pm4py.algo.discovery.ilp.variants package#

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/>.

Submodules#

pm4py.algo.discovery.ilp.variants.classic 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/>.

class pm4py.algo.discovery.ilp.variants.classic.Parameters(value)[source]#

Bases: Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'#
PARAM_ARTIFICIAL_START_ACTIVITY = 'pm4py:param:art_start_act'#
PARAM_ARTIFICIAL_END_ACTIVITY = 'pm4py:param:art_end_act'#
CAUSAL_RELATION = 'causal_relation'#
SHOW_PROGRESS_BAR = 'show_progress_bar'#
ALPHA = 'alpha'#
pm4py.algo.discovery.ilp.variants.classic.apply(log0: Union[EventLog, EventStream, DataFrame], parameters: Optional[Dict[Any, Any]] = None) Tuple[PetriNet, Marking, Marking][source]#

Discovers a Petri net using the ILP miner.

The implementation follows what is described in the scientific paper: van Zelst, Sebastiaan J., et al. “Discovering workflow nets using integer linear programming.” Computing 100.5 (2018): 529-556.

Parameters#

log0

Event log / Event stream / Pandas dataframe

parameters

Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY => the attribute to be used as activity - Parameters.SHOW_PROGRESS_BAR => decides if the progress bar should be shown

Returns#

net

Petri net

im

Initial marking

fm

Final marking