Fit the TabDPT in-context discriminator (freeze the training context)

Freezes the training context for TabDPT in-context classification. The training matrix is mapped to the per-sample robust CLR over the frozen feature universe (colnames(X_train)); if the context exceeds hp$max_context rows it is reduced to a deterministic, seeded, class-stratified subsample. A TabDPT classifier (pinned token-free checkpoint) is constructed and fit() on that frozen rCLR context (which, for TabDPT, simply loads the context into the model and fits the preprocessing scaler / optional PCA – no gradient training). The frozen R-side state needed to reproduce scores (the rCLR context, labels, feature universe, class order, device/config, hp) is stored on the model; the live python classifier is also kept for scoring but is NEVER part of the model digest.

Usage

fit_tabdpt(X_train, y_train, meta_train = NULL, hp = list())

Arguments

X_train

Numeric matrix (samples \(\times\) features) of non-negative abundances with unique, non-empty feature names (colnames).

y_train

Numeric / integer 0/1 labels (1 = case), length nrow(X_train), with at least one case and one control.

meta_train

Optional per-sample metadata. Accepted for interface uniformity and otherwise ignored.

hp

Optional list of hyperparameters. Allowed fields: device ("cpu" (default), "cuda", or "auto"; "cuda" / "auto" fall back to CPU when no GPU is available), temperature (TabDPT softmax temperature at predict, positive finite, default 1.0), normalizer (TabDPT input scaler, one of "standard" (default), "minmax", "robust", "power", "quantile-uniform", "quantile-normal", "log1p"), max_context (row cap on the frozen context, integer \(\ge 2\), default 4096L; larger contexts are seeded class-stratified subsampled), min_features (feature-overlap floor at scoring, positive integer, default 3L), seed (integer; default 42L), and score_batch (benchmark-only batched scoring flag, logical, default FALSE).

Value

Object of class tabdpt_model: a list with context_rclr (frozen rCLR context matrix), context_y, feature_universe, classes, case_col, n_context, device (resolved), checkpoint (pinned checkpoint version), hp, and the live python classifier clf (excluded from the digest).

References

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

Examples

if (FALSE) { # \dontrun{
set.seed(1)
n <- 120; p <- 20; k <- 6
L <- matrix(stats::rnorm(n * p, 4, 0.5), nrow = n,
            dimnames = list(NULL, paste0("miR-", seq_len(p))))
y <- rep(c(0, 1), each = n / 2)
L[y == 1, seq_len(k)] <- L[y == 1, seq_len(k)] + 1.2
X <- exp(L)
model <- fit_tabdpt(X, y)
score_tabdpt(model, X[1, , drop = FALSE])
} # }