Logistic是一种常用的分类方法,属于对数线性模型,利用Logistic回归,根据现有数据对分类边界建立回归公式,以此进行分类。
回归:假设现有一些数据点,我们用一条直线对这些点进行拟合,这个拟合过程就称为回归
下图为Sigmoid函数曲线图。z为0时,Sigmoid函数值为0.5。随着z的增大,Sigmoid函数值将趋近于1;随着x的减小,Sigmoid函数值将趋近于0.
为了实现Logistic回归分类器,我们在每个特征上都乘以一个回归系数,然后把结果相加,总和带入Sigmoid函数中,得到一个0~1的数值。若数值大于0.5,则被分为1类,否则,分为0类。
考虑n维特征\(x = (x_0,x_1,x_2,\cdots,x_n)\),参数向量\(w=(w_0,w_1,w_2,\cdots,w_n)\)我们对输入数据线性加权得:
\[z = w^Tx = w_0x_0+ w_1x_1 + w_2x_2 + \cdots \cdots + w_nx_n \tag{2} \]将z作为自变量带入Sigmoid函数中,得到一个0~1的数值。若数值大于0.5,则被分为1类,否则,分为0类。即
\[\sigma(z) = \frac{1}{1 + e^{-z}} = \frac{1}{1 + e^{-w^Tx}} = \sigma(w^Tx) \]现在的问题则是:如何确定最佳参数\(w\)从而使分类尽可能地准确。
梯度上升法即沿着该函数的梯度方向探寻,寻找最优解。记函数f(x,y)的梯度为
\[\nabla f(x,y) = \begin{bmatrix} \frac{\partial f(x,y)}{\partial x}\\ \frac{\partial f(x,y)}{\partial y} \end{bmatrix} \]梯度算法迭代公式如下:
\[w = w + \alpha \nabla_w f(w) \]改公式一直迭代执行,直至达到某个条件未知,如达到可以允许的误差范围,或迭代次数达到某个值。
PS: 梯度上升法用来求最大值,而梯度下降法是用来求最小值
使用梯度上升算法之前,我们需要知道如何优化,才能达到我们的目的,即目标函数是什么,根据目标函数来使用梯度上升算法。我们考虑二分类问题,其中包含类别1与类别0,可以得到预测函数,公式如下:
\[f_w(x)= \sigma(w^Tx) = \frac{1}{1 + e^{-w^Tx}} \]\(f_w(x)\)的值表示\(y=1\)的概率,因此分类结果为类别1与类别0的概率分别为:
\[P(y=1|x;w) = f_w(x)\\ \\ P(y=0|x;w) = 1 - f_w(x) \]即:
\[P(y|x;w) = (f_w(x))^y (1 - f_w(x))^{1-y} \]其似然函数为:
\[L(w) = \prod_{i=1}^{m}P(y^{(i)}|x^{(i)};w) = \prod_{i=1}^{m}(f_w(x^{(i)}))^{y^{(i)}} (1 - f_w(x^{(i)}))^{1-y^{(i)}} \]\(m\)为样本个数
对数似然函数为:
\[l(w) = lnL(w) = \sum_{i=1}^{m}\begin{Bmatrix}y^{(i)}ln(f_w(x^{(i)})) + (1-y^{(i)})ln(1 - f_w(x^{(i)}))\end{Bmatrix} \]最大似然估计就是要求使得\(l(w)\)达到最大值的\(w\),所以目标函数就是\(l(w)\)
import numpy as np def loadDataSet(): dataMat = [] labelMat = [] file = open('testSet.txt','r') # testSet.txt可在附录获取 for line in file: strLine = line.strip().split() dataMat.append([1.0,float(strLine[0]),float(strLine[1])]) labelMat.append([strLine[2]]) return dataMat,labelMat def sigmoid(x): return 1.0 / (1.0 + np.exp(-x)) def lossFunction(y,y_hat): # 梯度的相减部分 return y - y_hat def gradAscent(data,labels): dataMat = np.mat(data, dtype = 'float64') # 转换为numpy数据类型 labelMat = np.mat(labels, dtype = 'float64') m,n = dataMat.shape lr = 0.001 epochs = 500 weights = np.ones((n,1)) for epoch in range(epochs): labelEst = sigmoid(dataMat*weights) loss = lossFunction(labelMat,labelEst) # 目标函数 weights = weights + lr * dataMat.transpose() * loss return weights def plotBestFit(weight): weightArray = weight.getA() dataMat, labelMat = loadDataSet() dataArr = np.array(dataMat) n = dataArr.shape[0] xcord1 = []; ycord1 = [] xcord2 = []; ycord2 = [] for i in range(n): if int(labelMat[i][0]) == 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 = np.arange(-3.0,3.0,0.1) y = (-weightArray[0] - weightArray[1] * x) / weightArray[2] ax.plot(x,y) plt.xlabel('x1'); plt.ylabel('x2') plt.show() data,labels = loadDataSet() weights = gradAscent(data,labels) print(weights) plotBestFit(weights)
可视化:
随机梯度下降法不同于批量梯度下降,随机梯度下降是每次迭代使用一个样本来对参数进行更新。使得训练速度加快。推荐一篇讲的非常好的博文:批量梯度下降、随机梯度下降和小批量梯度下降
def stocGradAscent1(dataMatrix, classLabels,numIter=150): dataMatrix = np.array(data, dtype='float64') # 转换为numpy数据类型 classLabels = np.array(labels, dtype='float64') m,n = np.shape(dataMatrix) weights = np.ones(n) for j in range(numIter): dataIndex = list(range(m)) for i in range(m): lr = 4 / (1.0 +j + i) + 0.01 randIndex = int(random.uniform(0,len(dataIndex))) h = sigmoid(sum(dataMatrix[randIndex] * weights)) error = classLabels[randIndex] - h weights = weights + lr * error * dataMatrix[randIndex] del (dataIndex[randIndex]) return weights
testSet.txt文件:
-0.017612 14.053064 0 -1.395634 4.662541 1 -0.752157 6.538620 0 -1.322371 7.152853 0 0.423363 11.054677 0 0.406704 7.067335 1 0.667394 12.741452 0 -2.460150 6.866805 1 0.569411 9.548755 0 -0.026632 10.427743 0 0.850433 6.920334 1 1.347183 13.175500 0 1.176813 3.167020 1 -1.781871 9.097953 0 -0.566606 5.749003 1 0.931635 1.589505 1 -0.024205 6.151823 1 -0.036453 2.690988 1 -0.196949 0.444165 1 1.014459 5.754399 1 1.985298 3.230619 1 -1.693453 -0.557540 1 -0.576525 11.778922 0 -0.346811 -1.678730 1 -2.124484 2.672471 1 1.217916 9.597015 0 -0.733928 9.098687 0 -3.642001 -1.618087 1 0.315985 3.523953 1 1.416614 9.619232 0 -0.386323 3.989286 1 0.556921 8.294984 1 1.224863 11.587360 0 -1.347803 -2.406051 1 1.196604 4.951851 1 0.275221 9.543647 0 0.470575 9.332488 0 -1.889567 9.542662 0 -1.527893 12.150579 0 -1.185247 11.309318 0 -0.445678 3.297303 1 1.042222 6.105155 1 -0.618787 10.320986 0 1.152083 0.548467 1 0.828534 2.676045 1 -1.237728 10.549033 0 -0.683565 -2.166125 1 0.229456 5.921938 1 -0.959885 11.555336 0 0.492911 10.993324 0 0.184992 8.721488 0 -0.355715 10.325976 0 -0.397822 8.058397 0 0.824839 13.730343 0 1.507278 5.027866 1 0.099671 6.835839 1 -0.344008 10.717485 0 1.785928 7.718645 1 -0.918801 11.560217 0 -0.364009 4.747300 1 -0.841722 4.119083 1 0.490426 1.960539 1 -0.007194 9.075792 0 0.356107 12.447863 0 0.342578 12.281162 0 -0.810823 -1.466018 1 2.530777 6.476801 1 1.296683 11.607559 0 0.475487 12.040035 0 -0.783277 11.009725 0 0.074798 11.023650 0 -1.337472 0.468339 1 -0.102781 13.763651 0 -0.147324 2.874846 1 0.518389 9.887035 0 1.015399 7.571882 0 -1.658086 -0.027255 1 1.319944 2.171228 1 2.056216 5.019981 1 -0.851633 4.375691 1 -1.510047 6.061992 0 -1.076637 -3.181888 1 1.821096 10.283990 0 3.010150 8.401766 1 -1.099458 1.688274 1 -0.834872 -1.733869 1 -0.846637 3.849075 1 1.400102 12.628781 0 1.752842 5.468166 1 0.078557 0.059736 1 0.089392 -0.715300 1 1.825662 12.693808 0 0.197445 9.744638 0 0.126117 0.922311 1 -0.679797 1.220530 1 0.677983 2.556666 1 0.761349 10.693862 0 -2.168791 0.143632 1 1.388610 9.341997 0 0.317029 14.739025 0