pytorch=1.8.0=py3.8_cuda11.1_cudnn8.0.5_0
torchvision=0.9.0=py38_cu111
torch-scatter==2.0.8

log_level: INFO

data:
  batch_size: The batch size for training
  dataset_dir: The processed dataset directory
  val_batch_size: The batch size for validation and test
  event_type_num: Number of the event types in the dataset. {'MOOC': 97, "Stack OverFlow": 22, "Retweet": 3}

model:
  encoder_type: Used history encoder, chosen in [FNet, RNN, LSTM, GRU, Attention]
  intensity_type: Used intensity function, chosen in [LogNormMix, GomptMix, LogCauMix, ExpDecayMix, WeibMix, GaussianMix] and 
        [LogNormMixSingle, GomptMixSingle, LogCauMixSingle, ExpDecayMixSingle, WeibMixSingle, GaussianMixSingle, FNNIntegralSingle],
        where *Single means modeling the overall intensities
  time_embed_type: Time embedding, chosen in [Linear, Trigono]
  embed_dim: Embeded dimension
  lag_step: Predefined lag step, which is only used when intra_encoding is true
  atten_heads: Attention heads, only used in Attention encoder, must be a divisor of embed_dim.
  layer_num: The layers number in the encoder and history encoder
  dropout: Dropout ratio, must be in 0.0-1.0
  gumbel_tau: Initial temperature in Gumbel-max
  l1_lambda: Weight to control the sparsity of Granger causality graph
  use_prior_graph: Only be true when the ganger graph is given, chosen in [true, false]
  intra_encoding: Whether to use intra-type encoding,  chosen in [true, false]

train:
  epochs: Training epoches
  lr: Initial learning rate
  log_dir: Diretory for logger
  lr_decay_ratio: The decay ratio of learning rate
  max_grad_norm: Max gradient norm
  min_learning_rate: Min learning rate
  optimizer: The optimizer to use, chosen in [adam]
  patience: Epoch for early stopping 
  steps: Epoch numbers for learning rate decay. 
  test_every_n_epochs: 10
  experiment_name: 'stackoverflow'
  delayed_grad_epoch: 10
  relation_inference: Whether to use graph discovery, chosen in [true, false],
        if false, but intra_encoding is true, the graph will be complete.
  
gpu: The GPU number to use for training

seed: Random Seed