pm4py.algo.conformance.footprints.variants.log_model module

class pm4py.algo.conformance.footprints.variants.log_model.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'

class pm4py.algo.conformance.footprints.variants.log_model.Parameters(*values)

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'

STRICT = 'strict'

pm4py.algo.conformance.footprints.variants.log_model.apply_single(log_footprints: Dict[str, Any], model_footprints: Dict[str, Any], parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Any]

Apply footprints conformance between a log footprints object and a model footprints object

Parameters:

• log_footprints – Footprints of the log (NOT a list, but a single footprints object)

• model_footprints – Footprints of the model

• parameters –

  Parameters of the algorithm, including:

  • Parameters.STRICT => strict check of the footprints

Returns:

Set of all the violations between the log footprints and the model footprints

Return type:

violations

pm4py.algo.conformance.footprints.variants.log_model.apply(log_footprints: Dict[str, Any] | List[Dict[str, Any]], model_footprints: Dict[str, Any], parameters: Dict[str | Parameters, Any] | None = None) → List[Dict[str, Any]] | Dict[str, Any]

Apply footprints conformance between a log footprints object and a model footprints object

Parameters:

• log_footprints – Footprints of the log

• model_footprints – Footprints of the model

• parameters –

  Parameters of the algorithm, including:

  • Parameters.STRICT => strict check of the footprints

Returns:

Set of all the violations between the log footprints and the model footprints, OR list of case-per-case violations

Return type:

violations

pm4py.algo.conformance.footprints.variants.log_model.get_diagnostics_dataframe(log: EventLog, conf_result: List[Dict[str, Any]] | Dict[str, Any], parameters: Dict[str | Parameters, Any] | None = None) → DataFrame

Gets the diagnostics dataframe from the log and the results of footprints conformance checking (trace-by-trace)

Parameters:

• log – Event log

• conf_result – Conformance checking results (trace-by-trace)

Returns:

Diagnostics dataframe

Return type:

diagn_dataframe