使用sklearn实现中文文本分类

中文文本分类是一种基于机器学习的任务，sklearn是一个非常流行的机器学习库，可以用来实现中文文本分类。下面是一个使用sklearn实现中文文本分类的简单示例：

1. 准备数据集

首先需要准备一个中文文本分类的数据集，可以使用已有的数据集，如THUCNews、新闻分类数据集等。

2. 数据预处理

对于中文文本，需要进行分词、去停用词等预处理工作。可以使用jieba分词库和中文停用词库来进行预处理，代码如下：

import jieba
import codecs
import os

# 加载停用词表
def load_stopwords(stopwords_path):
    stopwords = []
    with codecs.open(stopwords_path, 'r', encoding='utf-8') as f:
        for line in f:
            stopwords.append(line.strip())
    return stopwords

# 对文本进行分词和去停用词
def cut_words(text_path, stopwords_path):
    stopwords = load_stopwords(stopwords_path)
    cut_text = []
    with codecs.open(text_path, 'r', encoding='utf-8') as f:
        for line in f:
            words = jieba.cut(line.strip())
            words = [word for word in words if word not in stopwords]
            cut_text.append(' '.join(words))
    return cut_text

3. 特征提取

对于文本分类任务，需要将文本转换成向量表示。可以使用TF-IDF等方法进行特征提取，代码如下：

from sklearn.feature_extraction.text import TfidfVectorizer

# 对文本进行TF-IDF特征提取
def tfidf_transformer(cut_text):
    tfidf_vectorizer = TfidfVectorizer()
    tfidf_matrix = tfidf_vectorizer.fit_transform(cut_text)
    return tfidf_matrix.toarray()

4. 模型训练和预测

使用sklearn的分类模型进行训练和预测，例如朴素贝叶斯分类器。代码如下：

from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

# 训练朴素贝叶斯分类器
def train(X_train, y_train):
    clf = MultinomialNB()
    clf.fit(X_train, y_train)
    return clf

# 预测
def predict(clf, X_test):
    y_pred = clf.predict(X_test)
    return y_pred

# 计算准确率
def evaluate(y_true, y_pred):
    acc = accuracy_score(y_true, y_pred)
    return acc

完整的代码如下：

import jieba
import codecs
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

# 加载停用词表
def load_stopwords(stopwords_path):
    stopwords = []
    with codecs.open(stopwords_path, 'r', encoding='utf-8') as f:
        for line in f:
            stopwords.append(line.strip())
    return stopwords

# 对文本进行分词和去停用词
def cut_words(text_path, stopwords_path):
    stopwords = load_stopwords(stopwords_path)
    cut_text = []
    with codecs.open(text_path, 'r', encoding='utf-8') as f:
        for line in f:
            words = jieba.cut(line.strip())
            words = [word for word in words if word not in stopwords]
            cut_text.append(' '.join(words))
    return cut_text

# 对文本进行TF-IDF特征提取
def tfidf_transformer(cut_text):
    tfidf_vectorizer = TfidfVectorizer()
    tfidf_matrix = tfidf_vectorizer.fit_transform(cut_text)
    return tfidf_matrix.toarray()

# 训练朴素贝叶斯分类器
def train(X_train, y_train):
    clf = MultinomialNB()
    clf.fit(X_train, y_train)
    return clf

# 预测
def predict(clf, X_test):
    y_pred = clf.predict(X_test)
    return y_pred

# 计算准确率
def evaluate(y_true, y_pred):
    acc = accuracy_score(y_true, y_pred)
    return acc

if __name__ == '__main__':
    # 准备数据集
    data_dir = './data'
    train_text_path = os.path.join(data_dir, 'train.txt')
    test_text_path = os.path.join(data_dir, 'test.txt')
    stopwords_path = os.path.join(data_dir, 'stopwords.txt')

    # 数据预处理
    X_train_cut = cut_words(train_text_path, stopwords_path)
    X_test_cut = cut_words(test_text_path, stopwords_path)

    # 特征提取
    X_train = tfidf_transformer(X_train_cut)
    X_test = tfidf_transformer(X_test_cut)

    # 训练模型
    y_train = [int(line.split('\t')[0]) for line in codecs.open(train_text_path, 'r', encoding='utf-8')]
    clf = train(X_train, y_train)

    # 预测和评估
    y_true = [int(line.split('\t')[0]) for line in codecs.open(test_text_path, 'r', encoding='utf-8')]
    y_pred = predict(clf, X_test)
    acc = evaluate(y_true, y_pred)
    print('accuracy: {:.2f}%'.format(acc * 100))

需要注意的是，上述示例中仅使用了朴素贝叶斯分类器进行分类，实际上可以使用其他的分类器，例如支持向量机、决策树等。同时也可以使用深度学习方法进行中文文本分类，例如使用卷积神经网络、循环神经网络等。