Check whether a surrogate-IES regime is favourable

Issues a warning when the ratio of training points to parameters falls below an empirical threshold, where the Gaussian-process surrogate inside pesto_surrogate_ies() and surrogate_ensemble_update() is unlikely to repay its training cost. The check is a soft guardrail — it does not modify the run, only flags an unfavourable regime so the caller can decide whether to fall back to pure IES.

Usage

check_surrogate_regime(n_params, n_train, threshold = 5)

Arguments

n_params: Integer. Number of estimated parameters.
n_train: Integer. Number of training samples available to the surrogate (typically the ensemble size).
threshold: Numeric. Minimum acceptable n_train / n_params ratio. Default 5, the empirical soft floor from the surrogate-IES vignette.

Value

Invisibly returns TRUE when the regime is favourable (n_train >= threshold * n_params) and FALSE otherwise. Called for the warning side-effect.

Details

The default threshold of 5 corresponds to the soft floor n_train >= 5 * n_params documented in vignette("surrogate-ies", package = "PESTO"). Below that floor the GP posterior variance typically stays above the uncertainty-driven switching threshold and surrogate savings collapse to near zero.

This is exposed as a stand-alone helper so users can call it explicitly before scheduling an expensive ensemble. It is not invoked automatically by pesto_surrogate_ies() in the current release; that wiring is tracked as a v0.2 enhancement candidate.

References

Rasmussen, C. E. & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

Examples

# Favourable regime: 100 training points for 10 parameters.
check_surrogate_regime(n_params = 10L, n_train = 100L)

# Unfavourable regime: 30 training points for 30 parameters
# (the curse-of-dimensionality case from Scenario C of the
# comparison-and-simulation vignette). Emits a warning.
suppressWarnings(
  check_surrogate_regime(n_params = 30L, n_train = 30L)
)

# Custom threshold for users with a smoother forward model.
check_surrogate_regime(n_params = 20L, n_train = 60L, threshold = 3)