text: import seaborn as sns
  from matplotlib.pyplot import figure
  
  from kts.feature_selection.selector import BuiltinImportance
  
  
  def plot_importances(experiment,
                       n_best=15,
                       sort_by="mean",
                       calculator=BuiltinImportance(),
                       fontsize=12):
      """Show feature importances of an experiment.
  
      Args:
        experiment: Experiment instance, like lb['012ABC']
        sort_by: one of 'max', 'mean', 'min', and 'std' (Default value = 'mean')
        calculator: ImportanceCalculator instance (Default value = BuiltinImportance())
        fontsize:  (Default value = 12)
        n_best: number of features to show (Default value = 15)
      """
  
      AGGS = ["max", "mean", "min", "std"]
      assert sort_by in AGGS
      importances = experiment.feature_importances(
          importance_calculator=calculator)
      n_best = min(n_best, importances.shape[1])
      tmp = importances.T.join(importances.agg(AGGS).T)
      tmp.sort_values(sort_by, ascending=False, inplace=True)
      tmp = tmp.head(n_best)
      tmp = tmp.drop(AGGS, axis=1).unstack().reset_index(level=1)
      tmp.columns = ['feature', 'importance']
      figure(figsize=(8, n_best / 2.5))
      a = sns.barplot(x="importance",
                      y="feature",
                      data=tmp,
                      capsize=0.2,
                      errwidth=1.5,
                      color='gray',
                      errcolor=sns.mpl.rcParams['axes.edgecolor'])
      a.spines['top'].set_visible(False)
      a.spines['right'].set_visible(False)
      a.spines['bottom'].set_visible(False)
      a.spines['left'].set_visible(False)
      a.tick_params(labelsize=fontsize)
      a.set_xlabel("importance", fontsize=fontsize)
      a.set_ylabel("feature", fontsize=fontsize)
      return a
  