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Merge pull request #633 from contramundum53/simplify-ep

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[Preferential] Simplify EP implementation
This commit is contained in:
Naoto Mizuno
2023-10-06 11:12:07 +09:00
committed by GitHub
parent 0e5398c11e f2f233c753
commit 6f79d7d9fb
1 changed files with 19 additions and 15 deletions
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optuna_dashboard/preferential/samplers/gp.py
+19 -15
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@@ -158,6 +158,18 @@ def _truncnorm_mean_var_logz(alpha: Tensor) -> tuple[Tensor, Tensor, Tensor]:
return mean, var, logz
def _observation(var0: Tensor, mean0: Tensor, noise_var: Tensor) -> tuple[Tensor, Tensor, Tensor]:
obs_var = var0 + noise_var
obs_sigma = torch.sqrt(obs_var)
alpha = -mean0 / torch.clamp_min(obs_sigma, min=1e-20)
mean_norm, var_norm, logz = _truncnorm_mean_var_logz(alpha)
denom_factor = 1 / torch.clamp_min(noise_var + var_norm * var0, min=1e-20)
da = (1 - var_norm) * denom_factor
db = (mean0 * (1 - var_norm) + obs_sigma * mean_norm) * denom_factor
return (da, db, logz)
def _orthants_MVN_EP(
cov0: Tensor, preferences: Tensor, noise_var: Tensor, cycles: int
) -> tuple[Tensor, Tensor, Tensor]:
@@ -178,25 +190,17 @@ def _orthants_MVN_EP(
r0 = (1 - var1 * virtual_obs_a[i]).reciprocal()
var0 = var1 * r0
mean0 = (mean1 + var1 * virtual_obs_b[i]) * r0
mean0 = (mean1 - var1 * virtual_obs_b[i]) * r0
obs_var = var0 + noise_var
obs_sigma = torch.sqrt(obs_var)
alpha = -mean0 / torch.clamp_min(obs_sigma, min=1e-20)
mean_norm, var_norm, logz = _truncnorm_mean_var_logz(alpha)
virtual_obs_a2, virtual_obs_b2, logz = _observation(var0, mean0, noise_var)
kalman_factor = var0 / torch.clamp_min(obs_var, min=1e-20)
mean2 = mean0 + obs_sigma * mean_norm * kalman_factor
var2 = kalman_factor * (noise_var + var_norm * var0)
var1_var2_inv = torch.clamp_min(var1 * var2, min=1e-20).reciprocal()
db = (mean1 * var2 - mean2 * var1) * var1_var2_inv
da = (var1 - var2) * var1_var2_inv
virtual_obs_b[i] = virtual_obs_b[i] + db
virtual_obs_a[i] = virtual_obs_a[i] + da
da = virtual_obs_a2 - virtual_obs_a[i]
db = virtual_obs_b2 - virtual_obs_b[i]
virtual_obs_a[i] = virtual_obs_a2
virtual_obs_b[i] = virtual_obs_b2
dr = (1 + var1 * da).reciprocal()
mu = mu - Sxy * ((db + mean1 * da) * dr)
mu = mu + Sxy * ((db - mean1 * da) * dr)
cov = cov - (Sxy[:, None] * (da * dr)) @ Sxy[None, :]
log_zs[i] = logz
return mu, cov, torch.sum(log_zs)