[keras] feature_importance: now uses more events + loss

keras.py

@@ -531,9 +531,9 @@ class PlotMulticlass(TFSummaryCallback):
         if self.plot_importance:
             importance = feature_importance(
                 self.model,
-                x=[feat[:5000] for feat in self.x],
-                y=self.truth[:5000],
-                sample_weight=self.sample_weight[:5000],
+                x=[feat[:20000] for feat in self.x],
+                y=self.truth[:20000],
+                sample_weight=self.sample_weight[:20000],
                 method="grad",
                 columns=[c for col in self.columns.values() for c in col],
             )
@@ -1332,9 +1332,9 @@ def feature_importance_grad(model, x=None, **kwargs):
 
 def feature_importance_perm(model, x=None, **kwargs):
     inp_list = list(x)
-    feat = 1  # acc
+    feat = 0  # loss
     ref = model.evaluate(x=x, **kwargs)[feat]
-    accs = []
+    vals = []
     for index, tensor in enumerate(inp_list):
         s = tensor.shape
         for i in range(np.prod(s[1:])):
@@ -1349,8 +1349,8 @@ def feature_importance_perm(model, x=None, **kwargs):
 
             valid = inp_list.copy()
             valid[index] = arr_shuffled_reshaped
-            accs.append(model.evaluate(x=valid, **kwargs)[feat])
-    return ref / np.array(accs)
+            vals.append(model.evaluate(x=valid, **kwargs)[feat])
+    return np.array(vals) / ref
 
 
 def feature_importance(*args, method="grad", columns=[], **kwargs):