code: def df_bias_amplification(
      y_true,
      y_pred,
      *,
      prot_attr=None,
      pos_label=1,
      concentration=1.0,
      sample_weight=None,
  ):
      r"""Differential fairness bias amplification.
  
      Measures the increase in unfairness attributable to a classifier compared to
      the original data. See [#foulds18]_ for more details.
  
      Args:
          y_true (pandas.Series): Ground truth (correct) target values.
          y_pred (array-like): Estimated targets as returned by a classifier.
          prot_attr (array-like, keyword-only): Protected attribute(s). If
              ``None``, all protected attributes in y_true are used.
          pos_label (scalar, optional): The label of the positive class.
          concentration (scalar, optional): Dirichlet smoothing concentration
              parameter :math:`|R_Y|\alpha` (must be non-negative).
          sample_weight (array-like, optional): Sample weights.
  
      Returns:
          float: Difference in smoothed EDF between the classifier and the
          original dataset, :math:`\epsilon_{\text{classifier}}
          - \epsilon_{\text{data}}`. Lower is better.
  
      References:
          .. [#foulds18] J. R. Foulds, R. Islam, K. N. Keya, and S. Pan,
             "An Intersectional Definition of Fairness," arXiv preprint
             arXiv:1807.08362, 2018.
      """
      eps_true = smoothed_edf(
          y_true,
          prot_attr=prot_attr,
          pos_label=pos_label,
          concentration=concentration,
          sample_weight=sample_weight,
      )
      eps_pred = smoothed_edf(
          y_true,
          y_pred,
          prot_attr=prot_attr,
          pos_label=pos_label,
          concentration=concentration,
          sample_weight=sample_weight,
      )
      return eps_pred - eps_true
  
imports: from itertools import permutations
  from typing import Union
  import warnings
  import numpy
  import pandas
  from scipy.special import rel_entr
  from sklearn.metrics import make_scorer, recall_score, precision_score
  from sklearn.metrics import multilabel_confusion_matrix
  from sklearn.metrics._classification import _prf_divide, _check_zero_division
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_X_y
  from aif360.metrics import ot_metric
  from aif360.sklearn.utils import check_inputs, check_groups
  from aif360.detectors.mdss.ScoringFunctions import BerkJones, Bernoulli
  from aif360.detectors.mdss.MDSS import MDSS
label_desc: Use image node_id 27 for calling a global function with example usage: df_bias_amplification(y_true, y_pred) and returns: unknown