Source code for pm4py.algo.evaluation.replay_fitness.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 pm4py.algo.evaluation.replay_fitness.variants import (
    alignment_based,
    token_replay,
)
from pm4py.algo.conformance import alignments
from pm4py.util import exec_utils
from pm4py.objects.petri_net.utils.check_soundness import (
    check_easy_soundness_net_in_fin_marking,
)
from enum import Enum
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
import pandas as pd




class Variants(Enum):
    ALIGNMENT_BASED = alignment_based
    TOKEN_BASED = token_replay





class Parameters(Enum):
    ALIGN_VARIANT = "align_variant"



ALIGNMENT_BASED = Variants.ALIGNMENT_BASED
TOKEN_BASED = Variants.TOKEN_BASED

VERSIONS = {ALIGNMENT_BASED, TOKEN_BASED}




def apply(
    log: Union[EventLog, pd.DataFrame],
    petri_net: PetriNet,
    initial_marking: Marking,
    final_marking: Marking,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
    variant=None,
    align_variant=None,
) -> Dict[str, Any]:
    """
    Apply fitness evaluation starting from an event log and a marked Petri net,
    by using one of the replay techniques provided by PM4Py

    Parameters
    -----------
    log
        Trace log object
    petri_net
        Petri net
    initial_marking
        Initial marking
    final_marking
        Final marking
    parameters
        Parameters related to the replay algorithm
    variant
        Chosen variant:
            - Variants.ALIGNMENT_BASED
            - Variants.TOKEN_BASED
    align_variant
        Alignments variant (for alignment-based replay)

    Returns
    ----------
    fitness_eval
        Fitness evaluation
    """
    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(
                petri_net, initial_marking, final_marking
            )
        ):
            # in the case the net is not a easy sound workflow net, we must
            # apply token-based replay
            variant = TOKEN_BASED
        else:
            # otherwise, use the align-etconformance approach (safer, in the
            # case the model contains duplicates)
            variant = ALIGNMENT_BASED

    if variant == TOKEN_BASED:
        # execute the token-based replay variant
        return exec_utils.get_variant(variant).apply(
            log,
            petri_net,
            initial_marking,
            final_marking,
            parameters=parameters,
        )
    else:
        # execute the alignments based variant, with the specification of the
        # alignments variant
        if align_variant is None:
            align_variant = alignments.petri_net.algorithm.DEFAULT_VARIANT
        return exec_utils.get_variant(variant).apply(
            log,
            petri_net,
            initial_marking,
            final_marking,
            align_variant=align_variant,
            parameters=parameters,
        )





def evaluate(results, parameters=None, variant=TOKEN_BASED):
    """
    Evaluate replay results when the replay algorithm has already been applied

    Parameters
    -----------
    results
        Results of the replay algorithm
    parameters
        Possible parameters passed to the evaluation
    variant
        Indicates which evaluator is called

    Returns
    -----------
    fitness_eval
        Fitness evaluation
    """
    return exec_utils.get_variant(variant).evaluate(
        results, parameters=parameters
    )