def test_redis_look_ahead_error():
"""Test whether the look-ahead mode fails as expected."""
model, prior, distance, obs = basic_testcase()
with tempfile.NamedTemporaryFile(mode='w', suffix='.csv') as fh:
sampler = RedisEvalParallelSamplerServerStarter(
look_ahead=True,
look_ahead_delay_evaluation=False,
log_file=fh.name)
args_list = [{
'eps': pyabc.MedianEpsilon()
}, {
'distance_function': pyabc.AdaptivePNormDistance()
}]
for args in args_list:
if 'distance_function' not in args:
args['distance_function'] = distance
try:
with pytest.raises(AssertionError) as e:
abc = pyabc.ABCSMC(model,
prior,
sampler=sampler,
population_size=10,
**args)
abc.new(pyabc.create_sqlite_db_id(), obs)
abc.run(max_nr_populations=3)
assert "cannot be used in look-ahead mode" in str(e.value)
finally:
sampler.shutdown()
def redis_starter_sampler(request):
s = RedisEvalParallelSamplerServerStarter(batch_size=5)
try:
yield s
finally:
# release all resources
s.shutdown()
def test_redis_look_ahead():
"""Test the redis sampler in look-ahead mode."""
model, prior, distance, obs = basic_testcase()
eps = pyabc.ListEpsilon([20, 10, 5])
# spice things up with an adaptive population size
pop_size = pyabc.AdaptivePopulationSize(start_nr_particles=50,
mean_cv=0.5,
max_population_size=50)
with tempfile.NamedTemporaryFile(mode='w', suffix='.csv') as fh:
sampler = RedisEvalParallelSamplerServerStarter(
look_ahead=True,
look_ahead_delay_evaluation=False,
log_file=fh.name)
try:
abc = pyabc.ABCSMC(model,
prior,
distance,
sampler=sampler,
population_size=pop_size,
eps=eps)
abc.new(pyabc.create_sqlite_db_id(), obs)
h = abc.run(max_nr_populations=3)
finally:
sampler.shutdown()
assert h.n_populations == 3
# read log file
df = pd.read_csv(fh.name, sep=',')
assert (df.n_lookahead > 0).any()
assert (df.n_lookahead_accepted > 0).any()
assert (df.n_preliminary == 0).all()
def test_redis_catch_error():
def model(pars):
if np.random.uniform() < 0.1:
raise ValueError("error")
return {'s0': pars['p0'] + 0.2 * np.random.uniform()}
def distance(s0, s1):
return abs(s0['s0'] - s1['s0'])
prior = pyabc.Distribution(p0=pyabc.RV("uniform", 0, 10))
sampler = RedisEvalParallelSamplerServerStarter(batch_size=3,
workers=1,
processes_per_worker=1)
try:
abc = pyabc.ABCSMC(model,
prior,
distance,
sampler=sampler,
population_size=10)
db_file = "sqlite:///" + os.path.join(tempfile.gettempdir(), "test.db")
data = {'s0': 2.8}
abc.new(db_file, data)
abc.run(minimum_epsilon=.1, max_nr_populations=3)
finally:
sampler.shutdown()
def test_redis_multiprocess():
sampler = RedisEvalParallelSamplerServerStarter(
batch_size=3, workers=1, processes_per_worker=1)
def simulate_one():
accepted = np.random.randint(2)
return Particle(0, {}, 0.1, [], [], accepted)
sample = sampler.sample_until_n_accepted(10, simulate_one)
assert 10 == len(sample.get_accepted_population())
sampler.cleanup()
def test_redis_pw_protection():
sampler = RedisEvalParallelSamplerServerStarter( # noqa: S106
password="******", port=8888)
def simulate_one():
accepted = np.random.randint(2)
return Particle(0, {}, 0.1, [], [], accepted)
sample = sampler.sample_until_n_accepted(10, simulate_one)
assert 10 == len(sample.get_accepted_population())
sampler.cleanup()
def test_redis_look_ahead():
"""Test the redis sampler in look-ahead mode."""
model, prior, distance, obs = basic_testcase()
eps = pyabc.ListEpsilon([20, 10, 5])
# spice things up with an adaptive population size
pop_size = pyabc.AdaptivePopulationSize(start_nr_particles=50,
mean_cv=0.5,
max_population_size=50)
with tempfile.NamedTemporaryFile(mode='w', suffix='.csv') as fh:
sampler = RedisEvalParallelSamplerServerStarter(
look_ahead=True,
look_ahead_delay_evaluation=False,
log_file=fh.name,
)
try:
abc = pyabc.ABCSMC(
model,
prior,
distance,
sampler=sampler,
population_size=pop_size,
eps=eps,
)
abc.new(pyabc.create_sqlite_db_id(), obs)
h = abc.run(max_nr_populations=3)
finally:
sampler.shutdown()
assert h.n_populations == 3
# read log file
df = pd.read_csv(fh.name, sep=',')
assert (df.n_lookahead > 0).any()
assert (df.n_lookahead_accepted > 0).any()
assert (df.n_preliminary == 0).all()
# check history proposal ids
for t in range(0, h.max_t + 1):
pop = h.get_population(t=t)
pop_size = len(pop)
n_lookahead_pop = len(
[p for p in pop.particles if p.proposal_id == -1])
assert (min(pop_size, int(
df.loc[df.t == t, 'n_lookahead_accepted'])) == n_lookahead_pop)
def test_redis_subprocess():
"""Test whether the instructed redis sampler allows worker subprocesses."""
# print worker output
logging.getLogger("Redis-Worker").addHandler(logging.StreamHandler())
def model_process(p, pipe):
"""The actual model."""
pipe.send({"y": p['p0'] + 0.1 * np.random.randn(10)})
def model(p):
"""Model calling a subprocess."""
parent, child = multiprocessing.Pipe()
proc = multiprocessing.Process(target=model_process, args=(p, child))
proc.start()
res = parent.recv()
proc.join()
return res
prior = pyabc.Distribution(p0=pyabc.RV('uniform', -5, 10),
p1=pyabc.RV('uniform', -2, 2))
def distance(y1, y2):
return np.abs(y1['y'] - y2['y']).sum()
obs = {'y': 1}
# False as daemon argument is ok, True and None are not allowed
sampler = RedisEvalParallelSamplerServerStarter(workers=1,
processes_per_worker=2,
daemon=False)
try:
abc = pyabc.ABCSMC(model,
prior,
distance,
sampler=sampler,
population_size=10)
abc.new(pyabc.create_sqlite_db_id(), obs)
# would just never return if model evaluation fails
abc.run(max_nr_populations=3)
finally:
sampler.shutdown()
def test_redis_pw_protection():
def simulate_one():
accepted = np.random.randint(2)
return pyabc.Particle(0, {}, 0.1, [], [], accepted)
sampler = RedisEvalParallelSamplerServerStarter( # noqa: S106
password="******")
try:
# needs to be always set
sampler.set_analysis_id("ana_id")
sample = sampler.sample_until_n_accepted(10, simulate_one, 0)
assert 10 == len(sample.get_accepted_population())
finally:
sampler.shutdown()
def test_redis_multiprocess():
def simulate_one():
accepted = np.random.randint(2)
return pyabc.Particle(0, {}, 0.1, [], [], accepted)
sampler = RedisEvalParallelSamplerServerStarter(batch_size=3,
workers=1,
processes_per_worker=2)
try:
# id needs to be set
sampler.set_analysis_id("ana_id")
sample = sampler.sample_until_n_accepted(10, simulate_one, 0)
assert 10 == len(sample.get_accepted_population())
finally:
sampler.shutdown()
def redis_starter_sampler():
s = RedisEvalParallelSamplerServerStarter(batch_size=5)
yield s
s.cleanup()
def RedisEvalParallelSamplerLookAheadDelayWrapper(**kwargs):
return RedisEvalParallelSamplerServerStarter(
look_ahead=True, look_ahead_delay_evaluation=True, **kwargs)
def RedisEvalParallelSamplerWaitForAllWrapper(**kwargs):
kwargs.setdefault('batch_size', 5)
kwargs.setdefault('catch', False)
return RedisEvalParallelSamplerServerStarter(wait_for_all_samples=True,
**kwargs)
def RedisEvalParallelSamplerWrapper(**kwargs):
kwargs.setdefault('batch_size', 5)
kwargs.setdefault('catch', False)
return RedisEvalParallelSamplerServerStarter(**kwargs)
def test_redis_continuous_analyses():
"""Test correct behavior of the redis server with multiple analyses."""
sampler = RedisEvalParallelSamplerServerStarter()
try:
sampler.set_analysis_id("id1")
# try "starting a new run while the old one has not finished yet"
with pytest.raises(AssertionError) as e:
sampler.set_analysis_id("id2")
assert "busy with an analysis " in str(e.value)
# after stopping it should work
sampler.stop()
sampler.set_analysis_id("id2")
finally:
sampler.shutdown()
def RedisEvalParallelSamplerServerStarterWrapper():
return RedisEvalParallelSamplerServerStarter(batch_size=5)
def RedisEvalParallelSamplerLookAheadDelayWrapper(**kwargs):
kwargs.setdefault('catch', False)
return RedisEvalParallelSamplerServerStarter(
look_ahead=True, look_ahead_delay_evaluation=True, **kwargs)
