Source code for pm4py.algo.enhancement.sna.variants.pandas.working_together

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from pm4py.util import xes_constants as xes
from pm4py.util import exec_utils
from pm4py.algo.enhancement.sna.parameters import Parameters
from pm4py.util import variants_util

[docs]def apply(log, parameters=None): """ Calculates the Working Together metric Parameters ------------ log Log parameters Possible parameters of the algorithm Returns ----------- tuple Tuple containing the metric matrix and the resources list. Moreover, last boolean indicates that the metric is not directed. """ if parameters is None: parameters = {} import numpy from pm4py.statistics.traces.generic.pandas import case_statistics resource_key = exec_utils.get_param_value(Parameters.RESOURCE_KEY, parameters, xes.DEFAULT_RESOURCE_KEY) parameters_variants = {case_statistics.Parameters.ACTIVITY_KEY: resource_key, case_statistics.Parameters.ATTRIBUTE_KEY: resource_key} variants_occ = {x["variant"]: x["case:concept:name"] for x in case_statistics.get_variant_statistics(log, parameters=parameters_variants)} variants_resources = list(variants_occ.keys()) resources = [variants_util.get_activities_from_variant(y) for y in variants_resources] flat_list = sorted(list(set([item for sublist in resources for item in sublist]))) metric_matrix = numpy.zeros((len(flat_list), len(flat_list))) for idx, rv in enumerate(resources): rvj = variants_resources[idx] ord_res_list = sorted(list(set(rv))) for i in range(len(ord_res_list) - 1): res_i = flat_list.index(ord_res_list[i]) for j in range(i + 1, len(ord_res_list)): res_j = flat_list.index(ord_res_list[j]) metric_matrix[res_i, res_j] += float(variants_occ[rvj]) / float(len(log)) metric_matrix[res_j, res_i] += float(variants_occ[rvj]) / float(len(log)) return [metric_matrix, flat_list, False]