pm4py.algo.conformance.footprints.util.evaluation module

class pm4py.algo.conformance.footprints.util.evaluation.Outputs(*values)

Bases: Enum

DFG = 'dfg'

SEQUENCE = 'sequence'

PARALLEL = 'parallel'

START_ACTIVITIES = 'start_activities'

END_ACTIVITIES = 'end_activities'

ACTIVITIES = 'activities'

SKIPPABLE = 'skippable'

ACTIVITIES_ALWAYS_HAPPENING = 'activities_always_happening'

MIN_TRACE_LENGTH = 'min_trace_length'

TRACE = 'trace'

pm4py.algo.conformance.footprints.util.evaluation.fp_fitness(fp_log, fp_model, conf_results, parameters=None)

Calculates the footprints fitness provided the footprints of the log, and the result of footprints conformance (applied to the entire log)

Parameters:

• fp_log – Footprints of the log

• fp_model – Footprints of the model

• conf_results – Footprints conformance (applied to the entire log)

• parameters – Parameters of the algorithm

Returns:

Fitness value (between 0.0 and 1.0)

Return type:

fitness

pm4py.algo.conformance.footprints.util.evaluation.fp_precision(fp_log, fp_model, parameters=None)

Calculates the footprints based precision provided the two footprints of the log and the model.

Parameters:

• fp_log – Footprints of the log

• fp_model – Footprints of the model

• parameters – Parameters of the algorithm

Returns:

Precision value (between 0 and 1)

Return type:

precision

pm4py.algo.conformance.footprints.util.evaluation.flatten_fp(fp: List[Dict[str, Any]]) → Dict[str, Any]

Flattens the trace-based footprints to the footprints of the overall log

Parameters:

fp – Trace-based footprints

Returns:

Overall log footprints

Return type:

log_fp

pm4py.algo.conformance.footprints.util.evaluation.flatten_conf(conf: List[Dict[str, Any]]) → Dict[str, Any]

Flattens the trace-based conformance checking results (obtained using footprints) to the conformance checking results on the overall log

Parameters:

conf – Trace-based conformance checking results

Returns:

Overall log conformance checking results

Return type:

log_conf