ML之分类预测之ElasticNet之PLoR：在二分类数据集上调用Glmnet库训练PLoR模型(T2)

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ML之分类预测之ElasticNet之PLoR：在二分类数据集上调用Glmnet库训练PLoR模型(T2)

输出结果

 

 

设计思路

核心代码

for iStep in range(nSteps):
    lam = lam * lamMult 

    betaIRLS = list(beta)
    beta0IRLS = beta0
    distIRLS = 100.0
    iterIRLS = 0
    while distIRLS > 0.01:
        iterIRLS += 1
        iterInner = 0.0

        betaInner = list(betaIRLS)
        beta0Inner = beta0IRLS
        distInner = 100.0
        while distInner > 0.01:
            iterInner += 1
            if iterInner > 100: break
            betaStart = list(betaInner)
            for iCol in range(ncol):

                sumWxr = 0.0
                sumWxx = 0.0
                sumWr = 0.0
                sumW = 0.0

                for iRow in range(nrow):
                    x = list(xNormalized[iRow])
                    y = labels[iRow]
                    p = Pr(beta0IRLS, betaIRLS, x)
                    if abs(p) < 1e-5:
                        p = 0.0
                        w = 1e-5
                    elif abs(1.0 - p) < 1e-5:
                        p = 1.0
                        w = 1e-5
                    else:
                        w = p * (1.0 - p)

                    z = (y - p) / w + beta0IRLS + sum([x[i] * betaIRLS[i] for i in range(ncol)])
                    r = z - beta0Inner - sum([x[i] * betaInner[i] for i in range(ncol)])
                    sumWxr += w * x[iCol] * r
                    sumWxx += w * x[iCol] * x[iCol]
                    sumWr += w * r
                    sumW += w

                avgWxr = sumWxr / nrow
                avgWxx = sumWxx / nrow

                beta0Inner = beta0Inner + sumWr / sumW
                uncBeta = avgWxr + avgWxx * betaInner[iCol]
                betaInner[iCol] = S(uncBeta, lam * alpha) / (avgWxx + lam * (1.0 - alpha))

            sumDiff = sum([abs(betaInner[n] - betaStart[n]) for n in range(ncol)])
            sumBeta = sum([abs(betaInner[n]) for n in range(ncol)])
            distInner = sumDiff/sumBeta

        a = sum([abs(betaIRLS[i] - betaInner[i]) for i in range(ncol)])
        b = sum([abs(betaIRLS[i]) for i in range(ncol)])
        distIRLS = a / (b + 0.0001)
        dBeta = [betaInner[i] - betaIRLS[i] for i in range(ncol)]
        gradStep = 1.0
        temp = [betaIRLS[i] + gradStep * dBeta[i] for i in range(ncol)]
        betaIRLS = list(temp)

    beta = list(betaIRLS)
    beta0 = beta0IRLS
    betaMat.append(list(beta))
    beta0List.append(beta0)

    nzBeta = [index for index in range(ncol) if beta[index] != 0.0]
    for q in nzBeta:
        if not(q in nzList):
            nzList.append(q)