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Cross-validate psvr_mape() with automatic warm-start across folds

psvr_cv.Rd

Fits a psvr_mape() model on each split in splits, carrying the converged dual variables (alpha, alpha_star) from one fold into the next as the SMO warm-start, so each solve starts from the previous fold's optimum instead of from zero. Because consecutive folds share most of their training rows, that starting point is already close to feasible; before each solve the carried vectors are projected back onto the constraint set (the equality \(\sum_k \beta_k = 0\) and the per-sample box), with the residual violation absorbed by the rows that are new to this fold. The warm-start procedure is Algorithm 1 of arXiv:2605.01446 v3. Returns a tibble with one row per fold.

Usage

psvr_cv(
  splits,
  ...,
  X_var = NULL,
  y_var = NULL,
  warm_start = TRUE,
  verbose = FALSE
)

Arguments

splits

Either an rsample::rset object (e.g. from rsample::vfold_cv()), or a list of named lists each containing analysis (data frame), assessment (data frame), and optionally row_ids (integer vector of original training-row indices used for warm-start alignment across folds; defaults to positional).

...

Arguments forwarded to psvr_mape(). Must specify kernel and the MAPE hyperparameters (C, eps). alpha_init and alpha_star_init are managed internally; supplying them via ... is an error, and so is loss, which is not an argument of psvr_mape().

X_var

Character vector of predictor column names.

y_var

Single character giving the target column name.

warm_start

Logical; if FALSE, each fold fits cold-start (useful for benchmarking the T5 speedup).

verbose

Logical; if TRUE, report per-fold progress via message() (suppressible with suppressMessages()).

Value

A tibble with one row per split and columns:

split_id

1-based fold index.

fit

A list-column of psvr_mape objects, or psvr_mape_sym when sym_type is "even" or "odd".

predictions

A list-column of numeric vectors (predictions on the assessment set).

metrics

A list-column of named numeric vectors (mape, rmspe, mse, r2).

iter_count

Integer; SMO iterations from fit$iterations.

elapsed_sec

Numeric; wall-clock seconds for the fit.

warm_started

Logical; TRUE for fold > 1 when warm_start = TRUE.

Details

This helper is MAPE-only, and there is no loss argument. That is a limitation of the implementation, not of the method: only psvr_mape() was ever wired to it. LS-SVR cross-validates perfectly well, it simply has no carryover state to exploit (each fold is a single linear-system solve), so for psvr_rmspe() use tune::tune_grid() with parallel cold-start.

Examples

if (requireNamespace("rsample", quietly = TRUE) &&
    requireNamespace("tibble",  quietly = TRUE)) {
  set.seed(2026)
  d <- data.frame(
    y  = stats::rlnorm(80, sdlog = 1.0),
    x1 = stats::rnorm(80),
    x2 = stats::rnorm(80)
  )
  folds <- rsample::vfold_cv(d, v = 5)
  res <- psvr_cv(folds, X_var = c("x1", "x2"), y_var = "y",
                 kernel = make_kernel("rbf", sigma = 1),
                 C = 10, eps = 5)
  median(vapply(res$metrics, function(m) m[["mape"]], numeric(1)))
}
#> Warning: SMO solver did not converge within max_iter = 100000 (final iter = 100000)
#> [1] 157.6322