Description of basic parameters

model_name : The name of model.
dataset_name: The name of dataset.
data_path : The folder path of dataset.
data_class : The name of data preprocessing module, default KGDataModule.
litmodel_name : The name of processing module of training, evaluation and testing, default KGELitModel.
train_sampler_class : Sampling method used in training, default UniSampler.
test_sampler_class : Sampling method used in validation and testing, default TestSampler.
loss_name : The name of loss function.
negative_adversarial_sampling : Use self-adversarial negative sampling.
optim_name : The name of optimizer.
seed: Random seed.
margin : The fixed margin in loss function.
adv_temp : The temperature of sampling in self-adversarial negative sampling.
emb_dim : The embedding dimension in KGE model.
out_dim : The output embedding dimmension in some KGE model.
max_epochs : The maximum epoch in training.
lr : Learning rate
train_bs : Batch size in training.
eval_bs : Bathc size in evaluation and testing.
num_neg : The number of negative samples corresponding to each positive sample
num_ent : The number of entity, autogenerate.
num_rel : The number of relation, autogenerate.
check_per_epoch : Evaluation per n epoch of training.
early_stop_patience : If the number of consecutive bad results is n, early stop.
num_layers : The number of layers in some GNN model.
decoder_model : The name of decoder model, in some model.
eval_task : The task of validation, default link_prediction.
calc_hits : Calculate the hit rate, default [1,3,10].
gpu : Select the GPU in training, default cuda:0.
filter_flag : Filter in negative sampling.
use_wandb : Use “weight and bias” to record the result.
use_weight : Use subsampling weight.
checkpoint_dir : The checkpoint model path.
save_config : Save parameters config file.
load_config : Load parameters config file.
config_path : The config path.