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

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from pm4py.algo.enhancement.sna.variants.log import handover as log_handover, jointactivities as log_jointactivities, \
    subcontracting as log_subcontracting, working_together as log_workingtogether
from pm4py.algo.enhancement.sna.variants.pandas import handover as pd_handover, subcontracting as pd_subcontracting, \
    working_together as pd_workingtogether, jointactivities as pd_jointactivities
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.algo.enhancement.sna.parameters import Parameters
from pm4py.util import exec_utils
from enum import Enum
import numpy as np
import deprecation

[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]@deprecation.deprecated('2.2.5', '3.0.0', details='use pm4py.algo.organizational_mining.sna.algorithm instead') def apply(log, parameters=None, variant=Variants.HANDOVER_LOG): """ 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, 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