M5-LightGBM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m5-forecasting-accuracy/calendar.csv\n",
      "m5-forecasting-accuracy/sell_prices.csv\n",
      "m5-forecasting-accuracy/sales_train_validation.csv\n",
      "m5-forecasting-accuracy/sample_submission.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import gc\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('m5-forecasting-accuracy'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CAL_COL_TYPES = {'event_name_1':'category', 'event_type_1':'category','event_name_2':'category', 'event_type_2':'category', \n",
    "            'weekday':'category', 'wm_yr_wk': 'int16', 'wday': 'int16', 'month': 'int16', 'year':'int16', \n",
    "            'snap_CA': 'float32', 'snap_TX': 'float32', 'snap_WI':'float32'}\n",
    "SELLP_COL_TYPES = {'store_id':'category', 'item_id': 'category', 'wm_yr_wk': 'int16', 'sell_price':'float32'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "# Read the data\n",
    "INPUT_DIR='m5-forecasting-accuracy/'\n",
    "cal_data=pd.read_csv(INPUT_DIR+'calendar.csv', dtype=CAL_COL_TYPES)\n",
    "sellp_data=pd.read_csv(INPUT_DIR+'sell_prices.csv', dtype=SELLP_COL_TYPES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1969, 14)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>weekday</th>\n",
       "      <th>wday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>d</th>\n",
       "      <th>event_name_1</th>\n",
       "      <th>event_type_1</th>\n",
       "      <th>event_name_2</th>\n",
       "      <th>event_type_2</th>\n",
       "      <th>snap_CA</th>\n",
       "      <th>snap_TX</th>\n",
       "      <th>snap_WI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>d_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-30</td>\n",
       "      <td>11101</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>d_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>11101</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>d_3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>11101</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>d_4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>11101</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>d_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  wm_yr_wk    weekday  wday  month  year    d event_name_1  \\\n",
       "0  2011-01-29     11101   Saturday     1      1  2011  d_1          NaN   \n",
       "1  2011-01-30     11101     Sunday     2      1  2011  d_2          NaN   \n",
       "2  2011-01-31     11101     Monday     3      1  2011  d_3          NaN   \n",
       "3  2011-02-01     11101    Tuesday     4      2  2011  d_4          NaN   \n",
       "4  2011-02-02     11101  Wednesday     5      2  2011  d_5          NaN   \n",
       "\n",
       "  event_type_1 event_name_2 event_type_2  snap_CA  snap_TX  snap_WI  \n",
       "0          NaN          NaN          NaN      0.0      0.0      0.0  \n",
       "1          NaN          NaN          NaN      0.0      0.0      0.0  \n",
       "2          NaN          NaN          NaN      0.0      0.0      0.0  \n",
       "3          NaN          NaN          NaN      1.0      1.0      0.0  \n",
       "4          NaN          NaN          NaN      1.0      0.0      1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(cal_data.shape)\n",
    "cal_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>weekday</th>\n",
       "      <th>wday</th>\n",
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       "      <th>event_type_2</th>\n",
       "      <th>snap_CA</th>\n",
       "      <th>snap_TX</th>\n",
       "      <th>snap_WI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>11613</td>\n",
       "      <td>Monday</td>\n",
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       "      <td>2016-04-26</td>\n",
       "      <td>11613</td>\n",
       "      <td>Tuesday</td>\n",
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       "      <td>2016</td>\n",
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       "      <th>1915</th>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>11613</td>\n",
       "      <td>Wednesday</td>\n",
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       "      <td>4</td>\n",
       "      <td>2016</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1916</th>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>11613</td>\n",
       "      <td>Thursday</td>\n",
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       "      <th>1917</th>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>11613</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>11614</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1919</td>\n",
       "      <td>Pesach End</td>\n",
       "      <td>Religious</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>11614</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1920</td>\n",
       "      <td>OrthodoxEaster</td>\n",
       "      <td>Religious</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>11614</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>11614</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>11614</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>11614</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1924</td>\n",
       "      <td>Cinco De Mayo</td>\n",
       "      <td>Cultural</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>2016-05-06</td>\n",
       "      <td>11614</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>2016-05-07</td>\n",
       "      <td>11615</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>11615</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1927</td>\n",
       "      <td>Mother's day</td>\n",
       "      <td>Cultural</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>11615</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>11615</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>11615</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>11615</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>11615</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>2016-05-14</td>\n",
       "      <td>11616</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>11616</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>11616</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>11616</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>11616</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>11616</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>2016-05-20</td>\n",
       "      <td>11616</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>2016-05-21</td>\n",
       "      <td>11617</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>11617</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>11617</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>11617</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>2016-05-25</td>\n",
       "      <td>11617</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>2016-05-26</td>\n",
       "      <td>11617</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>11617</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>11618</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>11618</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>11618</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1949</td>\n",
       "      <td>MemorialDay</td>\n",
       "      <td>National</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>11618</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>11618</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>2016-06-02</td>\n",
       "      <td>11618</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1952</td>\n",
       "      <td>NBAFinalsStart</td>\n",
       "      <td>Sporting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>11618</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>11619</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>11619</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>11619</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>11619</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1957</td>\n",
       "      <td>Ramadan starts</td>\n",
       "      <td>Religious</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>11619</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>2016-06-09</td>\n",
       "      <td>11619</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>2016-06-10</td>\n",
       "      <td>11619</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>2016-06-11</td>\n",
       "      <td>11620</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>11620</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>11620</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>11620</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>11620</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>11620</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>2016-06-17</td>\n",
       "      <td>11620</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>11621</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>11621</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1969</td>\n",
       "      <td>NBAFinalsEnd</td>\n",
       "      <td>Sporting</td>\n",
       "      <td>Father's day</td>\n",
       "      <td>Cultural</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  wm_yr_wk    weekday  wday  month  year       d  \\\n",
       "1913  2016-04-25     11613     Monday     3      4  2016  d_1914   \n",
       "1914  2016-04-26     11613    Tuesday     4      4  2016  d_1915   \n",
       "1915  2016-04-27     11613  Wednesday     5      4  2016  d_1916   \n",
       "1916  2016-04-28     11613   Thursday     6      4  2016  d_1917   \n",
       "1917  2016-04-29     11613     Friday     7      4  2016  d_1918   \n",
       "1918  2016-04-30     11614   Saturday     1      4  2016  d_1919   \n",
       "1919  2016-05-01     11614     Sunday     2      5  2016  d_1920   \n",
       "1920  2016-05-02     11614     Monday     3      5  2016  d_1921   \n",
       "1921  2016-05-03     11614    Tuesday     4      5  2016  d_1922   \n",
       "1922  2016-05-04     11614  Wednesday     5      5  2016  d_1923   \n",
       "1923  2016-05-05     11614   Thursday     6      5  2016  d_1924   \n",
       "1924  2016-05-06     11614     Friday     7      5  2016  d_1925   \n",
       "1925  2016-05-07     11615   Saturday     1      5  2016  d_1926   \n",
       "1926  2016-05-08     11615     Sunday     2      5  2016  d_1927   \n",
       "1927  2016-05-09     11615     Monday     3      5  2016  d_1928   \n",
       "1928  2016-05-10     11615    Tuesday     4      5  2016  d_1929   \n",
       "1929  2016-05-11     11615  Wednesday     5      5  2016  d_1930   \n",
       "1930  2016-05-12     11615   Thursday     6      5  2016  d_1931   \n",
       "1931  2016-05-13     11615     Friday     7      5  2016  d_1932   \n",
       "1932  2016-05-14     11616   Saturday     1      5  2016  d_1933   \n",
       "1933  2016-05-15     11616     Sunday     2      5  2016  d_1934   \n",
       "1934  2016-05-16     11616     Monday     3      5  2016  d_1935   \n",
       "1935  2016-05-17     11616    Tuesday     4      5  2016  d_1936   \n",
       "1936  2016-05-18     11616  Wednesday     5      5  2016  d_1937   \n",
       "1937  2016-05-19     11616   Thursday     6      5  2016  d_1938   \n",
       "1938  2016-05-20     11616     Friday     7      5  2016  d_1939   \n",
       "1939  2016-05-21     11617   Saturday     1      5  2016  d_1940   \n",
       "1940  2016-05-22     11617     Sunday     2      5  2016  d_1941   \n",
       "1941  2016-05-23     11617     Monday     3      5  2016  d_1942   \n",
       "1942  2016-05-24     11617    Tuesday     4      5  2016  d_1943   \n",
       "1943  2016-05-25     11617  Wednesday     5      5  2016  d_1944   \n",
       "1944  2016-05-26     11617   Thursday     6      5  2016  d_1945   \n",
       "1945  2016-05-27     11617     Friday     7      5  2016  d_1946   \n",
       "1946  2016-05-28     11618   Saturday     1      5  2016  d_1947   \n",
       "1947  2016-05-29     11618     Sunday     2      5  2016  d_1948   \n",
       "1948  2016-05-30     11618     Monday     3      5  2016  d_1949   \n",
       "1949  2016-05-31     11618    Tuesday     4      5  2016  d_1950   \n",
       "1950  2016-06-01     11618  Wednesday     5      6  2016  d_1951   \n",
       "1951  2016-06-02     11618   Thursday     6      6  2016  d_1952   \n",
       "1952  2016-06-03     11618     Friday     7      6  2016  d_1953   \n",
       "1953  2016-06-04     11619   Saturday     1      6  2016  d_1954   \n",
       "1954  2016-06-05     11619     Sunday     2      6  2016  d_1955   \n",
       "1955  2016-06-06     11619     Monday     3      6  2016  d_1956   \n",
       "1956  2016-06-07     11619    Tuesday     4      6  2016  d_1957   \n",
       "1957  2016-06-08     11619  Wednesday     5      6  2016  d_1958   \n",
       "1958  2016-06-09     11619   Thursday     6      6  2016  d_1959   \n",
       "1959  2016-06-10     11619     Friday     7      6  2016  d_1960   \n",
       "1960  2016-06-11     11620   Saturday     1      6  2016  d_1961   \n",
       "1961  2016-06-12     11620     Sunday     2      6  2016  d_1962   \n",
       "1962  2016-06-13     11620     Monday     3      6  2016  d_1963   \n",
       "1963  2016-06-14     11620    Tuesday     4      6  2016  d_1964   \n",
       "1964  2016-06-15     11620  Wednesday     5      6  2016  d_1965   \n",
       "1965  2016-06-16     11620   Thursday     6      6  2016  d_1966   \n",
       "1966  2016-06-17     11620     Friday     7      6  2016  d_1967   \n",
       "1967  2016-06-18     11621   Saturday     1      6  2016  d_1968   \n",
       "1968  2016-06-19     11621     Sunday     2      6  2016  d_1969   \n",
       "\n",
       "        event_name_1 event_type_1  event_name_2 event_type_2  snap_CA  \\\n",
       "1913             NaN          NaN           NaN          NaN      0.0   \n",
       "1914             NaN          NaN           NaN          NaN      0.0   \n",
       "1915             NaN          NaN           NaN          NaN      0.0   \n",
       "1916             NaN          NaN           NaN          NaN      0.0   \n",
       "1917             NaN          NaN           NaN          NaN      0.0   \n",
       "1918      Pesach End    Religious           NaN          NaN      0.0   \n",
       "1919  OrthodoxEaster    Religious           NaN          NaN      1.0   \n",
       "1920             NaN          NaN           NaN          NaN      1.0   \n",
       "1921             NaN          NaN           NaN          NaN      1.0   \n",
       "1922             NaN          NaN           NaN          NaN      1.0   \n",
       "1923   Cinco De Mayo     Cultural           NaN          NaN      1.0   \n",
       "1924             NaN          NaN           NaN          NaN      1.0   \n",
       "1925             NaN          NaN           NaN          NaN      1.0   \n",
       "1926    Mother's day     Cultural           NaN          NaN      1.0   \n",
       "1927             NaN          NaN           NaN          NaN      1.0   \n",
       "1928             NaN          NaN           NaN          NaN      1.0   \n",
       "1929             NaN          NaN           NaN          NaN      0.0   \n",
       "1930             NaN          NaN           NaN          NaN      0.0   \n",
       "1931             NaN          NaN           NaN          NaN      0.0   \n",
       "1932             NaN          NaN           NaN          NaN      0.0   \n",
       "1933             NaN          NaN           NaN          NaN      0.0   \n",
       "1934             NaN          NaN           NaN          NaN      0.0   \n",
       "1935             NaN          NaN           NaN          NaN      0.0   \n",
       "1936             NaN          NaN           NaN          NaN      0.0   \n",
       "1937             NaN          NaN           NaN          NaN      0.0   \n",
       "1938             NaN          NaN           NaN          NaN      0.0   \n",
       "1939             NaN          NaN           NaN          NaN      0.0   \n",
       "1940             NaN          NaN           NaN          NaN      0.0   \n",
       "1941             NaN          NaN           NaN          NaN      0.0   \n",
       "1942             NaN          NaN           NaN          NaN      0.0   \n",
       "1943             NaN          NaN           NaN          NaN      0.0   \n",
       "1944             NaN          NaN           NaN          NaN      0.0   \n",
       "1945             NaN          NaN           NaN          NaN      0.0   \n",
       "1946             NaN          NaN           NaN          NaN      0.0   \n",
       "1947             NaN          NaN           NaN          NaN      0.0   \n",
       "1948     MemorialDay     National           NaN          NaN      0.0   \n",
       "1949             NaN          NaN           NaN          NaN      0.0   \n",
       "1950             NaN          NaN           NaN          NaN      1.0   \n",
       "1951  NBAFinalsStart     Sporting           NaN          NaN      1.0   \n",
       "1952             NaN          NaN           NaN          NaN      1.0   \n",
       "1953             NaN          NaN           NaN          NaN      1.0   \n",
       "1954             NaN          NaN           NaN          NaN      1.0   \n",
       "1955             NaN          NaN           NaN          NaN      1.0   \n",
       "1956  Ramadan starts    Religious           NaN          NaN      1.0   \n",
       "1957             NaN          NaN           NaN          NaN      1.0   \n",
       "1958             NaN          NaN           NaN          NaN      1.0   \n",
       "1959             NaN          NaN           NaN          NaN      1.0   \n",
       "1960             NaN          NaN           NaN          NaN      0.0   \n",
       "1961             NaN          NaN           NaN          NaN      0.0   \n",
       "1962             NaN          NaN           NaN          NaN      0.0   \n",
       "1963             NaN          NaN           NaN          NaN      0.0   \n",
       "1964             NaN          NaN           NaN          NaN      0.0   \n",
       "1965             NaN          NaN           NaN          NaN      0.0   \n",
       "1966             NaN          NaN           NaN          NaN      0.0   \n",
       "1967             NaN          NaN           NaN          NaN      0.0   \n",
       "1968    NBAFinalsEnd     Sporting  Father's day     Cultural      0.0   \n",
       "\n",
       "      snap_TX  snap_WI  \n",
       "1913      0.0      0.0  \n",
       "1914      0.0      0.0  \n",
       "1915      0.0      0.0  \n",
       "1916      0.0      0.0  \n",
       "1917      0.0      0.0  \n",
       "1918      0.0      0.0  \n",
       "1919      1.0      0.0  \n",
       "1920      0.0      1.0  \n",
       "1921      1.0      1.0  \n",
       "1922      0.0      0.0  \n",
       "1923      1.0      1.0  \n",
       "1924      1.0      1.0  \n",
       "1925      1.0      0.0  \n",
       "1926      0.0      1.0  \n",
       "1927      1.0      1.0  \n",
       "1928      0.0      0.0  \n",
       "1929      1.0      1.0  \n",
       "1930      1.0      1.0  \n",
       "1931      1.0      0.0  \n",
       "1932      0.0      1.0  \n",
       "1933      1.0      1.0  \n",
       "1934      0.0      0.0  \n",
       "1935      0.0      0.0  \n",
       "1936      0.0      0.0  \n",
       "1937      0.0      0.0  \n",
       "1938      0.0      0.0  \n",
       "1939      0.0      0.0  \n",
       "1940      0.0      0.0  \n",
       "1941      0.0      0.0  \n",
       "1942      0.0      0.0  \n",
       "1943      0.0      0.0  \n",
       "1944      0.0      0.0  \n",
       "1945      0.0      0.0  \n",
       "1946      0.0      0.0  \n",
       "1947      0.0      0.0  \n",
       "1948      0.0      0.0  \n",
       "1949      0.0      0.0  \n",
       "1950      1.0      0.0  \n",
       "1951      0.0      1.0  \n",
       "1952      1.0      1.0  \n",
       "1953      0.0      0.0  \n",
       "1954      1.0      1.0  \n",
       "1955      1.0      1.0  \n",
       "1956      1.0      0.0  \n",
       "1957      0.0      1.0  \n",
       "1958      1.0      1.0  \n",
       "1959      0.0      0.0  \n",
       "1960      1.0      1.0  \n",
       "1961      1.0      1.0  \n",
       "1962      1.0      0.0  \n",
       "1963      0.0      1.0  \n",
       "1964      1.0      1.0  \n",
       "1965      0.0      0.0  \n",
       "1966      0.0      0.0  \n",
       "1967      0.0      0.0  \n",
       "1968      0.0      0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_data.loc[1913:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6841121, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>sell_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11325</td>\n",
       "      <td>9.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11326</td>\n",
       "      <td>9.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11327</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11328</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11329</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_id        item_id  wm_yr_wk  sell_price\n",
       "0     CA_1  HOBBIES_1_001     11325        9.58\n",
       "1     CA_1  HOBBIES_1_001     11326        9.58\n",
       "2     CA_1  HOBBIES_1_001     11327        8.26\n",
       "3     CA_1  HOBBIES_1_001     11328        8.26\n",
       "4     CA_1  HOBBIES_1_001     11329        8.26"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sellp_data.shape)\n",
    "sellp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2016, 4, 25, 0, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_lags = 57 # Number of days to look dack when predicting values\n",
    "tr_last = 1913 # Last training date\n",
    "fday = datetime(2016,4,25)\n",
    "fday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store_id      category\n",
       "item_id       category\n",
       "wm_yr_wk         int16\n",
       "sell_price     float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellp_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_df(is_train=True, nrows=None, first_day=1200):\n",
    "    # convert categorical colum values to numerical for sell_prices.csv \n",
    "    sellp_data_change = sellp_data.copy()\n",
    "    for col, col_dtype in SELLP_COL_TYPES.items():\n",
    "        if col_dtype == 'category':\n",
    "            sellp_data_change[col] = sellp_data_change[col].cat.codes.astype('int16')\n",
    "            sellp_data_change[col] -= sellp_data_change[col].min()\n",
    "    \n",
    "    cal_data_change = cal_data.copy()\n",
    "    cal_data_change['date'] = pd.to_datetime(cal_data_change['date'])\n",
    "    # convert categorical colum values to numerical for calendar.csv\n",
    "    for col, col_dtype in CAL_COL_TYPES.items():\n",
    "        if col_dtype == 'category':\n",
    "            cal_data_change[col] = cal_data_change[col].cat.codes.astype('int16')\n",
    "            cal_data_change[col] -= cal_data_change[col].min()\n",
    "            \n",
    "    start_day = max(1 if is_train else tr_last-max_lags, first_day)\n",
    "    dcols = [f'd_{day}' for day in range(start_day, tr_last+1)]\n",
    "    catcols = ['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id']\n",
    "    dtype = {dcol : 'float32' for dcol in dcols}\n",
    "    dtype.update({col:'category' for col in catcols if col!='id'})\n",
    "    salestv_data=pd.read_csv(INPUT_DIR+'sales_train_validation.csv', nrows=nrows, usecols= catcols+dcols, dtype=dtype)\n",
    "    \n",
    "    # convert categorical colum values to numerical for sales_train_validation.csv \n",
    "    for col in catcols:\n",
    "        if col != 'id':\n",
    "            salestv_data[col] = salestv_data[col].cat.codes.astype('int16')\n",
    "            salestv_data[col] -= salestv_data[col].min()\n",
    "    \n",
    "    if not is_train:\n",
    "        for day in range(tr_last+1, tr_last+28+1):\n",
    "            salestv_data[f'd_{day}'] = np.nan\n",
    "    \n",
    "    # Unpivot the datafame along d_cols\n",
    "    df = pd.melt(salestv_data, \n",
    "                 id_vars=catcols, \n",
    "                 value_vars=[col for col in salestv_data.columns if col.startswith('d_')],\n",
    "                 var_name='d',\n",
    "                 value_name='sales'\n",
    "                )\n",
    "    df = df.merge(cal_data_change, on='d', copy=False)\n",
    "    df = df.merge(sellp_data_change, on=['store_id', 'item_id', 'wm_yr_wk'], copy=False)\n",
    "    return df        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n",
      "Wall time: 6.2 µs\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "#df_test = create_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(df_test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_fea(df):\n",
    "    lags =[7, 28]\n",
    "    lag_cols = [f'd_{lag}' for lag in lags]\n",
    "    \n",
    "    # shift the sales by lag value and append a new column\n",
    "    for lag, lag_col in zip(lags, lag_cols):\n",
    "        df[lag_col] = df[['id', 'sales']].groupby('id')['sales'].shift(lag)\n",
    "        \n",
    "    wins = [7, 28]\n",
    "    for win in wins:\n",
    "        for lag, lag_col in zip(lags, lag_cols):\n",
    "            df[f'rmean_{lag}_{win}'] = df[['id', lag_col]].groupby('id')[lag_col].transform(lambda x: x.rolling(win).mean())\n",
    "    \n",
    "    date_features = {\n",
    "        'wday':'weekday',\n",
    "        'week':'weekofyear',\n",
    "        'month':'month',\n",
    "        'quarter':'quarter',\n",
    "        'year':'year',\n",
    "        'mday':'day'\n",
    "    }\n",
    "    \n",
    "    # This code will add missing features of date in the dataframe & for the existing features it \n",
    "    # will change their type to int16\n",
    "    for date_feature_name, date_feature_func in date_features.items():\n",
    "        if date_feature_name in df.columns:\n",
    "            df[date_feature_name] = df[date_feature_name].astype('int16')\n",
    "        else:\n",
    "            df[date_feature_name] = getattr(df['date'].dt, date_feature_func).astype('int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIRST_DAY=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store_id      category\n",
       "item_id       category\n",
       "wm_yr_wk         int16\n",
       "sell_price     float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellp_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46027957, 22)\n",
      "CPU times: user 28.5 s, sys: 15.4 s, total: 43.9 s\n",
      "Wall time: 47.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df = create_df(is_train=True, first_day = FIRST_DAY)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46027957, 31)\n",
      "CPU times: user 2min 25s, sys: 36 s, total: 3min 1s\n",
      "Wall time: 3min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "create_fea(df)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 46027957 entries, 0 to 46027956\n",
      "Data columns (total 31 columns):\n",
      " #   Column        Dtype         \n",
      "---  ------        -----         \n",
      " 0   id            object        \n",
      " 1   item_id       int16         \n",
      " 2   dept_id       int16         \n",
      " 3   cat_id        int16         \n",
      " 4   store_id      int16         \n",
      " 5   state_id      int16         \n",
      " 6   d             object        \n",
      " 7   sales         float32       \n",
      " 8   date          datetime64[ns]\n",
      " 9   wm_yr_wk      int16         \n",
      " 10  weekday       int16         \n",
      " 11  wday          int16         \n",
      " 12  month         int16         \n",
      " 13  year          int16         \n",
      " 14  event_name_1  int16         \n",
      " 15  event_type_1  int16         \n",
      " 16  event_name_2  int16         \n",
      " 17  event_type_2  int16         \n",
      " 18  snap_CA       float32       \n",
      " 19  snap_TX       float32       \n",
      " 20  snap_WI       float32       \n",
      " 21  sell_price    float32       \n",
      " 22  d_7           float32       \n",
      " 23  d_28          float32       \n",
      " 24  rmean_7_7     float32       \n",
      " 25  rmean_28_7    float32       \n",
      " 26  rmean_7_28    float32       \n",
      " 27  rmean_28_28   float32       \n",
      " 28  week          int16         \n",
      " 29  quarter       int16         \n",
      " 30  mday          int16         \n",
      "dtypes: datetime64[ns](1), float32(11), int16(17), object(2)\n",
      "memory usage: 4.7+ GB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>dept_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>state_id</th>\n",
       "      <th>d</th>\n",
       "      <th>sales</th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>...</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>d_7</th>\n",
       "      <th>d_28</th>\n",
       "      <th>rmean_7_7</th>\n",
       "      <th>rmean_28_7</th>\n",
       "      <th>rmean_7_28</th>\n",
       "      <th>rmean_28_28</th>\n",
       "      <th>week</th>\n",
       "      <th>quarter</th>\n",
       "      <th>mday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOBBIES_1_008_CA_1_validation</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBBIES_1_008_CA_1_validation</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2011-01-30</td>\n",
       "      <td>11101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBBIES_1_008_CA_1_validation</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>11101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOBBIES_1_008_CA_1_validation</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>11101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOBBIES_1_008_CA_1_validation</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>11101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              id  item_id  dept_id  cat_id  store_id  \\\n",
       "0  HOBBIES_1_008_CA_1_validation        7        0       0         0   \n",
       "1  HOBBIES_1_008_CA_1_validation        7        0       0         0   \n",
       "2  HOBBIES_1_008_CA_1_validation        7        0       0         0   \n",
       "3  HOBBIES_1_008_CA_1_validation        7        0       0         0   \n",
       "4  HOBBIES_1_008_CA_1_validation        7        0       0         0   \n",
       "\n",
       "   state_id    d  sales       date  wm_yr_wk  ...  sell_price  d_7  d_28  \\\n",
       "0         0  d_1   12.0 2011-01-29     11101  ...        0.46  NaN   NaN   \n",
       "1         0  d_2   15.0 2011-01-30     11101  ...        0.46  NaN   NaN   \n",
       "2         0  d_3    0.0 2011-01-31     11101  ...        0.46  NaN   NaN   \n",
       "3         0  d_4    0.0 2011-02-01     11101  ...        0.46  NaN   NaN   \n",
       "4         0  d_5    0.0 2011-02-02     11101  ...        0.46  NaN   NaN   \n",
       "\n",
       "   rmean_7_7  rmean_28_7  rmean_7_28  rmean_28_28  week  quarter  mday  \n",
       "0        NaN         NaN         NaN          NaN     4        1    29  \n",
       "1        NaN         NaN         NaN          NaN     4        1    30  \n",
       "2        NaN         NaN         NaN          NaN     5        1    31  \n",
       "3        NaN         NaN         NaN          NaN     5        1     1  \n",
       "4        NaN         NaN         NaN          NaN     5        1     2  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44351007, 31)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop missing value rows\n",
    "df.dropna(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sellp_data, cal_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features = ['item_id', 'store_id', 'cat_id', 'dept_id', 'state_id'] + ['event_name_1', 'event_type_1', 'event_name_2', 'event_type_2']\n",
    "useless_cols = ['id', 'date', 'sales', 'd', 'wm_yr_wk', 'weekday']\n",
    "train_cols = df.columns[~df.columns.isin(useless_cols)]\n",
    "X_train = df[train_cols]\n",
    "y_train = df['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.9 s, sys: 18.2 s, total: 38.1 s\n",
      "Wall time: 45.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "np.random.seed(777)\n",
    "\n",
    "# This is a random sample, we're not gonna apply any time series train-test-split tricks here!\n",
    "fake_valid_inds = np.random.choice(X_train.index.values, 2_000_000, replace=False) # Validation dataset\n",
    "train_inds = np.setdiff1d(X_train.index.values, fake_valid_inds) # Training dataset\n",
    "\n",
    "train_data = lgb.Dataset(X_train.loc[train_inds], label=y_train.loc[train_inds], categorical_feature=cat_features, free_raw_data=False)\n",
    "fake_valid_data = lgb.Dataset(X_train.loc[fake_valid_inds], label=y_train.loc[fake_valid_inds], categorical_feature=cat_features, free_raw_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df, X_train, y_train, fake_valid_inds,train_inds ; gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LGB parameters\n",
    "\n",
    "params={\n",
    "#    'device':'gpu',\n",
    "    'objective':'poisson',\n",
    "    'metric':['rmse'],\n",
    "    'force_row_wise':True,\n",
    "    'learning_rate':0.075,\n",
    "    'sub_row': 0.75,\n",
    "    'bagging_freq': 1,\n",
    "    'lambda_12':0.1,\n",
    "    'verbosity':1,\n",
    "    'num_iterations':1200,\n",
    "    'num_leaves':2**11-1,\n",
    "    'min_data_in_leaf':2**12-1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kunalarora/Desktop/LOreal/tracking-env/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n",
      "/Users/kunalarora/Desktop/LOreal/tracking-env/lib/python3.7/site-packages/lightgbm/basic.py:1291: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20]\tvalid_0's rmse: 2.86423\n",
      "[40]\tvalid_0's rmse: 2.53726\n",
      "[60]\tvalid_0's rmse: 2.4502\n",
      "[80]\tvalid_0's rmse: 2.42179\n",
      "[100]\tvalid_0's rmse: 2.40779\n",
      "[120]\tvalid_0's rmse: 2.39514\n",
      "[140]\tvalid_0's rmse: 2.3838\n",
      "[160]\tvalid_0's rmse: 2.37223\n",
      "[180]\tvalid_0's rmse: 2.36271\n",
      "[200]\tvalid_0's rmse: 2.35544\n",
      "[220]\tvalid_0's rmse: 2.3497\n",
      "[240]\tvalid_0's rmse: 2.34422\n",
      "[260]\tvalid_0's rmse: 2.33873\n",
      "[280]\tvalid_0's rmse: 2.33497\n",
      "[300]\tvalid_0's rmse: 2.33133\n",
      "[320]\tvalid_0's rmse: 2.32779\n",
      "[340]\tvalid_0's rmse: 2.3246\n",
      "[360]\tvalid_0's rmse: 2.3222\n",
      "[380]\tvalid_0's rmse: 2.31899\n",
      "[400]\tvalid_0's rmse: 2.31638\n",
      "[420]\tvalid_0's rmse: 2.31417\n",
      "[440]\tvalid_0's rmse: 2.31179\n",
      "[460]\tvalid_0's rmse: 2.30986\n",
      "[480]\tvalid_0's rmse: 2.30831\n",
      "[500]\tvalid_0's rmse: 2.30626\n",
      "[520]\tvalid_0's rmse: 2.30461\n",
      "[540]\tvalid_0's rmse: 2.30293\n",
      "[560]\tvalid_0's rmse: 2.30132\n",
      "[580]\tvalid_0's rmse: 2.29972\n",
      "[600]\tvalid_0's rmse: 2.2981\n",
      "[620]\tvalid_0's rmse: 2.29661\n",
      "[640]\tvalid_0's rmse: 2.29526\n",
      "[660]\tvalid_0's rmse: 2.2939\n",
      "[680]\tvalid_0's rmse: 2.2926\n",
      "[700]\tvalid_0's rmse: 2.29127\n",
      "[720]\tvalid_0's rmse: 2.29021\n",
      "[740]\tvalid_0's rmse: 2.28895\n",
      "[760]\tvalid_0's rmse: 2.28775\n",
      "[780]\tvalid_0's rmse: 2.28603\n",
      "[800]\tvalid_0's rmse: 2.28476\n",
      "[820]\tvalid_0's rmse: 2.28351\n",
      "[840]\tvalid_0's rmse: 2.28253\n",
      "[860]\tvalid_0's rmse: 2.28121\n",
      "[880]\tvalid_0's rmse: 2.28017\n",
      "[900]\tvalid_0's rmse: 2.27924\n",
      "[920]\tvalid_0's rmse: 2.27811\n",
      "[940]\tvalid_0's rmse: 2.27714\n",
      "[960]\tvalid_0's rmse: 2.2764\n",
      "[980]\tvalid_0's rmse: 2.27529\n",
      "[1000]\tvalid_0's rmse: 2.27438\n",
      "[1020]\tvalid_0's rmse: 2.27307\n",
      "[1040]\tvalid_0's rmse: 2.27216\n",
      "[1060]\tvalid_0's rmse: 2.27097\n",
      "[1080]\tvalid_0's rmse: 2.26993\n",
      "[1100]\tvalid_0's rmse: 2.2692\n",
      "[1120]\tvalid_0's rmse: 2.26811\n",
      "[1140]\tvalid_0's rmse: 2.26721\n",
      "[1160]\tvalid_0's rmse: 2.26628\n",
      "[1180]\tvalid_0's rmse: 2.26522\n",
      "[1200]\tvalid_0's rmse: 2.26438\n",
      "CPU times: user 22h 11min 55s, sys: 6min 4s, total: 22h 18min\n",
      "Wall time: 5h 55min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "m_lgb = lgb.train(params, train_data, valid_sets=[fake_valid_data], verbose_eval=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x1239eda58>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_lgb.save_model(\"model.lgb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_data=pd.read_csv(INPUT_DIR+'calendar.csv', dtype=CAL_COL_TYPES)\n",
    "sellp_data=pd.read_csv(INPUT_DIR+'sell_prices.csv', dtype=SELLP_COL_TYPES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2016-04-25 00:00:00\n",
      "1 2016-04-26 00:00:00\n",
      "2 2016-04-27 00:00:00\n",
      "3 2016-04-28 00:00:00\n",
      "4 2016-04-29 00:00:00\n",
      "5 2016-04-30 00:00:00\n",
      "6 2016-05-01 00:00:00\n",
      "7 2016-05-02 00:00:00\n",
      "8 2016-05-03 00:00:00\n",
      "9 2016-05-04 00:00:00\n",
      "10 2016-05-05 00:00:00\n",
      "11 2016-05-06 00:00:00\n",
      "12 2016-05-07 00:00:00\n",
      "13 2016-05-08 00:00:00\n",
      "14 2016-05-09 00:00:00\n",
      "15 2016-05-10 00:00:00\n",
      "16 2016-05-11 00:00:00\n",
      "17 2016-05-12 00:00:00\n",
      "18 2016-05-13 00:00:00\n",
      "19 2016-05-14 00:00:00\n",
      "20 2016-05-15 00:00:00\n",
      "21 2016-05-16 00:00:00\n",
      "22 2016-05-17 00:00:00\n",
      "23 2016-05-18 00:00:00\n",
      "24 2016-05-19 00:00:00\n",
      "25 2016-05-20 00:00:00\n",
      "26 2016-05-21 00:00:00\n",
      "27 2016-05-22 00:00:00\n",
      "0 1.028 0.3333333333333333\n",
      "0 2016-04-25 00:00:00\n",
      "1 2016-04-26 00:00:00\n",
      "2 2016-04-27 00:00:00\n",
      "3 2016-04-28 00:00:00\n",
      "4 2016-04-29 00:00:00\n",
      "5 2016-04-30 00:00:00\n",
      "6 2016-05-01 00:00:00\n",
      "7 2016-05-02 00:00:00\n",
      "8 2016-05-03 00:00:00\n",
      "9 2016-05-04 00:00:00\n",
      "10 2016-05-05 00:00:00\n",
      "11 2016-05-06 00:00:00\n",
      "12 2016-05-07 00:00:00\n",
      "13 2016-05-08 00:00:00\n",
      "14 2016-05-09 00:00:00\n",
      "15 2016-05-10 00:00:00\n",
      "16 2016-05-11 00:00:00\n",
      "17 2016-05-12 00:00:00\n",
      "18 2016-05-13 00:00:00\n",
      "19 2016-05-14 00:00:00\n",
      "20 2016-05-15 00:00:00\n",
      "21 2016-05-16 00:00:00\n",
      "22 2016-05-17 00:00:00\n",
      "23 2016-05-18 00:00:00\n",
      "24 2016-05-19 00:00:00\n",
      "25 2016-05-20 00:00:00\n",
      "26 2016-05-21 00:00:00\n",
      "27 2016-05-22 00:00:00\n",
      "1 1.023 0.3333333333333333\n",
      "0 2016-04-25 00:00:00\n",
      "1 2016-04-26 00:00:00\n",
      "2 2016-04-27 00:00:00\n",
      "3 2016-04-28 00:00:00\n",
      "4 2016-04-29 00:00:00\n",
      "5 2016-04-30 00:00:00\n",
      "6 2016-05-01 00:00:00\n",
      "7 2016-05-02 00:00:00\n",
      "8 2016-05-03 00:00:00\n",
      "9 2016-05-04 00:00:00\n",
      "10 2016-05-05 00:00:00\n",
      "11 2016-05-06 00:00:00\n",
      "12 2016-05-07 00:00:00\n",
      "13 2016-05-08 00:00:00\n",
      "14 2016-05-09 00:00:00\n",
      "15 2016-05-10 00:00:00\n",
      "16 2016-05-11 00:00:00\n",
      "17 2016-05-12 00:00:00\n",
      "18 2016-05-13 00:00:00\n",
      "19 2016-05-14 00:00:00\n",
      "20 2016-05-15 00:00:00\n",
      "21 2016-05-16 00:00:00\n",
      "22 2016-05-17 00:00:00\n",
      "23 2016-05-18 00:00:00\n",
      "24 2016-05-19 00:00:00\n",
      "25 2016-05-20 00:00:00\n",
      "26 2016-05-21 00:00:00\n",
      "27 2016-05-22 00:00:00\n",
      "2 1.018 0.3333333333333333\n",
      "CPU times: user 4h 15min 14s, sys: 2min 47s, total: 4h 18min 1s\n",
      "Wall time: 2h 27min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "alphas = [1.028, 1.023, 1.018]\n",
    "weights = [1/len(alphas)]*len(alphas)\n",
    "sub = 0.\n",
    "\n",
    "for icount, (alpha, weight) in enumerate(zip(alphas, weights)):\n",
    "\n",
    "    # create a dataframe that has values starting from tr_last-max_lags (1913 - 57) i.e, 57 days \n",
    "    # before the start of validation date\n",
    "    te = create_df(False)\n",
    "    cols = [f\"F{i}\" for i in range(1,29)]\n",
    "\n",
    "    for tdelta in range(0, 28):\n",
    "        day = fday + timedelta(days=tdelta)\n",
    "        print(tdelta, day)\n",
    "        # Create a dataframe starting from day to it's previous 57 days\n",
    "        tst = te[(te.date >= day - timedelta(days=max_lags)) & (te.date <= day)].copy()\n",
    "        create_fea(tst)\n",
    "        # Make tst exactly the same format as used for training for model to predict values\n",
    "        tst = tst.loc[tst.date == day , train_cols]\n",
    "        # Add the predicted values of sales to the dataframe starting from firstday of validation\n",
    "        te.loc[te.date == day, \"sales\"] = alpha*m_lgb.predict(tst) # magic multiplier by kyakovlev\n",
    "\n",
    "\n",
    "    # Select and create a sub-df starting from validation day\n",
    "    te_sub = te.loc[te.date >= fday, [\"id\", \"sales\"]].copy()\n",
    "    \n",
    "    # Number each item in each group from 0 to the length of that group - 1: cumcount()\n",
    "    te_sub[\"F\"] = [f\"F{rank}\" for rank in te_sub.groupby(\"id\")[\"id\"].cumcount()+1]\n",
    "    \n",
    "    # Unstack or pivot the values back to their original shape as per submission.csv\n",
    "    te_sub = te_sub.set_index([\"id\", \"F\" ]).unstack()[\"sales\"][cols].reset_index()\n",
    "    \n",
    "    #Replace all the Nan values to 0\n",
    "    te_sub.fillna(0., inplace = True)\n",
    "    \n",
    "    te_sub.sort_values(\"id\", inplace = True)\n",
    "    te_sub.reset_index(drop=True, inplace = True)\n",
    "    te_sub.to_csv(f\"submission_{icount}.csv\",index=False)\n",
    "    # Create the weigted average value of sales\n",
    "    if icount == 0 :\n",
    "        sub = te_sub\n",
    "        sub[cols] *= weight\n",
    "    else:\n",
    "        sub[cols] += te_sub[cols]*weight\n",
    "    print(icount, alpha, weight)\n",
    "\n",
    "\n",
    "sub2 = sub.copy()\n",
    "sub2[\"id\"] = sub2[\"id\"].str.replace(\"validation$\", \"evaluation\")\n",
    "sub = pd.concat([sub, sub2], axis=0, sort=False)\n",
    "sub.to_csv(\"submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the prediction value step-by-step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>item_id</th>\n",
       "      <th>dept_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>state_id</th>\n",
       "      <th>wday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>event_name_1</th>\n",
       "      <th>event_type_1</th>\n",
       "      <th>...</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>d_7</th>\n",
       "      <th>d_28</th>\n",
       "      <th>rmean_7_7</th>\n",
       "      <th>rmean_28_7</th>\n",
       "      <th>rmean_7_28</th>\n",
       "      <th>rmean_28_28</th>\n",
       "      <th>week</th>\n",
       "      <th>quarter</th>\n",
       "      <th>mday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1707442</th>\n",
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       "      <td>2016</td>\n",
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       "      <td>8.38</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>1.035714</td>\n",
       "      <td>17</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.97</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>...</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707463</th>\n",
       "      <td>3</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>2.071429</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>1.071429</td>\n",
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       "      <td>2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>3044</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
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       "      <td>0.250000</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>1920844</th>\n",
       "      <td>3045</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920851</th>\n",
       "      <td>3046</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920858</th>\n",
       "      <td>3047</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920865</th>\n",
       "      <td>3048</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>1.607143</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30490 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_id  dept_id  cat_id  store_id  state_id  wday  month  year  \\\n",
       "1707442        0        0       0         0         0     3      4  2016   \n",
       "1707449        1        0       0         0         0     3      4  2016   \n",
       "1707456        2        0       0         0         0     3      4  2016   \n",
       "1707463        3        0       0         0         0     3      4  2016   \n",
       "1707470        4        0       0         0         0     3      4  2016   \n",
       "...          ...      ...     ...       ...       ...   ...    ...   ...   \n",
       "1920837     3044        6       2         9         2     3      4  2016   \n",
       "1920844     3045        6       2         9         2     3      4  2016   \n",
       "1920851     3046        6       2         9         2     3      4  2016   \n",
       "1920858     3047        6       2         9         2     3      4  2016   \n",
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       "                                    id  item_id  dept_id  cat_id  store_id  \\\n",
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       "...                                ...      ...      ...     ...       ...   \n",
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       "1920864    FOODS_3_827_WI_3_validation     3048        6       2         9   \n",
       "1920865    FOODS_3_827_WI_3_validation     3048        6       2         9   \n",
       "\n",
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       "\n",
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    "pp = te[(te.date >= (fday - timedelta(days=max_lags))) & (te.date <= fday)]\n",
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  {
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   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/kunalarora/Desktop/LOreal/tracking-env/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/Users/kunalarora/Desktop/LOreal/tracking-env/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/Users/kunalarora/Desktop/LOreal/tracking-env/lib/python3.7/site-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/Users/kunalarora/Desktop/LOreal/tracking-env/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "create_fea(pp)"
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  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
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       "      <th>d</th>\n",
       "      <th>sales</th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>...</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>d_7</th>\n",
       "      <th>d_28</th>\n",
       "      <th>rmean_7_7</th>\n",
       "      <th>rmean_28_7</th>\n",
       "      <th>rmean_7_28</th>\n",
       "      <th>rmean_28_28</th>\n",
       "      <th>week</th>\n",
       "      <th>quarter</th>\n",
       "      <th>mday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_1857</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2016-02-28</td>\n",
       "      <td>11605</td>\n",
       "      <td>...</td>\n",
       "      <td>8.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_1858</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>11605</td>\n",
       "      <td>...</td>\n",
       "      <td>8.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_1859</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>11605</td>\n",
       "      <td>...</td>\n",
       "      <td>8.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_1860</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>11605</td>\n",
       "      <td>...</td>\n",
       "      <td>8.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d_1861</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>11605</td>\n",
       "      <td>...</td>\n",
       "      <td>8.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1920857</th>\n",
       "      <td>FOODS_3_826_WI_3_validation</td>\n",
       "      <td>3047</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.035714</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920858</th>\n",
       "      <td>FOODS_3_826_WI_3_validation</td>\n",
       "      <td>3047</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>d_1914</td>\n",
       "      <td>1.080357</td>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>11613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920863</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>3048</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>d_1912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-04-23</td>\n",
       "      <td>11613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>1.821429</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920864</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>3048</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>1.678571</td>\n",
       "      <td>1.964286</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920865</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>3048</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>d_1914</td>\n",
       "      <td>1.065993</td>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>11613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>1.607143</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1768420 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    id  item_id  dept_id  cat_id  store_id  \\\n",
       "1        HOBBIES_1_001_CA_1_validation        0        0       0         0   \n",
       "2        HOBBIES_1_001_CA_1_validation        0        0       0         0   \n",
       "3        HOBBIES_1_001_CA_1_validation        0        0       0         0   \n",
       "4        HOBBIES_1_001_CA_1_validation        0        0       0         0   \n",
       "5        HOBBIES_1_001_CA_1_validation        0        0       0         0   \n",
       "...                                ...      ...      ...     ...       ...   \n",
       "1920857    FOODS_3_826_WI_3_validation     3047        6       2         9   \n",
       "1920858    FOODS_3_826_WI_3_validation     3047        6       2         9   \n",
       "1920863    FOODS_3_827_WI_3_validation     3048        6       2         9   \n",
       "1920864    FOODS_3_827_WI_3_validation     3048        6       2         9   \n",
       "1920865    FOODS_3_827_WI_3_validation     3048        6       2         9   \n",
       "\n",
       "         state_id       d     sales       date  wm_yr_wk  ...  sell_price  \\\n",
       "1               0  d_1857  2.000000 2016-02-28     11605  ...        8.26   \n",
       "2               0  d_1858  0.000000 2016-02-29     11605  ...        8.26   \n",
       "3               0  d_1859  2.000000 2016-03-01     11605  ...        8.26   \n",
       "4               0  d_1860  0.000000 2016-03-02     11605  ...        8.26   \n",
       "5               0  d_1861  1.000000 2016-03-03     11605  ...        8.26   \n",
       "...           ...     ...       ...        ...       ...  ...         ...   \n",
       "1920857         2  d_1913  3.000000 2016-04-24     11613  ...        1.28   \n",
       "1920858         2  d_1914  1.080357 2016-04-25     11613  ...        1.28   \n",
       "1920863         2  d_1912  0.000000 2016-04-23     11613  ...        1.00   \n",
       "1920864         2  d_1913  0.000000 2016-04-24     11613  ...        1.00   \n",
       "1920865         2  d_1914  1.065993 2016-04-25     11613  ...        1.00   \n",
       "\n",
       "         d_7  d_28  rmean_7_7  rmean_28_7  rmean_7_28  rmean_28_28  week  \\\n",
       "1        NaN   NaN        NaN         NaN         NaN          NaN     8   \n",
       "2        NaN   NaN        NaN         NaN         NaN          NaN     9   \n",
       "3        NaN   NaN        NaN         NaN         NaN          NaN     9   \n",
       "4        NaN   NaN        NaN         NaN         NaN          NaN     9   \n",
       "5        NaN   NaN        NaN         NaN         NaN          NaN     9   \n",
       "...      ...   ...        ...         ...         ...          ...   ...   \n",
       "1920857  1.0   4.0   0.714286    1.571429    1.035714     1.250000    16   \n",
       "1920858  0.0   3.0   0.714286    1.857143    1.000000     1.285714    17   \n",
       "1920863  0.0   0.0   0.000000    2.285714    1.821429     1.785714    16   \n",
       "1920864  0.0   5.0   0.000000    2.428571    1.678571     1.964286    16   \n",
       "1920865  0.0   7.0   0.000000    3.142857    1.607143     2.142857    17   \n",
       "\n",
       "         quarter  mday  \n",
       "1              1    28  \n",
       "2              1    29  \n",
       "3              1     1  \n",
       "4              1     2  \n",
       "5              1     3  \n",
       "...          ...   ...  \n",
       "1920857        2    24  \n",
       "1920858        2    25  \n",
       "1920863        2    23  \n",
       "1920864        2    24  \n",
       "1920865        2    25  \n",
       "\n",
       "[1768420 rows x 31 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>1920837</th>\n",
       "      <td>3044</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920844</th>\n",
       "      <td>3045</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920851</th>\n",
       "      <td>3046</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920858</th>\n",
       "      <td>3047</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920865</th>\n",
       "      <td>3048</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>1.607143</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30490 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_id  dept_id  cat_id  store_id  state_id  wday  month  year  \\\n",
       "1707442        0        0       0         0         0     3      4  2016   \n",
       "1707449        1        0       0         0         0     3      4  2016   \n",
       "1707456        2        0       0         0         0     3      4  2016   \n",
       "1707463        3        0       0         0         0     3      4  2016   \n",
       "1707470        4        0       0         0         0     3      4  2016   \n",
       "...          ...      ...     ...       ...       ...   ...    ...   ...   \n",
       "1920837     3044        6       2         9         2     3      4  2016   \n",
       "1920844     3045        6       2         9         2     3      4  2016   \n",
       "1920851     3046        6       2         9         2     3      4  2016   \n",
       "1920858     3047        6       2         9         2     3      4  2016   \n",
       "1920865     3048        6       2         9         2     3      4  2016   \n",
       "\n",
       "         event_name_1  event_type_1  ...  sell_price  d_7  d_28  rmean_7_7  \\\n",
       "1707442             0             0  ...        8.38  1.0   1.0   0.857143   \n",
       "1707449             0             0  ...        3.97  0.0   1.0   0.000000   \n",
       "1707456             0             0  ...        2.97  1.0   0.0   1.285714   \n",
       "1707463             0             0  ...        4.64  4.0   0.0   1.857143   \n",
       "1707470             0             0  ...        2.88  0.0   1.0   0.857143   \n",
       "...               ...           ...  ...         ...  ...   ...        ...   \n",
       "1920837             0             0  ...        2.98  0.0   0.0   0.285714   \n",
       "1920844             0             0  ...        2.48  0.0   0.0   0.142857   \n",
       "1920851             0             0  ...        3.98  2.0   1.0   0.714286   \n",
       "1920858             0             0  ...        1.28  0.0   3.0   0.714286   \n",
       "1920865             0             0  ...        1.00  0.0   7.0   0.000000   \n",
       "\n",
       "         rmean_28_7  rmean_7_28  rmean_28_28  week  quarter  mday  \n",
       "1707442    1.142857    0.964286     1.035714    17        2    25  \n",
       "1707449    0.714286    0.178571     0.357143    17        2    25  \n",
       "1707456    0.285714    0.464286     0.535714    17        2    25  \n",
       "1707463    3.000000    2.071429     1.892857    17        2    25  \n",
       "1707470    0.714286    1.071429     1.107143    17        2    25  \n",
       "...             ...         ...          ...   ...      ...   ...  \n",
       "1920837    0.000000    0.142857     0.250000    17        2    25  \n",
       "1920844    0.000000    0.285714     0.000000    17        2    25  \n",
       "1920851    0.571429    0.928571     1.250000    17        2    25  \n",
       "1920858    1.857143    1.000000     1.285714    17        2    25  \n",
       "1920865    3.142857    1.607143     2.142857    17        2    25  \n",
       "\n",
       "[30490 rows x 25 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.loc[pp.date==fday, train_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 56s, sys: 1.14 s, total: 1min 57s\n",
      "Wall time: 41.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred = m_lgb.predict(tst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30490,)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30490,)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.loc[te.date==fday, 'sales'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = te.loc[te.date >= fday, [\"id\", \"sales\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th>F</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">HOBBIES_1_001_CA_1_validation</th>\n",
       "      <th>F1</th>\n",
       "      <td>0.88792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOODS_3_825_WI_3_validation</th>\n",
       "      <th>F28</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">FOODS_3_826_WI_3_validation</th>\n",
       "      <th>F27</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F28</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">FOODS_3_827_WI_3_validation</th>\n",
       "      <th>F27</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F28</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>853720 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     sales\n",
       "id                            F           \n",
       "HOBBIES_1_001_CA_1_validation F1   0.88792\n",
       "                              F2       NaN\n",
       "                              F3       NaN\n",
       "                              F4       NaN\n",
       "                              F5       NaN\n",
       "...                                    ...\n",
       "FOODS_3_825_WI_3_validation   F28      NaN\n",
       "FOODS_3_826_WI_3_validation   F27      NaN\n",
       "                              F28      NaN\n",
       "FOODS_3_827_WI_3_validation   F27      NaN\n",
       "                              F28      NaN\n",
       "\n",
       "[853720 rows x 1 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub[\"F\"] = [f\"F{rank}\" for rank in sub.groupby(\"id\")[\"id\"].cumcount()+1]\n",
    "sub = sub.set_index(['id','F'])\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>F</th>\n",
       "      <th>id</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>...</th>\n",
       "      <th>F19</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
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       "      <th>F23</th>\n",
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       "      <th>F25</th>\n",
       "      <th>F26</th>\n",
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       "      <th>F28</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_1_001_CA_1_validation</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_1_001_CA_2_validation</td>\n",
       "      <td>0.922160</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_1_001_CA_3_validation</td>\n",
       "      <td>0.903504</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_1_001_CA_4_validation</td>\n",
       "      <td>0.433709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_1_001_TX_1_validation</td>\n",
       "      <td>0.233760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>30485</th>\n",
       "      <td>HOUSEHOLD_2_516_TX_2_validation</td>\n",
       "      <td>0.255922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30486</th>\n",
       "      <td>HOUSEHOLD_2_516_TX_3_validation</td>\n",
       "      <td>0.166515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30487</th>\n",
       "      <td>HOUSEHOLD_2_516_WI_1_validation</td>\n",
       "      <td>0.061944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30488</th>\n",
       "      <td>HOUSEHOLD_2_516_WI_2_validation</td>\n",
       "      <td>0.036739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30489</th>\n",
       "      <td>HOUSEHOLD_2_516_WI_3_validation</td>\n",
       "      <td>0.061876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30490 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "F                                   id        F1  F2  F3  F4  F5  F6  F7  F8  \\\n",
       "0          FOODS_1_001_CA_1_validation  0.733849 NaN NaN NaN NaN NaN NaN NaN   \n",
       "1          FOODS_1_001_CA_2_validation  0.922160 NaN NaN NaN NaN NaN NaN NaN   \n",
       "2          FOODS_1_001_CA_3_validation  0.903504 NaN NaN NaN NaN NaN NaN NaN   \n",
       "3          FOODS_1_001_CA_4_validation  0.433709 NaN NaN NaN NaN NaN NaN NaN   \n",
       "4          FOODS_1_001_TX_1_validation  0.233760 NaN NaN NaN NaN NaN NaN NaN   \n",
       "...                                ...       ...  ..  ..  ..  ..  ..  ..  ..   \n",
       "30485  HOUSEHOLD_2_516_TX_2_validation  0.255922 NaN NaN NaN NaN NaN NaN NaN   \n",
       "30486  HOUSEHOLD_2_516_TX_3_validation  0.166515 NaN NaN NaN NaN NaN NaN NaN   \n",
       "30487  HOUSEHOLD_2_516_WI_1_validation  0.061944 NaN NaN NaN NaN NaN NaN NaN   \n",
       "30488  HOUSEHOLD_2_516_WI_2_validation  0.036739 NaN NaN NaN NaN NaN NaN NaN   \n",
       "30489  HOUSEHOLD_2_516_WI_3_validation  0.061876 NaN NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "F      F9  ...  F19  F20  F21  F22  F23  F24  F25  F26  F27  F28  \n",
       "0     NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1     NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2     NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3     NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4     NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...    ..  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "30485 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "30486 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "30487 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "30488 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "30489 NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[30490 rows x 29 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = sub.unstack()['sales'][cols].reset_index()\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_1_001_CA_4_validation</td>\n",
       "      <td>0.433709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>FOODS_1_001_TX_1_validation</td>\n",
       "      <td>0.233760</td>\n",
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      ],
      "text/plain": [
       "F                                   id        F1   F2   F3   F4   F5   F6  \\\n",
       "0          FOODS_1_001_CA_1_validation  0.733849  0.0  0.0  0.0  0.0  0.0   \n",
       "1          FOODS_1_001_CA_2_validation  0.922160  0.0  0.0  0.0  0.0  0.0   \n",
       "2          FOODS_1_001_CA_3_validation  0.903504  0.0  0.0  0.0  0.0  0.0   \n",
       "3          FOODS_1_001_CA_4_validation  0.433709  0.0  0.0  0.0  0.0  0.0   \n",
       "4          FOODS_1_001_TX_1_validation  0.233760  0.0  0.0  0.0  0.0  0.0   \n",
       "...                                ...       ...  ...  ...  ...  ...  ...   \n",
       "30485  HOUSEHOLD_2_516_TX_2_validation  0.255922  0.0  0.0  0.0  0.0  0.0   \n",
       "30486  HOUSEHOLD_2_516_TX_3_validation  0.166515  0.0  0.0  0.0  0.0  0.0   \n",
       "30487  HOUSEHOLD_2_516_WI_1_validation  0.061944  0.0  0.0  0.0  0.0  0.0   \n",
       "30488  HOUSEHOLD_2_516_WI_2_validation  0.036739  0.0  0.0  0.0  0.0  0.0   \n",
       "30489  HOUSEHOLD_2_516_WI_3_validation  0.061876  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
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     "execution_count": 131,
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    "sub = sub.fillna(0.)\n",
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  {
   "cell_type": "code",
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       "F                                   id        F1   F2   F3   F4   F5   F6  \\\n",
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       "...                                ...       ...  ...  ...  ...  ...  ...   \n",
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     },
     "execution_count": 132,
     "metadata": {},
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   "source": [
    "sub = sub.sort_values('id')\n",
    "sub"
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  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_1_001_CA_3_validation</td>\n",
       "      <td>0.903504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_1_001_CA_4_validation</td>\n",
       "      <td>0.433709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_1_001_TX_1_validation</td>\n",
       "      <td>0.233760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>30485</th>\n",
       "      <td>HOUSEHOLD_2_516_TX_2_validation</td>\n",
       "      <td>0.255922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30486</th>\n",
       "      <td>HOUSEHOLD_2_516_TX_3_validation</td>\n",
       "      <td>0.166515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30487</th>\n",
       "      <td>HOUSEHOLD_2_516_WI_1_validation</td>\n",
       "      <td>0.061944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30488</th>\n",
       "      <td>HOUSEHOLD_2_516_WI_2_validation</td>\n",
       "      <td>0.036739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30489</th>\n",
       "      <td>HOUSEHOLD_2_516_WI_3_validation</td>\n",
       "      <td>0.061876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30490 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "F                                   id        F1   F2   F3   F4   F5   F6  \\\n",
       "0          FOODS_1_001_CA_1_validation  0.733849  0.0  0.0  0.0  0.0  0.0   \n",
       "1          FOODS_1_001_CA_2_validation  0.922160  0.0  0.0  0.0  0.0  0.0   \n",
       "2          FOODS_1_001_CA_3_validation  0.903504  0.0  0.0  0.0  0.0  0.0   \n",
       "3          FOODS_1_001_CA_4_validation  0.433709  0.0  0.0  0.0  0.0  0.0   \n",
       "4          FOODS_1_001_TX_1_validation  0.233760  0.0  0.0  0.0  0.0  0.0   \n",
       "...                                ...       ...  ...  ...  ...  ...  ...   \n",
       "30485  HOUSEHOLD_2_516_TX_2_validation  0.255922  0.0  0.0  0.0  0.0  0.0   \n",
       "30486  HOUSEHOLD_2_516_TX_3_validation  0.166515  0.0  0.0  0.0  0.0  0.0   \n",
       "30487  HOUSEHOLD_2_516_WI_1_validation  0.061944  0.0  0.0  0.0  0.0  0.0   \n",
       "30488  HOUSEHOLD_2_516_WI_2_validation  0.036739  0.0  0.0  0.0  0.0  0.0   \n",
       "30489  HOUSEHOLD_2_516_WI_3_validation  0.061876  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "F       F7   F8   F9  ...  F19  F20  F21  F22  F23  F24  F25  F26  F27  F28  \n",
       "0      0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1      0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2      0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3      0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4      0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "30485  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "30486  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "30487  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "30488  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "30489  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[30490 rows x 29 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "salestv_data=pd.read_csv(INPUT_DIR+'sales_train_validation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>item_id</th>\n",
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       "      <th>30485</th>\n",
       "      <td>FOODS_3_823_WI_3_validation</td>\n",
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       "      <th>30486</th>\n",
       "      <td>FOODS_3_824_WI_3_validation</td>\n",
       "      <td>FOODS_3_824</td>\n",
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       "      <th>30487</th>\n",
       "      <td>FOODS_3_825_WI_3_validation</td>\n",
       "      <td>FOODS_3_825</td>\n",
       "      <td>FOODS_3</td>\n",
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       "      <td>WI_3</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30488</th>\n",
       "      <td>FOODS_3_826_WI_3_validation</td>\n",
       "      <td>FOODS_3_826</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>30489</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>FOODS_3_827</td>\n",
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       "      <td>FOODS</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1919 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                id      item_id  dept_id cat_id store_id  \\\n",
       "30485  FOODS_3_823_WI_3_validation  FOODS_3_823  FOODS_3  FOODS     WI_3   \n",
       "30486  FOODS_3_824_WI_3_validation  FOODS_3_824  FOODS_3  FOODS     WI_3   \n",
       "30487  FOODS_3_825_WI_3_validation  FOODS_3_825  FOODS_3  FOODS     WI_3   \n",
       "30488  FOODS_3_826_WI_3_validation  FOODS_3_826  FOODS_3  FOODS     WI_3   \n",
       "30489  FOODS_3_827_WI_3_validation  FOODS_3_827  FOODS_3  FOODS     WI_3   \n",
       "\n",
       "      state_id  d_1  d_2  d_3  d_4  ...  d_1904  d_1905  d_1906  d_1907  \\\n",
       "30485       WI    0    0    2    2  ...       2       0       0       0   \n",
       "30486       WI    0    0    0    0  ...       0       0       0       0   \n",
       "30487       WI    0    6    0    2  ...       2       1       0       2   \n",
       "30488       WI    0    0    0    0  ...       0       0       1       0   \n",
       "30489       WI    0    0    0    0  ...       0       0       0       0   \n",
       "\n",
       "       d_1908  d_1909  d_1910  d_1911  d_1912  d_1913  \n",
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       "30486       0       0       0       0       1       0  \n",
       "30487       0       1       0       0       1       0  \n",
       "30488       0       1       0       3       1       3  \n",
       "30489       0       0       0       0       0       0  \n",
       "\n",
       "[5 rows x 1919 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salestv_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>weekday</th>\n",
       "      <th>wday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
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       "      <th>snap_WI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>11613</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>11613</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1915</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>11613</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>11613</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>11613</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>11614</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1919</td>\n",
       "      <td>Pesach End</td>\n",
       "      <td>Religious</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>11614</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1920</td>\n",
       "      <td>OrthodoxEaster</td>\n",
       "      <td>Religious</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>11614</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>11614</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>11614</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>11614</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1924</td>\n",
       "      <td>Cinco De Mayo</td>\n",
       "      <td>Cultural</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>2016-05-06</td>\n",
       "      <td>11614</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>2016-05-07</td>\n",
       "      <td>11615</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>11615</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1927</td>\n",
       "      <td>Mother's day</td>\n",
       "      <td>Cultural</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>11615</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>11615</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>11615</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>11615</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>11615</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>2016-05-14</td>\n",
       "      <td>11616</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>11616</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>11616</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>11616</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>11616</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>11616</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>2016-05-20</td>\n",
       "      <td>11616</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>2016-05-21</td>\n",
       "      <td>11617</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>11617</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>11617</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>11617</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>2016-05-25</td>\n",
       "      <td>11617</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>2016-05-26</td>\n",
       "      <td>11617</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>11617</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>11618</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>11618</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>11618</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1949</td>\n",
       "      <td>MemorialDay</td>\n",
       "      <td>National</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>11618</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>11618</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>2016-06-02</td>\n",
       "      <td>11618</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1952</td>\n",
       "      <td>NBAFinalsStart</td>\n",
       "      <td>Sporting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>11618</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>11619</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>11619</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>11619</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>11619</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1957</td>\n",
       "      <td>Ramadan starts</td>\n",
       "      <td>Religious</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>11619</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>2016-06-09</td>\n",
       "      <td>11619</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>2016-06-10</td>\n",
       "      <td>11619</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>2016-06-11</td>\n",
       "      <td>11620</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>11620</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>11620</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>11620</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>11620</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>11620</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>2016-06-17</td>\n",
       "      <td>11620</td>\n",
       "      <td>Friday</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>11621</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>11621</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>d_1969</td>\n",
       "      <td>NBAFinalsEnd</td>\n",
       "      <td>Sporting</td>\n",
       "      <td>Father's day</td>\n",
       "      <td>Cultural</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  wm_yr_wk    weekday  wday  month  year       d  \\\n",
       "1913  2016-04-25     11613     Monday     3      4  2016  d_1914   \n",
       "1914  2016-04-26     11613    Tuesday     4      4  2016  d_1915   \n",
       "1915  2016-04-27     11613  Wednesday     5      4  2016  d_1916   \n",
       "1916  2016-04-28     11613   Thursday     6      4  2016  d_1917   \n",
       "1917  2016-04-29     11613     Friday     7      4  2016  d_1918   \n",
       "1918  2016-04-30     11614   Saturday     1      4  2016  d_1919   \n",
       "1919  2016-05-01     11614     Sunday     2      5  2016  d_1920   \n",
       "1920  2016-05-02     11614     Monday     3      5  2016  d_1921   \n",
       "1921  2016-05-03     11614    Tuesday     4      5  2016  d_1922   \n",
       "1922  2016-05-04     11614  Wednesday     5      5  2016  d_1923   \n",
       "1923  2016-05-05     11614   Thursday     6      5  2016  d_1924   \n",
       "1924  2016-05-06     11614     Friday     7      5  2016  d_1925   \n",
       "1925  2016-05-07     11615   Saturday     1      5  2016  d_1926   \n",
       "1926  2016-05-08     11615     Sunday     2      5  2016  d_1927   \n",
       "1927  2016-05-09     11615     Monday     3      5  2016  d_1928   \n",
       "1928  2016-05-10     11615    Tuesday     4      5  2016  d_1929   \n",
       "1929  2016-05-11     11615  Wednesday     5      5  2016  d_1930   \n",
       "1930  2016-05-12     11615   Thursday     6      5  2016  d_1931   \n",
       "1931  2016-05-13     11615     Friday     7      5  2016  d_1932   \n",
       "1932  2016-05-14     11616   Saturday     1      5  2016  d_1933   \n",
       "1933  2016-05-15     11616     Sunday     2      5  2016  d_1934   \n",
       "1934  2016-05-16     11616     Monday     3      5  2016  d_1935   \n",
       "1935  2016-05-17     11616    Tuesday     4      5  2016  d_1936   \n",
       "1936  2016-05-18     11616  Wednesday     5      5  2016  d_1937   \n",
       "1937  2016-05-19     11616   Thursday     6      5  2016  d_1938   \n",
       "1938  2016-05-20     11616     Friday     7      5  2016  d_1939   \n",
       "1939  2016-05-21     11617   Saturday     1      5  2016  d_1940   \n",
       "1940  2016-05-22     11617     Sunday     2      5  2016  d_1941   \n",
       "1941  2016-05-23     11617     Monday     3      5  2016  d_1942   \n",
       "1942  2016-05-24     11617    Tuesday     4      5  2016  d_1943   \n",
       "1943  2016-05-25     11617  Wednesday     5      5  2016  d_1944   \n",
       "1944  2016-05-26     11617   Thursday     6      5  2016  d_1945   \n",
       "1945  2016-05-27     11617     Friday     7      5  2016  d_1946   \n",
       "1946  2016-05-28     11618   Saturday     1      5  2016  d_1947   \n",
       "1947  2016-05-29     11618     Sunday     2      5  2016  d_1948   \n",
       "1948  2016-05-30     11618     Monday     3      5  2016  d_1949   \n",
       "1949  2016-05-31     11618    Tuesday     4      5  2016  d_1950   \n",
       "1950  2016-06-01     11618  Wednesday     5      6  2016  d_1951   \n",
       "1951  2016-06-02     11618   Thursday     6      6  2016  d_1952   \n",
       "1952  2016-06-03     11618     Friday     7      6  2016  d_1953   \n",
       "1953  2016-06-04     11619   Saturday     1      6  2016  d_1954   \n",
       "1954  2016-06-05     11619     Sunday     2      6  2016  d_1955   \n",
       "1955  2016-06-06     11619     Monday     3      6  2016  d_1956   \n",
       "1956  2016-06-07     11619    Tuesday     4      6  2016  d_1957   \n",
       "1957  2016-06-08     11619  Wednesday     5      6  2016  d_1958   \n",
       "1958  2016-06-09     11619   Thursday     6      6  2016  d_1959   \n",
       "1959  2016-06-10     11619     Friday     7      6  2016  d_1960   \n",
       "1960  2016-06-11     11620   Saturday     1      6  2016  d_1961   \n",
       "1961  2016-06-12     11620     Sunday     2      6  2016  d_1962   \n",
       "1962  2016-06-13     11620     Monday     3      6  2016  d_1963   \n",
       "1963  2016-06-14     11620    Tuesday     4      6  2016  d_1964   \n",
       "1964  2016-06-15     11620  Wednesday     5      6  2016  d_1965   \n",
       "1965  2016-06-16     11620   Thursday     6      6  2016  d_1966   \n",
       "1966  2016-06-17     11620     Friday     7      6  2016  d_1967   \n",
       "1967  2016-06-18     11621   Saturday     1      6  2016  d_1968   \n",
       "1968  2016-06-19     11621     Sunday     2      6  2016  d_1969   \n",
       "\n",
       "        event_name_1 event_type_1  event_name_2 event_type_2  snap_CA  \\\n",
       "1913             NaN          NaN           NaN          NaN      0.0   \n",
       "1914             NaN          NaN           NaN          NaN      0.0   \n",
       "1915             NaN          NaN           NaN          NaN      0.0   \n",
       "1916             NaN          NaN           NaN          NaN      0.0   \n",
       "1917             NaN          NaN           NaN          NaN      0.0   \n",
       "1918      Pesach End    Religious           NaN          NaN      0.0   \n",
       "1919  OrthodoxEaster    Religious           NaN          NaN      1.0   \n",
       "1920             NaN          NaN           NaN          NaN      1.0   \n",
       "1921             NaN          NaN           NaN          NaN      1.0   \n",
       "1922             NaN          NaN           NaN          NaN      1.0   \n",
       "1923   Cinco De Mayo     Cultural           NaN          NaN      1.0   \n",
       "1924             NaN          NaN           NaN          NaN      1.0   \n",
       "1925             NaN          NaN           NaN          NaN      1.0   \n",
       "1926    Mother's day     Cultural           NaN          NaN      1.0   \n",
       "1927             NaN          NaN           NaN          NaN      1.0   \n",
       "1928             NaN          NaN           NaN          NaN      1.0   \n",
       "1929             NaN          NaN           NaN          NaN      0.0   \n",
       "1930             NaN          NaN           NaN          NaN      0.0   \n",
       "1931             NaN          NaN           NaN          NaN      0.0   \n",
       "1932             NaN          NaN           NaN          NaN      0.0   \n",
       "1933             NaN          NaN           NaN          NaN      0.0   \n",
       "1934             NaN          NaN           NaN          NaN      0.0   \n",
       "1935             NaN          NaN           NaN          NaN      0.0   \n",
       "1936             NaN          NaN           NaN          NaN      0.0   \n",
       "1937             NaN          NaN           NaN          NaN      0.0   \n",
       "1938             NaN          NaN           NaN          NaN      0.0   \n",
       "1939             NaN          NaN           NaN          NaN      0.0   \n",
       "1940             NaN          NaN           NaN          NaN      0.0   \n",
       "1941             NaN          NaN           NaN          NaN      0.0   \n",
       "1942             NaN          NaN           NaN          NaN      0.0   \n",
       "1943             NaN          NaN           NaN          NaN      0.0   \n",
       "1944             NaN          NaN           NaN          NaN      0.0   \n",
       "1945             NaN          NaN           NaN          NaN      0.0   \n",
       "1946             NaN          NaN           NaN          NaN      0.0   \n",
       "1947             NaN          NaN           NaN          NaN      0.0   \n",
       "1948     MemorialDay     National           NaN          NaN      0.0   \n",
       "1949             NaN          NaN           NaN          NaN      0.0   \n",
       "1950             NaN          NaN           NaN          NaN      1.0   \n",
       "1951  NBAFinalsStart     Sporting           NaN          NaN      1.0   \n",
       "1952             NaN          NaN           NaN          NaN      1.0   \n",
       "1953             NaN          NaN           NaN          NaN      1.0   \n",
       "1954             NaN          NaN           NaN          NaN      1.0   \n",
       "1955             NaN          NaN           NaN          NaN      1.0   \n",
       "1956  Ramadan starts    Religious           NaN          NaN      1.0   \n",
       "1957             NaN          NaN           NaN          NaN      1.0   \n",
       "1958             NaN          NaN           NaN          NaN      1.0   \n",
       "1959             NaN          NaN           NaN          NaN      1.0   \n",
       "1960             NaN          NaN           NaN          NaN      0.0   \n",
       "1961             NaN          NaN           NaN          NaN      0.0   \n",
       "1962             NaN          NaN           NaN          NaN      0.0   \n",
       "1963             NaN          NaN           NaN          NaN      0.0   \n",
       "1964             NaN          NaN           NaN          NaN      0.0   \n",
       "1965             NaN          NaN           NaN          NaN      0.0   \n",
       "1966             NaN          NaN           NaN          NaN      0.0   \n",
       "1967             NaN          NaN           NaN          NaN      0.0   \n",
       "1968    NBAFinalsEnd     Sporting  Father's day     Cultural      0.0   \n",
       "\n",
       "      snap_TX  snap_WI  \n",
       "1913      0.0      0.0  \n",
       "1914      0.0      0.0  \n",
       "1915      0.0      0.0  \n",
       "1916      0.0      0.0  \n",
       "1917      0.0      0.0  \n",
       "1918      0.0      0.0  \n",
       "1919      1.0      0.0  \n",
       "1920      0.0      1.0  \n",
       "1921      1.0      1.0  \n",
       "1922      0.0      0.0  \n",
       "1923      1.0      1.0  \n",
       "1924      1.0      1.0  \n",
       "1925      1.0      0.0  \n",
       "1926      0.0      1.0  \n",
       "1927      1.0      1.0  \n",
       "1928      0.0      0.0  \n",
       "1929      1.0      1.0  \n",
       "1930      1.0      1.0  \n",
       "1931      1.0      0.0  \n",
       "1932      0.0      1.0  \n",
       "1933      1.0      1.0  \n",
       "1934      0.0      0.0  \n",
       "1935      0.0      0.0  \n",
       "1936      0.0      0.0  \n",
       "1937      0.0      0.0  \n",
       "1938      0.0      0.0  \n",
       "1939      0.0      0.0  \n",
       "1940      0.0      0.0  \n",
       "1941      0.0      0.0  \n",
       "1942      0.0      0.0  \n",
       "1943      0.0      0.0  \n",
       "1944      0.0      0.0  \n",
       "1945      0.0      0.0  \n",
       "1946      0.0      0.0  \n",
       "1947      0.0      0.0  \n",
       "1948      0.0      0.0  \n",
       "1949      0.0      0.0  \n",
       "1950      1.0      0.0  \n",
       "1951      0.0      1.0  \n",
       "1952      1.0      1.0  \n",
       "1953      0.0      0.0  \n",
       "1954      1.0      1.0  \n",
       "1955      1.0      1.0  \n",
       "1956      1.0      0.0  \n",
       "1957      0.0      1.0  \n",
       "1958      1.0      1.0  \n",
       "1959      0.0      0.0  \n",
       "1960      1.0      1.0  \n",
       "1961      1.0      1.0  \n",
       "1962      1.0      0.0  \n",
       "1963      0.0      1.0  \n",
       "1964      1.0      1.0  \n",
       "1965      0.0      0.0  \n",
       "1966      0.0      0.0  \n",
       "1967      0.0      0.0  \n",
       "1968      0.0      0.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_data.tail(56)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intuition behind using Lags and rolling window in the model"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(remember index starts from 0 and for simplicity a month is 28 days)\n",
    "\n",
    "***First off what each feature mathematically does***\n",
    "\n",
    "\n",
    "***lag_7:*** sales shifted 7 steps downwards for each group. The example above focuses on one group only as an example. That is why the first value appears on the 7th index.  \n",
    "***lag_28:*** sales shifted 28 steps downwads. That is why the first value appears on the 28th index.  \n",
    "***rmean_7_7:*** rolling mean sales of a window size of 7 over column lag_7. First value (0.2857) appears on the 13th index because means including nan are nan.  \n",
    "***rmean_7_28:*** rolling mean sales of a window size of 7 over column lag_28. First value (0.357) appears on the 34th index because that is the first time the mean formula gets all 7 non-nan values.  \n",
    "***rmean_28_7:*** rolling mean sales of a window size of 28 over column lag_7. First value (0.2857) appears on the 3th index because it is the first time the mean formula gets 28 non-nan values.  \n",
    "***rmean_28_28:*** rolling mean sales of a window size of 28 over column lag_28. First value appears on 55th index because that is the first time the formula here all non-nan values.  \n",
    "\n",
    "***The intuition as far as I can understand is the following:***\n",
    "\n",
    "\n",
    "1. Captures the week-on-week similarity and that too of just the past week. In other words, people are likely to shop this monday similar to the last monday (except it is some special occassion).\n",
    "2. Captures the weekly similarity from a month-to-month perspective. Example: people in the 1st weekend of a month shop more so that weekend looks more similar to first weeks of other months than the previous weekend. (Though 28 is arguable here. A month is generally 30. Interesting would be a variable window depending on when the comparative week starts. Dealing with edge cases like week divided into 2 months will be tricky).\n",
    "\n",
    "***Since individual data points are prone to erratic spikes or troughs, mean provides a more \"representative\" picture.***\n",
    "\n",
    "3. Captures the information regarding the sales of the whole previous week ending 7 days in the past i.e. if we are at day 14, then the average is of sales from days 1-7 NOT days 7-14. This provides the information about the whole week and not just a single day sale comparison like lag_7 to bring the lag_7 value into \"better weekly context\".\n",
    "4. Captures the information regarding the sales of the entire previous 4 weeks ending 7 days in the past i.e. if we are at day 35, then the average is sales from days 1-28.\n",
    "\n",
    "5. Captures the information regarding the sales of the whole week ending 4 weeks ago i.e. if we are on day 35, then the average is of sales from day 1-7. (Assuming for simplicity the month is 28 days), this provides the information of not just a month-to-month comparison of the same day (day 7 of month one vs day 7 of month two), but the entire week leading up to day 7. Again the idea I believe is to capture the whole week and not just a single day sale comparison like lag_28 to bring the lag_28 value into \"better weekly context\".\n",
    "6. Captures the information regarding the sales of the entire previous 4 weeks ending 4 weeks in the past i.e. if we are at day 56, then the average is of days 1-28. (Assuming for simplicity the month is 28 days), the idea again is to bring the point value of lag_28 into a better context (i.e. of day 28 when being compared to day 56) into a \"better monthly context\".\n",
    "\n",
    "***How would you \"talk\" about these features?***\n",
    "\n",
    "\n",
    "Hey let's see how the sales were last friday compared to this friday?\n",
    "Hey let's see how the sales were first weekend of the last month compared to first weekend of this month?\n",
    "May be comparing last saturday to this saturday is too specific. Week-on-week same day trends are more likely to be similar if the prior week went similar too. It would make sense to not just have the last saturday but also the mean of the whole week leading upto that day to give the model the \"hint\" how normal the whole week was."
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