{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Necessary Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:34:54.624167Z",
     "start_time": "2017-09-26T05:34:46.420964Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import sqlite3\n",
    "\n",
    "data_home = './'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Process the Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get more information about the Millionsong project from https://labrosa.ee.columbia.edu/millionsong/\n",
    "\n",
    "#### Refer to Chapter 10: Section 'The Million Song Dataset Taste Profile' for more details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Triplets data  [user, song, play_count]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the data from http://labrosa.ee.columbia.edu/millionsong/sites/default/files/challenge/train_triplets.txt.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T09:48:06.127761Z",
     "start_time": "2017-09-24T09:48:05.969929Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triplet_dataset = pd.read_csv(filepath_or_buffer=data_home+'train_triplets.txt', \n",
    "                              nrows=10000,sep='\\t', header=None, \n",
    "                              names=['user','song','play_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T09:48:29.300568Z",
     "start_time": "2017-09-24T09:48:29.275986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>song</th>\n",
       "      <th>play_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOAKIMP12A8C130995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOAPDEY12A81C210A9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBBMDR12A8C13253B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBFNSP12AF72A0E22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBFOVM12A58A7D494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBNZDC12A6D4FC103</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBSUJE12A6D4F8CF5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBVFZR12A6D4F8AE3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBXALG12A8C13C108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBXHDL12A81C204C0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user                song  play_count\n",
       "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAKIMP12A8C130995           1\n",
       "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAPDEY12A81C210A9           1\n",
       "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBBMDR12A8C13253B           2\n",
       "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBFNSP12AF72A0E22           1\n",
       "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBFOVM12A58A7D494           1\n",
       "5  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBNZDC12A6D4FC103           1\n",
       "6  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBSUJE12A6D4F8CF5           2\n",
       "7  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBVFZR12A6D4F8AE3           1\n",
       "8  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBXALG12A8C13C108           1\n",
       "9  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBXHDL12A81C204C0           1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplet_dataset.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get User and total play counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:00:40.238849Z",
     "start_time": "2017-09-24T09:57:54.875213Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_dict = {}\n",
    "with open(data_home+'train_triplets.txt') as f:\n",
    "    for line_number, line in enumerate(f):\n",
    "        user = line.split('\\t')[0]\n",
    "        play_count = int(line.split('\\t')[2])\n",
    "        if user in output_dict:\n",
    "            play_count +=output_dict[user]\n",
    "            output_dict.update({user:play_count})\n",
    "        output_dict.update({user:play_count})\n",
    "output_list = [{'user':k,'play_count':v} for k,v in output_dict.items()]\n",
    "play_count_df = pd.DataFrame(output_list)\n",
    "play_count_df = play_count_df.sort_values(by = 'play_count', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "play_count_df.to_csv(path_or_buf='user_playcount_df.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Song and total play counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:08:50.652416Z",
     "start_time": "2017-09-24T10:05:53.721519Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_dict = {}\n",
    "with open(data_home+'train_triplets.txt') as f:\n",
    "    for line_number, line in enumerate(f):\n",
    "        song = line.split('\\t')[1]\n",
    "        play_count = int(line.split('\\t')[2])\n",
    "        if song in output_dict:\n",
    "            play_count +=output_dict[song]\n",
    "            output_dict.update({song:play_count})\n",
    "        output_dict.update({song:play_count})\n",
    "output_list = [{'song':k,'play_count':v} for k,v in output_dict.items()]\n",
    "song_count_df = pd.DataFrame(output_list)\n",
    "song_count_df = song_count_df.sort_values(by = 'play_count', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "song_count_df.to_csv(path_or_buf='song_playcount_df.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View top users and songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:08:54.987662Z",
     "start_time": "2017-09-24T10:08:53.518248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>play_count</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13132</td>\n",
       "      <td>093cb74eb3c517c5179ae24caf0ebec51b24d2a2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9884</td>\n",
       "      <td>119b7c88d58d0c6eb051365c103da5caf817bea6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8210</td>\n",
       "      <td>3fa44653315697f42410a30cb766a4eb102080bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7015</td>\n",
       "      <td>a2679496cd0af9779a92a13ff7c6af5c81ea8c7b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6494</td>\n",
       "      <td>d7d2d888ae04d16e994d6964214a1de81392ee04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6472</td>\n",
       "      <td>4ae01afa8f2430ea0704d502bc7b57fb52164882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6150</td>\n",
       "      <td>b7c24f770be6b802805ac0e2106624a517643c17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5656</td>\n",
       "      <td>113255a012b2affeab62607563d03fbdf31b08e7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5620</td>\n",
       "      <td>6d625c6557df84b60d90426c0116138b617b9449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5602</td>\n",
       "      <td>99ac3d883681e21ea68071019dba828ce76fe94d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   play_count                                      user\n",
       "0       13132  093cb74eb3c517c5179ae24caf0ebec51b24d2a2\n",
       "1        9884  119b7c88d58d0c6eb051365c103da5caf817bea6\n",
       "2        8210  3fa44653315697f42410a30cb766a4eb102080bb\n",
       "3        7015  a2679496cd0af9779a92a13ff7c6af5c81ea8c7b\n",
       "4        6494  d7d2d888ae04d16e994d6964214a1de81392ee04\n",
       "5        6472  4ae01afa8f2430ea0704d502bc7b57fb52164882\n",
       "6        6150  b7c24f770be6b802805ac0e2106624a517643c17\n",
       "7        5656  113255a012b2affeab62607563d03fbdf31b08e7\n",
       "8        5620  6d625c6557df84b60d90426c0116138b617b9449\n",
       "9        5602  99ac3d883681e21ea68071019dba828ce76fe94d"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_count_df = pd.read_csv(filepath_or_buffer='user_playcount_df.csv')\n",
    "play_count_df.head(n =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:09:06.800300Z",
     "start_time": "2017-09-24T10:09:06.427869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>play_count</th>\n",
       "      <th>song</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>726885</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>648239</td>\n",
       "      <td>SOAUWYT12A81C206F1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>527893</td>\n",
       "      <td>SOSXLTC12AF72A7F54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>425463</td>\n",
       "      <td>SOFRQTD12A81C233C0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>389880</td>\n",
       "      <td>SOEGIYH12A6D4FC0E3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>356533</td>\n",
       "      <td>SOAXGDH12A8C13F8A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>292642</td>\n",
       "      <td>SONYKOW12AB01849C9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>274627</td>\n",
       "      <td>SOPUCYA12A8C13A694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>268353</td>\n",
       "      <td>SOUFTBI12AB0183F65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>244730</td>\n",
       "      <td>SOVDSJC12A58A7A271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   play_count                song\n",
       "0      726885  SOBONKR12A58A7A7E0\n",
       "1      648239  SOAUWYT12A81C206F1\n",
       "2      527893  SOSXLTC12AF72A7F54\n",
       "3      425463  SOFRQTD12A81C233C0\n",
       "4      389880  SOEGIYH12A6D4FC0E3\n",
       "5      356533  SOAXGDH12A8C13F8A1\n",
       "6      292642  SONYKOW12AB01849C9\n",
       "7      274627  SOPUCYA12A8C13A694\n",
       "8      268353  SOUFTBI12AB0183F65\n",
       "9      244730  SOVDSJC12A58A7A271"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song_count_df = pd.read_csv(filepath_or_buffer='song_playcount_df.csv')\n",
    "song_count_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:16:05.763243Z",
     "start_time": "2017-09-24T10:16:05.700186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.8807280500655"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_play_count = sum(song_count_df.play_count)\n",
    "(float(play_count_df.head(n=100000).play_count.sum())/total_play_count)*100\n",
    "play_count_subset = play_count_df.head(n=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:26:37.537061Z",
     "start_time": "2017-09-24T10:26:37.528055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.39315366645269"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(float(song_count_df.head(n=30000).play_count.sum())/total_play_count)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:26:38.311410Z",
     "start_time": "2017-09-24T10:26:38.306426Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "song_count_subset = song_count_df.head(n=30000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:26:39.337554Z",
     "start_time": "2017-09-24T10:26:39.326529Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_subset = list(play_count_subset.user)\n",
    "song_subset = list(song_count_subset.song)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:30:23.563673Z",
     "start_time": "2017-09-24T10:27:22.711377Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triplet_dataset = pd.read_csv(filepath_or_buffer=data_home+'train_triplets.txt',sep='\\t', \n",
    "                              header=None, names=['user','song','play_count'])\n",
    "triplet_dataset_sub = triplet_dataset[triplet_dataset.user.isin(user_subset) ]\n",
    "del(triplet_dataset)\n",
    "triplet_dataset_sub_song = triplet_dataset_sub[triplet_dataset_sub.song.isin(song_subset)]\n",
    "del(triplet_dataset_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet_dataset_sub_song.to_csv(path_or_buf=data_home+'triplet_dataset_sub_song.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:32:35.248710Z",
     "start_time": "2017-09-24T10:32:35.241709Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10774785, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplet_dataset_sub_song.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T10:33:25.958644Z",
     "start_time": "2017-09-24T10:33:25.939631Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>song</th>\n",
       "      <th>play_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOADQPP12A67020C82</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOAFTRR12AF72A8D4D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOANQFY12AB0183239</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOAYATB12A6701FD50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBOAFP12A8C131F36</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBZZDU12A6310D8A3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOCAHRT12A8C13A1A4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SODASIJ12A6D4F5D89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SODEAWL12AB0187032</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         user                song  play_count\n",
       "498  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOADQPP12A67020C82          12\n",
       "499  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOAFTRR12AF72A8D4D           1\n",
       "500  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOANQFY12AB0183239           1\n",
       "501  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOAYATB12A6701FD50           1\n",
       "502  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBOAFP12A8C131F36           7\n",
       "503  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBONKR12A58A7A7E0          26\n",
       "504  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBZZDU12A6310D8A3           7\n",
       "505  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOCAHRT12A8C13A1A4           5\n",
       "506  d6589314c0a9bcbca4fee0c93b14bc402363afea  SODASIJ12A6D4F5D89           1\n",
       "507  d6589314c0a9bcbca4fee0c93b14bc402363afea  SODEAWL12AB0187032           8"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplet_dataset_sub_song.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding songs metadata from million songs dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the data from http://labrosa.ee.columbia.edu/millionsong/sites/default/files/AdditionalFiles/track_metadata.db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T13:48:12.545636Z",
     "start_time": "2017-09-24T13:48:12.536631Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('songs',)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = sqlite3.connect(data_home+'track_metadata.db')\n",
    "cur = conn.cursor()\n",
    "cur.execute(\"SELECT name FROM sqlite_master WHERE type='table'\")\n",
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:00:06.135546Z",
     "start_time": "2017-09-24T14:00:06.091508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_id</th>\n",
       "      <th>title</th>\n",
       "      <th>song_id</th>\n",
       "      <th>release</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>artist_mbid</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>duration</th>\n",
       "      <th>artist_familiarity</th>\n",
       "      <th>artist_hotttnesss</th>\n",
       "      <th>year</th>\n",
       "      <th>track_7digitalid</th>\n",
       "      <th>shs_perf</th>\n",
       "      <th>shs_work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRMMMYQ128F932D901</td>\n",
       "      <td>Silent Night</td>\n",
       "      <td>SOQMMHC12AB0180CB8</td>\n",
       "      <td>Monster Ballads X-Mas</td>\n",
       "      <td>ARYZTJS1187B98C555</td>\n",
       "      <td>357ff05d-848a-44cf-b608-cb34b5701ae5</td>\n",
       "      <td>Faster Pussy cat</td>\n",
       "      <td>252.05506</td>\n",
       "      <td>0.649822</td>\n",
       "      <td>0.394032</td>\n",
       "      <td>2003</td>\n",
       "      <td>7032331</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRMMMKD128F425225D</td>\n",
       "      <td>Tanssi vaan</td>\n",
       "      <td>SOVFVAK12A8C1350D9</td>\n",
       "      <td>Karkuteillä</td>\n",
       "      <td>ARMVN3U1187FB3A1EB</td>\n",
       "      <td>8d7ef530-a6fd-4f8f-b2e2-74aec765e0f9</td>\n",
       "      <td>Karkkiautomaatti</td>\n",
       "      <td>156.55138</td>\n",
       "      <td>0.439604</td>\n",
       "      <td>0.356992</td>\n",
       "      <td>1995</td>\n",
       "      <td>1514808</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRMMMRX128F93187D9</td>\n",
       "      <td>No One Could Ever</td>\n",
       "      <td>SOGTUKN12AB017F4F1</td>\n",
       "      <td>Butter</td>\n",
       "      <td>ARGEKB01187FB50750</td>\n",
       "      <td>3d403d44-36ce-465c-ad43-ae877e65adc4</td>\n",
       "      <td>Hudson Mohawke</td>\n",
       "      <td>138.97098</td>\n",
       "      <td>0.643681</td>\n",
       "      <td>0.437504</td>\n",
       "      <td>2006</td>\n",
       "      <td>6945353</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRMMMCH128F425532C</td>\n",
       "      <td>Si Vos Querés</td>\n",
       "      <td>SOBNYVR12A8C13558C</td>\n",
       "      <td>De Culo</td>\n",
       "      <td>ARNWYLR1187B9B2F9C</td>\n",
       "      <td>12be7648-7094-495f-90e6-df4189d68615</td>\n",
       "      <td>Yerba Brava</td>\n",
       "      <td>145.05751</td>\n",
       "      <td>0.448501</td>\n",
       "      <td>0.372349</td>\n",
       "      <td>2003</td>\n",
       "      <td>2168257</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRMMMWA128F426B589</td>\n",
       "      <td>Tangle Of Aspens</td>\n",
       "      <td>SOHSBXH12A8C13B0DF</td>\n",
       "      <td>Rene Ablaze Presents Winter Sessions</td>\n",
       "      <td>AREQDTE1269FB37231</td>\n",
       "      <td></td>\n",
       "      <td>Der Mystic</td>\n",
       "      <td>514.29832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2264873</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             track_id              title             song_id  \\\n",
       "0  TRMMMYQ128F932D901       Silent Night  SOQMMHC12AB0180CB8   \n",
       "1  TRMMMKD128F425225D        Tanssi vaan  SOVFVAK12A8C1350D9   \n",
       "2  TRMMMRX128F93187D9  No One Could Ever  SOGTUKN12AB017F4F1   \n",
       "3  TRMMMCH128F425532C      Si Vos Querés  SOBNYVR12A8C13558C   \n",
       "4  TRMMMWA128F426B589   Tangle Of Aspens  SOHSBXH12A8C13B0DF   \n",
       "\n",
       "                                release           artist_id  \\\n",
       "0                 Monster Ballads X-Mas  ARYZTJS1187B98C555   \n",
       "1                           Karkuteillä  ARMVN3U1187FB3A1EB   \n",
       "2                                Butter  ARGEKB01187FB50750   \n",
       "3                               De Culo  ARNWYLR1187B9B2F9C   \n",
       "4  Rene Ablaze Presents Winter Sessions  AREQDTE1269FB37231   \n",
       "\n",
       "                            artist_mbid       artist_name   duration  \\\n",
       "0  357ff05d-848a-44cf-b608-cb34b5701ae5  Faster Pussy cat  252.05506   \n",
       "1  8d7ef530-a6fd-4f8f-b2e2-74aec765e0f9  Karkkiautomaatti  156.55138   \n",
       "2  3d403d44-36ce-465c-ad43-ae877e65adc4    Hudson Mohawke  138.97098   \n",
       "3  12be7648-7094-495f-90e6-df4189d68615       Yerba Brava  145.05751   \n",
       "4                                              Der Mystic  514.29832   \n",
       "\n",
       "   artist_familiarity  artist_hotttnesss  year  track_7digitalid  shs_perf  \\\n",
       "0            0.649822           0.394032  2003           7032331        -1   \n",
       "1            0.439604           0.356992  1995           1514808        -1   \n",
       "2            0.643681           0.437504  2006           6945353        -1   \n",
       "3            0.448501           0.372349  2003           2168257        -1   \n",
       "4            0.000000           0.000000     0           2264873        -1   \n",
       "\n",
       "   shs_work  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_metadata_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T13:59:00.464363Z",
     "start_time": "2017-09-24T13:58:47.127394Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "track_metadata_df = pd.read_sql(con=conn, sql='select * from songs')\n",
    "track_metadata_df_sub = track_metadata_df[track_metadata_df.song_id.isin(song_subset)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:00:17.841834Z",
     "start_time": "2017-09-24T14:00:17.450258Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "track_metadata_df_sub.to_csv(path_or_buf=data_home+'track_metadata_df_sub.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:00:26.155770Z",
     "start_time": "2017-09-24T14:00:26.149763Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30447, 14)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_metadata_df_sub.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load up the saved data subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:35:13.855787Z",
     "start_time": "2017-09-26T05:35:00.267829Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triplet_dataset_sub_song = pd.read_csv(filepath_or_buffer=data_home+'train_triplets_sub_song.csv')\n",
    "track_metadata_df_sub = pd.read_csv(filepath_or_buffer=data_home+'track_metadata_df_sub.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:35:26.643464Z",
     "start_time": "2017-09-26T05:35:14.956760Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del(track_metadata_df_sub['track_id'])\n",
    "del(track_metadata_df_sub['artist_mbid'])\n",
    "track_metadata_df_sub = track_metadata_df_sub.drop_duplicates(['song_id'])\n",
    "triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song, track_metadata_df_sub, how='left', left_on='song', right_on='song_id')\n",
    "triplet_dataset_sub_song_merged.rename(columns={'play_count':'listen_count'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:35:31.415206Z",
     "start_time": "2017-09-26T05:35:29.271749Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del(triplet_dataset_sub_song_merged['song_id'])\n",
    "del(triplet_dataset_sub_song_merged['artist_id'])\n",
    "del(triplet_dataset_sub_song_merged['duration'])\n",
    "del(triplet_dataset_sub_song_merged['artist_familiarity'])\n",
    "del(triplet_dataset_sub_song_merged['artist_hotttnesss'])\n",
    "del(triplet_dataset_sub_song_merged['track_7digitalid'])\n",
    "del(triplet_dataset_sub_song_merged['shs_perf'])\n",
    "del(triplet_dataset_sub_song_merged['shs_work'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:01:44.587532Z",
     "start_time": "2017-09-24T14:01:44.561014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>song</th>\n",
       "      <th>listen_count</th>\n",
       "      <th>title</th>\n",
       "      <th>release</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOADQPP12A67020C82</td>\n",
       "      <td>12</td>\n",
       "      <td>You And Me Jesus</td>\n",
       "      <td>Tribute To Jake Hess</td>\n",
       "      <td>Jake Hess</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOAFTRR12AF72A8D4D</td>\n",
       "      <td>1</td>\n",
       "      <td>Harder Better Faster Stronger</td>\n",
       "      <td>Discovery</td>\n",
       "      <td>Daft Punk</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOANQFY12AB0183239</td>\n",
       "      <td>1</td>\n",
       "      <td>Uprising</td>\n",
       "      <td>Uprising</td>\n",
       "      <td>Muse</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOAYATB12A6701FD50</td>\n",
       "      <td>1</td>\n",
       "      <td>Breakfast At Tiffany's</td>\n",
       "      <td>Home</td>\n",
       "      <td>Deep Blue Something</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBOAFP12A8C131F36</td>\n",
       "      <td>7</td>\n",
       "      <td>Lucky (Album Version)</td>\n",
       "      <td>We Sing.  We Dance.  We Steal Things.</td>\n",
       "      <td>Jason Mraz &amp; Colbie Caillat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>26</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>If There Was A Way</td>\n",
       "      <td>Dwight Yoakam</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBZZDU12A6310D8A3</td>\n",
       "      <td>7</td>\n",
       "      <td>Don't Dream It's Over</td>\n",
       "      <td>Recurring Dream_ Best Of Crowded House (Domest...</td>\n",
       "      <td>Crowded House</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOCAHRT12A8C13A1A4</td>\n",
       "      <td>5</td>\n",
       "      <td>S.O.S.</td>\n",
       "      <td>SOS</td>\n",
       "      <td>Jonas Brothers</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SODASIJ12A6D4F5D89</td>\n",
       "      <td>1</td>\n",
       "      <td>The Invisible Man</td>\n",
       "      <td>The Invisible Man</td>\n",
       "      <td>Michael Cretu</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SODEAWL12AB0187032</td>\n",
       "      <td>8</td>\n",
       "      <td>American Idiot [feat. Green Day &amp; The Cast Of ...</td>\n",
       "      <td>The Original Broadway Cast Recording 'American...</td>\n",
       "      <td>Green Day</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user                song  listen_count  \\\n",
       "0  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOADQPP12A67020C82            12   \n",
       "1  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOAFTRR12AF72A8D4D             1   \n",
       "2  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOANQFY12AB0183239             1   \n",
       "3  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOAYATB12A6701FD50             1   \n",
       "4  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBOAFP12A8C131F36             7   \n",
       "5  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBONKR12A58A7A7E0            26   \n",
       "6  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBZZDU12A6310D8A3             7   \n",
       "7  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOCAHRT12A8C13A1A4             5   \n",
       "8  d6589314c0a9bcbca4fee0c93b14bc402363afea  SODASIJ12A6D4F5D89             1   \n",
       "9  d6589314c0a9bcbca4fee0c93b14bc402363afea  SODEAWL12AB0187032             8   \n",
       "\n",
       "                                               title  \\\n",
       "0                                   You And Me Jesus   \n",
       "1                      Harder Better Faster Stronger   \n",
       "2                                           Uprising   \n",
       "3                             Breakfast At Tiffany's   \n",
       "4                              Lucky (Album Version)   \n",
       "5                                     You're The One   \n",
       "6                              Don't Dream It's Over   \n",
       "7                                             S.O.S.   \n",
       "8                                  The Invisible Man   \n",
       "9  American Idiot [feat. Green Day & The Cast Of ...   \n",
       "\n",
       "                                             release  \\\n",
       "0                               Tribute To Jake Hess   \n",
       "1                                          Discovery   \n",
       "2                                           Uprising   \n",
       "3                                               Home   \n",
       "4              We Sing.  We Dance.  We Steal Things.   \n",
       "5                                 If There Was A Way   \n",
       "6  Recurring Dream_ Best Of Crowded House (Domest...   \n",
       "7                                                SOS   \n",
       "8                                  The Invisible Man   \n",
       "9  The Original Broadway Cast Recording 'American...   \n",
       "\n",
       "                   artist_name  year  \n",
       "0                    Jake Hess  2004  \n",
       "1                    Daft Punk  2007  \n",
       "2                         Muse     0  \n",
       "3          Deep Blue Something  1993  \n",
       "4  Jason Mraz & Colbie Caillat     0  \n",
       "5                Dwight Yoakam  1990  \n",
       "6                Crowded House  1986  \n",
       "7               Jonas Brothers  2007  \n",
       "8                Michael Cretu  1985  \n",
       "9                    Green Day     0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplet_dataset_sub_song_merged.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most popular songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAN9CAYAAABLhdfQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADATf4P6UbZn1BvWrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc40359668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popular_songs = triplet_dataset_sub_song_merged[['title','listen_count']].groupby('title').sum().reset_index()\n",
    "popular_songs_top_20 = popular_songs.sort_values('listen_count', ascending=False).head(n=20)\n",
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "objects = (list(popular_songs_top_20['title']))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(popular_songs_top_20['listen_count'])\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Item count')\n",
    "plt.title('Most popular songs')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most popular releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:31:19.717845Z",
     "start_time": "2017-09-24T14:31:14.004246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAKKCAYAAADlZfXFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+QuqWfpbLeW/XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28591e43518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popular_release = triplet_dataset_sub_song_merged[['release','listen_count']].groupby('release').sum().reset_index()\n",
    "popular_release_top_20 = popular_release.sort_values('listen_count', ascending=False).head(n=20)\n",
    "\n",
    "objects = (list(popular_release_top_20['release']))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(popular_release_top_20['listen_count'])\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Item count')\n",
    "plt.title('Most popular Release')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most popular artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:34:20.212228Z",
     "start_time": "2017-09-24T14:34:15.653934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAJNCAYAAADpiNvJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x285a5b99080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popular_artist = triplet_dataset_sub_song_merged[['artist_name','listen_count']].groupby('artist_name').sum().reset_index()\n",
    "popular_artist_top_20 = popular_artist.sort_values('listen_count', ascending=False).head(n=20)\n",
    "\n",
    "objects = (list(popular_artist_top_20['artist_name']))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(popular_artist_top_20['listen_count'])\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Item count')\n",
    "plt.title('Most popular Artists')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Song count distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T14:55:04.690241Z",
     "start_time": "2017-09-24T14:54:59.848292Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_song_count_distribution = triplet_dataset_sub_song_merged[['user','title']].groupby('user').count().reset_index().sort_values(\n",
    "by='title',ascending = False)\n",
    "user_song_count_distribution.title.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T15:11:28.003535Z",
     "start_time": "2017-09-24T15:11:27.461150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAHJCAYAAACloWxtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x285a98df518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = user_song_count_distribution.title\n",
    "n, bins, patches = plt.hist(x, 50, facecolor='green', alpha=0.75)\n",
    "plt.xlabel('Play Counts')\n",
    "plt.ylabel('Num of Users')\n",
    "plt.title(r'$\\mathrm{Histogram\\ of\\ User\\ Play\\ Count\\ Distribution}\\ $')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommendation Engines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Recommenders as Recommenders\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Popularity based recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triplet_dataset_sub_song_merged_set = triplet_dataset_sub_song_merged\n",
    "train_data, test_data = train_test_split(triplet_dataset_sub_song_merged_set, test_size = 0.40, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>song</th>\n",
       "      <th>listen_count</th>\n",
       "      <th>title</th>\n",
       "      <th>release</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3031653</th>\n",
       "      <td>e120d4418d07d81a8efdb5cb9ab4328c09b28a95</td>\n",
       "      <td>SONYKOW12AB01849C9</td>\n",
       "      <td>8</td>\n",
       "      <td>Secrets</td>\n",
       "      <td>Waking Up</td>\n",
       "      <td>OneRepublic</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7890358</th>\n",
       "      <td>e9b13911d892a8ad58de6714b465aa5550fa3f61</td>\n",
       "      <td>SOYFFTM12AF72AA2AE</td>\n",
       "      <td>1</td>\n",
       "      <td>Gone</td>\n",
       "      <td>Some Mad Hope</td>\n",
       "      <td>Matt Nathanson</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6291377</th>\n",
       "      <td>359f2acd6a2da352521531d17de290573b0f04ca</td>\n",
       "      <td>SOOAFDW12A8C13325B</td>\n",
       "      <td>1</td>\n",
       "      <td>Bryn (Album)</td>\n",
       "      <td>Vampire Weekend</td>\n",
       "      <td>Vampire Weekend</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3124329</th>\n",
       "      <td>43259fdd657ced6341184945608c1bd5939bdb95</td>\n",
       "      <td>SOHFVJR12AF72A9805</td>\n",
       "      <td>1</td>\n",
       "      <td>Holdin' On Together</td>\n",
       "      <td>Alphabetical</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359286</th>\n",
       "      <td>b7ef66e5c94b0f03943f7352be7271c3b6a54908</td>\n",
       "      <td>SOWORSV12A6D4F9345</td>\n",
       "      <td>4</td>\n",
       "      <td>Mr Blue</td>\n",
       "      <td>Hurricane Glass</td>\n",
       "      <td>Catherine Feeny</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             user                song  \\\n",
       "3031653  e120d4418d07d81a8efdb5cb9ab4328c09b28a95  SONYKOW12AB01849C9   \n",
       "7890358  e9b13911d892a8ad58de6714b465aa5550fa3f61  SOYFFTM12AF72AA2AE   \n",
       "6291377  359f2acd6a2da352521531d17de290573b0f04ca  SOOAFDW12A8C13325B   \n",
       "3124329  43259fdd657ced6341184945608c1bd5939bdb95  SOHFVJR12AF72A9805   \n",
       "2359286  b7ef66e5c94b0f03943f7352be7271c3b6a54908  SOWORSV12A6D4F9345   \n",
       "\n",
       "         listen_count                title          release      artist_name  \\\n",
       "3031653             8              Secrets        Waking Up      OneRepublic   \n",
       "7890358             1                 Gone    Some Mad Hope   Matt Nathanson   \n",
       "6291377             1         Bryn (Album)  Vampire Weekend  Vampire Weekend   \n",
       "3124329             1  Holdin' On Together     Alphabetical          Phoenix   \n",
       "2359286             4              Mr Blue  Hurricane Glass  Catherine Feeny   \n",
       "\n",
       "         year  \n",
       "3031653  2009  \n",
       "7890358  2007  \n",
       "6291377  2007  \n",
       "3124329     0  \n",
       "2359286  2007  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_popularity_recommendation(train_data, user_id, item_id):\n",
    "    #Get a count of user_ids for each unique song as recommendation score\n",
    "    train_data_grouped = train_data.groupby([item_id]).agg({user_id: 'count'}).reset_index()\n",
    "    train_data_grouped.rename(columns = {user_id: 'score'},inplace=True)\n",
    "    \n",
    "    #Sort the songs based upon recommendation score\n",
    "    train_data_sort = train_data_grouped.sort_values(['score', item_id], ascending = [0,1])\n",
    "    \n",
    "    #Generate a recommendation rank based upon score\n",
    "    train_data_sort['Rank'] = train_data_sort['score'].rank(ascending=0, method='first')\n",
    "        \n",
    "    #Get the top 10 recommendations\n",
    "    popularity_recommendations = train_data_sort.head(20)\n",
    "    return popularity_recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "recommendations = create_popularity_recommendation(triplet_dataset_sub_song_merged,'user','title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>score</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19580</th>\n",
       "      <td>Sehr kosmisch</td>\n",
       "      <td>18629</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>Dog Days Are Over (Radio Edit)</td>\n",
       "      <td>17636</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27314</th>\n",
       "      <td>You're The One</td>\n",
       "      <td>16082</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19542</th>\n",
       "      <td>Secrets</td>\n",
       "      <td>15139</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18636</th>\n",
       "      <td>Revelry</td>\n",
       "      <td>14942</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25070</th>\n",
       "      <td>Undo</td>\n",
       "      <td>14682</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7531</th>\n",
       "      <td>Fireflies</td>\n",
       "      <td>13087</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9641</th>\n",
       "      <td>Hey_ Soul Sister</td>\n",
       "      <td>12991</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25216</th>\n",
       "      <td>Use Somebody</td>\n",
       "      <td>12790</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9922</th>\n",
       "      <td>Horn Concerto No. 4 in E flat K495: II. Romanc...</td>\n",
       "      <td>12343</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24291</th>\n",
       "      <td>Tive Sim</td>\n",
       "      <td>11825</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3629</th>\n",
       "      <td>Canada</td>\n",
       "      <td>11591</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23468</th>\n",
       "      <td>The Scientist</td>\n",
       "      <td>11534</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4194</th>\n",
       "      <td>Clocks</td>\n",
       "      <td>11358</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>Just Dance</td>\n",
       "      <td>11056</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26974</th>\n",
       "      <td>Yellow</td>\n",
       "      <td>10923</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16438</th>\n",
       "      <td>OMG</td>\n",
       "      <td>10824</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9845</th>\n",
       "      <td>Home</td>\n",
       "      <td>10511</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3296</th>\n",
       "      <td>Bulletproof</td>\n",
       "      <td>10382</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4760</th>\n",
       "      <td>Creep (Explicit)</td>\n",
       "      <td>10246</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  score  Rank\n",
       "19580                                      Sehr kosmisch  18629   1.0\n",
       "5780                      Dog Days Are Over (Radio Edit)  17636   2.0\n",
       "27314                                     You're The One  16082   3.0\n",
       "19542                                            Secrets  15139   4.0\n",
       "18636                                            Revelry  14942   5.0\n",
       "25070                                               Undo  14682   6.0\n",
       "7531                                           Fireflies  13087   7.0\n",
       "9641                                    Hey_ Soul Sister  12991   8.0\n",
       "25216                                       Use Somebody  12790   9.0\n",
       "9922   Horn Concerto No. 4 in E flat K495: II. Romanc...  12343  10.0\n",
       "24291                                           Tive Sim  11825  11.0\n",
       "3629                                              Canada  11591  12.0\n",
       "23468                                      The Scientist  11534  13.0\n",
       "4194                                              Clocks  11358  14.0\n",
       "12136                                         Just Dance  11056  15.0\n",
       "26974                                             Yellow  10923  16.0\n",
       "16438                                                OMG  10824  17.0\n",
       "9845                                                Home  10511  18.0\n",
       "3296                                         Bulletproof  10382  19.0\n",
       "4760                                    Creep (Explicit)  10246  20.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Item similarity  based recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "song_count_subset = song_count_df.head(n=5000)\n",
    "user_subset = list(play_count_subset.user)\n",
    "song_subset = list(song_count_subset.song)\n",
    "triplet_dataset_sub_song_merged_sub = triplet_dataset_sub_song_merged[triplet_dataset_sub_song_merged.song.isin(song_subset)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>song</th>\n",
       "      <th>listen_count</th>\n",
       "      <th>title</th>\n",
       "      <th>release</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6689</th>\n",
       "      <td>0d0f80a34807aab31a3521424d456d30bf2c93d9</td>\n",
       "      <td>SOAEHEX12A8C13EFA4</td>\n",
       "      <td>21</td>\n",
       "      <td>Dead And Gone [feat. Justin Timberlake] (Expli...</td>\n",
       "      <td>Paper Trail</td>\n",
       "      <td>T.I.</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6690</th>\n",
       "      <td>0d0f80a34807aab31a3521424d456d30bf2c93d9</td>\n",
       "      <td>SOCKJVP12A6D4F920C</td>\n",
       "      <td>3</td>\n",
       "      <td>Epic</td>\n",
       "      <td>The Real Thing</td>\n",
       "      <td>Faith No More</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6692</th>\n",
       "      <td>0d0f80a34807aab31a3521424d456d30bf2c93d9</td>\n",
       "      <td>SOCZKYZ12A8C13C748</td>\n",
       "      <td>1</td>\n",
       "      <td>Walking In The Air</td>\n",
       "      <td>Oceanborn</td>\n",
       "      <td>Nightwish</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6697</th>\n",
       "      <td>0d0f80a34807aab31a3521424d456d30bf2c93d9</td>\n",
       "      <td>SOZAFNE12AAF3B50E6</td>\n",
       "      <td>12</td>\n",
       "      <td>You Raise Me Up</td>\n",
       "      <td>Holidays &amp; Hits</td>\n",
       "      <td>Celtic Woman</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>4e11f45d732f4861772b2906f81a7d384552ad12</td>\n",
       "      <td>SOABJBU12A8C13F63F</td>\n",
       "      <td>2</td>\n",
       "      <td>Back Against The Wall</td>\n",
       "      <td>Cage The Elephant</td>\n",
       "      <td>Cage The Elephant</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           user                song  \\\n",
       "6689   0d0f80a34807aab31a3521424d456d30bf2c93d9  SOAEHEX12A8C13EFA4   \n",
       "6690   0d0f80a34807aab31a3521424d456d30bf2c93d9  SOCKJVP12A6D4F920C   \n",
       "6692   0d0f80a34807aab31a3521424d456d30bf2c93d9  SOCZKYZ12A8C13C748   \n",
       "6697   0d0f80a34807aab31a3521424d456d30bf2c93d9  SOZAFNE12AAF3B50E6   \n",
       "10680  4e11f45d732f4861772b2906f81a7d384552ad12  SOABJBU12A8C13F63F   \n",
       "\n",
       "       listen_count                                              title  \\\n",
       "6689             21  Dead And Gone [feat. Justin Timberlake] (Expli...   \n",
       "6690              3                                               Epic   \n",
       "6692              1                                 Walking In The Air   \n",
       "6697             12                                    You Raise Me Up   \n",
       "10680             2                              Back Against The Wall   \n",
       "\n",
       "                 release        artist_name  year  \n",
       "6689         Paper Trail               T.I.  2008  \n",
       "6690      The Real Thing      Faith No More  1989  \n",
       "6692           Oceanborn          Nightwish  1998  \n",
       "6697     Holidays & Hits       Celtic Woman  2005  \n",
       "10680  Cage The Elephant  Cage The Elephant  2008  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplet_dataset_sub_song_merged_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(triplet_dataset_sub_song_merged_sub, test_size = 0.30, random_state=0)\n",
    "is_model = Recommenders.item_similarity_recommender_py()\n",
    "is_model.create(train_data, 'user', 'title')\n",
    "user_id = list(train_data.user)[7]\n",
    "user_items = is_model.get_user_items(user_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Recommendation process going on:\n",
      "----------------------------------------------------------------------\n",
      "No. of unique songs for the user: 186\n",
      "no. of unique songs in the training set: 4833\n",
      "Non zero values in cooccurence_matrix :315344\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>song</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>0.099810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Coda</td>\n",
       "      <td>0.093718</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Tuesday Moon</td>\n",
       "      <td>0.084476</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Tron</td>\n",
       "      <td>0.083682</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Acadian Coast</td>\n",
       "      <td>0.081797</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Love Letter To Japan</td>\n",
       "      <td>0.081042</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Heavy Water</td>\n",
       "      <td>0.080742</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Balloons (Single version)</td>\n",
       "      <td>0.079459</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Blackbirds</td>\n",
       "      <td>0.078346</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>Diamond Dave</td>\n",
       "      <td>0.076680</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    user_id                       song  \\\n",
       "0  2a2f776cbac6df64d6cb505e7e834e01684673b6                     Meteor   \n",
       "1  2a2f776cbac6df64d6cb505e7e834e01684673b6                       Coda   \n",
       "2  2a2f776cbac6df64d6cb505e7e834e01684673b6               Tuesday Moon   \n",
       "3  2a2f776cbac6df64d6cb505e7e834e01684673b6                       Tron   \n",
       "4  2a2f776cbac6df64d6cb505e7e834e01684673b6              Acadian Coast   \n",
       "5  2a2f776cbac6df64d6cb505e7e834e01684673b6       Love Letter To Japan   \n",
       "6  2a2f776cbac6df64d6cb505e7e834e01684673b6                Heavy Water   \n",
       "7  2a2f776cbac6df64d6cb505e7e834e01684673b6  Balloons (Single version)   \n",
       "8  2a2f776cbac6df64d6cb505e7e834e01684673b6                 Blackbirds   \n",
       "9  2a2f776cbac6df64d6cb505e7e834e01684673b6               Diamond Dave   \n",
       "\n",
       "      score rank  \n",
       "0  0.099810    1  \n",
       "1  0.093718    2  \n",
       "2  0.084476    3  \n",
       "3  0.083682    4  \n",
       "4  0.081797    5  \n",
       "5  0.081042    6  \n",
       "6  0.080742    7  \n",
       "7  0.079459    8  \n",
       "8  0.078346    9  \n",
       "9  0.076680   10  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recommend songs for the user using personalized model\n",
    "is_model.recommend(user_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix factorization  based recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:35:54.570454Z",
     "start_time": "2017-09-26T05:35:47.751153Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index()\n",
    "triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True)\n",
    "triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df)\n",
    "triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged['total_listen_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:35:59.313745Z",
     "start_time": "2017-09-26T05:35:56.374249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>song</th>\n",
       "      <th>listen_count</th>\n",
       "      <th>fractional_play_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOADQPP12A67020C82</td>\n",
       "      <td>12</td>\n",
       "      <td>0.036474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOAFTRR12AF72A8D4D</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOANQFY12AB0183239</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOAYATB12A6701FD50</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBOAFP12A8C131F36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.021277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user                song  listen_count  \\\n",
       "0  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOADQPP12A67020C82            12   \n",
       "1  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOAFTRR12AF72A8D4D             1   \n",
       "2  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOANQFY12AB0183239             1   \n",
       "3  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOAYATB12A6701FD50             1   \n",
       "4  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBOAFP12A8C131F36             7   \n",
       "\n",
       "   fractional_play_count  \n",
       "0               0.036474  \n",
       "1               0.003040  \n",
       "2               0.003040  \n",
       "3               0.003040  \n",
       "4               0.021277  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplet_dataset_sub_song_merged[triplet_dataset_sub_song_merged.user =='d6589314c0a9bcbca4fee0c93b14bc402363afea'][['user','song','listen_count','fractional_play_count']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:36:21.506614Z",
     "start_time": "2017-09-26T05:36:02.556438Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import coo_matrix\n",
    "\n",
    "small_set = triplet_dataset_sub_song_merged\n",
    "user_codes = small_set.user.drop_duplicates().reset_index()\n",
    "song_codes = small_set.song.drop_duplicates().reset_index()\n",
    "user_codes.rename(columns={'index':'user_index'}, inplace=True)\n",
    "song_codes.rename(columns={'index':'song_index'}, inplace=True)\n",
    "song_codes['so_index_value'] = list(song_codes.index)\n",
    "user_codes['us_index_value'] = list(user_codes.index)\n",
    "small_set = pd.merge(small_set,song_codes,how='left')\n",
    "small_set = pd.merge(small_set,user_codes,how='left')\n",
    "mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']]\n",
    "data_array = mat_candidate.fractional_play_count.values\n",
    "row_array = mat_candidate.us_index_value.values\n",
    "col_array = mat_candidate.so_index_value.values\n",
    "\n",
    "data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:36:23.566197Z",
     "start_time": "2017-09-26T05:36:23.558192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<99996x30000 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 10774785 stored elements in COOrdinate format>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:36:28.138686Z",
     "start_time": "2017-09-26T05:36:28.108662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_index</th>\n",
       "      <th>user</th>\n",
       "      <th>us_index_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27513</th>\n",
       "      <td>2981069</td>\n",
       "      <td>2a2f776cbac6df64d6cb505e7e834e01684673b6</td>\n",
       "      <td>27513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_index                                      user  us_index_value\n",
       "27513     2981069  2a2f776cbac6df64d6cb505e7e834e01684673b6           27513"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_codes[user_codes.user =='2a2f776cbac6df64d6cb505e7e834e01684673b6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:36:35.965832Z",
     "start_time": "2017-09-26T05:36:31.399367Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math as mt\n",
    "from scipy.sparse.linalg import * #used for matrix multiplication\n",
    "from scipy.sparse.linalg import svds\n",
    "from scipy.sparse import csc_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:38:22.051129Z",
     "start_time": "2017-09-26T05:38:22.026115Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_svd(urm, K):\n",
    "    U, s, Vt = svds(urm, K)\n",
    "\n",
    "    dim = (len(s), len(s))\n",
    "    S = np.zeros(dim, dtype=np.float32)\n",
    "    for i in range(0, len(s)):\n",
    "        S[i,i] = mt.sqrt(s[i])\n",
    "\n",
    "    U = csc_matrix(U, dtype=np.float32)\n",
    "    S = csc_matrix(S, dtype=np.float32)\n",
    "    Vt = csc_matrix(Vt, dtype=np.float32)\n",
    "    \n",
    "    return U, S, Vt\n",
    "\n",
    "def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test):\n",
    "    rightTerm = S*Vt \n",
    "    max_recommendation = 250\n",
    "    estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16)\n",
    "    recomendRatings = np.zeros(shape=(MAX_UID,max_recommendation ), dtype=np.float16)\n",
    "    for userTest in uTest:\n",
    "        prod = U[userTest, :]*rightTerm\n",
    "        estimatedRatings[userTest, :] = prod.todense()\n",
    "        recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation]\n",
    "    return recomendRatings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:38:41.670740Z",
     "start_time": "2017-09-26T05:38:23.522055Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K=50\n",
    "urm = data_sparse\n",
    "MAX_PID = urm.shape[1]\n",
    "MAX_UID = urm.shape[0]\n",
    "\n",
    "U, S, Vt = compute_svd(urm, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:38:44.738094Z",
     "start_time": "2017-09-26T05:38:44.485691Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictied ratings:\n"
     ]
    }
   ],
   "source": [
    "uTest = [4,5,6,7,8,873,23]\n",
    "\n",
    "uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:38:50.376825Z",
     "start_time": "2017-09-26T05:38:48.031969Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendation for user with user id 4\n",
      "The number 1 recommended song is Fireflies BY Charttraxx Karaoke\n",
      "The number 2 recommended song is Hey_ Soul Sister BY Train\n",
      "The number 3 recommended song is OMG BY Usher featuring will.i.am\n",
      "The number 4 recommended song is Lucky (Album Version) BY Jason Mraz & Colbie Caillat\n",
      "The number 5 recommended song is Vanilla Twilight BY Owl City\n",
      "The number 6 recommended song is Billionaire [feat. Bruno Mars]  (Explicit Album Version) BY Travie McCoy\n",
      "The number 7 recommended song is Crumpshit BY Philippe Rochard\n",
      "The number 8 recommended song is Love Story BY Taylor Swift\n",
      "The number 9 recommended song is TULENLIEKKI BY M.A. Numminen\n",
      "The number 10 recommended song is Use Somebody BY Kings Of Leon\n",
      "Recommendation for user with user id 5\n",
      "The number 1 recommended song is Sehr kosmisch BY Harmonia\n",
      "The number 2 recommended song is Dog Days Are Over (Radio Edit) BY Florence + The Machine\n",
      "The number 3 recommended song is Ain't Misbehavin BY Sam Cooke\n",
      "The number 4 recommended song is Revelry BY Kings Of Leon\n",
      "The number 5 recommended song is Undo BY Björk\n",
      "The number 6 recommended song is Cosmic Love BY Florence + The Machine\n",
      "The number 7 recommended song is Home BY Edward Sharpe & The Magnetic Zeros\n",
      "The number 8 recommended song is You've Got The Love BY Florence + The Machine\n",
      "The number 9 recommended song is Tighten Up BY The Black Keys\n",
      "The number 10 recommended song is Bring Me To Life BY Evanescence\n",
      "Recommendation for user with user id 6\n",
      "The number 1 recommended song is Crumpshit BY Philippe Rochard\n",
      "The number 2 recommended song is Marry Me BY Train\n",
      "The number 3 recommended song is Hey_ Soul Sister BY Train\n",
      "The number 4 recommended song is Lucky (Album Version) BY Jason Mraz & Colbie Caillat\n",
      "The number 5 recommended song is One On One BY the bird and the bee\n",
      "The number 6 recommended song is Canada BY Five Iron Frenzy\n",
      "The number 7 recommended song is I Never Told You BY Colbie Caillat\n",
      "The number 8 recommended song is Fireflies BY Charttraxx Karaoke\n",
      "The number 9 recommended song is TULENLIEKKI BY M.A. Numminen\n",
      "The number 10 recommended song is Bring Me To Life BY Evanescence\n",
      "Recommendation for user with user id 7\n",
      "The number 1 recommended song is Behind The Sea [Live In Chicago] BY Panic At The Disco\n",
      "The number 2 recommended song is The City Is At War (Album Version) BY Cobra Starship\n",
      "The number 3 recommended song is Una Confusion BY LU\n",
      "The number 4 recommended song is Dead Souls BY Nine Inch Nails\n",
      "The number 5 recommended song is Home BY Edward Sharpe & The Magnetic Zeros\n",
      "The number 6 recommended song is Climbing Up The Walls BY Radiohead\n",
      "The number 7 recommended song is Tighten Up BY The Black Keys\n",
      "The number 8 recommended song is West One (Shine On Me) BY The Ruts\n",
      "The number 9 recommended song is Tive Sim BY Cartola\n",
      "The number 10 recommended song is Cosmic Love BY Florence + The Machine\n",
      "Recommendation for user with user id 8\n",
      "The number 1 recommended song is Undo BY Björk\n",
      "The number 2 recommended song is Canada BY Five Iron Frenzy\n",
      "The number 3 recommended song is Better To Reign In Hell BY Cradle Of Filth\n",
      "The number 4 recommended song is Unite (2009 Digital Remaster) BY Beastie Boys\n",
      "The number 5 recommended song is Behind The Sea [Live In Chicago] BY Panic At The Disco\n",
      "The number 6 recommended song is Rockin' Around The Christmas Tree BY Brenda Lee\n",
      "The number 7 recommended song is Tautou BY Brand New\n",
      "The number 8 recommended song is Revelry BY Kings Of Leon\n",
      "The number 9 recommended song is 16 Candles BY The Crests\n",
      "The number 10 recommended song is Catch You Baby (Steve Pitron & Max Sanna Radio Edit) BY Lonnie Gordon\n",
      "Recommendation for user with user id 873\n",
      "The number 1 recommended song is The City Is At War (Album Version) BY Cobra Starship\n",
      "The number 2 recommended song is Una Confusion BY LU\n",
      "The number 3 recommended song is Dead Souls BY Nine Inch Nails\n",
      "The number 4 recommended song is Home BY Edward Sharpe & The Magnetic Zeros\n",
      "The number 5 recommended song is Tighten Up BY The Black Keys\n",
      "The number 6 recommended song is Angel of Music BY Andrew Lloyd Webber\n",
      "The number 7 recommended song is Tive Sim BY Cartola\n",
      "The number 8 recommended song is What You Know BY Two Door Cinema Club\n",
      "The number 9 recommended song is Float On BY Modest Mouse\n",
      "The number 10 recommended song is The Funeral (Album Version) BY Band Of Horses\n",
      "Recommendation for user with user id 23\n",
      "The number 1 recommended song is Garden Of Eden BY Guns N' Roses\n",
      "The number 2 recommended song is Don't Speak BY John Dahlbäck\n",
      "The number 3 recommended song is Master Of Puppets BY Metallica\n",
      "The number 4 recommended song is TULENLIEKKI BY M.A. Numminen\n",
      "The number 5 recommended song is Bring Me To Life BY Evanescence\n",
      "The number 6 recommended song is Kryptonite BY 3 Doors Down\n",
      "The number 7 recommended song is Make Her Say BY Kid Cudi / Kanye West / Common\n",
      "The number 8 recommended song is Night Village BY Deep Forest\n",
      "The number 9 recommended song is Better To Reign In Hell BY Cradle Of Filth\n",
      "The number 10 recommended song is Xanadu BY Olivia Newton-John;Electric Light Orchestra\n"
     ]
    }
   ],
   "source": [
    "for user in uTest:\n",
    "    print(\"Recommendation for user with user id {}\". format(user))\n",
    "    rank_value = 1\n",
    "    for i in uTest_recommended_items[user,0:10]:\n",
    "        song_details = small_set[small_set.so_index_value == i].drop_duplicates('so_index_value')[['title','artist_name']]\n",
    "        print(\"The number {} recommended song is {} BY {}\".format(rank_value, list(song_details['title'])[0],list(song_details['artist_name'])[0]))\n",
    "        rank_value+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:37:37.485771Z",
     "start_time": "2017-09-26T05:37:37.387208Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictied ratings:\n"
     ]
    }
   ],
   "source": [
    "uTest = [27513]\n",
    "#Get estimated rating for test user\n",
    "print(\"Predictied ratings:\")\n",
    "uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-26T05:37:38.879475Z",
     "start_time": "2017-09-26T05:37:38.540521Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendation for user with user id 27513\n",
      "The number 1 recommended song is Behind The Sea [Live In Chicago] BY Panic At The Disco\n",
      "The number 2 recommended song is Una Confusion BY LU\n",
      "The number 3 recommended song is Home BY Edward Sharpe & The Magnetic Zeros\n",
      "The number 4 recommended song is Dead Souls BY Nine Inch Nails\n",
      "The number 5 recommended song is The City Is At War (Album Version) BY Cobra Starship\n",
      "The number 6 recommended song is Tighten Up BY The Black Keys\n",
      "The number 7 recommended song is Climbing Up The Walls BY Radiohead\n",
      "The number 8 recommended song is Yellow BY Coldplay\n",
      "The number 9 recommended song is Creep (Explicit) BY Radiohead\n",
      "The number 10 recommended song is West One (Shine On Me) BY The Ruts\n"
     ]
    }
   ],
   "source": [
    "for user in uTest:\n",
    "    print(\"Recommendation for user with user id {}\". format(user))\n",
    "    rank_value = 1\n",
    "    for i in uTest_recommended_items[user,0:10]:\n",
    "        song_details = small_set[small_set.so_index_value == i].drop_duplicates('so_index_value')[['title','artist_name']]\n",
    "        print(\"The number {} recommended song is {} BY {}\".format(rank_value, list(song_details['title'])[0],list(song_details['artist_name'])[0]))\n",
    "        rank_value+=1"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "nav_menu": {
    "height": "153px",
    "width": "160px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": true,
   "toc_position": {
    "height": "691px",
    "left": "0px",
    "right": "1405px",
    "top": "106px",
    "width": "303px"
   },
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}