torch_geometric.explain.algorithm.CaptumExplainer

class CaptumExplainer(attribution_method: Union[str, Any], **kwargs)[source]

Bases: ExplainerAlgorithm

A Captum-based explainer for identifying compact subgraph structures and node features that play a crucial role in the predictions made by a GNN.

This explainer algorithm uses Captum to compute attributions.

Currently, the following attribution methods are supported:

  • captum.attr.IntegratedGradients

  • captum.attr.Saliency

  • captum.attr.InputXGradient

  • captum.attr.Deconvolution

  • captum.attr.ShapleyValueSampling

  • captum.attr.GuidedBackprop

Parameters

  • attribution_method (Attribution or str) – The Captum attribution method to use. Can be a string or a captum.attr method.

  • **kwargs – Additional arguments for the Captum attribution method.

forward(model: Module, x: Union[Tensor, Dict[str, Tensor]], edge_index: Union[Tensor, Dict[Tuple[str, str, str], Tensor]], *, target: Tensor, index: Optional[Union[int, Tensor]] = None, **kwargs) → Union[Explanation, HeteroExplanation][source]

Computes the explanation.

Parameters

  • model (torch.nn.Module) – The model to explain.

  • x (Union[torch.Tensor, Dict[NodeType, torch.Tensor]]) – The input node features of a homogeneous or heterogeneous graph.

  • edge_index (Union[torch.Tensor, Dict[NodeType, torch.Tensor]]) – The input edge indices of a homogeneous or heterogeneous graph.

  • target (torch.Tensor) – The target of the model.

  • index (Union[int, Tensor], optional) – The index of the model output to explain. Can be a single index or a tensor of indices. (default: None)

  • **kwargs (optional) – Additional keyword arguments passed to model.

supports() → bool[source]

Checks if the explainer supports the user-defined settings provided in self.explainer_config, self.model_config.