pm4py.algo.discovery.inductive.variants.im_clean package

Submodules

pm4py.algo.discovery.inductive.variants.im_clean.algorithm module

class pm4py.algo.discovery.inductive.variants.im_clean.algorithm.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
DFG_ONLY = 'dfg_only'
NOISE_THRESHOLD = 'noise_threshold'
USE_MSD_PARALLEL_CUT = 'use_msd_par_cut'
pm4py.algo.discovery.inductive.variants.im_clean.algorithm.apply(event_log: Union[pandas.core.frame.DataFrame, pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream], parameters: Optional[Dict[str, Any]] = None) → Tuple[pm4py.objects.petri_net.obj.PetriNet, pm4py.objects.petri_net.obj.Marking, pm4py.objects.petri_net.obj.Marking][source]
pm4py.algo.discovery.inductive.variants.im_clean.algorithm.apply_tree(event_log: Union[pandas.core.frame.DataFrame, pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream], parameters: Optional[Dict[str, Any]] = None)pm4py.objects.process_tree.obj.ProcessTree[source]
pm4py.algo.discovery.inductive.variants.im_clean.algorithm.apply_tree_variants(variants, parameters=None)[source]
pm4py.algo.discovery.inductive.variants.im_clean.algorithm.apply_variants(variants, parameters=None)[source]
pm4py.algo.discovery.inductive.variants.im_clean.algorithm.inductive_miner(log, dfg, threshold, root, act_key, use_msd, remove_noise=False)[source]

pm4py.algo.discovery.inductive.variants.im_clean.d_types module

pm4py.algo.discovery.inductive.variants.im_clean.utils module

pm4py.algo.discovery.inductive.variants.im_clean.utils.transform_dfg_to_directed_nx_graph(dfg, alphabet)[source]

Module contents