From 80c778a06e0fa30b49cac29f3880870c7cc336d4 Mon Sep 17 00:00:00 2001
From: lbecker <lbecker@rhrk.uni-kl.de>
Date: Sun, 18 Aug 2024 06:06:30 +0200
Subject: [PATCH] Upload New File
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "74864fce-6ed8-4a3a-845f-67313da37576",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# COMPAS Race Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e984129a-f74d-4dbf-b0fc-78cb977d0f29",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import display,HTML\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import warnings\n",
+ "from sklearn.model_selection import train_test_split,StratifiedKFold\n",
+ "from datamodel import datamodel\n",
+ "from datetime import datetime\n",
+ "from logreg_builder import logreg_builder\n",
+ "from visualisation_model import visualisation_model\n",
+ "from measure_model import measure_model\n",
+ "pd.set_option('display.max_columns', None)\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e91a299b-2185-4753-9ca6-e388fb5728e6",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Pre-Processing Recommended by ProPublica and Barenstein\n",
+ "At first the data[^1] is loaded. Note that the dataset does not include all attributes but only the ones chosen by the ProPublica team and the attributes 'dob' and 'screening_date' for further processing as proposed by Barenstein.\n",
+ "\n",
+ "[^1]: https://github.com/propublica/compas-analysis/blob/master/compas-scores-two-years.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bd79d11e-0a7a-4e48-8b92-24fe74189939",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compas_data = pd.read_csv('compas-scores-two-years.csv',usecols=['age','compas_screening_date','dob', 'c_charge_degree', 'race', 'age_cat', 'score_text', 'sex', 'priors_count', \n",
+ " 'days_b_screening_arrest', 'decile_score', 'is_recid', 'two_year_recid', 'c_jail_in', 'c_jail_out'])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2cb41526-2eff-45ff-9df1-ac1743504dd0",
+ "metadata": {},
+ "source": [
+ "## Pre-Processing Recommended by ProPublica\n",
+ "The ProPublica team removes values that are invalid or assumed to be biased and the attribute 'length of stay' is extracted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a1beec52-b61a-4b82-8db0-6b83382d0f59",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></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>decile_score</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_charge_degree</th>\n",
+ " <th>is_recid</th>\n",
+ " <th>score_text</th>\n",
+ " <th>two_year_recid</th>\n",
+ " <th>length_of_stay</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\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>1</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-08-13</td>\n",
+ " <td>2013-08-14</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\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>3</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-01-26</td>\n",
+ " <td>2013-02-05</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Low</td>\n",
+ " <td>1</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\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>4</td>\n",
+ " <td>4</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-04-13</td>\n",
+ " <td>2013-04-14</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Low</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\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>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2013-11-30</td>\n",
+ " <td>2013-12-01</td>\n",
+ " <td>M</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\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>6</td>\n",
+ " <td>14</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-02-18</td>\n",
+ " <td>2014-02-24</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Medium</td>\n",
+ " <td>1</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7209</th>\n",
+ " <td>2013-11-23</td>\n",
+ " <td>Male</td>\n",
+ " <td>1992-07-17</td>\n",
+ " <td>23</td>\n",
+ " <td>Less than 25</td>\n",
+ " <td>African-American</td>\n",
+ " <td>7</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-11-22</td>\n",
+ " <td>2013-11-24</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Medium</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7210</th>\n",
+ " <td>2014-02-01</td>\n",
+ " <td>Male</td>\n",
+ " <td>1993-03-25</td>\n",
+ " <td>23</td>\n",
+ " <td>Less than 25</td>\n",
+ " <td>African-American</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-01-31</td>\n",
+ " <td>2014-02-02</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7211</th>\n",
+ " <td>2014-01-14</td>\n",
+ " <td>Male</td>\n",
+ " <td>1958-10-01</td>\n",
+ " <td>57</td>\n",
+ " <td>Greater than 45</td>\n",
+ " <td>Other</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-01-13</td>\n",
+ " <td>2014-01-14</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7212</th>\n",
+ " <td>2014-03-09</td>\n",
+ " <td>Female</td>\n",
+ " <td>1982-11-17</td>\n",
+ " <td>33</td>\n",
+ " <td>25 - 45</td>\n",
+ " <td>African-American</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-03-08</td>\n",
+ " <td>2014-03-09</td>\n",
+ " <td>M</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7213</th>\n",
+ " <td>2014-06-30</td>\n",
+ " <td>Female</td>\n",
+ " <td>1992-12-18</td>\n",
+ " <td>23</td>\n",
+ " <td>Less than 25</td>\n",
+ " <td>Hispanic</td>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>-2.0</td>\n",
+ " <td>2014-06-28</td>\n",
+ " <td>2014-06-30</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Low</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>6172 rows × 16 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " compas_screening_date sex dob age age_cat \\\n",
+ "0 2013-08-14 Male 1947-04-18 69 Greater than 45 \n",
+ "1 2013-01-27 Male 1982-01-22 34 25 - 45 \n",
+ "2 2013-04-14 Male 1991-05-14 24 Less than 25 \n",
+ "5 2013-11-30 Male 1971-08-22 44 25 - 45 \n",
+ "6 2014-02-19 Male 1974-07-23 41 25 - 45 \n",
+ "... ... ... ... ... ... \n",
+ "7209 2013-11-23 Male 1992-07-17 23 Less than 25 \n",
+ "7210 2014-02-01 Male 1993-03-25 23 Less than 25 \n",
+ "7211 2014-01-14 Male 1958-10-01 57 Greater than 45 \n",
+ "7212 2014-03-09 Female 1982-11-17 33 25 - 45 \n",
+ "7213 2014-06-30 Female 1992-12-18 23 Less than 25 \n",
+ "\n",
+ " race decile_score priors_count days_b_screening_arrest \\\n",
+ "0 Other 1 0 -1.0 \n",
+ "1 African-American 3 0 -1.0 \n",
+ "2 African-American 4 4 -1.0 \n",
+ "5 Other 1 0 0.0 \n",
+ "6 Caucasian 6 14 -1.0 \n",
+ "... ... ... ... ... \n",
+ "7209 African-American 7 0 -1.0 \n",
+ "7210 African-American 3 0 -1.0 \n",
+ "7211 Other 1 0 -1.0 \n",
+ "7212 African-American 2 3 -1.0 \n",
+ "7213 Hispanic 4 2 -2.0 \n",
+ "\n",
+ " c_jail_in c_jail_out c_charge_degree is_recid score_text \\\n",
+ "0 2013-08-13 2013-08-14 F 0 Low \n",
+ "1 2013-01-26 2013-02-05 F 1 Low \n",
+ "2 2013-04-13 2013-04-14 F 1 Low \n",
+ "5 2013-11-30 2013-12-01 M 0 Low \n",
+ "6 2014-02-18 2014-02-24 F 1 Medium \n",
+ "... ... ... ... ... ... \n",
+ "7209 2013-11-22 2013-11-24 F 0 Medium \n",
+ "7210 2014-01-31 2014-02-02 F 0 Low \n",
+ "7211 2014-01-13 2014-01-14 F 0 Low \n",
+ "7212 2014-03-08 2014-03-09 M 0 Low \n",
+ "7213 2014-06-28 2014-06-30 F 1 Low \n",
+ "\n",
+ " two_year_recid length_of_stay \n",
+ "0 0 1 \n",
+ "1 1 10 \n",
+ "2 1 1 \n",
+ "5 0 1 \n",
+ "6 1 6 \n",
+ "... ... ... \n",
+ "7209 0 2 \n",
+ "7210 0 2 \n",
+ "7211 0 1 \n",
+ "7212 0 1 \n",
+ "7213 1 2 \n",
+ "\n",
+ "[6172 rows x 16 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Rows that contain invalid values are removed\n",
+ "compas_data = compas_data[compas_data['is_recid']!=-1]\n",
+ "compas_data = compas_data[compas_data['c_charge_degree']!='O']\n",
+ "compas_data = compas_data[compas_data['score_text']!='N/A']\n",
+ "#The ProPublica team assumes a data failure if a screening does not occur within 30 days\n",
+ "compas_data = compas_data[(compas_data['days_b_screening_arrest']<=30) & (compas_data['days_b_screening_arrest']>=-30)]\n",
+ "#extraction of length of stay in days\n",
+ "compas_data['c_jail_in'] = compas_data['c_jail_in'].apply(lambda x: x[:10])\n",
+ "compas_data['c_jail_in'] = pd.to_datetime(compas_data['c_jail_in'])\n",
+ "compas_data['c_jail_out'] = compas_data['c_jail_out'].apply(lambda x: x[:10])\n",
+ "compas_data['c_jail_out'] = pd.to_datetime(compas_data['c_jail_out'])\n",
+ "compas_data['length_of_stay'] = abs((compas_data['c_jail_out'] - compas_data['c_jail_in'])).dt.days\n",
+ "compas_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "78496e0f-423e-4781-80da-94d2625cbb7d",
+ "metadata": {},
+ "source": [
+ "## Further Pre-Processing Recommended by Barenstein\n",
+ "As examined by Barenstein, the COMPAS data includes rows observed less than two years distorting the data since they can only be reoffending to be included in the data. Thus these rows are removed as well.\n",
+ "Moreover, Barenstein notes that the age attribute is calculated based on day of birth and the time of data collection instead of the screening date. He argues that this is counterintuitive since age might be a relevant attribute for COMPAS."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0d5cc19-03b7-4878-8b28-ccf61a054b0f",
+ "metadata": {},
+ "source": [
+ "## Remove Entries Screened Less Than Two Years Before End of Data Collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "894bebda-3c98-46fc-a04d-c121cd068179",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compas_data['compas_screening_date'] = pd.to_datetime(compas_data['compas_screening_date'])\n",
+ "compas_data = compas_data[compas_data['compas_screening_date']<datetime(2014, 4, 1)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a46bd64-c7f9-410d-a54a-88326a5c7acc",
+ "metadata": {},
+ "source": [
+ "## Retrieve Age at COMPAS Screening Date\n",
+ "The age is retrieved by calculating the difference of days between day of birth and the screening date by 365:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "8220c143-bfcf-40fd-b49d-753435daa53a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></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>decile_score</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_charge_degree</th>\n",
+ " <th>is_recid</th>\n",
+ " <th>score_text</th>\n",
+ " <th>two_year_recid</th>\n",
+ " <th>length_of_stay</th>\n",
+ " <th>age_at_c_screening_date</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\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>1</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-08-13</td>\n",
+ " <td>2013-08-14</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\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>3</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-01-26</td>\n",
+ " <td>2013-02-05</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Low</td>\n",
+ " <td>1</td>\n",
+ " <td>10</td>\n",
+ " <td>31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\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>4</td>\n",
+ " <td>4</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-04-13</td>\n",
+ " <td>2013-04-14</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Low</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\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>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2013-11-30</td>\n",
+ " <td>2013-12-01</td>\n",
+ " <td>M</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>42</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\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>6</td>\n",
+ " <td>14</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-02-18</td>\n",
+ " <td>2014-02-24</td>\n",
+ " <td>F</td>\n",
+ " <td>1</td>\n",
+ " <td>Medium</td>\n",
+ " <td>1</td>\n",
+ " <td>6</td>\n",
+ " <td>39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7208</th>\n",
+ " <td>2013-10-20</td>\n",
+ " <td>Male</td>\n",
+ " <td>1995-06-28</td>\n",
+ " <td>20</td>\n",
+ " <td>Less than 25</td>\n",
+ " <td>African-American</td>\n",
+ " <td>9</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-10-19</td>\n",
+ " <td>2013-10-20</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>High</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7209</th>\n",
+ " <td>2013-11-23</td>\n",
+ " <td>Male</td>\n",
+ " <td>1992-07-17</td>\n",
+ " <td>23</td>\n",
+ " <td>Less than 25</td>\n",
+ " <td>African-American</td>\n",
+ " <td>7</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2013-11-22</td>\n",
+ " <td>2013-11-24</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Medium</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7210</th>\n",
+ " <td>2014-02-01</td>\n",
+ " <td>Male</td>\n",
+ " <td>1993-03-25</td>\n",
+ " <td>23</td>\n",
+ " <td>Less than 25</td>\n",
+ " <td>African-American</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-01-31</td>\n",
+ " <td>2014-02-02</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7211</th>\n",
+ " <td>2014-01-14</td>\n",
+ " <td>Male</td>\n",
+ " <td>1958-10-01</td>\n",
+ " <td>57</td>\n",
+ " <td>Greater than 45</td>\n",
+ " <td>Other</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-01-13</td>\n",
+ " <td>2014-01-14</td>\n",
+ " <td>F</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7212</th>\n",
+ " <td>2014-03-09</td>\n",
+ " <td>Female</td>\n",
+ " <td>1982-11-17</td>\n",
+ " <td>33</td>\n",
+ " <td>25 - 45</td>\n",
+ " <td>African-American</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>2014-03-08</td>\n",
+ " <td>2014-03-09</td>\n",
+ " <td>M</td>\n",
+ " <td>0</td>\n",
+ " <td>Low</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>31</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5297 rows × 17 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " compas_screening_date sex dob age age_cat \\\n",
+ "0 2013-08-14 Male 1947-04-18 69 Greater than 45 \n",
+ "1 2013-01-27 Male 1982-01-22 34 25 - 45 \n",
+ "2 2013-04-14 Male 1991-05-14 24 Less than 25 \n",
+ "5 2013-11-30 Male 1971-08-22 44 25 - 45 \n",
+ "6 2014-02-19 Male 1974-07-23 41 25 - 45 \n",
+ "... ... ... ... ... ... \n",
+ "7208 2013-10-20 Male 1995-06-28 20 Less than 25 \n",
+ "7209 2013-11-23 Male 1992-07-17 23 Less than 25 \n",
+ "7210 2014-02-01 Male 1993-03-25 23 Less than 25 \n",
+ "7211 2014-01-14 Male 1958-10-01 57 Greater than 45 \n",
+ "7212 2014-03-09 Female 1982-11-17 33 25 - 45 \n",
+ "\n",
+ " race decile_score priors_count days_b_screening_arrest \\\n",
+ "0 Other 1 0 -1.0 \n",
+ "1 African-American 3 0 -1.0 \n",
+ "2 African-American 4 4 -1.0 \n",
+ "5 Other 1 0 0.0 \n",
+ "6 Caucasian 6 14 -1.0 \n",
+ "... ... ... ... ... \n",
+ "7208 African-American 9 0 -1.0 \n",
+ "7209 African-American 7 0 -1.0 \n",
+ "7210 African-American 3 0 -1.0 \n",
+ "7211 Other 1 0 -1.0 \n",
+ "7212 African-American 2 3 -1.0 \n",
+ "\n",
+ " c_jail_in c_jail_out c_charge_degree is_recid score_text \\\n",
+ "0 2013-08-13 2013-08-14 F 0 Low \n",
+ "1 2013-01-26 2013-02-05 F 1 Low \n",
+ "2 2013-04-13 2013-04-14 F 1 Low \n",
+ "5 2013-11-30 2013-12-01 M 0 Low \n",
+ "6 2014-02-18 2014-02-24 F 1 Medium \n",
+ "... ... ... ... ... ... \n",
+ "7208 2013-10-19 2013-10-20 F 0 High \n",
+ "7209 2013-11-22 2013-11-24 F 0 Medium \n",
+ "7210 2014-01-31 2014-02-02 F 0 Low \n",
+ "7211 2014-01-13 2014-01-14 F 0 Low \n",
+ "7212 2014-03-08 2014-03-09 M 0 Low \n",
+ "\n",
+ " two_year_recid length_of_stay age_at_c_screening_date \n",
+ "0 0 1 66 \n",
+ "1 1 10 31 \n",
+ "2 1 1 21 \n",
+ "5 0 1 42 \n",
+ "6 1 6 39 \n",
+ "... ... ... ... \n",
+ "7208 0 1 18 \n",
+ "7209 0 2 21 \n",
+ "7210 0 2 20 \n",
+ "7211 0 1 55 \n",
+ "7212 0 1 31 \n",
+ "\n",
+ "[5297 rows x 17 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "compas_data['dob'] = pd.to_datetime(compas_data['dob'])\n",
+ "compas_data['age_at_c_screening_date']= abs((compas_data['compas_screening_date']-compas_data['dob'])).dt.days // 365\n",
+ "compas_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c3074a26-661a-47df-bef8-56b0a51c6f88",
+ "metadata": {},
+ "source": [
+ "## Manual Encoding of Race and Charge Degree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "d7439b45-5acc-4d3f-b37e-8f4364eb3df5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['F', 'M'], dtype=object)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "compas_data['c_charge_degree'].unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ef281120-1fcb-4773-a8e1-9ee1a7c642d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compas_data['c_charge_degree']=(compas_data['c_charge_degree']=='F')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "12f67539-886a-4809-819d-6b81f17534d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compas_data['is_white']=(compas_data['race']=='Caucasian').astype('int8')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64061893-67eb-4dc5-9604-cec3e5ecc67d",
+ "metadata": {},
+ "source": [
+ "## Selection of Features of Interest\n",
+ "The features are chosen as by ProPublica. The only differences lie in the calculation of age and the fact that sensitive attributes were removed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "96e72dfa-05a7-413b-805b-a11f0328c83e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compas_data=compas_data[['is_white','priors_count','length_of_stay',\n",
+ " 'age_at_c_screening_date','two_year_recid','c_charge_degree']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c9be778-8a78-4767-ae15-f6105a26da0f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "1aed82c3-3c80-41ba-83f0-7d936afcd862",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>positive class</th>\n",
+ " <th>negative class</th>\n",
+ " <th>total</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>white</th>\n",
+ " <td>0.105027</td>\n",
+ " <td>0.243569</td>\n",
+ " <td>0.348596</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>not white</th>\n",
+ " <td>0.256313</td>\n",
+ " <td>0.395091</td>\n",
+ " <td>0.651404</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>total</th>\n",
+ " <td>0.361341</td>\n",
+ " <td>0.638659</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " positive class negative class total\n",
+ "white 0.105027 0.243569 0.348596\n",
+ "not white 0.256313 0.395091 0.651404\n",
+ "total 0.361341 0.638659 1.000000"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x_train,x_test,y_train,y_test = train_test_split(compas_data.drop(columns='two_year_recid'),compas_data['two_year_recid'],test_size=0.2,random_state=42)\n",
+ "cv = StratifiedKFold(n_splits=5,shuffle=True,random_state=42)\n",
+ "dm = datamodel(x_train,y_train,x_test,y_test,'two_year_recid',1,0,'is_white', 1,0,x_train['is_white'],x_test['is_white'])\n",
+ "white_x_train,white_y_train,not_white_x_train,not_white_y_train = dm.get_sensitive_train_data()\n",
+ "white_x_test,white_y_test,not_white_x_test,not_white_y_test = dm.get_sensitive_test_data()\n",
+ "white_dm = datamodel(white_x_train,white_y_train,white_x_test,white_y_test,'two_year_recid',1,0,'is_white', 1,0,white_x_train['is_white'],white_x_test['is_white'])\n",
+ "not_white_dm = datamodel(not_white_x_train,not_white_y_train,not_white_x_test,not_white_y_test,'two_year_recid',1,0,'is_white', 1,0,not_white_x_train['is_white'],not_white_x_test['is_white'])\n",
+ "dm.create_contingency_table('white','not white')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "ea72e5fa-adf4-4d14-82b6-be567d41b635",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "white base rate: 0.32352941176470584\n",
+ "not white base rate: 0.4\n"
+ ]
+ }
+ ],
+ "source": [
+ "white_base_rate = 0.11/0.34\n",
+ "not_white_base_rate = 0.26/0.65\n",
+ "print('white base rate:',white_base_rate)\n",
+ "print('not white base rate:',not_white_base_rate)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c93d474-9d46-4e95-a3b8-9b73b3ce52b7",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Feature Scaling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "ba33d759-f0dc-4aac-9945-3c8b0112cbd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>is_white</th>\n",
+ " <th>priors_count</th>\n",
+ " <th>length_of_stay</th>\n",
+ " <th>age_at_c_screening_date</th>\n",
+ " <th>c_charge_degree</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2020</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.154873</td>\n",
+ " <td>0.168831</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4181</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.467532</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1563</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.001335</td>\n",
+ " <td>0.480519</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2736</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.006676</td>\n",
+ " <td>0.090909</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5651</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.121212</td>\n",
+ " <td>0.001335</td>\n",
+ " <td>0.311688</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4187</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.060606</td>\n",
+ " <td>0.066756</td>\n",
+ " <td>0.051948</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5097</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.030303</td>\n",
+ " <td>0.001335</td>\n",
+ " <td>0.181818</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7065</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.402597</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7121</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.181818</td>\n",
+ " <td>0.002670</td>\n",
+ " <td>0.103896</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1165</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.005340</td>\n",
+ " <td>0.051948</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>4237 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " is_white priors_count length_of_stay age_at_c_screening_date \\\n",
+ "2020 1.0 0.000000 0.154873 0.168831 \n",
+ "4181 1.0 0.000000 0.000000 0.467532 \n",
+ "1563 1.0 0.000000 0.001335 0.480519 \n",
+ "2736 1.0 0.000000 0.006676 0.090909 \n",
+ "5651 1.0 0.121212 0.001335 0.311688 \n",
+ "... ... ... ... ... \n",
+ "4187 0.0 0.060606 0.066756 0.051948 \n",
+ "5097 0.0 0.030303 0.001335 0.181818 \n",
+ "7065 1.0 0.000000 0.000000 0.402597 \n",
+ "7121 0.0 0.181818 0.002670 0.103896 \n",
+ "1165 0.0 0.000000 0.005340 0.051948 \n",
+ "\n",
+ " c_charge_degree \n",
+ "2020 1.0 \n",
+ "4181 0.0 \n",
+ "1563 0.0 \n",
+ "2736 1.0 \n",
+ "5651 1.0 \n",
+ "... ... \n",
+ "4187 0.0 \n",
+ "5097 0.0 \n",
+ "7065 1.0 \n",
+ "7121 0.0 \n",
+ "1165 1.0 \n",
+ "\n",
+ "[4237 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dm.feature_scaler()\n",
+ "white_dm.feature_scaler()\n",
+ "not_white_dm.feature_scaler()\n",
+ "dm.x_train"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e29a6ec-25b1-4823-b2fb-89eb094ab851",
+ "metadata": {},
+ "source": [
+ "## Training Unaware of the Sensitive Attribute"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "c491a88b-24d3-4fb3-8a9f-5b8f06b83ac4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dm.remove_sensitive_feature()\n",
+ "white_dm.remove_sensitive_feature()\n",
+ "not_white_dm.remove_sensitive_feature()\n",
+ "not_white_x_test,not_white_y_test,white_x_test,white_y_test = dm.get_sensitive_test_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5613e1d6-9dfa-404c-9586-59ea89151ed0",
+ "metadata": {},
+ "source": [
+ "## Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "7034d819-24f7-42ee-932b-9f91414b54d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lb = logreg_builder(dm.x_train,dm.y_train,dm.x_test,dm.y_test,cv,{'penalty':['l2','l1'],'C':[0.01,0.1,1,10,100]})\n",
+ "lr,result_table,train_test_comparison=lb.make_logreg()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "29f759d3-92cc-40d2-ae85-17d64ec250ef",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>model</th>\n",
+ " <th>params</th>\n",
+ " <th>standard derivation</th>\n",
+ " <th>mean validation accuracy</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>default Logistic Regression</td>\n",
+ " <td>{'penalty': 'l2', 'C': 1.0}</td>\n",
+ " <td>0.009394</td>\n",
+ " <td>0.695066</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Logistic Regression</td>\n",
+ " <td>{'penalty': 'l2', 'C': 100}</td>\n",
+ " <td>0.012208</td>\n",
+ " <td>0.699313</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " model params \\\n",
+ "0 default Logistic Regression {'penalty': 'l2', 'C': 1.0} \n",
+ "0 Logistic Regression {'penalty': 'l2', 'C': 100} \n",
+ "\n",
+ " standard derivation mean validation accuracy \n",
+ "0 0.009394 0.695066 \n",
+ "0 0.012208 0.699313 "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "0a9c8d76-b82e-42a4-a80f-ec54568c7f61",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>model</th>\n",
+ " <th>Logistic Regression</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>baseline train accuracy</td>\n",
+ " <td>0.696247</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>tuned train accuracy</td>\n",
+ " <td>0.700260</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>baseline test accuracy</td>\n",
+ " <td>0.695283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>tuned test accuracy</td>\n",
+ " <td>0.697170</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " model Logistic Regression\n",
+ "0 baseline train accuracy 0.696247\n",
+ "1 tuned train accuracy 0.700260\n",
+ "2 baseline test accuracy 0.695283\n",
+ "3 tuned test accuracy 0.697170"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_test_comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "a4ff36fa-62bb-4ac4-8f32-8ca849b77255",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "white_x_test,white_y_test,not_white_x_test,not_white_y_test = dm.get_sensitive_test_data()\n",
+ "mm=measure_model(lr,lr,dm.x_test,dm.y_test,white_x_test,white_y_test,not_white_x_test,not_white_y_test)\n",
+ "labels,y,y_1,y_2,gqrs,single_white_cm,single_not_white_cm = mm.get_measures()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a07775fd-33bd-443b-93aa-0ca43368ff93",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Logistic Regression Visualisation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "966c4934-7262-4cb2-97a2-8364dd70d170",
+ "metadata": {},
+ "source": [
+ "## Visualisation Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "6ff1a157-afe4-44ce-9d3e-a0097a3890a4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "single_lr_vis = visualisation_model(labels,y,y_1,y_2,gqrs,'white','not white','COMPAS Logistic Regression','race_compas_lr')\n",
+ "single_lr_vis.create_final_visualisation()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d634cd79-11be-42f2-a851-7cef3305a271",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Performance Values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "e2593328-986c-4bd1-85ff-6e45c1a36916",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>★ Accuracy</th>\n",
+ " <th>Precision</th>\n",
+ " <th>FM</th>\n",
+ " <th>F1</th>\n",
+ " <th>TS</th>\n",
+ " <th>Recall</th>\n",
+ " <th>GM</th>\n",
+ " <th>BA</th>\n",
+ " <th>Specifity</th>\n",
+ " <th>TS*</th>\n",
+ " <th>F1*</th>\n",
+ " <th>FM*</th>\n",
+ " <th>NPV</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>total</th>\n",
+ " <td>0.7</td>\n",
+ " <td>0.7</td>\n",
+ " <td>0.51</td>\n",
+ " <td>0.48</td>\n",
+ " <td>0.32</td>\n",
+ " <td>0.37</td>\n",
+ " <td>0.58</td>\n",
+ " <td>0.64</td>\n",
+ " <td>0.9</td>\n",
+ " <td>0.65</td>\n",
+ " <td>0.79</td>\n",
+ " <td>0.79</td>\n",
+ " <td>0.7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>white</th>\n",
+ " <td>0.72</td>\n",
+ " <td>0.55</td>\n",
+ " <td>0.3</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.15</td>\n",
+ " <td>0.17</td>\n",
+ " <td>0.39</td>\n",
+ " <td>0.55</td>\n",
+ " <td>0.94</td>\n",
+ " <td>0.7</td>\n",
+ " <td>0.82</td>\n",
+ " <td>0.83</td>\n",
+ " <td>0.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>not white</th>\n",
+ " <td>0.69</td>\n",
+ " <td>0.72</td>\n",
+ " <td>0.56</td>\n",
+ " <td>0.55</td>\n",
+ " <td>0.38</td>\n",
+ " <td>0.44</td>\n",
+ " <td>0.62</td>\n",
+ " <td>0.66</td>\n",
+ " <td>0.87</td>\n",
+ " <td>0.62</td>\n",
+ " <td>0.76</td>\n",
+ " <td>0.77</td>\n",
+ " <td>0.68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>IGQR</th>\n",
+ " <td>0.96</td>\n",
+ " <td>0.76</td>\n",
+ " <td>0.53</td>\n",
+ " <td>0.46</td>\n",
+ " <td>0.39</td>\n",
+ " <td>0.38</td>\n",
+ " <td>0.64</td>\n",
+ " <td>0.84</td>\n",
+ " <td>0.93</td>\n",
+ " <td>0.88</td>\n",
+ " <td>0.93</td>\n",
+ " <td>0.93</td>\n",
+ " <td>0.92</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ★ Accuracy Precision FM F1 TS Recall GM BA Specifity \\\n",
+ "total 0.7 0.7 0.51 0.48 0.32 0.37 0.58 0.64 0.9 \n",
+ "white 0.72 0.55 0.3 0.25 0.15 0.17 0.39 0.55 0.94 \n",
+ "not white 0.69 0.72 0.56 0.55 0.38 0.44 0.62 0.66 0.87 \n",
+ "IGQR 0.96 0.76 0.53 0.46 0.39 0.38 0.64 0.84 0.93 \n",
+ "\n",
+ " TS* F1* FM* NPV \n",
+ "total 0.65 0.79 0.79 0.7 \n",
+ "white 0.7 0.82 0.83 0.73 \n",
+ "not white 0.62 0.76 0.77 0.68 \n",
+ "IGQR 0.88 0.93 0.93 0.92 "
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "single_lr_vis.get_table()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66c2aa98-eb41-4e75-a03b-3e5386180789",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Confusion Matrix: White"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "1b77900c-b89c-4484-8a3e-a13d8acc8184",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Predicted Positive</th>\n",
+ " <th>Predicted Negative</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Actual Positive</th>\n",
+ " <td>17</td>\n",
+ " <td>86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Actual Negative</th>\n",
+ " <td>14</td>\n",
+ " <td>234</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Predicted Positive Predicted Negative\n",
+ "Actual Positive 17 86\n",
+ "Actual Negative 14 234"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "single_white_cm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "82750928-a7ac-463f-8930-423cc2141772",
+ "metadata": {},
+ "source": [
+ "### Confusion Matrix: Not white"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "07f94185-d14a-4a65-9b23-80fb9ec512ac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Predicted Positive</th>\n",
+ " <th>Predicted Negative</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Actual Positive</th>\n",
+ " <td>133</td>\n",
+ " <td>170</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Actual Negative</th>\n",
+ " <td>51</td>\n",
+ " <td>355</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Predicted Positive Predicted Negative\n",
+ "Actual Positive 133 170\n",
+ "Actual Negative 51 355"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "single_not_white_cm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "c0a0f30b-9716-4397-a9e3-ea3ba85bcd22",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'tree_builder' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m tb \u001b[38;5;241m=\u001b[39m tree_builder(dm\u001b[38;5;241m.\u001b[39mx_train,dm\u001b[38;5;241m.\u001b[39my_train,dm\u001b[38;5;241m.\u001b[39mx_test,dm\u001b[38;5;241m.\u001b[39my_test,cv)\n\u001b[0;32m 2\u001b[0m tree,result_table,train_test_comparison \u001b[38;5;241m=\u001b[39m tb\u001b[38;5;241m.\u001b[39mmake_tree()\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'tree_builder' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "tb = tree_builder(dm.x_train,dm.y_train,dm.x_test,dm.y_test,cv)\n",
+ "tree,result_table,train_test_comparison = tb.make_tree()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ca9b198-f173-49b7-8fc5-9d19672b3755",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f0a8c5c-b717-494f-a4d9-d3d8e7888b6e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_test_comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "58989418-4cf7-42d2-8f59-55bbb54f5edb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mm=measure_model(tree,tree,dm.x_test,dm.y_test,white_x_test,white_y_test,not_white_x_test,not_white_y_test)\n",
+ "labels,y,y_1,y_2,gqrs,single_white_cm,single_not_white_cm = mm.get_measures()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "65956951-e347-42e7-8e65-7bdca3236300",
+ "metadata": {},
+ "source": [
+ "## Double Decision Tree Classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79b97b7a-018d-4530-aac9-73ed304bdd19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "white_dtb = tree_builder(white_dm.x_train,white_dm.y_train,white_dm.x_test,white_dm.y_test,cv)\n",
+ "white_dt,result_table,train_test_comparison=white_dtb.make_tree()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51b7b450-ca37-4e70-888b-035d507560f7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d32e25d2-7acf-4e61-9106-a3665b21c49b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_test_comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3db7a1af-d984-4ea3-953e-564330751051",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "not_white_dtb = tree_builder(not_white_dm.x_train,not_white_dm.y_train,not_white_dm.x_test,not_white_dm.y_test,cv)\n",
+ "not_white_dt,result_table,train_test_comparison=not_white_dtb.make_tree()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "500724a5-b109-495b-8353-85a79c1db3d5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0515aea6-446a-49f1-85be-806946a011a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_test_comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a4279712-39f0-4854-96f6-cab3dc7dd963",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mm = measure_model(white_dt,not_white_dt,dm.x_test,dm.y_test,white_dm.x_test,white_dm.y_test,not_white_dm.x_test,not_white_dm.y_test,single_classifier=False)\n",
+ "double_labels,double_y,double_y_1,double_y_2,double_gqrs,double_white_cm,double_not_white_cm = mm.get_measures()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e99ca2ea-97c8-4660-90fe-7b6655968241",
+ "metadata": {},
+ "source": [
+ "## Visualisation Single Decision Tree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d10974ea-2ac0-47e8-8deb-1e31f6443acd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tree_vis = visualisation_model(labels,y,y_1,y_2,gqrs,'white','not white','COMPAS Single Decision Tree','race_compas_single_dt')\n",
+ "tree_vis.create_final_visualisation()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d0e75215-c864-49dc-a2f0-cb910f1327c5",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Performance Values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3674131-d766-4e3f-a603-46abae01ceb9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tree_vis.get_table()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b5c01d7e-4e0a-4668-a420-ad71c4881a7a",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Confusion Matrix: White"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab9bcd81-c7a2-4a07-999f-70f6537fc738",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "single_white_cm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e671dbd-289a-40de-8e75-2196f8178a88",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Confusion Matrix: Not white"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a21d230-64fd-406c-9077-5de65e31d07f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "single_not_white_cm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26bb02ee-3b43-47f2-8ee9-8812ba2fd9ba",
+ "metadata": {},
+ "source": [
+ "## Visualisation Double Decision Tree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13eb40cd-6b9f-4174-9c47-256d7141e468",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "double_dt_vis = visualisation_model(double_labels,double_y,double_y_1,double_y_2,double_gqrs,'white','not white','COMPAS Double Decision Tree','race_compas_double_dt')\n",
+ "double_dt_vis.create_final_visualisation()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ecca4073-a0c2-4a4b-a0b3-c7dafab0e5b2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Performance Values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bbdabdcf-20ed-4d38-b9fc-400750eca662",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_dt_vis.get_table()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25381c75-590f-46ae-ac57-a641fc4be135",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Confusion Matrix: White"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f99c5fc5-c514-409b-ac56-9f7dc229a208",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_white_cm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4eab235e-d4dd-465f-8731-9108e414d6a5",
+ "metadata": {},
+ "source": [
+ "### Confusion Matrix: Not white"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd0ccbe4-1cd5-48e6-92e8-02f03bdd59a5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_not_white_cm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3fe0f0b5-6646-4517-89ee-54c043252a82",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sb = svm_builder(dm.x_train,dm.y_train,dm.x_test,dm.y_test,cv,{'C':[0.01,1,100],'kernel':['rbf'],'gamma':[0.001,0.01,1,10,1000]})\n",
+ "svm,result_table,train_test_comparison = sb.make_svm()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b276865a-3c6b-45a0-ac6c-95eec75197bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b1b2bf0-8fa0-4fc6-a9e0-632a8af43df8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_test_comparison "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79da5faf-7321-4ec6-95b7-b8ac8514666c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mm=measure_model(svm,svm,dm.x_test,dm.y_test,white_x_test,white_y_test,not_white_x_test,not_white_y_test)\n",
+ "labels,y,y_1,y_2,gqrs,single_white_cm,single_not_white_cm = mm.get_measures()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d8f212c-d842-4fa6-9c0b-9dad3221a973",
+ "metadata": {},
+ "source": [
+ "## Double SVM Classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54263686-f839-4470-a2a4-8280db1b2d1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "white_svmb = svm_builder(white_dm.x_train,white_dm.y_train,white_dm.x_test,white_dm.y_test,cv,{'C':[0.01,1,100],'kernel':['rbf'],'gamma':[0.001,0.01,1,10,1000]})\n",
+ "white_svm,result_table,train_test_comparison=white_svmb.make_svm()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "448f9daa-a649-40be-ace0-3e99b5d4f951",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60ea5ce6-0113-4cd9-9fa8-815d86038f93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_test_comparison "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c4b1a76-b05e-4d00-94e4-8a3bc50ef467",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "not_white_svmb = svm_builder(not_white_dm.x_train,not_white_dm.y_train,not_white_dm.x_test,not_white_dm.y_test,cv,{'C':[0.01,1,100],'kernel':['rbf'],'gamma':[0.001,0.01,1,10,1000]})\n",
+ "not_white_svm,result_table,train_test_comparison=not_white_svmb.make_svm()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38a45034-076e-480b-bd15-d0807fc9ded2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53e8e0bf-6169-4e57-91ba-3b3cf89a6972",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_test_comparison "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a0318e3e-af29-4787-a033-34923e1c2b8b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mm = measure_model(white_svm,not_white_svm,dm.x_test,dm.y_test,white_dm.x_test,white_dm.y_test,not_white_dm.x_test,not_white_dm.y_test,single_classifier=False)\n",
+ "double_labels,double_y,double_y_1,double_y_2,double_gqrs,double_white_cm,double_not_white_cm = mm.get_measures()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "018d5dd1-48a3-48fa-acd3-6d9fcf1baaf0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "svm_vis = visualisation_model(labels,y,y_1,y_2,gqrs,'white','not white','COMPAS Single SVM','race_compas_single_svm')\n",
+ "svm_vis.create_final_visualisation()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b955cd65-9201-46df-b4f8-3f189f00b2c8",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Performance Values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "896ef912-57f8-4e91-91f1-3505c1135f98",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "svm_vis.get_table()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc7ff218-c6ba-4882-8395-fc31873be2a5",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Confusion Matrix: White"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e9313b0-4968-461f-be66-ce760ba76f38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "single_white_cm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d217553f-ad86-46a4-9fdb-d7cd97bd284b",
+ "metadata": {},
+ "source": [
+ "### Confusion Matrix: Not white"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1f03efe4-a45a-4fc5-9f3a-d28400b54289",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "single_not_white_cm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "043468ed-f703-46c9-89ab-ed4ec6ed6a5f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_svm_vis = visualisation_model(labels,y,y_1,y_2,gqrs,'white','not white','COMPAS Double SVM','race_compas_double_svm')\n",
+ "double_svm_vis.create_final_visualisation()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20e3b5d9-ca9e-46dc-ae85-7e6a54784111",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Performance Values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e79c347c-88e6-49a5-b3c3-5d6dffc91c39",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_svm_vis.get_table()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39a024ab-7c81-4e9a-880d-6187a64c9aba",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Confusion Matrix: White"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "edca0873-081a-485a-86d7-3154855ca599",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_white_cm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cb77657-15d2-49dd-8db9-e975583c6ef9",
+ "metadata": {},
+ "source": [
+ "### Confusion Matrix: Not white"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a55f81fc-1766-4e39-b98a-3a5653e782f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "double_not_white_cm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac7227ba-2f65-49b9-b743-8aa51ef0e257",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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