{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Лабораторная 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Информация о диабете индейцев Пима"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    },
    {
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       "      <th>Pregnancies</th>\n",
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       "      <td>33.6</td>\n",
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       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
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       "      <td>27</td>\n",
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       "      <td>36.8</td>\n",
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       "      <td>0.315</td>\n",
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       "<p>768 rows × 9 columns</p>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import set_config\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "df = pd.read_csv(\".//scv//diabetes.csv\")\n",
    "print(df.columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формирование выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "196            1      105             58              0        0  24.3   \n",
       "69             4      146             85             27      100  28.9   \n",
       "494            3       80              0              0        0   0.0   \n",
       "463            5       88             78             30        0  27.6   \n",
       "653            2      120             54              0        0  26.8   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "322            0      124             70             20        0  27.4   \n",
       "109            0       95             85             25       36  37.4   \n",
       "27             1       97             66             15      140  23.2   \n",
       "651            1      117             60             23      106  33.8   \n",
       "197            3      107             62             13       48  22.9   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "196                     0.187   21        0  \n",
       "69                      0.189   27        0  \n",
       "494                     0.174   22        0  \n",
       "463                     0.258   37        0  \n",
       "653                     0.455   27        0  \n",
       "..                        ...  ...      ...  \n",
       "322                     0.254   36        1  \n",
       "109                     0.247   24        1  \n",
       "27                      0.487   22        0  \n",
       "651                     0.466   27        0  \n",
       "197                     0.678   23        1  \n",
       "\n",
       "[614 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "     Outcome\n",
       "196        0\n",
       "69         0\n",
       "494        0\n",
       "463        0\n",
       "653        0\n",
       "..       ...\n",
       "322        1\n",
       "109        1\n",
       "27         0\n",
       "651        0\n",
       "197        1\n",
       "\n",
       "[614 rows x 1 columns]"
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    {
     "data": {
      "text/plain": [
       "'X_test'"
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       "      <td>17</td>\n",
       "      <td>105</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.695</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>11</td>\n",
       "      <td>127</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.207</td>\n",
       "      <td>37</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.235</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>35.8</td>\n",
       "      <td>0.514</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "      <td>66</td>\n",
       "      <td>32</td>\n",
       "      <td>274</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.502</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.334</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.283</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "669            9      154             78             30      100  30.9   \n",
       "379            0       93            100             39       72  43.4   \n",
       "640            0      102             86             17      105  29.3   \n",
       "658           11      127            106              0        0  39.0   \n",
       "304            3      150             76              0        0  21.0   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "203            2       99             70             16       44  20.4   \n",
       "605            1      124             60             32        0  35.8   \n",
       "561            0      198             66             32      274  41.3   \n",
       "280            0      146             70              0        0  37.9   \n",
       "103            1       81             72             18       40  26.6   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "669                     0.164   45        0  \n",
       "379                     1.021   35        0  \n",
       "640                     0.695   27        0  \n",
       "658                     0.190   51        0  \n",
       "304                     0.207   37        0  \n",
       "..                        ...  ...      ...  \n",
       "203                     0.235   27        0  \n",
       "605                     0.514   21        0  \n",
       "561                     0.502   28        1  \n",
       "280                     0.334   28        1  \n",
       "103                     0.283   24        0  \n",
       "\n",
       "[154 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Outcome\n",
       "669        0\n",
       "379        0\n",
       "640        0\n",
       "658        0\n",
       "304        0\n",
       "..       ...\n",
       "203        0\n",
       "605        0\n",
       "561        1\n",
       "280        1\n",
       "103        0\n",
       "\n",
       "[154 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from typing import Tuple\n",
    "import pandas as pd\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",
    "    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",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\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",
    "    # 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",
    "    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",
    "    # 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",
    "    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\n",
    "\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"Outcome\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=9\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": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пропущенные значения по столбцам:\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n",
      "\n",
      "Статистический обзор данных:\n",
      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
      "\n",
      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
      "count  768.000000                768.000000  768.000000  768.000000  \n",
      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
      "std      7.884160                  0.331329   11.760232    0.476951  \n",
      "min      0.000000                  0.078000   21.000000    0.000000  \n",
      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
      "max     67.100000                  2.420000   81.000000    1.000000  \n"
     ]
    }
   ],
   "source": [
    "null_values = df.isnull().sum()\n",
    "print(\"Пропущенные значения по столбцам:\")\n",
    "print(null_values)\n",
    "\n",
    "stat_summary = df.describe()\n",
    "print(\"\\nСтатистический обзор данных:\")\n",
    "print(stat_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы в датасете:\n",
      "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "4              0      137             40             35      168  43.1   \n",
      "12            10      139             80              0        0  27.1   \n",
      "39             4      111             72             47      207  37.1   \n",
      "45             0      180             66             39        0  42.0   \n",
      "58             0      146             82              0        0  40.5   \n",
      "100            1      163             72              0        0  39.0   \n",
      "147            2      106             64             35      119  30.5   \n",
      "187            1      128             98             41       58  32.0   \n",
      "218            5       85             74             22        0  29.0   \n",
      "228            4      197             70             39      744  36.7   \n",
      "243            6      119             50             22      176  27.1   \n",
      "245            9      184             85             15        0  30.0   \n",
      "259           11      155             76             28      150  33.3   \n",
      "292            2      128             78             37      182  43.3   \n",
      "308            0      128             68             19      180  30.5   \n",
      "330            8      118             72             19        0  23.1   \n",
      "370            3      173             82             48      465  38.4   \n",
      "371            0      118             64             23       89   0.0   \n",
      "383            1       90             62             18       59  25.1   \n",
      "395            2      127             58             24      275  27.7   \n",
      "445            0      180             78             63       14  59.4   \n",
      "534            1       77             56             30       56  33.3   \n",
      "593            2       82             52             22      115  28.5   \n",
      "606            1      181             78             42      293  40.0   \n",
      "618            9      112             82             24        0  28.2   \n",
      "621            2       92             76             20        0  24.2   \n",
      "622            6      183             94              0        0  40.8   \n",
      "659            3       80             82             31       70  34.2   \n",
      "661            1      199             76             43        0  42.9   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  Outcome  \n",
      "4                       2.288   33        1  \n",
      "12                      1.441   57        0  \n",
      "39                      1.390   56        1  \n",
      "45                      1.893   25        1  \n",
      "58                      1.781   44        0  \n",
      "100                     1.222   33        1  \n",
      "147                     1.400   34        0  \n",
      "187                     1.321   33        1  \n",
      "218                     1.224   32        1  \n",
      "228                     2.329   31        0  \n",
      "243                     1.318   33        1  \n",
      "245                     1.213   49        1  \n",
      "259                     1.353   51        1  \n",
      "292                     1.224   31        1  \n",
      "308                     1.391   25        1  \n",
      "330                     1.476   46        0  \n",
      "370                     2.137   25        1  \n",
      "371                     1.731   21        0  \n",
      "383                     1.268   25        0  \n",
      "395                     1.600   25        0  \n",
      "445                     2.420   25        1  \n",
      "534                     1.251   24        0  \n",
      "593                     1.699   25        0  \n",
      "606                     1.258   22        1  \n",
      "618                     1.282   50        1  \n",
      "621                     1.698   28        0  \n",
      "622                     1.461   45        0  \n",
      "659                     1.292   27        1  \n",
      "661                     1.394   22        1  \n"
     ]
    },
    {
     "data": {
      "image/png": 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0tOCNN96IOD0s+tSofv36AQA2btwou4iJfs2YMWNkX9O3b18AQEpKiuJroqnNl6D09PSIMidMmID8/Hz8/e9/xxNPPKG7T7OysjrVccOGDYr1/Mtf/oKuXbuGAirkHH/88Zg/fz5GjhyJO++8E8OGDcMDDzyADz/8UPE9ehUXFyMQCGDr1q0Rpxru3LkTdXV1KC4uBqBtTIM2bdoEn8+Ho446SvE1/fr1wzvvvIMRI0ZEnNaoVffu3VFeXo5NmzaFFmWfffYZbr75ZtnXP/bYY9i1axfuuOMOxfoEAgFs2rSpU+iKUVdeeSX++Mc/4ocffkD//v0xbdq00Om24SRJwvTp03HuueeGThMmInfi6XZEJNzixYvR0tKC008/Xdf7Zs+ejYKCAlx33XWyzyclJQHo+KIR7pFHHjFUT6AjceyJJ54I/b21tRVPPPEEevTogaFDhypud+3atar/EnzllVfif//7H371q19pinLOz8/HL3/5S/zvf/8LJetFk6tLa2srHnvsMdXyw82ZMyfi73/7298AAGeeeabidurr60OnEAb9/Oc/R9euXVFWVtYphS/43iFDhqCkpASPPPJI6PSs6Nf07NkTp556Kp544olQbHK4H3/8sdNjavNFSWtrKw4cOBA6nU1Un8rZtm0b5s6dizvuuCPmAqGhoQGXXnopJkyYgD//+c8YM2ZMzGQ0I8466ywAnfeXv/71rwCAs88+G4C2MQU69p1//vOfOOmkk2LO74kTJ6K9vR133XVXp+cOHDjQaU7I8fv9GDhwIMaMGYMxY8aE9s1oe/fuxd13341p06Yp/rp1zjnnwO/348477+z0K2H0sUWrYLJfr169cN9992HRokWhOPFwS5Yswf/+9z+UlZUZ2g4R2Ye/JBGRMI2Njfjb3/6GO++8E0lJSZAkKXSaU9DOnTuxb98+LFq0CGeccUbEdSRvvfUWnnvuuVBIQrTs7GyMGjUK999/P9ra2nDIIYfgrbfe0v0rSrjgl5pt27bhyCOPxAsvvIANGzZg3rx5oVjiX/ziF3jllVdw7rnn4uyzz0ZlZSUef/xx9O/fPxQTLGfcuHH48ccfNS2QghYuXIg5c+YonqozfPhw5OXlYdKkSfjDH/4An8+HZ599VveXu8rKSkyYMAHjxo1DeXk5Fi1ahIsuugiDBg0C0PFFOTU1FePHj8fkyZOxb98+zJ8/Hz179oxYxGRnZ+Phhx/G1VdfjRNPPBEXXXQR8vLy8Nlnn6GpqQnPPPMM/H4/5s6di/Hjx2Pw4MG44oorUFRUhC1btuCLL77Af/7zHwAdC7dTTjkFxx57LK655hr07dsXO3fuRHl5Ob777jt89tlnEW1Qmy9BjY2NEafbPfvss2hubg5FiovqUznvvfcejjnmGMXry4KmTp2K/fv348knnzS9TSWDBg3CpEmTMG/evNAphh9//DGeeeYZnHPOOTjttNMAaBvTd955BzNnzsT//vc/LF26NOZ2R48ejcmTJ6OsrAwbNmzAz3/+c6SkpGDr1q146aWX8Oijj+KCCy4Q0sb169eje/fuuPXWWxVfc/jhh+NPf/oT7rrrLowcORLnnXce0tLS8Mknn6BXr16mFzDXXnstFi9ejOuuuw4bN25EZmZm6Lm33noL11xzTcxf3ojIJewP1COieBWMudX6JxipG4x0Hjx4cEQEr1xs7nfffSede+65Um5urpSTkyP96le/kn744QcJgDRr1qzQ67RGgA8YMED673//K5WWlkrp6elScXGx9Pe//z3idYFAQLrnnnuk4uJiKS0tTTr++OOlN998s1O8dLC+4RHfcv0jFwGuVE+55z/88ENp2LBhUkZGhtSrVy/p1ltvlf7zn/8oxhTLlbdp0ybpggsukLp27Srl5eVJv/vd7zrFPb/xxhvScccdJ6Wnp0t9+vSR7rvvPunpp5/uFHUdfO3w4cOljIwMKTs7WzrppJOk559/PuI1H3zwgXTGGWdIXbt2lbKysqTjjjtO+tvf/hbxmoqKCumyyy6TCgsLpZSUFOmQQw6RfvGLX0gvv/xy6DV65sukSZMi5lyXLl2kIUOGSM8++2zEdrX2qd4IcADSq6++GvHa6Hnz/PPPSz6fT1q+fHmn14mMAJckSWpra5Nmz54tlZSUSCkpKVLv3r2lGTNmyEbHxxrT3//+99KoUaM61VmSOkeAB82bN08aOnSolJGRIXXt2lU69thjpVtvvVX64YcfVNsYTSkCHID08MMPa6rP008/LR1//PFSWlqalJeXJ40ePVp6++23NW0rSG7OSZIkffnll1J6ero0bdq0iNdlZGRI33//fcRro6PiicgdfJIk4J/KiIjQcWpRSUkJVq1ahVNPPdX066x26qmnYvfu3di4caNjdbDbHXfcgdmzZ+PHH39U/LWKiIgo0fGaJCIiIiIiojBcJBGRMF26dMHFF1+seL8ava8jIiIicgKDG4hImO7du3cKajDzOiIiIiIn8JokIiIiIiKiMDzdjoiIiIiIKAwXSURERERERGHi/pqkQCCAH374AV27doXP53O6OkRERERE5BBJkrB371706tULfr/y70Vxv0j64Ycf0Lt3b6erQURERERELrFjxw4ceuihis/H/SKpa9euADo6Ijs72+HaEBERERGRUxoaGtC7d+/QGkFJ3C+SgqfYZWdnc5FERERERESql+EwuIGIiIiIiCgMF0lERERERERhuEgiIiIiIiIKw0USERERERFRGC6SiIiIiIiIwnCRREREREREFIaLJCIiIiIiojBcJBEREREREYXhIomIiIiIiCgMF0lERERERERhuEgiIiIiIiIKw0USERERERFRGC6SiIiIiIiIwiQ7XQEiIq9oD0j4uLIWu/Y2o2fXdJxUko8kv8/pahEREZFgXCQREWmwfGMVZi/dhKr65tBjRTnpmDW+P8YNLHKwZkRERCQaT7cjIlKxfGMVpixaH7FAAoDq+mZMWbQeyzdWOVQzIiIisgIXSUREMbQHJMxeugmSzHPBx2Yv3YT2gNwriIiIyIu4SCIiiuHjytpOvyCFkwBU1Tfj48pa+ypFREREluIiiYgohl17lRdIRl5HRERE7sdFEhFRDD27pgt9HREREbkfF0lERDGcVJKPopx0KAV9+9CRcndSSb6d1SIiIiILObpIam9vx8yZM1FSUoKMjAz069cPd911FyTppwugJUnC7bffjqKiImRkZGDMmDHYunWrg7UmokSS5Pdh1vj+ANBpoRT8+6zx/Xm/JCIiojji6CLpvvvuw9y5c/H3v/8dmzdvxn333Yf7778ff/vb30Kvuf/++/F///d/ePzxx7F27VpkZWVh7NixaG7m+f9EZI9xA4sw95IhKMyJPKWuMCcdcy8ZwvskERERxRmfFP6zjc1+8YtfoKCgAE899VTosfPPPx8ZGRlYtGgRJElCr169cNNNN+Hmm28GANTX16OgoAALFy7Eb37zG9VtNDQ0ICcnB/X19cjOzrasLUQU/9oDEj6urMWuvc3o2bXjFDv+gkREROQdWtcGjv6SNHz4cKxYsQJfffUVAOCzzz7DBx98gDPPPBMAUFlZierqaowZMyb0npycHJx88skoLy+XLbOlpQUNDQ0Rf4iIREjy+1Darxt+OfgQlPbrxgUSERFRnEp2cuPTp09HQ0MDjj76aCQlJaG9vR133303Lr74YgBAdXU1AKCgoCDifQUFBaHnopWVlWH27NnWVpyIiIiIiOKWo78kvfjii3juueewePFirF+/Hs888wwefPBBPPPMM4bLnDFjBurr60N/duzYIbDGREREREQU7xz9JemWW27B9OnTQ9cWHXvssdi+fTvKysowadIkFBYWAgB27tyJoqKfLozeuXMnBg8eLFtmWloa0tLSLK87ERERERHFJ0d/SWpqaoLfH1mFpKQkBAIBAEBJSQkKCwuxYsWK0PMNDQ1Yu3YtSktLba0rERERERElBkd/SRo/fjzuvvtuHHbYYRgwYAA+/fRT/PWvf8WVV14JAPD5fLjhhhvwl7/8BUcccQRKSkowc+ZM9OrVC+ecc46TVSciIiIiojjl6CLpb3/7G2bOnInf/va32LVrF3r16oXJkyfj9ttvD73m1ltvRWNjI6699lrU1dXhlFNOwfLly5Genh6jZCIiIiIiImMcvU+SHXifJCIiIiIiAjxynyQiIiIiIiK34SKJiIiIiIgoDBdJREREREREYbhIIiIiIiIiCsNFEhERERERURgukoiIiIiIiMJwkURERERERBSGiyQiIiIiIqIwXCQRERERERGF4SKJiIiIiIgoDBdJREREREREYbhIIiIiIiIiCsNFEhERERERURgukoiIiIiIiMJwkURERERERBSGiyQiIiIiIqIwXCQRERERERGF4SKJiIiIiIgoDBdJREREREREYbhIIiIiIiIiCsNFEhERERERURgukoiIiIiIiMJwkURERERERBSGiyQiIiIiIqIwXCQRERERERGF4SKJiIiIiIgoDBdJREREREREYbhIIiIiIiIiCsNFEhERERERURgukoiIiIiIiMJwkURERERERBSGiyQiIiIiIqIwXCQRERERERGF4SKJiIiIiIgoDBdJREREREREYbhIIiIiIiIiCsNFEhERERERURgukoiIiIiIiMJwkURERERERBSGiyQiIiIiIqIwXCQRERERERGF4SKJiIiIiIgoDBdJREREREREYbhIIiIiIiIiCpPsdAWIiIiIRGoPSPi4sha79jajZ9d0nFSSjyS/z+lqEZGHcJFEREREcWP5xirMXroJVfXNoceKctIxa3x/jBtY5GDNiMhLeLodERERxYXlG6swZdH6iAUSAFTXN2PKovVYvrHKoZoRkddwkURERESe1x6QMHvpJkgyzwUfm710E9oDcq8gIorERRIRERF53seVtZ1+QQonAaiqb8bHlbX2VYqIPIuLJCIiIvK8XXuVF0hGXkdEiY2LJCIiIvK8nl3Thb6OiBIbF0lERETkeSeV5KMoJx1KQd8+dKTcnVSSb2e1iMijuEgiIiIiz0vy+zBrfH8A6LRQCv591vj+vF8SEWnCRRIRERHFhXEDizD3kiEozIk8pa4wJx1zLxnC+yQRkWa8mSwRERHFjXEDi3BG/0J8XFmLXXub0bNrxyl2/AWJiPTgIomIiIjiSpLfh9J+3ZyuBhF5GE+3IyIiIiIiCsNFEhERERERURiebkcJqT0g8Xx1IiIiIpLl6C9Jffr0gc/n6/Rn6tSpAIDm5mZMnToV3bp1Q5cuXXD++edj586dTlaZ4sDyjVU45b6VuHD+Gly/ZAMunL8Gp9y3Ess3VjldNSIiIiJyAUcXSZ988gmqqqpCf95++20AwK9+9SsAwLRp07B06VK89NJLeO+99/DDDz/gvPPOc7LK5HHLN1ZhyqL1qKpvjni8ur4ZUxat50KJiIiIiOCTJElyuhJBN9xwA958801s3boVDQ0N6NGjBxYvXowLLrgAALBlyxYcc8wxKC8vx7BhwzSV2dDQgJycHNTX1yM7O9vK6pPLtQcknHLfyk4LpCAfOu6l8cFtP+Opd0RERERxSOvawDXBDa2trVi0aBGuvPJK+Hw+rFu3Dm1tbRgzZkzoNUcffTQOO+wwlJeXK5bT0tKChoaGiD9EAPBxZa3iAgkAJABV9c34uLLWvkoRERERkeu4ZpH02muvoa6uDpdffjkAoLq6GqmpqcjNzY14XUFBAaqrqxXLKSsrQ05OTuhP7969Law1ecmuvcoLJCOvIyIiIqL45JpF0lNPPYUzzzwTvXr1MlXOjBkzUF9fH/qzY8cOQTUkr+vZNV3o64iIiIgoPrkiAnz79u1455138Morr4QeKywsRGtrK+rq6iJ+Tdq5cycKCwsVy0pLS0NaWpqV1SUBnIjgPqkkH0U56aiub4bchXjBa5JOKsm3tB5E5CzeAoCIiNS4YpG0YMEC9OzZE2effXbosaFDhyIlJQUrVqzA+eefDwD48ssv8e2336K0tNSpqpIAyzdWYfbSTRHXBxXlpGPW+P4YN7DIsu0m+X2YNb4/pixaDx8QsVAKfj2aNb4/vywRxTGnjj9EROQtjp9uFwgEsGDBAkyaNAnJyT+t2XJycnDVVVfhxhtvxKpVq7Bu3TpcccUVKC0t1ZxsR+7jdAT3uIFFmHvJEBTmRJ5SV5iTjrmXDOGXJKI45vTxh4iIvMPxX5LeeecdfPvtt7jyyis7Pffwww/D7/fj/PPPR0tLC8aOHYvHHnvMgVqSCO0BCbOXbpI91U1Cx685s5duwhn9Cy39NWfcwCKc0b+Qp9sQJRC3HH+IiMgbXHWfJCvwPknuUV5Rgwvnr1F93fPXDENpv2421IiIEgWPP0REBHjwPkkU/xjBTURO4fGHiIj04CKJbMMIbiJyCo8/RESkBxdJZJtgBLfS2f4+dKRMMYKbKD61BySUV9Tg9Q3fo7yiBu0B+8725vGHiIj0cDy4gRIHI7iJEpfT0ds8/hARkR78JYlsxQhuosTjluhtHn+IiEgrptuRI3jHe6LE0B6QcMp9KzstkIJ86FikfHDbz2w7BvD4Q0SUuLSuDXi6HTkiye9jzC5RAvi4slZxgQR0nPZWVd+MjytrbTsm8PhDRERqeLodERFZhtHbRETkRVwkERGRZRi9TUREXsTT7YiIPE70NTYiywtGb1fXN0PuAtjgNUmM3rYOr8EiItKPiyQiIg8THa0tujxGbzvL6eh1IiKv4ul2REQeJTpa26qobkZvO8Mt0etERF7ECHAiIg8SHa1tR1Q3T/uyjxuj14mI3EDr2oC/JBEReZCeaG0nypMTjN7+5eBDUNqvG7+cW8iO8SQiimdcJBEReZDoaG1GdccXjicRkTlcJBEReZDoaG1GdccXjicRkTlMtyOK4rY4ZV7HQXJER2szqju+eHk8ecwjIjfgIokojNvilBnfS0pER2szqju+eHU8ecwjIrdguh3RQcG43OgdIvgVQm9UsdnyRNeH4pPbFvbkLl4aTx7ziMgOWtcGXCQRwX1xyozvJT3cdooouYsXxpPHPCKyi9a1AU+3I4K+uNzSft0sL090fSi+BaO13VoeOcsL48ljHhG5DdPtiOC+OGXG9xJRIuExj4jchoskIrgvTpnxvUSUSHjMIyK34SKJ4kZ7QEJ5RQ1e3/A9yitq0B7QfrldMC5X6Ux3H4BuWamort+vqWwt5RXFiN81+34iIi/hMY+I3IaLJIoLyzdW4ZT7VuLC+Wtw/ZINuHD+Gpxy30os31il6f3BuFwAsh/SEoCaxlZMe/EzTWXHKk9L/G74++VIACYMKuIFzEQUF8weM4mIROMiiTwvGBsbfdFvdX0zpixar3mhNG5gEeZeMgSFOeqnc2gpW6m8wpx0TVG24wYW4dpRJYrPz1tdqbltRERuZ/aYSUQkEiPAydOsiI0NxuVWNzTjrje/QG1jm6myjcbvMhKXiBKRFyLLici7GAFOCcGK2NhgXG55RY3iAklP2UbjdxmJS0SJyAuR5UQU/3i6HXmalbGxTkfSOr19IiIiokTFRRJ5mpWxsU5H0jq9fSIiIqJExUUSeZqVsbFOR9I6vX03MhPzTkRERKQVF0nkaVbGxjodSev09t3GbMw7ERERkVZcJJHnWRkb63QkrdPbdwtRMe9EREREWjACnOKGlbGxTkfSOr19JzEKnYiIiERhBDglHCtjY52OpHV6+05iFDoRERHZjafbEZGrMQqdiIiI7MZFEhG5GqPQiYiIyG483Y6EE3X9jFw5ABL22pxEFYxCr65vhtwFlMFrkgIBCa9v+J7zgoiIiEzjIomEWr6xCrOXboq4hqQoJx2zxvfXlcQmV05uZgoAoK6pzVTZ5C3BKPQpi9bDB0QslIJ/39/WjoufWht6nPOCiIiIzODpdiSMqJhmpXLqmtoiFkhGyiZvUopCl1s4A5wXREREZA4jwEkIUTHNauWYKZu8L/wUzO5d0nDTixtQ3dAi+1rOCyIiIoqmdW3AX5JICD0xzWbKMVM2eV8wCv2Xgw+B3+dTXCABnBdERERkHBdJJISomGYzMc6MgE4sjAYnIiIiq3CRREKIimk2E+PMCOjEwmhwIiIisgrT7UgIrTHNwRhvo+XI0Vq2KKIizskcUXOOxOL+ocwtfeOWehARuRkXSSSEWkwzAMwa31/1gzhWOXL0lC2CqIhzMk/UnCNxuH8oc0vfuKUeRERux3Q7Eiqe75MUjCaP3mGCX8HnXjKEXzIcwC997sD9Q5lb+sYt9SAicpLWtQEXSSScqFM55MoB4MhpIqIizskaPH3IWdw/lLmlb9xSDyIip2ldG/B0OxIuGNNsVTkiytZLT8S5E/VLdKLmHBnD/UOZW/rGLfUgIvIKptsRacC4aSJl3D+UuaVv3FIPIiKv4CKJSAPGTRMp4/6hzC1945Z6EBF5BRdJRBoE46aVztT3oSMsgHHT5AXtAQnlFTV4fcP3KK+oQXvA3KWp3D+UuaVv3FIPIiKv4CKJSINg3DSATl8yGDdNXrJ8YxVOuW8lLpy/Btcv2YAL56/BKfetxPKNVYbL5P6hLNg3SstQCfb0DceIiEgfLpKINBo3sAhzLxmCwpzI01EKc9IZnUueEIyAjr6Av7q+GVMWrTe1UOL+4X4cIyIi7RgBTqQT46bJi+yKgOb+EcmN0dscIyJKZIwAJ7II46bJi+yKgOb+EcmN0dscIyIidTzdjogoATAC2hnsdyIib+IiiYgoATAC2hnsdyIib+LpdmQaz29Xxr4htwhGQFfXNysmreVmpiAQkNAekITNUyv2AS/tV2r9HrwmidHbRETu4nhww/fff4/bbrsN//73v9HU1ITDDz8cCxYswAknnAAAkCQJs2bNwvz581FXV4cRI0Zg7ty5OOKIIzSVz+AGay3fWIXZSzdFnHNflJOOWeP7J3xSEvuG3CaYbgdAcaEEiJunVuwDXtyvlPo9uKxjshwRkX20rg0cPd1uz549GDFiBFJSUvDvf/8bmzZtwkMPPYS8vLzQa+6//3783//9Hx5//HGsXbsWWVlZGDt2LJqbef6206yME/Y69g25kVIEdDQR89SKfcCr+xWjt4mIvMfRX5KmT5+ODz/8EO+//77s85IkoVevXrjppptw8803AwDq6+tRUFCAhQsX4je/+Y3qNvhLkjXcGGvrFuwbcrv2gIQ139Rg6nPrUbe/TfY1ZuapFftAPOxXXjpNkIgoXnnil6Q33ngDJ5xwAn71q1+hZ8+eOP744zF//vzQ85WVlaiursaYMWNCj+Xk5ODkk09GeXm5bJktLS1oaGiI+EPi6Ym1TTTsG3K7JL8Pfp9PcYEEmJunVuwD8bBfBaO3fzn4EJT268YFEhGRizm6SPrmm29C1xf95z//wZQpU/CHP/wBzzzzDACguroaAFBQUBDxvoKCgtBz0crKypCTkxP607t3b2sbkaAYa6uMfUNeYOU8taJs7ldERGQnRxdJgUAAQ4YMwT333IPjjz8e1157La655ho8/vjjhsucMWMG6uvrQ3927NghsMYUxFhbZewb8gIr56kVZXO/IiIiOzm6SCoqKkL//v0jHjvmmGPw7bffAgAKCwsBADt37ox4zc6dO0PPRUtLS0N2dnbEH+o4F768ogavb/ge5RU1aA+YuxQtGGurdLKIDx2JU26KtRXdB0q82DduYtc4JTor56kVZXO/IiIiOzl6n6QRI0bgyy+/jHjsq6++QnFxMQCgpKQEhYWFWLFiBQYPHgyg42KrtWvXYsqUKXZX17OsiMxN8vswa3x/TFm0Hj7Ix9rOGt/fNefc2xkb7LW+cRMvxjt7lZXz1IqyuV8REZGdHP0ladq0aVizZg3uuecefP3111i8eDHmzZuHqVOnAgB8Ph9uuOEG/OUvf8Ebb7yBzz//HJdddhl69eqFc845x8mqe4aVkbleibV1IjbYK33jJl6Nd/YyK+epFWVzvyIiIrs4fjPZN998EzNmzMDWrVtRUlKCG2+8Eddcc03o+eDNZOfNm4e6ujqccsopeOyxx3DkkUdqKj+RI8Dtisx1c6yt07HBbu4bN3F6nBKdlfPUirK5XxERkVFa1waOL5KslsiLpPKKGlw4f43q656/ZhhK+3WzoUb2Yx94A8eJiIiI7OCJ+ySRtRiZyz7wCo4TERERuQkXSXGMkbnsA6/gOBEREZGbOJpuR9YKRuZW1zdD7pzK4HUebo3MFXHdgVofAEBuRgoCkoT2gMTrGhzixrmqZf65+doYN9eNyAncJ4jUcT/5CRdJcczLkbmioqBj9UFQ3f42XPzkWkZNO8htc1XL/HNzXLmb60bkBO4TROq4n0RicEMC8NqkD0ZBR0/M4NdjI1G/cn0QzUz5JIYb5qqW+QdA+BwVxYr9h8jLuE8QqUuk/YTpdgdxkdTBKz+fWhkF3R6QsKaiBlMXr0fd/jbh5ZMYTs5VrfNPkiRUN7TEfI0Tc4hR6kSRuE8QqUu0/YTpdhQhye9Dab9u+OXgQ1Dar5trJ/nHlbUxf+2RAFTVN+PjylrdZSf5ffD7fYoLJLPlkxhOzlWt809pgRT+GifmkJX7D5EXcZ8gUsf9RB4XSeQqVkdBM2qaYhE57k7MIc5vokjcJ4jUcT+Rx0USuYrVUdCMmqZYRI67E3OI85soEvcJInXcT+RxkUSuEoyCVjrByoeOC/mNRkFrKb9bViqq6/ejvKIG7YG4vmTPEu0BCeUVNXh9w/ee60Ot868wO83QHLW6b6zef+zm5bnkBYnQv/G2TxBZgfuJPEaAk6tYHQWtFgkuAahpbMW0Fz8D4O4UQDdyQzqdGVrnHwDdc9SOvnFblLoZXp9Lbpco/RtP+wSRVbifyGO6HbmS1R/gWiLBgfiMvrRKPMWHir5Pkt194/UvwPE0l9woEfvX6/sEkR0SZT9hBPhBXCR5l9VR0MHyqxuacdebX6C2kbHgRsVjfKiW+af1NU70jVdi/6PF41xyk0TuX6/uE0R2SoT9ROvagKfbkWsFo6CtLr+8okZxgQRERl9aWR8v0xMf6pU+1DL/tLzGqb6xev+xSjzOJTdJ5P716j5BZCfuJz9hcAMlPEZfmsc+VMa+0Yf9ZS32LxGRNlwkUcJj9KV57ENl7Bt92F/WYv8SEWnD0+0o4QWjL6vrmztdyAz8dI5+okVf6sE+VMa+iS36/PehxXnsLwvF63xMhOsoiMheXCRRwmP0pXnBPrxu0XrZ5yUkbh9yfilTSlKaMKgI81ZXsr8sEI/zMVESuYjIXjzdjgjAuIFFmHvJEBTmRJ5iUpiTHpdxuGQvzq/OgjHU0SEC1fXNmLe6EteOKmF/WSSe5mOseTRl0Xos31jlUM2IyOsYAU4UhqdsGJPIscJ6cH510Dpf3rvlNKzbvifh+8sqXp+PPO4QkRGMACcygNGXxiRyrLAenF8dtM6Xddv3sL8s5PX5yOMOEVmJp9sRkWmMFSY9OF9IBM4jIrISF0lEZBpjhUkPzhcSgfOIiKzE0+0oJrVz1r1+TrsobusHu+sTr7HCZA3OFxKB84iIrMRFEilSi1Vl7GoHt/WDE/WJx1hhsg7nC4nAeUREVmK6HckKxqpGT47gR821o0owb3Wl4vNei5E1Sq2f7O4Hp+vjtgUjuRvnC4nAeUREemhdG3CRRJ2oxaoCgN8HBBRmTqLErrotftYt9XHbqYfkbpwvJALnERFpxQhwMkwtVhVQXiABiRO76rb4WbfUx+uxwmQvzhcSgfOIiERjuh11IiouNd5jV90WP+u2+hARERF5FRdJ1ImouNR4j111W/ys2+pDRERE5FWGTrdbvXo1hg8fjuRknq3nRmbPzVaLVQU6rkmSJOiOXXX6vHGR23db/Kzb6kNEykQdi5w+pnoZ+46IYjG0yjnttNNQVVWFnj17iq4PmSQi5UdLrOo1IzvS7fTErjqdQCR6+26Ln3VbfYhInqhjkdPHVC9j3xGRGkPpdn6/H9XV1Z5YJCVSup3o+GeR90lyQzS1Vdt324et2+pDRD8RdSxy+pjqZew7osRmaQS43+/Hq6++iry8PNnnR40apbdIyyTKIsmq+Ge10xG0nK7gdDS1Hdt322kbbqsPEYk7Fjl9TPUy9h0RWR4Bfu6558o+7vP50N7ebrRYMsiq+Ge1WFUtsatOR1PbsX23xc+6rT5EJO5Y5PQx1cvYd0SkleF0u+rqagQCgU5/uEByhpvjn52um9PbJyICxB2LeEwzjn1HRFoZWiT5fPwJ2m3cHP/sdN2c3j4RESDuWMRjmnHsOyLSytDpdgYuYyKL2Rn/rPd6F6ejqU8qyUdhdhqqG1pkn2c0NpE6XudmnqhjodPH1HBemxdu6juyjtfmJbmToUVSIBAQXQ8yya74ZyPJaU5HU7+9qRrNB+TnLKOxidQxMVEMUcdCp4+pQV6cF27pO7KOF+cluZOh0+3Kysrw9NNPd3r86aefxn333We6UmTMuIFFmHvJEBTmRJ4mUJiTLiTSNBibGn3Ra3V9M6YsWo/lG6scq5taneua2mSfz8lMYdwrUQxm9nvqTNSx0KljapCX54XTfUfW8fK8JPcxFAHep08fLF68GMOHD494fO3atfjNb36DyspKYRU0K1EiwMNZ8TOzyOhau34CV6sz0PGvS4x6JZLHuGTriDoWOnFaUbzMC56SFV/iZV6S9SyNAK+urkZRUed/aenRoweqqrhKd5oV8c+iYlPtjKZWqzPAqFeiWBiXbB1Rx0In4v7jZV7wVgnxJV7mJbmHodPtevfujQ8//LDT4x9++CF69eplulLkPl6MTfVinYnchPsQyeG8IDfivCTRDP2SdM011+CGG25AW1sbfvaznwEAVqxYgVtvvRU33XST0AqSO3gxNtWLdSZyE+5DJIfzgtyI85JEM7RIuuWWW1BTU4Pf/va3aG1tBQCkp6fjtttuw4wZM4RWkJwVPGe7uqEZ+VkpqG2UD0BwY2yqiKjX6HPWhxbn4ZNttSivqAEgobRvdwzr143nNyvgOf/exrhkseJlf+C8sFa8zBO7cV4axzknz1BwQ9C+ffuwefNmZGRk4IgjjkBaWprIugmRiMENosjFaMoJ7kZuTAUKJt0A8lGvseos136fD4jeY3IzU3Dvece6ru1OYwxrfDCzD9FP4m1/4LywRrzNE7txXuqXiHNO69rA1CLJC7hIMiZ4oNEyOdy+Mxk5AOhpf9DjPPiGKPUfP6i8KRE/REWK1/2B80KseJ0nduO81C5R55zli6T//ve/ePHFF/Htt9+GTrkLeuWVV4wUaQkukvTTEqOZn5WKP599DApzMjzxs6yen5K1RIfLKcxOw4fTT3d9X1iNMazxiadjGBPv+wPnhRjxPk/sxnmpLpHnnKUR4EuWLMFll12GsWPH4q233sLPf/5zfPXVV9i5cyfOPfdcw5Umd9ASo1nT2IrCnAzPxGjqiXrVEh0up7qhhdGiYAxrvGJcsjHxvj9wXogR7/PEbpyX6jjn1BmKAL/nnnvw8MMPY+nSpUhNTcWjjz6KLVu2YOLEiTjssMNE15FslugxmmbaFa99okeizx+icNwfSAvOE7Ib55w6Q4ukiooKnH322QCA1NRUNDY2wufzYdq0aZg3b57QCpL9Ej1G00y74rVP9Ej0+UMUjvsDacF5QnbjnFNn6HS7vLw87N27FwBwyCGHYOPGjTj22GNRV1eHpqYmoRUk+9kdo+m2c4fV2q+kMDvNkmhRt/WPmqHFecjPSkVtY6vs84xhTVytBwJ4tnwbttc2oTg/E5eW9kFqsqF/qxPKyn2MscSkRSLPE699xsWLRJ5zWhlaJI0aNQpvv/02jj32WPzqV7/C9ddfj5UrV+Ltt9/G6aefLrqOZLMkvw+zxvfHlEXr4YN8jOas8f2FHMTcmEITq/2x3DFhgPADuxv7J5ZgfWMtkABx84e8o2zZJsx/vxKBsB3q7mWbcc3IEsw4q79j9bJ6H7PzeErelajzxGufcfEkUeecHobS7Wpra9Hc3IxevXohEAjg/vvvx0cffYQjjjgCf/7zn5GXl2dFXQ1hup1xVh+83B49uXxjFaa/8jnqmuRvoBtk1X2S3N4/0bTEpvPDLzGVLduEJ1ZXKj4/eZQzCyU79zF+GSQtEmmeeO0zLl4l0pwL4n2SDuIiyRyrfgb3QvRke0DCiHtXoLqhRfE1eZnJWPvHM4SfMuSF/gmnJTY9PysFa2aMccXpVWSf1gMBHD3z3xG/IEXz+4Atd51p69xwYh/jaUWkRSLME699xsW7RJhz4SyNAAeA9vZ2vPrqq9i8eTMAoH///vjlL3+J5GTDRZILWRWj6YXoyY8ra2MukABgT9MBrNu+R3gdvdA/4bTEptc2tlnSV+Ruz5Zvi7lAAoCA1PG6q0b2tadScGYfYywxaZEI88Rrn3HxLhHmnBGGVjRffPEFJkyYgOrqahx11FEAgPvuuw89evTA0qVLMXDgQKGVpPjjhehJJ+vohf4J57X6kn2212oL89H6OlE4Z4mcw/2PvMDQuQ1XX301BgwYgO+++w7r16/H+vXrsWPHDhx33HG49tprRdeR4pAXoiedrKMX+iec1+pL9inOzxT6OlE4Z4mcw/2PvMDQL0kbNmzAf//734iAhry8PNx999048cQThVWO4oNc7K/Z6Ek7zp91Mh7Tzm2L6Eu99U2085+9zOxYXVraB3cv26x6TdKlpX3MV/YgLXVm/C2Rc7j/kRcYWiQdeeSR2LlzJwYMGBDx+K5du3D44YdrLueOO+7A7NmzIx476qijsGXLFgBAc3MzbrrpJixZsgQtLS0YO3YsHnvsMRQUFBipNjkgVuyv0ehJu5JYnIzHtGvbovoyWN/rFq2XfV4Kq28iJul4lYixSk3245qRJTHT7a4ZWSIstEFrnRl/S+Qc7n/kBYY+lcrKyvCHP/wBL7/8Mr777jt89913ePnll3HDDTfgvvvuQ0NDQ+iPmgEDBqCqqir054MPPgg9N23aNCxduhQvvfQS3nvvPfzwww8477zzjFSZHBCM/Y3+F+SABDyxuhKffrsHcy8ZgsKcyJ/TC3PSFaM/g5Gh0Rd8Vtc3Y8qi9Vi+sUpoG8YNLNJdR69s2+6+dGqbZIzIsZpxVn9MHlWC6O87fp/Y+G+9dXZy/yZKdNz/yO0MRYD7/T+trXy+jk+9YDHhf/f5fGhvb1cs54477sBrr72GDRs2dHquvr4ePXr0wOLFi3HBBRcAALZs2YJjjjkG5eXlGDZsmKa6MgLcGXpif5P8Pk2n8zgZGerk6WFWbFt0X2otT5IkxcRARr66h1X7mtypt6J+QTJTZ57+SeQc7n9kN0sjwFetWmW4YtG2bt2KXr16IT09HaWlpSgrK8Nhhx2GdevWoa2tDWPGjAm99uijj8Zhhx0Wc5HU0tKClpafvoRp+TWLxNMb+6sletLJyFAn4zGt2LbovtRaXiyMfHUPq/a11GS/ZTHfZurM+Fsi53D/I7cytEgaPXq0kI2ffPLJWLhwIY466ihUVVVh9uzZGDlyJDZu3Ijq6mqkpqYiNzc34j0FBQWorq5WLLOsrKzTdU5kPytifxkZKo7ovhTZ5xw/53lxX/NinYmIyL0M3/l10aJFaG9vx2WXXYbVq1fjn//8J4YMGYLLL79ccxlnnnlm6P+PO+44nHzyySguLsaLL76IjIwMQ/WaMWMGbrzxxtDfGxoa0Lt3b0NlkXFWxP4yMlQc0X0pss85fs7z4r7mxToTEZF7GToZfPr06fjDH/6AP/3pT7jhhhswceJEfPfdd/j973+P++67z3BlcnNzceSRR+Lrr79GYWEhWltbUVdXF/GanTt3orCwULGMtLQ0ZGdnR/wh+11a2qfTRdrR9Mb+BiNDlYr1oSPFKp4iQ9sDEsoravD6hu9RXlGDdrVzGDXS0pfdslJRXb9f03a1jk1hdlpCjZ9XqY0nAORnpaC6oVnovDRDS50Ls9M4v4iISBNDi6RFixbh+eefx/vvv4+///3veOSRR/DKK69g7ty5ePrppw1XZt++faioqEBRURGGDh2KlJQUrFixIvT8l19+iW+//RalpaWGt0H2CMb+xqI39jcYGQqg0xeheIwMXb6xCqfctxIXzl+D65dswIXz1+CU+1YKSYCL1ZdAx/UbNY2tmPbiZ5q2GyxP6atyMAL8jgkDZLcZj+PnZWrzAwBqG9sw7QWx89IMLXVuPhDA25uUT9cmIiIKMrRI+vHHHzFgwACUlJQgPT0dQ4cOBQCMHDkSO3bs0FzOzTffjPfeew/btm3DRx99hHPPPRdJSUm48MILkZOTg6uuugo33ngjVq1ahXXr1uGKK65AaWmp5mQ7cpYVsb+JEhlqR1S2Ul/KEbXdRBm/eODE/DArWOeczBTZ5+ub2lxRTyIicj9DEeCHHHII/vWvf2Hw4MEoKyvDlClTkJubi02bNuG0007Dzp07NZXzm9/8BqtXr0ZNTQ169OiBU045BXfffTf69esH4KebyT7//PMRN5ONdbpdNEaAO8+K2N94jgy1O+o82JfVDc24680vUNvYpnu7euscz+MXb0Lzo34/7vrXZtQ2tsq+zi0R7u0BCSPuXYnqBvtvFUBERO5naQT4ZZddFrpWaMaMGaHH33vvPZx44omay1myZEnM59PT0zFnzhzMmTPHSDXJJayI/Y3nyFC7o86DfVleUaO4QFLbrt46x/P4xZvI+SG/QALcE+EeXPArcUs9iYjI3QwtksrKymQfnzJlCqZMmWKqQkSJzqkoYzPbZfxy/PPKGHulnkRE5G6GI8CBjmuTvvzySwDAUUcdhR49egipFFEicyrK2Mx2Gb8c/7wyxl6pJxERuZuhC0MaGxtx5ZVXolevXhg1ahRGjRqFXr164aqrrkJTk/abg5J3WBVF7RQ3t8epqHMz29UaGT20OE9EVckBXong90o97eKGY50b6kBEpJehX5JuvPFGvPfee3jjjTcwYsQIAMAHH3yAP/zhD7jpppswd+5coZUkZy3fWIXZSzdFXHNSlJOOWeP7ezKNzO3tCUYZT1m0Hj4gIlbbyqhsM9uN9d6g2sY2jH5glWv6mfRxal7q5ZV62sENxzo31IGIyAhD6Xbdu3fHyy+/jFNPPTXi8VWrVmHixIn48ccfRdXPNKbbmROMoo6eJMGvF16LbfZSe5z6cmFmu3LvDefGfiZ9vPKl1yv1tIobjnVuqAMRUTStawNDi6TMzEysW7cOxxxzTMTjX3zxBU466SQ0Njbqr7FFuEgyzu4oaqt5sT1ORWWb2W7rgQCGla1wfVQ0GeeVCHev1FM0Nxzr3FAHIiI5lkaAl5aWYtasWfjHP/6B9PSOi1/379+P2bNno7S01FiNyXXsjqK2mhfb41RUtpntrtu+xxNR0WScVyLcvVJP0dxwrHNDHYiIzDC0SHr00UcxduxYHHrooRg0aBAA4LPPPkNaWhreeustoRUk58RblG68tcet2M9EznLDPuiGOhARmWFokTRw4EBs3boVzz33HLZs2QIAuPDCC3HxxRcjIyNDaAXJOfEWpRtv7XEr9jORs9ywD7qhDkREZhhaJNXU1KBbt2645ppr8O233+LJJ5/El19+if/+978YOXKk6DqSQ4JRutX1zbJpZcFzyocW56G8osbwef92XTegtT1WRwPb1V4z2zHzXrf0M1GicsM+aGUdEvVaMyKyl67ghs8//xzjx4/Hjh07cMQRR2DJkiUYN24cGhsb4ff70djYiJdffhnnnHOOhVXWh8EN5gTTiQD5KN1rR5Xgjc+qDCdI2Z1ApdYeq9OW7Gqv6IQ6vXV0up+JEp0b9kEr6pDoqYVEZJ4l6XZnnnkmkpOTMX36dDz77LN48803MXbsWMyfPx8A8Pvf/x7r1q3DmjVrzLdAEC6SzFP6UJowqAjzVlcajnd1Kh7WyWhtO9prZjsi68gvM0TOcsM+KLIOjBQnIhEsWSR1794dK1euxHHHHYd9+/YhOzsbn3zyCYYOHQoA2LJlC4YNG4a6ujrTDRCFiyQxok9vGFqch9EPrDIc7+p0PKzdp2vY1V4z27GijjwthshZbtgHRdTB6c8MIooflkSA19bWorCwEADQpUsXZGVlIS8vL/R8Xl4e9u7da7DK5GbRUbrlFTWm4l2djoe1OxrYrvaa2Y4VdUzUCGYit3DDPiiiDk5/ZhBR4vHrfYPP54v5d0oMZuNdEy0e1q72mtlOoo0JEXkHj09EZDfd6XaXX3450tLSAADNzc247rrrkJWVBQBoaWkRWztyLbPxrokWD2tXe81sJ9HGhIi8g8cnIrKbrkXSpEmTIv5+ySWXdHrNZZddZq5GCcQN54obZTbeVe39AJCVmoQD7QG0ByRbrksaWpyHddv3WDIeevrL6vjtguw0BCQJr2/4PqJ8UZG9wfpX1+9HbWMr8rukoTBbbH96ed8B3FN/t9TDa9hv9nNDrDkRJRZdwQ1e5NbgBjekDpllNt5V6f3RcjNTcO95x1oeke33AYGwiogeDy39BcDS+G0JHf1Z19QmW76IMY2uv9F26NmGl/Ydt9TfLfXwGvabc9wQa05E3mdJup0XuXGRFE8xpma/MMT6Uh3tcYsjsqNZMR6x+guApfHb0YsjpfKNjqmWfvXpbIfWbXhl33FL/d1SD69hvzmPi1QiMouLpIPctkiKxxhTs6eetB4IYFjZCtQ2tsZ8XWF2Gj6cfrqlEdnRrBgPuf4CYGn8dvesNNz00meobtBWvt4x1dqvZvrT6/uOW+rvlnp4DfvNPXi6IxGZYUkEOJkXjzGmZuNd123fo7pAAoDqhhbLI7KjWTEecv1lNlJdbTvlFTWKCyS58vWOqdZ+NdOfXt933FJ/t9TDa9hv7uGGWHMiin+6I8DJHMaYdqanrXZFZIverqjyra6/Xf1jZDte33fcUn+31MNr2G9ERImFiySbMca0Mz1ttSsiW/R2RZVvdf3t6h8j2/H6vuOW+rulHl7DfiMiSiw83c5mbo8xNRLfrHZ+uNrzJ5XkozA7PebpYACQn5mC6vr9KK+oMXwOese20lDdoO2eXlrGQ8T58VbPC7vK13pNkpHtWNUGu65vEB2xbrS+bj8GOU2pf73ab7x+J/5xjEkrzhV9uEiyWZLfh1nj+2PKovWhSOag4DSdNb6/I5PWSHyzWtKQliSiJL8Pd0zoj+sORrsqqW1qw7QXP4tZHzVvb6pG84GAptdqGQ9RSUtWzwu7ylcbQzPbsaINdiZliai/iPq6+RjkNLX+9Vq/MQku/nGMSSvOFf2YbucQt01WI/HNanG4144qwbzVlZrjcpdvrML0Vz6XjamWq4tcGbFojf4OUhsPK+KArZ4XVpa/fGNVzEWSqPtdiWqDU3HOoiPWjdbXbccgp2ntX6/0G+PK4x/HmLTiXInECPCD3LpIAtzzs6eR+GZAPbLaF3VzVqWyok/NW/NNDcoraiBBwuK127Gn6YCuMsy0MahbVirKZ5yO1GT5y/asjAO2el5YUb6W/i0SGI9stg1OxzmLjlg3Wl+3HIOcprd/3d5vTs9vsh7HmLTiXOmMEeAe4JYYUyPxzTj4/7FeG2v5rRSXm+T3YcTh3THi8O4or6jBnFUVusuQozf6u6axFeu271Es18o4YKvnhRXla+lfkfHIZtvgdJyz6Ih1o/V1yzHIaXr71+395vT8JutxjEkrzhXjuEgiW+KbjZQlMnJXdOQ044Ajea0/WF8KF2/9G2/toc44xqQV54pxXCSRLfHNRsoSGbkrOnKaccCRvNYfrC+Fi7f+jbf2UGccY9KKc8U43icpgbUHJJRX1KC6fj/ys1JVX+9Dx3UlJ5Xkh+Jwlc5e9QGIdWpreFlKtGxDrQytZWkpN9hfr2/4HoGAhMJsMXVzg/C2lVfUoF3pYjIFIsdKVJ1isaK+VnJzfa0cJ7u4uX+NiLf2UGdaxrhbVmrothle3C9JDB4PjOMvSQkqVtx3LOERt2pxuNeM7Ei3g8LzanG5IqOKY5UVTqlcuf7KzUyBdPA9XogDViIyVlop3U6Cvv6wOkHMazHYbq2vV5Le1Li1f42Kt/ZQZ+FjLEdCx7W1Zm+bQd7H44Fx/CUpAQWjIPUskIpy0jtFRI4bWIS5lwxBYU7kT7SFB18746z+MZ/XcrBW24aeA75SWWrlKvVX/cGo8pzMFNN1c4pS26rrmzFl0Xos31gVt3USObfs4Lb6unHumOG2/jUr3tpDnY0bWIRrR5Voeq1X90sSg8cDYxgBnmC0RDXnZ6XgT2f1R11TK/K7pKEwO3bErVocroi4XJGRu+Fldc9KA3zA7n0tinVXi84syE7DQxMHK5bhViJjQUWV5URUqdvjnKO5ob7xHCnrhv4VKd7aQz/Re2sLL++XJAaPBx0YAU6ytEQ11za2oVduBs4feqimMtXicEXE5YqM3NVTlpbozOqGFvh9Pvxy8CFC6mcXkbGgospyIqrU7XHO0dxQ33iOlHVD/4oUb+2hn+i9tYWX90sSg8cDfXi6XYJhFKQ+8dxfTkSsq70unvs7nnCciJxndP/ifkmkDRdJCYZRkPrEc385EbGu9rp47u94wnEicp7R/Yv7JZE2PN0uQQTPQ61uaEZ+VgpqG9tkXxc8Z1ku/lrpPFYvneOqt67B6Mzq+mbZRDyl/vICkW0TVZZaOYCxKHG5MffSvHWbeN4vlHhtvnitvqSfluNlOLftl5yjYrAfrcNFUgLQE/ctF9UcK+YXgGcigI3EFYuOtnYTuyLW9ZSlFmsLABMGFZmOEp8wqAhvfFbliXnrRokWKeu1qHOv1ZeM0XprC8B9+yXnqBjsR2vxdLs4ZyTuW8v7q+ubcd2i9bjOIxHA8RZXLIodEet6y1KLtZ23ulLTeCmNeVV9M55YXcm5YFKiRMp67djhtfqSOUr7YfQ6yE37JeeoGOxH6zECPI7pjQcFIiNCAeh+v1w5Tv+rlZm44niOOg5nVcS6kbJE9LmRua+1bIoUz6d6eG3/91p9SZzo/XBocR7Wbd/juv2Sc1QM9qM5jAAn3fGgQGREKA7+vxFuiho1E1ccz1HH4ZyKWJcjos+NzH2tZVOkeI6U9dr+77X6kjhy+6Ebx5hzVAz2oz24SIpjZmI+RUWEuiFq1ExcMaOO7Seiz82OB8eTAO/t/16rLyUezlEx2I/24CIpjpmJ+RQVEeqGqFEzccWMOrafiD43Ox4cTwK8t/97rb6UeDhHxWA/2oOLpDimNx4U6BwRqvf9SuXEYvU1DWbiiocW58WMTAeAvMxkDC3OC/3dSHtaDwTwbPk2bK9tQnF+Ji4t7YPUZOdyVay4Rqm6oRm1+1qQn5WKwpwMxTJFxEsbmftB3bJSI8aTzNMzn9x0jZObo87l+snN9SUC3L1PeYlX+tFNx3MjGNwQ58qWbcITqyt1vefxsAScYHoK0DnmV5L5/+DfAWhK0rErvjJWO5Tqqic6PTczBfeedywA/ZHoZcs2Yf77lQiEVczvA64ZWYIZZ/VXb5xgIsckVh/GKtPIeGktQwtGqIqjZz65Mc5WxFy0ok6xbsvgtvoShXPjPuVFbu9HNx7Pg7SuDbhIimPBHUjvAD8etWNZdZ8kpfpZtYPr/bJmpO/kxGqP2iJ28ih7F0oix0RLH/pilCniAKvnPknR9UKMupE2euaT3ccDPdz0Ya+lnwDv3L+OEpOb9ikvc2s/uvl4DnCRFJKoiyTREcixfjI18nOqU/GVWupqtO9ikWtP64EAjp7574hfkKL5fcCWu8605dQ7kWOipw+LYpQp4qd6pTJaDwQwrOwdxVMpGaFqjp75BMS+3YAbxsINp43o7VOn60sUixv2qXjgtn70Qjw5I8ATnOgI5Fgxv0YigJ2Kr9RSV6N9F4tce54t3xZzgQQAAanjdVeN7Cu0PnJEjomePoxVpoh4aaUy1m3fE/NaM0aomqNnPgGxbzfghrFwQ9S53n3U6foSxeKGfSoeuK0f4ymenIukOOX2CGQ3x1dauc3wsrfXNml6j9bXmSVyTPT2oZvHmRGqxljRv4k+FpyzROR28XSc4iIpTrk9AtnN8ZVWbjO87OL8TE3v0fo6s0SOid4+dPM4M0LVGCv6N9HHgnOWiNwuno5TXCS5lNlzTI1GIFsZGxnepu5ZaSjMTsfOBu3xlVafdxuKqq7fj/ysVOxpbBUS3AB0tKcgOw0BScLrG75Hz67puOjkYty9bLPqNUkXnVyM8oqaTu0WHausZc5ojcfWM/+6ZaWiun4/yitqLD+XOmIOdklDYXYadja0KNYxNzMFgYCE9oDEc+V10htR64U4W6dp2a/yMpNxoD0QOs7Eun50aHEe1m3f45prGYjIPYx+5/JKPLkWDG5wIVFpJUYS2mKljZkh16bczBTUNbVpihC3OsFFT9y3Grn2SPipvUFFOekYeEg23t60S7GsM/r3xMbvGzSls4mIVdYSm202vTAWK1N59MxBO+sVz/RE1Lo9ztYt9Ebbx0oi9fsQ8Y80nOdEBJj/zuX24znT7Q7y2iJJdGyi3vskWRE5HatNSouH8B3R6ihJkXHfk0eVdFq8RLcvKFj/Mf17YsXmXZ3uk3T6MT3xzqZdum4EDJiPVVZbMJq9D5beNoigdw7aVa9E4PX7JLmRnv1K7R8Aol8LcJ4TJTJR37ncfDznIukgLy2SRMcm6o2ytiKWUUubCrLT8NDEwdi9r0X29BAroyRFx30X5aTjvVtOC53C0j0rDTe99BmqG2LXf+VNp2Lx2u3YXtuE4vxMXHRyMX720LumItwB47HKIuOxQ6cxNjSjdl8LcjNTcfeyTbZFb2udQ/effxx+//ynqNvPSHDRRJ8aSsF9dAVqG1uFlst5TpS4rPge6sbjOSPAPUh0bKLeKGsrYhm1tKm6oQV+nw+/HHyIofebqbPouO+q+mas274nVJfyihrFBRLwU/037KiLiPkur6gxHeEerI+W10b3nch47Oh40vKKGlujt7XOoa927lVcIFlRr0SiJ6LWbXG2btWxj4pdIAGc50SJTPR3Lq8fz7lIchHRsYlG4xVFxjKarauX2ipXptH62xnhLvdaK/vd7nhQreVojVr3QmwpxT+33KaBiOJHPMV3i8BFkouIjk00Gq8oMpbRbF291Fa5Mo3W384Id7nXWtnvdseDai1Ha9S6F2JLKf655TYNRBQ/4im+WwS/0xWgnwRjE5XO1vSh45oXrbGJauXJyc1IQUDqiDwWwWybRPdJuPaAhIAkITcjRfd7lUTXRU/92wMSyitq8PqG7xEISCjM1jd20eWZ6Tuz/R7elvKKmoj5ZLRPosvRSst+kJ+VgotOLrZsriUiEWOXCHUyysjxXQur5nk89T1RvLLyO5cXuSa44d5778WMGTNw/fXX45FHHgEANDc346abbsKSJUvQ0tKCsWPH4rHHHkNBQYHmcr0U3ACIj03UGxcbJDpe20ybrIiSFBn5HV4fpbQ4tfoDneN5tcZTy5UnIlbZ6Hu1JNoY7ROj81JrtPmEQUWYdzAN0o2xpV7hxlQjN9bJLC3zOvz4oXYssTJdMt76niheuT2+WwStawNX/JL0ySef4IknnsBxxx0X8fi0adOwdOlSvPTSS3jvvffwww8/4LzzznOolvYYN7AIcy8ZgsKcyJ8yC3PSDU1MpfLUwkWq65sxZdF6LN9YpWt7euqgtU2i+yR4ABC5QCqKURe1+gOQrU/9wUjqnMzIX7qKctIxeVQJijT0h5m+M/Jepb6Nnk9G+8TovFTaXnTZ81ZX4tpRJcLmWiLSOgcSvU4iaJnXhTnpePySIXhcw+eAFfM8XvueKF6J/s7lZY7/krRv3z4MGTIEjz32GP7yl79g8ODBeOSRR1BfX48ePXpg8eLFuOCCCwAAW7ZswTHHHIPy8nIMGzZMU/le+yUpSHRsotyd1j+prMXUxettizw22yYRfSI68tsH4B9XnoThh3fXHIUdXn9APaZbKSLdrlhlre81Eh1qtE+Mzku12ORg2eEx7m6KLXU7qyP746VOooXvR92z0gAfFG+pEP05YOU8T4S+J4pXbo3vFsEzEeBTp07F2WefjTFjxuAvf/lL6PF169ahra0NY8aMCT129NFH47DDDou5SGppaUFLS0vo7w0NDdZV3kKiYxPlyvP7fbZGHpttk4g+ER35LQH4audejDyyh+pr5eqvFvUdKyLdrlhlre81Eh1qtE+Mzku12ORg2eEx7qSd1ZH98VIn0bTuo3Kvs7LNidD3RPHK6/HdIji6SFqyZAnWr1+PTz75pNNz1dXVSE1NRW5ubsTjBQUFqK6uViyzrKwMs2fPFl3VuJSIUY9WtEVrdLSceBoDUW2Jp/jxROPG/nVjnRIF+56IvMyxa5J27NiB66+/Hs899xzS08VFCc6YMQP19fWhPzt27BBWdrxJxKhHK9qiNTpaTjyNgai2xFP8eKJxY/+6sU6Jgn1PRF7m2C9J69atw65duzBkyJDQY+3t7Vi9ejX+/ve/4z//+Q9aW1tRV1cX8WvSzp07UVhYqFhuWloa0tLSrKx63AhGPVbXN8smHgXPF48V8+y181XV2qyX3wdcWton9PfWAwE8W74N22ubUJyfiUtL+yA12a/YV2bHwE2MtEWuX6zsE6Nle3GuO0Fr/wYCEl7f8L0tfenVfSwe5pzb+j4e+lQE9gOJYNd10U5ybJF0+umn4/PPP4947IorrsDRRx+N2267Db1790ZKSgpWrFiB888/HwDw5Zdf4ttvv0VpaakTVY47SX4fZo3vjymL1neKhg1O3Vnj+8tOZK9GusZqsxHXjCxBanLHD7JlyzZh/vuVCL/9x93LNuP0Y3pi4/cNin1ldAzcRu98ijWHrOoTI3Peq3PdCWr9KwHY39aOi59aG3rc6r40c5xzSrzMOTf1fbz0qVnsBxJBzzzy8pxzPN0u3KmnnhpKtwOAKVOmYNmyZVi4cCGys7Px+9//HgDw0UcfaS7Tq+l2dtI7gYORrtETx0sZ+nJtlls05WWm4IQ+eVixeVfE4sfv61ggzTirP4COBdITB++vo0V0X3n5IBJNz32SYs0hQNx9kozUUWs9vTY+dpDr37zMFOxp6hwUY1dfemUfi8c553Tfx2OfGsF+IBH0zCO3zjmtawNXL5KCN5N9/vnnI24mG+t0u2hcJGljZcyzWynFopd/sxtAR6rLsL7dkOT3KZ5GB3ScYnf0zH9HLKK0iO4rr/4cLSdWW/TMIQCW9Ylaf8fTXHdCRCx1lzTc9OIGVDe0yL7Wrr50+z4Wz3POqb6P5z7Vg/1AIuj9/HbrnPNMBHi4d999N+Lv6enpmDNnDubMmeNMhRKIlTHPbiXX5hFHdMeII7p3em1qsh9XjewrW86z5dt0L5CAzn0VT3Gbsdqidw5Z1Sdq/R1Pc90J4f1bXlGjuEAC7OtLt+9j8TznnOr7eO5TPdgPJIKeeYSD/6/ltW6dc65aJJH7MdK1MzMR4EBi9RXgnTnklXp6AftSG/aTeOzTDuwHEsGKeeTmOcdFEunCSNfOzESAA4nVV4B35pBX6ukF7Ett2E/isU87sB9IBCvmkZvnHBdJDnP7OfLRRES6imqzXDmAtmtYRNQhWEZuZip8PsDI1X25mSkIBCS0ByRLxl2pnU7OOzfFAsfqBy1x8flZKahuaEZ5RY3r9109RM8PN425m9nVT1773DFD1K0JvN4/3AcTjxXzWO888vqcc1VwgxXcHNzgdOKPUcG0EkA+0jVWWomoNsuVk5uZAgCoC0vQUkosM1sHuTLMsGLcldo5YVAR3visytF5Z2YOiayD1hS+6HrK8cK+q4VVxyU3jLkXWN1PXv3cMUNPn8Zz/3AfTBxWzmO9+5Mb55wn0+2s4NZFkltjEbUysgOKarNSOXLkorbN1kHX9jX+wiR63PXU0Yrta+HklxG9EaZaFsRe2Xdjsfq4FM9fQEWyeqHq1c8dM0TdmsDr/cN9MP7ZMY+9fp8kLpIOcuMiKV6iOPXebVlEm9XKiVX2e7echtEPrDJVBy3bz05Pxi8H98Jh+Vl48v1vsHOvcqqX3u1rYaSPRG5fDydOazEyF4P1rK7fj7v+tRm1ja2a3+sVdh2X4vFUJiuI7qd4+dwxQ9StCbzeP9wH45ed81jvd0A3zTlPRoAniniJ4tQT6SqqzWrlxCr72fJtpuugZfsNzQdw1rG9AEDzAknr9rUw0kcit6+HE7HARuZisJ7lFTWKCySl93qFXcclt8dwu4XofoqXzx0zRN6awMu4D8YvO+exnnnk1TnHRZIDEjGKU1SbzfSJ1qjuWNuwY+zMjrvT73c7M2MYz/tuPLeNOL5q2D8UDziPxeIiyQGJGMUpqs1m+kRrVHesbdgxdmbH3en3u52ZMYznfTee20YcXzXsH4oHnMdicZHkADuiOO04/1PPNkTFsA4tzkN+VmrMU57k5Gam4MieXVGYnY6dDcb7XW37euIvjWxfCy3R1UoKs9MQkCS8vuF7V5w3bIRaNHz3rDTD8yCeY3TjuW1O0Hp8tOtcfY5vbOyfn7jt+hG7ebn9nMdicZHkgCS/D7PG98eURevhg3ws4qzx/Q3vlHYkiejdht42K0V8tx4IoKm1XXd965racOmCj5GbmQLp4Db19nuwTmrXpISXodRmLe81KlZfq2loPoCLn1wb+rvTCTR6aY2GNzoPkvw+TBhUhCdWVyrWQcQYOiF83siRAEwYVOTJttlN6/HRztQnqz93vI7908GNSWR28nr7OY/F8jtdgUQ1bmAR5l4yBIU5kT95Fuakm4pnDEY/Rl+4V13fjCmL1mP5xirDdTa7Da1tViq/rqkt5gIpKy0p9IVYSf3BL8o5Ua9T63elOqlRarPVlLZblJOOM/r3VHxfdP+KnDdWizVvwhdIgLl5MC/GAunaUSWe+CBVMm5gEa4dVaL4/LzVlZ6YC07Seny041gdzarPnXiR6P3jxJx0k3hpf6LPY5EYAe4wkT/r2hH9KGIbZmJYYynomor3bzsdn2yrxdTn1qNuf5vs63wACrLT8NDEwdi9r8V0fHl02XLtbw9IWPNNjWq9REfMRvf10OK8mDHodtVLNKPR8KLnQZHL+0lNIsUgW0Fr/4m4HYHZenr1dCI7JGL/JPq+H4/tT8R5rBUjwD1CZCyiHdGPIrZhJoY1lp17W7Fu+x74fT7FhUiwjtUNLfD7fPjl4ENUy9VTJ6X2J/l9muolOmI2uq/LK2oMR6i7OfrWaDS86Hng9n5Sk0gxyFbQ2n8ibkdghlfjeO2SiP2T6Pt+PLY/EeexaFwkxRE3xVMb3Yad8dVWtsWt8dFORpNbyU1z2s39pCYR2mglrf0i4nYERCIl+r6f6O0neVwkxRE3xVMb3Yad8dVWtsWt8dFORpNbyU1z2s39pCYR2mglrf0i4nYERCIl+r6f6O0neVwkxRE7oh/NbEPL+bFm4qsLuqZqit7WW8eTSvJRmJ2O6gbt1yQZiY8GgPysFFQ3NKO8osaS84eN9K+oyFCz50fHer8d7XJbtKoV55tb3Ua5a+TWbd8TF+fMtwckBAIScjNSVK87vLS0D578oNJ0Pzt1zQGvdYg/bju+2S1e28991RwukuKIHdGPRrehNVbTTHx1S7uEtzdVY9zAIqF1nDCoCM0H1GPHtcRHq7WttrEN017YENq26NhRLRHW4UTNG7Oxqmrv1ztvjLTLTdGqVsXUWtlGuTr7fUAgbCNeitoNJ9e2aOH9l5rsN93PTkUVez0imeS56fjmhHhsP/dV85huF4fcdp+kYKxm9EQLHmrkIinlys/LTEFLjPskRZcnoo565GWmoOy8Y1X7WMsXKiB2/xil1s7M1KSI/hUxb4yMv9H3K80bCZH3STLTLqc/eMz2p9ZtiGyj1v3LijlvNa1tE3mfJDvmgJu2S/Zx+vjmtHhpP/fV2LSuDbhIilN2/MSqZRtmYjXlym8PSBhWtkLxhq7R5Ymoo1aF2Wn4cPrpmvo5WK/q+v2461+bNbfHDC3tLNQRiS1im2rtM/J+uTEHIHR/cPI0J7tiakW1Ue/+5aWoXS1ty81IwZyLh2BY326Kc1xPPzsVVRyPEckkL9FP0fJ6+7mvqmMEeIKzI/pRyzbMxGrKlf9xZa3igkKuPBF11Kq6oUVzPGiwXuUVNbraY4aWduqJxBaxTbX2GXm/0piL3B+cila1M6ZWVBv17l9eitrV0ra6/W3w+3yKX0b09rNTUcXxGJFM8hI9Otrr7ee+Ko7f6QpQfHMqXtmKKHAryrIzdtSJiFOz22QsayQv9odTtwOwgxf3Ka9tl4j04b4qDhdJZCmn4pWtiAK3oiw7Y0ediDg1u03GskbyYn84dTsAO3hxn/LadolIH+6r4nCRRLq0BySUV9Tg9Q3fo7yiBu2B2Je0BWM1lc569aHjoki9Mcxay9NSX7UytdDbjmC9qhuakZ+VEvO1uRkpCEiSbN31jIeWdoZHkKuNrRZmx1/P+/XOTS8SvT/ZQe/+5cY2APL7mhPj4dQccHruJcL+TdpxPihzel+NJwxuIM3MJjEB8rGaelNWtJZnJN0uukwt9LZDa7pdtOi6GxkPPe0Ulehjdvy1vB9AXCQSaSF6f7KD1nnn1jbE2tcA2D4eTs0BJ7ebKPs3qeN8UOfFzwk7Md3uIC6SxBAR4yw6UjhWeaJix4P3SXrjsyrFRY3e+/0YjRqPXhQYHQ8nIsitvE8SYLwvvMqLXxK8ep8kLccSwP5FeqLcJ4lRxhSO80E7L35O2IWLpIO4SDJPVJyk6FhNpfJEx45HR4l375IGSMDuRn0x2VrqlZeZgnYJqN/fpviawpx0SJKE6oYW3e2LbqfdEeRmxl8p2jtRo069GFMbXeehxXlYt32Pa9ug51gCiI2Z11o/p6Lo7dguo4wpHOeDfl78nLADI8BJGFFxkqJjNZXKEx07HutxPbTUq7ZJfnEU/hq1X4C0jIcTEeRm+1Du/eUVNQkbderFmFq5Oru5DXqPJXa3xak5YNd2GWVM4Tgf9PPi54SbMLiBVHktTtKt9XXj9tzaV1p5vf7kbpxfzmL/UzjOB7IbF0mkymtxkm6trxu359a+0srr9Sd34/xyFvufwnE+kN14uh2pCsZJVtc3ywYOBM8DdkucpFvrO7Q4D/lZqTGv/ynITgPgw86G2HWXJAk7G1oUAyDCo7xjnYNsRV/ZeQ60iPon2jnb8dZeK9vjlmOJ0Ta2BySs+aYG5RU1ACSU9u2OYf26eWa89fa/yLkQb/tJPFCbDwCjrUksLpJIVZLfh1nj+2PKovXwQT5Octb4/q75AHFjfYMpM7EWSABwx4QB+PTbPXhidaViWdGxw3JqG9sw7YUNAGKn2YjuK7vTdMzWP9HSf+KtvVa3xw3HEjO3Xpj+yueoC7vO8e+rKpCbmYJ7zzvWE+Otp/9FzoV420/iRfh8UDJhUJFrvouQ9zHdjjTz2geHW+qrJfZbLc46aPKoEsw4q+N1Zcs2xVxMBRmNBdfbV05Gs5q5Z1SiRMnGW3vtbI+TcdtG2rh8YxWui/FFEgAe99B4W3HLh1jbiqf9JB7F+uzzgWNE6hgBfhAXSWJ57RQEp+urFlkKdJwat2bGGCT5faqvLQqLG1Z7bTg9seBG+soN0ax66u+G+top3trrRHvsPpYYbWN7QMKIe1eiuiH2saEwOw0fTj/dE+MNWHPLB7ltxNN+Eo84RiQCI8DJEl6Lk3S6vmqRpUDHqXHrtu8BoB7vHYw31fLacHpiwY1wQzSrnvq7ob52irf2OtEeu48lRtv4cWWt6gIJAKobWjwz3oA1t3ywsiyyBseI7MRFEpGFrIgsNRNvalU0qteiWb1WX7Pirb3x1h45Rtto17HELUTOhUSYV17HMSI7cZFEZCErIkvNxJtaFY3qtWhWr9XXrHhrb7y1R47RNtp1LHELkXMhEeaV13GMyE5cJFFI8Jzv6oZm1O5rQX5WKgpzMoSfe6/33H6nrysyUy8tkaXBuO6eXdNQmJ2mGO0dHXerVm6s95oh1+6TSvJRmJ2G6oYWy7cvgtZxGVqcZ2u9rOKWKGtR4q09coy2sWNfTNd0TZKX+ydI5Fywa1659TMNiF03N9Q7Efb9aG7o90TFRRIBkE8PChKZ4qQ3JcotCXVG6xUrwjYoPK47NzMFEqApblit3FjvNUqp3RMGFaH5QEDxfZKg7YuidVxGP7DK8bkmghuirEWKt/bIMdrGJL8Pd0zor5pud8eEAZ7unyCRc8GOeeXWzzQgdt0AuKLeibDvh3PzfEkETLcjTRHVImI19UarujWK1Ui9Yi1Co8uQ0LFYCr+/idJBUa5cvw8IhFVO1AFVyzyJ5bhDs/HG70aaqoNoauPi9FwTLd4+cOOtPXJE3icJgKfuk6SHF+6T5NbPNCB23ZSO+U7WO1H2fbfOF69jBPhBXCTFpiWiOigYP23kX2j0xna6NebTTL1CpzPW78dd/9oc88ayhTnpePCCQdjd2KIpzjr8p/ihxXlYt32P0J/m9cyTWDbeMRZd0t31A3brgQCGla1QHY94iZSNt1M34q09coy2sT0gYc03NSivqAEgobRvdwzr1y3u+idI5FwQPa/c+pmmpW6xOF3veN333Txf4gEjwEkTLRHVQWZiNfXGdro15tNMvYIRtuUVNYpfyMPL8Pt9+OXgQ1TrJBeNK7pP9MyTWKa98CnmTzpRQI3EWbd9j6bxiJdIWadj8UWLt/bIMdrGJL8PIw7vjhGHd7egVu4jci6Inldu/UwDzB3fnax3PO/7bp4vicTvdAXIWXpjMo3GauqN7XRrzKeIerm1bbGIqsu3e/YLKUckL44HEXmLm48zIrbJ46NYbp4viYSLpASnNybTaKym3thOt8Z8iqiXW9sWi6i6HJaXIaQckbw4HkTkLW4+zojYJo+PYrl5viQSnm7nECvPpdVTtpYo5KAiE7GaemM77Yj5NDIGIurlxQhTPfMklod/fbywOoliZDyMzB2191hxTIjnc/bdxIv9bHed43F7Ij9rnTzumz2+x0ucvJ3U5o6b50si4SLJAVamsugtOzxOMxYfzMVq6o3tDL5eKcbWbKS00TEQET/qxQhTLZHZao47NNt1oQ2A/vEwMnfU3mPFMSER0p/cwIv9bHed43F7Zj5r3XbcV6ubJPP/4ZoPBPD2pmrXzne30TJ33DxfEgnT7WxmZaSjmbLdeJ+k5RurYt7r43GDfSViDER8CMfTl6sJg4rwwn+/6xQ3HM7oeNlFy3gYjX+P9Z5rR5Vg3upKoccERsfaw4v9bHed43F7oj9r3XLcV7tPklykPODu+e42Rm6F4tb54mWMAD/ITYskKyMdRZQdiqhuaEbtvhbkZ6WiMCdD+GkKWk5RsKqvRJYr4nSOeDlNBwBG3LsS1Q3ejitVu9u83rmjJVo3+r5WamVqaQOjY63nxX62u87xuD2Rn7VuPO4r1a09IMXFMd5JRueOm+eLVzEC3IWsjHQUUbZdcZpatmNVX4ksV0R/eTHCVK7O5RU1ih+egHfiSmONh5G5oyVaV2mBpFSmGkbH2sOL/Wx3neNxe176rDVCqW7Bf0BV4sb57jZG546b50u8Y7qdjayMdIy3uEir2hNv/eQWidCvRtooqr16ykmEsXADL/az3XWOx+15cdxFSNR2i8Q+9B4ukmxkZaRjvMVFWtWeeOsnt0iEfjXSRlHt1VNOIoyFG3ixn+2uczxuz4vjLkKitlsk9qH38HQ7G2mJ2TQas+1EXGT4ebLds9IAH7B7X4uQc2atao/RcmOdp/1xZS2q6/ejtrEV+V3SUJjt/DnDRuoV3cahxXlYt32P5+NtRdGy/0ZH4Wp5j98HSJJyYmC3rFQMLc7TVc/C7DRUN7TIPq9lLNx4DrzI2HURZQ0tznPlnI/VNrv3U7vi9c1sT69EONZFaw9ICAQk5GakoG6/cjiPmduEJIJEnDtex0WSjbTEKO9vazcUpWl3XGSsNDzAfPqKVe0xUm6sRLc3PquyPBFQLyNJhXLviQ4U0BJva1Vkuxto2X+jo3C1zLdrRnak2ymVWdPYitEPrNI8n97eVI3mAwHZ57TsO25MUxIZuy6335opS27snIroVesnuz8n7IjXN7M9O9rkdWqf9eEmDCqKm3ZbIdHmTjxgup0Dlm+ssixK0677Q8hFWIYTFQlqVXu0lqulrUp8sD8SVevYhNdLaxvVxrRs2SY8sbpS8f2TR5Vgxln9Vbbifkb2XyP3SdJSrlzdYo1lbmYK7j3v2JiLAbdFWouMXVdiNsJd76LLCnr6yY33LRI599x4nyQvMrIfMQZcXSLMHbdjBPhBblwkdURprlA9HcZolKaVp8poiTQOEhUJalV71MrV01Y5dkeiaq1veL0A6GqjUptaDwRw9Mx/x0xq8/uALXedidRkb18KaTQKV22+tR4IYFjZO6htlD+dRW0+aRn/IhPvdyLi16rYdVFlBd/z3i2naT411QpG+8nOUypFx+ub2Z4objwtVRQj+xFjwLWL57njBYwAd7GOKE35BRJgPkrTyrhILZHGQaIiQa1qj1q5etoqx+5IVK31Da8XDv6/VkpterZ8W8wFEtBx6t6z5dtw1ci+mrfnRkajcNXm27rtexQXSLHKDa+X2liaeb8TEb9Wxa6LKiv4nnXb9zga0Wukn+yOFRYdr29me6LEczSzkf2IMeDaxfPciSdcJDnAyzGQVsaTu40T8c12bkdkDO722iZN79P6Ojdzazy90++3ghOx60bKcvoY55V6KvF6/eORHTHpRG7HRZIDvBwDaWU8uds4Ed9s53ZExuAW52dqep/W17mZW+PpnX6/FZyIXTdSltPHOK/UU4nX6x+P7IhJJ3I7LpIcoCcG0qnrcZQMLc5DflZKzNOC5NphtD566imiryJizbukoaBrKnbubdVVRpCeOHEApusenFdar0kKbjdWXLSc/KyUTpHUl5b2wd3LNsc85c4H4PCeXfDh1t3Y3SgmKt5KSvPJbfH0dr0fkB97Kxlpk5Z2yDFSlpnIXpHHdq9HC9tVf6uv142na0yM7Edun2dEejm6SJo7dy7mzp2Lbdu2AQAGDBiA22+/HWeeeSYAoLm5GTfddBOWLFmClpYWjB07Fo899hgKCgocrLV5WmMg395U7Wiym9L7tC6Qgu1Q+6AQEdUrIi1Grgyfyc84LXHiuZkpABCRlmZknMPnldqHWrBeyzdWKcZFK6ltbOsUSZ2a7Mc1I0tipttJACYt+CTiMbcm+qjNJ6vi6ScMKorZh7HKNRsvqyXiXG7srWSkTVraEc1IWWbGWnS6ldejhe2ov5WJYvGYVqZ3P/LCPCPSy9F0u6VLlyIpKQlHHHEEJEnCM888gwceeACffvopBgwYgClTpuBf//oXFi5ciJycHPzud7+D3+/Hhx9+qHkbbky3C4p1YAVgSRSv0ZhVvVGgWj8gRET1ioiONRP1LcdsnLiZcdZ6nyS1+sT6YFSqX9myTZj/fqVqiINaOU7SOp9EfzFSGw+tEepm6yUqjlwkt94nychYWxmz7vUv61be8sHKPndbbL5IVv7DHpFTPBsBnp+fjwceeAAXXHABevTogcWLF+OCCy4AAGzZsgXHHHMMysvLMWzYME3luXmRBCifemVFFK/RmFUtUaD5mSl49DfHo7apVdcd7M1G9QLm+8ps1He43IwUzLloCIb162Y6TtxMnGpwXlXX70dtYyvyu6ShMDvyNEa1+hRmp6G1XUJto/zphrHiwJ/5qBIPv7MVTa3tlrZTNL37iKhTbMzGd8uVZ6ZeHXHkK3SPvZWMtEnEabxmti9XhtUx614/7Ut0/a3sczfG5lvBqlPEiZziuQjw9vZ2vPTSS2hsbERpaSnWrVuHtrY2jBkzJvSao48+GocddljMRVJLSwtaWn66vqKhocHyupshFwNZXlFjSRSv0ZhVLVGgtU1tSE7y45eDDxFWHy31BGJHWGvpK7NR3+Hq9rfB7/d1+rCwO05VRLy52nVKSvVLTfZj4CG5mhZIscpxgt59RFSMq9n47mhm69URR658LZ4TY2akTUrvEVmWHnbErHs9Wlh0/a3sczfG5ltBaUy83CYiLRy/q+Pnn3+OLl26IC0tDddddx1effVV9O/fH9XV1UhNTUVubm7E6wsKClBdXa1YXllZGXJyckJ/evfubXELxHNbxLDT9Yn1fhF1Ex1XKlee2+JURZYpqr1uiI11KorYbRHIbqtPvGC/2s/KPud4EsU3xxdJRx11FDZs2IC1a9diypQpmDRpEjZt2mS4vBkzZqC+vj70Z8eOHQJraw+3RQw7XZ9Y7xdRN9FxpXLluS1OVWSZotrrhthYp6KI3RaB7Lb6xAv2q/2s7HOOJ1F8c/x0u9TUVBx++OEAgKFDh+KTTz7Bo48+il//+tdobW1FXV1dxK9JO3fuRGFhoWJ5aWlpSEtLs7ralnJTxHB7QEIgICE3IwV1++VT7YLvCwQkvL7he8Xzk6PPax5anGcoqhf4KYo4ye9DYXY6qhtinxMu11fh1+3kZ6ViT2OrqeCGWNsyGktcZFHsrZYo94KuqfD5/NjZoFznnIxkbPy+Hj/U7UddUyvys1JRmJOha2yD/XagPYAH//MlAAmlfbt3uq7LDmb2PTPXUnSMR6rqNUCio3XNxJznZ6Wiun4/yitqVNvq9etkRNE6v4YW56G8oibhrwERMW+sjBd3Inqd+xKRfRxfJEULBAJoaWnB0KFDkZKSghUrVuD8888HAHz55Zf49ttvUVpa6nAtrWVVHGqw3OsWrZd9XooqVy3lKlgfCcD+tnZc/NTa0OPRSTexkqbmra7UHNUbFIwinjCoCM0H5K99idVXWtqmlwRgwqAi2XExEksMdPTr25uqhaZdySUTyWlpl/DrE2KPT/3+A7h72eZOj2sd2+Bz9fvbcOnTH4ce//uqCuRmpuDe8461NS3J6L5nJpXrp2h95QWS0nbNMBpzjoN/r2lsxbQXP+v0Pr3bSSRa5teEQUUY/cCqhE8TEzVvrIwX1/uZahb3JSJ7OXq63YwZM7B69Wps27YNn3/+OWbMmIF3330XF198MXJycnDVVVfhxhtvxKpVq7Bu3TpcccUVKC0t1Zxs52XjBhZh7iVDUJgT+TN9YU66LZGiwVhTtUWE0hfu6vpmTFm0Hss3VimWVV3fjHmrK3HtqJJO7dSiqr4ZT6yuVPyyn5uZIttXWtumVOYZ/XsqPj9vdSWWb6ySfU5pTHMzU5CVmiT7nvqmtlA/6qXUzrqmNtUFUnDbRsenSuPYBuePXMhDXVMbrjPYdjP07nux5rfa2GmZi1bs81rqrNQPcpTaaqZv4lWs+XXtqBLMW12paZ+N5z4UPW+c/jwVgfsSkf0cjQC/6qqrsGLFClRVVSEnJwfHHXccbrvtNpxxxhkAfrqZ7PPPPx9xM9lYp9tFc3sEuBqRP61rjSt975bTOv1LZrTcjBT87cLjccvLnymmoPkAFGSnAfCpng638qZTMeK+lTETtfQqzE7Dh9NPNx3FHa4oJx2SJMVss5bI8ejTDkfdv9JUmXLbEBFrHj4nPqmsxdTF6xVPu5RTdPC967bvwa69zejeJQ2QgN2NLejeJQ03vrABO/fGTtGTG0c7aNn3zEQAa4rWz0rBmhljkJos7t+zjMacVzc04643v1A8RVPufYkQj2yU3HFA7bgbLR770OrIbq/EizuxHaJE4YkI8Keeeirm8+np6ZgzZw7mzJljU43cR2Qcqta40mfLt6l+UNftb8NXO/fGjImWoD1GevHa7UIXSDi4bSNx5rGovVdL5Gv0mJZX1Kj2o94YWVGx5sFtr9u+B36/T9cCCWHvlat3eUWN6gIJkB9HO2jZ98xEAGuK1m9sU+w/o4zGnJdX1MS8hi36fYkSj2yU3HHAztsEuJWV88ZL8eJObIeIIrnumiSyjtYY0u21TUJfZ3dZ4YzGmYverojXWlGmHeUpvdfJ9ohiZuy8Fjeu932MR9bHbbcJcIqX5o1ddfVSnxDFEy6SEojWGNLi/Eyhr7O7rHBG48xFb1fEa60o047ylN7rZHtEMTN2Xosb1/s+xiPr47bbBDjFS/PGrrp6qU+I4onj90ki+wTjSpXOWPah4xqSS0v7oCjGxdo+APmZKdi9twVZafKBA8HXFWanoaCrciR7MEp4977YZRlRmJ3WKXpVrQ9iCfZPYXbsiPn0FB9aD7SjPSB/uV97QEJ5RQ1e3/A9yitqQnHZsbZbmJ2GgCSF3qNUdpCZdkZvOxhDbqTMYFS7Uh0Ls9U/1OXG0YjoflfrQy207lOxYuFj9Wd+VgqqG5qF1VfLdpXqrPd9el+vND5WjJseWrevt556jwNyYs0vr3Jqv9AzfsHXBm8fYWT/18PMcUaNlnY7vQ8SOcXR4AY7eD24QbRgQg4gH4UaTPopW7YJT6yuNLydYHnXjirBC//9TlOammhKEdJG2hbeP59+u0fT+7NSk/DQxEER21eKcB14SDbe3rQrZlv0xv8qjbVW0XMivEy993qKFQ+tFJ8b9LiA9Ckro3PV2hCr/nrGSGTUr9bjgNn3aX19rFsEvPFZlWORx1rnjd75ZfQ4EE5trLzM7v1Cz/hpvX2E6PExus+qlanWbsaOUzzSujbgIikBqR30tHxxVRN+nxyzEyxYlt7Flqgv+cBPCy4Aut//eNgXQSPblqP1g1HrB3pWahJSkv2aFmJ67zGl5Yv39Fc+7zS2ou6TpNTvor7EmFkkBd/v1JcuI19+RC0Iwo83evYLuxYHWueN3vll9DiQlZqExrCo/Hj/omrXfqFn/PSMnRXjI3LBoqXdgPznXTwv0CkxcJF0EBdJ8pSiUNsDEkbcu1IxsltNbkYK5lw0BCeW5OuOs1Uqa9jBtJ4R965QTcuLFh6NCsBwLHYwytpIm8y8Nxatsa+tBwIYVrYiZnpgYXYaVt/6s1BUt1o8bngsdO2+FmRnpODONzdhb/MBQ3VtD0hY800NyitqAEgo7dsdww4mq5lhdXSuqPJD/Vm/H3f9a3PMG8uKjPo1Goms932xjjdG9kmrI49F3S5Bbyy6kuDtFB6aOBi797UIia/2Aqv3Cz37L6D++ZGflYKZvxiAwmzrxkdEjLnWdpu93QWRW3kiApycoxSFGvzia1Td/jb4/T6s277H9IIgWFaS36cak60kPBoVUI/wVqI1Gl30e2PRGvu6bvse1Xj16oYWXVHTcvHFSgskLXVN8vsw4vDuGHF4d03b18rq6FxR5UfGbCuPleioX6ORyHrfF+t4Y2S/sDryWNTtEvTGoscqp7qhBX6fD78cfIju93uV1fuFnv0XUP/8qG1sQ2F2uqUx3CJizLW2OxbGjlMi4CKJIoiIEBUZQ6o3TlitHDPMxJRbFXEOqLfNjvhYt0bUWl0v0eW7tR+t4ob92ky5WvdrNx3HvMiq/cILt1+wghWf0UTxiIskiiAiQlRkDKneOGG1cswwE1NuVcQ5oN42O+Jj3RpRa3W9RJfv1n60ihv2azPlat2v3XQc8yKr9gsv3H7BClZ8RhPFI0aAxxERMZ1aY5mV5GakIBCQQnG2Zs5Ujo4TVovelmM2wjq8jGA0ut73m3mvlnqpxb5qaXdmShI2fl+P1gMBxdfEml9q88ZsFK7RuS0iOjfWtocW5yE/K0XxvXrbbWXUL6Dej0b7Odb71OaNmX0yuh9ERRXrvV2CqFh0JUbGXWRss9MR0FbtF3rKtXrfFEXLWGltS2F2muvbS2Ql/pIUJ0Sl3iT5fbhjQn/D6XZ1+9tw8VNrI9LtfOgcWarlI3bW+P6hC0Lf3lSNPTpjxIMH9/ByZo3X17bwMlKT/Zg1vj+mLFqvuQ0AMGFQUei9erctyfx/dL3ULppN8vtU693U1o67l21G2b8345qRJZhxVv+I59Xm19ubqtF8oD262BBJY13lmJnbwbYr9btavWJtGwBmL92E2kb5ealnjETVNxYtKXOi0+4AxCxTy9yMptSvIpO/YtVL6zFBrp5G2ouDr5swqEjzuItOQXM6AlrreOjdL/SWa0UdRNI6VlrbDcDV7SWyGn9JigPBKM/oCy2r65sxZdF6LN9Ypau8cQOL8PglQ5Cb2flfyLNSk5CZqn7T1+r6ZsxbXYlrR5WgMOoGiYU56Zg8qiTm+yePKomIXb1u0Xq0xPiV47hDszvdiLEwJ910RGl0GeMGFmHuJUM6tSmWeasrNY1BdH8X5qTj8UuG4HGZ7eltm9Z6ByTgidWVKFu2KfSY2vwqW7YJUxatt+ReWKLntqhtX7doPa6TeS6ciPknitYx1NvPRvoouszg3MyROd7IketXK+aJ0j6j9ZigNP562xuk9Tgisi+c3P+i6e1nK8q1qg4i6B0rLW1xc3uJ7MAIcI+zMuJYKZYZANZU1OCjit1YWL4NjS3yvyCEx+SGR0sPLc5TjcMuCotdHV62Ajv3xk628/uAL2aPw4YddYrRqFrid/Myk/F/vxmC2qbWmPGqwb6Z+tx61O2PvTjQGqcaK+JXROxrsJyPtu7GZQs+jvmv2H4fsOWuM5Hk96nOL5+vY3EVi5F5KGJuGy3DaFRzULesVJTPOB2pyfr+HcqK/VlLmbHG0Io+kovGVov4z81MwZwLh3SKhrcj5l3LvqdnHzVyqwUr57vVZYkk6lhoplyr6mCUmbHS0ha3tZfILEaAJwgrI45jxTKPOKI7/H4f5rxbobrt6Gjp8ooa1S9W4bGragskoOML3uK123HVyL6Kr9ESv7un6QCSk/yqMbtJfh/8Pp/qAgnQHqcaK+JXROxrsJyvdu1VPc0nIAHPlm9D/145qvNLyz+zGJmHIua20TKMRjUH1TS26opU17pdq/ox1hha0Udy0dhqEf91TT/dEiCc1THvWvc9PfuokVstWDnfrS5LJFHHQjPlWlUHo8yMlZa2uK29RHbhIsnjnIwMNrptq+qsFsfrhRhmO+JUtcYWb69tQveu+sMyYrEiSjfW66yeo3rKFPkeJyKJrewjM+32Ymy6VXH7IvvCi/2aqDhWRNbgIsnjnIwMNrptq+qsFsfrhRhmO+JUtcYWF+dnCq+PFVG6sV5n9RzVU6bI9zgRSWxlH5lptxdj062K2xfZF17s10TFsSKyBhdJHheM8qyub5Y9hSp4LrIVMZ1Gt632PgDIy0zBB1//CEhAbkYy6vYfiFkXvw+4tLQPAOXzp/XUN9Y52MHnqhuakZ+VophuFl5uXlYq2tsDqG+Wb4faOCnVJ/zx7l3SAAnY3dgS8f/B68CC14UdWdAVfpXriIL9meT3qY6VlpQuvfOwPSAhIEnIzUiJeUpjfmYKDrQH8PqG72XPlbdyjipRmkfds9IAH2SvOdPV5qwUDC3Oi3gsen6Ej3f3rDQUZqdjZ4NyH2i5JsmqPtJSVqz5I+IYqPeaC7PXaBjpOy3tEPl54ORni5eZnRtK1wLHKoNjRWQNBjfEgWCqDSAf02llCo3RbQcT60SZPKojtlpLzLFafQHl2GK550R5PEZfydVnwqAivPFZlaa6RC+KMlOT0NSqHNkd7M/g9uX6TA8ftM9DufZqJRd3a2aOKr1PLZpdaR4p1VVvm9XeGz3euZkpqGtqU6zvtaNKMG91pWxbg+2xoo9EjJOI9+qJuBYViR2ss57Ycy37kMjPAyc/W7zI7NxYvrEK01/5vFNSaG5mCu4971jVKH6OFZE2WtcGXCTFCSfvZWFk26IWST4fcO3InxZIcl86oj8k1O7rolSG1TtK+MIkSM8XKZGiF2yiFy5KzLY31hdwO+8BBMjPI7m6Bhcoetqs973B+RtcLEXX16n7JOkpS8viwMhxSMsxw+jr1Wjdr/Qey+PtPkleYHZuaPlMVPqHtPAyOFZE6rhIOihRFkmAszGdeuNTjUYH52Yk4dQjC9DUdgAn9umGScP7IDXZrzsCVa6+AEzFPpsVjN0OxkabjaE2Klbks5bY89yMFEw9rR+6d01HYbb2eSiqvbHqb2T/0HLqpdF5pDVCXcR71WLmldpjRR9ZdSqbyOOQ3DHDikjs8NN3a/e1ID8rFT2z0yNOlzVyLBf5ecAI6NjMzg2tkfCF2Wn4cPrplp4KSpQIGAGegJyM6dQbf2v0i3Dd/nb8+qTDOm1LbwSqXH21RJNbKRi7HYwxNxtDbZRSXKzW2PO6/W0YeEiu8PhrrWLVX3QEtNl5pDVCXcR71WLmAfv6yGhZIt+r95hhVSS2GyKt7SwrHpmdG1oj4asbWlTnF8eKSBx9dzokEsBsDKlVMcBuiEcNj+d2uj52xy2Lbq9T/ef0uKlxe/3soncuM2aZlJidG05E+hOROi6SyHZmY0itigF2QzxqeDy30/WxO27ZybjxeNiuVm6vn130zmXGLJMSs3PDiUh/IlLH0+1cxKpziUWXq3QdhtZtmIkOLsxOMxQDDHRcwBr+3mA7ftjThA3f1UGSgK7pSdjbrJz6ZiW/D7jo5OLQ3830k1nRfRVep8LsdMVTQ3wAeman4eYXP8WepgPIz0rBq789BfldUmXnTHjU7cl9usWMqtYjNzMFgYCE9oBk+/n4esbNzDVJwffreavSuAL2X8vQeiCAZ8u3YXttE4rzM3Fp6U/XF5qNUNbyfi2xyflZqaiu34/yihoMLc7TFLM8tDgP5RU1cXFNiNY46kS/DsZsBLfacTVI6fNPTqKPCZEIDG5wCatSaUSXK1debmYKACimZskpW7YJTxyMHNYjVrqPWjpQZmoS/jpxkKHIZbtE95uI+G0j5JL2gvWRi6gFYn9hj34uNzMFrQcCnWLIg9HkotIEnUp20jpuPnQk1KntC6L6I9a42pmKVbZsE+a/XxmxOPT7gNOP6YmN3zeYilDWm6andf8Kxu7Hikq/dlRJp1h+r6aLaY2jZqJaB7MR3CLS7cLL4pgQKWO63UFeWCSJjpW1qlxR9/UwGvOs9AUvvFwtseKTDUQu20Wu30TcJ8nn03eRv9x9jeyMI4+Oqla7+a0SJ+8REmtBGTR5VAmOPywv5rydLPPF2yg942pV3+n9BxI9XzKNtEPrP5ioLYSCCyi7+tFKWr+wA8q3TAC81WYRnLxPUngZHBOi2LhIOsjtiyQrY2VFlmsknlluG0ZjnrVFqK5AdUOLprK0THq/D7jvvGNRtvxL1Da2aq5n1/RkTBjcC8X5mXjqg22aUouiy5DrN7lTJ8If794lLRQbHP3/Nzy/Dj82HjBcB7vjyAu6puKvvz4+FFU9tDgP67bv6dzOrDQEJAm/f/5TxdQ9o/uQWVpifQuz0wD4Yp6+WJiTjvduOQ2fbKtVjV/PSU9GUpJfcb7aFWutpPVAAEfP/LfuBa+W/d9sBHMwYeyuN79AbWPsufTeLaeF5mNwfo5+YJVt/WglrXHUBV1T4fP5VeeuF9oskojTRbWc4qj0Xjv3ZyKvYgS4R1gVKyu6XCPxzHLbMBrzrC1CVX2BFCxLi4AE/O/7es0LpGDZDc0HcPaxvQBA9wIpWEZ0W5ViXbXEvZZX1OhaIMnVwe448p17WztFVSu1s7yiJubCweg+ZJaWWF+1ORus+7rtezTFr9c3xx5nu2KtlTxbvs3QL4Ja9n8z7QjuR+UVNYoLpPBy1m3fE1GOWuy7U3PQCK1x1Dv3xj4ueqnNIpmN4E7y+zDi8O4YcXh33e+1e38mindcJDnMqlhZ0eWaiR0Nf68V8d8iylWyraZJ/UUyRNRHVJtEjJ0TsbNumsNGiNyeVfHodvddeMS9EWb3f7XXGS3HrXPQiHiJ4k9E8TQPidyAEeAOsypWVnS5ZmJHw99rRfy3iHKV9OmWqf4iGT27plvWVjvL0Rt/LJKb5rARIrcnYj5Flxf+X62vNys84t4Is/u/2uuMluPWOWhEvETxJ6J4modEbsBfkhymJVI5PDpUT7ytWqRokYZyQzHZdfvRNT0Ze1VO54mue0F2xzUjr2/4PnTuvpn47/Cywts+tDgPeZnJ2NOk77QytfpnpCYhPysVexpbNQdWhI+X0bbmZaZgaHEegJ/OUf/o6934vm4/euVmYMTh3TGsr7bz1E8qyUdB11TV02NitcPuOPLcjCSs/upHvPjJt+iVl4HhMuflh19Hkp+VonodSSAgP3esoiUWuODgNUlKsed651PwmiSl+ap3XNWii9VEH1cuOrkYdy/bbPiapKHFefhw626Uf7MbQMdpTcP6dtM0P2PFJ4fPpbzMFOxRCNtQ6g+tY610/Aqvg9ORzVrjqIPXJGmdu1ZwS5+5hdX7M1mD89i9GNzgMLVIZeCnNBo9yTlakrWCiTkATKenxdpGdDS4UoyumuiEtmDb5ervBKVkOqMR3rmZKfj1CYfihf9+JzuOehOPtCT/AcopSE7FkQeFt1dPIpkE+XloRxyullhgQD4hLCg81dHMGOgdVxHpmnLHlYGHZOPtTbt01/vaUSWy+0L4cSxW3yjtL3pvB6AUwxyrH9XmoNsim/Wm2wFi544Wbuszt7BqfyZrcB47g+l2B7l5kaQWqZyXmYKysC+FWmM97Yxq1itWjK6RstzUxvDxCqdlwWqGnntn3PjiZ53uTRRN7b4yItqSl5mCFpn7JGmhJ8Jd6VcBO78waPkQjBWLHR3bbfQeX2rjKvp+arGOV2P698SKzbs03ydpwqAi1djw4Jd2rf/oFKueatuJda82ufvIxarPtQrz2ekvtW6+TxJjrmPjF29v4Dx2DhdJB7l1kaQlUrkwOw0fTj8dADTHeqq91g3kYnS7Z6UBPmDX3hbcufQLxdNd3Cw4XnJ3o9caT25Ekc4o94+27sY/P/0OTa3tOLFPPi4ZVowNO+o0/dTfHpAwvGwFdu411pac9GQ8dslQDOvbkawUjLqVJAnPrd2Ouv3qp0vGumeSD0B+Vir+fPYx6Jmdjpte3KDY73bG4cY6ncJIbG97QMKaihpMXRw7Ejw/KwUzfzEAhdnqp3CIOuVDa3tW3nQqFq/dju21TSjOz8SlpX2QmuzvVI+hxXkYdf9K1f2n6OAxZdT9qzTFUgP6j5Va5kxELH9WGm566bOY9fGpzGcnI5u1xlHbeboQY6614Slc7sZ57CxGgLuclkjl6oYWfFxZCwCaYz3VXusGSjG6QEeUrhcXSMBP4xXdJj3x5EboiXRN8vsw8qgeGHlUj4jHtcbBflxZa3iBBHREVPt9vtBBPxh1W15RgznvVmgqI9b1LBKAmsZWFOZkAIgdsW1nHG6sWGAjsb1Jfh/8fvVI8NrGNhRmp2ueGyL6QWt7Nuyow1Uj+6rWo7yiRtP+U1XfjGfLY9+XzOyxUsucCa9/R91j1yfWP1M6HdmsNY5a1NzRgjHX2tg5JqQf57E3cJHkECuiOr0W6ylXX6+1IZpTbbKr36yKNncydtjpORdvsdNORrdrjRi36lYEorcjupx44NZ5T6QH57E3cJHkECuiOr0W6ylXX6+1IZpTbbKr30Rsx44+8tJ+E2+x005Gt2uNGLcrnt8NMf7xxq3znkgPzmNv4CLJIXqjOvW+1s2n3IXH+ZZX1HScu98lDZCAXftaYsY5691OrPP9RQuPVA83tDhPWJvk5GeloLqhGeUVNaauI4l1/nooIrl+f8yIZDVKMcwdscNpmk6r8h9MOVTaF/KzUlFdvx/du6TFjIXXEodrx3n9RmN7RcT9WtE+0THEWudGUU46Li3tgyc/qBRyXJUTq+7hfZmfmYot1Q3YXtuErunJ2Nd8QLE+Wq5J0tpX8XIdSqx2aIl7Dx4TP/x6NyABuxtbHO2PiOvUDn7WOV0nchbj2r2BiySHJPl9mDW+P6YsWt8ppS14uJw1vn/o4Bl8rRwJwIRBRZ1ea1cih1z9pRjPAR31Hf3AKmHpdkrbuWak9iQ0s8LHICiYMmTVAgnouO5k2gsbABhLMFJLQjKapibnjgkDZL8QJPl9uGPCAE0x5acf01MxQjp4TdK0Fz+LWYbcPhbNroQovccCs+8Lsqp9ZuslV56WuTFrfH+kJvsNHVf1JGXK1d3oPiIBGHNMT7xzcD6b6at4STRTa0es+RUUfkwM50R/qM0NL44RmSf6OEnWYLqdw/R8sOmNCbYydjrWPYrUngveJ0nvxItONdNSh3EDi2L2m5zM1CRD0dRyY6C2WFWKBzZKb3SoWgSpUjyxkqzUJABAY1T/ab2nk9XzNkjti4kT0axGv+QaeZ8d7RP9pV1tP35cJSbdzD3l1N5v9h+lJsvcEkFPX8VLlLDeW13oXZja3R9a5obXxojEipd/3PAaRoAf5PZFEqDtFAm9cZEdsdMrVe+arkV+Vgr+fFZ/7GlqRX5WKgpzMjpFGMeKN46O89X6C1J4nHNhTgaGFueFIsPVthN8zkj8dlFOOu4/7zis3VaDgAQs+WQHahtbNdVXT7xwfmYKUpOTDI1R9I11leqhFvmsNqfUTlfMy0zGRScXw4eOJKXoaO9YscFKWg8EcPI9byueJmdWt6xUlM84HanJftnnnYxmNXq6lJ732dk+u2PFo2PStWxbyzFCac5ouZWDFoXZaVh9688Uj2+xxEuUsJkxrq7fj7v+tVn3cdrK/tAzN7wyRmSNeDlN1ksYAe4hWqI69cZFdsROi7kuqbaxDUW5GThv6KGyz8eqv1ycr9YvFOFxzsEytG4nyEj8dlV9M5KT/bh57NEor6jBYxqjqfXGC9c2tQEw9ouJiNhgLXNK7Z9Q9jQdwCmH9+i0HS2xwUrWbd9j2QIJ6JhTcvHzQU5GsxqN7dXzPjvbZ3eseHRMupZtazlGKM0ZLbdy0KK6oSXmnIwlXqKEzYxxeUWNpgWSUjlW0DM3vDJGZA3GtbsXF0keoTcu0slIZdHlmNm20fea6Ue3RHaq1cOt8cROR6bHezSrF9tnZZ3NlC2yj8weq6wq3y52j4PV/eHGOhGRPlwkeYTeuEgnI5VFl2Nm20bfa6Yf3RLZqVYPt8YTOx2ZHu/RrF5sn5V1NlO2yD4ye6yyqny72D0OVveHG+tERPpwkeQgreehth4IYOP3daqBAuFR0EOL83TH28oJj6HUeu1U8FS/3XubUdvYiqr6ZvTKyUBeViryM1OQn5WKPY2tmuqVlZaEA+0BtAck1XN05eqnJ1o6KDzKW+/7s9KS8MHWHzGsbzcUZqdhZ0OLYrxnQXYaAB92Nugfo1gx2EDHtVw/7GnCU+/XI79LGgqz0ztd06U2R7TEExdkpyEgSXh9w/ehPm8PSHi2fBu21zahOD8Tl5b2kb3+R+56tXXb96C6fj/ys1I1nz6jV05GMgKSFJpT4dc11Da2IjczVXWOKsW969lHdu1tRvesjv5bW1kDSQJyM1PRvUvn6/6C71tTUYPyb3YDYdeAyZUffU3YiSX5obHvntUxH5TmnRXRs+HHhdp9LbLXNsZ838E5oTQmZurcEdEfe751y0rF0OK8To9riaPWQikaP5bwPo11iwErouDdFh2vZxzsiFbW+pltZ53IOm7Yf9wi3trG4AaHaE00KVu2CfPfr9R9r5/wFDlAe7ytnMcvGQIgdoIcYDwGVwu1dLRY/fnpt3t0pdtFp2QZTVvLSk1CY2u7Yrzn3IP9Gox21zNGRhL45NIBleZIdLqd3PMSOqfzZR2sV/hr/b6OKPYZZ/UPPSY3XtH1U6MntllOsP3RqWJaTB4V2R5A2z6tZx+JjmGXm4fR+4XS66L7KjhuseamqGSlWG2OleKkta/M1NnoeESXYWQfDve4zrqL7Bu96VpWpnEp9aXWdqiNgx1Jcno/s5lu521u2n+c5qW2Md3uIDcukrTGnOqNrpYr61qZaFm9X0YnK8RAy33Zt3oyyX2ZiNWfRuoT3IaIaF+g80LCzsVlLLHmiNp9koxElwcXFmb7dfKoEhx/WJ4tUeFKtMa9m91HfOgYH7XjQPAfMrTcZypYrtwiV/QHmtYI5OgviHrmiNE6652Hsb7MGt2HtUbjR29LVN/ojQ53e3S80/ckMvKZ7dYvkaTOjfuPU7zWNi6SDnLbIklrzOnKm07FgFnLVRcz+ZmpqG2SP00kWNZ7t5zW6TSrtd/U4NKnP9ZU51iLjeA2JEnSnSIHdJwiOPMXA9A9KxV/WPIp9mi4V0l0BKyICN5wwThesze7DSrKSceDFwyKeYf14KlUUxevR91++774K80RuVO9wk8Ru+mlz3SnJ/p9wBezx+FnD71ruF/D6zvq/lXCEhyN1kNL3LvZfUTLYr/w4KmbevojeLrkQxMHY/c+5blplJ59M3y/1vK+4HGjMNtYnY0eN2JFNYef/nbn0i9iHsu6pCVh7kVDMfyI7rrqruXzI/y2CSKj4L0SHR9xrOqSBkiIeewVpfVAAEfP/HfMz2wfgIVXnIhkv9+WOpF13Lz/2M2LbWMEuEtpjTm9Z9kmTb/2KC2QwsuSi5b9audejTWO/QUtuA2jahvbUJjdcbGq2gIJ6ByTKiqCN1x1QwueLd8mrNyq+mb4/T78cvAhiq9J8vvg9/tsXSABsedIuPCI0vKKGkOLk4AE3LPM3C9mwfo+W77NsQVSeD20xL2b3Ue0/CuWkcWXdPB9fl/suWmUnn0z+vYFqvH5B48bRmNzjR43YkU1h8dRqx3L9rW0IznZr/sLg5bPj+jbJpgpS8+4uCU63qk45WfLt6l+ZksAvt61D1eN7GtLncg6bt5/7BbPbeMiyWZaIz631TRZus3tteLKN0tv7Gn4662KTBXdP1rq6WT8q55tm6mnqHntlvkbD5G9VrXB6H5tR6S12TY7FSEvsmyrbisRD/uEEVqPSW45dpE53H9+Es9tk7/lPFlGa8Rnn26Zlm6zOF9c+Wb17JquK/o0/LVWRaaK7h8t9XQy/tVo/+slal67Zf7qnbtuZFX99ZarN3bfiVsDaHm/W+PKzZYVL1HjVtF6THLLsYvM4f7zk3huG39JsoneuNY/ntUfz639VvX85gKNUb7R53hfdHIx/vKvzbouWo4VhxwISNi519g1SdUNzeiemYq8zBTV01SyUpPwwiff4oOtP2J4v45oYxERvOEKs9Nw0cnFmPNuhZAYaqW4aKDjPPZgXHbvvEwUdE3Drr3yseFWiI6eVYrljo5VN9Lnfh/wx7P6453NuwyPV7C+IsfHiOh+0xJdLEmSYiS82rbU3pOXkYzU5CTd+2BB11QEJAmvrv8OtY2tinHxwWPIMx9twyfbapCZmozzhxyK4Yd3j7g+MHruaJ0nRTkdry+vqLE07ltrpLgSLdvWGmU9uHcunnr/G9WofCNla+kbPWW1ByQEAhJyM1IUTwkWEWPt1fjg9oCEI3t2Vd1X/T7g0tI+NtVKndH+9uo4iaR264Do/UHkvus28dw2BjfYwGhcq1pSTjDlSy0yFZCP7x54SDbe3rRLb3M6yUxNAgDdkdQi5Gam4NcnHCok6jxoskzamxlK8b5yUbG+g/c/skP0fNMSyx1MYgL0R5eHp9tpTWGTq69cGp/dlNLtAPFx75M1pNsBP0XO66H0pS563JXKzkxNwl8nDgIgf4wJRsyrpdtpHVO74r7NblttPozp3xMrNu+K6GO5qHwjZevpG63zVq3fRCRYeSk+OJyeeSV36wCnGO1vr46TSGpjrpZuB1h/6wW7ea1tTLc7yOlFktm4Vrkv0tEfprEOWoB87HD4h7WIhZLT9C5s5BYj4QsuUTuF0oeimXh3o5QWPHrizqO/POmJ4Q6PVjeySNL6pdsOdtwnKehxnX1t5B5aVtCyqNU7pnbEfSvdP0vvtpXmg9o/Tmn5Ii3yi6qRz49oZr8key0+OEjrvNK6ALaL0f726jiJpGXMeZ8k97eNi6SDnFwkiYprDT8lS+m0DLmfvwH1aOKC7I7Tu/TerFarzGQffj6wCL1yMpCXlYr8zBTc/e8twk+TKsxOw6qbT8OI+1YonsoIALkZKZhz8RCc2Ccfn2yrRXlFDQAJpX07Tt0TFfsNKMdeaomKNSM/KxWPThyM3Y0tqqdQaY1clmtXRwz3Ss3Janqj1aP3j6HFeULHx4zoKPogLaehhL8mPyMVf3hBOfo+fA61ByScfM87mmLy7z//OKytrIUECc+t2Ya6/c4smqIj5qsbmlG7rwX5Wamax9SOuG+5bYg4pSi6jMG9c1Vv7eD3AVvuOlP11DuRpzwZ+fwAfjqeDuvbzdS2vRYfDGibV5kpSZh2xpGYNFz9VEq7GO1vr46TSFqPJWtmjIk53vF8uqJX2sYIcBcQFdeamuxXjQyViz0tr6hR3b6R6GA9mg5I+PWJh0XER1txHUl1QwsWr90ec4EEAHX72+D3+ZCa7MeIw7tjxOHdQ8+p9ZdeSrGXWqJizahtbEVysh/nDjm003Ny80xvHHJkDLf2+aM3Wj16/xA9PmaoxUDHEh2nHmvREx01rjUmPznJj5vHHoXyihrMWVWh+h6rqEXMaxlTO+K+5bYhIko6uoyn3v9Gdd8PSB3HCCPHfKOMfH4APx1PzXwJ8mp8sJZ51dTWjoGH5LhmgQQY72+vjpNIWo8lem6pEW/irW1cJFnI6VhEt8Qt2hHZDWiPVlWqg13jYEcErB2R3kbaYeQ9eiNU7SKiPlYdI9zWZ2b3OTvivu3oKy/FRNvVb24aHz0Srd5eba9I7IPEw0WShZyORXRL3KIdkd2A9mhVpTrYNQ52RMDaEeltpB1G3qM3QtUuIupj1THCbX1mdp+zI+7bjr7yUky0Xf3mpvHRI9Hq7dX2isQ+SDzu+Q04DgVjEZVORPAhdjy0HdsvzE6DVaeLyrVPrU5yZWhRmJ2GS0v7mOrvYN1EUdrepaV9bO1zNUbGpCgnPdTfWmkZI7ntREeouuHsZlH7rZ5jxEkl+SjMTtNVN63vsYrWfc7KY6TTx+FwWvZ9t8RE29VvbhofPRKt3l5tr0jsg8Tj6CKprKwMJ554Irp27YqePXvinHPOwZdffhnxmubmZkydOhXdunVDly5dcP7552Pnzp0O1VifJL8vlBAUvVMF/z5rfH/LLmrTsv07JgzANSNLdJWr9Qsu0Ll9seoU/f5gNLAWd0wYgNRkv6n+DtZNxGjE2l5qsl93n5vdZixaxyR6G8H+1roltTFS2k6wLXrqGatMub/rLU/UfqvnGJHk9+GOCQN01U3re0Qws88Zfb8WTh+Hw2nZ968ZWeKKa1js6jc3jY8eiVZvr7ZXJPZB4nH0SPzee+9h6tSpWLNmDd5++220tbXh5z//ORobG0OvmTZtGpYuXYqXXnoJ7733Hn744Qecd955DtZan3EDizD3kiEojPoX98KcdFviMrVsf8ZZ/TF5VInqv3Ce0b8nHpcpKy8zBbmZKYrla62T3PtnnNUfj18ypFP5QbmZKRH3ITLb38H3m/1FSW17Sn3u9wHHHZodcyyKctIxeVRJpzqamVNK/RZdj+htaOkvrWOkpS1K783LTAndr0tu23LztjAnXfE5pfLyotoigp45O25gkeL+oFS3WO/xKcyz6HHPSuvcF0DH/ZNi9a+efc7KY6TTx+FwsfZ9N91HB7Cv39w0PnokWr292l6R2AeJxVUR4D/++CN69uyJ9957D6NGjUJ9fT169OiBxYsX44ILLgAAbNmyBccccwzKy8sxbNgw1TKdvk9SkNOxiFq2Hx41XpSThu9q9+PbPfvRp1sm/nhWf2Qc/NKoFBert33h5XTPSgN8wO59LbLvbw9IWFNRg4++2Y0f9uxHr9wMDO/XHcP6yUfPmu3v4Pur6/eHYrR7dklDQJKwtrIWgIST+3SDP8mH3ftaVOuvRCnePfzx3nmZOLqgK2r3t3aK7hY9p6LLVIoNj9Vfu/e1om5/K3wASvtqGyO9/ac0B9d8UxMR6x6+7Vj9pVheRQ3Kv9kNoCOxx0zUsRo94xncH/TUTe49J/bJ74jmDpvnSnHx7QEJz3y0DZ9sq0FmajLOH3Iohh/eXVP/im6/UU4fh8NpubWDW9jVb24aHz0Srd5eba9I7ANv8+R9kr7++mscccQR+PzzzzFw4ECsXLkSp59+Ovbs2YPc3NzQ64qLi3HDDTdg2rRpncpoaWlBS8tPscQNDQ3o3bu344skIiIiIiJyltZFkmv+2SoQCOCGG27AiBEjMHDgQABAdXU1UlNTIxZIAFBQUIDq6mrZcsrKypCTkxP607t3b6urTkREREREccQ1i6SpU6di48aNWLJkialyZsyYgfr6+tCfHTt2CKohERERERElAlfcJ+l3v/sd3nzzTaxevRqHHnpo6PHCwkK0trairq4u4teknTt3orCwULastLQ0pKU5F3lLRERERETe5ugvSZIk4Xe/+x1effVVrFy5EiUlkdGoQ4cORUpKClasWBF67Msvv8S3336L0tJSu6tLREREREQJwNFfkqZOnYrFixfj9ddfR9euXUPXGeXk5CAjIwM5OTm46qqrcOONNyI/Px/Z2dn4/e9/j9LSUk3JdkRERERERHo5mm7nU7hJx4IFC3D55ZcD6LiZ7E033YTnn38eLS0tGDt2LB577DHF0+2iuSUCnIiIiIiInOXJCHArcJFERERERESAByPAiYiIiIiI3ICLJCIiIiIiojBcJBEREREREYXhIomIiIiIiCgMF0lERERERERhuEgiIiIiIiIK4+jNZO0QTDhvaGhwuCZEREREROSk4JpA7S5Icb9I2rt3LwCgd+/eDteEiIiIiIjcYO/evcjJyVF8Pu5vJhsIBPDDDz+ga9eu8Pl8TleHdGpoaEDv3r2xY8cO3gw4wXDsExfHPjFx3BMXxz4xOTXukiRh79696NWrF/x+5SuP4v6XJL/fj0MPPdTpapBJ2dnZPHAmKI594uLYJyaOe+Li2CcmJ8Y91i9IQQxuICIiIiIiCsNFEhERERERURguksjV0tLSMGvWLKSlpTldFbIZxz5xcewTE8c9cXHsE5Pbxz3ugxuIiIiIiIj04C9JREREREREYbhIIiIiIiIiCsNFEhERERERURgukoiIiIiIiMJwkUSOmzNnDvr06YP09HScfPLJ+PjjjxVfO3/+fIwcORJ5eXnIy8vDmDFjYr6e3E3P2IdbsmQJfD4fzjnnHGsrSJbQO+51dXWYOnUqioqKkJaWhiOPPBLLli2zqbYkkt6xf+SRR3DUUUchIyMDvXv3xrRp09Dc3GxTbUmE1atXY/z48ejVqxd8Ph9ee+011fe8++67GDJkCNLS0nD44Ydj4cKFlteTxNM79q+88grOOOMM9OjRA9nZ2SgtLcV//vMfeyorg4skctQLL7yAG2+8EbNmzcL69esxaNAgjB07Frt27ZJ9/bvvvosLL7wQq1atQnl5OXr37o2f//zn+P77722uOZmld+yDtm3bhptvvhkjR460qaYkkt5xb21txRlnnIFt27bh5Zdfxpdffon58+fjkEMOsbnmZJbesV+8eDGmT5+OWbNmYfPmzXjqqafwwgsv4I9//KPNNSczGhsbMWjQIMyZM0fT6ysrK3H22WfjtNNOw4YNG3DDDTfg6quvdvTLMhmjd+xXr16NM844A8uWLcO6detw2mmnYfz48fj0008trqkCichBJ510kjR16tTQ39vb26VevXpJZWVlmt5/4MABqWvXrtIzzzxjVRXJIkbG/sCBA9Lw4cOlJ598Upo0aZL0y1/+0oaakkh6x33u3LlS3759pdbWVruqSBbRO/ZTp06Vfvazn0U8duONN0ojRoywtJ5kHQDSq6++GvM1t956qzRgwICIx379619LY8eOtbBmZDUtYy+nf//+0uzZs8VXSAP+kkSOaW1txbp16zBmzJjQY36/H2PGjEF5ebmmMpqamtDW1ob8/HyrqkkWMDr2d955J3r27ImrrrrKjmqSYEbG/Y033kBpaSmmTp2KgoICDBw4EPfccw/a29vtqjYJYGTshw8fjnXr1oVOyfvmm2+wbNkynHXWWbbUmZxRXl4eMU8AYOzYsZq/F1D8CAQC2Lt3r2Pf8ZId2SoRgN27d6O9vR0FBQURjxcUFGDLli2ayrjtttvQq1evTgdUcjcjY//BBx/gqaeewoYNG2yoIVnByLh/8803WLlyJS6++GIsW7YMX3/9NX7729+ira0Ns2bNsqPaJICRsb/ooouwe/dunHLKKZAkCQcOHMB1113H0+3iXHV1tew8aWhowP79+5GRkeFQzchuDz74IPbt24eJEyc6sn3+kkSede+992LJkiV49dVXkZ6e7nR1yEJ79+7FpZdeivnz56N79+5OV4dsFAgE0LNnT8ybNw9Dhw7Fr3/9a/zpT3/C448/7nTVyGLvvvsu7rnnHjz22GNYv349XnnlFfzrX//CXXfd5XTViMhiixcvxuzZs/Hiiy+iZ8+ejtSBvySRY7p3746kpCTs3Lkz4vGdO3eisLAw5nsffPBB3HvvvXjnnXdw3HHHWVlNsoDesa+oqMC2bdswfvz40GOBQAAAkJycjC+//BL9+vWzttJkmpF9vqioCCkpKUhKSgo9dswxx6C6uhqtra1ITU21tM4khpGxnzlzJi699FJcffXVAIBjjz0WjY2NuPbaa/GnP/0Jfj//nTceFRYWys6T7Oxs/oqUIJYsWYKrr74aL730kqNnCvEIQ45JTU3F0KFDsWLFitBjgUAAK1asQGlpqeL77r//ftx1111Yvnw5TjjhBDuqSoLpHfujjz4an3/+OTZs2BD6M2HChFD6Ue/eve2sPhlkZJ8fMWIEvv7669CiGAC++uorFBUVcYHkIUbGvqmpqdNCKLhYliTJusqSo0pLSyPmCQC8/fbbMb8XUPx4/vnnccUVV+D555/H2Wef7WxlHImLIDpoyZIlUlpamrRw4UJp06ZN0rXXXivl5uZK1dXVkiRJ0qWXXipNnz499Pp7771XSk1NlV5++WWpqqoq9Gfv3r1ONYEM0jv20Zhu5016x/3bb7+VunbtKv3ud7+TvvzyS+nNN9+UevbsKf3lL39xqglkkN6xnzVrltS1a1fp+eefl7755hvprbfekvr16ydNnDjRqSaQAXv37pU+/fRT6dNPP5UASH/961+lTz/9VNq+fbskSZI0ffp06dJLLw29/ptvvpEyMzOlW265Rdq8ebM0Z84cKSkpSVq+fLlTTSCD9I79c889JyUnJ0tz5syJ+I5XV1fnSP25SCLH/e1vf5MOO+wwKTU1VTrppJOkNWvWhJ4bPXq0NGnSpNDfi4uLJQCd/syaNcv+ipNpesY+GhdJ3qV33D/66CPp5JNPltLS0qS+fftKd999t3TgwAGba00i6Bn7trY26Y477pD69esnpaenS71795Z++9vfSnv27LG/4mTYqlWrZD+3g2M9adIkafTo0Z3eM3jwYCk1NVXq27evtGDBAtvrTebpHfvRo0fHfL3dfJLE36yJiIiIiIiCeE0SERERERFRGC6SiIiIiIiIwnCRREREREREFIaLJCIiIiIiojBcJBEREREREYXhIomIiIiIiCgMF0lERERERERhuEgiIiIi09ra2pyuAhGRMFwkERERuczw4cPx9ddfo7m5GSNHjsTGjRudrlInTz/9NH72s5/hsMMOQ2ZmJi699FKnq0REJEyy0xUgIvKayy67DHv27MHSpUudrgrFqd/97ncYOHAgDhw4gPHjx2PAgAFOVynC5MmTsXz5ctx999044YQTkJycjJ49ezpdLSIiYXySJElOV4KIyO2++OIL3Hnnnfjwww/x/fffAwC6dOmCU045BTfeeCPOOOMMh2tI8aapqQn79u1z3eLj/fffx/nnn4/PPvsMRUVFTleHiMgSPN2OiEjFq6++ikGDBqGlpQWLFi3CxIkTMW7cOPz73/9GYWEhfv7zn2POnDkAgNWrVyMlJQXV1dURZdxwww0YOXIkAGDhwoXIzc2NeH7btm3w+XzYsGEDAODdd9+Fz+dDXV0dAGDPnj047rjjcNlllyH4b1unnnoqbrjhhohy7rjjDgwePDj0d7ltjRo1KmJbAPDmm29i0KBByMjIgM/ng8/nwznnnKPYJ8HtPPHEE+jduzcyMzMxceJE1NfXh14TCARw55134tBDD0VaWhoGDx6M5cuXdyrr8ssvD20z+Ce6XXPnzkW/fv2QmpqKo446Cs8++2zE83V1dZg8eTIKCgqQnp6OgQMH4s0334x4TfQ2ovvgn//8JwYMGIC0tDT06dMHDz30UMT7+/TpE3pfVlYWhg8fjv/+97+h57WMh1qfhM+DzMxM9OzZEzNnzoTP58MjjzwiNxSd3hv9JziHgn3w2muvhf7+1FNPdervZ599FieccAK6du2KwsJCXHTRRdi1a1fo+TfffBPHHnssrr76auTm5iI/Px+XX355xNgDwJNPPoljjjkG6enpOProo/HYY49F1EPpz7vvvgsAuO2223DkkUciMzMTffv2xcyZM3ndExHZhoskIiIVN9xwA0499VS89tprOPXUU5GRkYG0tDSccsopWLBgAS6//HLceuutaGxsxKhRo9C3b9+IL/FtbW147rnncOWVVxra/r59+3DWWWehb9++ePrpp+Hz+Qy35ZVXXsGnn34a8VhdXR1+/etf49RTT8WmTZtQVVWFiRMnqpb19ddf48UXX8TSpUuxfPlyfPrpp/jtb38bev7RRx/FQw89hAcffBD/+9//MHbsWEyYMAFbt27tVNa4ceNQVVWFqqoqlJaWRjz36quv4vrrr8dNN92EjRs3YvLkybjiiiuwatUqAB0LjzPPPBMffvghFi1ahE2bNuHee+9FUlJSqIzgwnLBggWoqqrCxx9/HLGNdevWYeLEifjNb36Dzz//HHfccQdmzpyJhQsXRrzuzjvvRFVVFf773/8iKysLU6dOVe2ncHr6BAC+++47PPLII8jIyNC8jXfeeQdVVVX45z//GfN1jY2NmDlzJrp06RLxeFtbG+666y589tlneO2117Bt2zZcfvnloed//PFHrFy5Eunp6Xj//ffx2muvYc2aNRHz+7nnnsPtt9+Ou+++G5s3b8Y999yDmTNn4plnngGA0FhXVVUB6FigBv8+fPhwAEDXrl2xcOFCbNq0CY8++ijmz5+Phx9+WHM/EBGZwWuSiIhi2LlzJ7799ltMmzZN8TUTJkzAwoULsXHjRpx88sm46qqrsGDBAtxyyy0AgKVLl6K5uVnTwiNaS0sLLrjgAmRmZuKFF15AcrLxw3ZbWxtuu+023HbbbZg5c2bo8a+++gpNTU247bbb0KtXLwBARkYGWlpaYpbX3NyMf/zjHzjkkEMAAH/7299w9tln46GHHkJhYSEefPBB3HbbbfjNb34DALjvvvuwatUqPPLII6Ff3oJt7NKlCwoLCwEAqampEdv5//buPybq+g/g+BMJuOKO8zTjQgQFuoKRp39QGYOj1aq1XJsVrNg8iH74RzMnXmxhQeEYU4KWRrPabrFBtpk6wvxHZ21d3LDwNhyIKRizm1kIEjAjjvf3D3af3afjBCld6/t6bLdx78+P9/vzfo/t87rX+/P+1NfXU1JSogVgW7duxev1Ul9fz0MPPcTRo0fp7Oykt7cXm80GQFpaWti1Ayxbtgyr1crVq1d12xsaGnj44Ye1frHZbPT09LBr1y5dgBDMrixevBiLxXLdAet8+ySosrKSoqIijh49Oue5g+NltVqxWq0sWbLkmvvv3LmTrKwspqamdOWhwU5aWhrvv/8+OTk5jI2NYTQamZ6exmKx0NzcTHx8PDCTNcrLy+Ps2bNkZGRQVVXFu+++y4YNGwBYtWoVPT097N27F6fTqY110JIlS8LKtm/frv29cuVKtm3bxr59+3j99dfn7AshhPi7JJMkhBDXELxhn5iYiLhPcJvBYABmpo+dPXsWr9cLzEx5Kyws1G4oAa5cuYLRaNQ+kR7MLy4u5tixYzgcDuLi4v7WtXzwwQeYzWaKi4t15StWrOCWW27hs88+Y3p6et7nS0lJ0QIkgHXr1jE9PU1fXx+jo6P4/X5yc3N1x+Tm5tLb26srGxoaIiEhIWI9vb291zyPz+cjOTlZC5BmMzo6CqAbg/nU8eOPPxIIBLSyiooKjEYj8fHxdHZ2hgU2TU1NunGtra3VtWG+fQLQ1dXFwYMHqampiXhdoYaGhgCu2ZdBfr+fhoaGsCmFMJNVW79+PSkpKZhMJhwOBwCDg4PaPna7XdeXDzzwANHR0fT09DA+Ps65c+coKyvT9cWOHTs4d+7cvK4F4PPPPyc3Nxer1YrRaGT79u26NgghxI0kQZIQQlyDxWLh/vvvp7m5mfHx8bDtU1NT7N27l+TkZLKzswG44447WL9+PW63m19++YUjR46ETbUzmUz4fD7t89VXX81a/8WLF/niiy+ora2lu7t7wdcxPDxMTU0NDQ0NYdmPO++8kw8//JDa2loMBgNGo5GWlpYF13W9+vv7WbVq1YKPn89UNL/fD6BlyhbK5XLh8/no6uoiLy+PwsJCXRBVXFysG9dNmzYtuK7y8nK2bds278UR+vv7iY2Nndc1VlZW8uyzz2K323Xl4+PjPPbYYyQkJNDS0sKJEyc4ePAgAJOTk8DM/0QkUVFRjI2NAfDxxx/r+uLUqVPaDwdz6ejooLi4mCeeeIL29nZOnjxJZWWl1gYhhLjRZLqdEELM4ZNPPuHJJ58kMzOTsrIyBgYGmJiYoLa2lubmZi5dusShQ4d0z8C8+OKLPPfccyQnJ5Oenh6WPVi0aBEZGRna90jT6Nra2khLS+Oll16itLQUr9e7oCl3NTU15OXlkZ+fz/nz58O2O51O3G43a9euZcuWLVRUVOhu/mczODiI3+/Xbsq9Xi+LFi3i7rvvJiEhgaSkJDwej5aJAPB4PNx3333a9wsXLtDf368tajGbzMxMPB4PTqdTd56srCwAVq9ezYULFzhz5kzEbNKJEycwmUykp6dfs45QHo8Hm82mG9fbb79dG7eKigruvfdeBgYGtDKz2awb19Apb/PtE5gZ9zNnznD48OGI/fJX33zzDQ8++KCuvbPx+Xzs37+fvr6+sG2nT59maGiIuro6VqxYAaBbnALgnnvu4dNPP2V8fFzLJnm9XgKBAJmZmSQmJpKUlER/f39Y1nK+vvvuO1JTU6msrNTKfvrppwWdSwghFkKCJCGEmEN2djZ9fX243W6+/fZbent7+fPPP+no6OCFF16gtLSUZcuW6Y4J/hq/Y8cO3nnnnQXXHbzJrqurY/Xq1dTV1eme1QgEArrna6amplBKMTk5qZsq+NFHH9HV1RWxnvLycqKiomhsbCQmJgaTyaRbFW02BoMBp9NJfX09o6OjbN68mcLCQu3ZEpfLRVVVFenp6axZswa3243P59OyVMPDw1RUVJCamorNZtNWBJycnNSWvzYajbhcLgoLC1m7di2PPPIIX375JQcOHNCe03E4HOTn5/P000/T0NBARkYGp0+fJioqikcffZT29nbeeOMNNm7cGDGAKC8vJycnh5qaGoqKiujo6GDPnj26FdkAfv/9dy5evMjExAR79uzBZDLpphzOZa4+Cdq5cye7d+/mtttum/OcgUAAj8dDa2srdXV1Wj9evnwZgEuXLulWOKyvr6e8vHzWjFNKSgqxsbHs3r2bTZs2cerUqbDpfs8//zxvvfUWGzdupLq6mpGREV5++WU2bNigBYhvv/02mzdvxmw28/jjj/PHH3/w/fffMzw8zNatW+e8prvuuovBwUH27dtHTk4Ohw8f1jJaQghxUyghhBDXxel0qqeeemrO/d58800VHR2t/H6/rtztdiuz2awrGxgYUIA6efKkUkqp48ePK0ANDw9r+3z99dfKYDCo7u5upZRSDodDAbN+HA6HVhegXn311Yh1tba2qsTERPXzzz/P+xqrqqqU3W5XTU1NKikpSRkMBvXMM8+oy5cva/sEAgFVXV2tli9frmJiYpTdbldHjhzR1RGp/YCqqqrS9m1qalJpaWkqJiZG2Ww21dzcrGvP0NCQKi0tVUuXLlUGg0FlZ2er9vZ29dtvv6nly5crl8ulrl69GrEPlFJq//79KisrS8XExKiUlBS1a9cuXR2pqala22699VaVk5Ojjh07pm13OBzqtddem7Wf5tsnwXbZ7XYVCAR0dTc2Ns46FsFjrvUJApTValVjY2MR293a2qpWrlyp4uLi1Lp161RbW1tYX/3www/K4XCouLg4ZbFYVElJiRoZGdG1q6WlRa1Zs0bFxsYqi8Wi8vPz1YEDB8LaD6jjx4+HlbtcLrV06VJlNBpVUVGRamxsDPu/EUKIG0VeJiuEEDdIWVkZv/76K21tbTe1Xp/Px5YtW7T3zdwI1dXVHDp0SPeeoetVUlJCQUGBbvW4oPfee4+RkRGqq6sXfP7/F+fPn6egoGDWaZQAixcvnjMrKIQQQk+m2wkhxD/sypUrdHd309raetMDJJh53umvy2j/G5nN5oiLLsTHx4ctTS1mFx0dHTbdM1RiYuJNbI0QQvw3SCZJCCH+YQUFBXR2dvLKK6/8Z19++U9kkoQQQoh/KwmShBBCCCGEECKEvCdJCCGEEEIIIUJIkCSEEEIIIYQQISRIEkIIIYQQQogQEiQJIYQQQgghRAgJkoQQQgghhBAihARJQgghhBBCCBFCgiQhhBBCCCGECCFBkhBCCCGEEEKE+B+zzpjqEoIw+AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q1 = df[\"DiabetesPedigreeFunction\"].quantile(0.25)\n",
    "Q3 = df[\"DiabetesPedigreeFunction\"].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "threshold = 1.5 * IQR\n",
    "lower_bound = Q1 - threshold\n",
    "upper_bound = Q3 + threshold\n",
    "\n",
    "outliers = (df[\"DiabetesPedigreeFunction\"] < lower_bound) | (df[\"DiabetesPedigreeFunction\"] > upper_bound)\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы в датасете:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Заменяем выбросы на медианные значения\n",
    "median_score = df[\"DiabetesPedigreeFunction\"].median()\n",
    "df.loc[outliers, \"DiabetesPedigreeFunction\"] = median_score\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['DiabetesPedigreeFunction'], df['Age'])\n",
    "plt.xlabel('Функция родословной диабета')\n",
    "plt.ylabel('Возраст')\n",
    "plt.title('Диаграмма рассеивания после чистки')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Классификация данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "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",
    "\n",
    "\n",
    "columns_to_drop = [\"Pregnancies\", \"SkinThickness\", \"BloodPressure\", \"Outcome\", \"DiabetesPedigreeFunction\"]\n",
    "num_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if column not in columns_to_drop and df[column].dtype != \"object\"\n",
    "]\n",
    "cat_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if column not in columns_to_drop 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",
    "\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка работы конвеера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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>Glucose</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.478144</td>\n",
       "      <td>-0.688684</td>\n",
       "      <td>-0.946400</td>\n",
       "      <td>-1.029257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.818506</td>\n",
       "      <td>0.180416</td>\n",
       "      <td>-0.377190</td>\n",
       "      <td>-0.522334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>-1.268784</td>\n",
       "      <td>-0.688684</td>\n",
       "      <td>-3.953317</td>\n",
       "      <td>-0.944770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>-1.015779</td>\n",
       "      <td>-0.688684</td>\n",
       "      <td>-0.538054</td>\n",
       "      <td>0.322537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>-0.003760</td>\n",
       "      <td>-0.688684</td>\n",
       "      <td>-0.637047</td>\n",
       "      <td>-0.522334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.122742</td>\n",
       "      <td>-0.688684</td>\n",
       "      <td>-0.562802</td>\n",
       "      <td>0.238050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>-0.794400</td>\n",
       "      <td>-0.375808</td>\n",
       "      <td>0.674613</td>\n",
       "      <td>-0.775796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.731149</td>\n",
       "      <td>0.528056</td>\n",
       "      <td>-1.082516</td>\n",
       "      <td>-0.944770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>-0.098637</td>\n",
       "      <td>0.232562</td>\n",
       "      <td>0.229143</td>\n",
       "      <td>-0.522334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.414893</td>\n",
       "      <td>-0.271516</td>\n",
       "      <td>-1.119638</td>\n",
       "      <td>-0.860283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Glucose   Insulin       BMI       Age\n",
       "196 -0.478144 -0.688684 -0.946400 -1.029257\n",
       "69   0.818506  0.180416 -0.377190 -0.522334\n",
       "494 -1.268784 -0.688684 -3.953317 -0.944770\n",
       "463 -1.015779 -0.688684 -0.538054  0.322537\n",
       "653 -0.003760 -0.688684 -0.637047 -0.522334\n",
       "..        ...       ...       ...       ...\n",
       "322  0.122742 -0.688684 -0.562802  0.238050\n",
       "109 -0.794400 -0.375808  0.674613 -0.775796\n",
       "27  -0.731149  0.528056 -1.082516 -0.944770\n",
       "651 -0.098637  0.232562  0.229143 -0.522334\n",
       "197 -0.414893 -0.271516 -1.119638 -0.860283\n",
       "\n",
       "[614 rows x 4 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "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": [
    "Формирование набора моделей для классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree\n",
    "\n",
    "class_models = {\n",
    "    \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
    "    # \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")},\n",
    "    \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=9)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
    "    \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
    "    \"gradient_boosting\": {\n",
    "        \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
    "    },\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestClassifier(\n",
    "            max_depth=11, class_weight=\"balanced\", random_state=9\n",
    "        )\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPClassifier(\n",
    "            hidden_layer_sizes=(7,),\n",
    "            max_iter=500,\n",
    "            early_stopping=True,\n",
    "            random_state=9,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучение моделей на обучающем наборе данных и оценка на тестовом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n",
      "Model: ridge\n",
      "Model: decision_tree\n",
      "Model: knn\n",
      "Model: naive_bayes\n",
      "Model: gradient_boosting\n",
      "Model: random_forest\n",
      "Model: mlp\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\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\"] = metrics.precision_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
    "        y_test, y_test_probs\n",
    "    )\n",
    "    class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
    "        y_test, y_test_predict\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сводная таблица оценок качества для использованных моделей классификации\n",
    "\n",
    "Матрица неточностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
    "for index, key in enumerate(class_models.keys()):\n",
    "    c_matrix = class_models[key][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Healthy\", \"Sick\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "    disp.ax_.set_title(key)\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Точность, полнота, верность (аккуратность), F-мера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_09af8_row0_col0 {\n",
       "  background-color: #3aba76;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row0_col1, #T_09af8_row1_col0, #T_09af8_row1_col2, #T_09af8_row7_col3 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_09af8_row0_col2 {\n",
       "  background-color: #24878e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row0_col3 {\n",
       "  background-color: #23888e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row0_col4, #T_09af8_row4_col7 {\n",
       "  background-color: #ac2694;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row0_col5, #T_09af8_row1_col4, #T_09af8_row1_col5, #T_09af8_row1_col6, #T_09af8_row1_col7 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row0_col6 {\n",
       "  background-color: #6f00a8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row0_col7 {\n",
       "  background-color: #c6417d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row1_col1 {\n",
       "  background-color: #7fd34e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_09af8_row1_col3, #T_09af8_row4_col1, #T_09af8_row6_col3 {\n",
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       "}\n",
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       "#T_09af8_row2_col6 {\n",
       "  background-color: #6600a7;\n",
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       "}\n",
       "#T_09af8_row2_col7 {\n",
       "  background-color: #bf3984;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row3_col0 {\n",
       "  background-color: #95d840;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_09af8_row3_col1, #T_09af8_row3_col2 {\n",
       "  background-color: #6ece58;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_09af8_row3_col3 {\n",
       "  background-color: #21918c;\n",
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       "}\n",
       "#T_09af8_row3_col4 {\n",
       "  background-color: #d24f71;\n",
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       "}\n",
       "#T_09af8_row3_col5 {\n",
       "  background-color: #d04d73;\n",
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       "}\n",
       "#T_09af8_row3_col6 {\n",
       "  background-color: #cc4778;\n",
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       "}\n",
       "#T_09af8_row3_col7 {\n",
       "  background-color: #b7318a;\n",
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       "#T_09af8_row4_col0 {\n",
       "  background-color: #3bbb75;\n",
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       "#T_09af8_row4_col2 {\n",
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       "#T_09af8_row4_col3 {\n",
       "  background-color: #1fa188;\n",
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       "#T_09af8_row4_col4 {\n",
       "  background-color: #b42e8d;\n",
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       "}\n",
       "#T_09af8_row4_col5 {\n",
       "  background-color: #c7427c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row4_col6 {\n",
       "  background-color: #8e0ca4;\n",
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       "#T_09af8_row6_col2 {\n",
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       "#T_09af8_row6_col5 {\n",
       "  background-color: #c03a83;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row6_col7 {\n",
       "  background-color: #b22b8f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_09af8_row7_col2 {\n",
       "  background-color: #7ad151;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_09af8_row7_col4, #T_09af8_row7_col5, #T_09af8_row7_col6, #T_09af8_row7_col7 {\n",
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       "}\n",
       "</style>\n",
       "<table id=\"T_09af8\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_09af8_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_09af8_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_09af8_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_09af8_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_09af8_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_09af8_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_09af8_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_09af8_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_09af8_row0_col0\" class=\"data row0 col0\" >0.710843</td>\n",
       "      <td id=\"T_09af8_row0_col1\" class=\"data row0 col1\" >0.714286</td>\n",
       "      <td id=\"T_09af8_row0_col2\" class=\"data row0 col2\" >0.551402</td>\n",
       "      <td id=\"T_09af8_row0_col3\" class=\"data row0 col3\" >0.648148</td>\n",
       "      <td id=\"T_09af8_row0_col4\" class=\"data row0 col4\" >0.765472</td>\n",
       "      <td id=\"T_09af8_row0_col5\" class=\"data row0 col5\" >0.785714</td>\n",
       "      <td id=\"T_09af8_row0_col6\" class=\"data row0 col6\" >0.621053</td>\n",
       "      <td id=\"T_09af8_row0_col7\" class=\"data row0 col7\" >0.679612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row1\" class=\"row_heading level0 row1\" >random_forest</th>\n",
       "      <td id=\"T_09af8_row1_col0\" class=\"data row1 col0\" >0.977169</td>\n",
       "      <td id=\"T_09af8_row1_col1\" class=\"data row1 col1\" >0.666667</td>\n",
       "      <td id=\"T_09af8_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_09af8_row1_col3\" class=\"data row1 col3\" >0.777778</td>\n",
       "      <td id=\"T_09af8_row1_col4\" class=\"data row1 col4\" >0.991857</td>\n",
       "      <td id=\"T_09af8_row1_col5\" class=\"data row1 col5\" >0.785714</td>\n",
       "      <td id=\"T_09af8_row1_col6\" class=\"data row1 col6\" >0.988453</td>\n",
       "      <td id=\"T_09af8_row1_col7\" class=\"data row1 col7\" >0.717949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row2\" class=\"row_heading level0 row2\" >naive_bayes</th>\n",
       "      <td id=\"T_09af8_row2_col0\" class=\"data row2 col0\" >0.702532</td>\n",
       "      <td id=\"T_09af8_row2_col1\" class=\"data row2 col1\" >0.708333</td>\n",
       "      <td id=\"T_09af8_row2_col2\" class=\"data row2 col2\" >0.518692</td>\n",
       "      <td id=\"T_09af8_row2_col3\" class=\"data row2 col3\" >0.629630</td>\n",
       "      <td id=\"T_09af8_row2_col4\" class=\"data row2 col4\" >0.755700</td>\n",
       "      <td id=\"T_09af8_row2_col5\" class=\"data row2 col5\" >0.779221</td>\n",
       "      <td id=\"T_09af8_row2_col6\" class=\"data row2 col6\" >0.596774</td>\n",
       "      <td id=\"T_09af8_row2_col7\" class=\"data row2 col7\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
       "      <td id=\"T_09af8_row3_col0\" class=\"data row3 col0\" >0.941463</td>\n",
       "      <td id=\"T_09af8_row3_col1\" class=\"data row3 col1\" >0.642857</td>\n",
       "      <td id=\"T_09af8_row3_col2\" class=\"data row3 col2\" >0.901869</td>\n",
       "      <td id=\"T_09af8_row3_col3\" class=\"data row3 col3\" >0.666667</td>\n",
       "      <td id=\"T_09af8_row3_col4\" class=\"data row3 col4\" >0.946254</td>\n",
       "      <td id=\"T_09af8_row3_col5\" class=\"data row3 col5\" >0.753247</td>\n",
       "      <td id=\"T_09af8_row3_col6\" class=\"data row3 col6\" >0.921241</td>\n",
       "      <td id=\"T_09af8_row3_col7\" class=\"data row3 col7\" >0.654545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row4\" class=\"row_heading level0 row4\" >knn</th>\n",
       "      <td id=\"T_09af8_row4_col0\" class=\"data row4 col0\" >0.716346</td>\n",
       "      <td id=\"T_09af8_row4_col1\" class=\"data row4 col1\" >0.584615</td>\n",
       "      <td id=\"T_09af8_row4_col2\" class=\"data row4 col2\" >0.696262</td>\n",
       "      <td id=\"T_09af8_row4_col3\" class=\"data row4 col3\" >0.703704</td>\n",
       "      <td id=\"T_09af8_row4_col4\" class=\"data row4 col4\" >0.798046</td>\n",
       "      <td id=\"T_09af8_row4_col5\" class=\"data row4 col5\" >0.720779</td>\n",
       "      <td id=\"T_09af8_row4_col6\" class=\"data row4 col6\" >0.706161</td>\n",
       "      <td id=\"T_09af8_row4_col7\" class=\"data row4 col7\" >0.638655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row5\" class=\"row_heading level0 row5\" >ridge</th>\n",
       "      <td id=\"T_09af8_row5_col0\" class=\"data row5 col0\" >0.610442</td>\n",
       "      <td id=\"T_09af8_row5_col1\" class=\"data row5 col1\" >0.561644</td>\n",
       "      <td id=\"T_09af8_row5_col2\" class=\"data row5 col2\" >0.710280</td>\n",
       "      <td id=\"T_09af8_row5_col3\" class=\"data row5 col3\" >0.759259</td>\n",
       "      <td id=\"T_09af8_row5_col4\" class=\"data row5 col4\" >0.741042</td>\n",
       "      <td id=\"T_09af8_row5_col5\" class=\"data row5 col5\" >0.707792</td>\n",
       "      <td id=\"T_09af8_row5_col6\" class=\"data row5 col6\" >0.656587</td>\n",
       "      <td id=\"T_09af8_row5_col7\" class=\"data row5 col7\" >0.645669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row6\" class=\"row_heading level0 row6\" >decision_tree</th>\n",
       "      <td id=\"T_09af8_row6_col0\" class=\"data row6 col0\" >0.793860</td>\n",
       "      <td id=\"T_09af8_row6_col1\" class=\"data row6 col1\" >0.552632</td>\n",
       "      <td id=\"T_09af8_row6_col2\" class=\"data row6 col2\" >0.845794</td>\n",
       "      <td id=\"T_09af8_row6_col3\" class=\"data row6 col3\" >0.777778</td>\n",
       "      <td id=\"T_09af8_row6_col4\" class=\"data row6 col4\" >0.869707</td>\n",
       "      <td id=\"T_09af8_row6_col5\" class=\"data row6 col5\" >0.701299</td>\n",
       "      <td id=\"T_09af8_row6_col6\" class=\"data row6 col6\" >0.819005</td>\n",
       "      <td id=\"T_09af8_row6_col7\" class=\"data row6 col7\" >0.646154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09af8_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_09af8_row7_col0\" class=\"data row7 col0\" >0.379576</td>\n",
       "      <td id=\"T_09af8_row7_col1\" class=\"data row7 col1\" >0.376000</td>\n",
       "      <td id=\"T_09af8_row7_col2\" class=\"data row7 col2\" >0.920561</td>\n",
       "      <td id=\"T_09af8_row7_col3\" class=\"data row7 col3\" >0.870370</td>\n",
       "      <td id=\"T_09af8_row7_col4\" class=\"data row7 col4\" >0.447883</td>\n",
       "      <td id=\"T_09af8_row7_col5\" class=\"data row7 col5\" >0.448052</td>\n",
       "      <td id=\"T_09af8_row7_col6\" class=\"data row7 col6\" >0.537517</td>\n",
       "      <td id=\"T_09af8_row7_col7\" class=\"data row7 col7\" >0.525140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203b54857c0>"
      ]
     },
     "execution_count": 276,
     "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": [
    "ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4cc76_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_4cc76_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_4cc76_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_4cc76_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_4cc76_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row0\" class=\"row_heading level0 row0\" >random_forest</th>\n",
       "      <td id=\"T_4cc76_row0_col0\" class=\"data row0 col0\" >0.785714</td>\n",
       "      <td id=\"T_4cc76_row0_col1\" class=\"data row0 col1\" >0.717949</td>\n",
       "      <td id=\"T_4cc76_row0_col2\" class=\"data row0 col2\" >0.867222</td>\n",
       "      <td id=\"T_4cc76_row0_col3\" class=\"data row0 col3\" >0.546816</td>\n",
       "      <td id=\"T_4cc76_row0_col4\" class=\"data row0 col4\" >0.551041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row1\" class=\"row_heading level0 row1\" >gradient_boosting</th>\n",
       "      <td id=\"T_4cc76_row1_col0\" class=\"data row1 col0\" >0.753247</td>\n",
       "      <td id=\"T_4cc76_row1_col1\" class=\"data row1 col1\" >0.654545</td>\n",
       "      <td id=\"T_4cc76_row1_col2\" class=\"data row1 col2\" >0.845741</td>\n",
       "      <td id=\"T_4cc76_row1_col3\" class=\"data row1 col3\" >0.462725</td>\n",
       "      <td id=\"T_4cc76_row1_col4\" class=\"data row1 col4\" >0.462910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row2\" class=\"row_heading level0 row2\" >logistic</th>\n",
       "      <td id=\"T_4cc76_row2_col0\" class=\"data row2 col0\" >0.785714</td>\n",
       "      <td id=\"T_4cc76_row2_col1\" class=\"data row2 col1\" >0.679612</td>\n",
       "      <td id=\"T_4cc76_row2_col2\" class=\"data row2 col2\" >0.835556</td>\n",
       "      <td id=\"T_4cc76_row2_col3\" class=\"data row2 col3\" >0.519205</td>\n",
       "      <td id=\"T_4cc76_row2_col4\" class=\"data row2 col4\" >0.520588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
       "      <td id=\"T_4cc76_row3_col0\" class=\"data row3 col0\" >0.707792</td>\n",
       "      <td id=\"T_4cc76_row3_col1\" class=\"data row3 col1\" >0.645669</td>\n",
       "      <td id=\"T_4cc76_row3_col2\" class=\"data row3 col2\" >0.833889</td>\n",
       "      <td id=\"T_4cc76_row3_col3\" class=\"data row3 col3\" >0.406373</td>\n",
       "      <td id=\"T_4cc76_row3_col4\" class=\"data row3 col4\" >0.419772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row4\" class=\"row_heading level0 row4\" >naive_bayes</th>\n",
       "      <td id=\"T_4cc76_row4_col0\" class=\"data row4 col0\" >0.779221</td>\n",
       "      <td id=\"T_4cc76_row4_col1\" class=\"data row4 col1\" >0.666667</td>\n",
       "      <td id=\"T_4cc76_row4_col2\" class=\"data row4 col2\" >0.822593</td>\n",
       "      <td id=\"T_4cc76_row4_col3\" class=\"data row4 col3\" >0.502471</td>\n",
       "      <td id=\"T_4cc76_row4_col4\" class=\"data row4 col4\" >0.504419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row5\" class=\"row_heading level0 row5\" >knn</th>\n",
       "      <td id=\"T_4cc76_row5_col0\" class=\"data row5 col0\" >0.720779</td>\n",
       "      <td id=\"T_4cc76_row5_col1\" class=\"data row5 col1\" >0.638655</td>\n",
       "      <td id=\"T_4cc76_row5_col2\" class=\"data row5 col2\" >0.806296</td>\n",
       "      <td id=\"T_4cc76_row5_col3\" class=\"data row5 col3\" >0.414293</td>\n",
       "      <td id=\"T_4cc76_row5_col4\" class=\"data row5 col4\" >0.419023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row6\" class=\"row_heading level0 row6\" >decision_tree</th>\n",
       "      <td id=\"T_4cc76_row6_col0\" class=\"data row6 col0\" >0.701299</td>\n",
       "      <td id=\"T_4cc76_row6_col1\" class=\"data row6 col1\" >0.646154</td>\n",
       "      <td id=\"T_4cc76_row6_col2\" class=\"data row6 col2\" >0.794167</td>\n",
       "      <td id=\"T_4cc76_row6_col3\" class=\"data row6 col3\" >0.400271</td>\n",
       "      <td id=\"T_4cc76_row6_col4\" class=\"data row6 col4\" >0.417827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4cc76_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_4cc76_row7_col0\" class=\"data row7 col0\" >0.448052</td>\n",
       "      <td id=\"T_4cc76_row7_col1\" class=\"data row7 col1\" >0.525140</td>\n",
       "      <td id=\"T_4cc76_row7_col2\" class=\"data row7 col2\" >0.603333</td>\n",
       "      <td id=\"T_4cc76_row7_col3\" class=\"data row7 col3\" >0.069387</td>\n",
       "      <td id=\"T_4cc76_row7_col4\" class=\"data row7 col4\" >0.110298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203bb436870>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'random_forest'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
    "\n",
    "display(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод данных с ошибкой предсказания для оценки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Error items count: 33'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Pregnancies</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.564</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.178</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>80</td>\n",
       "      <td>15</td>\n",
       "      <td>176</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.443</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>72</td>\n",
       "      <td>27</td>\n",
       "      <td>228</td>\n",
       "      <td>33.9</td>\n",
       "      <td>0.255</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>99</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.496</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.709</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>114</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0.640</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>190</td>\n",
       "      <td>37.7</td>\n",
       "      <td>0.324</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>39</td>\n",
       "      <td>744</td>\n",
       "      <td>36.7</td>\n",
       "      <td>2.329</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.251</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.334</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "      <td>88</td>\n",
       "      <td>15</td>\n",
       "      <td>155</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.262</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>68</td>\n",
       "      <td>28</td>\n",
       "      <td>205</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.875</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>76</td>\n",
       "      <td>43</td>\n",
       "      <td>255</td>\n",
       "      <td>47.9</td>\n",
       "      <td>0.259</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.520</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.196</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.297</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.304</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.197</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>72</td>\n",
       "      <td>25</td>\n",
       "      <td>190</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.549</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>110</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.680</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>126</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0.338</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.9</td>\n",
       "      <td>0.693</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>84</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>46.8</td>\n",
       "      <td>0.925</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>230</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.733</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>1.461</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.4</td>\n",
       "      <td>0.732</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.164</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>78</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.236</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>88</td>\n",
       "      <td>37</td>\n",
       "      <td>140</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1.174</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>1.182</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Predicted  Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
       "46             1          1      146             56              0        0   \n",
       "86            13          1      106             72             54        0   \n",
       "91             4          1      123             80             15      176   \n",
       "95             6          1      144             72             27      228   \n",
       "125            1          0       88             30             42       99   \n",
       "167            4          1      120             68              0        0   \n",
       "188            8          0      109             76             39      114   \n",
       "204            6          1      103             72             32      190   \n",
       "228            4          1      197             70             39      744   \n",
       "274           13          1      106             70              0        0   \n",
       "280            0          0      146             70              0        0   \n",
       "282            7          1      133             88             15      155   \n",
       "309            2          0      124             68             28      205   \n",
       "335            0          1      165             76             43      255   \n",
       "363            4          0      146             78              0        0   \n",
       "397            0          0      131             66             40        0   \n",
       "510           12          0       84             72             31        0   \n",
       "517            7          1      125             86              0        0   \n",
       "536            0          1      105             90              0        0   \n",
       "541            3          0      128             72             25      190   \n",
       "549            4          1      189            110             31        0   \n",
       "568            4          1      154             72             29      126   \n",
       "577            2          0      118             80              0        0   \n",
       "583            8          1      100             76              0        0   \n",
       "590           11          0      111             84             40        0   \n",
       "594            6          1      123             72             45      230   \n",
       "622            6          1      183             94              0        0   \n",
       "630            7          0      114             64              0        0   \n",
       "658           11          1      127            106              0        0   \n",
       "669            9          1      154             78             30      100   \n",
       "725            4          1      112             78             40        0   \n",
       "744           13          1      153             88             37      140   \n",
       "750            4          0      136             70              0        0   \n",
       "\n",
       "      BMI  DiabetesPedigreeFunction  Age  Outcome  \n",
       "46   29.7                     0.564   29        0  \n",
       "86   36.6                     0.178   45        0  \n",
       "91   32.0                     0.443   34        0  \n",
       "95   33.9                     0.255   40        0  \n",
       "125  55.0                     0.496   26        1  \n",
       "167  29.6                     0.709   34        0  \n",
       "188  27.9                     0.640   31        1  \n",
       "204  37.7                     0.324   55        0  \n",
       "228  36.7                     2.329   31        0  \n",
       "274  34.2                     0.251   52        0  \n",
       "280  37.9                     0.334   28        1  \n",
       "282  32.4                     0.262   37        0  \n",
       "309  32.9                     0.875   30        1  \n",
       "335  47.9                     0.259   26        0  \n",
       "363  38.5                     0.520   67        1  \n",
       "397  34.3                     0.196   22        1  \n",
       "510  29.7                     0.297   46        1  \n",
       "517  37.6                     0.304   51        0  \n",
       "536  29.6                     0.197   46        0  \n",
       "541  32.4                     0.549   27        1  \n",
       "549  28.5                     0.680   37        0  \n",
       "568  31.3                     0.338   37        0  \n",
       "577  42.9                     0.693   21        1  \n",
       "583  38.7                     0.190   42        0  \n",
       "590  46.8                     0.925   45        1  \n",
       "594  33.6                     0.733   34        0  \n",
       "622  40.8                     1.461   45        0  \n",
       "630  27.4                     0.732   34        1  \n",
       "658  39.0                     0.190   51        0  \n",
       "669  30.9                     0.164   45        0  \n",
       "725  39.4                     0.236   38        0  \n",
       "744  40.6                     1.174   39        0  \n",
       "750  31.2                     1.182   22        1  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.transform(X_test)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "y_pred = class_models[best_model][\"preds\"]\n",
    "\n",
    "error_index = y_test[y_test[\"Outcome\"] != y_pred].index.tolist()\n",
    "display(f\"Error items count: {len(error_index)}\")\n",
    "\n",
    "error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
    "error_df = X_test.loc[error_index].copy()\n",
    "error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
    "error_df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пример использования обученной модели (конвейера) для предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.415</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "450          1.0     82.0           64.0           13.0     95.0  21.2   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  \n",
       "450                     0.415  23.0      0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>-1.205533</td>\n",
       "      <td>0.136961</td>\n",
       "      <td>-1.329999</td>\n",
       "      <td>-0.860283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Glucose   Insulin       BMI       Age\n",
       "450 -1.205533  0.136961 -1.329999 -0.860283"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'predicted: 0 (proba: [0.96 0.04])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'real: 0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = class_models[best_model][\"pipeline\"]\n",
    "\n",
    "example_id = 450\n",
    "test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
    "test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
    "display(test)\n",
    "display(test_preprocessed)\n",
    "result_proba = model.predict_proba(test)[0]\n",
    "result = model.predict(test)[0]\n",
    "real = int(y_test.loc[example_id].values[0])\n",
    "display(f\"predicted: {result} (proba: {result_proba})\")\n",
    "display(f\"real: {real}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подбор гиперпараметров методом поиска по сетке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Определяем числовые признаки\n",
    "numeric_features = X_train.select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
    "\n",
    "# Установка random_state\n",
    "random_state = 9\n",
    "\n",
    "# Определение трансформера\n",
    "pipeline_end = ColumnTransformer([\n",
    "    ('numeric', StandardScaler(), numeric_features),\n",
    "    # Добавьте другие трансформеры, если требуется\n",
    "])\n",
    "\n",
    "# Объявление модели\n",
    "optimized_model = RandomForestClassifier(\n",
    "    random_state=random_state,\n",
    "    criterion=\"gini\",\n",
    "    max_depth=5,\n",
    "    max_features=\"sqrt\",\n",
    "    n_estimators=10,\n",
    ")\n",
    "\n",
    "# Создание пайплайна с корректными шагами\n",
    "result = {}\n",
    "\n",
    "# Обучение модели\n",
    "result[\"pipeline\"] = Pipeline([\n",
    "    (\"pipeline\", pipeline_end),\n",
    "    (\"model\", optimized_model)\n",
    "]).fit(X_train, y_train.values.ravel())\n",
    "\n",
    "# Прогнозирование и расчет метрик\n",
    "result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
    "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
    "result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
    "\n",
    "# Метрики для оценки модели\n",
    "result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
    "result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
    "result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
    "result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
    "result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
    "result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
    "result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
    "result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
    "result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
    "result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
    "result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
    "result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формирование данных для оценки старой и новой версии модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_model_type = \"random_forest\"\n",
    "optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=class_models[optimized_model_type]\n",
    ")\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=result\n",
    ")\n",
    "optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
    "optimized_metrics = optimized_metrics.set_index(\"Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка параметров старой и новой модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cba1c_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_cba1c_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_cba1c_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
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       "      <th id=\"T_cba1c_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_cba1c_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cba1c_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_cba1c_row0_col0\" class=\"data row0 col0\" >0.977169</td>\n",
       "      <td id=\"T_cba1c_row0_col1\" class=\"data row0 col1\" >0.666667</td>\n",
       "      <td id=\"T_cba1c_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row0_col3\" class=\"data row0 col3\" >0.777778</td>\n",
       "      <td id=\"T_cba1c_row0_col4\" class=\"data row0 col4\" >0.991857</td>\n",
       "      <td id=\"T_cba1c_row0_col5\" class=\"data row0 col5\" >0.785714</td>\n",
       "      <td id=\"T_cba1c_row0_col6\" class=\"data row0 col6\" >0.988453</td>\n",
       "      <td id=\"T_cba1c_row0_col7\" class=\"data row0 col7\" >0.717949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cba1c_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_cba1c_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
       "      <td id=\"T_cba1c_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203bd82cda0>"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\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",
    "].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": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c86af_row0_col0, #T_c86af_row0_col1 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c86af_row0_col2, #T_c86af_row0_col3, #T_c86af_row0_col4 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c86af_row1_col0, #T_c86af_row1_col1 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c86af_row1_col2, #T_c86af_row1_col3, #T_c86af_row1_col4 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c86af\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c86af_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_c86af_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_c86af_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_c86af_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_c86af_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c86af_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_c86af_row0_col0\" class=\"data row0 col0\" >0.785714</td>\n",
       "      <td id=\"T_c86af_row0_col1\" class=\"data row0 col1\" >0.717949</td>\n",
       "      <td id=\"T_c86af_row0_col2\" class=\"data row0 col2\" >0.867222</td>\n",
       "      <td id=\"T_c86af_row0_col3\" class=\"data row0 col3\" >0.546816</td>\n",
       "      <td id=\"T_c86af_row0_col4\" class=\"data row0 col4\" >0.551041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c86af_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_c86af_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_c86af_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_c86af_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_c86af_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_c86af_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203b5ef2d20>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False\n",
    ")\n",
    "\n",
    "for index in range(0, len(optimized_metrics)):\n",
    "    c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Healthy\", \"Sick\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В желтом квадрате мы видим значение 79, что обозначает количество правильно классифицированных объектов, отнесенных к классу \"Sick\". Это свидетельствует о том, что модель успешно идентифицирует объекты этого класса, минимизируя количество ложных положительных срабатываний.\n",
    "\n",
    "В зеленом квадрате значение 42 указывает на количество правильно классифицированных объектов, отнесенных к классу \"Healthy\". Это также является показателем хорошей точности модели в определении объектов данного класса."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определение достижимого уровня качества модели для второй задачи"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
      "\n",
      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
      "count  768.000000                768.000000  768.000000  768.000000  \n",
      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
      "std      7.884160                  0.331329   11.760232    0.476951  \n",
      "min      0.000000                  0.078000   21.000000    0.000000  \n",
      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
      "max     67.100000                  2.420000   81.000000    1.000000  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import set_config\n",
    "\n",
    "\n",
    "random_state = 9\n",
    "set_config(transform_output=\"pandas\")\n",
    "df = pd.read_csv(\".//scv//diabetes.csv\")\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формирование выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
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       "      <td>0.247</td>\n",
       "      <td>24</td>\n",
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       "      <td>15</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 9 columns</p>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "196            1      105             58              0        0  24.3   \n",
       "69             4      146             85             27      100  28.9   \n",
       "494            3       80              0              0        0   0.0   \n",
       "463            5       88             78             30        0  27.6   \n",
       "653            2      120             54              0        0  26.8   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "322            0      124             70             20        0  27.4   \n",
       "109            0       95             85             25       36  37.4   \n",
       "27             1       97             66             15      140  23.2   \n",
       "651            1      117             60             23      106  33.8   \n",
       "197            3      107             62             13       48  22.9   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "196                     0.187   21        0  \n",
       "69                      0.189   27        0  \n",
       "494                     0.174   22        0  \n",
       "463                     0.258   37        0  \n",
       "653                     0.455   27        0  \n",
       "..                        ...  ...      ...  \n",
       "322                     0.254   36        1  \n",
       "109                     0.247   24        1  \n",
       "27                      0.487   22        0  \n",
       "651                     0.466   27        0  \n",
       "197                     0.678   23        1  \n",
       "\n",
       "[614 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "653        0\n",
       "..       ...\n",
       "322        1\n",
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "669            9      154             78             30      100  30.9   \n",
       "379            0       93            100             39       72  43.4   \n",
       "640            0      102             86             17      105  29.3   \n",
       "658           11      127            106              0        0  39.0   \n",
       "304            3      150             76              0        0  21.0   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "203            2       99             70             16       44  20.4   \n",
       "605            1      124             60             32        0  35.8   \n",
       "561            0      198             66             32      274  41.3   \n",
       "280            0      146             70              0        0  37.9   \n",
       "103            1       81             72             18       40  26.6   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "669                     0.164   45        0  \n",
       "379                     1.021   35        0  \n",
       "640                     0.695   27        0  \n",
       "658                     0.190   51        0  \n",
       "304                     0.207   37        0  \n",
       "..                        ...  ...      ...  \n",
       "203                     0.235   27        0  \n",
       "605                     0.514   21        0  \n",
       "561                     0.502   28        1  \n",
       "280                     0.334   28        1  \n",
       "103                     0.283   24        0  \n",
       "\n",
       "[154 rows x 9 columns]"
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       "     Outcome\n",
       "669        0\n",
       "379        0\n",
       "640        0\n",
       "658        0\n",
       "304        0\n",
       "..       ...\n",
       "203        0\n",
       "605        0\n",
       "561        1\n",
       "280        1\n",
       "103        0\n",
       "\n",
       "[154 rows x 1 columns]"
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     },
     "metadata": {},
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   ],
   "source": [
    "from typing import Tuple\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input: DataFrame,\n",
    "    stratify_colname: str = \"y\",\n",
    "    frac_train: float = 0.6,\n",
    "    frac_val: float = 0.15,\n",
    "    frac_test: float = 0.25,\n",
    "    random_state: int = None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "   \n",
    "\n",
    "    if not (0 < frac_train < 1) or not (0 <= frac_val <= 1) or not (0 <= frac_test <= 1):\n",
    "        raise ValueError(\"Fractions must be between 0 and 1 and the sum must equal 1.\")\n",
    "    \n",
    "    if not (frac_train + frac_val + frac_test == 1.0):\n",
    "        raise ValueError(\"fractions %f, %f, %f do not add up to 1.0\" %\n",
    "                         (frac_train, frac_val, frac_test))\n",
    "\n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(f\"{stratify_colname} is not a column in the DataFrame.\")\n",
    "\n",
    "    X = df_input\n",
    "    y = df_input[[stratify_colname]]\n",
    "\n",
    " \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",
    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
    "\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "\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",
    "    \n",
    "    return df_train, df_val, df_test, y_train, y_val, y_test\n",
    "\n",
    "\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"Outcome\", frac_train=0.80, frac_val=0.0, frac_test=0.20, random_state=random_state\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формирование конвейера для классификации данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "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",
    "class DiabetFeatures(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "\n",
    "columns_to_drop = [\"Pregnancies\", \"SkinThickness\", \"Insulin\", \"BMI\", \"Outcome\"]\n",
    "num_columns = [\"Glucose\", \"Age\", \"BloodPressure\", \"DiabetesPedigreeFunction\"]\n",
    "cat_columns = []\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",
    "\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",
    "features_postprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"prepocessing_cat\", preprocessing_cat, [\"Cabin_type\"]),\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": [
    "Демонстрация работы конвейера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "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>Glucose</th>\n",
       "      <th>Age</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.478144</td>\n",
       "      <td>-1.029257</td>\n",
       "      <td>-0.554050</td>\n",
       "      <td>-0.849205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.818506</td>\n",
       "      <td>-0.522334</td>\n",
       "      <td>0.804885</td>\n",
       "      <td>-0.843172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>-1.268784</td>\n",
       "      <td>-0.944770</td>\n",
       "      <td>-3.473244</td>\n",
       "      <td>-0.888421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>-1.015779</td>\n",
       "      <td>0.322537</td>\n",
       "      <td>0.452568</td>\n",
       "      <td>-0.635028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>-0.003760</td>\n",
       "      <td>-0.522334</td>\n",
       "      <td>-0.755374</td>\n",
       "      <td>-0.040763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.122742</td>\n",
       "      <td>0.238050</td>\n",
       "      <td>0.049921</td>\n",
       "      <td>-0.647095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>-0.794400</td>\n",
       "      <td>-0.775796</td>\n",
       "      <td>0.804885</td>\n",
       "      <td>-0.668211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.731149</td>\n",
       "      <td>-0.944770</td>\n",
       "      <td>-0.151403</td>\n",
       "      <td>0.055767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>-0.098637</td>\n",
       "      <td>-0.522334</td>\n",
       "      <td>-0.453388</td>\n",
       "      <td>-0.007581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.414893</td>\n",
       "      <td>-0.860283</td>\n",
       "      <td>-0.352726</td>\n",
       "      <td>0.631933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Glucose       Age  BloodPressure  DiabetesPedigreeFunction\n",
       "196 -0.478144 -1.029257      -0.554050                 -0.849205\n",
       "69   0.818506 -0.522334       0.804885                 -0.843172\n",
       "494 -1.268784 -0.944770      -3.473244                 -0.888421\n",
       "463 -1.015779  0.322537       0.452568                 -0.635028\n",
       "653 -0.003760 -0.522334      -0.755374                 -0.040763\n",
       "..        ...       ...            ...                       ...\n",
       "322  0.122742  0.238050       0.049921                 -0.647095\n",
       "109 -0.794400 -0.775796       0.804885                 -0.668211\n",
       "27  -0.731149 -0.944770      -0.151403                  0.055767\n",
       "651 -0.098637 -0.522334      -0.453388                 -0.007581\n",
       "197 -0.414893 -0.860283      -0.352726                  0.631933\n",
       "\n",
       "[614 rows x 4 columns]"
      ]
     },
     "execution_count": 289,
     "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": [
    "Формирование набора моделей для классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree\n",
    "\n",
    "class_models = {\n",
    "    \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
    "    \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")},\n",
    "    \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=random_state)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
    "    \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
    "    \"gradient_boosting\": {\n",
    "        \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
    "    },\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestClassifier(\n",
    "            max_depth=11, class_weight=\"balanced\", random_state=random_state\n",
    "        )\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPClassifier(\n",
    "            hidden_layer_sizes=(7,),\n",
    "            max_iter=500,\n",
    "            early_stopping=True,\n",
    "            random_state=random_state,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучение моделей на обучающем наборе данных и оценка на тестовом¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: ridge\n",
      "Model: decision_tree\n",
      "Model: knn\n",
      "Model: naive_bayes\n",
      "Model: gradient_boosting\n",
      "Model: random_forest\n",
      "Model: mlp\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\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\"] = metrics.precision_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
    "        y_test, y_test_probs\n",
    "    )\n",
    "    class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
    "        y_test, y_test_predict\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сводная таблица оценок качества для использованных моделей классификации¶\n",
    "\n",
    "Матрица неточностей\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
    "for index, key in enumerate(class_models.keys()):\n",
    "    c_matrix = class_models[key][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Healthy\", \"Sick\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "    disp.ax_.set_title(key)\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: #20a486;\n",
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       "}\n",
       "#T_0c53d_row0_col1, #T_0c53d_row3_col0, #T_0c53d_row3_col2, #T_0c53d_row5_col3 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
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       "#T_0c53d_row0_col2 {\n",
       "  background-color: #24868e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row0_col3, #T_0c53d_row3_col1 {\n",
       "  background-color: #2fb47c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row0_col4 {\n",
       "  background-color: #7b02a8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row0_col5, #T_0c53d_row0_col7, #T_0c53d_row3_col4, #T_0c53d_row3_col6 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row0_col6 {\n",
       "  background-color: #6700a8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row1_col0 {\n",
       "  background-color: #23a983;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row1_col1, #T_0c53d_row2_col0 {\n",
       "  background-color: #90d743;\n",
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       "  background-color: #50c46a;\n",
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       "}\n",
       "#T_0c53d_row3_col5 {\n",
       "  background-color: #b22b8f;\n",
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       "#T_0c53d_row4_col6 {\n",
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       "#T_0c53d_row5_col2 {\n",
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       "#T_0c53d_row7_col2 {\n",
       "  background-color: #23898e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0c53d_row7_col4, #T_0c53d_row7_col5, #T_0c53d_row7_col6, #T_0c53d_row7_col7 {\n",
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       "</style>\n",
       "<table id=\"T_0c53d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0c53d_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_0c53d_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_0c53d_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_0c53d_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_0c53d_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_0c53d_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_0c53d_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_0c53d_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row0\" class=\"row_heading level0 row0\" >naive_bayes</th>\n",
       "      <td id=\"T_0c53d_row0_col0\" class=\"data row0 col0\" >0.678571</td>\n",
       "      <td id=\"T_0c53d_row0_col1\" class=\"data row0 col1\" >0.734694</td>\n",
       "      <td id=\"T_0c53d_row0_col2\" class=\"data row0 col2\" >0.532710</td>\n",
       "      <td id=\"T_0c53d_row0_col3\" class=\"data row0 col3\" >0.666667</td>\n",
       "      <td id=\"T_0c53d_row0_col4\" class=\"data row0 col4\" >0.749186</td>\n",
       "      <td id=\"T_0c53d_row0_col5\" class=\"data row0 col5\" >0.798701</td>\n",
       "      <td id=\"T_0c53d_row0_col6\" class=\"data row0 col6\" >0.596859</td>\n",
       "      <td id=\"T_0c53d_row0_col7\" class=\"data row0 col7\" >0.699029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row1\" class=\"row_heading level0 row1\" >logistic</th>\n",
       "      <td id=\"T_0c53d_row1_col0\" class=\"data row1 col0\" >0.696774</td>\n",
       "      <td id=\"T_0c53d_row1_col1\" class=\"data row1 col1\" >0.717391</td>\n",
       "      <td id=\"T_0c53d_row1_col2\" class=\"data row1 col2\" >0.504673</td>\n",
       "      <td id=\"T_0c53d_row1_col3\" class=\"data row1 col3\" >0.611111</td>\n",
       "      <td id=\"T_0c53d_row1_col4\" class=\"data row1 col4\" >0.750814</td>\n",
       "      <td id=\"T_0c53d_row1_col5\" class=\"data row1 col5\" >0.779221</td>\n",
       "      <td id=\"T_0c53d_row1_col6\" class=\"data row1 col6\" >0.585366</td>\n",
       "      <td id=\"T_0c53d_row1_col7\" class=\"data row1 col7\" >0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row2\" class=\"row_heading level0 row2\" >gradient_boosting</th>\n",
       "      <td id=\"T_0c53d_row2_col0\" class=\"data row2 col0\" >0.949749</td>\n",
       "      <td id=\"T_0c53d_row2_col1\" class=\"data row2 col1\" >0.673469</td>\n",
       "      <td id=\"T_0c53d_row2_col2\" class=\"data row2 col2\" >0.883178</td>\n",
       "      <td id=\"T_0c53d_row2_col3\" class=\"data row2 col3\" >0.611111</td>\n",
       "      <td id=\"T_0c53d_row2_col4\" class=\"data row2 col4\" >0.942997</td>\n",
       "      <td id=\"T_0c53d_row2_col5\" class=\"data row2 col5\" >0.759740</td>\n",
       "      <td id=\"T_0c53d_row2_col6\" class=\"data row2 col6\" >0.915254</td>\n",
       "      <td id=\"T_0c53d_row2_col7\" class=\"data row2 col7\" >0.640777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row3\" class=\"row_heading level0 row3\" >random_forest</th>\n",
       "      <td id=\"T_0c53d_row3_col0\" class=\"data row3 col0\" >0.990741</td>\n",
       "      <td id=\"T_0c53d_row3_col1\" class=\"data row3 col1\" >0.633333</td>\n",
       "      <td id=\"T_0c53d_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_0c53d_row3_col3\" class=\"data row3 col3\" >0.703704</td>\n",
       "      <td id=\"T_0c53d_row3_col4\" class=\"data row3 col4\" >0.996743</td>\n",
       "      <td id=\"T_0c53d_row3_col5\" class=\"data row3 col5\" >0.753247</td>\n",
       "      <td id=\"T_0c53d_row3_col6\" class=\"data row3 col6\" >0.995349</td>\n",
       "      <td id=\"T_0c53d_row3_col7\" class=\"data row3 col7\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row4\" class=\"row_heading level0 row4\" >knn</th>\n",
       "      <td id=\"T_0c53d_row4_col0\" class=\"data row4 col0\" >0.730159</td>\n",
       "      <td id=\"T_0c53d_row4_col1\" class=\"data row4 col1\" >0.622642</td>\n",
       "      <td id=\"T_0c53d_row4_col2\" class=\"data row4 col2\" >0.644860</td>\n",
       "      <td id=\"T_0c53d_row4_col3\" class=\"data row4 col3\" >0.611111</td>\n",
       "      <td id=\"T_0c53d_row4_col4\" class=\"data row4 col4\" >0.793160</td>\n",
       "      <td id=\"T_0c53d_row4_col5\" class=\"data row4 col5\" >0.733766</td>\n",
       "      <td id=\"T_0c53d_row4_col6\" class=\"data row4 col6\" >0.684864</td>\n",
       "      <td id=\"T_0c53d_row4_col7\" class=\"data row4 col7\" >0.616822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row5\" class=\"row_heading level0 row5\" >ridge</th>\n",
       "      <td id=\"T_0c53d_row5_col0\" class=\"data row5 col0\" >0.602459</td>\n",
       "      <td id=\"T_0c53d_row5_col1\" class=\"data row5 col1\" >0.583333</td>\n",
       "      <td id=\"T_0c53d_row5_col2\" class=\"data row5 col2\" >0.686916</td>\n",
       "      <td id=\"T_0c53d_row5_col3\" class=\"data row5 col3\" >0.777778</td>\n",
       "      <td id=\"T_0c53d_row5_col4\" class=\"data row5 col4\" >0.732899</td>\n",
       "      <td id=\"T_0c53d_row5_col5\" class=\"data row5 col5\" >0.727273</td>\n",
       "      <td id=\"T_0c53d_row5_col6\" class=\"data row5 col6\" >0.641921</td>\n",
       "      <td id=\"T_0c53d_row5_col7\" class=\"data row5 col7\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row6\" class=\"row_heading level0 row6\" >decision_tree</th>\n",
       "      <td id=\"T_0c53d_row6_col0\" class=\"data row6 col0\" >0.848168</td>\n",
       "      <td id=\"T_0c53d_row6_col1\" class=\"data row6 col1\" >0.612245</td>\n",
       "      <td id=\"T_0c53d_row6_col2\" class=\"data row6 col2\" >0.757009</td>\n",
       "      <td id=\"T_0c53d_row6_col3\" class=\"data row6 col3\" >0.555556</td>\n",
       "      <td id=\"T_0c53d_row6_col4\" class=\"data row6 col4\" >0.868078</td>\n",
       "      <td id=\"T_0c53d_row6_col5\" class=\"data row6 col5\" >0.720779</td>\n",
       "      <td id=\"T_0c53d_row6_col6\" class=\"data row6 col6\" >0.800000</td>\n",
       "      <td id=\"T_0c53d_row6_col7\" class=\"data row6 col7\" >0.582524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c53d_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_0c53d_row7_col0\" class=\"data row7 col0\" >0.513158</td>\n",
       "      <td id=\"T_0c53d_row7_col1\" class=\"data row7 col1\" >0.532258</td>\n",
       "      <td id=\"T_0c53d_row7_col2\" class=\"data row7 col2\" >0.546729</td>\n",
       "      <td id=\"T_0c53d_row7_col3\" class=\"data row7 col3\" >0.611111</td>\n",
       "      <td id=\"T_0c53d_row7_col4\" class=\"data row7 col4\" >0.661238</td>\n",
       "      <td id=\"T_0c53d_row7_col5\" class=\"data row7 col5\" >0.675325</td>\n",
       "      <td id=\"T_0c53d_row7_col6\" class=\"data row7 col6\" >0.529412</td>\n",
       "      <td id=\"T_0c53d_row7_col7\" class=\"data row7 col7\" >0.568966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203b5bc8b60>"
      ]
     },
     "execution_count": 293,
     "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": [
    "\n",
    "\n",
    "Почти все модели, включая логистическую регрессию, ридж-регрессию, KNN, наивный байесовский классификатор, многослойную перцептронную сеть, случайный лес, дерево решений и градиентный бустинг, демонстрируют 100% точность (1.000000) на обучающей выборке. Это указывает на то, что модели смогли подстроиться под обучающие данные, что может указывать на возможное переобучение.\n",
    "\n",
    "ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
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       "  background-color: #9511a1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40ce9_row6_col1 {\n",
       "  background-color: #228b8d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40ce9_row6_col2, #T_40ce9_row7_col2, #T_40ce9_row7_col3, #T_40ce9_row7_col4 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40ce9_row6_col3, #T_40ce9_row6_col4 {\n",
       "  background-color: #7b02a8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40ce9_row7_col0, #T_40ce9_row7_col1 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_40ce9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_40ce9_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_40ce9_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_40ce9_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_40ce9_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_40ce9_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_40ce9_row0_col0\" class=\"data row0 col0\" >0.779221</td>\n",
       "      <td id=\"T_40ce9_row0_col1\" class=\"data row0 col1\" >0.660000</td>\n",
       "      <td id=\"T_40ce9_row0_col2\" class=\"data row0 col2\" >0.825370</td>\n",
       "      <td id=\"T_40ce9_row0_col3\" class=\"data row0 col3\" >0.498083</td>\n",
       "      <td id=\"T_40ce9_row0_col4\" class=\"data row0 col4\" >0.501593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "      <td id=\"T_40ce9_row1_col0\" class=\"data row1 col0\" >0.727273</td>\n",
       "      <td id=\"T_40ce9_row1_col1\" class=\"data row1 col1\" >0.666667</td>\n",
       "      <td id=\"T_40ce9_row1_col2\" class=\"data row1 col2\" >0.824444</td>\n",
       "      <td id=\"T_40ce9_row1_col3\" class=\"data row1 col3\" >0.443756</td>\n",
       "      <td id=\"T_40ce9_row1_col4\" class=\"data row1 col4\" >0.456930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row2\" class=\"row_heading level0 row2\" >naive_bayes</th>\n",
       "      <td id=\"T_40ce9_row2_col0\" class=\"data row2 col0\" >0.798701</td>\n",
       "      <td id=\"T_40ce9_row2_col1\" class=\"data row2 col1\" >0.699029</td>\n",
       "      <td id=\"T_40ce9_row2_col2\" class=\"data row2 col2\" >0.820556</td>\n",
       "      <td id=\"T_40ce9_row2_col3\" class=\"data row2 col3\" >0.548344</td>\n",
       "      <td id=\"T_40ce9_row2_col4\" class=\"data row2 col4\" >0.549805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
       "      <td id=\"T_40ce9_row3_col0\" class=\"data row3 col0\" >0.759740</td>\n",
       "      <td id=\"T_40ce9_row3_col1\" class=\"data row3 col1\" >0.640777</td>\n",
       "      <td id=\"T_40ce9_row3_col2\" class=\"data row3 col2\" >0.815741</td>\n",
       "      <td id=\"T_40ce9_row3_col3\" class=\"data row3 col3\" >0.460927</td>\n",
       "      <td id=\"T_40ce9_row3_col4\" class=\"data row3 col4\" >0.462155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_40ce9_row4_col0\" class=\"data row4 col0\" >0.753247</td>\n",
       "      <td id=\"T_40ce9_row4_col1\" class=\"data row4 col1\" >0.666667</td>\n",
       "      <td id=\"T_40ce9_row4_col2\" class=\"data row4 col2\" >0.808704</td>\n",
       "      <td id=\"T_40ce9_row4_col3\" class=\"data row4 col3\" >0.471650</td>\n",
       "      <td id=\"T_40ce9_row4_col4\" class=\"data row4 col4\" >0.473300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row5\" class=\"row_heading level0 row5\" >knn</th>\n",
       "      <td id=\"T_40ce9_row5_col0\" class=\"data row5 col0\" >0.733766</td>\n",
       "      <td id=\"T_40ce9_row5_col1\" class=\"data row5 col1\" >0.616822</td>\n",
       "      <td id=\"T_40ce9_row5_col2\" class=\"data row5 col2\" >0.776204</td>\n",
       "      <td id=\"T_40ce9_row5_col3\" class=\"data row5 col3\" >0.412870</td>\n",
       "      <td id=\"T_40ce9_row5_col4\" class=\"data row5 col4\" >0.412912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row6\" class=\"row_heading level0 row6\" >decision_tree</th>\n",
       "      <td id=\"T_40ce9_row6_col0\" class=\"data row6 col0\" >0.720779</td>\n",
       "      <td id=\"T_40ce9_row6_col1\" class=\"data row6 col1\" >0.582524</td>\n",
       "      <td id=\"T_40ce9_row6_col2\" class=\"data row6 col2\" >0.719167</td>\n",
       "      <td id=\"T_40ce9_row6_col3\" class=\"data row6 col3\" >0.373510</td>\n",
       "      <td id=\"T_40ce9_row6_col4\" class=\"data row6 col4\" >0.374505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40ce9_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_40ce9_row7_col0\" class=\"data row7 col0\" >0.675325</td>\n",
       "      <td id=\"T_40ce9_row7_col1\" class=\"data row7 col1\" >0.568966</td>\n",
       "      <td id=\"T_40ce9_row7_col2\" class=\"data row7 col2\" >0.719074</td>\n",
       "      <td id=\"T_40ce9_row7_col3\" class=\"data row7 col3\" >0.310530</td>\n",
       "      <td id=\"T_40ce9_row7_col4\" class=\"data row7 col4\" >0.312437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203be18ef60>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'naive_bayes'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
    "\n",
    "display(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод данных с ошибкой предсказания для оценки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Error items count: 31'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Pregnancies</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.258</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>110</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.153</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>99</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.496</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.272</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.180</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>114</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0.640</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>318</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.150</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>82</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.260</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>142</td>\n",
       "      <td>60</td>\n",
       "      <td>33</td>\n",
       "      <td>190</td>\n",
       "      <td>28.8</td>\n",
       "      <td>0.687</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>39</td>\n",
       "      <td>744</td>\n",
       "      <td>36.7</td>\n",
       "      <td>2.329</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.334</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.254</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.207</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>68</td>\n",
       "      <td>28</td>\n",
       "      <td>205</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.875</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>76</td>\n",
       "      <td>43</td>\n",
       "      <td>255</td>\n",
       "      <td>47.9</td>\n",
       "      <td>0.259</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>275</td>\n",
       "      <td>27.7</td>\n",
       "      <td>1.600</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.196</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>0.151</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.376</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.297</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>72</td>\n",
       "      <td>25</td>\n",
       "      <td>190</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.549</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>110</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.680</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>126</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0.338</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.9</td>\n",
       "      <td>0.693</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>1.461</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.4</td>\n",
       "      <td>0.732</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.164</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>125</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.439</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>78</td>\n",
       "      <td>23</td>\n",
       "      <td>79</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.323</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>88</td>\n",
       "      <td>37</td>\n",
       "      <td>140</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1.174</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Predicted  Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
       "64             7          0      114             66              0        0   \n",
       "88            15          0      136             70             32      110   \n",
       "125            1          0       88             30             42       99   \n",
       "143           10          0      108             66              0        0   \n",
       "170            6          0      102             82              0        0   \n",
       "188            8          0      109             76             39      114   \n",
       "199            4          0      148             60             27      318   \n",
       "214            9          0      112             82             32      175   \n",
       "223            7          1      142             60             33      190   \n",
       "228            4          1      197             70             39      744   \n",
       "280            0          0      146             70              0        0   \n",
       "294            0          1      161             50              0        0   \n",
       "304            3          1      150             76              0        0   \n",
       "309            2          0      124             68             28      205   \n",
       "335            0          1      165             76             43      255   \n",
       "395            2          1      127             58             24      275   \n",
       "397            0          0      131             66             40        0   \n",
       "401            6          1      137             61              0        0   \n",
       "406            4          0      115             72              0        0   \n",
       "510           12          0       84             72             31        0   \n",
       "541            3          0      128             72             25      190   \n",
       "549            4          1      189            110             31        0   \n",
       "568            4          1      154             72             29      126   \n",
       "577            2          0      118             80              0        0   \n",
       "622            6          1      183             94              0        0   \n",
       "630            7          0      114             64              0        0   \n",
       "658           11          1      127            106              0        0   \n",
       "669            9          1      154             78             30      100   \n",
       "693            7          0      129             68             49      125   \n",
       "730            3          0      130             78             23       79   \n",
       "744           13          1      153             88             37      140   \n",
       "\n",
       "      BMI  DiabetesPedigreeFunction  Age  Outcome  \n",
       "64   32.8                     0.258   42        1  \n",
       "88   37.1                     0.153   43        1  \n",
       "125  55.0                     0.496   26        1  \n",
       "143  32.4                     0.272   42        1  \n",
       "170  30.8                     0.180   36        1  \n",
       "188  27.9                     0.640   31        1  \n",
       "199  30.9                     0.150   29        1  \n",
       "214  34.2                     0.260   36        1  \n",
       "223  28.8                     0.687   61        0  \n",
       "228  36.7                     2.329   31        0  \n",
       "280  37.9                     0.334   28        1  \n",
       "294  21.9                     0.254   65        0  \n",
       "304  21.0                     0.207   37        0  \n",
       "309  32.9                     0.875   30        1  \n",
       "335  47.9                     0.259   26        0  \n",
       "395  27.7                     1.600   25        0  \n",
       "397  34.3                     0.196   22        1  \n",
       "401  24.2                     0.151   55        0  \n",
       "406  28.9                     0.376   46        1  \n",
       "510  29.7                     0.297   46        1  \n",
       "541  32.4                     0.549   27        1  \n",
       "549  28.5                     0.680   37        0  \n",
       "568  31.3                     0.338   37        0  \n",
       "577  42.9                     0.693   21        1  \n",
       "622  40.8                     1.461   45        0  \n",
       "630  27.4                     0.732   34        1  \n",
       "658  39.0                     0.190   51        0  \n",
       "669  30.9                     0.164   45        0  \n",
       "693  38.5                     0.439   43        1  \n",
       "730  28.4                     0.323   34        1  \n",
       "744  40.6                     1.174   39        0  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.transform(X_test)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "y_pred = class_models[best_model][\"preds\"]\n",
    "\n",
    "error_index = y_test[y_test[\"Outcome\"] != y_pred].index.tolist()\n",
    "display(f\"Error items count: {len(error_index)}\")\n",
    "\n",
    "error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
    "error_df = X_test.loc[error_index].copy()\n",
    "error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
    "error_df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пример использования обученной модели (конвейера) для предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>7.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.161</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "555          7.0    124.0           70.0           33.0    215.0  25.5   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  \n",
       "555                     0.161  37.0      0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Glucose</th>\n",
       "      <th>Age</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>0.122742</td>\n",
       "      <td>0.322537</td>\n",
       "      <td>0.049921</td>\n",
       "      <td>-0.927636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Glucose       Age  BloodPressure  DiabetesPedigreeFunction\n",
       "555  0.122742  0.322537       0.049921                 -0.927636"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'predicted: 0 (proba: [0.7669925 0.2330075])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'real: 0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = class_models[best_model][\"pipeline\"]\n",
    "\n",
    "example_id = 555\n",
    "test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
    "test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
    "display(test)\n",
    "display(test_preprocessed)\n",
    "result_proba = model.predict_proba(test)[0]\n",
    "result = model.predict(test)[0]\n",
    "real = int(y_test.loc[example_id].values[0])\n",
    "display(f\"predicted: {result} (proba: {result_proba})\")\n",
    "display(f\"real: {real}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подбор гиперпараметров методом поиска по сетке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model__criterion': 'entropy',\n",
       " 'model__max_depth': 7,\n",
       " 'model__max_features': 'sqrt',\n",
       " 'model__n_estimators': 50}"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "optimized_model_type = \"random_forest\"\n",
    "\n",
    "random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
    "\n",
    "param_grid = {\n",
    "    \"model__n_estimators\": [10, 50, 100],\n",
    "    \"model__max_features\": [\"sqrt\", \"log2\"],\n",
    "    \"model__max_depth\": [5, 7, 10],\n",
    "    \"model__criterion\": [\"gini\", \"entropy\"],\n",
    "}\n",
    "\n",
    "gs_optomizer = GridSearchCV(\n",
    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
    ")\n",
    "gs_optomizer.fit(X_train, y_train.values.ravel())\n",
    "gs_optomizer.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучение модели с новыми гиперпараметрами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_model = ensemble.RandomForestClassifier(\n",
    "    random_state=random_state,\n",
    "    criterion=\"gini\",\n",
    "    max_depth=5,\n",
    "    max_features=\"log2\",\n",
    "    n_estimators=10,\n",
    ")\n",
    "\n",
    "result = {}\n",
    "\n",
    "result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
    "result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
    "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
    "result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
    "\n",
    "result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
    "result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
    "result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
    "result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
    "result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
    "result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
    "result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
    "result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
    "result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
    "result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
    "result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
    "result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формирование данных для оценки старой и новой версии модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=class_models[optimized_model_type]\n",
    ")\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=result\n",
    ")\n",
    "optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
    "optimized_metrics = optimized_metrics.set_index(\"Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка параметров старой и новой модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "#T_dbff0_row0_col1, #T_dbff0_row1_col0, #T_dbff0_row1_col2, #T_dbff0_row1_col3 {\n",
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       "}\n",
       "#T_dbff0_row0_col4, #T_dbff0_row0_col6, #T_dbff0_row0_col7 {\n",
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       "  color: #f1f1f1;\n",
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       "#T_dbff0_row0_col5, #T_dbff0_row1_col5 {\n",
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       "#T_dbff0_row1_col4, #T_dbff0_row1_col6, #T_dbff0_row1_col7 {\n",
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       "</style>\n",
       "<table id=\"T_dbff0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dbff0_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_dbff0_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_dbff0_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_dbff0_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_dbff0_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_dbff0_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_dbff0_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_dbff0_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
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       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dbff0_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_dbff0_row0_col0\" class=\"data row0 col0\" >0.990741</td>\n",
       "      <td id=\"T_dbff0_row0_col1\" class=\"data row0 col1\" >0.633333</td>\n",
       "      <td id=\"T_dbff0_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_dbff0_row0_col3\" class=\"data row0 col3\" >0.703704</td>\n",
       "      <td id=\"T_dbff0_row0_col4\" class=\"data row0 col4\" >0.996743</td>\n",
       "      <td id=\"T_dbff0_row0_col5\" class=\"data row0 col5\" >0.753247</td>\n",
       "      <td id=\"T_dbff0_row0_col6\" class=\"data row0 col6\" >0.995349</td>\n",
       "      <td id=\"T_dbff0_row0_col7\" class=\"data row0 col7\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dbff0_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_dbff0_row1_col0\" class=\"data row1 col0\" >0.861842</td>\n",
       "      <td id=\"T_dbff0_row1_col1\" class=\"data row1 col1\" >0.673913</td>\n",
       "      <td id=\"T_dbff0_row1_col2\" class=\"data row1 col2\" >0.612150</td>\n",
       "      <td id=\"T_dbff0_row1_col3\" class=\"data row1 col3\" >0.574074</td>\n",
       "      <td id=\"T_dbff0_row1_col4\" class=\"data row1 col4\" >0.830619</td>\n",
       "      <td id=\"T_dbff0_row1_col5\" class=\"data row1 col5\" >0.753247</td>\n",
       "      <td id=\"T_dbff0_row1_col6\" class=\"data row1 col6\" >0.715847</td>\n",
       "      <td id=\"T_dbff0_row1_col7\" class=\"data row1 col7\" >0.620000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203c01dc0e0>"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\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",
    "].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": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_efa8e_row0_col0, #T_efa8e_row1_col0 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_efa8e_row0_col1 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_efa8e_row0_col2, #T_efa8e_row1_col3, #T_efa8e_row1_col4 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_efa8e_row0_col3, #T_efa8e_row0_col4, #T_efa8e_row1_col2 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_efa8e_row1_col1 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_efa8e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_efa8e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_efa8e_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_efa8e_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_efa8e_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_efa8e_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_efa8e_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_efa8e_row0_col0\" class=\"data row0 col0\" >0.753247</td>\n",
       "      <td id=\"T_efa8e_row0_col1\" class=\"data row0 col1\" >0.666667</td>\n",
       "      <td id=\"T_efa8e_row0_col2\" class=\"data row0 col2\" >0.808704</td>\n",
       "      <td id=\"T_efa8e_row0_col3\" class=\"data row0 col3\" >0.471650</td>\n",
       "      <td id=\"T_efa8e_row0_col4\" class=\"data row0 col4\" >0.473300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efa8e_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_efa8e_row1_col0\" class=\"data row1 col0\" >0.753247</td>\n",
       "      <td id=\"T_efa8e_row1_col1\" class=\"data row1 col1\" >0.620000</td>\n",
       "      <td id=\"T_efa8e_row1_col2\" class=\"data row1 col2\" >0.846111</td>\n",
       "      <td id=\"T_efa8e_row1_col3\" class=\"data row1 col3\" >0.439034</td>\n",
       "      <td id=\"T_efa8e_row1_col4\" class=\"data row1 col4\" >0.442128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203bd7f7530>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False\n",
    ")\n",
    "\n",
    "for index in range(0, len(optimized_metrics)):\n",
    "    c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Healthy\", \"Sick\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Регрессионная модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import set_config\n",
    "\n",
    "random_state=9\n",
    "set_config(transform_output=\"pandas\")\n",
    "df = pd.read_csv(\".//scv//diabetes.csv\")\n",
    "print(df.columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделение набора данных на обучающую и тестовые выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <td>0</td>\n",
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       "      <td>0.207</td>\n",
       "      <td>27</td>\n",
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       "      <th>270</th>\n",
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       "      <td>37</td>\n",
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       "      <td>38</td>\n",
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       "      <td>21</td>\n",
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       "</table>\n",
       "<p>614 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "60             2       84              0              0        0   0.0   \n",
       "618            9      112             82             24        0  28.2   \n",
       "346            1      139             46             19       83  28.7   \n",
       "294            0      161             50              0        0  21.9   \n",
       "231            6      134             80             37      370  46.2   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "71             5      139             64             35      140  28.6   \n",
       "106            1       96            122              0        0  22.4   \n",
       "270           10      101             86             37        0  45.6   \n",
       "435            0      141              0              0        0  42.4   \n",
       "102            0      125             96              0        0  22.5   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "60                      0.304   21  \n",
       "618                     1.282   50  \n",
       "346                     0.654   22  \n",
       "294                     0.254   65  \n",
       "231                     0.238   46  \n",
       "..                        ...  ...  \n",
       "71                      0.411   26  \n",
       "106                     0.207   27  \n",
       "270                     1.136   38  \n",
       "435                     0.205   29  \n",
       "102                     0.262   21  \n",
       "\n",
       "[614 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</table>\n",
       "<p>614 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Outcome\n",
       "60         0\n",
       "618        1\n",
       "346        0\n",
       "294        0\n",
       "231        1\n",
       "..       ...\n",
       "71         0\n",
       "106        0\n",
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       "435        1\n",
       "102        0\n",
       "\n",
       "[614 rows x 1 columns]"
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1.251</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.485</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2</td>\n",
       "      <td>146</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>360</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.337</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.705</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "668            6       98             58             33      190  34.0   \n",
       "324            2      112             75             32        0  35.7   \n",
       "624            2      108             64              0        0  30.8   \n",
       "690            8      107             80              0        0  24.6   \n",
       "473            7      136             90              0        0  29.9   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "355            9      165             88              0        0  30.4   \n",
       "534            1       77             56             30       56  33.3   \n",
       "344            8       95             72              0        0  36.8   \n",
       "296            2      146             70             38      360  28.0   \n",
       "462            8       74             70             40       49  35.3   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "668                     0.430   43  \n",
       "324                     0.148   21  \n",
       "624                     0.158   21  \n",
       "690                     0.856   34  \n",
       "473                     0.210   50  \n",
       "..                        ...  ...  \n",
       "355                     0.302   49  \n",
       "534                     1.251   24  \n",
       "344                     0.485   57  \n",
       "296                     0.337   29  \n",
       "462                     0.705   39  \n",
       "\n",
       "[154 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>154 rows × 1 columns</p>\n",
       "</div>"
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      "text/plain": [
       "     Outcome\n",
       "668        0\n",
       "324        0\n",
       "624        0\n",
       "690        0\n",
       "473        0\n",
       "..       ...\n",
       "355        1\n",
       "534        0\n",
       "344        0\n",
       "296        1\n",
       "462        0\n",
       "\n",
       "[154 rows x 1 columns]"
      ]
     },
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    }
   ],
   "source": [
    "from typing import Tuple\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_into_train_test(\n",
    "    df_input: DataFrame,\n",
    "    target_colname: str = \"Outcome\",\n",
    "    frac_train: float = 0.8,\n",
    "    random_state: int = None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "    \n",
    "    if not (0 < frac_train < 1):\n",
    "        raise ValueError(\"Fraction must be between 0 and 1.\")\n",
    "    \n",
    "    # Проверка наличия целевого признака\n",
    "    if target_colname not in df_input.columns:\n",
    "        raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
    "    \n",
    "    # Разделяем данные на признаки и целевую переменную\n",
    "    X = df_input.drop(columns=[target_colname])  # Признаки\n",
    "    y = df_input[[target_colname]]  # Целевая переменная\n",
    "\n",
    "    # Разделяем данные на обучающую и тестовую выборки\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y,\n",
    "        test_size=(1.0 - frac_train),\n",
    "        random_state=random_state\n",
    "    )\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "# Применение функции для разделения данных\n",
    "X_train, X_test, y_train, y_test = split_into_train_test(\n",
    "    df, \n",
    "    target_colname=\"Outcome\", \n",
    "    frac_train=0.8, \n",
    "    random_state=42  # Убедитесь, что вы задали нужное значение random_state\n",
    ")\n",
    "\n",
    "# Для отображения результатов\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": [
    "Определение перечня алгоритмов решения задачи аппроксимации (регрессии)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "models = {\n",
    "    \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
    "    \"linear_poly\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(degree=2),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"linear_interact\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(interaction_only=True),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=random_state)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestRegressor(\n",
    "            max_depth=7, random_state=random_state, n_jobs=-1\n",
    "        )\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPRegressor(\n",
    "            activation=\"tanh\",\n",
    "            hidden_layer_sizes=(3,),\n",
    "            max_iter=500,\n",
    "            early_stopping=True,\n",
    "            random_state=random_state,\n",
    "        )\n",
    "    },\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: linear\n",
      "Model: linear_poly\n",
      "Model: linear_interact\n",
      "Model: ridge\n",
      "Model: decision_tree\n",
      "Model: knn\n",
      "Model: random_forest\n",
      "Model: mlp\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from pandas import DataFrame\n",
    "from sklearn import metrics\n",
    "\n",
    "for model_name in models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "\n",
    "    fitted_model = models[model_name][\"model\"].fit(\n",
    "        X_train.values, y_train.values.ravel()\n",
    "    )\n",
    "    y_train_pred = fitted_model.predict(X_train.values)\n",
    "    y_test_pred = fitted_model.predict(X_test.values)\n",
    "    models[model_name][\"fitted\"] = fitted_model\n",
    "    models[model_name][\"train_preds\"] = y_train_pred\n",
    "    models[model_name][\"preds\"] = y_test_pred\n",
    "    models[model_name][\"RMSE_train\"] = math.sqrt(\n",
    "        metrics.mean_squared_error(y_train, y_train_pred)\n",
    "    )\n",
    "    models[model_name][\"RMSE_test\"] = math.sqrt(\n",
    "        metrics.mean_squared_error(y_test, y_test_pred)\n",
    "    )\n",
    "    models[model_name][\"RMAE_test\"] = math.sqrt(\n",
    "        metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    )\n",
    "    models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод результатов оценки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_222a8_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
       "      <th id=\"T_222a8_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
       "      <th id=\"T_222a8_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
       "      <th id=\"T_222a8_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row0\" class=\"row_heading level0 row0\" >random_forest</th>\n",
       "      <td id=\"T_222a8_row0_col0\" class=\"data row0 col0\" >0.240052</td>\n",
       "      <td id=\"T_222a8_row0_col1\" class=\"data row0 col1\" >0.405871</td>\n",
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       "      <td id=\"T_222a8_row0_col3\" class=\"data row0 col3\" >0.282505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row1\" class=\"row_heading level0 row1\" >linear</th>\n",
       "      <td id=\"T_222a8_row1_col0\" class=\"data row1 col0\" >0.396793</td>\n",
       "      <td id=\"T_222a8_row1_col1\" class=\"data row1 col1\" >0.413576</td>\n",
       "      <td id=\"T_222a8_row1_col2\" class=\"data row1 col2\" >0.590024</td>\n",
       "      <td id=\"T_222a8_row1_col3\" class=\"data row1 col3\" >0.255003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row2\" class=\"row_heading level0 row2\" >ridge</th>\n",
       "      <td id=\"T_222a8_row2_col0\" class=\"data row2 col0\" >0.396822</td>\n",
       "      <td id=\"T_222a8_row2_col1\" class=\"data row2 col1\" >0.414236</td>\n",
       "      <td id=\"T_222a8_row2_col2\" class=\"data row2 col2\" >0.590431</td>\n",
       "      <td id=\"T_222a8_row2_col3\" class=\"data row2 col3\" >0.252623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row3\" class=\"row_heading level0 row3\" >linear_poly</th>\n",
       "      <td id=\"T_222a8_row3_col0\" class=\"data row3 col0\" >0.370076</td>\n",
       "      <td id=\"T_222a8_row3_col1\" class=\"data row3 col1\" >0.422852</td>\n",
       "      <td id=\"T_222a8_row3_col2\" class=\"data row3 col2\" >0.584147</td>\n",
       "      <td id=\"T_222a8_row3_col3\" class=\"data row3 col3\" >0.221209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row4\" class=\"row_heading level0 row4\" >linear_interact</th>\n",
       "      <td id=\"T_222a8_row4_col0\" class=\"data row4 col0\" >0.380128</td>\n",
       "      <td id=\"T_222a8_row4_col1\" class=\"data row4 col1\" >0.426815</td>\n",
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       "      <td id=\"T_222a8_row4_col3\" class=\"data row4 col3\" >0.206543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row5\" class=\"row_heading level0 row5\" >decision_tree</th>\n",
       "      <td id=\"T_222a8_row5_col0\" class=\"data row5 col0\" >0.249880</td>\n",
       "      <td id=\"T_222a8_row5_col1\" class=\"data row5 col1\" >0.445708</td>\n",
       "      <td id=\"T_222a8_row5_col2\" class=\"data row5 col2\" >0.520376</td>\n",
       "      <td id=\"T_222a8_row5_col3\" class=\"data row5 col3\" >0.134743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_222a8_row6_col0\" class=\"data row6 col0\" >0.373319</td>\n",
       "      <td id=\"T_222a8_row6_col1\" class=\"data row6 col1\" >0.450285</td>\n",
       "      <td id=\"T_222a8_row6_col2\" class=\"data row6 col2\" >0.592157</td>\n",
       "      <td id=\"T_222a8_row6_col3\" class=\"data row6 col3\" >0.116883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222a8_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_222a8_row7_col0\" class=\"data row7 col0\" >0.623529</td>\n",
       "      <td id=\"T_222a8_row7_col1\" class=\"data row7 col1\" >0.544323</td>\n",
       "      <td id=\"T_222a8_row7_col2\" class=\"data row7 col2\" >0.658689</td>\n",
       "      <td id=\"T_222a8_row7_col3\" class=\"data row7 col3\" >-0.290498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x203c0120950>"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_metrics = pd.DataFrame.from_dict(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": [
    "\n",
    "Вывод реального и \"спрогнозированного\" результата для обучающей и тестовой выборок\n",
    "\n",
    "Получение лучшей модели\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'random_forest'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = str(reg_metrics.sort_values(by=\"RMSE_test\").iloc[0].name)\n",
    "\n",
    "display(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подбор гиперпараметров методом поиска по сетке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Лучший результат (MSE): 0.15427721639903466\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor  # Используем регрессор\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "df.dropna(inplace=True) \n",
    "# Предикторы и целевая переменная\n",
    "X = df[[\"Glucose\", \"Age\", \"BloodPressure\", \"DiabetesPedigreeFunction\"]]\n",
    "y = df['Outcome']  # Целевая переменная для регрессии\n",
    "\n",
    "\n",
    "model = RandomForestRegressor() \n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],  \n",
    "    'max_depth': [None, 10, 20, 30],  \n",
    "    'min_samples_split': [2, 5, 10]  \n",
    "}\n",
    "\n",
    "# 3. Подбор гиперпараметров с помощью Grid Search\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
    "                           scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Обучение модели на тренировочных данных\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 4. Результаты подбора гиперпараметров\n",
    "print(\"Лучшие параметры:\", grid_search.best_params_)\n",
    "print(\"Лучший результат (MSE):\", -grid_search.best_score_) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучение модели с новыми гиперпараметрами и сравнение новых и старых данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "d:\\5_semester\\AIM\\rep\\AIM-PIbd-31-Razubaev-S-M\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Старые параметры: {'max_depth': 30, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "Лучший результат (MSE) на старых параметрах: 0.1543002886456971\n",
      "\n",
      "Новые параметры: {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Лучший результат (MSE) на новых параметрах: 0.15791709286040012\n",
      "Среднеквадратическая ошибка (MSE) на тестовых данных: 0.16712438177283198\n",
      "Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 0.408808490338486\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "old_param_grid = {\n",
    "    'n_estimators': [50, 100, 200],  # Количество деревьев\n",
    "    'max_depth': [None, 10, 20, 30],  # Максимальная глубина дерева\n",
    "    'min_samples_split': [2, 5, 10]   # Минимальное количество образцов для разбиения узла\n",
    "}\n",
    "\n",
    "old_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
    "                                param_grid=old_param_grid,\n",
    "                                scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "old_grid_search.fit(X_train, y_train)\n",
    "\n",
    "old_best_params = old_grid_search.best_params_\n",
    "old_best_mse = -old_grid_search.best_score_  # Меняем знак, так как берем отрицательное значение MSE\n",
    "\n",
    "new_param_grid = {\n",
    "    'n_estimators': [200],\n",
    "    'max_depth': [20],\n",
    "    'min_samples_split': [10]\n",
    "}\n",
    "\n",
    "new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
    "                                param_grid=new_param_grid,\n",
    "                                scoring='neg_mean_squared_error', cv=2)\n",
    "\n",
    "new_grid_search.fit(X_train, y_train)\n",
    "\n",
    "new_best_params = new_grid_search.best_params_\n",
    "new_best_mse = -new_grid_search.best_score_  # Меняем знак, так как берем отрицательное значение MSE\n",
    "\n",
    "model_best = RandomForestRegressor(**new_best_params)\n",
    "model_best.fit(X_train, y_train)\n",
    "\n",
    "model_oldbest = RandomForestRegressor(**old_best_params)\n",
    "model_oldbest.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model_best.predict(X_test)\n",
    "y_oldpred = model_oldbest.predict(X_test)\n",
    "\n",
    "mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(\"Старые параметры:\", old_best_params)\n",
    "print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
    "print(\"\\nНовые параметры:\", new_best_params)\n",
    "print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
    "print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
    "print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(y_test.values, label='Истинные значения', color='blue', linewidth=2)\n",
    "plt.plot(y_oldpred, label='Предсказанные значения(после)', color='red', linestyle='--', linewidth=2)\n",
    "plt.plot(y_pred, label='Предсказанные значения(до)', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "plt.title('Сравнение предсказанных и истинных значений')\n",
    "plt.xlabel('Подбор параметров')\n",
    "plt.ylabel('Значения')\n",
    "plt.grid()\n",
    "plt.legend( loc ='lower right')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}