Hi Max,
Here's a bit more information regarding the 'memory not mapped' errors which
occur in caret.
1. The segfault only occurs when knitting a Markdown file in RStudio. When the
code is run 'normally' in R, everything's fine.
2. The error is very hard to replicate! It only occurs when the following block
(running train on C5.0) is run after several other calls to train; running the
block alone in a Markdown file runs fine:
```{r}
require(repmis) # For downloading from https
df <- source_data('https://dl.dropboxusercontent.com/u/47973221/data.csv',
sep=',')
require(caret)
c5.ms <- train(df[,-1],df[,1], method='C5.0Tree', subset=F, trials=10,
fuzzyThreshold=F, trControl=trainControl(method='repeatedcv', number=10,
repeats=10, classProbs=TRUE, allowParallel=F))
```
3. Note that I'm using caret-5.15-61, in order to replicate previous results
from SVM models (see earlier in thread).
I appreciate that this is probably insufficient information to identify the
issue, but perhaps the traceback below provides a clue. In the meantime, I'll
try to create a Markdown file which replicates the problem reliably.
Thanks in advance for any suggestions or advice.
Cheers,
Andrew
sessionInfo()
## R version 3.0.2 (2013-09-25)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
##
## locale:
## [1] en_NZ.UTF-8/en_NZ.UTF-8/en_NZ.UTF-8/C/en_NZ.UTF-8/en_NZ.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] C50_0.1.0-15 e1071_1.6-1 class_7.3-9 caret_5.15-61
## [5] reshape2_1.2.2 plyr_1.8 lattice_0.20-24 foreach_1.4.1
## [9] cluster_1.14.4 repmis_0.2.6.2 knitr_1.5
##
## loaded via a namespace (and not attached):
## [1] codetools_0.2-8 compiler_3.0.2 digest_0.6.3 evaluate_0.5.1
## [5] formatR_0.10 grid_3.0.2 httr_0.2 iterators_1.0.6
## [9] RCurl_1.95-4.1 stringr_0.6.2 tools_3.0.2
Error that appears when knitting:
*** caught segfault ***
address 0x1028f7000, cause 'memory not mapped'
Traceback:
1: .C("C50", as.character(namesString), as.character(dataString),
as.character(costString), as.logical(control$subset), as.logical(rules),
as.integer(control$bands), as.integer(trials), as.logical(control$winnow),
as.double(control$sample), as.integer(control$seed),
as.integer(control$noGlobalPruning), as.double(control$CF),
as.integer(control$minCases), as.logical(control$fuzzyThreshold),
as.logical(control$earlyStopping), tree = character(1), rules = character(1),
output = character(1), PACKAGE = "C50")
2: C5.0.default(x = trainX, y = trainY, rules = method == "C5.0Rules", ...)
3: C5.0(x = trainX, y = trainY, rules = method == "C5.0Rules", ...)
4: caret:::createModel(data = dat[modelIndex, , drop = FALSE], method =
method, tuneValue = info$loop[parm, , drop = FALSE], obsLevels = lev,
pp = ppp, custom = ctrl$custom$model, ...)
5: doTryCatch(return(expr), name, parentenv, handler)
6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
7: tryCatchList(expr, classes, parentenv, handlers)
8: tryCatch(expr, error = function(e) { call <- conditionCall(e) if
(!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <-
paste("Error in", dcall, ": ") LONG <- 75L msg <-
conditionMessage(e) sm <- strsplit(msg, "\n")[[1L]] w <- 14L +
nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type
= "b") if (w > LONG) prefix <- paste0(prefix, "\n ") }
else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L])) if (!silent &&
identical(getOption("show.error.messages"), TRUE)) { cat(msg,
file = stderr()) .Internal(printDeferredWarnings()) }
invisible(structure(msg, class = "try-error", condition = e))})
9: try(caret:::createModel(data = dat[modelIndex, , drop = FALSE], method
= method, tuneValue = info$loop[parm, , drop = FALSE], obsLevels = lev, pp
= ppp, custom = ctrl$custom$model, ...), silent = TRUE)
10: eval(expr, envir, enclos)
11: eval(xpr, envir = envir)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(eval(xpr, envir = envir), error = function(e) e)
16: doTryCatch(return(expr), name, parentenv, handler)
17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
18: tryCatchList(expr, classes, parentenv, handlers)
19: tryCatch({ repeat { args <- nextElem(it) if (obj$verbose)
{ cat(sprintf("evaluation # %d:\n", i)) print(args)
} for (a in names(args)) assign(a, args[[a]], pos = envir,
inherits = FALSE) r <- tryCatch(eval(xpr, envir = envir), error =
function(e) e) if (obj$verbose) { cat("result of evaluating
expression:\n") print(r) }
tryCatch(accumulator(list(r), i), error = function(e) { cat("error
calling combine function:\n") print(e) NULL })
i <- i + 1 }}, error = function(e) { if
(!identical(conditionMessage(e), "StopIteration"))
stop(simpleError(conditionMessage(e), expr))})
20: e$fun(obj, substitute(ex), parent.frame(), e$data)
21: foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose =
FALSE, .packages = "caret", .errorhandling = "stop") %:% foreach(parm =
1:nrow(info$loop), .combine = "c", .verbose = FALSE, .packages = "caret",
.errorhandling = "stop") %op% { library(caret) if
(ctrl$verboseIter) caret:::progress(printed[parm, , drop = FALSE],
names(resampleIndex), iter) if
(names(resampleIndex)[iter] != "AllData") { modelIndex <-
resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] }
else { modelIndex <- 1:nrow(dat) holdoutIndex <-
modelIndex } if (testing) cat("pre-model\n")
mod <- try(caret:::createModel(data = dat[modelIndex, , drop =
FALSE], method = method, tuneValue = info$loop[parm, , drop =
FALSE], obsLevels = lev, pp = ppp, custom = ctrl$custom$model,
...), silent = TRUE) if !
(class(mod)[1] != "try-error") { predicted <-
try(caret:::predictionFunction(method = method, modelFit =
mod$fit, newdata = dat[holdoutIndex, !(names(dat) %in%
c(".outcome", ".modelWeights")), drop = FALSE], preProc =
mod$preProc, param = info$seqParam[[parm]], custom =
ctrl$custom$prediction), silent = TRUE) if (class(predicted)[1] ==
"try-error") { wrn <- paste(colnames(printed[parm, , drop =
FALSE]), printed[parm, , drop = FALSE], sep = "=", collapse =
", ") wrn <- paste("predictions failed for ",
names(resampleIndex)[iter], ": ", wrn, sep = "")
if (ctrl$verboseIter) cat(wrn, "\n")
warning(wrn) rm(wrn) nPred <-
length(holdoutIndex) if (!is.null(lev)) {
predicted <- rep("", nPred) predicted[seq!
(along = predicted)] <- NA } else {
predicted <- rep(NA, nPred) } if
(!is.null(info$seqParam[[parm]])) { tmp <- predicted
predicted <- vector(mode = "list", length = nrow(info$seqParam[[parm]])
+ 1) for (i in seq(along = predicted))
predicted[[i]] <- tmp rm(tmp) } }
} else { wrn <- paste(colnames(printed[parm, , drop =
FALSE]), printed[parm, , drop = FALSE], sep = "=", collapse =
", ") wrn <- paste("model fit failed for ",
names(resampleIndex)[iter], ": ", wrn, sep = "") if
(ctrl$verboseIter) cat(wrn, "\n") warning(wrn)
rm(wrn) nPred <- length(holdoutIndex) if
(!is.null(lev)) { predicted <- rep("", nPred)
predicted[seq(along = predicted)] <- NA } else {
!
predicted <- rep(NA, nPred) } if
(!is.null(info$seqParam[[parm]])) { tmp <- predicted
predicted <- vector(mode = "list", length = nrow(info$seqParam[[parm]]) +
1) for (i in seq(along = predicted))
predicted[[i]] <- tmp rm(tmp) } } if
(testing) print(head(predicted)) if (ctrl$classProbs) {
if (class(mod)[1] != "try-error") { probValues <-
caret:::probFunction(method = method, modelFit = mod$fit,
newdata = dat[holdoutIndex, !(names(dat) %in% c(".outcome",
".modelWeights")), drop = FALSE], preProc = mod$preProc,
param = info$seqParam[[parm]], custom =
ctrl$custom$probability) } else {
probValues <- as.data.frame(matrix(NA, nrow = nPred, ncol =
length(lev))) colnames(!
probValues) <- lev if (!is.null(info$seqParam[[parm]]))
{ tmp <- probValues probValues <-
vector(mode = "list", length = nrow(info$seqParam[[parm]]) +
1) for (i in seq(along = probValues)) probValues[[i]] <- tmp
rm(tmp) } } if (testing)
print(head(probValues)) } if (!is.null(info$seq)) {
allParam <- caret:::expandParameters(info$loop[parm,
, drop = FALSE], info$seqParam[[parm]]) if (method == "ctree")
allParam <- allParam[!duplicated(allParam), , drop
= FALSE] if (method == "glmnet") allParam <-
allParam[complete.cases(allParam), , drop = FALSE]
predicted <- lapply(predicted, function(x, y, lv) { if
(!is.factor(x) & is.character(x)) x <-
factor(as.character(x), levels = lv) data.frame(pred = x, obs =
y, !
stringsAsFactors = FALSE) }, y = dat$.outcome[holdoutIndex], lv =
lev) if (testing) print(head(predicted))
if (ctrl$classProbs) { for (k in seq(along = predicted))
predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
} if (ctrl$savePredictions) { tmpPred <-
predicted for (modIndex in seq(along = tmpPred)) {
tmpPred[[modIndex]]$rowIndex <- holdoutIndex
tmpPred[[modIndex]] <- cbind(tmpPred[[modIndex]],
allParam[modIndex, , drop = FALSE]) } tmpPred <-
rbind.fill(tmpPred) tmpPred$Resample <-
names(resampleIndex)[iter] } else tmpPred <- NULL
thisResample <- lapply(predicted, ctrl$summaryFunction, lev
= lev, model = method) if (testing)
print(head(thisResample)) !
if (length(lev) > 1) { cells <- lapply(predicted, func
tion(x) caret:::flatTable(x$pred, x$obs)) for
(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]],
cells[[ind]]) } thisResample <-
do.call("rbind", thisResample) thisResample <- cbind(allParam,
thisResample) } else { if (is.factor(dat$.outcome))
predicted <- factor(as.character(predicted),
levels = lev) tmp <- data.frame(pred = predicted, obs =
dat$.outcome[holdoutIndex], stringsAsFactors = FALSE)
names(tmp)[1] <- "pred" if (ctrl$classProbs) tmp <-
cbind(tmp, probValues) if (ctrl$savePredictions) {
tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex
tmpPred <- cbind(tmpPred, info$loop[parm, , drop = FALSE])
tmpPred$Resample <- names(resampleIndex)[iter] } else tm!
pPred <- NULL thisResample <- ctrl$summaryFunction(tmp, lev = lev,
model = method) if (length(lev) > 1)
thisResample <- c(thisResample, caret:::flatTable(tmp$pred,
tmp$obs)) thisResample <- as.data.frame(t(thisResample))
thisResample <- cbind(thisResample, info$loop[parm, , drop =
FALSE]) } thisResample$Resample <- names(resampleIndex)[iter]
if (ctrl$verboseIter) caret:::progress(printed[parm, , drop =
FALSE], names(resampleIndex), iter, FALSE)
list(resamples = thisResample, pred = tmpPred) }
22: nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method,
ppOpts = preProcess, ctrl = trControl, lev = classLevels, ...)
23: train.default(syllm[, syllvars], syllm$Band, method = "C5.0Tree",
subset = F, trials = 10, fuzzyThreshold = F, trControl = trainControl(method =
"repeatedcv", number = 10, repeats = 10, classProbs = TRUE,
summaryFunction = multiClassSummary, allowParallel = F))
24: train(syllm[, syllvars], syllm$Band, method = "C5.0Tree", subset = F,
trials = 10, fuzzyThreshold = F, trControl = trainControl(method =
"repeatedcv", number = 10, repeats = 10, classProbs = TRUE,
summaryFunction = multiClassSummary, allowParallel = F))
25: eval(expr, envir, enclos)
26: eval(call, envir, enclos)
27: withVisible(eval(call, envir, enclos))
28: withCallingHandlers(withVisible(eval(call, envir, enclos)), warning =
wHandler, error = eHandler, message = mHandler)
29: doTryCatch(return(expr), name, parentenv, handler)
30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
31: tryCatchList(expr, classes, parentenv, handlers)
32: tryCatch(expr, error = function(e) { call <- conditionCall(e) if
(!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <-
paste("Error in", dcall, ": ") LONG <- 75L msg <-
conditionMessage(e) sm <- strsplit(msg, "\n")[[1L]] w <- 14L +
nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type
= "b") if (w > LONG) prefix <- paste0(prefix, "\n ") }
else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L])) if (!silent &&
identical(getOption("show.error.messages"), TRUE)) { cat(msg,
file = stderr()) .Internal(printDeferredWarnings()) }
invisible(structure(msg, class = "try-error", condition = e))})
33: try(f, silent = TRUE)
34: handle(ev <- withCallingHandlers(withVisible(eval(call, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
35: evaluate_call(expr, parsed$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)
36: 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)
37: in_dir(opts_knit$get("root.dir") %n% input_dir(), evaluate(code, envir
= env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L))
38: block_exec(params)
39: call_block(x)
40: process_group.block(group)
41: process_group(group)
42: withCallingHandlers(if (tangle) process_tangle(group) else
process_group(group), error = function(e) { cat(res, sep = "\n",
file = output %n% "") message("Quitting from lines ",
paste(current_lines(i), collapse = "-"), " (",
knit_concord$get("infile"), ") ") })
43: process_file(text, output)
44: knit("compareclassifiers3.Rmd", encoding = "UTF-8")
aborting ...
On 18/11/2013, at 14:42 , Max Kuhn <mxk...@gmail.com> wrote:
> Andrew,
>
>> What I still don't quite understand is which accuracy values from train() I
>> should trust: those using classProbs=T or classProbs=F?
>
> It depends on whether you need the class probabilities and class
> predictions to match (which they would if classProbs = TRUE).
>
> Another option is to use a model where this discrepancy does not exist.
>
>> train often crashes with 'memory map' errors!)?
>
> I've never seen that. You should describe it more.
>
> Max
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
