pm4py.discovery.discover_transition_system#
- pm4py.discovery.discover_transition_system(log: EventLog | DataFrame, direction: str = 'forward', window: int = 2, view: str = 'sequence', activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') TransitionSystem[source]#
Discovers a transition system as described in the process mining book “Process Mining: Data Science in Action”
- Parameters:
log – event log / Pandas dataframe
direction (
str) – direction in which the transition system is built (forward, backward)window (
int) – window (2, 3, …)view (
str) – view to use in the construction of the states (sequence, set, multiset)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:
TransitionSystem
import pm4py transition_system = pm4py.discover_transition_system(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')