def test_random(distributions):
np.random.seed(1337)
random = Random()
all_equals = np.all(
[np.allclose(random(distributions), random(distributions)) for _ in range(10)]
)
assert not all_equals
random = Random(threshold=0.1)
marg = random(distributions)
assert np.any(distributions[marg] <= 0.1)
def test_heuristics_reorder_list():
# we are just testing if given calculated uncertainty measures for chunks of data
# the `reorder_indices` would make correct decision. Here index 0 has the
# highest uncertainty chosen but both methods (uncertainties1 and uncertainties2)
streaming_prediction = [np.array([0.98]), np.array([0.87, 0.68]),
np.array([0.96, 0.54])]
heuristic = BALD()
ranks = heuristic.reorder_indices(streaming_prediction)
assert np.all(ranks == [0, 3, 1, 2, 4]), "reorder list for BALD is not right {}".format(ranks)
heuristic = Variance()
ranks = heuristic.reorder_indices(streaming_prediction)
assert np.all(ranks == [0, 3, 1, 2, 4]), "reorder list for Variance is not right {}".format(
ranks)
heuristic = Entropy()
ranks = heuristic.reorder_indices(streaming_prediction)
assert np.all(ranks == [0, 3, 1, 2, 4]), "reorder list for Entropy is not right {}".format(
ranks)
heuristic = Margin()
ranks = heuristic.reorder_indices(streaming_prediction)
assert np.all(ranks == [4, 2, 1, 3, 0]), "reorder list for Margin is not right {}".format(ranks)
heuristic = Certainty()
ranks = heuristic.reorder_indices(streaming_prediction)
assert np.all(ranks == [4, 2, 1, 3, 0]), "reorder list for Certainty is not right {}".format(
ranks)
heuristic = Random()
ranks = heuristic.reorder_indices(streaming_prediction)
assert ranks.size == 5, "reorder list for Random is not right {}".format(
ranks)
def test_random(distributions):
np.random.seed(1337)
random = Random()
all_equals = np.all(
[np.allclose(random(distributions), random(distributions)) for _ in range(10)]
)
assert not all_equals
variance_firstchunk = np.array([0.76])
variance_secondchunk = np.array([0.63, 0.48])
streaming_prediction = [[bald_firstchunk, variance_firstchunk],
[bald_secondchunk, variance_secondchunk]]
heuristics = CombineHeuristics([BALD(), Variance()],
weights=[0.5, 0.5],
reduction='mean')
ranks = heuristics.reorder_indices(streaming_prediction)
assert np.all(
ranks == [0, 1, 2]), "Combine Heuristics is not right {}".format(ranks)
@pytest.mark.parametrize("heur", [
Random(),
BALD(reduction='sum'),
Entropy(reduction='sum'),
Variance(reduction='sum')
])
@pytest.mark.parametrize("n_batch", [1, 10, 20])
def test_heuristics_works_with_generator(heur, n_batch):
BATCH_SIZE = 32
def predictions(n_batch):
for _ in range(n_batch):
yield np.random.randn(BATCH_SIZE, 3, 32, 32, 10)
preds = predictions(n_batch)
out = heur(preds)
assert out.shape[0] == n_batch * BATCH_SIZE
