torch_geometric.explain
Warning
This module is in active development and may not be stable. Access requires installing PyG from master.
Philoshopy
This module provides a set of tools to explain the predictions of a PyG model or to explain the underlying phenomenon of a dataset (see the “GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks” paper for more details).
We represent explanations using the torch_geometric.explain.Explanation
class, which is a Data
object containing masks for the nodes, edges, features and any attributes of the data.
The torch_geometric.explain.Explainer
class is designed to handle all explainability parameters (see the torch_geometric.explain.config.ExplainerConfig
class for more details):
which algorithm from the
torch_geometric.explain.algorithm
module to use (e.g.,GNNExplainer
)the type of explanation to compute (e.g.,
explanation_type="phenomenon"
orexplanation_type="model"
)the different type of masks for node and edges (e.g.,
mask="object"
ormask="attributes"
)any postprocessing of the masks (e.g.,
threshold_type="topk"
orthreshold_type="hard"
)
This class allows the user to easily compare different explainability methods and to easily switch between different types of masks, while making sure the highlevel framework stays the same.
Explainer
 class Explainer(model: Module, algorithm: ExplainerAlgorithm, explanation_type: Union[ExplanationType, str], model_config: Union[ModelConfig, Dict[str, Any]], node_mask_type: Optional[Union[MaskType, str]] = None, edge_mask_type: Optional[Union[MaskType, str]] = None, threshold_config: Optional[ThresholdConfig] = None)[source]
Bases:
object
An explainer class for instancelevel explanations of Graph Neural Networks.
 Parameters:
model (torch.nn.Module) – The model to explain.
algorithm (ExplainerAlgorithm) – The explanation algorithm.
explanation_type (ExplanationType or str) –
The type of explanation to compute. The possible values are:
"model"
: Explains the model prediction."phenomenon"
: Explains the phenomenon that the model is trying to predict.
In practice, this means that the explanation algorithm will either compute their losses with respect to the model output (
"model"
) or the target output ("phenomenon"
).model_config (ModelConfig) – The model configuration. See
ModelConfig
for available options. (default:None
)node_mask_type (MaskType or str, optional) –
The type of mask to apply on nodes. The possible values are (default:
None
):None
: Will not apply any mask on nodes."object"
: Will mask each node."common_attributes"
: Will mask each feature."attributes"
: Will mask each feature across all nodes.
edge_mask_type (MaskType or str, optional) – The type of mask to apply on edges. Has the sample possible values as
node_mask_type
. (default:None
)threshold_config (ThresholdConfig, optional) – The threshold configuration. See
ThresholdConfig
for available options. (default:None
)
 get_prediction(*args, **kwargs) Tensor [source]
Returns the prediction of the model on the input graph.
If the model mode is
"regression"
, the prediction is returned as a scalar value. If the model mode is"multiclass_classification"
or"binary_classification"
, the prediction is returned as the predicted class label. Parameters:
*args – Arguments passed to the model.
**kwargs (optional) – Additional keyword arguments passed to the model.
 get_masked_prediction(x: Union[Tensor, Dict[str, Tensor]], edge_index: Union[Tensor, Dict[Tuple[str, str, str], Tensor]], node_mask: Optional[Union[Tensor, Dict[str, Tensor]]] = None, edge_mask: Optional[Union[Tensor, Dict[Tuple[str, str, str], Tensor]]] = None, **kwargs) Tensor [source]
Returns the prediction of the model on the input graph with node and edge masks applied.
 __call__(x: Union[Tensor, Dict[str, Tensor]], edge_index: Union[Tensor, Dict[Tuple[str, str, str], Tensor]], *, target: Optional[Tensor] = None, index: Optional[Union[int, Tensor]] = None, **kwargs) Union[Explanation, HeteroExplanation] [source]
Computes the explanation of the GNN for the given inputs and target.
Note
If you get an error message like “Trying to backward through the graph a second time”, make sure that the target you provided was computed with
torch.no_grad()
. Parameters:
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. If the explanation type is
"phenomenon"
, the target has to be provided. If the explanation type is"model"
, the target should be set toNone
and will get automatically inferred. For classification tasks, the target needs to contain the class labels. (default:None
)index (Union[int, Tensor], optional) – The indices in the firstdimension of the model output to explain. Can be a single index or a tensor of indices. If set to
None
, all model outputs will be explained. (default:None
)**kwargs – additional arguments to pass to the GNN.
 get_target(prediction: Tensor) Tensor [source]
Returns the target of the model from a given prediction.
If the model mode is of type
"regression"
, the prediction is returned as it is. If the model mode is of type"multiclass_classification"
or"binary_classification"
, the prediction is returned as the predicted class label.
 class ExplainerConfig(explanation_type: Union[ExplanationType, str], node_mask_type: Optional[Union[MaskType, str]] = None, edge_mask_type: Optional[Union[MaskType, str]] = None)[source]
Configuration class to store and validate high level explanation parameters.
 Parameters:
explanation_type (ExplanationType or str) –
The type of explanation to compute. The possible values are:
"model"
: Explains the model prediction."phenomenon"
: Explains the phenomenon that the model is trying to predict.
In practice, this means that the explanation algorithm will either compute their losses with respect to the model output (
"model"
) or the target output ("phenomenon"
).node_mask_type (MaskType or str, optional) –
The type of mask to apply on nodes. The possible values are (default:
None
):None
: Will not apply any mask on nodes."object"
: Will mask each node."common_attributes"
: Will mask each feature."attributes"
: Will mask each feature across all nodes.
edge_mask_type (MaskType or str, optional) – The type of mask to apply on edges. Has the sample possible values as
node_mask_type
. (default:None
)
 class ModelConfig(mode: Union[ModelMode, str], task_level: Union[ModelTaskLevel, str], return_type: Optional[Union[ModelReturnType, str]] = None)[source]
Configuration class to store model parameters.
 Parameters:
mode (ModelMode or str) –
The mode of the model. The possible values are:
"binary_classification"
: A binary classification model."multiclass_classification"
: A multiclass classification model."regression"
: A regression model.
task_level (ModelTaskLevel or str) –
The tasklevel of the model. The possible values are:
"node"
: A nodelevel prediction model."edge"
: An edgelevel prediction model."graph"
: A graphlevel prediction model.
return_type (ModelReturnType or str, optional) –
The return type of the model. The possible values are (default:
None
):"raw"
: The model returns raw values."probs"
: The model returns probabilities."log_probs"
: The model returns logprobabilities.
 class ThresholdConfig(threshold_type: Union[ThresholdType, str], value: Union[float, int])[source]
Configuration class to store and validate threshold parameters.
 Parameters:
threshold_type (ThresholdType or str) –
The type of threshold to apply. The possible values are:
None
: No threshold is applied."hard"
: A hard threshold is applied to each mask. The elements of the mask with a value below thevalue
are set to0
, the others are set to1
."topk"
: A soft threshold is applied to each mask. The top obj:value elements of each mask are kept, the others are set to0
."topk_hard"
: Same as"topk"
but values are set to1
for all elements which are kept.
value (int or float, optional) – The value to use when thresholding. (default:
None
)
Explanations
 class Explanation(x: Optional[Tensor] = None, edge_index: Optional[Tensor] = None, edge_attr: Optional[Tensor] = None, y: Optional[Tensor] = None, pos: Optional[Tensor] = None, **kwargs)[source]
Bases:
Data
,ExplanationMixin
Holds all the obtained explanations of a homogeneous graph.
The explanation object is a
Data
object and can hold node attributions and edge attributions. It can also hold the original graph if needed. Parameters:
 validate(raise_on_error: bool = True) bool [source]
Validates the correctness of the
Explanation
object.
 get_explanation_subgraph() Explanation [source]
Returns the induced subgraph, in which all nodes and edges with zero attribution are masked out.
 get_complement_subgraph() Explanation [source]
Returns the induced subgraph, in which all nodes and edges with any attribution are masked out.
 visualize_feature_importance(path: Optional[str] = None, feat_labels: Optional[List[str]] = None, top_k: Optional[int] = None)[source]
Creates a bar plot of the node feature importances by summing up the node mask across all nodes.
 Parameters:
 visualize_graph(path: Optional[str] = None, backend: Optional[str] = None)[source]
Visualizes the explanation graph with edge opacity corresponding to edge importance.
 Parameters:
path (str, optional) – The path to where the plot is saved. If set to
None
, will visualize the plot onthefly. (default:None
)backend (str, optional) – The graph drawing backend to use for visualization (
"graphviz"
,"networkx"
). If set toNone
, will use the most appropriate visualization backend based on available system packages. (default:None
)
 class HeteroExplanation(_mapping: Optional[Dict[str, Any]] = None, **kwargs)[source]
Bases:
HeteroData
,ExplanationMixin
Holds all the obtained explanations of a heterogeneous graph.
The explanation object is a
HeteroData
object and can hold node attributions and edge attributions. It can also hold the original graph if needed. validate(raise_on_error: bool = True) bool [source]
Validates the correctness of the
Explanation
object.
 get_explanation_subgraph() HeteroExplanation [source]
Returns the induced subgraph, in which all nodes and edges with zero attribution are masked out.
 get_complement_subgraph() HeteroExplanation [source]
Returns the induced subgraph, in which all nodes and edges with any attribution are masked out.
 visualize_feature_importance(path: Optional[str] = None, feat_labels: Optional[Dict[str, List[str]]] = None, top_k: Optional[int] = None)[source]
Creates a bar plot of the node feature importances by summing up node masks across all nodes for each node type.
 Parameters:
path (str, optional) – The path to where the plot is saved. If set to
None
, will visualize the plot onthefly. (default:None
)feat_labels (Dict[NodeType, List[str]], optional) – The labels of features for each node type. (default
None
)top_k (int, optional) – Top k features to plot. If
None
plots all features. (default:None
)
Explainer Algorithms
An abstract base class for implementing explainer algorithms. 

A dummy explainer that returns random explanations (useful for testing purposes). 

The GNNExplainer model from the "GNNExplainer: Generating Explanations for Graph Neural Networks" paper for identifying compact subgraph structures and node features that play a crucial role in the predictions made by a GNN. 

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

The PGExplainer model from the "Parameterized Explainer for Graph Neural Network" paper. 

An explainer that uses the attention coefficients produced by an attentionbased GNN (e.g., 

The GraphMaskExplainer model from the "Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking" paper for identifying layerwise compact subgraph structures and node features that play a crucial role in the predictions made by a GNN. 
Explanation Metrics
The quality of an explanation can be judged by a variety of different methods. PyG supports the following metrics outofthebox:
Compares and evaluates an explanation mask with the groundtruth explanation mask. 

Evaluates the fidelity of an 

Returns the componentwise characterization score as described in the "GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks" paper. 

Returns the AUC for the fidelity curve as described in the "GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks" paper. 

Evaluates how faithful an 