Modeling_Ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7713c8e",
   "metadata": {},
   "source": [
    "# Reorder Prediction Using a CatBoost Ensemble Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a552995",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Relevant packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gc\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import *\n",
    "\n",
    "\n",
    "#import utility functions from script\n",
    "from utils import *\n",
    "from modeling_utils import *\n",
    "\n",
    "#Data file directory\n",
    "root = 'C:/Users/ALEXANDER/Documents/Machine Learning/Instacart/Data/'\n",
    "\n",
    "##Ignore Warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c4ea74a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of properties dataframe is : 3620.7658081054688  MB\n",
      "\n",
      "___MEMORY USAGE AFTER COMPLETION:___\n",
      "Memory usage is:  1301.2127122879028  MB\n",
      "This is  35.9375 % of the initial size\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_pickle(root + 'final_dataset.pkl')\n",
    "id_segment = pd.read_csv(\"id_segment_map.csv\")\n",
    "#reduce memory allocation\n",
    "data = reduce_memory(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3105f9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>total_product_orders_by_user</th>\n",
       "      <th>total_product_reorders_by_user</th>\n",
       "      <th>user_product_reorder_percentage</th>\n",
       "      <th>avg_add_to_cart_by_user</th>\n",
       "      <th>avg_days_since_last_bought</th>\n",
       "      <th>last_ordered_in</th>\n",
       "      <th>is_reorder_3</th>\n",
       "      <th>is_reorder_2</th>\n",
       "      <th>is_reorder_1</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>mean_add_to_cart_order</th>\n",
       "      <th>total_orders</th>\n",
       "      <th>total_reorders</th>\n",
       "      <th>reorder_percentage</th>\n",
       "      <th>unique_users</th>\n",
       "      <th>order_first_time_total_cnt</th>\n",
       "      <th>order_second_time_total_cnt</th>\n",
       "      <th>is_organic</th>\n",
       "      <th>second_time_percent</th>\n",
       "      <th>aisle_mean_add_to_cart_order</th>\n",
       "      <th>aisle_std_add_to_cart_order</th>\n",
       "      <th>aisle_total_orders</th>\n",
       "      <th>aisle_total_reorders</th>\n",
       "      <th>aisle_reorder_percentage</th>\n",
       "      <th>aisle_unique_users</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department_mean_add_to_cart_order</th>\n",
       "      <th>department_std_add_to_cart_order</th>\n",
       "      <th>department_total_orders</th>\n",
       "      <th>department_total_reorders</th>\n",
       "      <th>department_reorder_percentage</th>\n",
       "      <th>department_unique_users</th>\n",
       "      <th>department</th>\n",
       "      <th>avg_dow</th>\n",
       "      <th>std_dow</th>\n",
       "      <th>avg_doh</th>\n",
       "      <th>std_doh</th>\n",
       "      <th>avg_since_order</th>\n",
       "      <th>std_since_order</th>\n",
       "      <th>total_orders_by_user</th>\n",
       "      <th>total_products_by_user</th>\n",
       "      <th>total_unique_product_by_user</th>\n",
       "      <th>total_reorders_by_user</th>\n",
       "      <th>reorder_propotion_by_user</th>\n",
       "      <th>average_order_size</th>\n",
       "      <th>reorder_in_order</th>\n",
       "      <th>orders_3</th>\n",
       "      <th>orders_2</th>\n",
       "      <th>orders_1</th>\n",
       "      <th>reorder_3</th>\n",
       "      <th>reorder_2</th>\n",
       "      <th>reorder_1</th>\n",
       "      <th>order_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.899902</td>\n",
       "      <td>1.400391</td>\n",
       "      <td>17.593750</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.722656</td>\n",
       "      <td>35791</td>\n",
       "      <td>27791.0</td>\n",
       "      <td>0.776367</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>0.582520</td>\n",
       "      <td>6.453125</td>\n",
       "      <td>6.449219</td>\n",
       "      <td>357537</td>\n",
       "      <td>228406.0</td>\n",
       "      <td>0.638672</td>\n",
       "      <td>63506</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>6.976562</td>\n",
       "      <td>6.710938</td>\n",
       "      <td>2690129</td>\n",
       "      <td>1757892.0</td>\n",
       "      <td>0.653320</td>\n",
       "      <td>172795</td>\n",
       "      <td>beverages</td>\n",
       "      <td>2.644531</td>\n",
       "      <td>1.255859</td>\n",
       "      <td>10.539062</td>\n",
       "      <td>3.5</td>\n",
       "      <td>18.546875</td>\n",
       "      <td>10.5625</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.694824</td>\n",
       "      <td>5.898438</td>\n",
       "      <td>0.706055</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.888672</td>\n",
       "      <td>3.333984</td>\n",
       "      <td>19.562500</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.277344</td>\n",
       "      <td>1946</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>0.713867</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>0.552734</td>\n",
       "      <td>9.320312</td>\n",
       "      <td>7.906250</td>\n",
       "      <td>306487</td>\n",
       "      <td>159119.0</td>\n",
       "      <td>0.519043</td>\n",
       "      <td>76177</td>\n",
       "      <td>nuts seeds dried fruit</td>\n",
       "      <td>9.187500</td>\n",
       "      <td>7.691406</td>\n",
       "      <td>2887550</td>\n",
       "      <td>1657973.0</td>\n",
       "      <td>0.574219</td>\n",
       "      <td>174219</td>\n",
       "      <td>snacks</td>\n",
       "      <td>2.644531</td>\n",
       "      <td>1.255859</td>\n",
       "      <td>10.539062</td>\n",
       "      <td>3.5</td>\n",
       "      <td>18.546875</td>\n",
       "      <td>10.5625</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.694824</td>\n",
       "      <td>5.898438</td>\n",
       "      <td>0.706055</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.191406</td>\n",
       "      <td>5526</td>\n",
       "      <td>3603.0</td>\n",
       "      <td>0.651855</td>\n",
       "      <td>1923</td>\n",
       "      <td>1923</td>\n",
       "      <td>1003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.521484</td>\n",
       "      <td>7.144531</td>\n",
       "      <td>6.281250</td>\n",
       "      <td>3642188</td>\n",
       "      <td>2615469.0</td>\n",
       "      <td>0.718262</td>\n",
       "      <td>177141</td>\n",
       "      <td>fresh fruits</td>\n",
       "      <td>8.023438</td>\n",
       "      <td>6.660156</td>\n",
       "      <td>9479291</td>\n",
       "      <td>6160710.0</td>\n",
       "      <td>0.649902</td>\n",
       "      <td>193237</td>\n",
       "      <td>produce</td>\n",
       "      <td>2.644531</td>\n",
       "      <td>1.255859</td>\n",
       "      <td>10.539062</td>\n",
       "      <td>3.5</td>\n",
       "      <td>18.546875</td>\n",
       "      <td>10.5625</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.694824</td>\n",
       "      <td>5.898438</td>\n",
       "      <td>0.706055</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.899902</td>\n",
       "      <td>3.300781</td>\n",
       "      <td>17.593750</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.761719</td>\n",
       "      <td>6476</td>\n",
       "      <td>4797.0</td>\n",
       "      <td>0.740723</td>\n",
       "      <td>1679</td>\n",
       "      <td>1679</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>0.529297</td>\n",
       "      <td>8.460938</td>\n",
       "      <td>7.375000</td>\n",
       "      <td>163524</td>\n",
       "      <td>96804.0</td>\n",
       "      <td>0.591797</td>\n",
       "      <td>44854</td>\n",
       "      <td>popcorn jerky</td>\n",
       "      <td>9.187500</td>\n",
       "      <td>7.691406</td>\n",
       "      <td>2887550</td>\n",
       "      <td>1657973.0</td>\n",
       "      <td>0.574219</td>\n",
       "      <td>174219</td>\n",
       "      <td>snacks</td>\n",
       "      <td>2.644531</td>\n",
       "      <td>1.255859</td>\n",
       "      <td>10.539062</td>\n",
       "      <td>3.5</td>\n",
       "      <td>18.546875</td>\n",
       "      <td>10.5625</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.694824</td>\n",
       "      <td>5.898438</td>\n",
       "      <td>0.706055</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>6.332031</td>\n",
       "      <td>21.671875</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.621094</td>\n",
       "      <td>3751</td>\n",
       "      <td>2465.0</td>\n",
       "      <td>0.657227</td>\n",
       "      <td>1286</td>\n",
       "      <td>1286</td>\n",
       "      <td>617</td>\n",
       "      <td>0</td>\n",
       "      <td>0.479736</td>\n",
       "      <td>9.046875</td>\n",
       "      <td>7.566406</td>\n",
       "      <td>377586</td>\n",
       "      <td>215822.0</td>\n",
       "      <td>0.571777</td>\n",
       "      <td>77080</td>\n",
       "      <td>cereal</td>\n",
       "      <td>9.125000</td>\n",
       "      <td>7.601562</td>\n",
       "      <td>709569</td>\n",
       "      <td>398013.0</td>\n",
       "      <td>0.561035</td>\n",
       "      <td>114169</td>\n",
       "      <td>breakfast</td>\n",
       "      <td>2.644531</td>\n",
       "      <td>1.255859</td>\n",
       "      <td>10.539062</td>\n",
       "      <td>3.5</td>\n",
       "      <td>18.546875</td>\n",
       "      <td>10.5625</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.694824</td>\n",
       "      <td>5.898438</td>\n",
       "      <td>0.706055</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666504</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  total_product_orders_by_user  total_product_reorders_by_user  \\\n",
       "0        1                          10.0                             9.0   \n",
       "1        1                           9.0                             8.0   \n",
       "2        1                           1.0                             0.0   \n",
       "3        1                          10.0                             9.0   \n",
       "4        1                           3.0                             2.0   \n",
       "\n",
       "   user_product_reorder_percentage  avg_add_to_cart_by_user  \\\n",
       "0                         0.899902                 1.400391   \n",
       "1                         0.888672                 3.333984   \n",
       "2                         0.000000                 5.000000   \n",
       "3                         0.899902                 3.300781   \n",
       "4                         0.666504                 6.332031   \n",
       "\n",
       "   avg_days_since_last_bought  last_ordered_in  is_reorder_3  is_reorder_2  \\\n",
       "0                   17.593750             10.0           1.0           1.0   \n",
       "1                   19.562500             10.0           1.0           1.0   \n",
       "2                   28.000000              5.0           0.0           0.0   \n",
       "3                   17.593750             10.0           1.0           1.0   \n",
       "4                   21.671875             10.0           1.0           0.0   \n",
       "\n",
       "   is_reorder_1  order_number  order_dow  order_hour_of_day  \\\n",
       "0           1.0          11.0        4.0                8.0   \n",
       "1           1.0          11.0        4.0                8.0   \n",
       "2           0.0          11.0        4.0                8.0   \n",
       "3           1.0          11.0        4.0                8.0   \n",
       "4           0.0          11.0        4.0                8.0   \n",
       "\n",
       "   days_since_prior_order  reordered  mean_add_to_cart_order  total_orders  \\\n",
       "0                    14.0        1.0                3.722656         35791   \n",
       "1                    14.0        1.0                4.277344          1946   \n",
       "2                    14.0        0.0                4.191406          5526   \n",
       "3                    14.0        0.0                4.761719          6476   \n",
       "4                    14.0        1.0                5.621094          3751   \n",
       "\n",
       "   total_reorders  reorder_percentage  unique_users  \\\n",
       "0         27791.0            0.776367          8000   \n",
       "1          1389.0            0.713867           557   \n",
       "2          3603.0            0.651855          1923   \n",
       "3          4797.0            0.740723          1679   \n",
       "4          2465.0            0.657227          1286   \n",
       "\n",
       "   order_first_time_total_cnt  order_second_time_total_cnt  is_organic  \\\n",
       "0                        8000                         4660           0   \n",
       "1                         557                          308           0   \n",
       "2                        1923                         1003           0   \n",
       "3                        1679                          889           0   \n",
       "4                        1286                          617           0   \n",
       "\n",
       "   second_time_percent  aisle_mean_add_to_cart_order  \\\n",
       "0             0.582520                      6.453125   \n",
       "1             0.552734                      9.320312   \n",
       "2             0.521484                      7.144531   \n",
       "3             0.529297                      8.460938   \n",
       "4             0.479736                      9.046875   \n",
       "\n",
       "   aisle_std_add_to_cart_order  aisle_total_orders  aisle_total_reorders  \\\n",
       "0                     6.449219              357537              228406.0   \n",
       "1                     7.906250              306487              159119.0   \n",
       "2                     6.281250             3642188             2615469.0   \n",
       "3                     7.375000              163524               96804.0   \n",
       "4                     7.566406              377586              215822.0   \n",
       "\n",
       "   aisle_reorder_percentage  aisle_unique_users                   aisle  \\\n",
       "0                  0.638672               63506             soft drinks   \n",
       "1                  0.519043               76177  nuts seeds dried fruit   \n",
       "2                  0.718262              177141            fresh fruits   \n",
       "3                  0.591797               44854           popcorn jerky   \n",
       "4                  0.571777               77080                  cereal   \n",
       "\n",
       "   department_mean_add_to_cart_order  department_std_add_to_cart_order  \\\n",
       "0                           6.976562                          6.710938   \n",
       "1                           9.187500                          7.691406   \n",
       "2                           8.023438                          6.660156   \n",
       "3                           9.187500                          7.691406   \n",
       "4                           9.125000                          7.601562   \n",
       "\n",
       "   department_total_orders  department_total_reorders  \\\n",
       "0                  2690129                  1757892.0   \n",
       "1                  2887550                  1657973.0   \n",
       "2                  9479291                  6160710.0   \n",
       "3                  2887550                  1657973.0   \n",
       "4                   709569                   398013.0   \n",
       "\n",
       "   department_reorder_percentage  department_unique_users department  \\\n",
       "0                       0.653320                   172795  beverages   \n",
       "1                       0.574219                   174219     snacks   \n",
       "2                       0.649902                   193237    produce   \n",
       "3                       0.574219                   174219     snacks   \n",
       "4                       0.561035                   114169  breakfast   \n",
       "\n",
       "    avg_dow   std_dow    avg_doh  std_doh  avg_since_order  std_since_order  \\\n",
       "0  2.644531  1.255859  10.539062      3.5        18.546875          10.5625   \n",
       "1  2.644531  1.255859  10.539062      3.5        18.546875          10.5625   \n",
       "2  2.644531  1.255859  10.539062      3.5        18.546875          10.5625   \n",
       "3  2.644531  1.255859  10.539062      3.5        18.546875          10.5625   \n",
       "4  2.644531  1.255859  10.539062      3.5        18.546875          10.5625   \n",
       "\n",
       "   total_orders_by_user  total_products_by_user  total_unique_product_by_user  \\\n",
       "0                    10                      59                            18   \n",
       "1                    10                      59                            18   \n",
       "2                    10                      59                            18   \n",
       "3                    10                      59                            18   \n",
       "4                    10                      59                            18   \n",
       "\n",
       "   total_reorders_by_user  reorder_propotion_by_user  average_order_size  \\\n",
       "0                    41.0                   0.694824            5.898438   \n",
       "1                    41.0                   0.694824            5.898438   \n",
       "2                    41.0                   0.694824            5.898438   \n",
       "3                    41.0                   0.694824            5.898438   \n",
       "4                    41.0                   0.694824            5.898438   \n",
       "\n",
       "   reorder_in_order  orders_3  orders_2  orders_1  reorder_3  reorder_2  \\\n",
       "0          0.706055         6         6         9   0.666504        1.0   \n",
       "1          0.706055         6         6         9   0.666504        1.0   \n",
       "2          0.706055         6         6         9   0.666504        1.0   \n",
       "3          0.706055         6         6         9   0.666504        1.0   \n",
       "4          0.706055         6         6         9   0.666504        1.0   \n",
       "\n",
       "   reorder_1  order_diff  \n",
       "0   0.666504         1.0  \n",
       "1   0.666504         1.0  \n",
       "2   0.666504         6.0  \n",
       "3   0.666504         1.0  \n",
       "4   0.666504         1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#New feature generation\n",
    "data['order_diff'] = data['order_number'] - data['last_ordered_in']\n",
    "data.drop(['product_id'], axis = 1, inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "508a5e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8474661, 58)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Chech dataset size\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "939d3240",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get numerical columns\n",
    "num_cols = data._get_numeric_data().columns\n",
    "#GEt categorical columns\n",
    "cat_cols = list(set(data.columns) - set(num_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35f43e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.merge(id_segment, on = 'user_id', how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17d552a",
   "metadata": {},
   "source": [
    "- **After finding the possible cohort (clusters) among the different customers, I decided to substitute each user_id with the cluster to which they are assumed to belong. Hope this would eventually increase the predictive model performance.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f72aab",
   "metadata": {},
   "source": [
    "- By performing Iterative Feature selection, I deduced that these sets of features are useless. Hence I decided to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b5de5be",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"department_unique_users\",\"order_first_time_total_cnt\",\"department_total_reorders\",\"department_std_add_to_cart_order\",\n",
    "\"aisle_unique_users\",\"total_orders_by_user\",\"aisle_total_reorders\",\"is_reorder_3\",\"total_orders_by_user\",\"user_id\"],1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f2372eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate target and predictors\n",
    "Target = data['reordered']\n",
    "data_features = data.drop(['reordered'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f67f9cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into Train and test set\n",
    "Train, Test, y_train, y_test = train_test_split(data_features,Target, stratify=Target, random_state=21,shuffle=True,\n",
    "                                               train_size=0.85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc5cf1e",
   "metadata": {},
   "source": [
    "- Due to the size of all categories in `aisle` and `department`. `One hot encoding` and `label encoding` would not be ideal. So I decided to try `target encoding`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c09bf5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encode categorical features\n",
    "TE_encoder = TargetEncoder()\n",
    "a = Train[cat_cols]\n",
    "b = Test[cat_cols]\n",
    "\n",
    "X_target_encoded = TE_encoder.fit(a,y_train)\n",
    "Train =X_target_encoded.transform(Train)\n",
    "Test = X_target_encoded.transform(Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15a53513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train reorder distribution [0: 6498961], [1: 704500]\n",
      "Test reorder distribution [0: 1146876], [1: 124324]\n"
     ]
    }
   ],
   "source": [
    "#Check distribution of targets\n",
    "print(f\"Train reorder distribution [0: {np.bincount(y_train)[0]}], [1: {np.bincount(y_train)[1]}]\")\n",
    "print(f\"Test reorder distribution [0: {np.bincount(y_test)[0]}], [1: {np.bincount(y_test)[1]}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "275a82b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4445adcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6570655\ttest: 0.6569382\tbest: 0.6569382 (0)\ttotal: 12.2s\tremaining: 11h 49m 46s\n",
      "250:\tlearn: 0.2448812\ttest: 0.2449272\tbest: 0.2449272 (250)\ttotal: 27m 34s\tremaining: 5h 56m 53s\n",
      "500:\tlearn: 0.2429769\ttest: 0.2430169\tbest: 0.2430169 (500)\ttotal: 53m 52s\tremaining: 5h 22m 29s\n",
      "750:\tlearn: 0.2413847\ttest: 0.2414241\tbest: 0.2414241 (750)\ttotal: 1h 20m 18s\tremaining: 4h 53m 56s\n",
      "1000:\tlearn: 0.2400700\ttest: 0.2401090\tbest: 0.2401090 (1000)\ttotal: 1h 46m 21s\tremaining: 4h 25m 30s\n",
      "1250:\tlearn: 0.2388971\ttest: 0.2389375\tbest: 0.2389375 (1250)\ttotal: 2h 12m 11s\tremaining: 3h 57m 38s\n",
      "1500:\tlearn: 0.2378081\ttest: 0.2378494\tbest: 0.2378494 (1500)\ttotal: 2h 38m 42s\tremaining: 3h 31m 21s\n",
      "1750:\tlearn: 0.2367968\ttest: 0.2368379\tbest: 0.2368379 (1750)\ttotal: 3h 4m 24s\tremaining: 3h 4m 11s\n",
      "2000:\tlearn: 0.2358029\ttest: 0.2358444\tbest: 0.2358444 (2000)\ttotal: 3h 31m 21s\tremaining: 2h 38m 19s\n",
      "2250:\tlearn: 0.2348284\ttest: 0.2348698\tbest: 0.2348698 (2250)\ttotal: 3h 57m 7s\tremaining: 2h 11m 34s\n",
      "2500:\tlearn: 0.2339226\ttest: 0.2339649\tbest: 0.2339649 (2500)\ttotal: 4h 23m 12s\tremaining: 1h 45m 8s\n",
      "2750:\tlearn: 0.2329835\ttest: 0.2330261\tbest: 0.2330261 (2750)\ttotal: 4h 50m 39s\tremaining: 1h 19m 8s\n",
      "3000:\tlearn: 0.2321176\ttest: 0.2321608\tbest: 0.2321608 (3000)\ttotal: 5h 17m 13s\tremaining: 52m 44s\n",
      "3250:\tlearn: 0.2312823\ttest: 0.2313263\tbest: 0.2313263 (3250)\ttotal: 5h 42m 47s\tremaining: 26m 15s\n",
      "3499:\tlearn: 0.2304813\ttest: 0.2305248\tbest: 0.2305248 (3499)\ttotal: 6h 8m 22s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.2305247529\n",
      "bestIteration = 3499\n",
      "\n"
     ]
    }
   ],
   "source": [
    "catboost =  CatBoostClassifier(random_seed=10,use_best_model=True,l2_leaf_reg=9.5,max_depth=10,\n",
    "                               bagging_temperature=1,\n",
    "                          n_estimators=3500,silent=True,eval_metric='Logloss')\n",
    "\n",
    "catboost.fit(Train,y_train,eval_set=[(Train,y_train)],early_stopping_rounds=100,\n",
    "                           verbose=250,use_best_model=True)\n",
    "predictions = catboost.predict_proba(Test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0f7c5fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def evaluation_report(y_test, predictions,pred_prob):\n",
    "    print (\"\\n Classification report : \\n\",classification_report(y_test, predictions))\n",
    "    print (f\"Accuracy Score : {accuracy_score(y_test, predictions)}\")\n",
    "    print(f\"Weighted F1 Score: {f1_score(y_test, predictions,average='weighted')}\")\n",
    "    print (f\"Area under curve : {roc_auc_score(y_test,pred_prob)}\\n\")\n",
    "    print (f\"Logloss score : {log_loss(y_test,pred_prob)}\\n\")\n",
    "\n",
    "\n",
    "def plot_confusion(conf_matrix):\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.subplot(221)\n",
    "    sns.heatmap(conf_matrix, fmt = \"d\",annot=True, cmap='YlOrBr')\n",
    "    limit1, limit2 = plt.ylim()\n",
    "    plt.ylim(limit1 + 0.5, limit2 - 0.5)\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.ylabel('True Values')\n",
    "    plt.xlabel('Predicted Values')\n",
    "    plt.savefig(\"Confusion_Matrix.png\",bbox_inches=\"tight\")\n",
    "\n",
    "    \n",
    "def plot_roc_curve(y_true,pred_prob):\n",
    "\n",
    "    fpr,tpr,thresholds = roc_curve(y_true,pred_prob)\n",
    "    norm = np.sqrt(tpr * (1-fpr))\n",
    "    index = np.argmax(norm)\n",
    "    threshold = np.round(thresholds[index],3)\n",
    "\n",
    "    plt.subplot(222)\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=1, label = \"Auc : %.3f\" %roc_auc_score(y_test,pred_prob))\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.scatter(fpr[index], tpr[index], marker='o', color='black', label='Best Threshold:' + str(threshold))\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig(\"ROC_Curve.png\",bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    \n",
    "def plot_feature_importance(estimator,top_n):\n",
    "    fig, ax = plt.subplots(figsize = (10,15))\n",
    "    sorted_idx = catboost.feature_importances_.argsort()[:top_n]\n",
    "    ax.barh(Train.columns[sorted_idx], catboost.feature_importances_[sorted_idx])\n",
    "    plt.xlabel(\"CatBoost Feature Importance\")\n",
    "    plt.title(f\"Top {top_n} features and their importance\")\n",
    "    fig.savefig('CatBoost Feature Importance Plot.png',bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94a3fc9",
   "metadata": {},
   "source": [
    "### Optimize F1 score\n",
    "- The goal here is to find the optimal threshold to define class boundaries in predicted probabilities.\n",
    "- Here, I will make use of the precision-recall trade-off to find the optimal threshold that yields the greatest F1 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ed04cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_results(predictions,threshold):\n",
    "    pred_=[]\n",
    "    for pred in predictions:\n",
    "        if pred < threshold:\n",
    "            pred_.append(0)\n",
    "        else:\n",
    "            pred_.append(1)\n",
    "    return pred_\n",
    "\n",
    "#define the threshold space\n",
    "thresholds = np.arange(0.1,1.0,0.05)\n",
    "results={}\n",
    "precision,recall,f1 =[],[],[]\n",
    "#obtain the class label predicted for each probability threshold\n",
    "for threshold  in thresholds:\n",
    "    results[f\"{threshold}\"] = get_results(predictions,threshold)\n",
    "    \n",
    "#Get precision,f1_score and recall at each threshold.\n",
    "for thresh in results.keys():\n",
    "    precision.append(precision_score(y_test,results[thresh]))\n",
    "    recall.append(recall_score(y_test,results[thresh]))\n",
    "    f1.append(f1_score(y_test,results[thresh],average=\"weighted\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d3eb4b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot recall vs precision to get optimal threshold\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions, \"b--\", label=\"Precision\")\n",
    "    plt.plot(thresholds, recalls, \"g-\", label=\"Recall\")\n",
    "    plt.xlabel(\"Threshold\")\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.ylim([0, 1])\n",
    "    \n",
    "plot_precision_recall_vs_threshold(precision, recall, thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4dbc9085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal probability threshold is 0.35 and the corresponding f1 score is 0.8977624329393258\n"
     ]
    }
   ],
   "source": [
    "print(f\"The optimal probability threshold is {thresholds[np.argmax(f1)]:.2f} and the corresponding f1 score is {f1[np.argmax(f1)]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2370807",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_=[]\n",
    "threshold = 0.35\n",
    "for pred in predictions:\n",
    "    if pred < threshold:\n",
    "        pred_.append(0)\n",
    "    else:\n",
    "        pred_.append(1)\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test,pred_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c5dd446a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.97      0.95   1146876\n",
      "         1.0       0.54      0.34      0.42    124324\n",
      "\n",
      "    accuracy                           0.91   1271200\n",
      "   macro avg       0.74      0.66      0.68   1271200\n",
      "weighted avg       0.89      0.91      0.90   1271200\n",
      "\n",
      "Accuracy Score : 0.9073544682190057\n",
      "Weighted F1 Score: 0.8977624329393258\n",
      "Area under curve : 0.8428488481132919\n",
      "\n",
      "Logloss score : 0.23922647310184267\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluation_report(y_test, pred_,predictions)\n",
    "plot_confusion(conf_matrix)\n",
    "plot_roc_curve(y_test,pred_prob=predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc85ce72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x2700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(catboost,top_n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285ac984",
   "metadata": {},
   "source": [
    "- **The top 10 features with the most contribution to model prediction are:**\n",
    "    - total_reorders\n",
    "    - department_reorder_percentage\n",
    "    - order_second_time_total_cnt\n",
    "    - aisle\n",
    "    - department_total_order\n",
    "    - department\n",
    "    - aisle_std_add_to_cart_order\n",
    "    - total_orders\n",
    "    - department_mean_add_to_cart_order\n",
    "    - is_organic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6670c54",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}