a class to manipulate metadata for statistical analysis
$ npm i dataset-metadata
to import the package use
const METADATA = require('dataset-metadata');
or
import { METADATA } from 'ml-dataset-metadata';
to create a metadata object use
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
L.list()
returns an array with all the metadata headers.
Retrieve information (number of classes, counts for each classes) about a particular metadata using
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.
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.
L.sample('iris')
returns an object with four arrays: trainIndex, testIndex, mask (a boolean filter), and classVector (the original class).
To append another metadata.
let newMetadata = metadata;
L.append(NewMetadata, 'column', { header: 'duplicated' });
To remove the duplicated metadata.
L.remove('duplicated', 'column');
Import and export METADATA object.
let L = new METADATA([metadata], { headers: ['iris'] });
L = JSON.stringify(L.toJSON());
let newL = METADATA.load(JSON.parse(L));