pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc module

class pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.Parameters(*values)

Bases: Enum

ATTRIBUTE_KEY = 'pm4py:param:attribute_key'

ACTIVITY_KEY = 'pm4py:param:activity_key'

SINGLE = 'single'

BINARIZE = 'binarize'

POSITIVE = 'positive'

LOWER_PERCENT = 'lower_percent'

pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.occu_suc(dfg, filter_percent)

Parameters:

• dfg – a counter containing all the direct succession relationship with frequency

• filter_percent – clarify the percentage of direct succession one wants to preserve

Returns:

dataframe of direct succession relationship with frequency

pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.occu_var_suc(var_list, parameters=None)

return dataframe that shows the frequency of each element(direct succession) in each variant list :param var_list: :param parameters: binarize states if user wants to binarize the frequency, default is binarized :return:

pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.suc_sim(var_list_1, var_list_2, log1, log2, freq_thres, num, parameters=None)

this function compare the activity similarity between two sublogs via the two lists of variants. :param var_list_1: lists of variants in sublog 1 :param var_list_2: lists of variants in sublog 2 :param freq_thres: same as sublog2df() :param log1: input sublog1 of sublog2df(), which must correspond to var_list_1 :param log2: input sublog2 of sublog2df(), which must correspond to var_list_2 :return: the distance matrix between 2 sublogs in which each element is the distance between two variants.

pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.suc_sim_dual(var_list_1, var_list_2, log1, log2, freq_thres, num, parameters=None)

this function compare the activity similarity between two sublogs via the two lists of variants. :param var_list_1: lists of variants in sublog 1 :param var_list_2: lists of variants in sublog 2 :param freq_thres: same as sublog2df() :param log1: input sublog1 of sublog2df(), which must correspond to var_list_1 :param log2: input sublog2 of sublog2df(), which must correspond to var_list_2 :return: the distance matrix between 2 sublogs in which each element is the distance between two variants.

pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.suc_sim_percent(log1, log2, percent_1, percent_2)

this function compare the activity similarity between two sublogs via the two lists of variants. :param var_list_1: lists of variants in sublog 1 :param var_list_2: lists of variants in sublog 2 :param freq_thres: same as sublog2df() :param log1: input sublog1 of sublog2df(), which must correspond to var_list_1 :param log2: input sublog2 of sublog2df(), which must correspond to var_list_2 :return: the distance matrix between 2 sublogs in which each element is the distance between two variants.

pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.suc_sim_percent_avg(log1, log2, percent_1, percent_2)

this function compare the activity similarity between two sublogs via the two lists of variants. :param var_list_1: lists of variants in sublog 1 :param var_list_2: lists of variants in sublog 2 :param freq_thres: same as sublog2df() :param log1: input sublog1 of sublog2df(), which must correspond to var_list_1 :param log2: input sublog2 of sublog2df(), which must correspond to var_list_2 :return: the distance matrix between 2 sublogs in which each element is the distance between two variants.