pm4py.algo.discovery.inductive.cuts.sequence module#
PM4Py – A Process Mining Library for Python
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- class pm4py.algo.discovery.inductive.cuts.sequence.SequenceCut[source]#
Bases:
Cut[T],ABC,Generic[T]- classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) List[Collection[Any]] | None[source]#
This method finds a sequence cut in the dfg. Implementation follows function sequence on page 188 of “Robust Process Mining with Guarantees” by Sander J.J. Leemans (ISBN: 978-90-386-4257-4)
Basic Steps: 1. create a group per activity 2. merge pairwise reachable nodes (based on transitive relations) 3. merge pairwise unreachable nodes (based on transitive relations) 4. sort the groups based on their reachability
- class pm4py.algo.discovery.inductive.cuts.sequence.StrictSequenceCut[source]#
Bases:
SequenceCut[T],ABC,Generic[T]- classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) List[Collection[Any]] | None[source]#
This method implements the strict sequence cut as defined on page 233 of “Robust Process Mining with Guarantees” by Sander J.J. Leemans (ISBN: 978-90-386-4257-4) The function merges groups that together can be skipped.
- class pm4py.algo.discovery.inductive.cuts.sequence.SequenceCutUVCL[source]#
Bases:
SequenceCut[IMDataStructureUVCL]- classmethod project(obj: IMDataStructureUVCL, groups: List[Collection[Any]], parameters: Dict[str, Any] | None = None) List[IMDataStructureUVCL][source]#
Projection of the given data object (Generic type T). Returns a corresponding process tree and the projected sub logs according to the identified groups. A precondition of the project function is that it holds on the object for the given Object
- class pm4py.algo.discovery.inductive.cuts.sequence.StrictSequenceCutUVCL[source]#
Bases:
StrictSequenceCut[IMDataStructureUVCL],SequenceCutUVCL- classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) List[Collection[Any]] | None[source]#
This method implements the strict sequence cut as defined on page 233 of “Robust Process Mining with Guarantees” by Sander J.J. Leemans (ISBN: 978-90-386-4257-4) The function merges groups that together can be skipped.
- class pm4py.algo.discovery.inductive.cuts.sequence.SequenceCutDFG[source]#
Bases:
SequenceCut[IMDataStructureDFG]- classmethod project(obj: IMDataStructureDFG, groups: List[Collection[Any]], parameters: Dict[str, Any] | None = None) List[IMDataStructureDFG][source]#
Projection of the given data object (Generic type T). Returns a corresponding process tree and the projected sub logs according to the identified groups. A precondition of the project function is that it holds on the object for the given Object
- class pm4py.algo.discovery.inductive.cuts.sequence.StrictSequenceCutDFG[source]#
Bases:
StrictSequenceCut[IMDataStructureDFG],SequenceCutDFG- classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) List[Collection[Any]] | None[source]#
This method implements the strict sequence cut as defined on page 233 of “Robust Process Mining with Guarantees” by Sander J.J. Leemans (ISBN: 978-90-386-4257-4) The function merges groups that together can be skipped.
