def arg_parse():
parser = argparse.ArgumentParser(description='GNN arguments.')
utils.parse_optimizer(parser)
parser.add_argument('--model_type', type=str, help='Type of GNN model.')
parser.add_argument('--batch_size', type=int, help='Training batch size')
parser.add_argument('--num_layers',
type=int,
help='Number of graph conv layers')
parser.add_argument('--hidden_dim', type=int, help='Training hidden size')
parser.add_argument('--dropout', type=float, help='Dropout rate')
parser.add_argument('--epochs', type=int, help='Number of training epochs')
parser.add_argument('--dataset', type=str, help='Dataset')
parser.set_defaults(
model_type='GCN',
dataset='cora',
num_layers=2,
batch_size=32,
hidden_dim=32,
dropout=0.0,
epochs=200,
opt='adam', # opt_parser
opt_scheduler='none',
weight_decay=0.0,
lr=0.01)
return parser.parse_args()
def arg_parse():
parser = argparse.ArgumentParser(description='GNN arguments.')
utils.parse_optimizer(parser)
parser.add_argument('--model_type', type=str, help='Type of GNN model.')
parser.add_argument('--batch_size', type=int, help='Training batch size')
parser.add_argument('--num_layers',
type=int,
help='Number of graph conv layers')
parser.add_argument('--hidden_dim', type=int, help='Training hidden size')
parser.add_argument('--dropout', type=float, help='Dropout rate')
parser.add_argument('--dataset',
type=str,
help='Dataset, either hate or suspended')
parser.add_argument('--glove_only',
action='store_true',
help='Whether to use only glove features')
parser.set_defaults(
model_type='GraphSage',
dataset='hate',
num_layers=1,
batch_size=128,
hidden_dim=256,
dropout=0.0,
opt='adam', # opt_parser
opt_scheduler='none',
weight_decay=0.0,
lr=0.01)
return parser.parse_args()