import numpy as np
import matplotlib.pyplot as plt
def loadSimpData():
datMat = np.matrix([[ 1. , 2.1],
[ 1.5 , 1.6],
[ 1.3, 1. ],
[ 1. , 1. ],
[ 2. , 1. ]])
classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]
return datMat,classLabels
datMat,classLabels = loadSimpData()
##print(datMat)
##print(classLabels)
def showDataSet(dataMat, labelMat):
data_plus = [] #正样本
data_minus = [] #负样本
for i in range(len(dataMat)):
if labelMat[i] > 0:
data_plus.append(dataMat[i])
else:
data_minus.append(dataMat[i])
data_plus_np = np.array(data_plus) #转换为numpy矩阵
data_minus_np = np.array(data_minus) #转换为numpy矩阵
plt.scatter(np.transpose(data_plus_np)[0], np.transpose(data_plus_np)[1]) #正样本散点图
plt.scatter(np.transpose(data_minus_np)[0], np.transpose(data_minus_np)[1]) #负样本散点图
plt.show()
##showDataSet(datMat,classLabels)
def stumpClassify(dataMatrix,dimen,threshVal,threshIneq):#just classify the data
retArray = np.ones((np.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 = np.mat(dataArr); labelMat = np.mat(classLabels).T
m,n = np.shape(dataMatrix)
numSteps = 10.0; bestStump = {}; bestClasEst = np.mat(np.zeros((m,1)))
minError = np.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 = np.mat(np.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
##D = np.mat(np.ones((5,1))/5)
##buildStump(datMat,classLabels,D)
def adaBoostTrainDS(dataArr,classLabels,numIt=40):
from numpy import log,exp,sign
weakClassArr = []
m = np.shape(dataArr)[0]
D = np.mat(np.ones((m,1))/m) #init D to all equal
aggClassEst = np.mat(np.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 = np.multiply(-1*alpha*np.mat(classLabels).T,classEst) #exponent for D calc, getting messy
D = np.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 = np.multiply(sign(aggClassEst) != np.mat(classLabels).T,np.ones((m,1)))
errorRate = aggErrors.sum()/m
#print ("total error: ",errorRate)
if errorRate == 0.0: break
return weakClassArr,aggClassEst
##classifierArray,aggClassEst = adaBoostTrainDS(datMat,classLabels,9)
##print(classifierArray)
##print(aggClassEst)
def adaClassify(datToClass,classifierArr):
dataMatrix = np.mat(datToClass)#do stuff similar to last aggClassEst in adaBoostTrainDS
m = np.shape(dataMatrix)[0]
aggClassEst = np.mat(np.zeros((m,1)))
for i in range(len(classifierArr)):
classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],\
classifierArr[i]['thresh'],\
classifierArr[i]['ineq'])#call stump classify
#print(classEst)
aggClassEst += classifierArr[i]['alpha']*classEst
#print (aggClassEst)
return np.sign(aggClassEst)
##print(adaClassify([0,0],classifierArray))
def loadDataSet(fileName):
numFeat = len((open(fileName).readline().split('\t')))
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
datArr,labelArr = loadDataSet('horseColicTraining2.txt')
##print(np.shape(datArr))
##print(np.shape(labelArr))
#过拟合,过学习
##classifierArray,aggClassEst = adaBoostTrainDS(datArr,labelArr,1000)
classifierArray,aggClassEst = adaBoostTrainDS(datArr,labelArr,10)
##print(classifierArray)
testArr,testLabelArr = loadDataSet('horseColicTest2.txt')
##prediction1000 = adaClassify(testArr,classifierArray)
prediction10 = adaClassify(testArr,classifierArray)
errArr = np.mat(np.ones((67,1)))
##print(errArr[prediction1000 != np.mat(testLabelArr).T].sum())
##print("error rate:",errArr[prediction1000 != np.mat(testLabelArr).T].sum()/67)
print(errArr[prediction10 != np.mat(testLabelArr).T].sum())
print("error rate:",errArr[prediction10 != np.mat(testLabelArr).T].sum()/67)
def plotROC(predStrengths, classLabels):
import matplotlib.pyplot as plt
cur = (1.0,1.0) #cursor
ySum = 0.0 #variable to calculate AUC
numPosClas = sum(np.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)
#print(cur)
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)
plotROC(aggClassEst.T,labelArr)
《机器学习第七章Adaboost实践》
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转载自blog.csdn.net/weixin_43955530/article/details/88031920
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