tutorial-AIF360.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6ffe7c7",
   "metadata": {},
   "source": [
    "# <center> AIF360 Scikit-Learn Tutorial</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b994b6",
   "metadata": {},
   "source": [
    "# Get data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b306619",
   "metadata": {},
   "source": [
    "- If you have a dataset that you want to evaluate bias in it, you can simply load with pandas and input in the library\n",
    "- AIF360 has a module for loading datasets.\n",
    "    - fetch_adult() - Adult Census Income Dataset.\n",
    "    - fetch_german() - German Credit Dataset\n",
    "    - fetch_bank() - Bank Marketing Dataset\n",
    "    `from aif360.sklearn.datasets import fetch_adult`\n",
    "- For the didactic purposes, I've downloaded the original dataset from Kaggle https://www.kaggle.com/uciml/adult-census-income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "790e6a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.options.display.max_rows = 2000\n",
    "pd.options.display.max_columns = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59d943d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2753d9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>?</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   90         ?   77053       HS-grad              9        Widowed   \n",
       "1   82   Private  132870       HS-grad              9        Widowed   \n",
       "2   66         ?  186061  Some-college             10        Widowed   \n",
       "3   54   Private  140359       7th-8th              4       Divorced   \n",
       "4   41   Private  264663  Some-college             10      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0                  ?  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                  ?      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              40  United-States  <=50K  \n",
       "1          4356              18  United-States  <=50K  \n",
       "2          4356              40  United-States  <=50K  \n",
       "3          3900              40  United-States  <=50K  \n",
       "4          3900              40  United-States  <=50K  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce4c233",
   "metadata": {},
   "source": [
    "# Explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34d3027f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d54fe6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             32561 non-null  int64 \n",
      " 1   workclass       32561 non-null  object\n",
      " 2   fnlwgt          32561 non-null  int64 \n",
      " 3   education       32561 non-null  object\n",
      " 4   education.num   32561 non-null  int64 \n",
      " 5   marital.status  32561 non-null  object\n",
      " 6   occupation      32561 non-null  object\n",
      " 7   relationship    32561 non-null  object\n",
      " 8   race            32561 non-null  object\n",
      " 9   sex             32561 non-null  object\n",
      " 10  capital.gain    32561 non-null  int64 \n",
      " 11  capital.loss    32561 non-null  int64 \n",
      " 12  hours.per.week  32561 non-null  int64 \n",
      " 13  native.country  32561 non-null  object\n",
      " 14  income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d811fda1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61ebb046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>index</th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>?</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0      0   90         ?   77053       HS-grad              9        Widowed   \n",
       "1      1   82   Private  132870       HS-grad              9        Widowed   \n",
       "2      2   66         ?  186061  Some-college             10        Widowed   \n",
       "3      3   54   Private  140359       7th-8th              4       Divorced   \n",
       "4      4   41   Private  264663  Some-college             10      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0                  ?  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                  ?      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              40  United-States  <=50K  \n",
       "1          4356              18  United-States  <=50K  \n",
       "2          4356              40  United-States  <=50K  \n",
       "3          3900              40  United-States  <=50K  \n",
       "4          3900              40  United-States  <=50K  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068ff199",
   "metadata": {},
   "source": [
    "## > Explore column by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6f26ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'age', 'workclass', 'fnlwgt', 'education', 'education.num',\n",
       "       'marital.status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital.gain', 'capital.loss', 'hours.per.week', 'native.country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db27f6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32561.000000\n",
       "mean        38.581647\n",
       "std         13.640433\n",
       "min         17.000000\n",
       "25%         28.000000\n",
       "50%         37.000000\n",
       "75%         48.000000\n",
       "max         90.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c49735cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2410., 3160., 2461., 3429., 3465., 2583., 3198., 2965., 1828.,\n",
       "        2139., 1558., 1033.,  996.,  599.,  269.,  227.,  120.,   54.,\n",
       "          20.,   47.]),\n",
       " array([17.  , 20.65, 24.3 , 27.95, 31.6 , 35.25, 38.9 , 42.55, 46.2 ,\n",
       "        49.85, 53.5 , 57.15, 60.8 , 64.45, 68.1 , 71.75, 75.4 , 79.05,\n",
       "        82.7 , 86.35, 90.  ]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['age'], bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6adb4def",
   "metadata": {},
   "source": [
    "It is well distributed. Seems like most of population is around 30 years old with 37/38 being the ages with most people. \n",
    "\n",
    "No transformations are needed for this column, I will use the raw value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44ec265d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22696\n",
       "Self-emp-not-inc     2541\n",
       "Local-gov            2093\n",
       "?                    1836\n",
       "State-gov            1298\n",
       "Self-emp-inc         1116\n",
       "Federal-gov           960\n",
       "Without-pay            14\n",
       "Never-worked            7\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3d81f4f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             0.697030\n",
       "Self-emp-not-inc    0.078038\n",
       "Local-gov           0.064279\n",
       "?                   0.056386\n",
       "State-gov           0.039864\n",
       "Self-emp-inc        0.034274\n",
       "Federal-gov         0.029483\n",
       "Without-pay         0.000430\n",
       "Never-worked        0.000215\n",
       "Name: workclass, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b40bc6",
   "metadata": {},
   "source": [
    "Most people work in private companies.\n",
    "\n",
    "I can one hot encode these features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4ff83414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.256100e+04\n",
       "mean     1.897784e+05\n",
       "std      1.055500e+05\n",
       "min      1.228500e+04\n",
       "25%      1.178270e+05\n",
       "50%      1.783560e+05\n",
       "75%      2.370510e+05\n",
       "max      1.484705e+06\n",
       "Name: fnlwgt, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fnlwgt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b70f3ca",
   "metadata": {},
   "source": [
    "This seems like some sort of id. Let me check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f746ddb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21648"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fnlwgt'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "04771931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 16)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565be9eb",
   "metadata": {},
   "source": [
    "Nope. But I have to check what it can mean later. \n",
    "\n",
    "I searched on Google and I got this: \"The continuous variable fnlwgt represents final weight, which is the number of units in the target population that the responding unit represents\". Since I have no idea what this means, I will drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "05e0b74c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HS-grad         10501\n",
       "Some-college     7291\n",
       "Bachelors        5355\n",
       "Masters          1723\n",
       "Assoc-voc        1382\n",
       "11th             1175\n",
       "Assoc-acdm       1067\n",
       "10th              933\n",
       "7th-8th           646\n",
       "Prof-school       576\n",
       "9th               514\n",
       "12th              433\n",
       "Doctorate         413\n",
       "5th-6th           333\n",
       "1st-4th           168\n",
       "Preschool          51\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9d4371",
   "metadata": {},
   "source": [
    "Just education levels. I can make them into ordinal features and do a one hot encoding after."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f5aaa567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     10501\n",
       "10     7291\n",
       "13     5355\n",
       "14     1723\n",
       "11     1382\n",
       "7      1175\n",
       "12     1067\n",
       "6       933\n",
       "4       646\n",
       "15      576\n",
       "5       514\n",
       "8       433\n",
       "16      413\n",
       "3       333\n",
       "2       168\n",
       "1        51\n",
       "Name: education.num, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education.num'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026d2de2",
   "metadata": {},
   "source": [
    "Is this some sort of code for the previous column? Let me check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ecd71122",
   "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>education</th>\n",
       "      <th>education.num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st-4th</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5th-6th</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       education  education.num\n",
       "13     Preschool              1\n",
       "3        1st-4th              2\n",
       "4        5th-6th              3\n",
       "5        7th-8th              4\n",
       "6            9th              5\n",
       "0           10th              6\n",
       "1           11th              7\n",
       "2           12th              8\n",
       "11       HS-grad              9\n",
       "15  Some-college             10\n",
       "8      Assoc-voc             11\n",
       "7     Assoc-acdm             12\n",
       "9      Bachelors             13\n",
       "12       Masters             14\n",
       "14   Prof-school             15\n",
       "10     Doctorate             16"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education', 'education.num'], as_index=False).count()\\\n",
    "        [['education', 'education.num']].sort_values('education.num')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f1a0d8",
   "metadata": {},
   "source": [
    "Looks like it is. This way, I may not need this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "448fc35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       14976\n",
       "Never-married            10683\n",
       "Divorced                  4443\n",
       "Separated                 1025\n",
       "Widowed                    993\n",
       "Married-spouse-absent      418\n",
       "Married-AF-spouse           23\n",
       "Name: marital.status, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital.status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0c915617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       0.459937\n",
       "Never-married            0.328092\n",
       "Divorced                 0.136452\n",
       "Separated                0.031479\n",
       "Widowed                  0.030497\n",
       "Married-spouse-absent    0.012837\n",
       "Married-AF-spouse        0.000706\n",
       "Name: marital.status, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital.status'].value_counts(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977f529a",
   "metadata": {},
   "source": [
    "One hot encode this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e0e59835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       4140\n",
       "Craft-repair         4099\n",
       "Exec-managerial      4066\n",
       "Adm-clerical         3770\n",
       "Sales                3650\n",
       "Other-service        3295\n",
       "Machine-op-inspct    2002\n",
       "?                    1843\n",
       "Transport-moving     1597\n",
       "Handlers-cleaners    1370\n",
       "Farming-fishing       994\n",
       "Tech-support          928\n",
       "Protective-serv       649\n",
       "Priv-house-serv       149\n",
       "Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ae9dab2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f0e010",
   "metadata": {},
   "source": [
    "Occupation will probably impact on what we are trying to predict: income. Good thing is that the data is well distributed for this. \n",
    "\n",
    "Despite having so many occupations in the world this column has only these 15. I can one hot encode them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7bc9dc89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Husband           13193\n",
       "Not-in-family      8305\n",
       "Own-child          5068\n",
       "Unmarried          3446\n",
       "Wife               1568\n",
       "Other-relative      981\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d6f521ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Husband           0.405178\n",
       "Not-in-family     0.255060\n",
       "Own-child         0.155646\n",
       "Unmarried         0.105832\n",
       "Wife              0.048156\n",
       "Other-relative    0.030128\n",
       "Name: relationship, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relationship'].value_counts(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fd9efc",
   "metadata": {},
   "source": [
    "Don't know if this column means that the person is in a relationship with a \"husband\" or \"wife\" or if they are the 'husband' and 'wife'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876c4422",
   "metadata": {},
   "source": [
    "Let me see if I can find out by using the column 'sex'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3feab201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "relationship    sex   \n",
       "Husband         Female        1\n",
       "                Male      13192\n",
       "Not-in-family   Female     3875\n",
       "                Male       4430\n",
       "Other-relative  Female      430\n",
       "                Male        551\n",
       "Own-child       Female     2245\n",
       "                Male       2823\n",
       "Unmarried       Female     2654\n",
       "                Male        792\n",
       "Wife            Female     1566\n",
       "                Male          2\n",
       "Name: index, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['relationship', 'sex'])['index'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f413f579",
   "metadata": {},
   "source": [
    "It looks like this means that they are 'husbands' and 'wives'. However, I already have this information in the 'marital.status' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f160f1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "relationship    marital.status       \n",
       "Husband         Married-AF-spouse            9\n",
       "                Married-civ-spouse       13184\n",
       "Not-in-family   Divorced                  2404\n",
       "                Married-civ-spouse          17\n",
       "                Married-spouse-absent      211\n",
       "                Never-married             4706\n",
       "                Separated                  420\n",
       "                Widowed                    547\n",
       "Other-relative  Divorced                   110\n",
       "                Married-AF-spouse            1\n",
       "                Married-civ-spouse         124\n",
       "                Married-spouse-absent       32\n",
       "                Never-married              611\n",
       "                Separated                   55\n",
       "                Widowed                     48\n",
       "Own-child       Divorced                   328\n",
       "                Married-AF-spouse            1\n",
       "                Married-civ-spouse          95\n",
       "                Married-spouse-absent       45\n",
       "                Never-married             4485\n",
       "                Separated                   99\n",
       "                Widowed                     15\n",
       "Unmarried       Divorced                  1601\n",
       "                Married-spouse-absent      130\n",
       "                Never-married              881\n",
       "                Separated                  451\n",
       "                Widowed                    383\n",
       "Wife            Married-AF-spouse           12\n",
       "                Married-civ-spouse        1556\n",
       "Name: index, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['relationship', 'marital.status'])['index'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799d3cc0",
   "metadata": {},
   "source": [
    "Yep. I'll delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "0fe623c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                 27816\n",
       "Black                  3124\n",
       "Asian-Pac-Islander     1039\n",
       "Amer-Indian-Eskimo      311\n",
       "Other                   271\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "657adb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                 0.854274\n",
       "Black                 0.095943\n",
       "Asian-Pac-Islander    0.031909\n",
       "Amer-Indian-Eskimo    0.009551\n",
       "Other                 0.008323\n",
       "Name: race, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'].value_counts(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855012a2",
   "metadata": {},
   "source": [
    "There are much more white people than the rest. Maybe this can be a good `sens. attribute`.\n",
    "\n",
    "One hot encode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "bdd5bc7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      21790\n",
       "Female    10771\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "40cb952e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      0.669205\n",
       "Female    0.330795\n",
       "Name: sex, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8fde045",
   "metadata": {},
   "source": [
    "There are more men than women. Maybe this can be a good `sens. attribute`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c3321008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32561.000000\n",
       "mean      1077.648844\n",
       "std       7385.292085\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max      99999.000000\n",
       "Name: capital.gain, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital.gain'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "895c4e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        29849\n",
       "15024      347\n",
       "7688       284\n",
       "7298       246\n",
       "99999      159\n",
       "5178        97\n",
       "3103        97\n",
       "4386        70\n",
       "5013        69\n",
       "8614        55\n",
       "Name: capital.gain, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital.gain'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "568adfa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.916710\n",
       "15024    0.010657\n",
       "7688     0.008722\n",
       "7298     0.007555\n",
       "99999    0.004883\n",
       "5178     0.002979\n",
       "3103     0.002979\n",
       "4386     0.002150\n",
       "5013     0.002119\n",
       "8614     0.001689\n",
       "Name: capital.gain, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital.gain'].value_counts(1).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6a3908",
   "metadata": {},
   "source": [
    "Have to know what this column mean later. But why are there much more zeroes than the rest?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ae9660",
   "metadata": {},
   "source": [
    "I googled it and I got: \"Capital gain refers to an increase in a capital asset's value and is considered to be realized when the asset is sold\". This way, it may be an important feature for the model to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57a80ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32561.000000\n",
       "mean        87.303830\n",
       "std        402.960219\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max       4356.000000\n",
       "Name: capital.loss, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital.loss'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5fff984b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       31042\n",
       "1902      202\n",
       "1977      168\n",
       "1887      159\n",
       "1848       51\n",
       "1485       51\n",
       "2415       49\n",
       "1602       47\n",
       "1740       42\n",
       "1590       40\n",
       "Name: capital.loss, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital.loss'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d9874b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.953349\n",
       "1902    0.006204\n",
       "1977    0.005160\n",
       "1887    0.004883\n",
       "1848    0.001566\n",
       "1485    0.001566\n",
       "2415    0.001505\n",
       "1602    0.001443\n",
       "1740    0.001290\n",
       "1590    0.001228\n",
       "Name: capital.loss, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital.loss'].value_counts(1).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c1a0cc",
   "metadata": {},
   "source": [
    "Same as above. I can normalize them both and use the value as raw feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9d4b6163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40    0.467338\n",
       "50    0.086576\n",
       "45    0.056018\n",
       "60    0.045300\n",
       "35    0.039833\n",
       "20    0.037591\n",
       "30    0.035288\n",
       "55    0.021314\n",
       "25    0.020700\n",
       "48    0.015878\n",
       "Name: hours.per.week, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hours.per.week'].value_counts(1).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "135587cf",
   "metadata": {},
   "source": [
    "I can turn them into ordinal features and then one hot encode them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9fe3d9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United-States                 0.895857\n",
       "Mexico                        0.019748\n",
       "?                             0.017905\n",
       "Philippines                   0.006081\n",
       "Germany                       0.004207\n",
       "Canada                        0.003716\n",
       "Puerto-Rico                   0.003501\n",
       "El-Salvador                   0.003255\n",
       "India                         0.003071\n",
       "Cuba                          0.002918\n",
       "England                       0.002764\n",
       "Jamaica                       0.002488\n",
       "South                         0.002457\n",
       "China                         0.002303\n",
       "Italy                         0.002242\n",
       "Dominican-Republic            0.002150\n",
       "Vietnam                       0.002058\n",
       "Guatemala                     0.001966\n",
       "Japan                         0.001904\n",
       "Poland                        0.001843\n",
       "Columbia                      0.001812\n",
       "Taiwan                        0.001566\n",
       "Haiti                         0.001351\n",
       "Iran                          0.001321\n",
       "Portugal                      0.001136\n",
       "Nicaragua                     0.001044\n",
       "Peru                          0.000952\n",
       "France                        0.000891\n",
       "Greece                        0.000891\n",
       "Ecuador                       0.000860\n",
       "Ireland                       0.000737\n",
       "Hong                          0.000614\n",
       "Trinadad&Tobago               0.000584\n",
       "Cambodia                      0.000584\n",
       "Laos                          0.000553\n",
       "Thailand                      0.000553\n",
       "Yugoslavia                    0.000491\n",
       "Outlying-US(Guam-USVI-etc)    0.000430\n",
       "Honduras                      0.000399\n",
       "Hungary                       0.000399\n",
       "Scotland                      0.000369\n",
       "Holand-Netherlands            0.000031\n",
       "Name: native.country, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native.country'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bfbeccb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native.country'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab49ff5",
   "metadata": {},
   "source": [
    "Much more people from USA. This can also be a `sens attribute`.\n",
    "\n",
    "I can turn this into binary (if from USA 1, if not 0) or separate by region and then OHE them (NA, SA, Euro, Africa, Asia, etc). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fb2b9120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    24720\n",
       ">50K      7841\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b6e96da",
   "metadata": {},
   "source": [
    "I believe this is the label we can use. 1 would be >50K and 0 would be <=50K."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a38f15",
   "metadata": {},
   "source": [
    "## Check what does the '?' mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9662e007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index             0.000000\n",
       "age               0.000000\n",
       "workclass         0.056386\n",
       "fnlwgt            0.000000\n",
       "education         0.000000\n",
       "education.num     0.000000\n",
       "marital.status    0.000000\n",
       "occupation        0.056601\n",
       "relationship      0.000000\n",
       "race              0.000000\n",
       "sex               0.000000\n",
       "capital.gain      0.000000\n",
       "capital.loss      0.000000\n",
       "hours.per.week    0.000000\n",
       "native.country    0.017905\n",
       "income            0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df=='?'].count()/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac0c7ac",
   "metadata": {},
   "source": [
    "This is the percentage of appearances of '?' value. Maybe this can be considered a null value, this way I can just drop the rows with this value so I won't have to deal with them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f21fd07",
   "metadata": {},
   "source": [
    "# Evaluate bias in the original dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e786f04",
   "metadata": {},
   "source": [
    "The columns selected as good `sens attr` were:\n",
    "- age\n",
    "- sex\n",
    "- race\n",
    "- native.country\n",
    "\n",
    "For each, we'll run the fairness metric `disparate_impact_ratio` which can be applied BEFORE training a model. <br><br>\n",
    "<center>--- FORMULA ---</center>\n",
    "<center>P_unprivileged_class(1)/P_privileged_class(1) (_Probability of the unprivileged class to receive a potitive outcome  divided by the probability of the privileged class to receive a potitive outcome_)</center>\n",
    "<br>\n",
    "\n",
    "How to interpret this metric:\n",
    "- Its output value ranges from 0 to 1\n",
    "- The higher its value fairer is your dataset/model related to that protected attribute. \n",
    "- Despite 1 being the perfect value for it (which means your dataset/model is fairest), a value greater than 0.8 is considered an acceptable bias."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e395508f",
   "metadata": {},
   "source": [
    "Let's start with 'sex' column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be13dff",
   "metadata": {},
   "source": [
    "Import the AIF360 module which contains the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bd2e439d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aif360.sklearn import metrics as mt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da59821b",
   "metadata": {},
   "source": [
    "But first, we need to configure our dataset to the input accepted by the library. \n",
    "\n",
    "To use AIF360 with sklearn, we need to:\n",
    "1. Binarize the sensitive attribute with: 1 being set to be the privileged group and 0 as the unprivileged.\n",
    "2. Binarize the label column with: 1 being the positive outcome and 0 else.\n",
    "3. Turn the sensitive attribute column into the index of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510bec1a",
   "metadata": {},
   "source": [
    "Let's first binarize the y label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1f7ab1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['income'] = df['income'].apply(lambda x: 1 if x=='>50K' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b327dd4",
   "metadata": {},
   "source": [
    "Now let's binarize the sens. attr column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e194fa80",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sex'] = df['sex'].apply(lambda x: 1 if x=='Male' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f2d89d",
   "metadata": {},
   "source": [
    "Now, for the purpose of readibility, let's separate these two into one variable `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e8d293e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.set_index('sex')['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ca27dfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24720\n",
       "1     7841\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6bebc334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3580225496813511"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.disparate_impact_ratio(y, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a35ea1a",
   "metadata": {},
   "source": [
    "The metric presents a really low value for this attribute. Far even from 0.8 which is the acceptable value. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b754d153",
   "metadata": {},
   "source": [
    "Now, since we are going to repeat this for the other protected attribute columns, let's create this function to make the development easier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b0af03eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_sens_attr(df, label_col, prot_tuple):\n",
    "    \"\"\"To use AIF360 with sklearn, we need to turn the sensitive \n",
    "    attribute column into the index of the dataset. This function will\n",
    "    binarize the prot_attr column and set it as the index. \n",
    "    \n",
    "    :param df: [pd.DataFrame]\n",
    "    :param label_col: [str], the y label column name \n",
    "    :param prot_tuple: [tuple], tuple containing the protected attribute \n",
    "        column name and the threshold to make the binarization. Apply\n",
    "        needed configurations depending if the threshold is a category\n",
    "        or a number\n",
    "    :return y: [pd.DataFrame], a dataframe consisting of only the label\n",
    "        column and the sensitive attribute set as index\n",
    "    \"\"\"\n",
    "    \n",
    "    threshold = prot_tuple[1]\n",
    "    prot_attr = prot_tuple[0]\n",
    "    df = df.copy()\n",
    "    \n",
    "    if isinstance(threshold, str):\n",
    "        df[prot_attr] = df[prot_attr].apply(lambda x: 1 if x==threshold else 0)\n",
    "    else:\n",
    "        df[prot_attr] = df[prot_attr].apply(lambda x: 1 if x>=threshold else 0)\n",
    "    \n",
    "    y = df.set_index(prot_attr)[label_col]\n",
    "    \n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "997a5812",
   "metadata": {},
   "source": [
    "Let's do it for 'race'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5da50c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = set_sens_attr(df, 'income', ('race', 'White'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "56cfcc52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5963484039832201"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.disparate_impact_ratio(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "701d18f5",
   "metadata": {},
   "source": [
    "The metric for \"race\" is higher than for \"sex\", however, it still shows that the \"race\" is an attribute that has a lot of bias."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3b54f9",
   "metadata": {},
   "source": [
    "Let's do it for 'native.country'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5b01eecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = set_sens_attr(df, 'income', ('native.country', 'United-States'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2af7a5e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8037180456803203"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.disparate_impact_ratio(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af69367c",
   "metadata": {},
   "source": [
    "This feature presents an acceptable score, this way I will not pay attention to it in bias mitigation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f93d0d50",
   "metadata": {},
   "source": [
    "# Transform data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21386345",
   "metadata": {},
   "source": [
    "Let's apply the transformations I listed on the Exploration section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "a7ba5c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed = df.copy().drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cfc6474",
   "metadata": {},
   "source": [
    "Clean '?' and nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "b8d2850f",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed = transformed[transformed!='?']\n",
    "transformed = transformed.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee43e726",
   "metadata": {},
   "source": [
    "Drop not needed columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "e5bc6469",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['fnlwgt', 'education']\n",
    "transformed = transformed.drop(drop_cols, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb45d95",
   "metadata": {},
   "source": [
    "One hot encode the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "e22e8817",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_cols = ['workclass', 'marital.status',\n",
    "           'occupation', 'relationship']\n",
    "for col in ohe_cols:\n",
    "    transformed = transformed.join(pd.get_dummies(transformed[col], \n",
    "                                                  prefix=col))\\\n",
    "                                                .drop(col, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d00bb5",
   "metadata": {},
   "source": [
    "Normalize other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "d70574d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_cols = ['capital.loss', 'capital.gain']\n",
    "for col in norm_cols:\n",
    "    transformed[col] = transformed[col]/transformed[col].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb36e6a7",
   "metadata": {},
   "source": [
    "Binarize columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "9fb99ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed['sex'] = transformed['sex'].apply(lambda x: 1 if x=='Male' else 0)\n",
    "transformed['race'] = transformed['race'].apply(lambda x: 1 if x=='White' else 0)\n",
    "transformed['native.country'] = transformed['native.country'].apply(lambda x: 1 if x=='United-States' else 0)\n",
    "transformed['income'] = transformed['income'].apply(lambda x: 1 if x=='>50K' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "15b23c7a",
   "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>age</th>\n",
       "      <th>education.num</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "      <th>workclass_Federal-gov</th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <th>workclass_Without-pay</th>\n",
       "      <th>marital.status_Divorced</th>\n",
       "      <th>marital.status_Married-AF-spouse</th>\n",
       "      <th>marital.status_Married-civ-spouse</th>\n",
       "      <th>marital.status_Married-spouse-absent</th>\n",
       "      <th>marital.status_Never-married</th>\n",
       "      <th>marital.status_Separated</th>\n",
       "      <th>marital.status_Widowed</th>\n",
       "      <th>occupation_Adm-clerical</th>\n",
       "      <th>occupation_Armed-Forces</th>\n",
       "      <th>occupation_Craft-repair</th>\n",
       "      <th>occupation_Exec-managerial</th>\n",
       "      <th>occupation_Farming-fishing</th>\n",
       "      <th>occupation_Handlers-cleaners</th>\n",
       "      <th>occupation_Machine-op-inspct</th>\n",
       "      <th>occupation_Other-service</th>\n",
       "      <th>occupation_Priv-house-serv</th>\n",
       "      <th>occupation_Prof-specialty</th>\n",
       "      <th>occupation_Protective-serv</th>\n",
       "      <th>occupation_Sales</th>\n",
       "      <th>occupation_Tech-support</th>\n",
       "      <th>occupation_Transport-moving</th>\n",
       "      <th>relationship_Husband</th>\n",
       "      <th>relationship_Not-in-family</th>\n",
       "      <th>relationship_Other-relative</th>\n",
       "      <th>relationship_Own-child</th>\n",
       "      <th>relationship_Unmarried</th>\n",
       "      <th>relationship_Wife</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.895317</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.895317</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.865473</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.865473</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education.num  race  sex  capital.gain  capital.loss  hours.per.week  \\\n",
       "1   82              9     1    0           0.0      1.000000              18   \n",
       "3   54              4     1    0           0.0      0.895317              40   \n",
       "4   41             10     1    0           0.0      0.895317              40   \n",
       "5   34              9     1    0           0.0      0.865473              45   \n",
       "6   38              6     1    1           0.0      0.865473              40   \n",
       "\n",
       "   native.country  income  workclass_Federal-gov  workclass_Local-gov  \\\n",
       "1               1       0                      0                    0   \n",
       "3               1       0                      0                    0   \n",
       "4               1       0                      0                    0   \n",
       "5               1       0                      0                    0   \n",
       "6               1       0                      0                    0   \n",
       "\n",
       "   workclass_Private  workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
       "1                  1                       0                           0   \n",
       "3                  1                       0                           0   \n",
       "4                  1                       0                           0   \n",
       "5                  1                       0                           0   \n",
       "6                  1                       0                           0   \n",
       "\n",
       "   workclass_State-gov  workclass_Without-pay  marital.status_Divorced  \\\n",
       "1                    0                      0                        0   \n",
       "3                    0                      0                        1   \n",
       "4                    0                      0                        0   \n",
       "5                    0                      0                        1   \n",
       "6                    0                      0                        0   \n",
       "\n",
       "   marital.status_Married-AF-spouse  marital.status_Married-civ-spouse  \\\n",
       "1                                 0                                  0   \n",
       "3                                 0                                  0   \n",
       "4                                 0                                  0   \n",
       "5                                 0                                  0   \n",
       "6                                 0                                  0   \n",
       "\n",
       "   marital.status_Married-spouse-absent  marital.status_Never-married  \\\n",
       "1                                     0                             0   \n",
       "3                                     0                             0   \n",
       "4                                     0                             0   \n",
       "5                                     0                             0   \n",
       "6                                     0                             0   \n",
       "\n",
       "   marital.status_Separated  marital.status_Widowed  occupation_Adm-clerical  \\\n",
       "1                         0                       1                        0   \n",
       "3                         0                       0                        0   \n",
       "4                         1                       0                        0   \n",
       "5                         0                       0                        0   \n",
       "6                         1                       0                        1   \n",
       "\n",
       "   occupation_Armed-Forces  occupation_Craft-repair  \\\n",
       "1                        0                        0   \n",
       "3                        0                        0   \n",
       "4                        0                        0   \n",
       "5                        0                        0   \n",
       "6                        0                        0   \n",
       "\n",
       "   occupation_Exec-managerial  occupation_Farming-fishing  \\\n",
       "1                           1                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "5                           0                           0   \n",
       "6                           0                           0   \n",
       "\n",
       "   occupation_Handlers-cleaners  occupation_Machine-op-inspct  \\\n",
       "1                             0                             0   \n",
       "3                             0                             1   \n",
       "4                             0                             0   \n",
       "5                             0                             0   \n",
       "6                             0                             0   \n",
       "\n",
       "   occupation_Other-service  occupation_Priv-house-serv  \\\n",
       "1                         0                           0   \n",
       "3                         0                           0   \n",
       "4                         0                           0   \n",
       "5                         1                           0   \n",
       "6                         0                           0   \n",
       "\n",
       "   occupation_Prof-specialty  occupation_Protective-serv  occupation_Sales  \\\n",
       "1                          0                           0                 0   \n",
       "3                          0                           0                 0   \n",
       "4                          1                           0                 0   \n",
       "5                          0                           0                 0   \n",
       "6                          0                           0                 0   \n",
       "\n",
       "   occupation_Tech-support  occupation_Transport-moving  relationship_Husband  \\\n",
       "1                        0                            0                     0   \n",
       "3                        0                            0                     0   \n",
       "4                        0                            0                     0   \n",
       "5                        0                            0                     0   \n",
       "6                        0                            0                     0   \n",
       "\n",
       "   relationship_Not-in-family  relationship_Other-relative  \\\n",
       "1                           1                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "5                           0                            0   \n",
       "6                           0                            0   \n",
       "\n",
       "   relationship_Own-child  relationship_Unmarried  relationship_Wife  \n",
       "1                       0                       0                  0  \n",
       "3                       0                       1                  0  \n",
       "4                       1                       0                  0  \n",
       "5                       0                       1                  0  \n",
       "6                       0                       1                  0  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "7ff82d86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30162, 43)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c790f18",
   "metadata": {},
   "source": [
    "I got 43 features for my model. Let's just quick explore this data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c4f1a41",
   "metadata": {},
   "source": [
    "# Explore transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "364f77e3",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>38.437902</td>\n",
       "      <td>13.134665</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education.num</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>10.121312</td>\n",
       "      <td>2.549995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.859790</td>\n",
       "      <td>0.347210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.675685</td>\n",
       "      <td>0.468126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital.gain</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.010920</td>\n",
       "      <td>0.074064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital.loss</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>0.092814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours.per.week</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>40.931238</td>\n",
       "      <td>11.979984</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native.country</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.911876</td>\n",
       "      <td>0.283480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.248922</td>\n",
       "      <td>0.432396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_Federal-gov</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.031265</td>\n",
       "      <td>0.174035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.068530</td>\n",
       "      <td>0.252657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_Private</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.738877</td>\n",
       "      <td>0.439254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.035608</td>\n",
       "      <td>0.185313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>0.275664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.042404</td>\n",
       "      <td>0.201513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass_Without-pay</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.021540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Divorced</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.139712</td>\n",
       "      <td>0.346694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Married-AF-spouse</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Married-civ-spouse</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.466315</td>\n",
       "      <td>0.498872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Married-spouse-absent</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.012267</td>\n",
       "      <td>0.110077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Never-married</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.322459</td>\n",
       "      <td>0.467425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Separated</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.031132</td>\n",
       "      <td>0.173677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital.status_Widowed</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.027419</td>\n",
       "      <td>0.163303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Adm-clerical</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.123367</td>\n",
       "      <td>0.328864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Armed-Forces</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.017272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Craft-repair</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.133612</td>\n",
       "      <td>0.340240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Exec-managerial</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.132352</td>\n",
       "      <td>0.338879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Farming-fishing</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.032790</td>\n",
       "      <td>0.178088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Handlers-cleaners</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.044758</td>\n",
       "      <td>0.206776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Machine-op-inspct</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.065181</td>\n",
       "      <td>0.246850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Other-service</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.106492</td>\n",
       "      <td>0.308471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Priv-house-serv</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.068693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Prof-specialty</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.133877</td>\n",
       "      <td>0.340526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Protective-serv</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.021351</td>\n",
       "      <td>0.144555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Sales</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.118825</td>\n",
       "      <td>0.323588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Tech-support</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.030237</td>\n",
       "      <td>0.171241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation_Transport-moving</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.052119</td>\n",
       "      <td>0.222270</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship_Husband</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.413202</td>\n",
       "      <td>0.492417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship_Not-in-family</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.256150</td>\n",
       "      <td>0.436513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship_Other-relative</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.029474</td>\n",
       "      <td>0.169134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship_Own-child</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.148067</td>\n",
       "      <td>0.355172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship_Unmarried</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.106492</td>\n",
       "      <td>0.308471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship_Wife</th>\n",
       "      <td>30162.0</td>\n",
       "      <td>0.046615</td>\n",
       "      <td>0.210816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        count       mean        std   min  \\\n",
       "age                                   30162.0  38.437902  13.134665  17.0   \n",
       "education.num                         30162.0  10.121312   2.549995   1.0   \n",
       "race                                  30162.0   0.859790   0.347210   0.0   \n",
       "sex                                   30162.0   0.675685   0.468126   0.0   \n",
       "capital.gain                          30162.0   0.010920   0.074064   0.0   \n",
       "capital.loss                          30162.0   0.020288   0.092814   0.0   \n",
       "hours.per.week                        30162.0  40.931238  11.979984   1.0   \n",
       "native.country                        30162.0   0.911876   0.283480   0.0   \n",
       "income                                30162.0   0.248922   0.432396   0.0   \n",
       "workclass_Federal-gov                 30162.0   0.031265   0.174035   0.0   \n",
       "workclass_Local-gov                   30162.0   0.068530   0.252657   0.0   \n",
       "workclass_Private                     30162.0   0.738877   0.439254   0.0   \n",
       "workclass_Self-emp-inc                30162.0   0.035608   0.185313   0.0   \n",
       "workclass_Self-emp-not-inc            30162.0   0.082853   0.275664   0.0   \n",
       "workclass_State-gov                   30162.0   0.042404   0.201513   0.0   \n",
       "workclass_Without-pay                 30162.0   0.000464   0.021540   0.0   \n",
       "marital.status_Divorced               30162.0   0.139712   0.346694   0.0   \n",
       "marital.status_Married-AF-spouse      30162.0   0.000696   0.026378   0.0   \n",
       "marital.status_Married-civ-spouse     30162.0   0.466315   0.498872   0.0   \n",
       "marital.status_Married-spouse-absent  30162.0   0.012267   0.110077   0.0   \n",
       "marital.status_Never-married          30162.0   0.322459   0.467425   0.0   \n",
       "marital.status_Separated              30162.0   0.031132   0.173677   0.0   \n",
       "marital.status_Widowed                30162.0   0.027419   0.163303   0.0   \n",
       "occupation_Adm-clerical               30162.0   0.123367   0.328864   0.0   \n",
       "occupation_Armed-Forces               30162.0   0.000298   0.017272   0.0   \n",
       "occupation_Craft-repair               30162.0   0.133612   0.340240   0.0   \n",
       "occupation_Exec-managerial            30162.0   0.132352   0.338879   0.0   \n",
       "occupation_Farming-fishing            30162.0   0.032790   0.178088   0.0   \n",
       "occupation_Handlers-cleaners          30162.0   0.044758   0.206776   0.0   \n",
       "occupation_Machine-op-inspct          30162.0   0.065181   0.246850   0.0   \n",
       "occupation_Other-service              30162.0   0.106492   0.308471   0.0   \n",
       "occupation_Priv-house-serv            30162.0   0.004741   0.068693   0.0   \n",
       "occupation_Prof-specialty             30162.0   0.133877   0.340526   0.0   \n",
       "occupation_Protective-serv            30162.0   0.021351   0.144555   0.0   \n",
       "occupation_Sales                      30162.0   0.118825   0.323588   0.0   \n",
       "occupation_Tech-support               30162.0   0.030237   0.171241   0.0   \n",
       "occupation_Transport-moving           30162.0   0.052119   0.222270   0.0   \n",
       "relationship_Husband                  30162.0   0.413202   0.492417   0.0   \n",
       "relationship_Not-in-family            30162.0   0.256150   0.436513   0.0   \n",
       "relationship_Other-relative           30162.0   0.029474   0.169134   0.0   \n",
       "relationship_Own-child                30162.0   0.148067   0.355172   0.0   \n",
       "relationship_Unmarried                30162.0   0.106492   0.308471   0.0   \n",
       "relationship_Wife                     30162.0   0.046615   0.210816   0.0   \n",
       "\n",
       "                                       25%   50%   75%   max  \n",
       "age                                   28.0  37.0  47.0  90.0  \n",
       "education.num                          9.0  10.0  13.0  16.0  \n",
       "race                                   1.0   1.0   1.0   1.0  \n",
       "sex                                    0.0   1.0   1.0   1.0  \n",
       "capital.gain                           0.0   0.0   0.0   1.0  \n",
       "capital.loss                           0.0   0.0   0.0   1.0  \n",
       "hours.per.week                        40.0  40.0  45.0  99.0  \n",
       "native.country                         1.0   1.0   1.0   1.0  \n",
       "income                                 0.0   0.0   0.0   1.0  \n",
       "workclass_Federal-gov                  0.0   0.0   0.0   1.0  \n",
       "workclass_Local-gov                    0.0   0.0   0.0   1.0  \n",
       "workclass_Private                      0.0   1.0   1.0   1.0  \n",
       "workclass_Self-emp-inc                 0.0   0.0   0.0   1.0  \n",
       "workclass_Self-emp-not-inc             0.0   0.0   0.0   1.0  \n",
       "workclass_State-gov                    0.0   0.0   0.0   1.0  \n",
       "workclass_Without-pay                  0.0   0.0   0.0   1.0  \n",
       "marital.status_Divorced                0.0   0.0   0.0   1.0  \n",
       "marital.status_Married-AF-spouse       0.0   0.0   0.0   1.0  \n",
       "marital.status_Married-civ-spouse      0.0   0.0   1.0   1.0  \n",
       "marital.status_Married-spouse-absent   0.0   0.0   0.0   1.0  \n",
       "marital.status_Never-married           0.0   0.0   1.0   1.0  \n",
       "marital.status_Separated               0.0   0.0   0.0   1.0  \n",
       "marital.status_Widowed                 0.0   0.0   0.0   1.0  \n",
       "occupation_Adm-clerical                0.0   0.0   0.0   1.0  \n",
       "occupation_Armed-Forces                0.0   0.0   0.0   1.0  \n",
       "occupation_Craft-repair                0.0   0.0   0.0   1.0  \n",
       "occupation_Exec-managerial             0.0   0.0   0.0   1.0  \n",
       "occupation_Farming-fishing             0.0   0.0   0.0   1.0  \n",
       "occupation_Handlers-cleaners           0.0   0.0   0.0   1.0  \n",
       "occupation_Machine-op-inspct           0.0   0.0   0.0   1.0  \n",
       "occupation_Other-service               0.0   0.0   0.0   1.0  \n",
       "occupation_Priv-house-serv             0.0   0.0   0.0   1.0  \n",
       "occupation_Prof-specialty              0.0   0.0   0.0   1.0  \n",
       "occupation_Protective-serv             0.0   0.0   0.0   1.0  \n",
       "occupation_Sales                       0.0   0.0   0.0   1.0  \n",
       "occupation_Tech-support                0.0   0.0   0.0   1.0  \n",
       "occupation_Transport-moving            0.0   0.0   0.0   1.0  \n",
       "relationship_Husband                   0.0   0.0   1.0   1.0  \n",
       "relationship_Not-in-family             0.0   0.0   1.0   1.0  \n",
       "relationship_Other-relative            0.0   0.0   0.0   1.0  \n",
       "relationship_Own-child                 0.0   0.0   0.0   1.0  \n",
       "relationship_Unmarried                 0.0   0.0   0.0   1.0  \n",
       "relationship_Wife                      0.0   0.0   0.0   1.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "29e70b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(transformed.corr(), cmap='YlOrBr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dd23ebfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3I9FJRrkHSNF915C2l8YBU/K1Q20vQcpHfUCLrmYhRU9/E1iHpAq7Z6OKEUkdBMFg4clTSh29REdGatun2l7b9kbAi8DDdVXOBrbLr5u5dk0ExuWowsnAxeTc1g3eLyKpgyAYHGIFUQ1Ji+SfS5KkNc6RtFyhymjgwfz6UmD37M20PvBSds26jWSPWDjX24SUljQIgmDoMAxtEJ3KfV8gaUHgbVKO6UmSTlVKJjQV+AfJswngCpKdYgLwGil/NbanSPomcG32372DiKQOgmCo0WOrgzJ0GkldL9mN7e2a1DWwf5NrY4DVOhlLEATB9MQxQQRBEAQNiQkiCIIgaEiPeSiVoaWRWtJpkp6TdG+hbAFJYyQ9kn/On8vnlXSZpLuz/MZehTZTClmQLi2U31gof1rSxV3+jEEQBJ0TXkwN+S3vzkx0MHCt7eWAa+nTCdkfuN/26sDGwI+zGBXA64UsSDW9EWxvWCsHbgIubPfDBEEQTC9slzp6iZYThO2/AC/UFRezHZ1BX+YiA3Nnb6S5crvJZQaS049uQoqDCIIgGFrECqI0i+YYBoB/AYvm1yeQNJqeBu4BDrLfcQx+T46CvlnSNg363Ia0Kvlvg2tARFIHQTCIDMMJomMjddYor30rm5MkNzYBlgHGSLox3/SXsv1U1jC/TtI9th8tdLULSaq2v/d6J1NTqLkGQTCQDEc313ZXEM8qJcmuJct+LpfvBVyYFVsnAI8DKwLYfir/fAwYC6xZ6yznpF4X+FOb4wmCIJi+THa5o4dod4IoZjvag77MRf8ENgWQtCiwAvCYpPklzZ7LFwI+zLRyGtsDl9t+o83xBEEQTFc81aWOXqLlFpOk35M8khaSNBE4HDgGOE/S3iQ5jR1z9aOA30q6BxDwHdv/kfQh4DeSppImpWNsFyeInXOfQRAEQ5Meu/mXoeUEYXuXJpc2bVD3aWCzBuV/B1bt5z02bjWOIAiCQaW3dPhK0ZGaaxAEwXChm1tMkraQ9JCkCZIObnB9dknn5uu3SBqZyz8h6Q5J9+SfmxTajM191gKPF+n0M5eaIJpEUx8n6UFJ4yVdJGm+ujZLSnolK7XWyhp+KZI2lXRn/lB/lbRspx8sCIKgm3iySx2tyBkzfwl8ElgJ2CWnXi6yN/Ci7WWBn5KSswH8B9jK9qok++9Zde12LQQkP0eHlF1B/JZ3R1OPAVaxvRopUdAhddd/AlxZO2nxpZxI/mDAOcD3yn+EIAiCAWBqyaM16wITcpK0t4A/kIKPixSDkc8HNpUk23flrXyA+4D31hyApgelJohG0dS2r8kZ4ABuJmWIAyAHwj1O+gA1+vtSDMyTX89LCrQLgiAYMnQxX9DiwJOF84m5rGGdfJ99CViwrs52wJ223yyUnZ53Yg7LihYd0S01188D5wJImgv4DvAJUp7pGo2+lPXy6y8AV0h6HfgvsH6XxhUEQdAdShqpJe0D7FMoOikH+XYNSSuTtp2KTkG75mDkuYELgN2AMzt5n46N1JIOJektnZ2LjgB+avuVCt18DdjS9gjgdNL2VKP3CqmNIAgGhbIrCNsn2R5VOOonh6eAJQrnI3JZwzqSZiHtrDyfz0cAFwG7F9UoCsHIL5O26tft9DN3tIKQtCfwaWBT98kYrgdsL+lYYD5gqqQ3SKlE3/Wl5FzUq9u+JZefC1zV6P1CaiMIgkGje26utwHLSVqaNBHsDHy2rk4tGPkmUiDxdVnWaD6S4sTBtv9Wq5wnkfly3NmspPvynzsdaNsThKQtgG8DH7X9Wq28mIZU0hHAK7ZPyB+g0ZfyIjCvpOVtP0zamnqg3XEFQRBMD6aW0qVuje3Jkg4ArgZmBk6zfZ+kI4HbbV8KnAqcJWkCyf67c25+ALAs8H1J389lmwGvAlfnyWFm0uRwcqdjLTVBNImmPgSYnSTIB3Cz7X2b9dHsS8n9fxG4IEdav0iyaQRBEAwZShqgy/VlXwFcUVf2/cLrN4AdGrT7H+B/mnS7dvdGmNCMmuAitpiCICjLqIkXd+zR8+zGG5e65yw6dmzH7zVUiJzUQRAEJejmCmJGod2c1EflCOpxkq6R9P5cPn+Oqh4v6VZJqxTaPJHDw8dJur1Qvrqkm/K1y5QyywVBEAwpPFWljl6i3ZzUx9leLUc+Xw7U9s6+C4zL0dW7A8fXtftYDgEfVSg7hWSRX5XkuvWtah8hCIJg+tPFQLkZhrZyUtelBZ2TFAkNSULjulznQWBkzgvRH8sDf8mvx5CiA4MgCIYUU6eo1NFLtB0oJ+loSU8Cu9K3grgb2DZfXxdYij4JDgPXZAXCYpThffRJbuzAtLESQRAEQ4LYYqqA7UNtL0GKoD4gFx8DzCdpHPAV4C5gSr72EdtrkcT69pe0US7/PLCfpDuAuYG3mr1nRFIHQTBY2OWOXqIbXkxnk/x5D89bT3sBZKGox4HHYJow8OckXUQKA/9L3oraLLdZHvhUszeKSOogCAaLXlsdlKGtFYSk5Qqno4EHc/l8kmbL5V8gTQD/lTRnFpBC0pykCeHefL5I/jkTSeb71+2MKQiCYHoyHLeY2s1JvaWkFUjqJP8AahHUHwTOkGSSbWHvXL4ocFGOuJ4FOMd2TW9pF0n759cXksT6giAIhhS9ZoAuQ7s5qU9tUvcmkldSffljwOpN2hzPu91hgyAIhhR2TBBBEARBA3otxqEMMUEEQRCUYOowXEG0K7VxhKSnsmzGOElb5vKRkl4vlP+60OZoSU9KeqWu/9klnStpgqRbJI3s4ucLgiDoCrZKHb1Eu1IbkLLGrZGPomzto4Xyovz3ZTTOcLQ38KLtZYGfktLoBUEQDCmGoxdTW1Ib7WD7ZtvPNLg0Gjgjvz4f2LQbybaDIAi6SUhtVOOArNp6mqT5C+VLS7pL0g2SNmzauo/FgSchJRUCXgIW7GBcQRAEXWeqVeroJdqdIE4ElgHWAJ4BfpzLnwGWtL0m8HXgnG7Kd4fURhAEg0XYIEpi+1nbU2xPJeU9XTeXv2n7+fz6DuBRGsRF1PEUWaAv562eF3i+yfueZHuU7VHbzjmynaEHQRC0RTe1mCRtIemh7JxzcIPrTZ13JB2Syx+StHnZPtuhXamNxQqnn6FPNmNhSTPn1x8AliNrMfXDpcAe+fX2wHWeUfOgBkHQs3RriynfI39JEi5diaQmsVJdtYbOO7nezsDKJOehX0mauWSflWlXamNjSWuQJLyfAL6Uq28EHCnpbZIMx762X8j9HAt8Fpgj93OK7SNIUdlnSZpAMobv3OmHCoIg6DZd3D5aF5iQFSaQ9AeSs879hTqjgSPy6/OBE7LzzmjgD7bfBB7P982ad2irPivTbamNC4ALmlz7NvDtBuVvkPJABEEQDFmmdM+F9R3HnMxEYL1mdWxPllRz3lkcuLmu7eL5das+K9OJF1MQBMGwoayRuuhMk499Wvc+NGk3kvrcQrT0EzlBUO1aMwPKfJLOl/SgpAckbZDLd5B0n6SpkkYRBEEwBClrgyg60+TjpLqu3nHMyYzIZQ3r1DnvNGtbps/KtBVJbXunWrQ0aUvpQmhuQMnNjgeusr0iSdn1gVx+LylN6V8IgiAYorjkUYLbgOUkLZ3z5+xMctYp0sx551Jg5+zltDTJEejWkn1WpowN4i/N9JGy0WRHYJNc1NCAIul+kgF7z9znW+TUorYfyH119EGCIAimJ90Kgss2hQOAq4GZgdNs3yfpSOB225fSxHkn1zuPZHyeDOxvewpAoz47HWunaq4bAs/afiSfNzOgvA78Gzhd0urAHcBBtl/t8P2DIAgGhCldDILL+nVX1JV9v/C6qfOO7aOBo8v02SmdGql3AX5fot4swFrAiTnK+lWgciBHRFIHQTBYGJU6eom2J4hsONkWOLdQ3MxQMhGYaPuWXH4+acKoRERSB0EwWEx1uaOX6GQF8XHgQdsTC2UNDSi2/wU8qZTHGmBTOgzgCIIgGEimolJHL1HGzfX3wE3ACpImSto7X9qZuu2lbBSpGVCuomBAAb4CnC1pPEnk739z/5/JkdUbAH+SdHXHnyoIgqDLDMctJs2oske3j9hmxhx4EAQDzqiJF3d85x6z6E6l7jmfePbcnpklIid1EARBCab02OqgDO1GUq8h6eYcSX27pHUL1zbO5fdJuqG/fnL5cTm6erykiyTN16XPFgRB0DWmljx6iXZzUh8L/CBHUn8/n5Nv7r8Ctra9MtP68TbqB2AMsIrt1YCHgUNKjz4IgmCAGI42iHZzUhuoZYqbF3g6v/4scKHtf+a2z7XoB9vX5FSjkILsRlT5AEEQBAPBVJU7eol2bRBfBa6W9CPSJPOhXL48MKukscDcwPG2z6zQ7+eZNq4iCIJgSNBrLqxlaDcO4svA12wvAXyNvvwQswBrA58CNgcOk9Qq5SgAkg4laYuc3U+diKQOgmBQmFLy6CXanSD2ICu4An+kL6PRROBq26/a/g9JoXX1Vp1J2hP4NLBrf+lGI5I6CILBYqpU6ugl2p0gngY+ml9vAtTE+i4BPiJpFklzkDIaPdCg/TtI2oKUaW5r26+1OZ4gCILpShflvmcY2s1J/UXg+KzH9AawDyTpbklXAeNJHl+n2L63WT+2TwVOAGYHxmTJ75tt79vNDxkEQdApvebCWoZ2c1JDsjU0qn8ccFzZfmwv22oMQRAEg02veSiVISKpgyAISjAcvZhiggiCICjBlOE3P5SS2lhC0vWS7s/yGQfl8gUkjZH0SP45fy5fUdJNkt6U9M26vuaTdH6W1nhA0ga5/KgstTFO0jWS3j89PmwQBEG7hNRGYyYD37C9ErA+sL+klUgZ4a61vRxwLX0Z4l4ADgR+1KCv44GrbK9Icn+teTgdZ3u1LN1xOUm+IwiCYMgwUF5MzR6+G9TbI9d5RNIeuWwOSX/KD+H3STqmUH9PSf/OD+LjJH2h1VjKSG08Y/vO/Ppl0k19cWA0cEaudgawTa7znO3bgLfrPsy8wEbkoDrbb9melF//t1B1TnrPWywIghmcAZTaaPbw/Q6SFiB5lK5HikM7vDCR/Cg/hK8JfFjSJwtNz7W9Rj5OaTWQSnEQkkbmN70FWNT2M/nSv4BFWzRfGvg3cLqkuySdImnOQt9HS3oS2JVYQQRBMMQYwC2mhg/fdWwOjLH9gu0XSaKnW9h+zfb1kB7CgTvpQN+u9AQhaS7gAuCrdU/85OjnVk/9s5DyUJ9oe03gVQozo+1Ds3TH2cABTcYQUhtBEAwKAzhBlHn4Xhx4snA+MZe9Q1bX3oq0CqmxXbb3ni9piVYDKTVBSJqVNDmcbbsmsfGspMXy9cWA55q1L3yAibZvyefnkyaMes4GtmvUQUhtBEEwWExRuaP4IJuPfer7kvRnSfc2OEYX65V8+H4XOYj598DPbT+Wiy8DRubUCmPoW6U0pUwktUh2gwds/6Rw6VKSJtMx+ecl/fVj+1+SnpS0gu2HgE1JuauRtJztmlzHaODBVuMKgiAYSMquDmyfBJzUos7Hm12T9KykxWw/08/D91MkZYoaI4CxhfOTgEds/6zwns8Xrp9CzuPTH2XiID4M7AbcI2lcLvsuaWI4T9LewD+AHQEkvQ+4nZQvYqqkrwIr5W2prwBnS5oNeAzYK/d3jKQVSL+DfwAhtREEwZBiAD1nyjx8Xw38b8EwvRk52Zqk/yHl6ZnGS6k26eTTrWmhkwflpDb+Ck1DCDdtUP9fNDGK2B4HjGpQ3nBLKQiCYKgwgFIbzR6+RwH72v6C7RckHQXcltscmctGAIeSdmHuzPp2J2SPpQMlbU0KXXgB2LPVQCKSOgiCoAQDFQSXt4IaPXzfTmFVYPs04LS6OhNp8kBv+xAqpnTuJJJ6h3w+Nc9stfq7FgIxxuXra+RrR2c7xCtN3ms7SS72FwRBMBSIhEGNaRZJfS+wLSkp0DvYPrsWiEGyXTyet5YgWdHXpQGS5gYOIsVYBEEQDCmGY07qtiOpbT+QvZH6YxfgD4W+bi4YSeo5CvghKb9EEATBkCK0mFpQF0ldhp1Ivrit+l0LWML2n6qMJwiCYKAYjhnluhJJ3aT+esBrtYxy/dSbCfgJ8I0SfUYkdRAEg8JUXOroJTqJpG7FzpRYPQBzA6sAYyU9QbJzXNrIUB2R1EEQDBbDcYupk0jq/trMRPLd3bBVXdsvAQsV2o4FvplduoIgCIYEveahVIYyK4haJPUmBdfVLSV9RtJEYAPgT5KuLrTZCHiyoAECgKRjc5s5JE2UdESXPkcQBMF0ZTh6MXUaSX1RkzZjSVtF9eXfBr7d4v02bjWmIAiCgabX7AtliEjqIAiCEgy/6SEmiCAIglL0mgG6DJ1IbRyVE0+Mk3SNpPfn8l1z+T2S/i5p9UJf8+VEFQ9KekDSBrn8CElPFW0c0+sDB0EQtMNwdHMts4KoSW3cmeUw7pA0BjjO9mEAkg4kpQndF3gc+KjtF5VyoZ5EypsKcDxwle3ts+T3HIX3+antH3XnYwVBEHSX4ejFVMZI/QzwTH79sqSa1Mb9hWpzkrfobP+9UH4zWfpb0rwk76Y9c723gLc6/whBEATTn15bHZShI6mNmjorsCtpBVHP3sCV+fXSwL+B0yXdJekUSXMW6h6Qt6ZOKyTBqH//iKQOgmBQCKmNfmgktWH7UNtLkPJIH1BX/2OkCeI7uWgWUg7qE22vCbwKHJyvnQgsA6xBWq38uNEYIpI6CILBYjhGUndLauNsYLtC/dVIOU9HF/KgTgQm2q4J/Z1PmjCw/aztKbanAifTRBI8CIJgsHDJf71EGS+mhlIbkpYrVBtNSnGHpCWBC4HdbD9cq5BTkT6Zc09Dyph0f26zWKGvz5ByTQRBEAwZJuNSRy9RxoupJrVxj6Rxuey7wN75Zj+VlDd133zt+8CCwK9yPtTJtmvCe18Bzs4eTI8Be+XyY3PWOQNPAF9q/yMFQRB0n4G69UtaADgXGEm6H+5o+8UG9fYAvpdP/8f2Gbl8LLAY8Hq+tpnt5yTNDpwJrA08D+xk+4n+xtKJ1MYVTep/gULe1Lpr44BGKq27tRpHEATBYDKAXkwHA9faPkbSwfn8O8UKeRI5nHQ/NSn84NLCRLJrA8HTvYEXbS8raWdSgrad+htIJS+mIAiC4coAGqlHA2fk12cA2zSoszkwxvYLeVIYA2xRod/zgU2zCaEpbUdSF65/Q5IlLZTP55V0maS7c/29CnWvkjRJ0uV1fSi7zD6cI6wPbDWuIAiCgWQAjdSLFlIz/wtYtEGdxYEnC+cTc1mN07MqxWGFSeCdNrYnAy+RzAFNaTuS2vb9kpYANgP+Wai/P3C/7a0kLQw8JOnsHBh3HCl6ut7GsCewBLCi7amSFikxriAIggGj7OpA0j7APoWik2yfVFfnz8D7GjQ/tHhi25Kqzjq72n4q368vINmQz6zYB9BBJDXJA+mnJPnuS4pNgLnzrDUX8AJpksH2tZI2bvA2XwY+m91csf1cOx8mCIJgejGl5OogTwYntajz8WbXJD0raTHbz2QPz0b3w6eAjQvnI4Cxue+n8s+XJZ1DChs4M7dZApgoaRZgXpKxuiltR1JLGg08ZfvuumonAB8EngbuAQ6q3fj7YRlgpxwlfWWdC20QBMGgM9UudXSBS4E98us9mPYBvMbVwGaS5s/KE5sBV0uapbDdPyvwafrCBor9bg9cZ/c/4LYiqUkrgu/SWF5jc2Ac8H5SZPQJkuZp0f3swBvZHfZk4LQmYwipjSAIBoUBlNo4BviEpEeAj+dzJI2SdAqA7ReAo4Db8nFkLpudNFGMJ92HnyLdUyHFsy0oaQLwdfqULJpSKh9EfSS1pFVJ2kp3Z/vHCOBOSeuSYhuOyTPTBEmPAysCt/bzFhNJwXWQstSd3qhScel2+4hteisiJQiCIc1Aublm9YlNG5TfTiGEwPZp1D1M236VFOfQqN83gB2qjKWtSGrb99hexPZI2yNJN/i1crT0P8kfTtKiwAqkoLj+uBj4WH79UeDh5lWDIAgGnuEotdF2JLXthoFypGXPbyXdQwqw+47t/wBIupG0mphL0kRgb9tXk5ZQZ0v6GvAKTQLtgiAIBoteE+IrQyeR1MU6IwuvnyYZTBrV27BJ+STgU63GEgRBMFhMGYZTROSkDoIgKMHwmx46jKSW9BWl/NL3STo2l62rvtzSd0v6TKF+s5zU5xbaPFHYygqCIBgS2C519BKd5KRelKTtsbrtNwvRz/cCo2xPzkEed0u6LId2N8xJbfsdwShJPyaFgAdBEAwZhmPK0U4iqb9Icmd9M197Lv98rdD8PWTXYJXISZ09pnYENunkQwVBEHSb2GJqgabNSb08sKGkWyTdIGmdQr31JN1HiqTeN68eWuWkBtgQeNb2I+1/pCAIgu4zhamljl6ik5zUswALAOsD3wLOq6kG2r7F9srAOsAhkt5D/zmpa+wC/L6fMUQkdRAEg8JwtEF0kpN6InChE7eSVmALFdvZfoAU17AK/eSkzu8xC7AtKZNSQ2yfZHuU7VHbzjmyzNCDIAi6wgDmgxgytJ2TmkL0s6TlgdmA/0haOt/skbQUKTDuif5yUmc+Djxoe2JnHykIgqD7RCR1Y5rlpD4NOE3SvSRj8x5Zu/wjwMGS3iZNqPvVIqlpnpMaYGf62V4KgiAYTMKLqQEtIqk/16D+WcBZTfoaR4Oc1Pnanq3GEgRBMFj0mn2hDBFJHQRBUIJe81AqQ0wQQRAEJehSMqAZiralNprJY0iaTdLpku7JUhsbF/o6WtKTkl6pe48l83vcJWm8pC27+imDIAg6ZAATBg0Z2pba6Ece44sAtlfN8htXSlonpx29jJSStD4Q7nvAebZPlLQScAUwspMPFgRB0E3CSN2AfqQ27oeG8hgrAdfl+s9JmkQyTN9q++bc5l1vA9TSks5LymcdBEEwZBiOE0QnUhs16uUx7ga2VkqevTQp/d0SLbo+AvhcTiJ0BckdttH7RyR1EASDwhRPLXV0iqQFJI2R9Ej+OX+TenvkOo9I2iOXzV3Y+h8n6T+Sfpav7Snp34VrLROzdSK1UaNeHuM0UtT07cDPgL8DU1p0vwvwW9sjgC2BsyS9a2wRSR0EwWAxgIFyBwPX2l4OuJZ3SxIhaQHgcGA9YF3gcEnz237Z9hq1A/gHcGGh6bmF66e0GkgnUhsN5TFsT7b9tTyA0cB8tM4xvTdwXm5/E0kFdqF+WwRBEAwgA6jFNBo4I78+A9imQZ3NgTG2X7D9IjAG2KJYIStcLALc2O5AOpHagAbyGJLmqKm0SvoEMNn2/fTPP0nSG0j6IGmC+HfpTxEEQTCdmYpLHV1g0Wz7BfgXKfdOPYsDTxbOJ+ayIjuTVgzFQW2XPUXPl9Rq67/UCqImtbFJYe+q5obaSB5jEeDObMz+Tm4LgKRjs51hDkkTJR2RL30D+KKku3N/e7pLU3EQBEE3KLuCKNpK87FPfV+S/izp3gbH6Lr37MR7tv7+fBkw0vZqpBXHGQ1bFehIaqORPIbtJ4AV3lU5Xfs28O0G5feTJqIgCIIhSdnVge2TgJNa1Pl4s2uSnpW0mO1nlLJyPteg2lPAxoXzEcDYQh+rA7PYvqPwns8X6p8CHNvfGKGiF1MQBMFwZaC8mIBLgT3y6z2ASxrUuRrYTNL82ctps1xW4125dfJkU2Nr4IFWA+kkknp1STfliOnLJM1T125JSa9I+mahbL689/WgpAckbVCmryAIgsFmAL2YjgE+IekRkp33GABJoySdAmD7BeAo4LZ8HJnLauzIu7f/D8z38LuBA8npn/tDrbb686yzWDGSmmRVPwP4pu0bJH0eWNr2YYV255P2zm6x/aNcdgZwo+1TlCS/57A9SdJt/fXViNtHbBM2iiAISjFq4sXNFKlL88FF1i11z3nguVs7fq+hQssVhO1nbN+ZX79MWpYsTspJ/ZdcbQywXa2NpG2Ax4H7CmXzAhuRPKKw/ZbtSfly076CIAiGAsMxYVAnkdT3kfx1AXYgR0vngLrvAD+oa740yXX19CzKd0rNHbZZX0EQBEOFqXapo5foJJL688B+ku4A5iZllYMkm/FT26/UdTELKQf1ibbXBF6lL0KwWV/1YwipjSAIBoXhuIIolQ+iUSS17QdJlvNaxN6ncvX1gO0lHUuKop4q6Q3gfGCi7ZqO0/nkCaKfvqah6D4WNoggCAaSLnkozVC0nCCaRVJLWiSrtc5Ekuv+NYDtDQt1jgBesX1CPn9S0gq2HyJFTt/fX19BEARDBccE0ZBaJPU9ykmBgO8Cy0naP59fCJxeoq+vAGdnD6bHgL1y+S5t9BUEQTBgDEe575ZurkOV2GIKgqAs3XBzXXKBVUvdc/75wj094+YaOamDIAhKMBxXEGUiqd8j6Val/NL3SfpBLj9A0gRJlrRQob4k/TxfGy9prVy+Ro6Wvi+XF1OWLi3pltzm3LwFFQRBMGSYMnVqqaOXKOPm+iawie3VgTWALSStD/yNFAb+j7r6nwSWy8c+wIm5/DVgd9srk3TLfyZpvnzthyTX2GWBF0n5IYIgCIYMw9HNtUwktQsxDbPmw7bvysqt9YwGzsztbgbmy8qED9fSktp+mqRQuHD2ktqE5PYKzRNkBEEQDBoDmDBoyFA2o9zM2YPpOVIWo1v6qd4ykYWkdYHZgEeBBYFJtic3qx8EQTDYDGDCoCFDqQnC9pSc33QEsK6kVdp9wyz+dxawlys6FkckdRAEg0WsIFqQxfWupy73aR1PMa2W0ohcRpbx/hNwaN5+AnietA01S339Bu9/ku1RtkdtO+fIKkMPgiDoiNBiaoCkhWvGZEnvBT4BPNhPk0uB3bM30/rASzkz0mzARST7RM3eUEupdz2wfS5qliAjCIJg0BjAhEFDhjIriMWA6yWNJyWmGGP7ckkHKuWXHgGMryWyAK4gRUlPAE4G9svlO5LkvvdUX27rNfK17wBflzSBZJM4tQufLQiCoGsMxy2miKQOgqDn6UYk9VxzLF3qnvPKa49HJHUQBMFwotdiHMoQE0QQBEEJes0AXYZKXkxBEATDlYGyQUhaQNIYSY/kn/M3qXeVpEmSLq8rbyhdJGn2fD4hXx/ZaiwxQQRBEJRgqqeWOrrAwcC1tpcDrqUv82Y9x5FSMdTTTLpob+DFXP7TXK9fYoIIgiAowQB6MY0mSQ5BP9JDtq8FXi6WtZAuKvZ7PrBprt+csh96RjuAfaKPoTOGodLHUBhDfI6h91108yCJlN5eOCqNjyQ9VHut4nmDuhsDlxfOFwImFM6XAO7Nr+8FRhSuPQos1N9YenkFsU/0MaTGMFT6GApj6EYfQ2EMQ6WPboyha7ig+JCPk+rrSPqzpHsbHKPr+jIMnvtUeDEFQRAMMLY/3uyapGezAvYzWbvuuQpdvyNd5CSAWpQuqskgTczSRvPm+k3p5RVEEATBjMilJMkhqCg9lFcczaSLiv1uD1yX6zellyeIdy3rhnEfQ2EMQ6WPoTCGbvQxFMYwVProxhiGEscAn5D0CCkp2zEAkkYVJI2QdCPwR5KxeaKkzfOlZtJFpwIL5vKv09w76h1mWKmNIAiCYPrSyyuIIAiCoANiggiCIAgaEhNEEARB0JCenyBqOiRttJuj22Op8N6zlymb0ZA0U84qGMzANPn7XKBiH6t2b0TB9KKnJghJY4sCVJLWJSU5qtLHhyTdT86aJ2l1Sb+q0H6Z2n8gSRvnxErzVRkDcFPJsv7GsbykkyVdI+m62lGxj5klbZ0/w9drR8U+zpE0j6Q5SZGc90v6VpU+uoGk9+XPspWk97XZx1KSPp5fv1fS3BXa7l13PrOkwyu+fzd+pzvUxi3pe5IulLRWlT6ACyXNWuhzMWBMxT5+JelWSftJmrdi29p3ca2ke/P5apK+V7WfoH96aoIA/g+4Kv/RHQ38GtirYh8/BTYnB5DYvpuUCa8sFwBTJC1Lcr9bAjinTMN8E1sbeK+kNSWtlY+Ngaormj8CdwLfA75VOKpwGbAnyVVu7sJRhZVs/5ekB3MlsDSNBcYaImnbrGr5kqT/SnpZ0n+rDEDSF4BbgW1J/t83S/p8xT6+SNKv+U0uGgFcXKGLTSVdIWkxSSsDN1P9u+zG7/Qw2y9L+gjJhfJU4MSKfVwMnJcnuZHA1cAhVTqwvSGwK+n/xx35QeITFbo4Ob/n27m/8cDOVcYQlGCwdUumgw7KxqQ/mmeA97XR/pb8865C2d0V2t+Zf34L+Ep9Xy3a7kEKcnk5/6wdlwLbVvwcd3ThuxzfhT7uA2Yl3dw+2sb3OQH4YIdjeAhYsHC+IPBQxT7GAbPV/V3cU7GPnYD/AP8APtzG5+jG7/Su/PP/gM8Wyyr2sz/pAeIe4EMdjGdmYDtSlO8DpJV7y7914Lb6sQPjOv1+4pj26CmpDUmH0Zf7ejVgrKRv2P5ThW6elPQhwHkZfRDpD7csb0vahXSz3yqXzdpP/XewfQZwhqTtbF9Q4T0bcZmk/YCLgDcL7/FChT6ulLSZ7Ws6GMdvgCeAu4G/SFoKqLICeNZ2le+/Ec8zrerly7SQGGjAm7bfqolfZqmC0kFEkpYj/S1dAHwQ2E3SXbZfqzCGbvxOn5L0G+ATwA/zdmipnYS67UUBS5ImzvUlrW/7J2UHIWk10ur+U6Ttqa1s3ynp/aTt1AtbdPEfScuQfweStic9FAZdpKcC5ST9DDjE9uv5fCngFNull66SFgKOJy2/BVwDHGS71A1F0krAvsBNtn8vaWlgR9sttdcLfTTa53+J9AQ5rmQfjzcotu0PVBjHZ4DfkW4gb5O+D9vuyNCsPp2YMnWPB95H2tYo3hRb3UCKfZwJrEqSHDBJ9nh8PihzY5N0LDAJ2B34CrAfcL/tQ0uO4UHgANt/Vpplvg583vbKFT5HN36ncwBbkFY/j2T7waplHgJa2Uxs/6DCOG4ATgHOr/1/LVzbzfZZLdp/gLSF+yFSzoPHgc/ZfqLsGILW9NQEAcl4CCxp+6EhMJb5gSWc9kertDsHGEVawgN8mnQzGwn80fax3RxnP+N4nHQzvcdt/qFI+n6jcttHlmx/euPmLm1D6MaNTdJMpIQrm5EmyqtJDx+lvhdJ8zjZYoply9t+uEz7bpGfuifafjPbtlYDzrQ9qY2+5qi4Aqq1mxk4y/Znq7Zt0NecwEy2X25ZOahMT00QkrYCfgTMZntpSWsAR9reukIfP29Q/BJwu+2WolmSxgJbk5Ry7yApMf7NdmnvH0l/Aba0/Uo+nwv4E+nJ7w7bK5XoY1bgy/QZ2McCv7H9dsVxbGy3nyZL0jcKp+8hTXYPVLnBDwXyjegN21Py+czA7GVvkJIWBf4XWNz2FnmluYHtU1s0RdImtq+TtG2j6xVXU+NIDx8jgStIq6qVbW9ZoY8NSMbtuWwvKWl14Eu296vQx43AprbfKtumrv18pNXcSAqq1LYPbKe/oDE9ZYMAjgDWJd0MsT0uL0Wr8B5gRZJRFZIB7XFgdUkfs/3VFu3ntf3f7Dlzpu3DJVVaQQCLUNhOIW3vLGr7dUlvNmlTz4kk20fNRXe3XPaFCuN4jGTHuZJpt3dK7zXb/nHxXNKPSE/fpZA0AvgF8OFcdCNpy29iibY/s/1VSZfRwF5Q5cGBlPrx48Ar+fy9pO3HD5Vs/1vgdKC2JfUwcC59Qmr98VHgOvpsWkVM6/36IlNtT86TzS9s/0LSXRXaA/yM5Ol3KSRPP0lVPP0g/Z/6m6RLgVdrhRX+tq4geYLdA3Qlz2fwbnptgnjb9kuaNote1T+e1UgeJrUnxRNJN6WPkP4YWzFL3tfdkb6bQVXOBm6RVFuxbAWck59i7y/Zxzq2Vy+cXyfp7orjeDwfs+WjG8xBchEty+kkN+Ed8vnnclkZu1JtH/tHFd6vGe+pregAbL+iasGUC9k+T9Ihuf1kSVPKNLR9eP5Z1WW7ETUnit2p6ERRN6Yn6/6flfosBR7Nx0z0uftW2c54T5VVedAevTZB3Cfps8DM2WvkQODvFfuYH5iLtK0EMCewgO0pJZ/ejyQ9If/V9m15BfNIlQHYPio/tdeemve1fXt+vWvJbqZIWsb2o/COUa/Sf+IqRsdmSLqHvv/4MwMLA0dV6GJh20U7xG8lfbVMQ9t35J83VHi/ZrwqaS3bdwIoxau83qJNffsF6fO6WZ++v7HSSPoUsDJppQuUt+dk9iI5URxt+/HsRNGvQbgBnXr6QTLw/7FYIGmHZpUbcJZSbMrltO/RFbSg12wQc5Ce2ouGxKNsv1Ghj71JgUhjcx8bkfaOfw8cYXu6RwFLOgr4C/B326+2qt+kj01JT9qPkT7HUsBetq+v0Mf1NN6a2aRCH0sVTieT3FZLeTDl9teSPsfvc9EupM+xaYU+PkzaflyK9FBU88aq4v2zDvAH4Onc/n3ATrVJqET7tUhbZauQIsoXBrav4sAg6dekFdjHSB5A2wO32t6734bv7mc2YPl8+lAVu1Ru35GnX+7jTttrtSrrp/3+wNEkz7La32il32nQmp6aILpF9sXejfRUNBfJ6+MvJdu+h+TtUv+UV8XrZi9gQ2ADks/+jcBfyhjJ6/qZHVghnz5ku6z9otZ+7cLpe0j2mMm2v12hj7Ns79aqrJ/2S5FurBuQbgR/Bw60/c8KY3gQ+BrJaeCdVVSVG1ruZ1am/T6rGPx3ID2wLEH6HtcjRTXfWaGP8bZXK/ycC7jSKSq5bB8bA2eQYlOUx7NH2b/vTpH0SWBL0hbsuYVL85Ci7tct2c9jwLq2/9P9UQY1emKLqZkRskZFL6YvkJbMI8hBQKTAnbJPzWeRokE3J2037UrF5XfeUjldSTNoR+CbpMTsLaUZ+vF4WVZSJY+XBk/Hf5N0a9n2mWn8/JUCzNZuUrfRGP5B8grrhJdsX9lhH5Amh5VIk+Va+fs8s2Tbw2z/Mbs+f4xkFzmRNFGUpbal9Vp+iHkeWKxCe4AfA5vV3MAlLU9anZX+neQ2J5IcJ1ZRCnrb2vb/lGj+NHA76Xda/Pt6mTSJl2UCUNnFNqhGT0wQ9BkhtyUt/X+Xz3cBnq3Y10HAOsDNtj8maUXSFlNZlrW9g6TRts9Qimm4scoAlNIKrkQa+42krYSyT5pd83jRtAqdM5FuIqWE1bIx9rskXama/7+AtyiRIlLSt20fK+kXNN7maunOqD4RuuslHUf67MX96ipP74eTZFxWInnQfBL4K1B2gqitXD4FnGz7T5LK3FCLXJ7dO48j/T2YtNVUhVldiBGy/bAKwnslOZkkJfOb3Mf4/Hfe8vM4aZvdLemcqltbdbwKjMvboMXfabi5dpGemCBqRkhJP7Y9qnDpMkm3N2nWjDdsvyEJSbPbflDSCq2bvUPtj36SpFWAf5HcVquwIMmgOwl4AfhP2X37mscLKf5jmsjbbJCswh2km5BI9oPHSdtnZcbxf8D/Sfo/25WE3DK1VVfV31+RH9edF/82TPlVIaRJenWS9s9eSnENv2vRpkjbEhc1bNeM+xdIupzkyVPV0H17fgCpjX1Xqn/Hc9i+tc6LqbRdKbOupCNo3y50MdXEEoM26IkJosCckj5g+zF454Y4Z8U+JuantIuBMZJeJImrleWkvI1wGMlPfC6gYTRxM2x/BkDSB0lbVddLmtl2FffQC4B6g9/5VNveqTqhNOrjkPx9LMe0Npl+97xt16LIX2vX28X2xyoOtz/esD1V0mSlnBbPkfbvy7IjKdDxR7YnZVfoSg4PDbYNkfQSKdL9uZLdfJkktFd70r6RvliZsnRDB+lUGtiFypJX5x0Z24PW9JSRWtIWpO2LoufOl2yXDsyq6++jpC2Vq9xmxGeb7/tpkpF6I2A+UkDQjbZPK9F2RdK+/7FMewOaB/iWK2j/5P4+xLujVctuqzS16ZT1hOrU2yXXP4jkCfUyaXtkLeBglxQhVHpUPgX4BklS+hukgLlxXYpNKIWkP5GM9TVPtI1JN9ilSSvGqu6q7Y6jkQ7SrtleVLaPW2xXsb/Ut9+YQTS2Dxd6aoKAdzx3VsynD1b13OngffsN2nE1pcsTSE92N9p+uuI4RpNyL2xNjnTNvAz8wXbpuBBJZwHLkG7stac8V9nnVYqDqNl01qjZdGw3lI0otOuKt0vu627bq0vanBQD8D2SFlCVSeYe26vm1yOBeaq4qHYDSVcDu9t+Np8vSrKB7ELycluln7bFeJR3YXu1NsbTtg6SpGNI26ht2YUk3UGSK5/G2G679Ao5aE2vbTFB2kIZSfpsq1f0NOmEqslfmmL7gA7aXgJcImkD25Wy0DVgFOlm3MlTRLs2nW55u0B6woQ04Zxp+z7VbaCX4E5J69i+zYOnGLpEbXLIPJfLXpDUanvl090ahKRHyavafNzXRje11UO7dqFuGNuDFvTUCqIbT7yDiaSXafyUV1lmW92Jx/gjKeagbZ19SReRone/SvrP/yLpP3cpcThJs3a6t6ykCLs4aStmddKT69gqT5tKsRTLkuxRr9L3O6n85N0uSqlvl2RanbCJpK3Ey7tsc+lvHLOTbvAbkqL9VyAll/rMQLx/HsNpJBmdorF95ip/30Frem2CeIDOn3g7HcMZpKjSSfl8fuDHA/2Hm2/uDwKfpRCPYfugCn1cD6xBStdZ3AZoKy6hYNO5suxNX0ky5f/oiz+ojaFKFPRMpM/xWDYQL0hSVa0SxbxUo/Iq++6dklc925J0wQD+BlzQ6d+7pJNs71Oh/iykbcOP5rEsSJogvlShj05l4GcnGdtr38WNwK8Gakt5uNBrE0THT7xdGMNdttdsVVain5mBRZnWOFwlevgu22uqL+p2VpJNY/0KfXy0UbkraBup80jqvwKHk3KFb0Vajcxku5JnmKTF6XOpBFp7Ug0XJK3tkpIhuf5rJOHKnwB/dsWI9NxHRzLw6lB+PShHr9kgFgLuV4r27fiJt01mkjS/7RfhnWCzSt+zpK+QborP0qdGa5LSbFk6jseoMhH0Q30k9cxUcLUF3mv7WknKT+tHZANl6QlC0g9J+aDvp7D1SNK7mqGp+vTfiCqTQ2YX0pP7fsAXJP2dZCS/tsJ7diQDT+fy60EJem2COGKwB0AKzropr2YgyVRXicSG5Ba6QjtPZgVq8Rjfo814jEaUvSGpw0jqAm/mLaJHJB1ASm4/V8Vhb0P6Pntx++E3ZSqpi3I0BUeIFUkR5V8Fvk26SbdLVRn4TuXXgxL01BbTUEEpW1jNG+M622VzONTaXw98whVUTweKNrYj2o2krrVfhxRVPR9JJnxe4FjbN1fo40pgh+INZbjRbLuwRsVtwwtIxv5HSauwvwK3uJpqciMZ+CNtn1Cy/d+Ar3ha+fUTbG9QdgxBa3pigpD0V9sfaeAFVNn7pwtj2dt1aSQlHWP74Ap9nEryDPkTbWZyk/S/pBvppHw+P/AN298r20c3UJLaHmf7VUmfIwWpHV/VuKsUvew2fe5rN7RrmQF1e7r59N8NJI0iSY5UjoAu9NGpDHxH8utBOXpighhKSLoCONv22fn8l6TlcEsNo5rxVtIkklF2GlwhgU8TY3nVCOTlSS6U9cbdKvkgxpNuzquR0m6eAuxou98n2kL7UaQo6FqcyUvA5yuuYvZoVG77jLJ9DCZdfvrv2CusSb/vs/2vim1WJ7nKQrJhVAo8VAfy60E5es0GMRTYDrhU0lSS9s6kMpNDZm0lGed/knIgdMLMOTDtTQBJ7wVmr9jHH4Ffk+Qp2n1anGzbShHeJ9g+VSkpU1lOA/azfSOApI+QJozSBnvP4Lo9XXIWqHE6fV5hHyN7hXWh31NJSrWlUJI/+SJ96sJnZ/tWlb/7degLiq0qvx6UIFYQXULTSmPPTRL7+xvZMOwSqRAlHUgSU1uatHR+5xLVM6B9h+QWWkvXuRdwqe1jK/RxR5VgsiZ93ABcld9/I1L0793OshUl2ndjJbQxPaDb06WYkDtsr61ppUNK/56zF9qf3WFQXl5ZbuCcMTG7rd7kkoGHmsGDYmcUYgXRPWrS2DVEeqL6VC5v+Z/Y9s+Bn0s60faXOxmM7R/m/4S11JxHubpo4WWS9gMuov28vzuRgvX2tv0vSUuS8hmU5QYlmezfk77HnYCxyrkeXE67p+MkOUOEbjz9d+QV5pSbfaqkeV1daryImHZVOoU+SZQydEMGJmhBrCC6SP6Pt4Htvw32WLqBpMcbFFdayXRhDP3l0HYZe0gtWLBV2VCn06f/XL/eK2wekjPDLRX6uARYExhDkh0Bqhn9lcQt9yA9fEByRf6t7Z+VbD/oQbHDgVhBdBGnfAEnkP7zDCp1Hl2zAbMCr1bx6HIX8kE0GVvp4K5OtzIy3UiSMxToRkzISNu3kQLM9gJQyq9ReoIg2Q1KZyZshO2fSBpLn1TGXrbvqtDFUAiK7XliBdFlckToTcCFQ2X5K0nAaGD9iu62s5JsIhvlorHAbzo18FaJpVCHmj25j57Q7enS03/H+TVym/cCS7qgqFqx/frAfTW35ezG/MGyn6WZZ1eXDfrDnpggukx+cp+TtKf6OoMQi9GMRgbfFvVPIa08au6guwFTbH9heoyvyRg60uzpJSTt4AbZ9erLmrTtZn6NrUh54GezvbSkNUhBbqWf3iXdBaxVe4jKK6Pbq05UwfQltpi6jO2u5YXoBE2bnnImklGvdKRrZh3bqxfOr5N0d8n370pwlzvQ7JF0nu0d1SRZzoxmgwAOoU/qu7+yRnQzv8YRwLqkFSW2xyllmauCiivsvD3b8n40lIJihwMxQUwHJG1NYVvG9uWDMIytCq8nk1w8R1fsY4qkZWw/CpBvAmXjIX5U8b3KUkWzpyZt3rVkOYNB4el/cUk/L1yah/S7bYntu4G7JZ3tziVc3rb9kqbNuTS1WeUmPJbduk/M5/uRUgX3i+2P5J9D4kGs14kJossopVJcBzg7Fx0k6cPuQI+oHdydXMnfAq6XVMzxXarfbu0Fq4lmT8kx1Dxc9rP9nbp+fwh8592thiQdP/3XVlPAXZI6XU3dJ+mzpGDM5YADgdKpbDP7Aj8niUmaJIPSkSpt0H3CBtFlcuzBGran5vOZSbo1A7KdIekX9L+1UymQKBt4i3IGlQy7nQZ3qUPNntxHI8PsjOjmOku7T/+SFrP9jLqQ+EhJNfVQYDPSg8PVpDibqluYwRAnVhDTh/mAWjDZvAP83jX3zQ+Tbso1g+QOpHwILZG0ie3r6uwYAMtmOYMqLo6dBnctxrTeLnNLWqmMt4ukL5O2Lj6QJ+4ac5Oi3GcIuvH0X1tN2f6HpPeRbAgGbnNFDSWnpDyH5lWY3YaAYiMkfXqQtmODJsQKostI2hk4hmTAE8kWcbDtc/trNx3GcTPwkdoTpypklJP0A9uHK+VyrsdVPIi6IO3QtreLpHmB+UkrmKJ778uuFg0+qHT56f8LJPmX60h/nx8leSCdVqGPdUgaWW0LKDbp9we2D++kj6C7xATRZST9DngYeJFkGK78hNalcTxEiup+IZ/PD9xse4X+W07Tx9K2H29V1qKPv5PiD84n3ZSeAo4pOw5J42yvUVfW1vaQpEWYdpurdArXoUKnT//57+JDzsmolPJz/73i38V4YH9PK6D4qxltyy5oTTdUHINpqeWC2Bo4HvilknLlQHMMcKek30o6A7iT6pntLmhQdn7FPg4ieR4dSNI++hywe4X2j0k6UNKs+TiIEt4uRSRtJekR4HHgBtLEfWWVPoYC+en/VmBbYHvgZklV40GeJxm3a7ycy6owpTY5ANj+KyW9qWpImkPSYZJOzufLSZqhvc16kVhBTAeyYXod0p77vsDrtlcc4DGIFNj2VZLf+jjgfbZvLdF2RVIu6WNJnkw15gG+ZXvlhg0b99V2cFeuuwjJ22UT+rxdvmr7uQpjuDu3/7PtNSV9DPicy8uwDwm69PR/JrAqcAnp+xwNjM9HqaRUkn5GSi9aFFB8gyxl4hICipLOJXlk7W57lWz4/nv9ajEYXMJI3WUkXUuKpL6JJOmwTpWbWRf5Fck3/b22L81bTBeQJq5WrECKHZiPaeMpXiZp+Fehk+Au8ne3c8X3rOdt289LmknSTLavzze5GY1uPP0/mo8al+SfVeIKasGT9faCNUkTRpmEUsvY3knSLpAM36oLrAgGn5ggus940lbKKiTj3SRJN9l+fYDHsZ7ttbKRF9svKiXNaYn7ktJvYPumdt68G8FduZ8zgIM8berUH1eU2pgkaS5S/uSzJT1HQYV0BmICcIuSmuo7T/9Kyqilnv5dISthP310Q0DxLSU9p5rzwTIURPeCoUFMEF3G9tcguWMCe5LcPN9H9WxunfJ23uqq/QdcmOrRrndJ2p+03VQ07pa5OXdL2mG12uSQ3/tFSVXVckeTtkC+RlJynZeSwXZDjI6f/vPfwbd59++0dBrZJv2uVWZrqcARpERSS0g6m+SWvWcnYwi6T9gguoySDPOGpFXEE6RtphttXzfA49iVtDe8Fklsb3vge2X3/nMffwQeJCX8OZJ0c33AdmmjeyfBXbn93cDGtl/M5wsAN7hkRrpgWiRdQ4qN+SbJPrYH8O/6SPM2+j3ZdqXtx2xDWZ/kbnuz7f90Moag+8QE0WUkfZM0KdzRBc2bTseyIimjnIBrbT9Qsf1d2ag73vZqFWMpuiKUJ2l3UtTueflzbA8cbfusEm0bCrrVfnoGE3brxtN/IS7lHVdhSbfZLmOb6hpKYo7nkNLgzojbfcOC2GLqMranl0hdZWw/SFoBtEst78MkSasA/wIWKdm2K0J5ts+UNIGkRmtSYplSdhH3nqDb2aSn/09TePqv2Eftd/qMpE+RtgIX6Kf+u5D0YWCc7VclfY60Sj2+SsAeScxxJ+AYSbcBfwAuD7mOoUWsIIKmZL/7C0hukb8lZS/7vu1fl2zfcYL7HPfwBVIGM5FSU55s+xcV+/kIsJzt0yUtBMxdJeBvKNCNp/8ca3AjsATwC5LTwBG2L6vQx3iSJ9NqpL+LU4AdbTdM4tOir5lJXk9fBLaY0VZ1vU6sIIKm2D4lv/wLUDkPtbuT4H5vUia8V+EdFdabSDe3Ukg6nLQCWYHkNDAbyWf/w22OabDo+OmfpMn1V9v3Ah/LNp0fAaUnCGCybUsaDZxg+1RJlWNKshfTVkxrKwuGEDFBBE2R9L+klJaT8vn8wDdsf69CN68A90hqN8G9mDYHxZRcVoXPkHz078zv/XT2MpvR+B8lfalv0Pf0/9WKfdR7hb3QhlfYy5IOIUXFb6SkjzVrlQ4knUeSDLkKOIHkeFDVyy6YzsQEEfTHJ21/t3aSXUy3JGn4l6XTBPenk3z/L8rn29AnZ1KWt/ITb83ld84OxjOYdOPpfyZJ89d5hVW9D+xE8mzb2/a/JC0JHFexj1OBXWyXTUAVDAIxQQT9MbOk2Z1zQOQtgUrxHLY72jaw/RNJY0mCf5CM1HeVbZ+jcy+X9BtgPklfBD4PnNzJuAaJbjz9/xi4KbswQ5p0ji7bONsMfl+0K2XRwzNLtt8ku3zPCYyuD552NSn5YDoTE0TQH2cD16pP9nsvKu4Tq8OEQbnuneTtoarklcMOwNeB/5LsEN+3Paad/gaZjp/+s1fY7fTJYWxru1SekNy+U7vSR0mqvls1uGY6W20GXSa8mIJ+yZIZm+bTMbavrtj+r/QlDNqKnDDI9ve7OtD+x3AGyZh620C95/Qgx4R8lz4dqx0oGRPS5XFcQrLptGtX6oqUfDD9iQkimK6ow4RBXRrDg8CywD+Y9oY2w+UvkLQSfU//11V5+u/iGPZoVF5lO1GN08AO6N9F0JrYYgqaUheJPBvJU+XVir7qb2Yvl0eyDMlTpHiKgWTzAX6/6UaeEAZ8UqgbQ9t2JfVJyc+raVPazkNhCzIYGsQEETSlGImcjb2jSdo5VSgmDDqKlCOjSsKgjqkY4Ru0QNLjNJZPKWNX6qaUfDCdiS2moBI1faYK9UeRtJSWos9X3jPi9k6QyCJ7Nd5DsoUsUMWupA6k5IOBIyaIoCl1WwAzkaKRP2p7gwp9PETKSncPBbnxeKrvLaraDyS9hxQl346UfDBAxBZT0B/FLYDJJPny0RX7+LftS7s2omDQkVQ0LtceHKreS84iCUluTkFKvisDDLpGrCCC6YqkTYFdSLmk38kYFgFRMy6Sri+c1h4cfmT7oQp9tC0lHwwcsYII3oWkX9DACFmjir87Ke5hRZL9obbFFAFRMzCdqPMW6ERKPhggYoIIGnF7/vlhUgT0ufl8B6q7WK5je4VuDSwYfLJg4OHARrnoBuDIipHVJ2Xxx+8Bl5Jcnw/r6kCDjoktpqApkm4GPuKcGa+dbYAs03HcYAR0BdMHSRcA99Inu7IbsLrtbZu3elcfswPbASOZ1rttRswV3rPECiLoj/lJAUwv5PO5clkV1gfGZd/5N+lL9xlurjMuy9jernD+A0njKvZxCfAScAcF21QwtIgJIuiPY4A7s5qqSFsKR1TsY4sujykYfF6X9BHbf4V3UpC+XrGPEbbjb2OIExNE0B+/JSXo+SppYjgMeF+VDiLeoSf5MnBGtkUAvEjKj12Fv0ta1fY93R1a0E3CBhE0RdKJJM+jTWx/MBsVr6mSAznoPbL9YHtgGZJkxktUtB9Iup8koBhbj0OYWEEE/bGe7bUk3QXvZJSbbbAHFQw6lwCTSDk6nmqzj092bTTBdCMmiKA/3s4ZxGqpOhemIJcRDFs6th/E1uOMwUyDPYBgSPNz4CJgEUlHA38F/ndwhxQMAf4uadXBHkQw/QkbRNAvWb9/U9Ie8bW2Qy9nmCLpHtJqchZgOeAxwn7Q08QEEQRBKSQt1d/12DbqPWKCCIIgCBoSNoggCIKgITFBBEEQBA2JCSIIgiBoSEwQQRAEQUNiggiCIAga8v/4ZUGuv4RavQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5cc29d",
   "metadata": {},
   "source": [
    "All good."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a74205",
   "metadata": {},
   "source": [
    "# Separate data (train/test, X/y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2011774e",
   "metadata": {},
   "source": [
    "Set index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "63324c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed = transformed.set_index('sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67cdf065",
   "metadata": {},
   "source": [
    "Separate X and y (train and test)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "abd5ad79",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = transformed['income']\n",
    "X = transformed.drop('income', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "db2b6fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "9ddaf7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, X_test,\n",
    " y_train, y_test) = train_test_split(X, y, train_size=0.7, random_state=1234567)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c596a5d6",
   "metadata": {},
   "source": [
    "# Train a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "49f0de87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "3dc8c076",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "estimator = LogisticRegression(solver='lbfgs')\n",
    "y_pred = estimator.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76dba3b7",
   "metadata": {},
   "source": [
    "# Evaluate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "d6cc16a1",
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.869614</td>\n",
       "      <td>0.916093</td>\n",
       "      <td>0.892248</td>\n",
       "      <td>6829.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.691105</td>\n",
       "      <td>0.577477</td>\n",
       "      <td>0.629202</td>\n",
       "      <td>2220.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.833020</td>\n",
       "      <td>0.833020</td>\n",
       "      <td>0.833020</td>\n",
       "      <td>0.83302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.780359</td>\n",
       "      <td>0.746785</td>\n",
       "      <td>0.760725</td>\n",
       "      <td>9049.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.825820</td>\n",
       "      <td>0.833020</td>\n",
       "      <td>0.827715</td>\n",
       "      <td>9049.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score     support\n",
       "0              0.869614  0.916093  0.892248  6829.00000\n",
       "1              0.691105  0.577477  0.629202  2220.00000\n",
       "accuracy       0.833020  0.833020  0.833020     0.83302\n",
       "macro avg      0.780359  0.746785  0.760725  9049.00000\n",
       "weighted avg   0.825820  0.833020  0.827715  9049.00000"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14a328c",
   "metadata": {},
   "source": [
    "# Evaluate fairness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "6a6c1abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def fairness_report(y_test, y_pred, prot_attr, return_df=True):\n",
    "    fairness_report_dict = {\n",
    "        prot_attr: {\"disparate_impact_ratio\": \n",
    "                        mt.disparate_impact_ratio(y_test, y_pred, prot_attr=prot_attr),\n",
    "                    \"statistical_parity_difference\": \n",
    "                        mt.statistical_parity_difference(y_test, y_pred, prot_attr=prot_attr),\n",
    "                    \"equal_opportunity_difference\": \n",
    "                        mt.equal_opportunity_difference(y_test, y_pred, prot_attr=prot_attr),\n",
    "                    \"average_odds_difference\": \n",
    "                        mt.average_odds_difference(y_test, y_pred, prot_attr=prot_attr)}}\n",
    "    if return_df:\n",
    "        return pd.DataFrame(fairness_report_dict)\n",
    "    else:\n",
    "        return fairness_report_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "0ecc76e6",
   "metadata": {
    "scrolled": true
   },
   "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>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average_odds_difference</th>\n",
       "      <td>-0.093343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disparate_impact_ratio</th>\n",
       "      <td>0.338628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equal_opportunity_difference</th>\n",
       "      <td>-0.105511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistical_parity_difference</th>\n",
       "      <td>-0.171693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sex\n",
       "average_odds_difference       -0.093343\n",
       "disparate_impact_ratio         0.338628\n",
       "equal_opportunity_difference  -0.105511\n",
       "statistical_parity_difference -0.171693"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fairness_report(y_test, y_pred, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af21138",
   "metadata": {},
   "source": [
    "Seems like it is failing on 2 fairness metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6de061a",
   "metadata": {},
   "source": [
    "# Mitigate bias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f877cd88",
   "metadata": {},
   "source": [
    "## Using Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "1fb01de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aif360.sklearn.preprocessing import ReweighingMeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "4be8a449",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=ReweighingMeta(estimator=LogisticRegression()),\n",
       "             param_grid={'estimator__C': [1, 10],\n",
       "                         'reweigher__prot_attr': ['sex']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rew = ReweighingMeta(estimator=estimator)\n",
    "\n",
    "params = {'estimator__C': [1, 10], 'reweigher__prot_attr': ['sex']}\n",
    "\n",
    "clf = GridSearchCV(rew, params, scoring='accuracy', cv=5)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "0228d978",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d29922",
   "metadata": {},
   "source": [
    "### Evaluate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "a1b759bb",
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.863848</td>\n",
       "      <td>0.921658</td>\n",
       "      <td>0.891817</td>\n",
       "      <td>6829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.696540</td>\n",
       "      <td>0.553153</td>\n",
       "      <td>0.616621</td>\n",
       "      <td>2220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.831252</td>\n",
       "      <td>0.831252</td>\n",
       "      <td>0.831252</td>\n",
       "      <td>0.831252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.780194</td>\n",
       "      <td>0.737405</td>\n",
       "      <td>0.754219</td>\n",
       "      <td>9049.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.822803</td>\n",
       "      <td>0.831252</td>\n",
       "      <td>0.824303</td>\n",
       "      <td>9049.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.863848  0.921658  0.891817  6829.000000\n",
       "1              0.696540  0.553153  0.616621  2220.000000\n",
       "accuracy       0.831252  0.831252  0.831252     0.831252\n",
       "macro avg      0.780194  0.737405  0.754219  9049.000000\n",
       "weighted avg   0.822803  0.831252  0.824303  9049.000000"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97ad560",
   "metadata": {},
   "source": [
    "### Evaluate fairness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "3f1fa40e",
   "metadata": {
    "scrolled": true
   },
   "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>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average_odds_difference</th>\n",
       "      <td>0.051906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disparate_impact_ratio</th>\n",
       "      <td>0.609058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equal_opportunity_difference</th>\n",
       "      <td>0.127800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistical_parity_difference</th>\n",
       "      <td>-0.086982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sex\n",
       "average_odds_difference        0.051906\n",
       "disparate_impact_ratio         0.609058\n",
       "equal_opportunity_difference   0.127800\n",
       "statistical_parity_difference -0.086982"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fairness_report(y_test, y_pred, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b53ec4",
   "metadata": {},
   "source": [
    "## Using Inprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "33248b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aif360.sklearn.inprocessing import AdversarialDebiasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "931163f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdversarialDebiasing(prot_attr='sex', random_state=1234567)"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv_deb = AdversarialDebiasing(prot_attr='sex', random_state=1234567)\n",
    "adv_deb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "3af4b7dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = adv_deb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e2e3b2",
   "metadata": {},
   "source": [
    "### Evaluate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "112771e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.860893</td>\n",
       "      <td>0.946112</td>\n",
       "      <td>0.901493</td>\n",
       "      <td>6829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.761658</td>\n",
       "      <td>0.529730</td>\n",
       "      <td>0.624867</td>\n",
       "      <td>2220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.843961</td>\n",
       "      <td>0.843961</td>\n",
       "      <td>0.843961</td>\n",
       "      <td>0.843961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.811275</td>\n",
       "      <td>0.737921</td>\n",
       "      <td>0.763180</td>\n",
       "      <td>9049.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.836547</td>\n",
       "      <td>0.843961</td>\n",
       "      <td>0.833628</td>\n",
       "      <td>9049.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.860893  0.946112  0.901493  6829.000000\n",
       "1              0.761658  0.529730  0.624867  2220.000000\n",
       "accuracy       0.843961  0.843961  0.843961     0.843961\n",
       "macro avg      0.811275  0.737921  0.763180  9049.000000\n",
       "weighted avg   0.836547  0.843961  0.833628  9049.000000"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04fa5f6",
   "metadata": {},
   "source": [
    "### Evaluate fairness "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "d5c15287",
   "metadata": {
    "scrolled": true
   },
   "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>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average_odds_difference</th>\n",
       "      <td>0.102165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disparate_impact_ratio</th>\n",
       "      <td>0.704891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equal_opportunity_difference</th>\n",
       "      <td>0.203286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistical_parity_difference</th>\n",
       "      <td>-0.055569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sex\n",
       "average_odds_difference        0.102165\n",
       "disparate_impact_ratio         0.704891\n",
       "equal_opportunity_difference   0.203286\n",
       "statistical_parity_difference -0.055569"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fairness_report(y_test, y_pred, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3065849e",
   "metadata": {},
   "source": [
    "### Close instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "73c8a4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adv_deb.sess_.close()  # AdversarialDebiasing creates a TensorFlow session \n",
    "                       # which we should close when we're finished to free up resources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fb4e32",
   "metadata": {},
   "source": [
    "## Using Postprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "923c045d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aif360.sklearn.postprocessing import CalibratedEqualizedOdds, PostProcessingMeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "6b278554",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paollamagalhaes/miniconda3/envs/aif360/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PostProcessingMeta(estimator=LogisticRegression(),\n",
       "                   postprocessor=CalibratedEqualizedOdds(cost_constraint='fnr',\n",
       "                                                         prot_attr='sex',\n",
       "                                                         random_state=1234567))"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_eq_odds = CalibratedEqualizedOdds('sex', cost_constraint='fnr', random_state=1234567)\n",
    "postproc = PostProcessingMeta(estimator=estimator, postprocessor=cal_eq_odds, random_state=1234567)\n",
    "\n",
    "postproc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "b8bae811",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = postproc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48da0afe",
   "metadata": {},
   "source": [
    "### Evaluate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "01a60f1a",
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.813686</td>\n",
       "      <td>0.954166</td>\n",
       "      <td>0.878345</td>\n",
       "      <td>6829.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.699328</td>\n",
       "      <td>0.327928</td>\n",
       "      <td>0.446489</td>\n",
       "      <td>2220.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.800530</td>\n",
       "      <td>0.800530</td>\n",
       "      <td>0.800530</td>\n",
       "      <td>0.80053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.756507</td>\n",
       "      <td>0.641047</td>\n",
       "      <td>0.662417</td>\n",
       "      <td>9049.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.785631</td>\n",
       "      <td>0.800530</td>\n",
       "      <td>0.772397</td>\n",
       "      <td>9049.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score     support\n",
       "0              0.813686  0.954166  0.878345  6829.00000\n",
       "1              0.699328  0.327928  0.446489  2220.00000\n",
       "accuracy       0.800530  0.800530  0.800530     0.80053\n",
       "macro avg      0.756507  0.641047  0.662417  9049.00000\n",
       "weighted avg   0.785631  0.800530  0.772397  9049.00000"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4775a7",
   "metadata": {},
   "source": [
    "### Evaluate fairness "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "ddf4ed3e",
   "metadata": {
    "scrolled": true
   },
   "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>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average_odds_difference</th>\n",
       "      <td>0.047157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disparate_impact_ratio</th>\n",
       "      <td>0.564673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equal_opportunity_difference</th>\n",
       "      <td>0.125682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistical_parity_difference</th>\n",
       "      <td>-0.058128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sex\n",
       "average_odds_difference        0.047157\n",
       "disparate_impact_ratio         0.564673\n",
       "equal_opportunity_difference   0.125682\n",
       "statistical_parity_difference -0.058128"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fairness_report(y_test, y_pred, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcd499d",
   "metadata": {},
   "source": [
    "# Bias mitigation algoritms comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "73c3ab00",
   "metadata": {},
   "outputs": [],
   "source": [
    "algos = [estimator, clf, postproc, adv_deb]\n",
    "algo_names = ['suppression', 'preprocessing', 'inprocessing', 'postprocessing']\n",
    "sex_fairness = pd.DataFrame(index=fairness_report(y_test, y_pred, prot_attr='sex').index)\n",
    "for i, algo in enumerate(algos):\n",
    "    y_pred = algo.predict(X_test)\n",
    "    sex_fairness = pd.merge(sex_fairness, \n",
    "                             fairness_report(y_test, y_pred, prot_attr='sex'), \n",
    "                             left_index=True, right_index=True, suffixes=('', '_' + algo_names[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "38844873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>sex_preprocessing</th>\n",
       "      <th>sex_inprocessing</th>\n",
       "      <th>sex_postprocessing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average_odds_difference</th>\n",
       "      <td>-0.093343</td>\n",
       "      <td>0.051906</td>\n",
       "      <td>0.047157</td>\n",
       "      <td>0.102165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disparate_impact_ratio</th>\n",
       "      <td>0.338628</td>\n",
       "      <td>0.609058</td>\n",
       "      <td>0.564673</td>\n",
       "      <td>0.704891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equal_opportunity_difference</th>\n",
       "      <td>-0.105511</td>\n",
       "      <td>0.127800</td>\n",
       "      <td>0.125682</td>\n",
       "      <td>0.203286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistical_parity_difference</th>\n",
       "      <td>-0.171693</td>\n",
       "      <td>-0.086982</td>\n",
       "      <td>-0.058128</td>\n",
       "      <td>-0.055569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sex  sex_preprocessing  sex_inprocessing  \\\n",
       "average_odds_difference       -0.093343           0.051906          0.047157   \n",
       "disparate_impact_ratio         0.338628           0.609058          0.564673   \n",
       "equal_opportunity_difference  -0.105511           0.127800          0.125682   \n",
       "statistical_parity_difference -0.171693          -0.086982         -0.058128   \n",
       "\n",
       "                               sex_postprocessing  \n",
       "average_odds_difference                  0.102165  \n",
       "disparate_impact_ratio                   0.704891  \n",
       "equal_opportunity_difference             0.203286  \n",
       "statistical_parity_difference           -0.055569  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_fairness"
   ]
  }
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