pm4py.algo.evaluation.replay_fitness package#
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
Subpackages#
Submodules#
pm4py.algo.evaluation.replay_fitness.algorithm module#
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
- class pm4py.algo.evaluation.replay_fitness.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ALIGNMENT_BASED = <module 'pm4py.algo.evaluation.replay_fitness.variants.alignment_based' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\evaluation\\replay_fitness\\variants\\alignment_based.py'>#
- TOKEN_BASED = <module 'pm4py.algo.evaluation.replay_fitness.variants.token_replay' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\evaluation\\replay_fitness\\variants\\token_replay.py'>#
- class pm4py.algo.evaluation.replay_fitness.algorithm.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ALIGN_VARIANT = 'align_variant'#
- pm4py.algo.evaluation.replay_fitness.algorithm.apply(log: EventLog | DataFrame, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Dict[str | Parameters, Any] | None = None, variant=None, align_variant=None) Dict[str, Any][source]#
Apply fitness evaluation starting from an event log and a marked Petri net, by using one of the replay techniques provided by PM4Py
Parameters#
- log
Trace log object
- petri_net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- parameters
Parameters related to the replay algorithm
- variant
- Chosen variant:
Variants.ALIGNMENT_BASED
Variants.TOKEN_BASED
- align_variant
Alignments variant (for alignment-based replay)
Returns#
- fitness_eval
Fitness evaluation
- pm4py.algo.evaluation.replay_fitness.algorithm.evaluate(results, parameters=None, variant=Variants.TOKEN_BASED)[source]#
Evaluate replay results when the replay algorithm has already been applied
Parameters#
- results
Results of the replay algorithm
- parameters
Possible parameters passed to the evaluation
- variant
Indicates which evaluator is called
Returns#
- fitness_eval
Fitness evaluation