def test_get_float_value() -> None:
assert 1.0 == ChainerPruningExtension._get_float_value(1.0)
assert 1.0 == ChainerPruningExtension._get_float_value(chainer.Variable(np.array([1.0])))
assert math.isnan(ChainerPruningExtension._get_float_value(float("nan")))
with pytest.raises(TypeError):
ChainerPruningExtension._get_float_value([]) # type: ignore
def test_chainer_pruning_extension_trigger():
# type: () -> None
study = optuna.create_study()
trial = create_running_trial(study, 1.0)
extension = ChainerPruningExtension(trial, 'main/loss', (1, 'epoch'))
assert isinstance(extension.pruner_trigger, triggers.IntervalTrigger)
extension = ChainerPruningExtension(trial, 'main/loss', triggers.IntervalTrigger(1, 'epoch'))
assert isinstance(extension.pruner_trigger, triggers.IntervalTrigger)
extension = ChainerPruningExtension(trial, 'main/loss',
triggers.ManualScheduleTrigger(1, 'epoch'))
assert isinstance(extension.pruner_trigger, triggers.ManualScheduleTrigger)
with pytest.raises(TypeError):
ChainerPruningExtension(trial, 'main/loss', triggers.TimeTrigger(1.))
def test_chainer_pruning_extension_trigger() -> None:
study = optuna.create_study()
trial = study.ask()
extension = ChainerPruningExtension(trial, "main/loss", (1, "epoch"))
assert isinstance(extension._pruner_trigger, triggers.IntervalTrigger)
extension = ChainerPruningExtension(
trial, "main/loss", triggers.IntervalTrigger(1, "epoch") # type: ignore
)
assert isinstance(extension._pruner_trigger, triggers.IntervalTrigger)
extension = ChainerPruningExtension(
trial, "main/loss", triggers.ManualScheduleTrigger(1, "epoch") # type: ignore
)
assert isinstance(extension._pruner_trigger, triggers.ManualScheduleTrigger)
with pytest.raises(TypeError):
ChainerPruningExtension(trial, "main/loss", triggers.TimeTrigger(1.0)) # type: ignore
def test_chainer_pruning_extension_observation_isnan():
# type: () -> None
study = optuna.create_study()
trial = study._run_trial(func=lambda _: 1.0, catch=(Exception, ))
extension = ChainerPruningExtension(trial, 'main/loss', (1, 'epoch'))
MockTrainer = namedtuple('_MockTrainer', ('observation', ))
trainer = MockTrainer(observation={'main/loss': float('nan')})
with patch.object(extension, '_observation_exists',
Mock(return_value=True)) as mock:
extension(trainer)
assert mock.call_count == 1
def test_chainer_pruning_extension_observation_nan() -> None:
study = optuna.create_study(pruner=DeterministicPruner(True))
trial = study.ask()
extension = ChainerPruningExtension(trial, "main/loss", (1, "epoch"))
MockTrainer = namedtuple("MockTrainer", ("observation", "updater"))
MockUpdater = namedtuple("MockUpdater", ("epoch"))
trainer = MockTrainer(observation={"main/loss": float("nan")}, updater=MockUpdater(1))
with patch.object(extension, "_observation_exists", Mock(return_value=True)) as mock:
with pytest.raises(optuna.TrialPruned):
extension(trainer) # type: ignore
assert mock.call_count == 1
def test_chainer_pruning_extension_observation_nan():
# type: () -> None
study = optuna.create_study(pruner=DeterministicPruner(True))
trial = create_running_trial(study, 1.0)
extension = ChainerPruningExtension(trial, 'main/loss', (1, 'epoch'))
MockTrainer = namedtuple('_MockTrainer', ('observation', 'updater'))
MockUpdater = namedtuple('_MockUpdater', ('epoch'))
trainer = MockTrainer(observation={'main/loss': float('nan')}, updater=MockUpdater(1))
with patch.object(extension, '_observation_exists', Mock(return_value=True)) as mock:
with pytest.raises(TrialPruned):
extension(trainer)
assert mock.call_count == 1
def test_observation_exists():
# type: () -> None
study = optuna.create_study()
trial = create_running_trial(study, 1.0)
MockTrainer = namedtuple('_MockTrainer', ('observation', ))
trainer = MockTrainer(observation={'OK': 0})
# Trigger is deactivated. Return False whether trainer has observation or not.
with patch.object(triggers.IntervalTrigger, '__call__', Mock(return_value=False)) as mock:
extension = ChainerPruningExtension(trial, 'NG', (1, 'epoch'))
assert extension._observation_exists(trainer) is False
extension = ChainerPruningExtension(trial, 'OK', (1, 'epoch'))
assert extension._observation_exists(trainer) is False
assert mock.call_count == 2
# Trigger is activated. Return True if trainer has observation.
with patch.object(triggers.IntervalTrigger, '__call__', Mock(return_value=True)) as mock:
extension = ChainerPruningExtension(trial, 'NG', (1, 'epoch'))
assert extension._observation_exists(trainer) is False
extension = ChainerPruningExtension(trial, 'OK', (1, 'epoch'))
assert extension._observation_exists(trainer) is True
assert mock.call_count == 2
def test_observation_exists() -> None:
study = optuna.create_study()
trial = study.ask()
MockTrainer = namedtuple("MockTrainer", ("observation",))
trainer = MockTrainer(observation={"OK": 0})
# Trigger is deactivated. Return False whether trainer has observation or not.
with patch.object(triggers.IntervalTrigger, "__call__", Mock(return_value=False)) as mock:
extension = ChainerPruningExtension(trial, "NG", (1, "epoch"))
assert extension._observation_exists(trainer) is False # type: ignore
extension = ChainerPruningExtension(trial, "OK", (1, "epoch"))
assert extension._observation_exists(trainer) is False # type: ignore
assert mock.call_count == 2
# Trigger is activated. Return True if trainer has observation.
with patch.object(triggers.IntervalTrigger, "__call__", Mock(return_value=True)) as mock:
extension = ChainerPruningExtension(trial, "NG", (1, "epoch"))
assert extension._observation_exists(trainer) is False # type: ignore
extension = ChainerPruningExtension(trial, "OK", (1, "epoch"))
assert extension._observation_exists(trainer) is True # type: ignore
assert mock.call_count == 2
def test_get_float_value() -> None:
assert 1.0 == ChainerPruningExtension._get_float_value(1.0)
assert 1.0 == ChainerPruningExtension._get_float_value(chainer.Variable(np.array([1.0])))
assert math.isnan(ChainerPruningExtension._get_float_value(float("nan")))