=============
python-recsys
A python library for implementing a recommender system.
Installation
Dependencies
**python-recsys** is build on top of `Divisi2`_, with csc-pysparse (Divisi2 also requires `NumPy`_, and uses Networkx).
.. _`Divisi2`: https://github.com/commonsense/divisi2
.. _`NumPy`: http://numpy.scipy.org
**python-recsys** also requires `SciPy`_.
.. _`SciPy`: http://numpy.scipy.org
To install the dependencies do something like this (Ubuntu):
::
sudo apt-get install python-scipy python-numpy
sudo apt-get install python-pip
sudo pip install csc-pysparse networkx divisi2
# If you don't have pip installed then do:
# sudo easy_install csc-pysparse
# sudo easy_install networkx
# sudo easy_install divisi2
Download
~~~~~~~~
Download **python-recsys** from `github`_.
.. _`github`: http://github.com/ocelma/python-recsys
Install
~~~~~~~
::
tar xvfz python-recsys.tar.gz
cd python-recsys
sudo python setup.py install
Example
~~~~~~~
1. Load Movielens dataset:
::
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})
2. Compute Singular Value Decomposition (SVD), M=U Sigma V^t:
::
k = 100
svd.compute(k=k,
min_values=10,
pre_normalize=None,
mean_center=True,
post_normalize=True,
savefile='/tmp/movielens')
3. Get similarity between two movies:
::
ITEMID1 = 1 # Toy Story (1995)
ITEMID2 = 2355 # A bug's life (1998)
svd.similarity(ITEMID1, ITEMID2)
# 0.67706936677315799
4. Get movies similar to *Toy Story*:
::
svd.similar(ITEMID1)
# Returns: <ITEMID, Cosine Similarity Value>
[(1, 0.99999999999999978), # Toy Story
(3114, 0.87060391051018071), # Toy Story 2
(2355, 0.67706936677315799), # A bug's life
(588, 0.5807351496754426), # Aladdin
(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
5. Predict the rating a user (USERID) would give to a movie (ITEMID):
::
MIN_RATING = 0.0
MAX_RATING = 5.0
ITEMID = 1
USERID = 1
svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
# Predicted value 5.0
svd.get_matrix().value(ITEMID, USERID)
# Real value 5.0
6. 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
# Returns: <ITEMID, Predicted Rating>
[(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
7. 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)
# Returns: <USERID, Predicted Rating>
[(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)]
Documentation
Documentation and examples available here_.
.. _here: http://ocelma.net/software/python-recsys/build/html
To create the HTML documentation files from doc/source do:
::
cd doc
make html
HTML files are created here:
::
doc/build/html/index.html