fixed sample_weight=None bug and classes_ typo

aif360/sklearn/postprocessing/calibrated_equalized_odds.py

@@ -64,11 +64,11 @@ def __init__(self, prot_attr=None, cost_constraint='weighted',
         self.cost_constraint = cost_constraint
         self.random_state = random_state
 
-    def _weighted_cost(self, y_true, probas_pred, pos_label, sample_weight):
+    def _weighted_cost(self, y_true, probas_pred, sample_weight=None):
         """Evaluates the cost function specified by ``self.cost_constraint``."""
-        fpr = generalized_fpr(y_true, probas_pred, pos_label, sample_weight)
-        fnr = generalized_fnr(y_true, probas_pred, pos_label, sample_weight)
-        br = base_rate(y_true, probas_pred, pos_label, sample_weight)
+        fpr = generalized_fpr(y_true, probas_pred, self.pos_label_, sample_weight)
+        fnr = generalized_fnr(y_true, probas_pred, self.pos_label_, sample_weight)
+        br = base_rate(y_true, probas_pred, self.pos_label_, sample_weight)
         if self.cost_constraint == 'fpr':
             return fpr
         elif self.cost_constraint == 'fnr':
@@ -117,7 +117,7 @@ def _args(grp_idx, triv=False):
             idx = (groups == self.groups_[grp_idx])
             pred = (np.full_like(y_pred, self.base_rates_[grp_idx]) if triv else
                     y_pred)
-            return [y_true[idx], pred[idx], pos_label, sample_weight[idx]]
+            return [y_true[idx], pred[idx], sample_weight[idx]]
 
         self.base_rates_ = [base_rate(*_args(i)) for i in range(2)]
 
@@ -178,7 +178,7 @@ def predict(self, y_pred):
             numpy.ndarray: Predicted class label per sample.
         """
         scores = self.predict_proba(y_pred)
-        return self.classes[scores.argmax(axis=1)]
+        return self.classes_[scores.argmax(axis=1)]
 
     def score(self, y_pred, y_true, sample_weight=None):
         """Score the predictions according to the cost constraint specified.