Cleanup and some test fixes

    """Computes and returns statistics of this dataset

    This function assumes that the first task of a dataset holds the energy for

    an input system, and that the remaining tasks holds the gradient for the system.

    an input system, and that the remaining tasks holds the gradient for the

    system.

    TODO(rbharath, joegomes): It is unclear whether this should be a Dataset

    function. Might get refactored out.

    y_pred = np.reshape(y_pred, (n_samples, n_tasks))

    # Special case to handle singletasks.

    if n_tasks == 1:

      y_pred = np.squeeze(y_pred)

      y_pred = np.reshape(y_pred, (n_samples,)) 

    return y_pred

  def predict_grad(self, dataset, transformers=[]):

    Concatenates results from all singletask models.

    """

    n_tasks = len(self.tasks)

    n_samples = X.shape[0]

    n_samples = len(dataset) 

    y_pred = np.zeros((n_samples, n_tasks, n_classes))

    for ind, task in enumerate(self.tasks):

      if self.store_in_memory:

            verbosity=self.verbosity)

        task_model.reload()

      y_pred[:, ind] = task_model.predict_proba_on_batch(

          dataset, transformers, n_classes)

      y_pred[:, ind] = np.squeeze(task_model.predict_proba(

          dataset, transformers, n_classes))

    return y_pred

  def save(self):

      csvfile: Open file object.

    """

    mol_ids = self.dataset.get_ids()

    n_tasks = len(self.task_names)

    y_preds = np.reshape(y_preds, (len(y_preds), n_tasks))

    assert len(y_preds) == len(mol_ids)

    with open(csv_out, "wb") as csvfile:

      csvwriter = csv.writer(csvfile)