def __init__(
self,
scenario: Scenario,
tae_runner: typing.Union[ExecuteTARun, typing.Callable] = None,
runhistory: RunHistory = None,
intensifier: Intensifier = None,
acquisition_function: AbstractAcquisitionFunction = None,
acquisition_function_optimizer: AcquisitionFunctionMaximizer = None,
model: AbstractEPM = None,
runhistory2epm: AbstractRunHistory2EPM = None,
initial_design: InitialDesign = None,
initial_configurations: typing.List[Configuration] = None,
stats: Stats = None,
restore_incumbent: Configuration = None,
rng: typing.Union[np.random.RandomState, int] = None,
smbo_class: SMBO = None,
run_id: int = 1):
"""Constructor
Parameters
----------
scenario : ~smac.scenario.scenario.Scenario
Scenario object
tae_runner : ~smac.tae.execute_ta_run.ExecuteTARun or callable
Callable or implementation of
:class:`~smac.tae.execute_ta_run.ExecuteTARun`. In case a
callable is passed it will be wrapped by
:class:`~smac.tae.execute_func.ExecuteTAFuncDict`.
If not set, it will be initialized with the
:class:`~smac.tae.execute_ta_run_old.ExecuteTARunOld`.
runhistory : RunHistory
runhistory to store all algorithm runs
intensifier : Intensifier
intensification object to issue a racing to decide the current
incumbent
acquisition_function : ~smac.optimizer.acquisition.AbstractAcquisitionFunction
Object that implements the :class:`~smac.optimizer.acquisition.AbstractAcquisitionFunction`.
Will use :class:`~smac.optimizer.acquisition.EI` if not set.
acquisition_function_optimizer : ~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Object that implements the :class:`~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer`.
Will use :class:`smac.optimizer.ei_optimization.InterleavedLocalAndRandomSearch` if not set.
model : AbstractEPM
Model that implements train() and predict(). Will use a
:class:`~smac.epm.rf_with_instances.RandomForestWithInstances` if not set.
runhistory2epm : ~smac.runhistory.runhistory2epm.RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use :class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4Cost`
if objective is cost or
:class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost`
if objective is runtime.
initial_design : InitialDesign
initial sampling design
initial_configurations : typing.List[Configuration]
list of initial configurations for initial design --
cannot be used together with initial_design
stats : Stats
optional stats object
rng : np.random.RandomState
Random number generator
restore_incumbent : Configuration
incumbent used if restoring to previous state
smbo_class : ~smac.optimizer.smbo.SMBO
Class implementing the SMBO interface which will be used to
instantiate the optimizer class.
run_id: int, (default: 1)
Run ID will be used as subfolder for output_dir.
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
aggregate_func = average_cost
self.output_dir = create_output_directory(scenario, run_id)
scenario.write()
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
# initialize empty runhistory
if runhistory is None:
runhistory = RunHistory(aggregate_func=aggregate_func)
# inject aggr_func if necessary
if runhistory.aggregate_func is None:
runhistory.aggregate_func = aggregate_func
# initial random number generator
num_run, rng = self._get_rng(rng=rng)
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(rng.randint(MAXINT))
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=self.output_dir, stats=self.stats)
# initial EPM
types, bounds = get_types(scenario.cs, scenario.feature_array)
if model is None:
model = RandomForestWithInstances(
types=types,
bounds=bounds,
instance_features=scenario.feature_array,
seed=rng.randint(MAXINT),
pca_components=scenario.PCA_DIM)
# initial acquisition function
if acquisition_function is None:
if scenario.run_obj == "runtime":
acquisition_function = LogEI(model=model)
else:
acquisition_function = EI(model=model)
# inject model if necessary
if acquisition_function.model is None:
acquisition_function.model = model
# initialize optimizer on acquisition function
if acquisition_function_optimizer is None:
acquisition_function_optimizer = InterleavedLocalAndRandomSearch(
acquisition_function, scenario.cs,
np.random.RandomState(seed=rng.randint(MAXINT)))
elif not isinstance(
acquisition_function_optimizer,
AcquisitionFunctionMaximizer,
):
raise ValueError(
"Argument 'acquisition_function_optimizer' must be of type"
"'AcquisitionFunctionMaximizer', but is '%s'" %
type(acquisition_function_optimizer))
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
if tae_runner is None:
tae_runner = ExecuteTARunOld(
ta=scenario.ta,
stats=self.stats,
run_obj=scenario.run_obj,
runhistory=runhistory,
par_factor=scenario.par_factor,
cost_for_crash=scenario.cost_for_crash)
# Second case, the tae_runner is a function to be optimized
elif callable(tae_runner):
tae_runner = ExecuteTAFuncDict(
ta=tae_runner,
stats=self.stats,
run_obj=scenario.run_obj,
memory_limit=scenario.memory_limit,
runhistory=runhistory,
par_factor=scenario.par_factor,
cost_for_crash=scenario.cost_for_crash)
# Third case, if it is an ExecuteTaRun we can simply use the
# instance. Otherwise, the next check raises an exception
elif not isinstance(tae_runner, ExecuteTARun):
raise TypeError("Argument 'tae_runner' is %s, but must be "
"either a callable or an instance of "
"ExecuteTaRun. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file." %
type(tae_runner))
# Check that overall objective and tae objective are the same
if tae_runner.run_obj != scenario.run_obj:
raise ValueError("Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" % (tae_runner.run_obj, scenario.run_obj))
# inject stats if necessary
if tae_runner.stats is None:
tae_runner.stats = self.stats
# inject runhistory if necessary
if tae_runner.runhistory is None:
tae_runner.runhistory = runhistory
# inject cost_for_crash
if tae_runner.crash_cost != scenario.cost_for_crash:
tae_runner.crash_cost = scenario.cost_for_crash
# initialize intensification
if intensifier is None:
intensifier = Intensifier(tae_runner=tae_runner,
stats=self.stats,
traj_logger=traj_logger,
rng=rng,
instances=scenario.train_insts,
cutoff=scenario.cutoff,
deterministic=scenario.deterministic,
run_obj_time=scenario.run_obj == "runtime",
always_race_against=scenario.cs.get_default_configuration() \
if scenario.always_race_default else None,
instance_specifics=scenario.instance_specific,
minR=scenario.minR,
maxR=scenario.maxR)
# inject deps if necessary
if intensifier.tae_runner is None:
intensifier.tae_runner = tae_runner
if intensifier.stats is None:
intensifier.stats = self.stats
if intensifier.traj_logger is None:
intensifier.traj_logger = traj_logger
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both"
)
if initial_configurations is not None:
initial_design = MultiConfigInitialDesign(
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
runhistory=runhistory,
rng=rng,
configs=initial_configurations,
intensifier=intensifier,
aggregate_func=aggregate_func)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT":
initial_design = DefaultConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "RANDOM":
initial_design = RandomConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" % scenario.initial_incumbent)
# inject deps if necessary
if initial_design.tae_runner is None:
initial_design.tae_runner = tae_runner
if initial_design.scenario is None:
initial_design.scenario = scenario
if initial_design.stats is None:
initial_design.stats = self.stats
if initial_design.traj_logger is None:
initial_design.traj_logger = traj_logger
# initial conversion of runhistory into EPM data
if runhistory2epm is None:
num_params = len(scenario.cs.get_hyperparameters())
if scenario.run_obj == "runtime":
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(scenario.cutoff)
threshold = np.log10(scenario.cutoff * scenario.par_factor)
imputor = RFRImputator(rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=2)
runhistory2epm = RunHistory2EPM4LogCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.CAPPED,
],
imputor=imputor)
elif scenario.run_obj == 'quality':
runhistory2epm = RunHistory2EPM4Cost(
scenario=scenario,
num_params=num_params,
success_states=[StatusType.SUCCESS, StatusType.CRASHED],
impute_censored_data=False,
impute_state=None)
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
# inject scenario if necessary:
if runhistory2epm.scenario is None:
runhistory2epm.scenario = scenario
smbo_args = {
'scenario': scenario,
'stats': self.stats,
'initial_design': initial_design,
'runhistory': runhistory,
'runhistory2epm': runhistory2epm,
'intensifier': intensifier,
'aggregate_func': aggregate_func,
'num_run': num_run,
'model': model,
'acq_optimizer': acquisition_function_optimizer,
'acquisition_func': acquisition_function,
'rng': rng,
'restore_incumbent': restore_incumbent
}
if smbo_class is None:
self.solver = SMBO(**smbo_args)
else:
self.solver = smbo_class(**smbo_args)
def __init__(
self,
scenario: Scenario,
tae_runner: Optional[Union[Type[BaseRunner], Callable]] = None,
tae_runner_kwargs: Optional[Dict] = None,
runhistory: Optional[Union[Type[RunHistory], RunHistory]] = None,
runhistory_kwargs: Optional[Dict] = None,
intensifier: Optional[Type[AbstractRacer]] = None,
intensifier_kwargs: Optional[Dict] = None,
acquisition_function: Optional[
Type[AbstractAcquisitionFunction]] = None,
acquisition_function_kwargs: Optional[Dict] = None,
integrate_acquisition_function: bool = False,
acquisition_function_optimizer: Optional[
Type[AcquisitionFunctionMaximizer]] = None,
acquisition_function_optimizer_kwargs: Optional[Dict] = None,
model: Optional[Type[AbstractEPM]] = None,
model_kwargs: Optional[Dict] = None,
runhistory2epm: Optional[Type[AbstractRunHistory2EPM]] = None,
runhistory2epm_kwargs: Optional[Dict] = None,
multi_objective_algorithm: Optional[
Type[AbstractMultiObjectiveAlgorithm]] = None,
multi_objective_kwargs: Optional[Dict] = None,
initial_design: Optional[Type[InitialDesign]] = None,
initial_design_kwargs: Optional[Dict] = None,
initial_configurations: Optional[List[Configuration]] = None,
stats: Optional[Stats] = None,
restore_incumbent: Optional[Configuration] = None,
rng: Optional[Union[np.random.RandomState, int]] = None,
smbo_class: Optional[Type[SMBO]] = None,
run_id: Optional[int] = None,
random_configuration_chooser: Optional[
Type[RandomConfigurationChooser]] = None,
random_configuration_chooser_kwargs: Optional[Dict] = None,
dask_client: Optional[dask.distributed.Client] = None,
n_jobs: Optional[int] = 1,
):
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
self.scenario = scenario
self.output_dir = ""
if not restore_incumbent:
# restore_incumbent is used by the CLI interface which provides a method for restoring a SMAC run given an
# output directory. This is the default path.
# initial random number generator
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
self.output_dir = create_output_directory(scenario, run_id)
elif scenario.output_dir is not None: # type: ignore[attr-defined] # noqa F821
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
# output-directory is created in CLI when restoring from a
# folder. calling the function again in the facade results in two
# folders being created: run_X and run_X.OLD. if we are
# restoring, the output-folder exists already and we omit creating it,
# but set the self-output_dir to the dir.
# necessary because we want to write traj to new output-dir in CLI.
self.output_dir = cast(str, scenario.output_dir_for_this_run
) # type: ignore[attr-defined] # noqa F821
rng = cast(np.random.RandomState, rng)
if (scenario.deterministic is
True # type: ignore[attr-defined] # noqa F821
and getattr(scenario, "tuner_timeout", None) is None
and scenario.run_obj ==
"quality" # type: ignore[attr-defined] # noqa F821
):
self.logger.info(
"Optimizing a deterministic scenario for quality without a tuner timeout - will make "
"SMAC deterministic and only evaluate one configuration per iteration!"
)
scenario.intensification_percentage = 1e-10 # type: ignore[attr-defined] # noqa F821
scenario.min_chall = 1 # type: ignore[attr-defined] # noqa F821
scenario.write()
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
if self.scenario.run_obj == "runtime" and not self.scenario.transform_y == "LOG": # type: ignore[attr-defined] # noqa F821
self.logger.warning(
"Runtime as objective automatically activates log(y) transformation"
)
self.scenario.transform_y = "LOG" # type: ignore[attr-defined] # noqa F821
# initialize empty runhistory
num_obj = len(scenario.multi_objectives
) # type: ignore[attr-defined] # noqa F821
runhistory_def_kwargs = {}
if runhistory_kwargs is not None:
runhistory_def_kwargs.update(runhistory_kwargs)
if runhistory is None:
runhistory = RunHistory(**runhistory_def_kwargs)
elif inspect.isclass(runhistory):
runhistory = runhistory(
**runhistory_def_kwargs) # type: ignore[operator] # noqa F821
elif isinstance(runhistory, RunHistory):
pass
else:
raise ValueError(
"runhistory has to be a class or an object of RunHistory")
rand_conf_chooser_kwargs = {"rng": rng}
if random_configuration_chooser_kwargs is not None:
rand_conf_chooser_kwargs.update(
random_configuration_chooser_kwargs)
if random_configuration_chooser is None:
if "prob" not in rand_conf_chooser_kwargs:
rand_conf_chooser_kwargs[
"prob"] = scenario.rand_prob # type: ignore[attr-defined] # noqa F821
random_configuration_chooser_instance = ChooserProb(
**
rand_conf_chooser_kwargs # type: ignore[arg-type] # noqa F821 # type: RandomConfigurationChooser
)
elif inspect.isclass(random_configuration_chooser):
random_configuration_chooser_instance = random_configuration_chooser( # type: ignore # noqa F821
**
rand_conf_chooser_kwargs # type: ignore[arg-type] # noqa F821
)
elif not isinstance(random_configuration_chooser,
RandomConfigurationChooser):
raise ValueError(
"random_configuration_chooser has to be"
" a class or object of RandomConfigurationChooser")
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(
rng.randint(MAXINT)) # type: ignore[attr-defined] # noqa F821
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=self.output_dir, stats=self.stats)
# initial EPM
types, bounds = get_types(
scenario.cs,
scenario.feature_array) # type: ignore[attr-defined] # noqa F821
model_def_kwargs = {
"types": types,
"bounds": bounds,
"instance_features": scenario.feature_array,
"seed": rng.randint(MAXINT),
"pca_components": scenario.PCA_DIM,
}
if model_kwargs is not None:
model_def_kwargs.update(model_kwargs)
if model is None:
for key, value in {
"log_y": scenario.transform_y
in ["LOG",
"LOGS"], # type: ignore[attr-defined] # noqa F821
"num_trees": scenario.
rf_num_trees, # type: ignore[attr-defined] # noqa F821
"do_bootstrapping": scenario.
rf_do_bootstrapping, # type: ignore[attr-defined] # noqa F821
"ratio_features": scenario.
rf_ratio_features, # type: ignore[attr-defined] # noqa F821
"min_samples_split": scenario.
rf_min_samples_split, # type: ignore[attr-defined] # noqa F821
"min_samples_leaf": scenario.
rf_min_samples_leaf, # type: ignore[attr-defined] # noqa F821
"max_depth": scenario.
rf_max_depth, # type: ignore[attr-defined] # noqa F821
}.items():
if key not in model_def_kwargs:
model_def_kwargs[key] = value
model_def_kwargs[
"configspace"] = self.scenario.cs # type: ignore[attr-defined] # noqa F821
model_instance = RandomForestWithInstances(
**
model_def_kwargs # type: ignore[arg-type] # noqa F821 # type: AbstractEPM
)
elif inspect.isclass(model):
model_def_kwargs[
"configspace"] = self.scenario.cs # type: ignore[attr-defined] # noqa F821
model_instance = model(
**model_def_kwargs) # type: ignore # noqa F821
else:
raise TypeError("Model not recognized: %s" % (type(model)))
# initial acquisition function
acq_def_kwargs = {"model": model_instance}
if acquisition_function_kwargs is not None:
acq_def_kwargs.update(acquisition_function_kwargs)
acquisition_function_instance = (
None) # type: Optional[AbstractAcquisitionFunction]
if acquisition_function is None:
if scenario.transform_y in [
"LOG", "LOGS"
]: # type: ignore[attr-defined] # noqa F821
acquisition_function_instance = LogEI(
**acq_def_kwargs # type: ignore[arg-type] # noqa F821
)
else:
acquisition_function_instance = EI(
**acq_def_kwargs # type: ignore[arg-type] # noqa F821
)
elif inspect.isclass(acquisition_function):
acquisition_function_instance = acquisition_function(
**acq_def_kwargs)
else:
raise TypeError(
"Argument acquisition_function must be None or an object implementing the "
"AbstractAcquisitionFunction, not %s." %
type(acquisition_function))
if integrate_acquisition_function:
acquisition_function_instance = IntegratedAcquisitionFunction(
acquisition_function=
acquisition_function_instance, # type: ignore
**acq_def_kwargs,
)
# initialize optimizer on acquisition function
acq_func_opt_kwargs = {
"acquisition_function": acquisition_function_instance,
"config_space":
scenario.cs, # type: ignore[attr-defined] # noqa F821
"rng": rng,
}
if acquisition_function_optimizer_kwargs is not None:
acq_func_opt_kwargs.update(acquisition_function_optimizer_kwargs)
if acquisition_function_optimizer is None:
for key, value in {
"max_steps": scenario.
sls_max_steps, # type: ignore[attr-defined] # noqa F821
"n_steps_plateau_walk": scenario.
sls_n_steps_plateau_walk, # type: ignore[attr-defined] # noqa F821
}.items():
if key not in acq_func_opt_kwargs:
acq_func_opt_kwargs[key] = value
acquisition_function_optimizer_instance = LocalAndSortedRandomSearch(
**acq_func_opt_kwargs # type: ignore
)
elif inspect.isclass(acquisition_function_optimizer):
acquisition_function_optimizer_instance = acquisition_function_optimizer( # type: ignore # noqa F821
**acq_func_opt_kwargs) # type: ignore # noqa F821
else:
raise TypeError(
"Argument acquisition_function_optimizer must be None or an object implementing the "
"AcquisitionFunctionMaximizer, but is '%s'" %
type(acquisition_function_optimizer))
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
tae_def_kwargs = {
"stats": self.stats,
"run_obj": scenario.run_obj,
"par_factor":
scenario.par_factor, # type: ignore[attr-defined] # noqa F821
"cost_for_crash":
scenario.cost_for_crash, # type: ignore[attr-defined] # noqa F821
"abort_on_first_run_crash": scenario.
abort_on_first_run_crash, # type: ignore[attr-defined] # noqa F821
"multi_objectives": scenario.
multi_objectives, # type: ignore[attr-defined] # noqa F821
}
if tae_runner_kwargs is not None:
tae_def_kwargs.update(tae_runner_kwargs)
if "ta" not in tae_def_kwargs:
tae_def_kwargs[
"ta"] = scenario.ta # type: ignore[attr-defined] # noqa F821
if tae_runner is None:
tae_def_kwargs[
"ta"] = scenario.ta # type: ignore[attr-defined] # noqa F821
tae_runner_instance = ExecuteTARunOld(
**tae_def_kwargs
) # type: ignore[arg-type] # noqa F821 # type: BaseRunner
elif inspect.isclass(tae_runner):
tae_runner_instance = cast(
BaseRunner, tae_runner(**tae_def_kwargs)) # type: ignore
elif callable(tae_runner):
tae_def_kwargs["ta"] = tae_runner
tae_def_kwargs[
"use_pynisher"] = scenario.limit_resources # type: ignore[attr-defined] # noqa F821
tae_def_kwargs[
"memory_limit"] = scenario.memory_limit # type: ignore[attr-defined] # noqa F821
tae_runner_instance = ExecuteTAFuncDict(
**tae_def_kwargs) # type: ignore
else:
raise TypeError(
"Argument 'tae_runner' is %s, but must be "
"either None, a callable or an object implementing "
"BaseRunner. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file." % type(tae_runner))
# In case of a parallel run, wrap the single worker in a parallel
# runner
if n_jobs is None or n_jobs == 1:
_n_jobs = 1
elif n_jobs == -1:
_n_jobs = joblib.cpu_count()
elif n_jobs > 0:
_n_jobs = n_jobs
else:
raise ValueError(
"Number of tasks must be positive, None or -1, but is %s" %
str(n_jobs))
if _n_jobs > 1 or dask_client is not None:
tae_runner_instance = DaskParallelRunner( # type: ignore
tae_runner_instance,
n_workers=_n_jobs,
output_directory=self.output_dir,
dask_client=dask_client,
)
# Check that overall objective and tae objective are the same
# TODO: remove these two ignores once the scenario object knows all its attributes!
if tae_runner_instance.run_obj != scenario.run_obj: # type: ignore[union-attr] # noqa F821
raise ValueError(
"Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" %
(tae_runner_instance.run_obj,
scenario.run_obj)) # type: ignore[union-attr] # noqa F821
if intensifier is None:
intensifier = Intensifier
if isinstance(intensifier, AbstractRacer):
intensifier_instance = intensifier
elif inspect.isclass(intensifier):
# initialize intensification
intensifier_def_kwargs = {
"stats": self.stats,
"traj_logger": traj_logger,
"rng": rng,
"instances":
scenario.train_insts, # type: ignore[attr-defined] # noqa F821
"cutoff":
scenario.cutoff, # type: ignore[attr-defined] # noqa F821
"deterministic": scenario.
deterministic, # type: ignore[attr-defined] # noqa F821
"run_obj_time": scenario.run_obj ==
"runtime", # type: ignore[attr-defined] # noqa F821
"instance_specifics": scenario.
instance_specific, # type: ignore[attr-defined] # noqa F821
"adaptive_capping_slackfactor": scenario.
intens_adaptive_capping_slackfactor, # type: ignore[attr-defined] # noqa F821
"min_chall": scenario.
intens_min_chall, # type: ignore[attr-defined] # noqa F821
}
if issubclass(intensifier, Intensifier):
intensifier_def_kwargs[
"always_race_against"] = scenario.cs.get_default_configuration(
) # type: ignore[attr-defined] # noqa F821
intensifier_def_kwargs[
"use_ta_time_bound"] = scenario.use_ta_time # type: ignore[attr-defined] # noqa F821
intensifier_def_kwargs[
"minR"] = scenario.minR # type: ignore[attr-defined] # noqa F821
intensifier_def_kwargs[
"maxR"] = scenario.maxR # type: ignore[attr-defined] # noqa F821
if intensifier_kwargs is not None:
intensifier_def_kwargs.update(intensifier_kwargs)
intensifier_instance = intensifier(
**intensifier_def_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise TypeError(
"Argument intensifier must be None or an object implementing the AbstractRacer, but is '%s'"
% type(intensifier))
# initialize multi objective
# the multi_objective_algorithm_instance will be passed to the runhistory2epm object
multi_objective_algorithm_instance = (
None) # type: Optional[AbstractMultiObjectiveAlgorithm]
if scenario.multi_objectives is not None and num_obj > 1: # type: ignore[attr-defined] # noqa F821
# define any defaults here
_multi_objective_kwargs = {"rng": rng, "num_obj": num_obj}
if multi_objective_kwargs is not None:
_multi_objective_kwargs.update(multi_objective_kwargs)
if multi_objective_algorithm is None:
multi_objective_algorithm_instance = MeanAggregationStrategy(
**_multi_objective_kwargs
) # type: ignore[arg-type] # noqa F821
elif inspect.isclass(multi_objective_algorithm):
multi_objective_algorithm_instance = multi_objective_algorithm(
**_multi_objective_kwargs)
else:
raise TypeError(
"Multi-objective algorithm not recognized: %s" %
(type(multi_objective_algorithm)))
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both"
)
init_design_def_kwargs = {
"cs": scenario.cs, # type: ignore[attr-defined] # noqa F821
"traj_logger": traj_logger,
"rng": rng,
"ta_run_limit":
scenario.ta_run_limit, # type: ignore[attr-defined] # noqa F821
"configs": initial_configurations,
"n_configs_x_params": 0,
"max_config_fracs": 0.0,
}
if initial_design_kwargs is not None:
init_design_def_kwargs.update(initial_design_kwargs)
if initial_configurations is not None:
initial_design_instance = InitialDesign(**init_design_def_kwargs)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT": # type: ignore[attr-defined] # noqa F821
init_design_def_kwargs["max_config_fracs"] = 0.0
initial_design_instance = DefaultConfiguration(
**init_design_def_kwargs)
elif scenario.initial_incumbent == "RANDOM": # type: ignore[attr-defined] # noqa F821
init_design_def_kwargs["max_config_fracs"] = 0.0
initial_design_instance = RandomConfigurations(
**init_design_def_kwargs)
elif scenario.initial_incumbent == "LHD": # type: ignore[attr-defined] # noqa F821
initial_design_instance = LHDesign(**init_design_def_kwargs)
elif scenario.initial_incumbent == "FACTORIAL": # type: ignore[attr-defined] # noqa F821
initial_design_instance = FactorialInitialDesign(
**init_design_def_kwargs)
elif scenario.initial_incumbent == "SOBOL": # type: ignore[attr-defined] # noqa F821
initial_design_instance = SobolDesign(**init_design_def_kwargs)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" %
scenario.initial_incumbent # type: ignore
) # type: ignore[attr-defined] # noqa F821
elif inspect.isclass(initial_design):
initial_design_instance = initial_design(**init_design_def_kwargs)
else:
raise TypeError(
"Argument initial_design must be None or an object implementing the InitialDesign, but is '%s'"
% type(initial_design))
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
if scenario.transform_y in [
"LOG", "LOGS"
]: # type: ignore[attr-defined] # noqa F821
cutoff = np.log(np.nanmin([
np.inf, np.float_(scenario.cutoff)
])) # type: ignore[attr-defined] # noqa F821
threshold = cutoff + np.log(
scenario.par_factor) # type: ignore[attr-defined] # noqa F821
else:
cutoff = np.nanmin([np.inf, np.float_(scenario.cutoff)
]) # type: ignore[attr-defined] # noqa F821
threshold = cutoff * scenario.par_factor # type: ignore[attr-defined] # noqa F821
num_params = len(scenario.cs.get_hyperparameters()
) # type: ignore[attr-defined] # noqa F821
imputor = RFRImputator(
rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model_instance,
change_threshold=0.01,
max_iter=2,
)
r2e_def_kwargs = {
"scenario": scenario,
"num_params": num_params,
"success_states": [
StatusType.SUCCESS,
],
"impute_censored_data": True,
"impute_state": [
StatusType.CAPPED,
],
"imputor": imputor,
"scale_perc": 5,
}
# TODO: consider other sorts of multi-objective algorithms
if isinstance(multi_objective_algorithm_instance, AggregationStrategy):
r2e_def_kwargs.update({
"multi_objective_algorithm":
multi_objective_algorithm_instance
})
if scenario.run_obj == "quality":
r2e_def_kwargs.update({
"success_states": [
StatusType.SUCCESS,
StatusType.CRASHED,
StatusType.MEMOUT,
],
"impute_censored_data":
False,
"impute_state":
None,
})
if (isinstance(intensifier_instance, (SuccessiveHalving, Hyperband))
and scenario.run_obj == "quality"):
r2e_def_kwargs.update({
"success_states": [
StatusType.SUCCESS,
StatusType.CRASHED,
StatusType.MEMOUT,
StatusType.DONOTADVANCE,
],
"consider_for_higher_budgets_state": [
StatusType.DONOTADVANCE,
StatusType.TIMEOUT,
StatusType.CRASHED,
StatusType.MEMOUT,
],
})
if runhistory2epm_kwargs is not None:
r2e_def_kwargs.update(runhistory2epm_kwargs)
if runhistory2epm is None:
if scenario.run_obj == "runtime":
rh2epm = RunHistory2EPM4LogCost(
**r2e_def_kwargs # type: ignore
) # type: ignore[arg-type] # noqa F821 # type: AbstractRunHistory2EPM
elif scenario.run_obj == "quality":
if scenario.transform_y == "NONE": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4Cost(
**r2e_def_kwargs) # type: ignore # noqa F821
elif scenario.transform_y == "LOG": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4LogCost(
**r2e_def_kwargs) # type: ignore # noqa F821
elif scenario.transform_y == "LOGS": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4LogScaledCost(
**r2e_def_kwargs) # type: ignore # noqa F821
elif scenario.transform_y == "INVS": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4InvScaledCost(
**r2e_def_kwargs) # type: ignore # noqa F821
else:
raise ValueError(
"Unknown run objective: %s. Should be either "
"quality or runtime." %
self.scenario.run_obj # type: ignore # noqa F821
)
elif inspect.isclass(runhistory2epm):
rh2epm = runhistory2epm(**
r2e_def_kwargs) # type: ignore # noqa F821
else:
raise TypeError(
"Argument runhistory2epm must be None or an object implementing the RunHistory2EPM, but is '%s'"
% type(runhistory2epm))
smbo_args = {
"scenario": scenario,
"stats": self.stats,
"initial_design": initial_design_instance,
"runhistory": runhistory,
"runhistory2epm": rh2epm,
"intensifier": intensifier_instance,
"num_run": run_id,
"model": model_instance,
"acq_optimizer": acquisition_function_optimizer_instance,
"acquisition_func": acquisition_function_instance,
"rng": rng,
"restore_incumbent": restore_incumbent,
"random_configuration_chooser":
random_configuration_chooser_instance,
"tae_runner": tae_runner_instance,
} # type: Dict[str, Any]
if smbo_class is None:
self.solver = SMBO(**
smbo_args) # type: ignore[arg-type] # noqa F821
else:
self.solver = smbo_class(
**smbo_args) # type: ignore[arg-type] # noqa F821
def __init__(
self,
scenario: Scenario,
# TODO: once we drop python3.4 add type hint
# typing.Union[ExecuteTARun, callable]
tae_runner=None,
runhistory: RunHistory = None,
intensifier: Intensifier = None,
acquisition_function: AbstractAcquisitionFunction = None,
model: AbstractEPM = None,
runhistory2epm: AbstractRunHistory2EPM = None,
initial_design: InitialDesign = None,
initial_configurations: typing.List[Configuration] = None,
stats: Stats = None,
rng: np.random.RandomState = None,
run_id: int = 1):
"""Constructor"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
aggregate_func = average_cost
self.runhistory = None
self.trajectory = None
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
self.output_dir = create_output_directory(scenario, run_id)
scenario.write()
# initialize empty runhistory
if runhistory is None:
runhistory = RunHistory(aggregate_func=aggregate_func)
# inject aggr_func if necessary
if runhistory.aggregate_func is None:
runhistory.aggregate_func = aggregate_func
# initial random number generator
num_run, rng = self._get_rng(rng=rng)
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(rng.randint(MAXINT))
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=self.output_dir, stats=self.stats)
# initial EPM
types, bounds = get_types(scenario.cs, scenario.feature_array)
if model is None:
model = RandomForestWithInstances(
types=types,
bounds=bounds,
instance_features=scenario.feature_array,
seed=rng.randint(MAXINT),
pca_components=scenario.PCA_DIM,
num_trees=scenario.rf_num_trees,
do_bootstrapping=scenario.rf_do_bootstrapping,
ratio_features=scenario.rf_ratio_features,
min_samples_split=scenario.rf_min_samples_split,
min_samples_leaf=scenario.rf_min_samples_leaf,
max_depth=scenario.rf_max_depth)
# initial acquisition function
if acquisition_function is None:
if scenario.run_obj == "runtime":
acquisition_function = LogEI(model=model)
else:
acquisition_function = EI(model=model)
# inject model if necessary
if acquisition_function.model is None:
acquisition_function.model = model
# initialize optimizer on acquisition function
local_search = LocalSearch(
acquisition_function,
scenario.cs,
max_steps=scenario.sls_max_steps,
n_steps_plateau_walk=scenario.sls_n_steps_plateau_walk)
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
if tae_runner is None:
tae_runner = ExecuteTARunOld(
ta=scenario.ta,
stats=self.stats,
run_obj=scenario.run_obj,
runhistory=runhistory,
par_factor=scenario.par_factor,
cost_for_crash=scenario.cost_for_crash)
# Second case, the tae_runner is a function to be optimized
elif callable(tae_runner):
tae_runner = ExecuteTAFuncDict(
ta=tae_runner,
stats=self.stats,
run_obj=scenario.run_obj,
memory_limit=scenario.memory_limit,
runhistory=runhistory,
par_factor=scenario.par_factor,
cost_for_crash=scenario.cost_for_crash)
# Third case, if it is an ExecuteTaRun we can simply use the
# instance. Otherwise, the next check raises an exception
elif not isinstance(tae_runner, ExecuteTARun):
raise TypeError("Argument 'tae_runner' is %s, but must be "
"either a callable or an instance of "
"ExecuteTaRun. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file." %
type(tae_runner))
# Check that overall objective and tae objective are the same
if tae_runner.run_obj != scenario.run_obj:
raise ValueError("Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" % (tae_runner.run_obj, scenario.run_obj))
# inject stats if necessary
if tae_runner.stats is None:
tae_runner.stats = self.stats
# inject runhistory if necessary
if tae_runner.runhistory is None:
tae_runner.runhistory = runhistory
# inject cost_for_crash
if tae_runner.crash_cost != scenario.cost_for_crash:
tae_runner.crash_cost = scenario.cost_for_crash
# initialize intensification
if intensifier is None:
intensifier = Intensifier(
tae_runner=tae_runner,
stats=self.stats,
traj_logger=traj_logger,
rng=rng,
instances=scenario.train_insts,
cutoff=scenario.cutoff,
deterministic=scenario.deterministic,
run_obj_time=scenario.run_obj == "runtime",
always_race_against=scenario.cs.get_default_configuration()
if scenario.always_race_default else None,
instance_specifics=scenario.instance_specific,
minR=scenario.minR,
maxR=scenario.maxR,
adaptive_capping_slackfactor=scenario.
intens_adaptive_capping_slackfactor,
min_chall=scenario.intens_min_chall)
# inject deps if necessary
if intensifier.tae_runner is None:
intensifier.tae_runner = tae_runner
if intensifier.stats is None:
intensifier.stats = self.stats
if intensifier.traj_logger is None:
intensifier.traj_logger = traj_logger
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both"
)
if initial_configurations is not None:
initial_design = MultiConfigInitialDesign(
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
runhistory=runhistory,
rng=rng,
configs=initial_configurations,
intensifier=intensifier,
aggregate_func=aggregate_func)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT":
initial_design = DefaultConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "RANDOM":
initial_design = RandomConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" % scenario.initial_incumbent)
# inject deps if necessary
if initial_design.tae_runner is None:
initial_design.tae_runner = tae_runner
if initial_design.scenario is None:
initial_design.scenario = scenario
if initial_design.stats is None:
initial_design.stats = self.stats
if initial_design.traj_logger is None:
initial_design.traj_logger = traj_logger
# initial conversion of runhistory into EPM data
if runhistory2epm is None:
num_params = len(scenario.cs.get_hyperparameters())
if scenario.run_obj == "runtime":
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log(scenario.cutoff)
threshold = np.log(scenario.cutoff * scenario.par_factor)
imputor = RFRImputator(rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=2)
runhistory2epm = RunHistory2EPM4LogCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.CAPPED,
],
imputor=imputor)
elif scenario.run_obj == 'quality':
runhistory2epm = RunHistory2EPM4Cost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=False,
impute_state=None)
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
# inject scenario if necessary:
if runhistory2epm.scenario is None:
runhistory2epm.scenario = scenario
self.solver = EPILS_Solver(scenario=scenario,
stats=self.stats,
initial_design=initial_design,
runhistory=runhistory,
runhistory2epm=runhistory2epm,
intensifier=intensifier,
aggregate_func=aggregate_func,
num_run=num_run,
model=model,
acq_optimizer=local_search,
acquisition_func=acquisition_function,
rng=rng)
def __init__(self,
scenario: Scenario,
tae_runner: Optional[Union[Type[ExecuteTARun], Callable]] = None,
tae_runner_kwargs: Optional[dict] = None,
runhistory: Optional[Union[Type[RunHistory], RunHistory]] = None,
runhistory_kwargs: Optional[dict] = None,
intensifier: Optional[Type[Intensifier]] = None,
intensifier_kwargs: Optional[dict] = None,
acquisition_function: Optional[Type[AbstractAcquisitionFunction]] = None,
acquisition_function_kwargs: Optional[dict] = None,
integrate_acquisition_function: bool = False,
acquisition_function_optimizer: Optional[Type[AcquisitionFunctionMaximizer]] = None,
acquisition_function_optimizer_kwargs: Optional[dict] = None,
model: Optional[Type[AbstractEPM]] = None,
model_kwargs: Optional[dict] = None,
runhistory2epm: Optional[Type[AbstractRunHistory2EPM]] = None,
runhistory2epm_kwargs: Optional[dict] = None,
initial_design: Optional[Type[InitialDesign]] = None,
initial_design_kwargs: Optional[dict] = None,
initial_configurations: Optional[List[Configuration]] = None,
stats: Optional[Stats] = None,
restore_incumbent: Optional[Configuration] = None,
rng: Optional[Union[np.random.RandomState, int]] = None,
smbo_class: Optional[SMBO] = None,
run_id: Optional[int] = None,
random_configuration_chooser: Optional[Type[RandomConfigurationChooser]] = None,
random_configuration_chooser_kwargs: Optional[dict] = None
):
"""
Constructor
Parameters
----------
scenario : ~smac.scenario.scenario.Scenario
Scenario object
tae_runner : ~smac.tae.execute_ta_run.ExecuteTARun or callable
Callable or implementation of
:class:`~smac.tae.execute_ta_run.ExecuteTARun`. In case a
callable is passed it will be wrapped by
:class:`~smac.tae.execute_func.ExecuteTAFuncDict`.
If not set, it will be initialized with the
:class:`~smac.tae.execute_ta_run_old.ExecuteTARunOld`.
tae_runner_kwargs: Optional[dict]
arguments passed to constructor of '~tae_runner'
runhistory : RunHistory
runhistory to store all algorithm runs
runhistory_kwargs : Optional[dict]
arguments passed to constructor of runhistory.
We strongly advise against changing the aggregation function,
since it will break some code assumptions
intensifier : Intensifier
intensification object to issue a racing to decide the current
incumbent
intensifier_kwargs: Optional[dict]
arguments passed to the constructor of '~intensifier'
acquisition_function : ~smac.optimizer.acquisition.AbstractAcquisitionFunction
Class or object that implements the :class:`~smac.optimizer.acquisition.AbstractAcquisitionFunction`.
Will use :class:`~smac.optimizer.acquisition.EI` or :class:`~smac.optimizer.acquisition.LogEI` if not set.
`~acquisition_function_kwargs` is passed to the class constructor.
acquisition_function_kwargs : Optional[dict]
dictionary to pass specific arguments to ~acquisition_function
integrate_acquisition_function : bool, default=False
Whether to integrate the acquisition function. Works only with models which can sample their
hyperparameters (i.e. GaussianProcessMCMC).
acquisition_function_optimizer : ~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Object that implements the :class:`~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer`.
Will use :class:`smac.optimizer.ei_optimization.InterleavedLocalAndRandomSearch` if not set.
acquisition_function_optimizer_kwargs: Optional[dict]
Arguments passed to constructor of '~acquisition_function_optimizer'
model : AbstractEPM
Model that implements train() and predict(). Will use a
:class:`~smac.epm.rf_with_instances.RandomForestWithInstances` if not set.
model_kwargs : Optional[dict]
Arguments passed to constructor of '~model'
runhistory2epm : ~smac.runhistory.runhistory2epm.RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use :class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4Cost`
if objective is cost or
:class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost`
if objective is runtime.
runhistory2epm_kwargs: Optional[dict]
Arguments passed to the constructor of '~runhistory2epm'
initial_design : InitialDesign
initial sampling design
initial_design_kwargs: Optional[dict]
arguments passed to constructor of `~initial_design'
initial_configurations : List[Configuration]
list of initial configurations for initial design --
cannot be used together with initial_design
stats : Stats
optional stats object
rng : np.random.RandomState
Random number generator
restore_incumbent : Configuration
incumbent used if restoring to previous state
smbo_class : ~smac.optimizer.smbo.SMBO
Class implementing the SMBO interface which will be used to
instantiate the optimizer class.
run_id : int (optional)
Run ID will be used as subfolder for output_dir. If no ``run_id`` is given, a random ``run_id`` will be
chosen.
random_configuration_chooser : ~smac.optimizer.random_configuration_chooser.RandomConfigurationChooser
How often to choose a random configuration during the intensification procedure.
random_configuration_chooser_kwargs : Optional[dict]
arguments of constructor for '~random_configuration_chooser'
"""
self.logger = logging.getLogger(
self.__module__ + "." + self.__class__.__name__)
aggregate_func = average_cost
self.scenario = scenario
self.output_dir = ""
if not restore_incumbent:
# restore_incumbent is used by the CLI interface which provides a method for restoring a SMAC run given an
# output directory. This is the default path.
# initial random number generator
# run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
# run_id=datetime.now().strftime("%Y%m%d%H%M%S%f")
run_id=uuid1()
self.output_dir = create_output_directory(scenario, run_id) # fixme run_id
elif scenario.output_dir is not None:
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
# output-directory is created in CLI when restoring from a
# folder. calling the function again in the facade results in two
# folders being created: run_X and run_X.OLD. if we are
# restoring, the output-folder exists already and we omit creating it,
# but set the self-output_dir to the dir.
# necessary because we want to write traj to new output-dir in CLI.
self.output_dir = scenario.output_dir_for_this_run
if (
scenario.deterministic is True
and getattr(scenario, 'tuner_timeout', None) is None
and scenario.run_obj == 'quality'
):
self.logger.info('Optimizing a deterministic scenario for quality without a tuner timeout - will make '
'SMAC deterministic and only evaluate one configuration per iteration!')
scenario.intensification_percentage = 1e-10
scenario.min_chall = 1
scenario.write()
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario,file_system=scenario.file_system)
if self.scenario.run_obj == "runtime" and not self.scenario.transform_y == "LOG":
self.logger.warning("Runtime as objective automatically activates log(y) transformation")
self.scenario.transform_y = "LOG"
# initialize empty runhistory
runhistory_def_kwargs = {'aggregate_func': aggregate_func}
if runhistory_kwargs is not None:
runhistory_def_kwargs.update(runhistory_kwargs)
if runhistory is None:
runhistory = RunHistory(**runhistory_def_kwargs,file_system=scenario.file_system)
elif inspect.isclass(runhistory):
runhistory = runhistory(**runhistory_def_kwargs)
else:
if runhistory.aggregate_func is None:
runhistory.aggregate_func = aggregate_func
rand_conf_chooser_kwargs = {
'rng': rng
}
if random_configuration_chooser_kwargs is not None:
rand_conf_chooser_kwargs.update(random_configuration_chooser_kwargs)
if random_configuration_chooser is None:
if 'prob' not in rand_conf_chooser_kwargs:
rand_conf_chooser_kwargs['prob'] = scenario.rand_prob
random_configuration_chooser = ChooserProb(**rand_conf_chooser_kwargs)
elif inspect.isclass(random_configuration_chooser):
random_configuration_chooser = random_configuration_chooser(**rand_conf_chooser_kwargs)
elif not isinstance(random_configuration_chooser, RandomConfigurationChooser):
raise ValueError("random_configuration_chooser has to be"
" a class or object of RandomConfigurationChooser")
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(rng.randint(MAXINT))
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=self.output_dir, stats=self.stats,file_system=scenario.file_system)
# initial EPM
types, bounds = get_types(scenario.cs, scenario.feature_array)
model_def_kwargs = {
'types': types,
'bounds': bounds,
'instance_features': scenario.feature_array,
'seed': rng.randint(MAXINT),
'pca_components': scenario.PCA_DIM,
}
if model_kwargs is not None:
model_def_kwargs.update(model_kwargs)
if model is None:
for key, value in {
'log_y': scenario.transform_y in ["LOG", "LOGS"],
'num_trees': scenario.rf_num_trees,
'do_bootstrapping': scenario.rf_do_bootstrapping,
'ratio_features': scenario.rf_ratio_features,
'min_samples_split': scenario.rf_min_samples_split,
'min_samples_leaf': scenario.rf_min_samples_leaf,
'max_depth': scenario.rf_max_depth,
}.items():
if key not in model_def_kwargs:
model_def_kwargs[key] = value
model_def_kwargs['configspace'] = self.scenario.cs
model = RandomForestWithInstances(**model_def_kwargs)
elif inspect.isclass(model):
model_def_kwargs['configspace'] = self.scenario.cs
model = model(**model_def_kwargs)
else:
raise TypeError(
"Model not recognized: %s" %(type(model)))
# initial acquisition function
acq_def_kwargs = {'model': model}
if acquisition_function_kwargs is not None:
acq_def_kwargs.update(acquisition_function_kwargs)
if acquisition_function is None:
if scenario.transform_y in ["LOG", "LOGS"]:
acquisition_function = LogEI(**acq_def_kwargs)
else:
acquisition_function = EI(**acq_def_kwargs)
elif inspect.isclass(acquisition_function):
acquisition_function = acquisition_function(**acq_def_kwargs)
else:
raise TypeError(
"Argument acquisition_function must be None or an object implementing the "
"AbstractAcquisitionFunction, not %s."
% type(acquisition_function)
)
if integrate_acquisition_function:
acquisition_function = IntegratedAcquisitionFunction(
acquisition_function=acquisition_function,
**acq_def_kwargs
)
# initialize optimizer on acquisition function
acq_func_opt_kwargs = {
'acquisition_function': acquisition_function,
'config_space': scenario.cs,
'rng': rng,
}
if acquisition_function_optimizer_kwargs is not None:
acq_func_opt_kwargs.update(acquisition_function_optimizer_kwargs)
if acquisition_function_optimizer is None:
for key, value in {
'max_steps': scenario.sls_max_steps,
'n_steps_plateau_walk': scenario.sls_n_steps_plateau_walk,
}.items():
if key not in acq_func_opt_kwargs:
acq_func_opt_kwargs[key] = value
acquisition_function_optimizer = InterleavedLocalAndRandomSearch(**acq_func_opt_kwargs)
elif inspect.isclass(acquisition_function_optimizer):
acquisition_function_optimizer = acquisition_function_optimizer(**acq_func_opt_kwargs)
else:
raise TypeError(
"Argument acquisition_function_optimizer must be None or an object implementing the "
"AcquisitionFunctionMaximizer, but is '%s'" %
type(acquisition_function_optimizer)
)
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
tae_def_kwargs = {
'stats': self.stats,
'run_obj': scenario.run_obj,
'runhistory': runhistory,
'par_factor': scenario.par_factor,
'cost_for_crash': scenario.cost_for_crash,
'abort_on_first_run_crash': scenario.abort_on_first_run_crash
}
if tae_runner_kwargs is not None:
tae_def_kwargs.update(tae_runner_kwargs)
if 'ta' not in tae_def_kwargs:
tae_def_kwargs['ta'] = scenario.ta
if tae_runner is None:
tae_def_kwargs['ta'] = scenario.ta
tae_runner = ExecuteTARunOld(**tae_def_kwargs)
elif inspect.isclass(tae_runner):
tae_runner = tae_runner(**tae_def_kwargs)
elif callable(tae_runner):
tae_def_kwargs['ta'] = tae_runner
tae_runner = ExecuteTAFuncDict(**tae_def_kwargs)
else:
raise TypeError("Argument 'tae_runner' is %s, but must be "
"either None, a callable or an object implementing "
"ExecuteTaRun. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file."
% type(tae_runner))
# Check that overall objective and tae objective are the same
if tae_runner.run_obj != scenario.run_obj:
raise ValueError("Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" % (tae_runner.run_obj, scenario.run_obj))
# initialize intensification
intensifier_def_kwargs = {
'tae_runner': tae_runner,
'stats': self.stats,
'traj_logger': traj_logger,
'rng': rng,
'instances': scenario.train_insts,
'cutoff': scenario.cutoff,
'deterministic': scenario.deterministic,
'run_obj_time': scenario.run_obj == "runtime",
'always_race_against': scenario.cs.get_default_configuration()
if scenario.always_race_default else None,
'use_ta_time_bound': scenario.use_ta_time,
'instance_specifics': scenario.instance_specific,
'minR': scenario.minR,
'maxR': scenario.maxR,
'adaptive_capping_slackfactor': scenario.intens_adaptive_capping_slackfactor,
'min_chall': scenario.intens_min_chall
}
if intensifier_kwargs is not None:
intensifier_def_kwargs.update(intensifier_kwargs)
if intensifier is None:
intensifier = Intensifier(**intensifier_def_kwargs)
elif inspect.isclass(intensifier):
intensifier = intensifier(**intensifier_def_kwargs)
else:
raise TypeError(
"Argument intensifier must be None or an object implementing the Intensifier, but is '%s'" %
type(intensifier)
)
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both")
init_design_def_kwargs = {
'tae_runner': tae_runner,
'scenario': scenario,
'stats': self.stats,
'traj_logger': traj_logger,
'runhistory': runhistory,
'rng': rng,
'configs': initial_configurations,
'intensifier': intensifier,
'aggregate_func': aggregate_func,
'n_configs_x_params': 0,
'max_config_fracs': 0.0
}
if initial_design_kwargs is not None:
init_design_def_kwargs.update(initial_design_kwargs)
if initial_configurations is not None:
initial_design = InitialDesign(**init_design_def_kwargs)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT":
init_design_def_kwargs['max_config_fracs'] = 0.0
initial_design = DefaultConfiguration(**init_design_def_kwargs)
elif scenario.initial_incumbent == "RANDOM":
init_design_def_kwargs['max_config_fracs'] = 0.0
initial_design = RandomConfigurations(**init_design_def_kwargs)
elif scenario.initial_incumbent == "LHD":
initial_design = LHDesign(**init_design_def_kwargs)
elif scenario.initial_incumbent == "FACTORIAL":
initial_design = FactorialInitialDesign(**init_design_def_kwargs)
elif scenario.initial_incumbent == "SOBOL":
initial_design = SobolDesign(**init_design_def_kwargs)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" % scenario.initial_incumbent)
elif inspect.isclass(initial_design):
initial_design = initial_design(**init_design_def_kwargs)
else:
raise TypeError(
"Argument initial_design must be None or an object implementing the InitialDesign, but is '%s'" %
type(initial_design)
)
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
if scenario.transform_y in ["LOG", "LOGS"]:
cutoff = np.log(np.nanmin([np.inf, np.float_(scenario.cutoff)]))
threshold = cutoff + np.log(scenario.par_factor)
else:
cutoff = np.nanmin([np.inf, np.float_(scenario.cutoff)])
threshold = cutoff * scenario.par_factor
num_params = len(scenario.cs.get_hyperparameters())
imputor = RFRImputator(rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=2)
r2e_def_kwargs = {
'scenario': scenario,
'num_params': num_params,
'success_states': [StatusType.SUCCESS, ],
'impute_censored_data': True,
'impute_state': [StatusType.CAPPED, ],
'imputor': imputor,
'scale_perc': 5
}
if scenario.run_obj == 'quality':
r2e_def_kwargs.update({
'success_states': [StatusType.SUCCESS, StatusType.CRASHED],
'impute_censored_data': False,
'impute_state': None,
})
if runhistory2epm_kwargs is not None:
r2e_def_kwargs.update(runhistory2epm_kwargs)
if runhistory2epm is None:
if scenario.run_obj == 'runtime':
runhistory2epm = RunHistory2EPM4LogCost(**r2e_def_kwargs)
elif scenario.run_obj == 'quality':
if scenario.transform_y == "NONE":
runhistory2epm = RunHistory2EPM4Cost(**r2e_def_kwargs)
elif scenario.transform_y == "LOG":
runhistory2epm = RunHistory2EPM4LogCost(**r2e_def_kwargs)
elif scenario.transform_y == "LOGS":
runhistory2epm = RunHistory2EPM4LogScaledCost(**r2e_def_kwargs)
elif scenario.transform_y == "INVS":
runhistory2epm = RunHistory2EPM4InvScaledCost(**r2e_def_kwargs)
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
elif inspect.isclass(runhistory2epm):
runhistory2epm = runhistory2epm(**r2e_def_kwargs)
else:
raise TypeError(
"Argument runhistory2epm must be None or an object implementing the RunHistory2EPM, but is '%s'" %
type(runhistory2epm)
)
smbo_args = {
'scenario': scenario,
'stats': self.stats,
'initial_design': initial_design,
'runhistory': runhistory,
'runhistory2epm': runhistory2epm,
'intensifier': intensifier,
'aggregate_func': aggregate_func,
'num_run': run_id,
'model': model,
'acq_optimizer': acquisition_function_optimizer,
'acquisition_func': acquisition_function,
'rng': rng,
'restore_incumbent': restore_incumbent,
'random_configuration_chooser': random_configuration_chooser
}
if smbo_class is None:
self.solver = SMBO(**smbo_args)
else:
self.solver = smbo_class(**smbo_args)
def convert_data_for_epm(scenario: Scenario,
runhistory: RunHistory,
logger=None):
"""
converts data from runhistory into EPM format
Parameters
----------
scenario: Scenario
smac.scenario.scenario.Scenario Object
runhistory: RunHistory
smac.runhistory.runhistory.RunHistory Object with all necessary data
Returns
-------
X: np.array
X matrix with configuartion x features for all observed samples
y: np.array
y matrix with all observations
types: np.array
types of X cols -- necessary to train our RF implementation
"""
types, bounds = get_types(scenario.cs, scenario.feature_array)
model = RandomForestWithInstances(types, bounds)
params = scenario.cs.get_hyperparameters()
num_params = len(params)
run_obj = scenario.run_obj
if run_obj == "runtime":
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(scenario.cutoff)
threshold = np.log10(scenario.cutoff * scenario.par_factor)
imputor = RFRImputator(rng=np.random.RandomState(42),
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=10)
# TODO: Adapt runhistory2EPM object based on scenario
rh2EPM = RunHistory2EPM4LogCost(scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.TIMEOUT,
],
imputor=imputor)
X, Y = rh2EPM.transform(runhistory)
else:
rh2EPM = RunHistory2EPM4Cost(scenario=scenario,
num_params=num_params,
success_states=None,
impute_censored_data=False,
impute_state=None)
X, Y = rh2EPM.transform(runhistory)
return X, Y, types
def convert_data_for_epm(scenario: Scenario,
runhistory: RunHistory,
impute_inactive_parameters=False,
rng=None,
logger=None):
"""
converts data from runhistory into EPM format
Parameters
----------
scenario: Scenario
smac.scenario.scenario.Scenario Object
runhistory: RunHistory
smac.runhistory.runhistory.RunHistory Object with all necessary data
impute_inactive_parameters: bool
whether to impute all inactive parameters in all configurations - this is needed for random forests, as they do not accept nan-values
Returns
-------
X: np.array
X matrix with configuartion x features for all observed samples
y: np.array
y matrix with all observations
types: np.array
types of X cols -- necessary to train our RF implementation
"""
if rng is None:
rng = np.random.RandomState(42)
if impute_inactive_parameters:
runhistory = force_finite_runhistory(runhistory)
types, bounds = get_types(scenario.cs, scenario.feature_array)
if logger is not None:
logger.debug("Types: " + str(types) + ", Bounds: " + str(bounds))
model = RandomForestWithInstances(scenario.cs, types, bounds,
rng.randint(MAXINT))
params = scenario.cs.get_hyperparameters()
num_params = len(params)
run_obj = scenario.run_obj
if run_obj == "runtime":
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(scenario.cutoff)
threshold = np.log10(scenario.cutoff * scenario.par_factor)
imputor = RFRImputator(rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=10)
# TODO: Adapt runhistory2EPM object based on scenario
rh2EPM = RunHistory2EPM4LogCost(scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.TIMEOUT,
],
imputor=imputor)
X, Y = rh2EPM.transform(runhistory)
else:
rh2EPM = RunHistory2EPM4Cost(scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=False,
impute_state=None)
X, Y = rh2EPM.transform(runhistory)
return X, Y, types
def __init__(
self,
scenario: Scenario,
tae_runner: typing.Optional[typing.Union[ExecuteTARun,
typing.Callable]] = None,
runhistory: typing.Optional[RunHistory] = None,
intensifier: typing.Optional[Intensifier] = None,
acquisition_function: typing.
Optional[AbstractAcquisitionFunction] = None,
acquisition_function_optimizer: typing.
Optional[AcquisitionFunctionMaximizer] = None,
model: typing.Optional[AbstractEPM] = None,
runhistory2epm: typing.Optional[AbstractRunHistory2EPM] = None,
initial_design: typing.Optional[InitialDesign] = None,
initial_configurations: typing.Optional[
typing.List[Configuration]] = None,
stats: typing.Optional[Stats] = None,
restore_incumbent: typing.Optional[Configuration] = None,
rng: typing.Optional[typing.Union[np.random.RandomState, int]] = None,
smbo_class: typing.Optional[SMBO] = None,
run_id: typing.Optional[int] = None,
random_configuration_chooser: typing.
Optional[RandomConfigurationChooser] = None):
"""
Constructor
Parameters
----------
scenario : ~smac.scenario.scenario.Scenario
Scenario object
tae_runner : ~smac.tae.execute_ta_run.ExecuteTARun or callable
Callable or implementation of
:class:`~smac.tae.execute_ta_run.ExecuteTARun`. In case a
callable is passed it will be wrapped by
:class:`~smac.tae.execute_func.ExecuteTAFuncDict`.
If not set, it will be initialized with the
:class:`~smac.tae.execute_ta_run_old.ExecuteTARunOld`.
runhistory : RunHistory
runhistory to store all algorithm runs
intensifier : Intensifier
intensification object to issue a racing to decide the current
incumbent
acquisition_function : ~smac.optimizer.acquisition.AbstractAcquisitionFunction
Object that implements the :class:`~smac.optimizer.acquisition.AbstractAcquisitionFunction`.
Will use :class:`~smac.optimizer.acquisition.EI` if not set.
acquisition_function_optimizer : ~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Object that implements the :class:`~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer`.
Will use :class:`smac.optimizer.ei_optimization.InterleavedLocalAndRandomSearch` if not set.
model : AbstractEPM
Model that implements train() and predict(). Will use a
:class:`~smac.epm.rf_with_instances.RandomForestWithInstances` if not set.
runhistory2epm : ~smac.runhistory.runhistory2epm.RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use :class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4Cost`
if objective is cost or
:class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost`
if objective is runtime.
initial_design : InitialDesign
initial sampling design
initial_configurations : typing.List[Configuration]
list of initial configurations for initial design --
cannot be used together with initial_design
stats : Stats
optional stats object
rng : np.random.RandomState
Random number generator
restore_incumbent : Configuration
incumbent used if restoring to previous state
smbo_class : ~smac.optimizer.smbo.SMBO
Class implementing the SMBO interface which will be used to
instantiate the optimizer class.
run_id : int (optional)
Run ID will be used as subfolder for output_dir. If no ``run_id`` is given, a random ``run_id`` will be
chosen.
random_configuration_chooser : ~smac.optimizer.random_configuration_chooser.RandomConfigurationChooser
How often to choose a random configuration during the intensification procedure.
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
aggregate_func = average_cost
self.scenario = scenario
self.output_dir = ""
if not restore_incumbent:
# restore_incumbent is used by the CLI interface which provides a method for restoring a SMAC run given an
# output directory. This is the default path.
# initial random number generator
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
self.output_dir = create_output_directory(scenario, run_id)
elif scenario.output_dir is not None:
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
# output-directory is created in CLI when restoring from a
# folder. calling the function again in the facade results in two
# folders being created: run_X and run_X.OLD. if we are
# restoring, the output-folder exists already and we omit creating it,
# but set the self-output_dir to the dir.
# necessary because we want to write traj to new output-dir in CLI.
self.output_dir = scenario.output_dir_for_this_run
if (scenario.deterministic is True
and getattr(scenario, 'tuner_timeout', None) is None
and scenario.run_obj == 'quality'):
self.logger.info('Optimizing a deterministic scenario for '
'quality without a tuner timeout - will make '
'SMAC deterministic!')
scenario.intensification_percentage = 1e-10
scenario.write()
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
if self.scenario.run_obj == "runtime" and not self.scenario.transform_y == "LOG":
self.logger.warn(
"Runtime as objective automatically activates log(y) transformation"
)
self.scenario.transform_y = "LOG"
# initialize empty runhistory
if runhistory is None:
runhistory = RunHistory(aggregate_func=aggregate_func)
# inject aggr_func if necessary
if runhistory.aggregate_func is None:
runhistory.aggregate_func = aggregate_func
if not random_configuration_chooser:
random_configuration_chooser = ChooserProb(prob=scenario.rand_prob,
rng=rng)
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(rng.randint(MAXINT))
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=self.output_dir, stats=self.stats)
# initial EPM
types, bounds = get_types(scenario.cs, scenario.feature_array)
if model is None:
model = RandomForestWithInstances(
types=types,
bounds=bounds,
instance_features=scenario.feature_array,
seed=rng.randint(MAXINT),
pca_components=scenario.PCA_DIM,
log_y=scenario.transform_y in ["LOG", "LOGS"],
num_trees=scenario.rf_num_trees,
do_bootstrapping=scenario.rf_do_bootstrapping,
ratio_features=scenario.rf_ratio_features,
min_samples_split=scenario.rf_min_samples_split,
min_samples_leaf=scenario.rf_min_samples_leaf,
max_depth=scenario.rf_max_depth)
# initial acquisition function
if acquisition_function is None:
if scenario.transform_y in ["LOG", "LOGS"]:
acquisition_function = LogEI(model=model)
else:
acquisition_function = EI(model=model)
# inject model if necessary
if acquisition_function.model is None:
acquisition_function.model = model
# initialize optimizer on acquisition function
if acquisition_function_optimizer is None:
acquisition_function_optimizer = InterleavedLocalAndRandomSearch(
acquisition_function=acquisition_function,
config_space=scenario.cs,
rng=np.random.RandomState(seed=rng.randint(MAXINT)),
max_steps=scenario.sls_max_steps,
n_steps_plateau_walk=scenario.sls_n_steps_plateau_walk)
elif not isinstance(
acquisition_function_optimizer,
AcquisitionFunctionMaximizer,
):
raise ValueError(
"Argument 'acquisition_function_optimizer' must be of type"
"'AcquisitionFunctionMaximizer', but is '%s'" %
type(acquisition_function_optimizer))
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
if tae_runner is None:
tae_runner = ExecuteTARunOld(
ta=scenario.ta,
stats=self.stats,
run_obj=scenario.run_obj,
runhistory=runhistory,
par_factor=scenario.par_factor,
cost_for_crash=scenario.cost_for_crash,
abort_on_first_run_crash=scenario.abort_on_first_run_crash)
# Second case, the tae_runner is a function to be optimized
elif callable(tae_runner):
tae_runner = ExecuteTAFuncDict(
ta=tae_runner,
stats=self.stats,
run_obj=scenario.run_obj,
memory_limit=scenario.memory_limit,
runhistory=runhistory,
par_factor=scenario.par_factor,
cost_for_crash=scenario.cost_for_crash,
abort_on_first_run_crash=scenario.abort_on_first_run_crash)
# Third case, if it is an ExecuteTaRun we can simply use the
# instance. Otherwise, the next check raises an exception
elif not isinstance(tae_runner, ExecuteTARun):
raise TypeError("Argument 'tae_runner' is %s, but must be "
"either a callable or an instance of "
"ExecuteTaRun. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file." %
type(tae_runner))
# Check that overall objective and tae objective are the same
if tae_runner.run_obj != scenario.run_obj:
raise ValueError("Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" % (tae_runner.run_obj, scenario.run_obj))
# inject stats if necessary
if tae_runner.stats is None:
tae_runner.stats = self.stats
# inject runhistory if necessary
if tae_runner.runhistory is None:
tae_runner.runhistory = runhistory
# inject cost_for_crash
if tae_runner.crash_cost != scenario.cost_for_crash:
tae_runner.crash_cost = scenario.cost_for_crash
# initialize intensification
if intensifier is None:
intensifier = Intensifier(
tae_runner=tae_runner,
stats=self.stats,
traj_logger=traj_logger,
rng=rng,
instances=scenario.train_insts,
cutoff=scenario.cutoff,
deterministic=scenario.deterministic,
run_obj_time=scenario.run_obj == "runtime",
always_race_against=scenario.cs.get_default_configuration()
if scenario.always_race_default else None,
use_ta_time_bound=scenario.use_ta_time,
instance_specifics=scenario.instance_specific,
minR=scenario.minR,
maxR=scenario.maxR,
adaptive_capping_slackfactor=scenario.
intens_adaptive_capping_slackfactor,
min_chall=scenario.intens_min_chall)
# inject deps if necessary
if intensifier.tae_runner is None:
intensifier.tae_runner = tae_runner
if intensifier.stats is None:
intensifier.stats = self.stats
if intensifier.traj_logger is None:
intensifier.traj_logger = traj_logger
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both"
)
if initial_configurations is not None:
initial_design = MultiConfigInitialDesign(
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
runhistory=runhistory,
rng=rng,
configs=initial_configurations,
intensifier=intensifier,
aggregate_func=aggregate_func)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT":
initial_design = DefaultConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "RANDOM":
initial_design = RandomConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "LHD":
initial_design = LHDesign(runhistory=runhistory,
intensifier=intensifier,
aggregate_func=aggregate_func,
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "FACTORIAL":
initial_design = FactorialInitialDesign(
runhistory=runhistory,
intensifier=intensifier,
aggregate_func=aggregate_func,
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "SOBOL":
initial_design = SobolDesign(runhistory=runhistory,
intensifier=intensifier,
aggregate_func=aggregate_func,
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" % scenario.initial_incumbent)
# inject deps if necessary
if initial_design.tae_runner is None:
initial_design.tae_runner = tae_runner
if initial_design.scenario is None:
initial_design.scenario = scenario
if initial_design.stats is None:
initial_design.stats = self.stats
if initial_design.traj_logger is None:
initial_design.traj_logger = traj_logger
# initial conversion of runhistory into EPM data
if runhistory2epm is None:
num_params = len(scenario.cs.get_hyperparameters())
if scenario.run_obj == 'runtime':
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log(scenario.cutoff)
threshold = np.log(scenario.cutoff * scenario.par_factor)
imputor = RFRImputator(rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=2)
runhistory2epm = RunHistory2EPM4LogCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.CAPPED,
],
imputor=imputor)
elif scenario.run_obj == 'quality':
if scenario.transform_y == "NONE":
runhistory2epm = RunHistory2EPM4Cost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS, StatusType.CRASHED
],
impute_censored_data=False,
impute_state=None)
elif scenario.transform_y == "LOG":
runhistory2epm = RunHistory2EPM4LogCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS, StatusType.CRASHED
],
impute_censored_data=False,
impute_state=None)
elif scenario.transform_y == "LOGS":
runhistory2epm = RunHistory2EPM4LogScaledCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS, StatusType.CRASHED
],
impute_censored_data=False,
impute_state=None)
elif scenario.transform_y == "INVS":
runhistory2epm = RunHistory2EPM4InvScaledCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS, StatusType.CRASHED
],
impute_censored_data=False,
impute_state=None)
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
# inject scenario if necessary:
if runhistory2epm.scenario is None:
runhistory2epm.scenario = scenario
smbo_args = {
'scenario': scenario,
'stats': self.stats,
'initial_design': initial_design,
'runhistory': runhistory,
'runhistory2epm': runhistory2epm,
'intensifier': intensifier,
'aggregate_func': aggregate_func,
'num_run': run_id,
'model': model,
'acq_optimizer': acquisition_function_optimizer,
'acquisition_func': acquisition_function,
'rng': rng,
'restore_incumbent': restore_incumbent,
'random_configuration_chooser': random_configuration_chooser
}
if smbo_class is None:
self.solver = SMBO(**smbo_args)
else:
self.solver = smbo_class(**smbo_args)
def __init__(
self,
scenario: Scenario,
tae_runner: Optional[Union[Type[BaseRunner], Callable]] = None,
tae_runner_kwargs: Optional[Dict] = None,
runhistory: Optional[Union[Type[RunHistory], RunHistory]] = None,
runhistory_kwargs: Optional[Dict] = None,
intensifier: Optional[Type[AbstractRacer]] = None,
intensifier_kwargs: Optional[Dict] = None,
acquisition_function: Optional[
Type[AbstractAcquisitionFunction]] = None,
acquisition_function_kwargs: Optional[Dict] = None,
integrate_acquisition_function: bool = False,
acquisition_function_optimizer: Optional[
Type[AcquisitionFunctionMaximizer]] = None,
acquisition_function_optimizer_kwargs: Optional[Dict] = None,
model: Optional[Type[AbstractEPM]] = None,
model_kwargs: Optional[Dict] = None,
runhistory2epm: Optional[Type[AbstractRunHistory2EPM]] = None,
runhistory2epm_kwargs: Optional[Dict] = None,
initial_design: Optional[Type[InitialDesign]] = None,
initial_design_kwargs: Optional[Dict] = None,
initial_configurations: Optional[List[Configuration]] = None,
stats: Optional[Stats] = None,
restore_incumbent: Optional[Configuration] = None,
rng: Optional[Union[np.random.RandomState, int]] = None,
smbo_class: Optional[Type[SMBO]] = None,
run_id: Optional[int] = None,
random_configuration_chooser: Optional[
Type[RandomConfigurationChooser]] = None,
random_configuration_chooser_kwargs: Optional[Dict] = None,
dask_client: Optional[dask.distributed.Client] = None,
n_jobs: Optional[int] = 1,
):
"""
Constructor
Parameters
----------
scenario : ~smac.scenario.scenario.Scenario
Scenario object
tae_runner : ~smac.tae.base.BaseRunner or callable
Callable or implementation of
:class:`~smac.tae.base.BaseRunner`. In case a
callable is passed it will be wrapped by
:class:`~smac.tae.execute_func.ExecuteTAFuncDict`.
If not set, it will be initialized with the
:class:`~smac.tae.execute_ta_run_old.ExecuteTARunOld`.
tae_runner_kwargs: Optional[Dict]
arguments passed to constructor of '~tae_runner'
runhistory : RunHistory
runhistory to store all algorithm runs
runhistory_kwargs : Optional[Dict]
arguments passed to constructor of runhistory.
We strongly advise against changing the aggregation function,
since it will break some code assumptions
intensifier : Intensifier
intensification object to issue a racing to decide the current
incumbent
intensifier_kwargs: Optional[Dict]
arguments passed to the constructor of '~intensifier'
acquisition_function : ~smac.optimizer.acquisition.AbstractAcquisitionFunction
Class or object that implements the :class:`~smac.optimizer.acquisition.AbstractAcquisitionFunction`.
Will use :class:`~smac.optimizer.acquisition.EI` or :class:`~smac.optimizer.acquisition.LogEI` if not set.
`~acquisition_function_kwargs` is passed to the class constructor.
acquisition_function_kwargs : Optional[Dict]
dictionary to pass specific arguments to ~acquisition_function
integrate_acquisition_function : bool, default=False
Whether to integrate the acquisition function. Works only with models which can sample their
hyperparameters (i.e. GaussianProcessMCMC).
acquisition_function_optimizer : ~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Object that implements the :class:`~smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer`.
Will use :class:`smac.optimizer.ei_optimization.LocalAndSortedRandomSearch` if not set.
acquisition_function_optimizer_kwargs: Optional[Dict]
Arguments passed to constructor of '~acquisition_function_optimizer'
model : AbstractEPM
Model that implements train() and predict(). Will use a
:class:`~smac.epm.rf_with_instances.RandomForestWithInstances` if not set.
model_kwargs : Optional[Dict]
Arguments passed to constructor of '~model'
runhistory2epm : ~smac.runhistory.runhistory2epm.RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use :class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4Cost`
if objective is cost or
:class:`~smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost`
if objective is runtime.
runhistory2epm_kwargs: Optional[Dict]
Arguments passed to the constructor of '~runhistory2epm'
initial_design : InitialDesign
initial sampling design
initial_design_kwargs: Optional[Dict]
arguments passed to constructor of `~initial_design'
initial_configurations : List[Configuration]
list of initial configurations for initial design --
cannot be used together with initial_design
stats : Stats
optional stats object
rng : np.random.RandomState
Random number generator
restore_incumbent : Configuration
incumbent used if restoring to previous state
smbo_class : ~smac.optimizer.smbo.SMBO
Class implementing the SMBO interface which will be used to
instantiate the optimizer class.
run_id : int (optional)
Run ID will be used as subfolder for output_dir. If no ``run_id`` is given, a random ``run_id`` will be
chosen.
random_configuration_chooser : ~smac.optimizer.random_configuration_chooser.RandomConfigurationChooser
How often to choose a random configuration during the intensification procedure.
random_configuration_chooser_kwargs : Optional[Dict]
arguments of constructor for '~random_configuration_chooser'
dask_client : dask.distributed.Client
User-created dask client, can be used to start a dask cluster and then attach SMAC to it.
n_jobs : int, optional
Number of jobs. If > 1 or -1, this creates a dask client if ``dask_client`` is ``None``. Will
be ignored if ``dask_client`` is not ``None``.
If ``None``, this value will be set to 1, if ``-1``, this will be set to the number of cpu cores.
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
self.scenario = scenario
self.output_dir = ""
if not restore_incumbent:
# restore_incumbent is used by the CLI interface which provides a method for restoring a SMAC run given an
# output directory. This is the default path.
# initial random number generator
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
self.output_dir = create_output_directory(scenario, run_id)
elif scenario.output_dir is not None: # type: ignore[attr-defined] # noqa F821
run_id, rng = get_rng(rng=rng, run_id=run_id, logger=self.logger)
# output-directory is created in CLI when restoring from a
# folder. calling the function again in the facade results in two
# folders being created: run_X and run_X.OLD. if we are
# restoring, the output-folder exists already and we omit creating it,
# but set the self-output_dir to the dir.
# necessary because we want to write traj to new output-dir in CLI.
self.output_dir = cast(str, scenario.output_dir_for_this_run
) # type: ignore[attr-defined] # noqa F821
rng = cast(np.random.RandomState, rng)
if (scenario.deterministic is
True # type: ignore[attr-defined] # noqa F821
and getattr(scenario, 'tuner_timeout', None) is None
and scenario.run_obj ==
'quality' # type: ignore[attr-defined] # noqa F821
):
self.logger.info(
'Optimizing a deterministic scenario for quality without a tuner timeout - will make '
'SMAC deterministic and only evaluate one configuration per iteration!'
)
scenario.intensification_percentage = 1e-10 # type: ignore[attr-defined] # noqa F821
scenario.min_chall = 1 # type: ignore[attr-defined] # noqa F821
scenario.write()
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
if self.scenario.run_obj == "runtime" and not self.scenario.transform_y == "LOG": # type: ignore[attr-defined] # noqa F821
self.logger.warning(
"Runtime as objective automatically activates log(y) transformation"
)
self.scenario.transform_y = "LOG" # type: ignore[attr-defined] # noqa F821
# initialize empty runhistory
runhistory_def_kwargs = {}
if runhistory_kwargs is not None:
runhistory_def_kwargs.update(runhistory_kwargs)
if runhistory is None:
runhistory = RunHistory(**runhistory_def_kwargs)
elif inspect.isclass(runhistory):
runhistory = runhistory(
**runhistory_def_kwargs) # type: ignore[operator] # noqa F821
elif isinstance(runhistory, RunHistory):
pass
else:
raise ValueError(
'runhistory has to be a class or an object of RunHistory')
rand_conf_chooser_kwargs = {'rng': rng}
if random_configuration_chooser_kwargs is not None:
rand_conf_chooser_kwargs.update(
random_configuration_chooser_kwargs)
if random_configuration_chooser is None:
if 'prob' not in rand_conf_chooser_kwargs:
rand_conf_chooser_kwargs[
'prob'] = scenario.rand_prob # type: ignore[attr-defined] # noqa F821
random_configuration_chooser_instance = (
ChooserProb(**rand_conf_chooser_kwargs
) # type: ignore[arg-type] # noqa F821
) # type: RandomConfigurationChooser
elif inspect.isclass(random_configuration_chooser):
random_configuration_chooser_instance = random_configuration_chooser(
**
rand_conf_chooser_kwargs) # type: ignore[arg-type] # noqa F821
elif not isinstance(random_configuration_chooser,
RandomConfigurationChooser):
raise ValueError(
"random_configuration_chooser has to be"
" a class or object of RandomConfigurationChooser")
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(
rng.randint(MAXINT)) # type: ignore[attr-defined] # noqa F821
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=self.output_dir, stats=self.stats)
# initial EPM
types, bounds = get_types(
scenario.cs,
scenario.feature_array) # type: ignore[attr-defined] # noqa F821
model_def_kwargs = {
'types': types,
'bounds': bounds,
'instance_features': scenario.feature_array,
'seed': rng.randint(MAXINT),
'pca_components': scenario.PCA_DIM,
}
if model_kwargs is not None:
model_def_kwargs.update(model_kwargs)
if model is None:
for key, value in {
'log_y': scenario.transform_y
in ["LOG",
"LOGS"], # type: ignore[attr-defined] # noqa F821
'num_trees': scenario.
rf_num_trees, # type: ignore[attr-defined] # noqa F821
'do_bootstrapping': scenario.
rf_do_bootstrapping, # type: ignore[attr-defined] # noqa F821
'ratio_features': scenario.
rf_ratio_features, # type: ignore[attr-defined] # noqa F821
'min_samples_split': scenario.
rf_min_samples_split, # type: ignore[attr-defined] # noqa F821
'min_samples_leaf': scenario.
rf_min_samples_leaf, # type: ignore[attr-defined] # noqa F821
'max_depth': scenario.
rf_max_depth, # type: ignore[attr-defined] # noqa F821
}.items():
if key not in model_def_kwargs:
model_def_kwargs[key] = value
model_def_kwargs[
'configspace'] = self.scenario.cs # type: ignore[attr-defined] # noqa F821
model_instance = (
RandomForestWithInstances(
**model_def_kwargs) # type: ignore[arg-type] # noqa F821
) # type: AbstractEPM
elif inspect.isclass(model):
model_def_kwargs[
'configspace'] = self.scenario.cs # type: ignore[attr-defined] # noqa F821
model_instance = model(
**model_def_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise TypeError("Model not recognized: %s" % (type(model)))
# initial acquisition function
acq_def_kwargs = {'model': model_instance}
if acquisition_function_kwargs is not None:
acq_def_kwargs.update(acquisition_function_kwargs)
if acquisition_function is None:
if scenario.transform_y in [
"LOG", "LOGS"
]: # type: ignore[attr-defined] # noqa F821
acquisition_function_instance = (
LogEI(**
acq_def_kwargs) # type: ignore[arg-type] # noqa F821
) # type: AbstractAcquisitionFunction
else:
acquisition_function_instance = EI(
**acq_def_kwargs) # type: ignore[arg-type] # noqa F821
elif inspect.isclass(acquisition_function):
acquisition_function_instance = acquisition_function(
**acq_def_kwargs)
else:
raise TypeError(
"Argument acquisition_function must be None or an object implementing the "
"AbstractAcquisitionFunction, not %s." %
type(acquisition_function))
if integrate_acquisition_function:
acquisition_function_instance = IntegratedAcquisitionFunction(
acquisition_function=acquisition_function_instance,
**acq_def_kwargs)
# initialize optimizer on acquisition function
acq_func_opt_kwargs = {
'acquisition_function': acquisition_function_instance,
'config_space':
scenario.cs, # type: ignore[attr-defined] # noqa F821
'rng': rng,
}
if acquisition_function_optimizer_kwargs is not None:
acq_func_opt_kwargs.update(acquisition_function_optimizer_kwargs)
if acquisition_function_optimizer is None:
for key, value in {
'max_steps': scenario.
sls_max_steps, # type: ignore[attr-defined] # noqa F821
'n_steps_plateau_walk': scenario.
sls_n_steps_plateau_walk, # type: ignore[attr-defined] # noqa F821
}.items():
if key not in acq_func_opt_kwargs:
acq_func_opt_kwargs[key] = value
acquisition_function_optimizer_instance = (
LocalAndSortedRandomSearch(
**
acq_func_opt_kwargs) # type: ignore[arg-type] # noqa F821
) # type: AcquisitionFunctionMaximizer
elif inspect.isclass(acquisition_function_optimizer):
acquisition_function_optimizer_instance = acquisition_function_optimizer(
**acq_func_opt_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise TypeError(
"Argument acquisition_function_optimizer must be None or an object implementing the "
"AcquisitionFunctionMaximizer, but is '%s'" %
type(acquisition_function_optimizer))
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
tae_def_kwargs = {
'stats': self.stats,
'run_obj': scenario.run_obj,
'par_factor':
scenario.par_factor, # type: ignore[attr-defined] # noqa F821
'cost_for_crash':
scenario.cost_for_crash, # type: ignore[attr-defined] # noqa F821
'abort_on_first_run_crash': scenario.
abort_on_first_run_crash # type: ignore[attr-defined] # noqa F821
}
if tae_runner_kwargs is not None:
tae_def_kwargs.update(tae_runner_kwargs)
if 'ta' not in tae_def_kwargs:
tae_def_kwargs[
'ta'] = scenario.ta # type: ignore[attr-defined] # noqa F821
if tae_runner is None:
tae_def_kwargs[
'ta'] = scenario.ta # type: ignore[attr-defined] # noqa F821
tae_runner_instance = (
ExecuteTARunOld(
**tae_def_kwargs) # type: ignore[arg-type] # noqa F821
) # type: BaseRunner
elif inspect.isclass(tae_runner):
tae_runner_instance = cast(BaseRunner, tae_runner(
**tae_def_kwargs)) # type: ignore[arg-type] # noqa F821
elif callable(tae_runner):
tae_def_kwargs['ta'] = tae_runner
tae_def_kwargs[
'use_pynisher'] = scenario.limit_resources # type: ignore[attr-defined] # noqa F821
tae_runner_instance = ExecuteTAFuncDict(
**tae_def_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise TypeError(
"Argument 'tae_runner' is %s, but must be "
"either None, a callable or an object implementing "
"BaseRunner. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file." % type(tae_runner))
# In case of a parallel run, wrap the single worker in a parallel
# runner
if n_jobs is None or n_jobs == 1:
_n_jobs = 1
elif n_jobs == -1:
_n_jobs = joblib.cpu_count()
elif n_jobs > 0:
_n_jobs = n_jobs
else:
raise ValueError(
'Number of tasks must be positive, None or -1, but is %s' %
str(n_jobs))
if _n_jobs > 1 or dask_client is not None:
tae_runner_instance = DaskParallelRunner(
tae_runner_instance,
n_workers=_n_jobs,
output_directory=self.output_dir,
dask_client=dask_client,
)
# Check that overall objective and tae objective are the same
# TODO: remove these two ignores once the scenario object knows all its attributes!
if tae_runner_instance.run_obj != scenario.run_obj: # type: ignore[union-attr] # noqa F821
raise ValueError(
"Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" %
(tae_runner_instance.run_obj,
scenario.run_obj)) # type: ignore[union-attr] # noqa F821
# initialize intensification
intensifier_def_kwargs = {
'stats': self.stats,
'traj_logger': traj_logger,
'rng': rng,
'instances':
scenario.train_insts, # type: ignore[attr-defined] # noqa F821
'cutoff':
scenario.cutoff, # type: ignore[attr-defined] # noqa F821
'deterministic':
scenario.deterministic, # type: ignore[attr-defined] # noqa F821
'run_obj_time': scenario.run_obj ==
"runtime", # type: ignore[attr-defined] # noqa F821
'instance_specifics': scenario.
instance_specific, # type: ignore[attr-defined] # noqa F821
'adaptive_capping_slackfactor': scenario.
intens_adaptive_capping_slackfactor, # type: ignore[attr-defined] # noqa F821
'min_chall':
scenario.intens_min_chall # type: ignore[attr-defined] # noqa F821
}
if isinstance(intensifier, Intensifier) \
or (intensifier is not None and inspect.isclass(intensifier) and issubclass(intensifier, Intensifier)):
intensifier_def_kwargs[
'always_race_against'] = scenario.cs.get_default_configuration(
) # type: ignore[attr-defined] # noqa F821
intensifier_def_kwargs[
'use_ta_time_bound'] = scenario.use_ta_time # type: ignore[attr-defined] # noqa F821
intensifier_def_kwargs[
'minR'] = scenario.minR # type: ignore[attr-defined] # noqa F821
intensifier_def_kwargs[
'maxR'] = scenario.maxR # type: ignore[attr-defined] # noqa F821
if intensifier_kwargs is not None:
intensifier_def_kwargs.update(intensifier_kwargs)
if intensifier is None:
intensifier_instance = (
Intensifier(**intensifier_def_kwargs
) # type: ignore[arg-type] # noqa F821
) # type: AbstractRacer
elif inspect.isclass(intensifier):
intensifier_instance = intensifier(
**intensifier_def_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise TypeError(
"Argument intensifier must be None or an object implementing the AbstractRacer, but is '%s'"
% type(intensifier))
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both"
)
init_design_def_kwargs = {
'cs': scenario.cs, # type: ignore[attr-defined] # noqa F821
'traj_logger': traj_logger,
'rng': rng,
'ta_run_limit':
scenario.ta_run_limit, # type: ignore[attr-defined] # noqa F821
'configs': initial_configurations,
'n_configs_x_params': 0,
'max_config_fracs': 0.0
}
if initial_design_kwargs is not None:
init_design_def_kwargs.update(initial_design_kwargs)
if initial_configurations is not None:
initial_design_instance = InitialDesign(**init_design_def_kwargs)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT": # type: ignore[attr-defined] # noqa F821
init_design_def_kwargs['max_config_fracs'] = 0.0
initial_design_instance = DefaultConfiguration(
**init_design_def_kwargs)
elif scenario.initial_incumbent == "RANDOM": # type: ignore[attr-defined] # noqa F821
init_design_def_kwargs['max_config_fracs'] = 0.0
initial_design_instance = RandomConfigurations(
**init_design_def_kwargs)
elif scenario.initial_incumbent == "LHD": # type: ignore[attr-defined] # noqa F821
initial_design_instance = LHDesign(**init_design_def_kwargs)
elif scenario.initial_incumbent == "FACTORIAL": # type: ignore[attr-defined] # noqa F821
initial_design_instance = FactorialInitialDesign(
**init_design_def_kwargs)
elif scenario.initial_incumbent == "SOBOL": # type: ignore[attr-defined] # noqa F821
initial_design_instance = SobolDesign(**init_design_def_kwargs)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" % scenario.initial_incumbent
) # type: ignore[attr-defined] # noqa F821
elif inspect.isclass(initial_design):
initial_design_instance = initial_design(**init_design_def_kwargs)
else:
raise TypeError(
"Argument initial_design must be None or an object implementing the InitialDesign, but is '%s'"
% type(initial_design))
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
if scenario.transform_y in [
"LOG", "LOGS"
]: # type: ignore[attr-defined] # noqa F821
cutoff = np.log(np.nanmin([
np.inf, np.float_(scenario.cutoff)
])) # type: ignore[attr-defined] # noqa F821
threshold = cutoff + np.log(
scenario.par_factor) # type: ignore[attr-defined] # noqa F821
else:
cutoff = np.nanmin([np.inf, np.float_(scenario.cutoff)
]) # type: ignore[attr-defined] # noqa F821
threshold = cutoff * scenario.par_factor # type: ignore[attr-defined] # noqa F821
num_params = len(scenario.cs.get_hyperparameters()
) # type: ignore[attr-defined] # noqa F821
imputor = RFRImputator(rng=rng,
cutoff=cutoff,
threshold=threshold,
model=model_instance,
change_threshold=0.01,
max_iter=2)
r2e_def_kwargs = {
'scenario': scenario,
'num_params': num_params,
'success_states': [
StatusType.SUCCESS,
],
'impute_censored_data': True,
'impute_state': [
StatusType.CAPPED,
],
'imputor': imputor,
'scale_perc': 5
}
if scenario.run_obj == 'quality':
r2e_def_kwargs.update({
'success_states':
[StatusType.SUCCESS, StatusType.CRASHED, StatusType.MEMOUT],
'impute_censored_data':
False,
'impute_state':
None,
})
if isinstance(
intensifier_instance,
(SuccessiveHalving, Hyperband)) and scenario.run_obj == "quality":
r2e_def_kwargs.update({
'success_states': [
StatusType.SUCCESS,
StatusType.CRASHED,
StatusType.MEMOUT,
StatusType.DONOTADVANCE,
],
'consider_for_higher_budgets_state': [
StatusType.DONOTADVANCE,
StatusType.TIMEOUT,
StatusType.CRASHED,
StatusType.MEMOUT,
],
})
if runhistory2epm_kwargs is not None:
r2e_def_kwargs.update(runhistory2epm_kwargs)
if runhistory2epm is None:
if scenario.run_obj == 'runtime':
rh2epm = (
RunHistory2EPM4LogCost(
**r2e_def_kwargs) # type: ignore[arg-type] # noqa F821
) # type: AbstractRunHistory2EPM
elif scenario.run_obj == 'quality':
if scenario.transform_y == "NONE": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4Cost(
**r2e_def_kwargs) # type: ignore[arg-type] # noqa F821
elif scenario.transform_y == "LOG": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4LogCost(
**r2e_def_kwargs) # type: ignore[arg-type] # noqa F821
elif scenario.transform_y == "LOGS": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4LogScaledCost(
**r2e_def_kwargs) # type: ignore[arg-type] # noqa F821
elif scenario.transform_y == "INVS": # type: ignore[attr-defined] # noqa F821
rh2epm = RunHistory2EPM4InvScaledCost(
**r2e_def_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
elif inspect.isclass(runhistory2epm):
rh2epm = runhistory2epm(
**r2e_def_kwargs) # type: ignore[arg-type] # noqa F821
else:
raise TypeError(
"Argument runhistory2epm must be None or an object implementing the RunHistory2EPM, but is '%s'"
% type(runhistory2epm))
smbo_args = {
'scenario': scenario,
'stats': self.stats,
'initial_design': initial_design_instance,
'runhistory': runhistory,
'runhistory2epm': rh2epm,
'intensifier': intensifier_instance,
'num_run': run_id,
'model': model_instance,
'acq_optimizer': acquisition_function_optimizer_instance,
'acquisition_func': acquisition_function_instance,
'rng': rng,
'restore_incumbent': restore_incumbent,
'random_configuration_chooser':
random_configuration_chooser_instance,
'tae_runner': tae_runner_instance,
} # type: Dict[str, Any]
if smbo_class is None:
self.solver = SMBO(**
smbo_args) # type: ignore[arg-type] # noqa F821
else:
self.solver = smbo_class(
**smbo_args) # type: ignore[arg-type] # noqa F821
def __init__(self,
scenario,
tae_runner=None,
acquisition_function=None,
model=None,
runhistory2epm=None,
stats=None,
rng=None):
'''
Interface that contains the main Bayesian optimization loop
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
tae_runner: object
object that implements the following method to call the target
algorithm (or any other arbitrary function):
run(self, config)
If not set, it will be initialized with the tae.ExecuteTARunOld()
acquisition_function : AcquisitionFunction
Object that implements the AbstractAcquisitionFunction. Will use
EI if not set.
model : object
Model that implements train() and predict(). Will use a
RandomForest if not set.
runhistory2epm : RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use RunHistory2EPM4Cost if objective is cost or
RunHistory2EPM4LogCost if objective is runtime.
stats: Stats
optional stats object
rng: numpy.random.RandomState
Random number generator
'''
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
self.runhistory = RunHistory()
self.logger = logging.getLogger("smbo")
if rng is None:
self.num_run = np.random.randint(1234567980)
self.rng = np.random.RandomState(seed=self.num_run)
elif isinstance(rng, int):
self.num_run = rng
self.rng = np.random.RandomState(seed=rng)
elif isinstance(rng, np.random.RandomState):
self.num_run = rng.randint(1234567980)
self.rng = rng
else:
raise TypeError('Unknown type %s for argument rng. Only accepts '
'None, int or np.random.RandomState' %
str(type(rng)))
self.scenario = scenario
self.config_space = scenario.cs
self.traj_logger = TrajLogger(output_dir=self.scenario.output_dir,
stats=self.stats)
self.types = get_types(self.config_space, scenario.feature_array)
if model is None:
self.model = RandomForestWithInstances(
self.types,
scenario.feature_array,
seed=self.rng.randint(1234567980))
else:
self.model = model
if acquisition_function is None:
self.acquisition_func = EI(self.model)
else:
self.acquisition_func = acquisition_function
self.local_search = LocalSearch(self.acquisition_func,
self.config_space)
self.incumbent = None
if tae_runner is None:
self.executor = ExecuteTARunOld(ta=scenario.ta,
stats=self.stats,
run_obj=scenario.run_obj,
par_factor=scenario.par_factor)
else:
self.executor = tae_runner
self.inten = Intensifier(
executor=self.executor,
stats=self.stats,
traj_logger=self.traj_logger,
instances=self.scenario.train_insts,
cutoff=self.scenario.cutoff,
deterministic=self.scenario.deterministic,
run_obj_time=self.scenario.run_obj == "runtime",
instance_specifics=self.scenario.instance_specific)
num_params = len(self.config_space.get_hyperparameters())
self.objective = average_cost
if self.scenario.run_obj == "runtime":
if runhistory2epm is None:
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(self.scenario.cutoff)
threshold = np.log10(self.scenario.cutoff *
self.scenario.par_factor)
imputor = RFRImputator(cs=self.config_space,
rs=self.rng,
cutoff=cutoff,
threshold=threshold,
model=self.model,
change_threshold=0.01,
max_iter=10)
self.rh2EPM = RunHistory2EPM4LogCost(scenario=self.scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.TIMEOUT,
],
imputor=imputor)
else:
self.rh2EPM = runhistory2epm
elif self.scenario.run_obj == 'quality':
if runhistory2epm is None:
self.rh2EPM = RunHistory2EPM4Cost\
(scenario=self.scenario, num_params=num_params,
success_states=[StatusType.SUCCESS, ],
impute_censored_data=False, impute_state=None)
else:
self.rh2EPM = runhistory2epm
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
def __init__(
self,
scenario: Scenario,
# TODO: once we drop python3.4 add type hint
# typing.Union[ExecuteTARun, callable]
tae_runner=None,
runhistory: RunHistory = None,
intensifier: Intensifier = None,
acquisition_function: AbstractAcquisitionFunction = None,
model: AbstractEPM = None,
runhistory2epm: AbstractRunHistory2EPM = None,
initial_design: InitialDesign = None,
initial_configurations: typing.List[Configuration] = None,
stats: Stats = None,
rng: np.random.RandomState = None):
'''
Facade to use SMAC default mode
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
tae_runner: ExecuteTARun or callable
Callable or implementation of :class:`ExecuteTaRun`. In case a
callable is passed it will be wrapped by tae.ExecuteTaFunc().
If not set, tae_runner will be initialized with
the tae.ExecuteTARunOld()
runhistory: RunHistory
runhistory to store all algorithm runs
intensifier: Intensifier
intensification object to issue a racing to decide the current
incumbent
acquisition_function : AcquisitionFunction
Object that implements the AbstractAcquisitionFunction. Will use
EI if not set.
model : AbstractEPM
Model that implements train() and predict(). Will use a
RandomForest if not set.
runhistory2epm : RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use RunHistory2EPM4Cost if objective is cost or
RunHistory2EPM4LogCost if objective is runtime.
initial_design: InitialDesign
initial sampling design
initial_configurations: typing.List[Configuration]
list of initial configurations for initial design --
cannot be used together with initial_design
stats: Stats
optional stats object
rng: np.random.RandomState
Random number generator
'''
self.logger = logging.getLogger("SMAC")
aggregate_func = average_cost
# initialize stats object
if stats:
self.stats = stats
else:
self.stats = Stats(scenario)
# initialize empty runhistory
if runhistory is None:
runhistory = RunHistory(aggregate_func=aggregate_func)
# initial random number generator
num_run, rng = self._get_rng(rng=rng)
# reset random number generator in config space to draw different
# random configurations with each seed given to SMAC
scenario.cs.seed(rng.randint(MAXINT))
# initial Trajectory Logger
traj_logger = TrajLogger(output_dir=scenario.output_dir,
stats=self.stats)
# initial EPM
types = get_types(scenario.cs, scenario.feature_array)
if model is None:
model = RandomForestWithInstances(
types=types,
instance_features=scenario.feature_array,
seed=rng.randint(MAXINT))
# initial acquisition function
if acquisition_function is None:
acquisition_function = EI(model=model)
# initialize optimizer on acquisition function
local_search = LocalSearch(acquisition_function, scenario.cs)
# initialize tae_runner
# First case, if tae_runner is None, the target algorithm is a call
# string in the scenario file
if tae_runner is None:
tae_runner = ExecuteTARunOld(ta=scenario.ta,
stats=self.stats,
run_obj=scenario.run_obj,
runhistory=runhistory,
par_factor=scenario.par_factor)
# Second case, the tae_runner is a function to be optimized
elif callable(tae_runner):
tae_runner = ExecuteTAFuncDict(ta=tae_runner,
stats=self.stats,
run_obj=scenario.run_obj,
memory_limit=scenario.memory_limit,
runhistory=runhistory,
par_factor=scenario.par_factor)
# Third case, if it is an ExecuteTaRun we can simply use the
# instance. Otherwise, the next check raises an exception
elif not isinstance(tae_runner, ExecuteTARun):
raise TypeError("Argument 'tae_runner' is %s, but must be "
"either a callable or an instance of "
"ExecuteTaRun. Passing 'None' will result in the "
"creation of target algorithm runner based on the "
"call string in the scenario file." %
type(tae_runner))
# Check that overall objective and tae objective are the same
if tae_runner.run_obj != scenario.run_obj:
raise ValueError("Objective for the target algorithm runner and "
"the scenario must be the same, but are '%s' and "
"'%s'" % (tae_runner.run_obj, scenario.run_obj))
# inject stats if necessary
if tae_runner.stats is None:
tae_runner.stats = self.stats
# inject runhistory if necessary
if tae_runner.runhistory is None:
tae_runner.runhistory = runhistory
# initial intensification
if intensifier is None:
intensifier = Intensifier(
tae_runner=tae_runner,
stats=self.stats,
traj_logger=traj_logger,
rng=rng,
instances=scenario.train_insts,
cutoff=scenario.cutoff,
deterministic=scenario.deterministic,
run_obj_time=scenario.run_obj == "runtime",
instance_specifics=scenario.instance_specific,
minR=scenario.minR,
maxR=scenario.maxR)
# initial design
if initial_design is not None and initial_configurations is not None:
raise ValueError(
"Either use initial_design or initial_configurations; but not both"
)
if initial_configurations is not None:
initial_design = MultiConfigInitialDesign(
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
runhistory=runhistory,
rng=rng,
configs=initial_configurations,
intensifier=intensifier,
aggregate_func=aggregate_func)
elif initial_design is None:
if scenario.initial_incumbent == "DEFAULT":
initial_design = DefaultConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
elif scenario.initial_incumbent == "RANDOM":
initial_design = RandomConfiguration(tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
rng=rng)
else:
raise ValueError("Don't know what kind of initial_incumbent "
"'%s' is" % scenario.initial_incumbent)
# initial conversion of runhistory into EPM data
if runhistory2epm is None:
num_params = len(scenario.cs.get_hyperparameters())
if scenario.run_obj == "runtime":
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(scenario.cutoff)
threshold = np.log10(scenario.cutoff * scenario.par_factor)
imputor = RFRImputator(rs=rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=2)
runhistory2epm = RunHistory2EPM4LogCost(
scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=True,
impute_state=[
StatusType.TIMEOUT,
],
imputor=imputor)
elif scenario.run_obj == 'quality':
runhistory2epm = RunHistory2EPM4Cost\
(scenario=scenario, num_params=num_params,
success_states=[StatusType.SUCCESS, ],
impute_censored_data=False, impute_state=None)
else:
raise ValueError('Unknown run objective: %s. Should be either '
'quality or runtime.' % self.scenario.run_obj)
self.solver = SMBO(scenario=scenario,
stats=self.stats,
initial_design=initial_design,
runhistory=runhistory,
runhistory2epm=runhistory2epm,
intensifier=intensifier,
aggregate_func=aggregate_func,
num_run=num_run,
model=model,
acq_optimizer=local_search,
acquisition_func=acquisition_function,
rng=rng)
def convert_data(scenario: Scenario, runhistory: RunHistory):
'''
converts data from runhistory into EPM format
Parameters
----------
scenario: Scenario
smac.scenario.scenario.Scenario Object
runhistory: RunHistory
smac.runhistory.runhistory.RunHistory Object with all necessary data
Returns
-------
np.array
X matrix with configuartion x features for all observed samples
np.array
y matrix with all observations
np.array
types of X cols -- necessary to train our RF implementation
'''
types = np.zeros(len(scenario.cs.get_hyperparameters()), dtype=np.uint)
for i, param in enumerate(scenario.cs.get_hyperparameters()):
if isinstance(param, (CategoricalHyperparameter)):
n_cats = len(param.choices)
types[i] = n_cats
if scenario.feature_array is not None:
types = np.hstack((types, np.zeros((scenario.feature_array.shape[1]))))
types = np.array(types, dtype=np.uint)
model = RandomForestWithInstances(types, scenario.feature_array)
params = scenario.cs.get_hyperparameters()
num_params = len(params)
if scenario.run_obj == "runtime":
if scenario.run_obj == "runtime":
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(scenario.cutoff)
threshold = np.log10(scenario.cutoff * scenario.par_factor)
else:
cutoff = scenario.cutoff
threshold = scenario.cutoff * scenario.par_factor
imputor = RFRImputator(cs=scenario.cs,
rs=np.random.RandomState(42),
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=10)
# TODO: Adapt runhistory2EPM object based on scenario
rh2EPM = RunHistory2EPM4LogCost(scenario=scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=False,
impute_state=[
StatusType.TIMEOUT,
],
imputor=imputor)
else:
rh2EPM = RunHistory2EPM4Cost(scenario=scenario,
num_params=num_params,
success_states=None,
impute_censored_data=False,
impute_state=None)
X, Y = rh2EPM.transform(runhistory)
return X, Y, types