Score the deep-feature class-conditional Mahalanobis LRT discriminator

Scores each row of X independently with the frozen model from fit_lrt_deepmaha, 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 MLP forward, and scored by the class-conditional Mahalanobis log-likelihood ratio (case minus control) with the frozen tied covariance. 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_lrt_deepmaha(model, X, meta = NULL)

Arguments

model: A lrt_deepmaha_model object from fit_lrt_deepmaha.
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

Lee K, Lee K, Lee H, Shin J. (2018) A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. NeurIPS 31. arXiv:1807.03888.

Examples

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