https://github.com/mljs/dataset-metadata
a class to manipulate metadata for statistical analysis
https://github.com/mljs/dataset-metadata
metadata multivariate-analysis
Last synced: 4 months ago
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a class to manipulate metadata for statistical analysis
- Host: GitHub
- URL: https://github.com/mljs/dataset-metadata
- Owner: mljs
- License: mit
- Created: 2020-02-03T21:18:10.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-05T06:26:02.000Z (over 2 years ago)
- Last Synced: 2025-02-06T02:02:58.675Z (5 months ago)
- Topics: metadata, multivariate-analysis
- Language: JavaScript
- Homepage:
- Size: 2.31 MB
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Changelog: History.md
- License: LICENSE
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README
# dataset-metadata
[![NPM version][npm-image]][npm-url]
[![build status][travis-image]][travis-url]
[![Test coverage][codecov-image]][codecov-url]
[![David deps][david-image]][david-url]
[![npm download][download-image]][download-url]
a class to manipulate metadata for statistical analysis
## Installation
`$ npm i dataset-metadata`
## [API Documentation](https://mljs.github.io/dataset-metadata/)
## Examples
to import the package use
```js
const METADATA = require('dataset-metadata');
```
or
```js
import { METADATA } from 'ml-dataset-metadata';
```
to create a metadata object use
```js
import { getClasses } from 'ml-dataset-iris';
const metadata = getClasses();
let L = new METADATA([metadata], { headers: ['iris'] });
```
this will create an array with the class of the famous iris dataset and create a METADATA object L.
List all the available metadata
```js
L.list()
```
returns an array with all the metadata headers.
Retrieve information (number of classes, counts for each classes) about a particular metadata using
```js
L.get('iris');
```
Retrieve values of a particular metadata as a Matrix object. This will coerce any string class into a Matrix of number with first class being "0", second being "1", etc.
```js
L.get('iris', { format: 'matrix' }).values
```
For supervised method it is usual to sample a class to get a training set and a test set.
```js
L.sample('iris')
```
returns an object with four arrays: trainIndex, testIndex, mask (a boolean filter), and classVector (the original class).
To append another metadata.
```js
let newMetadata = metadata;
L.append(NewMetadata, 'column', { header: 'duplicated' });
```
To remove the duplicated metadata.
```js
L.remove('duplicated', 'column');
```
Import and export METADATA object.
```js
let L = new METADATA([metadata], { headers: ['iris'] });
L = JSON.stringify(L.toJSON());
let newL = METADATA.load(JSON.parse(L));
```
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/dataset-metadata.svg?style=flat-square
[npm-url]: https://www.npmjs.com/package/dataset-metadata
[travis-image]: https://img.shields.io/travis/com/mljs/dataset-metadata/master.svg?style=flat-square
[travis-url]: https://travis-ci.com/mljs/dataset-metadata
[codecov-image]: https://img.shields.io/codecov/c/github/mljs/dataset-metadata.svg?style=flat-square
[codecov-url]: https://codecov.io/gh/mljs/dataset-metadata
[david-image]: https://img.shields.io/david/mljs/dataset-metadata.svg?style=flat-square
[david-url]: https://david-dm.org/mljs/dataset-metadata
[download-image]: https://img.shields.io/npm/dm/dataset-metadata.svg?style=flat-square
[download-url]: https://www.npmjs.com/package/dataset-metadata