def example():
"""simple test and performance measure
"""
# reviews = movielens_extractor.get_ml_1m_dataset()
reviews = movielens_extractor.get_ml_100K_dataset()
ratings = movielens_extractor.reviews_to_numpy_matrix(reviews)
# suffle_data
np.random.seed(0)
np.set_printoptions(precision=16)
# print(NormalRandom.generate_matrix(1, 10))
# np.random.shuffle(ratings)
# split data to training & validation
train_pct = 0.9
train_size = int(train_pct * len(ratings))
train = ratings[:train_size]
validation = ratings[train_size:]
# params
num_features = 10
bmf_model = BayesianMatrixFactorization()
start_time = time.clock()
bmf_model.load(ratings, train, validation)
end_time = time.clock()
print("time spent = %.3f" % (end_time - start_time))
return bmf_model
def example():
"""simple test and performance measure
"""
reviews = movielens_extractor.get_ml_1m_dataset()
ratings = movielens_extractor.reviews_to_numpy_matrix(reviews)
# suffle_data
np.random.seed(0)
np.random.shuffle(ratings)
# split data to training & validation
train_pct = 0.9
train_size = int(train_pct * len(ratings))
train = ratings[:train_size]
validation = ratings[train_size:]
# params
bmf_model = ProbabilisticMatrixFactorization()
start_time = time.clock()
bmf_model.load(train, validation)
end_time = time.clock()
print "time spent = %.3f" % (end_time - start_time)
return bmf_model
def example():
"""simple test and performance measure
"""
reviews = movielens_extractor.get_ml_1m_dataset()
ratings = movielens_extractor.reviews_to_numpy_matrix(reviews)
# suffle_data
np.random.seed(0)
np.random.shuffle(ratings)
# split data to training & validation
train_pct = 0.9
train_size = int(train_pct * len(ratings))
train = ratings[:train_size]
validation = ratings[train_size:]
# params
bmf_model = ProbabilisticMatrixFactorization()
start_time = time.clock()
bmf_model.load(train, validation)
end_time = time.clock()
print "time spent = %.3f" % (end_time - start_time)
return bmf_model
