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Noisy pybads GP fit #57

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Merged
lacerbi merged 1 commit into from
Oct 16, 2025
Merged

Noisy pybads GP fit #57

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4 changes: 2 additions & 2 deletions pybads/bads/gaussian_process_train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -572,8 +572,8 @@ def _robust_gp_fit_(
X = X[~idx_drop_out]
Y = Y[~idx_drop_out]
# Remove also user specified noise
if tmp_gp.s2 is not None and tmp_gp.s2.size > 0:
tmp_gp.s2 = tmp_gp.s2[~idx_drop_out]
if s2 is not None and s2.size > 0:
s2 = s2[~idx_drop_out]

# Retry with random sample prior
old_hyp_gp = (
Expand Down
34 changes: 33 additions & 1 deletion pybads/testing/bads/test_bads_optimization.py
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Expand Up @@ -24,7 +24,6 @@ def run_bads(fun, x0, LB, UB, PLB, PUB, tol_errs, f_min,
if uncertainty_handling > 0:
options["uncertainty_handling"] = True
options["max_fun_evals"] = 200 if max_fun_evals is None else max_fun_evals
options["specify_target_noise"] = True
if uncertainty_handling > 1:
options["specify_target_noise"] = True
else:
Expand Down Expand Up @@ -114,6 +113,38 @@ def test_noisy_sphere_opt():
oracle_fun = lambda x: np.sum(np.atleast_2d(x)**2, axis=1) # True objective function
run_bads(fun, x0, LB, UB, PLB, PUB, tol_errs, f_min=0.0, oracle_fun=oracle_fun, assert_flag=True)

def test_small_noisy_func():
np.random.seed(42343)
def noisy_sphere(x, sigma=1.0):
"""Simple quadratic function with added noise."""
x_2d = np.atleast_2d(x)
f = np.sum(x_2d**2, axis=1)
noise = 1e-4 * sigma * np.random.normal(size=x_2d.shape[0])
return f + noise

x0 = np.array([-3, -3, -3])
LB = np.array([-5, -5, -5])
UB = np.array([5, 5, 5])
PLB = np.array([-2, -2, -2])
PUB = np.array([2, 2, 2])
tol_errs = np.array([0.1, 0.1, 0.1])

oracle_fun = lambda x: np.sum(np.atleast_2d(x)**2, axis=1)
run_bads(
noisy_sphere,
x0,
LB,
UB,
PLB,
PUB,
tol_errs,
f_min=0.0,
oracle_fun=oracle_fun,
uncertainty_handling=1,
assert_flag=True,
max_fun_evals=300
)

def he_noisy_sphere(x):
y = np.sum(np.atleast_2d(x)**2, axis=1)
s = 2 + 1*np.sqrt(y)
Expand All @@ -125,3 +156,4 @@ def test_he_noisy_sphere_opt():
fun = he_noisy_sphere
oracle_fun = lambda x: np.sum(np.atleast_2d(x)**2, axis=1) # True objective function
run_bads(fun, x0, LB, UB, PLB, PUB, tol_errs, f_min=0.0, oracle_fun=oracle_fun, uncertainty_handling=2, assert_flag=True)