swyft

swyft: Truncated Marginal Neural Ratio Estimation in Python - Published in JOSS (2022)

https://github.com/undark-lab/swyft

Keywords

Repository

A system for scientific simulation-based inference at scale.

README.rst

Swyft
=====

.. image:: https://raw.githubusercontent.com/undark-lab/swyft/v0.4.1/docs/source/_static/img/swyft_logo_wide.png
   :width: 800
   :align: center

*Swyft* is a system for scientific simulation-based inference at scale.

.. image:: https://badge.fury.io/py/swyft.svg
   :target: https://badge.fury.io/py/swyft
   :alt: PyPI version


.. .. image:: https://github.com/undark-lab/swyft/actions/workflows/tests.yml/badge.svg
..    :target: https://github.com/undark-lab/swyft/actions
..    :alt: Tests


.. .. image:: https://github.com/undark-lab/swyft/actions/workflows/syntax.yml/badge.svg
..    :target: https://github.com/undark-lab/swyft/actions
..    :alt: Syntax


.. image:: https://codecov.io/gh/undark-lab/swyft/branch/master/graph/badge.svg?token=E253LRJWWE
   :target: https://codecov.io/gh/undark-lab/swyft
   :alt: codecov


.. .. image:: https://readthedocs.org/projects/swyft/badge/?version=latest
..    :target: https://swyft.readthedocs.io/en/latest/?badge=latest
..    :alt: Documentation Status


.. .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
..    :target: https://github.com/undark-lab/swyft/blob/master/CONTRIBUTING.md
..    :alt: Contributions welcome


.. .. image:: https://colab.research.google.com/assets/colab-badge.svg
..    :target: https://colab.research.google.com/github/undark-lab/swyft/blob/master/notebooks/Quickstart.ipynb
..    :alt: colab


.. image:: https://joss.theoj.org/papers/10.21105/joss.04205/status.svg
   :target: https://doi.org/10.21105/joss.04205


.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5752734.svg
   :target: https://doi.org/10.5281/zenodo.5752734


*Swyft* is the official implementation of Truncated Marginal Neural Ratio Estimation (TMNRE),
a hyper-efficient, simulation-based inference technique for complex data and expensive simulators.


Swyft in action
---------------


.. image:: https://raw.githubusercontent.com/undark-lab/swyft/v0.4.1/docs/source/_static/img/SBI-curve.gif
   :width: 800
   :align: center



* Swyft makes it convenient to perform Bayesian or Frequentist inference of hundreds, thousands or millions of parameter posteriors by constructing optimal data summaries. 
* To this end, Swyft estimates likelihood-to-evidence ratios for arbitrary marginal posteriors; they typically require fewer simulations than the corresponding joint.
* Swyft performs targeted inference by prior truncation, combining simulation efficiency with empirical testability.
* Swyft is based on stochastic simulators, which map parameters stochastically to observational data. Swyft makes it convenient to define such simulators as graphical models.
* In scientific settings, a cost-benefit analysis often favors approximating the posterior marginality; *swyft* provides this functionality.
* The package additionally implements our prior truncation technique, routines to empirically test results by estimating the expected coverage, and a simulator manager with `zarr `_ storage to simplify use with complex simulators.


Papers using Swyft/TMNRE
------------------------

2021

- “Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation“ Cole+ https://arxiv.org/abs/2111.08030

2022

- “Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation” Anau Montel+, https://arxiv.org/abs/2205.09126
- “SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation” Karchev+ https://arxiv.org/abs/2209.06733
- “One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses” Coogan+ https://arxiv.org/abs/2209.09918
- “Detection is truncation: studying source populations with truncated marginal neural ratio estimation” Anau Montel+ https://arxiv.org/abs/2211.04291

2023

- “Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation” Gagnon-Hartman+ https://arxiv.org/abs/2301.05241
- “Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation” Saxena+ https://arxiv.org/abs/2303.07339
- “Peregrine: Sequential simulation-based inference for gravitational wave signals”, Bhardwaj+ https://arxiv.org/abs/2304.02035
- “Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way”, Alvey+ https://arxiv.org/abs/2304.02032


Further information
-------------------

* **Documentation & installation**: https://swyft.readthedocs.io/
* **Example usage**: https://swyft.readthedocs.io/en/latest/tutorial-notebooks.html
* **Source code**: https://github.com/undark-lab/swyft
* **Support & discussion**: https://github.com/undark-lab/swyft/discussions
* **Bug reports**: https://github.com/undark-lab/swyft/issues
* **Contributing**: https://swyft.readthedocs.io/en/latest/contributing-link.html
* **Citation**: https://swyft.readthedocs.io/en/latest/citation.html


*Swyft* history
---------------

* As of v0.4.0, *Swyft* is based on pytorch-lightning, with a completely updated
* `v0.3.2 `_ is the version that was submitted to `JOSS `_.
* `tmnre `_ is the implementation of the paper `Truncated Marginal Neural Ratio Estimation `_.
* `v0.1.2 `_ is the implementation of the paper `Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time `_.

Relevant packages
-----------------

* `sbi `_ is a collection of simulation-based inference methods. Unlike *Swyft*, the repository does not include our truncation scheme nor marginal estimation of posteriors.

* `lampe `_ is an implementation of amoritzed simulation-based inference methods aimed at simulation-based inference researchers due to its flexibility.

Owner

JOSS Publication

swyft: Truncated Marginal Neural Ratio Estimation in Python

Published

July 19, 2022

DOI

10.21105/joss.04205

Volume 7, Issue 75, Page 4205

Authors

Benjamin Kurt Miller
Gravitation Astroparticle Physics Amsterdam (GRAPPA), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Amsterdam Machine Learning Lab (AMLab), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, AI4Science Lab, University of Amsterdam, Science Park 904, 1098 XH Amsterdam

Alex Cole
Gravitation Astroparticle Physics Amsterdam (GRAPPA), University of Amsterdam, Science Park 904, 1098 XH Amsterdam

Christoph Weniger
Gravitation Astroparticle Physics Amsterdam (GRAPPA), University of Amsterdam, Science Park 904, 1098 XH Amsterdam

Francesco Nattino
Netherlands eScience Center, Science Park 140, 1098 XG Amsterdam, The Netherlands

Ou Ku
Netherlands eScience Center, Science Park 140, 1098 XG Amsterdam, The Netherlands

Meiert W. Grootes
Netherlands eScience Center, Science Park 140, 1098 XG Amsterdam, The Netherlands

Editor

Pierre de Buyl

Tags

simulation-based inference likelihood-free inference machine learning bayesian inference system identification parameter identification inverse problem

View PDF Review Thread Software Archive

GitHub Events

Committers

Last synced: 5 months ago

Issues and Pull Requests

Last synced: 4 months ago

Packages