Update metric_for_tp_fp_samples.py

metric_for_tp_fp_samples.py

@@ -97,8 +97,7 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 		elif prediction_strategy[0] == "threshold":
 			results = self.threshold(logits, prediction_strategy[1])
 		elif prediction_strategy[0] == "topk":
-			results = self.topk(logits, prediction_strategy[1])
-		print(results)        
+			results = self.topk(logits, prediction_strategy[1])     
 		return results
 	#Prediction strategy functions______________________________________________
 	def argmax_max(self, logits):
@@ -218,6 +217,7 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 				"coincidence count": samples,
 				})
 			for i, j in zip(predicted_labels, references):
+				#print(i)
 				if j[1] == 0:
 					TP_data.loc[TP_data["class"] == j[0], "number of samples"] += 1
 					if len(i) >> 0:	
@@ -227,6 +227,7 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 						else:
 							if FPifWrong:
 								for k in i:	
+									print(k)                                    
 									FP_data.loc[FP_data["class"] == k, "coincidence count"] += 1
 									FP_data = FP_data.sort_values(by=["class"], ignore_index  = True)
 				if j[1] == 2: