11.29作业

朴素贝叶斯应用：垃圾邮件分类

1. 数据准备：收集数据与读取

2. 数据预处理：处理数据

3. 训练集与测试集：将先验数据按一定比例进行拆分。

4. 提取数据特征，将文本解析为词向量 。

5. 训练模型：建立模型，用训练数据训练模型。即根据训练样本集，计算词项出现的概率P(xi|y)，后得到各类下词汇出现概率的向量 。

6. 测试模型：用测试数据集评估模型预测的正确率。

混淆矩阵

准确率、精确率、召回率、F值

7. 预测一封新邮件的类别。

8. 考虑如何进行中文的文本分类（期末作业之一）。 

 

要点：

理解朴素贝叶斯算法

理解机器学习算法建模过程

理解文本常用处理流程

理解模型评估方法

 

import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

#预处理
def processing(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

text = '''I've been searching for the right words to thank you for this breather. I promise i wont take your help for granted and will fulfil my promise. You have been wonderful and a blessing at all times.    
ham    I HAVE A DATE ON SUNDAY WITH WILL!!    '''

#读取文件
import csv
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) 

#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

# 输出不为0的列
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]

# 提取特征值
tfidf.get_feature_names()[630:650]