Run simple save/load test

    import pandas as pd

    print(pd.__version__)

  def get_dataset(self,

                  mode='classification',

                  featurizer='GraphConv',

                  num_tasks=2):

    from deepchem.molnet import load_bace_classification, load_delaney

    import numpy as np

    import deepchem as dc

    from deepchem.data import NumpyDataset

    data_points = 10

    if mode == 'classification':

      tasks, all_dataset, transformers = load_bace_classification(featurizer)

    else:

      tasks, all_dataset, transformers = load_delaney(featurizer)

    train, valid, test = all_dataset

    for i in range(1, num_tasks):

      tasks.append("random_task")

    w = np.ones(shape=(data_points, len(tasks)))

    if mode == 'classification':

      y = np.random.randint(0, 2, size=(data_points, len(tasks)))

      metric = dc.metrics.Metric(

        dc.metrics.roc_auc_score, np.mean, mode="classification")

    else:

      y = np.random.normal(size=(data_points, len(tasks)))

      metric = dc.metrics.Metric(

        dc.metrics.mean_absolute_error, mode="regression")

    ds = NumpyDataset(train.X[:data_points], y, w, train.ids[:data_points])

    return tasks, ds, transformers, metric

  def test_graph_conv_model(self):

    from deepchem.models import GraphConvModel, TensorGraph

    import numpy as np

    tasks, dataset, transformers, metric = self.get_dataset(

      'classification', 'GraphConv')

    batch_size = 50

    model = GraphConvModel(

      len(tasks), batch_size=batch_size, mode='classification')

    model.fit(dataset, nb_epoch=10)

    scores = model.evaluate(dataset, [metric], transformers)

    assert scores['mean-roc_auc_score'] >= 0.9

    model.save()

    model = TensorGraph.load_from_dir(model.model_dir)

    scores2 = model.evaluate(dataset, [metric], transformers)

    assert np.allclose(scores['mean-roc_auc_score'],

                       scores2['mean-roc_auc_score'])

if __name__ == '__main__':

  unittest.main()