Source code for pm4py.streaming.algo.conformance.temporal.algorithm

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from enum import Enum
from typing import Optional, Dict, Any

from pm4py.streaming.algo.conformance.temporal.variants import classic
from pm4py.util import exec_utils
from pm4py.util import typing


[docs] class Variants(Enum): CLASSIC = classic
[docs] def apply( temporal_profile: typing.TemporalProfile, variant=Variants.CLASSIC, parameters: Optional[Dict[Any, Any]] = None, ): """ Initialize the streaming conformance checking Parameters --------------- temporal_profile Temporal profile variant Variant of the algorithm, possible values: - Variants.CLASSIC 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 - Parameters.DICT_VARIANT => the variant of dictionary to use - Parameters.CASE_DICT_ID => the identifier of the case dictionary - Parameters.DEV_DICT_ID => the identifier of the deviations dictionary """ return exec_utils.get_variant(variant).apply( temporal_profile, parameters=parameters )