pm4py.discovery.discover_declare#

pm4py.discovery.discover_declare(log: EventLog | DataFrame, allowed_templates: Set[str] | None = None, considered_activities: Set[str] | None = None, min_support_ratio: float | None = None, min_confidence_ratio: float | None = None, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') → Dict[str, Dict[Any, Dict[str, int]]][source]#

Discovers a DECLARE model from an event log.

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 / Pandas dataframe

  • allowed_templates – (optional) collection of templates to consider for the discovery

  • considered_activities – (optional) collection of activities to consider for the discovery

  • min_support_ratio – (optional, decided automatically otherwise) minimum percentage of cases (over the entire set of cases of the log) for which the discovered rules apply

  • min_confidence_ratio – (optional, decided automatically otherwise) minimum percentage of cases (over the rule’s support) for which the discovered rules are valid

  • 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 type:

Dict[str, Any]

import pm4py

declare_model = pm4py.discover_declare(log)
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