pm4py.discovery.derive_minimum_self_distance#
- pm4py.discovery.derive_minimum_self_distance(log: DataFrame | EventLog | EventStream, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Dict[str, int][source]#
This algorithm computes the minimum self-distance for each activity observed in an event log. The self distance of a in <a> is infinity, of a in <a,a> is 0, in <a,b,a> is 1, etc. The activity key ‘concept:name’ is used.
- Parameters:
log – event log / Pandas dataframe
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:
Dict[str, int]
import pm4py msd = pm4py.derive_minimum_self_distance(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')