基于LSTM的新闻中文文本分类——基于textCNN与textRNN

构建词语字典

def build_vocab(file_path, tokenizer, max_size, min_freq):
    # 定义词汇表字典：使用 vocab_dic = {} 初始化一个空字典，用于存储每个词及其出现频率
    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]
            """
            分割与计数：对每行内容进行处理，先用 strip() 去除首尾空白符，然后分割出需要处理的文本内容（默认以制表符\t分割）。
            使用 tokenizer(content) 对内容进行分词，然后统计每个词的出现次数
            """
            for word in tokenizer(content):
                vocab_dic[word] = vocab_dic.get(word, 0) + 1
        """
        词汇按出现频率筛选（频率大于等于 min_freq）并排序（按频率降序），最多保留 max_size 个词汇
        """
        vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[:max_size]
        '''
          重构词汇表：将筛选并排序后的词汇列表转换为字典，每个词汇映射到一个唯一的索引。
          词汇表中还额外添加了特殊标记 UNK（未知词）和 PAD（填充符），它们分别在词汇表的末尾添加。
        '''
        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})
        #  k:v ===> 词:索引
    return vocab_dic

数据集构建

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)}")
    train = load_dataset(config.train_path,vocab, config.pad_size)
    dev = load_dataset(config.dev_path,vocab, config.pad_size)
    test = load_dataset(config.test_path,vocab, config.pad_size)
    return vocab, train, dev, test

数据预处理

数据格式

首先数据格式：
文本内容以及对应过的标签

data label

数据预处理

        数据预处理:
        去除空行: 忽略空行。
        分割行: 将每一行通过制表符\t分割为content（内容）和label（标签）。
        文本转换: 使用tokenizer函数将content分词。
        序列填充或截断: 根据pad_size参数（默认为32），
        如果分词后的序列长度小于pad_size，则用vocab字典中的PAD标记进行填充；如果长度大于pad_size，则进行截断。

def load_dataset(path,vocab,tokenizer, 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), ...]

数据接口类

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

加载预训练模型

1. 设置文件路径和参数：

   • vocab_dir：词汇表的文件路径，这个文件包含从词汇到索引的映射。
   • pretrain_dir：预训练词向量文件的路径。
   • emb_dim：词向量的维度，这里设为300。
   • filename_trimmed_dir：压缩后保存新词向量的文件路径。

2. 加载词汇表：

   • 使用pickle（pkl）加载词汇表，得到word_to_id字典，它将词汇映射到一个唯一的索引。

3. 初始化词向量矩阵：

   • 创建一个随机初始化的词向量矩阵embeddings，其形状为词汇表长度×词向量维度（len(word_to_id), emb_dim）。

4. 读取预训练的词向量：

   • 打开预训练词向量文件，按行读取。
   • 对于每一行，去掉首尾空白并分割空格，得到一个列表lin，其中lin[0]是词汇，lin[1:301]是对应的300维词向量。

5. 更新词向量矩阵：

   • 如果词汇lin[0]存在于word_to_id中，找到对应的索引idx。
   • 将lin[1:301]中的字符串转换为浮点数，形成新的词向量emb。
   • 更新embeddings矩阵中的idx行，即用新的词向量替换原来的随机向量。

6. 保存词向量矩阵：

   • 使用numpy的savez_compressed方法，将更新后的embeddings矩阵压缩保存到指定路径。

    '''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:(emb_dim+1)]]
            embeddings[idx] = np.asarray(emb, dtype='float32')
    f.close()
    np.savez_compressed(filename_trimmed_dir, embeddings=embeddings)

模型定义

输入文本先通过embedding层转换为词向量表示。
添加一个维度以适配卷积操作（unsqueeze(1)）。
应用多个卷积层和池化层（conv_and_pool），然后将结果拼接。
应用Dropout。
通过全连接层得到最终分类结果。

textcnn

# coding: UTF-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'TextCNN'
        self.train_path = dataset + '/data/train.txt'                                # 训练集
        self.dev_path = dataset + '/data/dev.txt'                                    # 验证集
        self.test_path = dataset + '/data/test.txt'                                  # 测试集
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]                                # 类别名单
        self.vocab_path = dataset + '/data/vocab.pkl'                                # 词表
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果
        self.log_path = dataset + '/log/' + self.model_name
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
            if embedding != 'random' else None                                       # 预训练词向量
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 20                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度
        self.filter_sizes = (2, 3, 4)                                   # 卷积核尺寸
        self.num_filters = 256                                          # 卷积核数量(channels数)


'''Convolutional Neural Networks for Sentence Classification'''


class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.convs = nn.ModuleList(
            [nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])
        self.dropout = nn.Dropout(config.dropout)
        self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x

    def forward(self, x):
        #print (x[0].shape)
        out = self.embedding(x[0])
        out = out.unsqueeze(1)
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc(out)
        return out

textRnn模型定义

# coding: UTF-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'TextRNN'
        self.train_path = dataset + '/data/train.txt'                                # 训练集
        self.dev_path = dataset + '/data/dev.txt'                                    # 验证集
        self.test_path = dataset + '/data/test.txt'                                  # 测试集
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]                                # 类别名单
        self.vocab_path = dataset + '/data/vocab.pkl'                                # 词表
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果
        self.log_path = dataset + '/log/' + self.model_name
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
            if embedding != 'random' else None                                       # 预训练词向量
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 10                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度, 若使用了预训练词向量，则维度统一
        self.hidden_size = 128                                          # lstm隐藏层
        self.num_layers = 2                                             # lstm层数


'''Recurrent Neural Network for Text Classification with Multi-Task Learning'''


class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        self.fc = nn.Linear(config.hidden_size * 2, config.num_classes)

    def forward(self, x):
        x, _ = x
        out = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        out, _ = self.lstm(out)
        out = self.fc(out[:, -1, :])  # 句子最后时刻的 hidden state
        return out

训练、测试、验证

# 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)

github项目地址