def impute_default_values(configuration_space: ConfigurationSpace,
configs_array: np.ndarray) -> np.ndarray:
"""Impute inactive hyperparameters in configuration array with their default.
Necessary to apply an EPM to the data.
Parameters
----------
configuration_space : ConfigurationSpace
configs_array : np.ndarray
Array of configurations.
Returns
-------
np.ndarray
Array with configuration hyperparameters. Inactive values are imputed
with their default value.
"""
for hp in configuration_space.get_hyperparameters():
default = hp.normalized_default_value
idx = configuration_space.get_idx_by_hyperparameter_name(hp.name)
nonfinite_mask = ~np.isfinite(configs_array[:, idx])
configs_array[nonfinite_mask, idx] = default
return configs_array
def config_space2string(config_space: ConfigurationSpace):
pattern = r'[,|{}\'=<>&]'
for hp in config_space.get_hyperparameters():
if re.search(pattern, hp.name):
raise NameError('Invalid character in hyperparameter name!')
if hasattr(hp, 'choices'):
for value in hp.choices:
if re.search(pattern, value):
raise NameError('Invalid character in categorical hyperparameter value!')
return str(config_space)
def __init__(self,
config_space: ConfigurationSpace,
size,
lower_bounds=None,
upper_bounds=None,
random_state=None):
"""
Parameters
----------
config_space : ConfigurationSpace
ConfigurationSpace to do sampling.
size : int N
Number of samples.
lower_bounds : lower bounds in [0, 1] for continuous dimensions (optional)
upper_bounds : upper bounds in [0, 1] for continuous dimensions (optional)
"""
self.config_space = config_space
self.search_dims = []
for i, param in enumerate(config_space.get_hyperparameters()):
if isinstance(param, UniformFloatHyperparameter):
self.search_dims.append((0.0, 1.0))
elif isinstance(param, UniformIntegerHyperparameter):
self.search_dims.append((0.0, 1.0))
else:
raise NotImplementedError(
'Only Integer and Float are supported in %s.' %
self.__class__.__name__)
self.size = size
default_lb, default_ub = zip(*self.search_dims)
self.lower_bounds = np.array(
default_lb) if lower_bounds is None else np.clip(
lower_bounds, default_lb, default_ub)
self.upper_bounds = np.array(
default_ub) if upper_bounds is None else np.clip(
upper_bounds, default_lb, default_ub)
self.rng = check_random_state(random_state)
