code: def plot_tmleqini(
      df,
      inference_col,
      learner=LGBMRegressor(
          num_leaves=64, learning_rate=0.05, n_estimators=300
      ),
      outcome_col="y",
      treatment_col="w",
      p_col="tau",
      n_segment=5,
      cv=None,
      calibrate_propensity=True,
      ci=False,
      figsize=(8, 8),
  ):
      """Plot the qini chart based of TMLE estimation
  
      Args:
          df (pandas.DataFrame): a data frame with model estimates and actual data as columns
          inferenece_col (list of str): a list of columns that used in learner for inference
          learner (optional): a model used by TMLE to estimate the outcome
          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)
          p_col (str, optional): the column name for propensity score
          n_segment (int, optional): number of segment that TMLE will estimated for each
          cv (sklearn.model_selection._BaseKFold, optional): sklearn CV object
          calibrate_propensity (bool, optional): whether calibrate propensity score or not
          ci (bool, optional): whether return confidence intervals for ATE or not
      """
      plot_df = get_tmleqini(
          df,
          learner=learner,
          inference_col=inference_col,
          outcome_col=outcome_col,
          treatment_col=treatment_col,
          p_col=p_col,
          n_segment=n_segment,
          cv=cv,
          calibrate_propensity=calibrate_propensity,
          ci=ci,
      )
      if ci:
          model_names = [x.replace(" LB", "") for x in plot_df.columns]
          model_names = list(
              set([x.replace(" UB", "") for x in model_names])
          )
  
          fig, ax = plt.subplots(figsize=figsize)
          cmap = plt.get_cmap("tab10")
          cindex = 0
  
          for col in model_names:
              lb_col = col + " LB"
              up_col = col + " UB"
  
              if col != "Random":
                  ax.plot(
                      plot_df.index, plot_df[col], color=cmap(cindex)
                  )
                  ax.fill_between(
                      plot_df.index,
                      plot_df[lb_col],
                      plot_df[up_col],
                      color=cmap(cindex),
                      alpha=0.25,
                  )
              else:
                  ax.plot(
                      plot_df.index, plot_df[col], color=cmap(cindex)
                  )
              cindex += 1
  
          ax.legend()
      else:
          plot_df.plot(figsize=figsize)
  
      plt.xlabel("Population")
      plt.ylabel("Qini")
      plt.show()
  
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 11 for calling a global function with example usage: plot_tmleqini(df, inference_col, learner, outcome_col, treatment_col, p_col, n_segment, cv, calibrate_propensity, ci, figsize) without return types