Merge pull request #840 from rbharath/error_bar

    return mu[:max_index + 1], sigma[:max_index + 1]

  def bayesian_predict_on_batch(self, X, transformers=[], n_passes=4):

    """ 

    Returns: 

      mu: numpy ndarray of shape (n_samples, n_tasks) 

      sigma: numpy ndarray of shape (n_samples, n_tasks)     

    """

    dataset = NumpyDataset(X=X, y=None, n_tasks=len(self.outputs))

    y_ = []

    for i in range(n_passes):

      generator = self.default_generator(

          dataset, predict=True, pad_batches=True)

      y_.append(self.predict_on_generator(generator, transformers))

    # Concatenates along 0-th dimension

    y_ = np.array(y_)

    mu = np.mean(y_, axis=0)

    sigma = np.std(y_, axis=0)

    return mu, sigma

  def predict_on_smiles(self, smiles, transformers=[], untransform=False):

    """Generates predictions on a numpy array of smile strings

  model.save()

  model = TensorGraph.load_from_dir(model.model_dir)

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

def test_graph_conv_error_bars():

  tasks, dataset, transformers, metric = get_dataset('regression', 'GraphConv')

  batch_size = 50

  model = GraphConvTensorGraph(

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

  model.fit(dataset, nb_epoch=1)

  mu, sigma = model.bayesian_predict(

      dataset, transformers, untransform=True, n_passes=24)

  assert mu.shape == (len(dataset), len(tasks))

  assert sigma.shape == (len(dataset), len(tasks))

model.load_from_dir('model_saves')

mu, sigma = model.bayesian_predict(

    valid_dataset.X, transformers, untransform=True, n_passes=24)

    valid_dataset, transformers, untransform=True, n_passes=24)

print(mu[:4])

print(sigma[:4])