Last updated on 2020-12-10 17:48:25 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.5 | 6.29 | 63.38 | 69.67 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.1.5 | 6.04 | 49.97 | 56.01 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.1.5 | 87.27 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.5 | 78.98 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 0.1.5 | 10.00 | 87.00 | 97.00 | ERROR | |
| r-patched-linux-x86_64 | 0.1.5 | 6.28 | 59.67 | 65.95 | OK | |
| r-patched-solaris-x86 | 0.1.5 | 121.70 | OK | |||
| r-release-linux-x86_64 | 0.1.5 | 6.61 | 58.25 | 64.86 | OK | |
| r-release-macos-x86_64 | 0.1.5 | OK | ||||
| r-release-windows-ix86+x86_64 | 0.1.5 | 11.00 | 64.00 | 75.00 | OK | |
| r-oldrel-macos-x86_64 | 0.1.5 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 0.1.5 | 11.00 | 82.00 | 93.00 | OK |
Version: 0.1.5
Check: tests
Result: ERROR
Running 'testthat.R' [10s/11s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(OptimClassifier)
>
> test_check("OptimClassifier")
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
model = FALSE, x = FALSE, y = FALSE, cp = 0)
n= 482
CP nsplit rel error xerror xstd
1 0.66515837 0 1.0000000 1.0000000 0.04949948
2 0.02262443 1 0.3348416 0.3348416 0.03581213
3 0.01357466 4 0.2669683 0.3438914 0.03620379
4 0.01131222 5 0.2533937 0.3438914 0.03620379
Variable importance
X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
36 16 16 13 10 6 2 1 1 1
Node number 1: 482 observations, complexity param=0.6651584
predicted class=0 expected loss=0.4585062 P(node) =1
class counts: 261 221
probabilities: 0.541 0.459
left son=2 (219 obs) right son=3 (263 obs)
Primary splits:
X8 splits as LR, improve=119.25990, (0 missing)
X10 < 2.5 to the left, improve= 52.61262, (0 missing)
X9 splits as LR, improve= 47.76803, (0 missing)
X14 < 396 to the left, improve= 32.46584, (0 missing)
X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
Surrogate splits:
X9 splits as LR, agree=0.701, adj=0.342, (0 split)
X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
Node number 2: 219 observations
predicted class=0 expected loss=0.07305936 P(node) =0.4543568
class counts: 203 16
probabilities: 0.927 0.073
Node number 3: 263 observations, complexity param=0.02262443
predicted class=1 expected loss=0.2205323 P(node) =0.5456432
class counts: 58 205
probabilities: 0.221 0.779
left son=6 (99 obs) right son=7 (164 obs)
Primary splits:
X9 splits as LR, improve=11.902240, (0 missing)
X10 < 0.5 to the left, improve=11.902240, (0 missing)
X14 < 216.5 to the left, improve=10.195680, (0 missing)
X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
X13 < 72.5 to the right, improve= 6.568284, (0 missing)
Surrogate splits:
X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
Node number 6: 99 observations, complexity param=0.02262443
predicted class=1 expected loss=0.4141414 P(node) =0.2053942
class counts: 41 58
probabilities: 0.414 0.586
left son=12 (61 obs) right son=13 (38 obs)
Primary splits:
X13 < 111 to the right, improve=6.520991, (0 missing)
X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
X14 < 388.5 to the left, improve=3.403553, (0 missing)
X3 < 2.52 to the left, improve=2.599301, (0 missing)
Surrogate splits:
X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
Node number 7: 164 observations
predicted class=1 expected loss=0.1036585 P(node) =0.340249
class counts: 17 147
probabilities: 0.104 0.896
Node number 12: 61 observations, complexity param=0.02262443
predicted class=0 expected loss=0.442623 P(node) =0.126556
class counts: 34 27
probabilities: 0.557 0.443
left son=24 (49 obs) right son=25 (12 obs)
Primary splits:
X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
X14 < 126 to the left, improve=3.7856330, (0 missing)
X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
X3 < 9.625 to the right, improve=1.0257110, (0 missing)
X2 < 24.5 to the right, improve=0.9861812, (0 missing)
Surrogate splits:
X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
Node number 13: 38 observations
predicted class=1 expected loss=0.1842105 P(node) =0.07883817
class counts: 7 31
probabilities: 0.184 0.816
Node number 24: 49 observations, complexity param=0.01357466
predicted class=0 expected loss=0.3469388 P(node) =0.1016598
class counts: 32 17
probabilities: 0.653 0.347
left son=48 (34 obs) right son=49 (15 obs)
Primary splits:
X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
X13 < 150 to the left, improve=2.3016430, (0 missing)
X14 < 126 to the left, improve=2.2040820, (0 missing)
X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
Surrogate splits:
X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
Node number 25: 12 observations
predicted class=1 expected loss=0.1666667 P(node) =0.02489627
class counts: 2 10
probabilities: 0.167 0.833
Node number 48: 34 observations
predicted class=0 expected loss=0.2352941 P(node) =0.07053942
class counts: 26 8
probabilities: 0.765 0.235
Node number 49: 15 observations
predicted class=1 expected loss=0.4 P(node) =0.03112033
class counts: 6 9
probabilities: 0.400 0.600
-- ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ----------
Error: arrange() failed at implicit mutate() step.
x Could not create a temporary column for `..1`.
i `..1` is `get(column)`.
Backtrace:
x
1. +-OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
2. | \-OptimClassifier:::OrderModels(summary_models, criteria)
3. | +-base::ifelse(...)
4. | +-dplyr::arrange(summary_table, get(column))
5. | \-dplyr:::arrange.data.frame(summary_table, get(column))
6. | \-dplyr:::arrange_rows(.data, dots)
7. | +-base::withCallingHandlers(...)
8. | +-dplyr::transmute(new_data_frame(.data), !!!quosures)
9. | \-dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
10. | +-dplyr::mutate(.data, ..., .keep = "none")
11. | \-dplyr:::mutate.data.frame(.data, ..., .keep = "none")
12. | \-dplyr:::mutate_cols(.data, ...)
13. | +-base::withCallingHandlers(...)
14. | \-mask$eval_all_mutate(dots[[i]])
15. +-base::get(column)
16. +-base::.handleSimpleError(...)
17. | \-dplyr:::h(simpleError(msg, call))
18. | \-rlang::abort(...)
19. | \-rlang:::signal_abort(cnd)
20. | \-base::signalCondition(cnd)
21. \-(function (cnd) ...
-- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
glm.fit: fitted probabilities numerically 0 or 1 occurred
-- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
glm.fit: fitted probabilities numerically 0 or 1 occurred
7 successful models have been tested and 21 thresholds evaluated
Model rmse Threshold success_rate ti_error tii_error
1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
[1] "\n"-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
# weights: 37
initial value 314.113022
iter 10 value 305.860086
iter 20 value 305.236595
iter 30 value 305.199531
final value 305.199440
converged
# weights: 73
initial value 331.204920
iter 10 value 277.703814
iter 20 value 264.464583
iter 30 value 235.346147
iter 40 value 213.675019
iter 50 value 204.512635
iter 60 value 187.308074
iter 70 value 147.436155
iter 80 value 134.805399
iter 90 value 128.131335
iter 100 value 127.540544
final value 127.539685
converged
# weights: 109
initial value 313.022965
iter 10 value 293.213546
iter 20 value 276.666037
iter 30 value 272.763270
iter 40 value 271.860014
iter 50 value 271.852731
final value 271.852717
converged
# weights: 145
initial value 342.699156
iter 10 value 266.493159
iter 20 value 248.027579
iter 30 value 202.156187
iter 40 value 181.633139
iter 50 value 165.106980
iter 60 value 146.805052
iter 70 value 131.481835
iter 80 value 118.560985
iter 90 value 113.589950
iter 100 value 111.521717
iter 110 value 109.315739
iter 120 value 108.445079
iter 130 value 107.625920
iter 140 value 106.688656
iter 150 value 105.653618
iter 160 value 105.376674
iter 170 value 105.364509
final value 105.364086
converged
# weights: 181
initial value 431.964891
iter 10 value 268.804053
iter 20 value 257.332978
iter 30 value 249.853001
iter 40 value 185.335385
iter 50 value 153.782864
iter 60 value 139.784881
iter 70 value 126.922475
iter 80 value 123.252145
iter 90 value 120.348628
iter 100 value 116.840543
iter 110 value 112.093661
iter 120 value 106.440800
iter 130 value 105.233650
iter 140 value 105.222728
final value 105.198137
converged
# weights: 217
initial value 364.112551
iter 10 value 274.040840
iter 20 value 260.171164
iter 30 value 252.654051
iter 40 value 245.185007
iter 50 value 239.105471
iter 60 value 225.547657
iter 70 value 208.619569
iter 80 value 196.251825
iter 90 value 188.803731
iter 100 value 187.520201
iter 110 value 180.509639
iter 120 value 173.309374
iter 130 value 162.696568
iter 140 value 158.906941
iter 150 value 157.467273
iter 160 value 157.011874
iter 170 value 156.566607
iter 180 value 154.830641
iter 190 value 154.706559
iter 200 value 154.475990
iter 210 value 153.619213
iter 220 value 149.786054
iter 230 value 147.795144
iter 240 value 147.083513
iter 250 value 145.964459
iter 260 value 145.865523
iter 270 value 145.820803
iter 280 value 145.792567
iter 290 value 145.721832
iter 300 value 145.610319
iter 310 value 145.570264
iter 320 value 145.522248
iter 330 value 145.455157
iter 340 value 145.350623
iter 350 value 145.244467
iter 360 value 145.203358
iter 370 value 145.184359
iter 380 value 145.176312
iter 390 value 145.169123
iter 400 value 145.154012
iter 410 value 145.147479
iter 420 value 145.127146
iter 430 value 145.091879
iter 440 value 145.074548
iter 450 value 145.026713
iter 460 value 145.016682
iter 470 value 145.010994
iter 480 value 144.949362
iter 490 value 144.786727
iter 500 value 144.760616
final value 144.760616
stopped after 500 iterations
6 models have been tested with differents levels of hidden layers
hiddenlayers rmse threshold success_rate ti_error tii_error
1 4 0.3236595 1 0.5950413 0.4049587 0
2 5 0.3279188 1 0.5950413 0.4049587 0
3 2 0.3613537 1 0.5950413 0.4049587 0
4 6 0.4056531 1 0.5950413 0.4049587 0
5 3 0.4793713 1 0.5950413 0.4049587 0
6 1 0.5048849 1 0.5950413 0.4049587 06 successful models have been tested
hiddenlayers rmse threshold success_rate ti_error tii_error
1 4 0.3236595 1 0.5950413 0.4049587 0
2 5 0.3279188 1 0.5950413 0.4049587 0
3 2 0.3613537 1 0.5950413 0.4049587 0
4 6 0.4056531 1 0.5950413 0.4049587 0
5 3 0.4793713 1 0.5950413 0.4049587 0
6 1 0.5048849 1 0.5950413 0.4049587 0
4 successful kernels have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
== testthat results ===========================================================
ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
[ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.1.5
Check: tests
Result: ERROR
Running ‘testthat.R’ [9s/14s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(OptimClassifier)
>
> test_check("OptimClassifier")
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
model = FALSE, x = FALSE, y = FALSE, cp = 0)
n= 482
CP nsplit rel error xerror xstd
1 0.66515837 0 1.0000000 1.0000000 0.04949948
2 0.02262443 1 0.3348416 0.3348416 0.03581213
3 0.01357466 4 0.2669683 0.3438914 0.03620379
4 0.01131222 5 0.2533937 0.3438914 0.03620379
Variable importance
X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
36 16 16 13 10 6 2 1 1 1
Node number 1: 482 observations, complexity param=0.6651584
predicted class=0 expected loss=0.4585062 P(node) =1
class counts: 261 221
probabilities: 0.541 0.459
left son=2 (219 obs) right son=3 (263 obs)
Primary splits:
X8 splits as LR, improve=119.25990, (0 missing)
X10 < 2.5 to the left, improve= 52.61262, (0 missing)
X9 splits as LR, improve= 47.76803, (0 missing)
X14 < 396 to the left, improve= 32.46584, (0 missing)
X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
Surrogate splits:
X9 splits as LR, agree=0.701, adj=0.342, (0 split)
X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
Node number 2: 219 observations
predicted class=0 expected loss=0.07305936 P(node) =0.4543568
class counts: 203 16
probabilities: 0.927 0.073
Node number 3: 263 observations, complexity param=0.02262443
predicted class=1 expected loss=0.2205323 P(node) =0.5456432
class counts: 58 205
probabilities: 0.221 0.779
left son=6 (99 obs) right son=7 (164 obs)
Primary splits:
X9 splits as LR, improve=11.902240, (0 missing)
X10 < 0.5 to the left, improve=11.902240, (0 missing)
X14 < 216.5 to the left, improve=10.195680, (0 missing)
X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
X13 < 72.5 to the right, improve= 6.568284, (0 missing)
Surrogate splits:
X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
Node number 6: 99 observations, complexity param=0.02262443
predicted class=1 expected loss=0.4141414 P(node) =0.2053942
class counts: 41 58
probabilities: 0.414 0.586
left son=12 (61 obs) right son=13 (38 obs)
Primary splits:
X13 < 111 to the right, improve=6.520991, (0 missing)
X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
X14 < 388.5 to the left, improve=3.403553, (0 missing)
X3 < 2.52 to the left, improve=2.599301, (0 missing)
Surrogate splits:
X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
Node number 7: 164 observations
predicted class=1 expected loss=0.1036585 P(node) =0.340249
class counts: 17 147
probabilities: 0.104 0.896
Node number 12: 61 observations, complexity param=0.02262443
predicted class=0 expected loss=0.442623 P(node) =0.126556
class counts: 34 27
probabilities: 0.557 0.443
left son=24 (49 obs) right son=25 (12 obs)
Primary splits:
X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
X14 < 126 to the left, improve=3.7856330, (0 missing)
X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
X3 < 9.625 to the right, improve=1.0257110, (0 missing)
X2 < 24.5 to the right, improve=0.9861812, (0 missing)
Surrogate splits:
X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
Node number 13: 38 observations
predicted class=1 expected loss=0.1842105 P(node) =0.07883817
class counts: 7 31
probabilities: 0.184 0.816
Node number 24: 49 observations, complexity param=0.01357466
predicted class=0 expected loss=0.3469388 P(node) =0.1016598
class counts: 32 17
probabilities: 0.653 0.347
left son=48 (34 obs) right son=49 (15 obs)
Primary splits:
X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
X13 < 150 to the left, improve=2.3016430, (0 missing)
X14 < 126 to the left, improve=2.2040820, (0 missing)
X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
Surrogate splits:
X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
Node number 25: 12 observations
predicted class=1 expected loss=0.1666667 P(node) =0.02489627
class counts: 2 10
probabilities: 0.167 0.833
Node number 48: 34 observations
predicted class=0 expected loss=0.2352941 P(node) =0.07053942
class counts: 26 8
probabilities: 0.765 0.235
Node number 49: 15 observations
predicted class=1 expected loss=0.4 P(node) =0.03112033
class counts: 6 9
probabilities: 0.400 0.600
── ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ──────────
Error: arrange() failed at implicit mutate() step.
✖ Could not create a temporary column for `..1`.
ℹ `..1` is `get(column)`.
Backtrace:
█
1. ├─OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
2. │ └─OptimClassifier:::OrderModels(summary_models, criteria)
3. │ ├─base::ifelse(...)
4. │ ├─dplyr::arrange(summary_table, get(column))
5. │ └─dplyr:::arrange.data.frame(summary_table, get(column))
6. │ └─dplyr:::arrange_rows(.data, dots)
7. │ ├─base::withCallingHandlers(...)
8. │ ├─dplyr::transmute(new_data_frame(.data), !!!quosures)
9. │ └─dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
10. │ ├─dplyr::mutate(.data, ..., .keep = "none")
11. │ └─dplyr:::mutate.data.frame(.data, ..., .keep = "none")
12. │ └─dplyr:::mutate_cols(.data, ...)
13. │ ├─base::withCallingHandlers(...)
14. │ └─mask$eval_all_mutate(dots[[i]])
15. ├─base::get(column)
16. ├─base::.handleSimpleError(...)
17. │ └─dplyr:::h(simpleError(msg, call))
18. │ └─rlang::abort(...)
19. │ └─rlang:::signal_abort(cnd)
20. │ └─base::signalCondition(cnd)
21. └─(function (cnd) ...
── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
glm.fit: fitted probabilities numerically 0 or 1 occurred
── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
glm.fit: fitted probabilities numerically 0 or 1 occurred
7 successful models have been tested and 21 thresholds evaluated
Model rmse Threshold success_rate ti_error tii_error
1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
[1] "\n"── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
# weights: 37
initial value 314.113022
iter 10 value 305.860086
iter 20 value 305.236595
iter 30 value 305.199531
final value 305.199440
converged
# weights: 73
initial value 331.204920
iter 10 value 277.703814
iter 20 value 264.464583
iter 30 value 235.346147
iter 40 value 213.675019
iter 50 value 204.513413
iter 60 value 188.579524
iter 70 value 151.606445
iter 80 value 135.045373
iter 90 value 127.862716
iter 100 value 127.540380
final value 127.539685
converged
# weights: 109
initial value 313.022965
iter 10 value 293.213546
iter 20 value 276.666037
iter 30 value 272.763270
iter 40 value 271.860014
iter 50 value 271.852731
final value 271.852717
converged
# weights: 145
initial value 342.699156
iter 10 value 266.493159
iter 20 value 248.027580
iter 30 value 202.137254
iter 40 value 182.841633
iter 50 value 163.406077
iter 60 value 153.712411
iter 70 value 143.761097
iter 80 value 136.791530
iter 90 value 131.883986
iter 100 value 128.256325
iter 110 value 123.054770
iter 120 value 119.373904
iter 130 value 117.935121
iter 140 value 115.543916
iter 150 value 113.870705
iter 160 value 112.397158
iter 170 value 110.659059
iter 180 value 110.265167
iter 190 value 109.914964
iter 200 value 109.465033
iter 210 value 109.365831
iter 220 value 109.309421
iter 230 value 109.221952
iter 240 value 109.183033
iter 250 value 109.077007
iter 260 value 109.024442
iter 270 value 108.979083
iter 280 value 108.966573
iter 290 value 108.946679
iter 300 value 108.928539
iter 310 value 108.906541
iter 320 value 108.903732
iter 330 value 108.881879
iter 340 value 108.860613
iter 350 value 108.852071
iter 360 value 108.843541
iter 370 value 108.833519
iter 380 value 108.826552
iter 390 value 108.704253
iter 400 value 108.679474
iter 410 value 108.667142
iter 420 value 108.661706
iter 430 value 108.659019
iter 440 value 108.653729
iter 450 value 108.633311
iter 460 value 108.613541
iter 470 value 108.584588
iter 480 value 108.566776
iter 490 value 108.527453
iter 500 value 108.484387
final value 108.484387
stopped after 500 iterations
# weights: 181
initial value 431.964891
iter 10 value 268.804053
iter 20 value 257.332978
iter 30 value 249.853001
iter 40 value 185.335385
iter 50 value 153.782864
iter 60 value 139.784881
iter 70 value 126.922476
iter 80 value 123.252215
iter 90 value 120.355265
iter 100 value 116.841582
iter 110 value 112.118113
iter 120 value 105.917923
iter 130 value 105.028943
iter 140 value 105.024535
final value 105.024530
converged
# weights: 217
initial value 364.112551
iter 10 value 274.040840
iter 20 value 260.171164
iter 30 value 252.654051
iter 40 value 245.185007
iter 50 value 239.105792
iter 60 value 228.177441
iter 70 value 209.068307
iter 80 value 189.216192
iter 90 value 179.285289
iter 100 value 177.101089
iter 110 value 177.096487
final value 177.096448
converged
# weights: 253
initial value 325.866420
iter 10 value 269.878024
iter 20 value 260.328850
iter 30 value 255.407336
iter 40 value 248.367432
iter 50 value 201.019882
iter 60 value 154.795327
iter 70 value 142.061451
iter 80 value 129.177931
iter 90 value 113.635713
iter 100 value 109.226495
iter 110 value 102.067007
iter 120 value 100.120314
iter 130 value 99.785251
iter 140 value 99.503240
iter 150 value 99.238235
iter 160 value 99.194327
iter 170 value 99.004752
iter 180 value 98.488715
iter 190 value 97.866264
iter 200 value 97.553015
iter 210 value 97.406696
iter 220 value 97.262658
iter 230 value 97.069177
iter 240 value 96.926948
iter 250 value 96.846568
iter 260 value 96.575699
iter 270 value 96.307219
iter 280 value 96.246913
iter 290 value 96.193544
iter 300 value 96.112864
iter 310 value 96.061954
iter 320 value 95.737456
iter 330 value 95.016696
iter 340 value 94.608414
iter 350 value 94.331090
iter 360 value 94.229239
iter 370 value 94.123306
iter 380 value 94.070268
iter 390 value 93.966009
iter 400 value 93.756340
iter 410 value 93.566368
iter 420 value 93.510538
iter 430 value 93.426889
iter 440 value 93.342668
iter 450 value 93.293375
iter 460 value 93.232193
iter 470 value 93.158243
iter 480 value 93.026257
iter 490 value 92.808916
iter 500 value 92.697079
final value 92.697079
stopped after 500 iterations
# weights: 289
initial value 308.589551
iter 10 value 276.114138
iter 20 value 271.523670
iter 30 value 263.359821
iter 40 value 252.965028
iter 50 value 215.194350
iter 60 value 197.931441
iter 70 value 189.556282
iter 80 value 162.533108
iter 90 value 148.780324
iter 100 value 127.237270
iter 110 value 113.271915
iter 120 value 103.681517
iter 130 value 100.506900
iter 140 value 99.473416
iter 150 value 96.419897
iter 160 value 95.875123
iter 170 value 95.582754
iter 180 value 94.802815
iter 190 value 93.800813
iter 200 value 93.415047
iter 210 value 93.113811
iter 220 value 92.848904
iter 230 value 92.216786
iter 240 value 91.199472
iter 250 value 91.064264
iter 260 value 90.940703
iter 270 value 90.613176
iter 280 value 90.580067
iter 290 value 90.552349
iter 300 value 90.469581
iter 310 value 90.125040
iter 320 value 89.834299
iter 330 value 89.634271
iter 340 value 89.477507
iter 350 value 89.278901
iter 360 value 89.110192
iter 370 value 89.034938
iter 380 value 88.514948
iter 390 value 88.368879
iter 400 value 88.232517
iter 410 value 88.189377
iter 420 value 88.178188
iter 430 value 88.169094
iter 440 value 88.156563
iter 450 value 88.136171
iter 460 value 88.106613
iter 470 value 87.790970
iter 480 value 87.676820
iter 490 value 87.599647
iter 500 value 87.568524
final value 87.568524
stopped after 500 iterations
# weights: 325
initial value 387.634927
iter 10 value 268.437459
iter 20 value 261.669527
iter 30 value 238.226127
iter 40 value 218.985628
iter 50 value 203.340002
iter 60 value 197.855735
iter 70 value 192.165234
iter 80 value 191.161086
iter 90 value 191.147630
iter 100 value 191.146459
iter 110 value 191.146013
final value 191.145998
converged
9 models have been tested with differents levels of hidden layers
hiddenlayers rmse threshold success_rate ti_error tii_error
1 5 0.3276663 1 0.5950413 0.4049587 0.00000000
2 7 0.3331272 1 0.5950413 0.4049587 0.00000000
3 4 0.3552631 1 0.5950413 0.4049587 0.00000000
4 8 0.3645871 1 0.7851240 0.1983471 0.01652893
5 2 0.3706125 1 0.5950413 0.4049587 0.00000000
6 9 0.4330809 1 0.5950413 0.4049587 0.00000000
7 6 0.4491394 1 0.5950413 0.4049587 0.00000000
8 3 0.4793713 1 0.5950413 0.4049587 0.00000000
9 1 0.5048849 1 0.5950413 0.4049587 0.000000009 successful models have been tested
hiddenlayers rmse threshold success_rate ti_error tii_error
1 5 0.3276663 1 0.5950413 0.4049587 0.00000000
2 7 0.3331272 1 0.5950413 0.4049587 0.00000000
3 4 0.3552631 1 0.5950413 0.4049587 0.00000000
4 8 0.3645871 1 0.7851240 0.1983471 0.01652893
5 2 0.3706125 1 0.5950413 0.4049587 0.00000000
6 9 0.4330809 1 0.5950413 0.4049587 0.00000000
7 6 0.4491394 1 0.5950413 0.4049587 0.00000000
8 3 0.4793713 1 0.5950413 0.4049587 0.00000000
9 1 0.5048849 1 0.5950413 0.4049587 0.00000000
4 successful kernels have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
══ testthat results ═══════════════════════════════════════════════════════════
ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
[ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.1.5
Check: tests
Result: ERROR
Running ‘testthat.R’ [11s/19s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(OptimClassifier)
>
> test_check("OptimClassifier")
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
model = FALSE, x = FALSE, y = FALSE, cp = 0)
n= 482
CP nsplit rel error xerror xstd
1 0.66515837 0 1.0000000 1.0000000 0.04949948
2 0.02262443 1 0.3348416 0.3348416 0.03581213
3 0.01357466 4 0.2669683 0.3438914 0.03620379
4 0.01131222 5 0.2533937 0.3438914 0.03620379
Variable importance
X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
36 16 16 13 10 6 2 1 1 1
Node number 1: 482 observations, complexity param=0.6651584
predicted class=0 expected loss=0.4585062 P(node) =1
class counts: 261 221
probabilities: 0.541 0.459
left son=2 (219 obs) right son=3 (263 obs)
Primary splits:
X8 splits as LR, improve=119.25990, (0 missing)
X10 < 2.5 to the left, improve= 52.61262, (0 missing)
X9 splits as LR, improve= 47.76803, (0 missing)
X14 < 396 to the left, improve= 32.46584, (0 missing)
X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
Surrogate splits:
X9 splits as LR, agree=0.701, adj=0.342, (0 split)
X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
Node number 2: 219 observations
predicted class=0 expected loss=0.07305936 P(node) =0.4543568
class counts: 203 16
probabilities: 0.927 0.073
Node number 3: 263 observations, complexity param=0.02262443
predicted class=1 expected loss=0.2205323 P(node) =0.5456432
class counts: 58 205
probabilities: 0.221 0.779
left son=6 (99 obs) right son=7 (164 obs)
Primary splits:
X9 splits as LR, improve=11.902240, (0 missing)
X10 < 0.5 to the left, improve=11.902240, (0 missing)
X14 < 216.5 to the left, improve=10.195680, (0 missing)
X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
X13 < 72.5 to the right, improve= 6.568284, (0 missing)
Surrogate splits:
X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
Node number 6: 99 observations, complexity param=0.02262443
predicted class=1 expected loss=0.4141414 P(node) =0.2053942
class counts: 41 58
probabilities: 0.414 0.586
left son=12 (61 obs) right son=13 (38 obs)
Primary splits:
X13 < 111 to the right, improve=6.520991, (0 missing)
X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
X14 < 388.5 to the left, improve=3.403553, (0 missing)
X3 < 2.52 to the left, improve=2.599301, (0 missing)
Surrogate splits:
X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
Node number 7: 164 observations
predicted class=1 expected loss=0.1036585 P(node) =0.340249
class counts: 17 147
probabilities: 0.104 0.896
Node number 12: 61 observations, complexity param=0.02262443
predicted class=0 expected loss=0.442623 P(node) =0.126556
class counts: 34 27
probabilities: 0.557 0.443
left son=24 (49 obs) right son=25 (12 obs)
Primary splits:
X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
X14 < 126 to the left, improve=3.7856330, (0 missing)
X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
X3 < 9.625 to the right, improve=1.0257110, (0 missing)
X2 < 24.5 to the right, improve=0.9861812, (0 missing)
Surrogate splits:
X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
Node number 13: 38 observations
predicted class=1 expected loss=0.1842105 P(node) =0.07883817
class counts: 7 31
probabilities: 0.184 0.816
Node number 24: 49 observations, complexity param=0.01357466
predicted class=0 expected loss=0.3469388 P(node) =0.1016598
class counts: 32 17
probabilities: 0.653 0.347
left son=48 (34 obs) right son=49 (15 obs)
Primary splits:
X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
X13 < 150 to the left, improve=2.3016430, (0 missing)
X14 < 126 to the left, improve=2.2040820, (0 missing)
X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
Surrogate splits:
X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
Node number 25: 12 observations
predicted class=1 expected loss=0.1666667 P(node) =0.02489627
class counts: 2 10
probabilities: 0.167 0.833
Node number 48: 34 observations
predicted class=0 expected loss=0.2352941 P(node) =0.07053942
class counts: 26 8
probabilities: 0.765 0.235
Node number 49: 15 observations
predicted class=1 expected loss=0.4 P(node) =0.03112033
class counts: 6 9
probabilities: 0.400 0.600
── ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ──────────
Error: arrange() failed at implicit mutate() step.
✖ Could not create a temporary column for `..1`.
ℹ `..1` is `get(column)`.
Backtrace:
█
1. ├─OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
2. │ └─OptimClassifier:::OrderModels(summary_models, criteria)
3. │ ├─base::ifelse(...)
4. │ ├─dplyr::arrange(summary_table, get(column))
5. │ └─dplyr:::arrange.data.frame(summary_table, get(column))
6. │ └─dplyr:::arrange_rows(.data, dots)
7. │ ├─base::withCallingHandlers(...)
8. │ ├─dplyr::transmute(new_data_frame(.data), !!!quosures)
9. │ └─dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
10. │ ├─dplyr::mutate(.data, ..., .keep = "none")
11. │ └─dplyr:::mutate.data.frame(.data, ..., .keep = "none")
12. │ └─dplyr:::mutate_cols(.data, ...)
13. │ ├─base::withCallingHandlers(...)
14. │ └─mask$eval_all_mutate(dots[[i]])
15. ├─base::get(column)
16. ├─base::.handleSimpleError(...)
17. │ └─dplyr:::h(simpleError(msg, call))
18. │ └─rlang::abort(...)
19. │ └─rlang:::signal_abort(cnd)
20. │ └─base::signalCondition(cnd)
21. └─(function (cnd) ...
── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
glm.fit: fitted probabilities numerically 0 or 1 occurred
── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
glm.fit: fitted probabilities numerically 0 or 1 occurred
7 successful models have been tested and 21 thresholds evaluated
Model rmse Threshold success_rate ti_error tii_error
1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
[1] "\n"── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
# weights: 37
initial value 314.113022
iter 10 value 305.860086
iter 20 value 305.236595
iter 30 value 305.199531
final value 305.199440
converged
# weights: 73
initial value 331.204920
iter 10 value 277.703814
iter 20 value 264.464583
iter 30 value 235.346147
iter 40 value 213.675019
iter 50 value 204.512635
iter 60 value 187.308074
iter 70 value 147.436155
iter 80 value 134.805399
iter 90 value 128.131335
iter 100 value 127.540544
final value 127.539685
converged
# weights: 109
initial value 313.022965
iter 10 value 293.213546
iter 20 value 276.666037
iter 30 value 272.763270
iter 40 value 271.860014
iter 50 value 271.852731
final value 271.852717
converged
# weights: 145
initial value 342.699156
iter 10 value 266.493159
iter 20 value 248.027579
iter 30 value 202.156187
iter 40 value 181.633139
iter 50 value 165.106980
iter 60 value 146.805052
iter 70 value 131.481835
iter 80 value 118.560985
iter 90 value 113.589950
iter 100 value 111.521717
iter 110 value 109.315739
iter 120 value 108.445079
iter 130 value 107.625920
iter 140 value 106.688656
iter 150 value 105.653618
iter 160 value 105.376674
iter 170 value 105.364509
final value 105.364086
converged
# weights: 181
initial value 431.964891
iter 10 value 268.804053
iter 20 value 257.332978
iter 30 value 249.853001
iter 40 value 185.335385
iter 50 value 153.782864
iter 60 value 139.784881
iter 70 value 126.922475
iter 80 value 123.252145
iter 90 value 120.348628
iter 100 value 116.840543
iter 110 value 112.093661
iter 120 value 106.440800
iter 130 value 105.233650
iter 140 value 105.222728
final value 105.198137
converged
# weights: 217
initial value 364.112551
iter 10 value 274.040840
iter 20 value 260.171164
iter 30 value 252.654051
iter 40 value 245.185007
iter 50 value 239.105471
iter 60 value 225.547657
iter 70 value 208.619569
iter 80 value 196.251825
iter 90 value 188.803731
iter 100 value 187.520201
iter 110 value 180.509639
iter 120 value 173.309374
iter 130 value 162.696568
iter 140 value 158.906941
iter 150 value 157.467273
iter 160 value 157.011874
iter 170 value 156.566607
iter 180 value 154.830641
iter 190 value 154.706559
iter 200 value 154.475990
iter 210 value 153.619213
iter 220 value 149.786054
iter 230 value 147.795144
iter 240 value 147.083513
iter 250 value 145.964459
iter 260 value 145.865523
iter 270 value 145.820803
iter 280 value 145.792567
iter 290 value 145.721832
iter 300 value 145.610319
iter 310 value 145.570264
iter 320 value 145.522248
iter 330 value 145.455157
iter 340 value 145.350623
iter 350 value 145.244467
iter 360 value 145.203358
iter 370 value 145.184359
iter 380 value 145.176312
iter 390 value 145.169123
iter 400 value 145.154012
iter 410 value 145.147479
iter 420 value 145.127146
iter 430 value 145.091879
iter 440 value 145.074548
iter 450 value 145.026713
iter 460 value 145.016682
iter 470 value 145.010994
iter 480 value 144.949362
iter 490 value 144.786727
iter 500 value 144.760616
final value 144.760616
stopped after 500 iterations
6 models have been tested with differents levels of hidden layers
hiddenlayers rmse threshold success_rate ti_error tii_error
1 4 0.3236595 1 0.5950413 0.4049587 0
2 5 0.3279188 1 0.5950413 0.4049587 0
3 2 0.3613537 1 0.5950413 0.4049587 0
4 6 0.4056531 1 0.5950413 0.4049587 0
5 3 0.4793713 1 0.5950413 0.4049587 0
6 1 0.5048849 1 0.5950413 0.4049587 06 successful models have been tested
hiddenlayers rmse threshold success_rate ti_error tii_error
1 4 0.3236595 1 0.5950413 0.4049587 0
2 5 0.3279188 1 0.5950413 0.4049587 0
3 2 0.3613537 1 0.5950413 0.4049587 0
4 6 0.4056531 1 0.5950413 0.4049587 0
5 3 0.4793713 1 0.5950413 0.4049587 0
6 1 0.5048849 1 0.5950413 0.4049587 0
4 successful kernels have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
══ testthat results ═══════════════════════════════════════════════════════════
ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
[ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.1.5
Check: tests
Result: ERROR
Running ‘testthat.R’ [12s/14s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(OptimClassifier)
>
> test_check("OptimClassifier")
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
model = FALSE, x = FALSE, y = FALSE, cp = 0)
n= 482
CP nsplit rel error xerror xstd
1 0.66515837 0 1.0000000 1.0000000 0.04949948
2 0.02262443 1 0.3348416 0.3348416 0.03581213
3 0.01357466 4 0.2669683 0.3438914 0.03620379
4 0.01131222 5 0.2533937 0.3438914 0.03620379
Variable importance
X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
36 16 16 13 10 6 2 1 1 1
Node number 1: 482 observations, complexity param=0.6651584
predicted class=0 expected loss=0.4585062 P(node) =1
class counts: 261 221
probabilities: 0.541 0.459
left son=2 (219 obs) right son=3 (263 obs)
Primary splits:
X8 splits as LR, improve=119.25990, (0 missing)
X10 < 2.5 to the left, improve= 52.61262, (0 missing)
X9 splits as LR, improve= 47.76803, (0 missing)
X14 < 396 to the left, improve= 32.46584, (0 missing)
X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
Surrogate splits:
X9 splits as LR, agree=0.701, adj=0.342, (0 split)
X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
Node number 2: 219 observations
predicted class=0 expected loss=0.07305936 P(node) =0.4543568
class counts: 203 16
probabilities: 0.927 0.073
Node number 3: 263 observations, complexity param=0.02262443
predicted class=1 expected loss=0.2205323 P(node) =0.5456432
class counts: 58 205
probabilities: 0.221 0.779
left son=6 (99 obs) right son=7 (164 obs)
Primary splits:
X9 splits as LR, improve=11.902240, (0 missing)
X10 < 0.5 to the left, improve=11.902240, (0 missing)
X14 < 216.5 to the left, improve=10.195680, (0 missing)
X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
X13 < 72.5 to the right, improve= 6.568284, (0 missing)
Surrogate splits:
X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
Node number 6: 99 observations, complexity param=0.02262443
predicted class=1 expected loss=0.4141414 P(node) =0.2053942
class counts: 41 58
probabilities: 0.414 0.586
left son=12 (61 obs) right son=13 (38 obs)
Primary splits:
X13 < 111 to the right, improve=6.520991, (0 missing)
X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
X14 < 388.5 to the left, improve=3.403553, (0 missing)
X3 < 2.52 to the left, improve=2.599301, (0 missing)
Surrogate splits:
X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
Node number 7: 164 observations
predicted class=1 expected loss=0.1036585 P(node) =0.340249
class counts: 17 147
probabilities: 0.104 0.896
Node number 12: 61 observations, complexity param=0.02262443
predicted class=0 expected loss=0.442623 P(node) =0.126556
class counts: 34 27
probabilities: 0.557 0.443
left son=24 (49 obs) right son=25 (12 obs)
Primary splits:
X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
X14 < 126 to the left, improve=3.7856330, (0 missing)
X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
X3 < 9.625 to the right, improve=1.0257110, (0 missing)
X2 < 24.5 to the right, improve=0.9861812, (0 missing)
Surrogate splits:
X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
Node number 13: 38 observations
predicted class=1 expected loss=0.1842105 P(node) =0.07883817
class counts: 7 31
probabilities: 0.184 0.816
Node number 24: 49 observations, complexity param=0.01357466
predicted class=0 expected loss=0.3469388 P(node) =0.1016598
class counts: 32 17
probabilities: 0.653 0.347
left son=48 (34 obs) right son=49 (15 obs)
Primary splits:
X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
X13 < 150 to the left, improve=2.3016430, (0 missing)
X14 < 126 to the left, improve=2.2040820, (0 missing)
X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
Surrogate splits:
X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
Node number 25: 12 observations
predicted class=1 expected loss=0.1666667 P(node) =0.02489627
class counts: 2 10
probabilities: 0.167 0.833
Node number 48: 34 observations
predicted class=0 expected loss=0.2352941 P(node) =0.07053942
class counts: 26 8
probabilities: 0.765 0.235
Node number 49: 15 observations
predicted class=1 expected loss=0.4 P(node) =0.03112033
class counts: 6 9
probabilities: 0.400 0.600
── ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ──────────
Error: arrange() failed at implicit mutate() step.
✖ Could not create a temporary column for `..1`.
ℹ `..1` is `get(column)`.
Backtrace:
█
1. ├─OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
2. │ └─OptimClassifier:::OrderModels(summary_models, criteria)
3. │ ├─base::ifelse(...)
4. │ ├─dplyr::arrange(summary_table, get(column))
5. │ └─dplyr:::arrange.data.frame(summary_table, get(column))
6. │ └─dplyr:::arrange_rows(.data, dots)
7. │ ├─base::withCallingHandlers(...)
8. │ ├─dplyr::transmute(new_data_frame(.data), !!!quosures)
9. │ └─dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
10. │ ├─dplyr::mutate(.data, ..., .keep = "none")
11. │ └─dplyr:::mutate.data.frame(.data, ..., .keep = "none")
12. │ └─dplyr:::mutate_cols(.data, ...)
13. │ ├─base::withCallingHandlers(...)
14. │ └─mask$eval_all_mutate(dots[[i]])
15. ├─base::get(column)
16. ├─base::.handleSimpleError(...)
17. │ └─dplyr:::h(simpleError(msg, call))
18. │ └─rlang::abort(...)
19. │ └─rlang:::signal_abort(cnd)
20. │ └─base::signalCondition(cnd)
21. └─(function (cnd) ...
── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
glm.fit: fitted probabilities numerically 0 or 1 occurred
── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
glm.fit: fitted probabilities numerically 0 or 1 occurred
7 successful models have been tested and 21 thresholds evaluated
Model rmse Threshold success_rate ti_error tii_error
1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
[1] "\n"── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
Some predictor variables are on very different scales: consider rescaling
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
# weights: 37
initial value 314.113022
iter 10 value 305.860086
iter 20 value 305.236595
iter 30 value 305.199531
final value 305.199440
converged
# weights: 73
initial value 331.204920
iter 10 value 277.703814
iter 20 value 264.464583
iter 30 value 235.346147
iter 40 value 213.675019
iter 50 value 204.512635
iter 60 value 187.308074
iter 70 value 147.436155
iter 80 value 134.805399
iter 90 value 128.131335
iter 100 value 127.540544
final value 127.539685
converged
# weights: 109
initial value 313.022965
iter 10 value 293.213546
iter 20 value 276.666037
iter 30 value 272.763270
iter 40 value 271.860014
iter 50 value 271.852731
final value 271.852717
converged
# weights: 145
initial value 342.699156
iter 10 value 266.493159
iter 20 value 248.027579
iter 30 value 202.156187
iter 40 value 181.633139
iter 50 value 165.106980
iter 60 value 146.805052
iter 70 value 131.481835
iter 80 value 118.560985
iter 90 value 113.589950
iter 100 value 111.521717
iter 110 value 109.315739
iter 120 value 108.445079
iter 130 value 107.625920
iter 140 value 106.688656
iter 150 value 105.653618
iter 160 value 105.376674
iter 170 value 105.364509
final value 105.364086
converged
# weights: 181
initial value 431.964891
iter 10 value 268.804053
iter 20 value 257.332978
iter 30 value 249.853001
iter 40 value 185.335385
iter 50 value 153.782864
iter 60 value 139.784881
iter 70 value 126.922475
iter 80 value 123.252145
iter 90 value 120.348628
iter 100 value 116.840543
iter 110 value 112.093661
iter 120 value 106.440800
iter 130 value 105.233650
iter 140 value 105.222728
final value 105.198137
converged
# weights: 217
initial value 364.112551
iter 10 value 274.040840
iter 20 value 260.171164
iter 30 value 252.654051
iter 40 value 245.185007
iter 50 value 239.105471
iter 60 value 225.547657
iter 70 value 208.619569
iter 80 value 196.251825
iter 90 value 188.803731
iter 100 value 187.520201
iter 110 value 180.509639
iter 120 value 173.309374
iter 130 value 162.696568
iter 140 value 158.906941
iter 150 value 157.467273
iter 160 value 157.011874
iter 170 value 156.566607
iter 180 value 154.830641
iter 190 value 154.706559
iter 200 value 154.475990
iter 210 value 153.619213
iter 220 value 149.786054
iter 230 value 147.795144
iter 240 value 147.083513
iter 250 value 145.964459
iter 260 value 145.865523
iter 270 value 145.820803
iter 280 value 145.792567
iter 290 value 145.721832
iter 300 value 145.610319
iter 310 value 145.570264
iter 320 value 145.522248
iter 330 value 145.455157
iter 340 value 145.350623
iter 350 value 145.244467
iter 360 value 145.203358
iter 370 value 145.184359
iter 380 value 145.176312
iter 390 value 145.169123
iter 400 value 145.154012
iter 410 value 145.147479
iter 420 value 145.127146
iter 430 value 145.091879
iter 440 value 145.074548
iter 450 value 145.026713
iter 460 value 145.016682
iter 470 value 145.010994
iter 480 value 144.949362
iter 490 value 144.786727
iter 500 value 144.760616
final value 144.760616
stopped after 500 iterations
6 models have been tested with differents levels of hidden layers
hiddenlayers rmse threshold success_rate ti_error tii_error
1 4 0.3236595 1 0.5950413 0.4049587 0
2 5 0.3279188 1 0.5950413 0.4049587 0
3 2 0.3613537 1 0.5950413 0.4049587 0
4 6 0.4056531 1 0.5950413 0.4049587 0
5 3 0.4793713 1 0.5950413 0.4049587 0
6 1 0.5048849 1 0.5950413 0.4049587 06 successful models have been tested
hiddenlayers rmse threshold success_rate ti_error tii_error
1 4 0.3236595 1 0.5950413 0.4049587 0
2 5 0.3279188 1 0.5950413 0.4049587 0
3 2 0.3613537 1 0.5950413 0.4049587 0
4 6 0.4056531 1 0.5950413 0.4049587 0
5 3 0.4793713 1 0.5950413 0.4049587 0
6 1 0.5048849 1 0.5950413 0.4049587 0
4 successful kernels have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
══ testthat results ═══════════════════════════════════════════════════════════
ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
[ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.1.5
Check: tests
Result: ERROR
Running 'testthat.R' [14s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(OptimClassifier)
>
> test_check("OptimClassifier")
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
6 successful models have been tested
CP rmse success_rate ti_error tii_error Nnodes
1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
model = FALSE, x = FALSE, y = FALSE, cp = 0)
n= 482
CP nsplit rel error xerror xstd
1 0.66515837 0 1.0000000 1.0000000 0.04949948
2 0.02262443 1 0.3348416 0.3348416 0.03581213
3 0.01357466 4 0.2669683 0.3438914 0.03620379
4 0.01131222 5 0.2533937 0.3438914 0.03620379
Variable importance
X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
36 16 16 13 10 6 2 1 1 1
Node number 1: 482 observations, complexity param=0.6651584
predicted class=0 expected loss=0.4585062 P(node) =1
class counts: 261 221
probabilities: 0.541 0.459
left son=2 (219 obs) right son=3 (263 obs)
Primary splits:
X8 splits as LR, improve=119.25990, (0 missing)
X10 < 2.5 to the left, improve= 52.61262, (0 missing)
X9 splits as LR, improve= 47.76803, (0 missing)
X14 < 396 to the left, improve= 32.46584, (0 missing)
X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
Surrogate splits:
X9 splits as LR, agree=0.701, adj=0.342, (0 split)
X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
Node number 2: 219 observations
predicted class=0 expected loss=0.07305936 P(node) =0.4543568
class counts: 203 16
probabilities: 0.927 0.073
Node number 3: 263 observations, complexity param=0.02262443
predicted class=1 expected loss=0.2205323 P(node) =0.5456432
class counts: 58 205
probabilities: 0.221 0.779
left son=6 (99 obs) right son=7 (164 obs)
Primary splits:
X9 splits as LR, improve=11.902240, (0 missing)
X10 < 0.5 to the left, improve=11.902240, (0 missing)
X14 < 216.5 to the left, improve=10.195680, (0 missing)
X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
X13 < 72.5 to the right, improve= 6.568284, (0 missing)
Surrogate splits:
X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
Node number 6: 99 observations, complexity param=0.02262443
predicted class=1 expected loss=0.4141414 P(node) =0.2053942
class counts: 41 58
probabilities: 0.414 0.586
left son=12 (61 obs) right son=13 (38 obs)
Primary splits:
X13 < 111 to the right, improve=6.520991, (0 missing)
X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
X14 < 388.5 to the left, improve=3.403553, (0 missing)
X3 < 2.52 to the left, improve=2.599301, (0 missing)
Surrogate splits:
X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
Node number 7: 164 observations
predicted class=1 expected loss=0.1036585 P(node) =0.340249
class counts: 17 147
probabilities: 0.104 0.896
Node number 12: 61 observations, complexity param=0.02262443
predicted class=0 expected loss=0.442623 P(node) =0.126556
class counts: 34 27
probabilities: 0.557 0.443
left son=24 (49 obs) right son=25 (12 obs)
Primary splits:
X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
X14 < 126 to the left, improve=3.7856330, (0 missing)
X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
X3 < 9.625 to the right, improve=1.0257110, (0 missing)
X2 < 24.5 to the right, improve=0.9861812, (0 missing)
Surrogate splits:
X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
Node number 13: 38 observations
predicted class=1 expected loss=0.1842105 P(node) =0.07883817
class counts: 7 31
probabilities: 0.184 0.816
Node number 24: 49 observations, complexity param=0.01357466
predicted class=0 expected loss=0.3469388 P(node) =0.1016598
class counts: 32 17
probabilities: 0.653 0.347
left son=48 (34 obs) right son=49 (15 obs)
Primary splits:
X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
X13 < 150 to the left, improve=2.3016430, (0 missing)
X14 < 126 to the left, improve=2.2040820, (0 missing)
X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
Surrogate splits:
X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
Node number 25: 12 observations
predicted class=1 expected loss=0.1666667 P(node) =0.02489627
class counts: 2 10
probabilities: 0.167 0.833
Node number 48: 34 observations
predicted class=0 expected loss=0.2352941 P(node) =0.07053942
class counts: 26 8
probabilities: 0.765 0.235
Node number 49: 15 observations
predicted class=1 expected loss=0.4 P(node) =0.03112033
class counts: 6 9
probabilities: 0.400 0.600
-- ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ----------
Error: arrange() failed at implicit mutate() step.
x Could not create a temporary column for `..1`.
i `..1` is `get(column)`.
Backtrace:
x
1. +-OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
2. | \-OptimClassifier:::OrderModels(summary_models, criteria)
3. | +-base::ifelse(...)
4. | +-dplyr::arrange(summary_table, get(column))
5. | \-dplyr:::arrange.data.frame(summary_table, get(column))
6. | \-dplyr:::arrange_rows(.data, dots)
7. | +-base::withCallingHandlers(...)
8. | +-dplyr::transmute(new_data_frame(.data), !!!quosures)
9. | \-dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
10. | +-dplyr::mutate(.data, ..., .keep = "none")
11. | \-dplyr:::mutate.data.frame(.data, ..., .keep = "none")
12. | \-dplyr:::mutate_cols(.data, ...)
13. | +-base::withCallingHandlers(...)
14. | \-mask$eval_all_mutate(dots[[i]])
15. +-base::get(column)
16. +-base::.handleSimpleError(...)
17. | \-dplyr:::h(simpleError(msg, call))
18. | \-rlang::abort(...)
19. | \-rlang:::signal_abort(cnd)
20. | \-base::signalCondition(cnd)
21. \-(function (cnd) ...
-- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
glm.fit: fitted probabilities numerically 0 or 1 occurred
-- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
glm.fit: fitted probabilities numerically 0 or 1 occurred
7 successful models have been tested and 21 thresholds evaluated
Model rmse Threshold success_rate ti_error tii_error
1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
3 successful models have been tested
Model rmse threshold success_rate ti_error tii_error
1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
[1] "\n"-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
Some predictor variables are on very different scales: consider rescaling
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308
8 random variables have been tested
Random_Variable aic bic rmse threshold success_rate ti_error
1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
tii_error
1 0.01923077
2 0.05769231
3 0.08653846
4 0.07692308
5 0.09615385
6 0.07692308
7 0.07692308
8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
# weights: 37
initial value 314.113022
iter 10 value 305.860086
iter 20 value 305.236595
iter 30 value 305.199531
final value 305.199440
converged
# weights: 73
initial value 331.204920
iter 10 value 277.703814
iter 20 value 264.464583
iter 30 value 235.346119
iter 40 value 213.143728
iter 50 value 198.653605
iter 60 value 189.722819
iter 70 value 147.320177
iter 80 value 136.263418
iter 90 value 128.270425
iter 100 value 127.663106
iter 110 value 127.661701
iter 110 value 127.661700
iter 110 value 127.661700
final value 127.661700
converged
# weights: 109
initial value 313.022965
iter 10 value 293.213546
iter 20 value 276.666037
iter 30 value 272.763270
iter 40 value 271.860014
iter 50 value 271.852731
final value 271.852717
converged
# weights: 145
initial value 342.699156
iter 10 value 266.493159
iter 20 value 248.027580
iter 30 value 202.137254
iter 40 value 182.841633
iter 50 value 163.406077
iter 60 value 153.712611
iter 70 value 143.751965
iter 80 value 136.924097
iter 90 value 132.451409
iter 100 value 128.008563
iter 110 value 121.143215
iter 120 value 118.825805
iter 130 value 118.697294
iter 140 value 118.365630
iter 150 value 118.026769
iter 160 value 117.290617
iter 170 value 116.989570
iter 180 value 115.674799
iter 190 value 114.569251
iter 200 value 114.484329
iter 210 value 114.166832
iter 220 value 113.601200
iter 230 value 112.748507
iter 240 value 111.069187
iter 250 value 109.623750
iter 260 value 109.507997
iter 270 value 109.319786
iter 280 value 109.173316
iter 290 value 109.098379
iter 300 value 109.080459
iter 310 value 109.071379
iter 320 value 109.054026
iter 330 value 109.038041
iter 340 value 109.023128
iter 350 value 109.009024
iter 360 value 109.002012
iter 370 value 108.997458
iter 380 value 108.985170
iter 390 value 108.968280
iter 400 value 108.947004
iter 410 value 108.927032
iter 420 value 108.841519
final value 108.836900
converged
# weights: 181
initial value 431.964891
iter 10 value 268.804053
iter 20 value 257.332978
iter 30 value 249.853001
iter 40 value 185.335385
iter 50 value 153.782804
iter 60 value 139.768644
iter 70 value 128.585804
iter 80 value 123.378576
iter 90 value 119.778357
iter 100 value 117.186852
iter 110 value 116.089822
iter 120 value 107.815280
iter 130 value 102.184831
iter 140 value 99.993525
iter 150 value 99.336193
iter 160 value 99.255967
iter 170 value 99.255697
iter 180 value 99.255239
iter 180 value 99.255239
iter 180 value 99.255239
final value 99.255239
converged
# weights: 217
initial value 364.112551
iter 10 value 274.040840
iter 20 value 260.171164
iter 30 value 252.654051
iter 40 value 245.185007
iter 50 value 239.105792
iter 60 value 228.177441
iter 70 value 209.068307
iter 80 value 189.216192
iter 90 value 179.285289
iter 100 value 177.101089
iter 110 value 177.096487
final value 177.096448
converged
# weights: 253
initial value 325.866420
iter 10 value 269.878024
iter 20 value 260.328850
iter 30 value 255.407336
iter 40 value 246.177487
iter 50 value 189.449891
iter 60 value 148.214287
iter 70 value 138.638245
iter 80 value 126.342161
iter 90 value 115.768794
iter 100 value 106.821478
iter 110 value 96.604319
iter 120 value 95.772744
iter 130 value 94.530406
iter 140 value 93.117235
iter 150 value 92.779526
iter 160 value 92.426473
iter 170 value 91.639027
iter 180 value 89.777304
iter 190 value 89.146231
iter 200 value 89.048580
iter 210 value 89.043519
iter 220 value 88.987536
iter 230 value 88.922038
iter 240 value 88.919203
iter 250 value 88.868516
iter 260 value 88.866360
iter 270 value 88.792197
iter 280 value 88.761927
iter 290 value 88.759643
iter 300 value 88.756572
iter 310 value 88.754814
iter 320 value 88.754232
iter 330 value 88.753935
iter 340 value 88.744635
iter 350 value 88.610024
final value 88.606398
converged
# weights: 289
initial value 308.589551
iter 10 value 276.114138
iter 20 value 271.523670
iter 30 value 263.359821
iter 40 value 253.586724
iter 50 value 213.816844
iter 60 value 191.744528
iter 70 value 186.738090
iter 80 value 172.224440
iter 90 value 150.368428
iter 100 value 113.936207
iter 110 value 104.523212
iter 120 value 102.944488
iter 130 value 101.630580
iter 140 value 97.269033
iter 150 value 92.837710
iter 160 value 89.914339
iter 170 value 86.641147
iter 180 value 81.772484
iter 190 value 81.083576
iter 200 value 80.411390
iter 210 value 80.328208
iter 220 value 80.291606
iter 230 value 80.277154
iter 240 value 80.274125
iter 250 value 80.272141
iter 260 value 80.266943
iter 270 value 80.264131
iter 280 value 80.263133
iter 290 value 80.262658
iter 300 value 80.262196
iter 310 value 80.260835
iter 320 value 80.256191
iter 330 value 80.247971
iter 340 value 80.239631
final value 80.238950
converged
# weights: 325
initial value 387.634927
iter 10 value 268.437459
iter 20 value 261.669527
iter 30 value 238.226415
iter 40 value 218.917605
iter 50 value 207.230019
iter 60 value 205.169683
iter 70 value 198.999367
iter 80 value 191.407467
iter 90 value 176.982664
iter 100 value 172.309578
iter 110 value 169.081819
iter 120 value 168.137239
iter 130 value 167.927451
iter 140 value 167.907049
iter 150 value 167.864890
iter 160 value 167.791594
iter 170 value 167.772690
iter 180 value 167.757983
iter 190 value 167.750873
iter 200 value 167.747380
iter 210 value 167.743021
iter 220 value 167.739851
iter 230 value 167.737052
iter 240 value 167.736204
iter 250 value 167.735055
iter 260 value 167.734493
iter 270 value 167.733498
iter 280 value 167.732784
iter 290 value 167.732492
iter 300 value 167.732100
iter 310 value 167.731155
iter 320 value 167.730510
iter 330 value 167.728127
iter 340 value 167.726124
iter 350 value 167.724631
iter 360 value 167.724583
iter 370 value 167.669584
iter 380 value 167.622086
iter 390 value 167.612038
iter 400 value 167.606902
iter 410 value 167.442066
iter 420 value 167.394360
iter 430 value 167.386859
iter 440 value 167.293435
iter 450 value 167.228548
iter 460 value 167.168939
iter 470 value 167.077454
iter 480 value 166.296543
iter 490 value 165.650435
iter 500 value 164.097554
final value 164.097554
stopped after 500 iterations
9 models have been tested with differents levels of hidden layers
hiddenlayers rmse threshold success_rate ti_error tii_error
1 2 0.3515271 1 0.5950413 0.4049587 0.000000000
2 5 0.3543909 1 0.5950413 0.4049587 0.000000000
3 4 0.3565713 1 0.5950413 0.4049587 0.000000000
4 7 0.3622068 1 0.6033058 0.3925620 0.004132231
5 8 0.3796342 1 0.5950413 0.4049587 0.000000000
6 9 0.4423661 1 0.5950413 0.4049587 0.000000000
7 6 0.4491394 1 0.5950413 0.4049587 0.000000000
8 3 0.4793713 1 0.5950413 0.4049587 0.000000000
9 1 0.5048849 1 0.5950413 0.4049587 0.0000000009 successful models have been tested
hiddenlayers rmse threshold success_rate ti_error tii_error
1 2 0.3515271 1 0.5950413 0.4049587 0.000000000
2 5 0.3543909 1 0.5950413 0.4049587 0.000000000
3 4 0.3565713 1 0.5950413 0.4049587 0.000000000
4 7 0.3622068 1 0.6033058 0.3925620 0.004132231
5 8 0.3796342 1 0.5950413 0.4049587 0.000000000
6 9 0.4423661 1 0.5950413 0.4049587 0.000000000
7 6 0.4491394 1 0.5950413 0.4049587 0.000000000
8 3 0.4793713 1 0.5950413 0.4049587 0.000000000
9 1 0.5048849 1 0.5950413 0.4049587 0.000000000
4 successful kernels have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
Kernels rmse threshold success_rate ti_error tii_error
1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
== testthat results ===========================================================
ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
[ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
Error: Test failures
Execution halted
Flavor: r-devel-windows-ix86+x86_64