pm4py.discovery.derive_minimum_self_distance(log: Union[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.

  • log – event log / Pandas dataframe

  • 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:

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')