def test_BO(ma2):
# Log transform of the distance usually smooths the distance surface
log_d = elfi.Operation(np.log, ma2['d'], name='log_d')
n_init = 20
res_init = elfi.Rejection(log_d, batch_size=5).sample(n_init, quantile=1)
bounds = {n: (-2, 2) for n in ma2.parameter_names}
bo = elfi.BayesianOptimization(
log_d, initial_evidence=res_init.outputs, update_interval=10, batch_size=5, bounds=bounds)
assert bo.target_model.n_evidence == n_init
assert bo.n_evidence == n_init
assert bo.n_precomputed_evidence == n_init
assert bo.n_initial_evidence == n_init
n1 = 5
bo.infer(n_init + n1)
assert bo.target_model.n_evidence == n_init + n1
assert bo.n_evidence == n_init + n1
assert bo.n_precomputed_evidence == n_init
assert bo.n_initial_evidence == n_init
n2 = 5
bo.infer(n_init + n1 + n2)
assert bo.target_model.n_evidence == n_init + n1 + n2
assert bo.n_evidence == n_init + n1 + n2
assert bo.n_precomputed_evidence == n_init
assert bo.n_initial_evidence == n_init
assert np.array_equal(bo.target_model._gp.X[:n_init, 0], res_init.samples_array[:, 0])
def test_bo(ma2):
bs = 2
upd_int = 1
n_evi = 16
init_evi = 10
bounds = {'t1': (-2, 2), 't2': (-1, 1)}
anv = .1
bo = elfi.BayesianOptimization(
ma2,
'd',
initial_evidence=init_evi,
update_interval=upd_int,
batch_size=bs,
bounds=bounds,
acq_noise_var=anv)
res = bo.infer(n_evidence=n_evi)
seed = bo.seed
bo = elfi.BayesianOptimization(
ma2,
'd',
seed=seed,
initial_evidence=init_evi,
update_interval=upd_int,
batch_size=bs,
bounds=bounds,
acq_noise_var=anv)
res_same = bo.infer(n_evidence=n_evi)
bo = elfi.BayesianOptimization(
ma2,
'd',
initial_evidence=init_evi,
update_interval=upd_int,
batch_size=bs,
bounds=bounds,
acq_noise_var=anv)
res_diff = bo.infer(n_evidence=n_evi)
check_consistent_sample(res, res_diff, res_same)
assert not np.array_equal(res.x_min, res_diff.x_min)
assert np.array_equal(res.x_min, res_same.x_min)
def test_async(ma2):
bounds = {n: (-2, 2) for n in ma2.parameter_names}
bo = elfi.BayesianOptimization(ma2,
'd',
initial_evidence=0,
update_interval=2,
batch_size=2,
bounds=bounds,
async=True)
samples = 5
bo.infer(samples)
def test_BO_works_with_zero_init_samples(ma2):
log_d = elfi.Operation(np.log, ma2['d'], name='log_d')
bounds = {n: (-2, 2) for n in ma2.parameter_names}
bo = elfi.BayesianOptimization(
log_d, initial_evidence=0, update_interval=4, batch_size=2, bounds=bounds)
assert bo.target_model.n_evidence == 0
assert bo.n_evidence == 0
assert bo.n_precomputed_evidence == 0
assert bo.n_initial_evidence == 0
n_samples = 4
bo.infer(n_samples)
assert bo.target_model.n_evidence == n_samples
assert bo.n_evidence == n_samples
assert bo.n_precomputed_evidence == 0
assert bo.n_initial_evidence == 0