def test_suggest_loguniform(storage_init_func):
# type: (Callable[[], storages.BaseStorage]) -> None
with pytest.raises(ValueError):
LogUniformDistribution(low=1.0, high=0.9)
with pytest.raises(ValueError):
LogUniformDistribution(low=0.0, high=0.9)
mock = Mock()
mock.side_effect = [1.0, 2.0, 3.0]
sampler = samplers.RandomSampler()
with patch.object(sampler, "sample_independent", mock) as mock_object:
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
distribution = LogUniformDistribution(low=0.1, high=4.0)
assert trial._suggest("x",
distribution) == 1.0 # Test suggesting a param.
assert trial._suggest(
"x", distribution) == 1.0 # Test suggesting the same param.
assert trial._suggest(
"y", distribution) == 3.0 # Test suggesting a different param.
assert trial.params == {"x": 1.0, "y": 3.0}
assert mock_object.call_count == 3
def _get_params(self, trial: trial_module.Trial) -> Dict[str, Any]:
params = self.params.copy() # type: Dict[str, Any]
if self.param_distributions is None:
params["feature_fraction"] = trial.suggest_discrete_uniform(
"feature_fraction", 0.1, 1.0, 0.05)
params["max_depth"] = trial.suggest_int("max_depth", 1, 7)
params["num_leaves"] = trial.suggest_int("num_leaves", 2,
2**params["max_depth"])
# See https://github.com/Microsoft/LightGBM/issues/907
params["min_data_in_leaf"] = trial.suggest_int(
"min_data_in_leaf",
1,
max(1, int(self.n_samples / params["num_leaves"])),
)
params["lambda_l1"] = trial.suggest_loguniform(
"lambda_l1", 1e-09, 10.0)
params["lambda_l2"] = trial.suggest_loguniform(
"lambda_l2", 1e-09, 10.0)
if params["boosting_type"] != "goss":
params["bagging_fraction"] = trial.suggest_discrete_uniform(
"bagging_fraction", 0.5, 0.95, 0.05)
params["bagging_freq"] = trial.suggest_int(
"bagging_freq", 1, 10)
return params
for name, distribution in self.param_distributions.items():
params[name] = trial._suggest(name, distribution)
return params
def _get_params(self, trial: trial_module.Trial) -> Dict[str, Any]:
params = self.params.copy() # type: Dict[str, Any]
if self.param_distributions is None:
params["colsample_bylevel"] = trial.suggest_discrete_uniform(
"colsample_bylevel", 0.1, 1.0, 0.05
)
params["max_depth"] = trial.suggest_int("max_depth", 1, 7)
# https://catboost.ai/docs/concepts/parameter-tuning.html#tree-growing-policy
# params["num_leaves"] = trial.suggest_int(
# "num_leaves", 2, 2 ** params["max_depth"]
# )
# See https://github.com/Microsoft/LightGBM/issues/907
params["num_leaves"] = 31
params["min_data_in_leaf"] = trial.suggest_int(
"min_data_in_leaf",
1,
max(1, int(self.n_samples / params["num_leaves"])),
)
params["l2_leaf_reg"] = trial.suggest_loguniform("lambda_l2", 1e-09, 10.0)
if params["bootstrap_type"] == "Bayesian":
params["bagging_temperature"] = trial.suggest_discrete_uniform(
"bagging_temperature", 0.5, 0.95, 0.05
)
elif (
params["bootstrap_type"] == "Bernoulli"
or params["bootstrap_type"] == "Poisson"
):
params["subsample"] = trial.suggest_uniform("subsample", 0.1, 1)
return params
for name, distribution in self.param_distributions.items():
params[name] = trial._suggest(name, distribution)
return params