pm4py.conformance.precision_alignments#

pm4py.conformance.precision_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') → float[source]#

Calculates the precision of the model w.r.t. the event log using alignments

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 reference paper for the alignments-based precision (Align-ETConformance) is: Adriansyah, Arya, et al. “Measuring precision of modeled behavior.” Information systems and e-Business Management 13.1 (2015): 37-67

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

  • 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

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_alignments = pm4py.precision_alignments(dataframe, net, im, fm, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')
On this page
Show Source