Package evaluation to test SDeMo on Julia 1.14.0-DEV.1414 (f2608a495e*) started at 2025-12-25T16:39:09.353 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 11.05s ################################################################################ # Installation # Installing SDeMo... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [3e5feb82] + SDeMo v1.7.1 Updating `~/.julia/environments/v1.14/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.3 [31c24e10] + Distributions v0.25.122 [ffbed154] + DocStringExtensions v0.9.5 [1a297f60] + FillArrays v1.15.0 [34004b35] + HypergeometricFunctions v0.3.28 [92d709cd] + IrrationalConstants v0.2.6 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v1.3.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [e1d29d7a] + Missings v1.2.0 [6f286f6a] + MultivariateStats v0.10.3 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.37 [69de0a69] + Parsers v2.8.3 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.1 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [189a3867] + Reexport v1.2.2 [79098fc4] + Rmath v0.9.0 [3e5feb82] + SDeMo v1.7.1 [a2af1166] + SortingAlgorithms v1.2.2 [276daf66] + SpecialFunctions v2.6.1 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.9 [4c63d2b9] + StatsFuns v1.5.2 [ec057cc2] + StructUtils v2.6.0 [1c621080] + TestItems v1.0.0 ⌅ [68821587] + Arpack_jll v3.5.2+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [56f22d72] + Artifacts v1.11.0 [ade2ca70] + Dates v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [9e88b42a] + Serialization v1.11.0 [2f01184e] + SparseArrays v1.13.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 6.15s ################################################################################ # Precompilation # ERROR: LoadError: MethodError: no method matching setindex!(::Base.ScopedValues.ScopedValue{IO}, ::Nothing) The function `setindex!` exists, but no method is defined for this combination of argument types. Stacktrace: [1] top-level scope @ /PkgEval.jl/scripts/precompile.jl:10 [2] include(mod::Module, _path::String) @ Base ./Base.jl:309 [3] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [4] _start() @ Base ./client.jl:577 in expression starting at /PkgEval.jl/scripts/precompile.jl:6 caused by: MethodError: no method matching setindex!(::Base.ScopedValues.ScopedValue{IO}, ::Base.DevNull) The function `setindex!` exists, but no method is defined for this combination of argument types. Stacktrace: [1] top-level scope @ /PkgEval.jl/scripts/precompile.jl:7 [2] include(mod::Module, _path::String) @ Base ./Base.jl:309 [3] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [4] _start() @ Base ./client.jl:577 Precompilation failed after 16.25s ################################################################################ # Testing # Testing SDeMo Status `/tmp/jl_VDIspf/Project.toml` [3e5feb82] SDeMo v1.7.1 [10745b16] Statistics v1.11.1 [f8b46487] TestItemRunner v1.1.4 [8dfed614] Test v1.11.0 Status `/tmp/jl_VDIspf/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.19.3 [31c24e10] Distributions v0.25.122 [ffbed154] DocStringExtensions v0.9.5 [1a297f60] FillArrays v1.15.0 [34004b35] HypergeometricFunctions v0.3.28 [92d709cd] IrrationalConstants v0.2.6 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v1.3.0 [2ab3a3ac] LogExpFunctions v0.3.29 [e1d29d7a] Missings v1.2.0 [6f286f6a] MultivariateStats v0.10.3 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.37 [69de0a69] Parsers v2.8.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.1 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 [189a3867] Reexport v1.2.2 [79098fc4] Rmath v0.9.0 [3e5feb82] SDeMo v1.7.1 [a2af1166] SortingAlgorithms v1.2.2 [276daf66] SpecialFunctions v2.6.1 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.8.0 [2913bbd2] StatsBase v0.34.9 [4c63d2b9] StatsFuns v1.5.2 [ec057cc2] StructUtils v2.6.0 [f8b46487] TestItemRunner v1.1.4 [1c621080] TestItems v1.0.0 ⌅ [68821587] Arpack_jll v3.5.2+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [f43a241f] Downloads v1.7.0 [7b1f6079] FileWatching v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.13.0 [b27032c2] LibCURL v1.0.0 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.14.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v1.0.0 [9e88b42a] Serialization v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.13.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.17.0+0 [e37daf67] LibGit2_jll v1.9.2+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.12.2 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.68.0+1 [3f19e933] p7zip_jll v17.7.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... [ Info: [ 0%] LOSS: training ≈ 0.496 validation ≈ 0.4993 (101%) [ Info: [ 1%] LOSS: training ≈ 0.4214 validation ≈ 0.4318 (102%) [ Info: [ 2%] LOSS: training ≈ 0.3846 validation ≈ 0.3981 (103%) [ Info: [ 2%] LOSS: training ≈ 0.3626 validation ≈ 0.378 (104%) [ Info: [ 2%] LOSS: training ≈ 0.3479 validation ≈ 0.3647 (105%) [ Info: [ 3%] LOSS: training ≈ 0.3373 validation ≈ 0.3553 (105%) [ Info: [ 4%] LOSS: training ≈ 0.3292 validation ≈ 0.3481 (106%) [ Info: [ 4%] LOSS: training ≈ 0.3228 validation ≈ 0.3425 (106%) [ Info: [ 4%] LOSS: training ≈ 0.3175 validation ≈ 0.3379 (106%) [ Info: [ 5%] LOSS: training ≈ 0.3131 validation ≈ 0.3341 (107%) [ Info: [ 6%] LOSS: training ≈ 0.3094 validation ≈ 0.3308 (107%) [ Info: [ 6%] LOSS: training ≈ 0.3061 validation ≈ 0.328 (107%) [ Info: [ 6%] LOSS: training ≈ 0.3033 validation ≈ 0.3255 (107%) [ Info: [ 7%] LOSS: training ≈ 0.3007 validation ≈ 0.3232 (107%) [ Info: [ 8%] LOSS: training ≈ 0.2984 validation ≈ 0.3212 (108%) [ Info: [ 8%] LOSS: training ≈ 0.2964 validation ≈ 0.3194 (108%) [ Info: [ 8%] LOSS: training ≈ 0.2945 validation ≈ 0.3177 (108%) [ Info: [ 9%] LOSS: training ≈ 0.2928 validation ≈ 0.3161 (108%) [ Info: [ 10%] LOSS: training ≈ 0.2912 validation ≈ 0.3146 (108%) [ Info: [ 10%] LOSS: training ≈ 0.2898 validation ≈ 0.3132 (108%) [ Info: [ 10%] LOSS: training ≈ 0.2884 validation ≈ 0.3119 (108%) [ Info: [ 11%] LOSS: training ≈ 0.2871 validation ≈ 0.3107 (108%) [ Info: [ 12%] LOSS: training ≈ 0.286 validation ≈ 0.3095 (108%) [ Info: [ 12%] LOSS: training ≈ 0.2848 validation ≈ 0.3084 (108%) [ Info: [ 12%] LOSS: training ≈ 0.2838 validation ≈ 0.3073 (108%) [ Info: [ 13%] LOSS: training ≈ 0.2828 validation ≈ 0.3063 (108%) [ Info: [ 14%] LOSS: training ≈ 0.2819 validation ≈ 0.3053 (108%) [ Info: [ 14%] LOSS: training ≈ 0.281 validation ≈ 0.3044 (108%) [ Info: [ 14%] LOSS: training ≈ 0.2801 validation ≈ 0.3035 (108%) [ Info: [ 15%] LOSS: training ≈ 0.2793 validation ≈ 0.3026 (108%) [ Info: [ 16%] LOSS: training ≈ 0.2786 validation ≈ 0.3017 (108%) [ Info: [ 16%] LOSS: training ≈ 0.2779 validation ≈ 0.3009 (108%) [ Info: [ 16%] LOSS: training ≈ 0.2772 validation ≈ 0.3001 (108%) [ Info: [ 17%] LOSS: training ≈ 0.2765 validation ≈ 0.2993 (108%) [ Info: [ 18%] LOSS: training ≈ 0.2759 validation ≈ 0.2986 (108%) [ Info: [ 18%] LOSS: training ≈ 0.2752 validation ≈ 0.2978 (108%) [ Info: [ 18%] LOSS: training ≈ 0.2747 validation ≈ 0.2971 (108%) [ Info: [ 19%] LOSS: training ≈ 0.2741 validation ≈ 0.2964 (108%) [ Info: [ 20%] LOSS: training ≈ 0.2735 validation ≈ 0.2957 (108%) [ Info: [ 20%] LOSS: training ≈ 0.273 validation ≈ 0.2951 (108%) [ Info: [ 20%] LOSS: training ≈ 0.2725 validation ≈ 0.2944 (108%) [ Info: [ 21%] LOSS: training ≈ 0.272 validation ≈ 0.2938 (108%) [ Info: [ 22%] LOSS: training ≈ 0.2716 validation ≈ 0.2932 (108%) [ Info: [ 22%] LOSS: training ≈ 0.2711 validation ≈ 0.2926 (108%) [ Info: [ 22%] LOSS: training ≈ 0.2707 validation ≈ 0.292 (108%) [ Info: [ 23%] LOSS: training ≈ 0.2703 validation ≈ 0.2914 (108%) [ Info: [ 24%] LOSS: training ≈ 0.2698 validation ≈ 0.2908 (108%) [ Info: [ 24%] LOSS: training ≈ 0.2694 validation ≈ 0.2903 (108%) [ Info: [ 24%] LOSS: training ≈ 0.2691 validation ≈ 0.2897 (108%) [ Info: [ 25%] LOSS: training ≈ 0.2687 validation ≈ 0.2892 (108%) [ Info: [ 26%] LOSS: training ≈ 0.2683 validation ≈ 0.2887 (108%) [ Info: [ 26%] LOSS: training ≈ 0.268 validation ≈ 0.2882 (108%) [ Info: [ 26%] LOSS: training ≈ 0.2677 validation ≈ 0.2877 (107%) [ Info: [ 27%] LOSS: training ≈ 0.2673 validation ≈ 0.2872 (107%) [ Info: [ 28%] LOSS: training ≈ 0.267 validation ≈ 0.2867 (107%) [ Info: [ 28%] LOSS: training ≈ 0.2667 validation ≈ 0.2862 (107%) [ Info: [ 28%] LOSS: training ≈ 0.2664 validation ≈ 0.2858 (107%) [ Info: [ 29%] LOSS: training ≈ 0.2661 validation ≈ 0.2853 (107%) [ Info: [ 30%] LOSS: training ≈ 0.2658 validation ≈ 0.2849 (107%) [ Info: [ 30%] LOSS: training ≈ 0.2656 validation ≈ 0.2845 (107%) [ Info: [ 30%] LOSS: training ≈ 0.2653 validation ≈ 0.284 (107%) [ Info: [ 31%] LOSS: training ≈ 0.265 validation ≈ 0.2836 (107%) [ Info: [ 32%] LOSS: training ≈ 0.2648 validation ≈ 0.2832 (107%) [ Info: [ 32%] LOSS: training ≈ 0.2646 validation ≈ 0.2828 (107%) [ Info: [ 32%] LOSS: training ≈ 0.2643 validation ≈ 0.2824 (107%) [ Info: [ 33%] LOSS: training ≈ 0.2641 validation ≈ 0.282 (107%) [ Info: [ 34%] LOSS: training ≈ 0.2639 validation ≈ 0.2817 (107%) [ Info: [ 34%] LOSS: training ≈ 0.2636 validation ≈ 0.2813 (107%) [ Info: [ 34%] LOSS: training ≈ 0.2634 validation ≈ 0.2809 (107%) [ Info: [ 35%] LOSS: training ≈ 0.2632 validation ≈ 0.2806 (107%) [ Info: [ 36%] LOSS: training ≈ 0.263 validation ≈ 0.2802 (107%) [ Info: [ 36%] LOSS: training ≈ 0.2628 validation ≈ 0.2799 (106%) [ Info: [ 36%] LOSS: training ≈ 0.2626 validation ≈ 0.2795 (106%) [ Info: [ 37%] LOSS: training ≈ 0.2625 validation ≈ 0.2792 (106%) [ Info: [ 38%] LOSS: training ≈ 0.2623 validation ≈ 0.2789 (106%) [ Info: [ 38%] LOSS: training ≈ 0.2621 validation ≈ 0.2785 (106%) [ Info: [ 38%] LOSS: training ≈ 0.2619 validation ≈ 0.2782 (106%) [ Info: [ 39%] LOSS: training ≈ 0.2618 validation ≈ 0.2779 (106%) [ Info: [ 40%] LOSS: training ≈ 0.2616 validation ≈ 0.2776 (106%) [ Info: [ 40%] LOSS: training ≈ 0.2614 validation ≈ 0.2773 (106%) [ Info: [ 40%] LOSS: training ≈ 0.2613 validation ≈ 0.277 (106%) [ Info: [ 41%] LOSS: training ≈ 0.2611 validation ≈ 0.2767 (106%) [ Info: [ 42%] LOSS: training ≈ 0.261 validation ≈ 0.2764 (106%) [ Info: [ 42%] LOSS: training ≈ 0.2609 validation ≈ 0.2761 (106%) [ Info: [ 42%] LOSS: training ≈ 0.2607 validation ≈ 0.2759 (106%) [ Info: [ 43%] LOSS: training ≈ 0.2606 validation ≈ 0.2756 (106%) [ Info: [ 44%] LOSS: training ≈ 0.2604 validation ≈ 0.2753 (106%) [ Info: [ 44%] LOSS: training ≈ 0.2603 validation ≈ 0.2751 (106%) [ Info: [ 44%] LOSS: training ≈ 0.2602 validation ≈ 0.2748 (106%) [ Info: [ 45%] LOSS: training ≈ 0.2601 validation ≈ 0.2746 (106%) [ Info: [ 46%] LOSS: training ≈ 0.2599 validation ≈ 0.2743 (106%) [ Info: [ 46%] LOSS: training ≈ 0.2598 validation ≈ 0.2741 (105%) [ Info: [ 46%] LOSS: training ≈ 0.2597 validation ≈ 0.2738 (105%) [ Info: [ 47%] LOSS: training ≈ 0.2596 validation ≈ 0.2736 (105%) [ Info: [ 48%] LOSS: training ≈ 0.2595 validation ≈ 0.2733 (105%) [ Info: [ 48%] LOSS: training ≈ 0.2594 validation ≈ 0.2731 (105%) [ Info: [ 48%] LOSS: training ≈ 0.2593 validation ≈ 0.2729 (105%) [ Info: [ 49%] LOSS: training ≈ 0.2592 validation ≈ 0.2727 (105%) [ Info: [ 50%] LOSS: training ≈ 0.2591 validation ≈ 0.2724 (105%) [ Info: [ 50%] LOSS: training ≈ 0.259 validation ≈ 0.2722 (105%) [ Info: [ 50%] LOSS: training ≈ 0.2589 validation ≈ 0.272 (105%) [ Info: [ 51%] LOSS: training ≈ 0.2588 validation ≈ 0.2718 (105%) [ Info: [ 52%] LOSS: training ≈ 0.2587 validation ≈ 0.2716 (105%) [ Info: [ 52%] LOSS: training ≈ 0.2586 validation ≈ 0.2714 (105%) [ Info: [ 52%] LOSS: training ≈ 0.2585 validation ≈ 0.2712 (105%) [ Info: [ 53%] LOSS: training ≈ 0.2584 validation ≈ 0.271 (105%) [ Info: [ 54%] LOSS: training ≈ 0.2584 validation ≈ 0.2708 (105%) [ Info: [ 54%] LOSS: training ≈ 0.2583 validation ≈ 0.2706 (105%) [ Info: [ 55%] LOSS: training ≈ 0.2582 validation ≈ 0.2704 (105%) [ Info: [ 55%] LOSS: training ≈ 0.2581 validation ≈ 0.2702 (105%) [ Info: [ 56%] LOSS: training ≈ 0.2581 validation ≈ 0.2701 (105%) [ Info: [ 56%] LOSS: training ≈ 0.258 validation ≈ 0.2699 (105%) [ Info: [ 56%] LOSS: training ≈ 0.2579 validation ≈ 0.2697 (105%) [ Info: [ 57%] LOSS: training ≈ 0.2578 validation ≈ 0.2695 (105%) [ Info: [ 57%] LOSS: training ≈ 0.2578 validation ≈ 0.2694 (104%) [ Info: [ 58%] LOSS: training ≈ 0.2577 validation ≈ 0.2692 (104%) [ Info: [ 58%] LOSS: training ≈ 0.2576 validation ≈ 0.269 (104%) [ Info: [ 59%] LOSS: training ≈ 0.2576 validation ≈ 0.2689 (104%) [ Info: [ 60%] LOSS: training ≈ 0.2575 validation ≈ 0.2687 (104%) [ Info: [ 60%] LOSS: training ≈ 0.2574 validation ≈ 0.2685 (104%) [ Info: [ 60%] LOSS: training ≈ 0.2574 validation ≈ 0.2684 (104%) [ Info: [ 61%] LOSS: training ≈ 0.2573 validation ≈ 0.2682 (104%) [ Info: [ 62%] LOSS: training ≈ 0.2573 validation ≈ 0.2681 (104%) [ Info: [ 62%] LOSS: training ≈ 0.2572 validation ≈ 0.2679 (104%) [ Info: [ 62%] LOSS: training ≈ 0.2572 validation ≈ 0.2678 (104%) [ Info: [ 63%] LOSS: training ≈ 0.2571 validation ≈ 0.2676 (104%) [ Info: [ 64%] LOSS: training ≈ 0.257 validation ≈ 0.2675 (104%) [ Info: [ 64%] LOSS: training ≈ 0.257 validation ≈ 0.2673 (104%) [ Info: [ 64%] LOSS: training ≈ 0.2569 validation ≈ 0.2672 (104%) [ Info: [ 65%] LOSS: training ≈ 0.2569 validation ≈ 0.2671 (104%) [ Info: [ 66%] LOSS: training ≈ 0.2568 validation ≈ 0.2669 (104%) [ Info: [ 66%] LOSS: training ≈ 0.2568 validation ≈ 0.2668 (104%) [ Info: [ 66%] LOSS: training ≈ 0.2567 validation ≈ 0.2666 (104%) [ Info: [ 67%] LOSS: training ≈ 0.2567 validation ≈ 0.2665 (104%) [ Info: [ 68%] LOSS: training ≈ 0.2567 validation ≈ 0.2664 (104%) [ Info: [ 68%] LOSS: training ≈ 0.2566 validation ≈ 0.2663 (104%) [ Info: [ 68%] LOSS: training ≈ 0.2566 validation ≈ 0.2661 (104%) [ Info: [ 69%] LOSS: training ≈ 0.2565 validation ≈ 0.266 (104%) [ Info: [ 70%] LOSS: training ≈ 0.2565 validation ≈ 0.2659 (104%) [ Info: [ 70%] LOSS: training ≈ 0.2564 validation ≈ 0.2658 (104%) [ Info: [ 70%] LOSS: training ≈ 0.2564 validation ≈ 0.2656 (104%) [ Info: [ 71%] LOSS: training ≈ 0.2564 validation ≈ 0.2655 (104%) [ Info: [ 72%] LOSS: training ≈ 0.2563 validation ≈ 0.2654 (104%) [ Info: [ 72%] LOSS: training ≈ 0.2563 validation ≈ 0.2653 (104%) [ Info: [ 72%] LOSS: training ≈ 0.2562 validation ≈ 0.2652 (103%) [ Info: [ 73%] LOSS: training ≈ 0.2562 validation ≈ 0.2651 (103%) [ Info: [ 74%] LOSS: training ≈ 0.2562 validation ≈ 0.2649 (103%) [ Info: [ 74%] LOSS: training ≈ 0.2561 validation ≈ 0.2648 (103%) [ Info: [ 74%] LOSS: training ≈ 0.2561 validation ≈ 0.2647 (103%) [ Info: [ 75%] LOSS: training ≈ 0.2561 validation ≈ 0.2646 (103%) [ Info: [ 76%] LOSS: training ≈ 0.256 validation ≈ 0.2645 (103%) [ Info: [ 76%] LOSS: training ≈ 0.256 validation ≈ 0.2644 (103%) [ Info: [ 76%] LOSS: training ≈ 0.256 validation ≈ 0.2643 (103%) [ Info: [ 77%] LOSS: training ≈ 0.2559 validation ≈ 0.2642 (103%) [ Info: [ 78%] LOSS: training ≈ 0.2559 validation ≈ 0.2641 (103%) [ Info: [ 78%] LOSS: training ≈ 0.2559 validation ≈ 0.264 (103%) [ Info: [ 78%] LOSS: training ≈ 0.2559 validation ≈ 0.2639 (103%) [ Info: [ 79%] LOSS: training ≈ 0.2558 validation ≈ 0.2638 (103%) [ Info: [ 80%] LOSS: training ≈ 0.2558 validation ≈ 0.2637 (103%) [ Info: [ 80%] LOSS: training ≈ 0.2558 validation ≈ 0.2636 (103%) [ Info: [ 80%] LOSS: training ≈ 0.2557 validation ≈ 0.2635 (103%) [ Info: [ 81%] LOSS: training ≈ 0.2557 validation ≈ 0.2634 (103%) [ Info: [ 82%] LOSS: training ≈ 0.2557 validation ≈ 0.2633 (103%) [ Info: [ 82%] LOSS: training ≈ 0.2557 validation ≈ 0.2632 (103%) [ Info: [ 82%] LOSS: training ≈ 0.2556 validation ≈ 0.2632 (103%) [ Info: [ 83%] LOSS: training ≈ 0.2556 validation ≈ 0.2631 (103%) [ Info: [ 84%] LOSS: training ≈ 0.2556 validation ≈ 0.263 (103%) [ Info: [ 84%] LOSS: training ≈ 0.2556 validation ≈ 0.2629 (103%) [ Info: [ 84%] LOSS: training ≈ 0.2555 validation ≈ 0.2628 (103%) [ Info: [ 85%] LOSS: training ≈ 0.2555 validation ≈ 0.2627 (103%) [ Info: [ 86%] LOSS: training ≈ 0.2555 validation ≈ 0.2626 (103%) [ Info: [ 86%] LOSS: training ≈ 0.2555 validation ≈ 0.2626 (103%) [ Info: [ 86%] LOSS: training ≈ 0.2554 validation ≈ 0.2625 (103%) [ Info: [ 87%] LOSS: training ≈ 0.2554 validation ≈ 0.2624 (103%) [ Info: [ 88%] LOSS: training ≈ 0.2554 validation ≈ 0.2623 (103%) [ Info: [ 88%] LOSS: training ≈ 0.2554 validation ≈ 0.2622 (103%) [ Info: [ 88%] LOSS: training ≈ 0.2554 validation ≈ 0.2622 (103%) [ Info: [ 89%] LOSS: training ≈ 0.2553 validation ≈ 0.2621 (103%) [ Info: [ 90%] LOSS: training ≈ 0.2553 validation ≈ 0.262 (103%) [ Info: [ 90%] LOSS: training ≈ 0.2553 validation ≈ 0.2619 (103%) [ Info: [ 90%] LOSS: training ≈ 0.2553 validation ≈ 0.2619 (103%) [ Info: [ 91%] LOSS: training ≈ 0.2553 validation ≈ 0.2618 (103%) [ Info: [ 92%] LOSS: training ≈ 0.2552 validation ≈ 0.2617 (103%) [ Info: [ 92%] LOSS: training ≈ 0.2552 validation ≈ 0.2616 (103%) [ Info: [ 92%] LOSS: training ≈ 0.2552 validation ≈ 0.2616 (102%) [ Info: [ 93%] LOSS: training ≈ 0.2552 validation ≈ 0.2615 (102%) [ Info: [ 94%] LOSS: training ≈ 0.2552 validation ≈ 0.2614 (102%) [ Info: [ 94%] LOSS: training ≈ 0.2552 validation ≈ 0.2614 (102%) [ Info: [ 94%] LOSS: training ≈ 0.2551 validation ≈ 0.2613 (102%) [ Info: [ 95%] LOSS: training ≈ 0.2551 validation ≈ 0.2612 (102%) [ Info: [ 96%] LOSS: training ≈ 0.2551 validation ≈ 0.2611 (102%) [ Info: [ 96%] LOSS: training ≈ 0.2551 validation ≈ 0.2611 (102%) [ Info: [ 96%] LOSS: training ≈ 0.2551 validation ≈ 0.261 (102%) [ Info: [ 97%] LOSS: training ≈ 0.2551 validation ≈ 0.261 (102%) [ Info: [ 98%] LOSS: training ≈ 0.255 validation ≈ 0.2609 (102%) [ Info: [ 98%] LOSS: training ≈ 0.255 validation ≈ 0.2608 (102%) [ Info: [ 98%] LOSS: training ≈ 0.255 validation ≈ 0.2608 (102%) [ Info: [ 99%] LOSS: training ≈ 0.255 validation ≈ 0.2607 (102%) [ Info: [100%] LOSS: training ≈ 0.255 validation ≈ 0.2606 (102%) [ Info: [100%] LOSS: training ≈ 0.255 validation ≈ 0.2606 (102%) [ Info: [ 0%] LOSS: training ≈ 0.4747 [ Info: [ 1%] LOSS: training ≈ 0.4038 [ Info: [ 2%] LOSS: training ≈ 0.371 [ Info: [ 2%] LOSS: training ≈ 0.352 [ Info: [ 2%] LOSS: training ≈ 0.3394 [ Info: [ 3%] LOSS: training ≈ 0.3303 [ Info: [ 4%] LOSS: training ≈ 0.3233 [ Info: [ 4%] LOSS: training ≈ 0.3178 [ Info: [ 4%] LOSS: training ≈ 0.3132 [ Info: [ 5%] LOSS: training ≈ 0.3093 [ Info: [ 6%] LOSS: training ≈ 0.3059 [ Info: [ 6%] LOSS: training ≈ 0.303 [ Info: [ 6%] LOSS: training ≈ 0.3004 [ Info: [ 7%] LOSS: training ≈ 0.298 [ Info: [ 8%] LOSS: training ≈ 0.2959 [ Info: [ 8%] LOSS: training ≈ 0.2939 [ Info: [ 8%] LOSS: training ≈ 0.2921 [ Info: [ 9%] LOSS: training ≈ 0.2905 [ Info: [ 10%] LOSS: training ≈ 0.289 [ Info: [ 10%] LOSS: training ≈ 0.2875 [ Info: [ 10%] LOSS: training ≈ 0.2862 [ Info: [ 11%] LOSS: training ≈ 0.2849 [ Info: [ 12%] LOSS: training ≈ 0.2838 [ Info: [ 12%] LOSS: training ≈ 0.2826 [ Info: [ 12%] LOSS: training ≈ 0.2816 [ Info: [ 13%] LOSS: training ≈ 0.2806 [ Info: [ 14%] LOSS: training ≈ 0.2796 [ Info: [ 14%] LOSS: training ≈ 0.2787 [ Info: [ 14%] LOSS: training ≈ 0.2779 [ Info: [ 15%] LOSS: training ≈ 0.277 [ Info: [ 16%] LOSS: training ≈ 0.2763 [ Info: [ 16%] LOSS: training ≈ 0.2755 [ Info: [ 16%] LOSS: training ≈ 0.2748 [ Info: [ 17%] LOSS: training ≈ 0.2741 [ Info: [ 18%] LOSS: training ≈ 0.2734 [ Info: [ 18%] LOSS: training ≈ 0.2728 [ Info: [ 18%] LOSS: training ≈ 0.2722 [ Info: [ 19%] LOSS: training ≈ 0.2716 [ Info: [ 20%] LOSS: training ≈ 0.271 [ Info: [ 20%] LOSS: training ≈ 0.2705 [ Info: [ 20%] LOSS: training ≈ 0.27 [ Info: [ 21%] LOSS: training ≈ 0.2695 [ Info: [ 22%] LOSS: training ≈ 0.269 [ Info: [ 22%] LOSS: training ≈ 0.2685 [ Info: [ 22%] LOSS: training ≈ 0.2681 [ Info: [ 23%] LOSS: training ≈ 0.2677 [ Info: [ 24%] LOSS: training ≈ 0.2673 [ Info: [ 24%] LOSS: training ≈ 0.2669 [ Info: [ 24%] LOSS: training ≈ 0.2665 [ Info: [ 25%] LOSS: training ≈ 0.2661 [ Info: [ 26%] LOSS: training ≈ 0.2657 [ Info: [ 26%] LOSS: training ≈ 0.2654 [ Info: [ 26%] LOSS: training ≈ 0.265 [ Info: [ 27%] LOSS: training ≈ 0.2647 [ Info: [ 28%] LOSS: training ≈ 0.2644 [ Info: [ 28%] LOSS: training ≈ 0.2641 [ Info: [ 28%] LOSS: training ≈ 0.2638 [ Info: [ 29%] LOSS: training ≈ 0.2635 [ Info: [ 30%] LOSS: training ≈ 0.2632 [ Info: [ 30%] LOSS: training ≈ 0.263 [ Info: [ 30%] LOSS: training ≈ 0.2627 [ Info: [ 31%] LOSS: training ≈ 0.2625 [ Info: [ 32%] LOSS: training ≈ 0.2622 [ Info: [ 32%] LOSS: training ≈ 0.262 [ Info: [ 32%] LOSS: training ≈ 0.2618 [ Info: [ 33%] LOSS: training ≈ 0.2615 [ Info: [ 34%] LOSS: training ≈ 0.2613 [ Info: [ 34%] LOSS: training ≈ 0.2611 [ Info: [ 34%] LOSS: training ≈ 0.2609 [ Info: [ 35%] LOSS: training ≈ 0.2607 [ Info: [ 36%] LOSS: training ≈ 0.2605 [ Info: [ 36%] LOSS: training ≈ 0.2603 [ Info: [ 36%] LOSS: training ≈ 0.2602 [ Info: [ 37%] LOSS: training ≈ 0.26 [ Info: [ 38%] LOSS: training ≈ 0.2598 [ Info: [ 38%] LOSS: training ≈ 0.2597 [ Info: [ 38%] LOSS: training ≈ 0.2595 [ Info: [ 39%] LOSS: training ≈ 0.2593 [ Info: [ 40%] LOSS: training ≈ 0.2592 [ Info: [ 40%] LOSS: training ≈ 0.259 [ Info: [ 40%] LOSS: training ≈ 0.2589 [ Info: [ 41%] LOSS: training ≈ 0.2588 [ Info: [ 42%] LOSS: training ≈ 0.2586 [ Info: [ 42%] LOSS: training ≈ 0.2585 [ Info: [ 42%] LOSS: training ≈ 0.2584 [ Info: [ 43%] LOSS: training ≈ 0.2582 [ Info: [ 44%] LOSS: training ≈ 0.2581 [ Info: [ 44%] LOSS: training ≈ 0.258 [ Info: [ 44%] LOSS: training ≈ 0.2579 [ Info: [ 45%] LOSS: training ≈ 0.2578 [ Info: [ 46%] LOSS: training ≈ 0.2577 [ Info: [ 46%] LOSS: training ≈ 0.2576 [ Info: [ 46%] LOSS: training ≈ 0.2574 [ Info: [ 47%] LOSS: training ≈ 0.2573 [ Info: [ 48%] LOSS: training ≈ 0.2572 [ Info: [ 48%] LOSS: training ≈ 0.2572 [ Info: [ 48%] LOSS: training ≈ 0.2571 [ Info: [ 49%] LOSS: training ≈ 0.257 [ Info: [ 50%] LOSS: training ≈ 0.2569 [ Info: [ 50%] LOSS: training ≈ 0.2568 [ Info: [ 50%] LOSS: training ≈ 0.2567 [ Info: [ 51%] LOSS: training ≈ 0.2566 [ Info: [ 52%] LOSS: training ≈ 0.2565 [ Info: [ 52%] LOSS: training ≈ 0.2565 [ Info: [ 52%] LOSS: training ≈ 0.2564 [ Info: [ 53%] LOSS: training ≈ 0.2563 [ Info: [ 54%] LOSS: training ≈ 0.2562 [ Info: [ 54%] LOSS: training ≈ 0.2562 [ Info: [ 55%] LOSS: training ≈ 0.2561 [ Info: [ 55%] LOSS: training ≈ 0.256 [ Info: [ 56%] LOSS: training ≈ 0.256 [ Info: [ 56%] LOSS: training ≈ 0.2559 [ Info: [ 56%] LOSS: training ≈ 0.2558 [ Info: [ 57%] LOSS: training ≈ 0.2558 [ Info: [ 57%] LOSS: training ≈ 0.2557 [ Info: [ 58%] LOSS: training ≈ 0.2556 [ Info: [ 58%] LOSS: training ≈ 0.2556 [ Info: [ 59%] LOSS: training ≈ 0.2555 [ Info: [ 60%] LOSS: training ≈ 0.2555 [ Info: [ 60%] LOSS: training ≈ 0.2554 [ Info: [ 60%] LOSS: training ≈ 0.2554 [ Info: [ 61%] LOSS: training ≈ 0.2553 [ Info: [ 62%] LOSS: training ≈ 0.2553 [ Info: [ 62%] LOSS: training ≈ 0.2552 [ Info: [ 62%] LOSS: training ≈ 0.2552 [ Info: [ 63%] LOSS: training ≈ 0.2551 [ Info: [ 64%] LOSS: training ≈ 0.2551 [ Info: [ 64%] LOSS: training ≈ 0.255 [ Info: [ 64%] LOSS: training ≈ 0.255 [ Info: [ 65%] LOSS: training ≈ 0.2549 [ Info: [ 66%] LOSS: training ≈ 0.2549 [ Info: [ 66%] LOSS: training ≈ 0.2548 [ Info: [ 66%] LOSS: training ≈ 0.2548 [ Info: [ 67%] LOSS: training ≈ 0.2548 [ Info: [ 68%] LOSS: training ≈ 0.2547 [ Info: [ 68%] LOSS: training ≈ 0.2547 [ Info: [ 68%] LOSS: training ≈ 0.2546 [ Info: [ 69%] LOSS: training ≈ 0.2546 [ Info: [ 70%] LOSS: training ≈ 0.2546 [ Info: [ 70%] LOSS: training ≈ 0.2545 [ Info: [ 70%] LOSS: training ≈ 0.2545 [ Info: [ 71%] LOSS: training ≈ 0.2545 [ Info: [ 72%] LOSS: training ≈ 0.2544 [ Info: [ 72%] LOSS: training ≈ 0.2544 [ Info: [ 72%] LOSS: training ≈ 0.2544 [ Info: [ 73%] LOSS: training ≈ 0.2543 [ Info: [ 74%] LOSS: training ≈ 0.2543 [ Info: [ 74%] LOSS: training ≈ 0.2543 [ Info: [ 74%] LOSS: training ≈ 0.2542 [ Info: [ 75%] LOSS: training ≈ 0.2542 [ Info: [ 76%] LOSS: training ≈ 0.2542 [ Info: [ 76%] LOSS: training ≈ 0.2541 [ Info: [ 76%] LOSS: training ≈ 0.2541 [ Info: [ 77%] LOSS: training ≈ 0.2541 [ Info: [ 78%] LOSS: training ≈ 0.2541 [ Info: [ 78%] LOSS: training ≈ 0.254 [ Info: [ 78%] LOSS: training ≈ 0.254 [ Info: [ 79%] LOSS: training ≈ 0.254 [ Info: [ 80%] LOSS: training ≈ 0.254 [ Info: [ 80%] LOSS: training ≈ 0.2539 [ Info: [ 80%] LOSS: training ≈ 0.2539 [ Info: [ 81%] LOSS: training ≈ 0.2539 [ Info: [ 82%] LOSS: training ≈ 0.2539 [ Info: [ 82%] LOSS: training ≈ 0.2538 [ Info: [ 82%] LOSS: training ≈ 0.2538 [ Info: [ 83%] LOSS: training ≈ 0.2538 [ Info: [ 84%] LOSS: training ≈ 0.2538 [ Info: [ 84%] LOSS: training ≈ 0.2537 [ Info: [ 84%] LOSS: training ≈ 0.2537 [ Info: [ 85%] LOSS: training ≈ 0.2537 [ Info: [ 86%] LOSS: training ≈ 0.2537 [ Info: [ 86%] LOSS: training ≈ 0.2537 [ Info: [ 86%] LOSS: training ≈ 0.2536 [ Info: [ 87%] LOSS: training ≈ 0.2536 [ Info: [ 88%] LOSS: training ≈ 0.2536 [ Info: [ 88%] LOSS: training ≈ 0.2536 [ Info: [ 88%] LOSS: training ≈ 0.2536 [ Info: [ 89%] LOSS: training ≈ 0.2535 [ Info: [ 90%] LOSS: training ≈ 0.2535 [ Info: [ 90%] LOSS: training ≈ 0.2535 [ Info: [ 90%] LOSS: training ≈ 0.2535 [ Info: [ 91%] LOSS: training ≈ 0.2535 [ Info: [ 92%] LOSS: training ≈ 0.2535 [ Info: [ 92%] LOSS: training ≈ 0.2534 [ Info: [ 92%] LOSS: training ≈ 0.2534 [ Info: [ 93%] LOSS: training ≈ 0.2534 [ Info: [ 94%] LOSS: training ≈ 0.2534 [ Info: [ 94%] LOSS: training ≈ 0.2534 [ Info: [ 94%] LOSS: training ≈ 0.2534 [ Info: [ 95%] LOSS: training ≈ 0.2533 [ Info: [ 96%] LOSS: training ≈ 0.2533 [ Info: [ 96%] LOSS: training ≈ 0.2533 [ Info: [ 96%] LOSS: training ≈ 0.2533 [ Info: [ 97%] LOSS: training ≈ 0.2533 [ Info: [ 98%] LOSS: training ≈ 0.2533 [ Info: [ 98%] LOSS: training ≈ 0.2533 [ Info: [ 98%] LOSS: training ≈ 0.2532 [ Info: [ 99%] LOSS: training ≈ 0.2532 [ Info: [100%] LOSS: training ≈ 0.2532 [ Info: [100%] LOSS: training ≈ 0.2532 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 [ Info: Baseline mcc: -2.8367742685852565e-17 [ Info: Optimal 1 variables model - mcc ≈ 0.7569 [ Info: Optimal 2 variables model - mcc ≈ 0.7777 [ Info: Returning model with 2 variables - mcc ≈ 0.7777 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90 [ Info: [ 2 vars.] MCC val. ≈ -0.0 [ Info: [ 3 vars.] MCC val. ≈ 0.807 [ Info: [ 4 vars.] MCC val. ≈ 0.835 [ Info: Optimal var. pool: [1, 12, 18, 19] ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44 ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90 [ Info: [ 0 vars.] MCC val. ≈ -0.0 [ Info: [ 1 vars.] MCC val. ≈ 0.732 [ Info: [ 2 vars.] MCC val. ≈ 0.773 [ Info: Optimal var. pool: [8, 3] ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44 [ Info: [19 vars.] MCC val. ≈ -0.0 [ Info: [18 vars.] MCC val. ≈ 0.731 [ Info: [17 vars.] MCC val. ≈ 0.738 [ Info: Optimal var. pool: [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18] ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44 [ Info: [ 9 vars.] MCC val. ≈ -0.0 [ Info: [ 8 vars.] MCC val. ≈ 0.784 [ Info: [ 7 vars.] MCC val. ≈ 0.803 [ Info: [ 6 vars.] MCC val. ≈ 0.81 [ Info: Optimal var. pool: [1, 3, 5, 6, 8, 9] ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90 [ Info: [ 2 vars.] MCC val. ≈ -0.0 [ Info: [ 3 vars.] MCC val. ≈ 0.711 [ Info: [ 4 vars.] MCC val. ≈ 0.739 [ Info: [ 5 vars.] MCC val. ≈ 0.759 [ Info: [ 6 vars.] MCC val. ≈ 0.78 [ Info: [ 7 vars.] MCC val. ≈ 0.781 [ Info: Optimal var. pool: [12, 13, 8, 4, 5, 7, 1] ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90 [ Info: [ 0 vars.] MCC val. ≈ -0.0 [ Info: [ 1 vars.] MCC val. ≈ 0.758 [ Info: [ 2 vars.] MCC val. ≈ 0.771 [ Info: [ 3 vars.] MCC val. ≈ 0.785 [ Info: Optimal var. pool: [8, 4, 7] Test Summary: | Pass Total Time Package | 375 375 7m07.3s Testing SDeMo tests passed Testing completed after 466.41s PkgEval succeeded after 512.6s