11月24日课堂作业（垃圾邮件过滤）

实验代码：

import os
import re
import string
import math

DATA_DIR = 'enron'
target_names = ['ham', 'spam']


def get_data(DATA_DIR):
    subfolders = ['enron%d' % i for i in range(1, 7)]
    data = []
    target = []
    for subfolder in subfolders:
        # spam
        spam_files = os.listdir(os.path.join(DATA_DIR, subfolder, 'spam'))
        for spam_file in spam_files:
            with open(os.path.join(DATA_DIR, subfolder, 'spam', spam_file), encoding="latin-1") as f:
                data.append(f.read())
                target.append(1)
        # ham
        ham_files = os.listdir(os.path.join(DATA_DIR, subfolder, 'ham'))
        for ham_file in ham_files:
            with open(os.path.join(DATA_DIR, subfolder, 'ham', ham_file), encoding="latin-1") as f:
                data.append(f.read())
                target.append(0)
    return data, target


X, y = get_data(DATA_DIR)


class SpamDetector_1(object):
    """Implementation of Naive Bayes for binary classification"""

    # 清除空格
    def clean(self, s):
        translator = str.maketrans("", "", string.punctuation)
        return s.translate(translator)

    # 分开每个单词
    def tokenize(self, text):
        text = self.clean(text).lower()
        return re.split("\W+", text)

    # 计算某个单词出现的次数
    def get_word_counts(self, words):
        word_counts = {}
        for word in words:
            word_counts[word] = word_counts.get(word, 0.0) + 1.0
        return word_counts


class SpamDetector_2(SpamDetector_1):
    # X:data,Y:target标签（垃圾邮件或正常邮件）
    def fit(self, X, Y):
        self.num_messages = {}
        self.log_class_priors = {}
        self.word_counts = {}
        # 建立一个集合存储所有出现的单词
        self.vocab = set()
        # 统计spam和ham邮件的个数
        self.num_messages['spam'] = sum(1 for label in Y if label == 1)
        self.num_messages['ham'] = sum(1 for label in Y if label == 0)

        # 计算先验概率，即所有的邮件中，垃圾邮件和正常邮件所占的比例
        self.log_class_priors['spam'] = math.log(
            self.num_messages['spam'] / (self.num_messages['spam'] + self.num_messages['ham']))
        self.log_class_priors['ham'] = math.log(
            self.num_messages['ham'] / (self.num_messages['spam'] + self.num_messages['ham']))

        self.word_counts['spam'] = {}
        self.word_counts['ham'] = {}

        for x, y in zip(X, Y):
            c = 'spam' if y == 1 else 'ham'
            # 构建一个字典存储单封邮件中的单词以及其个数
            counts = self.get_word_counts(self.tokenize(x))
            for word, count in counts.items():
                if word not in self.vocab:
                    self.vocab.add(word)  # 确保self.vocab中含有所有邮件中的单词
                # 下面语句是为了计算垃圾邮件和非垃圾邮件的词频，即给定词在垃圾邮件和非垃圾邮件中出现的次数。
                # c是0或1，垃圾邮件的标签
                if word not in self.word_counts[c]:
                    self.word_counts[c][word] = 0.0
                self.word_counts[c][word] += count


MNB = SpamDetector_2()
MNB.fit(X[100:], y[100:])


class SpamDetector(SpamDetector_2):
    def predict(self, X):
        result = []
        flag_1 = 0
        # 遍历所有的测试集
        for x in X:
            counts = self.get_word_counts(self.tokenize(x))  # 生成可以记录单词以及该单词出现的次数的字典
            spam_score = 0
            ham_score = 0
            flag_2 = 0
            for word, _ in counts.items():
                if word not in self.vocab:
                    continue

                # 下面计算P(内容|垃圾邮件)和P(内容|正常邮件),所有的单词都要进行拉普拉斯平滑
                else:
                    # 该单词存在于正常邮件的训练集和垃圾邮件的训练集当中
                    if word in self.word_counts['spam'].keys() and word in self.word_counts['ham'].keys():
                        log_w_given_spam = math.log(
                            (self.word_counts['spam'][word] + 1) / (
                                        sum(self.word_counts['spam'].values()) + len(self.vocab)))
                        log_w_given_ham = math.log(
                            (self.word_counts['ham'][word] + 1) / (sum(self.word_counts['ham'].values()) + len(
                                self.vocab)))
                    # 该单词存在于垃圾邮件的训练集当中,但不存在于正常邮件的训练集当中
                    if word in self.word_counts['spam'].keys() and word not in self.word_counts['ham'].keys():
                        log_w_given_spam = math.log(
                            (self.word_counts['spam'][word] + 1) / (
                                        sum(self.word_counts['spam'].values()) + len(self.vocab)))
                        log_w_given_ham = math.log(1 / (sum(self.word_counts['ham'].values()) + len(
                            self.vocab)))
                    # 该单词存在于正常邮件的训练集当中,但不存在于垃圾邮件的训练集当中
                    if word not in self.word_counts['spam'].keys() and word in self.word_counts['ham'].keys():
                        log_w_given_spam = math.log(1 / (sum(self.word_counts['spam'].values()) + len(self.vocab)))
                        log_w_given_ham = math.log(
                            (self.word_counts['ham'][word] + 1) / (sum(self.word_counts['ham'].values()) + len(
                                self.vocab)))

                # 把计算到的P(内容|垃圾邮件)和P(内容|正常邮件)加起来
                spam_score += log_w_given_spam
                ham_score += log_w_given_ham

                flag_2 += 1

                # 最后，还要把先验加上去，即P(垃圾邮件)和P(正常邮件)
                spam_score += self.log_class_priors['spam']
                ham_score += self.log_class_priors['ham']

            # 最后进行预测，如果spam_score > ham_score则标志为1，即垃圾邮件
            if spam_score > ham_score:
                result.append(1)
            else:
                result.append(0)

            flag_1 += 1

        return result


MNB = SpamDetector()
MNB.fit(X[100:], y[100:])
pred = MNB.predict(X[:100])
true = y[:100]

accuracy = 0
for i in range(100):
    if pred[i] == true[i]:
        accuracy += 1
print(accuracy)  # 0.98

实验结果：

1、只读取前3个文件夹时，测试分类的正确率只有27%，效果不太理想

 2、当读取前5个文件夹时，测试分类的正确率达到了96%，正确率大大提升，效果理想

3、当读取前全部文件夹时，测试分类的正确率达到了98%，效果很理想

 实验总结：

通过本实验对朴素贝叶斯的理解与应用来解决垃圾邮件过滤问题，体会到朴素贝叶斯处理大量垃圾邮件过滤等这类工作上的优越性与便利性。有效解决垃圾邮件过滤的问题。