前言：之前在朴素贝叶斯算法这篇文章中，对朴素贝叶斯分类算法的原理做了一个总结。这里我们就从实战的角度来看朴素贝叶斯类库。重点讲述朴素贝叶斯类库的使用要点和参数选择。

这里的实战是利用朴素贝叶斯来进行垃圾邮件的分类

实战部分：

from numpy import *
import codecs,re
from sklearn.naive_bayes import MultinomialNB

#创建一个包含在所有文档中出现的不重复词的列表
def createVocabList(dadaSet):
    vocabset = set([])
    for document in dadaSet:
        vocabset = vocabset | set(document)
    return list(vocabset)

def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)]=1
        else:
            print("the word:%s is not in my Vocabulary"%word)
    return returnVec

def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

def textParse(bigString):  #input is big string, #output is word list
    listOfTokens = re.split(r'\W*',bigString)
    return [tok.lower() for tok in listOfTokens if len(tok)>2]

# 这里是重点
def spamtest():
    clf = MultinomialNB()

    doclist =[]
    classlist = []
    fulllist = []
    for i in range(1,26):
        open_res = codecs.open('./spam/%d.txt'% i,mode = 'r',encoding = 'utf-8').read()
        # print(open_res)
        wordlist = textParse(open_res)
        # print(wordlist)
        doclist.append(wordlist)
        fulllist.extend(wordlist)
        classlist.append(1)
        open_rs = codecs.open('./ham/%d.txt'% i,mode = 'r',encoding = 'utf-8').read()
        wordlist = textParse(open_rs)
        doclist.append(wordlist)
        fulllist.extend(wordlist)
        classlist.append(0)

    vocabList = createVocabList(doclist)
    trainSet = list(range(50))
    testSet = []
    #拆分训练集与测试集，其中测试集选出10个样本，从总的样本中抽选出10个样本，注意抽取的方法，一定注意随机的思想
    for i in range(10):
        randindex = int(random.uniform(0,len(trainSet))) #这里是抽取的trainSet的索引的意思
        testSet.append(trainSet[randindex])
        trainSet.pop(randindex)

    trainMat = []
    trainClasses = []
    for docindex in trainSet:
        trainMat.append(bagOfWords2VecMN(vocabList,doclist[docindex]))
        trainClasses.append(classlist[docindex])

    #模型的训练
    clf.fit(array(trainMat),array(trainClasses))

    #拿切分出来的样本来测试
    errorCount = 0

    for testdocindex in testSet:
        testVecs = []
        testVecs.append(bagOfWords2VecMN(vocabList,doclist[testdocindex]))

        if clf.predict(array(testVecs))!=classlist[testdocindex]:
            errorCount +=1
    print("error rate is :",(errorCount/10))

spamtest()
程序运行结果：

FutureWarning: split() requires a non-empty pattern match.return _compile(pattern, flags).split(string, maxsplit)
error rate is : 0.0

Process finished with exit code 0
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作者：不曾走远~
原文：https://blog.csdn.net/qq_20412595/article/details/82467042