https://github.com/probsys/sppl
Probabilistic programming system for fast and exact symbolic inference
https://github.com/probsys/sppl
pldi probabilistic-inference probabilistic-programming programming-languages sum-product-networks
Last synced: 7 months ago
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Probabilistic programming system for fast and exact symbolic inference
- Host: GitHub
- URL: https://github.com/probsys/sppl
- Owner: probsys
- License: apache-2.0
- Created: 2019-08-14T23:30:58.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-04T00:41:06.000Z (about 1 year ago)
- Last Synced: 2024-05-17T20:54:52.174Z (about 1 year ago)
- Topics: pldi, probabilistic-inference, probabilistic-programming, programming-languages, sum-product-networks
- Language: Python
- Homepage:
- Size: 6.22 MB
- Stars: 74
- Watchers: 12
- Forks: 8
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
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[](https://pypi.org/project/sppl/)
Sum-Product Probabilistic Language
==================================
SPPL is a probabilistic programming language that delivers exact solutions
to a broad range of probabilistic inference queries. The language handles
continuous, discrete, and mixed-type probability distributions; many-to-one
numerical transformations; and a query language that includes general
predicates on random variables.
Users express generative models as probabilistic programs with standard
imperative constructs, such as arrays, if/else branches, for loops, etc.
The program is then translated to a sum-product expression (a
generalization of [sum-product networks](https://arxiv.org/pdf/2004.01167.pdf))
that statically represents the probability distribution of all random
variables in the program. This expression is used to deliver answers to
probabilistic inference queries.
A system description of SPPL is given in the following paper:
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference.
Saad, F. A.; Rinard, M. C.; and Mansinghka, V. K.
In PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference
on Programming Language Design and Implementation,
June 20-25, Virtual, Canada. ACM, New York, NY, USA. 2021.
https://doi.org/10.1145/3453483.3454078.
### Installation
This software is tested on Ubuntu 20.04 and Python 3.8.
SPPL is available on the PyPI repository
$ python -m pip install sppl
To install the Jupyter interface, first obtain the system-wide dependencies in
[requirements.sh](https://github.com/probcomp/sppl/blob/master/requirements.sh)
and then run
$ python -m pip install 'sppl[magics]'
### Examples
The easiest way to use SPPL is via the browser-based Jupyter interface, which
allows for interactive modeling, querying, and plotting.
Refer to the `.ipynb` notebooks under the
[examples](https://github.com/probcomp/sppl/tree/master/examples) directory.
### Benchmarks
Please refer to the artifact at the ACM Digital Library:
https://doi.org/10.1145/3453483.3454078
### Guide to Source Code
Please refer to [GUIDE.md](./GUIDE.md) for a description of the
main source files in this repository.
### Tests
To run the test suite as a user, first install the test dependencies:
$ python -m pip install 'sppl[tests]'
Then run the test suite:
$ python -m pytest --pyargs sppl
To run the test suite as a developer:
- To run crash tests: `$ ./check.sh`
- To run integration tests: `$ ./check.sh ci`
- To run a specific test: `$ ./check.sh [] /path/to/test.py`
- To run the examples: `$ ./check.sh examples`
- To build a docker image: `$ ./check.sh docker`
- To generate a coverage report: `$ ./check.sh coverage`
To view the coverage report, open `htmlcov/index.html` in the browser.
### Language Reference
Coming Soon!
### Citation
To cite this work, please use the following BibTeX.
```bibtex
@inproceedings{saad2021sppl,
title = {{SPPL:} Probabilistic Programming with Fast Exact Symbolic Inference},
author = {Saad, Feras A. and Rinard, Martin C. and Mansinghka, Vikash K.},
booktitle = {PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Design and Implementation},
pages = {804--819},
year = 2021,
location = {Virtual, Canada},
publisher = {ACM},
address = {New York, NY, USA},
doi = {10.1145/3453483.3454078},
address = {New York, NY, USA},
keywords = {probabilistic programming, symbolic execution, static analysis},
}
```
### License
Apache 2.0; see [LICENSE.txt](./LICENSE.txt)
### Acknowledgments
The [logo](https://github.com/probcomp/sppl/blob/master/sppl.png) was
designed by [McCoy R. Becker](https://femtomc.github.io/).