start interpreting using calf

exp/exp_interpret.py

@@ -139,12 +139,16 @@ def run_classifier(self, dataloader, name):
                 results.extend(rows)
                 if results_df.shape[0]>0: 
                     min_batch_index = results_df['batch_index'].max() + 1
+                else:
+                    min_batch_index = 0
             else:
                 min_batch_index = 0
                 # create and write header row if the file doesn't exists or it is to be overwritten
                 result_file = open(batch_filename, 'w', newline='')
                 writer = csv.writer(result_file) 
                 writer.writerow(results[0])
+        else:
+            min_batch_index = 0
 
         attrs = []
         if min_batch_index>0:
@@ -165,7 +169,11 @@ def run_classifier(self, dataloader, name):
             inputs = batch_x
             # baseline must be a scaler or tuple of tensors with same dimension as input
             baselines = get_baseline(inputs, mode=self.args.baseline_mode)
-            additional_forward_args = (padding_mask, None, None)
+
+            if self.args.model in ['CALF', 'OFA']:
+                additional_forward_args = None
+            else:
+                additional_forward_args = (padding_mask, None, None)
 
             # get attributions
             batch_results, batch_attr = self.evaluate(

interpret.py

@@ -40,9 +40,6 @@ def main(args):
 
         exp.load_best_model()
 
-        # Some models don't work with gradient based explainers
-        # explainers = ['deep_lift', 'gradient_shap', 'integrated_gradients']
-
         interpreter = Exp_Interpret(exp, dataloader) 
         interpreter.interpret(dataloader)
 

interpret_CALF.py

@@ -41,9 +41,6 @@ def main(args):
 
         exp.load_best_model()
 
-        # Some models don't work with gradient based explainers
-        # explainers = ['deep_lift', 'gradient_shap', 'integrated_gradients']
-
         interpreter = Exp_Interpret(exp, dataloader) 
         interpreter.interpret(dataloader)
 

interpret_TimeLLM.py

@@ -41,9 +41,6 @@ def main(args):
 
         exp.load_best_model()
 
-        # Some models don't work with gradient based explainers
-        # explainers = ['deep_lift', 'gradient_shap', 'integrated_gradients']
-
         interpreter = Exp_Interpret(exp, dataloader) 
         interpreter.interpret(dataloader)
 

results/electricity_CALF/1/augmented_occlusion.csv

@@ -0,0 +1,9 @@
+metric,area,comp,suff
+mae,0.05,7.97165,18.21702
+mae,0.075,9.332975,17.384995
+mae,0.1,10.038847,16.889594
+mae,0.15,11.445272,15.737271
+mse,0.05,6.148502,22.12033
+mse,0.075,7.665615,20.257282
+mse,0.1,8.547635,19.182259
+mse,0.15,10.432541,16.808907