Merge pull request #25 from fidelity/test_fix

update
fidelity · Sep 7, 2023 · fa968bc · fa968bc
2 parents 4e59100 + ffb37a9
commit fa968bc
Showing 1 changed file with 7 additions and 5 deletions.
diff --git a/jurity/fairness/for_difference.py b/jurity/fairness/for_difference.py
@@ -10,10 +10,11 @@
 
 from jurity.fairness.base import _BaseBinaryFairness
 from jurity.utils import check_and_convert_list_types
-from jurity.utils import check_inputs_validity
+from jurity.utils import check_inputs
 from jurity.utils import performance_measures
 from jurity.utils import split_array_based_on_membership_label
 
+
 class FORDifference(_BaseBinaryFairness):
 
     def __init__(self):
@@ -26,7 +27,7 @@ def __init__(self):
     @staticmethod
     def get_score(labels: Union[List, np.ndarray, pd.Series],
                   predictions: Union[List, np.ndarray, pd.Series],
-                  is_member: Union[List, np.ndarray, pd.Series],
+                  memberships: Union[List, np.ndarray, pd.Series],
                   membership_label: Union[str, float, int] = 1) -> float:
         """
         The equality (or lack thereof) of the false omission rates across groups is an important fairness metric.
@@ -43,7 +44,7 @@ def get_score(labels: Union[List, np.ndarray, pd.Series],
             Binary ground truth labels for the provided dataset (0/1).
         predictions: Union[List, np.ndarray, pd.Series]
             Binary predictions from some black-box classifier (0/1).
-        is_member: Union[List, np.ndarray, pd.Series]
+        memberships: Union[List, np.ndarray, pd.Series]
             Binary membership labels (0/1).
         membership_label: Union[str, float, int]
             Value indicating group membership.
@@ -54,10 +55,11 @@ def get_score(labels: Union[List, np.ndarray, pd.Series],
         False Omission Rate difference between groups.
         """
         # Logic to check input types.
-        check_inputs_validity(labels=labels, predictions=predictions, is_member=is_member, optional_labels=False)
+        check_inputs(predictions=predictions, memberships=memberships, membership_labels=membership_label,
+                     must_have_labels=True, labels=labels)
 
         # List needs to be converted to np for indexing
-        is_member = check_and_convert_list_types(is_member)
+        is_member = check_and_convert_list_types(memberships)
         predictions = check_and_convert_list_types(predictions)
         labels = check_and_convert_list_types(labels)