机器学习 作业12 垃圾邮件分类2

1.读取

def read_dataset():
    file_path = r'E:\大三下\1机器学习算法基础\12\SMSSpamCollection'
    sms = open(file_path, encoding='utf-8')  # 读取数据
    sms_label = []  # 存储标题
    sms_data = []  # 存储数据
    csv_reader = csv.reader(sms, delimiter='\t')
    for line in csv_reader:
        sms_label.append(line[0])  # 提取出标签
        sms_data.append(preprocessing(line[1]))  # 对每封邮件做预处理
    sms.close()
    return sms_data, sms_label

2.数据预处理

def preprocessing(text):
    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]  # 分词
    stops = stopwords.words('english')  # 使用英文的停用词表
    tokens = [token for token in tokens if token not in stops]  # 停用词
    tokens = [token.lower() for token in tokens if len(token) >= 3]  # 大小写，短词
    lmtzr = WordNetLemmatizer()
    tag = nltk.pos_tag(tokens)  # 词性
    tokens = [lmtzr.lemmatize(token, pos=get_wordnet_pos(tag[i][1])) for i, token in enumerate(tokens)]  # 词性还原
    preprocessed_text = ' '.join(tokens)
    return preprocessed_text

3.数据划分—训练集和测试集数据划分

from sklearn.model_selection import train_test_split

x_train,x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=0, stratify=y_train)

def split_dataset(data, label):
    x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=0, stratify=label)
    return x_train, x_test, y_train, y_test

4.文本特征提取

sklearn.feature_extraction.text.CountVectorizer

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html?highlight=sklearn%20feature_extraction%20text%20tfidfvectorizer

sklearn.feature_extraction.text.TfidfVectorizer

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=sklearn%20feature_extraction%20text%20tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf2 = TfidfVectorizer()

观察邮件与向量的关系

向量还原为邮件

# 把原始文本转化为tf-idf的特征矩阵
def tfidf_dataset(x_train, x_test):
    tfidf = TfidfVectorizer()
    X_train = tfidf.fit_transform(x_train)  # X_train用fit_transform生成词汇表
    X_test = tfidf.transform(x_test)  # X_test要与X_train词汇表相同，因此在X_train进行fit_transform基础上进行transform操作
    return X_train, X_test, tfidf

# 向量还原邮件
def revert_mail(x_train, X_train, model):
    s = X_train.toarray()[0]
    print("第一封邮件向量表示为：", s)
    # 该函数输入一个矩阵，返回扁平化后矩阵中非零元素的位置（index）
    a = np.flatnonzero(X_train.toarray()[0])  # 非零元素的位置（index）
    print("非零元素的位置:", a)
    print("向量的非零元素的值：", s[a])
    b = model.vocabulary_  # 词汇表
    key_list = []
    for key, value in b.items():
        if value in a:
            key_list.append(key)  # key非0元素对应的单词
    print("向量非零元素对应的单词：", key_list)
    print("向量化之前的邮件：", x_train[0])

5.模型选择

from sklearn.naive_bayes import GaussianNB

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from sklearn.naive_bayes import MultinomialNB

说明为什么选择这个模型？

def mnb_model(x_train, x_test, y_train, y_test):
    mnb = MultinomialNB()
    mnb.fit(x_train, y_train)
    ypre_mnb = mnb.predict(x_test)
    print("总数：", len(y_test))
    print("预测正确数：", (ypre_mnb == y_test).sum())
    return ypre_mnb

6.模型评价：混淆矩阵，分类报告

from sklearn.metrics import confusion_matrix

confusion_matrix = confusion_matrix(y_test, y_predict)

说明混淆矩阵的含义

from sklearn.metrics import classification_report

说明准确率、精确率、召回率、F值分别代表的意义 

def class_report(ypre_mnb, y_test):
    conf_matrix = confusion_matrix(y_test, ypre_mnb)
    print("混淆矩阵：\n", conf_matrix)
    c = classification_report(y_test, ypre_mnb)
    print("------------------------------------------")
    print("分类报告：\n", c)
    print("模型准确率：", (conf_matrix[0][0] + conf_matrix[1][1]) / np.sum(conf_matrix))

7.比较与总结

如果用CountVectorizer进行文本特征生成，与TfidfVectorizer相比，效果如何？

CountVectorizer:只考虑词汇在文本中出现的评率，属于词袋模型特征。

TfidfVectorizer：除了考量某词汇在文本出现的频率，还关注包含这个词汇的所有文本的数量，能够削减高频没有意义的词汇出现带来的影响，挖掘更有意义的特征。属于Tfidf特征。

如果训练文本比较多的情况下，TfidfVectorizer更有优势。与TfidfVectorizer相比，CountVectorizer对于负类的预测更加准确，而正类的预测则稍逊色。但总体预测正确率也比TfidfVectorizer稍高，相比之下似乎CountVectorizer更适合进行预测。