03_Notebooks/02_Desarrollo/04_Transformacion de datos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d31dea65",
   "metadata": {},
   "source": [
    "# TRANSFORMACIÓN DE DATOS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swiss-fault",
   "metadata": {},
   "source": [
    "## IMPORTAR PAQUETES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "adjacent-toddler",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from category_encoders import TargetEncoder\n",
    "\n",
    "#Automcompletar rápido\n",
    "%config IPCompleter.greedy=True\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-compensation",
   "metadata": {},
   "source": [
    "## IMPORTAR LOS DATOS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5b94ef",
   "metadata": {},
   "source": [
    "1.- Sustituir la ruta del proyecto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9d2de58",
   "metadata": {},
   "outputs": [],
   "source": [
    "ruta_proyecto = 'C:/Users/pelop/OneDrive/Desktop/Curso Data Science Pedro/2 CURSO DATA SCIENCE/03_MACHINE_LEARNING/07_CASOS/02_RETAIL/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3363765",
   "metadata": {},
   "source": [
    "2.- Nombrar los ficheros de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9e2e04a",
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre_cat = 'cat_resultado_eda.pickle'\n",
    "nombre_num = 'num_resultado_eda.pickle'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9825018",
   "metadata": {},
   "source": [
    "3.- Cargar los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cab6ebcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = pd.read_pickle(ruta_proyecto + '/02_Datos/03_Trabajo/' + nombre_cat)\n",
    "num = pd.read_pickle(ruta_proyecto + '/02_Datos/03_Trabajo/' + nombre_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9790d322",
   "metadata": {},
   "source": [
    "## NUEVAS VARIABLES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94062c60",
   "metadata": {},
   "source": [
    "En este caso ya nos vienen creadas:\n",
    "\n",
    "* componentes de la fecha\n",
    "* variables de calendario\n",
    "\n",
    "Vamos a crear:\n",
    "\n",
    "* las que identificamos en EDA\n",
    "* lags\n",
    "* ventanas móviles\n",
    "\n",
    "Para esta fase necesitamos volver a unir en un solo dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ebcf2d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>d</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>wday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>event_name_1</th>\n",
       "      <th>event_type_1</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>ventas</th>\n",
       "      <th>sell_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>NewYear</td>\n",
       "      <td>National</td>\n",
       "      <td>11249</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_120</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>NewYear</td>\n",
       "      <td>National</td>\n",
       "      <td>11249</td>\n",
       "      <td>33</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_202</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>NewYear</td>\n",
       "      <td>National</td>\n",
       "      <td>11249</td>\n",
       "      <td>0</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_252</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
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       "      <td>Tuesday</td>\n",
       "      <td>NewYear</td>\n",
       "      <td>National</td>\n",
       "      <td>11249</td>\n",
       "      <td>0</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_288</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>NewYear</td>\n",
       "      <td>National</td>\n",
       "      <td>11249</td>\n",
       "      <td>20</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_329</td>\n",
       "      <td>d_1767</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>9</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_555</td>\n",
       "      <td>d_1767</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>26</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_586</td>\n",
       "      <td>d_1767</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>13</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_587</td>\n",
       "      <td>d_1767</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>11</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>d_1767</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>9</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21280 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           store_id      item_id       d  year month wday  weekday  \\\n",
       "date                                                                 \n",
       "2013-01-01     CA_3  FOODS_3_090   d_704  2013     1    4  Tuesday   \n",
       "2013-01-01     CA_3  FOODS_3_120   d_704  2013     1    4  Tuesday   \n",
       "2013-01-01     CA_3  FOODS_3_202   d_704  2013     1    4  Tuesday   \n",
       "2013-01-01     CA_3  FOODS_3_252   d_704  2013     1    4  Tuesday   \n",
       "2013-01-01     CA_3  FOODS_3_288   d_704  2013     1    4  Tuesday   \n",
       "...             ...          ...     ...   ...   ...  ...      ...   \n",
       "2015-11-30     CA_4  FOODS_3_329  d_1767  2015    11    3   Monday   \n",
       "2015-11-30     CA_4  FOODS_3_555  d_1767  2015    11    3   Monday   \n",
       "2015-11-30     CA_4  FOODS_3_586  d_1767  2015    11    3   Monday   \n",
       "2015-11-30     CA_4  FOODS_3_587  d_1767  2015    11    3   Monday   \n",
       "2015-11-30     CA_4  FOODS_3_714  d_1767  2015    11    3   Monday   \n",
       "\n",
       "           event_name_1 event_type_1  wm_yr_wk  ventas  sell_price  \n",
       "date                                                                \n",
       "2013-01-01      NewYear     National     11249       0        1.25  \n",
       "2013-01-01      NewYear     National     11249      33        1.25  \n",
       "2013-01-01      NewYear     National     11249       0        4.98  \n",
       "2013-01-01      NewYear     National     11249       0        4.98  \n",
       "2013-01-01      NewYear     National     11249      20        4.28  \n",
       "...                 ...          ...       ...     ...         ...  \n",
       "2015-11-30   Sin_evento   Sin_evento     11544       9        1.68  \n",
       "2015-11-30   Sin_evento   Sin_evento     11544      26        2.48  \n",
       "2015-11-30   Sin_evento   Sin_evento     11544      13        2.48  \n",
       "2015-11-30   Sin_evento   Sin_evento     11544      11        1.58  \n",
       "2015-11-30   Sin_evento   Sin_evento     11544       9        1.58  \n",
       "\n",
       "[21280 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([cat,num], axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96660f64",
   "metadata": {},
   "source": [
    "### Variable demanda intermitente"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9471382",
   "metadata": {},
   "source": [
    "Esta variable va a identificar cuantos días seguidos han transcurrido con ventas cero.\n",
    "\n",
    "La definiremos como que si los últimos n días han tenido cero ventas entonces hay rotura de stock.\n",
    "\n",
    "Podemos crear varias cambiando el n.\n",
    "\n",
    "Nos servirá para modelizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffc41b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotura_stock(ventas, n = 5):\n",
    "    cero_ventas = pd.Series(np.where(ventas == 0,1,0))\n",
    "    num_ceros = cero_ventas.rolling(n).sum()\n",
    "    rotura_stock = np.where(num_ceros == n,1,0)\n",
    "    return(rotura_stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27ba0dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by = ['store_id','item_id','date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15c2fd28",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rotura_stock_3'] = df.groupby(['store_id','item_id']).ventas.transform(lambda x: rotura_stock(x, 3)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6eb102ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rotura_stock_7'] = df.groupby(['store_id','item_id']).ventas.transform(lambda x: rotura_stock(x,7)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ca8aedb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rotura_stock_15'] = df.groupby(['store_id','item_id']).ventas.transform(lambda x: rotura_stock(x,15)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ab92787-a97a-4239-ac68-a895369f030c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_id</th>\n",
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       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
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       "      <td>NewYear</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2013-01-02</th>\n",
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       "      <td>FOODS_3_090</td>\n",
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       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11249</td>\n",
       "      <td>224</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2013-01-03</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_706</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11249</td>\n",
       "      <td>241</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_707</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11249</td>\n",
       "      <td>232</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_708</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11250</td>\n",
       "      <td>301</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           store_id      item_id      d  year month wday    weekday  \\\n",
       "date                                                                  \n",
       "2013-01-01     CA_3  FOODS_3_090  d_704  2013     1    4    Tuesday   \n",
       "2013-01-02     CA_3  FOODS_3_090  d_705  2013     1    5  Wednesday   \n",
       "2013-01-03     CA_3  FOODS_3_090  d_706  2013     1    6   Thursday   \n",
       "2013-01-04     CA_3  FOODS_3_090  d_707  2013     1    7     Friday   \n",
       "2013-01-05     CA_3  FOODS_3_090  d_708  2013     1    1   Saturday   \n",
       "\n",
       "           event_name_1 event_type_1  wm_yr_wk  ventas  sell_price  \\\n",
       "date                                                                 \n",
       "2013-01-01      NewYear     National     11249       0        1.25   \n",
       "2013-01-02   Sin_evento   Sin_evento     11249     224        1.25   \n",
       "2013-01-03   Sin_evento   Sin_evento     11249     241        1.25   \n",
       "2013-01-04   Sin_evento   Sin_evento     11249     232        1.25   \n",
       "2013-01-05   Sin_evento   Sin_evento     11250     301        1.25   \n",
       "\n",
       "            rotura_stock_3  rotura_stock_7  rotura_stock_15  \n",
       "date                                                         \n",
       "2013-01-01               0               0                0  \n",
       "2013-01-02               0               0                0  \n",
       "2013-01-03               0               0                0  \n",
       "2013-01-04               0               0                0  \n",
       "2013-01-05               0               0                0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8dfac55",
   "metadata": {},
   "source": [
    "### Variables de lag"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f3b32c",
   "metadata": {},
   "source": [
    "Vamos a crear lags sobre las siguientes variables:\n",
    "\n",
    "* ventas: lags de 15 días\n",
    "* sell_price: lags de 7 días\n",
    "* rotura_stock: lag de un día\n",
    "\n",
    "**Nota.-** La variable rotura_stock es mejor crearla con solo un lag de un día. Esto es debido a que los modelos de forecasting solo pueden predecir al día siguiente (al contrario que los métodos tradicionales que pueden predecir varios días). La idea es usar el método recursivo y encadenar predicciones sobre las prediciiones para acceder a días posteriores. La particularidad de la variable rotura_stock está en que es muy raro que el modelo nos vaya a predecir exactamente 0, puede predecir valores muy bajos pero exactamente 0 sería muy raro y la variable no funcionaría bien (de acuerdo a como la hemos definido nosotros). Así pues, solo el primer lag utilizaría un dato real y los siguientes sería sobre los predecidos. De ahí que solo vayamos a usar lags de 1 día para ella."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c0f1daca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def crear_lags(df, variable, num_lags = 7):\n",
    "    \n",
    "    #Crea el objeto dataframe\n",
    "    lags = pd.DataFrame()\n",
    "    \n",
    "    #Crea todos los lags\n",
    "    for cada in range(1,num_lags+1):\n",
    "        lags[variable + '_lag_'+ str(cada)] = df[variable].shift(cada)\n",
    "    \n",
    "    #Devuelve el dataframe de lags\n",
    "    return(lags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3f3aa549",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags_sell_price_df = (df.groupby(['store_id', 'item_id'])\n",
    "                        .apply(lambda x: crear_lags(df = x, variable = 'sell_price', num_lags= 7))\n",
    "                        .reset_index()\n",
    "                        .set_index('date'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c4ffcaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags_rotura_stock_3_df = (df.groupby(['store_id','item_id'])\n",
    "                            .apply(lambda x: crear_lags(df = x, variable = 'rotura_stock_3', num_lags= 1))\n",
    "                            .reset_index()\n",
    "                            .set_index('date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "30d05719",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags_rotura_stock_7_df = (df.groupby(['store_id','item_id'])\n",
    "                            .apply(lambda x: crear_lags(df = x, variable = 'rotura_stock_7', num_lags= 1))\n",
    "                            .reset_index()\n",
    "                            .set_index('date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "19694f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags_rotura_stock_15_df = (df.groupby(['store_id','item_id'])\n",
    "                            .apply(lambda x: crear_lags(df = x, variable = 'rotura_stock_15', num_lags= 1))\n",
    "                            .reset_index()\n",
    "                            .set_index('date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03c5e6d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags_ventas_df = (df.groupby(['store_id','item_id'])\n",
    "                    .apply(lambda x: crear_lags(df = x, variable = 'ventas', num_lags= 15))\n",
    "                    .reset_index()\n",
    "                    .set_index('date'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fbeb70",
   "metadata": {},
   "source": [
    "### Variables de ventanas móviles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c381a4",
   "metadata": {},
   "source": [
    "Vamos a crear tres tipos de ventanas móviles sobre las ventas:\n",
    "\n",
    "* mínimo móvil\n",
    "* media móvil\n",
    "* máximo móvil\n",
    "\n",
    "Cada uno de ellos en el rango de 15 días."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d142f38-05b0-4a18-93ee-bf030af443c2",
   "metadata": {},
   "source": [
    "**Nota.-** Recordar que usamos el *shift(1)* para retrasar los datos un día hacia atrás. Para predecir el día actual necesito los datos de ayer, no los de hoy que aún no los tenemos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82a011ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def min_movil(df, variable, num_periodos = 7):\n",
    "\n",
    "    minm = pd.DataFrame()\n",
    "    \n",
    "    for cada in range(2,num_periodos+1):\n",
    "        minm[variable + '_minm_' + str(cada)] = df[variable].shift(1).rolling(cada).min()\n",
    "    \n",
    "    #Devuelve el dataframe de lags\n",
    "    return(minm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "35da6f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def media_movil(df, variable, num_periodos = 7):\n",
    "\n",
    "    mm = pd.DataFrame()\n",
    "    \n",
    "    for cada in range(2,num_periodos+1):\n",
    "        mm[variable + '_mm_' + str(cada)] = df[variable].shift(1).rolling(cada).mean()\n",
    "    \n",
    "    #Devuelve el dataframe de lags\n",
    "    return(mm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e554559",
   "metadata": {},
   "outputs": [],
   "source": [
    "def max_movil(df, variable, num_periodos = 7):\n",
    "\n",
    "    maxm = pd.DataFrame()\n",
    "    \n",
    "    for cada in range(2,num_periodos+1):\n",
    "        maxm[variable + '_maxm_' + str(cada)] = df[variable].shift(1).rolling(cada).max()\n",
    "    \n",
    "    #Devuelve el dataframe de lags\n",
    "    return(maxm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2694eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_movil_df = (df.groupby(['store_id','item_id'])\n",
    "                  .apply(lambda x: min_movil(df = x, variable = 'ventas', num_periodos= 15))\n",
    "                  .reset_index()\n",
    "                  .set_index('date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e00a410f",
   "metadata": {},
   "outputs": [],
   "source": [
    "media_movil_df = (df.groupby(['store_id','item_id'])\n",
    "                    .apply(lambda x: media_movil(df = x, variable = 'ventas', num_periodos= 15))\n",
    "                    .reset_index()\n",
    "                    .set_index('date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "92bf2a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_movil_df = (df.groupby(['store_id','item_id'])\n",
    "                    .apply(lambda x: max_movil(df = x, variable = 'ventas', num_periodos= 15))\n",
    "                    .reset_index()\n",
    "                    .set_index('date'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df95cb17",
   "metadata": {},
   "source": [
    "## PREPARAR LOS DATASETS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e151b5f9",
   "metadata": {},
   "source": [
    "### Unir todos los dataframes generados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6db645a6",
   "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>store_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>d</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>wday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>event_name_1</th>\n",
       "      <th>event_type_1</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>...</th>\n",
       "      <th>ventas_maxm_6</th>\n",
       "      <th>ventas_maxm_7</th>\n",
       "      <th>ventas_maxm_8</th>\n",
       "      <th>ventas_maxm_9</th>\n",
       "      <th>ventas_maxm_10</th>\n",
       "      <th>ventas_maxm_11</th>\n",
       "      <th>ventas_maxm_12</th>\n",
       "      <th>ventas_maxm_13</th>\n",
       "      <th>ventas_maxm_14</th>\n",
       "      <th>ventas_maxm_15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_704</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>NewYear</td>\n",
       "      <td>National</td>\n",
       "      <td>11249</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>2013-01-02</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_705</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11249</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>2013-01-03</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_706</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11249</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>2013-01-04</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_707</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11249</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>2013-01-05</th>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>d_708</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11250</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>...</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>2015-11-26</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>d_1763</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Thanksgiving</td>\n",
       "      <td>National</td>\n",
       "      <td>11543</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>d_1764</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11543</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-28</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>d_1765</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-29</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>d_1766</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>d_1767</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>Sin_evento</td>\n",
       "      <td>11544</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21280 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           store_id      item_id       d  year month wday    weekday  \\\n",
       "date                                                                   \n",
       "2013-01-01     CA_3  FOODS_3_090   d_704  2013     1    4    Tuesday   \n",
       "2013-01-02     CA_3  FOODS_3_090   d_705  2013     1    5  Wednesday   \n",
       "2013-01-03     CA_3  FOODS_3_090   d_706  2013     1    6   Thursday   \n",
       "2013-01-04     CA_3  FOODS_3_090   d_707  2013     1    7     Friday   \n",
       "2013-01-05     CA_3  FOODS_3_090   d_708  2013     1    1   Saturday   \n",
       "...             ...          ...     ...   ...   ...  ...        ...   \n",
       "2015-11-26     CA_4  FOODS_3_714  d_1763  2015    11    6   Thursday   \n",
       "2015-11-27     CA_4  FOODS_3_714  d_1764  2015    11    7     Friday   \n",
       "2015-11-28     CA_4  FOODS_3_714  d_1765  2015    11    1   Saturday   \n",
       "2015-11-29     CA_4  FOODS_3_714  d_1766  2015    11    2     Sunday   \n",
       "2015-11-30     CA_4  FOODS_3_714  d_1767  2015    11    3     Monday   \n",
       "\n",
       "            event_name_1 event_type_1  wm_yr_wk  ...  ventas_maxm_6  \\\n",
       "date                                             ...                  \n",
       "2013-01-01       NewYear     National     11249  ...            NaN   \n",
       "2013-01-02    Sin_evento   Sin_evento     11249  ...            NaN   \n",
       "2013-01-03    Sin_evento   Sin_evento     11249  ...            NaN   \n",
       "2013-01-04    Sin_evento   Sin_evento     11249  ...            NaN   \n",
       "2013-01-05    Sin_evento   Sin_evento     11250  ...            NaN   \n",
       "...                  ...          ...       ...  ...            ...   \n",
       "2015-11-26  Thanksgiving     National     11543  ...           15.0   \n",
       "2015-11-27    Sin_evento   Sin_evento     11543  ...           22.0   \n",
       "2015-11-28    Sin_evento   Sin_evento     11544  ...           22.0   \n",
       "2015-11-29    Sin_evento   Sin_evento     11544  ...           22.0   \n",
       "2015-11-30    Sin_evento   Sin_evento     11544  ...           22.0   \n",
       "\n",
       "            ventas_maxm_7  ventas_maxm_8  ventas_maxm_9  ventas_maxm_10  \\\n",
       "date                                                                      \n",
       "2013-01-01            NaN            NaN            NaN             NaN   \n",
       "2013-01-02            NaN            NaN            NaN             NaN   \n",
       "2013-01-03            NaN            NaN            NaN             NaN   \n",
       "2013-01-04            NaN            NaN            NaN             NaN   \n",
       "2013-01-05            NaN            NaN            NaN             NaN   \n",
       "...                   ...            ...            ...             ...   \n",
       "2015-11-26           15.0           15.0           15.0            15.0   \n",
       "2015-11-27           22.0           22.0           22.0            22.0   \n",
       "2015-11-28           22.0           22.0           22.0            22.0   \n",
       "2015-11-29           22.0           22.0           22.0            22.0   \n",
       "2015-11-30           22.0           22.0           22.0            22.0   \n",
       "\n",
       "            ventas_maxm_11  ventas_maxm_12  ventas_maxm_13  ventas_maxm_14  \\\n",
       "date                                                                         \n",
       "2013-01-01             NaN             NaN             NaN             NaN   \n",
       "2013-01-02             NaN             NaN             NaN             NaN   \n",
       "2013-01-03             NaN             NaN             NaN             NaN   \n",
       "2013-01-04             NaN             NaN             NaN             NaN   \n",
       "2013-01-05             NaN             NaN             NaN             NaN   \n",
       "...                    ...             ...             ...             ...   \n",
       "2015-11-26            17.0            17.0            17.0            17.0   \n",
       "2015-11-27            22.0            22.0            22.0            22.0   \n",
       "2015-11-28            22.0            22.0            22.0            22.0   \n",
       "2015-11-29            22.0            22.0            22.0            22.0   \n",
       "2015-11-30            22.0            22.0            22.0            22.0   \n",
       "\n",
       "            ventas_maxm_15  \n",
       "date                        \n",
       "2013-01-01             NaN  \n",
       "2013-01-02             NaN  \n",
       "2013-01-03             NaN  \n",
       "2013-01-04             NaN  \n",
       "2013-01-05             NaN  \n",
       "...                    ...  \n",
       "2015-11-26            17.0  \n",
       "2015-11-27            22.0  \n",
       "2015-11-28            22.0  \n",
       "2015-11-29            22.0  \n",
       "2015-11-30            22.0  \n",
       "\n",
       "[21280 rows x 82 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unido = pd.concat([df,\n",
    "                      lags_sell_price_df,\n",
    "                      lags_rotura_stock_3_df,\n",
    "                      lags_rotura_stock_7_df,\n",
    "                      lags_rotura_stock_15_df,\n",
    "                      lags_ventas_df,\n",
    "                      min_movil_df,\n",
    "                      media_movil_df,\n",
    "                      max_movil_df], axis = 1)\n",
    "\n",
    "# Eliminar columnas duplicadas\n",
    "df_unido = df_unido.loc[:,~df_unido.columns.duplicated()]\n",
    "df_unido"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdb1834",
   "metadata": {},
   "source": [
    "### Eliminar los nulos que han generado las nuevas variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fddc50e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_unido.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0fe27685-fc13-46fd-bbe0-ebb09e8584c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store_id                 0\n",
       "ventas_mm_9              0\n",
       "ventas_mm_7              0\n",
       "ventas_mm_6              0\n",
       "ventas_mm_5              0\n",
       "                        ..\n",
       "ventas_lag_2             0\n",
       "ventas_lag_1             0\n",
       "rotura_stock_15_lag_1    0\n",
       "rotura_stock_7_lag_1     0\n",
       "ventas_maxm_15           0\n",
       "Length: 82, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unido.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa11f9af",
   "metadata": {},
   "source": [
    "### Eliminar las variables que no vamos a necesitar para modelizar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8731ee-0a75-402b-ba12-e7a75f3cb57d",
   "metadata": {},
   "source": [
    "Eliminamos las variables que no vamos a usar y las originales de las que se han derivado los lags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d0022e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_eliminar = ['d','wm_yr_wk','sell_price','rotura_stock_3','rotura_stock_7','rotura_stock_15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "48540f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_unido.drop(columns=a_eliminar, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51958aca",
   "metadata": {},
   "source": [
    "### Identificar la target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ef06ec69",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df_unido.ventas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae37124",
   "metadata": {},
   "source": [
    "### Separar num y cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d92a8d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = df_unido.select_dtypes(include='O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a2364290",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = df_unido.select_dtypes(exclude='O')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e924c05a",
   "metadata": {},
   "source": [
    "## TRANSFORMACIÓN DE CATEGÓRICAS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1fa5201",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9b786a",
   "metadata": {},
   "source": [
    "#### Variables a aplicar OHE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6e36cf47",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_ohe = ['year',\n",
    "          'month',\n",
    "          'wday',\n",
    "          'weekday',\n",
    "          'event_name_1',\n",
    "          'event_type_1'\n",
    "        ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8d4634",
   "metadata": {},
   "source": [
    "#### Instanciar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e75c2ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder(sparse_output = False, handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c9f58f",
   "metadata": {},
   "source": [
    "#### Entrenar y aplicar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4162d5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_ohe = ohe.fit_transform(cat[var_ohe])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9131f15",
   "metadata": {},
   "source": [
    "#### Guardar como dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "783255d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_ohe = pd.DataFrame(cat_ohe, columns = ohe.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5ec0ae",
   "metadata": {},
   "source": [
    "### Target Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320bbcb7",
   "metadata": {},
   "source": [
    "#### Variables a aplicar TE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3544bb26",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_te = ['year',\n",
    "          'month',\n",
    "          'wday',\n",
    "          'weekday',\n",
    "          'event_name_1',\n",
    "          'event_type_1'\n",
    "        ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d66cf0d",
   "metadata": {},
   "source": [
    "#### Instanciar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0d214b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "te = TargetEncoder(min_samples_leaf=100, return_df = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ea9fb7",
   "metadata": {},
   "source": [
    "#### Entrenar y aplicar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aabd67d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_te = te.fit_transform(cat[var_te], y = target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8423376",
   "metadata": {},
   "source": [
    "#### Guardar como dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9a188ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Añadir sufijos a los nombres\n",
    "nombres_te = [variable + '_te' for variable in var_te]\n",
    "\n",
    "#Guardar como dataframe\n",
    "cat_te = pd.DataFrame(cat_te, columns = nombres_te)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87434e0c",
   "metadata": {},
   "source": [
    "## UNIFICAR DATASETS TRANSFORMADOS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d638486",
   "metadata": {},
   "source": [
    "### Meter en una lista todos los dataframes generados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44aeca4e",
   "metadata": {},
   "source": [
    "Rescatamos de df_unido las variables de segmentación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "19cfe9b5",
   "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>date</th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-17</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date store_id      item_id\n",
       "0 2013-01-16     CA_3  FOODS_3_090\n",
       "1 2013-01-17     CA_3  FOODS_3_090"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_df_unido = df_unido[['store_id','item_id']].reset_index()\n",
    "\n",
    "de_df_unido.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b90d8a5",
   "metadata": {},
   "source": [
    "### Unir todos los dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a63745d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframes = [de_df_unido, cat_ohe,cat_te,num.reset_index(drop=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1a0f6ea0",
   "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>date</th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>year_2013</th>\n",
       "      <th>year_2014</th>\n",
       "      <th>year_2015</th>\n",
       "      <th>month_1</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</th>\n",
       "      <th>month_4</th>\n",
       "      <th>...</th>\n",
       "      <th>ventas_maxm_6</th>\n",
       "      <th>ventas_maxm_7</th>\n",
       "      <th>ventas_maxm_8</th>\n",
       "      <th>ventas_maxm_9</th>\n",
       "      <th>ventas_maxm_10</th>\n",
       "      <th>ventas_maxm_11</th>\n",
       "      <th>ventas_maxm_12</th>\n",
       "      <th>ventas_maxm_13</th>\n",
       "      <th>ventas_maxm_14</th>\n",
       "      <th>ventas_maxm_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-17</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-18</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-19</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>281.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-20</td>\n",
       "      <td>CA_3</td>\n",
       "      <td>FOODS_3_090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>362.0</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>20975</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20976</th>\n",
       "      <td>2015-11-27</td>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20977</th>\n",
       "      <td>2015-11-28</td>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20978</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20979</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>CA_4</td>\n",
       "      <td>FOODS_3_714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20980 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date store_id      item_id  year_2013  year_2014  year_2015  \\\n",
       "0     2013-01-16     CA_3  FOODS_3_090        1.0        0.0        0.0   \n",
       "1     2013-01-17     CA_3  FOODS_3_090        1.0        0.0        0.0   \n",
       "2     2013-01-18     CA_3  FOODS_3_090        1.0        0.0        0.0   \n",
       "3     2013-01-19     CA_3  FOODS_3_090        1.0        0.0        0.0   \n",
       "4     2013-01-20     CA_3  FOODS_3_090        1.0        0.0        0.0   \n",
       "...          ...      ...          ...        ...        ...        ...   \n",
       "20975 2015-11-26     CA_4  FOODS_3_714        0.0        0.0        1.0   \n",
       "20976 2015-11-27     CA_4  FOODS_3_714        0.0        0.0        1.0   \n",
       "20977 2015-11-28     CA_4  FOODS_3_714        0.0        0.0        1.0   \n",
       "20978 2015-11-29     CA_4  FOODS_3_714        0.0        0.0        1.0   \n",
       "20979 2015-11-30     CA_4  FOODS_3_714        0.0        0.0        1.0   \n",
       "\n",
       "       month_1  month_2  month_3  month_4  ...  ventas_maxm_6  ventas_maxm_7  \\\n",
       "0          1.0      0.0      0.0      0.0  ...          351.0          351.0   \n",
       "1          1.0      0.0      0.0      0.0  ...          351.0          351.0   \n",
       "2          1.0      0.0      0.0      0.0  ...          351.0          351.0   \n",
       "3          1.0      0.0      0.0      0.0  ...          281.0          351.0   \n",
       "4          1.0      0.0      0.0      0.0  ...          362.0          362.0   \n",
       "...        ...      ...      ...      ...  ...            ...            ...   \n",
       "20975      0.0      0.0      0.0      0.0  ...           15.0           15.0   \n",
       "20976      0.0      0.0      0.0      0.0  ...           22.0           22.0   \n",
       "20977      0.0      0.0      0.0      0.0  ...           22.0           22.0   \n",
       "20978      0.0      0.0      0.0      0.0  ...           22.0           22.0   \n",
       "20979      0.0      0.0      0.0      0.0  ...           22.0           22.0   \n",
       "\n",
       "       ventas_maxm_8  ventas_maxm_9  ventas_maxm_10  ventas_maxm_11  \\\n",
       "0              351.0          351.0           351.0           351.0   \n",
       "1              351.0          351.0           351.0           351.0   \n",
       "2              351.0          351.0           351.0           351.0   \n",
       "3              351.0          351.0           351.0           351.0   \n",
       "4              362.0          362.0           362.0           362.0   \n",
       "...              ...            ...             ...             ...   \n",
       "20975           15.0           15.0            15.0            17.0   \n",
       "20976           22.0           22.0            22.0            22.0   \n",
       "20977           22.0           22.0            22.0            22.0   \n",
       "20978           22.0           22.0            22.0            22.0   \n",
       "20979           22.0           22.0            22.0            22.0   \n",
       "\n",
       "       ventas_maxm_12  ventas_maxm_13  ventas_maxm_14  ventas_maxm_15  \n",
       "0               351.0           351.0           351.0           351.0  \n",
       "1               351.0           351.0           351.0           351.0  \n",
       "2               351.0           351.0           351.0           351.0  \n",
       "3               351.0           351.0           351.0           351.0  \n",
       "4               362.0           362.0           362.0           362.0  \n",
       "...               ...             ...             ...             ...  \n",
       "20975            17.0            17.0            17.0            17.0  \n",
       "20976            22.0            22.0            22.0            22.0  \n",
       "20977            22.0            22.0            22.0            22.0  \n",
       "20978            22.0            22.0            22.0            22.0  \n",
       "20979            22.0            22.0            22.0            22.0  \n",
       "\n",
       "[20980 rows x 142 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tablon = pd.concat(dataframes, axis = 1)\n",
    "\n",
    "df_tablon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f098f8a",
   "metadata": {},
   "source": [
    "## GUARDAR DATASET TRAS TRANSFORMACIÓN DE DATOS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7656ba95",
   "metadata": {},
   "source": [
    "En formato pickle para no perder las modificaciones de metadatos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "782bf43c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definir los nombres del archivo\n",
    "ruta_df_tablon = ruta_proyecto + '/02_Datos/03_Trabajo/' + 'df_tablon.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "44c7dd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Guardar los archivos\n",
    "df_tablon.to_pickle(ruta_df_tablon)"
   ]
  }
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