def test_only_returns_new_points():
learner = AverageLearner(lambda x: x, atol=None, rtol=0.01)
# Only tell it n = 5...10
for i in range(5, 10):
learner.tell(i, 1)
learner.tell_pending(0) # This means it shouldn't return 0 anymore
assert learner.ask(1)[0][0] == 1
assert learner.ask(1)[0][0] == 2
assert learner.ask(1)[0][0] == 3
assert learner.ask(1)[0][0] == 4
assert learner.ask(1)[0][0] == 10
def test_min_npoints():
def constant_function(seed):
return 0.1
for min_npoints in [1, 2, 3]:
learner = AverageLearner(constant_function,
atol=0.01,
rtol=0.01,
min_npoints=min_npoints)
simple(learner, lambda l: l.loss() < 1)
assert learner.npoints >= max(2, min_npoints)
def test_avg_std_and_npoints():
learner = AverageLearner(lambda x: x, atol=None, rtol=0.01)
for i in range(300):
# This will add 5000 points at random values of n.
# It could try to readd already evaluated points.
n = random.randint(0, 2 * 300)
value = random.random()
# With 10% chance None is added to simulate asking that point.
if value < 0.9:
learner.tell(n, value)
else:
learner.tell_pending(n)
if i > 2 and i % 10 == 0:
# We need more than two points for 'learner.std' to be defined.
values = np.array(list(learner.data.values()))
std = np.sqrt(sum((values - values.mean())**2) / (len(values) - 1))
assert learner.npoints == len(learner.data)
assert abs(learner.sum_f - values.sum()) < 1e-13
assert abs(learner.std - std) < 1e-13
def test_zero_mean():
# see https://github.com/python-adaptive/adaptive/issues/275
learner = AverageLearner(None, rtol=0.01)
learner.tell(0, -1)
learner.tell(1, 1)
learner.loss()