Flip the sign of constraints in GPSampler#6213
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nabenabe0928 merged 2 commits intooptuna:masterfrom Aug 6, 2025
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@gen740 @kAIto47802 |
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import numpy as np
import optuna
def objective(trial: optuna.Trial) -> float:
x = trial.suggest_float("x", 0.0, 2 * np.pi)
y = trial.suggest_float("y", 0.0, 2 * np.pi)
c = float(np.sin(x) * np.sin(y) + 0.95)
trial.set_user_attr("c", c)
return float(np.sin(x) + y)
def constraints(trial: optuna.trial.FrozenTrial) -> tuple[float]:
c = trial.user_attrs["c"]
return (c, )
sampler = optuna.samplers.GPSampler(constraints_func=constraints, seed=42)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=30)This code yielded the identical results to the master branch. |
kAIto47802
approved these changes
Jul 25, 2025
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kAIto47802
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Thank you for the PR. LGTM!
gen740
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Aug 1, 2025
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gen740
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I confirmed the PR code is returning an identical result in the master branch.
LGTM!
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@nabenabe0928 |
auto-merge was automatically disabled
August 1, 2025 06:08
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Motivation
Since GPSampler conventionally trains regressors on
yto be maximized, we adapt constraints to this convention.Description of the changes
Math Background
Suppose$p(x | \mu, \sigma^2)$ is the probability density function of the Gauss distribution $\mathcal{N}(\mu, \sigma^2)$ .
The previous version (x is better when it is lower):
$\int_{-\infty}^{f_0} p(x | \mu, \sigma^2) dx = \int_{-\infty}^{\frac{f_0 - \mu}{\sigma}} p( t \coloneqq \frac{x - \mu}{\sigma} | 0, 1) dt$
This version (x is better when it is higher):
$\int_{f_0}^{\infty} p(x | \mu, \sigma^2) dx = \int_{\frac{f_0 - \mu}{\sigma}}^{\infty} p( t \coloneqq \frac{x - \mu}{\sigma} | 0, 1) dt = \int_{-\frac{f_0 - \mu}{\sigma}}^{-\infty} -p( u \coloneqq -t | 0, 1) du = \int_{-\infty}^{-\frac{f_0 - \mu}{\sigma}} p( u | 0, 1) du$