manifolds-rs

Dimensionality reduction for single-cell and computational biology. The Rust crate does the work; this is a scikit-learn shaped layer over it.

Seven algorithms on the CPU, three of them with GPU variants where the neighbour search and the Adam update move to the device. No CUDA runtime to install: the GPU backend is wgpu, so it runs on Metal, Vulkan or DX12 and ships in the ordinary wheel.

Install

uv pip install manifolds-rs

Wheels are built for Linux x86_64 and macOS on both architectures, against Python 3.10 and up.

Thirty seconds

import manifolds_rs as mf

X, labels = mf.datasets.clustered(20_000, dim=50, n_clusters=12)

embedding = mf.UMAP(n_neighbors=15, min_dist=0.1).fit_transform(X)

Every estimator takes its parameters in the constructor, its data in fit, and hands back an (n_samples, n_components) array. get_params and set_params are there, so clone and Pipeline work without scikit-learn being an install requirement.

What's here

Class What it is for
UMAP The default choice. Local structure, decent global structure from the spectral init.
DensMAP UMAP with relative density preserved, so a dense region stays dense.
TSNE Local structure above all else. Barnes-Hut, 2-D only.
DensNE t-SNE with the same density correction.
PHATE Continuous structure. Trajectories and branch points survive this.
PaCMAP Global structure without leaning on a spectral init. Three pair types.
DiffusionMaps The spectral embedding PHATE is built on.
UMAPGpu, DensMAPGpu, TSNEGpu The same, with the neighbour search on the device.

Parameters

The knobs people actually turn are ordinary constructor arguments. The rest live in frozen dataclasses, one per group, and anything you leave alone keeps the crate's default:

mf.UMAP(
    n_neighbors=30,
    ann="hnsw",
    nn_params=mf.NeighbourParams(m=32, ef_search=200),
    optim_params=mf.UmapOptim(gamma=1.5, neg_sample_rate=10),
)

Every parameter has exactly one home. If it is a constructor argument it is not in the group, so there is never a question of which wins.

Reusing the neighbour graph

Running several embeddings over the same data? Build the graph once. On anything large the search is most of the runtime.

ind, dist = mf.knn_graph(X, k=15, ann="hnsw")

a = mf.UMAP().fit_transform(X, knn_indices=ind, knn_distances=dist)
b = mf.PaCMAP().fit_transform(X, knn_indices=ind, knn_distances=dist)

No transform

None of these projects new points, so there is no transform. That is the crate's position rather than a gap in the bindings: embedding new data means refitting on the combined set, which moves the existing coordinates too. Anyone telling you otherwise is doing something to the new points that is not the same operation the old ones went through.

Precision

float32 in, float32 throughout; float64 in, float64 throughout. Anything else is promoted to float64 rather than narrowed, so precision is never lost by accident. The GPU estimators are the exception and cast to float32, because WGSL has no float64 and the alternative is a failure inside a kernel.

What is not in the wheel

FFT-accelerated t-SNE. It needs FFTW, a system library no manylinux container carries, so approx="fft" raises unless you build the extension yourself with the fft_tsne feature. Barnes-Hut is the default and is what the wheel does.

Parametric UMAP is in the crate but not yet bound.

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Credits

Author: Gregor Lueg

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License

MIT

Requires

Python >=3.10

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