{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Данные по инсультам\n",
    "\n",
    "Выведем информацию о столбцах датасета:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9046</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51676</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31112</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60182</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18234</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44873</th>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19723</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37544</th>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44679</th>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5110 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "id                                                                             \n",
       "9046     Male  67.0             0              1          Yes        Private   \n",
       "51676  Female  61.0             0              0          Yes  Self-employed   \n",
       "31112    Male  80.0             0              1          Yes        Private   \n",
       "60182  Female  49.0             0              0          Yes        Private   \n",
       "1665   Female  79.0             1              0          Yes  Self-employed   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "18234  Female  80.0             1              0          Yes        Private   \n",
       "44873  Female  81.0             0              0          Yes  Self-employed   \n",
       "19723  Female  35.0             0              0          Yes  Self-employed   \n",
       "37544    Male  51.0             0              0          Yes        Private   \n",
       "44679  Female  44.0             0              0          Yes       Govt_job   \n",
       "\n",
       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "id                                                                      \n",
       "9046           Urban             228.69  36.6  formerly smoked       1  \n",
       "51676          Rural             202.21   NaN     never smoked       1  \n",
       "31112          Rural             105.92  32.5     never smoked       1  \n",
       "60182          Urban             171.23  34.4           smokes       1  \n",
       "1665           Rural             174.12  24.0     never smoked       1  \n",
       "...              ...                ...   ...              ...     ...  \n",
       "18234          Urban              83.75   NaN     never smoked       0  \n",
       "44873          Urban             125.20  40.0     never smoked       0  \n",
       "19723          Rural              82.99  30.6     never smoked       0  \n",
       "37544          Rural             166.29  25.6  formerly smoked       0  \n",
       "44679          Urban              85.28  26.2          Unknown       0  \n",
       "\n",
       "[5110 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import set_config\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "random_state=9\n",
    "\n",
    "df = pd.read_csv(\"./csv/option4.csv\", index_col=\"id\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Бизнес-цели\n",
    "#### Классификация\n",
    "##### Цель: сделать модель, которая на основе данных про здоровье, образ жизни и соцдем-факторы будет предсказывать риск инсульта.\n",
    "\n",
    "Применение:\n",
    "Для врачей: чтобы находить пациентов с высоким риском и вовремя их спасать.\n",
    "Для медсистем: можно встроить в карты пациентов, чтобы система сама предупреждала врача.\n",
    "Для людей: повышать осведомленность о факторах риска и как их избежать.\n",
    "\n",
    "#### Регрессия\n",
    "##### Цель: сделать модель, которая будет предсказывать уровень глюкозы на основе тех же факторов. Поможет отслеживать изменения и оценивать риски.\n",
    "\n",
    "Применение:\n",
    "Для врачей: находить пациентов с риском диабета и другими проблемами, чтобы сразу назначать профилактику.\n",
    "Для медсистем: встроить в записи пациентов, чтобы врачи видели уровень глюкозы даже без лабораторий.\n",
    "Для населения: обучать, как lifestyle влияет на глюкозу, и рекомендовать изменения.\n",
    "\n",
    "### Качество моделей\n",
    "#### Классификация (инсульт):\n",
    "\n",
    "Признаки норм: возраст, гипертония, сердце — инфа полезная, модель будет неплохо различать риск.\n",
    "Проблемы: нет данных про гены, детальную историю болезней, питание, физнагрузки. Это режет потолок точности.\n",
    "\n",
    "#### Регрессия (глюкоза):\n",
    "Признаки норм: возраст, курение, давление — связь с глюкозой есть, что-то предскажет.\n",
    "Проблемы: не хватает данных про еду, активность, гормоны, поэтому точность ограничена."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Классификация\n",
    "\n",
    "Разделим набор данных на на обучающую и тестовые выборки (80/20). Целевой признак - stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "    \"\"\"\n",
    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
    "    following fractional ratios provided by the user, where each subset is\n",
    "    stratified by the values in a specific column (that is, each subset has\n",
    "    the same relative frequency of the values in the column). It performs this\n",
    "    splitting by running train_test_split() twice.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_input : Pandas dataframe\n",
    "        Input dataframe to be split.\n",
    "    stratify_colname : str\n",
    "        The name of the column that will be used for stratification. Usually\n",
    "        this column would be for the label.\n",
    "    frac_train : float\n",
    "    frac_val   : float\n",
    "    frac_test  : float\n",
    "        The ratios with which the dataframe will be split into train, val, and\n",
    "        test data. The values should be expressed as float fractions and should\n",
    "        sum to 1.0.\n",
    "    random_state : int, None, or RandomStateInstance\n",
    "        Value to be passed to train_test_split().\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    df_train, df_val, df_test :\n",
    "        Dataframes containing the three splits.\n",
    "    \"\"\"\n",
    "\n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "\n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "\n",
    "    X = df_input  # Contains all columns.\n",
    "    y = df_input[\n",
    "        [stratify_colname]\n",
    "    ]  # Dataframe of just the column on which to stratify.\n",
    "\n",
    "    # Split original dataframe into train and temp dataframes.\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
    "    )\n",
    "\n",
    "    if frac_val <= 0:\n",
    "        assert len(df_input) == len(df_train) + len(df_temp)\n",
    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
    "\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "    return df_train, df_val, df_test, y_train, y_val, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22159</th>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>97.06</td>\n",
       "      <td>28.5</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8920</th>\n",
       "      <td>Female</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.35</td>\n",
       "      <td>33.5</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65507</th>\n",
       "      <td>Male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>55.72</td>\n",
       "      <td>38.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43196</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>59.54</td>\n",
       "      <td>42.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59745</th>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>76.74</td>\n",
       "      <td>53.9</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66546</th>\n",
       "      <td>Female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>80.08</td>\n",
       "      <td>25.1</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68798</th>\n",
       "      <td>Female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>59.86</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61409</th>\n",
       "      <td>Male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.24</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69259</th>\n",
       "      <td>Female</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>100.85</td>\n",
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       "    <tr>\n",
       "      <th>17231</th>\n",
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       "      <td>No</td>\n",
       "      <td>Private</td>\n",
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       "      <td>90.42</td>\n",
       "      <td>24.3</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</div>"
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       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "id                                                                             \n",
       "22159  Female  54.0             1              0           No        Private   \n",
       "8920   Female  51.0             0              0          Yes  Self-employed   \n",
       "65507    Male  33.0             0              0          Yes        Private   \n",
       "43196  Female  52.0             0              0          Yes  Self-employed   \n",
       "59745  Female  27.0             0              0          Yes        Private   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "66546  Female  20.0             0              0           No        Private   \n",
       "68798  Female  58.0             0              0          Yes        Private   \n",
       "61409    Male  32.0             1              0           No       Govt_job   \n",
       "69259  Female  77.0             0              0          Yes        Private   \n",
       "17231  Female  24.0             0              0           No        Private   \n",
       "\n",
       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "id                                                                      \n",
       "22159          Urban              97.06  28.5  formerly smoked       0  \n",
       "8920           Rural              76.35  33.5  formerly smoked       0  \n",
       "65507          Rural              55.72  38.2     never smoked       0  \n",
       "43196          Urban              59.54  42.2          Unknown       0  \n",
       "59745          Urban              76.74  53.9          Unknown       0  \n",
       "...              ...                ...   ...              ...     ...  \n",
       "66546          Urban              80.08  25.1     never smoked       0  \n",
       "68798          Rural              59.86  28.0  formerly smoked       1  \n",
       "61409          Urban              58.24   NaN  formerly smoked       0  \n",
       "69259          Rural             100.85  29.5           smokes       0  \n",
       "17231          Urban              90.42  24.3     never smoked       0  \n",
       "\n",
       "[4088 rows x 11 columns]"
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       "      <td>NaN</td>\n",
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       "      <td>64.06</td>\n",
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       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "id                                                                             \n",
       "18072  Female  39.0             0              0          Yes       Govt_job   \n",
       "67063    Male  62.0             0              0          Yes  Self-employed   \n",
       "40387  Female  17.0             0              0           No        Private   \n",
       "18032    Male  62.0             0              1          Yes        Private   \n",
       "5478   Female  60.0             0              0          Yes  Self-employed   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "57710  Female  50.0             0              0          Yes        Private   \n",
       "63043  Female  27.0             0              0           No        Private   \n",
       "63986    Male  60.0             0              0          Yes        Private   \n",
       "28461    Male  15.0             0              0           No   Never_worked   \n",
       "54975    Male   7.0             0              0           No  Self-employed   \n",
       "\n",
       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "id                                                                      \n",
       "18072          Urban             107.47  21.3          Unknown       0  \n",
       "67063          Urban             130.56  36.1          Unknown       0  \n",
       "40387          Rural              77.46  24.0          Unknown       0  \n",
       "18032          Rural              90.61  25.8           smokes       0  \n",
       "5478           Urban             203.04   NaN           smokes       0  \n",
       "...              ...                ...   ...              ...     ...  \n",
       "57710          Rural             112.25  21.6          Unknown       0  \n",
       "63043          Urban              61.80  26.8  formerly smoked       0  \n",
       "63986          Rural             153.48  37.3     never smoked       0  \n",
       "28461          Rural              79.59  28.4          Unknown       0  \n",
       "54975          Rural              64.06  18.9          Unknown       0  \n",
       "\n",
       "[1022 rows x 11 columns]"
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       "       stroke\n",
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   ],
   "source": [
    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"stroke\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=random_state\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выберем ориентир для задачи классификации. Для этого применим алгоритм случайного предсказания, т.е. в каждом случае в качестве предсказания выберем случайный класс."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.5\n",
      "Baseline Precision: 0.05758157389635317\n",
      "Baseline Recall: 0.6\n",
      "Baseline F1 Score: 0.10507880910683012\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score\n",
    "\n",
    "# Получаем уникальные классы для целевого признака из тренировочного набора данных\n",
    "unique_classes = np.unique(y_train)\n",
    "\n",
    "# Генерируем случайные предсказания, выбирая случайное значение из области значений целевого признака\n",
    "random_predictions = np.random.choice(unique_classes, size=len(y_test))\n",
    "\n",
    "# Вычисление метрик для ориентира\n",
    "baseline_accuracy = accuracy_score(y_test, random_predictions)\n",
    "baseline_precision = precision_score(y_test, random_predictions)\n",
    "baseline_recall = recall_score(y_test, random_predictions)\n",
    "baseline_f1 = f1_score(y_test, random_predictions)\n",
    "\n",
    "print('Baseline Accuracy:', baseline_accuracy)\n",
    "print('Baseline Precision:', baseline_precision)\n",
    "print('Baseline Recall:', baseline_recall)\n",
    "print('Baseline F1 Score:', baseline_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Метрики модели\n",
    "- Accuracy: доля правильных предсказаний от общего числа примеров. Простая, но бесполезная метрика в задачах с дисбалансом классов — не учитывает, как модель работает с редким классом.\n",
    "- Precision: доля правильных предсказаний положительного класса среди всех предсказанных положительных. Полезна, если критичны ложные срабатывания (например, чтобы не ошибаться с инсультом).\n",
    "- Recall: доля найденных объектов положительного класса среди всех реальных примеров положительного класса. Помогает понять, насколько хорошо модель \"ловит\" положительный класс. Важна, чтобы минимизировать пропуски инсультов.\n",
    "- F1 Score: гармоническое среднее между precision и recall. Учитывает и точность, и полноту, что важно в задачах с несбалансированными классами.\n",
    "Эти метрики показывают разные аспекты работы модели: от общего уровня точности до способности находить редкие классы и балансировать между precision и recall. Это позволяет оценить модель всесторонне."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сформируем конвейер для классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.discriminant_analysis import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "columns_to_drop = [\"work_type\", \"stroke\"]\n",
    "columns_not_to_modify = [\"hypertension\", \"heart_disease\"]\n",
    "\n",
    "num_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if column not in columns_to_drop\n",
    "    and column not in columns_not_to_modify\n",
    "    and df[column].dtype != \"object\"\n",
    "]\n",
    "\n",
    "cat_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if column not in columns_to_drop\n",
    "    and column not in columns_not_to_modify\n",
    "    and df[column].dtype == \"object\"\n",
    "]\n",
    "\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "num_scaler = StandardScaler()\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer),\n",
    "        (\"scaler\", num_scaler),\n",
    "    ]\n",
    ")\n",
    "\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
    "preprocessing_cat = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", cat_imputer),\n",
    "        (\"encoder\", cat_encoder),\n",
    "    ]\n",
    ")\n",
    "\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "drop_columns = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь проверим работу конвейера:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
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       "      <th>id</th>\n",
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       "      <td>-0.123924</td>\n",
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       "<p>4088 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  avg_glucose_level       bmi  gender_Male  gender_Other  \\\n",
       "id                                                                        \n",
       "22159  0.472344          -0.194427 -0.059214          0.0           0.0   \n",
       "8920   0.339807          -0.653763  0.587887          0.0           0.0   \n",
       "65507 -0.455418          -1.111325  1.196162          1.0           0.0   \n",
       "43196  0.383986          -1.026600  1.713843          0.0           0.0   \n",
       "59745 -0.720492          -0.645113  3.228060          0.0           0.0   \n",
       "...         ...                ...       ...          ...           ...   \n",
       "66546 -1.029746          -0.571034 -0.499243          0.0           0.0   \n",
       "68798  0.649060          -1.019502 -0.123924          0.0           0.0   \n",
       "61409 -0.499597          -1.055433 -0.098040          1.0           0.0   \n",
       "69259  1.488464          -0.110367  0.070206          0.0           0.0   \n",
       "17231 -0.853030          -0.341699 -0.602779          0.0           0.0   \n",
       "\n",
       "       ever_married_Yes  Residence_type_Urban  smoking_status_formerly smoked  \\\n",
       "id                                                                              \n",
       "22159               0.0                   1.0                             1.0   \n",
       "8920                1.0                   0.0                             1.0   \n",
       "65507               1.0                   0.0                             0.0   \n",
       "43196               1.0                   1.0                             0.0   \n",
       "59745               1.0                   1.0                             0.0   \n",
       "...                 ...                   ...                             ...   \n",
       "66546               0.0                   1.0                             0.0   \n",
       "68798               1.0                   0.0                             1.0   \n",
       "61409               0.0                   1.0                             1.0   \n",
       "69259               1.0                   0.0                             0.0   \n",
       "17231               0.0                   1.0                             0.0   \n",
       "\n",
       "       smoking_status_never smoked  smoking_status_smokes  hypertension  \\\n",
       "id                                                                        \n",
       "22159                          0.0                    0.0             1   \n",
       "8920                           0.0                    0.0             0   \n",
       "65507                          1.0                    0.0             0   \n",
       "43196                          0.0                    0.0             0   \n",
       "59745                          0.0                    0.0             0   \n",
       "...                            ...                    ...           ...   \n",
       "66546                          1.0                    0.0             0   \n",
       "68798                          0.0                    0.0             0   \n",
       "61409                          0.0                    0.0             1   \n",
       "69259                          0.0                    1.0             0   \n",
       "17231                          1.0                    0.0             0   \n",
       "\n",
       "       heart_disease  \n",
       "id                    \n",
       "22159              0  \n",
       "8920               0  \n",
       "65507              0  \n",
       "43196              0  \n",
       "59745              0  \n",
       "...              ...  \n",
       "66546              0  \n",
       "68798              0  \n",
       "61409              0  \n",
       "69259              0  \n",
       "17231              0  \n",
       "\n",
       "[4088 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.fit_transform(X_train)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "preprocessed_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подберем оптимальные гиперпараметры для каждой из выбранных моделей методом поиска по сетке и сформируем их набор.\n",
    "\n",
    "knn -- k-ближайших соседей\n",
    "\n",
    "random_forest -- метод случайного леса (набор деревьев решений)\n",
    "\n",
    "mlp -- многослойный персептрон (нейронная сеть)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\mai\\labs\\AIM-PIbd-31-Bakalskaya-E-D\\lab_4\\venv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие параметры для knn: {'n_neighbors': 1, 'weights': 'uniform'}\n",
      "Лучшие параметры для random_forest: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 7, 'max_features': 'sqrt', 'n_estimators': 50, 'random_state': 9}\n",
      "Лучшие параметры для mlp: {'alpha': np.float64(0.1), 'early_stopping': True, 'hidden_layer_sizes': np.int64(14), 'max_iter': 1000, 'random_state': 9, 'solver': 'adam'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import neighbors, ensemble, neural_network\n",
    "\n",
    "# Словарь с вариантами гиперпараметров для каждой модели\n",
    "param_grids = {\n",
    "    \"knn\": {\n",
    "        \"n_neighbors\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], \n",
    "        \"weights\": ['uniform', 'distance']\n",
    "    },\n",
    "    \"random_forest\": {\n",
    "        \"n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
    "        \"max_features\": [\"sqrt\", \"log2\", 2],\n",
    "        \"max_depth\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "        \"criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
    "        \"random_state\": [random_state],\n",
    "        \"class_weight\": [\"balanced\", \"balanced_subsample\"]\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"solver\": ['adam'], \n",
    "        \"max_iter\": [1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000], \n",
    "        \"alpha\": 10.0 ** -np.arange(1, 10), \n",
    "        \"hidden_layer_sizes\":np.arange(10, 15), \n",
    "        \"early_stopping\": [True, False],\n",
    "        \"random_state\": [random_state]\n",
    "    }\n",
    "}\n",
    "\n",
    "# Создаем экземпляры моделей\n",
    "models = {\n",
    "    \"knn\": neighbors.KNeighborsClassifier(),\n",
    "    \"random_forest\": ensemble.RandomForestClassifier(),\n",
    "    \"mlp\": neural_network.MLPClassifier()\n",
    "}\n",
    "\n",
    "# Словарь для хранения моделей с их лучшими параметрами\n",
    "class_models = {}\n",
    "\n",
    "# Выполнение поиска по сетке для каждой модели\n",
    "for model_name, model in models.items():\n",
    "    # Создаем GridSearchCV для текущей модели\n",
    "    gs_optimizer = GridSearchCV(estimator=model, param_grid=param_grids[model_name], scoring=\"f1\", n_jobs=-1)\n",
    "    \n",
    "    # Обучаем GridSearchCV\n",
    "    gs_optimizer.fit(preprocessed_df, y_train.values.ravel())\n",
    "    \n",
    "    # Получаем лучшие параметры\n",
    "    best_params = gs_optimizer.best_params_\n",
    "    print(f\"Лучшие параметры для {model_name}: {best_params}\")\n",
    "    \n",
    "    class_models[model_name] = {\n",
    "        \"model\": model.set_params(**best_params)  # Настраиваем модель с лучшими параметрами\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ЖЕСТЬ ЭТА ХРЕНЬ 12 МИНУТ СОЗДАВАЛАСЬ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "что ж теперь... обучим модели разными способами модели и посмотрим на качество обучения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: knn\n",
      "Model: random_forest\n",
      "Model: mlp\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    model = class_models[model_name][\"model\"]\n",
    "\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
    "    y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
    "\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"probs\"] = y_test_probs\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    class_models[model_name][\"Precision_train\"] = precision_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Precision_test\"] = precision_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_train\"] = recall_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_test\"] = recall_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_train\"] = accuracy_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_test\"] = accuracy_score(\n",
    "        y_test, y_test_predict\n",
    "    ) \n",
    "    class_models[model_name][\"F1_train\"] = f1_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"F1_test\"] = f1_score(y_test, y_test_predict)\n",
    "    class_models[model_name][\"Confusion_matrix\"] = confusion_matrix(\n",
    "        y_test, y_test_predict\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Матрицы неточностей:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Цветовая схема: светло-голубой для правильных, бледно-красный для ошибок\n",
    "def custom_color_map(c_matrix):\n",
    "    colors = np.empty_like(c_matrix, dtype=str)\n",
    "    for i in range(c_matrix.shape[0]):\n",
    "        for j in range(c_matrix.shape[1]):\n",
    "            if i == j:\n",
    "                colors[i, j] = \"#add8e6\"  # Светло-голубой\n",
    "            else:\n",
    "                colors[i, j] = \"#f08080\"  # Бледно-красный\n",
    "    return colors\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "for index, (key, model_info) in enumerate(class_models.items()):\n",
    "    c_matrix = model_info[\"Confusion_matrix\"]\n",
    "\n",
    "    # Генерация кастомных цветов\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Not stroke\", \"Stroke\"]\n",
    "    )\n",
    "    disp.plot(ax=ax.flat[index], cmap=custom_color_map(c_matrix), colorbar=False)\n",
    "\n",
    "    disp.ax_.set_title(key)\n",
    "\n",
    "if len(class_models) < len(ax.flat):\n",
    "    for i in range(len(class_models), len(ax.flat)):\n",
    "        fig.delaxes(ax.flat[i])\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.1, hspace=0.4, wspace=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision, Recall, Accuracy, F1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0e668_row0_col0 {\n",
       "  background-color: #1f988b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row0_col1, #T_0e668_row1_col0, #T_0e668_row1_col2, #T_0e668_row2_col3 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_0e668_row0_col2, #T_0e668_row0_col3, #T_0e668_row1_col1, #T_0e668_row2_col0 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row0_col4 {\n",
       "  background-color: #b7318a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row0_col5, #T_0e668_row1_col4, #T_0e668_row1_col6, #T_0e668_row2_col7 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row0_col6, #T_0e668_row0_col7, #T_0e668_row2_col4, #T_0e668_row2_col5 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row1_col3, #T_0e668_row2_col1 {\n",
       "  background-color: #1f968b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row1_col5 {\n",
       "  background-color: #be3885;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row1_col7 {\n",
       "  background-color: #9c179e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e668_row2_col2 {\n",
       "  background-color: #86d549;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_0e668_row2_col6 {\n",
       "  background-color: #8808a6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0e668\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0e668_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_0e668_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_0e668_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_0e668_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_0e668_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_0e668_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_0e668_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_0e668_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0e668_level0_row0\" class=\"row_heading level0 row0\" >mlp</th>\n",
       "      <td id=\"T_0e668_row0_col0\" class=\"data row0 col0\" >0.400000</td>\n",
       "      <td id=\"T_0e668_row0_col1\" class=\"data row0 col1\" >0.200000</td>\n",
       "      <td id=\"T_0e668_row0_col2\" class=\"data row0 col2\" >0.020101</td>\n",
       "      <td id=\"T_0e668_row0_col3\" class=\"data row0 col3\" >0.020000</td>\n",
       "      <td id=\"T_0e668_row0_col4\" class=\"data row0 col4\" >0.950832</td>\n",
       "      <td id=\"T_0e668_row0_col5\" class=\"data row0 col5\" >0.948141</td>\n",
       "      <td id=\"T_0e668_row0_col6\" class=\"data row0 col6\" >0.038278</td>\n",
       "      <td id=\"T_0e668_row0_col7\" class=\"data row0 col7\" >0.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e668_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
       "      <td id=\"T_0e668_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_0e668_row1_col1\" class=\"data row1 col1\" >0.117647</td>\n",
       "      <td id=\"T_0e668_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_0e668_row1_col3\" class=\"data row1 col3\" >0.120000</td>\n",
       "      <td id=\"T_0e668_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_0e668_row1_col5\" class=\"data row1 col5\" >0.912916</td>\n",
       "      <td id=\"T_0e668_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
       "      <td id=\"T_0e668_row1_col7\" class=\"data row1 col7\" >0.118812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e668_level0_row2\" class=\"row_heading level0 row2\" >random_forest</th>\n",
       "      <td id=\"T_0e668_row2_col0\" class=\"data row2 col0\" >0.228869</td>\n",
       "      <td id=\"T_0e668_row2_col1\" class=\"data row2 col1\" >0.135135</td>\n",
       "      <td id=\"T_0e668_row2_col2\" class=\"data row2 col2\" >0.884422</td>\n",
       "      <td id=\"T_0e668_row2_col3\" class=\"data row2 col3\" >0.500000</td>\n",
       "      <td id=\"T_0e668_row2_col4\" class=\"data row2 col4\" >0.849315</td>\n",
       "      <td id=\"T_0e668_row2_col5\" class=\"data row2 col5\" >0.818982</td>\n",
       "      <td id=\"T_0e668_row2_col6\" class=\"data row2 col6\" >0.363636</td>\n",
       "      <td id=\"T_0e668_row2_col7\" class=\"data row2 col7\" >0.212766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bdc4916000>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(\n",
    "    by=\"Accuracy_test\", ascending=False\n",
    ").style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Анализ моделей:\n",
    "##### MLP (многослойный перцептрон):\n",
    "Точность (Accuracy): 95% (обучение и тест).\n",
    "Precision и Recall: крайне низкие (0.40 и 0.02 на обучении, 0.20 и 0.02 на тесте).\n",
    "F1-метрика: практически нулевая (0.038 и 0.037).\n",
    "Вывод: модель хорошо определяет общий класс, но почти не замечает положительные примеры.\n",
    "\n",
    "##### KNN (K-ближайшие соседи):\n",
    "Обучение: идеальные метрики (1.0).\n",
    "Тест: резкое падение (Precision 0.118, Recall 0.12, Accuracy 91%).\n",
    "Вывод: переобучение. Отлично работает на обучении, но плохо обобщает на новых данных.\n",
    "\n",
    "##### Random Forest (случайный лес):\n",
    "Accuracy: 85% (обучение) и 82% (тест).\n",
    "Precision и Recall: умеренные, но низкие на тесте (0.135 и 0.50).\n",
    "F1: лучше других моделей (0.213 на тесте).\n",
    "\n",
    "##### Вывод: баланс метрик лучше, чем у других, но точность распознавания положительных примеров всё еще оставляет желать лучшего.\n",
    "Сравнение с baseline:\n",
    "Baseline (простая модель):\n",
    "Accuracy 52%, Precision 0.058, Recall 0.58, F1 0.106.\n",
    "Победитель по Accuracy: все модели значительно превосходят baseline.\n",
    "Recall: Random Forest лучше baseline, MLP и KNN уступают.\n",
    "F1-метрика: Random Forest снова впереди, но до желаемого уровня ещё далеко.\n",
    "\n",
    "##### Заключение:\n",
    "MLP: сильно смещена, игнорирует положительные примеры.\n",
    "KNN: высокая дисперсия, сильно переобучена.\n",
    "Random Forest: самый сбалансированный вариант, но precision нужно улучшать.\n",
    "Итог: Random Forest – лучший выбор из предложенных, но требует доработки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Регрессия\n",
    "\n",
    "Разделим набор данных на на обучающую и тестовые выборки (80/20). Целевой признак - avg_glucose_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13276</th>\n",
       "      <td>Female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21346</th>\n",
       "      <td>Female</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>17.8</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59178</th>\n",
       "      <td>Female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Urban</td>\n",
       "      <td>22.3</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>NaN</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>26.1</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30463</th>\n",
       "      <td>Male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>29.4</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41935</th>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>33.9</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68483</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>41.2</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38617</th>\n",
       "      <td>Male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>29.9</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46527</th>\n",
       "      <td>Male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>41.9</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4088 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "id                                                                             \n",
       "13276  Female  38.0             0              0          Yes        Private   \n",
       "21346  Female  12.0             0              0           No       children   \n",
       "59178  Female   7.0             0              0           No       children   \n",
       "1679     Male  35.0             0              0          Yes        Private   \n",
       "1534   Female  61.0             0              0          Yes        Private   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "30463    Male  29.0             0              0           No        Private   \n",
       "41935    Male  34.0             0              0           No        Private   \n",
       "68483  Female  60.0             0              0          Yes        Private   \n",
       "38617    Male  28.0             0              0          Yes  Self-employed   \n",
       "46527    Male  53.0             1              1          Yes       Govt_job   \n",
       "\n",
       "      Residence_type   bmi   smoking_status  stroke  \n",
       "id                                                   \n",
       "13276          Urban  22.6          Unknown       0  \n",
       "21346          Rural  17.8          Unknown       0  \n",
       "59178          Urban  22.3          Unknown       0  \n",
       "1679           Rural   NaN  formerly smoked       0  \n",
       "1534           Rural  26.1           smokes       0  \n",
       "...              ...   ...              ...     ...  \n",
       "30463          Urban  29.4  formerly smoked       0  \n",
       "41935          Rural  33.9     never smoked       0  \n",
       "68483          Urban  41.2  formerly smoked       0  \n",
       "38617          Urban  29.9     never smoked       0  \n",
       "46527          Rural  41.9     never smoked       0  \n",
       "\n",
       "[4088 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "13276     71.06\n",
       "21346     70.13\n",
       "59178     86.75\n",
       "1679      77.48\n",
       "1534      99.35\n",
       "          ...  \n",
       "30463     82.93\n",
       "41935    125.29\n",
       "68483     65.38\n",
       "38617     73.98\n",
       "46527    109.51\n",
       "Name: avg_glucose_level, Length: 4088, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'X_test'"
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     "output_type": "display_data"
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       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>26.7</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
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       "      <th>23850</th>\n",
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       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
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       "      <td>33.1</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31156</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>29.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71010</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39518</th>\n",
       "      <td>Female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>20.7</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7780</th>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>30.7</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56137</th>\n",
       "      <td>Female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>36.3</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33175</th>\n",
       "      <td>Female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>28.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1022 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "id                                                                             \n",
       "8385     Male  37.0             0              0          Yes        Private   \n",
       "937      Male   7.0             0              0           No       children   \n",
       "3494   Female  80.0             0              0          Yes        Private   \n",
       "23850    Male  66.0             0              0          Yes        Private   \n",
       "31156  Female  49.0             0              0          Yes        Private   \n",
       "...       ...   ...           ...            ...          ...            ...   \n",
       "71010  Female  80.0             0              0           No  Self-employed   \n",
       "39518  Female  20.0             0              0           No        Private   \n",
       "7780     Male  51.0             0              0          Yes  Self-employed   \n",
       "56137  Female  62.0             0              0          Yes        Private   \n",
       "33175  Female  57.0             0              0          Yes       Govt_job   \n",
       "\n",
       "      Residence_type   bmi smoking_status  stroke  \n",
       "id                                                 \n",
       "8385           Urban  35.9        Unknown       0  \n",
       "937            Urban   NaN        Unknown       0  \n",
       "3494           Rural  26.7        Unknown       0  \n",
       "23850          Urban  33.1   never smoked       0  \n",
       "31156          Urban  29.8   never smoked       0  \n",
       "...              ...   ...            ...     ...  \n",
       "71010          Urban  22.8   never smoked       0  \n",
       "39518          Rural  20.7   never smoked       0  \n",
       "7780           Urban  30.7   never smoked       0  \n",
       "56137          Urban  36.3        Unknown       0  \n",
       "33175          Urban  28.5        Unknown       1  \n",
       "\n",
       "[1022 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "8385      90.78\n",
       "937       87.94\n",
       "3494     102.90\n",
       "23850    103.01\n",
       "31156    105.99\n",
       "          ...  \n",
       "71010     57.57\n",
       "39518     78.94\n",
       "7780      75.73\n",
       "56137     88.32\n",
       "33175    110.52\n",
       "Name: avg_glucose_level, Length: 1022, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'bmi', 'smoking_status', 'stroke']\n",
    "target = 'avg_glucose_level'\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=random_state)\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выберем ориентир для задачи регрессии. Для этого применим алгоритм правила нуля, т.е. в каждом случае в качестве предсказания выберем среднее значение из области значений целевого признака."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline RMSE: 44.12711275645952\n",
      "Baseline RMAE: 5.662154850745081\n",
      "Baseline R2: -0.0010729515309222393\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "# Базовое предсказание: среднее значение по y_train\n",
    "baseline_predictions = [y_train.mean()] * len(y_test)\n",
    "\n",
    "# Вычисление метрик качества для ориентира\n",
    "baseline_rmse = math.sqrt(\n",
    "        mean_squared_error(y_test, baseline_predictions)\n",
    "    )\n",
    "baseline_rmae = math.sqrt(\n",
    "        mean_absolute_error(y_test, baseline_predictions)\n",
    "    )\n",
    "baseline_r2 = r2_score(y_test, baseline_predictions)\n",
    "\n",
    "print('Baseline RMSE:', baseline_rmse)\n",
    "print('Baseline RMAE:', baseline_rmae)\n",
    "print('Baseline R2:', baseline_r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Метрики:\n",
    "##### RMSE: корень из MSE, измеряет среднеквадратическую ошибку. \n",
    "Удобен, так как результат в тех же единицах, что и данные. Штрафует за большие отклонения. Хорош для задач, где важна интерпретируемость.\n",
    "##### RMAE: корень из MAE, измеряет среднюю абсолютную ошибку. \n",
    "Менее чувствителен к выбросам, что полезно для данных с редкими сильными отклонениями.\n",
    "##### R²: коэффициент детерминации, показывает, насколько модель объясняет изменчивость данных. Значение ближе к 1 — модель хорошо описывает данные\n",
    "Используется для сравнения моделей на одинаковых данных.\n",
    "Эти метрики помогают оценить, насколько точна модель по сравнению с простым усреднением."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сформируем конвейер для регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_drop = []\n",
    "columns_not_to_modify = [\"hypertension\", \"heart_disease\", \"stroke\", \"avg_glucose_level\"]\n",
    "\n",
    "num_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if column not in columns_to_drop\n",
    "    and column not in columns_not_to_modify\n",
    "    and df[column].dtype != \"object\"\n",
    "]\n",
    "\n",
    "cat_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if column not in columns_to_drop\n",
    "    and column not in columns_not_to_modify\n",
    "    and df[column].dtype == \"object\"\n",
    "]\n",
    "\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "num_scaler = StandardScaler()\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer),\n",
    "        (\"scaler\", num_scaler),\n",
    "    ]\n",
    ")\n",
    "\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
    "preprocessing_cat = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", cat_imputer),\n",
    "        (\"encoder\", cat_encoder),\n",
    "    ]\n",
    ")\n",
    "\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "drop_columns = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "pipeline_end_reg = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь проверим работу конвейера:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>bmi</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13276</th>\n",
       "      <td>-0.236211</td>\n",
       "      <td>-0.826056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21346</th>\n",
       "      <td>-1.386874</td>\n",
       "      <td>-1.455413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59178</th>\n",
       "      <td>-1.608155</td>\n",
       "      <td>-0.865391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>-0.368980</td>\n",
       "      <td>-0.104918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>0.781682</td>\n",
       "      <td>-0.367150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30463</th>\n",
       "      <td>-0.634518</td>\n",
       "      <td>0.065532</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41935</th>\n",
       "      <td>-0.413236</td>\n",
       "      <td>0.655554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68483</th>\n",
       "      <td>0.737426</td>\n",
       "      <td>1.612701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38617</th>\n",
       "      <td>-0.678774</td>\n",
       "      <td>0.131090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46527</th>\n",
       "      <td>0.427632</td>\n",
       "      <td>1.704482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4088 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age       bmi  gender_Male  gender_Other  ever_married_Yes  \\\n",
       "id                                                                       \n",
       "13276 -0.236211 -0.826056          0.0           0.0               1.0   \n",
       "21346 -1.386874 -1.455413          0.0           0.0               0.0   \n",
       "59178 -1.608155 -0.865391          0.0           0.0               0.0   \n",
       "1679  -0.368980 -0.104918          1.0           0.0               1.0   \n",
       "1534   0.781682 -0.367150          0.0           0.0               1.0   \n",
       "...         ...       ...          ...           ...               ...   \n",
       "30463 -0.634518  0.065532          1.0           0.0               0.0   \n",
       "41935 -0.413236  0.655554          1.0           0.0               0.0   \n",
       "68483  0.737426  1.612701          0.0           0.0               1.0   \n",
       "38617 -0.678774  0.131090          1.0           0.0               1.0   \n",
       "46527  0.427632  1.704482          1.0           0.0               1.0   \n",
       "\n",
       "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "id                                                                          \n",
       "13276                     0.0                1.0                      0.0   \n",
       "21346                     0.0                0.0                      0.0   \n",
       "59178                     0.0                0.0                      0.0   \n",
       "1679                      0.0                1.0                      0.0   \n",
       "1534                      0.0                1.0                      0.0   \n",
       "...                       ...                ...                      ...   \n",
       "30463                     0.0                1.0                      0.0   \n",
       "41935                     0.0                1.0                      0.0   \n",
       "68483                     0.0                1.0                      0.0   \n",
       "38617                     0.0                0.0                      1.0   \n",
       "46527                     0.0                0.0                      0.0   \n",
       "\n",
       "       work_type_children  Residence_type_Urban  \\\n",
       "id                                                \n",
       "13276                 0.0                   1.0   \n",
       "21346                 1.0                   0.0   \n",
       "59178                 1.0                   1.0   \n",
       "1679                  0.0                   0.0   \n",
       "1534                  0.0                   0.0   \n",
       "...                   ...                   ...   \n",
       "30463                 0.0                   1.0   \n",
       "41935                 0.0                   0.0   \n",
       "68483                 0.0                   1.0   \n",
       "38617                 0.0                   1.0   \n",
       "46527                 0.0                   0.0   \n",
       "\n",
       "       smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "id                                                                   \n",
       "13276                             0.0                          0.0   \n",
       "21346                             0.0                          0.0   \n",
       "59178                             0.0                          0.0   \n",
       "1679                              1.0                          0.0   \n",
       "1534                              0.0                          0.0   \n",
       "...                               ...                          ...   \n",
       "30463                             1.0                          0.0   \n",
       "41935                             0.0                          1.0   \n",
       "68483                             1.0                          0.0   \n",
       "38617                             0.0                          1.0   \n",
       "46527                             0.0                          1.0   \n",
       "\n",
       "       smoking_status_smokes  hypertension  heart_disease  stroke  \n",
       "id                                                                 \n",
       "13276                    0.0             0              0       0  \n",
       "21346                    0.0             0              0       0  \n",
       "59178                    0.0             0              0       0  \n",
       "1679                     0.0             0              0       0  \n",
       "1534                     1.0             0              0       0  \n",
       "...                      ...           ...            ...     ...  \n",
       "30463                    0.0             0              0       0  \n",
       "41935                    0.0             0              0       0  \n",
       "68483                    0.0             0              0       0  \n",
       "38617                    0.0             0              0       0  \n",
       "46527                    0.0             1              1       0  \n",
       "\n",
       "[4088 rows x 16 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end_reg.fit_transform(X_train)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end_reg.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "preprocessed_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подберем оптимальные гиперпараметры для каждой из выбранных моделей методом поиска по сетке и сформируем их набор.\n",
    "\n",
    "knn -- k-ближайших соседей\n",
    "\n",
    "random_forest -- метод случайного леса (набор деревьев решений)\n",
    "\n",
    "mlp -- многослойный персептрон (нейронная сеть)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\mai\\labs\\AIM-PIbd-31-Bakalskaya-E-D\\lab_4\\venv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие параметры для knn: {'n_jobs': -1, 'n_neighbors': 30, 'weights': 'uniform'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\mai\\labs\\AIM-PIbd-31-Bakalskaya-E-D\\lab_4\\venv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие параметры для random_forest: {'criterion': 'squared_error', 'max_depth': 7, 'max_features': 'sqrt', 'n_estimators': 250, 'n_jobs': -1, 'random_state': 9}\n"
     ]
    }
   ],
   "source": [
    "# Словарь с вариантами гиперпараметров для каждой модели\n",
    "param_grids = {\n",
    "    \"knn\": {\n",
    "        \"n_neighbors\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], \n",
    "        \"weights\": ['uniform', 'distance'],\n",
    "        \"n_jobs\": [-1]\n",
    "    },\n",
    "    \"random_forest\": {\n",
    "        \"n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
    "        \"max_features\": [\"sqrt\", \"log2\", 2],\n",
    "        \"max_depth\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "        \"criterion\": [\"squared_error\", \"absolute_error\", \"poisson\"],\n",
    "        \"random_state\": [random_state],\n",
    "        \"n_jobs\": [-1]\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"solver\": ['adam'], \n",
    "        \"max_iter\": [1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000], \n",
    "        \"alpha\": 10.0 ** -np.arange(1, 10), \n",
    "        \"hidden_layer_sizes\":np.arange(10, 15), \n",
    "        \"early_stopping\": [True, False],\n",
    "        \"random_state\": [random_state]\n",
    "    }\n",
    "}\n",
    "\n",
    "# Создаем экземпляры моделей\n",
    "models = {\n",
    "    \"knn\": neighbors.KNeighborsRegressor(),\n",
    "    \"random_forest\": ensemble.RandomForestRegressor(),\n",
    "    \"mlp\": neural_network.MLPRegressor()\n",
    "}\n",
    "\n",
    "# Словарь для хранения моделей с их лучшими параметрами\n",
    "class_models = {}\n",
    "\n",
    "# Выполнение поиска по сетке для каждой модели\n",
    "for model_name, model in models.items():\n",
    "    # Создаем GridSearchCV для текущей модели\n",
    "    gs_optimizer = GridSearchCV(estimator=model, param_grid=param_grids[model_name], scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "    \n",
    "    # Обучаем GridSearchCV\n",
    "    gs_optimizer.fit(preprocessed_df, y_train.values.ravel())\n",
    "    \n",
    "    # Получаем лучшие параметры\n",
    "    best_params = gs_optimizer.best_params_\n",
    "    print(f\"Лучшие параметры для {model_name}: {best_params}\")\n",
    "    \n",
    "    class_models[model_name] = {\n",
    "        \"model\": model.set_params(**best_params)  # Настраиваем модель с лучшими параметрами\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее обучим модели и оценим их качество."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    \n",
    "    model = class_models[model_name][\"model\"]\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end_reg), (\"model\", model)])\n",
    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "    y_train_pred = model_pipeline.predict(X_train)\n",
    "    y_test_pred = model_pipeline.predict(X_test)\n",
    "\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"train_preds\"] = y_train_pred\n",
    "    class_models[model_name][\"preds\"] = y_test_pred\n",
    "    \n",
    "    class_models[model_name][\"RMSE_train\"] = math.sqrt(\n",
    "        mean_squared_error(y_train, y_train_pred)\n",
    "    )\n",
    "    class_models[model_name][\"RMSE_test\"] = math.sqrt(\n",
    "        mean_squared_error(y_test, y_test_pred)\n",
    "    )\n",
    "    class_models[model_name][\"RMAE_test\"] = math.sqrt(\n",
    "        mean_absolute_error(y_test, y_test_pred)\n",
    "    )\n",
    "    class_models[model_name][\"R2_test\"] = r2_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE, RMAE, R2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\"RMSE_train\", \"RMSE_test\", \"RMAE_test\", \"R2_test\"]\n",
    "]\n",
    "reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n",
    "    cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n",
    ").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"RMAE_test\", \"R2_test\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результаты графиками:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создаем графики для всех моделей\n",
    "for model_name, model_data in class_models.items():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    y_pred = model_data[\"preds\"]\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "    plt.xlabel('Фактический уровень глюкозы')\n",
    "    plt.ylabel('Прогнозируемый уровень глюкозы')\n",
    "    plt.title(f\"Model: {model_name}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На представленных графиках можно заметить, что модели в целом не демонстрируют высокого качества. Визуализация их предсказаний показывает сильное рассеивание вокруг идеальной линии y = x, что указывает на значительные отклонения предсказаний от фактических значений.\n",
    "\n",
    "Тем не менее ориентир, хоть возможно и не столь значительно, каждая из моделей превосходит по всем показателям. Особенно заметное улучшение в \n",
    "R2, которая переходит из отрицательного значения в положительное, что говорит о том, что модели хотя бы частично объясняют дисперсию данных. \n",
    "\n",
    "Кроме того, можно сказать, что все модели имеет умеренную дисперсию и не сильно подвержены переобучению, потому что разница между RMSE на обучении и тесте незначительна.\n",
    "\n",
    "Итоговые выводы:\n",
    "- Наиболее качественная модель: MLP, так как она показывает наименьшее значение RMSE и наибольшее значение R2, что указывает на лучшую точность и объяснение дисперсии целевой переменной.\n",
    "\n",
    "- Random Forest: Близок по производительности к MLP, с чуть большим RMSE, но является более устойчивой моделью с небольшими отклонениями между обучением и тестом.\n",
    "\n",
    "- KNN: Худшая модель, демонстрирующая наибольшие ошибки и низкое R2, что указывает на необходимость улучшения или использования другой модели для данной задачи."
   ]
  }
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