pm4py.algo.conformance.dcr package#

Subpackages#

Submodules#

pm4py.algo.conformance.dcr.algorithm module#

class pm4py.algo.conformance.dcr.algorithm.Variants(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

CLASSIC = <module 'pm4py.algo.conformance.dcr.variants.classic' from '/home/vco/Projects/pm4py-dcr4py/pm4py/algo/conformance/dcr/variants/classic.py'>#

pm4py.algo.conformance.dcr.algorithm.apply(log: DataFrame | EventLog, G, variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]][source]#

Applies rule based conformance checking against a DCR graph and an event log.

Parameters#

log

Event log / Pandas dataframe

G

DCR Graph

variant

Variant to be used: - Variants.CLASSIC

parameters

Variant-specific parameters

Returns#

conf_res

List containing dictionaries with the following keys and values: - no_constr_total: the total number of constraints of the DCR Graphs - deviations: the 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

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

Gets the diagnostics dataframe from a log and the conformance results

Parameters#

log

Event log

conf_result

Results of conformance checking

variant

Variant to be used: - Variants.CLASSIC

parameters

Variant-specific parameters

Returns#

diagn_dataframe

Diagnostics dataframe

Module contents#