def test_reset_classification(make_random_dataset):
"""Make sure the reset of the reclassification status works as expected."""
X, _ = make_random_dataset # pylint: disable=invalid-name
palinstance = PALBase(X, ["model"], 3)
lows = np.zeros((100, 3))
highs = np.zeros((100, 3))
means = np.full((100, 3), 1)
palinstance._means = means
palinstance.std = np.full((100, 3), 0.1)
pareto_optimal = np.array([False] * 98 + [True, True])
sampled = np.array([[False] * 3, [False] * 3, [False] * 3, [False] * 3])
unclassified = np.array([True] * 98 + [False, False])
palinstance.rectangle_lows = lows
palinstance.rectangle_ups = highs
palinstance.sampled = sampled
palinstance.pareto_optimal = pareto_optimal
palinstance.unclassified = unclassified
palinstance._reset_classification()
assert palinstance.number_unclassified_points == len(X)
assert palinstance.number_pareto_optimal_points == 0
assert palinstance.number_discarded_points == 0
def test_augment_design_space(make_random_dataset):
"""Testing the basic functionality of the augmentation method
Does NOT test the re-classification step, which needs a model"""
X, _ = make_random_dataset # pylint: disable=invalid-name
X_augmented = np.vstack([X, X]) # pylint: disable=invalid-name
palinstance = PALBase(X, ["model"], 3)
# Iteration count to low
with pytest.raises(ValueError):
palinstance.augment_design_space(X_augmented)
palinstance.iteration = 2
# Incorrect shape
with pytest.raises(AssertionError):
palinstance.augment_design_space(X_augmented[:, 2])
with pytest.raises(ValueError):
palinstance.augment_design_space(X_augmented[:, :2])
# Mock that we already ran that
lows = np.zeros((100, 3))
highs = np.zeros((100, 3))
means = np.full((100, 3), 1)
palinstance._means = means
palinstance.std = np.full((100, 3), 0.1)
pareto_optimal = np.array([False] * 98 + [True, True])
sampled = np.array([[False] * 3, [False] * 3, [False] * 3, [False] * 3])
unclassified = np.array([True] * 98 + [False, False])
palinstance.rectangle_lows = lows
palinstance.rectangle_ups = highs
palinstance.sampled = sampled
palinstance.pareto_optimal = pareto_optimal
palinstance.unclassified = unclassified
# As we do not have a model, we cannot test the classification
palinstance.augment_design_space(X_augmented, clean_classify=False)
assert palinstance.number_discarded_points == 0
assert palinstance.number_pareto_optimal_points == 2
assert palinstance.number_unclassified_points == 298
assert palinstance.number_sampled_points == 0
assert palinstance.number_design_points == 300
assert len(palinstance.means) == 300
assert len(palinstance.std) == 300
def test_sample(make_random_dataset):
"""Test the sampling"""
X, _ = make_random_dataset # pylint:disable=invalid-name
palinstance = PALBase(X, ["model"], 4)
lows = np.array([
[0.0, 0.0, 0.0, 0.0],
[-1.0, -1.0, -1.0, -1.0],
[-2.0, -2.0, -2.0, -2.0],
[2.0, 2.0, 2.0, 2.0],
])
highs = np.array([
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[2.0, 2.0, 2.0, 2.0],
])
means = np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
palinstance._means = means
pareto_optimal = np.array([False, False, True, True])
sampled = np.array([[False] * 4, [False] * 4, [False] * 4, [False] * 4])
unclassified = np.array([True, True, False, False])
palinstance.rectangle_lows = lows
palinstance.rectangle_ups = highs
palinstance.sampled = sampled
palinstance.pareto_optimal = pareto_optimal
palinstance.unclassified = unclassified
sampled_idx = palinstance.sample()
assert sampled_idx == 2
pareto_optimal = np.array([False, False, True, True])
sampled = np.array([[False] * 4, [False] * 4, [False] * 4, [False] * 4])
unclassified = np.array([True, True, False, False])
palinstance.sampled = sampled
palinstance.pareto_optimal = pareto_optimal
palinstance.unclassified = unclassified
sampled_idx = palinstance.sample()
assert sampled_idx == 2
pareto_optimal = np.array([False, False, True, True])
sampled = np.array([[False] * 4, [False] * 4, [True] * 4, [False] * 4])
unclassified = np.array([True, True, False, False])
palinstance.sampled = sampled
palinstance.pareto_optimal = pareto_optimal
palinstance.unclassified = unclassified
sampled_idx = palinstance.sample()
assert sampled_idx == 1
pareto_optimal = np.array([False, False, False, True])
sampled = np.array([[False] * 4, [False] * 4, [True] * 4, [False] * 4])
unclassified = np.array([True, True, False, False])
palinstance.sampled = sampled
palinstance.pareto_optimal = pareto_optimal
palinstance.unclassified = unclassified
sampled_idx = palinstance.sample()
assert sampled_idx == 1