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ml-pls

PLS regression algorithm

https://github.com/mljs/pls

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: wiley.com, zenodo.org
  • ✓
    Committers with academic emails
    3 of 12 committers (25.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.0%) to scientific vocabulary

Keywords

hacktoberfest
Last synced: 6 months ago · JSON representation ·

Repository

PLS regression algorithm

Basic Info
  • Host: GitHub
  • Owner: mljs
  • License: mit
  • Language: JavaScript
  • Default Branch: main
  • Homepage: http://mljs.github.io/pls/
  • Size: 6.89 MB
Statistics
  • Stars: 11
  • Watchers: 13
  • Forks: 5
  • Open Issues: 5
  • Releases: 7
Topics
hacktoberfest
Created over 10 years ago · Last pushed over 2 years ago
Metadata Files
Readme Changelog License Citation

README.md

Partial Least Squares (PLS), Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) and NIPALS based OPLS

NPM version build status DOI npm download

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

```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

```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

```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

```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

```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

License

MIT

Owner

  • Name: ml.js
  • Login: mljs
  • Kind: organization

Machine learning and numerical analysis tools in JavaScript for Node.js and the Browser

Citation (CITATION.cff) 

cff-version: 1.2.0
message: 'If you use this software, please cite it as below.'
title: 'Javascript implementation of Partial Least Squares (PLS), Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) and NIPALS based OPLS'
abstract: 'The tools in this package allows to model linear relationships between a descriptor and response matrix using projection to latent structures with different approches using Javascript.'
repository-artifact: http://cheminfo.github.io/jcampconverter/
repository-code: https://github.com/cheminfo/jcampconverter
authors:
  - family-names: 'Wist'
    given-names: 'Julien'
    affiliation: 'Universidad del Valle, Cali, Colombia'
    orcid: 'https://orcid.org/0000-0002-3416-2572'
  - family-names: 'Osorio'
    given-names: 'Javier'
    affiliation: 'Universidad del Valle, Cali, Colombia'
    orcid: 'https://orcid.org/0000-0002-8278-3103'
  - family-names: 'Zasso'
    given-names: 'Michaël'
    affiliation: 'Zakodium Sàrl, Switzerland'
    orcid: 'https://orcid.org/0000-0001-5295-2159'
  - family-names: 'Patiny'
    given-names: 'Luc'
    orcid: 'https://orcid.org/0000-0002-4943-2643'

license: MIT
keywords:
  - PLS
  - OPLS
  - K-OPLS
  - NIPALS
  - Machine learning
  - Supervised learning
doi: 10.5281/zenodo.7314529

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 117
  • Total Committers: 12
  • Avg Commits per committer: 9.75
  • Development Distribution Score (DDS): 0.65
Top Committers
Name Email Commits
Jefferson s****0@h****m 41
Michaël Zasso t****s@p****m 22
Jefferson Hernández j****4@g****m 18
Luc Patiny l****c@p****m 11
mljs-bot 7****t@u****m 6
Michaël Zasso m****e@g****m 4
Javier Osorio M 3****m@u****m 4
Luc Patiny l****y@u****m 3
jobo322 j****s@c****o 3
josoriom j****o@c****o 2
jul j****t@c****o 2
Wadjih Bencheikh j****h@e****z 1
Committer Domains (Top 20 + Academic)
correounivalle.edu.co: 3 esi.dz: 1 patiny.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 13
  • Total pull requests: 30
  • Average time to close issues: 9 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 8
  • Total pull request authors: 9
  • Average comments per issue: 0.46
  • Average comments per pull request: 1.13
  • Merged pull requests: 25
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • targos (3)
  • jobo322 (2)
  • JeffersonH44 (2)
  • lpatiny (2)
  • josoriom (1)
  • loldrup (1)
  • JiayiHong (1)
  • DavidMartinOnGitHub (1)
Pull Request Authors
  • mljs-bot (10)
  • josoriom (6)
  • JeffersonH44 (6)
  • jwist (2)
  • jobo322 (2)
  • wadjih-bencheikh18 (1)
  • santimirandarp (1)
  • targos (1)
  • lpatiny (1)
Top Labels
Issue Labels
Pull Request Labels
autorelease: tagged (7) autorelease: pending (2)

Packages

  • Total packages: 1
  • Total downloads:
    • npm 1,369 last-month
  • Total docker downloads: 16
  • Total dependent packages: 3
  • Total dependent repositories: 24
  • Total versions: 23
  • Total maintainers: 7
npmjs.org: ml-pls

Partial least squares library

  • Versions: 23
  • Dependent Packages: 3
  • Dependent Repositories: 24
  • Downloads: 1,369 Last month
  • Docker Downloads: 16
Rankings
Dependent repos count: 2.5%
Dependent packages count: 5.9%
Downloads: 6.2%
Average: 6.3%
Forks count: 7.3%
Stargazers count: 9.7%
Maintainers (7)
targos lpatiny stropitek mljs-bot andcastillo jeffersonh44 maasencioh
Last synced: 7 months ago

Dependencies

package.json npm
  • @types/jest ^27.4.1 development
  • benchmark ^2.1.4 development
  • cheminfo-build ^1.1.11 development
  • eslint ^8.10.0 development
  • eslint-config-cheminfo-typescript ^10.3.0 development
  • jest ^27.5.1 development
  • jest-matcher-deep-close-to ^3.0.2 development
  • ml-dataset-iris ^1.2.1 development
  • ml-dataset-metadata ^0.3.0 development
  • ml-kernel ^3.0.0 development
  • papaparse ^5.3.1 development
  • prettier ^2.5.1 development
  • rimraf ^3.0.2 development
  • ts-jest ^27.1.3 development
  • typescript ^4.6.2 development
  • ml-array-mean ^1.1.6
  • ml-confusion-matrix ^0.4.0
  • ml-cross-validation ^1.3.0
  • ml-matrix ^6.9.0
  • ml-roc-multiclass ^0.2.0
.github/workflows/documentationjs.yml actions
  • JamesIves/github-pages-deploy-action releases/v4 composite
  • actions/checkout v2 composite
  • zakodium/documentationjs-action v1 composite
.github/workflows/lactame.yml actions
  • actions/checkout v2 composite
  • actions/setup-node v2-beta composite
  • zakodium/lactame-action v1 composite