def restore_state_after_output_dir(self, scen, stats, traj_list_aclib,
traj_list_old):
"""Finish processing files for state-restoration. Trajectory
is read in, but needs to be written to new output-folder. Therefore, the
output-dir is created. This needs to be considered in the SMAC-facade."""
# write trajectory-list
traj_path_aclib = os.path.join(scen.output_dir, "traj_aclib2.json")
traj_path_old = os.path.join(scen.output_dir, "traj_old.csv")
with open(traj_path_aclib, 'w') as traj_fn:
traj_fn.writelines(traj_list_aclib)
with open(traj_path_old, 'w') as traj_fn:
traj_fn.writelines(traj_list_old)
# read trajectory to retrieve incumbent
# TODO replace this with simple traj_path_aclib?
trajectory = TrajLogger.read_traj_aclib_format(fn=traj_path_aclib, cs=scen.cs)
incumbent = trajectory[-1]["incumbent"]
self.logger.debug("Restored incumbent %s from %s", incumbent,
traj_path_aclib)
return incumbent
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 : ~dsmac.scenario.scenario.Scenario
Scenario object
tae_runner : ~dsmac.tae.execute_ta_run.ExecuteTARun or callable
Callable or implementation of
:class:`~dsmac.tae.execute_ta_run.ExecuteTARun`. In case a
callable is passed it will be wrapped by
:class:`~dsmac.tae.execute_func.ExecuteTAFuncDict`.
If not set, it will be initialized with the
:class:`~dsmac.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 : ~dsmac.optimizer.acquisition.AbstractAcquisitionFunction
Class or object that implements the :class:`~dsmac.optimizer.acquisition.AbstractAcquisitionFunction`.
Will use :class:`~dsmac.optimizer.acquisition.EI` or :class:`~dsmac.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 : ~dsmac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Object that implements the :class:`~dsmac.optimizer.ei_optimization.AcquisitionFunctionMaximizer`.
Will use :class:`dsmac.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:`~dsmac.epm.rf_with_instances.RandomForestWithInstances` if not set.
model_kwargs : Optional[dict]
Arguments passed to constructor of '~model'
runhistory2epm : ~dsmac.runhistory.runhistory2epm.RunHistory2EMP
Object that implements the AbstractRunHistory2EPM. If None,
will use :class:`~dsmac.runhistory.runhistory2epm.RunHistory2EPM4Cost`
if objective is cost or
:class:`~dsmac.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 : ~dsmac.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 : ~dsmac.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
self.output_dir = scenario.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,
db_type=scenario.db_type,
db_args=scenario.db_args,
db_kwargs=scenario.db_kwargs)
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 hasattr(scenario,
'filter_callback') and scenario.filter_callback is not None:
print('update callback')
intensifier_def_kwargs.update(
{'filter_callback': scenario.filter_callback})
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:
initial_design.initial_configurations = initial_configurations
initial_configurations = None
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,
'initial_configurations': initial_design.initial_configurations
}
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 validate(self,
config_mode='inc',
instance_mode='train+test',
repetitions=1,
use_epm=False,
n_jobs=-1,
backend='threading'):
"""Create validator-object and run validation, using
scenario-information, runhistory from smbo and tae_runner from intensify
Parameters
----------
config_mode: str or list<Configuration>
string or directly a list of Configuration
str from [def, inc, def+inc, wallclock_time, cpu_time, all]
time evaluates at cpu- or wallclock-timesteps of:
[max_time/2^0, max_time/2^1, max_time/2^3, ..., default]
with max_time being the highest recorded time
instance_mode: string
what instances to use for validation, from [train, test, train+test]
repetitions: int
number of repetitions in nondeterministic algorithms (in
deterministic will be fixed to 1)
use_epm: bool
whether to use an EPM instead of evaluating all runs with the TAE
n_jobs: int
number of parallel processes used by joblib
Returns
-------
runhistory: RunHistory
runhistory containing all specified runs
"""
if isinstance(config_mode, str):
traj_fn = os.path.join(self.scenario.output_dir_for_this_run,
"traj_aclib2.json")
trajectory = TrajLogger.read_traj_aclib_format(fn=traj_fn,
cs=self.scenario.cs)
else:
trajectory = None
if self.scenario.output_dir_for_this_run:
new_rh_path = os.path.join(self.scenario.output_dir_for_this_run,
"validated_runhistory.json")
else:
new_rh_path = None
validator = Validator(self.scenario, trajectory, self.rng)
if use_epm:
new_rh = validator.validate_epm(config_mode=config_mode,
instance_mode=instance_mode,
repetitions=repetitions,
runhistory=self.runhistory,
output_fn=new_rh_path)
else:
new_rh = validator.validate(config_mode,
instance_mode,
repetitions,
n_jobs,
backend,
self.runhistory,
self.intensifier.tae_runner,
output_fn=new_rh_path)
return new_rh
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, file_system=scenario.file_system)
self.output_dir = create_output_directory(scenario, run_id)
scenario.write()
# initialize empty runhistory
if runhistory is None:
runhistory = RunHistory(aggregate_func=aggregate_func,
file_system=scenario.file_system)
# 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,
file_system=scenario.file_system)
# initial EPM
types, bounds = get_types(scenario.cs, scenario.feature_array)
if model is None:
model = RandomForestWithInstances(
configspace=scenario.cs,
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,
distributer=scenario.distributer)
# 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 = InitialDesign(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,
runhistory=runhistory,
rng=rng,
intensifier=intensifier,
aggregate_func=aggregate_func,
max_config_fracs=0.0)
elif scenario.initial_incumbent == "RANDOM":
initial_design = RandomConfigurations(
tae_runner=tae_runner,
scenario=scenario,
stats=self.stats,
traj_logger=traj_logger,
runhistory=runhistory,
rng=rng,
intensifier=intensifier,
aggregate_func=aggregate_func,
max_config_fracs=0.0)
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 main_cli(self, commandline_arguments: typing.List[str]=None):
"""Main function of SMAC for CLI interface"""
self.logger.info("SMAC call: %s" % (" ".join(sys.argv)))
cmd_reader = CMDReader()
kwargs = {}
if commandline_arguments:
kwargs['commandline_arguments'] = commandline_arguments
main_args_, smac_args_, scen_args_ = cmd_reader.read_cmd(**kwargs)
root_logger = logging.getLogger()
root_logger.setLevel(main_args_.verbose_level)
logger_handler = logging.StreamHandler(
stream=sys.stdout)
if root_logger.level >= logging.INFO:
formatter = logging.Formatter(
"%(levelname)s:\t%(message)s")
else:
formatter = logging.Formatter(
"%(asctime)s:%(levelname)s:%(name)s:%(message)s",
"%Y-%m-%d %H:%M:%S")
logger_handler.setFormatter(formatter)
root_logger.addHandler(logger_handler)
# remove default handler
if len(root_logger.handlers) > 1:
root_logger.removeHandler(root_logger.handlers[0])
# Create defaults
rh = None
initial_configs = None
stats = None
incumbent = None
# Create scenario-object
scenario = {}
scenario.update(vars(smac_args_))
scenario.update(vars(scen_args_))
scen = Scenario(scenario=scenario)
# Restore state
if main_args_.restore_state:
root_logger.debug("Restoring state from %s...", main_args_.restore_state)
rh, stats, traj_list_aclib, traj_list_old = self.restore_state(scen, main_args_)
scen.output_dir_for_this_run = create_output_directory(
scen, main_args_.seed, root_logger,
)
scen.write()
incumbent = self.restore_state_after_output_dir(scen, stats,
traj_list_aclib, traj_list_old)
if main_args_.warmstart_runhistory:
aggregate_func = average_cost
rh = RunHistory(aggregate_func=aggregate_func)
scen, rh = merge_foreign_data_from_file(
scenario=scen,
runhistory=rh,
in_scenario_fn_list=main_args_.warmstart_scenario,
in_runhistory_fn_list=main_args_.warmstart_runhistory,
cs=scen.cs,
aggregate_func=aggregate_func)
if main_args_.warmstart_incumbent:
initial_configs = [scen.cs.get_default_configuration()]
for traj_fn in main_args_.warmstart_incumbent:
trajectory = TrajLogger.read_traj_aclib_format(
fn=traj_fn, cs=scen.cs)
initial_configs.append(trajectory[-1]["incumbent"])
if main_args_.mode == "SMAC4AC":
optimizer = SMAC4AC(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
runhistory=rh,
initial_configurations=initial_configs,
stats=stats,
restore_incumbent=incumbent,
run_id=main_args_.seed)
elif main_args_.mode == "SMAC4HPO":
optimizer = SMAC4HPO(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
runhistory=rh,
initial_configurations=initial_configs,
stats=stats,
restore_incumbent=incumbent,
run_id=main_args_.seed)
elif main_args_.mode == "SMAC4BO":
optimizer = SMAC4BO(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
runhistory=rh,
initial_configurations=initial_configs,
stats=stats,
restore_incumbent=incumbent,
run_id=main_args_.seed)
elif main_args_.mode == "ROAR":
optimizer = ROAR(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
runhistory=rh,
initial_configurations=initial_configs,
run_id=main_args_.seed)
elif main_args_.mode == "EPILS":
optimizer = EPILS(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
runhistory=rh,
initial_configurations=initial_configs,
run_id=main_args_.seed)
elif main_args_.mode == "Hydra":
optimizer = Hydra(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
runhistory=rh,
initial_configurations=initial_configs,
stats=stats,
restore_incumbent=incumbent,
run_id=main_args_.seed,
random_configuration_chooser=main_args_.random_configuration_chooser,
n_iterations=main_args_.hydra_iterations,
val_set=main_args_.hydra_validation,
incs_per_round=main_args_.hydra_incumbents_per_round,
n_optimizers=main_args_.hydra_n_optimizers)
elif main_args_.mode == "PSMAC":
optimizer = PSMAC(
scenario=scen,
rng=np.random.RandomState(main_args_.seed),
run_id=main_args_.seed,
shared_model=smac_args_.shared_model,
validate=main_args_.psmac_validate,
n_optimizers=main_args_.hydra_n_optimizers,
n_incs=main_args_.hydra_incumbents_per_round,
)
try:
optimizer.optimize()
except (TAEAbortException, FirstRunCrashedException) as err:
self.logger.error(err)