mlx-vis 0.1.0

Unified dimensionality reduction on Apple Silicon: UMAP, t-SNE, PaCMAP, NNDescent, PCA - all in MLX.

mlx-vis

Dimensionality reduction on Apple Silicon. UMAP, t-SNE, PaCMAP, and NNDescent - all running on Metal via MLX.

Install

uv pip install mlx-vis

From source:

git clone --recurse-submodules https://github.com/hanxiao/mlx-vis.git
cd mlx-vis
uv pip install .

Usage

import numpy as np
from mlx_vis import UMAP, TSNE, PaCMAP, NNDescent

X = np.random.randn(10000, 128).astype(np.float32)

# UMAP
Y = UMAP(n_components=2, n_neighbors=15).fit_transform(X)

# t-SNE
Y = TSNE(n_components=2, perplexity=30).fit_transform(X)

# PaCMAP
Y = PaCMAP(n_components=2, n_neighbors=10).fit_transform(X)

# NNDescent (approximate k-NN graph)
indices, distances = NNDescent(k=15).build(X)

Submodule imports also work:

from mlx_vis.umap import UMAP
from mlx_vis.tsne import TSNE
from mlx_vis.pacmap import PaCMAP
from mlx_vis.nndescent import NNDescent

Methods

Method	Class	Main API	Output
UMAP	UMAP(n_components, n_neighbors, min_dist, ...)	fit_transform(X)	np.ndarray (n, d)
t-SNE	TSNE(n_components, perplexity, ...)	fit_transform(X)	np.ndarray (n, d)
PaCMAP	PaCMAP(n_components, n_neighbors, ...)	fit_transform(X)	np.ndarray (n, d)
NNDescent	NNDescent(k, n_iters, ...)	build(X)	(indices, distances)

Dependencies

• mlx >= 0.20.0
• numpy >= 1.24.0

License

Apache-2.0