def test_bo_custom_model():
domain = Domain({"x": [-2., 2.]})
bayes_opt = bo.BayesianOptimisation(domain=domain, seed=7)
kernel = GPy.kern.RBF(1) + GPy.kern.Bias(1)
n_steps = 3
batch_size = 3
all_samples = []
all_evaluations = []
first_samples = bayes_opt.run_step(batch_size=batch_size, minimise=False)
xs = np.atleast_2d([s["x"] for s in first_samples])
ys = np.atleast_2d(
test_utils.continuous_heteroscedastic_1d(
np.array([s["x"] for s in first_samples])))
for i in range(n_steps):
custom_model = GPy.models.GPHeteroscedasticRegression(xs, ys, kernel)
samples = bayes_opt.run_step(batch_size,
minimise=False,
model=custom_model)
evaluations = test_utils.continuous_heteroscedastic_1d(
np.array([s["x"] for s in samples]))
bayes_opt.update(samples,
[base.EvaluationScore(ev) for ev in evaluations])
xs = np.concatenate([xs, np.atleast_2d([s["x"] for s in samples])],
axis=0)
ys = np.concatenate([ys, np.atleast_2d(evaluations)], axis=0)
# gather the samples and evaluations for later assessment
all_samples.extend([s["x"] for s in samples])
all_evaluations.extend(evaluations)
best_eval_index = int(np.argmax(all_evaluations))
best_sample = all_samples[best_eval_index]
assert np.isclose(best_sample,
test_utils.CONT_HETEROSCED_1D_ARGMAX,
atol=1e-1)
def test_bo_set_history():
n_samples = 10
domain = Domain({"a": {"b": [2, 3]}, "c": [0, 0.1]})
history = [
base.HistoryPoint(domain.sample(),
{"score": base.EvaluationScore(float(i))})
for i in range(n_samples)
]
bayes_opt = bo.BayesianOptimisation(domain, seed=7)
bayes_opt.history = history
assert bayes_opt.history == history
assert len(bayes_opt._data_x) == len(bayes_opt._data_fx) == len(history)
def test_bo_update_and_reset():
domain = Domain({"a": {"b": [2, 3], "d": {"f": [3, 4]}}, "c": [0, 0.1]})
bayes_opt = bo.BayesianOptimisation(domain, seed=7)
samples = []
n_reps = 3
for i in range(n_reps):
samples.extend(bayes_opt.run_step(batch_size=1, minimise=False))
bayes_opt.update(samples[-1], base.EvaluationScore(2. * i))
assert len(bayes_opt._data_x) == n_reps
assert len(bayes_opt._data_fx) == n_reps
assert np.all(bayes_opt._data_x == np.array(
[bayes_opt._convert_to_gpyopt_sample(s) for s in samples]))
assert np.all(bayes_opt._data_fx == 2. *
np.arange(n_reps).reshape(n_reps, 1))
bayes_opt.reset()
assert len(bayes_opt.history) == 0
def square(sample: Sample) -> base.EvaluationScore:
return base.EvaluationScore(sample["x"]**2)