corrects cr calc

src/read_logs.py

@@ -74,7 +74,7 @@ class LogAnalyser:
     def get_c1_plot(self):
         # c2_threshold=0
         c1_threshold_list = np.arange(0, 1, 0.01)
-        c1_reaction_weight_list = np.arange(0, 1, 0.1)
+        c1_reaction_weight_list = np.arange(0, 1.1, 0.1)
 
         # reaction_weight=0.5
         list_stories = self.df.Story.unique()
@@ -118,7 +118,7 @@ class LogAnalyser:
                         df_c1.at[index, 'c1'] = reaction_show
                         review = df_c1.e_review[index]
                         df_c1.at[index, 'c1r'] = self.get_criteria_review(
-                            reaction_show, review=review)
+                            reaction_show, review=review, neutral_emotion=row.Emotion == 'neutral')
                 c1r_sum = df_c1['c1r'].sum()
                 c1r_sum_list.append(c1r_sum)
                 c1_analysis_dict['c1_threshold']=c1_threshold
@@ -162,11 +162,11 @@ class LogAnalyser:
                 subset_condition = self.get_subset_condition(df_c2, story_id)
                 dfs = df_c2[subset_condition]
                 for i, (index, row) in enumerate(dfs.iterrows()):
-                    c2 = row.Emotion != 'neutral' and row.Score > c2_threshold
+                    c2 = row.Score > c2_threshold
                     df_c2.at[index, 'c2'] = c2
                     review = df_c2.e_review[index]
                     df_c2.at[index, 'c2r'] = self.get_criteria_review(
-                        c2, review=review)
+                        c2, review=review, neutral_emotion=row.Emotion == 'neutral')
             c2r_sum_list.append(df_c2['c2r'].sum())
         fig = px.line(x=c2_threshold_list, y=c2r_sum_list)
         fig.update_layout(
@@ -188,12 +188,15 @@ class LogAnalyser:
         return (data.Story == story_id) & (data.Turn == 'user')
 
     @staticmethod
-    def get_criteria_review(c, review):
+    def get_criteria_review(c, review, neutral_emotion=False):
         # printj.green(f'{c} {type(c)}')
         # printj.green(f'{review} {type(review)}')
-        result = int(c == True and (review == 'o' or review == None)) + int(
-            c == False and review == 'x')
-        return np.round(result, 0)
+        review_bool = True if (review == 'o' or review == None) else False if (c == False and review == 'x') else None
+        if neutral_emotion and review_bool:
+            result = True
+        else:
+            result = (c and review_bool) or (not c and not review_bool)
+        return np.round(int(result), 0)
         # return str(np.round(result, 0))
 
     def get_ngram_pattern(self, s, n=2):
@@ -269,9 +272,9 @@ class LogAnalyser:
                 self.df.at[index, 'c2'] = c2
                 review = self.df.e_review[index]
                 self.df.at[index, 'c1r'] = self.get_criteria_review(
-                    reaction_show, review=review)
+                    reaction_show, review=review, neutral_emotion=row.Emotion == 'neutral')
                 self.df.at[index, 'c2r'] = self.get_criteria_review(
-                    c2, review=review)
+                    c2, review=review, neutral_emotion=row.Emotion == 'neutral')
             s = ''
             df_edit = self.df[self.get_subset_condition(
                 self.df, story_id)].reaction_show.copy()