pm4py.algo.conformance.alignments.process_tree.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

Subpackages

• pm4py.algo.conformance.alignments.process_tree.variants.approximated package
  • Submodules
  • pm4py.algo.conformance.alignments.process_tree.variants.approximated.calculate_a_sa_ea_sets module
    • initialize_a_sa_ea_tau_sets()
  • pm4py.algo.conformance.alignments.process_tree.variants.approximated.matrix_lp module
    • Parameters
      • Parameters.MAX_TRACE_LENGTH
      • Parameters.MAX_PROCESS_TREE_HEIGHT
      • Parameters.PARAMETER_VARIANT_DELIMITER
      • Parameters.ACTIVITY_KEY
      • Parameters.PARAM_MAX_ALIGN_TIME_TRACE
      • Parameters.PARAM_MAX_ALIGN_TIME
      • Parameters.SUBTREE_ALIGN_CACHE
    • apply_from_variants_tree_string()
    • apply_from_variants_list()
    • apply()
  • pm4py.algo.conformance.alignments.process_tree.variants.approximated.original module
    • Parameters
      • Parameters.MAX_TRACE_LENGTH
      • Parameters.MAX_PROCESS_TREE_HEIGHT
      • Parameters.PARAMETER_VARIANT_DELIMITER
      • Parameters.ACTIVITY_KEY
      • Parameters.PARAM_MAX_ALIGN_TIME_TRACE
      • Parameters.PARAM_MAX_ALIGN_TIME
    • apply_from_variants_tree_string()
    • apply_from_variants_list()
    • apply()
  • pm4py.algo.conformance.alignments.process_tree.variants.approximated.utilities module
    • Parameters
      • Parameters.CLASSIC_ALIGNMENTS_VARIANT
      • Parameters.CONVERSION_VERSION
    • AlignmentNoneException
    • EfficientTree
    • concatenate_traces()
    • trace_to_list_of_str()
    • list_of_str_to_trace()
    • get_activity_labels_from_trace()
    • apply_standard_cost_function_to_alignment()
    • empty_sequence_accepted()
    • calculate_optimal_alignment()
    • add_fitness_and_cost_info_to_alignments()

Submodules

pm4py.algo.conformance.alignments.process_tree.variants.dynamic_programming 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

pm4py.algo.conformance.alignments.process_tree.variants.dynamic_programming.clear_global_caches()

Clear all global caches to free memory

class pm4py.algo.conformance.alignments.process_tree.variants.dynamic_programming.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'

SHOW_PROGRESS_BAR = 'show_progress_bar'

MAX_TOTAL_TIME = 'max_total_time'

USE_GLOBAL_CACHE = 'use_global_cache'

BATCH_SIZE = 'batch_size'

CLEAR_GLOBAL_CACHE = 'clear_global_cache'

pm4py.algo.conformance.alignments.process_tree.variants.dynamic_programming.align_trace_with_process_tree(trace, tree, use_global_cache=False)

Aligns a trace with a process tree using dynamic programming. Returns both the cost and the actual alignment.

Version based on: A Dynamic Programming Approach for Alignments on Process Trees http://processquerying.com/wp-content/uploads/2024/09/PQMI_2024_279_A_Dynamic_Programming_Approach_for_Alignments_on_Process_Trees.pdf

Enhanced with global caching and memory optimizations.

pm4py.algo.conformance.alignments.process_tree.variants.dynamic_programming.apply_list_tuple_activities(list_tuple_activities: List[Collection[str]], process_tree: ProcessTree, parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]]

Apply alignment to a list of trace activity tuples with memory optimization

pm4py.algo.conformance.alignments.process_tree.variants.dynamic_programming.apply(log: DataFrame | EventLog, process_tree: ProcessTree, parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]]

Optimized version that aligns an event log against a process tree model. Based on the approach described in: Schwanen, Christopher T., Wied Pakusa, and Wil MP van der Aalst. “Process tree alignments.” Enterprise Design, Operations, and Computing, ser. LNCS, Cham: Springer International Publishing (2024).

Parameters

log

Event log or Pandas dataframe

process_tree

Process tree

parameters

Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY => the attribute to be used as activity - Parameters.SHOW_PROGRESS_BAR => shows the progress bar - Parameters.MAX_TOTAL_TIME => maximum total time in seconds - Parameters.USE_GLOBAL_CACHE => use global caching to reuse results (default: False) - Parameters.BATCH_SIZE => size of batches for processing (default: 1000) - Parameters.CLEAR_GLOBAL_CACHE => clear global cache before processing (default: True)

Returns

aligned_traces

List that contains the alignment for each trace

pm4py.algo.conformance.alignments.process_tree.variants.milp 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.process_tree.variants.milp.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'

SHOW_PROGRESS_BAR = 'show_progress_bar'

class pm4py.algo.conformance.alignments.process_tree.variants.milp.ProcessTreeAligner(tree: ProcessTree)

Bases: object

Implementation of the approach described in:

Schwanen, Christopher T., Wied Pakusa, and Wil MP van der Aalst. “Process tree alignments.” Enterprise Design, Operations, and Computing, ser. LNCS, Cham: Springer International Publishing (2024).

align(trace: Tuple[str]) → Tuple[float, List[Tuple[str, str]]]

Align a trace with the process tree. Using tuple for trace to enable caching.

Args:

trace: A tuple of activity labels (converted from list for caching)

Returns:

A tuple containing (alignment cost, alignment moves)

pm4py.algo.conformance.alignments.process_tree.variants.milp.apply_list_tuple_activities(list_tuple_activities: List[Collection[str]], aligner: ProcessTreeAligner, parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]]

Apply the alignment algorithm to a list of activities. Optimized to use caching and more efficient data structures.

pm4py.algo.conformance.alignments.process_tree.variants.milp.apply(log: DataFrame | EventLog, process_tree: ProcessTree, parameters: Dict[Any, Any] | None = None) → List[Dict[str, Any]]

Aligns an event log against a process tree model, using the approach described in: Schwanen, Christopher T., Wied Pakusa, and Wil MP van der Aalst. “Process tree alignments.” Enterprise Design, Operations, and Computing, ser. LNCS, Cham: Springer International Publishing (2024).

Parameters

log

Event log or Pandas dataframe

process_tree

Process tree

parameters

Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY => the attribute to be used as activity - Parameters.SHOW_PROGRESS_BAR => shows the progress bar

Returns

aligned_traces

List that contains the alignment for each trace

pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt 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.process_tree.variants.search_graph_pt.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

CORES = 'cores'

ACTIVITY_KEY = 'pm4py:param:activity_key'

SHOW_PROGRESS_BAR = 'show_progress_bar'

CASE_ID_KEY = 'pm4py:param:case_id_key'

class pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt.SGASearchState(costs: float, index: int, state: Dict[Tuple[int, ProcessTree], OperatorState], leaves: List[ProcessTree] | None = None, parent: Any = None, children: List[Any] = None)

Bases: object

pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt.align_variant(variant, tree_leaf_set, pt)

pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt.apply_variant(variant, tree, parameters=None)

pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt.apply_from_variants_list(var_list, tree, parameters=None)

pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt.apply_multiprocessing(obj: EventLog | Trace | DataFrame, pt: ProcessTree, parameters: Dict[Any, Any] | None = None) → Dict[str, Any] | List[Dict[str, Any]]

pm4py.algo.conformance.alignments.process_tree.variants.search_graph_pt.apply(obj: EventLog | Trace | DataFrame, pt: ProcessTree, parameters: Dict[Any, Any] | None = None) → Dict[str, Any] | List[Dict[str, Any]]