def test_model_rmh_posterior_with_stop_and_resume(self):
posterior_num_runs = 100
posterior_num_traces_each_run = 20
posterior_num_traces_correct = posterior_num_traces_each_run * posterior_num_runs
true_posterior = Normal(7.25, math.sqrt(1/1.2))
posterior_mean_correct = float(true_posterior.mean)
posterior_stddev_correct = float(true_posterior.stddev)
prior_mean_correct = 1.
prior_stddev_correct = math.sqrt(5)
posteriors = []
initial_trace = None
for i in range(posterior_num_runs):
posterior = self._model.posterior_traces(num_traces=posterior_num_traces_each_run, inference_engine=InferenceEngine.RANDOM_WALK_METROPOLIS_HASTINGS, observe={'obs0': 8, 'obs1': 9}, initial_trace=initial_trace)
initial_trace = posterior[-1]
posteriors.append(posterior)
posterior = Empirical.combine(posteriors).map(lambda trace: trace.result)
posterior_num_traces = posterior.length
posterior_mean = float(posterior.mean)
posterior_mean_unweighted = float(posterior.unweighted().mean)
posterior_stddev = float(posterior.stddev)
posterior_stddev_unweighted = float(posterior.unweighted().stddev)
kl_divergence = float(pyprob.distributions.Distribution.kl_divergence(true_posterior, Normal(posterior.mean, posterior.stddev)))
util.eval_print('posterior_num_runs', 'posterior_num_traces_each_run', 'posterior_num_traces', 'posterior_num_traces_correct', 'prior_mean_correct', 'posterior_mean_unweighted', 'posterior_mean', 'posterior_mean_correct', 'prior_stddev_correct', 'posterior_stddev_unweighted', 'posterior_stddev', 'posterior_stddev_correct', 'kl_divergence')
self.assertEqual(posterior_num_traces, posterior_num_traces_correct)
self.assertAlmostEqual(posterior_mean, posterior_mean_correct, places=0)
self.assertAlmostEqual(posterior_stddev, posterior_stddev_correct, places=0)
self.assertLess(kl_divergence, 0.25)
def test_dist_empirical_combine_non_uniform_weights_use_initial(self):
samples1 = 10000
samples2 = 1000
observation = [8, 9]
posterior_mean_correct = 7.25
posterior_stddev_correct = math.sqrt(1/1.2)
posterior1 = self._model_gum.posterior_distribution(samples1, observation=observation)
posterior1_mean = float(posterior1.mean)
posterior1_mean_unweighted = float(posterior1.unweighted().mean)
posterior1_stddev = float(posterior1.stddev)
posterior1_stddev_unweighted = float(posterior1.unweighted().stddev)
kl_divergence1 = float(util.kl_divergence_normal(Normal(posterior_mean_correct, posterior_stddev_correct), Normal(posterior1.mean, posterior1_stddev)))
posterior2 = self._model_gum.posterior_distribution(samples2, observation=observation)
posterior2_mean = float(posterior2.mean)
posterior2_mean_unweighted = float(posterior2.unweighted().mean)
posterior2_stddev = float(posterior2.stddev)
posterior2_stddev_unweighted = float(posterior2.unweighted().stddev)
kl_divergence2 = float(util.kl_divergence_normal(Normal(posterior_mean_correct, posterior_stddev_correct), Normal(posterior2.mean, posterior2_stddev)))
posterior = Empirical.combine([posterior1, posterior2], use_initial_values_and_weights=True)
posterior_mean = float(posterior.mean)
posterior_mean_unweighted = float(posterior.unweighted().mean)
posterior_stddev = float(posterior.stddev)
posterior_stddev_unweighted = float(posterior.unweighted().stddev)
kl_divergence = float(util.kl_divergence_normal(Normal(posterior_mean_correct, posterior_stddev_correct), Normal(posterior.mean, posterior_stddev)))
util.debug('samples1', 'posterior1_mean_unweighted', 'posterior1_mean', 'posterior1_stddev_unweighted', 'posterior1_stddev', 'kl_divergence1', 'samples2', 'posterior2_mean_unweighted', 'posterior2_mean', 'posterior2_stddev_unweighted', 'posterior2_stddev', 'kl_divergence2', 'posterior_mean_unweighted', 'posterior_mean', 'posterior_mean_correct', 'posterior_stddev_unweighted', 'posterior_stddev', 'posterior_stddev_correct', 'kl_divergence')
self.assertAlmostEqual(posterior1_mean, posterior_mean_correct, places=0)
self.assertAlmostEqual(posterior1_stddev, posterior_stddev_correct, places=0)
self.assertLess(kl_divergence1, 0.25)
self.assertAlmostEqual(posterior2_mean, posterior_mean_correct, places=0)
self.assertAlmostEqual(posterior2_stddev, posterior_stddev_correct, places=0)
self.assertLess(kl_divergence2, 0.25)
self.assertAlmostEqual(posterior_mean, posterior_mean_correct, places=0)
self.assertAlmostEqual(posterior_stddev, posterior_stddev_correct, places=0)
self.assertLess(kl_divergence, 0.25)
def test_dist_empirical_combine_uniform_weights(self):
dist1_mean_correct = 1
dist1_stddev_correct = 3
dist2_mean_correct = 5
dist2_stddev_correct = 2
dist3_mean_correct = -2.5
dist3_stddev_correct = 1.2
dist_combined_mean_correct = 1.16667
dist_combined_stddev_correct = 3.76858
dist1 = Normal(dist1_mean_correct, dist1_stddev_correct)
dist1_empirical = Empirical([dist1.sample() for i in range(empirical_samples)])
dist1_empirical_mean = float(dist1_empirical.mean)
dist1_empirical_stddev = float(dist1_empirical.stddev)
dist2 = Normal(dist2_mean_correct, dist2_stddev_correct)
dist2_empirical = Empirical([dist2.sample() for i in range(empirical_samples)])
dist2_empirical_mean = float(dist2_empirical.mean)
dist2_empirical_stddev = float(dist2_empirical.stddev)
dist3 = Normal(dist3_mean_correct, dist3_stddev_correct)
dist3_empirical = Empirical([dist3.sample() for i in range(empirical_samples)])
dist3_empirical_mean = float(dist3_empirical.mean)
dist3_empirical_stddev = float(dist3_empirical.stddev)
dist_combined_empirical = Empirical.combine([dist1_empirical, dist2_empirical, dist3_empirical])
dist_combined_empirical_mean = float(dist_combined_empirical.mean)
dist_combined_empirical_stddev = float(dist_combined_empirical.stddev)
util.debug('dist1_empirical_mean', 'dist1_empirical_stddev', 'dist1_mean_correct', 'dist1_stddev_correct', 'dist2_empirical_mean', 'dist2_empirical_stddev', 'dist2_mean_correct', 'dist2_stddev_correct', 'dist3_empirical_mean', 'dist3_empirical_stddev', 'dist3_mean_correct', 'dist3_stddev_correct', 'dist_combined_empirical_mean', 'dist_combined_empirical_stddev', 'dist_combined_mean_correct', 'dist_combined_stddev_correct')
self.assertAlmostEqual(dist1_empirical_mean, dist1_mean_correct, places=1)
self.assertAlmostEqual(dist1_empirical_stddev, dist1_stddev_correct, places=1)
self.assertAlmostEqual(dist2_empirical_mean, dist2_mean_correct, places=1)
self.assertAlmostEqual(dist2_empirical_stddev, dist2_stddev_correct, places=1)
self.assertAlmostEqual(dist3_empirical_mean, dist3_mean_correct, places=1)
self.assertAlmostEqual(dist3_empirical_stddev, dist3_stddev_correct, places=1)
self.assertAlmostEqual(dist_combined_empirical_mean, dist_combined_mean_correct, places=1)
self.assertAlmostEqual(dist_combined_empirical_stddev, dist_combined_stddev_correct, places=1)