Skip to main content

A JAX implementation of BART

Project description

PyPI

BART vectoriZed

A branchless vectorized implementation of Bayesian Additive Regression Trees (BART) in JAX.

BART is a nonparametric Bayesian regression technique. Given predictors $X$ and responses $y$, BART finds a function to predict $y$ given $X$. The result of the inference is a sample of possible functions, representing the uncertainty over the determination of the function.

This Python module provides an implementation of BART that runs on GPU, to process large datasets faster. It is also good on CPU. Most other implementations of BART are for R, and run on CPU only.

On CPU, bartz runs at the speed of dbarts (the fastest implementation I know of), but using half the memory. On GPU, the speed premium depends on sample size; with 50000 datapoints and 5000 trees, on an Nvidia Tesla V100 GPU it's 12 times faster than an Apple M1 CPU, and this factor is linearly proportional to the number of datapoints.

Links

Other BART packages

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bartz-0.4.0.tar.gz (30.4 kB view details)

Uploaded Source

Built Distribution

bartz-0.4.0-py3-none-any.whl (35.5 kB view details)

Uploaded Python 3

File details

Details for the file bartz-0.4.0.tar.gz.

File metadata

  • Download URL: bartz-0.4.0.tar.gz
  • Upload date:
  • Size: 30.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.2 CPython/3.12.2 Darwin/23.4.0

File hashes

Hashes for bartz-0.4.0.tar.gz
Algorithm Hash digest
SHA256 728deedf86567576401ab39b677c34f92901673efc28aaad946125729dfbddb5
MD5 0528011db344d0aeefd312146dcec251
BLAKE2b-256 274db5b5f1c6b891d17395572b389a709212b537853997b539166144236db58e

See more details on using hashes here.

File details

Details for the file bartz-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: bartz-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 35.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.2 CPython/3.12.2 Darwin/23.4.0

File hashes

Hashes for bartz-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 0c4061ea5aa29c782fe989730c02e7e7d812ead05fa344c7bdaa6ab72e2ca015
MD5 2fce72ac0e53a7b7b3be0794b8208ced
BLAKE2b-256 d7f957d1c3f331ca5cdbdf74c55bce5290f9de876ace0b505b5f9c7a26c0e61d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page