import torch
from torch import Tensor
from torch_geometric.utils.loop import add_self_loops
from dig.version import debug
from ..models.utils import subgraph
from torch.nn.functional import cross_entropy
from .base_explainer import ExplainerBase
EPS = 1e-15
def cross_entropy_with_logit(y_pred: torch.Tensor, y_true: torch.Tensor, **kwargs):
return cross_entropy(y_pred, y_true.long(), **kwargs)
[docs]class GNNExplainer(ExplainerBase):
r"""The GNN-Explainer model from the `"GNNExplainer: Generating
Explanations for Graph Neural Networks"
<https://arxiv.org/abs/1903.03894>`_ paper for identifying compact subgraph
structures and small subsets node features that play a crucial role in a
GNN’s node-predictions.
.. note:: For an example, see `benchmarks/xgraph
<https://github.com/divelab/DIG/tree/dig/benchmarks/xgraph>`_.
Args:
model (torch.nn.Module): The GNN module to explain.
epochs (int, optional): The number of epochs to train.
(default: :obj:`100`)
lr (float, optional): The learning rate to apply.
(default: :obj:`0.01`)
explain_graph (bool, optional): Whether to explain graph classification model
(default: :obj:`False`)
"""
coeffs = {
'edge_size': 0.005,
'node_feat_size': 1.0,
'edge_ent': 1.0,
'node_feat_ent': 0.1,
}
def __init__(self, model, epochs=100, lr=0.01, explain_graph=False):
super(GNNExplainer, self).__init__(model, epochs, lr, explain_graph)
def __loss__(self, raw_preds, x_label):
if self.explain_graph:
loss = cross_entropy_with_logit(raw_preds, x_label)
else:
loss = cross_entropy_with_logit(raw_preds[self.node_idx].unsqueeze(0), x_label)
m = self.edge_mask.sigmoid()
loss = loss + self.coeffs['edge_size'] * m.sum()
ent = -m * torch.log(m + EPS) - (1 - m) * torch.log(1 - m + EPS)
loss = loss + self.coeffs['edge_ent'] * ent.mean()
if self.mask_features:
m = self.node_feat_mask.sigmoid()
loss = loss + self.coeffs['node_feat_size'] * m.sum()
ent = -m * torch.log(m + EPS) - (1 - m) * torch.log(1 - m + EPS)
loss = loss + self.coeffs['node_feat_ent'] * ent.mean()
return loss
def gnn_explainer_alg(self,
x: Tensor,
edge_index: Tensor,
ex_label: Tensor,
mask_features: bool = False,
**kwargs
) -> None:
# initialize a mask
self.to(x.device)
self.mask_features = mask_features
# train to get the mask
optimizer = torch.optim.Adam([self.node_feat_mask, self.edge_mask],
lr=self.lr)
for epoch in range(1, self.epochs + 1):
if mask_features:
h = x * self.node_feat_mask.view(1, -1).sigmoid()
else:
h = x
raw_preds = self.model(x=h, edge_index=edge_index, **kwargs)
loss = self.__loss__(raw_preds, ex_label)
if epoch % 20 == 0 and debug:
print(f'Loss:{loss.item()}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
return self.edge_mask.data
[docs] def forward(self, x, edge_index, mask_features=False, **kwargs):
r"""
Run the explainer for a specific graph instance.
Args:
x (torch.Tensor): The graph instance's input node features.
edge_index (torch.Tensor): The graph instance's edge index.
mask_features (bool, optional): Whether to use feature mask. Not recommended.
(Default: :obj:`False`)
**kwargs (dict):
:obj:`node_idx` （int): The index of node that is pending to be explained.
(for node classification)
:obj:`sparsity` (float): The Sparsity we need to control to transform a
soft mask to a hard mask. (Default: :obj:`0.7`)
:obj:`num_classes` (int): The number of task's classes.
:rtype: (None, list, list)
.. note::
(None, edge_masks, related_predictions):
edge_masks is a list of edge-level explanation for each class;
related_predictions is a list of dictionary for each class
where each dictionary includes 4 type predicted probabilities.
"""
super().forward(x=x, edge_index=edge_index, **kwargs)
self.model.eval()
self_loop_edge_index, _ = add_self_loops(edge_index, num_nodes=self.num_nodes)
# Only operate on a k-hop subgraph around `node_idx`.
# Get subgraph and relabel the node, mapping is the relabeled given node_idx.
if not self.explain_graph:
node_idx = kwargs.get('node_idx')
self.node_idx = node_idx
assert node_idx is not None
_, _, _, self.hard_edge_mask = subgraph(
node_idx, self.__num_hops__, self_loop_edge_index, relabel_nodes=True,
num_nodes=None, flow=self.__flow__())
# Assume the mask we will predict
labels = tuple(i for i in range(kwargs.get('num_classes')))
ex_labels = tuple(torch.tensor([label]).to(self.device) for label in labels)
# Calculate mask
edge_masks = []
for ex_label in ex_labels:
self.__clear_masks__()
self.__set_masks__(x, self_loop_edge_index)
edge_masks.append(self.control_sparsity(self.gnn_explainer_alg(x, edge_index, ex_label), sparsity=kwargs.get('sparsity')))
# edge_masks.append(self.gnn_explainer_alg(x, edge_index, ex_label))
with torch.no_grad():
related_preds = self.eval_related_pred(x, edge_index, edge_masks, **kwargs)
self.__clear_masks__()
return None, edge_masks, related_preds
def __repr__(self):
return f'{self.__class__.__name__}()'