/
utils.py
440 lines (385 loc) · 18.4 KB
/
utils.py
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# -*- coding: utf-8 -*-
"""
@author: ryuichi takanobu
@modified: anubhav sachan
"""
import time
import logging
import os
import numpy as np
import argparse
import torch
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--log_dir', type=str, default='log', help='Logging directory')
parser.add_argument('--data_dir', type=str, default='data', help='Data directory')
parser.add_argument('--save_dir', type=str, default='model_multi', help='Directory to store model')
parser.add_argument('--load', type=str, default='', help='File name to load trained model')
parser.add_argument('--pretrain', type=bool, default=False, help='Set to pretrain')
parser.add_argument('--test', type=bool, default=False, help='Set to inference')
parser.add_argument('--config', type=str, default='multiwoz', help='Dataset to use')
parser.add_argument('--test_case', type=int, default=1000, help='Number of test cases')
parser.add_argument('--save_per_epoch', type=int, default=4, help="Save model every XXX epoches")
parser.add_argument('--print_per_batch', type=int, default=200, help="Print log every XXX batches")
parser.add_argument('--epoch', type=int, default=48, help='Max number of epoch')
parser.add_argument('--process', type=int, default=8, help='Process number')
parser.add_argument('--batchsz', type=int, default=32, help='Batch size')
parser.add_argument('--batchsz_traj', type=int, default=512, help='Batch size to collect trajectories')
parser.add_argument('--policy_weight_sys', type=float, default=2.5, help='Pos weight on system policy pretraining')
parser.add_argument('--policy_weight_usr', type=float, default=4, help='Pos weight on user policy pretraining')
parser.add_argument('--lr_policy', type=float, default=1e-3, help='Learning rate of dialog policy')
parser.add_argument('--lr_vnet', type=float, default=3e-5, help='Learning rate of value network')
parser.add_argument('--weight_decay', type=float, default=1e-5, help='Weight decay (L2 penalty)')
parser.add_argument('--gamma', type=float, default=0.99, help='Discounted factor')
parser.add_argument('--clip', type=float, default=10, help='Gradient clipping')
parser.add_argument('--interval', type=int, default=400, help='Update interval of target network')
return parser
def discard(dic, key, value=None):
if key in dic:
if value is None or dic[key] == value:
del(dic[key])
def init_session(key, cfg):
# shared info
turn_data = {}
turn_data['others'] = {'session_id': key, 'turn': 0, 'terminal': False, 'change': False}
turn_data['sys_action'] = dict()
turn_data['user_action'] = dict()
# belief & goal state
turn_data['belief_state'] = {}
turn_data['goal_state'] = {}
for domain in cfg.belief_domains:
turn_data['belief_state'][domain] = {}
turn_data['goal_state'][domain] = {}
# user goal
session_data = {}
for domain in cfg.belief_domains:
session_data[domain] = {}
return turn_data, session_data
def init_session_dstc(key, cfg):
# shared info
turn_data = {}
turn_data['others'] = {'session_id': key, 'turn': 0, 'terminal': False, 'change': False}
turn_data['sys_action'] = dict()
turn_data['user_action'] = dict()
# belief & goal state
turn_data['belief_state'] = {}
turn_data['goal_state'] = {}
for domain in cfg.belief_domains:
turn_data['belief_state'][domain] = {}
turn_data['goal_state'][domain] = {}
# user goal
session_data = {}
for domain in cfg.belief_domains:
session_data[domain] = {}
return turn_data, session_data
def init_goal(goal, state, off_goal, cfg):
for domain in cfg.belief_domains:
if domain in off_goal and off_goal[domain]:
domain_data = off_goal[domain]
# constraint
if 'info' in domain_data:
for slot, value in domain_data['info'].items():
slot = cfg.map_inverse[domain][slot]
# single slot value for user goal
inform_da = domain+'-'+slot
if inform_da in cfg.inform_da_usr:
goal[domain][slot] = value
state[domain][slot] = value
if 'fail_info' in domain_data and domain_data['fail_info']:
goal[domain]['final'] = {}
for slot, value in domain_data['fail_info'].items():
slot = cfg.map_inverse[domain][slot]
# single slot value for user goal
inform_da = domain+'-'+slot
if inform_da in cfg.inform_da_usr:
goal[domain]['final'][slot] = goal[domain][slot]
goal[domain][slot] = value
state[domain][slot] = value
# booking
if 'book' in domain_data:
goal[domain]['book'] = True
for slot, value in domain_data['book'].items():
if slot in cfg.map_inverse[domain]:
slot = cfg.map_inverse[domain][slot]
# single slot value for user goal
inform_da = domain+'-'+slot
if inform_da in cfg.inform_da_usr:
goal[domain][slot] = value
state[domain][slot] = value
if 'fail_book' in domain_data and domain_data['fail_book']:
if 'final' not in goal[domain]:
goal[domain]['final'] = {}
for slot, value in domain_data['fail_book'].items():
if slot in cfg.map_inverse[domain]:
slot = cfg.map_inverse[domain][slot]
# single slot value for user goal
inform_da = domain+'-'+slot
if inform_da in cfg.inform_da_usr:
goal[domain]['final'][slot] = goal[domain][slot]
goal[domain][slot] = value
state[domain][slot] = value
# request
if 'reqt' in domain_data:
for slot in domain_data['reqt']:
slot = cfg.map_inverse[domain][slot]
request_da = domain+'-'+slot
if request_da in cfg.request_da_usr:
goal[domain][slot] = '?'
state[domain][slot] = '?'
def init_goal_dstc(goal, state, off_goal, cfg):
for domain in cfg.belief_domains:
if domain in off_goal and off_goal[domain]:
domain_data = off_goal[domain]
# for slot, value in
# # constraint
# if 'info' in domain_data:
# for slot, value in domain_data['info'].items():
# slot = cfg.map_inverse[domain][slot]
# # single slot value for user goal
# inform_da = domain+'-'+slot
# if inform_da in cfg.inform_da_usr:
# goal[domain][slot] = value
# state[domain][slot] = value
# if 'fail_info' in domain_data and domain_data['fail_info']:
# goal[domain]['final'] = {}
# for slot, value in domain_data['fail_info'].items():
# slot = cfg.map_inverse[domain][slot]
# # single slot value for user goal
# inform_da = domain+'-'+slot
# if inform_da in cfg.inform_da_usr:
# goal[domain]['final'][slot] = goal[domain][slot]
# goal[domain][slot] = value
# state[domain][slot] = value
# # booking
# if 'book' in domain_data:
# goal[domain]['book'] = True
# for slot, value in domain_data['book'].items():
# if slot in cfg.map_inverse[domain]:
# slot = cfg.map_inverse[domain][slot]
# # single slot value for user goal
# inform_da = domain+'-'+slot
# if inform_da in cfg.inform_da_usr:
# goal[domain][slot] = value
# state[domain][slot] = value
# if 'fail_book' in domain_data and domain_data['fail_book']:
# if 'final' not in goal[domain]:
# goal[domain]['final'] = {}
# for slot, value in domain_data['fail_book'].items():
# if slot in cfg.map_inverse[domain]:
# slot = cfg.map_inverse[domain][slot]
# # single slot value for user goal
# inform_da = domain+'-'+slot
# if inform_da in cfg.inform_da_usr:
# goal[domain]['final'][slot] = goal[domain][slot]
# goal[domain][slot] = value
# state[domain][slot] = value
# # request
# if 'reqt' in domain_data:
# for slot in domain_data['reqt']:
# slot = cfg.map_inverse[domain][slot]
# request_da = domain+'-'+slot
# if request_da in cfg.request_da_usr:
# goal[domain][slot] = '?'
# state[domain][slot] = '?'
def reload(state, goal, domain):
state[domain] = {}
for key in goal[domain]:
if key != 'final':
state[domain][key] = goal[domain][key]
if 'final' in goal[domain]:
for key in goal[domain]['final']:
goal[domain][key] = goal[domain]['final'][key]
state[domain][key] = goal[domain][key]
del(goal[domain]['final'])
def init_logging_handler(log_dir, extra=''):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
current_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
stderr_handler = logging.StreamHandler()
file_handler = logging.FileHandler('{}/log_{}.txt'.format(log_dir, current_time+extra))
logging.basicConfig(handlers=[stderr_handler, file_handler])
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
def to_device(data):
if type(data) == dict:
for k, v in data.items():
data[k] = v.to(device=DEVICE)
else:
for idx, item in enumerate(data):
data[idx] = item.to(device=DEVICE)
return data
def check_constraint(slot, val_usr, val_sys):
try:
if slot == 'arrive':
val1 = int(val_usr.split(':')[0]) * 100 + int(val_usr.split(':')[1])
val2 = int(val_sys.split(':')[0]) * 100 + int(val_sys.split(':')[1])
if val1 < val2:
return True
elif slot == 'leave':
val1 = int(val_usr.split(':')[0]) * 100 + int(val_usr.split(':')[1])
val2 = int(val_sys.split(':')[0]) * 100 + int(val_sys.split(':')[1])
if val1 > val2:
return True
else:
if val_usr != val_sys:
return True
return False
except:
return False
def state_vectorize(state, config, db, noisy=False):
"""
state: dict_keys(['user_action', 'sys_action', 'select_entity', 'belief_state', 'others'])
state_vec: [user_act, last_sys_act, inform, request, book, degree, entropy]
"""
user_act = np.zeros(len(config.da_usr))
for da in state['user_action']:
user_act[config.da2idx_u[da]] = 1.
last_sys_act = np.zeros(len(config.da))
for da in state['sys_action']:
last_sys_act[config.da2idx[da]] = 1.
inform = np.zeros(len(config.inform_da))
request = np.zeros(len(config.request_da))
for domain in state['belief_state']:
for slot, value in state['belief_state'][domain].items():
key = domain+'-'+slot
if value == '?':
if key in config.request2idx:
request[config.request2idx[key]] = 1.
else:
if key in config.inform2idx:
inform[config.inform2idx[key]] = 1.
# select entity
book = np.zeros(len(config.belief_domains))
for domain in state['belief_state']:
if 'booked' in state['belief_state'][domain]:
book[config.domain2idx[domain]] = 1.
degree, entropy = db.pointer(state['belief_state'], config.mapping, config.db_domains, config.requestable, noisy)
final = 1. if state['others']['terminal'] else 0.
state_vec = np.r_[user_act, last_sys_act, inform, request, book, degree, final]
assert len(state_vec) == config.s_dim
return state_vec
def state_vectorize_dstc(state, config):
"""
state: dict_keys(['user_action', 'sys_action', 'select_entity', 'belief_state', 'others'])
state_vec: [user_act, last_sys_act, final]
"""
user_act = np.zeros(len(config.da_usr))
for da in state['user_action']:
user_act[config.da2idx_u[da]] = 1.
last_sys_act = np.zeros(len(config.da))
for da in state['sys_action']:
last_sys_act[config.da2idx[da]] = 1.
final = 1. if state['others']['terminal'] else 0.
state_vec = np.r_[user_act, last_sys_act, final]
assert len(state_vec) == config.s_dim
return state_vec
def action_vectorize(action, config):
act_vec = np.zeros(config.a_dim)
for da in action:
act_vec[config.da2idx[da]] = 1
return act_vec
def state_vectorize_user_dstc(state, config):
"""
state: dict_keys(['user_action', 'sys_action', 'user_goal', 'goal_state', 'others'])
state_vec: [sys_act, last_user_act, inform, request, focus, inconsistency, nooffer]
"""
sys_act = np.zeros(len(config.da))
for da in state['sys_action']:
sys_act[config.da2idx[da]] = 1.
last_user_act = np.zeros(len(config.da_usr))
for da in state['user_action']:
last_user_act[config.da2idx_u[da]] = 1.
# inform = np.zeros(len(config.inform_da_usr))
# request = np.zeros(len(config.request_da_usr))
# for domain in state['goal_state']:
# if domain in state['invisible_domains']:
# continue
# for slot, value in state['goal_state'][domain].items():
# key = domain+'-'+slot
# if value == '?':
# if key in config.request2idx_u:
# request[config.request2idx_u[key]] = 1.
# else:
# if key in config.inform2idx_u and slot in state['user_goal'][domain]\
# and state['user_goal'][domain][slot] != '?':
# inform[config.inform2idx_u[key]] = 1.
# focus = np.zeros(len(config.belief_domains))
# if current_domain:
# focus[config.domain2idx[current_domain]] = 1.
# inconsistency = np.zeros(len(config.inform_da_usr))
# nooffer = np.zeros(len(config.belief_domains))
# for da, value in state['sys_action'].items():
# domain, intent, slot, p = da.split('-')
# if intent in ['inform', 'recommend', 'offerbook', 'offerbooked']:
# key = domain+'-'+slot
# if key in config.inform2idx_u and slot in state['user_goal'][domain]:
# refer = state['user_goal'][domain][slot]
# if refer != '?' and check_constraint(slot, refer, value):
# inconsistency[config.inform2idx_u[key]] = 1.
# if intent in ['nooffer', 'nobook'] and current_domain:
# nooffer[config.domain2idx[current_domain]] = 1.
state_vec = np.r_[sys_act, last_user_act]
assert len(state_vec) == config.s_dim_usr
return state_vec
def action_vectorize_user_dstc(action, terminal, config):
act_vec = np.zeros(config.a_dim_usr)
for da in action:
act_vec[config.da2idx_u[da]] = 1
if terminal:
act_vec[-1] = 1
return act_vec
def state_vectorize_user(state, config, current_domain):
"""
state: dict_keys(['user_action', 'sys_action', 'user_goal', 'goal_state', 'others'])
state_vec: [sys_act, last_user_act, inform, request, focus, inconsistency, nooffer]
"""
sys_act = np.zeros(len(config.da))
for da in state['sys_action']:
sys_act[config.da2idx[da]] = 1.
last_user_act = np.zeros(len(config.da_usr))
for da in state['user_action']:
last_user_act[config.da2idx_u[da]] = 1.
inform = np.zeros(len(config.inform_da_usr))
request = np.zeros(len(config.request_da_usr))
for domain in state['goal_state']:
if domain in state['invisible_domains']:
continue
for slot, value in state['goal_state'][domain].items():
key = domain+'-'+slot
if value == '?':
if key in config.request2idx_u:
request[config.request2idx_u[key]] = 1.
else:
if key in config.inform2idx_u and slot in state['user_goal'][domain]\
and state['user_goal'][domain][slot] != '?':
inform[config.inform2idx_u[key]] = 1.
focus = np.zeros(len(config.belief_domains))
if current_domain:
focus[config.domain2idx[current_domain]] = 1.
inconsistency = np.zeros(len(config.inform_da_usr))
nooffer = np.zeros(len(config.belief_domains))
for da, value in state['sys_action'].items():
domain, intent, slot, p = da.split('-')
if intent in ['inform', 'recommend', 'offerbook', 'offerbooked']:
key = domain+'-'+slot
if key in config.inform2idx_u and slot in state['user_goal'][domain]:
refer = state['user_goal'][domain][slot]
if refer != '?' and check_constraint(slot, refer, value):
inconsistency[config.inform2idx_u[key]] = 1.
if intent in ['nooffer', 'nobook'] and current_domain:
nooffer[config.domain2idx[current_domain]] = 1.
state_vec = np.r_[sys_act, last_user_act, inform, request, inconsistency, nooffer]
assert len(state_vec) == config.s_dim_usr
return state_vec
def action_vectorize_user(action, terminal, config):
act_vec = np.zeros(config.a_dim_usr)
for da in action:
act_vec[config.da2idx_u[da]] = 1
if terminal:
act_vec[-1] = 1
return act_vec
def reparameterize(mu, logvar):
std = (0.5*logvar).exp()
eps = torch.randn_like(std)
return eps.mul(std) + mu