class_docstr: Model is an algorithm for learning classifiers that are fair with respect
  to rich subgroups.
  
  Rich subgroups are defined by (linear) functions over the sensitive
  attributes, and fairness notions are statistical: false positive, false
  negative, and statistical parity rates. This implementation uses a max of
  two regressions as a cost-sensitive classification oracle, and supports
  linear regression, support vector machines, decision trees, and kernel
  regression. For details see:
  
  References:
      .. [1] "Preventing Fairness Gerrymandering: Auditing and Learning for
         Subgroup Fairness." Michale Kearns, Seth Neel, Aaron Roth, Steven Wu.
         ICML '18.
      .. [2] "An Empirical Study of Rich Subgroup Fairness for Machine
         Learning". Michael Kearns, Seth Neel, Aaron Roth, Steven Wu. FAT '19.
docstr: Helper function to print outputs at each iteration of fit.
  
  Args:
      iteration: current iter
      error: most recent error
      group: most recent group found by the auditor
docstr_tok: ["Helper","function","to","print","outputs","at","each","iteration","of","fit",".","Args",":","iteration",":","current","iter","error",":","most","recent","error","group",":","most","recent","group","found","by","the","auditor"]
code: def print_outputs(self, iteration, error, group):
      """Helper function to print outputs at each iteration of fit.
  
      Args:
          iteration: current iter
          error: most recent error
          group: most recent group found by the auditor
      """
  
      if self.printflag:
          print(
              "iteration: {}, error: {}, fairness violation: {}, violated group size: {}".format(
                  int(iteration),
                  error,
                  group.weighted_disparity,
                  group.group_size,
              )
          )
  
file_path: AIF360/aif360/algorithms/inprocessing/gerryfair_classifier.py
imports: import copy
  from aif360.algorithms.inprocessing.gerryfair import heatmap
  from aif360.algorithms.inprocessing.gerryfair.clean import array_to_tuple
  from aif360.algorithms.inprocessing.gerryfair.learner import Learner
  from aif360.algorithms.inprocessing.gerryfair.auditor import *
  from aif360.algorithms.inprocessing.gerryfair.classifier_history import ClassifierHistory
  from aif360.algorithms import Transformer
label_desc: Use image node_id 4 for calling the GerryFairClassifier obj's underlying member method code with example usage: obj.print_outputs(iteration, error, group) without return types