leave_n_out_setting

def get_params(self) -> dict[str, Any]:
    """Get the parameters of the setting."""
    # Get all instance attributes that don't start with underscore
    # and are not special attributes
    exclude_attrs = {"prediction_data_processor"}

    params = {}
    for attr_name, attr_value in vars(self).items():
        if not attr_name.startswith("_") and attr_name not in exclude_attrs:
            params[attr_name] = attr_value

    return params

def split(self, data: InteractionMatrix) -> None:
    """Split data according to the setting.

    Calling this method changes the state of the setting object to be ready
    for evaluation. The method splits data into training_data, ground_truth_data,
    and unlabeled_data.

    Note:
        SlidingWindowSetting will have an additional attribute incremental_data.

    Args:
        data: Interaction matrix to be split.
    """
    logger.debug("Splitting data...")
    self._num_full_interactions = data.num_interactions
    start = time.time()
    self._split(data)
    end = time.time()
    logger.info(f"{self.name} data split - Took {end - start:.3}s")

    logger.debug("Checking split attribute and sizes.")
    self._check_split()

    self._split_complete = True
    logger.info(f"{self.name} data split complete.")

def restore(self, n: int = 0) -> None:
    """Restore last run.

    Args:
        n: Iteration number to restore to. If None, restores to beginning.
    """
    logger.debug(f"Restoring setting to iteration {n}")
    self.current_index = n

def get_split_at(self, index: int) -> SplitResult:
    """Get the split data at a specific index.

    Args:
        index: The index of the split to retrieve.

    Returns:
        SplitResult with keys: 'unlabeled', 'ground_truth', 't_window', 'incremental'.

    Raises:
        IndexError: If index is out of range.
    """
    if index < 0 or index > self.num_split:
        raise IndexError(f"Index {index} out of range for {self.num_split} splits")

    if self._sliding_window_setting:
        if not (
            isinstance(self._unlabeled_data, list)
            and isinstance(self._ground_truth_data, list)
            and isinstance(self._t_window, list)
        ):
            raise ValueError("Expected list of InteractionMatrix for sliding window setting.")
        result = SplitResult(
            unlabeled=self._unlabeled_data[index],
            ground_truth=self._ground_truth_data[index],
            # TODO change this variable to training_data when refactoring
            incremental=(
                self._incremental_data[index - 1] if index < len(self._incremental_data) and index > 0 else None
            ),
            t_window=self._t_window[index],
        )
    else:
        if index != 0:
            raise IndexError("Non-sliding setting has only one split at index 0")
        if (
            isinstance(self._unlabeled_data, list)
            or isinstance(self._ground_truth_data, list)
            or isinstance(self._t_window, list)
        ):
            raise ValueError("Expected single data for non-sliding setting.")
        result = SplitResult(
            unlabeled=self._unlabeled_data,
            ground_truth=self._ground_truth_data,
            incremental=None,
            t_window=self._t_window,
        )

    return result