Package evaluation to test DecisionTree on Julia 1.14.0-DEV.30 (073666df8b*) started at 2025-11-04T13:12:13.448 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.72s ################################################################################ # Installation # Installing DecisionTree... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [7806a523] + DecisionTree v0.12.4 Updating `~/.julia/environments/v1.14/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 v1.13.0 [a63ad114] + Mmap v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [4536629a] + OpenBLAS_jll v0.3.29+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Installation completed after 1.15s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled │ exception = │ UndefVarError: `project_rel_path` not defined in `TestEnv` │ Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value. │ Stacktrace: │ [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:75 │ [2] test_dir_has_project_file │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:52 [inlined] │ [3] maybe_gen_project_override! │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:83 [inlined] │ [4] activate(pkg::String; allow_reresolve::Bool) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:12 │ [5] activate(pkg::String) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:9 │ [6] top-level scope │ @ /PkgEval.jl/scripts/precompile.jl:24 │ [7] include(mod::Module, _path::String) │ @ Base ./Base.jl:309 │ [8] exec_options(opts::Base.JLOptions) │ @ Base ./client.jl:344 │ [9] _start() │ @ Base ./client.jl:577 └ @ Main /PkgEval.jl/scripts/precompile.jl:26 Precompiling package dependencies... Precompiling packages... 1983.3 ms ✓ DecisionTree 1 dependency successfully precompiled in 2 seconds. 5 already precompiled. Precompilation completed after 15.56s ################################################################################ # Testing # Testing DecisionTree Status `/tmp/jl_efk51y/Project.toml` [1520ce14] AbstractTrees v0.4.5 [7806a523] DecisionTree v0.12.4 [8bb1440f] DelimitedFiles v1.9.1 [6e75b9c4] ScikitLearnBase v0.5.0 [860ef19b] StableRNGs v1.0.3 [10745b16] Statistics v1.11.1 [37e2e46d] LinearAlgebra v1.13.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_efk51y/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7806a523] DecisionTree v0.12.4 [8bb1440f] DelimitedFiles v1.9.1 [6e75b9c4] ScikitLearnBase v0.5.0 [860ef19b] StableRNGs v1.0.3 [10745b16] Statistics v1.11.1 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v1.0.0 [9e88b42a] Serialization v1.11.0 [f489334b] StyledStrings v1.11.0 [8dfed614] Test v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [4536629a] OpenBLAS_jll v0.3.29+0 [8e850b90] libblastrampoline_jll v5.15.0+0 Testing Running tests... Julia version: 1.14.0-DEV.30 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: 0.8658658658658659 Mean Accuracy: 0.8528528528528528 Mean Accuracy: 0.8658658658658659 Mean Accuracy: 0.8658658658658659 Mean Accuracy: 0.8648648648648649 Mean Accuracy: 0.8648648648648649 Mean Accuracy: 0.8608608608608609 Mean Accuracy: 0.8608608608608609 Mean Accuracy: 0.8658658658658659 Mean Accuracy: 0.8658658658658659 Mean Accuracy: 0.8688688688688688 Mean Accuracy: 0.8688688688688688 ##### nfoldCV Classification Forest ##### Testing nfoldCV_forest Mean Accuracy: 0.908908908908909 Mean Accuracy: 0.8948948948948949 Mean Accuracy: 0.908908908908909 Mean Accuracy: 0.908908908908909 Mean Accuracy: 0.8948948948948949 Mean Accuracy: 0.8948948948948949 Mean Accuracy: 0.903903903903904 Mean Accuracy: 0.903903903903904 Mean Accuracy: 0.8868868868868868 Mean Accuracy: 0.8868868868868868 Mean Accuracy: 0.9029029029029029 Mean Accuracy: 0.9029029029029029 Fold 1 Classes: [-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 2 1 0 0 1 152 4 0 0 18 149 0 0 0 4 2 Accuracy: 0.9159159159159159 Kappa: 0.8386907027438497 Fold 2 Classes: [-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 10 4 0 0 0 144 14 0 0 11 146 0 0 0 3 1 Accuracy: 0.9039039039039038 Kappa: 0.8224946695095948 Fold 3 Classes: [-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 5 7 0 0 0 128 8 0 0 12 163 2 0 0 7 1 Accuracy: 0.8918918918918919 Kappa: 0.7978653447316506 Mean Accuracy: 0.903903903903904 ##### nfoldCV Adaboosted Stumps ##### Testing nfoldCV_stumps Mean Accuracy: 0.8818818818818818 Mean Accuracy: 0.8828828828828829 Mean Accuracy: 0.8818818818818818 Mean Accuracy: 0.8818818818818818 Mean Accuracy: 0.881881881881882 Mean Accuracy: 0.881881881881882 Mean Accuracy: 0.8908908908908909 Mean Accuracy: 0.8908908908908909 Mean Accuracy: 0.881881881881882 Mean Accuracy: 0.881881881881882 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}: 17 4 0 0 2 144 5 0 0 5 130 3 0 0 2 21 Accuracy: 0.9369369369369369 Kappa: 0.8970542772600804 Fold 2 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 15 4 0 0 2 134 5 0 0 4 146 0 0 0 1 22 Accuracy: 0.9519519519519519 Kappa: 0.920759094559624 Fold 3 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 18 5 0 0 0 132 7 0 0 3 146 1 0 0 1 20 Accuracy: 0.948948948948949 Kappa: 0.9161010166879093 Mean Accuracy: 0.9459459459459459 ##### nfoldCV Classification Forest ##### Fold 1 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 12 9 0 0 0 142 9 0 0 3 131 4 0 0 0 23 Accuracy: 0.924924924924925 Kappa: 0.8765606002194478 Fold 2 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 13 6 0 0 2 131 8 0 0 9 139 2 0 0 3 20 Accuracy: 0.9099099099099099 Kappa: 0.8509222228854534 Fold 3 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 18 5 0 0 3 131 5 0 0 10 136 4 0 0 2 19 Accuracy: 0.9129129129129129 Kappa: 0.8583581454700127 Mean Accuracy: 0.9159159159159159 ##### nfoldCV Adaboosted Stumps ##### Fold 1 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 0 21 0 0 0 141 10 0 0 3 135 0 0 0 23 0 Accuracy: 0.8288288288288288 Kappa: 0.697706641184902 Fold 2 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 0 19 0 0 0 135 6 0 0 1 142 7 0 0 23 0 Accuracy: 0.8318318318318318 Kappa: 0.7050440504246872 Fold 3 Classes: Int32[-2, -1, 0, 1] Matrix: 4×4 Matrix{Int64}: 0 23 0 0 0 133 6 0 0 13 137 0 0 0 21 0 Accuracy: 0.8108108108108109 Kappa: 0.6659288512373007 Mean Accuracy: 0.8238238238238238 ================================================== TEST: classification/heterogeneous.jl ================================================== TEST: classification/digits.jl digits.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/digits.jl:1 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] #20 @ ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:564 [inlined] [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#20#21"{StableRNGs.LehmerRNG, Root{Float32, Int64}, Vector{Int64}, Matrix{Float32}, var"#6#7", Float64, SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar @ ./array.jl:763 [inlined] [8] map @ ./abstractarray.jl:3390 [inlined] [9] permutation_importance(trees::Root{Float32, Int64}, labels::Vector{Int64}, features::Matrix{Float32}, score::var"#6#7", n_iter::Int64; rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:563 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::Root{Float32, Int64}, labels::Vector{Int64}, features::Matrix{Float32}, score::Function, n_iter::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 [11] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::Root{Float32, Int64}, labels::Vector{Int64}, features::Matrix{Float32}, score::Function) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 [12] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/digits.jl:2 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [14] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/digits.jl:38 [inlined] [15] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [16] IncludeInto @ ./Base.jl:311 [inlined] [17] run_tests(list::Vector{String}) @ Main ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:23 [18] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:67 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [20] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:67 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [22] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:62 [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [24] top-level scope @ none:6 [25] eval(m::Module, e::Any) @ Core ./boot.jl:489 [26] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [27] _start() @ Base ./client.jl:577 ================================================== 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 1 < 6.95 ? ├─ 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 20 0 0 1 9 Accuracy: 0.98 Kappa: 0.9685534591194969 Fold 2 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 17 0 0 0 15 4 0 0 14 Accuracy: 0.92 Kappa: 0.8805256869772999 Fold 3 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 13 0 0 0 10 1 0 2 24 Accuracy: 0.94 Kappa: 0.9031633311814072 Mean Accuracy: 0.9466666666666667 ##### nfoldCV Classification Forest ##### Fold 1 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 20 0 0 0 20 0 0 1 9 Accuracy: 0.98 Kappa: 0.9685534591194969 Fold 2 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 17 0 0 0 17 2 0 0 14 Accuracy: 0.96 Kappa: 0.9399038461538461 Fold 3 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 13 0 0 0 11 0 0 4 22 Accuracy: 0.92 Kappa: 0.874529485570891 Mean Accuracy: 0.9533333333333333 ##### nfoldCV Classification Adaboosted Stumps ##### Fold 1 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 20 0 0 0 20 0 0 0 10 Accuracy: 1.0 Kappa: 1.0 Fold 2 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 17 0 0 0 16 3 0 0 14 Accuracy: 0.94 Kappa: 0.9101258238466147 Fold 3 Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] Matrix: 3×3 Matrix{Int64}: 13 0 0 0 10 1 0 2 24 Accuracy: 0.94 Kappa: 0.9031633311814072 Mean Accuracy: 0.96 ================================================== TEST: classification/adult.jl adult.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/adult.jl:4 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{String, 1, Matrix{String}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] #20 @ ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:564 [inlined] [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#20#21"{StableRNGs.LehmerRNG, Ensemble{String, String}, Vector{String}, Matrix{String}, var"#12#13", Float64, SubArray{String, 1, Matrix{String}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar @ ./array.jl:763 [inlined] [8] map @ ./abstractarray.jl:3390 [inlined] [9] permutation_importance(trees::Ensemble{String, String}, labels::Vector{String}, features::Matrix{String}, score::var"#12#13", n_iter::Int64; rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:563 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::Ensemble{String, String}, labels::Vector{String}, features::Matrix{String}, score::Function, n_iter::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 [11] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::Ensemble{String, String}, labels::Vector{String}, features::Matrix{String}, score::Function) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 [12] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/adult.jl:5 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [14] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/adult.jl:22 [inlined] [15] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [16] IncludeInto @ ./Base.jl:311 [inlined] [17] run_tests(list::Vector{String}) @ Main ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:23 [18] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:67 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [20] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:67 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [22] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:62 [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [24] top-level scope @ none:6 [25] eval(m::Module, e::Any) @ Core ./boot.jl:489 [26] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [27] _start() @ Base ./client.jl:577 ================================================== TEST: classification/scikitlearn.jl scikitlearn.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:18 Test threw exception Expression: isapprox((permutation_importance(model, features, labels; rng = StableRNG(1))).mean, (permutation_importance(model.root, labels, features, ((model, y, X)->begin #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:24 =# accuracy(y, apply_tree(model, X)) end); rng = StableRNG(1))).mean) UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] (::DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, DecisionTreeClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}})(i::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:664 [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, DecisionTreeClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, DecisionTreeClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}) @ Base ./array.jl:763 [8] map(f::Function, A::UnitRange{Int64}) @ Base ./abstractarray.jl:3390 [9] permutation_importance(trees::DecisionTreeClassifier, X::Matrix{Float64}, y::Vector{Int64}; score::typeof(accuracy), n_iter::Int64, rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:663 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::DecisionTreeClassifier, X::Matrix{Float64}, y::Vector{Int64}) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:650 [11] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:2 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [13] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:18 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] scikitlearn.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:33 Test threw exception Expression: isapprox((permutation_importance(model, features, labels; rng = StableRNG(1))).mean, (permutation_importance(model.ensemble, labels, features, ((model, y, X)->begin #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:39 =# accuracy(y, apply_forest(model, X)) end); rng = StableRNG(1))).mean) UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] (::DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, RandomForestClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}})(i::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:664 [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, RandomForestClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, RandomForestClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}) @ Base ./array.jl:763 [8] map(f::Function, A::UnitRange{Int64}) @ Base ./abstractarray.jl:3390 [9] permutation_importance(trees::RandomForestClassifier, X::Matrix{Float64}, y::Vector{Int64}; score::typeof(accuracy), n_iter::Int64, rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:663 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::RandomForestClassifier, X::Matrix{Float64}, y::Vector{Int64}) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:650 [11] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:2 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [13] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:33 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] scikitlearn.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:49 Test threw exception Expression: isapprox((permutation_importance(model, features, labels; rng = StableRNG(1))).mean, (permutation_importance((model.ensemble, model.coeffs), labels, features, ((model, y, X)->begin #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:55 =# accuracy(y, apply_adaboost_stumps(model, X)) end); rng = StableRNG(1))).mean) UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] (::DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, AdaBoostStumpClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}})(i::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:664 [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, AdaBoostStumpClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(accuracy), StableRNGs.LehmerRNG, AdaBoostStumpClassifier, Matrix{Float64}, Vector{Int64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}) @ Base ./array.jl:763 [8] map(f::Function, A::UnitRange{Int64}) @ Base ./abstractarray.jl:3390 [9] permutation_importance(trees::AdaBoostStumpClassifier, X::Matrix{Float64}, y::Vector{Int64}; score::typeof(accuracy), n_iter::Int64, rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:663 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::AdaBoostStumpClassifier, X::Matrix{Float64}, y::Vector{Int64}) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:650 [11] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:2 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [13] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/classification/scikitlearn.jl:49 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] ================================================== TEST: classification/adding_trees.jl ================================================== TEST: regression/random.jl scikitlearn.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:12 Test threw exception Expression: isapprox((permutation_importance(model, features, labels; rng = StableRNG(1))).mean, (permutation_importance(model.root, labels, features, ((model, y, X)->begin #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:18 =# R2(y, apply_tree(model, X)) end); rng = StableRNG(1))).mean) UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] (::DecisionTree.var"#104#105"{typeof(R2), StableRNGs.LehmerRNG, DecisionTreeRegressor, Matrix{Float64}, Vector{Float64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}})(i::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:664 [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(R2), StableRNGs.LehmerRNG, DecisionTreeRegressor, Matrix{Float64}, Vector{Float64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(R2), StableRNGs.LehmerRNG, DecisionTreeRegressor, Matrix{Float64}, Vector{Float64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}) @ Base ./array.jl:763 [8] map(f::Function, A::UnitRange{Int64}) @ Base ./abstractarray.jl:3390 [9] permutation_importance(trees::DecisionTreeRegressor, X::Matrix{Float64}, y::Vector{Float64}; score::typeof(R2), n_iter::Int64, rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:663 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::DecisionTreeRegressor, X::Matrix{Float64}, y::Vector{Float64}) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:650 [11] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:2 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [13] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:12 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] scikitlearn.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:31 Test threw exception Expression: isapprox((permutation_importance(model, features, labels; rng = StableRNG(1))).mean, (permutation_importance(model.ensemble, labels, features, ((model, y, X)->begin #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:37 =# R2(y, apply_forest(model, X)) end); rng = StableRNG(1))).mean) UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] (::DecisionTree.var"#104#105"{typeof(R2), StableRNGs.LehmerRNG, RandomForestRegressor, Matrix{Float64}, Vector{Float64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}})(i::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:664 [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(R2), StableRNGs.LehmerRNG, RandomForestRegressor, Matrix{Float64}, Vector{Float64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#104#105"{typeof(R2), StableRNGs.LehmerRNG, RandomForestRegressor, Matrix{Float64}, Vector{Float64}, Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}) @ Base ./array.jl:763 [8] map(f::Function, A::UnitRange{Int64}) @ Base ./abstractarray.jl:3390 [9] permutation_importance(trees::RandomForestRegressor, X::Matrix{Float64}, y::Vector{Float64}; score::typeof(R2), n_iter::Int64, rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:663 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::RandomForestRegressor, X::Matrix{Float64}, y::Vector{Float64}) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/scikitlearnAPI.jl:650 [11] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:2 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [13] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/regression/random.jl:31 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] ================================================== TEST: regression/low_precision.jl ##### nfoldCV Regression Tree ##### Fold 1 Mean Squared Error: 1.622037913795833 Correlation Coeff: 0.907366065160043 Coeff of Determination: 0.8193274778185934 Fold 2 Mean Squared Error: 2.2368616530123875 Correlation Coeff: 0.8819312624799148 Coeff of Determination: 0.7775218386536554 Fold 3 Mean Squared Error: 2.050826130887033 Correlation Coeff: 0.8857393750992809 Coeff of Determination: 0.7833517062458076 Mean Coeff of Determination: 0.7934003409060187 ##### nfoldCV Regression Forest ##### Fold 1 Mean Squared Error: 1.0458165148792802 Correlation Coeff: 0.9539097203124147 Coeff of Determination: 0.8835105481350719 Fold 2 Mean Squared Error: 1.4775037803851656 Correlation Coeff: 0.9446290502480413 Coeff of Determination: 0.8530475391718182 Fold 3 Mean Squared Error: 1.3371112491146366 Correlation Coeff: 0.9453151296956102 Coeff of Determination: 0.8587482057511487 Mean Coeff of Determination: 0.865102097686013 ================================================== TEST: regression/digits.jl ##### 3 foldCV Regression Tree ##### Mean Coeff of Determination: 0.6407141156969016 ##### 3 foldCV Regression Forest ##### Mean Coeff of Determination: 0.6138356140370572 ================================================== 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: 3 < 2.45 ├─ ================================================== TEST: miscellaneous/feature_importance_test.jl feature_importance_test.jl: Error During Test at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/miscellaneous/feature_importance_test.jl:1 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle! @ ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [inlined] [4] #20 @ ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:564 [inlined] [5] iterate @ ./generator.jl:48 [inlined] [6] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, DecisionTree.var"#20#21"{StableRNGs.LehmerRNG, Root{Float64, Int64}, Vector{Int64}, Matrix{Float64}, var"#45#46", Float64, SubArray{Float64, 1, Matrix{Float64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [7] collect_similar @ ./array.jl:763 [inlined] [8] map @ ./abstractarray.jl:3390 [inlined] [9] permutation_importance(trees::Root{Float64, Int64}, labels::Vector{Int64}, features::Matrix{Float64}, score::var"#45#46", n_iter::Int64; rng::StableRNGs.LehmerRNG) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:563 [10] kwcall(::@NamedTuple{rng::StableRNGs.LehmerRNG}, ::typeof(permutation_importance), trees::Root{Float64, Int64}, labels::Vector{Int64}, features::Matrix{Float64}, score::Function, n_iter::Int64) @ DecisionTree ~/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 [11] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/miscellaneous/feature_importance_test.jl:2 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [13] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/miscellaneous/feature_importance_test.jl:68 [inlined] [14] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [15] IncludeInto @ ./Base.jl:311 [inlined] [16] run_tests(list::Vector{String}) @ Main ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:23 [17] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:67 [inlined] [18] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [19] macro expansion @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:67 [inlined] [20] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [21] top-level scope @ ~/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:62 [22] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [23] top-level scope @ none:6 [24] eval(m::Module, e::Any) @ Core ./boot.jl:489 [25] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [26] _start() @ Base ./client.jl:577 ================================================== TEST: miscellaneous/ensemble_methods.jl ================================================== Test Summary: | Pass Error Total Time Test Suites | 9580 8 9588 4m44.7s Classification | 133 5 138 3m35.7s random.jl | 57 57 59.0s low_precision.jl | 20 20 45.3s heterogeneous.jl | 4 4 8.8s digits.jl | 6 1 7 44.9s iris.jl | 29 29 14.9s adult.jl | 2 1 3 20.7s scikitlearn.jl | 11 3 14 15.5s adding models in an ensemble | 4 4 5.5s Regression | 41 2 43 42.3s scikitlearn.jl | 8 2 10 11.1s low_precision.jl | 16 16 19.7s digits.jl | 11 11 9.8s scikitlearn.jl | 6 6 1.5s Miscellaneous | 9406 1 9407 26.6s convert.jl | 9 9 1.1s convert to text | 0 0.5s abstract_trees_test.jl | 9372 9372 8.6s abstract_trees - test misuse | 1 1 1.1s feature_importance_test.jl | 12 1 13 8.9s methods for `Ensemble` type | 12 12 5.7s RNG of the outermost testset: Xoshiro(0x761db5083dcb76f7, 0x0cf6b51568ff75da, 0x786f8131c12d747d, 0xfccc11178dc6f707, 0xe0c3d1e61f1de027) ERROR: LoadError: Some tests did not pass: 9580 passed, 0 failed, 8 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/test/runtests.jl:61 Testing failed after 292.45s ERROR: LoadError: Package DecisionTree errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:2946 [3] test @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:2795 [inlined] [4] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:572 [5] kwcall(::@NamedTuple{julia_args::Cmd, io::IOContext{IO}}, ::typeof(Pkg.API.test), ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:548 [6] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172 [7] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkgs::Vector{PackageSpec}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:161 [8] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [9] test @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [inlined] [10] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkg::String) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [11] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:219 [12] include(mod::Module, _path::String) @ Base ./Base.jl:309 [13] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [14] _start() @ Base ./client.jl:577 in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 PkgEval failed after 337.15s: package tests unexpectedly errored