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

Scores each row of X independently with the frozen model from fit_tabicl. 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 TabICL 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_tabicl(model, X, meta = NULL)

Arguments

model

A tabicl_model object from fit_tabicl.

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

The legacy score_batch option is retained for serialized-model and hyperparameter compatibility, but scoring always uses the row-by-row \(n=1\) forward path. This is intentional: fused GPU batch kernels produced small batch-size-dependent numerical differences on otherwise identical queries, which violates the operational single-sample contract.

References

Qu J, Holzmuller D, Varoquaux G, Le Morvan M. (2025) TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. ICML; arXiv:2502.05564.

Examples

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