pm4py.algo.conformance.alignments.decomposed.variants package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
Submodules#
pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
- class pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- BEST_WORST_COST = 'best_worst_cost'#
- PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#
- ICACHE = 'icache'#
- MCACHE = 'mcache'#
- PARAM_THRESHOLD_BORDER_AGREEMENT = 'thresh_border_agreement'#
- PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#
- PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#
- PARAM_SYNC_COST_FUNCTION = 'sync_cost_function'#
- PARAM_TRACE_NET_COSTS = 'trace_net_costs'#
- PARAM_MAX_ALIGN_TIME = 'max_align_time'#
- PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#
- SHOW_PROGRESS_BAR = 'show_progress_bar'#
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.get_best_worst_cost(petri_net, initial_marking, final_marking, parameters=None)[source]#
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.apply_from_variants_list_petri_string(var_list, petri_net_string, parameters=None)[source]#
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.apply_from_variants_list(var_list, petri_net, initial_marking, final_marking, parameters=None)[source]#
Apply the alignments from the specification of a list of variants in the log
Parameters#
- var_list
List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
- petri_net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- parameters
Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)
Returns#
- dictio_alignments
Dictionary that assigns to each variant its alignment
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.apply(log: EventLog, net: PetriNet, im: Marking, fm: Marking, parameters: Dict[str | Parameters, Any] | None = None) List[Dict[str, Any]][source]#
Apply the recomposition alignment approach to a log and a Petri net performing decomposition
Parameters#
- log
Event log
- net
Petri net
- im
Initial marking
- fm
Final marking
- parameters
Parameters of the algorithm
Returns#
- aligned_traces
For each trace, return its alignment
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.apply_log(log, list_nets, parameters=None)[source]#
Apply the recomposition alignment approach to a log and a decomposed Petri net
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.get_acache(cons_nets)[source]#
Calculates the A-Cache of the given decomposition
Parameters#
- cons_nets
List of considered nets
Returns#
- acache
A-Cache
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.get_alres(al)[source]#
Gets a description of the alignment for the border agreement
Parameters#
- al
Alignment
Returns#
- alres
Description of the alignment
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.order_nodes_second_round(to_visit, G0)[source]#
Orders the second round of nodes to visit to reconstruct the alignment
Optimized version with improved algorithm
- pm4py.algo.conformance.alignments.decomposed.variants.recompos_maximal.recompose_alignment(cons_nets, cons_nets_result)[source]#
Alignment recomposition
Optimized version with more efficient graph operations