update results

    help='Choice of random seed')

args = parser.parse_args()

#Datasets and models used in the benchmark test

splitters = args.splitter_args

splits = args.splitter_args

models = args.model_args

datasets = args.dataset_args

if len(args.seed_args) > 0:

else:

  seed = 123

if len(splitters) == 0:

  splitters = ['random']

if len(splits) == 0:

  splits = ['random']

if len(models) == 0:

  models = [

      'tf', 'tf_robust', 'logreg', 'graphconv', 'irv', 'tf_regression',

    }

out_path = '.'

for dataset in datasets:

  for split in splitters:

  for split in splits:

    for model in models:

      with open(os.path.join(out_path, dataset + model + '.pkl'), 'r') as f:

        hyper_parameters = pickle.load(f)