def preprocess_raw_data(raw_data, tokenized_data):
if not os.path.exists(raw_data):
print("数据集不存在，请添加数据集")
exit(0)
pairs = []
count = 0
config = get_config()
with open(raw_data, encoding="utf-8") as file:
pair = ""
for line in file:
line = line.strip("\n").replace('/', '')
if line == "":
pairs.append(pair)
count += 1
if count % 10000 == 0:
print('已读取：', count, '轮问答对')
pair = ""
continue
elif len(pair) == 0:
pair = config["cls_token"] + line + config["sep_token"]
else:
pair = pair + line + config["sep_token"]
print("数据读取完毕，正在处理中...")
with open(tokenized_data, 'w', encoding="utf-8") as file:
for i in range(len(pairs)):
file.write(" ".join(jieba.cut(pairs[i])) + "\n")
if i % 10000 == 0:
print(len(range(len(pairs))), '处理进度：', i)
def say(respuesta):
print respuesta
command = 'sudo pico2wave -w temp/out.wav -l es-ES "' + respuesta + '"'
os.system(command)
os.system("aplay temp/out.wav")
if get_config.get_config()["modo"] == "texto":
bot.sendMessage("351857770", respuesta)
def main(_):
this_dir = os.path.dirname(os.path.realpath(__file__))
if FLAGS.seq_id == 'None':
seq = [[FLAGS.config_id, 1]]
else:
seqs = json.load(open(os.path.join(this_dir, "seqs.json"), 'r'))
seq = seqs[FLAGS.seq_id]
print(seq)
summaries = []
for config_id, num_trials in seq:
if config_id == "None":
config = get_config(FLAGS.__flags, {})
else:
# TODO : create config file (.json)
configs_path = os.path.join(this_dir,
"configs%s" % FLAGS.config_ext)
config = get_config_from_file(FLAGS.__flags, configs_path,
config_id)
print("=" * 80)
print("Config ID {}, task {}, {} trials".format(
config.config_id, config.task, num_trials))
summary = _main(config, num_trials)
summaries.append(summary)
print("=" * 80)
print("SUMMARY")
for summary in summaries:
print(summary)
def load_data(tokenized_data, dict_fn, num_sample=0):
if not os.path.exists(tokenized_data):
print("没有检测到分词数据集，请先执行pre_treat模式")
exit(0)
with open(tokenized_data, 'r', encoding="utf-8") as file:
lines = file.read().strip().split("\n")
if num_sample == 0:
sentences = [line for line in lines]
else:
sentences = [line for line in lines[:num_sample]]
config = get_config()
tokenizer = tf.keras.preprocessing.text.Tokenizer(
filters='', oov_token=config["unk_token"])
tokenizer.fit_on_texts(sentences)
input_tensor = tokenizer.texts_to_sequences(sentences)
input_tensor = tf.keras.preprocessing.sequence.pad_sequences(
input_tensor, maxlen=config["max_length"], padding="post")
with open(dict_fn, "w", encoding="utf-8") as file:
file.write(
json.dumps(tokenizer.word_index, indent=4, ensure_ascii=False))
dataset = tf.data.Dataset.from_tensor_slices(input_tensor).cache().shuffle(
config["buffer_size"]).prefetch(tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(config["batch_size"], drop_remainder=True)
steps_per_epoch = len(input_tensor) // config["batch_size"]
return dataset, tokenizer, steps_per_epoch
def main(_):
this_dir = os.path.dirname(os.path.realpath(__file__))
if FLAGS.seq_id == 'None':
seq = [[FLAGS.config_id, 1]]
else:
seqs = json.load(open(os.path.join(this_dir, "seqs.json"), 'r'))
seq = seqs[FLAGS.seq_id]
print(seq)
summaries = []
for config_id, num_trials in seq:
if config_id == "None":
config = get_config(FLAGS.__flags, {})
else:
# TODO : create config file (.json)
configs_path = os.path.join(this_dir, "configs%s" % FLAGS.config_ext)
config = get_config_from_file(FLAGS.__flags, configs_path, config_id)
print("=" * 80)
print("Config ID {}, task {}, {} trials".format(config.config_id, config.task, num_trials))
summary = _main(config, num_trials)
summaries.append(summary)
print("=" * 80)
print("SUMMARY")
for summary in summaries:
print(summary)
def main(_):
this_dir = os.path.dirname(os.path.realpath(__file__))
if FLAGS.seq_id == 'None':
seq = [[FLAGS.config_id, FLAGS.num_trials]]
else:
seqs = json.load(open(os.path.join(this_dir, "seqs.json"), 'r'))
seq = seqs[FLAGS.seq_id]
summaries = []
for config_id, num_trials in seq:
if config_id == "None":
config = get_config(FLAGS.__flags, {})
else:
# TODO : create config file (.json)
configs_path = os.path.join(this_dir,
"configs_new%s" % FLAGS.config_ext)
config = get_config_from_file(FLAGS.__flags, configs_path,
config_id)
if config.task == "all":
tasks = list(map(str, range(1, 6)))
else:
tasks = [config.task]
for task in tasks:
# FIXME : this is bad way of setting task each time
config.task = task
print("=" * 80)
print("Config ID {}, task {}, {} trials".format(
config.config_id, config.task, num_trials))
summary = _main(config, num_trials)
summaries.append(summary)
print("=" * 80)
print("SUMMARY")
for summary in summaries:
print(summary)
def main(_):
this_dir = os.path.dirname(os.path.realpath(__file__))
if FLAGS.seq_id == 'None':
seq = [[FLAGS.config_id, FLAGS.num_trials]]
else:
seqs = json.load(open(os.path.join(this_dir, "seqs.json"), 'r'))
seq = seqs[FLAGS.seq_id]
summaries = []
for config_id, num_trials in seq:
if config_id == "None":
config = get_config(FLAGS.__flags, {})
else:
# TODO : create config file (.json)
configs_path = os.path.join(this_dir, "configs_new%s" % FLAGS.config_ext)
config = get_config_from_file(FLAGS.__flags, configs_path, config_id)
if config.task == "all":
tasks = list(map(str, range(1, 6)))
else:
tasks = [config.task]
for task in tasks:
# FIXME : this is bad way of setting task each time
config.task = task
print("=" * 80)
print("Config ID {}, task {}, {} trials".format(config.config_id, config.task, num_trials))
summary = _main(config, num_trials)
summaries.append(summary)
print("=" * 80)
print("SUMMARY")
for summary in summaries:
print(summary)
def get_noticias():
periodico = get_config.get_config()['periodico']
api_key = " ## KEY DE LA API ## "
if periodico == "el pais":
link = "https://api.rss2json.com/v1/api.json?rss_url=http%3A%2F%2Fep00.epimg.net%2Frss%2Felpais%2Fportada.xml&api_key=" + api_key
elif periodico = "el mundo":
link = "https://api.rss2json.com/v1/api.json?rss_url=http%3A%2F%2Festaticos.elmundo.es%2Felmundo%2Frss%2Fportada.xml&api_key=" + api_key
def ask():
modo = get_config.get_config()["modo"]
if modo == "texto":
bot.sendMessage("## ID DE LA CONVERSACION ##", "Di algo!")
response = bot.getUpdates()
num = len(response)
print response
while len(bot.getUpdates()) == num:
time.sleep(3)
print "Nuevo mensaje"
response = bot.getUpdates()
return response[-1]["message"]["text"]
elif modo == "audio":
r = sr.Recognizer()
with sr.Microphone() as source:
print("Di algo!")
audio = r.listen(source)
try:
rec = r.recognize_google(audio, language="es-ES")
print("Has dicho " + rec)
return rec.lower()
except sr.UnknownValueError:
print("No se ha entendido el audio")
except sr.RequestError as e:
print("Ha habido un error con el reconocimiento de voz {0}".format(
e))
else:
print "Hay un error con la configuración"
def main():
config = get_config()
args = train_args.setup_train_args()
if args.seed:
train_args.set_random_seed(args)
# 初始化tokenizer
tokenizer = BertTokenizer(vocab_file=args.vocab_path)
# tokenizer的字典大小
global pad_id
# pad_id = tokenizer.convert_tokens_to_ids(PAD)
# 创建对话模型的输出目录
if not os.path.exists(args.dialogue_model_output_path):
os.mkdir(args.dialogue_model_output_path)
# 加载GPT2模型
model, n_ctx, optimizer = create_model(args, config)
# 对原始数据进行预处理,将原始语料转换成对应的token_id
# 如果当前是要训练对话生成模型
print('开始产生token')
# 不修改数据集的情况下，没必要每次训练都运行preprocess_raw_data 因为 生成的data是一样的
if not os.path.exists(args.train_tokenized_path):
file = open(args.train_tokenized_path, 'w')
preprocess_data.preprocess_raw_data(args, tokenizer, n_ctx)
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='train_accuracy')
print('开始训练')
train(model, args, tokenizer, optimizer, train_loss, train_accuracy)
print('训练结束')
def loss_function(real, pred):
config = get_config()
real = tf.reshape(real, shape=(-1, config["max_length"] - 1))
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,
reduction="none")(
real, pred)
mask = tf.cast(tf.not_equal(real, 0), tf.float32)
loss = tf.multiply(loss, mask)
return tf.reduce_mean(loss)
def response(sentence):
inputs = " ".join(jieba.cut("cls" + sentence + "sep"))
config = get_config()
tokenizer = data_utils.load_dict(config["gpt2_dict"])
inputs = [tokenizer.get(i, 1) for i in inputs.split(' ')]
# inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=config["max_length"], padding="post")
inputs = tf.convert_to_tensor(inputs)
inputs = tf.cast(tf.expand_dims(inputs, axis=0), dtype=tf.int64)
checkpoint_dir = config["checkpoint_dir"]
model = gpt2.gpt2(vocab_size=config["vocab_size"],
num_layers=config["num_layers"],
units=config["units"],
deep=config["deep"],
num_heads=config["num_heads"],
dropout=config["dropout"])
learning_rate = gpt2.CustomSchedule(config["deep"])
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir, exist_ok=True)
if os.listdir(checkpoint_dir):
checkpoint.restore(
tf.train.latest_checkpoint(checkpoint_dir)).expect_partial()
result = []
for _ in range(config["max_length"]):
# print(inputs)
# exit(0)
predictions = model(inputs=inputs, training=False)
predictions = tf.nn.softmax(predictions, axis=-1)
predictions = predictions[:, -1:, :]
predictions = tf.squeeze(predictions, axis=1)
# print(predictions)
# exit(0)
pred = tf.argmax(input=predictions, axis=-1)
print(inputs)
print(pred)
# exit(0)
if pred.numpy()[0] == 2:
break
result.append(pred.numpy()[0])
inputs = tf.concat([inputs, tf.expand_dims(pred, axis=0)], axis=-1)
print(inputs)
print(result)
return data_utils.sequences_to_texts(result, tokenizer)
def __init__(self):
self._config = get_config()['urls']
self.TREND = self._config['TREND']
self.CUSTOM = self._config['CUSTOM']
def train():
print('训练开始，正在准备数据中...')
config = get_config()
dataset, tokenizer, steps_per_epoch = data_utils.load_data(
tokenized_data=config["tokenized_corpus"],
dict_fn=config["gpt2_dict"],
num_sample=35)
train_loss = tf.keras.metrics.Mean(name="train_loss")
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name="train_accuracy")
learning_rate = gpt2.CustomSchedule(config["deep"])
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
model = gpt2.gpt2(vocab_size=config["vocab_size"],
num_layers=config["num_layers"],
units=config["units"],
deep=config["deep"],
num_heads=config["num_heads"],
dropout=config["dropout"])
checkpoint_dir = config["checkpoint_dir"]
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir, exist_ok=True)
if os.listdir(checkpoint_dir):
checkpoint.restore(
tf.train.latest_checkpoint(checkpoint_dir)).expect_partial()
for epoch in range(config["epochs"]):
print('Epoch {}/{}'.format(epoch + 1, config["epochs"]))
start_time = time.time()
train_loss.reset_states()
train_accuracy.reset_states()
step_loss = 0
batch_sum = 0
sample_sum = 0
for batch, inputs in enumerate(dataset.take(steps_per_epoch)):
inputs_real = inputs[:, 1:]
inputs = inputs[:, :-1]
with tf.GradientTape() as tape:
predictions = model(inputs)
loss = loss_function(inputs_real, predictions)
gradient = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradient, model.trainable_variables))
train_loss(loss)
train_accuracy(inputs_real, predictions)
step_loss = train_loss.result()
batch_sum = batch_sum + len(inputs)
sample_sum = steps_per_epoch * len(inputs)
print('\r',
'{}/{} [==================================]'.format(
batch_sum, sample_sum),
end='',
flush=True)
step_time = (time.time() - start_time)
sys.stdout.write(' - {:.4f}s/step - loss: {:.4f}\n'.format(
step_time, step_loss))
sys.stdout.flush()
checkpoint.save(file_prefix=os.path.join(checkpoint_dir, "ckpt"))
print("训练结束")
# -*- coding: utf-8 -*-
# Modulo que utiliza la API de OpenWeatherMap para devolver información acerce del clima epor ciudades
import json
import urllib
import mtime
from config import get_config
city = get_config.get_config()["ciudad"]
def get_ciudad_from_frase(frase):
i = 0
frase = frase.split(" ")
for palabra in frase:
if palabra == "en" or palabra == "de":
return frase[i+1]
i += 1
def traducir_weather(frase):
return {"mist":"nublado" , "scattered clouds":"nubes dispersas", "broken clouds":"nubes rotas", "clear sky":"cielo claro", "few clouds":"pocas nubes", "shower rain":"aguaceros", "rain":"lluvia", "thunderstorm":"tormenta eléctrica", "drizzle":"llovizna", "sleet":"aguanieve", "clouds":"nublado" }[frase]
def get_tiempo_from_frase(frase):
frase = frase.split(" ")
dia_actual = mtime.current_day()
hora_actual = mtime.current_hour()
def get_clima_actual(frase):
ciudad = get_ciudad_from_frase(frase)
if ciudad == None:
ciudad = city
return mcalculator.raiz(q)
else:
return "No he entendido la operación, ¿puedes repetirla, por favor?"
if "que es" in q or "quien es" in q or "quien fue" in q:
return mwiki.buscar(q)
if "noticias" in q:
return mnoticias.get_noticias()
if "cambiar configuracion" in q:
return change_config.change_config()
if "cambiar" in q and "modo" in q:
return change_config.change_mode()
else:
return "Perdona, no te he entendido"
while 1:
if get_config.get_config()["modo"] == "audio":
hey = input.ask()
while( "hola %s" % (get_config.get_oonfig()["asistente"]) not in hey):
hey = telegram_input.ask()
#print "beeeep"
question = input.ask().lower()
output.say(search(question))
# -*- coding: utf-8 -*-
# Módulo que responde a despedirse, eligiendo una respuesta eleatoria entre posibilidades que cambian según la hora del día
import datetime
from config import get_config
from random import randint
nombre = get_config.get_config()["nombre"]
farewells = [
"Adios!", "Hasta otra!",
"Que vaya bien, %s ." % (nombre),
"Vale, %s , hablamos luego" % (nombre)
]
hora = datetime.datetime.now().hour
if hora >= 6 and hora < 12:
farewells.extend(["Que tengas un buen dia!"])
elif hora >= 12 and hora < 20:
farewells.extend(
["Adios, %s. que acabes de pasar bien la tarde!" % (nombre)])
else:
farewells.extend(["Buenas noches, " + nombre])
def farewell():
return farewells[randint(0, len(farewells) - 1)]
import pandas as pd
from cleaning_data.cleaning_data import CleaningData
from data_to_excel.data_to_excel import DataToExcel
from config.get_config import get_config
from parse_pdf.parse_pdf import ParsePdf
config = get_config()
contents = ParsePdf(
config['to_delete']
).get_pdf_data(
config['pdf_filename'], config['page']
)
new_content = CleaningData().clean(contents)
data_to_excel = DataToExcel(new_content)
data_to_excel.to_excel()
final_result = data_to_excel.final_data
df = pd.DataFrame(final_result)
df.to_csv(
config['output_filename'],
index=False,
columns=
[
'Session Title', 'Title', 'Position', 'First Name',
'Middle Name', 'Last Name', 'Workplace'
from config.get_config import get_config from logger.logging import Loader config = get_config(Loader)
def __init__(self):
self.driver = webdriver.Chrome()
self.config = get_config()
self.scrapy_config = self.config['scrapy']
import comet_ml, os, sys, torch
from data import load_data
from lib.model import Model
from util import Logger, train, validation, AdamOptimizer
# load the configuration files
config_name = None
from config.get_config import get_config
model_config, data_config, exp_config = get_config(config_name)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=str(exp_config['device'])
torch.cuda.set_device(0)
# data
train_data, val_data = load_data(data_config, exp_config['batch_size'])
eval_length = data_config['eval_length']
train_epoch_size = data_config['train_epoch_size']
val_epoch_size = data_config['val_epoch_size']
# model
model = Model(**model_config).to(0)
# optimizer
optimizer = AdamOptimizer(params=model.parameters(), lr=exp_config['lr'],
grad_clip_value=exp_config['grad_clip_value'],
grad_clip_norm=exp_config['grad_clip_norm'])
logger_on = True
