Source code for pm4py.algo.evaluation.precision.algorithm

from pm4py.algo.evaluation.precision.variants import etconformance_token
from pm4py.algo.evaluation.precision.variants import align_etconformance
from pm4py.objects.petri_net.utils.check_soundness import (
    check_easy_soundness_net_in_fin_marking,
)
from enum import Enum
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog, EventStream
from pm4py.objects.petri_net.obj import PetriNet, Marking
import pandas as pd




class Variants(Enum):
    ETCONFORMANCE_TOKEN = etconformance_token
    ALIGN_ETCONFORMANCE = align_etconformance



ETCONFORMANCE_TOKEN = Variants.ETCONFORMANCE_TOKEN
ALIGN_ETCONFORMANCE = Variants.ALIGN_ETCONFORMANCE

VERSIONS = {ETCONFORMANCE_TOKEN, ALIGN_ETCONFORMANCE}




def apply(
    log: Union[EventLog, EventStream, pd.DataFrame],
    net: PetriNet,
    marking: Marking,
    final_marking: Marking,
    parameters: Optional[Dict[Any, Any]] = None,
    variant=None,
) -> float:
    """
    Method to apply ET Conformance

    Parameters
    -----------
    log
        Trace log
    net
        Petri net
    marking
        Initial marking
    final_marking
        Final marking
    parameters
        Parameters of the algorithm, including:
            pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Activity key
    variant
        Variant of the algorithm that should be applied:
            - Variants.ETCONFORMANCE_TOKEN
            - Variants.ALIGN_ETCONFORMANCE
    """
    if parameters is None:
        parameters = {}

    # execute the following part of code when the variant is not specified by
    # the user
    if variant is None:
        if not (
            check_easy_soundness_net_in_fin_marking(
                net, marking, final_marking
            )
        ):
            # in the case the net is not a easy sound workflow net, we must
            # apply token-based replay
            variant = ETCONFORMANCE_TOKEN
        else:
            # otherwise, use the align-etconformance approach (safer, in the
            # case the model contains duplicates)
            variant = ALIGN_ETCONFORMANCE

    return exec_utils.get_variant(variant).apply(
        log, net, marking, final_marking, parameters=parameters
    )