使用朴素贝叶斯算法来进行垃圾邮件分类完整代码

好的，以下是使用朴素贝叶斯算法进行垃圾邮件分类的完整代码，供您参考。

import os
import random
import math
from collections import defaultdict

# 定义停用词列表
stopwords = ['a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'for', 'from',
             'has', 'he', 'in', 'is', 'it', 'its', 'of', 'on', 'that', 'the',
             'to', 'was', 'were', 'will', 'with']

# 定义数据集路径
data_path = 'data'

# 定义分类类别
categories = ['ham', 'spam']

# 定义训练数据和测试数据的比例
train_ratio = 0.8

# 定义词袋模型
bag_of_words = defaultdict(int)

# 定义类别计数
category_count = defaultdict(int)

# 定义条件计数
condition_count = defaultdict(lambda: defaultdict(int))

# 定义训练集和测试集
train_data = []
test_data = []


def load_data():
    """
    加载数据集
    """
    for category in categories:
        folder_path = os.path.join(data_path, category)
        files = os.listdir(folder_path)
        for file_name in files:
            file_path = os.path.join(folder_path, file_name)
            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                content = f.read()
                data = {'category': category, 'content': content}
                if random.random() < train_ratio:
                    train_data.append(data)
                else:
                    test_data.append(data)


def preprocess_data(data):
    """
    预处理数据
    """
    words = []
    content = data['content']
    for word in content.split():
        word = word.strip().lower()
        if word.isalpha() and word not in stopwords:
            words.append(word)
    return {'category': data['category'], 'words': words}


def train():
    """
    训练模型
    """
    for data in train_data:
        preprocessed_data = preprocess_data(data)
        category = preprocessed_data['category']
        words = preprocessed_data['words']
        category_count[category] += 1
        for word in words:
            bag_of_words[word] += 1
            condition_count[category][word] += 1


def predict(data):
    """
    预测分类
    """
    words = preprocess_data(data)['words']
    scores = {category: math.log(category_count[category] / len(train_data)) for category in categories}
    for word in words:
        if word in bag_of_words:
            for category in categories:
                score = math.log(condition_count[category][word] + 1) - math.log(bag_of_words[word] + len(bag_of_words))
                scores[category] += score
    return max(scores, key=scores.get)


def evaluate():
    """
    评估模型
    """
    correct = 0
    for data in test_data:
        predicted_category = predict(data)
        if predicted_category == data['category']:
            correct += 1
    accuracy = correct / len(test_data)
    print(f"Accuracy: {accuracy:.2f}")


if __name__ == '__main__':
    load_data()
    train()
    evaluate()

以上就是使用朴素贝叶斯算法进行垃圾邮件分类的完整代码，代码中包括数据加载、数据预处理、模型训练、预测分类和模型评估等步骤。