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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(mllrnrs) > > test_check("mllrnrs") CV fold: Fold1 CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 0.19 Round = 1 bagging_fraction = 0.6000 feature_fraction = 0.6000 min_data_in_leaf = 4.0000 learning_rate = 0.2000 num_leaves = 18.0000 Value = -0.4745914 elapsed = 0.13 Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.431546 elapsed = 0.10 Round = 3 bagging_fraction = 0.8000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 2.0000 Value = -0.4727251 elapsed = 0.12 Round = 4 bagging_fraction = 1.0000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 10.0000 Value = -0.4571104 elapsed = 0.12 Round = 5 bagging_fraction = 0.2466196 feature_fraction = 0.8656377 min_data_in_leaf = 7.0000 learning_rate = 0.1760741 num_leaves = 15.0000 Value = -0.4444332 elapsed = 0.09 Round = 6 bagging_fraction = 0.5481146 feature_fraction = 0.2547513 min_data_in_leaf = 9.0000 learning_rate = 0.1137251 num_leaves = 7.0000 Value = -0.5070443 Best Parameters Found: Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.431546 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 0.13 Round = 1 bagging_fraction = 0.6000 feature_fraction = 0.6000 min_data_in_leaf = 4.0000 learning_rate = 0.2000 num_leaves = 18.0000 Value = -0.5316282 elapsed = 0.08 Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.4985911 elapsed = 0.08 Round = 3 bagging_fraction = 0.8000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 2.0000 Value = -0.505255 elapsed = 0.11 Round = 4 bagging_fraction = 1.0000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 10.0000 Value = -0.535165 elapsed = 0.11 Round = 5 bagging_fraction = 0.7832345 feature_fraction = 0.2154829 min_data_in_leaf = 7.0000 learning_rate = 0.1873565 num_leaves = 16.0000 Value = -0.5855704 elapsed = 0.08 Round = 6 bagging_fraction = 0.7998778 feature_fraction = 0.6535149 min_data_in_leaf = 5.0000 learning_rate = 0.1301516 num_leaves = 3.0000 Value = -0.4888063 Best Parameters Found: Round = 6 bagging_fraction = 0.7998778 feature_fraction = 0.6535149 min_data_in_leaf = 5.0000 learning_rate = 0.1301516 num_leaves = 3.0000 Value = -0.4888063 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 0.14 Round = 1 bagging_fraction = 0.6000 feature_fraction = 0.6000 min_data_in_leaf = 4.0000 learning_rate = 0.2000 num_leaves = 18.0000 Value = -0.4832754 elapsed = 0.09 Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.4296056 elapsed = 0.07 Round = 3 bagging_fraction = 0.8000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 2.0000 Value = -0.4746145 elapsed = 0.13 Round = 4 bagging_fraction = 1.0000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 10.0000 Value = -0.4447323 elapsed = 0.11 Round = 5 bagging_fraction = 0.5157127 feature_fraction = 0.9291875 min_data_in_leaf = 9.0000 learning_rate = 0.1998166 num_leaves = 6.0000 Value = -0.4283087 elapsed = 0.10 Round = 6 bagging_fraction = 0.7445644 feature_fraction = 0.2223738 min_data_in_leaf = 9.0000 learning_rate = 0.1282084 num_leaves = 6.0000 Value = -0.4834545 Best Parameters Found: Round = 5 bagging_fraction = 0.5157127 feature_fraction = 0.9291875 min_data_in_leaf = 9.0000 learning_rate = 0.1998166 num_leaves = 6.0000 Value = -0.4283087 CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.64 Round = 1 alpha = 0.0500 Value = -0.03838112 elapsed = 1.32 Round = 2 alpha = 0.2000 Value = -0.03852748 elapsed = 1.33 Round = 3 alpha = 0.1500 Value = -0.03849621 elapsed = 1.16 Round = 4 alpha = 0.1000 Value = -0.03844983 elapsed = 1.39 Round = 5 alpha = 0.9927179 Value = -0.03865969 elapsed = 1.25 Round = 6 alpha = 0.6273975 Value = -0.03863518 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.03838112 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.50 Round = 1 alpha = 0.0500 Value = -0.03859583 elapsed = 1.24 Round = 2 alpha = 0.2000 Value = -0.03864684 elapsed = 1.35 Round = 3 alpha = 0.1500 Value = -0.03863035 elapsed = 1.28 Round = 4 alpha = 0.1000 Value = -0.03861402 elapsed = 1.21 Round = 5 alpha = 0.9927182 Value = -0.03871602 elapsed = 1.19 Round = 6 alpha = 0.6550449 Value = -0.03870422 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.03859583 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.19 Round = 1 alpha = 0.0500 Value = -0.04148682 elapsed = 1.21 Round = 2 alpha = 0.2000 Value = -0.04162914 elapsed = 1.18 Round = 3 alpha = 0.1500 Value = -0.04159226 elapsed = 1.19 Round = 4 alpha = 0.1000 Value = -0.04155432 elapsed = 1.39 Round = 5 alpha = 0.655018 Value = -0.04172817 elapsed = 1.17 Round = 6 alpha = 0.9927204 Value = -0.04175126 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.04148682 CV fold: Fold1 Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) 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. elapsed = 0.09 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.179965 elapsed = 0.11 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1661427 elapsed = 0.08 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1687842 elapsed = 0.07 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1946351 elapsed = 0.09 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1735466 elapsed = 0.08 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1672985 elapsed = 0.05 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2445919 elapsed = 0.08 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1870047 elapsed = 0.05 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2413571 elapsed = 0.09 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1741428 elapsed = 0.07 Round = 11 subsample = 0.5698177 colsample_bytree = 0.7931576 min_child_weight = 3.0000 learning_rate = 0.1965151 max_depth = 4.0000 Value = -0.167589 elapsed = 0.11 Round = 12 subsample = 0.9185694 colsample_bytree = 0.4773705 min_child_weight = 9.0000 learning_rate = 0.1919655 max_depth = 9.0000 Value = -0.1646158 Best Parameters Found: Round = 12 subsample = 0.9185694 colsample_bytree = 0.4773705 min_child_weight = 9.0000 learning_rate = 0.1919655 max_depth = 9.0000 Value = -0.1646158 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. elapsed = 0.08 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1780216 elapsed = 0.08 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1681675 elapsed = 0.10 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1634727 elapsed = 0.07 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.186193 elapsed = 0.07 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1720624 elapsed = 0.07 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1653245 elapsed = 0.05 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2425153 elapsed = 0.08 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1784738 elapsed = 0.04 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2385224 elapsed = 0.08 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1655399 elapsed = 0.07 Round = 11 subsample = 1.0000 colsample_bytree = 0.6357025 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 4.0000 Value = -0.1591217 elapsed = 0.16 Round = 12 subsample = 1.0000 colsample_bytree = 0.6333377 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 9.0000 Value = -0.1640883 Best Parameters Found: Round = 11 subsample = 1.0000 colsample_bytree = 0.6357025 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 4.0000 Value = -0.1591217 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. elapsed = 0.10 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1913157 elapsed = 0.10 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1782915 elapsed = 0.10 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.176422 elapsed = 0.08 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1991184 elapsed = 0.08 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1763187 elapsed = 0.08 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1754786 elapsed = 0.04 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2462612 elapsed = 0.08 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1919792 elapsed = 0.04 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2433922 elapsed = 0.08 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1831283 elapsed = 0.10 Round = 11 subsample = 0.8661782 colsample_bytree = 0.5152599 min_child_weight = 9.0000 learning_rate = 0.1902978 max_depth = 6.0000 Value = -0.1671077 elapsed = 0.14 Round = 12 subsample = 0.2000 colsample_bytree = 1.0000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.194026 Best Parameters Found: Round = 11 subsample = 0.8661782 colsample_bytree = 0.5152599 min_child_weight = 9.0000 learning_rate = 0.1902978 max_depth = 6.0000 Value = -0.1671077 CV fold: Fold1 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) [ FAIL 0 | WARN 3 | SKIP 3 | PASS 34 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (3): 'test-binary.R:54:3', 'test-lints.R:10:5', 'test-multiclass.R:54:3' [ FAIL 0 | WARN 3 | SKIP 3 | PASS 34 ] > > proc.time() user system elapsed 116.84 5.42 139.25