def test_can_specify_a_gaussian_proposal_distribution(net: BayesNet) -> None:
algo = MetropolisHastingsSampler(proposal_distribution="gaussian",
latents=net.iter_latent_vertices(),
proposal_distribution_sigma=np.array(1.))
generate_samples(net=net,
sample_from=net.iter_latent_vertices(),
sampling_algorithm=algo)
def test_can_iter_through_samples(algo: str, net: BayesNet) -> None:
draws = 10
samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), algo=algo, down_sample_interval=1)
count = 0
for sample in islice(samples, draws):
count += 1
assert count == draws
def test_iter_returns_same_result_as_sample() -> None:
draws = 100
model = thermometers.model()
net = BayesNet(model.temperature.iter_connected_graph())
set_starting_state(model)
sampler = MetropolisHastingsSampler(proposal_distribution='prior',
latents=net.iter_latent_vertices())
samples = sample(net=net,
sample_from=net.iter_latent_vertices(),
sampling_algorithm=sampler,
draws=draws)
set_starting_state(model)
sampler = MetropolisHastingsSampler(proposal_distribution='prior',
latents=net.iter_latent_vertices())
iter_samples = generate_samples(net=net,
sample_from=net.iter_latent_vertices(),
sampling_algorithm=sampler)
samples_dataframe = pd.DataFrame()
for iter_sample in islice(iter_samples, draws):
samples_dataframe = samples_dataframe.append(iter_sample,
ignore_index=True)
for vertex_label in samples_dataframe:
np.testing.assert_almost_equal(samples_dataframe[vertex_label].mean(),
np.average(samples[vertex_label]))
def test_iter_with_live_plot(net: BayesNet) -> None:
KeanuRandom.set_default_random_seed(1)
_, ax = plt.subplots(3, 1, squeeze=False)
samples = generate_samples(net=net,
sample_from=net.get_latent_vertices(),
live_plot=True,
refresh_every=5,
ax=ax)
for sample in islice(samples, 5):
pass
reorder_subplots(ax)
assert len(ax) == 3
assert all(len(ax[i][0].get_lines()) == 1 for i in range(3))
assert np.allclose(
ax[0][0].get_lines()[0].get_ydata(),
[0.49147822, 0.49147822, 0.49147822, 0.20033212, 0.20033212])
assert np.allclose(
ax[1][0].get_lines()[0].get_ydata(),
[0.87268333, 1.10409369, 1.10409369, 1.10409369, 0.69098161])
assert np.allclose(
ax[2][0].get_lines()[0].get_ydata(),
[-14.46166855, -14.46166855, 0.32305686, 0.32305686, 0.32305686])
def test_can_track_acceptance_rate_when_iterating(net: BayesNet) -> None:
acceptance_rate_tracker = AcceptanceRateTracker()
latents = list(net.get_latent_vertices())
algo = MetropolisHastingsSampler(proposal_distribution='prior', proposal_listeners=[acceptance_rate_tracker])
samples = generate_samples(net=net, sample_from=latents, sampling_algorithm=algo, drop=3)
draws = 100
for _ in islice(samples, draws):
for latent in latents:
rate = acceptance_rate_tracker.get_acceptance_rate(latent)
assert 0 <= rate <= 1
def test_generate_samples_throws_if_vertices_in_sample_from_are_missing_labels(
) -> None:
sigma = Gamma(1., 1)
vertex = Gaussian(0., sigma, label="gaussian")
assert sigma.get_label() is None
net = BayesNet([sigma, vertex])
with pytest.raises(ValueError,
match=r"Vertices in sample_from must be labelled."):
samples = generate_samples(net=net,
sample_from=net.iter_latent_vertices())
def test_iter_with_live_plot(net: BayesNet) -> None:
KeanuRandom.set_default_random_seed(1)
_, ax = plt.subplots(3, 1, squeeze=False)
samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), live_plot=True, refresh_every=5, ax=ax)
for sample in islice(samples, 5):
pass
reorder_subplots(ax)
assert len(ax) == 3
assert all(len(ax[i][0].get_lines()) == 1 for i in range(3))
assert all(len(ax[i][0].get_lines()[0].get_ydata() == 5) for i in range(3))
def test_can_iter_through_samples(algo: Callable[[BayesNet],
PosteriorSamplingAlgorithm],
net: BayesNet) -> None:
draws = 10
samples = generate_samples(net=net,
sample_from=net.iter_latent_vertices(),
sampling_algorithm=algo(net),
down_sample_interval=1)
count = 0
for sample in islice(samples, draws):
count += 1
assert count == draws
def test_iter_returns_same_result_as_sample(algo: str) -> None:
draws = 100
model = thermometers.model()
net = BayesNet(model.temperature.get_connected_graph())
set_starting_state(model)
samples = sample(net=net, sample_from=net.get_latent_vertices(), algo=algo, draws=draws)
set_starting_state(model)
iter_samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), algo=algo)
samples_dataframe = pd.DataFrame()
[samples_dataframe.append(pd.DataFrame(list(next_sample.items()))) for next_sample in islice(iter_samples, draws)]
for vertex_id in samples_dataframe:
np.testing.assert_almost_equal(samples_dataframe[vertex_id].mean(), np.average(samples[vertex_id]))
def test_can_iter_through_tensor_samples(algo: Callable[
[BayesNet], PosteriorSamplingAlgorithm], tensor_net: BayesNet) -> None:
draws = 10
samples = generate_samples(net=tensor_net,
sample_from=tensor_net.iter_latent_vertices(),
sampling_algorithm=algo(tensor_net),
down_sample_interval=1)
count = 0
for sample in islice(samples, draws):
count += 1
for distribution in ('exp', 'gamma'):
for i in (0, 1):
for j in (0, 1):
assert ((distribution, (i, j)) in sample)
assert count == draws
def test_iter_with_live_plot(net: BayesNet) -> None:
num_plots = 3
_, ax = plt.subplots(num_plots, 1, squeeze=False)
samples = generate_samples(net=net,
sample_from=net.iter_latent_vertices(),
live_plot=True,
refresh_every=5,
ax=ax)
for sample in islice(samples, 5):
pass
reorder_subplots(ax)
assert len(ax) == num_plots
assert all(len(ax[i][0].get_lines()) == 1 for i in range(num_plots))
assert all(
len(ax[i][0].get_lines()[0].get_ydata() == 5)
for i in range(num_plots))
def test_autocorrelation_same_for_streaming_as_batch() -> None:
with Model() as model:
model.uniform = Uniform(0, 1000)
net = model.to_bayes_net()
draws = 15
set_starting_state(model)
samples = sample(net=net, sample_from=net.get_latent_vertices(), algo="metropolis", draws=draws)
set_starting_state(model)
iter_samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), algo="metropolis")
samples_dataframe = pd.DataFrame()
for next_sample in islice(iter_samples, draws):
samples_dataframe = samples_dataframe.append(next_sample, ignore_index=True)
for vertex_id in samples_dataframe:
autocorr_streaming = stats.autocorrelation(list(samples_dataframe[vertex_id].values))
autocorr_batch = stats.autocorrelation(samples[vertex_id])
np.testing.assert_array_equal(autocorr_batch, autocorr_streaming)
def test_can_specify_a_gaussian_proposal_distribution(net: BayesNet) -> None:
generate_samples(net=net,
sample_from=net.get_latent_vertices(),
proposal_distribution="gaussian",
proposal_distribution_sigma=np.array(1.))
