pm4py.algo.clustering.trace_attribute_driven.variants package
Submodules
pm4py.algo.clustering.trace_attribute_driven.variants.act_dist_calc module
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
- 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.logslice_dist module
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc module
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
- 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.suc_dist_calc module
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
- 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
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.