def test_param_mean(self):
"""Check param_mean estimator."""
self.assertAlmostEqual(
e.param_mean(self.ns_run),
np.sum(self.w_rel * self.ns_run['theta'][:, 0]), places=12)
ndim = self.ns_run['theta'].shape[1]
self.assertRaises(
IndexError, e.param_mean, self.ns_run, param_ind=ndim)
self.assertTrue(np.isnan(
e.param_mean(self.ns_run, param_ind=ndim, handle_indexerror=True)))
def test_run_ci_bootstrap(self):
"""Check bootstrap ci equals estimator expected value when the
run only contains one thread."""
run = nestcheck.dummy_data.get_dummy_run(1, 10)
ci = nestcheck.error_analysis.run_ci_bootstrap(
run, [e.param_mean], n_simulate=10, cred_int=0.5)
self.assertAlmostEqual(ci[0], e.param_mean(run), places=12)
def test_dynamic_param(self):
"""Test numerical results for nested sampling with dynamic_goal=1."""
dynamic_goal = 1
dyPolyChord.run_dypolychord(
self.run_func, dynamic_goal, self.settings,
init_step=self.ninit, ninit=self.ninit)
run = nestcheck.data_processing.process_polychord_run(
self.settings['file_root'], self.settings['base_dir'])
first_logl = -137.231721859574
if not np.isclose(run['logl'][0], first_logl):
warnings.warn(
self.random_seed_msg.format(run['logl'][0], first_logl),
UserWarning)
else:
self.assertAlmostEqual(e.param_mean(run), -0.05323120028149568,
places=12)
def test_dynamic_evidence(self):
"""Test numerical results for nested sampling with dynamic_goal=0."""
dynamic_goal = 0
dyPolyChord.run_dypolychord(
self.run_func, dynamic_goal, self.settings,
init_step=self.ninit, ninit=self.ninit)
run = nestcheck.data_processing.process_polychord_run(
self.settings['file_root'], self.settings['base_dir'])
first_logl = -158.773632799691
if not np.isclose(run['logl'][0], first_logl):
warnings.warn(
self.random_seed_msg.format(run['logl'][0], first_logl),
UserWarning)
else:
self.assertEqual(e.count_samples(run), 1169)
self.assertAlmostEqual(e.param_mean(run), 0.026487985350451874,
places=12)
def test_dynamic_both_evidence_and_param(self):
"""Test numerical results for nested sampling with
dynamic_goal=0.25."""
dynamic_goal = 0.25
dyPolyChord.run_dypolychord(
self.run_func, dynamic_goal, self.settings,
init_step=self.ninit, ninit=self.ninit)
run = nestcheck.data_processing.process_polychord_run(
self.settings['file_root'], self.settings['base_dir'])
first_logl = -165.502617578541
if not np.isclose(run['logl'][0], first_logl):
warnings.warn(
self.random_seed_msg.format(run['logl'][0], first_logl),
UserWarning)
else:
self.assertEqual(e.count_samples(run), 1093)
self.assertAlmostEqual(e.param_mean(run), -0.0021307716191374263,
places=12)