Steps towards cached shape

    Returns

    -------

    List with values `[out_ids, out_X, out_y, out_w]` with filenames of locations to disk which these respective arrays were written.

    List with values `[out_ids, out_X, out_y, out_w, out_ids_shape, out_X_shape, out_y_shape, out_w_shape]` with filenames of locations to disk which these respective arrays were written.

    """

    if X is not None:

      out_X: Optional[str] = "%s-X.npy" % basename

      save_to_disk(X, os.path.join(data_dir, out_X))  # type: ignore

      out_X_shape = X.shape

    else:

      out_X = None

      out_X_shape = None

    if y is not None:

      out_y: Optional[str] = "%s-y.npy" % basename

      save_to_disk(y, os.path.join(data_dir, out_y))  # type: ignore

      out_y_shape = y.shape

    else:

      out_y = None

      out_y_shape = None

    if w is not None:

      out_w: Optional[str] = "%s-w.npy" % basename

      save_to_disk(w, os.path.join(data_dir, out_w))  # type: ignore

      out_w_shape = w.shape

    else:

      out_w = None

      out_w_shape = None

    if ids is not None:

      out_ids: Optional[str] = "%s-ids.npy" % basename

      save_to_disk(ids, os.path.join(data_dir, out_ids))  # type: ignore

      out_ids_shape = ids.shape

    else:

      out_ids = None

      out_ids_shape = None

    # note that this corresponds to the _construct_metadata column order

    return [out_ids, out_X, out_y, out_w]

    return [

        out_ids, out_X, out_y, out_w, out_ids_shape, out_X_shape, out_y_shape,

        out_w_shape

    ]

  def save_to_disk(self) -> None:

    """Save dataset to disk."""

      A DiskDataset with a single shard.

    """

    # Create temp directory to store shuffled version

    shuffle_dir = tempfile.mkdtemp()

    n_shards = self.get_number_shards()

    all_X = []

    all_y = []

    all_w = []

  def get_shape(self) -> Tuple[Shape, Shape, Shape, Shape]:

    """Finds shape of dataset."""

    n_tasks = len(self.get_task_names())

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

      if shard_num == 0:

        X_shape = np.array(X.shape)

        if n_tasks > 0:

          y_shape = np.array(y.shape)

          w_shape = np.array(w.shape)

        else:

          y_shape = tuple()

          w_shape = tuple()

        ids_shape = np.array(ids.shape)

      else:

        X_shape[0] += np.array(X.shape)[0]

        if n_tasks > 0:

          y_shape[0] += np.array(y.shape)[0]

          w_shape[0] += np.array(w.shape)[0]

        ids_shape[0] += np.array(ids.shape)[0]

    return tuple(X_shape), tuple(y_shape), tuple(w_shape), tuple(ids_shape)

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

    #  if shard_num == 0:

    #    X_shape = np.array(X.shape)

    #    if n_tasks > 0:

    #      y_shape = np.array(y.shape)

    #      w_shape = np.array(w.shape)

    #    else:

    #      y_shape = tuple()

    #      w_shape = tuple()

    #    ids_shape = np.array(ids.shape)

    #  else:

    #    X_shape[0] += np.array(X.shape)[0]

    #    if n_tasks > 0:

    #      y_shape[0] += np.array(y.shape)[0]

    #      w_shape[0] += np.array(w.shape)[0]

    #    ids_shape[0] += np.array(ids.shape)[0]

    #return tuple(X_shape), tuple(y_shape), tuple(w_shape), tuple(ids_shape)

  def get_label_means(self) -> pd.DataFrame:

    """Return pandas series of label means."""