example-svd.py

from ..scikits.crab.models import MatrixPreferenceDataModel
from ..scikits.crab.metrics.pairwise import cosine_distances
from ..scikits.crab.similarities import ItemSimilarity

from ..scikits.crab.recommenders.svd.classes import MatrixFactorBasedRecommender

movies = {'Marcel Caraciolo': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
 'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
 'The Night Listener': 3.0},
'Luciana Nunes': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
 'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
 'You, Me and Dupree': 3.5},
'Leopoldo Pires': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
 'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Lorena Abreu': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
 'The Night Listener': 4.5, 'Superman Returns': 4.0,
 'You, Me and Dupree': 2.5},
'Steve Gates': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
 'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
 'You, Me and Dupree': 2.0},
'Sheldom': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
 'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Penny Frewman': {'Snakes on a Plane': 4.5, 'You, Me and Dupree': 1.0, 'Superman Returns': 4.0},
'Maria Gabriela': {}}

model = MatrixPreferenceDataModel(movies)
recommender = MatrixFactorBasedRecommender(model, n_features=10, learning_rate=0.01, regularization=0.02)

userid = 'Leopoldo Pires'
print recommender.recommend(userid)