朴素贝叶斯应用：垃圾邮件分类

#导包
import nltk

from nltk.corpus import stopwords

from nltk.stem import WordNetLemmatizer

import csv

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.metrics import confusion_matrix

from sklearn.metrics import classification_report

# 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

 

file_path = r'C:UsersAdministratorDesktopsms.txt'

sms = open(file_path,'r',encoding='utf-8')

sms=open(file_path,'r',encoding='utf-8')

sms_data=[]

sms_label=[]

csv_reader=csv.reader(sms,delimiter='	')

for line in csv_reader:

    sms_label.append(line[0])

    sms_data.append(preprocessing(line[1]))

sms.close()

 

 

#按0.7：0.3比例分为训练集和测试集，再将其向量化

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=0.3,random_state=0,stratify=sms_label)

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

print('x_train',x_train)

print('y_train',y_train)

 

 

# 将其向量化

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)

 

#朴素贝叶斯分类器

clf = MultinomialNB().fit(X_train,y_train)

y_nb_pred = clf.predict(X_test)

 

# 分类结果显示

print(y_nb_pred.shape,y_nb_pred) # x-test预测结果

print('nb_confusion_matrix:')

cm = confusion_matrix(y_test,y_nb_pred) #混淆矩阵

print(cm)

print('nb_classification_repert:')

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_ily),6034 feature_log_preb

intercept = clf.intercept_ # P(y),class_log_prior : array,shape(n...

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('	%.4f	%-15s		%.4f	%-15s' % (coef_1,fn_1,coef_2,fn_2))

#预测一封新邮件的类别。

new_email=['新邮件']

vectorizer(new_email)

clf.predict(new_email)