def test_margin(distributions, reduction):
np.random.seed(1337)
margin = Margin(reduction=reduction)
marg = margin(distributions)
assert np.all(marg == [1, 2, 0]), "Margin is not right {}".format(marg)
margin = Margin(shuffle_prop=0.9, reduction=reduction)
marg = margin(distributions)
assert np.any(marg != [1, 2, 0])
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)