To help you get started, we've collected the most common ways that pm4py is being used within popular public projects.

nirdizati-research / predict-python / src / split / splitting.py View on Github

type=SplitTypes.SPLIT_DOUBLE.value,
        original_log=split.original_log,
        test_size=split.test_size,
        splitting_method=split.splitting_method
    ).exists() or split.splitting_method == SplitOrderingMethods.SPLIT_RANDOM.value):
        training_log, test_log = _split_single_log(split)
        additional_columns = get_additional_columns(get_log(split.original_log))

        if split.splitting_method != SplitOrderingMethods.SPLIT_RANDOM.value:
            _ = Split.objects.get_or_create(
                type=SplitTypes.SPLIT_DOUBLE.value,
                original_log=split.original_log,
                test_size=split.test_size,
                splitting_method=split.splitting_method,
                train_log=create_log(EventLog(training_log), '0-' + str(100 - int(split.test_size * 100)) + '.xes'),
                test_log=create_log(EventLog(test_log), str(100 - int(split.test_size * 100)) + '-100.xes'),
                additional_columns=split.additional_columns
            )[0]

        logger.info("\t\tLoaded single log from {}".format(split.original_log.path))
    else:
        # Have to use sklearn to convert some internal data types
        training_log = get_log(split.train_log)
        additional_columns = get_additional_columns(training_log)
        if split.additional_columns is None:
            split.additional_columns = split.train_log.name + split.test_log.name + '_ac.xes'
            split.save()
        training_log, train_log_to_append = train_test_split(training_log, test_size=0, shuffle=False)
        test_log, test_log_to_append = train_test_split(get_log(split.test_log), test_size=0, shuffle=False)
        logger.info("\t\tLoaded double logs from {} and {}.".format(split.train_log.path, split.test_log.path))
    if len(training_log) == 0:
        raise TypeError("Training log is empty. Create a new Split with better parameters")

View more ways to use pm4py

pm4py

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.

Documentation / API

The full documentation of pm4py can be found at https://pm4py.fit.fraunhofer.de

First Example

A very simple example, to whet your appetite:

import pm4py

if name == "main": log = pm4py.read_xes('') net, initial_marking, final_marking = pm4py.discover_petri_net_inductive(log) pm4py.view_petri_net(net, initial_marking, final_marking, format="svg")

Installation

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

Requirements

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

Release Notes

To track the incremental updates, please refer to the CHANGELOG file.

Third Party Dependencies

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.

Citing pm4py

If you are using pm4py in your scientific work, please cite pm4py as follows:

Alessandro Berti, Sebastiaan van Zelst, Daniel Schuster. (2023). PM4Py: A process mining library for Python. Software Impacts, 17, 100556. DOI | Article Link

BiBTeX:

@article{pm4py,
title = {PM4Py: A process mining library for Python},
journal = {Software Impacts},
volume = {17},
pages = {100556},
year = {2023},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2023.100556},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823000933},
author = {Alessandro Berti and Sebastiaan van Zelst and Daniel Schuster},
}