text: import datetime
  
  import trainer.corpora as crp
  import trainer.features as ftr
  import trainer.classifier_test_verbose as cls
  import os
  
  # vars
  type = "final"
  nltk_run = False
  sklearn_run = True
  COUNT = 25000
  cut = int((COUNT / 2) * 4 / 5)
  array = [15000]
  
  def run(dataset):
  
      nlt = dict()
      skl = dict()
  
      dir = "output/" + dataset + "/" + type + "/"
      os.makedirs(dir, exist_ok=True)
  
      # file
      for variable in array:
          var_name = str(variable)
  
          if nltk_run:
              nlt_file = dir + dataset + "-" + type + "-" + var_name + "-nlt.csv"
              nlt[var_name] = open(nlt_file, 'a')
              nlt[var_name].write(str(datetime.datetime.today()) + "\n")
  
          if sklearn_run:
              skl_file = dir + dataset + "-" + type + "-" + var_name + "-skl.csv"
              skl[var_name] = open(skl_file, 'a')
              skl[var_name].write(str(datetime.datetime.today()) + "\n")
  
      # cycle
      for x in range(0, 10):
          print(x)
          corpora = crp.Corpora(dataset, count=COUNT, shuffle=True)
  
          for variable in array:
              print(str(variable))
              var_name = str(variable)
              features = ftr.Features(corpora, total=COUNT, bigram=True, stop=False, stem="porter", lower=True, inf_count=variable)
  
              posfeats = features.get_features_pos()
              negfeats = features.get_fearures_neg()
  
              trainfeats = negfeats[:cut] + posfeats[:cut]
              testfeats = negfeats[cut:] + posfeats[cut:]
  
              nlt_output, skl_output = cls.train(trainfeats, testfeats, dataset, nlt=nltk_run, skl=sklearn_run)
  
              # if nltk_run:
              #     print(str(nlt_output))
              #     nlt[var_name].write(nlt_output)
              #     nlt[var_name].flush()
              # if sklearn_run:
              #     print(str(skl_output))
              #     skl[var_name].write(skl_output)
              #     skl[var_name].flush()
  
  
  
  dataset_array = ["stanford"]
  
  for dataset in dataset_array:
      run(dataset)