code: def calculate_metrics(molecules, ensemble_lookup, filename, options):
      
      metric_List = []    # [(auc, auclow, auchigh), (fpf, ef, eflow, efhigh), (fpf, ef, eflow, efhigh), ..., ]
      sort_order = 'asc'
  
      # set up the appropriate score_structure data
      score_structure = classification.make_score_structure(molecules, ensemble_lookup[filename])
  
      # calculate auc values
      auc_structure = classification.make_auc_structure(score_structure)
      auc = classification.calculate_auc(auc_structure, sort_order)
      metric_List.append(auc)
  
      # calculate enrichment factor values
      for fpf in make_fpfList(options):
          fpf = float(fpf)
          ef_structure = classification.make_ef_structure(score_structure, fpf, sort_order)
          if ef_structure:
              ef = classification.calculate_ef(ef_structure, fpf, None, 'include_intervals')
              metric_List.append(ef)
  
      if options.write_roc:
          output.write_roc(auc_structure, filename, options)
  
      return metric_List
docs: Determine the virtual screening performance of the ensemble
      :param molecules: list [mol_object_1, mol_object_2, .... ]
      :param ensemble: tuple (receptor_x, receptor_y, .... )
      :param options: interface object that makes command line arguments available.
      :return: