23_time_series_feature_engg.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strategies for time-series analysis, with bike-sharing-demand regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml \n",
    "\n",
    "bike_sharing = fetch_openml(\"Bike_Sharing_Demand\", version=2, as_frame=True, parser=\"pandas\")\n",
    "df = bike_sharing.frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>feel_temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  year  month  hour holiday  weekday workingday weather  temp  \\\n",
       "0  spring     0      1     0   False        6      False   clear  9.84   \n",
       "1  spring     0      1     1   False        6      False   clear  9.02   \n",
       "2  spring     0      1     2   False        6      False   clear  9.02   \n",
       "3  spring     0      1     3   False        6      False   clear  9.84   \n",
       "4  spring     0      1     4   False        6      False   clear  9.84   \n",
       "\n",
       "   feel_temp  humidity  windspeed  count  \n",
       "0     14.395      0.81        0.0     16  \n",
       "1     13.635      0.80        0.0     40  \n",
       "2     13.635      0.80        0.0     32  \n",
       "3     14.395      0.75        0.0     13  \n",
       "4     14.395      0.75        0.0      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   season      17379 non-null  category\n",
      " 1   year        17379 non-null  int64   \n",
      " 2   month       17379 non-null  int64   \n",
      " 3   hour        17379 non-null  int64   \n",
      " 4   holiday     17379 non-null  category\n",
      " 5   weekday     17379 non-null  int64   \n",
      " 6   workingday  17379 non-null  category\n",
      " 7   weather     17379 non-null  category\n",
      " 8   temp        17379 non-null  float64 \n",
      " 9   feel_temp   17379 non-null  float64 \n",
      " 10  humidity    17379 non-null  float64 \n",
      " 11  windspeed   17379 non-null  float64 \n",
      " 12  count       17379 non-null  int64   \n",
      "dtypes: category(4), float64(4), int64(5)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'year', 'month', 'hour', 'holiday', 'weekday', 'workingday',\n",
       "       'weather', 'temp', 'feel_temp', 'humidity', 'windspeed', 'count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Looking into the dataset \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "avg_week_demand = df.groupby([\"weekday\", \"hour\"])[\"count\"].mean()\n",
    "avg_week_demand.plot(ax = ax)\n",
    "\n",
    "viz = ax.set(title=\"Average hourly bike demand during the week\",\n",
    "            xticks = [i * 24 for i in range(7)],\n",
    "            xticklabels = [\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],\n",
    "            xlabel = \"Time of the week\", \n",
    "            ylabel = \"Number of bike rentals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "977"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"count\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rescaling target variable - count \n",
    "y = df[\"count\"] / df[\"count\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,4))\n",
    "y.hist(bins=30, ax=ax)\n",
    "vi = ax.set(xlabel=\"fraction of rented fleet demand\",ylabel=\"no of hours\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>feel_temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>spring</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>0.60</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>spring</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>0.60</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>spring</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>0.60</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>spring</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>spring</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  year  month  hour holiday  weekday workingday weather   temp  \\\n",
       "0      spring     0      1     0   False        6      False   clear   9.84   \n",
       "1      spring     0      1     1   False        6      False   clear   9.02   \n",
       "2      spring     0      1     2   False        6      False   clear   9.02   \n",
       "3      spring     0      1     3   False        6      False   clear   9.84   \n",
       "4      spring     0      1     4   False        6      False   clear   9.84   \n",
       "...       ...   ...    ...   ...     ...      ...        ...     ...    ...   \n",
       "17374  spring     1     12    19   False        1       True   misty  10.66   \n",
       "17375  spring     1     12    20   False        1       True   misty  10.66   \n",
       "17376  spring     1     12    21   False        1       True   clear  10.66   \n",
       "17377  spring     1     12    22   False        1       True   clear  10.66   \n",
       "17378  spring     1     12    23   False        1       True   clear  10.66   \n",
       "\n",
       "       feel_temp  humidity  windspeed  \n",
       "0         14.395      0.81     0.0000  \n",
       "1         13.635      0.80     0.0000  \n",
       "2         13.635      0.80     0.0000  \n",
       "3         14.395      0.75     0.0000  \n",
       "4         14.395      0.75     0.0000  \n",
       "...          ...       ...        ...  \n",
       "17374     12.880      0.60    11.0014  \n",
       "17375     12.880      0.60    11.0014  \n",
       "17376     12.880      0.60    11.0014  \n",
       "17377     13.635      0.56     8.9981  \n",
       "17378     13.635      0.65     8.9981  \n",
       "\n",
       "[17379 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(\"count\", axis=\"columns\")\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clear         11413\n",
       "misty          4544\n",
       "rain           1419\n",
       "heavy_rain        3\n",
       "Name: weather, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"weather\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clear    11413\n",
       "misty     4544\n",
       "rain      1422\n",
       "Name: weather, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"weather\"].replace(\"heavy_rain\", \"rain\", inplace=True)\n",
    "X[\"weather\"].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A time-sensitive cross-validation splitter is used to evaluate our demand forecasting model, gap of 2 days or 48 hours, and keeping 1000 datapoints for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit \n",
    "\n",
    "ts_cv =TimeSeriesSplit(n_splits=5, gap=48, max_train_size=10000, test_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_splits = list(ts_cv.split(X,y))\n",
    "train_0, test_0 = all_splits[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>feel_temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12379</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>22.14</td>\n",
       "      <td>25.760</td>\n",
       "      <td>0.68</td>\n",
       "      <td>27.9993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>summer</td>\n",
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       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>21.32</td>\n",
       "      <td>25.000</td>\n",
       "      <td>0.77</td>\n",
       "      <td>22.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12381</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>rain</td>\n",
       "      <td>21.32</td>\n",
       "      <td>25.000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>19.9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12382</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>rain</td>\n",
       "      <td>20.50</td>\n",
       "      <td>24.240</td>\n",
       "      <td>0.82</td>\n",
       "      <td>12.9980</td>\n",
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       "    <tr>\n",
       "      <th>12383</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "      <td>rain</td>\n",
       "      <td>20.50</td>\n",
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       "      <td>0.82</td>\n",
       "      <td>12.9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13374</th>\n",
       "      <td>fall</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>34.44</td>\n",
       "      <td>40.150</td>\n",
       "      <td>0.53</td>\n",
       "      <td>15.0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13375</th>\n",
       "      <td>fall</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>34.44</td>\n",
       "      <td>39.395</td>\n",
       "      <td>0.49</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13376</th>\n",
       "      <td>fall</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>34.44</td>\n",
       "      <td>39.395</td>\n",
       "      <td>0.49</td>\n",
       "      <td>19.0012</td>\n",
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       "    <tr>\n",
       "      <th>13377</th>\n",
       "      <td>fall</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>36.08</td>\n",
       "      <td>40.910</td>\n",
       "      <td>0.42</td>\n",
       "      <td>7.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13378</th>\n",
       "      <td>fall</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>clear</td>\n",
       "      <td>35.26</td>\n",
       "      <td>40.150</td>\n",
       "      <td>0.47</td>\n",
       "      <td>16.9979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  year  month  hour holiday  weekday workingday weather   temp  \\\n",
       "12379  summer     1      6     0   False        2       True   clear  22.14   \n",
       "12380  summer     1      6     1   False        2       True   misty  21.32   \n",
       "12381  summer     1      6     2   False        2       True    rain  21.32   \n",
       "12382  summer     1      6     3   False        2       True    rain  20.50   \n",
       "12383  summer     1      6     4   False        2       True    rain  20.50   \n",
       "...       ...   ...    ...   ...     ...      ...        ...     ...    ...   \n",
       "13374    fall     1      7    11   False        1       True   clear  34.44   \n",
       "13375    fall     1      7    12   False        1       True   clear  34.44   \n",
       "13376    fall     1      7    13   False        1       True   clear  34.44   \n",
       "13377    fall     1      7    14   False        1       True   clear  36.08   \n",
       "13378    fall     1      7    15   False        1       True   clear  35.26   \n",
       "\n",
       "       feel_temp  humidity  windspeed  \n",
       "12379     25.760      0.68    27.9993  \n",
       "12380     25.000      0.77    22.0028  \n",
       "12381     25.000      0.72    19.9995  \n",
       "12382     24.240      0.82    12.9980  \n",
       "12383     24.240      0.82    12.9980  \n",
       "...          ...       ...        ...  \n",
       "13374     40.150      0.53    15.0013  \n",
       "13375     39.395      0.49     8.9981  \n",
       "13376     39.395      0.49    19.0012  \n",
       "13377     40.910      0.42     7.0015  \n",
       "13378     40.150      0.47    16.9979  \n",
       "\n",
       "[1000 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[test_0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>feel_temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>summer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>25.42</td>\n",
       "      <td>31.060</td>\n",
       "      <td>0.50</td>\n",
       "      <td>6.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>summer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>24.60</td>\n",
       "      <td>31.060</td>\n",
       "      <td>0.53</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>summer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>23.78</td>\n",
       "      <td>27.275</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>summer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>22.96</td>\n",
       "      <td>26.515</td>\n",
       "      <td>0.64</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>summer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>misty</td>\n",
       "      <td>22.14</td>\n",
       "      <td>25.760</td>\n",
       "      <td>0.68</td>\n",
       "      <td>8.9981</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12326</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>26.24</td>\n",
       "      <td>31.060</td>\n",
       "      <td>0.36</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12327</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>25.42</td>\n",
       "      <td>31.060</td>\n",
       "      <td>0.35</td>\n",
       "      <td>19.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12328</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>24.60</td>\n",
       "      <td>31.060</td>\n",
       "      <td>0.40</td>\n",
       "      <td>7.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12329</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>23.78</td>\n",
       "      <td>27.275</td>\n",
       "      <td>0.46</td>\n",
       "      <td>8.9981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12330</th>\n",
       "      <td>summer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>clear</td>\n",
       "      <td>22.96</td>\n",
       "      <td>26.515</td>\n",
       "      <td>0.52</td>\n",
       "      <td>7.0015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  year  month  hour holiday  weekday workingday weather   temp  \\\n",
       "2331   summer     0      4     1   False        2       True   misty  25.42   \n",
       "2332   summer     0      4     2   False        2       True   misty  24.60   \n",
       "2333   summer     0      4     3   False        2       True   misty  23.78   \n",
       "2334   summer     0      4     4   False        2       True   misty  22.96   \n",
       "2335   summer     0      4     5   False        2       True   misty  22.14   \n",
       "...       ...   ...    ...   ...     ...      ...        ...     ...    ...   \n",
       "12326  summer     1      6    19   False        6      False   clear  26.24   \n",
       "12327  summer     1      6    20   False        6      False   clear  25.42   \n",
       "12328  summer     1      6    21   False        6      False   clear  24.60   \n",
       "12329  summer     1      6    22   False        6      False   clear  23.78   \n",
       "12330  summer     1      6    23   False        6      False   clear  22.96   \n",
       "\n",
       "       feel_temp  humidity  windspeed  \n",
       "2331      31.060      0.50     6.0032  \n",
       "2332      31.060      0.53     8.9981  \n",
       "2333      27.275      0.56     8.9981  \n",
       "2334      26.515      0.64     8.9981  \n",
       "2335      25.760      0.68     8.9981  \n",
       "...          ...       ...        ...  \n",
       "12326     31.060      0.36    11.0014  \n",
       "12327     31.060      0.35    19.0012  \n",
       "12328     31.060      0.40     7.0015  \n",
       "12329     27.275      0.46     8.9981  \n",
       "12330     26.515      0.52     7.0015  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[train_0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Gradient Boosting Regression` with decision trees is often flexible enough to effficiently handle heteorogenous tabular data with a mix of categorical and numerical features as long as the number of samples is large enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline \n",
    "from sklearn.preprocessing import OrdinalEncoder \n",
    "from sklearn.compose import ColumnTransformer \n",
    "from sklearn.ensemble import HistGradientBoostingRegressor \n",
    "from sklearn.model_selection import cross_validate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = [\"weather\", \"season\",\"holiday\",\"workingday\"]\n",
    "categories = [[\"clear\",\"misty\",\"rain\"],\n",
    "            [\"spring\",\"summer\",\"fall\",\"winter\"],\n",
    "            [\"False\",\"True\"],\n",
    "            [\"False\",\"True\"]]\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder(categories=categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt_pipeline = make_pipeline(ColumnTransformer(transformers=[(\"categorical\", ordinal_encoder, categorical_columns)],remainder=\"passthrough\",verbose_feature_names_out=False), HistGradientBoostingRegressor(categorical_features=categorical_columns)).set_output(transform=\"pandas\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating our gradient boosting pipeline model with the mean absolute error "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, X, y, cv):\n",
    "    cv_results = cross_validate(model, X, y, cv = cv, \n",
    "                                scoring=[\"neg_mean_absolute_error\", \"neg_root_mean_squared_error\"])\n",
    "    mae = -cv_results[\"test_neg_mean_absolute_error\"]\n",
    "    rmse = -cv_results[\"test_neg_root_mean_squared_error\"]\n",
    "    print(\n",
    "        f\"Mean Absolute Error:     {mae.mean():.3f} +/- {mae.std():.3f}\\n\"\n",
    "        f\"Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:     0.044 +/- 0.003\n",
      "Root Mean Squared Error: 0.068 +/- 0.005\n"
     ]
    }
   ],
   "source": [
    "evaluate(gbrt_pipeline, X, y, cv = ts_cv)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Naive Linear Regression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder \n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from sklearn.linear_model import RidgeCV \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:     0.142 +/- 0.014\n",
      "Root Mean Squared Error: 0.184 +/- 0.020\n"
     ]
    }
   ],
   "source": [
    "one_hot_encoder = OneHotEncoder(handle_unknown=\"ignore\",sparse_output=False)\n",
    "alphas = np.logspace(-6, 6, 25)\n",
    "naive_linear_pipeline = make_pipeline(ColumnTransformer(transformers=[(\"categorical\", one_hot_encoder, categorical_columns)], remainder=MinMaxScaler()), RidgeCV(alphas=alphas))\n",
    "evaluate(naive_linear_pipeline, X, y, cv = ts_cv)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time steps as categories "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Since, time features are encoded in a discrete manner using 24 unique values in the \"hours\" feature, we could decide to treat those as categorical variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:     0.099 +/- 0.011\n",
      "Root Mean Squared Error: 0.131 +/- 0.011\n"
     ]
    }
   ],
   "source": [
    "one_hot_linear_pipeline = make_pipeline(\n",
    "    ColumnTransformer(\n",
    "    transformers=[(\"categorical\",one_hot_encoder, categorical_columns), (\"one_hot_time\",one_hot_encoder, [\"hour\",\"weekday\",\"month\"])], remainder=MinMaxScaler()\n",
    "    ), RidgeCV(alphas=alphas)\n",
    ")\n",
    "evaluate(one_hot_linear_pipeline, X, y, cv=ts_cv)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trigonometric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sin_transformer(period):\n",
    "    return FunctionTransformer(lambda x: np.sin(x / period * 2 * np.pi))\n",
    "\n",
    "def cos_transformer(period):\n",
    "    return FunctionTransformer(lambda x: np.cos(x / period * 2 * np.pi)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "hour_df = pd.DataFrame(np.arange(26).reshape(-1,1), columns=[\"hour\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hour_df[\"hour_sin\"] = sin_transformer(24).fit_transform(hour_df)[\"hour\"]\n",
    "hour_df[\"hour_cos\"] = cos_transformer(24).fit_transform(hour_df)[\"hour\"]\n",
    "hour_df.plot(x = \"hour\")\n",
    "viz = plt.title(\"Trigonometric encoding for the 'hour' feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RlYZqXvmtAUbkzA8PyvZw903u3hjM3gIc29ltc/Yx3d3r3L1u8ODBRQm8EGP3O5rsd5i9JWLVHLH/cWWPR0S6r+6EQ/BM6/d1dXWCkz44NoSIii/qHTbCTF5zgTFmNtrMksBUsreC3sPMhubMXggsDR4/BpxjZv3NrD9wTlAWOQOqD+DkwVNIWPWeZoakVfP+vidyUK8jQo5ORLpiyNB+fHzaJKprEljwmV1Tm+CkU8Yy4ZhRocZWTFFOXqH1NnT3lJldTTbpxIHb3H2xmV0HzHP3GcC/mNmFQArYDFwebLvZzL5DNgECXOfum8v+T3TS2Qd8grH7HcOLW54k5Skm9DuZQ/tMxCy6v6EQkfZ96spTOfb4g5n50Es0Nac49cxxHHfSoXpfl4l5nnbbnqqurs7nzZsXdhgiIkVnZvPdva5Y+9v/8CF+8vSPF7TNw6f+pKgxtEcjbIiISF5RHmFDyUtERFpz3YxSREQqjO6kLCIiFUnJS0REKopuiSIiIhXJlbxERKTSqLehiIhUFFdvQxERqURqNhQRkQqjDhsiIlKBVPMSEZGKoh8pi4hI5fFsp42oUvISEZG81FVeREQqiqNrXiIiUnGi3dswFnYAIiIihVLNS0RE8lKHDRERqTi65iUiIhXFXclLREQqUJQ7bCh5FcDdeWX7UhZsfZHqWDUnDfwAQ2sPDDssEakw69e/y2MzF7F50w6OPXY0J514KPF49PrP6ZpXG8xsMvBjIA7c4u43tFj+FeCzQArYAFzh7quCZWlgUbDqm+5+YSljdXemr/wFL21bSGOmkRgxZq57jE+O/BQnD/5gKQ8tIj3I3Hmv861v30cm4zQ3p/nr4y9z8Oj3cdMPLiWZjFZ9IsrNhqGlejOLAz8DzgPGAZea2bgWq70I1Ln7UcC9wPdzltW7+8RgKmniAli0beGexAWQIUOzN/OHN3/HztTOUh9eRHqAdDrD9d+bQWNjiubmNAD19c0sX7GOhx5eGHJ0e3MM98Kmcgqznno8sNzdV7p7E3AXMCV3BXd/0t13BbNzgOFljnGP5zc/vydx5YpbnKXvLg4hIhGpNMuXryOVSrcqb2xMMXPWyyFE1D4vcCqnMJPXMOCtnPnVQVlbrgQeyZmvMbN5ZjbHzC4qQXx7ScYSWN5xvoy4RauqLyLRVJWIk8nk/5hPJCL2ORL0NlTNqxvM7DKgDvhBTvFB7l4HfAL4v2Z2SBvbXhUkuXkbNmzocgyTBp1CIpbIs8R5//5Hdnm/IrLvOHj0YPbfv1er8pqaBBdecHQIEXWgBFUvM7vNzNab2cs5Zd82szVmtiCYzu9oP2EmrzXAiJz54UHZXszsLOCbwIXuvqfdzt3XBH9XArOBvM+8u0939zp3rxs8eHCXgz2kz6FMHnI+CUuQjCWpjtVQHavmi4f+C8lYssv7FZF9h5lx/Xc+St++tfSqTVKdrKK6uopTTzmcM04/IuzwWilRzet2YHKe8ptz+jE83NFOwqynzgXGmNlosklrKtla1B5mdjTwK2Cyu6/PKe8P7HL3RjMbBExi784cJXHhsIuYNOgUFr+7iOpYNRP6TaQmXlvqw4pID3Lw6Pdxz51fZM5zK9i6dRdHHTWCUQcNCjusvErRVd7dnzazUd3dT2jJy91TZnY18BjZrvK3uftiM7sOmOfuM8g2E/YB/mhm8F6X+COAX5lZhmzt8QZ3X1KOuAdWD+SDg08rx6FEpIdKJqv44CmHhR1Gu0K4JcrVZvZpYB7wVXff0t7KoV4hDKqGD7co+1bO47Pa2O4fwPjSRicisg9zoPDkNcjM5uXMT3f36Z3Y7hfAd4Kjfge4CbiivQ0i1r1FRESiogvNhhuDjnQFHsfX7X5sZr8G/tLRNhXR21BEREJQph96mdnQnNkPAx3+6E01LxERKRszuxM4jWwT42rgWuA0M5tINgW+AXyuo/0oeYmISB6l+eGxu1+ap/jWQvej5CUiIvlV+qjyZlYHnAIcCNSTbY+c2VFXRhERqVARvxllux02zOwzZvYC8A2gFngVWA+cDDxuZneY2cjShykiImUX4ZF5O6p59QImuXt9voXBBbYxwJtFjktEREJXoTUvd/8Z0GRmX25j+QJ3n1WSyEREJFwRrnl1+Dsvd0/TYsxBERHZB0Q4eXW2t+EzZvZT4G5gz22D3f2FkkQlIiLh6trwUGXT2eQ1Mfh7XU6ZA2cUNRoREYmMUowqXyydSl7ufnqpAxERkYip9ORlZt/KV+7u1+UrFxGRHqAHNBvuzHlcA1wALC1+OCIiEhVW6TUvd78pd97Mfkj2JpIiItIThdCDsBBdHduwFzC8mIGIiEiUWOU3G5rZIt7LwXFgMHv3PBQRkZ6mB9S8Lsh5nALWuXuqBPGIiEhURDh5depOyu6+CugHfIjsXS7HlTAmERGRdnUqeZnZvwJ/AN4XTH8wsy+VMjAREQlZDxge6krgBHffCWBmNwLPAj8pVWAiIhKiHjI8lAHpnPk0RRgr38wmAz8m2wnkFne/ocXyauC3wLHAJuDj7v5GsOwbZJNqGvgXdy961/1Xt7/BU+vnkvEMpww+lnF9D8Esuk+miFSuZSvX8ejsxTQ2pjj1pLHUTTgo9M+biv+dF/Ab4DkzeyCYvwi4tTsHNrM48DPgbGA1MNfMZrj7kpzVrgS2uPuhZjYVuBH4uJmNA6YC7yd7d+fHzWxsMAJ+Ufz+jT8z4+0naco0A84T65/jzCEn8rlDLinWIUREALhnxjym//5vNDenybjz2FNLmHTcIVz7lQvCTWARTl6d7bDxI+AKYHMwfcbd/283j308sNzdV7p7E3AXMKXFOlOAO4LH9wJnWvaZnALc5e6N7v46sDzYX1GsqV/Hg28/QWOmCcdxoDHTxKx1z7Jix1vFOoyICJu37uRXv3uaxqYUmWAk3IaGZv4xdwXzFq4KObro6lTyCiwgm0D+BGwys5HdPPYwIDcTrA7K8q4TdM3fBgzs5LZdNn/zEjzPcMpNmRTPb3qpWIcREeH5F18nHm/9UVzf0Mzsf7waQkTvMS9sKqfO/kj5S8C1wDreu97lwFGlC604zOwq4CqAkSM7l28TsapsVb3FkxG3GNXxZLFDFJF9WDJRlbdpMGZGTXUihIhyRLjDRmdrXv8KHObu73f3o9x9vLt3N3GtAUbkzA8PyvKuY2ZVwP5kO250ZlsA3H26u9e5e93gwYM7FdhJAyfmLY9ZjEmDjunUPkREOuPEYw/O29KTSMQ574wjQ4goUGg3+TLXvDqbvN4i22RXTHOBMWY22sySZDtgzGixzgxgWvD4YuAJzz7LM4CpZlZtZqOBMcDzxQqsX3I/vjz20yRjCWrj1dTGqknGEnz+kI8zpGZgsQ4jIkKv2iT//Y2LqK1J0Ks2SW1NgkQizj9fdgqHjn5fuMFFOHm122xoZl8JHq4EZpvZQ0Dj7uVBR44ucfeUmV1NdnT6OHCbuy82s+uAee4+g2yPxt+Z2XKyHUWmBtsuNrN7gCVkh6v6YjF7GgJ8YNDRTOh3OPO3LCbjGY7pP46+iT7FPISICAB1E0bxp9/8L+bMX0lTc4rjJo5iYP/wP28quav8fsHfN4MpGUxF4e4PAw+3KPtWzuMG4GNtbHs9cH2xYsmnd1UtHxxcV8pDiIgA2RrYGScfHnYYe6vU5OXu/1WuQEREJGIinLzaveZlZr82s7xXDM2st5ldYWafLE1oIiISlkK7yUetq/zPgG+Z2XjgZWADUEO2g0Rf4DayA/aKiEhPE+Gu8h01Gy4ALjGzPkAdMBSoB5a6e7i/nhMRkdKKcLNhp36k7O47gNkAZtafvX9jJSIiPVCUext29n5es82sr5kNAF4Afm1mN5c2NBERkfw6+yPl/d39XeAjwG/d/QTgzNKFJSIioYvwj5Q7m7yqzGwocAnwlxLGIyIiURDx3oadTV7XkR0JY4W7zzWzg4FlpQtLRERCF+GaV2c7bPwR+GPO/Ergo6UKSkREIqAHdNgYbmYPmNn6YLrPzIaXOjgREQlPT2g2/A3ZkdwPDKY/B2UiIiJl19nkNdjdf+PuqWC6HejczbFERKQyRfiaV2eT1yYzu8zM4sF0GdmbQoqISE/UQ3obXkG2m/w7wFqyN4a8vEQxiYhIFES45tWp3oZku8pPc/ctAMFIGz8km9RERKQninBvw84mr6N2Jy4Ad99sZkeXKCYREQmZ0QPGNgRiwYC8wJ6aV2cTn4iIVKIe0Gx4E/Csme3+ofLHgOtLE5KIiISuRJ0wzOw24AJgvbsfGZQNAO4GRgFvAJfktvbl06mal7v/luygvOuC6SPu/ruuBi8iIhWgNDWv24HJLcquAWa5+xhgVjDfrk43/bn7EmBJp8MTEZHKVoKal7s/bWajWhRPAU4LHt9B9v6RX29vP7puJSIieZWxw8YQd18bPH4HGNLRBp3tsFFUZjbAzGaa2bLgb/8860w0s2fNbLGZvWRmH89ZdruZvW5mC4JpYln/ARERyWeQmc3Lma4qdAfu3qlGyLBqXrvbN28ws2uC+ZZVxF3Ap919mZkdCMw3s8fcfWuw/H+7+73lCxkynuEfG5cye/3L9Ion+acDj+OwvhqfWEQK8+Y7W3jwiZfYsGUHJ00YzZknjCWZiGBDWOE1r43uXteFI60zs6Huvja4d+T6jjYI62x12L7p7q/lPH7bzNaTHU9xa1kibCHtGa5ZeDsLtqykPt1EDOOht+fx+UPP42MjTw4jJBGpQE/PX85//vQh0ukMqXSGv81fwZ0Pz2P6tZdSU50IO7z3lLf7+wxgGnBD8PfBjjYIpdmQAts3zex4IAmsyCm+PmhOvNnMqksU5x5/37BkT+ICyOA0Zpr5xfKH2dq0s9SHF5EeIJVKc90vH6WxKUUqnQGgvrGZVWu3cP+shSFH11opxjY0szuBZ4HDzGy1mV1JNmmdbWbLgLOC+XaVLHmZ2eNm9nKeaUrueh21bwZVyN8Bn3H3TFD8DeBw4DhgAO30SjGzq3a3v27YsKHL/8/s9Yv2JK5cVRZn/mbdVFpEOvbqqvVkvPXHXWNTipn/eCWEiDpQgq7y7n6puw9194S7D3f3W919k7uf6e5j3P0sd9/c0X5K1mzo7me1tczMOtW+aWZ9gYeAb7r7nJx97661NZrZb4B/byeO6cB0gLq6ui5XgnvFqzEMz/MM1cSTXd2tiOxDaqsTZDKZvMt61Ubvc6QnDA9VbLvbN6GN9k0zSwIPAL9t2TEjSHiYmQEXAS+XMliAC4YdRzLWOtebGccNHFvqw4tIDzB62EAG9++D2d7lNdUJPnrWxFBialeEh4cKK3nlbd80szozuyVY5xLgg8DlebrE/8HMFgGLgEHAd0sd8OF9R/DPh5xLMlZFr3g1vePV9K6q4QcTr8ib1EREWjIzfvjVDzNw/970qknSqyZBMhHnQ6ceyenHjwk7vL0VmrjKnLzM87S/9lR1dXU+b968bu1jS9MO5m1eRm08yXEDxlIdj1DvIBGpCKl0hvmL32TL9nomjB3G0MF9u71PM5vfxW7qefUaMsIP/eRXCtpm0c1fKWoM7VGVoUD9k304+wDdDUZEuq4qHuOEo0aFHUbHIly3UfISEZG8otxhQ8lLRETyU/ISEZGKo+QlIiIVpUQ3oywWJS8REclPyUtERCqNal4iIlJ5lLxERKTSRLnmFdbwUCIiIl2mmpeIiLQWwniFhVDyEhGR/JS8RESkkhjRvual5CUiIvkpeYmISKWxCN8yS8lLRERaU4cNERGpRLrmJSIilUfJS0REKo1qXiIiUnmUvEREpKLofl4iIlKRlLz2ZmYDgLuBUcAbwCXuviXPemlgUTD7prtfGJSPBu4CBgLzgU+5e1PpI++a5kyau994nvtWvUBzJs0/DR/PtEMm0asqGXZoItJFqXSGe2cv5P6nF9GczjD5+MP41DnH0qumZ7yvoz7CRlijyl8DzHL3McCsYD6fenefGEwX5pTfCNzs7ocCW4ArSxtu9/zb83fy46WzWL59Pat2buLWZc8w7ZlbSWXSYYcmIl30tV/+hZ/c/wwr3t7Em+u2cPujc7nixrtpTvWg97V7YVMZhZW8pgB3BI/vAC7q7IZmZsAZwL1d2b7cFm9dw9xNb9CQbt5T1phJ8ebOzcx+59UQIxORrnrlzfU8t2QVDU2pPWVNzWnWbNjGky8uDzGy4jIvbCqnsJLXEHdfGzx+BxjSxno1ZjbPzOaY2UVB2UBgq7vvftWsBoaVLtTuWbD5LdJ5vpHsSjcxf/OqECISke5atHItnu993djMi8vWhBBRCXgXpjIq2TUvM3scOCDPom/mzri7m7WZsw9y9zVmdjDwhJktArYVGMdVwFUAI0eOLGTTonhfTV8SsRhNmb3Lq2NVDK3dv+zxiEj3De7Xh6p4nMbmvZsIqxNxDhjQN6So9i0lS17uflZby8xsnZkNdfe1ZjYUWN/GPtYEf1ea2WzgaOA+oJ+ZVQW1r+FAm1913H06MB2grq6u7JcfTx0ylupYgl004zlfTapiMT40fEK5wxGRIph05ChqklXsamza61JPPBbjgg8cEV5gRWaZjtcJS1jNhjOAacHjacCDLVcws/5mVh08HgRMApZ4tq7+JHBxe9tHRTJexe0nX8Gh+w2mOlZFTTzBsNp+TD9pGv2re4cdnoh0QaIqzi1fu4QxwwZRnYhTk6xi6MC+/OzLH2Fg3x70vt4Xmw07cANwj5ldCawCLgEwszrg8+7+WeAI4FdmliGbZG9w9yXB9l8H7jKz7wIvAreW+x8oxOg+g7j/9C+ydtdWUp5heK/+ZPudiEilGjmkP3de+yne2byd5lSa4YP373Hv6yh3lQ8lebn7JuDMPOXzgM8Gj/8BjG9j+5XA8aWMsRSG9uoXdggiUmQHDNgv7BBKwyl79/dCaIQNERHJSzUvERGpPEpeIiJSSaI+PJSSl4iItBbCkE+FUPISEZG8VPMSEZHKo+QlIiKVRjUvERGpLA5kopu9lLxERCS/6OYuJS8REclPzYYiIlJ51FVeREQqTSlqXmb2BrAdSAMpd6/ryn6UvEREpNxOd/eN3dmBkpeIiLQWwj26ChHWzShFRCTCsmMbekETMMjM5uVMV+XZtQN/NbP5bSzvFNW8REQkv0zBW2zsxDWsk919jZm9D5hpZq+4+9OFHkg1rx7g3aYG5m9czds7t4Udikgkbdq+kwWvv82m7TvDDqWidKHm1SF3XxP8XQ88QBdvLKyaVwVzd25aNJvbXn2OZLyKpnSaE943kp984CP0SVSHHZ5I6JrTaf7rrsd57MVXSVbFaUqlOffow/j21LOpiuu7e7tKcM3LzHoDMXffHjw+B7iuK/vSs1fBHnhjEbe/NpfGTJrtzY00ZlLMWb+Kb8x9KOzQRCLhl4/OYeaC12hKpdnR0ERTKs3MBa/xy0efDTu0CuDv3Rals1PHhgDPmNlC4HngIXd/tCvRKXlVsF+/Mof6dPNeZU2ZNI+veY3tzY0hRSUSHXc/s5CG5tReZQ3NKe56ZmFIEVUW88Kmjrj7SnefEEzvd/fruxqbklcF29K0K295zIwdSl4i7GjI/z7Y2dBU5kgqVPFrXkWj5FXBPjBkNDGsVfl+iWqG1O4XQkQi0fL+EQfkLR83YkiZI6lADpYpbCqnUJKXmQ0ws5lmtiz42z/POqeb2YKcqcHMLgqW3W5mr+csm1ju/yEKvnzkqfRJVJOw7NNoQE28iuuOPY+YtU5qIvuaaz56OrXJKuKx7PshHjNqk1V84+LTQ46sQkS45hVWb8NrgFnufoOZXRPMfz13BXd/EpgI2WQHLAf+mrPK/3b3e8sTbjSN6NOPhyf/M7e8Moe5G95kRJ/+fO7wkzhq4IFhhyYSCeMPOoA7v/pJfjNrLq+u2cBhwwbzmTOPY/SQAWGHVhkiPMJGWMlrCnBa8PgOYDYtklcLFwOPuHv+izz7sKG9+vKfx5wTdhgikTV6yACu+8S5YYdRkTr7260whHXNa4i7rw0ev0O2+2R7pgJ3tii73sxeMrObzUw/ahIRKbZ9sdnQzB4H8l0t/WbujLu7WdudLM1sKDAeeCyn+Btkk14SmE621pb3h27B2FlXAYwcObKA/0BEZB/mdGV4qLIpWfJy97PaWmZm68xsqLuvDZLT+nZ2dQnwgLvv+UFTTq2t0cx+A/x7O3FMJ5vgqKuri24dWEQkQozOD/kUhrCaDWcA04LH04AH21n3Ulo0GQYJDzMz4CLg5eKHKCKyj4tws2FYyesG4GwzWwacFcxjZnVmdsvulcxsFDACeKrF9n8ws0XAImAQ8N1yBC0iItEQSm9Dd98EnJmnfB7w2Zz5N4BhedY7o5TxiYgIZa9NFUKjyouISGv7aocNERGpbFHusKHkJSIi+Sl5iYhIZSl/D8JCKHmJiEhrjpKX9DwvbnibP61YTHMmwwWjD+ekA0ZiGsleOrB4zToenL+ExlSKc8eP5aRD9bqJNHXYkJ7k5hefYfrLz9GYTuM4f1q5mCkHj+N7H5gcdmgSYbfOnsvPn5hDUypNxp2HFr7KGUccwo0fn6wEFlFR7rChm1FKQd7cvpVfvvwc9ekUGRwHdqWa+dPKJbywfk3Y4UlEvbNtOz+d9SwNzSkywQdifVMzTyxdwfMrV4ccnbRJI2xIT/Hk6hXku8lPQ6qZmW8tL39AUhH+/toq4nlqVw1NzTy+eFkIEUmHHMh4YVMZqdlQClITryJmMSC9V3lVLEavqkQ4QUnkVSeq8jYNxmJGr6ReN9EU7d6GqnlJQc49aCye5wUdtxgXjj4ihIikEpx2+Oi8N+VNxON86Gi9biJLzYbSU/SrruWnp02htipB70SS3lUJquNVXHfi2RzUt3/Y4UlE9amp5sef/BC1yQS9qxP0Siaororz7+edwqFDBoUdnrQlwslLzYZSsLNGHMrzl3yRp9asJJXJcOqwg+lfUxt2WBJxk8YexFP/cRXPvPYGTak0k8YcxIA+vcIOS9qy+5pXRCl5SZfsl6zmAjUTSoF6Vyc5d/zYsMOQTnHw6P7QS8lLRETyU4cNERGR4lHNS0REWtM1L5HSWrFlMxt27eSIQYPZv7om7HAiqb65mcVr19EnWc1hQwZpOCbpnAg3Gyp5ScXaVL+LKx9+gFc2bSARi9GUznD1sSfwpbqTwg4tUh5YuJj/euRJ4jEjnXGG7Neb6Zd+mIMG9As7NIm6CCcvXfOSivWFx2bw8oZ1NKRSbG9qojGd4ucvPMdjKzXc0G6L167j2w8/QX1zMzsam6hvbmbV5q1c/vv79owxKJJfgb/x0o+URTq2dsd2Fq5bSyqzd1fe+lSKWxbOCymq6PmfeS/RlN57KC8HttXX8+Lqt8MJSiqDA5lMYVMZqdlQKtK2xgaqYnEaW3wwA2yurw8hor01pdO4O9VV4b7FNu7YmbeGZRhbdzWEEJFUlAjXzkOpeZnZx8xssZllzKyunfUmm9mrZrbczK7JKR9tZs8F5XebWbI8kUtUHNxvALE8nQ4SsRinHzQ6hIiyNu7cyVUP/InxP/4J43/8E6bedTevb9kSWjynjRlNbaJ1Am3OpDl6+NAQIpKKombDVl4GPgI83dYKZhYHfgacB4wDLjWzccHiG4Gb3f1QYAtwZWnDlahJxuNcd8qZ1FZVsTuFVcfj9K+p5fNHHx9KTOlMhkvuvJunVr5OKpMh7c681Wu4+A93sr2xMZSYPjzh/Qzvtz81OTXA2kQVn5t0PAN6a2gmaU+Bt0PZF26J4u5LgY666x4PLHf3lcG6dwFTzGwpcAbwiWC9O4BvA78oVbwSTR8+bBwH7d+PWxfO5+0d73LqyNFMG390aOMsPrNqFRt37iSV8w3UgcZUigeXLOWyoyeWPaaaRBX3XHEp97zwEo8uXUa/2ho+edxETjlkVNljkQrj4BoeqkuGAW/lzK8GTgAGAlvdPZVTPqytnZjZVcBVACNHjixNpBKaYw44kGMOODDsMABYtWVrqw4kkO1EsmLz5hAiyuqVTHD5icdy+YnHhhaDVKh98UfKZvY4cECeRd909wdLddyW3H06MB2grq4uus+EVLzDBg8iHotBi04kvRIJjhwyJKSoRLohwh02Spa83P2sbu5iDTAiZ354ULYJ6GdmVUHta3e5SKiOHz6cMQMHsnTDhj3d06tiMfrV1PBPh2kkdakw7mXv/l6IKP/Oay4wJuhZmASmAjM8exvfJ4GLg/WmAWWryYm0xcz43SUX84kJR7F/TQ29k0k+dPhhPHDZJ6hJ6Fb3UoEi3NswlGteZvZh4CfAYOAhM1vg7uea2YHALe5+vrunzOxq4DEgDtzm7ouDXXwduMvMvgu8CNwawr8h0krvZJL/PON0/vOM08MORaTbPMI1r7B6Gz4APJCn/G3g/Jz5h4GH86y3kmxvRBERKYny16YKEeXehiIiEhbdEkVERCpShH/nFeUOGyIiInkpeYmISCsOeMYLmjqjrTFrC6VmQxERac296M2GOWPWnk12dKS5ZjbD3ZcUui8lLxERyauztakC5B2zFlDyEhGRIil+h422xqwt2D6VvObPn7/RzFZ1YdNBwMZix9MNUYonSrFAtOKJUiwQrXiiFAtEK56uxnJQMYPYzpbHHvd7BxW4WY2Z5d7KfHowvmzR7VPJy90Hd2U7M5vn7m3eNLPcohRPlGKBaMUTpVggWvFEKRaIVjxRicXdJ5dgt22NWVsw9TYUEZFyyTtmbVd2tE/VvEREJDwdjFlbECWvzilJm203RCmeKMUC0YonSrFAtOKJUiwQrXiiFEvRtTVmbaHMIzzwooiISD665iUiIhVHyStgZh8zs8VmljGzNnv6tDW0SXAB8rmg/O7gYmRXYxlgZjPNbFnwt3+edU43swU5U4OZXRQsu93MXs9ZNrGrsXQ2nmC9dM4xZ+SUl/vcTDSzZ4Pn8yUz+3jOsqKcm46GuDGz6uB/XR7876Nyln0jKH/VzM7tyvELjOUrZrYkOBezzOygnGV5n7MSx3O5mW3IOe5nc5ZNC57bZWY2rQyx3JwTx2tmtjVnWVHPjZndZmbrzezlNpabmf2/INaXzOyYnGVFPS89grtryjadHgEcBswG6tpYJw6sAA4GksBCYFyw7B5gavD4l8AXuhHL94FrgsfXADd2sP4AYDPQK5i/Hbi4iOemU/EAO9ooL+u5AcYCY4LHBwJrgX7FOjftvQ5y1vlfwC+Dx1OBu4PH44L1q4HRwX7iJY7l9JzXxhd2x9Lec1bieC4HftrG63hl8Ld/8Lh/KWNpsf6XyHYgKNW5+SBwDPByG8vPBx4BDDgReK4U56WnTKp5Bdx9qbu/2sFqe4Y2cfcm4C5gipkZcAZwb7DeHcBF3QhnSrCPzu7rYuARd9/VjWMWM549wjg37v6auy8LHr8NrCd71+5iyfs6aCfOe4Ezg3MxBbjL3Rvd/XVgOd27sWqHsbj7kzmvjTlkf1tTKp05N205F5jp7pvdfQswE+jOb40KjeVS4M5uHK9d7v402S+ZbZkC/Naz5gD9zGwoxT8vPYKSV2HyDW0yDBgIbHX3VIvyrhri7muDx+8AQzpYfyqt33TXB00PN5tZdTdiKSSeGjObZ2ZzdjdhEvK5MbPjyX7rXpFT3N1z09brIO86wf++jey56My2xY4l15Vkv93vlu85647OxvPR4Dm418x2/2g1tHMTNKWOBp7IKS72uelIW/EW+7z0CPtUV3kzexw4IM+ib7r7g1GJJXfG3d3M2uwSGnwzG0/2dxO7fYPsB3uSbLfbrwPXlSGeg9x9jZkdDDxhZovIfmgXpMjn5nfANPc9g7QVfG56CjO7DKgDTs0pbvWcufuK/Hsomj8Dd7p7o5l9jmwN9YwSH7MjU4F73T2dUxbGuZFO2qeSl7uf1c1dtDW0ySayVfyq4Ft2h0OetBeLma0zs6Huvjb4AF7fzq4uAR5w9+acfe+umTSa2W+Af28vlmLF4+5rgr8rzWw2cDRwHyGcGzPrCzxE9ovJnJx9F3xu8ujMEDe711ltZlXA/mRfJ0UbHqeAWDCzs8gm/1PdvXF3eRvPWXc+oDuMx9035czeQvY65u5tT2ux7exSxpJjKvDFFnEW+9x0pK14i31eegQ1GxYm79Am7u7Ak2SvPQFMA7pTk5sR7KMz+2rVTh98qO++3nQRkLd3UzHjMbP+u5vgzGwQMAlYEsa5CZ6bB8heP7i3xbJinJvODHGTG+fFwBPBuZgBTLVsb8TRwBjg+S7E0OlYzOxo4FfAhe6+Pqc873PWjVg6G8/QnNkLgaXB48eAc4K4+gPnsHeLQtFjCeI5nGxHiGdzykpxbjoyA/h00OvwRGBb8GWr2OelZwi7x0hUJuDDZNuSG4F1wGNB+YHAwznrnQ+8RvYb2Ddzyg8m+yG0HPgjUN2NWAYCs4BlwOPAgKC8DrglZ71RZL+VxVps/wSwiOwH8++BPt08Nx3GA3wgOObC4O+VYZ0b4DKgGViQM00s5rnJ9zog2/x4YfC4Jvhflwf/+8E5234z2O5V4LwivHY7iuXx4DW9+1zM6Og5K3E83wMWB8d9Ejg8Z9srgnO2HPhMqWMJ5r8N3NBiu6KfG7JfMtcGr83VZK8/fh74fLDcyN6ocUVwzLqcbYt6XnrCpBE2RESk4qjZUEREKo6Sl4iIVBwlLxERqThKXiIiUnGUvEREpOIoeYmISMVR8hLJw8xuMbNxnVjv38zs08Hj2dbO7XS6Gc9dZjamFPsWqURKXiJ5uPtn3b3dERWCYZ+uAP6nlLGYWRz4BfC1Uh5HpJIoeck+z8x6m9lDZrbQzF42s4/n1qLMbIeZXR8sn2Nmu0eyPwN4wd8bMR/gY2b2vGVvbHhKsH2Nmf3GzBaZ2YtmdnpQfrmZ/TQnjr+Y2Wk5x7zJzBYCJwF/A84KEqbIPk/JSyR7b6S33X2Cux8JPNpieW9gjrtPAJ4G/jkonwTMb7FulbsfD/wbcG1Q9kWyg+CPJzsW5R1mVtNBTL3J3oxwgrs/49lR8ZcDEwr/90R6HiUvkew4cmeb2Y1mdoq7t7yNSxPwl+DxfLJjSgIMBTa0WPf+POudTHYcRdz9FWAV2bs9tydNdkT+XOvJjrUpss9TE4Ts89z9NTM7huwgrt81s1ktVmn29wYBTfPe+6ae7AC8uRrzrNeWFHt/gczdV4PvfW+p3cvrO9inyD5BNS/Z55nZgcAud/898APgmE5uuhQ4tBPr/Q34ZHCsscBIsiPKvwFMNLOYZe8mfHwH+xlL929vI9IjqOYlkr0T9Q/MLEP2dhVfAH7Yie0eIXun5o78HPiFZe8snQIu9+xdhP8OvE72PlFLgRfa2kHQSaTe3d/pxPFEejzdEkWkG8zsAeBr7r6sxMf5MvCuu99ayuOIVAo1G4p0zzVkO26U2lbgjjIcR6QiqOYlIiIVRzUvERGpOEpeIiJScZS8RESk4ih5iYhIxVHyEhGRivP/AcIZ6UjHT4/SAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making it anolog version of clock \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "sp = ax.scatter(hour_df[\"hour_sin\"], hour_df[\"hour_cos\"], c = hour_df[\"hour\"])\n",
    "ax.set(xlabel=\"sin(hour)\", ylabel=\"cos(hour)\")\n",
    "viz = fig.colorbar(sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:     0.125 +/- 0.014\n",
      "Root Mean Squared Error: 0.166 +/- 0.020\n"
     ]
    }
   ],
   "source": [
    "#building feature extraction pipeline using this strategy\n",
    "\n",
    "\n",
    "cyclic_cossin_transformer = ColumnTransformer(\n",
    "    transformers=[\n",
    "    (\"categorical\", one_hot_encoder, categorical_columns),\n",
    "    (\"month_sin\", sin_transformer(12), [\"month\"]),\n",
    "    (\"month_cos\", cos_transformer(12), [\"month\"]),\n",
    "    (\"weekday_sin\", sin_transformer(7), [\"weekday\"]),\n",
    "    (\"weekdaycos\", cos_transformer(7), [\"weekday\"]),\n",
    "    (\"hour_sin\", sin_transformer(24), [\"hour\"]),\n",
    "    (\"hour_cos\", cos_transformer(24), [\"hour\"])\n",
    "    ], remainder=MinMaxScaler()\n",
    ")\n",
    "\n",
    "cyclic_cossin_linear_pipeline = make_pipeline(cyclic_cossin_transformer, RidgeCV(alphas=alphas))\n",
    "\n",
    "evaluate(cyclic_cossin_linear_pipeline, X, y, cv = ts_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Periodic Spline Features \n",
    "\n",
    "from sklearn.preprocessing import SplineTransformer \n",
    "\n",
    "def periodic_spline_transformer(period, n_splines = None, degree = 3):\n",
    "    if n_splines is None:\n",
    "        n_splines = period \n",
    "    n_knots = n_splines + 1 \n",
    "    return SplineTransformer(degree=degree, n_knots=n_knots, knots=np.linspace(0, period, n_knots).reshape(n_knots, 1), extrapolation=\"periodic\", include_bias=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hour_df = pd.DataFrame(np.linspace(0, 26, 1000).reshape(-1, 1), columns=[\"hour\"])\n",
    "splines = periodic_spline_transformer(24, n_splines=12).fit_transform(hour_df)\n",
    "splines_df = pd.DataFrame(\n",
    "    splines,\n",
    "    columns=[f\"spline_{i}\" for i in range(splines.shape[1])],\n",
    ")\n",
    "pd.concat([hour_df, splines_df], axis=\"columns\").plot(x=\"hour\", cmap=plt.cm.tab20b)\n",
    "viz = plt.title(\"Periodic spline-based encoding for the 'hour' feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:     0.097 +/- 0.011\n",
      "Root Mean Squared Error: 0.132 +/- 0.013\n"
     ]
    }
   ],
   "source": [
    "cyclic_spline_transformer = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"categorical\", one_hot_encoder, categorical_columns),\n",
    "        (\"cyclic_month\", periodic_spline_transformer(12, n_splines=6), [\"month\"]),\n",
    "        (\"cyclic_weekday\", periodic_spline_transformer(7, n_splines=3), [\"weekday\"]),\n",
    "        (\"cyclic_hour\", periodic_spline_transformer(24, n_splines=12), [\"hour\"]),\n",
    "    ], remainder=MinMaxScaler())\n",
    "cyclic_spline_linear_pipeline = make_pipeline(cyclic_spline_transformer, RidgeCV(alphas=alphas))\n",
    "\n",
    "evaluate(cyclic_spline_linear_pipeline, X, y, cv=ts_cv)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing impact of features on linear model predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "naive_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0])\n",
    "naive_linear_predictions = naive_linear_pipeline.predict(X.iloc[test_0])\n",
    "\n",
    "one_hot_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0])\n",
    "one_hot_linear_predictions = one_hot_linear_pipeline.predict(X.iloc[test_0])\n",
    "\n",
    "cyclic_cossin_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0])\n",
    "cyclic_cossin_linear_predictions = cyclic_cossin_linear_pipeline.predict(X.iloc[test_0])\n",
    "\n",
    "cyclic_spline_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0])\n",
    "cyclic_spline_linear_predictions = cyclic_spline_linear_pipeline.predict(X.iloc[test_0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize those predictions by zooming on the last 96 hours (4 days) of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_hours = slice(-96, None)\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "fig.suptitle(\"Predictions by linear models\")\n",
    "ax.plot(\n",
    "    y.iloc[test_0].values[last_hours],\n",
    "    \"x-\",\n",
    "    alpha=0.2,\n",
    "    label=\"Actual demand\",\n",
    "    color=\"black\",\n",
    ")\n",
    "ax.plot(naive_linear_predictions[last_hours], \"x-\", label=\"Ordinal time features\")\n",
    "ax.plot(\n",
    "    cyclic_cossin_linear_predictions[last_hours],\n",
    "    \"x-\",\n",
    "    label=\"Trigonometric time features\",\n",
    ")\n",
    "ax.plot(\n",
    "    cyclic_spline_linear_predictions[last_hours],\n",
    "    \"x-\",\n",
    "    label=\"Spline-based time features\",\n",
    ")\n",
    "ax.plot(\n",
    "    one_hot_linear_predictions[last_hours],\n",
    "    \"x-\",\n",
    "    label=\"One-hot time features\",\n",
    ")\n",
    "_ = ax.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}