dgld.models.SLGAD.SL_GAD_utils
- dgld.models.SLGAD.SL_GAD_utils.loss_fun(pos_scores, neg_scores, criterion, device)
The function to compute loss function of Bayesian personalized ranking
- Parameters
pos_scores (Tensor.tensor) – anomaly score of postive subgraph
neg_scores (Torch.tensor) – anomaly score of negative subgrph
criterion (torch.nn.Functions) – functions to compute loss
device (string) – device of computation
- Returns
L – loss
- Return type
Torch.tensor
- dgld.models.SLGAD.SL_GAD_utils.loss_fun_BCE(pos_scores, neg_scores, criterion, device)[source]
The function to compute loss function of Bayesian personalized ranking
- Parameters
pos_scores (Tensor.tensor) – anomaly score of postive subgraph
neg_scores (Torch.tensor) – anomaly score of negative subgrph
criterion (torch.nn.Functions) – functions to compute loss
device (string) – device of computation
- Returns
L – loss
- Return type
Torch.tensor
- dgld.models.SLGAD.SL_GAD_utils.loss_fun_BPR(pos_scores, neg_scores, criterion, device)[source]
The function to compute loss function of Bayesian personalized ranking
- Parameters
pos_scores (Tensor.tensor) – anomaly score of postive subgraph
neg_scores (Torch.tensor) – anomaly score of negative subgrph
criterion (torch.nn.Functions) – functions to compute loss
device (string) – device of computation
- Returns
L – loss
- Return type
Torch.tensor
- dgld.models.SLGAD.SL_GAD_utils.test_epoch(epoch, args, loader, net, device, criterion, optimizer)[source]
The function to train
- Parameters
epoch (int) – the number of epoch to train
loader (GraphDataLoader) – get subgraph set
net (class) – model
device (string) – device of computation
criterion (torch.nn.Functions) – functions to compute loss
optimizer (optim.Adam) – optimizer to adjust model
- Returns
predict_scores – anomaly scores of anchor nodes
- Return type
Torch.tensor
- dgld.models.SLGAD.SL_GAD_utils.train_epoch(epoch, args, loader, net, device, criterion, optimizer)[source]
The function to train
- Parameters
epoch (int) – the number of epoch to train
loader (GraphDataLoader) – get subgraph set
net (class) – model
device (string) – device of computation
criterion (torch.nn.Functions) – functions to compute loss
optimizer (optim.Adam) – optimizer to adjust model
- Returns
L – loss
- Return type
Torch.tensor