Recommender System - YW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cheong\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import time\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = pd.read_csv(\"data/olist_customers_dataset.csv\")\n",
    "orders = pd.read_csv(\"data/olist_orders_dataset.csv\")\n",
    "items = pd.read_csv(\"data/olist_order_items_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
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       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 08:59:02</td>\n",
       "      <td>2017-09-13 09:45:35</td>\n",
       "      <td>2017-09-19 18:34:16</td>\n",
       "      <td>2017-09-20 23:43:48</td>\n",
       "      <td>2017-09-29 00:00:00</td>\n",
       "      <td>871766c5855e863f6eccc05f988b23cb</td>\n",
       "      <td>28013</td>\n",
       "      <td>campos dos goytacazes</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:13</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-04-26 10:53:06</td>\n",
       "      <td>2017-04-26 11:05:13</td>\n",
       "      <td>2017-05-04 14:35:00</td>\n",
       "      <td>2017-05-12 16:04:24</td>\n",
       "      <td>2017-05-15 00:00:00</td>\n",
       "      <td>eb28e67c4c0b83846050ddfb8a35d051</td>\n",
       "      <td>15775</td>\n",
       "      <td>santa fe do sul</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
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       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:30</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-14 14:33:31</td>\n",
       "      <td>2018-01-14 14:48:30</td>\n",
       "      <td>2018-01-16 12:36:48</td>\n",
       "      <td>2018-01-22 13:19:16</td>\n",
       "      <td>2018-02-05 00:00:00</td>\n",
       "      <td>3818d81c6709e39d06b2738a8d3a2474</td>\n",
       "      <td>35661</td>\n",
       "      <td>para de minas</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:18</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:00:35</td>\n",
       "      <td>2018-08-08 10:10:18</td>\n",
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       "      <td>2018-08-14 13:32:39</td>\n",
       "      <td>2018-08-20 00:00:00</td>\n",
       "      <td>af861d436cfc08b2c2ddefd0ba074622</td>\n",
       "      <td>12952</td>\n",
       "      <td>atibaia</td>\n",
       "      <td>SP</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:51</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:51</td>\n",
       "      <td>2017-02-04 14:10:13</td>\n",
       "      <td>2017-02-16 09:46:09</td>\n",
       "      <td>2017-03-01 16:42:31</td>\n",
       "      <td>2017-03-17 00:00:00</td>\n",
       "      <td>64b576fb70d441e8f1b2d7d446e483c5</td>\n",
       "      <td>13226</td>\n",
       "      <td>varzea paulista</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2  000229ec398224ef6ca0657da4fc703e              1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "   shipping_limit_date   price  freight_value order_status  \\\n",
       "0  2017-09-19 09:45:35   58.90          13.29    delivered   \n",
       "1  2017-05-03 11:05:13  239.90          19.93    delivered   \n",
       "2  2018-01-18 14:48:30  199.00          17.87    delivered   \n",
       "3  2018-08-15 10:10:18   12.99          12.79    delivered   \n",
       "4  2017-02-13 13:57:51  199.90          18.14    delivered   \n",
       "\n",
       "  order_purchase_timestamp    order_approved_at order_delivered_carrier_date  \\\n",
       "0      2017-09-13 08:59:02  2017-09-13 09:45:35          2017-09-19 18:34:16   \n",
       "1      2017-04-26 10:53:06  2017-04-26 11:05:13          2017-05-04 14:35:00   \n",
       "2      2018-01-14 14:33:31  2018-01-14 14:48:30          2018-01-16 12:36:48   \n",
       "3      2018-08-08 10:00:35  2018-08-08 10:10:18          2018-08-10 13:28:00   \n",
       "4      2017-02-04 13:57:51  2017-02-04 14:10:13          2017-02-16 09:46:09   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date  \\\n",
       "0           2017-09-20 23:43:48           2017-09-29 00:00:00   \n",
       "1           2017-05-12 16:04:24           2017-05-15 00:00:00   \n",
       "2           2018-01-22 13:19:16           2018-02-05 00:00:00   \n",
       "3           2018-08-14 13:32:39           2018-08-20 00:00:00   \n",
       "4           2017-03-01 16:42:31           2017-03-17 00:00:00   \n",
       "\n",
       "                 customer_unique_id  customer_zip_code_prefix  \\\n",
       "0  871766c5855e863f6eccc05f988b23cb                     28013   \n",
       "1  eb28e67c4c0b83846050ddfb8a35d051                     15775   \n",
       "2  3818d81c6709e39d06b2738a8d3a2474                     35661   \n",
       "3  af861d436cfc08b2c2ddefd0ba074622                     12952   \n",
       "4  64b576fb70d441e8f1b2d7d446e483c5                     13226   \n",
       "\n",
       "           customer_city customer_state  \n",
       "0  campos dos goytacazes             RJ  \n",
       "1        santa fe do sul             SP  \n",
       "2          para de minas             MG  \n",
       "3                atibaia             SP  \n",
       "4        varzea paulista             SP  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = items.merge(orders, on = \"order_id\")\n",
    "data = data.merge(customers, on = \"customer_id\").drop('customer_id', axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                 customer_unique_id                        product_id  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  372645c7439f9661fbbacfd129aa92ec   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  5099f7000472b634fea8304448d20825   \n",
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       "4  0004aac84e0df4da2b147fca70cf8255  c72e18b3fe2739b8d24ebf3102450f37   \n",
       "\n",
       "   purchase_count  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## For each customer ID, get the products they purchased and the number of times they purchased that product\n",
    "\n",
    "df = pd.melt(data, \n",
    "             id_vars=['customer_unique_id'],\n",
    "             value_vars=['product_id']).drop(['variable'], axis=1) \\\n",
    "            .rename(columns={'value': 'product_id'}) \\\n",
    "            .groupby(['customer_unique_id', 'product_id'])\\\n",
    "            .agg({'product_id': 'count'}) \\\n",
    "            .rename(columns={'product_id': 'purchase_count'}) \\\n",
    "            .reset_index()\n",
    "            \n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                 customer_unique_id                        product_id  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  372645c7439f9661fbbacfd129aa92ec   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  5099f7000472b634fea8304448d20825   \n",
       "2  0000f46a3911fa3c0805444483337064  64b488de448a5324c4134ea39c28a34b   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078  2345a354a6f2033609bbf62bf5be9ef6   \n",
       "4  0004aac84e0df4da2b147fca70cf8255  c72e18b3fe2739b8d24ebf3102450f37   \n",
       "\n",
       "   purchase_count  purchase_dummy  \n",
       "0               1               1  \n",
       "1               1               1  \n",
       "2               1               1  \n",
       "3               1               1  \n",
       "4               1               1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create dummy for whether the customer bought that product or not\n",
    "\n",
    "def create_data_dummy(data):\n",
    "    data_dummy = data.copy()\n",
    "    data_dummy['purchase_dummy'] = 1\n",
    "    return data_dummy\n",
    "data_dummy = create_data_dummy(df)\n",
    "\n",
    "data_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Affinity score: Higher score if more customers buy both of the products\n",
    "\n",
    "#Get list of unique products\n",
    "productList=list(set(df[\"product_id\"].tolist()))\n",
    "\n",
    "#Get count of customers\n",
    "custCount=len(set(df[\"customer_unique_id\"].tolist()))\n",
    "\n",
    "#Create an empty data frame to store item affinity scores for items.\n",
    "itemAffinity= pd.DataFrame(columns=('item1', 'item2', 'score'))\n",
    "rowCount=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "searchItem = \"372645c7439f9661fbbacfd129aa92ec\"\n",
    "\n",
    "#For each product in the product list, compare with other products to get the affinity score.\n",
    "for ind1 in range(len(productList)):\n",
    "    \n",
    "    if productList[ind1] != searchItem:\n",
    "        continue\n",
    "            \n",
    "    #Get list of users who bought this item 1.\n",
    "    item1Users = df[df.product_id==productList[ind1]][\"customer_unique_id\"].tolist()\n",
    "    #print(\"Item 1 \", item1Users)\n",
    "    \n",
    "    #Get item 2 - items that are not item 1 or those that are not analyzed already.\n",
    "    for ind2 in range(ind1, len(productList)):\n",
    "        \n",
    "        if ( ind1 == ind2):\n",
    "            continue\n",
    "                   \n",
    "        #Get list of users who bought item 2\n",
    "        item2Users=df[df.product_id==productList[ind2]][\"customer_unique_id\"].tolist()\n",
    "        #print(\"Item 2\",item2Users)\n",
    "        \n",
    "        #Find score. Find the common list of users and divide it by the total users.\n",
    "        commonUsers= len(set(item1Users).intersection(set(item2Users)))\n",
    "        score=commonUsers \n",
    "\n",
    "        #Add a score for item 1, item 2\n",
    "        itemAffinity.loc[rowCount] = [productList[ind1],productList[ind2],score]\n",
    "        rowCount +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for 372645c7439f9661fbbacfd129aa92ec \n",
      "                                   item2    score\n",
      "31044  42155695adbe665066ad812855fe523a  1.00000\n",
      "35188  525947dbe3304ac32bf51602f9557c12  1.00000\n",
      "27314  bd1aace4fad5609f005fd721b45dcec4  1.00000\n",
      "29220  cc1b0e67ffb98a08c886e8c3c27a915f  1.00000\n",
      "26635  3ea30fb7b4c6d17f44c1594a713c224c  1.00000\n",
      "30182  2b10e945dae5434075c8bb2be0d17325  1.00000\n",
      "10264  cc1b0e67ffb98a08c886e8c3c27a915f  0.00001\n",
      "5094   3ea30fb7b4c6d17f44c1594a713c224c  0.00001\n",
      "22200  525947dbe3304ac32bf51602f9557c12  0.00001\n",
      "6452   bd1aace4fad5609f005fd721b45dcec4  0.00001\n",
      "13912  42155695adbe665066ad812855fe523a  0.00001\n",
      "12188  2b10e945dae5434075c8bb2be0d17325  0.00001\n"
     ]
    }
   ],
   "source": [
    "## Sort items to recommend in descending order\n",
    "\n",
    "recoList=itemAffinity[itemAffinity.item1==searchItem]\\\n",
    "        [[\"item2\",\"score\"]]\\\n",
    "        .sort_values(\"score\", ascending=[0])\n",
    "        \n",
    "## Remove zero scores\n",
    "recoList = recoList[recoList[\"score\"] != 0]\n",
    "        \n",
    "print(\"Recommendations for\", searchItem, \"\\n\", recoList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cheong\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-5600fc32532c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m                 \u001b[1;31m#Add to existing row\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m                 \u001b[0mitemAffinity\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitemAffinity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem1\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mproducts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mitemAffinity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem2\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mproducts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'score'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;31m#Check final result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3368\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3369\u001b[0m             \u001b[1;31m# set column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3370\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3371\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3372\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3450\u001b[0m         \u001b[1;31m# value exception to occur first\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3451\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3452\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_setitem_copy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3454\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_duplicates\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_check_setitem_copy\u001b[1;34m(self, stacklevel, t, force)\u001b[0m\n\u001b[0;32m   3244\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3245\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3246\u001b[1;33m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_referents\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_copy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3247\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_copy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3248\u001b[0m                     \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "################# Deprecated ###############################\n",
    "\n",
    "#Get list of unique customers\n",
    "custList=list(set(df[\"customer_unique_id\"].tolist()))\n",
    "\n",
    "#For each customer in the customer list, get the products that he purchased, and add to the affinity score.\n",
    "for ind in range(len(custList)):\n",
    "            \n",
    "    #Get list of products that the customer bought.\n",
    "    products = df[df.customer_unique_id==custList[ind]][\"product_id\"].tolist()\n",
    "    \n",
    "    #Add to affinity score\n",
    "    for ind1 in range(len(products)):\n",
    "        for ind2 in range(len(products)):\n",
    "            \n",
    "            if ( ind1 == ind2):\n",
    "                continue\n",
    "                \n",
    "            if itemAffinity[(itemAffinity.item1==products[ind1]) & (itemAffinity.item2 == products[ind2])].shape[0] == 0:\n",
    "\n",
    "                #Add a score for item 1, item 2\n",
    "                itemAffinity.loc[rowCount] = [products[ind1],products[ind2],1]\n",
    "                rowCount +=1\n",
    "                #Add a score for item2, item 1. The same score would apply irrespective of the sequence.\n",
    "                itemAffinity.loc[rowCount] = [products[ind2],products[ind1],1]\n",
    "                rowCount +=1\n",
    "                \n",
    "            else:\n",
    "                \n",
    "                #Add to existing row\n",
    "                itemAffinity[(itemAffinity.item1==products[ind1]) & (itemAffinity.item2 == products[ind2])]['score'] += 1\n",
    "        \n",
    "#Check final result\n",
    "itemAffinity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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