pm4py.discovery.discover_transition_system(log: Union[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”

  • 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 activity

  • timestamp_key (str) – attribute to be used for the timestamp

  • case_id_key (str) – attribute to be used as case identifier

Return type:


import pm4py

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