text: import os
  import pathlib
  import pickle
  
  from config.load_config import get_config
  from config.config_utils import initialize_config_preproc
  from train_repeated_model_binary.run_repeated_training_binary import run_repeated_training_binary
  
  
  if __name__ == '__main__':
      # Configuring the files here for now
      cfg_template = get_config(filename=pathlib.Path(os.getcwd()).parent / 'config' / 'default_config.yml')
      cfg_template.user = 'jyao'
      cfg_template.load_mode = 'csv'
      cfg_template.overwrite = True
      cfg_template = initialize_config_preproc(cfg_template)
  
      # now load the actual cfg generated from the data
      vec_idx_patient = [1, 310]
      f_cfg_handle = "preproc_cfg_{}_{}.pkl".format(vec_idx_patient[0], vec_idx_patient[1])
      f_cfg = cfg_template.d_preproc / f_cfg_handle
      with open(str(f_cfg), 'rb') as handle:
          cfg_in = pickle.load(handle)
      cfg_in.vec_idx_patient = vec_idx_patient
  
      # name of particular feature that will be used
      # note if want to test for disease label then have to specify this to be 'disease'
      # otherwise it has to be one of ['IRF/SRF', 'Scar', 'GA', 'CNV', 'Large PED']
      cfg_in.str_feature = 'disease'
  
      # specify that here we are performing binary class training
      # binary_mode = 0 (normal vs NNV) / 1 (normal vs NV) / 2 (NNV vs NV)
      cfg_in.binary_class = True
      cfg_in.binary_mode = 1
  
      # whether or not to make the training set balanced - note this will give you imbalanced test set
      cfg_in.balanced = True
  
      # specify how many times to train the repeated models
      cfg_in.n_repeats = 10
  
      # now start the repeated training
      cfg_in.bool_debug = False
      cfg_in.str_arch = 'arch_025'
      run_repeated_training_binary(cfg_in)
  