Changelog¶

0.4.0 The real treasure was the Markov chain samples we made along the way (2024-04-16)¶

2x faster on GPU, due to parallelizing better the tree sampling step.
Uses less memory, now can do \(n=100\,000\) with \(10\,000\) trees on a V100. This was mostly an excessively large batch size for counting datapoints per leaf.
The Metropolis-Hastings ratio is saved only for the proposed move.
The grow and prune moves are merged into one object.

2-3x faster on CPU.
Uses less memory.
Add initkw argument to BART.gbart for advanced configuration of the MCMC initialization.
Modified the automatic determination of sigest in BART.gbart to match the one of the R package.
Add usequants=False option to BART.gbart, which is now the default.
New function prepcovars.uniform_splits_from_matrix.
Add sum_trees=False option to grove.evaluate_forest to evaluate separately each tree.
Support non-batched arguments in jaxext.autobatch.
Fix a bug with empty arrays in jaxext.autobatch.
New option in mcmcstep.init to save the acceptance ratios.
Separate batching options for residuals and counts.
Sweeping changes to the tree move internals, more computations are parallel across trees.
Added support for dbarts in the unit tests.

Rename bartz.BART to bartz.BART.gbart.
Expose submodule bartz.jaxext with auxiliary functions for jax.
Shorter compilation time if no burnin or saved samples are drawn. This is useful when using the interface only to create the initial MCMC state, or when saving all samples to inspect the warm-up phase.
20x faster on GPU.
2x faster on CPU.
Use less temporary memory to quantilize covariates, avoiding out-of-memory errors on GPU.

Optimize the MCMC step to only traverse each tree once.
Now bartz runs at the same speed as the R package BART (tested at \(p=10\), \(n=100\ldots 10000\)).
The MCMC functions are heavily changed, but the interface is the same.

First release.