def add_data(self, entity):
targetId = entity.get_raw(self.RAW_TARGETID, None)
relevance = entity.get_raw(self.RAW_RELEVANCE, 0)
if targetId:
target = self.targetStore.load_one_by_id(targetId)
relation = MatchingRelation()
relation.set_argument(0, entity)
relation.set_argument(1, target)
relation.compute()
relation.set_raw(self.RAW_RELEVANCE, relevance)
self.tigress.supervise(self, relation)
def add_data(self, entity):
targetId = entity.get_raw(self.RAW_TARGETID, None)
relevance = entity.get_raw(self.RAW_RELEVANCE, 0)
if targetId:
target = self.targetStore.load_one_by_id(targetId)
relation = MatchingRelation()
relation.set_argument(0, entity)
relation.set_argument(1, target)
relation.compute()
relation.set_raw(self.RAW_RELEVANCE, relevance)
self.tigress.supervise(self, relation)
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]
