def __init__(
self,
study_name: str,
storage: Union[str, storages.BaseStorage],
sampler: Optional["samplers.BaseSampler"] = None,
pruner: Optional[pruners.BasePruner] = None,
) -> None:
self.study_name = study_name
storage = storages.get_storage(storage)
study_id = storage.get_study_id_from_name(study_name)
super(Study, self).__init__(study_id, storage)
self.sampler = sampler or samplers.TPESampler()
self.pruner = pruner or pruners.MedianPruner()
self._optimize_lock = threading.Lock()
self._stop_flag = False
def __init__(
self,
study_name, # type: str
storage, # type: Union[str, storages.BaseStorage]
sampler=None, # type: samplers.BaseSampler
pruner=None # type: pruners.BasePruner
):
# type: (...) -> None
self.study_name = study_name
storage = storages.get_storage(storage)
study_id = storage.get_study_id_from_name(study_name)
super(Study, self).__init__(study_id, storage)
self.sampler = sampler or samplers.TPESampler()
self.pruner = pruner or pruners.MedianPruner()
self._optimize_lock = threading.Lock()
def test_suggest_low_equals_high(storage_init_func):
# type: (typing.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_id(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., 1.) == 1. # Suggesting a param.
assert trial.suggest_uniform('a', 1., 1.) == 1. # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_loguniform('b', 1., 1.) == 1. # Suggesting a param.
assert trial.suggest_loguniform('b', 1., 1.) == 1. # Suggesting the same param.
assert mock_object.call_count == 0
assert trial.suggest_discrete_uniform('c', 1., 1., 1.) == 1. # Suggesting a param.
assert trial.suggest_discrete_uniform('c', 1., 1., 1.) == 1. # 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
def test_suggest_low_equals_high(storage_init_func):
# type: (typing.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 __init__(
self,
study_name, # type: str
storage, # type: Union[str, storages.BaseStorage]
sampler=None, # type: samplers.BaseSampler
pruner=None, # type: pruners.BasePruner
force_garbage_collection=True, # type: bool
):
# type: (...) -> None
self.study_name = study_name
storage = storages.get_storage(storage)
study_id = storage.get_study_id_from_name(study_name)
super(Study, self).__init__(study_id, storage)
self.sampler = sampler or samplers.TPESampler()
self.pruner = pruner or pruners.MedianPruner()
self.logger = logging.get_logger(__name__)
self._optimize_lock = threading.Lock()
self.force_garbage_collection = force_garbage_collection
def _make_study(self, is_higher_better: bool) -> study_module.Study:
direction = "maximize" if is_higher_better else "minimize"
if self.study is None:
seed = self._get_random_state()
sampler = samplers.TPESampler(seed=seed)
return study_module.create_study(
direction=direction, sampler=sampler
)
_direction = (
study_module.StudyDirection.MAXIMIZE
if is_higher_better
else study_module.StudyDirection.MINIMIZE
)
if self.study.direction != _direction:
raise ValueError(
"direction of study must be '{}'.".format(direction)
)
return self.study
def fit(
self,
X, # type: TwoDimArrayLikeType
y=None, # type: Optional[Union[OneDimArrayLikeType, TwoDimArrayLikeType]]
groups=None, # type: Optional[OneDimArrayLikeType]
**fit_params # type: Any
):
# type: (...) -> 'OptunaSearchCV'
"""Run fit with all sets of parameters.
Args:
X:
Training data.
y:
Target variable.
groups:
Group labels for the samples used while splitting the dataset
into train/test set.
**fit_params:
Parameters passed to ``fit`` on the estimator.
Returns:
self:
Return self.
"""
self._check_params()
random_state = check_random_state(self.random_state)
max_samples = self.subsample
n_samples = _num_samples(X)
old_level = _logger.getEffectiveLevel()
if self.verbose > 1:
_logger.setLevel(DEBUG)
elif self.verbose > 0:
_logger.setLevel(INFO)
else:
_logger.setLevel(WARNING)
self.sample_indices_ = np.arange(n_samples)
if type(max_samples) is float:
max_samples = int(max_samples * n_samples)
if max_samples < n_samples:
self.sample_indices_ = random_state.choice(self.sample_indices_,
max_samples,
replace=False)
self.sample_indices_.sort()
X_res = _safe_indexing(X, self.sample_indices_)
y_res = _safe_indexing(y, self.sample_indices_)
groups_res = _safe_indexing(groups, self.sample_indices_)
fit_params_res = fit_params
if fit_params_res is not None:
fit_params_res = _check_fit_params(X, fit_params,
self.sample_indices_)
classifier = is_classifier(self.estimator)
cv = check_cv(self.cv, y_res, classifier)
self.n_splits_ = cv.get_n_splits(X_res, y_res, groups=groups_res)
self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
if self.study is None:
seed = random_state.randint(0, np.iinfo("int32").max)
sampler = samplers.TPESampler(seed=seed)
self.study_ = study_module.create_study(direction="maximize",
sampler=sampler)
else:
self.study_ = self.study
objective = _Objective(
self.estimator,
self.param_distributions,
X_res,
y_res,
cv,
self.enable_pruning,
self.error_score,
fit_params_res,
groups_res,
self.max_iter,
self.return_train_score,
self.scorer_,
)
_logger.info("Searching the best hyperparameters using {} "
"samples...".format(_num_samples(self.sample_indices_)))
self.study_.optimize(objective,
n_jobs=self.n_jobs,
n_trials=self.n_trials,
timeout=self.timeout)
_logger.info("Finished hyperparemeter search!")
if self.refit:
self._refit(X, y, **fit_params)
_logger.setLevel(old_level)
return self
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_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))
with patch.object(
optuna.distributions,
"_get_single_value",
wraps=optuna.distributions._get_single_value) as mock_object:
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
