Source code for pm4py.algo.organizational_mining.sna.algorithm

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py 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 General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
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
from enum import Enum
import numpy as np

from enum import Enum
from pm4py.util import constants

from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd


[docs]class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY METRIC_NORMALIZATION = "metric_normalization"
[docs]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
[docs]def apply(log: Union[EventLog, pd.DataFrame], parameters: Optional[Dict[Union[str, Parameters], Any]] = None, variant=Variants.HANDOVER_LOG) -> List[Any]: """ 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(sna[0])) if enable_metric_normalization and abs_max > 0: sna[0] = sna[0] / abs_max return sna