def add_component_deepcopy(config_space, prefix, component_space):
# We have to retrieve the configuration space every time because
# we change the objects it returns. If we reused it, we could not
# retrieve the conditions further down
# TODO implement copy for hyperparameters and forbidden and
# conditions!
component = config_space.get_hyperparameter("__choice__")
for parameter in component_space.get_hyperparameters():
new_parameter = copy.deepcopy(parameter)
new_parameter.name = "%s:%s" % (prefix, new_parameter.name)
config_space.add_hyperparameter(new_parameter)
# We must only add a condition if the hyperparameter is not
# conditional on something else
if len(component_space.get_parents_of(parameter)) == 0:
condition = EqualsCondition(new_parameter, component, prefix)
config_space.add_condition(condition)
space_copy = copy.deepcopy(component_space)
for condition in space_copy.get_conditions():
dlcs = condition.get_descendant_literal_conditions()
for dlc in dlcs:
if not dlc.child.name.startswith(prefix):
dlc.child.name = "%s:%s" % (prefix, dlc.child.name)
if not dlc.parent.name.startswith(prefix):
dlc.parent.name = "%s:%s" % (prefix, dlc.parent.name)
config_space.add_condition(condition)
space_copy = copy.deepcopy(component_space)
for forbidden_clause in space_copy.forbidden_clauses:
dlcs = forbidden_clause.get_descendant_literal_clauses()
for dlc in dlcs:
if not dlc.hyperparameter.name.startswith(prefix):
dlc.hyperparameter.name = "%s:%s" % (prefix,
dlc.hyperparameter.name)
config_space.add_forbidden_clause(forbidden_clause)
return config_space
def add_component_deepcopy(config_space, prefix, component_space):
# We have to retrieve the configuration space every time because
# we change the objects it returns. If we reused it, we could not
# retrieve the conditions further down
# TODO implement copy for hyperparameters and forbidden and
# conditions!
component = config_space.get_hyperparameter("__choice__")
for parameter in component_space.get_hyperparameters():
new_parameter = copy.deepcopy(parameter)
new_parameter.name = "%s:%s" % (prefix,
new_parameter.name)
config_space.add_hyperparameter(new_parameter)
# We must only add a condition if the hyperparameter is not
# conditional on something else
if len(component_space.get_parents_of(parameter)) == 0:
condition = EqualsCondition(new_parameter, component,
prefix)
config_space.add_condition(condition)
space_copy = copy.deepcopy(component_space)
for condition in space_copy.get_conditions():
dlcs = condition.get_descendant_literal_conditions()
for dlc in dlcs:
if not dlc.child.name.startswith(prefix):
dlc.child.name = "%s:%s" % (
prefix, dlc.child.name)
if not dlc.parent.name.startswith(prefix):
dlc.parent.name = "%s:%s" % (
prefix, dlc.parent.name)
config_space.add_condition(condition)
space_copy = copy.deepcopy(component_space)
for forbidden_clause in space_copy.forbidden_clauses:
dlcs = forbidden_clause.get_descendant_literal_clauses()
for dlc in dlcs:
if not dlc.hyperparameter.name.startswith(prefix):
dlc.hyperparameter.name = "%s:%s" % (prefix,
dlc.hyperparameter.name)
config_space.add_forbidden_clause(forbidden_clause)
return config_space
def get_hyperparameter_search_space(cls, dataset_properties,
default=None,
include=None,
exclude=None):
if include is not None and exclude is not None:
raise ValueError("The argument include and exclude cannot be used together.")
cs = ConfigurationSpace()
# Compile a list of all estimator objects for this problem
available_estimators = cls.get_available_components(
data_prop=dataset_properties,
include=include,
exclude=exclude)
if len(available_estimators) == 0:
raise ValueError("No regressors found")
if default is None:
defaults = ['random_forest', 'support_vector_regression'] + \
list(available_estimators.keys())
for default_ in defaults:
if default_ in available_estimators:
if include is not None and default_ not in include:
continue
if exclude is not None and default_ in exclude:
continue
default = default_
break
estimator = CategoricalHyperparameter('__choice__',
list(available_estimators.keys()),
default=default)
cs.add_hyperparameter(estimator)
for estimator_name in available_estimators.keys():
# We have to retrieve the configuration space every time because
# we change the objects it returns. If we reused it, we could not
# retrieve the conditions further down
# TODO implement copy for hyperparameters and forbidden and
# conditions!
estimator_configuration_space = available_estimators[
estimator_name]. \
get_hyperparameter_search_space(dataset_properties)
for parameter in estimator_configuration_space.get_hyperparameters():
new_parameter = copy.deepcopy(parameter)
new_parameter.name = "%s:%s" % (
estimator_name, new_parameter.name)
cs.add_hyperparameter(new_parameter)
# We must only add a condition if the hyperparameter is not
# conditional on something else
if len(estimator_configuration_space.
get_parents_of(parameter)) == 0:
condition = EqualsCondition(new_parameter, estimator,
estimator_name)
cs.add_condition(condition)
for condition in available_estimators[estimator_name]. \
get_hyperparameter_search_space(
dataset_properties).get_conditions():
dlcs = condition.get_descendant_literal_conditions()
for dlc in dlcs:
if not dlc.child.name.startswith(estimator_name):
dlc.child.name = "%s:%s" % (
estimator_name, dlc.child.name)
if not dlc.parent.name.startswith(estimator_name):
dlc.parent.name = "%s:%s" % (
estimator_name, dlc.parent.name)
cs.add_condition(condition)
for forbidden_clause in available_estimators[estimator_name]. \
get_hyperparameter_search_space(
dataset_properties).forbidden_clauses:
dlcs = forbidden_clause.get_descendant_literal_clauses()
for dlc in dlcs:
if not dlc.hyperparameter.name.startswith(estimator_name):
dlc.hyperparameter.name = "%s:%s" % (estimator_name,
dlc.hyperparameter.name)
cs.add_forbidden_clause(forbidden_clause)
return cs
def get_hyperparameter_search_space(cls,
dataset_properties,
default=None,
include=None,
exclude=None):
if include is not None and exclude is not None:
raise ValueError(
"The argument include and exclude cannot be used together.")
cs = ConfigurationSpace()
# Compile a list of all estimator objects for this problem
available_estimators = cls.get_available_components(
data_prop=dataset_properties, include=include, exclude=exclude)
if len(available_estimators) == 0:
raise ValueError("No regressors found")
if default is None:
defaults = ['random_forest', 'support_vector_regression'] + \
list(available_estimators.keys())
for default_ in defaults:
if default_ in available_estimators:
if include is not None and default_ not in include:
continue
if exclude is not None and default_ in exclude:
continue
default = default_
break
estimator = CategoricalHyperparameter('__choice__',
list(
available_estimators.keys()),
default=default)
cs.add_hyperparameter(estimator)
for estimator_name in available_estimators.keys():
# We have to retrieve the configuration space every time because
# we change the objects it returns. If we reused it, we could not
# retrieve the conditions further down
# TODO implement copy for hyperparameters and forbidden and
# conditions!
estimator_configuration_space = available_estimators[
estimator_name]. \
get_hyperparameter_search_space(dataset_properties)
for parameter in estimator_configuration_space.get_hyperparameters(
):
new_parameter = copy.deepcopy(parameter)
new_parameter.name = "%s:%s" % (estimator_name,
new_parameter.name)
cs.add_hyperparameter(new_parameter)
# We must only add a condition if the hyperparameter is not
# conditional on something else
if len(estimator_configuration_space.get_parents_of(
parameter)) == 0:
condition = EqualsCondition(new_parameter, estimator,
estimator_name)
cs.add_condition(condition)
for condition in available_estimators[estimator_name]. \
get_hyperparameter_search_space(
dataset_properties).get_conditions():
dlcs = condition.get_descendant_literal_conditions()
for dlc in dlcs:
if not dlc.child.name.startswith(estimator_name):
dlc.child.name = "%s:%s" % (estimator_name,
dlc.child.name)
if not dlc.parent.name.startswith(estimator_name):
dlc.parent.name = "%s:%s" % (estimator_name,
dlc.parent.name)
cs.add_condition(condition)
for forbidden_clause in available_estimators[estimator_name]. \
get_hyperparameter_search_space(
dataset_properties).forbidden_clauses:
dlcs = forbidden_clause.get_descendant_literal_clauses()
for dlc in dlcs:
if not dlc.hyperparameter.name.startswith(estimator_name):
dlc.hyperparameter.name = "%s:%s" % (
estimator_name, dlc.hyperparameter.name)
cs.add_forbidden_clause(forbidden_clause)
return cs
