pm4py.algo.evaluation.replay_fitness package

Submodules

pm4py.algo.evaluation.replay_fitness.algorithm module

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\\FRAUNHOFER\\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\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\evaluation\\replay_fitness\\variants\\token_replay.py'>
pm4py.algo.evaluation.replay_fitness.algorithm.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

pm4py.algo.evaluation.replay_fitness.algorithm.evaluate(results, parameters=None, variant=<Variants.TOKEN_BASED: <module 'pm4py.algo.evaluation.replay_fitness.variants.token_replay' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\evaluation\\replay_fitness\\variants\\token_replay.py'>>)[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

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\\FRAUNHOFER\\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\\FRAUNHOFER\\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: <module 'pm4py.algo.evaluation.replay_fitness.variants.token_replay' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\evaluation\\replay_fitness\\variants\\token_replay.py'>>)[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

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'

Module contents