{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная 4\n",
    "\n",
    "Датасет: Информация об онлайн обучении учеников\n",
    "\n",
    "## Бизнес-цель 1: \n",
    "Улучшение доступа к онлайн-образованию для учеников с низким уровнем финансового обеспечения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Education Level', 'Institution Type', 'Gender', 'Age', 'Device',\n",
      "       'IT Student', 'Location', 'Financial Condition', 'Internet Type',\n",
      "       'Network Type', 'Flexibility Level'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Tuple\n",
    "from pandas import DataFrame\n",
    "from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree, metrics, set_config\n",
    "from sklearn.model_selection import train_test_split\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",
    "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",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "df = pd.read_csv(\"..\\\\static\\\\csv\\\\students_adaptability_level_online_education.csv\")\n",
    "print(df.columns)\n",
    "\n",
    "map_flexibility_to_int = {'Low': 0, 'Moderate': 1, 'High': 2}\n",
    "\n",
    "df['Flexibility Level'] = df['Flexibility Level'].map(map_flexibility_to_int).astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Предварительно создадим колонку для работы с ней (ключевой фактор)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fincond_mapping = {'Poor': 2, 'Mid': 1, 'Rich': 0}\n",
    "internet_type_mapping = {'Mobile Data': 1, 'Wifi': 0}\n",
    "device_mapping = {'Mobile': 1, 'Computer': 0}\n",
    "network_type = {'2G': 2, '3G': 1, '4G': 0}\n",
    "\n",
    "df['Financial Score'] = df['Financial Condition'].map(fincond_mapping)\n",
    "df['Internet Score'] = df['Internet Type'].map(internet_type_mapping)\n",
    "df['Device Score'] = df['Device'].map(device_mapping)\n",
    "df['Network Score'] = df['Network Type'].map(network_type)\n",
    "\n",
    "df['Access Difficulty Score'] = df['Financial Score'] + df['Internet Score'] + df['Device Score'] + df['Network Score']\n",
    "\n",
    "df['Access Difficulty'] = (df['Access Difficulty Score'] >= 3).astype(int)\n",
    "df.drop(columns=['Financial Score', 'Device Score', 'Internet Score', 'Network Score', 'Access Difficulty Score'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формируем выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
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     "metadata": {},
     "output_type": "display_data"
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       "      <th>Education Level</th>\n",
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       "     Education Level Institution Type  Gender  Age    Device IT Student  \\\n",
       "649           School           Public    Male   18    Mobile         No   \n",
       "637           School          Private  Female    9    Mobile         No   \n",
       "68            School           Public  Female   11    Mobile         No   \n",
       "276       University          Private  Female   18    Mobile        Yes   \n",
       "547           School           Public    Male   11    Mobile         No   \n",
       "...              ...              ...     ...  ...       ...        ...   \n",
       "1097      University          Private    Male   23    Mobile        Yes   \n",
       "854           School           Public  Female   18    Mobile         No   \n",
       "756       University           Public    Male   18  Computer         No   \n",
       "133          College           Public    Male   18    Mobile         No   \n",
       "53        University           Public    Male   27    Mobile        Yes   \n",
       "\n",
       "     Location Financial Condition Internet Type Network Type  \\\n",
       "649      Town                 Mid          Wifi           4G   \n",
       "637      Town                 Mid   Mobile Data           4G   \n",
       "68       Town                 Mid          Wifi           4G   \n",
       "276      Town                 Mid   Mobile Data           3G   \n",
       "547      Town                 Mid          Wifi           4G   \n",
       "...       ...                 ...           ...          ...   \n",
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       "756      Town                 Mid          Wifi           3G   \n",
       "133      Town                Poor   Mobile Data           4G   \n",
       "53      Rural                Poor   Mobile Data           4G   \n",
       "\n",
       "      Flexibility Level  Access Difficulty  \n",
       "649                   1                  0  \n",
       "637                   1                  1  \n",
       "68                    0                  0  \n",
       "276                   0                  1  \n",
       "547                   1                  0  \n",
       "...                 ...                ...  \n",
       "1097                  0                  0  \n",
       "854                   0                  1  \n",
       "756                   1                  0  \n",
       "133                   0                  1  \n",
       "53                    1                  1  \n",
       "\n",
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       "      Access Difficulty\n",
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       "...                 ...\n",
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       "      <td>23</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>University</td>\n",
       "      <td>Private</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Computer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>University</td>\n",
       "      <td>Private</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Education Level Institution Type  Gender  Age    Device IT Student  \\\n",
       "265           School          Private  Female    9    Mobile         No   \n",
       "358           School          Private  Female   10    Mobile         No   \n",
       "316       University          Private    Male   23       Tab         No   \n",
       "907           School          Private  Female    9    Mobile         No   \n",
       "1042      University          Private    Male   23    Mobile         No   \n",
       "...              ...              ...     ...  ...       ...        ...   \n",
       "421           School          Private  Female   10    Mobile         No   \n",
       "936       University          Private    Male   23       Tab         No   \n",
       "722       University          Private    Male   23    Mobile        Yes   \n",
       "1075      University          Private    Male   23  Computer        Yes   \n",
       "577       University          Private    Male   23    Mobile        Yes   \n",
       "\n",
       "     Location Financial Condition Internet Type Network Type  \\\n",
       "265      Town                Poor          Wifi           4G   \n",
       "358      Town                 Mid   Mobile Data           3G   \n",
       "316      Town                 Mid          Wifi           4G   \n",
       "907      Town                Poor   Mobile Data           4G   \n",
       "1042     Town                 Mid   Mobile Data           3G   \n",
       "...       ...                 ...           ...          ...   \n",
       "421      Town                 Mid   Mobile Data           3G   \n",
       "936      Town                Rich          Wifi           4G   \n",
       "722     Rural                Poor   Mobile Data           3G   \n",
       "1075     Town                 Mid          Wifi           4G   \n",
       "577      Town                 Mid          Wifi           4G   \n",
       "\n",
       "      Flexibility Level  Access Difficulty  \n",
       "265                   1                  1  \n",
       "358                   1                  1  \n",
       "316                   1                  0  \n",
       "907                   1                  1  \n",
       "1042                  1                  1  \n",
       "...                 ...                ...  \n",
       "421                   1                  1  \n",
       "936                   2                  0  \n",
       "722                   1                  1  \n",
       "1075                  0                  0  \n",
       "577                   0                  0  \n",
       "\n",
       "[241 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Access Difficulty</th>\n",
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       "<p>241 rows × 1 columns</p>\n",
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      "text/plain": [
       "      Access Difficulty\n",
       "265                   1\n",
       "358                   1\n",
       "316                   0\n",
       "907                   1\n",
       "1042                  1\n",
       "...                 ...\n",
       "421                   1\n",
       "936                   0\n",
       "722                   1\n",
       "1075                  0\n",
       "577                   0\n",
       "\n",
       "[241 rows x 1 columns]"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "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\n",
    "    y = df_input[\n",
    "        [stratify_colname]\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",
    "    if frac_val <= 0:\n",
    "        assert len(df_input) == len(df_train) + len(df_temp)\n",
    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
    "    \n",
    "    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=\"Access Difficulty\", 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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пропущенные значения по столбцам:\n",
      "Education Level        0\n",
      "Institution Type       0\n",
      "Gender                 0\n",
      "Age                    0\n",
      "Device                 0\n",
      "IT Student             0\n",
      "Location               0\n",
      "Financial Condition    0\n",
      "Internet Type          0\n",
      "Network Type           0\n",
      "Flexibility Level      0\n",
      "Access Difficulty      0\n",
      "dtype: int64\n",
      "\n",
      "Статистический обзор данных:\n",
      "               Age  Flexibility Level  Access Difficulty\n",
      "count  1205.000000        1205.000000        1205.000000\n",
      "mean     17.065560           0.684647           0.624896\n",
      "std       5.830369           0.618221           0.484351\n",
      "min       9.000000           0.000000           0.000000\n",
      "25%      11.000000           0.000000           0.000000\n",
      "50%      18.000000           1.000000           1.000000\n",
      "75%      23.000000           1.000000           1.000000\n",
      "max      27.000000           2.000000           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": "markdown",
   "metadata": {},
   "source": [
    "Формируем конвеер для классификации данных и проверка конвеера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Access Difficulty</th>\n",
       "      <th>Institution Type_Public</th>\n",
       "      <th>Device_Mobile</th>\n",
       "      <th>Device_Tab</th>\n",
       "      <th>Location_Town</th>\n",
       "      <th>Financial Condition_Poor</th>\n",
       "      <th>Financial Condition_Rich</th>\n",
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       "      <th>Network Type_3G</th>\n",
       "      <th>Network Type_4G</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>547</th>\n",
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       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>-1.289567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>854</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>-1.289567</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.775454</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.775454</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>964 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Access Difficulty  Institution Type_Public  Device_Mobile  Device_Tab  \\\n",
       "649           -1.289567                      1.0            1.0         0.0   \n",
       "637            0.775454                      0.0            1.0         0.0   \n",
       "68            -1.289567                      1.0            1.0         0.0   \n",
       "276            0.775454                      0.0            1.0         0.0   \n",
       "547           -1.289567                      1.0            1.0         0.0   \n",
       "...                 ...                      ...            ...         ...   \n",
       "1097          -1.289567                      0.0            1.0         0.0   \n",
       "854            0.775454                      1.0            1.0         0.0   \n",
       "756           -1.289567                      1.0            0.0         0.0   \n",
       "133            0.775454                      1.0            1.0         0.0   \n",
       "53             0.775454                      1.0            1.0         0.0   \n",
       "\n",
       "      Location_Town  Financial Condition_Poor  Financial Condition_Rich  \\\n",
       "649             1.0                       0.0                       0.0   \n",
       "637             1.0                       0.0                       0.0   \n",
       "68              1.0                       0.0                       0.0   \n",
       "276             1.0                       0.0                       0.0   \n",
       "547             1.0                       0.0                       0.0   \n",
       "...             ...                       ...                       ...   \n",
       "1097            1.0                       0.0                       1.0   \n",
       "854             1.0                       0.0                       0.0   \n",
       "756             1.0                       0.0                       0.0   \n",
       "133             1.0                       1.0                       0.0   \n",
       "53              0.0                       1.0                       0.0   \n",
       "\n",
       "      Internet Type_Wifi  Network Type_3G  Network Type_4G  \n",
       "649                  1.0              0.0              1.0  \n",
       "637                  0.0              0.0              1.0  \n",
       "68                   1.0              0.0              1.0  \n",
       "276                  0.0              1.0              0.0  \n",
       "547                  1.0              0.0              1.0  \n",
       "...                  ...              ...              ...  \n",
       "1097                 1.0              0.0              1.0  \n",
       "854                  0.0              0.0              1.0  \n",
       "756                  1.0              1.0              0.0  \n",
       "133                  0.0              0.0              1.0  \n",
       "53                   0.0              0.0              1.0  \n",
       "\n",
       "[964 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop = ['Age', 'Education Level', 'Gender', 'IT Student', 'Flexibility Level']\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",
    ")\n",
    "\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": [
    "Формируем набор моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_models = {\n",
    "    \"logistic\": {\"model\": linear_model.LogisticRegression()},\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": [
    "Обучаем модели и тестируем их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "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, average=None)\n",
    "    class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict, average=None)\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": [
    "Матрица неточностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, 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=[\"Low dif-ty\", \"High dif-ty\"]\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision_train</th>\n",
       "      <th>Precision_test</th>\n",
       "      <th>Recall_train</th>\n",
       "      <th>Recall_test</th>\n",
       "      <th>Accuracy_train</th>\n",
       "      <th>Accuracy_test</th>\n",
       "      <th>F1_train</th>\n",
       "      <th>F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logistic</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision_tree</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naive_bayes</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gradient_boosting</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375519</td>\n",
       "      <td>0.373444</td>\n",
       "      <td>[0.5460030165912518, 0.0]</td>\n",
       "      <td>[0.5438066465256798, 0.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Precision_train  Precision_test  Recall_train  Recall_test  \\\n",
       "logistic                       1.0             1.0           1.0          1.0   \n",
       "ridge                          1.0             1.0           1.0          1.0   \n",
       "decision_tree                  1.0             1.0           1.0          1.0   \n",
       "knn                            1.0             1.0           1.0          1.0   \n",
       "naive_bayes                    1.0             1.0           1.0          1.0   \n",
       "gradient_boosting              1.0             1.0           1.0          1.0   \n",
       "random_forest                  1.0             1.0           1.0          1.0   \n",
       "mlp                            0.0             0.0           0.0          0.0   \n",
       "\n",
       "                   Accuracy_train  Accuracy_test                   F1_train  \\\n",
       "logistic                 1.000000       1.000000                 [1.0, 1.0]   \n",
       "ridge                    1.000000       1.000000                 [1.0, 1.0]   \n",
       "decision_tree            1.000000       1.000000                 [1.0, 1.0]   \n",
       "knn                      1.000000       1.000000                 [1.0, 1.0]   \n",
       "naive_bayes              1.000000       1.000000                 [1.0, 1.0]   \n",
       "gradient_boosting        1.000000       1.000000                 [1.0, 1.0]   \n",
       "random_forest            1.000000       1.000000                 [1.0, 1.0]   \n",
       "mlp                      0.375519       0.373444  [0.5460030165912518, 0.0]   \n",
       "\n",
       "                                     F1_test  \n",
       "logistic                          [1.0, 1.0]  \n",
       "ridge                             [1.0, 1.0]  \n",
       "decision_tree                     [1.0, 1.0]  \n",
       "knn                               [1.0, 1.0]  \n",
       "naive_bayes                       [1.0, 1.0]  \n",
       "gradient_boosting                 [1.0, 1.0]  \n",
       "random_forest                     [1.0, 1.0]  \n",
       "mlp                [0.5438066465256798, 0.0]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(\n",
    "    by=\"Accuracy_test\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Accuracy_test</th>\n",
       "      <th>F1_test</th>\n",
       "      <th>ROC_AUC_test</th>\n",
       "      <th>Cohen_kappa_test</th>\n",
       "      <th>MCC_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logistic</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision_tree</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naive_bayes</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gradient_boosting</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>0.373444</td>\n",
       "      <td>[0.5438066465256798, 0.0]</td>\n",
       "      <td>0.068065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Accuracy_test                    F1_test  ROC_AUC_test  \\\n",
       "logistic                1.000000                 [1.0, 1.0]      1.000000   \n",
       "ridge                   1.000000                 [1.0, 1.0]      1.000000   \n",
       "decision_tree           1.000000                 [1.0, 1.0]      1.000000   \n",
       "knn                     1.000000                 [1.0, 1.0]      1.000000   \n",
       "naive_bayes             1.000000                 [1.0, 1.0]      1.000000   \n",
       "gradient_boosting       1.000000                 [1.0, 1.0]      1.000000   \n",
       "random_forest           1.000000                 [1.0, 1.0]      1.000000   \n",
       "mlp                     0.373444  [0.5438066465256798, 0.0]      0.068065   \n",
       "\n",
       "                   Cohen_kappa_test  MCC_test  \n",
       "logistic                        1.0       1.0  \n",
       "ridge                           1.0       1.0  \n",
       "decision_tree                   1.0       1.0  \n",
       "knn                             1.0       1.0  \n",
       "naive_bayes                     1.0       1.0  \n",
       "gradient_boosting               1.0       1.0  \n",
       "random_forest                   1.0       1.0  \n",
       "mlp                             0.0       0.0  "
      ]
     },
     "execution_count": 11,
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Лучшая модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'logistic'"
      ]
     },
     "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": [
    "Находим ошибки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Error items count: 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>Education Level</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Institution Type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Device</th>\n",
       "      <th>IT Student</th>\n",
       "      <th>Location</th>\n",
       "      <th>Financial Condition</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Network Type</th>\n",
       "      <th>Flexibility Level</th>\n",
       "      <th>Access Difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Education Level, Predicted, Institution Type, Gender, Age, Device, IT Student, Location, Financial Condition, Internet Type, Network Type, Flexibility Level, Access Difficulty]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "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_new_pred = class_models[best_model][\"preds\"]\n",
    "\n",
    "error_index = y_test[y_test[\"Access Difficulty\"] != y_new_pred].index.tolist()\n",
    "display(f\"Error items count: {len(error_index)}\")\n",
    "\n",
    "error_predicted = pd.Series(y_new_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 модели (конвейера) для предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Institution Type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Device</th>\n",
       "      <th>IT Student</th>\n",
       "      <th>Location</th>\n",
       "      <th>Financial Condition</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Network Type</th>\n",
       "      <th>Flexibility Level</th>\n",
       "      <th>Access Difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>School</td>\n",
       "      <td>Private</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Education Level Institution Type  Gender Age  Device IT Student Location  \\\n",
       "450          School          Private  Female  11  Mobile         No     Town   \n",
       "\n",
       "    Financial Condition Internet Type Network Type Flexibility Level  \\\n",
       "450                Poor   Mobile Data           4G                 1   \n",
       "\n",
       "    Access Difficulty  \n",
       "450                 1  "
      ]
     },
     "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>Access Difficulty</th>\n",
       "      <th>Institution Type_Public</th>\n",
       "      <th>Device_Mobile</th>\n",
       "      <th>Device_Tab</th>\n",
       "      <th>Location_Town</th>\n",
       "      <th>Financial Condition_Poor</th>\n",
       "      <th>Financial Condition_Rich</th>\n",
       "      <th>Internet Type_Wifi</th>\n",
       "      <th>Network Type_3G</th>\n",
       "      <th>Network Type_4G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>0.775454</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Access Difficulty  Institution Type_Public  Device_Mobile  Device_Tab  \\\n",
       "450           0.775454                      0.0            1.0         0.0   \n",
       "\n",
       "     Location_Town  Financial Condition_Poor  Financial Condition_Rich  \\\n",
       "450            1.0                       1.0                       0.0   \n",
       "\n",
       "     Internet Type_Wifi  Network Type_3G  Network Type_4G  \n",
       "450                 0.0              0.0              1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'predicted: 1 (proba: [0.00310819 0.99689181])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'real: 1'"
      ]
     },
     "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": [
    "Создаем гиперпараметры методом поиска по сетке. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model__criterion': 'gini',\n",
       " 'model__max_depth': 2,\n",
       " 'model__max_features': 'sqrt',\n",
       " 'model__n_estimators': 10}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_model_type = 'random_forest'\n",
    "random_state = 9\n",
    "\n",
    "random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
    "\n",
    "param_grid = {\n",
    "    \"model__n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
    "    \"model__max_features\": [\"sqrt\", \"log2\", 2],\n",
    "    \"model__max_depth\": [2, 3, 4, 5, 6, 7, 8, 9 ,10],\n",
    "    \"model__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_model = ensemble.RandomForestClassifier(\n",
    "    random_state=random_state,\n",
    "    criterion=\"gini\",\n",
    "    max_depth=2,\n",
    "    max_features=\"sqrt\",\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 и сама оценка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision_train</th>\n",
       "      <th>Precision_test</th>\n",
       "      <th>Recall_train</th>\n",
       "      <th>Recall_test</th>\n",
       "      <th>Accuracy_train</th>\n",
       "      <th>Accuracy_test</th>\n",
       "      <th>F1_train</th>\n",
       "      <th>F1_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Precision_train Precision_test Recall_train Recall_test Accuracy_train  \\\n",
       "Name                                                                          \n",
       "Old              1.0            1.0          1.0         1.0            1.0   \n",
       "New              1.0            1.0          1.0         1.0            1.0   \n",
       "\n",
       "     Accuracy_test    F1_train     F1_test  \n",
       "Name                                        \n",
       "Old            1.0  [1.0, 1.0]  [1.0, 1.0]  \n",
       "New            1.0         1.0         1.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\")\n",
    "\n",
    "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",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Accuracy_test</th>\n",
       "      <th>F1_test</th>\n",
       "      <th>ROC_AUC_test</th>\n",
       "      <th>Cohen_kappa_test</th>\n",
       "      <th>MCC_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>1.0</td>\n",
       "      <td>[1.0, 1.0]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy_test     F1_test ROC_AUC_test Cohen_kappa_test MCC_test\n",
       "Name                                                                 \n",
       "Old            1.0  [1.0, 1.0]          1.0              1.0      1.0\n",
       "New            1.0         1.0          1.0              1.0      1.0"
      ]
     },
     "execution_count": 18,
     "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",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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=[\"Low dif-ty\", \"High dif-ty\"]\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": [
    "Модель идеально классифицировала объекты, которые относятся к \"High difficulty\" и \"Low difficulty\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Бизнес-цель 2: \n",
    "Повышение удовлетворенности учеников онлайн-обучением на основе их устройств, типу соединения, местоположения.\n",
    "\n",
    "Регрессионная модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "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>Education Level</th>\n",
       "      <th>Institution Type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Device</th>\n",
       "      <th>IT Student</th>\n",
       "      <th>Location</th>\n",
       "      <th>Financial Condition</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Network Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>School</td>\n",
       "      <td>Public</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Rich</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>School</td>\n",
       "      <td>Private</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>School</td>\n",
       "      <td>Private</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>University</td>\n",
       "      <td>Public</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>School</td>\n",
       "      <td>Public</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>College</td>\n",
       "      <td>Private</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>University</td>\n",
       "      <td>Private</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Computer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Town</td>\n",
       "      <td>Rich</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>School</td>\n",
       "      <td>Private</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>University</td>\n",
       "      <td>Private</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>No</td>\n",
       "      <td>Town</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>University</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>Rural</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>964 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Education Level Institution Type  Gender  Age    Device IT Student  \\\n",
       "294           School           Public  Female    9    Mobile         No   \n",
       "876           School          Private    Male   11    Mobile         No   \n",
       "382           School          Private    Male   11    Mobile         No   \n",
       "634       University           Public  Female   23    Mobile         No   \n",
       "906           School           Public  Female   11    Mobile         No   \n",
       "...              ...              ...     ...  ...       ...        ...   \n",
       "1044         College          Private  Female   18    Mobile         No   \n",
       "1095      University          Private  Female   23  Computer        Yes   \n",
       "1130          School          Private    Male   11    Mobile         No   \n",
       "860       University          Private    Male   23    Mobile         No   \n",
       "1126      University          Private    Male   23  Computer        Yes   \n",
       "\n",
       "     Location Financial Condition Internet Type Network Type  \n",
       "294      Town                Rich   Mobile Data           4G  \n",
       "876      Town                 Mid   Mobile Data           3G  \n",
       "382      Town                 Mid   Mobile Data           3G  \n",
       "634      Town                 Mid          Wifi           3G  \n",
       "906      Town                 Mid          Wifi           3G  \n",
       "...       ...                 ...           ...          ...  \n",
       "1044     Town                 Mid          Wifi           4G  \n",
       "1095     Town                Rich          Wifi           4G  \n",
       "1130     Town                Poor          Wifi           4G  \n",
       "860      Town                 Mid   Mobile Data           4G  \n",
       "1126    Rural                 Mid   Mobile Data           3G  \n",
       "\n",
       "[964 rows x 10 columns]"
      ]
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    {
     "data": {
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       "'y_train'"
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       "      Flexibility Level\n",
       "294                   0\n",
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       "382                   0\n",
       "634                   0\n",
       "906                   0\n",
       "...                 ...\n",
       "1044                  1\n",
       "1095                  2\n",
       "1130                  0\n",
       "860                   0\n",
       "1126                  0\n",
       "\n",
       "[964 rows x 1 columns]"
      ]
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       "     Education Level Institution Type  Gender  Age    Device IT Student  \\\n",
       "101           School          Private  Female   11  Computer         No   \n",
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       "...              ...              ...     ...  ...       ...        ...   \n",
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       "894           School          Private  Female   10    Mobile         No   \n",
       "866           School          Private    Male   11    Mobile         No   \n",
       "1006      University          Private  Female   23  Computer         No   \n",
       "\n",
       "     Location Financial Condition Internet Type Network Type  \n",
       "101      Town                 Mid          Wifi           4G  \n",
       "946      Town                 Mid          Wifi           4G  \n",
       "306      Town                 Mid          Wifi           4G  \n",
       "109      Town                 Mid          Wifi           3G  \n",
       "1061    Rural                 Mid   Mobile Data           3G  \n",
       "...       ...                 ...           ...          ...  \n",
       "908      Town                Rich          Wifi           4G  \n",
       "1135     Town                 Mid          Wifi           4G  \n",
       "894      Town                Poor   Mobile Data           3G  \n",
       "866      Town                 Mid   Mobile Data           3G  \n",
       "1006     Town                Rich          Wifi           4G  \n",
       "\n",
       "[241 rows x 10 columns]"
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      "text/plain": [
       "      Flexibility Level\n",
       "101                   1\n",
       "946                   1\n",
       "306                   1\n",
       "109                   2\n",
       "1061                  1\n",
       "...                 ...\n",
       "908                   1\n",
       "1135                  1\n",
       "894                   0\n",
       "866                   0\n",
       "1006                  1\n",
       "\n",
       "[241 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n",
    "\n",
    "random_state = 9\n",
    "map_flexibility_to_int = {'Low': 0, 'Moderate': 1, 'High': 2}\n",
    "\n",
    "df = pd.read_csv(\"..\\\\static\\\\csv\\\\students_adaptability_level_online_education.csv\")\n",
    "\n",
    "df['Flexibility Level'] = df['Flexibility Level'].map(map_flexibility_to_int).astype('int32')\n",
    "\n",
    "def split_into_train_test(\n",
    "    df_input: DataFrame,\n",
    "    target_colname: str,\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",
    "    if target_colname not in df_input.columns:\n",
    "        raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
    "    \n",
    "    X = df_input.drop(columns=[target_colname])\n",
    "    y = df_input[[target_colname]]\n",
    "\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",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_into_train_test(\n",
    "    df, \n",
    "    target_colname=\"Flexibility Level\", \n",
    "    frac_train=0.8, \n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполним one-hot encoding, чтобы избавиться от категориальных признаков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <th>634</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>964 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Education Level_School  Education Level_University  \\\n",
       "294     9                    True                       False   \n",
       "876    11                    True                       False   \n",
       "382    11                    True                       False   \n",
       "634    23                   False                        True   \n",
       "906    11                    True                       False   \n",
       "...   ...                     ...                         ...   \n",
       "1044   18                   False                       False   \n",
       "1095   23                   False                        True   \n",
       "1130   11                    True                       False   \n",
       "860    23                   False                        True   \n",
       "1126   23                   False                        True   \n",
       "\n",
       "      Institution Type_Public  Gender_Male  Device_Mobile  Device_Tab  \\\n",
       "294                      True        False           True       False   \n",
       "876                     False         True           True       False   \n",
       "382                     False         True           True       False   \n",
       "634                      True        False           True       False   \n",
       "906                      True        False           True       False   \n",
       "...                       ...          ...            ...         ...   \n",
       "1044                    False        False           True       False   \n",
       "1095                    False        False          False       False   \n",
       "1130                    False         True           True       False   \n",
       "860                     False         True           True       False   \n",
       "1126                    False         True          False       False   \n",
       "\n",
       "      IT Student_Yes  Location_Town  Financial Condition_Poor  \\\n",
       "294            False           True                     False   \n",
       "876            False           True                     False   \n",
       "382            False           True                     False   \n",
       "634            False           True                     False   \n",
       "906            False           True                     False   \n",
       "...              ...            ...                       ...   \n",
       "1044           False           True                     False   \n",
       "1095            True           True                     False   \n",
       "1130           False           True                      True   \n",
       "860            False           True                     False   \n",
       "1126            True          False                     False   \n",
       "\n",
       "      Financial Condition_Rich  Internet Type_Wifi  Network Type_3G  \\\n",
       "294                       True               False            False   \n",
       "876                      False               False             True   \n",
       "382                      False               False             True   \n",
       "634                      False                True             True   \n",
       "906                      False                True             True   \n",
       "...                        ...                 ...              ...   \n",
       "1044                     False                True            False   \n",
       "1095                      True                True            False   \n",
       "1130                     False                True            False   \n",
       "860                      False               False            False   \n",
       "1126                     False               False             True   \n",
       "\n",
       "      Network Type_4G  \n",
       "294              True  \n",
       "876             False  \n",
       "382             False  \n",
       "634             False  \n",
       "906             False  \n",
       "...               ...  \n",
       "1044             True  \n",
       "1095             True  \n",
       "1130             True  \n",
       "860              True  \n",
       "1126            False  \n",
       "\n",
       "[964 rows x 14 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features = ['Education Level', 'Institution Type', 'Gender', 'Device', 'IT Student', 'Location', 'Financial Condition', 'Internet Type', 'Network Type']\n",
    "\n",
    "X_test = pd.get_dummies(X_test, columns=cat_features, drop_first=True)\n",
    "X_train = pd.get_dummies(X_train, columns=cat_features, drop_first=True)\n",
    "\n",
    "X_test\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определение перечня алгоритмов решения задачи регрессии."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "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",
    "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": [
    "Выводим результаты оценки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_a8fc9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a8fc9_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
       "      <th id=\"T_a8fc9_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
       "      <th id=\"T_a8fc9_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
       "      <th id=\"T_a8fc9_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row0\" class=\"row_heading level0 row0\" >random_forest</th>\n",
       "      <td id=\"T_a8fc9_row0_col0\" class=\"data row0 col0\" >0.383913</td>\n",
       "      <td id=\"T_a8fc9_row0_col1\" class=\"data row0 col1\" >0.415442</td>\n",
       "      <td id=\"T_a8fc9_row0_col2\" class=\"data row0 col2\" >0.564953</td>\n",
       "      <td id=\"T_a8fc9_row0_col3\" class=\"data row0 col3\" >0.581728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
       "      <td id=\"T_a8fc9_row1_col0\" class=\"data row1 col0\" >0.402696</td>\n",
       "      <td id=\"T_a8fc9_row1_col1\" class=\"data row1 col1\" >0.460020</td>\n",
       "      <td id=\"T_a8fc9_row1_col2\" class=\"data row1 col2\" >0.582800</td>\n",
       "      <td id=\"T_a8fc9_row1_col3\" class=\"data row1 col3\" >0.487148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
       "      <td id=\"T_a8fc9_row2_col0\" class=\"data row2 col0\" >0.431006</td>\n",
       "      <td id=\"T_a8fc9_row2_col1\" class=\"data row2 col1\" >0.465811</td>\n",
       "      <td id=\"T_a8fc9_row2_col2\" class=\"data row2 col2\" >0.582463</td>\n",
       "      <td id=\"T_a8fc9_row2_col3\" class=\"data row2 col3\" >0.474156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row3\" class=\"row_heading level0 row3\" >linear_interact</th>\n",
       "      <td id=\"T_a8fc9_row3_col0\" class=\"data row3 col0\" >0.437974</td>\n",
       "      <td id=\"T_a8fc9_row3_col1\" class=\"data row3 col1\" >0.476828</td>\n",
       "      <td id=\"T_a8fc9_row3_col2\" class=\"data row3 col2\" >0.604217</td>\n",
       "      <td id=\"T_a8fc9_row3_col3\" class=\"data row3 col3\" >0.448987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row4\" class=\"row_heading level0 row4\" >linear_poly</th>\n",
       "      <td id=\"T_a8fc9_row4_col0\" class=\"data row4 col0\" >0.437146</td>\n",
       "      <td id=\"T_a8fc9_row4_col1\" class=\"data row4 col1\" >0.476920</td>\n",
       "      <td id=\"T_a8fc9_row4_col2\" class=\"data row4 col2\" >0.605206</td>\n",
       "      <td id=\"T_a8fc9_row4_col3\" class=\"data row4 col3\" >0.448773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row5\" class=\"row_heading level0 row5\" >ridge</th>\n",
       "      <td id=\"T_a8fc9_row5_col0\" class=\"data row5 col0\" >0.536685</td>\n",
       "      <td id=\"T_a8fc9_row5_col1\" class=\"data row5 col1\" >0.564421</td>\n",
       "      <td id=\"T_a8fc9_row5_col2\" class=\"data row5 col2\" >0.682269</td>\n",
       "      <td id=\"T_a8fc9_row5_col3\" class=\"data row5 col3\" >0.227951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row6\" class=\"row_heading level0 row6\" >linear</th>\n",
       "      <td id=\"T_a8fc9_row6_col0\" class=\"data row6 col0\" >0.536652</td>\n",
       "      <td id=\"T_a8fc9_row6_col1\" class=\"data row6 col1\" >0.564834</td>\n",
       "      <td id=\"T_a8fc9_row6_col2\" class=\"data row6 col2\" >0.682842</td>\n",
       "      <td id=\"T_a8fc9_row6_col3\" class=\"data row6 col3\" >0.226821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8fc9_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_a8fc9_row7_col0\" class=\"data row7 col0\" >0.582720</td>\n",
       "      <td id=\"T_a8fc9_row7_col1\" class=\"data row7 col1\" >0.620961</td>\n",
       "      <td id=\"T_a8fc9_row7_col2\" class=\"data row7 col2\" >0.727896</td>\n",
       "      <td id=\"T_a8fc9_row7_col3\" class=\"data row7 col3\" >0.065525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x239cb3282d0>"
      ]
     },
     "execution_count": 42,
     "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": [
    "Выводим лучшую модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "Подбираем гиперпараметры методом поиска по сетке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Лучшие параметры: {'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "Лучший результат (MSE): 0.15015918754440927\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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"
     ]
    }
   ],
   "source": [
    "X = df[['Device', 'Financial Condition', 'Internet Type']]\n",
    "y = df['Flexibility Level']  # Целевая переменная для регрессии\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",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
    "                           scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие параметры:\", grid_search.best_params_)\n",
    "print(\"Лучший результат (MSE):\", -grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучаем модель с новыми гиперпараметрами и сравниваем новых данных со старыми."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.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': 2, 'n_estimators': 200}\n",
      "Лучший результат (MSE) на старых параметрах: 0.14998947697586934\n",
      "\n",
      "Новые параметры: {'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "Лучший результат (MSE) на новых параметрах: 0.18737177399159283\n",
      "Среднеквадратическая ошибка (MSE) на тестовых данных: 0.13671335461532685\n",
      "Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 0.3697476904800446\n"
     ]
    }
   ],
   "source": [
    "# Old data\n",
    "\n",
    "old_param_grid = param_grid\n",
    "old_grid_search = grid_search\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_ \n",
    "\n",
    "# New data\n",
    "\n",
    "new_param_grid = {\n",
    "    'n_estimators': [50],\n",
    "    'max_depth': [30],\n",
    "    'min_samples_split': [2]\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_\n",
    "\n",
    "new_best_model = RandomForestRegressor(**new_best_params)\n",
    "new_best_model.fit(X_train, y_train)\n",
    "\n",
    "old_best_model = RandomForestRegressor(**old_best_params)\n",
    "old_best_model.fit(X_train, y_train)\n",
    "\n",
    "y_new_pred = new_best_model.predict(X_test)\n",
    "y_old_pred = old_best_model.predict(X_test)\n",
    "\n",
    "mse = metrics.mean_squared_error(y_test, y_new_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 данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test.values, label='Истинные значения', color='blue', linewidth=2)\n",
    "plt.plot(y_old_pred, label='Предсказанные значения (старые данные)', color='red', linestyle='--', linewidth=2)\n",
    "plt.plot(y_new_pred, label='Предсказанные значения (новые данные)', 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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}