def test_triangulate_no_tie_break(self, huang_darwiche_moralized):
# Now lets see what happens if
# we dont enforce the tie-breakers...
# It seems the triangulated graph is
# different adding edges from d to c
# and b to c
# Will be interesting to see whether
# inference will still be correct.
triangulate(huang_darwiche_moralized)
nodes = {node.name: node for node in huang_darwiche_moralized.nodes}
assert set(nodes["f_a"].neighbours) == set(
[nodes["f_b"], nodes["f_c"]])
assert set(nodes["f_b"].neighbours) == set(
[nodes["f_a"], nodes["f_d"], nodes["f_c"]])
assert set(nodes["f_c"].neighbours) == set([
nodes["f_a"], nodes["f_e"], nodes["f_g"], nodes["f_b"],
nodes["f_d"]
])
assert set(nodes["f_d"].neighbours) == set(
[nodes["f_b"], nodes["f_f"], nodes["f_e"], nodes["f_c"]])
assert set(nodes["f_e"].neighbours) == set([
nodes["f_c"], nodes["f_f"], nodes["f_h"], nodes["f_d"],
nodes["f_g"]
])
assert set(nodes["f_f"].neighbours) == set(
[nodes["f_d"], nodes["f_e"]])
assert set(nodes["f_g"].neighbours) == set(
[nodes["f_c"], nodes["f_h"], nodes["f_e"]])
assert set(nodes["f_h"].neighbours) == set(
[nodes["f_e"], nodes["f_g"]])
def test_insert_duplicate_clique(huang_darwiche_moralized):
cliques, _ = triangulate(huang_darwiche_moralized, priority_func)
forest = set()
for clique in cliques:
jt_node = JoinTreeCliqueNode(clique)
clique.node = jt_node
tree = JoinTree([jt_node])
forest.add(tree)
s = SepSet(cliques[0], cliques[0])
assert s.insertable(forest) is False
s_copy = copy.deepcopy(s)
s.insert(forest)
assert len(s.X.node.neighbours) > len(s_copy.X.node.neighbours)
def test_triangulate(self, huang_darwiche_moralized):
# Because of ties in the priority q we will
# override the priority function here to
# insert tie breakers to ensure the same
# elimination ordering as Darwich Huang.
def priority_func_override(node):
introduced_arcs = 0
cluster = [node] + node.neighbours
for node_a, node_b in combinations(cluster, 2):
if node_a not in node_b.neighbours:
assert node_b not in node_a.neighbours
introduced_arcs += 1
introduced_arcs_dict = {
"f_h": [introduced_arcs, 0],
"f_g": [introduced_arcs, 1],
"f_c": [introduced_arcs, 2],
"f_b": [introduced_arcs, 3],
"f_d": [introduced_arcs, 4],
"f_e": [introduced_arcs, 5],
"others": [introduced_arcs, 10],
}
if node.name in introduced_arcs_dict:
return introduced_arcs_dict[node.name]
return introduced_arcs_dict["others"]
cliques, elimination_ordering = triangulate(huang_darwiche_moralized,
priority_func_override)
nodes = {node.name: node for node in huang_darwiche_moralized.nodes}
assert len(cliques) == 6
assert cliques[0].nodes == set(
[nodes["f_e"], nodes["f_g"], nodes["f_h"]])
assert cliques[1].nodes == set(
[nodes["f_c"], nodes["f_e"], nodes["f_g"]])
assert cliques[2].nodes == set(
[nodes["f_d"], nodes["f_e"], nodes["f_f"]])
assert cliques[3].nodes == set(
[nodes["f_a"], nodes["f_c"], nodes["f_e"]])
assert cliques[4].nodes == set(
[nodes["f_a"], nodes["f_b"], nodes["f_d"]])
assert cliques[5].nodes == set(
[nodes["f_a"], nodes["f_d"], nodes["f_e"]])
assert elimination_ordering == [
"f_h",
"f_g",
"f_f",
"f_c",
"f_b",
"f_d",
"f_e",
"f_a",
]
# Now lets ensure the triangulated graph is
# the same as Darwiche Huang fig. 2 pg. 13
nodes = {node.name: node for node in huang_darwiche_moralized.nodes}
assert set(nodes["f_a"].neighbours) == set(
[nodes["f_b"], nodes["f_c"], nodes["f_d"], nodes["f_e"]])
assert set(nodes["f_b"].neighbours) == set(
[nodes["f_a"], nodes["f_d"]])
assert set(nodes["f_c"].neighbours) == set(
[nodes["f_a"], nodes["f_e"], nodes["f_g"]])
assert set(nodes["f_d"].neighbours) == set(
[nodes["f_b"], nodes["f_f"], nodes["f_e"], nodes["f_a"]])
assert set(nodes["f_e"].neighbours) == set([
nodes["f_c"],
nodes["f_f"],
nodes["f_h"],
nodes["f_d"],
nodes["f_g"],
nodes["f_a"],
])
assert set(nodes["f_f"].neighbours) == set(
[nodes["f_d"], nodes["f_e"]])
assert set(nodes["f_g"].neighbours) == set(
[nodes["f_c"], nodes["f_h"], nodes["f_e"]])
assert set(nodes["f_h"].neighbours) == set(
[nodes["f_e"], nodes["f_g"]])