pm4py.discovery.discover_petri_net_ilp#

pm4py.discovery.discover_petri_net_ilp(log: EventLog | DataFrame, alpha: float = 1.0, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') → Tuple[PetriNet, Marking, Marking][source]#

Discovers a Petri net using the ILP Miner.

Parameters:

log – Event log or Pandas DataFrame.
alpha (float) – Noise threshold for the sequence encoding graph (1.0=no filtering, 0.0=maximum filtering) (default: 1.0).
activity_key (str) – Attribute to be used for the activity (default: “concept:name”).
timestamp_key (str) – Attribute to be used for the timestamp (default: “time:timestamp”).
case_id_key (str) – Attribute to be used as case identifier (default: “case:concept:name”).

Returns:

A tuple containing the Petri net, initial marking, and final marking.

Return type:

Tuple[PetriNet, Marking, Marking]

import pm4py

net, im, fm = pm4py.discover_petri_net_ilp(
    dataframe,
    activity_key='concept:name',
    case_id_key='case:concept:name',
    timestamp_key='time:timestamp'
)