Declare4Py: a Python package for declarative Process Mining and Machine LearningΒΆ

Declare4Py is the first Python package for declarative Process Mining with core functionalities to easily implement Machine Learning applications for Process Mining. Declarative process mining uses declarative behavioural rules (based on Linear Temporal Logic) for defining process models. This results in a high flexibility of the business process model definition without neglecting hard constraints that must be satisfied. Moreover, declarative languages can be used as a bridge between Process Mining and Machine learning with the DECLARE encoding that encodes the traces in a log into a numeric format suitable as input to Machine Learning algorithms. Declare4Py implements such a bridge by including standard algorithms for

  1. declarative Process Mining (e.g., conformance checking, model discovery, trace generation, query checking);

  2. log encodings (e.g., complex-index, aggregate, Declare);

  3. log labelling according to filtering or declarative rules.

All the Declare4Py data formats are compatible with the main Machine Learning Python packages: scikit-learn, Tensorflow and PyTorch.