pm4py.conformance.conformance_declare#
- pm4py.conformance.conformance_declare(log: EventLog | DataFrame, declare_model: Dict[str, Dict[Any, Dict[str, int]]], activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name', return_diagnostics_dataframe: bool = False) List[Dict[str, Any]] [source]#
Apply conformance checking against a DECLARE model.
Reference 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.
declare_model – DECLARE model represented as a nested dictionary.
activity_key (
str
) – Attribute to be used for the activity (default is “concept:name”).timestamp_key (
str
) – Attribute to be used for the timestamp (default is “time:timestamp”).case_id_key (
str
) – Attribute to be used as the case identifier (default is “case:concept:name”).return_diagnostics_dataframe (
bool
) – If possible, returns a dataframe with the diagnostics instead of the usual output (default is constants.DEFAULT_RETURN_DIAGNOSTICS_DATAFRAME).
- Returns:
A list of dictionaries containing diagnostics for each trace.
- Return type:
List[Dict[str, Any]]