pm4py.algo.conformance.temporal_profile.variants.log module

class pm4py.algo.conformance.temporal_profile.variants.log.Parameters(*values)

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'

ZETA = 'zeta'

BUSINESS_HOURS = 'business_hours'

BUSINESS_HOUR_SLOTS = 'business_hour_slots'

WORKCALENDAR = 'workcalendar'

pm4py.algo.conformance.temporal_profile.variants.log.apply(log: EventLog, temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Dict[Any, Any] | None = None) → List[List[Tuple[float, float, float, float]]]

Checks the conformance of the log using the provided temporal profile.

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).

Parameters:

• log – Event log

• temporal_profile – Temporal profile

• parameters –

  Parameters of the algorithm, including:

  • Parameters.ACTIVITY_KEY => the attribute to use as activity

  • Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp

  • Parameters.TIMESTAMP_KEY => the attribute to use as timestamp

  • Parameters.ZETA => multiplier for the standard deviation

  • Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time.

    Default: False

  • Parameters.BUSINESS_HOURS_SLOTS =>

  work schedule of the company, provided as a list of tuples where each tuple represents one time slot of business hours. One slot i.e. one tuple consists of one start and one end time given in seconds since week start, e.g. [

  (7 * 60 * 60, 17 * 60 * 60), ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60), ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),

  ] meaning that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00

Returns:

A list containing, for each trace, all the deviations. Each deviation is a tuple with four elements: - 1) The source activity of the recorded deviation - 2) The target activity of the recorded deviation - 3) The time passed between the occurrence of the source activity and the target activity - 4) The value of (time passed - mean)/std for this occurrence (zeta).

Return type:

list_dev