def test_mistake_hooks(self):
construct_universe()
num_traces_to_make = 5
while not num_traces_to_make == ExperimentAdvice.invoked_count:
run_model(([
(ExperimentAdvice, 'all actors', list(), dict()),
(CompetenceModel, 'get_next_task', list(), dict()),
]),
num_start_messages=1,
num_ticks=100)
if not ExperimentAdvice.invoked:
action_log.pop()
if 'fuzzed tasks' in experimental_environment.keys(
) and experimental_environment['fuzzed tasks'][-1] == []:
experimental_environment['fuzzed tasks'].pop()
new_trace()
reset_universe()
# We have one too many traces, so we have to delete the one made by the last new_trace call.
action_log.pop()
generate_XES()
generate_CSV()
def test_massive_data_gen(self):
construct_universe()
ticks = 350
iterations = 5
simulate = partial(run_model, ([
(CompetenceModel, 'get_next_task',
[0.05 * iterations, ticks / 2 / iterations], dict()),
(ExperimentAdvice, 'all workflows', list(), dict()),
(NewTasksOnEnd, 'end', list(), dict()),
]),
num_start_messages=1,
num_ticks=ticks)
# run_model(([
# # (ActAbovePermissions, 'get_next_task', list(), dict()),
# (CompetenceModel, 'get_next_task', [0.05*iterations, ticks/2/iterations], dict()),
# (ExperimentAdvice, 'all workflows', list(), dict()),
# (NewTasksOnEnd, 'end', list(), dict()),
# ]),
# num_start_messages=1,
# num_ticks=ticks,
# )
self.play_simulation(simulate, discard_work=True)
while len(action_log) < 10000:
self.play_simulation(simulate, discard_work=False)
print(len(action_log))
# We also stopped halfway through a simulation...so unless the last action in the last action log is one the simulation should end on, we should remove that trace (and its fuzzing effects)
try:
if action_log[-1][-1] != "A_declined" \
and action_log[-1][-1] != "W_beoordelen_fraude_complete" \
and action_log[-1][-1] != "W_wijzigen_contractgegevens_schedule":
action_log.pop()
if len(experimental_environment["fuzzed tasks"]) != len(
action_log):
experimental_environment["fuzzed tasks"].pop()
except:
pass
generate_XES(log_path="10000.xes")
generate_CSV(csv_path="10000.csv")
return behaviour_function(attribute, actor)
super(ChangingBehaviour, self).__init__(select_behaviour, "",
times_to_trigger)
def remove_previous_action(_, __):
action_log[-1].pop()
SkippingAdvice = partial(ExperimentAdvice, remove_previous_action, 'skipping')
RepeatingAdvice = partial(ExperimentAdvice, manually_log, 'repeating')
AlternatingBehaviour = partial(ChangingBehaviour,
[(remove_previous_action, "skipping"),
(manually_log, "repeating")])
bpi_13_experimental_frontend.action_log = []
construct_universe()
while len(action_log) < 25:
run_model(FuzzerClass=AlternatingBehaviour,
num_ticks=250,
num_start_messages=1)
new_trace()
print(len(action_log))
action_log.pop()
generate_XES()
generate_CSV()
def test_replay_data_generation(self):
construct_universe()
ticks = 50000
min_dataset_required = 1000 # We want at least 1000 traces.
iterations = 1
class MonteCarlo:
traces = []
class Governor:
def encore(self, _attribute, _actor, _res):
MonteCarlo.traces.append(list())
class Collector:
def encore(self, attribute, _actor, _res):
MonteCarlo.traces[-1].append(attribute.func_name)
simulate = partial(
run_model,
([
# (ActAbovePermissions, 'get_next_task', list(), dict()),
# (CompetenceModel, 'get_next_task', [0.05*iterations, ticks/2/iterations], dict()),
# (ExperimentAdvice, 'all workflows', list(), dict()),
(NewTasksOnEnd, 'end', list(), dict()),
(MonteCarlo.Governor, 'start', list(), dict()),
(MonteCarlo.Collector, 'all workflows', list(), dict()),
]),
num_start_messages=1,
num_ticks=ticks)
self.play_simulation(simulate)
while len(MonteCarlo.traces) < min_dataset_required:
self.play_simulation(simulate, discard_work=False)
while [] in MonteCarlo.traces:
MonteCarlo.traces.remove([])
try:
if MonteCarlo.traces[-1][-1] != "A_declined" \
and MonteCarlo.traces[-1][-1] != "W_beoordelen_fraude_complete" \
and MonteCarlo.traces[-1][-1] != "W_wijzigen_contractgegevens_schedule":
MonteCarlo.traces.pop()
except:
pass
print(len(MonteCarlo.traces))
# We also stopped halfway through a simulation...so unless the last action in the last action log is one the simulation should end on, we should remove that trace (and its fuzzing effects)
try:
if action_log[-1][-1] != "A_declined" \
and action_log[-1][-1] != "W_beoordelen_fraude_complete" \
and action_log[-1][-1] != "W_wijzigen_contractgegevens_schedule":
action_log.pop()
if len(experimental_environment["fuzzed tasks"]) != len(
action_log):
experimental_environment["fuzzed tasks"].pop()
except:
pass
def completes_trace(incomplete, sample):
inc_position = 0
for event in sample:
if event == incomplete[inc_position]:
inc_position += 1
if inc_position == len(incomplete):
return True
return False
to_complete = copy(choice(MonteCarlo.traces))
num_gaps = len(to_complete) - 3
for _ in range(num_gaps):
to_complete.remove(choice(to_complete))
completes_sample = partial(completes_trace, to_complete)
MonteCarlo.traces = map(tuple, MonteCarlo.traces)
histogram = {
trace: MonteCarlo.traces.count(trace)
for trace in MonteCarlo.traces if completes_sample(trace)
}
lim = randint(0, sum(histogram.values()))
total = 0
for completing_trace, count in histogram.items():
total += count
if total > lim:
break
print("We complete the trace: \n\t" + str(to_complete) +
"\n...with the trace...\n\t" + str(completing_trace))
def test_competence_application(self):
construct_universe()
run_model([(CompetenceModel, 'get_next_task', list(), dict())],
num_start_messages=1,
num_ticks=150)