Lightfm_hybrid_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "import warnings\r\n",
    "warnings.filterwarnings('ignore')\r\n",
    "import pprint"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "cust_item = pd.read_excel('C:\\\\Users\\\\dell\\\\Desktop\\\\Recommendation_project\\\\COding\\\\custitem.xlsx')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "custitem = cust_item.copy()\r\n",
    "custitem.drop(['Unnamed: 0'], axis =1, inplace = True)\r\n",
    "custitem.rename(columns = {'Unnamed: 0_level_1':'customer'}, inplace=True)\r\n",
    "custitem.drop_duplicates(inplace=True)\r\n",
    "custitem"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "        customer  badmimango  blacksapota  citronmelon  custardapple  \\\n",
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       "107410   CUST87Q           0            0            0             0   \n",
       "107420  CUST293Q           0            0            0             0   \n",
       "\n",
       "        dasherimango  dholkapomogranates  galaapple  gigerinemelon  \\\n",
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       "107410            0  ...                   0                        0   \n",
       "107420            0  ...                   0                        0   \n",
       "\n",
       "        premiumbrownrice  safeharvestsonamasuririce  \\\n",
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       "        safeharvestunpolishedbrownsonamasuririce  sonamasuriboiledrice  \\\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>badmimango</th>\n",
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       "      <td>CUST230</td>\n",
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       "      <th>2</th>\n",
       "      <td>CUST289</td>\n",
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       "      <th>3</th>\n",
       "      <td>CUST193</td>\n",
       "      <td>0</td>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Grouping by customer and taking all transactions of customer into one row\r\n",
    "custitem = custitem.groupby('customer').sum()\r\n",
    "for i in custitem.columns:\r\n",
    "    custitem[i].loc[custitem.loc[:,i] > 1] = 1\r\n",
    "custitem"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          badmimango  blacksapota  citronmelon  custardapple  dasherimango  \\\n",
       "customer                                                                     \n",
       "CUST1              0            0            0             1             0   \n",
       "CUST10             0            0            0             0             0   \n",
       "CUST100            0            1            1             0             1   \n",
       "CUST100A           0            1            1             0             1   \n",
       "CUST100B           0            1            1             0             1   \n",
       "...              ...          ...          ...           ...           ...   \n",
       "CUST9M             0            0            0             0             0   \n",
       "CUST9N             0            0            0             0             0   \n",
       "CUST9O             0            0            0             0             0   \n",
       "CUST9P             0            0            0             0             0   \n",
       "CUST9Q             0            0            0             0             0   \n",
       "\n",
       "          dholkapomogranates  galaapple  gigerinemelon  greensapota  \\\n",
       "customer                                                              \n",
       "CUST1                      0          0              0            0   \n",
       "CUST10                     0          0              0            0   \n",
       "CUST100                    0          1              0            0   \n",
       "CUST100A                   0          1              0            0   \n",
       "CUST100B                   0          1              0            0   \n",
       "...                      ...        ...            ...          ...   \n",
       "CUST9M                     0          0              0            0   \n",
       "CUST9N                     0          0              0            0   \n",
       "CUST9O                     0          0              0            0   \n",
       "CUST9P                     0          0              0            0   \n",
       "CUST9Q                     0          0              0            0   \n",
       "\n",
       "          guineagoldpapaya  ...  premiumbasmatirice  premiumbasmatiricebrown  \\\n",
       "customer                    ...                                                \n",
       "CUST1                    0  ...                   0                        0   \n",
       "CUST10                   0  ...                   0                        0   \n",
       "CUST100                  0  ...                   0                        0   \n",
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       "CUST9P                   0  ...                   0                        0   \n",
       "CUST9Q                   0  ...                   0                        0   \n",
       "\n",
       "          premiumbrownrice  safeharvestsonamasuririce  \\\n",
       "customer                                                \n",
       "CUST1                    0                          0   \n",
       "CUST10                   0                          0   \n",
       "CUST100                  0                          0   \n",
       "CUST100A                 0                          0   \n",
       "CUST100B                 0                          0   \n",
       "...                    ...                        ...   \n",
       "CUST9M                   0                          0   \n",
       "CUST9N                   0                          0   \n",
       "CUST9O                   0                          0   \n",
       "CUST9P                   0                          0   \n",
       "CUST9Q                   0                          0   \n",
       "\n",
       "          safeharvestunpolishedbrownsonamasuririce  sonamasuriboiledrice  \\\n",
       "customer                                                                   \n",
       "CUST1                                            0                     0   \n",
       "CUST10                                           0                     1   \n",
       "CUST100                                          0                     0   \n",
       "CUST100A                                         0                     0   \n",
       "CUST100B                                         0                     0   \n",
       "...                                            ...                   ...   \n",
       "CUST9M                                           0                     0   \n",
       "CUST9N                                           0                     0   \n",
       "CUST9O                                           0                     0   \n",
       "CUST9P                                           0                     0   \n",
       "CUST9Q                                           0                     0   \n",
       "\n",
       "          sonamasurinewrice  sonamasuririce  specialbasmatirice  \\\n",
       "customer                                                          \n",
       "CUST1                     0               1                   0   \n",
       "CUST10                    0               1                   0   \n",
       "CUST100                   0               0                   0   \n",
       "CUST100A                  0               0                   0   \n",
       "CUST100B                  0               0                   0   \n",
       "...                     ...             ...                 ...   \n",
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       "CUST9O                    0               0                   0   \n",
       "CUST9P                    0               0                   0   \n",
       "CUST9Q                    0               0                   0   \n",
       "\n",
       "          vmdsonamasuriricesupreme  \n",
       "customer                            \n",
       "CUST1                            0  \n",
       "CUST10                           0  \n",
       "CUST100                          0  \n",
       "CUST100A                         0  \n",
       "CUST100B                         0  \n",
       "...                            ...  \n",
       "CUST9M                           0  \n",
       "CUST9N                           0  \n",
       "CUST9O                           0  \n",
       "CUST9P                           0  \n",
       "CUST9Q                           0  \n",
       "\n",
       "[6966 rows x 116 columns]"
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       "<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>badmimango</th>\n",
       "      <th>blacksapota</th>\n",
       "      <th>citronmelon</th>\n",
       "      <th>custardapple</th>\n",
       "      <th>dasherimango</th>\n",
       "      <th>dholkapomogranates</th>\n",
       "      <th>galaapple</th>\n",
       "      <th>gigerinemelon</th>\n",
       "      <th>greensapota</th>\n",
       "      <th>guineagoldpapaya</th>\n",
       "      <th>...</th>\n",
       "      <th>premiumbasmatirice</th>\n",
       "      <th>premiumbasmatiricebrown</th>\n",
       "      <th>premiumbrownrice</th>\n",
       "      <th>safeharvestsonamasuririce</th>\n",
       "      <th>safeharvestunpolishedbrownsonamasuririce</th>\n",
       "      <th>sonamasuriboiledrice</th>\n",
       "      <th>sonamasurinewrice</th>\n",
       "      <th>sonamasuririce</th>\n",
       "      <th>specialbasmatirice</th>\n",
       "      <th>vmdsonamasuriricesupreme</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CUST1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST100</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST100A</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST100B</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST9M</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST9N</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST9O</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST9P</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST9Q</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6966 rows × 116 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### LIGHT FM HYBRID MODEL FOR E-GROCERY REC. SYSTEM\n",
    "\n",
    "#### Advantages of Light FM \n",
    "\n",
    "A hybrid matrix factorization model by Lyst.\n",
    "\n",
    "LightFM is a hybrid matrix factorisation model representing users and items as linear combinations of their content features’ latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant.\n",
    "\n",
    "LightFM model learns embeddings (latent representations in a high-dimensional space) for users and items in a way that encodes user preferences over items. When multiplied together, these representations produce scores for every item for a given user; items scored highly are more likely to be interesting to the user\n",
    "\n",
    "#### Benefit of LightFM python library\n",
    "\n",
    "The biggest benefit of lightfm library is that it implements WARP (Weighted Approximate-Rank Pairwise) loss for implicit feedback learning-to-rank.\n",
    "\n",
    "WARP works like these:\n",
    "\n",
    "For a given (user, positive item pair), sample a negative item at random from all the remaining items. Compute predictions for both items; if the negative item’s prediction exceeds that of the positive item plus a margin, perform a gradient update to rank the positive item higher and the negative item lower. If there is no rank violation, continue sampling negative items until a violation is found.\n",
    "\n",
    "If you found a violating negative example at the first try, make a large gradient update: this indicates that a lot of negative items are ranked higher than positives items given the current state of the model, and the model must be updated by a large amount. If it took a lot of sampling to find a violating example, perform a small update: the model is likely close to the optimum and should be updated at a low rate."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "from lightfm.cross_validation import random_train_test_split\r\n",
    "from lightfm.evaluation import auc_score, precision_at_k, recall_at_k\r\n",
    "from lightfm import LightFM\r\n",
    "from scipy.sparse import csr_matrix"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "cust_id = list(custitem.index)\r\n",
    "cust_dict = {}\r\n",
    "counter = 0\r\n",
    "for i in cust_id:\r\n",
    "    cust_dict[i] = counter\r\n",
    "    counter += 1\r\n",
    "\r\n",
    "item_list = list(custitem.columns)\r\n",
    "item_dict ={}\r\n",
    "counter = 0\r\n",
    "for i in item_list:\r\n",
    "        item_dict[counter] =i\r\n",
    "        counter += 1\r\n",
    "    \r\n",
    "# convert to csr matrix\r\n",
    "\r\n",
    "# Light FM needs data to be in matrix..\r\n",
    "cust_item_csr = csr_matrix(custitem.values)\r\n",
    "cust_item_csr"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<6966x116 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 108961 stored elements in Compressed Sparse Row format>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "filename = 'custdic.pkl'\r\n",
    "pickle.dump(cust_dict, open(filename, 'wb'))\r\n",
    "\r\n",
    "filename = 'itemdic.pkl'\r\n",
    "pickle.dump(item_dict, open(filename, 'wb'))\r\n",
    "\r\n",
    "filename = 'custitem.pkl'\r\n",
    "pickle.dump(custitem, open(filename, 'wb'))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### no_components (int, optional) \n",
    "– the dimensionality of the feature latent embeddings.\n",
    "\n",
    "#### learning_rate (float, optional)\n",
    "– initial learning rate for the adagrad learning schedule.\n",
    "\n",
    "#### loss (string, optional) \n",
    "– one of (‘logistic’, ‘bpr’, ‘warp’, ‘warp-kos’): the loss function.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Model Training\r\n",
    "model = LightFM(loss = 'warp',random_state=2016, learning_rate=0.9, no_components = 150, user_alpha=0.000005)\r\n",
    "model = model.fit(cust_item_csr,epochs = 100, num_threads = 16, verbose = False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Item recommendation to a user\n",
    "\n",
    "##### we want to show a user, items he might be interested in buying/viewing based on his/her interactions done in the past. Typical industry examples for this are like “Deals recommended for you”.."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "def sample_Recommendation_user(cust_id,threshold = 0, nrec_items = 10):\r\n",
    "    n_cust,n_items = custitem.shape\r\n",
    "    cust_x = cust_dict[cust_id]\r\n",
    "    \r\n",
    "    scores = pd.Series(model.predict(cust_x,np.arange(n_items)))\r\n",
    "    scores.index = custitem.columns\r\n",
    "    \r\n",
    "    scores = list(pd.Series(scores.sort_values(ascending = False).index))\r\n",
    "    \r\n",
    "    known_items = list(pd.Series(custitem.loc[cust_id,:][custitem.loc[cust_id,:] > threshold].index).sort_values(ascending=False))\r\n",
    "    scores = [x for x in scores if x not in known_items]\r\n",
    "    return_score_list = scores[0:nrec_items]\r\n",
    "    known_items = list(pd.Series(known_items))\r\n",
    "    scores = list(pd.Series(return_score_list)) \r\n",
    "            \r\n",
    "    print('\\n recommended items:')\r\n",
    "    return scores"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## User recommendation to a item\n",
    "#### we can recommend a list of users specific to a particular item. Example of such cases is when you are running a promotion on an item and want to run an e-mail campaign around this promotional item to only 10,000 users who might be interested in this item."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "def sample_recommendation_item(item_id,number_of_user):\r\n",
    "    n_users,n_items = custitem.shape\r\n",
    "    x = np.array(list(item_dict.keys()))\r\n",
    "    scores = pd.Series(model.predict(np.arange(n_users),np.repeat(x.searchsorted(item_id),n_users)))\r\n",
    "    users_list = list(custitem.index[scores.sort_values(ascending = False).head(number_of_user).index])\r\n",
    "    return users_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "sample_recommendation_item(50,5)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['CUST88B', 'CUST161P', 'CUST242K', 'CUST24I', 'CUST119J']"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Item recommendation to items\n",
    "\n",
    "#### we will discuss how we can recommend a list of items specific to a particular item. This kind of models will help you to find similar/related items or items which can be bundled together. \n",
    "#### Typical industry use case for such models are in cross-selling and up-selling opportunities on product page like “Products related to this item”, “Frequently bought together”, “Customers who bought this also bought this” and “Customers who viewed this item also viewed”."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\r\n",
    "\r\n",
    "def item_item_Recommendation(item,n_items):\r\n",
    "    #create_item_embed_dist_matrix\r\n",
    "    item_norm_sparse = csr_matrix(model.item_embeddings)\r\n",
    "    similarities = cosine_similarity(item_norm_sparse)\r\n",
    "    item_embed_dist_matrix = pd.DataFrame(similarities)\r\n",
    "    item_embed_dist_matrix.columns = custitem.columns\r\n",
    "    item_embed_dist_matrix.index = custitem.columns\r\n",
    "    # recommend\r\n",
    "    recommend_items = list(pd.Series(item_embed_dist_matrix.loc[item,:].sort_values(ascending = False).head(n_items+1).index[1:n_items+1]))\r\n",
    "    print('Item similar to above item:')\r\n",
    "    return recommend_items"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "item_item_Recommendation('blacksapota',10)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Item similar to above item:\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['kashmirapple',\n",
       " 'organicbasmatiricewhite',\n",
       " 'redonion',\n",
       " 'brownonion',\n",
       " 'capsicum',\n",
       " 'buffalomilk',\n",
       " 'yellowbellpepper',\n",
       " 'rasthalibanana',\n",
       " 'rubypomogranates',\n",
       " 'muskatpomogranates']"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder\r\n",
    "from mlxtend.frequent_patterns import apriori\r\n",
    "from mlxtend.frequent_patterns import association_rules"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "frequent_items = apriori(custitem, min_support=0.1, use_colnames= True)\r\n",
    "\r\n",
    "pd.set_option('display.max_rows',None)\r\n",
    "popularitems = frequent_items.sort_values('support',ascending = False)\r\n",
    "popularitems.head(16)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     support          itemsets\n",
       "30  0.558140     (greenchilli)\n",
       "27  0.547804          (carrot)\n",
       "38  0.534884     (buffalomilk)\n",
       "43  0.532300  (sonamasuririce)\n",
       "26  0.503876        (capsicum)\n",
       "41  0.489233     (basmatirice)\n",
       "36  0.485788     (tomatolocal)\n",
       "33  0.475452        (redonion)\n",
       "24  0.457364         (brocoli)\n",
       "32  0.436693       (redchilli)\n",
       "23  0.426357      (watermelon)\n",
       "12  0.413437    (kashmirapple)\n",
       "34  0.410853     (springonion)\n",
       "35  0.405685    (tomatohybrid)\n",
       "39  0.400517         (cowmilk)\n",
       "25  0.390181      (brownonion)"
      ],
      "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.558140</td>\n",
       "      <td>(greenchilli)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.547804</td>\n",
       "      <td>(carrot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.534884</td>\n",
       "      <td>(buffalomilk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.532300</td>\n",
       "      <td>(sonamasuririce)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.503876</td>\n",
       "      <td>(capsicum)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.489233</td>\n",
       "      <td>(basmatirice)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.485788</td>\n",
       "      <td>(tomatolocal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.475452</td>\n",
       "      <td>(redonion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.457364</td>\n",
       "      <td>(brocoli)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.436693</td>\n",
       "      <td>(redchilli)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.426357</td>\n",
       "      <td>(watermelon)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.413437</td>\n",
       "      <td>(kashmirapple)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.410853</td>\n",
       "      <td>(springonion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.405685</td>\n",
       "      <td>(tomatohybrid)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.400517</td>\n",
       "      <td>(cowmilk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.390181</td>\n",
       "      <td>(brownonion)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "x = list(popularitems['itemsets'].head(16))\r\n",
    "y = [list(s) for s in x]\r\n",
    "mostbought = [y[0] for y in y]\r\n",
    "mostbought"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['greenchilli',\n",
       " 'carrot',\n",
       " 'buffalomilk',\n",
       " 'sonamasuririce',\n",
       " 'capsicum',\n",
       " 'basmatirice',\n",
       " 'tomatolocal',\n",
       " 'redonion',\n",
       " 'brocoli',\n",
       " 'redchilli',\n",
       " 'watermelon',\n",
       " 'kashmirapple',\n",
       " 'springonion',\n",
       " 'tomatohybrid',\n",
       " 'cowmilk',\n",
       " 'brownonion']"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "import pickle\r\n",
    "\r\n",
    "filename = 'mostbought.pkl'\r\n",
    "pickle.dump(mostbought, open(filename, 'wb'))\r\n",
    "\r\n",
    "filename = 'model.pkl'\r\n",
    "pickle.dump(model, open(filename, 'wb'))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
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