1b6587331ec77d295e2261de12e13974b7d1a46a,aif360/algorithms/inprocessing/meta_fair_classifier.py,MetaFairClassifier,fit,#MetaFairClassifier#,46

Before Change

            PrejudiceRemover: Returns self.
                

        data = np.column_stack([dataset.features, dataset.labels])
        columns = dataset.feature_names + dataset.label_names
        train_df = pd.DataFrame(data=data, columns=columns)


        x_train = dataset.features
        //print([dataset.favorable_label])
        y_train = np.array([1 if y == [dataset.favorable_label] else -1 for y in dataset.labels])
        x_control_train = np.array(train_df[self.sensitive_attr])
        //print(x_train, y_train, x_control_train)

        all_sensitive_attributes = dataset.protected_attribute_names

        if not self.sensitive_attr:
            self.sensitive_attr = all_sensitive_attributes[0]

        model_name = self.obj.getModel(self.tau, x_train, y_train, x_control_train)


        self.model_name = model_name
        return self

    def predict(self, dataset):

After Change

        x_train = dataset.features
        y_train = np.array([1 if y == [dataset.favorable_label] else
                           -1 for y in dataset.labels])
        x_control_train = x_train[:, sens_index].copy()

        self.model = self.obj.getModel(self.tau, x_train, y_train,
            x_control_train)

Instances