torch_geometric.explain.algorithm.PGExplainer
- class PGExplainer(epochs: int, lr: float = 0.003, **kwargs)[source]
Bases:
ExplainerAlgorithm
The PGExplainer model from the “Parameterized Explainer for Graph Neural Network” paper.
Internally, it utilizes a neural network to identify subgraph structures that play a crucial role in the predictions made by a GNN. Importantly, the
PGExplainer
needs to be trained viatrain()
before being able to generate explanations:explainer = Explainer( model=model, algorithm=PGExplainer(epochs=30, lr=0.003), explanation_type='phenomenon', edge_mask_type='object', model_config=ModelConfig(...), ) # Train against a variety of node-level or graph-level predictions: for epoch in range(30): for index in [...]: # Indices to train against. loss = explainer.algorithm.train(epoch, model, x, edge_index, target=target, index=index) # Get the final explanations: explanation = explainer(x, edge_index, target=target, index=0)
- Parameters:
- train(epoch: int, model: Module, x: Tensor, edge_index: Tensor, *, target: Tensor, index: Optional[Union[int, Tensor]] = None, **kwargs)[source]
Trains the underlying explainer model. Needs to be called before being able to make predictions.
- Parameters:
epoch (int) – The current epoch of the training phase.
model (torch.nn.Module) – The model to explain.
x (torch.Tensor) – The input node features of a homogeneous graph.
edge_index (torch.Tensor) – The input edge indices of a homogeneous graph.
target (torch.Tensor) – The target of the model.
index (int or torch.Tensor, optional) – The index of the model output to explain. Needs to be a single index. (default:
None
)**kwargs (optional) – Additional keyword arguments passed to
model
.
- forward(model: Module, x: Tensor, edge_index: Tensor, *, target: Tensor, index: Optional[Union[int, Tensor]] = None, **kwargs) Explanation [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
.