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 arguments include_estimators and "
"exclude_estimators 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 classifiers found")
if default is None:
defaults = ['random_forest', 'liblinear_svc', 'sgd', 'libsvm_svc'
] + 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
def get_hyperparameter_search_space(cls, estimator_name,
default_estimator,
estimator_components,
preprocessor_components,
dataset_properties,
always_active):
"""Return the configuration space for the CASH problem.
This method should be called by the method
get_hyperparameter_search_space of a subclass. After the subclass
assembles a list of available estimators and preprocessor components,
_get_hyperparameter_search_space can be called to do the work of
creating the actual
HPOlibConfigSpace.configuration_space.ConfigurationSpace object.
Parameters
----------
estimator_name : str
Name of the estimator hyperparameter which will be used in the
configuration space. For a classification task, this would be
'classifier'.
estimator_components : dict {name: component}
Dictionary with all estimator components to be included in the
configuration space.
preprocessor_components : dict {name: component}
Dictionary with all preprocessor components to be included in the
configuration space. .
always_active : list of str
A list of components which will always be active in the pipeline.
This is useful for components like imputation which have
hyperparameters to be configured, but which do not have any parent.
default_estimator : str
Default value for the estimator hyperparameter.
Returns
-------
cs : HPOlibConfigSpace.configuration_space.Configuration
The configuration space describing the AutoSklearnClassifier.
"""
cs = ConfigurationSpace()
available_estimators = estimator_components
available_preprocessors = preprocessor_components
if default_estimator is None:
default_estimator = available_estimators.keys()[0]
estimator = CategoricalHyperparameter(estimator_name,
available_estimators.keys(), default=default_estimator)
cs.add_hyperparameter(estimator)
for 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[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" % (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, name)
cs.add_condition(condition)
for condition in available_estimators[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(name):
dlc.child.name = "%s:%s" % (name, dlc.child.name)
if not dlc.parent.name.startswith(name):
dlc.parent.name = "%s:%s" % (name, dlc.parent.name)
cs.add_condition(condition)
for forbidden_clause in available_estimators[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(name):
dlc.hyperparameter.name = "%s:%s" % (name,
dlc.hyperparameter.name)
cs.add_forbidden_clause(forbidden_clause)
preprocessor_choices = filter(lambda app: app not in always_active,
available_preprocessors.keys())
preprocessor = CategoricalHyperparameter("preprocessor",
["None"] + preprocessor_choices, default='None')
cs.add_hyperparameter(preprocessor)
for name in available_preprocessors.keys():
preprocessor_configuration_space = available_preprocessors[name]. \
get_hyperparameter_search_space(dataset_properties)
for parameter in preprocessor_configuration_space.get_hyperparameters():
new_parameter = copy.deepcopy(parameter)
new_parameter.name = "%s:%s" % (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(preprocessor_configuration_space.
get_parents_of(
parameter)) == 0 and name not in always_active:
condition = EqualsCondition(new_parameter, preprocessor,
name)
cs.add_condition(condition)
for condition in available_preprocessors[name]. \
get_hyperparameter_search_space(dataset_properties).get_conditions():
dlcs = condition.get_descendent_literal_conditions()
for dlc in dlcs:
if not dlc.child.name.startswith(name):
dlc.child.name = "%s:%s" % (name, dlc.child.name)
if not dlc.parent.name.startswith(name):
dlc.parent.name = "%s:%s" % (name, dlc.parent.name)
cs.add_condition(condition)
for forbidden_clause in available_preprocessors[name]. \
get_hyperparameter_search_space(dataset_properties).forbidden_clauses:
dlcs = forbidden_clause.get_descendant_literal_clauses()
for dlc in dlcs:
if not dlc.hyperparameter.startwith(name):
dlc.hyperparameter.name = "%s:%s" % (name,
dlc.hyperparameter.name)
cs.add_forbidden_clause(forbidden_clause)
# Now try to add things for which we know that they don't work
try:
cs.add_forbidden_clause(ForbiddenAndConjunction(
ForbiddenEqualsClause(cs.get_hyperparameter(
"select_percentile_classification:score_func"), "chi2"),
ForbiddenEqualsClause(cs.get_hyperparameter(
"rescaling:strategy"), "standard")
))
except:
pass
return cs