ONE#

class pygod.detector.ONE(hid_a=36, hid_s=36, alpha=1.0, beta=1.0, gamma=1.0, weight_decay=0.0, contamination=0.1, lr=0.004, epoch=5, gpu=-1, verbose=0)[source]#

Bases: Detector

Outlier Aware Network Embedding for Attributed Networks

Note

This detector is transductive only. Using predict with unseen data will train the detector from scratch.

See [BLM19] for details.

Parameters:

  • hid_a (int, optional) – Hidden dimension for the attribute. Default: 36.

  • hid_s (int, optional) – Hidden dimension for the structure. Default: 36.

  • alpha (float, optional) – Weight for the attribute loss. Default: 1..

  • beta (float, optional) – Weight for the structural loss. Default: 1..

  • gamma (float, optional) – Weight for the combined loss. Default: 1..

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

  • contamination (float, optional) – Valid in (0., 0.5). The proportion of outliers in the data set. 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: 5.

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

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

fit(data, label=None)[source]#

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)#

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.

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.