Labs/lab_4_trees_6131_Gershevskiy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Лабораторная работа 4\n",
    "\n",
    "Деревья решений\n",
    "\n",
    "Выполнил: Гершевский Егор Владимирович, гр. 6131-010402D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "0.  Выберите набор данных для классификации (бинарной или многоклассовой — не важно). Опишите данные, что обозначает каждый столбец, какого типа (категориальный, вещественный, целочисленный, бинарный) данный столбец, при необходимости для категориальных в том числе бинарных признаков привести расшифровку их значений. В общем, проведите \"визуальный\" анализ ваших данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/egorgersevskij/Desktop/University/Магистратура/2/Инструменты анализа данных/tasks/datasets/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Этот набор данных изначально получен из Национального института диабета, болезней органов пищеварения и почек. Целью набора данных является диагностическое прогнозирование наличия у пациента диабета на основе определенных диагностических измерений, включенных в набор данных. На выбор этих экземпляров из большей базы данных было наложено несколько ограничений. В частности, все пациенты здесь — женщины не моложе 21 года индийского происхождения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наборы данных состоят из нескольких переменных медицинских предикторов и одной целевой переменной «Результат». Переменные-предикторы включают количество беременностей, которые были у пациентки, их ИМТ, уровень инсулина, возраст и так далее."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25, 10))\n",
    "sns.countplot(df[\"Age\"], palette=\"Set2\")\n",
    "ax.set(title=\"Количество людей в различных возрастных категориях\")\n",
    "plt.ylabel('Кол-во людей')\n",
    "plt.xlabel('Возраст')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из графика видно, что в датасете большинство людей, чей возраст меньше 30 лет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.0. Для вашего датасета преобразовать категориальные признаки в бинарные с помощью `pd.factorize` / `pd.get_dummies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Outcome\"] = df[\"Outcome\"].replace({1: \"Yes\", 0: \"No\"}) #создадим категориальный признак для преобразования"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age Outcome  \n",
       "0                     0.627   50      No  \n",
       "1                     0.351   31     Yes  \n",
       "2                     0.672   32      No  \n",
       "3                     0.167   21     Yes  \n",
       "4                     2.288   33      No  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "codes, uniques = pd.factorize(df[\"Outcome\"])\n",
    "df[\"Outcome\"] = codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        0  \n",
       "1                     0.351   31        1  \n",
       "2                     0.672   32        0  \n",
       "3                     0.167   21        1  \n",
       "4                     2.288   33        0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1. С помощью sklearn.preprocessing.StandardScaler нормализовать значения признаков и построить для нормализованного датасета `KNeighborsClassifier` (без использования конвееров)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = df.drop(\"Outcome\", axis=1), df[\"Outcome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scaler = MinMaxScaler()\n",
    "X_scaler = Scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_knn_train, X_knn_test, y_knn_train, y_knn_test = train_test_split(X_scaler, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.36 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.36      0.99      0.53        70\n",
      "           1       0.50      0.01      0.02       122\n",
      "\n",
      "    accuracy                           0.36       192\n",
      "   macro avg       0.43      0.50      0.27       192\n",
      "weighted avg       0.45      0.36      0.20       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "KNeighbors = KNeighborsClassifier()\n",
    "KNeighbors.fit(X_knn_train, y_knn_train)\n",
    "KNeighborsPredict = KNeighbors.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, KNeighborsPredict), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, KNeighborsPredict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.2. То же самое и использованием `sklearn.pipeline.Pipeline`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.72 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.54      0.59        70\n",
      "           1       0.76      0.83      0.79       122\n",
      "\n",
      "    accuracy                           0.72       192\n",
      "   macro avg       0.70      0.69      0.69       192\n",
      "weighted avg       0.72      0.72      0.72       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "KNeighborsPipe = Pipeline([('Scaler', MinMaxScaler()), ('kNN', KNeighborsClassifier())])\n",
    "KNeighborsPipe.fit(X_train, y_train)\n",
    "KNeighborsPredictPipe = KNeighborsPipe.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, KNeighborsPredictPipe), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, KNeighborsPredictPipe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "1.3. С помощью `GridSearchCV` произвести подбор гиперпараметра `KNeighborsClassifier` (`n_neighbors`) и выведите значение гиперпараметра и метрику для наилучшей модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.36 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.36      1.00      0.53        70\n",
      "           1       0.00      0.00      0.00       122\n",
      "\n",
      "    accuracy                           0.36       192\n",
      "   macro avg       0.18      0.50      0.27       192\n",
      "weighted avg       0.13      0.36      0.19       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "KNeighborsParams = {'n_neighbors': range(1, 35)}\n",
    "KNeighborsGCV = GridSearchCV(KNeighborsClassifier(), KNeighborsParams, scoring=\"roc_auc\")\n",
    "KNeighborsGCV.fit(X_knn_train, y_knn_train)\n",
    "KNeighborsPredictGCV = KNeighborsGCV.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, KNeighborsPredictGCV), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, KNeighborsPredictGCV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=34)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNeighborsGCV.best_estimator_ \n",
    "#KNeighborsClassifier(n_neighbors=34)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7976062403867281"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNeighborsGCV.best_score_\n",
    "#0.7976062403867281"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1. Продемонстрировать работу `DecisionTreeClassifier`, посчитать метрики качества на отложенной выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.74 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.59      0.62        70\n",
      "           1       0.78      0.83      0.80       122\n",
      "\n",
      "    accuracy                           0.74       192\n",
      "   macro avg       0.72      0.71      0.71       192\n",
      "weighted avg       0.73      0.74      0.74       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "classifier_predict = classifier.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, classifier_predict), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, classifier_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2. С помощью `GridSearchCV` произвести подбор гиперпараметров для поиска наилучшего дерева (перебрать `max_depth`, `max_features` и может быть что-то ещё)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator: DecisionTreeClassifier(max_depth=7.0, min_samples_leaf=0.020000000000000004,\n",
      "                       min_samples_split=0.1)\n",
      "Accuracy: 0.71 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.60      0.60        70\n",
      "           1       0.77      0.77      0.77       122\n",
      "\n",
      "    accuracy                           0.71       192\n",
      "   macro avg       0.69      0.69      0.69       192\n",
      "weighted avg       0.71      0.71      0.71       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "parametrs = {\n",
    "    \"max_depth\": np.linspace(1, 32, 32, endpoint=True),\n",
    "    \"min_samples_split\": np.linspace(0.01, 0.1, 10, endpoint=True),\n",
    "    \"min_samples_leaf\": np.linspace(0.01, 0.1, 10, endpoint=True),\n",
    "}\n",
    "\n",
    "classifier_GCV = GridSearchCV(DecisionTreeClassifier(), parametrs)\n",
    "classifier_GCV.fit(X_train, y_train)\n",
    "print('Best estimator:', classifier_GCV.best_estimator_)\n",
    "classifier_GCV_predict = classifier_GCV.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, classifier_GCV_predict), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, classifier_GCV_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "2.3. Для полученного наилучшего дерева построить график feature_importances и дополнительно напечатать 3 наиболее важных признака, отсортировав их по убыванию важности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   feature  importance\n",
      "1  Glucose    0.499429\n",
      "5      BMI    0.229376\n",
      "7      Age    0.122237\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='importance', ylabel='feature'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance_df = pd.DataFrame(data={\"feature\": X_train.columns, 'importance': classifier_GCV.best_estimator_.feature_importances_})\n",
    "print(features_importance_df.sort_values(by=\"importance\", ascending=False).head(3))\n",
    "sns.barplot(data=features_importance_df.sort_values(by=\"importance\", ascending=False).head(3), y=\"feature\", x=\"importance\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "3.1. Продемонстрировать работу случайного леса `RandomForestClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.77 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.60      0.65        70\n",
      "           1       0.79      0.86      0.82       122\n",
      "\n",
      "    accuracy                           0.77       192\n",
      "   macro avg       0.75      0.73      0.74       192\n",
      "weighted avg       0.76      0.77      0.76       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "RFC = RandomForestClassifier()\n",
    "RFC.fit(X_train, y_train)\n",
    "RFC_predict = RFC.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, RFC_predict), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, RFC_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2. С помощью `GridSearchCV` произвести подбор гиперпараметров для поиск наилучшего леса, посмотреть насколько улучшились метрики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator: RandomForestClassifier(n_estimators=31)\n",
      "Accuracy: 0.76 %\n",
      "Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.64      0.66        70\n",
      "           1       0.80      0.83      0.81       122\n",
      "\n",
      "    accuracy                           0.76       192\n",
      "   macro avg       0.74      0.74      0.74       192\n",
      "weighted avg       0.76      0.76      0.76       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "parametrs = {\"n_estimators\": range(1, 65)}\n",
    "\n",
    "RFC_GCV = GridSearchCV(RandomForestClassifier(), parametrs)\n",
    "RFC_GCV.fit(X_train, y_train)\n",
    "print('Best estimator:', RFC_GCV.best_estimator_)\n",
    "RFC_GCV_predict = RFC_GCV.predict(X_test)\n",
    "print('Accuracy: %2.2f' %accuracy_score(y_test, RFC_GCV_predict), '%')\n",
    "print('Classification report: \\n', classification_report(y_test, RFC_GCV_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   feature  importance\n",
      "1  Glucose    0.252766\n",
      "5      BMI    0.179787\n",
      "7      Age    0.135378\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='importance', ylabel='feature'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance_df = pd.DataFrame(data={\"feature\": X_train.columns, 'importance': RFC_GCV.best_estimator_.feature_importances_})\n",
    "print(features_importance_df.sort_values(by=\"importance\", ascending=False).head(3))\n",
    "sns.barplot(data=features_importance_df.sort_values(by=\"importance\", ascending=False).head(3), y=\"feature\", x=\"importance\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "4. Сделать выводы. Какой классификатор и с камими гиперпараметрами дал в итоге наилучший результат для выбранного датасета на отложенной выборке."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "Выводы по лабораторной работе:\n",
    "\n",
    " - По точности примерно одинаковыми оказались Random Forest Classifier (Accuracy = 77%) и DecisionTree (Accuracy = 74%), совсем неудачно отработал KNeighborsClassifier (Accuracy = 36%)\n",
    " - Для каждой модели важны различные признаки\n",
    " - GridSearchCV (и Pipeline) положительно влияет на точность модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}