R Under development (unstable) (2023-07-16 r84696 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> # 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
> 
> 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)...  2.57 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.33 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.23 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)...  2.64 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.42 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.2 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)...  2.69 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.29 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.22 seconds

CV fold: Fold1

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)...  6.61 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.39 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.9 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)...  6.33 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.72 seconds
  3) Running FUN 2 times in 2 thread(s)...  1.1 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)...  6.77 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.78 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.84 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)...  2.45 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.28 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.19 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)...  2.51 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.28 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.21 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)...  2.43 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.3 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.2 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)...  2.86 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        2.85 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.26 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)...  3.66 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        5.92 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.82 seconds

CV fold: Fold1

Registering parallel backend using 2 cores.

Running initial scoring function 10 times in 2 thread(s)...  3.35 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        5.62 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.29 seconds

CV fold: Fold1

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)...  3.56 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        2.09 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.36 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)...  3.77 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        5.66 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.2 seconds

 Errors encountered in FUN 

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)...  3.61 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        3.1 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.45 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)...  6.92 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        5.56 seconds
  3) Running FUN 2 times in 2 thread(s)...  1.03 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)...  6.94 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        1.72 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.58 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)...  6.75 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        1.54 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.73 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)...  3.36 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.61 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.31 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)...  3.3 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.69 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.3 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)...  3.56 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        0.74 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.3 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)...  2.44 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        4.27 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.22 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)...  2.82 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        2.84 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.2 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)...  2.56 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        5.25 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.22 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)...  5.58 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        1.32 seconds
  3) Running FUN 2 times in 2 thread(s)...  1.26 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)...  5.2 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        6.81 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.87 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)...  5.97 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        6.44 seconds
  3) Running FUN 2 times in 2 thread(s)...  1.49 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)...  2.99 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        11.77 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.41 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)...  2.99 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        1.53 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.28 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)...  2.91 seconds

Starting Epoch 1 
  1) Fitting Gaussian Process...
  2) Running local optimum search...        5.23 seconds
  3) Running FUN 2 times in 2 thread(s)...  0.32 seconds

CV fold: Fold1

CV fold: Fold2

CV fold: Fold3
[ FAIL 4 | WARN 3 | SKIP 1 | PASS 37 ]

══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'

══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-lightgbm_binary.R:106:5'): test nested cv, bayesian, binary - lightgbm ──
Error in `predict.lgb.Booster(object = <environment>, data = structure(c(1, 
10, 4, 7, 2, 7, 2, 2, 1, 6, 1, 3, 8, 4, 1, 2, 0, 2, 1, 2, 17, 
3, 0, 8, 6, 8, 1, 9, 5, 6, 0, 1, 4, 3, 4, 6, 0, 12, 1, 11, 5, 
2, 2, 10, 5, 1, 4, 1, 6, 2, 14, 10, 3, 3, 1, 1, 6, 1, 9, 3, 1, 
12, 1, 3, 0, 1, 1, 1, 1, 4, 2, 1, 0, 3, 2, 0, 4, 0, 0, 0, 2, 
8, 6, 7, 8, 3, 4, 0, 0, 6, 0, 2, 3, 0, 1, 8, 3, 4, 1, 1, 3, 1, 
4, 0, 3, 1, 1, 11, 1, 2, 2, 0, 11, 2, 8, 9, 3, 1, 4, 2, 2, 2, 
1, 2, 2, 1, 3, 1, 1, 2, 10, 103, 125, 111, 187, 100, 83, 110, 
100, 71, 93, 81, 83, 155, 99, 117, 125, 100, 108, 109, 88, 163, 
111, 129, 109, 103, 196, 96, 112, 109, 125, 177, 87, 197, 74, 
91, 119, 165, 92, 193, 155, 96, 108, 108, 129, 155, 119, 96, 
128, 151, 146, 100, 161, 113, 99, 157, 122, 105, 95, 152, 99, 
90, 88, 196, 173, 84, 107, 109, 100, 131, 116, 123, 112, 138, 
129, 94, 102, 184, 135, 95, 104, 100, 74, 154, 114, 126, 158, 
85, 84, 173, 154, 139, 68, 116, 117, 77, 100, 128, 84, 88, 164, 
84, 88, 154, 121, 111, 119, 109, 138, 128, 94, 157, 179, 136, 
155, 167, 145, 123, 144, 127, 122, 127, 93, 108, 174, 106, 81, 
187, 121, 128, 88, 101, 30, 70, 72, 68, 66, 78, 74, 68, 48, 50, 
72, 58, 62, 76, 88, 60, 70, 52, 56, 74, 72, 90, 110, 76, 72, 
76, 64, 82, 62, 68, 60, 78, 70, 68, 70, 50, 90, 62, 50, 76, 74, 
62, 62, 76, 84, 86, 56, 48, 62, 70, 78, 68, 50, 80, 72, 64, 70, 
60, 78, 62, 62, 74, 76, 82, 64, 72, 60, 66, 64, 72, 48, 72, 60, 
64, 68, 64, 78, 94, 80, 64, 54, 70, 78, 76, 74, 70, 58, 82, 78, 
74, 62, 70, 74, 66, 56, 74, 72, 90, 78, 82, 68, 62, 72, 66, 58, 
44, 38, 74, 82, 76, 74, 50, 84, 52, 106, 80, 100, 82, 88, 76, 
46, 64, 60, 88, 56, 74, 70, 78, 88, 58, 76, 38, 26, 47, 39, 20, 
26, 29, 25, 18, 30, 18, 31, 26, 15, 24, 20, 26, 26, 21, 19, 41, 
12, 46, 39, 32, 29, 27, 32, 41, 30, 29, 27, 39, 28, 32, 22, 33, 
7, 16, 28, 18, 32, 10, 28, 44, 39, 17, 45, 31, 38, 25, 23, 10, 
11, 21, 32, 32, 18, 34, 19, 12, 40, 36, 48, 22, 30, 8, 29, 14, 
12, 32, 30, 35, 29, 18, 46, 39, 46, 45, 37, 28, 40, 41, 17, 38, 
30, 22, 31, 32, 32, 17, 32, 15, 31, 30, 40, 25, 23, 29, 43, 30, 
24, 29, 30, 31, 47, 18, 26, 17, 18, 35, 36, 35, 27, 46, 46, 35, 
46, 11, 27, 21, 32, 46, 37, 27, 41, 22, 39, 39, 26, 48, 83, 115, 
207, 304, 90, 71, 125, 71, 76, 64, 40, 18, 495, 51, 145, 140, 
50, 63, 135, 53, 114, 78, 130, 114, 190, 280, 87, 175, 129, 120, 
478, 32, 744, 45, 88, 176, 680, 258, 375, 150, 67, 56, 278, 122, 
545, 220, 49, 194, 120, 360, 184, 132, 85, 64, 168, 156, 68, 
58, 171, 74, 43, 54, 249, 465, 66, 82, 182, 196, 415, 87, 165, 
176, 167, 115, 76, 78, 277, 145, 92, 64, 105, 49, 140, 110, 75, 
328, 49, 125, 265, 193, 210, 66, 105, 188, 56, 215, 190, 56, 
76, 67, 106, 44, 126, 165, 44, 63, 120, 144, 183, 66, 440, 159, 
130, 540, 231, 130, 240, 180, 155, 200, 335, 160, 178, 120, 165, 
57, 200, 74, 110, 16, 180, 43.3, 31.1, 37.1, 37.7, 32.9, 29.3, 
32.4, 38.5, 20.4, 28.7, 26.6, 34.3, 34, 23.2, 34.5, 33.8, 30.8, 
32.5, 25.2, 29, 40.9, 28.4, 67.1, 27.9, 37.7, 37.5, 33.2, 34.2, 
35.8, 30, 34.6, 34.6, 36.7, 29.7, 33.1, 27.1, 52.3, 27.6, 25.9, 
33.3, 33.6, 25.2, 25.3, 35.9, 38.7, 45.6, 20.8, 40.5, 35.5, 28, 
36.6, 25.5, 29.5, 19.3, 25.6, 35.1, 30.8, 23.9, 34.2, 21.8, 27.2, 
35.3, 36.5, 38.4, 35.8, 30.8, 25.4, 32, 23.7, 22.1, 42.1, 34.4, 
34.6, 26.4, 26, 40.6, 37, 40.6, 36.5, 33.6, 37.8, 35.3, 46.1, 
23.8, 25.9, 35.5, 27.8, 38.2, 46.5, 29.3, 22.1, 25, 26.3, 30.8, 
33.3, 39.4, 32.4, 39.5, 32, 32.8, 31.9, 29.9, 31.3, 34.3, 29.5, 
35.5, 23.1, 36.1, 27.5, 31.6, 39.4, 37.8, 28.3, 38.7, 37.6, 37.9, 
57.3, 46.1, 34.5, 35.9, 34.4, 38, 35.5, 44.5, 29, 46.3, 36.4, 
39, 36.5, 28.4, 32.9, 0.183, 0.205, 1.39, 0.254, 0.867, 0.767, 
0.698, 0.324, 0.323, 0.356, 0.283, 0.336, 0.543, 0.223, 0.403, 
0.088, 0.597, 0.318, 0.833, 0.229, 0.817, 0.495, 0.319, 0.64, 
0.324, 0.605, 0.289, 0.26, 0.514, 0.464, 1.072, 0.101, 2.329, 
0.293, 0.446, 1.318, 0.427, 0.926, 0.655, 1.353, 0.997, 0.128, 
0.881, 0.28, 0.619, 0.808, 0.34, 0.613, 0.692, 0.337, 0.412, 
0.326, 0.626, 0.284, 0.123, 0.692, 0.122, 0.26, 0.893, 0.279, 
0.58, 0.378, 0.875, 2.137, 0.545, 0.821, 0.947, 0.444, 0.389, 
0.463, 0.52, 0.528, 0.534, 0.219, 0.561, 0.496, 0.264, 0.284, 
0.33, 0.51, 0.498, 0.705, 0.571, 0.466, 0.162, 0.344, 0.306, 
0.233, 1.159, 0.839, 0.207, 0.187, 0.107, 0.493, 1.251, 0.661, 
0.549, 0.159, 0.365, 0.341, 0.591, 0.422, 0.338, 0.203, 0.43, 
0.28, 0.407, 0.557, 0.115, 0.649, 0.134, 0.455, 0.26, 0.24, 0.165, 
0.637, 0.88, 0.335, 0.598, 0.483, 0.176, 0.674, 0.415, 0.646, 
0.426, 1.096, 0.408, 0.261, 1.057, 0.766, 0.171, 33, 41, 56, 
41, 28, 36, 27, 26, 22, 23, 24, 25, 46, 21, 40, 31, 21, 22, 23, 
22, 47, 29, 26, 31, 55, 57, 21, 36, 25, 32, 21, 22, 31, 23, 22, 
33, 23, 44, 24, 51, 43, 21, 22, 39, 34, 29, 26, 24, 28, 29, 46, 
47, 25, 30, 24, 30, 37, 22, 33, 26, 24, 48, 29, 25, 21, 24, 21, 
42, 21, 37, 26, 25, 21, 28, 21, 21, 31, 26, 26, 22, 24, 39, 27, 
31, 39, 35, 28, 23, 58, 39, 21, 25, 24, 22, 24, 43, 27, 25, 29, 
50, 25, 23, 37, 33, 22, 25, 26, 50, 22, 23, 30, 22, 42, 25, 43, 
40, 22, 46, 28, 26, 22, 23, 24, 24, 22, 32, 36, 28, 37, 22, 63
), dim = c(131L, 8L), dimnames = list(NULL, c("pregnant", "glucose", 
"pressure", "triceps", "insulin", "mass", "pedigree", "age"))))`: argument "newdata" is missing, with no default
Backtrace:
     ▆
  1. └─lightgbm_optimizer$execute() at test-lightgbm_binary.R:106:4
  2.   └─mlexperiments:::.run_cv(self = self, private = private)
  3.     └─mlexperiments:::.fold_looper(self, private)
  4.       ├─base::do.call(private$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.           └─mlexperiments:::.cv_fit_model(...)
  9.             ├─base::do.call(self$learner$predict, pred_args)
 10.             └─mlexperiments (local) `<fn>`(model = `<lgb.Bstr>`, newdata = `<dbl[,8]>`, ncores = 2L)
 11.               ├─base::do.call(private$fun_predict, kwargs)
 12.               └─mllrnrs (local) `<fn>`(model = `<lgb.Bstr>`, newdata = `<dbl[,8]>`, ncores = 2L)
 13.                 ├─base::do.call(stats::predict, args)
 14.                 ├─stats (local) `<fn>`(object = `<lgb.Bstr>`, data = `<dbl[,8]>`)
 15.                 └─lightgbm:::predict.lgb.Booster(object = `<lgb.Bstr>`, data = `<dbl[,8]>`)
 16.                   └─object$predict(...)
 17.                     └─predictor$predict(...)
 18.                       └─base::identical(class(data), "character")
── Error ('test-lightgbm_multiclass.R:107:5'): test nested cv, bayesian, multiclass - lightgbm ──
Error in `predict.lgb.Booster(object = <environment>, data = structure(c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 
1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 
0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 
0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 
1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
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1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 
1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0), dim = c(1062L, 22L), dimnames = list(
    NULL, c("V1600", "V1601", "V1611", "V1621", "V1631", "V1641", 
    "V1651", "V1661", "V1671", "V1681", "V1691", "V1701", "V1711", 
    "V1721", "V1731", "V1741", "V1751", "V1761", "V1771", "V1781", 
    "V1791", "V1801"))), reshape = TRUE)`: 'reshape' argument is no longer supported.
Backtrace:
     ▆
  1. └─lightgbm_optimizer$execute() at test-lightgbm_multiclass.R:107:4
  2.   └─mlexperiments:::.run_cv(self = self, private = private)
  3.     └─mlexperiments:::.fold_looper(self, private)
  4.       ├─base::do.call(private$cv_run_model, run_args)
  5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
  6.         ├─base::do.call(.cv_run_nested_model, args)
  7.         └─mlexperiments (local) `<fn>`(...)
  8.           └─mlexperiments:::.cv_fit_model(...)
  9.             ├─base::do.call(self$learner$predict, pred_args)
 10.             └─mlexperiments (local) `<fn>`(...)
 11.               ├─base::do.call(private$fun_predict, kwargs)
 12.               └─mllrnrs (local) `<fn>`(...)
 13.                 ├─base::do.call(stats::predict, args)
 14.                 ├─stats (local) `<fn>`(object = `<lgb.Bstr>`, data = `<dbl[,22]>`, reshape = TRUE)
 15.                 └─lightgbm:::predict.lgb.Booster(...)
── Error ('test-lightgbm_regression.R:106:5'): test nested cv, bayesian, regression - lightgbm ──
Error in `predict.lgb.Booster(object = <environment>, data = structure(c(0.02729, 
0.17004, 0.22489, 0.09378, 0.62976, 0.80271, 0.85204, 1.38799, 
0.08014, 0.03359, 0.1415, 0.15936, 0.12269, 0.22927, 0.0536, 
0.01311, 0.04379, 0.13554, 0.15876, 0.19539, 0.08387, 0.03659, 
0.03551, 0.05302, 0.03932, 0.02875, 0.12083, 0.11432, 0.21161, 
0.13262, 0.1712, 0.13117, 0.12802, 0.14231, 0.14476, 0.06899, 
0.07165, 0.38735, 0.88125, 0.34006, 0.32982, 0.55778, 0.54452, 
0.2909, 1.62864, 2.77974, 1.6566, 1.12658, 1.41385, 2.44668, 
1.22358, 1.42502, 1.27346, 1.51902, 2.44953, 2.3139, 0.09178, 
0.06664, 0.07022, 0.05425, 0.0578, 0.06588, 0.10008, 0.08308, 
0.12579, 0.01381, 0.04011, 0.0315, 0.03445, 0.0351, 0.22969, 
0.25199, 0.43571, 0.21719, 0.07013, 0.11425, 0.35809, 0.62356, 
0.52693, 0.41238, 0.46296, 0.44791, 0.11329, 0.10612, 0.33983, 
0.1403, 0.66351, 0.54011, 0.53412, 0.76162, 0.7857, 0.16211, 
0.1146, 0.22188, 0.07978, 0.03705, 0.00906, 0.03871, 0.07886, 
0.08199, 0.04417, 0.05479, 0.07503, 0.49298, 2.63548, 0.79041, 
0.26169, 0.31827, 0.40202, 0.1676, 0.18159, 0.28392, 0.06617, 
0.06724, 0.03738, 0.03961, 0.03041, 0.05497, 0.02498, 0.03049, 
0.03113, 4.26131, 3.67822, 3.47428, 13.5222, 9.2323, 15.288, 
9.82349, 17.8667, 88.9762, 14.3337, 6.96215, 5.29305, 13.3598, 
38.3518, 25.0461, 14.2362, 41.5292, 20.7162, 7.40389, 18.811, 
28.6558, 45.7461, 25.9406, 11.0874, 12.0482, 7.05042, 8.79212, 
8.49213, 6.44405, 11.1604, 10.6718, 6.28807, 9.92485, 6.71772, 
8.24809, 4.66883, 7.75223, 5.82115, 15.5757, 13.0751, 5.82401, 
3.67367, 4.83567, 0.15086, 0.20746, 0.23912, 0.06076, 0, 12.5, 
12.5, 12.5, 0, 0, 0, 0, 0, 75, 0, 0, 0, 0, 21, 90, 80, 12.5, 
0, 0, 0, 25, 25, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 45, 80, 80, 95, 82.5, 95, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 30, 30, 22, 22, 20, 20, 20, 20, 20, 20, 20, 20, 
40, 20, 90, 52.5, 80, 0, 70, 33, 33, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.07, 7.87, 7.87, 
7.87, 8.14, 8.14, 8.14, 8.14, 5.96, 2.95, 6.91, 6.91, 6.91, 6.91, 
5.64, 1.22, 3.37, 6.07, 10.81, 10.81, 12.83, 4.86, 4.86, 3.41, 
3.41, 15.04, 2.89, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 10.01, 
10.01, 25.65, 25.65, 25.65, 21.89, 21.89, 21.89, 21.89, 21.89, 
21.89, 21.89, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 
19.58, 19.58, 19.58, 19.58, 4.05, 4.05, 4.05, 4.05, 2.46, 2.46, 
2.46, 2.46, 3.44, 0.46, 1.52, 1.47, 2.03, 2.68, 10.59, 10.59, 
10.59, 10.59, 13.89, 13.89, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 4.93, 
4.93, 5.86, 5.86, 3.97, 3.97, 3.97, 3.97, 3.97, 6.96, 6.96, 6.96, 
6.41, 3.33, 2.97, 5.32, 4.95, 13.92, 2.24, 2.18, 2.18, 9.9, 9.9, 
9.9, 9.9, 9.9, 9.9, 7.38, 7.38, 7.38, 3.24, 3.24, 5.19, 5.19, 
5.19, 5.19, 1.89, 3.78, 4.39, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 27.74, 27.74, 9.69, 11.93, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.469, 0.524, 
0.524, 0.524, 0.538, 0.538, 0.538, 0.538, 0.499, 0.428, 0.448, 
0.448, 0.448, 0.448, 0.439, 0.403, 0.398, 0.409, 0.413, 0.413, 
0.437, 0.426, 0.426, 0.489, 0.489, 0.464, 0.445, 0.52, 0.52, 
0.52, 0.52, 0.52, 0.52, 0.547, 0.547, 0.581, 0.581, 0.581, 0.624, 
0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.871, 0.871, 0.871, 
0.871, 0.871, 0.605, 0.871, 0.605, 0.605, 0.605, 0.605, 0.51, 
0.51, 0.51, 0.51, 0.488, 0.488, 0.488, 0.488, 0.437, 0.422, 0.404, 
0.403, 0.415, 0.4161, 0.489, 0.489, 0.489, 0.489, 0.55, 0.55, 
0.507, 0.507, 0.504, 0.504, 0.504, 0.507, 0.428, 0.428, 0.431, 
0.431, 0.647, 0.647, 0.647, 0.647, 0.647, 0.464, 0.464, 0.464, 
0.447, 0.4429, 0.4, 0.405, 0.411, 0.437, 0.4, 0.472, 0.472, 0.544, 
0.544, 0.544, 0.544, 0.544, 0.544, 0.493, 0.493, 0.493, 0.46, 
0.46, 0.515, 0.515, 0.515, 0.515, 0.518, 0.484, 0.442, 0.77, 
0.77, 0.718, 0.631, 0.631, 0.671, 0.671, 0.671, 0.671, 0.7, 0.7, 
0.7, 0.693, 0.693, 0.693, 0.693, 0.693, 0.659, 0.597, 0.597, 
0.597, 0.693, 0.679, 0.718, 0.614, 0.614, 0.584, 0.584, 0.584, 
0.74, 0.74, 0.74, 0.74, 0.713, 0.713, 0.713, 0.713, 0.713, 0.58, 
0.58, 0.532, 0.583, 0.583, 0.609, 0.609, 0.585, 0.573, 7.185, 
6.004, 6.377, 5.889, 5.949, 5.456, 5.965, 5.95, 5.85, 7.024, 
6.169, 6.211, 6.069, 6.03, 6.511, 7.249, 5.787, 5.594, 5.961, 
6.245, 5.874, 6.302, 6.167, 7.079, 6.405, 6.211, 8.069, 6.781, 
6.137, 5.851, 5.836, 6.127, 6.474, 6.254, 5.731, 5.87, 6.004, 
5.613, 5.637, 6.458, 5.822, 6.335, 6.151, 6.174, 5.019, 4.903, 
6.122, 5.012, 6.129, 5.272, 6.943, 6.51, 6.25, 8.375, 6.402, 
5.88, 6.416, 6.546, 6.02, 6.315, 6.98, 7.765, 6.563, 5.604, 6.556, 
7.875, 7.287, 6.975, 6.162, 7.853, 6.326, 5.783, 5.344, 5.807, 
6.642, 6.373, 6.951, 6.879, 8.725, 7.163, 7.412, 6.726, 6.897, 
6.095, 6.108, 6.487, 7.333, 7.203, 7.52, 5.56, 7.014, 6.24, 6.538, 
7.691, 6.482, 6.968, 7.088, 6.209, 7.148, 6.009, 6.871, 6.616, 
7.42, 6.635, 4.973, 6.122, 6.023, 5.914, 6.382, 6.426, 6.376, 
5.708, 5.868, 6.333, 6.31, 6.037, 5.895, 5.985, 6.54, 6.874, 
6.014, 6.112, 5.362, 8.78, 3.863, 6.216, 6.649, 6.794, 6.223, 
6.968, 4.88, 5.713, 6.051, 5.887, 5.453, 5.987, 6.343, 5.531, 
4.138, 5.617, 4.628, 5.155, 4.519, 5.304, 6.411, 5.648, 6.103, 
5.565, 6.348, 6.425, 6.629, 6.459, 6.341, 6.251, 6.749, 7.393, 
5.976, 6.301, 6.513, 5.926, 5.713, 6.242, 6.312, 5.905, 5.454, 
5.093, 6.019, 6.976, 61.1, 85.9, 94.3, 39, 61.8, 36.6, 89.2, 
82, 41.5, 15.8, 6.6, 6.5, 40, 85.5, 21.1, 21.9, 31.1, 36.8, 17.5, 
6.2, 36.6, 32.2, 46.7, 63.1, 73.9, 28.9, 76, 71.3, 87.4, 96.7, 
91.9, 85.2, 97.1, 84.2, 65.2, 69.7, 84.1, 95.6, 94.7, 98.9, 95.4, 
98.2, 97.9, 93.6, 100, 97.8, 97.3, 88, 96, 94, 97.4, 100, 92.6, 
93.9, 95.2, 97.3, 84.1, 33.1, 47.2, 73.4, 58.4, 83.3, 95.6, 89.8, 
29.1, 32, 34.1, 15.3, 38.4, 33.2, 52.5, 72.7, 100, 53.8, 85.1, 
92.4, 88.5, 77.7, 83, 79.9, 76.9, 66.5, 54.3, 65.1, 34.9, 13, 
100, 81.8, 89.4, 62.8, 84.6, 16.3, 58.7, 51.8, 32.1, 37.2, 20.8, 
31.3, 27.7, 42.3, 47.4, 58.1, 71.9, 82.5, 37.8, 52.8, 90.4, 83.2, 
67.2, 52.3, 54.3, 74.3, 25.8, 17.2, 38.5, 34.5, 59.6, 45.4, 59.7, 
28.1, 48.5, 81.3, 96.2, 82.9, 100, 100, 93.3, 98.8, 100, 91.9, 
100, 97, 82.5, 94.7, 100, 100, 100, 85.4, 100, 97.9, 100, 100, 
100, 89.1, 100, 87.6, 85.1, 70.6, 86.1, 74.8, 94.6, 94.8, 96.4, 
96.6, 92.6, 99.3, 87.9, 83.7, 89.9, 71, 56.7, 64.7, 51.9, 53.2, 
92.7, 98, 65.3, 91, 4.9671, 6.5921, 6.3467, 5.4509, 4.7075, 3.7965, 
4.0123, 3.99, 3.9342, 5.4011, 5.7209, 5.7209, 5.7209, 5.6894, 
6.8147, 8.6966, 6.6115, 6.498, 5.2873, 5.2873, 4.5026, 5.4007, 
5.4007, 3.4145, 3.0921, 3.6659, 3.4952, 2.8561, 2.7147, 2.1069, 
2.211, 2.1224, 2.4329, 2.2565, 2.7592, 2.2577, 2.1974, 1.7572, 
1.9799, 2.1185, 2.4699, 2.1107, 1.6687, 1.6119, 1.4394, 1.3459, 
1.618, 1.6102, 1.7494, 1.7364, 1.8773, 1.7659, 1.7984, 2.162, 
2.2625, 2.3887, 2.6463, 3.1323, 3.5549, 3.3175, 2.829, 2.741, 
2.847, 2.9879, 4.5667, 5.6484, 7.309, 7.6534, 6.27, 5.118, 4.3549, 
4.3549, 3.875, 3.6526, 3.4211, 3.3633, 2.8617, 3.2721, 2.8944, 
3.2157, 3.6715, 3.6519, 6.3361, 6.3361, 8.0555, 7.3967, 1.8946, 
2.1121, 2.1398, 1.9865, 2.1329, 4.429, 3.9175, 4.3665, 4.1403, 
5.2447, 7.3073, 7.3172, 5.1167, 5.5027, 7.8278, 3.37, 3.0992, 
3.3175, 2.5194, 2.6403, 2.834, 3.9986, 3.5325, 4.5404, 4.5404, 
4.7211, 5.2146, 5.2146, 6.4584, 5.9853, 5.615, 4.8122, 6.2669, 
6.4654, 8.0136, 2.5091, 2.1036, 1.9047, 1.5106, 1.1691, 1.3449, 
1.358, 1.3861, 1.4165, 1.5895, 1.9265, 2.1678, 1.7821, 1.4896, 
1.5888, 1.5741, 1.6074, 1.1781, 1.4547, 1.5539, 1.5894, 1.6582, 
1.6475, 1.8589, 1.9512, 2.0218, 2.0635, 2.0527, 2.2004, 2.1247, 
1.9879, 2.072, 2.198, 2.3236, 2.4527, 2.5806, 2.7831, 2.8016, 
2.9084, 2.8237, 3.4242, 3.9917, 3.1523, 1.8209, 1.8226, 2.4091, 
2.1675, 2, 5, 5, 5, 4, 4, 4, 4, 5, 3, 3, 3, 3, 3, 4, 5, 4, 4, 
4, 4, 5, 4, 4, 2, 2, 4, 2, 5, 5, 5, 5, 5, 5, 6, 6, 2, 2, 2, 4, 
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
3, 3, 3, 3, 5, 4, 2, 3, 2, 4, 4, 4, 4, 4, 5, 5, 8, 8, 8, 8, 8, 
8, 6, 6, 7, 7, 5, 5, 5, 5, 5, 3, 3, 3, 4, 5, 1, 6, 4, 4, 5, 7, 
7, 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 5, 5, 5, 5, 1, 5, 3, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 4, 4, 6, 1, 242, 311, 
311, 311, 307, 307, 307, 307, 279, 252, 233, 233, 233, 233, 243, 
226, 337, 345, 305, 305, 398, 281, 281, 270, 270, 270, 276, 384, 
384, 384, 384, 384, 384, 432, 432, 188, 188, 188, 437, 437, 437, 
437, 437, 437, 437, 403, 403, 403, 403, 403, 403, 403, 403, 403, 
403, 403, 296, 296, 296, 296, 193, 193, 193, 193, 398, 255, 329, 
402, 348, 224, 277, 277, 277, 277, 276, 276, 307, 307, 307, 307, 
307, 307, 300, 300, 330, 330, 264, 264, 264, 264, 264, 223, 223, 
223, 254, 216, 285, 293, 245, 289, 358, 222, 222, 304, 304, 304, 
304, 304, 304, 287, 287, 287, 430, 430, 224, 224, 224, 224, 422, 
370, 352, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 
666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 
666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 
666, 666, 666, 666, 666, 666, 711, 711, 391, 273, 17.8, 15.2, 
15.2, 15.2, 21, 21, 21, 21, 19.2, 18.3, 17.9, 17.9, 17.9, 17.9, 
16.8, 17.9, 16.1, 18.9, 19.2, 19.2, 18.7, 19, 19, 17.8, 17.8, 
18.2, 18, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 17.8, 17.8, 19.1, 
19.1, 19.1, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 14.7, 14.7, 
14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 16.6, 16.6, 
16.6, 16.6, 17.8, 17.8, 17.8, 17.8, 15.2, 14.4, 12.6, 17, 14.7, 
14.7, 18.6, 18.6, 18.6, 18.6, 16.4, 16.4, 17.4, 17.4, 17.4, 17.4, 
17.4, 17.4, 16.6, 16.6, 19.1, 19.1, 13, 13, 13, 13, 13, 18.6, 
18.6, 18.6, 17.6, 14.9, 15.3, 16.6, 19.2, 16, 14.8, 18.4, 18.4, 
18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 19.6, 19.6, 19.6, 16.9, 16.9, 
20.2, 20.2, 20.2, 20.2, 15.9, 17.6, 18.8, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.1, 20.1, 19.2, 21, 392.83, 
386.71, 392.52, 390.5, 396.9, 288.99, 392.53, 232.6, 396.9, 395.62, 
383.37, 394.46, 389.39, 392.74, 396.9, 395.93, 396.9, 396.9, 
376.94, 377.17, 396.06, 396.9, 390.64, 396.06, 393.55, 396.33, 
396.9, 395.58, 394.47, 394.05, 395.67, 387.69, 395.24, 388.74, 
391.5, 389.15, 377.67, 359.29, 396.9, 395.04, 388.69, 394.67, 
396.9, 388.08, 396.9, 396.9, 372.8, 343.28, 321.02, 88.63, 363.43, 
364.31, 338.92, 388.45, 330.04, 348.13, 395.5, 390.96, 393.23, 
395.6, 396.9, 395.56, 396.9, 391, 382.84, 394.23, 396.9, 396.9, 
393.77, 392.78, 394.87, 389.43, 396.9, 390.94, 392.78, 393.74, 
391.7, 390.39, 382, 372.08, 376.14, 360.2, 391.25, 394.62, 390.18, 
396.28, 383.29, 392.8, 388.37, 392.4, 384.07, 396.9, 394.96, 
390.77, 396.9, 392.23, 394.72, 396.9, 396.9, 396.9, 390.86, 393.36, 
396.9, 396.9, 350.45, 396.9, 396.3, 390.7, 395.21, 396.9, 396.9, 
391.13, 382.44, 375.21, 389.4, 396.9, 394.81, 396.9, 389.96, 
387.97, 385.64, 390.74, 380.79, 354.55, 131.42, 366.15, 363.02, 
396.9, 393.74, 396.9, 372.92, 394.43, 378.38, 396.9, 396.9, 396.9, 
396.9, 329.46, 370.22, 314.64, 28.79, 210.97, 88.27, 127.36, 
318.75, 291.55, 2.52, 3.65, 83.45, 97.95, 109.85, 43.06, 318.01, 
388.52, 0.32, 375.87, 10.48, 272.21, 393.82, 368.74, 396.9, 396.9, 
388.62, 388.22, 395.09, 318.43, 396.9, 396.9, 4.03, 17.1, 20.45, 
15.71, 8.26, 11.69, 13.83, 27.71, 8.77, 1.98, 5.81, 7.44, 9.55, 
18.8, 5.28, 4.81, 10.24, 13.09, 9.88, 7.54, 9.1, 6.72, 7.51, 
5.7, 8.2, 6.21, 4.21, 7.67, 13.44, 16.47, 18.66, 14.09, 12.27, 
10.45, 13.61, 14.37, 14.27, 27.26, 18.34, 12.6, 15.03, 16.96, 
18.46, 24.16, 34.41, 29.29, 14.1, 12.12, 15.12, 16.14, 4.59, 
7.39, 5.5, 3.32, 11.32, 12.03, 9.04, 5.33, 10.11, 6.29, 5.04, 
7.56, 5.68, 13.98, 4.56, 2.97, 4.08, 4.56, 7.43, 3.81, 10.97, 
18.06, 23.09, 16.03, 9.69, 10.5, 9.71, 9.93, 4.63, 6.36, 5.25, 
8.05, 11.38, 12.4, 9.16, 5.9, 7.79, 9.59, 7.26, 10.45, 14.79, 
6.59, 7.73, 6.58, 7.19, 4.59, 7.85, 7.14, 3.56, 10.4, 6.07, 8.93, 
6.47, 4.54, 12.64, 5.98, 11.72, 18.33, 10.36, 7.2, 6.87, 11.74, 
9.97, 7.34, 6.75, 8.01, 10.56, 9.74, 8.65, 4.61, 10.53, 12.67, 
10.19, 5.29, 13.33, 9.53, 23.24, 21.24, 21.78, 17.21, 30.62, 
17.11, 18.76, 16.35, 30.59, 26.77, 20.32, 27.38, 23.34, 26.4, 
34.37, 20.08, 36.98, 26.64, 15.02, 14.1, 23.29, 17.16, 17.64, 
12.03, 23.27, 23.98, 17.79, 16.44, 17.44, 16.74, 19.01, 16.23, 
10.29, 18.13, 14.76, 10.74, 10.58, 11.45, 18.06, 29.68, 12.92, 
5.64), dim = c(168L, 14L), dimnames = list(NULL, c("crim", "zn", 
"indus", "chas0", "chas1", "nox", "rm", "age", "dis", "rad", 
"tax", "ptratio", "b", "lstat"))))`: argument "newdata" is missing, with no default
Backtrace:
     ▆
  1. └─lightgbm_optimization$execute() at test-lightgbm_regression.R:106:4
  2.   └─mlexperiments:::.run_cv(self = self, private = private)
  3.     └─mlexperiments:::.fold_looper(self, private)
  4.       ├─base::do.call(private$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.           └─mlexperiments:::.cv_fit_model(...)
  9.             ├─base::do.call(self$learner$predict, pred_args)
 10.             └─mlexperiments (local) `<fn>`(model = `<lgb.Bstr>`, newdata = `<dbl[,14]>`, ncores = 2L)
 11.               ├─base::do.call(private$fun_predict, kwargs)
 12.               └─mllrnrs (local) `<fn>`(model = `<lgb.Bstr>`, newdata = `<dbl[,14]>`, ncores = 2L)
 13.                 ├─base::do.call(stats::predict, args)
 14.                 ├─stats (local) `<fn>`(object = `<lgb.Bstr>`, data = `<dbl[,14]>`)
 15.                 └─lightgbm:::predict.lgb.Booster(object = `<lgb.Bstr>`, data = `<dbl[,14]>`)
 16.                   └─object$predict(...)
 17.                     └─predictor$predict(...)
 18.                       └─base::identical(class(data), "character")
── Error ('test-lightgbm_regression.R:149:5'): test nested cv, grid - lightgbm ──
Error in `predict.lgb.Booster(object = <environment>, data = structure(c(0.02729, 
0.17004, 0.22489, 0.09378, 0.62976, 0.80271, 0.85204, 1.38799, 
0.08014, 0.03359, 0.1415, 0.15936, 0.12269, 0.22927, 0.0536, 
0.01311, 0.04379, 0.13554, 0.15876, 0.19539, 0.08387, 0.03659, 
0.03551, 0.05302, 0.03932, 0.02875, 0.12083, 0.11432, 0.21161, 
0.13262, 0.1712, 0.13117, 0.12802, 0.14231, 0.14476, 0.06899, 
0.07165, 0.38735, 0.88125, 0.34006, 0.32982, 0.55778, 0.54452, 
0.2909, 1.62864, 2.77974, 1.6566, 1.12658, 1.41385, 2.44668, 
1.22358, 1.42502, 1.27346, 1.51902, 2.44953, 2.3139, 0.09178, 
0.06664, 0.07022, 0.05425, 0.0578, 0.06588, 0.10008, 0.08308, 
0.12579, 0.01381, 0.04011, 0.0315, 0.03445, 0.0351, 0.22969, 
0.25199, 0.43571, 0.21719, 0.07013, 0.11425, 0.35809, 0.62356, 
0.52693, 0.41238, 0.46296, 0.44791, 0.11329, 0.10612, 0.33983, 
0.1403, 0.66351, 0.54011, 0.53412, 0.76162, 0.7857, 0.16211, 
0.1146, 0.22188, 0.07978, 0.03705, 0.00906, 0.03871, 0.07886, 
0.08199, 0.04417, 0.05479, 0.07503, 0.49298, 2.63548, 0.79041, 
0.26169, 0.31827, 0.40202, 0.1676, 0.18159, 0.28392, 0.06617, 
0.06724, 0.03738, 0.03961, 0.03041, 0.05497, 0.02498, 0.03049, 
0.03113, 4.26131, 3.67822, 3.47428, 13.5222, 9.2323, 15.288, 
9.82349, 17.8667, 88.9762, 14.3337, 6.96215, 5.29305, 13.3598, 
38.3518, 25.0461, 14.2362, 41.5292, 20.7162, 7.40389, 18.811, 
28.6558, 45.7461, 25.9406, 11.0874, 12.0482, 7.05042, 8.79212, 
8.49213, 6.44405, 11.1604, 10.6718, 6.28807, 9.92485, 6.71772, 
8.24809, 4.66883, 7.75223, 5.82115, 15.5757, 13.0751, 5.82401, 
3.67367, 4.83567, 0.15086, 0.20746, 0.23912, 0.06076, 0, 12.5, 
12.5, 12.5, 0, 0, 0, 0, 0, 75, 0, 0, 0, 0, 21, 90, 80, 12.5, 
0, 0, 0, 25, 25, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 45, 80, 80, 95, 82.5, 95, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 30, 30, 22, 22, 20, 20, 20, 20, 20, 20, 20, 20, 
40, 20, 90, 52.5, 80, 0, 70, 33, 33, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.07, 7.87, 7.87, 
7.87, 8.14, 8.14, 8.14, 8.14, 5.96, 2.95, 6.91, 6.91, 6.91, 6.91, 
5.64, 1.22, 3.37, 6.07, 10.81, 10.81, 12.83, 4.86, 4.86, 3.41, 
3.41, 15.04, 2.89, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 10.01, 
10.01, 25.65, 25.65, 25.65, 21.89, 21.89, 21.89, 21.89, 21.89, 
21.89, 21.89, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 
19.58, 19.58, 19.58, 19.58, 4.05, 4.05, 4.05, 4.05, 2.46, 2.46, 
2.46, 2.46, 3.44, 0.46, 1.52, 1.47, 2.03, 2.68, 10.59, 10.59, 
10.59, 10.59, 13.89, 13.89, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 4.93, 
4.93, 5.86, 5.86, 3.97, 3.97, 3.97, 3.97, 3.97, 6.96, 6.96, 6.96, 
6.41, 3.33, 2.97, 5.32, 4.95, 13.92, 2.24, 2.18, 2.18, 9.9, 9.9, 
9.9, 9.9, 9.9, 9.9, 7.38, 7.38, 7.38, 3.24, 3.24, 5.19, 5.19, 
5.19, 5.19, 1.89, 3.78, 4.39, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 
18.1, 18.1, 18.1, 18.1, 27.74, 27.74, 9.69, 11.93, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.469, 0.524, 
0.524, 0.524, 0.538, 0.538, 0.538, 0.538, 0.499, 0.428, 0.448, 
0.448, 0.448, 0.448, 0.439, 0.403, 0.398, 0.409, 0.413, 0.413, 
0.437, 0.426, 0.426, 0.489, 0.489, 0.464, 0.445, 0.52, 0.52, 
0.52, 0.52, 0.52, 0.52, 0.547, 0.547, 0.581, 0.581, 0.581, 0.624, 
0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.871, 0.871, 0.871, 
0.871, 0.871, 0.605, 0.871, 0.605, 0.605, 0.605, 0.605, 0.51, 
0.51, 0.51, 0.51, 0.488, 0.488, 0.488, 0.488, 0.437, 0.422, 0.404, 
0.403, 0.415, 0.4161, 0.489, 0.489, 0.489, 0.489, 0.55, 0.55, 
0.507, 0.507, 0.504, 0.504, 0.504, 0.507, 0.428, 0.428, 0.431, 
0.431, 0.647, 0.647, 0.647, 0.647, 0.647, 0.464, 0.464, 0.464, 
0.447, 0.4429, 0.4, 0.405, 0.411, 0.437, 0.4, 0.472, 0.472, 0.544, 
0.544, 0.544, 0.544, 0.544, 0.544, 0.493, 0.493, 0.493, 0.46, 
0.46, 0.515, 0.515, 0.515, 0.515, 0.518, 0.484, 0.442, 0.77, 
0.77, 0.718, 0.631, 0.631, 0.671, 0.671, 0.671, 0.671, 0.7, 0.7, 
0.7, 0.693, 0.693, 0.693, 0.693, 0.693, 0.659, 0.597, 0.597, 
0.597, 0.693, 0.679, 0.718, 0.614, 0.614, 0.584, 0.584, 0.584, 
0.74, 0.74, 0.74, 0.74, 0.713, 0.713, 0.713, 0.713, 0.713, 0.58, 
0.58, 0.532, 0.583, 0.583, 0.609, 0.609, 0.585, 0.573, 7.185, 
6.004, 6.377, 5.889, 5.949, 5.456, 5.965, 5.95, 5.85, 7.024, 
6.169, 6.211, 6.069, 6.03, 6.511, 7.249, 5.787, 5.594, 5.961, 
6.245, 5.874, 6.302, 6.167, 7.079, 6.405, 6.211, 8.069, 6.781, 
6.137, 5.851, 5.836, 6.127, 6.474, 6.254, 5.731, 5.87, 6.004, 
5.613, 5.637, 6.458, 5.822, 6.335, 6.151, 6.174, 5.019, 4.903, 
6.122, 5.012, 6.129, 5.272, 6.943, 6.51, 6.25, 8.375, 6.402, 
5.88, 6.416, 6.546, 6.02, 6.315, 6.98, 7.765, 6.563, 5.604, 6.556, 
7.875, 7.287, 6.975, 6.162, 7.853, 6.326, 5.783, 5.344, 5.807, 
6.642, 6.373, 6.951, 6.879, 8.725, 7.163, 7.412, 6.726, 6.897, 
6.095, 6.108, 6.487, 7.333, 7.203, 7.52, 5.56, 7.014, 6.24, 6.538, 
7.691, 6.482, 6.968, 7.088, 6.209, 7.148, 6.009, 6.871, 6.616, 
7.42, 6.635, 4.973, 6.122, 6.023, 5.914, 6.382, 6.426, 6.376, 
5.708, 5.868, 6.333, 6.31, 6.037, 5.895, 5.985, 6.54, 6.874, 
6.014, 6.112, 5.362, 8.78, 3.863, 6.216, 6.649, 6.794, 6.223, 
6.968, 4.88, 5.713, 6.051, 5.887, 5.453, 5.987, 6.343, 5.531, 
4.138, 5.617, 4.628, 5.155, 4.519, 5.304, 6.411, 5.648, 6.103, 
5.565, 6.348, 6.425, 6.629, 6.459, 6.341, 6.251, 6.749, 7.393, 
5.976, 6.301, 6.513, 5.926, 5.713, 6.242, 6.312, 5.905, 5.454, 
5.093, 6.019, 6.976, 61.1, 85.9, 94.3, 39, 61.8, 36.6, 89.2, 
82, 41.5, 15.8, 6.6, 6.5, 40, 85.5, 21.1, 21.9, 31.1, 36.8, 17.5, 
6.2, 36.6, 32.2, 46.7, 63.1, 73.9, 28.9, 76, 71.3, 87.4, 96.7, 
91.9, 85.2, 97.1, 84.2, 65.2, 69.7, 84.1, 95.6, 94.7, 98.9, 95.4, 
98.2, 97.9, 93.6, 100, 97.8, 97.3, 88, 96, 94, 97.4, 100, 92.6, 
93.9, 95.2, 97.3, 84.1, 33.1, 47.2, 73.4, 58.4, 83.3, 95.6, 89.8, 
29.1, 32, 34.1, 15.3, 38.4, 33.2, 52.5, 72.7, 100, 53.8, 85.1, 
92.4, 88.5, 77.7, 83, 79.9, 76.9, 66.5, 54.3, 65.1, 34.9, 13, 
100, 81.8, 89.4, 62.8, 84.6, 16.3, 58.7, 51.8, 32.1, 37.2, 20.8, 
31.3, 27.7, 42.3, 47.4, 58.1, 71.9, 82.5, 37.8, 52.8, 90.4, 83.2, 
67.2, 52.3, 54.3, 74.3, 25.8, 17.2, 38.5, 34.5, 59.6, 45.4, 59.7, 
28.1, 48.5, 81.3, 96.2, 82.9, 100, 100, 93.3, 98.8, 100, 91.9, 
100, 97, 82.5, 94.7, 100, 100, 100, 85.4, 100, 97.9, 100, 100, 
100, 89.1, 100, 87.6, 85.1, 70.6, 86.1, 74.8, 94.6, 94.8, 96.4, 
96.6, 92.6, 99.3, 87.9, 83.7, 89.9, 71, 56.7, 64.7, 51.9, 53.2, 
92.7, 98, 65.3, 91, 4.9671, 6.5921, 6.3467, 5.4509, 4.7075, 3.7965, 
4.0123, 3.99, 3.9342, 5.4011, 5.7209, 5.7209, 5.7209, 5.6894, 
6.8147, 8.6966, 6.6115, 6.498, 5.2873, 5.2873, 4.5026, 5.4007, 
5.4007, 3.4145, 3.0921, 3.6659, 3.4952, 2.8561, 2.7147, 2.1069, 
2.211, 2.1224, 2.4329, 2.2565, 2.7592, 2.2577, 2.1974, 1.7572, 
1.9799, 2.1185, 2.4699, 2.1107, 1.6687, 1.6119, 1.4394, 1.3459, 
1.618, 1.6102, 1.7494, 1.7364, 1.8773, 1.7659, 1.7984, 2.162, 
2.2625, 2.3887, 2.6463, 3.1323, 3.5549, 3.3175, 2.829, 2.741, 
2.847, 2.9879, 4.5667, 5.6484, 7.309, 7.6534, 6.27, 5.118, 4.3549, 
4.3549, 3.875, 3.6526, 3.4211, 3.3633, 2.8617, 3.2721, 2.8944, 
3.2157, 3.6715, 3.6519, 6.3361, 6.3361, 8.0555, 7.3967, 1.8946, 
2.1121, 2.1398, 1.9865, 2.1329, 4.429, 3.9175, 4.3665, 4.1403, 
5.2447, 7.3073, 7.3172, 5.1167, 5.5027, 7.8278, 3.37, 3.0992, 
3.3175, 2.5194, 2.6403, 2.834, 3.9986, 3.5325, 4.5404, 4.5404, 
4.7211, 5.2146, 5.2146, 6.4584, 5.9853, 5.615, 4.8122, 6.2669, 
6.4654, 8.0136, 2.5091, 2.1036, 1.9047, 1.5106, 1.1691, 1.3449, 
1.358, 1.3861, 1.4165, 1.5895, 1.9265, 2.1678, 1.7821, 1.4896, 
1.5888, 1.5741, 1.6074, 1.1781, 1.4547, 1.5539, 1.5894, 1.6582, 
1.6475, 1.8589, 1.9512, 2.0218, 2.0635, 2.0527, 2.2004, 2.1247, 
1.9879, 2.072, 2.198, 2.3236, 2.4527, 2.5806, 2.7831, 2.8016, 
2.9084, 2.8237, 3.4242, 3.9917, 3.1523, 1.8209, 1.8226, 2.4091, 
2.1675, 2, 5, 5, 5, 4, 4, 4, 4, 5, 3, 3, 3, 3, 3, 4, 5, 4, 4, 
4, 4, 5, 4, 4, 2, 2, 4, 2, 5, 5, 5, 5, 5, 5, 6, 6, 2, 2, 2, 4, 
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
3, 3, 3, 3, 5, 4, 2, 3, 2, 4, 4, 4, 4, 4, 5, 5, 8, 8, 8, 8, 8, 
8, 6, 6, 7, 7, 5, 5, 5, 5, 5, 3, 3, 3, 4, 5, 1, 6, 4, 4, 5, 7, 
7, 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 5, 5, 5, 5, 1, 5, 3, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 4, 4, 6, 1, 242, 311, 
311, 311, 307, 307, 307, 307, 279, 252, 233, 233, 233, 233, 243, 
226, 337, 345, 305, 305, 398, 281, 281, 270, 270, 270, 276, 384, 
384, 384, 384, 384, 384, 432, 432, 188, 188, 188, 437, 437, 437, 
437, 437, 437, 437, 403, 403, 403, 403, 403, 403, 403, 403, 403, 
403, 403, 296, 296, 296, 296, 193, 193, 193, 193, 398, 255, 329, 
402, 348, 224, 277, 277, 277, 277, 276, 276, 307, 307, 307, 307, 
307, 307, 300, 300, 330, 330, 264, 264, 264, 264, 264, 223, 223, 
223, 254, 216, 285, 293, 245, 289, 358, 222, 222, 304, 304, 304, 
304, 304, 304, 287, 287, 287, 430, 430, 224, 224, 224, 224, 422, 
370, 352, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 
666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 
666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 
666, 666, 666, 666, 666, 666, 711, 711, 391, 273, 17.8, 15.2, 
15.2, 15.2, 21, 21, 21, 21, 19.2, 18.3, 17.9, 17.9, 17.9, 17.9, 
16.8, 17.9, 16.1, 18.9, 19.2, 19.2, 18.7, 19, 19, 17.8, 17.8, 
18.2, 18, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 17.8, 17.8, 19.1, 
19.1, 19.1, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 14.7, 14.7, 
14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 16.6, 16.6, 
16.6, 16.6, 17.8, 17.8, 17.8, 17.8, 15.2, 14.4, 12.6, 17, 14.7, 
14.7, 18.6, 18.6, 18.6, 18.6, 16.4, 16.4, 17.4, 17.4, 17.4, 17.4, 
17.4, 17.4, 16.6, 16.6, 19.1, 19.1, 13, 13, 13, 13, 13, 18.6, 
18.6, 18.6, 17.6, 14.9, 15.3, 16.6, 19.2, 16, 14.8, 18.4, 18.4, 
18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 19.6, 19.6, 19.6, 16.9, 16.9, 
20.2, 20.2, 20.2, 20.2, 15.9, 17.6, 18.8, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 
20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.1, 20.1, 19.2, 21, 392.83, 
386.71, 392.52, 390.5, 396.9, 288.99, 392.53, 232.6, 396.9, 395.62, 
383.37, 394.46, 389.39, 392.74, 396.9, 395.93, 396.9, 396.9, 
376.94, 377.17, 396.06, 396.9, 390.64, 396.06, 393.55, 396.33, 
396.9, 395.58, 394.47, 394.05, 395.67, 387.69, 395.24, 388.74, 
391.5, 389.15, 377.67, 359.29, 396.9, 395.04, 388.69, 394.67, 
396.9, 388.08, 396.9, 396.9, 372.8, 343.28, 321.02, 88.63, 363.43, 
364.31, 338.92, 388.45, 330.04, 348.13, 395.5, 390.96, 393.23, 
395.6, 396.9, 395.56, 396.9, 391, 382.84, 394.23, 396.9, 396.9, 
393.77, 392.78, 394.87, 389.43, 396.9, 390.94, 392.78, 393.74, 
391.7, 390.39, 382, 372.08, 376.14, 360.2, 391.25, 394.62, 390.18, 
396.28, 383.29, 392.8, 388.37, 392.4, 384.07, 396.9, 394.96, 
390.77, 396.9, 392.23, 394.72, 396.9, 396.9, 396.9, 390.86, 393.36, 
396.9, 396.9, 350.45, 396.9, 396.3, 390.7, 395.21, 396.9, 396.9, 
391.13, 382.44, 375.21, 389.4, 396.9, 394.81, 396.9, 389.96, 
387.97, 385.64, 390.74, 380.79, 354.55, 131.42, 366.15, 363.02, 
396.9, 393.74, 396.9, 372.92, 394.43, 378.38, 396.9, 396.9, 396.9, 
396.9, 329.46, 370.22, 314.64, 28.79, 210.97, 88.27, 127.36, 
318.75, 291.55, 2.52, 3.65, 83.45, 97.95, 109.85, 43.06, 318.01, 
388.52, 0.32, 375.87, 10.48, 272.21, 393.82, 368.74, 396.9, 396.9, 
388.62, 388.22, 395.09, 318.43, 396.9, 396.9, 4.03, 17.1, 20.45, 
15.71, 8.26, 11.69, 13.83, 27.71, 8.77, 1.98, 5.81, 7.44, 9.55, 
18.8, 5.28, 4.81, 10.24, 13.09, 9.88, 7.54, 9.1, 6.72, 7.51, 
5.7, 8.2, 6.21, 4.21, 7.67, 13.44, 16.47, 18.66, 14.09, 12.27, 
10.45, 13.61, 14.37, 14.27, 27.26, 18.34, 12.6, 15.03, 16.96, 
18.46, 24.16, 34.41, 29.29, 14.1, 12.12, 15.12, 16.14, 4.59, 
7.39, 5.5, 3.32, 11.32, 12.03, 9.04, 5.33, 10.11, 6.29, 5.04, 
7.56, 5.68, 13.98, 4.56, 2.97, 4.08, 4.56, 7.43, 3.81, 10.97, 
18.06, 23.09, 16.03, 9.69, 10.5, 9.71, 9.93, 4.63, 6.36, 5.25, 
8.05, 11.38, 12.4, 9.16, 5.9, 7.79, 9.59, 7.26, 10.45, 14.79, 
6.59, 7.73, 6.58, 7.19, 4.59, 7.85, 7.14, 3.56, 10.4, 6.07, 8.93, 
6.47, 4.54, 12.64, 5.98, 11.72, 18.33, 10.36, 7.2, 6.87, 11.74, 
9.97, 7.34, 6.75, 8.01, 10.56, 9.74, 8.65, 4.61, 10.53, 12.67, 
10.19, 5.29, 13.33, 9.53, 23.24, 21.24, 21.78, 17.21, 30.62, 
17.11, 18.76, 16.35, 30.59, 26.77, 20.32, 27.38, 23.34, 26.4, 
34.37, 20.08, 36.98, 26.64, 15.02, 14.1, 23.29, 17.16, 17.64, 
12.03, 23.27, 23.98, 17.79, 16.44, 17.44, 16.74, 19.01, 16.23, 
10.29, 18.13, 14.76, 10.74, 10.58, 11.45, 18.06, 29.68, 12.92, 
5.64), dim = c(168L, 14L), dimnames = list(NULL, c("crim", "zn", 
"indus", "chas0", "chas1", "nox", "rm", "age", "dis", "rad", 
"tax", "ptratio", "b", "lstat"))))`: argument "newdata" is missing, with no default
Backtrace:
     ▆
  1. └─lightgbm_optimization$execute() at test-lightgbm_regression.R:149:4
  2.   └─mlexperiments:::.run_cv(self = self, private = private)
  3.     └─mlexperiments:::.fold_looper(self, private)
  4.       ├─base::do.call(private$cv_run_model, run_args)
  5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
  6.         ├─base::do.call(.cv_run_nested_model, args)
  7.         └─mlexperiments (local) `<fn>`(...)
  8.           └─mlexperiments:::.cv_fit_model(...)
  9.             ├─base::do.call(self$learner$predict, pred_args)
 10.             └─mlexperiments (local) `<fn>`(model = `<lgb.Bstr>`, newdata = `<dbl[,14]>`, ncores = 2L)
 11.               ├─base::do.call(private$fun_predict, kwargs)
 12.               └─mllrnrs (local) `<fn>`(model = `<lgb.Bstr>`, newdata = `<dbl[,14]>`, ncores = 2L)
 13.                 ├─base::do.call(stats::predict, args)
 14.                 ├─stats (local) `<fn>`(object = `<lgb.Bstr>`, data = `<dbl[,14]>`)
 15.                 └─lightgbm:::predict.lgb.Booster(object = `<lgb.Bstr>`, data = `<dbl[,14]>`)
 16.                   └─object$predict(...)
 17.                     └─predictor$predict(...)
 18.                       └─base::identical(class(data), "character")

[ FAIL 4 | WARN 3 | SKIP 1 | PASS 37 ]
Error: Test failures
Execution halted