def test_not_equals_condition(self):
hp1 = CategoricalHyperparameter("parent", [0, 1])
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cond = NotEqualsCondition(hp2, hp1, 0)
cond_ = NotEqualsCondition(hp2, hp1, 0)
self.assertEqual(cond, cond_)
cond_reverse = NotEqualsCondition(hp1, hp2, 0)
self.assertNotEqual(cond, cond_reverse)
self.assertNotEqual(cond, dict())
self.assertEqual("child | parent != 0", str(cond))
def test_not_equals_condition(self):
hp1 = CategoricalHyperparameter("parent", [0, 1])
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cond = NotEqualsCondition(hp2, hp1, 0)
cond_ = NotEqualsCondition(hp2, hp1, 0)
self.assertEqual(cond, cond_)
# Test vector value:
self.assertEqual(cond.vector_value, hp1._inverse_transform(0))
self.assertEqual(cond.vector_value, cond_.vector_value)
cond_reverse = NotEqualsCondition(hp1, hp2, 0)
self.assertNotEqual(cond, cond_reverse)
self.assertNotEqual(cond, dict())
self.assertEqual("child | parent != 0", str(cond))
def get_complete_configspace():
"""Creates a configspace that includes all kinds of parameters with
complicated values. The idea is to provide a configspace that can be
used to check modules using ConfigSpace as a dependency to check
compatibility with e.g. Constants, log-scale, etc.
Returns
-------
cs: ConfigurationSpace
cs containing all kinds of parameters
"""
cs = ConfigurationSpace()
hp = {}
# Add Constants for all allowed types ('int', 'float', 'string')
hp['alpha'] = Constant("alpha", 0.0001)
hp['tol'] = Constant("tol", '0.0001')
hp['verbose'] = Constant("verbose", 1)
# Add numericals
# Add Floats
hp['beta1'] = UniformFloatHyperparameter("beta1", 0.85, 0.95, log=False)
hp['power_t'] = NormalFloatHyperparameter("power_t", mu=0.5, sigma=0.1, log=False)
# Add Ints
hp['momentum'] = UniformIntegerHyperparameter("momentum", 0, 100, False)
hp['beta2'] = NormalIntegerHyperparameter("beta2", mu=1, sigma=0.001, log=False)
# Add Floats (log)
hp['learning_rate_init'] = UniformFloatHyperparameter("learning_rate_init", 0.0001, 0.1, log=True)
hp['random1'] = NormalFloatHyperparameter("NormalFloat", mu=0, sigma=1, default_value=1, log=True)
# Add Ints (log)
hp['random2'] = UniformIntegerHyperparameter("UniformInt", 2, 100, log=True)
hp['random3'] = NormalIntegerHyperparameter("NormalInt", mu=0, sigma=1, default_value=1, log=True)
# Add Categorical for allowed types
hp['activation'] = CategoricalHyperparameter('activation', choices=['identity', 'logistic', 'tanh', 'relu'])
hp['solver'] = CategoricalHyperparameter('solver', choices=[-2, 0, 2]) # corrresponds to: ‘lbfgs’, ‘sgd’, ‘adam’
hp['batch_size_auto'] = CategoricalHyperparameter('batch_size_auto', choices=[True, False])
hp['learning_rate'] = CategoricalHyperparameter('learning_rate', choices=[-0.5, 0.0, 0.5]) # corresponds to {‘constant’, ‘invscaling’, ‘adaptive’}
# Add Ordinal
hp['batch_size'] = OrdinalHyperparameter('batch_size', sequence=[32, 64.0, '128'])
for k, v in hp.items():
cs.add_hyperparameter(v)
# learning_rate only with sgd
c = InCondition(hp['learning_rate'], hp['solver'], [0])
c = EqualsCondition(hp['momentum'], hp['solver'], 0)
# learning_rate_init only with sgd or adam
cs.add_condition(OrConjunction(EqualsCondition(hp['learning_rate'], hp['solver'], 0), # sgd
EqualsCondition(hp['learning_rate'], hp['solver'], 2))) # adam
# batch_size only with not batch_size_auto
cs.add_condition(NotEqualsCondition(hp['batch_size'], hp['batch_size_auto'], True))
# complicated way for solver == sgd
#cs.add_condition(AndConjunction(LessThanCondition(hp['power_t'], hp['solver'], 1),
# GreaterThanCondition(hp['power_t'], hp['solver'], -1)))
# betas with adam
cs.add_condition(EqualsCondition(hp['beta1'], hp['solver'], 2))
cs.add_condition(EqualsCondition(hp['beta2'], hp['solver'], 2))
return cs
def _construct_neq_condition(
condition: Dict,
cs: ConfigurationSpace,
) -> NotEqualsCondition:
return NotEqualsCondition(
child=cs.get_hyperparameter(condition['child']),
parent=cs.get_hyperparameter(condition['parent']),
value=condition['value'],
)
def condition_specification(child_name, condition, configuration_space):
# specifies the condition type
child = configuration_space.get_hyperparameter(child_name)
parent_name = condition[0]
parent = configuration_space.get_hyperparameter(parent_name)
operation = condition[1]
if operation == 'in':
restrictions = condition[3:-1:2]
for i in range(len(restrictions)):
if isinstance(parent, FloatHyperparameter):
restrictions[i] = float(restrictions[i])
elif isinstance(parent, IntegerHyperparameter):
restrictions[i] = int(restrictions[i])
if len(restrictions) == 1:
condition = EqualsCondition(child, parent, restrictions[0])
else:
condition = InCondition(child, parent, values=restrictions)
return condition
else:
restrictions = condition[2]
if isinstance(parent, FloatHyperparameter):
restrictions = float(restrictions)
elif isinstance(parent, IntegerHyperparameter):
restrictions = int(restrictions)
if operation == '==':
condition = EqualsCondition(child, parent, restrictions)
elif operation == '!=':
condition = NotEqualsCondition(child, parent, restrictions)
else:
if isinstance(parent, FloatHyperparameter):
restrictions = float(restrictions)
elif isinstance(parent, IntegerHyperparameter):
restrictions = int(restrictions)
elif isinstance(parent, OrdinalHyperparameter):
pass
else:
raise ValueError('The parent of a conditional hyperparameter '
'must be either a float, int or ordinal '
'hyperparameter, but is %s.' % type(parent))
if operation == '<':
condition = LessThanCondition(child, parent, restrictions)
elif operation == '>':
condition = GreaterThanCondition(child, parent, restrictions)
return condition
def test_read_new_configuration_space_complex_conditionals(self):
classi = OrdinalHyperparameter(
"classi",
[
"random_forest", "extra_trees", "k_nearest_neighbors",
"something"
],
)
knn_weights = CategoricalHyperparameter("knn_weights",
["uniform", "distance"])
weather = OrdinalHyperparameter(
"weather", ["sunny", "rainy", "cloudy", "snowing"])
temperature = CategoricalHyperparameter("temperature", ["high", "low"])
rain = CategoricalHyperparameter("rain", ["yes", "no"])
gloves = OrdinalHyperparameter("gloves",
["none", "yarn", "leather", "gortex"])
heur1 = CategoricalHyperparameter("heur1", ["off", "on"])
heur2 = CategoricalHyperparameter("heur2", ["off", "on"])
heur_order = CategoricalHyperparameter("heur_order",
["heur1then2", "heur2then1"])
gloves_condition = OrConjunction(
EqualsCondition(gloves, rain, "yes"),
EqualsCondition(gloves, temperature, "low"))
heur_condition = AndConjunction(
EqualsCondition(heur_order, heur1, "on"),
EqualsCondition(heur_order, heur2, "on"))
and_conjunction = AndConjunction(
NotEqualsCondition(knn_weights, classi, "extra_trees"),
EqualsCondition(knn_weights, classi, "random_forest"))
Cl_condition = OrConjunction(
EqualsCondition(knn_weights, classi, "k_nearest_neighbors"),
and_conjunction, EqualsCondition(knn_weights, classi, "something"))
and1 = AndConjunction(EqualsCondition(temperature, weather, "rainy"),
EqualsCondition(temperature, weather, "cloudy"))
and2 = AndConjunction(
EqualsCondition(temperature, weather, "sunny"),
NotEqualsCondition(temperature, weather, "snowing"))
another_condition = OrConjunction(and1, and2)
complex_conditional_space = ConfigurationSpace()
complex_conditional_space.add_hyperparameter(classi)
complex_conditional_space.add_hyperparameter(knn_weights)
complex_conditional_space.add_hyperparameter(weather)
complex_conditional_space.add_hyperparameter(temperature)
complex_conditional_space.add_hyperparameter(rain)
complex_conditional_space.add_hyperparameter(gloves)
complex_conditional_space.add_hyperparameter(heur1)
complex_conditional_space.add_hyperparameter(heur2)
complex_conditional_space.add_hyperparameter(heur_order)
complex_conditional_space.add_condition(gloves_condition)
complex_conditional_space.add_condition(heur_condition)
complex_conditional_space.add_condition(Cl_condition)
complex_conditional_space.add_condition(another_condition)
complex_cs = list()
complex_cs.append(
"classi ordinal {random_forest,extra_trees,k_nearest_neighbors, something} "
"[random_forest]")
complex_cs.append(
"knn_weights categorical {uniform, distance} [uniform]")
complex_cs.append(
"weather ordinal {sunny, rainy, cloudy, snowing} [sunny]")
complex_cs.append("temperature categorical {high, low} [high]")
complex_cs.append("rain categorical { yes, no } [yes]")
complex_cs.append(
"gloves ordinal { none, yarn, leather, gortex } [none]")
complex_cs.append("heur1 categorical { off, on } [off]")
complex_cs.append("heur2 categorical { off, on } [off]")
complex_cs.append(
"heur_order categorical { heur1then2, heur2then1 } [heur1then2]")
complex_cs.append("gloves | rain == yes || temperature == low")
complex_cs.append("heur_order | heur1 == on && heur2 == on")
complex_cs.append(
"knn_weights | classi == k_nearest_neighbors || "
"classi != extra_trees && classi == random_forest || classi == something"
)
complex_cs.append(
"temperature | weather == rainy && weather == cloudy || "
"weather == sunny && weather != snowing")
cs_new = pcs_new.read(complex_cs)
self.assertEqual(cs_new, complex_conditional_space)
