pm4py.discovery.discover_temporal_profile#
- pm4py.discovery.discover_temporal_profile(log: EventLog | DataFrame, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Dict[Tuple[str, str], Tuple[float, float]][source]#
Discovers a temporal profile from a log object.
Implements the approach described in: Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. “Temporal Conformance Checking at Runtime based on Time-infused Process Models.” arXiv preprint arXiv:2008.07262 (2020).
The output is a dictionary containing, for every couple of activities eventually following in at least a case of the log, the average and the standard deviation of the difference of the timestamps.
E.g. if the log has two cases:
A (timestamp: 1980-01) B (timestamp: 1980-03) C (timestamp: 1980-06) A (timestamp: 1990-01) B (timestamp: 1990-02) D (timestamp: 1990-03)
The returned dictionary will contain: {(‘A’, ‘B’): (1.5 months, 0.5 months), (‘A’, ‘C’): (5 months, 0), (‘A’, ‘D’): (2 months, 0)}
- Parameters:
log – event log / Pandas dataframe
activity_key (
str) – attribute to be used for the activitytimestamp_key (
str) – attribute to be used for the timestampcase_id_key (
str) – attribute to be used as case identifier
- Return type:
Dict[Tuple[str, str], Tuple[float, float]]
import pm4py temporal_profile = pm4py.discover_temporal_profile(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')