RatingShades.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set(font_scale=1.3)\n",
    "sns.set_style('white')\n",
    "mpl.rcParams['text.usetex'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from polara.recommender.data import RecommenderData\n",
    "from polara.recommender.models import CoffeeModel\n",
    "from polara.tools.movielens import get_movielens_data\n",
    "from polara.tools.printing import print_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ml_data, ml_genres = get_movielens_data(local_file=\"ml-1m.zip\", get_genres=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rating_model_data = RecommenderData(ml_data, 'userid', 'movieid', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing data\n"
     ]
    }
   ],
   "source": [
    "rating_model_data.holdout_size = 1\n",
    "rating_model_data.random_holdout = False\n",
    "# rating_model_data.test_ratio = 0.000017\n",
    "rating_model_data.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'holdout_size': 1,\n",
       " 'negative_prediction': False,\n",
       " 'permute_tops': False,\n",
       " 'random_holdout': False,\n",
       " 'shuffle_data': False,\n",
       " 'test_fold': 5,\n",
       " 'test_ratio': 0.2,\n",
       " 'test_sample': None}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_model_data.get_configuration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CoFFee model training time: 1.98389791159s\n"
     ]
    }
   ],
   "source": [
    "coffee = CoffeeModel(rating_model_data)\n",
    "coffee.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "core, v, w = coffee._core, coffee._items_factors, coffee._feedback_factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3687L, 10L) (5L, 2L)\n"
     ]
    }
   ],
   "source": [
    "print v.shape, w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieid</th>\n",
       "      <th>movienm</th>\n",
       "      <th>genreid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieid              movienm genreid\n",
       "507      296  Pulp Fiction (1994)   Crime\n",
       "508      296  Pulp Fiction (1994)   Drama"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_genres[ml_genres.movienm.str.lower().str.contains('pulp fiction')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new</th>\n",
       "      <th>old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>285</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     new  old\n",
       "285  285  296"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_model_data.index.itemid.query('old == 296')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1  2  3  4\n",
       "new  0  1  2  3  4\n",
       "old  1  2  3  4  5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_model_data.index.feedback.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = np.zeros((v.shape[0], w.shape[0]))\n",
    "p[285, 4] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = v.dot(v.T.dot(p.dot(w).dot(w.T)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "colors = ['white',\n",
    "          'light blue grey',\n",
    "          'grey blue',\n",
    "          'dark slate blue',\n",
    "          'dark blue grey']\n",
    "custom_colors = [colors[i] for i in [4, 2, 0, 2, 3, 4, 1, 2, 3, 1, 3, 1, 4, 1]]\n",
    "cmap = mpl.colors.ListedColormap(sns.xkcd_palette(custom_colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0xb9792e8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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8fzx6WFG9LUPA/fv3/X97nqdLly7pwIEDGhgY8MtmZ2f1gx/QmQDOB68F7jGASjlOZV/i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\nihAAAEBE/T8KNrXIrmHhGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0d5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Shades of ratings')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Movie id')\n",
    "plt.ylim([0, 1000])\n",
    "\n",
    "plt.pcolor(r, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plt.savefig(\"shades.pdf\")\n",
    "# plt.show()\n",
    "# plt.close()\n",
    "# plt.clf()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}