New fixes and new features

explaining_framework/explainers/wrappers/from_captum.py

@@ -1,3 +1,5 @@
+import logging
+
 import torch
 from captum.attr import (LRP, DeepLift, DeepLiftShap, FeatureAblation,
                          FeaturePermutation, GradientShap, GuidedBackprop,
@@ -96,7 +98,7 @@ class CaptumWrapper(ExplainerAlgorithm):
             "Occlusion",
             "Saliency",
         ]:
-            if task_level not in [ModelTaskLevel.node, ModelTaskLevel.graph]:
+            if task_level not in [ModelTaskLevel.graph]:
                 logging.error(f"Task level '{task_level.value}' not supported")
                 return False
 
@@ -209,11 +211,12 @@ class CaptumWrapper(ExplainerAlgorithm):
         model: torch.nn.Module,
         x: Tensor,
         edge_index: Tensor,
+        index: int,
         target: int,
         **kwargs,
     ):
         mask_type = self._get_mask_type()
-        converted_model = to_captum_model(model, mask_type=mask_type, output_idx=target)
+        converted_model = to_captum_model(model, mask_type=mask_type, output_idx=index)
         self.captum_method = self._load_captum_method(converted_model)
         inputs, additional_forward_args = to_captum_input(
             x, edge_index, mask_type=mask_type

explaining_framework/explainers/wrappers/from_graphxai.py

@@ -225,17 +225,6 @@ class GraphXAIWrapper(ExplainerAlgorithm):
 
         return node_mask, edge_mask, node_feat_mask, edge_feat_mask
 
-    def _parse_method_args(self, method, **kwargs):
-        signature = inspect.signature(method)
-        args = tuple(
-            [
-                kwargs[k.name]
-                for k in signature.parameters.values()
-                if k.name in kwargs.keys()
-            ]
-        )
-        return args
-
     def forward(
         self,
         model: torch.nn.Module,
@@ -243,47 +232,27 @@ class GraphXAIWrapper(ExplainerAlgorithm):
         edge_index: Tensor,
         target: Tensor,
         index: Optional[Union[int, Tensor]] = None,
-        target_index: Optional[int] = None,
         **kwargs,
     ):
         mask_type = self._get_mask_type()
         self.graphxai_method = self._load_graphxai_method(model)
-        # IF CRITERION = MSE:
-        # if (
-        # self.name in ["IntegratedGradients", "GradExplainer"]
-        # and "label" in kwargs.keys()
-        # ):
-        # kwargs["label"] = kwargs["label"].float()
-        if (
-            self.name in ["PGMExplainer", "RandomExplainer"]
-            and "label" in kwargs.keys()
-        ):
-            kwargs.pop("label")
 
         if self.model_config.task_level == ModelTaskLevel.node:
-            args = self._parse_method_args(
-                self.graphxai_method.get_explanation_node,
+            attr = self.graphxai_method.get_explanation_node(
                 x=x,
                 edge_index=edge_index,
-                node_idx=target,
+                label=target,
+                node_idx=index,
+                y=target,
             )
-
-            attr = self.graphxai_method.get_explanation_node(*args, **kwargs)
         elif self.model_config.task_level == ModelTaskLevel.graph:
-            args = self._parse_method_args(
-                self.graphxai_method.get_explanation_graph,
+            attr = self.graphxai_method.get_explanation_graph(
                 x=x,
                 edge_index=edge_index,
+                label=target,
+                y=target,
             )
-
-            attr = self.graphxai_method.get_explanation_graph(*args, **kwargs)
         elif self.model_config.task_level == ModelTaskLevel.edge:
-            args = self._parse_method_args(
-                self.graphxai_method.get_explanation_link,
-                x=x,
-                edge_index=edge_index,
-            )
-
             attr = self.graphxai_method.get_explanation_link(*args, **kwargs)
         else:
             raise ValueError(f"{self.model_config.task_level} is not supported yet")

explaining_framework/explainers/wrappers/test.py

@@ -0,0 +1,114 @@
+import traceback
+
+import torch
+import torch.nn as nn
+from torch_geometric.data import Batch, Data
+from torch_geometric.explain import Explainer
+from torch_geometric.nn import GATConv, GCNConv, GINConv, global_mean_pool
+
+from from_captum import CaptumWrapper
+from from_graphxai import GraphXAIWrapper
+
+__all__captum = [
+    "LRP",
+    "DeepLift",
+    "DeepLiftShap",
+    "FeatureAblation",
+    "FeaturePermutation",
+    "GradientShap",
+    "GuidedBackprop",
+    "GuidedGradCam",
+    "InputXGradient",
+    "IntegratedGradients",
+    "Lime",
+    "Occlusion",
+    "Saliency",
+]
+
+__all__graphxai = [
+    "CAM",
+    "GradCAM",
+    "GNN_LRP",
+    "GradExplainer",
+    "GuidedBackPropagation",
+    "IntegratedGradients",
+    "PGExplainer",
+    "PGMExplainer",
+    "RandomExplainer",
+    "SubgraphX",
+    "GraphMASK",
+]
+
+
+edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)
+size_F = 4
+size_O = in_channels = 6
+x = torch.ones((3, size_F))
+y = torch.tensor([1], dtype=torch.long)
+loss = nn.CrossEntropyLoss()
+
+data = Data(x=x, edge_index=edge_index, y=y)
+batch = Batch().from_data_list([data])
+
+
+class Model(torch.nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.dim_in = dim_in
+        self.dim_out = dim_out
+        self.conv = GCNConv(dim_in, dim_out)
+
+    def forward(self, x, edge_index):
+        x = self.conv(x, edge_index)
+        x = global_mean_pool(x, torch.LongTensor([0]))
+        return x
+
+
+model = Model(size_F, size_O)
+optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
+
+for epoch in range(1, 2):
+    model.train()
+    optimizer.zero_grad()
+    out = model(batch.x, batch.edge_index)
+    # lossee = loss(out, torch.ones(x.shape[0], size_O))
+    lossee = loss(out, torch.ones(1, size_O))
+    lossee.backward()
+    optimizer.step()
+target = torch.LongTensor([[0]])
+
+for kind in ["node"]:
+    for name in __all__captum + __all__graphxai:
+        if name in __all__captum:
+            explaining_algorithm = CaptumWrapper(name)
+        elif name in __all__graphxai:
+            explaining_algorithm = GraphXAIWrapper(
+                name, in_channels=in_channels, criterion="cross-entropy"
+            )
+
+        print(name)
+        try:
+            explainer = Explainer(
+                model=model,
+                algorithm=explaining_algorithm,
+                explainer_config=dict(
+                    explanation_type="phenomenon",
+                    node_mask_type="object",
+                    edge_mask_type="object",
+                ),
+                model_config=dict(
+                    mode="classification",
+                    task_level=kind,
+                    return_type="raw",
+                ),
+            )
+            explanation = explainer(
+                x=batch.x,
+                edge_index=batch.edge_index,
+                index=int(target),
+                target=batch.y,
+            )
+            print(explanation.__dict__)
+        except Exception as e:
+            print(str(e))
+            pass

explaining_framework/metric/base.py

@@ -0,0 +1,19 @@
+from abc import ABC
+
+
+class Metric(ABC):
+    def __init__(self, name: str, model: torch.nn.Module = None, **kwargs):
+        self.name = name
+        self.model = model
+        if is_model_needed and model is None:
+            raise ValueError(f"{self.name} needs model to perform measurements")
+
+    def is_model_needed(self):
+        if "fidelity" in self.name:
+            return True
+        else:
+            return False
+
+    @abstractmethod
+    def __call__(self, exp: Explanation, **kwargs) -> float:
+        pass

explaining_framework/stats/graph/graph_stat.py

@@ -140,6 +140,15 @@ class GraphStat(object):
             name: lambda x, name=name: x.__getattr__(name) if hasattr(x, name) else None
             for name in names
         }
+        maps_add_assortativity = {
+            "assortativity": lambda x: torch_geometric.utils.assortativity(x.edge_index)
+        }
+        maps_add_homophily = {
+            f"homophily_{approach}": lambda x: torch_geometric.utils.homophily(
+                edge_index=x.edge_index, y=x.y, method=approach
+            )
+            for approach in ["edge", "node", "edge_insensitive"]
+        }
         return maps
 
     def __call__(self, data):