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argument_test_without_pretraining.py
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argument_test_without_pretraining.py
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import argparse
def parse_args():
parser = argparse.ArgumentParser(description="Run LiteralKG.")
parser.add_argument('--exp_name', type=str, default="run")
parser.add_argument('--seed', type=int, default=2022,
help='Random seed.')
parser.add_argument('--data_name', nargs='?', default='Balance_800',
help='Choose a dataset')
parser.add_argument('--data_dir', nargs='?', default='data/',
help='Input data path.')
parser.add_argument('--use_pretrain', type=int, default=1,
help='0: No pretrain, 1: Pretrain with stored model.')
parser.add_argument('--pretrain_embedding_dir', nargs='?', default='data/pretrain/',
help='Path of learned embeddings.')
parser.add_argument('--pretrain_model_path', nargs='?', default='fine-tuning_model_epoch48.pth',
help='Path of stored model.')
parser.add_argument('--test_model', nargs='?', default="training",
help='The model to test.')
parser.add_argument('--model_epoch', nargs='?', default=48,
help='The epoch to test.')
parser.add_argument('--fine_tuning_batch_size', type=int, default=2048,
help='Fine Tuning batch size.')
parser.add_argument('--pre_training_batch_size', type=int, default=2048,
help='KG batch size.')
parser.add_argument('--test_batch_size', type=int, default=2048,
help='Test batch size (the head number to test every batch).')
parser.add_argument('--total_ent', type=int, default=1000,
help='Total entities.')
parser.add_argument('--total_rel', type=int, default=100,
help='Total relations.')
parser.add_argument('--embed_dim', type=int, default=300,
help='head / entity Embedding size.')
parser.add_argument('--relation_dim', type=int, default=300,
help='Relation Embedding size.')
parser.add_argument('--scale_gat_dim', type=int, default=256,
help='Scale gat concatenation.')
parser.add_argument('--num_lit_dim', type=int, default=2,
help='Numerical Literal Embedding size.')
parser.add_argument('--txt_lit_dim', type=int, default=300,
help='Text Literal Embedding size.')
parser.add_argument('--use_num_lit', type=bool, default=True,
help='Using Numerical Literal Embedding.')
parser.add_argument('--use_txt_lit', type=bool, default=True,
help='Using Text Literal Embedding.')
parser.add_argument('--laplacian_type', type=str, default='random-walk',
help='Specify the type of the adjacency (laplacian) matrix from {symmetric, random-walk}.')
parser.add_argument('--aggregation_type', type=str, default='bi-interaction',
help='Specify the type of the aggregation layer from {gcn, graphsage, bi-interaction, gin}.')
parser.add_argument('--conv_dim_list', nargs='?', default='[32, 32, 32, 32, 32, 32, 32, 32, 32]',
help='Output sizes of every aggregation layer.')
parser.add_argument('--conv_dim', type=int, default=32,
help='Output sizes of every aggregation layer.')
parser.add_argument('--n_conv_layers', type=int, default=8,
help='Output sizes of every aggregation layer.')
parser.add_argument('--mess_dropout_list', nargs='?', default='[0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]',
help='Dropout probability w.r.t. message dropout for each deep layer. 0: no dropout.')
parser.add_argument('--mess_dropout', type=float, default=0.1,
help='Dropout probability w.r.t. message dropout for each deep layer. 0: no dropout.')
parser.add_argument('--kg_l2loss_lambda', type=float, default=1e-5,
help='Lambda when calculating KG l2 loss.')
parser.add_argument('--fine_tuning_l2loss_lambda', type=float, default=1e-5,
help='Lambda when calculating Fine Tuning l2 loss.')
parser.add_argument('--lr', type=float, default=0.0002,
help='Learning rate.')
parser.add_argument('--milestone_score', type=float, default=0.5,
help='The condition of link score.')
parser.add_argument('--n_epoch', type=int, default=100,
help='Number of epoch.')
parser.add_argument('--epoch_data_rate', type=float, default=1,
help='Sampling data rate for each epoch.')
parser.add_argument('--stopping_steps', type=int, default=10,
help='Number of epoch for early stopping')
parser.add_argument('--fine_tuning_print_every', type=int, default=500,
help='Iter interval of printing Fine Tuning loss.')
parser.add_argument('--kg_print_every', type=int, default=500,
help='Iter interval of printing KG loss.')
parser.add_argument('--evaluate_every', type=int, default=1,
help='Epoch interval of evaluating Fine Tuning.')
# parser.add_argument('--Ks', nargs='?', default='[20, 40, 60, 80, 100]',
# help='Calculate metric@K when evaluating.')
parser.add_argument('--pre_training_neg_rate', type=int, default=3,
help='The pre-training negative rate.')
parser.add_argument('--fine_tuning_neg_rate', type=int, default=3,
help='The fine tuning negative rate.')
parser.add_argument('--test_neg_rate', type=int, default=1,
help='The fine tuning negative rate.')
parser.add_argument('--train_data_rate', type=int, default=0.8,
help='The (train data/all train data) rate. Validate data rate = 1 - train_data_rate.')
parser.add_argument('--device', nargs='?', default='cuda:0',
help='Choose a device to run')
parser.add_argument('--prediction_dict_file', nargs='?', default='disease_dict.pickle',
help='Disease dictionary file')
parser.add_argument('--use_residual', type=bool, default=False,
help='Use residual connection.')
parser.add_argument('--use_parallel_gpu', type=bool, default=False,
help='Use many GPUs.')
parser.add_argument('--alpha', type=float, default=0.1, help='alpha_l')
parser.add_argument('--lamda', type=float, default=0.5, help='lamda.')
parser.add_argument('--n_mlp_layers', type=int, default=2, help='The number of GIN hidden layer.')
parser.add_argument('--mlp_hidden_dim', type=int, default=64, help='The dimension of GIN hidden layer.')
parser.add_argument('--evaluation_file', nargs='?', default='outputs/evaluation.xlsx',
help='The evaluation results file')
parser.add_argument('--evaluation_row', type=int, default=0, help='The evaluation row in excel result file')
args = parser.parse_args()
args.data_name = args.data_name.replace("'", "")
save_dir = 'trained_model/LiteralKG/{}/embed-dim{}_relation-dim{}_{}_n-layers{}_gat{}_conv{}_bs{}_num{}_txt{}_lr{}_dropout{}_without_pretrain/{}/'.format(
args.data_name, args.embed_dim, args.relation_dim, args.aggregation_type,
args.n_conv_layers, args.scale_gat_dim, args.conv_dim, args.pre_training_batch_size, args.use_num_lit, args.use_txt_lit, args.lr,
args.mess_dropout, args.exp_name)
args.save_dir = save_dir
args.pretrain_model_path = f"{args.save_dir}{args.test_model}_model_epoch{args.model_epoch}.pth"
return args