jaxsne
A library for doing dimensionality reduction in different metric spaces, or using different distributions.
In addition to regular t-SNE for MNIST
this can also be used to embed points on the sphere
or even into hierarchical hyperbolic space
The downside is that this is generally less performant than the t-SNE provided by scikit-learn, so should only be used if you want to tweak the metrics or measures.
Installation
pip install jaxsne
Basic Usage
import jaxsne
data = ... # n x d
reduced = jaxsne.sne(data)
# or
reduced = jaxsne.scaling(data)
Advanced Usage
import jaxsne
import jax
from jax import Array
from jax import numpy as jnp
@jax.jit
def manhattan(left: Array, right: Array) -> Array:
return jnp.abs(left - right).sum(-1)
data = ... # n x d
reduced = jaxsne.sne(data, in_metric=manhattan, out_metric=manhattan)
Development
uv run ruff format --check
uv run ruff check
uv run pyright
uv run pytest
Publishing
rm -rf dist
uv build
uv publish --username __token__
Tasks
- Extend to Barnes-Hut-SNE
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