垃圾邮件2

1.读取

file_path=r'C:\Users\we\Desktop\SMSSpamCollection'
sms=open(file_path,'r',encoding='utf-8')
sms_data=[]
sms_label=[]
csv_reader=csv.reader(sms,delimiter='\t')
for line in csv_reader:
    sms_label.append(line[0])
    sms_data.append(preprocessing(line[1]))
sms.close()

2.数据预处理

def cixing(word):#根据词性，生成还原参数
    if word.startswith('J'):
        return nltk.corpus.wordnet.ADJ
    elif word.startswith('V'):
        return nltk.corpus.wordnet.VERB
    elif word.startswith('N'):
        return nltk.corpus.wordnet.NOUN
    elif word.startswith('R'):
        return nltk.corpus.wordnet.ADV
    else:
        return nltk.corpus.wordnet.NOUN

def preprocessing(text):
    # 分词
    fenge = []
    for sent in nltk.sent_tokenize(text):
        for word in nltk.word_tokenize(sent):
            fenge.append(word)
    # 停用词
    stops = stopwords.words("english")
    tingyong = [i for i in fenge if i not in stops]
    # 磁性标注
    tag = nltk.pos_tag(tingyong)
    # 磁性还原
    lemmatizer = WordNetLemmatizer()
    huanyuan = []
    # huanyuan = [lemmatizer.lemmatize(i, pos=cixing(tag[index][1])) for index, i in enumerate(tingyong)]
    for index,i in enumerate(tingyong):
        huanyuan.append(lemmatizer.lemmatize(i,pos=cixing(tag[index][1])))
    return huanyuan

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)

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(sms_data, sms_label, test_size=0.2, random_state=0,
                                                    stratify=sms_label)

print('原数据长度:', len(sms_data), '\n训练数据长度:', len(x_train), '\n测试数据长度:', len(x_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

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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

4.模型选择

from sklearn.naive_bayes import GaussianNB

from sklearn.naive_bayes import MultinomialNB

说明为什么选择这个模型？

这个模型适合离散的数据

# 向量化
from sklearn.feature_extraction.text import TfidfVectorizer

tfidf2 = TfidfVectorizer()
X_train = tfidf2.fit_transform(x_train)
X_test = tfidf2.transform(x_test)
print('X_train矩阵长度：', X_train.toarray().shape, '\nX_test矩阵长度：', X_test.toarray().shape)
print('邮件以及向量关系矩阵：\n', X_train.toarray())
print('词汇表：\n', tfidf2.vocabulary_)

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

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

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？