def test_uniform_acceptor():
"""Test the uniform acceptor."""
def dist(x, x_0):
return sum(abs(x[key] - x_0[key]) for key in x_0)
distance = pyabc.SimpleFunctionDistance(dist)
acceptor = pyabc.UniformAcceptor()
eps = pyabc.ListEpsilon([1, 4, 2])
x = {'s0': 1.5}
x_0 = {'s0': 0}
ret = acceptor(distance_function=distance,
eps=eps,
x=x,
x_0=x_0,
t=2,
par=None)
assert ret.accept
# now let's test again, including previous time points
acceptor = pyabc.UniformAcceptor(use_complete_history=True)
ret = acceptor(distance_function=distance,
eps=eps,
x=x,
x_0=x_0,
t=2,
par=None)
assert not ret.accept
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_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_listepsilon():
eps = pyabc.ListEpsilon([3.5, 2.3, 1, 0.3])
with pytest.raises(Exception):
eps(4)
matplotlib.rc('font', **font)
# In[2]:
nodes = 8
noisevar = 0.03
sleepvars = [1, 1.5, 2]
basepath = "/p/home/jusers/reck1/juwels/scripts/Batch_pyABC/programs/ODE"
pop_sizes = [64, 256, 1024, 4096]
psize = pop_sizes[-1]
eps_list = [8, 4, 2, 1, 0.7, 0.5, 0.33, 0.25]
eps = pyabc.ListEpsilon(eps_list)
iters_LA = 25
iters_ORI = 25
iters_STAT = 3
# In[3]:
histories_LA = []
for i in range(iters_LA):
histories_LA.append(
pyabc.History("sqlite:///" + os.path.join(
basepath, "results/Var" + str(sleepvars[0]) +
"/database", "DYNLA" + str(nodes) + "_" + str(psize) + "_" +
str(i) + ".db")))
