pymc-bart

https://github.com/pymc-devs/pymc-bart

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 1 DOI reference(s) in README
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Academic publication links
Links to: arxiv.org
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (15.2%) to scientific vocabulary

Keywords from Contributors

closember optimizing-compiler automatic-differentiation bayesian-statistics

Last synced: 6 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: pymc-devs
License: other
Language: Python
Default Branch: main
Homepage: https://www.pymc.io/projects/bart
Size: 626 KB

Statistics

Stars: 122
Watchers: 20
Forks: 22
Open Issues: 23
Releases: 33

Created over 3 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

Bayesian Additive Regression Trees for Probabilistic Programming with PyMC

pymc-bart logo

PyMC-BART extends PyMC probabilistic programming framework to be able to define and solve models including a BART random variable. PyMC-BART also includes a few helpers function to aid with the interpretation of those models and perform variable selection.

Installation
Usage
Contributions
Code of Conduct
Citation
License
Donations
Sponsors

Installation

PyMC-BART is available on Conda-Forge. If you magange your Python dependencies and environments with Conda, this is your best option. You may also perfer to install this way if you want an easy-to-use, isolated setup in a seperate environment. This helps avoid interfering with other projects or system-wide Python installations. To set up a suitable Conda environment, run:

bash conda create --name=pymc-bart --channel=conda-forge pymc-bart conda activate pymc-bart

Alternatively, you can use pip installation. This installation is generally perfered by users who use pip, Python's package installer. This is the best choice for users who are not using Conda or for those who want to install PyMC-BART into a virtual environment managed by venv or virtualenv. In this case, run:

bash pip install pymc-bart

In case you want to upgrade to the bleeding edge version of the package you can install from GitHub:

bash pip install git+https://github.com/pymc-devs/pymc-bart.git

Usage

Get started by using PyMC-BART to set up a BART model:

```python import pymc as pm import pymc_bart as pmb

X, y = ... # Your data replaces "..." with pm.Model() as model: bart = pmb.BART('bart', X, y) ... idata = pm.sample() ```

Contributions

PyMC-BART is a community project and welcomes contributions. Additional information can be found in the Contributing Readme

Code of Conduct

PyMC-BART wishes to maintain a positive community. Additional details can be found in the Code of Conduct

Citation

If you use PyMC-BART and want to cite it please use

Here is the citation in BibTeX format

@misc{quiroga2023bayesian, title={Bayesian additive regression trees for probabilistic programming}, author={Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A}, year={2023}, doi={10.48550/ARXIV.2206.03619}, archivePrefix={arXiv}, primaryClass={stat.CO} }

License

Apache License, Version 2.0

Donations

PyMC-BART , as other pymc-devs projects, is a non-profit project under the NumFOCUS umbrella. If you want to support PyMC-BART financially, you can donate here.

Sponsors

Owner

Name: PyMC
Login: pymc-devs
Kind: organization

Website: https://www.pymc.io
Twitter: pymc_devs
Repositories: 34
Profile: https://github.com/pymc-devs

Citation (CITATION.bib)

@misc{quiroga2023bayesian,
  doi = {10.48550/ARXIV.2206.03619},
  url = {https://arxiv.org/abs/2206.03619},
  author = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
  keywords = {Computation (stat.CO), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Bayesian additive regression trees for probabilistic programming},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

GitHub Events

Total

Create event: 33
Release event: 8
Issues event: 10
Watch event: 29
Delete event: 27
Member event: 1
Issue comment event: 32
Push event: 78
Pull request review comment event: 11
Pull request review event: 18
Pull request event: 71
Fork event: 4

Last Year

Create event: 33
Release event: 8
Issues event: 10
Watch event: 29
Delete event: 27
Member event: 1
Issue comment event: 32
Push event: 78
Pull request review comment event: 11
Pull request review event: 18
Pull request event: 71
Fork event: 4

Committers

Last synced: 8 months ago

All Time

Total Commits: 206
Total Committers: 12
Avg Commits per committer: 17.167
Development Distribution Score (DDS): 0.383

Past Year

Commits: 58
Committers: 5
Avg Commits per committer: 11.6
Development Distribution Score (DDS): 0.534

Top Committers

Name	Email	Commits
Osvaldo A Martin	a**a@g**m	127
pre-commit-ci[bot]	6****]	32
Juan Orduz	j**z@g**m	18
Pablo G Garay	g**1@g**m	10
Franco Joaquín Loyola	f****a	6
Ben Mares	s**1@t**m	4
Alexandre Andorra	a**e@g**m	3
Margus Niitsoo	v**y@g**m	2
howsiyu	h**u@g**m	1
Ryan Duecker	r**r@y**m	1
Oriol Abril-Pla	o**a@g**m	1
Nicholas L	6****r	1

Committer Domains (Top 20 + Academic)

tensorial.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 61
Total pull requests: 187
Average time to close issues: 3 months
Average time to close pull requests: 7 days
Total issue authors: 31
Total pull request authors: 13
Average comments per issue: 2.33
Average comments per pull request: 0.66
Merged pull requests: 169
Bot issues: 1
Bot pull requests: 44

Past Year

Issues: 7
Pull requests: 60
Average time to close issues: 6 days
Average time to close pull requests: 5 days
Issue authors: 7
Pull request authors: 6
Average comments per issue: 1.0
Average comments per pull request: 0.43
Merged pull requests: 50
Bot issues: 1
Bot pull requests: 28

View more stats

Top Authors

Issue Authors

aloctavodia (17)
juanitorduz (10)
fonnesbeck (3)
j-adamczyk (2)
twj8CDC (2)
jacobpascalcoblentz (1)
DevenVGokhale (1)
ggmirandac (1)
ricardoV94 (1)
elanmart (1)
flyaflya (1)
fatihbozdag (1)
gzhai001 (1)
Nilavro (1)
maresb (1)

Pull Request Authors

aloctavodia (96)
pre-commit-ci[bot] (71)
juanitorduz (28)
PabloGGaray (13)
fjloyola (10)
AlexAndorra (6)
maresb (5)
derekpowell (2)
OriolAbril (2)
NicholasLindner (2)
howsiyu (1)
velochy (1)
RyanAugust (1)

Top Labels

Issue Labels

documentation (4) enhancement (1)

Pull Request Labels

maintenance (17) bug (6) documentation (2) enhancement (2)

Packages

Total packages: 2
Total downloads:
- pypi 2,442 last-month

Total dependent packages: 2
(may contain duplicates)
Total dependent repositories: 2
(may contain duplicates)
Total versions: 31
Total maintainers: 2

pypi.org: pymc-bart

Bayesian Additive Regression Trees for Probabilistic programming with PyMC

Homepage: http://github.com/pymc-devs/pymc-bart
Documentation: https://pymc-bart.readthedocs.io/
License: Apache License, Version 2.0
Latest release: 0.10.0
published 8 months ago

Versions: 30
Dependent Packages: 2
Dependent Repositories: 2
Downloads: 2,442 Last month

Rankings

Dependent packages count: 7.4%

Stargazers count: 8.8%

Average: 10.3%

Downloads: 11.3%

Dependent repos count: 11.9%

Forks count: 12.0%

Maintainers (2)

aloctavodia juanitorduz

Last synced: 6 months ago

conda-forge.org: pymc-bart

Homepage: http://github.com/pymc-devs/pymc-bart
License: Apache-2.0
Latest release: 0.2.1
published over 3 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 34.0%

Average: 49.5%

Dependent packages count: 51.2%

Stargazers count: 51.9%

Forks count: 61.1%

Last synced: 6 months ago

Dependencies

requirements-docs.txt pypi

nbsphinx >=0.4.2
pydata-sphinx-theme >=0.6.3
sphinx >=4

requirements.txt pypi

arviz >=0.12.1
numba >=0.55.1
pymc >=4.1.7

requirements-dev.txt pypi

black ==22.3.0 development
click ==8.0.4 development
pylint ==2.10.2 development
pytest >=4.4.0 development
pytest-cov >=2.6.1 development

.github/workflows/publish-to-test-pypi.yml actions

actions/checkout master composite
actions/setup-python v3 composite
pypa/gh-action-pypi-publish release/v1 composite

.github/workflows/test.yml actions

actions/checkout v2 composite
codecov/codecov-action v1 composite
conda-incubator/setup-miniconda v2 composite

pymc-bart

Science Score: 67.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

Bayesian Additive Regression Trees for Probabilistic Programming with PyMC

Table of Contents

Installation

Usage

Contributions

Code of Conduct

Citation

License

Donations

Sponsors

Owner

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: pymc-bart

Rankings

Maintainers (2)

conda-forge.org: pymc-bart

Rankings

Dependencies