Fit the 1D wavelet-scattering single-sample discriminator

Maps the TRAINING matrix to the per-sample robust CLR over the frozen feature universe (colnames(X_train)), freezes the scattering signal length to shape = next_pow2(ncol(X_train)), computes each training specimen's float64 1D wavelet scattering coefficients (FORCED ROW-BY-ROW) via reticulate-python torch + kymatio, freezes a per-coordinate coefficient standardization, and fits a FROZEN ridge-logistic head over the standardized coefficients. If hp$lambda is NULL the ridge penalty is selected by TRAINING-ONLY deterministic class-stratified k-fold CV (no RNG). The fitted model holds NO external pointer – score rebuilds ScatteringTorch1D from the stored config.

Usage

fit_inv_scatter(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: J (scattering scale, positive integer; default 3L), Q (wavelets per octave, positive integer; default 4L), max_order (scattering order, 1L or 2L; default 2L), lambda (ridge penalty for the logistic head; NULL (default) selects it by training-only deterministic k-fold CV, otherwise a non-negative finite number), min_features (feature-overlap floor at scoring, positive integer; default 3L), device ("cpu" (default), "cuda", or "auto"; "cuda"/"auto" fall back to CPU with no GPU), and seed (integer; default 42L).

Value

Object of class inv_scatter_model: a list with feature_universe, J, Q, shape, max_order, coef_mean, coef_sd (frozen standardization), intercept, weights (frozen linear head), lambda (resolved ridge penalty), device (resolved), seed, and hp. No python pointer.

References

Anden J, Mallat S. (2014) Deep Scattering Spectrum. IEEE TSP 62(16):4114-4128.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
n <- 80; p <- 16; 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_inv_scatter(X, y)
score_inv_scatter(model, X[1, , drop = FALSE])
} # }