Source code for pm4py.algo.organizational_mining.roles.variants.pandas

'''
    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.organizational_mining.roles.common import algorithm
from pm4py.util import xes_constants as xes
from collections import Counter
from pm4py.util import exec_utils

from enum import Enum
from pm4py.util import constants
from typing import Optional, Dict, Any, Union, List
import pandas as pd
from pm4py.objects.org.roles.obj import Role




class Parameters(Enum):
    ROLES_THRESHOLD_PARAMETER = "roles_threshold_parameter"
    RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY





def apply(
    df: pd.DataFrame,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> List[Role]:
    """
    Gets the roles (group of different activities done by similar resources)
    out of the log

    Parameters
    -------------
    df
        Pandas dataframe
    parameters
        Possible parameters of the algorithm

    Returns
    ------------
    roles
        List of different roles inside the log
    """
    if parameters is None:
        parameters = {}

    resource_key = exec_utils.get_param_value(
        Parameters.RESOURCE_KEY, parameters, xes.DEFAULT_RESOURCE_KEY
    )
    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY
    )
    activity_resource_couples = Counter(
        df.groupby([resource_key, activity_key]).size().to_dict()
    )

    return algorithm.apply(activity_resource_couples, parameters=parameters)