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)