Data_explore_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./1_Data_source/datasets_hi4/train-data.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id_product</th>\n",
       "      <th>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Site</th>\n",
       "      <th>Operations</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Reference proxy</th>\n",
       "      <th>Product  Line proxy</th>\n",
       "      <th>Division proxy</th>\n",
       "      <th>Customer Persona proxy</th>\n",
       "      <th>Strategic Product Family proxy</th>\n",
       "      <th>Product Life cycel status</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month 1</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Month 3</th>\n",
       "      <th>Month 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>2033855</td>\n",
       "      <td>74999</td>\n",
       "      <td>NAM</td>\n",
       "      <td>US</td>\n",
       "      <td>US_DC_Mechanicsburg</td>\n",
       "      <td>North America Operations</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>reference-8957</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>EOL</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>1986720</td>\n",
       "      <td>27864</td>\n",
       "      <td>EAJP</td>\n",
       "      <td>AU</td>\n",
       "      <td>AU_DC_Adelaide</td>\n",
       "      <td>International Operations</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Australia</td>\n",
       "      <td>reference-1958</td>\n",
       "      <td>Product Line-1</td>\n",
       "      <td>Division-1</td>\n",
       "      <td>Customer Segmentation-16</td>\n",
       "      <td>Strategic Product Family-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>544209</td>\n",
       "      <td>54495</td>\n",
       "      <td>NAM</td>\n",
       "      <td>US</td>\n",
       "      <td>US_DC_Athens</td>\n",
       "      <td>North America Operations</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>reference-5210</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-44</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>87</td>\n",
       "      <td>39</td>\n",
       "      <td>57</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>956394</td>\n",
       "      <td>221823</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>CN</td>\n",
       "      <td>CN_DC_Shanghai</td>\n",
       "      <td>China Operations</td>\n",
       "      <td>China &amp; HK</td>\n",
       "      <td>China</td>\n",
       "      <td>reference-6300</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-1</td>\n",
       "      <td>Strategic Product Family-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sep-dec 2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>1630426</td>\n",
       "      <td>161284</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>DE</td>\n",
       "      <td>NL_DC_HLD</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>DACH</td>\n",
       "      <td>Germany</td>\n",
       "      <td>reference-3737</td>\n",
       "      <td>Product Line-3</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-50</td>\n",
       "      <td>Strategic Product Family-7</td>\n",
       "      <td>NPI</td>\n",
       "      <td>sep-dec 2022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>1846567</td>\n",
       "      <td>132568</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>DK</td>\n",
       "      <td>SE_DC_Orebro</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>Nordic &amp; Baltics</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>reference-9455</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-13</td>\n",
       "      <td>Strategic Product Family-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jan-apr 2023</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>872266</td>\n",
       "      <td>137695</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>ES</td>\n",
       "      <td>ES_DC_SPL</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>Iberia</td>\n",
       "      <td>Spain</td>\n",
       "      <td>reference-2660</td>\n",
       "      <td>Product Line-2</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-13</td>\n",
       "      <td>Strategic Product Family-5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sep-dec 2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>719865</td>\n",
       "      <td>230151</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>CN</td>\n",
       "      <td>CN_DC_Beijing</td>\n",
       "      <td>China Operations</td>\n",
       "      <td>China &amp; HK</td>\n",
       "      <td>China</td>\n",
       "      <td>reference-7418</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-13</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>1319278</td>\n",
       "      <td>94993</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>GB</td>\n",
       "      <td>NL_DC_HLD</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>UK and Ireland</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>reference-2673</td>\n",
       "      <td>Product Line-2</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-16</td>\n",
       "      <td>Strategic Product Family-5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>606719</td>\n",
       "      <td>117005</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>DK</td>\n",
       "      <td>SE_DC_Orebro</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>Nordic &amp; Baltics</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>reference-6996</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  id_product  Region Country                 Site  \\\n",
       "1000  2033855       74999     NAM      US  US_DC_Mechanicsburg   \n",
       "1001  1986720       27864    EAJP      AU       AU_DC_Adelaide   \n",
       "1002   544209       54495     NAM      US         US_DC_Athens   \n",
       "1003   956394      221823   CHINA      CN       CN_DC_Shanghai   \n",
       "1004  1630426      161284  EUROPE      DE            NL_DC_HLD   \n",
       "1005  1846567      132568  EUROPE      DK         SE_DC_Orebro   \n",
       "1006   872266      137695  EUROPE      ES            ES_DC_SPL   \n",
       "1007   719865      230151   CHINA      CN        CN_DC_Beijing   \n",
       "1008  1319278       94993  EUROPE      GB            NL_DC_HLD   \n",
       "1009   606719      117005  EUROPE      DK         SE_DC_Orebro   \n",
       "\n",
       "                    Operations              Zone         Cluster  \\\n",
       "1000  North America Operations                US             USA   \n",
       "1001  International Operations           Pacific       Australia   \n",
       "1002  North America Operations                US             USA   \n",
       "1003          China Operations        China & HK           China   \n",
       "1004         Europe Operations              DACH         Germany   \n",
       "1005         Europe Operations  Nordic & Baltics         Denmark   \n",
       "1006         Europe Operations            Iberia           Spain   \n",
       "1007          China Operations        China & HK           China   \n",
       "1008         Europe Operations    UK and Ireland  United Kingdom   \n",
       "1009         Europe Operations  Nordic & Baltics         Denmark   \n",
       "\n",
       "     Reference proxy Product  Line proxy Division proxy  \\\n",
       "1000  reference-8957      Product Line-4     Division-3   \n",
       "1001  reference-1958      Product Line-1     Division-1   \n",
       "1002  reference-5210      Product Line-4     Division-3   \n",
       "1003  reference-6300      Product Line-4     Division-3   \n",
       "1004  reference-3737      Product Line-3     Division-2   \n",
       "1005  reference-9455      Product Line-4     Division-3   \n",
       "1006  reference-2660      Product Line-2     Division-2   \n",
       "1007  reference-7418      Product Line-4     Division-3   \n",
       "1008  reference-2673      Product Line-2     Division-2   \n",
       "1009  reference-6996      Product Line-4     Division-3   \n",
       "\n",
       "        Customer Persona proxy Strategic Product Family proxy  \\\n",
       "1000   Customer Segmentation-4     Strategic Product Family-9   \n",
       "1001  Customer Segmentation-16     Strategic Product Family-4   \n",
       "1002  Customer Segmentation-44     Strategic Product Family-9   \n",
       "1003   Customer Segmentation-1    Strategic Product Family-12   \n",
       "1004  Customer Segmentation-50     Strategic Product Family-7   \n",
       "1005  Customer Segmentation-13    Strategic Product Family-12   \n",
       "1006  Customer Segmentation-13     Strategic Product Family-5   \n",
       "1007  Customer Segmentation-13     Strategic Product Family-9   \n",
       "1008  Customer Segmentation-16     Strategic Product Family-5   \n",
       "1009   Customer Segmentation-4     Strategic Product Family-9   \n",
       "\n",
       "     Product Life cycel status          Date Month 1 Month 2 Month 3 Month 4  \n",
       "1000                       EOL  may-jul 2023     NaN       0       0       0  \n",
       "1001                       NaN  may-jul 2023     NaN       0       0       0  \n",
       "1002                       NaN  may-aug 2021      87      39      57     103  \n",
       "1003                       NaN  sep-dec 2021       0       0       0       0  \n",
       "1004                       NPI  sep-dec 2022       0       0       1       0  \n",
       "1005                       NaN  jan-apr 2023       0       0       0       0  \n",
       "1006                       NaN  sep-dec 2021       0       0       0       0  \n",
       "1007                       NaN  may-aug 2021       0       0       0       0  \n",
       "1008                       NaN  may-aug 2022       0       0       0       0  \n",
       "1009                       NaN  may-aug 2021       0       0       0       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1000:1010]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index  -  1762970\n",
      "id_product  -  244857\n",
      "Region  -  4\n",
      "Country  -  31\n",
      "Site  -  88\n",
      "Operations  -  5\n",
      "Zone  -  12\n",
      "Cluster  -  18\n",
      "Reference proxy  -  22406\n",
      "Product  Line proxy  -  6\n",
      "Division proxy  -  3\n",
      "Customer Persona proxy  -  72\n",
      "Strategic Product Family proxy  -  19\n",
      "Product Life cycel status  -  4\n",
      "Date  -  9\n",
      "Month 1  -  4620\n",
      "Month 2  -  4471\n",
      "Month 3  -  5040\n",
      "Month 4  -  4360\n"
     ]
    }
   ],
   "source": [
    "for col in data.columns.values:\n",
    "    print(f\"{col}  -  {len(data[col].unique())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ = data.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_prep_train(df):\n",
    "    \"\"\" Prepare data for ML tasks \"\"\"\n",
    "    # Transform volume data into float64 data and discarding NaN\n",
    "    for month in [\"Month 1\", \"Month 2\", \"Month 3\", \"Month 4\"]:\n",
    "        df[month] = df[month].str.replace(\" \", \"\").fillna(\"0\").astype(float)\n",
    "    \n",
    "    # # Clean Date infos\n",
    "    # month_lib = {\n",
    "    #     \"jan\": 0,\n",
    "    #     \"feb\": 1,    \n",
    "    #     \"mar\": 2,\n",
    "    #     \"apr\": 3,    \n",
    "    #     \"may\": 4,\n",
    "    #     \"jun\": 5,    \n",
    "    #     \"jul\": 6,\n",
    "    #     \"aug\": 7,    \n",
    "    #     \"sep\": 8,\n",
    "    #     \"oct\": 9,    \n",
    "    #     \"nov\": 10,\n",
    "    #     \"dec\": 11\n",
    "    # }\n",
    "    # date = df[\"Date\"].str.split(n=1, expand=True)\n",
    "    # df[\"Year\"] = date[1]\n",
    "    # df[\"Quadr\"] = pd.to_numeric(date[0].str.slice(stop=3).replace(month_lib))//3\n",
    "    # df[\"Quadr\"] = df[\"Quadr\"].astype(str)\n",
    "    \n",
    "    # Get rid off duplicates\n",
    "    df.drop_duplicates(inplace=True)\n",
    "\n",
    "def data_prep_test(df):\n",
    "    \"\"\" Prepare data for ML tasks \"\"\"\n",
    "    # Transform volume data into float64 data and discarding NaN\n",
    "    for month in [\"Month 1\", \"Month 2\", \"Month 3\"]:\n",
    "        df[month] = df[month].str.replace(\" \", \"\").fillna(\"0\").astype(float)\n",
    "        \n",
    "    # # Clean Date infos\n",
    "    # month_lib = {\n",
    "    #     \"jan\": 0,\n",
    "    #     \"feb\": 1,    \n",
    "    #     \"mar\": 2,\n",
    "    #     \"apr\": 3,    \n",
    "    #     \"may\": 4,\n",
    "    #     \"jun\": 5,    \n",
    "    #     \"jul\": 6,\n",
    "    #     \"aug\": 7,    \n",
    "    #     \"sep\": 8,\n",
    "    #     \"oct\": 9,    \n",
    "    #     \"nov\": 10,\n",
    "    #     \"dec\": 11\n",
    "    # }\n",
    "    # date = df[\"Date\"].str.split(n=1, expand=True)\n",
    "    # df[\"Year\"] = date[1]\n",
    "    # df[\"Quadr\"] = pd.to_numeric(date[0].str.slice(stop=3).replace(month_lib))//3\n",
    "    # df[\"Quadr\"] = df[\"Quadr\"].astype(str)\n",
    "\n",
    "    # KEEP duplicates\n",
    "    #df.drop_duplicates(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prep_train(data_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                               int64\n",
       "id_product                          int64\n",
       "Region                             object\n",
       "Country                            object\n",
       "Site                               object\n",
       "Operations                         object\n",
       "Zone                               object\n",
       "Cluster                            object\n",
       "Reference proxy                    object\n",
       "Product  Line proxy                object\n",
       "Division proxy                     object\n",
       "Customer Persona proxy             object\n",
       "Strategic Product Family proxy     object\n",
       "Product Life cycel status          object\n",
       "Date                               object\n",
       "Month 1                           float64\n",
       "Month 2                           float64\n",
       "Month 3                           float64\n",
       "Month 4                           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id_product</th>\n",
       "      <th>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Site</th>\n",
       "      <th>Operations</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Reference proxy</th>\n",
       "      <th>Product  Line proxy</th>\n",
       "      <th>Division proxy</th>\n",
       "      <th>Customer Persona proxy</th>\n",
       "      <th>Strategic Product Family proxy</th>\n",
       "      <th>Product Life cycel status</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month 1</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Month 3</th>\n",
       "      <th>Month 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>2033855</td>\n",
       "      <td>74999</td>\n",
       "      <td>NAM</td>\n",
       "      <td>US</td>\n",
       "      <td>US_DC_Mechanicsburg</td>\n",
       "      <td>North America Operations</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>reference-8957</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>EOL</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>1986720</td>\n",
       "      <td>27864</td>\n",
       "      <td>EAJP</td>\n",
       "      <td>AU</td>\n",
       "      <td>AU_DC_Adelaide</td>\n",
       "      <td>International Operations</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Australia</td>\n",
       "      <td>reference-1958</td>\n",
       "      <td>Product Line-1</td>\n",
       "      <td>Division-1</td>\n",
       "      <td>Customer Segmentation-16</td>\n",
       "      <td>Strategic Product Family-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>544209</td>\n",
       "      <td>54495</td>\n",
       "      <td>NAM</td>\n",
       "      <td>US</td>\n",
       "      <td>US_DC_Athens</td>\n",
       "      <td>North America Operations</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>reference-5210</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-44</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>87.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>956394</td>\n",
       "      <td>221823</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>CN</td>\n",
       "      <td>CN_DC_Shanghai</td>\n",
       "      <td>China Operations</td>\n",
       "      <td>China &amp; HK</td>\n",
       "      <td>China</td>\n",
       "      <td>reference-6300</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-1</td>\n",
       "      <td>Strategic Product Family-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sep-dec 2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>1630426</td>\n",
       "      <td>161284</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>DE</td>\n",
       "      <td>NL_DC_HLD</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>DACH</td>\n",
       "      <td>Germany</td>\n",
       "      <td>reference-3737</td>\n",
       "      <td>Product Line-3</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-50</td>\n",
       "      <td>Strategic Product Family-7</td>\n",
       "      <td>NPI</td>\n",
       "      <td>sep-dec 2022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>1846567</td>\n",
       "      <td>132568</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>DK</td>\n",
       "      <td>SE_DC_Orebro</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>Nordic &amp; Baltics</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>reference-9455</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-13</td>\n",
       "      <td>Strategic Product Family-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jan-apr 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>872266</td>\n",
       "      <td>137695</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>ES</td>\n",
       "      <td>ES_DC_SPL</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>Iberia</td>\n",
       "      <td>Spain</td>\n",
       "      <td>reference-2660</td>\n",
       "      <td>Product Line-2</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-13</td>\n",
       "      <td>Strategic Product Family-5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sep-dec 2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>719865</td>\n",
       "      <td>230151</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>CN</td>\n",
       "      <td>CN_DC_Beijing</td>\n",
       "      <td>China Operations</td>\n",
       "      <td>China &amp; HK</td>\n",
       "      <td>China</td>\n",
       "      <td>reference-7418</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-13</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>1319278</td>\n",
       "      <td>94993</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>GB</td>\n",
       "      <td>NL_DC_HLD</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>UK and Ireland</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>reference-2673</td>\n",
       "      <td>Product Line-2</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-16</td>\n",
       "      <td>Strategic Product Family-5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>606719</td>\n",
       "      <td>117005</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>DK</td>\n",
       "      <td>SE_DC_Orebro</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>Nordic &amp; Baltics</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>reference-6996</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  id_product  Region Country                 Site  \\\n",
       "1000  2033855       74999     NAM      US  US_DC_Mechanicsburg   \n",
       "1001  1986720       27864    EAJP      AU       AU_DC_Adelaide   \n",
       "1002   544209       54495     NAM      US         US_DC_Athens   \n",
       "1003   956394      221823   CHINA      CN       CN_DC_Shanghai   \n",
       "1004  1630426      161284  EUROPE      DE            NL_DC_HLD   \n",
       "1005  1846567      132568  EUROPE      DK         SE_DC_Orebro   \n",
       "1006   872266      137695  EUROPE      ES            ES_DC_SPL   \n",
       "1007   719865      230151   CHINA      CN        CN_DC_Beijing   \n",
       "1008  1319278       94993  EUROPE      GB            NL_DC_HLD   \n",
       "1009   606719      117005  EUROPE      DK         SE_DC_Orebro   \n",
       "\n",
       "                    Operations              Zone         Cluster  \\\n",
       "1000  North America Operations                US             USA   \n",
       "1001  International Operations           Pacific       Australia   \n",
       "1002  North America Operations                US             USA   \n",
       "1003          China Operations        China & HK           China   \n",
       "1004         Europe Operations              DACH         Germany   \n",
       "1005         Europe Operations  Nordic & Baltics         Denmark   \n",
       "1006         Europe Operations            Iberia           Spain   \n",
       "1007          China Operations        China & HK           China   \n",
       "1008         Europe Operations    UK and Ireland  United Kingdom   \n",
       "1009         Europe Operations  Nordic & Baltics         Denmark   \n",
       "\n",
       "     Reference proxy Product  Line proxy Division proxy  \\\n",
       "1000  reference-8957      Product Line-4     Division-3   \n",
       "1001  reference-1958      Product Line-1     Division-1   \n",
       "1002  reference-5210      Product Line-4     Division-3   \n",
       "1003  reference-6300      Product Line-4     Division-3   \n",
       "1004  reference-3737      Product Line-3     Division-2   \n",
       "1005  reference-9455      Product Line-4     Division-3   \n",
       "1006  reference-2660      Product Line-2     Division-2   \n",
       "1007  reference-7418      Product Line-4     Division-3   \n",
       "1008  reference-2673      Product Line-2     Division-2   \n",
       "1009  reference-6996      Product Line-4     Division-3   \n",
       "\n",
       "        Customer Persona proxy Strategic Product Family proxy  \\\n",
       "1000   Customer Segmentation-4     Strategic Product Family-9   \n",
       "1001  Customer Segmentation-16     Strategic Product Family-4   \n",
       "1002  Customer Segmentation-44     Strategic Product Family-9   \n",
       "1003   Customer Segmentation-1    Strategic Product Family-12   \n",
       "1004  Customer Segmentation-50     Strategic Product Family-7   \n",
       "1005  Customer Segmentation-13    Strategic Product Family-12   \n",
       "1006  Customer Segmentation-13     Strategic Product Family-5   \n",
       "1007  Customer Segmentation-13     Strategic Product Family-9   \n",
       "1008  Customer Segmentation-16     Strategic Product Family-5   \n",
       "1009   Customer Segmentation-4     Strategic Product Family-9   \n",
       "\n",
       "     Product Life cycel status          Date  Month 1  Month 2  Month 3  \\\n",
       "1000                       EOL  may-jul 2023      0.0      0.0      0.0   \n",
       "1001                       NaN  may-jul 2023      0.0      0.0      0.0   \n",
       "1002                       NaN  may-aug 2021     87.0     39.0     57.0   \n",
       "1003                       NaN  sep-dec 2021      0.0      0.0      0.0   \n",
       "1004                       NPI  sep-dec 2022      0.0      0.0      1.0   \n",
       "1005                       NaN  jan-apr 2023      0.0      0.0      0.0   \n",
       "1006                       NaN  sep-dec 2021      0.0      0.0      0.0   \n",
       "1007                       NaN  may-aug 2021      0.0      0.0      0.0   \n",
       "1008                       NaN  may-aug 2022      0.0      0.0      0.0   \n",
       "1009                       NaN  may-aug 2021      0.0      0.0      0.0   \n",
       "\n",
       "      Month 4  \n",
       "1000      0.0  \n",
       "1001      0.0  \n",
       "1002    103.0  \n",
       "1003      0.0  \n",
       "1004      0.0  \n",
       "1005      0.0  \n",
       "1006      0.0  \n",
       "1007      0.0  \n",
       "1008      0.0  \n",
       "1009      0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_[1000:1010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id_product</th>\n",
       "      <th>Month 1</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Month 3</th>\n",
       "      <th>Month 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.762970e+06</td>\n",
       "      <td>1.762970e+06</td>\n",
       "      <td>1.762970e+06</td>\n",
       "      <td>1.762970e+06</td>\n",
       "      <td>1.762970e+06</td>\n",
       "      <td>1.762970e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.101680e+06</td>\n",
       "      <td>1.224159e+05</td>\n",
       "      <td>2.887069e+01</td>\n",
       "      <td>2.899735e+01</td>\n",
       "      <td>3.587396e+01</td>\n",
       "      <td>2.679187e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.362992e+05</td>\n",
       "      <td>7.068119e+04</td>\n",
       "      <td>7.409607e+02</td>\n",
       "      <td>7.189398e+02</td>\n",
       "      <td>1.155221e+03</td>\n",
       "      <td>6.927619e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.507065e+05</td>\n",
       "      <td>6.119400e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.101724e+06</td>\n",
       "      <td>1.223860e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.652811e+06</td>\n",
       "      <td>1.836208e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.203712e+06</td>\n",
       "      <td>2.448560e+05</td>\n",
       "      <td>1.875400e+05</td>\n",
       "      <td>2.711010e+05</td>\n",
       "      <td>4.812900e+05</td>\n",
       "      <td>2.452200e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index    id_product       Month 1       Month 2       Month 3  \\\n",
       "count  1.762970e+06  1.762970e+06  1.762970e+06  1.762970e+06  1.762970e+06   \n",
       "mean   1.101680e+06  1.224159e+05  2.887069e+01  2.899735e+01  3.587396e+01   \n",
       "std    6.362992e+05  7.068119e+04  7.409607e+02  7.189398e+02  1.155221e+03   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    5.507065e+05  6.119400e+04  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    1.101724e+06  1.223860e+05  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    1.652811e+06  1.836208e+05  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max    2.203712e+06  2.448560e+05  1.875400e+05  2.711010e+05  4.812900e+05   \n",
       "\n",
       "            Month 4  \n",
       "count  1.762970e+06  \n",
       "mean   2.679187e+01  \n",
       "std    6.927619e+02  \n",
       "min   -1.000000e+00  \n",
       "25%    0.000000e+00  \n",
       "50%    0.000000e+00  \n",
       "75%    0.000000e+00  \n",
       "max    2.452200e+05  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100000.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_[\"Month 3\"], data_[\"Month 4\"], alpha=.2)\n",
    "plt.xlim(0, 100000)\n",
    "plt.ylim(0, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_.drop(columns=\"Month 4\")\n",
    "y = data_[\"Month 4\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train / Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y,\n",
    "    random_state=42, # keep 42\n",
    "    test_size=0.2, \n",
    ")\n",
    "\n",
    "# to be refined to obtain train / val  / test ? or cross-val ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get rid off all records with zero values over the 4 months (for the training only)\n",
    "X_train_NZ = X_train[X_train[\"Month 1\"] + X_train[\"Month 2\"] + X_train[\"Month 3\"] !=0]\n",
    "y_train_NZ = y_train[X_train[\"Month 1\"] + X_train[\"Month 2\"] + X_train[\"Month 3\"]!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of each set: \n",
      "Total (X, y): 1762970      =   Train_tot: 1410376   +  Test_tot: 352594\n",
      "Total Non Zero Train  522800\n",
      "36734046.0\n"
     ]
    }
   ],
   "source": [
    "print('Size of each set: ')\n",
    "print('Total (X, y):', y.count(), '     =   Train_tot:',y_train.count(), '  +  Test_tot:', y_test.count())\n",
    "print('Total Non Zero Train ', y_train_NZ.count())\n",
    "print(y_train_NZ.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def mse(y_hat, y, squared_ = False):\n",
    "    \"\"\" Compute the Root Mean Squared Error \"\"\"\n",
    "    return mean_squared_error(y_hat, y, squared = squared_)\n",
    "\n",
    "def norm_mse(y_hat, y, squared_ = False):\n",
    "    \"\"\" compute the Root Mean Squared Error normalized by a null prediction\"\"\"\n",
    "    return mean_squared_error(y_hat, y, squared = squared_) / mean_squared_error(np.zeros(y.shape), y, squared = squared_)\n",
    "\n",
    "def RMSE_hfactory(y_hat, y, squared_ = False):\n",
    "    R_0 = mean_squared_error(np.zeros(y.shape), y, squared = squared_) \n",
    "    rmse_h = (R_0- 0.8 * mse(y_hat, y))/R_0\n",
    "    return rmse_h\n",
    "\n",
    "def score_func(y_hat, y, w, squared_ = False):\n",
    "    \"\"\" Compute \n",
    "    w = penalize loss for y_hat > y \"\"\"\n",
    "    score = (mean_squared_error(y_hat[y_hat>y], y[y_hat>y], squared=True) * w + mean_squared_error(y_hat[y_hat>y], y[y_hat>y], squared=True))\n",
    "    if not squared_:\n",
    "        score = np.sqrt(score)\n",
    "    score /= mean_squared_error(np.zeros(y.shape), y, squared = squared_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare models pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "sklearn.set_config(display='diagram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import make_column_transformer, make_column_selector\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "numeric_transformer = make_pipeline(\n",
    "    SimpleImputer(strategy=\"median\"), \n",
    "    #StandardScaler(),\n",
    ")\n",
    "categorical_transformer = make_pipeline(\n",
    "    SimpleImputer(strategy=\"constant\"),\n",
    "    OneHotEncoder(handle_unknown='ignore'),\n",
    ")\n",
    "\n",
    "\n",
    "cat_col = ['Date', 'Region']\n",
    "cat_col_1 = ['Product Life cycel status', 'Date', 'Site']#, 'Region']\n",
    "cat_col_2 = ['Product Life cycel status', 'Date', 'Site', 'Strategic Product Family proxy']\n",
    "cat_col_3 = ['Product Life cycel status', 'Date', 'Site', 'Country', 'Strategic Product Family proxy']\n",
    "\n",
    "\n",
    "\n",
    "# cat_col = ['Product Life cycel status', 'Date', 'Region', 'Product  Line proxy']\n",
    "\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (numeric_transformer, make_column_selector(dtype_include=\"float64\")),\n",
    "    (categorical_transformer, cat_col),\n",
    "    remainder=\"drop\",\n",
    ")\n",
    "preprocessor_1 = make_column_transformer(\n",
    "    (numeric_transformer, make_column_selector(dtype_include=\"float64\")),\n",
    "    (categorical_transformer, cat_col_1),\n",
    "    remainder=\"drop\",\n",
    ")\n",
    "preprocessor_2 = make_column_transformer(\n",
    "    (numeric_transformer, make_column_selector(dtype_include=\"float64\")),\n",
    "    (categorical_transformer, cat_col_2),\n",
    "    remainder=\"drop\",\n",
    ")\n",
    "preprocessor_3 = make_column_transformer(\n",
    "    (numeric_transformer, make_column_selector(dtype_include=\"float64\")),\n",
    "    (categorical_transformer, cat_col_3),\n",
    "    remainder=\"drop\",\n",
    ")\n",
    "\n",
    "model_LR =make_pipeline(\n",
    "    preprocessor,\n",
    "    LinearRegression(),\n",
    ")\n",
    "\n",
    "n_est=500\n",
    "m_d=7\n",
    "\n",
    "model_RF_1 = make_pipeline(\n",
    "    preprocessor_1,\n",
    "    RandomForestRegressor(\n",
    "        n_estimators=n_est,\n",
    "        max_depth=m_d,\n",
    "        random_state=42,\n",
    "        ),\n",
    ")\n",
    "model_RF_2 = make_pipeline(\n",
    "    preprocessor_2,\n",
    "    RandomForestRegressor(\n",
    "        n_estimators=n_est,\n",
    "        max_depth=m_d,\n",
    "        random_state=42,\n",
    "        ),\n",
    ")\n",
    "model_RF_3 = make_pipeline(\n",
    "    preprocessor_3,\n",
    "    RandomForestRegressor(\n",
    "        n_estimators=n_est,\n",
    "        max_depth=m_d,\n",
    "        random_state=42,\n",
    "        ),\n",
    ")\n",
    "\n",
    "model_AB = make_pipeline(\n",
    "    preprocessor,\n",
    "    AdaBoostRegressor(\n",
    "        random_state=42,\n",
    "        ),\n",
    ")\n",
    "\n",
    "model_XGB_2 = make_pipeline(\n",
    "    preprocessor_2,\n",
    "    XGBRegressor(\n",
    "        random_state=42,\n",
    "        max_depth=7,\n",
    "        n_estimators=500\n",
    "        ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2e2274c90&gt;),\n",
       "                                                 (&#x27;pipeline-2&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Product Life cycel status&#x27;,\n",
       "                                                   &#x27;Date&#x27;, &#x27;Site&#x27;,\n",
       "                                                   &#x27;Strategic Product Family &#x27;\n",
       "                                                   &#x27;proxy&#x27;])])),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(max_depth=7, n_estimators=500,\n",
       "                                       random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2e2274c90&gt;),\n",
       "                                                 (&#x27;pipeline-2&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Product Life cycel status&#x27;,\n",
       "                                                   &#x27;Date&#x27;, &#x27;Site&#x27;,\n",
       "                                                   &#x27;Strategic Product Family &#x27;\n",
       "                                                   &#x27;proxy&#x27;])])),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(max_depth=7, n_estimators=500,\n",
       "                                       random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2e2274c90&gt;),\n",
       "                                (&#x27;pipeline-2&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Product Life cycel status&#x27;, &#x27;Date&#x27;, &#x27;Site&#x27;,\n",
       "                                  &#x27;Strategic Product Family proxy&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-1</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x2e2274c90&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-2</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Product Life cycel status&#x27;, &#x27;Date&#x27;, &#x27;Site&#x27;, &#x27;Strategic Product Family proxy&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=7, n_estimators=500, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('pipeline-1',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median'))]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x2e2274c90>),\n",
       "                                                 ('pipeline-2',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='constant')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  ['Product Life cycel status',\n",
       "                                                   'Date', 'Site',\n",
       "                                                   'Strategic Product Family '\n",
       "                                                   'proxy'])])),\n",
       "                ('randomforestregressor',\n",
       "                 RandomForestRegressor(max_depth=7, n_estimators=500,\n",
       "                                       random_state=42))])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RF_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "538.8500203426303\n",
      "484.80024192028554\n",
      "649.2042895583725\n",
      "0.7467606879955702\n",
      "\n",
      "\n",
      "358.2714254463043\n",
      "455.82215460306367\n",
      "649.2042895583725\n",
      "0.7021243727658378\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_list = [model_LR, model_RF_2]#, model_RF_2, model_RF_3]#, model_XGB]\n",
    "\n",
    "for model_ in model_list:\n",
    "    # model_.fit(X_train, y_train)\n",
    "\n",
    "    # y_train_hat = model_.predict(X_train)\n",
    "    # y_test_hat = model_.predict(X_test)\n",
    "    \n",
    "    # print(mse(y_train_hat, y_train))\n",
    "    # print(mse(y_test_hat, y_test))\n",
    "    # print(mse(np.zeros(y_test.shape), y_test))\n",
    "    # print(norm_mse(y_test_hat, y_test))\n",
    "    # print(\"\\n\")\n",
    "\n",
    "    #----- Non Zero Training -------\n",
    "    model_.fit(X_train_NZ, y_train_NZ)\n",
    "\n",
    "    y_train_hat = model_.predict(X_train)\n",
    "    y_test_hat = model_.predict(X_test)\n",
    "\n",
    "    y_train_hat[X_train[\"Month 1\"] + X_train[\"Month 2\"] + X_train[\"Month 3\"]==0] = 0\n",
    "    y_test_hat[X_test[\"Month 1\"] + X_test[\"Month 2\"] + X_test[\"Month 3\"]==0] = 0\n",
    "    print(mse(y_train_hat, y_train))\n",
    "    print(mse(y_test_hat, y_test))\n",
    "    print(mse(np.zeros(y_test.shape), y_test))\n",
    "    print(norm_mse(y_test_hat, y_test))\n",
    "    print(RMSE_hfactory(y_test_hat, y_test))\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "538.8500203426303\n",
      "484.80024192028554\n",
      "649.2042895583725\n",
      "0.7467606879955702\n",
      "0.40259144960354387\n",
      "\n",
      "\n",
      "323.3107832292425\n",
      "504.4020588829874\n",
      "649.2042895583725\n",
      "0.7769542915776354\n",
      "0.3784365667378916\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_list = [model_LR, model_XGB_2]#, model_RF_2, model_RF_3]#, model_XGB]\n",
    "\n",
    "for model_ in model_list:\n",
    "    # model_.fit(X_train, y_train)\n",
    "\n",
    "    # y_train_hat = model_.predict(X_train)\n",
    "    # y_test_hat = model_.predict(X_test)\n",
    "    \n",
    "    # print(mse(y_train_hat, y_train))\n",
    "    # print(mse(y_test_hat, y_test))\n",
    "    # print(mse(np.zeros(y_test.shape), y_test))\n",
    "    # print(norm_mse(y_test_hat, y_test))\n",
    "    # print(\"\\n\")\n",
    "\n",
    "    #----- Non Zero Training -------\n",
    "    model_.fit(X_train_NZ, y_train_NZ)\n",
    "\n",
    "    y_train_hat = model_.predict(X_train)\n",
    "    y_test_hat = model_.predict(X_test)\n",
    "\n",
    "    y_train_hat[X_train[\"Month 1\"] + X_train[\"Month 2\"] + X_train[\"Month 3\"]==0] = 0\n",
    "    y_test_hat[X_test[\"Month 1\"] + X_test[\"Month 2\"] + X_test[\"Month 3\"]==0] = 0\n",
    "    print(mse(y_train_hat, y_train))\n",
    "    print(mse(y_test_hat, y_test))\n",
    "    print(mse(np.zeros(y_test.shape), y_test))\n",
    "    print(norm_mse(y_test_hat, y_test))\n",
    "    print(RMSE_hfactory(y_test_hat, y_test))\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.157419014503933"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b5b89c10>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train, y_train_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10000.0)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_test_hat, alpha=.1)\n",
    "plt.xlim(-100,10000)\n",
    "plt.ylim(-100,10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tune model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('columntransformer',\n",
       "   ColumnTransformer(transformers=[('pipeline-1',\n",
       "                                    Pipeline(steps=[('simpleimputer',\n",
       "                                                     SimpleImputer(strategy='median'))]),\n",
       "                                    <sklearn.compose._column_transformer.make_column_selector object at 0x2b5b8cbd0>),\n",
       "                                   ('pipeline-2',\n",
       "                                    Pipeline(steps=[('simpleimputer',\n",
       "                                                     SimpleImputer(strategy='constant')),\n",
       "                                                    ('onehotencoder',\n",
       "                                                     OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                    ['Product Life cycel status', 'Date'])])),\n",
       "  ('randomforestregressor',\n",
       "   RandomForestRegressor(max_depth=5, n_estimators=200, random_state=42))],\n",
       " 'verbose': False,\n",
       " 'columntransformer': ColumnTransformer(transformers=[('pipeline-1',\n",
       "                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                   SimpleImputer(strategy='median'))]),\n",
       "                                  <sklearn.compose._column_transformer.make_column_selector object at 0x2b5b8cbd0>),\n",
       "                                 ('pipeline-2',\n",
       "                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                   SimpleImputer(strategy='constant')),\n",
       "                                                  ('onehotencoder',\n",
       "                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                  ['Product Life cycel status', 'Date'])]),\n",
       " 'randomforestregressor': RandomForestRegressor(max_depth=5, n_estimators=200, random_state=42),\n",
       " 'columntransformer__n_jobs': None,\n",
       " 'columntransformer__remainder': 'drop',\n",
       " 'columntransformer__sparse_threshold': 0.3,\n",
       " 'columntransformer__transformer_weights': None,\n",
       " 'columntransformer__transformers': [('pipeline-1',\n",
       "   Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))]),\n",
       "   <sklearn.compose._column_transformer.make_column_selector at 0x2b5b8cbd0>),\n",
       "  ('pipeline-2',\n",
       "   Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='constant')),\n",
       "                   ('onehotencoder', OneHotEncoder(handle_unknown='ignore'))]),\n",
       "   ['Product Life cycel status', 'Date'])],\n",
       " 'columntransformer__verbose': False,\n",
       " 'columntransformer__verbose_feature_names_out': True,\n",
       " 'columntransformer__pipeline-1': Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))]),\n",
       " 'columntransformer__pipeline-2': Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='constant')),\n",
       "                 ('onehotencoder', OneHotEncoder(handle_unknown='ignore'))]),\n",
       " 'columntransformer__pipeline-1__memory': None,\n",
       " 'columntransformer__pipeline-1__steps': [('simpleimputer',\n",
       "   SimpleImputer(strategy='median'))],\n",
       " 'columntransformer__pipeline-1__verbose': False,\n",
       " 'columntransformer__pipeline-1__simpleimputer': SimpleImputer(strategy='median'),\n",
       " 'columntransformer__pipeline-1__simpleimputer__add_indicator': False,\n",
       " 'columntransformer__pipeline-1__simpleimputer__copy': True,\n",
       " 'columntransformer__pipeline-1__simpleimputer__fill_value': None,\n",
       " 'columntransformer__pipeline-1__simpleimputer__keep_empty_features': False,\n",
       " 'columntransformer__pipeline-1__simpleimputer__missing_values': nan,\n",
       " 'columntransformer__pipeline-1__simpleimputer__strategy': 'median',\n",
       " 'columntransformer__pipeline-2__memory': None,\n",
       " 'columntransformer__pipeline-2__steps': [('simpleimputer',\n",
       "   SimpleImputer(strategy='constant')),\n",
       "  ('onehotencoder', OneHotEncoder(handle_unknown='ignore'))],\n",
       " 'columntransformer__pipeline-2__verbose': False,\n",
       " 'columntransformer__pipeline-2__simpleimputer': SimpleImputer(strategy='constant'),\n",
       " 'columntransformer__pipeline-2__onehotencoder': OneHotEncoder(handle_unknown='ignore'),\n",
       " 'columntransformer__pipeline-2__simpleimputer__add_indicator': False,\n",
       " 'columntransformer__pipeline-2__simpleimputer__copy': True,\n",
       " 'columntransformer__pipeline-2__simpleimputer__fill_value': None,\n",
       " 'columntransformer__pipeline-2__simpleimputer__keep_empty_features': False,\n",
       " 'columntransformer__pipeline-2__simpleimputer__missing_values': nan,\n",
       " 'columntransformer__pipeline-2__simpleimputer__strategy': 'constant',\n",
       " 'columntransformer__pipeline-2__onehotencoder__categories': 'auto',\n",
       " 'columntransformer__pipeline-2__onehotencoder__drop': None,\n",
       " 'columntransformer__pipeline-2__onehotencoder__dtype': numpy.float64,\n",
       " 'columntransformer__pipeline-2__onehotencoder__feature_name_combiner': 'concat',\n",
       " 'columntransformer__pipeline-2__onehotencoder__handle_unknown': 'ignore',\n",
       " 'columntransformer__pipeline-2__onehotencoder__max_categories': None,\n",
       " 'columntransformer__pipeline-2__onehotencoder__min_frequency': None,\n",
       " 'columntransformer__pipeline-2__onehotencoder__sparse': 'deprecated',\n",
       " 'columntransformer__pipeline-2__onehotencoder__sparse_output': True,\n",
       " 'randomforestregressor__bootstrap': True,\n",
       " 'randomforestregressor__ccp_alpha': 0.0,\n",
       " 'randomforestregressor__criterion': 'squared_error',\n",
       " 'randomforestregressor__max_depth': 5,\n",
       " 'randomforestregressor__max_features': 1.0,\n",
       " 'randomforestregressor__max_leaf_nodes': None,\n",
       " 'randomforestregressor__max_samples': None,\n",
       " 'randomforestregressor__min_impurity_decrease': 0.0,\n",
       " 'randomforestregressor__min_samples_leaf': 1,\n",
       " 'randomforestregressor__min_samples_split': 2,\n",
       " 'randomforestregressor__min_weight_fraction_leaf': 0.0,\n",
       " 'randomforestregressor__n_estimators': 200,\n",
       " 'randomforestregressor__n_jobs': None,\n",
       " 'randomforestregressor__oob_score': False,\n",
       " 'randomforestregressor__random_state': 42,\n",
       " 'randomforestregressor__verbose': 0,\n",
       " 'randomforestregressor__warm_start': False}"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RF_1.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit\n",
    "\n",
    "# Select the model to tune\n",
    "model_ = model_RF_1\n",
    "\n",
    "cv_ = StratifiedShuffleSplit(n_splits=5, test_size=.2)\n",
    "param_grid = {\n",
    "    \n",
    "}\n",
    "\n",
    "grid_pipeline = GridSearchCV(model_, param_grid, cv=cv_, scoring=my_scorer)\n",
    "grid_pipeline.fit(X_train, y_train)\n",
    "grid_pipeline.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "358.2714254463043\n",
      "455.82215460306367\n",
      "649.2042895583725\n",
      "0.7021243727658378\n",
      "0.4383005017873296\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_ = model_RF_2\n",
    "y_train_hat = model_.predict(X_train)\n",
    "y_test_hat = model_.predict(X_test)\n",
    "\n",
    "y_train_hat[X_train[\"Month 1\"] + X_train[\"Month 2\"] + X_train[\"Month 3\"]==0] = 0\n",
    "y_test_hat[X_test[\"Month 1\"] + X_test[\"Month 2\"] + X_test[\"Month 3\"]==0] = 0\n",
    "print(mse(y_train_hat, y_train))\n",
    "print(mse(y_test_hat, y_test))\n",
    "print(mse(np.zeros(y_test.shape), y_test))\n",
    "print(norm_mse(y_test_hat, y_test))\n",
    "print(RMSE_hfactory(y_test_hat, y_test))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8968083.172047125"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_hat.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the selected model on the test data to be submitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_submit = pd.read_csv(\"./1_Data_source/datasets_hi4/X_test.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_submit_ = data_submit.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prep_test(data_submit_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                               int64\n",
       "id_product                          int64\n",
       "Region                             object\n",
       "Country                            object\n",
       "Site                               object\n",
       "Operations                         object\n",
       "Zone                               object\n",
       "Cluster                            object\n",
       "Reference proxy                    object\n",
       "Product  Line proxy                object\n",
       "Division proxy                     object\n",
       "Customer Persona proxy             object\n",
       "Strategic Product Family proxy     object\n",
       "Product Life cycel status          object\n",
       "Date                               object\n",
       "Month 1                           float64\n",
       "Month 2                           float64\n",
       "Month 3                           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_submit_.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id_product</th>\n",
       "      <th>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Site</th>\n",
       "      <th>Operations</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Reference proxy</th>\n",
       "      <th>Product  Line proxy</th>\n",
       "      <th>Division proxy</th>\n",
       "      <th>Customer Persona proxy</th>\n",
       "      <th>Strategic Product Family proxy</th>\n",
       "      <th>Product Life cycel status</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month 1</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Month 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>490548</td>\n",
       "      <td>834</td>\n",
       "      <td>EAJP</td>\n",
       "      <td>AU</td>\n",
       "      <td>AU_DC_Sydney</td>\n",
       "      <td>International Operations</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Australia</td>\n",
       "      <td>reference-145</td>\n",
       "      <td>Product Line-1</td>\n",
       "      <td>Division-1</td>\n",
       "      <td>Customer Segmentation-8</td>\n",
       "      <td>Strategic Product Family-1</td>\n",
       "      <td>EOL</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>20435.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>4270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2167957</td>\n",
       "      <td>209101</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR_DC_Evreux</td>\n",
       "      <td>France Operations</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>reference-13121</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-66</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999013</td>\n",
       "      <td>40157</td>\n",
       "      <td>EAJP</td>\n",
       "      <td>NZ</td>\n",
       "      <td>AU_DC_ALD</td>\n",
       "      <td>International Operations</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>reference-3409</td>\n",
       "      <td>Product Line-3</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>469221</td>\n",
       "      <td>224364</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>CN</td>\n",
       "      <td>CN_DC_Shanghai</td>\n",
       "      <td>China Operations</td>\n",
       "      <td>China &amp; HK</td>\n",
       "      <td>China</td>\n",
       "      <td>reference-19450</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jan-apr 2021</td>\n",
       "      <td>7.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1171716</td>\n",
       "      <td>192288</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>BE</td>\n",
       "      <td>NL_DC_HLD</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>BeNe</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>reference-3686</td>\n",
       "      <td>Product Line-3</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-7</td>\n",
       "      <td>RaMPDOWN</td>\n",
       "      <td>jan-apr 2022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  id_product  Region Country            Site  \\\n",
       "0   490548         834    EAJP      AU    AU_DC_Sydney   \n",
       "1  2167957      209101  EUROPE      FR    FR_DC_Evreux   \n",
       "2  1999013       40157    EAJP      NZ       AU_DC_ALD   \n",
       "3   469221      224364   CHINA      CN  CN_DC_Shanghai   \n",
       "4  1171716      192288  EUROPE      BE       NL_DC_HLD   \n",
       "\n",
       "                 Operations        Zone      Cluster  Reference proxy  \\\n",
       "0  International Operations     Pacific    Australia    reference-145   \n",
       "1         France Operations      France       France  reference-13121   \n",
       "2  International Operations     Pacific  New Zealand   reference-3409   \n",
       "3          China Operations  China & HK        China  reference-19450   \n",
       "4         Europe Operations        BeNe      Belgium   reference-3686   \n",
       "\n",
       "  Product  Line proxy Division proxy    Customer Persona proxy  \\\n",
       "0      Product Line-1     Division-1   Customer Segmentation-8   \n",
       "1      Product Line-4     Division-3  Customer Segmentation-66   \n",
       "2      Product Line-3     Division-2   Customer Segmentation-4   \n",
       "3      Product Line-4     Division-3   Customer Segmentation-4   \n",
       "4      Product Line-3     Division-2   Customer Segmentation-4   \n",
       "\n",
       "  Strategic Product Family proxy Product Life cycel status          Date  \\\n",
       "0     Strategic Product Family-1                       EOL  may-aug 2021   \n",
       "1     Strategic Product Family-9                       NaN  may-jul 2023   \n",
       "2     Strategic Product Family-7                       NaN  may-jul 2023   \n",
       "3    Strategic Product Family-13                       NaN  jan-apr 2021   \n",
       "4     Strategic Product Family-7                  RaMPDOWN  jan-apr 2022   \n",
       "\n",
       "   Month 1  Month 2  Month 3  \n",
       "0  20435.0    610.0   4270.0  \n",
       "1      0.0      0.0      0.0  \n",
       "2      0.0      0.0      4.0  \n",
       "3      7.0    150.0    136.0  \n",
       "4      0.0      0.0      1.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_submit_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_submit = data_submit_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_submit = model_RF_2.predict(X_submit)\n",
    "y_submit[X_submit[\"Month 1\"] + X_submit[\"Month 2\"] + X_submit[\"Month 3\"]==0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_submit_ = pd.concat([X_submit[\"index\"], pd.DataFrame(y_submit, columns=[\"Month 4\"])], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id_product</th>\n",
       "      <th>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Site</th>\n",
       "      <th>Operations</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Reference proxy</th>\n",
       "      <th>Product  Line proxy</th>\n",
       "      <th>Division proxy</th>\n",
       "      <th>Customer Persona proxy</th>\n",
       "      <th>Strategic Product Family proxy</th>\n",
       "      <th>Product Life cycel status</th>\n",
       "      <th>Date</th>\n",
       "      <th>Month 1</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Month 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>490548</td>\n",
       "      <td>834</td>\n",
       "      <td>EAJP</td>\n",
       "      <td>AU</td>\n",
       "      <td>AU_DC_Sydney</td>\n",
       "      <td>International Operations</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>Australia</td>\n",
       "      <td>reference-145</td>\n",
       "      <td>Product Line-1</td>\n",
       "      <td>Division-1</td>\n",
       "      <td>Customer Segmentation-8</td>\n",
       "      <td>Strategic Product Family-1</td>\n",
       "      <td>EOL</td>\n",
       "      <td>may-aug 2021</td>\n",
       "      <td>20435.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>4270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2167957</td>\n",
       "      <td>209101</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR_DC_Evreux</td>\n",
       "      <td>France Operations</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>reference-13121</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-66</td>\n",
       "      <td>Strategic Product Family-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999013</td>\n",
       "      <td>40157</td>\n",
       "      <td>EAJP</td>\n",
       "      <td>NZ</td>\n",
       "      <td>AU_DC_ALD</td>\n",
       "      <td>International Operations</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>reference-3409</td>\n",
       "      <td>Product Line-3</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>may-jul 2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>469221</td>\n",
       "      <td>224364</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>CN</td>\n",
       "      <td>CN_DC_Shanghai</td>\n",
       "      <td>China Operations</td>\n",
       "      <td>China &amp; HK</td>\n",
       "      <td>China</td>\n",
       "      <td>reference-19450</td>\n",
       "      <td>Product Line-4</td>\n",
       "      <td>Division-3</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jan-apr 2021</td>\n",
       "      <td>7.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1171716</td>\n",
       "      <td>192288</td>\n",
       "      <td>EUROPE</td>\n",
       "      <td>BE</td>\n",
       "      <td>NL_DC_HLD</td>\n",
       "      <td>Europe Operations</td>\n",
       "      <td>BeNe</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>reference-3686</td>\n",
       "      <td>Product Line-3</td>\n",
       "      <td>Division-2</td>\n",
       "      <td>Customer Segmentation-4</td>\n",
       "      <td>Strategic Product Family-7</td>\n",
       "      <td>RaMPDOWN</td>\n",
       "      <td>jan-apr 2022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  id_product  Region Country            Site  \\\n",
       "0   490548         834    EAJP      AU    AU_DC_Sydney   \n",
       "1  2167957      209101  EUROPE      FR    FR_DC_Evreux   \n",
       "2  1999013       40157    EAJP      NZ       AU_DC_ALD   \n",
       "3   469221      224364   CHINA      CN  CN_DC_Shanghai   \n",
       "4  1171716      192288  EUROPE      BE       NL_DC_HLD   \n",
       "\n",
       "                 Operations        Zone      Cluster  Reference proxy  \\\n",
       "0  International Operations     Pacific    Australia    reference-145   \n",
       "1         France Operations      France       France  reference-13121   \n",
       "2  International Operations     Pacific  New Zealand   reference-3409   \n",
       "3          China Operations  China & HK        China  reference-19450   \n",
       "4         Europe Operations        BeNe      Belgium   reference-3686   \n",
       "\n",
       "  Product  Line proxy Division proxy    Customer Persona proxy  \\\n",
       "0      Product Line-1     Division-1   Customer Segmentation-8   \n",
       "1      Product Line-4     Division-3  Customer Segmentation-66   \n",
       "2      Product Line-3     Division-2   Customer Segmentation-4   \n",
       "3      Product Line-4     Division-3   Customer Segmentation-4   \n",
       "4      Product Line-3     Division-2   Customer Segmentation-4   \n",
       "\n",
       "  Strategic Product Family proxy Product Life cycel status          Date  \\\n",
       "0     Strategic Product Family-1                       EOL  may-aug 2021   \n",
       "1     Strategic Product Family-9                       NaN  may-jul 2023   \n",
       "2     Strategic Product Family-7                       NaN  may-jul 2023   \n",
       "3    Strategic Product Family-13                       NaN  jan-apr 2021   \n",
       "4     Strategic Product Family-7                  RaMPDOWN  jan-apr 2022   \n",
       "\n",
       "   Month 1  Month 2  Month 3  \n",
       "0  20435.0    610.0   4270.0  \n",
       "1      0.0      0.0      0.0  \n",
       "2      0.0      0.0      4.0  \n",
       "3      7.0    150.0    136.0  \n",
       "4      0.0      0.0      1.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11025.604814\n",
       "1        0.000000\n",
       "2        8.215690\n",
       "3       93.758169\n",
       "4        8.215690\n",
       "Name: Month 4, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_submit_['Month 4'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_submit_.to_csv(\"y_test_team21_2.csv\", index=False, sep=\";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CO2 Emissions impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn. metrics import make_scorer\n",
    "my_scorer = make_scorer(score_func, greater_is_better=False, needs_proba=False, needs_threshold=False, **kwargs)"
   ]
  }
 ],
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