pm4py.discovery.discover_petri_net_heuristics#
- pm4py.discovery.discover_petri_net_heuristics(log: EventLog | DataFrame, dependency_threshold: float = 0.5, and_threshold: float = 0.65, loop_two_threshold: float = 0.5, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Tuple[PetriNet, Marking, Marking][source]#
Discover a Petri net using the Heuristics Miner
Heuristics Miner is an algorithm that acts on the Directly-Follows Graph, providing way to handle with noise and to find common constructs (dependency between two activities, AND). The output of the Heuristics Miner is an Heuristics Net, so an object that contains the activities and the relationships between them. The Heuristics Net can be then converted into a Petri net. The paper can be visited by clicking on the upcoming link: this link).
- Parameters:
log – event log / Pandas dataframe
dependency_threshold (
float) – dependency threshold (default: 0.5)and_threshold (
float) – AND threshold (default: 0.65)loop_two_threshold (
float) – loop two threshold (default: 0.5)activity_key (
str) – attribute to be used for the activitytimestamp_key (
str) – attribute to be used for the timestampcase_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_heuristics(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')