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_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)
