/
tracker.py
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/
tracker.py
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# -*- coding: utf-8 -*-
"""
@author: truthless
"""
import random
import torch
from copy import deepcopy
from dbquery import DBQuery
from datamanager import expand_da
class StateTracker(object):
def __init__(self, data_dir, config):
self.time_step = 0
self.cfg = config
self.db = DBQuery(data_dir)
self.topic = 'NONE'
def _action_to_dict(self, das):
da_dict = {}
for da, value in das.items():
domain, intent, slot, p = da.split('-')
domint = '-'.join((domain, intent))
if domint not in da_dict:
da_dict[domint] = []
da_dict[domint].append([slot, value])
return da_dict
def _dict_to_vec(self, das):
da_vector = torch.zeros(self.cfg.a_dim_usr, dtype=torch.int32)
expand_da(das)
for domint in das:
pairs = das[domint]
for slot, p, value in pairs:
da = '-'.join((domint, slot, p)).lower()
if da in self.cfg.dau2idx:
idx = self.cfg.dau2idx[da]
da_vector[idx] = 1
return da_vector
def _mask_user_goal(self, goal):
domain_ordering = list(goal['domain_ordering'])
if 'hospital' in goal:
del(goal['hospital'])
domain_ordering.remove('hospital')
if 'police' in goal:
del(goal['police'])
domain_ordering.remove('police')
goal['domain_ordering'] = tuple(domain_ordering)
def get_entities(self, s, domain):
origin = s['belief_state']['inform'][domain].items()
constraint = []
for k, v in origin:
if k in self.cfg.mapping[domain]:
constraint.append((self.cfg.mapping[domain][k], v))
entities = self.db.query(domain, constraint)
random.shuffle(entities)
return entities
def update_belief_sys(self, old_s, a):
"""
update belief state with sys action
"""
s = deepcopy(old_s)
a_index = torch.nonzero(a) # get multiple da indices
self.time_step += 1
s['others']['turn'] = self.time_step
# update sys/user dialog act
s['history']['sys'] = dict(s['history']['sys'], **s['last_sys_action'])
del(s['last_sys_action'])
s['last_user_action'] = s['user_action']
s['user_action'] = dict()
# update belief part
das = [self.cfg.idx2da[idx.item()] for idx in a_index]
das = [da.split('-') for da in das]
sorted(das, key=lambda x:x[0]) # sort by domain
entities = [] if self.topic == 'NONE' else self.get_entities(s, self.topic)
for domain, intent, slot, p in das:
_domain = self.topic if domain == 'booking' else domain
if domain in self.cfg.belief_domains and domain != self.topic:
self.topic = domain
entities = self.get_entities(s, domain)
da = '-'.join((domain, intent, slot, p))
if intent in ['nooffer', 'nobook']:
if slot in s['belief_state']['inform'][_domain]:
s['sys_action'][da] = s['belief_state']['inform'][_domain][slot]
else:
s['sys_action'][da] = 'none'
elif slot == 'choice':
s['sys_action'][da] = str(len(entities))
elif p == 'none':
s['sys_action'][da] = 'none'
elif p == '?':
s['sys_action'][da] = '?'
else:
num = int(p) - 1
if len(entities) > num and slot in self.cfg.mapping[_domain]:
typ = self.cfg.mapping[_domain][slot]
s['sys_action'][da] = entities[num][typ]
else:
s['sys_action'][da] = 'none'
if intent == 'inform' and _domain != 'NONE':
s['belief_state']['request'][_domain].discard(slot)
# booked
if intent == 'inform' and slot == 'car': # taxi
if not s['belief_state']['booked']['taxi']:
s['belief_state']['booked']['taxi'] == 'booked'
elif intent == 'offerbooked' and slot == 'ref': # train
s['belief_state']['request']['train'].discard('ref')
if not s['belief_state']['booked']['train'] and entities:
s['belief_state']['booked']['train'] = entities[0]['ref']
elif intent == 'book' and slot == 'ref': # attraction, hotel, restaurant
if _domain not in ['attraction', 'hotel', 'restaurant']:
continue
s['belief_state']['request'][_domain].discard('ref')
if not s['belief_state']['booked'][_domain] and entities:
# save entity id
s['belief_state']['booked'][_domain] = entities[0]['ref']
return s
def update_belief_usr(self, old_s, a, terminal):
"""
update belief state with user action
"""
s = deepcopy(old_s)
a_index = torch.nonzero(a) # get multiple da indices
self.time_step += 1
s['others']['turn'] = self.time_step
s['others']['terminal'] = terminal
# update sys/user dialog act
s['history']['user'] = dict(s['history']['user'], **s['last_user_action'])
del(s['last_user_action'])
s['last_sys_action'] = s['sys_action']
s['sys_action'] = dict()
# update belief part
das = [self.cfg.idx2dau[idx.item()] for idx in a_index]
das = [da.split('-') for da in das]
sorted(das, key=lambda x:x[0]) # sort by domain
for domain, intent, slot, p in das:
if domain in self.cfg.belief_domains and domain != self.topic:
self.topic = domain
da = '-'.join((domain, intent, slot, p))
if p == 'none':
s['user_action'][da] = 'none'
elif p == '?':
s['user_action'][da] = '?'
else:
if slot in s['user_goal']['inform'][domain]:
s['user_action'][da] = s['user_goal']['inform'][domain][slot]
else:
s['user_action'][da] = 'dont care'
if slot != 'none':
if intent == 'inform':
# update constraints with reasonable value according to user goal
if slot in s['user_goal']['inform'][domain]:
s['belief_state']['inform'][domain][slot] = s['user_goal']['inform'][domain][slot] # value
else:
s['belief_state']['inform'][domain][slot] = 'dont care'
elif intent == 'request':
s['belief_state']['request'][domain].add(slot)
return s
def reset(self, random_seed=None):
"""
Args:
random_seed (int):
Returns:
init_state (dict):
"""
pass
def step(self, s, sys_a):
"""
Args:
s (dict):
sys_a (vector):
Returns:
next_s (dict):
terminal (bool):
"""
pass