Package evaluation to load DecisionTree on Julia 1.14.0-DEV.2226 (797a5ef2b0*) started at 2026-05-23T18:28:31.931 ################################################################################ # Set-up # Set-up completed after 0.13s ################################################################################ # Installation # Installing DecisionTree... Resolving package versions... Installed Statistics ────── v1.11.1 Installed AbstractTrees ─── v0.4.5 Installed DelimitedFiles ── v1.9.1 Installed ScikitLearnBase ─ v0.5.0 Installed DecisionTree ──── v0.12.4 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 v1.13.0 [e66e0078] + CompilerSupportLibraries_jll v1.5.1+0 [4536629a] + OpenBLAS_jll v0.3.33+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Installation completed after 5.8s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Project No packages added to or removed from `~/.julia/environments/pkgeval/Project.toml` Manifest No packages added to or removed from `~/.julia/environments/pkgeval/Manifest.toml` Precompiling package dependencies... Precompiling project... 84.9 s ✓ AbstractTrees 1.0 s ✓ Statistics 0.9 s ✓ DelimitedFiles 0.8 s ✓ ScikitLearnBase ┌ Info: JuliaLowering threw given input: │ code = │ :(#= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 =# Core.@doc " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" function permutation_importance(trees::U, labels::AbstractVector{T}, features::AbstractVecOrMat{S}, score::Function, n_iter::Int = 3; rng = Random.GLOBAL_RNG) where {S, T, U <: Union{<:Ensemble{S, T}, <:Root{S, T}, <:DecisionTree.LeafOrNode{S, T}, Tuple{<:Ensemble{S, T}, AbstractVector{Float64}}}} │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 =# │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:558 =# │ base = score(trees, labels, features) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:559 =# │ scores = Matrix{Float64}(undef, size(features, 2), n_iter) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:560 =# │ rng = mk_rng(rng)::Random.AbstractRNG │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:561 =# │ for (i, col) = enumerate(eachcol(features)) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:562 =# │ origin = copy(col) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:563 =# │ scores[i, :] = map(1:n_iter) do i │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:564 =# │ shuffle!(rng, col) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:565 =# │ base - score(trees, labels, features) │ end │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:567 =# │ features[:, i] = origin │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:568 =# │ end │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:570 =# │ (mean = reshape(mapslices(scores; dims = 2) do im │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:573 =# │ mean(im) │ end, :), std = reshape(mapslices(scores; dims = 2) do im │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:579 =# │ std(im) │ end, :), scores = scores) │ end) │ st0 = │ SyntaxTree with attributes mod,kind,var_id,toplevel_pure,scope_type,macro_source,name_val,syntax_flags,meta,scope_layer,value,jl_source,is_toplevel_thunk,source,__macro_ctx__ │ [macrocall] │ │ @doc :: Identifier │ mod │ :(#= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 =#) :: Value │ │ " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" :: Value │ │ [function] │ │ [where] │ │ [call] │ │ permutation_importance :: Identifier │ │ [parameters] │ │ [kw] │ │ rng :: Identifier │ │ [.] │ │ Random :: Identifier │ │ [inert] │ │ GLOBAL_RNG :: Identifier │ │ [::] │ │ trees :: Identifier │ │ U :: Identifier │ │ [::] │ │ labels :: Identifier │ │ [curly] │ │ AbstractVector :: Identifier │ │ T :: Identifier │ │ [::] │ │ features :: Identifier │ │ [curly] │ │ AbstractVecOrMat :: Identifier │ │ S :: Identifier │ │ [::] │ │ score :: Identifier │ │ Function :: Identifier │ │ [kw] │ │ [::] │ │ n_iter :: Identifier │ │ Int :: Identifier │ │ 3 :: Value │ │ S :: Identifier │ │ T :: Identifier │ │ [<:] │ │ U :: Identifier │ │ [curly] │ │ Union :: Identifier │ │ [<:] │ │ [curly] │ │ Ensemble :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [<:] │ │ [curly] │ │ Root :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [<:] │ │ [curly] │ │ [.] │ │ DecisionTree :: Identifier │ │ [inert] │ │ LeafOrNode :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [curly] │ │ Tuple :: Identifier │ │ [<:] │ │ [curly] │ │ Ensemble :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [curly] │ │ AbstractVector :: Identifier │ │ Float64 :: Identifier │ │ [block] │ │ [=] │ │ base :: Identifier │ │ [call] │ │ score :: Identifier │ │ trees :: Identifier │ │ labels :: Identifier │ │ features :: Identifier │ │ [=] │ │ scores :: Identifier │ │ [call] │ │ [curly] │ │ Matrix :: Identifier │ │ Float64 :: Identifier │ │ undef :: Identifier │ │ [call] │ │ size :: Identifier │ │ features :: Identifier │ │ 2 :: Value │ │ n_iter :: Identifier │ │ [=] │ │ rng :: Identifier │ │ [::] │ │ [call] │ │ mk_rng :: Identifier │ │ rng :: Identifier │ │ [.] │ │ Random :: Identifier │ │ [inert] │ │ AbstractRNG :: Identifier │ │ [for] │ │ [=] │ │ [tuple] │ │ i :: Identifier │ │ col :: Identifier │ │ [call] │ │ enumerate :: Identifier │ │ [call] │ │ eachcol :: Identifier │ │ features :: Identifier │ │ [block] │ │ [=] │ │ origin :: Identifier │ │ [call] │ │ copy :: Identifier │ │ col :: Identifier │ │ [=] │ │ [ref] │ │ scores :: Identifier │ │ i :: Identifier │ │ : :: Identifier │ │ [do] │ │ [call] │ │ map :: Identifier │ │ [call] │ │ : :: Identifier │ │ 1 :: Value │ │ n_iter :: Identifier │ │ [->] │ │ [tuple] │ │ i :: Identifier │ │ [block] │ │ [call] │ │ shuffle! :: Identifier │ │ rng :: Identifier │ │ col :: Identifier │ │ [call] │ │ - :: Identifier │ │ base :: Identifier │ │ [call] │ │ score :: Identifier │ │ trees :: Identifier │ │ labels :: Identifier │ │ features :: Identifier │ │ [=] │ │ [ref] │ │ features :: Identifier │ │ : :: Identifier │ │ i :: Identifier │ │ origin :: Identifier │ │ [tuple] │ │ [=] │ │ mean :: Identifier │ │ [call] │ │ reshape :: Identifier │ │ [do] │ │ [call] │ │ mapslices :: Identifier │ │ [parameters] │ │ [kw] │ │ dims :: Identifier │ │ 2 :: Value │ │ scores :: Identifier │ │ [->] │ │ [tuple] │ │ im :: Identifier │ │ [block] │ │ [call] │ │ mean :: Identifier │ │ im :: Identifier │ │ : :: Identifier │ │ [=] │ │ std :: Identifier │ │ [call] │ │ reshape :: Identifier │ │ [do] │ │ [call] │ │ mapslices :: Identifier │ │ [parameters] │ │ [kw] │ │ dims :: Identifier │ │ 2 :: Value │ │ scores :: Identifier │ │ [->] │ │ [tuple] │ │ im :: Identifier │ │ [block] │ │ [call] │ │ std :: Identifier │ │ im :: Identifier │ │ : :: Identifier │ │ [=] │ │ scores :: Identifier │ │ scores :: Identifier │ │ │ st1 = │ SyntaxTree with attributes mod,kind,var_id,toplevel_pure,scope_type,macro_source,name_val,syntax_flags,meta,scope_layer,value,jl_source,is_toplevel_thunk,source │ [block] │ │ [=] │ │ val :: Identifier │ scope_layer=3 │ [function] │ │ [where] │ │ [call] │ │ permutation_importance :: Identifier │ scope_layer=1 │ [parameters] │ │ [kw] │ │ rng :: Identifier │ scope_layer=1 │ [.] │ │ Random :: Identifier │ scope_layer=1 │ [inert] │ │ GLOBAL_RNG :: Identifier │ │ [::] │ │ trees :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [::] │ │ labels :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [::] │ │ features :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVecOrMat :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ [::] │ │ score :: Identifier │ scope_layer=1 │ Function :: Identifier │ scope_layer=1 │ [kw] │ │ [::] │ │ n_iter :: Identifier │ scope_layer=1 │ Int :: Identifier │ scope_layer=1 │ 3 :: Value │ macro_source=194 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ Union :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Root :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ [.] │ │ DecisionTree :: Identifier │ scope_layer=1 │ [inert] │ │ LeafOrNode :: Identifier │ │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ Float64 :: Identifier │ scope_layer=1 │ [block] │ │ [=] │ │ base :: Identifier │ scope_layer=1 │ [call] │ │ score :: Identifier │ scope_layer=1 │ trees :: Identifier │ scope_layer=1 │ labels :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ [=] │ │ scores :: Identifier │ scope_layer=1 │ [call] │ │ [curly] │ │ Matrix :: Identifier │ scope_layer=1 │ Float64 :: Identifier │ scope_layer=1 │ undef :: Identifier │ scope_layer=1 │ [call] │ │ size :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ 2 :: Value │ macro_source=194 │ n_iter :: Identifier │ scope_layer=1 │ [=] │ │ rng :: Identifier │ scope_layer=1 │ [::] │ │ [call] │ │ mk_rng :: Identifier │ scope_layer=1 │ rng :: Identifier │ scope_layer=1 │ [.] │ │ Random :: Identifier │ scope_layer=1 │ [inert] │ │ AbstractRNG :: Identifier │ │ [for] │ │ [=] │ │ [tuple] │ │ i :: Identifier │ scope_layer=1 │ col :: Identifier │ scope_layer=1 │ [call] │ │ enumerate :: Identifier │ scope_layer=1 │ [call] │ │ eachcol :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ [block] │ │ [=] │ │ origin :: Identifier │ scope_layer=1 │ [call] │ │ copy :: Identifier │ scope_layer=1 │ col :: Identifier │ scope_layer=1 │ [=] │ │ [ref] │ │ scores :: Identifier │ scope_layer=1 │ i :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ [do] │ │ [call] │ │ map :: Identifier │ scope_layer=1 │ [call] │ │ : :: Identifier │ scope_layer=1 │ 1 :: Value │ macro_source=194 │ n_iter :: Identifier │ scope_layer=1 │ [->] │ │ [tuple] │ │ i :: Identifier │ scope_layer=1 │ [block] │ │ [call] │ │ shuffle! :: Identifier │ scope_layer=1 │ rng :: Identifier │ scope_layer=1 │ col :: Identifier │ scope_layer=1 │ [call] │ │ - :: Identifier │ scope_layer=1 │ base :: Identifier │ scope_layer=1 │ [call] │ │ score :: Identifier │ scope_layer=1 │ trees :: Identifier │ scope_layer=1 │ labels :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ [=] │ │ [ref] │ │ features :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ i :: Identifier │ scope_layer=1 │ origin :: Identifier │ scope_layer=1 │ [tuple] │ │ [=] │ │ mean :: Identifier │ scope_layer=1 │ [call] │ │ reshape :: Identifier │ scope_layer=1 │ [do] │ │ [call] │ │ mapslices :: Identifier │ scope_layer=1 │ [parameters] │ │ [kw] │ │ dims :: Identifier │ scope_layer=1 │ 2 :: Value │ macro_source=194 │ scores :: Identifier │ scope_layer=1 │ [->] │ │ [tuple] │ │ im :: Identifier │ scope_layer=1 │ [block] │ │ [call] │ │ mean :: Identifier │ scope_layer=1 │ im :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ [=] │ │ std :: Identifier │ scope_layer=1 │ [call] │ │ reshape :: Identifier │ scope_layer=1 │ [do] │ │ [call] │ │ mapslices :: Identifier │ scope_layer=1 │ [parameters] │ │ [kw] │ │ dims :: Identifier │ scope_layer=1 │ 2 :: Value │ macro_source=194 │ scores :: Identifier │ scope_layer=1 │ [->] │ │ [tuple] │ │ im :: Identifier │ scope_layer=1 │ [block] │ │ [call] │ │ std :: Identifier │ scope_layer=1 │ im :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ [=] │ │ scores :: Identifier │ scope_layer=1 │ scores :: Identifier │ scope_layer=1 │ [call] │ │ Base.Docs.doc! :: Value │ macro_source=194 │ DecisionTree :: Value │ macro_source=194 │ [call] │ │ Base.Docs.Binding :: Value │ macro_source=194 │ DecisionTree :: Value │ │ [inert] │ jl_source=L65 │ permutation_importance :: Identifier │ │ [call] │ macro_source=194 │ Base.Docs.docstr :: Value │ macro_source=194 │ [call] │ macro_source=194 │ Core.svec :: Value │ macro_source=194 │ " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" :: Value │ macro_source=194 │ [call] │ macro_source=194 │ Dict{Symbol, Any} :: Value │ macro_source=194 │ :path => "/home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl" :: Value │ macro_source=194 │ :linenumber => 514 :: Value │ macro_source=194 │ :module => DecisionTree :: Value │ macro_source=194 │ [where] │ │ [where] │ │ [where] │ │ [curly] │ │ Union :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVecOrMat :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ Function :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVecOrMat :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ Function :: Identifier │ scope_layer=1 │ Int :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ Union :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Root :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ [.] │ │ DecisionTree :: Identifier │ scope_layer=1 │ [inert] │ │ LeafOrNode :: Identifier │ │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ Float64 :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ Any :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ Any :: Identifier │ scope_layer=1 │ val :: Identifier │ scope_layer=3 │ │ file = "/home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl" │ line = 514 └ mod = DecisionTree ERROR: LoadError: internal lowering bug: #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 =# - `jl_assert(!(haskey(ssa_rewrites, lhs_id)), _)`: multiple assignments to ssavalue Expression:  (= #₂ (call core.TypeVar :#T1 #₃₀₀)) Containing expressions:  (= #₂ (call core.TypeVar :#T1 #₃₀₀))  Detailed provenance:  (= #₂ (call core.TypeVar :#T1 #₃₀₀)) @#= /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:366 =#  └─ (= #₂ (call core.TypeVar :#T1 (call core.apply_type #₄₂/Ensemble #₉₃/S #₉₄/T))) @#= /source/usr/share/julia/JuliaLowering/src/closure_conversion.jl:197 =#  └─ (= #₂ (call core.TypeVar :#T1 (call core.apply_type #₄₂/Ensemble #₉₃/S #₉₄/T))) @#= /source/usr/share/julia/JuliaLowering/src/desugaring.jl:231 =#  └─ (= #₂ (call core.TypeVar :#T1 (call core.apply_type Ensemble S T))) @#= /source/usr/share/julia/JuliaLowering/src/desugaring.jl:231 =#  └─ (= #₂ (call core.TypeVar :#T1 (curly Ensemble S T)))  └─ (call core.TypeVar :#T1 (curly Ensemble S T)) @#= /source/usr/share/julia/JuliaLowering/src/desugaring.jl:3025 =#  └─ (<: (curly Ensemble S T))  └─ (<: (curly Ensemble S T))  └─ (<: (curly Ensemble S T))  └─ (<: (curly Ensemble S T))  ├─ @ /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541  └─ (macrocall @doc :(#= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 =#) " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" (function (where (call permutation_importance (parameters (kw rng (. Random (inert GLOBAL_RNG)))) (:: trees U) (:: labels (curly AbstractVector T)) (:: features (curly AbstractVecOrMat S)) (:: score Function) (kw (:: n_iter Int) 3)) S T (<: U (curly Union (<: (curly Ensemble S T)) (<: (curly Root S T)) (<: (curly (. DecisionTree (inert LeafOrNode)) S T)) (curly Tuple (<: (curly Ensemble S T)) (curly AbstractVector Float64))))) (block (= base (call score trees labels features)) (= scores (call (curly Matrix Float64) undef (call size features 2) n_iter)) (= rng (:: (call mk_rng rng) (. Random (inert AbstractRNG)))) (for (= (tuple i col) (call enumerate (call eachcol features))) (block (= origin (call copy col)) (= (ref scores i :) (do (call map (call : 1 n_iter)) (-> (tuple i) (block (call shuffle! rng col) (call - base (call score trees labels features)))))) (= (ref features : i) origin))) (tuple (= mean (call reshape (do (call mapslices (parameters (kw dims 2)) scores) (-> (tuple im) (block (call mean im)))) :)) (= std (call reshape (do (call mapslices (parameters (kw dims 2)) scores) (-> (tuple im) (block (call std im)))) :)) (= scores scores)))))  └─ @ /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514  Stacktrace:  [1] iterate(A::Base.JuliaSyntax.SyntaxList{Dict{Symbol, Dict{Int64, Any}}, Vector{Int64}})  @ Base /source/usr/share/julia/JuliaLowering/src/ast.jl:23 [inlined]  [2] renumber_body(ctx::Base.JuliaLowering.LinearIRContext{Dict{Symbol, Dict{Int64, Any}}}, input_code::Base.JuliaSyntax.SyntaxList{Dict{Symbol, Dict{Int64, Any}}, Vector{Int64}}, slot_rewrites::Dict{Int64, Int64})  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:1106  [3] compile_lambda(outer_ctx::Base.JuliaLowering.LinearIRContext{Dict{Symbol, Dict{Int64, Any}}}, ex::Base.JuliaSyntax.SyntaxTree{Dict{Symbol, Dict{Int64, Any}}})  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:1223  [4] linearize_ir(ctx::Base.JuliaLowering.ClosureConversionCtx{Dict{Symbol, Dict{Int64, Any}}}, ex::Base.JuliaSyntax.SyntaxTree{Dict{Symbol, Dict{Int64, Any}}})  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:1253  [5] core_lowering_hook(code::Any, mod::Module, file::String, line::UInt64, world::UInt64, _warn::Bool)  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/hooks.jl:33  [6] include(mapexpr::Function, mod::Module, _path::String)  @ Base ./Base.jl:327  [7] top-level scope  @ ~/.julia/packages/DecisionTree/0Dw1P/src/DecisionTree.jl:164  [8] include(mod::Module, _path::String)  @ Base ./Base.jl:326  [9] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3271  [10] top-level scope  @ stdin:5  [11] eval(m::Module, e::Any)  @ Core ./boot.jl:517  [12] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3113  [13] push!(a::Vector{SubString{String}}, item::String)  @ Base ./loading.jl:3123 [inlined]  [14] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:353  [15] _start()  @ Base ./client.jl:596 in expression starting at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 in expression starting at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/DecisionTree.jl:1 in expression starting at stdin:5 ✗ DecisionTree 4 dependencies successfully precompiled in 114 seconds. 1 already precompiled. Precompilation completed after 133.76s ################################################################################ # Loading # Loading DecisionTree... ┌ Info: JuliaLowering threw given input: │ code = │ :(#= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 =# Core.@doc " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" function permutation_importance(trees::U, labels::AbstractVector{T}, features::AbstractVecOrMat{S}, score::Function, n_iter::Int = 3; rng = Random.GLOBAL_RNG) where {S, T, U <: Union{<:Ensemble{S, T}, <:Root{S, T}, <:DecisionTree.LeafOrNode{S, T}, Tuple{<:Ensemble{S, T}, AbstractVector{Float64}}}} │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 =# │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:558 =# │ base = score(trees, labels, features) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:559 =# │ scores = Matrix{Float64}(undef, size(features, 2), n_iter) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:560 =# │ rng = mk_rng(rng)::Random.AbstractRNG │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:561 =# │ for (i, col) = enumerate(eachcol(features)) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:562 =# │ origin = copy(col) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:563 =# │ scores[i, :] = map(1:n_iter) do i │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:564 =# │ shuffle!(rng, col) │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:565 =# │ base - score(trees, labels, features) │ end │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:567 =# │ features[:, i] = origin │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:568 =# │ end │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:570 =# │ (mean = reshape(mapslices(scores; dims = 2) do im │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:573 =# │ mean(im) │ end, :), std = reshape(mapslices(scores; dims = 2) do im │ #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:579 =# │ std(im) │ end, :), scores = scores) │ end) │ st0 = │ SyntaxTree with attributes mod,kind,var_id,toplevel_pure,scope_type,macro_source,name_val,syntax_flags,meta,scope_layer,value,jl_source,is_toplevel_thunk,source,__macro_ctx__ │ [macrocall] │ │ @doc :: Identifier │ mod │ :(#= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 =#) :: Value │ │ " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" :: Value │ │ [function] │ │ [where] │ │ [call] │ │ permutation_importance :: Identifier │ │ [parameters] │ │ [kw] │ │ rng :: Identifier │ │ [.] │ │ Random :: Identifier │ │ [inert] │ │ GLOBAL_RNG :: Identifier │ │ [::] │ │ trees :: Identifier │ │ U :: Identifier │ │ [::] │ │ labels :: Identifier │ │ [curly] │ │ AbstractVector :: Identifier │ │ T :: Identifier │ │ [::] │ │ features :: Identifier │ │ [curly] │ │ AbstractVecOrMat :: Identifier │ │ S :: Identifier │ │ [::] │ │ score :: Identifier │ │ Function :: Identifier │ │ [kw] │ │ [::] │ │ n_iter :: Identifier │ │ Int :: Identifier │ │ 3 :: Value │ │ S :: Identifier │ │ T :: Identifier │ │ [<:] │ │ U :: Identifier │ │ [curly] │ │ Union :: Identifier │ │ [<:] │ │ [curly] │ │ Ensemble :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [<:] │ │ [curly] │ │ Root :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [<:] │ │ [curly] │ │ [.] │ │ DecisionTree :: Identifier │ │ [inert] │ │ LeafOrNode :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [curly] │ │ Tuple :: Identifier │ │ [<:] │ │ [curly] │ │ Ensemble :: Identifier │ │ S :: Identifier │ │ T :: Identifier │ │ [curly] │ │ AbstractVector :: Identifier │ │ Float64 :: Identifier │ │ [block] │ │ [=] │ │ base :: Identifier │ │ [call] │ │ score :: Identifier │ │ trees :: Identifier │ │ labels :: Identifier │ │ features :: Identifier │ │ [=] │ │ scores :: Identifier │ │ [call] │ │ [curly] │ │ Matrix :: Identifier │ │ Float64 :: Identifier │ │ undef :: Identifier │ │ [call] │ │ size :: Identifier │ │ features :: Identifier │ │ 2 :: Value │ │ n_iter :: Identifier │ │ [=] │ │ rng :: Identifier │ │ [::] │ │ [call] │ │ mk_rng :: Identifier │ │ rng :: Identifier │ │ [.] │ │ Random :: Identifier │ │ [inert] │ │ AbstractRNG :: Identifier │ │ [for] │ │ [=] │ │ [tuple] │ │ i :: Identifier │ │ col :: Identifier │ │ [call] │ │ enumerate :: Identifier │ │ [call] │ │ eachcol :: Identifier │ │ features :: Identifier │ │ [block] │ │ [=] │ │ origin :: Identifier │ │ [call] │ │ copy :: Identifier │ │ col :: Identifier │ │ [=] │ │ [ref] │ │ scores :: Identifier │ │ i :: Identifier │ │ : :: Identifier │ │ [do] │ │ [call] │ │ map :: Identifier │ │ [call] │ │ : :: Identifier │ │ 1 :: Value │ │ n_iter :: Identifier │ │ [->] │ │ [tuple] │ │ i :: Identifier │ │ [block] │ │ [call] │ │ shuffle! :: Identifier │ │ rng :: Identifier │ │ col :: Identifier │ │ [call] │ │ - :: Identifier │ │ base :: Identifier │ │ [call] │ │ score :: Identifier │ │ trees :: Identifier │ │ labels :: Identifier │ │ features :: Identifier │ │ [=] │ │ [ref] │ │ features :: Identifier │ │ : :: Identifier │ │ i :: Identifier │ │ origin :: Identifier │ │ [tuple] │ │ [=] │ │ mean :: Identifier │ │ [call] │ │ reshape :: Identifier │ │ [do] │ │ [call] │ │ mapslices :: Identifier │ │ [parameters] │ │ [kw] │ │ dims :: Identifier │ │ 2 :: Value │ │ scores :: Identifier │ │ [->] │ │ [tuple] │ │ im :: Identifier │ │ [block] │ │ [call] │ │ mean :: Identifier │ │ im :: Identifier │ │ : :: Identifier │ │ [=] │ │ std :: Identifier │ │ [call] │ │ reshape :: Identifier │ │ [do] │ │ [call] │ │ mapslices :: Identifier │ │ [parameters] │ │ [kw] │ │ dims :: Identifier │ │ 2 :: Value │ │ scores :: Identifier │ │ [->] │ │ [tuple] │ │ im :: Identifier │ │ [block] │ │ [call] │ │ std :: Identifier │ │ im :: Identifier │ │ : :: Identifier │ │ [=] │ │ scores :: Identifier │ │ scores :: Identifier │ │ │ st1 = │ SyntaxTree with attributes mod,kind,var_id,toplevel_pure,scope_type,macro_source,name_val,syntax_flags,meta,scope_layer,value,jl_source,is_toplevel_thunk,source │ [block] │ │ [=] │ │ val :: Identifier │ scope_layer=3 │ [function] │ │ [where] │ │ [call] │ │ permutation_importance :: Identifier │ scope_layer=1 │ [parameters] │ │ [kw] │ │ rng :: Identifier │ scope_layer=1 │ [.] │ │ Random :: Identifier │ scope_layer=1 │ [inert] │ │ GLOBAL_RNG :: Identifier │ │ [::] │ │ trees :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [::] │ │ labels :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [::] │ │ features :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVecOrMat :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ [::] │ │ score :: Identifier │ scope_layer=1 │ Function :: Identifier │ scope_layer=1 │ [kw] │ │ [::] │ │ n_iter :: Identifier │ scope_layer=1 │ Int :: Identifier │ scope_layer=1 │ 3 :: Value │ macro_source=194 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ Union :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Root :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ [.] │ │ DecisionTree :: Identifier │ scope_layer=1 │ [inert] │ │ LeafOrNode :: Identifier │ │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ Float64 :: Identifier │ scope_layer=1 │ [block] │ │ [=] │ │ base :: Identifier │ scope_layer=1 │ [call] │ │ score :: Identifier │ scope_layer=1 │ trees :: Identifier │ scope_layer=1 │ labels :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ [=] │ │ scores :: Identifier │ scope_layer=1 │ [call] │ │ [curly] │ │ Matrix :: Identifier │ scope_layer=1 │ Float64 :: Identifier │ scope_layer=1 │ undef :: Identifier │ scope_layer=1 │ [call] │ │ size :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ 2 :: Value │ macro_source=194 │ n_iter :: Identifier │ scope_layer=1 │ [=] │ │ rng :: Identifier │ scope_layer=1 │ [::] │ │ [call] │ │ mk_rng :: Identifier │ scope_layer=1 │ rng :: Identifier │ scope_layer=1 │ [.] │ │ Random :: Identifier │ scope_layer=1 │ [inert] │ │ AbstractRNG :: Identifier │ │ [for] │ │ [=] │ │ [tuple] │ │ i :: Identifier │ scope_layer=1 │ col :: Identifier │ scope_layer=1 │ [call] │ │ enumerate :: Identifier │ scope_layer=1 │ [call] │ │ eachcol :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ [block] │ │ [=] │ │ origin :: Identifier │ scope_layer=1 │ [call] │ │ copy :: Identifier │ scope_layer=1 │ col :: Identifier │ scope_layer=1 │ [=] │ │ [ref] │ │ scores :: Identifier │ scope_layer=1 │ i :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ [do] │ │ [call] │ │ map :: Identifier │ scope_layer=1 │ [call] │ │ : :: Identifier │ scope_layer=1 │ 1 :: Value │ macro_source=194 │ n_iter :: Identifier │ scope_layer=1 │ [->] │ │ [tuple] │ │ i :: Identifier │ scope_layer=1 │ [block] │ │ [call] │ │ shuffle! :: Identifier │ scope_layer=1 │ rng :: Identifier │ scope_layer=1 │ col :: Identifier │ scope_layer=1 │ [call] │ │ - :: Identifier │ scope_layer=1 │ base :: Identifier │ scope_layer=1 │ [call] │ │ score :: Identifier │ scope_layer=1 │ trees :: Identifier │ scope_layer=1 │ labels :: Identifier │ scope_layer=1 │ features :: Identifier │ scope_layer=1 │ [=] │ │ [ref] │ │ features :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ i :: Identifier │ scope_layer=1 │ origin :: Identifier │ scope_layer=1 │ [tuple] │ │ [=] │ │ mean :: Identifier │ scope_layer=1 │ [call] │ │ reshape :: Identifier │ scope_layer=1 │ [do] │ │ [call] │ │ mapslices :: Identifier │ scope_layer=1 │ [parameters] │ │ [kw] │ │ dims :: Identifier │ scope_layer=1 │ 2 :: Value │ macro_source=194 │ scores :: Identifier │ scope_layer=1 │ [->] │ │ [tuple] │ │ im :: Identifier │ scope_layer=1 │ [block] │ │ [call] │ │ mean :: Identifier │ scope_layer=1 │ im :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ [=] │ │ std :: Identifier │ scope_layer=1 │ [call] │ │ reshape :: Identifier │ scope_layer=1 │ [do] │ │ [call] │ │ mapslices :: Identifier │ scope_layer=1 │ [parameters] │ │ [kw] │ │ dims :: Identifier │ scope_layer=1 │ 2 :: Value │ macro_source=194 │ scores :: Identifier │ scope_layer=1 │ [->] │ │ [tuple] │ │ im :: Identifier │ scope_layer=1 │ [block] │ │ [call] │ │ std :: Identifier │ scope_layer=1 │ im :: Identifier │ scope_layer=1 │ : :: Identifier │ scope_layer=1 │ [=] │ │ scores :: Identifier │ scope_layer=1 │ scores :: Identifier │ scope_layer=1 │ [call] │ │ Base.Docs.doc! :: Value │ macro_source=194 │ DecisionTree :: Value │ macro_source=194 │ [call] │ │ Base.Docs.Binding :: Value │ macro_source=194 │ DecisionTree :: Value │ │ [inert] │ jl_source=L65 │ permutation_importance :: Identifier │ │ [call] │ macro_source=194 │ Base.Docs.docstr :: Value │ macro_source=194 │ [call] │ macro_source=194 │ Core.svec :: Value │ macro_source=194 │ " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" :: Value │ macro_source=194 │ [call] │ macro_source=194 │ Dict{Symbol, Any} :: Value │ macro_source=194 │ :path => "/home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl" :: Value │ macro_source=194 │ :linenumber => 514 :: Value │ macro_source=194 │ :module => DecisionTree :: Value │ macro_source=194 │ [where] │ │ [where] │ │ [where] │ │ [curly] │ │ Union :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVecOrMat :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ Function :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVecOrMat :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ Function :: Identifier │ scope_layer=1 │ Int :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ U :: Identifier │ scope_layer=1 │ [curly] │ │ Union :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Root :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ [.] │ │ DecisionTree :: Identifier │ scope_layer=1 │ [inert] │ │ LeafOrNode :: Identifier │ │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ Tuple :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ [curly] │ │ Ensemble :: Identifier │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ [curly] │ │ AbstractVector :: Identifier │ scope_layer=1 │ Float64 :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ T :: Identifier │ scope_layer=1 │ Any :: Identifier │ scope_layer=1 │ [<:] │ scope_layer=1 │ S :: Identifier │ scope_layer=1 │ Any :: Identifier │ scope_layer=1 │ val :: Identifier │ scope_layer=3 │ │ file = "/home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl" │ line = 514 └ mod = DecisionTree ERROR: LoadError: internal lowering bug: #= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541 =# - `jl_assert(!(haskey(ssa_rewrites, lhs_id)), _)`: multiple assignments to ssavalue Expression:  (= #₂ (call core.TypeVar :#T1 #₃₀₀)) Containing expressions:  (= #₂ (call core.TypeVar :#T1 #₃₀₀))  Detailed provenance:  (= #₂ (call core.TypeVar :#T1 #₃₀₀)) @#= /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:366 =#  └─ (= #₂ (call core.TypeVar :#T1 (call core.apply_type #₄₂/Ensemble #₉₃/S #₉₄/T))) @#= /source/usr/share/julia/JuliaLowering/src/closure_conversion.jl:197 =#  └─ (= #₂ (call core.TypeVar :#T1 (call core.apply_type #₄₂/Ensemble #₉₃/S #₉₄/T))) @#= /source/usr/share/julia/JuliaLowering/src/desugaring.jl:231 =#  └─ (= #₂ (call core.TypeVar :#T1 (call core.apply_type Ensemble S T))) @#= /source/usr/share/julia/JuliaLowering/src/desugaring.jl:231 =#  └─ (= #₂ (call core.TypeVar :#T1 (curly Ensemble S T)))  └─ (call core.TypeVar :#T1 (curly Ensemble S T)) @#= /source/usr/share/julia/JuliaLowering/src/desugaring.jl:3025 =#  └─ (<: (curly Ensemble S T))  └─ (<: (curly Ensemble S T))  └─ (<: (curly Ensemble S T))  └─ (<: (curly Ensemble S T))  ├─ @ /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:541  └─ (macrocall @doc :(#= /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 =#) " permutation_importances(\n trees :: U,\n labels :: AbstractVector{T},\n features:: AbstractVecOrMat{S},\n score :: Function,\n n_iter :: Int = 3;\n rng = Random.GLOBAL_RNG\n )\n\nCalculate feature importance by shuffling each feature.\n\n* `trees`: a `DecisionTree.Leaf` object, `DecisionTree.Node` object, `DecisionTree.Root`\n object, `DecisionTree.Ensemble` object or `Tuple{DecisionTree.Ensemble, AbstractVector}`\n object (for adaboost model)\n* `score`: a function for evaluating model performance with the form of `score(model, y, X)`\n\n# Return a `NamedTuple`\n\n* Fields\n1. `mean`: mean of feature importance of each shuffle\n2. `std`: standard deviation of feature importance of each shuffle\n3. `scores`: scores of each shuffle\n\nFor algorithm details, please see [Permutation feature importanc](https://scikit-learn.org/stable/modules/permutation_importance.html).\n\n" (function (where (call permutation_importance (parameters (kw rng (. Random (inert GLOBAL_RNG)))) (:: trees U) (:: labels (curly AbstractVector T)) (:: features (curly AbstractVecOrMat S)) (:: score Function) (kw (:: n_iter Int) 3)) S T (<: U (curly Union (<: (curly Ensemble S T)) (<: (curly Root S T)) (<: (curly (. DecisionTree (inert LeafOrNode)) S T)) (curly Tuple (<: (curly Ensemble S T)) (curly AbstractVector Float64))))) (block (= base (call score trees labels features)) (= scores (call (curly Matrix Float64) undef (call size features 2) n_iter)) (= rng (:: (call mk_rng rng) (. Random (inert AbstractRNG)))) (for (= (tuple i col) (call enumerate (call eachcol features))) (block (= origin (call copy col)) (= (ref scores i :) (do (call map (call : 1 n_iter)) (-> (tuple i) (block (call shuffle! rng col) (call - base (call score trees labels features)))))) (= (ref features : i) origin))) (tuple (= mean (call reshape (do (call mapslices (parameters (kw dims 2)) scores) (-> (tuple im) (block (call mean im)))) :)) (= std (call reshape (do (call mapslices (parameters (kw dims 2)) scores) (-> (tuple im) (block (call std im)))) :)) (= scores scores)))))  └─ @ /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514  Stacktrace:  [1] iterate(A::Base.JuliaSyntax.SyntaxList{Dict{Symbol, Dict{Int64, Any}}, Vector{Int64}})  @ Base /source/usr/share/julia/JuliaLowering/src/ast.jl:23 [inlined]  [2] renumber_body(ctx::Base.JuliaLowering.LinearIRContext{Dict{Symbol, Dict{Int64, Any}}}, input_code::Base.JuliaSyntax.SyntaxList{Dict{Symbol, Dict{Int64, Any}}, Vector{Int64}}, slot_rewrites::Dict{Int64, Int64})  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:1106  [3] compile_lambda(outer_ctx::Base.JuliaLowering.LinearIRContext{Dict{Symbol, Dict{Int64, Any}}}, ex::Base.JuliaSyntax.SyntaxTree{Dict{Symbol, Dict{Int64, Any}}})  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:1223  [4] linearize_ir(ctx::Base.JuliaLowering.ClosureConversionCtx{Dict{Symbol, Dict{Int64, Any}}}, ex::Base.JuliaSyntax.SyntaxTree{Dict{Symbol, Dict{Int64, Any}}})  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/linear_ir.jl:1253  [5] core_lowering_hook(code::Any, mod::Module, file::String, line::UInt64, world::UInt64, _warn::Bool)  @ Base.JuliaLowering /source/usr/share/julia/JuliaLowering/src/hooks.jl:33  [6] include(mapexpr::Function, mod::Module, _path::String)  @ Base ./Base.jl:327  [7] top-level scope  @ ~/.julia/packages/DecisionTree/0Dw1P/src/DecisionTree.jl:164  [8] include(mod::Module, _path::String)  @ Base ./Base.jl:326  [9] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3271  [10] top-level scope  @ stdin:5  [11] eval(m::Module, e::Any)  @ Core ./boot.jl:517  [12] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3113  [13] push!(a::Vector{SubString{String}}, item::String)  @ Base ./loading.jl:3123 [inlined]  [14] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:353  [15] _start()  @ Base ./client.jl:596 in expression starting at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/measures.jl:514 in expression starting at /home/pkgeval/.julia/packages/DecisionTree/0Dw1P/src/DecisionTree.jl:1 in expression starting at stdin:5 1 dependency had output during precompilation: ┌ DecisionTree │ [Output was shown above] └ ERROR: The following 1 package failed to precompile: DecisionTree Failed to precompile DecisionTree [7806a523-6efd-50cb-b5f6-3fa6f1930dbb] to "/home/pkgeval/.julia/compiled/v1.14/DecisionTree/jl_jIfLha" (ProcessExited(1)). Loading failed after 34.73s ERROR: LoadError: failed process: Process(`/opt/julia/bin/julia -C native -J/opt/julia/lib/julia/sys.so -g1 --check-bounds=yes --inline=yes --check-bounds=yes --pkgimages=existing -e 'using DecisionTree'`, ProcessExited(1)) [1] Stacktrace: [1] spawn_opts_inherit() @ Base ./process.jl:612 [inlined] [2] run(::Cmd; wait::Bool) @ Base ./process.jl:525 [3] run(::Cmd) @ Base ./process.jl:522 [4] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:197 [5] include(mod::Module, _path::String) @ Base ./Base.jl:326 [6] exec_options(opts::Base.JLOptions) @ Base ./client.jl:355 [7] _start() @ Base ./client.jl:596 in expression starting at /PkgEval.jl/scripts/evaluate.jl:188 PkgEval failed after 194.11s: package fails to precompile