docstr: Calculates feature importances for each treatment group, based on specified method in __init__.
docstr_tok: ["Calculates","feature","importances","for","each","treatment","group",",","based","on","specified","method","in","__init__","."]
code: def get_importance(self):
      """
      Calculates feature importances for each treatment group, based on specified method in __init__.
      """
      importance_catalog = {
          "auto": self.default_importance,
          "permutation": self.perm_importance,
      }
      importance_dict = importance_catalog[self.method]()
  
      importance_dict = {
          group: pd.Series(array, index=self.features).sort_values(
              ascending=False
          )
          for group, array in importance_dict.items()
      }
      return importance_dict
  
imports: import pandas
  import shap
  import matplotlib.pyplot
  from lightgbm import LGBMRegressor
  from sklearn.inspection import permutation_importance
  from sklearn.model_selection import train_test_split
  from copy import deepcopy
  from causalml.inference.meta.utils import convert_pd_to_np
label_desc: Use image node_id 5 for calling the Explainer obj's underlying member method code with example usage: obj.get_importance() and returns: importance_dict