GAAN#
- class pygod.detector.GAAN(noise_dim=16, hid_dim=64, num_layers=4, dropout=0.0, weight_decay=0.0, act=<function relu>, backbone=None, contamination=0.1, lr=0.004, epoch=100, gpu=-1, batch_size=0, num_neigh=0, weight=0.5, verbose=0, save_emb=False, compile_model=False, **kwargs)[source]#
Bases:
DeepDetector
Generative Adversarial Attributed Network Anomaly Detection
GAAN is a generative adversarial attribute network anomaly detection framework, including a generator module, an encoder module, a discriminator module, and uses anomaly evaluation measures that consider sample reconstruction error and real sample recognition confidence to make predictions. This model is transductive only.
See [CLW+20] for details.
- Parameters:
noise_dim (int, optional) – Input dimension of the Gaussian random noise. Defaults:
16
.hid_dim (int, optional) – Hidden dimension of model. Default:
64
.num_layers (int, optional) – Total number of layers in model. A half (floor) of the layers are for the generator, the other half (ceil) of the layers are for encoder. 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 GAAN is fixed to be MLP. Changing of this parameter will not affect the model. Default:
None
.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
.weight (float, optional) – Weight between reconstruction of node feature and structure. Default:
0.5
.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.
- 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:
- threshold_#
The threshold is based on
contamination
. It is the \(N`*``contamination`\) most abnormal samples indecision_score_
. The threshold is calculated for generating binary outlier labels.- Type:
- label_#
The binary labels of the training data. 0 stands for inliers and 1 for outliers. It is generated by applying
threshold_
ondecision_score_
.- Type:
- emb#
The learned node hidden embeddings of shape \(N \times\)
hid_dim
. Only available whensave_emb
isTrue
. When the detector has not been fitted,emb
isNone
. 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:
- 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
.