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from __future__ import annotations
from typing import Sequence
import pytest
from optuna import create_study
from optuna import trial
from optuna.study import StudyDirection
from optuna.study._multi_objective import _get_pareto_front_trials_by_trials
from optuna.trial import FrozenTrial
def _trial_to_values(t: FrozenTrial) -> tuple[float, ...]:
assert t.values is not None
return tuple(t.values)
def assert_is_output_equal_to_ans(
trials: list[FrozenTrial], directions: Sequence[StudyDirection], ans: set[tuple[int]]
) -> None:
res = {_trial_to_values(t) for t in _get_pareto_front_trials_by_trials(trials, directions)}
assert res == ans
@pytest.mark.parametrize(
"directions,values_set,ans_set",
[
(
["minimize", "maximize"],
[[2, 2], [1, 1], [3, 1], [3, 2], [1, 3]],
[{(2, 2)}] + [{(1, 1), (2, 2)}] * 3 + [{(1, 3)}],
),
(
["minimize", "maximize", "minimize"],
[[2, 2, 2], [1, 1, 1], [3, 1, 3], [3, 2, 3], [1, 3, 1]],
[{(2, 2, 2)}] + [{(1, 1, 1), (2, 2, 2)}] * 3 + [{(1, 3, 1)}],
),
],
)
def test_get_pareto_front_trials(
directions: list[str], values_set: list[list[int]], ans_set: list[set[tuple[int]]]
) -> None:
study = create_study(directions=directions)
assert_is_output_equal_to_ans(study.trials, study.directions, set())
for values, ans in zip(values_set, ans_set):
study.optimize(lambda t: values, n_trials=1)
assert_is_output_equal_to_ans(study.trials, study.directions, ans)
assert len(_get_pareto_front_trials_by_trials(study.trials, study.directions)) == 1
# The trial result is the same as the last one.
study.optimize(lambda t: values_set[-1], n_trials=1)
assert_is_output_equal_to_ans(study.trials, study.directions, ans_set[-1])
assert len(_get_pareto_front_trials_by_trials(study.trials, study.directions)) == 2
@pytest.mark.parametrize(
"directions,values_set,ans_set",
[
(["minimize", "maximize"], [[1, 1], [2, 2], [3, 2]], [{(1, 1), (2, 2)}, {(1, 1), (3, 2)}]),
(
["minimize", "maximize", "minimize"],
[[1, 1, 1], [2, 2, 2], [3, 2, 3]],
[{(1, 1, 1), (2, 2, 2)}, {(1, 1, 1), (3, 2, 3)}],
),
],
)
def test_get_pareto_front_trials_with_constraint(
directions: list[str], values_set: list[list[int]], ans_set: list[set[tuple[int]]]
) -> None:
study = create_study(directions=directions)
trials = [
trial.create_trial(values=values, system_attrs={"constraints": [i % 2]})
for i, values in enumerate(values_set)
]
study.add_trials(trials)
for consider_constraint, ans in zip([False, True], ans_set):
trials = study.trials
study_dirs = study.directions
assert ans == {
_trial_to_values(t)
for t in _get_pareto_front_trials_by_trials(trials, study_dirs, consider_constraint)
}
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