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_sample_with_plot(net: BayesNet) -> None:
KeanuRandom.set_default_random_seed(1)
_, ax = plt.subplots(3, 1, squeeze=False)
sample(net=net, sample_from=net.get_latent_vertices(), draws=5, plot=True, ax=ax)
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_get_acceptance_rates(net: BayesNet) -> None:
acceptance_rate_tracker = AcceptanceRateTracker()
latents = list(net.get_latent_vertices())
algo = MetropolisHastingsSampler(proposal_distribution='prior', proposal_listeners=[acceptance_rate_tracker])
samples = sample(net=net, sample_from=latents, sampling_algorithm=algo, drop=3)
for latent in latents:
rate = acceptance_rate_tracker.get_acceptance_rate(latent)
assert 0 <= rate <= 1
def test_it_throws_if_you_pass_in_a_proposal_listener_but_you_didnt_specify_the_proposal_type(
net: BayesNet) -> None:
with pytest.raises(TypeError) as excinfo:
sample(net=net,
sample_from=net.get_latent_vertices(),
proposal_listeners=[AcceptanceRateTracker()],
drop=3)
assert str(
excinfo.value
) == "If you pass in proposal_listeners you must also specify proposal_distribution"
def test_it_throws_if_you_pass_in_a_proposal_listener_but_the_algo_isnt_metropolis(
net: BayesNet) -> None:
with pytest.raises(TypeError) as excinfo:
sample(net=net,
sample_from=net.get_latent_vertices(),
algo="hamiltonian",
proposal_listeners=[AcceptanceRateTracker()],
drop=3)
assert str(
excinfo.value
) == "Only Metropolis Hastings supports the proposal_listeners parameter"
def test_it_throws_if_you_pass_in_a_proposal_distribution_but_the_algo_isnt_metropolis(
net: BayesNet) -> None:
with pytest.raises(TypeError) as excinfo:
sample(net=net,
sample_from=net.get_latent_vertices(),
algo="hamiltonian",
proposal_distribution="prior",
drop=3)
assert str(
excinfo.value
) == "Only Metropolis Hastings supports the proposal_distribution parameter"
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_construct_bayes_net() -> None:
uniform = UniformInt(0, 1)
graph = set(uniform.get_connected_graph())
vertex_ids = [vertex.get_id() for vertex in graph]
assert len(vertex_ids) == 3
assert uniform.get_id() in vertex_ids
net = BayesNet(graph)
latent_vertex_ids = [
vertex.get_id() for vertex in net.get_latent_vertices()
]
assert len(latent_vertex_ids) == 1
assert uniform.get_id() in latent_vertex_ids
def test_can_track_acceptance_rate_when_iterating(net: BayesNet) -> None:
acceptance_rate_tracker = AcceptanceRateTracker()
latents = list(net.get_latent_vertices())
samples = generate_samples(net=net,
sample_from=latents,
proposal_distribution='prior',
proposal_listeners=[acceptance_rate_tracker],
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_sampling_returns_dict_of_list_of_ndarrays_for_vertices_in_sample_from(algo: PosteriorSamplingAlgorithm,
net: BayesNet) -> None:
draws = 5
sample_from = list(net.get_latent_vertices())
vertex_labels = [vertex.get_label() for vertex in sample_from]
samples = sample(net=net, sample_from=sample_from, sampling_algorithm=algo, draws=draws)
assert len(samples) == len(sample_from)
assert type(samples) == dict
for label, vertex_samples in samples.items():
assert label in vertex_labels
assert len(vertex_samples) == draws
assert type(vertex_samples) == list
assert all(type(sample) == np.ndarray for sample in vertex_samples)
assert all(sample.dtype == float for sample in vertex_samples)
assert all(sample.shape == () for sample in vertex_samples)
def test_coalmining() -> None:
KeanuRandom.set_default_random_seed(1)
coal_mining = CoalMining()
model = coal_mining.model()
model.disasters.observe(coal_mining.training_data())
net = BayesNet(model.switchpoint.get_connected_graph())
samples = sample(net=net, sample_from=net.get_latent_vertices(), draws=50000, drop=10000, down_sample_interval=5)
vertex_samples: List[numpy_types] = samples["switchpoint"]
vertex_samples_primitive: List[List[primitive_types]] = list(map(
lambda a: a.tolist(), vertex_samples)) # because you can't concatenate 0-d arrays
vertex_samples_concatentated: np.ndarray = np.array(vertex_samples_primitive)
switch_year = np.argmax(np.bincount(vertex_samples_concatentated))
assert switch_year == 1890
def test_can_specify_a_gaussian_proposal_distribution(net: BayesNet) -> None:
algo = MetropolisHastingsSampler(proposal_distribution="gaussian", proposal_distribution_sigma=np.array(1.))
generate_samples(net=net, sample_from=net.get_latent_vertices(), sampling_algorithm=algo)
def test_can_specify_nuts_params(net: BayesNet) -> None:
algo = NUTSSampler(1000, 0.65, True, 0.1, 10)
samples = sample(net, list(net.get_latent_vertices()), algo, draws=500, drop=100)
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.))
