def test_get_conditions(self):
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("parent", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cs.add_hyperparameter(hp2)
self.assertEqual([], cs.get_conditions())
cond1 = EqualsCondition(hp2, hp1, 0)
cs.add_condition(cond1)
self.assertEqual([cond1], cs.get_conditions())
def _get_configuration_space(self) -> ConfigurationSpace:
"""Get the configuration space for the random forest.
Returns
-------
ConfigurationSpace
"""
cfg = ConfigurationSpace()
cfg.seed(int(self.rs.randint(0, 1000)))
num_trees = Constant("num_trees", value=N_TREES)
bootstrap = CategoricalHyperparameter(
"do_bootstrapping", choices=(self.bootstrap,), default_value=self.bootstrap,
)
max_feats = CategoricalHyperparameter("max_features", choices=(3 / 6, 4 / 6, 5 / 6, 1), default_value=1)
min_split = UniformIntegerHyperparameter("min_samples_to_split", lower=1, upper=10, default_value=2)
min_leavs = UniformIntegerHyperparameter("min_samples_in_leaf", lower=1, upper=10, default_value=1)
cfg.add_hyperparameters([num_trees, bootstrap, max_feats, min_split, min_leavs])
return cfg
def test_add_second_condition_wo_conjunction(self):
hp1 = CategoricalHyperparameter("input1", [0, 1])
hp2 = CategoricalHyperparameter("input2", [0, 1])
hp3 = Constant("And", "True")
cond1 = EqualsCondition(hp3, hp1, 1)
cond2 = EqualsCondition(hp3, hp2, 1)
cs = ConfigurationSpace()
cs.add_hyperparameter(hp1)
cs.add_hyperparameter(hp2)
cs.add_hyperparameter(hp3)
cs.add_condition(cond1)
self.assertRaisesRegexp(
ValueError, "Adding a second condition \(different\) for a "
"hyperparameter is ambigouos and "
"therefore forbidden. Add a conjunction "
"instead!", cs.add_condition, cond2)
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 test_illegal_default_configuration(self):
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("loss", ["l1", "l2"],
default_value='l1')
hp2 = CategoricalHyperparameter("penalty", ["l1", "l2"],
default_value='l1')
cs.add_hyperparameter(hp1)
cs.add_hyperparameter(hp2)
forb1 = ForbiddenEqualsClause(hp1, "l1")
forb2 = ForbiddenEqualsClause(hp2, "l1")
forb3 = ForbiddenAndConjunction(forb1, forb2)
# cs.add_forbidden_clause(forb3)
self.assertRaisesRegex(
ValueError,
r"Given vector violates forbidden clause \(Forbidden: loss == \'l1\' && "
r"Forbidden: penalty == \'l1\'\)",
cs.add_forbidden_clause,
forb3,
)
def test_random_neighbor_failing(self):
hp = Constant('a', 'b')
self.assertRaisesRegex(ValueError, 'Probably caught in an infinite '
'loop.',
self._test_random_neigbor, hp)
hp = CategoricalHyperparameter('a', ['a'])
self.assertRaisesRegex(ValueError, 'Probably caught in an infinite '
'loop.',
self._test_random_neigbor, hp)
def test_add_configuration_space_conjunctions(self):
cs1 = ConfigurationSpace()
cs2 = ConfigurationSpace()
hp1 = cs1.add_hyperparameter(CategoricalHyperparameter("input1", [0, 1]))
hp2 = cs1.add_hyperparameter(CategoricalHyperparameter("input2", [0, 1]))
hp3 = cs1.add_hyperparameter(UniformIntegerHyperparameter("child1", 0, 10))
hp4 = cs1.add_hyperparameter(UniformIntegerHyperparameter("child2", 0, 10))
cond1 = EqualsCondition(hp2, hp3, 0)
cond2 = EqualsCondition(hp1, hp3, 5)
cond3 = EqualsCondition(hp1, hp4, 1)
andCond = AndConjunction(cond2, cond3)
cs1.add_conditions([cond1, andCond])
cs2.add_configuration_space(prefix='test', configuration_space=cs1)
self.assertEqual(str(cs2).count('test:'), 10)
# Check that they're equal except for the "test:" prefix
self.assertEqual(str(cs1), str(cs2).replace('test:', ''))
def choice(label: str, options: List, default=None):
if len(options) == 1:
return Constant(label, _encode(options[0]))
choices = []
for option in options:
choices.append(_encode(option))
kwargs = {}
if default:
kwargs.update({'default_value': _encode(default)})
hp = CategoricalHyperparameter(label, choices, **kwargs)
return hp
def test_add_conjunction(self):
hp1 = CategoricalHyperparameter("input1", [0, 1])
hp2 = CategoricalHyperparameter("input2", [0, 1])
hp3 = CategoricalHyperparameter("input3", [0, 1])
hp4 = Constant("And", "True")
cond1 = EqualsCondition(hp4, hp1, 1)
cond2 = EqualsCondition(hp4, hp2, 1)
cond3 = EqualsCondition(hp4, hp3, 1)
andconj1 = AndConjunction(cond1, cond2, cond3)
cs = ConfigurationSpace()
cs.add_hyperparameter(hp1)
cs.add_hyperparameter(hp2)
cs.add_hyperparameter(hp3)
cs.add_hyperparameter(hp4)
cs.add_condition(andconj1)
self.assertNotIn(hp4, cs.get_all_unconditional_hyperparameters())
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 test_estimate_size(self):
cs = ConfigurationSpace()
self.assertEqual(cs.estimate_size(), 0)
cs.add_hyperparameter(Constant('constant', 0))
self.assertEqual(cs.estimate_size(), 1)
cs.add_hyperparameter(UniformIntegerHyperparameter('integer', 0, 5))
self.assertEqual(cs.estimate_size(), 6)
cs.add_hyperparameter(CategoricalHyperparameter('cat', [0, 1, 2]))
self.assertEqual(cs.estimate_size(), 18)
cs.add_hyperparameter(UniformFloatHyperparameter('float', 0, 1))
self.assertTrue(np.isinf(cs.estimate_size()))
def set_training_space(cs: ConfigurationSpace):
'''
Set hyperparameters for training
'''
batch_size = CategoricalHyperparameter('batch_size', [16, 32],
default_value=32)
keep_prob = UniformFloatHyperparameter('keep_prob',
0,
0.99,
default_value=0.5)
cs.add_hyperparameters([batch_size, keep_prob])
def test_condition_with_cycles(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)
cond2 = EqualsCondition(hp1, hp2, 0)
self.assertRaisesRegex(ValueError, r"Hyperparameter configuration "
r"contains a cycle \[\['child', 'parent'\]\]",
cs.add_condition, cond2)
def test_get_hyperparameter(self):
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("parent", [0, 1])
cs.add_hyperparameter(hp1)
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cs.add_hyperparameter(hp2)
retval = cs.get_hyperparameter("parent")
self.assertEqual(hp1, retval)
retval = cs.get_hyperparameter("child")
self.assertEqual(hp2, retval)
self.assertRaises(KeyError, cs.get_hyperparameter, "grandfather")
def set_optimizer_space(cs: ConfigurationSpace):
'''
Set hyperparameters for optimizers
'''
optimizer = CategoricalHyperparameter('optimizer', ['SGD', 'Adam'],
default_value='Adam')
sgd_lr = UniformFloatHyperparameter('sgd_lr',
0.00001,
0.1,
default_value=0.005,
log=True) # log scale
sgd_decay = UniformFloatHyperparameter('sgd_decay',
0.0001,
0.1,
default_value=0.05,
log=True) # log scale
sgd_momentum = UniformFloatHyperparameter('sgd_momentum',
0.3,
0.99,
default_value=0.9)
adam_lr = UniformFloatHyperparameter('adam_lr',
0.00001,
0.1,
default_value=0.005,
log=True) # log scale
adam_decay = UniformFloatHyperparameter('adam_decay',
0.0001,
0.1,
default_value=0.05,
log=True) # log scale
sgd_lr_cond = InCondition(child=sgd_lr,
parent=optimizer,
values=['SGD'])
sgd_decay_cond = InCondition(child=sgd_decay,
parent=optimizer,
values=['SGD'])
sgd_momentum_cond = InCondition(child=sgd_momentum,
parent=optimizer,
values=['SGD'])
adam_lr_cond = InCondition(child=adam_lr,
parent=optimizer,
values=['Adam'])
adam_decay_cond = InCondition(child=adam_decay,
parent=optimizer,
values=['Adam'])
cs.add_hyperparameters(
[optimizer, sgd_lr, sgd_decay, sgd_momentum, adam_lr, adam_decay])
cs.add_conditions([
sgd_lr_cond, sgd_decay_cond, sgd_momentum_cond, adam_lr_cond,
adam_decay_cond
])
def test_get_hyperparamforbidden_clauseseters(self):
cs = ConfigurationSpace()
self.assertEqual(0, len(cs.get_hyperparameters()))
hp1 = CategoricalHyperparameter("parent", [0, 1])
cs.add_hyperparameter(hp1)
self.assertEqual([hp1], cs.get_hyperparameters())
hp2 = UniformIntegerHyperparameter("child", 0, 10)
cs.add_hyperparameter(hp2)
cond1 = EqualsCondition(hp2, hp1, 1)
cs.add_condition(cond1)
self.assertEqual([hp1, hp2], cs.get_hyperparameters())
# TODO: I need more tests for the topological sort!
self.assertEqual([hp1, hp2], cs.get_hyperparameters())
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"))
def test_sample_configuration_with_or_conjunction(self):
cs = ConfigurationSpace(seed=1)
hyper_params = {}
hyper_params["hp5"] = CategoricalHyperparameter("hp5", ['0', '1', '2'])
hyper_params["hp7"] = CategoricalHyperparameter("hp7", ['3', '4', '5'])
hyper_params["hp8"] = CategoricalHyperparameter("hp8", ['6', '7', '8'])
for key in hyper_params:
cs.add_hyperparameter(hyper_params[key])
cs.add_condition(
InCondition(hyper_params["hp5"], hyper_params["hp8"], ['6']))
cs.add_condition(
OrConjunction(
InCondition(hyper_params["hp7"], hyper_params["hp8"], ['7']),
InCondition(hyper_params["hp7"], hyper_params["hp5"], ['1'])))
for cfg, fixture in zip(cs.sample_configuration(10),
[[1, np.NaN, 2], [0, 2, np.NaN], [0, 1, 1],
[1, np.NaN, 2], [1, np.NaN, 2]]):
np.testing.assert_array_almost_equal(cfg.get_array(), fixture)
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
BaseImageClassificationModel.set_training_space(cs)
BaseImageClassificationModel.set_optimizer_space(cs)
vgg_kernel_size = CategoricalHyperparameter('vgg_kernel_size', [3, 5], default_value=3)
vgg_keep_prob = UniformFloatHyperparameter('vgg_keep_prob', 0, 0.99, default_value=0.5)
vgg_block2_layer = UniformIntegerHyperparameter('vgg_block2_layer', 2, 3, default_value=2)
vgg_block3_layer = UniformIntegerHyperparameter('vgg_block3_layer', 2, 5, default_value=3)
vgg_block4_layer = UniformIntegerHyperparameter('vgg_block4_layer', 2, 5, default_value=3)
vgg_block5_layer = UniformIntegerHyperparameter('vgg_block5_layer', 2, 5, default_value=3)
cs.add_hyperparameters(
[vgg_kernel_size, vgg_keep_prob, vgg_block2_layer, vgg_block3_layer, vgg_block4_layer, vgg_block5_layer])
return cs
def test_add_conditions(self):
cs1 = ConfigurationSpace()
cs2 = ConfigurationSpace()
hp1 = cs1.add_hyperparameter(CategoricalHyperparameter("input1", [0, 1]))
cs2.add_hyperparameter(hp1)
hp2 = cs1.add_hyperparameter(CategoricalHyperparameter("input2", [0, 1]))
cs2.add_hyperparameter(hp2)
hp3 = cs1.add_hyperparameter(UniformIntegerHyperparameter("child1", 0, 10))
cs2.add_hyperparameter(hp3)
hp4 = cs1.add_hyperparameter(UniformIntegerHyperparameter("child2", 0, 10))
cs2.add_hyperparameter(hp4)
cond1 = EqualsCondition(hp2, hp3, 0)
cond2 = EqualsCondition(hp1, hp3, 5)
cond3 = EqualsCondition(hp1, hp4, 1)
andCond = AndConjunction(cond2, cond3)
cs1.add_conditions([cond1, andCond])
cs2.add_condition(cond1)
cs2.add_condition(andCond)
self.assertEqual(str(cs1), str(cs2))
def test_add_forbidden_clause(self):
cs = ConfigurationSpace()
hp1 = CategoricalHyperparameter("input1", [0, 1])
cs.add_hyperparameter(hp1)
forb = ForbiddenEqualsClause(hp1, 1)
# TODO add checking whether a forbidden clause makes sense at all
cs.add_forbidden_clause(forb)
# TODO add something to properly retrieve the forbidden clauses
self.assertEqual(str(cs), "Configuration space object:\n "
"Hyperparameters:\n input1, "
"Type: Categorical, Choices: {0, 1}, "
"Default: 0\n"
" Forbidden Clauses:\n"
" Forbidden: input1 == 1\n")
def test_with_ordinal(self):
cs = smac.configspace.ConfigurationSpace()
_ = cs.add_hyperparameter(
CategoricalHyperparameter('a', [0, 1], default_value=0))
_ = cs.add_hyperparameter(
OrdinalHyperparameter('b', [0, 1], default_value=1))
_ = cs.add_hyperparameter(
UniformFloatHyperparameter('c',
lower=0.,
upper=1.,
default_value=1))
_ = cs.add_hyperparameter(
UniformIntegerHyperparameter('d',
lower=0,
upper=10,
default_value=1))
cs.seed(1)
feat_array = np.array([0, 0, 0]).reshape(1, -1)
types, bounds = get_types(cs, feat_array)
model = RandomForestWithInstances(
configspace=cs,
types=types,
bounds=bounds,
instance_features=feat_array,
seed=1,
ratio_features=1.0,
pca_components=9,
)
self.assertEqual(bounds[0][0], 2)
self.assertTrue(bounds[0][1] is np.nan)
self.assertEqual(bounds[1][0], 0)
self.assertEqual(bounds[1][1], 1)
self.assertEqual(bounds[2][0], 0.)
self.assertEqual(bounds[2][1], 1.)
self.assertEqual(bounds[3][0], 0.)
self.assertEqual(bounds[3][1], 1.)
X = np.array(
[[0., 0., 0., 0., 0., 0., 0.], [0., 0., 1., 0., 0., 0., 0.],
[0., 1., 0., 9., 0., 0., 0.], [0., 1., 1., 4., 0., 0., 0.]],
dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
X_train = np.vstack((X, X, X, X, X, X, X, X, X, X))
y_train = np.vstack((y, y, y, y, y, y, y, y, y, y))
model.train(X_train, y_train.reshape((-1, 1)))
mean, _ = model.predict(X)
for idx, m in enumerate(mean):
self.assertAlmostEqual(y[idx], m, 0.05)
def string2hyperparameter(hp_desc: str):
# Only support type, range, default_value, log, q
# Sample: x2, Type: UniformInteger, Range: [1, 15], Default: 4, on log-scale, Q: 2
q = -1
log = None
default_value = None
params = hp_desc.split(',')
cur_idx = -1
while default_value is None:
if q == -1:
if 'Q:' in params[cur_idx]:
q = float(params[cur_idx][4:])
cur_idx -= 1
continue
else:
q = None
if log is None:
if 'log-scale' in params[cur_idx]:
log = True
cur_idx -= 1
continue
else:
log = False
if default_value is None:
default_value = str(params[cur_idx][10:])
cur_idx -= 1
prefix_params = ','.join(params[:cur_idx + 1])
range_str = prefix_params.split(':')[-1]
if range_str[-1] == ']':
element_list = range_str[2:-1].split(',')
range = [float(element_list[0]), float(element_list[1])]
else:
element_list = range_str[1:-1].split(',')
range = [element[1:] for element in element_list]
type_str = prefix_params.split(':')[-2].split(',')[0][1:]
name_str = ':'.join(prefix_params.split(':')[:-2])
name = ','.join(name_str.split(',')[:-1])[4:]
if type_str == 'UniformFloat':
return UniformFloatHyperparameter(name, range[0], range[1], default_value=float(default_value), log=log, q=q)
elif type_str == 'UniformInteger':
return UniformIntegerHyperparameter(name, range[0], range[1], default_value=int(default_value), log=log, q=q)
elif type_str == 'Categorical':
return CategoricalHyperparameter(name, range, default_value=default_value)
else:
raise ValueError('Hyperparameter type %s not supported!' % type)
def get_hyperparameter_search_space(dataset_properties=None):
# TODO add hyperparameter to gbdt binning
cs = ConfigurationSpace()
binning_method = CategoricalHyperparameter()
# shrinkage = UniformFloatHyperparameter(
# name="shrinkage", lower=0.0, upper=1.0, default_value=0.5
# )
# n_components = UniformIntegerHyperparameter(
# name="n_components", lower=1, upper=29, default_value=10
# )
# tol = UniformFloatHyperparameter(
# name="tol", lower=0.0001, upper=1, default_value=0.0001
# )
cs.add_hyperparameters([binning_method])
return cs
def get_cash_cs(include_algorithms=None, task_type=CLASSIFICATION):
_candidates = get_combined_candidtates(_classifiers, _addons)
if include_algorithms is not None:
_candidates = set(include_algorithms).intersection(set(_candidates.keys()))
if len(_candidates) == 0:
raise ValueError("No algorithms included! Please check the spelling of the included algorithms!")
cs = ConfigurationSpace()
algo = CategoricalHyperparameter('algorithm', list(_candidates))
cs.add_hyperparameter(algo)
for estimator_id in _candidates:
estimator_cs = get_hpo_cs(estimator_id)
parent_hyperparameter = {'parent': algo,
'value': estimator_id}
cs.add_configuration_space(estimator_id, estimator_cs, parent_hyperparameter=parent_hyperparameter)
return cs
def test_meta_field(self):
cs = ConfigurationSpace()
cs.add_hyperparameter(
UniformIntegerHyperparameter("uihp",
lower=1,
upper=10,
meta=dict(uihp=True)))
cs.add_hyperparameter(
NormalIntegerHyperparameter("nihp",
mu=0,
sigma=1,
meta=dict(nihp=True)))
cs.add_hyperparameter(
UniformFloatHyperparameter("ufhp",
lower=1,
upper=10,
meta=dict(ufhp=True)))
cs.add_hyperparameter(
NormalFloatHyperparameter("nfhp",
mu=0,
sigma=1,
meta=dict(nfhp=True)))
cs.add_hyperparameter(
CategoricalHyperparameter("chp",
choices=['1', '2', '3'],
meta=dict(chp=True)))
cs.add_hyperparameter(
OrdinalHyperparameter("ohp",
sequence=['1', '2', '3'],
meta=dict(ohp=True)))
cs.add_hyperparameter(Constant("const", value=1,
meta=dict(const=True)))
parent = ConfigurationSpace()
parent.add_configuration_space("sub", cs, delimiter=':')
self.assertEqual(
parent.get_hyperparameter("sub:uihp").meta, dict(uihp=True))
self.assertEqual(
parent.get_hyperparameter("sub:nihp").meta, dict(nihp=True))
self.assertEqual(
parent.get_hyperparameter("sub:ufhp").meta, dict(ufhp=True))
self.assertEqual(
parent.get_hyperparameter("sub:nfhp").meta, dict(nfhp=True))
self.assertEqual(
parent.get_hyperparameter("sub:chp").meta, dict(chp=True))
self.assertEqual(
parent.get_hyperparameter("sub:ohp").meta, dict(ohp=True))
self.assertEqual(
parent.get_hyperparameter("sub:const").meta, dict(const=True))
def maxsat(n_eval, n_variables, random_seed):
assert n_variables in [28, 43, 60]
if n_variables == 28:
evaluator = MaxSAT28(random_seed)
elif n_variables == 43:
evaluator = MaxSAT43(random_seed)
elif n_variables == 60:
evaluator = MaxSAT60(random_seed)
name_tag = 'maxsat' + str(n_variables) + '_' + datetime.now().strftime(
"%Y-%m-%d-%H:%M:%S:%f")
cs = ConfigurationSpace()
for i in range(n_variables):
car_var = CategoricalHyperparameter('x' + str(i + 1).zfill(2),
[str(elm) for elm in range(2)],
default_value='0')
cs.add_hyperparameter(car_var)
init_points_numpy = evaluator.suggested_init.long().numpy()
init_points = []
for i in range(init_points_numpy.shape[0]):
init_points.append(
Configuration(
cs, {
'x' + str(j + 1).zfill(2): str(init_points_numpy[i][j])
for j in range(n_variables)
}))
def evaluate(x):
x_tensor = torch.LongTensor(
[int(x['x' + str(j + 1).zfill(2)]) for j in range(n_variables)])
return evaluator.evaluate(x_tensor).item()
print('Began at ' + datetime.now().strftime("%H:%M:%S"))
scenario = Scenario({
"run_obj": "quality",
"runcount-limit": n_eval,
"cs": cs,
"deterministic": "true",
'output_dir': os.path.join(EXP_DIR, name_tag)
})
smac = SMAC(scenario=scenario,
tae_runner=evaluate,
initial_configurations=init_points)
smac.optimize()
evaluations, optimum = evaluations_from_smac(smac)
print('Finished at ' + datetime.now().strftime("%H:%M:%S"))
return optimum
def pest_control(n_eval, random_seed):
evaluator = PestControl(random_seed)
name_tag = 'pestcontrol_' + datetime.now().strftime("%Y-%m-%d-%H:%M:%S:%f")
cs = ConfigurationSpace()
for i in range(PESTCONTROL_N_STAGES):
car_var = CategoricalHyperparameter(
'x' + str(i + 1).zfill(2),
[str(elm) for elm in range(PESTCONTROL_N_CHOICE)],
default_value='0')
cs.add_hyperparameter(car_var)
init_points_numpy = sample_init_points([PESTCONTROL_N_CHOICE] *
PESTCONTROL_N_STAGES, 20,
random_seed).long().numpy()
init_points = []
for i in range(init_points_numpy.shape[0]):
init_points.append(
Configuration(
cs, {
'x' + str(j + 1).zfill(2): str(init_points_numpy[i][j])
for j in range(PESTCONTROL_N_STAGES)
}))
def evaluate(x):
x_tensor = torch.LongTensor([
int(x['x' + str(j + 1).zfill(2)])
for j in range(PESTCONTROL_N_STAGES)
])
return evaluator.evaluate(x_tensor).item()
print('Began at ' + datetime.now().strftime("%H:%M:%S"))
scenario = Scenario({
"run_obj": "quality",
"runcount-limit": n_eval,
"cs": cs,
"deterministic": "true",
'output_dir': os.path.join(EXP_DIR, name_tag)
})
smac = SMAC(scenario=scenario,
tae_runner=evaluate,
initial_configurations=init_points)
smac.optimize()
evaluations, optimum = evaluations_from_smac(smac)
print('Finished at ' + datetime.now().strftime("%H:%M:%S"))
return optimum
def test_add_configuration_space(self):
cs = ConfigurationSpace()
hp1 = cs.add_hyperparameter(CategoricalHyperparameter("input1", [0, 1]))
cs.add_forbidden_clause(ForbiddenEqualsClause(hp1, 1))
hp2 = cs.add_hyperparameter(UniformIntegerHyperparameter("child", 0, 10))
cs.add_condition(EqualsCondition(hp2, hp1, 0))
cs2 = ConfigurationSpace()
cs2.add_configuration_space('prefix', cs, delimiter='__')
self.assertEqual(str(cs2), '''Configuration space object:
Hyperparameters:
prefix__child, Type: UniformInteger, Range: [0, 10], Default: 5
prefix__input1, Type: Categorical, Choices: {0, 1}, Default: 0
Conditions:
prefix__child | prefix__input1 == 0
Forbidden Clauses:
Forbidden: prefix__input1 == 1
''')
def contamination(n_eval, lamda, random_seed_pair):
evaluator = Contamination(lamda, random_seed_pair)
name_tag = '_'.join([
'contamination', ('%.2E' % lamda),
datetime.now().strftime("%Y-%m-%d-%H:%M:%S:%f")
])
cs = ConfigurationSpace()
for i in range(CONTAMINATION_N_STAGES):
car_var = CategoricalHyperparameter('x' + str(i + 1).zfill(2),
[str(elm) for elm in range(2)],
default_value='0')
cs.add_hyperparameter(car_var)
init_points_numpy = evaluator.suggested_init.long().numpy()
init_points = []
for i in range(init_points_numpy.shape[0]):
init_points.append(
Configuration(
cs, {
'x' + str(j + 1).zfill(2): str(init_points_numpy[i][j])
for j in range(CONTAMINATION_N_STAGES)
}))
def evaluate(x):
x_tensor = torch.LongTensor([
int(x['x' + str(j + 1).zfill(2)])
for j in range(CONTAMINATION_N_STAGES)
])
return evaluator.evaluate(x_tensor).item()
print('Began at ' + datetime.now().strftime("%H:%M:%S"))
scenario = Scenario({
"run_obj": "quality",
"runcount-limit": n_eval,
"cs": cs,
"deterministic": "true",
'output_dir': os.path.join(EXP_DIR, name_tag)
})
smac = SMAC(scenario=scenario,
tae_runner=evaluate,
initial_configurations=init_points)
smac.optimize()
evaluations, optimum = evaluations_from_smac(smac)
print('Finished at ' + datetime.now().strftime("%H:%M:%S"))
return optimum
