def predict(self, entity, fields=None):
panda = self.pandas[0]
score = panda.predict(entity)
if sign0(score) > 0:
self.tigress.measure(entity, panda.name)
monitor_accuracy(panda.name, self.creator, score, 'True')
return panda.name
else:
self.tigress.measure(entity, cons.DEFAULT_NONE)
monitor_accuracy(panda.name, self.creator, score, 'False')
return cons.DEFAULT_NONE
def predict(self, entity, fields=None):
panda = self.pandas[0]
score = panda.predict(entity)
if sign0(score) > 0:
self.tigress.measure(entity, panda.name)
monitor_accuracy(panda.name, self.creator, score, 'True')
return panda.name
else:
self.tigress.measure(entity, cons.DEFAULT_NONE)
monitor_accuracy(panda.name, self.creator, score, 'False')
return cons.DEFAULT_NONE
def predict(self, entity, fields=None):
query = eval(self.queryFunc)(entity)
targetIds = self.targetStore.load_all_in_ids(query, 0, self.windowSize)
targets = self.targetStore.load_all_by_ids(targetIds)
relation = MatchingRelation()
relation.set_argument(0, entity)
results = []
for target in targets:
relation.set_argument(1, target)
relation.compute()
rank = self.invertedMapping[[sign0(panda.predict(relation)) for panda in self.pandas]]
results.append((rank, target))
results.sort(reverse=True)
return results[:self.beamSize]
def predict(self, entity, fields=None):
query = eval(self.queryFunc)(entity)
targetIds = self.targetStore.load_all_in_ids(query, 0, self.windowSize)
targets = self.targetStore.load_all_by_ids(targetIds)
relation = MatchingRelation()
relation.set_argument(0, entity)
results = []
for target in targets:
relation.set_argument(1, target)
relation.compute()
rank = self.invertedMapping[[
sign0(panda.predict(relation)) for panda in self.pandas
]]
results.append((rank, target))
results.sort(reverse=True)
return results[:self.beamSize]
[ent.save() for ent in ents]
likeTS = ms.yaml2json('turtle_scripts/turtle_like.yml')
# print likeTS
likeT = ms.create_turtle(likeTS)
likeT.save()
ent = ents[0]
# print ents[0].generic()
ent._setattr('likeTravel', 'Y')
ms.crane.entityStore.save_one(ent)
ms.add_data('likeTravel', 'monk', str(ents[0]._id))
print likeT.tigress.p
print likeT.pandas[0].mantis.data
likeT.tigress.defaulting=True
likeT.save()
likeT = ms.load_turtle('likeTravel','monk')
likeT.train()
for i in ents:
ent = ms.load_entity(i._id)
print likeT.pandas[0].predict(ent)
print sign0(likeT.pandas[0].predict(ent))
# likeTS = ms.yaml2json('turtle_scripts/turtle_like.yml')
# # print likeTS
# likeT = ms.create_turtle(likeTS)
# likeT.save()
# ent = ents[0]
# # print ents[0].generic()
# ent._setattr('likeTravel', 'Y')
# ms.crane.entityStore.save_all(ents)
# print ent.generic()