Score the spectral-normalized neural Gaussian process discriminator (SNGP)

Scores each row of X independently with the frozen model from fit_unc_sngp, in PURE R (no python). Each query is mapped to the per-sample robust CLR over the frozen feature universe (absent universe features carry the neutral rCLR value 0), embedded by the exported spectral-normalized LayerNorm encoder forward, projected by the fixed random-Fourier-feature map, and scored by the mean-field-adjusted RFF-GP posterior logit. Larger = more case-like.

Queries with fewer than model$hp$min_features universe features present (a column-overlap floor), or empty positive support over the frozen universe on the (pre-rCLR) aligned abundances (!any(X_use[i, ] > 0)), return the neutral score 0. A FLAT all-equal-positive composition maps to the rCLR origin but is a VALID specimen and is scored normally. The score of a row depends only on that row and the frozen model and is exactly invariant to per-specimen positive scaling.

Usage

score_unc_sngp(model, X, meta = NULL)

Arguments

model: A unc_sngp_model object from fit_unc_sngp.
X: Numeric matrix (samples $\times$ features) of non-negative abundances with named feature columns.
meta: Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

Value

Plain finite numeric vector of length nrow(X); larger values are more case-like.

References

Liu JZ, Lin Z, Padhy S, Tran D, Bedrax-Weiss T, Lakshminarayanan B. (2020) Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS 33. arXiv:2006.10108.

Examples

if (FALSE) { # \dontrun{
model <- fit_unc_sngp(X, y)
score_unc_sngp(model, X[1, , drop = FALSE])
} # }