>2013-11-26 08:26:57</td>\n",
       "      <td>13016402CF10A</td>\n",
       "      <td>2013-11-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Poss 3,4 MDMA (Ecstasy)</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>2013-11-25</td>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>ellyaher lanza</td>\n",
       "      <td>ellyaher</td>\n",
       "      <td>lanza</td>\n",
       "      <td>2013-10-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992-08-18</td>\n",
       "      <td>23</td>\n",
       "      <td>Less than 25</td>\n",
       "      <td>African-American</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-10-03 04:07:35</td>\n",
       "      <td>2013-10-07 08:17:30</td>\n",
       "      <td>13018837MM10A</td>\n",
       "      <td>2013-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Battery</td>\n",
       "      <td>1</td>\n",
       "      <td>14010414TC40A</td>\n",
       "      <td>(M2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-02-08</td>\n",
       "      <td>Driving License Suspended</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2013-10-03</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-10-03</td>\n",
       "      <td>2013-10-03</td>\n",
       "      <td>2013-10-07</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>kortney coleman</td>\n",
       "      <td>kortney</td>\n",
       "      <td>coleman</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Female</td>\n",
       "      <td>1978-08-22</td>\n",
       "      <td>37</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-01 03:28:03</td>\n",
       "      <td>2013-01-02 01:12:19</td>\n",
       "      <td>13000053MM10A</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Battery</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risk of Recidivism</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Risk of Violence</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              name      first       last compas_screening_date     sex  \\\n",
       "0   1  miguel hernandez     miguel  hernandez            2013-08-14    Male   \n",
       "1   3       kevon dixon      kevon      dixon            2013-01-27    Male   \n",
       "2   4          ed philo         ed      philo            2013-04-14    Male   \n",
       "3   7      marsha miles     marsha      miles            2013-11-30    Male   \n",
       "4   8     edward riddle     edward     riddle            2014-02-19    Male   \n",
       "5   9    steven stewart     steven    stewart            2013-08-30    Male   \n",
       "6  10  elizabeth thieme  elizabeth     thieme            2014-03-16  Female   \n",
       "7  14    benjamin franc   benjamin      franc            2013-11-26    Male   \n",
       "8  15    ellyaher lanza   ellyaher      lanza            2013-10-03    Male   \n",
       "9  16   kortney coleman    kortney    coleman            2013-01-01  Female   \n",
       "\n",
       "          dob  age          age_cat              race  juv_fel_count  \\\n",
       "0  1947-04-18   69  Greater than 45             Other              0   \n",
       "1  1982-01-22   34          25 - 45  African-American              0   \n",
       "2  1991-05-14   24     Less than 25  African-American              0   \n",
       "3  1971-08-22   44          25 - 45             Other              0   \n",
       "4  1974-07-23   41          25 - 45         Caucasian              0   \n",
       "5  1973-02-25   43          25 - 45             Other              0   \n",
       "6  1976-06-03   39          25 - 45         Caucasian              0   \n",
       "7  1988-06-01   27          25 - 45         Caucasian              0   \n",
       "8  1992-08-18   23     Less than 25  African-American              0   \n",
       "9  1978-08-22   37          25 - 45         Caucasian              0   \n",
       "\n",
       "   decile_score  juv_misd_count  juv_other_count  priors_count  \\\n",
       "0             1               0                0             0   \n",
       "1             3               0                0             0   \n",
       "2             4               0                1             4   \n",
       "3             1               0                0             0   \n",
       "4             6               0                0            14   \n",
       "5             4               0                0             3   \n",
       "6             1               0                0             0   \n",
       "7             4               0                0             0   \n",
       "8             6               0                0             3   \n",
       "9             1               0                0             0   \n",
       "\n",
       "   days_b_screening_arrest            c_jail_in           c_jail_out  \\\n",
       "0                     -1.0  2013-08-13 06:03:42  2013-08-14 05:41:20   \n",
       "1                     -1.0  2013-01-26 03:45:27  2013-02-05 05:36:53   \n",
       "2                     -1.0  2013-04-13 04:58:34  2013-04-14 07:02:04   \n",
       "3                      0.0  2013-11-30 04:50:18  2013-12-01 12:28:56   \n",
       "4                     -1.0  2014-02-18 05:08:24  2014-02-24 12:18:30   \n",
       "5                     -1.0  2013-08-29 08:55:23  2013-08-30 08:42:13   \n",
       "6                     -1.0  2014-03-15 05:35:34  2014-03-18 04:28:46   \n",
       "7                     -1.0  2013-11-25 06:31:06  2013-11-26 08:26:57   \n",
       "8                      0.0  2013-10-03 04:07:35  2013-10-07 08:17:30   \n",
       "9                      0.0  2013-01-01 03:28:03  2013-01-02 01:12:19   \n",
       "\n",
       "   c_case_number c_offense_date c_arrest_date  c_days_from_compas  \\\n",
       "0  13011352CF10A     2013-08-13           NaN                 1.0   \n",
       "1  13001275CF10A     2013-01-26           NaN                 1.0   \n",
       "2  13005330CF10A     2013-04-13           NaN                 1.0   \n",
       "3  13022355MM10A     2013-11-30           NaN                 0.0   \n",
       "4  14002304CF10A     2014-02-18           NaN                 1.0   \n",
       "5  13012216CF10A            NaN    2013-08-29                 1.0   \n",
       "6  14004524MM10A     2014-03-15           NaN                 1.0   \n",
       "7  13016402CF10A     2013-11-25           NaN                 1.0   \n",
       "8  13018837MM10A     2013-10-03           NaN                 0.0   \n",
       "9  13000053MM10A     2013-01-01           NaN                 0.0   \n",
       "\n",
       "  c_charge_degree                   c_charge_desc  is_recid  r_case_number  \\\n",
       "0               F    Aggravated Assault w/Firearm         0            NaN   \n",
       "1               F  Felony Battery w/Prior Convict         1  13009779CF10A   \n",
       "2               F           Possession of Cocaine         1  13011511MM10A   \n",
       "3               M                         Battery         0            NaN   \n",
       "4               F       Possession Burglary Tools         1  14004485CF10A   \n",
       "5               F           arrest case no charge         0            NaN   \n",
       "6               M                         Battery         0            NaN   \n",
       "7               F         Poss 3,4 MDMA (Ecstasy)         0            NaN   \n",
       "8               M                         Battery         1  14010414TC40A   \n",
       "9               M                         Battery         0            NaN   \n",
       "\n",
       "  r_charge_degree  r_days_from_arrest r_offense_date  \\\n",
       "0             NaN                 NaN            NaN   \n",
       "1            (F3)                 NaN     2013-07-05   \n",
       "2            (M1)                 0.0     2013-06-16   \n",
       "3             NaN                 NaN            NaN   \n",
       "4            (F2)                 0.0     2014-03-31   \n",
       "5             NaN                 NaN            NaN   \n",
       "6             NaN                 NaN            NaN   \n",
       "7             NaN                 NaN            NaN   \n",
       "8            (M2)                 NaN     2014-02-08   \n",
       "9             NaN                 NaN            NaN   \n",
       "\n",
       "                    r_charge_desc   r_jail_in  r_jail_out  violent_recid  \\\n",
       "0                             NaN         NaN         NaN            NaN   \n",
       "1     Felony Battery (Dom Strang)         NaN         NaN            NaN   \n",
       "2     Driving Under The Influence  2013-06-16  2013-06-16            NaN   \n",
       "3                             NaN         NaN         NaN            NaN   \n",
       "4  Poss of Firearm by Convic Felo  2014-03-31  2014-04-18            NaN   \n",
       "5                             NaN         NaN         NaN            NaN   \n",
       "6                             NaN         NaN         NaN            NaN   \n",
       "7                             NaN         NaN         NaN            NaN   \n",
       "8       Driving License Suspended         NaN         NaN            NaN   \n",
       "9                             NaN         NaN         NaN            NaN   \n",
       "\n",
       "   is_violent_recid vr_case_number vr_charge_degree vr_offense_date  \\\n",
       "0                 0            NaN              NaN             NaN   \n",
       "1                 1  13009779CF10A             (F3)      2013-07-05   \n",
       "2                 0            NaN              NaN             NaN   \n",
       "3                 0            NaN              NaN             NaN   \n",
       "4                 0            NaN              NaN             NaN   \n",
       "5                 0            NaN              NaN             NaN   \n",
       "6                 0            NaN              NaN             NaN   \n",
       "7                 0            NaN              NaN             NaN   \n",
       "8                 0            NaN              NaN             NaN   \n",
       "9                 0            NaN              NaN             NaN   \n",
       "\n",
       "                vr_charge_desc  type_of_assessment  decile_score.1 score_text  \\\n",
       "0                          NaN  Risk of Recidivism               1        Low   \n",
       "1  Felony Battery (Dom Strang)  Risk of Recidivism               3        Low   \n",
       "2                          NaN  Risk of Recidivism               4        Low   \n",
       "3                          NaN  Risk of Recidivism               1        Low   \n",
       "4                          NaN  Risk of Recidivism               6     Medium   \n",
       "5                          NaN  Risk of Recidivism               4        Low   \n",
       "6                          NaN  Risk of Recidivism               1        Low   \n",
       "7                          NaN  Risk of Recidivism               4        Low   \n",
       "8                          NaN  Risk of Recidivism               6     Medium   \n",
       "9                          NaN  Risk of Recidivism               1        Low   \n",
       "\n",
       "  screening_date v_type_of_assessment  v_decile_score v_score_text  \\\n",
       "0     2013-08-14     Risk of Violence               1          Low   \n",
       "1     2013-01-27     Risk of Violence               1          Low   \n",
       "2     2013-04-14     Risk of Violence               3          Low   \n",
       "3     2013-11-30     Risk of Violence               1          Low   \n",
       "4     2014-02-19     Risk of Violence               2          Low   \n",
       "5     2013-08-30     Risk of Violence               3          Low   \n",
       "6     2014-03-16     Risk of Violence               1          Low   \n",
       "7     2013-11-26     Risk of Violence               4          Low   \n",
       "8     2013-10-03     Risk of Violence               4          Low   \n",
       "9     2013-01-01     Risk of Violence               1          Low   \n",
       "\n",
       "  v_screening_date  in_custody out_custody  priors_count.1  start   end  \\\n",
       "0       2013-08-14  2014-07-07  2014-07-14               0      0   327   \n",
       "1       2013-01-27  2013-01-26  2013-02-05               0      9   159   \n",
       "2       2013-04-14  2013-06-16  2013-06-16               4      0    63   \n",
       "3       2013-11-30  2013-11-30  2013-12-01               0      1   853   \n",
       "4       2014-02-19  2014-03-31  2014-04-18              14      5    40   \n",
       "5       2013-08-30  2014-05-22  2014-06-03               3      0   265   \n",
       "6       2014-03-16  2014-03-15  2014-03-18               0      2   747   \n",
       "7       2013-11-26  2013-11-25  2013-11-26               0      0   857   \n",
       "8       2013-10-03  2013-10-03  2013-10-07               3      4   128   \n",
       "9       2013-01-01  2013-01-01  2013-01-02               0      1  1186   \n",
       "\n",
       "   event  two_year_recid  \n",
       "0      0               0  \n",
       "1      1               1  \n",
       "2      0               1  \n",
       "3      0               0  \n",
       "4      1               1  \n",
       "5      0               0  \n",
       "6      0               0  \n",
       "7      0               0  \n",
       "8      1               1  \n",
       "9      0               0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(dataset))\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kG49svx1jkH4"
   },
   "source": [
    "### Basic analysis of demographics\n",
    "A series of plots is given below which look at the distribution of the data, the decile scores (the scores in the COMPAS dataset that measure likelihood of reoffense), and the risk category, which is based on the decile score and is determined in the following manner:\n",
    "\n",
    " 1 – 4: scale score is low relative to other offenders in norm group.\n",
    "\n",
    " 5 – 7: scale score is medium relative to other offenders in norm group.\n",
    "\n",
    " 8 – 10: scale score is high relative to other offenders in norm group.\n",
    "\n",
    "\n",
    "After looking at the plots, you will be asked to describe the imbalances of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "X7Q-o2A8jkH4",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def get_basic_stats(column_name,check_values=None,\n",
    "                    check_NaN=False,group_count=True,plot_hist = False):\n",
    "    '''\n",
    "    basic stats on a given column of the dataset\n",
    "    @param column_name: column name to compute stats on\n",
    "    '''\n",
    "    \n",
    "    result = {}\n",
    "    \n",
    "    if column_name not in dataset.columns:\n",
    "        raise ValueError(\"column_name must be set to a value from the the available values\")\n",
    "        \n",
    "    #values in the column\n",
    "    column_values = np.unique(dataset[column_name].values)\n",
    "    \n",
    "    if check_values:\n",
    "        print('-',column_name,\"in dataset:\")\n",
    "        [print(value) for value in column_values]\n",
    "\n",
    "    if check_NaN:\n",
    "        print(\"-NaN  present in the column?\",dataset[column_name].isnull().any())\n",
    "    \n",
    "    if group_count:\n",
    "        values_count = dataset[column_name].value_counts()   \n",
    "        result.update(values_count=values_count)\n",
    "        \n",
    "    #histogram of values\n",
    "    if plot_hist:\n",
    "        fig = hist(dataset[column_name], column_name) \n",
    "        fig.show()        \n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "6J1JjYj9jkH4",
    "scrolled": false
   },
   "outputs": [
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       "        if (typeof require !== 'undefined') {\n",
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       "        requirejs.config({\n",
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       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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\"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"race\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"race\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('57bc9c40-c155-42fb-8c88-fe1529cf8885');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = get_basic_stats(\"race\",plot_hist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "QpVT42IAjkH5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"age\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"age\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('668df64b-46ed-420f-bab0-b3691bb757f4');\n",
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       "            observer.disconnect();\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
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       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = get_basic_stats(\"age\", plot_hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like most of the imprisoned are young (20-37) and african-american."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "lsEIRtpnjkH5"
   },
   "outputs": [],
   "source": [
    "sex_race_df = pd.crosstab(dataset.race, dataset.sex)\n",
    "sex_race_df = sex_race_df.stack().reset_index().rename(columns={0:\"value\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "B2znoGtOjkH5"
   },
   "outputs": [
    {
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race = np.sort(dataset.race.unique())\n",
    "males = sex_race_df.value[sex_race_df.sex == 'Male']\n",
    "females = sex_race_df.value[sex_race_df.sex == 'Female']\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Male', x=race, y= males),\n",
    "    go.Bar(name='Female', x=race, y=females)\n",
    "])\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Men are imprisoned at a rate 4-5x as much as women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ccH85bhdjkH6",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "decile_score_per_race = pd.crosstab(dataset.decile_score, dataset.race)\n",
    "decile_score_per_race = decile_score_per_race.stack().reset_index().rename(columns = {0:'values'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "MWWO5wX0jkH6",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dec_score = dataset.decile_score.unique()\n",
    "afrac_amer = decile_score_per_race.values[decile_score_per_race.race == \"African-American\"][:,2]\n",
    "hispanic = decile_score_per_race.values[decile_score_per_race.race == \"Hispanic\"][:,2]\n",
    "asian = decile_score_per_race.values[decile_score_per_race.race == \"Asian\"][:,2]\n",
    "causcasian = decile_score_per_race.values[decile_score_per_race.race == \"Caucasian\"][:,2]\n",
    "native = decile_score_per_race.values[decile_score_per_race.race == \"Native American\"][:,2]\n",
    "other = decile_score_per_race.values[decile_score_per_race.race == \"Other\"][:,2]\n",
    "\n",
    "fig = go.Figure(data = [\n",
    "    go.Bar(name = 'African-American', x = dec_score, y = afrac_amer),\n",
    "    go.Bar(name = 'Hispanic', x = dec_score, y = hispanic),\n",
    "    go.Bar(name = 'Asian', x = dec_score, y = asian),\n",
    "    go.Bar(name = 'Causcasian', x = dec_score, y = causcasian),\n",
    "    go.Bar(name = 'Native-American', x = dec_score, y = native),\n",
    "    go.Bar(name = 'Other', x = dec_score, y = other)\n",
    "])\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though the number of african-american prisoners stays relatively steady across all four deciles, the total number prisoners at risk for recitivism decreases as the risk level increases. This means that african-americans represent a much higher proportion of high-risk deciles than low-risk deciles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LRyYvb_XjkH7"
   },
   "source": [
    "The *low*, *medium* and *high* values below are categories for risk based on the **decile_score** computed. This will be discussed in more detail in the upcoming sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "SzgksdT3jkH7",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "score_text_per_race = pd.crosstab(dataset.score_text, dataset.race)\n",
    "score_text_per_race = score_text_per_race.stack().reset_index().rename(columns = {0:'values'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "rIG87rosjkH7",
    "scrolled": false
   },
   "outputs": [
    {
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       "                            \n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dec_score = dataset.score_text.unique()\n",
    "afrac_amer = score_text_per_race.values[score_text_per_race.race == \"African-American\"][:,2]\n",
    "hispanic = score_text_per_race.values[score_text_per_race.race == \"Hispanic\"][:,2]\n",
    "asian = score_text_per_race.values[score_text_per_race.race == \"Asian\"][:,2]\n",
    "causcasian = score_text_per_race.values[score_text_per_race.race == \"Caucasian\"][:,2]\n",
    "native = score_text_per_race.values[score_text_per_race.race == \"Native American\"][:,2]\n",
    "other = score_text_per_race.values[score_text_per_race.race == \"Other\"][:,2]\n",
    "\n",
    "fig = go.Figure(data = [\n",
    "    go.Bar(name = 'African-American', x = dec_score, y = afrac_amer),\n",
    "    go.Bar(name = 'Hispanic', x = dec_score, y = hispanic),\n",
    "    go.Bar(name = 'Asian', x = dec_score, y = asian),\n",
    "    go.Bar(name = 'Causcasian', x = dec_score, y = causcasian),\n",
    "    go.Bar(name = 'Native-American', x = dec_score, y = native),\n",
    "    go.Bar(name = 'Other', x = dec_score, y = other)\n",
    "])\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sort of disproves my point earlier, because for low risk african americans make up a very high proportion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D8KEGlKLjkH7"
   },
   "source": [
    "### Decile score vs Race\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "IUnpp9LgjkH7"
   },
   "outputs": [],
   "source": [
    "def scores_stats(category, value):\n",
    "    '''\n",
    "    @param category: vategory to perform stats on\n",
    "    @param value: entry in the specefied category\n",
    "    '''\n",
    "    scores = dataset[dataset[category] == value]\n",
    "    scores = scores[['decile_score','score_text']]\n",
    "    histogram,_ = np.histogram(scores['decile_score'])\n",
    "    fig = px.histogram(scores['decile_score'], 'decile_score',\n",
    "                       title = 'Decile score of '+ value + 's',\n",
    "                       opacity = 0.8,\n",
    "                       color_discrete_sequence = ['indianred'])\n",
    "    fig.add_shape(dict(\n",
    "        type=\"line\",\n",
    "        x0 = scores['decile_score'].mean(),\n",
    "        y0 = 0,\n",
    "        x1 = scores['decile_score'].mean(),\n",
    "        y1 = max(histogram) + 20,\n",
    "        line=dict(\n",
    "            color = \"LightSeaGreen\",\n",
    "            width = 4,\n",
    "            dash = \"dashdot\")\n",
    "    ))\n",
    "    fig.show()\n",
    "    print('mean decile score: %.2f'%scores['decile_score'].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "cTqEh1YtjkH7"
   },
   "outputs": [
    {
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     "text": [
      "mean decile score: 5.28\n"
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean decile score: 3.64\n"
     ]
    }
   ],
   "source": [
    "scores_stats('race','Caucasian')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4lMZG3zSjkH8"
   },
   "source": [
    "### Decile score vs Gender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "_g5rz9v-jkH8",
    "scrolled": false
   },
   "outputs": [
    {
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     "text": [
      "mean decile score: 4.50\n"
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   ],
   "source": [
    "scores_stats('sex','Male')"
   ]
  },
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   "execution_count": 21,
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean decile score: 4.06\n"
     ]
    }
   ],
   "source": [
    "scores_stats('sex','Female')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary**:\n",
    "From what we've seen so far, we have reason to believe that the risk assessment of african-americans is higher than the risk assessment for caucasians. There is also a small amount of evidence that there is a risk gap in gender, but this is a smaller gap. Now we're going to check how accurate the risk assessment is by looking at the ground-truth data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tcktxvv5sqGr"
   },
   "source": [
    "### Ground truth data - How much recidivism is there in our dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C5XPlttgjkH9"
   },
   "source": [
    "Now, we are going to look at COMPAS's distribution with regards to the number of people who were re-arrested in two years (i.e. what COMPAS aims to predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FyilqZdwjkH9"
   },
   "source": [
    "#### Number of people who were re-arrested\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "g9eyF05yjkH9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people in the dataset whom got re-arrested in the span of two years:  2809\n",
      "Percentage of people re-arrested within two-years: 0.4551198963058976%\n"
     ]
    }
   ],
   "source": [
    "#people whom got re-arrested in two years span\n",
    "print('Number of people in the dataset whom got re-arrested in the span of two years: ',\n",
    "      dataset['two_year_recid'].sum())\n",
    "print(f\"Percentage of people re-arrested within two-years: {dataset['two_year_recid'].sum()/len(dataset)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBzw2KnujkH9"
   },
   "source": [
    "#### Number of people who were rearrested, split by race and gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "_OwdQs2tjkH_"
   },
   "outputs": [],
   "source": [
    "two_recide_sex_race_df = pd.crosstab(dataset.race[dataset.two_year_recid == 1], dataset.sex[dataset.two_year_recid == 1])\n",
    "two_recide_sex_race_df = two_recide_sex_race_df.stack().reset_index().rename(columns={0:\"value\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "pYKrXmq5jkH_"
   },
   "outputs": [
    {
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   ],
   "source": [
    "race = dataset.race.unique()\n",
    "race = np.sort(race)\n",
    "males = two_recide_sex_race_df.value[two_recide_sex_race_df.sex == 'Male']\n",
    "females = two_recide_sex_race_df.value[two_recide_sex_race_df.sex == 'Female']\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Male', x=race, y= males),\n",
    "    go.Bar(name='Female', x=race, y=females)\n",
    "], layout_title_text = \"Male/Female Recidivism by Race\")\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "FCw5cptNjkH_"
   },
   "outputs": [],
   "source": [
    "# Plot confusion matrix without passing the classifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from itertools import cycle, product\n",
    "def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Blues):\n",
    "    print(cm)\n",
    "    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    \n",
    "    fmt = 'd'\n",
    "    thresh = cm.max() / 2\n",
    "    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                horizontalalignment=\"center\",\n",
    "                color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "    \n",
    "def plot_conf_matrix(y_test, y_pred, class_names):\n",
    "    print(\"plotting the Confusion Matrix\")\n",
    "    cnf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    np.set_printoptions(precision=2)\n",
    "    print('CCR = {}'.format(np.trace(cnf_matrix) / len(y_test)))\n",
    "    print('Precision = {}'.format(precision_macro_average(cnf_matrix)))\n",
    "    print('Recall = {}'.format(recall_macro_average(cnf_matrix)))\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion Matrix')\n",
    "    plt.show()\n",
    "    \n",
    "def recall_macro_average(confusion_matrix):\n",
    "    rows, columns = confusion_matrix.shape\n",
    "    sum_of_recalls = 0\n",
    "    for label in range(columns):\n",
    "        sum_of_recalls += recall(label, confusion_matrix)\n",
    "    return sum_of_recalls / columns\n",
    "\n",
    "def precision_macro_average(confusion_matrix):\n",
    "    rows, columns = confusion_matrix.shape\n",
    "    sum_of_precisions = 0\n",
    "    for label in range(rows):\n",
    "        sum_of_precisions += precision(label, confusion_matrix)\n",
    "    return sum_of_precisions / rows\n",
    "\n",
    "def precision(label, confusion_matrix):\n",
    "    col = confusion_matrix[:, label]\n",
    "    return confusion_matrix[label, label] / col.sum()\n",
    "    \n",
    "def recall(label, confusion_matrix):\n",
    "    row = confusion_matrix[label, :]\n",
    "    return confusion_matrix[label, label] / row.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ldPTjIuExHC"
   },
   "source": [
    "### **Question 1: Takeaways from the distributions**\n",
    "Now that you have looked at the way this data is distributed, what do you notice? What imbalances (if any) do you see and how might they affect the results of a model that uses this dataset? Answer these questions in the cell below:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking over some of the distributions across some of our data attributes, we notice that:\n",
    "1. African-americans are imprisoned at a higher rate than other races. They are imprisoned at a rate of 1.5-2x as much as caucasians, and no other race is imprisoned near as much as these two races.\n",
    "    - We know though that caucasians make up a far larger proportion of the population, so this should be surprising, as it is not representative of our population.\n",
    "2. Men are imprisoned at a much higher rate than women, about 4-5x as much.\n",
    "3. African-americans are graded to be a higher risk for recidivism than caucasians. Men are also graded to be higher risk, but too a lesser extent. This grade comes as a score from another part of our dataset.\n",
    "4. The score may or may not be an effective assessment of risk. We saw that african-americans had a higher recidivism than caucasians, but this may be because they make up a larger proportion of the imprisoned population (see point 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gKIyya28jkIB"
   },
   "source": [
    "### Error Analysis of COMPAS Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o8bCokgWmv1z"
   },
   "source": [
    "Below, we look at the error rates, specifically the false positive and false negative rates, for Caucasians and African Americans. In order to calculate these error rates, we need some sort of binary classification from these decile scores. To get that, we can set a *threshold* such that everything above that threshold is classified as a prediction that the person will commit a reoffense in two years. For instance, if we picked 7, we can say any decile score of 7 or higher will be a prediction that the person will reoffend. Here, we plot the false positive and false negative rates for every possible threshold based on the decile scores.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "20UECkmOiRtu"
   },
   "outputs": [],
   "source": [
    "#get false negative and false positive rates \n",
    "compas = pd.read_csv(COMPAS_INPUT_FILE).query('days_b_screening_arrest <= 30 & days_b_screening_arrest >= -30')\n",
    "\n",
    "compas_violent = compas[compas['race'].isin(['African-American', 'Caucasian'])].groupby(['race', 'v_decile_score'], as_index=False)\n",
    "compas = compas[compas['race'].isin(['African-American', 'Caucasian'])].groupby(['race', 'decile_score'], as_index=False)\n",
    "\n",
    "compas_stats = compas['two_year_recid'].agg({'recid_count': recid_count_fn, 'non_recid_count': non_recid_count_fn, 'total_count': total_count_fn})\n",
    "compas_violent_stats = compas_violent['two_year_recid'].agg({'recid_count': recid_count_fn, 'non_recid_count': non_recid_count_fn, 'total_count': total_count_fn})\n",
    "\n",
    "#add columns to compas stats so we can find tpr, fpr, tnr, fnr\n",
    "for race in ['African-American', 'Caucasian']:\n",
    "    data = compas_stats['race'] == race\n",
    "    compas_stats.loc[data, 'TP'] = TP(compas_stats[data]) \n",
    "    compas_stats.loc[data, 'TN'] = TN(compas_stats[data]) \n",
    "    compas_stats.loc[data, 'FP'] = FP(compas_stats[data]) \n",
    "    compas_stats.loc[data, 'FN'] = FN(compas_stats[data])\n",
    "    compas_stats.fillna(0 , inplace=True)\n",
    "\n",
    "fpr = compas_stats['FP'] / (compas_stats['FP'] + compas_stats['TN']).tolist()\n",
    "fnr = compas_stats['FN'] / (compas_stats['FN'] + compas_stats['TP']).tolist()\n",
    "fpr_length = int(len(fpr)/2); fnr_length = int(len(fnr)/2)\n",
    "fpr_african_americans = fpr[:fpr_length]; fpr_caucasians = fpr[fpr_length:]\n",
    "fnr_african_americans = fnr[:fnr_length]; fnr_caucasians = fnr[fnr_length:]"
   ]
  },
  {
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   "execution_count": 27,
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   ],
   "source": [
    "#visualize false positive rates\n",
    "\n",
    "decileScores = np.arange(1,11)\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Caucasian', x=decileScores, y=fpr_caucasians[::-1]),\n",
    "    go.Bar(name='African American', x=decileScores, y=fpr_african_americans[::-1]),\n",
    "], layout_title_text = \"False Positive Rate for Diff. Thresholds: African Americans vs. Caucasians\")\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "Mb71tMGJmU3V"
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Thresholds: African Americans vs. Caucasians\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize false negative rates\n",
    "\n",
    "decileScores = np.arange(1,11)\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Caucasian', x=decileScores, y=fnr_caucasians),\n",
    "    go.Bar(name='African American', x=decileScores, y=fnr_african_americans),\n",
    "], layout_title_text = \"False Negative Rate for Diff. Thresholds: African Americans vs. Caucasians\")\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='stack')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CktV3nVGqe-b"
   },
   "source": [
    "### **Question 2: What do you notice about these error rates? Do you see any biases? What could potentially happen as a result of these biases?**\n",
    "Answer this question in the cell below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BFA7_9t6oz_G"
   },
   "source": [
    "These two charts show that african-americans tend to make up a larger proportion of false positives than caucasians, while making up a smaller proportion of false negatives.\n",
    "\n",
    "This effect makes it seem as though the african-american recividism rate is higher than it really is. This is because, on average, african-americans are more likely to be mistakenly reported as recitivizing, whereas caucasians are more likely to be mistakenly reported as non-recitivizing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true,
    "id": "orRbC45ojkIC"
   },
   "source": [
    "## Part 2.2 - Standard classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T_u4IUHNjkID"
   },
   "source": [
    "In this section, we are going to train a simple SVM classifier on the \"two_years_recid\" ground truth data and plot confusion matrices in order to investigate the bias in the classifiers towards different attributes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "U8jMAte8jkIA",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decile score ranges in score text\n",
      "low: 1 4\n",
      "medium: 5 7\n",
      "high 8 10\n"
     ]
    }
   ],
   "source": [
    "#mapping of low medium high to decile score\n",
    "print('decile score ranges in score text')\n",
    "scores = dataset[['decile_score','score_text']]\n",
    "low_range = scores[scores['score_text'] == 'Low']['decile_score']\n",
    "print('low:',low_range.min(),low_range.max())\n",
    "medium_range = scores[scores['score_text'] == 'Medium']['decile_score']\n",
    "print('medium:',medium_range.min(),medium_range.max())\n",
    "High_range = scores[scores['score_text'] == 'High']['decile_score']\n",
    "print('high',High_range.min(),High_range.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3xhoeZufjkIA"
   },
   "source": [
    "To be able to compare compas scores in range [1,10], to the ground truth binary 0/1, the COMPAS scores are grouped into two groups to transfer them into binary labels\n",
    "- *recidivate* means *high* = label *1*\n",
    "- *will not recidivate* means *low/medium* = label *0*\n",
    "\n",
    "Thus, here, our threshold is .8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "emlLK4-djkIA"
   },
   "outputs": [],
   "source": [
    "# split scores to new classes, high to 1 and medium/ low to zero\n",
    "new_scores = dataset.copy()\n",
    "new_scores['decile_score'].replace(to_replace=range(1,8),value=0,inplace=True)\n",
    "new_scores['decile_score'].replace(to_replace=range(8,11),value=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3VNQ0CwTjkID"
   },
   "source": [
    "- Preparing for the next part, we start using IBM aif360 package, a collection of fairness algorithms and performance metrics  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-vyz6nxTjkID"
   },
   "source": [
    "- We use the following set of features out of the 137 questions collected by northepoint, we notice that we are including the charge degree(Felony/murder), the priors count(number of crimes commited before), and age category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "vIUB1Pt_jkID",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set of features to be used out of the dataset\n",
      "['sex',\n",
      " 'race',\n",
      " 'age_cat=25 to 45',\n",
      " 'age_cat=Greater than 45',\n",
      " 'age_cat=Less than 25',\n",
      " 'priors_count=0',\n",
      " 'priors_count=1 to 3',\n",
      " 'priors_count=More than 3',\n",
      " 'c_charge_degree=F',\n",
      " 'c_charge_degree=M']\n"
     ]
    }
   ],
   "source": [
    "#note: to run this line, you may need to put the compas-scores-two-years.csv file in the following directory:\n",
    "#/usr/local/lib/python3.6/dist-packages/aif360/data/raw/compas\n",
    "\n",
    "dataset_conditioned = load_preproc_data_compas()\n",
    "print(\"set of features to be used out of the dataset\")\n",
    "pprint(dataset_conditioned.feature_names)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q2lcWAVxjkID"
   },
   "source": [
    "- As a normalization step, all of the data values are turned into binary values, in order to equalize the effect of features with respect to the classifier.\n",
    "\n",
    "- The race has more than one value, but since we want to look at the bias between African American and Caucasians, we drop the other races from the dataset. This does not introduce a problem, since the size of the dataset drops from 6127 entries to 5278 entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "g-iEfCldjkID"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example samples from the dataset\n"
     ]
    },
    {
     "data": {
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       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>age_cat=25 to 45</th>\n",
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       "      <th>two_year_recid</th>\n",
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      "text/plain": [
       "    sex  race  age_cat=25 to 45  age_cat=Greater than 45  \\\n",
       "3   0.0   0.0               1.0                      0.0   \n",
       "4   0.0   0.0               0.0                      0.0   \n",
       "8   0.0   1.0               1.0                      0.0   \n",
       "10  1.0   1.0               1.0                      0.0   \n",
       "14  0.0   1.0               1.0                      0.0   \n",
       "\n",
       "    age_cat=Less than 25  priors_count=0  priors_count=1 to 3  \\\n",
       "3                    0.0             1.0                  0.0   \n",
       "4                    1.0             0.0                  0.0   \n",
       "8                    0.0             0.0                  0.0   \n",
       "10                   0.0             1.0                  0.0   \n",
       "14                   0.0             1.0                  0.0   \n",
       "\n",
       "    priors_count=More than 3  c_charge_degree=F  c_charge_degree=M  \\\n",
       "3                        0.0                1.0                0.0   \n",
       "4                        1.0                1.0                0.0   \n",
       "8                        1.0                1.0                0.0   \n",
       "10                       0.0                0.0                1.0   \n",
       "14                       0.0                1.0                0.0   \n",
       "\n",
       "    two_year_recid  \n",
       "3              1.0  \n",
       "4              1.0  \n",
       "8              1.0  \n",
       "10             0.0  \n",
       "14             0.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_as_df,_ = dataset_conditioned.convert_to_dataframe()\n",
    "print(\"Example samples from the dataset\")\n",
    "dataset_as_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kV6r9UdqjkIE"
   },
   "source": [
    "### Train-validate-test split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iIHuG6PyjkIE"
   },
   "source": [
    "- We divide our dataset into 3 folds to train/test different classifers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "VVEnZK_HjkIE"
   },
   "outputs": [],
   "source": [
    "dataset_conditioned_train, dataset_conditioned_validate_test = dataset_conditioned.split([0.7], shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "ktN8IwHZjkIE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of traning and testing samples\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((3694, 11), (1584, 11))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('size of traning and testing samples')\n",
    "dataset_conditioned_train.convert_to_dataframe()[0].shape,dataset_conditioned_validate_test.convert_to_dataframe()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "rCwi-cudzITz"
   },
   "outputs": [],
   "source": [
    "test_df = dataset_conditioned_validate_test.convert_to_dataframe()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZyBnF_GdjkIE"
   },
   "source": [
    "### Splitting based on gender "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "OPiLmCq7jkIE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of female samples in test set: (333, 10), number of male samples in test set: (1251, 10)\n"
     ]
    }
   ],
   "source": [
    "# get male samples from the dataset\n",
    "male_samples = test_df[test_df['sex'] == 0.0]\n",
    "male_samples_label = male_samples.filter(['two_year_recid'])\n",
    "male_samples.drop(columns=['two_year_recid'],inplace=True)\n",
    "\n",
    "# get female samples from the dataset\n",
    "female_samples = test_df[test_df['sex'] == 1.0]\n",
    "female_samples_label = female_samples.filter(['two_year_recid'])\n",
    "female_samples.drop(columns=['two_year_recid'],inplace=True)\n",
    "print('Number of female samples in test set: {0}, number of male samples in test set: {1}'.format(female_samples.shape,\n",
    "                                                                                      male_samples.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eHYYcwzSjkIE"
   },
   "source": [
    "### Splitting based on race - African American and Caucasian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "yP-1vKqWjkIF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of African American samples in test set: (955, 10), number of Caucasian samples in test set: (629, 10)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#get caucasians samples from the dataset\n",
    "caucasian_samples = test_df[test_df['race'] == 1.0]\n",
    "caucasian_samples_label = caucasian_samples.filter(['two_year_recid'])\n",
    "caucasian_samples.drop(columns=['two_year_recid'],inplace=True)\n",
    "\n",
    "#get african american samples from the dataset\n",
    "african_american_samples = test_df[test_df['race'] == 0.0]\n",
    "african_american_samples_label = african_american_samples.filter(['two_year_recid'])\n",
    "african_american_samples.drop(columns=['two_year_recid'],inplace=True)\n",
    "print('Number of African American samples in test set: {0}, number of Caucasian samples in test set: {1}'.format(african_american_samples.shape,\n",
    "                                                                                      caucasian_samples.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4woIPw9njkIF"
   },
   "source": [
    "### Accuracy of COMPAS Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3r0skLaZjkIF"
   },
   "source": [
    "Before delving into the performance of our SVM classifier, let's look at the confusion matrices with respect to race and gender for our sample and chosen threshold in order to inspect the true positive rate and false positive rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "5QqtCzqrjkIB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COMPAS overall performance against the true labels: 63.399222%\n"
     ]
    }
   ],
   "source": [
    "overal_accuracy = performance.accuracy_score(new_scores['two_year_recid'],\n",
    "                                             new_scores['decile_score'],normalize=True)\n",
    "\n",
    "print('COMPAS overall performance against the true labels: {0:3f}%'.format(overal_accuracy *100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n6v_TU8CjkIF"
   },
   "source": [
    "#### Confusion Matrix - African American\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "cTIQ5Q46jkIF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6100787401574803\n",
      "Precision = 0.6547616628996622\n",
      "Recall = 0.6211659271642475\n",
      "[[1303  211]\n",
      " [1027  634]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_scores_african_american = new_scores[new_scores['race'] == 'African-American']\n",
    "performance.accuracy_score(new_scores_african_american['two_year_recid'],new_scores_african_american['decile_score'],normalize=True)\n",
    "\n",
    "# performance.confusion_matrix(new_scores_african_american['two_year_recid'],new_scores_african_american['decile_score'])\n",
    "plot_conf_matrix(new_scores_african_american['two_year_recid'], new_scores_african_american['decile_score'], class_names=['Not Recedivate', 'Recedivate'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, two things:\n",
    "1. Accuracy wasn't great.\n",
    "2. The model really under-guessed on \"recedivate.\" The model only guessed ~800 cases of recedivism, whereas it was really almost double that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xSjWa9wzi8AG"
   },
   "source": [
    "#### Confusion Matrix - Caucasian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "9fD3KjYYi5-U"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6571564431764146\n",
      "Precision = 0.6876967846579525\n",
      "Recall = 0.5747306221758777\n",
      "[[1220   61]\n",
      " [ 660  162]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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6ulZ43neB/SLikFyRDncAY6q9qZlZX6qwpTxS0j0lr/3LLrMncGVEjAb+TmqcDsvHhgEzSIG4vKwqlQbl1YDbOyl/q6QCZmbFU3lOeVpEjC15TfjglXg9b0/Lf3akb7chNVJv76SsKpUG5RfpZNVnYAtSnsXMrJBUu9Ws/wzsJmkysAdwOjBb0i1Ae0TcFRH3lZdVW99KR/RNAH4lad+8/xFJmwMnAEdVe1Mzs77UrwZd4iJiBrBtWfEHurwtSDe4UhUF5Yg4QdISpKeKrcCNwLvASRHx64WpgJlZb2ugsSOVz30RET+QdCzwMVLa498R8Vav1czMrAbULHNfdCGA2Xm7vcZ1MTPrFbVIX/SVSvspD5J0KunJ44PAQ8Drkk6T1NqL9TMzW2g1GtHXJyptKf8W+DSwL+93jdsUOA5YHPh67atmZrbwROqB0SgqDcpfBHaJiOtKyp6W9ArwVxyUzazAGih7UXFQfhuY0kn5FGBW7apjZlZjqngYdSFUOnjkdOAnkgZ3FOTtH+VjZmaFJKBF6vFVFN2tPHJFWdE4YIqkh/L+2vn9i2FmVmAFirk96i598VrZ/l/L9v9b47qYmdWc1CQLp0bE3n1ZETOz3lKk9ERPvJq1mTW9xgnJVQRlSXsD/wOsAAwsPRYRH61xvczMaqaRhllXOqLvcOCXwL2klUcuAx4BPgSc20t1MzNbaJLo19Lzqygq7RK3H7B/RBwBtAFnRMTnSIF6xd6qnJlZLTTSMOtKg/KHSau3Qhos0rHayIXAF2pdKTOzWqrRJPd9otKgPBUYmbef5f0VWlclzRxnZlZIafBIz6+iqDQo3wB8Lm+fA5ws6UZgIvC33qiYmVmtNMWIvjL7kwN4RJwpaTrwSdKAkt/1Ut3MzBaa1IT9lCNiLjC3ZH8iqZVsZlZ4DRSTu537Yv1KL5JXcDUzK6SmGGYN3EN6iNfTTxNAv5rVyMyshkSxcsY96S4or9xntTAz6y0F64fck+4mJHq2LyuyqFly2VHsfOQB9a6GZa+/9V69q2C9qEj9kHviCYnMrKkJ6OegbGZWHA30nM9B2cyaXyMF5UpH9JmZNSSJms0SJ+lrkq6XNFnS8pJOkXSLpNNKzvlAWTWqCsqSRkraWNKgBbmZmVk91GKWOEnLA1tGxPiIGAcsDQyNiM2BgZI2zOM75iurtq4VpS8kLU6a82JXUr/k1YCnJZ0JTI2Io6q9sZlZX+hYzboGtgX6Sboe+DfwOHBdPjaJNFHbnE7K7q7mJpW2lI8HlgfWJ03d2eEqYOdqbmhm1tdaKngBIyXdU/Lav+wySwMDI2I88A6wBPBGPjYTGJ5f5WVVqfRB3+eAnSPiAUmlU3U+BngpKDMrrI6VRyowLSLGdnN8JnBT3r4BGMv7c8sPA2YA7Z2UVaXSlvII4LVOyhfPlTAzK6warTzyL2CdvD2GlModn/e3Ae4Abu+krCqVBuW7eX8+ZXh/Yvtv5IqamRVWLSa5j4gHgFmSJgMbAicBsyXdArRHxF15crb5yqqta6XpiyOBaySNzu/5Tt7eCNii2puamfUVQc0WRo2Iw8qKDurknA+UVaOilnJE/Av4BDAQeIrUPH8R2NTTdppZoVXQSi7S4JKKR/RFxMPAnr1YFzOzXqEeZyAujkr7KX+ou+MR8XptqmNmVlsdC6c2ikpbytPoftVqT3JvZoVVq5xyX6g0KG9Vtj8AWA/4JvDDmtbIzKyGmrKlHBE3dVI8SdLTwL7An2taKzOzWmmWlUcq9ADuEmdmBdcsa/R1S9JQ4GDg+ZrVxsysxlI/5XrXonKV9r54k/kf9AkYArwN7NEL9TIzqxHR0mxd4oBvl+3PBV4F7oyI6bWtkplZ7YgmyylL6g8sBlwWES/2fpXMzGpI0L+Bul/0mGmJiDnAiaRucGZmDaWjpVyDWeL6RKXpizuADYBne7EuZma9ohl7X5wFnCRpBeBe0gO+eTwpkZkVWQPF5O6DsqRzSd3eOgaHnNzJaYGHWZtZQUnQr4Gick8t5T2B7wMr90FdzMx6ReOE5J6DsgAiwrlkM2tINVzNuk9UklPubnY4M7PCa6AecRUF5anq4VsmIpxTNrOCEj3FsCKpJCjvzwIsk21mVgSi8hWii6CSoHxlRLzS6zUxM+slzdRSdj7ZzBqbmutBX+P8JGZmnWiq9EVENNLPYmbWqWZKX5iZNbxm6xJnZtawUvqicaKy0xNm1vRqOXWnpEMk3Zq3T5F0i6TTSo5/oKwaDspm1uRU0X8VXUkaBIzJ2+sDQyNic2CgpA07K6u2tg7KZtbURJolrqdXhfYB/pC3NwGuy9uTgE27KKuKg7KZNbcKUheVxGRJA4BxEXFDLhoOvJG3Z+b9zsqq4gd9Ztb0KmwIj5R0T8n+hIiYULL/Vd6fWx5S0B2Wt4eRpqNo76SsKg7KVpFNVhzOpisOp0XinDufZ82lh863P2P2HHZbdxlWHDGY52bMYuIDU+td5aY19aUX2fNLO/OfJx7jyRdep3///lz8lz9x8YXnM7d9LqdPOI+XXpzCUUceTktLC+uuvwFH//ykele7rirMGU+LiLHdHF8DGCPpAGA0MBJYB7gI2AY4D5gDfKOsrCoOytaj4a39WX3UYpxy8zOd7gOsMLyVQf1bOHHyf/ny+suy4ojBPDt9Vn0q3OSGj/gQEy//J/t+ZTcAXnpxCnfcdgsXXX7NvHP69evHRVdcQ2trK9/eb08ee/QR1hr98XpVua46csoLKyK+N++a0q0RcbSk0yTdAjwQEXflY7PLy6rhoGw9Gr3MUFoEh2yxEi+98S7Pz5g93/7EB15i5SWH8O+X3wLg8ZffZpUlHZR7S2trK62trfP2b7rhOtrb29nt89uy+hprcfRxv2SppZeZd7z/gAH067doz65b6wF9EbFZ/vOgTo59oKwaftBnPVq8tT/9WsQpNz/De+1zWaJsf8zywxgyoB+z2+YCMKutncEDFu0g0JdefeUV2t57j4suv4bBg4dwzdVXzjv270ce5rVpr7L6mmvVsYb1V6sucX2hYYKypHGSnpV0o6TrJC1Zo+tOzn/uJWmDKuvz0VrUoehmtc3lP6+mBcwffyX9Wbq/7OKDmNXWTuuA9M+pdUA/ZrW116eyi6DFhw1jk09uDsAntxjHf554HIDp01/nh989mF+e/rt6Vq/uRM/d4Yq0sGrDBOXs/IjYitRP8H9qeeGIOC8i7q3iLeOARSIoPzXtHZZfIv26/JHhrQTMtz/t7fd4+rV3WGupoQCstdRiPP2aUxd9ZezGm/LYow8D8OjDD7HCiisxZ84c/t/+e/GjY34xXypjkVSjLnF9pdGCcofhAJJ+LGmypBskrZTLfibp1lw2XNKqkq6VdJOkH+ZzNpR0n6SLgBG57ChJ20iaIGmtXHagpN0kbZfvc4+kr0kaCOwF/FLSLyUNlnRhvufE3J+xabwwczZt7cGhW67MSh8azLVPTJtv/94X3uC5GbNpmzuXw8etzNyAZ5xP7jVtbW3svtN2/PuRh/jyFz7De+++S+vgwey646d44P57+Mznd+Gqy/7KA/ffy7E/OZJdd/wU99x1R72rXVeq4FUUjfag76uStgOGkFrK34qIcTmIHiHpTOCjEbGZ3p+r73fAPhHxfA6cHwZ+DOwEvA6Ur9R9CbArcAywQ96OiPinpP7ATRHxR0nnAbdGxCRJBwJXRMSFkr6Z33NheeUl7U9aXouhI5et2YfSFy55aGq3+4C7wfWRAQMGMPGyf85Xtv7Yjebb32nX3dlp1937slqF1WirWTdaS/n83I/wLlJQHZdzwr8lddReHfgXpCgaEUHqW3h+Pm8tYHlgeEQ8FxFvAU+W3eMGYCtJSwFvRcTbwAaSJgHXAx/rpF5rAQfne+wJLNVZ5SNiQkSMjYixrcNGLOBHYGbVaqT0RaO1lDscB5wKXBsRB8K8IZCjgc8CZ+QyAU8AB0fES5L6kZa4mplbzNOB1UovHBFzJP0XOBy4NBd/F9gXmML7QbwN6Ohi8ARwfUT8taQuZlYQRepd0ZOGDMoR8UQOfFNz6zSACyNiQu6hcRvwLrAL8APg3Dy7UxvwBVJq4gpSgH2uk1v8lTQipyPHcClwOfAA7w+bnAwcJ2lj4ETgLEnfIv22dARwZw1/ZDNbCEVqCfekYYJyREwmBcKO/W3y5rFl5/2g7K0zgO3Lyu4E1i9731El21cDQ0v2zwXOLTv/NmCLkqKv9PQzmFl9OCibmRVE6l3ROFHZQdnMmlvBHuT1xEHZzJpeA8VkB2Uza3ZCDdRUdlA2s6bXQDHZQdnMmlvRhlH3xEHZzJqe0xdmZgXSQDHZQdnMml8DxWQHZTNrcg2WVHZQNrOm1mhTdzoom1nTa5yQ7KBsZouCBorKDspm1vQ8IZGZWYG0NE5MdlA2s0WAg7KZWTF4PmUzsyJRY6UvGm01azOz6qmCV0+XkDaW9C9Jt0o6JZcdnvcv6FgwubOyajgom1mTU0X/VeBZYOuI2AxYStKWwFZ5/yFgJ0lLlZdVW1sHZTNrelLPr55ExNSImJ1324DRvL+Y8yRgU2BsJ2VVcU7ZzJqaqHiWuJGS7inZnxAREz5wPWkdYBQwA5ibi2cCw/PrjbKyqjgom1nTqzA9MS0ixnZ7HelDwBnAbsAGwIfzoWGkID2zk7KqOH1hZk2vFukLSf2BPwGHRcRU4G5gy3x4G+COLsqq4qBsZs0td4nr6VWBLwIbAidImgysAtws6VZgDHBZRLxSXlZtdZ2+MLNFwMJ3VI6IC4ELy4pvB44vO+/48rJqOCibWVOr4kFfITgom1nTa6CY7KBsZs3PK4+YmRVJ48RkB2Uza34NFJMdlM2suUlOX5iZFUvjxGQHZTNrfg0Ukx2Uzaz5NVD2wkHZzJqbUEPllD33hZlZgbilbGZNr4Eayg7KZtb8vJq1mVlBqMFWs3ZQNrPm56BsZlYcTl+YmRWI0xdmZkXioGxmVhyNlL5QRNS7DoskSa8Cz9a7HjUwEphW70rYPM3y97FiRIyqxYUk/ZP0ufRkWkRsV4t7LgwHZVsoku6JiLH1rocl/vtofB5mbWZWIA7KZmYF4qBsC2tCvStg8/HfR4NzTtnMrEDcUjYzKxAHZTOzAnFQNjMrEAdlqytJ/Top87/LXubPuLj8oM/qRpIiIiR9GNgJeC8i3Hugl0lqiYi5kkYCywMvRsSr9a6XJf62tLrJAXkY8DfSkPMdJJ3TcVxqpEV8GkcOyMsCfwK+CBwiaes6V8syB2Xrc5KWz38OAJYBrgImAa3ABEkrQAradatkE5I0sGR3L2Ai8BdgG+D5/Pdhdeb0hfUpSSOAHwMPk351PofUYhsNbAwsC3wP2M+/UteOpI8D7wJzgY+QGmSbA1sDBwBLAh8Hzo6IOfWqp7mlbH0sIqYDlwMnAqtExIvAvsCjwO7AycARDsg1Nxg4iNQyXg54GvgocCMpGP8MuMkBuf7cUrY+IalfRLTn3hYjgU8BewJnRMTlkkaRWmvTI+Lleta1mXR87nn7R6SW8fci4i5JqwBrAGOBiRHxRB2rapmDsvW6kqf9ywM/Av4O3EYKzhOAu4HpwIkR0Va/mjaXks99FLAC6beRNYHdgCtIn/+rEXFnHatpZZy+sF6XA8NSwB+Bh4D1SL8uzyY9cBoIXOyAXFv5c1+O9BB1Z+B64BnSg9XtgQMBp4kKxi1l63WSBgFDSQ+W7iAFhZuAIcAvgafc06J2Svp/DwL2IAXeycD/AVOA7SLiFUlLRMTMOlbVOuGWsvUKJf3yyLE/AJtGxGWknhVHA5cCbcCbDsi1lQPyEGBH4FrgX6S+4Bvl7TNyasMBuYC8cKr1ihwYlB/u/QZYNx/6F+nX5sHAXn6o12tWBnYArgNmAf8BNiE9TD0iIubWsW7WDbeUraYk/VjSKvlX55MkrQ88BXxE0jIRcTGpn/LXIuKpula2iUhaJ/85TNKoiHiUlCI6JefqnwTWAX7mz73YnFO2mpG0MvA86ct+K+AdUqv4YuDrpIdM34mIWfWqYzPKn/tywO3Ad4GOHP45pC5wl+Qc8rzucVZcbilbTUjalDQ44RPAKNIDvDnAfqRA/TKwCuChvDUk6XigLSJuA75BGin5e+BW4ATgW8BhAA7IjcEtZasZSbsAmwGnkb7wzwVOjogr87wLoyJiSj3r2Gwk7UmaVOirpJF5nwJui4hrJH2ENDDk8Yh4rI7VtCo4KFtNSFoS+A0pZTEdOIv0gOlvwDERcWkdq9d0SgaG9CM9zBsFfJo0n8jOpK6Hk5wqajxOX9hCkdSSu70dRvrVeR/Sr857k1IVOwP31a+GzSkH5MWAG4Dfkn4r+SPwAmm03hj8/3dDcpc4WyClD41ygLgK+BywVET8TdKOpMD8E4/Uq52OFjJARLwt6f7cowVJ7wH3kGbcezAiZtexqraAHJStaiX9jz8C/FTSXaSh0g8C35b0CrA48EMH5NopWzFkS1IrebakP0TEnqS5LR4FFvfAkMblX2+sanlgyFDgFFLOeCap69WjwC2kXhY/yNNyWo2UrBhyGfAxUpe3K4Dpks4AfgF8OyJeqF8tbWH5QZ9VrKSlJlJL+KekeSwOBw4lPdh7Pc+ZbDVSmrKQtAMwnBSM7wU+FRHP5WPDIuKNulXUasItZatISUBeGvgO6d/OU8CvgTNJSzmdlv+0GilNWeQpONtIi8xeCuwCrCbpbEkDHJCbg4OyVSQHhiVJq4a8EhEzSJPd/BbYlDR0+nsR8VL9atl88ue+DPBPYJOIuI60yOwbpGWdfsD7Q6mtCTh9Yd3qmAYyb68CfD0iflByfAQQQP+ImFanajalnCYScCTwbkScWHLs8/nYY14xpLm4pWxdyt3eQtLQ3FprAzaWtGY+/lnSMOo3HZBrJw8IIZK5wAPAMpKG5+NbArdHxGUOyM3HLWXrVu72djIwDbiatNr07qTuWLsCu0XEf+pXw+ZSspbhisC3gcdIoyQHAquT5hH5X+CzEfFs/WpqvcVB2T5A0uCImCWpP+kh3kWkmceOJ82vsAqpj/vbHU/+beHkVMUqpFVC3gEmAheQZn/bnLR81lqkeal/7y/C5uXBIzZPHi59KtAi6ZKImCzpcWAJ4GvAV0gTDrVExOS6VbTJ5M/9fFJu/nHgNeC/+XUAcAjpwd7lwEVeqaW5OadswLyW2h9JU2z+GfhN7oL1H9KghAtJa+odA0ytVz2b1EWkFPJXSHNXTAWeILWWjyb1CT+ZNFLPAbnJuaVsHcaRpnncOyLaJD1I6gd7BWme5DXyOd+MiMfrVclmI2kAabXpJfODvA1JQ6gvI3V924w0R/WREeGVpxcBzinbPJL+lzQf7/8BnyRNv7kzcA3pQd9Et9RqL8/2tgPwTVI3t28BqwJfJo2S/K57tyw6HJSNPBqsLW9/jRQUdomIF3P3t+HAc57LovdIGkzq1bIW8KuImJIftI7ygJxFi4PyIkzS8p2tBCLpW8A2wMHuXdF3cot5e2AL4LyI8DzUiyAH5UWUpOWA9YGlSIucHgpML2kxf4eU3/yK13brO3n2vW1ISzo5h7wIclBeROUHTLsBxwFXRMS3O8pLAvMIz/jW90qHttuix0F5EVM2DWR/0rSbLwOvRcTlkoZGxFt1raTZIsxBeRGUJ0o/FngIuJ+02OYIYDXSyL1DvJSQWX24n/IiomMC9PyU/w/AxaQJhtYhjSJ7AlgMuMwB2ax+HJSbWEduUtIBwMqSfhER0yXdDlwVES9J+gqwcUT8jLSShZnVkYdZN7GSh0XnAG8DB+fURRuwcx5G/S7wsdwdy8zqzDnlJibpGtLcFZdGxPWSTiAtbnovaQj1QNKQ3v08dNqsGJy+aFJ5ovTrgc8AG0q6hDRU+iuk3PFvI+JVd3szKxa3lJuYpFbg06Tl6KeTelr8mDS/xZURsWsdq2dmnXBLuYlFxGxJNwIDgA2AqyNiR0mrkia+MbOCcUt5EZAf4n2GNPPb7yPigfrWyMy64pbyIiAi3pZ0NfAe8IEJiMysONxSXoR4TgWz4nNQNjMrEA8eMTMrEAdlM7MCcVA2MysQB2UrPEm7SoqS/b0k1WXOZ0lXSTqvm+PjJIWkkVVcc7KkMxayXivl+45dmOtY/Tko2wKRdF4OAiGpTdLTkk7qo4mNJgIfrfRkSc9IOqwX62NWM+6nbAtjEvBV0ojBzYGzSfNqfLP8xLzKSXstuuRFxCxg1sJex6yI3FK2hfFuREyNiOcj4s/ABcBOAJKOkvRITjU8RZoidDFJS0iaIOkVSW9Kuqn8V25JX5P0rKR3JF0FLF12/APpC0k7SLpT0ixJr0m6UlKrpMnAisCJHS37kvd8It//HUlTJP1W0rCS40PybwRvSXpZ0pHVfkCSlpR0oaQXct0elbR3J6f2l3SapOn5daKklpLrDJR0fL7OO5LulrRttfWx4nNQtlqaRWo1d1gZ+DLwRWBdUmD+O2n5qR2B9YCbgRvyPM9I2hg4D5gAjAGuBH7a3U0lbQdcAVxHmuNjK+Am0r/vXYAX8jWWzS8krQ1cm9+3bj5vDHBuyaVPIk3e9AVgfK7vFhV/GkkrcF/+eUcDpwG/kzS+7Lw9cn03Bb4B7A8cXHL896RpVr8MfJy0esyVktatsj5WdBHhl19Vv0iB86qS/Y1IU4NOzPtHkSbTX7rknK2Bt4DBZdd6APhu3v4zcF3Z8bPJc/bn/b2At0r2bwP+0k1dnwEOKyv7I3BOWdkYIIClgKGkL5E9So4PBWYA53Vzr3H5GiO7OecvwNkl+5OBJ8mDuXLZD4EX8vYqwFxghbLrXAb8Jm+vlO87tt7/NvxauJdzyrYwtstphP6kFvLlwIElx1+IiJdL9jcAhgCvSvNNUtdKCjwAa5Fax6VuB/bpph7rkb4kqrEBsKqk3UvKOiq1CvAOaRGA2zsORsRbkh6u5iZ5XuvvA7uTfkMYlK87uezUOyJH1+x24JicTlk/1+3fZZ/bIOCGaupjxeegbAvjZtKv2W3AixHRVnb87bL9FuBl0kPBcm/UvnrdaiG1wE/p5NgUYPUa3ecw4FDgIOBh0m8KPye1xivVQmoFb0j6rEv5gWeTcVC2hfFORPxfFeffR3poNzcinu7inMeATcrKyvfL3U/K+Z7VxfH3gH6d1GV0V/XPDyfb8r2fzmWLkfK5T/VQn1KbkRYUOD9fQ6SAP6PsvI3LJozahPRF94ak+0kt5WUi4sYq7m0NyA/6rC9NIuV/L5e0vaSVJW0q6WhJHa3nXwHbSDpC0mqS9gN27uG6xwJflPQzSR+TNFrSIZKG5OPPAJtLWr5kUMfxwEaSzpS0nqRVJe0o6XeQUhWkBWePl/QpSaNJDwHLg3tPngTGS9pM0prAGaQHoOWWA06VtIakXYHDya34iHiS1LPlPKWBNB+VNFbSYZJ2qbI+VnAOytZncitwB1Ie9CzgCeAiYA3gxXzOHaT88TeBh0i9Io7q4bpXkwL39qRW802kHhhz8yk/Bj5CauG+mt/zEKknxUr5/AeB40jplQ6HATcCl+Y/HyGlbKrxM+Au4B/5vW+TAmy5C0gB/07SZ3MO86dW9ib1wDgBeBy4Ktf/2SrrYwXnqTvNzArELWUzswJxUDYzKxAHZTOzAnFQNjMrEAdlM7MCcVA2MysQB2UzswJxUDYzKxAHZTOzAvn/VzyJBvZVZdEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_scores_Caucasian = new_scores[new_scores['race'] == 'Caucasian']\n",
    "# performance.accuracy_score(new_scores_Caucasian['two_year_recid'],new_scores_Caucasian['decile_score'],normalize=True)\n",
    "plot_conf_matrix(new_scores_Caucasian.two_year_recid, new_scores_Caucasian.decile_score, class_names=['Not Recedivate', 'Recedivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is similarly unbalanced here, interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sLjABvThjkIF"
   },
   "source": [
    "#### Confusion Matrix - Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "nK_6aGkijkIF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6868085106382978\n",
      "Precision = 0.6706947175082781\n",
      "Recall = 0.5838671649094711\n",
      "[[709  53]\n",
      " [315  98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_scores_female = new_scores[new_scores['sex'] == 'Female']\n",
    "plot_conf_matrix(new_scores_female.two_year_recid, new_scores_female.decile_score, class_names=['Not Recedivate', 'Recedivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "diiv5IQzppNF"
   },
   "source": [
    "#### Confusion Matrix - Male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "RMZZI4ACp0sX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6215729437662597\n",
      "Precision = 0.6714706491896825\n",
      "Recall = 0.6093971337593926\n",
      "[[2357  244]\n",
      " [1647  749]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_scores_male = new_scores[new_scores['sex'] == 'Male']\n",
    "plot_conf_matrix(new_scores_male.two_year_recid, new_scores_male.decile_score, class_names=['Not Recedivate', 'Recedivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U-T0AKG-qCrQ"
   },
   "source": [
    "Next, we examine the results of running three simple classifiers on the COMPAS data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgIqgQvQjkIG"
   },
   "source": [
    "### SVM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "aFG5NarwjkIG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the SVM on testing set 64.46% \n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm_classifier = SVC(gamma=0.001, C=100.)\n",
    "svm_classifier.fit(dataset_conditioned_train.features, dataset_conditioned_train.labels.ravel())\n",
    "\n",
    "pred = svm_classifier.predict(dataset_conditioned_validate_test.features)\n",
    "\n",
    "print('Accuracy of the SVM on testing set {0:.2f}% '\n",
    "      .format(performance.accuracy_score(dataset_conditioned_validate_test.labels, pred, normalize=True)*100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a3YuSu4ujkIG"
   },
   "source": [
    "#### Confusion Matrix for African Americans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "WbPuVcIVjkIG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6492146596858639\n",
      "Precision = 0.652818721112203\n",
      "Recall = 0.6473876340406115\n",
      "[[260 208]\n",
      " [127 360]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "african_american_prediction = svm_classifier.predict(african_american_samples)\n",
    "plot_conf_matrix(african_american_samples_label.to_numpy().ravel(), african_american_prediction, class_names=['Not Recidivate', 'Recidivate'] )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mcy34QV4jkIG"
   },
   "source": [
    "#### Confusion Matrix for Caucasians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "yIYPw9WKjkIG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6375198728139905\n",
      "Precision = 0.6256913746342923\n",
      "Recall = 0.6221616853466169\n",
      "[[262 103]\n",
      " [125 139]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caucasian_prediction = svm_classifier.predict(caucasian_samples)\n",
    "plot_conf_matrix(caucasian_samples_label.to_numpy().ravel(), caucasian_prediction, class_names=['Not Recidivate', 'Recidivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nABuudxc0Gpf"
   },
   "source": [
    "#### Confusion Matrix for Females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "r_m68drL0Ppg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.6306306306306306\n",
      "Precision = 0.6056083220262325\n",
      "Recall = 0.6100290583523129\n",
      "[[146  68]\n",
      " [ 55  64]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_prediction = svm_classifier.predict(female_samples)\n",
    "plot_conf_matrix(female_samples_label.to_numpy().ravel(), female_prediction, class_names=['Not Recidivate', 'Recidivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TH4CbSF20K7j"
   },
   "source": [
    "#### Confusion Matrix for Males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "MuaM3Mve0WPG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting the Confusion Matrix\n",
      "CCR = 0.64828137490008\n",
      "Precision = 0.6488941914160836\n",
      "Recall = 0.6478612400564405\n",
      "[[376 243]\n",
      " [197 435]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_prediction = svm_classifier.predict(male_samples)\n",
    "plot_conf_matrix(male_samples_label.to_numpy().ravel(), male_prediction, class_names=['Not Recidivate', 'Recidivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFc1QZGtjkIK"
   },
   "source": [
    "### **Question 3: What biases did the classifier show?**\n",
    "After training and testing this SVM classifier, what did you notice about the results above? How did the confusion matrices from the output of the classifiers compare to those from the COMPAS data? What kinds of biases did both show? Answer these questions in the cell below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GtQA1g3kjkIK"
   },
   "source": [
    "**Compas and Unbalanced Error**\n",
    "\n",
    "Across all race and gender splits, the Compas rates really underestimated overall recedivism rates, relative to their actual rates. We notice this because the bottom-left quadrant is consistently more filled than the top right quadrant. This means that the balance of false positive vs. false negative errors is off by anywhere from 5-10 points in the four categories. Because this misbalance results in more false negatives and less false positives, this misbalance causes recetivism to be less visible in each of these categories. Thus, the larger the misbalance, the less visible the recetivism rate is for that group. \n",
    "Applying this rule to the confusion matrices for our different populations reveals obvious bias. \n",
    "\n",
    "One way of analyzing this disaprity is precision and recall. In the Compas risk assessment, precision is more than 10% higher than recall for caucasians, whereas it precision is only 3% higher than recall for african-americans. This gap makes it appear that the african-american recedivism rate is higher than caucasians, but really the error types are unequally unbalanced. There is also a 2-3 point difference between men and women. Women have a 10.5% difference, whereas men only have a 6% difference. This confirms our graphical intuition that african-american and male recedivism is more visible than caucasian and white recedivism.\n",
    "\n",
    "**SVM**\n",
    "\n",
    "The SVM classification algorithm does a much better job of balancing precision and recall, with these two statistics being within 1% of each other across all four categories. The improved balancing in this algorithm also boosts the overall CCR rate about 1%. This comes in part from a 4% increase from african-americans and a 3% increase from males. Interestingly though, this is offset by a 3% and 6% loss in CCR by caucasians and females respectively.\n",
    "\n",
    "Another way to recognize the false positive/false negative disparity is the false positive rate (FPR) and the false negative rate (FNR). For example, although african-americans have an FPR that is 18% higher than their FNR, caucasians have an FPR that is actually 19% *less* than their FNR. However, unlike precision and recall, the SVM model does not improve this lapse in FPR and FNR. In the SVR models, both FPRs are far below the FNR rates. That being said, the FPR for african-americans is only 46% lower than the FNR, whereas the FPR for caucasians is a staggering 80% lower than their FNR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TD:\n",
    "1. calc fpr/fnr for each one\n",
    "2. rewrite svm\n",
    "3. tweak classifiers\n",
    "4. implement neural model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nryVxMw_jkIL"
   },
   "source": [
    "## Building fairer classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HLQPqbB8jkIL"
   },
   "source": [
    "### **Question 4: How can these classifiers be improved?**\n",
    "What modifications can be made on the above classifiers that would result in fairer outcomes? Write some potential modifications in the cell below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SNW-kYdYKCI8"
   },
   "source": [
    "**Improving Our Models**\n",
    "\n",
    "As we learned from our talk with Dr. Heidari, there are a number of ways of defining statistical fairness in criminal justice. The Compas and SVM models embrace just one of many different definitions of fairness. These models define fairness as having equal recedivism rates across categories like race and gender. Proponents of other definitions of fairness argue that having equal recedivism rates across categories is actually a harmful assumption because it is known to be untrue--african-americans are subject to systemic racism that say, caucasians, simply are not. To resolve this, a fairer model might utilize a definition of fairness that specifies equality in error: that the false positive rate should be equal to the false negative rate for each race and gender. This was a place in which there was a huge disparity across races for both models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P15azilhjkIQ"
   },
   "source": [
    "### **Question 5 - Implement a Fairer classifier**\n",
    "Using your suggested modifications above, modify one of the simple classifiers in order to produce a fairer model. Implement this model and plot the resulting confusion matrices below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "id": "ocjxkuy-Kxd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the SVM on testing set 61.74% \n"
     ]
    }
   ],
   "source": [
    "#training new model\n",
    "new_svm_classifier = SVC(gamma='auto', C=1, kernel='sigmoid') #using defaults\n",
    "new_svm_classifier.fit(dataset_conditioned_train.features, dataset_conditioned_train.labels.ravel())\n",
    "\n",
    "new_pred = new_svm_classifier.predict(dataset_conditioned_validate_test.features)\n",
    "\n",
    "print('Accuracy of the SVM on testing set {0:.2f}% '\n",
    "      .format(performance.accuracy_score(dataset_conditioned_validate_test.labels, new_pred, normalize=True)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREVIOUS SVM MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.6492146596858639\n",
      "Precision = 0.652818721112203\n",
      "Recall = 0.6473876340406115\n",
      "[[260 208]\n",
      " [127 360]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NEW SVM MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.612565445026178\n",
      "Precision = 0.6126206140350877\n",
      "Recall = 0.6126621211323471\n",
      "[[289 179]\n",
      " [191 296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#old model\n",
    "print(\"PREVIOUS SVM MODEL\")\n",
    "african_american_prediction = svm_classifier.predict(african_american_samples)\n",
    "plot_conf_matrix(african_american_samples_label.to_numpy().ravel(), african_american_prediction, class_names=['Not Recidivate', 'Recidivate'] )\n",
    "\n",
    "#new model\n",
    "print(\"NEW SVM MODEL\")\n",
    "new_african_american_prediction = new_svm_classifier.predict(african_american_samples)\n",
    "plot_conf_matrix(african_american_samples_label.to_numpy().ravel(), new_african_american_prediction, class_names=['Not Recidivate', 'Recidivate'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREVIOUS SVM MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.6375198728139905\n",
      "Precision = 0.6256913746342923\n",
      "Recall = 0.6221616853466169\n",
      "[[262 103]\n",
      " [125 139]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NEW SVM MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.6248012718600954\n",
      "Precision = 0.6105772450277908\n",
      "Recall = 0.6028175591531756\n",
      "[[270  95]\n",
      " [141 123]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#old model\n",
    "print(\"PREVIOUS SVM MODEL\")\n",
    "caucasian_prediction = svm_classifier.predict(caucasian_samples)\n",
    "plot_conf_matrix(caucasian_samples_label.to_numpy().ravel(), caucasian_prediction, class_names=['Not Recidivate', 'Recidivate'])\n",
    "\n",
    "#new model\n",
    "print(\"NEW SVM MODEL\")\n",
    "new_caucasian_prediction = new_svm_classifier.predict(caucasian_samples)\n",
    "plot_conf_matrix(caucasian_samples_label.to_numpy().ravel(), new_caucasian_prediction, class_names=['Not Recidivate', 'Recidivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**New Model Performance**\n",
    "\n",
    "With just three tweaks to the model*, we were able to more effectively balance the false positive and false negative rates. Although we raised the FNRs for both groups, the FPRs came down. For the old model, african-americans had a net 18% between the FPR and FNR, and caucasians had a net -19%. For the new SVM model, african-americans had a net -1% between the FPR and FNR, and caucasians had a net -27%. The total difference between rates dropped from 37% to 26%, while the accuracy only dropped 3%\n",
    "\n",
    "* We dropped the regularization hyperparameter to 1, changed the kernel coefficient to 1/num_of_features, and specified the kernel type to a sigmoid function. These parameters were changed with the help of sklearn's documentation at: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KtdSHHFrXhn3"
   },
   "source": [
    "### **Extra Credit: Implement a deep learning based model**\n",
    "For extra credit, implement a fair deep learning based model in the cell below and measure its performance using confusion matrices (you can add extra cells if needed). How did its results compare to those of the fair SVM classifier? Write a one-sentence analysis of the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "id": "5wSCclGVX9u9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the MLP on testing set 64.65% \n"
     ]
    }
   ],
   "source": [
    "model = MLPClassifier(random_state=1, max_iter=1000)\n",
    "model.fit(dataset_conditioned_train.features, dataset_conditioned_train.labels.ravel())\n",
    "preds = model.predict(dataset_conditioned_validate_test.features)\n",
    "\n",
    "print('Accuracy of the MLP on testing set {0:.2f}% '\n",
    "      .format(performance.accuracy_score(dataset_conditioned_validate_test.labels, preds, normalize=True)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.612565445026178\n",
      "Precision = 0.6126206140350877\n",
      "Recall = 0.6126621211323471\n",
      "[[289 179]\n",
      " [191 296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.6523560209424084\n",
      "Precision = 0.6528823100634951\n",
      "Recall = 0.6527602274522193\n",
      "[[315 153]\n",
      " [179 308]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.6248012718600954\n",
      "Precision = 0.6105772450277908\n",
      "Recall = 0.6028175591531756\n",
      "[[270  95]\n",
      " [141 123]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP MODEL\n",
      "plotting the Confusion Matrix\n",
      "CCR = 0.6375198728139905\n",
      "Precision = 0.6253866442872558\n",
      "Recall = 0.608535699460357\n",
      "[[288  77]\n",
      " [151 113]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#african-american predictions\n",
    "#new model\n",
    "print(\"SVM MODEL\")\n",
    "new_african_american_prediction = new_svm_classifier.predict(african_american_samples)\n",
    "plot_conf_matrix(african_american_samples_label.to_numpy().ravel(), new_african_american_prediction, class_names=['Not Recidivate', 'Recidivate'] )\n",
    "\n",
    "print(\"MLP MODEL\")\n",
    "#mlp model\n",
    "mlp_african_american_prediction = model.predict(african_american_samples)\n",
    "plot_conf_matrix(african_american_samples_label.to_numpy().ravel(), mlp_african_american_prediction, class_names=['Not Recidivate', 'Recidivate'] )\n",
    "\n",
    "\n",
    "#caucasian predictions\n",
    "#new model\n",
    "print(\"SVM MODEL\")\n",
    "new_caucasian_prediction = new_svm_classifier.predict(caucasian_samples)\n",
    "plot_conf_matrix(caucasian_samples_label.to_numpy().ravel(), new_caucasian_prediction, class_names=['Not Recidivate', 'Recidivate'])\n",
    "\n",
    "print(\"MLP MODEL\")\n",
    "#mlp model\n",
    "mlp_caucasian_prediction = model.predict(caucasian_samples)\n",
    "plot_conf_matrix(caucasian_samples_label.to_numpy().ravel(), mlp_caucasian_prediction, class_names=['Not Recidivate', 'Recidivate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model I chose is the relatively simple multi-layer perceptron model. Interestingly, this model performed similarly to the original SVM model, with an increase in accuracy but subsequent loss of FPR/FNR balance."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Homework 1.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.8.7"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
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