def create_dataset_global(self, part, file_dir, data_dir, cfg, db):
datas = self.data[part]
goals = self.goal[part]
s_usr, s_sys, r_g, next_s_usr, next_s_sys, t = [], [], [], [], [], []
evaluator = MultiWozEvaluator(data_dir)
for idx, turn_data in enumerate(datas):
if turn_data['others']['turn'] % 2 == 0:
if turn_data['others']['turn'] == 0:
current_goal = goals[turn_data['others']['session_id']]
evaluator.add_goal(current_goal)
else:
next_s_usr.append(s_usr[-1])
if turn_data['others']['change'] and evaluator.cur_domain:
if 'final' in current_goal[evaluator.cur_domain]:
for key in current_goal[evaluator.cur_domain]['final']:
current_goal[evaluator.cur_domain][key] = current_goal[evaluator.cur_domain]['final'][key]
del(current_goal[evaluator.cur_domain]['final'])
turn_data['user_goal'] = deepcopy(current_goal)
s_usr.append(torch.Tensor(state_vectorize_user(turn_data, cfg, evaluator.cur_domain)))
evaluator.add_usr_da(turn_data['trg_user_action'])
if turn_data['others']['terminal']:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = turn_data['trg_user_action']
next_turn_data['sys_action'] = datas[idx+1]['trg_sys_action']
next_turn_data['trg_user_action'] = {}
next_turn_data['goal_state'] = datas[idx+1]['final_goal_state']
next_s_usr.append(torch.Tensor(state_vectorize_user(next_turn_data, cfg, evaluator.cur_domain)))
else:
if turn_data['others']['turn'] != 1:
next_s_sys.append(s_sys[-1])
s_sys.append(torch.Tensor(state_vectorize(turn_data, cfg, db, True)))
evaluator.add_sys_da(turn_data['trg_sys_action'])
if turn_data['others']['terminal']:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = {}
next_turn_data['sys_action'] = turn_data['trg_sys_action']
next_turn_data['trg_sys_action'] = {}
next_turn_data['belief_state'] = turn_data['final_belief_state']
next_s_sys.append(torch.Tensor(state_vectorize(next_turn_data, cfg, db, True)))
reward_g = 20 if evaluator.task_success() else -5
r_g.append(reward_g)
t.append(1)
else:
reward_g = 5 if evaluator.cur_domain and evaluator.domain_success(evaluator.cur_domain) else -1
r_g.append(reward_g)
t.append(0)
torch.save((s_usr, s_sys, r_g, next_s_usr, next_s_sys, t), file_dir)
def create_dataset_sys(self, part, file_dir, data_dir, cfg, db):
datas = self.data[part]
goals = self.goal[part]
s, a, r, next_s, t = [], [], [], [], []
evaluator = MultiWozEvaluator(data_dir)
for idx, turn_data in enumerate(datas):
if turn_data['others']['turn'] % 2 == 0:
if turn_data['others']['turn'] == 0:
evaluator.add_goal(
goals[turn_data['others']['session_id']])
evaluator.add_usr_da(turn_data['trg_user_action'])
continue
if turn_data['others']['turn'] != 1:
next_s.append(s[-1])
s.append(torch.Tensor(state_vectorize(turn_data, cfg, db, True)))
a.append(
torch.Tensor(action_vectorize(turn_data['trg_sys_action'],
cfg)))
evaluator.add_sys_da(turn_data['trg_sys_action'])
if turn_data['others']['terminal']:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = {}
next_turn_data['sys_action'] = turn_data['trg_sys_action']
next_turn_data['trg_sys_action'] = {}
next_turn_data['belief_state'] = turn_data[
'final_belief_state']
next_s.append(
torch.Tensor(state_vectorize(next_turn_data, cfg, db,
True)))
reward = 20 if evaluator.task_success(False) else -5
r.append(reward)
t.append(1)
else:
reward = 0
if evaluator.cur_domain:
for slot, value in turn_data['belief_state'][
evaluator.cur_domain].items():
if value == '?':
for da in turn_data['trg_sys_action']:
d, i, k, p = da.split('-')
if i in [
'inform', 'recommend', 'offerbook',
'offerbooked'
] and k == slot:
break
else:
# not answer request
reward -= 1
if not turn_data['trg_sys_action']:
reward -= 5
r.append(reward)
t.append(0)
torch.save((s, a, r, next_s, t), file_dir)
def create_dataset_usr(self, part, file_dir, data_dir, cfg, db):
datas = self.data[part]
goals = self.goal[part]
s, a, r, next_s, t = [], [], [], [], []
evaluator = MultiWozEvaluator(data_dir)
current_goal = None
for idx, turn_data in enumerate(datas):
if turn_data['others']['turn'] % 2 == 1:
evaluator.add_sys_da(turn_data['trg_sys_action'])
continue
if turn_data['others']['turn'] == 0:
current_goal = goals[turn_data['others']['session_id']]
evaluator.add_goal(current_goal)
else:
next_s.append(s[-1])
if turn_data['others']['change'] and evaluator.cur_domain:
if 'final' in current_goal[evaluator.cur_domain]:
for key in current_goal[evaluator.cur_domain]['final']:
current_goal[evaluator.cur_domain][key] = current_goal[evaluator.cur_domain]['final'][key]
del(current_goal[evaluator.cur_domain]['final'])
turn_data['user_goal'] = deepcopy(current_goal)
s.append(torch.Tensor(state_vectorize_user(turn_data, cfg, evaluator.cur_domain)))
a.append(torch.Tensor(action_vectorize_user(turn_data['trg_user_action'], turn_data['others']['terminal'], cfg)))
evaluator.add_usr_da(turn_data['trg_user_action'])
if turn_data['others']['terminal']:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = turn_data['trg_user_action']
next_turn_data['sys_action'] = datas[idx+1]['trg_sys_action']
next_turn_data['trg_user_action'] = {}
next_turn_data['goal_state'] = datas[idx+1]['final_goal_state']
next_s.append(torch.Tensor(state_vectorize_user(next_turn_data, cfg, evaluator.cur_domain)))
reward = 20 if evaluator.inform_F1(ansbysys=False)[1] == 1. else -5
r.append(reward)
t.append(1)
else:
reward = 0
if evaluator.cur_domain:
for da in turn_data['trg_user_action']:
d, i, k = da.split('-')
if i == 'request':
for slot, value in turn_data['goal_state'][d].items():
if value != '?' and slot in turn_data['user_goal'][d]\
and turn_data['user_goal'][d][slot] != '?':
# request before express constraint
reward -= 1
if not turn_data['trg_user_action']:
reward -= 5
r.append(reward)
t.append(0)
torch.save((s, a, r, next_s, t), file_dir)
def create_dataset_global(self, part, file_dir, data_dir, cfg, db):
"""
创建global数据，这个数据记录了用户侧和系统侧的所有状态以及奖励
"""
datas = self.data[part]
goals = self.goal[part]
s_usr, s_sys, r_g, next_s_usr, next_s_sys, t = [], [], [], [], [], []
evaluator = MultiWozEvaluator(data_dir, cfg.d)
for idx, turn_data in enumerate(datas):
if turn_data['others']['turn'] % 2 == 0:
if turn_data['others']['turn'] == 0:
current_goal = goals[turn_data['others']['session_id']]
evaluator.add_goal(current_goal)
else:
next_s_usr.append(s_usr[-1])
# 当用户目标无法满足时，切换用户目标
if turn_data['others']['change'] and evaluator.cur_domain:
if 'final' in current_goal[evaluator.cur_domain]:
for key in current_goal[evaluator.cur_domain]['final']:
current_goal[
evaluator.cur_domain][key] = current_goal[
evaluator.cur_domain]['final'][key]
del (current_goal[evaluator.cur_domain]['final'])
turn_data['user_goal'] = deepcopy(current_goal)
s_usr.append(
torch.Tensor(
state_vectorize_user(turn_data, cfg,
evaluator.cur_domain)))
evaluator.add_usr_da(turn_data['trg_user_action'])
if turn_data['others']['terminal']:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = turn_data[
'trg_user_action']
next_turn_data['sys_action'] = datas[idx +
1]['trg_sys_action']
next_turn_data['trg_user_action'] = {}
next_turn_data['goal_state'] = datas[idx +
1]['final_goal_state']
next_s_usr.append(
torch.Tensor(
state_vectorize_user(next_turn_data, cfg,
evaluator.cur_domain)))
else:
if turn_data['others']['turn'] != 1:
next_s_sys.append(s_sys[-1])
s_sys.append(
torch.Tensor(state_vectorize(turn_data, cfg, db, True)))
evaluator.add_sys_da(turn_data['trg_sys_action'])
if turn_data['others']['terminal']:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = {}
next_turn_data['sys_action'] = turn_data['trg_sys_action']
next_turn_data['trg_sys_action'] = {}
next_turn_data['belief_state'] = turn_data[
'final_belief_state']
next_s_sys.append(
torch.Tensor(
state_vectorize(next_turn_data, cfg, db, True)))
# 由于多轮对话系统，默认最终都是系统说结束语，因此通过系统判断任务是否成功作为整体的奖励
reward_g = 20 if evaluator.task_success() else -5
r_g.append(reward_g)
t.append(1)
else:
# 增加domain_success的奖励，其他的则每增加一轮减少一点损失，用于缩短轮数 todo 什么是 domain_success
reward_g = 5 if evaluator.cur_domain and evaluator.domain_success(
evaluator.cur_domain) else -1
r_g.append(reward_g)
t.append(0)
torch.save((s_usr, s_sys, r_g, next_s_usr, next_s_sys, t), file_dir)
def create_dataset_sys(self, part, file_dir, data_dir, cfg, db):
"""
创建sys的训练数据
"""
datas = self.data[part]
goals = self.goal[part]
# 系统状态+系统动作+回报+上一轮系统状态+末轮标志位
s, a, r, next_s, t = [], [], [], [], []
# evaluator 全称记录数据
evaluator = MultiWozEvaluator(data_dir, cfg.d)
for idx, turn_data in enumerate(datas):
# user
# 用户侧并没有做数据的更新操作
if turn_data['others']['turn'] % 2 == 0:
# 首轮对话加载用户目标
if turn_data['others']['turn'] == 0:
evaluator.add_goal(
goals[turn_data['others']['session_id']])
#
evaluator.add_usr_da(turn_data['trg_user_action'])
continue
# 错位了，确实表示的下一轮状态
if turn_data['others']['turn'] != 1:
next_s.append(s[-1])
# 将当前数据转化为状态向量
s.append(torch.Tensor(state_vectorize(turn_data, cfg, db, True)))
# 将当前动作转化为动作向量
a.append(
torch.Tensor(action_vectorize(turn_data['trg_sys_action'],
cfg)))
evaluator.add_sys_da(turn_data['trg_sys_action'])
if turn_data['others']['terminal']:
# 结束轮
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = {}
next_turn_data['sys_action'] = turn_data['trg_sys_action']
next_turn_data['trg_sys_action'] = {}
next_turn_data['belief_state'] = turn_data[
'final_belief_state']
# 统计next_s
next_s.append(
torch.Tensor(state_vectorize(next_turn_data, cfg, db,
True)))
# 统计奖励, 对于系统动作，判决任务是否完成作为最终奖励依据,
# 系统是否完成了真实用户动作所提出的订阅请求，且系统是否回答了真实用户动作所咨询的所有问题
reward = 20 if evaluator.task_success(False) else -5
r.append(reward)
# 结束标志位
t.append(1)
else:
reward = 0
if evaluator.cur_domain:
for slot, value in turn_data['belief_state'][
evaluator.cur_domain].items():
if value == '?':
for da in turn_data['trg_sys_action']:
d, i, k, p = da.split('-')
if i in [
'inform', 'recommend', 'offerbook',
'offerbooked'
] and k == slot:
break
else:
# not answer request
# 没有完成对belief_state中的提问，奖励减一
reward -= 1
if not turn_data['trg_sys_action']:
# 本轮没有回复奖励减五
reward -= 5
r.append(reward)
t.append(0)
torch.save((s, a, r, next_s, t), file_dir)