Source code for pm4py.algo.label_splitting.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 typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from enum import Enum
from pm4py.util import exec_utils
from pm4py.algo.label_splitting.variants import contextual




class Variants(Enum):
    CONTEXTUAL = contextual





def apply(
    log: Union[EventLog, EventStream, pd.DataFrame],
    variant=Variants.CONTEXTUAL,
    parameters: Optional[Dict[Any, Any]] = None,
) -> pd.DataFrame:
    """
    Applies a technique of label-splitting, to distinguish between different meanings of the same
    activity. The result is a Pandas dataframe where the label-splitting has been applied.

    Minimum Viable Example:

        import pm4py
        from pm4py.algo.label_splitting import algorithm as label_splitter

        log = pm4py.read_xes("tests/input_data/receipt.xes")
        log2 = label_splitter.apply(log)


    Parameters
    ---------------
    log
        Event log
    parameters
        Variant-specific parameters

    Returns
    ---------------
    dataframe
        Pandas dataframe with the re-labeling
    """
    return exec_utils.get_variant(variant).apply(log, parameters)