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,
  extreme_transform = NULL
)

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.

extreme_transform

Optional function of a numeric matrix that creates extreme neighbours within the input domain, preserving dimensions and names. The default NULL multiplies values by one million. For raw Ct, shift detected values down by log(1e6) / log(ct_base) cycles and leave non-detects unchanged. Only neighbours are transformed; target rows retain their original measurements.

Value

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

See also

[singlesample_assert_row_equivariant()]