朴素贝叶斯应用：垃圾邮件分类

三、 垃圾邮件分类
数据准备：
用csv读取邮件数据，分解出邮件类别及邮件内容。
对邮件内容进行预处理：去掉长度小于3的词，去掉没有语义的词等
尝试使用nltk库：
pip install nltk
nltk.download
不成功：就使用词频统计的处理方法
训练集和测试集数据划分
from sklearn.model_selection import train_test_split

from nltk.corpue import stopwords
stops=stopwords(‘english‘)
stops
tokens=[token for tokens if token not in stops]
‘ ‘.join(tokens)

text

#pip install nltk
#nltk.download
from sklearn.model_selection import train_test_split
import nltk
from nltk.stem import WordNetLemmatizer
#lemmatizer=WordNetLemmatizer()
#lemmatizer.lemmatize(‘leaves‘)

#垃圾邮件分类
text=‘‘‘Yes i think so. I am in office but my lap is in room i think thats on for the last few days. I didnt shut that down‘‘‘

import nltk
from nltk.stem import WordNetLemmatizer
#lemmatizer=WordNetLemmatizer()
#lemmatizer.lemmatize(‘leaves‘)

#预处理
def preprocessing(text):
    #text=text.decode("utf-8")
    tokens=[word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
    stops=stopwords.words(‘english‘)
    tokens=[token for token in tokens if token not in stops]
 
    tokens=[token.lower() for token in tokens if len(token)>=3]
    lmtzr=WordNetLemmatizer()
    tokens=[lmtzr.lemmatize(token) for  token in tokens]
    preprocessed_text=‘ ‘.join(tokens)
    return preprocessed_text

text

#读取数据集
import csv    #用csv读取邮件数据，分解出邮件类别及邮件内容
file_path = r‘C:\Users\Administrator\Desktop\SMSSpamCollectionjsn.txt‘
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(line[1])
sms.close()

#按0.7:0.3比例分为训练集和测试集
import numpy as np
sms_data=np.array(sms_data)
sms_label=np.array(sms_label)

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=0.3,random_state=0,stratify=sms_label) #训练集，测试集

#将其向量化
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df = 2,ngram_range=(1,2),stop_words=‘english‘,strip_accents=‘unicode‘,norm=‘l2‘)
X_train = vectorizer.fit_transform(x_train)
X_test = vectorizer.transform(x_test)

#朴素贝叶斯分类群
from sklearn.naive_bayes import MultinomialNB
clf=MultinomialNB().fit(X_train,y_train)
y_nb_pred=clf.predict(X_test)

#分类结果显示
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

print(y_nb_pred.shape,y_nb_pred)#x_test预测结果
print(‘nb_confusion_matrik：‘)
cm=confusion_matrix(y_test,y_nb_pred)#混淆矩阵
print(cm)
print(‘nb_classification_report:‘)
cr=classification_report(y_test,y_nb_pred)#主要分类指标的文本报告
print(cr)
feature_names=vectorizer.get_feature_names()#出现过的单词列表
coefs=clf.coef_#先验概率 P(x_i|y),6034 feature_log_prob_
intercept=clf.intercept_#P(y),class_log_prior_:array,shape(n_classes,)
coefs_with_fns=sorted(zip(coefs[0],feature_names))#对数概率P(x_i|y)与单词x_i映射

n=10
top=zip(coefs_with_fns[:n],coefs_with_fns[:-(n+1):-1])
for(coef_1,fn_1),(coef_2,fn_2) in top:
    print(‘\t%.4f\t%-15s\t\t%.4f\t%-15s‘%(coef_1,fn_1,coef_2,fn_2))

sms_label

print(len(x_train),len(x_test))

print(X_train.shape,X_test.shape)

x_train

X_train

a=X_train.toarray()
a

for i in range(1000):
    for j in range(5984):
        if a[i,j]!=0:
            print(i,j,a[i,j])
            
vectorizer.get_feature_names()[1610]