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.
yarn add umap-jsimport { UMAP } from 'umap-js';
const umap = new UMAP();
const embedding = umap.fit(data);import { UMAP } from 'umap-js';
const umap = new UMAP();
const embedding = await umap.fitAsync(data, callback);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();The UMAP constructor can accept a number of parameters via a UMAPParameters object:
| 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 |
const umap = new UMAP({
nComponents: 2,
nEpochs: 400,
nNeighbors: 15,
});umap-js uses jest for testing.
yarn test
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