Score within-cohort ILR balance discriminator — score_ws_balance

Scores each row of X independently with the frozen model learned by fit_ws_balance_ilr. The scorer first restricts the specimen panel to features from the frozen training universe that are present in X. If fewer than model$min_sbp_features canonical SBP features are present, the specimen/panel receives the documented neutral score 0. Otherwise, ws_balance_ilr computes the ILR balance matrix and columns with any non-NA value are summed per row with na.rm = TRUE; rows with no valid balance therefore also receive 0. The stored orientation sign is then applied so higher scores are more case-like.

A specimen with fewer than the minimum canonical SBP features has a degenerate low-information score. This is the single-sample analogue of the benchmark's panel-coverage ineligibility, surfaced as finite 0 instead of NA so foundation validators can require finite outputs.

Usage

score_ws_balance_ilr(model, X, meta = NULL)

Arguments

model: A ws_balance_ilr_model object returned by fit_ws_balance_ilr.
X: Numeric matrix (samples $\times$ features) of non-negative abundance values. Columns must be named feature ids.
meta: Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

Value

Plain finite numeric vector of length nrow(X). Scores use only each row's own values plus the frozen model, with no scored-batch centering, quantiles, or renormalization.

References

Egozcue J. J., Pawlowsky-Glahn V. (2005) Groups of parts and their balances in compositional data analysis. Mathematical Geology 37(7): 795-828.

Examples

if (FALSE) { # \dontrun{
sbp <- ws_default_sbp()
features <- unique(unlist(lapply(sbp, function(b) {
  c(b$numerator, b$denominator)
}), use.names = FALSE))
features <- setdiff(features, c("ALL_NON_RBC", "ALL_NON_PLT",
                                "TOP_K_BY_ABUNDANCE", "TAIL_BY_ABUNDANCE"))
X <- matrix(stats::rgamma(40 * length(features), shape = 2), nrow = 40,
            dimnames = list(NULL, features))
y <- rep(c(0, 1), each = 20)
model <- fit_ws_balance_ilr(X, y)
score_ws_balance_ilr(model, X[1, , drop = FALSE])
} # }