Removed a test for a model that no longer exists

    scores = model.evaluate(dataset, [regression_metric])

    assert scores[regression_metric.name] < .2

  @attr('slow')

  def test_ANI_multitask_regression_overfit(self):

    """Test ANI-1 regression overfits tiny data."""

    input_file = os.path.join(self.current_dir, "example_DTNN.mat")

    np.random.seed(123)

    tf.random.set_seed(123)

    dataset = scipy.io.loadmat(input_file)

    X = np.concatenate([np.expand_dims(dataset['Z'], 2), dataset['R']], axis=2)

    X = X[:, :13, :]

    y = dataset['T']

    w = np.ones_like(y)

    dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids=None)

    regression_metric = dc.metrics.Metric(

        dc.metrics.pearson_r2_score, mode="regression")

    n_tasks = y.shape[1]

    batch_size = 10

    transformers = [

        dc.trans.NormalizationTransformer(transform_y=True, dataset=dataset),

    ]

    for transformer in transformers:

      dataset = transformer.transform(dataset)

    model = dc.models.ANIRegression(

        n_tasks,

        13,

        atom_number_cases=[1, 6, 7, 8],

        batch_size=batch_size,

        learning_rate=ExponentialDecay(0.01, 0.7, 100),

        mode="regression")

    # Fit trained model

    model.fit(dataset, nb_epoch=500)

    # Eval model on train

    scores = model.evaluate(dataset, [regression_metric], transformers[0:1])

    assert scores[regression_metric.name] > .7

  @attr('slow')

  def test_DAG_singletask_regression_overfit(self):

    """Test DAG regressor multitask overfits tiny data."""