def test_distribute_first_points_over_learners(strategy):
for initial_points in [0, 3]:
learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]
learner = BalancingLearner(learners, strategy=strategy)
points = learner.ask(initial_points)[0]
learner.tell_many(points, points)
points, _ = learner.ask(100)
i_learner, xs = zip(*points)
# assert that are all learners in the suggested points
assert len(set(i_learner)) == len(learners)
def test_balancing_learner(learner_type, f, learner_kwargs):
"""Test if the BalancingLearner works with the different types of learners."""
learners = [
learner_type(generate_random_parametrization(f), **learner_kwargs)
for i in range(4)
]
learner = BalancingLearner(learners)
# Emulate parallel execution
stash = []
for i in range(100):
n = random.randint(1, 10)
m = random.randint(0, n)
xs, _ = learner.ask(n, tell_pending=False)
# Save 'm' random points out of `xs` for later
random.shuffle(xs)
for _ in range(m):
stash.append(xs.pop())
for x in xs:
learner.tell(x, learner.function(x))
# Evaluate and add 'm' random points from `stash`
random.shuffle(stash)
for _ in range(m):
x = stash.pop()
learner.tell(x, learner.function(x))
assert all(l.npoints > 10 for l in learner.learners), [
l.npoints for l in learner.learners
]
def test_ask_0(strategy):
learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]
learner = BalancingLearner(learners, strategy=strategy)
points, _ = learner.ask(0)
assert len(points) == 0