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Partial Least Squares (PLS), Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) and NIPALS based OPLS
PLS regression algorithm based on the Yi Cao implementation:
K-OPLS regression algorithm based onthis paper.
OPLS implementation based on the R packageMetabomate using NIPALS factorization loop.
$ npm i ml-pls
importPLSfrom'ml-pls';constX=[[0.1,0.02],[0.25,1.01],[0.95,0.01],[1.01,0.96],];constY=[[1,0],[1,0],[1,0],[0,1],];constoptions={latentVectors:10,tolerance:1e-4,};constpls=newPLS(options);pls.train(X,Y);
import{getNumbers,getClassesAsNumber,getCrossValidationSets,}from'ml-dataset-iris';import{OPLS}from'ml-pls';constcvFolds=getCrossValidationSets(7,{idx:0,by:'trainTest'});constdata=getNumbers();constirisLabels=getClassesAsNumber();constmodel=newOPLS(data,irisLabels,{ cvFolds});console.log(model.mode);// 'regression'
The OPLS class is intended for exploratory modeling, that is not for the creation of predictors. Therefore there is a built-in k-fold cross-validation loop and Q2y is an average over the folds.
console.log(model.model[0].Q2y);
should give 0.9209227614652857
import{getNumbers,getClasses,getCrossValidationSets,}from'ml-dataset-iris';import{OPLS}from'ml-pls';constcvFolds=getCrossValidationSets(7,{idx:0,by:'trainTest'});constdata=getNumbers();constirisLabels=getClasses();constmodel=newOPLS(data,irisLabels,{ cvFolds});console.log(model.mode);// 'discriminantAnalysis'console.log(model.model[0].auc);// 0.5366666666666665,
If for some reason a predictor is necessary the following code may serve as an example
import{getNumbers,getClassesAsNumber,getCrossValidationSets,}from'ml-dataset-iris';import{OPLS}from'ml-pls';// get frozen folds for testing purposesconst{ testIndex, trainIndex}=getCrossValidationSets(7,{idx:0,by:'trainTest',})[0];// Getting the data of selected foldconstirisNumbers=getNumbers();consttestData=irisNumbers.filter((el,idx)=>testIndex.includes(idx));consttrainingData=irisNumbers.filter((el,idx)=>trainIndex.includes(idx));// Getting the labels of selected foldconstirisLabels=getClassesAsNumber();consttestLabels=irisLabels.filter((el,idx)=>testIndex.includes(idx));consttrainingLabels=irisLabels.filter((el,idx)=>trainIndex.includes(idx));constmodel=newOPLS(trainingData,trainingLabels);console.log(model.mode);// 'discriminantAnalysis'constprediction=model.predict(testData,{trueLabels:testLabels});// Get the predicted Q2 valueconsole.log(prediction.Q2y);// 0.9247698398971457
importKernelfrom'ml-kernel';import{KOPLS}from'ml-pls';constkernel=newKernel('gaussian',{sigma:25,});constX=[[0.1,0.02],[0.25,1.01],[0.95,0.01],[1.01,0.96],];constY=[[1,0],[1,0],[1,0],[0,1],];constcls=newKOPLS({orthogonalComponents:10,predictiveComponents:1,kernel:kernel,});cls.train(X,Y);const{ prediction,// prediction predScoreMat,// Score matrix over prediction predYOrthVectors,// Y-Orthogonal vectors over prediction}=cls.predict(X);console.log(prediction);console.log(predScoreMat);console.log(predYOrthVectors);
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PLS regression algorithm