Add an alpha to GroupThreshold to control balance between original score and fairness

src/aequitas/audit.py

@@ -6,6 +6,7 @@
 from .bias import Bias
 from .group import Group
 from .plot import summary, disparity, absolute
+from .flow.methods.postprocessing import Threshold
 
 
 class Audit:
@@ -49,7 +50,7 @@ class is a wrapper around the Group and Bias classes.
             the keys are the sensitive attribute columns and the values are the
             reference groups. By default, 'maj'.
         """
-        self.df = df
+        self.df = df.copy(deep=True)
         self.score_column = score_column
         self.threshold = threshold
         self.label_column = label_column
@@ -253,6 +254,14 @@ def _validate_score_column(self):
         # If not binarized and a threshold is not passed, raise an error
         if not self.binarized and self.threshold is None:
             raise ValueError("Scores are not binarized. Please pass a threshold.")
+        if not self.binarized:
+            self.threshold_object = Threshold(**self.threshold)
+            self.threshold_object.fit(
+                None, self.df[self.score_column], self.df[self.label_column]
+            )
+            self.df[self.score_column] = self.threshold_object.transform(
+                None, self.df[self.score_column]
+            )
 
     def _validate_label_column(self):
         # Check if column exists

src/aequitas/flow/methods/postprocessing/balanced_group_threshold.py

@@ -14,6 +14,7 @@ def __init__(
         threshold_type: str,
         threshold_value: Union[float, int],
         fairness_metric: str,
+        alpha: float = 1,
     ):
         """Initialize a new instance of the BalancedGroupThreshold class.
 
@@ -35,11 +36,14 @@ def __init__(
                 - tpr: true positive rate
                 - fpr: false positive rate
                 - pprev: predicted prevalence
+        alpha : float, optional
+            The alpha value to use for the model score correction. The default is 1.
         """
         self.logger = create_logger("methods.postprocessing.BalancedGroupThreshold")
         self.threshold_type = threshold_type
         self.threshold_value = threshold_value
         self.fairness_metric = fairness_metric
+        self.alpha = alpha
 
         self.thresholds = {}
 
@@ -100,6 +104,11 @@ def process_group(group_df):
             # Forward fill the 'value' column
             group_df["value"].fillna(method="ffill", inplace=True)
             group_df["value"].fillna(0, inplace=True)
+
+            # Apply model score correction
+            group_df["value"] = group_df["value"] * self.alpha + (
+               1 - group_df["y_hat"]
+            ) * (1 - self.alpha)
             return group_df
 
         # Create a single DataFrame