parser.add_argument('-search', type=str, help='input a search method name')
parser.add_argument('-batch_size', type=int, help='batch size')
parser.add_argument('-epochs', type=int, help='epochs')
parser.add_argument('-early_stop', type=int, help='early stop')
parser.add_argument('-trials', type=int, help='try number')
args = parser.parse_args()
if args.sep == None:
args.sep = '::'
# Step 1: Preprocess data
if args.data == "ml":
data = MovielensPreprocessor(csv_path=args.data_path,
validate_percentage=0.1,
test_percentage=0.1)
train_X, train_y, val_X, val_y, test_X, test_y = data.preprocess()
train_X_categorical = data.get_x_categorical(train_X)
val_X_categorical = data.get_x_categorical(val_X)
test_X_categorical = data.get_x_categorical(test_X)
user_num, item_num = data.get_hash_size()
# Step 2: Build the recommender, which provides search space
if args.model == 'mf':
model = build_mf(user_num, item_num)
if args.model == 'mlp':
model = build_mlp(user_num, item_num)
if args.model == 'gmf':
model = build_gmf(user_num, item_num)
if args.model == 'neumf':
model = build_neumf(user_num, item_num)
from autorecsys.pipeline.preprocessor import MovielensPreprocessor
from autorecsys.recommender import RPRecommender
# logging setting
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# load dataset
##Netflix Dataset
# dataset_paths = ["./examples/datasets/netflix-prize-data/combined_data_" + str(i) + ".txt" for i in range(1, 5)]
# data = NetflixPrizePreprocessor(dataset_paths)
# Step 1: Preprocess data
movielens = MovielensPreprocessor()
train_X, train_y, val_X, val_y, test_X, test_y = movielens.preprocess()
train_X_categorical = movielens.get_x_categorical(train_X)
val_X_categorical = movielens.get_x_categorical(val_X)
test_X_categorical = movielens.get_x_categorical(test_X)
user_num, item_num = movielens.get_hash_size()
# Step 2: Build the recommender, which provides search space
# Step 2.1: Setup mappers to handle inputs
input = Input(shape=[2])
user_emb_gmf = LatentFactorMapper(column_id=0,
num_of_entities=user_num,
embedding_dim=64)(input)
item_emb_gmf = LatentFactorMapper(column_id=1,
num_of_entities=item_num,
embedding_dim=64)(input)
user_emb_mlp = LatentFactorMapper(column_id=0,
def test_MovielensPreprocessor(self):
movielens = MovielensPreprocessor(csv_path=os.path.join(
dataset_directory, 'movielens/ratings-10k.dat'))
movielens.preprocess()
assert movielens.data_df.shape == (10000, 3)