update

fidelity · Sep 7, 2023 · f8acba9 · f8acba9
1 parent 1475e9a
commit f8acba9
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Showing 3 changed files with 15 additions and 3 deletions.
diff --git a/jurity/fairness/__init__.py b/jurity/fairness/__init__.py
@@ -100,7 +100,8 @@ def get_all_scores(labels: Union[List, np.ndarray, pd.Series],
                 membership_labels = [1]
 
             if bootstrap_results is None:
-                bootstrap_results = get_bootstrap_results(predictions, memberships, surrogates, membership_labels)
+                bootstrap_results = get_bootstrap_results(predictions, memberships, surrogates,
+                                                          membership_labels, labels)
 
         # Output df
         df = pd.DataFrame(columns=["Metric", "Value", "Ideal Value", "Lower Bound", "Upper Bound"])
@@ -144,7 +145,7 @@ def _get_score_logic(metric, name,
                 score = metric.get_score(labels, predictions, memberships, membership_labels)
         else:
             if name == "StatisticalParity":
-                score = metric.get_score(predictions, memberships, membership_labels, bootstrap_results)
+                score = metric.get_score(predictions, memberships, surrogates, membership_labels, bootstrap_results)
             elif name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality"]:
                 score = metric.get_score(labels, predictions, memberships, surrogates,
                                          membership_labels, bootstrap_results)

diff --git a/jurity/fairness/average_odds.py b/jurity/fairness/average_odds.py
@@ -124,7 +124,6 @@ def get_score(labels: Union[List, np.ndarray, pd.Series],
                                    must_have_labels=True, labels=labels)
                 bootstrap_results = get_bootstrap_results(predictions, memberships, surrogates, membership_labels,
                                                           labels)
-            print(bootstrap_results)
 
             tpr_group_1, tpr_group_2 = unpack_bootstrap(bootstrap_results, Constants.TPR, membership_labels)
             fpr_group_1, fpr_group_2 = unpack_bootstrap(bootstrap_results, Constants.FPR, membership_labels)

diff --git a/tests/test_fairness_proba.py b/tests/test_fairness_proba.py
@@ -23,6 +23,18 @@ def test_quick_start(self):
         metric = BinaryFairnessMetrics.StatisticalParity()
         score = metric.get_score(predictions, memberships, surrogates)
 
+    def test_quick_start_avg_odds(self):
+        # Data
+        labels = [1, 1, 1, 0, 1, 0, 1, 1, 1, 1]
+        predictions = [0, 0, 0, 1, 0, 0, 0, 0, 0, 1]
+        memberships = [[0.2, 0.8], [0.4, 0.6], [0.2, 0.8], [0.9, 0.1], [0.3, 0.7],
+                       [0.8, 0.2], [0.6, 0.4], [0.8, 0.2], [0.1, 0.9], [0.7, 0.3]]
+        surrogates = [0, 2, 0, 1, 3, 0, 0, 1, 1, 2]
+        # membership_labels = [1]
+        metric = BinaryFairnessMetrics.AverageOdds()
+        score = metric.get_score(labels, predictions, memberships, surrogates)
+        print(score)
+
     def test_all_scores(self):
         labels = [1, 1, 1, 0, 1, 0, 1, 1, 1, 1]
         predictions = [0, 0, 0, 1, 0, 0, 0, 0, 0, 1]