def _ens_pipeline(self):
ens_pl = Pipeline(ensemble=self)
ens_pl.planner_result = self.train_result
ens_pl.finished = True
return ens_pl
def _create_subpipelines(self, primitive, prim_requirements):
mainlst = []
requirement_permutations = list(
itertools.permutations(prim_requirements))
for requirements in requirement_permutations:
#print("%s requirement: %s" % (primitive.name, requirements))
xpipe = Pipeline()
lst = [xpipe]
# Fulfill all requirements of the primitive
for requirement in requirements:
reqvalue = prim_requirements[requirement]
glues = self.glues.getPrimitivesByEffect(requirement, reqvalue)
if len(glues) == 1:
prim = glues[0]
#print("-> Adding one %s" % prim.name)
xpipe.insertPrimitiveAt(0, prim)
elif len(glues) > 1:
newlst = []
for pipe in lst:
# lst.remove(pipe)
for prim in glues:
cpipe = pipe.clone()
#print("-> Adding %s" % prim.name)
cpipe.insertPrimitiveAt(0, prim)
newlst.append(cpipe)
lst = newlst
mainlst += lst
return mainlst
def l1_to_proxy_pipeline(self, l1_pipeline):
pipeline = Pipeline()
ok = True
for prim in l1_pipeline.get_primitives():
l2prim = self.primitive_hash.get(prim.name, None)
if not l2prim:
ok = False
break
pipeline.addPrimitive(l2prim)
if ok:
return pipeline
return None
def generate_pipelines_with_hierarchy(self, level=2) -> typing.List[Pipeline]:
# ignore level for now, since only have one level hierarchy
results = []
families = self.primitive_family_mappings.get_families_by_task_type(self.task_type.value)
if self.task_type == TaskType.GRAPH_MATCHING:
# if GRAPH_MATCHING then add LINK_PREDICTION
families = families + self.primitive_family_mappings.get_families_by_task_type(TaskType.LINK_PREDICTION)
# moved to get_child_nodes
#families = sebt(families)-set(self.exclude_families
family_nodes = [self.ontology.get_family(f) for f in families]
# include / exclude family and type checks
child_nodes = self._get_child_nodes(family_nodes)
for node in child_nodes:
primitives = self.ontology.hierarchy.get_primitives_as_list(node)
if not primitives:
continue
# Inclusion checks
primitives = [p for p in primitives if self._check_primitive_include(p)]
if not primitives:
continue
weights = [p.weight for p in primitives]
# Set task type for execute_pipeline
for p in primitives:
p.task = 'Modeling'
primitive = random_choices(primitives, weights)
pipe = Pipeline(primitives=[primitive])
results.append(pipe)
return results