Cleaning cruft

    # Eval model on test

    evaluator = Evaluator(model, test_dataset, transformers, verbosity=True)

    _ = evaluator.compute_model_performance(classification_metrics)

  '''

  # TODO(rbharath): This fails on many systems with an Illegal Instruction

  # error. Need to debug.

  from deepchem.featurizers.nnscore import NNScoreComplexFeaturizer

  def test_singletask_keras_mlp_NNScore_regression_API(self):

    """Test of singletask MLP NNScore regression API."""

    from deepchem.models.keras_models.fcnet import SingleTaskDNN

    splittype = "scaffold"

    compound_featurizers = []

    complex_featurizers = [NNScoreComplexFeaturizer()]

    input_transformers = [NormalizationTransformer, ClippingTransformer]

    output_transformers = [NormalizationTransformer]

    tasks = ["label"]

    task_type = "regression"

    task_types = {task: task_type for task in tasks}

    model_params = {"nb_hidden": 10, "activation": "relu",

                    "dropout": .5, "learning_rate": .01,

                    "momentum": .9, "nesterov": False,

                    "decay": 1e-4, "batch_size": 5,

                    "nb_epoch": 2, "init": "glorot_uniform",

                    "nb_layers": 1, "batchnorm": False}

    input_file = "nnscore_example.pkl.gz"

    protein_pdb_field = "protein_pdb"

    ligand_pdb_field = "ligand_pdb"

    train_dataset, test_dataset, _, transformers = self._featurize_train_test_split(

        splittype, compound_featurizers, 

        complex_featurizers, input_transformers,

        output_transformers, input_file, tasks,

        protein_pdb_field=protein_pdb_field,

        ligand_pdb_field=ligand_pdb_field)

    model_params["data_shape"] = train_dataset.get_data_shape()

    model = SingleTaskDNN(task_types, model_params)

    self._create_model(train_dataset, test_dataset, model, transformers)

  #TODO(enf/rbharath): This should be uncommented now that 3D CNNs are in

  #                    keras. Need to upgrade the base version of keras for

  #                    deepchem.

  def test_singletask_cnn_GridFeaturizer_regression_API(self):

    """Test of singletask 3D ConvNet regression API."""

    splittype = "scaffold"

    compound_featurizers = []

    complex_featurizers = [GridFeaturizer(feature_types=["voxel_combined"],

                                          voxel_feature_types=["ecfp", "splif", "hbond",

                                                               "pi_stack", "cation_pi",

                                                               "salt_bridge", "charge"],

                                          voxel_width=0.5)]

    input_transforms = ["normalize", "truncate"]

    output_transforms = ["normalize"]

    task_type = "regression"

    model_params = {"nb_hidden": 10, "activation": "relu",

                    "dropout": .5, "learning_rate": .01,

                    "momentum": .9, "nesterov": False,

                    "decay": 1e-4, "batch_size": 5,

                    "nb_epoch": 2, "init": "glorot_uniform",

                    "loss_function": "mean_squared_error"}

    model_name = "convolutional_3D_regressor"

    protein_pdb_field = "protein_pdb"

    ligand_pdb_field = "ligand_pdb"

    input_file = "nnscore_example.pkl.gz"

    tasks = ["label"]

    self._create_model(splittype, compound_featurizers, complex_featurizers, input_transforms,

                       output_transforms, task_type, model_params, model_name,

                       input_file=input_file,

                       protein_pdb_field=protein_pdb_field,

                       ligand_pdb_field=ligand_pdb_field,

                       tasks=tasks)

    '''