Score the truncated path-signature discriminator (pure base R, row-by-row)

Scores each row of X independently with the frozen model from fit_sig_path. Each query is mapped to the per-sample robust CLR over the frozen feature universe (absent universe features carry the neutral rCLR value 0), lifted to the time-augmented 2D path, its closed-form truncated path signature computed in PURE BASE R ONE ROW AT A TIME, standardized with the FROZEN training statistics, and passed through the FROZEN ridge-logistic linear head. The score is $\mathrm{intercept} + \sum_d w_d \cdot \mathrm{std\_sig}_d(x)$; larger = more case-like. The signature is a deterministic pure-R function of the row alone, so a row's score is bit-identical whether scored alone or in any batch.

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 ORIGINAL abundances (!any(X_use[i, ] > 0)), return the neutral score 0. A FLAT all-equal-positive composition maps to the rCLR origin (all-zero $v$) but is a VALID specimen scored normally (NOT floored): the time coordinate still moves, so its path signature is non-degenerate. 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_sig_path(model, X, meta = NULL)

Arguments

model: A sig_path_model object from fit_sig_path.
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

Chevyrev I, Kormilitzin A. (2016) A primer on the signature method in machine learning. arXiv:1603.03788.

Examples

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