Portable

    metadata_filename = os.path.join(self.data_dir, "metadata.joblib")

    if os.path.exists(metadata_filename):

      tasks, metadata_df = load_from_disk(metadata_filename)

      del metadata_df['task_names']

      save_metadata(tasks, metadata_df, self.data_dir)

      return tasks, metadata_df

    raise ValueError("No Metadata Found On Disk")

    metadata_entries should have elements returned by write_data_to_disk

    above.

    """

    columns = ('basename', 'task_names', 'ids', 'X', 'y', 'w')

    columns = ('basename', 'ids', 'X', 'y', 'w')

    metadata_df = pd.DataFrame(metadata_entries, columns=columns)

    return metadata_df

      out_ids = None

    # note that this corresponds to the _construct_metadata column order

    return [basename, tasks, out_ids, out_X, out_y, out_w]

    return [basename, out_ids, out_X, out_y, out_w]

  def save_to_disk(self):

    """Save dataset to disk."""

        self.default_generator(dataset, predict=True),

        metrics,

        labels=self.my_labels,

        transformers=transformers,

        weights=[self.my_task_weights],

        per_task_metrics=per_task_metrics)

  Returns

  -------

  """

  if isinstance(tasks, np.ndarray):

    tasks = tasks.tolist()

  metadata_filename = os.path.join(data_dir, "metadata.hd5")

  with open(tasks_filename, 'w') as fout:

    json.dump(tasks, fout)

  hdf = pd.HDFStore(metadata_filename)

  hdf.put('metadata', metadata_df)

  hdf.put('metadata', metadata_df, format='table')

  hdf.close()