text: import argparse
  from jnt.isas.taxo import TaxonomyFeatures, TaxonomyResources
  from jnt.isas.predictors import TaxonomyPredictor
  from glob import glob
  from traceback import format_exc
  from os.path import join
  
  
  
  RES_DIR = "./resources"
  CLASSIFIERS_DIR = join(RES_DIR,"models/release/2features-new/*")
  
  
  def load_res(language, mode, test_en=False):
      if language == "en":
          if mode == "simple": freq_fpaths=[""]
          else: freq_fpaths = [join(RES_DIR,"en_freq-59g-mwe62m.csv.gz")]
  
          if test_en:
              isa_common_fpaths = [join(RES_DIR,"en_ps.csv.gz")]
          else:
              isa_common_fpaths = [
                     join(RES_DIR,"en_ma.csv.gz"),
                     join(RES_DIR,"en_pm.csv.gz"),
                     join(RES_DIR,"en_ps.csv.gz"),
                     join(RES_DIR,"en_cc.csv.gz"),
                     join(RES_DIR,"en_ps59g.csv.gz")]
  
          isa_domain_fpaths = {
              "food": [join(RES_DIR,"en_food.csv.gz")],
              "science": [join(RES_DIR,"en_science.csv.gz")],
              "environment": [join(RES_DIR,"en_environment.csv.gz")]}
  
      elif language == "fr":
          freq_fpaths=[""]
          isa_common_fpaths = [join(RES_DIR,"fr.csv.gz")]
          isa_domain_fpaths = {
              "food": [join(RES_DIR,"fr_food.csv.gz")],
              "science": [join(RES_DIR,"fr_science.csv.gz")],
              "environment": [join(RES_DIR,"fr_environment.csv.gz")]}
  
      elif language == "nl":
          freq_fpaths=[""]
          isa_common_fpaths = [join(RES_DIR,"nl.csv.gz")]
          isa_domain_fpaths = {
              "food": [join(RES_DIR,"nl_food.csv.gz")],
              "science": [join(RES_DIR,"nl_science.csv.gz")],
              "environment": [join(RES_DIR,"nl_environment.csv.gz")]}
  
      elif language == "it":
          freq_fpaths=[""]
          isa_common_fpaths = [join(RES_DIR,"it.csv.gz")]
          isa_domain_fpaths = {
              "food": [join(RES_DIR,"it_food.csv.gz")],
              "science": [join(RES_DIR,"it_science.csv.gz")],
              "environment": [join(RES_DIR,"it_environment.csv.gz")]}
  
      taxo_res_domain = {}
      for domain in isa_domain_fpaths:
          taxo_res_domain[domain] = TaxonomyResources(freq_fpaths=[], isa_fpaths=isa_domain_fpaths[domain])
      taxo_res_common = TaxonomyResources(freq_fpaths=freq_fpaths, isa_fpaths=isa_common_fpaths)
  
      return taxo_res_common, taxo_res_domain
  
  
  def get_taxo_res_domain_voc(taxo_res_domain, voc_fpath):
      for domain in taxo_res_domain.keys():
          if domain in voc_fpath:
              print(voc_fpath, "is", domain)
              return taxo_res_domain[domain]
  
      print("Warning: domain not found for", voc_fpath)
      return TaxonomyResources()
  
  
  def combine_taxo_res(taxo_res1, taxo_res2):
      taxo_res12 = TaxonomyResources()
  
      taxo_res12._isas = taxo_res1._isas.copy()
      taxo_res12._isas.update(taxo_res2._isas)
  
      taxo_res12._freqs = taxo_res1._freqs.copy()
      taxo_res12._freqs.update(taxo_res2._freqs)
  
      return taxo_res12
  
  
  def evaluate_on_trial_taxo():
      relations_fpath = join(RES_DIR,"relations.csv")  # assuming features "hyper_in_hypo_i" and "hypo2hyper_substract"
      taxo_fpath = relations_fpath + "-taxo.csv"
      print("Relations:", relations_fpath)
      print("Unpruned taxonomy:", taxo_fpath)
  
      taxo_features = TaxonomyFeatures(TaxonomyResources(), relations_fpath=relations_fpath, lang="en")
      taxo_predict = TaxonomyPredictor(taxo_features)
      taxo_predict.predict_by_global_threshold(threshold=0, field="hypo2hyper_substract", or_correct_predict=False)
      taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
      taxo_predict.save(taxo_fpath)
      taxo_predict.evaluate(field="correct_predict")
  
      for max_knn in [1, 2, 3, 5]:
          taxo_knn_fpath = relations_fpath + "-taxo-knn" + str(max_knn) + ".csv"
          taxo_predict.predict_by_local_threshold(threshold=0, max_knn=max_knn, field="hypo2hyper_substract", or_correct_predict=False)
          taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
          taxo_predict.save(taxo_knn_fpath)
          taxo_predict.evaluate(field="correct_predict")
  
  
  def extract_semeval_taxo(input_voc_pattern, language, mode, classifiers_pattern, test_en):
      #Laedt alle Datensaetze(auch alle Domaenen, aus vocabularies)
      taxo_res_common, taxo_res_domain = load_res(language, mode, test_en)
  
      for voc_fpath in sorted(glob(input_voc_pattern)):
          for space in [False, True]:
              s = "-space" if space else ""
              relations_fpath = voc_fpath + s + "-relations.csv"
              taxo_fpath = relations_fpath + "-taxo.csv"
              print("\n", voc_fpath, "\n", "="*50)
              print("Relations:", relations_fpath)
              print("Unpruned taxonomy:", taxo_fpath)
  
              #Laedt domain-datenset und kombiniert sie mit dem allgemeinen Datenset
              taxo_res_domain_voc = get_taxo_res_domain_voc(taxo_res_domain, voc_fpath)
              taxo_res_voc = combine_taxo_res(taxo_res_common, taxo_res_domain_voc)
              taxo_features = TaxonomyFeatures(taxo_res_voc, voc_fpath, lang=language)
  
              if mode == "simple":
                  taxo_features.fill_direct_isas()
                  taxo_features.fill_substrings(must_have_space=space)
                  taxo_features.hypo2hyper_ratio()
                  taxo_predict = TaxonomyPredictor(taxo_features)
                  taxo_predict.predict_by_global_threshold(threshold=0, field="hypo2hyper_substract", or_correct_predict=False)
                  taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
                  taxo_predict.save(taxo_fpath)
  
                  for max_knn in [1, 2, 3, 5]:
                      #hypo2hyper fuer pattern
                      #hyperinhypoi feur substring
                      taxo_knn_fpath = relations_fpath + "-taxo-knn" + str(max_knn) + ".csv"
                      taxo_predict.predict_by_local_threshold(threshold=0, max_knn=max_knn, field="hypo2hyper_substract", or_correct_predict=False)
                      taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
                      taxo_predict.save(taxo_knn_fpath)
  
              elif mode == "super":
                  taxo_features.fill_super_features()
  
                  for classifier_dir in glob(classifiers_pattern):
                      try:
                          print("Predicting with:", classifier_dir)
                          taxo_predict = TaxonomyPredictor(taxo_features)
                          method = taxo_predict.predict_by_classifier(classifier_dir)
                          taxo_predict.save(taxo_fpath + "-" + method + ".csv")
                          taxo_predict.save(taxo_fpath + "-" + method + "-conf.csv", conf=True)
                      except:
                          print(format_exc())
  
  
  def main():
      parser = argparse.ArgumentParser(description="Apply classifiers to the trial data.")
      parser.add_argument('input', help='Input vocabulary pattern e.g. "/home/en/*_en.csv"')
      parser.add_argument('language', type=str, default='en', choices=['en', 'fr', 'nl', 'it'], help='Path to an input file.')
      parser.add_argument('mode', type=str, default='simple', choices=['simple', 'super'], help="Mode of the taxonomy induction system. Use 'simple' for the unsupervised method, 'super' for supervised method and 'test' for a quick test.")
      parser.add_argument('--test', action='store_true', help="Load only few resouses, but do it quickly (works only for English).")
      parser.add_argument('-c', help='Path to the classifier or a pattern to the classifiers e.g. "/home/*".', default=CLASSIFIERS_DIR)
      args = parser.parse_args()
  
      print("Input: ", args.input)
      print("Language: ", args.language)
      print("Mode: ", args.mode)
      print("Classifiers: ", args.c)
      print("Test model: ", args.test)
  
      if args.mode in ["simple", "super"]:
          extract_semeval_taxo(args.input, args.language, args.mode, args.c, args.test)
      else:
          evaluate_on_trial_taxo()
  
  if __name__ == '__main__':
      main()
  