Source code for pm4py.algo.organizational_mining.sna.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.organizational_mining.sna.variants.log import (
    working_together as log_workingtogether,
    handover as log_handover,
    jointactivities as log_jointactivities,
    subcontracting as log_subcontracting,
)
from pm4py.algo.organizational_mining.sna.variants.pandas import (
    jointactivities as pd_jointactivities,
    handover as pd_handover,
    subcontracting as pd_subcontracting,
    working_together as pd_workingtogether,
)
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.util import exec_utils
import numpy as np

from enum import Enum
from pm4py.util import constants

from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
import pandas as pd
from pm4py.objects.org.sna.obj import SNA




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY
    METRIC_NORMALIZATION = "metric_normalization"





class Variants(Enum):
    HANDOVER_LOG = log_handover
    WORKING_TOGETHER_LOG = log_workingtogether
    SUBCONTRACTING_LOG = log_subcontracting
    JOINTACTIVITIES_LOG = log_jointactivities
    HANDOVER_PANDAS = pd_handover
    WORKING_TOGETHER_PANDAS = pd_workingtogether
    SUBCONTRACTING_PANDAS = pd_subcontracting
    JOINTACTIVITIES_PANDAS = pd_jointactivities





def apply(
    log: Union[EventLog, pd.DataFrame],
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
    variant=Variants.HANDOVER_LOG,
) -> SNA:
    """
    Calculates a SNA metric

    Parameters
    ------------
    log
        Log
    parameters
        Possible parameters of the algorithm
    variant
        Variant of the algorithm to apply. Possible values:
            - Variants.HANDOVER_LOG
            - Variants.WORKING_TOGETHER_LOG
            - Variants.SUBCONTRACTING_LOG
            - Variants.JOINTACTIVITIES_LOG
            - Variants.HANDOVER_PANDAS
            - Variants.WORKING_TOGETHER_PANDAS
            - Variants.SUBCONTRACTING_PANDAS
            - Variants.JOINTACTIVITIES_PANDAS

    Returns
    -----------
    tuple
        Tuple containing the metric matrix and the resources list
    """
    if parameters is None:
        parameters = {}

    enable_metric_normalization = exec_utils.get_param_value(
        Parameters.METRIC_NORMALIZATION, parameters, False
    )

    if variant in [
        Variants.HANDOVER_LOG,
        Variants.WORKING_TOGETHER_LOG,
        Variants.JOINTACTIVITIES_LOG,
        Variants.SUBCONTRACTING_LOG,
    ]:
        log = log_conversion.apply(
            log,
            variant=log_conversion.Variants.TO_EVENT_LOG,
            parameters=parameters,
        )
    sna = exec_utils.get_variant(variant).apply(log, parameters=parameters)
    abs_max = np.max(np.abs(list(sna.connections.values())))
    if enable_metric_normalization and abs_max > 0:
        for key in sna.connections:
            sna.connections[key] = sna.connections[key] / abs_max
    return sna