class_docstr: Reject option based classification (ROC) post-processor.
  
  Reject option classification is a post-processing technique that gives
  favorable outcomes to unprivileged groups and unfavorable outcomes to
  privileged groups in a confidence band around the decision boundary with the
  highest uncertainty [#kamiran12]_.
  
  Note:
      A :class:`~sklearn.pipeline.Pipeline` expects a single estimation step
      but this class requires an estimator's predictions as input. See
      :class:`PostProcessingMeta` for a workaround.
  
  See also:
      :class:`PostProcessingMeta`, :class:`RejectOptionClassifierCV`
  
  References:
      .. [#kamiran12] `F. Kamiran, A. Karim, and X. Zhang, "Decision Theory
         for Discrimination-Aware Classification," IEEE International
         Conference on Data Mining, 2012.
         <https://ieeexplore.ieee.org/abstract/document/6413831>`_
  
  Attributes:
      prot_attr_ (str or list(str)): Protected attribute(s) used for post-
          processing.
      groups_ (array, shape (2,)): A list of group labels known to the
          classifier. Note: this algorithm require a binary division of the
          data.
      classes_ (array, shape (num_classes,)): A list of class labels known to
          the classifier. Note: this algorithm treats all non-positive
          outcomes as negative (binary classification only).
      pos_label_ (scalar): The label of the positive class.
      priv_group_ (scalar): The label of the privileged group.
  
  Examples:
      RejectOptionClassifier can be easily paired with GridSearchCV to
      find the best threshold and margin with respect to a fairness measure:
  
      >>> from sklearn.model_selection import GridSearchCV
      >>> roc = RejectOptionClassifier()
      >>> param = [{'threshold': [t],
                    'margin': np.arange(0.05, min(t, 1-t)+0.025, 0.05)}
      ...          for t in np.arange(0.05, 1., 0.05)]
      >>> stat_par = make_scorer(statistical_parity_difference)
      >>> scoring = {'bal_acc': 'balanced_accuracy', 'stat_par': stat_par}
      >>> def refit(cv_res):
      ...     return np.ma.array(cv_res['mean_test_bal_acc'],
      ...             mask=cv_res['mean_test_stat_par'] < -0.1).argmax()
      ...
      >>> grid = GridSearchCV(roc, param, scoring=scoring, refit=refit)
  
      Or, alternatively, this can be done in one step with
      RejectOptionClassifierCV:
  
      >>> grid = RejectOptionClassifierCV(scoring='statistical_parity')
docstr: Predict class labels for the given scores.
  
  In general, it is not necessary to fit and predict separately so this
  method may be used instead. For subsequent predicts, it may be easier
  to use the `predict` method, though.
  
  Args:
      X (pandas.DataFrame): Probability estimates of the targets as
          returned by a ``predict_proba()`` call or equivalent. Note: must
          include protected attributes in the index.
      y (array-like, optional): Ground-truth (correct) target values.
          Note: if not provided, `labels` must be provided in
          `**fit_params`. See `fit` for details.
      **fit_params: See `fit` for details.
  
  Returns:
      numpy.ndarray: Predicted class label per sample.
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code: def fit_predict(self, X, y=None, **fit_params):
      """Predict class labels for the given scores.
  
      In general, it is not necessary to fit and predict separately so this
      method may be used instead. For subsequent predicts, it may be easier
      to use the `predict` method, though.
  
      Args:
          X (pandas.DataFrame): Probability estimates of the targets as
              returned by a ``predict_proba()`` call or equivalent. Note: must
              include protected attributes in the index.
          y (array-like, optional): Ground-truth (correct) target values.
              Note: if not provided, `labels` must be provided in
              `**fit_params`. See `fit` for details.
          **fit_params: See `fit` for details.
  
      Returns:
          numpy.ndarray: Predicted class label per sample.
      """
      return self.fit(X, y, **fit_params).predict(X)
  
imports: import warnings
  import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.model_selection import GridSearchCV
  from sklearn.utils.validation import check_is_fitted
  from aif360.sklearn.metrics import statistical_parity_difference
  from aif360.sklearn.metrics import average_odds_error
  from aif360.sklearn.metrics import equal_opportunity_difference
  from aif360.sklearn.metrics import disparate_impact_ratio
  from aif360.sklearn.metrics import make_scorer
  from aif360.sklearn.utils import check_groups
label_desc: Use image node_id 6 for calling the RejectOptionClassifier obj's underlying member method code with example usage: obj.fit_predict(X, y) and returns: self