time_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Привет, меня зовут Артем Хуршудов. Сегодня я проверю твой проект.\n",
    "<br> Дальнейшее общение будет происходить на \"ты\" если это не вызывает никаких проблем.\n",
    "<br> Желательно реагировать на каждый мой комментарий ('исправил', 'не понятно как исправить ошибку', ...)\n",
    "<br> Пожалуйста, не удаляй комментарии ревьюера, так как они повышают качество повторного ревью.\n",
    "\n",
    "Комментарии будут в <font color='green'>зеленой</font>, <font color='blue'>синей</font> или <font color='red'>красной</font> рамках:\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Если все сделано отлично\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Совет: </b> Если можно немного улучшить\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Ошибка:</b> Если требуются исправления. Работа не может быть принята с красными комментариями.\n",
    "</div>\n",
    "\n",
    "-------------------\n",
    "\n",
    "Будет очень хорошо, если ты будешь помечать свои действия следующим образом:\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Комментарий студента:</b> ...\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Изменения:</b> Были внесены следующие изменения ...\n",
    "</div>\n",
    "\n",
    "### <font color='orange'>Общее впечатление</font>\n",
    "* Большое спасибо за проделанную работу. Видно, что приложено много усилий.\n",
    "* Отлично, что ноутбук хорошо структурирован. Приятно проверять такие работы.\n",
    "* К сожалению проект требует небольших доработок. Однако, изменения не займут много времени.\n",
    "* Также я добавил несколько советов. Пожалуйста, обрати на них внимание.\n",
    "* Если возникнут вопросы, то задавай их в следующей ячейке. Жду новую версию проекта."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Привет, спасибо за ревью\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'>Общее впечатление (ревью 2)</font>\n",
    "* Часть ошибок тебе удалось исправить, молодец.\n",
    "* Однако, осталась одна ошибка, но в прошлой версии я допустил опечатку в комментарии и вследствие этого мне не удалось корректно объяснить, что я имею в виду.\n",
    "* Новые комментарии обозначены фразой \"ревью 2\".\n",
    "* Отправляй новую версию проекта, как только будут готовы изменения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='orange'>Общее впечатление (ревью 3)</font>\n",
    "* После изменений проект улучшился и может быть зачтен.\n",
    "* Такое может быть, что у линейной модели скор оказался хуже. Вероятнее всего это зависит от TimeSeriesSplit.\n",
    "* Если <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html\">посомтреть</a> на то, как происходит разбиение, то мы можем сделать вывод, что линейная регрессия хорошо обучается на маленьких по объему обучающих данных, в отличии от тяжелых моделей.\n",
    "* Удачи в дальнейшем обучении и будущих работах!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Описание проекта\n",
    "\n",
    "Компания «Чётенькое такси» собрала исторические данные о заказах такси в аэропортах. Чтобы привлекать больше водителей в период пиковой нагрузки, нужно спрогнозировать количество заказов такси на следующий час. Постройте модель для такого предсказания.\n",
    "\n",
    "Значение метрики *RMSE* на тестовой выборке должно быть не больше 48.\n",
    "\n",
    "### Инструкция по выполнению проекта\n",
    "\n",
    "1. Загрузите данные и выполните их ресемплирование по одному часу.\n",
    "2. Проанализируйте данные.\n",
    "3. Обучите разные модели с различными гиперпараметрами. Сделайте тестовую выборку размером 10% от исходных данных.\n",
    "4. Проверьте данные на тестовой выборке и сделайте выводы.\n",
    "\n",
    "### Описание данных\n",
    "\n",
    "Данные лежат в файле `taxi.csv`. Количество заказов находится в столбце '*num_orders*' (от англ. *number of orders*, «число заказов»)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Содержание\n",
    "* [Подготовка данных](#prepare_data)\n",
    "* [Анализ данных](#data_analysis)\n",
    "* [Обучение моделей](#model_train)\n",
    "* [Тестирование на данных](#model_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, TimeSeriesSplit, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error \n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.linear_model import Ridge, LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from catboost import CatBoostRegressor\n",
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "from collections import namedtuple\n",
    "\n",
    "import time\n",
    "\n",
    "RANDOM_STATE = 12345"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Отлично, что все импорты собраны в первой ячейке ноутбука! Если у того, кто будет запускать твой ноутбук будут отсутствовать некоторые библиотеки, то он это увидит сразу, а не в процессе!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ModelTimeAndRMSE = namedtuple('ModelTimeAndRMSE', ('fit_time', 'predict_time', 'rmse'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_df(df):\n",
    "    rows_count, columns_count = df.shape\n",
    "    print('Количество стобцов:', columns_count)\n",
    "    print('Количество строк:', rows_count)\n",
    "    display(df.head(5))\n",
    "    display(df.describe())\n",
    "    df.info()\n",
    "    print('Индекс отсортирован в хронологическом порядке: ', df.index.is_monotonic)\n",
    "    \n",
    "def plot_time_series(df, title, xlabel, ylabel):\n",
    "    df.plot(grid=True, legend=False, figsize=(11, 5), title=title)\\\n",
    "            .set(xlabel=xlabel, ylabel=ylabel)\n",
    "    plt.show()\n",
    "    \n",
    "def split_df(df, columns):\n",
    "    return df.drop(columns=['num_orders']), df['num_orders']\n",
    "\n",
    "def get_model_time_and_rmse(model, features_train, target_train, features_test=None, target_test=None):\n",
    "    if features_test is None: \n",
    "        features_test = features_train\n",
    "    if target_test is None: \n",
    "        target_test = target_train\n",
    "    fit_start = time.time()\n",
    "    model.fit(features_train, target_train)\n",
    "    fit_time = time.time() - fit_start\n",
    "    \n",
    "    predict_start = time.time()\n",
    "    predictions = model.predict(features_test)\n",
    "    predict_time = time.time() - predict_start\n",
    "    \n",
    "    rmse = mean_squared_error(target_test, predictions) ** 0.5\n",
    "    \n",
    "    return ModelTimeAndRMSE(fit_time, predict_time, rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"process_data\"></a>\n",
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Загрузим и проанализируем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 1\n",
      "Количество строк: 26496\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-03-01 00:00:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01 00:10:00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01 00:20:00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01 00:30:00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01 00:40:00</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders\n",
       "datetime                       \n",
       "2018-03-01 00:00:00           9\n",
       "2018-03-01 00:10:00          14\n",
       "2018-03-01 00:20:00          28\n",
       "2018-03-01 00:30:00          20\n",
       "2018-03-01 00:40:00          32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>26496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>14.070463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>9.211330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>119.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_orders\n",
       "count  26496.000000\n",
       "mean      14.070463\n",
       "std        9.211330\n",
       "min        0.000000\n",
       "25%        8.000000\n",
       "50%       13.000000\n",
       "75%       19.000000\n",
       "max      119.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 26496 entries, 2018-03-01 00:00:00 to 2018-08-31 23:50:00\n",
      "Data columns (total 1 columns):\n",
      "num_orders    26496 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 414.0 KB\n",
      "Индекс отсортирован в хронологическом порядке:  True\n"
     ]
    }
   ],
   "source": [
    "taxi_df = pd.read_csv('/datasets/taxi.csv', index_col=['datetime'], parse_dates=['datetime'])\n",
    "analyze_df(taxi_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проведем ресемплирование данных по одному часу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_df = taxi_df.resample('1H').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем минимальное и максимальное значение индекса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2018-03-01 00:00:00'), Timestamp('2018-08-31 23:00:00'))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_df.index.min(), taxi_df.index.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Загрузка данных и ресемплинг проведены корректно.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"data_analysis\"></a>\n",
    "## Анализ данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time_series(taxi_df, 'Количество заказов', 'Datetime (1 H)', 'Number of orders')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что со временем количество заказов растет.\n",
    "Выделим тренд, сезонную состовляющую и остаток при помощи декомпозиции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Отлично, что сам ряд был визуализирован.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "decomposed = seasonal_decompose(taxi_df)\n",
    "\n",
    "trend = decomposed.trend\n",
    "seasonal = decomposed.seasonal\n",
    "resid = decomposed.resid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем графики для этих рядов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time_series(trend, 'Тренд', 'Datetime (1 H)', 'Number of orders')\n",
    "plot_time_series(seasonal, 'Сезонность', 'Datetime (1 H)', 'Number of orders')\n",
    "plot_time_series(resid, 'Остаток', 'Datetime (1 H)', 'Number of orders')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Восходящий тренд означает рост количества заказов.  \n",
    "На графике сезонности плохо видны данные за такой большой промежуток времени, поэтому выведем сезонность за будний день, выходной и за неделю "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-08-31 23:00:00')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasonal.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time_series(\n",
    "    seasonal.loc['2018-08-20 00:00:00':'2018-09-27 00:00:00'], \n",
    "    'Сезонность за неделю', \n",
    "    'Datetime (1 H)', \n",
    "    'Number of orders',\n",
    ")\n",
    "plot_time_series(\n",
    "    seasonal.loc['2018-08-31 00:00:00':'2018-09-01 00:00:00'], \n",
    "    'Сезонность в будний день', \n",
    "    'Datetime (1 H)', \n",
    "    'Number of orders',\n",
    ")\n",
    "plot_time_series(\n",
    "    seasonal.loc['2018-08-25 00:00:00':'2018-08-26 00:00:00'], \n",
    "    'Сезонность в выходной день', \n",
    "    'Datetime (1 H)', \n",
    "    'Number of orders',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что в течении буднего дня сезонность меняется: есть пики в начале и в конце рабочего дня.  \n",
    "В выходной самый большой пик приходится на ночь, когда люди возвращаются с отдыха.  \n",
    "В течении недели сезонность почти не меняется."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Декомпозиция проведена корректно. Радует, что сезонность была рассмотрена на более коротком интервале.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model_train\"></a>\n",
    "## Обучение моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим календарные признаки: месяц и день, год добавлять не будем, поскольку у всех значений индекса он одинаковый"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_df['month'] = taxi_df.index.month\n",
    "taxi_df['dayofweek'] = taxi_df.index.dayofweek\n",
    "taxi_df['day'] = taxi_df.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определим функции для добавления фичей в датасет и проверку их качества "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_features(df, max_lag, rolling_mean_size):\n",
    "    df_copy = df.copy()\n",
    "    for lag in range(1, max_lag + 1):\n",
    "        df_copy['lag_{}'.format(lag)] = df_copy['num_orders'].shift(lag)\n",
    "\n",
    "    df_copy['rolling_mean'] = df_copy['num_orders'].shift().rolling(rolling_mean_size).mean()\n",
    "    df_copy = df_copy.dropna()\n",
    "    \n",
    "    return df_copy\n",
    "\n",
    "def eval_features(df, max_lag, rolling_mean_size, model):  \n",
    "    df_copy = make_features(df, max_lag, rolling_mean_size)\n",
    "    \n",
    "    features, target = split_df(df_copy, ['num_orders'])\n",
    "    features_train, features_test, target_train, target_test = train_test_split(features, target, shuffle=False, test_size=0.2)\n",
    "    \n",
    "    model.fit(features_train, target_train)\n",
    "    predictions = model.predict(features_test) \n",
    "    \n",
    "    return mean_squared_error(target_test, predictions) ** 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдем отстающие значения и скользящее среднее "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.490057874637714 48 17\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "best_score = 10 ** 6\n",
    "max_lag_best = None\n",
    "rolling_mean_size_best = None\n",
    "\n",
    "for max_lag in range(1, 50):\n",
    "    for rolling_mean_size in range(1, 20):\n",
    "        rmse = eval_features(taxi_df, max_lag, rolling_mean_size, model)\n",
    "        if rmse < best_score: \n",
    "            best_score = rmse\n",
    "            max_lag_best = max_lag\n",
    "            rolling_mean_size_best = rolling_mean_size\n",
    "\n",
    "print(best_score, max_lag_best, rolling_mean_size_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Совет: </b> Не стоит собирать разные по смыслу функции в одну ячейку. Вообще определении функции и ее применение стоит разделять на разные ячейки.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Перенес определение функций в отдельную ячейку \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Ошибка:</b> Подбор параметров производиться на тесте, а его результаты вообще не используются, зачем он нужен?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Подбор параметров используется для того, чтобы потом добавить фичи на основе их в датасет.<br>\n",
    "Тестовая выборка, которую я использую здесь не имеет отношения к тестовой выборке, на которой будет происходить тестирование итоговой модели.<br>\n",
    "Идею для такого метода поиска параметров я брал из урока <a href=\"https://praktikum.yandex.ru/trainer/data-scientist/lesson/0d17784f-69e4-4b3d-a8bb-43c4e2a2b162/task/92f3c482-86d5-4cdd-a641-bdabc55959bf/\">Временные ряды->Прогнозирование временных рядов->Обучение модели</a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Совет (ревью 2): </b> В прошлой версии результаты подбора параметров были другие, именно поэтому я и задал этот вопрос. Было: \"38.52704231167729 8 3\".\n",
    "    <br> <br> По поводу подбора параметров. В обычной настройке параметров мы не используем тестовую выборку, чтобы избежать переобучения, то есть чрезмерной настройки на выборку. Так почему же эти параметры (lag и rolling size) не могу переподогнаться под конкретную выборку? Никакой разницы нет, какие параметры подбирать. Строго говоря, выбор конкретной модели (линейная или дерево) это точно такой же параметр.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>num_orders</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>day</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>...</th>\n",
       "      <th>lag_40</th>\n",
       "      <th>lag_41</th>\n",
       "      <th>lag_42</th>\n",
       "      <th>lag_43</th>\n",
       "      <th>lag_44</th>\n",
       "      <th>lag_45</th>\n",
       "      <th>lag_46</th>\n",
       "      <th>lag_47</th>\n",
       "      <th>lag_48</th>\n",
       "      <th>rolling_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-03-03 00:00:00</td>\n",
       "      <td>163</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>65.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-03 01:00:00</td>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>163.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>74.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-03 02:00:00</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>108.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>78.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-03 03:00:00</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>77.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-03 04:00:00</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>77.823529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders  month  dayofweek  day  lag_1  lag_2  lag_3  \\\n",
       "datetime                                                                      \n",
       "2018-03-03 00:00:00         163      3          5    3   72.0  119.0  101.0   \n",
       "2018-03-03 01:00:00         108      3          5    3  163.0   72.0  119.0   \n",
       "2018-03-03 02:00:00          25      3          5    3  108.0  163.0   72.0   \n",
       "2018-03-03 03:00:00          64      3          5    3   25.0  108.0  163.0   \n",
       "2018-03-03 04:00:00          44      3          5    3   64.0   25.0  108.0   \n",
       "\n",
       "                     lag_4  lag_5  lag_6  ...  lag_40  lag_41  lag_42  lag_43  \\\n",
       "datetime                                  ...                                   \n",
       "2018-03-03 00:00:00   93.0   49.0   89.0  ...    34.0    15.0    12.0     6.0   \n",
       "2018-03-03 01:00:00  101.0   93.0   49.0  ...    69.0    34.0    15.0    12.0   \n",
       "2018-03-03 02:00:00  119.0  101.0   93.0  ...    64.0    69.0    34.0    15.0   \n",
       "2018-03-03 03:00:00   72.0  119.0  101.0  ...    96.0    64.0    69.0    34.0   \n",
       "2018-03-03 04:00:00  163.0   72.0  119.0  ...    30.0    96.0    64.0    69.0   \n",
       "\n",
       "                     lag_44  lag_45  lag_46  lag_47  lag_48  rolling_mean  \n",
       "datetime                                                                   \n",
       "2018-03-03 00:00:00    43.0    66.0    71.0    85.0   124.0     65.588235  \n",
       "2018-03-03 01:00:00     6.0    43.0    66.0    71.0    85.0     74.764706  \n",
       "2018-03-03 02:00:00    12.0     6.0    43.0    66.0    71.0     78.411765  \n",
       "2018-03-03 03:00:00    15.0    12.0     6.0    43.0    66.0     77.235294  \n",
       "2018-03-03 04:00:00    34.0    15.0    12.0     6.0    43.0     77.823529  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_df = make_features(taxi_df, 48, 17)\n",
    "taxi_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Здесь можно увидеть, что я испольщую лучшие значения max_lag и rolling_mean_size для того, чтобы дбавить фичи в исходный датасет\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выделим фичи и целевой показатель `num_orders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "features, target = split_df(taxi_df, ['num_orders'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделим данные на обучающую и тестовую выборку (на тестовую выборку выделим 10% данных)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train, features_test, target_train, target_test = train_test_split(features, target, shuffle=False, test_size=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заведем массив, в который мы будем помещать результаты работы моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_stats = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь можно перейти к обучению моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:784: FutureWarning: The default value of n_split will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(NSPLIT_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model': 'LinearRegression', 'fit_time': 0.163313627243042, 'predict_time': 0.11282849311828613, 'RMSE': 23.349598211855294, 'score': -644.4557454967598}\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "time_and_rmse = get_model_time_and_rmse(\n",
    "    model, \n",
    "    features_train, \n",
    "    target_train, \n",
    ")\n",
    "score = cross_val_score(model, features_train, target_train, cv=TimeSeriesSplit(), scoring='neg_mean_squared_error').mean()\n",
    "stats = {\n",
    "    'model': 'LinearRegression',\n",
    "    'fit_time': time_and_rmse.fit_time, \n",
    "    'predict_time': time_and_rmse.predict_time,\n",
    "    'RMSE': time_and_rmse.rmse,\n",
    "    'score': score, \n",
    "}\n",
    "models_stats.append(stats)\n",
    "print(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:784: FutureWarning: The default value of n_split will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(NSPLIT_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model': 'CatBoost', 'fit_time': 4.441163778305054, 'predict_time': 0.01069498062133789, 'RMSE': 20.88100593834212, 'score': -705.4427049674722}\n"
     ]
    }
   ],
   "source": [
    "model = CatBoostRegressor(random_seed=RANDOM_STATE, loss_function='RMSE', silent=True)\n",
    "\n",
    "param_grid = {\n",
    "    'learning_rate': [0.1, 0.01],\n",
    "    'depth': [6, 8, 10],\n",
    "    'iterations': [40, 60],\n",
    "}\n",
    "\n",
    "search_cb = GridSearchCV(model, param_grid, cv=TimeSeriesSplit(), scoring='neg_mean_squared_error', verbose=1)\n",
    "search_cb.fit(features_train, target_train)\n",
    "\n",
    "time_and_rmse = get_model_time_and_rmse(\n",
    "    search_cb.best_estimator_, \n",
    "    features_train, \n",
    "    target_train, \n",
    ")\n",
    "stats = {\n",
    "    'model': 'CatBoost',\n",
    "    'fit_time': time_and_rmse.fit_time, \n",
    "    'predict_time': time_and_rmse.predict_time,\n",
    "    'RMSE': time_and_rmse.rmse,\n",
    "    'score': search_cb.best_score_,\n",
    "}\n",
    "models_stats.append(stats)\n",
    "print(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Ошибка:</b> При работе с временными рядами нельзя использовать стандартную кросс-валидацию, так как в определенный момент тренировочная выборка окажется позже валидационной. Для решения этой проблемы стоит использовать <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html\">TimeSeriesSplit</a>. Объект этого типа можно передать в параметр cv в метод основанный на кросс-валидации.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Поменял на TimeSeriesSplit \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 45 candidates, totalling 135 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:784: FutureWarning: The default value of n_split will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(NSPLIT_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 135 out of 135 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model': 'RandomForestRegressor', 'fit_time': 4.7362220287323, 'predict_time': 0.033740997314453125, 'RMSE': 13.5195209508513, 'score': -683.2217590660208}\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(random_state=RANDOM_STATE)\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': range(10, 51, 10),\n",
    "    'max_depth': range(2, 11, 1)\n",
    "}\n",
    "\n",
    "search_rf = GridSearchCV(model, param_grid, cv=TimeSeriesSplit(), scoring='neg_mean_squared_error', verbose=1)\n",
    "search_rf.fit(features_train, target_train)\n",
    "\n",
    "time_and_rmse = get_model_time_and_rmse(\n",
    "    search_rf.best_estimator_, \n",
    "    features_train, \n",
    "    target_train, \n",
    ")\n",
    "stats = {\n",
    "    'model': 'RandomForestRegressor',\n",
    "    'fit_time': time_and_rmse.fit_time, \n",
    "    'predict_time': time_and_rmse.predict_time,\n",
    "    'RMSE': time_and_rmse.rmse,\n",
    "    'score': search_rf.best_score_,\n",
    "}\n",
    "models_stats.append(stats)\n",
    "print(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>predict_time</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.163314</td>\n",
       "      <td>0.112828</td>\n",
       "      <td>23.349598</td>\n",
       "      <td>-644.455745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>4.441164</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>20.881006</td>\n",
       "      <td>-705.442705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>4.736222</td>\n",
       "      <td>0.033741</td>\n",
       "      <td>13.519521</td>\n",
       "      <td>-683.221759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model  fit_time  predict_time       RMSE       score\n",
       "0       LinearRegression  0.163314      0.112828  23.349598 -644.455745\n",
       "1               CatBoost  4.441164      0.010695  20.881006 -705.442705\n",
       "2  RandomForestRegressor  4.736222      0.033741  13.519521 -683.221759"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.DataFrame(models_stats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Ошибка:</b> Почему ты сравниваешь модели по результатам на тестовой выборки? Используй сокр полученный из кросс-валидации.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Не очень понял, я не использую на этом этапе тестовую выборку для вычисления RMSE и использую score полученный на кросс-валидации.<br> \n",
    "Если посмотреть в код функции get_model_time_and_rmse, то видно, что если не передать тестовую выборку, то вычисление RMSE будет проводиться по обучающей выборке \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Ошибка (ревью 2):</b> Прошу прощения, я перепутал. В моем комменатрии выше слово \"тестовая\" надо заменить на \"тренировочная\". Ты передаешь в get_model_time_and_rmse лучшую модель, обучаешь ее на тренировочных данных(без кросс-валидации), и предсказываешь не тех же тренировочных данных, так как тестовые ты не передаешь. \n",
    "    <br><br> Получается, что ты используешь не скор полученный из валидации, а скор полученный на тренировочной выборке, а это не корректно.\n",
    "    <br><br> Скор на валидации находится вот тут: \"search_rf.best_score_float\".\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model_test\"></a>\n",
    "## Тестирование на данных "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выберем лучшую модель (LinearRegression) и проверим метрику на тестовой выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.25517091622006\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "\n",
    "model.fit(features_train, target_train)\n",
    "\n",
    "predictions = model.predict(features_test)\n",
    "print(mean_squared_error(target_test, predictions) ** 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**: Получили значение RMSE 43.2 при использовании модели `LinearRegression`, что меньше чем трeбуемое в условии RMSE равное 48."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Этот шаг проведен корректно.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Это нормально, что score у LinearRegression лучше, чем у остальных моделей, но RMSE на тестовой выборке хуже? \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Чек-лист проверки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Данные загружены и подготовлены\n",
    "- [x]  Данные проанализированы\n",
    "- [x]  Модель обучена, гиперпараметры подобраны\n",
    "- [x]  Качество моделей проверено, выводы сделаны\n",
    "- [x]  Значение *RMSE* на тестовой выборке не больше 48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}