fix typo

convlab/policy/emoUS/evaluate.py

@@ -9,6 +9,7 @@ import torch
 from datasets import load_metric
 from sklearn import metrics
 from tqdm import tqdm
+from pprint import pprint
 
 from convlab.nlg.evaluate import fine_SER
 from convlab.policy.emoUS.emoUS import UserActionPolicy
@@ -194,13 +195,13 @@ class Evaluator:
 
     def dialog_result(self, dialog):
         x = {"gen_acts": [], "golden_acts": [],
-             "gen_emotions": [], "golden_emotions": []}
+             "gen_emotion": [], "golden_emotion": []}
 
         for d in dialog:
             x["gen_acts"].append(d["gen_acts"])
             x["golden_acts"].append(d["golden_acts"])
-            x["gen_emotions"].append(d["gen_emotion"])
-            x["golden_emotions"].append(d["golden_emotion"])
+            x["gen_emotion"].append(d["gen_emotion"])
+            x["golden_emotion"].append(d["golden_emotion"])
         return x
 
     def semantic_evaluation(self, x):
@@ -246,8 +247,8 @@ class Evaluator:
                 self.evaluation_result["semantic action prediction"][metric] = score
 
         if not golden_emotion and not golden_action:
-            r = emotion_score(x["golden_emotions"],
-                              x["gen_emotions"],
+            r = emotion_score(x["golden_emotion"],
+                              x["gen_emotion"],
                               self.model_checkpoint)
             self.evaluation_result["emotion prediction"]["emotion"] = {}
             self.evaluation_result["emotion prediction"]["emotion"]["macro_f1"] = r["macro_f1"]
@@ -260,9 +261,9 @@ class Evaluator:
             else:
                 # transfer emotions to sentiment if the model do not generate sentiment
                 golden_sentiment = [self.emo2sent[emo]
-                                    for emo in self.r["golden_emotions"]]
+                                    for emo in self.r["golden_emotion"]]
                 gen_sentiment = [self.emo2sent[emo]
-                                 for emo in self.r["gen_emotions"]]
+                                 for emo in self.r["gen_emotion"]]
             r = sentiment_score(
                 golden_sentiment,
                 gen_sentiment,
@@ -273,7 +274,7 @@ class Evaluator:
             self.evaluation_result["emotion prediction"]["sentiment"]["sep_f1"] = {
                 emo: f1 for emo, f1 in zip(r["label"], r["sep_f1"])}
 
-        print(self.evaluation_result)
+        pprint(self.evaluation_result)
 
     # def save_results(self):