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_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_can_get_acceptance_rates(net: BayesNet) -> None:
acceptance_rate_tracker = AcceptanceRateTracker()
latents = list(net.iter_latent_vertices())
algo = MetropolisHastingsSampler(
proposal_distribution='prior',
latents=net.iter_latent_vertices(),
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_sample_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 = sample(net=net, sample_from=net.iter_latent_vertices())
def test_sampling_returns_multi_indexed_dict_of_list_of_scalars_for_tensor_in_sample_from(
algo: Callable[[BayesNet], PosteriorSamplingAlgorithm],
tensor_net: BayesNet) -> None:
draws = 5
sample_from = list(tensor_net.iter_latent_vertices())
samples = sample(net=tensor_net,
sample_from=sample_from,
sampling_algorithm=algo(tensor_net),
draws=draws)
assert type(samples) == dict
__assert_valid_samples(draws, samples)
def test_sampling_returns_dict_of_list_of_ndarrays_for_vertices_in_sample_from(
algo: Callable[[BayesNet], PosteriorSamplingAlgorithm],
net: BayesNet) -> None:
draws = 5
sample_from = list(net.iter_latent_vertices())
samples = sample(net=net,
sample_from=sample_from,
sampling_algorithm=algo(net),
draws=draws)
assert len(samples) == len(sample_from)
assert type(samples) == dict
__assert_valid_samples(draws, samples)
def test_can_specify_nuts_params(net: BayesNet) -> None:
algo = NUTSSampler(adapt_count=1000,
target_acceptance_prob=0.65,
adapt_step_size_enabled=True,
adapt_potential_enabled=True,
initial_step_size=0.1,
max_tree_height=10)
samples = sample(net,
list(net.iter_latent_vertices()),
algo,
draws=500,
drop=100)
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_down_sample_interval(net: BayesNet) -> None:
draws = 10
down_sample_interval = 2
samples = sample(net=net,
sample_from=net.iter_latent_vertices(),
draws=draws,
down_sample_interval=down_sample_interval)
expected_num_samples = draws / down_sample_interval
assert all(
len(vertex_samples) == expected_num_samples
for label, vertex_samples in samples.items())
def test_dropping_samples(net: BayesNet) -> None:
draws = 10
drop = 3
samples = sample(net=net,
sample_from=net.iter_latent_vertices(),
draws=draws,
drop=drop)
expected_num_samples = draws - drop
assert all(
len(vertex_samples) == expected_num_samples
for label, vertex_samples in samples.items())
def test_sample_dict_can_be_loaded_in_to_dataframe(net: BayesNet) -> None:
sample_from = list(net.iter_latent_vertices())
vertex_labels = [vertex.get_label() for vertex in sample_from]
samples = sample(net=net, sample_from=sample_from, draws=5)
df = pd.DataFrame(samples)
for column in df:
header = df[column].name
vertex_label = header
assert vertex_label in vertex_labels
assert len(df[column]) == 5
assert type(df[column][0]) == np.float64
def test_multi_indexed_sample_dict_can_be_loaded_in_to_dataframe(
tensor_net: BayesNet) -> None:
sample_from = list(tensor_net.iter_latent_vertices())
vertex_labels = [vertex.get_label() for vertex in sample_from]
samples = sample(net=tensor_net, sample_from=sample_from, draws=5)
df = pd.DataFrame(samples)
for parent_column in df.columns.levels[0]:
assert parent_column in vertex_labels
for child_column in df.columns.levels[1]:
assert type(child_column) == tuple
assert len(df[parent_column][child_column]) == 5
assert type(df[parent_column][child_column][0]) == np.float64
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_coalmining() -> None:
coal_mining = CoalMining()
model = coal_mining.model()
model.disasters.observe(coal_mining.training_data())
net = BayesNet(model.switchpoint.iter_connected_graph())
samples = sample(net=net, sample_from=net.iter_latent_vertices(), draws=2000, drop=100, down_sample_interval=5)
vertex_samples: List[primitive_types] = samples["switchpoint"]
vertex_samples_concatentated: np.ndarray = np.array(vertex_samples)
switch_year = np.argmax(np.bincount(vertex_samples_concatentated))
assert switch_year == 1890
def test_sampling_returns_multi_indexed_dict_of_list_of_scalars_for_mixed_net(
algo: Callable[[BayesNet], PosteriorSamplingAlgorithm]) -> None:
exp = Exponential(1.)
add_rank_2 = exp + np.array([1., 2., 3., 4.]).reshape((2, 2))
add_rank_3 = exp + np.array([1., 2., 3., 4., 1., 2., 3., 4.]).reshape(
(2, 2, 2))
gaussian_rank_2 = Gaussian(add_rank_2, 2.)
gaussian_rank_3 = Gaussian(add_rank_3, 1.)
exp.set_label("exp")
gaussian_rank_2.set_label("gaussian")
gaussian_rank_3.set_label("gaussian2")
mixed_net = BayesNet(exp.iter_connected_graph())
draws = 5
sample_from = list(mixed_net.iter_latent_vertices())
vertex_labels = [vertex.get_label() for vertex in sample_from]
samples = sample(net=mixed_net,
sample_from=sample_from,
sampling_algorithm=algo(mixed_net),
draws=draws)
assert type(samples) == dict
__assert_valid_samples(draws, samples)
assert ('exp', (0, )) in samples
for i in (0, 1):
for j in (0, 1):
assert (('gaussian', (i, j)) in samples)
df = pd.DataFrame(samples)
expected_num_columns = {"exp": 1, "gaussian": 4, "gaussian2": 8}
expected_tuple_size = {"exp": 1, "gaussian": 2, "gaussian2": 3}
assert len(df.columns.levels[0]) == 3
for parent_column in df.columns.levels[0]:
assert parent_column in vertex_labels
assert len(
df[parent_column].columns) == expected_num_columns[parent_column]
for child_column in df[parent_column].columns:
assert type(child_column) == tuple
assert len(child_column) == expected_tuple_size[parent_column]
assert len(df[parent_column][child_column]) == 5
assert type(df[parent_column][child_column][0]) == np.float64
def test_sample_with_plot(net: BayesNet) -> None:
num_plots = 3
_, ax = plt.subplots(num_plots, 1, squeeze=False)
sample(net=net,
sample_from=net.iter_latent_vertices(),
draws=5,
plot=True,
ax=ax)
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_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_it_throws_if_you_specify_gaussian_with_not_enough_sigmas_for_each_latent(net: BayesNet) -> None:
with pytest.raises(
TypeError, match=r"Gaussian Proposal Distribution requires a sigma or a list of sigmas for each latent"):
ProposalDistribution("gaussian", latents=list(net.iter_latent_vertices()), sigma=[1.])
def test_you_can_create_a_gaussian_proposal_distribution(sigma: tensor_arg_types, net: BayesNet) -> None:
ProposalDistribution("gaussian", latents=list(net.iter_latent_vertices()), sigma=sigma)