bike-sharing-demand/bike-sharing-demand.ipynb

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    "# Bike Sharing Demand\n",
    "\n",
    "Forecast use of a city bikeshare system\n",
    "\n",
    "[Get started on this competition through Kaggle Scripts](https://www.kaggle.com/c/bike-sharing-demand/forums/t/13228/kaggle-scripts/69563#post69563)\n",
    "\n",
    "Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.\n",
    "\n",
    "The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.\n",
    "\n",
    "![Bikes](https://storage.googleapis.com/kaggle-competitions/kaggle/3948/media/bikes.png)\n",
    "\n",
    "Acknowledgements\n",
    "----------------\n",
    "\n",
    "Kaggle is hosting this competition for the machine learning community to use for fun and practice. This dataset was provided by Hadi Fanaee Tork using data from [Capital Bikeshare](http://www.capitalbikeshare.com/system-data). We also thank the UCI machine learning repository for [hosting the dataset](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset). If you use the problem in publication, please cite:\n",
    "\n",
    "Fanaee-T, Hadi, and Gama, Joao, _Event labeling combining ensemble detectors and background knowledge_, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.\n",
    "\n",
    "Dataset Description\n",
    "-------------------\n",
    "\n",
    "[See, fork, and run a random forest benchmark model through Kaggle Scripts](https://www.kaggle.com/users/993/ben-hamner/bike-sharing-demand/random-forest-benchmark)\n",
    "\n",
    "You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period.\n",
    "\n",
    "Data Fields\n",
    "-----------\n",
    "\n",
    "**datetime** - hourly date + timestamp    \n",
    "**season** -  1 = spring, 2 = summer, 3 = fall, 4 = winter   \n",
    "**holiday** - whether the day is considered a holiday  \n",
    "**workingday** - whether the day is neither a weekend nor holiday  \n",
    "**weather** - 1: Clear, Few clouds, Partly cloudy, Partly cloudy  \n",
    "2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist  \n",
    "3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds  \n",
    "4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog   \n",
    "**temp** - temperature in Celsius  \n",
    "**atemp** - \"feels like\" temperature in Celsius  \n",
    "**humidity** - relative humidity  \n",
    "**windspeed** - wind speed  \n",
    "**casual** - number of non-registered user rentals initiated  \n",
    "**registered** - number of registered user rentals initiated  \n",
    "**count** - number of total rentals\n",
    "\n",
    "Link: https://www.kaggle.com/c/bike-sharing-demand"
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    "import numpy as np\n",
    "import pandas as pd\n",
    "from catboost import (\n",
    "    CatBoostRegressor,\n",
    "    Pool,\n",
    "    sum_models,\n",
    "    to_regressor,\n",
    "    EFeaturesSelectionAlgorithm,\n",
    "    EShapCalcType,\n",
    ")\n",
    "from sklearn.model_selection import TimeSeriesSplit, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from tqdm.notebook import tqdm"
   ]
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       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
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       "2012-12-19 23:00:00       4        0           1        1  13.12  16.665   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  \n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00        81     0.0000       3          13     16  \n",
       "2011-01-01 01:00:00        80     0.0000       8          32     40  \n",
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       "2011-01-01 04:00:00        75     0.0000       0           1      1  \n",
       "...                       ...        ...     ...         ...    ...  \n",
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       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
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       "...                     ...      ...         ...      ...    ...     ...   \n",
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       "\n",
       "                     humidity  windspeed  casual  registered  count  isTest  \n",
       "datetime                                                                     \n",
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       "...                       ...        ...     ...         ...    ...     ...  \n",
       "2012-12-31 19:00:00        60    11.0014     NaN         NaN    NaN       1  \n",
       "2012-12-31 20:00:00        60    11.0014     NaN         NaN    NaN       1  \n",
       "2012-12-31 21:00:00        60    11.0014     NaN         NaN    NaN       1  \n",
       "2012-12-31 22:00:00        56     8.9981     NaN         NaN    NaN       1  \n",
       "2012-12-31 23:00:00        65     8.9981     NaN         NaN    NaN       1  \n",
       "\n",
       "[17379 rows x 12 columns]"
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     "execution_count": 6,
     "metadata": {},
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    "df[\"isTest\"] = df.index.isin(test_df.index).astype(int)\n",
    "df"
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   "id": "8c73d225",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 17379 entries, 2011-01-01 00:00:00 to 2012-12-31 23:00:00\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  int64  \n",
      " 1   holiday     17379 non-null  int64  \n",
      " 2   workingday  17379 non-null  int64  \n",
      " 3   weather     17379 non-null  int64  \n",
      " 4   temp        17379 non-null  float64\n",
      " 5   atemp       17379 non-null  float64\n",
      " 6   humidity    17379 non-null  int64  \n",
      " 7   windspeed   17379 non-null  float64\n",
      " 8   casual      10886 non-null  float64\n",
      " 9   registered  10886 non-null  float64\n",
      " 10  count       10886 non-null  float64\n",
      " 11  isTest      17379 non-null  int64  \n",
      "dtypes: float64(6), int64(6)\n",
      "memory usage: 1.7 MB\n"
     ]
    },
    {
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
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    {
     "data": {
      "text/plain": [
       "casual        0.373612\n",
       "registered    0.373612\n",
       "count         0.373612\n",
       "season        0.000000\n",
       "holiday       0.000000\n",
       "workingday    0.000000\n",
       "weather       0.000000\n",
       "temp          0.000000\n",
       "atemp         0.000000\n",
       "humidity      0.000000\n",
       "windspeed     0.000000\n",
       "isTest        0.000000\n",
       "dtype: float64"
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     },
     "execution_count": 8,
     "metadata": {},
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      "application/javascript": [
       "\n",
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  {
   "cell_type": "markdown",
   "id": "3504b7e7",
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    "id": "3504b7e7"
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   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41a6ee7d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:24:13.501709Z",
     "start_time": "2022-12-04T12:24:13.466841Z"
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   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>week</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>quarter</th>\n",
       "      <th>is_weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.66</td>\n",
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       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>65</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2012-12-31 19:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 20:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 21:00:00       1        0           1        1  10.66  12.880   \n",
       "2012-12-31 22:00:00       1        0           1        1  10.66  13.635   \n",
       "2012-12-31 23:00:00       1        0           1        1  10.66  13.635   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0  ...  2011   \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0  ...  2011   \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0  ...  2011   \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0  ...  2011   \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0  ...  2011   \n",
       "...                       ...        ...     ...         ...  ...   ...   \n",
       "2012-12-31 19:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 20:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 21:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 22:00:00        56     8.9981     NaN         NaN  ...  2012   \n",
       "2012-12-31 23:00:00        65     8.9981     NaN         NaN  ...  2012   \n",
       "\n",
       "                     month  day  hour  minute  day_of_year  week  day_of_week  \\\n",
       "datetime                                                                        \n",
       "2011-01-01 00:00:00      1    1     0       0            1    52            5   \n",
       "2011-01-01 01:00:00      1    1     1       0            1    52            5   \n",
       "2011-01-01 02:00:00      1    1     2       0            1    52            5   \n",
       "2011-01-01 03:00:00      1    1     3       0            1    52            5   \n",
       "2011-01-01 04:00:00      1    1     4       0            1    52            5   \n",
       "...                    ...  ...   ...     ...          ...   ...          ...   \n",
       "2012-12-31 19:00:00     12   31    19       0          366     1            0   \n",
       "2012-12-31 20:00:00     12   31    20       0          366     1            0   \n",
       "2012-12-31 21:00:00     12   31    21       0          366     1            0   \n",
       "2012-12-31 22:00:00     12   31    22       0          366     1            0   \n",
       "2012-12-31 23:00:00     12   31    23       0          366     1            0   \n",
       "\n",
       "                     quarter  is_weekend  \n",
       "datetime                                  \n",
       "2011-01-01 00:00:00        1           1  \n",
       "2011-01-01 01:00:00        1           1  \n",
       "2011-01-01 02:00:00        1           1  \n",
       "2011-01-01 03:00:00        1           1  \n",
       "2011-01-01 04:00:00        1           1  \n",
       "...                      ...         ...  \n",
       "2012-12-31 19:00:00        4           0  \n",
       "2012-12-31 20:00:00        4           0  \n",
       "2012-12-31 21:00:00        4           0  \n",
       "2012-12-31 22:00:00        4           0  \n",
       "2012-12-31 23:00:00        4           0  \n",
       "\n",
       "[17379 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"# https://www.aboutdatablog.com/post/extracting-features-from-dates-in-pandas\\ndf[\\\"year\\\"] = df.index.year\\ndf[\\\"month\\\"] = df.index.month\\ndf[\\\"day\\\"] = df.index.day\\ndf[\\\"hour\\\"] = df.index.hour\\ndf[\\\"minute\\\"] = df.index.minute\\ndf[\\\"day_of_year\\\"] = df.index.day_of_year\\ndf[\\\"week\\\"] = df.index.isocalendar().week\\ndf[\\\"day_of_week\\\"] = df.index.day_of_week\\ndf[\\\"quarter\\\"] = df.index.quarter\\ndf[\\\"is_weekend\\\"] = df.index.weekday.isin([5, 6]).astype(int)\\n\\ndf\";\n",
       "                var nbb_formatted_code = \"# https://www.aboutdatablog.com/post/extracting-features-from-dates-in-pandas\\ndf[\\\"year\\\"] = df.index.year\\ndf[\\\"month\\\"] = df.index.month\\ndf[\\\"day\\\"] = df.index.day\\ndf[\\\"hour\\\"] = df.index.hour\\ndf[\\\"minute\\\"] = df.index.minute\\ndf[\\\"day_of_year\\\"] = df.index.day_of_year\\ndf[\\\"week\\\"] = df.index.isocalendar().week\\ndf[\\\"day_of_week\\\"] = df.index.day_of_week\\ndf[\\\"quarter\\\"] = df.index.quarter\\ndf[\\\"is_weekend\\\"] = df.index.weekday.isin([5, 6]).astype(int)\\n\\ndf\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://www.aboutdatablog.com/post/extracting-features-from-dates-in-pandas\n",
    "df[\"year\"] = df.index.year\n",
    "df[\"month\"] = df.index.month\n",
    "df[\"day\"] = df.index.day\n",
    "df[\"hour\"] = df.index.hour\n",
    "df[\"minute\"] = df.index.minute\n",
    "df[\"day_of_year\"] = df.index.day_of_year\n",
    "df[\"week\"] = df.index.isocalendar().week\n",
    "df[\"day_of_week\"] = df.index.day_of_week\n",
    "df[\"quarter\"] = df.index.quarter\n",
    "df[\"is_weekend\"] = df.index.weekday.isin([5, 6]).astype(int)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e992b44d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:24:13.570371Z",
     "start_time": "2022-12-04T12:24:13.503917Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_lag_1</th>\n",
       "      <th>temp_lag_2</th>\n",
       "      <th>temp_lag_3</th>\n",
       "      <th>temp_lag_4</th>\n",
       "      <th>temp_lag_5</th>\n",
       "      <th>temp_lag_6</th>\n",
       "      <th>temp_lag_7</th>\n",
       "      <th>temp_rolling_mean</th>\n",
       "      <th>temp_expanding_mean</th>\n",
       "      <th>atemp_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>humidity_expanding_mean</th>\n",
       "      <th>windspeed_lag_1</th>\n",
       "      <th>windspeed_lag_2</th>\n",
       "      <th>windspeed_lag_3</th>\n",
       "      <th>windspeed_lag_4</th>\n",
       "      <th>windspeed_lag_5</th>\n",
       "      <th>windspeed_lag_6</th>\n",
       "      <th>windspeed_lag_7</th>\n",
       "      <th>windspeed_rolling_mean</th>\n",
       "      <th>windspeed_expanding_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>9.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.430000</td>\n",
       "      <td>14.395</td>\n",
       "      <td>...</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>9.02</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.293333</td>\n",
       "      <td>13.635</td>\n",
       "      <td>...</td>\n",
       "      <td>80.333333</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>9.02</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.430000</td>\n",
       "      <td>13.635</td>\n",
       "      <td>...</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>9.84</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.512000</td>\n",
       "      <td>14.395</td>\n",
       "      <td>...</td>\n",
       "      <td>78.200000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.48</td>\n",
       "      <td>11.48</td>\n",
       "      <td>10.66</td>\n",
       "      <td>9.84</td>\n",
       "      <td>10.66</td>\n",
       "      <td>20.378711</td>\n",
       "      <td>13.635</td>\n",
       "      <td>...</td>\n",
       "      <td>62.723453</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>9.750175</td>\n",
       "      <td>12.737170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.48</td>\n",
       "      <td>11.48</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>20.378151</td>\n",
       "      <td>12.880</td>\n",
       "      <td>...</td>\n",
       "      <td>62.723297</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>9.251025</td>\n",
       "      <td>12.737070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.48</td>\n",
       "      <td>11.48</td>\n",
       "      <td>10.66</td>\n",
       "      <td>20.377592</td>\n",
       "      <td>12.880</td>\n",
       "      <td>...</td>\n",
       "      <td>62.723140</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>10.500575</td>\n",
       "      <td>12.736970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.48</td>\n",
       "      <td>10.66</td>\n",
       "      <td>20.377033</td>\n",
       "      <td>12.880</td>\n",
       "      <td>...</td>\n",
       "      <td>62.722753</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>10.500575</td>\n",
       "      <td>12.736755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.66</td>\n",
       "      <td>20.376474</td>\n",
       "      <td>13.635</td>\n",
       "      <td>...</td>\n",
       "      <td>62.722884</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>9.999750</td>\n",
       "      <td>12.736540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     temp_lag_1  temp_lag_2  temp_lag_3  temp_lag_4  \\\n",
       "datetime                                                              \n",
       "2011-01-01 00:00:00        0.00        0.00        0.00        0.00   \n",
       "2011-01-01 01:00:00        9.84        0.00        0.00        0.00   \n",
       "2011-01-01 02:00:00        9.02        9.84        0.00        0.00   \n",
       "2011-01-01 03:00:00        9.02        9.02        9.84        0.00   \n",
       "2011-01-01 04:00:00        9.84        9.02        9.02        9.84   \n",
       "...                         ...         ...         ...         ...   \n",
       "2012-12-31 19:00:00       10.66       10.66       10.66       11.48   \n",
       "2012-12-31 20:00:00       10.66       10.66       10.66       10.66   \n",
       "2012-12-31 21:00:00       10.66       10.66       10.66       10.66   \n",
       "2012-12-31 22:00:00       10.66       10.66       10.66       10.66   \n",
       "2012-12-31 23:00:00       10.66       10.66       10.66       10.66   \n",
       "\n",
       "                     temp_lag_5  temp_lag_6  temp_lag_7  temp_rolling_mean  \\\n",
       "datetime                                                                     \n",
       "2011-01-01 00:00:00        0.00        0.00        0.00               0.00   \n",
       "2011-01-01 01:00:00        0.00        0.00        0.00               0.00   \n",
       "2011-01-01 02:00:00        0.00        0.00        0.00               0.00   \n",
       "2011-01-01 03:00:00        0.00        0.00        0.00               9.43   \n",
       "2011-01-01 04:00:00        0.00        0.00        0.00               9.43   \n",
       "...                         ...         ...         ...                ...   \n",
       "2012-12-31 19:00:00       11.48       10.66        9.84              10.66   \n",
       "2012-12-31 20:00:00       11.48       11.48       10.66              10.66   \n",
       "2012-12-31 21:00:00       10.66       11.48       11.48              10.66   \n",
       "2012-12-31 22:00:00       10.66       10.66       11.48              10.66   \n",
       "2012-12-31 23:00:00       10.66       10.66       10.66              10.66   \n",
       "\n",
       "                     temp_expanding_mean  atemp_lag_1  ...  \\\n",
       "datetime                                               ...   \n",
       "2011-01-01 00:00:00             0.000000        0.000  ...   \n",
       "2011-01-01 01:00:00             9.430000       14.395  ...   \n",
       "2011-01-01 02:00:00             9.293333       13.635  ...   \n",
       "2011-01-01 03:00:00             9.430000       13.635  ...   \n",
       "2011-01-01 04:00:00             9.512000       14.395  ...   \n",
       "...                                  ...          ...  ...   \n",
       "2012-12-31 19:00:00            20.378711       13.635  ...   \n",
       "2012-12-31 20:00:00            20.378151       12.880  ...   \n",
       "2012-12-31 21:00:00            20.377592       12.880  ...   \n",
       "2012-12-31 22:00:00            20.377033       12.880  ...   \n",
       "2012-12-31 23:00:00            20.376474       13.635  ...   \n",
       "\n",
       "                     humidity_expanding_mean  windspeed_lag_1  \\\n",
       "datetime                                                        \n",
       "2011-01-01 00:00:00                 0.000000           0.0000   \n",
       "2011-01-01 01:00:00                80.500000           0.0000   \n",
       "2011-01-01 02:00:00                80.333333           0.0000   \n",
       "2011-01-01 03:00:00                79.000000           0.0000   \n",
       "2011-01-01 04:00:00                78.200000           0.0000   \n",
       "...                                      ...              ...   \n",
       "2012-12-31 19:00:00                62.723453           8.9981   \n",
       "2012-12-31 20:00:00                62.723297          11.0014   \n",
       "2012-12-31 21:00:00                62.723140          11.0014   \n",
       "2012-12-31 22:00:00                62.722753          11.0014   \n",
       "2012-12-31 23:00:00                62.722884           8.9981   \n",
       "\n",
       "                     windspeed_lag_2  windspeed_lag_3  windspeed_lag_4  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 01:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 02:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 03:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 04:00:00           0.0000           0.0000           0.0000   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-31 19:00:00           6.0032          12.9980           8.9981   \n",
       "2012-12-31 20:00:00           8.9981           6.0032          12.9980   \n",
       "2012-12-31 21:00:00          11.0014           8.9981           6.0032   \n",
       "2012-12-31 22:00:00          11.0014          11.0014           8.9981   \n",
       "2012-12-31 23:00:00          11.0014          11.0014          11.0014   \n",
       "\n",
       "                     windspeed_lag_5  windspeed_lag_6  windspeed_lag_7  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 01:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 02:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 03:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 04:00:00           0.0000           0.0000           0.0000   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-31 19:00:00          15.0013          11.0014          12.9980   \n",
       "2012-12-31 20:00:00           8.9981          15.0013          11.0014   \n",
       "2012-12-31 21:00:00          12.9980           8.9981          15.0013   \n",
       "2012-12-31 22:00:00           6.0032          12.9980           8.9981   \n",
       "2012-12-31 23:00:00           8.9981           6.0032          12.9980   \n",
       "\n",
       "                     windspeed_rolling_mean  windspeed_expanding_mean  \n",
       "datetime                                                               \n",
       "2011-01-01 00:00:00                0.000000                  0.000000  \n",
       "2011-01-01 01:00:00                0.000000                  0.000000  \n",
       "2011-01-01 02:00:00                0.000000                  0.000000  \n",
       "2011-01-01 03:00:00                0.000000                  0.000000  \n",
       "2011-01-01 04:00:00                0.000000                  0.000000  \n",
       "...                                     ...                       ...  \n",
       "2012-12-31 19:00:00                9.750175                 12.737170  \n",
       "2012-12-31 20:00:00                9.251025                 12.737070  \n",
       "2012-12-31 21:00:00               10.500575                 12.736970  \n",
       "2012-12-31 22:00:00               10.500575                 12.736755  \n",
       "2012-12-31 23:00:00                9.999750                 12.736540  \n",
       "\n",
       "[17379 rows x 36 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"# https://www.analyticsvidhya.com/blog/2019/12/6-powerful-feature-engineering-techniques-time-series/\\n# https://habr.com/ru/company/ods/blog/327242/\\n\\n\\ndef extract_ts_features(df, max_lag=7, rolling_size=4, expanding_size=2):\\n    ts_df = pd.DataFrame()\\n\\n    for col_name in df.columns:\\n        # shift\\n        for lag in range(1, max_lag + 1):\\n            ts_df[\\\"{}_lag_{}\\\".format(col_name, lag)] = df[col_name].shift(lag)\\n\\n        # rolling\\n        ts_df[\\\"{}_rolling_mean\\\".format(col_name)] = (\\n            df[col_name].rolling(rolling_size).mean()\\n        )\\n\\n        # expanding window\\n        ts_df[\\\"{}_expanding_mean\\\".format(col_name)] = (\\n            df[col_name].expanding(expanding_size).mean()\\n        )\\n\\n    return ts_df.fillna(0)\\n\\n\\ndf1 = df[[\\\"temp\\\", \\\"atemp\\\", \\\"humidity\\\", \\\"windspeed\\\"]].copy()\\nts_df = extract_ts_features(df1)\\nts_df\";\n",
       "                var nbb_formatted_code = \"# https://www.analyticsvidhya.com/blog/2019/12/6-powerful-feature-engineering-techniques-time-series/\\n# https://habr.com/ru/company/ods/blog/327242/\\n\\n\\ndef extract_ts_features(df, max_lag=7, rolling_size=4, expanding_size=2):\\n    ts_df = pd.DataFrame()\\n\\n    for col_name in df.columns:\\n        # shift\\n        for lag in range(1, max_lag + 1):\\n            ts_df[\\\"{}_lag_{}\\\".format(col_name, lag)] = df[col_name].shift(lag)\\n\\n        # rolling\\n        ts_df[\\\"{}_rolling_mean\\\".format(col_name)] = (\\n            df[col_name].rolling(rolling_size).mean()\\n        )\\n\\n        # expanding window\\n        ts_df[\\\"{}_expanding_mean\\\".format(col_name)] = (\\n            df[col_name].expanding(expanding_size).mean()\\n        )\\n\\n    return ts_df.fillna(0)\\n\\n\\ndf1 = df[[\\\"temp\\\", \\\"atemp\\\", \\\"humidity\\\", \\\"windspeed\\\"]].copy()\\nts_df = extract_ts_features(df1)\\nts_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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     },
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   ],
   "source": [
    "# https://www.analyticsvidhya.com/blog/2019/12/6-powerful-feature-engineering-techniques-time-series/\n",
    "# https://habr.com/ru/company/ods/blog/327242/\n",
    "\n",
    "\n",
    "def extract_ts_features(df, max_lag=7, rolling_size=4, expanding_size=2):\n",
    "    ts_df = pd.DataFrame()\n",
    "\n",
    "    for col_name in df.columns:\n",
    "        # shift\n",
    "        for lag in range(1, max_lag + 1):\n",
    "            ts_df[\"{}_lag_{}\".format(col_name, lag)] = df[col_name].shift(lag)\n",
    "\n",
    "        # rolling\n",
    "        ts_df[\"{}_rolling_mean\".format(col_name)] = (\n",
    "            df[col_name].rolling(rolling_size).mean()\n",
    "        )\n",
    "\n",
    "        # expanding window\n",
    "        ts_df[\"{}_expanding_mean\".format(col_name)] = (\n",
    "            df[col_name].expanding(expanding_size).mean()\n",
    "        )\n",
    "\n",
    "    return ts_df.fillna(0)\n",
    "\n",
    "\n",
    "df1 = df[[\"temp\", \"atemp\", \"humidity\", \"windspeed\"]].copy()\n",
    "ts_df = extract_ts_features(df1)\n",
    "ts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5de3b29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:24:13.594211Z",
     "start_time": "2022-12-04T12:24:13.571669Z"
    }
   },
   "outputs": [
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       "      <th>season</th>\n",
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       "      <td>1</td>\n",
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       "      <td>11.0014</td>\n",
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       "      <td>11.0014</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>9.750175</td>\n",
       "      <td>12.737170</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
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       "      <td>12.9980</td>\n",
       "      <td>9.999750</td>\n",
       "      <td>12.736540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2012-12-31 19:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 20:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 21:00:00       1        0           1        1  10.66  12.880   \n",
       "2012-12-31 22:00:00       1        0           1        1  10.66  13.635   \n",
       "2012-12-31 23:00:00       1        0           1        1  10.66  13.635   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  \\\n",
       "datetime                                                      ...   \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0  ...   \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0  ...   \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0  ...   \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0  ...   \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0  ...   \n",
       "...                       ...        ...     ...         ...  ...   \n",
       "2012-12-31 19:00:00        60    11.0014     NaN         NaN  ...   \n",
       "2012-12-31 20:00:00        60    11.0014     NaN         NaN  ...   \n",
       "2012-12-31 21:00:00        60    11.0014     NaN         NaN  ...   \n",
       "2012-12-31 22:00:00        56     8.9981     NaN         NaN  ...   \n",
       "2012-12-31 23:00:00        65     8.9981     NaN         NaN  ...   \n",
       "\n",
       "                     humidity_expanding_mean  windspeed_lag_1  \\\n",
       "datetime                                                        \n",
       "2011-01-01 00:00:00                 0.000000           0.0000   \n",
       "2011-01-01 01:00:00                80.500000           0.0000   \n",
       "2011-01-01 02:00:00                80.333333           0.0000   \n",
       "2011-01-01 03:00:00                79.000000           0.0000   \n",
       "2011-01-01 04:00:00                78.200000           0.0000   \n",
       "...                                      ...              ...   \n",
       "2012-12-31 19:00:00                62.723453           8.9981   \n",
       "2012-12-31 20:00:00                62.723297          11.0014   \n",
       "2012-12-31 21:00:00                62.723140          11.0014   \n",
       "2012-12-31 22:00:00                62.722753          11.0014   \n",
       "2012-12-31 23:00:00                62.722884           8.9981   \n",
       "\n",
       "                     windspeed_lag_2  windspeed_lag_3  windspeed_lag_4  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 01:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 02:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 03:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 04:00:00           0.0000           0.0000           0.0000   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-31 19:00:00           6.0032          12.9980           8.9981   \n",
       "2012-12-31 20:00:00           8.9981           6.0032          12.9980   \n",
       "2012-12-31 21:00:00          11.0014           8.9981           6.0032   \n",
       "2012-12-31 22:00:00          11.0014          11.0014           8.9981   \n",
       "2012-12-31 23:00:00          11.0014          11.0014          11.0014   \n",
       "\n",
       "                     windspeed_lag_5  windspeed_lag_6  windspeed_lag_7  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 01:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 02:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 03:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 04:00:00           0.0000           0.0000           0.0000   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-31 19:00:00          15.0013          11.0014          12.9980   \n",
       "2012-12-31 20:00:00           8.9981          15.0013          11.0014   \n",
       "2012-12-31 21:00:00          12.9980           8.9981          15.0013   \n",
       "2012-12-31 22:00:00           6.0032          12.9980           8.9981   \n",
       "2012-12-31 23:00:00           8.9981           6.0032          12.9980   \n",
       "\n",
       "                     windspeed_rolling_mean  windspeed_expanding_mean  \n",
       "datetime                                                               \n",
       "2011-01-01 00:00:00                0.000000                  0.000000  \n",
       "2011-01-01 01:00:00                0.000000                  0.000000  \n",
       "2011-01-01 02:00:00                0.000000                  0.000000  \n",
       "2011-01-01 03:00:00                0.000000                  0.000000  \n",
       "2011-01-01 04:00:00                0.000000                  0.000000  \n",
       "...                                     ...                       ...  \n",
       "2012-12-31 19:00:00                9.750175                 12.737170  \n",
       "2012-12-31 20:00:00                9.251025                 12.737070  \n",
       "2012-12-31 21:00:00               10.500575                 12.736970  \n",
       "2012-12-31 22:00:00               10.500575                 12.736755  \n",
       "2012-12-31 23:00:00                9.999750                 12.736540  \n",
       "\n",
       "[17379 rows x 58 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
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    "df = df.join(ts_df)\n",
    "df"
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  },
  {
   "cell_type": "markdown",
   "id": "a87f1e3e",
   "metadata": {
    "ExecuteTime": {
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    "id": "mwearqjFGsjs",
    "outputId": "4c3c941b-d850-4228-ba36-9ae73333b0e0"
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    "# Prepare"
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    "ExecuteTime": {
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>...</th>\n",
       "      <th>humidity_expanding_mean</th>\n",
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       "      <th>windspeed_lag_2</th>\n",
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       "      <th>windspeed_lag_4</th>\n",
       "      <th>windspeed_lag_5</th>\n",
       "      <th>windspeed_lag_6</th>\n",
       "      <th>windspeed_lag_7</th>\n",
       "      <th>windspeed_rolling_mean</th>\n",
       "      <th>windspeed_expanding_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-20 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>61.885919</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>14.001325</td>\n",
       "      <td>12.800608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>61.885378</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>10.251000</td>\n",
       "      <td>12.799433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>61.884838</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>8.9981</td>\n",
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       "      <td>15.0013</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>8.750200</td>\n",
       "      <td>12.798257</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-20 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>61.884298</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
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       "      <td>15.0013</td>\n",
       "      <td>9.251025</td>\n",
       "      <td>12.798092</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-20 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>5.500700</td>\n",
       "      <td>12.797927</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>62.723453</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>9.750175</td>\n",
       "      <td>12.737170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
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       "      <td>62.723297</td>\n",
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       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>9.251025</td>\n",
       "      <td>12.737070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>62.723140</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>10.500575</td>\n",
       "      <td>12.736970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>62.722753</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>10.500575</td>\n",
       "      <td>12.736755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>65</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>62.722884</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>9.999750</td>\n",
       "      <td>12.736540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6493 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2012-12-31 19:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 20:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 21:00:00       1        0           1        1  10.66  12.880   \n",
       "2012-12-31 22:00:00       1        0           1        1  10.66  13.635   \n",
       "2012-12-31 23:00:00       1        0           1        1  10.66  13.635   \n",
       "\n",
       "                     humidity  windspeed  year  month  ...  \\\n",
       "datetime                                               ...   \n",
       "2011-01-20 00:00:00        56    26.0027  2011      1  ...   \n",
       "2011-01-20 01:00:00        56     0.0000  2011      1  ...   \n",
       "2011-01-20 02:00:00        56     0.0000  2011      1  ...   \n",
       "2011-01-20 03:00:00        56    11.0014  2011      1  ...   \n",
       "2011-01-20 04:00:00        56    11.0014  2011      1  ...   \n",
       "...                       ...        ...   ...    ...  ...   \n",
       "2012-12-31 19:00:00        60    11.0014  2012     12  ...   \n",
       "2012-12-31 20:00:00        60    11.0014  2012     12  ...   \n",
       "2012-12-31 21:00:00        60    11.0014  2012     12  ...   \n",
       "2012-12-31 22:00:00        56     8.9981  2012     12  ...   \n",
       "2012-12-31 23:00:00        65     8.9981  2012     12  ...   \n",
       "\n",
       "                     humidity_expanding_mean  windspeed_lag_1  \\\n",
       "datetime                                                        \n",
       "2011-01-20 00:00:00                61.885919           8.9981   \n",
       "2011-01-20 01:00:00                61.885378          26.0027   \n",
       "2011-01-20 02:00:00                61.884838           0.0000   \n",
       "2011-01-20 03:00:00                61.884298           0.0000   \n",
       "2011-01-20 04:00:00                61.883757          11.0014   \n",
       "...                                      ...              ...   \n",
       "2012-12-31 19:00:00                62.723453           8.9981   \n",
       "2012-12-31 20:00:00                62.723297          11.0014   \n",
       "2012-12-31 21:00:00                62.723140          11.0014   \n",
       "2012-12-31 22:00:00                62.722753          11.0014   \n",
       "2012-12-31 23:00:00                62.722884           8.9981   \n",
       "\n",
       "                     windspeed_lag_2  windspeed_lag_3  windspeed_lag_4  \\\n",
       "datetime                                                                 \n",
       "2011-01-20 00:00:00           6.0032          15.0013          15.0013   \n",
       "2011-01-20 01:00:00           8.9981           6.0032          15.0013   \n",
       "2011-01-20 02:00:00          26.0027           8.9981           6.0032   \n",
       "2011-01-20 03:00:00           0.0000          26.0027           8.9981   \n",
       "2011-01-20 04:00:00           0.0000           0.0000          26.0027   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-31 19:00:00           6.0032          12.9980           8.9981   \n",
       "2012-12-31 20:00:00           8.9981           6.0032          12.9980   \n",
       "2012-12-31 21:00:00          11.0014           8.9981           6.0032   \n",
       "2012-12-31 22:00:00          11.0014          11.0014           8.9981   \n",
       "2012-12-31 23:00:00          11.0014          11.0014          11.0014   \n",
       "\n",
       "                     windspeed_lag_5  windspeed_lag_6  windspeed_lag_7  \\\n",
       "datetime                                                                 \n",
       "2011-01-20 00:00:00          26.0027          23.9994          26.0027   \n",
       "2011-01-20 01:00:00          15.0013          26.0027          23.9994   \n",
       "2011-01-20 02:00:00          15.0013          15.0013          26.0027   \n",
       "2011-01-20 03:00:00           6.0032          15.0013          15.0013   \n",
       "2011-01-20 04:00:00           8.9981           6.0032          15.0013   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-31 19:00:00          15.0013          11.0014          12.9980   \n",
       "2012-12-31 20:00:00           8.9981          15.0013          11.0014   \n",
       "2012-12-31 21:00:00          12.9980           8.9981          15.0013   \n",
       "2012-12-31 22:00:00           6.0032          12.9980           8.9981   \n",
       "2012-12-31 23:00:00           8.9981           6.0032          12.9980   \n",
       "\n",
       "                     windspeed_rolling_mean  windspeed_expanding_mean  \n",
       "datetime                                                               \n",
       "2011-01-20 00:00:00               14.001325                 12.800608  \n",
       "2011-01-20 01:00:00               10.251000                 12.799433  \n",
       "2011-01-20 02:00:00                8.750200                 12.798257  \n",
       "2011-01-20 03:00:00                9.251025                 12.798092  \n",
       "2011-01-20 04:00:00                5.500700                 12.797927  \n",
       "...                                     ...                       ...  \n",
       "2012-12-31 19:00:00                9.750175                 12.737170  \n",
       "2012-12-31 20:00:00                9.251025                 12.737070  \n",
       "2012-12-31 21:00:00               10.500575                 12.736970  \n",
       "2012-12-31 22:00:00               10.500575                 12.736755  \n",
       "2012-12-31 23:00:00                9.999750                 12.736540  \n",
       "\n",
       "[6493 rows x 54 columns]"
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       "      <th>season</th>\n",
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       "      <th>2011-01-01 00:00:00</th>\n",
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       "      <th>2011-01-01 01:00:00</th>\n",
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       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
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       "      <td>80.500000</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-19 19:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>19.695</td>\n",
       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>61.886694</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>25.001050</td>\n",
       "      <td>12.799965</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-19 20:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
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       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>12.9980</td>\n",
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       "      <td>22.751525</td>\n",
       "      <td>12.800167</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-19 21:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>61.886163</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>20.001175</td>\n",
       "      <td>12.800369</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-19 22:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>13.94</td>\n",
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       "      <td>61</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>61.886082</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>15.502125</td>\n",
       "      <td>12.799745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 23:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>61.886460</td>\n",
       "      <td>6.0032</td>\n",
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       "      <td>15.0013</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>11.250975</td>\n",
       "      <td>12.799395</td>\n",
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       "</table>\n",
       "<p>10886 rows × 54 columns</p>\n",
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      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2012-12-19 19:00:00       4        0           1        1  15.58  19.695   \n",
       "2012-12-19 20:00:00       4        0           1        1  14.76  17.425   \n",
       "2012-12-19 21:00:00       4        0           1        1  13.94  15.910   \n",
       "2012-12-19 22:00:00       4        0           1        1  13.94  17.425   \n",
       "2012-12-19 23:00:00       4        0           1        1  13.12  16.665   \n",
       "\n",
       "                     humidity  windspeed  year  month  ...  \\\n",
       "datetime                                               ...   \n",
       "2011-01-01 00:00:00        81     0.0000  2011      1  ...   \n",
       "2011-01-01 01:00:00        80     0.0000  2011      1  ...   \n",
       "2011-01-01 02:00:00        80     0.0000  2011      1  ...   \n",
       "2011-01-01 03:00:00        75     0.0000  2011      1  ...   \n",
       "2011-01-01 04:00:00        75     0.0000  2011      1  ...   \n",
       "...                       ...        ...   ...    ...  ...   \n",
       "2012-12-19 19:00:00        50    26.0027  2012     12  ...   \n",
       "2012-12-19 20:00:00        57    15.0013  2012     12  ...   \n",
       "2012-12-19 21:00:00        61    15.0013  2012     12  ...   \n",
       "2012-12-19 22:00:00        61     6.0032  2012     12  ...   \n",
       "2012-12-19 23:00:00        66     8.9981  2012     12  ...   \n",
       "\n",
       "                     humidity_expanding_mean  windspeed_lag_1  \\\n",
       "datetime                                                        \n",
       "2011-01-01 00:00:00                 0.000000           0.0000   \n",
       "2011-01-01 01:00:00                80.500000           0.0000   \n",
       "2011-01-01 02:00:00                80.333333           0.0000   \n",
       "2011-01-01 03:00:00                79.000000           0.0000   \n",
       "2011-01-01 04:00:00                78.200000           0.0000   \n",
       "...                                      ...              ...   \n",
       "2012-12-19 19:00:00                61.886694          23.9994   \n",
       "2012-12-19 20:00:00                61.886245          26.0027   \n",
       "2012-12-19 21:00:00                61.886163          15.0013   \n",
       "2012-12-19 22:00:00                61.886082          15.0013   \n",
       "2012-12-19 23:00:00                61.886460           6.0032   \n",
       "\n",
       "                     windspeed_lag_2  windspeed_lag_3  windspeed_lag_4  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 01:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 02:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 03:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 04:00:00           0.0000           0.0000           0.0000   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-19 19:00:00          26.0027          23.9994          19.0012   \n",
       "2012-12-19 20:00:00          23.9994          26.0027          23.9994   \n",
       "2012-12-19 21:00:00          26.0027          23.9994          26.0027   \n",
       "2012-12-19 22:00:00          15.0013          26.0027          23.9994   \n",
       "2012-12-19 23:00:00          15.0013          15.0013          26.0027   \n",
       "\n",
       "                     windspeed_lag_5  windspeed_lag_6  windspeed_lag_7  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 01:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 02:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 03:00:00           0.0000           0.0000           0.0000   \n",
       "2011-01-01 04:00:00           0.0000           0.0000           0.0000   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-19 19:00:00          12.9980          12.9980          19.0012   \n",
       "2012-12-19 20:00:00          19.0012          12.9980          12.9980   \n",
       "2012-12-19 21:00:00          23.9994          19.0012          12.9980   \n",
       "2012-12-19 22:00:00          26.0027          23.9994          19.0012   \n",
       "2012-12-19 23:00:00          23.9994          26.0027          23.9994   \n",
       "\n",
       "                     windspeed_rolling_mean  windspeed_expanding_mean  \n",
       "datetime                                                               \n",
       "2011-01-01 00:00:00                0.000000                  0.000000  \n",
       "2011-01-01 01:00:00                0.000000                  0.000000  \n",
       "2011-01-01 02:00:00                0.000000                  0.000000  \n",
       "2011-01-01 03:00:00                0.000000                  0.000000  \n",
       "2011-01-01 04:00:00                0.000000                  0.000000  \n",
       "...                                     ...                       ...  \n",
       "2012-12-19 19:00:00               25.001050                 12.799965  \n",
       "2012-12-19 20:00:00               22.751525                 12.800167  \n",
       "2012-12-19 21:00:00               20.001175                 12.800369  \n",
       "2012-12-19 22:00:00               15.502125                 12.799745  \n",
       "2012-12-19 23:00:00               11.250975                 12.799395  \n",
       "\n",
       "[10886 rows x 54 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"X_train = (\\n    df[df[\\\"isTest\\\"] == 0]\\n    .drop([\\\"casual\\\", \\\"registered\\\", \\\"count\\\", \\\"isTest\\\"], axis=1)\\n    .copy()\\n)\\nX_train\";\n",
       "                var nbb_formatted_code = \"X_train = (\\n    df[df[\\\"isTest\\\"] == 0]\\n    .drop([\\\"casual\\\", \\\"registered\\\", \\\"count\\\", \\\"isTest\\\"], axis=1)\\n    .copy()\\n)\\nX_train\";\n",
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       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "X_train = (\n",
    "    df[df[\"isTest\"] == 0]\n",
    "    .drop([\"casual\", \"registered\", \"count\", \"isTest\"], axis=1)\n",
    "    .copy()\n",
    ")\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a95ebaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:24:13.648969Z",
     "start_time": "2022-12-04T12:24:13.638469Z"
    }
   },
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>2.833213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>3.713572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>3.496508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>2.639057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 19:00:00</th>\n",
       "      <td>5.820083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 20:00:00</th>\n",
       "      <td>5.488938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 21:00:00</th>\n",
       "      <td>5.129899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 22:00:00</th>\n",
       "      <td>4.867534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 23:00:00</th>\n",
       "      <td>4.488636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10886 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count\n",
       "datetime                     \n",
       "2011-01-01 00:00:00  2.833213\n",
       "2011-01-01 01:00:00  3.713572\n",
       "2011-01-01 02:00:00  3.496508\n",
       "2011-01-01 03:00:00  2.639057\n",
       "2011-01-01 04:00:00  0.693147\n",
       "...                       ...\n",
       "2012-12-19 19:00:00  5.820083\n",
       "2012-12-19 20:00:00  5.488938\n",
       "2012-12-19 21:00:00  5.129899\n",
       "2012-12-19 22:00:00  4.867534\n",
       "2012-12-19 23:00:00  4.488636\n",
       "\n",
       "[10886 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"y_train = df[df[\\\"isTest\\\"] == 0][[\\\"count\\\"]].copy()\\ny_train[\\\"count\\\"] = np.log1p(y_train[\\\"count\\\"])  # RMSLE\\ny_train\";\n",
       "                var nbb_formatted_code = \"y_train = df[df[\\\"isTest\\\"] == 0][[\\\"count\\\"]].copy()\\ny_train[\\\"count\\\"] = np.log1p(y_train[\\\"count\\\"])  # RMSLE\\ny_train\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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   "source": [
    "y_train = df[df[\"isTest\"] == 0][[\"count\"]].copy()\n",
    "y_train[\"count\"] = np.log1p(y_train[\"count\"])  # RMSLE\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce1d8a23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:24:13.659396Z",
     "start_time": "2022-12-04T12:24:13.649859Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9797, 54), (1089, 54), (9797, 1), (1089, 1))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"X_train, X_true, y_train, y_true = train_test_split(\\n    X_train, y_train, test_size=0.1, shuffle=False, random_state=42\\n)\\nX_train.shape, X_true.shape, y_train.shape, y_true.shape\";\n",
       "                var nbb_formatted_code = \"X_train, X_true, y_train, y_true = train_test_split(\\n    X_train, y_train, test_size=0.1, shuffle=False, random_state=42\\n)\\nX_train.shape, X_true.shape, y_train.shape, y_true.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "X_train, X_true, y_train, y_true = train_test_split(\n",
    "    X_train, y_train, test_size=0.1, shuffle=False, random_state=42\n",
    ")\n",
    "X_train.shape, X_true.shape, y_train.shape, y_true.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf830df",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e850ba",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9955749c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:24:13.664731Z",
     "start_time": "2022-12-04T12:24:13.660682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"tscv = TimeSeriesSplit(n_splits=5)\\ntscv\";\n",
       "                var nbb_formatted_code = \"tscv = TimeSeriesSplit(n_splits=5)\\ntscv\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=5)\n",
    "tscv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "530b9244",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:25:28.962773Z",
     "start_time": "2022-12-04T12:24:13.665505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2abcbf4a4d54c12b5519e8d711b3a68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"model = CatBoostRegressor(logging_level=\\\"Silent\\\")\\n\\n# https://docs.aws.amazon.com/sagemaker/latest/dg/catboost-tuning.html\\ntuned_params = {\\n    \\\"learning_rate\\\": [\\n        0.001,\\n        0.002,\\n        0.003,\\n        0.004,\\n        0.005,\\n        0.006,\\n        0.007,\\n        0.008,\\n        0.009,\\n        0.01,\\n    ],\\n    \\\"depth\\\": [4, 5, 6, 7, 8, 9, 10],\\n    \\\"l2_leaf_reg\\\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\\n    \\\"random_strength\\\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],\\n    \\\"iterations\\\": [500, 600, 700, 800, 900, 1000],\\n}\\n\\ngrid_search_result = model.randomized_search(\\n    tuned_params, Pool(X_train, y_train), cv=tscv, verbose=False, plot=True\\n)\";\n",
       "                var nbb_formatted_code = \"model = CatBoostRegressor(logging_level=\\\"Silent\\\")\\n\\n# https://docs.aws.amazon.com/sagemaker/latest/dg/catboost-tuning.html\\ntuned_params = {\\n    \\\"learning_rate\\\": [\\n        0.001,\\n        0.002,\\n        0.003,\\n        0.004,\\n        0.005,\\n        0.006,\\n        0.007,\\n        0.008,\\n        0.009,\\n        0.01,\\n    ],\\n    \\\"depth\\\": [4, 5, 6, 7, 8, 9, 10],\\n    \\\"l2_leaf_reg\\\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\\n    \\\"random_strength\\\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],\\n    \\\"iterations\\\": [500, 600, 700, 800, 900, 1000],\\n}\\n\\ngrid_search_result = model.randomized_search(\\n    tuned_params, Pool(X_train, y_train), cv=tscv, verbose=False, plot=True\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = CatBoostRegressor(logging_level=\"Silent\")\n",
    "\n",
    "# https://docs.aws.amazon.com/sagemaker/latest/dg/catboost-tuning.html\n",
    "tuned_params = {\n",
    "    \"learning_rate\": [\n",
    "        0.001,\n",
    "        0.002,\n",
    "        0.003,\n",
    "        0.004,\n",
    "        0.005,\n",
    "        0.006,\n",
    "        0.007,\n",
    "        0.008,\n",
    "        0.009,\n",
    "        0.01,\n",
    "    ],\n",
    "    \"depth\": [4, 5, 6, 7, 8, 9, 10],\n",
    "    \"l2_leaf_reg\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "    \"random_strength\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],\n",
    "    \"iterations\": [500, 600, 700, 800, 900, 1000],\n",
    "}\n",
    "\n",
    "grid_search_result = model.randomized_search(\n",
    "    tuned_params, Pool(X_train, y_train), cv=tscv, verbose=False, plot=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b333bb4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:25:28.971905Z",
     "start_time": "2022-12-04T12:25:28.964918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'depth': 9,\n",
       " 'l2_leaf_reg': 8,\n",
       " 'iterations': 1000,\n",
       " 'random_strength': 3.0,\n",
       " 'learning_rate': 0.008}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"best_model_params = grid_search_result[\\\"params\\\"]\\nbest_model_params\";\n",
       "                var nbb_formatted_code = \"best_model_params = grid_search_result[\\\"params\\\"]\\nbest_model_params\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model_params = grid_search_result[\"params\"]\n",
    "best_model_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07715ddb",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7b6d29a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:25:29.013707Z",
     "start_time": "2022-12-04T12:25:28.973413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9797, 54), (9797, 1))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"importance_df = pd.DataFrame(\\n    {\\n        \\\"Column\\\": X_train.columns,\\n        \\\"Score\\\": model.get_feature_importance(),\\n    }\\n).sort_values(by=\\\"Score\\\", ascending=False)\\n\\nX_sf = X_train[importance_df[\\\"Column\\\"]]\\ny_sf = y_train\\n\\nX_sf.shape, y_sf.shape\";\n",
       "                var nbb_formatted_code = \"importance_df = pd.DataFrame(\\n    {\\n        \\\"Column\\\": X_train.columns,\\n        \\\"Score\\\": model.get_feature_importance(),\\n    }\\n).sort_values(by=\\\"Score\\\", ascending=False)\\n\\nX_sf = X_train[importance_df[\\\"Column\\\"]]\\ny_sf = y_train\\n\\nX_sf.shape, y_sf.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance_df = pd.DataFrame(\n",
    "    {\n",
    "        \"Column\": X_train.columns,\n",
    "        \"Score\": model.get_feature_importance(),\n",
    "    }\n",
    ").sort_values(by=\"Score\", ascending=False)\n",
    "\n",
    "X_sf = X_train[importance_df[\"Column\"]]\n",
    "y_sf = y_train\n",
    "\n",
    "X_sf.shape, y_sf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec4f848e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:25:29.022973Z",
     "start_time": "2022-12-04T12:25:29.015369Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49, 52]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"num_list = list(range(10, X_sf.shape[1], 3))\\nnum_list\";\n",
       "                var nbb_formatted_code = \"num_list = list(range(10, X_sf.shape[1], 3))\\nnum_list\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_list = list(range(10, X_sf.shape[1], 3))\n",
    "num_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37483345",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:26:44.135044Z",
     "start_time": "2022-12-04T12:25:29.024490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acafc7f62f1e4c2b826036f88b811b40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "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>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_features</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.445987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.452033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.454561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.455287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.455546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 score\n",
       "num_features          \n",
       "46            0.445987\n",
       "43            0.452033\n",
       "25            0.454561\n",
       "52            0.455287\n",
       "49            0.455546"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"def select_features_loop(X, y, num_features=10):\\n    X = X.iloc[:, :num_features]\\n\\n    X_sub_train, X_sub_val, y_sub_train, y_sub_val = train_test_split(\\n        X, y, test_size=0.1, shuffle=False, random_state=42\\n    )\\n\\n    model = CatBoostRegressor(**best_model_params, logging_level=\\\"Silent\\\")\\n    model.fit(\\n        Pool(X_sub_train, y_sub_train),\\n        eval_set=Pool(X_sub_val, y_sub_val),\\n        verbose=False,\\n    )\\n\\n    score = mean_squared_error(y_true, model.predict(X_true), squared=False)\\n\\n    return [num_features, score]\\n\\n\\nloss_list = []\\nfor num_features in tqdm(num_list):\\n    loss_values = select_features_loop(X_sf, y_sf, num_features)\\n    loss_list.append(loss_values)\\n\\npd.DataFrame(loss_list, columns=[\\\"num_features\\\", \\\"score\\\"]).set_index(\\n    \\\"num_features\\\"\\n).sort_values(by=\\\"score\\\").head()\";\n",
       "                var nbb_formatted_code = \"def select_features_loop(X, y, num_features=10):\\n    X = X.iloc[:, :num_features]\\n\\n    X_sub_train, X_sub_val, y_sub_train, y_sub_val = train_test_split(\\n        X, y, test_size=0.1, shuffle=False, random_state=42\\n    )\\n\\n    model = CatBoostRegressor(**best_model_params, logging_level=\\\"Silent\\\")\\n    model.fit(\\n        Pool(X_sub_train, y_sub_train),\\n        eval_set=Pool(X_sub_val, y_sub_val),\\n        verbose=False,\\n    )\\n\\n    score = mean_squared_error(y_true, model.predict(X_true), squared=False)\\n\\n    return [num_features, score]\\n\\n\\nloss_list = []\\nfor num_features in tqdm(num_list):\\n    loss_values = select_features_loop(X_sf, y_sf, num_features)\\n    loss_list.append(loss_values)\\n\\npd.DataFrame(loss_list, columns=[\\\"num_features\\\", \\\"score\\\"]).set_index(\\n    \\\"num_features\\\"\\n).sort_values(by=\\\"score\\\").head()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def select_features_loop(X, y, num_features=10):\n",
    "    X = X.iloc[:, :num_features]\n",
    "\n",
    "    X_sub_train, X_sub_val, y_sub_train, y_sub_val = train_test_split(\n",
    "        X, y, test_size=0.1, shuffle=False, random_state=42\n",
    "    )\n",
    "\n",
    "    model = CatBoostRegressor(**best_model_params, logging_level=\"Silent\")\n",
    "    model.fit(\n",
    "        Pool(X_sub_train, y_sub_train),\n",
    "        eval_set=Pool(X_sub_val, y_sub_val),\n",
    "        verbose=False,\n",
    "    )\n",
    "\n",
    "    score = mean_squared_error(y_true, model.predict(X_true), squared=False)\n",
    "\n",
    "    return [num_features, score]\n",
    "\n",
    "\n",
    "loss_list = []\n",
    "for num_features in tqdm(num_list):\n",
    "    loss_values = select_features_loop(X_sf, y_sf, num_features)\n",
    "    loss_list.append(loss_values)\n",
    "\n",
    "pd.DataFrame(loss_list, columns=[\"num_features\", \"score\"]).set_index(\n",
    "    \"num_features\"\n",
    ").sort_values(by=\"score\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "15fab3fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:26:44.142762Z",
     "start_time": "2022-12-04T12:26:44.136244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp',\n",
       "       'humidity', 'windspeed', 'year', 'month', 'day', 'hour', 'minute',\n",
       "       'day_of_year', 'week', 'day_of_week', 'quarter', 'is_weekend',\n",
       "       'temp_lag_1', 'temp_lag_2', 'temp_lag_3', 'temp_lag_4', 'temp_lag_5',\n",
       "       'temp_lag_6', 'temp_lag_7', 'temp_rolling_mean', 'temp_expanding_mean',\n",
       "       'atemp_lag_1', 'atemp_lag_2', 'atemp_lag_3', 'atemp_lag_4',\n",
       "       'atemp_lag_5', 'atemp_lag_6', 'atemp_lag_7', 'atemp_rolling_mean',\n",
       "       'atemp_expanding_mean', 'humidity_lag_1', 'humidity_lag_2',\n",
       "       'humidity_lag_3', 'humidity_lag_4', 'humidity_lag_5', 'humidity_lag_6',\n",
       "       'humidity_lag_7', 'humidity_rolling_mean', 'humidity_expanding_mean',\n",
       "       'windspeed_lag_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"X_train = X_train.iloc[:, :46]\\nX_train.columns\";\n",
       "                var nbb_formatted_code = \"X_train = X_train.iloc[:, :46]\\nX_train.columns\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = X_train.iloc[:, :46]\n",
    "X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ae5f07",
   "metadata": {},
   "source": [
    "## Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b49ac2df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:07.218899Z",
     "start_time": "2022-12-04T12:26:44.144116Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learn': {'RMSE': 0.3691517750318588}, 'validation': {'RMSE': 0.7724490462121082}}\n",
      "{'learn': {'RMSE': 0.34902052907150627}, 'validation': {'RMSE': 0.5194982514177947}}\n",
      "{'learn': {'RMSE': 0.33727648760437695}, 'validation': {'RMSE': 0.6617173371175511}}\n",
      "{'learn': {'RMSE': 0.3306123350788192}, 'validation': {'RMSE': 0.4758906974556173}}\n",
      "{'learn': {'RMSE': 0.3269694855362372}, 'validation': {'RMSE': 0.42524697144876444}}\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"def train_loop(X_train, y_train):\\n    ensemble = []\\n\\n    for i, (train_index, val_index) in enumerate(tscv.split(X_train)):\\n        X_sub_train, X_sub_val = X_train.iloc[train_index], X_train.iloc[val_index]\\n        y_sub_train, y_sub_val = y_train.iloc[train_index], y_train.iloc[val_index]\\n\\n        model = CatBoostRegressor(**best_model_params, logging_level=\\\"Silent\\\")\\n\\n        model.fit(\\n            Pool(X_sub_train, y_sub_train),\\n            eval_set=Pool(X_sub_val, y_sub_val),\\n            verbose=False,\\n        )\\n\\n        ensemble.append(model)\\n        print(model.best_score_)\\n\\n    return ensemble\\n\\n\\nensemble = train_loop(X_train, y_train)\";\n",
       "                var nbb_formatted_code = \"def train_loop(X_train, y_train):\\n    ensemble = []\\n\\n    for i, (train_index, val_index) in enumerate(tscv.split(X_train)):\\n        X_sub_train, X_sub_val = X_train.iloc[train_index], X_train.iloc[val_index]\\n        y_sub_train, y_sub_val = y_train.iloc[train_index], y_train.iloc[val_index]\\n\\n        model = CatBoostRegressor(**best_model_params, logging_level=\\\"Silent\\\")\\n\\n        model.fit(\\n            Pool(X_sub_train, y_sub_train),\\n            eval_set=Pool(X_sub_val, y_sub_val),\\n            verbose=False,\\n        )\\n\\n        ensemble.append(model)\\n        print(model.best_score_)\\n\\n    return ensemble\\n\\n\\nensemble = train_loop(X_train, y_train)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train_loop(X_train, y_train):\n",
    "    ensemble = []\n",
    "\n",
    "    for i, (train_index, val_index) in enumerate(tscv.split(X_train)):\n",
    "        X_sub_train, X_sub_val = X_train.iloc[train_index], X_train.iloc[val_index]\n",
    "        y_sub_train, y_sub_val = y_train.iloc[train_index], y_train.iloc[val_index]\n",
    "\n",
    "        model = CatBoostRegressor(**best_model_params, logging_level=\"Silent\")\n",
    "\n",
    "        model.fit(\n",
    "            Pool(X_sub_train, y_sub_train),\n",
    "            eval_set=Pool(X_sub_val, y_sub_val),\n",
    "            verbose=False,\n",
    "        )\n",
    "\n",
    "        ensemble.append(model)\n",
    "        print(model.best_score_)\n",
    "\n",
    "    return ensemble\n",
    "\n",
    "\n",
    "ensemble = train_loop(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8152ab90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:07.279302Z",
     "start_time": "2022-12-04T12:27:07.220949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7f5fcb046e50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"models_avrg = to_regressor(\\n    sum_models(ensemble, weights=[1.0 / len(ensemble)] * len(ensemble))\\n)\\nmodels_avrg\";\n",
       "                var nbb_formatted_code = \"models_avrg = to_regressor(\\n    sum_models(ensemble, weights=[1.0 / len(ensemble)] * len(ensemble))\\n)\\nmodels_avrg\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models_avrg = to_regressor(\n",
    "    sum_models(ensemble, weights=[1.0 / len(ensemble)] * len(ensemble))\n",
    ")\n",
    "models_avrg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b45a69e",
   "metadata": {},
   "source": [
    "# Validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd067198",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:07.293394Z",
     "start_time": "2022-12-04T12:27:07.280769Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.33364941, 5.36218346, 5.4966825 , ..., 4.76573145, 4.51426993,\n",
       "       4.24447422])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"y_preds_1 = models_avrg.predict(X_true)\\ny_preds_1\";\n",
       "                var nbb_formatted_code = \"y_preds_1 = models_avrg.predict(X_true)\\ny_preds_1\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds_1 = models_avrg.predict(X_true)\n",
    "y_preds_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc431c0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:07.302214Z",
     "start_time": "2022-12-04T12:27:07.294817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6878338095465699"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"mean_squared_error(y_true, y_preds_1, squared=False)\";\n",
       "                var nbb_formatted_code = \"mean_squared_error(y_true, y_preds_1, squared=False)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "mean_squared_error(y_true, y_preds_1, squared=False)"
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  },
  {
   "cell_type": "markdown",
   "id": "172ad3a1",
   "metadata": {},
   "source": [
    "# Combine frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "518bb4ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:07.312376Z",
     "start_time": "2022-12-04T12:27:07.303467Z"
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    {
     "data": {
      "text/plain": [
       "((10886, 54), (10886, 1))"
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"X_train_1 = pd.concat([X_train, X_true])\\ny_train_1 = pd.concat([y_train, y_true])\\n\\nX_train_1.shape, y_train_1.shape\";\n",
       "                var nbb_formatted_code = \"X_train_1 = pd.concat([X_train, X_true])\\ny_train_1 = pd.concat([y_train, y_true])\\n\\nX_train_1.shape, y_train_1.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
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       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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     "metadata": {},
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   "source": [
    "X_train_1 = pd.concat([X_train, X_true])\n",
    "y_train_1 = pd.concat([y_train, y_true])\n",
    "\n",
    "X_train_1.shape, y_train_1.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9bb57f59",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:30.609066Z",
     "start_time": "2022-12-04T12:27:07.314552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learn': {'RMSE': 0.3694597209692936}, 'validation': {'RMSE': 0.7610691264188832}}\n",
      "{'learn': {'RMSE': 0.3441705950051839}, 'validation': {'RMSE': 0.6047196462406584}}\n",
      "{'learn': {'RMSE': 0.3315627863476515}, 'validation': {'RMSE': 0.6854839170811264}}\n",
      "{'learn': {'RMSE': 0.33447700662826485}, 'validation': {'RMSE': 0.41764445181691007}}\n",
      "{'learn': {'RMSE': 0.32019396463354305}, 'validation': {'RMSE': 0.42252799903395305}}\n"
     ]
    },
    {
     "data": {
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"ensemble = train_loop(X_train_1, y_train_1)\";\n",
       "                var nbb_formatted_code = \"ensemble = train_loop(X_train_1, y_train_1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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   "source": [
    "ensemble = train_loop(X_train_1, y_train_1)"
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  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4988594c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:30.663783Z",
     "start_time": "2022-12-04T12:27:30.610791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7f5fcb091c40>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"models_avrg = to_regressor(\\n    sum_models(ensemble, weights=[1.0 / len(ensemble)] * len(ensemble))\\n)\\nmodels_avrg\";\n",
       "                var nbb_formatted_code = \"models_avrg = to_regressor(\\n    sum_models(ensemble, weights=[1.0 / len(ensemble)] * len(ensemble))\\n)\\nmodels_avrg\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
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    "    sum_models(ensemble, weights=[1.0 / len(ensemble)] * len(ensemble))\n",
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    "models_avrg"
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   "metadata": {},
   "source": [
    "# Submission"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68b08af6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:30.691452Z",
     "start_time": "2022-12-04T12:27:30.665448Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.80635494, 2.30283064, 1.93065592, ..., 4.48283304, 4.25620547,\n",
       "       4.03190534])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"y_preds_avrg = models_avrg.predict(X_test)\\ny_preds_avrg\";\n",
       "                var nbb_formatted_code = \"y_preds_avrg = models_avrg.predict(X_test)\\ny_preds_avrg\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds_avrg = models_avrg.predict(X_test)\n",
    "y_preds_avrg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1224e5a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:30.703959Z",
     "start_time": "2022-12-04T12:27:30.692624Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
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       "      <th>datetime</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2011-01-20 00:00:00</th>\n",
       "      <td>16.549484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 01:00:00</th>\n",
       "      <td>10.002456</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-20 02:00:00</th>\n",
       "      <td>6.894031</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-20 03:00:00</th>\n",
       "      <td>5.271689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 04:00:00</th>\n",
       "      <td>5.326224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>140.586401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>111.740839</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>88.484999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>70.541802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>56.368209</td>\n",
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       "  </tbody>\n",
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       "<p>6493 rows × 1 columns</p>\n",
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       "                          count\n",
       "datetime                       \n",
       "2011-01-20 00:00:00   16.549484\n",
       "2011-01-20 01:00:00   10.002456\n",
       "2011-01-20 02:00:00    6.894031\n",
       "2011-01-20 03:00:00    5.271689\n",
       "2011-01-20 04:00:00    5.326224\n",
       "...                         ...\n",
       "2012-12-31 19:00:00  140.586401\n",
       "2012-12-31 20:00:00  111.740839\n",
       "2012-12-31 21:00:00   88.484999\n",
       "2012-12-31 22:00:00   70.541802\n",
       "2012-12-31 23:00:00   56.368209\n",
       "\n",
       "[6493 rows x 1 columns]"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "application/javascript": [
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"submission = pd.DataFrame(\\n    {\\\"datetime\\\": X_test.index, \\\"count\\\": np.exp(y_preds_avrg)}\\n).set_index(\\\"datetime\\\")\\nsubmission\";\n",
       "                var nbb_formatted_code = \"submission = pd.DataFrame(\\n    {\\\"datetime\\\": X_test.index, \\\"count\\\": np.exp(y_preds_avrg)}\\n).set_index(\\\"datetime\\\")\\nsubmission\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "source": [
    "submission = pd.DataFrame(\n",
    "    {\"datetime\": X_test.index, \"count\": np.exp(y_preds_avrg)}\n",
    ").set_index(\"datetime\")\n",
    "submission"
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  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03a4aa6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-04T12:27:30.725540Z",
     "start_time": "2022-12-04T12:27:30.705023Z"
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"submission.to_csv(\\\"../../data/bike-sharing-demand/submission.csv\\\")\";\n",
       "                var nbb_formatted_code = \"submission.to_csv(\\\"../../data/bike-sharing-demand/submission.csv\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
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   "source": [
    "submission.to_csv(\"../../data/bike-sharing-demand/submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "685c2485",
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   "id": "3a921150",
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   "execution_count": null,
   "id": "20329618",
   "metadata": {
    "id": "20329618"
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