def trans_alpha(log, parameters=None):
dfg = {k: v for k, v in dfg_inst.apply(log).items() if v > 0}
if parameters is None:
parameters = {}
if pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:
parameters[pm_util.constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] = xes_util.DEFAULT_NAME_KEY
start_activities = endpoints.derive_start_activities_from_log(
log, parameters[pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY])
end_activities = endpoints.derive_end_activities_from_log(
log, parameters[pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY])
labels = set()
for el in dfg:
labels.add(el[0])
labels.add(el[1])
for a in start_activities:
labels.add(a)
for a in end_activities:
labels.add(a)
labels = list(labels)
alpha_abstraction = alpha_classic_abstraction.ClassicAlphaAbstraction(
start_activities, end_activities, dfg)
new_parallel_set = set()
loop_set = set()
for par in alpha_abstraction.parallel_relation:
for trace in log:
for i in range(len(trace) - 1):
if trace[i]['concept:name'] == par[0] and trace[
i + 1]['concept:name'] == par[1]:
if trace[i + 1]['concept:name'] in trace[i].enabled:
new_parallel_set.add(par)
pairs = list(
map(
lambda p: ({p[0]}, {p[1]}),
filter(
lambda p: classic.__initial_filter(
alpha_abstraction.parallel_relation, p),
alpha_abstraction.causal_relation)))
for par in alpha_abstraction.parallel_relation:
if par not in new_parallel_set and (
par[1], par[0]) not in new_parallel_set and par[1] != par[0]:
loop_set.add(par)
pairs.append(({par[0]}, {par[1]}))
else:
new_parallel_set.add(par)
for i in range(0, len(pairs)):
t1 = pairs[i]
for j in range(i, len(pairs)):
t2 = pairs[j]
if t1 != t2:
if t1[0].issubset(t2[0]) or t1[1].issubset(t2[1]):
if not (classic.__check_is_unrelated(
(new_parallel_set.union(loop_set)),
alpha_abstraction.causal_relation, t1[0], t2[0])
or classic.__check_is_unrelated(
(new_parallel_set.union(loop_set)),
alpha_abstraction.causal_relation, t1[1],
t2[1])):
new_alpha_pair = (t1[0] | t2[0], t1[1] | t2[1])
if new_alpha_pair not in pairs:
pairs.append((t1[0] | t2[0], t1[1] | t2[1]))
internal_places = filter(lambda p: classic.__pair_maximizer(pairs, p),
pairs)
net = petri.petrinet.PetriNet('alpha_classic_net_' + str(time.time()))
label_transition_dict = {}
for i in range(0, len(labels)):
label_transition_dict[labels[i]] = petri.petrinet.PetriNet.Transition(
labels[i], labels[i])
net.transitions.add(label_transition_dict[labels[i]])
src = classic.__add_source(net, alpha_abstraction.start_activities,
label_transition_dict)
sink = classic.__add_sink(net, alpha_abstraction.end_activities,
label_transition_dict)
for pair in internal_places:
place = petri.petrinet.PetriNet.Place(str(pair))
net.places.add(place)
for in_arc in pair[0]:
petri.utils.add_arc_from_to(label_transition_dict[in_arc], place,
net)
for out_arc in pair[1]:
petri.utils.add_arc_from_to(place, label_transition_dict[out_arc],
net)
return net, Marking({src: 1}), Marking({sink: 1})
def apply_dfg_sa_ea(dfg, start_activities, end_activities, parameters=None):
"""
Applying Alpha Miner starting from the knowledge of the Directly Follows graph,
and of the start activities and end activities in the log (possibly inferred from the DFG)
Parameters
------------
dfg
Directly-Follows graph
start_activities
Start activities
end_activities
End activities
parameters
Parameters of the algorithm including:
activity key -> name of the attribute that contains the activity
Returns
-------
net : :class:`pm4py.entities.petri.petrinet.PetriNet`
A Petri net describing the event log that is provided as an input
initial marking : :class:`pm4py.models.net.Marking`
marking object representing the initial marking
final marking : :class:`pm4py.models.net.Marking`
marking object representing the final marking, not guaranteed that it is actually reachable!
"""
if parameters is None:
parameters = {}
if pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:
parameters[
pm_util.constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] = log_util.xes.DEFAULT_NAME_KEY
if start_activities is None:
start_activities = dfg_utils.infer_start_activities(dfg)
if end_activities is None:
end_activities = dfg_utils.infer_end_activities(dfg)
labels = set()
for el in dfg:
labels.add(el[0])
labels.add(el[1])
for a in start_activities:
labels.add(a)
for a in end_activities:
labels.add(a)
labels = list(labels)
alpha_abstraction = alpha_classic_abstraction.ClassicAlphaAbstraction(
start_activities,
end_activities,
dfg,
activity_key=parameters[PARAMETER_CONSTANT_ACTIVITY_KEY])
pairs = list(
map(
lambda p: ({p[0]}, {p[1]}),
filter(
lambda p: __initial_filter(alpha_abstraction.parallel_relation,
p),
alpha_abstraction.causal_relation)))
for i in range(0, len(pairs)):
t1 = pairs[i]
for j in range(i, len(pairs)):
t2 = pairs[j]
if t1 != t2:
if t1[0].issubset(t2[0]) or t1[1].issubset(t2[1]):
if not (__check_is_unrelated(
alpha_abstraction.parallel_relation,
alpha_abstraction.causal_relation, t1[0], t2[0])
or __check_is_unrelated(
alpha_abstraction.parallel_relation,
alpha_abstraction.causal_relation, t1[1],
t2[1])):
new_alpha_pair = (t1[0] | t2[0], t1[1] | t2[1])
if new_alpha_pair not in pairs:
pairs.append((t1[0] | t2[0], t1[1] | t2[1]))
internal_places = filter(lambda p: __pair_maximizer(pairs, p), pairs)
net = petri.petrinet.PetriNet('alpha_classic_net_' + str(time.time()))
label_transition_dict = {}
for i in range(0, len(labels)):
label_transition_dict[labels[i]] = petri.petrinet.PetriNet.Transition(
labels[i], labels[i])
net.transitions.add(label_transition_dict[labels[i]])
src = __add_source(net, alpha_abstraction.start_activities,
label_transition_dict)
sink = __add_sink(net, alpha_abstraction.end_activities,
label_transition_dict)
for pair in internal_places:
place = petri.petrinet.PetriNet.Place(str(pair))
net.places.add(place)
for in_arc in pair[0]:
petri.utils.add_arc_from_to(label_transition_dict[in_arc], place,
net)
for out_arc in pair[1]:
petri.utils.add_arc_from_to(place, label_transition_dict[out_arc],
net)
return net, Marking({src: 1}), Marking({sink: 1})
def trans_alpha(log, parameters=None):
dfg = {k: v for k, v in dfg_inst.apply(log).items() if v > 0}
if parameters is None:
parameters = {}
if pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:
parameters[pm_util.constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] = xes_util.DEFAULT_NAME_KEY
start_activities = endpoints.derive_start_activities_from_log(
log, parameters[pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY])
end_activities = endpoints.derive_end_activities_from_log(
log, parameters[pm_util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY])
labels = set()
for el in dfg:
labels.add(el[0])
labels.add(el[1])
for a in start_activities:
labels.add(a)
for a in end_activities:
labels.add(a)
labels = list(labels)
alpha_abstraction = alpha_classic_abstraction.ClassicAlphaAbstraction(
start_activities, end_activities, dfg)
pairs = list(
map(
lambda p: ({p[0]}, {p[1]}),
filter(
lambda p: classic.__initial_filter(
alpha_abstraction.parallel_relation, p),
alpha_abstraction.causal_relation)))
#this part added
parallel_set = alpha_abstraction.parallel_relation
loop_cand_set = set()
for rel in parallel_set.copy():
not_loop_flag = False
pre_act = rel[0]
post_act = rel[1]
for trace in log:
for i in range(len(trace) - 1):
if trace[i]['concept:name'] == pre_act and trace[
i + 1]['concept:name'] == post_act:
pre_en = trace[i]['enabled']
if post_act in pre_en: #not loop
not_loop_flag = True
break
else: #loop
continue
break
if not not_loop_flag:
loop_cand_set.add((pre_act, post_act))
loop_set = set()
for loop_cand in loop_cand_set:
if loop_cand[::-1] in loop_cand_set and loop_cand[0] != loop_cand[1]:
loop_set.add(loop_cand)
#find loops based on enabling information
#this part added
for i in range(0, len(pairs)):
t1 = pairs[i]
for j in range(i, len(pairs)):
t2 = pairs[j]
if t1 != t2:
if t1[0].issubset(t2[0]) or t1[1].issubset(t2[1]):
if not (classic.__check_is_unrelated(
alpha_abstraction.parallel_relation,
alpha_abstraction.causal_relation, t1[0], t2[0])
or classic.__check_is_unrelated(
alpha_abstraction.parallel_relation,
alpha_abstraction.causal_relation, t1[1],
t2[1])):
new_alpha_pair = (t1[0] | t2[0], t1[1] | t2[1])
if new_alpha_pair not in pairs:
pairs.append((t1[0] | t2[0], t1[1] | t2[1]))
internal_places = filter(lambda p: classic.__pair_maximizer(pairs, p),
pairs)
net = petri.petrinet.PetriNet('alpha_classic_net_' + str(time.time()))
label_transition_dict = {}
for i in range(0, len(labels)):
label_transition_dict[labels[i]] = petri.petrinet.PetriNet.Transition(
labels[i], labels[i])
net.transitions.add(label_transition_dict[labels[i]])
for pair in internal_places:
place = petri.petrinet.PetriNet.Place(str(pair))
net.places.add(place)
for in_arc in pair[0]:
petri.utils.add_arc_from_to(label_transition_dict[in_arc], place,
net)
for out_arc in pair[1]:
petri.utils.add_arc_from_to(place, label_transition_dict[out_arc],
net)
src = classic.__add_source(net, alpha_abstraction.start_activities,
label_transition_dict)
sink = classic.__add_sink(net, alpha_abstraction.end_activities,
label_transition_dict)
loop_tail_set = set()
for t in label_transition_dict.values(): #check if two-length-loop
if len(t.in_arcs) == 0 and len(t.out_arcs) == 0:
loop_tail_set.add(t)
for loop_tail in loop_tail_set:
if loop_set is not None:
loop_body = None
for loop in loop_set:
if loop[0] == loop_tail.name:
loop_body = label_transition_dict[loop[1]]
if loop_body is not None:
for place in net.places:
for in_arc in place.in_arcs:
if in_arc.source == loop_body:
petri.utils.add_arc_from_to(
place, label_transition_dict[loop_tail.name],
net)
break
for out_arc in place.out_arcs:
if out_arc.target == loop_body:
petri.utils.add_arc_from_to(
label_transition_dict[loop_tail.name], place,
net)
return net, Marking({src: 1}), Marking({sink: 1})
