- pm4py.discovery.discover_bpmn_inductive(log: Union[EventLog, DataFrame, DirectlyFollowsGraph], noise_threshold: float = 0.0, multi_processing: bool = False, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') BPMN [source]#
Discovers a BPMN 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
float) – noise threshold (default: 0.0)
bool) – boolean that enables/disables multiprocessing in inductive miner
str) – attribute to be used for the activity
str) – attribute to be used for the timestamp
str) – attribute to be used as case identifier
- Return type:
import pm4py bpmn_graph = pm4py.discover_bpmn_inductive(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')