pm4py.algo.conformance.declare.algorithm module#

PM4Py – A Process Mining Library for Python

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

Bases: Enum

CLASSIC = <module 'pm4py.algo.conformance.declare.variants.classic' from '/home/berti/pm4py/pm4py/algo/conformance/declare/variants/classic.py'>#
pm4py.algo.conformance.declare.algorithm.apply(log: EventLog | DataFrame, model: Dict[str, Dict[Any, Dict[str, int]]], variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]][source]#

Applies conformance checking against a DECLARE model.

Parameters:

  • log – Event log / Pandas dataframe

  • model – DECLARE model

  • variant – Variant to be used: - Variants.CLASSIC

  • parameters – Variant-specific parameters

Returns:

List containing for every case a dictionary with different keys: - no_constr_total => the total number of constraints of the DECLARE model - deviations => a list of deviations - no_dev_total => the total number of deviations - dev_fitness => the fitness (1 - no_dev_total / no_constr_total) - is_fit => True if the case is perfectly fit

Return type:

lst_conf_res

pm4py.algo.conformance.declare.algorithm.get_diagnostics_dataframe(log, conf_result, variant=Variants.CLASSIC, parameters=None) → DataFrame[source]#

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

Parameters:

  • log – Event log

  • conf_result – Results of conformance checking

  • variant – Variant to be used: - Variants.CLASSIC

  • parameters – Variant-specific parameters

Returns:

Diagnostics dataframe

Return type:

diagn_dataframe