12 Прогнозирование заказов такси/Прогнозирование заказов такси.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Прогнозирование заказов такси"
   ]
  },
  {
   "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=\"#Описание-проекта\" data-toc-modified-id=\"Описание-проекта-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Описание проекта</a></span></li><li><span><a href=\"#Описание-данных\" data-toc-modified-id=\"Описание-данных-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Описание данных</a></span></li><li><span><a href=\"#Импорт\" data-toc-modified-id=\"Импорт-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Импорт</a></span></li><li><span><a href=\"#Подготовка\" data-toc-modified-id=\"Подготовка-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Подготовка</a></span><ul class=\"toc-item\"><li><span><a href=\"#Загрузка-данных\" data-toc-modified-id=\"Загрузка-данных-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Загрузка данных</a></span></li><li><span><a href=\"#Знакомство-с-данными\" data-toc-modified-id=\"Знакомство-с-данными-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Знакомство с данными</a></span></li><li><span><a href=\"#Ресемплинг\" data-toc-modified-id=\"Ресемплинг-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Ресемплинг</a></span></li></ul></li><li><span><a href=\"#Анализ\" data-toc-modified-id=\"Анализ-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Анализ</a></span><ul class=\"toc-item\"><li><span><a href=\"#Исследование-данных\" data-toc-modified-id=\"Исследование-данных-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Исследование данных</a></span></li><li><span><a href=\"#Создание-признаков\" data-toc-modified-id=\"Создание-признаков-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Создание признаков</a></span></li></ul></li><li><span><a href=\"#Обучение\" data-toc-modified-id=\"Обучение-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Обучение</a></span><ul class=\"toc-item\"><li><span><a href=\"#Разделение-на-тестовый-и-обучающий-наборы\" data-toc-modified-id=\"Разделение-на-тестовый-и-обучающий-наборы-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Разделение на тестовый и обучающий наборы</a></span></li><li><span><a href=\"#Подготовка-данных-для-коросс-валидации\" data-toc-modified-id=\"Подготовка-данных-для-коросс-валидации-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>Подготовка данных для коросс-валидации</a></span></li><li><span><a href=\"#Базовая-модель\" data-toc-modified-id=\"Базовая-модель-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>Базовая модель</a></span></li><li><span><a href=\"#Модели-с-параметрами-по-умолчанию\" data-toc-modified-id=\"Модели-с-параметрами-по-умолчанию-6.4\"><span class=\"toc-item-num\">6.4&nbsp;&nbsp;</span>Модели с параметрами по умолчанию</a></span><ul class=\"toc-item\"><li><span><a href=\"#Линейная-регрессия\" data-toc-modified-id=\"Линейная-регрессия-6.4.1\"><span class=\"toc-item-num\">6.4.1&nbsp;&nbsp;</span>Линейная регрессия</a></span></li><li><span><a href=\"#RandomForestRegressor\" data-toc-modified-id=\"RandomForestRegressor-6.4.2\"><span class=\"toc-item-num\">6.4.2&nbsp;&nbsp;</span>RandomForestRegressor</a></span></li><li><span><a href=\"#LightGBM\" data-toc-modified-id=\"LightGBM-6.4.3\"><span class=\"toc-item-num\">6.4.3&nbsp;&nbsp;</span>LightGBM</a></span></li></ul></li><li><span><a href=\"#Настройка-гиперпараметров\" data-toc-modified-id=\"Настройка-гиперпараметров-6.5\"><span class=\"toc-item-num\">6.5&nbsp;&nbsp;</span>Настройка гиперпараметров</a></span></li></ul></li><li><span><a href=\"#Тестирование\" data-toc-modified-id=\"Тестирование-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Тестирование</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": [
    "## Описание проекта"
   ]
  },
  {
   "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.\n",
    "\n",
    "**Цель:** Построить модель, предсказывающую количество заказов такси на следующий час.\n",
    "\n",
    "**Целевая метрика:** *RMSE* на тестовой выборке должно быть не больше 48."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Описание данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Файл с данными:** `/datasets/taxi.csv`\n",
    "\n",
    "**Признаки:**\n",
    "- `datetime` - Дата и время заказа,\n",
    "- `num_orders` -Число заказов (целевой признак)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Импорт"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: category_encoders in /opt/conda/envs/practicum/lib/python3.9/site-packages (2.6.0)\n",
      "Requirement already satisfied: pandas>=1.0.5 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from category_encoders) (1.2.4)\n",
      "Requirement already satisfied: numpy>=1.14.0 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from category_encoders) (1.20.1)\n",
      "Requirement already satisfied: statsmodels>=0.9.0 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from category_encoders) (0.13.2)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from category_encoders) (0.24.1)\n",
      "Requirement already satisfied: scipy>=1.0.0 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from category_encoders) (1.8.0)\n",
      "Requirement already satisfied: patsy>=0.5.1 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from category_encoders) (0.5.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from pandas>=1.0.5->category_encoders) (2022.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
      "Requirement already satisfied: six in /opt/conda/envs/practicum/lib/python3.9/site-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from scikit-learn>=0.20.0->category_encoders) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n",
      "Requirement already satisfied: packaging>=21.3 in /opt/conda/envs/practicum/lib/python3.9/site-packages (from statsmodels>=0.9.0->category_encoders) (23.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install category_encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from category_encoders.target_encoder import TargetEncoder\n",
    "from lightgbm import LGBMRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, TimeSeriesSplit, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "SEED = 42\n",
    "N_SPLITS = 10\n",
    "N_JOBS = -1\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    data = pd.read_csv('/datasets/taxi.csv')\n",
    "    \n",
    "except FileNotFoundError:\n",
    "    data = pd.read_csv('https://code.s3.yandex.net/datasets/taxi.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Знакомство с данными"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем общую информацию о датафрейме."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26496 entries, 0 to 26495\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   datetime    26496 non-null  object\n",
      " 1   num_orders  26496 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 414.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим содержат ли данные пропуски и полные дубликаты:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные содержат 0 пропусков.\n"
     ]
    }
   ],
   "source": [
    "print(f'Данные содержат {data.isna().sum().sum()} пропусков.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные содержат 0 дубликатов.\n"
     ]
    }
   ],
   "source": [
    "print(f'Данные содержат {data.duplicated().sum()} дубликатов.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Столбец `datetime` содержит дату и время заказов, преобразуем его в `datetime object`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['datetime'] = pd.to_datetime(data.datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Назначим столбец с датой и временем индексом датафрейма, отсортируем индекс и проверим в хронологическом ли порядке расположены даты и время."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные расположены в хронологическом порядке.\n"
     ]
    }
   ],
   "source": [
    "data = data.set_index('datetime').sort_index()\n",
    "\n",
    "if data.index.is_monotonic:\n",
    "    print('Данные расположены в хронологическом порядке.')\n",
    "else:\n",
    "    print('Хронологический порядок нарушен.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем первые 10 строк получившегося датафрейма."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:00:00</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:10:00</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:20:00</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:30:00</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:40:00</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders\n",
       "datetime                       \n",
       "2018-03-01 00:00:00           9\n",
       "2018-03-01 00:10:00          14\n",
       "2018-03-01 00:20:00          28\n",
       "2018-03-01 00:30:00          20\n",
       "2018-03-01 00:40:00          32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ресемплинг"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные в таблице приведены с интервалом в 10 минут. Выполним ресемплинг, чтобы получить данные с интервалом в 1 час, будем использовать среднее значение за час и округлим результат до целых."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:00:00</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 01:00:00</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 02:00:00</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 03:00:00</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 04:00:00</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders\n",
       "datetime                       \n",
       "2018-03-01 00:00:00         124\n",
       "2018-03-01 01:00:00          85\n",
       "2018-03-01 02:00:00          71\n",
       "2018-03-01 03:00:00          66\n",
       "2018-03-01 04:00:00          43"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.resample('1H').sum().round()\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Исследование данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определим даты первого и последнего наблюдений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Дата и время начала наблюдений: 2018-03-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(f'Дата и время начала наблюдений: {data.index.min()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Дата и время окончания наблюдений: 2018-08-31 23:00:00\n"
     ]
    }
   ],
   "source": [
    "print(f'Дата и время окончания наблюдений: {data.index.max()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим графики, иллюстрирующие поведение количества заказов во времени, тренд, сезонность и остатки. График сезонности построим за последние 120 часов, чтобы периодичность колебаний была различима."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposed = seasonal_decompose(data)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(4, 1, figsize=(10, 12))\n",
    "\n",
    "data.plot(title='Динамика количества заказов', \n",
    "          ylabel='Количество заказов', \n",
    "          ax = ax[0]\n",
    "         )\n",
    "\n",
    "decomposed.trend.plot(title='Тренд', \n",
    "                      ylabel='Количество заказов',\n",
    "                      ax = ax[1]\n",
    "                     )\n",
    "\n",
    "decomposed.seasonal.tail(120).plot(title='Сезонность', \n",
    "                         ylabel='Количество заказов',\n",
    "                         ax = ax[2]\n",
    "                        )\n",
    "\n",
    "decomposed.resid.plot(title='Остатки', \n",
    "                         ylabel='Количество заказов',\n",
    "                         ax = ax[3]\n",
    "                        )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На графике заказов наблюдается тренд. Также присутствует сезонность c периодичностью примерно 24 часа; у суточной сезонности присутствует ряд пиков. На графике остатков можно заметить, что стандартное отклонение в июле и августе немного выше, чем в другие месяцы, присутствуют выбросы.\n",
    "\n",
    "Посмотрим гистограмму остатков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposed.resid.hist(bins=30, density=True)\n",
    "decomposed.resid.plot.kde()\n",
    "\n",
    "plt.title(\"Гистограмма распределения значений остатков\")\n",
    "plt.xlabel(\"Значения остатков\")\n",
    "plt.ylabel(\"Плотность вероятности\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Распределение значений остатков по форме близко к нормальному при этом на гистограмме присутствуют выбросы - значения, лежащие за пределами -20 и 20."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Создание признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим отдельные признки для месяца, числа месяца, дня недели и часа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-31 19:00:00</th>\n",
       "      <td>136</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 20:00:00</th>\n",
       "      <td>154</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 21:00:00</th>\n",
       "      <td>159</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 22:00:00</th>\n",
       "      <td>223</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 23:00:00</th>\n",
       "      <td>205</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders  month  day  dayofweek  hour\n",
       "datetime                                                    \n",
       "2018-08-31 19:00:00         136      8   31          4    19\n",
       "2018-08-31 20:00:00         154      8   31          4    20\n",
       "2018-08-31 21:00:00         159      8   31          4    21\n",
       "2018-08-31 22:00:00         223      8   31          4    22\n",
       "2018-08-31 23:00:00         205      8   31          4    23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['month'] = data.index.month\n",
    "data['day'] = data.index.day\n",
    "data['dayofweek'] = data.index.dayofweek\n",
    "data['hour'] = data.index.hour\n",
    "\n",
    "display(data.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы выделить тренд, добавим к данным недельное скользящее среднее."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>moving_average</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:00:00</th>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 01:00:00</th>\n",
       "      <td>85</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 02:00:00</th>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 03:00:00</th>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 04:00:00</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_orders  month  day  dayofweek  hour  moving_average\n",
       "datetime                                                                    \n",
       "2018-03-01 00:00:00         124      3    1          3     0             NaN\n",
       "2018-03-01 01:00:00          85      3    1          3     1             NaN\n",
       "2018-03-01 02:00:00          71      3    1          3     2             NaN\n",
       "2018-03-01 03:00:00          66      3    1          3     3             NaN\n",
       "2018-03-01 04:00:00          43      3    1          3     4             NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['moving_average'] = data.num_orders.shift(1).rolling(24 * 7).mean()\n",
    "\n",
    "data.moving_average.plot(figsize=(10, 3), \n",
    "                         title='Недельное скользящее среднее', \n",
    "                         ylabel='Количество заказов'\n",
    "                        )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как было отмечено ранее в данных присутствует сезонность равная примерно 24 часам, также возможно наличие недельной сезонности, поэтому также добавим 168 запаздывающих значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(24 * 7):\n",
    "    data[f'lag_{i}'] = data.num_orders.shift(i + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В начале набора данных образовались пропущенные значения, удалим их."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разделение на тестовый и обучающий наборы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Прежде чем приступать к разарботке моделей раделим датасет на тестовую и обучающую выборки, а также вынесем целевой признак в отдельную переменную."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.num_orders.copy()\n",
    "X = data.drop(columns=['num_orders'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, shuffle=False, random_state=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подготовка данных для коросс-валидации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполним разделение тренировочного множества на группы с помощью `sklearn.model_selection.TimeSeriesSplit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_splits = list(TimeSeriesSplit(n_splits=N_SPLITS).split(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Базовая модель\n",
    "В качестве базовой модели используем модель, предсказывающую новое значение $x(t)$ предыдущим значением ряда $x(t-1)$. Оценим качество её предсказаний."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DummyTimeSeriesRegressor(BaseEstimator, RegressorMixin):  \n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return X.lag_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель на тречнировочной выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DummyTimeSeriesRegressor (оценки на обучающей выборке):\n",
      "RMSE = 36.49\n",
      "R2 = 0.12\n"
     ]
    }
   ],
   "source": [
    "def evaluate_on_train(estimator, X, y, decimals=2, **kwargs):\n",
    "    y_pred = estimator.fit(X, y, **kwargs).predict(X)\n",
    "    print(f'RMSE = {mean_squared_error(y, y_pred, squared=False).round(decimals)}')\n",
    "    print(f'R2 = {r2_score(y, y_pred).round(decimals)}')\n",
    "    \n",
    "\n",
    "print('DummyTimeSeriesRegressor (оценки на обучающей выборке):')\n",
    "evaluate_on_train(estimator=DummyTimeSeriesRegressor(), \n",
    "                  X=X_train, \n",
    "                  y=y_train,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы получить оценки, близкие к результатам на тестовом множесте, выполним кросс-валидацию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>35.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RMSE\n",
       "min     29.05\n",
       "mean    36.78\n",
       "median  35.14\n",
       "max     48.18\n",
       "std      5.99"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rmse_cv(estimator, X, y, cv=cv_splits, fit_params=None, n_jobs=N_JOBS, decimals=2):\n",
    "    \n",
    "    scores = cross_val_score(estimator=estimator,\n",
    "                             X=X,\n",
    "                             y=y,\n",
    "                             scoring='neg_mean_squared_error',\n",
    "                             fit_params=fit_params,\n",
    "                             cv=cv,\n",
    "                             n_jobs=n_jobs\n",
    "                            )\n",
    "    \n",
    "    scores = pd.DataFrame(np.sqrt(-scores), columns=['RMSE'])\n",
    "    return scores.agg(['min', 'mean', 'median', 'max', 'std']).round(decimals)\n",
    "\n",
    "\n",
    "rmse_cv(estimator=DummyTimeSeriesRegressor(),\n",
    "        X=X_train, \n",
    "        y=y_train\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R2\n",
       "min    -0.32\n",
       "mean   -0.11\n",
       "median -0.06\n",
       "max     0.04\n",
       "std     0.14"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def r2_cv(estimator, X, y, cv=cv_splits, fit_params=None, n_jobs=N_JOBS, decimals=2):\n",
    "    \n",
    "    scores = cross_val_score(estimator=estimator,\n",
    "                             X=X,\n",
    "                             y=y,\n",
    "                             scoring='r2',\n",
    "                             fit_params=fit_params,\n",
    "                             cv=cv,\n",
    "                             n_jobs=n_jobs\n",
    "                            )\n",
    "    \n",
    "    scores = pd.DataFrame(scores, columns=['R2'])\n",
    "    return scores.agg(['min', 'mean', 'median', 'max', 'std']).round(decimals)\n",
    "    \n",
    "    \n",
    "r2_cv(estimator=DummyTimeSeriesRegressor(), \n",
    "      X=X_train, \n",
    "      y=y_train\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модели с параметрами по умолчанию"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Линейная регрессия"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем применить линейную регрессию с регуляризацией. Чтобы избежать искажений расстояний в духе \"11 часов больше 0 часов\" закодируем категориальные переменные `category_encoders.target_encoder.TargetEncoder`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_regr = Pipeline([\n",
    "    ('encoder', TargetEncoder(cols=['month', 'day', 'dayofweek', 'hour'])),\n",
    "    ('standard_scaler', StandardScaler()),\n",
    "    ('regressor', Ridge())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель на тречнировочной выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeRegressor (оценки на обучающей выборке):\n",
      "RMSE = 20.22\n",
      "R2 = 0.73\n"
     ]
    }
   ],
   "source": [
    "print('RidgeRegressor (оценки на обучающей выборке):')\n",
    "evaluate_on_train(estimator=ridge_regr, \n",
    "                  X=X_train, \n",
    "                  y=y_train,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы получить оценки, близкие к результатам на тестовом множесте, выполним кросс-валидацию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>21.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RMSE\n",
       "min     20.73\n",
       "mean    23.63\n",
       "median  21.76\n",
       "max     33.08\n",
       "std      3.82"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(estimator=ridge_regr, \n",
    "        X=X_train, \n",
    "        y=y_train\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R2\n",
       "min     0.24\n",
       "mean    0.53\n",
       "median  0.57\n",
       "max     0.64\n",
       "std     0.11"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_cv(estimator=ridge_regr, \n",
    "      X=X_train, \n",
    "      y=y_train\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для древовидных моделей не важны масштабы признаков, поэтому можем использовать случайный лес без предобработки данных.\n",
    "\n",
    "Оценим модель на тречнировочной выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestRegressor().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor (оценки на обучающей выборке):\n",
      "RMSE = 8.0\n",
      "R2 = 0.96\n"
     ]
    }
   ],
   "source": [
    "print('RandomForestRegressor (оценки на обучающей выборке):')\n",
    "evaluate_on_train(estimator=forest, \n",
    "                  X=X_train, \n",
    "                  y=y_train,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель с помощью кросс-валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>21.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RMSE\n",
       "min     17.85\n",
       "mean    22.11\n",
       "median  21.17\n",
       "max     30.48\n",
       "std      3.77"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(estimator=forest, \n",
    "        X=X_train, \n",
    "        y=y_train\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R2\n",
       "min     0.55\n",
       "mean    0.60\n",
       "median  0.59\n",
       "max     0.66\n",
       "std     0.03"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_cv(estimator=forest, \n",
    "      X=X_train, \n",
    "      y=y_train\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Случайный лес демонстрирует оверфитинг, при этом, результаты на кросс-валидации не сильно лучше, чем у линейной регрессии."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LightGBM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель на тречнировочной выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_regressor = LGBMRegressor(n_jobs=N_JOBS, num_threads=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor (оценки на обучающей выборке):\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 8.72\n",
      "R2 = 0.95\n"
     ]
    }
   ],
   "source": [
    "print('LGBMRegressor (оценки на обучающей выборке):')\n",
    "evaluate_on_train(estimator=lgbm_regressor, \n",
    "                  X=X_train, \n",
    "                  y=y_train,\n",
    "                  categorical_feature=['month', 'day', 'dayofweek', 'hour'],\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель с помощью кросс-валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>21.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RMSE\n",
       "min     19.29\n",
       "mean    22.48\n",
       "median  21.62\n",
       "max     29.88\n",
       "std      3.41"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(estimator=lgbm_regressor, \n",
    "        X=X_train, \n",
    "        y=y_train,\n",
    "        fit_params=dict(categorical_feature=['month', 'day', 'dayofweek', 'hour']),\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R2\n",
       "min     0.53\n",
       "mean    0.59\n",
       "median  0.59\n",
       "max     0.63\n",
       "std     0.03"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_cv(estimator=lgbm_regressor, \n",
    "      X=X_train, \n",
    "      y=y_train,\n",
    "      fit_params=dict(categorical_feature=['month', 'day', 'dayofweek', 'hour']),\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`lgbm_regressor` демонстрирует чуть более высокую точность, чем линейная регрессия и чуть меньший оверфитинг, чем случайный лес. Попробуем настроить гиперпараметры для `ridge_regr` и `lgbm_regressor`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Настройка гиперпараметров"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем подобрать \"силу\" регуляризации для линейной регрессии."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regressor__alpha': 150}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {'regressor__alpha': [0.1, 1.0, 10, 50, 100, 150]}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=ridge_regr,\n",
    "                           param_grid=params,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           cv=cv_splits,\n",
    "                           n_jobs=N_JOBS,\n",
    "                          )\n",
    "\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "best_ridge = grid_search.best_estimator_\n",
    "\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим получившуюся модель на полной обучающей выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_ridge (оценки на обучающей выборке):\n",
      "RMSE = 20.24\n",
      "R2 = 0.73\n"
     ]
    }
   ],
   "source": [
    "print('best_ridge (оценки на обучающей выборке):')\n",
    "evaluate_on_train(estimator=best_ridge, \n",
    "                  X=X_train, \n",
    "                  y=y_train,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель с помощью кросс-валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>20.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RMSE\n",
       "min     18.66\n",
       "mean    22.21\n",
       "median  20.97\n",
       "max     30.84\n",
       "std      3.65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linreg_rmse = rmse_cv(estimator=best_ridge, \n",
    "                      X=X_train, \n",
    "                      y=y_train\n",
    "                     )\n",
    "\n",
    "display(linreg_rmse)\n",
    "\n",
    "models_performance = pd.DataFrame(index=['min', 'mean', 'median', 'max', 'std'])\n",
    "models_performance['best_ridge'] = linreg_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R2\n",
       "min     0.54\n",
       "mean    0.60\n",
       "median  0.59\n",
       "max     0.65\n",
       "std     0.03"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_cv(estimator=best_ridge, \n",
    "      X=X_train, \n",
    "      y=y_train\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подберём гиперпараметры для `LGBMRegressor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/practicum/lib/python3.9/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.05, 'max_depth': 50, 'n_estimators': 150, 'num_leaves': 31}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {'num_leaves': [26, 31, 36],\n",
    "          'max_depth': [50, 100, 150],\n",
    "          'learning_rate': [0.05, 0.10, 0.15],\n",
    "          'n_estimators': [50, 100, 150],\n",
    "         }\n",
    "\n",
    "grid_search = GridSearchCV(estimator=lgbm_regressor,\n",
    "                           param_grid=params,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           cv=cv_splits,\n",
    "                           n_jobs=N_JOBS,\n",
    "                          )\n",
    "\n",
    "\n",
    "grid_search.fit(X_train, y_train, categorical_feature=['month', 'day', 'dayofweek', 'hour'])\n",
    "\n",
    "best_lgbm = grid_search.best_estimator_\n",
    "\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим получившуюся модель на полной обучающей выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_lgbm (оценки на обучающей выборке):\n",
      "RMSE = 10.84\n",
      "R2 = 0.92\n"
     ]
    }
   ],
   "source": [
    "print('best_lgbm (оценки на обучающей выборке):')\n",
    "evaluate_on_train(estimator=best_lgbm, \n",
    "                  X=X_train, \n",
    "                  y=y_train,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим модель с помощью кросс-валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>21.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RMSE\n",
       "min     17.64\n",
       "mean    22.05\n",
       "median  21.13\n",
       "max     29.65\n",
       "std      3.63"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_rmse = rmse_cv(estimator=best_lgbm, \n",
    "                    X=X_train, \n",
    "                    y=y_train\n",
    "                   )\n",
    "\n",
    "display(lgbm_rmse)\n",
    "\n",
    "models_performance['best_lgbm'] = lgbm_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R2\n",
       "min     0.53\n",
       "mean    0.60\n",
       "median  0.60\n",
       "max     0.66\n",
       "std     0.04"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_cv(estimator=best_lgbm, \n",
    "      X=X_train, \n",
    "      y=y_train\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем сводную таблицу, содержащую оценки RMSE с помощью кросс-валидации после настройки гиперпараметров для обеих моделей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>best_ridge</th>\n",
       "      <th>best_lgbm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.66</td>\n",
       "      <td>17.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.21</td>\n",
       "      <td>22.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>20.97</td>\n",
       "      <td>21.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.84</td>\n",
       "      <td>29.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.65</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        best_ridge  best_lgbm\n",
       "min          18.66      17.64\n",
       "mean         22.21      22.05\n",
       "median       20.97      21.13\n",
       "max          30.84      29.65\n",
       "std           3.65       3.63"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Линейная регрессия и `LGBMRegressor` показывают примерно одинаковые результаты на кросс-валидации после настройки гиперпараметров, при этом, `LGBMRegressor` демонстрирует оверфитинг, поэтому остановимся на линейной регрессии."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Тестирование"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим лучшую модель на тестовой выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результаты лучшей модели на тестовой выборке\n",
      "RMSE: 34.681134342240064\n",
      "R2: 0.6532436053473931\n"
     ]
    }
   ],
   "source": [
    "y_pred = best_ridge.predict(X_test)\n",
    "\n",
    "print('Результаты лучшей модели на тестовой выборке')\n",
    "print(f'RMSE: {mean_squared_error(y_test, y_pred, squared=False)}')\n",
    "print(f'R2: {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Полученный резлуьтат удовлетворяет условию задачи: RMSE на тестовой выборке меньше 48."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Совместим на одном графике фактические и спрогнозированные значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n",
      "[LightGBM] [Warning] num_threads is set=4, n_jobs=-1 will be ignored. Current value: num_threads=4\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.DataFrame(y_test)\n",
    "test_data['num_orders_pred'] = y_pred\n",
    "\n",
    "test_data.plot(title='Сравнение фактических и спрогнозированных значений', \n",
    "               ylabel='Количество заказов',\n",
    "               figsize=(10, 3)\n",
    "              )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Чек-лист проверки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Данные загружены и подготовлены\n",
    "- [x]  Данные проанализированы\n",
    "- [x]  Модель обучена, гиперпараметры подобраны\n",
    "- [x]  Качество моделей проверено, выводы сделаны\n",
    "- [x]  Значение *RMSE* на тестовой выборке не больше 48"
   ]
  }
 ],
 "metadata": {
  "ExecuteTimeLog": [
   {
    "duration": 419,
    "start_time": "2023-03-13T12:16:32.953Z"
   },
   {
    "duration": 89,
    "start_time": "2023-03-13T12:20:50.131Z"
   },
   {
    "duration": 13,
    "start_time": "2023-03-13T12:20:59.509Z"
   },
   {
    "duration": 68,
    "start_time": "2023-03-13T12:27:30.630Z"
   },
   {
    "duration": 6,
    "start_time": "2023-03-13T12:27:30.897Z"
   },
   {
    "duration": 44,
    "start_time": "2023-03-13T12:29:36.933Z"
   },
   {
    "duration": 6,
    "start_time": "2023-03-13T12:29:37.159Z"
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   {
    "duration": 10,
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   },
   {
    "duration": 4,
    "start_time": "2023-03-13T12:32:28.756Z"
   },
   {
    "duration": 8,
    "start_time": "2023-03-13T12:32:46.517Z"
   },
   {
    "duration": 44,
    "start_time": "2023-03-13T12:33:32.659Z"
   },
   {
    "duration": 399,
    "start_time": "2023-03-13T12:33:37.364Z"
   },
   {
    "duration": 42,
    "start_time": "2023-03-13T12:33:37.765Z"
   },
   {
    "duration": 13,
    "start_time": "2023-03-13T12:33:37.808Z"
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   {
    "duration": 13,
    "start_time": "2023-03-13T12:33:37.822Z"
   },
   {
    "duration": 13,
    "start_time": "2023-03-13T12:33:50.705Z"
   },
   {
    "duration": 365,
    "start_time": "2023-03-13T12:35:03.197Z"
   },
   {
    "duration": 41,
    "start_time": "2023-03-13T12:35:03.564Z"
   },
   {
    "duration": 11,
    "start_time": "2023-03-13T12:35:03.607Z"
   },
   {
    "duration": 18,
    "start_time": "2023-03-13T12:35:03.619Z"
   },
   {
    "duration": 44,
    "start_time": "2023-03-13T12:35:03.639Z"
   },
   {
    "duration": 383,
    "start_time": "2023-03-13T12:35:31.081Z"
   },
   {
    "duration": 42,
    "start_time": "2023-03-13T12:35:31.466Z"
   },
   {
    "duration": 11,
    "start_time": "2023-03-13T12:35:31.510Z"
   },
   {
    "duration": 17,
    "start_time": "2023-03-13T12:35:31.523Z"
   },
   {
    "duration": 46,
    "start_time": "2023-03-13T12:35:31.542Z"
   },
   {
    "duration": 395,
    "start_time": "2023-03-13T12:35:51.033Z"
   },
   {
    "duration": 41,
    "start_time": "2023-03-13T12:35:51.430Z"
   },
   {
    "duration": 12,
    "start_time": "2023-03-13T12:35:51.473Z"
   },
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