from typing import Callable
from typing import List
from typing import Optional
from typing import Sequence
from typing import Union
import warnings
from optuna.distributions import CategoricalDistribution
from optuna.distributions import LogUniformDistribution
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.visualization import _plotly_imports
__all__ = ["is_available"]
[文档]def is_available() -> bool:
"""Returns whether visualization with plotly is available or not.
.. note::
:mod:`~optuna.visualization` module depends on plotly version 4.0.0 or higher. If a
supported version of plotly isn't installed in your environment, this function will return
:obj:`False`. In such case, please execute ``$ pip install -U plotly>=4.0.0`` to install
plotly.
Returns:
:obj:`True` if visualization with plotly is available, :obj:`False` otherwise.
"""
return _plotly_imports._imports.is_successful()
def _check_plot_args(
study: Union[Study, Sequence[Study]],
target: Optional[Callable[[FrozenTrial], float]],
target_name: str,
) -> None:
studies: Sequence[Study]
if isinstance(study, Study):
studies = [study]
else:
studies = study
if target is None and any(study._is_multi_objective() for study in studies):
raise ValueError(
"If the `study` is being used for multi-objective optimization, "
"please specify the `target`."
)
if target is not None and target_name == "Objective Value":
warnings.warn(
"`target` is specified, but `target_name` is the default value, 'Objective Value'."
)
def _is_log_scale(trials: List[FrozenTrial], param: str) -> bool:
return any(
isinstance(t.distributions[param], LogUniformDistribution)
for t in trials
if param in t.params
)
def _is_categorical(trials: List[FrozenTrial], param: str) -> bool:
return any(
isinstance(t.distributions[param], CategoricalDistribution)
for t in trials
if param in t.params
)