code: def auuc_score(
      df,
      outcome_col="y",
      treatment_col="w",
      treatment_effect_col="tau",
      normalize=True,
      tmle=False,
      *args,
      **kwarg,
  ):
      """Calculate the AUUC (Area Under the Uplift Curve) score.
  
       Args:
          df (pandas.DataFrame): a data frame with model estimates and actual data as columns
          outcome_col (str, optional): the column name for the actual outcome
          treatment_col (str, optional): the column name for the treatment indicator (0 or 1)
          treatment_effect_col (str, optional): the column name for the true treatment effect
          normalize (bool, optional): whether to normalize the y-axis to 1 or not
  
      Returns:
          (float): the AUUC score
      """
  
      if not tmle:
          cumgain = get_cumgain(
              df,
              outcome_col,
              treatment_col,
              treatment_effect_col,
              normalize,
          )
      else:
          cumgain = get_tmlegain(
              df,
              outcome_col=outcome_col,
              treatment_col=treatment_col,
              *args,
              **kwarg,
          )
      return cumgain.sum() / cumgain.shape[0]
  
imports: from matplotlib import pyplot
  import logging
  import numpy
  import pandas
  import seaborn
  from lightgbm import LGBMRegressor
  from ..inference.meta.tmle import TMLELearner
label_desc: Use image node_id 12 for calling a global function with example usage: auuc_score(df, outcome_col, treatment_col, treatment_effect_col, normalize, tmle) and returns: unknown