https://github.com/replikativ/umap-rstr

UMAP (Uniform Manifold Approximation and Projection) for Clojure — a port of umap-learn on the raster typed-dispatch compiler.
https://github.com/replikativ/umap-rstr

clojure data-science raster umap visualization

Last synced: 1 day ago
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UMAP (Uniform Manifold Approximation and Projection) for Clojure — a port of umap-learn on the raster typed-dispatch compiler.

• Host: GitHub
• URL: https://github.com/replikativ/umap-rstr
• Owner: replikativ
• License: bsd-3-clause
• Created: 2026-06-18T00:04:43.000Z (28 days ago)
• Default Branch: main
• Last Pushed: 2026-07-05T08:50:36.000Z (10 days ago)
• Last Synced: 2026-07-05T10:13:29.714Z (10 days ago)
• Topics: clojure, data-science, raster, umap, visualization
• Language: Clojure
• Homepage: https://simm.is
• Size: 1.25 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Notice: NOTICE

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README

          
# umap-rstr

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UMAP (Uniform Manifold Approximation and Projection) for Clojure, built on

[raster](https://github.com/replikativ/raster) — typed multiple dispatch with

devirtualizing bytecode compilation. The `-rstr` suffix in the repo/artifact name

(`umap-rstr`) marks the raster substrate, à la Julia's `.jl` repo suffix; the

namespace you require is just `umap`.

A faithful port of `umap-learn` (Python+numba is the gold standard): cosine/

euclidean kNN → fuzzy simplicial set → spectral/random init → negative-sampled

SGD layout. The numeric kernels are `deftm` functions that JIT-compile to

primitive-speed JVM bytecode; the orchestration is plain Clojure.

## Status

Validated against `umap-learn` on MNIST-70k and Fashion-MNIST-70k (cosine):

| dataset | trust (rstr) | trust (umap-learn) | fit (rstr) | fit (umap-learn) |

|---------|--------------|--------------------|------------|------------------|

| MNIST-70k   | 0.951 | 0.950 | 83 s  | 109 s |

| Fashion-70k | 0.901 | 0.906 | 67 s  | 181 s |

Trustworthiness matches; wall-clock is faster (incl. cold JVM/JIT). See

`dev/` for the reproducible comparison harness.

Embeddings side-by-side (left: umap-rstr, right: umap-learn), coloured by the

true class label:

![MNIST: umap-rstr vs umap-learn](img/mnist_compare.png)

![Fashion-MNIST: umap-rstr vs umap-learn](img/fashion_compare.png)

## Installation

raster depends on a typedclojure fork via git, and git deps resolve transitively

only through `deps.edn` (not a Maven POM), so pin this lib as a git dependency:

```clojure

io.github.replikativ/umap-rstr

{:git/url "https://github.com/replikativ/umap-rstr"

 :git/sha ""}

```

(Once typedclojure ships its fixes in a Maven release upstream, this moves to a

`org.replikativ/umap-rstr {:mvn/version "..."}` Clojars coordinate.)

## Usage

```clojure

(require '[umap :as umap])

;; X: flat row-major double[] (or float[]) of n*dim

(def result (umap/fit X n dim :k 15 :metric :cosine :init :auto :seed 42))

(:emb result)   ;; => double[n*2] embedding

```

Options: `:k` (neighbors, 15), `:out-dim` (2), `:n-epochs` (auto 500/200),

`:neg-rate` (5.0), `:gamma` (1.0), `:init` (`:auto`/`:spectral`/`:random`),

`:metric` (`:cosine`/`:euclidean`), `:seed` (42).

## Namespaces

- `umap` — public `fit` orchestrator

- `umap.layout` — negative-sampled SGD (the hot kernel)

- `umap.graph` — fuzzy simplicial set (smooth-knn-dist, membership, symmetrize)

- `umap.spectral` — spectral init (matrix-free Lanczos, disconnected-graph handling)

kNN / RP-trees / Tausworthe RNG live in raster (`raster.knn`,

`raster.spatial.*`, `raster.tausworthe`) since they're shared with clustering.

## Requirements

Runs on the Valhalla JDK (raster's `deftm` kernels use preview features). See

raster's README for JDK setup and run with the `:valhalla` alias.

```bash

clojure -M:valhalla:test          # run tests

```

## License

BSD 3-Clause (see `LICENSE` and `NOTICE`).

umap-rstr is a derivative work — a Clojure / raster port of

[umap-learn](https://github.com/lmcinnes/umap) (© 2017 Leland McInnes, BSD

3-Clause). It follows umap-learn's algorithm and numerical behaviour but is an

independent reimplementation, not endorsed by or affiliated with the original

authors. The port is © 2026 Christian Weilbach, released under the same license.

> McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold

> Approximation and Projection for Dimension Reduction. arXiv:1802.03426.