class_docstr: A collection of algorithms for construction of fair ranked candidate lists. [1]_ .
  
  References:
      .. [1] Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi,
          "Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search,"
          KDD '19: Proceedings of the 25th ACM SIGKDD International Conference
          on Knowledge Discovery & Data Mining, July 2019, Pages 2221-2231.
docstr: Args:
      unprivileged_groups (list(dict)): Representation for the unprivileged
          group.
      privileged_groups (list(dict)): Representation for the privileged
          group.
code: def __init__(self, unprivileged_groups, privileged_groups):
      """
      Args:
          unprivileged_groups (list(dict)): Representation for the unprivileged
              group.
          privileged_groups (list(dict)): Representation for the privileged
              group.
      """
  
      super(DeterministicReranking, self).__init__(
          unprivileged_groups=unprivileged_groups,
          privileged_groups=privileged_groups,
      )
  
      self.unprivileged_groups = unprivileged_groups
      self.privileged_groups = privileged_groups
      self._n_groups = len(unprivileged_groups) + len(privileged_groups)
      self.s = set(unprivileged_groups[0].keys())
      self.s_vals = set(self.unprivileged_groups[0].values()).union(
          set(self.privileged_groups[0].values())
      )
  
file_path: AIF360/aif360/algorithms/postprocessing/deterministic_reranking.py
imports: import numpy
  import pandas
  from aif360.algorithms import Transformer
  from aif360.datasets import StructuredDataset, RegressionDataset
label_desc: Use image node_id 1 to create a new DeterministicReranking object from inherited base classes: Transformer with example: obj = DeterministicReranking(unprivileged_groups, privileged_groups)