Last updated on 2019-11-26 00:51:46 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0.0 | 11.97 | 209.35 | 221.32 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 2.0.0 | 12.36 | 151.40 | 163.76 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.0.0 | 472.86 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0.0 | 440.97 | OK | |||
| r-devel-windows-ix86+x86_64 | 2.0.0 | 52.00 | 426.00 | 478.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 2.0.0 | 27.00 | 360.00 | 387.00 | OK | |
| r-patched-linux-x86_64 | 2.0.0 | 13.33 | 361.44 | 374.77 | OK | |
| r-patched-solaris-x86 | 2.0.0 | 672.80 | OK | |||
| r-release-linux-x86_64 | 2.0.0 | 13.17 | 364.82 | 377.99 | OK | |
| r-release-windows-ix86+x86_64 | 2.0.0 | 42.00 | 329.00 | 371.00 | OK | |
| r-release-osx-x86_64 | 2.0.0 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 2.0.0 | 19.00 | 425.00 | 444.00 | OK | |
| r-oldrel-osx-x86_64 | 2.0.0 | OK |
Version: 2.0.0
Check: tests
Result: ERROR
Running 'testthat.R' [63s/63s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(caretEnsemble)
>
> test_check("caretEnsemble")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
caretEnsemble
--- call from context ---
predict.caretList(models)
--- call from argument ---
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
--- R stacktrace ---
where 1: predict.caretList(models)
where 2: predict(models)
where 3: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
where 4: withCallingHandlers(code, warning = function(condition) {
out$push(condition)
maybe_restart("muffleWarning")
})
where 5: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
...)
where 6: quasi_capture(enquo(object), label, capture_warnings)
where 7 at testthat/test-caretList.R#109: expect_warning(p1 <- predict(models))
where 8: eval(code, test_env)
where 9: eval(code, test_env)
where 10: withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error)
where 11: doTryCatch(return(expr), name, parentenv, handler)
where 12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 13: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
where 14: doTryCatch(return(expr), name, parentenv, handler)
where 15: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
names[nh], parentenv, handlers[[nh]])
where 16: tryCatchList(expr, classes, parentenv, handlers)
where 17: tryCatch(withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error), error = handle_fatal,
skip = function(e) {
})
where 18: test_code(desc, code, env = parent.frame())
where 19 at testthat/test-caretList.R#94: test_that("caretList predictions", {
skip_if_not_installed("randomForest")
skip_if_not_installed("nnet")
skip_if_not_installed("plyr")
expect_warning({
models <- caretList(iris[, 1:2], iris[, 5], tuneLength = 1,
verbose = FALSE, methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet",
trace = FALSE)), trControl = trainControl(method = "cv",
number = 2, savePredictions = "final", classProbs = FALSE))
})
expect_warning(p1 <- predict(models))
p2 <- predict(models, newdata = iris[100, c(1:2)])
p3 <- predict(models, newdata = iris[110, c(1:2)])
expect_is(p1, "matrix")
expect_is(p1[, 1], "character")
expect_is(p1[, 2], "character")
expect_equal(names(models), colnames(p1))
expect_is(p2, "matrix")
expect_is(p2[, 1], "character")
expect_is(p2[, 2], "character")
expect_equal(names(models), colnames(p2))
expect_is(p3, "matrix")
expect_is(p3[, 1], "character")
expect_is(p3[, 2], "character")
expect_equal(names(models), colnames(p3))
expect_warning({
models <- caretList(iris[, 1:2], iris[, 5], tuneLength = 1,
verbose = FALSE, methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet",
trace = FALSE)), trControl = trainControl(method = "cv",
number = 2, savePredictions = "final", classProbs = TRUE))
})
expect_warning(p2 <- predict(models))
p3 <- predict(models, newdata = iris[100, c(1:2)])
expect_is(p2, "matrix")
expect_is(p2[, 1], "numeric")
expect_is(p2[, 2], "numeric")
expect_is(p3, "matrix")
expect_is(p3[, 1], "numeric")
expect_is(p3[, 2], "numeric")
expect_equal(names(models), colnames(p3))
models[[1]]$modelType <- "Bogus"
expect_error(expect_warning(predict(models)))
})
where 20: eval(code, test_env)
where 21: eval(code, test_env)
where 22: withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error)
where 23: doTryCatch(return(expr), name, parentenv, handler)
where 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 25: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
where 26: doTryCatch(return(expr), name, parentenv, handler)
where 27: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
names[nh], parentenv, handlers[[nh]])
where 28: tryCatchList(expr, classes, parentenv, handlers)
where 29: tryCatch(withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error), error = handle_fatal,
skip = function(e) {
})
where 30: test_code(NULL, exprs, env)
where 31: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
where 32: force(code)
where 33: doWithOneRestart(return(expr), restart)
where 34: withOneRestart(expr, restarts[[1L]])
where 35: withRestarts(testthat_abort_reporter = function() NULL, force(code))
where 36: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
{
reporter$start_file(basename(path))
lister$start_file(basename(path))
source_file(path, new.env(parent = env), chdir = TRUE,
wrap = wrap)
reporter$.end_context()
reporter$end_file()
})
where 37: FUN(X[[i]], ...)
where 38: lapply(paths, test_file, env = env, reporter = current_reporter,
start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
where 39: force(code)
where 40: doWithOneRestart(return(expr), restart)
where 41: withOneRestart(expr, restarts[[1L]])
where 42: withRestarts(testthat_abort_reporter = function() NULL, force(code))
where 43: with_reporter(reporter = current_reporter, results <- lapply(paths,
test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
load_helpers = FALSE, wrap = wrap))
where 44: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
stop_on_warning = stop_on_warning, wrap = wrap)
where 45: test_dir(path = test_path, reporter = reporter, env = env, filter = filter,
..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
wrap = wrap)
where 46: test_package_dir(package = package, test_path = test_path, filter = filter,
reporter = reporter, ..., stop_on_failure = stop_on_failure,
stop_on_warning = stop_on_warning, wrap = wrap)
where 47: test_check("caretEnsemble")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (object, newdata = NULL, ..., verbose = FALSE)
{
if (is.null(newdata)) {
warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
newdata <- object[[1]]$trainingData
if (is.null(newdata)) {
stop("Could not find training data in the first model in the ensemble.")
}
}
if (verbose == TRUE) {
pboptions(type = "txt", char = "*")
}
else if (verbose == FALSE) {
pboptions(type = "none")
}
preds <- pbsapply(object, function(x) {
type <- x$modelType
if (type == "Classification") {
if (x$control$classProbs) {
caret::predict.train(x, type = "prob", newdata = newdata,
...)[, 2]
}
else {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
}
else if (type == "Regression") {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
else {
stop(paste("Unknown model type:", type))
}
})
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
colnames(preds) <- make.names(sapply(object, function(x) x$method),
unique = TRUE)
return(preds)
}
<bytecode: 0xd4233a0>
<environment: namespace:caretEnsemble>
--- function search by body ---
Function predict.caretList in namespace caretEnsemble has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.0.0
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building 'caretEnsemble-intro.Rmd' using rmarkdown
Warning in train.default(x, y, weights = w, ...) :
The metric "Accuracy" was not in the result set. ROC will be used instead.
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning in train.default(x, y, weights = w, ...) :
The metric "Accuracy" was not in the result set. ROC will be used instead.
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
caretEnsemble
--- call from context ---
predict.caretList(model_list, newdata = head(testing))
--- call from argument ---
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
--- R stacktrace ---
where 1: predict.caretList(model_list, newdata = head(testing))
where 2: predict(model_list, newdata = head(testing))
where 3: as.data.frame(predict(model_list, newdata = head(testing)))
where 4: eval(expr, envir, enclos)
where 5: eval(expr, envir, enclos)
where 6: withVisible(eval(expr, envir, enclos))
where 7: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 8: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 9: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 10: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 11: evaluate::evaluate(...)
where 12: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 13: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 14: block_exec(params)
where 15: call_block(x)
where 16: process_group.block(group)
where 17: process_group(group)
where 18: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 19: process_file(text, output)
where 20: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet,
encoding = encoding)
where 21: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
...)
where 22: vweave_rmarkdown(...)
where 23: engine$weave(file, quiet = quiet, encoding = enc)
where 24: doTryCatch(return(expr), name, parentenv, handler)
where 25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 26: tryCatchList(expr, classes, parentenv, handlers)
where 27: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
outputs <- c(outputs, output)
}, error = function(e) {
thisOK <<- FALSE
fails <<- c(fails, file)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 28: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/caretEnsemble.Rcheck/vign_test/caretEnsemble",
ser_elibs = "/tmp/RtmpEfMT31/file4def10a2de2d.rds")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (object, newdata = NULL, ..., verbose = FALSE)
{
if (is.null(newdata)) {
warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
newdata <- object[[1]]$trainingData
if (is.null(newdata)) {
stop("Could not find training data in the first model in the ensemble.")
}
}
if (verbose == TRUE) {
pboptions(type = "txt", char = "*")
}
else if (verbose == FALSE) {
pboptions(type = "none")
}
preds <- pbsapply(object, function(x) {
type <- x$modelType
if (type == "Classification") {
if (x$control$classProbs) {
caret::predict.train(x, type = "prob", newdata = newdata,
...)[, 2]
}
else {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
}
else if (type == "Regression") {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
else {
stop(paste("Unknown model type:", type))
}
})
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
colnames(preds) <- make.names(sapply(object, function(x) x$method),
unique = TRUE)
return(preds)
}
<bytecode: 0x146a8b58>
<environment: namespace:caretEnsemble>
--- function search by body ---
Function predict.caretList in namespace caretEnsemble has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [45s/70s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(caretEnsemble)
>
> test_check("caretEnsemble")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
caretEnsemble
--- call from context ---
predict.caretList(models)
--- call from argument ---
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
--- R stacktrace ---
where 1: predict.caretList(models)
where 2: predict(models)
where 3: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
where 4: withCallingHandlers(code, warning = function(condition) {
out$push(condition)
maybe_restart("muffleWarning")
})
where 5: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
...)
where 6: quasi_capture(enquo(object), label, capture_warnings)
where 7 at testthat/test-caretList.R#109: expect_warning(p1 <- predict(models))
where 8: eval(code, test_env)
where 9: eval(code, test_env)
where 10: withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error)
where 11: doTryCatch(return(expr), name, parentenv, handler)
where 12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 13: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
where 14: doTryCatch(return(expr), name, parentenv, handler)
where 15: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
names[nh], parentenv, handlers[[nh]])
where 16: tryCatchList(expr, classes, parentenv, handlers)
where 17: tryCatch(withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error), error = handle_fatal,
skip = function(e) {
})
where 18: test_code(desc, code, env = parent.frame())
where 19 at testthat/test-caretList.R#94: test_that("caretList predictions", {
skip_if_not_installed("randomForest")
skip_if_not_installed("nnet")
skip_if_not_installed("plyr")
expect_warning({
models <- caretList(iris[, 1:2], iris[, 5], tuneLength = 1,
verbose = FALSE, methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet",
trace = FALSE)), trControl = trainControl(method = "cv",
number = 2, savePredictions = "final", classProbs = FALSE))
})
expect_warning(p1 <- predict(models))
p2 <- predict(models, newdata = iris[100, c(1:2)])
p3 <- predict(models, newdata = iris[110, c(1:2)])
expect_is(p1, "matrix")
expect_is(p1[, 1], "character")
expect_is(p1[, 2], "character")
expect_equal(names(models), colnames(p1))
expect_is(p2, "matrix")
expect_is(p2[, 1], "character")
expect_is(p2[, 2], "character")
expect_equal(names(models), colnames(p2))
expect_is(p3, "matrix")
expect_is(p3[, 1], "character")
expect_is(p3[, 2], "character")
expect_equal(names(models), colnames(p3))
expect_warning({
models <- caretList(iris[, 1:2], iris[, 5], tuneLength = 1,
verbose = FALSE, methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet",
trace = FALSE)), trControl = trainControl(method = "cv",
number = 2, savePredictions = "final", classProbs = TRUE))
})
expect_warning(p2 <- predict(models))
p3 <- predict(models, newdata = iris[100, c(1:2)])
expect_is(p2, "matrix")
expect_is(p2[, 1], "numeric")
expect_is(p2[, 2], "numeric")
expect_is(p3, "matrix")
expect_is(p3[, 1], "numeric")
expect_is(p3[, 2], "numeric")
expect_equal(names(models), colnames(p3))
models[[1]]$modelType <- "Bogus"
expect_error(expect_warning(predict(models)))
})
where 20: eval(code, test_env)
where 21: eval(code, test_env)
where 22: withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error)
where 23: doTryCatch(return(expr), name, parentenv, handler)
where 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 25: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
where 26: doTryCatch(return(expr), name, parentenv, handler)
where 27: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
names[nh], parentenv, handlers[[nh]])
where 28: tryCatchList(expr, classes, parentenv, handlers)
where 29: tryCatch(withCallingHandlers({
eval(code, test_env)
if (!handled && !is.null(test)) {
skip_empty()
}
}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
message = handle_message, error = handle_error), error = handle_fatal,
skip = function(e) {
})
where 30: test_code(NULL, exprs, env)
where 31: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
where 32: force(code)
where 33: doWithOneRestart(return(expr), restart)
where 34: withOneRestart(expr, restarts[[1L]])
where 35: withRestarts(testthat_abort_reporter = function() NULL, force(code))
where 36: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
{
reporter$start_file(basename(path))
lister$start_file(basename(path))
source_file(path, new.env(parent = env), chdir = TRUE,
wrap = wrap)
reporter$.end_context()
reporter$end_file()
})
where 37: FUN(X[[i]], ...)
where 38: lapply(paths, test_file, env = env, reporter = current_reporter,
start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
where 39: force(code)
where 40: doWithOneRestart(return(expr), restart)
where 41: withOneRestart(expr, restarts[[1L]])
where 42: withRestarts(testthat_abort_reporter = function() NULL, force(code))
where 43: with_reporter(reporter = current_reporter, results <- lapply(paths,
test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
load_helpers = FALSE, wrap = wrap))
where 44: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
stop_on_warning = stop_on_warning, wrap = wrap)
where 45: test_dir(path = test_path, reporter = reporter, env = env, filter = filter,
..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
wrap = wrap)
where 46: test_package_dir(package = package, test_path = test_path, filter = filter,
reporter = reporter, ..., stop_on_failure = stop_on_failure,
stop_on_warning = stop_on_warning, wrap = wrap)
where 47: test_check("caretEnsemble")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (object, newdata = NULL, ..., verbose = FALSE)
{
if (is.null(newdata)) {
warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
newdata <- object[[1]]$trainingData
if (is.null(newdata)) {
stop("Could not find training data in the first model in the ensemble.")
}
}
if (verbose == TRUE) {
pboptions(type = "txt", char = "*")
}
else if (verbose == FALSE) {
pboptions(type = "none")
}
preds <- pbsapply(object, function(x) {
type <- x$modelType
if (type == "Classification") {
if (x$control$classProbs) {
caret::predict.train(x, type = "prob", newdata = newdata,
...)[, 2]
}
else {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
}
else if (type == "Regression") {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
else {
stop(paste("Unknown model type:", type))
}
})
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
colnames(preds) <- make.names(sapply(object, function(x) x$method),
unique = TRUE)
return(preds)
}
<bytecode: 0x55a5ccb3d468>
<environment: namespace:caretEnsemble>
--- function search by body ---
Function predict.caretList in namespace caretEnsemble has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.0.0
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building ‘caretEnsemble-intro.Rmd’ using rmarkdown
Warning in train.default(x, y, weights = w, ...) :
The metric "Accuracy" was not in the result set. ROC will be used instead.
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning in train.default(x, y, weights = w, ...) :
The metric "Accuracy" was not in the result set. ROC will be used instead.
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
caretEnsemble
--- call from context ---
predict.caretList(model_list, newdata = head(testing))
--- call from argument ---
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
--- R stacktrace ---
where 1: predict.caretList(model_list, newdata = head(testing))
where 2: predict(model_list, newdata = head(testing))
where 3: as.data.frame(predict(model_list, newdata = head(testing)))
where 4: eval(expr, envir, enclos)
where 5: eval(expr, envir, enclos)
where 6: withVisible(eval(expr, envir, enclos))
where 7: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 8: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 9: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 10: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 11: evaluate::evaluate(...)
where 12: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 13: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 14: block_exec(params)
where 15: call_block(x)
where 16: process_group.block(group)
where 17: process_group(group)
where 18: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 19: process_file(text, output)
where 20: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet,
encoding = encoding)
where 21: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
...)
where 22: vweave_rmarkdown(...)
where 23: engine$weave(file, quiet = quiet, encoding = enc)
where 24: doTryCatch(return(expr), name, parentenv, handler)
where 25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 26: tryCatchList(expr, classes, parentenv, handlers)
where 27: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
outputs <- c(outputs, output)
}, error = function(e) {
thisOK <<- FALSE
fails <<- c(fails, file)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 28: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/caretEnsemble.Rcheck/vign_test/caretEnsemble",
ser_elibs = "/home/hornik/tmp/scratch/RtmpuJuW1s/file19e7105b067f.rds")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (object, newdata = NULL, ..., verbose = FALSE)
{
if (is.null(newdata)) {
warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
newdata <- object[[1]]$trainingData
if (is.null(newdata)) {
stop("Could not find training data in the first model in the ensemble.")
}
}
if (verbose == TRUE) {
pboptions(type = "txt", char = "*")
}
else if (verbose == FALSE) {
pboptions(type = "none")
}
preds <- pbsapply(object, function(x) {
type <- x$modelType
if (type == "Classification") {
if (x$control$classProbs) {
caret::predict.train(x, type = "prob", newdata = newdata,
...)[, 2]
}
else {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
}
else if (type == "Regression") {
caret::predict.train(x, type = "raw", newdata = newdata,
...)
}
else {
stop(paste("Unknown model type:", type))
}
})
if (class(preds) != "matrix" & class(preds) != "data.frame") {
if (class(preds) == "character" | class(preds) == "factor") {
preds <- as.character(preds)
}
preds <- as.matrix(t(preds))
}
colnames(preds) <- make.names(sapply(object, function(x) x$method),
unique = TRUE)
return(preds)
}
<bytecode: 0x5619be7f1728>
<environment: namespace:caretEnsemble>
--- function search by body ---
Function predict.caretList in namespace caretEnsemble has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc