Source code for pm4py.algo.conformance.footprints.algorithm

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or 
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from enum import Enum
from pm4py.algo.conformance.footprints.variants import (
    log_model,
    log_extensive,
    trace_extensive,
)
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union, List


[docs] class Variants(Enum): LOG_MODEL = log_model LOG_EXTENSIVE = log_extensive TRACE_EXTENSIVE = trace_extensive
[docs] def apply( log_footprints: Union[Dict[str, Any], List[Dict[str, Any]]], model_footprints: Dict[str, Any], variant=Variants.LOG_MODEL, parameters: Optional[Dict[Any, Any]] = None, ) -> Union[List[Dict[str, Any]], Dict[str, Any]]: """ Apply footprints conformance between a log footprints object and a model footprints object Parameters ----------------- log_footprints Footprints of the log model_footprints Footprints of the model parameters Parameters of the algorithm, including: - Parameters.STRICT => strict check of the footprints Returns ------------------ violations Set/dictionary of all the violations between the log footprints and the model footprints, OR list of case-per-case violations """ if parameters is None: parameters = {} return exec_utils.get_variant(variant).apply( log_footprints, model_footprints, parameters=parameters )