Bug fixes in pre-processing methods

Author: reluzita

src/aequitas/flow/datasets/folktables.py

@@ -64,7 +64,7 @@
 
 BOOL_COLUMNS = {
     "ACSIncome": ["SEX"],
-    "ACSEmployment": ["SEX", "DIS", "NATIVTY", "DEAR", "DEYE", "DREM"],
+    "ACSEmployment": ["SEX", "DIS", "NATIVITY", "DEAR", "DEYE", "DREM"],
     "ACSMobility": [
         "SEX",
         "DIS",

src/aequitas/flow/methods/preprocessing/data_repairer.py

@@ -54,6 +54,7 @@ def __init__(
         self.repair_level = repair_level
         self.columns = columns
         self.definition = definition
+        self.used_in_inference = True
 
     def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series] = None) -> None:
         """
@@ -72,7 +73,11 @@ def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series] = None) -> N
         super().fit(X, y, s)
 
         if self.columns is None:
-            self.columns = X.columns.tolist()
+            self.columns = [
+                column
+                for column in X.columns
+                if (X[column].dtype != "category" and X[column].dtype != "bool")
+            ]
         if s is None:
             raise ValueError("s must be passed.")
         self._quantile_points = np.linspace(0, 1, self.definition)
@@ -141,7 +146,7 @@ def transform(
             Transformed features, labels, and sensitive attribute.
         """
         super().transform(X, y, s)
-        
+
         if s is None:
             raise ValueError("s must be passed.")
 

src/aequitas/flow/methods/preprocessing/feature_importance_suppression.py

@@ -16,6 +16,7 @@ def __init__(
         feature_importance_threshold: Optional[float] = 0.1,
         n_estimators: Optional[int] = 10,
         seed: int = 0,
+        n_jobs: int = 1,
     ):
         """Iterively removes the most important features with respect to the sensitive
         attribute.
@@ -32,6 +33,8 @@ def __init__(
             The number of trees in the random forest. Defaults to 10.
         seed : int, optional
             The seed for the random forest. Defaults to 0.
+        n_jobs : int, optional
+            The number of jobs to run in parallel. Defaults to 1.
         """
         self.logger = create_logger(
             "methods.preprocessing.FeatureImportanceSuppression"
@@ -45,6 +48,7 @@ def __init__(
         self.feature_importance_threshold = feature_importance_threshold
         self.n_estimators = n_estimators
         self.seed = seed
+        self.n_jobs = n_jobs
 
     def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series]) -> None:
         """Iteratively removes the most important features to predict the sensitive
@@ -64,7 +68,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series]) -> None:
         self.logger.info("Identifying features to remove.")
 
         rf = RandomForestClassifier(
-            n_estimators=self.n_estimators, random_state=self.seed
+            n_estimators=self.n_estimators, random_state=self.seed, n_jobs=self.n_jobs
         )
 
         features = pd.concat([X, y], axis=1)

src/aequitas/flow/methods/preprocessing/massaging.py

@@ -24,6 +24,7 @@ def __init__(
 
         self.classifier = instantiate_object(classifier, **classifier_args)
         self.logger.info(f"Created base estimator {self.classifier}")
+        self.used_in_inference = False
 
     def _rank(
         self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series]