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]#

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

Return type:

List[Dict[str, Any]]

import pm4py

log = pm4py.read_xes("C:/receipt.xes")
declare_model = pm4py.discover_declare(log)
conf_result = pm4py.conformance_declare(log, declare_model)