def test_plot_intermediate_values(
plotter: Callable[[_IntermediatePlotInfo], Any], info: _IntermediatePlotInfo
) -> None:
figure = plotter(info)
if isinstance(figure, go.Figure):
figure.write_image(BytesIO())
else:
plt.savefig(BytesIO())
def test_plot_param_importances(
plot_param_importances: Callable[..., Any],
specific_create_study: Callable[[], Study],
params: Optional[List[str]],
) -> None:
study = specific_create_study()
figure = plot_param_importances(study, params=params)
if isinstance(figure, go.Figure):
figure.write_image(BytesIO())
else:
plt.savefig(BytesIO())
def test_get_optimization_history_plot(
target_name: str, info_list: List[_OptimizationHistoryInfo]) -> None:
figure = _get_optimization_history_plot(info_list, target_name=target_name)
assert figure.get_ylabel() == target_name
expected_legends = []
for info in info_list:
expected_legends.append(info.values_info.label_name)
if info.best_values_info is not None:
expected_legends.append(info.best_values_info.label_name)
legends = [legend.get_text() for legend in figure.legend().get_texts()]
assert sorted(legends) == sorted(expected_legends)
plt.savefig(BytesIO())
def test_get_pareto_front_plot(
plotter: Callable[[_ParetoFrontInfo], Any],
info_template: _ParetoFrontInfo,
include_dominated_trials: bool,
has_constraints_func: bool,
) -> None:
info = info_template
if not include_dominated_trials:
info = info._replace(include_dominated_trials=False, non_best_trials_with_values=[])
if not has_constraints_func:
info = info._replace(has_constraints_func=False, infeasible_trials_with_values=[])
figure = plotter(info)
if isinstance(figure, go.Figure):
figure.write_image(BytesIO())
else:
plt.savefig(BytesIO())
def save_static_image(figure: Union[go.Figure, Axes, np.ndarray]) -> None:
if isinstance(figure, go.Figure):
figure.write_image(BytesIO())
else:
plt.savefig(BytesIO())