def test_noisy_artificial_function_loss() -> None:
func = ArtificialFunction(name="sphere", block_dimension=5, noise_level=.3)
seed = np.random.randint(99999)
xp = xpbase.Experiment(func, optimizer="OnePlusOne", budget=5, seed=seed)
xp.run()
loss_ref = xp.result["loss"]
# now with copy
reco = xp.recommendation
assert reco is not None
np.random.seed(seed)
pfunc = func.copy()
np.testing.assert_equal(pfunc.evaluation_function(*reco.args, **reco.kwargs), loss_ref)
np.random.seed(None)
def test_noisy_artificial_function_loss() -> None:
func = ArtificialFunction(name="sphere", block_dimension=5, noise_level=0.3)
seed = np.random.randint(99999)
xp = xpbase.Experiment(func, optimizer="OnePlusOne", budget=5, seed=seed)
# Because copy() can have different random initialization for the parameters
# The function should be copied early.
np.random.seed(seed)
pfunc = func.copy()
xp.run()
loss_ref = xp.result["loss"]
# now with copy
assert xp._optimizer is not None
reco = xp._optimizer.provide_recommendation()
assert reco is not None
np.testing.assert_equal(pfunc.evaluation_function(reco), loss_ref)
np.random.seed(None)
