def test_dist_truncated_normal_batched(self):
dist_sample_shape_correct = [2, 1]
dist_means_non_truncated_correct = [[0], [2]]
dist_stddevs_non_truncated_correct = [[1], [3]]
dist_means_correct = [[0], [0.901189]]
dist_stddevs_correct = [[0.53956], [1.95118]]
dist_lows_correct = [[-1], [-4]]
dist_highs_correct = [[1], [4]]
dist_log_probs_correct = [[-0.537223], [-1.69563]]
dist = TruncatedNormal(dist_means_non_truncated_correct, dist_stddevs_non_truncated_correct, dist_lows_correct, dist_highs_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_non_truncated_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_mixture(self):
dist_sample_shape_correct = [1]
dist_1 = Normal(0, 0.1)
dist_2 = Normal(2, 0.1)
dist_3 = Normal(3, 0.1)
dist_means_correct = [0.7]
dist_stddevs_correct = [1.10454]
dist_log_probs_correct = [-23.473]
dist = Mixture([dist_1, dist_2, dist_3], probs=[0.7, 0.2, 0.1])
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
# print(dist.log_prob([2,2]))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_mixture_batched(self):
dist_sample_shape_correct = [2, 1]
dist_1 = Normal([[0], [1]], [[0.1], [1]])
dist_2 = Normal([[2], [5]], [[0.1], [1]])
dist_3 = Normal([[3], [10]], [[0.1], [1]])
dist_means_correct = [[0.7], [8.1]]
dist_stddevs_correct = [[1.10454], [3.23883]]
dist_log_probs_correct = [[-23.473], [-3.06649]]
dist = Mixture([dist_1, dist_2, dist_3], probs=[[0.7, 0.2, 0.1], [0.1, 0.2, 0.7]])
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_kumaraswamy_batched(self):
dist_sample_shape_correct = [4, 1]
dist_shape1s_correct = [[0.5], [7.5], [7.5], [7.5]]
dist_shape2s_correct = [[0.75], [2.5], [2.5], [2.5]]
dist_means_correct = [[0.415584], [0.807999], [0.807999], [0.807999]]
dist_stddevs_correct = [[0.327509], [0.111605], [0.111605], [0.111605]]
dist_values = [[0.415584], [0.807999], [0.], [1.]]
dist_log_probs_correct = [[-0.283125], [1.20676], [float('-inf')], [float('-inf')]]
dist = Kumaraswamy(dist_shape1s_correct, dist_shape2s_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_values))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_values', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_normal(self):
dist_sample_shape_correct = [1]
dist_means_correct = [0]
dist_stddevs_correct = [1]
dist_log_probs_correct = [-0.918939]
dist = Normal(dist_means_correct, dist_stddevs_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical(
[dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct',
'dist_means', 'dist_means_empirical', 'dist_means_correct',
'dist_stddevs', 'dist_stddevs_empirical',
'dist_stddevs_correct', 'dist_log_probs',
'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(
np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(
np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(
np.allclose(dist_stddevs_empirical, dist_stddevs_correct,
atol=0.1))
self.assertTrue(
np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_inference_branching_random_walk_metropolis_hastings(self):
samples = importance_sampling_samples
posterior_correct = util.empirical_to_categorical(
self.true_posterior(), max_val=40)
start = time.time()
posterior = util.empirical_to_categorical(
self._model.posterior_distribution(
samples,
inference_engine=InferenceEngine.
RANDOM_WALK_METROPOLIS_HASTINGS,
observe={'obs': 6}),
max_val=40)
add_random_walk_metropolis_hastings_duration(time.time() - start)
posterior_probs = util.to_numpy(posterior._probs)
posterior_probs_correct = util.to_numpy(posterior_correct._probs)
kl_divergence = float(
pyprob.distributions.Distribution.kl_divergence(
posterior, posterior_correct))
util.eval_print('samples', 'posterior_probs',
'posterior_probs_correct', 'kl_divergence')
add_random_walk_metropolis_hastings_kl_divergence(kl_divergence)
self.assertLess(kl_divergence, 0.75)
def test_dist_kumaraswamy(self):
dist_sample_shape_correct = [1]
dist_shape1s_correct = [2]
dist_shape2s_correct = [5]
dist_means_correct = [0.369408]
dist_stddevs_correct = [0.173793]
dist_log_probs_correct = [0.719861]
dist = Kumaraswamy(dist_shape1s_correct, dist_shape2s_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_normal_multivariate_batched(self):
dist_sample_shape_correct = [2, 3]
dist_means_correct = [[0, 2, 0], [2, 0, 2]]
dist_stddevs_correct = [[1, 3, 1], [3, 1, 3]]
dist_log_probs_correct = [[sum([-0.918939, -2.01755, -0.918939])],
[sum([-2.01755, -0.918939, -2.01755])]]
dist = Normal(dist_means_correct, dist_stddevs_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical(
[dist.sample() for i in range(empirical_samples)])
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct',
'dist_means', 'dist_means_empirical', 'dist_means_correct',
'dist_stddevs', 'dist_stddevs_empirical',
'dist_stddevs_correct', 'dist_log_probs',
'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(
np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(
np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(
np.allclose(dist_stddevs_empirical, dist_stddevs_correct,
atol=0.1))
self.assertTrue(
np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_empirical_resample(self):
dist_means_correct = [2]
dist_stddevs_correct = [5]
dist = Normal(dist_means_correct, dist_stddevs_correct)
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_empirical = dist_empirical.resample(int(empirical_samples/2))
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
util.debug('dist_means_empirical', 'dist_means_correct', 'dist_stddevs_empirical', 'dist_stddevs_correct')
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.25))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.25))
def test_inference_remote_branching_importance_sampling(self):
samples = importance_sampling_samples
posterior_correct = util.empirical_to_categorical(self.true_posterior(), max_val=40)
start = time.time()
posterior = util.empirical_to_categorical(self._model.posterior_results(samples, observe={'obs': 6}), max_val=40)
add_importance_sampling_duration(time.time() - start)
posterior_probs = util.to_numpy(posterior._probs)
posterior_probs_correct = util.to_numpy(posterior_correct._probs)
kl_divergence = float(pyprob.distributions.Distribution.kl_divergence(posterior, posterior_correct))
util.eval_print('samples', 'posterior_probs', 'posterior_probs_correct', 'kl_divergence')
add_importance_sampling_kl_divergence(kl_divergence)
self.assertLess(kl_divergence, 0.75)
def test_dist_uniform(self):
dist_sample_shape_correct = [1]
dist_means_correct = [0.5]
dist_stddevs_correct = [0.288675]
dist_lows_correct = [0]
dist_highs_correct = [1]
dist_log_probs_correct = [0]
dist = Uniform(dist_lows_correct, dist_highs_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_lows = util.to_numpy(dist.low)
dist_highs = util.to_numpy(dist.high)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_lows', 'dist_lows_correct', 'dist_highs', 'dist_highs_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_lows, dist_lows_correct, atol=0.1))
self.assertTrue(np.allclose(dist_highs, dist_highs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_uniform_batched(self):
dist_sample_shape_correct = [4, 1]
dist_means_correct = [[0.5], [7.5], [0.5], [0.5]]
dist_stddevs_correct = [[0.288675], [1.44338], [0.288675], [0.288675]]
dist_lows_correct = [[0], [5], [0], [0]]
dist_highs_correct = [[1], [10], [1], [1]]
dist_values = [[0.5], [7.5], [0], [1]]
dist_log_probs_correct = [[0], [-1.60944], [float('-inf')], [float('-inf')]]
dist = Uniform(dist_lows_correct, dist_highs_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_lows = util.to_numpy(dist.low)
dist_highs = util.to_numpy(dist.high)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_values))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_lows', 'dist_lows_correct', 'dist_highs', 'dist_highs_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_values', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_lows, dist_lows_correct, atol=0.1))
self.assertTrue(np.allclose(dist_highs, dist_highs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_model_remote_branching_importance_sampling(self):
observation = 6
posterior_correct = util.empirical_to_categorical(
self.true_posterior(observation), max_val=40)
posterior = util.empirical_to_categorical(
self._model.posterior_distribution(samples,
observation=observation),
max_val=40)
posterior_probs = util.to_numpy(posterior._probs[0])
posterior_probs_correct = util.to_numpy(posterior_correct._probs[0])
kl_divergence = float(
util.kl_divergence_categorical(posterior_correct, posterior))
util.debug('samples', 'posterior_probs', 'posterior_probs_correct',
'kl_divergence')
self.assertLess(kl_divergence, 0.25)
def test_dist_truncated_normal_clamped_batched(self):
dist_sample_shape_correct = [2, 1]
dist_means_non_truncated = [[0], [2]]
dist_means_non_truncated_correct = [[0.5], [1]]
dist_stddevs_non_truncated = [[1], [3]]
dist_means_correct = [[0.744836], [-0.986679]]
dist_stddevs_correct = [[0.143681], [1.32416]]
dist_lows_correct = [[0.5], [-4]]
dist_highs_correct = [[1], [1]]
dist_log_prob_arguments = [[0.75], [-3]]
dist_log_probs_correct = [[0.702875], [-2.11283]]
dist = TruncatedNormal(dist_means_non_truncated, dist_stddevs_non_truncated, dist_lows_correct, dist_highs_correct, clamp_mean_between_low_high=True)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_means_non_truncated = util.to_numpy(dist._mean_non_truncated)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_log_prob_arguments))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_means_non_truncated', 'dist_means_non_truncated_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means_non_truncated, dist_means_non_truncated_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_poisson_batched(self):
dist_sample_shape_correct = [2, 1]
dist_means_correct = [[4], [100]]
dist_stddevs_correct = [[math.sqrt(4)], [math.sqrt(100)]]
dist_rates_correct = [[4], [100]]
dist_log_probs_correct = [[-1.63288], [-3.22236]]
dist = Poisson(dist_rates_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_rates = util.to_numpy(dist.rate)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_rates', 'dist_rates_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_rates, dist_rates_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_poisson_multivariate_batched(self):
dist_sample_shape_correct = [2, 3]
dist_means_correct = [[1, 2, 15], [100, 200, 300]]
dist_stddevs_correct = [[math.sqrt(1), math.sqrt(2), math.sqrt(15)], [math.sqrt(100), math.sqrt(200), math.sqrt(300)]]
dist_rates_correct = [[1, 2, 15], [100, 200, 300]]
dist_log_probs_correct = [[sum([-1, -1.30685, -2.27852])], [sum([-3.22236, -3.56851, -3.77110])]]
dist = Poisson(dist_rates_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_rates = util.to_numpy(dist.rate)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_rates', 'dist_rates_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.25))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.25))
self.assertTrue(np.allclose(dist_rates, dist_rates_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_poisson_multivariate_from_flat_params(self):
dist_sample_shape_correct = [1, 3]
dist_means_correct = [[1, 2, 15]]
dist_stddevs_correct = [[math.sqrt(1), math.sqrt(2), math.sqrt(15)]]
dist_rates_correct = [[1, 2, 15]]
dist_log_probs_correct = [sum([-1, -1.30685, -2.27852])]
dist = Poisson(dist_rates_correct[0])
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_rates = util.to_numpy(dist.rate)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_rates', 'dist_rates_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_rates, dist_rates_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_categorical(self):
dist_sample_shape_correct = [1]
dist_log_probs_correct = [-2.30259]
dist = Categorical([0.1, 0.2, 0.7])
dist_sample_shape = list(dist.sample().size())
dist_log_probs = util.to_numpy(dist.log_prob(0))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_model_remote_branching_random_walk_metropolis_hastings(self):
observation = 6
posterior_correct = util.empirical_to_categorical(
self.true_posterior(observation), max_val=40)
posterior = util.empirical_to_categorical(
self._model.posterior_distribution(
samples,
observation=observation,
inference_engine=pyprob.InferenceEngine.
RANDOM_WALK_METROPOLIS_HASTINGS),
max_val=40)
posterior_probs = util.to_numpy(posterior._probs[0])
posterior_probs_correct = util.to_numpy(posterior_correct._probs[0])
kl_divergence = float(
util.kl_divergence_categorical(posterior_correct, posterior))
util.debug('samples', 'posterior_probs', 'posterior_probs_correct',
'kl_divergence')
self.assertLess(kl_divergence, 0.25)
def test_dist_categorical_batched(self):
dist_sample_shape_correct = [2]
dist_log_probs_correct = [[-2.30259], [-0.693147]]
dist = Categorical([[0.1, 0.2, 0.7],
[0.2, 0.5, 0.3]])
dist_sample_shape = list(dist.sample().size())
dist_log_probs = util.to_numpy(dist.log_prob([[0, 1]]))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_distributions_remote(self):
num_samples = 4000
prior_normal_mean_correct = Normal(1.75, 0.5).mean
prior_uniform_mean_correct = Uniform(1.2, 2.5).mean
prior_categorical_mean_correct = 1. # Categorical([0.1, 0.5, 0.4])
prior_poisson_mean_correct = Poisson(4.0).mean
prior_bernoulli_mean_correct = Bernoulli(0.2).mean
prior_beta_mean_correct = Beta(1.2, 2.5).mean
prior_exponential_mean_correct = Exponential(2.2).mean
prior_gamma_mean_correct = Gamma(0.5, 1.2).mean
prior_log_normal_mean_correct = LogNormal(0.5, 0.2).mean
prior_binomial_mean_correct = Binomial(10, 0.72).mean
prior_weibull_mean_correct = Weibull(1.1, 0.6).mean
prior = self._model.prior(num_samples)
prior_normal = prior.map(
lambda trace: trace.named_variables['normal'].value)
prior_uniform = prior.map(
lambda trace: trace.named_variables['uniform'].value)
prior_categorical = prior.map(
lambda trace: trace.named_variables['categorical'].value)
prior_poisson = prior.map(
lambda trace: trace.named_variables['poisson'].value)
prior_bernoulli = prior.map(
lambda trace: trace.named_variables['bernoulli'].value)
prior_beta = prior.map(
lambda trace: trace.named_variables['beta'].value)
prior_exponential = prior.map(
lambda trace: trace.named_variables['exponential'].value)
prior_gamma = prior.map(
lambda trace: trace.named_variables['gamma'].value)
prior_log_normal = prior.map(
lambda trace: trace.named_variables['log_normal'].value)
prior_binomial = prior.map(
lambda trace: trace.named_variables['binomial'].value)
prior_weibull = prior.map(
lambda trace: trace.named_variables['weibull'].value)
prior_normal_mean = util.to_numpy(prior_normal.mean)
prior_uniform_mean = util.to_numpy(prior_uniform.mean)
prior_categorical_mean = util.to_numpy(int(prior_categorical.mean))
prior_poisson_mean = util.to_numpy(prior_poisson.mean)
prior_bernoulli_mean = util.to_numpy(prior_bernoulli.mean)
prior_beta_mean = util.to_numpy(prior_beta.mean)
prior_exponential_mean = util.to_numpy(prior_exponential.mean)
prior_gamma_mean = util.to_numpy(prior_gamma.mean)
prior_log_normal_mean = util.to_numpy(prior_log_normal.mean)
prior_binomial_mean = util.to_numpy(prior_binomial.mean)
prior_weibull_mean = util.to_numpy(prior_weibull.mean)
util.eval_print('num_samples', 'prior_normal_mean',
'prior_normal_mean_correct', 'prior_uniform_mean',
'prior_uniform_mean_correct', 'prior_categorical_mean',
'prior_categorical_mean_correct', 'prior_poisson_mean',
'prior_poisson_mean_correct', 'prior_bernoulli_mean',
'prior_bernoulli_mean_correct', 'prior_beta_mean',
'prior_beta_mean_correct', 'prior_exponential_mean',
'prior_exponential_mean_correct', 'prior_gamma_mean',
'prior_gamma_mean_correct', 'prior_log_normal_mean',
'prior_log_normal_mean_correct', 'prior_binomial_mean',
'prior_binomial_mean_correct', 'prior_weibull_mean',
'prior_weibull_mean_correct')
self.assertTrue(
np.allclose(prior_normal_mean, prior_normal_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_uniform_mean,
prior_uniform_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_categorical_mean,
prior_categorical_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_poisson_mean,
prior_poisson_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_bernoulli_mean,
prior_bernoulli_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_beta_mean, prior_beta_mean_correct, atol=0.1))
self.assertTrue(
np.allclose(prior_exponential_mean,
prior_exponential_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_gamma_mean, prior_gamma_mean_correct, atol=0.1))
self.assertTrue(
np.allclose(prior_log_normal_mean,
prior_log_normal_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_binomial_mean,
prior_binomial_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_weibull_mean,
prior_weibull_mean_correct,
atol=0.1))