def test_orchestration_run_one_step(make_random_dataset):
"""Test if the orchestration works.
In the base class it should raise an error as without
prediction function we cannot do anything
"""
X, y = make_random_dataset # pylint:disable=invalid-name
palinstance = PALBase(X, ["model"], 3, beta_scale=1)
sample_idx = np.array([1, 2, 3, 4])
palinstance.update_train_set(sample_idx, y[sample_idx])
with pytest.raises(NotImplementedError):
_ = palinstance.run_one_step()
def test__replace_by_measurements(make_random_dataset):
"""Test that replacing the mean/std by the measured ones works"""
X, y = make_random_dataset # pylint:disable=invalid-name
palinstance = PALBase(X, ["model"], 3, beta_scale=1)
assert palinstance.measurement_uncertainty.sum() == 0
sample_idx = np.array([1, 2, 3, 4])
palinstance.update_train_set(sample_idx, y[sample_idx], y[sample_idx])
palinstance._means = palinstance.measurement_uncertainty
palinstance.std = palinstance.measurement_uncertainty
palinstance._replace_by_measurements()
assert (palinstance.y == palinstance.std).all()
def test_update_train_set(make_random_dataset):
"""Check if the update of the training set works"""
X, y = make_random_dataset # pylint:disable=invalid-name
palinstance = PALBase(X, ["model"], 3)
assert not palinstance._has_train_set
assert palinstance.sampled.sum() == 0
palinstance.update_train_set(np.array([0]), y[0, :].reshape(-1, 3))
assert palinstance.sampled_indices == np.array([0])
assert palinstance.number_sampled_points == 1
assert (palinstance.y[0] == y[0, :]).all()
def test_turn_to_maximization(make_random_dataset):
"""Test that flipping the sign for minimization problems works"""
X, y = make_random_dataset # pylint:disable=invalid-name
palinstance = PALBase(X, ["model"], 3)
palinstance.update_train_set(np.array([0]), y[0, :].reshape(-1, 3))
assert (palinstance.y[0] == y[0, :]).all()
assert (palinstance._y[0] == y[0, :]).all()
palinstance = PALBase(X, ["model"], 3, goals=[1, 1, -1])
palinstance.update_train_set(np.array([0]), y[0, :].reshape(-1, 3))
assert (palinstance.y[0] == y[0, :] * np.array([1, 1, -1])).all()
assert (palinstance._y[0] == y[0, :]).all()