def test_target_is_none_and_study_is_multi_obj() -> None:
study = create_study(directions=["minimize", "minimize"])
with pytest.raises(ValueError):
_get_optimization_history_info_list(study,
target=None,
target_name="Objective Value",
error_bar=False)
def test_info_with_no_trials(direction: str, error_bar: bool) -> None:
# Single study.
study = create_study(direction=direction)
info_list = _get_optimization_history_info_list(
study, target=None, target_name="Objective Value", error_bar=error_bar)
assert info_list == []
# Multiple studies.
studies = [create_study(direction=direction) for _ in range(10)]
info_list = _get_optimization_history_info_list(
studies,
target=None,
target_name="Objective Value",
error_bar=error_bar)
assert info_list == []
def test_warn_default_target_name_with_customized_target(
direction: str, error_bar: bool) -> None:
# Single study.
study = create_study(direction=direction)
with pytest.warns(UserWarning):
_get_optimization_history_info_list(study,
target=lambda t: t.number,
target_name="Objective Value",
error_bar=error_bar)
# Multiple studies.
studies = [create_study(direction=direction) for _ in range(10)]
with pytest.warns(UserWarning):
_get_optimization_history_info_list(studies,
target=lambda t: t.number,
target_name="Objective Value",
error_bar=error_bar)
def test_get_optimization_history_info_list_with_error_bar(
direction: str, target_name: str) -> None:
n_studies = 10
def objective(trial: Trial) -> float:
if trial.number == 0:
return 1.0
elif trial.number == 1:
return 2.0
elif trial.number == 2:
return 0.0
return 0.0
# Test with trials.
studies = [create_study(direction=direction) for _ in range(n_studies)]
for study in studies:
study.optimize(objective, n_trials=3)
info_list = _get_optimization_history_info_list(study,
target=None,
target_name=target_name,
error_bar=True)
best_values = [1.0, 1.0, 0.0
] if direction == "minimize" else [1.0, 2.0, 2.0]
assert info_list == [
_OptimizationHistoryInfo(
[0, 1, 2],
_ValuesInfo([1.0, 2.0, 0.0], [0.0, 0.0, 0.0], target_name),
_ValuesInfo(best_values, [0.0, 0.0, 0.0], "Best Value"),
)
]
# Test customized target.
info_list = _get_optimization_history_info_list(study,
target=lambda t: t.number,
target_name=target_name,
error_bar=True)
assert info_list == [
_OptimizationHistoryInfo(
[0, 1, 2],
_ValuesInfo([0.0, 1.0, 2.0], [0.0, 0.0, 0.0], target_name),
None,
)
]
def test_ignore_failed_trials(direction: str, error_bar: bool) -> None:
# Single study.
study = create_study(direction=direction)
study.optimize(fail_objective, n_trials=1, catch=(ValueError, ))
info_list = _get_optimization_history_info_list(
study, target=None, target_name="Objective Value", error_bar=error_bar)
assert info_list == []
# Multiple studies.
studies = [create_study(direction=direction) for _ in range(10)]
for study in studies:
study.optimize(fail_objective, n_trials=1, catch=(ValueError, ))
info_list = _get_optimization_history_info_list(
studies,
target=None,
target_name="Objective Value",
error_bar=error_bar)
assert info_list == []
def test_get_optimization_history_info_list_with_multiple_studies(
direction: str, target_name: str) -> None:
n_studies = 10
base_values = [1.0, 2.0, 0.0]
base_best_values = [1.0, 1.0, 0.0
] if direction == "minimize" else [1.0, 2.0, 2.0]
# Test with trials.
studies = [create_study(direction=direction) for _ in range(n_studies)]
for i, study in enumerate(studies):
study.optimize(lambda t: base_values[t.number] + i, n_trials=3)
info_list = _get_optimization_history_info_list(studies,
target=None,
target_name=target_name,
error_bar=False)
for i, info in enumerate(info_list):
values_i = [v + i for v in base_values]
best_values_i = [v + i for v in base_best_values]
assert info == _OptimizationHistoryInfo(
[0, 1, 2],
_ValuesInfo(values_i, None,
f"{target_name} of {studies[i].study_name}"),
_ValuesInfo(best_values_i, None,
f"Best Value of {studies[i].study_name}"),
)
# Test customized target.
info_list = _get_optimization_history_info_list(studies,
target=lambda t: t.number,
target_name=target_name,
error_bar=False)
for i, info in enumerate(info_list):
assert info == _OptimizationHistoryInfo(
[0, 1, 2],
_ValuesInfo([0.0, 1.0, 2.0], None,
f"{target_name} of {studies[i].study_name}"),
None,
)
def test_error_bar_in_optimization_history(direction: str) -> None:
def objective(trial: Trial) -> float:
return trial.suggest_float("x", 0, 1)
studies = [create_study(direction=direction) for _ in range(3)]
suggested_params = [0.1, 0.3, 0.2]
for x, study in zip(suggested_params, studies):
study.enqueue_trial({"x": x})
study.optimize(objective, n_trials=1)
info_list = _get_optimization_history_info_list(
studies, target=None, target_name="Objective Value", error_bar=True)
mean = np.mean(suggested_params).item()
std = np.std(suggested_params).item()
assert info_list == [
_OptimizationHistoryInfo(
[0],
_ValuesInfo([mean], [std], "Objective Value"),
_ValuesInfo([mean], [std], "Best Value"),
)
]
def plot_optimization_history(
study: Union[Study, Sequence[Study]],
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
error_bar: bool = False,
) -> "Axes":
"""Plot optimization history of all trials in a study with Matplotlib.
.. seealso::
Please refer to :func:`optuna.visualization.plot_optimization_history` for an example.
Example:
The following code snippet shows how to plot optimization history.
.. plot::
import optuna
import matplotlib.pyplot as plt
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x ** 2 + y
sampler = optuna.samplers.TPESampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=10)
optuna.visualization.matplotlib.plot_optimization_history(study)
plt.tight_layout()
.. note::
You need to adjust the size of the plot by yourself using ``plt.tight_layout()`` or
``plt.savefig(IMAGE_NAME, bbox_inches='tight')``.
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their target values.
You can pass multiple studies if you want to compare those optimization histories.
target:
A function to specify the value to display. If it is :obj:`None` and ``study`` is being
used for single-objective optimization, the objective values are plotted.
.. note::
Specify this argument if ``study`` is being used for multi-objective optimization.
target_name:
Target's name to display on the axis label and the legend.
error_bar:
A flag to show the error bar.
Returns:
A :class:`matplotlib.axes.Axes` object.
"""
_imports.check()
info_list = _get_optimization_history_info_list(study, target, target_name,
error_bar)
return _get_optimization_history_plot(info_list, target_name)