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

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

Bases: Enum

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

Transforms the alignment result to a simple dictionary including the percentage of fit traces and the average fitness

Parameters:

  • aligned_traces – Alignments calculated for the traces in the log

  • parameters – Possible parameters of the evaluation

Returns:

Containing two keys (percFitTraces and averageFitness)

Return type:

dictionary

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

Evaluate fitness based on alignments

Parameters:

  • log – Event log

  • petri_net – Petri net

  • initial_marking – Initial marking

  • final_marking – Final marking

  • align_variant – Variants of the alignments to apply

  • parameters – Parameters of the algorithm

Returns:

Containing two keys (percFitTraces and averageFitness)

Return type:

dictionary

pm4py.algo.evaluation.replay_fitness.variants.alignment_based.apply_trace(trace: Trace, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, best_worst: Any, activity_key: str) → Dict[str, Any][source]#

Performs the basic alignment search, given a trace, a net and the costs of the “best of the worst”. The costs of the best of the worst allows us to deduce the fitness of the trace. We compute the fitness by means of 1 - alignment costs / best of worst costs (i.e. costs of 0 => fitness 1)

Parameters:

  • trace (list input trace, assumed to be a list of events (i.e. the code will use the activity key to)

  • get the attributes)

  • petri_net (pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment)

  • initial_marking (pm4py.objects.petri.net.Marking initial marking in the Petri net)

  • final_marking (pm4py.objects.petri.net.Marking final marking in the Petri net)

  • best_worst (cost of the best worst alignment of a trace (empty trace aligned to the model))

  • activity_key (str (optional) key to use to identify the activity described by the events)

Returns:

dictionary

Return type:

dict with keys alignment, cost, visited_states, queued_states and traversed_arcs