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更新2.KNN文件位置
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jiangzhonglian
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src/python/7.AdaBoost/adaboost.py
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304
src/python/7.AdaBoost/adaboost.py
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#!/usr/bin/python
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# coding:utf8
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'''
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Created on Nov 28, 2010
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Adaboost is short for Adaptive Boosting
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@author: Peter/jiangzhonglian
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'''
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from numpy import *
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def loadSimpData():
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""" 测试数据
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Returns:
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dataArr feature对应的数据集
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labelArr feature对应的分类标签
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"""
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dataArr = array([[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]])
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labelArr = [1.0, 1.0, -1.0, -1.0, 1.0]
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return dataArr, labelArr
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# general function to parse tab -delimited floats
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def loadDataSet(fileName):
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# get number of fields
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numFeat = len(open(fileName).readline().split('\t'))
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dataArr = []
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labelArr = []
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fr = open(fileName)
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for line in fr.readlines():
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lineArr = []
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curLine = line.strip().split('\t')
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for i in range(numFeat-1):
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lineArr.append(float(curLine[i]))
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dataArr.append(lineArr)
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labelArr.append(float(curLine[-1]))
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return dataArr, labelArr
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def stumpClassify(dataMat, dimen, threshVal, threshIneq):
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"""stumpClassify(将数据集，按照feature列的value进行 二分法切分比较来赋值分类)
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Args:
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dataMat Matrix数据集
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dimen 特征列
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threshVal 特征列要比较的值
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Returns:
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retArray 结果集
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"""
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# 默认都是1
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retArray = ones((shape(dataMat)[0], 1))
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# dataMat[:, dimen] 表示数据集中第dimen列的所有值
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# threshIneq == 'lt'表示修改左边的值，gt表示修改右边的值
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# print '-----', threshIneq, dataMat[:, dimen], threshVal
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if threshIneq == 'lt':
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retArray[dataMat[:, dimen] <= threshVal] = -1.0
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else:
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retArray[dataMat[:, dimen] > threshVal] = -1.0
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return retArray
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def buildStump(dataArr, labelArr, D):
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"""buildStump(得到决策树的模型)
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Args:
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dataArr 特征标签集合
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labelArr 分类标签集合
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D 最初的特征权重值
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Returns:
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bestStump 最优的分类器模型
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minError 错误率
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bestClasEst 训练后的结果集
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"""
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# 转换数据
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dataMat = mat(dataArr)
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labelMat = mat(labelArr).T
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# m行 n列
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m, n = shape(dataMat)
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# 初始化数据
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numSteps = 10.0
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bestStump = {}
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bestClasEst = mat(zeros((m, 1)))
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# 初始化的最小误差为无穷大
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minError = inf
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# 循环所有的feature列，将列切分成 若干份，每一段以最左边的点作为分类节点
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for i in range(n):
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rangeMin = dataMat[:, i].min()
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rangeMax = dataMat[:, i].max()
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# print 'rangeMin=%s, rangeMax=%s' % (rangeMin, rangeMax)
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# 计算每一份的元素个数
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stepSize = (rangeMax-rangeMin)/numSteps
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# 例如： 4=(10-1)/2 那么 1-4(-1次) 1(0次) 1+1*4(1次) 1+2*4(2次)
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# 所以： 循环 -1/0/1/2
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for j in range(-1, int(numSteps)+1):
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# go over less than and greater than
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for inequal in ['lt', 'gt']:
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# 如果是-1，那么得到rangeMin-stepSize; 如果是numSteps，那么得到rangeMax
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threshVal = (rangeMin + float(j) * stepSize)
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# 对单层决策树进行简单分类，得到预测的分类值
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predictedVals = stumpClassify(dataMat, i, threshVal, inequal)
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# print predictedVals
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errArr = mat(ones((m, 1)))
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# 正确为0，错误为1
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errArr[predictedVals == labelMat] = 0
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# 计算 平均每个特征的概率0.2*错误概率的总和为多少，就知道错误率多高
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# 例如： 一个都没错，那么错误率= 0.2*0=0 ， 5个都错，那么错误率= 0.2*5=1， 只错3个，那么错误率= 0.2*3=0.6
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weightedError = D.T*errArr
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'''
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dim 表示 feature列
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threshVal 表示树的分界值
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inequal 表示计算树左右颠倒的错误率的情况
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weightedError 表示整体结果的错误率
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bestClasEst 预测的最优结果
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'''
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# print "split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError)
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if weightedError < minError:
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minError = weightedError
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bestClasEst = predictedVals.copy()
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bestStump['dim'] = i
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bestStump['thresh'] = threshVal
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bestStump['ineq'] = inequal
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# bestStump 表示分类器的结果，在第几个列上，用大于／小于比较，阈值是多少
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return bestStump, minError, bestClasEst
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def adaBoostTrainDS(dataArr, labelArr, numIt=40):
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"""adaBoostTrainDS(adaBoost训练过程放大)
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Args:
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dataArr 特征标签集合
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labelArr 分类标签集合
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numIt 实例数
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Returns:
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weakClassArr 弱分类器的集合
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aggClassEst 预测的分类结果值
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"""
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weakClassArr = []
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m = shape(dataArr)[0]
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# 初始化 D，设置每个特征的权重值，平均分为m份
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D = mat(ones((m, 1))/m)
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aggClassEst = mat(zeros((m, 1)))
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for i in range(numIt):
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# 得到决策树的模型
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bestStump, error, classEst = buildStump(dataArr, labelArr, D)
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# alpha目的主要是计算每一个分类器实例的权重(组合就是分类结果)
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# 计算每个分类器的alpha权重值
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alpha = float(0.5*log((1.0-error)/max(error, 1e-16)))
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bestStump['alpha'] = alpha
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# store Stump Params in Array
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weakClassArr.append(bestStump)
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# print "alpha=%s, classEst=%s, bestStump=%s, error=%s " % (alpha, classEst.T, bestStump, error)
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# -1主要是下面求e的-alpha次方； 如果判断正确，乘积为1，否则成绩为-1，这样就可以算出分类的情况了
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expon = multiply(-1*alpha*mat(labelArr).T, classEst)
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print '\n'
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print 'labelArr=', labelArr
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print 'classEst=', classEst.T
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print '\n'
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print '乘积: ', multiply(mat(labelArr).T, classEst).T
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# 判断正确的，就乘以-1，否则就乘以1， 为什么？ 书上的公式。
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print '(-1取反)预测值expon=', expon.T
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# 计算e的expon次方，然后计算得到一个综合的概率的值
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# 结果发现： 判断错误的特征，D对于的特征的权重值会变大。
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D = multiply(D, exp(expon))
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D = D/D.sum()
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print "D: ", D.T
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print '\n'
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# 预测的分类结果值，在上一轮结果的基础上，进行加和操作
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print '当前的分类结果：', alpha*classEst.T
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aggClassEst += alpha*classEst
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print "叠加后的分类结果aggClassEst: ", aggClassEst.T
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# 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。
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# 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
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aggErrors = multiply(sign(aggClassEst) != mat(labelArr).T, ones((m, 1)))
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errorRate = aggErrors.sum()/m
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# print "total error=%s " % (errorRate)
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if errorRate == 0.0:
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break
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return weakClassArr, aggClassEst
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def adaClassify(datToClass, classifierArr):
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# do stuff similar to last aggClassEst in adaBoostTrainDS
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dataMat = mat(datToClass)
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m = shape(dataMat)[0]
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aggClassEst = mat(zeros((m, 1)))
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# 循环 多个分类器
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for i in range(len(classifierArr)):
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# 前提： 我们已经知道了最佳的分类器的实例
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# 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。
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classEst = stumpClassify(dataMat, classifierArr[i]['dim'], classifierArr[i]['thresh'], classifierArr[i]['ineq'])
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aggClassEst += classifierArr[i]['alpha']*classEst
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# print aggClassEst
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return sign(aggClassEst)
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def plotROC(predStrengths, classLabels):
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"""plotROC(打印ROC曲线，并计算AUC的面积大小)
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Args:
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predStrengths 最终预测结果的权重值
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classLabels 原始数据的分类结果集
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"""
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import matplotlib.pyplot as plt
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# variable to calculate AUC
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ySum = 0.0
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# 对正样本的进行求和
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numPosClas = sum(array(classLabels)==1.0)
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# 正样本的概率
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yStep = 1/float(numPosClas)
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# 负样本的概率
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xStep = 1/float(len(classLabels)-numPosClas)
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# argsort函数返回的是数组值从小到大的索引值
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# get sorted index, it's reverse
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sortedIndicies = predStrengths.argsort()
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# 开始创建模版对象
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fig = plt.figure()
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fig.clf()
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ax = plt.subplot(111)
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# cursor光标值
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cur = (1.0, 1.0)
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# loop through all the values, drawing a line segment at each point
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for index in sortedIndicies.tolist()[0]:
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if classLabels[index] == 1.0:
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delX = 0
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delY = yStep
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else:
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delX = xStep
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delY = 0
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ySum += cur[1]
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# draw line from cur to (cur[0]-delX, cur[1]-delY)
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# 画点连线 (x1, x2, y1, y2)
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# print cur[0], cur[0]-delX, cur[1], cur[1]-delY
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ax.plot([cur[0], cur[0]-delX], [cur[1], cur[1]-delY], c='b')
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cur = (cur[0]-delX, cur[1]-delY)
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# 画对角的虚线线
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ax.plot([0, 1], [0, 1], 'b--')
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plt.xlabel('False positive rate')
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plt.ylabel('True positive rate')
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plt.title('ROC curve for AdaBoost horse colic detection system')
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# 设置画图的范围区间 (x1, x2, y1, y2)
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ax.axis([0, 1, 0, 1])
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plt.show()
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'''
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参考说明：http://blog.csdn.net/wenyusuran/article/details/39056013
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为了计算AUC，我们需要对多个小矩形的面积进行累加。这些小矩形的宽度是xStep，因此
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可以先对所有矩形的高度进行累加，最后再乘以xStep得到其总面积。所有高度的和(ySum)随
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着x轴的每次移动而渐次增加。
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'''
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print "the Area Under the Curve is: ", ySum*xStep
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if __name__ == "__main__":
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# 我们要将5个点进行分类
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dataArr, labelArr = loadSimpData()
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print 'dataArr', dataArr, 'labelArr', labelArr
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# D表示最初值，对1进行均分为5份，平均每一个初始的概率都为0.2
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# D的目的是为了计算错误概率： weightedError = D.T*errArr
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D = mat(ones((5, 1))/5)
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print 'D=', D.T
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# bestStump, minError, bestClasEst = buildStump(dataArr, labelArr, D)
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# print 'bestStump=', bestStump
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# print 'minError=', minError
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# print 'bestClasEst=', bestClasEst.T
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# 分类器：weakClassArr
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# 历史累计的分类结果集
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weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9)
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print '\nweakClassArr=', weakClassArr, '\naggClassEst=', aggClassEst.T
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"""
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发现:
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分类的权重值：最大的值，为alpha的加和，最小值为-最大值
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特征的权重值：如果一个值误判的几率越小，那么D的特征权重越少
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"""
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# 测试数据的分类结果, 观测：aggClassEst分类的最终权重
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print adaClassify([0, 0], weakClassArr).T
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print adaClassify([[5, 5], [0, 0]], weakClassArr).T
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# # 马疝病数据集
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# # 训练集合
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# dataArr, labelArr = loadDataSet("input/07.AdaBoost/horseColicTraining2.txt")
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# weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 40)
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# print weakClassArr, '\n-----\n', aggClassEst.T
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# # 计算ROC下面的AUC的面积大小
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# plotROC(aggClassEst.T, labelArr)
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# # 测试集合
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# dataArrTest, labelArrTest = loadDataSet("input/07.AdaBoost/horseColicTest2.txt")
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# m = shape(dataArrTest)[0]
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# predicting10 = adaClassify(dataArrTest, weakClassArr)
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# errArr = mat(ones((m, 1)))
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# # 测试：计算总样本数，错误样本数，错误率
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# print m, errArr[predicting10 != mat(labelArrTest).T].sum(), errArr[predicting10 != mat(labelArrTest).T].sum()/m
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