def test_plot_pareto_front_2d() -> None:
# Test with no trial.
study = optuna.multi_objective.create_study(["minimize", "minimize"])
figure = plot_pareto_front(study)
assert len(figure.data) == 1
assert figure.data[0]["x"] == ()
assert figure.data[0]["y"] == ()
# Test with three trials.
study.enqueue_trial({"x": 1, "y": 1})
study.enqueue_trial({"x": 1, "y": 0})
study.enqueue_trial({"x": 0, "y": 1})
study.optimize(lambda t: [t.suggest_int("x", 0, 1), t.suggest_int("y", 0, 1)], n_trials=3)
figure = plot_pareto_front(study)
assert len(figure.data) == 1
assert figure.data[0]["x"] == (1, 0)
assert figure.data[0]["y"] == (0, 1)
assert figure.layout.xaxis.title.text == "Objective 0"
assert figure.layout.yaxis.title.text == "Objective 1"
# Test with `names` argument.
with pytest.raises(ValueError):
plot_pareto_front(study, names=[])
with pytest.raises(ValueError):
plot_pareto_front(study, names=["Foo"])
with pytest.raises(ValueError):
plot_pareto_front(study, names=["Foo", "Bar", "Baz"])
figure = plot_pareto_front(study, names=["Foo", "Bar"])
assert figure.layout.xaxis.title.text == "Foo"
assert figure.layout.yaxis.title.text == "Bar"
def test_plot_pareto_front_unsupported_dimensions(
include_dominated_trials: bool) -> None:
# Unsupported: n_objectives == 1.
with pytest.raises(ValueError):
study = optuna.multi_objective.create_study(["minimize"])
study.optimize(lambda t: [0], n_trials=1)
plot_pareto_front(study,
include_dominated_trials=include_dominated_trials)
# Supported: n_objectives == 2.
study = optuna.multi_objective.create_study(["minimize", "minimize"])
study.optimize(lambda t: [0, 0], n_trials=1)
plot_pareto_front(study, include_dominated_trials=include_dominated_trials)
# Supported: n_objectives == 3.
study = optuna.multi_objective.create_study(
["minimize", "minimize", "minimize"])
study.optimize(lambda t: [0, 0, 0], n_trials=1)
plot_pareto_front(study, include_dominated_trials=include_dominated_trials)
# Unsupported: n_objectives == 4.
with pytest.raises(ValueError):
study = optuna.multi_objective.create_study(
["minimize", "minimize", "minimize", "minimize"])
study.optimize(lambda t: [0, 0, 0, 0], n_trials=1)
plot_pareto_front(study,
include_dominated_trials=include_dominated_trials)
def test_plot_pareto_front_2d(include_dominated_trials: bool) -> None:
# Test with no trial.
study = optuna.multi_objective.create_study(["minimize", "minimize"])
figure = plot_pareto_front(study, include_dominated_trials=include_dominated_trials)
assert len(figure.data) == 1
assert figure.data[0]["x"] == ()
assert figure.data[0]["y"] == ()
# Test with three trials.
study.enqueue_trial({"x": 1, "y": 1})
study.enqueue_trial({"x": 1, "y": 0})
study.enqueue_trial({"x": 0, "y": 1})
study.optimize(lambda t: [t.suggest_int("x", 0, 1), t.suggest_int("y", 0, 1)], n_trials=3)
figure = plot_pareto_front(study, include_dominated_trials=include_dominated_trials)
assert len(figure.data) == 1
if include_dominated_trials:
# The last elements come from dominated trial that is enqueued firstly.
assert figure.data[0]["x"] == (1, 0, 1)
assert figure.data[0]["y"] == (0, 1, 1)
else:
assert figure.data[0]["x"] == (1, 0)
assert figure.data[0]["y"] == (0, 1)
assert figure.layout.xaxis.title.text == "Objective 0"
assert figure.layout.yaxis.title.text == "Objective 1"
# Test with `names` argument.
with pytest.raises(ValueError):
plot_pareto_front(study, names=[], include_dominated_trials=include_dominated_trials)
with pytest.raises(ValueError):
plot_pareto_front(study, names=["Foo"], include_dominated_trials=include_dominated_trials)
with pytest.raises(ValueError):
plot_pareto_front(
study,
names=["Foo", "Bar", "Baz"],
include_dominated_trials=include_dominated_trials,
)
figure = plot_pareto_front(
study,
names=["Foo", "Bar"],
include_dominated_trials=include_dominated_trials,
)
assert figure.layout.xaxis.title.text == "Foo"
assert figure.layout.yaxis.title.text == "Bar"
def test_plot_pareto_front_invalid_axis_order(
dimension: int, include_dominated_trials: bool
) -> None:
study = optuna.multi_objective.create_study(["minimize"] * dimension)
# Invalid: len(axis_order) != dimension
with pytest.raises(ValueError):
invalid_axis_order = list(range(dimension + 1))
assert len(invalid_axis_order) != dimension
plot_pareto_front(
study,
include_dominated_trials=include_dominated_trials,
axis_order=invalid_axis_order,
)
# Invalid: np.unique(axis_order).size != dimension
with pytest.raises(ValueError):
invalid_axis_order = list(range(dimension))
invalid_axis_order[1] = invalid_axis_order[0]
assert np.unique(invalid_axis_order).size != dimension
plot_pareto_front(
study,
include_dominated_trials=include_dominated_trials,
axis_order=invalid_axis_order,
)
# Invalid: max(axis_order) > (dimension - 1)
with pytest.raises(ValueError):
invalid_axis_order = list(range(dimension))
invalid_axis_order[-1] += 1
assert max(invalid_axis_order) > (dimension - 1)
plot_pareto_front(
study,
include_dominated_trials=include_dominated_trials,
axis_order=invalid_axis_order,
)
# Invalid: min(axis_order) < 0
with pytest.raises(ValueError):
study = optuna.multi_objective.create_study(["minimize", "minimize"])
invalid_axis_order = list(range(dimension))
invalid_axis_order[0] -= 1
assert min(invalid_axis_order) < 0
plot_pareto_front(
study,
include_dominated_trials=include_dominated_trials,
axis_order=invalid_axis_order,
)
def test_plot_pareto_front_2d(
include_dominated_trials: bool, axis_order: Optional[List[int]]
) -> None:
# Test with no trial.
study = optuna.multi_objective.create_study(["minimize", "minimize"])
figure = plot_pareto_front(
study,
include_dominated_trials=include_dominated_trials,
axis_order=axis_order,
)
assert len(figure.data) == 1
assert figure.data[0]["x"] == ()
assert figure.data[0]["y"] == ()
# Test with three trials.
study.enqueue_trial({"x": 1, "y": 1})
study.enqueue_trial({"x": 1, "y": 0})
study.enqueue_trial({"x": 0, "y": 1})
study.optimize(lambda t: [t.suggest_int("x", 0, 1), t.suggest_int("y", 0, 1)], n_trials=3)
figure = plot_pareto_front(
study,
include_dominated_trials=include_dominated_trials,
axis_order=axis_order,
)
assert len(figure.data) == 1
data: List[Tuple[int, ...]]
if include_dominated_trials:
# The last elements come from dominated trial that is enqueued firstly.
data = [(1, 0, 1), (0, 1, 1)]
if axis_order is None:
assert figure.data[0]["x"] == data[0]
assert figure.data[0]["y"] == data[1]
else:
assert figure.data[0]["x"] == data[axis_order[0]]
assert figure.data[0]["y"] == data[axis_order[1]]
else:
data = [(1, 0), (0, 1)]
if axis_order is None:
assert figure.data[0]["x"] == data[0]
assert figure.data[0]["y"] == data[1]
else:
assert figure.data[0]["x"] == data[axis_order[0]]
assert figure.data[0]["y"] == data[axis_order[1]]
titles = ["Objective {}".format(i) for i in range(2)]
if axis_order is None:
assert figure.layout.xaxis.title.text == titles[0]
assert figure.layout.yaxis.title.text == titles[1]
else:
assert figure.layout.xaxis.title.text == titles[axis_order[0]]
assert figure.layout.yaxis.title.text == titles[axis_order[1]]
# Test with `names` argument.
with pytest.raises(ValueError):
plot_pareto_front(study, names=[], include_dominated_trials=include_dominated_trials)
with pytest.raises(ValueError):
plot_pareto_front(study, names=["Foo"], include_dominated_trials=include_dominated_trials)
with pytest.raises(ValueError):
plot_pareto_front(
study,
names=["Foo", "Bar", "Baz"],
include_dominated_trials=include_dominated_trials,
axis_order=axis_order,
)
names = ["Foo", "Bar"]
figure = plot_pareto_front(
study,
names=names,
include_dominated_trials=include_dominated_trials,
axis_order=axis_order,
)
if axis_order is None:
assert figure.layout.xaxis.title.text == names[0]
assert figure.layout.yaxis.title.text == names[1]
else:
assert figure.layout.xaxis.title.text == names[axis_order[0]]
assert figure.layout.yaxis.title.text == names[axis_order[1]]
