Update bias_auc.py

bias_auc.py

@@ -9,12 +9,9 @@ _DESCRIPTION = """\
 Suite of threshold-agnostic metrics that provide a nuanced view
 of this unintended bias, by considering the various ways that a
 classifier’s score distribution can vary across designated groups.
-
 The following are computed:
-
 - BNSP (Background Negative, Subgroup Positive); and 
 - BPSN (Background Positive, Subgroup Negative) AUC 
-
 """
 
 _CITATION = """\
@@ -28,18 +25,14 @@ _CITATION = """\
 """
 
 _KWARGS_DESCRIPTION = """\
-
 Args:
     target list[list[str]]:  list containing list of group targeted for each item
     label list[int]: list containing label index for each item
     output list[list[float]]: list of model output values for each
-
 Returns (for each subgroup in target):
     'Subgroup' : Subgroup AUC score,
     'BPSN' : BPSN (Background Positive, Subgroup Negative) AUC,
     'BNSP' : BNSP (Background Negative, Subgroup Positive) AUC score,
-
-
 Example:
     >>> from evaluate import load
     
@@ -54,7 +47,6 @@ Example:
     ... [0.4341845214366913, 0.5658154487609863],
     ... [0.400595098733902, 0.5994048714637756],
     ... [0.3840397894382477, 0.6159601807594299]]
-
     >>> metric = load('Intel/bias_auc')
     >>> metric.add_batch(target=target,
                      label=label,
@@ -68,7 +60,7 @@ Example:
 
 class BiasAUC(evaluate.Metric):
     def _info(self):
-        return datasets.MetricInfo(
+        return evaluate.MetricInfo(
             description=_DESCRIPTION,
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
@@ -165,4 +157,4 @@ class BiasAUC(evaluate.Metric):
                     for subgroup in subgroups}
         result = self._update_overall(result, label, output)
         return result
-        
+