第五章 Logistic回归

假设现在有一些数据点，我们利用一条直线对这些点进行拟合（该线称为最佳拟合直线），这个拟合过程就称作回归。

为了实现Logistic回归分类器，我们可以在每个特征上都乘以一个回归系数，然后把所有的结果值相加，将这个结果代入Sigmoid函数中，进而得到一个范围在0-1之间的数值。任何大于0.5的数据被分入1类，小于0.5即被归入0类。

1.`Sigmoid`函数的输入记为 (z),由下面的公式得出：

\[z = {\omega_0}{x_0} + {\omega _1}{x _1} + {\omega _2}{x _2} + ... + {\omega _n}{x _n} \]

2.梯度算法的迭代公式为：

\[\omega = \omega + \alpha {\nabla _\omega }f(\omega ) \]

\[\theta _j = \theta _j + \alpha({y^i - h({x^i})}) * {x _j^i} \]

3.`Logistic`回归梯度上升优化算法

原始数据：

Logistic回归梯度上升优化算法-python代码:

def loadDataSet():
dataMat = []; labelMat = []
fr = open('testSet.txt')
for line in fr.readlines():
    lineArr = line.strip().split()
    dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
    labelMat.append(int(lineArr[2]))
return dataMat,labelMat

def sigmoid(inX):
    return 1.0/(1+exp(-inX))

def gradAscent(dataMatIn, classLabels):
    dataMatrix = mat(dataMatIn)             #convert to NumPy matrix
    labelMat = mat(classLabels).transpose() #convert to NumPy matrix
    m,n = shape(dataMatrix)
    alpha = 0.001
    maxCycles = 500                         #迭代次数
    weights = ones((n,1))
    for k in range(maxCycles):              #heavy on matrix operations
        h = sigmoid(dataMatrix*weights)     #matrix mult
        error = (labelMat - h)              #vector subtraction
        weights = weights + alpha * dataMatrix.transpose()* error #matrix mult 这是梯度上升公式更新参数
    return weights                          #返回值为[w0,w1,w2]

在python提示符下，敲下如下代码：

dataArr, labelMat = logRegres.loadDataSet()
logRegres.gradAscent(dataArr, labelMat)
Out[12]: 
matrix([[ 4.12414349],
        [ 0.48007329],
        [-0.6168482 ]])

4.分析数据：画出决策边界

** 画出数据集和Logistic回归最佳拟合直线的函数 **

def plotBestFit(weights):              #此处weights即为上面求出的[w0,w1,w2]
   import matplotlib.pyplot as plt
   dataMat,labelMat=loadDataSet()
   dataArr = array(dataMat)
   n = shape(dataArr)[0] 
   xcord1 = []; ycord1 = []
   xcord2 = []; ycord2 = []
   for i in range(n):
       if int(labelMat[i])== 1:
           xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])
       else:
           xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])
   fig = plt.figure()
   ax = fig.add_subplot(111)
   ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
   ax.scatter(xcord2, ycord2, s=30, c='green')
   x = arange(-3.0, 3.0, 0.1)
   y = (-weights[0]-weights[1]*x)/weights[2] # 0=w0x0+w1x1+w2x2 (x0=1) 此处x=x1；y=x2
   ax.plot(x, y)
   plt.xlabel('X1'); plt.ylabel('X2');
   plt.show()

在python提示符下，敲下如下代码：

logRegres.plotBestFit(weights.getA())  #getA() 将矩阵转化为数组array

运行结果为：

5.训练算法：随机梯度上升

改进算法，一次仅用一个样本点来更新回归系数——在线学习算法

** 随机梯度上升算法 **

def stocGradAscent0(dataMatrix, classLabels):
    m,n = shape(dataMatrix)
    alpha = 0.01
    weights = ones(n)   #initialize to all ones
    for i in range(m):
        h = sigmoid(sum(dataMatrix[i]*weights))
        error = classLabels[i] - h
        weights = weights + alpha * error * dataMatrix[i]
    return weights

在python提示符下，敲下如下代码：

weights = logRegres.stocGradAscent0(array(dataArr), labelMat)

运行结果为：

**改进的随机梯度上升算法 **

def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m,n = shape(dataMatrix)
weights = ones(n)   #initialize to all ones
for j in range(numIter):
    dataIndex = range(m)
    for i in range(m):
        alpha = 4/(1.0+j+i)+0.0001    #apha decreases with iteration, does not 
        randIndex = int(random.uniform(0,len(dataIndex)))#go to 0 because of the constant
        h = sigmoid(sum(dataMatrix[randIndex]*weights))
        error = classLabels[randIndex] - h
        weights = weights + alpha * error * dataMatrix[randIndex]
        del(dataIndex[randIndex])
return weights

步长alpha每次迭代时都会调整，可以缓解数据波动或者高频扰动。且alpha会随着迭代次数不断减小，但永远不会减小到0（常数项）——可以保证多次迭代后新数据仍然有影响。如果要处理的问题是动态变化的，可以适当加大常数项，确保新值获得更大的回归系数。当( j << max(i) )时，步长alpha不是严格下降的。避免参数严格下降也常见于模拟退火等其他优化算法中。
随机选取样本值来更新回归系数。这种方法将减少周期性的波动——具体实现方法：每次随机从样本中选出一个值，更新回归系数后将其删除，再进行下一次迭代。

改进后回归系数变化情况

收敛速度更快，波动更小

在python提示符下，敲下如下代码：

dataArr, labelMat = logRegres.loadDataSet()
weights = logRegres.stocGradAscent1(array(dataArr), labelMat)
logRegres.plotBestFit(weights)

运行结果为：

6. 示例：从疝气病症预测病马的死亡率

Logistic回归分类函数

def classifyVector(inX, weights):
    prob = sigmoid(sum(inX*weights))
    if prob > 0.5: return 1.0
    else: return 0.0

def colicTest():
    frTrain = open('horseColicTraining.txt'); frTest = open('horseColicTest.txt')
    trainingSet = []; trainingLabels = []
    for line in frTrain.readlines():
        currLine = line.strip().split('\t')
        lineArr =[]
        for i in range(21):
            lineArr.append(float(currLine[i]))
        trainingSet.append(lineArr)
        trainingLabels.append(float(currLine[21]))
    trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 1000)  #计算回归系数
    errorCount = 0; numTestVec = 0.0
    for line in frTest.readlines():
        numTestVec += 1.0
        currLine = line.strip().split('\t')
        lineArr =[]
        for i in range(21):
            lineArr.append(float(currLine[i]))
        if int(classifyVector(array(lineArr), trainWeights))!= int(currLine[21]):
            errorCount += 1
    errorRate = (float(errorCount)/numTestVec)
    print "the error rate of this test is: %f" % errorRate
    return errorRate

def multiTest():           #调用colicTest()10次并求平均值
    numTests = 10; errorSum=0.0
    for k in range(numTests):
        errorSum += colicTest()
    print "after %d iterations the average error rate is: %f" % (numTests, errorSum/float(numTests))

在python提示符下，敲下如下代码：

logRegres.multiTest()

输出：

the error rate of this test is: 0.313433
the error rate of this test is: 0.402985
the error rate of this test is: 0.298507
the error rate of this test is: 0.402985
the error rate of this test is: 0.388060
the error rate of this test is: 0.313433
the error rate of this test is: 0.328358
the error rate of this test is: 0.343284
the error rate of this test is: 0.283582
the error rate of this test is: 0.388060
after 10 iterations the average error rate is: 0.346269

posted @ 2016-09-19 20:30 arrowway 阅读(1179) 评论(0) 编辑收藏举报

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第五章 Logistic回归