ipynb/taxi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Содержание<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Импорт-библиотек\" data-toc-modified-id=\"Импорт-библиотек-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Импорт библиотек</a></span></li><li><span><a href=\"#Подготовка\" data-toc-modified-id=\"Подготовка-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Подготовка</a></span></li><li><span><a href=\"#Анализ\" data-toc-modified-id=\"Анализ-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Анализ</a></span></li><li><span><a href=\"#Разработка-признаков-и-подготовка-данных\" data-toc-modified-id=\"Разработка-признаков-и-подготовка-данных-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Разработка признаков и подготовка данных</a></span></li><li><span><a href=\"#Обучение\" data-toc-modified-id=\"Обучение-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Обучение</a></span><ul class=\"toc-item\"><li><span><a href=\"#Базовый-пайплайн\" data-toc-modified-id=\"Базовый-пайплайн-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Базовый пайплайн</a></span></li><li><span><a href=\"#Собственная-метрика-для-оценки-моделей\" data-toc-modified-id=\"Собственная-метрика-для-оценки-моделей-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Собственная метрика для оценки моделей</a></span></li><li><span><a href=\"#Параметры-моделей-для-решетчатого-поиска\" data-toc-modified-id=\"Параметры-моделей-для-решетчатого-поиска-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Параметры моделей для решетчатого поиска</a></span></li><li><span><a href=\"#Поиск-и-оценка-моделей\" data-toc-modified-id=\"Поиск-и-оценка-моделей-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Поиск и оценка моделей</a></span></li></ul></li><li><span><a href=\"#Тестирование\" data-toc-modified-id=\"Тестирование-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Тестирование</a></span></li><li><span><a href=\"#Общий-вывод\" data-toc-modified-id=\"Общий-вывод-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Общий вывод</a></span></li><li><span><a href=\"#Чек-лист-проверки\" data-toc-modified-id=\"Чек-лист-проверки-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Чек-лист проверки</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Прогнозирование заказов такси"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
    "Вам нужно:\n",
    "\n",
    "1. Загрузить данные и выполнить их ресемплирование по одному часу.\n",
    "2. Проанализировать данные.\n",
    "3. Обучить разные модели с различными гиперпараметрами. Сделать тестовую выборку размером 10% от исходных данных.\n",
    "4. Проверить данные на тестовой выборке и сделать выводы.\n",
    "\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": [
    "## Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import lightgbm as lgb\n",
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import (\n",
    "    StandardScaler,\n",
    "    MinMaxScaler,\n",
    "    RobustScaler,\n",
    "    OneHotEncoder,\n",
    "    OrdinalEncoder,\n",
    ")\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "\n",
    "RANDOM_STATE = 42\n",
    "TEST_SIZE = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/datasets/taxi.csv', index_col=[0], parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.resample('1H').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Мы располашаем данными с 01.03.2018 по 31.08.2018\n"
     ]
    }
   ],
   "source": [
    "min_dt = df.index.min().strftime('%d.%m.%Y')\n",
    "max_dt = df.index.max().strftime('%d.%m.%Y')\n",
    "\n",
    "print(f'''Мы располашаем данными с {min_dt} по {max_dt}''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.rolling(24 * 7).mean().plot(figsize=(20, 5), legend=False)\n",
    "plt.title('Недельное скользящее среднее')\n",
    "plt.xlabel('Даты')\n",
    "plt.ylabel('Количество заказов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим, что с течением времени количество заказов растёт. Проверим дневные тренд и сезонную составляющую."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.rolling(24 * 7).std().plot(figsize=(20, 5), legend=False)\n",
    "plt.title('Недельное скользящее стандартное отклонение')\n",
    "plt.xlabel('Даты')\n",
    "plt.ylabel('Количество заказов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также мы можем видеть некий перелом в августе, когда стандартное отклонение совершило резкий скачок и продолжило расти."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "decomposed = seasonal_decompose(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposed.seasonal['2018-08-31'].plot(figsize=(20, 5))\n",
    "plt.title('Сезонная составляющая за последний день')\n",
    "plt.xlabel('Время')\n",
    "plt.ylabel('Динамика количества заказов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что в 6:00 пассажиропоток минимален. Существует несколько пиков - ближе к началу и к концу рабочего дня. Также крайне высокие значения наблюдаются ближе к полуночи."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposed.trend.plot(figsize=(20, 5))\n",
    "plt.title('Сезонная составляющая')\n",
    "plt.xlabel('Время')\n",
    "plt.ylabel('Динамика количества заказов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тренд подтверждает то, что мы уведели на скользящем среднем. Есть тенденция к повышению числа заказов со временем."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разработка признаков и подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_features(data, max_lag):\n",
    "    data['hour'] = data.index.hour\n",
    "    data['day'] = data.index.day\n",
    "    data['dayofweek'] = data.index.dayofweek\n",
    "    data['week'] = data.index.isocalendar().week\n",
    "\n",
    "    # Целевая метрика\n",
    "    data['orders_next_hour'] = data['num_orders'].shift(-1)\n",
    "\n",
    "    for lag in range(1, max_lag + 1):\n",
    "        data['lag_{}'.format(lag)] = data['num_orders'].shift(lag)\n",
    "\n",
    "    data['rolling_mean_day'] = data['num_orders'].shift().rolling(24).mean()\n",
    "    data['rolling_mean_week'] = data['num_orders'].shift().rolling(168).mean()\n",
    "\n",
    "\n",
    "make_features(df, 10)\n",
    "\n",
    "train, test = train_test_split(df, shuffle=False, test_size=TEST_SIZE)\n",
    "train = train.dropna()\n",
    "test = test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop('orders_next_hour', axis=1)\n",
    "y_train = train['orders_next_hour']\n",
    "\n",
    "X_test = test.drop('orders_next_hour', axis=1)\n",
    "y_test = test['orders_next_hour']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Базовый пайплайн"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>week</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>lag_7</th>\n",
       "      <th>lag_8</th>\n",
       "      <th>lag_9</th>\n",
       "      <th>lag_10</th>\n",
       "      <th>rolling_mean_day</th>\n",
       "      <th>rolling_mean_week</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-08 00:00:00</th>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>94.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.041667</td>\n",
       "      <td>54.226190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08 01:00:00</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>143.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>61.833333</td>\n",
       "      <td>54.339286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08 02:00:00</th>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>60.041667</td>\n",
       "      <td>54.297619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08 03:00:00</th>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>61.750000</td>\n",
       "      <td>54.261905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08 04:00:00</th>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>68.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>61.833333</td>\n",
       "      <td>54.273810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13 09:00:00</th>\n",
       "      <td>137</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>91.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>134.041667</td>\n",
       "      <td>113.821429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13 10:00:00</th>\n",
       "      <td>156</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>137.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>135.250000</td>\n",
       "      <td>113.827381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13 11:00:00</th>\n",
       "      <td>144</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>156.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>137.250000</td>\n",
       "      <td>114.017857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13 12:00:00</th>\n",
       "      <td>92</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>144.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>139.875000</td>\n",
       "      <td>114.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13 13:00:00</th>\n",
       "      <td>119</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>92.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>138.583333</td>\n",
       "      <td>114.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3806 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders  hour  day  dayofweek  week  lag_1  lag_2  \\\n",
       "datetime                                                                    \n",
       "2018-03-08 00:00:00         143     0    8          3    10   94.0  127.0   \n",
       "2018-03-08 01:00:00          78     1    8          3    10  143.0   94.0   \n",
       "2018-03-08 02:00:00          65     2    8          3    10   78.0  143.0   \n",
       "2018-03-08 03:00:00          68     3    8          3    10   65.0   78.0   \n",
       "2018-03-08 04:00:00          60     4    8          3    10   68.0   65.0   \n",
       "...                         ...   ...  ...        ...   ...    ...    ...   \n",
       "2018-08-13 09:00:00         137     9   13          0    33   91.0   39.0   \n",
       "2018-08-13 10:00:00         156    10   13          0    33  137.0   91.0   \n",
       "2018-08-13 11:00:00         144    11   13          0    33  156.0  137.0   \n",
       "2018-08-13 12:00:00          92    12   13          0    33  144.0  156.0   \n",
       "2018-08-13 13:00:00         119    13   13          0    33   92.0  144.0   \n",
       "\n",
       "                     lag_3  lag_4  lag_5  lag_6  lag_7  lag_8  lag_9  lag_10  \\\n",
       "datetime                                                                       \n",
       "2018-03-08 00:00:00  108.0   76.0   50.0   99.0   48.0   46.0   51.0    43.0   \n",
       "2018-03-08 01:00:00  127.0  108.0   76.0   50.0   99.0   48.0   46.0    51.0   \n",
       "2018-03-08 02:00:00   94.0  127.0  108.0   76.0   50.0   99.0   48.0    46.0   \n",
       "2018-03-08 03:00:00  143.0   94.0  127.0  108.0   76.0   50.0   99.0    48.0   \n",
       "2018-03-08 04:00:00   78.0  143.0   94.0  127.0  108.0   76.0   50.0    99.0   \n",
       "...                    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "2018-08-13 09:00:00   66.0   83.0  143.0  168.0  437.0  273.0  160.0   185.0   \n",
       "2018-08-13 10:00:00   39.0   66.0   83.0  143.0  168.0  437.0  273.0   160.0   \n",
       "2018-08-13 11:00:00   91.0   39.0   66.0   83.0  143.0  168.0  437.0   273.0   \n",
       "2018-08-13 12:00:00  137.0   91.0   39.0   66.0   83.0  143.0  168.0   437.0   \n",
       "2018-08-13 13:00:00  156.0  137.0   91.0   39.0   66.0   83.0  143.0   168.0   \n",
       "\n",
       "                     rolling_mean_day  rolling_mean_week  \n",
       "datetime                                                  \n",
       "2018-03-08 00:00:00         60.041667          54.226190  \n",
       "2018-03-08 01:00:00         61.833333          54.339286  \n",
       "2018-03-08 02:00:00         60.041667          54.297619  \n",
       "2018-03-08 03:00:00         61.750000          54.261905  \n",
       "2018-03-08 04:00:00         61.833333          54.273810  \n",
       "...                               ...                ...  \n",
       "2018-08-13 09:00:00        134.041667         113.821429  \n",
       "2018-08-13 10:00:00        135.250000         113.827381  \n",
       "2018-08-13 11:00:00        137.250000         114.017857  \n",
       "2018-08-13 12:00:00        139.875000         114.095238  \n",
       "2018-08-13 13:00:00        138.583333         114.125000  \n",
       "\n",
       "[3806 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [\n",
    "    'num_orders',\n",
    "    'lag_1',\n",
    "    'lag_2',\n",
    "    'lag_3',\n",
    "    'lag_4',\n",
    "    'lag_5',\n",
    "    'lag_6',\n",
    "    'lag_7',\n",
    "    'lag_8',\n",
    "    'lag_9',\n",
    "    'lag_10',\n",
    "    'rolling_mean_day',\n",
    "    'rolling_mean_week',\n",
    "]\n",
    "cat_cols = ['hour', 'day', 'dayofweek', 'week']\n",
    "\n",
    "data_preprocessor = ColumnTransformer(\n",
    "    # Пайплайн обработки\n",
    "    [\n",
    "        ('num', StandardScaler(), num_cols),\n",
    "        (\n",
    "            'cat',\n",
    "            OrdinalEncoder(\n",
    "                handle_unknown='use_encoded_value', unknown_value=-1\n",
    "            ),\n",
    "            cat_cols,\n",
    "        ),\n",
    "    ],\n",
    "    remainder='passthrough',\n",
    ")\n",
    "\n",
    "pipe_final = Pipeline(\n",
    "    # Финальный пайплайн\n",
    "    [\n",
    "        ('preprocessor', data_preprocessor),\n",
    "        ('model', DecisionTreeRegressor(random_state=RANDOM_STATE)),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Собственная метрика для оценки моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(predict, actual):\n",
    "    predict = np.array(predict)\n",
    "    actual = np.array(actual)\n",
    "\n",
    "    distance = predict - actual\n",
    "\n",
    "    square_distance = distance**2\n",
    "\n",
    "    mean_square_distance = square_distance.mean()\n",
    "\n",
    "    score = np.sqrt(mean_square_distance)\n",
    "\n",
    "    return score\n",
    "\n",
    "\n",
    "rmse_score = make_scorer(rmse, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Параметры моделей для решетчатого поиска"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Параметры логистической регрессии\n",
    "lin_params = {}\n",
    "\n",
    "lin_params['preprocessor__num'] = [\n",
    "    StandardScaler(),\n",
    "    MinMaxScaler(),\n",
    "    RobustScaler(),\n",
    "]\n",
    "lin_params['preprocessor__cat'] = [OneHotEncoder(drop='first')]\n",
    "\n",
    "lin_params['model'] = [LinearRegression()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Параметры для KNeighborsRegressor\n",
    "knn_params = {}\n",
    "\n",
    "knn_params['preprocessor__num'] = [\n",
    "    StandardScaler(),\n",
    "    MinMaxScaler(),\n",
    "    RobustScaler(),\n",
    "]\n",
    "\n",
    "knn_params['model__n_neighbors'] = [2, 5]\n",
    "knn_params['model'] = [KNeighborsRegressor()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Параметры для DecisionTreeRegressor\n",
    "tree_params = {}\n",
    "\n",
    "tree_params['preprocessor__num'] = [RobustScaler()]\n",
    "\n",
    "tree_params['model__max_depth'] = range(1, 2)\n",
    "tree_params['model__min_samples_leaf'] = range(14, 15)\n",
    "tree_params['model'] = [DecisionTreeRegressor(random_state=RANDOM_STATE)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Параметры для SVR\n",
    "svr_params = {}\n",
    "\n",
    "svr_params['preprocessor__num'] = [StandardScaler(), MinMaxScaler()]\n",
    "\n",
    "svr_params['model__C'] = [10**-2, 10**-1]\n",
    "svr_params['model'] = [SVR()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Параметры для SVR\n",
    "lgbm_params = {}\n",
    "\n",
    "lgbm_params['model'] = [lgb.LGBMRegressor()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [svr_params, tree_params, knn_params, lin_params, lgbm_params]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Поиск и оценка моделей "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_reg = GridSearchCV(\n",
    "    pipe_final, params, cv=5, n_jobs=-1, scoring=rmse_score\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [10.0, 11.0, 12.0, 13.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [15.0, 16.0, 17.0, 18.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [20.0, 21.0, 22.0, 23.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [25.0, 26.0, 27.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [29.0, 30.0, 31.0, 32.0, 33.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [10.0, 11.0, 12.0, 13.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [15.0, 16.0, 17.0, 18.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [20.0, 21.0, 22.0, 23.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [25.0, 26.0, 27.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [29.0, 30.0, 31.0, 32.0, 33.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [10.0, 11.0, 12.0, 13.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [15.0, 16.0, 17.0, 18.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [20.0, 21.0, 22.0, 23.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [25.0, 26.0, 27.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:988: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 977, in _score\n",
      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n",
      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 345, in _score\n",
      "    y_pred = method_caller(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 87, in _cached_call\n",
      "    result, _ = _get_response_values(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_response.py\", line 238, in _get_response_values\n",
      "    y_pred, pos_label = prediction_method(X), None\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 602, in predict\n",
      "    Xt = transform.transform(Xt)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 1014, in transform\n",
      "    Xs = self._call_func_on_transformers(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\", line 823, in _call_func_on_transformers\n",
      "    return Parallel(n_jobs=self.n_jobs)(jobs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 67, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1863, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/joblib/parallel.py\", line 1792, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 129, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/pipeline.py\", line 1283, in _transform_one\n",
      "    res = transformer.transform(X, **params.transform)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/utils/_set_output.py\", line 273, in wrapped\n",
      "    data_to_wrap = f(self, X, *args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 1023, in transform\n",
      "    X_int, X_mask = self._transform(\n",
      "  File \"/opt/conda/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py\", line 213, in _transform\n",
      "    raise ValueError(msg)\n",
      "ValueError: Found unknown categories [29.0, 30.0, 31.0, 32.0, 33.0] in column 3 during transform\n",
      "\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_search.py:1051: UserWarning: One or more of the test scores are non-finite: [-39.66943397 -39.73304167 -38.78865544 -39.33802571 -35.61103688\n",
      " -37.37815822 -38.88487236 -37.54394465 -35.39908419 -36.90365055\n",
      " -35.58727586          nan          nan          nan -24.68978101]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;num&#x27;,\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         [&#x27;num_orders&#x27;,\n",
       "                                                                          &#x27;lag_1&#x27;,\n",
       "                                                                          &#x27;lag_2&#x27;,\n",
       "                                                                          &#x27;lag_3&#x27;,\n",
       "                                                                          &#x27;lag_4&#x27;,\n",
       "                                                                          &#x27;lag_5&#x27;,\n",
       "                                                                          &#x27;lag_6&#x27;,\n",
       "                                                                          &#x27;lag_7&#x27;,\n",
       "                                                                          &#x27;lag_8&#x27;,\n",
       "                                                                          &#x27;lag_9&#x27;,\n",
       "                                                                          &#x27;lag_10&#x27;,\n",
       "                                                                          &#x27;rolling_mean_day&#x27;,\n",
       "                                                                          &#x27;rolling_mean_week&#x27;]),\n",
       "                                                                        (&#x27;cat&#x27;,\n",
       "                                                                         OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                        unknown_value=-1)...\n",
       "                          &#x27;model__n_neighbors&#x27;: [2, 5],\n",
       "                          &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;model&#x27;: [LinearRegression()],\n",
       "                          &#x27;preprocessor__cat&#x27;: [OneHotEncoder(drop=&#x27;first&#x27;)],\n",
       "                          &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;model&#x27;: [LGBMRegressor()]}],\n",
       "             scoring=make_scorer(rmse, greater_is_better=False, response_method=&#x27;predict&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;num&#x27;,\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         [&#x27;num_orders&#x27;,\n",
       "                                                                          &#x27;lag_1&#x27;,\n",
       "                                                                          &#x27;lag_2&#x27;,\n",
       "                                                                          &#x27;lag_3&#x27;,\n",
       "                                                                          &#x27;lag_4&#x27;,\n",
       "                                                                          &#x27;lag_5&#x27;,\n",
       "                                                                          &#x27;lag_6&#x27;,\n",
       "                                                                          &#x27;lag_7&#x27;,\n",
       "                                                                          &#x27;lag_8&#x27;,\n",
       "                                                                          &#x27;lag_9&#x27;,\n",
       "                                                                          &#x27;lag_10&#x27;,\n",
       "                                                                          &#x27;rolling_mean_day&#x27;,\n",
       "                                                                          &#x27;rolling_mean_week&#x27;]),\n",
       "                                                                        (&#x27;cat&#x27;,\n",
       "                                                                         OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                        unknown_value=-1)...\n",
       "                          &#x27;model__n_neighbors&#x27;: [2, 5],\n",
       "                          &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;model&#x27;: [LinearRegression()],\n",
       "                          &#x27;preprocessor__cat&#x27;: [OneHotEncoder(drop=&#x27;first&#x27;)],\n",
       "                          &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;model&#x27;: [LGBMRegressor()]}],\n",
       "             scoring=make_scorer(rmse, greater_is_better=False, response_method=&#x27;predict&#x27;))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;num_orders&#x27;, &#x27;lag_1&#x27;,\n",
       "                                                   &#x27;lag_2&#x27;, &#x27;lag_3&#x27;, &#x27;lag_4&#x27;,\n",
       "                                                   &#x27;lag_5&#x27;, &#x27;lag_6&#x27;, &#x27;lag_7&#x27;,\n",
       "                                                   &#x27;lag_8&#x27;, &#x27;lag_9&#x27;, &#x27;lag_10&#x27;,\n",
       "                                                   &#x27;rolling_mean_day&#x27;,\n",
       "                                                   &#x27;rolling_mean_week&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [&#x27;hour&#x27;, &#x27;day&#x27;, &#x27;dayofweek&#x27;,\n",
       "                                                   &#x27;week&#x27;])])),\n",
       "                (&#x27;model&#x27;, DecisionTreeRegressor(random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;num_orders&#x27;, &#x27;lag_1&#x27;, &#x27;lag_2&#x27;, &#x27;lag_3&#x27;,\n",
       "                                  &#x27;lag_4&#x27;, &#x27;lag_5&#x27;, &#x27;lag_6&#x27;, &#x27;lag_7&#x27;, &#x27;lag_8&#x27;,\n",
       "                                  &#x27;lag_9&#x27;, &#x27;lag_10&#x27;, &#x27;rolling_mean_day&#x27;,\n",
       "                                  &#x27;rolling_mean_week&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=-1),\n",
       "                                 [&#x27;hour&#x27;, &#x27;day&#x27;, &#x27;dayofweek&#x27;, &#x27;week&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;num_orders&#x27;, &#x27;lag_1&#x27;, &#x27;lag_2&#x27;, &#x27;lag_3&#x27;, &#x27;lag_4&#x27;, &#x27;lag_5&#x27;, &#x27;lag_6&#x27;, &#x27;lag_7&#x27;, &#x27;lag_8&#x27;, &#x27;lag_9&#x27;, &#x27;lag_10&#x27;, &#x27;rolling_mean_day&#x27;, &#x27;rolling_mean_week&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;hour&#x27;, &#x27;day&#x27;, &#x27;dayofweek&#x27;, &#x27;week&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre></pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('num',\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         ['num_orders',\n",
       "                                                                          'lag_1',\n",
       "                                                                          'lag_2',\n",
       "                                                                          'lag_3',\n",
       "                                                                          'lag_4',\n",
       "                                                                          'lag_5',\n",
       "                                                                          'lag_6',\n",
       "                                                                          'lag_7',\n",
       "                                                                          'lag_8',\n",
       "                                                                          'lag_9',\n",
       "                                                                          'lag_10',\n",
       "                                                                          'rolling_mean_day',\n",
       "                                                                          'rolling_mean_week']),\n",
       "                                                                        ('cat',\n",
       "                                                                         OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                                                                        unknown_value=-1)...\n",
       "                          'model__n_neighbors': [2, 5],\n",
       "                          'preprocessor__num': [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'model': [LinearRegression()],\n",
       "                          'preprocessor__cat': [OneHotEncoder(drop='first')],\n",
       "                          'preprocessor__num': [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'model': [LGBMRegressor()]}],\n",
       "             scoring=make_scorer(rmse, greater_is_better=False, response_method='predict'))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kernel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LGBMRegressor()</th>\n",
       "      <td>{'model': LGBMRegressor()}</td>\n",
       "      <td>1</td>\n",
       "      <td>24.689781</td>\n",
       "      <td>6.175852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsRegressor()_5_StandardScaler()</th>\n",
       "      <td>{'model': KNeighborsRegressor(), 'model__n_nei...</td>\n",
       "      <td>2</td>\n",
       "      <td>35.399084</td>\n",
       "      <td>11.505489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsRegressor()_5_RobustScaler()</th>\n",
       "      <td>{'model': KNeighborsRegressor(), 'model__n_nei...</td>\n",
       "      <td>3</td>\n",
       "      <td>35.587276</td>\n",
       "      <td>11.648978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeRegressor(random_state=42)_1_14_RobustScaler()</th>\n",
       "      <td>{'model': DecisionTreeRegressor(random_state=4...</td>\n",
       "      <td>4</td>\n",
       "      <td>35.611037</td>\n",
       "      <td>6.389465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsRegressor()_5_MinMaxScaler()</th>\n",
       "      <td>{'model': KNeighborsRegressor(), 'model__n_nei...</td>\n",
       "      <td>5</td>\n",
       "      <td>36.903651</td>\n",
       "      <td>12.652100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                               params  \\\n",
       "kernel                                                                                                  \n",
       "LGBMRegressor()                                                            {'model': LGBMRegressor()}   \n",
       "KNeighborsRegressor()_5_StandardScaler()            {'model': KNeighborsRegressor(), 'model__n_nei...   \n",
       "KNeighborsRegressor()_5_RobustScaler()              {'model': KNeighborsRegressor(), 'model__n_nei...   \n",
       "DecisionTreeRegressor(random_state=42)_1_14_Rob...  {'model': DecisionTreeRegressor(random_state=4...   \n",
       "KNeighborsRegressor()_5_MinMaxScaler()              {'model': KNeighborsRegressor(), 'model__n_nei...   \n",
       "\n",
       "                                                    rank_test_score  \\\n",
       "kernel                                                                \n",
       "LGBMRegressor()                                                   1   \n",
       "KNeighborsRegressor()_5_StandardScaler()                          2   \n",
       "KNeighborsRegressor()_5_RobustScaler()                            3   \n",
       "DecisionTreeRegressor(random_state=42)_1_14_Rob...                4   \n",
       "KNeighborsRegressor()_5_MinMaxScaler()                            5   \n",
       "\n",
       "                                                    mean_test_score  \\\n",
       "kernel                                                                \n",
       "LGBMRegressor()                                           24.689781   \n",
       "KNeighborsRegressor()_5_StandardScaler()                  35.399084   \n",
       "KNeighborsRegressor()_5_RobustScaler()                    35.587276   \n",
       "DecisionTreeRegressor(random_state=42)_1_14_Rob...        35.611037   \n",
       "KNeighborsRegressor()_5_MinMaxScaler()                    36.903651   \n",
       "\n",
       "                                                    std_test_score  \n",
       "kernel                                                              \n",
       "LGBMRegressor()                                           6.175852  \n",
       "KNeighborsRegressor()_5_StandardScaler()                 11.505489  \n",
       "KNeighborsRegressor()_5_RobustScaler()                   11.648978  \n",
       "DecisionTreeRegressor(random_state=42)_1_14_Rob...        6.389465  \n",
       "KNeighborsRegressor()_5_MinMaxScaler()                   12.652100  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_reg = pd.DataFrame(grid_reg.cv_results_)\n",
    "results_df_reg = results_df_reg.sort_values(by=[\"rank_test_score\"])\n",
    "results_df_reg = results_df_reg.set_index(\n",
    "    results_df_reg[\"params\"].apply(\n",
    "        lambda x: \"_\".join(str(val) for val in x.values())\n",
    "    )\n",
    ").rename_axis(\"kernel\")\n",
    "results_df_reg['mean_test_score'] *= -1\n",
    "results_df_reg[\n",
    "    [\"params\", \"rank_test_score\", \"mean_test_score\", \"std_test_score\"]\n",
    "].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Тестирование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = grid_reg.best_estimator_.steps[1][1]\n",
    "data_preprocessor.fit(X_train)\n",
    "X_test_prepared = data_preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.71907073626491"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred_test = best_model.predict(X_test_prepared)\n",
    "\n",
    "rmse(X_pred_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_plot_data = pd.DataFrame(y_test).copy()\n",
    "test_plot_data['prediced_orders'] = X_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_plot_data.plot(figsize=(20, 10))\n",
    "plt.title('Реальное и предсказанное количество заказов')\n",
    "plt.xlabel('Время')\n",
    "plt.ylabel('Динамика количества заказов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Общий вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Перед нами стояла задача:**\n",
    "\n",
    "- Разработать модель, которая бы предсказывала нагруженность службы такси в следующий час;\n",
    "\n",
    "**Что мы сделали:**\n",
    "\n",
    "- Загрузили и обработали данные: выделили временной индекс, отсортировали и выделили семплы в 1 час;\n",
    "- Провели исследовательский анализ данных: посмотрели на тренд и сезонную составляющую;\n",
    "- Создали новые признаки, которые помогут в прогнозировании;\n",
    "- Создали пайплайн, который обрабатывает количественные переменные и обучает модель;\n",
    "- Протестировали несколько моделей, оценив время выполнения и RMSE;\n",
    "\n",
    "**Результат:**\n",
    "- Нам удалось снизить RMSE до приемлемого качества RMSE = 43;\n",
    "- В ходе исследования нам удалось выяснить, что наболее значимый эффект оказывают правильно подобранные признаки. При верных признаках любая модель справляется с данными хорошо, а при их отсутствии - любая модель справляется плохо;\n",
    "- В данном случае лучше всего себя показал градиентный бустинг с гиперпараметрами по умолчанию;"
   ]
  }
 ],
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