（3）机器学习实战笔记：朴素贝叶斯
优点：数据比较少的时候仍然有效，可以处理多类别问题
缺点：对于输入数据的准备方式比较敏感
适用的数据类型：标称型数据
将一组单词转换为一组数字
使用数字计算概率
著名应用：使用朴素贝叶斯过滤垃圾邮件
分类思路：
（1）收集数据：提供文本文件
（2）准备数据：将文本文件解析成词条向量
（3）分析数据：检查词条确保解析的正确性
（4）训练算法：使用我们之前建立的trainNB0（）函数
（5）测试算法：使用classifyNB（），构建一个新的测试函数来计算文档的错误率
（6）使用算法：构建一个完整的程度对一组文档进行分类，将错分的文档输出到屏幕上
切分文本：使用String.split（）方法切分
为了更加精确地估计分类器错误率，需要进行多次迭代后求出平均错误率
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简单实例：通过朴素贝叶斯分类实现垃圾邮件分类
通过对一邮件文本数据集进行处理（转化为向量）
经过朴素贝叶斯分类器进行分类可以判定是否为垃圾邮件
代码实现了简单的朴素贝叶斯分类器、文本向量转换器
详细备注见解释，下载数据集点这里
import numpy as np
from functools import reduce
#准备数据：从文本中构建词向量
def loadDataSet():
# 切分的词条
postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
['stop', 'posting', 'stupid', 'worthless', 'garbage'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
# 类别标签向量，1代表侮辱性词汇，0代表不是
classVec = [0, 1, 0, 1, 0, 1]
# 返回实验样本切分的词条、类别标签向量
return postingList, classVec
def createVocabList(dataSet):
#无重复提取单祠
vocabSet = set([])
for document in dataSet:
vocabSet = vocabSet |set(document)#创建两个集合的并集
return list(vocabSet)
#检查单词在第几篇文档（文档为inputSet向量）中出现
def setOfWords2Vec(vocabList,inputSet):
#创建一个元素都为0的向量
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
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
# print(myVocabList)
# print(setOfWords2Vec(myVocabList,listOPosts[0]))
#训练算法：词向量计算概率
#输入：文档矩阵trainMatrix、每类文档类别标签所构成向量trainCategory
def trainNB0(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])#无重复单词表有几个单词
#概率初始化
pAbusive=sum(trainCategory)/float(numTrainDocs) #文档里脏话文档的概率
p0Num = np.ones(numWords)#正向词汇列
p1Num = np.ones(numWords)#脏话词汇列，都初始化为0
#降低由概率值为0导致最后乘积为0的影响
p0Denom=2.0
p1Denom=2.0
for i in range(numTrainDocs):
#计算文档属于侮辱性文档(class=1)的概率P(1)
#对于二分类问题可以通过1-P（1）得到P(0)
#一旦某个词语在某文档中出现，该词对应的个数就加1
#在所有的文档中，文档的总词数也相应+1，
if trainCategory[i]==1: #对于脏话类词汇统计
p1Num += trainMatrix[i] #统计脏话词汇数量，对应位置数量+1（单位就是1）
p1Denom += sum(trainMatrix[i]) #总脏话词汇+出现次数
else:
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p1Vect=p1Num/p1Denom
p0Vect=p0Num/p0Denom
return p0Vect,p1Vect,pAbusive
trainMat=[]
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList,postinDoc))
#统计经过处理的无重复词汇表中在对应第（postinDoc）文档中是否出现，是标记1，
#返回len为文档长度的向量组
# print(trainMat)
p0V,p1V,pAb=trainNB0(trainMat,listClasses)
# print("0")
# print(p0V)
# print("1")
# print(p1V)
# print("A")
# print(pAb)
# print(myVocabList)
#朴素贝叶斯分类函数/输入向量（要分类的向量，使用函数trainNB0计算得到三个概率
def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
p1 = sum(vec2Classify*p1Vec)+np.log(pClass1)
p0 = sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)
if p1>p0:
return 1
else:
return 0
#简单的分类测试
def testingNB():
listOposts,listClasses = loadDataSet()
myVocabList = createVocabList(listOposts)
trainMat=[]
for postinDoc in listOposts:
trainMat.append(setOfWords2Vec(myVocabList,postinDoc))
p0V,p1V,pAb = trainNB0(np.array(trainMat),np.array(listClasses))
testEntry=['love','my','dalmation']
thisDoc = np.array(setOfWords2Vec(myVocabList,testEntry))
print(testEntry,'classified as:',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry=['stupid','garbage']
thisDoc = np.array(setOfWords2Vec(myVocabList,testEntry))
print(testEntry,'classified as:',classifyNB(thisDoc,p0V,p1V,pAb))
testingNB()
#词袋模型：遇到每一个单词时，会增加词向量中对应值，而不是将对应数值设为0
def bagOfWords2VecMN(vocabList,inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)]+=1
return returnVec
#应用：进行垃圾邮件的过滤
#切分文本
#test!
# mySent = 'This book is the best book on python or M.L. I have laid eyes upon.'
#
# import re
# regEx = re.compile('\\W*')
# listOfTokens = regEx.split(mySent)
#测试：使用朴素贝叶斯进行交叉验证
def textParse(bigString):
import re
listOfTokens =re.split(r'\W*',bigString)
return[tok.lower() for tok in listOfTokens if len(tok)>2]
#返回长度大于2的词，而且全部小写化
#该函数对贝叶斯垃圾邮件分类进行自动化处理，导入spam与ham下的文本文件，并为他们解析词列表。（*1）
#分离器所需要的概率计算只利用训练集中的文档来完成
#python变量trainingSet是一个整数列表，数值范围是0到49；（*2）
def spamTest():
docList=[]
classList=[]
fullText=[]
main_email=[]
for i in range(1,26):
#（*1）
wordList = textParse(open('email/spam/%d.txt'%i).read())
main_e=open('email/spam/%d.txt'%i).read()
main_email.append(main_e)
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt'%i).read())
docList.append(wordList)
main_e= open('email/spam/%d.txt' % i).read()
main_email.append(main_e)
fullText.extend(wordList)
classList.append(0)
#构建词汇表
vocabList = createVocabList(docList)
# print("构建的vocabList：")
# print(vocabList)
# print("=========================================================")
#进行测试集的划分 （*2）
trainingSet = list(range(50))
testSet=[]
for i in range(10): #随机选择10个文件
randIndex = int(np.random.uniform(0,len(trainingSet))) #随机构建测试集，获取随机数作为index
testSet.append(trainingSet[randIndex])#把index对应的邮件index添加到测试集中
del(trainingSet[randIndex])#并且把该index从待挑选名单中删除
trainMat=[]
trainClasses =[]
for docIndex in trainingSet:
#对于每一个训练集里的训练单位进行词向量的构建
trainMat.append(setOfWords2Vec(vocabList,docList[docIndex]))
trainClasses.append(classList[docIndex]) #安上对应标签！
#针对训练集进行训练
#
# print(trainMat)
# print(trainClasses)
p0V,p1V,pSpam=trainNB0(np.array(trainMat),np.array(trainClasses))
# print(p0V)
errorCount = 0
for docIndex in testSet:
#提取训练名单上对应的邮件信息，查看滴docIndex条元素里对应的单词是否在vocablist词汇表中出现，出现则+1，返回信息向量wordVector
# print("train")
# print(docList[docIndex])
wordVector = setOfWords2Vec(vocabList,docList[docIndex])
# print(wordVector)
if classifyNB(np.array(wordVector),p0V,p1V,pSpam)!=classList[docIndex]:
errorCount +=1
print(main_email[docIndex])
print(classifyNB(np.array(wordVector), p0V, p1V, pSpam))
print(classList[docIndex])
print('the error rate is :',float(errorCount)/len(testSet))
# spamTest()
#寻找最优参数
def findthebest_Data_test():
docList = []
classList = []
fullText = []
main_email = []
for i in range(1, 26):
# （*1）
wordList = textParse(open('email/spam/%d.txt' % i).read())
main_e = open('email/spam/%d.txt' % i).read()
main_email.append(main_e)
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
main_e = open('email/spam/%d.txt' % i).read()
main_email.append(main_e)
fullText.extend(wordList)
classList.append(0)
# 构建词汇表
vocabList = createVocabList(docList)
# print("构建的vocabList：")
# print(vocabList)
# print("=========================================================")
# 进行测试集的划分 （*2）
trainingSet = list(range(50))
testSet = []
for i in range(10): # 随机选择10个文件
randIndex = int(np.random.uniform(0, len(trainingSet))) # 随机构建测试集，获取随机数作为index
testSet.append(trainingSet[randIndex]) # 把index对应的邮件index添加到测试集中
del (trainingSet[randIndex]) # 并且把该index从待挑选名单中删除
trainMat = []
trainClasses = []
for docIndex in trainingSet:
# 对于每一个训练集里的训练单位进行词向量的构建
trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex]) # 安上对应标签！
# 针对训练集进行训练
#
# print(trainMat)
# print(trainClasses)
p0V, p1V, pSpam = trainNB0(np.array(trainMat), np.array(trainClasses))
# print(p0V)
errorCount = 0
for docIndex in testSet:
# 提取训练名单上对应的邮件信息，查看滴docIndex条元素里对应的单词是否在vocablist词汇表中出现，出现则+1，返回信息向量wordVector
# print("train")
# print(docList[docIndex])
wordVector = setOfWords2Vec(vocabList, docList[docIndex])
# print(wordVector)
if classifyNB(np.array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
# print(main_email[docIndex])
# print(classifyNB(np.array(wordVector), p0V, p1V, pSpam))
# print(classList[docIndex])
# print('the error rate is :', float(errorCount) / len(testSet))
error_rate=float(errorCount) / len(testSet)
return p0V, p1V, pSpam,error_rate
def find_the_data():
p0Num = np.ones(10)
p1Num = np.ones(10)
PA = 0.0
err=1
for i in range(50):
a,b,c,d=findthebest_Data_test()
if d<err:
err = d
p0Num=a
p1Num=b
PA=c
return p0Num,p1Num,PA
def final_test():
p0,p1,pA =find_the_data()
docList = []
classList = []
fullText = []
main_email = []
for i in range(1, 26):
# （*1）
wordList = textParse(open('email/spam/%d.txt' % i).read())
main_e = open('email/spam/%d.txt' % i).read()
main_email.append(main_e)
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
main_e = open('email/spam/%d.txt' % i).read()
main_email.append(main_e)
fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)
errorCount = 0
for i in range(len(docList)):
# 提取训练名单上对应的邮件信息，查看滴docIndex条元素里对应的单词是否在vocablist词汇表中出现，出现则+1，返回信息向量wordVector
# print("train")
# print(docList[docIndex])
wordVector = setOfWords2Vec(vocabList, docList[i])
# print(wordVector)
if classifyNB(np.array(wordVector), p0, p1, pA) != classList[i]:
errorCount += 1
# print(main_email[i])
# print(classifyNB(np.array(wordVector), p0, p1, pA))
# print(classList[i])
print('the error rate is :', float(errorCount) / len(docList))
final_test()
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