def test_uncertainty_sampling_shapes(model, model_with_density):
# test both with Lebesgue measure and with a probability measure because the gradients are computed differently
x = np.array([[-1, 1], [0, 0], [-2, 0.1]])
aq = UncertaintySampling(model)
aq_with_density = UncertaintySampling(model_with_density)
# value
res = aq.evaluate(x)
assert res.shape == (3, 1)
# gradient
res = aq.evaluate_with_gradients(x)
assert res[0].shape == (3, 1)
assert res[1].shape == (3, 2)
# value
res = aq_with_density.evaluate(x)
assert res.shape == (3, 1)
# gradient
res = aq_with_density.evaluate_with_gradients(x)
assert res[0].shape == (3, 1)
assert res[1].shape == (3, 2)
def uncertainty_sampling(model_test_list_fixture):
return UncertaintySampling(model_test_list_fixture)
def uncertainty_sampling_acquisition(vanilla_bq_model):
return UncertaintySampling(vanilla_bq_model)
def test_uncertainty_sampling_gradients(model, model_with_density):
aq = UncertaintySampling(model)
aq_with_density = UncertaintySampling(model_with_density)
x = np.array([[-2.5, 1.5]])
_check_grad(aq, x)
_check_grad(aq_with_density, x)