def run_matrix_factorization():
""" Executes the Matrix Factorization model on the ratings dataset. """
ratings = pd.read_csv('../data/ratings2.csv', sep='\t')
num_users = len(ratings.buyer_id.unique())
num_items = ratings.product_id.max() + 1
train, val = train_test_split(ratings)
model = MatrixFactorization(train,
num_users,
num_items,
num_latent_factors=20)
model.train(max_iter=20,
learning_rate=0.01,
regularize=0.5,
val=val,
lr_scheduler=True)
def main():
"""
main function for test
"""
# For command line arguments
psr = argparse.ArgumentParser()
psr.add_argument("--data_dir", default="ml-100k/")
psr.add_argument("--base", default="u1.base")
psr.add_argument("--test", default="u1.test")
psr.add_argument("--a", default=0.01, type=float)
psr.add_argument("--b", default=0.2, type=float)
psr.add_argument("--K", default=50, type=int)
psr.add_argument("--tol", default=3.e-2, type=float)
args = psr.parse_args()
# Get rating matrix
R, R_test, user_size, item_size = get_rating(data_dir=args.data_dir,
base=args.base,
test=args.test,
debug=True)
# Training
mf = MatrixFactorization(R=R,
R_test=R_test,
user_size=user_size,
item_size=item_size,
K=args.K,
a=args.a,
b=args.b)
print("training...")
mf.train(tol=args.tol, debug=True)
print("The number of test data is {}.".format(R_test.shape[0]))
icor = 0
for r in R_test:
iu = int(r[0]) - 1
ii = int(r[1]) - 1
Rhatui = mf.rating(iu=iu, ii=ii)
if np.round(Rhatui) == r[2]: icor = icor + 1
print("The number of correct predictions is {}.".format(icor))
#!/usr/bin/env python3
from matrix_factorization import MatrixFactorization
DATA = {
"DATA_DIR": "./ml-latest-small",
"RATING_FILE": "ratings.csv",
"MOVIE_FILE": "movies.csv",
"TAG_FILE": "tags.csv"
}
if __name__ == "__main__":
mf_cf = MatrixFactorization(DATA)
mf_cf.train()
mf_cf.predict_single(1, 110)