def predict(trainer,ent_id,creator='monk'):#How?
ent = ms.load_entity(ent_id)
print ent.desc
# return sign0(trainer.pandas[0].predict(ent))
score=trainer.pandas[0].predict(ent)
print score
return score
def get_recommended_place(collection_name,skip_num=0,request_num=5):
#print collection_name
ents = ms.load_entities(collectionName = collection_name)
user_turtle = 'likeTravel'
trainer = train(user_turtle,'monk')
rank=[]
for ent in ents:
rank.append((ent._id,predict(trainer,ent._id)))
sorted_by_score = sorted(rank, key=lambda tup: tup[1])
rst = []
for r in sorted_by_score:
ent = ms.load_entity(r[0])
e = {}
# e['place_id'] = ent.place_id
e['_id'] = ent._id
e['img_url'] = ent.img_url
e['desc'] = ent.desc
rst.append(e)
# print len(ents)
return rst[skip_num:skip_num+request_num]
[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()
def add_label(ent_id,field,value):
ent = ms.load_entity(ent_id)
ent._setattr(field,value)
ms.crane.entityStore.save_one(ent)
