ML之PLiR之LARS：利用LARS算法求解ElasticNet回归类型问题(实数值评分预测)

ML之PLiR之LARS：利用LARS算法求解ElasticNet回归类型问题(实数值评分预测)

目录

设计思路

输出结果

1、LARS

2、10-fold cross validation

实现代码

设计思路

更新……

输出结果

['"alcohol"', '"volatile acidity"', '"sulphates"', '"total sulfur dioxide"', '"chlorides"', '"fixed acidity"', '"pH"', '"free sulfur dioxide"', '"citric acid"', '"residual sugar"', '"density"']

1、LARS

2、10-fold cross validation

Minimum Mean Square Error 0.5873018933136459
Index of Minimum Mean Square Error 311

实现代码

  

   

    

     

    
    

     

      #initialize a vector of coefficients beta
     
    
   

    

     

    
    

     

      beta = [0.0] * ncols
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #initialize matrix of betas at each step
     
    
   

    

     

    
    

     

      betaMat = []
     
    
   

    

     

    
    

     

      betaMat.append(list(beta))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #number of steps to take
     
    
   

    

     

    
    

     

      nSteps = 350
     
    
   

    

     

    
    

     

      stepSize = 0.004
     
    
   

    

     

    
    

     

      nzList = []
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      for i in range(nSteps):
     
    
   

    

     

    
    

     
 #calculate residuals
     
    
   

    

     

    
    

     

       residuals = [0.0] * nrows
     
    
   

    

     

    
    

     
 for j in range(nrows):
     
    
   

    

     

    
    

     

       labelsHat = sum([xNormalized[j][k] * beta[k] for k in range(ncols)])
     
    
   

    

     

    
    

     

       residuals[j] = labelNormalized[j] - labelsHat
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #calculate correlation between attribute columns from normalized wine and residual
     
    
   

    

     

    
    

     

       corr = [0.0] * ncols
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 for j in range(ncols):
     
    
   

    

     

    
    

     

       corr[j] = sum([xNormalized[k][j] * residuals[k] for k in range(nrows)]) / nrows
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       iStar = 0
     
    
   

    

     

    
    

     

       corrStar = corr[0]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 for j in range(1, (ncols)):
     
    
   

    

     

    
    

     
 if abs(corrStar) < abs(corr[j]):
     
    
   

    

     

    
    

     

       iStar = j; corrStar = corr[j]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       beta[iStar] += stepSize * corrStar / abs(corrStar)
     
    
   

    

     

    
    

     

       betaMat.append(list(beta))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       nzBeta = [index for index in range(ncols) if beta[index] != 0.0]
     
    
   

    

     

    
    

     
 for q in nzBeta:
     
    
   

    

     

    
    

     
 if (q in nzList) == False:
     
    
   

    

     

    
    

     

       nzList.append(q)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      nameList = [names[nzList[i]] for i in range(len(nzList))]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print(nameList)
     
    
   

    

     

    
    

     

      for i in range(ncols):
     
    
   

    

     

    
    

     
 #plot range of beta values for each attribute
     
    
   

    

     

    
    

     

       coefCurve = [betaMat[k][i] for k in range(nSteps)]
     
    
   

    

     

    
    

     

       xaxis = range(nSteps)
     
    
   

    

     

    
    

     

       plot.plot(xaxis, coefCurve)
     
    
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/85041666