SuccessiveHalving(
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,
)
Bases: AbstractIntensifier
Implementation of Succesive Halving supporting multi-fidelity, multi-objective, and multi-processing.
Internally, a tracker keeps track of configurations and their bracket and stage.
The behaviour of this intensifier is as follows:
Note
The implementation natively supports brackets from Hyperband. However, in the case of Successive Halving,
only one bracket is used.
Parameters
eta : int, defaults to 3
Input that controls the proportion of configurations discarded in each round of Successive Halving.
n_seeds : int, defaults to 1
How many seeds to use for each instance.
instance_seed_order : str, defaults to "shuffle_once"
How to order the instance-seed pairs. Can be set to:
- `None`: No shuffling at all and use the instance-seed order provided by the user.
- `shuffle_once`: Shuffle the instance-seed keys once and use the same order across all runs.
- `shuffle`: Shuffles the instance-seed keys for each bracket individually.
incumbent_selection : str, defaults to "highest_observed_budget"
How to select the incumbent when using budgets. Can be set to:
- `any_budget`: Incumbent is the best on any budget i.e., best performance regardless of budget.
- `highest_observed_budget`: Incumbent is the best in the highest budget run so far.
- `highest_budget`: Incumbent is selected only based on the highest budget.
max_incumbents : int, defaults to 10
How many incumbents to keep track of in the case of multi-objective.
seed : int, defaults to None
Internal seed used for random events like shuffle seeds.
Source code in smac/intensifier/successive_halving.py
| 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
|
property
config_generator: Iterator[Configuration]
Based on the configuration selector, an iterator is returned that generates configurations.
property
writable
The configuration selector for the intensifier.
property
How often the incumbents have changed.
property
writable
Runhistory of the intensifier.
property
Returns the trajectory (changes of incumbents) of the optimization run.
property
writable
Returns used wallclock time.
Post initialization steps after the runhistory has been set.
Source code in smac/intensifier/successive_halving.py
| def __post_init__(self) -> None:
"""Post initialization steps after the runhistory has been set."""
super().__post_init__()
# We generate our instance seed pairs once
is_keys = self.get_instance_seed_keys_of_interest()
# Budgets, followed by lots of sanity-checking
eta = self._eta
min_budget = self._min_budget
max_budget = self._max_budget
if max_budget is not None and min_budget is not None and max_budget < min_budget:
raise ValueError("Max budget has to be larger than min budget.")
if self.uses_instances:
if isinstance(min_budget, float) or isinstance(max_budget, float):
raise ValueError("Successive Halving requires integer budgets when using instances.")
min_budget = min_budget if min_budget is not None else 1
max_budget = max_budget if max_budget is not None else len(is_keys)
if max_budget > len(is_keys):
raise ValueError(
f"Max budget of {max_budget} can not be greater than the number of instance-seed "
f"keys ({len(is_keys)})."
)
if max_budget < len(is_keys):
logger.warning(
f"Max budget {max_budget} does not include all instance seed " f"pairs ({len(is_keys)})."
)
else:
if min_budget is None or max_budget is None:
raise ValueError(
"Successive Halving requires the parameters min_budget and max_budget defined in the scenario."
)
if len(is_keys) != 1:
raise ValueError("Successive Halving supports only one seed when using budgets.")
if min_budget is None or min_budget <= 0:
raise ValueError("Min budget has to be larger than 0.")
budget_type = "INSTANCES" if self.uses_instances else "BUDGETS"
logger.info(
f"Successive Halving uses budget type {budget_type} with eta {eta}, "
f"min budget {min_budget}, and max budget {max_budget}."
)
# Pre-computing Successive Halving variables
max_iter = self._get_max_iterations(eta, max_budget, min_budget)
budgets, n_configs = self._compute_configs_and_budgets_for_stages(eta, max_budget, max_iter)
# Global variables
self._min_budget = min_budget
self._max_budget = max_budget
# Stage variables, depending on the bracket (0 is the bracket here since SH only has one bracket)
self._max_iterations: dict[int, int] = {0: max_iter + 1}
self._n_configs_in_stage: dict[int, list] = {0: n_configs}
self._budgets_in_stage: dict[int, list] = {0: budgets}
|
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.
Source code in smac/intensifier/abstract_intensifier.py
| 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() -> Configuration | None
Returns the current incumbent in a single-objective setting.
Source code in smac/intensifier/abstract_intensifier.py
| 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]
|
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.
Source code in smac/intensifier/abstract_intensifier.py
| 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 []
|
Find the lowest intersection of instance-seed-budget keys for all incumbents.
Source code in smac/intensifier/abstract_intensifier.py
| 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(
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).
Source code in smac/intensifier/abstract_intensifier.py
| 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}.")
|
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.
Source code in smac/intensifier/successive_halving.py
| 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
|
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.
Source code in smac/intensifier/abstract_intensifier.py
| 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() -> list[Configuration]
Returns rejected configurations when racing against the incumbent failed.
Source code in smac/intensifier/abstract_intensifier.py
| 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
|
Loads the latest state of the intensifier including the incumbents and trajectory.
Source code in smac/intensifier/abstract_intensifier.py
| 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"])
|
Prints the number of configurations in each bracket/stage.
Source code in smac/intensifier/successive_halving.py
| 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 the internal variables of the intensifier including the tracker.
Source code in smac/intensifier/successive_halving.py
| def reset(self) -> None:
"""Reset the internal variables of the intensifier including the tracker."""
super().reset()
# States
# dict[tuple[bracket, stage], list[tuple[seed to shuffle instance-seed keys, list[config_id]]]
self._tracker: dict[tuple[int, int], list[tuple[int | None, list[Configuration]]]] = defaultdict(list)
|
Saves the current state of the intensifier. In addition to the state (retrieved by get_state), this
method also saves the incumbents and trajectory.
Source code in smac/intensifier/abstract_intensifier.py
| 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(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.
Source code in smac/intensifier/abstract_intensifier.py
| 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)
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