def get_config_space_from_dict(space_dict: dict):
cs = ConfigurationSpace()
params_dict = space_dict['parameters']
for key in params_dict:
param_dict = params_dict[key]
param_type = param_dict['type']
if param_type in ['float', 'int']:
bound = param_dict['bound']
optional_args = dict()
if 'default' in param_dict:
optional_args['default_value'] = param_dict['default']
elif 'log' in param_dict:
optional_args['log'] = parse_bool(param_dict['log'])
elif 'q' in param_dict:
optional_args['q'] = param_dict['q']
if param_type == 'float':
param = UniformFloatHyperparameter(key, bound[0], bound[1], **optional_args)
else:
param = UniformIntegerHyperparameter(key, bound[0], bound[1], **optional_args)
elif param_type == 'cat':
choices = param_dict['choice']
optional_args = dict()
if 'default' in param_dict:
optional_args['default_value'] = param_dict['default']
param = CategoricalHyperparameter(key, choices, **optional_args)
else:
raise ValueError("Parameter type %s not supported!" % param_type)
cs.add_hyperparameter(param)
return cs
def string2config_space(space_desc: str):
line_list = space_desc.split('\n')
cur_line = 2
cs = ConfigurationSpace()
status = 'hp'
hp_list = list()
while cur_line != len(line_list) - 1:
line_content = line_list[cur_line]
if line_content == ' Conditions:':
hp_dict = {hp.name: hp for hp in hp_list}
status = 'cond'
elif line_content == ' Forbidden Clauses:':
status = 'bid'
else:
if status == 'hp':
hp = string2hyperparameter(line_content)
hp_list.append(hp)
cs.add_hyperparameter(hp)
elif status == 'cond':
cond = string2condition(line_content, hp_dict)
cs.add_condition(cond)
else:
forbid = string2forbidden(line_content, hp_dict)
cs.add_forbidden_clause(forbid)
cur_line += 1
return cs
def __init__(self, bounds=None, noise_std=0, random_state=None):
if bounds is None:
bounds = [0, 20]
lb, ub = bounds
config_space = ConfigurationSpace()
config_space.add_hyperparameter(UniformFloatHyperparameter('x1', lb, ub, 1))
config_space.add_hyperparameter(UniformIntegerHyperparameter('x2', lb, ub, 1))
super().__init__(config_space, noise_std,
optimal_value=-31.9998,
optimal_point=[(5.333, 4)],
random_state=random_state)
clf = svm.SVC(**cfg, random_state=42)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
return 1 - np.mean(scores) # Minimize!
logging.basicConfig(level=logging.INFO)
# Build Configuration Space which defines all parameters and their ranges
cs = ConfigurationSpace()
# We define a few possible types of SVM-kernels and add them as "kernel" to our cs
kernel = CategoricalHyperparameter("kernel",
["linear", "rbf", "poly", "sigmoid"],
default_value="poly")
cs.add_hyperparameter(kernel)
# There are some hyperparameters shared by all kernels
C = UniformFloatHyperparameter("C", 0.001, 1000.0, default_value=1.0)
shrinking = CategoricalHyperparameter("shrinking", ["true", "false"],
default_value="true")
cs.add_hyperparameters([C, shrinking])
# Others are kernel-specific, so we can add conditions to limit the searchspace
degree = UniformIntegerHyperparameter(
"degree", 1, 5, default_value=3) # Only used by kernel poly
coef0 = UniformFloatHyperparameter("coef0", 0.0, 10.0,
default_value=0.0) # poly, sigmoid
cs.add_hyperparameters([degree, coef0])
use_degree = InCondition(child=degree, parent=kernel, values=["poly"])
use_coef0 = InCondition(child=coef0, parent=kernel, values=["poly", "sigmoid"])