def __init__(self):
self.history_savedir = None
self.detail_savedir = None
self.logger = None
self.user = self.create_user()
self.rule_policy = RulePolicy()
self.dst = DialogStateTracker(UserPersonal, FLAGS.print, self.logger)
self.data_manager = DataManager(os.path.join(BASE_DIR, 'data/tmp'))
self.nlu_manager = NLUManager(NLU_save_path_dict)
# self.nlg_template = NLG_template
self.turn_num = 1
self.dialog_history = []
class OnlineStoreApp(App):
dataManager = DataManager()
ordersList = ListProperty([])
def app_func(self):
'''Wrapper functions for the async processes.
'''
def run_wrapper():
# Run the Kivy UI
self.async_run()
exit(0)
return asyncio.gather(run_wrapper())
def __init__(self, **kwargs):
super().__init__(**kwargs)
def build(self):
return OrderScreen()
def setUp(self):
removeLeftOverDbFiles()
DataManagerUnitTest.dm = DataManager(
configObj=DataManagerUnitTest.testConfig)
if 'chatting' in SysAct.keys():
strategy = random.choice([0,1,2])
if strategy == 0:
nl += '您好，我是中国移动业务咨询机器人，可以帮助您进行套餐查找、业务咨询、个人信息查询等，请问您需要什么帮助吗？'
elif strategy == 1:
nl += '抱歉，您说的这些我不太理解，但我可以在套餐查找、业务咨询、个人信息查询等方面为您提供帮助的~'
else:
nl += '请不要再调戏我了，问一些套餐查找、业务咨询、个人信息查询方面的问题吧'
# if 'repeat' in SysAct.keys():
# nl += "抱歉，能再重述一遍么？"
return nl
if __name__ == '__main__':
data_manager = DataManager('../data/tmp')
offer_entity = data_manager.SearchingByConstraints('套餐', {"功能费": [700, 900]})[0]
compared_entities = data_manager.SearchingByConstraints('套餐', {"功能费": [700, 900]})
domain = '套餐'
SysAct = {
'ex0': {
'offer': offer_entity,
'inform':['产品介绍', '套餐内容_国内主叫', '套餐内容_国内流量', '套餐内容_国内短信', '结转规则', '是否包含港澳台地区']},
'ex1': #self.UsrAct == "告知":
{'offer': offer_entity,
'inform': ["产品介绍"],
'reqmore': None, # None 是因为 reqmore 没有参数
'domain':domain},
'ex2':
{'request': ["套餐内容_国内流量", "套餐内容_国内主叫"],
'domain': domain},
def train_requestable(data_tmp_path):
"""
用于训练模型，先训练完存好了才能用
训练用 early stopping
:param data_tmp_path: data tmp 文件夹位置
"""
print('载入数据管理器...')
data_manager = DataManager(data_tmp_path)
print('载入训练数据...')
informable_slot_datasets, requestable_slot_datasets = generate_dataset(
data_manager.DialogData)
print('载入 requestable slot detector...')
init_learning_rate = 0.005
graph = tf.Graph()
with graph.as_default():
requestable_slots_models = {}
for k, v in All_requestable_slots_order.items():
requestable_slots_models[k] = RequestableSlotDector(
str(v), learning_rate=init_learning_rate)
with tf.Session(graph=graph,
config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.group(tf.global_variables_initializer()))
saver = tf.train.Saver()
# saver.restore(sess, "./ckpt/requestable/model.ckpt")
# 训练 requestable slots
requestable_slots_F1s = {}
for slot, model in requestable_slots_models.items():
average_loss = 0
learning_rate = init_learning_rate
best_F1 = 0
tolerance = 30
tolerance_count = 0
display_step = 10
for step in range(5000):
step += 1
batch_data, batch_output = requestable_slot_datasets[
slot].next_batch()
char_emb_matrix, word_emb_matrix, seqlen = data_manager.sent2num(
batch_data)
_, training_loss = sess.run(
[model.train_op, model.final_loss],
feed_dict={
model.char_emb_matrix: char_emb_matrix,
model.word_emb_matrix: word_emb_matrix,
model.output: batch_output
})
average_loss += training_loss / display_step
if step % display_step == 0:
batch_data, batch_output = requestable_slot_datasets[
slot].get_testset()
char_emb_matrix, word_emb_matrix, seqlen = data_manager.sent2num(
batch_data)
pred, accu = sess.run(
[model.predict, model.accuracy],
feed_dict={
model.char_emb_matrix: char_emb_matrix,
model.word_emb_matrix: word_emb_matrix,
model.output: batch_output
})
F1 = get_F1score(batch_output, pred.tolist())
if best_F1 < F1:
best_F1 = F1
tolerance_count = 0
if not os.path.exists("./ckpt/requestable/"):
os.makedirs("./ckpt/requestable/")
saver.save(sess, "./ckpt/requestable/model.ckpt")
if tolerance_count == tolerance:
break
print("%s, step % 4d, loss %0.4f, F1 %0.4f, accu %0.4f" %
(slot, step, average_loss, F1, accu))
average_loss = 0
tolerance_count += 1
learning_rate = max(learning_rate * 0.98, 0.001)
sess.run(model.update_lr,
feed_dict={model.new_lr: learning_rate})
print("requestable slot: %s, best F1 %0.4f" % (slot, best_F1))
requestable_slots_F1s[slot] = best_F1
print(requestable_slots_F1s)
print(
sum(requestable_slots_F1s.values()) /
len(requestable_slots_F1s.values()))
def train_informable(data_tmp_path):
"""
用于训练模型，先训练完存好了才能用
训练用 early stopping
:param data_tmp_path: data tmp 文件夹位置
"""
print('载入数据管理器...')
data_manager = DataManager(data_tmp_path)
print('载入训练数据...')
informable_slot_datasets, requestable_slot_datasets = generate_dataset(
data_manager.DialogData)
print('载入 informable slot detector ...')
init_learning_rate = 0.005
informable_batch_ratios = { # 不同slot 的minibatch ratio
"通话时长": [2, 8, 8, 8],
"流量": [4, 8, 8, 8],
"功能费": [4, 8, 8, 8]
}
graph = tf.Graph()
with graph.as_default():
informable_slots_models = {
"功能费": InformableSlotDector('cost',
learning_rate=init_learning_rate),
"流量": InformableSlotDector('data',
learning_rate=init_learning_rate),
"通话时长": InformableSlotDector('time',
learning_rate=init_learning_rate),
}
with tf.Session(graph=graph,
config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.group(tf.global_variables_initializer()))
saver = tf.train.Saver()
# saver.restore(sess, "./ckpt/informable/model.ckpt")
# 训练 informable slots
informable_slots_accus = []
for slot, model in informable_slots_models.items():
learning_rate = init_learning_rate
average_loss = 0
best_accu = 0
tolerance = 20
tolerance_count = 0
display_step = 10
for step in range(5000):
step += 1
batch_data, batch_output = informable_slot_datasets[
slot].next_batch(informable_batch_ratios[slot])
char_emb_matrix, word_emb_matrix, seqlen = data_manager.sent2num(
batch_data)
_, training_loss = sess.run(
[model.train_op, model.final_loss],
feed_dict={
model.char_emb_matrix: char_emb_matrix,
model.word_emb_matrix: word_emb_matrix,
model.output: batch_output
})
average_loss += training_loss / display_step
if step % display_step == 0:
batch_data, batch_output = informable_slot_datasets[
slot].get_testset()
char_emb_matrix, word_emb_matrix, seqlen = data_manager.sent2num(
batch_data)
pred, accu = sess.run(
[model.predict, model.accuracy],
feed_dict={
model.char_emb_matrix: char_emb_matrix,
model.word_emb_matrix: word_emb_matrix,
model.output: batch_output
})
if best_accu < accu:
best_accu = accu
tolerance_count = 0
if not os.path.exists("./ckpt/informable/"):
os.makedirs("./ckpt/informable/")
saver.save(sess, "./ckpt/informable/model.ckpt")
if tolerance_count == tolerance:
break
print("%s, step % 4d, loss %0.4f, accu %0.4f" %
(slot, step, average_loss, accu))
average_loss = 0
tolerance_count += 1
learning_rate = max(learning_rate * 0.95, 0.0001)
sess.run(model.update_lr,
feed_dict={model.new_lr: learning_rate})
print("informable slot: %s, best accu %0.4f" % (slot, best_accu))
informable_slots_accus.append(best_accu)
from datetime import datetime
from data.DataManager import DataManager
from structures.NeuralNetwork import NeuralNetwork
from structures.functions import sigmoid, d_sigmoid, same, d_same
from structures.layers.DenseLayer import DenseLayer
def classify(arr):
a, b = max((x, y) for y, x in enumerate(arr))
return b
if __name__ == '__main__':
dm = DataManager('mnist.pkl.gz')
nn = NeuralNetwork()
nn.add_layer(DenseLayer(784, 400, sigmoid, d_sigmoid))
nn.add_layer(DenseLayer(400, 150, sigmoid, d_sigmoid))
nn.add_layer(DenseLayer(150, 50, sigmoid, d_sigmoid))
nn.add_layer(DenseLayer(50, 10, same, d_same))
curtime = datetime.now()
nn.train(dm.train_set, epochs=30, epoch_range=1)
t = datetime.now() - curtime
print('{} seconds for training'.format(t.seconds))
print('correct for {}% on train set'.format(nn.calculate_correct(dm.test_set)))
results = []
for x, y in dm.test_set[:75]:
res = nn.feed_forward(x)
res = dm.classify(res)
self.batch_id[user_act] += test_batchsize
batch_output = []
user_act_id = user_acts.index(user_act)
batch_output += [user_act_id] * len(batch_data)
return batch_data, batch_output, end_flag
def generate_user_act_dataset(DialogData, user_act_data_ids):
dialog_data = copy.deepcopy(DialogData)
user_act_datas = {}
for user_act, data_ids in user_act_data_ids.items():
if user_act in ['问询费用选项', '问询通话时长选项', '问询流量选项']:
user_act = '问询说明'
if user_act not in user_act_datas.keys():
user_act_datas[user_act] = []
for data_id in data_ids:
user_act_datas[user_act] += dialog_data[data_id]["用户回复示例"]
user_act_dataset = UserActDataset(user_act_datas)
return user_act_dataset
if __name__ == '__main__':
data_manager = DataManager(os.path.join(BASE_DIR, '../../../data/tmp'))
user_act_dataset = generate_user_act_dataset(data_manager.DialogData,
user_act_data_ids)
for user_act, data in user_act_dataset.data.items():
print(user_act)
print(len(data))
class PassageFetcher(object):
DATA_MANAGER = DataManager()
LINE_CACHE_SIZE = 4
LOOK_FORWARD_SIZE = 4
TAG_LIMIT = 4
NEGATORY_QUOTE = "'Remember not only to say the right thing in the right place, but far more difficult still, to leave unsaid the wrong thing at the tempting moment.' - Benjamin Franklin"
@staticmethod
def FetchAndCompilePassages(tags):
print(tags)
tags = [str(tag) for tag in tags]
# Validate tags
valid_tags = PassageFetcher.ValidateTags(tags)
print(valid_tags)
feed = valid_tags if len(
valid_tags) < PassageFetcher.TAG_LIMIT else valid_tags[
0:PassageFetcher.TAG_LIMIT]
authors = []
# Get passages
result = "\n\n".join(PassageFetcher.FetchPassages(feed, authors))
if result == None or result.isspace():
return PassageFetcher.NEGATORY_QUOTE
else:
return tuple([result, feed, authors])
@staticmethod
def FetchPassages(tags, authors):
return [PassageFetcher.PassagePipe(tag, authors) for tag in tags]
@staticmethod
def PassagePipe(tag, authors):
locations = PassageFetcher.GetLocations(tag)
if locations == None: return ""
coordinates = PassageFetcher.GetRandomLocation(locations)
try:
authors.append(Novels[coordinates[0]])
except Exception as e:
print(e)
raw_passage = PassageFetcher.ExtractPassage(coordinates)
treated_passage = PassageFetcher.TreatPassage(raw_passage)
return treated_passage
@staticmethod
def ValidateTags(tags):
### ADD THE SORT HERE ###
valid_tags = []
for tag in tags:
try:
PassageFetcher.DATA_MANAGER.DATA[tag.lower()]
valid_tags.append(tag)
except:
print(tag + " was not found.")
return valid_tags
@staticmethod
def GetLocations(tag):
try:
locations = PassageFetcher.DATA_MANAGER.DATA[tag.lower()]
except Exception as e:
print("Error: ")
return None
return locations
@staticmethod
def GetRandomLocation(locations):
return random.choice(locations)
@staticmethod
def ExtractPassage(coordinates):
line_index = 0
word_index = 0
# Open up the book
with open("./data/books/" + coordinates[0]) as file:
# Compile regex sentence pattern
line_cache = []
found_flag = False
local_timer = PassageFetcher.LOOK_FORWARD_SIZE
# Find line
for line in file:
words = line.split()
# Find word
for word in words:
# If the word is found, compile the passage response
if word_index == coordinates[2]:
found_flag = True
word_index += 1
line_index += 1
if line != '\r\n' and line != '\n' and line != '\r':
line_cache.append(line)
if len(
line_cache
) > PassageFetcher.LINE_CACHE_SIZE and not found_flag:
line_cache.pop(0)
if found_flag:
local_timer -= 1
if local_timer <= 0:
break
return "".join(line_cache)
@staticmethod
def TreatPassage(raw_passage):
pattern = re.compile(r'([A-Z][^\.!?]*[\.!?])', re.M)
sentences = pattern.findall(raw_passage)
if len(sentences) > 3:
sentences.pop(0)
sentences.pop(len(sentences) - 1)
return " ".join(sentences)