Hyperband

smac.intensifier.hyperband

Hyperband

Hyperband(
    scenario: Scenario,
    eta: int = 3,
    n_seeds: int = 1,
    instance_seed_order: str | None = "shuffle_once",
    max_incumbents: int = 10,
    incumbent_selection: str = "highest_observed_budget",
    seed: int | None = None,
)

基类: SuccessiveHalving

请参阅 SuccessiveHalving 的文档。

源代码位于 smac/intensifier/successive_halving.py 中

def __init__(
    self,
    scenario: Scenario,
    eta: int = 3,
    n_seeds: int = 1,
    instance_seed_order: str | None = "shuffle_once",
    max_incumbents: int = 10,
    incumbent_selection: str = "highest_observed_budget",
    seed: int | None = None,
):
    super().__init__(
        scenario=scenario,
        n_seeds=n_seeds,
        max_incumbents=max_incumbents,
        seed=seed,
    )

    self._eta = eta
    self._instance_seed_order = instance_seed_order
    self._incumbent_selection = incumbent_selection
    self._highest_observed_budget_only = False if incumbent_selection == "any_budget" else True

    # Global variables derived from scenario
    self._min_budget = self._scenario.min_budget
    self._max_budget = self._scenario.max_budget

config_generator 属性

config_generator: Iterator[Configuration]

根据配置选择器，返回一个迭代器，该迭代器生成配置。

config_selector 属性 可写

config_selector: ConfigSelector

强化器的配置选择器。

incumbents_changed 属性

incumbents_changed: int

最优配置变更的次数。

runhistory 属性 可写

runhistory: RunHistory

强化器的运行历史记录。

trajectory 属性

trajectory: list[TrajectoryItem]

返回优化运行的轨迹（最优配置的变化）。

used_walltime 属性 可写

used_walltime: float

返回已使用的实际时间。

get_callback

get_callback() -> Callback

强化器利用回调函数根据运行历史记录高效地更新最优配置（每当新信息可用时）。此外，在此处集成回调函数也为开发者提供了未来更多的选择。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_callback(self) -> Callback:
    """The intensifier makes use of a callback to efficiently update the incumbent based on the runhistory
    (every time new information is available). Moreover, incorporating the callback here allows developers
    more options in the future.
    """

    class RunHistoryCallback(Callback):
        def __init__(self, intensifier: AbstractIntensifier):
            self.intensifier = intensifier

        def on_tell_end(self, smbo: smac.main.smbo.SMBO, info: TrialInfo, value: TrialValue) -> None:
            self.intensifier.update_incumbents(info.config)

    return RunHistoryCallback(self)

get_incumbent

get_incumbent() -> Configuration | None

在单目标设置中返回当前最优配置。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_incumbent(self) -> Configuration | None:
    """Returns the current incumbent in a single-objective setting."""
    if self._scenario.count_objectives() > 1:
        raise ValueError("Cannot get a single incumbent for multi-objective optimization.")

    if len(self._incumbents) == 0:
        return None

    assert len(self._incumbents) == 1
    return self._incumbents[0]

get_incumbent_instance_seed_budget_key_differences

get_incumbent_instance_seed_budget_key_differences(
    compare: bool = False,
) -> list[InstanceSeedBudgetKey]

在某些情况下，最优配置会在比其他配置更多的试验中进行评估。此方法返回不属于所有最优配置最低实例交集的实例。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_incumbent_instance_seed_budget_key_differences(self, compare: bool = False) -> list[InstanceSeedBudgetKey]:
    """There are situations in which incumbents are evaluated on more trials than others. This method returns the
    instances that are not part of the lowest intersection of instances for all incumbents.
    """
    incumbents = self.get_incumbents()

    if len(incumbents) > 0:
        # We want to calculate the differences so that we can evaluate the other incumbents on the same instances
        incumbent_isb_keys = [self.get_instance_seed_budget_keys(incumbent, compare) for incumbent in incumbents]

        if len(incumbent_isb_keys) <= 1:
            return []

        # Compute the actual differences
        intersection_isb_keys = set.intersection(*map(set, incumbent_isb_keys))  # type: ignore
        union_isb_keys = set.union(*map(set, incumbent_isb_keys))  # type: ignore
        incumbent_isb_keys = list(union_isb_keys - intersection_isb_keys)  # type: ignore

        if len(incumbent_isb_keys) == 0:
            return []

        return incumbent_isb_keys  # type: ignore

    return []

get_incumbent_instance_seed_budget_keys

get_incumbent_instance_seed_budget_keys(
    compare: bool = False,
) -> list[InstanceSeedBudgetKey]

找到所有最优配置的 instance-seed-budget 键的最低交集。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_incumbent_instance_seed_budget_keys(self, compare: bool = False) -> list[InstanceSeedBudgetKey]:
    """Find the lowest intersection of instance-seed-budget keys for all incumbents."""
    incumbents = self.get_incumbents()

    if len(incumbents) > 0:
        # We want to calculate the smallest set of trials that is used by all incumbents
        # Reason: We can not fairly compare otherwise
        incumbent_isb_keys = [self.get_instance_seed_budget_keys(incumbent, compare) for incumbent in incumbents]
        instances = list(set.intersection(*map(set, incumbent_isb_keys)))  # type: ignore

        return instances  # type: ignore

    return []

get_incumbents

get_incumbents(
    sort_by: str | None = None,
) -> list[Configuration]

返回运行历史记录中的最优配置（帕累托前沿上的点）的副本。在单目标优化的情况下，只返回一个最优配置（如果存在）。

返回

configs : list[Configuration] 帕累托前沿的配置。 sort_by : str，默认为 None 按 cost（成本最低优先）或 num_trials（试验次数最少的配置优先）对试验进行排序。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_incumbents(self, sort_by: str | None = None) -> list[Configuration]:
    """Returns the incumbents (points on the pareto front) of the runhistory as copy. In case of a single-objective
    optimization, only one incumbent (if is) is returned.

    Returns
    -------
    configs : list[Configuration]
        The configs of the Pareto front.
    sort_by : str, defaults to None
        Sort the trials by ``cost`` (lowest cost first) or ``num_trials`` (config with lowest number of trials
        first).
    """
    rh = self.runhistory

    if sort_by == "cost":
        return list(sorted(self._incumbents, key=lambda config: rh._cost_per_config[rh.get_config_id(config)]))
    elif sort_by == "num_trials":
        return list(sorted(self._incumbents, key=lambda config: len(rh.get_trials(config))))
    elif sort_by is None:
        return list(self._incumbents)
    else:
        raise ValueError(f"Unknown sort_by value: {sort_by}.")

get_instance_seed_budget_keys

get_instance_seed_budget_keys(
    config: Configuration, compare: bool = False
) -> list[InstanceSeedBudgetKey]

返回给定配置的 instance-seed-budget 键。此方法支持 highest_budget，它只返回最高预算（如果指定）的 instance-seed-budget 键。在这种情况下，update_incumbents 中的最优配置只有在最高预算下的成本更低时才会改变。

参数

config: Configuration 要查询的配置 compare : bool，默认为 False 在比较配置是否在相同的 instance-seed 键上进行评估时，去除预算信息。

源代码位于 smac/intensifier/successive_halving.py 中

def get_instance_seed_budget_keys(
    self, config: Configuration, compare: bool = False
) -> list[InstanceSeedBudgetKey]:
    """Returns the instance-seed-budget keys for a given configuration. This method supports ``highest_budget``,
    which only returns the instance-seed-budget keys for the highest budget (if specified). In this case, the
    incumbents in ``update_incumbents`` are only changed if the costs on the highest budget are lower.

    Parameters
    ----------
    config: Configuration
        The Configuration to be queried
    compare : bool, defaults to False
        Get rid of the budget information for comparing if the configuration was evaluated on the same
        instance-seed keys.
    """
    isb_keys = self.runhistory.get_instance_seed_budget_keys(
        config, highest_observed_budget_only=self._highest_observed_budget_only
    )

    # If incumbent should only be changed on the highest budget, we have to kick out all budgets below the highest
    if self.uses_budgets and self._incumbent_selection == "highest_budget":
        isb_keys = [key for key in isb_keys if key.budget == self._max_budget]

    if compare:
        # Get rid of duplicates
        isb_keys = list(
            set([InstanceSeedBudgetKey(instance=key.instance, seed=key.seed, budget=None) for key in isb_keys])
        )

    return isb_keys

get_instance_seed_keys_of_interest

get_instance_seed_keys_of_interest(
    *, validate: bool = False, seed: int | None = None
) -> list[InstanceSeedKey]

返回 instance-seed 键列表。考虑运行历史记录中的种子和实例（self._tf_seeds 和 self._tf_instances）。如果未找到种子或实例，则根据全局强化器种子生成新的种子和实例。

警告

传入的种子仅用于验证。对于训练，使用全局强化器种子。

参数

validate : bool，默认为 False 是否获取验证试验或训练试验。唯一的区别在于不同的种子。 seed : int | None，默认为 None 用于验证试验的种子。

返回

instance_seed_keys : list[InstanceSeedKey] 相关的 Instance-seed 键。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_instance_seed_keys_of_interest(
    self,
    *,
    validate: bool = False,
    seed: int | None = None,
) -> list[InstanceSeedKey]:
    """Returns a list of instance-seed keys. Considers seeds and instances from the
    runhistory (``self._tf_seeds`` and ``self._tf_instances``). If no seeds or instances were found, new
    seeds and instances are generated based on the global intensifier seed.

    Warning
    -------
    The passed seed is only used for validation. For training, the global intensifier seed is used.

    Parameters
    ----------
    validate : bool, defaults to False
        Whether to get validation trials or training trials. The only difference lies in different seeds.
    seed : int | None, defaults to None
        The seed used for the validation trials.

    Returns
    -------
    instance_seed_keys : list[InstanceSeedKey]
        Instance-seed keys of interest.
    """
    if self._runhistory is None:
        raise RuntimeError("Please set the runhistory before calling this method.")

    if len(self._tf_instances) == 0:
        raise RuntimeError("Please call __post_init__ before calling this method.")

    if seed is None:
        seed = 0

    # We cache the instance-seed keys for efficiency and consistency reasons
    if (self._instance_seed_keys is None and not validate) or (
        self._instance_seed_keys_validation is None and validate
    ):
        instance_seed_keys: list[InstanceSeedKey] = []
        if validate:
            rng = np.random.RandomState(seed)
        else:
            rng = self._rng

        i = 0
        while True:
            found_enough_configs = (
                self._max_config_calls is not None and len(instance_seed_keys) >= self._max_config_calls
            )
            used_enough_seeds = self._n_seeds is not None and i >= self._n_seeds

            if found_enough_configs or used_enough_seeds:
                break

            if validate:
                next_seed = int(rng.randint(low=0, high=MAXINT, size=1)[0])
            else:
                try:
                    next_seed = self._tf_seeds[i]
                    logger.info(f"Added existing seed {next_seed} from runhistory to the intensifier.")
                except IndexError:
                    # Use global random generator for a new seed and mark it so it will be reused for another config
                    next_seed = int(rng.randint(low=0, high=MAXINT, size=1)[0])

                    # This line here is really important because we don't want to add the same seed twice
                    if next_seed in self._tf_seeds:
                        continue

                    self._tf_seeds.append(next_seed)
                    logger.debug(f"Added a new random seed {next_seed} to the intensifier.")

            # If no instances are used, tf_instances includes None
            for instance in self._tf_instances:
                instance_seed_keys.append(InstanceSeedKey(instance, next_seed))

            # Only use one seed in deterministic case
            if self._scenario.deterministic:
                logger.info("Using only one seed for deterministic scenario.")
                break

            # Seed counter
            i += 1

        # Now we cut so that we only have max_config_calls instance_seed_keys
        # We favor instances over seeds here: That makes sure we always work with the same instance/seed pairs
        if self._max_config_calls is not None:
            if len(instance_seed_keys) > self._max_config_calls:
                instance_seed_keys = instance_seed_keys[: self._max_config_calls]
                logger.info(f"Cut instance-seed keys to {self._max_config_calls} entries.")

        # Set it globally
        if not validate:
            self._instance_seed_keys = instance_seed_keys
        else:
            self._instance_seed_keys_validation = instance_seed_keys

    if not validate:
        assert self._instance_seed_keys is not None
        instance_seed_keys = self._instance_seed_keys
    else:
        assert self._instance_seed_keys_validation is not None
        instance_seed_keys = self._instance_seed_keys_validation

    return instance_seed_keys.copy()

get_rejected_configs

get_rejected_configs() -> list[Configuration]

返回与最优配置竞争失败而被拒绝的配置。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def get_rejected_configs(self) -> list[Configuration]:
    """Returns rejected configurations when racing against the incumbent failed."""
    configs = []
    for rejected_config_id in self._rejected_config_ids:
        configs.append(self.runhistory._ids_config[rejected_config_id])

    return configs

load

load(filename: str | Path) -> None

加载强化器的最新状态，包括最优配置和轨迹。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def load(self, filename: str | Path) -> None:
    """Loads the latest state of the intensifier including the incumbents and trajectory."""
    if isinstance(filename, str):
        filename = Path(filename)

    try:
        with open(filename) as fp:
            data = json.load(fp)
    except Exception as e:
        logger.warning(
            f"Encountered exception {e} while reading runhistory from {filename}. Not adding any trials!"
        )
        return

    # We reset the intensifier and then reset the runhistory
    self.reset()
    if self._runhistory is not None:
        self.runhistory = self._runhistory

    self._incumbents = [self.runhistory.get_config(config_id) for config_id in data["incumbent_ids"]]
    self._incumbents_changed = data["incumbents_changed"]
    self._rejected_config_ids = data["rejected_config_ids"]
    self._trajectory = [TrajectoryItem(**item) for item in data["trajectory"]]
    self.set_state(data["state"])

print_tracker

print_tracker() -> None

打印每个 bracket/阶段中的配置数量。

源代码位于 smac/intensifier/successive_halving.py 中

def print_tracker(self) -> None:
    """Prints the number of configurations in each bracket/stage."""
    messages = []
    for (bracket, stage), others in self._tracker.items():
        counter = 0
        for _, config_ids in others:
            counter += len(config_ids)

        if counter > 0:
            messages.append(f"--- Bracket {bracket} / Stage {stage}: {counter} configs")

    if len(messages) > 0:
        logger.debug(f"{self.__class__.__name__} statistics:")

    for message in messages:
        logger.debug(message)

reset

reset() -> None

重置强化器的内部变量，包括 tracker 和下一个 bracket。

源代码位于 smac/intensifier/hyperband.py 中

def reset(self) -> None:
    """Resets the internal variables of the intensifier, including the tracker and the next bracket."""
    super().reset()

    # Reset current bracket
    self._next_bracket: int = 0

save

save(filename: str | Path) -> None

保存强化器的当前状态。除了状态（通过 get_state 获取）外，此方法还保存最优配置和轨迹。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def save(self, filename: str | Path) -> None:
    """Saves the current state of the intensifier. In addition to the state (retrieved by ``get_state``), this
    method also saves the incumbents and trajectory.
    """
    if isinstance(filename, str):
        filename = Path(filename)

    assert str(filename).endswith(".json")
    filename.parent.mkdir(parents=True, exist_ok=True)

    data = {
        "incumbent_ids": [self.runhistory.get_config_id(config) for config in self._incumbents],
        "rejected_config_ids": self._rejected_config_ids,
        "incumbents_changed": self._incumbents_changed,
        "trajectory": [dataclasses.asdict(item) for item in self._trajectory],
        "state": self.get_state(),
    }

    with open(filename, "w") as fp:
        json.dump(data, fp, indent=2, cls=NumpyEncoder)

update_incumbents

update_incumbents(config: Configuration) -> None

更新最优配置。每次将试验添加到运行历史记录时，都会调用此方法。由于只使用了受影响的配置和当前最优配置，因此此方法效率很高。此外，一个配置只有在所有最优配置实例上的性能更好时才被认为是最优配置。

关键是，如果（开始时）没有最优配置，则第一个配置将获得最优配置状态。对于下一个配置，我们需要检查该配置在已针对最优配置进行评估的所有实例上是否更好。如果是这样，我们就可以替换最优配置。否则，a) 我们需要重新排队该配置以获取缺失的 instance-seed-budget 组合，或 b) 将此配置标记为劣等（“被拒绝”）以不再考虑它。比较行为由 self.get_instance_seed_budget_keys() 和 self.get_incumbent_instance_seed_budget_keys() 控制。

值得注意的是，此方法旨在支持多保真度和多目标优化。get_instance_seed_budget_keys() 方法和 self.get_incumbent_instance_seed_budget_keys() 用于实现多保真度行为，而 calculate_pareto_front() 作为硬编码方式用于支持多目标优化，包括将单目标作为特例。calculate_pareto_front() 在所有（在 MO 情况下）最优配置集合上调用，该集合补充了挑战者配置，前提是它在 seed-instance-budget 组合中有足够的重叠。

最后，如果我们有一个 self._max_incumbents 并且帕累托前沿提供的最优配置数量超过此指定值，我们将使用拥挤距离截断最优配置。

源代码位于 smac/intensifier/abstract_intensifier.py 中

def update_incumbents(self, config: Configuration) -> None:
    """Updates the incumbents. This method is called everytime a trial is added to the runhistory. Since only
    the affected config and the current incumbents are used, this method is very efficient. Furthermore, a
    configuration is only considered incumbent if it has a better performance on all incumbent instances.

    Crucially, if there is no incumbent (at the start) then, the first configuration assumes
    incumbent status. For the next configuration, we need to check if the configuration
    is better on all instances that have been evaluated for the incumbent. If this is the
    case, then we can replace the incumbent. Otherwise, a) we need to requeue the config to
    obtain the missing instance-seed-budget combination or b) mark this configuration as
    inferior ("rejected") to not consider it again. The comparison behaviour is controlled by
    self.get_instance_seed_budget_keys() and self.get_incumbent_instance_seed_budget_keys().

    Notably, this method is written to support both multi-fidelity and multi-objective
    optimization. While the get_instance_seed_budget_keys() method and
    self.get_incumbent_instance_seed_budget_keys() are used for the multi-fidelity behaviour,
    calculate_pareto_front() is used as a hard coded way to support multi-objective
    optimization, including the single objective as special case. calculate_pareto_front()
    is called on the set of all (in case of MO) incumbents amended with the challenger
    configuration, provided it has a sufficient overlap in seed-instance-budget combinations.

    Lastly, if we have a self._max_incumbents and the pareto front provides more than this
    specified amount, we cut the incumbents using crowding distance.
    """
    rh = self.runhistory

    # What happens if a config was rejected, but it appears again? Give it another try even if it
    # has already been evaluated? Yes!

    # Associated trials and id
    config_isb_keys = self.get_instance_seed_budget_keys(config)
    config_id = rh.get_config_id(config)
    config_hash = get_config_hash(config)

    # We skip updating incumbents if no instances are available
    # Note: This is especially the case if trials of a config are still running
    # because if trials are running, the runhistory does not update the trials in the fast data structure
    if len(config_isb_keys) == 0:
        logger.debug(f"No relevant instances evaluated for config {config_hash}. Updating incumbents is skipped.")
        return

    # Now we get the incumbents and see which trials have been used
    incumbents = self.get_incumbents()
    incumbent_ids = [rh.get_config_id(c) for c in incumbents]
    # Find the lowest intersection of instance-seed-budget keys for all incumbents.
    incumbent_isb_keys = self.get_incumbent_instance_seed_budget_keys()

    # Save for later
    previous_incumbents = incumbents.copy()
    previous_incumbent_ids = incumbent_ids.copy()

    # Little sanity check here for consistency
    if len(incumbents) > 0:
        assert incumbent_isb_keys is not None
        assert len(incumbent_isb_keys) > 0

    # If there are no incumbents at all, we just use the new config as new incumbent
    # Problem: We can add running incumbents
    if len(incumbents) == 0:  # incumbent_isb_keys is None and len(incumbents) == 0:
        logger.info(f"Added config {config_hash} as new incumbent because there are no incumbents yet.")
        self._update_trajectory([config])

        # Nothing else to do
        return

    # Comparison keys
    # This one is a bit tricky: We would have problems if we compare with budgets because we might have different
    # scenarios (depending on the incumbent selection specified in Successive Halving).
    # 1) Any budget/highest observed budget: We want to get rid of the budgets because if we know it is calculated
    # on the same instance-seed already then we are ready to go. Imagine we would check for the same budgets,
    # then the configs can not be compared although the user does not care on which budgets configurations have
    # been evaluated.
    # 2) Highest budget: We only want to compare the configs if they are evaluated on the highest budget.
    # Here we do actually care about the budgets. Please see the ``get_instance_seed_budget_keys`` method from
    # Successive Halving to get more information.
    # Noitce: compare=True only takes effect when subclass implemented it. -- e.g. in SH it
    # will remove the budgets from the keys.
    config_isb_comparison_keys = self.get_instance_seed_budget_keys(config, compare=True)
    # Find the lowest intersection of instance-seed-budget keys for all incumbents.
    config_incumbent_isb_comparison_keys = self.get_incumbent_instance_seed_budget_keys(compare=True)

    # Now we have to check if the new config has been evaluated on the same keys as the incumbents
    if not all([key in config_isb_comparison_keys for key in config_incumbent_isb_comparison_keys]):
        # We can not tell if the new config is better/worse than the incumbents because it has not been
        # evaluated on the necessary trials
        logger.debug(
            f"Could not compare config {config_hash} with incumbents because it's evaluated on "
            f"different trials."
        )

        # The config has to go to a queue now as it is a challenger and a potential incumbent
        return
    else:
        # If all instances are available and the config is incumbent and even evaluated on more trials
        # then there's nothing we can do
        if config in incumbents and len(config_isb_keys) > len(incumbent_isb_keys):
            logger.debug(
                "Config is already an incumbent but can not be compared to other incumbents because "
                "the others are missing trials."
            )
            return

    # Add config to incumbents so that we compare only the new config and existing incumbents
    if config not in incumbents:
        incumbents.append(config)
        incumbent_ids.append(config_id)

    # Now we get all instance-seed-budget keys for each incumbent (they might be different when using budgets)
    all_incumbent_isb_keys = []
    for incumbent in incumbents:
        all_incumbent_isb_keys.append(self.get_instance_seed_budget_keys(incumbent))

    # We compare the incumbents now and only return the ones on the pareto front
    new_incumbents = calculate_pareto_front(rh, incumbents, all_incumbent_isb_keys)
    new_incumbent_ids = [rh.get_config_id(c) for c in new_incumbents]

    if len(previous_incumbents) == len(new_incumbents):
        if previous_incumbents == new_incumbents:
            # No changes in the incumbents, we need this clause because we can't use set difference then
            if config_id in new_incumbent_ids:
                self._remove_rejected_config(config_id)
            else:
                # config worse than incumbents and thus rejected
                self._add_rejected_config(config_id)
            return
        else:
            # In this case, we have to determine which config replaced which incumbent and reject it
            removed_incumbent_id = list(set(previous_incumbent_ids) - set(new_incumbent_ids))[0]
            removed_incumbent_hash = get_config_hash(rh.get_config(removed_incumbent_id))
            self._add_rejected_config(removed_incumbent_id)

            if removed_incumbent_id == config_id:
                logger.debug(
                    f"Rejected config {config_hash} because it is not better than the incumbents on "
                    f"{len(config_isb_keys)} instances."
                )
            else:
                self._remove_rejected_config(config_id)
                logger.info(
                    f"Added config {config_hash} and rejected config {removed_incumbent_hash} as incumbent because "
                    f"it is not better than the incumbents on {len(config_isb_keys)} instances: "
                )
                print_config_changes(rh.get_config(removed_incumbent_id), config, logger=logger)
    elif len(previous_incumbents) < len(new_incumbents):
        # Config becomes a new incumbent; nothing is rejected in this case
        self._remove_rejected_config(config_id)
        logger.info(
            f"Config {config_hash} is a new incumbent. " f"Total number of incumbents: {len(new_incumbents)}."
        )
    else:
        # There might be situations that the incumbents might be removed because of updated cost information of
        # config
        for incumbent in previous_incumbents:
            if incumbent not in new_incumbents:
                self._add_rejected_config(incumbent)
                logger.debug(
                    f"Removed incumbent {get_config_hash(incumbent)} because of the updated costs from config "
                    f"{config_hash}."
                )

    # Cut incumbents: We only want to keep a specific number of incumbents
    # We use the crowding distance for that
    if len(new_incumbents) > self._max_incumbents:
        new_incumbents = sort_by_crowding_distance(rh, new_incumbents, all_incumbent_isb_keys)
        new_incumbents = new_incumbents[: self._max_incumbents]

        # or random?
        # idx = self._rng.randint(0, len(new_incumbents))
        # del new_incumbents[idx]
        # del new_incumbent_ids[idx]

        logger.info(
            f"Removed one incumbent using crowding distance because more than {self._max_incumbents} are "
            "available."
        )

    self._update_trajectory(new_incumbents)