Last updated on 2025-09-08 09:50:24 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.0.5 | 5.96 | 190.22 | 196.18 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.0.5 | 3.75 | 90.24 | 93.99 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.0.5 | 192.48 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.0.5 | 262.13 | ERROR | |||
| r-devel-windows-x86_64 | 0.0.5 | 7.00 | 628.00 | 635.00 | OK | |
| r-patched-linux-x86_64 | 0.0.5 | 5.22 | 195.34 | 200.56 | OK | |
| r-release-linux-x86_64 | 0.0.5 | 5.06 | 197.48 | 202.54 | OK | |
| r-release-macos-arm64 | 0.0.5 | 322.00 | OK | |||
| r-release-macos-x86_64 | 0.0.5 | 354.00 | OK | |||
| r-release-windows-x86_64 | 0.0.5 | 7.00 | 639.00 | 646.00 | OK | |
| r-oldrel-macos-arm64 | 0.0.5 | 205.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.0.5 | 359.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.0.5 | 9.00 | 898.00 | 907.00 | OK |
Version: 0.0.5
Check: examples
Result: ERROR
Running examples in ‘mllrnrs-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: LearnerGlmnet
> ### Title: R6 Class to construct a Glmnet learner
> ### Aliases: LearnerGlmnet
>
> ### ** Examples
>
> # binary classification
>
> library(mlbench)
> data("PimaIndiansDiabetes2")
> dataset <- PimaIndiansDiabetes2 |>
+ data.table::as.data.table() |>
+ na.omit()
>
> seed <- 123
> feature_cols <- colnames(dataset)[1:8]
>
> train_x <- model.matrix(
+ ~ -1 + .,
+ dataset[, .SD, .SDcols = feature_cols]
+ )
> train_y <- as.integer(dataset[, get("diabetes")]) - 1L
>
> fold_list <- splitTools::create_folds(
+ y = train_y,
+ k = 3,
+ type = "stratified",
+ seed = seed
+ )
> glmnet_cv <- mlexperiments::MLCrossValidation$new(
+ learner = mllrnrs::LearnerGlmnet$new(
+ metric_optimization_higher_better = FALSE
+ ),
+ fold_list = fold_list,
+ ncores = 2,
+ seed = 123
+ )
> glmnet_cv$learner_args <- list(
+ alpha = 1,
+ lambda = 0.1,
+ family = "binomial",
+ type.measure = "class",
+ standardize = TRUE
+ )
> glmnet_cv$predict_args <- list(type = "response")
> glmnet_cv$performance_metric_args <- list(positive = "1")
> glmnet_cv$performance_metric <- mlexperiments::metric("auc")
Error: Package "measures" must be installed to use function 'metric()'.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.0.5
Check: tests
Result: ERROR
Running ‘testthat.R’ [3s/5s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
> # https://github.com/Rdatatable/data.table/issues/5658
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mllrnrs)
>
> test_check("mllrnrs")
[ FAIL 17 | WARN 0 | SKIP 1 | PASS 0 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet_binary.R:88:5'): test nested cv, bayesian, binary - glmnet ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("prauc") at test-glmnet_binary.R:88:5
── Error ('test-glmnet_binary.R:134:5'): test nested cv, grid - glmnet, errors ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("prauc") at test-glmnet_binary.R:134:5
── Error ('test-glmnet_multiclass.R:89:5'): test nested cv, bayesian, multiclass - glmnet ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-glmnet_multiclass.R:89:5
── Error ('test-glmnet_regression.R:87:5'): test nested cv, bayesian, regression - glmnet ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("rmsle") at test-glmnet_regression.R:87:5
── Error ('test-glmnet_regression.R:134:5'): test nested cv, grid - glmnet ─────
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("rmsle") at test-glmnet_regression.R:134:5
── Error ('test-lightgbm_binary.R:99:5'): test nested cv, bayesian, binary - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("auc") at test-lightgbm_binary.R:99:5
── Error ('test-lightgbm_multiclass.R:101:5'): test nested cv, bayesian, multiclass - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-lightgbm_multiclass.R:101:5
── Error ('test-lightgbm_regression.R:99:5'): test nested cv, bayesian, regression - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-lightgbm_regression.R:99:5
── Error ('test-lightgbm_regression.R:141:5'): test nested cv, grid - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-lightgbm_regression.R:141:5
── Error ('test-ranger_binary.R:92:5'): test nested cv, bayesian, binary - ranger ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("auc") at test-ranger_binary.R:92:5
── Error ('test-ranger_multiclass.R:93:5'): test nested cv, bayesian, regression - ranger ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-ranger_multiclass.R:93:5
── Error ('test-ranger_regression.R:87:5'): test nested cv, bayesian, regression - ranger ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-ranger_regression.R:87:5
── Error ('test-ranger_regression.R:125:5'): test nested cv, grid - ranger ─────
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-ranger_regression.R:125:5
── Error ('test-xgboost_binary.R:83:5'): test nested cv, bayesian, binary:logistic - xgboost ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("auc") at test-xgboost_binary.R:83:5
── Error ('test-xgboost_multiclass.R:86:5'): test nested cv, bayesian, multi:softprob - xgboost, with weights ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-xgboost_multiclass.R:86:5
── Error ('test-xgboost_regression.R:82:5'): test nested cv, bayesian, reg:squarederror - xgboost ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-xgboost_regression.R:82:5
── Error ('test-xgboost_regression.R:125:5'): test nested cv, grid - xgboost ───
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-xgboost_regression.R:125:5
[ FAIL 17 | WARN 0 | SKIP 1 | PASS 0 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.0.5
Check: examples
Result: ERROR
Running examples in ‘mllrnrs-Ex.R’ failed
The error most likely occurred in:
> ### Name: LearnerGlmnet
> ### Title: R6 Class to construct a Glmnet learner
> ### Aliases: LearnerGlmnet
>
> ### ** Examples
>
> # binary classification
>
> library(mlbench)
> data("PimaIndiansDiabetes2")
> dataset <- PimaIndiansDiabetes2 |>
+ data.table::as.data.table() |>
+ na.omit()
>
> seed <- 123
> feature_cols <- colnames(dataset)[1:8]
>
> train_x <- model.matrix(
+ ~ -1 + .,
+ dataset[, .SD, .SDcols = feature_cols]
+ )
> train_y <- as.integer(dataset[, get("diabetes")]) - 1L
>
> fold_list <- splitTools::create_folds(
+ y = train_y,
+ k = 3,
+ type = "stratified",
+ seed = seed
+ )
> glmnet_cv <- mlexperiments::MLCrossValidation$new(
+ learner = mllrnrs::LearnerGlmnet$new(
+ metric_optimization_higher_better = FALSE
+ ),
+ fold_list = fold_list,
+ ncores = 2,
+ seed = 123
+ )
> glmnet_cv$learner_args <- list(
+ alpha = 1,
+ lambda = 0.1,
+ family = "binomial",
+ type.measure = "class",
+ standardize = TRUE
+ )
> glmnet_cv$predict_args <- list(type = "response")
> glmnet_cv$performance_metric_args <- list(positive = "1")
> glmnet_cv$performance_metric <- mlexperiments::metric("auc")
Error: Package "measures" must be installed to use function 'metric()'.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.0.5
Check: tests
Result: ERROR
Running ‘testthat.R’
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
> # https://github.com/Rdatatable/data.table/issues/5658
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mllrnrs)
>
> test_check("mllrnrs")
[ FAIL 17 | WARN 0 | SKIP 1 | PASS 0 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet_binary.R:88:5'): test nested cv, bayesian, binary - glmnet ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("prauc") at test-glmnet_binary.R:88:5
── Error ('test-glmnet_binary.R:134:5'): test nested cv, grid - glmnet, errors ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("prauc") at test-glmnet_binary.R:134:5
── Error ('test-glmnet_multiclass.R:89:5'): test nested cv, bayesian, multiclass - glmnet ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-glmnet_multiclass.R:89:5
── Error ('test-glmnet_regression.R:87:5'): test nested cv, bayesian, regression - glmnet ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("rmsle") at test-glmnet_regression.R:87:5
── Error ('test-glmnet_regression.R:134:5'): test nested cv, grid - glmnet ─────
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("rmsle") at test-glmnet_regression.R:134:5
── Error ('test-lightgbm_binary.R:99:5'): test nested cv, bayesian, binary - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("auc") at test-lightgbm_binary.R:99:5
── Error ('test-lightgbm_multiclass.R:101:5'): test nested cv, bayesian, multiclass - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-lightgbm_multiclass.R:101:5
── Error ('test-lightgbm_regression.R:99:5'): test nested cv, bayesian, regression - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-lightgbm_regression.R:99:5
── Error ('test-lightgbm_regression.R:141:5'): test nested cv, grid - lightgbm ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-lightgbm_regression.R:141:5
── Error ('test-ranger_binary.R:92:5'): test nested cv, bayesian, binary - ranger ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("auc") at test-ranger_binary.R:92:5
── Error ('test-ranger_multiclass.R:93:5'): test nested cv, bayesian, regression - ranger ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-ranger_multiclass.R:93:5
── Error ('test-ranger_regression.R:87:5'): test nested cv, bayesian, regression - ranger ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-ranger_regression.R:87:5
── Error ('test-ranger_regression.R:125:5'): test nested cv, grid - ranger ─────
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-ranger_regression.R:125:5
── Error ('test-xgboost_binary.R:83:5'): test nested cv, bayesian, binary:logistic - xgboost ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("auc") at test-xgboost_binary.R:83:5
── Error ('test-xgboost_multiclass.R:86:5'): test nested cv, bayesian, multi:softprob - xgboost, with weights ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-xgboost_multiclass.R:86:5
── Error ('test-xgboost_regression.R:82:5'): test nested cv, bayesian, reg:squarederror - xgboost ──
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-xgboost_regression.R:82:5
── Error ('test-xgboost_regression.R:125:5'): test nested cv, grid - xgboost ───
Error: Package "measures" must be installed to use function 'metric()'.
Backtrace:
▆
1. └─mlexperiments::metric("msle") at test-xgboost_regression.R:125:5
[ FAIL 17 | WARN 0 | SKIP 1 | PASS 0 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.0.5
Check: tests
Result: ERROR
Running ‘testthat.R’ [1m/11m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
> # https://github.com/Rdatatable/data.table/issues/5658
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mllrnrs)
>
> test_check("mllrnrs")
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 18.377 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.961 seconds
3) Running FUN 2 times in 2 thread(s)... 1.236 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 14.076 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.039 seconds
3) Running FUN 2 times in 2 thread(s)... 1.222 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 14.338 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.05 seconds
3) Running FUN 2 times in 2 thread(s)... 1.085 seconds
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 16.241 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 27.775 seconds
3) Running FUN 2 times in 2 thread(s)... 1.249 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 15.659 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 22.895 seconds
3) Running FUN 2 times in 2 thread(s)... 0.753 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 12.79 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 38.675 seconds
3) Running FUN 2 times in 2 thread(s)... 0.968 seconds
CV fold: Fold1
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 12.992 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 23.837 seconds
3) Running FUN 2 times in 2 thread(s)... 1.05 seconds
CV fold: Fold2
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 11.24 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 29.711 seconds
3) Running FUN 2 times in 2 thread(s)... 0.895 seconds
CV fold: Fold3
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 10.93 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 32.119 seconds
3) Running FUN 2 times in 2 thread(s)... 0.79 seconds
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 9.538 seconds
num.trees mtry min.node.size max.depth sample.fraction
<num> <num> <num> <num> <num>
1: 1000 6 1 9 0.5
2: 1000 2 5 1 0.8
3: 1000 6 9 9 0.8
4: 1000 4 1 9 0.5
5: 1000 4 5 9 0.8
6: 1000 2 9 9 0.8
7: 500 2 9 1 0.8
8: 500 4 1 9 0.8
9: 1000 6 5 9 0.8
10: 500 6 1 1 0.5
errorMessage
<char>
1: `name` is not a function exported from R package {measures}
2: `name` is not a function exported from R package {measures}
3: `name` is not a function exported from R package {measures}
4: `name` is not a function exported from R package {measures}
5: `name` is not a function exported from R package {measures}
6: `name` is not a function exported from R package {measures}
7: `name` is not a function exported from R package {measures}
8: `name` is not a function exported from R package {measures}
9: `name` is not a function exported from R package {measures}
10: `name` is not a function exported from R package {measures}
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 9.594 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.704 seconds
3) Running FUN 2 times in 2 thread(s)... 0.753 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 9.062 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.846 seconds
3) Running FUN 2 times in 2 thread(s)... 0.777 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 8.811 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.125 seconds
3) Running FUN 2 times in 2 thread(s)... 0.888 seconds
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 6.065 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 13.027 seconds
3) Running FUN 2 times in 2 thread(s)... 0.52 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 5.982 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 9.738 seconds
3) Running FUN 2 times in 2 thread(s)... 0.441 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 5.971 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 16.539 seconds
3) Running FUN 2 times in 2 thread(s)... 0.588 seconds
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 6.979 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 34.856 seconds
3) Running FUN 2 times in 2 thread(s)... 0.734 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 7.295 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 4.398 seconds
3) Running FUN 2 times in 2 thread(s)... 0.61 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 7.21 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 18.203 seconds
3) Running FUN 2 times in 2 thread(s)... 0.62 seconds
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
[ FAIL 7 | WARN 3 | SKIP 1 | PASS 30 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet_binary.R:88:5'): test nested cv, bayesian, binary - glmnet ──
Error in `mlexperiments::metric("prauc")`: `name` is not a function exported from R package {measures}
Backtrace:
▆
1. └─mlexperiments::metric("prauc") at test-glmnet_binary.R:88:5
── Error ('test-glmnet_binary.R:134:5'): test nested cv, grid - glmnet, errors ──
Error in `mlexperiments::metric("prauc")`: `name` is not a function exported from R package {measures}
Backtrace:
▆
1. └─mlexperiments::metric("prauc") at test-glmnet_binary.R:134:5
── Error ('test-glmnet_multiclass.R:89:5'): test nested cv, bayesian, multiclass - glmnet ──
Error in `mlexperiments::metric("bacc")`: `name` is not a function exported from R package {measures}
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-glmnet_multiclass.R:89:5
── Error ('test-lightgbm_multiclass.R:101:5'): test nested cv, bayesian, multiclass - lightgbm ──
Error in `mlexperiments::metric("bacc")`: `name` is not a function exported from R package {measures}
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-lightgbm_multiclass.R:101:5
── Error ('test-ranger_binary.R:100:5'): test nested cv, bayesian, binary - ranger ──
Error in `(function (FUN, bounds, saveFile = NULL, initGrid, initPoints = 4,
iters.n = 3, iters.k = 1, otherHalting = list(timeLimit = Inf,
minUtility = 0), acq = "ucb", kappa = 2.576, eps = 0,
parallel = FALSE, gsPoints = pmax(100, length(bounds)^3),
convThresh = 1e+08, acqThresh = 1, errorHandling = "stop",
plotProgress = FALSE, verbose = 1, ...)
{
startT <- Sys.time()
optObj <- list()
class(optObj) <- "bayesOpt"
optObj$FUN <- FUN
optObj$bounds <- bounds
optObj$iters <- 0
optObj$initPars <- list()
optObj$optPars <- list()
optObj$GauProList <- list()
optObj <- changeSaveFile(optObj, saveFile)
checkParameters(bounds, iters.n, iters.k, otherHalting, acq,
acqThresh, errorHandling, plotProgress, parallel, verbose)
boundsDT <- boundsToDT(bounds)
otherHalting <- formatOtherHalting(otherHalting)
if (missing(initGrid) + missing(initPoints) != 1)
stop("Please provide 1 of initGrid or initPoints, but not both.")
if (!missing(initGrid)) {
setDT(initGrid)
inBounds <- checkBounds(initGrid, bounds)
inBounds <- as.logical(apply(inBounds, 1, prod))
if (any(!inBounds))
stop("initGrid not within bounds.")
optObj$initPars$initialSample <- "User Provided Grid"
initPoints <- nrow(initGrid)
}
else {
initGrid <- randParams(boundsDT, initPoints)
optObj$initPars$initialSample <- "Latin Hypercube Sampling"
}
optObj$initPars$initGrid <- initGrid
if (nrow(initGrid) <= 2)
stop("Cannot initialize with less than 3 samples.")
optObj$initPars$initPoints <- nrow(initGrid)
if (initPoints <= length(bounds))
stop("initPoints must be greater than the number of FUN inputs.")
sinkFile <- file()
on.exit({
while (sink.number() > 0) sink()
close(sinkFile)
})
`%op%` <- ParMethod(parallel)
if (parallel)
Workers <- getDoParWorkers()
else Workers <- 1
if (verbose > 0)
cat("\nRunning initial scoring function", nrow(initGrid),
"times in", Workers, "thread(s)...")
sink(file = sinkFile)
tm <- system.time(scoreSummary <- foreach(iter = 1:nrow(initGrid),
.options.multicore = list(preschedule = FALSE), .combine = list,
.multicombine = TRUE, .inorder = FALSE, .errorhandling = "pass",
.verbose = FALSE) %op% {
Params <- initGrid[get("iter"), ]
Elapsed <- system.time(Result <- tryCatch({
do.call(what = FUN, args = as.list(Params))
}, error = function(e) e))
if (any(class(Result) %in% c("simpleError", "error",
"condition")))
return(Result)
if (!inherits(x = Result, what = "list"))
stop("Object returned from FUN was not a list.")
resLengths <- lengths(Result)
if (!any(names(Result) == "Score"))
stop("FUN must return list with element 'Score' at a minimum.")
if (!is.numeric(Result$Score))
stop("Score returned from FUN was not numeric.")
if (any(resLengths != 1)) {
badReturns <- names(Result)[which(resLengths != 1)]
stop("FUN returned these elements with length > 1: ",
paste(badReturns, collapse = ","))
}
data.table(Params, Elapsed = Elapsed[[3]], as.data.table(Result))
})[[3]]
while (sink.number() > 0) sink()
if (verbose > 0)
cat(" ", tm, "seconds\n")
se <- which(sapply(scoreSummary, function(cl) any(class(cl) %in%
c("simpleError", "error", "condition"))))
if (length(se) > 0) {
print(data.table(initGrid[se, ], errorMessage = sapply(scoreSummary[se],
function(x) x$message)))
stop("Errors encountered in initialization are listed above.")
}
else {
scoreSummary <- rbindlist(scoreSummary)
}
scoreSummary[, `:=`(("gpUtility"), rep(as.numeric(NA), nrow(scoreSummary)))]
scoreSummary[, `:=`(("acqOptimum"), rep(FALSE, nrow(scoreSummary)))]
scoreSummary[, `:=`(("Epoch"), rep(0, nrow(scoreSummary)))]
scoreSummary[, `:=`(("Iteration"), 1:nrow(scoreSummary))]
scoreSummary[, `:=`(("inBounds"), rep(TRUE, nrow(scoreSummary)))]
scoreSummary[, `:=`(("errorMessage"), rep(NA, nrow(scoreSummary)))]
extraRet <- setdiff(names(scoreSummary), c("Epoch", "Iteration",
boundsDT$N, "inBounds", "Elapsed", "Score", "gpUtility",
"acqOptimum"))
setcolorder(scoreSummary, c("Epoch", "Iteration", boundsDT$N,
"gpUtility", "acqOptimum", "inBounds", "Elapsed", "Score",
extraRet))
if (any(scoreSummary$Elapsed < 1) & acq == "eips") {
cat("\n FUN elapsed time is too low to be precise. Switching acq to 'ei'.\n")
acq <- "ei"
}
optObj$optPars$acq <- acq
optObj$optPars$kappa <- kappa
optObj$optPars$eps <- eps
optObj$optPars$parallel <- parallel
optObj$optPars$gsPoints <- gsPoints
optObj$optPars$convThresh <- convThresh
optObj$optPars$acqThresh <- acqThresh
optObj$scoreSummary <- scoreSummary
optObj$GauProList$gpUpToDate <- FALSE
optObj$iters <- nrow(scoreSummary)
optObj$stopStatus <- "OK"
optObj$elapsedTime <- as.numeric(difftime(Sys.time(), startT,
units = "secs"))
saveSoFar(optObj, 0)
optObj <- addIterations(optObj, otherHalting = otherHalting,
iters.n = iters.n, iters.k = iters.k, parallel = parallel,
plotProgress = plotProgress, errorHandling = errorHandling,
saveFile = saveFile, verbose = verbose, ...)
return(optObj)
})(FUN = function (...)
{
kwargs <- list(...)
args <- .method_params_refactor(kwargs, method_helper)
set.seed(self$seed)
res <- do.call(private$fun_bayesian_scoring_function, args)
if (isFALSE(self$metric_optimization_higher_better)) {
res$Score <- as.numeric(I(res$Score * -1L))
}
return(res)
}, bounds = list(num.trees = c(100L, 1000L), mtry = c(2L, 9L),
min.node.size = c(1L, 20L), max.depth = c(1L, 40L), sample.fraction = c(0.3,
1)), initGrid = structure(list(num.trees = c(1000, 1000,
1000, 1000, 1000, 1000, 500, 500, 1000, 500), mtry = c(6, 2,
6, 4, 4, 2, 2, 4, 6, 6), min.node.size = c(1, 5, 9, 1, 5, 9,
9, 1, 5, 1), max.depth = c(9, 1, 9, 9, 9, 9, 1, 9, 9, 1), sample.fraction = c(0.5,
0.8, 0.8, 0.5, 0.8, 0.8, 0.8, 0.8, 0.8, 0.5)), out.attrs = list(
dim = c(num.trees = 2L, mtry = 3L, min.node.size = 3L, max.depth = 3L,
sample.fraction = 2L), dimnames = list(num.trees = c("num.trees= 500",
"num.trees=1000"), mtry = c("mtry=2", "mtry=4", "mtry=6"),
min.node.size = c("min.node.size=1", "min.node.size=5",
"min.node.size=9"), max.depth = c("max.depth=1", "max.depth=5",
"max.depth=9"), sample.fraction = c("sample.fraction=0.5",
"sample.fraction=0.8"))), row.names = c(NA, -10L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x834b550>), iters.n = 2L,
iters.k = 2L, otherHalting = list(timeLimit = Inf, minUtility = 0),
acq = "ucb", kappa = 3.5, eps = 0, parallel = TRUE, gsPoints = 125,
convThresh = 1e+08, acqThresh = 1, errorHandling = "stop",
plotProgress = FALSE, verbose = 1)`: Errors encountered in initialization are listed above.
Backtrace:
▆
1. └─ranger_optimizer$execute() at test-ranger_binary.R:100:5
2. └─mlexperiments:::.run_cv(self = self, private = private)
3. └─mlexperiments:::.fold_looper(self, private)
4. ├─base::do.call(private$cv_run_model, run_args)
5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
6. ├─base::do.call(.cv_run_nested_model, args)
7. └─mlexperiments (local) `<fn>`(...)
8. └─hparam_tuner$execute(k = self$k_tuning)
9. └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
10. └─mlexperiments:::.run_optimizer(...)
11. └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper)
12. ├─base::do.call(...)
13. └─mlexperiments (local) `<fn>`(...)
14. ├─base::do.call(ParBayesianOptimization::bayesOpt, args)
15. └─ParBayesianOptimization (local) `<fn>`(...)
── Error ('test-ranger_multiclass.R:93:5'): test nested cv, bayesian, regression - ranger ──
Error in `mlexperiments::metric("bacc")`: `name` is not a function exported from R package {measures}
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-ranger_multiclass.R:93:5
── Error ('test-xgboost_multiclass.R:86:5'): test nested cv, bayesian, multi:softprob - xgboost, with weights ──
Error in `mlexperiments::metric("bacc")`: `name` is not a function exported from R package {measures}
Backtrace:
▆
1. └─mlexperiments::metric("bacc") at test-xgboost_multiclass.R:86:5
[ FAIL 7 | WARN 3 | SKIP 1 | PASS 30 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc