def test_check_distribution_suggest_float(
storage_init_func: Callable[[], storages.BaseStorage]) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
x1 = trial.suggest_float("x1", 10, 20)
x2 = trial.suggest_uniform("x1", 10, 20)
assert x1 == x2
x3 = trial.suggest_float("x2", 1e-5, 1e-3, log=True)
x4 = trial.suggest_loguniform("x2", 1e-5, 1e-3)
assert x3 == x4
x5 = trial.suggest_float("x3", 10, 20, step=1.0)
x6 = trial.suggest_discrete_uniform("x3", 10, 20, 1.0)
assert x5 == x6
with pytest.raises(ValueError):
trial.suggest_float("x4", 1e-5, 1e-2, step=1e-5, log=True)
with pytest.raises(ValueError):
trial.suggest_int("x1", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x1", 10, 20)
def test_suggest_low_equals_high(storage_init_func):
# type: (Callable[[], storages.BaseStorage]) -> None
study = create_study(storage_init_func(), sampler=samplers.TPESampler(n_startup_trials=0))
trial = Trial(study, study._storage.create_new_trial(study._study_id))
# Parameter values are determined without suggestion when low == high.
with patch.object(trial, "_suggest", wraps=trial._suggest) as mock_object:
assert trial.suggest_uniform("a", 1.0, 1.0) == 1.0 # Suggesting a param.
assert trial.suggest_uniform("a", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_loguniform("b", 1.0, 1.0) == 1.0 # Suggesting a param.
assert trial.suggest_loguniform("b", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_discrete_uniform("c", 1.0, 1.0, 1.0) == 1.0 # Suggesting a param.
assert (
trial.suggest_discrete_uniform("c", 1.0, 1.0, 1.0) == 1.0
) # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_int("d", 1, 1) == 1 # Suggesting a param.
assert trial.suggest_int("d", 1, 1) == 1 # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_float("e", 1.0, 1.0) == 1.0 # Suggesting a param.
assert trial.suggest_float("e", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_float("f", 0.5, 0.5, log=True) == 0.5 # Suggesting a param.
assert trial.suggest_float("f", 0.5, 0.5, log=True) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_float("g", 0.5, 0.5, log=False) == 0.5 # Suggesting a param.
assert trial.suggest_float("g", 0.5, 0.5, log=False) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 0
def objective(trial: Trial) -> float:
ret: float = trial.suggest_int("x1", 0, 10)
ret += trial.suggest_int("x2", 1, 10, log=True)
ret += trial.suggest_float("x3", 0, 10)
ret += trial.suggest_float("x4", 1, 10, log=True)
ret += trial.suggest_float("x5", 1, 10, step=3)
_ = trial.suggest_categorical("x6", [1, 4, 7, 10])
return ret
def obj(t: Trial) -> float:
t.suggest_float("a", 1.0, 100.0)
t.suggest_float("b", 1.0, 100.0, log=True)
t.suggest_float("c", 1.0, 100.0, step=3.0)
t.suggest_int("d", 1, 100)
t.suggest_int("e", 0, 100, step=2)
t.suggest_int("f", 1, 100, log=True)
t.suggest_categorical("g", ["x", "y", "z"])
return 0.0
def objective(trial: Trial) -> float:
x0 = trial.suggest_float("x0", 0, 1)
x1 = trial.suggest_float("x1", 0.1, 1, log=True)
x2 = trial.suggest_float("x2", 0, 1, step=0.1)
x3 = trial.suggest_int("x3", 0, 2)
x4 = trial.suggest_int("x4", 2, 4, log=True)
x5 = trial.suggest_int("x5", 0, 4, step=2)
x6 = cast(float, trial.suggest_categorical("x6", [0.1, 0.2, 0.3]))
return x0 + x1 + x2 + x3 + x4 + x5 + x6
def objective_func_branched_search_space(trial: Trial) -> float:
c = trial.suggest_categorical("c", ("A", "B"))
if c == "A":
x = trial.suggest_float("x", -10, 10)
return (x + 5) ** 2
else:
y = trial.suggest_float("y", -10, 10)
return (y + 5) ** 2
def objective(trial: Trial) -> float:
a = trial.suggest_float("a", 1, 9)
b = trial.suggest_float("b", 1, 9, log=True)
c = trial.suggest_float("c", 1, 9, step=1)
d = trial.suggest_int("d", 1, 9)
e = trial.suggest_int("e", 1, 9, log=True)
f = trial.suggest_int("f", 1, 9, step=2)
g = cast(int, trial.suggest_categorical("g", range(1, 10)))
return a + b + c + d + e + f + g
def configure(cfg: DictConfig, trial: Trial) -> None:
x_value = trial.params["x"]
trial.suggest_float(
"z",
x_value - cfg.max_z_difference_from_x,
x_value + cfg.max_z_difference_from_x,
)
trial.suggest_float("+w", 0.0,
1.0) # note +w here, not w as w is a new parameter
def objective(trial: Trial) -> float:
if trial.number == 0:
trial.suggest_float("param1", 0, 1)
raise optuna.exceptions.TrialPruned()
if trial.number == 1:
trial.suggest_float("param2", 0, 1)
return 0
return 0
def objective(trial: Trial) -> float:
x1 = trial.suggest_float("x1", 0.1, 3)
x2 = trial.suggest_float("x2", 0.1, 3, log=True)
x3 = trial.suggest_float("x3", 0, 3, step=1)
if trial.number % 2 == 0:
x4 = trial.suggest_float("x4", 0.1, 3)
value = x1**4 + x2 + x3
if trial.number % 2 == 0:
value += x4
return value
def objective(trial: Trial) -> float:
# Predefined parameters are sampled by `sample_relative()` method.
assert trial.suggest_float("a", 0, 5) == 3.2
assert trial.suggest_categorical("b", ["foo", "bar", "baz"]) == "baz"
# Other parameters are sampled by `sample_independent()` method.
assert trial.suggest_int("c", 20, 50) == unknown_param_value
assert trial.suggest_float("d", 1, 100, log=True) == unknown_param_value
assert trial.suggest_float("e", 20, 40) == unknown_param_value
return 0.0
def objective(trial: Trial) -> float:
a = trial.suggest_int("a", 0, 100)
b = trial.suggest_float("b", -0.1, 0.1)
c = trial.suggest_categorical("c", ("x", "y", None, 1, 2.0))
d = trial.suggest_float("d", -5, 5, step=1)
e = trial.suggest_float("e", 0.0001, 1, log=True)
if c == "x":
return a * d
else:
return b * e
def objective(trial: Trial) -> Tuple[float, float]:
p0 = trial.suggest_float("p0", -10, 10)
p1 = trial.suggest_float("p1", 3, 5)
p2 = trial.suggest_float("p2", 0.00001, 0.1, log=True)
p3 = trial.suggest_float("p3", 100, 200, step=5)
p4 = trial.suggest_int("p4", -20, -15)
p5 = cast(int, trial.suggest_categorical("p5", [7, 1, 100]))
p6 = trial.suggest_float("p6", -10, 10, step=1.0)
p7 = trial.suggest_int("p7", 1, 7, log=True)
return (
p0 + p1 + p2,
p3 + p4 + p5 + p6 + p7,
)
def test_check_distribution_suggest_float(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
x1 = trial.suggest_float("x1", 10, 20)
x2 = trial.suggest_uniform("x1", 10, 20)
assert x1 == x2
x3 = trial.suggest_float("x2", 1e-5, 1e-3, log=True)
x4 = trial.suggest_loguniform("x2", 1e-5, 1e-3)
assert x3 == x4
def objective(trial: Trial):
args = gen_args()
# acquisition hyperparam's
args.cluster = bool(trial.suggest_int('cluster', 0, 1))
if not args.cluster and random() > 0.5:
args.epsilon = trial.suggest_float('epsilon', 0.00, 0.2, step=0.05)
args.fps = None
if args.model in {'rf', 'nn'} or args.cluster:
args.encoder = trial.suggest_categorical('encoder',
{'morgan', 'pair', 'rdkit'})
try:
exp = Explorer(**vars(args))
except (IncompatibilityError, NotImplementedError) as e:
print(e)
return float('-inf')
start = time()
exp.run()
total = time() - start
m, s = divmod(total, 60)
h, m = divmod(int(m), 60)
print(f'Total time for trial #{trial.number}: {h}h {m}m {s:0.2f}s\n')
return exp.top_k_avg
def objective(trial: Trial) -> float:
x1 = trial.suggest_float("x1", 0.1, 3)
x2 = trial.suggest_float("x2", 0.1, 3, log=True)
x3 = trial.suggest_float("x3", 0, 3, step=1)
x4 = trial.suggest_int("x4", -3, 3)
x5 = trial.suggest_int("x5", 1, 5, log=True)
x6 = trial.suggest_categorical("x6", [1.0, 1.1, 1.2])
if trial.number % 2 == 0:
# Conditional parameters are ignored unless `params` is specified and is not `None`.
x7 = trial.suggest_float("x7", 0.1, 3)
assert isinstance(x6, float)
value = x1**4 + x2 + x3 - x4**2 - x5 + x6
if trial.number % 2 == 0:
value += x7
return value
def suggest_from_config(trial: Trial, configuration: str):
"""
Suggest From Config
===================
Allows to generalize the `suggest_*` functions taking
information from a YAML configuration file.
Parameters
----------
trial : optuna.trial.Trial
`Trial` object deriving from an Ask-and-Tell interface.
configuration : str
YAML file containing hyperparameters configuration.
Returns
-------
None
"""
with open(configuration) as file:
params = yaml.full_load(file)
for par in params.values():
if par['type'] == 'categorical':
trial.suggest_categorical(
name=str(par['name']),
choices=list(par['choices']),
)
elif par['type'] == 'float':
trial.suggest_float(
name=str(par['name']),
low=float(par['low']),
high=float(par['high']),
step=float(par['step']) if par['step'] else None,
log=bool(par['log']) if par['log'] else False,
)
elif par['type'] == 'int':
trial.suggest_int(
name=str(par['name']),
low=float(par['low']),
high=float(par['high']),
step=float(par['step']) if par['step'] else 1,
log=bool(par['log']) if par['log'] else False,
)
else:
raise ValueError('Trial suggestion not implemented.')
def test_suggest_low_equals_high(storage_mode: str) -> None:
with patch.object(
distributions, "_get_single_value", wraps=distributions._get_single_value
) as mock_object, StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage, sampler=samplers.TPESampler(n_startup_trials=0))
trial = Trial(study, study._storage.create_new_trial(study._study_id))
assert trial.suggest_uniform("a", 1.0, 1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 1
assert trial.suggest_uniform("a", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 1
assert trial.suggest_loguniform("b", 1.0, 1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 2
assert trial.suggest_loguniform("b", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 2
assert trial.suggest_discrete_uniform("c", 1.0, 1.0, 1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 3
assert (
trial.suggest_discrete_uniform("c", 1.0, 1.0, 1.0) == 1.0
) # Suggesting the same param.
assert mock_object.call_count == 3
assert trial.suggest_int("d", 1, 1) == 1 # Suggesting a param.
assert mock_object.call_count == 4
assert trial.suggest_int("d", 1, 1) == 1 # Suggesting the same param.
assert mock_object.call_count == 4
assert trial.suggest_float("e", 1.0, 1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 5
assert trial.suggest_float("e", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 5
assert trial.suggest_float("f", 0.5, 0.5, log=True) == 0.5 # Suggesting a param.
assert mock_object.call_count == 6
assert trial.suggest_float("f", 0.5, 0.5, log=True) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 6
assert trial.suggest_float("g", 0.5, 0.5, log=False) == 0.5 # Suggesting a param.
assert mock_object.call_count == 7
assert trial.suggest_float("g", 0.5, 0.5, log=False) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 7
assert trial.suggest_float("h", 0.5, 0.5, step=1.0) == 0.5 # Suggesting a param.
assert mock_object.call_count == 8
assert trial.suggest_float("h", 0.5, 0.5, step=1.0) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 8
assert trial.suggest_int("i", 1, 1, log=True) == 1 # Suggesting a param.
assert mock_object.call_count == 9
assert trial.suggest_int("i", 1, 1, log=True) == 1 # Suggesting the same param.
assert mock_object.call_count == 9
def test_check_distribution_suggest_int(storage_init_func: Callable[
[], storages.BaseStorage], enable_log: bool) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.warns(None) as record:
trial.suggest_int("x", 10, 20, log=enable_log)
trial.suggest_int("x", 10, 20, log=enable_log)
trial.suggest_int("x", 10, 22, log=enable_log)
# We expect exactly one warning.
assert len(record) == 1
with pytest.raises(ValueError):
trial.suggest_float("x", 10, 20, log=enable_log)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_float("x", 10, 20, log=enable_log)
def test_check_distribution_suggest_int(storage_mode: str,
enable_log: bool) -> None:
sampler = samplers.RandomSampler()
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage, sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.warns() as record:
trial.suggest_int("x", 10, 20, log=enable_log)
trial.suggest_int("x", 10, 20, log=enable_log)
trial.suggest_int("x", 10, 22, log=enable_log)
# We expect exactly one warning (not counting ones caused by deprecation).
assert len([r for r in record if r.category != FutureWarning]) == 1
with pytest.raises(ValueError):
trial.suggest_float("x", 10, 20, log=enable_log)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_float("x", 10, 20, log=enable_log)
def objective(trial: Trial) -> float:
trial.suggest_float("a", 0, 10)
trial.suggest_float("b", 0.1, 10, log=True)
trial.suggest_float("c", 0, 10, step=1)
trial.suggest_int("d", 0, 10)
trial.suggest_categorical("e", ["foo", "bar", "baz"])
trial.suggest_int("f", 1, 10, log=True)
return 1.0
def objective(trial: Trial) -> float:
x = trial.suggest_float("x", -1, 1)
y = trial.suggest_int("y", -1, 1)
z = trial.suggest_float("z", -1, 1)
return x + y + z
def objective(trial: Trial, base_value: float) -> float:
return trial.suggest_float("x", 0.1, 0.2) + base_value
def objective(trial: Trial, exception: Exception) -> float:
trial.suggest_float("z", 0, 1)
raise exception
def _multi_objective_function(trial: Trial) -> Tuple[float, float]:
x1 = trial.suggest_float("x1", 0.1, 3)
x2 = trial.suggest_float("x2", 0.1, 3, log=True)
x3 = trial.suggest_float("x3", 2, 4, log=True)
return x1, x2 * x3
def _objective(trial: Trial) -> float:
x1 = trial.suggest_float("x1", 0.1, 3)
x2 = trial.suggest_float("x2", 0.1, 3, log=True)
x3 = trial.suggest_float("x3", 2, 4, log=True)
return x1 + x2 * x3
def objective(trial: Trial, low: int, high: int) -> float:
v = trial.suggest_float("x", low, high)
v += trial.suggest_int("y", low, high)
return v
def objective(trial: Trial) -> float:
x = trial.suggest_categorical("x", [True, False])
if x:
return trial.suggest_float("y", 0, 1)
return trial.suggest_float("z", 0, 1)
def objective(trial: Trial) -> float:
x = trial.suggest_float("x", 0, 5)
y = trial.suggest_float("y", 1, 1)
return 4 * x**2 + 4 * y**2
def objective(trial: Trial) -> float:
x1 = trial.suggest_float("x1", 0.1, trial.number + 0.1)
return x1**2
