pm4py.conformance.conformance_dcr#

pm4py.conformance.conformance_dcr(log: EventLog | DataFrame, dcr_graph: DcrGraph, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name', group_key: str = 'org:group', resource_key: str = 'org:resource', return_diagnostics_dataframe: bool = False) → DataFrame | List[Tuple[str, Dict[str, Any]]][source]#

Applies rule based conformance checking against a DCR model. reference: C. Josep et al., “Conformance Checking Software”, Springer International Publishing, 65-74, 2018., https://doi.org/10.1007/978-3-319-99414-7.

Parameters:

• log – event log

• dcr_graph (DcrGraph) – DCR graph

• activity_key (str) – attribute to be used for the activity

• timestamp_key (str) – attribute to be used for the timestamp

• case_id_key (str) – attribute to be used as case identifier

• group_key (str) – attribute to be used as role identifier

• resource_key (str) – attribute to be used as resource identifier

• return_diagnostics_dataframe (bool) – if possible, returns a dataframe with the diagnostics (instead of the usual output)

Return type:

DataFrame | List[Tuple[str,Dict[str, Any]]]