Experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = pd.read_csv('data/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.columns = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'cholesterol', \n",
    "               'fasting_blood_sugar', 'rest_ecg', 'max_heart_rate_achieved', \n",
    "               'exercise_induced_angina', 'st_depression', 'st_slope', \n",
    "               'num_major_vessels', 'thalassemia', 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting_blood_sugar</th>\n",
       "      <th>rest_ecg</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "      <th>st_depression</th>\n",
       "      <th>st_slope</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>thalassemia</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>37</td>\n",
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       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
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       "      <td>1</td>\n",
       "      <td>187</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest_pain_type  resting_blood_pressure  cholesterol  \\\n",
       "0   63    1                3                     145          233   \n",
       "1   37    1                2                     130          250   \n",
       "2   41    0                1                     130          204   \n",
       "3   56    1                1                     120          236   \n",
       "4   57    0                0                     120          354   \n",
       "\n",
       "   fasting_blood_sugar  rest_ecg  max_heart_rate_achieved  \\\n",
       "0                    1         0                      150   \n",
       "1                    0         1                      187   \n",
       "2                    0         0                      172   \n",
       "3                    0         1                      178   \n",
       "4                    0         1                      163   \n",
       "\n",
       "   exercise_induced_angina  st_depression  st_slope  num_major_vessels  \\\n",
       "0                        0            2.3         0                  0   \n",
       "1                        0            3.5         0                  0   \n",
       "2                        0            1.4         2                  0   \n",
       "3                        0            0.8         2                  0   \n",
       "4                        1            0.6         2                  0   \n",
       "\n",
       "   thalassemia  target  \n",
       "0            1       1  \n",
       "1            2       1  \n",
       "2            2       1  \n",
       "3            2       1  \n",
       "4            2       1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt['sex'][dt['sex'] == 0] = 'female'\n",
    "dt['sex'][dt['sex'] == 1] = 'male'\n",
    "\n",
    "dt['chest_pain_type'][dt['chest_pain_type'] == 1] = 'typical angina'\n",
    "dt['chest_pain_type'][dt['chest_pain_type'] == 2] = 'atypical angina'\n",
    "dt['chest_pain_type'][dt['chest_pain_type'] == 3] = 'non-anginal pain'\n",
    "dt['chest_pain_type'][dt['chest_pain_type'] == 4] = 'asymptomatic'\n",
    "\n",
    "dt['fasting_blood_sugar'][dt['fasting_blood_sugar'] == 0] = 'lower than 120mg/ml'\n",
    "dt['fasting_blood_sugar'][dt['fasting_blood_sugar'] == 1] = 'greater than 120mg/ml'\n",
    "\n",
    "dt['rest_ecg'][dt['rest_ecg'] == 0] = 'normal'\n",
    "dt['rest_ecg'][dt['rest_ecg'] == 1] = 'ST-T wave abnormality'\n",
    "dt['rest_ecg'][dt['rest_ecg'] == 2] = 'left ventricular hypertrophy'\n",
    "\n",
    "dt['exercise_induced_angina'][dt['exercise_induced_angina'] == 0] = 'no'\n",
    "dt['exercise_induced_angina'][dt['exercise_induced_angina'] == 1] = 'yes'\n",
    "\n",
    "dt['st_slope'][dt['st_slope'] == 1] = 'upsloping'\n",
    "dt['st_slope'][dt['st_slope'] == 2] = 'flat'\n",
    "dt['st_slope'][dt['st_slope'] == 3] = 'downsloping'\n",
    "\n",
    "dt['thalassemia'][dt['thalassemia'] == 1] = 'normal'\n",
    "dt['thalassemia'][dt['thalassemia'] == 2] = 'fixed defect'\n",
    "dt['thalassemia'][dt['thalassemia'] == 3] = 'reversable defect'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting_blood_sugar</th>\n",
       "      <th>rest_ecg</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "      <th>st_depression</th>\n",
       "      <th>st_slope</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>thalassemia</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>male</td>\n",
       "      <td>non-anginal pain</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>greater than 120mg/ml</td>\n",
       "      <td>normal</td>\n",
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       "      <th>1</th>\n",
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       "      <td>atypical angina</td>\n",
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       "      <td>250</td>\n",
       "      <td>lower than 120mg/ml</td>\n",
       "      <td>ST-T wave abnormality</td>\n",
       "      <td>187</td>\n",
       "      <td>no</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fixed defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>typical angina</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>lower than 120mg/ml</td>\n",
       "      <td>normal</td>\n",
       "      <td>172</td>\n",
       "      <td>no</td>\n",
       "      <td>1.4</td>\n",
       "      <td>flat</td>\n",
       "      <td>0</td>\n",
       "      <td>fixed defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>typical angina</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>lower than 120mg/ml</td>\n",
       "      <td>ST-T wave abnormality</td>\n",
       "      <td>178</td>\n",
       "      <td>no</td>\n",
       "      <td>0.8</td>\n",
       "      <td>flat</td>\n",
       "      <td>0</td>\n",
       "      <td>fixed defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>lower than 120mg/ml</td>\n",
       "      <td>ST-T wave abnormality</td>\n",
       "      <td>163</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.6</td>\n",
       "      <td>flat</td>\n",
       "      <td>0</td>\n",
       "      <td>fixed defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex   chest_pain_type  resting_blood_pressure  cholesterol  \\\n",
       "0   63    male  non-anginal pain                     145          233   \n",
       "1   37    male   atypical angina                     130          250   \n",
       "2   41  female    typical angina                     130          204   \n",
       "3   56    male    typical angina                     120          236   \n",
       "4   57  female                 0                     120          354   \n",
       "\n",
       "     fasting_blood_sugar               rest_ecg  max_heart_rate_achieved  \\\n",
       "0  greater than 120mg/ml                 normal                      150   \n",
       "1    lower than 120mg/ml  ST-T wave abnormality                      187   \n",
       "2    lower than 120mg/ml                 normal                      172   \n",
       "3    lower than 120mg/ml  ST-T wave abnormality                      178   \n",
       "4    lower than 120mg/ml  ST-T wave abnormality                      163   \n",
       "\n",
       "  exercise_induced_angina  st_depression st_slope  num_major_vessels  \\\n",
       "0                      no            2.3        0                  0   \n",
       "1                      no            3.5        0                  0   \n",
       "2                      no            1.4     flat                  0   \n",
       "3                      no            0.8     flat                  0   \n",
       "4                     yes            0.6     flat                  0   \n",
       "\n",
       "    thalassemia  target  \n",
       "0        normal       1  \n",
       "1  fixed defect       1  \n",
       "2  fixed defect       1  \n",
       "3  fixed defect       1  \n",
       "4  fixed defect       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = pd.get_dummies(dt, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>st_depression</th>\n",
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       "      <th>chest_pain_type_typical angina</th>\n",
       "      <th>fasting_blood_sugar_lower than 120mg/ml</th>\n",
       "      <th>rest_ecg_left ventricular hypertrophy</th>\n",
       "      <th>rest_ecg_normal</th>\n",
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       "      <th>st_slope_flat</th>\n",
       "      <th>st_slope_upsloping</th>\n",
       "      <th>thalassemia_fixed defect</th>\n",
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       "      <th>thalassemia_reversable defect</th>\n",
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       "      <td>3.5</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>1.4</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
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       "      <td>0.8</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>163</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  resting_blood_pressure  cholesterol  max_heart_rate_achieved  \\\n",
       "0   63                     145          233                      150   \n",
       "1   37                     130          250                      187   \n",
       "2   41                     130          204                      172   \n",
       "3   56                     120          236                      178   \n",
       "4   57                     120          354                      163   \n",
       "\n",
       "   st_depression  num_major_vessels  target  sex_male  \\\n",
       "0            2.3                  0       1         1   \n",
       "1            3.5                  0       1         1   \n",
       "2            1.4                  0       1         0   \n",
       "3            0.8                  0       1         1   \n",
       "4            0.6                  0       1         0   \n",
       "\n",
       "   chest_pain_type_atypical angina  chest_pain_type_non-anginal pain  \\\n",
       "0                                0                                 1   \n",
       "1                                1                                 0   \n",
       "2                                0                                 0   \n",
       "3                                0                                 0   \n",
       "4                                0                                 0   \n",
       "\n",
       "   chest_pain_type_typical angina  fasting_blood_sugar_lower than 120mg/ml  \\\n",
       "0                               0                                        0   \n",
       "1                               0                                        1   \n",
       "2                               1                                        1   \n",
       "3                               1                                        1   \n",
       "4                               0                                        1   \n",
       "\n",
       "   rest_ecg_left ventricular hypertrophy  rest_ecg_normal  \\\n",
       "0                                      0                1   \n",
       "1                                      0                0   \n",
       "2                                      0                1   \n",
       "3                                      0                0   \n",
       "4                                      0                0   \n",
       "\n",
       "   exercise_induced_angina_yes  st_slope_flat  st_slope_upsloping  \\\n",
       "0                            0              0                   0   \n",
       "1                            0              0                   0   \n",
       "2                            0              1                   0   \n",
       "3                            0              1                   0   \n",
       "4                            1              1                   0   \n",
       "\n",
       "   thalassemia_fixed defect  thalassemia_normal  thalassemia_reversable defect  \n",
       "0                         0                   1                              0  \n",
       "1                         1                   0                              0  \n",
       "2                         1                   0                              0  \n",
       "3                         1                   0                              0  \n",
       "4                         1                   0                              0  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.to_csv(path_or_buf='data/modified_heart_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                                        0\n",
       "resting_blood_pressure                     0\n",
       "cholesterol                                0\n",
       "max_heart_rate_achieved                    0\n",
       "st_depression                              0\n",
       "num_major_vessels                          0\n",
       "target                                     0\n",
       "sex_male                                   0\n",
       "chest_pain_type_atypical angina            0\n",
       "chest_pain_type_non-anginal pain           0\n",
       "chest_pain_type_typical angina             0\n",
       "fasting_blood_sugar_lower than 120mg/ml    0\n",
       "rest_ecg_left ventricular hypertrophy      0\n",
       "rest_ecg_normal                            0\n",
       "exercise_induced_angina_yes                0\n",
       "st_slope_flat                              0\n",
       "st_slope_upsloping                         0\n",
       "thalassemia_fixed defect                   0\n",
       "thalassemia_normal                         0\n",
       "thalassemia_reversable defect              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.target.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(29, 77)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.boxplot(dt.age)\n",
    "plt.show()\n",
    "dt.age.min(), dt.age.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zT5I0kRZaQBwC3DqwvqmrSZLGbFHfDeyuJKuAVd3qj5N8u89+HmQWAz/ou4mFIO9d2XcLujf/Nue9LXvjUx43zE4LLSA2A4cNrB/a1X6pqlYDq8fZ1KRIMlNV0333Ie3Iv81+LLQhpm8Ay5IcnuShwIuBS3ruSZIm0oI6g6iq7UleA3wJ2AdYU1XX9dyWJE2kBRUQAFV1KXBp331MKIfutFD5t9mDVFXfPUiSFqCFdg1CkrRAGBACfMSJFqYka5JsS7Kh714mkQEhH3Gihexc4IS+m5hUBoTAR5xogaqqK4Hb++5jUhkQAh9xIqnBgJAkNRkQgiEecSJp8hgQAh9xIqnBgBBVtR2Yf8TJDcCFPuJEC0GSC4CrgCcm2ZTk9L57miT+klqS1OQZhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASEtQEl+3HcPkgEhSWoyIDTRkjwiyb8n+a8kG5K8KMnRSa5IsjbJl5IcnGRRkm8kOa477l1JzryPz72522ddkpkkT+s+68Ykr+722S/J5UmuSXJtkuYj1pO8sfvu9UneMZL/EVLDor4bkHp2AvA/VfVHAEkeDXwRWFFVs0leBJxZVa9MchrwmSSv7Y773V189veqanmS9zM38c0zgYcDG4CPAD8HTqqqO5IsBq5OckkNPN4gyXOBZczN2RHgkiTHdvMkSCNlQGjSXQucleTvgH8Dfgg8CbgsCcA+wBaAqrouyce7/Z7RTa50X+YfeHgtsF9V3QncmeSuJPsDPwH+NsmxwD3MzcFxEPD9gc94bvf6Zre+H3OBYUBo5AwITbSq+k6SpwEnAn8D/CdwXVU9YyeHPBn4X+AxQ3z8Xd37PQPL8+uLgJcAU8DRVfWLJDczd4YxKMC7quqfh/g+aa/yGoQmWpLHAj+tqn8B3sPcsNFUkmd02/dNclS3fDJwIHAs8KHuLGBPPBrY1oXDs4HHNfb5EvDKJPt1PRySZJhwkvaYZxCadE8G3pPkHuAXwF8A24EPdtcjFgH/kGQr8G7g+Kq6NcmHgQ8AK/fguz8BfCHJtcAM8K0dd6iqLyf5beCqbsjrx8BLgW178L3SUHzctySpySEmSVKTQ0zSHkhyMXD4DuW/qqov9dGPtDc5xCRJanKISZLUZEBIkpoMCElSkwEhSWoyICRJTf8PLhaayHVmUbsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(dt.sex_male)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(dt[dt.target == 1].sex_male)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(dt[dt.target == 0].sex_male)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(dt.corr(), vmin=-1, vmax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fairness Evaluation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "code_folding": [
     1,
     23,
     40
    ]
   },
   "outputs": [],
   "source": [
    "# Demographic Parity\n",
    "def demo_parity(model, X_test, y_test):\n",
    "    X_test_male = X_test[X_test.sex_male == 1]\n",
    "    X_test_female = X_test[X_test.sex_male == 0]\n",
    "    \n",
    "    y_pred_male = model.predict_proba(X_test_male)\n",
    "    y_pred_female = model.predict_proba(X_test_female)\n",
    "    \n",
    "    prob_male_pos = np.sum(y_pred_male[:,1])/y_pred_male.shape[0]\n",
    "    prob_male_neg = 1 - prob_male_pos\n",
    "    \n",
    "    prob_female_pos = np.sum(y_pred_female[:,1])/y_pred_female.shape[0]\n",
    "    prob_female_neg = 1 - prob_female_pos\n",
    "    \n",
    "    print(\"Prob. of heart disease given male: %f, Prob. of heart disease given female: %f\" %\n",
    "          (prob_male_pos, prob_female_pos))\n",
    "    \n",
    "    pos_prob_ratio = min(prob_female_pos/prob_male_pos, prob_male_pos/prob_female_pos)\n",
    "    \n",
    "    print(\"Positive Prob. Ratio: %f\"%(pos_prob_ratio))\n",
    "    \n",
    "\n",
    "# Equalized Odds\n",
    "def equal_odds(model, X_test, y_test):\n",
    "    X_test_pos = X_test[y_test == 1]\n",
    "\n",
    "    X_test_male_pos = X_test_pos[X_test_pos.sex_male == 1]\n",
    "    X_test_female_pos = X_test_pos[X_test_pos.sex_male == 0]\n",
    "    \n",
    "    y_pred_male = model.predict(X_test_male_pos)\n",
    "    y_pred_female = model.predict(X_test_female_pos)\n",
    "    \n",
    "    prob_male_pos = np.sum(y_pred_male)/y_pred_male.shape[0]\n",
    "    prob_female_pos = np.sum(y_pred_female)/y_pred_female.shape[0]\n",
    "    \n",
    "    print(\"Prob. of heart disease given male w/ Y=1: %f, Prob. of heart disease given female w/ Y=1: %f\"\n",
    "              %(prob_male_pos, prob_female_pos))\n",
    "    \n",
    "\n",
    "# Predictive Parity\n",
    "def pred_parity(model, X_test, y_test):\n",
    "    pass\n",
    "\n",
    "# Discrimination\n",
    "def discrim(model, X_test, y_test):\n",
    "    X_test_male = X_test[X_test[:,sex_male_idx] == 1]\n",
    "    X_test_fem = X_test[X_test[:,sex_male_idx] == 0]\n",
    "    \n",
    "    y_pred_male = model.predict(X_test_male)\n",
    "    y_pred_fem = model.predict(X_test_fem)\n",
    "    \n",
    "    discrim = y_pred_male.sum()/y_pred_male.shape[0] - y_pred_fem.sum()/y_pred_fem.shape[0]\n",
    "    discrim = np.abs(discrim)\n",
    "    \n",
    "    return discrim\n",
    "\n",
    "# Accuracy\n",
    "def accuracy(model, X, y):\n",
    "    y_pred = model.predict(X)\n",
    "    return (1 - (1/X.shape[0])*np.sum(np.abs(y_pred-y)))\n",
    "\n",
    "# Max Delta\n",
    "def max_delta(model, X, y):\n",
    "    return accuracy(model, X, y) - discrim(model, X, y)\n",
    "\n",
    "# Consistency\n",
    "def consistency(model, X_test, y_test):\n",
    "    k = 5\n",
    "    knn_model = KNeighborsClassifier().fit(X_test, y_test)\n",
    "    nn = knn_model.kneighbors(X_test, k, False)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    consist_score = 0\n",
    "    for i in range(X_test.shape[0]):\n",
    "        nn_sum = 0\n",
    "        for j in nn[i][1:]:\n",
    "            nn_sum += y_pred[j]\n",
    "            \n",
    "        consist_score += np.abs(y_pred[i] - nn_sum)\n",
    "    \n",
    "    return (1 - 1/(y_pred.shape[0]*k)*consist_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, confusion_matrix, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read dataframe\n",
    "dataset = pd.read_csv('data/modified_heart_df').drop(columns='Unnamed: 0')\n",
    "\n",
    "# Shuffle dataset\n",
    "dataset = dataset.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "# Split into train/test\n",
    "train_df = dataset.iloc[:int(np.floor(0.7*dataset.shape[0])),:]\n",
    "test_df = dataset.iloc[int(np.floor(0.7*dataset.shape[0])):, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns='target')\n",
    "y_train = train_df.target\n",
    "X_test = test_df.drop(columns='target')\n",
    "y_test = test_df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_male_idx = list(X_train.columns).index('sex_male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mmscaler = MinMaxScaler().fit(X_train)\n",
    "X_train = mmscaler.transform(X_train)\n",
    "X_test = mmscaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((212, 19), (212,), (91, 19), (91,))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123, 42)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sum(), y_test.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7692307692307693"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_model = RandomForestClassifier().fit(X_train, y_train)\n",
    "rf_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6835164835164835"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consistency(rf_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.494079254079254"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_delta(rf_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27515151515151515"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrim(rf_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8651603498542274"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf_model.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7058823529411765, 0.8571428571428571, 0.7741935483870968)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf_model.predict(X_test)\n",
    "precision_score(y_test, y_pred), recall_score(y_test, y_pred), f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 15],\n",
       "       [ 6, 36]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reg. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "lr_model = LogisticRegressionCV(Cs=[2,1,0.66,0.33], scoring=max_delta, solver='lbfgs', cv=5, max_iter=1000)\n",
    "lr_model = lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model.C_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.02893823, -0.02893823,  0.00147322, -0.06966221],\n",
       "        [ 0.08124031,  0.08124031,  0.08124031,  0.08124031],\n",
       "        [-0.16404101, -0.27506877, -0.38609652, -0.17291823],\n",
       "        [ 0.61150677,  0.62840385,  0.39896662,  0.39896662],\n",
       "        [ 0.20664701,  0.2292951 ,  0.2292951 ,  0.2292951 ]])}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7912087912087912"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(lr_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6791208791208792"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consistency(lr_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40575424575424573"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_delta(lr_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38545454545454544"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrim(lr_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8760932944606414"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob = lr_model.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7254901960784313, 0.8809523809523809, 0.7956989247311828)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr_model.predict(X_test)\n",
    "precision_score(y_test, y_pred), recall_score(y_test, y_pred), f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[35, 14],\n",
       "       [ 5, 37]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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