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
)