更新加入LSTM的推特文本情感分类

import torch
import torchtext
import torch.nn as nn
import torch.nn.functional as F
from torchtext.vocab import GloVe
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
from sklearn.model_selection import train_test_split
import time

start=time.time()

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

#读取文件
data = pd.read_csv('D:/pycharmworkspace/ISLR-master/Tweets.csv')

#取指定的两列
data = data[['airline_sentiment','text']]

#去掉重复值
data.drop_duplicates(inplace=True)

#返回每一种类型的数量
data.airline_sentiment.value_counts()
#print(data.airline_sentiment.value_counts())

#在数据后面新建名字为review的一列数据 #neutral编号为0 positive编号为1 negative编号为2 取前一部分
data['review'] = pd.factorize(data.airline_sentiment)[0]

#删除airline_sentiment这一列数据
del data['airline_sentiment']

#保留规则 保留A-Z a-z 加号是按原来句子样式表达
token = re.compile('[A-Za-z]+|[!?,.()]')

def reg_text(text):
    # 保留所有大写和小写字母
    new_text = token.findall(text)
    # 将所有大写变成小写
    new_text = [word.lower() for word in new_text]
    return new_text

#对指定的text这列数据进行操作
data['text'] = data.text.apply(reg_text)

#创建词表 词表就是每一个单词给它编码的数值
word_set = set()

#x是列表的集合 t是每一条文本的列表
for text in data.text:
    # 对文本列表内继续迭代
    for word in text:
        # 重复单词不添加进去
        word_set.add(word)

#需要额外加1个
max_word = len(word_set) + 1

#将()转换成列表[]
word_list = list(word_set)

#word_list.index(w)+1是找到单词w的位置并加1
#这样词表就创建好了 word_index是词表
word_index = dict((word, word_list.index(word) + 1) for word in word_list)

#将文本转换成数值表示
#现在文本是x apply是应用函数 lambda是匿名函数 t代表文本的每一行元素 取不到则置为0
text = data.text.apply(lambda x: [word_index.get(word, 0) for word in x])

#每行文本长度不齐故获取最大长度的文本
maxlen = max(len(x) for x in text)

#将每行文本用0进行填充
pad_text = [l + (maxlen-len(l))*[0] for l in text]

#将列表转换成数组
pad_text = np.array(pad_text)

#制作标签
labels = data.review.values

#划分训练集合测试集
x_train, x_test, y_train, y_test = train_test_split(pad_text, labels)


class Mydataset(torch.utils.data.Dataset):
    def __init__(self, text_list, label_list):
        self.text_list = text_list
        self.label_list = label_list

    # index是序号 因为要进行切片
    def __getitem__(self, index):
        # 切片并装换成长类型
        text = torch.LongTensor(self.text_list[index])
        label = self.label_list[index]
        return text, label

    def __len__(self):
        return len(self.text_list)

train_ds = Mydataset(x_train, y_train)
test_ds = Mydataset(x_test, y_test)
BTACH_SIZE = 16
train_dl = torch.utils.data.DataLoader(
                                       train_ds,
                                       batch_size=BTACH_SIZE,
                                       shuffle=True
)
test_dl = torch.utils.data.DataLoader(
                                       test_ds,
                                       batch_size=BTACH_SIZE
)

em_dim = 100
hidden_size = 200

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # max_word是总共单词的数量 将这些单词映射到100的张量上
        #batch*maxlen*em_dim
        self.em = nn.Embedding(max_word, em_dim)
        #em_dim是输入特征 200是隐藏单元数 batch在第一维故bacth_first为True
        self.lstm = nn.LSTM(em_dim, hidden_size, batch_first=True)
        self.fc1 = nn.Linear(hidden_size, 256)
        self.fc2 = nn.Linear(256, 3)

    def forward(self, x):
        x = self.em(x)
        #lstm有两个输出 第一个是当前状态输出 第二个是当前隐藏输出 不要第二个
        x, _ = self.lstm(x)
        #取出最后一个的输出 经过lstm的x数据形式是 batch,time_step,output
        #第一个维度是batch 第二个维度是每一次时间步的输出 第三个维度是预测结果
        #对于第一个维度全要 第二个维度只要最后一个 第三个维度全要
        x = F.relu(self.fc1(x[:, -1, :]))
        x = self.fc2(x)
        return x

model = Net()
model = model.to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)


def fit(epoch, model, trainloader, testloader):
    correct = 0
    total = 0
    running_loss = 0

    model.train()
    for x, y in trainloader:
        x, y = x.to(device), y.to(device)
        y_pred = model(x)
        loss = loss_fn(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        with torch.no_grad():
            y_pred = torch.argmax(y_pred, dim=1)
            correct += (y_pred == y).sum().item()
            total += y.size(0)
            running_loss += loss.item()
    #    exp_lr_scheduler.step()
    epoch_loss = running_loss / len(trainloader.dataset)
    epoch_acc = correct / total

    test_correct = 0
    test_total = 0
    test_running_loss = 0

    model.eval()
    with torch.no_grad():
        for x, y in testloader:
            x, y = x.to(device), y.to(device)
            y_pred = model(x)
            loss = loss_fn(y_pred, y)
            y_pred = torch.argmax(y_pred, dim=1)
            test_correct += (y_pred == y).sum().item()
            test_total += y.size(0)
            test_running_loss += loss.item()

    epoch_test_loss = test_running_loss / len(testloader.dataset)
    epoch_test_acc = test_correct / test_total

    print('epoch: ', epoch,
          'loss： ', round(epoch_loss, 3),
          'accuracy:', round(epoch_acc, 3),
          'test_loss： ', round(epoch_test_loss, 3),
          'test_accuracy:', round(epoch_test_acc, 3)
          )

    return epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc

epochs = 30

train_loss = []
train_acc = []
test_loss = []
test_acc = []

for epoch in range(epochs):
    epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc = fit(epoch,
                                                                 model,
                                                                 train_dl,
                                                                 test_dl)
    train_loss.append(epoch_loss)
    train_acc.append(epoch_acc)
    test_loss.append(epoch_test_loss)
    test_acc.append(epoch_test_acc)

end = time.time()
print(end-start)

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有略微改善 但过拟合仍然严重