def test_sample(self):
# use the PMCABC scheme for T = 1
T, n_sample, n_simulate, eps_arr, eps_percentile = 1, 10, 1, [10], 10
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
journal = sampler.sample([self.observation], T, eps_arr, n_sample,
n_simulate, eps_percentile)
mu_post_sample, sigma_post_sample, post_weights = np.array(
journal.get_parameters()['mu']), np.array(
journal.get_parameters()['sigma']), np.array(
journal.get_weights())
# Compute posterior mean
mu_post_mean, sigma_post_mean = np.average(mu_post_sample,
weights=post_weights,
axis=0), np.average(
sigma_post_sample,
weights=post_weights,
axis=0)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(
mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10, 1))
self.assertEqual(sigma_sample_shape, (10, 1))
self.assertEqual(weights_sample_shape, (10, 1))
self.assertLess(mu_post_mean - 0.03713, 10e-2)
self.assertLess(sigma_post_mean - 7.727, 10e-2)
#self.assertEqual((mu_post_mean, sigma_post_mean), (,))
# use the PMCABC scheme for T = 2
T, n_sample, n_simulate, eps_arr, eps_percentile = 2, 10, 1, [10,
5], 10
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal = sampler.sample([self.observation], T, eps_arr, n_sample,
n_simulate, eps_percentile)
mu_post_sample, sigma_post_sample, post_weights = np.array(
journal.get_parameters()['mu']), np.array(
journal.get_parameters()['sigma']), np.array(
journal.get_weights())
# Compute posterior mean
mu_post_mean, sigma_post_mean = np.average(mu_post_sample,
weights=post_weights,
axis=0), np.average(
sigma_post_sample,
weights=post_weights,
axis=0)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(
mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10, 1))
self.assertEqual(sigma_sample_shape, (10, 1))
self.assertEqual(weights_sample_shape, (10, 1))
self.assertLess(mu_post_mean - 0.9356, 10e-2)
self.assertLess(sigma_post_mean - 7.819, 10e-2)
self.assertFalse(journal.number_of_simulations == 0)
def test_restart_from_journal(self):
# test with value of eps_arr_2 > percentile of distances
n_sample, n_simulate, eps_arr, eps_percentile = 10, 1, [10, 5], 10
# 2 steps with intermediate journal:
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_intermediate = sampler.sample([self.observation], 1,
[eps_arr[0]], n_sample,
n_simulate, eps_percentile)
journal_intermediate.save("tmp.jnl")
journal_final_1 = sampler.sample([self.observation],
1, [eps_arr[1]],
n_sample,
n_simulate,
eps_percentile,
journal_file="tmp.jnl")
# 2 steps directly
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_final_2 = sampler.sample([self.observation], 2, eps_arr,
n_sample, n_simulate, eps_percentile)
self.assertEqual(journal_final_1.configuration["epsilon_arr"],
journal_final_2.configuration["epsilon_arr"])
self.assertEqual(journal_final_1.posterior_mean()['mu'],
journal_final_2.posterior_mean()['mu'])
# test with value of eps_arr_2 < percentile of distances
n_sample, n_simulate, eps_arr, eps_percentile = 10, 1, [10, 1], 10
# 2 steps with intermediate journal:
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_intermediate = sampler.sample([self.observation], 1,
[eps_arr[0]], n_sample,
n_simulate, eps_percentile)
journal_intermediate.save("tmp.jnl")
journal_final_1 = sampler.sample([self.observation],
1, [eps_arr[1]],
n_sample,
n_simulate,
eps_percentile,
journal_file="tmp.jnl")
# 2 steps directly
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_final_2 = sampler.sample([self.observation], 2, eps_arr,
n_sample, n_simulate, eps_percentile)
self.assertEqual(journal_final_1.configuration["epsilon_arr"],
journal_final_2.configuration["epsilon_arr"])
self.assertEqual(journal_final_1.posterior_mean()['mu'],
journal_final_2.posterior_mean()['mu'])
