pm4py.algo.discovery.ilp.variants package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.algo.discovery.ilp.variants.classic module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.discovery.ilp.variants.classic.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

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: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = 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