pm4py.discovery.discover_performance_dfg#
- pm4py.discovery.discover_performance_dfg(log: EventLog | DataFrame, business_hours: bool = False, business_hour_slots=[(25200, 61200), (111600, 147600), (198000, 234000), (284400, 320400), (370800, 406800)], workcalendar=None, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Tuple[dict, dict, dict][source]#
Discovers a performance directly-follows graph from an event log.
This method returns a dictionary with the couples of directly-following activities (in the log) as keys and the performance of relation as value.
- Parameters:
log – event log / Pandas dataframe
business_hours (
bool) – enables/disables the computation based on the business hours (default: False)business_hour_slots – work schedule of the company, provided as a list of tuples where each tuple represents one time slot of business hours. One slot i.e. one tuple consists of one start and one end time given in seconds since week start, e.g. [(7 * 60 * 60, 17 * 60 * 60), ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60), ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),] meaning that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00
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[dict, dict, dict]
import pm4py performance_dfg, start_activities, end_activities = pm4py.discover_performance_dfg(dataframe, case_id_key='case:concept:name', activity_key='concept:name', timestamp_key='time:timestamp')