pm4py.algo.discovery.inductive.cuts.xor module#

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class pm4py.algo.discovery.inductive.cuts.xor.ExclusiveChoiceCut[source]#

Bases: Cut[T], ABC, Generic[T]

classmethod operator(parameters: Dict[str, Any] | None = None) → ProcessTree[source]#
classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) → List[Collection[Any]] | None[source]#

This method finds a xor cut in the dfg. Implementation follows function XorCut on page 188 of “Robust Process Mining with Guarantees” by Sander J.J. Leemans (ISBN: 978-90-386-4257-4)

Basic Steps: 1.) the dfg is transformed to its undirected equivalent. 2.) we detect the connected components in the graph. 3.) if there are more than one connected components, the cut exists and is non-minimal.

class pm4py.algo.discovery.inductive.cuts.xor.ExclusiveChoiceCutUVCL[source]#

Bases: ExclusiveChoiceCut[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.xor.ExclusiveChoiceCutDFG[source]#

Bases: ExclusiveChoiceCut[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