def test_challenger_list_callback(self, patch_sample, patch_ei,
patch_impute):
values = (10, 1, 9, 2, 8, 3, 7, 4, 6, 5)
patch_sample.return_value = ConfigurationMock(1)
patch_ei.return_value = np.array([[_] for _ in values], dtype=float)
patch_impute.side_effect = lambda l: values
cs = ConfigurationSpace()
ei = EI(None)
rs = RandomSearch(ei, cs)
rs._maximize = unittest.mock.Mock()
rs._maximize.return_value = [(0, 0)]
rval = rs.maximize(
runhistory=None,
stats=None,
num_points=10,
)
self.assertEqual(rs._maximize.call_count, 0)
next(rval)
self.assertEqual(rs._maximize.call_count, 1)
random_configuration_chooser = unittest.mock.Mock()
random_configuration_chooser.check.side_effect = [
True, False, False, False
]
rs._maximize = unittest.mock.Mock()
rs._maximize.return_value = [(0, 0), (1, 1)]
rval = rs.maximize(
runhistory=None,
stats=None,
num_points=10,
random_configuration_chooser=random_configuration_chooser,
)
self.assertEqual(rs._maximize.call_count, 0)
# The first configuration is chosen at random (see the random_configuration_chooser mock)
conf = next(rval)
self.assertIsInstance(conf, ConfigurationMock)
self.assertEqual(rs._maximize.call_count, 0)
# The 2nd configuration triggers the call to the callback (see the random_configuration_chooser mock)
conf = next(rval)
self.assertEqual(rs._maximize.call_count, 1)
self.assertEqual(conf, 0)
# The 3rd configuration doesn't trigger the callback any more
conf = next(rval)
self.assertEqual(rs._maximize.call_count, 1)
self.assertEqual(conf, 1)
with self.assertRaises(StopIteration):
next(rval)
class EPMChooser(object):
def __init__(self,
scenario: Scenario,
stats: Stats,
runhistory: RunHistory,
runhistory2epm: AbstractRunHistory2EPM,
model: RandomForestWithInstances,
acq_optimizer: AcquisitionFunctionMaximizer,
acquisition_func: AbstractAcquisitionFunction,
rng: np.random.RandomState,
restore_incumbent: Configuration = None,
random_configuration_chooser: typing.
Union[RandomConfigurationChooser] = ChooserNoCoolDown(2.0),
predict_x_best: bool = True,
min_samples_model: int = 1):
"""
Interface to train the EPM and generate next configurations
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
stats: smac.stats.stats.Stats
statistics object with configuration budgets
runhistory: smac.runhistory.runhistory.RunHistory
runhistory with all runs so far
model: smac.epm.rf_with_instances.RandomForestWithInstances
empirical performance model (right now, we support only
RandomForestWithInstances)
acq_optimizer: smac.optimizer.ei_optimization.AcquisitionFunctionMaximizer
Optimizer of acquisition function.
restore_incumbent: Configuration
incumbent to be used from the start. ONLY used to restore states.
rng: np.random.RandomState
Random number generator
random_configuration_chooser:
Chooser for random configuration -- one of
* ChooserNoCoolDown(modulus)
* ChooserLinearCoolDown(start_modulus, modulus_increment, end_modulus)
predict_x_best: bool
Choose x_best for computing the acquisition function via the model instead of via the observations.
min_samples_model: int
Minimum number of samples to build a model
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
self.incumbent = restore_incumbent
self.scenario = scenario
self.stats = stats
self.runhistory = runhistory
self.rh2EPM = runhistory2epm
self.model = model
self.acq_optimizer = acq_optimizer
self.acquisition_func = acquisition_func
self.rng = rng
self.random_configuration_chooser = random_configuration_chooser
self._random_search = RandomSearch(
acquisition_func,
self.scenario.cs, # type: ignore[attr-defined] # noqa F821
rng,
)
self.initial_design_configs = [] # type: typing.List[Configuration]
self.predict_x_best = predict_x_best
self.min_samples_model = min_samples_model
self.currently_considered_budgets = [
0.0,
]
def _collect_data_to_train_model(
self) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray]:
# if we use a float value as a budget, we want to train the model only on the highest budget
available_budgets = []
for run_key in self.runhistory.data.keys():
available_budgets.append(run_key.budget)
# Sort available budgets from highest to lowest budget
available_budgets = sorted(list(set(available_budgets)), reverse=True)
# Get #points per budget and if there are enough samples, then build a model
for b in available_budgets:
X, Y = self.rh2EPM.transform(self.runhistory, budget_subset=[
b,
])
if X.shape[0] >= self.min_samples_model:
self.currently_considered_budgets = [
b,
]
configs_array = self.rh2EPM.get_configurations(
self.runhistory,
budget_subset=self.currently_considered_budgets)
return X, Y, configs_array
return np.empty(shape=[0, 0]), np.empty(shape=[
0,
]), np.empty(shape=[0, 0])
def _get_evaluated_configs(self) -> typing.List[Configuration]:
return self.runhistory.get_all_configs_per_budget(
budget_subset=self.currently_considered_budgets)
def choose_next(
self,
incumbent_value: float = None) -> typing.Iterator[Configuration]:
"""Choose next candidate solution with Bayesian optimization. The
suggested configurations depend on the argument ``acq_optimizer`` to
the ``SMBO`` class.
Parameters
----------
incumbent_value: float
Cost value of incumbent configuration (required for acquisition function);
If not given, it will be inferred from runhistory or predicted;
if not given and runhistory is empty, it will raise a ValueError.
Returns
-------
Iterator
"""
self.logger.debug("Search for next configuration")
X, Y, X_configurations = self._collect_data_to_train_model()
if X.shape[0] == 0:
# Only return a single point to avoid an overly high number of
# random search iterations
return self._random_search.maximize(runhistory=self.runhistory,
stats=self.stats,
num_points=1)
self.model.train(X, Y)
if incumbent_value is not None:
best_observation = incumbent_value
x_best_array = None # type: typing.Optional[np.ndarray]
else:
if self.runhistory.empty():
raise ValueError("Runhistory is empty and the cost value of "
"the incumbent is unknown.")
x_best_array, best_observation = self._get_x_best(
self.predict_x_best, X_configurations)
self.acquisition_func.update(
model=self.model,
eta=best_observation,
incumbent_array=x_best_array,
num_data=len(self._get_evaluated_configs()),
X=X_configurations,
)
challengers = self.acq_optimizer.maximize(
runhistory=self.runhistory,
stats=self.stats,
num_points=self.scenario.
acq_opt_challengers, # type: ignore[attr-defined] # noqa F821
random_configuration_chooser=self.random_configuration_chooser)
return challengers
def _get_x_best(self, predict: bool,
X: np.ndarray) -> typing.Tuple[float, np.ndarray]:
"""Get value, configuration, and array representation of the "best" configuration.
The definition of best varies depending on the argument ``predict``. If set to ``True``,
this function will return the stats of the best configuration as predicted by the model,
otherwise it will return the stats for the best observed configuration.
Parameters
----------
predict : bool
Whether to use the predicted or observed best.
Returns
-------
float
np.ndarry
Configuration
"""
if predict:
costs = list(
map(
lambda x: (
self.model.predict_marginalized_over_instances(
x.reshape((1, -1)))[0][0][0],
x,
),
X,
))
costs = sorted(costs, key=lambda t: t[0])
x_best_array = costs[0][1]
best_observation = costs[0][0]
# won't need log(y) if EPM was already trained on log(y)
else:
all_configs = self.runhistory.get_all_configs_per_budget(
budget_subset=self.currently_considered_budgets)
x_best = self.incumbent
x_best_array = convert_configurations_to_array(all_configs)
best_observation = self.runhistory.get_cost(x_best)
best_observation_as_array = np.array(best_observation).reshape(
(1, 1))
# It's unclear how to do this for inv scaling and potential future scaling.
# This line should be changed if necessary
best_observation = self.rh2EPM.transform_response_values(
best_observation_as_array)
best_observation = best_observation[0][0]
return x_best_array, best_observation
class SMBO(object):
"""Interface that contains the main Bayesian optimization loop
Attributes
----------
logger
incumbent
scenario
config_space
stats
initial_design
runhistory
rh2EPM
intensifier
aggregate_func
num_run
model
acq_optimizer
acquisition_func
rng
"""
def __init__(self,
scenario: Scenario,
stats: Stats,
initial_design: InitialDesign,
runhistory: RunHistory,
runhistory2epm: AbstractRunHistory2EPM,
intensifier: Intensifier,
aggregate_func: callable,
num_run: int,
model: RandomForestWithInstances,
acq_optimizer: AcquisitionFunctionMaximizer,
acquisition_func: AbstractAcquisitionFunction,
rng: np.random.RandomState,
restore_incumbent: Configuration=None):
"""
Interface that contains the main Bayesian optimization loop
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
stats: Stats
statistics object with configuration budgets
initial_design: InitialDesign
initial sampling design
runhistory: RunHistory
runhistory with all runs so far
runhistory2epm : AbstractRunHistory2EPM
Object that implements the AbstractRunHistory2EPM to convert runhistory
data into EPM data
intensifier: Intensifier
intensification of new challengers against incumbent configuration
(probably with some kind of racing on the instances)
aggregate_func: callable
how to aggregate the runs in the runhistory to get the performance of a
configuration
num_run: int
id of this run (used for pSMAC)
model: RandomForestWithInstances
empirical performance model (right now, we support only
RandomForestWithInstances)
acq_optimizer: AcquisitionFunctionMaximizer
Optimizer of acquisition function.
acquisition_function : AcquisitionFunction
Object that implements the AbstractAcquisitionFunction (i.e., infill
criterion for acq_optimizer)
restore_incumbent: Configuration
incumbent to be used from the start. ONLY used to restore states.
rng: np.random.RandomState
Random number generator
"""
self.logger = logging.getLogger(
self.__module__ + "." + self.__class__.__name__)
self.incumbent = restore_incumbent
self.scenario = scenario
self.config_space = scenario.cs
self.stats = stats
self.initial_design = initial_design
self.runhistory = runhistory
self.rh2EPM = runhistory2epm
self.intensifier = intensifier
self.aggregate_func = aggregate_func
self.num_run = num_run
self.model = model
self.acq_optimizer = acq_optimizer
self.acquisition_func = acquisition_func
self.rng = rng
self._random_search = RandomSearch(
acquisition_func, self.config_space, rng
)
def start(self):
"""Starts the Bayesian Optimization loop.
Detects whether we the optimization is restored from previous state.
"""
self.stats.start_timing()
# Initialization, depends on input
if self.stats.ta_runs == 0 and self.incumbent is None:
try:
self.incumbent = self.initial_design.run()
except FirstRunCrashedException as err:
if self.scenario.abort_on_first_run_crash:
raise
elif self.stats.ta_runs > 0 and self.incumbent is None:
raise ValueError("According to stats there have been runs performed, "
"but the optimizer cannot detect an incumbent. Did "
"you set the incumbent (e.g. after restoring state)?")
elif self.stats.ta_runs == 0 and self.incumbent is not None:
raise ValueError("An incumbent is specified, but there are no runs "
"recorded in the Stats-object. If you're restoring "
"a state, please provide the Stats-object.")
else:
# Restoring state!
self.logger.info("State Restored! Starting optimization with "
"incumbent %s", self.incumbent)
self.logger.info("State restored with following budget:")
self.stats.print_stats()
def run(self):
"""Runs the Bayesian optimization loop
Returns
----------
incumbent: np.array(1, H)
The best found configuration
"""
self.start()
# Main BO loop
while True:
if self.scenario.shared_model:
pSMAC.read(run_history=self.runhistory,
output_dirs=self.scenario.input_psmac_dirs,
configuration_space=self.config_space,
logger=self.logger)
start_time = time.time()
X, Y = self.rh2EPM.transform(self.runhistory)
self.logger.debug("Search for next configuration")
# get all found configurations sorted according to acq
challengers = self.choose_next(X, Y)
time_spent = time.time() - start_time
time_left = self._get_timebound_for_intensification(time_spent)
self.logger.debug("Intensify")
self.incumbent, inc_perf = self.intensifier.intensify(
challengers=challengers,
incumbent=self.incumbent,
run_history=self.runhistory,
aggregate_func=self.aggregate_func,
time_bound=max(self.intensifier._min_time, time_left))
if self.scenario.shared_model:
pSMAC.write(run_history=self.runhistory,
output_directory=self.scenario.output_dir_for_this_run)
logging.debug("Remaining budget: %f (wallclock), %f (ta costs), %f (target runs)" % (
self.stats.get_remaing_time_budget(),
self.stats.get_remaining_ta_budget(),
self.stats.get_remaining_ta_runs()))
if self.stats.is_budget_exhausted():
break
self.stats.print_stats(debug_out=True)
return self.incumbent
def choose_next(self, X: np.ndarray, Y: np.ndarray, incumbent_value: float=None):
"""Choose next candidate solution with Bayesian optimization. The
suggested configurations depend on the argument ``acq_optimizer`` to
the ``SMBO`` class.
Parameters
----------
X : (N, D) numpy array
Each row contains a configuration and one set of
instance features.
Y : (N, O) numpy array
The function values for each configuration instance pair.
incumbent_value: float
Cost value of incumbent configuration
(required for acquisition function);
if not given, it will be inferred from runhistory;
if not given and runhistory is empty,
it will raise a ValueError
Returns
-------
Iterable
"""
import pdb
pdb.set_trace()
if X.shape[0] == 0:
# Only return a single point to avoid an overly high number of
# random search iterations
return self._random_search.maximize(runhistory=self.runhistory, stats=self.stats, num_points=1)
self.model.train(X, Y)
if incumbent_value is None:
if self.runhistory.empty():
raise ValueError("Runhistory is empty and the cost value of the incumbent is unknown.")
incumbent_value = self.runhistory.get_cost(self.incumbent)
self.acquisition_func.update(model=self.model, eta=incumbent_value)
challengers = self.acq_optimizer.maximize(self.runhistory, self.stats, 5000)
return challengers
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
"""
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)
new_rh_path = os.path.join(self.scenario.output_dir_for_this_run, "validated_runhistory.json")
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=new_rh_path)
else:
new_rh = validator.validate(config_mode, instance_mode, repetitions,
n_jobs, backend, self.runhistory,
self.intensifier.tae_runner,
new_rh_path)
return new_rh
def _get_timebound_for_intensification(self, time_spent):
"""Calculate time left for intensify from the time spent on
choosing challengers using the fraction of time intended for
intensification (which is specified in
scenario.intensification_percentage).
Parameters
----------
time_spent : float
Returns
-------
time_left : float
"""
frac_intensify = self.scenario.intensification_percentage
if frac_intensify <= 0 or frac_intensify >= 1:
raise ValueError("The value for intensification_percentage-"
"option must lie in (0,1), instead: %.2f" %
(frac_intensify))
total_time = time_spent / (1 - frac_intensify)
time_left = frac_intensify * total_time
self.logger.debug("Total time: %.4f, time spent on choosing next "
"configurations: %.4f (%.2f), time left for "
"intensification: %.4f (%.2f)" %
(total_time, time_spent, (1 - frac_intensify), time_left, frac_intensify))
return time_left
class SMBO(object):
"""Interface that contains the main Bayesian optimization loop
Attributes
----------
logger
incumbent
scenario
config_space
stats
initial_design
runhistory
rh2EPM
intensifier
aggregate_func
num_run
model
acq_optimizer
acquisition_func
rng
random_configuration_chooser
"""
def __init__(self,
scenario: Scenario,
stats: Stats,
initial_design: InitialDesign,
runhistory: RunHistory,
runhistory2epm: AbstractRunHistory2EPM,
intensifier: Intensifier,
aggregate_func: callable,
num_run: int,
model: RandomForestWithInstances,
acq_optimizer: AcquisitionFunctionMaximizer,
acquisition_func: AbstractAcquisitionFunction,
rng: np.random.RandomState,
restore_incumbent: Configuration = None,
random_configuration_chooser: typing.Union[
ChooserNoCoolDown,
ChooserLinearCoolDown] = ChooserNoCoolDown(2.0),
predict_incumbent: bool = True):
"""
Interface that contains the main Bayesian optimization loop
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
stats: Stats
statistics object with configuration budgets
initial_design: InitialDesign
initial sampling design
runhistory: RunHistory
runhistory with all runs so far
runhistory2epm : AbstractRunHistory2EPM
Object that implements the AbstractRunHistory2EPM to convert runhistory
data into EPM data
intensifier: Intensifier
intensification of new challengers against incumbent configuration
(probably with some kind of racing on the instances)
aggregate_func: callable
how to aggregate the runs in the runhistory to get the performance of a
configuration
num_run: int
id of this run (used for pSMAC)
model: RandomForestWithInstances
empirical performance model (right now, we support only
RandomForestWithInstances)
acq_optimizer: AcquisitionFunctionMaximizer
Optimizer of acquisition function.
acquisition_function : AcquisitionFunction
Object that implements the AbstractAcquisitionFunction (i.e., infill
criterion for acq_optimizer)
restore_incumbent: Configuration
incumbent to be used from the start. ONLY used to restore states.
rng: np.random.RandomState
Random number generator
random_configuration_chooser
Chooser for random configuration -- one of
* ChooserNoCoolDown(modulus)
* ChooserLinearCoolDown(start_modulus, modulus_increment, end_modulus)
predict_incumbent: bool
Use predicted performance of incumbent instead of observed performance
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
self.incumbent = restore_incumbent
self.scenario = scenario
self.config_space = scenario.cs
self.stats = stats
self.initial_design = initial_design
self.runhistory = runhistory
self.rh2EPM = runhistory2epm
self.intensifier = intensifier
self.aggregate_func = aggregate_func
self.num_run = num_run
self.model = model
self.acq_optimizer = acq_optimizer
self.acquisition_func = acquisition_func
self.rng = rng
self.random_configuration_chooser = random_configuration_chooser
self._random_search = RandomSearch(acquisition_func, self.config_space,
rng)
self.predict_incumbent = predict_incumbent
def start(self):
"""Starts the Bayesian Optimization loop.
Detects whether we the optimization is restored from previous state.
"""
self.stats.start_timing()
# Initialization, depends on input
if self.stats.ta_runs == 0 and self.incumbent is None:
self.incumbent = self.initial_design.run()
elif self.stats.ta_runs > 0 and self.incumbent is None:
raise ValueError(
"According to stats there have been runs performed, "
"but the optimizer cannot detect an incumbent. Did "
"you set the incumbent (e.g. after restoring state)?")
elif self.stats.ta_runs == 0 and self.incumbent is not None:
raise ValueError(
"An incumbent is specified, but there are no runs "
"recorded in the Stats-object. If you're restoring "
"a state, please provide the Stats-object.")
else:
# Restoring state!
self.logger.info(
"State Restored! Starting optimization with "
"incumbent %s", self.incumbent)
self.logger.info("State restored with following budget:")
self.stats.print_stats()
# To be on the safe side -> never return "None" as incumbent
if not self.incumbent:
self.incumbent = self.scenario.cs.get_default_configuration()
def run(self):
"""Runs the Bayesian optimization loop
Returns
----------
incumbent: np.array(1, H)
The best found configuration
"""
self.start()
# Main BO loop
while True:
if self.scenario.shared_model:
pSMAC.read(run_history=self.runhistory,
output_dirs=self.scenario.input_psmac_dirs,
configuration_space=self.config_space,
logger=self.logger)
start_time = time.time()
X, Y = self.rh2EPM.transform(self.runhistory)
self.logger.debug("Search for next configuration")
# get all found configurations sorted according to acq
challengers = self.choose_next(X, Y)
time_spent = time.time() - start_time
time_left = self._get_timebound_for_intensification(time_spent)
self.logger.debug("Intensify")
self.incumbent, inc_perf = self.intensifier.intensify(
challengers=challengers,
incumbent=self.incumbent,
run_history=self.runhistory,
aggregate_func=self.aggregate_func,
time_bound=max(self.intensifier._min_time, time_left))
if self.scenario.shared_model:
pSMAC.write(
run_history=self.runhistory,
output_directory=self.scenario.output_dir_for_this_run,
logger=self.logger)
logging.debug(
"Remaining budget: %f (wallclock), %f (ta costs), %f (target runs)"
% (self.stats.get_remaing_time_budget(),
self.stats.get_remaining_ta_budget(),
self.stats.get_remaining_ta_runs()))
if self.stats.is_budget_exhausted():
break
self.stats.print_stats(debug_out=True)
return self.incumbent
def choose_next(self,
X: np.ndarray,
Y: np.ndarray,
incumbent_value: float = None):
"""Choose next candidate solution with Bayesian optimization. The
suggested configurations depend on the argument ``acq_optimizer`` to
the ``SMBO`` class.
Parameters
----------
X : (N, D) numpy array
Each row contains a configuration and one set of
instance features.
Y : (N, O) numpy array
The function values for each configuration instance pair.
incumbent_value: float
Cost value of incumbent configuration
(required for acquisition function);
if not given, it will be inferred from runhistory;
if not given and runhistory is empty,
it will raise a ValueError
Returns
-------
Iterable
"""
if X.shape[0] == 0:
# Only return a single point to avoid an overly high number of
# random search iterations
return self._random_search.maximize(runhistory=self.runhistory,
stats=self.stats,
num_points=1)
self.model.train(X, Y)
if incumbent_value is None:
if self.runhistory.empty():
raise ValueError("Runhistory is empty and the cost value of "
"the incumbent is unknown.")
incumbent_value = self._get_incumbent_value()
self.acquisition_func.update(model=self.model,
eta=incumbent_value,
num_data=len(self.runhistory.data))
challengers = self.acq_optimizer.maximize(
runhistory=self.runhistory,
stats=self.stats,
num_points=self.scenario.acq_opt_challengers,
random_configuration_chooser=self.random_configuration_chooser)
return challengers
def _get_incumbent_value(self):
''' get incumbent value either from runhistory
or from best predicted performance on configs in runhistory
(depends on self.predict_incumbent)"
Return
------
float
'''
if self.predict_incumbent:
configs = convert_configurations_to_array(
self.runhistory.get_all_configs())
costs = list(
map(
lambda config: self.model.
predict_marginalized_over_instances(config.reshape(
(1, -1)))[0][0][0],
configs,
))
incumbent_value = np.min(costs)
# won't need log(y) if EPM was already trained on log(y)
else:
if self.runhistory.empty():
raise ValueError("Runhistory is empty and the cost value of "
"the incumbent is unknown.")
incumbent_value = self.runhistory.get_cost(self.incumbent)
# It's unclear how to do this for inv scaling and potential future scaling. This line should be changed if
# necessary
incumbent_value_as_array = np.array(incumbent_value).reshape(
(1, 1))
incumbent_value = self.rh2EPM.transform_response_values(
incumbent_value_as_array)
incumbent_value = incumbent_value[0][0]
return incumbent_value
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 _get_timebound_for_intensification(self, time_spent: float):
"""Calculate time left for intensify from the time spent on
choosing challengers using the fraction of time intended for
intensification (which is specified in
scenario.intensification_percentage).
Parameters
----------
time_spent : float
Returns
-------
time_left : float
"""
frac_intensify = self.scenario.intensification_percentage
if frac_intensify <= 0 or frac_intensify >= 1:
raise ValueError("The value for intensification_percentage-"
"option must lie in (0,1), instead: %.2f" %
(frac_intensify))
total_time = time_spent / (1 - frac_intensify)
time_left = frac_intensify * total_time
self.logger.debug("Total time: %.4f, time spent on choosing next "
"configurations: %.4f (%.2f), time left for "
"intensification: %.4f (%.2f)" %
(total_time, time_spent,
(1 - frac_intensify), time_left, frac_intensify))
return time_left
def _component_builder(self, conf:typing.Union[Configuration, dict]) \
-> typing.Tuple[AbstractAcquisitionFunction, AbstractEPM]:
"""
builds new Acquisition function object
and EPM object and returns these
Parameters
----------
conf: typing.Union[Configuration, dict]
configuration specificing "model" and "acq_func"
Returns
-------
typing.Tuple[AbstractAcquisitionFunction, AbstractEPM]
"""
types, bounds = get_types(
self.config_space, instance_features=self.scenario.feature_array)
if conf["model"] == "RF":
model = RandomForestWithInstances(
configspace=self.config_space,
types=types,
bounds=bounds,
instance_features=self.scenario.feature_array,
seed=self.rng.randint(MAXINT),
pca_components=conf.get("pca_dim", self.scenario.PCA_DIM),
log_y=conf.get("log_y", self.scenario.transform_y
in ["LOG", "LOGS"]),
num_trees=conf.get("num_trees", self.scenario.rf_num_trees),
do_bootstrapping=conf.get("do_bootstrapping",
self.scenario.rf_do_bootstrapping),
ratio_features=conf.get("ratio_features",
self.scenario.rf_ratio_features),
min_samples_split=conf.get("min_samples_split",
self.scenario.rf_min_samples_split),
min_samples_leaf=conf.get("min_samples_leaf",
self.scenario.rf_min_samples_leaf),
max_depth=conf.get("max_depth", self.scenario.rf_max_depth),
)
elif conf["model"] == "GP":
from smac.epm.gp_kernels import ConstantKernel, HammingKernel, WhiteKernel, Matern
cov_amp = ConstantKernel(
2.0,
constant_value_bounds=(np.exp(-10), np.exp(2)),
prior=LognormalPrior(mean=0.0, sigma=1.0, rng=self.rng),
)
cont_dims = np.nonzero(types == 0)[0]
cat_dims = np.nonzero(types != 0)[0]
if len(cont_dims) > 0:
exp_kernel = Matern(
np.ones([len(cont_dims)]),
[(np.exp(-10), np.exp(2)) for _ in range(len(cont_dims))],
nu=2.5,
operate_on=cont_dims,
)
if len(cat_dims) > 0:
ham_kernel = HammingKernel(
np.ones([len(cat_dims)]),
[(np.exp(-10), np.exp(2)) for _ in range(len(cat_dims))],
operate_on=cat_dims,
)
noise_kernel = WhiteKernel(
noise_level=1e-8,
noise_level_bounds=(np.exp(-25), np.exp(2)),
prior=HorseshoePrior(scale=0.1, rng=self.rng),
)
if len(cont_dims) > 0 and len(cat_dims) > 0:
# both
kernel = cov_amp * (exp_kernel * ham_kernel) + noise_kernel
elif len(cont_dims) > 0 and len(cat_dims) == 0:
# only cont
kernel = cov_amp * exp_kernel + noise_kernel
elif len(cont_dims) == 0 and len(cat_dims) > 0:
# only cont
kernel = cov_amp * ham_kernel + noise_kernel
else:
raise ValueError()
n_mcmc_walkers = 3 * len(kernel.theta)
if n_mcmc_walkers % 2 == 1:
n_mcmc_walkers += 1
model = GaussianProcessMCMC(
self.config_space,
types=types,
bounds=bounds,
kernel=kernel,
n_mcmc_walkers=n_mcmc_walkers,
chain_length=250,
burnin_steps=250,
normalize_y=True,
seed=self.rng.randint(low=0, high=10000),
)
if conf["acq_func"] == "EI":
acq = EI(model=model, par=conf.get("par_ei", 0))
elif conf["acq_func"] == "LCB":
acq = LCB(model=model, par=conf.get("par_lcb", 0))
elif conf["acq_func"] == "PI":
acq = PI(model=model, par=conf.get("par_pi", 0))
elif conf["acq_func"] == "LogEI":
# par value should be in log-space
acq = LogEI(model=model, par=conf.get("par_logei", 0))
return acq, model
def _get_acm_cs(self):
"""
returns a configuration space
designed for querying ~smac.optimizer.smbo._component_builder
Returns
-------
ConfigurationSpace
"""
cs = ConfigurationSpace()
cs.seed(self.rng.randint(0, 2**20))
if 'gp' in smac.extras_installed:
model = CategoricalHyperparameter("model", choices=("RF", "GP"))
else:
model = Constant("model", value="RF")
num_trees = Constant("num_trees", value=10)
bootstrap = CategoricalHyperparameter("do_bootstrapping",
choices=(True, False),
default_value=True)
ratio_features = CategoricalHyperparameter("ratio_features",
choices=(3 / 6, 4 / 6,
5 / 6, 1),
default_value=1)
min_split = UniformIntegerHyperparameter("min_samples_to_split",
lower=1,
upper=10,
default_value=2)
min_leaves = UniformIntegerHyperparameter("min_samples_in_leaf",
lower=1,
upper=10,
default_value=1)
cs.add_hyperparameters([
model, num_trees, bootstrap, ratio_features, min_split, min_leaves
])
inc_num_trees = InCondition(num_trees, model, ["RF"])
inc_bootstrap = InCondition(bootstrap, model, ["RF"])
inc_ratio_features = InCondition(ratio_features, model, ["RF"])
inc_min_split = InCondition(min_split, model, ["RF"])
inc_min_leavs = InCondition(min_leaves, model, ["RF"])
cs.add_conditions([
inc_num_trees, inc_bootstrap, inc_ratio_features, inc_min_split,
inc_min_leavs
])
acq = CategoricalHyperparameter("acq_func",
choices=("EI", "LCB", "PI", "LogEI"))
par_ei = UniformFloatHyperparameter("par_ei", lower=-10, upper=10)
par_pi = UniformFloatHyperparameter("par_pi", lower=-10, upper=10)
par_logei = UniformFloatHyperparameter("par_logei",
lower=0.001,
upper=100,
log=True)
par_lcb = UniformFloatHyperparameter("par_lcb",
lower=0.0001,
upper=0.9999)
cs.add_hyperparameters([acq, par_ei, par_pi, par_logei, par_lcb])
inc_par_ei = InCondition(par_ei, acq, ["EI"])
inc_par_pi = InCondition(par_pi, acq, ["PI"])
inc_par_logei = InCondition(par_logei, acq, ["LogEI"])
inc_par_lcb = InCondition(par_lcb, acq, ["LCB"])
cs.add_conditions([inc_par_ei, inc_par_pi, inc_par_logei, inc_par_lcb])
return cs
class SMBO(object):
"""Interface that contains the main Bayesian optimization loop
Attributes
----------
logger
incumbent
scenario
config_space
stats
initial_design
runhistory
rh2EPM
intensifier
aggregate_func
num_run
model
acq_optimizer
acquisition_func
rng
"""
def __init__(
self,
scenario: Scenario,
stats: Stats,
initial_design: InitialDesign,
runhistory: RunHistory,
runhistory2epm: AbstractRunHistory2EPM,
intensifier: Intensifier,
aggregate_func: callable,
num_run: int,
model: AbstractEPM,
acq_optimizer: AcquisitionFunctionMaximizer,
acquisition_func: AbstractAcquisitionFunction,
rng: np.random.RandomState,
restore_incumbent: Configuration = None,
# 强行在smbo中加入训练集和验证集
hoag: AbstractHOAG = None,
# 参数服务器worker的脚本文件路径
#server: Server = None,
bayesian_optimization: bool = False):
"""
Interface that contains the main Bayesian optimization loop
Parameters
----------
scenario: smac.scenario.scenario.Scenario
Scenario object
stats: Stats
statistics object with configuration budgets
initial_design: InitialDesign
initial sampling design
runhistory: RunHistory
runhistory with all runs so far
runhistory2epm : AbstractRunHistory2EPM
Object that implements the AbstractRunHistory2EPM to convert runhistory
data into EPM data
intensifier: Intensifier
intensification of new challengers against incumbent configuration
(probably with some kind of racing on the instances)
aggregate_func: callable
how to aggregate the runs in the runhistory to get the performance of a
configuration
num_run: int
id of this run (used for pSMAC)
model: AbstractEPM
empirical performance model (right now, we support only
AbstractEPM)
acq_optimizer: AcquisitionFunctionMaximizer
Optimizer of acquisition function.
acquisition_function : AcquisitionFunction
Object that implements the AbstractAcquisitionFunction (i.e., infill
criterion for acq_optimizer)
restore_incumbent: Configuration
incumbent to be used from the start. ONLY used to restore states.
rng: np.random.RandomState
Random number generator
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
self.incumbent = restore_incumbent
self.scenario = scenario
self.config_space = scenario.cs
self.stats = stats
self.initial_design = initial_design
self.runhistory = runhistory
self.rh2EPM = runhistory2epm
self.intensifier = intensifier
self.aggregate_func = aggregate_func
self.num_run = num_run
self.model = model
self.acq_optimizer = acq_optimizer
self.acquisition_func = acquisition_func
self.rng = rng
# hoag的类，直接使用hoag的fit，predict等
self.hoag = hoag
# 保存server端进程
#self.server = server
self.server = None
self.bayesian_optimization = bayesian_optimization
self._random_search = RandomSearch(acquisition_func, self.config_space,
rng)
def start(self):
"""Starts the Bayesian Optimization loop.
Detects whether we the optimization is restored from previous state.
"""
self.stats.start_timing()
# Initialization, depends on input
if self.stats.ta_runs == 0 and self.incumbent is None:
try:
if self.server is None:
self.incumbent = self.initial_design.run()
else:
# 由worker自己产生第一个incumbent，然后由server接收其中的一个
self.incumbent, new_runhistory = self.server.pull()
self.runhistory.update(new_runhistory)
except FirstRunCrashedException as err:
if self.scenario.abort_on_first_run_crash:
raise
elif self.stats.ta_runs > 0 and self.incumbent is None:
raise ValueError(
"According to stats there have been runs performed, "
"but the optimizer cannot detect an incumbent. Did "
"you set the incumbent (e.g. after restoring state)?")
elif self.stats.ta_runs == 0 and self.incumbent is not None:
raise ValueError(
"An incumbent is specified, but there are no runs "
"recorded in the Stats-object. If you're restoring "
"a state, please provide the Stats-object.")
else:
# Restoring state!
self.logger.info(
"State Restored! Starting optimization with "
"incumbent %s", self.incumbent)
self.logger.info("State restored with following budget:")
self.stats.print_stats()
def run(self):
"""Runs the Bayesian optimization loop
Returns
----------
incumbent: np.array(1, H)
The best found configuration
"""
self.start()
# 设置一个counter
counter = 0
# Main BO loop
while True:
# 打印每轮SMBO的最优结果(包括首轮SMBO 0)
print('SMBO ' + str(counter) + ': ' +
str(self.runhistory.get_cost(self.incumbent)))
counter += 1
if self.scenario.shared_model:
pSMAC.read(run_history=self.runhistory,
output_dirs=self.scenario.input_psmac_dirs,
configuration_space=self.config_space,
logger=self.logger)
start_time = time.time()
X, Y = self.rh2EPM.transform(self.runhistory)
self.logger.debug("Search for next configuration")
# get all found configurations sorted according to acq
challengers = self.choose_next(X, Y)
time_spent = time.time() - start_time
time_left = self._get_timebound_for_intensification(time_spent)
self.logger.debug("Intensify")
if self.server is None:
self.incumbent, inc_perf = self.intensifier.intensify(
challengers=challengers,
incumbent=self.incumbent,
run_history=self.runhistory,
aggregate_func=self.aggregate_func,
time_bound=max(self.intensifier._min_time, time_left))
else:
# 从worker读取loss，加入history再运行新的challengers
print(time_left)
self.server.push(incumbent=self.incumbent,
runhistory=self.runhistory,
challengers=challengers.challengers,
time_left=time_left)
# 从worker读取runhistory，并merge到self.runhistory
incumbent, new_runhistory = self.server.pull()
self.runhistory.update(new_runhistory)
# 更新了runhistory之后，应该找寻是否存在新的incumbent
# 因为worker没有完整的
runhistory_old = self.runhistory.get_history_for_config(
self.incumbent)
runhistory_new = self.runhistory.get_history_for_config(
incumbent)
# 找寻cost最小值
lowest_cost_old = min([cost[0] for cost in runhistory_old])
lowest_cost_new = min([cost[0] for cost in runhistory_new])
if lowest_cost_new < lowest_cost_old:
# 替换为新的incumbent
self.incumbent = incumbent
"""可以考虑用这个函数
new_incumbent = self._compare_configs(
incumbent=incumbent, challenger=challenger,
run_history=run_history,
aggregate_func=aggregate_func,
log_traj=log_traj)
"""
if self.scenario.shared_model:
pSMAC.write(
run_history=self.runhistory,
output_directory=self.scenario.output_dir_for_this_run)
logging.debug(
"Remaining budget: %f (wallclock), %f (ta costs), %f (target runs)"
% (self.stats.get_remaing_time_budget(),
self.stats.get_remaining_ta_budget(),
self.stats.get_remaining_ta_runs()))
if self.stats.is_budget_exhausted():
break
self.stats.print_stats(debug_out=True)
return self.incumbent
def choose_next(self,
X: np.ndarray,
Y: np.ndarray,
incumbent_value: float = None):
"""Choose next candidate solution with Bayesian optimization. The
suggested configurations depend on the argument ``acq_optimizer`` to
the ``SMBO`` class.
Parameters
----------
X : (N, D) numpy array
Each row contains a configuration and one set of
instance features.
Y : (N, O) numpy array
The function values for each configuration instance pair.
incumbent_value: float
Cost value of incumbent configuration
(required for acquisition function);
if not given, it will be inferred from runhistory;
if not given and runhistory is empty,
it will raise a ValueError
Returns
-------
Iterable
"""
if X.shape[0] == 0:
# Only return a single point to avoid an overly high number of
# random search iterations
return self._random_search.maximize(runhistory=self.runhistory,
stats=self.stats,
num_points=1)
# 消去完全相同的行
X, Y = remove_same_values(X, Y)
print(X.shape)
# 如果指定了hoag函数，则进行调用
if self.hoag is not None:
# 初始化梯度数组
gradient = np.zeros(X.shape)
# 对每组X，计算对应的梯度(此处有大量重复计算)
for i in range(X.shape[0]):
self.hoag.fit(X[i, :])
gradient[i, :] = self.hoag.predict_gradient()
X = X.flatten()
ind = np.argsort(X)
gradient = gradient.flatten()[ind].reshape(-1, 1)
X = X[ind].reshape(-1, 1)
Y = Y.flatten()[ind].reshape(-1, 1)
self.model.train(X, Y, gradient=gradient)
elif self.bayesian_optimization:
# gpr使用的参数
gp_params = {"alpha": 1e-5, "n_restarts_optimizer": 2}
# 从configspace读取超参的范围
pbounds = {}
for key in self.scenario.cs._hyperparameters.keys():
# 只处理float类型的超参
hyperparamter = self.scenario.cs._hyperparameters[key],
if isinstance(hyperparamter.default_value, float):
pbounds[key] = (hyperparamter.lower, hyperparamter.upper)
# 初始化bayesian_optimization
bo = BayesianOptimization(X, Y, pbounds=pbounds, verbose=False)
# 预测下一个ei取得点
newX = bo.maximize(acq="ei", **gp_params)
# 将超参数组再转化为Configuration
challengers = [Configuration(self.scenario.cs, x) for x in newX]
return challengers
else:
self.model.train(X, Y)
# 打印X和Y的值
# print("X: ", X.flatten())
# print("Y: ", Y.flatten())
# print("Y_pred: ", self.model.predict(X))
# if self.hoag is not None:
# print("G: ", gradient)
if incumbent_value is None:
if self.runhistory.empty():
raise ValueError("Runhistory is empty and the cost value of "
"the incumbent is unknown.")
incumbent_value = self.runhistory.get_cost(self.incumbent)
self.acquisition_func.update(model=self.model, eta=incumbent_value)
challengers = self.acq_optimizer.maximize(
# 初始为5000，提升速度调成500
self.runhistory,
self.stats,
500)
return challengers
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
"""
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)
new_rh_path = os.path.join(self.scenario.output_dir_for_this_run,
"validated_runhistory.json")
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=new_rh_path)
else:
new_rh = validator.validate(config_mode,
instance_mode,
repetitions,
n_jobs,
backend,
self.runhistory,
self.intensifier.tae_runner,
output=new_rh_path)
return new_rh
def _get_timebound_for_intensification(self, time_spent):
"""Calculate time left for intensify from the time spent on
choosing challengers using the fraction of time intended for
intensification (which is specified in
scenario.intensification_percentage).
Parameters
----------
time_spent : float
Returns
-------
time_left : float
"""
frac_intensify = self.scenario.intensification_percentage
if frac_intensify <= 0 or frac_intensify >= 1:
raise ValueError("The value for intensification_percentage-"
"option must lie in (0,1), instead: %.2f" %
(frac_intensify))
total_time = time_spent / (1 - frac_intensify)
time_left = frac_intensify * total_time
self.logger.debug("Total time: %.4f, time spent on choosing next "
"configurations: %.4f (%.2f), time left for "
"intensification: %.4f (%.2f)" %
(total_time, time_spent,
(1 - frac_intensify), time_left, frac_intensify))
return time_left
