pm4py.conformance.fitness_alignments#

pm4py.conformance.fitness_alignments(log: EventLog | DataFrame, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, multi_processing: bool = False, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name', variant_str: str | None = None) → Dict[str, float][source]#

Calculates the fitness using alignments The output dictionary contains the following keys: - average_trace_fitness (between 0.0 and 1.0; computed as average of the trace fitnesses) - log_fitness (between 0.0 and 1.0) - percentage_of_fitting_traces (the percentage of fit traces (from 0.0 to 100.0)

Alignment-based replay aims to find one of the best alignment between the trace and the model. For each trace, the output of an alignment is a list of couples where the first element is an event (of the trace) or » and the second element is a transition (of the model) or ». For each couple, the following classification could be provided:

  • Sync move: the classification of the event corresponds to the transition label; in this case, both the trace and the model advance in the same way during the replay.

  • Move on log: for couples where the second element is », it corresponds to a replay move in the trace that is not mimicked in the model. This kind of move is unfit and signal a deviation between the trace and the model.

  • Move on model: for couples where the first element is », it corresponds to a replay move in the model that is not mimicked in the trace. For moves on model, we can have the following distinction:

    • Moves on model involving hidden transitions: in this case, even if it is not a sync move, the move is fit.

    • Moves on model not involving hidden transitions: in this case, the move is unfit and signals a deviation between the trace and the model.

The calculation of the replay fitness aim to calculate how much of the behavior in the log is admitted by the process model. We propose two methods to calculate replay fitness, based on token-based replay and alignments respectively.

For alignments, the percentage of traces that are completely fit is returned, along with a fitness value that is calculated as the average of the fitness values of the single traces.

Parameters:

  • log – event log

  • petri_net (PetriNet) – petri net

  • initial_marking (Marking) – initial marking

  • final_marking (Marking) – final marking

  • multi_processing (bool) – boolean value that enables the multiprocessing

  • activity_key (str) – attribute to be used for the activity

  • timestamp_key (str) – attribute to be used for the timestamp

  • case_id_key (str) – attribute to be used as case identifier

  • variant_str – variant specification

Return type:

Dict[str, float]

import pm4py

net, im, fm = pm4py.discover_petri_net_inductive(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')
fitness_alignments = pm4py.fitness_alignments(dataframe, net, im, fm, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')
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