Quick start¶

Once you’ve built and installed pyrecsys, you can:

1. Set VERBOSE mode, to see some messages:

>>> import recsys.algorithm
>>> recsys.algorithm.VERBOSE = True


>>> from recsys.algorithm.factorize import SVD
>>> svd = SVD()
>>> svd.load_data(filename='./data/movielens/ratings.dat', sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
..........|

1. Compute SVD, :

>>> k = 100
>>> svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True)
Creating matrix (1000209 tuples)
Matrix density is: 4.4684%
Updating matrix: squish to at least 10 values
Computing svd k=100, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True


you can also save the output SVD model (in a zip file):

>>> k = 100
>>> svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True, savefile='/tmp/movielens')
Creating matrix (1000209 tuples)
Matrix density is: 4.4684%
Updating matrix: squish to at least 10 values
Computing svd k=100, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True
Saving svd model to /tmp/movielens


Note

once the SVD model has been saved (to a zip file) you can load it anytime, thus there’s not need to svd.compute() it again:

>>> from recsys.algorithm.factorize import SVD
>>> # Get two movies, and compute its similarity:
>>> ITEMID1 = 1    # Toy Story (1995)
>>> ITEMID2 = 2355 # A bug's life (1998)
>>> svd2.similarity(ITEMID1, ITEMID2)
0.67706936677315799

1. Compute similarity between two movies

>>> ITEMID1 = 1    # Toy Story (1995)
>>> ITEMID2 = 2355 # A bug's life (1998)
>>> svd.similarity(ITEMID1, ITEMID2)
0.67706936677315799

2. Get movies similar to Toy Story:

>>> svd.similar(ITEMID1)
[(1,    0.99999999999999978), # Toy Story
(3114, 0.87060391051018071), # Toy Story 2
(2355, 0.67706936677315799), # A bug's life
(595,  0.46031829709743477), # Beauty and the Beast
(1907, 0.44589398718134365), # Mulan
(364,  0.42908159895574161), # The Lion King
(2081, 0.42566581277820803), # The Little Mermaid
(3396, 0.42474056361935913), # The Muppet Movie
(2761, 0.40439361857585354)] # The Iron Giant

3. Predict rating for a given user and movie, >>> MIN_RATING = 0.0
>>> MAX_RATING = 5.0
>>> ITEMID = 1
>>> USERID = 1
>>> svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
5.0 #Predicted value
>>> svd.get_matrix().value(ITEMID, USERID)
5.0 #Real value

4. Recommend (non–rated) movies to a user:

>>> svd.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False
[(2905, 5.2133848204673416), # Shaggy D.A., The
(318,  5.2052108435956033), # Shawshank Redemption, The
(2019, 5.1037438278755474), # Seven Samurai (The Magnificent Seven)
(1178, 5.0962756861447023), # Paths of Glory (1957)
(904,  5.0771405690055724), # Rear Window (1954)
(1250, 5.0744156653222436), # Bridge on the River Kwai, The
(858,  5.0650911066862907), # Godfather, The
(922,  5.0605327279819408), # Sunset Blvd.
(1198, 5.0554543765500419), # Raiders of the Lost Ark
(1148, 5.0548789542105332)] # Wrong Trousers, The

5. Which users should see Toy Story? (e.g. which users -that have not rated Toy Story- would give it a high rating?)

>>> svd.recommend(ITEMID)
[(283,  5.716264440514446),
(3604, 5.6471765418323141),
(5056, 5.6218800339214496),
(446,  5.5707524860615738),
(3902, 5.5494529168484652),
(4634, 5.51643364021289),
(3324, 5.5138903299082802),
(4801, 5.4947999354188548),
(1131, 5.4941438045650068),
(2339, 5.4916048051511659)]

6. For large datasets (say more than 10M tuples), it might be better to run SVDLIBC directly (divisi2 -that also uses SVDLIBC- is way too slow creating the matrix and computing SVD):

>>> from recsys.utils.svdlibc import SVDLIBC
>>> svdlibc = SVDLIBC('./data/movielens/ratings.dat')
>>> svdlibc.to_sparse_matrix(sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
>>> svdlibc.compute(k=100)
>>> svd = svdlibc.export()
>>> svd.similar(ITEMID1) # results might be different than example 4. as there's no min_values=10 set here
[(1, 0.99999999999999978),
(3114, 0.84099896392054219),
(588, 0.79191433686817747),
(2355, 0.7772760704844065),
(1265, 0.74946256379033827),
(364, 0.73730970556786068),
(2321, 0.73652131961235268),
(595, 0.71665833726881523),
(3253, 0.7075696829413568),
(1923, 0.69687698887991523)]


Installation

Data model