Tested and working with python 3.11.0
Create a python virtual environment and:
pip install -r requirements.txt
Check out and run the jupyter notebook from notebooks/pm4py_dcr_example.ipynb
The running example is on the sepsis event log.
pm4py is a python library that supports (state-of-the-art) process mining algorithms in python. It is open source (licensed under GPL) and intended to be used in both academia and industry projects. pm4py is a product of the Fraunhofer Institute for Applied Information Technology.
The full documentation of pm4py can be found at https://pm4py.fit.fraunhofer.de
A very simple example, to whet your appetite:
import pm4py
if __name__ == "__main__":
log = pm4py.read_xes('<path-to-xes-log-file.xes>')
net, initial_marking, final_marking = pm4py.discover_petri_net_inductive(log)
pm4py.view_petri_net(net, initial_marking, final_marking, format="svg")pm4py can be installed on Python 3.9.x / 3.10.x / 3.11.x / 3.12.x by invoking: pip install -U pm4py
pm4py is also running on older Python environments with different requirements sets, including:
- Python 3.8 (3.8.10): third_party/old_python_deps/requirements_py38.txt
pm4py depends on some other Python packages, with different levels of importance:
- Essential requirements: numpy, pandas, deprecation, networkx
- Normal requirements (installed by default with the pm4py package, important for mainstream usage): graphviz, intervaltree, lxml, matplotlib, pydotplus, pytz, scipy, stringdist, tqdm
- Optional requirements (not installed by default): scikit-learn, pyemd, pyvis, jsonschema, polars, openai, pywin32, python-dateutil, requests, workalendar
To track the incremental updates, please refer to the CHANGELOG file.
As scientific library in the Python ecosystem, we rely on external libraries to offer our features. In the /third_party folder, we list all the licenses of our direct dependencies. Please check the /third_party/LICENSES_TRANSITIVE file to get a full list of all transitive dependencies and the corresponding license.
If you are using pm4py in your scientific work, please cite pm4py as follows:
Berti, A., van Zelst, S.J., van der Aalst, W.M.P. (2019): Process Mining for Python (PM4Py): Bridging the Gap Between Process-and Data Science. In: Proceedings of the ICPM Demo Track 2019, co-located with 1st International Conference on Process Mining (ICPM 2019), Aachen, Germany, June 24-26, 2019. pp. 13-16 (2019). http://ceur-ws.org/Vol-2374/