13-垃圾邮件分类2

1.读取

代码：

#1、读取
file_path=r'E:\作业\大三上\人工智能\works\robot\7\SMSSpamCollection'
sms=open(file_path,'r',encoding='utf-8')
sms_data=[]#字符串
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]))#对每封邮件做预处理,生成有效词的字符串
sms.close()

2.数据预处理

代码：

#2、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")
    tokens = [token for token in tokens if token not in stops]# 去停用词
    tokens = [token.lower() for token in tokens if len(token) >= 3]

    tag=nltk.pos_tag(tokens)#词性
    lmtzr = WordNetLemmatizer()
    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)

代码：

#3、划分数据
import numpy as np
from sklearn.model_selection import train_test_split
sms_data = np.array(sms_data)
sms_label = np.array(sms_label)
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) 

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

代码：

# 4、向量化
from sklearn.feature_extraction.text import TfidfVectorizer
TfidfVectorizer = TfidfVectorizer()
X_train = TfidfVectorizer.fit_transform(x_train)
X_test = TfidfVectorizer.transform(x_test)
print(X_train.toarray().shape)
print(X_test.toarray().shape)

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？

代码：

#4、模型选择
from sklearn.naive_bayes import MultinomialNB
mnb = MultinomialNB()
mnb.fit(X_train, y_train)
y_mnb_pred = mnb.predict(X_test)
print('预测结果',y_mnb_pred)

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

 

 

 

代码：

#5、模型评价
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
# 混淆矩阵
cm = confusion_matrix(y_test, y_mnb_pred)
print('混淆矩阵:',cm)
# 分类报告
cr = classification_report(y_test, y_mnb_pred)
print('分类报告:',cr)
print("模型准确率：", (cm[0][0] + cm[1][1]) / np.sum(cm))

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只考虑每种词汇在该训练文本中出现的频率，而TfidfVectorizer除了考量某一词汇在当前训练文本中出现的频率之外，同时关注包含这个词汇的其它训练文本数目的倒数。

  所以，如果训练文本的数据比较少，CountVectorizer的效果更好