Score the DeepCoDA zero-sum log-contrast single-sample discriminator

Scores each row of X independently with the frozen model from fit_coda_deepcoda, 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), projected through the frozen zero-sum log-contrast bottleneck, passed through the self-explaining $\theta$-net, and scored by the DeepCoDA logit $\sum_k \theta_k(x)\,b_k(x) + c$. 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 (its bottlenecks are all 0, so the logit reduces to the learned bias – a genuine computed value, not a floored 0). 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_coda_deepcoda(model, X, meta = NULL)

Arguments

model: A coda_deepcoda_model object from fit_coda_deepcoda.
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

Quinn TP, Nguyen D, Rana S, Gupta S, Venkatesh S. (2020) DeepCoDA: personalized interpretability for compositional health data. ICML, PMLR 119. arXiv:2006.01392.

Examples

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