def test_uniformfloat_transform(self):
"""This checks whether a value sampled through the configuration
space (it does not happend when the variable is sampled alone) stays
equal when it is serialized via JSON and the deserialized again."""
cs = ConfigurationSpace()
a = cs.add_hyperparameter(UniformFloatHyperparameter('a', -5, 10))
b = cs.add_hyperparameter(
NormalFloatHyperparameter('b', 1, 2, log=True))
for i in range(100):
config = cs.sample_configuration()
value = OrderedDict(sorted(config.get_dictionary().items()))
string = json.dumps(value)
saved_value = json.loads(string)
saved_value = OrderedDict(sorted(byteify(saved_value).items()))
self.assertEqual(repr(value), repr(saved_value))
# Next, test whether the truncation also works when initializing the
# Configuration with a dictionary
for i in range(100):
rs = np.random.RandomState(1)
value_a = a.sample(rs)
value_b = b.sample(rs)
values_dict = {'a': value_a, 'b': value_b}
config = Configuration(cs, values=values_dict)
string = json.dumps(config.get_dictionary())
saved_value = json.loads(string)
saved_value = byteify(saved_value)
self.assertEqual(values_dict, saved_value)
def impute_inactive_values(configuration: Configuration, strategy: Union[str, float]='default') -> Configuration:
"""Impute inactive parameters.
Parameters
----------
strategy : string, optional (default='default')
The imputation strategy.
- If 'default', replace inactive parameters by their default.
- If float, replace inactive parameters by the given float value,
which should be able to be splitted apart by a tree-based model.
"""
values = dict()
for hp in configuration.configuration_space.get_hyperparameters():
value = configuration.get(hp.name)
if value is None:
if strategy == 'default':
new_value = hp.default_value
elif isinstance(strategy, float):
new_value = strategy
else:
raise ValueError('Unknown imputation strategy %s' % str(strategy))
value = new_value
values[hp.name] = value
new_configuration = Configuration(configuration.configuration_space,
values=values,
allow_inactive_with_values=True)
return new_configuration
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 = ConfigSpaceNNI.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_forbidden_with_sampled_vector_configuration(self):
cs = ConfigurationSpace()
metric = CategoricalHyperparameter("metric", ["minkowski", "other"])
cs.add_hyperparameter(metric)
forbidden = ForbiddenEqualsClause(metric, "other")
cs.add_forbidden_clause(forbidden)
configuration = Configuration(cs, vector=np.ones(1, dtype=float))
self.assertRaisesRegexp(ValueError, "violates forbidden clause",
cs._check_forbidden, configuration.get_array())
def deactivate_inactive_hyperparameters(
configuration: Dict,
configuration_space: ConfigurationSpace,
):
hyperparameters = configuration_space.get_hyperparameters()
configuration = Configuration(configuration_space=configuration_space,
values=configuration,
allow_inactive_with_values=True)
hps = deque()
unconditional_hyperparameters = configuration_space.get_all_unconditional_hyperparameters()
hyperparameters_with_children = list()
for uhp in unconditional_hyperparameters:
children = configuration_space._children_of[uhp]
if len(children) > 0:
hyperparameters_with_children.append(uhp)
hps.extendleft(hyperparameters_with_children)
inactive = set()
while len(hps) > 0:
hp = hps.pop()
children = configuration_space._children_of[hp]
for child in children:
conditions = configuration_space._parent_conditions_of[child.name]
for condition in conditions:
if not condition.evaluate_vector(configuration.get_array()):
dic = configuration.get_dictionary()
try:
del dic[child.name]
except KeyError:
continue
configuration = Configuration(
configuration_space=configuration_space,
values=dic,
allow_inactive_with_values=True)
inactive.add(child.name)
hps.appendleft(child.name)
for hp in hyperparameters:
if hp.name in inactive:
dic = configuration.get_dictionary()
try:
del dic[hp.name]
except KeyError:
continue
configuration = Configuration(
configuration_space=configuration_space,
values=dic,
allow_inactive_with_values=True)
return Configuration(configuration_space, values=configuration.get_dictionary())
def test_check_configuration2(self):
# Test that hyperparameters which are not active must not be set and
# that evaluating forbidden clauses does not choke on missing
# hyperparameters
cs = ConfigurationSpace()
classifier = CategoricalHyperparameter(
"classifier", ["k_nearest_neighbors", "extra_trees"])
metric = CategoricalHyperparameter("metric", ["minkowski", "other"])
p = CategoricalHyperparameter("k_nearest_neighbors:p", [1, 2])
metric_depends_on_classifier = EqualsCondition(metric, classifier,
"k_nearest_neighbors")
p_depends_on_metric = EqualsCondition(p, metric, "minkowski")
cs.add_hyperparameter(metric)
cs.add_hyperparameter(p)
cs.add_hyperparameter(classifier)
cs.add_condition(metric_depends_on_classifier)
cs.add_condition(p_depends_on_metric)
forbidden = ForbiddenEqualsClause(metric, "other")
cs.add_forbidden_clause(forbidden)
configuration = Configuration(cs, dict(classifier="extra_trees"))
# check backward compatibility with checking configurations instead of vectors
cs.check_configuration(configuration)
def generate_config(cs, rs):
i = rs.randint(-10, 10)
f = rs.rand(1)[0]
seed = rs.randint(0, 10000)
# 'a' occurs more often than 'b'
c = 'a' if rs.binomial(1, 0.2) == 0 else 'b'
# We have 100 instance, but prefer the middle ones
instance_id = int(rs.normal(loc=50, scale=20, size=1)[0])
instance_id = min(max(0, instance_id), 100)
status = StatusType.SUCCESS
runtime = 10**(numpy.sin(i)+f) + seed/10000 - numpy.sin(instance_id)
if runtime > 40:
status = StatusType.TIMEOUT
runtime = 40
elif instance_id > 50 and runtime > 15:
# This is a timeout with probability 0.5
status = StatusType.TIMEOUT
runtime /= 2.0
config = Configuration(cs, values={'cat_a_b': c, 'float_0_1': f,
'integer_0_100': i})
return config, seed, runtime, status, instance_id
def setUp(self):
unittest.TestCase.setUp(self)
self.rh = runhistory.RunHistory(aggregate_func=average_cost)
self.cs = get_config_space()
self.config1 = Configuration(self.cs,
values={'a': 0, 'b': 100})
self.config2 = Configuration(self.cs,
values={'a': 100, 'b': 0})
self.config3 = Configuration(self.cs,
values={'a': 100, 'b': 100})
self.scen = Scenario({'run_obj': 'runtime', 'cutoff_time': 20,
'cs': self.cs})
self.types, self.bounds = get_types(self.cs, None)
self.scen = Scenario({'run_obj': 'runtime', 'cutoff_time': 20, 'cs': self.cs,
'output_dir': ''})
def import_data(self, data):
"""
Import additional data for tuning.
Parameters
----------
data : list of dict
Each of which has at least two keys, ``parameter`` and ``value``.
"""
_completed_num = 0
for trial_info in data:
self.logger.info(
"Importing data, current processing progress %s / %s",
_completed_num, len(data))
# simply validate data format
assert "parameter" in trial_info
_params = trial_info["parameter"]
assert "value" in trial_info
_value = trial_info['value']
if not _value:
self.logger.info(
"Useless trial data, value is %s, skip this trial data.",
_value)
continue
_value = extract_scalar_reward(_value)
# convert the keys in loguniform and categorical types
valid_entry = True
for key, value in _params.items():
if key in self.loguniform_key:
_params[key] = np.log(value)
elif key in self.categorical_dict:
if value in self.categorical_dict[key]:
_params[key] = self.categorical_dict[key].index(value)
else:
self.logger.info(
"The value %s of key %s is not in search space.",
str(value), key)
valid_entry = False
break
if not valid_entry:
continue
# start import this data entry
_completed_num += 1
config = Configuration(self.cs, values=_params)
if self.optimize_mode is OptimizeMode.Maximize:
_value = -_value
if self.first_one:
self.smbo_solver.nni_smac_receive_first_run(config, _value)
self.first_one = False
else:
self.smbo_solver.nni_smac_receive_runs(config, _value)
self.logger.info(
"Successfully import data to smac tuner, total data: %d, imported data: %d.",
len(data), _completed_num)
def test_get_hyperparameters_topological_sort_simple(self):
for iteration in range(10):
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("parent", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cs.add_hyperparameter(hp2)
cond1 = EqualsCondition(hp2, hp1, 0)
cs.add_condition(cond1)
# This automatically checks the configuration!
Configuration(cs, dict(parent=0, child=5))
def setUp(self):
unittest.TestCase.setUp(self)
self.rh = RunHistory(aggregate_func=average_cost)
self.cs = get_config_space()
self.config1 = Configuration(self.cs, values={'a': 0, 'b': 100})
self.config2 = Configuration(self.cs, values={'a': 100, 'b': 0})
self.config3 = Configuration(self.cs, values={'a': 100, 'b': 100})
self.scen = Scenario({
"cutoff_time": 2,
'cs': self.cs,
"run_obj": 'runtime',
"output_dir": ''
})
self.stats = Stats(scenario=self.scen)
self.stats.start_timing()
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
def test_sample_configuration(self):
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("parent", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cs.add_hyperparameter(hp2)
cond1 = EqualsCondition(hp2, hp1, 0)
cs.add_condition(cond1)
# This automatically checks the configuration!
Configuration(cs, dict(parent=0, child=5))
# and now for something more complicated
cs = ConfigurationSpace(seed=1)
hp1 = CategoricalHyperparameter("input1", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = CategoricalHyperparameter("input2", [0, 1])
cs.add_hyperparameter(hp2)
hp3 = CategoricalHyperparameter("input3", [0, 1])
cs.add_hyperparameter(hp3)
hp4 = CategoricalHyperparameter("input4", [0, 1])
cs.add_hyperparameter(hp4)
hp5 = CategoricalHyperparameter("input5", [0, 1])
cs.add_hyperparameter(hp5)
hp6 = Constant("AND", "True")
cs.add_hyperparameter(hp6)
cond1 = EqualsCondition(hp6, hp1, 1)
cond2 = NotEqualsCondition(hp6, hp2, 1)
cond3 = InCondition(hp6, hp3, [1])
cond4 = EqualsCondition(hp5, hp3, 1)
cond5 = EqualsCondition(hp4, hp5, 1)
cond6 = EqualsCondition(hp6, hp4, 1)
cond7 = EqualsCondition(hp6, hp5, 1)
conj1 = AndConjunction(cond1, cond2)
conj2 = OrConjunction(conj1, cond3)
conj3 = AndConjunction(conj2, cond6, cond7)
cs.add_condition(cond4)
cs.add_condition(cond5)
cs.add_condition(conj3)
samples = []
for i in range(5):
cs.seed(1)
samples.append([])
for j in range(100):
sample = cs.sample_configuration()
samples[-1].append(sample)
if i > 0:
for j in range(100):
self.assertEqual(samples[-1][j], samples[-2][j])
def test_get_config_runs(self):
'''
get some config runs from runhistory
'''
rh = RunHistory(aggregate_func=average_cost)
cs = get_config_space()
config1 = Configuration(cs, values={'a': 1, 'b': 2})
config2 = Configuration(cs, values={'a': 1, 'b': 3})
rh.add(config=config1,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=1,
seed=1)
rh.add(config=config2,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=1,
seed=1)
rh.add(config=config1,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=2,
seed=2)
ist = rh.get_runs_for_config(config=config1)
#print(ist)
#print(ist[0])
#print(ist[1])
self.assertEqual(len(ist), 2)
self.assertEqual(ist[0].instance, 1)
self.assertEqual(ist[1].instance, 2)
def test_init_with_values(self):
c1 = Configuration(self.cs,
values={
'parent': 1,
'child': 2,
'friend': 3
})
# Pay attention that the vector does not necessarily has an intuitive
# sorting!
# Values are a little bit higher than one would expect because,
# an integer range of [0,10] is transformed to [-0.499,10.499].
vector_values = {
'parent': 1,
'child': 0.22727223140405708,
'friend': 0.583333611112037
}
vector = [None] * 3
for name in self.cs._hyperparameter_idx:
vector[self.cs._hyperparameter_idx[name]] = vector_values[name]
c2 = Configuration(self.cs, vector=vector)
# This tests
# a) that the vector representation of both are the same
# b) that the dictionary representation of both are the same
self.assertEqual(c1, c2)
def test_full_update(self):
rh = RunHistory(aggregate_func=average_cost)
cs = get_config_space()
config1 = Configuration(cs, values={'a': 1, 'b': 2})
config2 = Configuration(cs, values={'a': 1, 'b': 3})
rh.add(config=config1,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=1,
seed=1)
rh.add(config=config2,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=1,
seed=1)
rh.add(config=config2,
cost=20,
time=20,
status=StatusType.SUCCESS,
instance_id=2,
seed=2)
cost_config2 = rh.get_cost(config2)
rh.compute_all_costs()
updated_cost_config2 = rh.get_cost(config2)
self.assertTrue(cost_config2 == updated_cost_config2)
rh.compute_all_costs(instances=[2])
updated_cost_config2 = rh.get_cost(config2)
self.assertTrue(cost_config2 != updated_cost_config2)
self.assertTrue(updated_cost_config2 == 20)
def test_add_multiple_times(self):
rh = RunHistory(aggregate_func=average_cost)
cs = get_config_space()
config = Configuration(cs, values={'a': 1, 'b': 2})
for i in range(5):
rh.add(config=config,
cost=i + 1,
time=i + 1,
status=StatusType.SUCCESS,
instance_id=None,
seed=12345,
additional_info=None)
self.assertEqual(len(rh.data), 1)
self.assertEqual(len(rh.get_runs_for_config(config)), 1)
self.assertEqual(len(rh._configid_to_inst_seed[1]), 1)
self.assertEqual(list(rh.data.values())[0].cost, 1)
def test_incremental_update(self):
rh = RunHistory(aggregate_func=average_cost)
cs = get_config_space()
config1 = Configuration(cs, values={'a': 1, 'b': 2})
rh.add(config=config1,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=1,
seed=1)
self.assertTrue(rh.get_cost(config1) == 10)
rh.add(config=config1,
cost=20,
time=20,
status=StatusType.SUCCESS,
instance_id=2,
seed=1)
self.assertTrue(rh.get_cost(config1) == 15)
def test_add_and_pickle(self):
'''
simply adding some rundata to runhistory, then pickle it
'''
rh = RunHistory(aggregate_func=average_cost)
cs = get_config_space()
config = Configuration(cs, values={'a': 1, 'b': 2})
self.assertTrue(rh.empty())
rh.add(config=config,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=None,
seed=None,
additional_info=None)
rh.add(config=config,
cost=10,
time=20,
status=StatusType.SUCCESS,
instance_id=1,
seed=12354,
additional_info={"start_time": 10})
self.assertFalse(rh.empty())
tmpfile = tempfile.NamedTemporaryFile(mode='wb', delete=False)
pickle.dump(rh, tmpfile, -1)
name = tmpfile.name
tmpfile.close()
with open(name, 'rb') as fh:
loaded_rh = pickle.load(fh)
self.assertEqual(loaded_rh.data, rh.data)
def test_merge_foreign_data(self):
''' test smac.utils.merge_foreign_data '''
scenario = Scenario(self.test_scenario_dict)
scenario_2 = Scenario(self.test_scenario_dict)
scenario_2.feature_dict = {"inst_new": [4]}
# init cs
cs = ConfigurationSpace()
cs.add_hyperparameter(
UniformIntegerHyperparameter(name='a', lower=0, upper=100))
cs.add_hyperparameter(
UniformIntegerHyperparameter(name='b', lower=0, upper=100))
# build runhistory
rh_merge = RunHistory(aggregate_func=average_cost)
config = Configuration(cs, values={'a': 1, 'b': 2})
rh_merge.add(config=config,
instance_id="inst_new",
cost=10,
time=20,
status=StatusType.SUCCESS,
seed=None,
additional_info=None)
# "d" is an instance in <scenario>
rh_merge.add(config=config,
instance_id="d",
cost=5,
time=20,
status=StatusType.SUCCESS,
seed=None,
additional_info=None)
# build empty rh
rh_base = RunHistory(aggregate_func=average_cost)
merge_foreign_data(scenario=scenario,
runhistory=rh_base,
in_scenario_list=[scenario_2],
in_runhistory_list=[rh_merge])
# both runs should be in the runhistory
# but we should not use the data to update the cost of config
self.assertTrue(len(rh_base.data) == 2)
self.assertTrue(np.isnan(rh_base.get_cost(config)))
# we should not get direct access to external run data
runs = rh_base.get_runs_for_config(config)
self.assertTrue(len(runs) == 0)
rh_merge.add(config=config,
instance_id="inst_new_2",
cost=10,
time=20,
status=StatusType.SUCCESS,
seed=None,
additional_info=None)
self.assertRaises(
ValueError, merge_foreign_data, **{
"scenario": scenario,
"runhistory": rh_base,
"in_scenario_list": [scenario_2],
"in_runhistory_list": [rh_merge]
})
def get_random_neighbor(configuration: Configuration, seed: int) -> Configuration:
"""Draw a random neighbor by changing one parameter of a configuration.
* If the parameter is categorical, it changes it to another value.
* If the parameter is ordinal, it changes it to the next higher or lower
value.
* If parameter is a float, draw a random sample
If changing a parameter activates new parameters or deactivates
previously active parameters, the configuration will be rejected. If more
than 10000 configurations were rejected, this function raises a
ValueError.
Parameters
----------
configuration : Configuration
seed : int
Used to generate a random state.
Returns
-------
Configuration
The new neighbor.
"""
random = np.random.RandomState(seed)
rejected = True
values = copy.deepcopy(configuration.get_dictionary())
while rejected:
# First, choose an active hyperparameter
active = False
iteration = 0
while not active:
iteration += 1
if configuration._num_hyperparameters > 1:
rand_idx = random.randint(0,
configuration._num_hyperparameters - 1)
else:
rand_idx = 0
value = configuration.get_array()[rand_idx]
if np.isfinite(value):
active = True
hp_name = configuration.configuration_space \
.get_hyperparameter_by_idx(rand_idx)
hp = configuration.configuration_space.get_hyperparameter(hp_name)
# Only choose if there is a possibility of finding a neigboor
if not hp.has_neighbors():
active = False
if iteration > 10000:
raise ValueError('Probably caught in an infinite loop.')
# Get a neighboor and adapt the rest of the configuration if necessary
neighbor = hp.get_neighbors(value, random, number=1, transform=True)[0]
previous_value = values[hp.name]
values[hp.name] = neighbor
try:
new_configuration = Configuration(
configuration.configuration_space, values=values)
rejected = False
except ValueError as e:
values[hp.name] = previous_value
return new_configuration
def get_one_exchange_neighbourhood(
configuration: Configuration,
seed: int,
num_neighbors: int=4,
stdev: float=0.2,
) -> Generator[Configuration, None, None]:
"""Return all configurations in a one-exchange neighborhood.
The method is implemented as defined by:
Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown
Sequential Model-Based Optimization for General Algorithm Configuration
In: Proceedings of the conference on Learning and Intelligent OptimizatioN (LION 5)
"""
random = np.random.RandomState(seed)
hyperparameters_list = list(
list(configuration.configuration_space._hyperparameters.keys())
)
hyperparameters_list_length = len(hyperparameters_list)
hyperparameters_used = [hp.name for hp in configuration.configuration_space.get_hyperparameters()
if hp.get_num_neighbors(configuration.get(hp.name)) == 0 and configuration.get(hp.name) is not None]
number_of_usable_hyperparameters = sum(np.isfinite(configuration.get_array()))
n_neighbors_per_hp = {
hp.name: num_neighbors if np.isinf(hp.get_num_neighbors(configuration.get(hp.name))) else hp.get_num_neighbors(configuration.get(hp.name))
for hp in configuration.configuration_space.get_hyperparameters()
}
finite_neighbors_stack = {} # type: Dict
configuration_space = configuration.configuration_space # type: ConfigSpaceNNI
while len(hyperparameters_used) < number_of_usable_hyperparameters:
index = int(random.randint(hyperparameters_list_length))
hp_name = hyperparameters_list[index]
if n_neighbors_per_hp[hp_name] == 0:
continue
else:
neighbourhood = []
number_of_sampled_neighbors = 0
array = configuration.get_array() # type: np.ndarray
value = array[index] # type: float
# Check for NaNs (inactive value)
if value != value:
continue
iteration = 0
hp = configuration_space.get_hyperparameter(hp_name) # type: Hyperparameter
num_neighbors_for_hp = hp.get_num_neighbors(configuration.get(hp_name))
while True:
# Obtain neigbors differently for different possible numbers of
# neighbors
if num_neighbors_for_hp == 0:
break
# No infinite loops
elif iteration > 100:
break
elif np.isinf(num_neighbors_for_hp):
if number_of_sampled_neighbors >= 1:
break
# TODO if code becomes slow remove the isinstance!
if isinstance(hp, (UniformFloatHyperparameter, UniformIntegerHyperparameter)):
neighbor = hp.get_neighbors(value, random,
number=1, std=stdev)[0]
else:
neighbor = hp.get_neighbors(value, random,
number=1)[0]
else:
if iteration > 0:
break
if hp_name not in finite_neighbors_stack:
neighbors = hp.get_neighbors(value, random)
random.shuffle(neighbors)
finite_neighbors_stack[hp_name] = neighbors
else:
neighbors = finite_neighbors_stack[hp_name]
neighbor = neighbors.pop()
# Check all newly obtained neigbors
new_array = array.copy()
new_array = ConfigSpaceNNI.c_util.change_hp_value(
configuration_space=configuration_space,
configuration_array=new_array,
hp_name=hp_name,
hp_value=neighbor,
index=index)
try:
# Populating a configuration from an array does not check
# if it is a legal configuration - check this (slow)
new_configuration = Configuration(configuration_space,
vector=new_array) # type: Configuration
# Only rigorously check every tenth configuration (
# because moving around in the neighborhood should
# just work!)
if np.random.random() > 0.95:
new_configuration.is_valid_configuration()
else:
configuration_space._check_forbidden(new_array)
neighbourhood.append(new_configuration)
except ForbiddenValueError as e:
pass
iteration += 1
if len(neighbourhood) > 0:
number_of_sampled_neighbors += 1
# Some infinite loop happened and no valid neighbor was found OR
# no valid neighbor is available for a categorical
if len(neighbourhood) == 0:
hyperparameters_used.append(hp_name)
n_neighbors_per_hp[hp_name] = 0
hyperparameters_used.append(hp_name)
else:
if hp_name not in hyperparameters_used:
n_ = neighbourhood.pop()
n_neighbors_per_hp[hp_name] -= 1
if n_neighbors_per_hp[hp_name] == 0:
hyperparameters_used.append(hp_name)
yield n_
def test_check_configuration(self):
# TODO this is only a smoke test
# TODO actually, this rather tests the evaluate methods in the
# conditions module!
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("parent", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cs.add_hyperparameter(hp2)
cond1 = EqualsCondition(hp2, hp1, 0)
cs.add_condition(cond1)
# This automatically checks the configuration!
Configuration(cs, dict(parent=0, child=5))
# and now for something more complicated
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("input1", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = CategoricalHyperparameter("input2", [0, 1])
cs.add_hyperparameter(hp2)
hp3 = CategoricalHyperparameter("input3", [0, 1])
cs.add_hyperparameter(hp3)
hp4 = CategoricalHyperparameter("input4", [0, 1])
cs.add_hyperparameter(hp4)
hp5 = CategoricalHyperparameter("input5", [0, 1])
cs.add_hyperparameter(hp5)
hp6 = Constant("AND", "True")
cs.add_hyperparameter(hp6)
cond1 = EqualsCondition(hp6, hp1, 1)
cond2 = NotEqualsCondition(hp6, hp2, 1)
cond3 = InCondition(hp6, hp3, [1])
cond4 = EqualsCondition(hp6, hp4, 1)
cond5 = EqualsCondition(hp6, hp5, 1)
conj1 = AndConjunction(cond1, cond2)
conj2 = OrConjunction(conj1, cond3)
conj3 = AndConjunction(conj2, cond4, cond5)
cs.add_condition(conj3)
expected_outcomes = [
False, False, False, False, False, False, False, True, False,
False, False, False, False, False, False, True, False, False,
False, True, False, False, False, True, False, False, False, False,
False, False, False, True
]
for idx, values in enumerate(product([0, 1], repeat=5)):
# The hyperparameters aren't sorted, but the test assumes them to
# be sorted.
hyperparameters = sorted(cs.get_hyperparameters(),
key=lambda t: t.name)
instantiations = {
hyperparameters[jdx + 1].name: values[jdx]
for jdx in range(len(values))
}
evaluation = conj3.evaluate(instantiations)
self.assertEqual(expected_outcomes[idx], evaluation)
if evaluation == False:
self.assertRaisesRegexp(
ValueError, "Inactive hyperparameter 'AND' must "
"not be specified, but has the vector value: "
"'0.0'.",
Configuration,
cs,
values={
"input1": values[0],
"input2": values[1],
"input3": values[2],
"input4": values[3],
"input5": values[4],
"AND": "True"
})
else:
Configuration(cs,
values={
"input1": values[0],
"input2": values[1],
"input3": values[2],
"input4": values[3],
"input5": values[4],
"AND": "True"
})
