I do not like yapf

from deepchem.data import DiskDataset

from deepchem.trans import undo_transforms

class SingletaskToMultitask(Model):

  """

  Convenience class to let singletask models be fit on multitask data.

  Warning: This current implementation is only functional for sklearn models. 

  """

  def __init__(self, tasks, model_builder, model_dir=None, verbose=True):

    super().__init__(self, model_dir=model_dir, verbose=verbose)

    self.tasks = tasks

      task_data_dirs.append(task_data_dir)

    task_datasets = self._to_singletask(dataset, task_data_dirs)

    for task, task_dataset in zip(self.tasks, task_datasets):

      log("Dataset for task %s has shape %s"

          % (task, str(task_dataset.get_shape())), self.verbose)

      log("Dataset for task %s has shape %s" %

          (task, str(task_dataset.get_shape())), self.verbose)

    return task_datasets

  @staticmethod

    tasks = dataset.get_task_names()

    assert len(tasks) == len(task_dirs)

    log("Splitting multitask dataset into singletask datasets", dataset.verbose)

    task_datasets = [DiskDataset.create_dataset([], task_dirs[task_num], [task])

                    for (task_num, task) in enumerate(tasks)]

    task_datasets = [

        DiskDataset.create_dataset([], task_dirs[task_num], [task])

        for (task_num, task) in enumerate(tasks)

    ]

    #task_metadata_rows = {task: [] for task in tasks}

    for shard_num, (X, y, w, ids) in enumerate(dataset.itershards()):

      log("Processing shard %d" % shard_num, dataset.verbose)

    return task_datasets

  def fit(self, dataset, **kwargs):

    """

    Updates all singletask models with new information.

    task_datasets = self._create_task_datasets(dataset)

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

      log("Fitting model for task %s" % task, self.verbose)

      task_model = self.model_builder(

          self.task_model_dirs[task])

      task_model = self.model_builder(self.task_model_dirs[task])

      task_model.fit(task_datasets[ind], **kwargs)

      task_model.save()

      task_model = self.model_builder(self.task_model_dirs[task])

      task_model.reload()

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

          dataset, transformers, n_classes))

      y_pred[:, ind] = np.squeeze(

          task_model.predict_proba(dataset, transformers, n_classes))

    return y_pred

  def save(self):