Last updated on 2025-12-19 20:49:57 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.7 | 6.54 | 84.07 | 90.61 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.1.7 | 4.70 | 51.11 | 55.81 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.1.7 | 12.00 | 110.03 | 122.03 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.1.7 | 10.00 | 99.34 | 109.34 | ERROR | |
| r-devel-windows-x86_64 | 0.1.7 | 7.00 | 84.00 | 91.00 | OK | |
| r-patched-linux-x86_64 | 0.1.7 | 7.07 | 72.49 | 79.56 | OK | |
| r-release-linux-x86_64 | 0.1.7 | 6.53 | 75.26 | 81.79 | ERROR | |
| r-release-macos-arm64 | 0.1.7 | OK | ||||
| r-release-macos-x86_64 | 0.1.7 | 4.00 | 59.00 | 63.00 | OK | |
| r-release-windows-x86_64 | 0.1.7 | 8.00 | 83.00 | 91.00 | OK | |
| r-oldrel-macos-arm64 | 0.1.7 | OK | ||||
| r-oldrel-macos-x86_64 | 0.1.7 | 4.00 | 71.00 | 75.00 | OK | |
| r-oldrel-windows-x86_64 | 0.1.7 | 12.00 | 111.00 | 123.00 | OK |
Version: 0.1.7
Check: examples
Result: ERROR
Running examples in ‘mlr3benchmark-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: autoplot.BenchmarkAggr
> ### Title: Plots for BenchmarkAggr
> ### Aliases: autoplot.BenchmarkAggr
>
> ### ** Examples
>
> if (requireNamespaces(c("mlr3learners", "mlr3", "rpart", "xgboost"))) {
+ library(mlr3)
+ library(mlr3learners)
+ library(ggplot2)
+
+ set.seed(1)
+ task = tsks(c("iris", "sonar", "wine", "zoo"))
+ learns = lrns(c("classif.featureless", "classif.rpart", "classif.xgboost"))
+ learns$classif.xgboost$param_set$values$nrounds = 50
+ bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 3)))
+ obj = as_benchmark_aggr(bm)
+
+ # mean and error bars
+ autoplot(obj, type = "mean", level = 0.95)
+
+ if (requireNamespace("PMCMRplus", quietly = TRUE)) {
+ # critical differences
+ autoplot(obj, type = "cd",style = 1)
+ autoplot(obj, type = "cd",style = 2)
+
+ # post-hoc friedman-nemenyi
+ autoplot(obj, type = "fn")
+ }
+
+ }
INFO [04:30:32.783] [mlr3] Running benchmark with 36 resampling iterations
INFO [04:30:32.845] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3)
INFO [04:30:32.905] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3)
INFO [04:30:32.936] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3)
INFO [04:30:33.033] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
INFO [04:30:33.072] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
INFO [04:30:33.111] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
INFO [04:30:33.149] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 1/3)
INFO [04:30:33.215] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 2/3)
INFO [04:30:33.268] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 3/3)
INFO [04:30:33.326] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3)
INFO [04:30:33.360] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3)
INFO [04:30:33.393] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3)
INFO [04:30:33.426] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3)
INFO [04:30:33.493] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3)
INFO [04:30:33.546] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3)
INFO [04:30:33.606] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 1/3)
INFO [04:30:33.716] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 2/3)
INFO [04:30:33.833] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 3/3)
INFO [04:30:33.928] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 1/3)
INFO [04:30:33.957] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 2/3)
INFO [04:30:33.985] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 3/3)
INFO [04:30:34.018] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 1/3)
INFO [04:30:34.056] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 2/3)
INFO [04:30:34.093] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 3/3)
INFO [04:30:34.131] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 1/3)
INFO [04:30:34.189] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 2/3)
INFO [04:30:34.246] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 3/3)
INFO [04:30:34.302] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 1/3)
INFO [04:30:34.340] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 2/3)
INFO [04:30:34.404] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 3/3)
INFO [04:30:34.435] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 1/3)
INFO [04:30:34.474] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 2/3)
INFO [04:30:34.514] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 3/3)
INFO [04:30:34.553] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 1/3)
INFO [04:30:34.627] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 2/3)
INFO [04:30:34.692] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 3/3)
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
INFO [04:30:34.778] [mlr3] Finished benchmark
Error: Global Friedman test non-significant (p > 0.05), try type = 'mean' instead.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.1.7
Check: examples
Result: ERROR
Running examples in ‘mlr3benchmark-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: BenchmarkAggr
> ### Title: Aggregated Benchmark Result Object
> ### Aliases: BenchmarkAggr
>
> ### ** Examples
>
> # Not restricted to mlr3 objects
> df = data.frame(tasks = factor(rep(c("A", "B"), each = 5),
+ levels = c("A", "B")),
+ learners = factor(paste0("L", 1:5)),
+ RMSE = runif(10), MAE = runif(10))
> as_benchmark_aggr(df, task_id = "tasks", learner_id = "learners")
<BenchmarkAggr> of 10 rows with 2 tasks, 5 learners and 2 measures
tasks learners RMSE MAE
<fctr> <fctr> <num> <num>
1: A L1 0.26550866 0.2059746
2: A L2 0.37212390 0.1765568
3: A L3 0.57285336 0.6870228
4: A L4 0.90820779 0.3841037
5: A L5 0.20168193 0.7698414
6: B L1 0.89838968 0.4976992
7: B L2 0.94467527 0.7176185
8: B L3 0.66079779 0.9919061
9: B L4 0.62911404 0.3800352
10: B L5 0.06178627 0.7774452
>
> if (requireNamespaces(c("mlr3", "rpart"))) {
+ library(mlr3)
+ task = tsks(c("pima", "spam"))
+ learns = lrns(c("classif.featureless", "classif.rpart"))
+ bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 2)))
+
+ # coercion
+ as_benchmark_aggr(bm)
+ }
INFO [17:13:03.108] [mlr3] Running benchmark with 8 resampling iterations
INFO [17:13:03.309] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/2)
INFO [17:13:03.496] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 2/2)
INFO [17:13:03.539] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/2)
INFO [17:13:03.629] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 2/2)
INFO [17:13:03.656] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/2)
INFO [17:13:03.776] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 2/2)
INFO [17:13:03.914] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/2)
INFO [17:13:04.035] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 2/2)
INFO [17:13:04.408] [mlr3] Finished benchmark
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
Calls: benchmark ... initialize -> .__ResultData__initialize -> [ -> [.data.table
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.1.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [5s/7s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("testthat")
+ library("checkmate") # for more expect_*() functions
+ library("mlr3benchmark")
+ test_check("mlr3benchmark")
+ }
INFO [17:13:08.955] [mlr3] Running benchmark with 4 resampling iterations
INFO [17:13:09.094] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/1)
INFO [17:13:09.175] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/1)
INFO [17:13:09.235] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/1)
INFO [17:13:09.321] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/1)
INFO [17:13:09.594] [mlr3] Finished benchmark
Saving _problems/test_BenchmarkAggr-101.R
INFO [17:13:10.669] [mlr3] Running benchmark with 18 resampling iterations
INFO [17:13:10.825] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3)
INFO [17:13:10.854] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3)
INFO [17:13:10.903] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3)
INFO [17:13:10.958] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
INFO [17:13:10.992] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
INFO [17:13:11.026] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
INFO [17:13:11.063] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 1/3)
INFO [17:13:11.103] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 2/3)
INFO [17:13:11.153] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 3/3)
INFO [17:13:11.216] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3)
INFO [17:13:11.261] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3)
INFO [17:13:11.335] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3)
INFO [17:13:11.501] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3)
INFO [17:13:11.644] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3)
INFO [17:13:11.736] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3)
INFO [17:13:11.785] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 1/3)
INFO [17:13:11.833] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 2/3)
INFO [17:13:11.871] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 3/3)
INFO [17:13:11.934] [mlr3] Finished benchmark
Saving _problems/test_autoplot_BenchmarkAggr-48.R
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 47 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_BenchmarkAggr.R:101:3'): mlr3 coercions ────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(task, learns, rsmp("holdout"))) at test_BenchmarkAggr.R:101:3
2. └─ResultData$new(grid, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_autoplot_BenchmarkAggr.R:48:3'): autoplot with BenchmarkAggr from mlr3::benchmark() ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 3))) at test_autoplot_BenchmarkAggr.R:48:3
2. └─ResultData$new(grid, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 47 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.1.7
Check: examples
Result: ERROR
Running examples in ‘mlr3benchmark-Ex.R’ failed
The error most likely occurred in:
> ### Name: BenchmarkAggr
> ### Title: Aggregated Benchmark Result Object
> ### Aliases: BenchmarkAggr
>
> ### ** Examples
>
> # Not restricted to mlr3 objects
> df = data.frame(tasks = factor(rep(c("A", "B"), each = 5),
+ levels = c("A", "B")),
+ learners = factor(paste0("L", 1:5)),
+ RMSE = runif(10), MAE = runif(10))
> as_benchmark_aggr(df, task_id = "tasks", learner_id = "learners")
<BenchmarkAggr> of 10 rows with 2 tasks, 5 learners and 2 measures
tasks learners RMSE MAE
<fctr> <fctr> <num> <num>
1: A L1 0.26550866 0.2059746
2: A L2 0.37212390 0.1765568
3: A L3 0.57285336 0.6870228
4: A L4 0.90820779 0.3841037
5: A L5 0.20168193 0.7698414
6: B L1 0.89838968 0.4976992
7: B L2 0.94467527 0.7176185
8: B L3 0.66079779 0.9919061
9: B L4 0.62911404 0.3800352
10: B L5 0.06178627 0.7774452
>
> if (requireNamespaces(c("mlr3", "rpart"))) {
+ library(mlr3)
+ task = tsks(c("pima", "spam"))
+ learns = lrns(c("classif.featureless", "classif.rpart"))
+ bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 2)))
+
+ # coercion
+ as_benchmark_aggr(bm)
+ }
INFO [17:44:25.371] [mlr3] Running benchmark with 8 resampling iterations
INFO [17:44:26.040] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/2)
INFO [17:44:26.228] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 2/2)
INFO [17:44:26.356] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/2)
INFO [17:44:26.618] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 2/2)
INFO [17:44:26.766] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/2)
INFO [17:44:26.846] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 2/2)
INFO [17:44:26.964] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/2)
INFO [17:44:27.253] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 2/2)
INFO [17:44:27.454] [mlr3] Finished benchmark
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
Calls: benchmark ... initialize -> .__ResultData__initialize -> [ -> [.data.table
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.1.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [13s/22s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("testthat")
+ library("checkmate") # for more expect_*() functions
+ library("mlr3benchmark")
+ test_check("mlr3benchmark")
+ }
INFO [17:44:40.388] [mlr3] Running benchmark with 4 resampling iterations
INFO [17:44:41.177] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/1)
INFO [17:44:41.430] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/1)
INFO [17:44:41.553] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/1)
INFO [17:44:41.696] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/1)
INFO [17:44:42.056] [mlr3] Finished benchmark
Saving _problems/test_BenchmarkAggr-101.R
INFO [17:44:46.341] [mlr3] Running benchmark with 18 resampling iterations
INFO [17:44:46.843] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3)
INFO [17:44:46.962] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3)
INFO [17:44:47.104] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3)
INFO [17:44:47.223] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
INFO [17:44:47.345] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
INFO [17:44:47.413] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
INFO [17:44:47.468] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 1/3)
INFO [17:44:47.536] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 2/3)
INFO [17:44:47.655] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 3/3)
INFO [17:44:47.796] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3)
INFO [17:44:47.861] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3)
INFO [17:44:47.936] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3)
INFO [17:44:48.041] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3)
INFO [17:44:48.264] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3)
INFO [17:44:48.447] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3)
INFO [17:44:48.632] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 1/3)
INFO [17:44:48.866] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 2/3)
INFO [17:44:49.048] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 3/3)
INFO [17:44:49.252] [mlr3] Finished benchmark
Saving _problems/test_autoplot_BenchmarkAggr-48.R
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 47 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_BenchmarkAggr.R:101:3'): mlr3 coercions ────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(task, learns, rsmp("holdout"))) at test_BenchmarkAggr.R:101:3
2. └─ResultData$new(grid, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_autoplot_BenchmarkAggr.R:48:3'): autoplot with BenchmarkAggr from mlr3::benchmark() ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 3))) at test_autoplot_BenchmarkAggr.R:48:3
2. └─ResultData$new(grid, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 47 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.1.7
Check: examples
Result: ERROR
Running examples in ‘mlr3benchmark-Ex.R’ failed
The error most likely occurred in:
> ### Name: BenchmarkAggr
> ### Title: Aggregated Benchmark Result Object
> ### Aliases: BenchmarkAggr
>
> ### ** Examples
>
> # Not restricted to mlr3 objects
> df = data.frame(tasks = factor(rep(c("A", "B"), each = 5),
+ levels = c("A", "B")),
+ learners = factor(paste0("L", 1:5)),
+ RMSE = runif(10), MAE = runif(10))
> as_benchmark_aggr(df, task_id = "tasks", learner_id = "learners")
<BenchmarkAggr> of 10 rows with 2 tasks, 5 learners and 2 measures
tasks learners RMSE MAE
<fctr> <fctr> <num> <num>
1: A L1 0.26550866 0.2059746
2: A L2 0.37212390 0.1765568
3: A L3 0.57285336 0.6870228
4: A L4 0.90820779 0.3841037
5: A L5 0.20168193 0.7698414
6: B L1 0.89838968 0.4976992
7: B L2 0.94467527 0.7176185
8: B L3 0.66079779 0.9919061
9: B L4 0.62911404 0.3800352
10: B L5 0.06178627 0.7774452
>
> if (requireNamespaces(c("mlr3", "rpart"))) {
+ library(mlr3)
+ task = tsks(c("pima", "spam"))
+ learns = lrns(c("classif.featureless", "classif.rpart"))
+ bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 2)))
+
+ # coercion
+ as_benchmark_aggr(bm)
+ }
INFO [12:26:41.519] [mlr3] Running benchmark with 8 resampling iterations
INFO [12:26:42.233] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/2)
INFO [12:26:42.500] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 2/2)
INFO [12:26:42.589] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/2)
INFO [12:26:42.737] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 2/2)
INFO [12:26:42.817] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/2)
INFO [12:26:42.882] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 2/2)
INFO [12:26:43.061] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/2)
INFO [12:26:43.561] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 2/2)
INFO [12:26:44.522] [mlr3] Finished benchmark
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
Calls: benchmark ... initialize -> .__ResultData__initialize -> [ -> [.data.table
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.1.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [11s/16s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("testthat")
+ library("checkmate") # for more expect_*() functions
+ library("mlr3benchmark")
+ test_check("mlr3benchmark")
+ }
INFO [12:26:55.417] [mlr3] Running benchmark with 4 resampling iterations
INFO [12:26:55.925] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/1)
INFO [12:26:56.054] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/1)
INFO [12:26:56.119] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/1)
INFO [12:26:56.203] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/1)
INFO [12:26:56.597] [mlr3] Finished benchmark
Saving _problems/test_BenchmarkAggr-101.R
INFO [12:26:59.282] [mlr3] Running benchmark with 18 resampling iterations
INFO [12:26:59.396] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3)
INFO [12:26:59.481] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3)
INFO [12:26:59.548] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3)
INFO [12:26:59.616] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
INFO [12:26:59.690] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
INFO [12:26:59.827] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
INFO [12:26:59.902] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 1/3)
INFO [12:26:59.956] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 2/3)
INFO [12:27:00.011] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 3/3)
INFO [12:27:00.064] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3)
INFO [12:27:00.119] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3)
INFO [12:27:00.163] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3)
INFO [12:27:00.209] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3)
INFO [12:27:00.293] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3)
INFO [12:27:00.385] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3)
INFO [12:27:00.479] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 1/3)
INFO [12:27:00.621] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 2/3)
INFO [12:27:00.804] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 3/3)
INFO [12:27:00.998] [mlr3] Finished benchmark
Saving _problems/test_autoplot_BenchmarkAggr-48.R
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 47 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_BenchmarkAggr.R:101:3'): mlr3 coercions ────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(task, learns, rsmp("holdout"))) at test_BenchmarkAggr.R:101:3
2. └─ResultData$new(grid, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_autoplot_BenchmarkAggr.R:48:3'): autoplot with BenchmarkAggr from mlr3::benchmark() ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 3))) at test_autoplot_BenchmarkAggr.R:48:3
2. └─ResultData$new(grid, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 47 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.1.7
Check: examples
Result: ERROR
Running examples in ‘mlr3benchmark-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: autoplot.BenchmarkAggr
> ### Title: Plots for BenchmarkAggr
> ### Aliases: autoplot.BenchmarkAggr
>
> ### ** Examples
>
> if (requireNamespaces(c("mlr3learners", "mlr3", "rpart", "xgboost"))) {
+ library(mlr3)
+ library(mlr3learners)
+ library(ggplot2)
+
+ set.seed(1)
+ task = tsks(c("iris", "sonar", "wine", "zoo"))
+ learns = lrns(c("classif.featureless", "classif.rpart", "classif.xgboost"))
+ learns$classif.xgboost$param_set$values$nrounds = 50
+ bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 3)))
+ obj = as_benchmark_aggr(bm)
+
+ # mean and error bars
+ autoplot(obj, type = "mean", level = 0.95)
+
+ if (requireNamespace("PMCMRplus", quietly = TRUE)) {
+ # critical differences
+ autoplot(obj, type = "cd",style = 1)
+ autoplot(obj, type = "cd",style = 2)
+
+ # post-hoc friedman-nemenyi
+ autoplot(obj, type = "fn")
+ }
+
+ }
INFO [16:03:29.849] [mlr3] Running benchmark with 36 resampling iterations
INFO [16:03:29.906] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3)
INFO [16:03:29.974] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3)
INFO [16:03:30.046] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3)
INFO [16:03:30.122] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
INFO [16:03:30.168] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
INFO [16:03:30.205] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
INFO [16:03:30.247] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 1/3)
INFO [16:03:30.330] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 2/3)
INFO [16:03:30.408] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 3/3)
INFO [16:03:30.481] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3)
INFO [16:03:30.529] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3)
INFO [16:03:30.561] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3)
INFO [16:03:30.591] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3)
INFO [16:03:30.657] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3)
INFO [16:03:30.707] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3)
INFO [16:03:30.759] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 1/3)
INFO [16:03:30.948] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 2/3)
INFO [16:03:31.069] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 3/3)
INFO [16:03:31.180] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 1/3)
INFO [16:03:31.214] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 2/3)
INFO [16:03:31.242] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 3/3)
INFO [16:03:31.295] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 1/3)
INFO [16:03:31.331] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 2/3)
INFO [16:03:31.367] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 3/3)
INFO [16:03:31.402] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 1/3)
INFO [16:03:31.511] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 2/3)
INFO [16:03:31.591] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 3/3)
INFO [16:03:31.674] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 1/3)
INFO [16:03:31.707] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 2/3)
INFO [16:03:31.755] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 3/3)
INFO [16:03:31.807] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 1/3)
INFO [16:03:31.844] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 2/3)
INFO [16:03:31.909] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 3/3)
INFO [16:03:31.947] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 1/3)
INFO [16:03:32.037] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 2/3)
INFO [16:03:32.138] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 3/3)
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
Warning in check.custom.obj(params, objective) :
Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
INFO [16:03:32.248] [mlr3] Finished benchmark
Error: Global Friedman test non-significant (p > 0.05), try type = 'mean' instead.
Execution halted
Flavor: r-release-linux-x86_64