pm4py.algo.conformance.log_skeleton.algorithm module#

class pm4py.algo.conformance.log_skeleton.algorithm.Variants(*values)[source]#

Bases: Enum

CLASSIC = <module 'pm4py.algo.conformance.log_skeleton.variants.classic' from '/Users/chris/Desktop/PIS/pm4py2/pm4py/pm4py/algo/conformance/log_skeleton/variants/classic.py'>#
pm4py.algo.conformance.log_skeleton.algorithm.apply(obj: EventLog | Trace | DataFrame, model: Dict[str, Any], variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) → List[Set[Any]][source]#

Apply log-skeleton based conformance checking given an event log/trace and a log-skeleton model

Parameters:

  • obj – Object (event log/trace)

  • model – Log-skeleton model

  • variant – Variant of the algorithm, possible values: Variants.CLASSIC

  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.CONSIDERED_CONSTRAINTS, among: equivalence, always_after, always_before, never_together, directly_follows, activ_freq

Returns:

Conformance checking results for each trace: - Outputs.IS_FIT => boolean that tells if the trace is perfectly fit according to the model - Outputs.DEV_FITNESS => deviation based fitness (between 0 and 1; the more the trace is near to 1 the more fit is) - Outputs.DEVIATIONS => list of deviations in the model

Return type:

aligned_traces

pm4py.algo.conformance.log_skeleton.algorithm.apply_from_variants_list(var_list: List[List[str]], model: Dict[str, Any], variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) → List[Set[Any]][source]#

Performs conformance checking using the log skeleton, applying it from a list of variants

Parameters:

  • var_list – List of variants

  • model – Log skeleton model

  • variant – Variant of the algorithm, possible values: Variants.CLASSIC

  • parameters – Parameters

Returns:

Dictionary containing, for each variant, the result of log skeleton checking

Return type:

conformance_dictio

pm4py.algo.conformance.log_skeleton.algorithm.get_diagnostics_dataframe(log: EventLog, conf_result: List[Set[Any]], variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) → DataFrame[source]#

Gets the diagnostics dataframe from a log and the results of log skeleton-based conformance checking

Parameters:

  • log – Event log

  • conf_result – Results of conformance checking

Returns:

Diagnostics dataframe

Return type:

diagn_dataframe