code: def do_sparsity_check(self, cfs, query_instance, sparsity_weight):
      cfs = cfs.assign(sparsity=np.nan, distancesparsity=np.nan)
      for index, row in cfs.iterrows():
          cnt = 0
          for column in self.data_interface.continuous_feature_names:
              if not np.isclose(
                  row[column], query_instance[column].values[0]
              ):
                  cnt += 1
          for column in self.data_interface.categorical_feature_names:
              if row[column] != query_instance[column].values[0]:
                  cnt += 1
  
          cfs.at[index, "sparsity"] = cnt
  
      cfs["distance"] = (cfs["distance"] - cfs["distance"].min()) / (
          cfs["distance"].max() - cfs["distance"].min()
      )
      cfs["sparsity"] = (cfs["sparsity"] - cfs["sparsity"].min()) / (
          cfs["sparsity"].max() - cfs["sparsity"].min()
      )
      cfs["distancesparsity"] = (
          cfs["distance"] + sparsity_weight * cfs["sparsity"]
      )
      cfs = cfs.sort_values(by="distancesparsity")
      cfs = cfs.drop(
          ["distance", "sparsity", "distancesparsity"], axis=1
      )
  
      return cfs
  
file_path: DiCE/dice_ml/explainer_interfaces/dice_KD.py
imports: import copy
  import timeit
  import numpy
  import pandas
  from dice_ml import diverse_counterfactuals
  from dice_ml.constants import ModelTypes
  from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
label_desc: Use image node_id 5 for calling the DiceKD obj's underlying member method code with example usage: obj.do_sparsity_check(cfs, query_instance, sparsity_weight) and returns: cfs