pm4py.algo.evaluation.replay_fitness.variants.token_replay module#

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class pm4py.algo.evaluation.replay_fitness.variants.token_replay.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
TOKEN_REPLAY_VARIANT = 'token_replay_variant'#
CLEANING_TOKEN_FLOOD = 'cleaning_token_flood'#
MULTIPROCESSING = 'multiprocessing'#
SHOW_PROGRESS_BAR = 'show_progress_bar'#
pm4py.algo.evaluation.replay_fitness.variants.token_replay.evaluate(aligned_traces: List[Dict[str, Any]], parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, float][source]#

Gets a dictionary expressing fitness in a synthetic way from the list of boolean values saying if a trace in the log is fit, and the float values of fitness associated to each trace

Parameters:

  • aligned_traces – Result of the token-based replayer

  • parameters – Possible parameters of the evaluation

Returns:

Containing two keys (percFitTraces and averageFitness)

Return type:

dictionary

pm4py.algo.evaluation.replay_fitness.variants.token_replay.apply(log: EventLog, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, float][source]#

Apply token replay fitness evaluation

Parameters:

  • log – Trace log

  • petri_net – Petri net

  • initial_marking – Initial marking

  • final_marking – Final marking

  • parameters – Parameters

Returns:

Containing two keys (percFitTraces and averageFitness)

Return type:

dictionary