def __init__(self, initial, tester, hlconfig):
self.initial = initial
if random.seed:
self.members = [initial.clone()]
else:
self.members = []
self.best = None
self.notadded = []
self.removed = []
self.failed = set()
self.testers = [tester]
self.roundNumber = -1
self.firstRound = True
self.hlconfig = hlconfig
if config.candidatelog:
self.candidatelog = storagedirs.openCsvStats("candidatelog",
['time',
'candidates',
'tests_complete',
'tests_timeout',
'tests_crashed',
'config_path',
'input_size',
'end_of_round',
'round_number'])
self.candidateloglast = None
self.starttime = time.time()
self.onMembersChanged(True)
self.members[0].log_mutation(MutationLog.seed)
def __init__(self, initial, tester, hlconfig):
self.initial = initial
if random.seed:
self.members = [initial.clone()]
else:
self.members = []
self.best = None
self.notadded = []
self.removed = []
self.failed = set()
self.testers = [tester]
self.roundNumber = -1
self.firstRound = True
self.hlconfig = hlconfig
if config.candidatelog:
self.candidatelog = storagedirs.openCsvStats(
"candidatelog", [
'time', 'candidates', 'tests_complete', 'tests_timeout',
'tests_crashed', 'config_path', 'input_size',
'end_of_round', 'round_number'
])
self.candidateloglast = None
self.starttime = time.time()
self.onMembersChanged(True)
self.members[0].log_mutation(MutationLog.seed)
def __init__(self, initial, tester, baseline=None):
self.members = [initial]
self.best = None
self.notadded = []
self.removed = []
self.failed = set()
self.testers = [tester]
self.baseline = baseline
self.roundNumber = -1
self.firstRound = True
if config.candidatelog:
self.candidatelog = storagedirs.openCsvStats("candidatelog",
['time',
'candidates',
'tests_complete',
'tests_timeout',
'tests_crashed',
'config_path',
'input_size',
'end_of_round',
'round_number'])
self.candidateloglast = None
self.starttime = time.time()
self.onMembersChanged(True)
def autotuneInner(benchmark, returnBest=None, tester_lambda=None, pop_lambda=None, hlconfig_lambda=None, config_lambda=None):
"""Function running the autotuning process.
If returnBest is specified, it should be a list. The best candidate found will
be added to that list"""
with progress.Scope("autotuning "+benchmark,
config.rounds_per_input_size*math.log(config.max_input_size,2)+config.final_rounds) as pr:
config.benchmark = benchmark
candidate, tester, hlconfig = init(benchmark, tester_lambda, pop_lambda, hlconfig_lambda, config_lambda)
try:
if pop_lambda is not None:
pop = pop_lambda(candidate, tester, hlconfig)
else:
pop = Population(candidate, tester, hlconfig)
stats = storagedirs.openCsvStats("roundstats",
("round",
"input_size",
"cumulative_sec",
"incremental_sec",
"testing_sec",
"inputgen_sec")+pop.statsHeader())
timers.total.start()
config.end_time = time.time() + config.max_time
def generation():
pop.generation()
pr()
stats.writerow((pop.roundNumber,
pop.inputSize(),
timers.total.total(),
timers.total.lap(),
timers.testing.lap(),
timers.inputgen.lap())+pop.stats())
try:
while pop.inputSize() < config.max_input_size:
for z in xrange(config.rounds_per_input_size):
generation()
pop.nextInputSize()
for z in xrange(config.final_rounds):
generation()
except TrainingTimeout:
pass
timers.total.stop()
#check to make sure we did something:
if pop.firstRound:
warnings.warn(tunerwarnings.AlwaysCrashes())
logging.info("TODO: using acc target: "+str(config.accuracy_target))
return pop.best
except:
traceback.print_exc() # Connelly: Print exceptions (are not otherwise displayed...not sure why)
raise
finally:
if pop.best and config.output_cfg:
print pop.best.cfgfile(),"=>" , config.output_cfg
shutil.copyfile(pop.best.cfgfile(), config.output_cfg)
if pop.best and returnBest is not None:
returnBest.append(pop.best)
at = storagedirs.getactivetimers()
if len(at):
storagedirs.openCsvStats("timers", at.keys()).writerow(at.values())
if tester and hasattr(tester, 'cleanup'): # Connelly: only call if has cleanup attr
tester.cleanup()
def onlinelearnInner(benchmark):
candidate, tester = sgatuner.init(benchmark, createChoiceSiteMutatorsOnline)
pop = OnlinePopulation()
objectives = ObjectiveTuner(pop)
''' mutators in the last time window that produced improved candidates,
ordered by descending fitness of the candidates'''
mutatorLog = MutatorLog(name = "acc and time log")
ostats = storagedirs.openCsvStats("onlinestats", ObjectiveTuner.statsHeader)
pstats = storagedirs.openCsvStats("population", OnlinePopulation.statsHeader)
clog = storagedirs.openCsvStats("onlinecandidates", ['gen',
'timesafe','accsafe','timeexp','accexp',
'safe','seed','experimental',
])
try:
timers.total.start()
'''seed first round'''
p = candidate
if config.online_baseline:
c = None
else:
c = p.clone()
if not tester.race(p, c):
raise Exception()
if not p.wasTimeout:
pop.add(p)
if c and not c.wasTimeout:
pop.add(c)
if not config.online_baseline:
mlog = MutatorLogFile(c.mutators)
'''now normal rounds'''
for gen in itertools.count(1):
if config.max_time and objectives.elapsed>config.max_time:
break
if config.max_gen and gen>config.max_gen:
break
if gen%config.reweight_interval==0:
pop.reweight()
p = pop.select(objectives.fitness)
#s = pop.choice(parentlimit(p), getacc)
s = p
if config.fixed_safe_alg:
p = candidate
if config.online_baseline:
c = None
else:
if(objectives.needAccuracy()):
mfilter = lambda x: x.accuracyHint
else:
mfilter = lambda x: True
c = s.cloneAndMutate(tester.n,
adaptive = True,
mutatorLog = mutatorLog,
objectives = objectives,
mutatorFilter = mfilter)
tlim, atarg = objectives.getlimits(p, s, c)
if tester.race(p, c, tlim, atarg) and not (p.wasTimeout and c.wasTimeout):
p.discardResults(config.max_trials)
if c and not c.wasTimeout:
pop.add(c)
pop.prune()
if c is None:
c=p
logging.debug("Child vs parent, better=%d, %f vs. %f" % (int(gettime(c) < gettime(p)), gettime(c), gettime(p)))
clog.writerow([gen, lasttime(p), lastacc(p), lasttime(c), lastacc(c)]
+map(storagedirs.relpath,[p.cfgfile(), s.cfgfile(), c.cfgfile()]))
dtime = gettime(c) - gettime(p)
dacc = None if c.wasTimeout else (getacc(c) - getacc(p))
if c is not None:
mutatorLog.add(c, dtime, dacc, gettime(c), None if c.wasTimeout else getacc(c));
if not config.online_baseline:
mlog.logPerformance(gen, gettime(c), "None" if c.wasTimeout else getacc(c), dtime, dacc, str(c.lastMutator));
mlog.logScores(gen, c.mutatorScores)
t,a = resultingTimeAcc(p, c)
print "Generation", gen, "elapsed",objectives.elapsed,"time", t,"accuracy",a, getconf(p)
print "Objectives", objectives
if a is not None and t is not None:
objectives.result(t,a)
pop.output((p,c,s))
ostats.writerow(objectives.stats(gen))
pstats.writerow(pop.stats(gen))
else:
print 'error'
timers.total.stop()
finally:
at = storagedirs.getactivetimers()
if len(at):
storagedirs.openCsvStats("timers", at.keys()).writerow(at.values())
tester.cleanup()
def __init__(self, mutators):
self.mutators = sorted(mutators, key=str)
self.mutatorPerf = storagedirs.openCsvStats("mutatorperf", ["gen", "time", "accuracy", "dtime", "daccuracy," "selected_mutator"])
self.mutatorScores = storagedirs.openCsvStats("mutatorscores", ["gen"] + map(str, mutators))
def onlinelearnInner(benchmark):
candidate, tester = sgatuner.init(benchmark,
createChoiceSiteMutatorsOnline)
pop = OnlinePopulation()
objectives = ObjectiveTuner(pop)
''' mutators in the last time window that produced improved candidates,
ordered by descending fitness of the candidates'''
mutatorLog = MutatorLog(name="acc and time log")
ostats = storagedirs.openCsvStats("onlinestats",
ObjectiveTuner.statsHeader)
pstats = storagedirs.openCsvStats("population",
OnlinePopulation.statsHeader)
clog = storagedirs.openCsvStats("onlinecandidates", [
'gen',
'timesafe',
'accsafe',
'timeexp',
'accexp',
'safe',
'seed',
'experimental',
])
try:
timers.total.start()
'''seed first round'''
p = candidate
if config.online_baseline:
c = None
else:
c = p.clone()
if not tester.race(p, c):
raise Exception()
if not p.wasTimeout:
pop.add(p)
if c and not c.wasTimeout:
pop.add(c)
if not config.online_baseline:
mlog = MutatorLogFile(c.mutators)
'''now normal rounds'''
for gen in itertools.count(1):
if config.max_time and objectives.elapsed > config.max_time:
break
if config.max_gen and gen > config.max_gen:
break
if gen % config.reweight_interval == 0:
pop.reweight()
p = pop.select(objectives.fitness)
#s = pop.choice(parentlimit(p), getacc)
s = p
if config.fixed_safe_alg:
p = candidate
if config.online_baseline:
c = None
else:
if (objectives.needAccuracy()):
mfilter = lambda x: x.accuracyHint
else:
mfilter = lambda x: True
c = s.cloneAndMutate(tester.n,
adaptive=True,
mutatorLog=mutatorLog,
objectives=objectives,
mutatorFilter=mfilter)
tlim, atarg = objectives.getlimits(p, s, c)
if tester.race(p, c, tlim,
atarg) and not (p.wasTimeout and c.wasTimeout):
p.discardResults(config.max_trials)
if c and not c.wasTimeout:
pop.add(c)
pop.prune()
if c is None:
c = p
logging.debug(
"Child vs parent, better=%d, %f vs. %f" %
(int(gettime(c) < gettime(p)), gettime(c), gettime(p)))
clog.writerow(
[gen,
lasttime(p),
lastacc(p),
lasttime(c),
lastacc(c)] +
map(storagedirs.relpath,
[p.cfgfile(), s.cfgfile(),
c.cfgfile()]))
dtime = gettime(c) - gettime(p)
dacc = None if c.wasTimeout else (getacc(c) - getacc(p))
if c is not None:
mutatorLog.add(c, dtime, dacc, gettime(c),
None if c.wasTimeout else getacc(c))
if not config.online_baseline:
mlog.logPerformance(gen, gettime(c),
"None" if c.wasTimeout else getacc(c),
dtime, dacc, str(c.lastMutator))
mlog.logScores(gen, c.mutatorScores)
t, a = resultingTimeAcc(p, c)
print "Generation", gen, "elapsed", objectives.elapsed, "time", t, "accuracy", a, getconf(
p)
print "Objectives", objectives
if a is not None and t is not None:
objectives.result(t, a)
pop.output((p, c, s))
ostats.writerow(objectives.stats(gen))
pstats.writerow(pop.stats(gen))
else:
print 'error'
timers.total.stop()
finally:
at = storagedirs.getactivetimers()
if len(at):
storagedirs.openCsvStats("timers", at.keys()).writerow(at.values())
tester.cleanup()
def __init__(self, mutators):
self.mutators = sorted(mutators, key=str)
self.mutatorPerf = storagedirs.openCsvStats("mutatorperf", ["gen", "time", "accuracy", "dtime", "daccuracy," "selected_mutator"])
self.mutatorScores = storagedirs.openCsvStats("mutatorscores", ["gen"] + map(str, mutators))
def autotuneInner(benchmark):
progress.push()
config.benchmark = benchmark
candidate, tester = init(benchmark)
try:
pop = Population(candidate, tester, None)
if not pop.isVariableAccuracy() and config.accuracy_target:
logging.info("clearing accuracy_target")
config.accuracy_target = None
stats = storagedirs.openCsvStats("roundstats",
("round",
"input_size",
"cumulative_sec",
"incremental_sec",
"testing_sec",
"inputgen_sec")+pop.statsHeader())
timers.total.start()
config.end_time = time.time() + config.max_time
try:
progress.remaining(config.max_input_size*(1+config.final_rounds))
while pop.inputSize() < config.max_input_size:
progress.status("autotuning %s: input %d of %d" % (config.benchmark, pop.inputSize(), config.max_input_size))
pop.generation()
stats.writerow((pop.roundNumber,
pop.inputSize(),
timers.total.total(),
timers.total.lap(),
timers.testing.lap(),
timers.inputgen.lap())+pop.stats())
pop.nextInputSize()
progress.remaining(config.max_input_size - pop.inputSize() + config.max_input_size*config.final_rounds)
for z in xrange(config.final_rounds):
pop.generation()
stats.writerow((pop.roundNumber,
pop.inputSize(),
timers.total.total(),
timers.total.lap(),
timers.testing.lap(),
timers.inputgen.lap())+pop.stats())
progress.remaining((config.final_rounds - z)*config.max_input_size)
except TrainingTimeout:
pass
timers.total.stop()
#check to make sure we did something:
if pop.firstRound:
warnings.warn(tunerwarnings.AlwaysCrashes())
logging.info("TODO: using acc target: "+str(config.accuracy_target))
return pop.best
finally:
if pop.best and config.output_cfg:
print pop.best.cfgfile(),"=>" , config.output_cfg
shutil.copyfile(pop.best.cfgfile(), config.output_cfg)
at = storagedirs.getactivetimers()
if len(at):
storagedirs.openCsvStats("timers", at.keys()).writerow(at.values())
if tester:
tester.cleanup()
progress.pop()
def autotuneInner(benchmark,
returnBest=None,
tester_lambda=None,
pop_lambda=None,
hlconfig_lambda=None,
config_lambda=None):
"""Function running the autotuning process.
If returnBest is specified, it should be a list. The best candidate found will
be added to that list"""
with progress.Scope(
"autotuning " + benchmark,
config.rounds_per_input_size * math.log(config.max_input_size, 2) +
config.final_rounds) as pr:
config.benchmark = benchmark
candidate, tester, hlconfig = init(benchmark, tester_lambda,
pop_lambda, hlconfig_lambda,
config_lambda)
try:
if pop_lambda is not None:
pop = pop_lambda(candidate, tester, hlconfig)
else:
pop = Population(candidate, tester, hlconfig)
stats = storagedirs.openCsvStats(
"roundstats",
("round", "input_size", "cumulative_sec", "incremental_sec",
"testing_sec", "inputgen_sec") + pop.statsHeader())
timers.total.start()
config.end_time = time.time() + config.max_time
def generation():
pop.generation()
pr()
stats.writerow((pop.roundNumber, pop.inputSize(),
timers.total.total(), timers.total.lap(),
timers.testing.lap(), timers.inputgen.lap()) +
pop.stats())
try:
while pop.inputSize() < config.max_input_size:
for z in xrange(config.rounds_per_input_size):
generation()
pop.nextInputSize()
for z in xrange(config.final_rounds):
generation()
except TrainingTimeout:
pass
timers.total.stop()
#check to make sure we did something:
if pop.firstRound:
warnings.warn(tunerwarnings.AlwaysCrashes())
logging.info("TODO: using acc target: " +
str(config.accuracy_target))
return pop.best
except:
traceback.print_exc(
) # Connelly: Print exceptions (are not otherwise displayed...not sure why)
raise
finally:
if pop.best and config.output_cfg:
print pop.best.cfgfile(), "=>", config.output_cfg
shutil.copyfile(pop.best.cfgfile(), config.output_cfg)
if pop.best and returnBest is not None:
returnBest.append(pop.best)
at = storagedirs.getactivetimers()
if len(at):
storagedirs.openCsvStats("timers",
at.keys()).writerow(at.values())
if tester and hasattr(
tester,
'cleanup'): # Connelly: only call if has cleanup attr
tester.cleanup()