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escape one frollsd tests for valgrind #7548

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escape one frollsd tests for valgrind #7548

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30f2a4f
escape one tests for valgrind
jangorecki Dec 29, 2025
9b91c2c
increment skipped count
jangorecki Dec 29, 2025
5d50ca9
escape proper one
jangorecki Dec 29, 2025
c524ccf
make test robust to valgrind numerical issues
MichaelChirico Dec 31, 2025
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11 changes: 7 additions & 4 deletions inst/tests/froll.Rraw
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ if (exists("test.data.table", .GlobalEnv, inherits=FALSE)) {

exact_NaN = isTRUE(capabilities()["long.double"]) && identical(as.integer(.Machine$longdouble.digits), 64L)
if (!exact_NaN) {
cat("\n**** Skipping 7 NaN/NA algo='exact' tests because .Machine$longdouble.digits==", .Machine$longdouble.digits, " (!=64); e.g. under valgrind\n\n", sep="")
cat("\n**** Skipping 8 NaN/NA algo='exact' tests because .Machine$longdouble.digits==", .Machine$longdouble.digits, " (!=64); e.g. under valgrind\n\n", sep="")
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@MichaelChirico MichaelChirico Dec 29, 2025

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Maybe we could do something like nskip for foreign messages to avoid incrementing this manually

data.table/R/test.data.table.R

Line 336 in 7e9907f

if (foreign && nskip > 0L) catf("Skipped %d tests for translated messages. ", nskip) # nocov

# for Matt when he runs valgrind it is 53, but 64 when running regular R
# froll.c uses long double and appears to require full long double accuracy in the algo='exact'
}
Expand Down Expand Up @@ -1448,9 +1448,12 @@ test(6001.727, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact"), c(NA_
test(6001.728, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact", na.rm=TRUE), c(NA_real_,NA_real_,NA_real_))
test(6001.729, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), algo="exact", na.rm=TRUE, partial=TRUE), c(NA_real_,NA_real_,NA_real_))
test(6001.730, frollvar(adaptive=TRUE, c(1:2,NA), c(2,0,2), fill=99, algo="exact", na.rm=TRUE), c(99,NA,NA))
y = c(1e8+2.980232e-8, 1e8, 1e8, 1e8) # CLAMP0 test
test(6001.731, frollvar(y, 3)[4L], 0)
test(6001.732, frollsd(y, 3)[4L], 0)
# numerical stability: we need to guarantee frollvar(x, n) >= 0 for all x, n
# the exact epsilon here is a bit implementation-dependent (as in #7546), but what's
# crucial is the output is never negative (or NaN after sqrt() for frollsd).
y = c(1e8+2.980232e-8, 1e8, 1e8, 1e8)
test(6001.731, between(frollvar(y, 3)[4L], 0, 1e-7))
test(6001.732, between(frollsd(y, 3)[4L], 0, 1e-7))
test(6001.733, frollvar(y, c(3,3,3,3), adaptive=TRUE)[4L], 0)
test(6001.734, frollsd(y, c(3,3,3,3), adaptive=TRUE)[4L], 0)
test(6001.740, frollvar(c(1.5,2.5,2,NA), c(3,3)), list(c(NA,NA,0.25,NA), c(NA,NA,0.25,NA)), output="running sequentially, because outer parallelism has been used", options=c(datatable.verbose=TRUE)) # ensure no nested parallelism in rolling functions #7352
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