def test_expected_improvement_and_gradient(self):
"""Test EI by comparing the vectorized and "naive" versions.
With the same RNG state, these two functions should return identical output.
We use a fairly low number of monte-carlo iterations since we are not
trying to converge; just check for consistency.
.. Note:: this is not a particularly good test. It relies on the "naive"
version being easier to verify manually and only checks for consistency
between the naive and vectorized versions.
"""
num_points_p_q_list = ((1, 0), (1, 1), (2, 1), (1, 4), (5, 3))
ei_tolerance = 10.0 * numpy.finfo(numpy.float64).eps
grad_ei_tolerance = 1.0e-13
numpy.random.seed(78532)
for test_case in self.gp_test_environments:
domain, gaussian_process = test_case
for num_to_sample, num_being_sampled in num_points_p_q_list:
points_to_sample = domain.generate_uniform_random_points_in_domain(num_to_sample)
points_being_sampled = domain.generate_uniform_random_points_in_domain(num_being_sampled)
union_of_points = numpy.reshape(numpy.append(points_to_sample, points_being_sampled), (num_to_sample + num_being_sampled, self.dim))
ei_eval = ExpectedImprovement(
gaussian_process,
points_to_sample,
points_being_sampled=points_being_sampled,
num_mc_iterations=self.num_mc_iterations,
)
# Compute quantities required for EI
mu_star = ei_eval._gaussian_process.compute_mean_of_points(union_of_points)
var_star = ei_eval._gaussian_process.compute_variance_of_points(union_of_points)
# Check EI
# Save state first to restore at the end (o/w other "random" events will get screwed up)
rng_state = numpy.random.get_state()
numpy.random.seed(self.rng_seed)
ei_vectorized = ei_eval._compute_expected_improvement_monte_carlo(mu_star, var_star)
numpy.random.seed(self.rng_seed)
ei_naive = ei_eval._compute_expected_improvement_monte_carlo_naive(mu_star, var_star)
self.assert_scalar_within_relative(ei_vectorized, ei_naive, ei_tolerance)
# Compute quantities required for grad EI
grad_mu = ei_eval._gaussian_process.compute_grad_mean_of_points(
union_of_points,
num_derivatives=num_to_sample,
)
grad_chol_decomp = ei_eval._gaussian_process.compute_grad_cholesky_variance_of_points(
union_of_points,
num_derivatives=num_to_sample,
)
# Check grad EI
numpy.random.seed(self.rng_seed)
grad_ei_vectorized = ei_eval._compute_grad_expected_improvement_monte_carlo(
mu_star,
var_star,
grad_mu,
grad_chol_decomp,
)
numpy.random.seed(self.rng_seed)
grad_ei_naive = ei_eval._compute_grad_expected_improvement_monte_carlo_naive(
mu_star,
var_star,
grad_mu,
grad_chol_decomp,
)
self.assert_vector_within_relative(grad_ei_vectorized, grad_ei_naive, grad_ei_tolerance)
# Restore state
numpy.random.set_state(rng_state)
