pm4py.discovery.discover_petri_net_inductive(log: Union[EventLog, DataFrame, DirectlyFollowsGraph], multi_processing: bool = False, noise_threshold: float = 0.0, 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 inductive miner algorithm.

The basic idea of Inductive Miner is about detecting a ‘cut’ in the log (e.g. sequential cut, parallel cut, concurrent cut and loop cut) and then recur on sublogs, which were found applying the cut, until a base case is found. The Directly-Follows variant avoids the recursion on the sublogs but uses the Directly Follows graph.

Inductive miner models usually make extensive use of hidden transitions, especially for skipping/looping on a portion on the model. Furthermore, each visible transition has a unique label (there are no transitions in the model that share the same label).

  • log – event log / Pandas dataframe / typed DFG

  • noise_threshold (float) – noise threshold (default: 0.0)

  • multi_processing (bool) – boolean that enables/disables multiprocessing in inductive miner

  • activity_key (str) – attribute to be used for the activity

  • timestamp_key (str) – attribute to be used for the timestamp

  • case_id_key (str) – attribute to be used as case identifier

Return type:

Tuple[PetriNet, Marking, Marking]

import pm4py

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