https://github.com/facebookresearch/pplbench

Evaluation Framework for Probabilistic Programming Languages
https://github.com/facebookresearch/pplbench

bayesian-logistic-regression benchmark ppl probabilistic-programming-languages

Last synced: 4 months ago
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Evaluation Framework for Probabilistic Programming Languages

• Host: GitHub
• URL: https://github.com/facebookresearch/pplbench
• Owner: facebookresearch
• License: mit
• Created: 2020-04-07T23:24:59.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2023-08-30T21:35:41.000Z (10 months ago)
• Last Synced: 2023-12-04T13:40:17.353Z (7 months ago)
• Topics: bayesian-logistic-regression, benchmark, ppl, probabilistic-programming-languages
• Language: Python
• Homepage: https://pplbench.org/
• Size: 4.41 MB
• Stars: 91
• Watchers: 22
• Forks: 23
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Lists

• awesome-stars - facebookresearch/pplbench - Evaluation Framework for Probabilistic Programming Languages (Python)

README

        
[![](docs/assets/logo.png)]((https://facebookresearch.github.io/pplbench/))

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# Getting Started with PPL Bench

* [What is PPL Bench?](#what-is-ppl-bench)

* [Installing](#installation)

* [Getting Started](#getting-started)

## What is PPL Bench?

PPL Bench is a new benchmark framework for evaluating probabilistic programming languages (PPLs).

## Installation

1. Enter a virtual (or conda) environment

2. Install PPL Bench core via pip:

```

pip install pplbench

```

3. Install PPLs that you wish to benchmark. For PPL-specific instructions, see [Installing PPLs](docs/working_with_ppls.md).

You could also run the following command to install all PPLs that are currently supported by PPL Bench (except for Jags):

```

pip install pplbench[ppls]

```

Alternatively, you could also install PPL Bench from source. Please refer to [Installing PPLs](docs/working_with_ppls.md)

for instructions.

## Getting Started

Let's dive right in with a benchmark run of Bayesian Logistic Regression. To run this, you'll need to install

PyStan (if you haven't already):

```

pip install pystan==2.19.1.1

```

Then, run PPL Bench with example config:

```

pplbench examples/example.json

```

This will create a benchmark run with two trials of Stan on the Bayesian Logistic Regression model. The results of the run are saved in the `outputs/` directory.

This is what the Predictive Log Likelihood (PLL) plot should look like:

![PLL plot of example run](website/static/img/example_pystan_pll.svg)

![PLL half plot of example run](website/static/img/example_pystan_pll_half.svg)

Please see the [examples/example.json](examples/example.json) file to understand the schema for specifying benchmark runs. The schema is documented in [pplbench/main.py](pplbench/main.py) and can be printed by running the help command:

```

pplbench -h

```

A number of models is available in the `pplbench/models` directory and the PPL implementations are available in the `pplbench/ppls` directory.

Please feel free to submit pull requests to modify an existing PPL implementation or to add a new PPL or model.

## Join the PPL Bench community

 For more information about PPL Bench, refer to

1. Blog post: [link](https://ai.facebook.com/blog/ppl-bench-creating-a-standard-for-benchmarking-probabilistic-programming-languages)

2. Paper: [link](https://arxiv.org/abs/2010.08886)

3. Website: [link](https://facebookresearch.github.io/pplbench/)

See the [CONTRIBUTING.md](CONTRIBUTING.md) file for how to help out.

## License

This source code is licensed under the MIT license found in the

LICENSE file in the root directory of this source tree.