Fit Mondrian-conformal class-conditional LRT discriminator

Learns a frozen two-class discriminator from training data using class-conditional conformal nonconformity scores in per-sample robust centered log-ratio (rCLR) coordinates. Each class stores raw training anchors, a centroid in within-sample rCLR space, and a calibration set of class-specific nonconformity scores. No test data or test-time batch statistics are used.

Usage

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

Arguments

X_train

Numeric matrix (samples \(\times\) features) of non-negative abundance values. Columns must be uniquely named feature ids.

y_train

Integer/numeric 0/1 labels aligned to X_train; 1 is case/disease and 0 is control.

meta_train

Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

hp

List of frozen hyperparameters: nonconformity, either "euclid" (default) or "mahalanobis" for diagonal class-variance weighting; max_anchors_per_class positive integer anchor cap per class (default 200L); min_features positive integer feature-overlap floor at scoring (default 3L); eps positive finite variance floor used by the Mahalanobis path (default 1e-6); and seed non-negative integer used only for deterministic anchor subsampling while restoring the global RNG state (default 1L).

Value

A plain list of class conf_mondrian_model containing feature_universe, raw case_anchors and control_anchors, full-universe repr for inspection, and the resolved hp.

Details

The conformal p-value is used here as a discriminative atypicality contrast, not as an exact coverage-calibrated guarantee: the same training anchors define the centroids and the calibration nonconformity scores, so exchangeability is approximate.

References

Vovk V, Gammerman A, Shafer G. (2005) Algorithmic Learning in a Random World. Springer.

Shafer G, Vovk V. (2008) A tutorial on conformal prediction. Journal of Machine Learning Research 9: 371-421.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X <- matrix(stats::rgamma(60 * 30, shape = 2), nrow = 60,
            dimnames = list(NULL, paste0("miR-", seq_len(30))))
y <- rep(c(0, 1), each = 30)
X[y == 1, 1:5] <- X[y == 1, 1:5] + 20
X[y == 0, 6:10] <- X[y == 0, 6:10] + 20
model <- fit_conf_mondrian(X, y)
score <- score_conf_mondrian(model, X)
} # }