Package evaluation of DecisionTree on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T08:07:05.274 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.52s ################################################################################ # Installation # Installing DecisionTree... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [7806a523] + DecisionTree v0.12.4 Updating `~/.julia/environments/v1.11/Manifest.toml` [1520ce14] + AbstractTrees v0.4.5 [7806a523] + DecisionTree v0.12.4 [8bb1440f] + DelimitedFiles v1.9.1 [6e75b9c4] + ScikitLearnBase v0.5.0 [10745b16] + Statistics v1.11.1 [56f22d72] + Artifacts v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [a63ad114] + Mmap v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [4536629a] + OpenBLAS_jll v0.3.27+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 Installation completed after 1.66s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 13.89s ################################################################################ # Testing # Testing DecisionTree Status `/tmp/jl_VE9xs2/Project.toml` [1520ce14] AbstractTrees v0.4.5 [7806a523] DecisionTree v0.12.4 [8bb1440f] DelimitedFiles v1.9.1 [6e75b9c4] ScikitLearnBase v0.5.0 [860ef19b] StableRNGs v1.0.2 [10745b16] Statistics v1.11.1 [37e2e46d] LinearAlgebra v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_VE9xs2/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7806a523] DecisionTree v0.12.4 [8bb1440f] DelimitedFiles v1.9.1 [6e75b9c4] ScikitLearnBase v0.5.0 [860ef19b] StableRNGs v1.0.2 [10745b16] Statistics v1.11.1 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [4536629a] OpenBLAS_jll v0.3.27+1 [8e850b90] libblastrampoline_jll v5.11.0+0 Testing Running tests... Julia version: 1.11.4 TEST: classification/random.jl Feature 1 < 0.4006 ? ├─ Feature 2 < 0.5183 ? ├─ Feature 5 < 0.4131 ? ├─ 0 : 62/73 └─ -1 : 107/121 └─ Feature 5 < 0.7296 ? ├─ -1 : 133/140 └─ -1 : 27/54 └─ Feature 5 < 0.5163 ? ├─ Feature 2 < 0.6788 ? ├─ 0 : 204/223 └─ 0 : 70/91 └─ Feature 2 < 0.4592 ? ├─ 0 : 127/148 └─ -1 : 130/150 ##### nfoldCV Classification Tree ##### Testing nfoldCV_tree Mean Accuracy: 0.8648648648648649 Mean Accuracy: 0.8598598598598599 Mean Accuracy: 0.8648648648648649 Mean Accuracy: 0.8648648648648649 Mean Accuracy: 0.8668668668668668 Mean Accuracy: 0.8668668668668668 Mean Accuracy: 0.8568568568568568 Mean Accuracy: 0.8568568568568568 Mean Accuracy: 0.8678678678678678 Mean Accuracy: 0.8678678678678678 Mean Accuracy: 0.8688688688688688 Mean Accuracy: 0.8688688688688688 ##### nfoldCV Classification Forest ##### Testing nfoldCV_forest Mean Accuracy: 0.8998998998998999 Mean Accuracy: 0.9019019019019018 Mean Accuracy: 0.8998998998998999 Mean Accuracy: 0.8998998998998999 Mean Accuracy: 0.9039039039039038 Mean Accuracy: 0.9039039039039038 Mean Accuracy: 0.908908908908909 Mean Accuracy: 0.908908908908909 Mean Accuracy: 0.8988988988988988 Mean Accuracy: 0.8988988988988988 Mean Accuracy: 0.9009009009009009 Mean Accuracy: 0.9009009009009009 Fold 1 Classes: [-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 5 4 0 0 1 134 9 0 0 9 160 0 0 0 11 0 Accuracy: 0.8978978978978979 Kappa: 0.8089017165426098 Fold 2 Classes: [-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 3 2 0 0 1 146 11 0 0 7 160 0 0 0 2 1 Accuracy: 0.9309309309309309 Kappa: 0.8667652431068974 Fold 3 Classes: [-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 3 12 0 0 0 131 18 0 0 3 161 1 0 0 3 1 Accuracy: 0.8888888888888888 Kappa: 0.7913533834586465 Mean Accuracy: 0.9059059059059059 ##### nfoldCV Adaboosted Stumps ##### Testing nfoldCV_stumps Mean Accuracy: 0.8818818818818818 Mean Accuracy: 0.8828828828828829 Mean Accuracy: 0.8818818818818818 Mean Accuracy: 0.8818818818818818 Mean Accuracy: 0.9009009009009009 Mean Accuracy: 0.9009009009009009 Mean Accuracy: 0.8908908908908909 Mean Accuracy: 0.8908908908908909 Mean Accuracy: 0.8898898898898899 Mean Accuracy: 0.8898898898898899 Mean Accuracy: 0.883883883883884 Mean Accuracy: 0.883883883883884 ================================================== TEST: classification/low_precision.jl ##### nfoldCV Classification Tree ##### Fold 1 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 23 0 0 0 2 136 1 0 0 7 144 1 0 0 1 18 Accuracy: 0.963963963963964 Kappa: 0.9411348771433623 Fold 2 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 19 4 0 0 1 149 6 0 0 8 126 0 0 0 3 17 Accuracy: 0.933933933933934 Kappa: 0.8904654396483414 Fold 3 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 17 0 0 0 0 134 3 0 0 0 151 0 0 0 0 28 Accuracy: 0.990990990990991 Kappa: 0.9853527652337104 Mean Accuracy: 0.9629629629629629 ##### nfoldCV Classification Forest ##### Fold 1 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 12 11 0 0 0 133 6 0 0 4 148 0 0 0 7 12 Accuracy: 0.9159159159159159 Kappa: 0.8573024594052737 Fold 2 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 14 9 0 0 0 140 16 0 0 4 128 2 0 0 3 17 Accuracy: 0.8978978978978979 Kappa: 0.8300535867068942 Fold 3 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 14 3 0 0 0 132 5 0 0 2 145 4 0 0 4 24 Accuracy: 0.9459459459459459 Kappa: 0.911650256470727 Mean Accuracy: 0.91991991991992 ##### nfoldCV Adaboosted Stumps ##### Fold 1 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 0 23 0 0 0 137 2 0 0 5 147 0 0 0 19 0 Accuracy: 0.8528528528528528 Kappa: 0.7385850235508987 Fold 2 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 0 23 0 0 0 143 13 0 0 5 129 0 0 0 20 0 Accuracy: 0.8168168168168168 Kappa: 0.6750179985601152 Fold 3 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 0 17 0 0 0 135 2 0 0 4 147 0 0 0 28 0 Accuracy: 0.8468468468468469 Kappa: 0.7295269947443861 Mean Accuracy: 0.8388388388388389 ================================================== TEST: classification/heterogeneous.jl ================================================== TEST: classification/digits.jl ================================================== TEST: classification/iris.jl Feature 3 < 2.45 ? ├─ Iris-setosa : 50/50 └─ Feature 4 < 1.75 ? ├─ Feature 3 < 4.95 ? ├─ Feature 4 < 1.65 ? ├─ Iris-versicolor : 47/47 └─ Iris-virginica : 1/1 └─ Feature 4 < 1.55 ? ├─ Iris-virginica : 3/3 └─ Feature 3 < 5.45 ? ├─ Iris-versicolor : 2/2 └─ Iris-virginica : 1/1 └─ Feature 3 < 4.85 ? ├─ Feature 2 < 3.1 ? ├─ Iris-virginica : 2/2 └─ Iris-versicolor : 1/1 └─ Iris-virginica : 43/43 ##### nfoldCV Classification Tree ##### Fold 1 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 20 0 0 0 18 3 0 0 9 Accuracy: 0.94 Kappa: 0.9070631970260222 Fold 2 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 15 0 0 0 15 1 0 1 18 Accuracy: 0.96 Kappa: 0.9396863691194209 Fold 3 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 15 0 0 0 13 0 0 6 16 Accuracy: 0.88 Kappa: 0.8210023866348449 Mean Accuracy: 0.9266666666666666 ##### nfoldCV Classification Forest ##### Fold 1 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 20 0 0 0 19 2 0 0 9 Accuracy: 0.96 Kappa: 0.9375780274656679 Fold 2 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 15 0 0 0 15 1 0 3 16 Accuracy: 0.92 Kappa: 0.8798076923076925 Fold 3 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 15 0 0 0 13 0 0 6 16 Accuracy: 0.88 Kappa: 0.8210023866348449 Mean Accuracy: 0.9199999999999999 ##### nfoldCV Classification Adaboosted Stumps ##### Fold 1 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 20 0 0 0 19 2 0 0 9 Accuracy: 0.96 Kappa: 0.9375780274656679 Fold 2 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 15 0 0 0 14 2 0 2 17 Accuracy: 0.92 Kappa: 0.879372738238842 Fold 3 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 15 0 0 0 13 0 0 6 16 Accuracy: 0.88 Kappa: 0.8210023866348449 Mean Accuracy: 0.9199999999999999 ================================================== TEST: classification/adult.jl ##### 3 foldCV Classification Tree ##### Mean Accuracy: 0.8109892809975735 ##### 3 foldCV Classification Forest ##### Mean Accuracy: 0.8444669676587119 ##### nfoldCV Classification Adaboosted Stumps ##### Mean Accuracy: 0.8350686446143923 ================================================== TEST: classification/scikitlearn.jl ================================================== TEST: classification/adding_trees.jl ================================================== TEST: regression/random.jl ================================================== TEST: regression/low_precision.jl ##### nfoldCV Regression Tree ##### Fold 1 Mean Squared Error: 1.90132078391519 Correlation Coeff: 0.8974037382149185 Coeff of Determination: 0.8045191633321014 Fold 2 Mean Squared Error: 1.6017569471454187 Correlation Coeff: 0.9157999659106489 Coeff of Determination: 0.8384365213936864 Fold 3 Mean Squared Error: 1.5986344693657724 Correlation Coeff: 0.9123272177000765 Coeff of Determination: 0.8214814920817384 Mean Coeff of Determination: 0.821479058935842 ##### nfoldCV Regression Forest ##### Fold 1 Mean Squared Error: 1.2353634596621743 Correlation Coeff: 0.9467692443993198 Coeff of Determination: 0.8729883538187402 Fold 2 Mean Squared Error: 1.3297177364601998 Correlation Coeff: 0.9564527935419953 Coeff of Determination: 0.8658761409152053 Fold 3 Mean Squared Error: 1.1170134745442588 Correlation Coeff: 0.950751446536586 Coeff of Determination: 0.8752638063163086 Mean Coeff of Determination: 0.8713761003500847 ================================================== TEST: regression/digits.jl ##### 3 foldCV Regression Tree ##### Mean Coeff of Determination: 0.6349826429860214 ##### 3 foldCV Regression Forest ##### Mean Coeff of Determination: 0.6324059967649163 ================================================== TEST: regression/scikitlearn.jl ================================================== TEST: miscellaneous/convert.jl ================================================== TEST: miscellaneous/abstract_trees_test.jl [ Info: Test base functionality [ Info: -- Tree with feature names and class labels firstFt < 0.7 ├─ a (2/3) └─ secondFt < 0.5 ├─ b (2/3) └─ c (2/3) [ Info: -- Tree with feature names firstFt < 0.7 ├─ Class: 1 (2/3) └─ secondFt < 0.5 ├─ Class: 2 (2/3) └─ Class: 3 (2/3) [ Info: -- Tree with class labels Feature: 1 < 0.7 ├─ a (2/3) └─ Feature: 2 < 0.5 ├─ b (2/3) └─ c (2/3) [ Info: -- Tree with ids only (nonsense parameters) Feature: 1 < 0.7 ├─ Class: 1 (2/3) └─ Feature: 2 < 0.5 ├─ Class: 2 (2/3) └─ Class: 3 (2/3) [ Info: -- Tree with ids only Feature: 1 < 0.7 ├─ Class: 1 (2/3) └─ Feature: 2 < 0.5 ├─ Class: 2 (2/3) └─ Class: 3 (2/3) [ Info: Test `children` with 'adult' decision tree [ Info: -- Preparing test data [ Info: -- Test `children` [ Info: Test misuse of `classlabel` information [ Info: Create test data - a decision tree based on the iris data set [ Info: Try to replace the exisitng class labels Feature: 4 < 0.8 ├─ ================================================== TEST: miscellaneous/feature_importance_test.jl ================================================== TEST: miscellaneous/ensemble_methods.jl ================================================== Test Summary: | Pass Total Time Test Suites | 9612 9612 6m05.5s Testing DecisionTree tests passed Testing completed after 369.98s PkgEval succeeded after 400.65s