class_docstr: Double Ensemble Model
docstr: get feature importance
  
  Notes
  -----
      parameters reference:
      https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html?highlight=feature_importance#lightgbm.Booster.feature_importance
docstr_tok: ["get","feature","importance","Notes","--","--","-","parameters","reference",":","https",":","\/\/lightgbm.readthedocs.io\/en\/latest\/pythonapi\/lightgbm.Booster.html","?","highlight=feature_importance","#","lightgbm.Booster.feature_importance"]
code: def get_feature_importance(self, *args, **kwargs) -> pd.Series:
      """get feature importance
  
      Notes
      -----
          parameters reference:
          https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html?highlight=feature_importance#lightgbm.Booster.feature_importance
      """
      res = []
      for _model, _weight in zip(self.ensemble, self.sub_weights):
          res.append(
              pd.Series(
                  _model.feature_importance(*args, **kwargs),
                  index=_model.feature_name(),
              )
              * _weight
          )
      return (
          pd.concat(res, axis=1, sort=False)
          .sum(axis=1)
          .sort_values(ascending=False)
      )
  
imports: import lightgbm
  import numpy
  import pandas
  from typing import Text, Union
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...model.interpret.base import FeatureInt
  from ...log import get_module_logger
label_desc: Use image node_id 11 for calling the DEnsembleModel obj's underlying member method code with example usage: obj.get_feature_importance() and returns: pd