umap-js

UMAP-JS

This is a JavaScript reimplementation of UMAP from the python implementation found at https://github.com/lmcinnes/umap.

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.

There are a few important differences between the python implementation and the JS port.

• The optimization step is seeded with a random embedding rather than a spectral embedding. This gives comparable results for smaller datasets. The spectral embedding computation relies on efficient eigenvalue / eigenvector computations that are not easily done in JS.
• There is no implementation of any supervised dimension reduction or adding new points to an existing embedding.
• The only distance function used is euclidean distance.
• There is no specialized functionality for angular distances or sparse data representations.

Usage

Synchronous fitting

import { UMAP } from 'umap-js';

const umap = new UMAP();
const embedding = umap.fit(data);

Asynchronous fitting

import { UMAP } from 'umap-js';

const umap = new UMAP();
const embedding = await umap.fitAsync(data, callback);

Step-by-step fitting

import { UMAP } from 'umap-js';

const umap = new UMAP();
const nEpochs = umap.initializeFit(data);
for (let i = 0; i < nEpochs; i++) {
  umap.step();
}
const embedding = umap.getEmbedding();

Parameters

The UMAP constructor can accept a number of parameters:

Parameter	Description	default
nComponents	The number of components (dimensions) to project the data to	2
nEpochs	The number of epochs to optimize embeddings via SGD	(computed automatically)
nNeigbors	The number of nearest neigbors to construct the fuzzy manifold	15
random	A pseudo-random-number generator for controlling stochastic processes	Math.random

Testing

umap-js uses jest for testing.

yarn test

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