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Fix linear GP posterior variance #20

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Merged
huangyz0918 merged 1 commit into from
Sep 28, 2026
Merged

Fix linear GP posterior variance #20

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2 changes: 2 additions & 0 deletions proeval/generator/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -405,6 +405,8 @@ def get_posterior_embedding(
return posterior_mean.detach().cpu().numpy(), posterior_cov.detach().cpu().numpy()
else:
K_test_diag = torch.ones(phi_test_t.shape[0], device=device)
if kernel_type == "linear":
K_test_diag = torch.sum(phi_test_t ** 2, dim=1)
v = torch.cholesky_solve(K_test_train.t(), L)
posterior_var = K_test_diag - torch.sum(K_test_train * v.t(), dim=1)
posterior_var = torch.clamp(posterior_var, min=1e-10)
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15 changes: 15 additions & 0 deletions tests/test_bq_sampling.py
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Expand Up @@ -48,6 +48,21 @@ def parameters(self):
yield self._anchor


def test_linear_encoder_marginal_variance_matches_covariance():
torch = pytest.importorskip("torch")
from proeval.generator.core import get_posterior_embedding

phi = np.array([[0.0, 0.0], [0.2, -0.2], [1.0, -1.0]], dtype=np.float32)
args = (
phi[[2]], np.array([1.0]), phi, 0.3, [2],
np.full(3, 0.5), _DummyLinearEncoder(torch),
)
_, variance = get_posterior_embedding(*args)
_, covariance = get_posterior_embedding(*args, full_cov=True)

np.testing.assert_allclose(variance, np.diag(covariance), atol=1e-6)


def _run_active(kind, budget, n_init=0):
features, labels, prior_mean, prior_cov = _synthetic_case()
np.random.seed(7)
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