pm4py.algo.conformance.temporal_profile.variants package

Submodules

pm4py.algo.conformance.temporal_profile.variants.dataframe module

class pm4py.algo.conformance.temporal_profile.variants.dataframe.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
CASE_ID_KEY = 'case_id_glue'
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
ZETA = 'zeta'
pm4py.algo.conformance.temporal_profile.variants.dataframe.apply(df: pandas.core.frame.DataFrame, temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Optional[Dict[Any, Any]] = None) → List[List[Tuple[float, float, float, float]]][source]

Checks the conformance of the dataframe 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
  • df – Pandas dataframe

  • 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.CASE_ID_KEY => column to use as case identifier

Returns

A list containing, for each case, 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.variants.log module

class pm4py.algo.conformance.temporal_profile.variants.log.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
BUSINESS_HOURS = 'business_hours'
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
WEEKENDS = 'weekends'
WORKTIMING = 'worktiming'
ZETA = 'zeta'
pm4py.algo.conformance.temporal_profile.variants.log.apply(log: pm4py.objects.log.obj.EventLog, temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Optional[Dict[Any, Any]] = 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
  • 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.WORKTIMING => work schedule of the company (provided as a list where the first number is the start
      of the work time, and the second number is the end of the work time), if business hours are enabled

      Default: [7, 17] (work shift from 07:00 to 17:00)

    • Parameters.WEEKENDS => indexes of the days of the week that are weekend

      Default: [6, 7] (weekends are Saturday and Sunday)

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

Module contents