New fixes and starting to develop GraphXAI wrapper
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araison
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2aacd9fddd
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8067185d1a
@ -185,19 +185,23 @@ class CaptumWrapper(ExplainerAlgorithm):
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def _parse_attr(self, attr):
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if self.mask_type == "node":
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node_mask = attr
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node_mask = attr[0].squeeze(0)
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edge_mask = None
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if "edge" == mask_type:
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if self.mask_type == "edge":
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node_mask = None
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edge_mask = attr
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edge_mask = attr[0]
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if "node_and_mask" == mask_type:
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node_mask = attr[0]
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if self.mask_type == "node_and_edge":
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node_mask = attr[0].squeeze(0)
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edge_mask = attr[1]
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else:
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raise ValueError
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# TODO
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pass
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edge_feat_mask = None
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node_feat_mask = None
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return node_mask, edge_mask, node_feat_mask, edge_feat_mask
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def forward(
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self,
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@ -213,19 +217,25 @@ class CaptumWrapper(ExplainerAlgorithm):
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inputs, additional_forward_args = to_captum_input(
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x, edge_index, mask_type=mask_type
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)
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if self.name in ["InputXGradient", "Lime", "Saliency", "GuidedBackPropagation"]:
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if self.name in [
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"InputXGradient",
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"Lime",
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"Saliency",
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"GuidedBackPropagation",
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"FeatureAblation",
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]:
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attr = self.captum_method.attribute(
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inputs=inputs,
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additional_forward_args=additional_forward_args,
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target=target,
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)
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elif self.name == "FeatureAblation":
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attr = self.captum_method.attribute(
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inputs=inputs,
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additional_forward_args=additional_forward_args,
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)
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node_mask, edge_mask = self._parse_attr(attr)
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node_mask, edge_mask, node_feat_mask, edge_feat_mask = self._parse_attr(attr)
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return Explanation(
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x=x, edge_index=edge_index, edge_mask=edge_mask, node_mask=node_mask
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x=x,
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edge_index=edge_index,
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edge_mask=edge_mask,
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node_mask=node_mask,
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node_feat_mask=node_feat_mask,
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edge_feat_mask=edge_feat_mask,
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)
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@ -0,0 +1,231 @@
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from torch_geometric.data import Data
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from torch_geometric.explain import Explanation
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from torch_geometric.explain.algorithm.base import ExplainerAlgorithm
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from torch_geometric.explain.config import (ExplainerConfig, MaskType,
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ModelConfig, ModelMode,
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ModelTaskLevel)
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from graphxai.explainers.cam import CAM, GradCAM
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from graphxai.explainers.gnn_explainer import GNNExplainer
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from graphxai.explainers.gnn_lrp import GNN_LRP
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from graphxai.explainers.grad import GradExplainer
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from graphxai.explainers.graphlime import GraphLIME
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from graphxai.explainers.guidedbp import GuidedBP
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from graphxai.explainers.integrated_grad import IntegratedGradExplainer
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from graphxai.explainers.pg_explainer import PGExplainer
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from graphxai.explainers.pgm_explainer import PGMExplainer
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from graphxai.explainers.random import RandomExplainer
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from graphxai.explainers.subgraphx import SubgraphX
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def _load_CAM(model):
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return lambda model: CAM(model)
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def _load_GradCAM(model):
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return lambda model: GradCAM(model)
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def _load_GNN_LRP(model):
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# return lambda model: GNN_LRP(model)
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raise ValueError("GraphXAI GNN_LRP is not supported yet")
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def _load_GuidedBackPropagation(model, criterion):
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# return lambda model: GuidedBP(model, criterion)
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raise ValueError(
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"GraphXAI GuidedBackPropagation is discarded since already available in Captum for Pytorch Geometric (see CaptumWrapper)"
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)
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def _load_IntegratedGradients(model, criterion):
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return lambda model: IntegratedGradExplainer(model, criterion)
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def _load_GradExplainer(model, criterion):
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return lambda model: GradExplainer(model, criterion)
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def _load_PGExplainer(model, explain_graph=None, in_channels=None):
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return lambda model: PGExplainer(
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model, explain_graph=explain_graph, in_channels=in_channels
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)
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def _load_PGMExplainer(model, explain_graph=None):
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return lambda model: PGMExplainer(model, explain_graph)
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def _load_RandomExplainer(model):
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return lambda model: RandomExplainer(model)
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def _load_SubgraphX(model):
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return lambda model: SubgraphX(model)
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def _load_GNNExplainer(model):
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return lambda model: GNNExplainer(model)
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def _load_GraphLIME(model):
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return lambda model: GraphLIME(model)
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class GraphXAIWrapper(ExplainerAlgorithm):
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def __init__(self, name, criterion=None, in_channels=None):
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super().__init__()
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self.name = name
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self.criterion = criterion
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self.explain_graph = (
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True if self.model_config.task_level == ModelTaskLevel.graph else False
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)
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self.in_channels = in_channels
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def supports(self) -> bool:
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task_level = self.model_config.task_level
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if self.name in [
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"CAM",
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"GradCAM",
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"GNN_LRP",
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"GradExplainer",
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"GuidedBP",
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"IntegratedGradExplainer",
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"PGExplainer",
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"PGMExplainer",
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"RandomExplainer",
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"SubgraphX",
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"GNNExplainer",
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]:
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if task_level not in [ModelTaskLevel.node, ModelTaskLevel.graph]:
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logging.error(f"Task level '{task_level.value}' not supported")
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return False
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edge_mask_type = self.explainer_config.edge_mask_type
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if edge_mask_type not in [MaskType.object, None]:
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logging.error(
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f"Edge mask type '{edge_mask_type.value}' not " f"supported"
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)
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return False
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node_mask_type = self.explainer_config.node_mask_type
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if node_mask_type not in [
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MaskType.common_attributes,
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MaskType.object,
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MaskType.attributes,
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None,
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]:
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logging.error(
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f"Node mask type '{node_mask_type.value}' not " f"supported."
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)
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return False
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if self.name == "GraphLIME" and task_level == ModelTaskLevel.graph:
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return False
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return True
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def _get_mask_type(self):
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edge_mask_type = self.explainer_config.edge_mask_type
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node_mask_type = self.explainer_config.node_mask_type
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if edge_mask_type is None and node_mask_type is None:
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raise ValueError("You need to provide a masking config")
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if not edge_mask_type is None and node_mask_type is None:
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self.mask_type = "edge"
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if edge_mask_type is None and not node_mask_type is None:
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self.mask_type = "node"
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if not edge_mask_type is None and not node_mask_type is None:
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self.mask_type = "node_and_edge"
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return self.mask_type
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def _load_graphxai_method(self, model):
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if self.name == "CAM":
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return _load_CAM(model)
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elif self.name == "GradCAM":
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return _load_GradCAM(model)
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elif self.name == "GNNExplainer":
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return _load_GNNExplainer(model)
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elif self.name == "GNN_LRP":
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return _load_GNN_LRP(model)
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elif self.name == "GradExplainer":
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return _load_GradExplainer(model,self.criterion)
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elif self.name == "GraphLIME":
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return _load_GraphLIME(model)
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elif self.name == "GuidedBackPropagation":
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return _load_GuidedBackPropagation(model,self.criterion)
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elif self.name == "IntegratedGradients":
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return _load_IntegratedGradients(model,self.criterion)
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elif self.name == "PGExplainer":
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return _load_PGExplainer(model,explain_graph=self.explain_graph,in_channels=self.in_channels)
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elif self.name == "PGMExplainer":
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return _load_PGMExplainer(model,explain_graph=self.explain_graph)
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elif self.name == "RandomExplainer":
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return _load_RandomExplainer(model)
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elif self.name == "SubgraphX":
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return _load_SubgraphX(model)
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else:
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raise ValueError(f"{self.name} is not a supported Captum method yet !")
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def _parse_attr(self, attr):
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if self.mask_type == "node":
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node_mask = attr[0].squeeze(0)
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edge_mask = None
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if self.mask_type == "edge":
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node_mask = None
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edge_mask = attr[0]
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if self.mask_type == "node_and_edge":
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node_mask = attr[0].squeeze(0)
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edge_mask = attr[1]
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else:
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raise ValueError
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edge_feat_mask = None
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node_feat_mask = None
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return node_mask, edge_mask, node_feat_mask, edge_feat_mask
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def forward(
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self,
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model: torch.nn.Module,
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x: Tensor,
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edge_index: Tensor,
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target: int,
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**kwargs,
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):
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mask_type = self._get_mask_type()
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self.graphxai_method = self._load_graphxai_method(model)
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if self.explain_graph:
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attr = self.graphxai_method.get_explanation_graph(
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attr = self.captum_method.attribute(
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inputs=inputs,
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additional_forward_args=additional_forward_args,
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target=target,
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)
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node_mask, edge_mask, node_feat_mask, edge_feat_mask = self._parse_attr(attr)
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return Explanation(
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x=x,
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edge_index=edge_index,
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edge_mask=edge_mask,
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node_mask=node_mask,
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node_feat_mask=node_feat_mask,
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edge_feat_mask=edge_feat_mask,
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)
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