text: import os 
  from random_forest import random_forest
  from evaluate import evaluate
  
  cwd             = os.path.dirname(os.path.realpath(__file__))
  path_train      = os.path.join(cwd,"example","semcor_mfs.csv")
  path_test       = os.path.join(cwd,"example","semeval2013_mfs.csv")
  general_results = os.path.join(cwd,"example",'results.csv')
  delimiter       = "\t"
  name_identifiers= "ID"
  name_targets    = "is_MFS"
  
  for features in [ ["MFS_confidence_UKB", "Entropy_sense_ranking_UKB","sense_entropy","pos","num_senses"],
                    ["MFS_confidence_UKB", "Entropy_sense_ranking_UKB", "sense_entropy"],
                    ["num_senses","pos"],
                    ["MFS_confidence_UKB", "pos"],
                    ["Entropy_sense_ranking_UKB","pos"]
                  ]:
      
      classifier_output,evaluation = random_forest(path_train,
                                                   path_test,
                                                   name_identifiers,
                                                   name_targets,
                                                   features,
                                                   delimiter,
                                                   num_cores=4)
      
      
      #add to general results
      headers = "\t".join(['settings'] + evaluation.keys())
      values  = "\t".join(["-".join(features)] + 
                          [str(value) for value in evaluation.values()
                           if isinstance(value,float)
                           ])
      
      if not os.path.isfile(general_results):
          with open(general_results,"w") as outfile:
              outfile.write(headers+"\n")
              
              classifier_output,baseline_values = evaluate(evaluation['gold'],
                                                           [1 for item in classifier_output], 
                                                           evaluation.keys())
              baseline  = "\t".join(['baseline'] + 
                          [str(value) for value in baseline_values.values()
                           if isinstance(value,float)])
              outfile.write(baseline+"\n")
              
      with open(general_results,"a") as outfile:
          outfile.write(values+"\n")
          