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,classLabels

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

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
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,labelMat

datArr,labelArr=loadDataSet('horseColicTest2.txt')
classifierArray=adaBoostTrainDS(datArr,labelArr,10)
print(classifierArray)