pm4py.algo.conformance.temporal_profile.algorithm module#

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class pm4py.algo.conformance.temporal_profile.algorithm.Parameters(*values)[source]#

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'#
pm4py.algo.conformance.temporal_profile.algorithm.apply(elog: EventLog | DataFrame, temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Dict[Any, Any] | None = None) → List[List[Tuple[float, float, float, float]]][source]#

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:

  • elog – 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

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

pm4py.algo.conformance.temporal_profile.algorithm.get_diagnostics_dataframe(elog: EventLog | DataFrame, conf_result: List[List[Tuple[float, float, float, float]]], parameters: Dict[Any, Any] | None = None) → DataFrame[source]#

Gets the diagnostics dataframe from a log and the results of temporal profle-based conformance checking

Parameters:

  • log – Event log

  • conf_result – Results of conformance checking

Returns:

Diagnostics dataframe

Return type:

diagn_dataframe