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 test_check_distribution_suggest_categorical(storage_mode: str) -> 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))
trial.suggest_categorical("x", [10, 20, 30])
with pytest.raises(ValueError):
trial.suggest_categorical("x", [10, 20])
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
def test_check_distribution_suggest_categorical(
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))
trial.suggest_categorical("x", [10, 20, 30])
with pytest.raises(ValueError):
trial.suggest_categorical("x", [10, 20])
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
def objective(trial: Trial) -> float:
a = trial.suggest_int("a", 0, 100)
b = trial.suggest_uniform("b", -0.1, 0.1)
c = trial.suggest_categorical("c", ("x", "y"))
d = trial.suggest_discrete_uniform("d", -5, 5, 1)
e = trial.suggest_loguniform("e", 0.0001, 1)
if c == "x":
return a * d
else:
return b * e
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) -> float:
# Predefined parameters are sampled by `sample_relative()` method.
assert trial.suggest_uniform("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_loguniform("d", 1, 100) == unknown_param_value
assert trial.suggest_uniform("e", 20, 40) == unknown_param_value
return 0.0
def sample_params_values(self, trial: Trial, suggested_params: Dict,
estimated_n_trials: int) -> Dict:
"""Sample hyperparameters from suggested.
Args:
trial: Optuna trial object.
suggested_params: Dict with parameters.
estimated_n_trials: Maximum number of hyperparameter estimation.
Returns:
Dict with sampled hyperparameters.
"""
trial_values = copy(suggested_params)
try:
nan_rate = getattr(self, '_nan_rate')
except AttributeError:
nan_rate = 0
trial_values['max_depth'] = trial.suggest_int(name='max_depth',
low=3,
high=7)
if nan_rate > 0:
trial_values['nan_mode'] = trial.suggest_categorical(
name='nan_mode', choices=['Max', 'Min'])
if estimated_n_trials > 20:
trial_values['l2_leaf_reg'] = trial.suggest_loguniform(
name='l2_leaf_reg',
low=1e-8,
high=10.0,
)
# trial_values['bagging_temperature'] = trial.suggest_loguniform(
# name='bagging_temperature',
# low=0.01,
# high=10.0,
# )
if estimated_n_trials > 50:
trial_values['min_data_in_leaf'] = trial.suggest_int(
name='min_data_in_leaf', low=1, high=20)
# the only case when used this parameter is when categorical columns more than 0
if len(self._le_cat_features) > 0:
trial_values['one_hot_max_size'] = trial.suggest_int(
name='one_hot_max_size', low=3, high=10)
return trial_values
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 define_hyperparameters(trial: Trial) -> Pipeline:
ngram_range = trial.suggest_categorical("vectorizer__ngram_range",
["11", "12"])
vectorizer = TfidfVectorizer(
stop_words="english",
min_df=2,
ngram_range=string_to_tuple(ngram_range),
)
clf = trial.suggest_categorical(
"clf", ["SVC", "RandomForestClassifier", "MultinomialNB"])
if clf == "SVC":
C = trial.suggest_uniform("clf__C", 0.1, 0.2)
classifier = SVC(C=C)
elif clf == "RandomForestClassifier":
max_depth = trial.suggest_int("clf__max_depth", 2, 4)
classifier = RandomForestClassifier(max_depth=max_depth)
else:
alpha = trial.suggest_loguniform("clf__alpha", 1e-2, 1e-1)
classifier = MultinomialNB(alpha=alpha)
return Pipeline([("vectorizer", vectorizer), ("clf", classifier)])
def objective(trial: Trial) -> float:
x1 = trial.suggest_uniform("x1", 0.1, 3)
x2 = trial.suggest_loguniform("x2", 0.1, 3)
x3 = trial.suggest_discrete_uniform("x3", 0, 3, 1)
x4 = trial.suggest_int("x4", -3, 3)
x5 = trial.suggest_categorical("x5", [1.0, 1.1, 1.2])
if trial.number % 2 == 0:
# Conditional parameters are ignored unless `params` is specified and is not `None`.
x6 = trial.suggest_uniform("x6", 0.1, 3)
assert isinstance(x5, float)
value = x1**4 + x2 + x3 - x4**2 - x5
if trial.number % 2 == 0:
value += x6
return value
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 objective(trial: Trial) -> float:
x = trial.suggest_int("x", 5, 5)
z = trial.suggest_categorical("z", [None])
if trial.number == 0:
return x * int(z is None)
elif trial.number == 1:
trial.report(1, 4)
trial.report(2, 7)
raise TrialPruned()
elif trial.number == 2:
trial.report(float("nan"), 3)
raise TrialPruned()
elif trial.number == 3:
raise TrialPruned()
else:
raise RuntimeError()
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_int("x", -1, 1)
y = trial.suggest_categorical("y", [-1, 0, 1])
y = cast(int, y)
return x**2 + y**2
def objective(trial: Trial) -> float:
x = trial.suggest_categorical("x", ["a", "b"])
if x == "a":
return trial.suggest_float("y", 0, 1)
else:
return trial.suggest_float("z", 0, 1)
def __call__(self, name: str, trial: Trial):
return trial.suggest_categorical(name, self.choices)
