def test_4(self):
query = Graph()
n11 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n12 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n13 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
query.add_node(n11)
query.add_node(n12)
query.add_node(n13)
query.add_edge(Edge(n11, n12, 0))
query.add_edge(Edge(n11, n13, 1))
graph = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n3 = Node(label=3, entity=DEFAULT_ENTITY,
value=SYMBOLIC_VALUE) # 3, not 2
n4 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n5 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n6 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n7 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
graph.add_node(n1)
graph.add_node(n2)
graph.add_node(n3)
graph.add_node(n4)
graph.add_node(n5)
graph.add_node(n6)
graph.add_node(n7)
graph.add_edge(Edge(n1, n2, 0))
graph.add_edge(Edge(n1, n3, 1))
graph.add_edge(Edge(n4, n5, 0))
graph.add_edge(Edge(n4, n6, 1))
graph.add_edge(Edge(n4, n7, 1))
mappings = list(query.get_subgraph_mappings(graph))
self.assertEqual(2, len(mappings))
def _create_node(self, label: int, value: Any):
if pd.isnull(value):
value = MY_NAN
node = Node(label=label, entity=self.entity, value=value)
self._all_nodes.append(node)
return node
def test_1(self):
g1 = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g1.add_node(n1)
g1.add_node(n2)
g1.add_edge(Edge(n1, n2, 0))
g2 = Graph()
n3 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g2.add_node(n3)
mappings_21 = list(g2.get_subgraph_mappings(g1))
self.assertEqual(1, len(mappings_21))
self.assertEqual(mappings_21[0].m_node[n3], n1)
self.assertEqual(mappings_21[0].m_ent[n3.entity], n1.entity)
mappings_12 = list(g1.get_subgraph_mappings(g2))
self.assertEqual(0, len(mappings_12))
def test_1(self):
from gauss.graphs.python.subgraph import _get_candidate_mappings
g1 = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g1.add_node(n1)
g1.add_node(n2)
g1.add_edge(Edge(n1, n2, 0))
g2 = Graph()
n3 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g2.add_node(n3)
m1 = _get_candidate_mappings(g2, g1)
m2 = _get_candidate_mappings(g1, g2)
self.assertIsNotNone(m1)
self.assertIsNone(m2)
self.assertIn(n3, m1.m_node)
self.assertSetEqual({n1}, m1.m_node[n3])
def deepcopy(self) -> Tuple['Graph', Dict[Entity, Entity], Dict[Node, Node]]:
ent_mapping: Dict[Entity, Entity] = {}
node_mapping: Dict[Node, Node] = {}
for entity in self._entities:
ent_mapping[entity] = Entity(value=entity.value)
for node in self._nodes:
node_mapping[node] = Node(label=node.label, entity=ent_mapping[node.entity], value=node.value)
new_graph = self.from_nodes_and_edges(nodes=set(node_mapping.values()),
edges={Edge(src=node_mapping[e.src], dst=node_mapping[e.dst],
label=e.label) for e in self._edges})
return new_graph, ent_mapping, node_mapping
def test_4(self):
from gauss.graphs.python.subgraph import _get_candidate_mappings
query = Graph()
n11 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n12 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n13 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
query.add_node(n11)
query.add_node(n12)
query.add_node(n13)
query.add_edge(Edge(n11, n12, 0))
query.add_edge(Edge(n11, n13, 1))
graph = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n3 = Node(label=3, entity=DEFAULT_ENTITY,
value=SYMBOLIC_VALUE) # 3, not 2
n4 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n5 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n6 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n7 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
graph.add_node(n1)
graph.add_node(n2)
graph.add_node(n3)
graph.add_node(n4)
graph.add_node(n5)
graph.add_node(n6)
graph.add_node(n7)
graph.add_edge(Edge(n1, n2, 0))
graph.add_edge(Edge(n1, n3, 1))
graph.add_edge(Edge(n4, n5, 0))
graph.add_edge(Edge(n4, n6, 1))
graph.add_edge(Edge(n4, n7, 1))
m = _get_candidate_mappings(query, graph)
self.assertIsNotNone(m)
def create_symbolic_copy(graph: Graph) -> Tuple[Graph, GraphMapping]:
mapping = GraphMapping()
for entity in graph.iter_entities():
mapping.m_ent[entity] = Entity(value=SYMBOLIC_VALUE)
for node in graph.iter_nodes():
mapping.m_node[node] = Node(label=node.label,
entity=mapping.m_ent[node.entity],
value=SYMBOLIC_VALUE)
new_graph = Graph.from_nodes_and_edges(nodes=set(mapping.m_node.values()),
edges={
Edge(src=mapping.m_node[e.src],
dst=mapping.m_node[e.dst],
label=e.label)
for e in graph.iter_edges()
})
return new_graph, mapping
def create_intermediate_node(self, value):
node = Node(label=NLabel.INTERM, entity=DEFAULT_ENTITY, value=value)
self.add_node(node)
return node
def _create_node(self, label: int, value: Any):
node = Node(label=label, entity=self.entity, value=value)
self._all_nodes.append(node)
return node
def test_greatest_common_universal_subgraph_1(self):
g1 = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n3 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n4 = Node(label=3, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g1.add_nodes_and_edges(nodes=[n1, n2, n3, n4])
g1.add_tags(["TAG_1", "TAG_2"])
g1.add_tagged_edges(
[TaggedEdge(n2, n2, "TAG_L1"),
TaggedEdge(n3, n3, "TAG_L2")])
# Linear chain from n1 to n2 and n2 to n3 and n3 to n4
g1.add_edge(Edge(src=n1, dst=n2, label=10))
g1.add_edge(Edge(src=n2, dst=n3, label=11))
g1.add_edge(Edge(src=n3, dst=n4, label=12))
g2 = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n3 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n4 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n5 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n6 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n7 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n8 = Node(label=3, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g2.add_nodes_and_edges(nodes=[n1, n2, n3, n4, n5, n6, n7, n8])
g2.add_tags(["TAG_2", "TAG_3"])
g2.add_tagged_edges(
[TaggedEdge(n2, n2, "TAG_L1"),
TaggedEdge(n3, n3, "TAG_L2")])
# Only one of label=2 has an edge to a label=3
g2.add_edge(Edge(src=n1, dst=n2, label=10))
g2.add_edge(Edge(src=n2, dst=n3, label=11))
g2.add_edge(Edge(src=n2, dst=n4, label=11))
g2.add_edge(Edge(src=n2, dst=n5, label=11))
g2.add_edge(Edge(src=n2, dst=n6, label=11))
g2.add_edge(Edge(src=n2, dst=n7, label=11))
g2.add_edge(Edge(src=n7, dst=n8, label=12))
query = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
query.add_nodes_and_edges(nodes=[n1, n2])
query.add_edge(Edge(n1, n2, 10))
supergraph, mapping = query.get_greatest_common_universal_supergraph(
[g1])
# We expect the supergraph to be equivalent to g1
self.assertEqual(3, supergraph.get_num_edges())
self.assertEqual(4, supergraph.get_num_nodes())
self.assertSetEqual({0, 1, 2, 3},
{n.label
for n in supergraph.iter_nodes()})
self.assertSetEqual({10, 11, 12},
{e.label
for e in supergraph.iter_edges()})
self.assertSetEqual({"TAG_1", "TAG_2"}, set(supergraph.iter_tags()))
self.assertEqual({"TAG_L1", "TAG_L2"},
{e.tag
for e in supergraph.iter_tagged_edges()})
for node in mapping.m_node:
self.assertIn(node, query.get_all_nodes())
supergraph, mapping = query.get_greatest_common_universal_supergraph(
[g1, g2])
# We expect the supergraph to be the linear chain 0 to 1 and 1 to 2
self.assertEqual(2, supergraph.get_num_edges())
self.assertEqual(3, supergraph.get_num_nodes())
self.assertSetEqual({0, 1, 2},
{n.label
for n in supergraph.iter_nodes()})
self.assertSetEqual({10, 11},
{e.label
for e in supergraph.iter_edges()})
self.assertSetEqual({"TAG_2"}, set(supergraph.iter_tags()))
self.assertEqual({"TAG_L1"},
{e.tag
for e in supergraph.iter_tagged_edges()})
for node in mapping.m_node:
self.assertIn(node, query.get_all_nodes())
def test_3(self):
from gauss.graphs.python.subgraph import _get_candidate_mappings
g1 = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=10)
n2 = Node(label=0, entity=DEFAULT_ENTITY, value=20)
n3 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n4 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n5 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n6 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g1.add_node(n1)
g1.add_node(n2)
g1.add_node(n3)
g1.add_node(n4)
g1.add_node(n5)
g1.add_node(n6)
g1.add_edge(Edge(n1, n3, 0))
g1.add_edge(Edge(n3, n5, 1))
g1.add_edge(Edge(n2, n4, 0))
g1.add_edge(Edge(n4, n6, 1))
g2 = Graph()
n21 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n22 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n23 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g2.add_node(n21)
g2.add_node(n22)
g2.add_node(n23)
g2.add_edge(Edge(n21, n22, 0))
g2.add_edge(Edge(n22, n23, 1))
m21 = _get_candidate_mappings(g2, g1)
self.assertIsNotNone(m21)
self.assertSetEqual({n1, n2}, m21.m_node[n21])
self.assertSetEqual({n3, n4}, m21.m_node[n22])
self.assertSetEqual({n5, n6}, m21.m_node[n23])
g3 = Graph()
n31 = Node(label=0, entity=DEFAULT_ENTITY, value=10)
n32 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n33 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g3.add_node(n31)
g3.add_node(n32)
g3.add_node(n33)
g3.add_edge(Edge(n31, n32, 0))
g3.add_edge(Edge(n32, n33, 1))
m31 = _get_candidate_mappings(g3, g1)
self.assertIsNotNone(m31)
self.assertSetEqual({n1}, m31.m_node[n31])
self.assertSetEqual({n3}, m31.m_node[n32])
self.assertSetEqual({n5}, m31.m_node[n33])
g4 = Graph()
n41 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n42 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n43 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g4.add_node(n41)
g4.add_node(n42)
g4.add_node(n43)
g4.add_edge(Edge(n41, n42, 0))
g4.add_edge(Edge(n42, n43, 1))
m41 = _get_candidate_mappings(g4, g1, GraphMapping(m_node={n41: n1}))
self.assertIsNotNone(m41)
self.assertSetEqual({n1}, m41.m_node[n41])
self.assertSetEqual({n3}, m41.m_node[n42])
self.assertSetEqual({n5}, m41.m_node[n43])
def test_5(self):
# Stress-tests the intelligence of back-tracking
query = Graph()
n11 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n12 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n13 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n14 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n15 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n16 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
query.add_node(n11)
query.add_node(n12)
query.add_node(n13)
query.add_node(n14)
query.add_node(n15)
query.add_node(n16)
query.add_edge(Edge(n13, n15, 0))
query.add_edge(Edge(n13, n15, 1))
query.add_edge(Edge(n14, n16, 0))
query.add_edge(Edge(n14, n16, 1))
graph = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n2 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n3 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n4 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n5 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n6 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n7 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n8 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n9 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n10 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
graph.add_node(n1)
graph.add_node(n2)
graph.add_node(n3)
graph.add_node(n4)
graph.add_node(n5)
graph.add_node(n6)
graph.add_node(n7)
graph.add_node(n8)
graph.add_node(n9)
graph.add_node(n10)
graph.add_edge(Edge(n3, n5, 0))
graph.add_edge(Edge(n3, n6, 1))
graph.add_edge(Edge(n4, n5, 1))
graph.add_edge(Edge(n4, n6, 0))
mappings = list(
query.get_subgraph_mappings(
graph, _worklist_order=[n11, n12, n13, n14, n15, n16]))
self.assertEqual(0, len(mappings))
def test_3(self):
g1 = Graph()
n1 = Node(label=0, entity=DEFAULT_ENTITY, value=10)
n2 = Node(label=0, entity=DEFAULT_ENTITY, value=20)
n3 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n4 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n5 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n6 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g1.add_node(n1)
g1.add_node(n2)
g1.add_node(n3)
g1.add_node(n4)
g1.add_node(n5)
g1.add_node(n6)
g1.add_edge(Edge(n1, n3, 0))
g1.add_edge(Edge(n3, n5, 1))
g1.add_edge(Edge(n2, n4, 0))
g1.add_edge(Edge(n4, n6, 1))
g2 = Graph()
n21 = Node(label=0, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n22 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n23 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g2.add_node(n21)
g2.add_node(n22)
g2.add_node(n23)
g2.add_edge(Edge(n21, n22, 0))
g2.add_edge(Edge(n22, n23, 1))
mappings_21 = list(g2.get_subgraph_mappings(g1))
self.assertEqual(2, len(mappings_21))
g3 = Graph()
n31 = Node(label=0, entity=DEFAULT_ENTITY, value=10)
n32 = Node(label=1, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
n33 = Node(label=2, entity=DEFAULT_ENTITY, value=SYMBOLIC_VALUE)
g3.add_node(n31)
g3.add_node(n32)
g3.add_node(n33)
g3.add_edge(Edge(n31, n32, 0))
g3.add_edge(Edge(n32, n33, 1))
mappings_31 = list(g3.get_subgraph_mappings(g1))
self.assertEqual(1, len(mappings_31))