class_docstr: A post-processing class to adjust the prediction scores based on a threshold for
  multiple groups in the dataset.
  
  Parameters
  ----------
  threshold_type : str
      The type of threshold to apply. It can be one of the following:
          - fixed: applies a fixed threshold for all samples.
          - fpr: applies a threshold to obtain a specific false positive rate.
          - tpr: applies a threshold to obtain a specific true positive rate.
          - top_pct: applies a threshold to obtain the top percentage of predicted
                     scores.
          - top_k: applies a threshold to obtain the top k predicted scores.
  threshold_value : Union[float, int]
      The value to use for the threshold, depending on the threshold_type parameter.
  
  Attributes
  ----------
  thresholds : dict[str, Threshold]
      A dictionary of Threshold objects, one for each group in the dataset.
  
  Methods
  -------
  fit(X, y_hat, y, s=None)
      Fit the threshold for each group in the dataset.
  
  transform(X, y_hat, s=None)
      Transform the prediction scores based on the threshold for each group in the
      dataset.
docstr: Transform the prediction scores based on the threshold for each group in the
  dataset.
  
  Parameters
  ----------
  X : numpy.ndarray
      The input data.
  y_hat : pandas.Series
      The pre-transformed predictions.
  s : pandas.Series, optional
      The protected attribute.
  
  Returns
  -------
  pd.Series
      Transformed predicted scores.
code: def transform(
      self,
      X: pd.DataFrame,
      y_hat: pd.Series,
      s: Optional[pd.Series] = None,
  ) -> pd.Series:
      """Transform the prediction scores based on the threshold for each group in the
      dataset.
  
      Parameters
      ----------
      X : numpy.ndarray
          The input data.
      y_hat : pandas.Series
          The pre-transformed predictions.
      s : pandas.Series, optional
          The protected attribute.
  
      Returns
      -------
      pd.Series
          Transformed predicted scores.
      """
      if s is None:
          raise ValueError(
              "`s` must be provided to transform with a GroupThreshold."
          )
      predictions = []
      for group in s.unique():
          predictions.append(
              self.thresholds[group].transform(
                  X[s == group],
                  y_hat[s == group],
                  s[s == group],
              )
          )
      return pd.concat(predictions)
  
file_path: aequitas/src/aequitas/fairflow/methods/postprocessing/group_threshold.py
imports: from typing import Optional, Union
  import pandas
  from ...utils import create_logger
  from .threshold import Threshold
label_desc: Use image node_id 3 for calling the GroupThreshold obj's underlying member method code with example usage: obj.transform(X, y_hat, s) and returns: pd