Removing left-over test

import tempfile

import shutil

import tensorflow as tf

from keras import backend as K

import deepchem as dc

from sklearn.ensemble import RandomForestRegressor

    _ = model.evaluate(train_dataset, regression_metrics, transformers)

    _ = model.evaluate(test_dataset, regression_metrics, transformers)

  def test_multitask_keras_mlp_ECFP_classification_API(self):

    """Test of Keras multitask deepchem classification API."""

    g = tf.Graph()

    sess = tf.Session(graph=g)

    K.set_session(sess)

    with g.as_default():

      task_type = "classification"

      current_dir = os.path.dirname(os.path.abspath(__file__))

      input_file = os.path.join(current_dir, "multitask_example.csv")

      tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",

               "task7", "task8", "task9", "task10", "task11", "task12",

               "task13", "task14", "task15", "task16"]

      n_features = 1024

      featurizer = dc.feat.CircularFingerprint(size=n_features)

      loader = dc.load.DataLoader(

          tasks=tasks, smiles_field="smiles",

          featurizer=featurizer, verbosity="low")

      dataset = loader.featurize(input_file)

      splitter = dc.splits.ScaffoldSplitter()

      train_dataset, test_dataset = splitter.train_test_split(dataset)

      metrics = [dc.metrics.Metric(dc.metrics.roc_auc_score),

                 dc.metrics.Metric(dc.metrics.matthews_corrcoef),

                 dc.metrics.Metric(dc.metrics.recall_score),

                 dc.metrics.Metric(dc.metrics.accuracy_score)]

      keras_model = dc.models.MultiTaskDNN(

          len(tasks), n_features, "classification", dropout=0.)

      model = dc.models.KerasModel(keras_model)

      # Fit trained model

      model.fit(train_dataset)

      model.save()

      # Eval model on train/test

      _ = model.evaluate(train_dataset, metrics)

      _ = model.evaluate(test_dataset, metrics)

  #def test_multitask_keras_mlp_ECFP_classification_API(self):

  #  """Test of Keras multitask deepchem classification API."""

  #  g = tf.Graph()

  #  sess = tf.Session(graph=g)

  #  K.set_session(sess)

  #  with g.as_default():

  #    task_type = "classification"

  #    current_dir = os.path.dirname(os.path.abspath(__file__))

  #    input_file = os.path.join(current_dir, "multitask_example.csv")

  #    tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",

  #             "task7", "task8", "task9", "task10", "task11", "task12",

  #             "task13", "task14", "task15", "task16"]

  #    n_features = 1024

  #    featurizer = dc.feat.CircularFingerprint(size=n_features)

  #    loader = dc.load.DataLoader(

  #        tasks=tasks, smiles_field="smiles",

  #        featurizer=featurizer, verbosity="low")

  #    dataset = loader.featurize(input_file)

  #    splitter = dc.splits.ScaffoldSplitter()

  #    train_dataset, test_dataset = splitter.train_test_split(dataset)

  #    metrics = [dc.metrics.Metric(dc.metrics.roc_auc_score),

  #               dc.metrics.Metric(dc.metrics.matthews_corrcoef),

  #               dc.metrics.Metric(dc.metrics.recall_score),

  #               dc.metrics.Metric(dc.metrics.accuracy_score)]

  #    

  #    keras_model = dc.models.MultiTaskDNN(

  #        len(tasks), n_features, "classification", dropout=0.)

  #    model = dc.models.KerasModel(keras_model)

  #    # Fit trained model

  #    model.fit(train_dataset)

  #    model.save()

  #    # Eval model on train/test

  #    _ = model.evaluate(train_dataset, metrics)

  #    _ = model.evaluate(test_dataset, metrics)

  def test_singletask_tf_mlp_ECFP_classification_API(self):

    """Test of Tensorflow singletask deepchem classification API."""