请写一段代码用LDA对文本进行主题提取，并用主题提取的结果使用逻辑回归进行文本分类

以下是一个Python示例，使用gensim库实现LDA主题建模和sklearn库实现逻辑回归文本分类：

# 导入所需库
import pandas as pd
import numpy as np
import re
import nltk
from gensim import corpora, models
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# 读取文本数据
data = pd.read_csv('text.csv', encoding='utf-8')

# 数据清洗和预处理
def clean_text(text):
    text = re.sub(r'[^\w\s]', '', text)  # 去除标点符号
    text = re.sub(r'\d+', '', text)  # 去除数字
    text = text.lower()  # 转换为小写
    text = nltk.word_tokenize(text)  # 分词
    text = [word for word in text if len(word) > 2]  # 去除长度小于3的单词
    text = [word for word in text if word not in nltk.corpus.stopwords.words('english')]  # 去除停用词
    return text

data['text_clean'] = data['text'].apply(lambda x: clean_text(x))

# 创建词袋模型
dictionary = corpora.Dictionary(data['text_clean'])
corpus = [dictionary.doc2bow(text) for text in data['text_clean']]

# 训练LDA模型
lda_model = models.LdaModel(corpus=corpus, id2word=dictionary, num_topics=10)

# 提取主题分布
def get_topic_distribution(lda_model, text):
    topic_distribution = np.array([0.0] * lda_model.num_topics)
    bow_vector = dictionary.doc2bow(text)
    if bow_vector:
        for topic in lda_model[bow_vector]:
            topic_distribution[topic[0]] = topic[1]
    return topic_distribution

data['topic_distribution'] = data['text_clean'].apply(lambda x: get_topic_distribution(lda_model, x))

# 将主题分布作为特征
X = np.array([x for x in data['topic_distribution']])
y = np.array(data['category'])

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练逻辑回归模型
clf = LogisticRegression()
clf.fit(X_train, y_train)

# 预测
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = clf.score(X_test, y_test)
print('Accuracy:', accuracy)

其中，`text.csv`是包含文本和类别信息的CSV文件，`clean_text()`函数用于清洗和预处理文本数据，`get_topic_distribution()`函数用于提取文本的主题分布，`X`和`y`分别是特征和标签，`train_test_split()`函数用于划分训练集和测试集，`LogisticRegression()`函数用于创建逻辑回归模型，`clf.fit()`函数用于训练模型，`clf.predict()`函数用于预测测试集标签，`clf.score()`函数用于计算模型的准确率。