config.yaml

name: BT3                              # folder name to save model
batch_size: 32                         # batch size
warm_up: 20                            # warm-up epochs
epochs: 150                            # total number of epochs
patience: 20                           # early stopping patience
loss_lambda: 0.01                      # loss weight of BTloss

load_model: None                        # resume training
eval_every_n_epochs: 1                  # validation frequency
save_every_n_epochs: 50                  # automatic model saving frequecy
log_every_n_steps: 50                   # print training log frequency

fp16_precision: False                   # float precision 16 (i.e. True/False)
init_lr: 0.0005                         # initial learning rate for Adam
weight_decay: 5e-5                      # weight decay for Adam
gpu: cuda:0                             # training GPU 

model_type: gcn                         # GNN backbone (i.e., gin/gcn)
model: 
  num_layer: 3                          # number of graph conv layers
  emb_dim: 300                          # embedding dimension in graph conv layers
  feat_dim: 512                         # output feature dimention
  drop_ratio: 0                         # dropout ratio
  pool: mean                            # readout pooling (i.e., mean/max/add)

aug: subgraph                           # molecule graph augmentation strategy (i.e., node/subgraph/mix)
dataset:
  num_workers: 4                        # dataloader number of workers
  valid_size: 0.05                      # ratio of validation data
  data_path: GNN_BT_Data/Smiles.csv # path of pre-training data

loss:
  temperature: 0.1                      # temperature of NT-Xent loss
  use_cosine_similarity: True           # whether to use cosine similarity in NT-Xent loss (i.e. True/False)