def update_search_space(self, search_space):
"""
Update search space definition in tuner by search_space in parameters.
Will called when first setup experiemnt or update search space in WebUI.
Parameters
----------
search_space : dict
"""
self.json = search_space
randint_to_quniform(self.json)
search_space_instance = json2space(self.json)
rstate = np.random.RandomState()
trials = hp.Trials()
domain = hp.Domain(None,
search_space_instance,
pass_expr_memo_ctrl=None)
algorithm = self._choose_tuner(self.algorithm_name)
self.rval = hp.FMinIter(algorithm,
domain,
trials,
max_evals=-1,
rstate=rstate,
verbose=0)
self.rval.catch_eval_exceptions = False
def update_search_space(self, search_space):
"""TODO: this is urgly, we put all the initialization work in this method, because initialization relies
on search space, also because update_search_space is called at the beginning.
NOTE: updating search space is not supported.
Parameters
----------
search_space:
search space
"""
randint_to_quniform(search_space)
if not self.update_ss_done:
self.categorical_dict = generate_scenario(search_space)
if self.categorical_dict is None:
raise RuntimeError(
'categorical dict is not correctly returned after parsing search space.'
)
self.optimizer = self._main_cli()
self.smbo_solver = self.optimizer.solver
self.loguniform_key = {
key
for key in search_space.keys()
if search_space[key]['_type'] == 'loguniform'
}
self.update_ss_done = True
else:
self.logger.warning('update search space is not supported.')
def handle_update_search_space(self, data):
"""data: JSON object, which is search space
Parameters
----------
data: int
number of trial jobs
"""
self.searchspace_json = data
randint_to_quniform(self.searchspace_json)
self.random_state = np.random.RandomState()
def update_search_space(self, search_space):
"""Update search space.
Search_space contains the information that user pre-defined.
Parameters
----------
search_space : dict
"""
self.searchspace_json = search_space
randint_to_quniform(self.searchspace_json)
self.space = json2space(self.searchspace_json)
self.random_state = np.random.RandomState()
self.population = []
is_rand = dict()
for item in self.space:
is_rand[item] = True
for _ in range(self.population_size):
config = json2parameter(self.searchspace_json, is_rand,
self.random_state)
self.population.append(Individual(config=config))
def handle_update_search_space(self, data):
"""change json format to ConfigSpace format dict<dict> -> configspace
Parameters
----------
data: JSON object
search space of this experiment
"""
search_space = data
randint_to_quniform(search_space)
cs = CS.ConfigurationSpace()
for var in search_space:
_type = str(search_space[var]["_type"])
if _type == 'choice':
cs.add_hyperparameter(
CSH.CategoricalHyperparameter(
var, choices=search_space[var]["_value"]))
elif _type == 'randint':
cs.add_hyperparameter(
CSH.UniformIntegerHyperparameter(
var, lower=0, upper=search_space[var]["_value"][0]))
elif _type == 'uniform':
cs.add_hyperparameter(
CSH.UniformFloatHyperparameter(
var,
lower=search_space[var]["_value"][0],
upper=search_space[var]["_value"][1]))
elif _type == 'quniform':
cs.add_hyperparameter(
CSH.UniformFloatHyperparameter(
var,
lower=search_space[var]["_value"][0],
upper=search_space[var]["_value"][1],
q=search_space[var]["_value"][2]))
elif _type == 'loguniform':
cs.add_hyperparameter(
CSH.UniformFloatHyperparameter(
var,
lower=search_space[var]["_value"][0],
upper=search_space[var]["_value"][1],
log=True))
elif _type == 'qloguniform':
cs.add_hyperparameter(
CSH.UniformFloatHyperparameter(
var,
lower=search_space[var]["_value"][0],
upper=search_space[var]["_value"][1],
q=search_space[var]["_value"][2],
log=True))
elif _type == 'normal':
cs.add_hyperparameter(
CSH.NormalFloatHyperparameter(
var,
mu=search_space[var]["_value"][1],
sigma=search_space[var]["_value"][2]))
elif _type == 'qnormal':
cs.add_hyperparameter(
CSH.NormalFloatHyperparameter(
var,
mu=search_space[var]["_value"][1],
sigma=search_space[var]["_value"][2],
q=search_space[var]["_value"][3]))
elif _type == 'lognormal':
cs.add_hyperparameter(
CSH.NormalFloatHyperparameter(
var,
mu=search_space[var]["_value"][1],
sigma=search_space[var]["_value"][2],
log=True))
elif _type == 'qlognormal':
cs.add_hyperparameter(
CSH.NormalFloatHyperparameter(
var,
mu=search_space[var]["_value"][1],
sigma=search_space[var]["_value"][2],
q=search_space[var]["_value"][3],
log=True))
else:
raise ValueError(
'unrecognized type in search_space, type is {}'.format(
_type))
self.search_space = cs