{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ba3d2c87-eedc-4bcd-8769-cbc1cb9d82d4",
   "metadata": {},
   "source": [
    "# Искусственные нейронные сети и Model Selection в scikit-learn\n",
    "\n",
    "## Цель работы\n",
    "\n",
    "Цель работы: познакомиться с базовыми принципами построения и обучения моделей в scikit-learn, а также изучить раздел **Model Selection**.\n",
    "В работе используются:\n",
    "\n",
    "- базовая нейронная сеть `MLPClassifier`;\n",
    "- инструменты выбора модели `GridSearchCV` и `RandomizedSearchCV`;\n",
    "- сгенерированный датасет;\n",
    "- внешний CSV-датасет.\n",
    "\n",
    "## Выбранный раздел scikit-learn examples\n",
    "\n",
    "Раздел: **Model Selection**\n",
    "Выбранный пример: **Comparing randomized search and grid search for hyperparameter estimation**\n",
    "Ссылка: https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html\n",
    "\n",
    "## Идея выбранного примера\n",
    "\n",
    "В выбранном примере сравниваются два способа подбора гиперпараметров модели:\n",
    "\n",
    "1. `GridSearchCV` — полный перебор всех заданных комбинаций параметров.\n",
    "2. `RandomizedSearchCV` — случайный перебор ограниченного количества комбинаций параметров.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ffce83-5675-4449-a9fa-38d580c7467c",
   "metadata": {},
   "source": [
    "1. Базовая нейросеть"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cb505bc-dc47-407a-b6eb-4ccb118989ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      0.89      0.94         9\n",
      "           2       0.91      1.00      0.95        10\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.97      0.96      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cahek\\Documents\\work\\практика\\lab4\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (700) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Загрузка и разбиение данных\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Модель MLP — многослойный перцептрон\n",
    "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=700)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Отчёт о точности\n",
    "print(classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6023685b-baba-4f12-a594-6f08da41fefd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      0.09      0.17        11\n",
      "           2       0.44      1.00      0.62         8\n",
      "\n",
      "    accuracy                           0.67        30\n",
      "   macro avg       0.81      0.70      0.59        30\n",
      "weighted avg       0.85      0.67      0.59        30\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cahek\\Documents\\work\\практика\\lab4\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Загрузка и разбиение данных\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Модель MLP — многослойный перцептрон\n",
    "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=100)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Отчёт о точности\n",
    "print(classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a6f534-73e2-4c69-bce7-8627238ec2c7",
   "metadata": {},
   "source": [
    "При `max_iter=500` появилось предупреждение `ConvergenceWarning`.  \n",
    "Оно означает, что модель достигла максимального числа итераций, но оптимизация ещё не завершилась полностью.\n",
    "При увеличении `max_iter` модель получает больше шагов обучения, и предупреждение может исчезнуть\n",
    "При `max_iter=100` accuracy было на уровне в среднем 0.50, что недостаточно для полного обучения\n",
    "При `max_iter=2500` модель получает больше шагов оптимизации. Вероятность сходимости выше, но качество не всегда сильно увеличивается, если модель уже обучилась достаточно хорошо."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10abdc54-4fd3-4b96-92ba-c70f48d34858",
   "metadata": {},
   "source": [
    "2. Самостоятельное задание: Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3bf444-1272-4c32-b66e-a095c2d4b8d4",
   "metadata": {},
   "source": [
    "Самостоятельное задание: Model Selection\n",
    "\n",
    "Случайным образом был выбран пример **Comparing randomized search and grid search for hyperparameter estimation** из раздела **Model Selection**:\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html\n",
    "\n",
    "Первоначально идея официального примера была воспроизведена с целью понимания различий между двумя подходами к подбору гиперпараметров:\n",
    "\n",
    "- `GridSearchCV`\n",
    "- `RandomizedSearchCV`\n",
    "\n",
    "**Цель задачи:**  \n",
    "Продемонстрировать применение методов выбора модели и подбора гиперпараметров в scikit-learn.\n",
    "\n",
    "1. **Использовать** алгоритмы:\n",
    "   - `GridSearchCV`;\n",
    "   - `RandomizedSearchCV`.\n",
    "\n",
    "2. **Протестировать** подход на двух типах данных:\n",
    "   - **Сгенерированный датасет**: `make_classification`;\n",
    "   - **Реальный датасет**:  Titanic, загруженный через pandas.read_csv из файла data/titanic.csv
    "\n",
    "3. **Сравнить** результаты:\n",
    "   - качество модели;\n",
    "   - лучшие найденные гиперпараметры;\n",
    "   - время работы каждого метода.\n",
    "\n",
    "В качестве модели используется `RandomForestClassifier`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ba4ad4-a608-4417-96d7-d008c037fc28",
   "metadata": {},
   "source": [
    "2.1. Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebea2cda-e2ea-4798-a212-c19bcf897600",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.stats import randint\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import (\n",
    "    GridSearchCV,\n",
    "    RandomizedSearchCV,\n",
    "    train_test_split\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff694921-7072-42ce-8ee7-14bee3371178",
   "metadata": {},
   "source": [
    "Эксперимент 1. Generated dataset\n",
    "\n",
    "Цель\n",
    "\n",
    "Проверить GridSearchCV и RandomizedSearchCV на сгенерированном датасете.\n",
    "\n",
    "Данные\n",
    "\n",
    "Используется make_classification из sklearn.datasets  \n",
    "Это generated dataset для задачи классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ebebae3-0174-4833-81ad-1320d31be348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (750, 10)\n",
      "Test: (250, 10)\n"
     ]
    }
   ],
   "source": [
    "X_generated, y_generated = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=10,\n",
    "    n_informative=6,\n",
    "    n_redundant=2,\n",
    "    n_classes=2,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(\n",
    "    X_generated,\n",
    "    y_generated,\n",
    "    test_size=0.25,\n",
    "    random_state=42,\n",
    "    stratify=y_generated\n",
    ")\n",
    "\n",
    "print(\"Train:\", X_gen_train.shape)\n",
    "print(\"Test:\", X_gen_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbcddcf2-8892-4f1e-b5be-98ced1058ea3",
   "metadata": {},
   "source": [
    "## Визуализация\n",
    "\n",
    "Для простой визуализации используем первые два признака."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2de9c7ec-ab66-423a-bfbe-14b92b9da7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.scatter(\n",
    "    X_generated[:, 0],\n",
    "    X_generated[:, 1],\n",
    "    c=y_generated,\n",
    "    cmap=\"coolwarm\",\n",
    "    edgecolors=\"k\",\n",
    "    alpha=0.7\n",
    ")\n",
    "plt.title(\"make_classification\")\n",
    "plt.xlabel(\"Признак 1\")\n",
    "plt.ylabel(\"Признак 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9f9f1e-f2f3-4c80-ac44-13700eea30a1",
   "metadata": {},
   "source": [
    "## Препроцессинг\n",
    "\n",
    "Дополнительный препроцессинг не требуется.  \n",
    "Данные уже числовые.  \n",
    "Модель RandomForestClassifier не требует масштабирования признаков.\n",
    "\n",
    "## Модель\n",
    "\n",
    "Используется RandomForestClassifier.\n",
    "\n",
    "Подбираемые параметры:\n",
    "\n",
    "- n_estimators\n",
    "- max_depth\n",
    "- min_samples_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "78b34b33-b4e7-4065-9b46-87dc85d8af28",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [None, 5, 10, 20],\n",
    "    \"min_samples_split\": [2, 5, 10]\n",
    "}\n",
    "\n",
    "param_distributions = {\n",
    "    \"n_estimators\": randint(50, 201),\n",
    "    \"max_depth\": [None, 5, 10, 20],\n",
    "    \"min_samples_split\": randint(2, 11)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55e8d6b-ecc0-4fba-bda0-8a08a5628c62",
   "metadata": {},
   "source": [
    "## Обучение GridSearchCV\n",
    "\n",
    "GridSearchCV перебирает все комбинации параметров из param_grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d58b51b-49e0-4ec5-9996-ed677dd4775d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV\n",
      "Время: 3.34\n",
      "Лучшие параметры: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Лучшее CV accuracy: 0.9013333333333334\n"
     ]
    }
   ],
   "source": [
    "grid_search_gen = GridSearchCV(\n",
    "    estimator=RandomForestClassifier(random_state=42),\n",
    "    param_grid=param_grid,\n",
    "    cv=3,\n",
    "    scoring=\"accuracy\",\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "grid_search_gen.fit(X_gen_train, y_gen_train)\n",
    "grid_time_gen = time.time() - start_time\n",
    "\n",
    "print(\"GridSearchCV\")\n",
    "print(\"Время:\", round(grid_time_gen, 2))\n",
    "print(\"Лучшие параметры:\", grid_search_gen.best_params_)\n",
    "print(\"Лучшее CV accuracy:\", grid_search_gen.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23312fd5-de05-4428-ad9a-84d2d4e57f64",
   "metadata": {},
   "source": [
    "## Обучение RandomizedSearchCV\n",
    "RandomizedSearchCV проверяет случайные комбинации параметров.\n",
    "\n",
    "Количество проверок задаётся через n_iter.-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe48786f-c219-4b7d-b8cb-613fc39b363a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV\n",
      "Время: 1.53\n",
      "Лучшие параметры: {'max_depth': 10, 'min_samples_split': 4, 'n_estimators': 157}\n",
      "Лучшее CV accuracy: 0.9\n"
     ]
    }
   ],
   "source": [
    "random_search_gen = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(random_state=42),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=10,\n",
    "    cv=3,\n",
    "    scoring=\"accuracy\",\n",
    "    n_jobs=-1,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "random_search_gen.fit(X_gen_train, y_gen_train)\n",
    "random_time_gen = time.time() - start_time\n",
    "\n",
    "print(\"RandomizedSearchCV\")\n",
    "print(\"Время:\", round(random_time_gen, 2))\n",
    "print(\"Лучшие параметры:\", random_search_gen.best_params_)\n",
    "print(\"Лучшее CV accuracy:\", random_search_gen.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8268f790-8541-45e0-a243-d1a9ad5d54f4",
   "metadata": {},
   "source": [
    "## Результаты\n",
    "\n",
    "Проверим лучшие модели на тестовой выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a25e22f6-b955-41cf-9979-81e293c48fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV test accuracy: 0.9\n",
      "RandomizedSearchCV test accuracy: 0.884\n"
     ]
    }
   ],
   "source": [
    "grid_model_gen = grid_search_gen.best_estimator_\n",
    "random_model_gen = random_search_gen.best_estimator_\n",
    "\n",
    "grid_pred_gen = grid_model_gen.predict(X_gen_test)\n",
    "random_pred_gen = random_model_gen.predict(X_gen_test)\n",
    "\n",
    "grid_acc_gen = accuracy_score(y_gen_test, grid_pred_gen)\n",
    "random_acc_gen = accuracy_score(y_gen_test, random_pred_gen)\n",
    "\n",
    "print(\"GridSearchCV test accuracy:\", grid_acc_gen)\n",
    "print(\"RandomizedSearchCV test accuracy:\", random_acc_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e0f1d63-257c-4bba-9669-f0b563abcf7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90       125\n",
      "           1       0.89      0.91      0.90       125\n",
      "\n",
      "    accuracy                           0.90       250\n",
      "   macro avg       0.90      0.90      0.90       250\n",
      "weighted avg       0.90      0.90      0.90       250\n",
      "\n",
      "RandomizedSearchCV report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.86      0.88       125\n",
      "           1       0.86      0.91      0.89       125\n",
      "\n",
      "    accuracy                           0.88       250\n",
      "   macro avg       0.89      0.88      0.88       250\n",
      "weighted avg       0.89      0.88      0.88       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"GridSearchCV report\")\n",
    "print(classification_report(y_gen_test, grid_pred_gen))\n",
    "\n",
    "print(\"RandomizedSearchCV report\")\n",
    "print(classification_report(y_gen_test, random_pred_gen))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3294f9-7066-4126-aa57-c6ed429176ee",
   "metadata": {},
   "source": [
    "## Сравнение методов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f4a1197-d3c3-4174-8b35-9013008283fd",
   "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>method</th>\n",
       "      <th>cv_accuracy</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>time_seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GridSearchCV</td>\n",
       "      <td>0.901333</td>\n",
       "      <td>0.900</td>\n",
       "      <td>3.335908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomizedSearchCV</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.884</td>\n",
       "      <td>1.527387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               method  cv_accuracy  test_accuracy  time_seconds\n",
       "0        GridSearchCV     0.901333          0.900      3.335908\n",
       "1  RandomizedSearchCV     0.900000          0.884      1.527387"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_gen = pd.DataFrame({\n",
    "    \"method\": [\"GridSearchCV\", \"RandomizedSearchCV\"],\n",
    "    \"cv_accuracy\": [\n",
    "        grid_search_gen.best_score_,\n",
    "        random_search_gen.best_score_\n",
    "    ],\n",
    "    \"test_accuracy\": [\n",
    "        grid_acc_gen,\n",
    "        random_acc_gen\n",
    "    ],\n",
    "    \"time_seconds\": [\n",
    "        grid_time_gen,\n",
    "        random_time_gen\n",
    "    ]\n",
    "})\n",
    "\n",
    "comparison_gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4571db82-184c-4c99-865b-4bdfc8ce23b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(comparison_gen[\"method\"], comparison_gen[\"time_seconds\"])\n",
    "plt.title(\"Время работы на make_classification\")\n",
    "plt.ylabel(\"Секунды\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f7fded-4ee1-48e4-93be-d41f3ffec0c4",
   "metadata": {},
   "source": [
    "## Интерпретация\n",
    "\n",
    "GridSearchCV проверяет все комбинации параметров.\n",
    "RandomizedSearchCV проверяет только часть комбинаций.\n",
    "На сгенерированном датасете оба метода подобрали параметры для RandomForestClassifier.\n",
    "RandomizedSearchCV обычно работает быстрее, а GridSearchCV делает более полный перебор."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a1b02d-cf5c-4505-91f6-eadf63e69c5d",
   "metadata": {},
   "source": [
    "# 4. Эксперимент 2. Внешний датасет\n",
    "\n",
    "## Данные\n",
    "\n",
    "Используется популярный внешний CSV-датасет Titanic.\n",
    "Источник: https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\n",
    "\n",
    "Данные загружаются через pandas.read_csv.\n",
    "survived - переменная отбора\n",
    "## Цель\n",
    "\n",
    "Предсказать, выжил пассажир или нет.\n",
    "Классы:\n",
    "\n",
    "- 0: не выжил\n",
    "- 1: выжил"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21f40c8c-ec6a-4b24-940d-5c2a4cc0c023",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df = pd.read_csv(\"data/titanic.csv\")\n",
    "\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d8caec-5b59-42e3-b049-277e3c399d09",
   "metadata": {},
   "source": [
    "## Первичный анализ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "557bafc8-b224-41fd-993a-89cbf0f1c9c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    str    \n",
      " 4   Sex          891 non-null    str    \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    str    \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    str    \n",
      " 11  Embarked     889 non-null    str    \n",
      "dtypes: float64(2), int64(5), str(5)\n",
      "memory usage: 118.9 KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e7912ec3-1e31-40b3-b921-a0b3b5f65f08",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7c93e3-5c1b-4eb6-acb5-28a6e857aaa9",
   "metadata": {},
   "source": [
    "## Визуализация\n",
    "\n",
    "Посмотрим распределение целевой переменной Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51c3ba63-6eb4-416c-82cd-25cb0d948e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "titanic_df[\"Survived\"].value_counts().sort_index().plot(kind=\"bar\")\n",
    "plt.title(\"Распределение Survived\")\n",
    "plt.xlabel(\"Класс\")\n",
    "plt.ylabel(\"Количество\")\n",
    "plt.xticks(ticks=[0, 1], labels=[\"Не выжил\", \"Выжил\"], rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c5c436-1bf4-45f5-abe2-dbfe41b9945c",
   "metadata": {},
   "source": [
    "## Препроцессинг\n",
    "\n",
    "Удаляем признаки, которые неудобны для простой модели:\n",
    "\n",
    "- PassengerId\n",
    "- Name\n",
    "- Ticket\n",
    "- Cabin\n",
    "\n",
    "Пропуски в Age заменяем медианой, а пропуски в Embarked заменяем самым частым значением.\n",
    "\n",
    "Категориальные признаки Sex и Embarked кодируем через get_dummies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2cba0262-ccf1-4cc9-886e-ad93342b6bc6",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  Sex_male  Embarked_Q  \\\n",
       "0         0       3  22.0      1      0   7.2500      True       False   \n",
       "1         1       1  38.0      1      0  71.2833     False       False   \n",
       "2         1       3  26.0      0      0   7.9250     False       False   \n",
       "3         1       1  35.0      1      0  53.1000     False       False   \n",
       "4         0       3  35.0      0      0   8.0500      True       False   \n",
       "\n",
       "   Embarked_S  \n",
       "0        True  \n",
       "1       False  \n",
       "2        True  \n",
       "3        True  \n",
       "4        True  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data = titanic_df.copy()\n",
    "\n",
    "titanic_data = titanic_data.drop(\n",
    "    columns=[\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"]\n",
    ")\n",
    "\n",
    "titanic_data[\"Age\"] = titanic_data[\"Age\"].fillna(\n",
    "    titanic_data[\"Age\"].median()\n",
    ")\n",
    "\n",
    "titanic_data[\"Embarked\"] = titanic_data[\"Embarked\"].fillna(\n",
    "    titanic_data[\"Embarked\"].mode()[0]\n",
    ")\n",
    "\n",
    "titanic_data = pd.get_dummies(\n",
    "    titanic_data,\n",
    "    columns=[\"Sex\", \"Embarked\"],\n",
    "    drop_first=True\n",
    ")\n",
    "\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf1f8b9-091d-4b5c-9003-3c5979d13ef0",
   "metadata": {},
   "source": [
    "## Разделение данных\n",
    "\n",
    "Разделяем данные на признаки X и целевую переменную y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "10cd15ac-94ac-4ea6-990c-3320563875aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (668, 8)\n",
      "Test: (223, 8)\n"
     ]
    }
   ],
   "source": [
    "X_titanic = titanic_data.drop(\"Survived\", axis=1)\n",
    "y_titanic = titanic_data[\"Survived\"]\n",
    "\n",
    "X_titanic_train, X_titanic_test, y_titanic_train, y_titanic_test = train_test_split(\n",
    "    X_titanic,\n",
    "    y_titanic,\n",
    "    test_size=0.25,\n",
    "    random_state=42,\n",
    "    stratify=y_titanic\n",
    ")\n",
    "\n",
    "print(\"Train:\", X_titanic_train.shape)\n",
    "print(\"Test:\", X_titanic_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61336dc-aa64-466d-924e-94ace83e502b",
   "metadata": {},
   "source": [
    "## Обучение GridSearchCV\n",
    "\n",
    "Повторяем подбор параметров на внешнем датасете Titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b840f8ff-6229-4bd8-aa11-ac24409d82c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV\n",
      "Время: 6.42\n",
      "Лучшие параметры: {'max_depth': 10, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Лучшее CV accuracy: 0.8308622523869161\n"
     ]
    }
   ],
   "source": [
    "grid_search_titanic = GridSearchCV(\n",
    "    estimator=RandomForestClassifier(random_state=42),\n",
    "    param_grid=param_grid,\n",
    "    cv=3,\n",
    "    scoring=\"accuracy\",\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "grid_search_titanic.fit(X_titanic_train, y_titanic_train)\n",
    "grid_time_titanic = time.time() - start_time\n",
    "\n",
    "print(\"GridSearchCV\")\n",
    "print(\"Время:\", round(grid_time_titanic, 2))\n",
    "print(\"Лучшие параметры:\", grid_search_titanic.best_params_)\n",
    "print(\"Лучшее CV accuracy:\", grid_search_titanic.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a1b98d-d3e7-4d82-9095-b18459fef8a0",
   "metadata": {},
   "source": [
    "## Обучение RandomizedSearchCV\n",
    "\n",
    "Повторяем случайный поиск параметров на Titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d77b6f5-247c-4dd4-9376-d4049a190c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV\n",
      "Время: 0.93\n",
      "Лучшие параметры: {'max_depth': 20, 'min_samples_split': 6, 'n_estimators': 107}\n",
      "Лучшее CV accuracy: 0.8278592493839131\n"
     ]
    }
   ],
   "source": [
    "random_search_titanic = RandomizedSearchCV(\n",
    "    estimator=RandomForestClassifier(random_state=42),\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=10,\n",
    "    cv=3,\n",
    "    scoring=\"accuracy\",\n",
    "    n_jobs=-1,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "random_search_titanic.fit(X_titanic_train, y_titanic_train)\n",
    "random_time_titanic = time.time() - start_time\n",
    "\n",
    "print(\"RandomizedSearchCV\")\n",
    "print(\"Время:\", round(random_time_titanic, 2))\n",
    "print(\"Лучшие параметры:\", random_search_titanic.best_params_)\n",
    "print(\"Лучшее CV accuracy:\", random_search_titanic.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035f9a8c-fb02-443d-b848-b47c22c20ffd",
   "metadata": {},
   "source": [
    "## Результаты\n",
    "\n",
    "Проверим лучшие модели на тестовой выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "56955547-08ad-4ae1-b355-3a24fa1dbe9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV test accuracy: 0.7802690582959642\n",
      "RandomizedSearchCV test accuracy: 0.7802690582959642\n"
     ]
    }
   ],
   "source": [
    "grid_model_titanic = grid_search_titanic.best_estimator_\n",
    "random_model_titanic = random_search_titanic.best_estimator_\n",
    "\n",
    "grid_pred_titanic = grid_model_titanic.predict(X_titanic_test)\n",
    "random_pred_titanic = random_model_titanic.predict(X_titanic_test)\n",
    "\n",
    "grid_acc_titanic = accuracy_score(y_titanic_test, grid_pred_titanic)\n",
    "random_acc_titanic = accuracy_score(y_titanic_test, random_pred_titanic)\n",
    "\n",
    "print(\"GridSearchCV test accuracy:\", grid_acc_titanic)\n",
    "print(\"RandomizedSearchCV test accuracy:\", random_acc_titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a30c58de-da1d-4e8b-9961-194e0f2abf17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.86      0.83       137\n",
      "           1       0.75      0.65      0.70        86\n",
      "\n",
      "    accuracy                           0.78       223\n",
      "   macro avg       0.77      0.76      0.76       223\n",
      "weighted avg       0.78      0.78      0.78       223\n",
      "\n",
      "RandomizedSearchCV report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.88      0.83       137\n",
      "           1       0.76      0.63      0.69        86\n",
      "\n",
      "    accuracy                           0.78       223\n",
      "   macro avg       0.78      0.75      0.76       223\n",
      "weighted avg       0.78      0.78      0.78       223\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"GridSearchCV report\")\n",
    "print(classification_report(y_titanic_test, grid_pred_titanic))\n",
    "\n",
    "print(\"RandomizedSearchCV report\")\n",
    "print(classification_report(y_titanic_test, random_pred_titanic))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c68d86-4569-461e-b664-6c1b4434b733",
   "metadata": {},
   "source": [
    "## Сравнение методов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f70b90e2-af7a-4276-9322-5ae5031b5170",
   "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>method</th>\n",
       "      <th>cv_accuracy</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>time_seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GridSearchCV</td>\n",
       "      <td>0.830862</td>\n",
       "      <td>0.780269</td>\n",
       "      <td>6.419291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomizedSearchCV</td>\n",
       "      <td>0.827859</td>\n",
       "      <td>0.780269</td>\n",
       "      <td>0.926053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               method  cv_accuracy  test_accuracy  time_seconds\n",
       "0        GridSearchCV     0.830862       0.780269      6.419291\n",
       "1  RandomizedSearchCV     0.827859       0.780269      0.926053"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_titanic = pd.DataFrame({\n",
    "    \"method\": [\"GridSearchCV\", \"RandomizedSearchCV\"],\n",
    "    \"cv_accuracy\": [\n",
    "        grid_search_titanic.best_score_,\n",
    "        random_search_titanic.best_score_\n",
    "    ],\n",
    "    \"test_accuracy\": [\n",
    "        grid_acc_titanic,\n",
    "        random_acc_titanic\n",
    "    ],\n",
    "    \"time_seconds\": [\n",
    "        grid_time_titanic,\n",
    "        random_time_titanic\n",
    "    ]\n",
    "})\n",
    "\n",
    "comparison_titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3aa12a3c-c459-4a34-bd00-98a641b776fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(comparison_titanic[\"method\"], comparison_titanic[\"time_seconds\"])\n",
    "plt.title(\"Время работы на Titanic датасете\")\n",
    "plt.ylabel(\"Секунды\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95855d97-f43e-449e-b88a-059fcbf1ce57",
   "metadata": {},
   "source": [
    "## Интерпретация\n",
    "\n",
    "Датасет Titanic был загружен через pandas.read_csv из локального файла data/titanic.csv.\n",
    "Перед обучением были обработаны пропуски и категориальные признаки.\n",
    "GridSearchCV выполнил полный перебор заданной сетки параметров.\n",
    "RandomizedSearchCV проверил 10 случайных комбинаций.\n",
    "- Оба метода подобрали параметры для RandomForestClassifier.\n",
    "- Так как качество близкое, RandomizedSearchCV выгоднее по времени,но если сетка маленькая, GridSearchCV удобен для полного перебора в других примерах."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aad359e-d303-46cb-b7dd-d74fcd78dd43",
   "metadata": {},
   "source": [
    "# Общий вывод\n",
    "\n",
    "В работе был выбран пример из раздела Model Selection:\n",
    "\n",
    "Comparing randomized search and grid search for hyperparameter estimation.\n",
    "\n",
    "Были использованы два типа данных:\n",
    "\n",
    "1. Generated dataset: make_classification.\n",
    "2. Внешний CSV-датасет: Titanic через pandas.read_csv из файла data/titanic.csv.\n",
    "\n",
    "Были применены два метода подбора гиперпараметров:\n",
    "\n",
    "- GridSearchCV\n",
    "- RandomizedSearchCV\n",
    "\n",
    "GridSearchCV выполняет полный перебор параметров.\n",
    "RandomizedSearchCV проверяет ограниченное количество случайных комбинаций.\n",
    "\n",
    "Оба метода относятся к выбору модели и помогают подобрать лучшие параметры по валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "822406e9-8786-4b2a-9cdb-3d9ad1b68c2d",
   "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": "3.13.13"
  }
 },
 "nbformat": 4,
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}