class_docstr: It is a rolling based on DDG-DA
  
  **NOTE**
  before running the example, please clean your previous results with following command
  - `rm -r mlruns`
class_docstr_tok: ["It","is","a","rolling","based","on","DDG-DA","*","*","NOTE","*","*","before","running","the","example",",","please","clean","your","previous","results","with","following","command","-","`","rm","-r","mlruns","`"]
code: def _get_feature_importance(self):
      # this must be lightGBM, because it needs to get the feature importance
      task = self.basic_task(enable_handler_cache=False)
      task = self._adjust_task(task, astype="gbdt")
      task = replace_task_handler_with_cache(task, self.working_dir)
  
      with R.start(experiment_name="feature_importance"):
          model = init_instance_by_config(task["model"])
          dataset = init_instance_by_config(task["dataset"])
          model.fit(dataset)
  
      fi = model.get_feature_importance()
      # Because the model use numpy instead of dataframe for training lightgbm
      # So the we must use following extra steps to get the right feature importance
      df = dataset.prepare(
          segments=slice(None),
          col_set="feature",
          data_key=DataHandlerLP.DK_R,
      )
      cols = df.columns
      fi_named = {
          cols[int(k.split("_")[1])]: imp
          for k, imp in fi.to_dict().items()
      }
  
      return pd.Series(fi_named)
  
imports: from pathlib import Path
  import pickle
  from typing import Optional, Union
  import pandas
  import yaml
  from qlib.contrib.meta.data_selection.dataset import InternalData, MetaDatasetDS
  from qlib.contrib.meta.data_selection.model import MetaModelDS
  from qlib.data.dataset.handler import DataHandlerLP
  from qlib.model.meta.task import MetaTask
  from qlib.model.trainer import TrainerR
  from qlib.typehint import Literal
  from qlib.utils import init_instance_by_config
  from qlib.workflow import R
  from qlib.workflow.task.utils import replace_task_handler_with_cache
  from .base import Rolling
label_desc: Use image node_id 3 for calling the DDGDA obj's underlying member method code with example usage: obj._get_feature_importance() and returns: pd