"max_iter": args.max_iter,
"use_logit":
False if args.loss in ["pairwise", "pairwise+utility"] else True,
"num_workers": args.num_workers
}
print("Reading dataset")
if args.dataset == "movielens":
data_dir = cfg.vals['movielens_dir'] + "/preprocessed_choice/"
elif args.dataset == "amazon":
data_dir = cfg.vals['amazon_dir'] + "/preprocessed_choice/"
else:
raise ValueError("--dataset must be 'amazon' or 'movielens'")
X_train, X_test, y_train, y_test = read_train_test_dir(data_dir)
print("Dataset read complete...")
user_item_rating_map = load_dict_output(data_dir, "user_item_rating.json",
True)
item_rating_map = load_dict_output(data_dir, "item_rating.json", True)
stats = load_dict_output(data_dir, "stats.json")
print("n users: {}".format(stats['n_users']))
print("n items: {}".format(stats['n_items']))
n_test = get_test_sample_size(X_test.shape[0], k=TEST_BATCH_SIZE)
X_test = X_test[:n_test, :]
y_test = y_test[:n_test, :]
"lambda": args.lmbda,
"seq_len": args.seq_len,
"max_iter": args.max_iter,
"grad_clip": args.grad_clip
}
print("Reading dataset")
if args.dataset == "movielens":
data_dir = cfg.vals['movielens_dir'] + "/preprocessed/"
elif args.dataset == "amazon":
data_dir = cfg.vals['amazon_dir'] + "/preprocessed/"
else:
raise ValueError("--dataset must be 'amazon' or 'movielens'")
X_train, X_test, y_train, y_test = read_train_test_dir(data_dir, drop_ts=False)
user_item_rating_map = load_dict_output(data_dir, "user_item_rating.json",
True)
item_rating_map = load_dict_output(data_dir, "item_rating.json", True)
stats = load_dict_output(data_dir, "stats.json")
print("Dataset read complete...")
print("n users: {}".format(stats['n_users']))
print("n items: {}".format(stats['n_items']))
interactions = Interactions(user_ids=X_train[:, 0],
item_ids=X_train[:, 1],
ratings=y_train.flatten(),
timestamps=X_train[:, 2],
num_users=stats['n_users'],
