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

class pm4py.algo.discovery.ilp.variants.classic.Parameters(*values)[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