class_docstr: Prejudice remover is an in-processing technique that adds a
  discrimination-aware regularization term to the learning objective [6]_.
  
  References:
      .. [6] T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, "Fairness-Aware
         Classifier with Prejudice Remover Regularizer," Joint European
         Conference on Machine Learning and Knowledge Discovery in Databases,
         2012.
class_docstr_tok: ["Prejudice","remover","is","an","in-processing","technique","that","adds","a","discrimination-aware","regularization","term","to","the","learning","objective","[","6","]","_.","References",":","..","[","6","]","T.","Kamishima",",","S.","Akaho",",","H.","Asoh",",","and","J.","Sakuma",",","``","Fairness-Aware","Classifier","with","Prejudice","Remover","Regularizer",",","''","Joint","European","Conference","on","Machine","Learning","and","Knowledge","Discovery","in","Databases",",","2012","."]
docstr: Args:
      eta (double, optional): fairness penalty parameter
      sensitive_attr (str, optional): name of protected attribute
      class_attr (str, optional): label name
docstr_tok: ["Args",":","eta","(","double",",","optional",")",":","fairness","penalty","parameter","sensitive_attr","(","str",",","optional",")",":","name","of","protected","attribute","class_attr","(","str",",","optional",")",":","label","name"]
code: def __init__(self, eta=1.0, sensitive_attr="", class_attr=""):
      """
      Args:
          eta (double, optional): fairness penalty parameter
          sensitive_attr (str, optional): name of protected attribute
          class_attr (str, optional): label name
      """
      super(PrejudiceRemover, self).__init__(
          eta=eta, sensitive_attr=sensitive_attr, class_attr=class_attr
      )
      self.eta = eta
      self.sensitive_attr = sensitive_attr
      self.class_attr = class_attr
  
file_path: AIF360/aif360/algorithms/inprocessing/prejudice_remover.py
imports: import numpy
  import pandas
  import tempfile
  import os
  import subprocess
  from aif360.algorithms import Transformer
label_desc: Use image node_id 1 to create a new PrejudiceRemover object from inherited base classes: Transformer with example: obj = PrejudiceRemover(eta, sensitive_attr, class_attr)