API CheatSheet#

The following APIs are applicable for all detectors for easy use.

Key Attributes of a fitted detector:

For the inductive setting:

Input of PyGOD: Please pass in a PyTorch Geometric (PyG) data object. See PyG data processing examples.

Base Detector#

Detector is the abstract class for all detectors:

class pygod.detector.Detector(contamination=0.1, verbose=0)[source]#

Bases: ABC

Abstract class for all outlier detection algorithms.

Parameters:

  • 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.

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

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 \(N`*``contamination`\) 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

abstract decision_function(data, label=None)[source]#

Predict raw outlier scores of testing data using the fitted detector. Outliers are assigned with higher outlier scores.

Parameters:
Returns:

score – The outlier scores of shape \(N\).

Return type:

torch.Tensor

abstract 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)[source]#

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.

abstract process_graph(data)[source]#

Data preprocessing for the input graph.

Parameters:

data (torch_geometric.data.Data) – The input graph.

Deep Detector#

By inherit Detector class, we also provide base deep detector class for deep learning based detectors to ease the implementation.

class pygod.detector.DeepDetector(hid_dim=64, num_layers=2, dropout=0.0, weight_decay=0.0, act=<function relu>, backbone=<class 'torch_geometric.nn.models.basic_gnn.GIN'>, contamination=0.1, lr=0.004, epoch=100, gpu=-1, batch_size=0, num_neigh=-1, verbose=0, gan=False, save_emb=False, compile_model=False, **kwargs)[source]#

Abstract class for deep outlier detection algorithms.

Parameters:

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

  • num_layers (int, optional) – Total number of layers in model. Default: 2.

  • 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 the deep detector implemented in PyG. Default: torch_geometric.nn.GIN.

  • 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.

  • gan (bool, optional) – Whether using adversarial training. Default: False.

  • 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:

torch.Tensor

threshold_#

The threshold is based on contamination. It is the \(N`*``contamination`\) 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

abstract forward_model(data)[source]#

Forward pass of the neural network detector.

Parameters:

data (torch_geometric.data.Data) – The input graph.

Returns:

  • loss (torch.Tensor) – The loss of the current batch.

  • score (torch.Tensor) – The outlier scores of the current batch.

abstract init_model()[source]#

Initialize the neural network detector.

Returns:

model – The initialized neural network detector.

Return type:

torch.nn.Module

abstract process_graph(data)#

Data preprocessing for the input graph.

Parameters:

data (torch_geometric.data.Data) – The input graph.