pm4py.algo.transformation.log_to_target.algorithm module#

class pm4py.algo.transformation.log_to_target.algorithm.Variants(*values)[source]#

Bases: Enum

NEXT_ACTIVITY = <module 'pm4py.algo.transformation.log_to_target.variants.next_activity' from '/Users/chris/Desktop/PIS/pm4py2/pm4py/pm4py/algo/transformation/log_to_target/variants/next_activity.py'>#
NEXT_TIME = <module 'pm4py.algo.transformation.log_to_target.variants.next_time' from '/Users/chris/Desktop/PIS/pm4py2/pm4py/pm4py/algo/transformation/log_to_target/variants/next_time.py'>#
REMAINING_TIME = <module 'pm4py.algo.transformation.log_to_target.variants.remaining_time' from '/Users/chris/Desktop/PIS/pm4py2/pm4py/pm4py/algo/transformation/log_to_target/variants/remaining_time.py'>#
pm4py.algo.transformation.log_to_target.algorithm.apply(log: EventLog | EventStream | DataFrame, variant=None, parameters: Dict[Any, Any] | None = None) → Tuple[Any, List[str]][source]#

Extracts from the event log the target vector for a specific ML use case

Parameters:

  • log – Event log / Event stream / Pandas dataframe

  • variant – Specification of the target vector: - Variants.NEXT_ACTIVITY => encodes the next activity - Variants.NEXT_TIME => encodes the next timestamp - Variants.REMAINING_TIME => encodes the remaining time

Returns:

  • vector – Target vector for the specified ML use case

  • classes – Classes (for every column of the target vector)