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垃圾邮件分类2

作者:互联网

1.读取

def read_dataset():
     file_path = r'C:\Users\D。\SMSSpamCollection'
     sms = open(file_path, encoding='utf-8')
     sms_data = []
     sms_label = []
     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]))  #提取出特征
     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]  #去除停用词
     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
#向量还原成邮件
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])

4.模型选择

from sklearn.naive_bayes import GaussianNB

from sklearn.naive_bayes import MultinomialNB

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

说明为什么选择这个模型?

垃圾邮件分类重点在于文档中单词出现的频率以及文档的重要性,不符合正态分布的特征,所以选择多项式分布模型。

5.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

 TP(True Positive):真实为0,预测也为0

 FN(False Negative):真实为0,预测为1

 FP(False Positive):真实为1,预测为0

 TN(True Negative):真实为1,预测也为1

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 

 准确率:被分对的样本数除以所有的样本数,通常来说,正确率越高,分类器越好。(TP+TN)/总

 精确率:表示被分为正例的示例中实际为正例的比例。 TP/(TP+FP)

 召回率 :召回率是覆盖面的度量,度量有多个正例被分为正例。TP/(TP+FN)

 F值 : 精确率 * 召回率 * 2 / ( 精确率 + 召回率) 。F值就是准确率(P)和召回率(R)的加权调和平均。

def class_report(ypre_mnb, y_test):
    conf_matrix = confusion_matrix(y_test, ypre_mnb)
    print("=====================================================")
    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))

if __name__ == '__main__':
    sms_data, sms_label = read_dataset() # 读取数据集
    x_train, x_test, y_train, y_test = split_dataset(sms_data, sms_label) # 划分数据集
    X_train, X_test,tfidf = tfidf_dataset(x_train, x_test) # 把原始文本转化为tf-idf的特征矩阵
    revert_mail(x_train, X_train, tfidf) # 向量还原成邮件
    y_mnb = mnb_model(X_train, X_test, y_train,y_test) # 模型选择
    class_report(y_mnb, y_test) # 模型评价

6.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频率,属于词袋模型特;TfidfVectorizer除了考滤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。相比之下,文本条目越多,Tfid的效果会越显著。

标签:垃圾邮件,分类,sms,print,train,test,mnb,sklearn
来源: https://www.cnblogs.com/MRJ1/p/12976905.html