CRAN Package Check Results for Package mlr3benchmark

Last updated on 2025-12-24 09:49:45 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.1.7	6.37	72.79	79.16	ERROR
r-devel-linux-x86_64-debian-gcc	0.1.7	5.09	52.20	57.29	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	12.00	114.85	126.85	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	11.00	112.00	123.00	OK

Check Details

Version: 0.1.7
Check: tests
Result: ERROR Running ‘testthat.R’ [9s/11s] 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 [04:33:33.861] [mlr3] Running benchmark with 4 resampling iterations INFO [04:33:34.174] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/1) INFO [04:33:34.268] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/1) INFO [04:33:34.313] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/1) INFO [04:33:34.353] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/1) INFO [04:33:34.466] [mlr3] Finished benchmark Saving _problems/test_BenchmarkAggr-101.R INFO [04:33:37.162] [mlr3] Running benchmark with 18 resampling iterations INFO [04:33:37.290] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3) INFO [04:33:37.324] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3) INFO [04:33:37.360] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3) INFO [04:33:37.394] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3) INFO [04:33:37.442] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3) INFO [04:33:37.536] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3) INFO [04:33:37.616] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 1/3) INFO [04:33:37.655] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 2/3) INFO [04:33:37.700] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 3/3) INFO [04:33:37.802] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3) INFO [04:33:37.846] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3) INFO [04:33:37.876] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3) INFO [04:33:37.923] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3) INFO [04:33:37.987] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3) INFO [04:33:38.050] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3) INFO [04:33:38.109] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 1/3) INFO [04:33:38.177] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 2/3) INFO [04:33:38.235] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 3/3) INFO [04:33:38.335] [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-clang

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 [17:15:19.903] [mlr3] Running benchmark with 4 resampling iterations INFO [17:15:20.155] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/1) INFO [17:15:20.232] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/1) INFO [17:15:20.263] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/1) INFO [17:15:20.309] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/1) INFO [17:15:20.465] [mlr3] Finished benchmark Saving _problems/test_BenchmarkAggr-101.R INFO [17:15:21.686] [mlr3] Running benchmark with 18 resampling iterations INFO [17:15:21.796] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3) INFO [17:15:21.852] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3) INFO [17:15:21.902] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3) INFO [17:15:21.998] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3) INFO [17:15:22.057] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3) INFO [17:15:22.087] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3) INFO [17:15:22.113] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 1/3) INFO [17:15:22.140] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 2/3) INFO [17:15:22.166] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 3/3) INFO [17:15:22.203] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3) INFO [17:15:22.269] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3) INFO [17:15:22.321] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3) INFO [17:15:22.358] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3) INFO [17:15:22.404] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3) INFO [17:15:22.443] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3) INFO [17:15:22.483] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 1/3) INFO [17:15:22.532] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 2/3) INFO [17:15:22.579] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 3/3) INFO [17:15:22.623] [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 [00:03:51.217] [mlr3] Running benchmark with 8 resampling iterations INFO [00:03:51.748] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/2) INFO [00:03:51.853] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 2/2) INFO [00:03:51.947] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/2) INFO [00:03:52.083] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 2/2) INFO [00:03:52.213] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/2) INFO [00:03:52.315] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 2/2) INFO [00:03:52.443] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/2) INFO [00:03:53.079] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 2/2) INFO [00:03:53.615] [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’ [13s/19s] 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 [00:04:04.375] [mlr3] Running benchmark with 4 resampling iterations INFO [00:04:04.837] [mlr3] Applying learner 'classif.featureless' on task 'pima' (iter 1/1) INFO [00:04:05.045] [mlr3] Applying learner 'classif.rpart' on task 'pima' (iter 1/1) INFO [00:04:05.277] [mlr3] Applying learner 'classif.featureless' on task 'spam' (iter 1/1) INFO [00:04:05.400] [mlr3] Applying learner 'classif.rpart' on task 'spam' (iter 1/1) INFO [00:04:05.772] [mlr3] Finished benchmark Saving _problems/test_BenchmarkAggr-101.R INFO [00:04:09.990] [mlr3] Running benchmark with 18 resampling iterations INFO [00:04:10.101] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3) INFO [00:04:10.169] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3) INFO [00:04:10.239] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3) INFO [00:04:10.307] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3) INFO [00:04:10.422] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3) INFO [00:04:10.523] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3) INFO [00:04:10.639] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 1/3) INFO [00:04:10.754] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 2/3) INFO [00:04:10.896] [mlr3] Applying learner 'rpart2' on task 'iris' (iter 3/3) INFO [00:04:11.033] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3) INFO [00:04:11.203] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3) INFO [00:04:11.343] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3) INFO [00:04:11.477] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3) INFO [00:04:11.658] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3) INFO [00:04:11.837] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3) INFO [00:04:12.070] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 1/3) INFO [00:04:12.247] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 2/3) INFO [00:04:12.443] [mlr3] Applying learner 'rpart2' on task 'sonar' (iter 3/3) INFO [00:04:12.652] [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