flowMC

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX - Published in JOSS (2023)

https://github.com/kazewong/flowmc

Science Score: 95.0%

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

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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 4 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: arxiv.org
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Committers with academic emails
3 of 9 committers (33.3%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

jax mcmc normalizing-flows stat

Last synced: 6 months ago · JSON representation

Repository

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

Basic Info

Host: GitHub
Owner: kazewong
License: mit
Language: Python
Default Branch: main
Homepage: https://flowmc.readthedocs.io/en/main/
Size: 33.9 MB

Statistics

Stars: 247
Watchers: 5
Forks: 30
Open Issues: 22
Releases: 17

Topics

jax mcmc normalizing-flows stat

Created about 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Code of conduct

flowMC

Normalizing-flow enhanced sampling package for probabilistic inference

[!WARNING] Note that flowMC has not reached v1.0.0, meaning the API could subject to changes. In general, the higher level the API, the less likely it is going to change. However, intermediate level API such as the resource strategy interface could subject to major revision for performance concerns.

flowMC_logo

flowMC is a Jax-based python package for normalizing-flow enhanced Markov chain Monte Carlo (MCMC) sampling. The code is open source under MIT license, and it is under active development.

Just-in-time compilation is supported.
Native support for GPU acceleration.
Suit for problems with multi-modality.
Minimal tuning.

Installation

The simplest way to install the package is to do it through pip

pip install flowMC

This will install the latest stable release and its dependencies. flowMC is based on Jax and Equinox. By default, installing flowMC will automatically install Jax and Equinox available on PyPI. By default this install the CPU version of Jax. If you have a GPU and want to use it, you can install the GPU version of Jax by running:

pip install flowMC[cuda]

If you want to install the latest version of flowMC, you can clone this repo and install it locally:

git clone https://github.com/kazewong/flowMC.git cd flowMC pip install -e .

There are a couple more extras that you can install with flowMC, including: - flowMC[docs]: Install the documentation dependencies. - flowMC[codeqa]: Install the code quality dependencies. - flowMC[visualize]: Install the visualization dependencies.

On top of pip installation, we highly encourage you to use uv to manage your python environment. Once you clone the repo, you can run uv sync to create a virtual environment with all the dependencies installed.

Attribution

If you used flowMC in your research, we would really appericiate it if you could at least cite the following papers:

``` @article{Wong:2022xvh, author = "Wong, Kaze W. k. and Gabri\'e, Marylou and Foreman-Mackey, Daniel", title = "{flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX}", eprint = "2211.06397", archivePrefix = "arXiv", primaryClass = "astro-ph.IM", doi = "10.21105/joss.05021", journal = "J. Open Source Softw.", volume = "8", number = "83", pages = "5021", year = "2023" }

@article{Gabrie:2021tlu, author = "Gabri\'e, Marylou and Rotskoff, Grant M. and Vanden-Eijnden, Eric", title = "{Adaptive Monte Carlo augmented with normalizing flows}", eprint = "2105.12603", archivePrefix = "arXiv", primaryClass = "physics.data-an", doi = "10.1073/pnas.2109420119", journal = "Proc. Nat. Acad. Sci.", volume = "119", number = "10", pages = "e2109420119", year = "2022" } ```

This will help flowMC getting more recognition, and the main benefit for you is this means the flowMC community will grow and it will be continuously improved. If you believe in the magic of open-source software, please support us by attributing our software in your work.

flowMC is a Jax implementation of methods described in:

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods Gabrié M., Rotskoff G. M., Vanden-Eijnden E. - ICML INNF+ workshop 2021 - pdf

Adaptive Monte Carlo augmented with normalizing flows. Gabrié M., Rotskoff G. M., Vanden-Eijnden E. - PNAS 2022 - doi, arxiv

Contributors

_{Hajime Kawahara} 🐛	_{Daniel Dodd} 📖 👀 ⚠️ 🐛	_{Matt Graham} 🐛 ⚠️ 👀 📖	_{Kaze Wong} 🐛 📝 💻 🖋 📖 💡 🚇 🚧 🔬 👀 ⚠️ ✅	_{Marylou Gabrié} 🐛 💻 🖋 📖 💡 🚧 🔬 ⚠️ ✅	_{Meesum Qazalbash} 💻 🚧	_{Thomas Ng} 💻 🚧
_{Thomas Edwards} 🐛 💻

Owner

Name: Kaze Wong
Login: kazewong
Kind: user

Repositories: 13
Profile: https://github.com/kazewong

JOSS Publication

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX

Published

March 09, 2023

DOI

10.21105/joss.05021

Volume 8, Issue 83, Page 5021

Authors

Kaze W. k. Wong

Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US

Marylou Gabrié

École Polytechnique, Palaiseau 91120, France, Center for Computational Mathematics, Flatiron Institute, New York, NY 10010, US

Daniel Foreman-Mackey

Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US

Editor

Rachel Kurchin

GitHub Events

Total

Create event: 30
Release event: 8
Issues event: 33
Watch event: 52
Delete event: 22
Issue comment event: 59
Push event: 242
Pull request review comment event: 15
Pull request review event: 32
Pull request event: 43
Fork event: 5

Last Year

Create event: 30
Release event: 8
Issues event: 33
Watch event: 52
Delete event: 22
Issue comment event: 59
Push event: 242
Pull request review comment event: 15
Pull request review event: 32
Pull request event: 43
Fork event: 5

Committers

Last synced: 7 months ago

All Time

Total Commits: 1,056
Total Committers: 9
Avg Commits per committer: 117.333
Development Distribution Score (DDS): 0.098

Past Year

Commits: 290
Committers: 3
Avg Commits per committer: 96.667
Development Distribution Score (DDS): 0.069

Top Committers

Name	Email	Commits
Kaze Wong	k**s@g**m	952
marylou	m**e@g**m	65
Thomas Ng	t**g@l**k	16
ThibeauWouters	t**s@l**u	7
allcontributors[bot]	4****]	6
Thibeau Wouters	t**s@w**l	6
Qazalbash	m**h@g**m	2
tedwards2412	t**2@g**m	1
Thibeau Wouters	t**s@l**u	1

Committer Domains (Top 20 + Academic)

ldas-pcdev11.ligo.caltech.edu: 1 wn-lot-008.nikhef.nl: 1 ldas-pcdev12.ligo.caltech.edu: 1 link.cuhk.edu.hk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 112
Total pull requests: 130
Average time to close issues: 3 months
Average time to close pull requests: 11 days
Total issue authors: 18
Total pull request authors: 12
Average comments per issue: 1.09
Average comments per pull request: 0.62
Merged pull requests: 118
Bot issues: 0
Bot pull requests: 7

Past Year

Issues: 23
Pull requests: 55
Average time to close issues: 15 days
Average time to close pull requests: 4 days
Issue authors: 7
Pull request authors: 5
Average comments per issue: 0.91
Average comments per pull request: 1.09
Merged pull requests: 47
Bot issues: 0
Bot pull requests: 7

View more stats

Top Authors

Issue Authors

kazewong (65)
Qazalbash (11)
matt-graham (8)
marylou-gabrie (8)
daniel-dodd (4)
thomasckng (2)
zeeshan5885 (2)
ColCarroll (2)
dfm (1)
HarrisonWilde (1)
ahnitz (1)
Felipe-4 (1)
ThibeauWouters (1)
HajimeKawahara (1)
bjricketts (1)

Pull Request Authors

kazewong (110)
marylou-gabrie (11)
Qazalbash (9)
allcontributors[bot] (6)
ThibeauWouters (4)
thomasckng (4)
tedwards2412 (2)
matt-graham (1)
google-labs-jules[bot] (1)
jasonmcewen (1)
alicjapolanska (1)
daniel-dodd (1)

Top Labels

Issue Labels

enhancement (22) Joss (7) documentation (5) good first issue (2) bug (2) question (1)

Pull Request Labels

enhancement (2) bug (2) documentation (1)

Packages

Total packages: 1
Total downloads:
- pypi 2,970 last-month

Total dependent packages: 1
Total dependent repositories: 0
Total versions: 29
Total maintainers: 1

pypi.org: flowmc

Normalizing flow exhanced sampler in jax

Documentation: https://github.com/kazewong/flowMC
License: MIT License Copyright (c) 2022 Kaze Wong & contributor Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Latest release: 0.4.5
published 8 months ago

Versions: 29
Dependent Packages: 1
Dependent Repositories: 0
Downloads: 2,970 Last month

Rankings

Dependent packages count: 6.6%

Stargazers count: 7.5%

Forks count: 12.8%

Average: 14.5%

Downloads: 14.8%

Dependent repos count: 30.6%

Maintainers (1)

kazewong.physics

Last synced: 6 months ago

Dependencies

.github/workflows/workflowsjoss.yml actions

actions/checkout v2 composite
actions/upload-artifact v1 composite
openjournals/openjournals-draft-action master composite

docs/requirements.txt pypi

distrax *
flax *
jax *
jaxlib *
myst-nb *
sphinx-book-theme *
tqdm *

.github/workflows/python-package.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite

.github/workflows/python-publish.yml actions

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pyproject.toml pypi

flowMC

Science Score: 95.0%

Keywords

Repository

Basic Info

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Metadata Files

README.md

flowMC

Installation

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JOSS Publication

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX

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