def METHOD_NAME(self):
    """
    Testing split method of SkLearnDataManager
     - Test _subset_loader
    """
    with self.assertRaises(FedbiomedTypeError):
        self.sklearn_data_manager.split(test_ratio=-1.)
    with self.assertRaises(FedbiomedTypeError):
        self.sklearn_data_manager.split(test_ratio=2.)
    with self.assertRaises(FedbiomedTypeError):
        self.sklearn_data_manager.split(test_ratio='not-float')
    # Get number of samples
    n_samples = len(self.sklearn_data_manager.dataset()[0])
    ratio = 0.5
    n_test = math.floor(n_samples * ratio)
    n_train = n_samples - n_test
    loader_train, loader_test = self.sklearn_data_manager.split(test_ratio=ratio)
    msg_test = 'Number of samples of test loader is not as expected'
    msg_train = 'Number of samples of train loader is not as expected'
    self.assertEqual(len(loader_test.dataset), n_test, msg_test)
    self.assertEqual(len(loader_train.dataset), n_train, msg_train)
    # Test if test ratio is 1
    ratio = 1.
    loader_train, loader_test = self.sklearn_data_manager.split(test_ratio=ratio)
    self.assertEqual(len(loader_test.dataset), n_samples, msg_test)
    self.assertEqual(len(loader_train.dataset), 0)
    # Test if test ratio is 0
    ratio = 0.
    loader_train, loader_test = self.sklearn_data_manager.split(test_ratio=ratio)
    self.assertEqual(len(loader_test.dataset), 0, msg_test)
    self.assertEqual(len(loader_train.dataset), n_samples, msg_train)