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
use_degree = InCondition(child=degree, parent=kernel, values=["poly"])
use_coef0 = InCondition(child=coef0, parent=kernel, values=["poly", "sigmoid"])
cs.add_conditions([use_degree, use_coef0])
# This also works for parameters that are a mix of categorical and values from a range of numbers
# For example, gamma can be either "auto" or a fixed float
gamma = CategoricalHyperparameter(
"gamma", ["auto", "value"],
default_value="auto") # only rbf, poly, sigmoid
gamma_value = UniformFloatHyperparameter("gamma_value",
0.0001,
8,
default_value=1)
cs.add_hyperparameters([gamma, gamma_value])
# We only activate gamma_value if gamma is set to "value"
cs.add_condition(InCondition(child=gamma_value, parent=gamma,
values=["value"]))
# And again we can restrict the use of gamma in general to the choice of the kernel
cs.add_condition(
InCondition(child=gamma, parent=kernel, values=["rbf", "poly", "sigmoid"]))
# Example call of the function
# It returns: Status, Cost, Runtime, Additional Infos
def_value = svm_from_cfg(cs.get_default_configuration())
print("Default Value: %.2f" % (def_value))
# Optimize, using a SMAC-object
print("Optimizing! Depending on your machine, this might take a few minutes.")
bo = BayesianOptimization(svm_from_cfg,
cs,
max_runs=30,
time_limit_per_trial=30,