Package Exports

ml-pls
ml-pls/lib-esm/index.js
ml-pls/lib/index.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (ml-pls) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

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:

PLS Matlab code

K-OPLS regression algorithm based on this paper.

K-OPLS Matlab code

OPLS implementation based on the R package Metabomate using NIPALS factorization loop.

installation

$ npm i ml-pls

Usage

PLS

import PLS from 'ml-pls';

const X = [
  [0.1, 0.02],
  [0.25, 1.01],
  [0.95, 0.01],
  [1.01, 0.96],
];
const Y = [
  [1, 0],
  [1, 0],
  [1, 0],
  [0, 1],
];
const options = {
  latentVectors: 10,
  tolerance: 1e-4,
};

const pls = new PLS(options);
pls.train(X, Y);

OPLS-R

import {
  getNumbers,
  getClassesAsNumber,
  getCrossValidationSets,
} from 'ml-dataset-iris';
import { OPLS } from 'ml-pls';

const cvFolds = getCrossValidationSets(7, { idx: 0, by: 'trainTest' });
const data = getNumbers();
const irisLabels = getClassesAsNumber();

const model = new OPLS(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

OPLS-DA

import {
  getNumbers,
  getClasses,
  getCrossValidationSets,
} from 'ml-dataset-iris';
import { OPLS } from 'ml-pls';

const cvFolds = getCrossValidationSets(7, { idx: 0, by: 'trainTest' });
const data = getNumbers();
const irisLabels = getClasses();

const model = new OPLS(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

Prediction

import {
  getNumbers,
  getClassesAsNumber,
  getCrossValidationSets,
} from 'ml-dataset-iris';
import { OPLS } from 'ml-pls';

// get frozen folds for testing purposes
const { testIndex, trainIndex } = getCrossValidationSets(7, {
  idx: 0,
  by: 'trainTest',
})[0];

// Getting the data of selected fold
const irisNumbers = getNumbers();
const testData = irisNumbers.filter((el, idx) => testIndex.includes(idx));
const trainingData = irisNumbers.filter((el, idx) => trainIndex.includes(idx));

// Getting the labels of selected fold
const irisLabels = getClassesAsNumber();
const testLabels = irisLabels.filter((el, idx) => testIndex.includes(idx));
const trainingLabels = irisLabels.filter((el, idx) => trainIndex.includes(idx));

const model = new OPLS(trainingData, trainingLabels);
console.log(model.mode); // 'discriminantAnalysis'
const prediction = model.predict(testData, { trueLabels: testLabels });
// Get the predicted Q2 value
console.log(prediction.Q2y); // 0.9247698398971457

K-OPLS

import Kernel from 'ml-kernel';
import { KOPLS } from 'ml-pls';

const kernel = new Kernel('gaussian', {
  sigma: 25,
});

const X = [
  [0.1, 0.02],
  [0.25, 1.01],
  [0.95, 0.01],
  [1.01, 0.96],
];
const Y = [
  [1, 0],
  [1, 0],
  [1, 0],
  [0, 1],
];

const cls = new KOPLS({
  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);

API Documentation

License

MIT