def test_process_graph_1_factor_se(self):
"""
Test that the get_posterior_joint returns the correct joint distribution (after the graph has been processed)
for a graph constructed with a single factor and special evidence.
"""
factors = [self.p_a_b_c]
cga = ClusterGraph(factors, special_evidence={"a": 0.3})
cga.process_graph()
actual_posterior_joint = cga.get_posterior_joint()
expected_posterior_joint = self.p_a_b_c.observe(["a"], [0.3])
self.assertTrue(
actual_posterior_joint.equals(expected_posterior_joint))
def test_get_posterior_joint(self):
"""
Test that the get_posterior_joint returns the correct joint distribution after the graph has been processed.
"""
factors = [self.p_a, self.p_b_g_a, self.p_c_g_a]
cga = ClusterGraph(factors)
cga.process_graph()
actual_posterior_joint = cga.get_posterior_joint()
actual_posterior_joint._update_covform()
expected_posterior_joint = self.p_a_b_c.copy()
self.assertTrue(
actual_posterior_joint.equals(expected_posterior_joint))
def test_process_graph_1_factor(self):
"""
Test that the get_posterior_joint returns the correct joint distribution (after the graph has been processed)
for a graph constructed with a single factor.
"""
factors = [self.p_a_b_c]
cga = ClusterGraph(factors)
cga.process_graph()
actual_posterior_joint = cga.get_posterior_joint()
expected_posterior_joint = self.p_a_b_c.copy()
self.assertTrue(
actual_posterior_joint.equals(expected_posterior_joint))
def get_cg2(seed=0, process=False):
"""
Helper function for making a cluster graph.
"""
np.random.seed(seed)
factors = [
make_random_gaussian(["a", "b"]),
make_random_gaussian(["c", "b"]),
make_random_gaussian(["c", "d"]),
make_random_gaussian(["e", "d"]),
make_random_gaussian(["e", "f"]),
]
cluster_graph = ClusterGraph(factors)
if process:
cluster_graph.process_graph()
return cluster_graph
def test_correct_marginal_special_evidence(self):
"""
Test that the get_marginal function returns the correct marginal after a graph with special evidence has been
processed.
"""
factors = [self.p_a, self.p_b_g_a, self.p_c_g_a]
cga = ClusterGraph(factors, special_evidence={"a": 3.0})
cga.process_graph(max_iter=1)
vrs = ["b"]
cov = [[1.9]]
mean = [[2.7002]]
log_weight = -2.5202640960492313
expected_posterior_marginal = Gaussian(var_names=vrs,
cov=cov,
mean=mean,
log_weight=log_weight)
actual_posterior_marginal = cga.get_marginal(vrs=["b"])
actual_posterior_marginal._update_covform()
self.assertTrue(
expected_posterior_marginal.equals(actual_posterior_marginal))
def test_correct_marginal_fails_vars(self):
"""
Test that the get_marginal function fails when there is no factor containing the variables to keep.
"""
g_1 = make_random_gaussian(["a", "b"])
g_2 = make_random_gaussian(["b", "c"])
cga = ClusterGraph([g_1, g_2])
cga.process_graph()
with self.assertRaises(ValueError):
cga.get_marginal(["a", "c"])
def test_make_animation_gif(self):
"""
Test that the make_message_passing_animation_gif creates a file with the correct name.
"""
# TODO: improve this test.
factors = [self.p_a, self.p_b_g_a, self.p_c_g_a]
cga = ClusterGraph(factors)
cga.process_graph(make_animation_gif=True)
filename = "my_graph_animation_now.gif"
self.assertFalse(filename in os.listdir())
cga.make_message_passing_animation_gif(filename=filename)
self.assertTrue(filename in os.listdir())
os.remove(filename)
def test_init_fail_duplicate_cluster_ids(self):
"""
Test that the initializer fails when clusters have duplicate cluster_ids and returns the correct error message.
"""
with mock.patch(
"veroku.cluster_graph.Cluster.cluster_id",
new_callable=unittest.mock.PropertyMock) as mock_cluster_id:
mock_cluster_id.return_value = "same_id"
with self.assertRaises(ValueError) as error_context:
ClusterGraph([
make_random_gaussian(["a", "b"]),
make_random_gaussian(["b", "c"]),
make_random_gaussian(["c", "d"]),
])
exception_msg = error_context.exception.args[0]
self.assertTrue("non-unique" in exception_msg.lower())
expected_num_same_id_cluster = 3
actual_same_id_clusters = exception_msg.count("same_id")
self.assertTrue(expected_num_same_id_cluster,
actual_same_id_clusters)
def get_cg1_and_factors():
"""
Helper function for making a cluster graph.
"""
factor_a = Gaussian(var_names=["a"],
cov=[[0.5]],
mean=[0.0],
log_weight=3.0)
factor_ab = Gaussian(var_names=["a", "b"],
cov=[[10, 9], [9, 10]],
mean=[0, 0],
log_weight=0.0)
factor_ac = Gaussian(var_names=["a", "c"],
cov=[[10, 3], [3, 10]],
mean=[0, 0],
log_weight=0.0)
factor_bd = Gaussian(var_names=["b", "d"],
cov=[[15, 4], [4, 15]],
mean=[0, 0],
log_weight=0.0)
factors = [factor_a, factor_ab, factor_ac, factor_bd]
cluster_graph_factors = [factor.copy() for factor in factors]
cluster_graph = ClusterGraph(factors)
return cluster_graph, cluster_graph_factors
def test_correct_message_passing(self): # pylint: disable=too-many-locals
"""
Check that the correct messages are passed in the correct order.
"""
factor_a = Gaussian(var_names=["a"],
cov=[[0.5]],
mean=[0.0],
log_weight=3.0)
factor_ab = Gaussian(var_names=["a", "b"],
cov=[[10, 9], [9, 10]],
mean=[0, 0],
log_weight=0.0)
factor_abfa = factor_ab.absorb(factor_a)
factor_ac = Gaussian(var_names=["a", "c"],
cov=[[10, 3], [3, 10]],
mean=[0, 0],
log_weight=0.0)
factor_bd = Gaussian(var_names=["b", "d"],
cov=[[15, 4], [4, 15]],
mean=[0, 0],
log_weight=0.0)
cluster_graph = ClusterGraph(
[factor_a, factor_ab, factor_ac, factor_bd])
# expected messages
# from cluster 0 (factor_abfa) to cluster 1 (factor_ac)
msg_1_factor = factor_abfa.marginalize(vrs=["a"], keep=True)
msg_1 = Message(msg_1_factor, "c0#a,b", "c1#a,c")
# from cluster 0 (factor_abfa) to cluster 2 (factor_bd)
msg_2_factor = factor_abfa.marginalize(vrs=["b"], keep=True)
msg_2 = Message(msg_2_factor, "c0#a,b", "c2#b,d")
# from cluster 1 (factor_ac) to cluster 0 (factor_abfa)
msg_3_factor = factor_ac.marginalize(vrs=["a"], keep=True)
msg_3 = Message(msg_3_factor, "c1#a,c", "c0#a,b")
# from cluster 2 (factor_bd) to cluster 0 (factor_abfa)
msg_4_factor = factor_bd.marginalize(vrs=["b"], keep=True)
msg_4 = Message(msg_4_factor, "c2#b,d", "c0#a,b")
expected_messages = [msg_1, msg_2, msg_3, msg_4]
# Test that the factors of the cluster in the cluster graph are correct
expected_cluster_factors = [
factor_abfa.copy(),
factor_ac.copy(),
factor_bd.copy()
]
actual_cluster_factors = [c._factor for c in cluster_graph._clusters]
def key_func(factor):
return "".join(factor.var_names)
actual_cluster_factors = sorted(actual_cluster_factors, key=key_func)
expected_cluster_factors = sorted(expected_cluster_factors,
key=key_func)
for actual_f, expect_f in zip(actual_cluster_factors,
expected_cluster_factors):
self.assertEqual(actual_f, expect_f)
# See note below
for gmp in cluster_graph.graph_message_paths:
receiver_cluster_id = gmp.receiver_cluster._cluster_id
sender_cluster_id = gmp.sender_cluster._cluster_id
message_vars = gmp.sender_cluster.get_sepset(receiver_cluster_id)
dim = len(message_vars)
almost_vacuous = Gaussian(var_names=message_vars,
cov=np.eye(dim) * 1e10,
mean=np.zeros([dim, 1]),
log_weight=0.0)
gmp.previously_sent_message = Message(
sender_id=sender_cluster_id,
receiver_id=receiver_cluster_id,
factor=almost_vacuous)
gmp.update_next_information_gain()
cluster_graph.debug = True
cluster_graph.process_graph(tol=0, max_iter=1)
# Note
# Now we want to ensure and check a certain message order. The problem is that if more than one KLD is inf,
# there is no correct sorting order. This potentially points to a trade-off between easy 'distance from vacuous'
# calculations at the start of message passing (and not ensuring that the most informative message is sent) and
# maybe rather calculating a distance from almost vacuous and ensuring that the most informative messages are
# sent first. Infinities might not be sortable, but that does not mean they are equal.
actual_messages = cluster_graph.passed_messages
self.assertEqual(len(expected_messages), len(actual_messages))
for actual_message, expected_message in zip(actual_messages,
expected_messages):
self.assertEqual(actual_message.sender_id,
expected_message.sender_id)
self.assertEqual(actual_message.receiver_id,
expected_message.receiver_id)
self.assertTrue(
actual_message.equals(expected_message, rtol=1e-03,
atol=1e-03))