args = parser.parse_args()
prefix_model_ckpt = args.model_name
ckpt_dir = args.ckpt_dir
data_dir = args.data_dir
real_adj_matrix = sp.load_npz(data_dir + 'adj_matrix/r_matrix_'+ str(args.nb_hop) + 'w.npz')
ckpt_path = ckpt_dir + '/' + prefix_model_ckpt + '/' + 'epoch_' + str(args.epoch) + '/' + prefix_model_ckpt + '_checkpoint.pt'
config_param_file = ckpt_dir + '/' + prefix_model_ckpt + '/' + prefix_model_ckpt + '_config.json'
load_param = check_point.load_config_param(config_param_file)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_data_type = torch.float32
train_data_path = data_dir + 'train.txt'
train_instances = utils.read_instances_lines_from_file(train_data_path)
nb_train = len(train_instances)
print(nb_train)
validate_data_path = data_dir + 'validate.txt'
validate_instances = utils.read_instances_lines_from_file(validate_data_path)
nb_validate = len(validate_instances)
print(nb_validate)
test_data_path = data_dir + 'test.txt'
test_instances = utils.read_instances_lines_from_file(test_data_path)
nb_test = len(test_instances)
print(nb_test)
### build knowledge ###
parser.add_argument('--example_file',
help='Example_file',
type=str,
default=None)
args = parser.parse_args()
f_dir = args.input_dir
o_dir = args.output_dir
model_name = args.model_name
nb_predict = args.nb_predict
topk = args.topk
ex_file = args.example_file
data_dir = f_dir
train_data_path = data_dir + 'train_lines.txt'
train_instances = utils.read_instances_lines_from_file(train_data_path)
nb_train = len(train_instances)
# print(nb_train)
test_data_path = data_dir + 'test_lines.txt'
test_instances = utils.read_instances_lines_from_file(test_data_path)
nb_test = len(test_instances)
# print(nb_test)
# print("---------------------@Build knowledge-------------------------------")
MAX_SEQ_LENGTH, item_dict, reversed_item_dict, item_probs, item_freq_dict, user_dict = utils.build_knowledge(
train_instances + test_instances)
if not os.path.exists(o_dir):
os.makedirs(o_dir)
saved_file = os.path.join(o_dir, model_name)
# print("Save model in ", saved_file)