/
datamanager.py
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/
datamanager.py
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# -*- coding: utf-8 -*-
"""
@author: truthless
"""
import os
import json
import logging
import torch
import torch.utils.data as data
from copy import deepcopy
from utils import init_session, init_goal, state_vectorize, action_vectorize
def expand_da(meta):
for k, v in meta.items():
domain, intent = k.split('-')
if intent.lower() == "request":
for pair in v:
pair.insert(1, '?')
else:
counter = {}
for pair in v:
if pair[0] == 'none':
pair.insert(1, 'none')
else:
if pair[0] in counter:
counter[pair[0]] += 1
else:
counter[pair[0]] = 1
pair.insert(1, str(counter[pair[0]]))
class DataManager():
"""Offline data manager"""
def __init__(self, data_dir, cfg):
self.data = {}
self.goal = {}
self.data_dir_new = data_dir + '/processed_data'
if os.path.exists(self.data_dir_new):
logging.info('Load processed data file')
for part in ['train','valid','test']:
with open(self.data_dir_new + '/' + part + '.json', 'r') as f:
self.data[part] = json.load(f)
with open(self.data_dir_new + '/' + part + '_goal.json', 'r') as f:
self.goal[part] = json.load(f)
else:
from dbquery import DBQuery
db = DBQuery(data_dir)
logging.info('Start preprocessing the dataset')
self._build_data(data_dir, self.data_dir_new, cfg, db)
def _build_data(self, data_dir, data_dir_new, cfg, db):
data_filename = data_dir + '/' + cfg.data_file
with open(data_filename, 'r') as f:
origin_data = json.load(f)
for part in ['train','valid','test']:
self.data[part] = []
self.goal[part] = {}
valList = []
with open(data_dir + '/' + cfg.val_file) as f:
for line in f:
valList.append(line.split('.')[0])
testList = []
with open(data_dir + '/' + cfg.test_file) as f:
for line in f:
testList.append(line.split('.')[0])
for k_sess in origin_data:
sess = origin_data[k_sess]
if k_sess in valList:
part = 'valid'
elif k_sess in testList:
part = 'test'
else:
part = 'train'
turn_data, session_data = init_session(k_sess, cfg)
init_goal(session_data, sess['goal'], cfg)
self.goal[part][k_sess] = session_data
belief_state = turn_data['belief_state']
for i, turn in enumerate(sess['log']):
turn_data['others']['turn'] = i
turn_data['others']['terminal'] = i + 2 >= len(sess['log'])
da_origin = turn['dialog_act']
expand_da(da_origin)
turn_data['belief_state'] = deepcopy(belief_state) # from previous turn
if i % 2 == 0: # user
if 'last_sys_action' in turn_data:
turn_data['history']['sys'] = dict(turn_data['history']['sys'], **turn_data['last_sys_action'])
del(turn_data['last_sys_action'])
turn_data['last_user_action'] = deepcopy(turn_data['user_action'])
turn_data['user_action'] = dict()
for domint in da_origin:
domain_intent = da_origin[domint]
_domint = domint.lower()
_domain, _intent = _domint.split('-')
if _intent == 'thank':
_intent = 'welcome'
_domint = _domain+'-'+_intent
for slot, p, value in domain_intent:
_slot = slot.lower()
_value = value.strip()
_da = '-'.join((_domint, _slot, p))
if _da in cfg.da_usr:
turn_data['user_action'][_da] = _value
if _intent == 'inform':
inform_da = _domain+'-'+_slot+'-1'
if inform_da in cfg.inform_da:
belief_state['inform'][_domain][_slot] = _value
elif _intent == 'request':
request_da = _domain+'-'+_slot
if request_da in cfg.request_da:
belief_state['request'][_domain].add(_slot)
else: # sys
if 'last_user_action' in turn_data:
turn_data['history']['user'] = dict(turn_data['history']['user'], **turn_data['last_user_action'])
del(turn_data['last_user_action'])
turn_data['last_sys_action'] = deepcopy(turn_data['sys_action'])
turn_data['sys_action'] = dict()
for domint in da_origin:
domain_intent = da_origin[domint]
_domint = domint.lower()
_domain, _intent = _domint.split('-')
for slot, p, value in domain_intent:
_slot = slot.lower()
_value = value.strip()
_da = '-'.join((_domint, _slot, p))
if _da in cfg.da:
turn_data['sys_action'][_da] = _value
if _intent == 'inform' and _domain in belief_state['request']:
belief_state['request'][_domain].discard(_slot)
elif _intent == 'book' and _slot == 'ref':
for domain in belief_state['request']:
if _slot in belief_state['request'][domain]:
belief_state['request'][domain].remove(_slot)
break
book_status = turn['metadata']
for domain in cfg.belief_domains:
if book_status[domain]['book']['booked']:
entity = book_status[domain]['book']['booked'][0]
if domain == 'taxi':
belief_state['booked'][domain] = 'booked'
elif domain == 'train':
found = db.query(domain, [('trainID', entity['trainID'])])
belief_state['booked'][domain] = found[0]['ref']
else:
found = db.query(domain, [('name', entity['name'])])
belief_state['booked'][domain] = found[0]['ref']
if i + 1 == len(sess['log']):
turn_data['next_belief_state'] = belief_state
self.data[part].append(deepcopy(turn_data))
def _set_default(obj):
if isinstance(obj, set):
return list(obj)
raise TypeError
os.makedirs(data_dir_new)
for part in ['train','valid','test']:
with open(data_dir_new + '/' + part + '.json', 'w') as f:
self.data[part] = json.dumps(self.data[part], default=_set_default)
f.write(self.data[part])
self.data[part] = json.loads(self.data[part])
with open(data_dir_new + '/' + part + '_goal.json', 'w') as f:
self.goal[part] = json.dumps(self.goal[part], default=_set_default)
f.write(self.goal[part])
self.goal[part] = json.loads(self.goal[part])
def create_dataset(self, part, file_dir, cfg, db):
datas = self.data[part]
goals = self.goal[part]
s, a, next_s = [], [], []
for idx, turn_data in enumerate(datas):
if turn_data['others']['turn'] % 2 == 0:
continue
turn_data['user_goal'] = goals[turn_data['others']['session_id']]
s.append(torch.Tensor(state_vectorize(turn_data, cfg, db, True)))
a.append(torch.Tensor(action_vectorize(turn_data, cfg)))
if not int(turn_data['others']['terminal']):
next_s.append(torch.Tensor(state_vectorize(datas[idx+2], cfg, db, True)))
else:
next_turn_data = deepcopy(turn_data)
next_turn_data['others']['turn'] = -1
next_turn_data['user_action'] = {}
next_turn_data['last_sys_action'] = next_turn_data['sys_action']
next_turn_data['sys_action'] = {}
next_turn_data['belief_state'] = next_turn_data['next_belief_state']
next_s.append(torch.Tensor(state_vectorize(next_turn_data, cfg, db, True)))
torch.save((s, a, next_s), file_dir)
def create_dataset_rl(self, part, batchsz, cfg, db):
logging.debug('start loading rl {}'.format(part))
file_dir = self.data_dir_new + '/' + part + '.pt'
if not os.path.exists(file_dir):
self.create_dataset(part, file_dir, cfg, db)
s, a, _ = torch.load(file_dir)
dataset = DatasetRL(s, a)
dataloader = data.DataLoader(dataset, batchsz, True)
logging.debug('finish loading rl {}'.format(part))
return dataloader
def create_dataset_irl(self, part, batchsz, cfg, db):
logging.debug('start loading irl {}'.format(part))
file_dir = self.data_dir_new + '/' + part + '.pt'
if not os.path.exists(file_dir):
self.create_dataset(part, file_dir, cfg, db)
s, a, next_s = torch.load(file_dir)
dataset = DatasetIrl(s, a, next_s)
dataloader = data.DataLoader(dataset, batchsz, True)
logging.debug('finish loading irl {}'.format(part))
return dataloader
class DatasetRL(data.Dataset):
def __init__(self, s_s, a_s):
self.s_s = s_s
self.a_s = a_s
self.num_total = len(s_s)
def __getitem__(self, index):
s = self.s_s[index]
a = self.a_s[index]
return s, a
def __len__(self):
return self.num_total
class DatasetIrl(data.Dataset):
def __init__(self, s_s, a_s, next_s_s):
self.s_s = s_s
self.a_s = a_s
self.next_s_s = next_s_s
self.num_total = len(s_s)
def __getitem__(self, index):
s = self.s_s[index]
a = self.a_s[index]
next_s = self.next_s_s[index]
return s, a, next_s
def __len__(self):
return self.num_total