class_docstr: Reweighing is a preprocessing technique that Weights the examples in each
  (group, label) combination differently to ensure fairness before
  classification [4]_.
  
  References:
      .. [4] F. Kamiran and T. Calders,  "Data Preprocessing Techniques for
         Classification without Discrimination," Knowledge and Information
         Systems, 2012.
docstr: Obtain the necessary conditioning boolean vectors to compute
  instance level weights.
code: def _obtain_conditionings(self, dataset):
      """Obtain the necessary conditioning boolean vectors to compute
      instance level weights.
      """
      # conditioning
      priv_cond = utils.compute_boolean_conditioning_vector(
          dataset.protected_attributes,
          dataset.protected_attribute_names,
          condition=self.privileged_groups,
      )
      unpriv_cond = utils.compute_boolean_conditioning_vector(
          dataset.protected_attributes,
          dataset.protected_attribute_names,
          condition=self.unprivileged_groups,
      )
      fav_cond = dataset.labels.ravel() == dataset.favorable_label
      unfav_cond = dataset.labels.ravel() == dataset.unfavorable_label
  
      # combination of label and privileged/unpriv. groups
      cond_p_fav = np.logical_and(fav_cond, priv_cond)
      cond_p_unfav = np.logical_and(unfav_cond, priv_cond)
      cond_up_fav = np.logical_and(fav_cond, unpriv_cond)
      cond_up_unfav = np.logical_and(unfav_cond, unpriv_cond)
  
      return (
          priv_cond,
          unpriv_cond,
          fav_cond,
          unfav_cond,
          cond_p_fav,
          cond_p_unfav,
          cond_up_fav,
          cond_up_unfav,
      )
  
file_path: AIF360/aif360/algorithms/preprocessing/reweighing.py
imports: import numpy
  from aif360.algorithms import Transformer
  from aif360.metrics import utils
label_desc: Use image node_id 4 for calling the Reweighing obj's underlying member method code with example usage: obj._obtain_conditionings(dataset) and returns: priv_cond,  unpriv_cond,  fav_cond,  unfav_cond,  cond_p_fav,  cond_p_unfav,  cond_up_fav,  cond_up_unfav