class_docstr: Class for computing metrics based on a single
  :obj:`~aif360.datasets.BinaryLabelDataset`.
class_docstr_tok: ["Class","for","computing","metrics","based","on","a","single",":","obj",":","`","~aif360.datasets.BinaryLabelDataset","`","."]
docstr: .. math::
     \frac{Pr(Y = 1 | D = \text{unprivileged})}
     {Pr(Y = 1 | D = \text{privileged})}
docstr_tok: ["..","math",":",":","\\frac","{","Pr","(","Y","=","1","|","D","=","\\text","{","unprivileged","}",")","}","{","Pr","(","Y","=","1","|","D","=","\\text","{","privileged","}",")","}"]
code: def disparate_impact(self):
      r"""
      .. math::
         \frac{Pr(Y = 1 | D = \text{unprivileged})}
         {Pr(Y = 1 | D = \text{privileged})}
      """
      return self.ratio(self.base_rate)
  
imports: import numpy
  from sklearn.neighbors import NearestNeighbors
  from aif360.algorithms.inprocessing.gerryfair.auditor import Auditor
  from aif360.datasets import BinaryLabelDataset
  from aif360.datasets.multiclass_label_dataset import MulticlassLabelDataset
  from aif360.metrics import DatasetMetric, utils
  from aif360.algorithms.inprocessing.gerryfair.clean import *
label_desc: Use image node_id 5 for calling the BinaryLabelDatasetMetric obj's underlying member method code with example usage: obj.disparate_impact() and returns: self