AIM-PIbd-31-Potapov-N-S/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Вариант 2. Показатели сердечных заболеваний"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any\n",
    "from math import ceil\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import ADASYN, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузим данные из датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('csv\\\\heart_2022_no_nans.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Посмотрим общие сведения о датасете"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246022 entries, 0 to 246021\n",
      "Data columns (total 40 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   State                      246022 non-null  object \n",
      " 1   Sex                        246022 non-null  object \n",
      " 2   GeneralHealth              246022 non-null  object \n",
      " 3   PhysicalHealthDays         246022 non-null  float64\n",
      " 4   MentalHealthDays           246022 non-null  float64\n",
      " 5   LastCheckupTime            246022 non-null  object \n",
      " 6   PhysicalActivities         246022 non-null  object \n",
      " 7   SleepHours                 246022 non-null  float64\n",
      " 8   RemovedTeeth               246022 non-null  object \n",
      " 9   HadHeartAttack             246022 non-null  object \n",
      " 10  HadAngina                  246022 non-null  object \n",
      " 11  HadStroke                  246022 non-null  object \n",
      " 12  HadAsthma                  246022 non-null  object \n",
      " 13  HadSkinCancer              246022 non-null  object \n",
      " 14  HadCOPD                    246022 non-null  object \n",
      " 15  HadDepressiveDisorder      246022 non-null  object \n",
      " 16  HadKidneyDisease           246022 non-null  object \n",
      " 17  HadArthritis               246022 non-null  object \n",
      " 18  HadDiabetes                246022 non-null  object \n",
      " 19  DeafOrHardOfHearing        246022 non-null  object \n",
      " 20  BlindOrVisionDifficulty    246022 non-null  object \n",
      " 21  DifficultyConcentrating    246022 non-null  object \n",
      " 22  DifficultyWalking          246022 non-null  object \n",
      " 23  DifficultyDressingBathing  246022 non-null  object \n",
      " 24  DifficultyErrands          246022 non-null  object \n",
      " 25  SmokerStatus               246022 non-null  object \n",
      " 26  ECigaretteUsage            246022 non-null  object \n",
      " 27  ChestScan                  246022 non-null  object \n",
      " 28  RaceEthnicityCategory      246022 non-null  object \n",
      " 29  AgeCategory                246022 non-null  object \n",
      " 30  HeightInMeters             246022 non-null  float64\n",
      " 31  WeightInKilograms          246022 non-null  float64\n",
      " 32  BMI                        246022 non-null  float64\n",
      " 33  AlcoholDrinkers            246022 non-null  object \n",
      " 34  HIVTesting                 246022 non-null  object \n",
      " 35  FluVaxLast12               246022 non-null  object \n",
      " 36  PneumoVaxEver              246022 non-null  object \n",
      " 37  TetanusLast10Tdap          246022 non-null  object \n",
      " 38  HighRiskLastYear           246022 non-null  object \n",
      " 39  CovidPos                   246022 non-null  object \n",
      "dtypes: float64(6), object(34)\n",
      "memory usage: 75.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<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>PhysicalHealthDays</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>4.119026</td>\n",
       "      <td>8.405844</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MentalHealthDays</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>4.167140</td>\n",
       "      <td>8.102687</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SleepHours</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>7.021331</td>\n",
       "      <td>1.440681</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeightInMeters</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>1.705150</td>\n",
       "      <td>0.106654</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeightInKilograms</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>83.615179</td>\n",
       "      <td>21.323156</td>\n",
       "      <td>28.12</td>\n",
       "      <td>68.04</td>\n",
       "      <td>81.65</td>\n",
       "      <td>95.25</td>\n",
       "      <td>292.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>28.668136</td>\n",
       "      <td>6.513973</td>\n",
       "      <td>12.02</td>\n",
       "      <td>24.27</td>\n",
       "      <td>27.46</td>\n",
       "      <td>31.89</td>\n",
       "      <td>97.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std    min    25%    50%  \\\n",
       "PhysicalHealthDays  246022.0   4.119026   8.405844   0.00   0.00   0.00   \n",
       "MentalHealthDays    246022.0   4.167140   8.102687   0.00   0.00   0.00   \n",
       "SleepHours          246022.0   7.021331   1.440681   1.00   6.00   7.00   \n",
       "HeightInMeters      246022.0   1.705150   0.106654   0.91   1.63   1.70   \n",
       "WeightInKilograms   246022.0  83.615179  21.323156  28.12  68.04  81.65   \n",
       "BMI                 246022.0  28.668136   6.513973  12.02  24.27  27.46   \n",
       "\n",
       "                      75%     max  \n",
       "PhysicalHealthDays   3.00   30.00  \n",
       "MentalHealthDays     4.00   30.00  \n",
       "SleepHours           8.00   24.00  \n",
       "HeightInMeters       1.78    2.41  \n",
       "WeightInKilograms   95.25  292.57  \n",
       "BMI                 31.89   97.65  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Получим информацию о пустых значениях в колонках датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_null_columns_info(df: DataFrame) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Возвращает информацию о пропущенных значениях в колонках датасета\n",
    "    \"\"\"\n",
    "    w = []\n",
    "    df_len = len(df)\n",
    "\n",
    "    for column in df.columns:\n",
    "        column_nulls = df[column].isnull()\n",
    "        w.append([column, column_nulls.any(), column_nulls.sum() / df_len])\n",
    "\n",
    "    null_df = DataFrame(w).rename(columns={0: \"Column\", 1: \"Has Null\", 2: \"Null Percent\"})\n",
    "\n",
    "    return null_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Column</th>\n",
       "      <th>Has Null</th>\n",
       "      <th>Null Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GeneralHealth</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PhysicalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MentalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LastCheckupTime</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PhysicalActivities</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SleepHours</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RemovedTeeth</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HadHeartAttack</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HadAngina</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HadStroke</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HadAsthma</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HadSkinCancer</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HadCOPD</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HadDepressiveDisorder</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HadKidneyDisease</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HadArthritis</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HadDiabetes</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DeafOrHardOfHearing</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BlindOrVisionDifficulty</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DifficultyConcentrating</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DifficultyWalking</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DifficultyDressingBathing</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DifficultyErrands</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SmokerStatus</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ECigaretteUsage</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ChestScan</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RaceEthnicityCategory</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>AgeCategory</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HeightInMeters</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>WeightInKilograms</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>BMI</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>AlcoholDrinkers</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>HIVTesting</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>FluVaxLast12</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PneumoVaxEver</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TetanusLast10Tdap</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>HighRiskLastYear</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CovidPos</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Column  Has Null  Null Percent\n",
       "0                       State     False           0.0\n",
       "1                         Sex     False           0.0\n",
       "2               GeneralHealth     False           0.0\n",
       "3          PhysicalHealthDays     False           0.0\n",
       "4            MentalHealthDays     False           0.0\n",
       "5             LastCheckupTime     False           0.0\n",
       "6          PhysicalActivities     False           0.0\n",
       "7                  SleepHours     False           0.0\n",
       "8                RemovedTeeth     False           0.0\n",
       "9              HadHeartAttack     False           0.0\n",
       "10                  HadAngina     False           0.0\n",
       "11                  HadStroke     False           0.0\n",
       "12                  HadAsthma     False           0.0\n",
       "13              HadSkinCancer     False           0.0\n",
       "14                    HadCOPD     False           0.0\n",
       "15      HadDepressiveDisorder     False           0.0\n",
       "16           HadKidneyDisease     False           0.0\n",
       "17               HadArthritis     False           0.0\n",
       "18                HadDiabetes     False           0.0\n",
       "19        DeafOrHardOfHearing     False           0.0\n",
       "20    BlindOrVisionDifficulty     False           0.0\n",
       "21    DifficultyConcentrating     False           0.0\n",
       "22          DifficultyWalking     False           0.0\n",
       "23  DifficultyDressingBathing     False           0.0\n",
       "24          DifficultyErrands     False           0.0\n",
       "25               SmokerStatus     False           0.0\n",
       "26            ECigaretteUsage     False           0.0\n",
       "27                  ChestScan     False           0.0\n",
       "28      RaceEthnicityCategory     False           0.0\n",
       "29                AgeCategory     False           0.0\n",
       "30             HeightInMeters     False           0.0\n",
       "31          WeightInKilograms     False           0.0\n",
       "32                        BMI     False           0.0\n",
       "33            AlcoholDrinkers     False           0.0\n",
       "34                 HIVTesting     False           0.0\n",
       "35               FluVaxLast12     False           0.0\n",
       "36              PneumoVaxEver     False           0.0\n",
       "37          TetanusLast10Tdap     False           0.0\n",
       "38           HighRiskLastYear     False           0.0\n",
       "39                   CovidPos     False           0.0"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_null_columns_info(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Получим информацию о выбросах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_numeric_columns(df: DataFrame) -> list[str]:\n",
    "    \"\"\"\n",
    "    Возвращает список числовых колонок\n",
    "    \"\"\"\n",
    "    return list(filter(lambda column: pd.api.types.is_numeric_dtype(df[column]), df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_outliers_info(df: DataFrame) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Возаращает информацию о выбросах в числовых колонках датасета\n",
    "    \"\"\"\n",
    "    data = {\n",
    "        \"Column\": [],\n",
    "        \"Has Outliers\": [],\n",
    "        \"Outliers Count\": [],\n",
    "        \"Min Value\": [],\n",
    "        \"Max Value\": [],\n",
    "        \"Q1\": [],\n",
    "        \"Q3\": []\n",
    "    }\n",
    "\n",
    "    info = DataFrame(data)\n",
    "\n",
    "    for column in get_numeric_columns(df):\n",
    "        Q1: float = df[column].quantile(0.25)\n",
    "        Q3: float = df[column].quantile(0.75)\n",
    "        IQR: float = Q3 - Q1\n",
    "\n",
    "        lower_bound: float = Q1 - 1.5 * IQR\n",
    "        upper_bound: float = Q3 + 1.5 * IQR\n",
    "\n",
    "        outliers: DataFrame = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count: int = outliers.shape[0]\n",
    "\n",
    "        info.loc[len(info)] = [column, outlier_count > 0, outlier_count, df[column].min(), df[column].max(), Q1, Q3]\n",
    "\n",
    "    return info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим данные по выбросам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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>Column</th>\n",
       "      <th>Has Outliers</th>\n",
       "      <th>Outliers Count</th>\n",
       "      <th>Min Value</th>\n",
       "      <th>Max Value</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PhysicalHealthDays</td>\n",
       "      <td>True</td>\n",
       "      <td>38810</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MentalHealthDays</td>\n",
       "      <td>True</td>\n",
       "      <td>32714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SleepHours</td>\n",
       "      <td>True</td>\n",
       "      <td>3488</td>\n",
       "      <td>1.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HeightInMeters</td>\n",
       "      <td>True</td>\n",
       "      <td>830</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2.41</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WeightInKilograms</td>\n",
       "      <td>True</td>\n",
       "      <td>5940</td>\n",
       "      <td>28.12</td>\n",
       "      <td>292.57</td>\n",
       "      <td>68.04</td>\n",
       "      <td>95.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BMI</td>\n",
       "      <td>True</td>\n",
       "      <td>7563</td>\n",
       "      <td>12.02</td>\n",
       "      <td>97.65</td>\n",
       "      <td>24.27</td>\n",
       "      <td>31.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Column  Has Outliers  Outliers Count  Min Value  Max Value  \\\n",
       "0  PhysicalHealthDays          True           38810       0.00      30.00   \n",
       "1    MentalHealthDays          True           32714       0.00      30.00   \n",
       "2          SleepHours          True            3488       1.00      24.00   \n",
       "3      HeightInMeters          True             830       0.91       2.41   \n",
       "4   WeightInKilograms          True            5940      28.12     292.57   \n",
       "5                 BMI          True            7563      12.02      97.65   \n",
       "\n",
       "      Q1     Q3  \n",
       "0   0.00   3.00  \n",
       "1   0.00   4.00  \n",
       "2   6.00   8.00  \n",
       "3   1.63   1.78  \n",
       "4  68.04  95.25  \n",
       "5  24.27  31.89  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_info = get_outliers_info(df)\n",
    "outliers_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_outliers(df: DataFrame) -> None:\n",
    "    \"\"\"\n",
    "    Генерирует диаграммы BoxPlot для числовых колонок датасета\n",
    "    \"\"\"\n",
    "    columns = get_numeric_columns(df)\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    rows: int = ceil(len(columns) / 3)\n",
    "    for index, column in enumerate(columns, 1):\n",
    "        plt.subplot(rows, 3, index)\n",
    "        plt.boxplot(df[column], vert=True, patch_artist=True)\n",
    "        plt.title(f\"Диаграмма размахов\\n\\\"{column}\\\"\")\n",
    "        plt.xlabel(column)\n",
    "            \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализируем выбросы с помощью диаграмм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df: DataFrame, columns: list[str]) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Устраняет выбросы в заданных колонках:\n",
    "    задает значениям выше максимального значение максимума, ниже минимального - значение минимума\n",
    "    \"\"\"\n",
    "    for column in columns:\n",
    "        Q1: float = df[column].quantile(0.25)\n",
    "        Q3: float = df[column].quantile(0.75)\n",
    "        IQR: float = Q3 - Q1\n",
    "\n",
    "        lower_bound: float = Q1 - 1.5 * IQR\n",
    "        upper_bound: float = Q3 + 1.5 * IQR\n",
    "\n",
    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаляем выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_columns = list(outliers_info[outliers_info[\"Has Outliers\"] == True][\"Column\"])\n",
    "df = remove_outliers(df, outliers_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Снова получим данные о выбросах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>Column</th>\n",
       "      <th>Has Outliers</th>\n",
       "      <th>Outliers Count</th>\n",
       "      <th>Min Value</th>\n",
       "      <th>Max Value</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PhysicalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MentalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SleepHours</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HeightInMeters</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>2.005</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WeightInKilograms</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>28.120</td>\n",
       "      <td>136.065</td>\n",
       "      <td>68.04</td>\n",
       "      <td>95.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BMI</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>12.840</td>\n",
       "      <td>43.320</td>\n",
       "      <td>24.27</td>\n",
       "      <td>31.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Column  Has Outliers  Outliers Count  Min Value  Max Value  \\\n",
       "0  PhysicalHealthDays         False               0      0.000      7.500   \n",
       "1    MentalHealthDays         False               0      0.000     10.000   \n",
       "2          SleepHours         False               0      3.000     11.000   \n",
       "3      HeightInMeters         False               0      1.405      2.005   \n",
       "4   WeightInKilograms         False               0     28.120    136.065   \n",
       "5                 BMI         False               0     12.840     43.320   \n",
       "\n",
       "      Q1     Q3  \n",
       "0   0.00   3.00  \n",
       "1   0.00   4.00  \n",
       "2   6.00   8.00  \n",
       "3   1.63   1.78  \n",
       "4  68.04  95.25  \n",
       "5  24.27  31.89  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_outliers_info(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что выбросов не осталось - проверим через диаграммы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Нормализация числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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>PhysicalHealthDaysNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.253306</td>\n",
       "      <td>0.385378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MentalHealthDaysNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.244973</td>\n",
       "      <td>0.378598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SleepHoursNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.501124</td>\n",
       "      <td>0.165569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeightInMetersNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.500401</td>\n",
       "      <td>0.176240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeightInKilogramsNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.510963</td>\n",
       "      <td>0.186742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.369818</td>\n",
       "      <td>0.495901</td>\n",
       "      <td>0.621891</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMINorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.513599</td>\n",
       "      <td>0.194556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.479659</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count      mean       std  min       25%       50%  \\\n",
       "PhysicalHealthDaysNorm  246022.0  0.253306  0.385378  0.0  0.000000  0.000000   \n",
       "MentalHealthDaysNorm    246022.0  0.244973  0.378598  0.0  0.000000  0.000000   \n",
       "SleepHoursNorm          246022.0  0.501124  0.165569  0.0  0.375000  0.500000   \n",
       "HeightInMetersNorm      246022.0  0.500401  0.176240  0.0  0.375000  0.491667   \n",
       "WeightInKilogramsNorm   246022.0  0.510963  0.186742  0.0  0.369818  0.495901   \n",
       "BMINorm                 246022.0  0.513599  0.194556  0.0  0.375000  0.479659   \n",
       "\n",
       "                             75%  max  \n",
       "PhysicalHealthDaysNorm  0.400000  1.0  \n",
       "MentalHealthDaysNorm    0.400000  1.0  \n",
       "SleepHoursNorm          0.625000  1.0  \n",
       "HeightInMetersNorm      0.625000  1.0  \n",
       "WeightInKilogramsNorm   0.621891  1.0  \n",
       "BMINorm                 0.625000  1.0  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "df_norm = df.copy()\n",
    "\n",
    "numeric_columns = get_numeric_columns(df)\n",
    "\n",
    "for column in numeric_columns:\n",
    "    norm_column = column + \"Norm\"\n",
    "    df_norm[norm_column] = min_max_scaler.fit_transform(\n",
    "        df_norm[column].to_numpy().reshape(-1, 1)\n",
    "    ).reshape(df_norm[column].shape)\n",
    "\n",
    "df_norm = df_norm.drop(columns=numeric_columns)\n",
    "\n",
    "\n",
    "df_norm.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Конструирование признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Автоматическое конструирование признаков с помощью фреймворка FeatureTools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "import featuretools as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Преобразуем датасет с помощью фремйворка\n",
    "# https://featuretools.alteryx.com/en/stable/getting_started/afe.html\n",
    "\n",
    "entity_set = ft.EntitySet().add_dataframe(df, \"df\", make_index=True, index=\"id\")\n",
    "\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=entity_set,\n",
    "    target_dataframe_name=\"df\",\n",
    "    max_depth=2\n",
    ")\n",
    "\n",
    "feature_matrix: DataFrame\n",
    "feature_defs: list[ft.Feature]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполняем категориальное и унитарное кодирование признаков с помощью FeatureTools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Было признаков: 40\n",
      "Стало признаков: 99\n",
      "<Feature: State = Washington>\n",
      "<Feature: State = Maryland>\n",
      "<Feature: State = Minnesota>\n",
      "<Feature: State = Ohio>\n",
      "<Feature: State = New York>\n",
      "<Feature: State = Texas>\n",
      "<Feature: State = Florida>\n",
      "<Feature: State = Kansas>\n",
      "<Feature: State = Wisconsin>\n",
      "<Feature: State = Maine>\n",
      "<Feature: State is unknown>\n",
      "<Feature: Sex = Female>\n",
      "<Feature: Sex = Male>\n",
      "<Feature: Sex is unknown>\n",
      "<Feature: GeneralHealth = Very good>\n",
      "<Feature: GeneralHealth = Good>\n",
      "<Feature: GeneralHealth = Excellent>\n",
      "<Feature: GeneralHealth = Fair>\n",
      "<Feature: GeneralHealth = Poor>\n",
      "<Feature: GeneralHealth is unknown>\n",
      "<Feature: PhysicalHealthDays>\n",
      "<Feature: MentalHealthDays>\n",
      "<Feature: LastCheckupTime = Within past year (anytime less than 12 months ago)>\n",
      "<Feature: LastCheckupTime = Within past 2 years (1 year but less than 2 years ago)>\n",
      "<Feature: LastCheckupTime = Within past 5 years (2 years but less than 5 years ago)>\n",
      "<Feature: LastCheckupTime = 5 or more years ago>\n",
      "<Feature: LastCheckupTime is unknown>\n",
      "<Feature: PhysicalActivities>\n",
      "<Feature: SleepHours>\n",
      "<Feature: RemovedTeeth = None of them>\n",
      "<Feature: RemovedTeeth = 1 to 5>\n",
      "<Feature: RemovedTeeth = 6 or more, but not all>\n",
      "<Feature: RemovedTeeth = All>\n",
      "<Feature: RemovedTeeth is unknown>\n",
      "<Feature: HadHeartAttack>\n",
      "<Feature: HadAngina>\n",
      "<Feature: HadStroke>\n",
      "<Feature: HadAsthma>\n",
      "<Feature: HadSkinCancer>\n",
      "<Feature: HadCOPD>\n",
      "<Feature: HadDepressiveDisorder>\n",
      "<Feature: HadKidneyDisease>\n",
      "<Feature: HadArthritis>\n",
      "<Feature: HadDiabetes = No>\n",
      "<Feature: HadDiabetes = Yes>\n",
      "<Feature: HadDiabetes = No, pre-diabetes or borderline diabetes>\n",
      "<Feature: HadDiabetes = Yes, but only during pregnancy (female)>\n",
      "<Feature: HadDiabetes is unknown>\n",
      "<Feature: DeafOrHardOfHearing>\n",
      "<Feature: BlindOrVisionDifficulty>\n",
      "<Feature: DifficultyConcentrating>\n",
      "<Feature: DifficultyWalking>\n",
      "<Feature: DifficultyDressingBathing>\n",
      "<Feature: DifficultyErrands>\n",
      "<Feature: SmokerStatus = Never smoked>\n",
      "<Feature: SmokerStatus = Former smoker>\n",
      "<Feature: SmokerStatus = Current smoker - now smokes every day>\n",
      "<Feature: SmokerStatus = Current smoker - now smokes some days>\n",
      "<Feature: SmokerStatus is unknown>\n",
      "<Feature: ECigaretteUsage = Never used e-cigarettes in my entire life>\n",
      "<Feature: ECigaretteUsage = Not at all (right now)>\n",
      "<Feature: ECigaretteUsage = Use them some days>\n",
      "<Feature: ECigaretteUsage = Use them every day>\n",
      "<Feature: ECigaretteUsage is unknown>\n",
      "<Feature: ChestScan>\n",
      "<Feature: RaceEthnicityCategory = White only, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Black only, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Other race only, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Multiracial, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory is unknown>\n",
      "<Feature: AgeCategory = Age 65 to 69>\n",
      "<Feature: AgeCategory = Age 60 to 64>\n",
      "<Feature: AgeCategory = Age 70 to 74>\n",
      "<Feature: AgeCategory = Age 55 to 59>\n",
      "<Feature: AgeCategory = Age 50 to 54>\n",
      "<Feature: AgeCategory = Age 75 to 79>\n",
      "<Feature: AgeCategory = Age 80 or older>\n",
      "<Feature: AgeCategory = Age 40 to 44>\n",
      "<Feature: AgeCategory = Age 45 to 49>\n",
      "<Feature: AgeCategory = Age 35 to 39>\n",
      "<Feature: AgeCategory is unknown>\n",
      "<Feature: HeightInMeters>\n",
      "<Feature: WeightInKilograms>\n",
      "<Feature: BMI>\n",
      "<Feature: AlcoholDrinkers>\n",
      "<Feature: HIVTesting>\n",
      "<Feature: FluVaxLast12>\n",
      "<Feature: PneumoVaxEver>\n",
      "<Feature: TetanusLast10Tdap = No, did not receive any tetanus shot in the past 10 years>\n",
      "<Feature: TetanusLast10Tdap = Yes, received tetanus shot but not sure what type>\n",
      "<Feature: TetanusLast10Tdap = Yes, received Tdap>\n",
      "<Feature: TetanusLast10Tdap = Yes, received tetanus shot, but not Tdap>\n",
      "<Feature: TetanusLast10Tdap is unknown>\n",
      "<Feature: HighRiskLastYear>\n",
      "<Feature: CovidPos = No>\n",
      "<Feature: CovidPos = Yes>\n",
      "<Feature: CovidPos = Tested positive using home test without a health professional>\n",
      "<Feature: CovidPos is unknown>\n"
     ]
    }
   ],
   "source": [
    "# Сгенерируем новые признаки\n",
    "# https://featuretools.alteryx.com/en/stable/guides/tuning_dfs.html\n",
    "\n",
    "feature_matrix_enc, features_enc = ft.encode_features(feature_matrix, feature_defs)\n",
    "feature_matrix_enc.to_csv(\"./csv/generated_features.csv\", index=False)\n",
    "\n",
    "print(\"Было признаков:\", len(feature_defs))\n",
    "print(\"Стало признаков:\", len(features_enc))\n",
    "print(*features_enc, sep='\\n')"
   ]
  }
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