机器学习实战读书笔记----利用Adaboost元算法提高分类性能

总结一下boosting与bagging：boosting是一种通过串行训练得到的分类器，每个新分类器都根据已经训练出的分类器性能进行训练，boosting是集中关注被已有分类器错分的那些数据来获得的新的分类器。由于boosting分类的结果是基于所有分类起的加权求和结果的，因此二者不太一样。bagging中的分类器权重是相等的，而boosting中的分类器权重不相等，每个权重代表的是其对应分类器在上一轮迭代中的成功度。

下面附上利用决策树桩构造的Adaboost算法代码：

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 loadDataSet(fileName):      #general function to parse tab -delimited floats    numFeat = len(open(fileName).readline().split('\t')) #get number of fields     dataMat = []; labelMat = []    fr = open(fileName)    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,labelMat#这里使用的分类器为决策树桩（Decision Stump）def stumpClassify(dataMatrix,dimen,threshVal,threshIneq):#just classify the data    retArray = ones((shape(dataMatrix)[0],1))    if threshIneq == 'lt':        retArray[dataMatrix[:,dimen] <= threshVal] = -1.0    else:        retArray[dataMatrix[:,dimen] > threshVal] = -1.0    return retArray    #利用三个循环选择在当前数据权值D下最好的分类器#第一个循环用来遍历所有的特征，确定特征的上界和下界，根据设置的步数算出每一步的长度#第二个循环用来遍历当前特征下用来分类的阈值，相当于在N个样本中插入N+1个点#第三个循环用来遍历判断方向，是大于阈值判断为正例还是小于阈值判断为正例，#调用stumpClassify()函数返回预测结果并与实际结果比较，结合权重向量D计算出损失代价，选择循环中是损失代价最小的决策树桩def 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 #init error sum, to +infinity    for i in range(n):#loop over all dimensions        rangeMin = dataMatrix[:,i].min(); rangeMax = dataMatrix[:,i].max();        stepSize = (rangeMax-rangeMin)/numSteps        for j in range(-1,int(numSteps)+1):#loop over all range in current dimension            for inequal in ['lt', 'gt']: #go over less than and greater than                threshVal = (rangeMin + float(j) * stepSize)                predictedVals = stumpClassify(dataMatrix,i,threshVal,inequal)#call stump classify with i, j, lessThan                errArr = mat(ones((m,1)))                errArr[predictedVals == labelMat] = 0                weightedError = D.T*errArr  #calc total error multiplied by D                #print "split: dim %d, thresh %.2f, thresh ineqal: %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#初始权重设为1/N，设置停止条件为达到最大分类器个数或errorRate为0#每次增加错误分类样本的权重，减少正确分类样本的权重#正确率高的分类器权重分配大，错误率高的分类器权重分配小def adaBoostTrainDS(dataArr,classLabels,numIt=40):    weakClassArr = []    m = shape(dataArr)[0]    D = mat(ones((m,1))/m)   #init D to all equal    aggClassEst = mat(zeros((m,1)))    for i in range(numIt):        bestStump,error,classEst = buildStump(dataArr,classLabels,D)#build Stump        #print "D:",D.T        alpha = float(0.5*log((1.0-error)/max(error,1e-16)))#calc alpha, throw in max(error,eps) to account for error=0        bestStump['alpha'] = alpha          weakClassArr.append(bestStump)                  #store Stump Params in Array        #print "classEst: ",classEst.T        expon = multiply(-1*alpha*mat(classLabels).T,classEst) #exponent for D calc, getting messy        D = multiply(D,exp(expon))                              #Calc New D for next iteration        D = D/D.sum()        #calc training error of all classifiers, if this is 0 quit for loop early (use break)        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        if errorRate == 0.0: break    return weakClassArr,aggClassEstdef adaClassify(datToClass,classifierArr):    dataMatrix = mat(datToClass)#do stuff similar to last aggClassEst in adaBoostTrainDS    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'])#call stump classify        aggClassEst += classifierArr[i]['alpha']*classEst        print aggClassEst    return sign(aggClassEst)