class_docstr: This class computes a set of feature balance measures that allow us to see
  whether each combination of a feature is receiving the positive outcome
  (true prediction) at balanced probability.
docstr: Returns feature balance measures for the given dataset,
  columns of interest, and label column.
  
  The following measures are computed:
  * Statistical Parity -
      https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29
  * Pointwise Mutual Information -
      https://en.wikipedia.org/wiki/Pointwise_mutual_information
  * Sorensen-Dice Coefficient -
      https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
  * Jaccard Index -
      https://en.wikipedia.org/wiki/Jaccard_index
  * Kendall Rank Correlation -
      https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
  * Log-Likelihood Ratio -
      https://en.wikipedia.org/wiki/Likelihood_function#Likelihood_ratio
  * t-test -
      https://en.wikipedia.org/wiki/Student%27s_t-test
  
  :param dataset: The dataset to compute the measures on.
  :type df: pd.DataFrame
  :return: A pandas DataFrame that contains four columns and has the
      format: <Feature Name>, <Class A value>, <Class B value>, <dict of
      measure name -> measure value key-value pairs>
  :rtype: pd.DataFrame
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code: def measures(self, dataset: pd.DataFrame) -> pd.DataFrame:
      """
      Returns feature balance measures for the given dataset,
      columns of interest, and label column.
  
      The following measures are computed:
      * Statistical Parity -
          https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29
      * Pointwise Mutual Information -
          https://en.wikipedia.org/wiki/Pointwise_mutual_information
      * Sorensen-Dice Coefficient -
          https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
      * Jaccard Index -
          https://en.wikipedia.org/wiki/Jaccard_index
      * Kendall Rank Correlation -
          https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
      * Log-Likelihood Ratio -
          https://en.wikipedia.org/wiki/Likelihood_function#Likelihood_ratio
      * t-test -
          https://en.wikipedia.org/wiki/Student%27s_t-test
  
      :param dataset: The dataset to compute the measures on.
      :type df: pd.DataFrame
      :return: A pandas DataFrame that contains four columns and has the
          format: <Feature Name>, <Class A value>, <Class B value>, <dict of
          measure name -> measure value key-value pairs>
      :rtype: pd.DataFrame
      """
      feature_balance_measures = [
          FeatureBalanceMeasures._get_measure_gaps_for_col(
              df=dataset,
              col_of_interest=col,
              label_col=self.label_col,
              pos_label=self.pos_label,
          )
          for col in self.cols_of_interest
      ]
      return pd.concat(feature_balance_measures)
  
imports: import itertools
  import warnings
  from typing import Callable, Dict, List, Tuple
  import numpy
  import pandas
  import scipy
  from responsibleai.databalanceanalysis.balance_measures import BalanceMeasures
  from responsibleai.databalanceanalysis.constants import Constants, Measures
label_desc: Use image node_id 2 for calling the FeatureBalanceMeasures obj's underlying member method code with example usage: obj.measures(dataset) and returns: pd