《機器學習實戰》這上面有AdaBoost的源碼，這裏直接摘抄給妳，順帶安利壹下這本書
'''Created?on?Nov?28,?2010
Adaboost?is?short?for?Adaptive?Boosting
@author:?Peter
'''
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?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
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
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
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,aggClassEst
def?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?classifyaggClassEst?+=?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)
yStep?=?1/float(numPosClas);?xStep?=?1/float(len(classLabels)-numPosClas)
sortedIndicies?=?predStrengths.argsort()#get?sorted?index,?it's?reverse
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