pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut module#

pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.generate_initial_order(nodes, efg)[source]#
pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.remove(blocks, g)[source]#
pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.contains(blocks, g)[source]#
pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.is_valid_order(po, efg, start_activities, end_activities)[source]#
pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.cluster_order(binary_relation)[source]#
class pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.MaximalPartialOrderCut[source]#

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

classmethod operator(parameters: Dict[str, Any] | None = None) → StrictPartialOrder[source]#
classmethod holds(obj: T, parameters: Dict[str, Any] | None = None) → BinaryRelation | None[source]#
classmethod apply(obj: T, parameters: Dict[str, Any] | None = None) → Tuple[StrictPartialOrder, List[POWL]] | None[source]#
pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.project_on_groups_with_unique_activities(log: Counter, groups: List[Collection[Any]])[source]#
class pm4py.algo.discovery.powl.inductive.variants.maximal.maximal_partial_order_cut.MaximalPartialOrderCutUVCL[source]#

Bases: MaximalPartialOrderCut[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