Machine Learning in action --AdaBoost(已勘误)
最近在自学机器学习，应导师要求，先把《Machine Learning with R》动手刷了一遍，感觉R真不能算是一门计算机语言，感觉也就是一个功能复杂的计算器。所以这次就决定使用经典教材《Machine Learning in action》。因为开学得换work station ，怕到时候代码又丢了，所以就索性开个博客，把代码上传上来。
因为书上的原代码有很多错误，并且网上的许多博客的代码也是没有改正的，这次我把修正过的代码po上来
version：python3.5
talk is cheap show me the code
函数定义代码
#coding=utf-8
from numpy import *
def loadSimpleData():
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):
m = shape(dataMatrix)[0]
retArray = ones((m, 1))
#分类正确
if threshIneq == 'lt':
retArray[dataMatrix[:, dimen] <= threshVal] = -1.0
else:
retArray[dataMatrix[:, dimen] > threshVal] = -1.0
return retArray
#单层决策树生成函数
def buildStump(dataArr, classLabels, D):
dataMatrix = mat(dataArr)
#print(dataMatrix)
labelMat = mat(classLabels).T
#print(labelMat)
m, n = shape(dataMatrix)
numSteps = 10.0 ; bestStump = {} ; bestClassEst = mat(zeros((m, 1)))
minError = inf
for i in range(n):
rangeMin = dataMatrix[:, i].min()
#print(dataMatrix)
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 #计算加权错误率
#print("split: dim %d ,thresh % .2f, thresh ineqal: %s ,the "
#"weighted error is %.3f " %(i, threshVal, inequal, weightedError))
if weightedError < minError :
minError = weightedError
bestClassEst = predictedVals.copy()
bestStump['dim'] = i
bestStump['thresh'] = threshVal
bestStump['ineq'] = inequal
return bestStump, minError, bestClassEst
#基于单层决策树的AdaBoost训练过程
def adaBoostTrainDS(dataArr, classLabels, numlt):
weakClassArr =[]
m = shape(dataArr)[0]
D = mat(ones((m, 1))/m)
aggClassEst = mat(zeros((m, 1)))
for i in range(numlt):
bestStump, error, classEst = buildStump(dataArr, classLabels, D)
print("D.T", D.T)
alpha = float(0.5 * log((1.0 - error) /max(error, 1e-16)))
bestStump['alpha'] = alpha
weakClassArr.append(bestStump)
#print("weakClassArr:",weakClassArr)
#print("classEst:", classEst.T)
#迭代计算 *D*
expon = multiply(-1 * alpha * mat(classLabels).T, classEst)
D = multiply(D, exp(expon))
D = D / D.sum()
#错误率累加计算
aggClassEst += alpha * classEst
#print("aggClassEst:",aggClassEst.T)
#print("sign(aggClassEst):",sign(aggClassEst))
#print("mat(classLabels).T:",mat(classLabels).T)
aggErrors = multiply(sign(aggClassEst) != mat(classLabels).T, ones((m, 1)))
print("aggError",aggErrors)
errorRate = aggErrors.sum()/m
print("total error:",errorRate,"\n")
if errorRate == 0.0:
break
#print("----weakClassArr", weakClassArr)
return weakClassArr, aggClassEst
#AdaBoost 分类函数
def adaClassify(datToClass, classifierArr):
dataMatrix = mat(datToClass)
m = shape(dataMatrix)[0]
aggClassEst = mat(zeros((m, 1)))
for i in range(len(classifierArr)): # 行数
classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'], classifierArr[i]['thresh'],
classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha'] * classEst
print("aggClassEst:",aggClassEst)
#print("--->",sign(aggClassEst))
return sign(aggClassEst)
def loadDataSet(fileName):
fr = open(fileName)
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
def plotROC(predStrengths, classLabels):
import matplotlib.pyplot as plt
cur = (1.0, 1.0)
ySum =0.0 #AUC的值
numPosClas = sum(array(classLabels) == 1.0)
yStep = 1/float(numPosClas)
xStep = 1/float(len(classLabels) - numPosClas)
#获取排好序的索引
sortedIndicies = predStrengths.argsort()
fig = plt.figure()
fig.clf()
ax = plt.subplot(111)
for index in sortedIndicies.tolist()[0]: #tolist() convert Array to List
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("the Area Under the Curve is:",ySum * xStep)
上面代码块只是定义了主要的函数，离运行还差一点。由于书原文中，采用了使用 iPython 命令行的运行方式，但是博主比较懒，所以干脆舍弃掉原来的方式。
废话不多少，直接上代码
实验1
if __name__=="__main__":
datMat, classLabels = loadSimpleData()
D = mat(ones((5, 1)) / 5)
buildStump(datMat, classLabels, D)实验2 ：
if __name__ == "__main__":
datArr, labelArr = loadSimpleData()
classifierArr = adaBoostTrainDS(datArr, labelArr, 30)
print(classifierArr)
实验3 ：
if __name__ == "__main__":
datArr, labelArr = loadSimpleData()
classifierArr = adaBoostTrainDS(datArr, labelArr, 30)
print("classifierArr",classifierArr)
print("len(classifierArr):",len(classifierArr))
adaClassify([[5,5], [0,0]], classifierArr)
print(adaClassify([[5,5], [0,0]], classifierArr))
实验4 ：
if __name__ == "__main__":
datArr, labelArr = loadDataSet("horseColicTraining2.txt")
classifierArr = adaBoostTrainDS(datArr, labelArr, 10)
print("classifierArr", classifierArr)
testArr, testLabelArr = loadDataSet("horseColicTest2.txt")
prediction10 = adaClassify(testArr, classifierArr)
print("prediction10:",prediction10)
errArr = mat(ones((67, 1)))
totalErr = errArr[prediction10 != mat(testLabelArr).T].sum()
totalErrRate = totalErr /67
print("totalErrRate:",totalErrRate)
实验5 ：
if __name__ == "__main__":
datArr, labelArr = loadDataSet("horseColicTraining2.txt")
classifierArr, aggClassEst = adaBoostTrainDS(datArr, labelArr, 10)
plotROC(aggClassEst.T, labelArr)
更多请戳github
https://github.com/Edgis/Machine-learning-in-action/blob/master/adaboost.py
