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.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

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 token-replay

Returns

Generalization measure

Return type

generalization

Module contents

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.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.