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_cannot_set_none_label() -> None:
vertex = Gaussian(0., 1., label="gaussian")
with pytest.raises(ValueError, match=r"label cannot be None"):
vertex.set_label(None)
def test_set_label() -> None:
label = "gaussian_vertex"
vertex = Gaussian(0., 1.)
vertex.set_label(label)
assert vertex.get_label() == label