Data_Exploration and Model_Based_Recommender.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie recommendation using MovieLens data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to ritu.tak@columbia.edu and will expire on November 01, 2018.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1510172966.log\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import graphlab\n",
    "from sklearn.model_selection import train_test_split,KFold\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing datasets from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "links = pd.read_csv(\"links.csv\")\n",
    "movies = pd.read_csv(\"movies.csv\")\n",
    "ratings = pd.read_csv(\"ratings.csv\")\n",
    "tags = pd.read_csv(\"tags.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Links dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "      <td>8844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "      <td>15602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "      <td>31357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "      <td>11862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the head of the links dataset (at movie ID level)\n",
    "links.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of links dataset = (9125, 3)\n"
     ]
    }
   ],
   "source": [
    "#checking the shape of the links dataset\n",
    "links_shape = links.shape\n",
    "print(\"Shape of links dataset = {}\".format(links_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of unique movieIds in links dataset = 9125\n"
     ]
    }
   ],
   "source": [
    "#number of unique movie Ids in the links dataset\n",
    "uni_movieIds_links = links[\"movieId\"].nunique()\n",
    "print(\"No of unique movieIds in links dataset = {}\".format(uni_movieIds_links))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    False\n",
       "imdbId     False\n",
       "tmdbId      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if a column has null values \n",
    "links.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>720</td>\n",
       "      <td>118114</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>721</td>\n",
       "      <td>114103</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>769</td>\n",
       "      <td>116992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>1133</td>\n",
       "      <td>111357</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>2851</td>\n",
       "      <td>81454</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId  imdbId  tmdbId\n",
       "607       720  118114     NaN\n",
       "608       721  114103     NaN\n",
       "640       769  116992     NaN\n",
       "910      1133  111357     NaN\n",
       "2282     2851   81454     NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at rows with missing tmdbid\n",
    "links[links[\"tmdbId\"].isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 movies have missing tmdbId\n"
     ]
    }
   ],
   "source": [
    "#number of movies from which tmdbid is missing (13)\n",
    "missing_tmdbId = links.loc[links[\"tmdbId\"].isnull(), \"movieId\"].nunique()\n",
    "print(\"{} movies have missing tmdbId\".format(missing_tmdbId))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Replacing the missing TmdbId with Zero\n",
    "links.loc[(links.tmdbId.isnull()) , \"tmdbId\"] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the analysis above we can see that the links dataset contains three columns: movieId, tmdbId and imdbId for 9125 movies. Of the three columns tmdbId has missing values. The 13 movies for which tmdbId is missing, we replaced with it with zero for our analysis.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Movies Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the head of the movies dataset (at movie ID level)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of movies dataset = (9125, 3)\n"
     ]
    }
   ],
   "source": [
    "#checking the shape of the movies dataset\n",
    "movies_shape = movies.shape\n",
    "print(\"Shape of movies dataset = {}\".format(movies_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of unique movies in movies dataset = 9125\n"
     ]
    }
   ],
   "source": [
    "#Data is unique at movie Id level\n",
    "no_movies = movies[\"movieId\"].nunique()\n",
    "print(\"No of unique movies in movies dataset = {}\".format(no_movies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    False\n",
       "title      False\n",
       "genres     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for mising values \n",
    "movies.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 movies dont have genres listed\n"
     ]
    }
   ],
   "source": [
    "#checking if the genres have not been listed\n",
    "no_genre = movies.loc[movies.genres == \"(no genres listed)\"].movieId.count()\n",
    "print(\"{} movies dont have genres listed\".format(no_genre) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#creating a new dataset with genres split into different columns\n",
    "movies2 = pd.DataFrame(movies.genres.str.split('|').tolist(), columns = [\"genres1\",\"genres2\",\"genres3\",\"genres4\",\"genres5\",\"genres6\",\"genres7\",\"genres8\",\"genres9\",\"genres10\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>genres1</th>\n",
       "      <th>genres2</th>\n",
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       "      <th>genres10</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres    genres1    genres2  \\\n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  Adventure  Animation   \n",
       "1                   Adventure|Children|Fantasy  Adventure   Children   \n",
       "2                               Comedy|Romance     Comedy    Romance   \n",
       "3                         Comedy|Drama|Romance     Comedy      Drama   \n",
       "4                                       Comedy     Comedy       None   \n",
       "\n",
       "    genres3 genres4  genres5 genres6 genres7 genres8 genres9 genres10  \n",
       "0  Children  Comedy  Fantasy    None    None    None    None     None  \n",
       "1   Fantasy    None     None    None    None    None    None     None  \n",
       "2      None    None     None    None    None    None    None     None  \n",
       "3   Romance    None     None    None    None    None    None     None  \n",
       "4      None    None     None    None    None    None    None     None  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenating the two datasets to create the final movies dataset\n",
    "movies_new = pd.concat([movies,movies2],axis = 1)\n",
    "movies_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>genres1</th>\n",
       "      <th>genres2</th>\n",
       "      <th>genres3</th>\n",
       "      <th>genres4</th>\n",
       "      <th>genres5</th>\n",
       "      <th>genres6</th>\n",
       "      <th>genres7</th>\n",
       "      <th>genres8</th>\n",
       "      <th>genres9</th>\n",
       "      <th>genres10</th>\n",
       "      <th>Year_release</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres    genres1    genres2  \\\n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  Adventure  Animation   \n",
       "1                   Adventure|Children|Fantasy  Adventure   Children   \n",
       "2                               Comedy|Romance     Comedy    Romance   \n",
       "3                         Comedy|Drama|Romance     Comedy      Drama   \n",
       "4                                       Comedy     Comedy       None   \n",
       "\n",
       "    genres3 genres4  genres5 genres6 genres7 genres8 genres9 genres10  \\\n",
       "0  Children  Comedy  Fantasy    None    None    None    None     None   \n",
       "1   Fantasy    None     None    None    None    None    None     None   \n",
       "2      None    None     None    None    None    None    None     None   \n",
       "3   Romance    None     None    None    None    None    None     None   \n",
       "4      None    None     None    None    None    None    None     None   \n",
       "\n",
       "  Year_release  \n",
       "0         1995  \n",
       "1         1995  \n",
       "2         1995  \n",
       "3         1995  \n",
       "4         1995  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the year of release dataset from the title column\n",
    "movies_new[\"Year_release\"] = movies_new['title'].str.extract('(\\d\\d\\d\\d)', expand=True)\n",
    "movies_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId         False\n",
       "title           False\n",
       "genres          False\n",
       "genres1         False\n",
       "genres2          True\n",
       "genres3          True\n",
       "genres4          True\n",
       "genres5          True\n",
       "genres6          True\n",
       "genres7          True\n",
       "genres8          True\n",
       "genres9          True\n",
       "genres10         True\n",
       "Year_release     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_new.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 movies have missing year of release\n"
     ]
    }
   ],
   "source": [
    "#No of movies with missing year of release \n",
    "missing_year = movies_new.loc[movies_new['Year_release'].isnull(),'movieId'].nunique()\n",
    "print(\"{} movies have missing year of release\".format(missing_year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Adding the missing Year_release for 4 movies manually\n",
    "movies_new.loc[9017,\"Year_release\"] = \"2015\"\n",
    "movies_new.loc[9063,\"Year_release\"] = \"2016\"\n",
    "movies_new.loc[9118,\"Year_release\"] = \"2016\"\n",
    "movies_new.loc[9124,\"Year_release\"] = \"2014\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that movies dataset contains three columns: movieId, title and genre for 9125 unique movies. The genre is missing for 18 movies which is listed as \"(no genre listed)\". The genre is split into different columns and saved for analyses.\n",
    "The year of release is missing for 4 movies which we inserted in the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging movies and links data - Movie Level Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of movies_links dataset = (9125, 16)\n"
     ]
    }
   ],
   "source": [
    "#merging the the links and movies datasets as both are on movie ID level\n",
    "movies_links = pd.merge(movies_new,links, on = \"movieId\")\n",
    "movies_links.head()\n",
    "movies_links.shape\n",
    "movies_links_shape = movies_links.shape\n",
    "print(\"Shape of movies_links dataset = {}\".format(movies_links_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId         False\n",
       "title           False\n",
       "genres          False\n",
       "genres1         False\n",
       "genres2          True\n",
       "genres3          True\n",
       "genres4          True\n",
       "genres5          True\n",
       "genres6          True\n",
       "genres7          True\n",
       "genres8          True\n",
       "genres9          True\n",
       "genres10         True\n",
       "Year_release    False\n",
       "imdbId          False\n",
       "tmdbId          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_links.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to dh2863@columbia.edu and will expire on November 02, 2018.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\\Users\\hp\\AppData\\Local\\Temp\\graphlab_server_1509991816.log.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\movies_links.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\movies_links.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.086417 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.086417 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[long,long,str,str,str,str,str,str,str,str,str,str,str,str,long,long,float]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\movies_links.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\movies_links.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 9125 lines in 0.053039 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 9125 lines in 0.053039 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#storing movie and links data as Sframe\n",
    "movies_links.to_csv(\"movies_links.csv\")\n",
    "movie_metadata = graphlab.SFrame.read_csv('movies_links.csv' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Ratings Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the head of the ratings data (unique at userID, movieID, timestamp level)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100004, 4) is the shape of ratings data\n"
     ]
    }
   ],
   "source": [
    "#checking the shape of the ratings data\n",
    "ratings_shape = ratings.shape\n",
    "print(\"{} is the shape of ratings data\".format(ratings_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       False\n",
       "movieId      False\n",
       "rating       False\n",
       "timestamp    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No missing entries in ratings data\n",
    "ratings.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId         671\n",
      "movieId       9066\n",
      "rating          10\n",
      "timestamp    78141\n",
      "dtype: int64\n",
      "No of unique users who have rated a movie = 671 \n",
      "No of unique movies that have been rated = 9066 \n",
      "No of unique values of ratings = 10 \n"
     ]
    }
   ],
   "source": [
    "#No of unique users 671\n",
    "m = ratings.nunique()\n",
    "uni_user = m[0]\n",
    "uni_movies = m[1]\n",
    "uni_ratings = m[2]\n",
    "print(\"No of unique users who have rated a movie = {} \".format(uni_user))\n",
    "print(\"No of unique movies that have been rated = {} \".format(uni_movies))\n",
    "print(\"No of unique values of ratings = {} \".format(uni_ratings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values of ratings =  [ 0.5  1.   1.5  2.   2.5  3.   3.5  4.   4.5  5. ]\n"
     ]
    }
   ],
   "source": [
    "#unique values of ratings \n",
    "k = ratings['rating'].unique()\n",
    "k = np.sort(k, kind= \"quicksort\")\n",
    "print(\"Values of ratings =  {}\".format(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2cJt3yDpW5ImSdop3yZJ+hbwuxbmUdOuz0invQ7Q3v1FWz8jQ/qM+hYYI+ks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\nmJB8S0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19f15050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#overall distribution of ratings \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "count_ratings = pd.value_counts(ratings['rating'], sort = False).sort_index()\n",
    "count_ratings.plot(kind = 'bar')\n",
    "plt.rc(\"font\",size=15)\n",
    "plt.title(\"Ratings Distribution\")\n",
    "plt.xlabel(\"Rating\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    100004\n",
       "Name: userId, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Every customer has rated a movie only once\n",
    "ratings.groupby(['userId','movieId']).userId.count().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1970-01-15 14:12:39.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1970-01-15 14:12:39.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1970-01-15 14:12:39.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1970-01-15 14:12:39.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1970-01-15 14:12:39.205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating               timestamp\n",
       "0       1       31     2.5 1970-01-15 14:12:39.144\n",
       "1       1     1029     3.0 1970-01-15 14:12:39.179\n",
       "2       1     1061     3.0 1970-01-15 14:12:39.182\n",
       "3       1     1129     2.0 1970-01-15 14:12:39.185\n",
       "4       1     1172     4.0 1970-01-15 14:12:39.205"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting time since epoch to datetime \n",
    "ratings[\"timestamp\"] = pd.to_datetime(ratings['timestamp'], unit='ms')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratings data consists of 671 users who have rated a total of 9066 movies. The rating is in a range of 0.5 to 5 with an increment of 0.5. Each user has rated a particular movie only once. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Tags data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>339</td>\n",
       "      <td>sandra 'boring' bullock</td>\n",
       "      <td>1138537770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>1955</td>\n",
       "      <td>dentist</td>\n",
       "      <td>1193435061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>7478</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>1170560997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>32892</td>\n",
       "      <td>Russian</td>\n",
       "      <td>1170626366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>34162</td>\n",
       "      <td>forgettable</td>\n",
       "      <td>1141391765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId                      tag   timestamp\n",
       "0      15      339  sandra 'boring' bullock  1138537770\n",
       "1      15     1955                  dentist  1193435061\n",
       "2      15     7478                 Cambodia  1170560997\n",
       "3      15    32892                  Russian  1170626366\n",
       "4      15    34162              forgettable  1141391765"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking tags data \n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
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       "    }\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>339</td>\n",
       "      <td>sandra 'boring' bullock</td>\n",
       "      <td>1970-01-14 04:15:37.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>1955</td>\n",
       "      <td>dentist</td>\n",
       "      <td>1970-01-14 19:30:35.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>7478</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>1970-01-14 13:09:20.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>32892</td>\n",
       "      <td>Russian</td>\n",
       "      <td>1970-01-14 13:10:26.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>34162</td>\n",
       "      <td>forgettable</td>\n",
       "      <td>1970-01-14 05:03:11.765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId                      tag               timestamp\n",
       "0      15      339  sandra 'boring' bullock 1970-01-14 04:15:37.770\n",
       "1      15     1955                  dentist 1970-01-14 19:30:35.061\n",
       "2      15     7478                 Cambodia 1970-01-14 13:09:20.997\n",
       "3      15    32892                  Russian 1970-01-14 13:10:26.366\n",
       "4      15    34162              forgettable 1970-01-14 05:03:11.765"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting time since epoch to datetime \n",
    "tags[\"timestamp\"] = pd.to_datetime(tags['timestamp'], unit='ms')\n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of tags dataset = (1296, 4)\n"
     ]
    }
   ],
   "source": [
    "#shape of tags data\n",
    "tags_shape = tags.shape\n",
    "print(\"Shape of tags dataset = {}\".format(tags_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of unique users who have tagged a movie = 61 \n",
      "No of unique movies that have been tagged = 689 \n",
      "No of unique values of tags = 582 \n"
     ]
    }
   ],
   "source": [
    "#No of unique values in tags dataset\n",
    "m = tags.nunique()\n",
    "uni_user = m[0]\n",
    "uni_movies = m[1]\n",
    "uni_tags = m[2]\n",
    "print(\"No of unique users who have tagged a movie = {} \".format(uni_user))\n",
    "print(\"No of unique movies that have been tagged = {} \".format(uni_movies))\n",
    "print(\"No of unique values of tags = {} \".format(uni_tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       False\n",
       "movieId      False\n",
       "tag          False\n",
       "timestamp    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking Null values in the tags dataset\n",
    "tags.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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></th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">138</th>\n",
       "      <th>48780</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79132</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109487</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">212</th>\n",
       "      <th>64957</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66097</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tag  timestamp\n",
       "userId movieId                \n",
       "138    48780      6          6\n",
       "       79132      7          7\n",
       "       109487    12         12\n",
       "212    64957      8          8\n",
       "       66097      7          7"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at data with more than 5 tags for a movie\n",
    "df = tags.groupby(['userId','movieId']).count()\n",
    "df.loc[df['tag'] > 5].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#creating combined tag for a particular user movie combination\n",
    "tags_new =tags.groupby(['userId','movieId'], as_index=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>339</td>\n",
       "      <td>sandra 'boring' bullock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>1955</td>\n",
       "      <td>dentist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>7478</td>\n",
       "      <td>Cambodia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>32892</td>\n",
       "      <td>Russian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>34162</td>\n",
       "      <td>forgettable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId                      tag\n",
       "0      15      339  sandra 'boring' bullock\n",
       "1      15     1955                  dentist\n",
       "2      15     7478                 Cambodia\n",
       "3      15    32892                  Russian\n",
       "4      15    34162              forgettable"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of tags dataset = (772, 3)\n"
     ]
    }
   ],
   "source": [
    "#New shape of tags dataset\n",
    "tags_shape = tags_new.shape\n",
    "print(\"shape of tags dataset = {}\".format(tags_shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging ratings and tags data - User level Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merging the ratings and tags data : information at user level\n",
    "ratings_tags = pd.merge(ratings,tags_new[['userId','movieId','tag']],how = 'left', left_on = ['userId','movieId'], right_on = ['userId','movieId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of ratings_tags dataset = (100004, 5)\n"
     ]
    }
   ],
   "source": [
    "#shape of the new ratings _tags dataset\n",
    "ratings_tags_shape = ratings_tags.shape\n",
    "print(\"shape of ratings_tags dataset = {}\".format(ratings_tags_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>15</td>\n",
       "      <td>339</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1970-01-13 23:49:36.622</td>\n",
       "      <td>sandra 'boring' bullock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId  rating               timestamp                      tag\n",
       "1048      15      339     2.5 1970-01-13 23:49:36.622  sandra 'boring' bullock"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking data for a particular entry\n",
    "ratings_tags.loc[(ratings_tags.userId == 15) & (ratings_tags.movieId == 339)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\ratings_tags.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\ratings_tags.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.1351 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.1351 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[long,long,long,float,str,str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\ratings_tags.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\ratings_tags.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100004 lines in 0.148604 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100004 lines in 0.148604 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#storing ratings and tags data as Sframe\n",
    "ratings_tags.to_csv(\"ratings_tags.csv\")\n",
    "user_metadata = graphlab.SFrame.read_csv('ratings_tags.csv' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining all datasets to create the final data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Including the movie information to the ratings_tags data\n",
    "all_data = pd.merge(ratings_tags, movies_links,how = 'left', left_on = ['movieId'], right_on = ['movieId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>tag</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>genres1</th>\n",
       "      <th>genres2</th>\n",
       "      <th>genres3</th>\n",
       "      <th>genres4</th>\n",
       "      <th>genres5</th>\n",
       "      <th>genres6</th>\n",
       "      <th>genres7</th>\n",
       "      <th>genres8</th>\n",
       "      <th>genres9</th>\n",
       "      <th>genres10</th>\n",
       "      <th>Year_release</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1970-01-15 14:12:39.144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1995</td>\n",
       "      <td>112792</td>\n",
       "      <td>9909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1970-01-15 14:12:39.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dumbo (1941)</td>\n",
       "      <td>Animation|Children|Drama|Musical</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Musical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1941</td>\n",
       "      <td>33563</td>\n",
       "      <td>11360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1970-01-15 14:12:39.182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleepers (1996)</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1996</td>\n",
       "      <td>117665</td>\n",
       "      <td>819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1970-01-15 14:12:39.185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Escape from New York (1981)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1981</td>\n",
       "      <td>82340</td>\n",
       "      <td>1103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1970-01-15 14:12:39.205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cinema Paradiso (Nuovo cinema Paradiso) (1989)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1989</td>\n",
       "      <td>95765</td>\n",
       "      <td>11216.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating               timestamp  tag  \\\n",
       "0       1       31     2.5 1970-01-15 14:12:39.144  NaN   \n",
       "1       1     1029     3.0 1970-01-15 14:12:39.179  NaN   \n",
       "2       1     1061     3.0 1970-01-15 14:12:39.182  NaN   \n",
       "3       1     1129     2.0 1970-01-15 14:12:39.185  NaN   \n",
       "4       1     1172     4.0 1970-01-15 14:12:39.205  NaN   \n",
       "\n",
       "                                            title  \\\n",
       "0                          Dangerous Minds (1995)   \n",
       "1                                    Dumbo (1941)   \n",
       "2                                 Sleepers (1996)   \n",
       "3                     Escape from New York (1981)   \n",
       "4  Cinema Paradiso (Nuovo cinema Paradiso) (1989)   \n",
       "\n",
       "                             genres    genres1    genres2 genres3   genres4  \\\n",
       "0                             Drama      Drama       None    None      None   \n",
       "1  Animation|Children|Drama|Musical  Animation   Children   Drama   Musical   \n",
       "2                          Thriller   Thriller       None    None      None   \n",
       "3  Action|Adventure|Sci-Fi|Thriller     Action  Adventure  Sci-Fi  Thriller   \n",
       "4                             Drama      Drama       None    None      None   \n",
       "\n",
       "  genres5 genres6 genres7 genres8 genres9 genres10 Year_release  imdbId  \\\n",
       "0    None    None    None    None    None     None         1995  112792   \n",
       "1    None    None    None    None    None     None         1941   33563   \n",
       "2    None    None    None    None    None     None         1996  117665   \n",
       "3    None    None    None    None    None     None         1981   82340   \n",
       "4    None    None    None    None    None     None         1989   95765   \n",
       "\n",
       "    tmdbId  \n",
       "0   9909.0  \n",
       "1  11360.0  \n",
       "2    819.0  \n",
       "3   1103.0  \n",
       "4  11216.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the head of the combined data\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of combined dataset = (100004, 20)\n"
     ]
    }
   ],
   "source": [
    "#shape of the combined data\n",
    "all_data_shape = all_data.shape\n",
    "print(\"shape of combined dataset = {}\".format(all_data_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#converting the tags column to string type\n",
    "all_data['tag'] = all_data['tag'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId                   int64\n",
       "movieId                  int64\n",
       "rating                 float64\n",
       "timestamp       datetime64[ns]\n",
       "tag                     object\n",
       "title                   object\n",
       "genres                  object\n",
       "genres1                 object\n",
       "genres2                 object\n",
       "genres3                 object\n",
       "genres4                 object\n",
       "genres5                 object\n",
       "genres6                 object\n",
       "genres7                 object\n",
       "genres8                 object\n",
       "genres9                 object\n",
       "genres10                object\n",
       "Year_release            object\n",
       "imdbId                   int64\n",
       "tmdbId                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking data type of all columns \n",
    "all_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model based recommendation with Graphlabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#splitting the data into test and train 80/20\n",
    "train_data, test_data  = train_test_split(all_data , test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train dataset = (80003, 20)\n"
     ]
    }
   ],
   "source": [
    "#shape of train data\n",
    "train_shape = train_data.shape\n",
    "print(\"shape of train dataset = {}\".format(train_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of unique users who have rated a movie = 671 \n",
      "No of unique movies that have been rated = 8399 \n",
      "No of unique values of ratings = 10 \n"
     ]
    }
   ],
   "source": [
    "#No of unique values in train data\n",
    "m = train_data.nunique()\n",
    "uni_user = m[0]\n",
    "uni_movies = m[1]\n",
    "uni_ratings = m[2]\n",
    "print(\"No of unique users who have rated a movie = {} \".format(uni_user))\n",
    "print(\"No of unique movies that have been rated = {} \".format(uni_movies))\n",
    "print(\"No of unique values of ratings = {} \".format(uni_ratings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train dataset = (20001, 20)\n"
     ]
    }
   ],
   "source": [
    "#shape of test dataset\n",
    "test_shape = test_data.shape\n",
    "print(\"shape of train dataset = {}\".format(test_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of unique users who have rated a movie = 671 \n",
      "No of unique movies that have been rated = 4901 \n",
      "No of unique values of ratings = 10 \n"
     ]
    }
   ],
   "source": [
    "#No of unique values in test data\n",
    "m = test_data.nunique()\n",
    "uni_user = m[0]\n",
    "uni_movies = m[1]\n",
    "uni_ratings = m[2]\n",
    "print(\"No of unique users who have rated a movie = {} \".format(uni_user))\n",
    "print(\"No of unique movies that have been rated = {} \".format(uni_movies))\n",
    "print(\"No of unique values of ratings = {} \".format(uni_ratings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80003, 20)\n",
      "(20001, 20)\n"
     ]
    }
   ],
   "source": [
    "#Keeping a copy of the test and train dataframes to use for sampling later\n",
    "trainData = train_data\n",
    "testData = test_data\n",
    "#verifying\n",
    "print(trainData.shape)\n",
    "print(testData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#exporting the test and train dataset to csv \n",
    "train_data.to_csv('train_data.csv')\n",
    "test_data.to_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 80003 lines in 0.425811 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 80003 lines in 0.425811 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\test_data.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\test_data.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 20001 lines in 0.111292 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 20001 lines in 0.111292 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#converting the test and train dataset to Sframe\n",
    "hints = [int,int ,int ,float,str, str, str, str, str, str,str,str,str,str,str,str, str,str,float, int, float]\n",
    "train = graphlab.SFrame.read_csv('train_data.csv',column_type_hints = hints )\n",
    "test = graphlab.SFrame.read_csv('test_data.csv',column_type_hints = hints )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the follwoing three approaches to solve the regression.\n",
    "* Adaptive Gradient Stochastic Gradient Descent\n",
    "* Stochastic Gradient Descent\n",
    "* Alternating Least Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.817622s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.817622s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | 0.142317                                 |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | 0.142317                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0381098         | 0.209166                                 |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0381098         | 0.209166                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0190549         | 0.345443                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0190549         | 0.345443                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.0762195         | 0.142317                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.0762195         | 0.142317                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 501us        | 1.13362           | 1.06471               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 501us        | 1.13362           | 1.06471               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 289.214ms    | 0.90004           | 0.948688              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 1       | 289.214ms    | 0.90004           | 0.948688              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 621.951ms    | 0.579026          | 0.760916              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 2       | 621.951ms    | 0.579026          | 0.760916              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 888.638ms    | 0.440058          | 0.66334               | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 3       | 888.638ms    | 0.440058          | 0.66334               | 0.0762195   |"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1.13s        | 0.356746          | 0.597246              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 4       | 1.13s        | 0.356746          | 0.597246              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.45s        | 0.29896           | 0.546728              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.45s        | 0.29896           | 0.546728              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.71s        | 0.253861          | 0.503794              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.71s        | 0.253861          | 0.503794              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.83s        | 0.149613          | 0.386709              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.83s        | 0.149613          | 0.386709              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 3.14s        | 0.133534          | 0.365324              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 11      | 3.14s        | 0.133534          | 0.365324              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 4.30s        | 0.0880508         | 0.296594              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 15      | 4.30s        | 0.0880508         | 0.296594              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 5.71s        | 0.0578569         | 0.240344              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 20      | 5.71s        | 0.0578569         | 0.240344              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 7.15s        | 0.0416145         | 0.203756              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 25      | 7.15s        | 0.0416145         | 0.203756              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 8.26s        | 0.0326064         | 0.180289              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 30      | 8.26s        | 0.0326064         | 0.180289              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 9.31s        | 0.0262197         | 0.161599              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 35      | 9.31s        | 0.0262197         | 0.161599              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 10.29s       | 0.0221742         | 0.148548              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 40      | 10.29s       | 0.0221742         | 0.148548              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 11.30s       | 0.0192449         | 0.13833               | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 45      | 11.30s       | 0.0192449         | 0.13833               | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 12.28s       | 0.0170617         | 0.130195              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 50      | 12.28s       | 0.0170617         | 0.130195              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.0196614</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.0196614"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.139823</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.139823"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using adaptive gradient stochastic descent \n",
    "m1 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', \n",
    "                                               item_id='movieId',solver= \"adagrad\" , user_data = user_metadata, \n",
    "                                               item_data = movie_metadata, num_factors=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.828639s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.828639s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.748272                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.748272                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.801807                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.801807                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.862337                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.862337                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.922701                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.922701                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.748272                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.748272                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.13364           | 1.06472               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.13364           | 1.06472               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 296.716ms    | DIVERGED          | DIVERGED              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 296.716ms    | DIVERGED          | DIVERGED              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 376.272ms    | 1.13351           | 1.06466               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 376.272ms    | 1.13351           | 1.06466               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 653.469ms    | DIVERGED          | DIVERGED              | 0.00238186  |</pre>"
      ],
      "text/plain": [
       "| 1       | 653.469ms    | DIVERGED          | DIVERGED              | 0.00238186  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 744.033ms    | 1.13357           | 1.06469               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 744.033ms    | 1.13357           | 1.06469               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 940.171ms    | 1.03667           | 1.01817               | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 940.171ms    | 1.03667           | 1.01817               | 0.00119093  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.09s        | 0.869429          | 0.932432              | 0.000708131 |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.09s        | 0.869429          | 0.932432              | 0.000708131 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1.27s        | 0.83924           | 0.9161                | 0.000522451 |</pre>"
      ],
      "text/plain": [
       "| 3       | 1.27s        | 0.83924           | 0.9161                | 0.000522451 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1.53s        | 0.805275          | 0.897371              | 0.000421057 |</pre>"
      ],
      "text/plain": [
       "| 4       | 1.53s        | 0.805275          | 0.897371              | 0.000421057 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.77s        | 0.805123          | 0.897287              | 0.000356171 |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.77s        | 0.805123          | 0.897287              | 0.000356171 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.95s        | 0.834335          | 0.913419              | 0.000310651 |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.95s        | 0.834335          | 0.913419              | 0.000310651 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 2.35s        | 0.772065          | 0.878672              | 0.000177033 |</pre>"
      ],
      "text/plain": [
       "| 8       | 2.35s        | 0.772065          | 0.878672              | 0.000177033 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.97s        | 0.751733          | 0.867025              |             |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.97s        | 0.751733          | 0.867025              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 13      | 3.35s        | 0.787718          | 0.887535              |             |</pre>"
      ],
      "text/plain": [
       "| 13      | 3.35s        | 0.787718          | 0.887535              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 18      | 4.34s        | 0.86531           | 0.93022               |             |</pre>"
      ],
      "text/plain": [
       "| 18      | 4.34s        | 0.86531           | 0.93022               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 23      | 5.25s        | 0.881704          | 0.938991              |             |</pre>"
      ],
      "text/plain": [
       "| 23      | 5.25s        | 0.881704          | 0.938991              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 28      | 5.95s        | 0.883365          | 0.939875              |             |</pre>"
      ],
      "text/plain": [
       "| 28      | 5.95s        | 0.883365          | 0.939875              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 33      | 6.77s        | 0.883773          | 0.940092              |             |</pre>"
      ],
      "text/plain": [
       "| 33      | 6.77s        | 0.883773          | 0.940092              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 38      | 7.46s        | 0.883501          | 0.939947              |             |</pre>"
      ],
      "text/plain": [
       "| 38      | 7.46s        | 0.883501          | 0.939947              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 43      | 8.25s        | 0.883492          | 0.939942              |             |</pre>"
      ],
      "text/plain": [
       "| 43      | 8.25s        | 0.883492          | 0.939942              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 48      | 9.00s        | 0.883113          | 0.939741              | 2.8691e-009 |</pre>"
      ],
      "text/plain": [
       "| 48      | 9.00s        | 0.883113          | 0.939741              | 2.8691e-009 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.882943</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.882943"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.93965</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.93965"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using stochastic gradient descent \n",
    "m2 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId', \n",
    "                                               user_data = user_metadata, item_data = movie_metadata ,\n",
    "                                               solver= \"sgd\" , num_factors=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Warning: This solver currently does not support side features. Proceeding with training without side features.</pre>"
      ],
      "text/plain": [
       "Warning: This solver currently does not support side features. Proceeding with training without side features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 547 users and 7950 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 547 users and 7950 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.540398s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.540398s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                    | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                    | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                  | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                  | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization               | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization               | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations               | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations               | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                       | Solver used for training                         | als      |</pre>"
      ],
      "text/plain": [
       "| solver                       | Solver used for training                         | als      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed time | RMSE                   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed time | RMSE                   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | NA                     |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | NA                     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 160.124ms    | 0.868927               |</pre>"
      ],
      "text/plain": [
       "| 0       | 160.124ms    | 0.868927               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 234.176ms    | 0.828776               |</pre>"
      ],
      "text/plain": [
       "| 1       | 234.176ms    | 0.828776               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 318.736ms    | 0.809089               |</pre>"
      ],
      "text/plain": [
       "| 2       | 318.736ms    | 0.809089               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 397.292ms    | 0.796711               |</pre>"
      ],
      "text/plain": [
       "| 3       | 397.292ms    | 0.796711               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 479.351ms    | 0.786859               |</pre>"
      ],
      "text/plain": [
       "| 4       | 479.351ms    | 0.786859               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 561.407ms    | 0.777935               |</pre>"
      ],
      "text/plain": [
       "| 5       | 561.407ms    | 0.777935               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 634.46ms     | 0.770774               |</pre>"
      ],
      "text/plain": [
       "| 6       | 634.46ms     | 0.770774               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 718.019ms    | 0.764788               |</pre>"
      ],
      "text/plain": [
       "| 7       | 718.019ms    | 0.764788               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 803.08ms     | 0.759848               |</pre>"
      ],
      "text/plain": [
       "| 8       | 803.08ms     | 0.759848               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9       | 908.153ms    | 0.756008               |</pre>"
      ],
      "text/plain": [
       "| 9       | 908.153ms    | 0.756008               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 1.01s        | 0.752738               |</pre>"
      ],
      "text/plain": [
       "| 10      | 1.01s        | 0.752738               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 1.09s        | 0.749811               |</pre>"
      ],
      "text/plain": [
       "| 11      | 1.09s        | 0.749811               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12      | 1.18s        | 0.747065               |</pre>"
      ],
      "text/plain": [
       "| 12      | 1.18s        | 0.747065               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 13      | 1.26s        | 0.744589               |</pre>"
      ],
      "text/plain": [
       "| 13      | 1.26s        | 0.744589               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 14      | 1.34s        | 0.742308               |</pre>"
      ],
      "text/plain": [
       "| 14      | 1.34s        | 0.742308               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 1.42s        | 0.740174               |</pre>"
      ],
      "text/plain": [
       "| 15      | 1.42s        | 0.740174               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 16      | 1.51s        | 0.738229               |</pre>"
      ],
      "text/plain": [
       "| 16      | 1.51s        | 0.738229               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 17      | 1.61s        | 0.736685               |</pre>"
      ],
      "text/plain": [
       "| 17      | 1.61s        | 0.736685               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 18      | 1.71s        | 0.735423               |</pre>"
      ],
      "text/plain": [
       "| 18      | 1.71s        | 0.735423               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 19      | 1.80s        | 0.734196               |</pre>"
      ],
      "text/plain": [
       "| 19      | 1.80s        | 0.734196               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 1.88s        | 0.733147               |</pre>"
      ],
      "text/plain": [
       "| 20      | 1.88s        | 0.733147               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 21      | 1.95s        | 0.732162               |</pre>"
      ],
      "text/plain": [
       "| 21      | 1.95s        | 0.732162               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 22      | 2.04s        | 0.731219               |</pre>"
      ],
      "text/plain": [
       "| 22      | 2.04s        | 0.731219               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 23      | 2.12s        | 0.730364               |</pre>"
      ],
      "text/plain": [
       "| 23      | 2.12s        | 0.730364               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 24      | 2.22s        | 0.729586               |</pre>"
      ],
      "text/plain": [
       "| 24      | 2.22s        | 0.729586               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 2.33s        | 0.728882               |</pre>"
      ],
      "text/plain": [
       "| 25      | 2.33s        | 0.728882               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 26      | 2.43s        | 0.728234               |</pre>"
      ],
      "text/plain": [
       "| 26      | 2.43s        | 0.728234               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 27      | 2.51s        | 0.727602               |</pre>"
      ],
      "text/plain": [
       "| 27      | 2.51s        | 0.727602               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 28      | 2.59s        | 0.727009               |</pre>"
      ],
      "text/plain": [
       "| 28      | 2.59s        | 0.727009               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 29      | 2.67s        | 0.726458               |</pre>"
      ],
      "text/plain": [
       "| 29      | 2.67s        | 0.726458               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 2.76s        | 0.725954               |</pre>"
      ],
      "text/plain": [
       "| 30      | 2.76s        | 0.725954               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 31      | 2.86s        | 0.725493               |</pre>"
      ],
      "text/plain": [
       "| 31      | 2.86s        | 0.725493               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 32      | 2.96s        | 0.725027               |</pre>"
      ],
      "text/plain": [
       "| 32      | 2.96s        | 0.725027               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 33      | 3.06s        | 0.724625               |</pre>"
      ],
      "text/plain": [
       "| 33      | 3.06s        | 0.724625               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 34      | 3.13s        | 0.724242               |</pre>"
      ],
      "text/plain": [
       "| 34      | 3.13s        | 0.724242               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 3.21s        | 0.723861               |</pre>"
      ],
      "text/plain": [
       "| 35      | 3.21s        | 0.723861               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 36      | 3.30s        | 0.723487               |</pre>"
      ],
      "text/plain": [
       "| 36      | 3.30s        | 0.723487               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 37      | 3.39s        | 0.723113               |</pre>"
      ],
      "text/plain": [
       "| 37      | 3.39s        | 0.723113               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 38      | 3.47s        | 0.72276                |</pre>"
      ],
      "text/plain": [
       "| 38      | 3.47s        | 0.72276                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 39      | 3.56s        | 0.722421               |</pre>"
      ],
      "text/plain": [
       "| 39      | 3.56s        | 0.722421               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 3.64s        | 0.722084               |</pre>"
      ],
      "text/plain": [
       "| 40      | 3.64s        | 0.722084               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 41      | 3.72s        | 0.721749               |</pre>"
      ],
      "text/plain": [
       "| 41      | 3.72s        | 0.721749               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 42      | 3.80s        | 0.721404               |</pre>"
      ],
      "text/plain": [
       "| 42      | 3.80s        | 0.721404               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 43      | 3.89s        | 0.721039               |</pre>"
      ],
      "text/plain": [
       "| 43      | 3.89s        | 0.721039               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 44      | 3.99s        | 0.720682               |</pre>"
      ],
      "text/plain": [
       "| 44      | 3.99s        | 0.720682               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 4.08s        | 0.720287               |</pre>"
      ],
      "text/plain": [
       "| 45      | 4.08s        | 0.720287               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 46      | 4.15s        | 0.719848               |</pre>"
      ],
      "text/plain": [
       "| 46      | 4.15s        | 0.719848               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 47      | 4.23s        | 0.719476               |</pre>"
      ],
      "text/plain": [
       "| 47      | 4.23s        | 0.719476               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 48      | 4.31s        | 0.719109               |</pre>"
      ],
      "text/plain": [
       "| 48      | 4.31s        | 0.719109               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 49      | 4.39s        | 0.718778               |</pre>"
      ],
      "text/plain": [
       "| 49      | 4.39s        | 0.718778               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| FINAL   | 4.39s        | 0.718778               |</pre>"
      ],
      "text/plain": [
       "| FINAL   | 4.39s        | 0.718778               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Implicit Alternating Least Squares \n",
    "m3 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"als\" ,user_data = user_metadata,\n",
    "                                               item_data = movie_metadata , num_factors=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the performance of the three models on the test data, to finalize the approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Evaluate model M0\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 9.75609756098e-06 |\n",
      "|   3    | 0.00266666666667 | 9.75609756098e-06 |\n",
      "|   4    |      0.006       | 0.000248083623693 |\n",
      "|   5    |      0.0048      | 0.000248083623693 |\n",
      "|   6    |      0.004       | 0.000248083623693 |\n",
      "|   7    | 0.00342857142857 | 0.000248083623693 |\n",
      "|   8    |      0.004       | 0.000257839721254 |\n",
      "|   9    | 0.00355555555556 | 0.000257839721254 |\n",
      "|   10   |      0.0032      | 0.000257839721254 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-----------------+-------------------+\n",
      "| cutoff |  mean_precision |    mean_recall    |\n",
      "+--------+-----------------+-------------------+\n",
      "|   1    |       0.0       |        0.0        |\n",
      "|   2    |      0.008      | 0.000425651867512 |\n",
      "|   3    | 0.0186666666667 | 0.000654588427343 |\n",
      "|   4    |      0.018      | 0.000794792598691 |\n",
      "|   5    |      0.016      | 0.000799403549699 |\n",
      "|   6    | 0.0133333333333 | 0.000799403549699 |\n",
      "|   7    | 0.0137142857143 |  0.00103773107583 |\n",
      "|   8    |      0.012      |  0.00103773107583 |\n",
      "|   9    | 0.0115555555556 |  0.00104748717339 |\n",
      "|   10   |      0.0104     |  0.00104748717339 |\n",
      "+--------+-----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M2\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 4.61095100865e-06 |\n",
      "|   3    | 0.00533333333333 | 9.22190201729e-06 |\n",
      "|   4    |      0.008       | 2.92471112148e-05 |\n",
      "|   5    |      0.0064      | 2.92471112148e-05 |\n",
      "|   6    |      0.008       | 8.03696901304e-05 |\n",
      "|   7    |      0.008       |  8.4980641139e-05 |\n",
      "|   8    |      0.007       |  8.4980641139e-05 |\n",
      "|   9    | 0.00622222222222 |  8.4980641139e-05 |\n",
      "|   10   |      0.0064      | 8.95915921477e-05 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "Model compare metric: precision_recall\n"
     ]
    }
   ],
   "source": [
    "model_comp1 = graphlab.compare(test, [m1,m2,m3],target='rating')\n",
    "model_comp1_df = model_comp1.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the models with varying solver paramaters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Evaluate model M0\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 9.75609756098e-06 |\n",
      "|   3    | 0.00266666666667 | 9.75609756098e-06 |\n",
      "|   4    |      0.006       | 0.000248083623693 |\n",
      "|   5    |      0.0048      | 0.000248083623693 |\n",
      "|   6    |      0.004       | 0.000248083623693 |\n",
      "|   7    | 0.00342857142857 | 0.000248083623693 |\n",
      "|   8    |      0.004       | 0.000257839721254 |\n",
      "|   9    | 0.00355555555556 | 0.000257839721254 |\n",
      "|   10   |      0.0032      | 0.000257839721254 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-----------------+-------------------+\n",
      "| cutoff |  mean_precision |    mean_recall    |\n",
      "+--------+-----------------+-------------------+\n",
      "|   1    |       0.0       |        0.0        |\n",
      "|   2    |      0.008      | 0.000425651867512 |\n",
      "|   3    | 0.0186666666667 | 0.000654588427343 |\n",
      "|   4    |      0.018      | 0.000794792598691 |\n",
      "|   5    |      0.016      | 0.000799403549699 |\n",
      "|   6    | 0.0133333333333 | 0.000799403549699 |\n",
      "|   7    | 0.0137142857143 |  0.00103773107583 |\n",
      "|   8    |      0.012      |  0.00103773107583 |\n",
      "|   9    | 0.0115555555556 |  0.00104748717339 |\n",
      "|   10   |      0.0104     |  0.00104748717339 |\n",
      "+--------+-----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M2\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 4.61095100865e-06 |\n",
      "|   3    | 0.00533333333333 | 9.22190201729e-06 |\n",
      "|   4    |      0.008       | 2.92471112148e-05 |\n",
      "|   5    |      0.0064      | 2.92471112148e-05 |\n",
      "|   6    |      0.008       | 8.03696901304e-05 |\n",
      "|   7    |      0.008       |  8.4980641139e-05 |\n",
      "|   8    |      0.007       |  8.4980641139e-05 |\n",
      "|   9    | 0.00622222222222 |  8.4980641139e-05 |\n",
      "|   10   |      0.0064      | 8.95915921477e-05 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "Model compare metric: precision_recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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On4dS50gDRWNyYq/1GDKt+rINQWkxnDp05ss/2x4Ayh6nDoGtwj0BgiOgeSdo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\nicLMzAo5UZiZWSEnCjMzK/QfjImTaUGynQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9381748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_comp1 = graphlab.compare(test, [m1,m2,m3],target='rating')\n",
    "model_comp1_df = model_comp1.to_dataframe()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "for i in range(0,len(model_comp1_df.index)):\n",
    "    xy_dict= model_comp1_df.loc[i,\"results\"]['precision_recall']\n",
    "    xy = pd.DataFrame(xy_dict)        \n",
    "    if i ==0 :\n",
    "        label1= \"adaptive gradient decent\"\n",
    "    if i ==1 :\n",
    "        label1= \"stochastic gradient descent\"\n",
    "    if i==2 :\n",
    "        label1= \"alternating least squares\"\n",
    "        \n",
    "    xy.plot(x='recall',y= 'precision',ax=ax,label=label1)  \n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         model      rmse\n",
      "0    adaptive gradient descent  0.997656\n",
      "1  stochastic gradient descent  1.379256\n",
      "2    alternating least squares  5.258273\n"
     ]
    },
    {
     "data": {
      "image/png": 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IaG3aVcbYvHlUVdeQn5tN9/atgi5JmjiFroiE0o69FYzLm8e23RVMn5RNv4yUoEuSENC1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\nCl0REZEoUeiKiIhEyf8H4vfHw0en3wQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26fa9ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_comp1 =graphlab.recommender.util.compare_models(test, [m1,m2,m3], model_names=None,\n",
    "                                         user_sample=1.0, metric='rmse',\n",
    "                                         target='rating', exclude_known_for_precision_recall=True, \n",
    "                                         make_plot=False, verbose=False)\n",
    "rmse_data = {'model':['adaptive gradient descent','stochastic gradient descent','alternating least squares'], 'rmse':[]}\n",
    "for i in range(0,len(model_comp1)):\n",
    "    rmse_data['rmse'].append(model_comp1[i]['rmse_overall'])\n",
    "    \n",
    "\n",
    "rmse_df=pd.DataFrame(rmse_data)\n",
    "rmse_df.plot(x='model',y='rmse')\n",
    "print(rmse_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the results above, the Model with solver as Stochastic Gradient Descent(SGD) has the best performance with highest area under the curve in the \"precision-recall\" curve and an rmse of 1.37 . Therefore we set the solver to be SGD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuning model performance for different values of the Regularization parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.912698s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.912698s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.746401                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.746401                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.815797                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.815797                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.866439                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.866439                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.941608                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.941608                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.746401                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.746401                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.13368           | 1.06474               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.13368           | 1.06474               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 326.733ms    | DIVERGED          | DIVERGED              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 326.733ms    | DIVERGED          | DIVERGED              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 429.304ms    | 1.13342           | 1.06462               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 429.304ms    | 1.13342           | 1.06462               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 744.027ms    | DIVERGED          | DIVERGED              | 0.00238186  |</pre>"
      ],
      "text/plain": [
       "| 1       | 744.027ms    | DIVERGED          | DIVERGED              | 0.00238186  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 832.089ms    | 1.13353           | 1.06468               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 832.089ms    | 1.13353           | 1.06468               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.02s        | 1.05628           | 1.02776               | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.02s        | 1.05628           | 1.02776               | 0.00119093  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.35s        | DIVERGED          | DIVERGED              | 0.000708131 |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.35s        | DIVERGED          | DIVERGED              | 0.000708131 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 1.46s        | 1.13349           | 1.06465               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 1.46s        | 1.13349           | 1.06465               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.67s        | 1.00281           | 1.00141               | 0.000354066 |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.67s        | 1.00281           | 1.00141               | 0.000354066 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.86s        | 0.914768          | 0.956435              | 0.000210529 |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.86s        | 0.914768          | 0.956435              | 0.000210529 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 2.13s        | 0.907424          | 0.952588              | 0.000155325 |</pre>"
      ],
      "text/plain": [
       "| 3       | 2.13s        | 0.907424          | 0.952588              | 0.000155325 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 2.36s        | 0.946059          | 0.972655              | 0.000125181 |</pre>"
      ],
      "text/plain": [
       "| 4       | 2.36s        | 0.946059          | 0.972655              | 0.000125181 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 2.54s        | 0.905882          | 0.951778              |             |</pre>"
      ],
      "text/plain": [
       "| 5       | 2.54s        | 0.905882          | 0.951778              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 2.82s        | 0.876764          | 0.936357              |             |</pre>"
      ],
      "text/plain": [
       "| 6       | 2.82s        | 0.876764          | 0.936357              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 3.69s        | 0.849482          | 0.921674              |             |</pre>"
      ],
      "text/plain": [
       "| 10      | 3.69s        | 0.849482          | 0.921674              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 3.89s        | 0.850997          | 0.922495              | 2.0725e-005 |</pre>"
      ],
      "text/plain": [
       "| 11      | 3.89s        | 0.850997          | 0.922495              | 2.0725e-005 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 16      | 4.92s        | 0.844733          | 0.919093              |             |</pre>"
      ],
      "text/plain": [
       "| 16      | 4.92s        | 0.844733          | 0.919093              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 21      | 5.89s        | 0.84015           | 0.916597              |             |</pre>"
      ],
      "text/plain": [
       "| 21      | 5.89s        | 0.84015           | 0.916597              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 26      | 6.64s        | 0.839155          | 0.916054              |             |</pre>"
      ],
      "text/plain": [
       "| 26      | 6.64s        | 0.839155          | 0.916054              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 31      | 7.35s        | 0.838565          | 0.915732              |             |</pre>"
      ],
      "text/plain": [
       "| 31      | 7.35s        | 0.838565          | 0.915732              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 36      | 8.11s        | 0.838536          | 0.915716              |             |</pre>"
      ],
      "text/plain": [
       "| 36      | 8.11s        | 0.838536          | 0.915716              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 41      | 9.02s        | 0.838341          | 0.915609              |             |</pre>"
      ],
      "text/plain": [
       "| 41      | 9.02s        | 0.838341          | 0.915609              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 46      | 9.96s        | 0.838391          | 0.915637              |             |</pre>"
      ],
      "text/plain": [
       "| 46      | 9.96s        | 0.838391          | 0.915637              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 10.65s       | 0.838325          | 0.915601              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 10.65s       | 0.838325          | 0.915601              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.838294</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.838294"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.915584</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.915584"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.990762s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.990762s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-006   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-006   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
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       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
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       "  Optimizing model using SGD; tuning step size."
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       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
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       "  Using 10000 / 80004 points for tuning the step size."
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
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       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
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      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
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      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
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      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
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     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | Not Viable                               |</pre>"
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      "text/plain": [
       "| 4       | 0.00476372        | Not Viable                               |"
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00119093        | 0.889919                                 |</pre>"
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      "text/plain": [
       "| 5       | 0.00119093        | 0.889919                                 |"
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<pre>| 6       | 0.000595465       | 0.967497                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.000595465       | 0.967497                                 |"
      ]
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    },
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     "data": {
      "text/html": [
       "<pre>| 7       | 0.000297732       | 0.974762                                 |</pre>"
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      "text/plain": [
       "| 7       | 0.000297732       | 0.974762                                 |"
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<pre>| 8       | 0.000148866       | 1.00532                                  |</pre>"
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      "text/plain": [
       "| 8       | 0.000148866       | 1.00532                                  |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00119093        | 0.889919                                 |</pre>"
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      "text/plain": [
       "| Final   | 0.00119093        | 0.889919                                 |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
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      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
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       "<pre>Starting Optimization.</pre>"
      ],
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       "Starting Optimization."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
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     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.13364           | 1.06473               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.13364           | 1.06473               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 262.686ms    | 1.01936           | 1.00963               | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 262.686ms    | 1.01936           | 1.00963               | 0.00119093  |"
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 468.332ms    | 0.884765          | 0.94062               | 0.000708131 |</pre>"
      ],
      "text/plain": [
       "| 2       | 468.332ms    | 0.884765          | 0.94062               | 0.000708131 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 728.516ms    | 0.824937          | 0.90826               | 0.000522451 |</pre>"
      ],
      "text/plain": [
       "| 3       | 728.516ms    | 0.824937          | 0.90826               | 0.000522451 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1.03s        | 0.811359          | 0.900755              | 0.000421057 |</pre>"
      ],
      "text/plain": [
       "| 4       | 1.03s        | 0.811359          | 0.900755              | 0.000421057 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.41s        | 0.804411          | 0.89689               | 0.000356171 |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.41s        | 0.804411          | 0.89689               | 0.000356171 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.70s        | 0.807989          | 0.898882              | 0.000310651 |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.70s        | 0.807989          | 0.898882              | 0.000310651 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.94s        | 0.766998          | 0.875784              | 0.000125926 |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.94s        | 0.766998          | 0.875784              | 0.000125926 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 3.17s        | 0.769105          | 0.876985              | 0.000117238 |</pre>"
      ],
      "text/plain": [
       "| 11      | 3.17s        | 0.769105          | 0.876985              | 0.000117238 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 4.06s        | 0.752023          | 0.867191              |             |</pre>"
      ],
      "text/plain": [
       "| 15      | 4.06s        | 0.752023          | 0.867191              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 5.80s        | 0.758751          | 0.871061              |             |</pre>"
      ],
      "text/plain": [
       "| 20      | 5.80s        | 0.758751          | 0.871061              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 7.31s        | 0.760613          | 0.872128              | 6.6575e-006 |</pre>"
      ],
      "text/plain": [
       "| 25      | 7.31s        | 0.760613          | 0.872128              | 6.6575e-006 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 8.37s        | 0.753504          | 0.868043              |             |</pre>"
      ],
      "text/plain": [
       "| 30      | 8.37s        | 0.753504          | 0.868043              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 9.38s        | 0.754901          | 0.868847              |             |</pre>"
      ],
      "text/plain": [
       "| 35      | 9.38s        | 0.754901          | 0.868847              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 10.40s       | 0.754828          | 0.868805              |             |</pre>"
      ],
      "text/plain": [
       "| 40      | 10.40s       | 0.754828          | 0.868805              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 11.53s       | 0.754346          | 0.868528              |             |</pre>"
      ],
      "text/plain": [
       "| 45      | 11.53s       | 0.754346          | 0.868528              |             |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 12.53s       | 0.754445          | 0.868585              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 12.53s       | 0.754445          | 0.868585              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
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       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
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       "Computing final objective value and training RMSE."
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.754468</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.754468"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.868598</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.868598"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
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       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 1.02128s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 1.02128s"
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     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 0.0001   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 0.0001   |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
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      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
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       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
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      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | Not Viable                               |</pre>"
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      "text/plain": [
       "| 4       | 0.00476372        | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00119093        | Not Viable                               |</pre>"
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      "text/plain": [
       "| 5       | 0.00119093        | Not Viable                               |"
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    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.000297732       | 0.991227                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.000297732       | 0.991227                                 |"
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000148866       | 1.00744                                  |</pre>"
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      "text/plain": [
       "| 7       | 0.000148866       | 1.00744                                  |"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 7.44331e-005      | 1.02369                                  |</pre>"
      ],
      "text/plain": [
       "| 8       | 7.44331e-005      | 1.02369                                  |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 9       | 3.72166e-005      | 1.0404                                   |</pre>"
      ],
      "text/plain": [
       "| 9       | 3.72166e-005      | 1.0404                                   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.000297732       | 0.991227                                 |</pre>"
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      "text/plain": [
       "| Final   | 0.000297732       | 0.991227                                 |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
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      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
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      "text/html": [
       "<pre>Starting Optimization.</pre>"
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       "Starting Optimization."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
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     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
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      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 499us        | 1.13366           | 1.06474               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 499us        | 1.13366           | 1.06474               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 246.174ms    | 0.985709          | 0.992829              | 0.000297732 |</pre>"
      ],
      "text/plain": [
       "| 1       | 246.174ms    | 0.985709          | 0.992829              | 0.000297732 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 476.337ms    | 0.925702          | 0.962134              | 0.000177033 |</pre>"
      ],
      "text/plain": [
       "| 2       | 476.337ms    | 0.925702          | 0.962134              | 0.000177033 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 677.479ms    | 0.899789          | 0.94857               | 0.000130613 |</pre>"
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      "text/plain": [
       "| 3       | 677.479ms    | 0.899789          | 0.94857               | 0.000130613 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 901.638ms    | 0.883986          | 0.940197              | 0.000105264 |</pre>"
      ],
      "text/plain": [
       "| 4       | 901.638ms    | 0.883986          | 0.940197              | 0.000105264 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.13s        | 0.882616          | 0.939454              |             |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.13s        | 0.882616          | 0.939454              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.34s        | 0.874314          | 0.935008              |             |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.34s        | 0.874314          | 0.935008              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.22s        | 0.862738          | 0.928741              |             |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.22s        | 0.862738          | 0.928741              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.41s        | 0.851735          | 0.922784              |             |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.41s        | 0.851735          | 0.922784              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 3.07s        | 0.843724          | 0.918407              |             |</pre>"
      ],
      "text/plain": [
       "| 15      | 3.07s        | 0.843724          | 0.918407              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 4.18s        | 0.842266          | 0.917598              |             |</pre>"
      ],
      "text/plain": [
       "| 20      | 4.18s        | 0.842266          | 0.917598              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 5.42s        | 0.843368          | 0.918193              |             |</pre>"
      ],
      "text/plain": [
       "| 25      | 5.42s        | 0.843368          | 0.918193              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 6.96s        | 0.843283          | 0.918144              |             |</pre>"
      ],
      "text/plain": [
       "| 30      | 6.96s        | 0.843283          | 0.918144              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 8.13s        | 0.843241          | 0.918121              |             |</pre>"
      ],
      "text/plain": [
       "| 35      | 8.13s        | 0.843241          | 0.918121              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 9.34s        | 0.843296          | 0.918151              |             |</pre>"
      ],
      "text/plain": [
       "| 40      | 9.34s        | 0.843296          | 0.918151              |             |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 10.46s       | 0.843244          | 0.918123              |             |</pre>"
      ],
      "text/plain": [
       "| 45      | 10.46s       | 0.843244          | 0.918123              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Convergence on objective within bounds.</pre>"
      ],
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       "Optimization Complete: Convergence on objective within bounds."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
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       "Computing final objective value and training RMSE."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.84322</pre>"
      ],
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       "       Final objective value: 0.84322"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.918109</pre>"
      ],
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       "       Final training RMSE: 0.918109"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.958483s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.958483s"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 0.01     |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 0.01     |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 0.0920564         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 0.0920564         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.0230141         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.0230141         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.00575353        | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.00575353        | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.00143838        | 0.881227                                 |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.00143838        | 0.881227                                 |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.000719191       | 0.930701                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.000719191       | 0.930701                                 |"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.000359595       | 0.978334                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.000359595       | 0.978334                                 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.000179798       | 1.01483                                  |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.000179798       | 1.01483                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00143838        | 0.881227                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00143838        | 0.881227                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.13361           | 1.06471               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.13361           | 1.06471               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 176.635ms    | 1.05747           | 1.02817               | 0.00143838  |</pre>"
      ],
      "text/plain": [
       "| 1       | 176.635ms    | 1.05747           | 1.02817               | 0.00143838  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 325.25ms     | 0.860375          | 0.92745               | 0.000668777 |</pre>"
      ],
      "text/plain": [
       "| 2       | 325.25ms     | 0.860375          | 0.92745               | 0.000668777 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 486.022ms    | 0.833728          | 0.91297               | 0.000435672 |</pre>"
      ],
      "text/plain": [
       "| 3       | 486.022ms    | 0.833728          | 0.91297               | 0.000435672 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 659.145ms    | 0.816584          | 0.903504              | 0.000323066 |</pre>"
      ],
      "text/plain": [
       "| 4       | 659.145ms    | 0.816584          | 0.903504              | 0.000323066 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 876.299ms    | 0.797898          | 0.89302               | 0.000256714 |</pre>"
      ],
      "text/plain": [
       "| 5       | 876.299ms    | 0.797898          | 0.89302               | 0.000256714 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.10s        | 0.78621           | 0.886331              | 0.000212973 |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.10s        | 0.78621           | 0.886331              | 0.000212973 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 1.94s        | 0.767967          | 0.875179              | 0.000126653 |</pre>"
      ],
      "text/plain": [
       "| 10      | 1.94s        | 0.767967          | 0.875179              | 0.000126653 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.20s        | 0.767346          | 0.874457              | 0.000115    |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.20s        | 0.767346          | 0.874457              | 0.000115    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 4.35s        | 0.760732          | 0.866042              |             |</pre>"
      ],
      "text/plain": [
       "| 20      | 4.35s        | 0.760732          | 0.866042              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 6.42s        | 0.768681          | 0.86758               |             |</pre>"
      ],
      "text/plain": [
       "| 30      | 6.42s        | 0.768681          | 0.86758               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 8.51s        | 0.769115          | 0.867711              |             |</pre>"
      ],
      "text/plain": [
       "| 40      | 8.51s        | 0.769115          | 0.867711              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 10.83s       | 0.769224          | 0.867762              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 10.83s       | 0.769224          | 0.867762              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.769229</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.769229"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.867765</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.867765"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sgd_R2 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-08,num_factors=4)\n",
    "sgd_R3 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-06,num_factors=4)\n",
    "sgd_R4 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-04,num_factors=4)\n",
    "sgd_R5 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-02,num_factors=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Evaluate model M0\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+----------------+------------------+\n",
      "| cutoff | mean_precision |   mean_recall    |\n",
      "+--------+----------------+------------------+\n",
      "|   1    |      0.0       |       0.0        |\n",
      "|   2    |     0.036      | 0.00120597321378 |\n",
      "|   3    |     0.064      | 0.00349376891356 |\n",
      "|   4    |     0.078      | 0.00495793403057 |\n",
      "|   5    |     0.096      | 0.00713748353419 |\n",
      "|   6    | 0.106666666667 | 0.00888194099003 |\n",
      "|   7    | 0.105142857143 | 0.0104237451419  |\n",
      "|   8    |     0.104      | 0.0110617684109  |\n",
      "|   9    |     0.104      | 0.0123912123833  |\n",
      "|   10   |     0.1008     | 0.0130689864238  |\n",
      "+--------+----------------+------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-----------------+-------------------+\n",
      "| cutoff |  mean_precision |    mean_recall    |\n",
      "+--------+-----------------+-------------------+\n",
      "|   1    |       0.0       |        0.0        |\n",
      "|   2    |       0.0       |        0.0        |\n",
      "|   3    |      0.024      | 0.000812831357025 |\n",
      "|   4    |       0.02      | 0.000817442308034 |\n",
      "|   5    |      0.0192     |  0.00122719840559 |\n",
      "|   6    | 0.0333333333333 |  0.00251744977798 |\n",
      "|   7    | 0.0331428571429 |  0.00265518516514 |\n",
      "|   8    |      0.038      |  0.00329720975621 |\n",
      "|   9    | 0.0355555555556 |  0.00340494549873 |\n",
      "|   10   |      0.0376     |  0.00379103661127 |\n",
      "+--------+-----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M2\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 4.61095100865e-06 |\n",
      "|   3    | 0.0106666666667  | 0.000437872647873 |\n",
      "|   4    |       0.01       | 0.000497131907132 |\n",
      "|   5    |      0.008       | 0.000497131907132 |\n",
      "|   6    |      0.008       | 0.000513004923005 |\n",
      "|   7    | 0.00685714285714 | 0.000513004923005 |\n",
      "|   8    |      0.007       | 0.000522761020566 |\n",
      "|   9    | 0.00622222222222 | 0.000522761020566 |\n",
      "|   10   |      0.0064      | 0.000532517118127 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M3\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-----------------+-------------------+\n",
      "| cutoff |  mean_precision |    mean_recall    |\n",
      "+--------+-----------------+-------------------+\n",
      "|   1    |       0.0       |        0.0        |\n",
      "|   2    |      0.032      | 0.000677238136686 |\n",
      "|   3    | 0.0213333333333 | 0.000677238136686 |\n",
      "|   4    |      0.022      | 0.000827198405595 |\n",
      "|   5    |      0.0368     |  0.00162832843664 |\n",
      "|   6    |       0.04      |  0.00183834926961 |\n",
      "|   7    |      0.048      |  0.00284930533674 |\n",
      "|   8    |      0.048      |  0.00331496005106 |\n",
      "|   9    | 0.0515555555556 |  0.00365008015618 |\n",
      "|   10   |      0.0512     |  0.00407489002862 |\n",
      "+--------+-----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "Model compare metric: precision_recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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mxuxs9IkTpzZ2dcU9IgK3mGi8+vTB0K7d6Q141feTZ/EGX1+Ue+u80rcnGUQA2TV+z8F2\n9l/bNhFa6xSl1HNAFnACWKG1XtGAeJ1eSkYhH6Vkc+fFnekR5u/ocEQb1CcygG9mDec/y/bw1poM\nVqcX8OL1icRHtHN0aI1GWyyYDh0+/Qw/M7Oq8c9GG43V2yo3N9yionCPjsYneQhu0dG4R8fgHhuD\nW3j4+btd2pgm/RSUUu2xXTV0AoqAT5RSN2utF59j2+nAdIDo6JZ5sxcpRCechZe7gScn9uaSHiE8\ntGQbV85dw5/HxHHnxZ1bzD0UtNmM6eDBc5/h5+SAyVS9rfLwwD06CveYWHyHX4x7TAzuMdG4R0fj\nGhaGMrSdK6P6sicZ5AI1O74jqx6zZ5vRwAGtdT6AUuozYChwVjLQWi8AFoCtNpGd8TuVhb8eIDWv\nhAVTB8gtDoVTuDgumOX3X8zjX+zg2eWp/LjnCP+9rq/TFL/TRiPG3NxTDX2WreE3ZmZiyj14Wv+9\n8vLCPToaj65d8Rs96tQZfkw0riEhKJeWkeSclT0t1iagm1KqE7YG/gbgpjO2WQrMrBpPGAwc11of\nUkplAUOUUt7YuolGAa2rAl2V3KITvPhDOqN7hkpFUuFUArzdefXGflzaK5S/frGD8S+t5m9X9OL6\ngVHNcu8Ea2Ulppycc57hmw4ePG32jYuPD+4xMXj26oX/uPG4R0fjHhONW3Q0rsHBcq+HJlRrMtBa\nm5VSM4Hl2KaWLtRa71RKzah6fj7wLbZppXuxTS29req5DUqpJcAWwAz8RtXZf2szZ9keNFoK0Qmn\npJRiUmIEA2MDefCTbTzy2XZ+2J3Hv6/uQ7Bfw++ypo1GjDm5GDMzbN04mZmYMjMxZmRiOnQIalRH\ndvH3xz0mBq++fWk3ccJpZ/iGwEBp8B1ESlg3gp0Hj3P5y79y98guzB7Xw9HhCHFBVqvmrbUZ/GfZ\nHvw93fhy5jAiAmpfuXyqD9/WyJ9s9I0ZGbYzfIulelsXf3/cY2NtfffR0bjH2r67RUfj2r59U769\nNktKWDuB55an0s7LjTtHdHF0KELUysVFcftFnRjapQOT56/j3g9+48PpQ3AzuNimZR46dFpDX93w\n5+aeNmh7skvHKyEe/ysurxq0jcE9NhZDgNy+s6WRZNBAG/YfZVVqPo+M70E7L+dcTCJETbaFV0eI\nzszgVf9MVn+zhdUr/0dMxdGzp2WeHLSNi8NvzBjbGf7JBr9DB2nwWxFJBg2gtWbO8lRC/T2Ylhzr\n6HCEqKa1xlJQUOMMv0a3TlZW9cKrUOAqVzdyvAIp6dWN8ItHVDX4sbjHxthm6UiD3yZIMmiAlbuP\nsDnzGE9flYCXu8xjFs1La42lqMjWlXPGoK0xMxNrWdmpjV1dcY+MxD02Fp8hQ06d4cfEYO4QzF/m\nraegtJJvZwynvb+n496UcBhJBvVksWrmLN9DpyAfJidFOjoc0YpZiotPP7uv0fifVrrYYMAtIgL3\nmBja9e9f1Z1ja/DdOnY870pbN2DulH5MeGUN9334G+/dMQSDlFFpcyQZ1NMXv+WSllfKqzf1k5vW\niAbRWmMpLKwxDz+7avFVFqasLCxFRac2Vgq38HDcY2Pwv/yyU4O2MbG4R0bUu25O1xA//nFlPA9+\nso1Xfkzn/tFxjfTuREshyaAeKs0W/vt9GvER/lwWH177DqLN01Yr5iNHzt3gZ2ZiLS8/tbGLi63B\nj4nGc9xY3KOiT53hR0Xh4tHwdQHncu2ASNbuK+CllekM6hTI0C5S8bQtkWRQD+9vyCK36AT/vjpB\nqpKKatXz8LOybQ1+ZhbG7Kqfs3PQlZWnNnZzq66W6Z2UhHtUVPVKW/eI+p/hN9Q/JsWzNbuI+z/c\nyrf3DSfIt2kSj3A+kgzqqLTSzKs/7mVolw4Ml1rxbU51aYWqLhxjzQb/zFo6np5VC65i8b14hO3n\n6CjcomNwC3fO4mk+Hq7Mvak/k+au4YGPt7Ho1oFywtNGSDKoozdXH+BomZHZ43rIlLtWylpWVtXA\nZ51eQC07C/Ohw6eXVvDzwz06+vRaOlUNvmtIy6yl0zPcnycm9OLxz3fwv1/2c9dIWUzZFkgyqIOj\npZW8vno/43qHkRgV4OhwRANorTEfOkRFWhqVaekY9++vbvAt+QWnbWsIDLTVwh84ELeo6OrSyG7R\n0a12pe1Ng6JZu+8oz61IZVCn9gyICXR0SKKJSTKog9d+2ke50cyDY2WmRUtiKSmhMj2dytTU6sa/\nMi3NdvPxKq4hIbjHxOA7YoRtwLZmg+/r68DoHUMpxb+vTmB7znFmvf8b3943nADv1nmHL2EjycBO\nuUUneHddJtcOiKRriJ+jwxHnoE0mjBkZVKSmUZlm+6pIS8V88FD1Ni6+vnh0747/FZfjGReHR/fu\neHTrhsFP/qZn8vd049Wb+nHNvLU8+MnvvH7LgFZ5FSRsJBnY6cXv00DBfTL/2uFO1tapTE2tavDT\nqExNw7h/P/pkITVXVzw6dcK7X388ro/Do3scnnFxuIaHS4NWB30iA3h0fE+e+noXb63J4I8XdXJ0\nSKKJSDKwQ3peCZ9uyeGPwzrZVepXNB5LaRmV6ae6dipTU6lIT8d6/Hj1Nq6hoXh0j8N3+EW2M/24\nODw6dWq1Ny5vbrcNi2XtvqP8+7vd9I1qJ+MHrZQkAzs8tyIVb3dX7v5DV0eH0mpprTFlZ1Oxc2f1\nmX5lWhqmnJzqbVy8vfGIi8N/7Fg84uLw7B5n6+IJkMH8pqSU4vnJfZk091emv7OZL+4ZRlSgt6PD\nEo1MkkEtfss6xvKdeTwwJo5AHznTbEzmY8coX7eO0rVrKV+7znaDFAAXF9xjY/FMiCfgmqurz/bd\nOnaU+9w6SDtvN968dSBXzl3DHW+n8OndQ/GV+3y3KvLXvACtNf9ZtocgX3dul77SBrNWVFC+eXN1\nAqjctRuwzdX3HjyIwDtux6tvXzy6dm2ykgui/roE+/LalP7c+tYm7vvgNxbckiQF7VoRSQYXsDq9\ngPX7C/n7xN74yFlQnWmrlYpduylbt5aytWs5sXmL7cYpbm549+1L8H334pOcjGd8/HkragrnMrxb\nME9O6MX/fbmT/yzbw2OX9XR0SKKRyP/A87BabVcFke29uHFQtKPDaTGMOTmUrV1L2dp1lK9fX11x\n06NbN9rfeCM+Q5PxTkrCxcfHwZGK+pqaHEv6kVIW/LKfrsG+XDcwytEhiUYgyeA8vtl+iJ0Hi3nh\n+r64u0o/9flYjh+nbP0GWwJYtw5TVhZgW8TlO3KkrfEfMgS3kBAHRyoa09+u6MX+/DIe/2I7MR28\nGdy5g6NDEg2kdI06K84iKSlJp6SkOOz1TRYrY/77M55uBr65d7j0i9ZgNRo5seU3ytato2ztWip2\n7gSrFRdvb7wHDcJn6FB8hibj3qWLzOdv5Y6Xm7hq3hqOlRn58p6LiO4gM4wcSSm1WWudVN/95crg\nHD5OySbjaDlvTpMBMm21UpmWRtlaW+NfnpKCrqgAgwGvPn0IuusufIYm49WnD8rNzdHhimbUztuN\nN6fZZhjd/vYmPr17KP6e8m+gpZJkcIYTRgsv/ZBOUkx7LunRNrs2TIcOVTf+ZevXYzl6FAD3zp0J\nuPZaW9fPoEFtsmaPOF2nIB/m3dyfW97cyKz3f+PNaUm4yp3/WiRJBmf4dEsOR0oqeeXGfm2mm8NS\nUkL5xo2UrbH1+xsPHADAEBRk6/ZJTsZnaDJuYWEOjlQ4o6Fdgvj7pN48/vkOnvxqJ09NjJd7ILRA\nkgxq0Frz7rpM4iP8GdSp9S651yYTJ7Ztqz77P7F9O1gsKC8vvAcmEXDddfgMHYpHXLc2kxBFw0wZ\nHEPm0XIW/LKfw8crefGGRFmU1sLIX6uGTRnHSM0r4T/XJLS6RlBrTfm6dRS+u5jyDRts99x1ccEz\nPp4Of7oDn+ShePVLxEXq+Yh6enR8Dzq28+Qf3+zm6tfW8MYtA2VQuQWRZFDDu+sz8fd0ZWLfCEeH\n0qjKN20i/6WXKU9JwTUkBP9JE21dP4MHY2jXztHhiVZCKcWtwzrRNcSPe97fwsS5v/LalP4M7SK3\nh20J7BrpUUqNU0qlKqX2KqUeOcfzSin1ctXzvyul+td4LkAptUQptUcptVspldyYb6CxHCmpYNmO\nQ0xOisLL3fnuTVsfJ7ZuJeuPfyRz6i0YMzMJ/b+/0uWH7wl/4gn8L71UEoFoEhd1C+LLe4YR5OvB\n1Dc38u66DEeHJOxQ65WBUsoAzAXGADnAJqXUUq31rhqbjQe6VX0NBuZVfQd4CVimtb5WKeUOOOV1\n40cbszFZNFMGt/zVxid27CT/lZcp+/kXDB06EPLIw7S/4QZcPD0dHZpoI2KDfPj87qHc9+FW/u/L\nnew5XMITE3rLAk4nZk830SBgr9Z6P4BS6kNgElAzGUwC3tG2FWzrq64GwoFy4GLgVgCttREwNl74\njcNssfL+xiyGdwuic3DLnS5ZkZpK/iuvUPrDSgzt2hH8lwcInDIFF2+nzL+ilfPzdOP1W5J4bkUq\n837ax94jpbw2pT8dfKUIoTOyJ01HANk1fs+pesyebToB+cBbSqnflFJvKKWcrijND7uPcOh4BTcP\niXF0KPVSuW8fOX/+MwcmXUn5ho0E3TuLLit/IOhPf5JEIBzK4KJ4eFwPXrohka3ZRUyau4bdh4od\nHZY4h6a+ZnMF+gPztNb9gDLgrDEHAKXUdKVUilIqJT8/v4nDOt3i9Zl0bOfJqBa2yMyYmUnu7Nns\nnzCRsp9/ocOMO+n6w/cE3323LAgTTmVSYgQf35mMyWLlmnlrWb7zsKNDEmewJxnkAjXLEkZWPWbP\nNjlAjtZ6Q9XjS7Alh7NorRdorZO01knBwcH2xN4o9uWX8uveAm4aHN1iVk4ac3I5+Pjj7LvsckpW\nfE/gbbfSZeUPhNx/vwwKC6fVNyqApTMvoluoH3e+u5lXVqbjjLXR2ip7xgw2Ad2UUp2wNfA3ADed\nsc1SYGbVeMJg4LjW+hCAUipbKdVda50KjOL0sQaHW7w+EzeD4vqBzj9wbDp8mIL58yn69DOUUgTe\nPIUOd9yBazMmTyEaItTfk4+mD+Gxz7bz/Pdp7M0v5cXrE1vdup6WqNZkoLU2K6VmAssBA7BQa71T\nKTWj6vn5wLfAZcBebIPGt9U4xCzgvaqZRPvPeM6hyo1mlmzOYXx8OMF+zjuoZc7Pp2DB6xR99BFa\na9pPvpYOd96JW2ioo0MTos483Qw8f11fgvw8WPDLfu4fHUenIKcbSmxz7Fp0prX+FluDX/Ox+TV+\n1sA959l3K1DvsqpNaenWg5RUmJma7JwDx+bCQo6+8SbH3n8fbTLR7qorCb7rLtwiWteiONH2KKW4\nPCGcBb/sJ/VwiSQDJ9BmVyBrrXlnXSY9wvxIimnv6HBOYykq4uhbiyh89110RQXtJlxB0N134x7j\nnElLiProFuqLUpB6uIRx8VIE0dHabDLYklXErkPF/OuqeKfpr7SUlFD49jsULlqEtbQU/8vGEzRz\nJh6dOzs6NCEanbe7K9GB3qTmyVRTZ9Bmk8Hi9Zn4erhyZaLju1ysZWUULn6PowsXYj1+HL8xowma\nOQvP7nGODk2IJtU91I/UwyWODkPQRpPB0dJKvvn9EDcOisLHgWV2rSdOcOyDDzn6xhtYCgvxHTGC\noHtn4dW7t8NiEqI59QjzY+WeI1SYLHi6tY6aYC1Vm0wGH6VkY7RYHTZwbDUaKfroYwoW/A9LfgE+\nQ4cSfO8svBITHRKPEI4SF+aHxarZe6SU+AhZI+NIbS4ZWKya99Znkdy5A11D/Jr1tbXRSNFnn1Mw\nfz7mw4fxHjiQ4BdewDvJKSdbCdHkeoTZ/g+m5ZVIMnCwNpcMfko9Qm7RCR6/vGezvaY2mzn+5VIK\nXnsNU24uXomJdPz303gPGeI0g9dCOEJsBx/cDS4ybuAE2lwyeGddJqH+Hozp1fQLtrTFQvG331Lw\n6lyMmZl4xscT9sTf8Bk+XJKAEICrwYUuIb6k5kkycLQ2lQwyj5bxc1o+94/uhlsT1iHSVislK1aQ\n/+qrGPfuw6N7dyLnvorvJZdIEhDiDA9eGoe3e5tqipxSm/oLvLchC1cXxY2Daq9DVJmejjE7B4+u\nXXCLjEQ8blzlAAAgAElEQVS5nJ48tMWCpagIy7FjWI4dw1x4rOrnQoqXr6Byzx7cu3Qh4sUX8Lv0\n0rP2F0LYjOopZVWcQZtJBhUmCx+nZDO2dxih/he+45e1ooLM2/6IpaAAAOXpiXvnTrh4etka/MJC\nLMXFcJ6Ki+6dOtHx2Tn4X3YZyiDT5YQQzq/NJIOvth2kqNxk1w1sij79FEtBAWF//zvK4ELl3n1U\n7t2LNhrx6N4d18D2GALaYwgMxNA+ANf2J39uj6F9e1zc3ZvhHQkhRONpM8lg8fpMuoX4MqRz4AW3\n0yYTR998E69+/Qi4brL08Qsh2oQ20ZG9LbuIbTnHmZocU2vjfvyrrzEfPESHO6dLIhBCtBltIhm8\nuz4Tb3cDV/W7cB0ibbFwdMECPHr2xHfEiGaKTgghHK/VJ4NjZUa+2naQq/pF4OfpdsFtS77/HmNG\nBkFyVSCEaGNa/ZjBJ5uzqTSfXYeoIi2NvH89jfXEierHTFlZuHfqhN+YMc0dphBCOFSrTgZWq2bx\n+iwGxQbSI8y/+nFtMnHw4UcwHzyIZ58+1Y8b+iQQOG2aTAcVQrQ5rToZ/JKeT1ZhOQ+O7X7a40cX\nLaJy924iXn4J/0svdVB0QgjhPFr1mMG76zIJ8vVgXO9Tt9QzZmRQ8Opc/MaMkUQghBBVWm0yyC4s\n58fUI9w4KAp3V9vb1Fpz6G9PoNzdCf3rXx0coRBCOI9Wmwze35iFgtPqEBUtWUL5xo2EzH4It9AQ\nxwUnhBBOplUmgwqThY82ZTO6ZygdA7wAMOUd4cicZ/EeNIiAa691cIRCCOFcWmUy+G7HIQrLjNyS\nHFv9WN4//4k2Ggl/6u+yhkAIIc7QKpPBu+sy6Rzkw9AuHQAoXrGCku+/J2jmPbjHxjo2OCGEcEKt\nLhnsyD3OlqwipgyJwcVFYSku5vA//oFHz550uPVWR4cnhBBOqdWtM1i8PhNPNxeuHRAJwJFnn8VS\neIyo+fNRbhcuRyGEEG1Vq7oyOH7CxBdbc7kyMYJ2Xm6Urd9A0SdLCLx1Gl69ezs6PCGEcFp2XRko\npcYBLwEG4A2t9TNnPK+qnr8MKAdu1VpvqfG8AUgBcrXWVzRS7Gf5dHMOFSYrNw+JwVpRwaEn/oZb\ndDTBM2c21Us6ltZQkAb7foR9q6Asv+aTp29X6+NnPlfjYXcfuPw5CEtoeMxCCKdUazKoasjnAmOA\nHGCTUmqp1npXjc3GA92qvgYD86q+n3QfsBvwp4nY6hBl0j86gPiIdhx5/nlMmVlEL3oLFy+vpnrZ\n5ldeCPtXnUoAxbm2xwO7QGDn07c9bdaUquPjNZ7L3gCfTYfpP4GrR4PCF0I4J3uuDAYBe7XW+wGU\nUh8Ck4CayWAS8I7WWgPrlVIBSqlwrfUhpVQkcDnwL+CBxg3/lLX7jrK/oIwXru/LiZ07ObrwLdpd\nczU+Q4Y01Us2D7MRcjZVNf4/wsHfAA2e7aDzSOgyGzr/AdrXfjvPektbDu9fB788B5c83nSvI4Rw\nGHuSQQSQXeP3HE4/6z/fNhHAIeBFYDbgV/8wa/fu+gwCfdwZHx/O4akPYWjfntDZs5vyJZuG1lC4\n39bw710JGavBWArKAJEDYeSj0HUUdOwHLs1UXTVuLPS9EVY/Dz2vgPC+zfO6Qohm06SziZRSVwBH\ntNablVIja9l2OjAdIDo6+kKbnuVg0Qm+35XHnSO64HIwhxNbtxIyezaGdu3qG/q5Hd4BO5ZAx/7Q\n5Q/g0Uj57cQxOPDLqbP/oizb4+1joc/10OUS6DTcdjXgKGOftsX2xT3wpx/B1d1xsQghGp09ySAX\niKrxe2TVY/Zscw0wUSl1GeAJ+CulFmutbz7zRbTWC4AFAElJSWeObF7QBxuz0MBNg6Ip/mQxAP7j\nxtblEBemNfz2Lnz7EJgrbI+5uEFMMnQbC90uhaBu5+hzPw+LGXI3n2r8c1NAW8HdDzqPgGH32RLA\nmWMAjuQdCFe8AB/eBL++ACMfdnREQohGZE8y2AR0U0p1wtbA3wDcdMY2S4GZVeMJg4HjWutDwKNV\nX1RdGTx4rkTQEEazlQ82ZnNJ9xCiAr3Zv2wZXn374taxY90PdnSf7Wy8ZveLsQy++Qts+8DWR3/l\nfFs3TvpySP8eVjxu+2ofa0sK3cZC7DBwO2PQ+liGrdtn34+2q4DKYlAutquM4Q/aun4iBoDBiddC\n9Lgc4q+FX561/RwW7+iIhBCNpNZkoLU2K6VmAsuxTS1dqLXeqZSaUfX8fOBbbNNK92KbWnpb04V8\numU7D1NQWsnU5BiMmZlU7t5NyMP1OGvd+wMsvgYmzYV+Vfnq6D74cArk77H11V/8kC1R+IfbGvwx\nT0FRNqSvsCWG3xbDxgXg6gWdLrad5Z/s/y/cbztmuyjofVVV18/FtjPulmT8HDjwM3x5N9yx0rmT\nlxDCbkqfNdfc8ZKSknRKSopd2143fx2Hiyv46cGRFL7+OvkvvEDXH1fW7crgRBG8lgwlB20DpVfN\nh2OZ8NZ4MJ2AaxfaxghqY6qAzF9tiSFtORw7AG4+tv7+LpfYvjp0tb87yVnt/AI+mQajn4SL/uzo\naIQQgFJqs9Y6qb77t+hyFHsOF7Mxo5DHLuuBi4uiZPlyPPv2qXsX0fLHofQwBHWH7I1QfBDenmDr\nIrr1a/sXW7l5QtfRtq9xz9iO4xPc+gZbe18J26+An/4Dva6EwE6OjkgI0UAtuhzF4vWZeLi6MHlA\nFOaCAip27cLvD5fU7SBpy2HrYtsZbuKNULgP3rrMtrhr6mf1X3WrFLSLaH2J4KTxc8DFFb554Bwr\nmYUQLU2LTgar9uQzqmcI7X3cKVu7FgCfYcPsP8CJY7D0XgjpBSMehqiq5ROleTDlE9uArji3dhEw\n6v9s4yHblzg6GiFEA7XYZFBSYSK36AS9O9rm3petWYMhIADP3r3sP8h3j0B5AVw5z1ZmISIJ+k+D\nmz62TRsVFzbwDlvCXPYIZK0Hq9XREQkh6qnFJoP0I6UAxIX6obWmdM1afIYORbnY+Zb2fAO/f2ib\n1tkx0faYqztMfNk24Ctq52KACS+BuRIWjoUX42HZo5C1QRKDEC1Mix1ATjtcAkD3UD8qU1OxFBTg\nc9FF9u1cXghf3W8bDxj+lyaMsg0IS4AHdkLqMtj1BWx6A9a/Bn4dodck2zTayIFgb5IWQjhEi00G\nqXkleLkZiGzvxbEv1gDgM2zohXcqSLetJM7bZRsvmPpZ6x3gbU6e7aDv9bavimJIW2abfpqyEDbM\nO5UYel5hSwxS+VQIp9Nik0F6XindQn1xcVGU/vorHt264RYaeuGdlj9mWyAG8Ie/Sn3+puDpD32u\ns32dKzG4ekH0YIgdDp1G2AruGVrsP0MhWo0W879QG41gMKAMtlIRqXkljIgLxnriBCdSNtN+ypQL\nHyBvpy0RXPyQ7Sw1VEopNLkzE0PGajiw2vb9x38A/wB3X4hOtq3G7jQcwvo0XzVWIUS1FpMMsmfN\nwi0klPB/PMWxMiP5JZXEhfpSvmkT2mSqfbxgzcu21cBD7m55JSBaA09/Wz2jHpfbfi8rgIxfbXWa\nMlbD9/9ne3zsvyH5bsfFKUQb1WKSQeWu3aiqO2+l5dkGj+NC/Sj7bA3K3R3vpAusCSjKtpWeHjRd\nEoGz8AmyrWTufaXt9+JDtuQQWe/V9EKIBmgRyUCbTJgLCqp/P5kMuof5UfrrGryTknDx9Dz/AdbN\ntX0fImecTss/HPpMPuthk8lETk4OFRUVDghKCOfj6elJZGQkbm6NWySyRSQDc0HBaSUP0vJK8fNw\npUPZMYr27SPgmmvOv3N5IWx521Z6OSDq/NsJp5STk4Ofnx+xsbGoll7gT4gG0lpz9OhRcnJy6NSp\ncWuCtYjJ36bDh0/7PTWvhLgwP8rXrQNqKUGx8XUwldtuGCNanIqKCjp06CCJQAhAKUWHDh2a5Eq5\nRSQDc96R6p+11qTnlRAX6kvpr7/iGhyMR1y3c+9oLIeN/7PdcCa0DmUqhFORRCDEKU31/6GFJINT\nVwb5pZUcKzcRF+xN+dp1+Awbdv4PZ+t7UH4ULrq/mSIVrZHBYCAxMZH4+HgmTJhAUVFRo7/GTz/9\nxBVXXFGnfQ4ePMi1115b59cqKiritddea/BxzmXkyJF0796dvn37MmzYMFJTUxvluA21aNEiDh48\n2KjHHDduHAEBAXX+u520bNkyunfvTteuXXnmmWeqH3/ooYfo0aMHffr04aqrrmqSf2/n0iKSganG\nlUF6nq0mUa/ig1iOHz//lFKLGda+DJGDbPPYhagnLy8vtm7dyo4dOwgMDGTu3LmODgmz2UzHjh1Z\nsqTuFWPPTAb1Pc75vPfee2zbto1p06bx0EMP2b2f2WxutBjOVJ9kUFs8Dz30EO+++2694rFYLNxz\nzz1899137Nq1iw8++IBdu3YBMGbMGHbs2MHvv/9OXFwc//73v+v1GnXVIpKBucaYQWpVTaKw9N9B\nKXyGnqeh3/UFFGXZxgqkm0E0kuTkZHJzc6t/f/bZZxk4cCB9+vThiSeeqH78H//4B927d+eiiy7i\nxhtv5LnnngNsZ84n7+JXUFBAbGzsWa+xceNGkpOT6devH0OHDq0+u160aBETJ07kkksuYdSoUWRk\nZBAfb1s8eccdd5CYmEhiYiLBwcH8/e9/p7S0lFGjRtG/f38SEhL48ssvAXjkkUfYt28fiYmJPPTQ\nQ6cdp6Kigttuu42EhAT69evHqlWrql/76quvZty4cXTr1o3Zs2fX+lldfPHF7N27F4CnnnqKgQMH\nEh8fz/Tp0zl5h8WRI0dy//33k5SUxEsvvcRXX33F4MGD6devH6NHjyYvLw+AJ598kmnTpjF8+HBi\nYmL47LPPmD17NgkJCYwbNw6TyQTA5s2bGTFiBAMGDGDs2LEcOnSIJUuWkJKSwpQpU0hMTOTEiRPn\n3O5c8VzIqFGj8PPzO+vx8x37zL9x165d6dy5M+7u7txwww3Vf59LL70UV1fb3J4hQ4aQk5NT62fd\nGFrEbCJT1T8IsE0rDfRxR29ah2evXrgGnmPdgNbw64sQFAfdL2vGSEVT+vtXO9l1sLhRj9mroz9P\nTOht17YWi4WVK1dy++23A7BixQrS09PZuHEjWmsmTpzIL7/8gpeXF59++inbtm3DZDLRv39/Bgyw\n/94YPXr0YPXq1bi6uvLDDz/w2GOP8emnnwKwZcsWfv/9dwIDA8nIyKje54033gAgMzOTcePGceut\nt+Lp6cnnn3+Ov78/BQUFDBkyhIkTJ/LMM8+wY8cOtm7dCnDacebOnYtSiu3bt7Nnzx4uvfRS0tLS\nANi6dSu//fYbHh4edO/enVmzZhEVdf4Zel999RUJCbaSLzNnzuRvf/sbAFOnTuXrr79mwoQJABiN\nxuoEeezYMdavX49SijfeeIM5c+bw/PPPA7Bv3z5WrVrFrl27SE5O5tNPP2XOnDlcddVVfPPNN1x+\n+eXMmjWLL7/8kuDgYD766CMef/xxFi5cyKuvvspzzz1HUlISJpPpvNudGc/SpUtJSUnhqaeesutv\nV9uxT8rNzT3ts4uMjGTDhg1nHW/hwoVcf/31dr12Q7WIZGA+IxnEtzNwYus2OlT9pzzLvh8hbztM\nfFWqZYoGO3HiBImJieTm5tKzZ0/GjBkD2JLBihUr6NevHwClpaWkp6dTUlLCpEmT8PT0xNPTs7rR\ns9fx48eZNm0a6enpKKWqz3rB1oUQeK4TIGxn9ZMnT+aVV14hJiYGk8nEY489xi+//IKLiwu5ubnV\nZ9rn8+uvvzJr1izAlpRiYmKqk8GoUaNo1852/5BevXqRmZl5zmQwZcoUvLy8iI2N5ZVXXgFg1apV\nzJkzh/LycgoLC+ndu3f151KzscvJyeH666/n0KFDGI3G06ZPjh8/Hjc3NxISErBYLIwbNw6AhIQE\nMjIySE1NZceOHdV/H4vFQnh4+Fnx1bZdzXgmTpzIxIkTL/iZ1eXYdfGvf/0LV1dXptRWaqeROH0y\n0FpXJwONbcxgpsdBsFjwueg8U0rXvAh+4baaOKLVsPcMvrGdHDMoLy9n7NixzJ07l3vvvRetNY8+\n+ih33nnnadu/+OKL5z2Wq6sr1qp7PZxveuD//d//8Yc//IHPP/+cjIwMRo4cWf2cj4/PeY89Y8YM\nrr76akaPHg3Y+u7z8/PZvHkzbm5uxMbGNmhKoofHqWqzBoPhvH3q7733HklJp1aSV1RUcPfdd5OS\nkkJUVBRPPvnkaXHUfE+zZs3igQceYOLEifz00088+eSTZ72+i4sLbm5u1RNHXFxcMJvNaK3p3bs3\n66qmnJ9Pbdtd6DOuzfmOnZ2dXZ38ZsyYQd++fcnOzq5+Picnh4iIiOrfFy1axNdff83KlSubbTad\n0582W44dQ1edGVWaLZRUmumRswsXb2+8ExPP3iF3i63ezZC7pVSyaFTe3t68/PLLPP/885jNZsaO\nHcvChQspLbVNasjNzeXIkSMMGzaMr776ioqKCkpLS/n666+rjxEbG8vmzZsBzjtoe/z48eqGYdGi\nRXbFNnfuXEpKSnjkkUdOO05ISAhubm6sWrWKzMxMAPz8/CgpKTnncYYPH857770HQFpaGllZWXTv\n3t2uGM7nZMMfFBREaWnpBQera773t99+u06v0717d/Lz86sbYpPJxM6dO4HT3/OFtmuo8x07KiqK\nrVu3snXrVmbMmMHAgQNJT0/nwIEDGI1GPvzww+orkGXLljFnzhyWLl2Kt7d3o8RlD6dPBjUHj0sr\nLQAE7f4N78GDUe7nuBfBmhfBox0MuLWZIhRtSb9+/ejTpw8ffPABl156KTfddBPJyckkJCRw7bXX\nUlJSwsCBA5k4cSJ9+vRh/PjxJCQkVHevPPjgg8ybN49+/fpRUKPESk2zZ8/m0UcfpV+/fnbPsHnu\nuefYvn179SDy/PnzmTJlCikpKSQkJPDOO+/Qo0cPADp06MCwYcOIj48/a7bP3XffjdVqJSEhgeuv\nv55FixaddkVQHwEBAfzpT38iPj6esWPHMnDgwPNu++STTzJ58mQGDBhAUFBQnV7H3d2dJUuW8PDD\nD9O3b18SExNZW3Vv9FtvvZUZM2aQmJiIxWI573ZnWrp0afVYx5mGDx/O5MmTWblyJZGRkSxfvvyC\nMdTk6urKq6++ytixY+nZsyfXXXcdvXvbrnxnzpxJSUkJY8aMITExkRkzZtTpc6gvpWuUeXAWSUlJ\n+uQATsmqVeTcdTcoRWHvfrwY3o+nvn+T0L/+lcCbz+hLO7oPXhlgW1cw+slmj1s0vt27d9OzZ09H\nh1FnpaWl+Pr6Ul5ezsUXX8yCBQvo37+/o8MSrcS5/l8opTZrretd6dHpxwxOjhcYgjoQXPQ7Czx+\n5DAB576r2dpXwOAOg+9q5iiFON306dPZtWsXFRUVTJs2TRKBcHpOnwxMeXlgMOAaFIzb8RxKD3vg\nFhGB+5nzs0vyYOv7kHgj+NVyxzMhmtj777/v6BCEqJMWMGaQh2tQELi4oK1Qnudx7hIUG+aDxQjJ\nsxwTqBBCtGDOnwzy8nANC8VkMnGi0B2r2eXsKaUVxbDpTeg5AYK6OiZQIYRowexKBkqpcUqpVKXU\nXqXUI+d4XimlXq56/nelVP+qx6OUUquUUruUUjuVUnWuI23Ky8MtJBRTRTlWowuunhZ8ks8oQbF5\nEVQel4J0QghRT7UmA6WUAZgLjAd6ATcqpc6sBz0e6Fb1NR2YV/W4GfiL1roXMAS45xz7XpDtyiAM\ns7vG3d9EzOgCDD415t6ajbD+NYgdDhH2L/kXQghxij1XBoOAvVrr/VprI/AhMOmMbSYB72ib9UCA\nUipca31Ia70FQGtdAuwGIrCTpbQMa2kpbqEhHB/Zkc7j8nH3tZx21zO2fwwlh+SqQDQZKWFtPylh\nbb/zlbA+6fnnn0cpdd71KI3NnmQQAWTX+D2Hsxv0WrdRSsUC/YCzqzGdh/mIbVqpa2gYARX7USej\n/fW/cDwXrFZY8xKEJkCXUfYeVog6kRLWdSMlrGt3oRLWYCtfsWLFCqKjo+t1/PpolgFkpZQv8Clw\nv9b6nGUnlVLTlVIpSqmU/Px84NTqYxUURLQpg2y/vrbuoFX/ghfj4Y1RUJAmZapFs5ES1lLC+qSm\nKmEN8Oc//5k5c+Y0613+7FlnkAvULE0YWfWYXdsopdywJYL3tNafne9FtNYLgAVgW4EMYDps+4eQ\nr610USVkxYwn6tpHofCA7S5mW9+HoO7Q+yo73oZo8b57BA5vb9xjhiXA+LMv0c9FSlhLCevaNEYJ\n6y+//JKIiAj69u1r12s2FnuSwSagm1KqE7YG/gbgpjO2WQrMVEp9CAwGjmutDylbWnsT2K21/m9d\ngzvZTVRwPJsugH90H9sTgZ3gkr/CyMdAW8Hg9GvnRAsmJaylhLW9GlrCury8nKeffpoVK1bYvU9j\nqbUV1VqblVIzgeWAAViotd6plJpR9fx84FvgMmAvUA7cVrX7MGAqsF0ptbXqsce01t/aE5zp8GEM\nAQGcOLIbgPBuZyzpd3GhBSyVEI3FzjP4xiYlrG2khHXtGlrCet++fRw4cKD6qiAnJ4f+/fuzceNG\nwsLC6h2XPexqSbXW32qt47TWXbTW/6p6bH5VIqBqFtE9Vc8naK1Tqh7/VWuttNZ9tNaJVV92JQIA\nc94RXENDcTuaShH+eAY07YchxIVICev6kRLW9pewTkhI4MiRI2RkZJCRkUFkZCRbtmxp8kQATn5a\nbco7jGtYKO1L93LYs7MMEguHkxLWdSclrOtWwtpRnLqEddqwi/AeOZIA19dJC59I/xmvOzo00cyk\nhLUQZ2tTJaytRiOWo0cp83Al0lqBIcyxWVOIupAS1qKlcdpkYD5iW2tQarX1xwbENu80KyEaQkpY\ni5bGaccMzHm2BWdGSyEA4V3Pcb9jIYQQjcKJk0HVHc7MhzmignD3be/giIQQovVy2mRwcvVxgCGH\nfK8uDo5GCCFaN6dNBua8PJSXF9EuuZxoH+focIQQolVz2mRgystDt2+Hp4sZ944yk0g4jpSwtp+U\nsLbfhUpYv/LKK/To0YPevXvbVRSwMThtMjDn5WHydgegfScZPBaOIyWs60ZKWNfuQiWsV61axZdf\nfsm2bdvYuXMnDz74YL1eo66cNhmY8g5jcTNj1Yrwzn0cHY4QgJSwlhLWpzRVCet58+bxyCOPVK/8\nDgkJqfWzbgxOvc5ABfnycvuOXGMsJMqz/sWjROvwn43/YU/hnkY9Zo/AHjw86GG7tpUS1lLCujaN\nUcI6LS2N1atX8/jjj+Pp6clzzz13wRIejcUpk4E2m8FsptyriDfbu7Ls+z+x7Jpljg5LtFFSwlpK\nWNuroSWswdY9VVhYyPr169m0aRPXXXcd+/fvb/Ib3ThnMqj6x7+vfSXgSm5pLlZtxUU5ba+WaAb2\nnsE3NilhbSMlrGvX0BLWYLtKuPrqq1FKMWjQIFxcXCgoKCA4OLjecdnDOVvXqn9kvwQ1rFqiEI1J\nSljXj5Swtr+ENcCVV15ZPVaTlpaG0WiscwXX+nDKZGCtujLY3d5AqEfTfwhC2EtKWNedlLCuWwnr\nP/7xj+zfv5/4+HhuuOEG3n777Wa5F7JTlrBOjIrS7/n5cvNDLlzfcwrvpX7A5ps3425wd3RooplJ\nCWshztZmSlhrk4kiX0iodMHb3ReAnNIcOrfr7ODIhLCPlLAWLY1TJgOLyUi+HyRaQ2nnYbu8Lqoo\ngnYODkwIO0kJa9HSOOWYgcVYyTE/RV+fRMpN5QAs3bfUwVEJIUTr5ZTJQJmtuHmZCY1K4qKIiwDY\ndXSXg6MSQojWyym7iVw0dHQ3EtI1kZDgHvi4+dAjsIejwxJCiFbLKa8MADobLARHdgOgb3BfVmat\nZFXWKgdHJYQQrZPTJgOjbzDKxQDAXX3vothYzL2r7uWHzB8cHJloa6SEtf2khLX9zlfCeuvWrQwZ\nMoTExESSkpLYuHFjY4V8QU6bDIqDT9UkSQxJ5OurvqZvcF/CfepW50OIhpIS1nUjJaxrd6ES1rNn\nz+aJJ55g69atPPXUU3I/A2tMwmm/x/jHsPiyxfQOkhvdCMeREtZSwvqkpiphrZSiuLgYsJXn6Nix\nY62fdWNwygFk7aLx6dTP0WEIJ3P46aep3N24Jaw9evYg7LHH7NpWSlhLCevaNEYJ6xdffJGxY8fy\n4IMPYrVaz1sqo7E5aTKAsDhJBsI5SAlrKWFtr8YoYT1v3jxeeOEFrrnmGj7++GNuv/12fvih6cdK\n7UoGSqlxwEuAAXhDa/3MGc+rqucvA8qBW7XWW+zZ91y0C3QIOf8Zh2ib7D2Db2xSwtpGSljXrjFK\nWL/99tvVXVSTJ0/mjjvuqHc8dVHrmIFSygDMBcYDvYAblVK9zthsPNCt6ms6MK8O+57FanCBZqjS\nJ0RdSAnr+pES1nUrYd2xY0d+/vlnAH788Ue6devWKLHVxp4rg0HAXq31fgCl1IfAJKDmkuBJwDva\nNiq0XikVoJQKB2Lt2PcsVoPTjmuLNq5mCeupU6eye/dukpOTAfD19WXx4sWnlbAODQ09q4T1dddd\nx4IFC7j88svP+RqzZ89m2rRp/POf/zzvNmd67rnncHNzIzExEbCdfU6ZMoUJEyaQkJBAUlLSOUtY\njx8/nnvuuaf6OHfffTd33XUXCQkJuLq6NnoJ67CwMLtKWLdv355LLrmEAwcO2P06J8tH33vvvRw/\nfhyz2cz9999P7969q0tYe3l5sW7duvNud6YLjRkMHz6cPXv2UFpaSmRkJG+++SZjx46169g1S1hb\nLBb++Mc/Vm/z+uuvc99992E2m/H09GTBggV2fwYNUWsJa6XUtcA4rfUdVb9PBQZrrWfW2OZr4Bmt\n9a9Vv68EHsaWDC6477l0CWuv9x0+Vu83JVoPKWEtxNladQlrpdR0bF1MRIV1cHA0QjSMlLAWLY09\nySrxePwAAAZbSURBVCAXqDmaG1n1mD3buNmxLwBa6wXAAoCkpCTnu+OOEHUgJaxFS2NP5/wmoJtS\nqpNSyh24ATiznvRS4BZlMwQ4rrU+ZOe+QgghHKzWKwOttVkpNRNYjm166EKt9U6l1Iyq5+cD32Kb\nVroX29TS2y60b5O8E9Fqaa2b5R6wQrQETXWrYrvGDLTW32Jr8Gs+Nr/Gzxq458z9zrevEPby9PTk\n6NGjdOjQQRKCaPO01hw9ehRPT89GP7bTDCALcS6RkZHk5OSQn5/v6FCEcAqenp5ERkY2+nElGQin\n5ubmdlpJAiFE05DVXUIIISQZCCGEkGQghBACO8pROIJSqgRwjvvl1V0QUODoIOpJYncMid0xWlvs\nMVrr4Poe0FkHkFMbUmPDkZRSKRJ785PYHUNid4ymiF26iYQQQkgyEEII4bzJoHkKeDcNid0xJHbH\nkNgdo9Fjd8oBZCGEEM3LWa8MhBBCNKMmTwZKqXFKqVSl1F6l1CPneF4ppV6uev53pVT/2vZVSgUq\npb5XSqVXfW/fgmJ/UimVq5TaWvV1mRPGvlApdUQpteOMfVrC536+2J36c1dKRSmlVimldimldiql\n7quxj1N/7rXE7uyfu6dSaqNSaltV7H+vsU+zfO5NGH/dPnutdZN9YStbvQ/oDLgD24BeZ2xzGfAd\noIAhwIba9gXmAI9U/fwI8J8WFPuTwIPO+rlXPXcx0B/YccY+Tv251xK7U3/uQDjQv+pnPyCtBf17\nv1Dszv65K8C36mc3YAMwpLk+9yaOv06ffVNfGQwC9mqt92utjcCHwKQztpkEvKNt1gMBSqnwWvad\nBPx/e/cTYlUZh3H8+5BKklYkIqKBQWMiqRNoRLZokRDRolbWwiZo40YoNy6C1m500x8XomARIpLg\n0M4iSExpYLJJMKtNVExjEP0TBdFfi/c1D9Pcc+/13nPvOczzgcvMnPu+wzO/uePvvud433skf34E\neKFB2Qehl+xExOfA73N837rXvSz7INxx9oiYjohJgIj4G7gIrCrMqW3d22QfhF6yR0T8k8cszLco\nzKm67lXm70rVzWAV8FPh65/5/4Ok1ZiyuSsivZMawK/Ain4F7iBXJ2Pazd2Vl3qHK1p69pK9TN3r\n3k4j6i5pDfAY6VkeNKjuc2SHmtdd0l2SzgOXgVMRMci6l2brZExJfuii9o2/gBxpPdSk/xJ1gLQc\nHAWmgX3DjXNnXPdqSFoCfAS8HhF/zb6/znVvkb32dY+IGxExSnqP9sclPTrHmNrWvSR/V7Wvuhn8\nAjxY+Hp1PtbJmLK5M7dOC+SPl/uYuV2uTsa0nBsRM/mXdxM4SFoi9lsv2cvUve4tNaHukhaS/jH9\nMCJOFMbUvu6tsjeh7oWsfwCfAc/mQ4Ooe0fZOhkzO3+3ta+6GUwAI5IekrQIeAkYnzVmHHglXy1/\nAvgzL83K5o4DY/nzMeBkU7LfenBlLwIX6L9espepe91bqnvdJQk4BFyMiP1zzKlt3cuyN6DuyyXd\nn7MuBrYB3xbmVF33yvJ3XfvZV5T7fSNdBf+OdLX8zXxsJ7Azbl8Nfzff/w2wuWxuPr4M+BT4HvgE\neKBB2T/IY6fyL3hlDbMfJS0rr5POTb7WoLq3yl7rugNPkU5DTAHn8+25JtS9Tfa6130j8FXOdwF4\nq/A9B1L3CvN3VXu/AtnMzJp/AdnMzHrnZmBmZm4GZmbmZmBmZrgZmJkZbgZmfSVpjfKOqZKelvTx\nsDOZdcLNwIz/tgj234PNW37w27yVn8VfkvQ+6QU7OySdlTQp6XjeawdJWyR9obRn/JeSlua5p/PY\nSUlPDvenMevNgmEHMBuyEdJWAz8AJ4BnIuKKpD3Abkl7gWPA9oiYkHQvcJW0T822iLgmaYT0yufN\nw/kRzHrnZmDz3Y8RcU7S88B64EzaaodFwFngEWA6IiYAIu/GKeke4B1Jo8ANYO0wwpv1i5uBzXdX\n8keR9oJ/uXinpA0t5r0BzACbSKdbr1WW0GwAfM3ALDkHbJX0MKRn/pLWApeAlZK25ONLJS0A7iOt\nGG4CO0hvXWjWWG4GZkBE/Aa8ChyVNEU6RbQu0tsQbgfelvQ1cAq4G3gPGMvH1nF7hWHWSN611MzM\nvDIwMzM3AzMzw83AzMxwMzAzM9wMzMwMNwMzM8PNwMzMcDMwMzPgX6cH0kjB+7MTAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae94cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       area                            model\n",
      "0  1.366007  Regularization Parameter: 1e-02\n",
      "1  0.522146  Regularization Parameter: 1e-04\n",
      "2  0.104511  Regularization Parameter: 1e-06\n",
      "3  0.878415  Regularization Parameter: 1e-08\n"
     ]
    }
   ],
   "source": [
    "model_comp1 = graphlab.compare(test, [sgd_R2,sgd_R3,sgd_R4,sgd_R5],target='rating')\n",
    "model_comp1_df = model_comp1.to_dataframe()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "auc={'model':[], 'area':[]}\n",
    "for i in range(0,len(model_comp1_df.index)):\n",
    "    xy_dict= model_comp1_df.loc[i,\"results\"]['precision_recall']\n",
    "    xy = pd.DataFrame(xy_dict)    \n",
    "    #xy.plot(x='recall',y= 'precision',ax=ax)\n",
    "    label1= \"Regularization Parameter: 1e-0{0}\".format(2*(i+1))\n",
    "    xy.plot(x='recall',y= 'precision',ax=ax,label=label1)\n",
    "    auc['model'].append(label1)\n",
    "    auc['area'].append(np.trapz(xy['precision']))\n",
    "    \n",
    "plt.show()\n",
    "auc_df= pd.DataFrame(auc)\n",
    "print(auc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LqFs1ll9frO90iJSVwkMqhQk/bmXe+l3c37cNVWNL0qWbiJyMwkMqvJy8AkZO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa99462b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_comp1 =graphlab.recommender.util.compare_models(test, [sgd_R2,sgd_R3,sgd_R4,sgd_R5], model_names=None,\n",
    "                                         user_sample=1.0, metric='rmse',\n",
    "                                         target='rating', exclude_known_for_precision_recall=True, \n",
    "                                         make_plot=False, verbose=False)\n",
    "\n",
    "rmse_data = {'Regularization Parameter':['1e-02','1e-04','1e-06','1e-08'], 'rmse':[]}\n",
    "for i in range(0,len(model_comp1)):\n",
    "    rmse_data['rmse'].append(model_comp1[i]['rmse_overall'])\n",
    "    \n",
    "\n",
    "rmse_df=pd.DataFrame(rmse_data)\n",
    "rmse_df.plot(x='Regularization Parameter',y='rmse')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the RMSE and Precision-Recall Curves plotted above, we see that a regularization parameter of 1e-08 has the best performance therefore we set our model to that value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuning the model by changing number of latent factors in the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.740286s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.740286s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 2        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 2        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00119093        | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00119093        | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.000297732       | No Decrease (2.95529 >= 1.12287)         |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.000297732       | No Decrease (2.95529 >= 1.12287)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 7.44331e-005      | 1.02761                                  |</pre>"
      ],
      "text/plain": [
       "| 7       | 7.44331e-005      | 1.02761                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 3.72166e-005      | 1.04402                                  |</pre>"
      ],
      "text/plain": [
       "| 8       | 3.72166e-005      | 1.04402                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9       | 1.86083e-005      | 1.06099                                  |</pre>"
      ],
      "text/plain": [
       "| 9       | 1.86083e-005      | 1.06099                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 9.30414e-006      | 1.07689                                  |</pre>"
      ],
      "text/plain": [
       "| 10      | 9.30414e-006      | 1.07689                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 7.44331e-005      | 1.02761                                  |</pre>"
      ],
      "text/plain": [
       "| Final   | 7.44331e-005      | 1.02761                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.1336            | 1.06471               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.1336            | 1.06471               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 202.642ms    | 1.00903           | 1.0045                |             |</pre>"
      ],
      "text/plain": [
       "| 1       | 202.642ms    | 1.00903           | 1.0045                |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 374.765ms    | 0.990163          | 0.995069              |             |</pre>"
      ],
      "text/plain": [
       "| 2       | 374.765ms    | 0.990163          | 0.995069              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 601.925ms    | 0.977344          | 0.988607              |             |</pre>"
      ],
      "text/plain": [
       "| 3       | 601.925ms    | 0.977344          | 0.988607              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 792.561ms    | 0.967644          | 0.983689              |             |</pre>"
      ],
      "text/plain": [
       "| 4       | 792.561ms    | 0.967644          | 0.983689              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.00s        | 0.965004          | 0.982346              |             |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.00s        | 0.965004          | 0.982346              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.26s        | 0.954358          | 0.976912              |             |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.26s        | 0.954358          | 0.976912              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.12s        | 0.941927          | 0.970529              |             |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.12s        | 0.941927          | 0.970529              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.36s        | 0.937948          | 0.968477              |             |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.36s        | 0.937948          | 0.968477              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 3.01s        | 0.928072          | 0.963365              |             |</pre>"
      ],
      "text/plain": [
       "| 15      | 3.01s        | 0.928072          | 0.963365              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 3.76s        | 0.919447          | 0.958878              |             |</pre>"
      ],
      "text/plain": [
       "| 20      | 3.76s        | 0.919447          | 0.958878              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 4.46s        | 0.910965          | 0.954445              | 6.6575e-006 |</pre>"
      ],
      "text/plain": [
       "| 25      | 4.46s        | 0.910965          | 0.954445              | 6.6575e-006 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 5.34s        | 0.902938          | 0.950231              |             |</pre>"
      ],
      "text/plain": [
       "| 30      | 5.34s        | 0.902938          | 0.950231              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 6.40s        | 0.897049          | 0.947127              |             |</pre>"
      ],
      "text/plain": [
       "| 35      | 6.40s        | 0.897049          | 0.947127              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 7.44s        | 0.892367          | 0.944652              |             |</pre>"
      ],
      "text/plain": [
       "| 40      | 7.44s        | 0.892367          | 0.944652              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 8.47s        | 0.890336          | 0.943576              |             |</pre>"
      ],
      "text/plain": [
       "| 45      | 8.47s        | 0.890336          | 0.943576              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 9.58s        | 0.889962          | 0.943378              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 9.58s        | 0.889962          | 0.943378              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.889877</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.889877"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.943333</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.943333"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 1.0764s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 1.0764s"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00119093        | 0.89242                                  |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00119093        | 0.89242                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.000595465       | 0.953485                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.000595465       | 0.953485                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000297732       | 1.03091                                  |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000297732       | 1.03091                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 0.000148866       | 1.03028                                  |</pre>"
      ],
      "text/plain": [
       "| 8       | 0.000148866       | 1.03028                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00119093        | 0.89242                                  |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00119093        | 0.89242                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.13357           | 1.06469               |             |</pre>"
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      "text/plain": [
       "| Initial | 0us          | 1.13357           | 1.06469               |             |"
      ]
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    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
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      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
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    {
     "data": {
      "text/html": [
       "<pre>| 1       | 251.679ms    | 1.02812           | 1.01396               | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 251.679ms    | 1.02812           | 1.01396               | 0.00119093  |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 518.37ms     | 0.878893          | 0.937493              | 0.000708131 |</pre>"
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      "text/plain": [
       "| 2       | 518.37ms     | 0.878893          | 0.937493              | 0.000708131 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 691.993ms    | 0.835567          | 0.914094              | 0.000522451 |</pre>"
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      "text/plain": [
       "| 3       | 691.993ms    | 0.835567          | 0.914094              | 0.000522451 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 958.68ms     | 0.824429          | 0.907981              | 0.000421057 |</pre>"
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      "text/plain": [
       "| 4       | 958.68ms     | 0.824429          | 0.907981              | 0.000421057 |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.17s        | 0.849946          | 0.921925              | 0.000356171 |</pre>"
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      "text/plain": [
       "| 5       | 1.17s        | 0.849946          | 0.921925              | 0.000356171 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.35s        | 0.778696          | 0.882438              | 0.000261225 |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.35s        | 0.778696          | 0.882438              | 0.000261225 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.19s        | 0.762176          | 0.873027              |             |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.19s        | 0.762176          | 0.873027              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.36s        | 0.777859          | 0.881963              |             |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.36s        | 0.777859          | 0.881963              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 2.96s        | 0.812065          | 0.901146              |             |</pre>"
      ],
      "text/plain": [
       "| 15      | 2.96s        | 0.812065          | 0.901146              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 3.94s        | 0.813333          | 0.90185               |             |</pre>"
      ],
      "text/plain": [
       "| 20      | 3.94s        | 0.813333          | 0.90185               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 4.96s        | 0.81768           | 0.904257              |             |</pre>"
      ],
      "text/plain": [
       "| 25      | 4.96s        | 0.81768           | 0.904257              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 5.76s        | 0.818419          | 0.904665              |             |</pre>"
      ],
      "text/plain": [
       "| 30      | 5.76s        | 0.818419          | 0.904665              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 6.83s        | 0.818924          | 0.904944              |             |</pre>"
      ],
      "text/plain": [
       "| 35      | 6.83s        | 0.818924          | 0.904944              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 7.71s        | 0.818933          | 0.904949              |             |</pre>"
      ],
      "text/plain": [
       "| 40      | 7.71s        | 0.818933          | 0.904949              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 8.36s        | 0.818741          | 0.904843              |             |</pre>"
      ],
      "text/plain": [
       "| 45      | 8.36s        | 0.818741          | 0.904843              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 9.03s        | 0.818569          | 0.904748              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 9.03s        | 0.818569          | 0.904748              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.818542</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.818542"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.904733</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.904733"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.857628s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.857628s"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 6        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 6        |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.787087                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.787087                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.863499                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.863499                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.878738                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.878738                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.912818                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.912818                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.787087                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.787087                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 500us        | 1.13369           | 1.06475               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 500us        | 1.13369           | 1.06475               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 398.283ms    | DIVERGED          | DIVERGED              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 398.283ms    | DIVERGED          | DIVERGED              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 508.861ms    | 1.13393           | 1.06486               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 508.861ms    | 1.13393           | 1.06486               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 910.146ms    | DIVERGED          | DIVERGED              | 0.00238186  |</pre>"
      ],
      "text/plain": [
       "| 1       | 910.146ms    | DIVERGED          | DIVERGED              | 0.00238186  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 1.05s        | 1.1337            | 1.06475               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 1.05s        | 1.1337            | 1.06475               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.41s        | DIVERGED          | DIVERGED              | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.41s        | DIVERGED          | DIVERGED              | 0.00119093  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 1.54s        | 1.1339            | 1.06485               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 1.54s        | 1.1339            | 1.06485               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.82s        | 0.982754          | 0.99134               | 0.000595465 |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.82s        | 0.982754          | 0.99134               | 0.000595465 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 2.03s        | 0.890145          | 0.943475              | 0.000354066 |</pre>"
      ],
      "text/plain": [
       "| 2       | 2.03s        | 0.890145          | 0.943475              | 0.000354066 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 2.26s        | 0.877174          | 0.936576              | 0.000261225 |</pre>"
      ],
      "text/plain": [
       "| 3       | 2.26s        | 0.877174          | 0.936576              | 0.000261225 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 2.56s        | 0.842318          | 0.917779              | 0.000210529 |</pre>"
      ],
      "text/plain": [
       "| 4       | 2.56s        | 0.842318          | 0.917779              | 0.000210529 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 2.78s        | 0.890865          | 0.943857              | 0.000178086 |</pre>"
      ],
      "text/plain": [
       "| 5       | 2.78s        | 0.890865          | 0.943857              | 0.000178086 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 3.07s        | 0.823476          | 0.907456              | 0.000130613 |</pre>"
      ],
      "text/plain": [
       "| 6       | 3.07s        | 0.823476          | 0.907456              | 0.000130613 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 3.32s        | 0.80958           | 0.899767              |             |</pre>"
      ],
      "text/plain": [
       "| 7       | 3.32s        | 0.80958           | 0.899767              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 4.26s        | 0.803462          | 0.89636               |             |</pre>"
      ],
      "text/plain": [
       "| 11      | 4.26s        | 0.803462          | 0.89636               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12      | 4.53s        | 0.79924           | 0.894002              |             |</pre>"
      ],
      "text/plain": [
       "| 12      | 4.53s        | 0.79924           | 0.894002              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 17      | 5.56s        | 0.799719          | 0.89427               |             |</pre>"
      ],
      "text/plain": [
       "| 17      | 5.56s        | 0.799719          | 0.89427               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 22      | 6.67s        | 0.802116          | 0.895609              |             |</pre>"
      ],
      "text/plain": [
       "| 22      | 6.67s        | 0.802116          | 0.895609              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 27      | 7.77s        | 0.802075          | 0.895587              |             |</pre>"
      ],
      "text/plain": [
       "| 27      | 7.77s        | 0.802075          | 0.895587              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 32      | 9.18s        | 0.802334          | 0.895731              |             |</pre>"
      ],
      "text/plain": [
       "| 32      | 9.18s        | 0.802334          | 0.895731              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 37      | 10.27s       | 0.802273          | 0.895697              |             |</pre>"
      ],
      "text/plain": [
       "| 37      | 10.27s       | 0.802273          | 0.895697              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 42      | 11.31s       | 0.802287          | 0.895705              |             |</pre>"
      ],
      "text/plain": [
       "| 42      | 11.31s       | 0.802287          | 0.895705              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 47      | 12.40s       | 0.802296          | 0.89571               |             |</pre>"
      ],
      "text/plain": [
       "| 47      | 12.40s       | 0.802296          | 0.89571               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.802297</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.802297"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.89571</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.89571"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sgd_NF1 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-08,num_factors=2)\n",
    "sgd_NF2 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-08,num_factors=4)\n",
    "sgd_NF3 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId', item_id='movieId',user_data = user_metadata, item_data = movie_metadata,solver= \"sgd\" ,regularization=1e-08,num_factors=6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Evaluate model M0\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |      0.008       | 4.61095100865e-06 |\n",
      "|   2    |      0.004       | 4.61095100865e-06 |\n",
      "|   3    | 0.00533333333333 | 0.000140204171348 |\n",
      "|   4    |       0.01       | 0.000497131907132 |\n",
      "|   5    |      0.0336      |  0.00326223617515 |\n",
      "|   6    | 0.0293333333333  |  0.00327199227271 |\n",
      "|   7    | 0.0285714285714  |  0.00353709295423 |\n",
      "|   8    |       0.04       |  0.00512713615469 |\n",
      "|   9    |      0.048       |  0.00616957198244 |\n",
      "|   10   |      0.0528      |  0.0072840212073  |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-----------------+-------------------+\n",
      "| cutoff |  mean_precision |    mean_recall    |\n",
      "+--------+-----------------+-------------------+\n",
      "|   1    |       0.0       |        0.0        |\n",
      "|   2    |      0.036      | 0.000716842097082 |\n",
      "|   3    |      0.024      | 0.000716842097082 |\n",
      "|   4    |      0.022      |  0.00085704626843 |\n",
      "|   5    |      0.0176     |  0.00085704626843 |\n",
      "|   6    | 0.0146666666667 |  0.00085704626843 |\n",
      "|   7    | 0.0125714285714 |  0.00085704626843 |\n",
      "|   8    |      0.012      | 0.000865723058017 |\n",
      "|   9    | 0.0115555555556 | 0.000875479155578 |\n",
      "|   10   |      0.0128     | 0.000941635181363 |\n",
      "+--------+-----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M2\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-----------------+-------------------+\n",
      "| cutoff |  mean_precision |    mean_recall    |\n",
      "+--------+-----------------+-------------------+\n",
      "|   1    |       0.0       |        0.0        |\n",
      "|   2    |      0.008      | 0.000140204171348 |\n",
      "|   3    |      0.032      | 0.000866802365991 |\n",
      "|   4    |       0.05      |  0.00174491987752 |\n",
      "|   5    |      0.0688     |  0.00466200195374 |\n",
      "|   6    | 0.0693333333333 |  0.00553811658646 |\n",
      "|   7    | 0.0674285714286 |  0.00602140748038 |\n",
      "|   8    |      0.066      |  0.00683503036852 |\n",
      "|   9    | 0.0648888888889 |  0.00725044498729 |\n",
      "|   10   |      0.0624     |  0.00733056975031 |\n",
      "+--------+-----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "Model compare metric: precision_recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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cTLtAH5bMGM5dC7fwxbb03xOxPdkG+xqJCvap/HxGIvbyxN/797anr8B9jbZ3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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9915ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_comp1 = graphlab.compare(test, [sgd_NF1,sgd_NF2,sgd_NF3],target='rating')\n",
    "model_comp1_df = model_comp1.to_dataframe()\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "for i in range(0,len(model_comp1_df.index)):\n",
    "    xy_dict= model_comp1_df.loc[i,\"results\"]['precision_recall']\n",
    "    xy = pd.DataFrame(xy_dict)    \n",
    "    #xy.plot(x='recall',y= 'precision',ax=ax)\n",
    "    label1= 2*(i+1)\n",
    "    xy.plot(x='recall',y= 'precision',ax=ax,label=\"{0} latent factors\".format(label1))\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7IvSyTaW8QKOQQBZMiGVApyY8uGw9H284aHUkt/H0i7uXGWMiKTkddJ+IXFHW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae9610f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  number_of_latent_factors      rmse\n",
      "0                        2  0.965563\n",
      "1                        4  1.194703\n",
      "2                        6  1.003914\n"
     ]
    }
   ],
   "source": [
    "model_comp1 =graphlab.recommender.util.compare_models(test, [sgd_NF1,sgd_NF2,sgd_NF3], model_names=None,\n",
    "                                         user_sample=1.0, metric='rmse',\n",
    "                                         target='rating', exclude_known_for_precision_recall=True, \n",
    "                                         make_plot=False, verbose=False)\n",
    "#fig, ax = plt.subplots(1, 1)\n",
    "rmse_data = {'number_of_latent_factors':['2','4','6'], 'rmse':[]}\n",
    "for i in range(0,len(model_comp1)):\n",
    "    rmse_data['rmse'].append(model_comp1[i]['rmse_overall'])\n",
    "    \n",
    "\n",
    "rmse_df=pd.DataFrame(rmse_data)\n",
    "rmse_df.plot(x='number_of_latent_factors',y='rmse')\n",
    "plt.show()\n",
    "print(rmse_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the RMSE and 'Precision-Recall' curves above, we see that a model with 6 latent factors has the best performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After tuning the model for best perormance by varying the 'solver','Regularization Parameter' and ' Number of Latent Factors' we finally set the following values for the parameters:\n",
    "1.Solver : Stochatic Gradient Descent\n",
    "2.Regularization : 1e-08\n",
    "3.Number of latent factors : 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Model Performance For Different Sample Sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the training data into 3 samples of 0.25, 0.5 and 0.75 fractions of the initial traiing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training sample-1 shape - (20001, 20)\n",
      "Training sample-2 shape - (40002, 20)\n",
      "Training sample-3 shape - (60002, 20)\n"
     ]
    }
   ],
   "source": [
    "train_sample1 = trainData.sample(frac =0.25,replace =False)\n",
    "train_sample2 = trainData.sample(frac =0.5,replace =False)\n",
    "train_sample3 = trainData.sample(frac =0.75,replace =False)\n",
    "print(\"Training sample-1 shape - {}\".format(train_sample1.shape))\n",
    "print(\"Training sample-2 shape - {}\".format(train_sample2.shape))\n",
    "print(\"Training sample-3 shape - {}\".format(train_sample3.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#converting data to csv for Sframe parsing\n",
    "train_sample1.to_csv(\"train_data1.csv\")\n",
    "train_sample2.to_csv(\"train_data2.csv\")\n",
    "train_sample3.to_csv(\"train_data3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data1.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data1.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 20001 lines in 0.14912 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 20001 lines in 0.14912 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data2.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data2.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 40002 lines in 0.265736 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 40002 lines in 0.265736 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data3.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_data3.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 60002 lines in 0.369281 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 60002 lines in 0.369281 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#converting the train datasets to Sframe\n",
    "hints = [int,int ,int ,float,str, str, str, str, str, str,str,str,str,str,str,str, str,str,float, int, float]\n",
    "train1 = graphlab.SFrame.read_csv('train_data1.csv',column_type_hints = hints )\n",
    "train2 = graphlab.SFrame.read_csv('train_data2.csv',column_type_hints = hints )\n",
    "train3 = graphlab.SFrame.read_csv('train_data3.csv',column_type_hints = hints )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building a factorization_recommender model using the the 3 samples and the full training dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 20001 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 20001 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.612458s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.612458s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 6        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 6        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 20001 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 20001 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00119093        | 0.901003                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00119093        | 0.901003                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.000595465       | No Decrease (1.26557 >= 1.10382)         |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.000595465       | No Decrease (1.26557 >= 1.10382)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000148866       | 0.990808                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000148866       | 0.990808                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 7.44331e-005      | 1.01091                                  |</pre>"
      ],
      "text/plain": [
       "| 8       | 7.44331e-005      | 1.01091                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00119093        | 0.901003                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00119093        | 0.901003                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 499us        | 1.10165           | 1.0496                |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 499us        | 1.10165           | 1.0496                |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 95.567ms     | 1.11265           | 1.05482               | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 95.567ms     | 1.11265           | 1.05482               | 0.00119093  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 186.631ms    | 0.947619          | 0.973457              | 0.000708131 |</pre>"
      ],
      "text/plain": [
       "| 2       | 186.631ms    | 0.947619          | 0.973457              | 0.000708131 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 264.688ms    | 0.914488          | 0.956289              | 0.000522451 |</pre>"
      ],
      "text/plain": [
       "| 3       | 264.688ms    | 0.914488          | 0.956289              | 0.000522451 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 366.26ms     | 0.882542          | 0.939437              | 0.000354066 |</pre>"
      ],
      "text/plain": [
       "| 4       | 366.26ms     | 0.882542          | 0.939437              | 0.000354066 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 462.251ms    | 0.913824          | 0.955941              | 0.000299503 |</pre>"
      ],
      "text/plain": [
       "| 5       | 462.251ms    | 0.913824          | 0.955941              | 0.000299503 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 569.327ms    | 0.829365          | 0.910695              | 0.000219663 |</pre>"
      ],
      "text/plain": [
       "| 6       | 569.327ms    | 0.829365          | 0.910695              | 0.000219663 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 1.02s        | 0.806673          | 0.89815               |             |</pre>"
      ],
      "text/plain": [
       "| 11      | 1.02s        | 0.806673          | 0.89815               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 2.24s        | 0.789065          | 0.888293              |             |</pre>"
      ],
      "text/plain": [
       "| 25      | 2.24s        | 0.789065          | 0.888293              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 4.21s        | 0.785231          | 0.886132              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 4.21s        | 0.785231          | 0.886132              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.785112</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.785112"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.886066</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.886066"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 40002 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 40002 observations with 671 users and 9125 items."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.986247s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.986247s"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 6        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 6        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 40002 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 40002 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.77306                                  |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.77306                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.832885                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.832885                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.890843                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.890843                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.952967                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.952967                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.77306                                  |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.77306                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
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       "Starting Optimization."
      ]
     },
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     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
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    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.1184            | 1.05754               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.1184            | 1.05754               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 218.184ms    | DIVERGED          | DIVERGED              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 218.184ms    | DIVERGED          | DIVERGED              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 296.741ms    | 1.1184            | 1.05755               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 296.741ms    | 1.1184            | 1.05755               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 450.35ms     | 1.0704            | 1.0346                | 0.00238186  |</pre>"
      ],
      "text/plain": [
       "| 1       | 450.35ms     | 1.0704            | 1.0346                | 0.00238186  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 589.448ms    | 0.934825          | 0.966863              | 0.00141626  |</pre>"
      ],
      "text/plain": [
       "| 2       | 589.448ms    | 0.934825          | 0.966863              | 0.00141626  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 707.533ms    | 0.898069          | 0.947665              | 0.0010449   |</pre>"
      ],
      "text/plain": [
       "| 3       | 707.533ms    | 0.898069          | 0.947665              | 0.0010449   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 892.665ms    | DIVERGED          | DIVERGED              | 0.000842115 |</pre>"
      ],
      "text/plain": [
       "| 4       | 892.665ms    | DIVERGED          | DIVERGED              | 0.000842115 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 949.703ms    | 1.11863           | 1.05765               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 949.703ms    | 1.11863           | 1.05765               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.12s        | 0.992117          | 0.996051              | 0.000421057 |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.12s        | 0.992117          | 0.996051              | 0.000421057 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.26s        | 0.939082          | 0.969062              | 0.000250362 |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.26s        | 0.939082          | 0.969062              | 0.000250362 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1.38s        | 0.918104          | 0.958177              | 0.000184714 |</pre>"
      ],
      "text/plain": [
       "| 3       | 1.38s        | 0.918104          | 0.958177              | 0.000184714 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.66s        | 0.891198          | 0.944033              | 0.000125926 |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.66s        | 0.891198          | 0.944033              | 0.000125926 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 2.04s        | 0.866788          | 0.931014              |             |</pre>"
      ],
      "text/plain": [
       "| 8       | 2.04s        | 0.866788          | 0.931014              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 2.94s        | 0.848416          | 0.921095              |             |</pre>"
      ],
      "text/plain": [
       "| 15      | 2.94s        | 0.848416          | 0.921095              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 3.98s        | 0.842695          | 0.917984              |             |</pre>"
      ],
      "text/plain": [
       "| 25      | 3.98s        | 0.842695          | 0.917984              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 4.90s        | 0.842285          | 0.917761              |             |</pre>"
      ],
      "text/plain": [
       "| 35      | 4.90s        | 0.842285          | 0.917761              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 5.96s        | 0.842222          | 0.917727              |             |</pre>"
      ],
      "text/plain": [
       "| 45      | 5.96s        | 0.842222          | 0.917727              |             |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 48      | 6.36s        | 0.842177          | 0.917702              |             |</pre>"
      ],
      "text/plain": [
       "| 48      | 6.36s        | 0.842177          | 0.917702              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached (hard limit).</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached (hard limit)."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
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       "Computing final objective value and training RMSE."
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.842195</pre>"
      ],
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       "       Final objective value: 0.842195"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.917712</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.917712"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 60002 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 60002 observations with 671 users and 9125 items."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 1.0728s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 1.0728s"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 6        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 6        |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 60002 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 60002 points for tuning the step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.731206                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.731206                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.853568                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.853568                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.89739                                  |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.89739                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.94374                                  |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.94374                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.731206                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.731206                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 499us        | 1.12291           | 1.05968               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 499us        | 1.12291           | 1.05968               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 330.808ms    | DIVERGED          | DIVERGED              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 330.808ms    | DIVERGED          | DIVERGED              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 424.374ms    | 1.12346           | 1.05994               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 424.374ms    | 1.12346           | 1.05994               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 604.502ms    | 1.11363           | 1.05529               | 0.00238186  |</pre>"
      ],
      "text/plain": [
       "| 1       | 604.502ms    | 1.11363           | 1.05529               | 0.00238186  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 904.716ms    | DIVERGED          | DIVERGED              | 0.00141626  |</pre>"
      ],
      "text/plain": [
       "| 2       | 904.716ms    | DIVERGED          | DIVERGED              | 0.00141626  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 996.28ms     | 1.12306           | 1.05975               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 996.28ms     | 1.12306           | 1.05975               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.24s        | 0.995908          | 0.997952              | 0.000708131 |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.24s        | 0.995908          | 0.997952              | 0.000708131 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.43s        | 0.900206          | 0.948792              | 0.000421057 |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.43s        | 0.900206          | 0.948792              | 0.000421057 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1.65s        | 0.866932          | 0.931092              | 0.000310651 |</pre>"
      ],
      "text/plain": [
       "| 3       | 1.65s        | 0.866932          | 0.931092              | 0.000310651 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1.84s        | 0.849653          | 0.921766              | 0.000250362 |</pre>"
      ],
      "text/plain": [
       "| 4       | 1.84s        | 0.849653          | 0.921766              | 0.000250362 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 2.03s        | 0.837172          | 0.914971              | 0.000211781 |</pre>"
      ],
      "text/plain": [
       "| 5       | 2.03s        | 0.837172          | 0.914971              | 0.000211781 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 2.42s        | 0.815627          | 0.903121              | 0.000164547 |</pre>"
      ],
      "text/plain": [
       "| 7       | 2.42s        | 0.815627          | 0.903121              | 0.000164547 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.97s        | 0.802727          | 0.89595               | 0.000125926 |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.97s        | 0.802727          | 0.89595               | 0.000125926 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12      | 3.39s        | 0.804929          | 0.897178              | 0.000109832 |</pre>"
      ],
      "text/plain": [
       "| 12      | 3.39s        | 0.804929          | 0.897178              | 0.000109832 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 17      | 4.71s        | 0.828825          | 0.910398              |             |</pre>"
      ],
      "text/plain": [
       "| 17      | 4.71s        | 0.828825          | 0.910398              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 22      | 6.04s        | 0.830299          | 0.911208              | 1.6482e-006 |</pre>"
      ],
      "text/plain": [
       "| 22      | 6.04s        | 0.830299          | 0.911208              | 1.6482e-006 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 27      | 7.39s        | 0.833019          | 0.912698              |             |</pre>"
      ],
      "text/plain": [
       "| 27      | 7.39s        | 0.833019          | 0.912698              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 32      | 8.51s        | 0.833523          | 0.912975              |             |</pre>"
      ],
      "text/plain": [
       "| 32      | 8.51s        | 0.833523          | 0.912975              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 37      | 9.76s        | 0.83338           | 0.912896              |             |</pre>"
      ],
      "text/plain": [
       "| 37      | 9.76s        | 0.83338           | 0.912896              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 42      | 11.18s       | 0.833421          | 0.912919              |             |</pre>"
      ],
      "text/plain": [
       "| 42      | 11.18s       | 0.833421          | 0.912919              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 47      | 12.26s       | 0.833514          | 0.91297               |             |</pre>"
      ],
      "text/plain": [
       "| 47      | 12.26s       | 0.833514          | 0.91297               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 12.94s       | 0.833486          | 0.912955              |             |</pre>"
      ],
      "text/plain": [
       "| 50      | 12.94s       | 0.833486          | 0.912955              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.83348</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.83348"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.912951</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.912951"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 1.21779s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 1.21779s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 6        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 6        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.776878                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.776878                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.804083                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.804083                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.925912                                 |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.925912                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.943805                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.943805                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.776878                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.776878                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 500us        | 1.13362           | 1.06472               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 500us        | 1.13362           | 1.06472               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 571.905ms    | DIVERGED          | DIVERGED              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 571.905ms    | DIVERGED          | DIVERGED              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 735.6ms      | 1.13336           | 1.06459               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 735.6ms      | 1.13336           | 1.06459               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.21s        | DIVERGED          | DIVERGED              | 0.00238186  |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.21s        | DIVERGED          | DIVERGED              | 0.00238186  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 1.36s        | 1.13364           | 1.06472               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 1.36s        | 1.13364           | 1.06472               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 1.57s        | 1.00662           | 1.00331               | 0.00119093  |</pre>"
      ],
      "text/plain": [
       "| 1       | 1.57s        | 1.00662           | 1.00331               | 0.00119093  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.75s        | 0.874157          | 0.934964              | 0.000708131 |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.75s        | 0.874157          | 0.934964              | 0.000708131 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1.95s        | 0.852755          | 0.923447              | 0.000522451 |</pre>"
      ],
      "text/plain": [
       "| 3       | 1.95s        | 0.852755          | 0.923447              | 0.000522451 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 2.14s        | 0.816572          | 0.903644              | 0.000421057 |</pre>"
      ],
      "text/plain": [
       "| 4       | 2.14s        | 0.816572          | 0.903644              | 0.000421057 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 2.34s        | 0.788359          | 0.887896              | 0.000356171 |</pre>"
      ],
      "text/plain": [
       "| 5       | 2.34s        | 0.788359          | 0.887896              | 0.000356171 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 2.56s        | 0.770358          | 0.8777                | 0.000310651 |</pre>"
      ],
      "text/plain": [
       "| 6       | 2.56s        | 0.770358          | 0.8777                | 0.000310651 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 2.91s        | 0.749676          | 0.865838              | 0.000250362 |</pre>"
      ],
      "text/plain": [
       "| 8       | 2.91s        | 0.749676          | 0.865838              | 0.000250362 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 3.44s        | 1.24664           | 1.11653               | 0.0001658   |</pre>"
      ],
      "text/plain": [
       "| 11      | 3.44s        | 1.24664           | 1.11653               | 0.0001658   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12      | 3.73s        | DIVERGED          | DIVERGED              | 0.000130613 |</pre>"
      ],
      "text/plain": [
       "| 12      | 3.73s        | DIVERGED          | DIVERGED              | 0.000130613 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| RESET   | 3.84s        | 1.13367           | 1.06474               |             |</pre>"
      ],
      "text/plain": [
       "| RESET   | 3.84s        | 1.13367           | 1.06474               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 5.03s        | 0.962593          | 0.981118              | 1.7035e-005 |</pre>"
      ],
      "text/plain": [
       "| 6       | 5.03s        | 0.962593          | 0.981118              | 1.7035e-005 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 16      | 6.92s        | 0.934619          | 0.966757              |             |</pre>"
      ],
      "text/plain": [
       "| 16      | 6.92s        | 0.934619          | 0.966757              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 26      | 8.98s        | 0.917939          | 0.958091              |             |</pre>"
      ],
      "text/plain": [
       "| 26      | 8.98s        | 0.917939          | 0.958091              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 36      | 11.15s       | 0.911545          | 0.954749              |             |</pre>"
      ],
      "text/plain": [
       "| 36      | 11.15s       | 0.911545          | 0.954749              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 11.79s       | 0.910309          | 0.954101              |             |</pre>"
      ],
      "text/plain": [
       "| 40      | 11.79s       | 0.910309          | 0.954101              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached (hard limit).</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached (hard limit)."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.910177</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.910177"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.954032</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.954032"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m1_t1 = graphlab.factorization_recommender.create(train1, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"sgd\" , user_data = user_metadata,\n",
    "                                               item_data = movie_metadata,regularization=1e-08, num_factors=6)\n",
    "m1_t2 = graphlab.factorization_recommender.create(train2, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"sgd\" , user_data = user_metadata,\n",
    "                                               item_data = movie_metadata, regularization=1e-08, num_factors=6)\n",
    "m1_t3 = graphlab.factorization_recommender.create(train3, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"sgd\" , user_data = user_metadata,\n",
    "                                               item_data = movie_metadata,regularization=1e-08, num_factors=6)\n",
    "m1 = graphlab.factorization_recommender.create(train, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"sgd\" , user_data = user_metadata,\n",
    "                                               item_data = movie_metadata,regularization=1e-08, num_factors=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8cg6bd5Xy+JgsTuveOuiSEo6CX0TqzNzPN3H1tFySk+oxa/wQ+mQ2DbqkhBTRURQzG2Zm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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae651b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  data_fraction      rmse\n",
      "0          0.25  0.906291\n",
      "1          0.50  0.909104\n",
      "2          0.75  0.934679\n",
      "3             1  0.962714\n"
     ]
    }
   ],
   "source": [
    "model_comp1 =graphlab.recommender.util.compare_models(test, [m1_t1,m1_t2,m1_t3,m1], model_names=None,\n",
    "                                         user_sample=1.0, metric='rmse',\n",
    "                                         target='rating', exclude_known_for_precision_recall=True, \n",
    "                                         make_plot=False, verbose=False)\n",
    "#fig, ax = plt.subplots(1, 1)\n",
    "rmse_data = {'data_fraction':[\"0.25\",\"0.50\",\"0.75\",\"1\"], 'rmse':[]}\n",
    "for i in range(0,len(model_comp1)):\n",
    "    rmse_data['rmse'].append(model_comp1[i]['rmse_overall'])\n",
    "    \n",
    "\n",
    "rmse_df=pd.DataFrame(rmse_data)\n",
    "rmse_df.plot(x='data_fraction',y='rmse')\n",
    "plt.show()\n",
    "print(rmse_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Evaluate model M0\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 4.61095100865e-06 |\n",
      "|   3    | 0.00533333333333 | 0.000140204171348 |\n",
      "|   4    |      0.008       | 0.000239067391003 |\n",
      "|   5    |      0.0064      | 0.000239067391003 |\n",
      "|   6    | 0.0186666666667  |  0.00103577843882 |\n",
      "|   7    | 0.0171428571429  |  0.00109292129596 |\n",
      "|   8    |      0.015       |  0.00109292129596 |\n",
      "|   9    |      0.016       |  0.00112158527729 |\n",
      "|   10   |      0.0144      |  0.00112158527729 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |      0.004       | 4.61095100865e-06 |\n",
      "|   3    | 0.00266666666667 | 4.61095100865e-06 |\n",
      "|   4    |      0.006       | 0.000302279427534 |\n",
      "|   5    |      0.0048      | 0.000302279427534 |\n",
      "|   6    |      0.004       | 0.000302279427534 |\n",
      "|   7    |      0.008       |  0.00103382446407 |\n",
      "|   8    |      0.011       |  0.00207255594038 |\n",
      "|   9    | 0.0133333333333  |  0.00231978337793 |\n",
      "|   10   |      0.032       |  0.00546009838952 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M2\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    | 0.00533333333333 | 2.51703557498e-05 |\n",
      "|   4    |      0.008       | 0.000105971782949 |\n",
      "|   5    |      0.0064      | 0.000105971782949 |\n",
      "|   6    | 0.0146666666667  | 0.000822502167559 |\n",
      "|   7    | 0.0137142857143  | 0.000909458689298 |\n",
      "|   8    |      0.018       |  0.00125415811713 |\n",
      "|   9    | 0.0168888888889  |  0.00131665811713 |\n",
      "|   10   |      0.0152      |  0.00131665811713 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M3\n",
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+----------------+-------------------+\n",
      "| cutoff | mean_precision |    mean_recall    |\n",
      "+--------+----------------+-------------------+\n",
      "|   1    |     0.008      | 4.61095100865e-06 |\n",
      "|   2    |     0.008      | 3.62315043683e-05 |\n",
      "|   3    |     0.096      |  0.00525120398926 |\n",
      "|   4    |     0.106      |  0.0077884031266  |\n",
      "|   5    |     0.1216     |  0.00974797176805 |\n",
      "|   6    | 0.137333333333 |  0.0120315881535  |\n",
      "|   7    |     0.152      |  0.0158037734507  |\n",
      "|   8    |     0.162      |  0.0189552291029  |\n",
      "|   9    | 0.164444444444 |  0.0213821080236  |\n",
      "|   10   |     0.1512     |  0.0214850576754  |\n",
      "+--------+----------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "Model compare metric: precision_recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qdzsf7a3fWq2kOj+ffXNfJvjMMwkafKrV4SiljoF3dAfw8Wm3g/c0YbSS3Jdf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\naMJQSinlkv8HkRTAWeVhbSkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa999ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_comp1 = graphlab.compare(test, [m1_t1,m1_t2,m1_t3,m1],target='rating')\n",
    "model_comp1_df = model_comp1.to_dataframe()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "for i in range(0,len(model_comp1_df.index)):\n",
    "    xy_dict= model_comp1_df.loc[i,\"results\"]['precision_recall']\n",
    "    xy = pd.DataFrame(xy_dict)    \n",
    "    #xy.plot(x='recall',y= 'precision',ax=ax)\n",
    "    label1= model_comp1_df.loc[i,\"model\"]\n",
    "    xy.plot(x='recall',y= 'precision',ax=ax,label=label1)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plots above, we see that the area under the precision-recall curve of the model trained with the complete dataset is the highest while the area under the curve of the model trained with only 0.25 of the data is the lowest which shows that a larger training dataset improves the accuracy of the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\full_dataset.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\full_dataset.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100004 lines in 0.702013 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100004 lines in 0.702013 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80003 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80003 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.979205s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.979205s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80003 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80003 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | 0.174514                                 |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | 0.174514                                 |"
      ]
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     "data": {
      "text/html": [
       "<pre>| 2       | 0.152439          | 0.0770773                                |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.152439          | 0.0770773                                |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0762195         | 0.089405                                 |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0762195         | 0.089405                                 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0381098         | 0.190627                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0381098         | 0.190627                                 |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.152439          | 0.0770773                                |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.152439          | 0.0770773                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.1152            | 1.05603               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.1152            | 1.05603               |             |"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
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     "data": {
      "text/html": [
       "<pre>| 1       | 303.222ms    | 1.13659           | 1.06606               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 1       | 303.222ms    | 1.13659           | 1.06606               | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 2       | 592.927ms    | 0.59575           | 0.771787              | 0.152439    |</pre>"
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       "| 2       | 592.927ms    | 0.59575           | 0.771787              | 0.152439    |"
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      "text/html": [
       "<pre>| 3       | 834.099ms    | 0.380439          | 0.616719              | 0.152439    |</pre>"
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       "| 3       | 834.099ms    | 0.380439          | 0.616719              | 0.152439    |"
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      "text/html": [
       "<pre>| 4       | 1.12s        | 0.282647          | 0.531551              | 0.152439    |</pre>"
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       "| 4       | 1.12s        | 0.282647          | 0.531551              | 0.152439    |"
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       "<pre>| 5       | 1.31s        | 0.220924          | 0.469912              | 0.152439    |</pre>"
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       "| 5       | 1.31s        | 0.220924          | 0.469912              | 0.152439    |"
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       "<pre>| 6       | 1.52s        | 0.176118          | 0.419529              | 0.152439    |</pre>"
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       "| 6       | 1.52s        | 0.176118          | 0.419529              | 0.152439    |"
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       "<pre>| 10      | 2.30s        | 0.0828882         | 0.287677              | 0.152439    |</pre>"
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       "| 10      | 2.30s        | 0.0828882         | 0.287677              | 0.152439    |"
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       "<pre>| 11      | 2.50s        | 0.0712256         | 0.266631              | 0.152439    |</pre>"
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       "| 11      | 2.50s        | 0.0712256         | 0.266631              | 0.152439    |"
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      "text/html": [
       "<pre>| 15      | 3.34s        | 0.0417238         | 0.203916              | 0.152439    |</pre>"
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       "| 15      | 3.34s        | 0.0417238         | 0.203916              | 0.152439    |"
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       "<pre>| 20      | 4.30s        | 0.0263733         | 0.161942              | 0.152439    |</pre>"
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       "| 20      | 4.30s        | 0.0263733         | 0.161942              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 25      | 5.26s        | 0.0191332         | 0.137773              | 0.152439    |</pre>"
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       "| 25      | 5.26s        | 0.0191332         | 0.137773              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 30      | 6.27s        | 0.0149688         | 0.121715              | 0.152439    |</pre>"
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       "| 30      | 6.27s        | 0.0149688         | 0.121715              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 35      | 7.31s        | 0.0126361         | 0.111715              | 0.152439    |</pre>"
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       "| 35      | 7.31s        | 0.0126361         | 0.111715              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 40      | 8.37s        | 0.0108139         | 0.103231              | 0.152439    |</pre>"
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       "| 40      | 8.37s        | 0.0108139         | 0.103231              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 45      | 9.38s        | 0.00974516        | 0.0979124             | 0.152439    |</pre>"
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       "| 45      | 9.38s        | 0.00974516        | 0.0979124             | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 50      | 10.38s       | 0.00888857        | 0.0934309             | 0.152439    |</pre>"
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       "| 50      | 10.38s       | 0.00888857        | 0.0934309             | 0.152439    |"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.00747672</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.00747672"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.0855423</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.0855423"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    | 0.00751879699248 | 4.42282176028e-06 |\n",
      "|   2    | 0.00375939849624 | 4.42282176028e-06 |\n",
      "|   3    | 0.00250626566416 | 4.42282176028e-06 |\n",
      "|   4    | 0.00375939849624 | 4.37882510403e-05 |\n",
      "|   5    | 0.00300751879699 | 4.37882510403e-05 |\n",
      "|   6    | 0.00250626566416 | 4.37882510403e-05 |\n",
      "|   7    | 0.00322234156821 | 5.81920766964e-05 |\n",
      "|   8    | 0.00281954887218 | 5.81920766964e-05 |\n",
      "|   9    | 0.00334168755221 | 0.000200056170894 |\n",
      "|   10   | 0.00451127819549 | 0.000209149052898 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "('\\nOverall RMSE: ', 0.9962849698640356)\n",
      "\n",
      "Per User RMSE (best)\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|   72   |  191  | 0.491125180379 |\n",
      "+--------+-------+----------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+--------+-------+---------------+\n",
      "| userId | count |      rmse     |\n",
      "+--------+-------+---------------+\n",
      "|   35   |   20  | 1.81034029692 |\n",
      "+--------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+---------+-------+-------------------+\n",
      "| movieId | count |        rmse       |\n",
      "+---------+-------+-------------------+\n",
      "|   2582  |   1   | 0.000734053088157 |\n",
      "+---------+-------+-------------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+---------+-------+---------------+\n",
      "| movieId | count |      rmse     |\n",
      "+---------+-------+---------------+\n",
      "|  161155 |   1   | 3.73106394646 |\n",
      "+---------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "{'rmse_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 133\n",
      "\n",
      "Data:\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  118   |  189  | 0.830539474052 |\n",
      "|   49   |   99  | 0.978842906119 |\n",
      "|   13   |   53  | 0.699788739682 |\n",
      "|   60   |   55  | 0.727862931136 |\n",
      "|  132   |   94  | 1.01582937083  |\n",
      "|   36   |  104  | 0.69535024899  |\n",
      "|  116   |   25  | 1.26242972644  |\n",
      "|  105   |  525  | 0.657812936087 |\n",
      "|   24   |   21  | 0.680719542426 |\n",
      "|   2    |   76  | 0.936587406619 |\n",
      "+--------+-------+----------------+\n",
      "[133 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_overall': Columns:\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\n",
      "Rows: 18\n",
      "\n",
      "Data:\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |    precision     |       recall      |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    | 0.00751879699248 | 4.42282176028e-06 |\n",
      "|   2    | 0.00375939849624 | 4.42282176028e-06 |\n",
      "|   3    | 0.00250626566416 | 4.42282176028e-06 |\n",
      "|   4    | 0.00375939849624 | 4.37882510403e-05 |\n",
      "|   5    | 0.00300751879699 | 4.37882510403e-05 |\n",
      "|   6    | 0.00250626566416 | 4.37882510403e-05 |\n",
      "|   7    | 0.00322234156821 | 5.81920766964e-05 |\n",
      "|   8    | 0.00281954887218 | 5.81920766964e-05 |\n",
      "|   9    | 0.00334168755221 | 0.000200056170894 |\n",
      "|   10   | 0.00451127819549 | 0.000209149052898 |\n",
      "+--------+------------------+-------------------+\n",
      "[18 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_by_item': Columns:\n",
      "\tmovieId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 4722\n",
      "\n",
      "Data:\n",
      "+---------+-------+----------------+\n",
      "| movieId | count |      rmse      |\n",
      "+---------+-------+----------------+\n",
      "|  101076 |   1   | 1.47370090503  |\n",
      "|   2779  |   3   | 1.19026149789  |\n",
      "|   118   |   1   | 0.104204001886 |\n",
      "|   3988  |   6   | 1.13174900753  |\n",
      "|  64614  |   9   | 0.663847053899 |\n",
      "|   2925  |   3   | 0.500307548929 |\n",
      "|  108689 |   1   |  1.0176947076  |\n",
      "|  102753 |   1   | 2.27602257122  |\n",
      "|   2871  |   10  | 0.544337391254 |\n",
      "|  93272  |   1   | 0.948816516858 |\n",
      "+---------+-------+----------------+\n",
      "[4722 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\tcount\tint\n",
      "\n",
      "Rows: 2394\n",
      "\n",
      "Data:\n",
      "+--------+--------+-----------+--------+-------+\n",
      "| userId | cutoff | precision | recall | count |\n",
      "+--------+--------+-----------+--------+-------+\n",
      "|   1    |   1    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   2    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   3    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   4    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   5    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   6    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   7    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   8    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   9    |    0.0    |  0.0   |   20  |\n",
      "|   1    |   10   |    0.0    |  0.0   |   20  |\n",
      "+--------+--------+-----------+--------+-------+\n",
      "[2394 rows x 5 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_overall': 0.9962849698640356}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80003 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80003 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 1.07974s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 1.07974s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80003 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80003 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | 0.147313                                 |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | 0.147313                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.152439          | 0.0758132                                |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.152439          | 0.0758132                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0762195         | 0.0973169                                |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0762195         | 0.0973169                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0381098         | 0.179463                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0381098         | 0.179463                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.152439          | 0.0758132                                |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.152439          | 0.0758132                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 501us        | 1.11573           | 1.05628               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 501us        | 1.11573           | 1.05628               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 311.721ms    | 1.13704           | 1.06627               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 1       | 311.721ms    | 1.13704           | 1.06627               | 0.152439    |"
      ]
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     "data": {
      "text/html": [
       "<pre>| 2       | 629.446ms    | 0.598242          | 0.7734                | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 2       | 629.446ms    | 0.598242          | 0.7734                | 0.152439    |"
      ]
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     "data": {
      "text/html": [
       "<pre>| 3       | 903.141ms    | 0.38019           | 0.616517              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 3       | 903.141ms    | 0.38019           | 0.616517              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 4       | 1.19s        | 0.283875          | 0.532703              | 0.152439    |</pre>"
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      "text/plain": [
       "| 4       | 1.19s        | 0.283875          | 0.532703              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 5       | 1.47s        | 0.220886          | 0.46987               | 0.152439    |</pre>"
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      "text/plain": [
       "| 5       | 1.47s        | 0.220886          | 0.46987               | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 6       | 1.76s        | 0.177116          | 0.420716              | 0.152439    |</pre>"
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      "text/plain": [
       "| 6       | 1.76s        | 0.177116          | 0.420716              | 0.152439    |"
      ]
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     "data": {
      "text/html": [
       "<pre>| 10      | 2.80s        | 0.082111          | 0.286319              | 0.152439    |</pre>"
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      "text/plain": [
       "| 10      | 2.80s        | 0.082111          | 0.286319              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 11      | 3.07s        | 0.0711789         | 0.266539              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 11      | 3.07s        | 0.0711789         | 0.266539              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 15      | 4.13s        | 0.0417349         | 0.203937              | 0.152439    |</pre>"
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      "text/plain": [
       "| 15      | 4.13s        | 0.0417349         | 0.203937              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 20      | 5.46s        | 0.027225          | 0.164542              | 0.152439    |</pre>"
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      "text/plain": [
       "| 20      | 5.46s        | 0.027225          | 0.164542              | 0.152439    |"
      ]
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    {
     "data": {
      "text/html": [
       "<pre>| 25      | 6.79s        | 0.0192092         | 0.138038              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 25      | 6.79s        | 0.0192092         | 0.138038              | 0.152439    |"
      ]
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    },
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     "data": {
      "text/html": [
       "<pre>| 30      | 8.08s        | 0.0151815         | 0.122574              | 0.152439    |</pre>"
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      "text/plain": [
       "| 30      | 8.08s        | 0.0151815         | 0.122574              | 0.152439    |"
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     "data": {
      "text/html": [
       "<pre>| 35      | 9.41s        | 0.0129029         | 0.112889              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 35      | 9.41s        | 0.0129029         | 0.112889              | 0.152439    |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 10.78s       | 0.0134812         | 0.115417              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 40      | 10.78s       | 0.0134812         | 0.115417              | 0.152439    |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 12.09s       | 0.00978259        | 0.0980882             | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 45      | 12.09s       | 0.00978259        | 0.0980882             | 0.152439    |"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 13.36s       | 0.00876494        | 0.0927506             | 0.152439    |</pre>"
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      "text/plain": [
       "| 50      | 13.36s       | 0.00876494        | 0.0927506             | 0.152439    |"
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.00784071</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.00784071"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.0876267</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.0876267"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    |       0.0        |        0.0        |\n",
      "|   4    |       0.0        |        0.0        |\n",
      "|   5    |       0.0        |        0.0        |\n",
      "|   6    |       0.0        |        0.0        |\n",
      "|   7    |       0.0        |        0.0        |\n",
      "|   8    |    0.00078125    |   2.44140625e-05  |\n",
      "|   9    | 0.00138888888889 | 0.000144606370192 |\n",
      "|   10   |     0.00125      | 0.000144606370192 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "('\\nOverall RMSE: ', 0.988664345839456)\n",
      "\n",
      "Per User RMSE (best)\n",
      "+--------+-------+---------------+\n",
      "| userId | count |      rmse     |\n",
      "+--------+-------+---------------+\n",
      "|  229   |   33  | 0.45917555157 |\n",
      "+--------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+--------+-------+---------------+\n",
      "| userId | count |      rmse     |\n",
      "+--------+-------+---------------+\n",
      "|  207   |   46  | 2.05143980879 |\n",
      "+--------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+---------+-------+-------------------+\n",
      "| movieId | count |        rmse       |\n",
      "+---------+-------+-------------------+\n",
      "|  117444 |   1   | 0.000575393398643 |\n",
      "+---------+-------+-------------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+---------+-------+---------------+\n",
      "| movieId | count |      rmse     |\n",
      "+---------+-------+---------------+\n",
      "|  65188  |   1   | 3.93491745885 |\n",
      "+---------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "{'rmse_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 160\n",
      "\n",
      "Data:\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  232   |  682  |  1.1263701847  |\n",
      "|  187   |  324  | 0.989744459311 |\n",
      "|  159   |  148  | 0.856766030223 |\n",
      "|  136   |   50  | 0.882963946748 |\n",
      "|  160   |  100  | 0.913829305644 |\n",
      "|  273   |   92  | 0.838548707289 |\n",
      "|  140   |   46  | 1.19443066286  |\n",
      "|  253   |  205  | 0.972831154464 |\n",
      "|  287   |  254  | 1.34416828771  |\n",
      "|  196   |   99  | 0.982989652974 |\n",
      "+--------+-------+----------------+\n",
      "[160 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_overall': Columns:\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\n",
      "Rows: 18\n",
      "\n",
      "Data:\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |    precision     |       recall      |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    |       0.0        |        0.0        |\n",
      "|   4    |       0.0        |        0.0        |\n",
      "|   5    |       0.0        |        0.0        |\n",
      "|   6    |       0.0        |        0.0        |\n",
      "|   7    |       0.0        |        0.0        |\n",
      "|   8    |    0.00078125    |   2.44140625e-05  |\n",
      "|   9    | 0.00138888888889 | 0.000144606370192 |\n",
      "|   10   |     0.00125      | 0.000144606370192 |\n",
      "+--------+------------------+-------------------+\n",
      "[18 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_by_item': Columns:\n",
      "\tmovieId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 4446\n",
      "\n",
      "Data:\n",
      "+---------+-------+-----------------+\n",
      "| movieId | count |       rmse      |\n",
      "+---------+-------+-----------------+\n",
      "|   7899  |   1   | 0.0564701060793 |\n",
      "|  101076 |   1   |  1.19639280414  |\n",
      "|   3143  |   1   |  1.01256052377  |\n",
      "|   2779  |   2   |  0.552541641562 |\n",
      "|   118   |   1   |  0.356983227232 |\n",
      "|   3988  |   6   |  0.836845100844 |\n",
      "|   2847  |   1   |  0.630042174773 |\n",
      "|  64614  |   4   |  0.608872695573 |\n",
      "|   5531  |   1   |  0.447537838727 |\n",
      "|   2925  |   1   |  1.76407549542  |\n",
      "+---------+-------+-----------------+\n",
      "[4446 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\tcount\tint\n",
      "\n",
      "Rows: 2880\n",
      "\n",
      "Data:\n",
      "+--------+--------+----------------+-----------------+-------+\n",
      "| userId | cutoff |   precision    |      recall     | count |\n",
      "+--------+--------+----------------+-----------------+-------+\n",
      "|  133   |   1    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   2    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   3    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   4    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   5    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   6    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   7    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   8    |      0.0       |       0.0       |   52  |\n",
      "|  133   |   9    | 0.111111111111 | 0.0192307692308 |   52  |\n",
      "|  133   |   10   |      0.1       | 0.0192307692308 |   52  |\n",
      "+--------+--------+----------------+-----------------+-------+\n",
      "[2880 rows x 5 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_overall': 0.988664345839456}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80003 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80003 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.711037s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.711037s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80003 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80003 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | 0.184349                                 |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | 0.184349                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.152439          | 0.0868174                                |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.152439          | 0.0868174                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0762195         | 0.0902738                                |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0762195         | 0.0902738                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0381098         | 0.178149                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0381098         | 0.178149                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.152439          | 0.0868174                                |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.152439          | 0.0868174                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.12328           | 1.05985               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.12328           | 1.05985               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 220.657ms    | 1.12833           | 1.06218               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 1       | 220.657ms    | 1.12833           | 1.06218               | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 408.29ms     | 0.600296          | 0.774727              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 2       | 408.29ms     | 0.600296          | 0.774727              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 601.427ms    | 0.383088          | 0.618863              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 3       | 601.427ms    | 0.383088          | 0.618863              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 793.563ms    | 0.292675          | 0.540901              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 4       | 793.563ms    | 0.292675          | 0.540901              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 987.7ms      | 0.23295           | 0.482538              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 5       | 987.7ms      | 0.23295           | 0.482538              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.19s        | 0.190114          | 0.435891              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.19s        | 0.190114          | 0.435891              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 1.93s        | 0.0936936         | 0.305878              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 10      | 1.93s        | 0.0936936         | 0.305878              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.13s        | 0.0809201         | 0.284226              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.13s        | 0.0809201         | 0.284226              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 3.84s        | 0.0308536         | 0.175216              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 20      | 3.84s        | 0.0308536         | 0.175216              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 5.99s        | 0.0172138         | 0.13059               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 30      | 5.99s        | 0.0172138         | 0.13059               | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 8.36s        | 0.0122911         | 0.110126              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 40      | 8.36s        | 0.0122911         | 0.110126              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 10.36s       | 0.0114319         | 0.106142              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 50      | 10.36s       | 0.0114319         | 0.106142              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.0109033</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.0109033"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.103622</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.103622"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    | 0.00234741784038 | 1.91887016924e-05 |\n",
      "|   4    | 0.00352112676056 | 3.37088120453e-05 |\n",
      "|   5    | 0.00281690140845 | 3.37088120453e-05 |\n",
      "|   6    | 0.00234741784038 | 3.37088120453e-05 |\n",
      "|   7    | 0.00201207243461 | 3.37088120453e-05 |\n",
      "|   8    | 0.00264084507042 | 5.08016603975e-05 |\n",
      "|   9    | 0.00234741784038 | 5.08016603975e-05 |\n",
      "|   10   | 0.00211267605634 | 5.08016603975e-05 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "('\\nOverall RMSE: ', 0.9822991827766332)\n",
      "\n",
      "Per User RMSE (best)\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  397   |   59  | 0.520065851382 |\n",
      "+--------+-------+----------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+--------+-------+--------------+\n",
      "| userId | count |     rmse     |\n",
      "+--------+-------+--------------+\n",
      "|  364   |   36  | 2.3856896672 |\n",
      "+--------+-------+--------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+---------+-------+-------------------+\n",
      "| movieId | count |        rmse       |\n",
      "+---------+-------+-------------------+\n",
      "|  45726  |   1   | 0.000393823477787 |\n",
      "+---------+-------+-------------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+---------+-------+---------------+\n",
      "| movieId | count |      rmse     |\n",
      "+---------+-------+---------------+\n",
      "|   6219  |   1   | 4.05601778621 |\n",
      "+---------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "{'rmse_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 142\n",
      "\n",
      "Data:\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  310   |   20  | 1.45800798517  |\n",
      "|  363   |  201  | 0.974953085218 |\n",
      "|  431   |  268  | 0.900680468478 |\n",
      "|  390   |  153  | 1.00719341029  |\n",
      "|  318   |   59  | 1.23750979986  |\n",
      "|  349   |   23  | 0.95605941791  |\n",
      "|  374   |   33  | 1.44999362428  |\n",
      "|  354   |   52  | 1.09710811841  |\n",
      "|  312   |  284  | 1.20300456463  |\n",
      "|  392   |   25  | 1.07470584549  |\n",
      "+--------+-------+----------------+\n",
      "[142 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_overall': Columns:\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\n",
      "Rows: 18\n",
      "\n",
      "Data:\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |    precision     |       recall      |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    | 0.00234741784038 | 1.91887016924e-05 |\n",
      "|   4    | 0.00352112676056 | 3.37088120453e-05 |\n",
      "|   5    | 0.00281690140845 | 3.37088120453e-05 |\n",
      "|   6    | 0.00234741784038 | 3.37088120453e-05 |\n",
      "|   7    | 0.00201207243461 | 3.37088120453e-05 |\n",
      "|   8    | 0.00264084507042 | 5.08016603975e-05 |\n",
      "|   9    | 0.00234741784038 | 5.08016603975e-05 |\n",
      "|   10   | 0.00211267605634 | 5.08016603975e-05 |\n",
      "+--------+------------------+-------------------+\n",
      "[18 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_by_item': Columns:\n",
      "\tmovieId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 4812\n",
      "\n",
      "Data:\n",
      "+---------+-------+----------------+\n",
      "| movieId | count |      rmse      |\n",
      "+---------+-------+----------------+\n",
      "|   7899  |   1   | 0.249426090122 |\n",
      "|   5684  |   1   | 1.45969434661  |\n",
      "|   2779  |   7   | 0.812190620437 |\n",
      "|   3988  |   4   | 0.973569476393 |\n",
      "|   2847  |   1   | 0.702053830459 |\n",
      "|  64614  |   9   | 0.998126394193 |\n",
      "|   2871  |   4   | 0.159649646581 |\n",
      "|   2043  |   1   | 0.210991911016 |\n",
      "|   2445  |   1   | 0.349391747933 |\n",
      "|   1029  |   6   | 0.70952440695  |\n",
      "+---------+-------+----------------+\n",
      "[4812 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\tcount\tint\n",
      "\n",
      "Rows: 2556\n",
      "\n",
      "Data:\n",
      "+--------+--------+-----------+--------+-------+\n",
      "| userId | cutoff | precision | recall | count |\n",
      "+--------+--------+-----------+--------+-------+\n",
      "|  292   |   1    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   2    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   3    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   4    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   5    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   6    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   7    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   8    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   9    |    0.0    |  0.0   |   72  |\n",
      "|  292   |   10   |    0.0    |  0.0   |   72  |\n",
      "+--------+--------+-----------+--------+-------+\n",
      "[2556 rows x 5 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_overall': 0.9822991827766332}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80003 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80003 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.70253s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.70253s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80003 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80003 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | 0.105144                                 |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | 0.105144                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0381098         | 0.202962                                 |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0381098         | 0.202962                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0190549         | 0.324701                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0190549         | 0.324701                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.0762195         | 0.105144                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.0762195         | 0.105144                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.10935           | 1.05326               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.10935           | 1.05326               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 325.731ms    | 0.89105           | 0.943938              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 1       | 325.731ms    | 0.89105           | 0.943938              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 700.497ms    | 0.567516          | 0.753314              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 2       | 700.497ms    | 0.567516          | 0.753314              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1.05s        | 0.433463          | 0.65835               | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 3       | 1.05s        | 0.433463          | 0.65835               | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1.42s        | 0.35139           | 0.592744              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 4       | 1.42s        | 0.35139           | 0.592744              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.75s        | 0.293331          | 0.541555              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.75s        | 0.293331          | 0.541555              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 2.09s        | 0.250896          | 0.500841              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 6       | 2.09s        | 0.250896          | 0.500841              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 3.34s        | 0.148027          | 0.384652              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 10      | 3.34s        | 0.148027          | 0.384652              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 3.65s        | 0.131547          | 0.362593              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 11      | 3.65s        | 0.131547          | 0.362593              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 4.78s        | 0.0869844         | 0.294788              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 15      | 4.78s        | 0.0869844         | 0.294788              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 6.35s        | 0.0570447         | 0.238645              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 20      | 6.35s        | 0.0570447         | 0.238645              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 7.66s        | 0.0408758         | 0.201931              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 25      | 7.66s        | 0.0408758         | 0.201931              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 9.22s        | 0.0312204         | 0.176398              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 30      | 9.22s        | 0.0312204         | 0.176398              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 10.73s       | 0.0250691         | 0.157993              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 35      | 10.73s       | 0.0250691         | 0.157993              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 12.18s       | 0.0214625         | 0.146126              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 40      | 12.18s       | 0.0214625         | 0.146126              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 13.56s       | 0.018141          | 0.134274              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 45      | 13.56s       | 0.018141          | 0.134274              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 14.79s       | 0.0159373         | 0.125795              | 0.0762195   |</pre>"
      ],
      "text/plain": [
       "| 50      | 14.79s       | 0.0159373         | 0.125795              | 0.0762195   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.0145459</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.0145459"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.120137</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.120137"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    | 0.00289855072464 | 1.21958656016e-05 |\n",
      "|   4    | 0.00652173913043 |  0.00045251358071 |\n",
      "|   5    | 0.00521739130435 |  0.00045251358071 |\n",
      "|   6    | 0.00434782608696 |  0.00045251358071 |\n",
      "|   7    | 0.00372670807453 |  0.00045251358071 |\n",
      "|   8    | 0.00326086956522 |  0.00045251358071 |\n",
      "|   9    | 0.00289855072464 |  0.00045251358071 |\n",
      "|   10   | 0.00260869565217 |  0.00045251358071 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "('\\nOverall RMSE: ', 0.9822081976486008)\n",
      "\n",
      "Per User RMSE (best)\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  521   |   49  | 0.608490734743 |\n",
      "+--------+-------+----------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+--------+-------+---------------+\n",
      "| userId | count |      rmse     |\n",
      "+--------+-------+---------------+\n",
      "|  477   |   21  | 1.91834426074 |\n",
      "+--------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+---------+-------+-------------------+\n",
      "| movieId | count |        rmse       |\n",
      "+---------+-------+-------------------+\n",
      "|   1769  |   1   | 0.000776773128298 |\n",
      "+---------+-------+-------------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+---------+-------+---------------+\n",
      "| movieId | count |      rmse     |\n",
      "+---------+-------+---------------+\n",
      "|  106471 |   1   | 3.56118785873 |\n",
      "+---------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "{'rmse_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 115\n",
      "\n",
      "Data:\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  435   |   23  | 0.888545268003 |\n",
      "|  537   |  307  | 0.947674514861 |\n",
      "|  526   |   27  | 0.886386701411 |\n",
      "|  511   |   31  | 0.799455846991 |\n",
      "|  454   |   27  | 0.907831832891 |\n",
      "|  444   |   20  | 0.985681077702 |\n",
      "|  499   |   33  | 0.823520418079 |\n",
      "|  517   |   42  | 1.18861988192  |\n",
      "|  496   |  126  | 0.979601225271 |\n",
      "|  498   |   20  | 1.23453296661  |\n",
      "+--------+-------+----------------+\n",
      "[115 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_overall': Columns:\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\n",
      "Rows: 18\n",
      "\n",
      "Data:\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |    precision     |       recall      |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    | 0.00289855072464 | 1.21958656016e-05 |\n",
      "|   4    | 0.00652173913043 |  0.00045251358071 |\n",
      "|   5    | 0.00521739130435 |  0.00045251358071 |\n",
      "|   6    | 0.00434782608696 |  0.00045251358071 |\n",
      "|   7    | 0.00372670807453 |  0.00045251358071 |\n",
      "|   8    | 0.00326086956522 |  0.00045251358071 |\n",
      "|   9    | 0.00289855072464 |  0.00045251358071 |\n",
      "|   10   | 0.00260869565217 |  0.00045251358071 |\n",
      "+--------+------------------+-------------------+\n",
      "[18 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_by_item': Columns:\n",
      "\tmovieId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 4914\n",
      "\n",
      "Data:\n",
      "+---------+-------+-----------------+\n",
      "| movieId | count |       rmse      |\n",
      "+---------+-------+-----------------+\n",
      "|   5288  |   2   |  1.00328454042  |\n",
      "|  42728  |   1   | 0.0151954322155 |\n",
      "|   2779  |   3   |  0.311247551628 |\n",
      "|   118   |   1   |  0.630240940964 |\n",
      "|   3988  |   7   |  1.85280863573  |\n",
      "|   2847  |   1   |  0.113622454564 |\n",
      "|  64614  |   8   |  0.786902061017 |\n",
      "|   2925  |   2   |  0.687758909305 |\n",
      "|   2871  |   12  |  0.915043142401 |\n",
      "|   3913  |   1   |  0.71082388208  |\n",
      "+---------+-------+-----------------+\n",
      "[4914 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\tcount\tint\n",
      "\n",
      "Rows: 2070\n",
      "\n",
      "Data:\n",
      "+--------+--------+-----------+--------+-------+\n",
      "| userId | cutoff | precision | recall | count |\n",
      "+--------+--------+-----------+--------+-------+\n",
      "|  433   |   1    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   2    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   3    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   4    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   5    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   6    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   7    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   8    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   9    |    0.0    |  0.0   |   55  |\n",
      "|  433   |   10   |    0.0    |  0.0   |   55  |\n",
      "+--------+--------+-----------+--------+-------+\n",
      "[2070 rows x 5 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_overall': 0.9822081976486008}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 80004 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 80004 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 1.11739s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 1.11739s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 4        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 4        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-008   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-008   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 10000 / 80004 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 10000 / 80004 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | 0.172465                                 |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | 0.172465                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.152439          | 0.0772441                                |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.152439          | 0.0772441                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0762195         | 0.104447                                 |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0762195         | 0.104447                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0381098         | 0.191235                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0381098         | 0.191235                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.152439          | 0.0772441                                |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.152439          | 0.0772441                                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 1.13363           | 1.06472               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 1.13363           | 1.06472               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 359.253ms    | 1.12788           | 1.06196               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 1       | 359.253ms    | 1.12788           | 1.06196               | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 625.943ms    | 0.621564          | 0.788334              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 2       | 625.943ms    | 0.621564          | 0.788334              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 901.639ms    | 0.420362          | 0.64828               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 3       | 901.639ms    | 0.420362          | 0.64828               | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1.16s        | 0.310513          | 0.557146              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 4       | 1.16s        | 0.310513          | 0.557146              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 1.44s        | 0.234919          | 0.484575              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 5       | 1.44s        | 0.234919          | 0.484575              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 1.73s        | 0.193281          | 0.439511              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 6       | 1.73s        | 0.193281          | 0.439511              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 2.72s        | 0.104771          | 0.323487              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 10      | 2.72s        | 0.104771          | 0.323487              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 2.99s        | 0.0923913         | 0.303744              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 11      | 2.99s        | 0.0923913         | 0.303744              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 3.97s        | 0.0586997         | 0.241989              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 15      | 3.97s        | 0.0586997         | 0.241989              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 5.23s        | 0.0377799         | 0.193988              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 20      | 5.23s        | 0.0377799         | 0.193988              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 6.64s        | 0.027247          | 0.164601              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 25      | 6.64s        | 0.027247          | 0.164601              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 7.88s        | 0.0216253         | 0.146521              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 30      | 7.88s        | 0.0216253         | 0.146521              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 9.26s        | 0.0176039         | 0.132078              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 35      | 9.26s        | 0.0176039         | 0.132078              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 10.53s       | 0.0155832         | 0.124185              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 40      | 10.53s       | 0.0155832         | 0.124185              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 11.61s       | 0.0130278         | 0.113424              | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 45      | 11.61s       | 0.0130278         | 0.113424              | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 12.67s       | 0.0114892         | 0.10642               | 0.152439    |</pre>"
      ],
      "text/plain": [
       "| 50      | 12.67s       | 0.0114892         | 0.10642               | 0.152439    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.0130447</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.0130447"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.113493</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.113493"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |  mean_precision  |    mean_recall    |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    |       0.0        |        0.0        |\n",
      "|   4    |      0.004       | 0.000357582184517 |\n",
      "|   5    |      0.0048      | 0.000367338282078 |\n",
      "|   6    |      0.004       | 0.000367338282078 |\n",
      "|   7    | 0.00457142857143 | 0.000377094379639 |\n",
      "|   8    |      0.005       |  0.0003868504772  |\n",
      "|   9    | 0.00622222222222 | 0.000625178003333 |\n",
      "|   10   |      0.0064      | 0.000629788954341 |\n",
      "+--------+------------------+-------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "('\\nOverall RMSE: ', 1.0051670610378534)\n",
      "\n",
      "Per User RMSE (best)\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  574   |  342  | 0.593023343635 |\n",
      "+--------+-------+----------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+--------+-------+---------------+\n",
      "| userId | count |      rmse     |\n",
      "+--------+-------+---------------+\n",
      "|  579   |   21  | 2.37143383901 |\n",
      "+--------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+---------+-------+------------------+\n",
      "| movieId | count |       rmse       |\n",
      "+---------+-------+------------------+\n",
      "|   7792  |   1   | 0.00027701375007 |\n",
      "+---------+-------+------------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+---------+-------+---------------+\n",
      "| movieId | count |      rmse     |\n",
      "+---------+-------+---------------+\n",
      "|   2483  |   1   | 4.51295114214 |\n",
      "+---------+-------+---------------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "{'rmse_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 125\n",
      "\n",
      "Data:\n",
      "+--------+-------+----------------+\n",
      "| userId | count |      rmse      |\n",
      "+--------+-------+----------------+\n",
      "|  621   |   95  | 1.36391847103  |\n",
      "|  614   |   99  | 1.09553679442  |\n",
      "|  565   |   27  | 1.14607302812  |\n",
      "|  640   |   49  | 1.16427510123  |\n",
      "|  631   |   31  | 0.961876367017 |\n",
      "|  573   |   38  | 0.839788913727 |\n",
      "|  617   |   75  | 1.23583021677  |\n",
      "|  578   |   34  | 1.73209452183  |\n",
      "|  550   |  135  | 0.853759327547 |\n",
      "|  607   |  427  | 0.929504727516 |\n",
      "+--------+-------+----------------+\n",
      "[125 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_overall': Columns:\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\n",
      "Rows: 18\n",
      "\n",
      "Data:\n",
      "+--------+------------------+-------------------+\n",
      "| cutoff |    precision     |       recall      |\n",
      "+--------+------------------+-------------------+\n",
      "|   1    |       0.0        |        0.0        |\n",
      "|   2    |       0.0        |        0.0        |\n",
      "|   3    |       0.0        |        0.0        |\n",
      "|   4    |      0.004       | 0.000357582184517 |\n",
      "|   5    |      0.0048      | 0.000367338282078 |\n",
      "|   6    |      0.004       | 0.000367338282078 |\n",
      "|   7    | 0.00457142857143 | 0.000377094379639 |\n",
      "|   8    |      0.005       |  0.0003868504772  |\n",
      "|   9    | 0.00622222222222 | 0.000625178003333 |\n",
      "|   10   |      0.0064      | 0.000629788954341 |\n",
      "+--------+------------------+-------------------+\n",
      "[18 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_by_item': Columns:\n",
      "\tmovieId\tint\n",
      "\tcount\tint\n",
      "\trmse\tfloat\n",
      "\n",
      "Rows: 5583\n",
      "\n",
      "Data:\n",
      "+---------+-------+-----------------+\n",
      "| movieId | count |       rmse      |\n",
      "+---------+-------+-----------------+\n",
      "|   7899  |   1   |  0.218707043165 |\n",
      "|   6769  |   1   |  2.57447469746  |\n",
      "|   2779  |   6   |  0.771920701583 |\n",
      "|   118   |   2   |  1.44953036259  |\n",
      "|   3988  |   6   |  1.23908380888  |\n",
      "|   8727  |   1   |   1.2656896859  |\n",
      "|  64614  |   6   |  0.738662678263 |\n",
      "|   2925  |   1   | 0.0311383260004 |\n",
      "|   2871  |   5   |  0.411068815999 |\n",
      "|   3913  |   1   | 0.0811173303234 |\n",
      "+---------+-------+-----------------+\n",
      "[5583 rows x 3 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision_recall_by_user': Columns:\n",
      "\tuserId\tint\n",
      "\tcutoff\tint\n",
      "\tprecision\tfloat\n",
      "\trecall\tfloat\n",
      "\tcount\tint\n",
      "\n",
      "Rows: 2250\n",
      "\n",
      "Data:\n",
      "+--------+--------+----------------+------------------+-------+\n",
      "| userId | cutoff |   precision    |      recall      | count |\n",
      "+--------+--------+----------------+------------------+-------+\n",
      "|  547   |   1    |      0.0       |       0.0        |  820  |\n",
      "|  547   |   2    |      0.0       |       0.0        |  820  |\n",
      "|  547   |   3    |      0.0       |       0.0        |  820  |\n",
      "|  547   |   4    |      0.25      | 0.00121951219512 |  820  |\n",
      "|  547   |   5    |      0.4       | 0.00243902439024 |  820  |\n",
      "|  547   |   6    | 0.333333333333 | 0.00243902439024 |  820  |\n",
      "|  547   |   7    | 0.428571428571 | 0.00365853658537 |  820  |\n",
      "|  547   |   8    |      0.5       | 0.00487804878049 |  820  |\n",
      "|  547   |   9    | 0.555555555556 | 0.00609756097561 |  820  |\n",
      "|  547   |   10   |      0.5       | 0.00609756097561 |  820  |\n",
      "+--------+--------+----------------+------------------+-------+\n",
      "[2250 rows x 5 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'rmse_overall': 1.0051670610378534}\n"
     ]
    }
   ],
   "source": [
    "#K-Fold data split\n",
    "all_data.to_csv(\"full_dataset.csv\")\n",
    "hints = [int,int ,int ,float,str, str, str, str, str, str,str,str,str,str,str,str, str,str,float, int, float]\n",
    "data = graphlab.SFrame.read_csv('full_dataset.csv',column_type_hints= hints)\n",
    "\n",
    "data.head()\n",
    "\n",
    "kfolds = graphlab.cross_validation.KFold(data, 5)\n",
    "for train, test in kfolds:    \n",
    "    model = graphlab.factorization_recommender.create(train, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"adagrad\" , user_data = user_metadata,\n",
    "                                              item_data = movie_metadata, num_factors=4)\n",
    "    print(model.evaluate(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Columns:\n",
       " \tX1\tint\n",
       " \tuserId\tint\n",
       " \tmovieId\tint\n",
       " \trating\tfloat\n",
       " \ttimestamp\tstr\n",
       " \ttag\tstr\n",
       " \ttitle\tstr\n",
       " \tgenres\tstr\n",
       " \tgenres1\tstr\n",
       " \tgenres2\tstr\n",
       " \tgenres3\tstr\n",
       " \tgenres4\tstr\n",
       " \tgenres5\tstr\n",
       " \tgenres6\tstr\n",
       " \tgenres7\tstr\n",
       " \tgenres8\tstr\n",
       " \tgenres9\tstr\n",
       " \tgenres10\tstr\n",
       " \tYear_release\tfloat\n",
       " \timdbId\tint\n",
       " \ttmdbId\tfloat\n",
       " \n",
       " Rows: 80003\n",
       " \n",
       " Data:\n",
       " +----+--------+---------+--------+-------------------------+-----+\n",
       " | X1 | userId | movieId | rating |        timestamp        | tag |\n",
       " +----+--------+---------+--------+-------------------------+-----+\n",
       " | 0  |   1    |    31   |  2.5   | 1970-01-15 14:12:39.144 | nan |\n",
       " | 1  |   1    |   1029  |  3.0   | 1970-01-15 14:12:39.179 | nan |\n",
       " | 2  |   1    |   1061  |  3.0   | 1970-01-15 14:12:39.182 | nan |\n",
       " | 3  |   1    |   1129  |  2.0   | 1970-01-15 14:12:39.185 | nan |\n",
       " | 4  |   1    |   1172  |  4.0   | 1970-01-15 14:12:39.205 | nan |\n",
       " | 5  |   1    |   1263  |  2.0   | 1970-01-15 14:12:39.151 | nan |\n",
       " | 6  |   1    |   1287  |  2.0   | 1970-01-15 14:12:39.187 | nan |\n",
       " | 7  |   1    |   1293  |  2.0   | 1970-01-15 14:12:39.148 | nan |\n",
       " | 8  |   1    |   1339  |  3.5   | 1970-01-15 14:12:39.125 | nan |\n",
       " | 9  |   1    |   1343  |  2.0   | 1970-01-15 14:12:39.131 | nan |\n",
       " +----+--------+---------+--------+-------------------------+-----+\n",
       " +-------------------------------+-------------------------------+-----------+\n",
       " |             title             |             genres            |  genres1  |\n",
       " +-------------------------------+-------------------------------+-----------+\n",
       " |     Dangerous Minds (1995)    |             Drama             |   Drama   |\n",
       " |          Dumbo (1941)         | Animation|Children|Drama|M... | Animation |\n",
       " |        Sleepers (1996)        |            Thriller           |  Thriller |\n",
       " |  Escape from New York (1981)  | Action|Adventure|Sci-Fi|Th... |   Action  |\n",
       " | Cinema Paradiso (Nuovo cin... |             Drama             |   Drama   |\n",
       " |    Deer Hunter, The (1978)    |           Drama|War           |   Drama   |\n",
       " |         Ben-Hur (1959)        |     Action|Adventure|Drama    |   Action  |\n",
       " |         Gandhi (1982)         |             Drama             |   Drama   |\n",
       " | Dracula (Bram Stoker's Dra... | Fantasy|Horror|Romance|Thr... |  Fantasy  |\n",
       " |        Cape Fear (1991)       |            Thriller           |  Thriller |\n",
       " +-------------------------------+-------------------------------+-----------+\n",
       " +-----------+---------+----------+---------+---------+---------+---------+---------+\n",
       " |  genres2  | genres3 | genres4  | genres5 | genres6 | genres7 | genres8 | genres9 |\n",
       " +-----------+---------+----------+---------+---------+---------+---------+---------+\n",
       " |           |         |          |         |         |         |         |         |\n",
       " |  Children |  Drama  | Musical  |         |         |         |         |         |\n",
       " |           |         |          |         |         |         |         |         |\n",
       " | Adventure |  Sci-Fi | Thriller |         |         |         |         |         |\n",
       " |           |         |          |         |         |         |         |         |\n",
       " |    War    |         |          |         |         |         |         |         |\n",
       " | Adventure |  Drama  |          |         |         |         |         |         |\n",
       " |           |         |          |         |         |         |         |         |\n",
       " |   Horror  | Romance | Thriller |         |         |         |         |         |\n",
       " |           |         |          |         |         |         |         |         |\n",
       " +-----------+---------+----------+---------+---------+---------+---------+---------+\n",
       " +----------+--------------+--------+-----+\n",
       " | genres10 | Year_release | imdbId | ... |\n",
       " +----------+--------------+--------+-----+\n",
       " |          |    1995.0    | 112792 | ... |\n",
       " |          |    1941.0    | 33563  | ... |\n",
       " |          |    1996.0    | 117665 | ... |\n",
       " |          |    1981.0    | 82340  | ... |\n",
       " |          |    1989.0    | 95765  | ... |\n",
       " |          |    1978.0    | 77416  | ... |\n",
       " |          |    1959.0    | 52618  | ... |\n",
       " |          |    1982.0    | 83987  | ... |\n",
       " |          |    1992.0    | 103874 | ... |\n",
       " |          |    1991.0    | 101540 | ... |\n",
       " +----------+--------------+--------+-----+\n",
       " [80003 rows x 21 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.,\n",
       " Columns:\n",
       " \tX1\tint\n",
       " \tuserId\tint\n",
       " \tmovieId\tint\n",
       " \trating\tfloat\n",
       " \ttimestamp\tstr\n",
       " \ttag\tstr\n",
       " \ttitle\tstr\n",
       " \tgenres\tstr\n",
       " \tgenres1\tstr\n",
       " \tgenres2\tstr\n",
       " \tgenres3\tstr\n",
       " \tgenres4\tstr\n",
       " \tgenres5\tstr\n",
       " \tgenres6\tstr\n",
       " \tgenres7\tstr\n",
       " \tgenres8\tstr\n",
       " \tgenres9\tstr\n",
       " \tgenres10\tstr\n",
       " \tYear_release\tfloat\n",
       " \timdbId\tint\n",
       " \ttmdbId\tfloat\n",
       " \n",
       " Rows: 20001\n",
       " \n",
       " Data:\n",
       " +-------+--------+---------+--------+-------------------------+-----+\n",
       " |   X1  | userId | movieId | rating |        timestamp        | tag |\n",
       " +-------+--------+---------+--------+-------------------------+-----+\n",
       " | 20001 |  133   |  72641  |  4.0   | 1970-01-17 09:22:28.353 | nan |\n",
       " | 20002 |  133   |  73290  |  5.0   | 1970-01-17 09:22:29.511 | nan |\n",
       " | 20003 |  133   |  73321  |  1.5   | 1970-01-17 09:22:32.072 | nan |\n",
       " | 20004 |  133   |  79132  |  1.0   | 1970-01-17 09:22:31.620 | nan |\n",
       " | 20005 |  133   |  80969  |  4.0   | 1970-01-17 09:22:30.601 | nan |\n",
       " | 20006 |  133   |  81562  |  4.0   | 1970-01-17 09:22:46.749 | nan |\n",
       " | 20007 |  133   |  81591  |  3.0   | 1970-01-17 09:22:28.004 | nan |\n",
       " | 20008 |  133   |  81834  |  4.0   | 1970-01-17 09:22:28.157 | nan |\n",
       " | 20009 |  133   |  82459  |  0.5   | 1970-01-17 09:22:31.654 | nan |\n",
       " | 20010 |  133   |  85438  |  2.5   | 1970-01-17 09:22:47.104 | nan |\n",
       " +-------+--------+---------+--------+-------------------------+-----+\n",
       " +-------------------------------+-------------------------------+-----------+\n",
       " |             title             |             genres            |  genres1  |\n",
       " +-------------------------------+-------------------------------+-----------+\n",
       " |    Blind Side, The  (2009)    |             Drama             |   Drama   |\n",
       " | Hachiko: A Dog's Story (a.... |             Drama             |   Drama   |\n",
       " |    Book of Eli, The (2010)    |     Action|Adventure|Drama    |   Action  |\n",
       " |        Inception (2010)       | Action|Crime|Drama|Mystery... |   Action  |\n",
       " |     Never Let Me Go (2010)    |      Drama|Romance|Sci-Fi     |   Drama   |\n",
       " |        127 Hours (2010)       |    Adventure|Drama|Thriller   | Adventure |\n",
       " |       Black Swan (2010)       |         Drama|Thriller        |   Drama   |\n",
       " | Harry Potter and the Death... | Action|Adventure|Fantasy|IMAX |   Action  |\n",
       " |        True Grit (2010)       |            Western            |  Western  |\n",
       " |        Jane Eyre (2011)       |         Drama|Romance         |   Drama   |\n",
       " +-------------------------------+-------------------------------+-----------+\n",
       " +-----------+----------+---------+---------+----------+---------+---------+---------+\n",
       " |  genres2  | genres3  | genres4 | genres5 | genres6  | genres7 | genres8 | genres9 |\n",
       " +-----------+----------+---------+---------+----------+---------+---------+---------+\n",
       " |           |          |         |         |          |         |         |         |\n",
       " |           |          |         |         |          |         |         |         |\n",
       " | Adventure |  Drama   |         |         |          |         |         |         |\n",
       " |   Crime   |  Drama   | Mystery |  Sci-Fi | Thriller |   IMAX  |         |         |\n",
       " |  Romance  |  Sci-Fi  |         |         |          |         |         |         |\n",
       " |   Drama   | Thriller |         |         |          |         |         |         |\n",
       " |  Thriller |          |         |         |          |         |         |         |\n",
       " | Adventure | Fantasy  |   IMAX  |         |          |         |         |         |\n",
       " |           |          |         |         |          |         |         |         |\n",
       " |  Romance  |          |         |         |          |         |         |         |\n",
       " +-----------+----------+---------+---------+----------+---------+---------+---------+\n",
       " +----------+--------------+---------+-----+\n",
       " | genres10 | Year_release |  imdbId | ... |\n",
       " +----------+--------------+---------+-----+\n",
       " |          |    2009.0    |  878804 | ... |\n",
       " |          |    2009.0    | 1028532 | ... |\n",
       " |          |    2010.0    | 1037705 | ... |\n",
       " |          |    2010.0    | 1375666 | ... |\n",
       " |          |    2010.0    | 1334260 | ... |\n",
       " |          |    2010.0    | 1542344 | ... |\n",
       " |          |    2010.0    |  947798 | ... |\n",
       " |          |    2010.0    |  926084 | ... |\n",
       " |          |    2010.0    | 1403865 | ... |\n",
       " |          |    2011.0    | 1229822 | ... |\n",
       " +----------+--------------+---------+-----+\n",
       " [20001 rows x 21 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sample fold\n",
    "(train, test) = kfolds[1]\n",
    "(train, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the performance of the model on dense data vs sparse data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     total_ratings\n",
      "0             2391\n",
      "1             1868\n",
      "2             1735\n",
      "3             1700\n",
      "4             1610\n",
      "5             1340\n",
      "6             1291\n",
      "7             1063\n",
      "8             1019\n",
      "9             1011\n",
      "10             947\n",
      "11             923\n",
      "12             922\n",
      "13             910\n",
      "14             876\n",
      "15             830\n",
      "16             792\n",
      "17             726\n",
      "18             713\n",
      "19             707\n",
      "20             696\n",
      "21             682\n",
      "22             678\n",
      "23             676\n",
      "24             655\n",
      "25             645\n",
      "26             641\n",
      "27             626\n",
      "28             617\n",
      "29             604\n",
      "..             ...\n",
      "641             21\n",
      "642             21\n",
      "643             20\n",
      "644             20\n",
      "645             20\n",
      "646             20\n",
      "647             20\n",
      "648             20\n",
      "649             20\n",
      "650             20\n",
      "651             20\n",
      "652             20\n",
      "653             20\n",
      "654             20\n",
      "655             20\n",
      "656             20\n",
      "657             20\n",
      "658             20\n",
      "659             20\n",
      "660             20\n",
      "661             20\n",
      "662             20\n",
      "663             20\n",
      "664             20\n",
      "665             20\n",
      "666             20\n",
      "667             20\n",
      "668             20\n",
      "669             20\n",
      "670             20\n",
      "\n",
      "[671 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "A=ratings.groupby(['userId']).userId.count()\n",
    "agg_ratings=list(A)\n",
    "user_Id=range(1,672)\n",
    "dict_pair=dict(zip(user_Id,agg_ratings))\n",
    "agg_ratings_sorted=sorted(agg_ratings, reverse=True)\n",
    "agg_ratings_df = pd.DataFrame({'total_ratings':agg_ratings_sorted})\n",
    "print (agg_ratings_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "258 0.384500745156\n",
      "80487\n",
      "[514, 4, 598, 518, 519, 8, 522, 523, 525, 15, 528, 17, 19, 21, 22, 23, 536, 516, 26, 30, 544, 33, 34, 547, 36, 605, 38, 41, 43, 558, 559, 48, 520, 562, 563, 564, 56, 57, 570, 607, 572, 61, 574, 575, 577, 67, 68, 582, 609, 72, 73, 75, 77, 78, 548, 592, 81, 83, 84, 85, 86, 599, 88, 602, 91, 92, 93, 94, 95, 608, 97, 99, 102, 529, 105, 615, 620, 110, 111, 624, 626, 531, 118, 119, 120, 553, 125, 533, 128, 641, 130, 133, 134, 647, 584, 652, 654, 655, 656, 659, 148, 149, 150, 527, 152, 665, 623, 157, 159, 165, 585, 168, 169, 534, 175, 176, 177, 178, 627, 182, 185, 187, 189, 603, 195, 199, 200, 201, 205, 212, 213, 214, 217, 219, 220, 550, 232, 234, 235, 236, 239, 240, 242, 243, 245, 247, 250, 251, 253, 254, 255, 257, 537, 262, 263, 265, 268, 270, 275, 282, 283, 285, 287, 292, 646, 294, 295, 297, 299, 303, 304, 648, 306, 309, 311, 312, 313, 316, 324, 328, 339, 342, 344, 345, 346, 350, 353, 355, 358, 362, 363, 367, 371, 373, 378, 380, 381, 382, 384, 386, 387, 388, 390, 391, 394, 396, 664, 402, 405, 407, 408, 580, 412, 416, 417, 418, 423, 426, 427, 428, 430, 431, 433, 434, 439, 441, 442, 671, 596, 450, 587, 452, 457, 460, 461, 463, 466, 468, 561, 471, 472, 475, 480, 481, 483, 486, 487, 493, 496, 497, 595, 500, 501, 502, 505, 509, 510, 597]\n"
     ]
    }
   ],
   "source": [
    "dict_pair_dense_100={k: v for k, v in dict_pair.iteritems() if v > 100} #filtering users who have rated >100 movies\n",
    "print len(dict_pair_dense_100), 258/671.0\n",
    "ratings_sum_dense_100=sum(dict_pair_dense_100.values()) #agg. no of ratings these users casted\n",
    "print ratings_sum_dense_100\n",
    "userId_dense=dict_pair_dense_100.keys() # extracting user_ids from dense dictionary\n",
    "print userId_dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dense_data = all_data.loc[userId_dense,:]\n",
    "train_dense, test_dense  = train_test_split(dense_data , test_size=0.2, random_state=42)\n",
    "train_dense.to_csv(\"train_dense_data.csv\")\n",
    "test_dense.to_csv(\"test_dense_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_dense_data.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\train_dense_data.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 206 lines in 0.021013 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 206 lines in 0.021013 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\test_dense_data.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\hp\\Desktop\\Jupyter Notebooks\\Personalisation-master\\Personalisation-master\\test_dense_data.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 52 lines in 0.020983 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 52 lines in 0.020983 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_dense_Sframe= graphlab.SFrame.read_csv(\"train_dense_data.csv\",column_type_hints=hints)\n",
    "test_dense_Sframe= graphlab.SFrame.read_csv(\"test_dense_data.csv\",column_type_hints=hints)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Recsys training: model = factorization_recommender</pre>"
      ],
      "text/plain": [
       "Recsys training: model = factorization_recommender"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 206 observations with 671 users and 9125 items.</pre>"
      ],
      "text/plain": [
       "    Data has 206 observations with 671 users and 9125 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.323745s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.323745s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 6        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 6        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-006   |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-006   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-010   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| side_data_factorization        | Assign Factors for Side Data                     | True     |</pre>"
      ],
      "text/plain": [
       "| side_data_factorization        | Assign Factors for Side Data                     | True     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 206 / 206 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 206 / 206 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 1.21951           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 1.21951           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 0.304878          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 0.304878          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 0.0762195         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 0.0762195         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.0190549         | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.0190549         | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.00476372        | 0.458953                                 |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.00476372        | 0.458953                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.00238186        | 0.568837                                 |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.00238186        | 0.568837                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.00119093        | 0.65903                                  |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.00119093        | 0.65903                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.000595465       | 0.752389                                 |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.000595465       | 0.752389                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.00476372        | 0.458953                                 |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.00476372        | 0.458953                                 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 0us          | 0.971717          | 0.985757              |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 0us          | 0.971717          | 0.985757              |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 15.51ms      | 0.929609          | 0.964162              | 0.00476372  |</pre>"
      ],
      "text/plain": [
       "| 1       | 15.51ms      | 0.929609          | 0.964162              | 0.00476372  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 20.015ms     | 0.678876          | 0.823939              | 0.00283252  |</pre>"
      ],
      "text/plain": [
       "| 2       | 20.015ms     | 0.678876          | 0.823939              | 0.00283252  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 25.017ms     | 0.592121          | 0.769494              | 0.0020898   |</pre>"
      ],
      "text/plain": [
       "| 3       | 25.017ms     | 0.592121          | 0.769494              | 0.0020898   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 30.023ms     | 0.539935          | 0.734803              | 0.00168423  |</pre>"
      ],
      "text/plain": [
       "| 4       | 30.023ms     | 0.539935          | 0.734803              | 0.00168423  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 34.024ms     | 0.515992          | 0.718326              | 0.00142468  |</pre>"
      ],
      "text/plain": [
       "| 5       | 34.024ms     | 0.515992          | 0.718326              | 0.00142468  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 55.538ms     | 0.479352          | 0.692352              | 0.0012426   |</pre>"
      ],
      "text/plain": [
       "| 6       | 55.538ms     | 0.479352          | 0.692352              | 0.0012426   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 91.064ms     | 0.410824          | 0.640955              | 0.000788682 |</pre>"
      ],
      "text/plain": [
       "| 11      | 91.064ms     | 0.410824          | 0.640955              | 0.000788682 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 0.333824</pre>"
      ],
      "text/plain": [
       "       Final objective value: 0.333824"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.577775</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.577775"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_dense = graphlab.factorization_recommender.create(train_dense_Sframe, target='rating', user_id='userId',\n",
    "                                               item_id='movieId',solver= \"sgd\" , user_data = user_metadata,\n",
    "                                               item_data = movie_metadata,regularization=1e-06, num_factors=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse_by_item': Columns:\n",
       " \tmovieId\tint\n",
       " \tcount\tint\n",
       " \trmse\tfloat\n",
       " \n",
       " Rows: 50\n",
       " \n",
       " Data:\n",
       " +---------+-------+----------------+\n",
       " | movieId | count |      rmse      |\n",
       " +---------+-------+----------------+\n",
       " |   431   |   1   | 1.03994908227  |\n",
       " |   454   |   1   | 0.835352705907 |\n",
       " |   3108  |   1   | 0.997145956157 |\n",
       " |   2959  |   1   | 1.58823122281  |\n",
       " |   208   |   1   | 0.522173361251 |\n",
       " |   356   |   2   |  1.1004675673  |\n",
       " |   616   |   1   |  1.3417596253  |\n",
       " |   112   |   1   | 1.61649025026  |\n",
       " |   1240  |   1   | 0.953923327843 |\n",
       " |   1358  |   1   | 3.33852477309  |\n",
       " +---------+-------+----------------+\n",
       " [50 rows x 3 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.,\n",
       " 'rmse_by_user': Columns:\n",
       " \tuserId\tint\n",
       " \tcount\tint\n",
       " \trmse\tfloat\n",
       " \n",
       " Rows: 8\n",
       " \n",
       " Data:\n",
       " +--------+-------+----------------+\n",
       " | userId | count |      rmse      |\n",
       " +--------+-------+----------------+\n",
       " |   2    |   10  | 0.769126366779 |\n",
       " |   8    |   4   |  1.7333954471  |\n",
       " |   5    |   4   | 0.793192543864 |\n",
       " |   4    |   17  | 1.15984022681  |\n",
       " |   3    |   3   | 1.06448474032  |\n",
       " |   1    |   1   | 1.99849247485  |\n",
       " |   7    |   10  |  1.2111678722  |\n",
       " |   6    |   3   | 0.725260172866 |\n",
       " +--------+-------+----------------+\n",
       " [8 rows x 3 columns],\n",
       " 'rmse_overall': 1.1347682580388412}"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_dense.evaluate_rmse(test_dense_Sframe,target ='rating')"
   ]
  }
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