def test_crossover_max_length_exceeded(SS_RBS_SS_BNB, RS_MNB):
""" Raise ValueError if either provided individual exceeds `max_length`. """
with pytest.raises(ValueError) as _:
random_crossover(SS_RBS_SS_BNB, RS_MNB, max_length=2)
with pytest.raises(ValueError) as _:
random_crossover(RS_MNB, SS_RBS_SS_BNB, max_length=2)
def test_crossover_max_length_exceeded(BernoulliNBThreeScalers, MultinomialNBRobustScaler):
""" Raise ValueError if either provided individual exceeds `max_length`. """
with pytest.raises(ValueError) as error:
random_crossover(BernoulliNBThreeScalers, MultinomialNBRobustScaler, max_length=2)
with pytest.raises(ValueError) as error:
random_crossover(MultinomialNBRobustScaler, BernoulliNBThreeScalers, max_length=2)
def test_crossover(SS_BNB, RS_MNB):
""" Two eligible individuals should produce two new individuals with crossover. """
ind1_copy, ind2_copy = SS_BNB.copy_as_new(), RS_MNB.copy_as_new()
# Cross-over is in-place
random_crossover(SS_BNB, RS_MNB)
# Both parents and children should be unique
all_individuals = [SS_BNB, RS_MNB, ind1_copy, ind2_copy]
assert 4 == len({ind.pipeline_str() for ind in all_individuals})
assert ind1_copy.pipeline_str() != SS_BNB.pipeline_str()
def test_crossover(BernoulliNBStandardScaler, MultinomialNBRobustScaler):
""" Two eligible individuals should produce two new individuals with crossover. """
ind1_copy, ind2_copy = BernoulliNBStandardScaler.copy_as_new(), MultinomialNBRobustScaler.copy_as_new()
# Cross-over is in-place
random_crossover(BernoulliNBStandardScaler, MultinomialNBRobustScaler)
# Both parents and children should be unique
assert len({ind.pipeline_str() for ind in [
BernoulliNBStandardScaler, MultinomialNBRobustScaler, ind1_copy, ind2_copy]}) == 4
assert ind1_copy.pipeline_str() != BernoulliNBStandardScaler.pipeline_str()
def test_crossover_max_length(BernoulliNBThreeScalers):
""" Setting `max_length` affects maximum produced length, and maximum length only. """
primitives_in_parent = len(BernoulliNBThreeScalers.primitives)
produced_lengths = []
for _ in range(60): # guarantees all allowed length pipelines are produced with probability >0.999
ind1, ind2 = random_crossover(BernoulliNBThreeScalers.copy_as_new(),
BernoulliNBThreeScalers.copy_as_new(),
max_length=primitives_in_parent)
# Only the first child is guaranteed to contain at most `max_length` primitives.
produced_lengths.append(len(ind1.primitives))
assert set(produced_lengths) == {2, 3, 4}
def test_crossover_max_length(SS_RBS_SS_BNB):
""" Setting `max_length` affects only maximum produced length. """
primitives_in_parent = len(SS_RBS_SS_BNB.primitives)
produced_lengths = []
for _ in range(
60
): # guarantees all length pipelines are produced with prob >0.999
ind1, ind2 = random_crossover(
SS_RBS_SS_BNB.copy_as_new(),
SS_RBS_SS_BNB.copy_as_new(),
max_length=primitives_in_parent,
)
# Only the first child is guaranteed to contain at most `max_length` primitives.
produced_lengths.append(len(ind1.primitives))
assert {2, 3, 4} == set(produced_lengths)