class_docstr: This is the Auditor class. It is used in the training algorithm to repeatedly find subgroups that break the
  fairness disparity constraint. You can also use it independently as a stand alone auditor.
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docstr: Return the baseline FP or FN rate of the classifier predictions.
  
  Args:
      :param y: true labels (binary)
      :param predictions: predictions of classifier (soft predictions)
  
  Returns:
      :return: The baseline FP or FN rate of the classifier predictions
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code: def get_baseline(self, y, predictions):
      """Return the baseline FP or FN rate of the classifier predictions.
  
      Args:
          :param y: true labels (binary)
          :param predictions: predictions of classifier (soft predictions)
  
      Returns:
          :return: The baseline FP or FN rate of the classifier predictions
      """
      if self.fairness_def == "FP":
          return np.mean(
              [predictions[i] for i, c in enumerate(y) if c == 0]
          )
      elif self.fairness_def == "FN":
          return np.mean(
              [(1 - predictions[i]) for i, c in enumerate(y) if c == 1]
          )
  
file_path: AIF360/aif360/algorithms/inprocessing/gerryfair/auditor.py
imports: import numpy
  import pandas
  from sklearn import linear_model
  from aif360.algorithms.inprocessing.gerryfair.reg_oracle_class import RegOracle
  from aif360.algorithms.inprocessing.gerryfair import clean
label_desc: Use image node_id 3 for calling the Auditor obj's underlying member method code with example usage: obj.get_baseline(y, predictions) and returns: np, np