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","`"]
docstr: Dump data for training meta model.
  The meta model will be trained upon the proxy forecasting model.
  This dataset is for the proxy forecasting model.
docstr_tok: ["Dump","data","for","training","meta","model",".","The","meta","model","will","be","trained","upon","the","proxy","forecasting","model",".","This","dataset","is","for","the","proxy","forecasting","model","."]
code: def _dump_data_for_proxy_model(self):
      """
      Dump data for training meta model.
      The meta model will be trained upon the proxy forecasting model.
      This dataset is for the proxy forecasting model.
      """
      topk = 30
      fi = self._get_feature_importance()
      col_selected = fi.nlargest(topk)
      # NOTE: adjusting to `self.sim_task_model` just for aligning with previous implementation.
      task = self._adjust_task(
          self.basic_task(enable_handler_cache=False),
          self.sim_task_model,
      )
      task = replace_task_handler_with_cache(task, self.working_dir)
  
      dataset = init_instance_by_config(task["dataset"])
      prep_ds = dataset.prepare(
          slice(None),
          col_set=["feature", "label"],
          data_key=DataHandlerLP.DK_L,
      )
  
      feature_df = prep_ds["feature"]
      label_df = prep_ds["label"]
  
      feature_selected = feature_df.loc[:, col_selected.index]
  
      feature_selected = feature_selected.groupby(
          "datetime", group_keys=False
      ).apply(lambda df: (df - df.mean()).div(df.std()))
      feature_selected = feature_selected.fillna(0.0)
  
      df_all = {
          "label": label_df.reindex(feature_selected.index),
          "feature": feature_selected,
      }
      df_all = pd.concat(df_all, axis=1)
      df_all.to_pickle(self.working_dir / "fea_label_df.pkl")
  
      # dump data in handler format for aligning the interface
      handler = DataHandlerLP(
          data_loader={
              "class": "qlib.data.dataset.loader.StaticDataLoader",
              "kwargs": {
                  "config": self.working_dir / "fea_label_df.pkl"
              },
          }
      )
      handler.to_pickle(self.working_dir / self.proxy_hd, dump_all=True)
  
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 4 for calling the DDGDA obj's underlying member method code with example usage: obj._dump_data_for_proxy_model() without return types