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机器学习 — AdaBoost算法（手稿+代码） 阅读原文 一.Adaboost理论部分 1.1 adaboost运行过程 注释：算法是利用指数函数降低误差，运行过程通过迭代进行。其中函数的算法怎么来的，你不用知道！当然你也可以尝试使用其它的函数代替指数函数，看看效果如何。 1.2 举例说明算法流程 略，花几分钟就可以看懂的例子。见：《统计学习方法》李航大大 1.3 算法误差界的证明 注释：误差的上界限由Zm约束，然而Zm又是由Gm(xi)约束，所以选择适当的Gm(xi)可以加快误差的减小。 二.代码实现 注释：这里参考大神博客http://blog.csdn.net/guyuealian/article/details/70995333，举例子很详细。 2.1程序流程图 2.2基本程序实现 注释：真是倒霉玩意，本来代码全部注释好了，突然Ubuntu奔溃了，全部程序就GG了。。。下面的代码就是官网的代码，部分补上注释。现在使用Deepin桌面版了，其它方面都比Ubuntu好，但是有点点卡。 from numpy import *def 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,labelMatdef stumpClassify(dataMatrix,dimen,threshVal,threshIneq):#just classify the data retArray = ones((shape(dataMatrix)[0],1)) if threshIneq == 'lt': retArray[dataMatrix[:,dimen] threshVal] = -1.0 return retArray 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,bestClasEstdef 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)def plotROC(predStrengths, classLabels): import matplotlib.pyplot as plt cur = (1.0,1.0) #cursor ySum = 0.0 #variable to calculate AUC numPosClas = sum(array(classLabels)==1.0)#标签等于1的和（也等于个数） yStep = 1/float(numPosClas); xStep = 1/float(len(classLabels)-numPosClas) sortedIndicies = predStrengths.argsort()#get sorted index, it's reverse sortData = sorted(predStrengths.tolist()[0]) fig = plt.figure() fig.clf() ax = plt.subplot(111) #loop through all the values, drawing a line segment at each point for index in sortedIndicies.tolist()[0]: if classLabels[index] == 1.0: delX = 0; delY = yStep; else: delX = xStep; delY = 0; ySum += cur[1] #draw line from cur to (cur[0]-delX,cur[1]-delY) ax.plot([cur[0],cur[0]-delX],[cur[1],cur[1]-delY], c='b') cur = (cur[0]-delX,cur[1]-delY) ax.plot([0,1],[0,1],'b--') plt.xlabel('False positive rate'); plt.ylabel('True positive rate') plt.title('ROC curve for AdaBoost horse colic detection system') ax.axis([0,1,0,1]) plt.show() print ('the Area Under the Curve is: ',ySum*xStep)复制代码注释：重点说明一下非均衡分类的图像绘制问题，想了很久才想明白！ 都是相对而言的，其中本文说的曲线在左上方就为好，也是相对而言的，看你怎么定义个理解！ |
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