Score the counterfactual class-conditional VAE single-sample discriminator

Scores each row of X independently with the frozen model from fit_cvae, 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), then scored by the counterfactual evidence log-likelihood ratio $s(x) = \mathrm{ELBO}(x,\mathrm{case}) - \mathrm{ELBO}(x,\mathrm{control})$ using the latent MEAN $z = \mu$ (DETERMINISTIC – no sampling, no RNG). 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_cvae(model, X, meta = NULL)

Arguments

model: A cvae_model object from fit_cvae.
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

Kingma DP, Welling M. (2014) Auto-Encoding Variational Bayes. ICLR. arXiv:1312.6114.

Examples

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