def connect_matching_pair_edges(self, Gu: nx.MultiGraph,
ls, rs, obj_node_id='obj',
connect_obj_rel_edges=False) -> nx.Graph:
"""
Ground truth (_gt_)generator function for a combined graph. Used for training S_0
:param Gu: A (unconnected) composed graph of Gs, Gt. No relational links between head nodes,
thus, the number_connected_components(Gs|Gt) = number of object nodes in each graph.
:param Gs: The source Graph, i.e., the text graph representation
:param Gt: The target Graph, i.e., the grounding (image features) graph representation
:param obj_node_id: the identifier determining a obj (or head) node
:return: matching_pairs: List of matching pair tuples between Gs, Gt
"""
matching_pairs, unmatched_pairs = self.get_matching_pairs_in_bipartite_graph(Gu, ls, rs, obj_node_id)
NDV = Gu.nodes(data=True)
# Connect the matching pairs if not connected #
for pair in matching_pairs:
s_node, t_node = pair
if not Gu.has_edge(s_node, t_node):
Gu.add_edge(s_node, t_node, '<gt>')
# Connect Attr Nodes #
is_head_node = lambda x: obj_node_id in x
Ns = nx.neighbors(Gu, s_node)
Nt = list(nx.neighbors(Gu, t_node))
for ns in Ns:
if is_head_node(ns):
continue
# Check label equality only, 'val' equality already verified
ns_label = NDV[ns]['label']
#logger.debug(f'Source attr node label = {ns_label}')
# TODO: potential issue here, Nt should always have a matching attr node
for nt in filter(lambda x: NDV[x]['label'] == ns_label, Nt):
if not Gu.has_edge(ns, nt):
Gu.add_edge(ns, nt, key='<gt>')
if connect_obj_rel_edges:
# TODO: Add a obj relation edge among all Gs, Gt obj nodes. Actually
# should be done earlier in the parse cycle.
pass
return Gu
def add_weak_gene(gene: EdgeGene, graph: nx.MultiGraph) -> bool:
key = gene.A1 + gene.A2
edge = (gene.P1, gene.P2, key)
if any(starmap(
lambda p, a: p in graph and not any(x == a for x in get_node_space(graph, p)),
[(gene.P1, gene.A1), (gene.P2, gene.A2)])):
return False
if graph.has_edge(gene.P1, gene.P2):
keys = graph[gene.P1][gene.P2]
if len(keys) > 1:
return False
graph.remove_edge(gene.P1, gene.P2, next(iter(keys)))
graph.add_edge(*edge, gene=gene)
return True
def add_strong_gene(gene: EdgeGene, graph: nx.MultiGraph, coupling_threshold: float) -> bool:
key = gene.A1 + gene.A2
edge = (gene.P1, gene.P2, key)
if not graph.has_edge(gene.P1, gene.P2):
graph.add_edge(*edge, gene=gene)
return True
keys = graph[gene.P1][gene.P2]
if key in keys:
return False
if len(keys) > 1:
graph.add_edge(*edge, gene=gene)
return True
if graph[gene.P1][gene.P2][next(iter(keys))]['gene'].C < coupling_threshold:
graph.remove_edge(gene.P1, gene.P2, next(iter(keys)))
graph.add_edge(*edge, gene=gene)
return True
def is_independent_set(g: MultiGraph, f: set) -> bool: for edge in itertools.combinations(f, 2): if g.has_edge(edge[0], edge[1]): return False return True
def is_independent_set(g: MultiGraph, f: set) -> bool:
for edge in itertools.combinations(f, 2):
if g.has_edge(edge[0], edge[1]):
return False
return True
