Adding initial (failing) multitask hyperparam opt test

    self._hyperparam_opt(rf_model_builder, params_dict, train_dataset,

                         valid_dataset, output_transformers, task_types,

                         metric)

  def test_multitask_keras_mlp_ECFP_classification_hyperparam_opt(self):

    """Straightforward test of Keras multitask deepchem classification API."""

    from deepchem.models.keras_models.fcnet import MultiTaskDNN

    splittype = "scaffold"

    output_transformers = []

    input_transformers = []

    task_type = "classification"

    # TODO(rbharath): There should be some automatic check to ensure that all

    # required model_params are specified.

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

        splittype, compound_featurizers, 

        complex_featurizers, input_transformers,

        output_transformers, input_file, task_types.keys())

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

    metric = Metric(metrics.mean_roc_auc_score)

    self._hyperparam_opt(MultiTaskDNN, params_dict, train_dataset,

                         valid_dataset, output_transformers, task_types,

                         metric)

# limitations under the License.

"""Evaluation metrics."""

import collections

import numpy as np

import warnings

from sklearn.metrics import roc_auc_score