# pm4py.algo.evaluation.replay_fitness package

## pm4py.algo.evaluation.replay_fitness.algorithm module

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.evaluation.replay_fitness.algorithm.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ALIGN_VARIANT = 'align_variant'
class pm4py.algo.evaluation.replay_fitness.algorithm.Variants(value)[source]

Bases: enum.Enum

An enumeration.

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'>
pm4py.algo.evaluation.replay_fitness.algorithm.apply(log: Union[pm4py.objects.log.obj.EventLog, pandas.core.frame.DataFrame], petri_net: pm4py.objects.petri_net.obj.PetriNet, initial_marking: pm4py.objects.petri_net.obj.Marking, final_marking: pm4py.objects.petri_net.obj.Marking, parameters: Optional[Dict[Union[str, pm4py.algo.evaluation.replay_fitness.algorithm.Parameters], Any]] = None, 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

Returns

Fitness evaluation

Return type

fitness_eval

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 evaluation

Return type

fitness_eval

## pm4py.algo.evaluation.replay_fitness.evaluator module

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.evaluation.replay_fitness.evaluator.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ALIGN_VARIANT = 'align_variant'
class pm4py.algo.evaluation.replay_fitness.evaluator.Variants(value)[source]

Bases: enum.Enum

An enumeration.

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'>
pm4py.algo.evaluation.replay_fitness.evaluator.apply(log, petri_net, initial_marking, final_marking, parameters=None, variant=None)[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

Returns

Fitness evaluation

Return type

fitness_eval

Deprecated since version 2.2.5: This will be removed in 3.0.0. please use pm4py.algo.evaluation.replay_fitness.algorithm instead

pm4py.algo.evaluation.replay_fitness.evaluator.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 evaluation

Return type

fitness_eval

Deprecated since version 2.2.5: This will be removed in 3.0.0. please use pm4py.algo.evaluation.replay_fitness.algorithm instead

## pm4py.algo.evaluation.replay_fitness.parameters module

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.evaluation.replay_fitness.parameters.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'
CLEANING_TOKEN_FLOOD = 'cleaning_token_flood'
MULTIPROCESSING = 'multiprocessing'
TOKEN_REPLAY_VARIANT = 'token_replay_variant'