Python get_folder_pathの例

コード例 #1

ファイル: nfm_solver_bce.py プロジェクト: ecom-research/graph_recsys_benchmark

parser.add_argument('--opt', type=str, default='adam', help='')
parser.add_argument('--lr', type=float, default=0.001, help='')
parser.add_argument('--weight_decay', type=float, default=0, help='')
parser.add_argument('--early_stopping', type=int, default=20, help='')
parser.add_argument('--save_epochs',
                    type=str,
                    default='5,10,15,20,25',
                    help='')
parser.add_argument('--save_every_epoch', type=int, default=26,
                    help='')  #26(for MovieLens), 16(only for Yelp)

args = parser.parse_args()

# Setup data and weights file path
data_folder, weights_folder, logger_folder = \
    get_folder_path(model=MODEL, dataset=args.dataset + args.dataset_name, loss_type=LOSS_TYPE)

# Setup device
if not torch.cuda.is_available() or args.device == 'cpu':
    device = 'cpu'
else:
    device = 'cuda:{}'.format(args.gpu_idx)

# Setup args
dataset_args = {
    'root': data_folder,
    'dataset': args.dataset,
    'name': args.dataset_name,
    'if_use_features': args.if_use_features.lower() == 'true',
    'num_negative_samples': args.num_negative_samples,
    'num_core': args.num_core,

コード例 #2

ファイル: mf_solver.py プロジェクト: kiminh/graph_recsys_benchmark

parser.add_argument("--batch_size", type=int, default=128, help="")
parser.add_argument("--num_workers", type=int, default=16, help="")
parser.add_argument("--opt", type=str, default='adam', help="")
parser.add_argument("--loss", type=str, default='mse', help="")
parser.add_argument("--lr", type=float, default=1e-3, help="")
parser.add_argument("--weight_decay", type=float, default=1e-3, help="")
parser.add_argument("--early_stopping", type=int, default=60, help="")
parser.add_argument("--save_epochs", type=list, default=[10, 40, 80], help="")
parser.add_argument("--save_every_epoch", type=int, default=40, help="")

args = parser.parse_args()


# Setup data and weights file path
data_folder, weights_folder, logger_folder = \
    get_folder_path(model=MODEL, dataset=args.dataset + args.dataset_name)

# Setup device
if not torch.cuda.is_available() or args.device == 'cpu':
    device = 'cpu'
else:
    device = 'cuda:{}'.format(args.gpu_idx)

# Setup args
dataset_args = {
    'root': data_folder, 'dataset': args.dataset, 'name': args.dataset_name,
    'if_use_features': args.if_use_features,
    'num_core': args.num_core, 'num_feat_core': args.num_feat_core,
    'train_ratio': args.train_ratio
}
model_args = {