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from collections import defaultdict
from typing import Dict, Optional, Union
import torch
from torch import Tensor
from torch_geometric.explain import Explanation, HeteroExplanation
from torch_geometric.explain.algorithm import ExplainerAlgorithm
from torch_geometric.explain.config import MaskType
from torch_geometric.typing import EdgeType, NodeType
class DummyExplainer(ExplainerAlgorithm):
r"""A dummy explainer that returns random explanations (useful for testing
purposes).
"""
def forward(
self,
model: torch.nn.Module,
x: Union[Tensor, Dict[NodeType, Tensor]],
edge_index: Union[Tensor, Dict[EdgeType, Tensor]],
edge_attr: Optional[Union[Tensor, Dict[EdgeType, Tensor]]] = None,
**kwargs,
) -> Union[Explanation, HeteroExplanation]:
assert isinstance(x, (Tensor, dict))
node_mask_type = self.explainer_config.node_mask_type
edge_mask_type = self.explainer_config.edge_mask_type
if isinstance(x, Tensor): # Homogeneous graph.
assert isinstance(edge_index, Tensor)
node_mask = None
if node_mask_type == MaskType.object:
node_mask = torch.rand(x.size(0), 1, device=x.device)
elif node_mask_type == MaskType.common_attributes:
node_mask = torch.rand(1, x.size(1), device=x.device)
elif node_mask_type == MaskType.attributes:
node_mask = torch.rand_like(x)
edge_mask = None
if edge_mask_type == MaskType.object:
edge_mask = torch.rand(edge_index.size(1), device=x.device)
return Explanation(node_mask=node_mask, edge_mask=edge_mask)
else: # isinstance(x, dict): # Heterogeneous graph.
assert isinstance(edge_index, dict)
node_dict = defaultdict(dict)
for k, v in x.items():
node_mask = None
if node_mask_type == MaskType.object:
node_mask = torch.rand(v.size(0), 1, device=v.device)
elif node_mask_type == MaskType.common_attributes:
node_mask = torch.rand(1, v.size(1), device=v.device)
elif node_mask_type == MaskType.attributes:
node_mask = torch.rand_like(v)
if node_mask is not None:
node_dict[k]['node_mask'] = node_mask
edge_dict = defaultdict(dict)
for k, v in edge_index.items():
edge_mask = None
if edge_mask_type == MaskType.object:
edge_mask = torch.rand(v.size(1), device=v.device)
if edge_mask is not None:
edge_dict[k]['edge_mask'] = edge_mask
return HeteroExplanation({**node_dict, **edge_dict})
def supports(self) -> bool:
return True
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