机器学习实战（7） ——利用AdaBoost元算法提高分类性能（python实现）

这是学习机器学习算法实战这本书时，写的代码实战。让自己对各个算法有更直观的了解，不能一直不写啊。不管简单还是不简单都亲自一行一行的敲一遍啊。
具体的源码和和数据链接：https://pan.baidu.com/s/1G2S2pb5gfBnxGNNTFgTkEA 密码：fov0
下面是是第七章写的adaboost.py程序# -*- coding: utf-8 -*-
# author: Yufeng Song
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 #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 les
a6fc
s 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
print ("predictedVals",predictedVals.T,"errArr",errArr.T)
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)
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 eror:",errorRate,"\n")
if errorRate == 0.0: break
return weakClassArr

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)

def loadDataSet(fileName):
numFeat = len(open(fileName).readlines().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(float(curLine[-1]))
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
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)
if __name__ == '__main__':
dataMatrix,classlabels=loadSimpData()
# print(dataMatrix)
# print(stumpClassify(dataMatrix,0,1.4,'lt'))
D = mat(ones((5,1))/5)
print(D)
print(buildStump(dataMatrix,classlabels,D))
print(adaBoostTrainDS(dataMatrix,classlabels,9))