torch_geometric.nn.models.to_captum_model

class to_captum_model(model: Module, mask_type: Union[str, MaskLevelType] = MaskLevelType.edge, output_idx: Optional[int] = None, metadata: Optional[Tuple[List[str], List[Tuple[str, str, str]]]] = None)

基础：

将模型转换为可用于Captum归因方法的模型。

同构图示例代码：

from captum.attr import IntegratedGradients

from torch_geometric.data import Data
from torch_geometric.nn import GCN
from torch_geometric.nn import to_captum_model, to_captum_input

data = Data(x=(...), edge_index(...))
model = GCN(...)
...  # Train the model.

# Explain predictions for node `10`:
mask_type="edge"
output_idx = 10
captum_model = to_captum_model(model, mask_type, output_idx)
inputs, additional_forward_args = to_captum_input(data.x,
                                    data.edge_index,mask_type)

ig = IntegratedGradients(captum_model)
ig_attr = ig.attribute(inputs = inputs,
                       target=int(y[output_idx]),
                       additional_forward_args=additional_forward_args,
                       internal_batch_size=1)

异构图的示例代码：

from captum.attr import IntegratedGradients

from torch_geometric.data import HeteroData
from torch_geometric.nn import HeteroConv
from torch_geometric.nn import (captum_output_to_dicts,
                                to_captum_model, to_captum_input)

data = HeteroData(...)
model = HeteroConv(...)
...  # Train the model.

# Explain predictions for node `10`:
mask_type="edge"
metadata = data.metadata
output_idx = 10
captum_model = to_captum_model(model, mask_type, output_idx, metadata)
inputs, additional_forward_args = to_captum_input(data.x_dict,
                                    data.edge_index_dict, mask_type)

ig = IntegratedGradients(captum_model)
ig_attr = ig.attribute(inputs=inputs,
                       target=int(y[output_idx]),
                       additional_forward_args=additional_forward_args,
                       internal_batch_size=1)
edge_attr_dict = captum_output_to_dicts(ig_attr, mask_type, metadata)

注意

有关在 PyG 中使用 Captum 归因方法的示例，请参见 examples/explain/captum_explainer.py。

Parameters:

• model (torch.nn.Module) – 需要解释的模型。

• mask_type (str, optional) – 表示使用Captum解释器创建的掩码类型。有效输入为"edge"、"node"和"node_and_edge"。（默认值："edge"）

• output_idx (int, 可选) – 要解释的输出元素（节点或链接索引）的索引。设置了output_idx后，前向函数将返回模型在指定索引处的元素的输出。（默认值：None）

• metadata (Metadata, optional) – 异构图（heterogeneous graph）的元数据。 仅在解释 HeteroData 对象时需要。 (默认值: None)

Return type:

Union[CaptumModel, CaptumHeteroModel]