pm4py.algo.conformance.declare.variants.classic module#

class pm4py.algo.conformance.declare.variants.classic.Parameters(*values)[source]#

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
pm4py.algo.conformance.declare.variants.classic.apply_list(projected_log: List[List[str]], model: Dict[str, Dict[Any, Dict[str, int]]], parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]][source]#
pm4py.algo.conformance.declare.variants.classic.apply(log: EventLog | DataFrame, model: Dict[str, Dict[Any, Dict[str, int]]], parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]][source]#

Applies conformance checking against a DECLARE model.

Paper: F. M. Maggi, A. J. Mooij and W. M. P. van der Aalst, “User-guided discovery of declarative process models,” 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, France, 2011, pp. 192-199, doi: 10.1109/CIDM.2011.5949297.

Parameters:

  • log – Event log / Pandas dataframe

  • model – DECLARE model

  • parameters – Possible parameters of the algorithm, including: - Parameters.ACTIVITY_KEY => the attribute to be used as activity - Parameters.CASE_ID_KEY => the attribute to be used as case identifier

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.variants.classic.get_diagnostics_dataframe(log, conf_result, 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

Returns:

Diagnostics dataframe

Return type:

diagn_dataframe