New fixes and new features

explaining_framework/metric/fidelity.py

@@ -1,8 +1,16 @@
 import torch
+import torch.nn.functional as F
 from torch.nn import KLDivLoss, Softmax
 
 from base import Metric
 
+print("NUM CLASSES cfg dataset")
+NUM_CLASS = 5
+
+
+def softmax(data):
+    return Softmax(dim=1)(data)
+
 
 class Fidelity(Metric):
     def __init__(name: str, model: torch.nn.Module, mask_type: str):
@@ -12,10 +20,6 @@ class Fidelity(Metric):
             "fidelity_minus",
             "fidelity_plus_prob",
             "fidelity_minus_prob",
-            "fidelity_plus_model",
-            "fidelity_minus_model",
-            "fidelity_plus_prob_model",
-            "fidelity_minus_prob_model",
             "infidelity_KL",
         ]
 
@@ -26,7 +30,7 @@ class Fidelity(Metric):
         self.s_exp_sub_c = None
         self.s_initial_data = None
 
-    def _fidelity_plus(self, exp) -> float:
+    def _score_check(self):
         if any(
             [
                 attr is None
@@ -40,8 +44,51 @@ class Fidelity(Metric):
             ]
         ):
             self.score(exp)
-        else:
+
-            fid = self.s_initial_data - self.s_exp_sub_c
+    def _fidelity_plus(self, exp: Explanation) -> float:
+        self._score_check()
+        inferred_class_initial = torch.argmax(self.s_initial_data, dim=1)
+        inferred_class_exp = torch.argmax(self.s_exp_sub_c, dim=1)
+        return torch.mean(
+            (exp.y == inferred_class_initial).long()
+            - (exp.y == inferred_class_exp).long()
+        ).item()
+
+    def _fidelity_minus(self, exp: Explanation) -> float:
+        self._score_check()
+        inferred_class_initial = torch.argmax(self.s_initial_data, dim=1)
+        inferred_class_exp = torch.argmax(self.s_exp_sub, dim=1)
+        return torch.mean(
+            (exp.y == inferred_class_initial).long()
+            - (exp.y == inferred_class_exp).long()
+        ).item()
+
+    def _fidelity_plus_prob(self, exp: Explanation) -> float:
+        self._score_check()
+        # one_hot_emb = F.one_hot(exp.y, num_classes=NUM_CLASS)
+        prob_initial = softmax(self.s_initial_data)
+        prob_exp = softmax(self.s_exp_sub_c)
+
+        size = int(exp.y.size(0))
+        prob_initial = prob_initial[torch.arange(size), exp.y]
+        prob_exp = prob_exp[torch.arange(size), exp.y]
+
+        return torch.mean(
+            torch.norm(1 - prob_initial, p=1) - torch.norm(1 - prob_exp, p=1)
+        ).item()
+
+    def _fidelity_minus_prob(self, exp: Explanation) -> float:
+        self._score_check()
+        prob_initial = softmax(self.s_initial_data)
+        prob_exp = softmax(self.s_exp_sub)
+
+        size = int(exp.y.size(0))
+        prob_initial = prob_initial[torch.arange(size), exp.y]
+        prob_exp = prob_exp[torch.arange(size), exp.y]
+
+        return torch.mean(
+            torch.norm(1 - prob_initial, p=1) - torch.norm(1 - prob_exp, p=1)
+        ).item()
 
     def score(self, exp):
         self.exp_sub = exp.get_explanation_subgraph()
@@ -53,14 +100,16 @@ class Fidelity(Metric):
     def load_metric(name):
         if name in self.authorized_metric:
             if name == "fidelity_plus":
-
+                self.metric = lambda exp: self._fidelity_plus(exp)
-                self.metric = eval("sklearn.metric.{name}")
+            if name == "fidelity_minus":
+                self.metric = lambda exp: self._fidelity_minus(exp)
+            if name == "fidelity_plus_prob":
+                self.metric = lambda exp: self._fidelity_plus_prob(exp)
+            if name == "fidelity_minus_prob":
+                self.metric = lambda exp: self._fidelity_minus_prob(exp)
         else:
             raise ValueError(f"{name} is not supported")
-
+        return self.metric
-    def compute(self, mask, target: Tensor) -> float:
-        if mask.type() == torch.bool and target.type() == torch.bool:
-            return self.metric(y_pred=mask, y_true=target)
 
     def __call__(self, exp: Explanation):
-        pass
+        return self.metric(exp)

explaining_framework/metric/sparsity.py

@@ -0,0 +1,8 @@
+import torch
+
+
+class Sparsity(Metric):
+    def __init__(self,name):
+        super().__init__(name=name,model=None)
+
+    def