Metadata-Version: 2.1
Name: SynBPS
Version: 1.0.0
Summary: Synthetic Business Process Simulation
Author-email: Mike Riess <mike@riess.no>
Project-URL: Homepage, https://github.com/pypa/sampleproject
Project-URL: Issues, https://github.com/pypa/sampleproject/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pomegranate ==0.14.8
Requires-Dist: numpy ==1.26.2
Requires-Dist: pandas ==2.1.4
Requires-Dist: scikit-learn >=0.22.2

# SynBPS
[![Downloads](https://static.pepy.tech/badge/synbps)](https://pepy.tech/project/synbps) [![Documentation Status](https://readthedocs.org/projects/synbps/badge/?version=latest)](https://synbps.readthedocs.io/en/latest/?badge=latest)

SynBPS is short for Synthetic Business Process Simulation, as it is intended for the simulation of **synthetic** (i.e. *multiple*, *hypothetical*) business processes from parametric distributions.

The intended usage of this software is to benchmark models within predictive process monitoring research. It is not intended for the simulation of real-world business processes, but rather as an addition to existing benchmark data, such as the BPI Challenge datasets. 

The benefit of SynBPS is the full transparency of the data generating process, which can help further understand the effects of different process characteristics on predictive performance. 

![header image](https://github.com/Mikeriess/SynBPS/blob/main/docs/illustration.png)

# Getting Started
You can install SynBPS using pip:

    pip install SynBPS

SynBPS requires pomegranate 0.14.8 and python 3.9 or higher.

## Example usage
See the [example notebook](https://github.com/Mikeriess/SynBPS/blob/main/tests/test_pypi.ipynb) for a short demo of SynBPS.

## Documentation
See the [official documentation here](https://synbps.readthedocs.io/en/latest/).

# Todos
- Extend HOMC to include h > 4

## Citation
If you use SynBPS, please cite the corresponding paper. The paper can be cited as:

```
	@inbook{Riess2023Framework,
	author = {Riess, Mike},
	title = {A Parametric Simulation Framework for the Generation of Event-Log Data},
	booktitle = {Essays on Predictive and Prescriptive Process Monitoring},
	publisher = {Norwegian University of Life Sciences},
	year = {2023},
	pages = {75-98},
	}
```

## Contributing
If you would like to contribute to SynBPS, you are welcome to submit your suggestions, bug reports, or pull requests. Follow the guidelines below to ensure smooth collaboration:

- Before submitting a new feature request or bug report, please check the existing issues to avoid duplicates.
- If you have a new feature idea, open an issue to discuss it with the maintainers and get feedback.
- For bug reports, provide a clear and concise description of the issue, including steps to reproduce it.
- If your contribution requires documentation changes, please update the documentation accordingly.
- Be respectful and considerate towards others in your interactions on the project.

