Dataset and task analysis
Project theme : topic classification of news, 10 classification tasks
generally require data preprocessing for NLP projects, but since the data of this project has been processed, data preprocessing is not required, but data preprocessing Processing is central to NLP projects.
THUCNews folder
train.txt (training set)
dev.txt (verification set)
test.txt (test set)
class.txt
Basic process analysis of data processing for text tasks
step1: word segmentation or word segmentation
step2: ID replacement
Corpus (known in vocab.pkl)
step3 vector mapping
Embedding (map a word into a vector embedding_SougouNews.npz
embedding_Tencent.npz)
overall process
Command line parameters and debug
#--model TextRNN
parser = argparse.ArgumentParser(description='Chinese Text Classification')
parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer')
parser.add_argument('--embedding', default='pre_trained', type=str, help='random or pre_trained')
parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
args = parser.parse_args()
Parsing of the above command line parameter code
parser = argparse.ArgumentParser(description='Chinese Text Classification'): Create an argument parser object for parsing command line parameters.
parser.add_argument('–model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer'): Add a command line named --model Parameter, used to specify the text classification model to use, it needs to provide a value of type string, which is one of the following models: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer.
parser.add_argument('–embedding', default='pre_trained', type=str, help='random or pre_trained'): Add a command line parameter named --embedding to specify the type of word embedding, which can Either 'random' (randomly initialized word vectors) or 'pre_trained' (pretrained word vectors).
parser.add_argument('--word', default=False, type=bool, help='True for word, False for char'): Add a command-line parameter named --word to specify is based on word (True ) to classify or to classify based on characters (False).
args = parser.parse_args(): parse the command line arguments and store the result in the args object, you can access the values specified by the user on the command line through args.model, args.embedding and args.word.
What this code does is allow the user to select different models, word embedding types, and text classification based on words or characters from the command line. Users need to provide corresponding parameters when running the script, for example: python script.py --model TextCNN --embedding pre_trained --word True.
run.py
import time
import torch
import numpy as np
from train_eval import train, init_network
from importlib import import_module
import argparse
from tensorboardX import SummaryWriter
#--model TextRNN
parser = argparse.ArgumentParser(description='Chinese Text Classification')
parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer')
parser.add_argument('--embedding', default='pre_trained', type=str, help='random or pre_trained')
parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
args = parser.parse_args()
if __name__ == '__main__':
dataset = 'THUCNews' # 数据集
# 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:random
embedding = 'embedding_SougouNews.npz'
if args.embedding == 'random':
embedding = 'random'
model_name = args.model #TextCNN, TextRNN,
if model_name == 'FastText':
from utils_fasttext import build_dataset, build_iterator, get_time_dif
embedding = 'random'
else:
from utils import build_dataset, build_iterator, get_time_dif
x = import_module('models.' + model_name)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data, config)
test_iter = build_iterator(test_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# train
config.n_vocab = len(vocab)
model = x.Model(config).to(config.device)
writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime()))
if model_name != 'Transformer':
init_network(model)
print(model.parameters)
train(config, model, train_iter, dev_iter, test_iter,writer)
train_eval.py
# coding: UTF-8
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn import metrics
import time
from utils import get_time_dif
from tensorboardX import SummaryWriter
# 权重初始化，默认xavier
def init_network(model, method='xavier', exclude='embedding', seed=123):
for name, w in model.named_parameters():
if exclude not in name:
if 'weight' in name:
if method == 'xavier':
nn.init.xavier_normal_(w)
elif method == 'kaiming':
nn.init.kaiming_normal_(w)
else:
nn.init.normal_(w)
elif 'bias' in name:
nn.init.constant_(w, 0)
else:
pass
def train(config, model, train_iter, dev_iter, test_iter,writer):
start_time = time.time()
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
# 学习率指数衰减，每次epoch：学习率 = gamma * 学习率
# scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf')
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
#writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime()))
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
# scheduler.step() # 学习率衰减
for i, (trains, labels) in enumerate(train_iter):
#print (trains[0].shape)
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
if total_batch % 100 == 0:
# 每多少轮输出在训练集和验证集上的效果
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
writer.add_scalar("loss/train", loss.item(), total_batch)
writer.add_scalar("loss/dev", dev_loss, total_batch)
writer.add_scalar("acc/train", train_acc, total_batch)
writer.add_scalar("acc/dev", dev_acc, total_batch)
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
writer.close()
test(config, model, test_iter)
def test(config, model, test_iter):
# test
model.load_state_dict(torch.load(config.save_path))
model.eval()
start_time = time.time()
test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True)
msg = 'Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}'
print(msg.format(test_loss, test_acc))
print("Precision, Recall and F1-Score...")
print(test_report)
print("Confusion Matrix...")
print(test_confusion)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
def evaluate(config, model, data_iter, test=False):
model.eval()
loss_total = 0
predict_all = np.array([], dtype=int)
labels_all = np.array([], dtype=int)
with torch.no_grad():
for texts, labels in data_iter:
outputs = model(texts)
loss = F.cross_entropy(outputs, labels)
loss_total += loss
labels = labels.data.cpu().numpy()
predic = torch.max(outputs.data, 1)[1].cpu().numpy()
labels_all = np.append(labels_all, labels)
predict_all = np.append(predict_all, predic)
acc = metrics.accuracy_score(labels_all, predict_all)
if test:
report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
confusion = metrics.confusion_matrix(labels_all, predict_all)
return acc, loss_total / len(data_iter), report, confusion
return acc, loss_total / len(data_iter)
utils.py
# coding: UTF-8
import os
import torch
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from datetime import timedelta
MAX_VOCAB_SIZE = 10000 # 词表长度限制
UNK, PAD = '<UNK>', '<PAD>' # 未知字，padding符号
def build_vocab(file_path, tokenizer, max_size, min_freq):
vocab_dic = {
}
with open(file_path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content = lin.split('\t')[0]
for word in tokenizer(content):
vocab_dic[word] = vocab_dic.get(word, 0) + 1
vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[:max_size]
vocab_dic = {
word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
vocab_dic.update({
UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
return vocab_dic
def build_dataset(config, ues_word):
if ues_word:
tokenizer = lambda x: x.split(' ') # 以空格隔开，word-level
else:
tokenizer = lambda x: [y for y in x] # char-level
if os.path.exists(config.vocab_path):
vocab = pkl.load(open(config.vocab_path, 'rb'))
else:
vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(vocab, open(config.vocab_path, 'wb'))
print(f"Vocab size: {
len(vocab)}")
def load_dataset(path, pad_size=32):
contents = []
with open(path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content, label = lin.split('\t')
words_line = []
token = tokenizer(content)
seq_len = len(token)
if pad_size:
if len(token) < pad_size:
token.extend([vocab.get(PAD)] * (pad_size - len(token)))
else:
token = token[:pad_size]
seq_len = pad_size
# word to id
for word in token:
words_line.append(vocab.get(word, vocab.get(UNK)))
contents.append((words_line, int(label), seq_len))
return contents # [([...], 0), ([...], 1), ...]
train = load_dataset(config.train_path, config.pad_size)
dev = load_dataset(config.dev_path, config.pad_size)
test = load_dataset(config.test_path, config.pad_size)
return vocab, train, dev, test
class DatasetIterater(object):
def __init__(self, batches, batch_size, device):
self.batch_size = batch_size
self.batches = batches
self.n_batches = len(batches) // batch_size
self.residue = False # 记录batch数量是否为整数
if len(batches) % self.n_batches != 0:
self.residue = True
self.index = 0
self.device = device
def _to_tensor(self, datas):
x = torch.LongTensor([_[0] for _ in datas]).to(self.device)
y = torch.LongTensor([_[1] for _ in datas]).to(self.device)
# pad前的长度(超过pad_size的设为pad_size)
seq_len = torch.LongTensor([_[2] for _ in datas]).to(self.device)
return (x, seq_len), y
def __next__(self):
if self.residue and self.index == self.n_batches:
batches = self.batches[self.index * self.batch_size: len(self.batches)]
self.index += 1
batches = self._to_tensor(batches)
return batches
elif self.index > self.n_batches:
self.index = 0
raise StopIteration
else:
batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
self.index += 1
batches = self._to_tensor(batches)
return batches
def __iter__(self):
return self
def __len__(self):
if self.residue:
return self.n_batches + 1
else:
return self.n_batches
def build_iterator(dataset, config):
iter = DatasetIterater(dataset, config.batch_size, config.device)
return iter
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
if __name__ == "__main__":
'''提取预训练词向量'''
# 下面的目录、文件名按需更改。
train_dir = "./THUCNews/data/train.txt"
vocab_dir = "./THUCNews/data/vocab.pkl"
pretrain_dir = "./THUCNews/data/sgns.sogou.char"
emb_dim = 300
filename_trimmed_dir = "./THUCNews/data/embedding_SougouNews"
if os.path.exists(vocab_dir):
word_to_id = pkl.load(open(vocab_dir, 'rb'))
else:
# tokenizer = lambda x: x.split(' ') # 以词为单位构建词表(数据集中词之间以空格隔开)
tokenizer = lambda x: [y for y in x] # 以字为单位构建词表
word_to_id = build_vocab(train_dir, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(word_to_id, open(vocab_dir, 'wb'))
embeddings = np.random.rand(len(word_to_id), emb_dim)
f = open(pretrain_dir, "r", encoding='UTF-8')
for i, line in enumerate(f.readlines()):
# if i == 0: # 若第一行是标题，则跳过
# continue
lin = line.strip().split(" ")
if lin[0] in word_to_id:
idx = word_to_id[lin[0]]
emb = [float(x) for x in lin[1:301]]
embeddings[idx] = np.asarray(emb, dtype='float32')
f.close()
np.savez_compressed(filename_trimmed_dir, embeddings=embeddings)
utils_fasttext.py
# coding: UTF-8
import os
import torch
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from datetime import timedelta
MAX_VOCAB_SIZE = 10000
UNK, PAD = '<UNK>', '<PAD>'
def build_vocab(file_path, tokenizer, max_size, min_freq):
vocab_dic = {
}
with open(file_path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content = lin.split('\t')[0]
for word in tokenizer(content):
vocab_dic[word] = vocab_dic.get(word, 0) + 1
vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[:max_size]
vocab_dic = {
word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
vocab_dic.update({
UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
return vocab_dic
def build_dataset(config, ues_word):
if ues_word:
tokenizer = lambda x: x.split(' ') # 以空格隔开，word-level
else:
tokenizer = lambda x: [y for y in x] # char-level
if os.path.exists(config.vocab_path):
vocab = pkl.load(open(config.vocab_path, 'rb'))
else:
vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
pkl.dump(vocab, open(config.vocab_path, 'wb'))
print(f"Vocab size: {
len(vocab)}")
def biGramHash(sequence, t, buckets):
t1 = sequence[t - 1] if t - 1 >= 0 else 0
return (t1 * 14918087) % buckets
def triGramHash(sequence, t, buckets):
t1 = sequence[t - 1] if t - 1 >= 0 else 0
t2 = sequence[t - 2] if t - 2 >= 0 else 0
return (t2 * 14918087 * 18408749 + t1 * 14918087) % buckets
def load_dataset(path, pad_size=32):
contents = []
with open(path, 'r', encoding='UTF-8') as f:
for line in tqdm(f):
lin = line.strip()
if not lin:
continue
content, label = lin.split('\t')
words_line = []
token = tokenizer(content)
seq_len = len(token)
if pad_size:
if len(token) < pad_size:
token.extend([vocab.get(PAD)] * (pad_size - len(token)))
else:
token = token[:pad_size]
seq_len = pad_size
# word to id
for word in token:
words_line.append(vocab.get(word, vocab.get(UNK)))
# fasttext ngram
buckets = config.n_gram_vocab
bigram = []
trigram = []
# ------ngram------
for i in range(pad_size):
bigram.append(biGramHash(words_line, i, buckets))
trigram.append(triGramHash(words_line, i, buckets))
# -----------------
contents.append((words_line, int(label), seq_len, bigram, trigram))
return contents # [([...], 0), ([...], 1), ...]
train = load_dataset(config.train_path, config.pad_size)
dev = load_dataset(config.dev_path, config.pad_size)
test = load_dataset(config.test_path, config.pad_size)
return vocab, train, dev, test
class DatasetIterater(object):
def __init__(self, batches, batch_size, device):
self.batch_size = batch_size
self.batches = batches
self.n_batches = len(batches) // batch_size
self.residue = False # 记录batch数量是否为整数
if len(batches) % self.n_batches != 0:
self.residue = True
self.index = 0
self.device = device
def _to_tensor(self, datas):
# xx = [xxx[2] for xxx in datas]
# indexx = np.argsort(xx)[::-1]
# datas = np.array(datas)[indexx]
x = torch.LongTensor([_[0] for _ in datas]).to(self.device)
y = torch.LongTensor([_[1] for _ in datas]).to(self.device)
bigram = torch.LongTensor([_[3] for _ in datas]).to(self.device)
trigram = torch.LongTensor([_[4] for _ in datas]).to(self.device)
# pad前的长度(超过pad_size的设为pad_size)
seq_len = torch.LongTensor([_[2] for _ in datas]).to(self.device)
return (x, seq_len, bigram, trigram), y
def __next__(self):
if self.residue and self.index == self.n_batches:
batches = self.batches[self.index * self.batch_size: len(self.batches)]
self.index += 1
batches = self._to_tensor(batches)
return batches
elif self.index > self.n_batches:
self.index = 0
raise StopIteration
else:
batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
self.index += 1
batches = self._to_tensor(batches)
return batches
def __iter__(self):
return self
def __len__(self):
if self.residue:
return self.n_batches + 1
else:
return self.n_batches
def build_iterator(dataset, config):
iter = DatasetIterater(dataset, config.batch_size, config.device)
return iter
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
if __name__ == "__main__":
'''提取预训练词向量'''
vocab_dir = "./THUCNews/data/vocab.pkl"
pretrain_dir = "./THUCNews/data/sgns.sogou.char"
emb_dim = 300
filename_trimmed_dir = "./THUCNews/data/vocab.embedding.sougou"
word_to_id = pkl.load(open(vocab_dir, 'rb'))
embeddings = np.random.rand(len(word_to_id), emb_dim)
f = open(pretrain_dir, "r", encoding='UTF-8')
for i, line in enumerate(f.readlines()):
# if i == 0: # 若第一行是标题，则跳过
# continue
lin = line.strip().split(" ")
if lin[0] in word_to_id:
idx = word_to_id[lin[0]]
emb = [float(x) for x in lin[1:301]]
embeddings[idx] = np.asarray(emb, dtype='float32')
f.close()
np.savez_compressed(filename_trimmed_dir, embeddings=embeddings)