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: Initialize the costs for CSC problem that corresponds to auditing. See paper for details.
  
  Args:
      :param self: object of class Auditor
      :param n: size of the dataset
  
  Return:
      :return The costs for labeling a point 0, for labeling a point 1, as tuples.
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code: def initialize_costs(self, n):
      """Initialize the costs for CSC problem that corresponds to auditing. See paper for details.
  
      Args:
          :param self: object of class Auditor
          :param n: size of the dataset
  
      Return:
          :return The costs for labeling a point 0, for labeling a point 1, as tuples.
      """
      costs_0 = None
      costs_1 = None
      if self.fairness_def == "FP":
          costs_0 = [0.0] * n
          costs_1 = [-1.0 / n * (2 * i - 1) for i in self.y_input]
  
      elif self.fairness_def == "FN":
          costs_1 = [0.0] * n
          costs_0 = [1.0 / n * (2 * i - 1) for i in self.y_input]
      return tuple(costs_0), tuple(costs_1), self.X_prime_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 2 for calling the Auditor obj's underlying member method code with example usage: obj.initialize_costs(n) and returns: tuple,  tuple,  self