Adding parsing files

explaining_framework/utils/explanation/adjust.py

@@ -0,0 +1,16 @@
+import copy
+
+from torch import FloatTensor
+from torch.nn import ReLU
+
+
+def relu_mask(explanation: Explanation) -> Explanation:
+    relu = ReLU()
+    explanation_store = explanation._store
+    raw_data = copy.copy(explanation._store)
+    for k, v in explanation_store.items():
+        if "mask" in k:
+            explanation_store[k] = relu(v)
+    explanation.__setattr__("raw_explanation", raw_data)
+    explanation.__setattr__("raw_explanation_transform", "relu")
+    return explanation

explaining_framework/utils/explanation/io.py

@@ -0,0 +1,50 @@
+import copy
+import json
+import os
+
+from torch_geometric.data import Data
+from torch_geometric.explain.explanation import Explanation
+
+
+def explanation_verification(exp: Explanation) -> bool:
+    is_good = True
+    masks = [v for k, v in exp.items() if "_mask" in k and isinstance(v, torch.Tensor)]
+    for mask in masks:
+        is_nan = mask.isnan().any().item()
+        is_inf = mask.isinf().any().item()
+        is_ok = exp.validate()
+        if is_nan or is_inf or not is_ok:
+            is_good = False
+            return is_good
+        else:
+            continue
+    return is_good
+
+
+def save_explanation(exp: Explanation, path: str) -> None:
+    data = copy.copy(exp).to_dict()
+    for k, v in data.items():
+        if isinstance(v, torch.Tensor):
+            data[k] = v.detach().cpu().tolist()
+    with open(path, "w") as f:
+        json.dump(data, f)
+
+
+def load_explanation(path: str) -> Explanation:
+    with open(path, "r") as f:
+        data = json.load(f)
+    for k, v in data.items():
+        if isinstance(v, list):
+            if k == "edge_index" or k == "y":
+                data[k] = torch.LongTensor(v)
+            else:
+                data[k] = torch.FloatTensor(v)
+    return Explanation.from_dict(data)
+
+
+def normalize_explanation_masks(exp: Explanation, p: str = "inf") -> Explanation:
+    data = exp.to_dict()
+    for k, v in data.items():
+        if "_mask" in k and isinstance(v, torch.FloatTensor):
+            data[k] = data[k] / torch.norm(input=data[k], p=p, dim=None).item()
+    return exp