4a1b752726028f02bd04fc7b766b048a859e7f97,deepchem/models/tests/test_api.py,TestModelAPI,test_singletask_keras_mlp_USF_regression_API,#TestModelAPI#,185

Before Change

  def test_singletask_keras_mlp_USF_regression_API(self):
    Test of singletask MLP User Specified Features regression API.
    from deepchem.models.keras_models.fcnet import SingleTaskDNN
    splittype = "scaffold"
    featurizers = []
    input_transformers = [NormalizationTransformer, ClippingTransformer]
    output_transformers = [NormalizationTransformer]
    feature_types = ["user_specified_features"]
    user_specified_features = ["evals"]
    tasks = ["u0"]
    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 = "gbd3k.pkl.gz"
    protein_pdb_field = None
    ligand_pdb_field = None
    train_dataset, test_dataset, _, transformers = self._featurize_train_test_split(
        splittype, featurizers,
        complex_featurizers, input_transformers,
        output_transformers, input_file, tasks,
        protein_pdb_field=protein_pdb_field,
        ligand_pdb_field=ligand_pdb_field,
        user_specified_features=user_specified_features)
    model_params["data_shape"] = train_dataset.get_data_shape()
    regression_metrics = [Metric(metrics.r2_score),
                          Metric(metrics.mean_squared_error),
                          Metric(metrics.mean_absolute_error)]

After Change

                    "nb_epoch": 2, "init": "glorot_uniform",
                    "nb_layers": 1, "batchnorm": False}

    input_file = os.path.join(self.current_dir, "gbd3k.pkl.gz")
    featurizer = DataFeaturizer(tasks=tasks,
                                smiles_field=self.smiles_field,
                                featurizers=featurizers,
                                verbosity="low")
    dataset = featurizer.featurize(input_file, self.data_dir)

    splitter = ScaffoldSplitter()
    train_dataset, test_dataset = splitter.train_test_split(
        dataset, self.train_dir, self.test_dir)

    input_transformers = [
      NormalizationTransformer(transform_X=True, dataset=train_dataset),
      ClippingTransformer(transform_X=True, dataset=train_dataset)]

Instances