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Created April 22, 2013 16:48
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MOVIE RECOMMENDER Buiiding a python based recommendation system by wrangling the movieLens database with Pandas. MovieLens data is available at http://www.grouplens.org/node/73
Raw
MLENS_1MILLION.ipynb.json
{
"metadata": {
"name": "MLENS_1MILLION"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recommending Movies from the MovieLens Database \n",
"=============================\n",
"*** \n",
"\n",
"Here we work with the 1 million ratings dataset from 60,000 users for 3,900 movies. We use an item-based filtering approach to recommend \n",
"movies to an user. We explore implement Pearson correlation based recoomendation scheme. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading in Files \n",
"----\n",
"\n",
"Frist we read in rating and movie data using Pandas."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"rcols = ['uid','mid','rating','timestamp']\n",
"ratings = pd.read_table('ml-1m/ratings.dat', sep='::', header=None, names=rcols)\n",
"ratings[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uid</th>\n",
" <th>mid</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 1</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978300760</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 1</td>\n",
" <td> 661</td>\n",
" <td> 3</td>\n",
" <td> 978302109</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 1</td>\n",
" <td> 914</td>\n",
" <td> 3</td>\n",
" <td> 978301968</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 1</td>\n",
" <td> 3408</td>\n",
" <td> 4</td>\n",
" <td> 978300275</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 1</td>\n",
" <td> 2355</td>\n",
" <td> 5</td>\n",
" <td> 978824291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 205,
"text": [
" uid mid rating timestamp\n",
"0 1 1193 5 978300760\n",
"1 1 661 3 978302109\n",
"2 1 914 3 978301968\n",
"3 1 3408 4 978300275\n",
"4 1 2355 5 978824291"
]
}
],
"prompt_number": 205
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mcols = ['mid','title','genres']\n",
"movies = pd.read_table('ml-1m/movies.dat', sep='::', header=None, names=mcols)\n",
"movies[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mid</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 2</td>\n",
" <td> Jumanji (1995)</td>\n",
" <td> Adventure|Children's|Fantasy</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 3</td>\n",
" <td> Grumpier Old Men (1995)</td>\n",
" <td> Comedy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 4</td>\n",
" <td> Waiting to Exhale (1995)</td>\n",
" <td> Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\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>"
],
"output_type": "pyout",
"prompt_number": 206,
"text": [
" mid title genres\n",
"0 1 Toy Story (1995) Animation|Children's|Comedy\n",
"1 2 Jumanji (1995) Adventure|Children's|Fantasy\n",
"2 3 Grumpier Old Men (1995) Comedy|Romance\n",
"3 4 Waiting to Exhale (1995) Comedy|Drama\n",
"4 5 Father of the Bride Part II (1995) Comedy"
]
}
],
"prompt_number": 206
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Meging movie table with rating table \n",
"---- \n",
"\n",
"Next, we merge movies with rating over the mid key "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings = pd.merge(movies, ratings)\n",
"allratings"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 207,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 1000209 entries, 0 to 1000208\n",
"Data columns:\n",
"mid 1000209 non-null values\n",
"title 1000209 non-null values\n",
"genres 1000209 non-null values\n",
"uid 1000209 non-null values\n",
"rating 1000209 non-null values\n",
"timestamp 1000209 non-null values\n",
"dtypes: int64(4), object(2)"
]
}
],
"prompt_number": 207
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mid</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" <th>uid</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" <td> 978824268</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 6</td>\n",
" <td> 4</td>\n",
" <td> 978237008</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 8</td>\n",
" <td> 4</td>\n",
" <td> 978233496</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 9</td>\n",
" <td> 5</td>\n",
" <td> 978225952</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 10</td>\n",
" <td> 5</td>\n",
" <td> 978226474</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>5</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 18</td>\n",
" <td> 4</td>\n",
" <td> 978154768</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>6</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 19</td>\n",
" <td> 5</td>\n",
" <td> 978555994</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>7</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 21</td>\n",
" <td> 3</td>\n",
" <td> 978139347</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>8</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 23</td>\n",
" <td> 4</td>\n",
" <td> 978463614</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>9</strong></td>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" <td> 26</td>\n",
" <td> 3</td>\n",
" <td> 978130703</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 208,
"text": [
" mid title genres uid rating timestamp\n",
"0 1 Toy Story (1995) Animation|Children's|Comedy 1 5 978824268\n",
"1 1 Toy Story (1995) Animation|Children's|Comedy 6 4 978237008\n",
"2 1 Toy Story (1995) Animation|Children's|Comedy 8 4 978233496\n",
"3 1 Toy Story (1995) Animation|Children's|Comedy 9 5 978225952\n",
"4 1 Toy Story (1995) Animation|Children's|Comedy 10 5 978226474\n",
"5 1 Toy Story (1995) Animation|Children's|Comedy 18 4 978154768\n",
"6 1 Toy Story (1995) Animation|Children's|Comedy 19 5 978555994\n",
"7 1 Toy Story (1995) Animation|Children's|Comedy 21 3 978139347\n",
"8 1 Toy Story (1995) Animation|Children's|Comedy 23 4 978463614\n",
"9 1 Toy Story (1995) Animation|Children's|Comedy 26 3 978130703"
]
}
],
"prompt_number": 208
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets see which movie has how many ratings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings['title'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 209,
"text": [
"American Beauty (1999) 3428\n",
"Star Wars: Episode IV - A New Hope (1977) 2991\n",
"Star Wars: Episode V - The Empire Strikes Back (1980) 2990\n",
"Star Wars: Episode VI - Return of the Jedi (1983) 2883\n",
"Jurassic Park (1993) 2672\n",
"Saving Private Ryan (1998) 2653\n",
"Terminator 2: Judgment Day (1991) 2649\n",
"Matrix, The (1999) 2590\n",
"Back to the Future (1985) 2583\n",
"Silence of the Lambs, The (1991) 2578\n",
"Men in Black (1997) 2538\n",
"Raiders of the Lost Ark (1981) 2514\n",
"Fargo (1996) 2513\n",
"Sixth Sense, The (1999) 2459\n",
"Braveheart (1995) 2443\n",
"...\n",
"Boy Called Hate, A (1995) 1\n",
"Bloody Child, The (1996) 1\n",
"Blood and Sand (Sangre y Arena) (1989) 1\n",
"Bittersweet Motel (2000) 1\n",
"Billy's Holiday (1995) 1\n",
"Beloved/Friend (Amigo/Amado) (1999) 1\n",
"Beauty (1998) 1\n",
"Bat Whispers, The (1930) 1\n",
"Back Stage (2000) 1\n",
"Baby, The (1973) 1\n",
"Another Man's Poison (1952) 1\n",
"Anna (1996) 1\n",
"An Unforgettable Summer (1994) 1\n",
"Alley Cats, The (1968) 1\n",
"Aiqing wansui (1994) 1\n",
"Length: 3706"
]
}
],
"prompt_number": 209
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keeping only movies with more than 50 Reviews \n",
"---\n",
"\n",
"We use the slicing operation in Pandas to keep only movies that had more than 50 ratings. This gives us 2500 movies to work with. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings_by_index = allratings.set_index('title')\n",
"allratings_filtered = allratings_by_index.ix[allratings['title'].value_counts()>50]\n",
"movie_titles = allratings_filtered.index.unique()\n",
"print len(movie_titles)\n",
"movie_titles"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2499\n"
]
},
{
"output_type": "pyout",
"prompt_number": 210,
"text": [
"array([Toy Story (1995), Jumanji (1995), Grumpier Old Men (1995), ...,\n",
" Requiem for a Dream (2000), Tigerland (2000), Contender, The (2000)], dtype=object)"
]
}
],
"prompt_number": 210
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets drop extraneous columns."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings_new = allratings_filtered.drop(['timestamp','genres','mid'],axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 211
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings_new"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 212,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 977089 entries, Toy Story (1995) to Contender, The (2000)\n",
"Data columns:\n",
"uid 977089 non-null values\n",
"rating 977089 non-null values\n",
"dtypes: int64(2)"
]
}
],
"prompt_number": 212
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets look at a litte slice of our dataset so far:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allratings_new[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uid</th>\n",
" <th>rating</th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 6</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 8</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 9</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 10</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 18</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 19</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 21</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 23</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>Toy Story (1995)</strong></td>\n",
" <td> 26</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 213,
"text": [
" uid rating\n",
"title \n",
"Toy Story (1995) 1 5\n",
"Toy Story (1995) 6 4\n",
"Toy Story (1995) 8 4\n",
"Toy Story (1995) 9 5\n",
"Toy Story (1995) 10 5\n",
"Toy Story (1995) 18 4\n",
"Toy Story (1995) 19 5\n",
"Toy Story (1995) 21 3\n",
"Toy Story (1995) 23 4\n",
"Toy Story (1995) 26 3"
]
}
],
"prompt_number": 213
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding Avg. User Ratings column \n",
"---\n",
"\n",
"I would like add a column for the average rating for each user across all movies that user rated. This is not required for the Pearson \n",
"correlation, but is needed by the Adjusted Cosine Similary metric, which we will explore later. To do this lets aggregate the ratings \n",
"of the user from the ratings table. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"avgUserRating = ratings.groupby('uid').mean().drop(['mid','timestamp'],axis=1)\n",
"avgUserRating.columns = ['avgRating']\n",
"avgUserRating[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avgRating</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 4.188679</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 3.713178</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 3.901961</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 4.190476</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>5</strong></td>\n",
" <td> 3.146465</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>6</strong></td>\n",
" <td> 3.901408</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>7</strong></td>\n",
" <td> 4.322581</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>8</strong></td>\n",
" <td> 3.884892</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>9</strong></td>\n",
" <td> 3.735849</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>10</strong></td>\n",
" <td> 4.114713</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 214,
"text": [
" avgRating\n",
"uid \n",
"1 4.188679\n",
"2 3.713178\n",
"3 3.901961\n",
"4 4.190476\n",
"5 3.146465\n",
"6 3.901408\n",
"7 4.322581\n",
"8 3.884892\n",
"9 3.735849\n",
"10 4.114713"
]
}
],
"prompt_number": 214
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets merge this with the `allratings_new` table. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tura = pd.merge(allratings_new.reset_index(),avgUserRating.reset_index())\n",
"tura = tura.set_index('title')\n",
"tura"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 215,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 977089 entries, Toy Story (1995) to Tampopo (1986)\n",
"Data columns:\n",
"uid 977089 non-null values\n",
"rating 977089 non-null values\n",
"avgRating 977089 non-null values\n",
"dtypes: float64(1), int64(2)"
]
}
],
"prompt_number": 215
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*** \n",
"Creating a heirarchical index \n",
"--- \n",
"We take the above table and reindex it so that we can select by movie title to get a table with columns [uid, rating] for that movie. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rs = tura.reset_index().set_index(['title','uid'])\n",
"rs.index\n",
"rs.ix['Sudden Death (1995)']#['rating']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rating</th>\n",
" <th>avgRating</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>65</strong></td>\n",
" <td> 5</td>\n",
" <td> 4.347107</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>73</strong></td>\n",
" <td> 3</td>\n",
" <td> 3.364706</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>103</strong></td>\n",
" <td> 4</td>\n",
" <td> 4.052174</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>149</strong></td>\n",
" <td> 3</td>\n",
" <td> 3.940878</td>\n",
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" <td><strong>185</strong></td>\n",
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" <td><strong>1230</strong></td>\n",
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" <td><strong>1414</strong></td>\n",
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" <td><strong>1527</strong></td>\n",
" <td> 2</td>\n",
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" <td><strong>1639</strong></td>\n",
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" <td><strong>1958</strong></td>\n",
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" <td><strong>4699</strong></td>\n",
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" <td><strong>4725</strong></td>\n",
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" <td><strong>5227</strong></td>\n",
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" <td><strong>5249</strong></td>\n",
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" <td><strong>5795</strong></td>\n",
" <td> 1</td>\n",
" <td> 3.056382</td>\n",
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" <td><strong>5837</strong></td>\n",
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" <td><strong>5879</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.781818</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"output_type": "pyout",
"prompt_number": 216,
"text": [
" rating avgRating\n",
"uid \n",
"65 5 4.347107\n",
"73 3 3.364706\n",
"103 4 4.052174\n",
"149 3 3.940878\n",
"185 3 3.588235\n",
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"1088 1 3.337585\n",
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"1377 2 2.821497\n",
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"1841 2 2.718062\n",
"1880 2 3.344503\n",
"1958 3 3.274648\n",
"2015 3 3.127353\n",
"2074 2 3.472222\n",
"2077 3 3.230994\n",
"2116 4 3.555098\n",
"2143 1 3.125000\n",
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"2453 3 3.534181\n",
"2777 3 3.498286\n",
"2857 3 3.159748\n",
"2907 3 3.293286\n",
"2909 3 3.822734\n",
"2983 5 4.363636\n",
"2996 2 3.146751\n",
"3000 1 3.264151\n",
"3182 3 3.078275\n",
"3209 1 2.060870\n",
"3242 4 3.590476\n",
"3311 2 2.778261\n",
"3336 2 2.748126\n",
"3416 3 3.578723\n",
"3507 1 3.261105\n",
"3558 3 4.310000\n",
"3579 3 3.323420\n",
"3589 2 2.494186\n",
"3624 1 3.985876\n",
"3626 4 3.598830\n",
"3650 3 3.560510\n",
"3669 2 3.194954\n",
"3768 2 3.359155\n",
"3792 3 3.576127\n",
"3834 4 4.254448\n",
"3859 3 3.414239\n",
"3962 4 3.471429\n",
"4113 4 3.515152\n",
"4156 4 3.780612\n",
"4213 4 4.060759\n",
"4227 3 2.701309\n",
"4238 2 3.424908\n",
"4261 1 3.187726\n",
"4279 3 3.373469\n",
"4284 2 2.743590\n",
"4323 3 3.576642\n",
"4404 1 3.067204\n",
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"4725 2 3.158052\n",
"4747 2 3.466667\n",
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"5026 2 2.858124\n",
"5046 2 3.649770\n",
"5227 3 3.159236\n",
"5249 1 3.263314\n",
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"5555 3 3.114964\n",
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"5879 4 3.781818"
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}
],
"prompt_number": 216
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rs.ix['Men in Black (1997)'][:10]"
],
"language": "python",
"metadata": {},
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{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rating</th>\n",
" <th>avgRating</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 3</td>\n",
" <td> 3.901961</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>5</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.146465</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>7</strong></td>\n",
" <td> 4</td>\n",
" <td> 4.322581</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>8</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.884892</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>10</strong></td>\n",
" <td> 5</td>\n",
" <td> 4.114713</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>13</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.388889</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>15</strong></td>\n",
" <td> 2</td>\n",
" <td> 3.323383</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>17</strong></td>\n",
" <td> 4</td>\n",
" <td> 4.075829</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>18</strong></td>\n",
" <td> 5</td>\n",
" <td> 3.649180</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>19</strong></td>\n",
" <td> 5</td>\n",
" <td> 3.572549</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 217,
"text": [
" rating avgRating\n",
"uid \n",
"3 3 3.901961\n",
"5 4 3.146465\n",
"7 4 4.322581\n",
"8 4 3.884892\n",
"10 5 4.114713\n",
"13 4 3.388889\n",
"15 2 3.323383\n",
"17 4 4.075829\n",
"18 5 3.649180\n",
"19 5 3.572549"
]
}
],
"prompt_number": 217
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Building a Similarity Function \n",
"----\n",
"\n",
"Now, we build the pieces necessary for creating a movie, movie similarity function. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mov1 = rs.ix['Jurassic Park (1993)'].reset_index()\n",
"mov2 = rs.ix['Saving Private Ryan (1998)'].reset_index()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 218
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mov1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 219,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2672 entries, 0 to 2671\n",
"Data columns:\n",
"uid 2672 non-null values\n",
"rating 2672 non-null values\n",
"avgRating 2672 non-null values\n",
"dtypes: float64(1), int64(2)"
]
}
],
"prompt_number": 219
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mov2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 220,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2653 entries, 0 to 2652\n",
"Data columns:\n",
"uid 2653 non-null values\n",
"rating 2653 non-null values\n",
"avgRating 2653 non-null values\n",
"dtypes: float64(1), int64(2)"
]
}
],
"prompt_number": 220
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An *inner join* on uid is a cute way for getting co-rated (rated by the same user) ratings. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mm = pd.merge(mov1,mov2,on='uid').drop('uid',axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 221
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mm['rating_x'] = mm['rating_x'].astype('float32')\n",
"mm['rating_y'] = mm['rating_y'].astype('float32')\n",
"mm[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rating_x</th>\n",
" <th>avgRating_x</th>\n",
" <th>rating_y</th>\n",
" <th>avgRating_y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 5</td>\n",
" <td> 3.713178</td>\n",
" <td> 4</td>\n",
" <td> 3.713178</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 4</td>\n",
" <td> 4.190476</td>\n",
" <td> 5</td>\n",
" <td> 4.190476</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 4</td>\n",
" <td> 4.322581</td>\n",
" <td> 5</td>\n",
" <td> 4.322581</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 5</td>\n",
" <td> 3.884892</td>\n",
" <td> 5</td>\n",
" <td> 3.884892</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.735849</td>\n",
" <td> 5</td>\n",
" <td> 3.735849</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>5</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.388889</td>\n",
" <td> 4</td>\n",
" <td> 3.388889</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>6</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.323383</td>\n",
" <td> 4</td>\n",
" <td> 3.323383</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>7</strong></td>\n",
" <td> 4</td>\n",
" <td> 4.075829</td>\n",
" <td> 5</td>\n",
" <td> 4.075829</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>8</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.572549</td>\n",
" <td> 4</td>\n",
" <td> 3.572549</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>9</strong></td>\n",
" <td> 4</td>\n",
" <td> 3.741176</td>\n",
" <td> 5</td>\n",
" <td> 3.741176</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 222,
"text": [
" rating_x avgRating_x rating_y avgRating_y\n",
"0 5 3.713178 4 3.713178\n",
"1 4 4.190476 5 4.190476\n",
"2 4 4.322581 5 4.322581\n",
"3 5 3.884892 5 3.884892\n",
"4 4 3.735849 5 3.735849\n",
"5 4 3.388889 4 3.388889\n",
"6 4 3.323383 4 3.323383\n",
"7 4 4.075829 5 4.075829\n",
"8 4 3.572549 4 3.572549\n",
"9 4 3.741176 5 3.741176"
]
}
],
"prompt_number": 222
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With Pandas getting the **Pearson correlation** is a breeze! "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pcorr = mm.corr(method='pearson')\n",
"pcorr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rating_x</th>\n",
" <th>avgRating_x</th>\n",
" <th>rating_y</th>\n",
" <th>avgRating_y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>rating_x</strong></td>\n",
" <td> 1.000000</td>\n",
" <td> 0.249931</td>\n",
" <td> 0.228763</td>\n",
" <td> 0.249931</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>avgRating_x</strong></td>\n",
" <td> 0.249931</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.229232</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>rating_y</strong></td>\n",
" <td> 0.228763</td>\n",
" <td> 0.229232</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.229232</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>avgRating_y</strong></td>\n",
" <td> 0.249931</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.229232</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 223,
"text": [
" rating_x avgRating_x rating_y avgRating_y\n",
"rating_x 1.000000 0.249931 0.228763 0.249931\n",
"avgRating_x 0.249931 1.000000 0.229232 1.000000\n",
"rating_y 0.228763 0.229232 1.000000 0.229232\n",
"avgRating_y 0.249931 1.000000 0.229232 1.000000"
]
}
],
"prompt_number": 223
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pcorr.ix['rating_x']['rating_y']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 224,
"text": [
"0.22876340496239664"
]
}
],
"prompt_number": 224
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pearson Based Movie Similarity\n",
"--- \n",
"\n",
"Putting the above elements together I can now create a Pearson correlation-based moveie similarity function \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def moviePairSimilarityPearson(movx, movy, rs,verbose=False):\n",
" m1 = rs.ix[movx].reset_index() \n",
" m2 = rs.ix[movy].reset_index() \n",
" mmerge = pd.merge(m1,m2,on='uid').drop('uid',axis=1)\n",
" mmerge['rating_x'] = mmerge['rating_x'].astype('float32')\n",
" mmerge['rating_y'] = mmerge['rating_y'].astype('float32')\n",
" pcorr = mmerge.corr(method='pearson')\n",
" if verbose: print movx, movy, mmerge\n",
" return pcorr.ix['rating_x']['rating_y']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 225
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"moviePairSimilarityPearson(movie_titles[23],movie_titles[10],rs,verbose=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 226,
"text": [
"0.31345982510030213"
]
}
],
"prompt_number": 226
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adjusted Cosine Based Similarity \n",
"--- \n",
"\n",
"This is a variation based on *Serwer et al.* http://www.ra.ethz.ch/cdstore/www10/papers/pdf/p519.pdf using the adjusted cosine function. \n",
"$$ \\frac{ \\sum__{u \\in U} (R_{u,i} - \\bar{R_u})(R_{u,j} - \\bar{R_u})}{\\sqrt{ \\sum (R_{u,i} - \\bar{R_u})^2}\\sqrt{ \\sum (R_{u,j} - \\bar{R_u})^2} } $$\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def moviePairSimilarityAdjCos(movx, movy, rs,verbose=False):\n",
" m1 = rs.ix[movx].reset_index() \n",
" m2 = rs.ix[movy].reset_index() \n",
" mm = pd.merge(m1,m2,on='uid').drop('uid',axis=1)\n",
" num = (mm['rating_x'] - mm['avgRating_x']).dot((mm['rating_y'] - mm['avgRating_y']))\n",
" den1 = (mm['rating_x'] - mm['avgRating_x']).dot((mm['rating_x'] - mm['avgRating_x']))\n",
" den2 = (mm['rating_y'] - mm['avgRating_y']).dot((mm['rating_y'] - mm['avgRating_y']))\n",
"\n",
" sim = num/numpy.sqrt(den1*den2)\n",
" if sim > 1.0: sim = 1.0 \n",
" return sim "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 227
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy\n",
"def bestMatches(movx, movie_tls,rs,nTop=100, simFunc = moviePairSimilarityPearson):\n",
" score = []\n",
" for movy in movie_tls:\n",
" #print movy\n",
" if movx == movy: continue\n",
" corr = simFunc(movx, movy, rs)\n",
" if pd.isnull(corr): continue\n",
" score += [(corr, movy.decode('unicode-escape'))] \n",
" score.sort()\n",
" score.reverse()\n",
" return score[0:nTop]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 228
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time score = bestMatches('Deer Hunter, The (1978)', movie_titles,rs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 59.75 s, sys: 0.19 s, total: 59.94 s\n",
"Wall time: 59.98 s\n"
]
}
],
"prompt_number": 229
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"score"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 230,
"text": [
"[(0.72040000656691172, u'Guinevere (1999)'),\n",
" (0.7096661947545746, u'Map of the World, A (1999)'),\n",
" (0.70808645033567741, u'It Came from Beneath the Sea (1955)'),\n",
" (0.68519671766984935, u'Bank Dick, The (1940)'),\n",
" (0.68290059141067172, u'General, The (1998)'),\n",
" (0.6773411933868696, u'Going My Way (1944)'),\n",
" (0.66237952453807258, u'Bride of the Monster (1956)'),\n",
" (0.65561006810718592, u'Andre (1994)'),\n",
" (0.65062675646110124, u'Saboteur (1942)'),\n",
" (0.64236405483757286, u'Bye Bye, Love (1995)'),\n",
" (0.62709155667593708, u'Good Earth, The (1937)'),\n",
" (0.62552033799622597, u'Destination Moon (1950)'),\n",
" (0.62457415833267238, u'Down in the Delta (1998)'),\n",
" (0.6175499629807264, u'Ninotchka (1939)'),\n",
" (0.61266313250846061, u'Duel in the Sun (1946)'),\n",
" (0.60704525120280273, u'Repulsion (1965)'),\n",
" (0.59878684908290802, u'Sanjuro (1962)'),\n",
" (0.58662343545497486, u'Prince of the City (1981)'),\n",
" (0.58642924708134436, u'Frankenstein Meets the Wolf Man (1943)'),\n",
" (0.584518768471054, u'Carnival of Souls (1962)'),\n",
" (0.58112810762940426, u'Sunshine (1999)'),\n",
" (0.57845353732929095, u'It Came from Outer Space (1953)'),\n",
" (0.57544630581407419, u'Long Walk Home, The (1990)'),\n",
" (0.57004087035588769, u'Return of the Fly (1959)'),\n",
" (0.56932079497565802, u'Killer, The (Die xue shuang xiong) (1989)'),\n",
" (0.56729534576268936, u'House of the Spirits, The (1993)'),\n",
" (0.56017489980125601, u'Trial, The (Le Proc\\xe8s) (1963)'),\n",
" (0.55797924072492511,\n",
" u'Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)'),\n",
" (0.55301956479715642, u'Sugarland Express, The (1974)'),\n",
" (0.54630664158851105, u'Farinelli: il castrato (1994)'),\n",
" (0.54617513401994722,\n",
" u'Fantastic Planet, The (La Plan\\xe8te sauvage) (1973)'),\n",
" (0.53851196036005333, u'Chungking Express (1994)'),\n",
" (0.53357837507993255, u'House Party 2 (1991)'),\n",
" (0.53263053965876617, u'Van, The (1996)'),\n",
" (0.53241390564966795, u'Carrington (1995)'),\n",
" (0.529808066253034, u'Bridge at Remagen, The (1969)'),\n",
" (0.52656766771370489, u'Crucible, The (1996)'),\n",
" (0.52629989901984342, u'Murder, My Sweet (1944)'),\n",
" (0.52556544432949681, u\"All the King's Men (1949)\"),\n",
" (0.52450195448557069, u'Red Sorghum (Hong Gao Liang) (1987)'),\n",
" (0.52101192016201348, u'Holy Smoke (1999)'),\n",
" (0.51608782605151282, u'Brokedown Palace (1999)'),\n",
" (0.515841221121351, u'Marty (1955)'),\n",
" (0.51576899775442131, u'Fighting Seabees, The (1944)'),\n",
" (0.51214446752571519, u'Whatever (1998)'),\n",
" (0.51082644554829892, u'Faster Pussycat! Kill! Kill! (1965)'),\n",
" (0.50877005128137509, u'House of Frankenstein (1944)'),\n",
" (0.50709719633499062, u'Girlfight (2000)'),\n",
" (0.50672844621884616, u'Invisible Man, The (1933)'),\n",
" (0.50664915892853168, u\"Wes Craven's New Nightmare (1994)\"),\n",
" (0.50593744301616372, u'187 (1997)'),\n",
" (0.5026246899500344, u'Tex (1982)'),\n",
" (0.50065652153557327, u'Taking of Pelham One Two Three, The (1974)'),\n",
" (0.50001156965511029, u'Madame Sousatzka (1988)'),\n",
" (0.49793634606326415, u'True Crime (1995)'),\n",
" (0.4903332116500338, u'Suspicion (1941)'),\n",
" (0.49025023976741067, u'Bless the Child (2000)'),\n",
" (0.48720657037290932, u'Outside Providence (1999)'),\n",
" (0.48474246994898695, u'Coming Home (1978)'),\n",
" (0.48328498581179685, u'Mummy, The (1959)'),\n",
" (0.48200696640357771, u\"She's So Lovely (1997)\"),\n",
" (0.48191825736121108, u'Killing Fields, The (1984)'),\n",
" (0.47962098975636341, u'Cry in the Dark, A (1988)'),\n",
" (0.47907921311958807, u'Clockers (1995)'),\n",
" (0.47849054439508332, u'Big Country, The (1958)'),\n",
" (0.47761497616186926, u'Children of the Damned (1963)'),\n",
" (0.47534072881784528, u'How to Make an American Quilt (1995)'),\n",
" (0.47399704916210089, u'Masque of the Red Death, The (1964)'),\n",
" (0.47378507247519402, u'Manon of the Spring (Manon des sources) (1986)'),\n",
" (0.47128818643447057, u'Geronimo: An American Legend (1993)'),\n",
" (0.47062717121619646, u'Nineteen Eighty-Four (1984)'),\n",
" (0.4680548967049587, u'Dersu Uzala (1974)'),\n",
" (0.46692263096638603, u'Blue in the Face (1995)'),\n",
" (0.46636456273214061, u'Flying Tigers (1942)'),\n",
" (0.46415947199644159, u'Gingerbread Man, The (1998)'),\n",
" (0.46020293675043689, u\"They Shoot Horses, Don't They? (1969)\"),\n",
" (0.46000832095241295, u'Picnic (1955)'),\n",
" (0.45650898242085874, u'Wings of the Dove, The (1997)'),\n",
" (0.45584980799007374,\n",
" u'Final Conflict, The (a.k.a. Omen III: The Final Conflict) (1981)'),\n",
" (0.45570760383539738, u'Phantoms (1998)'),\n",
" (0.45523323128545995, u'Lifeboat (1944)'),\n",
" (0.45305973659729831, u'Raven, The (1963)'),\n",
" (0.45216509937499233, u'Long Goodbye, The (1973)'),\n",
" (0.451538810541803, u'Last Detail, The (1973)'),\n",
" (0.44622877498452035, u'Spellbound (1945)'),\n",
" (0.44469966399810401, u'Terms of Endearment (1983)'),\n",
" (0.44394960846425152, u'Love and Basketball (2000)'),\n",
" (0.44337032668173454, u\"'Night Mother (1986)\"),\n",
" (0.44253720601966473, u'Dracula (1931)'),\n",
" (0.44136741475237468, u'Pallbearer, The (1996)'),\n",
" (0.44102642817049831, u'Distinguished Gentleman, The (1992)'),\n",
" (0.43974782052737277, u'For Whom the Bell Tolls (1943)'),\n",
" (0.43929768510697947, u'Governess, The (1998)'),\n",
" (0.43850657857392222, u'Mis\\xe9rables, Les (1995)'),\n",
" (0.43779402783218158, u'Here on Earth (2000)'),\n",
" (0.43669144466767074, u'Romper Stomper (1992)'),\n",
" (0.43609187753530676, u'Wrong Man, The (1956)'),\n",
" (0.43539052198925687, u\"Smilla's Sense of Snow (1997)\"),\n",
" (0.43474525463323571, u'Bad Seed, The (1956)'),\n",
" (0.43332232846155949, u'Bread and Chocolate (Pane e cioccolata) (1973)')]"
]
}
],
"prompt_number": 230
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time score2 = bestMatches( 'Sudden Death (1995)', movie_titles,rs,simFunc = moviePairSimilarityAdjCos,nTop=1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 55.71 s, sys: 0.14 s, total: 55.85 s\n",
"Wall time: 55.85 s\n"
]
}
],
"prompt_number": 231
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarity Matrix \n",
"----\n",
"\n",
"Now, we build the item-item similarity matrix. This can be a long job, so I would do it in chunks over multiple processors. The `iStart` and `iStop` params\n",
"let me chose the chunk I want to work on. Note that I use a unicode-decode option so that the table can later be dumped in a JSON format. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def makeSimilarityTable(movie_tls, rs,n=1000, simFunc = moviePairSimilarityAdjCos,iStart=0, iStop=None):\n",
"\tscores = {}\n",
"\tif iStop == None:\n",
"\t\tiStop = len(movie_tls)\n",
"\n",
"\tfor movx in movie_tls[iStart:iStop]:\n",
"\t\tscore = bestMatches(movx, movie_tls,rs,nTop=n, simFunc = simFunc)\n",
"\t\tscores[movx.decode('unicode-escape')] = score\n",
"\n",
"\treturn scores "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 232
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I ran this on a 12 core workstation, and later combined the dicts into a single pickle file. Now, I will use the precomputed similarity table to recommend movies to a user. \n",
"\n",
"First, I need some ratings from a user. Lets chose a random user from our database. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"simTable = pickle.load(open('./movieSimilarityMatchesCombined.pkl'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 339
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ur = tura.reset_index().set_index('uid')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 233
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"testRatings = ur.ix[45][:100].drop('avgRating',axis=1)\n",
"otherRatings = ur.ix[45][100:].drop('avgRating',axis=1)\n",
"testRatings.shape,otherRatings.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 378,
"text": [
"((100, 2), (175, 2))"
]
}
],
"prompt_number": 378
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pickle\n",
"def recommendMovies(userRatings,simTable):\n",
" \n",
" scores = {} \n",
" totalSim = {} \n",
" hits = {}\n",
" for (item, rating) in zip(userRatings.title, userRatings.rating): \n",
" for (similarity, item2) in simTable[item]:\n",
" if item2 in userRatings: continue \n",
" scores.setdefault(item2,0) \n",
" scores[item2] += similarity*rating \n",
" totalSim.setdefault(item2,0) \n",
" totalSim[item2] += similarity\n",
" hits.setdefault(item2,0) \n",
" hits[item2] += 1\n",
" for tit, hit in hits.items():\n",
" if hit < 20:\n",
" scores.pop(tit)\n",
" hits.pop(tit)\n",
" totalSim.pop(tit)\n",
" \n",
" rankings = [(score/totalSim[item], item) for item, score in scores.items()]\n",
" rankings.sort()\n",
" rankings.reverse()\n",
" return rankings, hits "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 379
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rankings, hits = recommendMovies(testRatings,simTable)\n",
"#hits\n",
"for score, title in rankings:\n",
" print score, title, hits[title]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3.87024583871 Risky Business (1983) 40\n",
"3.78015508174 Contact (1997) 37\n",
"3.77768596683 Fatal Attraction (1987) 28\n",
"3.77564198544 Hud (1963) 37\n",
"3.75583427069 Out of Africa (1985) 44\n",
"3.74259815572 Boys from Brazil, The (1978) 41\n",
"3.74223291923 Thomas Crown Affair, The (1999) 35\n",
"3.72729877725 Heaven Can Wait (1978) 46\n",
"3.72383543959 Brief Encounter (1946) 37\n",
"3.72316955857 Ghost and Mrs. Muir, The (1947) 48\n",
"3.70752162039 Keeping the Faith (2000) 41\n",
"3.70514070171 Crimson Tide (1995) 38\n",
"3.70404948436 My Favorite Year (1982) 42\n",
"3.70264877532 Shane (1953) 42\n",
"3.69377524357 Dead Again (1991) 49\n",
"3.6928643141 Gone with the Wind (1939) 41\n",
"3.68558875745 Rosemary's Baby (1968) 25\n",
"3.68405875759 Adventures of Robin Hood, The (1938) 44\n",
"3.68322467667 Face in the Crowd, A (1957) 42\n",
"3.68046755857 Christmas Carol, A (1938) 47\n",
"3.67636534453 Sleepless in Seattle (1993) 43\n",
"3.67511745408 If.... (1968) 47\n",
"3.67438502443 My Fair Lady (1964) 43\n",
"3.67192393146 Eat Drink Man Woman (1994) 45\n",
"3.67066400109 Little Mermaid, The (1989) 48\n",
"3.67041407768 Defending Your Life (1991) 38\n",
"3.66789984653 Cocoon (1985) 42\n",
"3.6650543828 Mary Poppins (1964) 44\n",
"3.66407640136 Planet of the Apes (1968) 34\n",
"3.66361761287 Once Upon a Time in America (1984) 37\n",
"3.66279951541 American in Paris, An (1951) 41\n",
"3.66269800974 Melvin and Howard (1980) 36\n",
"3.66265773975 Bell, Book and Candle (1958) 49\n",
"3.66254044086 Star Trek: First Contact (1996) 39\n",
"3.66234526204 Murder, My Sweet (1944) 44\n",
"3.65883430545 Born Yesterday (1950) 42\n",
"3.65856017961 Top Hat (1935) 44\n",
"3.65834013898 Clueless (1995) 44\n",
"3.65814751026 Aladdin (1992) 45\n",
"3.65471430979 Cabaret (1972) 40\n",
"3.65381610767 Star Trek IV: The Voyage Home (1986) 41\n",
"3.65139133945 Man Who Would Be King, The (1975) 44\n",
"3.65022792695 From Russia with Love (1963) 43\n",
"3.64893528836 Notting Hill (1999) 51\n",
"3.6483606395 Big Chill, The (1983) 48\n",
"3.64719548013 Help! (1965) 35\n",
"3.64704196005 Schindler's List (1993) 46\n",
"3.64572648132 Atlantic City (1980) 39\n",
"3.64337024527 My Man Godfrey (1957) 38\n",
"3.64264839802 Harvey (1950) 47\n",
"3.64176643808 League of Their Own, A (1992) 43\n",
"3.64134795485 Cinderella (1950) 48\n",
"3.63976680443 Shine (1996) 42\n",
"3.63729552929 Miracle on 34th Street (1947) 47\n",
"3.63707570842 Pale Rider (1985) 34\n",
"3.63572604744 Sabrina (1954) 49\n",
"3.63340300885 Dave (1993) 49\n",
"3.63307612553 Erin Brockovich (2000) 48\n",
"3.63261449959 Romancing the Stone (1984) 42\n",
"3.6313387091 Buddy Holly Story, The (1978) 48\n",
"3.63010927519 Parenthood (1989) 44\n",
"3.62978732738 Poltergeist (1982) 42\n",
"3.62972132923 My Cousin Vinny (1992) 45\n",
"3.62938661811 Moonstruck (1987) 46\n",
"3.62865153289 Old Yeller (1957) 45\n",
"3.62835478622 Red Rock West (1992) 48\n",
"3.62614432035 Shall We Dance? (1937) 44\n",
"3.6255604852 American Graffiti (1973) 45\n",
"3.62520397411 Green Mile, The (1999) 42\n",
"3.6251730761 Shadow of a Doubt (1943) 46\n",
"3.62499940948 Peter Pan (1953) 42\n",
"3.62476527728 Mrs. Brown (Her Majesty, Mrs. Brown) (1997) 43\n",
"3.62439399111 His Girl Friday (1940) 41\n",
"3.62277785648 Stalag 17 (1953) 37\n",
"3.62272136773 Wizard of Oz, The (1939) 43\n",
"3.62236562028 Babe (1995) 44\n",
"3.62197603586 Casablanca (1942) 45\n",
"3.62110952681 Fried Green Tomatoes (1991) 52\n",
"3.62096761509 Stripes (1981) 35\n",
"3.62083667907 Working Girl (1988) 39\n",
"3.61999604242 Back to the Future (1985) 48\n",
"3.61922940903 Goodbye Girl, The (1977) 47\n",
"3.61882326533 Funny Face (1957) 44\n",
"3.61609994458 Murphy's Romance (1985) 44\n",
"3.61563017434 Palm Beach Story, The (1942) 38\n",
"3.61457781656 Shop Around the Corner, The (1940) 45\n",
"3.61442912256 To Be or Not to Be (1942) 50\n",
"3.61318622937 Jesus' Son (1999) 37\n",
"3.61313031267 Sound of Music, The (1965) 47\n",
"3.61308362222 One False Move (1991) 44\n",
"3.61104509117 M*A*S*H (1970) 43\n",
"3.61007871046 Breakfast Club, The (1985) 48\n",
"3.6099145746 Arsenic and Old Lace (1944) 45\n",
"3.60797804536 Thin Blue Line, The (1988) 42\n",
"3.60776783064 My Man Godfrey (1936) 43\n",
"3.60713196629 Guns of Navarone, The (1961) 46\n",
"3.60669160069 Singin' in the Rain (1952) 43\n",
"3.60660939313 Big (1988) 47\n",
"3.6056899848 Shallow Grave (1994) 42\n",
"3.60550826924 West Side Story (1961) 45\n",
"3.60541746229 Roman Holiday (1953) 47\n",
"3.60484912119 Thin Man, The (1934) 44\n",
"3.60484006706 King and I, The (1956) 41\n",
"3.60422679887 Lady Eve, The (1941) 42\n",
"3.60354225833 Tao of Steve, The (2000) 44\n",
"3.60205721212 Midnight Run (1988) 41\n",
"3.60167431551 Father of the Bride (1950) 50\n",
"3.60125481441 Shall We Dance? (Shall We Dansu?) (1996) 39\n",
"3.60081483531 Last Emperor, The (1987) 43\n",
"3.60059485234 Charade (1963) 47\n",
"3.59939815548 Quiet Man, The (1952) 51\n",
"3.59836019974 Hunt for Red October, The (1990) 47\n",
"3.59823048436 Men in Black (1997) 36\n",
"3.59802888409 Shakespeare in Love (1998) 49\n",
"3.59782036945 Mansfield Park (1999) 51\n",
"3.59770754929 Rebel Without a Cause (1955) 41\n",
"3.59717580565 Four Weddings and a Funeral (1994) 48\n",
"3.59692584966 On Golden Pond (1981) 49\n",
"3.59673580246 When Harry Met Sally... (1989) 47\n",
"3.59578956673 Gypsy (1962) 45\n",
"3.59578525932 Last Detail, The (1973) 43\n",
"3.59576701116 It Happened One Night (1934) 46\n",
"3.59554115934 Children of Paradise (Les enfants du paradis) (1945) 37\n",
"3.59452174431 American President, The (1995) 43\n",
"3.59401581456 Odd Couple, The (1968) 46\n",
"3.59357727257 Thelma & Louise (1991) 46\n",
"3.59350879468 Notorious (1946) 42\n",
"3.59350608609 Mr. Smith Goes to Washington (1939) 45\n",
"3.59175550909 Full Monty, The (1997) 47\n",
"3.59137409458 What's Love Got to Do with It? (1993) 47\n",
"3.59134220886 Lady and the Tramp (1955) 45\n",
"3.58826710654 Lion King, The (1994) 42\n",
"3.58677461508 Strictly Ballroom (1992) 45\n",
"3.58674280274 You Can't Take It With You (1938) 46\n",
"3.58632040887 Toy Story 2 (1999) 46\n",
"3.58602762088 Heart and Souls (1993) 38\n",
"3.5858590921 People vs. Larry Flynt, The (1996) 52\n",
"3.58560070506 Manchurian Candidate, The (1962) 46\n",
"3.58552847863 It's a Wonderful Life (1946) 50\n",
"3.58515699894 Apollo 13 (1995) 45\n",
"3.58442438163 Sneakers (1992) 41\n",
"3.58434968129 To Catch a Thief (1955) 46\n",
"3.58253204732 Body Heat (1981) 41\n",
"3.58247256784 F/X (1986) 27\n",
"3.58224752004 Gaslight (1944) 42\n",
"3.58125043842 Trust (1990) 40\n",
"3.58125006619 Witness (1985) 47\n",
"3.58122620584 In the Name of the Father (1993) 38\n",
"3.58063843742 African Queen, The (1951) 41\n",
"3.58002358346 Gods and Monsters (1998) 44\n",
"3.57901372324 Primal Fear (1996) 47\n",
"3.57738010604 Vanya on 42nd Street (1994) 36\n",
"3.57725305472 Terminator, The (1984) 43\n",
"3.5765631628 Black Beauty (1994) 43\n",
"3.57634061134 White Christmas (1954) 45\n",
"3.57627641811 42 Up (1998) 42\n",
"3.57562952544 Fugitive, The (1993) 46\n",
"3.57479616543 Joy Luck Club, The (1993) 46\n",
"3.57432011203 Women on the Verge of a Nervous Breakdown (1988) 43\n",
"3.57416792268 Sullivan's Travels (1942) 42\n",
"3.5740141337 Swept Away (Travolti da un insolito destino nell'azzurro mare d'Agosto) (1975) 39\n",
"3.574007164 Close Encounters of the Third Kind (1977) 46\n",
"3.5734089757 Grand Hotel (1932) 51\n",
"3.57330568774 Treasure of the Sierra Madre, The (1948) 40\n",
"3.57310132048 Pretty Woman (1990) 40\n",
"3.57301129428 Bull Durham (1988) 46\n",
"3.57250782905 All About Eve (1950) 45\n",
"3.57227412881 Breakfast at Tiffany's (1961) 48\n",
"3.57206911573 Sting, The (1973) 41\n",
"3.5719640229 Women, The (1939) 42\n",
"3.57185503224 Big Sleep, The (1946) 43\n",
"3.5716776878 Next Stop, Wonderland (1998) 43\n",
"3.57164991139 Metropolis (1926) 43\n",
"3.57163175392 Gladiator (2000) 38\n",
"3.5712853569 Guys and Dolls (1955) 42\n",
"3.5711395042 Jerry Maguire (1996) 46\n",
"3.57099396895 Persuasion (1995) 46\n",
"3.57065348578 Lethal Weapon (1987) 36\n",
"3.56970528179 Living in Oblivion (1995) 40\n",
"3.56966713945 Butch Cassidy and the Sundance Kid (1969) 43\n",
"3.56946152662 Lady Vanishes, The (1938) 43\n",
"3.56936032367 Ever After: A Cinderella Story (1998) 47\n",
"3.56928575633 Willy Wonka and the Chocolate Factory (1971) 46\n",
"3.56894214348 Ninotchka (1939) 48\n",
"3.5689240414 Mildred Pierce (1945) 44\n",
"3.56887290013 Where Eagles Dare (1969) 45\n",
"3.56836801379 From Here to Eternity (1953) 43\n",
"3.56818286873 Killing, The (1956) 42\n",
"3.56758598034 Twin Falls Idaho (1999) 43\n",
"3.56622663213 Producers, The (1968) 43\n",
"3.56619461407 Star Wars: Episode IV - A New Hope (1977) 45\n",
"3.56613495148 Apartment, The (1960) 43\n",
"3.56613105285 General, The (1927) 37\n",
"3.5660319161 Bringing Up Baby (1938) 44\n",
"3.56589496947 Hard Day's Night, A (1964) 45\n",
"3.56575968322 Speed (1994) 39\n",
"3.56533468138 Toy Story (1995) 48\n",
"3.5646236712 Indiana Jones and the Last Crusade (1989) 48\n",
"3.56454199773 Beauty and the Beast (1991) 49\n",
"3.56407480289 Band Wagon, The (1953) 45\n",
"3.56370356918 Sixth Sense, The (1999) 46\n",
"3.56282953454 Commitments, The (1991) 44\n",
"3.56277117036 Hard 8 (a.k.a. Sydney, a.k.a. Hard Eight) (1996) 45\n",
"3.5621529697 Kramer Vs. Kramer (1979) 49\n",
"3.5620766069 39 Steps, The (1935) 44\n",
"3.56198929521 King Kong (1933) 41\n",
"3.56164341526 Topsy-Turvy (1999) 34\n",
"3.56093087366 Vertigo (1958) 45\n",
"3.55995204286 Day the Earth Stood Still, The (1951) 42\n",
"3.55985882268 Harold and Maude (1971) 42\n",
"3.55910289378 Open Your Eyes (Abre los ojos) (1997) 48\n",
"3.55854819095 North by Northwest (1959) 48\n",
"3.55848226416 Maltese Falcon, The (1941) 47\n",
"3.55812501838 Galaxy Quest (1999) 45\n",
"3.55759250773 King of Masks, The (Bian Lian) (1996) 48\n",
"3.55694040388 Double Indemnity (1944) 45\n",
"3.55642711627 Birdy (1984) 41\n",
"3.55637321948 Bound (1996) 45\n",
"3.5563667353 Great Escape, The (1963) 45\n",
"3.55606459401 Best Years of Our Lives, The (1946) 42\n",
"3.55596694007 Strangers on a Train (1951) 43\n",
"3.55567468883 Marty (1955) 48\n",
"3.55544288248 Marathon Man (1976) 47\n",
"3.55468129063 Sense and Sensibility (1995) 49\n",
"3.55454401678 Dr. No (1962) 43\n",
"3.55375267502 Jurassic Park (1993) 39\n",
"3.55328854611 Ideal Husband, An (1999) 50\n",
"3.55318803474 Mrs. Miniver (1942) 50\n",
"3.5526845995 Children of a Lesser God (1986) 56\n",
"3.55225660786 Starman (1984) 34\n",
"3.55215233009 Central Station (Central do Brasil) (1998) 47\n",
"3.55158906464 Pleasantville (1998) 47\n",
"3.5514783612 Last Supper, The (1995) 43\n",
"3.55147147812 Run Lola Run (Lola rennt) (1998) 47\n",
"3.55033748839 Dersu Uzala (1974) 42\n",
"3.54949857252 Fish Called Wanda, A (1988) 46\n",
"3.54938114166 Last of the Mohicans, The (1992) 44\n",
"3.54851830818 Wings of the Dove, The (1997) 39\n",
"3.54806995702 Pinocchio (1940) 46\n",
"3.54765597579 Lost Weekend, The (1945) 46\n",
"3.54693659274 Die Hard (1988) 44\n",
"3.54611129002 Mulan (1998) 50\n",
"3.54610185346 Mister Roberts (1955) 44\n",
"3.5460810139 Some Like It Hot (1959) 44\n",
"3.54595700825 Safe (1995) 35\n",
"3.54560014622 Little Women (1994) 52\n",
"3.54465675583 E.T. the Extra-Terrestrial (1982) 46\n",
"3.54430509815 Raiders of the Lost Ark (1981) 47\n",
"3.54401907085 To Kill a Mockingbird (1962) 45\n",
"3.54360698474 French Connection, The (1971) 42\n",
"3.54351780017 In the Heat of the Night (1967) 43\n",
"3.54339188475 Driving Miss Daisy (1989) 51\n",
"3.54306404508 Conversation, The (1974) 43\n",
"3.54293220967 Lucas (1986) 43\n",
"3.54162680363 Hidden, The (1987) 43\n",
"3.54162658477 Graduate, The (1967) 44\n",
"3.54097793852 Three Colors: Blue (1993) 42\n",
"3.53966304062 Philadelphia Story, The (1940) 45\n",
"3.53953254901 Rear Window (1954) 47\n",
"3.53918669839 Midnight Express (1978) 47\n",
"3.53910500149 Haunting, The (1963) 42\n",
"3.5385246881 Hoosiers (1986) 43\n",
"3.53789551855 Auntie Mame (1958) 48\n",
"3.53659182251 Grapes of Wrath, The (1940) 45\n",
"3.53634445387 Manon of the Spring (Manon des sources) (1986) 45\n",
"3.53631561434 As Good As It Gets (1997) 50\n",
"3.53621469811 Life Is Beautiful (La Vita \u00e8 bella) (1997) 50\n",
"3.53575356064 L.A. Confidential (1997) 48\n",
"3.53551392834 Repulsion (1965) 46\n",
"3.53504191814 Amadeus (1984) 47\n",
"3.53394126182 Dangerous Liaisons (1988) 44\n",
"3.53293031002 On the Waterfront (1954) 38\n",
"3.53279851177 101 Dalmatians (1961) 50\n",
"3.53218420752 Cape Fear (1962) 42\n",
"3.5313360623 Gay Divorcee, The (1934) 44\n",
"3.53088437784 October Sky (1999) 52\n",
"3.53075723006 Place in the Sun, A (1951) 49\n",
"3.5307519789 Superman (1978) 39\n",
"3.53067867286 Ghostbusters (1984) 45\n",
"3.53065303897 Much Ado About Nothing (1993) 52\n",
"3.5305213925 Nights of Cabiria (Le Notti di Cabiria) (1957) 36\n",
"3.53030568684 Man Who Knew Too Much, The (1934) 51\n",
"3.5302716785 Goldfinger (1964) 43\n",
"3.52992466241 Psycho (1960) 46\n",
"3.52984052825 Wrong Trousers, The (1993) 44\n",
"3.52950859685 Hilary and Jackie (1998) 50\n",
"3.5292095253 Bonnie and Clyde (1967) 45\n",
"3.52907812542 Sling Blade (1996) 47\n",
"3.52890172609 Lawrence of Arabia (1962) 43\n",
"3.52881985141 Stand by Me (1986) 51\n",
"3.52869576891 Blood Simple (1984) 40\n",
"3.52835840467 Ferris Bueller's Day Off (1986) 49\n",
"3.52811969444 Right Stuff, The (1983) 47\n",
"3.5279218513 Guess Who's Coming to Dinner (1967) 55\n",
"3.52769075702 Laura (1944) 46\n",
"3.52736032225 Cider House Rules, The (1999) 52\n",
"3.52735066668 Spy Who Loved Me, The (1977) 31\n",
"3.52682184605 High Plains Drifter (1972) 48\n",
"3.52665627948 Man Who Knew Too Much, The (1956) 48\n",
"3.52655336998 Local Hero (1983) 39\n",
"3.52559893073 Chinatown (1974) 45\n",
"3.52491760799 Happy, Texas (1999) 36\n",
"3.524832443 Untouchables, The (1987) 49\n",
"3.52464652509 Last Picture Show, The (1971) 43\n",
"3.5243836787 Star Wars: Episode VI - Return of the Jedi (1983) 46\n",
"3.52389827394 Three Days of the Condor (1975) 48\n",
"3.52373402859 Playing by Heart (1998) 44\n",
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"3.47109881734 Jungle Book, The (1967) 45\n",
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"3.46849029035 Affair to Remember, An (1957) 44\n",
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"3.45829538678 And Now for Something Completely Different (1971) 41\n",
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"3.40075009512 Fabulous Baker Boys, The (1989) 29\n",
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"3.3611062514 Flirting With Disaster (1996) 37\n",
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"3.13310504209 For Whom the Bell Tolls (1943) 53\n",
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"3.11891222754 Duel in the Sun (1946) 57\n",
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"2.86481918679 Go Fish (1994) 37\n",
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"2.83106606508 Night of the Comet (1984) 31\n",
"2.8308612275 Curse of Frankenstein, The (1957) 33\n",
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"2.81662709291 Down by Law (1986) 32\n",
"2.8164518436 Slums of Beverly Hills, The (1998) 32\n",
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"2.81491062784 Quatermass and the Pit (1967) 37\n",
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"2.81212762568 Scream 2 (1997) 45\n",
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"2.80810774769 Get on the Bus (1996) 49\n",
"2.80803937375 Higher Learning (1995) 42\n",
"2.80671112299 Mephisto (1981) 44\n",
"2.80668713177 GoldenEye (1995) 28\n",
"2.80536218248 Bad Lieutenant (1992) 30\n",
"2.80489639712 Blue Sky (1994) 35\n",
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"2.80065900704 Pete's Dragon (1977) 39\n",
"2.80006175808 Blown Away (1994) 37\n",
"2.79981307238 History of the World: Part I (1981) 26\n",
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" Blue Chips (1994) 39\n",
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"2.252520365 Scary Movie (2000) 28\n",
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"2.16978295389 Chill Factor (1999) 35\n",
"2.13378988397 NeverEnding Story III, The (1994) 35\n"
]
}
],
"prompt_number": 380
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ratPred = []\n",
"len(otherRatings.title)\n",
"i = 0 \n",
"for title, rating in zip(otherRatings.title,otherRatings.rating):\n",
" for score, item in rankings:\n",
" if item == title:\n",
" i += 1 \n",
" print score, rating, title, i\n",
" ratPred += [[rating,score]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
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"3.36770132116 4 Manhattan (1979) 1\n",
"3.54162658477 3 Graduate, The (1967) 2\n",
"3.49738378617 4 Groundhog Day (1993) 3\n",
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"3.61999604242 5 Back to the Future (1985) 5\n",
"3.51850864819 3 Young Frankenstein (1974) 6\n",
"2.4903380994 2 Night on Earth (1991) 7\n",
"3.3155666271 4 Somewhere in Time (1980) 8\n",
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"3.40821236749 1 Room with a View, A (1986) 10\n",
"3.56966713945 3 Butch Cassidy and the Sundance Kid (1969) 11\n",
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"3.36253992099 1 Grosse Pointe Blank (1997) 22\n",
"3.60125481441 3 Shall We Dance? (Shall We Dansu?) (1996) 23\n",
"2.43832607926 3 Addicted to Love (1997) 24\n",
"2.95263999149 3 Hercules (1997) 25\n",
"3.59823048436 4 Men in Black (1997) 26\n",
"2.31593598169 1 Picture Perfect (1997) 27\n",
"2.28795109825 1 She's So Lovely (1997) 28\n",
"3.30286991745 3 Chasing Amy (1997) 29\n",
"3.59175550909 3 Full Monty, The (1997) 30\n",
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{
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"3.67066400109 3 Little Mermaid, The (1989) 55\n",
"3.41470565285 4 Splash (1984) 56\n",
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"3.62972132923 3 My Cousin Vinny (1992) 74\n",
"2.52012741862 "
]
},
{
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"2 Practical Magic (1998) 75\n",
"3.55158906464 3 Pleasantville (1998) 76\n",
"2.61676504177 2 Living Out Loud (1998) 77\n",
"2.55603097107 2 Meet Joe Black (1998) 78\n",
"3.6483606395 4 Big Chill, The (1983) 79\n",
"3.52012722481 1 Bug's Life, A (1998) 80\n",
"2.61067663816 3 Celebrity (1998) 81\n",
"2.74882849984 5 Desperately Seeking Susan (1985) 82\n",
"2.62453783857 2 Money Pit, The (1986) 83\n",
"2.39966116347 2 Home Fries (1998) 84\n",
"3.59802888409 3 Shakespeare in Love (1998) 85\n",
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"3.52835840467 3 Ferris Bueller's Day Off (1986) 121\n",
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"3.419417115 3 Little Big Man (1970) 127\n",
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"3.4635070825 2 Holiday Inn (1942) 131\n",
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"3.64264839802 5 Harvey (1950) 134\n",
"3.61457781656 2 Shop Around the Corner, The (1940) 135\n",
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"3.12641578523 1 Fisher King, The (1991) 137\n",
"3.58632040887 4 Toy Story 2 (1999) 138\n",
"3.55812501838 5 Galaxy Quest (1999) 139\n",
"3.61922940903 4 Goodbye Girl, The (1977) 140\n",
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"2.90716525402 1 Wayne's World (1992) 142\n",
"3.64176643808 3 League of Their Own, A (1992) 143\n",
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"2.92098997437 4 Far and Away (1992) 146\n",
"2.87723605283 5 Forever Young (1992) 147\n",
"3.65883430545 4 Born Yesterday (1950) 148\n",
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"2.49250417045 2 Dying Young (1991) 150\n",
"2.96147929188 2 Good Morning, Vietnam (1987) 151\n",
"3.17795489892 4 Grumpy Old Men (1993) 152\n",
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"3.49482804617 3 Modern Times (1936) 154\n",
"3.58602762088 4 Heart and Souls (1993) 155\n",
"3.61609994458 4 Murphy's Romance (1985) 156\n",
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"3.59401581456 4 Odd Couple, The (1968) 158\n",
"3.66265773975 4 Bell, Book and Candle (1958) 159\n",
"3.31742950476 4 Arthur (1981) 160\n",
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"3.28519842442 3 Mr. Mom (1983) 164\n",
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"3.49145866193 3 Blazing Saddles (1974) 167\n",
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"2.67648755865 1 Prizzi's Honor (1985) 169\n",
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"2.49852782024 2 Bird on a Wire (1990) 171\n",
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"3.60354225833 4 Tao of Steve, The (2000) 174\n",
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