class_docstr: A class for feature importance methods.
class_docstr_tok: ["A","class","for","feature","importance","methods","."]
docstr: Calculate the multi-treatment unconditional D (one node)
  with Euclidean Distance as split Evaluation function.
  
  Args:
      nodeSummary (dict): a dictionary containing the statistics for a tree node sample
      control_group (string, optional, default='control'): the name for control group
docstr_tok: ["Calculate","the","multi-treatment","unconditional","D","(","one","node",")","with","Euclidean","Distance","as","split","Evaluation","function",".","Args",":","nodeSummary","(","dict",")",":","a","dictionary","containing","the","statistics","for","a","tree","node","sample","control_group","(","string",",","optional",",","default='control","'",")",":","the","name","for","control","group"]
code: def _evaluate_ED(nodeSummary, control_group="control"):
      """
      Calculate the multi-treatment unconditional D (one node)
      with Euclidean Distance as split Evaluation function.
  
      Args:
          nodeSummary (dict): a dictionary containing the statistics for a tree node sample
          control_group (string, optional, default='control'): the name for control group
      """
      if control_group not in nodeSummary:
          return 0
      pc = nodeSummary[control_group][0]
      d_res = 0
      for treatment_group in nodeSummary:
          if treatment_group != control_group:
              d_res += 2 * (nodeSummary[treatment_group][0] - pc) ** 2
      return d_res
  
imports: import numpy
  import pandas
  import statsmodels.api
  from scipy import stats
  from sklearn.impute import SimpleImputer
  import warnings
label_desc: Use image node_id 9 for calling the FilterSelect obj's underlying member method code with example usage: obj._evaluate_ED(nodeSummary, control_group) and returns: d_res, int