CRAN Package Check Results for Package MlBayesOpt

Last updated on 2019-10-19 01:50:17 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.3.4 11.32 215.67 226.99 OK
r-devel-linux-x86_64-debian-gcc 0.3.4 9.28 147.00 156.28 OK
r-devel-linux-x86_64-fedora-clang 0.3.4 246.67 ERROR
r-devel-linux-x86_64-fedora-gcc 0.3.4 223.56 NOTE
r-devel-windows-ix86+x86_64 0.3.4 29.00 236.00 265.00 OK
r-patched-linux-x86_64 0.3.4 10.52 181.33 191.85 OK
r-patched-solaris-x86 0.3.4 331.20 NOTE
r-release-linux-x86_64 0.3.4 10.90 180.34 191.24 OK
r-release-windows-ix86+x86_64 0.3.4 23.00 196.00 219.00 OK
r-release-osx-x86_64 0.3.4 NOTE
r-oldrel-windows-ix86+x86_64 0.3.4 12.00 166.00 178.00 OK
r-oldrel-osx-x86_64 0.3.4 NOTE

Check Details

Version: 0.3.4
Check: dependencies in R code
Result: NOTE
    Namespaces in Imports field not imported from:
     ‘data.table’ ‘foreach’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.3.4
Check: tests
Result: ERROR
     Running ‘testthat.R’ [114s/272s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(MlBayesOpt)
     >
     > test_check("MlBayesOpt")
     elapsed = 0.22 Round = 1 mtry_opt = 3.6634 min_node_size = 7.0000 Value = 0.1300
     elapsed = 0.11 Round = 2 mtry_opt = 5.4408 min_node_size = 4.0000 Value = 0.1400
     elapsed = 0.14 Round = 3 mtry_opt = 3.6190 min_node_size = 7.0000 Value = 0.1000
     elapsed = 0.12 Round = 4 mtry_opt = 2.6933 min_node_size = 3.0000 Value = 0.1700
     elapsed = 0.16 Round = 5 mtry_opt = 3.5290 min_node_size = 3.0000 Value = 0.1800
     elapsed = 0.13 Round = 6 mtry_opt = 8.5781 min_node_size = 5.0000 Value = 0.1600
     elapsed = 0.14 Round = 7 mtry_opt = 6.2937 min_node_size = 5.0000 Value = 0.1500
     elapsed = 0.15 Round = 8 mtry_opt = 8.1154 min_node_size = 4.0000 Value = 0.1500
     elapsed = 0.11 Round = 9 mtry_opt = 3.7041 min_node_size = 4.0000 Value = 0.1700
     elapsed = 0.12 Round = 10 mtry_opt = 4.4780 min_node_size = 9.0000 Value = 0.1600
     elapsed = 0.11 Round = 11 mtry_opt = 1.9407 min_node_size = 1.0000 Value = 0.1800
     elapsed = 0.14 Round = 12 mtry_opt = 7.0937 min_node_size = 6.0000 Value = 0.1600
     elapsed = 0.16 Round = 13 mtry_opt = 2.1344 min_node_size = 8.0000 Value = 0.1600
     elapsed = 0.14 Round = 14 mtry_opt = 7.1353 min_node_size = 2.0000 Value = 0.1500
     elapsed = 0.12 Round = 15 mtry_opt = 7.7371 min_node_size = 8.0000 Value = 0.1500
     elapsed = 0.17 Round = 16 mtry_opt = 7.2140 min_node_size = 9.0000 Value = 0.1300
     elapsed = 0.13 Round = 17 mtry_opt = 2.0706 min_node_size = 5.0000 Value = 0.1200
     elapsed = 0.14 Round = 18 mtry_opt = 7.4475 min_node_size = 3.0000 Value = 0.1500
     elapsed = 0.14 Round = 19 mtry_opt = 8.1743 min_node_size = 5.0000 Value = 0.1400
     elapsed = 0.13 Round = 20 mtry_opt = 8.4158 min_node_size = 1.0000 Value = 0.1200
     elapsed = 0.10 Round = 21 mtry_opt = 8.9925 min_node_size = 9.0000 Value = 0.1600
    
     Best Parameters Found:
     Round = 5 mtry_opt = 3.5290 min_node_size = 3.0000 Value = 0.1800
     List of 4
     $ Best_Par : Named num [1:2] 3.53 3
     ..- attr(*, "names")= chr [1:2] "mtry_opt" "min_node_size"
     $ Best_Value: num 0.18
     $ History :Classes 'data.table' and 'data.frame': 21 obs. of 4 variables:
     ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ...
     ..$ mtry_opt : num [1:21] 3.66 5.44 3.62 2.69 3.53 ...
     ..$ min_node_size: num [1:21] 7 4 7 3 3 5 5 4 4 9 ...
     ..$ Value : num [1:21] 0.13 0.14 0.1 0.17 0.18 0.16 0.15 0.15 0.17 0.16 ...
     ..- attr(*, ".internal.selfref")=<externalptr>
     $ Pred :Classes 'data.table' and 'data.frame': 1 obs. of 21 variables:
     ..$ V1 : num 0.13
     ..$ V2 : num 0.14
     ..$ V3 : num 0.1
     ..$ V4 : num 0.17
     ..$ V5 : num 0.18
     ..$ V6 : num 0.16
     ..$ V7 : num 0.15
     ..$ V8 : num 0.15
     ..$ V9 : num 0.17
     ..$ V10: num 0.16
     ..$ V11: num 0.18
     ..$ V12: num 0.16
     ..$ V13: num 0.16
     ..$ V14: num 0.15
     ..$ V15: num 0.15
     ..$ V16: num 0.13
     ..$ V17: num 0.12
     ..$ V18: num 0.15
     ..$ V19: num 0.14
     ..$ V20: num 0.12
     ..$ V21: num 0.16
     ..- attr(*, ".internal.selfref")=<externalptr>
     elapsed = 0.10 Round = 1 gamma_opt = 3.3299 cost_opt = 61.5259 Value = 0.1900
     elapsed = 0.04 Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2100
     elapsed = 0.05 Round = 3 gamma_opt = 3.2744 cost_opt = 70.8278 Value = 0.1700
     elapsed = 0.05 Round = 4 gamma_opt = 2.1175 cost_opt = 21.9740 Value = 0.1600
     elapsed = 0.07 Round = 5 gamma_opt = 3.1619 cost_opt = 19.3146 Value = 0.1600
     elapsed = 0.02 Round = 6 gamma_opt = 9.4727 cost_opt = 46.3378 Value = 0.1600
     elapsed = 0.05 Round = 7 gamma_opt = 6.6175 cost_opt = 41.6790 Value = 0.1400
     elapsed = 0.08 Round = 8 gamma_opt = 8.8943 cost_opt = 33.0888 Value = 0.1300
     elapsed = 0.09 Round = 9 gamma_opt = 3.3808 cost_opt = 29.9110 Value = 0.0800
     elapsed = 0.07 Round = 10 gamma_opt = 4.3481 cost_opt = 88.7062 Value = 0.1500
     elapsed = 0.08 Round = 11 gamma_opt = 1.1767 cost_opt = 5.2563 Value = 0.1300
     elapsed = 0.05 Round = 12 gamma_opt = 7.6174 cost_opt = 60.4227 Value = 0.1500
     elapsed = 0.07 Round = 13 gamma_opt = 1.4188 cost_opt = 79.6450 Value = 0.1700
     elapsed = 0.04 Round = 14 gamma_opt = 7.6693 cost_opt = 6.2103 Value = 0.0900
     elapsed = 0.05 Round = 15 gamma_opt = 8.4215 cost_opt = 78.2717 Value = 0.1300
     elapsed = 0.06 Round = 16 gamma_opt = 7.7677 cost_opt = 83.7658 Value = 0.1800
     elapsed = 0.02 Round = 17 gamma_opt = 1.3391 cost_opt = 45.6691 Value = 0.1100
     elapsed = 0.07 Round = 18 gamma_opt = 8.0596 cost_opt = 22.1903 Value = 0.1500
     elapsed = 0.07 Round = 19 gamma_opt = 8.9679 cost_opt = 46.9767 Value = 0.2000
     elapsed = 0.07 Round = 20 gamma_opt = 9.2699 cost_opt = 3.9481 Value = 0.1100
     elapsed = 0.09 Round = 21 gamma_opt = 5.8138 cost_opt = 28.6718 Value = 0.1700
    
     Best Parameters Found:
     Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2100
     List of 4
     $ Best_Par : Named num [1:2] 5.55 28.76
     ..- attr(*, "names")= chr [1:2] "gamma_opt" "cost_opt"
     $ Best_Value: num 0.21
     $ History :Classes 'data.table' and 'data.frame': 21 obs. of 4 variables:
     ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ...
     ..$ gamma_opt: num [1:21] 3.33 5.55 3.27 2.12 3.16 ...
     ..$ cost_opt : num [1:21] 61.5 28.8 70.8 22 19.3 ...
     ..$ Value : num [1:21] 0.19 0.21 0.17 0.16 0.16 0.16 0.14 0.13 0.08 0.15 ...
     ..- attr(*, ".internal.selfref")=<externalptr>
     $ Pred :Classes 'data.table' and 'data.frame': 100 obs. of 21 variables:
     ..$ V1 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V2 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V3 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V4 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V5 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V6 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V7 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V8 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V9 : Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V10: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V11: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V12: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V13: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V14: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V15: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V16: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V17: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V18: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V19: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 10 6 ...
     ..$ V20: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..$ V21: Factor w/ 10 levels "0","1","2","3",..: 6 6 6 6 6 6 6 1 6 6 ...
     ..- attr(*, ".internal.selfref")=<externalptr>
     elapsed = 0.06 Round = 1 gamma_opt = 3.3299 cost_opt = 61.5259 Value = 0.1900
     elapsed = 0.08 Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2300
     elapsed = 0.07 Round = 3 gamma_opt = 3.2744 cost_opt = 70.8278 Value = 0.1900
     elapsed = 0.05 Round = 4 gamma_opt = 2.1175 cost_opt = 21.9740 Value = 0.1900
     elapsed = 0.05 Round = 5 gamma_opt = 3.1619 cost_opt = 19.3146 Value = 0.1900
     elapsed = 0.04 Round = 6 gamma_opt = 9.4727 cost_opt = 46.3378 Value = 0.2200
     elapsed = 0.09 Round = 7 gamma_opt = 6.6175 cost_opt = 41.6790 Value = 0.2200
     elapsed = 0.08 Round = 8 gamma_opt = 8.8943 cost_opt = 33.0888 Value = 0.2200
     elapsed = 0.06 Round = 9 gamma_opt = 3.3808 cost_opt = 29.9110 Value = 0.1900
     elapsed = 0.08 Round = 10 gamma_opt = 4.3481 cost_opt = 88.7062 Value = 0.2300
     elapsed = 0.06 Round = 11 gamma_opt = 1.1767 cost_opt = 5.2563 Value = 0.2000
     elapsed = 0.07 Round = 12 gamma_opt = 7.6174 cost_opt = 60.4227 Value = 0.2200
     elapsed = 0.09 Round = 13 gamma_opt = 1.4188 cost_opt = 79.6450 Value = 0.1800
     elapsed = 0.07 Round = 14 gamma_opt = 7.6693 cost_opt = 6.2103 Value = 0.2200
     elapsed = 0.08 Round = 15 gamma_opt = 8.4215 cost_opt = 78.2717 Value = 0.2300
     elapsed = 0.09 Round = 16 gamma_opt = 7.7677 cost_opt = 83.7658 Value = 0.2200
     elapsed = 0.07 Round = 17 gamma_opt = 1.3391 cost_opt = 45.6691 Value = 0.1800
     elapsed = 0.08 Round = 18 gamma_opt = 8.0596 cost_opt = 22.1903 Value = 0.2200
     elapsed = 0.06 Round = 19 gamma_opt = 8.9679 cost_opt = 46.9767 Value = 0.2200
     elapsed = 0.06 Round = 20 gamma_opt = 9.2699 cost_opt = 3.9481 Value = 0.1800
     elapsed = 0.06 Round = 21 gamma_opt = 9.6352 cost_opt = 14.7148 Value = 0.2200
    
     Best Parameters Found:
     Round = 2 gamma_opt = 5.5515 cost_opt = 28.7558 Value = 0.2300
     List of 4
     $ Best_Par : Named num [1:2] 5.55 28.76
     ..- attr(*, "names")= chr [1:2] "gamma_opt" "cost_opt"
     $ Best_Value: num 0.23
     $ History :Classes 'data.table' and 'data.frame': 21 obs. of 4 variables:
     ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ...
     ..$ gamma_opt: num [1:21] 3.33 5.55 3.27 2.12 3.16 ...
     ..$ cost_opt : num [1:21] 61.5 28.8 70.8 22 19.3 ...
     ..$ Value : num [1:21] 0.19 0.23 0.19 0.19 0.19 0.22 0.22 0.22 0.19 0.23 ...
     ..- attr(*, ".internal.selfref")=<externalptr>
     $ Pred :Classes 'data.table' and 'data.frame': 1 obs. of 21 variables:
     ..$ V1 : num 0.19
     ..$ V2 : num 0.23
     ..$ V3 : num 0.19
     ..$ V4 : num 0.19
     ..$ V5 : num 0.19
     ..$ V6 : num 0.22
     ..$ V7 : num 0.22
     ..$ V8 : num 0.22
     ..$ V9 : num 0.19
     ..$ V10: num 0.23
     ..$ V11: num 0.2
     ..$ V12: num 0.22
     ..$ V13: num 0.18
     ..$ V14: num 0.22
     ..$ V15: num 0.23
     ..$ V16: num 0.22
     ..$ V17: num 0.18
     ..$ V18: num 0.22
     ..$ V19: num 0.22
     ..$ V20: num 0.18
     ..$ V21: num 0.22
     ..- attr(*, ".internal.selfref")=<externalptr>
     OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead.
     OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).
     elapsed = 0.14 Round = 1 eta_opt = 0.2854 max_depth_opt = 5.0000 nrounds_opt = 112.9858 subsample_opt = 0.4052 bytree_opt = 0.5438 Value = -0.2697
     elapsed = 0.05 Round = 2 eta_opt = 0.2589 max_depth_opt = 5.0000 nrounds_opt = 147.5089 subsample_opt = 0.8555 bytree_opt = 0.4354 Value = -0.1228
     elapsed = 0.03 Round = 3 eta_opt = 0.7183 max_depth_opt = 5.0000 nrounds_opt = 109.4287 subsample_opt = 0.4120 bytree_opt = 0.7854 Value = -0.0614
     elapsed = 0.03 Round = 4 eta_opt = 0.4457 max_depth_opt = 4.0000 nrounds_opt = 92.0318 subsample_opt = 0.4004 bytree_opt = 0.9258 Value = -0.0614
     elapsed = 0.06 Round = 5 eta_opt = 0.7929 max_depth_opt = 6.0000 nrounds_opt = 76.3611 subsample_opt = 0.5287 bytree_opt = 0.8673 Value = -0.0351
     elapsed = 0.08 Round = 6 eta_opt = 0.5479 max_depth_opt = 5.0000 nrounds_opt = 78.9520 subsample_opt = 0.9030 bytree_opt = 0.8784 Value = -0.0351
     elapsed = 0.09 Round = 7 eta_opt = 0.7459 max_depth_opt = 6.0000 nrounds_opt = 98.4645 subsample_opt = 0.8779 bytree_opt = 0.6732 Value = -0.0263
     elapsed = 0.03 Round = 8 eta_opt = 0.9927 max_depth_opt = 4.0000 nrounds_opt = 116.6771 subsample_opt = 0.4510 bytree_opt = 0.6461 Value = -0.0892
     elapsed = 0.10 Round = 9 eta_opt = 0.4420 max_depth_opt = 5.0000 nrounds_opt = 129.5805 subsample_opt = 0.7996 bytree_opt = 0.8865 Value = -0.0351
     elapsed = 0.03 Round = 10 eta_opt = 0.7997 max_depth_opt = 5.0000 nrounds_opt = 106.6147 subsample_opt = 0.9646 bytree_opt = 0.7630 Value = -0.0088
     elapsed = 0.08 Round = 11 eta_opt = 0.9412 max_depth_opt = 6.0000 nrounds_opt = 152.1588 subsample_opt = 0.4912 bytree_opt = 0.7928 Value = -0.0526
     elapsed = 0.09 Round = 12 eta_opt = 0.2909 max_depth_opt = 5.0000 nrounds_opt = 96.4243 subsample_opt = 0.7413 bytree_opt = 0.6119 Value = -0.0351
     elapsed = 0.09 Round = 13 eta_opt = 0.6865 max_depth_opt = 6.0000 nrounds_opt = 111.3159 subsample_opt = 0.4600 bytree_opt = 0.5622 Value = -0.1243
     elapsed = 0.08 Round = 14 eta_opt = 0.2130 max_depth_opt = 5.0000 nrounds_opt = 99.9155 subsample_opt = 0.3928 bytree_opt = 0.9956 Value = -0.1140
     elapsed = 0.07 Round = 15 eta_opt = 0.3405 max_depth_opt = 5.0000 nrounds_opt = 128.5783 subsample_opt = 0.7814 bytree_opt = 0.7801 Value = -0.0351
     elapsed = 0.07 Round = 16 eta_opt = 0.4475 max_depth_opt = 6.0000 nrounds_opt = 93.2215 subsample_opt = 0.2824 bytree_opt = 0.5279 Value = -0.3465
     elapsed = 0.09 Round = 17 eta_opt = 0.1121 max_depth_opt = 4.0000 nrounds_opt = 113.0691 subsample_opt = 0.7400 bytree_opt = 0.4776 Value = -0.1294
     elapsed = 0.03 Round = 18 eta_opt = 0.4441 max_depth_opt = 5.0000 nrounds_opt = 138.9680 subsample_opt = 0.2095 bytree_opt = 0.6869 Value = -0.4269
     elapsed = 0.05 Round = 19 eta_opt = 0.8827 max_depth_opt = 5.0000 nrounds_opt = 77.5822 subsample_opt = 0.3209 bytree_opt = 0.9544 Value = -0.1681
     elapsed = 0.06 Round = 20 eta_opt = 0.4063 max_depth_opt = 5.0000 nrounds_opt = 148.7789 subsample_opt = 0.2290 bytree_opt = 0.7593 Value = -0.4167
     ── 1. Error: (unknown) (@test-xgb_cv_opt.R#10) ────────────────────────────────
     task 14 failed - "non-finite value supplied by optim"
     1: xgb_cv_opt(data = tr, label = y, objectfun = "multi:softmax", evalmetric = "merror",
     n_folds = 3, classes = 10, init_points = 20, n_iter = 1) at testthat/test-xgb_cv_opt.R:10
     2: BayesianOptimization(xgb_cv, bounds = list(eta_opt = eta_range, max_depth_opt = max_depth_range,
     nrounds_opt = nrounds_range, subsample_opt = subsample_range, bytree_opt = bytree_range),
     init_points, init_grid_dt = NULL, n_iter, acq, kappa, eps, optkernel, verbose = TRUE)
     3: Utility_Max(DT_bounds, GP, acq = acq, y_max = max(DT_history[, Value]), kappa = kappa,
     eps = eps) %>% Min_Max_Inverse_Scale_Vec(., lower = DT_bounds[, Lower], upper = DT_bounds[,
     Upper]) %>% magrittr::set_names(., DT_bounds[, Parameter]) %>% inset(., DT_bounds[Type ==
     "integer", Parameter], round(extract(., DT_bounds[Type == "integer", Parameter])))
     4: eval(lhs, parent, parent)
     5: eval(lhs, parent, parent)
     6: Utility_Max(DT_bounds, GP, acq = acq, y_max = max(DT_history[, Value]), kappa = kappa,
     eps = eps)
     7: foreach(i = 1:nrow(Mat_tries), .combine = "rbind") %do% {
     optim_result <- optim(par = Mat_tries[i, ], fn = Utility, GP = GP, acq = acq,
     y_max = y_max, kappa = kappa, eps = eps, method = "L-BFGS-B", lower = rep(0,
     length(DT_bounds[, Lower])), upper = rep(1, length(DT_bounds[, Upper])),
     control = list(maxit = 100, factr = 5e+11))
     c(optim_result$par, optim_result$value)
     } %>% data.table(.) %>% setnames(., old = names(.), new = c(DT_bounds[, Parameter],
     "Negetive_Utility"))
     8: eval(lhs, parent, parent)
     9: eval(lhs, parent, parent)
     10: foreach(i = 1:nrow(Mat_tries), .combine = "rbind") %do% {
     optim_result <- optim(par = Mat_tries[i, ], fn = Utility, GP = GP, acq = acq,
     y_max = y_max, kappa = kappa, eps = eps, method = "L-BFGS-B", lower = rep(0,
     length(DT_bounds[, Lower])), upper = rep(1, length(DT_bounds[, Upper])),
     control = list(maxit = 100, factr = 5e+11))
     c(optim_result$par, optim_result$value)
     }
     11: e$fun(obj, substitute(ex), parent.frame(), e$data)
    
     elapsed = 0.24 Round = 1 eta_opt = 0.3996 max_depth_opt = 5.0000 nrounds_opt = 103.8797 subsample_opt = 0.6901 bytree_opt = 0.5783 Value = 1.0000
     elapsed = 0.37 Round = 2 eta_opt = 0.5996 max_depth_opt = 5.0000 nrounds_opt = 125.7482 subsample_opt = 0.3096 bytree_opt = 0.6693 Value = 1.0000
     elapsed = 0.24 Round = 3 eta_opt = 0.3946 max_depth_opt = 5.0000 nrounds_opt = 73.3337 subsample_opt = 0.1606 bytree_opt = 0.8845 Value = 0.1800
     elapsed = 0.35 Round = 4 eta_opt = 0.2905 max_depth_opt = 4.0000 nrounds_opt = 129.3648 subsample_opt = 0.1475 bytree_opt = 0.5431 Value = 0.1900
     elapsed = 0.25 Round = 5 eta_opt = 0.3845 max_depth_opt = 4.0000 nrounds_opt = 106.4619 subsample_opt = 0.3976 bytree_opt = 0.4083 Value = 1.0000
     elapsed = 0.35 Round = 6 eta_opt = 0.9525 max_depth_opt = 5.0000 nrounds_opt = 127.4542 subsample_opt = 0.2646 bytree_opt = 0.4167 Value = 1.0000
     elapsed = 0.29 Round = 7 eta_opt = 0.6955 max_depth_opt = 5.0000 nrounds_opt = 119.2315 subsample_opt = 0.5751 bytree_opt = 0.4965 Value = 1.0000
     elapsed = 0.21 Round = 8 eta_opt = 0.9005 max_depth_opt = 5.0000 nrounds_opt = 81.0287 subsample_opt = 0.8342 bytree_opt = 0.6838 Value = 1.0000
     elapsed = 0.20 Round = 9 eta_opt = 0.4042 max_depth_opt = 5.0000 nrounds_opt = 73.5520 subsample_opt = 0.5461 bytree_opt = 0.6483 Value = 1.0000
     elapsed = 0.35 Round = 10 eta_opt = 0.4913 max_depth_opt = 6.0000 nrounds_opt = 144.0938 subsample_opt = 0.1334 bytree_opt = 0.6559 Value = 0.9900
     elapsed = 0.19 Round = 11 eta_opt = 0.2058 max_depth_opt = 4.0000 nrounds_opt = 72.1364 subsample_opt = 0.4510 bytree_opt = 0.4659 Value = 1.0000
     elapsed = 0.28 Round = 12 eta_opt = 0.7855 max_depth_opt = 5.0000 nrounds_opt = 81.2798 subsample_opt = 0.3255 bytree_opt = 0.7891 Value = 1.0000
     elapsed = 0.45 Round = 13 eta_opt = 0.2276 max_depth_opt = 6.0000 nrounds_opt = 124.3278 subsample_opt = 0.9381 bytree_opt = 0.7298 Value = 1.0000
     elapsed = 0.34 Round = 14 eta_opt = 0.7902 max_depth_opt = 4.0000 nrounds_opt = 115.3598 subsample_opt = 0.6396 bytree_opt = 0.9333 Value = 1.0000
     elapsed = 0.34 Round = 15 eta_opt = 0.8579 max_depth_opt = 6.0000 nrounds_opt = 155.7652 subsample_opt = 0.9330 bytree_opt = 0.6380 Value = 1.0000
     elapsed = 0.37 Round = 16 eta_opt = 0.7991 max_depth_opt = 6.0000 nrounds_opt = 159.1933 subsample_opt = 0.9602 bytree_opt = 0.7328 Value = 1.0000
     elapsed = 0.35 Round = 17 eta_opt = 0.2204 max_depth_opt = 5.0000 nrounds_opt = 112.8439 subsample_opt = 0.8948 bytree_opt = 0.4939 Value = 1.0000
     elapsed = 0.31 Round = 18 eta_opt = 0.8253 max_depth_opt = 4.0000 nrounds_opt = 126.4373 subsample_opt = 0.6642 bytree_opt = 0.4461 Value = 0.9900
     elapsed = 0.26 Round = 19 eta_opt = 0.9071 max_depth_opt = 5.0000 nrounds_opt = 129.1942 subsample_opt = 0.6238 bytree_opt = 0.6919 Value = 1.0000
     elapsed = 0.16 Round = 20 eta_opt = 0.9343 max_depth_opt = 4.0000 nrounds_opt = 86.8685 subsample_opt = 0.9110 bytree_opt = 0.5663 Value = 1.0000
     elapsed = 0.15 Round = 21 eta_opt = 0.9550 max_depth_opt = 6.0000 nrounds_opt = 74.9652 subsample_opt = 0.2873 bytree_opt = 0.4000 Value = 1.0000
    
     Best Parameters Found:
     Round = 1 eta_opt = 0.3996 max_depth_opt = 5.0000 nrounds_opt = 103.8797 subsample_opt = 0.6901 bytree_opt = 0.5783 Value = 1.0000
     List of 4
     $ Best_Par : Named num [1:5] 0.4 5 103.88 0.69 0.578
     ..- attr(*, "names")= chr [1:5] "eta_opt" "max_depth_opt" "nrounds_opt" "subsample_opt" ...
     $ Best_Value: num 1
     $ History :Classes 'data.table' and 'data.frame': 21 obs. of 7 variables:
     ..$ Round : int [1:21] 1 2 3 4 5 6 7 8 9 10 ...
     ..$ eta_opt : num [1:21] 0.4 0.6 0.395 0.291 0.385 ...
     ..$ max_depth_opt: num [1:21] 5 5 5 4 4 5 5 5 5 6 ...
     ..$ nrounds_opt : num [1:21] 103.9 125.7 73.3 129.4 106.5 ...
     ..$ subsample_opt: num [1:21] 0.69 0.31 0.161 0.147 0.398 ...
     ..$ bytree_opt : num [1:21] 0.578 0.669 0.885 0.543 0.408 ...
     ..$ Value : num [1:21] 1 1 0.18 0.19 1 1 1 1 1 0.99 ...
     ..- attr(*, ".internal.selfref")=<externalptr>
     $ Pred :Classes 'data.table' and 'data.frame': 1 obs. of 21 variables:
     ..$ V1 : num 1
     ..$ V2 : num 1
     ..$ V3 : num 0.18
     ..$ V4 : num 0.19
     ..$ V5 : num 1
     ..$ V6 : num 1
     ..$ V7 : num 1
     ..$ V8 : num 1
     ..$ V9 : num 1
     ..$ V10: num 0.99
     ..$ V11: num 1
     ..$ V12: num 1
     ..$ V13: num 1
     ..$ V14: num 1
     ..$ V15: num 1
     ..$ V16: num 1
     ..$ V17: num 1
     ..$ V18: num 0.99
     ..$ V19: num 1
     ..$ V20: num 1
     ..$ V21: num 1
     ..- attr(*, ".internal.selfref")=<externalptr>
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 0 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 1 ]
     1. Error: (unknown) (@test-xgb_cv_opt.R#10)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang