modified test_training

import unittest

import pandas as pd

from deepchem.data import NumpyDataset

from deepchem.datasets import NumpyDataset

from deepchem.feat.molecule_featurizers import MolGanFeaturizer

from deepchem.models import BasicMolGANModel as MolGAN

from deepchem.models.optimizers import ExponentialDecay

  """

  def setUp(self):

    self.training_attempts = 6

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

    self.vertices = 9

    self.nodes = 5

    featurized = feat.featurize(molecules)

    dataset = NumpyDataset([x.adjacency_matrix for x in featurized],

                           [x.node_features for x in featurized])

    valid_attempts = 0

    for _ in range(10):

    # True will be assigned up successful training attempt

    success = False

    for _ in range(self.training_attempts):

      # force clear tensor flow backend

      keras_clear_session()

      # create new model

      valid_molecules_count = len(

          list(filter(lambda x: x is not None, generated_molecules)))

      if valid_molecules_count:

        valid_attempts = valid_attempts + 1

        success = True

        break

    # finally test if there was at least one valid training session

    # as the model structure improves this should become more and more strict

    assert valid_attempts > 0

    assert success

if __name__ == '__main__':