pm4py.algo.concept_drift.algorithm module#
- class pm4py.algo.concept_drift.algorithm.Variants(*values)[source]#
Bases:
Enum- BOSE = <module 'pm4py.algo.concept_drift.variants.bose' from '/Users/chris/Desktop/PIS/pm4py2/pm4py/pm4py/algo/concept_drift/variants/bose.py'>#
- pm4py.algo.concept_drift.algorithm.apply(log: EventLog | DataFrame, variant=Variants.BOSE, parameters: Dict[Any, Any] | None = None) Tuple[List[DataFrame], List[int], List[float]][source]#
- Parameters:
log – Event log or Pandas dataframe
variant – Variant of the algorithm (available: Variants.BOSE)
parameters – Variant-specific parameters
- Returns:
returned_sublogs (List[EventLog]) – A list of sub-logs, where each sub-log is an EventLog object representing the cumulative segment of the original event log from the start up to each detected change point (and the final sub-log up to the end). Note: Due to a potential implementation issue, these sub-logs are not segments between change points but rather cumulative logs up to each change point.
change_timestamps (List[float]) – A list of timestamps where concept drifts are detected. Each timestamp corresponds to the start time of the first trace in the sub-log where a change point occurs, based on case start timestamps.
p_values (List[float]) – A list of p-values associated with each detected change point, indicating the statistical significance of the drift (lower values suggest stronger evidence of a change).
