https://github.com/mljs/pls
PLS regression algorithm
https://github.com/mljs/pls
hacktoberfest
Last synced: 2 months ago
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PLS regression algorithm
- Host: GitHub
- URL: https://github.com/mljs/pls
- Owner: mljs
- License: mit
- Created: 2015-06-15T07:11:44.000Z (about 10 years ago)
- Default Branch: main
- Last Pushed: 2023-10-02T07:53:10.000Z (over 1 year ago)
- Last Synced: 2024-09-19T15:18:34.677Z (9 months ago)
- Topics: hacktoberfest
- Language: JavaScript
- Homepage: http://mljs.github.io/pls/
- Size: 6.89 MB
- Stars: 10
- Watchers: 14
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Partial Least Squares (PLS), Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) and NIPALS based OPLS
[![NPM version][npm-image]][npm-url]
[![build status][ci-image]][ci-url]
[](https://doi.org/10.5281/zenodo.7314529)
[![npm download][download-image]][download-url]
PLS regression algorithm based on the Yi Cao implementation:
[PLS Matlab code](http://www.mathworks.com/matlabcentral/fileexchange/18760-partial-least-squares-and-discriminant-analysis)
K-OPLS regression algorithm based on [this paper](http://onlinelibrary.wiley.com/doi/10.1002/cem.1071/abstract).
[K-OPLS Matlab code](http://kopls.sourceforge.net/download.shtml)
OPLS implementation based on the R package [Metabomate](https://github.com/kimsche/MetaboMate) using NIPALS factorization loop.
## installation
`$ npm i ml-pls`
## Usage
### [PLS](./src/PLS.js)
```js
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](./src/OPLS.js)
```js
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.
```js
console.log(model.model[0].Q2y);
```
should give 0.9209227614652857
### [OPLS-DA](./src/OPLS.js)
```js
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](./src/OPLS.js)
```js
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](./src/KOPLS.js)
```js
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](http://mljs.github.io/pls/)
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/ml-pls.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-pls
[ci-image]: https://github.com/mljs/pls/workflows/Node.js%20CI/badge.svg?branch=main
[ci-url]: https://github.com/mljs/pls/actions?query=workflow%3A%22Node.js+CI%22
[download-image]: https://img.shields.io/npm/dm/ml-pls.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-pls