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 specialized functionality for angular distances or sparse data representations.

$3

#### Installation

``sh
yarn add umap-js
`

#### Synchronous fitting

`javascript
import { UMAP } from 'umap-js';

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

#### Asynchronous fitting

`javascript
import { UMAP } from 'umap-js';

const umap = new UMAP();
const embedding = await umap.fitAsync(data, epochNumber => {
// check progress and give user feedback, or return false to stop
});
`

#### Step-by-step fitting

`javascript
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();
`

#### Supervised projection using labels

`javascript
import { UMAP } from 'umap-js';

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

#### Transforming additional points after fitting

`javascript
import { UMAP } from 'umap-js';

const umap = new UMAP();
umap.fit(data);
const transformed = umap.transform(additionalData);
`

#### Parameters

The UMAP constructor can accept a number of hyperparameters via a UMAPParameters object, with the most common described below. See umap.ts for more details.

| 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) |
| nNeighbors | The number of nearest neighbors to construct the fuzzy manifold | 15 |
| minDist | The effective minimum distance between embedded points, used with spread to control the clumped/dispersed nature of the embedding | 0.1 |
| spread | The effective scale of embedded points, used with minDist to control the clumped/dispersed nature of the embedding | 1.0 |
| random | A pseudo-random-number generator for controlling stochastic processes | Math.random |
| distanceFn | A custom distance function to use | euclidean |

`typescript
const umap = new UMAP({
nComponents: 2,
nEpochs: 400,
nNeighbors: 15,
});
`

$3

umap-js uses jest for testing.

`
yarn test
``

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