def test_pal_base(make_random_dataset):
"""Testing basic functionality of the PAL base class"""
palinstance = PALBase(make_random_dataset[0], ["model"], 3)
assert palinstance.number_discarded_points == 0
assert palinstance.number_pareto_optimal_points == 0
assert palinstance.number_unclassified_points == 100
assert palinstance.number_sampled_points == 0
assert palinstance.number_design_points == 100
assert palinstance.should_cross_validate()
assert len(palinstance.discarded_points) == 0
assert len(palinstance.pareto_optimal_points) == 0
assert len(palinstance.unclassified_points) == 100
with pytest.raises(ValueError):
palinstance.hyperrectangle_sizes # pylint:disable=pointless-statement
assert (str(palinstance) == "pyepal at iteration 1. \
0 Pareto optimal points, \
0 discarded points, \
100 unclassified points.")
assert palinstance._should_optimize_hyperparameters()
assert not palinstance._has_train_set
with pytest.raises(ValueError):
palinstance.sample()
assert palinstance.y.shape == (100, 3)
assert not palinstance.uses_fixed_epsilon
palinstance = PALBase(make_random_dataset[0], ["model"],
3,
ranges=[1, 1, 1])
assert palinstance.uses_fixed_epsilon
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