class_docstr: Weighted ensemble meta-model that implements Ensemble Selection: https://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
  
  A :class:`autogluon.core.models.GreedyWeightedEnsembleModel` must be specified as the `model_base` to properly function.
code: def compute_feature_importance(
      self, X, y, features=None, is_oof=True, **kwargs
  ) -> pd.DataFrame:
      logger.warning(
          "Warning: non-raw feature importance calculation is not valid for weighted ensemble since it does not have features, returning ensemble weights instead..."
      )
      if is_oof:
          fi = pd.Series(self._get_model_weights()).sort_values(
              ascending=False
          )
      else:
          logger.warning(
              "Warning: Feature importance calculation is not yet implemented for WeightedEnsembleModel on unseen data, returning generic feature importance..."
          )
          fi = pd.Series(self._get_model_weights()).sort_values(
              ascending=False
          )
  
      fi_df = fi.to_frame(name="importance")
      fi_df["stddev"] = np.nan
      fi_df["p_score"] = np.nan
      fi_df["n"] = np.nan
  
      # TODO: Rewrite preprocess() in greedy_weighted_ensemble_model to enable
      # fi_df = super().compute_feature_importance(X=X, y=y, features_to_use=features_to_use, preprocess=preprocess, is_oof=is_oof, **kwargs)
      return fi_df
  
file_path: autogluon/core/src/autogluon/core/models/ensemble/weighted_ensemble_model.py
imports: import logging
  from collections import defaultdict
  import numpy
  import pandas
  from ..greedy_ensemble.greedy_weighted_ensemble_model import GreedyWeightedEnsembleModel
  from .stacker_ensemble_model import StackerEnsembleModel
label_desc: Use image node_id 4 for calling the WeightedEnsembleModel obj's underlying member method code with example usage: obj.compute_feature_importance(X, y, features, is_oof) and returns: fi_df