def test_check_neighbouring_config_diamond_str(self):
diamond = ConfigurationSpace()
head = CategoricalHyperparameter('head', ['red', 'green'])
left = CategoricalHyperparameter('left', ['red', 'green'])
right = CategoricalHyperparameter('right', ['red', 'green', 'blue', 'yellow'])
bottom = CategoricalHyperparameter('bottom', ['red', 'green'])
diamond.add_hyperparameters([head, left, right, bottom])
diamond.add_condition(EqualsCondition(left, head, 'red'))
diamond.add_condition(EqualsCondition(right, head, 'red'))
diamond.add_condition(AndConjunction(EqualsCondition(bottom, left, 'green'),
EqualsCondition(bottom, right, 'green')))
config = Configuration(diamond, {'bottom': 'red', 'head': 'red', 'left': 'green', 'right': 'green'})
hp_name = "head"
index = diamond.get_idx_by_hyperparameter_name(hp_name)
neighbor_value = 1
new_array = ConfigSpace.c_util.change_hp_value(
diamond,
config.get_array(),
hp_name,
neighbor_value,
index
)
expected_array = np.array([1, np.nan, np.nan, np.nan])
np.testing.assert_almost_equal(new_array, expected_array)
def test_check_neighbouring_config_diamond_str(self):
diamond = ConfigurationSpace()
head = CategoricalHyperparameter('head', ['red', 'green'])
left = CategoricalHyperparameter('left', ['red', 'green'])
right = CategoricalHyperparameter('right',
['red', 'green', 'blue', 'yellow'])
bottom = CategoricalHyperparameter('bottom', ['red', 'green'])
diamond.add_hyperparameters([head, left, right, bottom])
diamond.add_condition(EqualsCondition(left, head, 'red'))
diamond.add_condition(EqualsCondition(right, head, 'red'))
diamond.add_condition(
AndConjunction(EqualsCondition(bottom, left, 'green'),
EqualsCondition(bottom, right, 'green')))
config = Configuration(diamond, {
'bottom': 'red',
'head': 'red',
'left': 'green',
'right': 'green'
})
hp_name = "head"
index = diamond.get_idx_by_hyperparameter_name(hp_name)
neighbor_value = 1
new_array = ConfigSpace.c_util.change_hp_value(diamond,
config.get_array(),
hp_name, neighbor_value,
index)
expected_array = np.array([1, np.nan, np.nan, np.nan])
np.testing.assert_almost_equal(new_array, expected_array)
def test_check_neighbouring_config_diamond(self):
diamond = ConfigurationSpace()
head = CategoricalHyperparameter('head', [0, 1])
left = CategoricalHyperparameter('left', [0, 1])
right = CategoricalHyperparameter('right', [0, 1, 2, 3])
bottom = CategoricalHyperparameter('bottom', [0, 1])
diamond.add_hyperparameters([head, left, right, bottom])
diamond.add_condition(EqualsCondition(left, head, 0))
diamond.add_condition(EqualsCondition(right, head, 0))
diamond.add_condition(
AndConjunction(EqualsCondition(bottom, left, 1),
EqualsCondition(bottom, right, 1)))
config = Configuration(diamond, {
'bottom': 0,
'head': 0,
'left': 1,
'right': 1
})
hp_name = "head"
index = diamond.get_idx_by_hyperparameter_name(hp_name)
neighbor_value = 1
new_array = change_hp_value(diamond, config.get_array(), hp_name,
neighbor_value, index)
expected_array = np.array([1, np.nan, np.nan, np.nan])
np.testing.assert_almost_equal(new_array, expected_array)
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 change_hp_value(configuration_space: ConfigurationSpace,
configuration_array: np.ndarray, hp_name: str,
hp_value: float, index: int) -> np.ndarray:
"""Change hyperparameter value in configuration array to given value.
Does not check if the new value is legal. Activates and deactivates other
hyperparameters if necessary. Does not check if new hyperparameter value
results in the violation of any forbidden clauses.
Parameters
----------
configuration_space : ConfigurationSpace
configuration_array : np.ndarray
hp_name : str
hp_value : float
index : int
Returns
-------
np.ndarray
"""
configuration_array[index] = hp_value
# Hyperparameters which are going to be set to inactive
disabled = []
# Activate hyperparameters if their parent node got activated
children = configuration_space._children_of[hp_name]
if len(children) > 0:
to_visit = deque() # type: deque
to_visit.extendleft(children)
visited = set() # type: Set[str]
activated_values = dict() # type: Dict[str, Union[int, float, str]]
while len(to_visit) > 0:
current = to_visit.pop()
if current.name in visited:
continue
visited.add(current.name)
if current.name in disabled:
continue
current_idx = configuration_space.get_idx_by_hyperparameter_name(
current.name)
current_value = configuration_array[current_idx]
conditions = configuration_space._parent_conditions_of[
current.name]
active = True
for condition in conditions:
if not condition.evaluate_vector(configuration_array):
active = False
break
if active and (current_value is None
or not np.isfinite(current_value)):
default = current._inverse_transform(current.default)
configuration_array[current_idx] = default
children_ = configuration_space._children_of[current.name]
if len(children_) > 0:
to_visit.extendleft(children_)
# If the hyperparameter was made inactive,
# all its children need to be deactivade as well
if not active and (current_value is not None
or np.isfinite(current_value)):
configuration_array[current_idx] = np.NaN
children = configuration_space._children_of[current.name]
if len(children) > 0:
to_disable = set()
for ch in children:
to_disable.add(ch.name)
while len(to_disable) > 0:
child = to_disable.pop()
child_idx = configuration_space. \
get_idx_by_hyperparameter_name(child)
disabled.append(child_idx)
children = configuration_space._children_of[child]
for ch in children:
to_disable.add(ch.name)
for idx in disabled:
configuration_array[idx] = np.NaN
return configuration_array
