recommendation.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataFile='u.data'\n",
    "data=pd.read_csv(dataFile,sep=\"\\t\",header=None,\n",
    "                 names=['userId','itemId','rating','timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>rating</th>\n",
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      "text/plain": [
       "   userId  itemId  rating  timestamp\n",
       "0     196     242       3  881250949\n",
       "1     186     302       3  891717742\n",
       "2      22     377       1  878887116\n",
       "3     244      51       2  880606923\n",
       "4     166     346       1  886397596"
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     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "data.head()\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movieInfoFile=\"u.item\"\n",
    "movieInfo=pd.read_csv(movieInfoFile,sep=\"|\", header=None, index_col=False,\n",
    "                     names=[\"itemId\",\"title\"], usecols=[0,1],encoding = 'latin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Four Rooms (1995)</td>\n",
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      ],
      "text/plain": [
       "   itemId              title\n",
       "0       1   Toy Story (1995)\n",
       "1       2   GoldenEye (1995)\n",
       "2       3  Four Rooms (1995)\n",
       "3       4  Get Shorty (1995)\n",
       "4       5     Copycat (1995)"
      ]
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     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "movieInfo.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.merge(data,movieInfo,left_on='itemId',right_on=\"itemId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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       "      <th></th>\n",
       "      <th>userId</th>\n",
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       "   userId  itemId  rating  timestamp         title\n",
       "0     196     242       3  881250949  Kolya (1996)\n",
       "1      63     242       3  875747190  Kolya (1996)\n",
       "2     226     242       5  883888671  Kolya (1996)\n",
       "3     154     242       3  879138235  Kolya (1996)\n",
       "4     306     242       5  876503793  Kolya (1996)"
      ]
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     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "data.head()"
   ]
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  {
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   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       userId  itemId  rating  timestamp  \\\n",
       "99995     840    1674       4  891211682   \n",
       "99996     655    1640       3  888474646   \n",
       "99997     655    1637       3  888984255   \n",
       "99998     655    1630       3  887428735   \n",
       "99999     655    1641       3  887427810   \n",
       "\n",
       "                                                   title  \n",
       "99995                                  Mamma Roma (1962)  \n",
       "99996                             Eighth Day, The (1996)  \n",
       "99997                                  Girls Town (1996)  \n",
       "99998  Silence of the Palace, The (Saimt el Qusur) (1...  \n",
       "99999                                    Dadetown (1995)  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.tail()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "userIds=data.userId # a Pandas series object\n",
    "userIds2=data[['userId']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   userId\n",
       "0     196\n",
       "1      63\n",
       "2     226\n",
       "3     154\n",
       "4     306"
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    "userIds.head()\n",
    "userIds2.head()\n"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "   userId\n",
       "0     196\n",
       "1      63\n",
       "2     226\n",
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       "4     306"
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    "userIds2.head()\n"
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   "execution_count": 16,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    userId\n",
       "0      196\n",
       "1       63\n",
       "2      226\n",
       "3      154\n",
       "4      306\n",
       "5      296\n",
       "6       34\n",
       "7      271\n",
       "8      201\n",
       "9      209\n",
       "10      35"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0:10,['userId']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>891700426</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>883601324</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  itemId  rating  timestamp             title\n",
       "3397     308       1       4  887736532  Toy Story (1995)\n",
       "3398     287       1       5  875334088  Toy Story (1995)\n",
       "3399     148       1       4  877019411  Toy Story (1995)\n",
       "3400     280       1       4  891700426  Toy Story (1995)\n",
       "3401      66       1       3  883601324  Toy Story (1995)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toyStoryUsers=data[data.title==\"Toy Story (1995)\"]\n",
    "# This will give us a subset dataframe with only the users who have rated Toy Story\n",
    "toyStoryUsers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.DataFrame.sort_values(data,['userId','itemId'],ascending=[0,1]) #Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23781</th>\n",
       "      <td>943</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>888639953</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65410</th>\n",
       "      <td>943</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>875501960</td>\n",
       "      <td>Dead Man Walking (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35098</th>\n",
       "      <td>943</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>888639000</td>\n",
       "      <td>Seven (Se7en) (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43773</th>\n",
       "      <td>943</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>888639093</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57040</th>\n",
       "      <td>943</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>888639042</td>\n",
       "      <td>Braveheart (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       userId  itemId  rating  timestamp                       title\n",
       "23781     943       2       5  888639953            GoldenEye (1995)\n",
       "65410     943       9       3  875501960     Dead Man Walking (1995)\n",
       "35098     943      11       4  888639000        Seven (Se7en) (1995)\n",
       "43773     943      12       5  888639093  Usual Suspects, The (1995)\n",
       "57040     943      22       4  888639042           Braveheart (1995)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numUsers=max(data.userId)\n",
    "numMovies=max(data.itemId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "943"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numUsers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1682"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "moviesPerUser=data.userId.value_counts()\n",
    "usersPerMovie=data.title.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Star Wars (1977)                                 583\n",
       "Contact (1997)                                   509\n",
       "Fargo (1996)                                     508\n",
       "Return of the Jedi (1983)                        507\n",
       "Liar Liar (1997)                                 485\n",
       "English Patient, The (1996)                      481\n",
       "Scream (1996)                                    478\n",
       "Toy Story (1995)                                 452\n",
       "Air Force One (1997)                             431\n",
       "Independence Day (ID4) (1996)                    429\n",
       "Raiders of the Lost Ark (1981)                   420\n",
       "Godfather, The (1972)                            413\n",
       "Pulp Fiction (1994)                              394\n",
       "Twelve Monkeys (1995)                            392\n",
       "Silence of the Lambs, The (1991)                 390\n",
       "Jerry Maguire (1996)                             384\n",
       "Chasing Amy (1997)                               379\n",
       "Rock, The (1996)                                 378\n",
       "Empire Strikes Back, The (1980)                  367\n",
       "Star Trek: First Contact (1996)                  365\n",
       "Titanic (1997)                                   350\n",
       "Back to the Future (1985)                        350\n",
       "Mission: Impossible (1996)                       344\n",
       "Fugitive, The (1993)                             336\n",
       "Indiana Jones and the Last Crusade (1989)        331\n",
       "Willy Wonka and the Chocolate Factory (1971)     326\n",
       "Princess Bride, The (1987)                       324\n",
       "Forrest Gump (1994)                              321\n",
       "Monty Python and the Holy Grail (1974)           316\n",
       "Saint, The (1997)                                316\n",
       "                                                ... \n",
       "Lady of Burlesque (1943)                           1\n",
       "Eye of Vichy, The (Oeil de Vichy, L') (1993)       1\n",
       "Eighth Day, The (1996)                             1\n",
       "Vermont Is For Lovers (1992)                       1\n",
       "Wend Kuuni (God's Gift) (1982)                     1\n",
       "Dadetown (1995)                                    1\n",
       "Promise, The (Versprechen, Das) (1994)             1\n",
       "Woman in Question, The (1950)                      1\n",
       "Invitation, The (Zaproszenie) (1986)               1\n",
       "They Made Me a Criminal (1939)                     1\n",
       "Witness (1985)                                     1\n",
       "Gate of Heavenly Peace, The (1995)                 1\n",
       "Nobody Loves Me (Keiner liebt mich) (1994)         1\n",
       "Ripe (1996)                                        1\n",
       "I, Worst of All (Yo, la peor de todas) (1990)      1\n",
       "Tokyo Fist (1995)                                  1\n",
       "Hana-bi (1997)                                     1\n",
       "Getting Away With Murder (1996)                    1\n",
       "Baton Rouge (1988)                                 1\n",
       "Leopard Son, The (1996)                            1\n",
       "Two Friends (1986)                                 1\n",
       "Death in Brunswick (1991)                          1\n",
       "Liebelei (1933)                                    1\n",
       "All Things Fair (1996)                             1\n",
       "Spanish Prisoner, The (1997)                       1\n",
       "Land and Freedom (Tierra y libertad) (1995)        1\n",
       "Paris Was a Woman (1995)                           1\n",
       "Sleepover (1995)                                   1\n",
       "Power 98 (1995)                                    1\n",
       "Wedding Bell Blues (1996)                          1\n",
       "Name: title, Length: 1664, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usersPerMovie\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def favoriteMovies(activeUser,N):\n",
    "    #1. subset the dataframe to have the rows corresponding to the active user\n",
    "    # 2. sort by the rating in descending order\n",
    "    # 3. pick the top N rows\n",
    "    topMovies=pd.DataFrame.sort_values(\n",
    "        data[data.userId==activeUser],['rating'],ascending=[0])[:N]\n",
    "    return list(topMovies.title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Men in Black (1997)', 'Blade Runner (1982)', 'Empire Strikes Back, The (1980)']\n"
     ]
    }
   ],
   "source": [
    "print(favoriteMovies(5,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "userItemRatingMatrix=pd.pivot_table(data, values='rating',\n",
    "                                    index=['userId'], columns=['itemId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1682 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "itemId  1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "userId                                                              ...    \n",
       "1        5.0   3.0   4.0   3.0   3.0   5.0   4.0   1.0   5.0   3.0  ...    \n",
       "2        4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   2.0  ...    \n",
       "3        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "4        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "5        4.0   3.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "itemId  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \n",
       "userId                                                              \n",
       "1        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "5        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 1682 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userItemRatingMatrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import correlation \n",
    "def similarity(user1,user2):\n",
    "    user1=np.array(user1)-np.nanmean(user1) # I am first normalizing user1 by \n",
    "    # the mean rating of user 1 for any movie\n",
    "    user2=np.array(user2)-np.nanmean(user2)\n",
    "    commonItemIds=[i for i in range(len(user1)) if user1[i]>0 and user2[i]>0]\n",
    "    # Gives us movies for which both users have non NaN ratings \n",
    "    if len(commonItemIds)==0:\n",
    "        # If there are no movies in common \n",
    "        return 0\n",
    "    else:\n",
    "        user1=np.array([user1[i] for i in commonItemIds])\n",
    "        user2=np.array([user2[i] for i in commonItemIds])\n",
    "        return correlation(user1,user2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Using this similarity function, let's find the nearest neighbours of the active user\n",
    "def nearestNeighbourRatings(activeUser,K):\n",
    "    # This function will find the K Nearest neighbours of the active user, then \n",
    "    # use their ratings to predict the activeUsers ratings for other movies \n",
    "    similarityMatrix=pd.DataFrame(index=userItemRatingMatrix.index,\n",
    "                                  columns=['Similarity'])\n",
    "    for i in userItemRatingMatrix.index:\n",
    "        similarityMatrix.loc[i]=similarity(userItemRatingMatrix.loc[activeUser],\n",
    "                                          userItemRatingMatrix.loc[i])\n",
    "    similarityMatrix=pd.DataFrame.sort_values(similarityMatrix,\n",
    "                                              ['Similarity'],ascending=[0])\n",
    "    # Sort the similarity matrix in the descending order of similarity \n",
    "    nearestNeighbours=similarityMatrix[:K]\n",
    "    neighbourItemRatings=userItemRatingMatrix.loc[nearestNeighbours.index]\n",
    "    predictItemRating=pd.DataFrame(index=userItemRatingMatrix.columns, columns=['Rating'])\n",
    "    for i in userItemRatingMatrix.columns:\n",
    "        # for each item \n",
    "        predictedRating=np.nanmean(userItemRatingMatrix.loc[activeUser])\n",
    "        # start with the average rating of the user\n",
    "        for j in neighbourItemRatings.index:\n",
    "            # for each neighbour in the neighbour list \n",
    "            if userItemRatingMatrix.loc[j,i]>0:\n",
    "                # If the neighbour has rated that item\n",
    "                # Add the rating of the neighbour for that item\n",
    "                #    adjusted by \n",
    "                #    the average rating of the neighbour \n",
    "                #    weighted by \n",
    "                #    the similarity of the neighbour to the active user\n",
    "                predictedRating += (userItemRatingMatrix.loc[j,i]\n",
    "                                    -np.nanmean(userItemRatingMatrix.loc[j]))*nearestNeighbours.loc[j,'Similarity']\n",
    "        # rating to the predicted Rating matrix\n",
    "        predictItemRating.loc[i,'Rating']=predictedRating\n",
    "    return predictItemRating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def topNRecommendations(activeUser,N):\n",
    "    predictItemRating=nearestNeighbourRatings(activeUser,10)\n",
    "    # Use the 10 nearest neighbours to find the predicted ratings\n",
    "    moviesAlreadyWatched=list(userItemRatingMatrix.loc[activeUser]\n",
    "                              .loc[userItemRatingMatrix.loc[activeUser]>0].index)\n",
    "    # find the list of items whose ratings which are not NaN\n",
    "    predictItemRating=predictItemRating.drop(moviesAlreadyWatched)\n",
    "    topRecommendations=pd.DataFrame.sort_values(predictItemRating,\n",
    "                                                ['Rating'],ascending=[0])[:N]\n",
    "    # This will give us the list of itemIds which are the top recommendations \n",
    "    # Let's find the corresponding movie titles \n",
    "    topRecommendationTitles=(movieInfo.loc[movieInfo.itemId.isin(topRecommendations.index)])\n",
    "    return list(topRecommendationTitles.title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aditya/Desktop/anaconda/lib/python3.6/site-packages/scipy/spatial/distance.py:543: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist = 1.0 - np.dot(um, vm) / (norm(um) * norm(vm))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Men in Black (1997)', 'Blade Runner (1982)', 'Empire Strikes Back, The (1980)', 'Wrong Trousers, The (1993)', 'Blues Brothers, The (1980)'] \n",
      " ['Truth About Cats & Dogs, The (1996)', 'Scream (1996)', 'First Wives Club, The (1996)']\n"
     ]
    }
   ],
   "source": [
    "activeUser=5\n",
    "print(favoriteMovies(activeUser,5),\"\\n\",topNRecommendations(activeUser,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
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     "output_type": "stream",
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   "source": [
    "\n",
    "def matrixFactorization(R, K, steps=10, gamma=0.001,lamda=0.02):\n",
    "    N=len(R.index)# Number of users\n",
    "    M=len(R.columns) # Number of items \n",
    "    P=pd.DataFrame(np.random.rand(N,K),index=R.index)\n",
    "    # This is the user factor matrix i want to find. It will have N rows \n",
    "    Q=pd.DataFrame(np.random.rand(M,K),index=R.columns)\n",
    "    # This is the product factor matrix i want to find. It will have M rows, \n",
    "    # one for each product/item/movie. \n",
    "    for step in range(steps):\n",
    "        for i in R.index:\n",
    "            for j in R.columns:\n",
    "                if R.loc[i,j]>0:\n",
    "                    # For each rating that exists in the training set \n",
    "                    eij=R.loc[i,j]-np.dot(P.loc[i],Q.loc[j])\n",
    "                    P.loc[i]=P.loc[i]+gamma*(eij*Q.loc[j]-lamda*P.loc[i])\n",
    "                    # The value in the brackets is the partial derivative of the \n",
    "                    # error function ie the slope. Lamda is the value of the \n",
    "                    # regularization parameter which penalizes the model for the \n",
    "                    # number of factors we are finding. \n",
    "                    Q.loc[j]=Q.loc[j]+gamma*(eij*P.loc[i]-lamda*Q.loc[j])\n",
    "        # At the end of this i have looped through all the ratings once. \n",
    "        # Let's check the value of the error function to see if we have reached \n",
    "        # the threshold at which we want to stop, else we will repeat the process\n",
    "        e=0\n",
    "        for i in R.index:\n",
    "            for j in R.columns:\n",
    "                if R.loc[i,j]>0:\n",
    "                    #Sum of squares of the errors in the rating\n",
    "                    e= e + pow(R.loc[i,j]-np.dot(P.loc[i],Q.loc[j]),2)+lamda*(pow(np.linalg.norm(P.loc[i]),2)+pow(np.linalg.norm(Q.loc[j]),2))\n",
    "        if e<0.001:\n",
    "            break\n",
    "        print(step)\n",
    "    return P,Q\n",
    "\n",
    "# Let's call this function now \n",
    "(P,Q)=matrixFactorization(userItemRatingMatrix.iloc[:100,:100],K=2,gamma=0.001,lamda=0.02, steps=100)\n",
    "# This will be pretty expensive computationally. For now lets just do it over a \n",
    "# part of the rating matrix to see how it works. I've kept the steps to 100 too. \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Star Wars (1977)', 'Shawshank Redemption, The (1994)', 'Fargo (1996)']\n"
     ]
    }
   ],
   "source": [
    "activeUser=1\n",
    "predictItemRating=pd.DataFrame(np.dot(P.loc[activeUser],Q.T),index=Q.index,columns=['Rating'])\n",
    "topRecommendations=pd.DataFrame.sort_values(predictItemRating,['Rating'],ascending=[0])[:3]\n",
    "# I found the ratings of all movies by the active user and then sorted them to find the top 3 movies \n",
    "topRecommendationTitles=movieInfo.loc[movieInfo.itemId.isin(topRecommendations.index)]\n",
    "print(list(topRecommendationTitles.title))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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