def test_study_system_attr_for_population_cache() -> None:
sampler = multi_objective.samplers.NSGAIIMultiObjectiveSampler(
population_size=10)
study = multi_objective.create_study(["minimize"], sampler=sampler)
def get_cached_entries(
study: multi_objective.study.MultiObjectiveStudy,
) -> List[Tuple[int, List[int]]]:
return [
v for k, v in study.system_attrs.items() if k.startswith(
multi_objective.samplers._nsga2._POPULATION_CACHE_KEY_PREFIX)
]
study.optimize(lambda t: [t.suggest_float("x", 0, 9)], n_trials=10)
cached_entries = get_cached_entries(study)
assert len(cached_entries) == 0
study.optimize(lambda t: [t.suggest_float("x", 0, 9)], n_trials=1)
cached_entries = get_cached_entries(study)
assert len(cached_entries) == 1
assert cached_entries[0][0] == 0 # Cached generation.
assert len(cached_entries[0][1]) == 10 # Population size.
study.optimize(lambda t: [t.suggest_float("x", 0, 9)], n_trials=10)
cached_entries = get_cached_entries(study)
assert len(cached_entries) == 1
assert cached_entries[0][0] == 1 # Cached generation.
assert len(cached_entries[0][1]) == 10 # Population size.
def test_population_size() -> None:
# Set `population_size` to 10.
sampler = multi_objective.samplers.NSGAIIMultiObjectiveSampler(
population_size=10)
study = multi_objective.create_study(["minimize"], sampler=sampler)
study.optimize(lambda t: [t.suggest_uniform("x", 0, 9)], n_trials=40)
generations = Counter([
t.system_attrs[multi_objective.samplers._nsga2._GENERATION_KEY]
for t in study.trials
])
assert generations == {0: 10, 1: 10, 2: 10, 3: 10}
# Set `population_size` to 2.
sampler = multi_objective.samplers.NSGAIIMultiObjectiveSampler(
population_size=2)
study = multi_objective.create_study(["minimize"], sampler=sampler)
study.optimize(lambda t: [t.suggest_uniform("x", 0, 9)], n_trials=40)
generations = Counter([
t.system_attrs[multi_objective.samplers._nsga2._GENERATION_KEY]
for t in study.trials
])
assert generations == {i: 2 for i in range(20)}
# Invalid population size.
with pytest.raises(ValueError):
# Less than 2.
multi_objective.samplers.NSGAIIMultiObjectiveSampler(population_size=1)
with pytest.raises(TypeError):
# Not an integer.
multi_objective.samplers.NSGAIIMultiObjectiveSampler(
population_size=2.5) # type: ignore