pm4py.evaluation.generalization.variants package¶

Submodules¶

pm4py.evaluation.generalization.variants.token_based module¶

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

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pm4py.evaluation.generalization.variants.token_based.apply(log, petri_net, initial_marking, final_marking, parameters=None)[source]¶

Calculates generalization on the provided log and Petri net.

The approach has been suggested by the paper Buijs, Joos CAM, Boudewijn F. van Dongen, and Wil MP van der Aalst. “Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity.” International Journal of Cooperative Information Systems 23.01 (2014): 1440001.

A token replay is applied and, for each transition, we can measure the number of occurrences in the replay. The following formula is applied for generalization

sum_{t in transitions} (math.sqrt(1.0/(n_occ_replay(t)))

1 - ———————————————————-: # transitions

Parameters:	log – Trace log petri_net – Petri net initial_marking – Initial marking final_marking – Final marking parameters – Algorithm parameters
Returns:	Generalization measure
Return type:	generalization

pm4py.evaluation.generalization.variants.token_based.get_generalization(petri_net, aligned_traces)[source]¶

Gets the generalization from the Petri net and the list of activated transitions during the replay