Recommendation Systems_R5_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make new Product Recommendations¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import time\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read and explore the given dataset.  ( Rename column/add headers, plot histograms, find data characteristics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1334707200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1QGNMC6O1VW39</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1397433600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   productId  ratings   timestamp\n",
       "0  A2CX7LUOHB2NDG  0321732944      5.0  1341100800\n",
       "1  A2NWSAGRHCP8N5  0439886341      1.0  1367193600\n",
       "2  A2WNBOD3WNDNKT  0439886341      3.0  1374451200\n",
       "3  A1GI0U4ZRJA8WN  0439886341      1.0  1334707200\n",
       "4  A1QGNMC6O1VW39  0511189877      5.0  1397433600"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_df = pd.read_csv(\"ratings_Electronics.csv\")\n",
    "product_df.columns = ['userId', 'productId', 'ratings','timestamp']\n",
    "product_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7824481"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(product_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 7824481 user - product details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping timestamp column\n",
    "product_df.drop('timestamp',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7824481 entries, 0 to 7824480\n",
      "Data columns (total 3 columns):\n",
      "userId       object\n",
      "productId    object\n",
      "ratings      float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 179.1+ MB\n"
     ]
    }
   ],
   "source": [
    "product_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Take a subset of the dataset to make it less sparse/ denser. ( For example, keep the users only who has given 50 or more number of ratings )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_grouped = product_df.groupby(['userId']).agg({'ratings': 'count'}).reset_index()\n",
    "grp_df = user_grouped.sort_values(['ratings'], ascending = [0])\n",
    "filtered_Users = grp_df[grp_df['ratings']>=50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3263531</th>\n",
       "      <td>A5JLAU2ARJ0BO</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3512451</th>\n",
       "      <td>ADLVFFE4VBT8</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989526</th>\n",
       "      <td>A3OXHLG6DIBRW8</td>\n",
       "      <td>498</td>\n",
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       "    <tr>\n",
       "      <th>3291008</th>\n",
       "      <td>A6FIAB28IS79</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3284634</th>\n",
       "      <td>A680RUE1FDO8B</td>\n",
       "      <td>406</td>\n",
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      ],
      "text/plain": [
       "                 userId  ratings\n",
       "3263531   A5JLAU2ARJ0BO      520\n",
       "3512451    ADLVFFE4VBT8      501\n",
       "2989526  A3OXHLG6DIBRW8      498\n",
       "3291008    A6FIAB28IS79      431\n",
       "3284634   A680RUE1FDO8B      406"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_Users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_50 = list(filtered_Users['userId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1540, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_Users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1540"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df = product_df[product_df['userId'].isin(filtered_Users['userId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125871, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1540"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sample_df['userId'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Split the data randomly into train and test dataset. ( For example, split it in 70/30 ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(sample_df, test_size = 0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Build Popularity Recommender model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Helper as Helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm = Helper.popularity_recommender_py()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm.create(train_data, 'userId', 'productId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = sample_df['userId'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>productId</th>\n",
       "      <th>score</th>\n",
       "      <th>Rank</th>\n",
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       "      <th>33726</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>103</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>19238</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>95</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>24884</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
       "      <td>B004CLYEDC</td>\n",
       "      <td>95</td>\n",
       "      <td>10.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              user_id   productId  score  Rank\n",
       "33726  A3TAS1AG6FMBQW  B0088CJT4U    160   1.0\n",
       "21503  A3TAS1AG6FMBQW  B003ES5ZUU    146   2.0\n",
       "33101  A3TAS1AG6FMBQW  B007WTAJTO    134   3.0\n",
       "9568   A3TAS1AG6FMBQW  B000N99BBC    128   4.0\n",
       "33402  A3TAS1AG6FMBQW  B00829TIEK    118   5.0\n",
       "34011  A3TAS1AG6FMBQW  B008DWCRQW    113   6.0\n",
       "33398  A3TAS1AG6FMBQW  B00829THK0    104   7.0\n",
       "19040  A3TAS1AG6FMBQW  B002R5AM7C    103   8.0\n",
       "19238  A3TAS1AG6FMBQW  B002SZEOLG     95   9.0\n",
       "24884  A3TAS1AG6FMBQW  B004CLYEDC     95  10.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_id = users[5]\n",
    "pm.recommend(user_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Popularity based we need not provide UserID as its is same for all users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Build Collaborative Filtering model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_model = Helper.item_similarity_recommender_py()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_model.create(train_data, 'userId', 'productId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>userId</th>\n",
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       "      <th>ratings</th>\n",
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      "text/plain": [
       "                 userId   productId  ratings\n",
       "2782420  A3TAS1AG6FMBQW  B002QC17VG      5.0\n",
       "7426090  A3TAS1AG6FMBQW  B00DQZQPNM      4.0\n",
       "7523405  A3TAS1AG6FMBQW  B00ECGX9NS      2.0\n",
       "2842002  A3TAS1AG6FMBQW  B002SZEOLG      5.0\n",
       "5708330  A3TAS1AG6FMBQW  B007BYLKFW      5.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data = train_data[train_data['userId'] == users[5]]\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       " 'B00JXECZIY',\n",
       " 'B002WE6D44',\n",
       " 'B001VKQR5U',\n",
       " 'B003YKG2UK',\n",
       " 'B008S4TSAI',\n",
       " 'B007IO38MI',\n",
       " 'B00G6CLN3Y',\n",
       " 'B00HWMPSF6',\n",
       " 'B00JZAB8OI',\n",
       " 'B0009SHDGC',\n",
       " 'B0063705PE',\n",
       " 'B0001OHH0Q',\n",
       " 'B007HOHV9U',\n",
       " 'B00002EQC2',\n",
       " 'B00A1DJO12',\n",
       " 'B00ATZ9I9U',\n",
       " 'B007ZTKWFM',\n",
       " 'B001KN6WL2',\n",
       " 'B001963NZI',\n",
       " 'B00DT04I9W',\n",
       " 'B009SG71MC',\n",
       " 'B003X26PMO',\n",
       " 'B00CBCUS1G',\n",
       " '0972683275',\n",
       " 'B00006I5NE',\n",
       " 'B001M4XCHQ',\n",
       " 'B002MAPRYU',\n",
       " 'B0073FE1F0',\n",
       " 'B00IWQ3Y20',\n",
       " 'B00HRQB28Y',\n",
       " 'B008LHUTKA',\n",
       " 'B0055TEQH4',\n",
       " 'B004LEAYXY',\n",
       " 'B00603RTC8',\n",
       " 'B00IVPU6AA',\n",
       " 'B00894YWD0',\n",
       " 'B001U5R9RG',\n",
       " 'B0090CVJZ4',\n",
       " 'B008X9Z528',\n",
       " 'B004NBL9WK',\n",
       " 'B004HW67MW',\n",
       " 'B00A6YOUWE',\n",
       " 'B0019HGU0M',\n",
       " 'B00HF3X5RU',\n",
       " 'B003ES5ZUU',\n",
       " 'B004LSNF04',\n",
       " 'B00C97AF16',\n",
       " 'B005CG2AX2',\n",
       " 'B009NHWVIA',\n",
       " 'B003CIBCX6',\n",
       " 'B00934CQ56',\n",
       " 'B00D6HBVIO',\n",
       " 'B0061S1INS',\n",
       " 'B000X23I22',\n",
       " 'B0011NVMO8',\n",
       " 'B00A750SCI',\n",
       " 'B000VZS2EU',\n",
       " 'B001PI09SE',\n",
       " 'B0028BBEBI',\n",
       " 'B006GWO5WK',\n",
       " 'B00D8X9FSY',\n",
       " 'B003ELYQGG',\n",
       " 'B0097CZHAU',\n",
       " 'B004J1G2MQ',\n",
       " 'B001F51G16',\n",
       " 'B00CALM17C',\n",
       " 'B000WYVBR0',\n",
       " 'B00005OOJZ',\n",
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       " 'B00000K0JC',\n",
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       " 'B006DY3CRK',\n",
       " 'B004Q61H6A',\n",
       " 'B00752RAWW',\n",
       " 'B000GW5J58',\n",
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       " 'B009RQW3BC',\n",
       " 'B002IW99AU',\n",
       " 'B003IBB9CG',\n",
       " 'B00002EQCW',\n",
       " 'B00B4OCWE8',\n",
       " 'B0002KR0SI',\n",
       " 'B00DOWCQL2',\n",
       " 'B0002J2TLC',\n",
       " 'B006ZP8UOW',\n",
       " 'B00D6XW62I',\n",
       " 'B002C97CAI',\n",
       " 'B00634PLTW',\n",
       " 'B003ZX8B3W',\n",
       " 'B00681RUR6',\n",
       " 'B004CLYEFK',\n",
       " 'B004K1EOCA',\n",
       " 'B005KGOMI0',\n",
       " 'B0017AO5AS',\n",
       " 'B008X9Z7N0',\n",
       " 'B00BD7UXXS',\n",
       " 'B00COF7DGS',\n",
       " 'B0001VGFKW',\n",
       " 'B00A7PPLP2',\n",
       " 'B00EU9V3S0',\n",
       " 'B005CLMJLU',\n",
       " 'B007AHJNF4',\n",
       " 'B001542X64',\n",
       " 'B0079UAT0A',\n",
       " 'B003GZDB1Q']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_items = list(user_data['productId'].unique())\n",
    "user_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_products = list(train_data['productId'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_products = user_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A3TAS1AG6FMBQW', 'A2SH6A32BE6NEV', 'A2J8T58Z4X15IO',\n",
       "       'A37D1ZP8GBHE38', 'A25QJBK33C4O0R'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_dat = train_data[train_data['productId'] == user_products[0]]\n",
    "item_use = item_dat['userId'].unique()\n",
    "item_use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------\n",
      "Training data products for the user userid: A3TAS1AG6FMBQW:\n",
      "------------------------------------------------------------------------------------\n",
      "B002QC17VG\n",
      "B00DQZQPNM\n",
      "B00ECGX9NS\n",
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      "B000I68BD4\n",
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      "B00752RAWW\n",
      "B000GW5J58\n",
      "B00DY9UFB8\n",
      "B009RQW3BC\n",
      "B002IW99AU\n",
      "B003IBB9CG\n",
      "B00002EQCW\n",
      "B00B4OCWE8\n",
      "B0002KR0SI\n",
      "B00DOWCQL2\n",
      "B0002J2TLC\n",
      "B006ZP8UOW\n",
      "B00D6XW62I\n",
      "B002C97CAI\n",
      "B00634PLTW\n",
      "B003ZX8B3W\n",
      "B00681RUR6\n",
      "B004CLYEFK\n",
      "B004K1EOCA\n",
      "B005KGOMI0\n",
      "B0017AO5AS\n",
      "B008X9Z7N0\n",
      "B00BD7UXXS\n",
      "B00COF7DGS\n",
      "B0001VGFKW\n",
      "B00A7PPLP2\n",
      "B00EU9V3S0\n",
      "B005CLMJLU\n",
      "B007AHJNF4\n",
      "B001542X64\n",
      "B0079UAT0A\n",
      "B003GZDB1Q\n",
      "----------------------------------------------------------------------\n",
      "Recommendation process going on:\n",
      "----------------------------------------------------------------------\n",
      "No. of unique products for the user: 136\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :140950\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
       "      <td>B008ULPAT2</td>\n",
       "      <td>0.016576</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
       "      <td>B008ULPE70</td>\n",
       "      <td>0.016115</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>0.015957</td>\n",
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       "    <tr>\n",
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       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>0.015647</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
       "      <td>B008ULPCB8</td>\n",
       "      <td>0.015342</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
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       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>0.015102</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>0.013769</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
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       "      <td>0.013557</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>B004CLYEDC</td>\n",
       "      <td>0.013193</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   productId     score rank\n",
       "0  A3TAS1AG6FMBQW  B008ULPAT2  0.016576    1\n",
       "1  A3TAS1AG6FMBQW  B008ULPE70  0.016115    2\n",
       "2  A3TAS1AG6FMBQW  B008X9ZBVI  0.015957    3\n",
       "3  A3TAS1AG6FMBQW  B005QCDY50  0.015753    4\n",
       "4  A3TAS1AG6FMBQW  B008X9Z8NE  0.015647    5\n",
       "5  A3TAS1AG6FMBQW  B008ULPCB8  0.015342    6\n",
       "6  A3TAS1AG6FMBQW  B008X9Z3UC  0.015102    7\n",
       "7  A3TAS1AG6FMBQW  B008X9Z6EA  0.013769    8\n",
       "8  A3TAS1AG6FMBQW  B009WZRCO6  0.013557    9\n",
       "9  A3TAS1AG6FMBQW  B004CLYEDC  0.013193   10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the Product for the user in training data\n",
    "user_id = users[5]\n",
    "user_items = is_model.get_user_items(user_id)\n",
    "#\n",
    "print(\"------------------------------------------------------------------------------------\")\n",
    "print(\"Training data products for the user userid: %s:\" % user_id)\n",
    "print(\"------------------------------------------------------------------------------------\")\n",
    "\n",
    "for user_item in user_items:\n",
    "    print(user_item)\n",
    "\n",
    "print(\"----------------------------------------------------------------------\")\n",
    "print(\"Recommendation process going on:\")\n",
    "print(\"----------------------------------------------------------------------\")\n",
    "\n",
    "#Recommend songs for the user using personalized model\n",
    "is_model.recommend(user_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Evaluate both the models. ( Once the model is trained on the training data, it can be used to compute the error (RMSE) on predictions made on the test data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of user_test_and_training:1540\n",
      "Length of user sample:77\n",
      "Getting recommendations for user:A2MOJANFPX353G\n",
      "No. of unique products for the user: 41\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :23945\n",
      "Getting recommendations for user:A3VBZDYGHF4NK8\n",
      "No. of unique products for the user: 53\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :59233\n",
      "Getting recommendations for user:A3TRY87JJCA5Y8\n",
      "No. of unique products for the user: 66\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :29318\n",
      "Getting recommendations for user:A64LEAZ2QF8L2\n",
      "No. of unique products for the user: 50\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :27389\n",
      "Getting recommendations for user:A2U3OEIK1CUPIK\n",
      "No. of unique products for the user: 56\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :6329\n",
      "Getting recommendations for user:A19RGDJPNZ7NZ\n",
      "No. of unique products for the user: 72\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :28558\n",
      "Getting recommendations for user:A203OCQQ12MAVT\n",
      "No. of unique products for the user: 198\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :119808\n",
      "Getting recommendations for user:A298GL2D0BHGKZ\n",
      "No. of unique products for the user: 47\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :46944\n",
      "Getting recommendations for user:A3HC7NWLKCPX4X\n",
      "No. of unique products for the user: 36\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :16758\n",
      "Getting recommendations for user:AG2YXYIQ8TLTA\n",
      "No. of unique products for the user: 53\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :61652\n",
      "Getting recommendations for user:AC9QEWWN4W9MC\n",
      "No. of unique products for the user: 45\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :29314\n",
      "Getting recommendations for user:A1AZQCVCDBQP6\n",
      "No. of unique products for the user: 40\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :15241\n",
      "Getting recommendations for user:A1C47ROOK7BSVL\n",
      "No. of unique products for the user: 54\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :26957\n",
      "Getting recommendations for user:ATDQ9CGCWTT8D\n",
      "No. of unique products for the user: 39\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :12529\n",
      "Getting recommendations for user:A14NA0W8ESGDSI\n",
      "No. of unique products for the user: 46\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :60395\n",
      "Getting recommendations for user:ARTA78TP6H73D\n",
      "No. of unique products for the user: 54\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :34703\n",
      "Getting recommendations for user:A3SMD3T0QTN109\n",
      "No. of unique products for the user: 70\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :71088\n",
      "Getting recommendations for user:A3KNGMX2RVQG91\n",
      "No. of unique products for the user: 50\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :32796\n",
      "Getting recommendations for user:AFM7FK7NFEKFZ\n",
      "No. of unique products for the user: 39\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :17204\n",
      "Getting recommendations for user:ATZJNEXX7LWD3\n",
      "No. of unique products for the user: 61\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :71183\n",
      "Getting recommendations for user:A3E68EBGXTWDNP\n",
      "No. of unique products for the user: 50\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :20623\n",
      "Getting recommendations for user:A1XE3M22N91451\n",
      "No. of unique products for the user: 63\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :24239\n",
      "Getting recommendations for user:A2AFTRU43PY9P5\n",
      "No. of unique products for the user: 42\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :19383\n",
      "Getting recommendations for user:AVU1ILDDYW301\n",
      "No. of unique products for the user: 46\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :45453\n",
      "Getting recommendations for user:A3OA4DV4L81N1D\n",
      "No. of unique products for the user: 160\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :69629\n",
      "Getting recommendations for user:A1TR1R2QKWRSRA\n",
      "No. of unique products for the user: 40\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :55804\n",
      "Getting recommendations for user:A13WREJ05GMRA6\n",
      "No. of unique products for the user: 50\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :28306\n",
      "Getting recommendations for user:AZNUHQSHZHSUE\n",
      "No. of unique products for the user: 42\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :29493\n",
      "Getting recommendations for user:A2PSD2R9A1CXG7\n",
      "No. of unique products for the user: 104\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :95132\n",
      "Getting recommendations for user:A1JZFGZEZVWQPY\n",
      "No. of unique products for the user: 87\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :73927\n",
      "Getting recommendations for user:A23GFTVIETX7DS\n",
      "No. of unique products for the user: 214\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :244080\n",
      "Getting recommendations for user:A35C2IVVQR1HTK\n",
      "No. of unique products for the user: 56\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :39477\n",
      "Getting recommendations for user:AA3FTJYJYX55C\n",
      "No. of unique products for the user: 46\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :20601\n",
      "Getting recommendations for user:A3FKQQTSMHOCE0\n",
      "No. of unique products for the user: 43\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :11382\n",
      "Getting recommendations for user:A6VPK7X53QNAQ\n",
      "No. of unique products for the user: 122\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :135344\n",
      "Getting recommendations for user:A2L4ZGN7GZJ95T\n",
      "No. of unique products for the user: 61\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :91332\n",
      "Getting recommendations for user:A1ZVFCPHCWFV71\n",
      "No. of unique products for the user: 48\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :43330\n",
      "Getting recommendations for user:A3V8P0O224OBDB\n",
      "No. of unique products for the user: 53\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :30301\n",
      "Getting recommendations for user:A1522TN5FVJL0Y\n",
      "No. of unique products for the user: 46\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :34157\n",
      "Getting recommendations for user:A2SNE4QQGVP13U\n",
      "No. of unique products for the user: 46\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :30681\n",
      "Getting recommendations for user:A17NVM7IAPF2NS\n",
      "No. of unique products for the user: 56\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :45344\n",
      "Getting recommendations for user:A3SRDVCD2ATIIS\n",
      "No. of unique products for the user: 45\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :35028\n",
      "Getting recommendations for user:A2QDOJFFLFGF18\n",
      "No. of unique products for the user: 75\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :86924\n",
      "Getting recommendations for user:A2XJMQ2COPGWJH\n",
      "No. of unique products for the user: 42\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :29972\n",
      "Getting recommendations for user:A2XAKGRUHIE2ZA\n",
      "No. of unique products for the user: 45\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :26472\n",
      "Getting recommendations for user:A1UNJ46NSB352E\n",
      "No. of unique products for the user: 40\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :42409\n",
      "Getting recommendations for user:A2CWIYIETNBAK3\n",
      "No. of unique products for the user: 44\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :57831\n",
      "Getting recommendations for user:A5CDMTW6JKV5G\n",
      "No. of unique products for the user: 45\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :32446\n",
      "Getting recommendations for user:A3Q6ZPW60CVF2L\n",
      "No. of unique products for the user: 42\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :15962\n",
      "Getting recommendations for user:A141OPVE376YFI\n",
      "No. of unique products for the user: 53\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :39926\n",
      "Getting recommendations for user:AQCJWSMSURQI2\n",
      "No. of unique products for the user: 51\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :79396\n",
      "Getting recommendations for user:AIFLY2HF8NS8U\n",
      "No. of unique products for the user: 68\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :65040\n",
      "Getting recommendations for user:A2HPJKM7L4EOQB\n",
      "No. of unique products for the user: 60\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :14317\n",
      "Getting recommendations for user:A2DXENZNO2JT4F\n",
      "No. of unique products for the user: 44\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :42918\n",
      "Getting recommendations for user:A1NGFGBX2VCQS8\n",
      "No. of unique products for the user: 116\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :131514\n",
      "Getting recommendations for user:A17W0GMB0YY83M\n",
      "No. of unique products for the user: 51\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :48771\n",
      "Getting recommendations for user:A2AX2UGB167J31\n",
      "No. of unique products for the user: 58\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :20616\n",
      "Getting recommendations for user:A2SPV96SGOEO2\n",
      "No. of unique products for the user: 49\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :51860\n",
      "Getting recommendations for user:A11P105WVK5240\n",
      "No. of unique products for the user: 45\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :26258\n",
      "Getting recommendations for user:A206CGM6J75UJY\n",
      "No. of unique products for the user: 56\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :36960\n",
      "Getting recommendations for user:A3F3B6HY9RJI04\n",
      "No. of unique products for the user: 86\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :84466\n",
      "Getting recommendations for user:A11ULAZ4B5ZXC8\n",
      "No. of unique products for the user: 48\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :14199\n",
      "Getting recommendations for user:AG0PM9IAHQ06C\n",
      "No. of unique products for the user: 42\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :19269\n",
      "Getting recommendations for user:A2UWPPU5GZLMQZ\n",
      "No. of unique products for the user: 102\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :66246\n",
      "Getting recommendations for user:A3EZFFPVS34AMO\n",
      "No. of unique products for the user: 97\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :72372\n",
      "Getting recommendations for user:A2RTVGR9B1M600\n",
      "No. of unique products for the user: 43\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :7431\n",
      "Getting recommendations for user:A2LW5AL0KQ9P1M\n",
      "No. of unique products for the user: 75\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :83821\n",
      "Getting recommendations for user:A3BACUOZV1M0WM\n",
      "No. of unique products for the user: 76\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :44923\n",
      "Getting recommendations for user:A2NN1KIYEM2SKS\n",
      "No. of unique products for the user: 44\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :45351\n",
      "Getting recommendations for user:ALRCAGJOJ9583\n",
      "No. of unique products for the user: 63\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :67019\n",
      "Getting recommendations for user:AT09WGFUM934H\n",
      "No. of unique products for the user: 89\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :53817\n",
      "Getting recommendations for user:A1Z16630QMH8Q6\n",
      "No. of unique products for the user: 77\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :72034\n",
      "Getting recommendations for user:A3T8WOFS5RD2IR\n",
      "No. of unique products for the user: 50\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :32418\n",
      "Getting recommendations for user:AH3LF9RAD1O2V\n",
      "No. of unique products for the user: 41\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :25159\n",
      "Getting recommendations for user:A1J87SYB3JPQWH\n",
      "No. of unique products for the user: 45\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :16896\n",
      "Getting recommendations for user:A307Y3LMCVEZS1\n",
      "No. of unique products for the user: 47\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :20454\n",
      "Getting recommendations for user:A3KZ7OH4VRL048\n",
      "No. of unique products for the user: 46\n",
      "no. of unique products in the training set: 41753\n",
      "Non zero values in cooccurence_matrix :66849\n",
      "90078.5609998703\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "#Define what percentage of users to use for precision recall calculation\n",
    "user_sample = 0.05\n",
    "\n",
    "#Instantiate the precision_recall_calculator class\n",
    "pr = Evaluation.precision_recall_calculator(test_data, train_data, pm, is_model)\n",
    "\n",
    "#Call method to calculate precision and recall values\n",
    "(pm_avg_precision_list, pm_avg_recall_list, ism_avg_precision_list, ism_avg_recall_list) = pr.calculate_measures(user_sample)\n",
    "\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as pl\n",
    "\n",
    "#Method to generate precision and recall curve\n",
    "def plot_precision_recall(m1_precision_list, m1_recall_list, m1_label, m2_precision_list, m2_recall_list, m2_label):\n",
    "    pl.clf()    \n",
    "    pl.plot(m1_recall_list, m1_precision_list, label=m1_label)\n",
    "    pl.plot(m2_recall_list, m2_precision_list, label=m2_label)\n",
    "    pl.xlabel('Recall')\n",
    "    pl.ylabel('Precision')\n",
    "    pl.ylim([0.0, 0.2])\n",
    "    pl.xlim([0.0, 0.2])\n",
    "    pl.title('Precision-Recall curve')\n",
    "    #pl.legend(loc=\"upper right\")\n",
    "    pl.legend(loc=9, bbox_to_anchor=(0.5, -0.2))\n",
    "    pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### print(\"Plotting precision recall curves.\")\n",
    "\n",
    "plot_precision_recall(pm_avg_precision_list, pm_avg_recall_list, \"popularity_model\",\n",
    "                      ism_avg_precision_list, ism_avg_recall_list, \"item_similarity_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Summarise your insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Precision Recall curve, we can see that Popularity based model does not do relevant recommendations. There is a fall in Precision frequently.\n",
    "The Collabrative recommendation does a steady relevant recommendation in the begining and tends to fall after few recommendation. But the fall is not that suddent to make irrelavent recommendation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Get top - K ( K = 5) recommendations. Since our goal is to recommend new products to each user based on his/her habits, we will recommend 5 new products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4405: 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",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "trainDF, tempDF = train_test_split(sample_df, test_size=0.2, random_state=1)\n",
    "#creating a copy of tempDF as testDF\n",
    "testDF = tempDF.copy()\n",
    "#Assigning ratings of tempDF to nan\n",
    "tempDF.ratings = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1476087</th>\n",
       "      <td>A2CIQEY05SPHTZ</td>\n",
       "      <td>B000W9UYL4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043225</th>\n",
       "      <td>A370XYLIZCSK1Y</td>\n",
       "      <td>B00347L6I6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3337659</th>\n",
       "      <td>ANTN61S4L7WG9</td>\n",
       "      <td>B003FMVPFY</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560303</th>\n",
       "      <td>A2HPJKM7L4EOQB</td>\n",
       "      <td>B002DW97WK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725957</th>\n",
       "      <td>A1MRPX3RM48T2I</td>\n",
       "      <td>B0015HYPOO</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 userId   productId  ratings\n",
       "1476087  A2CIQEY05SPHTZ  B000W9UYL4      NaN\n",
       "3043225  A370XYLIZCSK1Y  B00347L6I6      NaN\n",
       "3337659   ANTN61S4L7WG9  B003FMVPFY      NaN\n",
       "2560303  A2HPJKM7L4EOQB  B002DW97WK      NaN\n",
       "1725957  A1MRPX3RM48T2I  B0015HYPOO      NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1476087</th>\n",
       "      <td>A2CIQEY05SPHTZ</td>\n",
       "      <td>B000W9UYL4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043225</th>\n",
       "      <td>A370XYLIZCSK1Y</td>\n",
       "      <td>B00347L6I6</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3337659</th>\n",
       "      <td>ANTN61S4L7WG9</td>\n",
       "      <td>B003FMVPFY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560303</th>\n",
       "      <td>A2HPJKM7L4EOQB</td>\n",
       "      <td>B002DW97WK</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725957</th>\n",
       "      <td>A1MRPX3RM48T2I</td>\n",
       "      <td>B0015HYPOO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 userId   productId  ratings\n",
       "1476087  A2CIQEY05SPHTZ  B000W9UYL4      2.0\n",
       "3043225  A370XYLIZCSK1Y  B00347L6I6      5.0\n",
       "3337659   ANTN61S4L7WG9  B003FMVPFY      5.0\n",
       "2560303  A2HPJKM7L4EOQB  B002DW97WK      4.0\n",
       "1725957  A1MRPX3RM48T2I  B0015HYPOO      4.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove missing values in testDF\n",
    "testDF = testDF.dropna()\n",
    "testDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating ratings with trainDF and tempDF\n",
    "ratings = pd.concat([trainDF, tempDF]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125871, 4)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying Matrix Factorization via Singular Value Decomposition¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "productId      0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "userId                                                                      \n",
       "AZBXKUH4AIW3X         0.0         0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId      1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "userId                                                                      \n",
       "AZBXKUH4AIW3X         0.0         0.0         0.0         0.0         0.0   \n",
       "AZCE11PSTCH1L         0.0         0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId         ...      B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  \\\n",
       "userId            ...                                                       \n",
       "AZBXKUH4AIW3X     ...             0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId      B00LBZ1Z7K  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  \\\n",
       "userId                                                                      \n",
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       "AZOK5STV85FBJ         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId      B00LKG1MC8  \n",
       "userId                     \n",
       "AZBXKUH4AIW3X         0.0  \n",
       "AZCE11PSTCH1L         0.0  \n",
       "AZMY6E8B52L2T         0.0  \n",
       "AZNUHQSHZHSUE         0.0  \n",
       "AZOK5STV85FBJ         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_df = ratings.pivot(index = 'userId', columns ='productId', values = 'ratings').fillna(0)\n",
    "R_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import svds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The singular values - sigma with K = 5.\n",
    "U, sigma, Vt = svds(R_df, k = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#diag\n",
    "sigma = np.diag(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 86.15135881,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ],\n",
       "       [  0.        ,  88.88499251,   0.        ,   0.        ,\n",
       "          0.        ],\n",
       "       [  0.        ,   0.        ,  89.28798192,   0.        ,\n",
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       "          0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "        148.41344865]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) \n",
    "preds_df = pd.DataFrame(all_user_predicted_ratings, columns = R_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.000026</td>\n",
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       "      <td>0.000058</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.011240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001621</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>-0.014349</td>\n",
       "      <td>-0.010065</td>\n",
       "      <td>-0.002778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId  0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "0            0.001341         0.0    0.004106    0.005752    0.000590   \n",
       "1            0.002063         0.0    0.005888    0.015223    0.000595   \n",
       "2           -0.000686         0.0    0.002077    0.013008   -0.000139   \n",
       "3            0.001894         0.0    0.002377    0.003217    0.000510   \n",
       "4            0.000455         0.0    0.001653    0.009631    0.000148   \n",
       "\n",
       "productId  1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "0            0.002527    0.002638    0.000235    0.008144    0.002143   \n",
       "1            0.002231    0.013009    0.001540    0.005430    0.001884   \n",
       "2           -0.001783    0.007072    0.002552   -0.001830   -0.001482   \n",
       "3            0.002501    0.004678   -0.000372    0.004418    0.002098   \n",
       "4           -0.000042    0.005630    0.001443    0.000842   -0.000026   \n",
       "\n",
       "productId     ...      B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  \\\n",
       "0             ...        0.000395    0.055904    0.004024    0.041704   \n",
       "1             ...        0.000425    0.014243    0.002598   -0.010899   \n",
       "2             ...       -0.000050    0.001463   -0.002642   -0.018384   \n",
       "3             ...        0.000301    0.003343    0.003150   -0.000522   \n",
       "4             ...        0.000058    0.000572   -0.000349   -0.011240   \n",
       "\n",
       "productId  B00LBZ1Z7K  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  \\\n",
       "0                 0.0    0.008147    0.001396    0.119630    0.065704   \n",
       "1                 0.0   -0.000786    0.004046   -0.020849   -0.007816   \n",
       "2                 0.0   -0.003092    0.000179   -0.026653   -0.011597   \n",
       "3                 0.0    0.000556    0.002677   -0.006864   -0.005356   \n",
       "4                 0.0   -0.001621    0.001029   -0.014349   -0.010065   \n",
       "\n",
       "productId  B00LKG1MC8  \n",
       "0            0.018699  \n",
       "1            0.002395  \n",
       "2           -0.007698  \n",
       "3            0.004845  \n",
       "4           -0.002778  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return the products with the highest predicted rating that the specified user hasn’t already rated\n",
    "#Take specific user row from matrix from predictions\n",
    "def recommend_movies_simple(predictions_df, userID, original_ratings_df, num_recommendations=5):\n",
    "    # Get and sort the user's predictions\n",
    "    someUser = ratings.index[ratings['userId'] == userID].tolist()\n",
    "    user_row_number = someUser[0]\n",
    "    sorted_user_predictions = predictions_df.iloc[user_row_number].sort_values(ascending=False)\n",
    "    \n",
    "    # Get the user's data and merge in the movie information.\n",
    "    user_data = original_ratings_df[original_ratings_df.userId == (userID)]\n",
    "    \n",
    "    print ('User {0} has already rated {1} movies.'.format(userID, user_data.shape[0]))\n",
    "    print ('Recommending the highest {0} predicted ratings movies not already rated.'.format(num_recommendations))\n",
    "    recommendations = (sample_df[~sample_df['productId'].isin(user_data['productId'])].\n",
    "         merge(pd.DataFrame(sorted_user_predictions).reset_index(), how = 'left', left_on = 'productId', right_on = 'productId').\n",
    "         rename(columns = {user_row_number: 'Predictions'}))\n",
    "    \n",
    "    uniquesProducts = recommendations.groupby(['productId']).agg({'Predictions': 'count'}).reset_index()\n",
    "    \n",
    "    recommendations = (uniquesProducts.sort_values('Predictions', ascending = False).\n",
    "                       iloc[:num_recommendations, :-1]\n",
    "                      )\n",
    "    \n",
    "    return recommendations, sorted_user_predictions, user_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User A3EP26M3QHINXT has already rated 50 movies.\n",
      "Recommending the highest 5 predicted ratings movies not already rated.\n"
     ]
    }
   ],
   "source": [
    "recommendations, sorted_user_predictions, user_data = recommend_movies_simple(preds_df, 'A3EP26M3QHINXT', ratings, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>productId</th>\n",
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       "      <th>38971</th>\n",
       "      <td>B0088CJT4U</td>\n",
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       "      <th>24812</th>\n",
       "      <td>B003ES5ZUU</td>\n",
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       "        productId\n",
       "38971  B0088CJT4U\n",
       "24812  B003ES5ZUU\n",
       "38219  B007WTAJTO\n",
       "38584  B00829TIEK\n",
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      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations"
   ]
  }
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