def test_fast_non_dominated_sort_constrained_feasible_infeasible() -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = NSGAIISampler(constraints_func=lambda _: [0])
# Single objective.
directions = [StudyDirection.MINIMIZE]
trials = [
_create_frozen_trial(0, [10], [0]),
_create_frozen_trial(1, [20], [-1]),
_create_frozen_trial(2, [20], [-2]),
_create_frozen_trial(3, [30], [1]),
]
population_per_rank = sampler._fast_non_dominated_sort(trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{0},
{1, 2},
{3},
]
# Two objective.
directions = [StudyDirection.MAXIMIZE, StudyDirection.MAXIMIZE]
trials = [
_create_frozen_trial(0, [10, 30], [-1, -1]),
_create_frozen_trial(1, [10, 10], [-2, -2]),
_create_frozen_trial(2, [20, 20], [3, 3]),
_create_frozen_trial(3, [30, 10], [6, -1]),
_create_frozen_trial(4, [15, 15], [4, 4]),
]
population_per_rank = sampler._fast_non_dominated_sort(trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{0},
{1},
{2, 3},
{4},
]
# Three objective.
directions = [
StudyDirection.MAXIMIZE, StudyDirection.MAXIMIZE,
StudyDirection.MINIMIZE
]
trials = [
_create_frozen_trial(0, [5, 5, 4], [-1, -1, -1]),
_create_frozen_trial(1, [5, 5, 5], [1, 1, -1]),
_create_frozen_trial(2, [9, 9, 0], [1, -1, -1]),
_create_frozen_trial(3, [5, 7, 5], [-1, -1, -1]),
_create_frozen_trial(4, [0, 0, 9], [-1, -1, -1]),
_create_frozen_trial(5, [0, 9, 9], [-1, -1, -1]),
]
population_per_rank = sampler._fast_non_dominated_sort(trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{0, 3, 5},
{4},
{2},
{1},
]
def test_fast_non_dominated_sort() -> None:
sampler = NSGAIISampler()
# Single objective.
directions = [StudyDirection.MINIMIZE]
trials = [
_create_frozen_trial(0, [10]),
_create_frozen_trial(1, [20]),
_create_frozen_trial(2, [20]),
_create_frozen_trial(3, [30]),
]
population_per_rank = sampler._fast_non_dominated_sort(trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{0},
{1, 2},
{3},
]
# Two objective.
directions = [StudyDirection.MAXIMIZE, StudyDirection.MAXIMIZE]
trials = [
_create_frozen_trial(0, [10, 30]),
_create_frozen_trial(1, [10, 10]),
_create_frozen_trial(2, [20, 20]),
_create_frozen_trial(3, [30, 10]),
_create_frozen_trial(4, [15, 15]),
]
population_per_rank = sampler._fast_non_dominated_sort(trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{0, 2, 3},
{4},
{1},
]
# Three objective.
directions = [
StudyDirection.MAXIMIZE, StudyDirection.MAXIMIZE,
StudyDirection.MINIMIZE
]
trials = [
_create_frozen_trial(0, [5, 5, 4]),
_create_frozen_trial(1, [5, 5, 5]),
_create_frozen_trial(2, [9, 9, 0]),
_create_frozen_trial(3, [5, 7, 5]),
_create_frozen_trial(4, [0, 0, 9]),
_create_frozen_trial(5, [0, 9, 9]),
]
population_per_rank = sampler._fast_non_dominated_sort(trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{2},
{0, 3, 5},
{1},
{4},
]
def test_fast_non_dominated_sort_empty(n_dims: int) -> None:
for directions in itertools.product(
[StudyDirection.MINIMIZE, StudyDirection.MAXIMIZE], repeat=n_dims):
trials: List[FrozenTrial] = []
sampler = NSGAIISampler()
population_per_rank = sampler._fast_non_dominated_sort(
trials, list(directions))
assert population_per_rank == []
def test_fast_non_dominated_sort_missing_constraint_values() -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = NSGAIISampler(constraints_func=lambda _: [0])
# Single objective.
directions = [StudyDirection.MINIMIZE]
trials = [
_create_frozen_trial(0, [10]),
_create_frozen_trial(1, [20]),
_create_frozen_trial(2, [20], [0]),
_create_frozen_trial(3, [20], [1]),
_create_frozen_trial(4, [30], [-1]),
]
with pytest.warns(UserWarning):
population_per_rank = sampler._fast_non_dominated_sort(
trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{2},
{4},
{3},
{0},
{1},
]
# Two objectives.
directions = [StudyDirection.MAXIMIZE, StudyDirection.MAXIMIZE]
trials = [
_create_frozen_trial(0, [50, 30]),
_create_frozen_trial(1, [30, 50]),
_create_frozen_trial(2, [20, 20], [3, 3]),
_create_frozen_trial(3, [30, 10], [0, -1]),
_create_frozen_trial(4, [15, 15], [4, 4]),
]
with pytest.warns(UserWarning):
population_per_rank = sampler._fast_non_dominated_sort(
trials, directions)
assert [{t.number
for t in population} for population in population_per_rank] == [
{3},
{2},
{4},
{0, 1},
]
def test_fast_non_dominated_sort_no_constraints(
direction1: StudyDirection, direction2: StudyDirection) -> None:
sampler = NSGAIISampler()
directions = [direction1, direction2]
value_list = [10, 20, 20, 30, float("inf"), float("inf"), -float("inf")]
values = [[v1, v2] for v1 in value_list for v2 in value_list]
trials = [_create_frozen_trial(i, v) for i, v in enumerate(values)]
population_per_rank = sampler._fast_non_dominated_sort(
copy.copy(trials), directions)
_assert_population_per_rank(trials, directions, population_per_rank)
def test_fast_non_dominated_sort_missing_constraint_values(
values_and_constraints: List[Tuple[List[float], List[float]]]) -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = NSGAIISampler(constraints_func=lambda _: [0])
values_dim = len(values_and_constraints[0][0])
for directions in itertools.product(
[StudyDirection.MINIMIZE, StudyDirection.MAXIMIZE], repeat=values_dim):
trials = [
_create_frozen_trial(i, v, c)
for i, (v, c) in enumerate(values_and_constraints)
]
with pytest.warns(UserWarning):
population_per_rank = sampler._fast_non_dominated_sort(
copy.copy(trials), list(directions))
_assert_population_per_rank(trials, list(directions),
population_per_rank)
def test_fast_non_dominated_sort_with_constraints() -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = NSGAIISampler(constraints_func=lambda _: [0])
value_list = [10, 20, 20, 30, float("inf"), float("inf"), -float("inf")]
values = [[v1, v2] for v1 in value_list for v2 in value_list]
constraint_list = [-float("inf"), -2, 0, 1, 2, 3, float("inf")]
constraints = [[c1, c2] for c1 in constraint_list
for c2 in constraint_list]
trials = [
_create_frozen_trial(i, v, c)
for i, (v, c) in enumerate(itertools.product(values, constraints))
]
directions = [StudyDirection.MINIMIZE, StudyDirection.MAXIMIZE]
population_per_rank = sampler._fast_non_dominated_sort(
copy.copy(trials), directions)
_assert_population_per_rank(trials, directions, population_per_rank)
