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:
- 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
.