pm4py.algo.clustering.trace_attribute_driven.variants package

Submodules

pm4py.algo.clustering.trace_attribute_driven.variants.act_dist_calc module

class pm4py.algo.clustering.trace_attribute_driven.variants.act_dist_calc.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'
BINARIZE = 'binarize'
LOWER_PERCENT = 'lower_percent'
POSITIVE = 'positive'
SINGLE = 'single'
pm4py.algo.clustering.trace_attribute_driven.variants.act_dist_calc.act_sim(var_list_1, var_list_2, log1, log2, freq_thres, num, parameters=None)[source]

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.act_dist_calc.act_sim_dual(var_list_1, var_list_2, log1, log2, freq_thres, num, parameters=None)[source]

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.act_dist_calc.act_sim_med(var_list_1, var_list_2, log1, log2, freq_thres, num, parameters=None)[source]

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.act_dist_calc.act_sim_percent(log1, log2, percent_1, percent_2)[source]

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.act_dist_calc.act_sim_percent_avg(log1, log2, percent_1, percent_2)[source]

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.act_dist_calc.act_sim_percent_avg_actset(log1, log2, percent_1, percent_2, actset)[source]

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.act_dist_calc.occu_var_act(var_list)[source]

return dataframe that shows the frequency of each element(activity) in each variant list :param var_list: :return:

pm4py.algo.clustering.trace_attribute_driven.variants.logslice_dist module

pm4py.algo.clustering.trace_attribute_driven.variants.logslice_dist.log2sublog(log, str)[source]
pm4py.algo.clustering.trace_attribute_driven.variants.logslice_dist.slice_dist_act(log_1, log_2, unit, parameters=None)[source]
pm4py.algo.clustering.trace_attribute_driven.variants.logslice_dist.slice_dist_suc(log_1, log_2, unit)[source]

pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc module

class pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'
BINARIZE = 'binarize'
LOWER_PERCENT = 'lower_percent'
POSITIVE = 'positive'
SINGLE = 'single'
pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc.dist_calc(var_list_1, var_list_2, log1, log2, freq_thres, num, alpha, parameters=None)[source]

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 :param alpha: the weight parameter between activity similarity and succession similarity, which belongs to (0,1) :param parameters: state which linkage method to use :return: the similarity value between two sublogs

pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc.inner_prod_calc(df)[source]

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

class pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'
BINARIZE = 'binarize'
LOWER_PERCENT = 'lower_percent'
POSITIVE = 'positive'
SINGLE = 'single'
pm4py.algo.clustering.trace_attribute_driven.variants.suc_dist_calc.occu_suc(dfg, filter_percent)[source]
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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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.

Module contents