Changelog¶

0.6.0 bruv bernoulli got gauss beat any time (2025-05-29)¶

binary regression with probit link
allow to interrupt the MCMC with ^C
logging shows how much the tree heaps are filled; if it’s above 50% you should definitely increase the number of trees and/or the maximum depth
BART.gbart(..., run_mcmc_kw=dict(...)) allows to pass additional arguments to mcmcloop.run_mcmc
option to disable logging
refactor internals
- set offset in the MCMC state to avoid centering the responses
- set leaf variance in the MCMC state to avoid rescaling responses
- immutable dataclasses instead of dicts
- improvements to mcmcloop.run_mcmc
  - simpler to use signature with only three required parameters
  - the callback is allowed to carry a state and to modify the chain state (opt-in)
  - custom functions to change what is extracted from the state and put into the traces
  - two distinct callbacks, one invoked under jit and one out of it
  - more sophisticate default logging callback
- complete type hints
- improved documentation
- jaxext.split is a less error-prone alternative to jax.random.split

Heteroskedasticity with fixed weights.
The internal MCMC functions now follow the jax convention of having the key parameter first in the signature.
Fixed a bug where the MCMC callback would hang indefinitely.
The library is now routinely tested with the least recent supported dependencies.

Somehow 1 year went by before I had some time to spend on this software.

Somehow 1 year was sufficient to break most of my dependency specifications, despite having only 4 dependencies.

This release provides no new features, it’s only a quick fix to make bartz go well with the latest jax and numpy versions.

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.