Test whether a scorer is row-equivariant (non-throwing)

Convenience wrapper around [singlesample_assert_row_equivariant()] returning a logical instead of erroring. Used both for affirmative checks and for the negative test that a deliberately batch-coupled scorer (e.g. the batchwise Sinkhorn plan) is *not* row-equivariant.

Usage

singlesample_is_row_equivariant(
  score_fun,
  model,
  X,
  meta = NULL,
  n_perm = 4L,
  tol = 1e-08,
  n_singletons = 4L,
  model_digest = NULL,
  seed = 1L
)

Arguments

score_fun

A function `function(model, X, meta)` returning one numeric score per row of `X`.

model

A fitted model object.

X

Numeric matrix (samples x features), at least 4 rows.

meta

Optional per-sample metadata (data frame with `nrow(X)` rows).

n_perm

Number of random row permutations to test.

tol

Absolute tolerance for score agreement (default 1e-8).

n_singletons

Number of distinct rows to test as singletons (always includes the first and last row). Pass `nrow(X)` for an exhaustive sweep.

model_digest

Optional `function(model)` returning a comparable snapshot of the model's mutable state. Defaults to a serialization snapshot; supply a custom closure for torch / external-pointer models that do not serialize deterministically.

seed

Random seed for reproducible permutations/subsets.

Value

`TRUE` if row-equivariant, `FALSE` otherwise.

See also

[singlesample_assert_row_equivariant()]