Last updated on 2025-12-05 18:49:36 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.4.4 | 6.49 | 128.08 | 134.57 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.4.4 | 4.21 | 101.16 | 105.37 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.4.4 | 35.00 | 170.35 | 205.35 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.4.4 | 33.00 | 156.97 | 189.97 | OK | |
| r-devel-windows-x86_64 | 1.4.4 | 8.00 | 136.00 | 144.00 | OK | |
| r-patched-linux-x86_64 | 1.4.4 | 6.46 | 123.74 | 130.20 | OK | |
| r-release-linux-x86_64 | 1.4.4 | 5.72 | 122.76 | 128.48 | OK | |
| r-release-macos-arm64 | 1.4.4 | OK | ||||
| r-release-macos-x86_64 | 1.4.4 | 5.00 | 117.00 | 122.00 | OK | |
| r-release-windows-x86_64 | 1.4.4 | 8.00 | 134.00 | 142.00 | OK | |
| r-oldrel-macos-arm64 | 1.4.4 | OK | ||||
| r-oldrel-macos-x86_64 | 1.4.4 | 5.00 | 113.00 | 118.00 | OK | |
| r-oldrel-windows-x86_64 | 1.4.4 | 10.00 | 177.00 | 187.00 | OK |
Version: 1.4.4
Check: examples
Result: ERROR
Running examples in ‘BatchExperiments-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: addExperiments
> ### Title: Add experiemts to the registry.
> ### Aliases: addExperiments Experiment
>
> ### ** Examples
>
> ### EXAMPLE 1 ###
> reg = makeExperimentRegistry(id = "example1", file.dir = tempfile())
Creating dir: /tmp/RtmpuuZGsr/file2bc0ead3b1310
Saving registry: /tmp/RtmpuuZGsr/file2bc0ead3b1310/registry.RData
>
> # Define a problem:
> # Subsampling from the iris dataset.
> data(iris)
> subsample = function(static, ratio) {
+ n = nrow(static)
+ train = sample(n, floor(n * ratio))
+ test = setdiff(seq(n), train)
+ list(test = test, train = train)
+ }
> addProblem(reg, id = "iris", static = iris,
+ dynamic = subsample, seed = 123)
Writing problem files: /tmp/RtmpuuZGsr/file2bc0ead3b1310/problems/iris_static.RData, /tmp/RtmpuuZGsr/file2bc0ead3b1310/problems/iris_dynamic.RData
>
> # Define algorithm "tree":
> # Decision tree on the iris dataset, modeling Species.
> tree.wrapper = function(static, dynamic, ...) {
+ library(rpart)
+ mod = rpart(Species ~ ., data = static[dynamic$train, ], ...)
+ pred = predict(mod, newdata = static[dynamic$test, ], type = "class")
+ table(static$Species[dynamic$test], pred)
+ }
> addAlgorithm(reg, id = "tree", fun = tree.wrapper)
Writing algorithm file: /tmp/RtmpuuZGsr/file2bc0ead3b1310/algorithms/tree.RData
>
> # Define algorithm "forest":
> # Random forest on the iris dataset, modeling Species.
> forest.wrapper = function(static, dynamic, ...) {
+ library(randomForest)
+ mod = randomForest(Species ~ ., data = static, subset = dynamic$train, ...)
+ pred = predict(mod, newdata = static[dynamic$test, ])
+ table(static$Species[dynamic$test], pred)
+ }
> addAlgorithm(reg, id = "forest", fun = forest.wrapper)
Writing algorithm file: /tmp/RtmpuuZGsr/file2bc0ead3b1310/algorithms/forest.RData
>
> # Define problem parameters:
> pars = list(ratio = c(0.67, 0.9))
> iris.design = makeDesign("iris", exhaustive = pars)
>
> # Define decision tree parameters:
> pars = list(minsplit = c(10, 20), cp = c(0.01, 0.1))
> tree.design = makeDesign("tree", exhaustive = pars)
>
> # Define random forest parameters:
> pars = list(ntree = c(100, 500))
> forest.design = makeDesign("forest", exhaustive = pars)
>
> # Add experiments to the registry:
> # Use previously defined experimental designs.
> addExperiments(reg, prob.designs = iris.design,
+ algo.designs = list(tree.design, forest.design),
+ repls = 2) # usually you would set repls to 100 or more.
Adding 12 experiments / 24 jobs to DB.
>
> # Optional: Short summary over problems and algorithms.
> summarizeExperiments(reg)
prob algo .count
1 iris forest 8
2 iris tree 16
>
> # Optional: Test one decision tree job and one expensive (ntree = 1000)
> # random forest job. Use findExperiments to get the right job ids.
> do.tests = FALSE
> if (do.tests) {
+ id1 = findExperiments(reg, algo.pattern = "tree")[1]
+ id2 = findExperiments(reg, algo.pattern = "forest",
+ algo.pars = (ntree == 1000))[1]
+ testJob(reg, id1)
+ testJob(reg, id2)
+ }
>
> # Submit the jobs to the batch system
> submitJobs(reg)
Saving conf: /tmp/RtmpuuZGsr/file2bc0ead3b1310/conf.RData
Submitting 24 chunks / 24 jobs.
Cluster functions: Interactive.
Auto-mailer settings: start=none, done=none, error=none.
SubmitJobs |+ | 0% (00:00:00)
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SubmitJobs |++++ | 8% (00:00:11)
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SubmitJobs |+++++++++++++++++++++++++++++++++++++++++++++++++| 100% (00:00:00)
Sending 24 submit messages...
Might take some time, do not interrupt this!
>
> # Calculate the misclassification rate for all (already done) jobs.
> reduce = function(job, res) {
+ n = sum(res)
+ list(mcr = (n-sum(diag(res)))/n)
+ }
> res = reduceResultsExperiments(reg, fun = reduce)
Syncing registry ...
Reducing 24 results...
reduceResultsExperiments |+ | 0% (00:00:00)
reduceResultsExperiments |+++++++++++++++++++++++++++++++++++| 100% (00:00:00)
> print(res)
id prob ratio algo cp minsplit repl mcr ntree
1 1 iris 0.67 tree 0.01 10 1 0.04000000 NA
2 2 iris 0.67 tree 0.01 10 2 0.04000000 NA
3 3 iris 0.67 tree 0.01 20 1 0.04000000 NA
4 4 iris 0.67 tree 0.01 20 2 0.04000000 NA
5 5 iris 0.67 tree 0.10 10 1 0.04000000 NA
6 6 iris 0.67 tree 0.10 10 2 0.04000000 NA
7 7 iris 0.67 tree 0.10 20 1 0.04000000 NA
8 8 iris 0.67 tree 0.10 20 2 0.04000000 NA
9 9 iris 0.67 forest NA NA 1 0.04000000 100
10 10 iris 0.67 forest NA NA 2 0.06000000 100
11 11 iris 0.67 forest NA NA 1 0.04000000 500
12 12 iris 0.67 forest NA NA 2 0.04000000 500
13 13 iris 0.90 tree 0.01 10 1 0.00000000 NA
14 14 iris 0.90 tree 0.01 10 2 0.06666667 NA
15 15 iris 0.90 tree 0.01 20 1 0.00000000 NA
16 16 iris 0.90 tree 0.01 20 2 0.06666667 NA
17 17 iris 0.90 tree 0.10 10 1 0.00000000 NA
18 18 iris 0.90 tree 0.10 10 2 0.06666667 NA
19 19 iris 0.90 tree 0.10 20 1 0.00000000 NA
20 20 iris 0.90 tree 0.10 20 2 0.06666667 NA
21 21 iris 0.90 forest NA NA 1 0.00000000 100
22 22 iris 0.90 forest NA NA 2 0.06666667 100
23 23 iris 0.90 forest NA NA 1 0.00000000 500
24 24 iris 0.90 forest NA NA 2 0.06666667 500
>
> # Aggregate results using 'ddply' from package 'plyr':
> # Calculate the mean over all replications of identical experiments
> # (same problem, same algorithm and same parameters)
> library(plyr)
Error in library(plyr) : there is no package called ‘plyr’
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc