adaboost.py

#에이다부스트 코드 구현 예제
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 dataMat, classLabels



# 약한 학습기 생성 함수
# 7.1 의사결정 스텀프 생성 함수
def stumClassify(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 weigthedError < minError:
                    minError = weightedError
                    minError = weightedError
                    bestClasEst = predictedVals.copy()
                    bestStump['dim'] = i
                    bestStump['thresh'] = threshVal
                    bestStump['ineq'] = inequal
    return bestStump, minError, bestClasEst

# 전체 에이다부스트 알고리즘 구현하기
#7.2 의사결정 스텀프로 에이다부스트 검사
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 = flat(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

##에이다부스트로 분류하기
## 7.3 에이다부스트 분류 함수
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 += c;assofoerArr[i]['alpha'] * classEst
        print (aggClassEst)
    return sign(aggClassEst)


# 에이다부스트에 복잡한 데이터 집합 적용하기
# 7.4 에이다부스트를 위해 데이터를 불러오는 함수
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


# 분류 불균형
# 7.5 ROC 플롯과 AUC 계산 함수
def plotROC(predStrengths, 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 = predStrengths.argsort()
    fig = plt.figure()
    fig.clf()
    ax = plt.subplot(111)
    for index in sortedIndicies.tolist()[0] :
        if classLabels[index] == 1.0 :
            delX = 0
            delY = 0
        else : 
            delX = xStep
            delY = 0
            ySum += cur[1]
        ax.plot([cur[0], cur[0] - delX], [cul[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 AdaBoot Horse Colic Detection System')
    ax.axis([0,1,0,1])
    plt.show()
    print ("The Area Under the Curve is : ", ySum * xStep)