import scipy as sp
from scipy.sparse import spmatrix
from optuna import distributions
from optuna import logging
from optuna import samplers
from optuna import study as study_module
from optuna import TrialPruned
from optuna._experimental import experimental
from optuna._imports import try_import
from optuna.study import StudyDirection
from optuna.trial import FrozenTrial
from optuna.trial import Trial
with try_import() as _imports:
import pandas as pd
import sklearn
from sklearn.base import BaseEstimator
from sklearn.base import clone
from sklearn.base import is_classifier
from sklearn.metrics import check_scoring
from sklearn.model_selection import BaseCrossValidator
from sklearn.model_selection import check_cv
from sklearn.model_selection import cross_validate
from sklearn.utils import check_random_state
from sklearn.utils.metaestimators import _safe_split
if sklearn.__version__ >= "0.22":
from sklearn.utils import _safe_indexing as sklearn_safe_indexing
else:
if type_checking.TYPE_CHECKING:
from typing import Any # NOQA
from typing import Callable
from typing import Dict # NOQA
from typing import List # NOQA
from typing import Optional # NOQA
from typing import Set # NOQA
from typing import Tuple # NOQA
from typing import Type # NOQA
from typing import Union # NOQA
from optuna.distributions import BaseDistribution # NOQA
ObjectiveFuncType = Callable[[trial_module.Trial], float]
with try_import() as _pandas_imports:
# `trials_dataframe` is disabled if pandas is not available.
import pandas as pd # NOQA
_logger = logging.get_logger(__name__)
class BaseStudy(object):
def __init__(self, study_id, storage):
# type: (int, storages.BaseStorage) -> None
self._study_id = study_id
self._storage = storage
@property
def best_params(self):
