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

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class pm4py.algo.conformance.footprints.variants.log_model.Outputs(*values)[source]#

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)[source]#

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][source]#

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][source]#

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[source]#

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