Function test_constant_liar

tests/test_acquisition.py:241–268

(gp, target_space, target_func, random_state, strategy)

@pytest.mark.parametrize("strategy", [0.0, "mean", "min", "max"])
def test_constant_liar(gp, target_space, target_func, random_state, strategy):
    base_acq = acquisition.UpperConfidenceBound()
    acq = acquisition.ConstantLiar(base_acquisition=base_acq, strategy=strategy)

    target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0)
    target_space.register(params={"x": 1.0, "y": 1.5}, target=2.5)
    base_samples = np.array([base_acq.suggest(gp=gp, target_space=target_space) for _ in range(10)])
    samples = []

    assert len(acq.dummies) == 0
    for _ in range(10):
        samples.append(acq.suggest(gp=gp, target_space=target_space, random_state=random_state))
        assert len(acq.dummies) == len(samples)

    samples = np.array(samples)
    print(samples)

    base_distance = pdist(base_samples, "sqeuclidean").mean()
    distance = pdist(samples, "sqeuclidean").mean()

    assert base_distance < distance

    for i in range(10):
        target_space.register(params={"x": samples[i][0], "y": samples[i][1]}, target=target_func(samples[i]))

    acq.suggest(gp=gp, target_space=target_space, random_state=random_state)

    assert len(acq.dummies) == 1


def test_constant_liar_invalid_strategy():