def test_predict(self):
data = np.array([[0, 0, 5], [3, 0, 5], [3, 1, 2]])
ratings = csr_matrix(data)
train = ds.array(x=ratings, block_size=(1, 1))
als = ALS(tol=0.01, random_state=666, n_f=5, verbose=False)
als.fit(train)
predictions = als.predict_user(user_id=0)
# Check that the ratings for user 0 are similar to user 1 because they
# share preferences (third movie), thus it is expected that user 0
# will rate movie 1 similarly to user 1.
self.assertTrue(2.75 < predictions[0] < 3.25 and
predictions[1] < 1 and
predictions[2] > 4.5)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--num_subsets", type=int, default=48)
parser.add_argument("--num_factors", type=int, default=100)
parser.add_argument("--data_path", type=str, default='./tests/files/')
args = parser.parse_args()
num_subsets = args.num_subsets
data_path = args.data_path
n_f = args.num_factors
train, test = load_movielens(data_path=data_path)
exec_start = time()
als = ALS(tol=0.0001, n_f=n_f, max_iter=2, verbose=True)
# Fit using training data to check convergence
# als.fit(train)
# Fit using test data to check convergence
als.fit(train, test)
exec_end = time()
print("Ratings for user 0:\n%s" % als.predict_user(0))
print("Execution time: %.2f" % (exec_end - exec_start))