def get_largest_matching_pattern(
pattern: PerceptionGraphPattern,
graph: PerceptionGraph,
*,
debug_callback: Optional[DebugCallableType] = None,
graph_logger: Optional[GraphLogger] = None,
ontology: Ontology,
match_ratio: Optional[float] = None,
match_mode: MatchMode,
trim_after_match: Optional[Callable[[PerceptionGraphPattern],
PerceptionGraphPattern]] = None,
allowed_matches: ImmutableSetMultiDict[Any, Any] = immutablesetmultidict(),
) -> Optional[PerceptionGraphPattern]:
""" Helper function to return the largest matching `PerceptionGraphPattern`
for learner from a perception pattern and graph pair."""
matching = pattern.matcher(
graph,
debug_callback=debug_callback,
match_mode=match_mode,
allowed_matches=allowed_matches,
)
return matching.relax_pattern_until_it_matches(
graph_logger=graph_logger,
ontology=ontology,
min_ratio=match_ratio,
trim_after_match=trim_after_match,
)
def test_successfully_extending_partial_match():
"""
Tests whether we can match a perception pattern against a perception graph
when initializing the search from a partial match.
"""
target_object = BOX
# Create train and test templates for the target objects
train_obj_object = object_variable("obj-with-color", target_object)
obj_template = Phase1SituationTemplate(
"colored-obj-object", salient_object_variables=[train_obj_object])
template = all_possible(obj_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY)
train_curriculum = phase1_instances("all obj situations",
situations=template)
perceptual_representation = only(train_curriculum.instances())[2]
# Original perception graph
perception = PerceptionGraph.from_frame(
perceptual_representation.frames[0])
# Create a perception pattern for the whole thing
# and also a perception pattern for a subset of the whole pattern
whole_perception_pattern = PerceptionGraphPattern.from_graph(
perception).perception_graph_pattern
partial_digraph = whole_perception_pattern.copy_as_digraph()
partial_digraph.remove_nodes_from([
node for node in partial_digraph.nodes
if isinstance(node, IsColorNodePredicate)
])
partial_perception_pattern = PerceptionGraphPattern(partial_digraph)
# get our initial match by matching the partial pattern
matcher = partial_perception_pattern.matcher(
perception, match_mode=MatchMode.NON_OBJECT)
partial_match: PerceptionGraphPatternMatch = first(
matcher.matches(use_lookahead_pruning=True))
partial_mapping = partial_match.pattern_node_to_matched_graph_node
# Try to extend the partial mapping, to create a complete mapping
matcher_2 = whole_perception_pattern.matcher(
perception, match_mode=MatchMode.NON_OBJECT)
complete_match: PerceptionGraphPatternMatch = first(
matcher_2.matches(initial_partial_match=partial_mapping,
use_lookahead_pruning=True),
None,
)
complete_mapping = complete_match.pattern_node_to_matched_graph_node
assert len(complete_mapping) == len(perception.copy_as_digraph().nodes)
assert len(complete_mapping) == len(
whole_perception_pattern.copy_as_digraph().nodes)