from typing import List
from optuna._experimental import experimental
from optuna.distributions import CategoricalDistribution
from optuna.distributions import FloatDistribution
from optuna.distributions import IntDistribution
from optuna.trial import FrozenTrial
from optuna.visualization.matplotlib import _matplotlib_imports
__all__ = ["is_available"]
[docs]@experimental("2.2.0")
def is_available() -> bool:
"""Returns whether visualization with Matplotlib is available or not.
.. note::
:mod:`~optuna.visualization.matplotlib` module depends on Matplotlib version 3.0.0 or
higher. If a supported version of Matplotlib isn't installed in your environment, this
function will return :obj:`False`. In such a case, please execute ``$ pip install -U
matplotlib>=3.0.0`` to install Matplotlib.
Returns:
:obj:`True` if visualization with Matplotlib is available, :obj:`False` otherwise.
"""
return _matplotlib_imports._imports.is_successful()
def _is_log_scale(trials: List[FrozenTrial], param: str) -> bool:
for trial in trials:
if param in trial.params:
dist = trial.distributions[param]
if isinstance(dist, (FloatDistribution, IntDistribution)):
if dist.log:
return True
return False
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
)
def _is_numerical(trials: List[FrozenTrial], param: str) -> bool:
return all(
(isinstance(t.params[param], int) or isinstance(t.params[param], float))
and not isinstance(t.params[param], bool)
for t in trials
if param in t.params
)