torch_geometric.explain.algorithm.PGExplainer

class PGExplainer(epochs: int, lr: float = 0.003, **kwargs)

Bases: ExplainerAlgorithm

PGExplainer模型来自“Parameterized Explainer for Graph Neural Network”论文。

在内部，它利用神经网络来识别在GNN预测中起关键作用的子图结构。重要的是，PGExplainer需要通过train()进行训练，然后才能生成解释：

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:

• epochs (int) – 训练的轮数。

• lr (float, optional) – 应用的学习率。 (默认: 0.003).

• **kwargs (可选) – 用于覆盖默认设置的其他超参数 在 coeffs中。

reset_parameters()

重置模块的所有可学习参数。

train(epoch: int, model: Module, x: Tensor, edge_index: Tensor, *, target: Tensor, index: Optional[Union[int, Tensor]] = None, **kwargs)

训练底层解释器模型。 在能够进行预测之前需要调用。

Parameters:

• epoch (int) – 训练阶段的当前周期。

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

• x (torch.Tensor) – 同构图中的输入节点特征。

• edge_index (torch.Tensor) – 同质图的输入边索引。

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

• index (int 或 torch.Tensor, 可选) – 要解释的模型输出的索引。需要是单个索引。 (默认: 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

计算解释。

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.

Return type:

Explanation

supports() → bool

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

Return type:

bool 翻译后的内容： bool 在这个例子中，`bool` 是一个Python函数名称，根据翻译规则1，不需要翻译。因此，翻译后的内容保持不变。