使用python语言采用KNN算法帮我做一个非常详细的文本分类停用词表哈工大停用词表txt帮我下载好数据集帮我写一个非常好并且详细文本分类代码进一步优化代码例如使用交叉验证来选择最佳的K值或者使用其他特征提取方法

对于文本分类任务，我们首先需要准备好数据集和停用词表。这里我们选择使用20 Newsgroups数据集和哈工大停用词表。

1. 准备数据集和停用词表

20 Newsgroups数据集可以在sklearn库中直接下载，也可以在官网上下载：http://qwone.com/~jason/20Newsgroups/。我们选择使用sklearn库中的数据集。

from sklearn.datasets import fetch_20newsgroups

newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
newsgroups_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))

X_train = newsgroups_train.data
y_train = newsgroups_train.target
X_test = newsgroups_test.data
y_test = newsgroups_test.target

# 加载停用词表
stopwords_file = open('哈工大停用词表.txt', 'r', encoding='utf-8')
stopwords = stopwords_file.readlines()
stopwords = [word.strip() for word in stopwords]
stopwords_file.close()

2. 数据预处理

我们需要对文本进行预处理，包括分词、去停用词、提取特征等。

import jieba

def preprocess(text, stopwords):
    # 分词
    words = jieba.cut(text)
    # 去停用词
    words = [word for word in words if word not in stopwords]
    # 提取特征
    features = {}
    for word in words:
        features[word] = features.get(word, 0) + 1
    return features

X_train_preprocessed = [preprocess(text, stopwords) for text in X_train]
X_test_preprocessed = [preprocess(text, stopwords) for text in X_test]

3. 特征提取

我们使用TF-IDF进行特征提取，同时使用交叉验证选择最佳的K值。

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier

vectorizer = TfidfVectorizer()

X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

k_range = range(1, 10)
k_scores = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X_train_tfidf, y_train, cv=5, scoring='accuracy')
    k_scores.append(scores.mean())

best_k = k_range[k_scores.index(max(k_scores))]

print('Best K:', best_k)

4. 训练模型

我们使用KNN算法进行分类，选择最佳的K值。

knn = KNeighborsClassifier(n_neighbors=best_k)
knn.fit(X_train_tfidf, y_train)

y_pred = knn.predict(X_test_tfidf)

print('Accuracy:', knn.score(X_test_tfidf, y_test))

完整代码如下：

import jieba
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier

# 加载数据集和停用词表
newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
newsgroups_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))

X_train = newsgroups_train.data
y_train = newsgroups_train.target
X_test = newsgroups_test.data
y_test = newsgroups_test.target

stopwords_file = open('哈工大停用词表.txt', 'r', encoding='utf-8')
stopwords = stopwords_file.readlines()
stopwords = [word.strip() for word in stopwords]
stopwords_file.close()

# 数据预处理
def preprocess(text, stopwords):
    # 分词
    words = jieba.cut(text)
    # 去停用词
    words = [word for word in words if word not in stopwords]
    # 提取特征
    features = {}
    for word in words:
        features[word] = features.get(word, 0) + 1
    return features

X_train_preprocessed = [preprocess(text, stopwords) for text in X_train]
X_test_preprocessed = [preprocess(text, stopwords) for text in X_test]

# 特征提取
vectorizer = TfidfVectorizer()

X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

# 交叉验证选择最佳K值
k_range = range(1, 10)
k_scores = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X_train_tfidf, y_train, cv=5, scoring='accuracy')
    k_scores.append(scores.mean())

best_k = k_range[k_scores.index(max(k_scores))]

print('Best K:', best_k)

# 训练模型
knn = KNeighborsClassifier(n_neighbors=best_k)
knn.fit(X_train_tfidf, y_train)

y_pred = knn.predict(X_test_tfidf)

print('Accuracy:', knn.score(X_test_tfidf, y_test))
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