ee2bc2a8daaba5717e49917831ddf8229d1ebe70,deepchem/hyperparameters/tests/test_hyperparam_opt.py,TestHyperparamOptAPI,test_multitask_tf_mlp_ECFP_classification_hyperparam_opt,#TestHyperparamOptAPI#,172

Before Change

  def test_multitask_tf_mlp_ECFP_classification_hyperparam_opt(self):
    Straightforward test of Tensorflow multitask deepchem classification API.
    splittype = "scaffold"
    output_transformers = []
    input_transformers = []
    task_type = "classification"

    input_file = os.path.join(self.current_dir, "multitask_example.csv")
    tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",
             "task7", "task8", "task9", "task10", "task11", "task12",
             "task13", "task14", "task15", "task16"]
    task_types = {task: task_type for task in tasks}

    compound_featurizers = [CircularFingerprint(size=1024)]
    complex_featurizers = []

    train_dataset, valid_dataset, _, transformers = self._featurize_train_test_split(
        splittype, compound_featurizers, 
        complex_featurizers, input_transformers,
        output_transformers, input_file, tasks)
    metric = Metric(metrics.matthews_corrcoef, np.mean, mode="classification")
    params_dict = {"activation": ["relu"],
                    "momentum": [.9],
                    "batch_size": [50],

After Change

      params_dict, train_dataset, valid_dataset, transformers,
      metric, logdir=None)

  def test_multitask_tf_mlp_ECFP_classification_hyperparam_opt(self):
    Straightforward test of Tensorflow multitask deepchem classification API.
    splittype = "scaffold"
    task_type = "classification"

    input_file = os.path.join(self.current_dir, "multitask_example.csv")
    tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",
             "task7", "task8", "task9", "task10", "task11", "task12",
             "task13", "task14", "task15", "task16"]
    task_types = {task: task_type for task in tasks}

    featurizers = [CircularFingerprint(size=1024)]

    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, valid_dataset, test_dataset = splitter.train_valid_test_split(
        dataset, self.train_dir, self.valid_dir, self.test_dir)

    transformers = []

    metric = Metric(metrics.matthews_corrcoef, np.mean, mode="classification")

Instances