This repo implements Particle Gibbs sampler for Bayesian Additive Regression Trees (BART) written in Rust.

This implementation is ~10x faster than a vanilla python version, which makes it feasible to run the algorithm on much larger datasets.

Python bindings and integration with PyMC and Numpyro (coming soon) are also provided.

To use the model in PyMC, you can simply write

with pm.Model() as model_coal:
    μ_ = rust_pgbart.BART("μ_", X=x_data, Y=y_data, m=20)
    μ = pm.Deterministic("μ", np.abs(μ_))
    y_pred = pm.Poisson("y_pred", mu=μ, observed=y_data)
    idata_coal = pm.sample()

See examples for the full code.

• pgbart/src contains the generic implementation of the Particle Gibbs sampler. It does not have any notion of Python or PyMC. It only assumes you can provide the input data (X, y) and a function logp(predictions) -> float.
• bindings/ contains the Rust + Python + PyMC bindings code. See its README.md for details.

See ./bindings/README.md

This is still work in progress, and not all features are supported. Most notably out-of-sample prediction and variable importance plots are not yet available in Python (but the Rust code is mostly there).

I've developed this code as part of my effort to re-write Bayesian Modeling and Computation in Python into numpyro.

The implementation was based on:

• BART implementation paper
• Original PyMC implementation
• nutpie