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更新完AdaBoost的测试代码和案例

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jiangzhonglian
2017-03-15 20:04:27 +08:00
parent 12a19d1d6e
commit 6c2d2ac329
8 changed files with 466 additions and 54 deletions
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135
src/python/07.AdaBoost/adaboost.py
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@@ -9,14 +9,31 @@ Adaboost is short for Adaptive Boosting
from numpy import *
def loadSimpData():
""" 测试数据
Returns:
dataArr feature对应的数据集
labelArr feature对应的分类标签
"""
dataArr = array([[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]])
labelArr = [1.0, 1.0, -1.0, -1.0, 1.0]
# def loadSimpData():
# """ 测试数据
# Returns:
# dataArr feature对应的数据集
# labelArr feature对应的分类标签
# """
# dataArr = array([[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]])
# labelArr = [1.0, 1.0, -1.0, -1.0, 1.0]
# return dataArr, labelArr
# general function to parse tab -delimited floats
def loadDataSet(fileName):
# get number of fields
numFeat = len(open(fileName).readline().split('\t'))
dataArr = []
labelArr = []
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]))
dataArr.append(lineArr)
labelArr.append(float(curLine[-1]))
return dataArr, labelArr
@@ -117,11 +134,11 @@ def adaBoostTrainDS(dataArr, labelArr, numIt=40):
# 结果发现： 正确的alpha的权重值变小了，错误的变大了。也就说D里面分类的权重值变了。（可以举例验证，假设：alpha=0.6，什么的）
D = multiply(D, exp(expon))
D = D/D.sum()
print "D: ", D.T
# print "D: ", D.T
# 计算分类结果的值，在上一轮结果的基础上，进行加和操作
# calc training error of all classifiers, if this is 0 quit for loop early (use break)
aggClassEst += alpha*classEst
print "aggClassEst: ", aggClassEst.T
# print "aggClassEst: ", aggClassEst.T
# sign 判断正为1， 0为0， 负为-1，通过最终加和的权重值，判断符号。
# 结果为：错误的样本标签集合，因为是 !=,那么结果就是0 正, 1 负
aggErrors = multiply(sign(aggClassEst) != mat(labelArr).T, ones((m, 1)))
@@ -132,54 +149,27 @@ def adaBoostTrainDS(dataArr, labelArr, numIt=40):
return weakClassArr, aggClassEst
if __name__ == "__main__":
dataArr, labelArr = loadSimpData()
print '-----\n', dataArr, '\n', labelArr
def adaClassify(datToClass, classifierArr):
# do stuff similar to last aggClassEst in adaBoostTrainDS
dataMat = mat(datToClass)
m = shape(dataMat)[0]
aggClassEst = mat(zeros((m, 1)))
# D表示最初，对1进行均分为5份，平均每一个初始的概率都为0.2
D = mat(ones((5, 1))/5)
# print '-----', D
# print buildStump(dataArr, labelArr, D)
weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9)
print weakClassArr
def loadDataSet(fileName): #general function to parse tab -delimited floats
numFeat = len(open(fileName).readline().split('\t')) #get number of fields
dataArr = []
labelArr = []
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]))
dataArr.append(lineArr)
labelArr.append(float(curLine[-1]))
return dataArr, labelArr
def adaClassify(datToClass,classifierArr):
dataMatrix = mat(datToClass)#do stuff similar to last aggClassEst in adaBoostTrainDS
m = shape(dataMatrix)[0]
aggClassEst = mat(zeros((m,1)))
# 循环 多个分类器
for i in range(len(classifierArr)):
classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],\
classifierArr[i]['thresh'],\
classifierArr[i]['ineq'])#call stump classify
# 通过分类器来核算每一次的分类结果，然后通过alpha*每一次的结果 得到最后的权重加和的值。
classEst = stumpClassify(dataMat, classifierArr[i]['dim'], classifierArr[i]['thresh'], classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha']*classEst
print aggClassEst
# 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
# cursor
cur = (1.0, 1.0)
# variable to calculate AUC
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
@@ -189,9 +179,11 @@ def plotROC(predStrengths, classLabels):
#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;
delX = 0
delY = yStep
else:
delX = xStep; delY = 0;
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')
@@ -202,3 +194,40 @@ def plotROC(predStrengths, classLabels):
ax.axis([0,1,0,1])
plt.show()
print "the Area Under the Curve is: ",ySum*xStep
if __name__ == "__main__":
# dataArr, labelArr = loadSimpData()
# print '-----\n', dataArr, '\n', labelArr
# # D表示最初，对1进行均分为5份，平均每一个初始的概率都为0.2
# D = mat(ones((5, 1))/5)
# # print '-----', D
# # print buildStump(dataArr, labelArr, D)
# # 分类器：weakClassArr
# # 历史累计的分类结果集
# weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9)
# print weakClassArr, '\n-----\n', aggClassEst.T
# # 测试数据的分类结果
# print adaClassify([0, 0], weakClassArr)
# print adaClassify([[5, 5], [0, 0]], weakClassArr)
# 马疝病数据集
# 训练集合
dataArr, labelArr = loadDataSet("testData/AB_horseColicTraining2.txt")
weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 50)
# 测试集合
dataArrTest, labelArrTest = loadDataSet("testData/AB_horseColicTest2.txt")
m = shape(dataArrTest)[0]
predicting10 = adaClassify(dataArrTest, weakClassArr)
errArr = mat(ones((m, 1)))
# 测试：计算总样本数，错误样本数，错误率
print m, errArr[predicting10 != mat(labelArrTest).T].sum(), errArr[predicting10 != mat(labelArrTest).T].sum()/m