Nothing
npindex_fit_progress_time_counter <- function(start = 0, by = 1.7) {
current <- start
function() {
current <<- current + by
current
}
}
npindex_fit_progress_time_values <- function(values) {
force(values)
i <- 0L
function() {
i <<- min(i + 1L, length(values))
values[[i]]
}
}
npindex_fit_progress_lines <- function(shadow) {
vapply(shadow$trace, `[[`, character(1L), "line")
}
make_npindex_fit_progress_fixture <- function() {
set.seed(20260404)
n <- 24L
dat <- data.frame(
x1 = runif(n, -1, 1),
x2 = runif(n, -1, 1)
)
index <- dat$x1 + 0.6 * dat$x2
dat$y <- sin(index) + 0.2 * index^2 + rnorm(n, sd = 0.05)
list(
dat = dat,
tx = dat[c("x1", "x2")],
y = dat$y,
bw = npindexbw(
xdat = dat[c("x1", "x2")],
ydat = dat$y,
bws = c(1, 0.6, 0.35),
method = "ichimura",
regtype = "lp",
degree = 1L,
bernstein.basis = TRUE,
bwtype = "fixed",
bandwidth.compute = FALSE
),
bw_lc_fixed = npindexbw(
xdat = dat[c("x1", "x2")],
ydat = dat$y,
bws = c(1, 0.6, 0.35),
method = "ichimura",
regtype = "lc",
bwtype = "fixed",
bandwidth.compute = FALSE
)
)
}
test_that("npindex direct lp fit emits known-total fit progress without handoff", {
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
bws = fixture$bw,
txdat = fixture$tx,
tydat = fixture$y
),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
expect_s3_class(actual$value, "singleindex")
expect_false(any(grepl(": starting$", lines)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 1/24 \\([0-9]+\\.[0-9]%, elapsed [0-9]+\\.[0-9]s, eta [0-9]+\\.[0-9]s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)))
})
test_that("npindex direct lp fit stays silent below start grace without handoff", {
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0.75,
np.progress.interval.known.sec = 0.5
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
bws = fixture$bw,
txdat = fixture$tx,
tydat = fixture$y
),
force_renderer = "single_line",
now = npindex_fit_progress_time_values(c(0, 0.2, 0.4, 0.6))
)
expect_length(actual$trace, 0L)
})
test_that("npindex lp bw to fit route hands off into the regression fit surface", {
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
y ~ x1 + x2,
data = fixture$dat,
method = "ichimura",
regtype = "lp",
degree.select = "coordinate",
search.engine = "cell",
degree.min = 0L,
degree.max = 1L,
bwtype = "fixed",
nmulti = 1L
),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
bandwidth.pos <- grep("^\\[np\\] Bandwidth selection \\(", lines)
fit.start.pos <- grep(
"^\\[np\\] Fitting regression 0/24 \\(0\\.0%, elapsed 0\\.0s, eta 0\\.0s\\): starting$",
lines
)
fit.finish.pos <- grep(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)
expect_s3_class(actual$value, "singleindex")
expect_true(length(bandwidth.pos) > 0L)
expect_true(length(fit.start.pos) == 1L)
expect_true(length(fit.finish.pos) >= 1L)
expect_lt(max(bandwidth.pos), fit.start.pos[[1L]])
expect_lt(fit.start.pos[[1L]], fit.finish.pos[[1L]])
})
test_that("npindex lp nomad to powell to fit route preserves single-line handoff", {
skip_if_not_installed("crs")
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
y ~ x1 + x2,
data = fixture$dat,
method = "ichimura",
nomad = TRUE,
degree.min = 0L,
degree.max = 1L,
nmulti = 1L
),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
bandwidth.pos <- grep("^\\[np\\] Selecting degree and bandwidth", lines)
powell.pos <- grep("^\\[np\\] Refining bandwidth \\(", lines)
fit.start.pos <- grep(
"^\\[np\\] Fitting regression 0/24 \\(0\\.0%, elapsed 0\\.0s, eta 0\\.0s\\): starting$",
lines
)
fit.finish.pos <- grep(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)
expect_s3_class(actual$value, "singleindex")
expect_true(length(bandwidth.pos) > 0L)
expect_true(length(powell.pos) > 0L)
expect_true(length(fit.start.pos) == 1L)
expect_true(length(fit.finish.pos) >= 1L)
expect_lt(max(bandwidth.pos), fit.start.pos[[1L]])
expect_lt(max(powell.pos), fit.start.pos[[1L]])
expect_lt(fit.start.pos[[1L]], fit.finish.pos[[1L]])
})
test_that("predict.singleindex lp re-entry emits evaluation and training fit progress without handoff", {
fixture <- make_npindex_fit_progress_fixture()
fit <- npindex(
bws = fixture$bw,
txdat = fixture$tx,
tydat = fixture$y
)
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
predict(fit, newdata = fixture$dat[c(2L, 7L), c("x1", "x2")]),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
expect_false(any(grepl(": starting$", lines)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 1/2 \\(50\\.0%, elapsed [0-9]+\\.[0-9]s, eta [0-9]+\\.[0-9]s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 2/2 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 1/24 \\([0-9]+\\.[0-9]%, elapsed [0-9]+\\.[0-9]s, eta [0-9]+\\.[0-9]s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)))
})
test_that("npindex direct fixed lc fit emits known-total fit progress without handoff", {
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
bws = fixture$bw_lc_fixed,
txdat = fixture$tx,
tydat = fixture$y
),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
expect_s3_class(actual$value, "singleindex")
expect_false(any(grepl(": starting$", lines)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 1/24 \\([0-9]+\\.[0-9]%, elapsed [0-9]+\\.[0-9]s, eta [0-9]+\\.[0-9]s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)))
})
test_that("npindex direct fixed lc fit stays silent below start grace without handoff", {
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0.75,
np.progress.interval.known.sec = 0.5
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
bws = fixture$bw_lc_fixed,
txdat = fixture$tx,
tydat = fixture$y
),
force_renderer = "single_line",
now = npindex_fit_progress_time_values(c(0, 0.2, 0.4, 0.6))
)
expect_length(actual$trace, 0L)
})
test_that("npindex fixed lc bw to fit route hands off into the regression fit surface", {
fixture <- make_npindex_fit_progress_fixture()
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
npindex(
y ~ x1 + x2,
data = fixture$dat,
method = "ichimura",
regtype = "lc",
bwtype = "fixed",
nmulti = 1L
),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
bandwidth.pos <- grep("^\\[np\\] Bandwidth selection \\(", lines)
fit.start.pos <- grep(
"^\\[np\\] Fitting regression 0/24 \\(0\\.0%, elapsed 0\\.0s, eta 0\\.0s\\): starting$",
lines
)
fit.finish.pos <- grep(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)
expect_s3_class(actual$value, "singleindex")
expect_true(length(bandwidth.pos) > 0L)
expect_true(length(fit.start.pos) == 1L)
expect_true(length(fit.finish.pos) >= 1L)
expect_lt(max(bandwidth.pos), fit.start.pos[[1L]])
expect_lt(fit.start.pos[[1L]], fit.finish.pos[[1L]])
})
test_that("predict.singleindex fixed lc re-entry emits evaluation and training fit progress without handoff", {
fixture <- make_npindex_fit_progress_fixture()
fit <- npindex(
bws = fixture$bw_lc_fixed,
txdat = fixture$tx,
tydat = fixture$y
)
old_opts <- options(
np.messages = TRUE,
np.tree = FALSE,
np.progress.start.grace.known.sec = 0,
np.progress.interval.known.sec = 0
)
on.exit(options(old_opts), add = TRUE)
actual <- capture_progress_shadow_trace(
predict(fit, newdata = fixture$dat[c(2L, 7L), c("x1", "x2")]),
force_renderer = "single_line",
now = npindex_fit_progress_time_counter()
)
lines <- npindex_fit_progress_lines(actual)
expect_false(any(grepl(": starting$", lines)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 1/2 \\(50\\.0%, elapsed [0-9]+\\.[0-9]s, eta [0-9]+\\.[0-9]s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 2/2 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 1/24 \\([0-9]+\\.[0-9]%, elapsed [0-9]+\\.[0-9]s, eta [0-9]+\\.[0-9]s\\)$",
lines
)))
expect_true(any(grepl(
"^\\[np\\] Fitting regression 24/24 \\(100\\.0%, elapsed [0-9]+\\.[0-9]s, eta 0\\.0s\\)$",
lines
)))
})
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.