机器学习之利用AdaBoost元算法提高分类性能

    本文主要记录本人在学习机器学习过程中的相关代码实现，参考《机器学习实战》

from numpy import *def loadSimpData():    datMat=matrix([[1.,2.1],        [2.,1.1],        [1.3,1.],        [1.,1.],        [2.,1.]])    classLabels=[1.0,1.0,-1.0,-1.0,1.0]    return datMat,classLabelsdef stumpClassify(dataMatrix,dimen,threshVal,threshIneq):    retArray=ones((shape(dataMatrix)[0],1))    if threshIneq=='lt':        retArray[dataMatrix[:,dimen]<=threshVal]=-1.0    else:        retArray[dataMatrix[:,dimen]>threshVal]=-1.0    return retArraydef buildStump(dataArr,classLabels,D):    dataMatrix=mat(dataArr);labelMat=mat(classLabels).T    m,n=shape(dataMatrix)    numSteps=10.0;bestStump={};bestClasEst=mat(zeros((m,1)))    minError=inf    for i in range(n):        rangeMin=dataMatrix[:,i].min()        rangeMax=dataMatrix[:,i].max()        stepSize=(rangeMax-rangeMin)/numSteps        for j in range(-1,int(numSteps)+1):            for inequal in ['lt','gt']:                threshVal=(rangeMin+float(j)*stepSize)                predictedVals=stumpClassify(dataMatrix,i,threshVal,inequal)                errArr=mat(ones((m,1)))                errArr[predictedVals==labelMat]=0                weightedError=D.T*errArr                #~ print("split:dim %d,thresh %.2f,thresh inequal: %s,the \                    #~ weighted error is %.3f" % (i,threshVal,inequal,weightedError))                if weightedError<minError:                    minError=weightedError                    bestClasEst=predictedVals.copy()                    bestStump['dim']=i                    bestStump['thresh']=threshVal                    bestStump['ineq']=inequal    return bestStump,minError,bestClasEst#~ datMat,classLabels=loadSimpData()#~ D=mat(ones((5,1))/5)#~ print(buildStump(datMat,classLabels,D))def adaBoostTrainDS(dataArr,classLabels,numIt=40):    weakClassArr=[]    m=shape(dataArr)[0]    D=mat(ones((m,1))/m)    aggClassEst=mat(zeros((m,1)))    for i in range(numIt):        bestStump,error,classEst=buildStump(dataArr,classLabels,D)        print('D:',D.T)        alpha=float(0.5*log((1.0-error)/max(error,1e-16)))        bestStump['alpha']=alpha        weakClassArr.append(bestStump)        print('classEst:',classEst.T)        expon=multiply(-1*alpha*mat(classLabels).T,classEst)        D=multiply(D,exp(expon))        D=D/D.sum()        aggClassEst+=alpha*classEst        print('aggClassEst:',aggClassEst.T)        aggErrors=multiply(sign(aggClassEst)!=mat(classLabels).T,ones((m,1)))        errorRate=aggErrors.sum()/m        print('total error:',errorRate,'\n')        if errorRate==0.0: break    return weakClassArr#~ datMat,classLabels=loadSimpData()#~ classifierArray=adaBoostTrainDS(datMat,classLabels,9)#~ print(classifierArray)def adaClassify(datToClass,classifierArr):    dataMatrix=mat(datToClass)    m=shape(dataMatrix)[0]    aggClassEst=mat(zeros((m,1)))    for i in range(len(classifierArr)):        classEst=stumpClassify(dataMatrix,classifierArr[i]['dim'],            classifierArr[i]['thresh'],classifierArr[i]['ineq'])        aggClassEst+=classifierArr[i]['alpha']*classEst        print(aggClassEst)    return sign(aggClassEst)#~ datMat,classLabels=loadSimpData()#~ classifierArray=adaBoostTrainDS(datMat,classLabels,30)#~ print(adaClassify([0,0],classifierArray))def loadDataSet(filename):    with open(filename) as fr:        numFeat=len(fr.readline().split('\t'))        dataMat=[];labelMat=[]        for line in fr.readlines():            lineArr=[]            curLine=line.strip().split('\t')            for i in range(numFeat-1):                lineArr.append(float(curLine[i]))            dataMat.append(lineArr)            labelMat.append(float(curLine[-1]))    return dataMat,labelMatdatArr,labelArr=loadDataSet('horseColicTest2.txt')classifierArray=adaBoostTrainDS(datArr,labelArr,10)print(classifierArray)