Package evaluation to test SDeMo on Julia 1.14.0-DEV.1372 (893635dc59*) started at 2025-12-16T16:23:29.719 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 9.81s ################################################################################ # 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 v0.11.36 [69de0a69] + Parsers v2.8.3 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [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 4.73s ################################################################################ # 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 13.43s ################################################################################ # Testing # Testing SDeMo Status `/tmp/jl_2m2dhF/Project.toml` [3e5feb82] SDeMo v1.7.1 [10745b16] Statistics v1.11.1 [f8b46487] TestItemRunner v1.1.1 [8dfed614] Test v1.11.0 Status `/tmp/jl_2m2dhF/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 v0.11.36 [69de0a69] Parsers v2.8.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 [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 v1.1.1 [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 ≈ 0.5003 validation ≈ 0.4822 ( 96%) [ Info: [ 1%] LOSS: training ≈ 0.4271 validation ≈ 0.4129 ( 97%) [ Info: [ 2%] LOSS: training ≈ 0.3912 validation ≈ 0.3778 ( 97%) [ Info: [ 2%] LOSS: training ≈ 0.37 validation ≈ 0.3566 ( 96%) [ Info: [ 2%] LOSS: training ≈ 0.3558 validation ≈ 0.3422 ( 96%) [ Info: [ 3%] LOSS: training ≈ 0.3456 validation ≈ 0.3317 ( 96%) [ Info: [ 4%] LOSS: training ≈ 0.3379 validation ≈ 0.3235 ( 96%) [ Info: [ 4%] LOSS: training ≈ 0.3318 validation ≈ 0.317 ( 96%) [ Info: [ 4%] LOSS: training ≈ 0.3268 validation ≈ 0.3115 ( 95%) [ Info: [ 5%] LOSS: training ≈ 0.3226 validation ≈ 0.3068 ( 95%) [ Info: [ 6%] LOSS: training ≈ 0.319 validation ≈ 0.3027 ( 95%) [ Info: [ 6%] LOSS: training ≈ 0.3158 validation ≈ 0.2991 ( 95%) [ Info: [ 6%] LOSS: training ≈ 0.3131 validation ≈ 0.2958 ( 94%) [ Info: [ 7%] LOSS: training ≈ 0.3106 validation ≈ 0.2929 ( 94%) [ Info: [ 8%] LOSS: training ≈ 0.3084 validation ≈ 0.2902 ( 94%) [ Info: [ 8%] LOSS: training ≈ 0.3063 validation ≈ 0.2877 ( 94%) [ Info: [ 8%] LOSS: training ≈ 0.3045 validation ≈ 0.2853 ( 94%) [ Info: [ 9%] LOSS: training ≈ 0.3028 validation ≈ 0.2832 ( 94%) [ Info: [ 10%] LOSS: training ≈ 0.3012 validation ≈ 0.2812 ( 93%) [ Info: [ 10%] LOSS: training ≈ 0.2997 validation ≈ 0.2793 ( 93%) [ Info: [ 10%] LOSS: training ≈ 0.2983 validation ≈ 0.2775 ( 93%) [ Info: [ 11%] LOSS: training ≈ 0.297 validation ≈ 0.2758 ( 93%) [ Info: [ 12%] LOSS: training ≈ 0.2958 validation ≈ 0.2742 ( 93%) [ Info: [ 12%] LOSS: training ≈ 0.2946 validation ≈ 0.2727 ( 93%) [ Info: [ 12%] LOSS: training ≈ 0.2935 validation ≈ 0.2713 ( 92%) [ Info: [ 13%] LOSS: training ≈ 0.2925 validation ≈ 0.2699 ( 92%) [ Info: [ 14%] LOSS: training ≈ 0.2915 validation ≈ 0.2686 ( 92%) [ Info: [ 14%] LOSS: training ≈ 0.2906 validation ≈ 0.2674 ( 92%) [ Info: [ 14%] LOSS: training ≈ 0.2897 validation ≈ 0.2662 ( 92%) [ Info: [ 15%] LOSS: training ≈ 0.2888 validation ≈ 0.265 ( 92%) [ Info: [ 16%] LOSS: training ≈ 0.288 validation ≈ 0.2639 ( 92%) [ Info: [ 16%] LOSS: training ≈ 0.2872 validation ≈ 0.2629 ( 92%) [ Info: [ 16%] LOSS: training ≈ 0.2864 validation ≈ 0.2619 ( 91%) [ Info: [ 17%] LOSS: training ≈ 0.2857 validation ≈ 0.2609 ( 91%) [ Info: [ 18%] LOSS: training ≈ 0.285 validation ≈ 0.26 ( 91%) [ Info: [ 18%] LOSS: training ≈ 0.2843 validation ≈ 0.2591 ( 91%) [ Info: [ 18%] LOSS: training ≈ 0.2837 validation ≈ 0.2582 ( 91%) [ Info: [ 19%] LOSS: training ≈ 0.283 validation ≈ 0.2574 ( 91%) [ Info: [ 20%] LOSS: training ≈ 0.2824 validation ≈ 0.2566 ( 91%) [ Info: [ 20%] LOSS: training ≈ 0.2818 validation ≈ 0.2558 ( 91%) [ Info: [ 20%] LOSS: training ≈ 0.2813 validation ≈ 0.2551 ( 91%) [ Info: [ 21%] LOSS: training ≈ 0.2807 validation ≈ 0.2544 ( 91%) [ Info: [ 22%] LOSS: training ≈ 0.2802 validation ≈ 0.2537 ( 91%) [ Info: [ 22%] LOSS: training ≈ 0.2797 validation ≈ 0.253 ( 90%) [ Info: [ 22%] LOSS: training ≈ 0.2792 validation ≈ 0.2523 ( 90%) [ Info: [ 23%] LOSS: training ≈ 0.2787 validation ≈ 0.2517 ( 90%) [ Info: [ 24%] LOSS: training ≈ 0.2782 validation ≈ 0.2511 ( 90%) [ Info: [ 24%] LOSS: training ≈ 0.2778 validation ≈ 0.2505 ( 90%) [ Info: [ 24%] LOSS: training ≈ 0.2773 validation ≈ 0.25 ( 90%) [ Info: [ 25%] LOSS: training ≈ 0.2769 validation ≈ 0.2494 ( 90%) [ Info: [ 26%] LOSS: training ≈ 0.2765 validation ≈ 0.2489 ( 90%) [ Info: [ 26%] LOSS: training ≈ 0.2761 validation ≈ 0.2484 ( 90%) [ Info: [ 26%] LOSS: training ≈ 0.2757 validation ≈ 0.2479 ( 90%) [ Info: [ 27%] LOSS: training ≈ 0.2753 validation ≈ 0.2474 ( 90%) [ Info: [ 28%] LOSS: training ≈ 0.275 validation ≈ 0.2469 ( 90%) [ Info: [ 28%] LOSS: training ≈ 0.2746 validation ≈ 0.2465 ( 90%) [ Info: [ 28%] LOSS: training ≈ 0.2743 validation ≈ 0.246 ( 90%) [ Info: [ 29%] LOSS: training ≈ 0.2739 validation ≈ 0.2456 ( 90%) [ Info: [ 30%] LOSS: training ≈ 0.2736 validation ≈ 0.2452 ( 90%) [ Info: [ 30%] LOSS: training ≈ 0.2733 validation ≈ 0.2448 ( 90%) [ Info: [ 30%] LOSS: training ≈ 0.273 validation ≈ 0.2444 ( 90%) [ Info: [ 31%] LOSS: training ≈ 0.2727 validation ≈ 0.244 ( 90%) [ Info: [ 32%] LOSS: training ≈ 0.2724 validation ≈ 0.2437 ( 89%) [ Info: [ 32%] LOSS: training ≈ 0.2721 validation ≈ 0.2433 ( 89%) [ Info: [ 32%] LOSS: training ≈ 0.2718 validation ≈ 0.243 ( 89%) [ Info: [ 33%] LOSS: training ≈ 0.2715 validation ≈ 0.2427 ( 89%) [ Info: [ 34%] LOSS: training ≈ 0.2713 validation ≈ 0.2423 ( 89%) [ Info: [ 34%] LOSS: training ≈ 0.271 validation ≈ 0.242 ( 89%) [ Info: [ 34%] LOSS: training ≈ 0.2708 validation ≈ 0.2417 ( 89%) [ Info: [ 35%] LOSS: training ≈ 0.2705 validation ≈ 0.2414 ( 89%) [ Info: [ 36%] LOSS: training ≈ 0.2703 validation ≈ 0.2411 ( 89%) [ Info: [ 36%] LOSS: training ≈ 0.27 validation ≈ 0.2408 ( 89%) [ Info: [ 36%] LOSS: training ≈ 0.2698 validation ≈ 0.2406 ( 89%) [ Info: [ 37%] LOSS: training ≈ 0.2696 validation ≈ 0.2403 ( 89%) [ Info: [ 38%] LOSS: training ≈ 0.2694 validation ≈ 0.24 ( 89%) [ Info: [ 38%] LOSS: training ≈ 0.2692 validation ≈ 0.2398 ( 89%) [ Info: [ 38%] LOSS: training ≈ 0.269 validation ≈ 0.2395 ( 89%) [ Info: [ 39%] LOSS: training ≈ 0.2688 validation ≈ 0.2393 ( 89%) [ Info: [ 40%] LOSS: training ≈ 0.2686 validation ≈ 0.2391 ( 89%) [ Info: [ 40%] LOSS: training ≈ 0.2684 validation ≈ 0.2388 ( 89%) [ Info: [ 40%] LOSS: training ≈ 0.2682 validation ≈ 0.2386 ( 89%) [ Info: [ 41%] LOSS: training ≈ 0.268 validation ≈ 0.2384 ( 89%) [ Info: [ 42%] LOSS: training ≈ 0.2678 validation ≈ 0.2382 ( 89%) [ Info: [ 42%] LOSS: training ≈ 0.2677 validation ≈ 0.238 ( 89%) [ Info: [ 42%] LOSS: training ≈ 0.2675 validation ≈ 0.2378 ( 89%) [ Info: [ 43%] LOSS: training ≈ 0.2673 validation ≈ 0.2376 ( 89%) [ Info: [ 44%] LOSS: training ≈ 0.2672 validation ≈ 0.2374 ( 89%) [ Info: [ 44%] LOSS: training ≈ 0.267 validation ≈ 0.2372 ( 89%) [ Info: [ 44%] LOSS: training ≈ 0.2669 validation ≈ 0.237 ( 89%) [ Info: [ 45%] LOSS: training ≈ 0.2667 validation ≈ 0.2369 ( 89%) [ Info: [ 46%] LOSS: training ≈ 0.2666 validation ≈ 0.2367 ( 89%) [ Info: [ 46%] LOSS: training ≈ 0.2664 validation ≈ 0.2365 ( 89%) [ Info: [ 46%] LOSS: training ≈ 0.2663 validation ≈ 0.2364 ( 89%) [ Info: [ 47%] LOSS: training ≈ 0.2662 validation ≈ 0.2362 ( 89%) [ Info: [ 48%] LOSS: training ≈ 0.266 validation ≈ 0.236 ( 89%) [ Info: [ 48%] LOSS: training ≈ 0.2659 validation ≈ 0.2359 ( 89%) [ Info: [ 48%] LOSS: training ≈ 0.2658 validation ≈ 0.2357 ( 89%) [ Info: [ 49%] LOSS: training ≈ 0.2656 validation ≈ 0.2356 ( 89%) [ Info: [ 50%] LOSS: training ≈ 0.2655 validation ≈ 0.2354 ( 89%) [ Info: [ 50%] LOSS: training ≈ 0.2654 validation ≈ 0.2353 ( 89%) [ Info: [ 50%] LOSS: training ≈ 0.2653 validation ≈ 0.2352 ( 89%) [ Info: [ 51%] LOSS: training ≈ 0.2652 validation ≈ 0.235 ( 89%) [ Info: [ 52%] LOSS: training ≈ 0.2651 validation ≈ 0.2349 ( 89%) [ Info: [ 52%] LOSS: training ≈ 0.265 validation ≈ 0.2348 ( 89%) [ Info: [ 52%] LOSS: training ≈ 0.2648 validation ≈ 0.2347 ( 89%) [ Info: [ 53%] LOSS: training ≈ 0.2647 validation ≈ 0.2345 ( 89%) [ Info: [ 54%] LOSS: training ≈ 0.2646 validation ≈ 0.2344 ( 89%) [ Info: [ 54%] LOSS: training ≈ 0.2645 validation ≈ 0.2343 ( 89%) [ Info: [ 55%] LOSS: training ≈ 0.2644 validation ≈ 0.2342 ( 89%) [ Info: [ 55%] LOSS: training ≈ 0.2643 validation ≈ 0.2341 ( 89%) [ Info: [ 56%] LOSS: training ≈ 0.2643 validation ≈ 0.234 ( 89%) [ Info: [ 56%] LOSS: training ≈ 0.2642 validation ≈ 0.2339 ( 89%) [ Info: [ 56%] LOSS: training ≈ 0.2641 validation ≈ 0.2338 ( 89%) [ Info: [ 57%] LOSS: training ≈ 0.264 validation ≈ 0.2337 ( 89%) [ Info: [ 57%] LOSS: training ≈ 0.2639 validation ≈ 0.2336 ( 89%) [ Info: [ 58%] LOSS: training ≈ 0.2638 validation ≈ 0.2335 ( 88%) [ Info: [ 58%] LOSS: training ≈ 0.2637 validation ≈ 0.2334 ( 88%) [ Info: [ 59%] LOSS: training ≈ 0.2636 validation ≈ 0.2333 ( 88%) [ Info: [ 60%] LOSS: training ≈ 0.2636 validation ≈ 0.2332 ( 88%) [ Info: [ 60%] LOSS: training ≈ 0.2635 validation ≈ 0.2331 ( 88%) [ Info: [ 60%] LOSS: training ≈ 0.2634 validation ≈ 0.233 ( 88%) [ Info: [ 61%] LOSS: training ≈ 0.2633 validation ≈ 0.2329 ( 88%) [ Info: [ 62%] LOSS: training ≈ 0.2633 validation ≈ 0.2328 ( 88%) [ Info: [ 62%] LOSS: training ≈ 0.2632 validation ≈ 0.2328 ( 88%) [ Info: [ 62%] LOSS: training ≈ 0.2631 validation ≈ 0.2327 ( 88%) [ Info: [ 63%] LOSS: training ≈ 0.263 validation ≈ 0.2326 ( 88%) [ Info: [ 64%] LOSS: training ≈ 0.263 validation ≈ 0.2325 ( 88%) [ Info: [ 64%] LOSS: training ≈ 0.2629 validation ≈ 0.2325 ( 88%) [ Info: [ 64%] LOSS: training ≈ 0.2628 validation ≈ 0.2324 ( 88%) [ Info: [ 65%] LOSS: training ≈ 0.2628 validation ≈ 0.2323 ( 88%) [ Info: [ 66%] LOSS: training ≈ 0.2627 validation ≈ 0.2322 ( 88%) [ Info: [ 66%] LOSS: training ≈ 0.2627 validation ≈ 0.2322 ( 88%) [ Info: [ 66%] LOSS: training ≈ 0.2626 validation ≈ 0.2321 ( 88%) [ Info: [ 67%] LOSS: training ≈ 0.2625 validation ≈ 0.232 ( 88%) [ Info: [ 68%] LOSS: training ≈ 0.2625 validation ≈ 0.232 ( 88%) [ Info: [ 68%] LOSS: training ≈ 0.2624 validation ≈ 0.2319 ( 88%) [ Info: [ 68%] LOSS: training ≈ 0.2624 validation ≈ 0.2318 ( 88%) [ Info: [ 69%] LOSS: training ≈ 0.2623 validation ≈ 0.2318 ( 88%) [ Info: [ 70%] LOSS: training ≈ 0.2622 validation ≈ 0.2317 ( 88%) [ Info: [ 70%] LOSS: training ≈ 0.2622 validation ≈ 0.2317 ( 88%) [ Info: [ 70%] LOSS: training ≈ 0.2621 validation ≈ 0.2316 ( 88%) [ Info: [ 71%] LOSS: training ≈ 0.2621 validation ≈ 0.2316 ( 88%) [ Info: [ 72%] LOSS: training ≈ 0.262 validation ≈ 0.2315 ( 88%) [ Info: [ 72%] LOSS: training ≈ 0.262 validation ≈ 0.2314 ( 88%) [ Info: [ 72%] LOSS: training ≈ 0.2619 validation ≈ 0.2314 ( 88%) [ Info: [ 73%] LOSS: training ≈ 0.2619 validation ≈ 0.2313 ( 88%) [ Info: [ 74%] LOSS: training ≈ 0.2618 validation ≈ 0.2313 ( 88%) [ Info: [ 74%] LOSS: training ≈ 0.2618 validation ≈ 0.2312 ( 88%) [ Info: [ 74%] LOSS: training ≈ 0.2617 validation ≈ 0.2312 ( 88%) [ Info: [ 75%] LOSS: training ≈ 0.2617 validation ≈ 0.2311 ( 88%) [ Info: [ 76%] LOSS: training ≈ 0.2617 validation ≈ 0.2311 ( 88%) [ Info: [ 76%] LOSS: training ≈ 0.2616 validation ≈ 0.231 ( 88%) [ Info: [ 76%] LOSS: training ≈ 0.2616 validation ≈ 0.231 ( 88%) [ Info: [ 77%] LOSS: training ≈ 0.2615 validation ≈ 0.231 ( 88%) [ Info: [ 78%] LOSS: training ≈ 0.2615 validation ≈ 0.2309 ( 88%) [ Info: [ 78%] LOSS: training ≈ 0.2614 validation ≈ 0.2309 ( 88%) [ Info: [ 78%] LOSS: training ≈ 0.2614 validation ≈ 0.2308 ( 88%) [ Info: [ 79%] LOSS: training ≈ 0.2614 validation ≈ 0.2308 ( 88%) [ Info: [ 80%] LOSS: training ≈ 0.2613 validation ≈ 0.2307 ( 88%) [ Info: [ 80%] LOSS: training ≈ 0.2613 validation ≈ 0.2307 ( 88%) [ Info: [ 80%] LOSS: training ≈ 0.2612 validation ≈ 0.2307 ( 88%) [ Info: [ 81%] LOSS: training ≈ 0.2612 validation ≈ 0.2306 ( 88%) [ Info: [ 82%] LOSS: training ≈ 0.2612 validation ≈ 0.2306 ( 88%) [ Info: [ 82%] LOSS: training ≈ 0.2611 validation ≈ 0.2306 ( 88%) [ Info: [ 82%] LOSS: training ≈ 0.2611 validation ≈ 0.2305 ( 88%) [ Info: [ 83%] LOSS: training ≈ 0.2611 validation ≈ 0.2305 ( 88%) [ Info: [ 84%] LOSS: training ≈ 0.261 validation ≈ 0.2304 ( 88%) [ Info: [ 84%] LOSS: training ≈ 0.261 validation ≈ 0.2304 ( 88%) [ Info: [ 84%] LOSS: training ≈ 0.261 validation ≈ 0.2304 ( 88%) [ Info: [ 85%] LOSS: training ≈ 0.2609 validation ≈ 0.2303 ( 88%) [ Info: [ 86%] LOSS: training ≈ 0.2609 validation ≈ 0.2303 ( 88%) [ Info: [ 86%] LOSS: training ≈ 0.2609 validation ≈ 0.2303 ( 88%) [ Info: [ 86%] LOSS: training ≈ 0.2608 validation ≈ 0.2303 ( 88%) [ Info: [ 87%] LOSS: training ≈ 0.2608 validation ≈ 0.2302 ( 88%) [ Info: [ 88%] LOSS: training ≈ 0.2608 validation ≈ 0.2302 ( 88%) [ Info: [ 88%] LOSS: training ≈ 0.2607 validation ≈ 0.2302 ( 88%) [ Info: [ 88%] LOSS: training ≈ 0.2607 validation ≈ 0.2301 ( 88%) [ Info: [ 89%] LOSS: training ≈ 0.2607 validation ≈ 0.2301 ( 88%) [ Info: [ 90%] LOSS: training ≈ 0.2607 validation ≈ 0.2301 ( 88%) [ Info: [ 90%] LOSS: training ≈ 0.2606 validation ≈ 0.2301 ( 88%) [ Info: [ 90%] LOSS: training ≈ 0.2606 validation ≈ 0.23 ( 88%) [ Info: [ 91%] LOSS: training ≈ 0.2606 validation ≈ 0.23 ( 88%) [ Info: [ 92%] LOSS: training ≈ 0.2605 validation ≈ 0.23 ( 88%) [ Info: [ 92%] LOSS: training ≈ 0.2605 validation ≈ 0.23 ( 88%) [ Info: [ 92%] LOSS: training ≈ 0.2605 validation ≈ 0.2299 ( 88%) [ Info: [ 93%] LOSS: training ≈ 0.2605 validation ≈ 0.2299 ( 88%) [ Info: [ 94%] LOSS: training ≈ 0.2604 validation ≈ 0.2299 ( 88%) [ Info: [ 94%] LOSS: training ≈ 0.2604 validation ≈ 0.2299 ( 88%) [ Info: [ 94%] LOSS: training ≈ 0.2604 validation ≈ 0.2298 ( 88%) [ Info: [ 95%] LOSS: training ≈ 0.2604 validation ≈ 0.2298 ( 88%) [ Info: [ 96%] LOSS: training ≈ 0.2603 validation ≈ 0.2298 ( 88%) [ Info: [ 96%] LOSS: training ≈ 0.2603 validation ≈ 0.2298 ( 88%) [ Info: [ 96%] LOSS: training ≈ 0.2603 validation ≈ 0.2297 ( 88%) [ Info: [ 97%] LOSS: training ≈ 0.2603 validation ≈ 0.2297 ( 88%) [ Info: [ 98%] LOSS: training ≈ 0.2602 validation ≈ 0.2297 ( 88%) [ Info: [ 98%] LOSS: training ≈ 0.2602 validation ≈ 0.2297 ( 88%) [ Info: [ 98%] LOSS: training ≈ 0.2602 validation ≈ 0.2297 ( 88%) [ Info: [ 99%] LOSS: training ≈ 0.2602 validation ≈ 0.2296 ( 88%) [ Info: [100%] LOSS: training ≈ 0.2602 validation ≈ 0.2296 ( 88%) [ Info: [100%] LOSS: training ≈ 0.2601 validation ≈ 0.2296 ( 88%) [ 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 ≈ 0.7529 [ Info: Optimal 2 variables model - mcc ≈ 0.7745 [ Info: Optimal 3 variables model - mcc ≈ 0.7766 [ Info: Optimal 4 variables model - mcc ≈ 0.8008 [ Info: Returning model with 4 variables - mcc ≈ 0.8008 ┌ 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. ≈ 0.788 [ Info: [ 4 vars.] MCC val. ≈ 0.815 [ Info: [ 5 vars.] MCC val. ≈ 0.822 [ Info: [ 6 vars.] MCC val. ≈ 0.829 [ Info: [ 7 vars.] MCC val. ≈ 0.836 [ Info: Optimal var. pool: [1, 12, 2, 4, 14, 17, 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. ≈ 0.714 [ Info: [ 2 vars.] MCC val. ≈ 0.741 [ Info: [ 3 vars.] MCC val. ≈ 0.747 [ Info: [ 4 vars.] MCC val. ≈ 0.754 [ Info: [ 5 vars.] MCC val. ≈ 0.767 [ Info: [ 6 vars.] MCC val. ≈ 0.773 [ Info: [ 7 vars.] MCC val. ≈ 0.794 [ Info: [ 8 vars.] MCC val. ≈ 0.801 [ Info: Optimal var. pool: [8, 3, 18, 19, 1, 13, 4, 16] ┌ 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. ≈ 0.808 [ Info: [17 vars.] MCC val. ≈ 0.815 [ Info: [16 vars.] MCC val. ≈ 0.822 [ Info: [15 vars.] MCC val. ≈ 0.869 [ Info: Optimal var. pool: [1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 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. ≈ 0.793 [ Info: [ 7 vars.] MCC val. ≈ 0.8 [ Info: [ 6 vars.] MCC val. ≈ 0.807 [ 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. ≈ 0.759 [ Info: [ 4 vars.] MCC val. ≈ 0.767 [ Info: [ 5 vars.] MCC val. ≈ 0.814 [ Info: [ 6 vars.] MCC val. ≈ 0.814 [ Info: Optimal var. pool: [12, 13, 8, 1, 3, 5] ┌ 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. ≈ 0.76 [ Info: [ 2 vars.] MCC val. ≈ 0.763 [ Info: [ 3 vars.] MCC val. ≈ 0.794 [ Info: [ 4 vars.] MCC val. ≈ 0.809 [ Info: [ 5 vars.] MCC val. ≈ 0.837 [ Info: [ 6 vars.] MCC val. ≈ 0.85 [ Info: Optimal var. pool: [8, 4, 7, 5, 14, 19] Test Summary: | Pass Total Time Package | 374 374 12m03.4s Testing SDeMo tests passed Testing completed after 766.47s PkgEval succeeded after 809.11s