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: Format the data for the Kamishima code and save it.
docstr_tok: ["Format","the","data","for","the","Kamishima","code","and","save","it","."]
code: def _create_file_in_kamishima_format(
      self,
      df,
      class_attr,
      positive_class_val,
      sensitive_attrs,
      single_sensitive,
      privileged_vals,
  ):
      """Format the data for the Kamishima code and save it."""
      x = []
      for col in df:
          if col != class_attr and col not in sensitive_attrs:
              x.append(np.array(df[col].values, dtype=np.float64))
      x.append(
          np.array(
              single_sensitive.isin(privileged_vals), dtype=np.float64
          )
      )
      x.append(
          np.array(
              df[class_attr] == positive_class_val, dtype=np.float64
          )
      )
  
      fd, name = tempfile.mkstemp()
      os.close(fd)
      np.savetxt(name, np.array(x).T)
      return name
  
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 2 for calling the PrejudiceRemover obj's underlying member method code with example usage: obj._create_file_in_kamishima_format(df, class_attr, positive_class_val, sensitive_attrs, single_sensitive, privileged_vals) and returns: name