pm4py.evaluation.generalization.variants package¶
Submodules¶
pm4py.evaluation.generalization.variants.token_based module¶

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
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
petri_net – Petri net
aligned_traces – Result of the tokenreplay
 Returns
Generalization measure
 Return type
generalization