{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n",
    "\n",
    "df = pd.read_csv(\"..//static//csv//balanced_neo.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1-я бизнес-цель (регрессия)**: \n",
    "\n",
    "Предсказание скорости космического объекта для принятия решения о том, насколько опасным он может быть и стоит ли вести за ним наблюдения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Целевой признак: скорость космического объекта relative_velocity\n",
    "\n",
    "Вход: минимальный радиус est_diameter_min, максимальный радиус est_diameter_max, яркость объекта absolute_magnitude, расстояние от Земли miss_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Достижимый уровень качества: предсказания должны иметь погрешность в среднем не более 10000 км/с. Для проверки будет использоваться метрика MAE (средняя абсолютная ошибка)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\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.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import sklearn.preprocessing as preproc\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from mlxtend.evaluate import bias_variance_decomp\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//balanced_neo.csv\")\n",
    "data = df[['est_diameter_min', 'est_diameter_max', 'absolute_magnitude', 'miss_distance', 'relative_velocity']]\n",
    "\n",
    "X = data.drop('relative_velocity', axis=1)\n",
    "y = data['relative_velocity']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Преобразование числовых данных\n",
    "#заполнение пустых значений медианой\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer)\n",
    "    ]\n",
    ")\n",
    "\n",
    "#Категориальных данных нет, поэтому преобразовывать их не надо\n",
    "\n",
    "\n",
    "# Общая предобработка (только числовые данные)\n",
    "preprocessing = ColumnTransformer(\n",
    "    [\n",
    "        (\"nums\", preprocessing_num, X.columns)\n",
    "    ]\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Линейная регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'preprocessing': MinMaxScaler()}\n",
      "Cредняя абсолютная ошибка (MAE) = 19241.554618019443\n",
      "Смещение: -24344.57878426918\n",
      "Дисперсия: 219.3206565410472\n",
      "R^2 = 0.18832948575910047\n"
     ]
    }
   ],
   "source": [
    "pipeline_lin_reg = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('model', LinearRegression())]\n",
    ")\n",
    "\n",
    "# Определение сетки гиперпараметров (возможных знач-ий гиперпараметров) для перебора\n",
    "param_grid = {\n",
    "    #как будут масштабироваться признаки\n",
    "    'preprocessing': [StandardScaler(), preproc.MinMaxScaler(), preproc.MaxAbsScaler(), None]\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV для поиска лучших гиперпараметров по сетке с максимальным знач-ием \n",
    "# отрицательного корня из среднеквадратичной ошибки (отриц., чтобы искался не минимум, а максимум)\n",
    "grid_search = GridSearchCV(pipeline_lin_reg, param_grid, cv=5, scoring='neg_root_mean_squared_error', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель лин. регрессии\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "print(f'Cредняя абсолютная ошибка (MAE) = {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "\n",
    "# Оценка дисперсии и смещения\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(f'R^2 = {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Гребневая регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'model__alpha': 10.0, 'preprocessing': MinMaxScaler()}\n",
      "Cредняя абсолютная ошибка (MAE) = 19239.098934204343\n",
      "Смещение: -24500.751070720406\n",
      "Дисперсия: 399.3445953588631\n",
      "R^2 = 0.18843191913477164\n"
     ]
    }
   ],
   "source": [
    "pipeline_ridge = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('model', Ridge())]\n",
    ")\n",
    "\n",
    "# Определение сетки гиперпараметров (возможных знач-ий гиперпараметров) для перебора\n",
    "param_grid = {\n",
    "    #как будут масштабироваться признаки\n",
    "    'preprocessing': [StandardScaler(), preproc.MinMaxScaler(), preproc.MaxAbsScaler(), None],\n",
    "    #сила регуляризации\n",
    "    'model__alpha': [0, 0.5, 1.0, 1.5, 2.0, 5.0, 10.0] \n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV для поиска лучших гиперпараметров по сетке с максимальным знач-ием \n",
    "# отрицательного корня из среднеквадратичной ошибки (отриц., чтобы искался не минимум, а максимум)\n",
    "grid_search = GridSearchCV(pipeline_ridge, param_grid, cv=5, scoring='neg_root_mean_squared_error', n_jobs=-1, verbose=0)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель регрессии\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "print(f'Cредняя абсолютная ошибка (MAE) = {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n",
    "\n",
    "print(f'R^2 = {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Гребневая регрессия показала почти такие же результаты, что и линейная регрессия"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Метод градиентного бустинга (набор деревьев решений)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 100, 'preprocessing': None}\n",
      "Cредняя абсолютная ошибка (MAE) = 18906.41250012098\n",
      "Смещение: -24465.30968285963\n",
      "Дисперсия: 186.25822491864383\n",
      "R^2 = 0.21038509874388833\n"
     ]
    }
   ],
   "source": [
    "# Конвейер\n",
    "pipeline_grad = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('model', GradientBoostingRegressor())\n",
    "])\n",
    "\n",
    "# Определение сетки гиперпараметров\n",
    "param_grid = {\n",
    "    'preprocessing': [StandardScaler(), preproc.MinMaxScaler(), preproc.MaxAbsScaler(), None],\n",
    "    'model__n_estimators': [100, 200, 300],\n",
    "    #Скорость обучения\n",
    "    'model__learning_rate': [0.1, 0.2],\n",
    "    #Максимальная глубина дерева\n",
    "    'model__max_depth': [3, 5, 7]\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV\n",
    "grid_search = GridSearchCV(pipeline_grad, param_grid, cv=2, scoring='neg_root_mean_squared_error', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель случайного леса\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "\n",
    "print(f'Cредняя абсолютная ошибка (MAE) = {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "\n",
    "# Получение предсказаний на кросс-валидации\n",
    "y_cv_pred = cross_val_predict(best_model, X_train, y_train, cv=3)\n",
    "\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n",
    "\n",
    "print(f'R^2 = {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**: \n",
    "\n",
    "Все 3 модели регрессии не показали необходимого уровня \"погрешности\". Также у всех моделей большое значение смещения, т.е. эти модели для задачи слишком простые. Необходимо использовать более сложные модели. Также возможно, что по доступным в датасете данным нельзя достичь необходимой погрешности.\n",
    "\n",
    "Также низкое значение метрики R^2 у всех моделей показывает, что эти модели плохо объясняют вариацию целевой переменной.\n",
    "\n",
    "Из всех моделей градиентный бустинг показал самую низкую \"погрешность\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2-я бизнес-цель (классификация):** \n",
    "\n",
    "Определение опасности космиеского объекта для увеличения безопасности Земли"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Целевой признак: опасность объекта hazardous\n",
    "\n",
    "Вход: минимальный радиус est_diameter_min, максимальный радиус est_diameter_max, яркость объекта absolute_magnitude, скорость relative_velocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Достижимый уровень качества: необходимо, чтобы точность предсказания модели составляла не менее 90%. Для проверки этого будет использована метрика Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\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.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//balanced_neo.csv\")\n",
    "data = df[['est_diameter_min', 'est_diameter_max', 'absolute_magnitude', 'relative_velocity', 'hazardous']]\n",
    "\n",
    "X = data.drop('hazardous', axis=1)\n",
    "y = data['hazardous']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Преобразование числовых данных\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",
    "# Общая предобработка (только числовые данные)\n",
    "preprocessing = ColumnTransformer(\n",
    "    [\n",
    "        (\"nums\", preprocessing_num, X.columns),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Логистическая регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'classifier__C': 0.1, 'classifier__penalty': 'l1', 'classifier__solver': 'liblinear'}\n",
      "ROC у логистической регрессии = 0.8670873798838691\n",
      "Точность = 0.8591628959276018\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Смещение: 0.8529941124698746\n",
      "Дисперсия: 0.0065558753718589465\n"
     ]
    }
   ],
   "source": [
    "# Конвейер для логистической регрессии\n",
    "pipeline_logreg = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('classifier', LogisticRegression())\n",
    "])\n",
    "\n",
    "# Определение сетки гиперпараметров (возможных знач-ий гиперпараметров) для перебора\n",
    "param_grid = {\n",
    "    # Параметр регуляризации (сила регуляризации), чем меньше, тем сильнее регуляризация\n",
    "    'classifier__C': [0.1, 0.5, 1],\n",
    "    # Тип регуляризации (ф-ия штрафов)\n",
    "    'classifier__penalty': ['l1', 'l2'],\n",
    "    # Решатель (сам алгоритм?)\n",
    "    'classifier__solver': ['liblinear', 'saga']\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV для поиска лучших гиперпараметров по сетке с максимальным знач-ием ROC-кривой\n",
    "grid_search = GridSearchCV(pipeline_logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель логистической регрессии\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Использование и оценка лучшей логистической модели\n",
    "y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "print(f'ROC у логистической регрессии = {roc_auc_score(y_test, y_pred_proba)}')\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(f'Точность = {accuracy_score(y_test, y_pred)}')\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "\n",
    "# построение ROC кривой\n",
    "plt.plot(fpr, tpr)\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "# Построение матрицы ошибок\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Визуализация матрицы ошибок\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=['Предсказанный \"безопасный\"', 'Предсказанный \"опасный\"'], \n",
    "            yticklabels=['Действительно \"безопасный\"', 'Действительно \"опасный\"'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "# Оценка дисперсии и смещения\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Метод случаного леса (набор деревьев решений)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\AI labs\\aimenv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'classifier__max_depth': 10, 'classifier__min_samples_leaf': 4, 'classifier__n_estimators': 200}\n",
      "ROC у метода случайного леса = 0.9009594886141752\n",
      "Точность = 0.8721719457013575\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Смещение: 0.8684929985365014\n",
      "Дисперсия: 0.003137100883428496\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Конвейер для случайного леса\n",
    "pipeline_ranfor = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('classifier', RandomForestClassifier())\n",
    "])\n",
    "\n",
    "# Определение сетки гиперпараметров\n",
    "param_grid = {\n",
    "    #Количество деревьев в лесу\n",
    "    'classifier__n_estimators': [50, 100, 200],\n",
    "    #Максимальная глубина дерева\n",
    "    'classifier__max_depth': [10, 20, 30],\n",
    "    #Минимальное количество образцов для листового узла\n",
    "    'classifier__min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV\n",
    "grid_search = GridSearchCV(pipeline_ranfor, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель случайного леса\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Использование и оценка лучшей модели\n",
    "y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "print(f'ROC у метода случайного леса = {roc_auc_score(y_test, y_pred_proba)}')\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(f'Точность = {accuracy_score(y_test, y_pred)}')\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "\n",
    "# построение ROC кривой\n",
    "plt.plot(fpr, tpr)\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "# Построение матрицы ошибок\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Визуализация матрицы ошибок\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=['Предсказанный \"безопасный\"', 'Предсказанный \"опасный\"'], \n",
    "            yticklabels=['Действительно \"безопасный\"', 'Действительно \"опасный\"'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "# Оценка дисперсии и смещения\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Градиентный бустинг"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'classifier__learning_rate': 0.1, 'classifier__max_depth': 3, 'classifier__n_estimators': 300, 'classifier__subsample': 0.5}\n",
      "ROC у метода градиентного спуска = 0.9012421336337971\n",
      "Точность = 0.872737556561086\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица ошибок:\n",
      "[[1326  400]\n",
      " [  50 1760]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Смещение: 0.8811650848575816\n",
      "Дисперсия: 0.008658656436943876\n"
     ]
    }
   ],
   "source": [
    "# Конвейер\n",
    "pipeline_grad = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('classifier', GradientBoostingClassifier())\n",
    "])\n",
    "\n",
    "# Определение сетки гиперпараметров\n",
    "param_grid = {\n",
    "    'classifier__n_estimators': [100, 200, 300],\n",
    "    #Скорость обучения\n",
    "    'classifier__learning_rate': [0.1, 0.2],\n",
    "    #Максимальная глубина дерева\n",
    "    'classifier__max_depth': [3, 5, 7],\n",
    "    'classifier__subsample': [0.1, 0.5, 1.0],\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV\n",
    "grid_search = GridSearchCV(pipeline_grad, param_grid, cv=2, scoring='roc_auc', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Использование и оценка лучшей модели\n",
    "y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "print(f'ROC у метода градиентного спуска = {roc_auc_score(y_test, y_pred_proba)}')\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(f'Точность = {accuracy_score(y_test, y_pred)}')\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "\n",
    "# построение ROC кривой\n",
    "plt.plot(fpr, tpr)\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "# Построение матрицы ошибок\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Визуализация матрицы ошибок\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=['Предсказанный \"безопасный\"', 'Предсказанный \"опасный\"'], \n",
    "            yticklabels=['Действительно \"безопасный\"', 'Действительно \"опасный\"'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "# Оценка дисперсии и смещения\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**:\n",
    "\n",
    "Все модели классификации показали хорошие результаты, но лучший показатель точности у случайного леса. При этом все рассмотренные модели немного не дотянули до показателя точности в 90%. Дополнительая настройка гиперпараметров могла бы приблизить значение оценки ещё ближе к 90%  "
   ]
  }
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