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

```
'''
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/>.
'''
import copy
from typing import List, Optional, Set, 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 Cut, DFG
from pm4py.objects.log.obj import EventLog, Trace
[docs]def detect(dfg: DFG, alphabet: Dict[str, int], start_activities: Dict[str, int],
end_activities: Dict[str, int]) -> Optional[Cut]:
'''
This method finds a loop cut in the dfg.
Implementation follows function LoopCut on page 190 of
"Robust Process Mining with Guarantees" by Sander J.J. Leemans (ISBN: 978-90-386-4257-4)
Basic Steps:
1. merge all start and end activities in one group ('do' group)
2. remove start/end activities from the dfg
3. detect connected components in (undirected representative) of the reduced graph
4. check if each component meets the start/end criteria of the loop cut definition (merge with the 'do' group if not)
5. return the cut if at least two groups remain
Parameters
----------
dfg
directly follows graph
alphabet
alphabet of the dfg / log
start_activities
multiset of start activities of the dfg / log
end_activities
multiset of end activities of the dfg / log
Returns
-------
A list of sets of activities, i.e., forming a maximal loop cut
None if no cut is found.
'''
if len(dfg) == 0:
return None
groups = [set(start_activities.keys()).union(set(end_activities.keys()))]
for c in _compute_connected_components(dfg, alphabet, start_activities, end_activities, groups[0]):
groups.append(set(c.nodes))
groups = _exclude_sets_non_reachable_from_start(dfg, start_activities, end_activities, groups)
groups = _exclude_sets_no_reachable_from_end(dfg, start_activities, end_activities, groups)
groups = _check_start_completeness(dfg, start_activities, end_activities, groups)
groups = _check_end_completeness(dfg, start_activities, end_activities, groups)
groups = list(filter(lambda g: len(g) > 0, groups))
return groups if len(groups) > 1 else None
def _check_start_completeness(dfg, start_activities, end_activities, groups):
i = 1
while i < len(groups):
merge = False
for a in groups[i]:
if merge:
break
for (x, b) in dfg:
if x == a and b in start_activities:
for s in start_activities:
if not (a, s) in dfg:
merge = True
if merge:
groups[0] = groups[0].union(groups[i])
del groups[i]
continue
i = i + 1
return groups
def _check_end_completeness(dfg, start_activities, end_activities, groups):
i = 1
while i < len(groups):
merge = False
for a in groups[i]:
if merge:
break
for (b, x) in dfg:
if x == a and b in end_activities:
for e in end_activities:
if not (e, a) in dfg:
merge = True
if merge:
groups[0] = groups[0].union(groups[i])
del groups[i]
continue
i = i + 1
return groups
def _exclude_sets_non_reachable_from_start(dfg: DFG, start_activities: Dict[str, int], end_activities: Dict[str, int],
groups: Cut) -> Cut:
for a in set(start_activities).difference(set(end_activities)):
for (x, b) in dfg:
if x == a:
group_a, group_b = None, None
for group in groups:
group_a = group if a in group else group_a
group_b = group if b in group else group_b
groups = [group for group in groups if group != group_a and group != group_b]
# we are always merging on the do-part
groups.insert(0, group_a.union(group_b))
return groups
def _exclude_sets_no_reachable_from_end(dfg: DFG, start_activities: Dict[str, int], end_activities: Dict[str, int],
groups: Cut) -> Cut:
for b in set(end_activities).difference(set(start_activities)):
for (a, x) in dfg:
if x == b:
group_a, group_b = None, None
for group in groups:
group_a = group if a in group else group_a
group_b = group if b in group else group_b
groups = [group for group in groups if group != group_a and group != group_b]
groups.insert(0, group_a.union(group_b))
return groups
def _compute_connected_components(dfg: DFG, alphabet: Set[str], start_activities: Dict[str, int],
end_activities: Dict[str, int], do_set: Set[str]):
import networkx as nx
reduced_dfg = copy.copy(dfg)
for (a, b) in dfg:
if a in start_activities or a in end_activities or b in start_activities or b in end_activities:
del reduced_dfg[(a, b)]
reduced_alphabet = set(alphabet).difference(do_set)
nx_directed = im_utils.transform_dfg_to_directed_nx_graph(reduced_dfg, reduced_alphabet)
nx_undirected = nx_directed.to_undirected()
return [nx_undirected.subgraph(c).copy() for c in nx.connected_components(nx_undirected)]
[docs]def project(log: EventLog, cut: Cut, activity_key: str) -> List[EventLog]:
do = cut[0]
redo = cut[1:]
redo_activities = [y for x in redo for y in x]
do_log = EventLog()
redo_logs = []
for i in range(len(redo)):
redo_logs.append(EventLog())
for t in log:
do_trace = Trace()
redo_trace = Trace()
for e in t:
if e[activity_key] in do:
do_trace.append(e)
if len(redo_trace) > 0:
redo_logs = _append_trace_to_redo_log(redo_trace, redo_logs, redo, activity_key)
redo_trace = Trace()
else:
if e[activity_key] in redo_activities:
redo_trace.append(e)
if len(do_trace) > 0:
do_log.append(do_trace)
do_trace = Trace()
if len(redo_trace) > 0:
redo_logs = _append_trace_to_redo_log(redo_trace, redo_logs, redo, activity_key)
do_log.append(do_trace)
logs = [do_log]
logs.extend(redo_logs)
return logs
def _append_trace_to_redo_log(redo_trace: Trace, redo_logs: List[List[Trace]], redo_groups: List[Set[str]],
activity_key: str) -> List[List[Trace]]:
activities = set(x[activity_key] for x in redo_trace)
inte = [(i, len(activities.intersection(redo_groups[i]))) for i in range(len(redo_groups))]
inte = sorted(inte, key=lambda x: (x[1], x[0]), reverse=True)
redo_logs[inte[0][0]].append(redo_trace)
return redo_logs
[docs]def project_dfg(dfg_sa_ea_actcount, groups):
dfgs = []
skippable = [False, False]
for gind, g in enumerate(groups):
activities = {}
start_activities = {}
end_activities = {}
paths_frequency = {}
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]
if arc[1] in dfg_sa_ea_actcount.start_activities and arc[0] in dfg_sa_ea_actcount.end_activities:
skippable[1] = True
if gind == 0:
for act in dfg_sa_ea_actcount.start_activities:
if act in g:
start_activities[act] = dfg_sa_ea_actcount.start_activities[act]
else:
skippable[0] = True
for act in dfg_sa_ea_actcount.end_activities:
if act in g:
end_activities[act] = dfg_sa_ea_actcount.end_activities[act]
else:
skippable[1] = True
elif gind == 1:
for act in g:
start_activities[act] = 1
end_activities[act] = 1
dfgs.append(im_utils.DfgSaEaActCount(paths_frequency, start_activities, end_activities, activities))
return [dfgs, skippable]
```