adaboost分类算法

xiaoxiao2021-02-28  7

#!usr/bin/env python #-*- encoding:utf-8 -*- from numpy import * #创建数据 def loadSimpleData(): datMax = matrix( [[1.0,2.1], [2.0,1.1], [1.3,1.0], [1.0,1.0], [2.0,1.0]] ) classLables = [1.0,1.0,-1.0,-1.0,1.0] return datMax,classLables dataMax,classLables = loadSimpleData() #单层决策胡生成函数 def 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 retArray #datArr,labelArr = loadSimpleData() def buildStump(dataArr,classLabels,D): dataMatrix = mat(dataArr) labelMat = mat(classLables).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 bestStump['dim'] = i bestStump['thresh'] = threshVal bestStump['ineq'] = inequal return bestStump,minError,bestClasEst 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,classLables,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(classLables).T,classEst) D = multiply(D,exp(expon)) D = D / D.sum() aggClassEst += alpha*classEst print "aggClassEst: ",aggClassEst.T aggErrors = multiply(sign(aggClassEst) != mat(classLables).T,ones((m,1))) errorRate = aggErrors.sum() / m print "total error: ",errorRate,"\n" if errorRate == 0.0:break #return weakClassArr return weakClassArr 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) #ROC曲线的控制及AUC计算函数 def plotROC(predStrenghts,classLabels): import matplotlib.pyplot as plt cur = (1.0,1.0) ySum = 0.0 numPosCLas = sum(array(classLabels) == 1.0) yStep = 1 / float(numPosCLas) xStep = 1 / float(len(classLabels) - numPosCLas) sortedIndicies = predStrenghts.argsort() fig = plt.figure() fig.clf() ax = plt.subplot(111) for index in sortedIndicies.tolist()[0]: if classLabels[index] == 1.0: delX = 0 delY = yStep else: delX = xStep delY = 0 ySum += cur[1] 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 "this Ares Under the Curve is: ",ySum*xStep #D = mat(ones((5,1)) / 5) # print buildStump(dataMax,classLables,D) #datArr,labelArr = loadSimpleData() dataMax,classLables = loadSimpleData() classifierArr = adaBoostTrainDS(dataMax,classLables,30) adaClassify([5,5],classifierArr)
转载请注明原文地址: https://www.6miu.com/read-2250213.html

最新回复(0)