Source code for torch_geometric.explain.metric.faithfulness

from typing import Optional

import torch
import torch.nn.functional as F

from torch_geometric.explain import Explainer, Explanation
from torch_geometric.explain.config import MaskType, ModelMode, ModelReturnType

[docs]def unfaithfulness( explainer: Explainer, explanation: Explanation, top_k: Optional[int] = None, ) -> float: r"""Evaluates how faithful an :class:`~torch_geometric.explain.Explanation` is to an underyling GNN predictor, as described in the `"Evaluating Explainability for Graph Neural Networks" <>`_ paper. In particular, the graph explanation unfaithfulness metric is defined as .. math:: \textrm{GEF}(y, \hat{y}) = 1 - \exp(- \textrm{KL}(y || \hat{y})) where :math:`y` refers to the prediction probability vector obtained from the original graph, and :math:`\hat{y}` refers to the prediction probability vector obtained from the masked subgraph. Finally, the Kullback-Leibler (KL) divergence score quantifies the distance between the two probability distributions. Args: explainer (Explainer): The explainer to evaluate. explanation (Explanation): The explanation to evaluate. top_k (int, optional): If set, will only keep the original values of the top-:math:`k` node features identified by an explanation. If set to :obj:`None`, will use :obj:`explanation.node_mask` as it is for masking node features. (default: :obj:`None`) """ if explainer.model_config.mode == ModelMode.regression: raise ValueError("Fidelity not defined for 'regression' models") if top_k is not None and explainer.node_mask_type == MaskType.object: raise ValueError("Cannot apply top-k feature selection based on a " "node mask of type 'object'") node_mask = explanation.get('node_mask') edge_mask = explanation.get('edge_mask') x, edge_index = explanation.x, explanation.edge_index kwargs = {key: explanation[key] for key in explanation._model_args} y = explanation.get('prediction') if y is None: # == ExplanationType.phenomenon y = explainer.get_prediction(x, edge_index, **kwargs) if node_mask is not None and top_k is not None: feat_importance = node_mask.sum(dim=0) _, top_k_index = feat_importance.topk(top_k) node_mask = torch.zeros_like(node_mask) node_mask[:, top_k_index] = 1.0 y_hat = explainer.get_masked_prediction(x, edge_index, node_mask, edge_mask, **kwargs) if explanation.get('index') is not None: y, y_hat = y[explanation.index], y_hat[explanation.index] if explainer.model_config.return_type == ModelReturnType.raw: y, y_hat = y.softmax(dim=-1), y_hat.softmax(dim=-1) elif explainer.model_config.return_type == ModelReturnType.log_probs: y, y_hat = y.exp(), y_hat.exp() kl_div = F.kl_div(y.log(), y_hat, reduction='batchmean') return 1 - float(torch.exp(-kl_div))