Score the TabDPT in-context discriminator (default row-by-row)

Scores each row of X independently with the frozen model from fit_tabdpt. 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 classified by TabDPT against the FROZEN training context. By default, queries are passed to predict_proba ONE ROW AT A TIME (the \(n=1\) forward path used uniformly); the score is the case posterior \(P(y = 1)\) in \([0, 1]\), larger = more case-like.

Queries with fewer than model$hp$min_features present universe features, or an all-zero / empty specimen, return the neutral probability 0.5. 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_tabdpt(model, X, meta = NULL)

Arguments

model

A tabdpt_model object from fit_tabdpt.

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) in \([0, 1]\); larger values are more case-like.

Details

A benchmark-only score_batch option evaluates predict_proba once over balanced chunks of query rows. This path is leakage-free and AUC-faithful (\(|dAUC| = 0\); specimen ranks/verdicts unchanged), but is not bit-identical to the default \(n = 1\) row-by-row path because of a deterministic batch-size numerical effect. The default row-by-row single-specimen deployment path is unchanged.

References

Ma J, Thomas V, Hosseinzadeh R, et al. (2024) TabDPT: Scaling Tabular Foundation Models. arXiv:2410.18164.

Examples

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