查做外贸客户的网站,有哪些网站可以做代理,网站图片alt属性,基于html5的电商网站开发1. 惩罚线性回归模型概述
线性回归在实际应用时需要对普通最小二乘法进行一些修改。普通最小二乘法只在训练数据上最小化错误#xff0c;难以顾及所有数据。
惩罚线性回归方法是一族用于克服最小二乘法#xff08; OLS#xff09;过拟合问题的方法。岭回归是惩罚线性回归的…1. 惩罚线性回归模型概述
线性回归在实际应用时需要对普通最小二乘法进行一些修改。普通最小二乘法只在训练数据上最小化错误难以顾及所有数据。
惩罚线性回归方法是一族用于克服最小二乘法 OLS过拟合问题的方法。岭回归是惩罚线性回归的一个特例。岭回归通过对回归系数的平方和进行惩罚来避免过拟合。其他惩罚回归算法使用不同形式的惩罚项。
下面几个特点使得惩罚线性回归方法非常有效
--模型训练足够快速。
--变量的重要性信息。
--部署时的预测足够快速。
--在各种问题上性能可靠尤其对样本并不明显多于属性的属性矩阵或者非常稀疏的矩阵。希望模型为稀疏解即只使用部分属性进行预测的吝啬模型。
--问题可能适合使用线性模型来解决。 公式 4-6 可以用如下语言描述向量 beta 是以及常量 beta 零星是使期望预测的均方
错误最小的值期望预测的均方错误是指在所有数据行i1,...,n上计算 yi 与预测生成
yi 之间的错误平方的平均。 岭惩罚项对于惩罚回归来说并不是唯一有用的惩罚项。任何关于向量长度的指标都可以。使用不同的长度指标可以改变解的重要性。岭回归应用欧式几何的指标即 β 的平方和。另外一个有用的算法称作套索Lasso回归该回归源于出租车的几何路径被称作曼哈顿距离或者 L1 正则化即 β 的绝对值的和。ElasticNet 惩罚项包含套索惩罚项以及岭惩罚项。
2. 求解惩罚线性回归问题
有大量通用的数值优化算法可以求解公式 4-6、公式 4-8 以及公式 4-11 对应的优化问题但是惩罚线性回归问题的重要性促使研究人员开发专用算法从而能够非常快地生成解。本文将对这些算法进行介绍并且运行相关代码重点介绍2种算法最小角度回归 LARS 以及 Glmnet。
LARS 算法可以理解为一种改进的前向逐步回归算法。 之所以介绍 LARS 算法是因为该算法非常接近于套索以及前向逐步回归 LARS 算法很容易理解并且实现起来相对紧凑。通过研究 LARS 的代码你会理解针对更一般的 ElasticNet 回归求解的具体过程并且会了解惩罚回归求解的细节。
3. 完整代码code
from math import sqrt
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdmdef x_normalized(xList, xMeans, xSD):nrows len(xList)ncols len(xList[0])xNormalized []for i in range(nrows):rowNormalized [(xList[i][j] - xMeans[j]) / xSD[j] for j in range(ncols)]xNormalized.append(rowNormalized)def data_normalized(wine):nrows, ncols wine.shapewineNormalized winefor i in range(ncols):mean summary.iloc[1, i]sd summary.iloc[2, i]wineNormalized.iloc[:, i:(i 1)] (wineNormalized.iloc[:, i:(i 1)] - mean) / sdreturn wineNormalizeddef calculate_betaMat(nSteps, stepSize, wineNormalized):nrows, ncols wineNormalized.shape# initialize a vector of coefficients beta系数初始化beta [0.0] * (ncols - 1)# initialize matrix of betas at each step系数矩阵初始化betaMat []betaMat.append(list(beta))# initialize residuals list误差初始化residuals [0.0] * nrowsfor i in tqdm(range(nSteps)):# calculate residuals计算误差for j in range(nrows):residuals[j] wineNormalized.iloc[j, (ncols - 1)]for k in range(ncols - 1):residuals[j] - wineNormalized.iloc[j, k] * beta[k]# calculate correlation between attribute columns from normalized wine and residual变量与误差相关系数corr [0.0] * (ncols - 1)for j in range(ncols - 1):for k in range(nrows):corr[j] wineNormalized.iloc[k, j] * residuals[k] / nrowsiStar 0corrStar corr[0]for j in range(1, (ncols - 1)):if abs(corrStar) abs(corr[j]): # 相关性大的放前面iStar jcorrStar corr[j]beta[iStar] stepSize * corrStar / abs(corrStar) # 系数betaMat.append(list(beta))return betaMatdef plot_betaMat1(betaMat):ncols len(betaMat[0])for i in range(ncols):# plot range of beta values for each attributecoefCurve betaMat[0:nSteps][i]plt.plot(coefCurve)plt.xlabel(Attribute Index)plt.ylabel((Attribute Values))plt.show()def plot_betaMat2(nSteps, betaMat):ncols len(betaMat[0])for i in range(ncols):# plot range of beta values for each attributecoefCurve [betaMat[k][i] for k in range(nSteps)]xaxis range(nSteps)plt.plot(xaxis, coefCurve)plt.xlabel(Steps Taken)plt.ylabel((Coefficient Values))plt.show()def S(z, gamma):if gamma abs(z):return 0.0return (z / abs(z)) * (abs(z) - gamma)if __name__ __main__:target_url http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csvwine pd.read_csv(target_url, header0, sep;)# normalize the wine datasummary wine.describe()print(summary)# 数据标准化wineNormalized data_normalized(wine)# number of steps to take训练步数nSteps 100stepSize 0.1betaMat calculate_betaMat(nSteps, stepSize, wineNormalized)plot_betaMat1(betaMat)
# ----------------------------larsWine---------------------------------------------------# read data into iterablenames wine.columnsxList []labels []firstLine Truefor i in range(len(wine)):row wine.iloc[i]# put labels in separate arraylabels.append(float(row[-1]))# convert row to floatsfloatRow row[:-1]xList.append(floatRow)# Normalize columns in x and labelsnrows len(xList)ncols len(xList[0])# calculate means and variances(计算均值和方差)xMeans []xSD []for i in range(ncols):col [xList[j][i] for j in range(nrows)]mean sum(col) / nrowsxMeans.append(mean)colDiff [(xList[j][i] - mean) for j in range(nrows)]sumSq sum([colDiff[i] * colDiff[i] for i in range(nrows)])stdDev sqrt(sumSq / nrows)xSD.append(stdDev)# use calculate mean and standard deviation to normalize xListX标准化xNormalized x_normalized(xList, xMeans, xSD)# Normalize labels: 将属性及标签进行归一化meanLabel sum(labels) / nrowssdLabel sqrt(sum([(labels[i] - meanLabel) * (labels[i] - meanLabel) for i in range(nrows)]) / nrows)labelNormalized [(labels[i] - meanLabel) / sdLabel for i in range(nrows)]# initialize a vector of coefficients betabeta [0.0] * ncols# initialize matrix of betas at each stepbetaMat []betaMat.append(list(beta))# number of steps to takenSteps 350stepSize 0.004nzList []for i in range(nSteps):# calculate residualsresiduals [0.0] * nrowsfor j in range(nrows):labelsHat sum([xNormalized[j][k] * beta[k] for k in range(ncols)]) residuals[j] labelNormalized[j] - labelsHat # 计算残差# calculate correlation between attribute columns from normalized wine and residualcorr [0.0] * ncolsfor j in range(ncols):corr[j] sum([xNormalized[k][j] * residuals[k] for k in range(nrows)]) / nrows # 每个属性和残差的关联iStar 0corrStar corr[0]for j in range(1, (ncols)): # 逐个判断哪个属性对降低残差贡献最大if abs(corrStar) abs(corr[j]): # 好的最大关联特征会排到列表前面应该保留不太好的特征会排到最后iStar jcorrStar corr[j]beta[iStar] stepSize * corrStar / abs(corrStar) # 固定增加beta变量值关联为正增量为正关联为负增量为负betaMat.append(list(beta)) # 求解得到参数结果nzBeta [index for index in range(ncols) if beta[index] ! 0.0]for q in nzBeta:if q not in nzList: # 对于每一迭代步记录非零系数对应索引nzList.append(q)nameList [names[nzList[i]] for i in range(len(nzList))]print(nameList)plot_betaMat2(nSteps, betaMat) # 绘制系数曲线# -------------------------------larsWine 10折交叉------------------------------------------------# Build cross-validation loop to determine best coefficient values.# number of cross validation foldsnxval 10# number of steps and step sizenSteps 350stepSize 0.004# initialize list for storing errors.errors [] # 记录每一步迭代的错误for i in range(nSteps):b []errors.append(b)for ixval in range(nxval): # 10折交叉验证# Define test and training index setsidxTrain [a for a in range(nrows) if a % nxval ! ixval * nxval]idxTest [a for a in range(nrows) if a % nxval ixval * nxval]# Define test and training attribute and label setsxTrain [xNormalized[r] for r in idxTrain] # 训练集labelTrain [labelNormalized[r] for r in idxTrain]xTest [xNormalized[r] for r in idxTest] # 测试集labelTest [labelNormalized[r] for r in idxTest]# Train LARS regression on Training DatanrowsTrain len(idxTrain)nrowsTest len(idxTest)# initialize a vector of coefficients betabeta [0.0] * ncols# initialize matrix of betas at each stepbetaMat []betaMat.append(list(beta))for iStep in range(nSteps):# calculate residualsresiduals [0.0] * nrowsfor j in range(nrowsTrain):labelsHat sum([xTrain[j][k] * beta[k] for k in range(ncols)])residuals[j] labelTrain[j] - labelsHat# calculate correlation between attribute columns from normalized wine and residualcorr [0.0] * ncolsfor j in range(ncols):corr[j] sum([xTrain[k][j] * residuals[k] for k in range(nrowsTrain)]) / nrowsTrainiStar 0corrStar corr[0]for j in range(1, (ncols)):if abs(corrStar) abs(corr[j]):iStar jcorrStar corr[j]beta[iStar] stepSize * corrStar / abs(corrStar)betaMat.append(list(beta))# Use beta just calculated to predict and accumulate out of sample error - not being used in the calc of betafor j in range(nrowsTest):labelsHat sum([xTest[j][k] * beta[k] for k in range(ncols)])err labelTest[j] - labelsHaterrors[iStep].append(err)cvCurve []for errVect in errors:mse sum([x * x for x in errVect]) / len(errVect)cvCurve.append(mse)minMse min(cvCurve)minPt [i for i in range(len(cvCurve)) if cvCurve[i] minMse][0]print(Minimum Mean Square Error, minMse)print(Index of Minimum Mean Square Error, minPt)xaxis range(len(cvCurve))plt.plot(xaxis, cvCurve)plt.xlabel(Steps Taken)plt.ylabel((Mean Square Error))plt.show()# -------------------------------glmnet larsWine2------------------------------------------------# select value for alpha parameteralpha 1.0# make a pass through the data to determine value of lambda that# just suppresses all coefficients.# start with betas all equal to zero.xy [0.0] * ncolsfor i in range(nrows):for j in range(ncols):xy[j] xNormalized[i][j] * labelNormalized[i]maxXY 0.0for i in range(ncols):val abs(xy[i]) / nrowsif val maxXY:maxXY val# calculate starting value for lambdalam maxXY / alpha# this value of lambda corresponds to beta list of 0s# initialize a vector of coefficients betabeta [0.0] * ncols# initialize matrix of betas at each stepbetaMat []betaMat.append(list(beta))# begin iterationnSteps 100lamMult 0.93 # 100 steps gives reduction by factor of 1000 in# lambda (recommended by authors)nzList []for iStep in range(nSteps):# make lambda smaller so that some coefficient becomes non-zerolam lam * lamMultdeltaBeta 100.0eps 0.01iterStep 0betaInner list(beta)while deltaBeta eps:iterStep 1if iterStep 100:break# cycle through attributes and update one-at-a-time# record starting value for comparisonbetaStart list(betaInner)for iCol in range(ncols):xyj 0.0for i in range(nrows):# calculate residual with current value of betalabelHat sum([xNormalized[i][k] * betaInner[k] for k in range(ncols)])residual labelNormalized[i] - labelHatxyj xNormalized[i][iCol] * residualuncBeta xyj / nrows betaInner[iCol]betaInner[iCol] S(uncBeta, lam * alpha) / (1 lam * (1 - alpha))sumDiff sum([abs(betaInner[n] - betaStart[n]) for n in range(ncols)])sumBeta sum([abs(betaInner[n]) for n in range(ncols)])deltaBeta sumDiff / sumBetaprint(iStep, iterStep)beta betaInner# add newly determined beta to listbetaMat.append(beta)# keep track of the order in which the betas become non-zeronzBeta [index for index in range(ncols) if beta[index] ! 0.0]for q in nzBeta:if q not in nzList:nzList.append(q)# print out the ordered list of betasnameList [names[nzList[i]] for i in range(len(nzList))]print(nameList)nPts len(betaMat)plot_betaMat2(nPts, betaMat) # 绘制系数曲线
