ML之分類預測之ElasticNet之PLoR：在二分類數據集上調用Glmnet庫訓練PLoR模型(T2)

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目錄

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設計思路

核心代碼

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輸出結果

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設計思路

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核心代碼

for iStep in range(nSteps):lam = lam * lamMult betaIRLS = list(beta)beta0IRLS = beta0distIRLS = 100.0iterIRLS = 0while distIRLS > 0.01:iterIRLS += 1iterInner = 0.0betaInner = list(betaIRLS)beta0Inner = beta0IRLSdistInner = 100.0while distInner > 0.01:iterInner += 1if iterInner > 100: breakbetaStart = list(betaInner)for iCol in range(ncol):sumWxr = 0.0sumWxx = 0.0sumWr = 0.0sumW = 0.0for iRow in range(nrow):x = list(xNormalized[iRow])y = labels[iRow]p = Pr(beta0IRLS, betaIRLS, x)if abs(p) < 1e-5:p = 0.0w = 1e-5elif abs(1.0 - p) < 1e-5:p = 1.0w = 1e-5else: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] * rsumWxx += w * x[iCol] * x[iCol]sumWr += w * rsumW += wavgWxr = sumWxr / nrowavgWxx = sumWxx / nrowbeta0Inner = beta0Inner + sumWr / sumWuncBeta = 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/sumBetaa = 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 = 1.0temp = [betaIRLS[i] + gradStep * dBeta[i] for i in range(ncol)]betaIRLS = list(temp)beta = list(betaIRLS)beta0 = beta0IRLSbetaMat.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)

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