text: # global settings
  data_root = "../../data/winequality_dataset/"
  feature_save_dir = data_root + "features/"
  img_save_dir = data_root + "imgs/"
  
  # split chunk settings
  split_chunk_path = data_root + 'chunk/'
  raw_train_file = dict(
      file_path=data_root + 'raw_data/train.csv',
      size=3397,
      split_mode=['train', 'valA', 'valB'],
      split_ratio=[0.6, 0.2, 0.2],
      chunk_size=200000,
      features_names=[
          dict(name='quality', type='int'),
          dict(name='fixed acidity', type='float'),
          dict(name='volatile acidity', type='float'),
          dict(name='citric acid', type='float'),
          dict(name='residual sugar', type='float'),
          dict(name='chlorides', type='float'),
          dict(name='free sulfur dioxide', type='float'),
          dict(name='total sulfur dioxide', type='float'),
          dict(name='density', type='float'),
          dict(name='pH', type='float'),
          dict(name='sulphates', type='float'),
          dict(name='alcohol', type='float'),
      ]
  )
  raw_test_file = dict(
      file_path=data_root + 'raw_data/test.csv',
      size=1500,
      split_mode=['test'],
      split_ratio=[1.0],
      chunk_size=200000,
      features_names=[
          dict(name='fixed acidity', type='float'),
          dict(name='volatile acidity', type='float'),
          dict(name='citric acid', type='float'),
          dict(name='residual sugar', type='float'),
          dict(name='chlorides', type='float'),
          dict(name='free sulfur dioxide', type='float'),
          dict(name='total sulfur dioxide', type='float'),
          dict(name='density', type='float'),
          dict(name='pH', type='float'),
          dict(name='sulphates', type='float'),
          dict(name='alcohol', type='float'),
      ]
  )
  other_train_files = []
  
  # features settings
  feature_mode = ['train']
  target_name = 'quality'
  id_name = 'id'
  feature_dict_file = feature_save_dir + "feature_dict_train.json"
  draw_feature = True
  style = 'darkgrid'
  
  # train settings
  train_type = 'Regressor'
  work_dirs = "./work_dirs/"
  dataset_name = "alcohol"
  balanced_data = False
  normalization = 'none'  # global, local, none
  random_state = 666
  train_mode = ['train', 'valA', 'valB']
  val_mode = ['valA', 'valB']
  train_models = [
      dict(name='ABT', random_state=random_state, params=dict()),
      dict(name='RF', random_state=random_state, params=dict()),
      dict(name='XGB', random_state=random_state, params=dict()),
      dict(name='GBT', random_state=random_state, params=dict()),
      dict(name='LGB', random_state=random_state, params=dict()),
      dict(name='DT', random_state=random_state, params=dict()),
      dict(name='ET', random_state=random_state, params=dict()),
      dict(name='KNN', random_state=random_state, params=None),
      dict(name='LR', random_state=random_state, params=dict()),
      # dict(name='GNB', random_state=random_state, params=dict()),
      # dict(name='SVM', random_state=random_state, params=dict()),
  ]
  