实验代码：import osimport reimport stringimport mathDATA_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:# spamspam_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)# hamham_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, targetX, 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) + 1.0return word_countsclass 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] = 0.0self.word_counts[c][word] += countMNB = 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 = 0ham_score = 0flag_2 = 0for 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_spamham_score += log_w_given_hamflag_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 += 1return resultMNB = SpamDetector()MNB.fit(X[100:], y[100:])pred = MNB.predict(X[:100])true = y[:100]accuracy = 0for i in range(100):if pred[i] == true[i]:accuracy += 1print(accuracy) # 0.98
以上是对1-6文件夹的运行结果
对文件夹的范围进行修改，运行结果也有所不同
