# Source code for pm4py.algo.discovery.inductive.variants.im_clean.cuts.xor

```
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
from typing import Optional, Dict
from pm4py.algo.discovery.inductive.variants.im_clean import utils as im_utils
from pm4py.algo.discovery.inductive.variants.im_clean.d_types import DFG, Cut
from pm4py.objects.log.obj import EventLog, Trace
[docs]def detect(dfg: DFG, alphabet: Dict[str, int]) -> Optional[Cut]:
'''
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.
Parameters
----------
dfg
input directly follows graph
alphabet
alphabet of the dfg
Returns
-------
A list of sets of activities, i.e., forming a maximal xor cut
None if no cut is found.
'''
import networkx as nx
nx_dfg = im_utils.transform_dfg_to_directed_nx_graph(dfg, alphabet)
nx_und = nx_dfg.to_undirected()
conn_comps = [nx_und.subgraph(c).copy() for c in nx.connected_components(nx_und)]
if len(conn_comps) > 1:
cuts = list()
for comp in conn_comps:
cuts.append(set(comp.nodes))
return cuts
else:
return None
[docs]def project(log, groups, activity_key):
# refactored to support both IM and IMf
logs = list()
for group in groups:
logs.append(EventLog())
for t in log:
count = {i: 0 for i in range(len(groups))}
for index, group in enumerate(groups):
for e in t:
if e[activity_key] in group:
count[index] += 1
count = sorted(list((x, y) for x, y in count.items()), key=lambda x: (x[1], x[0]), reverse=True)
new_trace = Trace()
for e in t:
if e[activity_key] in groups[count[0][0]]:
new_trace.append(e)
logs[count[0][0]].append(new_trace)
return logs
[docs]def project_dfg(dfg_sa_ea_actcount, groups):
dfgs = []
skippable = []
for gind, g in enumerate(groups):
start_activities = {}
end_activities = {}
activities = {}
paths_frequency = {}
for act in dfg_sa_ea_actcount.start_activities:
if act in g:
start_activities[act] = dfg_sa_ea_actcount.start_activities[act]
for act in dfg_sa_ea_actcount.end_activities:
if act in g:
end_activities[act] = dfg_sa_ea_actcount.end_activities[act]
for act in dfg_sa_ea_actcount.act_count:
if act in g:
activities[act] = dfg_sa_ea_actcount.act_count[act]
for arc in dfg_sa_ea_actcount.dfg:
if arc[0] in g and arc[1] in g:
paths_frequency[arc] = dfg_sa_ea_actcount.dfg[arc]
dfgs.append(im_utils.DfgSaEaActCount(paths_frequency, start_activities, end_activities, activities))
skippable.append(False)
return [dfgs, skippable]
```