def test_inference_gum_marsaglia_posterior_inference_compilation(self):
observation = [8, 9]
posterior_mean_correct = 7.25
posterior_stddev_correct = math.sqrt(1 / 1.2)
self._model.learn_inference_network(observation=[1, 1],
early_stop_traces=training_traces)
posterior = self._model.posterior_distribution(
samples, use_inference_network=True, observation=observation)
posterior_mean = float(posterior.mean)
posterior_mean_unweighted = float(posterior.mean_unweighted)
posterior_stddev = float(posterior.stddev)
posterior_stddev_unweighted = float(posterior.stddev_unweighted)
kl_divergence = float(
util.kl_divergence_normal(posterior_mean_correct,
posterior_stddev_correct, posterior.mean,
posterior_stddev))
util.debug('training_traces', 'samples', 'posterior_mean_unweighted',
'posterior_mean', 'posterior_mean_correct',
'posterior_stddev_unweighted', 'posterior_stddev',
'posterior_stddev_correct', 'kl_divergence')
add_perf_score_inference_compilation(kl_divergence)
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_model_remote_gum_marsaglia_posterior_random_walk_metropolis_hastings(
self):
observation = [8, 9]
posterior_mean_correct = 7.25
posterior_stddev_correct = math.sqrt(1 / 1.2)
posterior = self._model.posterior_distribution(
samples,
inference_engine=pyprob.InferenceEngine.
RANDOM_WALK_METROPOLIS_HASTINGS,
observation=observation)
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('samples', 'posterior_mean_unweighted', 'posterior_mean',
'posterior_mean_correct', 'posterior_stddev_unweighted',
'posterior_stddev', 'posterior_stddev_correct',
'kl_divergence')
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_inference_gum_marsaglia_posterior_importance_sampling(self):
observation = [8, 9]
posterior_mean_correct = 7.25
posterior_stddev_correct = math.sqrt(1 / 1.2)
posterior = self._model.posterior_distribution(samples,
observation=observation)
posterior_mean = float(posterior.mean)
posterior_mean_unweighted = float(posterior.mean_unweighted)
posterior_stddev = float(posterior.stddev)
posterior_stddev_unweighted = float(posterior.stddev_unweighted)
kl_divergence = float(
util.kl_divergence_normal(posterior_mean_correct,
posterior_stddev_correct, posterior.mean,
posterior_stddev))
util.debug('samples', 'posterior_mean_unweighted', 'posterior_mean',
'posterior_mean_correct', 'posterior_stddev_unweighted',
'posterior_stddev', 'posterior_stddev_correct',
'kl_divergence')
add_perf_score_importance_sampling(kl_divergence)
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)