def test_saving_of_balancing_learner(learner_type, f, learner_kwargs):
f = generate_random_parametrization(f)
learner = BalancingLearner([learner_type(f, **learner_kwargs)])
control = BalancingLearner([learner_type(f, **learner_kwargs)])
if learner_type is Learner1D:
for l, c in zip(learner.learners, control.learners):
l._recompute_losses_factor = 1
c._recompute_losses_factor = 1
simple(learner, lambda l: l.learners[0].npoints > 100)
folder = tempfile.mkdtemp()
def fname(learner):
return folder + "test"
try:
learner.save(fname=fname)
control.load(fname=fname)
np.testing.assert_almost_equal(learner.loss(), control.loss())
# Try if the control is runnable
simple(control, lambda l: l.learners[0].npoints > 200)
finally:
shutil.rmtree(folder)
def test_balancing_learner_loss_cache():
learner = Learner1D(lambda x: x, bounds=(-1, 1))
learner.tell(-1, -1)
learner.tell(1, 1)
learner.tell_pending(0)
real_loss = learner.loss(real=True)
pending_loss = learner.loss(real=False)
# Test if the real and pending loss are cached correctly
bl = BalancingLearner([learner])
assert bl.loss(real=True) == real_loss
assert bl.loss(real=False) == pending_loss
# Test if everything is still fine when executed in the reverse order
bl = BalancingLearner([learner])
assert bl.loss(real=False) == pending_loss
assert bl.loss(real=True) == real_loss