Traditional mode shows fair errors

FairEval.py

@@ -180,7 +180,7 @@ class FairEval(evaluate.Metric):
             for k in weights:
                 print(k, ":", weights[k])
 
-        config = {"labels": "all", "eval_method": [mode], "weights": weights,}
+        config = {"labels": "all", "eval_method": ['traditional', 'fair', 'weighted'], "weights": weights,}
         results = calculate_results(total_errors, config)
         del results['conf']
 
@@ -204,36 +204,32 @@ class FairEval(evaluate.Metric):
         assert mode in ['traditional', 'fair', 'weighted'], 'mode must be \'traditional\', \'fair\' or \'weighted\''
         assert error_format in ['count', 'error_ratio', 'entity_ratio'], 'error_format must be \'count\', \'error_ratio\' or \'entity_ratio\''
 
-        # append entity-level errors and scores
+        # append entity-level errors (always fair)
+        for k, v in results['per_label']['fair'].items():
+            output[k] = {'TP': v['TP'] / fair_divider if error_format == 'entity_ratio' else v['TP'],
+                         'FP': v['FP'] / fair_divider, 'FN': v['FN'] / fair_divider,
+                         'LE': v['LE'] / fair_divider, 'BE': v['BE'] / fair_divider, 'LBE': v['LBE'] / fair_divider,}
+
+        # append entity-level scores (depending on mode)
         if mode == 'traditional':
             for k, v in results['per_label'][mode].items():
-                output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'],
-                             'TP': v['TP'] / trad_divider if error_format == 'entity_ratio' else v['TP'],
-                             'FP': v['FP'] / trad_divider, 'FN': v['FN'] / trad_divider}
+                output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'],}
         elif mode == 'fair' or mode == 'weighted':
             for k, v in results['per_label'][mode].items():
-                output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'],
-                             'TP': v['TP'] / fair_divider if error_format == 'entity_ratio' else v['TP'],
-                             'FP': v['FP'] / fair_divider, 'FN': v['FN'] / fair_divider,
-                             'LE': v['LE'] / fair_divider, 'BE': v['BE'] / fair_divider, 'LBE': v['LBE'] / fair_divider}
+                output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'],}
 
-        # append overall scores
+        # append overall scores (depending on mode)
         output['overall_precision'] = results['overall'][mode]['Prec']
         output['overall_recall'] = results['overall'][mode]['Rec']
         output['overall_f1'] = results['overall'][mode]['F1']
 
-        # append overall error counts
-        if mode == 'traditional':
-            output['TP'] = results['overall'][mode]['TP'] / trad_divider if error_format == 'entity_ratio' else results['overall'][mode]['TP']
-            output['FP'] = results['overall'][mode]['FP'] / trad_divider
-            output['FN'] = results['overall'][mode]['FN'] / trad_divider
-        elif mode == 'fair' or 'weighted':
-            output['TP'] = results['overall'][mode]['TP'] / fair_divider if error_format == 'entity_ratio' else results['overall'][mode]['TP']
-            output['FP'] = results['overall'][mode]['FP'] / fair_divider
-            output['FN'] = results['overall'][mode]['FN'] / fair_divider
-            output['LE'] = results['overall'][mode]['LE'] / fair_divider
-            output['BE'] = results['overall'][mode]['BE'] / fair_divider
-            output['LBE'] = results['overall'][mode]['LBE'] / fair_divider
+        # append overall error counts (always fair)
+        output['TP'] = results['overall']['fair']['TP'] / fair_divider if error_format == 'entity_ratio' else results['overall'][mode]['TP']
+        output['FP'] = results['overall']['fair']['FP'] / fair_divider
+        output['FN'] = results['overall']['fair']['FN'] / fair_divider
+        output['LE'] = results['overall']['fair']['LE'] / fair_divider
+        output['BE'] = results['overall']['fair']['BE'] / fair_divider
+        output['LBE'] = results['overall']['fair']['LBE'] / fair_divider
 
         return output