def run():
'''
Runs the Z3-translator on each pair (file, numInstances) in tests,
and ensures that the number of generated models equals numInstances.
'''
clock = Clock()
tests = getTestSet()
num_passed = 0
exceptions = 0
exception_list = []
failed_list = []
temp_model_count = Options.NUM_INSTANCES
for t in tests:
(file, expected_model_count) = t
try:
if expected_model_count == Options.INFINITE and Options.NUM_INSTANCES < 0:
#will change it back after the test runs
Options.NUM_INSTANCES = 5
module = file.getModule()
print_separate("Attempting: " + str(file.__name__))
clock.tick("Total Z3 Run Time")
cfr = ClaferModel(module)
actual_model_count = cfr.run()
clock.tack("Total Z3 Run Time")
clock = clock.combineClocks(cfr.clock)
if(expected_model_count == actual_model_count or
(expected_model_count == Options.INFINITE and actual_model_count == Options.NUM_INSTANCES)):
print("PASSED: " + str(file.__name__))
num_passed = num_passed + 1
else:
failed_list.append(str(file.__name__))
print("FAILED: " + str(file.__name__) + " " + str(expected_model_count) + " " + str(actual_model_count))
except:
print("FAILED: " + str(file.__name__) + " " + "\nException raised.")
traceback.print_exc()
exception_list.append(str(file.__name__))
exceptions = exceptions + 1
Options.NUM_INSTANCES = temp_model_count
print_separate("Results: " + str(num_passed) + "/" + str(len(tests)) + "\n| " +
"Failed List: " + str(failed_list) + "\n| " +
"Exceptions: " + str(exceptions) + "/" + str(len(tests)) + "\n| " +
"Exception List: " + str(exception_list))
clock.printEvents()
class ParSolver():
def __init__(self, cfr, module, solver, metrics_variables, metrics_objective_direction):
self.cfr = cfr
self.module = module
self.solvers = replicateSolver(solver, Options.CORES)
self.metrics_variables = metrics_variables
self.metrics_objective_direction = metrics_objective_direction
self.clock = Clock()
self.consumerConstraints = self.splitter()
def run(self):
if not self.consumerConstraints:
self.cfr.metric = Common.BOUND
return []
mgr = multiprocessing.Manager()
taskQueue = mgr.Queue()
solutions = mgr.Queue()
timeQueue = mgr.Queue()
# Enqueue initial tasks
for i in range(Options.NUM_SPLIT):
taskQueue.put(i)
for i in range(Options.CORES):
taskQueue.put("Poison")
# Start consumers
#case: objectives
if self.metrics_variables:
self.consumers = [ Consumer.GIAConsumer(taskQueue, solutions, self.cfr, timeQueue, i, "out", Options.CORES, j, self.metrics_variables, self.metrics_objective_direction, self.consumerConstraints)
for i,j in zip(range(Options.CORES), self.solvers)]
#case: no objectives
else:
self.consumers = [ Consumer.StandardConsumer(taskQueue, solutions, self.cfr, timeQueue, i, "out", Options.CORES, j, self.consumerConstraints)
for i,j in zip(range(Options.CORES), self.solvers)]
self.clock.tick("ParSolver")
for w in self.consumers:
w.start()
TERMINATED = False
for w in self.consumers:
if Options.TIME_OUT != 0:
w.join(Options.TIME_OUT)
else:
w.join()
if w.is_alive():
TERMINATED = True
w.terminate()
if TERMINATED:
GeneralHeuristics.safe_raise_heuristic_failure_exception("Heuristic Timed Out")
results = []
while not solutions.empty():
result = solutions.get()
results.append(result)
while not timeQueue.empty():
clock = timeQueue.get()
self.clock = self.clock.combineClocks(clock)
self.clock.tick("Merge")
merged_results = self.merge(results)
self.clock.tock("Merge")
self.clock.tock("ParSolver")
self.clock.getParallelStats(self.cfr)
return merged_results
def merge(self, results):
if self.metrics_variables:
results = self.removeDominatedAndEqual(results)
return [i for (i,_) in results]
else:
return list(set(results))
def checkDominated(self, l, r):
worseInOne = False
betterInOne = False
for i in range(len(l)):
ml = l[i]
mr = r[i]
if self.metrics_objective_direction[i] == METRICS_MAXIMIZE:
if ml < mr:
worseInOne = True
elif ml > mr:
betterInOne = True
elif self.metrics_objective_direction[i] == METRICS_MINIMIZE:
if ml > mr:
worseInOne = True
elif ml < mr:
betterInOne = True
if (worseInOne and not betterInOne):
return True
return False
def removeDominatedAndEqual(self, results):
count = len(results)
removalList = [False for _ in range(count)]
for i in range(count):
for j in range(count):
if i != j:
if self.checkDominated(results[i][1], results[j][1]):
removalList[i] = True
elif i < j and (results[i][0] == results[j][0] or results[i][1] == results[j][1]):
removalList[i] = True
nonDominated = []
for i in range(len(removalList)):
if not removalList[i]:
nonDominated.append(results[i])
return nonDominated
def splitter(self):
heuristic = GeneralHeuristics.heuristics[Options.SPLIT]
return heuristic(self.cfr, self.module, Options.NUM_SPLIT)
class ParSolver():
def __init__(self, cfr, module, solver, metrics_variables,
metrics_objective_direction):
self.cfr = cfr
self.module = module
self.solvers = replicateSolver(solver, Options.CORES)
self.metrics_variables = metrics_variables
self.metrics_objective_direction = metrics_objective_direction
self.clock = Clock()
self.consumerConstraints = self.splitter()
def run(self):
if not self.consumerConstraints:
self.cfr.metric = Common.BOUND
return []
mgr = multiprocessing.Manager()
taskQueue = mgr.Queue()
solutions = mgr.Queue()
timeQueue = mgr.Queue()
# Enqueue initial tasks
for i in range(Options.NUM_SPLIT):
taskQueue.put(i)
for i in range(Options.CORES):
taskQueue.put("Poison")
# Start consumers
#case: objectives
if self.metrics_variables:
self.consumers = [
Consumer.GIAConsumer(taskQueue, solutions, self.cfr, timeQueue,
i, "out", Options.CORES, j,
self.metrics_variables,
self.metrics_objective_direction,
self.consumerConstraints)
for i, j in zip(range(Options.CORES), self.solvers)
]
#case: no objectives
else:
self.consumers = [
Consumer.StandardConsumer(taskQueue, solutions, self.cfr,
timeQueue, i, "out", Options.CORES,
j, self.consumerConstraints)
for i, j in zip(range(Options.CORES), self.solvers)
]
self.clock.tick("ParSolver")
for w in self.consumers:
w.start()
TERMINATED = False
for w in self.consumers:
if Options.TIME_OUT != 0:
w.join(Options.TIME_OUT)
else:
w.join()
if w.is_alive():
TERMINATED = True
w.terminate()
if TERMINATED:
GeneralHeuristics.safe_raise_heuristic_failure_exception(
"Heuristic Timed Out")
results = []
while not solutions.empty():
result = solutions.get()
results.append(result)
while not timeQueue.empty():
clock = timeQueue.get()
self.clock = self.clock.combineClocks(clock)
self.clock.tick("Merge")
merged_results = self.merge(results)
self.clock.tock("Merge")
self.clock.tock("ParSolver")
self.clock.getParallelStats(self.cfr)
return merged_results
def merge(self, results):
if self.metrics_variables:
results = self.removeDominatedAndEqual(results)
return [i for (i, _) in results]
else:
return list(set(results))
def checkDominated(self, l, r):
worseInOne = False
betterInOne = False
for i in range(len(l)):
ml = l[i]
mr = r[i]
if self.metrics_objective_direction[i] == METRICS_MAXIMIZE:
if ml < mr:
worseInOne = True
elif ml > mr:
betterInOne = True
elif self.metrics_objective_direction[i] == METRICS_MINIMIZE:
if ml > mr:
worseInOne = True
elif ml < mr:
betterInOne = True
if (worseInOne and not betterInOne):
return True
return False
def removeDominatedAndEqual(self, results):
count = len(results)
removalList = [False for _ in range(count)]
for i in range(count):
for j in range(count):
if i != j:
if self.checkDominated(results[i][1], results[j][1]):
removalList[i] = True
elif i < j and (results[i][0] == results[j][0]
or results[i][1] == results[j][1]):
removalList[i] = True
nonDominated = []
for i in range(len(removalList)):
if not removalList[i]:
nonDominated.append(results[i])
return nonDominated
def splitter(self):
heuristic = GeneralHeuristics.heuristics[Options.SPLIT]
return heuristic(self.cfr, self.module, Options.NUM_SPLIT)