type=int,
default=4,
help="sample negative items for training")
parser.add_argument("--test_num_ng",
type=int,
default=99,
help="sample part of negative items for testing")
parser.add_argument("--out", default=True, help="save model or not")
parser.add_argument("--gpu", type=str, default="0", help="gpu card ID")
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
cudnn.benchmark = True
############################## PREPARE DATASET ##########################
train_data, test_data, user_num, item_num, train_mat = data_utils.load_all()
# construct the train and test datasets
train_dataset = data_utils.BPRData(train_data, item_num, train_mat,
args.num_ng, True)
test_dataset = data_utils.BPRData(test_data, item_num, train_mat, 0, False)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=16)
test_loader = data.DataLoader(test_dataset,
batch_size=args.test_num_ng + 1,
shuffle=False,
num_workers=0)
########################### CREATE MODEL #################################
help="compute metrics@top_k")
parser.add_argument("--gpu", type=str, default="1", help="gpu card ID")
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
cudnn.benchmark = True
data_path = '../data/{}/'.format(args.dataset)
model_path = './models/{}/'.format(args.dataset)
print("arguments: %s " % (args))
print("config model", args.model)
print("config data path", data_path)
print("config model path", model_path)
############################## PREPARE DATASET ##########################
train_data, test_data, user_num, item_num, train_mat, user_neg = data_utils.load_all(
args.dataset, data_path)
test_dataset = data_utils.NCFData(test_data, item_num, train_mat, user_neg,
False, 0, False)
test_loader = data.DataLoader(test_dataset,
batch_size=args.test_num_ng + 1,
shuffle=False,
num_workers=0)
print("data loaded! user_num:{}, item_num:{} test_data_len:{}".format(
user_num, item_num,
len(test_data) // (args.test_num_ng + 1)))
########################### CREATE MODEL #################################
test_model = torch.load('{}{}_{}.pth'.format(model_path, args.model,
args.alpha))
test_model.cuda()
random.seed(2019) #random and transforms
torch.backends.cudnn.deterministic=True # cudnn
def worker_init_fn(worker_id):
np.random.seed(2019 + worker_id)
data_path = '../data/{}/'.format(args.dataset)
model_path = './models/{}/'.format(args.dataset)
print("arguments: %s " %(args))
print("config model", args.model)
print("config data path", data_path)
print("config model path", model_path)
############################## PREPARE DATASET ##########################
train_data, valid_data, test_data_pos, user_pos, user_num ,item_num, train_mat, train_data_noisy = data_utils.load_all(args.dataset, data_path)
# construct the train and test datasets
train_dataset = data_utils.NCFData(
train_data, item_num, train_mat, args.num_ng, 0, train_data_noisy)
valid_dataset = data_utils.NCFData(
valid_data, item_num, train_mat, args.num_ng, 1)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)
valid_loader = data.DataLoader(valid_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)
print("data loaded! user_num:{}, item_num:{} train_data_len:{} test_user_num:{}".format(user_num, item_num, len(train_data), len(test_data_pos)))
########################### CREATE MODEL #################################
# paths
main_path = adaptdl.env.share_path()
train_rating = os.path.join(main_path, '{}.train.rating'.format(dataset))
test_rating = os.path.join(main_path, '{}.test.rating'.format(dataset))
test_negative = os.path.join(main_path, '{}.test.negative'.format(dataset))
model_path = os.path.join(main_path, 'models')
GMF_model_path = os.path.join(model_path, 'GMF.pth')
MLP_model_path = os.path.join(model_path, 'MLP.pth')
NeuMF_model_path = os.path.join(model_path, 'NeuMF.pth')
############################## PREPARE DATASET ##########################
train_data, test_data, user_num, item_num, train_mat = \
data_utils.load_all(main_path, train_rating, test_negative, dataset)
# construct the train and test datasets
train_dataset = data_utils.NCFData(
train_data, item_num, train_mat, args.num_ng, True)
test_dataset = data_utils.NCFData(
test_data, item_num, train_mat, 0, False)
train_loader = adl.AdaptiveDataLoader(
train_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=4, drop_last=True)
test_loader = adl.AdaptiveDataLoader(
test_dataset,
batch_size=args.test_num_ng+1, shuffle=False, num_workers=0)
if args.autoscale_bsz:
train_loader.autoscale_batch_size(
