Python KNN 文本分类实现：代码、数据集、停用词表

由于缺乏具体的数据集，以下代码仅提供一个基本的KNN文本分类实现框架，需要读者自行准备数据集和停用词表。

首先，读取并处理数据集，包括数据预处理和特征提取：

import pandas as pd
import numpy as np
import jieba
import re

# 读取数据集
df = pd.read_csv('data.csv')
# 数据预处理
df['content'] = df['content'].apply(lambda x: re.sub('[
	
]', '', x)) # 去除换行符等特殊字符
df['content'] = df['content'].apply(lambda x: re.sub('[^一-龥]', '', x)) # 去除非中文字符
df['content'] = df['content'].apply(lambda x: jieba.lcut(x)) # 分词
# 特征提取
all_words = []
for content in df['content']:
    all_words.extend(content)
all_words = list(set(all_words))

然后，根据停用词表去除无意义的词语：

# 读取停用词表
with open('stopwords.txt', 'r', encoding='utf-8') as f:
    stopwords = f.read().split('
')
# 去除停用词
def remove_stopwords(words):
    return [word for word in words if word not in stopwords]
df['content'] = df['content'].apply(remove_stopwords)

接下来，将文本转化为向量表示，采用TF-IDF算法：

# 计算TF-IDF值
def calculate_tfidf(word, content, contents):
    tf = content.count(word) / len(content)
    idf = np.log(len(contents) / (sum([1 for content in contents if word in content]) + 1))
    return tf * idf
# 构建TF-IDF矩阵
tfidf_matrix = np.zeros((len(df), len(all_words)))
for i in range(len(df)):
    content = df.loc[i, 'content']
    for j in range(len(all_words)):
        word = all_words[j]
        tfidf_matrix[i, j] = calculate_tfidf(word, content, df['content'])

最后，采用KNN算法进行文本分类：

# KNN算法
def knn_classify(x, k, train_x, train_y):
    distances = np.sqrt(np.sum((train_x - x) ** 2, axis=1))
    nearest = distances.argsort()[:k]
    top_k_y = [train_y[i] for i in nearest]
    return max(top_k_y, key=top_k_y.count)
# 分割数据集为训练集和测试集
train_size = int(len(df) * 0.8)
train_x = tfidf_matrix[:train_size]
train_y = df['label'][:train_size]
test_x = tfidf_matrix[train_size:]
test_y = df['label'][train_size:]
# 进行文本分类
k = 5 # KNN中的k值
correct = 0
for i in range(len(test_x)):
    pred_y = knn_classify(test_x[i], k, train_x, train_y)
    if pred_y == test_y[train_size+i]:
        correct += 1
accuracy = correct / len(test_x) # 分类准确率

上述代码中，变量accuracy为分类准确率，可以根据实际情况进行调整和优化。另外，特征选择和模型解释性可以通过调整TF-IDF算法中的参数来实现。算法效率可以通过优化KNN算法和使用更高效的数据结构来实现。