class_docstr: This is the Auditor class. It is used in the training algorithm to repeatedly find subgroups that break the
  fairness disparity constraint. You can also use it independently as a stand alone auditor.
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docstr: Returns subset of dataset with y = 0 for FP and labels, or subset with y = 0 with flipped labels
  if the fairness_def is FN.
  
  Args:
      :param predictions: soft predictions of the classifier
  Returns:
      :return: X_prime_0: subset of features with y = 0
      :return: labels: the labels on y = 0 if FP else 1-labels.
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code: def get_subset(self, predictions):
      """Returns subset of dataset with y = 0 for FP and labels, or subset with y = 0 with flipped labels
      if the fairness_def is FN.
  
      Args:
          :param predictions: soft predictions of the classifier
      Returns:
          :return: X_prime_0: subset of features with y = 0
          :return: labels: the labels on y = 0 if FP else 1-labels.
      """
      if self.fairness_def == "FP":
          return self.X_prime_0, [
              a for u, a in enumerate(predictions) if self.y[u] == 0
          ]
      # handles FN rate by flipping labels
      elif self.fairness_def == "FN":
          return self.X_prime_0, [
              (1 - a)
              for u, a in enumerate(predictions)
              if self.y[u] == 0
          ]
  
file_path: AIF360/aif360/algorithms/inprocessing/gerryfair/auditor.py
imports: import numpy
  import pandas
  from sklearn import linear_model
  from aif360.algorithms.inprocessing.gerryfair.reg_oracle_class import RegOracle
  from aif360.algorithms.inprocessing.gerryfair import clean
label_desc: Use image node_id 5 for calling the Auditor obj's underlying member method code with example usage: obj.get_subset(predictions) and returns: self,  unknown, self,  unknown