Debugging

explaining_framework/explainers/wrappers/test.py

@@ -2,12 +2,16 @@ import traceback
 
 import torch
 import torch.nn as nn
+from from_captum import CaptumWrapper
+from from_graphxai import GraphXAIWrapper
 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
+from explaining_framework.explaining_framework.metric.accuracy import Accuracy
+from explaining_framework.explaining_framework.metric.fidelity import Fidelity
+from explaining_framework.explaining_framework.metric.robust import Attack
+from explaining_framework.explaining_framework.metric.sparsity import Sparsity
 
 __all__captum = [
     "LRP",
@@ -27,16 +31,16 @@ __all__captum = [
 
 __all__graphxai = [
     "CAM",
-    "GradCAM",
-    "GNN_LRP",
-    "GradExplainer",
-    "GuidedBackPropagation",
-    "IntegratedGradients",
-    "PGExplainer",
-    "PGMExplainer",
-    "RandomExplainer",
-    "SubgraphX",
-    "GraphMASK",
+    #  "GradCAM",
+    #  "GNN_LRP",
+    #  "GradExplainer",
+    #  "GuidedBackPropagation",
+    #  "IntegratedGradients",
+    #  "PGExplainer",
+    #  "PGMExplainer",
+    #  "RandomExplainer",
+    #  "SubgraphX",
+    #  "GraphMASK",
 ]
 
 
@@ -78,7 +82,7 @@ for epoch in range(1, 2):
 target = torch.LongTensor([[0]])
 
 for kind in ["node"]:
-    for name in __all__captum + __all__graphxai:
+    for name in __all__graphxai:
         if name in __all__captum:
             explaining_algorithm = CaptumWrapper(name)
         elif name in __all__graphxai:
@@ -101,6 +105,7 @@ for kind in ["node"]:
                     task_level=kind,
                     return_type="raw",
                 ),
+                threshold_config=dict(threshold_type="hard", value=0.5),
             )
             explanation = explainer(
                 x=batch.x,
@@ -108,7 +113,22 @@ for kind in ["node"]:
                 index=int(target),
                 target=batch.y,
             )
-            print(explanation.__dict__)
+            explanation_threshold = explanation._apply_mask(
+                node_mask=explanation.node_mask, edge_mask=explanation.edge_mask
+            )
+            
+            for f_name in ["precision_score",
+            "precision_score",
+            "jaccard_score",
+            "roc_auc_score",
+            "f1_score",
+                           "accuracy_score"]:
+                acc = Accuracy(f_name)
+                gt = torch.ones_like(x)/2 
+                out = acc.forward(mask=explanation_threshold.node_mask,target=gt)
+                print(out)
+
+
         except Exception as e:
             print(str(e))
             pass