2. TP2 Redes - Normalización.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f422fd3-14ea-431a-b772-471e0cc7760b",
   "metadata": {},
   "source": [
    "# Rossmann Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb87b3fa-3dc2-42c2-bf35-25a31639e848",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 0. Entorno de ejecución\n",
    "* pip install pandas\n",
    "* pip install pandas_summary\n",
    "* pip install pyarrow\n",
    "* pip install scikit-learn\n",
    "* pip install sklearn-pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6bce19e7-8fb2-4beb-9502-4523fac24af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas_summary import DataFrameSummary\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn_pandas import DataFrameMapper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebaf81f1-0973-4808-b447-2c1fdd71778b",
   "metadata": {},
   "source": [
    "## 1. Levantamos los datos preprocesados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4eeb1d04-c06e-4625-9a27-cf35ca926a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_feather('all_preprocessed_train.fth')\n",
    "df_test = pd.read_feather('all_preprocessed_test.fth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "70ed7734-8497-4d8e-94eb-a9e6d1f3984d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1017209, 81), (41088, 80))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de40fa10-6bbb-4e49-b656-8f56cedc0dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Customers', 'Sales'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificación de que se hicieron bien las cosas en testa también\n",
    "set(df_train.columns) - set(df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "218fe5a2-f218-4804-b155-dd05eab176b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'DayOfWeek', 'Date', 'Sales', 'Customers', 'Open', 'Promo',\n",
       "       'StateHoliday', 'SchoolHoliday', 'Year', 'Month', 'Week', 'Day',\n",
       "       'StoreType', 'Assortment', 'CompetitionDistance',\n",
       "       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2',\n",
       "       'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval',\n",
       "       'CompetitionOpenSince', 'Promo2Since', 'State', 'file', 'week', 'trend',\n",
       "       'Date_y', 'Month_y', 'Day_y', 'file_DE', 'week_DE', 'trend_DE',\n",
       "       'Date_DE', 'State_DE', 'Month_DE', 'Day_DE', 'file_y',\n",
       "       'Max_TemperatureC', 'Mean_TemperatureC', 'Min_TemperatureC',\n",
       "       'Dew_PointC', 'MeanDew_PointC', 'Min_DewpointC', 'Max_Humidity',\n",
       "       'Mean_Humidity', 'Min_Humidity', 'Max_Sea_Level_PressurehPa',\n",
       "       'Mean_Sea_Level_PressurehPa', 'Min_Sea_Level_PressurehPa',\n",
       "       'Max_VisibilityKm', 'Mean_VisibilityKm', 'Min_VisibilitykM',\n",
       "       'Max_Wind_SpeedKm_h', 'Mean_Wind_SpeedKm_h', 'Max_Gust_SpeedKm_h',\n",
       "       'Precipitationmm', 'CloudCover', 'Events', 'WindDirDegrees',\n",
       "       'StateName', 'Year_y', 'Week_y', 'CompetitionDaysOpen',\n",
       "       'CompetitionMonthsOpen', 'Promo2Days', 'Promo2Weeks',\n",
       "       'StateHoliday_bool', 'AfterSchoolHoliday', 'BeforeSchoolHoliday',\n",
       "       'AfterStateHoliday_bool', 'BeforeStateHoliday_bool', 'AfterPromo',\n",
       "       'BeforePromo', 'SchoolHoliday_bw', 'StateHoliday_bool_bw', 'Promo_bw',\n",
       "       'SchoolHoliday_fw', 'StateHoliday_bool_fw', 'Promo_fw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ae3c28-40f1-4932-92a2-a08752c36ba1",
   "metadata": {},
   "source": [
    "## 2. Variables categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "121af0df-d78a-45d4-859d-4dd74a1f68c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = ['Store', 'DayOfWeek', 'Year', 'Month', 'Day', 'StateHoliday', \n",
    "            'CompetitionMonthsOpen', 'Promo2Weeks', \n",
    "            'StoreType', 'Assortment', 'PromoInterval', \n",
    "            'CompetitionOpenSinceYear', 'Promo2SinceYear', \n",
    "            'State', 'Week', 'Events', 'Promo_fw', 'Promo_bw', \n",
    "            'StateHoliday_bool_fw', 'StateHoliday_bool_bw', 'SchoolHoliday_fw', \n",
    "            'SchoolHoliday_bw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "657413ff-0e75-44a6-83cc-99949ca4fba6",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uniques</th>\n",
       "      <th>types</th>\n",
       "      <th>missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store</th>\n",
       "      <td>1115</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayOfWeek</th>\n",
       "      <td>7</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>3</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>12</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>31</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday</th>\n",
       "      <td>4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionMonthsOpen</th>\n",
       "      <td>25</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2Weeks</th>\n",
       "      <td>26</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StoreType</th>\n",
       "      <td>4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assortment</th>\n",
       "      <td>3</td>\n",
       "      <td>categorical</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PromoInterval</th>\n",
       "      <td>4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <td>21</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <td>8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <td>12</td>\n",
       "      <td>categorical</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <td>52</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Events</th>\n",
       "      <td>22</td>\n",
       "      <td>categorical</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo_fw</th>\n",
       "      <td>6</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo_bw</th>\n",
       "      <td>6</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday_bool_fw</th>\n",
       "      <td>3</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday_bool_bw</th>\n",
       "      <td>3</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchoolHoliday_fw</th>\n",
       "      <td>8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchoolHoliday_bw</th>\n",
       "      <td>8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         uniques        types missing\n",
       "Store                       1115      numeric       0\n",
       "DayOfWeek                      7      numeric       0\n",
       "Year                           3      numeric       0\n",
       "Month                         12      numeric       0\n",
       "Day                           31      numeric       0\n",
       "StateHoliday                   4  categorical       0\n",
       "CompetitionMonthsOpen         25      numeric       0\n",
       "Promo2Weeks                   26      numeric       0\n",
       "StoreType                      4  categorical       0\n",
       "Assortment                     3  categorical       0\n",
       "PromoInterval                  4  categorical       0\n",
       "CompetitionOpenSinceYear      21      numeric       0\n",
       "Promo2SinceYear                8      numeric       0\n",
       "State                         12  categorical       0\n",
       "Week                          52      numeric       0\n",
       "Events                        22  categorical       0\n",
       "Promo_fw                       6      numeric       0\n",
       "Promo_bw                       6      numeric       0\n",
       "StateHoliday_bool_fw           3      numeric       0\n",
       "StateHoliday_bool_bw           3      numeric       0\n",
       "SchoolHoliday_fw               8      numeric       0\n",
       "SchoolHoliday_bw               8      numeric       0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrameSummary(df_train[cat_vars]).summary().loc[['uniques', 'types', 'missing']].T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c004b14-a4dc-485c-bd4d-e0bf92870afd",
   "metadata": {},
   "source": [
    "No hay datos faltantes en las variables categóricas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da784b3-e153-40e2-b6c3-3e6566ea6c98",
   "metadata": {},
   "source": [
    "## 3. Variables continuas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5e18c211-5c60-4dc1-bcc6-8b8c83584353",
   "metadata": {},
   "outputs": [],
   "source": [
    "contin_vars = ['CompetitionDistance', \n",
    "               'Max_TemperatureC', 'Mean_TemperatureC', 'Min_TemperatureC', \n",
    "               'Precipitationmm', 'Max_Humidity', 'Mean_Humidity', 'Min_Humidity', \n",
    "               'Max_Wind_SpeedKm_h', 'Mean_Wind_SpeedKm_h', 'CloudCover', 'trend', \n",
    "               'trend_DE', 'AfterStateHoliday_bool', 'BeforeStateHoliday_bool', \n",
    "               'Promo', 'SchoolHoliday', 'StateHoliday_bool']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4734c267-bb5c-474b-99e6-53be4ef6c32b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>uniques</th>\n",
       "      <th>types</th>\n",
       "      <th>missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <td>654</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_TemperatureC</th>\n",
       "      <td>50</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_TemperatureC</th>\n",
       "      <td>45</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_TemperatureC</th>\n",
       "      <td>40</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precipitationmm</th>\n",
       "      <td>40</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_Humidity</th>\n",
       "      <td>52</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_Humidity</th>\n",
       "      <td>71</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_Humidity</th>\n",
       "      <td>93</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_Wind_SpeedKm_h</th>\n",
       "      <td>42</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_Wind_SpeedKm_h</th>\n",
       "      <td>27</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CloudCover</th>\n",
       "      <td>9</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trend</th>\n",
       "      <td>67</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trend_DE</th>\n",
       "      <td>38</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AfterStateHoliday_bool</th>\n",
       "      <td>136</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeforeStateHoliday_bool</th>\n",
       "      <td>147</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo</th>\n",
       "      <td>2</td>\n",
       "      <td>bool</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <td>2</td>\n",
       "      <td>bool</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday_bool</th>\n",
       "      <td>2</td>\n",
       "      <td>bool</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        uniques    types missing\n",
       "CompetitionDistance         654  numeric       0\n",
       "Max_TemperatureC             50  numeric       0\n",
       "Mean_TemperatureC            45  numeric       0\n",
       "Min_TemperatureC             40  numeric       0\n",
       "Precipitationmm              40  numeric       0\n",
       "Max_Humidity                 52  numeric       0\n",
       "Mean_Humidity                71  numeric       0\n",
       "Min_Humidity                 93  numeric       0\n",
       "Max_Wind_SpeedKm_h           42  numeric       0\n",
       "Mean_Wind_SpeedKm_h          27  numeric       0\n",
       "CloudCover                    9  numeric       0\n",
       "trend                        67  numeric       0\n",
       "trend_DE                     38  numeric       0\n",
       "AfterStateHoliday_bool      136  numeric       0\n",
       "BeforeStateHoliday_bool     147  numeric       0\n",
       "Promo                         2     bool       0\n",
       "SchoolHoliday                 2     bool       0\n",
       "StateHoliday_bool             2     bool       0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrameSummary(df_train[contin_vars]).summary().loc[['uniques', 'types', 'missing']].T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a76a3d2-d1f5-4a34-ad53-95e8da5b9d58",
   "metadata": {},
   "source": [
    "No hay datos faltantes en las variables continuas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce65ae7-a947-4b11-8ca4-800daa38989a",
   "metadata": {},
   "source": [
    "## 4. Normalización de las variales continuas y LabelEncode de las categóricas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7277ec61-964f-4ff6-8615-266a49d423c4",
   "metadata": {},
   "source": [
    "Lista de tuplas, donde cada tupla contiene: nombre de la variable y función Encoder/Normalizadora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "62aeb845-efd6-4200-8ed0-b17365f9a6f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_maps = [(o, LabelEncoder()) for o in cat_vars]\n",
    "contin_maps = [([o], StandardScaler()) for o in contin_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e9b1c87a-9122-4fd3-a89b-25d58585ad4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper_cat = DataFrameMapper(cat_maps)\n",
    "_ = mapper_cat.fit(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779110e0-673d-4249-b0e3-cf18b20bc4d8",
   "metadata": {},
   "source": [
    "Vemos algunos ejemplos de cómo se realiza el LabelEncode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1347f753-be34-489a-b732-327f172b1cc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('a', 0), ('a', 0), ('a', 0), ('c', 2), ('a', 0), ('a', 0), ('c', 2), ('a', 0), ('c', 2), ('a', 0)]\n",
      "[('Fog', 0), ('Fog', 0), ('Fog', 0), ('Sunny', 20), ('Sunny', 20), ('Sunny', 20), ('Rain', 10), ('Rain', 10), ('Fog', 0), ('Sunny', 20)]\n",
      "[(2015, 2), (2015, 2), (2015, 2), (2015, 2), (2015, 2), (2015, 2), (2015, 2), (2015, 2), (2015, 2), (2015, 2)]\n"
     ]
    }
   ],
   "source": [
    "# Assortment es la novena posición en cat_maps\n",
    "N = 10\n",
    "print(list(zip(df_train['Assortment'].values[:N], mapper_cat.transform(df_train)[:,9][:N])))\n",
    "print(list(zip(df_train['Events'].values[:N], mapper_cat.transform(df_train)[:,15][:N])))\n",
    "print(list(zip(df_train['Year'].values[:N], mapper_cat.transform(df_train)[:,2][:N])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7cd9754-a8b4-426b-aadc-961143ec4fef",
   "metadata": {},
   "source": [
    "Vemos que para el tipo de tienda, la 'a' se codifica con 0 y la 'c' con 2.\n",
    "Para Events, 'Fog' se codifica con 0 y 'Sunny' con 1.\n",
    "Por último, para la columna de año, 2015 se codifica como 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c7a881b8-9535-4cd5-bc10-9d06f06c225b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper_cont = DataFrameMapper(contin_maps)\n",
    "_ = mapper_cont.fit(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eded137-8950-4bcb-81d8-fdd720656471",
   "metadata": {},
   "source": [
    "Vemos ejemplos de la normalización de las variables continuas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "64fc09cf-e4cf-4276-89ad-7858a2d90bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1270.   570. 14130.   620. 29910.   310. 24000.  7520.  2030.  3160.]\n",
      "[-0.51104292 -0.59341201  1.00219483 -0.5875285   2.85902934 -0.62400624\n",
      "  2.16359893  0.22439533 -0.42161363 -0.28864639]\n"
     ]
    }
   ],
   "source": [
    "N = 10\n",
    "print(df_train['CompetitionDistance'].values[:N])\n",
    "print(mapper_cont.transform(df_train)[:, 0][:N])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb66028-d4c7-4731-93f1-2de7458f01ee",
   "metadata": {},
   "source": [
    "Ahora lo hacemos con todas las variables y todos los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e0d678d-4cd3-4917-a7f7-b0ae13055b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# La hacemos con todas ahora\n",
    "df_train[cat_vars] = mapper_cat.transform(df_train)\n",
    "df_test[cat_vars] = mapper_cat.transform(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb4531e1-d840-4be8-b26b-f98c1122e738",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>CompetitionMonthsOpen</th>\n",
       "      <th>Promo2Weeks</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>...</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>State</th>\n",
       "      <th>Week</th>\n",
       "      <th>Events</th>\n",
       "      <th>Promo_fw</th>\n",
       "      <th>Promo_bw</th>\n",
       "      <th>StateHoliday_bool_fw</th>\n",
       "      <th>StateHoliday_bool_bw</th>\n",
       "      <th>SchoolHoliday_fw</th>\n",
       "      <th>SchoolHoliday_bw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek  Year  Month  Day  StateHoliday  CompetitionMonthsOpen  \\\n",
       "0      0          4     2      6   30             0                     24   \n",
       "1      1          4     2      6   30             0                     24   \n",
       "2      2          4     2      6   30             0                     24   \n",
       "3      3          4     2      6   30             0                     24   \n",
       "4      4          4     2      6   30             0                      3   \n",
       "\n",
       "   Promo2Weeks  StoreType  Assortment  ...  Promo2SinceYear  State  Week  \\\n",
       "0           25          2           0  ...                0      4    30   \n",
       "1           25          0           0  ...                2     11    30   \n",
       "2           25          0           0  ...                3      6    30   \n",
       "3           25          2           2  ...                0      0    30   \n",
       "4           25          0           0  ...                0      9    30   \n",
       "\n",
       "   Events  Promo_fw  Promo_bw  StateHoliday_bool_fw  StateHoliday_bool_bw  \\\n",
       "0       0         1         5                     0                     0   \n",
       "1       0         1         5                     0                     0   \n",
       "2       0         1         5                     0                     0   \n",
       "3      20         1         5                     0                     0   \n",
       "4      20         1         5                     0                     0   \n",
       "\n",
       "   SchoolHoliday_fw  SchoolHoliday_bw  \n",
       "0                 1                 5  \n",
       "1                 1                 5  \n",
       "2                 1                 5  \n",
       "3                 1                 5  \n",
       "4                 1                 5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[cat_vars].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fac2c9-2edd-46a7-a646-f565af5d61e4",
   "metadata": {},
   "source": [
    "Vemos que todas las categorías están codificadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "195847df-9e4d-4c01-9c00-968fe512773b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>StateHoliday</th>\n",
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       "      <th>...</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>State</th>\n",
       "      <th>Week</th>\n",
       "      <th>Events</th>\n",
       "      <th>Promo_fw</th>\n",
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       "      <th>SchoolHoliday_bw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>uniques</th>\n",
       "      <td>1115</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
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       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>types</th>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>...</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Store DayOfWeek     Year    Month      Day StateHoliday  \\\n",
       "uniques     1115         7        3       12       31            4   \n",
       "types    numeric   numeric  numeric  numeric  numeric      numeric   \n",
       "\n",
       "        CompetitionMonthsOpen Promo2Weeks StoreType Assortment  ...  \\\n",
       "uniques                    25          26         4          3  ...   \n",
       "types                 numeric     numeric   numeric    numeric  ...   \n",
       "\n",
       "        Promo2SinceYear    State     Week   Events Promo_fw Promo_bw  \\\n",
       "uniques               8       12       52       22        6        6   \n",
       "types           numeric  numeric  numeric  numeric  numeric  numeric   \n",
       "\n",
       "        StateHoliday_bool_fw StateHoliday_bool_bw SchoolHoliday_fw  \\\n",
       "uniques                    3                    3                8   \n",
       "types                numeric              numeric          numeric   \n",
       "\n",
       "        SchoolHoliday_bw  \n",
       "uniques                8  \n",
       "types            numeric  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrameSummary(df_train[cat_vars]).summary().loc[['uniques', 'types']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a00087f0-ce7a-4378-848b-c52d995c0a57",
   "metadata": {},
   "source": [
    "Todas las variables ahora son numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bd31cc3e-fa8a-4f93-adc8-3ee87ba4b9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train[contin_vars] = mapper_cont.transform(df_train)\n",
    "df_test[contin_vars] = mapper_cont.transform(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a77ca9fe-1fc1-4cd9-8796-340e5a43940b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>Max_TemperatureC</th>\n",
       "      <th>Mean_TemperatureC</th>\n",
       "      <th>Min_TemperatureC</th>\n",
       "      <th>Precipitationmm</th>\n",
       "      <th>Max_Humidity</th>\n",
       "      <th>Mean_Humidity</th>\n",
       "      <th>Min_Humidity</th>\n",
       "      <th>Max_Wind_SpeedKm_h</th>\n",
       "      <th>Mean_Wind_SpeedKm_h</th>\n",
       "      <th>CloudCover</th>\n",
       "      <th>trend</th>\n",
       "      <th>trend_DE</th>\n",
       "      <th>AfterStateHoliday_bool</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.511043</td>\n",
       "      <td>1.024446</td>\n",
       "      <td>0.818632</td>\n",
       "      <td>0.325047</td>\n",
       "      <td>-0.315778</td>\n",
       "      <td>0.611120</td>\n",
       "      <td>-1.510789</td>\n",
       "      <td>-1.626560</td>\n",
       "      <td>0.148437</td>\n",
       "      <td>-0.147662</td>\n",
       "      <td>-1.859165</td>\n",
       "      <td>1.744361</td>\n",
       "      <td>1.743049</td>\n",
       "      <td>0.644376</td>\n",
       "      <td>1.072424</td>\n",
       "      <td>1.273237</td>\n",
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       "      <td>0.870277</td>\n",
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       "      <td>-1.269897</td>\n",
       "      <td>-0.970080</td>\n",
       "      <td>-0.147662</td>\n",
       "      <td>-0.493858</td>\n",
       "      <td>1.303439</td>\n",
       "      <td>1.743049</td>\n",
       "      <td>0.965073</td>\n",
       "      <td>1.072424</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.788172</td>\n",
       "      <td>0.404215</td>\n",
       "      <td>0.015437</td>\n",
       "      <td>-0.315778</td>\n",
       "      <td>0.870277</td>\n",
       "      <td>-0.980745</td>\n",
       "      <td>-1.320848</td>\n",
       "      <td>-0.970080</td>\n",
       "      <td>-1.165096</td>\n",
       "      <td>-1.404063</td>\n",
       "      <td>1.832546</td>\n",
       "      <td>1.743049</td>\n",
       "      <td>0.644376</td>\n",
       "      <td>1.072424</td>\n",
       "      <td>1.273237</td>\n",
       "      <td>2.144211</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.587528</td>\n",
       "      <td>0.551899</td>\n",
       "      <td>0.542354</td>\n",
       "      <td>0.479853</td>\n",
       "      <td>-0.315778</td>\n",
       "      <td>0.092806</td>\n",
       "      <td>-0.980745</td>\n",
       "      <td>-1.015137</td>\n",
       "      <td>0.036585</td>\n",
       "      <td>0.700200</td>\n",
       "      <td>0.416347</td>\n",
       "      <td>0.774331</td>\n",
       "      <td>1.743049</td>\n",
       "      <td>0.965073</td>\n",
       "      <td>1.072424</td>\n",
       "      <td>1.273237</td>\n",
       "      <td>2.144211</td>\n",
       "      <td>-0.177442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.859029</td>\n",
       "      <td>0.670036</td>\n",
       "      <td>0.680493</td>\n",
       "      <td>0.634658</td>\n",
       "      <td>-0.315778</td>\n",
       "      <td>-1.462136</td>\n",
       "      <td>-1.435069</td>\n",
       "      <td>-1.218945</td>\n",
       "      <td>-0.970080</td>\n",
       "      <td>-0.147662</td>\n",
       "      <td>-0.493858</td>\n",
       "      <td>1.479808</td>\n",
       "      <td>1.743049</td>\n",
       "      <td>0.644376</td>\n",
       "      <td>1.072424</td>\n",
       "      <td>1.273237</td>\n",
       "      <td>2.144211</td>\n",
       "      <td>-0.177442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CompetitionDistance  Max_TemperatureC  Mean_TemperatureC  Min_TemperatureC  \\\n",
       "0            -0.511043          1.024446           0.818632          0.325047   \n",
       "1            -0.593412          0.551899           0.404215          0.170242   \n",
       "2             1.002195          0.788172           0.404215          0.015437   \n",
       "3            -0.587528          0.551899           0.542354          0.479853   \n",
       "4             2.859029          0.670036           0.680493          0.634658   \n",
       "\n",
       "   Precipitationmm  Max_Humidity  Mean_Humidity  Min_Humidity  \\\n",
       "0        -0.315778      0.611120      -1.510789     -1.626560   \n",
       "1        -0.315778      0.870277      -0.905024     -1.269897   \n",
       "2        -0.315778      0.870277      -0.980745     -1.320848   \n",
       "3        -0.315778      0.092806      -0.980745     -1.015137   \n",
       "4        -0.315778     -1.462136      -1.435069     -1.218945   \n",
       "\n",
       "   Max_Wind_SpeedKm_h  Mean_Wind_SpeedKm_h  CloudCover     trend  trend_DE  \\\n",
       "0            0.148437            -0.147662   -1.859165  1.744361  1.743049   \n",
       "1           -0.970080            -0.147662   -0.493858  1.303439  1.743049   \n",
       "2           -0.970080            -1.165096   -1.404063  1.832546  1.743049   \n",
       "3            0.036585             0.700200    0.416347  0.774331  1.743049   \n",
       "4           -0.970080            -0.147662   -0.493858  1.479808  1.743049   \n",
       "\n",
       "   AfterStateHoliday_bool  BeforeStateHoliday_bool     Promo  SchoolHoliday  \\\n",
       "0                0.644376                 1.072424  1.273237       2.144211   \n",
       "1                0.965073                 1.072424  1.273237       2.144211   \n",
       "2                0.644376                 1.072424  1.273237       2.144211   \n",
       "3                0.965073                 1.072424  1.273237       2.144211   \n",
       "4                0.644376                 1.072424  1.273237       2.144211   \n",
       "\n",
       "   StateHoliday_bool  \n",
       "0          -0.177442  \n",
       "1          -0.177442  \n",
       "2          -0.177442  \n",
       "3          -0.177442  \n",
       "4          -0.177442  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[contin_vars].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d461a0-d805-4c62-8e51-84266ce9dc04",
   "metadata": {},
   "source": [
    "Vemos que todos los valores están normalizados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c218934-3e9e-4057-9485-37dadfbb3233",
   "metadata": {},
   "source": [
    "## 5. Procesamiento final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a6d7a321-0df8-41d2-b6fc-a0c45e69d725",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales = df_train[df_train.Sales!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c15e753e-a1bc-4518-9ad1-8bc59695aca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844338, 81)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "94caf8da-262b-424e-9873-0ce57e7d1976",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "985225eb-574d-471a-a7b2-784b692adeaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ae7d8a35-daa0-4a83-9ccc-0cfe59344b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales.to_feather('train_normalized_data.fth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ca86dab6-f57b-4838-8992-cf5ccd0d48ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.to_feather('test_normalized_data.fth')"
   ]
  }
 ],
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