rustypca 0.2.0

Probabilistic PCA with missing value support using Rust and PyO3

rustypca

A Python library for Probabilistic PCA that handles missing data without asking you to impute first. It uses the EM algorithm under the hood, with the number-crunching done in Rust.

Why?

Regular PCA doesn't cope well when your data has holes in it. Probabilistic PCA treats missing values as latent variables in a generative model — a more principled approach than patching NaNs and hoping for the best.

For the full story, see Tipping & Bishop (1999), "Probabilistic Principal Component Analysis", Journal of the Royal Statistical Society, 61(3), 611–622.

Features

• Rust backend — keeps things snappy on larger datasets
• Missing value support — the main reason this exists
• Scikit-learn compatible — fits in wherever you'd use sklearn.decomposition.PCA

Installation

pip install rustypca

You'll need a Rust toolchain to build from source. Python >= 3.10.

Quick start

import numpy as np
from rustypca import PPCA

X = np.random.randn(100, 10)
X[np.random.rand(100, 10) < 0.1] = np.nan  # Introduce some missing values

model = PPCA(n_components=2)
X_transformed = model.fit_transform(X)
X_reconstructed = model.inverse_transform(X_transformed)

No preprocessing or imputation needed.

Testing

make test

Disclaimer

This project was built with the help of Claude.

License

MIT