def get_hpo_cs(estimator_id, task_type=CLASSIFICATION):
_candidates = get_combined_candidtates(_classifiers, _addons)
if estimator_id in _candidates:
clf_class = _candidates[estimator_id]
else:
raise ValueError("Algorithm %s not supported!" % estimator_id)
cs = clf_class.get_hyperparameter_search_space()
return cs
def get_hpo_cs(estimator_id, task_type=REGRESSION):
_candidates = get_combined_candidtates(_regressors, _addons)
if estimator_id in _candidates:
rgs_class = _candidates[estimator_id]
else:
raise ValueError("Algorithm %s not supported!" % estimator_id)
cs = rgs_class.get_hyperparameter_search_space()
return cs
def get_cash_cs(include_algorithms=None, task_type=CLASSIFICATION):
_candidates = get_combined_candidtates(_classifiers, _addons)
if include_algorithms is not None:
_candidates = set(include_algorithms).intersection(set(_candidates.keys()))
if len(_candidates) == 0:
raise ValueError("No algorithms included! Please check the spelling of the included algorithms!")
cs = ConfigurationSpace()
algo = CategoricalHyperparameter('algorithm', list(_candidates))
cs.add_hyperparameter(algo)
for estimator_id in _candidates:
estimator_cs = get_hpo_cs(estimator_id)
parent_hyperparameter = {'parent': algo,
'value': estimator_id}
cs.add_configuration_space(estimator_id, estimator_cs, parent_hyperparameter=parent_hyperparameter)
return cs
def get_estimator(config, estimator_id):
classifier_type = estimator_id
config_ = config.copy()
config_['%s:random_state' % classifier_type] = 1
hpo_config = dict()
for key in config_:
key_name = key.split(':')[0]
if classifier_type == key_name:
act_key = key.split(':')[1]
hpo_config[act_key] = config_[key]
_candidates = get_combined_candidtates(_classifiers, _addons)
estimator = _candidates[classifier_type](**hpo_config)
if hasattr(estimator, 'n_jobs'):
setattr(estimator, 'n_jobs', 1)
return classifier_type, estimator