- pm4py.conformance.fitness_token_based_replay(log: Union[EventLog, DataFrame], petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Dict[str, float] #
Calculates the fitness using token-based replay. The fitness is calculated on a log-based level.
Token-based replay matches a trace and a Petri net model, starting from the initial place, in order to discover which transitions are executed and in which places we have remaining or missing tokens for the given process instance. Token-based replay is useful for Conformance Checking: indeed, a trace is fitting according to the model if, during its execution, the transitions can be fired without the need to insert any missing token. If the reaching of the final marking is imposed, then a trace is fitting if it reaches the final marking without any missing or remaining tokens.
In PM4Py there is an implementation of a token replayer that is able to go across hidden transitions (calculating shortest paths between places) and can be used with any Petri net model with unique visible transitions and hidden transitions. When a visible transition needs to be fired and not all places in the preset are provided with the correct number of tokens, starting from the current marking it is checked if for some place there is a sequence of hidden transitions that could be fired in order to enable the visible transition. The hidden transitions are then fired and a marking that permits to enable the visible transition is reached. The approach is described in: Berti, Alessandro, and Wil MP van der Aalst. “Reviving Token-based Replay: Increasing Speed While Improving Diagnostics.” ATAED@ Petri Nets/ACSD. 2019.
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 token-based replay, the percentage of traces that are completely fit is returned, along with a fitness value that is calculated as indicated in the scientific contribution
log – event log
PetriNet) – petri net
Marking) – initial marking
Marking) – final marking
str) – attribute to be used for the activity
str) – attribute to be used for the timestamp
str) – attribute to be used as case identifier
- Return type:
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_tbr = pm4py.fitness_token_based_replay(dataframe, net, im, fm, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')