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

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from pm4py.util import xes_constants as xes
from enum import Enum
from pm4py.util import constants, exec_utils
from pm4py.util import variants_util

BETA = Parameters.BETA
[docs]def apply(log, parameters=None): """ Calculates the HW metric Parameters ------------ log Log parameters Possible parameters of the algorithm: Paramters.BETA -> beta value as described in the Wil SNA paper Returns ----------- tuple Tuple containing the metric matrix and the resources list. Moreover, last boolean indicates that the metric is 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) beta = exec_utils.get_param_value(Parameters.BETA, parameters, 0) 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))) sum_i_to_j = {} for idx, rv in enumerate(resources): rvj = variants_resources[idx] for i in range(len(rv) - 1): res_i = flat_list.index(rv[i]) if not res_i in sum_i_to_j: sum_i_to_j[res_i] = {} for j in range(i + 1, len(rv)): res_j = flat_list.index(rv[j]) if not res_j in sum_i_to_j[res_i]: sum_i_to_j[res_i][res_j] = 0 if beta == 0: sum_i_to_j[res_i][res_j] += variants_occ[rvj] break else: sum_i_to_j[res_i][res_j] += variants_occ[rvj] * (beta ** (j - i - 1)) dividend = 0 for idx, rv in enumerate(resources): rvj = variants_resources[idx] if beta == 0: dividend = dividend + variants_occ[rvj] * (len(rv) - 1) else: dividend = dividend + variants_occ[rvj] * (len(rv) - 1) for key1 in sum_i_to_j: for key2 in sum_i_to_j[key1]: metric_matrix[key1][key2] = sum_i_to_j[key1][key2] / dividend return [metric_matrix, flat_list, True]