exploration_script.ipynb

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  "metadata": {
    "colab": {
      "name": "exploration_script.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NL7wRldqw0t"
      },
      "source": [
        "# **Datathon Cajamar UniversityHack 2021**\n",
        "\n",
        "The competition consists of performing a data analysis and feature extraction in order to obtain a model capable of **estimating sales for each product and date.**\n",
        "\n",
        "The **data** we work with is provided by the Spanish company PcComponentes that sells technological products.\n",
        "\n",
        "This notebook is the final work done and summarized in the most important parts.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "## **Evaluation**\n",
        "\n",
        "Projects are evaluated following two rules:\n",
        "\n",
        "\n",
        "1.   The quality of the code, the analytical techniques used and the description of the project presented.\n",
        "2. Error metric between real values and stock outs (this is, when predicted sales are lower than real demand.\n",
        "\n",
        "This is  the metric to minimize:\n",
        "(0.7 * rRMSE) + (0.3 * (1 - CF)),\n",
        "- CF is the % of favorable cases, defined as those in which there has been no lack of stock, that is, the demand has been equal or less than forecasts.\n",
        "- rRMSE is defined as \n",
        "\n",
        ">![](https://www.cajamardatalab.com/datathon-cajamar-universityhack-2021/retos/predictivo/formula.jpg)\n",
        "\n",
        "Being:\n",
        "* \"n\": number of cases\n",
        "* \"Y<sub>i</sub>\": real value\n",
        "* \"Ŷ<sub>i</sub>\": estimated value\n",
        "* \"Ӯ\": mean of the real values\n",
        "\n",
        "## **Authors**\n",
        "\n",
        "1. **Anton Chernysh**: anton_chernysh@outlook.es &  https://github.com/Visn0\n",
        "2. **Arismendi Sánchez, Carlos Eduardo**: carlos.arismendisanchez@gmail.com & https://github.com/carlosarismendi\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJOTWshJfbum"
      },
      "source": [
        "#0.Imports\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXUUEvvrePAM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45ebdadf-26dc-4962-9b5d-93032cf44473"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "from datetime import date\n",
        "import holidays\n",
        "import pickle\n",
        "import seaborn as sns\n",
        "import math\n",
        "import time\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split, KFold\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PowerTransformer, QuantileTransformer, Normalizer, RobustScaler, Normalizer\n",
        "from scipy.stats import zscore\n",
        "from sklearn.ensemble import VotingRegressor, GradientBoostingRegressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Conv2D, BatchNormalization, Flatten, LSTM, Activation, Input, GRU, LeakyReLU,  ReLU\n",
        "from tensorflow.keras.initializers import he_uniform\n",
        "from tensorflow.keras.regularizers import L1, L2\n",
        "\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.python.framework import ops\n",
        "from tensorflow.python.ops import math_ops\n",
        "\n",
        "from os.path import join\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "print(f'tensorflow version: {tf.__version__}')\n",
        "print(f'sklearn version: {sklearn.__version__}')\n",
        "\n",
        "tf.random.set_seed(0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "tensorflow version: 2.6.0\n",
            "sklearn version: 0.22.2.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxoXuJL9jqb6"
      },
      "source": [
        "#1.Constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flVvpUNDjtQQ"
      },
      "source": [
        "DIR_DRIVE_BASE      = '/content/drive/My Drive'\n",
        "DIR_COMPETICIONES   = join( DIR_DRIVE_BASE, 'UniversityHack')\n",
        "DIR_DATASET_BASE    = join( DIR_COMPETICIONES, 'dataset')\n",
        "DIR_LEARNING_CURVES = join( DIR_COMPETICIONES, 'learning_curves')\n",
        "DIR_SCORES          = join( DIR_COMPETICIONES, 'scores')\n",
        "DIR_CHECKPOINTS     = join( DIR_COMPETICIONES, 'checkpoints')\n",
        "DIR_LOGS            = join( DIR_COMPETICIONES, 'logs')\n",
        "\n",
        "LABEL_LSTM         = 'lstm'\n",
        "LABEL_CONVOLUTION  = 'convolution'\n",
        "LABEL_ALL_ITEM_ROW = 'all_item_row'\n",
        "LABEL_ONE_ITEM_ROW = 'one_item_row'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_yWzmvuc6Lk"
      },
      "source": [
        "# 2.Definitions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPw6bldykYcD"
      },
      "source": [
        "## 2.1. Preprocess"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjVWhvlxkbLj"
      },
      "source": [
        "### 2.1.1. Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooDfrfSqkr6g"
      },
      "source": [
        "def import_data(file, submission):\n",
        "  \"\"\"\n",
        "  file: url to the file.\n",
        "  submission: boolean indicating whether to import file to predict or to model.\n",
        "  \"\"\"\n",
        "  drive.mount('/content/drive')\n",
        "\n",
        "  # Read file\n",
        "  df = pd.read_csv(file, sep='|', header=0, decimal=',')  \n",
        "  df.drop_duplicates(inplace=True, keep='first', ignore_index=True)\n",
        "\n",
        "  # Transform date format to datetime\n",
        "  if submission:  \n",
        "    df['fecha'] = pd.to_datetime(df['fecha'], format='%Y/%m/%d')\n",
        "  else:\n",
        "    df['fecha'] = pd.to_datetime(df['fecha'], format='%d/%m/%Y %H:%M:%S')\n",
        "  \n",
        "  # Extract day, month and year\n",
        "  df['day']   = df['fecha'].dt.day.astype('float64', errors='ignore')\n",
        "  df['month'] = df['fecha'].dt.month.astype('float64', errors='ignore')\n",
        "  df['year']  = df['fecha'].dt.year.astype('float64', errors='ignore')  \n",
        "  \n",
        "  # Transform date into sine and cosine waves\n",
        "  df = parse_date_to_sin_cos(df)\n",
        "  # Get timestamp from date\n",
        "  df['timestamp'] = df['fecha'].map(datetime.datetime.timestamp)\n",
        "\n",
        "  # OneHotEncoder\n",
        "  df.loc[df['estado'] == 'Rotura', 'estado']    = 1.0\n",
        "  df.loc[df['estado'] == 'Transito', 'estado']  = 2.0\n",
        "  df.loc[df['estado'] == 'No Rotura', 'estado'] = 3.0\n",
        "\n",
        "  # Extract holidays in Spain (main clients)\n",
        "  sp_holidays    = holidays.Spain(expand=True, observed=True)\n",
        "  df['fiestaSP'] = df['fecha'].map(lambda x: x in sp_holidays).astype(int)\n",
        "\n",
        "  # Extract holidays in Portugal (second main clients)\n",
        "  pt_holidays      = holidays.Portugal(expand=True, observed=True)\n",
        "  df['fiestaPT']   = df['fecha'].map(lambda x: x in pt_holidays).astype(int)  \n",
        "  \n",
        "  # Another custom OneHotEncoder\n",
        "  df['categoria_uno'] = df['categoria_uno'].map(ord)\n",
        "\n",
        "  # This data can have null values (represented with '-')\n",
        "  # We replace them with nulls to be able to work with them later\n",
        "  df['categoria_dos'] = df['categoria_dos'].apply(lambda x: np.nan if x == \"-\" else float(x))\n",
        "  df['antiguedad']    = df['antiguedad'].apply(lambda x: np.nan if x == \"-\" else float(x))\n",
        "\n",
        "  df = df.astype({ \"id\": float, \"categoria_uno\": float, \"categoria_dos\": float , \"antiguedad\": float , \"precio\": float, \"estado\": float, \"visitas\": float, \"dia_atipico\": float, \"campaña\": float })\n",
        "  if not submission:\n",
        "    df = df.astype({ \"unidades_vendidas\": float })\n",
        "\n",
        "  return df "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFanWCZQklF8"
      },
      "source": [
        "### 2.1.2.  Sin/Cos extraction from date"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyKmS7Dgk3wi"
      },
      "source": [
        "def parse_date_to_sin_cos(df):\n",
        "  day = 24.0 * 60.0* 60.0\n",
        "  week = 7 * day\n",
        "  year = 365.2425 * day\n",
        "\n",
        "  timestamp_s = df['fecha'].map(datetime.datetime.timestamp)\n",
        "  df['Year sin'] = np.sin(timestamp_s * (2.0 * np.pi / year))\n",
        "  df['Week sin'] = np.sin(timestamp_s * (2.0 * np.pi / week))\n",
        "  df['Year cos'] = np.cos(timestamp_s * (2.0 * np.pi / year))\n",
        "  df['Week cos'] = np.cos(timestamp_s * (2.0 * np.pi / week))  \n",
        "\n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOgozd3vIbuO"
      },
      "source": [
        "### 2.1.3. Custom Null values fill\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_jgKVsZIu3A"
      },
      "source": [
        "def fill_nan(df_nan, pad_columns, backfill_columns):\n",
        "  \"\"\"\n",
        "    df_nan: dataframe to work with\n",
        "    pad_columns: list of columns to fill with pad method\n",
        "    backfill_columns: list of columns to fill with backfill method    \n",
        "  \"\"\"\n",
        "  df = df_nan.copy()\n",
        "  df.sort_values(['fecha'], axis=0, inplace=True)\n",
        "\n",
        "  unicos = df['id'].unique()\n",
        "  df_filled = pd.DataFrame(columns=df.columns)\n",
        "  for id in unicos:\n",
        "    df_ids_cols = df[df['id'] == id].copy()\n",
        "    df_ids_cols[pad_columns]      = df_ids_cols[pad_columns].interpolate(method='pad', inplace=False)\n",
        "    df_ids_cols[backfill_columns] = df_ids_cols[backfill_columns].interpolate(method='backfill', inplace=False)  \n",
        "    df_filled = pd.concat([df_filled, df_ids_cols])\n",
        "  \n",
        "  df_filled = df_filled.dropna(subset=['precio'])\n",
        "\n",
        "  df_filled.sort_values(['id','fecha'], axis=0, inplace=True)\n",
        "  df_filled['antiguedad'].interpolate(method='pad', inplace=True)\n",
        "  df_filled['antiguedad'].interpolate(method='backfill', inplace=True)\n",
        "\n",
        "  df_filled.sort_values(['id', 'precio'], axis=0, inplace=True)\n",
        "  df_filled['categoria_dos'].interpolate(method='pad', inplace=True)\n",
        "  df_filled['categoria_dos'].interpolate(method='backfill', inplace=True)\n",
        "\n",
        "  df_filled.sort_values(['fecha', 'id'], axis=0, inplace=True)\n",
        "  return df_filled\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ZFhuKK9hF3k"
      },
      "source": [
        "### 2.1.4. Age preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhQs7HcghJF7"
      },
      "source": [
        "def antiguedad_estimate_apply(row, fecha_max, antiguedad_max, categoria_max):\n",
        "  date_diff = row['fecha'] - fecha_max\n",
        "  row['antiguedad'] = date_diff.days + antiguedad_max\n",
        "  row['categoria_dos'] = categoria_max\n",
        "  return row\n",
        "\n",
        "def antiguedad_estimate(df_model, df_estimate):\n",
        "  df_final = pd.DataFrame(columns=df_estimate.columns)\n",
        "  estimate_ids = df_estimate['id'].unique()\n",
        "\n",
        "  for id in estimate_ids:\n",
        "    df_id_estimate = df_estimate[df_estimate['id'] == id].copy()\n",
        "    df_id_model = df_model[df_model['id'] == id].copy()\n",
        "    \n",
        "    fecha_max = df_id_model['fecha'].max()\n",
        "    antiguedad_max = df_id_model['antiguedad'].max()\n",
        "    categoria_max = df_id_model['categoria_dos'].max()\n",
        "    \n",
        "    df_id_estimate = df_id_estimate.apply(antiguedad_estimate_apply, args=(fecha_max, antiguedad_max, categoria_max), axis=1)\n",
        "    df_final = pd.concat([df_final, df_id_estimate])\n",
        "\n",
        "  df_final.sort_values(['id', 'fecha'], inplace=True, axis=0, ignore_index=True)\n",
        "  return df_final\n",
        "\n",
        "\n",
        "def antiguedad_model(df_model):\n",
        "  df_final = pd.DataFrame(columns=df_model.columns)\n",
        "  model_ids = df_model['id'].unique()\n",
        "\n",
        "  for id in model_ids:\n",
        "    df_id_model = df_model[df_model['id'] == id].copy()\n",
        "    \n",
        "    fechamin = df_id_model['fecha'].min()\n",
        "    df_id_model['date_diff'] = (df_id_model['fecha'] - fechamin).map(lambda x: x.days)\n",
        "    df_id_model['antiguedad'] = df_id_model['antiguedad'] + df_id_model['date_diff']\n",
        "    df_id_model.drop(['date_diff'], axis=1, inplace=True)\n",
        "        \n",
        "    df_final = pd.concat([df_final, df_id_model])\n",
        "\n",
        "  df_final.sort_values(['id', 'fecha'], inplace=True, axis=0, ignore_index=True)\n",
        "  return df_final"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lNBSwcqCb1C"
      },
      "source": [
        "### 2.1.5. Drop duplicates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEXFi_BPCeer"
      },
      "source": [
        "def drop_duplicates(df_raw):\n",
        "  df = df_raw.copy()\n",
        "\n",
        "  df.drop_duplicates(subset=['id', 'fecha'], keep='last', inplace=True, ignore_index=True)  \n",
        "  df.sort_values(['fecha','id'], axis=0, inplace=True, ignore_index=True)  \n",
        "\n",
        "  print(\"DF raw: \\t\", df_raw.shape)\n",
        "  print(\"DF dups clean: \\t\", df.shape)\n",
        "  \n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCArcK1eOA_S"
      },
      "source": [
        "### 2.1.6. Scale data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbKbFJFOODFO"
      },
      "source": [
        "def scale(df, cols_to_scale, scaler, fit=False, append_scaled=False):\n",
        "  df_copy = df.copy()\n",
        "\n",
        "  if len(cols_to_scale) == 0:\n",
        "    return df_copy\n",
        "  \n",
        "  # Save not scaled columns\n",
        "  cols_not_scaled = [col for col in df_copy.columns if col not in cols_to_scale] \n",
        "  \n",
        "  # Fit scaler\n",
        "  if fit:    \n",
        "    scaler = scaler.fit(df_copy[cols_to_scale])\n",
        "    print(f'Fit: {fit}')\n",
        "\n",
        "  # Transform dataset\n",
        "  scaled = scaler.transform(df_copy[cols_to_scale])\n",
        "  scaled = pd.DataFrame(data=scaled, index=df_copy.index, columns=cols_to_scale)\n",
        "\n",
        "  if append_scaled:\n",
        "    scaled = df_copy.join(scaled, rsuffix='_scaled')\n",
        "  else:    \n",
        "    scaled = scaled.join(df_copy[cols_not_scaled])  \n",
        "    \n",
        "  return scaler, scaled"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4v7Pcxjhkm2j"
      },
      "source": [
        "#### 2.1.6.1. Todos los items en una fila"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svOq5SUhlow3"
      },
      "source": [
        "def reordenar_dataframe(df, train=False):\n",
        "  unique_ids   = len(df['id'].unique())\n",
        "  unique_dates = len(df['fecha'].unique())\n",
        "\n",
        "  df.sort_values(['id','fecha'], axis=0, inplace=True, ignore_index=True)\n",
        "  df_reorden = []\n",
        "  i = 1\n",
        "  if train:\n",
        "    while i <= unique_ids :\n",
        "      df_reorden.append(df[(i-1)*unique_dates : (i-1)*unique_dates + unique_dates])\n",
        "      i = i + 1\n",
        "  \n",
        "  columns_important = ['visitas','precio','campaña'] #id ,'antiguedad''estado','categoria_dos','dia_atipico','categoria_uno',\n",
        "  df_final_X = df_reorden[0][columns_important + ['unidades_vendidas','Year sin','Week sin']] #'day','month','year',\n",
        "\n",
        "  if train: \n",
        "    df_final_X.drop('unidades_vendidas', axis=1, inplace=True)\n",
        "    df_final_X.reset_index(drop=True, inplace=True)\n",
        "    df_final_y = df_reorden[0][['unidades_vendidas']]\n",
        "    df_final_y.reset_index(drop=True, inplace=True)\n",
        "\n",
        "  for i in range(1, len(df_reorden), 1):\n",
        "    df_copy = df_reorden[i].copy()\n",
        "    df_copy.reset_index(drop=True, inplace=True)\n",
        "\n",
        "    for c in columns_important:\n",
        "      df_final_X[f'{c}_{i}'] = df_copy[f'{c}']    \n",
        "\n",
        "    if train:\n",
        "      df_final_y[f'unidades_vendidas{i}'] = df_copy['unidades_vendidas']\n",
        "\n",
        "  print(f\"Reorden shape: {df_final_X.shape}\")\n",
        "  display(df_final_X)\n",
        "  if train: \n",
        "    return df_final_X, df_final_y\n",
        "  \n",
        "  return df_final_X, None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TC4BHhObLGQn"
      },
      "source": [
        "### 2.1.7. Data format "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAH3WVIZ8cS3"
      },
      "source": [
        "#### 2.1.7.1. Item and date per row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUJcXY-K8izH"
      },
      "source": [
        "def individual_item_per_row(df):\n",
        "  df.sort_values(['fecha', 'id'], axis=0, inplace=True, ignore_index=True)\n",
        "  X = df.drop(labels=['unidades_vendidas', 'fecha', 'timestamp'], axis=1)\n",
        "  y = pd.DataFrame(df.pop('unidades_vendidas').values, columns=['unidades_vendidas'])\n",
        "  return X, y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2Vzs-lHTNIE"
      },
      "source": [
        "#### 2.1.7.2. LSTM: all ids in the same row\n",
        "The intention was to create a general LSTM model that would unify the information of all the products and be ordered by date. This model was not adequate for this case, since we did not have data for all dates, and the delivery of our predictions was over a long period of dates, so our models would be accumulating the error for later dates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9_N0einTRL9"
      },
      "source": [
        "def parse_for_lstm(X, y, train=False, look_back_size=1, past_size=3):    \n",
        "  X_copy = X.copy()\n",
        "  if {'timestamp', 'fecha'}.issubset(X.columns):\n",
        "    X_copy = X_copy.drop(['timestamp','fecha'], axis=1)\n",
        "\n",
        "  X_final = X_copy.copy()\n",
        "  for i in range(past_size):\n",
        "    X_shifted = X_copy[X_copy.columns].shift( -(look_back_size+i) )\n",
        "    cols = []\n",
        "    for c in X_copy.columns:\n",
        "      cols.append(f'{c}_{i-1}_{i}')\n",
        "\n",
        "    X_shifted.columns = cols\n",
        "\n",
        "    X_final = X_final.join(X_shifted)\n",
        "    X_final = X_final.dropna()\n",
        "  \n",
        "  if train:\n",
        "    y = y[look_back_size+past_size-1:]\n",
        "  \n",
        "  X_final = np.reshape( X_final.to_numpy(), (len(X_final), past_size+1, int(X_final.shape[-1]/(past_size+1))) )\n",
        "  return X_final, y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrD1_YYEm48M"
      },
      "source": [
        "#### 2.1.7.X. Format data selector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6xSSU9Fm48M"
      },
      "source": [
        "def split_xy(df_raw, kind=1, train=False, cols_to_scale=None):\n",
        "  X = None\n",
        "  y = None  \n",
        "  kind_label = None\n",
        "  df_copy = df_raw.copy()          \n",
        "\n",
        "  # deprecated\n",
        "  # if kind == 1:\n",
        "  #   X, y = reordenar_dataframe(df_copy, train=train)\n",
        "  #   kind_label = 'all_item_row'\n",
        "  # elif kind == 2:\n",
        "  #   X, y = parse_data_for_convolutions(df_copy, train)\n",
        "  #   kind_label = 'convolutions'\n",
        "  if kind == 3:\n",
        "    if train:\n",
        "      X, y = individual_item_per_row(df_copy)\n",
        "    else:\n",
        "      X = df_copy\n",
        "    kind_label = 'one_item_row'\n",
        "  elif kind == 4:\n",
        "    X, y = reordenar_dataframe(df_copy, train=train)        \n",
        "    X, y = parse_for_lstm(X, y, train, past_size=30)\n",
        "    kind_label = 'lstm'\n",
        "  \n",
        "  return  X, y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDVsOFmGZ0_m"
      },
      "source": [
        "## 2.2. Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDR2xFp3SUiZ"
      },
      "source": [
        "### 2.2.1. Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj_ltE4sZ240"
      },
      "source": [
        "def correlation(df):\n",
        "  corr_mat = df.corr()\n",
        "\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
        "  sns.heatmap(corr_mat, vmin=-1.0, vmax=1.0, cmap=\"jet\")\n",
        "  ax.set_title('Correlation map', fontsize=23)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7LDNgq9SXYQ"
      },
      "source": [
        "### 2.2.2. Generic (lines)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjC6hPcwh-eA"
      },
      "source": [
        "def plots(X, y, label, ax=None, fig=None):\n",
        "  if len(X) != len(y):\n",
        "    raise Exception('len(X) != len(y)')\n",
        "\n",
        "  n_plots = len(y)\n",
        "  \n",
        "  if ax is None or fig is None:\n",
        "    fig = plt.figure(figsize=(int(25), int(5)))\n",
        "    ax1 = fig.add_subplot(2, 1, 1)\n",
        "  else:\n",
        "    ax1 = fig.add_subplot(2, 1, 2, sharex=ax)\n",
        "\n",
        "  for i in range(0, n_plots, 1):\n",
        "    name = label[i]\n",
        "\n",
        "    ax1.plot(X[i], y[i], label=name) # x, y\n",
        "    ax1.set_title(f'Plot: {i}, {name}')\n",
        "\n",
        "    plt.subplots_adjust(top=0.97, bottom=0.03, left=0.03, right=0.97, hspace=0.1, wspace=0.15)\n",
        "\n",
        "  plt.legend()\n",
        "  plt.grid()\n",
        "  plt.plot()\n",
        "  return fig, ax1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24Lf1L1ofW-_"
      },
      "source": [
        "## 2.3. ZScore"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ujqTjpPfW_H"
      },
      "source": [
        "class ZScore:  \n",
        "  def __init__(self):\n",
        "    self.variance           = None\n",
        "    self.standard_deviation = None\n",
        "    self.fitted_cols        = None\n",
        "  \n",
        "  def is_fit(self):\n",
        "    if self.variance is None or self.standard_deviation is None:\n",
        "      raise Exception('ZScore is not fitted.')\n",
        "\n",
        "  def check_columns(self, df):\n",
        "    for col in self.fitted_cols:\n",
        "      if col not in df.columns:\n",
        "        raise Exception(f'Column: {col} not found in Dataframe.')\n",
        "\n",
        "  def fit(self, df, axis=0):\n",
        "    \"\"\"\n",
        "      axis: default 1 (columns).\n",
        "    \"\"\"\n",
        "    self.variance           = df.var(axis=axis)\n",
        "    self.standard_deviation = df.std(axis=axis)\n",
        "    self.fitted_cols        = df.columns    \n",
        "    return self\n",
        "\n",
        "  def transform(self, df):\n",
        "    self.is_fit()\n",
        "    self.check_columns(df)\n",
        "    return (df - self.variance) / self.standard_deviation\n",
        "\n",
        "  def inverse_transform(self, df_zscores):\n",
        "    self.is_fit()\n",
        "    self.check_columns(df_zscores)\n",
        "    return df_zscores * self.standard_deviation + self.variance\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFzX_1PmY2wA"
      },
      "source": [
        "## 2.4. Save and load pickle files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsiYnwIdY1yQ"
      },
      "source": [
        "def save_pickle(dir, filename, data):  \n",
        "  pickle_out = open( join(dir, f'{filename}.pckl'), 'wb')\n",
        "  pickle.dump(data, pickle_out)\n",
        "  pickle_out.close()\n",
        "\n",
        "def load_pickle(dir, filename):  \n",
        "  pickle_in = open( join(dir, f'{filename}.pckl'), 'rb' )\n",
        "  data = pickle.load(pickle_in)\n",
        "  pickle_in.close()\n",
        "  return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIt27uQ0g7m_"
      },
      "source": [
        "# 3. Preprocess pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxK8lFjtmTjl"
      },
      "source": [
        "## 3.1. Import and basic preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQd9t7BzZJyl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "40d7e0c9-7863-414f-cc3a-afba89523143"
      },
      "source": [
        "model_route    = join( DIR_DATASET_BASE, 'raw', 'Modelar_UH2021.txt' )\n",
        "estimate_route = join( DIR_DATASET_BASE, 'raw', 'Estimar2.txt' )\n",
        "\n",
        "df_model_raw    = import_data(model_route, submission=False)\n",
        "df_estimate_raw = import_data(estimate_route, submission=True)\n",
        "\n",
        "MODEL_IDS      = df_model_raw['id'].unique() #4168\n",
        "MODEL_DATES    = df_model_raw['fecha'].unique() # 487\n",
        "ESTIMATE_IDS   = df_estimate_raw['id'].unique() #2736\n",
        "ESTIMATE_DATES = df_estimate_raw['fecha'].unique() #2736\n",
        "\n",
        "df_model_raw.sort_values(['fecha', 'id'], inplace=True, ignore_index=True)\n",
        "display(df_model_raw.shape)\n",
        "display(df_estimate_raw.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2822: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  if self.run_code(code, result):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(2040037, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(212841, 20)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEm8Eq8Bzth8"
      },
      "source": [
        "### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEXFFRM1zg_A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        },
        "outputId": "0e03e7cc-5d4f-48de-954c-dff08c783739"
      },
      "source": [
        "display(df_model_raw)\n",
        "display(df_estimate_raw)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5241.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>23910.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5241.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>24306.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>27144.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4064.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>27504.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>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>2040032</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>456982.0</td>\n",
              "      <td>6575.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>81.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040033</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>457416.0</td>\n",
              "      <td>1395.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50.38</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040034</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>457422.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040035</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458650.0</td>\n",
              "      <td>1385.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040036</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1915.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2040037 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "0       2015-06-01   21972.0      0.0  ...  1.433117e+09         0         0\n",
              "1       2015-06-01   23910.0      5.0  ...  1.433117e+09         0         0\n",
              "2       2015-06-01   24306.0     13.0  ...  1.433117e+09         0         0\n",
              "3       2015-06-01   27144.0     15.0  ...  1.433117e+09         0         0\n",
              "4       2015-06-01   27504.0      7.0  ...  1.433117e+09         0         0\n",
              "...            ...       ...      ...  ...           ...       ...       ...\n",
              "2040032 2016-09-30  456982.0   6575.0  ...  1.475194e+09         0         0\n",
              "2040033 2016-09-30  457416.0   1395.0  ...  1.475194e+09         0         0\n",
              "2040034 2016-09-30  457422.0   1080.0  ...  1.475194e+09         0         0\n",
              "2040035 2016-09-30  458650.0   1385.0  ...  1.475194e+09         0         0\n",
              "2040036 2016-09-30  458660.0   1915.0  ...  1.475194e+09         0         0\n",
              "\n",
              "[2040037 rows x 21 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5150.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999999</td>\n",
              "      <td>9.749279e-01</td>\n",
              "      <td>-0.001494</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.475280e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-10-02</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5150.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999877</td>\n",
              "      <td>4.338837e-01</td>\n",
              "      <td>0.015708</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475366e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-10-03</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5150.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999458</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>0.032905</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475453e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-10-04</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5150.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.998745</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>0.050093</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.475539e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-10-05</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5150.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.997735</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>0.067266</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475626e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212836</th>\n",
              "      <td>2016-12-26</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.092719</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>0.995692</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.482710e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212837</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>965.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.075577</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>0.997140</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.482797e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212838</th>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.058413</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>0.998292</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.482883e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212839</th>\n",
              "      <td>2016-12-29</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>745.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.041232</td>\n",
              "      <td>7.920970e-13</td>\n",
              "      <td>0.999150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.482970e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212840</th>\n",
              "      <td>2016-12-30</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.024039</td>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>0.999711</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.483056e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>212841 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "0      2016-10-01   21972.0      5.0  ...  1.475280e+09         0         0\n",
              "1      2016-10-02   21972.0     15.0  ...  1.475366e+09         0         0\n",
              "2      2016-10-03   21972.0      5.0  ...  1.475453e+09         0         0\n",
              "3      2016-10-04   21972.0      5.0  ...  1.475539e+09         0         0\n",
              "4      2016-10-05   21972.0      0.0  ...  1.475626e+09         0         1\n",
              "...           ...       ...      ...  ...           ...       ...       ...\n",
              "212836 2016-12-26  458660.0   1085.0  ...  1.482710e+09         1         0\n",
              "212837 2016-12-27  458660.0    965.0  ...  1.482797e+09         0         0\n",
              "212838 2016-12-28  458660.0   1005.0  ...  1.482883e+09         0         0\n",
              "212839 2016-12-29  458660.0    745.0  ...  1.482970e+09         0         0\n",
              "212840 2016-12-30  458660.0   1225.0  ...  1.483056e+09         0         0\n",
              "\n",
              "[212841 rows x 20 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CleSy0REz-Ya",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "outputId": "61f84f73-35e4-4d13-d8a8-611d5d0830ef"
      },
      "source": [
        "display(df_model_raw.describe())\n",
        "display(df_estimate_raw.describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.035644e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>640151.000000</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>1.559222e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "      <td>2.040037e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.697231e+05</td>\n",
              "      <td>1.244349e+02</td>\n",
              "      <td>6.917907e+01</td>\n",
              "      <td>1.891781e+02</td>\n",
              "      <td>2.679813e+00</td>\n",
              "      <td>31.213647</td>\n",
              "      <td>6.752427e-02</td>\n",
              "      <td>4.483252e-03</td>\n",
              "      <td>1.073822e+03</td>\n",
              "      <td>3.694655e+00</td>\n",
              "      <td>1.572379e+01</td>\n",
              "      <td>6.779839e+00</td>\n",
              "      <td>2.015560e+03</td>\n",
              "      <td>-1.032934e-01</td>\n",
              "      <td>-2.551265e-03</td>\n",
              "      <td>-1.825014e-01</td>\n",
              "      <td>4.282819e-03</td>\n",
              "      <td>1.454142e+09</td>\n",
              "      <td>2.044571e-02</td>\n",
              "      <td>2.453485e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.802673e+04</td>\n",
              "      <td>5.947592e+02</td>\n",
              "      <td>3.821744e+00</td>\n",
              "      <td>1.223170e+02</td>\n",
              "      <td>7.243964e-01</td>\n",
              "      <td>23.937821</td>\n",
              "      <td>3.639783e-01</td>\n",
              "      <td>6.680685e-02</td>\n",
              "      <td>7.871654e+02</td>\n",
              "      <td>1.942196e+01</td>\n",
              "      <td>8.800808e+00</td>\n",
              "      <td>3.065335e+00</td>\n",
              "      <td>4.964436e-01</td>\n",
              "      <td>6.786111e-01</td>\n",
              "      <td>7.073210e-01</td>\n",
              "      <td>7.039255e-01</td>\n",
              "      <td>7.068753e-01</td>\n",
              "      <td>1.217995e+07</td>\n",
              "      <td>1.415192e-01</td>\n",
              "      <td>1.547026e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.197200e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>6.500000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.570000</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.260000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.015000e+03</td>\n",
              "      <td>-9.999964e-01</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>-9.999926e-01</td>\n",
              "      <td>-9.009689e-01</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.076360e+05</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.500000e+01</td>\n",
              "      <td>7.500000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>13.670000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.240000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>2.015000e+03</td>\n",
              "      <td>-7.604384e-01</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>-8.657726e-01</td>\n",
              "      <td>-9.009689e-01</td>\n",
              "      <td>1.443571e+09</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.877520e+05</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>6.900000e+01</td>\n",
              "      <td>1.920000e+02</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>22.230000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.310000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>2.016000e+03</td>\n",
              "      <td>-1.738070e-01</td>\n",
              "      <td>-1.644440e-13</td>\n",
              "      <td>-3.509460e-01</td>\n",
              "      <td>-2.225209e-01</td>\n",
              "      <td>1.454112e+09</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.378480e+05</td>\n",
              "      <td>8.500000e+01</td>\n",
              "      <td>7.200000e+01</td>\n",
              "      <td>3.070000e+02</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>42.830000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.359000e+03</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>2.016000e+03</td>\n",
              "      <td>5.014426e-01</td>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>4.958041e-01</td>\n",
              "      <td>6.234898e-01</td>\n",
              "      <td>1.464739e+09</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.586600e+05</td>\n",
              "      <td>1.200450e+05</td>\n",
              "      <td>7.900000e+01</td>\n",
              "      <td>3.430000e+02</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>175.780000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>5.310000e+03</td>\n",
              "      <td>4.881000e+03</td>\n",
              "      <td>3.100000e+01</td>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>2.016000e+03</td>\n",
              "      <td>9.999679e-01</td>\n",
              "      <td>9.749279e-01</td>\n",
              "      <td>9.999964e-01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id       visitas  ...      fiestaSP      fiestaPT\n",
              "count  2.040037e+06  2.040037e+06  ...  2.040037e+06  2.040037e+06\n",
              "mean   2.697231e+05  1.244349e+02  ...  2.044571e-02  2.453485e-02\n",
              "std    8.802673e+04  5.947592e+02  ...  1.415192e-01  1.547026e-01\n",
              "min    2.197200e+04  0.000000e+00  ...  0.000000e+00  0.000000e+00\n",
              "25%    2.076360e+05  5.000000e+00  ...  0.000000e+00  0.000000e+00\n",
              "50%    2.877520e+05  2.300000e+01  ...  0.000000e+00  0.000000e+00\n",
              "75%    3.378480e+05  8.500000e+01  ...  0.000000e+00  0.000000e+00\n",
              "max    4.586600e+05  1.200450e+05  ...  1.000000e+00  1.000000e+00\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212403.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.0</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>2.128410e+05</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>2.128410e+05</td>\n",
              "      <td>212841.000000</td>\n",
              "      <td>212841.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>254764.373124</td>\n",
              "      <td>305.879624</td>\n",
              "      <td>68.442147</td>\n",
              "      <td>163.750498</td>\n",
              "      <td>2.989001</td>\n",
              "      <td>26.928288</td>\n",
              "      <td>0.277837</td>\n",
              "      <td>0.014471</td>\n",
              "      <td>1059.632839</td>\n",
              "      <td>15.760709</td>\n",
              "      <td>10.970170</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.650191</td>\n",
              "      <td>9.456863e-03</td>\n",
              "      <td>0.622400</td>\n",
              "      <td>-0.006188</td>\n",
              "      <td>1.479126e+09</td>\n",
              "      <td>0.053641</td>\n",
              "      <td>0.053364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>97368.478179</td>\n",
              "      <td>1534.355917</td>\n",
              "      <td>3.686537</td>\n",
              "      <td>113.427619</td>\n",
              "      <td>0.104297</td>\n",
              "      <td>21.127655</td>\n",
              "      <td>0.517923</td>\n",
              "      <td>0.119422</td>\n",
              "      <td>844.078251</td>\n",
              "      <td>8.849119</td>\n",
              "      <td>0.817147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.304984</td>\n",
              "      <td>7.107248e-01</td>\n",
              "      <td>0.311216</td>\n",
              "      <td>0.703383</td>\n",
              "      <td>2.280639e+06</td>\n",
              "      <td>0.225308</td>\n",
              "      <td>0.224758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>21972.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999999</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>-0.001494</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475280e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>177232.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>449.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.929786</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>0.368102</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.477181e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>264804.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>19.440000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>826.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.727978</td>\n",
              "      <td>-3.115288e-14</td>\n",
              "      <td>0.685600</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.479082e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>335178.000000</td>\n",
              "      <td>225.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.720000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.407491</td>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>0.913209</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.481069e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>458660.000000</td>\n",
              "      <td>289620.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>343.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>141.180000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5219.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.006838</td>\n",
              "      <td>9.749279e-01</td>\n",
              "      <td>0.999977</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.483142e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  id        visitas  ...       fiestaSP       fiestaPT\n",
              "count  212841.000000  212841.000000  ...  212841.000000  212841.000000\n",
              "mean   254764.373124     305.879624  ...       0.053641       0.053364\n",
              "std     97368.478179    1534.355917  ...       0.225308       0.224758\n",
              "min     21972.000000       0.000000  ...       0.000000       0.000000\n",
              "25%    177232.000000      25.000000  ...       0.000000       0.000000\n",
              "50%    264804.000000      75.000000  ...       0.000000       0.000000\n",
              "75%    335178.000000     225.000000  ...       0.000000       0.000000\n",
              "max    458660.000000  289620.000000  ...       1.000000       1.000000\n",
              "\n",
              "[8 rows x 19 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcKWBhNSzw64"
      },
      "source": [
        "### Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJujG05Yzz2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "outputId": "f38bfc33-35ac-4ab0-f7ed-e3c89e1c86e5"
      },
      "source": [
        "arrays = df_model_raw.hist(figsize = (15, 15), bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YnJKXRc1K5u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "outputId": "030ff052-7b1b-457c-f91b-2d2c7fe933ce"
      },
      "source": [
        "correlation(df_model_raw)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxBpULX8bXi-"
      },
      "source": [
        "## 3.2. Fill NaNs\n",
        "\n",
        "There were specifically 3 columns that had null values. One of them was `precio` (price).The official documentation told us that the price had to be filled in with the value of the closest price to an earlier date for the given product. However, when applying this method, we found that for some products there was no previous price to fill with. At the beginning we decided to fill them with later values, so that we propagate the value to the oldest dates. But almost at the end, we realized that those cases occurred in products that had sales only in the last dates, and their age it was less than the others. So we conclude that those records were only noise and that the product was actually put into the catalog since its first price. So the final decision was deleting records from dates prior to their first prices.\n",
        "\n",
        "The second column that had nulls was `categoria_dos` (category_two). The documentation did not tell us nothing of how to fill them, so we decided to find out which columns it was related to more, in order to deduce the values. In the correlation map, we observe that the column most related to the latter was the price. This seemed to indicate that the category had to do with how expensive the product was. So our strategy was to order the data by id and price and fill the empty category_two with their close values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POiXINZOgPbP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "24da2826-8d43-49ec-88d0-02d73b930f25"
      },
      "source": [
        "pad_col = ['precio','antiguedad']\n",
        "backfill_col = ['antiguedad']\n",
        "\n",
        "# rellena antiguedad por [fecha, id], categoria 2 por [id, precio]\n",
        "df_model_filled = fill_nan(df_nan=df_model_raw, pad_columns=pad_col, backfill_columns=backfill_col)\n",
        "display(df_model_filled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>23910.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5241.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>30014.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2954.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>31180.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5310.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>35732.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3069.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>40850.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5159.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040032</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>456982.0</td>\n",
              "      <td>6575.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>81.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040033</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>457416.0</td>\n",
              "      <td>1395.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50.38</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040034</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>457422.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.77</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040035</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458650.0</td>\n",
              "      <td>1385.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2040036</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1915.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1779590 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "1       2015-06-01   23910.0      5.0  ...  1.433117e+09         0         0\n",
              "5       2015-06-01   30014.0      5.0  ...  1.433117e+09         0         0\n",
              "7       2015-06-01   31180.0     11.0  ...  1.433117e+09         0         0\n",
              "9       2015-06-01   35732.0      8.0  ...  1.433117e+09         0         0\n",
              "13      2015-06-01   40850.0     12.0  ...  1.433117e+09         0         0\n",
              "...            ...       ...      ...  ...           ...       ...       ...\n",
              "2040032 2016-09-30  456982.0   6575.0  ...  1.475194e+09         0         0\n",
              "2040033 2016-09-30  457416.0   1395.0  ...  1.475194e+09         0         0\n",
              "2040034 2016-09-30  457422.0   1080.0  ...  1.475194e+09         0         0\n",
              "2040035 2016-09-30  458650.0   1385.0  ...  1.475194e+09         0         0\n",
              "2040036 2016-09-30  458660.0   1915.0  ...  1.475194e+09         0         0\n",
              "\n",
              "[1779590 rows x 21 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNDcbgFrkX-R"
      },
      "source": [
        "## 3.3. Drop duplicates\n",
        "\n",
        "After filling in NaNs, there were duplicate rows that were not deleted before because of the nulls."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DUej0rgMpOQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "a5d28710-29a3-4cca-b8c9-303c7847f54d"
      },
      "source": [
        "df_model_nodups = drop_duplicates(df_model_filled)\n",
        "display(df_model_nodups)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DF raw: \t (1779590, 21)\n",
            "DF dups clean: \t (1769628, 21)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-06-01</td>\n",
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              "      <td>2015.0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>30014.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2954.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
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              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>31180.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5310.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>35732.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3069.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>40850.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5159.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769623</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>456982.0</td>\n",
              "      <td>6575.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>81.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769624</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>457416.0</td>\n",
              "      <td>1395.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50.38</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769625</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>457422.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>336.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.77</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769626</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458650.0</td>\n",
              "      <td>1385.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769627</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1915.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>0.781831</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1769628 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "0       2015-06-01   23910.0      5.0  ...  1.433117e+09         0         0\n",
              "1       2015-06-01   30014.0      5.0  ...  1.433117e+09         0         0\n",
              "2       2015-06-01   31180.0     11.0  ...  1.433117e+09         0         0\n",
              "3       2015-06-01   35732.0      8.0  ...  1.433117e+09         0         0\n",
              "4       2015-06-01   40850.0     12.0  ...  1.433117e+09         0         0\n",
              "...            ...       ...      ...  ...           ...       ...       ...\n",
              "1769623 2016-09-30  456982.0   6575.0  ...  1.475194e+09         0         0\n",
              "1769624 2016-09-30  457416.0   1395.0  ...  1.475194e+09         0         0\n",
              "1769625 2016-09-30  457422.0   1080.0  ...  1.475194e+09         0         0\n",
              "1769626 2016-09-30  458650.0   1385.0  ...  1.475194e+09         0         0\n",
              "1769627 2016-09-30  458660.0   1915.0  ...  1.475194e+09         0         0\n",
              "\n",
              "[1769628 rows x 21 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsqQuMl1KAqc"
      },
      "source": [
        "## 3.4. Age preprocess\n",
        "\n",
        "Finally, the last column is `antiguedad` (age). The strategy used is similar to the one used for categoria_dos but ordering by date and id. The reason for this that during the analysis, we saw that the number of the id increased as its age decreased, so the older ids are the newest products. So ordering that way, ids that had no antiquity were filled with their neighbors. Then, for each product, was taken its first date and it was increased by 1 for each day that passed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuQoDWpyTinW"
      },
      "source": [
        "### 3.4.1. Data for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uofe5SxJ0mPp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "bbfc97a1-6939-49dc-af06-0653baed7c62"
      },
      "source": [
        "df_model_final = df_model_nodups.copy()\n",
        "df_model_final = antiguedad_model(df_model_final)\n",
        "display(df_model_final)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
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              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-06-06</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5241.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.440768</td>\n",
              "      <td>9.749279e-01</td>\n",
              "      <td>-0.897621</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.433549e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-06-07</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5242.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.425262</td>\n",
              "      <td>4.338837e-01</td>\n",
              "      <td>-0.905070</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433635e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-06-08</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5243.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.409630</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>-0.912252</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433722e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-06-09</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5244.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.393877</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>-0.919163</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.433808e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-06-10</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5245.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0.378007</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>-0.925803</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.433894e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769623</th>\n",
              "      <td>2016-09-26</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>890.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.88</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>155.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.996174</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>-0.087397</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.474848e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769624</th>\n",
              "      <td>2016-09-27</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.997530</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>-0.070248</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.474934e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769625</th>\n",
              "      <td>2016-09-28</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.998590</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>-0.053078</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475021e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769626</th>\n",
              "      <td>2016-09-29</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1325.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999356</td>\n",
              "      <td>-5.192230e-13</td>\n",
              "      <td>-0.035892</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.475107e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769627</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1915.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1769628 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "0       2015-06-06   21972.0      1.0  ...  1.433549e+09         0         0\n",
              "1       2015-06-07   21972.0      1.0  ...  1.433635e+09         0         0\n",
              "2       2015-06-08   21972.0      1.0  ...  1.433722e+09         0         0\n",
              "3       2015-06-09   21972.0      1.0  ...  1.433808e+09         0         0\n",
              "4       2015-06-10   21972.0      0.0  ...  1.433894e+09         0         1\n",
              "...            ...       ...      ...  ...           ...       ...       ...\n",
              "1769623 2016-09-26  458660.0    890.0  ...  1.474848e+09         0         0\n",
              "1769624 2016-09-27  458660.0   1375.0  ...  1.474934e+09         0         0\n",
              "1769625 2016-09-28  458660.0   1085.0  ...  1.475021e+09         0         0\n",
              "1769626 2016-09-29  458660.0   1325.0  ...  1.475107e+09         0         0\n",
              "1769627 2016-09-30  458660.0   1915.0  ...  1.475194e+09         0         0\n",
              "\n",
              "[1769628 rows x 21 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s94Pc6NVKGKP"
      },
      "source": [
        "### 3.4.2. Data to predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "SlHAf-H-jPxO",
        "outputId": "c214762d-3edc-4942-cec4-20169d85b0b9"
      },
      "source": [
        "df_estimate_final = antiguedad_estimate(df_model_final, df_estimate_raw)\n",
        "display(df_estimate_final)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>5724.0</td>\n",
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              "      <td>2016.0</td>\n",
              "      <td>-0.999999</td>\n",
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              "      <td>-0.001494</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-10-02</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5725.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999877</td>\n",
              "      <td>4.338837e-01</td>\n",
              "      <td>0.015708</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475366e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-10-03</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5726.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999458</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>0.032905</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475453e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-10-04</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5727.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.998745</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>0.050093</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.475539e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-10-05</td>\n",
              "      <td>21972.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5728.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.997735</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>0.067266</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475626e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212836</th>\n",
              "      <td>2016-12-26</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>246.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.092719</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>0.995692</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.482710e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212837</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>965.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>247.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.075577</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>0.997140</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.482797e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212838</th>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>248.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.058413</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>0.998292</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.482883e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212839</th>\n",
              "      <td>2016-12-29</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>745.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>249.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.041232</td>\n",
              "      <td>7.920970e-13</td>\n",
              "      <td>0.999150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.482970e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212840</th>\n",
              "      <td>2016-12-30</td>\n",
              "      <td>458660.0</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>70.85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.024039</td>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>0.999711</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.483056e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>212841 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "0      2016-10-01   21972.0      5.0  ...  1.475280e+09         0         0\n",
              "1      2016-10-02   21972.0     15.0  ...  1.475366e+09         0         0\n",
              "2      2016-10-03   21972.0      5.0  ...  1.475453e+09         0         0\n",
              "3      2016-10-04   21972.0      5.0  ...  1.475539e+09         0         0\n",
              "4      2016-10-05   21972.0      0.0  ...  1.475626e+09         0         1\n",
              "...           ...       ...      ...  ...           ...       ...       ...\n",
              "212836 2016-12-26  458660.0   1085.0  ...  1.482710e+09         1         0\n",
              "212837 2016-12-27  458660.0    965.0  ...  1.482797e+09         0         0\n",
              "212838 2016-12-28  458660.0   1005.0  ...  1.482883e+09         0         0\n",
              "212839 2016-12-29  458660.0    745.0  ...  1.482970e+09         0         0\n",
              "212840 2016-12-30  458660.0   1225.0  ...  1.483056e+09         0         0\n",
              "\n",
              "[212841 rows x 20 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUvxN5o3O0gO"
      },
      "source": [
        "## 3.5. Save the preprocessed data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1IcX6RXL125"
      },
      "source": [
        "save_pickle(DIR_DATASET_BASE, f'df_model_filled_nodups_antiguedad', df_model_final)\n",
        "save_pickle(DIR_DATASET_BASE, f'df_estimate_filled_nodups_antiguedad', df_estimate_final)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fUEsIcRO3cW"
      },
      "source": [
        "## 3.6. Import the preprocessed data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dOUS58KO-QL",
        "outputId": "624d8477-dab7-48d3-9840-a3e5309bfcf0"
      },
      "source": [
        "drive.mount('/content/drive', force_remount=True)\n",
        "df_model_final    = load_pickle(DIR_DATASET_BASE, f'df_model_filled_nodups_antiguedad')\n",
        "df_estimate_final = load_pickle(DIR_DATASET_BASE, f'df_estimate_filled_nodups_antiguedad')\n",
        "\n",
        "print(df_model_final.shape)\n",
        "print(df_estimate_final.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "(1769628, 21)\n",
            "(212841, 20)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpxUfdm6WbZl"
      },
      "source": [
        "## 3.7. Split train, val, test\n",
        "\n",
        "Split data in 60% train, 20% valid, 20% test and grouped by id in order to have all ids in each slice"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "hMC312g1AoKt",
        "outputId": "69d45e0b-55e1-4bf9-81cd-f32667b1f1ea"
      },
      "source": [
        "pct = 0.2 # valid and test %\n",
        "\n",
        "df_model_final.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "\n",
        "df_train = pd.DataFrame(columns=df_model_final.columns)\n",
        "df_val   = pd.DataFrame(columns=df_model_final.columns)\n",
        "df_test  = pd.DataFrame(columns=df_model_final.columns)\n",
        "\n",
        "id_uniques = df_model_final['id'].unique()\n",
        "\n",
        "for id in id_uniques:\n",
        "  df_id = df_model_final[df_model_final['id'] == id].copy()\n",
        "  val_size    = int(pct * len(df_id)) \n",
        "  train_end   = int(len(df_id) - val_size * 2)\n",
        "  \n",
        "  df_train_id = df_id[ :train_end]\n",
        "  df_val_id   = df_id[train_end : train_end+val_size]\n",
        "  df_test_id  = df_id[train_end+val_size: ]\n",
        "\n",
        "  df_train = pd.concat([df_train, df_train_id])\n",
        "  df_val   = pd.concat([df_val, df_val_id])\n",
        "  df_test  = pd.concat([df_test, df_test_id])\n",
        "\n",
        "df_train.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "df_val.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "df_test.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "\n",
        "display(df_train.shape)\n",
        "display(df_val.shape)\n",
        "display(df_test.shape)\n",
        "print(len(df_train['id'].unique()), len(df_val['id'].unique()), len(df_test['id'].unique()))\n",
        "print(len(df_train['fecha'].unique()), len(df_val['fecha'].unique()), len(df_test['fecha'].unique()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(1065158, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(352235, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(352235, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "4168 4168 4168\n",
            "477 189 97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFXy5mRZXZ5F"
      },
      "source": [
        "### 3.7.1. Save splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUAM7xNzXea3"
      },
      "source": [
        "save_pickle(DIR_DATASET_BASE, 'df_train', df_train)\n",
        "save_pickle(DIR_DATASET_BASE, 'df_val', df_val)\n",
        "save_pickle(DIR_DATASET_BASE, 'df_test', df_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hq_MpZgabCIm"
      },
      "source": [
        "## 3.8. Import splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "ym5XFmWTZ1Rt",
        "outputId": "4e921892-78c6-4753-b9de-95388dcb3d5e"
      },
      "source": [
        "df_train = load_pickle(DIR_DATASET_BASE, 'df_train')\n",
        "df_val = load_pickle(DIR_DATASET_BASE, 'df_val')\n",
        "df_test = load_pickle(DIR_DATASET_BASE, 'df_test')\n",
        "\n",
        "display(df_train.shape)\n",
        "display(df_val.shape)\n",
        "display(df_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(1065158, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(352235, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(352235, 21)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02PH4XH0UqPx"
      },
      "source": [
        "## 3.9. Scale data\n",
        "I would like to mention an approach that we did not realize was to scale the data by seasons since there were seasons with higher average visits and sales. In our case we always scale all the data together and try to eliminate the extreme values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G32wKyRwV0iD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "604a9be8-9e83-46ac-ee14-8e9ba23d2936"
      },
      "source": [
        "APPEND_SCALED = False # if append or replace inplace scaled data\n",
        "SCALE_X = True\n",
        "SCALE_Y = False\n",
        "scalerX = None\n",
        "scalerY = None\n",
        "\n",
        "cols_to_scale = df_train.columns \n",
        "cols_to_scale = cols_to_scale.drop(['fecha', \"unidades_vendidas\", 'timestamp', 'Year sin', 'Week sin', 'Year cos', 'Week cos'])\n",
        "\n",
        "df_train.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "df_val.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "df_test.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "df_estimate_final.sort_values(['fecha','id'], inplace=True, axis=0, ignore_index=True)\n",
        "\n",
        "df_train_scaled    = df_train.copy()\n",
        "df_val_scaled      = df_val.copy()\n",
        "df_test_scaled     = df_test.copy()\n",
        "df_estimate_scaled = df_estimate_final.copy()\n",
        "\n",
        "if SCALE_X:  \n",
        "  scalerX = MinMaxScaler(feature_range=(0, 1))\n",
        "  scalerX, df_train_scaled     = scale(df_train_scaled, cols_to_scale=cols_to_scale, scaler=scalerX, fit=True, append_scaled=APPEND_SCALED)\n",
        "  scalerX, df_val_scaled       = scale(df_val_scaled, cols_to_scale=cols_to_scale, scaler=scalerX, fit=False, append_scaled=APPEND_SCALED)\n",
        "  scalerX, df_test_scaled      = scale(df_test_scaled, cols_to_scale=cols_to_scale, scaler=scalerX, fit=False, append_scaled=APPEND_SCALED)\n",
        "  scalerX, df_estimate_scaled  = scale(df_estimate_scaled, cols_to_scale=cols_to_scale, scaler=scalerX, fit=False, append_scaled=APPEND_SCALED)\n",
        "\n",
        "if SCALE_Y:\n",
        "  scalerY = ZScore()\n",
        "  scalerY, df_train_scaled = scale(df_train_scaled, cols_to_scale=['unidades_vendidas'], scaler=scalerY, fit=True)\n",
        "  scalerY, df_val_scaled   = scale(  df_val_scaled, cols_to_scale=['unidades_vendidas'], scaler=scalerY, fit=False)\n",
        "  scalerY, df_test_scaled  = scale( df_test_scaled, cols_to_scale=['unidades_vendidas'], scaler=scalerY, fit=False)\n",
        "\n",
        "display(df_train_scaled.shape)\n",
        "display(df_val_scaled.shape)\n",
        "display(df_test_scaled.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fit: True\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(1065158, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(352235, 21)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(352235, 21)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "BgnpOGzJCuds",
        "outputId": "7b3bc1a3-4197-4907-f984-ceec50a11627"
      },
      "source": [
        "display(df_train_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "      <th>fecha</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.004438</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.492669</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.011187</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.933905</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.018416</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.178886</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.011478</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.516341</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.021086</td>\n",
              "      <td>0.000096</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.651026</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.022723</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.946504</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.031510</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.765396</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.128707</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.537338</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.043230</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.093842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.058440</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.918934</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015-06-01</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.433117e+09</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>1065153</th>\n",
              "      <td>0.997165</td>\n",
              "      <td>0.056800</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.979472</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.522170</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003286</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-09-18</td>\n",
              "      <td>42.0</td>\n",
              "      <td>-0.974765</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>-0.223234</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.474157e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065154</th>\n",
              "      <td>0.999977</td>\n",
              "      <td>0.013636</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.991202</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.388627</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003652</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-09-18</td>\n",
              "      <td>15.0</td>\n",
              "      <td>-0.974765</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>-0.223234</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.474157e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065155</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010813</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.991202</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.388627</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003834</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-09-18</td>\n",
              "      <td>21.0</td>\n",
              "      <td>-0.974765</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>-0.223234</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.474157e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065156</th>\n",
              "      <td>0.997165</td>\n",
              "      <td>0.052284</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.979472</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.522170</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003469</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-09-19</td>\n",
              "      <td>21.0</td>\n",
              "      <td>-0.978461</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.206433</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.474243e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065157</th>\n",
              "      <td>0.997165</td>\n",
              "      <td>0.024145</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.979472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.522170</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003652</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-09-20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.981867</td>\n",
              "      <td>-0.974928</td>\n",
              "      <td>-0.189571</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.474330e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1065158 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               id   visitas  categoria_uno  ...  Year cos  Week cos     timestamp\n",
              "0        0.004438  0.000043       0.142857  ... -0.856437 -0.900969  1.433117e+09\n",
              "1        0.018416  0.000043       0.142857  ... -0.856437 -0.900969  1.433117e+09\n",
              "2        0.021086  0.000096       0.071429  ... -0.856437 -0.900969  1.433117e+09\n",
              "3        0.031510  0.000069       0.000000  ... -0.856437 -0.900969  1.433117e+09\n",
              "4        0.043230  0.000104       0.000000  ... -0.856437 -0.900969  1.433117e+09\n",
              "...           ...       ...            ...  ...       ...       ...           ...\n",
              "1065153  0.997165  0.056800       0.357143  ... -0.223234 -0.900969  1.474157e+09\n",
              "1065154  0.999977  0.013636       0.714286  ... -0.223234 -0.900969  1.474157e+09\n",
              "1065155  1.000000  0.010813       0.714286  ... -0.223234 -0.900969  1.474157e+09\n",
              "1065156  0.997165  0.052284       0.357143  ... -0.206433 -0.900969  1.474243e+09\n",
              "1065157  0.997165  0.024145       0.357143  ... -0.189571 -0.222521  1.474330e+09\n",
              "\n",
              "[1065158 rows x 21 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJTMmYXX0RcF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "3e28ad5f-e923-4dc0-f34c-ab8502ec2396"
      },
      "source": [
        "fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(7, 3))\n",
        "_ = axs[0].plot(df_train[df_train['id'] == df_train.loc[0,'id']]['visitas'], '.', label='raw')\n",
        "_ = axs[1].plot(df_train_scaled[df_train_scaled['id'] == df_train_scaled.loc[0,'id']]['visitas'], '.', label='scaled')\n",
        "for ax in axs:\n",
        "  ax.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOzNdhKCK2zb"
      },
      "source": [
        "## 3.10. Split labels from data. X,y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI2BxrVLD3dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "631ad236-2833-4b00-e0c6-dc5af5c1f60c"
      },
      "source": [
        "df_train_xy = df_train_scaled.copy()\n",
        "df_val_xy   = df_val_scaled.copy()\n",
        "df_test_xy  = df_test_scaled.copy()\n",
        "\n",
        "df_train_xy.sort_values(['id','fecha'], axis=0, inplace=True, ignore_index=True)\n",
        "df_val_xy.sort_values(['id','fecha'], axis=0, inplace=True, ignore_index=True)\n",
        "df_test_xy.sort_values(['id','fecha'], axis=0, inplace=True, ignore_index=True)\n",
        "\n",
        "tipo_red = 3\n",
        "X_train, y_train = split_xy(df_train_xy, kind=tipo_red, train=True)\n",
        "X_val, y_val     = split_xy(df_val_xy, kind=tipo_red, train=True)\n",
        "X_test, y_test   = split_xy(df_test_xy, kind=tipo_red,train=True)\n",
        "\n",
        "print(f'X_train.shape: {X_train.shape}, y_train.shape: {y_train.shape}')\n",
        "print(f'X_val.shape: {X_val.shape}, y_val.shape: {y_val.shape}')\n",
        "print(f'X_test.shape: {X_test.shape}, y_test.shape: {y_test.shape}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train.shape: (1065158, 18), y_train.shape: (1065158, 1)\n",
            "X_val.shape: (352235, 18), y_val.shape: (352235, 1)\n",
            "X_test.shape: (352235, 18), y_test.shape: (352235, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2dkM9nMnQes"
      },
      "source": [
        "# 4. Show final training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5u2CIJCknS1E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "fc3bc7c4-d3b4-4700-986c-b0c049149515"
      },
      "source": [
        "print(f'train.min: {np.amin(X_train.values)}, train.max: {np.amax(X_train.values)}')\n",
        "print(f'val.min: {np.amin(X_val.values)}, val.max: {np.amax(X_val.values)}')\n",
        "print(f'test.min: {np.amin(X_test.values)}, test.max: {np.amax(X_test.values)}')\n",
        "\n",
        "display(X_train)\n",
        "display(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train.min: -0.9999964163499203, train.max: 1.0\n",
            "val.min: -0.9999925921414804, val.max: 1.0426003126628451\n",
            "test.min: -0.9999925921414804, test.max: 1.0354208508307468\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.000043</td>\n",
              "      <td>0.142857</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>-0.856437</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.018416</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.178886</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.021086</td>\n",
              "      <td>0.000096</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.651026</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.946504</td>\n",
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              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.031510</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.765396</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.128707</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.537338</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.516251</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>-0.856437</td>\n",
              "      <td>-0.900969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.043230</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.093842</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.058440</td>\n",
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              "      <td>0.918934</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.454545</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1065154</th>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.5</td>\n",
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              "      <td>0.003652</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.974765</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>-0.223234</td>\n",
              "      <td>-0.900969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065155</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010813</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.991202</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.388627</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003834</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.974765</td>\n",
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              "      <td>-0.223234</td>\n",
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              "      <td>-0.433884</td>\n",
              "      <td>-0.206433</td>\n",
              "      <td>-0.900969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065157</th>\n",
              "      <td>0.997165</td>\n",
              "      <td>0.024145</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.979472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.522170</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003652</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.981867</td>\n",
              "      <td>-0.974928</td>\n",
              "      <td>-0.189571</td>\n",
              "      <td>-0.222521</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1065158 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               id   visitas  categoria_uno  ...  Week sin  Year cos  Week cos\n",
              "0        0.004438  0.000043       0.142857  ... -0.433884 -0.856437 -0.900969\n",
              "1        0.018416  0.000043       0.142857  ... -0.433884 -0.856437 -0.900969\n",
              "2        0.021086  0.000096       0.071429  ... -0.433884 -0.856437 -0.900969\n",
              "3        0.031510  0.000069       0.000000  ... -0.433884 -0.856437 -0.900969\n",
              "4        0.043230  0.000104       0.000000  ... -0.433884 -0.856437 -0.900969\n",
              "...           ...       ...            ...  ...       ...       ...       ...\n",
              "1065153  0.997165  0.056800       0.357143  ...  0.433884 -0.223234 -0.900969\n",
              "1065154  0.999977  0.013636       0.714286  ...  0.433884 -0.223234 -0.900969\n",
              "1065155  1.000000  0.010813       0.714286  ...  0.433884 -0.223234 -0.900969\n",
              "1065156  0.997165  0.052284       0.357143  ... -0.433884 -0.206433 -0.900969\n",
              "1065157  0.997165  0.024145       0.357143  ... -0.974928 -0.189571 -0.222521\n",
              "\n",
              "[1065158 rows x 18 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unidades_vendidas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
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              "      <td>3.0</td>\n",
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              "      <td>42.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>9.0</td>\n",
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              "      <td>3.0</td>\n",
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              "         unidades_vendidas\n",
              "0                      3.0\n",
              "1                      3.0\n",
              "2                     42.0\n",
              "3                      9.0\n",
              "4                      3.0\n",
              "...                    ...\n",
              "1065153               42.0\n",
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              "1065155               21.0\n",
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              "\n",
              "[1065158 rows x 1 columns]"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ur2Y33PpXUaJ"
      },
      "source": [
        "# 5. Training pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_fVr6OfZScd"
      },
      "source": [
        "## 5.1. Definitions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZr7XWGdaqgC"
      },
      "source": [
        "### 5.1.1. Metrics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RnUyZ-ZZX6F"
      },
      "source": [
        "def SMAPE(y_true, y_pred):\n",
        "  y_pred = ops.convert_to_tensor_v2_with_dispatch(y_pred)\n",
        "  y_true = math_ops.cast(y_true, y_pred.dtype)\n",
        "\n",
        "  nom = tf.math.abs(y_pred-y_true)                 \n",
        "  denom = tf.maximum(tf.math.abs(y_pred) + tf.math.abs(y_true), tf.constant([0.00001], dtype=nom.dtype))\n",
        "  denom = tf.divide(denom, 2.0)\n",
        "  results = tf.divide(nom, denom)    \n",
        "  results = 100.0 * tf.reduce_mean(results)\n",
        "\n",
        "  return results \n",
        "\n",
        "def casos_favorables(y_true, y_pred):\n",
        "  cfs = tf.math.less_equal(y_true, y_pred)  \n",
        "  cfs = tf.cast(cfs, tf.int32)  \n",
        "\n",
        "  cfs = tf.math.reduce_sum(cfs)    \n",
        "  cfs = tf.cast(cfs, y_true.dtype)\n",
        "  \n",
        "  size = tf.cast( tf.size(y_true), y_true.dtype )\n",
        "  return tf.divide(cfs, size)\n",
        "\n",
        "def rRMSE(y_true, y_pred):\n",
        "  y_pred = tf.convert_to_tensor(y_pred, dtype=tf.float32)\n",
        "  y_true = tf.cast(y_true, y_pred.dtype)\n",
        "\n",
        "  pows = tf.math.pow( (y_true-y_pred), 2.0 )\n",
        "  sum  = tf.reduce_sum(pows)\n",
        "  mse  = tf.divide( sum, tf.cast(tf.size(y_pred), y_true.dtype) )\n",
        "  rmse = tf.sqrt( mse )\n",
        "  mean = tf.reduce_mean(y_true)\n",
        "\n",
        "  rrmse   = tf.divide(rmse, mean)\n",
        "  cf      = casos_favorables(y_true, y_pred)\n",
        "  results = (0.7 * rrmse) + (0.3 * (1.0 - cf))\n",
        "\n",
        "  return results    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LM-QB85-afm9"
      },
      "source": [
        "###5.1.2. Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DPDf5MmahlX"
      },
      "source": [
        "def histories_mean(histories, divisor, mean):  \n",
        "  history_array = np.array(histories)\n",
        "  history_mean = history_array[0]\n",
        "  size = len(histories[0].history['loss'])\n",
        "\n",
        "  if (not mean):\n",
        "    return history_mean\n",
        "\n",
        "  for i in range(size):\n",
        "    for j in range(1, divisor):    \n",
        "      history_mean.history['loss'][i] += history_array[j].history['loss'][i]\n",
        "      history_mean.history['val_loss'][i] += history_array[j].history['val_loss'][i]\n",
        "\n",
        "  for i in range(size):\n",
        "    history_mean.history['loss'][i] /= divisor\n",
        "    history_mean.history['val_loss'][i] /= divisor\n",
        "\n",
        "  return history_mean\n",
        "\n",
        "# plot diagnostic learning curves\n",
        "def plot_learning_curves(histories, title, mean_divisor, mean=False):\t\t \n",
        "  history_mean = histories_mean(histories, mean_divisor, mean)\t\t\n",
        " \n",
        "  fig = plt.figure(figsize=(15, 5))\n",
        "\n",
        "  # Loss plots\n",
        "  ax1 = fig.add_subplot(1, 3, 1)\n",
        "  ax1.set_title('Loss')\n",
        "  ax1.set_xlabel('epochs')\n",
        "  ax1.set_ylabel('loss')\n",
        "  ax1.plot(history_mean.history['loss'], color='blue', label='train')\n",
        "  ax1.plot(history_mean.history['val_loss'], color='orange', label='validation')\n",
        "\n",
        "  # SMAPE metric plot\n",
        "  ax2 = fig.add_subplot(1, 3, 2)\n",
        "  ax2.set_title('Metric SMAPE')\n",
        "  ax2.set_xlabel('epochs')\n",
        "  ax2.set_ylabel('smape')\n",
        "  ax2.plot(history_mean.history['SMAPE'], color='blue', label='train')\n",
        "  ax2.plot(history_mean.history['val_SMAPE'], color='orange', label='validation')\n",
        "\n",
        "  ax2 = fig.add_subplot(1, 3, 3)\n",
        "  ax2.set_title('Metric rRMSE')\n",
        "  ax2.set_xlabel('epochs')\n",
        "  ax2.set_ylabel('rRMSE')\n",
        "  ax2.plot(history_mean.history['rRMSE'], color='blue', label='train')\n",
        "  ax2.plot(history_mean.history['val_rRMSE'], color='orange', label='validation')\n",
        "\n",
        "  plt.subplots_adjust(top=0.85, bottom=0.10, left=0.05, right=0.95, hspace=0.5, wspace=0.40)\n",
        "\n",
        "  plt.legend()\n",
        "  plt.suptitle(title)\n",
        "  # plt.savefig(f'{DIR_LEARNING_CURVES}/lc_{title}.png')\n",
        "  plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qRbe6Iar5gf"
      },
      "source": [
        "## 5.2. Neural Architectures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIfuMhJJb1Cu"
      },
      "source": [
        "### 5.0. BaseModel. DenseBlock."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0qDyLZJb4UN"
      },
      "source": [
        "class BaseModel:\n",
        "  def __init__(self, input_size, output_size, kernel_regularizer=None, batch_norm=False):\n",
        "    self.input_size           = input_size\n",
        "    self.output_size          = output_size\n",
        "    self.batch_norm           = batch_norm\n",
        "    self.kernel_regularizer   = kernel_regularizer \n",
        "\n",
        "  def custom_activation(self, activation_function):\n",
        "    if isinstance(activation_function, str):\n",
        "      return Activation(activation_function)\n",
        "    else:\n",
        "      return activation_function  \n",
        "\n",
        "  def seq_dense_block(self, size, activation, kernel_regularizer=None, batch_norm=False, use_bias=True, drop_out=None):\n",
        "    initializer = he_uniform(518)\n",
        "\n",
        "    result = keras.Sequential()\n",
        "    result.add(\n",
        "      Dense(\n",
        "          size\n",
        "        , kernel_regularizer=kernel_regularizer\n",
        "        , kernel_initializer=initializer\n",
        "        , use_bias=use_bias\n",
        "      )\n",
        "    )\n",
        "    \n",
        "    if batch_norm:\n",
        "      result.add(BatchNormalization())\n",
        "    \n",
        "    result.add(self.custom_activation(activation))\n",
        "\n",
        "    if drop_out is not None:\n",
        "      result.add(Dropout(drop_out))\n",
        "    return result  \n",
        "\n",
        "  def generator(self):\n",
        "    return self.seq_generator()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsMWTW_sb4yv"
      },
      "source": [
        "### 5.2.1. Feedforward."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0zowWBn4OfV"
      },
      "source": [
        "class Feedforward(BaseModel):\n",
        "  def __init__(self, input_size, output_size, kernel_regularizer=None, batch_norm=False):    \n",
        "    BaseModel.__init__(self, input_size, output_size, kernel_regularizer, batch_norm)  \n",
        "\n",
        "  def seq_generator(self):\n",
        "    dense_stack = [\n",
        "        self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.1)  \n",
        "      , self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.1)  \n",
        "      , self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.1)  \n",
        "      , self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.1)  \n",
        "    ]\n",
        "\n",
        "    inputs = tf.keras.layers.Input(shape=self.input_size)\n",
        "    x = inputs \n",
        "\n",
        "    for dense in dense_stack:\n",
        "      x = dense(x)  \n",
        "\n",
        "    last = self.seq_dense_block(self.output_size, ReLU())\n",
        "    x = last(x)\n",
        "    \n",
        "    return keras.Model(inputs=inputs, outputs=x, name='SeqFeedforward')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJC2QjX0pqPG"
      },
      "source": [
        "### 5.2.2. DenseResidual."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0rP_M6za_82"
      },
      "source": [
        "class DenseResidual(BaseModel):\n",
        "  def __init__(self, input_size, output_size, kernel_regularizer=None, batch_norm=False):    \n",
        "    BaseModel.__init__(self, input_size, output_size, kernel_regularizer, batch_norm)  \n",
        "\n",
        "  def seq_generator(self):\n",
        "    down_stack = [\n",
        "        self.seq_dense_block(512, LeakyReLU(), self.kernel_regularizer, self.batch_norm, use_bias=False, drop_out=0.2)  \n",
        "      , self.seq_dense_block(512, LeakyReLU(), self.kernel_regularizer, self.batch_norm, use_bias=False, drop_out=0.2)        \n",
        "      , self.seq_dense_block(256, LeakyReLU(), self.kernel_regularizer, self.batch_norm, use_bias=False, drop_out=0.2)  \n",
        "      , self.seq_dense_block(128, LeakyReLU(), self.kernel_regularizer, self.batch_norm, use_bias=False, drop_out=0.2)  \n",
        "      , self.seq_dense_block(64,  LeakyReLU(), self.kernel_regularizer, self.batch_norm, use_bias=False, drop_out=0.2)      \n",
        "    ]\n",
        "\n",
        "    up_stack = [\n",
        "        self.seq_dense_block(64,  ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.2)  \n",
        "      , self.seq_dense_block(128, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.2)  \n",
        "      , self.seq_dense_block(256, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.2)  \n",
        "      , self.seq_dense_block(512, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=0.2)  \n",
        "    ]\n",
        "\n",
        "    inputs = tf.keras.layers.Input(shape=self.input_size)\n",
        "    x = inputs\n",
        "\n",
        "    # Downsampling through the model\n",
        "    skips = []\n",
        "    for down in down_stack:\n",
        "      x = down(x)\n",
        "      skips.append(x)\n",
        "\n",
        "    skips = reversed(skips[:-1])\n",
        "\n",
        "    # Upsampling and establishing the skip connections\n",
        "    for up, skip in zip(up_stack, skips):\n",
        "      x = up(x)\n",
        "      concat = tf.keras.layers.Concatenate()\n",
        "      x = concat([x, skip])\n",
        "\n",
        "    last = self.seq_dense_block(self.output_size, ReLU())\n",
        "    x = last(x)\n",
        "    \n",
        "    return keras.Model(inputs=inputs, outputs=x, name='SeqDenseResidual')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hIXvgXUOkY_"
      },
      "source": [
        "### 5.2.3. LSTM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4x2jtjROkZS"
      },
      "source": [
        "class RecurrentLSTM(BaseModel):\n",
        "  def __init__(self, input_size, output_size, kernel_regularizer=None, batch_norm=False):    \n",
        "    BaseModel.__init__(self, input_size, output_size, kernel_regularizer, batch_norm) \n",
        "\n",
        "  def seq_lstm_block(self, size, activation, return_sequences=False, batch_norm=False, drop_out=None):\n",
        "    initializer = he_uniform(518) \n",
        "\n",
        "    result = keras.Sequential()\n",
        "    result.add(LSTM(\n",
        "          size\n",
        "        , activation=None\n",
        "        , kernel_initializer=initializer\n",
        "        , recurrent_initializer=initializer\n",
        "        , return_sequences=return_sequences\n",
        "        , kernel_regularizer=self.kernel_regularizer\n",
        "    ))\n",
        "    \n",
        "    result.add(self.custom_activation(activation))\n",
        "\n",
        "    if self.batch_norm:\n",
        "      result.add(BatchNormalization())\n",
        "\n",
        "    if drop_out is not None:\n",
        "      result.add(layers.Dropout(drop_out))\n",
        "\n",
        "    return result\n",
        "\n",
        "  def seq_generator(self):\n",
        "    recurrent_stack = [\n",
        "        self.seq_lstm_block(8, ReLU(), True, self.batch_norm, drop_out=None)\n",
        "      , self.seq_lstm_block(8, ReLU(), True, self.batch_norm, drop_out=None)\n",
        "      , self.seq_lstm_block(8, ReLU(), True, self.batch_norm, drop_out=None)\n",
        "      , self.seq_lstm_block(8, ReLU(), False, self.batch_norm, drop_out=None)\n",
        "    ] \n",
        "\n",
        "    dense_stack = [\n",
        "        self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=None)  \n",
        "      , self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=None)  \n",
        "      # , self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=None)  \n",
        "      # , self.seq_dense_block(8, ReLU(), self.kernel_regularizer, self.batch_norm, drop_out=None)  \n",
        "    ]\n",
        "\n",
        "    inputs = tf.keras.layers.Input(shape=self.input_size)\n",
        "    x = inputs\n",
        "\n",
        "    for recurrent in recurrent_stack:\n",
        "      x = recurrent(x)    \n",
        "\n",
        "    for dense in dense_stack:\n",
        "      x = dense(x)    \n",
        "\n",
        "    last = self.seq_dense_block(self.output_size, ReLU())\n",
        "    x = last(x)\n",
        "    \n",
        "    return keras.Model(inputs=inputs, outputs=x, name='SeqRecurrentLSTM')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pu42gOJ1yUHl"
      },
      "source": [
        "### 5.2.4. Residual LSTM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmK7DLy-yS6f"
      },
      "source": [
        "class ResidualLSTM(RecurrentLSTM):\n",
        "  def __init__(self, input_size, output_size, kernel_regularizer=None, batch_norm=False):    \n",
        "    BaseModel.__init__(self, input_size, output_size, kernel_regularizer, batch_norm) \n",
        "\n",
        "  def seq_generator(self):\n",
        "    down_stack = [\n",
        "        self.seq_lstm_block(512, ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(512, ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(256, ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(128, ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(64,  ReLU(), True, self.batch_norm)\n",
        "    ]\n",
        "\n",
        "    up_stack = [\n",
        "        self.seq_lstm_block(64,  ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(128, ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(256, ReLU(), True, self.batch_norm)\n",
        "      , self.seq_lstm_block(512, ReLU(), True, self.batch_norm)\n",
        "    ]\n",
        "\n",
        "    inputs = tf.keras.layers.Input(shape=self.input_size)\n",
        "    x = inputs\n",
        "\n",
        "    # Downsampling through the model\n",
        "    skips = []\n",
        "    for down in down_stack:\n",
        "      x = down(x)\n",
        "      skips.append(x)\n",
        "\n",
        "    skips = reversed(skips[:-1])\n",
        "\n",
        "    # Upsampling and establishing the skip connections\n",
        "    for up, skip in zip(up_stack, skips):\n",
        "      x = up(x)\n",
        "      concat = tf.keras.layers.Concatenate()\n",
        "      x = concat([x, skip])    \n",
        "\n",
        "    x = self.seq_lstm_block(512, ReLU(), False, self.batch_norm)(x)    \n",
        "    last = self.seq_dense_block(self.output_size, ReLU())\n",
        "    x = last(x)\n",
        "    \n",
        "    return keras.Model(inputs=inputs, outputs=x, name='SeqResidualLSTM')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "So0A0qh2BdgE"
      },
      "source": [
        "##5.3 Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGlNKoI2sJlb"
      },
      "source": [
        "###5.3.1. Neural networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "K7Uic8Y0sgzd",
        "outputId": "6ad28fe1-d028-4e02-f21b-035194c968de"
      },
      "source": [
        "LR_DECAY      = tf.optimizers.schedules.ExponentialDecay(initial_learning_rate=2e-4, decay_steps=1e5, decay_rate=0.8)\n",
        "OPTIMIZER     = tf.keras.optimizers.Adam(LR_DECAY, beta_1=0.5, beta_2=0.999, epsilon=1e-07, amsgrad=False) \n",
        "KERNEL_REGULARIZER  = tf.keras.regularizers.l2(5e-5)\n",
        "OUTPUT_SIZE = y_train.shape[-1] if len(y_train.shape) > 1 else 1\n",
        "\n",
        "epochs = 10\n",
        "batch_size = 256\n",
        "LOSS          = 'mse'\n",
        "METRICS       = [ SMAPE,rRMSE ]\n",
        "\n",
        "model = Feedforward(X_train.shape[1:], OUTPUT_SIZE, kernel_regularizer=KERNEL_REGULARIZER, batch_norm=True).generator()\n",
        "model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=METRICS) \n",
        "\n",
        "history = model.fit(\n",
        "      X_train\n",
        "    , y_train\n",
        "    , batch_size=batch_size\n",
        "    , epochs=epochs\n",
        "    # , validation_split=0.1\n",
        "    , validation_data=(X_val, y_val)\n",
        "    , verbose=1, shuffle=True\n",
        "    )\n",
        "\n",
        "histories = list()\n",
        "histories.append(history)\n",
        "\n",
        "plot_learning_curves(histories, \"learning curves\", 1, mean=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "4161/4161 [==============================] - 16s 3ms/step - loss: 427.1993 - SMAPE: 144.4138 - rRMSE: 2.3067 - val_loss: 193.4310 - val_SMAPE: 164.6281 - val_rRMSE: 2.3925\n",
            "Epoch 2/10\n",
            "4161/4161 [==============================] - 14s 3ms/step - loss: 417.4230 - SMAPE: 146.5076 - rRMSE: 2.2381 - val_loss: 191.7535 - val_SMAPE: 159.6304 - val_rRMSE: 2.4805\n",
            "Epoch 3/10\n",
            "4161/4161 [==============================] - 12s 3ms/step - loss: 413.6184 - SMAPE: 143.3133 - rRMSE: 2.2313 - val_loss: 189.7740 - val_SMAPE: 158.6857 - val_rRMSE: 2.4636\n",
            "Epoch 4/10\n",
            "4161/4161 [==============================] - 14s 3ms/step - loss: 410.8059 - SMAPE: 141.5279 - rRMSE: 2.2305 - val_loss: 188.3569 - val_SMAPE: 158.4156 - val_rRMSE: 2.4302\n",
            "Epoch 5/10\n",
            "4161/4161 [==============================] - 14s 3ms/step - loss: 408.7843 - SMAPE: 140.1015 - rRMSE: 2.2255 - val_loss: 187.6532 - val_SMAPE: 154.5244 - val_rRMSE: 2.4256\n",
            "Epoch 6/10\n",
            "4161/4161 [==============================] - 12s 3ms/step - loss: 407.5015 - SMAPE: 139.3155 - rRMSE: 2.2295 - val_loss: 186.8597 - val_SMAPE: 153.3702 - val_rRMSE: 2.4217\n",
            "Epoch 7/10\n",
            "4161/4161 [==============================] - 14s 3ms/step - loss: 406.1744 - SMAPE: 139.1463 - rRMSE: 2.2227 - val_loss: 186.1005 - val_SMAPE: 153.8769 - val_rRMSE: 2.4260\n",
            "Epoch 8/10\n",
            "4161/4161 [==============================] - 13s 3ms/step - loss: 405.9342 - SMAPE: 138.7928 - rRMSE: 2.2280 - val_loss: 184.9341 - val_SMAPE: 154.6930 - val_rRMSE: 2.4199\n",
            "Epoch 9/10\n",
            "4161/4161 [==============================] - 12s 3ms/step - loss: 404.4193 - SMAPE: 139.0218 - rRMSE: 2.2221 - val_loss: 182.9217 - val_SMAPE: 154.3278 - val_rRMSE: 2.4121\n",
            "Epoch 10/10\n",
            "4161/4161 [==============================] - 12s 3ms/step - loss: 401.8186 - SMAPE: 139.1163 - rRMSE: 2.2120 - val_loss: 178.7433 - val_SMAPE: 155.3502 - val_rRMSE: 2.3955\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hfOKfUcsO4d"
      },
      "source": [
        "### 5.3.2 Trees "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXnxE34-Hq10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c697769-b473-4974-c5f9-c2ed34c107f7"
      },
      "source": [
        "from sklearn.ensemble import VotingRegressor, GradientBoostingRegressor, RandomForestRegressor, ExtraTreesRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# !pip install catboost\n",
        "# from catboost import CatBoostRegressor\n",
        "# import xgboost\n",
        "# from xgboost import XGBRegressor\n",
        "\n",
        "# model = CatBoostRegressor(iterations=1000, task_type=\"GPU\", devices='0:1') #2.7 / 2.4\n",
        "# model = XGBRegressor(max_depth=5, min_child_weight=1,  n_estimators=100, n_jobs=-1 , verbose=2,learning_rate=0.16)\n",
        "# gbm = XGBRegressor()\n",
        "# model = GridSearchCV(gbm, {\"colsample_bytree\":[1.0],\n",
        "#                             \"min_child_weight\":[1.0,1.2] ,\n",
        "#                             'max_depth': [3,4,5,6], \n",
        "#                             'n_estimators': [100, 200, 500],\n",
        "#                            'learning_rate' : [0.05, 0.1, 0.15]}, verbose=1, n_jobs=-1)\n",
        "\n",
        "# model = DecisionTreeRegressor(random_state=0,max_depth=10)\n",
        "# model = MLPRegressor(random_state=1, max_iter=500, early_stopping=True, verbose=True) # 2.52 val, 2.3 train aun se podria mejorar\n",
        "\n",
        "# model = GradientBoostingRegressor(max_depth=19, learning_rate=0.1, random_state=1, verbose=2) #depth=3\n",
        "# reg2 = RandomForestRegressor(max_depth=4,  random_state=517, verbose=2, n_jobs=-1, bootstrap=True) #depth=4\n",
        "# reg3 = LinearRegression( n_jobs=-1)\n",
        "# model = VotingRegressor(estimators=[('gb', reg1), ('rf', reg2 ), ('ln', reg3 )], n_jobs=-1)\n",
        "\n",
        "model = ExtraTreesRegressor(max_depth=19, n_estimators=95 ,random_state=40, verbose=2, n_jobs=-1, bootstrap=True)\n",
        "\n",
        "start_time = time.time()\n",
        "model = model.fit(X_train, y_train)\n",
        "print(f\"--- {(time.time() - start_time)/60.} minutes ---\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:31: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "building tree 1 of 95\n",
            "building tree 2 of 95\n",
            "building tree 3 of 95\n",
            "building tree 4 of 95\n",
            "building tree 5 of 95\n",
            "building tree 6 of 95\n",
            "building tree 7 of 95\n",
            "building tree 8 of 95\n",
            "building tree 9 of 95\n",
            "building tree 10 of 95\n",
            "building tree 11 of 95\n",
            "building tree 12 of 95\n",
            "building tree 13 of 95\n",
            "building tree 14 of 95\n",
            "building tree 15 of 95\n",
            "building tree 16 of 95\n",
            "building tree 17 of 95\n",
            "building tree 18 of 95\n",
            "building tree 19 of 95\n",
            "building tree 20 of 95\n",
            "building tree 21 of 95\n",
            "building tree 22 of 95\n",
            "building tree 23 of 95\n",
            "building tree 24 of 95\n",
            "building tree 25 of 95\n",
            "building tree 26 of 95\n",
            "building tree 27 of 95\n",
            "building tree 28 of 95\n",
            "building tree 29 of 95\n",
            "building tree 30 of 95\n",
            "building tree 31 of 95\n",
            "building tree 32 of 95\n",
            "building tree 33 of 95\n",
            "building tree 34 of 95\n",
            "building tree 35 of 95\n",
            "building tree 36 of 95\n",
            "building tree 37 of 95\n",
            "building tree 38 of 95\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  2.0min\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "building tree 39 of 95\n",
            "building tree 40 of 95\n",
            "building tree 41 of 95\n",
            "building tree 42 of 95\n",
            "building tree 43 of 95\n",
            "building tree 44 of 95\n",
            "building tree 45 of 95\n",
            "building tree 46 of 95\n",
            "building tree 47 of 95\n",
            "building tree 48 of 95\n",
            "building tree 49 of 95\n",
            "building tree 50 of 95\n",
            "building tree 51 of 95\n",
            "building tree 52 of 95\n",
            "building tree 53 of 95\n",
            "building tree 54 of 95\n",
            "building tree 55 of 95\n",
            "building tree 56 of 95\n",
            "building tree 57 of 95\n",
            "building tree 58 of 95\n",
            "building tree 59 of 95\n",
            "building tree 60 of 95\n",
            "building tree 61 of 95\n",
            "building tree 62 of 95\n",
            "building tree 63 of 95\n",
            "building tree 64 of 95\n",
            "building tree 65 of 95\n",
            "building tree 66 of 95\n",
            "building tree 67 of 95\n",
            "building tree 68 of 95\n",
            "building tree 69 of 95\n",
            "building tree 70 of 95\n",
            "building tree 71 of 95\n",
            "building tree 72 of 95\n",
            "building tree 73 of 95\n",
            "building tree 74 of 95\n",
            "building tree 75 of 95\n",
            "building tree 76 of 95\n",
            "building tree 77 of 95\n",
            "building tree 78 of 95\n",
            "building tree 79 of 95\n",
            "building tree 80 of 95\n",
            "building tree 81 of 95\n",
            "building tree 82 of 95\n",
            "building tree 83 of 95\n",
            "building tree 84 of 95\n",
            "building tree 85 of 95\n",
            "building tree 86 of 95\n",
            "building tree 87 of 95\n",
            "building tree 88 of 95\n",
            "building tree 89 of 95\n",
            "building tree 90 of 95\n",
            "building tree 91 of 95\n",
            "building tree 92 of 95\n",
            "building tree 93 of 95\n",
            "building tree 94 of 95\n",
            "building tree 95 of 95\n",
            "--- 5.033627458413442 minutes ---\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  95 out of  95 | elapsed:  5.0min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eenH8eL-aPbO"
      },
      "source": [
        "## 5.4. Predictions and visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9_EcBffblVO"
      },
      "source": [
        "### 5.4.1. Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTFEKPXxdxxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a47382a1-01e9-4968-9bf4-5ca53e7bd942"
      },
      "source": [
        "y_pred_train  = model.predict(X_train) \n",
        "y_pred        = model.predict(X_val) \n",
        "\n",
        "y_pred = pd.DataFrame(y_pred, columns=['unidades_vendidas'])\n",
        "y_pred_train = pd.DataFrame(y_pred_train, columns=['unidades_vendidas'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  37 tasks      | elapsed:    4.8s\n",
            "[Parallel(n_jobs=2)]: Done  95 out of  95 | elapsed:   11.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  37 tasks      | elapsed:    1.5s\n",
            "[Parallel(n_jobs=2)]: Done  95 out of  95 | elapsed:    3.8s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyJaIcs0EwSI"
      },
      "source": [
        "### 5.4.2. Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWkYw_obcM-s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "outputId": "66305c2f-b33a-48ee-b18e-e779b35b61be"
      },
      "source": [
        "df_y_pred = pd.DataFrame(data=y_pred)\n",
        "df_y_pred_train = pd.DataFrame(data=y_pred_train)\n",
        "df_y_val = pd.DataFrame(data=y_val)\n",
        "df_y_train = pd.DataFrame(data=y_train)\n",
        "df_y_val.reset_index(inplace=True, drop=True)\n",
        "\n",
        "print(f'SCALED Y: {SCALE_Y}')\n",
        "if SCALE_Y:\n",
        "  df_y_pred       = pd.DataFrame(scalerY.inverse_transform(df_y_pred), columns=df_y_pred.columns)\n",
        "  df_y_pred_train = pd.DataFrame(scalerY.inverse_transform(df_y_pred_train), columns=df_y_pred.columns)\n",
        "  df_y_val        = pd.DataFrame(scalerY.inverse_transform(df_y_val), columns=df_y_pred.columns)\n",
        "  df_y_train      = pd.DataFrame(scalerY.inverse_transform(y_train.copy()), columns=df_y_pred.columns)\n",
        "\n",
        "df_y_pred = pd.DataFrame(np.around(tf.keras.activations.relu(df_y_pred).numpy()), columns=['unidades_vendidas'])\n",
        "df_y_pred_train = pd.DataFrame(np.around(tf.keras.activations.relu(df_y_pred_train).numpy()), columns=['unidades_vendidas'])\n",
        "\n",
        "print(\"TRAIN\")\n",
        "print(f'mse: {tf.reduce_mean(tf.keras.losses.mean_squared_error(df_y_train, df_y_pred_train)):.5f}')\n",
        "print(f'smape: {SMAPE(df_y_train, df_y_pred_train):.5f}')\n",
        "print(f'rrmse: {rRMSE(df_y_train, df_y_pred_train):.5f}')\n",
        "\n",
        "print(\"VAL\")\n",
        "print(f'mse: {tf.reduce_mean(tf.keras.losses.mean_squared_error(df_y_val, df_y_pred)):.5f}')\n",
        "print(f'smape: {SMAPE(df_y_val, df_y_pred):.5f}')\n",
        "print(f'rrmse: {rRMSE(df_y_val, df_y_pred):.5f}')\n",
        "\n",
        "unscale = False\n",
        "start = 100\n",
        "amount = 300\n",
        "\n",
        "display(df_y_pred.iloc[:10,0].values.T)\n",
        "display(df_y_val.iloc[:10,0].values.T)\n",
        "\n",
        "fig = plt.figure(figsize=(60, 8))\n",
        "# ax1 = fig.add_subplot(1, 2, 1)\n",
        "# ax1.plot(df_X_val['visitas'][:amount], label='visitas')\n",
        "# ax1.set_title(\"val visitas\")\n",
        "\n",
        "ax2 = fig.add_subplot(1, 2, 2)\n",
        "ax2.plot(df_y_pred.iloc[start:start+amount, 0], label='pred')\n",
        "ax2.plot(df_y_val.iloc[start:start+amount, 0], label='val')\n",
        "ax2.set_title(\"pred vs val\")\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SCALED Y: False\n",
            "TRAIN\n",
            "mse: 88.49332\n",
            "smape: 105.80892\n",
            "rrmse: 1.50387\n",
            "VAL\n",
            "mse: 108.74516\n",
            "smape: 102.84485\n",
            "rrmse: 2.33607\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([11.,  4., 39.,  2.,  8.,  0.,  3.,  5.,  7.,  3.])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([ 6.,  0., 24.,  6.,  0.,  0.,  0.,  3.,  6.,  9.])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 4320x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8L6-oetvzEj"
      },
      "source": [
        "## 5.5. Predictions and visualization Test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQ7ystL4vzEo"
      },
      "source": [
        "### 5.5.1. Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGsolB_qvzEp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3922d923-fc95-4199-f49f-f11850b84de3"
      },
      "source": [
        "y_pred_test = model.predict(X_test) \n",
        "y_pred_test = pd.DataFrame(y_pred_test, columns=['unidades_vendidas'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  37 tasks      | elapsed:    1.4s\n",
            "[Parallel(n_jobs=2)]: Done  95 out of  95 | elapsed:    3.3s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0g9G3rrvzEq"
      },
      "source": [
        "### 5.5.2. Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "id": "_2j42Zp-vzEr",
        "outputId": "e69d0b1f-3451-4ae6-8d9c-6ffdb4c1e607"
      },
      "source": [
        "df_y_test      = pd.DataFrame(data=y_test)\n",
        "df_y_pred_test = pd.DataFrame(data=y_pred_test)\n",
        "\n",
        "print(f'SCALED Y: {SCALE_Y}')\n",
        "if SCALE_Y:\n",
        "  df_y_test      = pd.DataFrame(scalerY.inverse_transform(df_y_test), columns=df_y_test.columns)  \n",
        "  df_y_pred_test = pd.DataFrame(scalerY.inverse_transform(df_y_pred_test), columns=df_y_pred_test.columns)\n",
        "\n",
        "df_y_pred_test = pd.DataFrame( np.around(tf.keras.activations.relu(df_y_pred_test).numpy()), columns=df_y_pred_test.columns)\n",
        "\n",
        "print(\"Test\")\n",
        "print(f'mse: {tf.reduce_mean(tf.keras.losses.mean_squared_error(df_y_test, df_y_pred_test)):.5f}')\n",
        "print(f'smape: {SMAPE(df_y_test, df_y_pred_test):.5f}')\n",
        "print(f'rrmse: {rRMSE(df_y_test, df_y_pred_test):.5f}')\n",
        "\n",
        "unscale = False\n",
        "start = 100\n",
        "amount = 300\n",
        "\n",
        "fig = plt.figure(figsize=(60, 8))\n",
        "\n",
        "ax2 = fig.add_subplot(1, 2, 2)\n",
        "ax2.plot(df_y_pred_test.iloc[start:start+amount, 0], label='pred')\n",
        "ax2.plot(df_y_test.iloc[start:start+amount, 0], label='val')\n",
        "ax2.set_title(\"pred vs val\")\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SCALED Y: False\n",
            "Test\n",
            "mse: 168.60041\n",
            "smape: 89.31135\n",
            "rrmse: 3.06800\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 4320x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBcoGdpDaLld"
      },
      "source": [
        "# 6. Submission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGM-vEClVae2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a740c5f0-d5ff-44b5-dee9-6e720b86bd15"
      },
      "source": [
        "df_estimate_final.sort_values(['fecha', 'id'], inplace=True, ignore_index=True)\n",
        "df_estimate_scaled.sort_values(['fecha', 'id'], inplace=True, ignore_index=True)\n",
        "\n",
        "# Predictions\n",
        "df_estimate_pred = df_estimate_scaled[X_train.columns].copy()\n",
        "df_estimate_pred['unidades_vendidas'] = np.around( tf.keras.activations.relu( model.predict(df_estimate_pred) ) )\n",
        "df_estimate_pred['fecha'] = df_estimate_final['fecha'].copy()\n",
        "df_estimate_pred['id']    = df_estimate_final['id'].copy()\n",
        "\n",
        "# Extract required columns for submission\n",
        "df_estimate_send = df_estimate_pred[['fecha', 'id', 'unidades_vendidas']].copy()\n",
        "\n",
        "df_estimate_send['fecha'] = df_estimate_send['fecha'].dt.strftime('%d/%m/%Y')\n",
        "df_estimate_send = df_estimate_send.astype({ \"fecha\": str , \"id\": int ,\"unidades_vendidas\": int})\n",
        "df_estimate_send.rename(columns={\"fecha\": \"FECHA\", \"id\": \"ID\", \"unidades_vendidas\": \"UNIDADES\"}, inplace=True)\n",
        "\n",
        "# Submission\n",
        "df_estimate_send.to_csv(path_or_buf=join(DIR_DATASET_BASE, f'Estimaciones_tmp.txt'),encoding=\"utf-8\", sep='|', index=False)\n",
        "\n",
        "df_estimate_send = pd.read_csv(filepath_or_buffer=join(DIR_DATASET_BASE, f'Estimaciones_tmp.txt'), sep='|')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  37 tasks      | elapsed:    1.0s\n",
            "[Parallel(n_jobs=2)]: Done  95 out of  95 | elapsed:    2.5s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yax_BHTsy7or"
      },
      "source": [
        "# 7. Display estimates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "9L-L4L9zv1Tn",
        "outputId": "d01be9b1-17d1-4edb-c7a5-1846aa127cd2"
      },
      "source": [
        "id = df_estimate_final.loc[1000,'id']\n",
        "\n",
        "df_estimate_con_ventas = df_estimate_final.copy()\n",
        "df_estimate_con_ventas['unidades_vendidas'] = df_estimate_pred['unidades_vendidas']\n",
        "\n",
        "df_train_id = df_model_final[df_model_final['id'] == id]\n",
        "df_estim_id = df_estimate_con_ventas[df_estimate_con_ventas['id'] == id]\n",
        "\n",
        "display(df_train_id.tail())\n",
        "display(df_estim_id.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1750193</th>\n",
              "      <td>2016-09-26</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1509.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.996174</td>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>-0.087397</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.474848e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1754361</th>\n",
              "      <td>2016-09-27</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.997530</td>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>-0.070248</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.474934e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1758529</th>\n",
              "      <td>2016-09-28</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1511.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.998590</td>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>-0.053078</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475021e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1762697</th>\n",
              "      <td>2016-09-29</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999356</td>\n",
              "      <td>-5.192230e-13</td>\n",
              "      <td>-0.035892</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.475107e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1766865</th>\n",
              "      <td>2016-09-30</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1513.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999825</td>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>-0.018696</td>\n",
              "      <td>0.623490</td>\n",
              "      <td>1.475194e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             fecha        id  visitas  ...     timestamp  fiestaSP  fiestaPT\n",
              "1750193 2016-09-26  239010.0     20.0  ...  1.474848e+09         0         0\n",
              "1754361 2016-09-27  239010.0     30.0  ...  1.474934e+09         0         0\n",
              "1758529 2016-09-28  239010.0      5.0  ...  1.475021e+09         0         0\n",
              "1762697 2016-09-29  239010.0     25.0  ...  1.475107e+09         0         0\n",
              "1766865 2016-09-30  239010.0     50.0  ...  1.475194e+09         0         0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "    .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>fecha</th>\n",
              "      <th>id</th>\n",
              "      <th>visitas</th>\n",
              "      <th>categoria_uno</th>\n",
              "      <th>categoria_dos</th>\n",
              "      <th>estado</th>\n",
              "      <th>precio</th>\n",
              "      <th>dia_atipico</th>\n",
              "      <th>campaña</th>\n",
              "      <th>antiguedad</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Week sin</th>\n",
              "      <th>Year cos</th>\n",
              "      <th>Week cos</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>fiestaSP</th>\n",
              "      <th>fiestaPT</th>\n",
              "      <th>unidades_vendidas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1000</th>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1514.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999999</td>\n",
              "      <td>0.974928</td>\n",
              "      <td>-0.001494</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.475280e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3423</th>\n",
              "      <td>2016-10-02</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1515.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999877</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>0.015708</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475366e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5839</th>\n",
              "      <td>2016-10-03</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.999458</td>\n",
              "      <td>-0.433884</td>\n",
              "      <td>0.032905</td>\n",
              "      <td>-0.900969</td>\n",
              "      <td>1.475453e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8243</th>\n",
              "      <td>2016-10-04</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1517.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.998745</td>\n",
              "      <td>-0.974928</td>\n",
              "      <td>0.050093</td>\n",
              "      <td>-0.222521</td>\n",
              "      <td>1.475539e+09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10634</th>\n",
              "      <td>2016-10-05</td>\n",
              "      <td>239010.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1518.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>-0.997735</td>\n",
              "      <td>-0.781831</td>\n",
              "      <td>0.067266</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           fecha        id  visitas  ...  fiestaSP  fiestaPT  unidades_vendidas\n",
              "1000  2016-10-01  239010.0     35.0  ...         0         0                4.0\n",
              "3423  2016-10-02  239010.0     25.0  ...         0         0                4.0\n",
              "5839  2016-10-03  239010.0     85.0  ...         0         0                8.0\n",
              "8243  2016-10-04  239010.0     45.0  ...         0         0                3.0\n",
              "10634 2016-10-05  239010.0     50.0  ...         0         1                3.0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "3Mf1uDVtQdQe",
        "outputId": "378257f2-9cb9-4215-d10e-26bbd2a1b604"
      },
      "source": [
        "fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(25, 7), sharex=True)\n",
        "axs.plot(df_train_id['fecha'], df_train_id['visitas'], label='train visitas')\n",
        "axs.plot(df_estim_id['fecha'], df_estim_id['visitas'], label='sub visitas', color='green')\n",
        "# axs[0].set_yscale('symlog')\n",
        "\n",
        "ax2=axs.twinx()\n",
        "ax2.plot(df_train_id['fecha'], df_train_id['unidades_vendidas'], label='train', color='orange')\n",
        "ax2.plot(df_estim_id['fecha'], df_estim_id['unidades_vendidas'], label='pred', color='red')\n",
        "\n",
        "axs.legend(loc='upper left')\n",
        "ax2.legend()\n",
        "axs.grid(which='both')\n",
        "fig.show()\n",
        "# axs[1].legend()\n",
        "# axs[1].grid(which='both')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1800x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "gd5_hsxjG8Q8",
        "outputId": "905433db-76d0-4941-d0ef-61a0939a4a34"
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "# Create figure with secondary y-axis\n",
        "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
        "\n",
        "# Add traces\n",
        "fig.add_trace(\n",
        "    go.Scatter(x = df_train_id['fecha'],y= df_train_id['visitas'], name=\"train visitas\", marker_color='blue'), secondary_y=False,\n",
        ")\n",
        "fig.add_trace(\n",
        "    go.Scatter(x = df_estim_id['fecha'],y= df_estim_id['visitas'], name=\"sub visitas\", marker_color='green'),secondary_y=False,\n",
        ")\n",
        "fig.add_trace(\n",
        "    go.Scatter(x = df_train_id['fecha'],y= df_train_id['unidades_vendidas'], name=\"train unidades\", marker_color='orange'),secondary_y=True,\n",
        ")\n",
        "fig.add_trace(\n",
        "    go.Scatter(x = df_estim_id['fecha'], y= df_estim_id['unidades_vendidas'], name=\"sub unidades\", marker_color='red'),secondary_y=True,\n",
        ")\n",
        "fig.update_layout(\n",
        "    autosize=False,\n",
        "    width=2000,\n",
        "    height=500,\n",
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