pm4py.conformance.precision_token_based_replay#

pm4py.conformance.precision_token_based_replay(log: 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') → float[source]#

Calculates the precision precision using token-based replay

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 reference paper for the TBR-based precision (ETConformance) is: Muñoz-Gama, Jorge, and Josep Carmona. “A fresh look at precision in process conformance.” International Conference on Business Process Management. Springer, Berlin, Heidelberg, 2010.

In this approach, the different prefixes of the log are replayed (whether possible) on the model. At the reached marking, the set of transitions that are enabled in the process model is compared with the set of activities that follow the prefix. The more the sets are different, the more the precision value is low. The more the sets are similar, the more the precision value is high.

Parameters:

  • log – event log

  • petri_net (PetriNet) – petri net

  • initial_marking (Marking) – initial marking

  • final_marking (Marking) – final marking

  • 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

Return type:

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