def _old_sampling_of_one_exchange_neighborhood(self, param, array, index):
neighbourhood = []
number_of_sampled_neighbors = 0
iteration = 0
checked_neighbors = []
checked_neighbors_non_unit_cube = []
while True:
hp = self.incumbent.configuration_space.get_hyperparameter(param)
num_neighbors = hp.get_num_neighbors(self.incumbent.get(param))
self.logger.debug('\t' + str(num_neighbors))
# Obtain neigbors differently for different possible numbers of
# neighbors
if num_neighbors == 0:
self.logger.debug('\tNo neighbors!')
break
# No infinite loops
elif iteration > 500:
self.logger.debug('\tMax iter')
break
elif np.isinf(num_neighbors):
num_samples_to_go = min(
10, self._continous_param_neighbor_samples -
number_of_sampled_neighbors)
if number_of_sampled_neighbors >= self._continous_param_neighbor_samples or num_samples_to_go <= 0:
break
neighbors = hp.get_neighbors(array[index],
self.rng,
number=num_samples_to_go)
else:
if iteration > 0:
break
neighbors = hp.get_neighbors(array[index], self.rng)
# self.logger.debug('\t\t' + str(neighbors))
# Check all newly obtained neighbors
for neighbor in neighbors:
if neighbor in checked_neighbors:
iteration += 1
continue
new_array = array.copy()
new_array = change_hp_value(self.incumbent.configuration_space,
new_array, param, neighbor, index)
try:
new_configuration = Configuration(
self.incumbent.configuration_space, vector=new_array)
neighbourhood.append(new_configuration)
new_configuration.is_valid_configuration()
check_forbidden(self.cs.forbidden_clauses, new_array)
number_of_sampled_neighbors += 1
checked_neighbors.append(neighbor)
checked_neighbors_non_unit_cube.append(
new_configuration[param])
except (ForbiddenValueError, ValueError) as e:
pass
iteration += 1
return checked_neighbors, checked_neighbors_non_unit_cube
def _get_one_exchange_neighborhood_by_parameter(self):
"""
Slight modification of ConfigSpace's get_one_exchange neighborhood. This orders the parameter values and samples
more neighbors in one go. Further we need to rigorously check each and every neighbor if it is forbidden or not.
"""
neighborhood_dict = {}
params = list(self.incumbent.keys())
self.logger.debug('params: ' + str(params))
for index, param in enumerate(params):
self.logger.info('Sampling neighborhood of %s' % param)
array = self.incumbent.get_array()
if not np.isfinite(array[index]):
self.logger.info('>'.join(['-' * 50, ' Not active!']))
continue
if self.old_sampling:
checked_neighbors, checked_neighbors_non_unit_cube = self._old_sampling_of_one_exchange_neighborhood(
param, array, index)
else:
neighbourhood = []
checked_neighbors = []
checked_neighbors_non_unit_cube = []
hp = self.incumbent.configuration_space.get_hyperparameter(
param)
num_neighbors = hp.get_num_neighbors(self.incumbent.get(param))
self.logger.debug('\t' + str(num_neighbors))
if num_neighbors == 0:
self.logger.debug('\tNo neighbors!')
continue
elif np.isinf(num_neighbors): # Continous Parameters
if hp.log:
base = np.e
log_lower = np.log(hp.lower) / np.log(base)
log_upper = np.log(hp.upper) / np.log(base)
neighbors = np.logspace(
log_lower,
log_upper,
self._continous_param_neighbor_samples,
endpoint=True,
base=base)
else:
neighbors = np.linspace(
hp.lower, hp.upper,
self._continous_param_neighbor_samples)
neighbors = list(
map(lambda x: hp._inverse_transform(x), neighbors))
else:
neighbors = hp.get_neighbors(array[index], self.rng)
for neighbor in neighbors:
if neighbor in checked_neighbors:
continue
new_array = array.copy()
new_array = change_hp_value(
self.incumbent.configuration_space, new_array, param,
neighbor, index)
try:
new_configuration = Configuration(
self.incumbent.configuration_space,
vector=new_array)
neighbourhood.append(new_configuration)
new_configuration.is_valid_configuration()
check_forbidden(self.cs.forbidden_clauses, new_array)
checked_neighbors.append(neighbor)
checked_neighbors_non_unit_cube.append(
new_configuration[param])
except (ForbiddenValueError, ValueError) as e:
pass
self.logger.info('>'.join([
'-' * 50,
' Found {:>3d} valid neighbors'.format(len(checked_neighbors))
]))
self._sampled_neighbors += len(checked_neighbors) + 1
sort_idx = list(
map(lambda x: x[0],
sorted(enumerate(checked_neighbors), key=lambda y: y[1])))
if isinstance(self.cs.get_hyperparameter(param),
CategoricalHyperparameter):
checked_neighbors_non_unit_cube = list(
np.array(checked_neighbors_non_unit_cube)[sort_idx])
else:
checked_neighbors_non_unit_cube = np.array(
checked_neighbors_non_unit_cube)[sort_idx]
neighborhood_dict[param] = [
np.array(checked_neighbors)[sort_idx],
checked_neighbors_non_unit_cube
]
return neighborhood_dict