Source code for pm4py.algo.discovery.genetic.algorithm

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2026 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 pm4py.util import exec_utils
from pm4py.algo.discovery.genetic.variants import classic
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from pm4py.util import constants
from typing import Union, Optional, Dict, Any, Tuple




[docs]
class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
    POPULATION_SIZE = "population_size"
    ELITISM_RATE = "elitism_rate"
    CROSSOVER_RATE = "crossover_rate"
    MUTATION_RATE = "mutation_rate"
    GENERATIONS = "generations"
    ELITISM_MIN_SAMPLE = "elitism_min_sample"
    LOG_CSV = "log_csv"





[docs]
class Variants(Enum):
    CLASSIC = classic






[docs]
def apply(
    log: Union[EventLog, EventStream, pd.DataFrame],
    variant=Variants.CLASSIC,
    parameters: Optional[Dict[Any, Any]] = None,
) -> Tuple[PetriNet, Marking, Marking]:
    """
    Discovers a Petri net using the genetic miner.

    Parameters
    ---------------
    log
        Event log / Event stream / Pandas dataframe
    variant
        Variant of the algorithm to be used, possible values:
        - Variants.CLASSIC
    parameters
        Variant-specific parameters

    Returns
    ---------------
    net
        Petri net
    im
        Initial marking
    fm
        Final marking
    """
    return exec_utils.get_variant(variant).apply(log, parameters)