基于torchtext的文本分类

%matplotlib inline

基于torchtext的文本分类

在这个项目，我们展示怎样去使用torchtext库建立数据集用于文本分类统计，用户可以去灵活调用，

通过iterator访问原始数据
建立数据处理的pipeline,将原始文本字符串转换为torch.tensor，用于模型训练.
Shuffle and iterate the data with torch.utils.data.DataLoader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>__

访问原始数据集iterator

torchtext库提供了几个原始数据集iterator,他们提供原始文本，例如AG_NEWS 数据集itrators 提供原始数据以元组的形式（label，text）

import torch
from torchtext.datasets import AG_NEWS
# train_iter = AG_NEWS(split='train')

# 如果不给root参数，会自动从网站下载。
train_iter = AG_NEWS(root='./data/ag_news_csv/', split='train')

train_iter

<torchtext.data.datasets_utils._RawTextIterableDataset at 0x7f125d5ba100>

next(train_iter)
>>> (3, "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - 
Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green 
again.")

next(train_iter)
>>> (3, 'Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private 
investment firm Carlyle Group,\\which has a reputation for making well-timed 
and occasionally\\controversial plays in the defense industry, has quietly 
placed\\its bets on another part of the market.')

next(train_iter)
>>> (3, "Oil and Economy Cloud Stocks' Outlook (Reuters) Reuters - Soaring 
crude prices plus worries\\about the economy and the outlook for earnings are 
expected to\\hang over the stock market next week during the depth of 
the\\summer doldrums.")

准备数据处理pipelines

我们已经重新定义了Torchtext库的非常基本的组件，包括vocab，词向量，tokrnizer。这些是原始文本字符串的基本数据处理构建块。
这是使用tokenizer和vocab进行典型NLP数据处理的示例。第一步是使用原始训练数据集构建词汇表。通过在Vocab类的构造函数中设置参数，用户可以拥有自定义的vocab。例如，要包含toknes的最小频率``min_freq''。

from torchtext.data.utils import get_tokenizer
from collections import Counter
from torchtext.vocab import Vocab
"""
get_tokenizer函数的作用是创建一个分词器，将语料喂给相应的分词器，可以根据不同分词函数的规则完成分词，
分词器支持’basic_english’，‘spacy’，‘moses’，‘toktok’，‘revtok’，'subword’等规则
"""

tokenizer = get_tokenizer('basic_english')
# train_iter = AG_NEWS(split='train')
train_iter = AG_NEWS(root='./data/ag_news_csv/', split='train')

#实例化一个计数器
counter = Counter()
for (label, line) in train_iter:
    #update后的参数可以是：可迭代对象或者映射操作原理：如果要更新的关键字已存在，则对它的值进行求和；如果不存在，则添加
    counter.update(tokenizer(line))
vocab = Vocab(counter, min_freq=1)

[vocab[token] for token in ['here', 'is', 'an', 'example']]

[476, 22, 31, 5298]

vacab块将token列表转换为整数
::

[vocab[token] for token in ['here', 'is', 'an', 'example']]
>>> [476, 22, 31, 5298]

使用分词器（tokenizer）和词汇表（vocablary）准备文本处理管道（pipeline）。文本和标签管道将用于处理来自数据集迭代器（iterator）的原始数据字符串。

text_pipeline = lambda x: [vocab[token] for token in tokenizer(x)]
label_pipeline = lambda x: int(x) - 1

The text pipeline converts a text string into a list of integers based on the lookup table defined in the vocabulary. The label pipeline converts the label into integers. For example,
text_pipeline根据vocab中定义的查找表将文本字符串转换为整数列表。 label_pipeline将标签转换为整数。例如
::

text_pipeline('here is the an example')
>>> [475, 21, 2, 30, 5286]
label_pipeline('10')
>>> 9

生成数据批处理和迭代器

torch.utils.data.DataLoader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>__
被推荐给pytorch用户(使用说明 here <https://pytorch.org/tutorials/beginner/data_loading_tutorial.html>__).

它与实现getitem()和len()协议的map-style数据集一起使用，并表示成map从索引/键到数据样本。它也适用于shutffle argument False的可迭代数据集
在发送到模型之前，colate_fn函数对从DataLoader生成的批样本起作用。 colate_fn的输入是一批数据，其批大小在DataLoader中，而colate_fn根据先前声明的数据处理管道对其进行处理。请注意此处，并确保将“ collate_fn”声明为顶级def。这样可以确保该功能在每个工作程序中均可用。
在此示例中，原始数据批处理输入中的文本条目被打包到一个列表中，并作为单个张量连接到nn.EmbeddingBag输入中。 offset是定界符的张量，表示文本张量中各个序列的起始索引。 Label是一个张量，用于保存单个文本条目的标签。

from torch.utils.data import DataLoader
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def collate_batch(batch):
    label_list, text_list, offsets = [], [], [0]
    for (_label, _text) in batch:
         label_list.append(label_pipeline(_label))
         processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)
         text_list.append(processed_text)
         offsets.append(processed_text.size(0))
    label_list = torch.tensor(label_list, dtype=torch.int64)
    #cusum 返回维度dim中输入元素的累计和
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    #orch.cat是将两个张量（tensor）拼接在一起，cat是concatnate的意思，即拼接，联系在一起。
    text_list = torch.cat(text_list)
    return label_list.to(device), text_list.to(device), offsets.to(device)    

# train_iter = AG_NEWS(split='train')
train_iter = AG_NEWS(root='./data/ag_news_csv/', split='train')
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)

定义模型

该模型由nn.EmbeddingBag <https://pytorch.org/docs/stable/nn.html?highlight=embeddingbag#torch.nn.EmbeddingBag>__层以及用于分类目的的线性层组成。nn.EmbeddingBag，默认模式为“ mean”，计算嵌入的“袋”的平均值。尽管此处的文本条目具有不同的长度，但是nn.EmbeddingBag模块此处不需要填充，因为文本长度以偏移量保存。

此外，由于nn.EmbeddingBag累积在fly的嵌入平均跨度
嵌入中的

nn.EmbeddingBag可以提升表现和内存效率来处理一系列张量。

image

from torch import nn

class TextClassificationModel(nn.Module):

    def __init__(self, vocab_size, embed_dim, num_class):
        # vocab_size: 代表整个语料包含的单词总数
        # embed_dim: 代表词嵌入的维度
        # num_class: 代表是文本分类的类别数
        super(TextClassificationModel, self).__init__()
        
        # 实例化EMbeddingBag层的对象, 传入3个参数, 分别代表单词总数, 词嵌入的维度, 进行梯度求解时只更新部分权重
        self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
        # 实例化全连接线性层的对象, 两个参数分别代表输入的维度和输出的维度
        self.fc = nn.Linear(embed_dim, num_class)
        # 对定义的所有层权重进行初始化
        self.init_weights()

    def init_weights(self):
        # 首先给定初始化权重的值域范围
        initrange = 0.5
        # 各层的权重使用均匀分布进行初始化
        self.embedding.weight.data.uniform_(-initrange, initrange)
        self.fc.weight.data.uniform_(-initrange, initrange)
        self.fc.bias.data.zero_()

    def forward(self, text, offsets):
        # text: 代表文本进过数字化映射后的张量
        embedded = self.embedding(text, offsets)
        return self.fc(embedded)

启动实例

AG_NEWS数据集有4个标签，因此num_class = 4
::

1 : World
2 : Sports
3 : Business
4 : Sci/Tec

我们建立一个嵌入维度为64的模型。vcab_size的大小等于词汇实例的长度。 num_class等于标签的数量，

# train_iter = AG_NEWS(split='train')
train_iter = AG_NEWS(root='./data/ag_news_csv/', split='train')
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModel(vocab_size, emsize, num_class).to(device)

Define functions to train the model and evaluate results.

import time

def train(dataloader):
    model.train()
    # 初始化训练损失值和准确率
    total_acc, total_count = 0, 0
    log_interval = 500
    start_time = time.time()

    for idx, (label, text, offsets) in enumerate(dataloader):
        # 训练模型的第一步: 将优化器的梯度清零
        optimizer.zero_grad()
        # 将一个批次的数据输入模型中, 进行预测
        predited_label = model(text, offsets)
        # 用损失函数来计算预测值和真实标签之间的损失
        loss = criterion(predited_label, label)
        # 进行反向传播的计算
        loss.backward()
        
        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
        # 参数更新
        optimizer.step()
        # 计算该批次的准确率并加到总准确率上, 注意一点这里加的是准确的数字
        total_acc += (predited_label.argmax(1) == label).sum().item()
        total_count += label.size(0)
        # 500轮打印一次
        if idx % log_interval == 0 and idx > 0:
            elapsed = time.time() - start_time
            print('| epoch {:3d} | {:5d}/{:5d} batches '
                  '| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
                                              total_acc/total_count))
            total_acc, total_count = 0, 0
            start_time = time.time()

def evaluate(dataloader):
    '''
    在model(test_datasets)之前，需要加上model.eval(). 
    否则的话，有输入数据，即使不训练，它也会改变权值。
    这是model中含有batch normalization层所带来的的性质。
    '''
    model.eval()
    total_acc, total_count = 0, 0
    # 注意: 在验证阶段, 一定要保证模型的参数不发生改变, 也就是不求梯度
    with torch.no_grad():
        for idx, (label, text, offsets) in enumerate(dataloader):
            # 将验证数据输入模型进行预测
            predited_label = model(text, offsets)
            #计算损失值
            loss = criterion(predited_label, label)
            # 将该批次的损失值累加到总损失值中
            total_acc += (predited_label.argmax(1) == label).sum().item()
            total_count += label.size(0)
    return total_acc/total_count

分割数据集并运行模型

由于原始的AG_NEWS没有验证数据集，因此我们拆分了训练
将数据集划分为训练/验证集，其分割比率为0.95（train），0.05（valid）。
我们使用torch.utils.data.dataset.random_split <https://pytorch.org/docs/stable/data.html?highlight=random_split#torch.utils.data.random_split>__
Pytorch核心库中的函数

CrossEntropyLoss <https://pytorch.org/docs/stable/nn.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss>__
criterionnn.LogSoftmax()和nn.NLLLoss()合并到一个类中。
在训练带有C类的分类问题时很有用。

SGD <https://pytorch.org/docs/stable/_modules/torch/optim/sgd.html>__
implements stochastic gradient descent method as the optimizer.
实现随机梯度下降法作为优化程序。最初的
学习率设置为5.0。
StepLR <https://pytorch.org/docs/master/_modules/torch/optim/lr_scheduler.html#StepLR>__
在这里，StepLR用于通过epochs调整学习率。

from torch.utils.data.dataset import random_split
# Hyperparameters
EPOCHS = 10 # epoch  指定训练的轮次
LR = 5  # learning rate 
BATCH_SIZE = 64 # batch size for training

# 定义损失函数, 定义交叉熵损失函数
criterion = torch.nn.CrossEntropyLoss()
# 定义优化器, 定义随机梯度下降优化器
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
# 定义优化器步长的一个优化器, 专门用于学习率的衰减
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
# train_iter, test_iter = AG_NEWS()
train_iter, test_iter = AG_NEWS(root='./data/ag_news_csv/')

train_dataset = list(train_iter)
test_dataset = list(test_iter)

# 选择全部训练数据的95%作为训练集数据, 剩下的5%作为验证数据
num_train = int(len(train_dataset) * 0.95)
# 子集1，子集2=random_split(数据集,[长度1，长度2])
split_train_, split_valid_ = \
    random_split(train_dataset, [num_train, len(train_dataset) - num_train])

train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE,
                              shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE,
                              shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE,
                             shuffle=True, collate_fn=collate_batch)
#训练10轮
for epoch in range(1, EPOCHS + 1):
    epoch_start_time = time.time()
    train(train_dataloader)
    accu_val = evaluate(valid_dataloader)
    # 如果total_accu!=0 且大于当前返回的准确率 调整学习率
    if total_accu is not None and total_accu > accu_val:
        # 进行整个轮次的优化器学习率的调整
      scheduler.step()
    else:
       total_accu = accu_val
    print('-' * 59)
    print('| end of epoch {:3d} | time: {:5.2f}s | '
          'valid accuracy {:8.3f} '.format(epoch,
                                           time.time() - epoch_start_time,
                                           accu_val))
    print('-' * 59)

| epoch   1 |   500/ 1782 batches | accuracy    0.683
| epoch   1 |  1000/ 1782 batches | accuracy    0.852
| epoch   1 |  1500/ 1782 batches | accuracy    0.879
-----------------------------------------------------------
| end of epoch   1 | time: 10.02s | valid accuracy    0.886 
-----------------------------------------------------------
| epoch   2 |   500/ 1782 batches | accuracy    0.895
| epoch   2 |  1000/ 1782 batches | accuracy    0.901
| epoch   2 |  1500/ 1782 batches | accuracy    0.904
-----------------------------------------------------------
| end of epoch   2 | time:  9.54s | valid accuracy    0.898 
-----------------------------------------------------------
| epoch   3 |   500/ 1782 batches | accuracy    0.914
| epoch   3 |  1000/ 1782 batches | accuracy    0.915
| epoch   3 |  1500/ 1782 batches | accuracy    0.914
-----------------------------------------------------------
| end of epoch   3 | time:  8.67s | valid accuracy    0.904 
-----------------------------------------------------------
| epoch   4 |   500/ 1782 batches | accuracy    0.923
| epoch   4 |  1000/ 1782 batches | accuracy    0.924
| epoch   4 |  1500/ 1782 batches | accuracy    0.924
-----------------------------------------------------------
| end of epoch   4 | time:  8.88s | valid accuracy    0.910 
-----------------------------------------------------------
| epoch   5 |   500/ 1782 batches | accuracy    0.931
| epoch   5 |  1000/ 1782 batches | accuracy    0.930
| epoch   5 |  1500/ 1782 batches | accuracy    0.929
-----------------------------------------------------------
| end of epoch   5 | time:  8.39s | valid accuracy    0.901 
-----------------------------------------------------------
| epoch   6 |   500/ 1782 batches | accuracy    0.940
| epoch   6 |  1000/ 1782 batches | accuracy    0.941
| epoch   6 |  1500/ 1782 batches | accuracy    0.944
-----------------------------------------------------------
| end of epoch   6 | time:  8.31s | valid accuracy    0.913 
-----------------------------------------------------------
| epoch   7 |   500/ 1782 batches | accuracy    0.943
| epoch   7 |  1000/ 1782 batches | accuracy    0.944
| epoch   7 |  1500/ 1782 batches | accuracy    0.941
-----------------------------------------------------------
| end of epoch   7 | time:  8.75s | valid accuracy    0.915 
-----------------------------------------------------------
| epoch   8 |   500/ 1782 batches | accuracy    0.944
| epoch   8 |  1000/ 1782 batches | accuracy    0.944
| epoch   8 |  1500/ 1782 batches | accuracy    0.944
-----------------------------------------------------------
| end of epoch   8 | time:  8.85s | valid accuracy    0.914 
-----------------------------------------------------------
| epoch   9 |   500/ 1782 batches | accuracy    0.946
| epoch   9 |  1000/ 1782 batches | accuracy    0.945
| epoch   9 |  1500/ 1782 batches | accuracy    0.947
-----------------------------------------------------------
| end of epoch   9 | time:  8.88s | valid accuracy    0.914 
-----------------------------------------------------------
| epoch  10 |   500/ 1782 batches | accuracy    0.946
| epoch  10 |  1000/ 1782 batches | accuracy    0.948
| epoch  10 |  1500/ 1782 batches | accuracy    0.946
-----------------------------------------------------------
| end of epoch  10 | time:  9.13s | valid accuracy    0.915 
-----------------------------------------------------------

Running the model on GPU with the following printout:

   | epoch   1 |   500/ 1782 batches | accuracy    0.684
   | epoch   1 |  1000/ 1782 batches | accuracy    0.852
   | epoch   1 |  1500/ 1782 batches | accuracy    0.877
   -----------------------------------------------------------
   | end of epoch   1 | time:  8.33s | valid accuracy    0.867
   -----------------------------------------------------------
   | epoch   2 |   500/ 1782 batches | accuracy    0.895
   | epoch   2 |  1000/ 1782 batches | accuracy    0.900
   | epoch   2 |  1500/ 1782 batches | accuracy    0.903
   -----------------------------------------------------------
   | end of epoch   2 | time:  8.18s | valid accuracy    0.890
   -----------------------------------------------------------
   | epoch   3 |   500/ 1782 batches | accuracy    0.914
   | epoch   3 |  1000/ 1782 batches | accuracy    0.914
   | epoch   3 |  1500/ 1782 batches | accuracy    0.916
   -----------------------------------------------------------
   | end of epoch   3 | time:  8.20s | valid accuracy    0.897
   -----------------------------------------------------------
   | epoch   4 |   500/ 1782 batches | accuracy    0.926
   | epoch   4 |  1000/ 1782 batches | accuracy    0.924
   | epoch   4 |  1500/ 1782 batches | accuracy    0.921
   -----------------------------------------------------------
   | end of epoch   4 | time:  8.18s | valid accuracy    0.895
   -----------------------------------------------------------
   | epoch   5 |   500/ 1782 batches | accuracy    0.938
   | epoch   5 |  1000/ 1782 batches | accuracy    0.935
   | epoch   5 |  1500/ 1782 batches | accuracy    0.937
   -----------------------------------------------------------
   | end of epoch   5 | time:  8.16s | valid accuracy    0.902
   -----------------------------------------------------------
   | epoch   6 |   500/ 1782 batches | accuracy    0.939
   | epoch   6 |  1000/ 1782 batches | accuracy    0.939
   | epoch   6 |  1500/ 1782 batches | accuracy    0.938
   -----------------------------------------------------------
   | end of epoch   6 | time:  8.16s | valid accuracy    0.906
   -----------------------------------------------------------
   | epoch   7 |   500/ 1782 batches | accuracy    0.941
   | epoch   7 |  1000/ 1782 batches | accuracy    0.939
   | epoch   7 |  1500/ 1782 batches | accuracy    0.939
   -----------------------------------------------------------
   | end of epoch   7 | time:  8.19s | valid accuracy    0.903
   -----------------------------------------------------------
   | epoch   8 |   500/ 1782 batches | accuracy    0.942
   | epoch   8 |  1000/ 1782 batches | accuracy    0.941
   | epoch   8 |  1500/ 1782 batches | accuracy    0.942
   -----------------------------------------------------------
   | end of epoch   8 | time:  8.16s | valid accuracy    0.904
   -----------------------------------------------------------
   | epoch   9 |   500/ 1782 batches | accuracy    0.942
   | epoch   9 |  1000/ 1782 batches | accuracy    0.941
   | epoch   9 |  1500/ 1782 batches | accuracy    0.942
   -----------------------------------------------------------
     end of epoch   9 | time:  8.16s | valid accuracy    0.904
   -----------------------------------------------------------
   | epoch  10 |   500/ 1782 batches | accuracy    0.940
   | epoch  10 |  1000/ 1782 batches | accuracy    0.942
   | epoch  10 |  1500/ 1782 batches | accuracy    0.942
   -----------------------------------------------------------
   | end of epoch  10 | time:  8.15s | valid accuracy    0.904
   -----------------------------------------------------------

Evaluate the model with test dataset

Checking the results of the test dataset…

print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))

Checking the results of test dataset.
test accuracy    0.908

   test accuracy    0.906

Test on a random news

Use the best model so far and test a golf news.

ag_news_label = {1: "World",
                 2: "Sports",
                 3: "Business",
                 4: "Sci/Tec"}

def predict(text, text_pipeline):
    with torch.no_grad():
        text = torch.tensor(text_pipeline(text))
        output = model(text, torch.tensor([0]))
        return output.argmax(1).item() + 1

ex_text_str = "MEMPHIS, Tenn. – Four days ago, Jon Rahm was \
    enduring the season’s worst weather conditions on Sunday at The \
    Open on his way to a closing 75 at Royal Portrush, which \
    considering the wind and the rain was a respectable showing. \
    Thursday’s first round at the WGC-FedEx St. Jude Invitational \
    was another story. With temperatures in the mid-80s and hardly any \
    wind, the Spaniard was 13 strokes better in a flawless round. \
    Thanks to his best putting performance on the PGA Tour, Rahm \
    finished with an 8-under 62 for a three-stroke lead, which \
    was even more impressive considering he’d never played the \
    front nine at TPC Southwind."

model = model.to("cpu")

print("This is a %s news" %ag_news_label[predict(ex_text_str, text_pipeline)])

This is a Sports news

   This is a Sports news

模型保存于加载

MODEL_PATH = './news_model.pth'
torch.save(model.state_dict(), MODEL_PATH)
# print('The model saved epoch {}'.format(epoch))

# 如果未来要重新加载模型,在实例化model后直接执行下面命令即可
model.load_state_dict(torch.load(MODEL_PATH))

The model saved epoch 10





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基于torchtext的文本分类

基于torchtext的文本分类

访问原始数据集iterator

准备数据处理pipelines

生成数据批处理和迭代器

定义模型

启动实例

Define functions to train the model and evaluate results.

分割数据集并运行模型

Evaluate the model with test dataset

Test on a random news

模型保存于加载

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