pm4py.discovery.discover_eventually_follows_graph

pm4py.discovery.discover_eventually_follows_graph(log: EventLog | DataFrame, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') → Dict[Tuple[str, str], int]

Gets the eventually follows graph from a log object.

The eventually follows graph is a dictionary associating to every couple of activities which are eventually following each other the number of occurrences of this relation.

Parameters:

• 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[Tuple[str, str], int]

import pm4py

efg = pm4py.discover_eventually_follows_graph(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')