AnomalyDAE

class pygod.detector.AnomalyDAE(emb_dim=64, hid_dim=64, num_layers=4, dropout=0.0, weight_decay=0.0, act=<function relu>, backbone=None, alpha=0.5, theta=1.0, eta=1.0, contamination=0.1, lr=0.004, epoch=5, gpu=-1, batch_size=0, num_neigh=-1, verbose=0, save_emb=False, compile_model=False, **kwargs)

Bases: DeepDetector

Dual Autoencoder for Anomaly Detection on Attributed Networks

AnomalyDAE is an anomaly detector that consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. The structural autoencoder uses Graph Attention layers. The reconstruction mean square error of the decoders are defined as structure anomaly score and attribute anomaly score, respectively, with two additional penalties on the reconstructed adj matrix and node attributes (force entries to be nonzero).

See [FZL20] for details.

Parameters:

• emb_dim (int, optional) – Embedding dimension of model. Default: 64.

• hid_dim (int, optional) – Hidden dimension of model. Default: 64.

• num_layers (int, optional) – Total number of layers of AnomalyDAE is fixed to be 4. Changing of this parameter will not affect the model. Default: 4.

• dropout (float, optional) – Dropout rate. Default: 0..

• weight_decay (float, optional) – Weight decay (L2 penalty). Default: 0..

• act (callable activation function or None, optional) – Activation function if not None. Default: torch.nn.functional.relu.

• backbone (torch.nn.Module) – The backbone of AnomalyDAE is fixed. Changing of this parameter will not affect the model. Default: None.

• alpha (float, optional) – Weight between reconstruction of node feature and structure. Default: 0.5.

• theta (float, optional) – The additional penalty for nonzero attribute. Default: 1..

• eta (float, optional) – The additional penalty for nonzero structure. Default: 1..

• contamination (float, optional) – The amount of contamination of the dataset in (0., 0.5], i.e., the proportion of outliers in the dataset. Used when fitting to define the threshold on the decision function. Default: 0.1.

• lr (float, optional) – Learning rate. Default: 0.004.

• epoch (int, optional) – Maximum number of training epoch. Default: 100.

• gpu (int) – GPU Index, -1 for using CPU. Default: -1.

• batch_size (int, optional) – Minibatch size, 0 for full batch training. Default: 0.

• num_neigh (int, optional) – Number of neighbors in sampling, -1 for all neighbors. Default: -1.

• verbose (int, optional) – Verbosity mode. Range in [0, 3]. Larger value for printing out more log information. Default: 0.

• save_emb (bool, optional) – Whether to save the embedding. Default: False.

• compile_model (bool, optional) – Whether to compile the model with torch_geometric.compile. Default: False.

• **kwargs – Other parameters for the backbone model.

decision_score_

The outlier scores of the training data. Outliers tend to have higher scores. This value is available once the detector is fitted.

Type:

torch.Tensor

threshold_

The threshold is based on contamination. It is the \(N \times\) contamination most abnormal samples in decision_score_. The threshold is calculated for generating binary outlier labels.

Type:

float

label_

The binary labels of the training data. 0 stands for inliers and 1 for outliers. It is generated by applying threshold_ on decision_score_.

Type:

torch.Tensor

emb

The learned node hidden embeddings of shape \(N \times\) hid_dim. Only available when save_emb is True. When the detector has not been fitted, emb is None. When the detector has multiple embeddings, emb is a tuple of torch.Tensor.

Type:

torch.Tensor or tuple of torch.Tensor or None

fit(data, label=None)

Fit detector with training data.

Parameters:

• data (torch_geometric.data.Data) – The training graph.

• label (torch.Tensor, optional) – The optional outlier ground truth labels used to monitor the training progress. They are not used to optimize the unsupervised model. Default: None.

Returns:

self – Fitted detector.

Return type:

object

predict(data=None, label=None, return_pred=True, return_score=False, return_prob=False, prob_method='linear', return_conf=False, return_emb=False)

Prediction for testing data using the fitted detector. Return predicted labels by default.

Parameters:

• data (torch_geometric.data.Data, optional) – The testing graph. If None, the training data is used. Default: None.

• label (torch.Tensor, optional) – The optional outlier ground truth labels used for testing. Default: None.

• return_pred (bool, optional) – Whether to return the predicted binary labels. The labels are determined by the outlier contamination on the raw outlier scores. Default: True.

• return_score (bool, optional) – Whether to return the raw outlier scores. Default: False.

• return_prob (bool, optional) – Whether to return the outlier probabilities. Default: False.

• prob_method (str, optional) –

  The method to convert the outlier scores to probabilities. Two approaches are possible:

  1. 'linear': simply use min-max conversion to linearly transform the outlier scores into the range of [0,1]. The model must be fitted first.

  2. 'unify': use unifying scores, see [KKSZ11].

  Default: 'linear'.

• return_conf (boolean, optional) – Whether to return the model’s confidence in making the same prediction under slightly different training sets. See [PVD20]. Default: False.

• return_emb (bool, optional) – Whether to return the learned node representations. Default: False.

Returns:

• pred (torch.Tensor) – The predicted binary outlier labels of shape \(N\). 0 stands for inliers and 1 for outliers. Only available when return_label=True.

• score (torch.Tensor) – The raw outlier scores of shape \(N\). Only available when return_score=True.

• prob (torch.Tensor) – The outlier probabilities of shape \(N\). Only available when return_prob=True.

• conf (torch.Tensor) – The prediction confidence of shape \(N\). Only available when return_conf=True.