Package evaluation of SDeMo on Julia 1.13.0-DEV.955 (13f6b3b5c0*) started at 2025-08-03T17:07:05.144 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 10.05s ################################################################################ # Installation # Installing SDeMo... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [3e5feb82] + SDeMo v1.5.1 Updating `~/.julia/environments/v1.13/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [34da2185] + Compat v4.18.0 [9a962f9c] + DataAPI v1.16.0 ⌅ [864edb3b] + DataStructures v0.18.22 [31c24e10] + Distributions v0.25.120 [ffbed154] + DocStringExtensions v0.9.5 [1a297f60] + FillArrays v1.13.0 [34004b35] + HypergeometricFunctions v0.3.28 [92d709cd] + IrrationalConstants v0.2.4 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v0.21.4 [2ab3a3ac] + LogExpFunctions v0.3.29 [e1d29d7a] + Missings v1.2.0 [6f286f6a] + MultivariateStats v0.10.3 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [69de0a69] + Parsers v2.8.3 [aea7be01] + PrecompileTools v1.3.2 [21216c6a] + Preferences v1.4.3 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [189a3867] + Reexport v1.2.2 [79098fc4] + Rmath v0.8.0 [3e5feb82] + SDeMo v1.5.1 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.1 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.5 [4c63d2b9] + StatsFuns v1.5.0 [1c621080] + TestItems v1.0.0 ⌅ [68821587] + Arpack_jll v3.5.1+1 [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 v1.12.0 [b27032c2] + LibCURL v0.6.4 [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 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.15.0+0 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.7.15 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.5+0 [458c3c95] + OpenSSL_jll v3.5.1+0 [efcefdf7] + PCRE2_jll v10.45.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.65.0+0 [3f19e933] + p7zip_jll v17.5.0+2 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 4.96s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 32.66s ################################################################################ # Testing # Testing SDeMo Status `/tmp/jl_6ujgyi/Project.toml` [3e5feb82] SDeMo v1.5.1 [10745b16] Statistics v1.11.1 [f8b46487] TestItemRunner v1.1.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_6ujgyi/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [34da2185] Compat v4.18.0 [9a962f9c] DataAPI v1.16.0 ⌅ [864edb3b] DataStructures v0.18.22 [31c24e10] Distributions v0.25.120 [ffbed154] DocStringExtensions v0.9.5 [1a297f60] FillArrays v1.13.0 [34004b35] HypergeometricFunctions v0.3.28 [92d709cd] IrrationalConstants v0.2.4 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v0.21.4 [2ab3a3ac] LogExpFunctions v0.3.29 [e1d29d7a] Missings v1.2.0 [6f286f6a] MultivariateStats v0.10.3 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.35 [69de0a69] Parsers v2.8.3 [aea7be01] PrecompileTools v1.3.2 [21216c6a] Preferences v1.4.3 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 [189a3867] Reexport v1.2.2 [79098fc4] Rmath v0.8.0 [3e5feb82] SDeMo v1.5.1 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.1 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 [2913bbd2] StatsBase v0.34.5 [4c63d2b9] StatsFuns v1.5.0 [f8b46487] TestItemRunner v1.1.0 [1c621080] TestItems v1.0.0 ⌅ [68821587] Arpack_jll v3.5.1+1 [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 v1.12.0 [b27032c2] LibCURL v0.6.4 [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 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.11.0 [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 v8.15.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.7.15 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.5+0 [458c3c95] OpenSSL_jll v3.5.1+0 [efcefdf7] PCRE2_jll v10.45.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.13.1+0 [8e850ede] nghttp2_jll v1.65.0+0 [3f19e933] p7zip_jll v17.5.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:90 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:90 [ Info: [ 2 vars.] MCC val. ≈ -0.0 [ Info: [ 3 vars.] MCC val. ≈ 0.507 [ Info: Optimal var. pool: [1, 12, 19] ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:44 ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:44 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:90 [ Info: [ 0 vars.] MCC val. ≈ -0.0 [ Info: [ 1 vars.] MCC val. ≈ 0.794 [ Info: [ 2 vars.] MCC val. ≈ 0.814 [ Info: [ 3 vars.] MCC val. ≈ 0.821 [ Info: [ 4 vars.] MCC val. ≈ 0.828 [ Info: Optimal var. pool: [8, 4, 17, 14] ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:44 [ Info: [19 vars.] MCC val. ≈ -0.0 [ Info: [18 vars.] MCC val. ≈ 0.519 [ Info: [17 vars.] MCC val. ≈ 0.718 [ Info: [16 vars.] MCC val. ≈ 0.734 [ Info: [15 vars.] MCC val. ≈ 0.746 [ Info: [14 vars.] MCC val. ≈ 0.752 [ Info: Optimal var. pool: [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 15, 16, 17, 18] ┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:44 [ Info: [ 9 vars.] MCC val. ≈ -0.0 [ Info: [ 8 vars.] MCC val. ≈ 0.801 [ Info: [ 7 vars.] MCC val. ≈ 0.807 [ Info: [ 6 vars.] MCC val. ≈ 0.822 [ Info: Optimal var. pool: [2, 3, 4, 7, 8, 9] ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:90 [ Info: [ 2 vars.] MCC val. ≈ -0.0 [ Info: [ 3 vars.] MCC val. ≈ 0.48 [ Info: [ 4 vars.] MCC val. ≈ 0.488 [ Info: Optimal var. pool: [12, 13, 1, 4] ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/selection.jl:90 [ Info: [ 0 vars.] MCC val. ≈ -0.0 [ Info: [ 1 vars.] MCC val. ≈ 0.731 [ Info: [ 2 vars.] MCC val. ≈ 0.752 [ Info: Optimal var. pool: [8, 7] [ Info: [ 0%] LOSS: training ≈ 0.4976 validation ≈ 0.4893 ( 98%) [ Info: [ 1%] LOSS: training ≈ 0.4252 validation ≈ 0.4212 ( 99%) [ Info: [ 2%] LOSS: training ≈ 0.3895 validation ≈ 0.3864 ( 99%) [ Info: [ 2%] LOSS: training ≈ 0.3683 validation ≈ 0.3653 ( 99%) [ Info: [ 2%] LOSS: training ≈ 0.354 validation ≈ 0.3511 ( 99%) [ Info: [ 3%] LOSS: training ≈ 0.3438 validation ≈ 0.3407 ( 99%) [ Info: [ 4%] LOSS: training ≈ 0.336 validation ≈ 0.3327 ( 99%) [ Info: [ 4%] LOSS: training ≈ 0.3297 validation ≈ 0.3262 ( 99%) [ Info: [ 4%] LOSS: training ≈ 0.3247 validation ≈ 0.3209 ( 99%) [ Info: [ 5%] LOSS: training ≈ 0.3204 validation ≈ 0.3164 ( 99%) [ Info: [ 6%] LOSS: training ≈ 0.3167 validation ≈ 0.3124 ( 99%) [ Info: [ 6%] LOSS: training ≈ 0.3135 validation ≈ 0.309 ( 99%) [ Info: [ 6%] LOSS: training ≈ 0.3107 validation ≈ 0.3058 ( 98%) [ Info: [ 7%] LOSS: training ≈ 0.3082 validation ≈ 0.303 ( 98%) [ Info: [ 8%] LOSS: training ≈ 0.306 validation ≈ 0.3005 ( 98%) [ Info: [ 8%] LOSS: training ≈ 0.3039 validation ≈ 0.2981 ( 98%) [ Info: [ 8%] LOSS: training ≈ 0.302 validation ≈ 0.2959 ( 98%) [ Info: [ 9%] LOSS: training ≈ 0.3003 validation ≈ 0.2939 ( 98%) [ Info: [ 10%] LOSS: training ≈ 0.2987 validation ≈ 0.292 ( 98%) [ Info: [ 10%] LOSS: training ≈ 0.2972 validation ≈ 0.2902 ( 98%) [ Info: [ 10%] LOSS: training ≈ 0.2958 validation ≈ 0.2885 ( 98%) [ Info: [ 11%] LOSS: training ≈ 0.2945 validation ≈ 0.2869 ( 97%) [ Info: [ 12%] LOSS: training ≈ 0.2933 validation ≈ 0.2854 ( 97%) [ Info: [ 12%] LOSS: training ≈ 0.2922 validation ≈ 0.284 ( 97%) [ Info: [ 12%] LOSS: training ≈ 0.2911 validation ≈ 0.2826 ( 97%) [ Info: [ 13%] LOSS: training ≈ 0.29 validation ≈ 0.2813 ( 97%) [ Info: [ 14%] LOSS: training ≈ 0.289 validation ≈ 0.28 ( 97%) [ Info: [ 14%] LOSS: training ≈ 0.2881 validation ≈ 0.2789 ( 97%) [ Info: [ 14%] LOSS: training ≈ 0.2872 validation ≈ 0.2777 ( 97%) [ Info: [ 15%] LOSS: training ≈ 0.2864 validation ≈ 0.2766 ( 97%) [ Info: [ 16%] LOSS: training ≈ 0.2855 validation ≈ 0.2756 ( 97%) [ Info: [ 16%] LOSS: training ≈ 0.2848 validation ≈ 0.2745 ( 96%) [ Info: [ 16%] LOSS: training ≈ 0.284 validation ≈ 0.2736 ( 96%) [ Info: [ 17%] LOSS: training ≈ 0.2833 validation ≈ 0.2726 ( 96%) [ Info: [ 18%] LOSS: training ≈ 0.2826 validation ≈ 0.2717 ( 96%) [ Info: [ 18%] LOSS: training ≈ 0.2819 validation ≈ 0.2708 ( 96%) [ Info: [ 18%] LOSS: training ≈ 0.2813 validation ≈ 0.27 ( 96%) [ Info: [ 19%] LOSS: training ≈ 0.2807 validation ≈ 0.2692 ( 96%) [ Info: [ 20%] LOSS: training ≈ 0.2801 validation ≈ 0.2684 ( 96%) [ Info: [ 20%] LOSS: training ≈ 0.2795 validation ≈ 0.2676 ( 96%) [ Info: [ 20%] LOSS: training ≈ 0.2789 validation ≈ 0.2669 ( 96%) [ Info: [ 21%] LOSS: training ≈ 0.2784 validation ≈ 0.2662 ( 96%) [ Info: [ 22%] LOSS: training ≈ 0.2779 validation ≈ 0.2655 ( 96%) [ Info: [ 22%] LOSS: training ≈ 0.2774 validation ≈ 0.2648 ( 95%) [ Info: [ 22%] LOSS: training ≈ 0.2769 validation ≈ 0.2641 ( 95%) [ Info: [ 23%] LOSS: training ≈ 0.2764 validation ≈ 0.2635 ( 95%) [ Info: [ 24%] LOSS: training ≈ 0.2759 validation ≈ 0.2629 ( 95%) [ Info: [ 24%] LOSS: training ≈ 0.2755 validation ≈ 0.2623 ( 95%) [ Info: [ 24%] LOSS: training ≈ 0.2751 validation ≈ 0.2617 ( 95%) [ Info: [ 25%] LOSS: training ≈ 0.2746 validation ≈ 0.2612 ( 95%) [ Info: [ 26%] LOSS: training ≈ 0.2742 validation ≈ 0.2606 ( 95%) [ Info: [ 26%] LOSS: training ≈ 0.2738 validation ≈ 0.2601 ( 95%) [ Info: [ 26%] LOSS: training ≈ 0.2735 validation ≈ 0.2596 ( 95%) [ Info: [ 27%] LOSS: training ≈ 0.2731 validation ≈ 0.2591 ( 95%) [ Info: [ 28%] LOSS: training ≈ 0.2727 validation ≈ 0.2586 ( 95%) [ Info: [ 28%] LOSS: training ≈ 0.2724 validation ≈ 0.2581 ( 95%) [ Info: [ 28%] LOSS: training ≈ 0.272 validation ≈ 0.2576 ( 95%) [ Info: [ 29%] LOSS: training ≈ 0.2717 validation ≈ 0.2572 ( 95%) [ Info: [ 30%] LOSS: training ≈ 0.2714 validation ≈ 0.2568 ( 95%) [ Info: [ 30%] LOSS: training ≈ 0.2711 validation ≈ 0.2563 ( 95%) [ Info: [ 30%] LOSS: training ≈ 0.2708 validation ≈ 0.2559 ( 95%) [ Info: [ 31%] LOSS: training ≈ 0.2705 validation ≈ 0.2555 ( 94%) [ Info: [ 32%] LOSS: training ≈ 0.2702 validation ≈ 0.2551 ( 94%) [ Info: [ 32%] LOSS: training ≈ 0.2699 validation ≈ 0.2548 ( 94%) [ Info: [ 32%] LOSS: training ≈ 0.2697 validation ≈ 0.2544 ( 94%) [ Info: [ 33%] LOSS: training ≈ 0.2694 validation ≈ 0.254 ( 94%) [ Info: [ 34%] LOSS: training ≈ 0.2691 validation ≈ 0.2537 ( 94%) [ Info: [ 34%] LOSS: training ≈ 0.2689 validation ≈ 0.2533 ( 94%) [ Info: [ 34%] LOSS: training ≈ 0.2686 validation ≈ 0.253 ( 94%) [ Info: [ 35%] LOSS: training ≈ 0.2684 validation ≈ 0.2527 ( 94%) [ Info: [ 36%] LOSS: training ≈ 0.2682 validation ≈ 0.2524 ( 94%) [ Info: [ 36%] LOSS: training ≈ 0.268 validation ≈ 0.252 ( 94%) [ Info: [ 36%] LOSS: training ≈ 0.2677 validation ≈ 0.2517 ( 94%) [ Info: [ 37%] LOSS: training ≈ 0.2675 validation ≈ 0.2514 ( 94%) [ Info: [ 38%] LOSS: training ≈ 0.2673 validation ≈ 0.2512 ( 94%) [ Info: [ 38%] LOSS: training ≈ 0.2671 validation ≈ 0.2509 ( 94%) [ Info: [ 38%] LOSS: training ≈ 0.2669 validation ≈ 0.2506 ( 94%) [ Info: [ 39%] LOSS: training ≈ 0.2667 validation ≈ 0.2503 ( 94%) [ Info: [ 40%] LOSS: training ≈ 0.2665 validation ≈ 0.2501 ( 94%) [ Info: [ 40%] LOSS: training ≈ 0.2663 validation ≈ 0.2498 ( 94%) [ Info: [ 40%] LOSS: training ≈ 0.2662 validation ≈ 0.2496 ( 94%) [ Info: [ 41%] LOSS: training ≈ 0.266 validation ≈ 0.2493 ( 94%) [ Info: [ 42%] LOSS: training ≈ 0.2658 validation ≈ 0.2491 ( 94%) [ Info: [ 42%] LOSS: training ≈ 0.2657 validation ≈ 0.2489 ( 94%) [ Info: [ 42%] LOSS: training ≈ 0.2655 validation ≈ 0.2486 ( 94%) [ Info: [ 43%] LOSS: training ≈ 0.2653 validation ≈ 0.2484 ( 94%) [ Info: [ 44%] LOSS: training ≈ 0.2652 validation ≈ 0.2482 ( 94%) [ Info: [ 44%] LOSS: training ≈ 0.265 validation ≈ 0.248 ( 94%) [ Info: [ 44%] LOSS: training ≈ 0.2649 validation ≈ 0.2478 ( 94%) [ Info: [ 45%] LOSS: training ≈ 0.2647 validation ≈ 0.2476 ( 94%) [ Info: [ 46%] LOSS: training ≈ 0.2646 validation ≈ 0.2474 ( 93%) [ Info: [ 46%] LOSS: training ≈ 0.2645 validation ≈ 0.2472 ( 93%) [ Info: [ 46%] LOSS: training ≈ 0.2643 validation ≈ 0.247 ( 93%) [ Info: [ 47%] LOSS: training ≈ 0.2642 validation ≈ 0.2468 ( 93%) [ Info: [ 48%] LOSS: training ≈ 0.2641 validation ≈ 0.2466 ( 93%) [ Info: [ 48%] LOSS: training ≈ 0.264 validation ≈ 0.2464 ( 93%) [ Info: [ 48%] LOSS: training ≈ 0.2638 validation ≈ 0.2463 ( 93%) [ Info: [ 49%] LOSS: training ≈ 0.2637 validation ≈ 0.2461 ( 93%) [ Info: [ 50%] LOSS: training ≈ 0.2636 validation ≈ 0.2459 ( 93%) [ Info: [ 50%] LOSS: training ≈ 0.2635 validation ≈ 0.2457 ( 93%) [ Info: [ 50%] LOSS: training ≈ 0.2634 validation ≈ 0.2456 ( 93%) [ Info: [ 51%] LOSS: training ≈ 0.2633 validation ≈ 0.2454 ( 93%) [ Info: [ 52%] LOSS: training ≈ 0.2632 validation ≈ 0.2453 ( 93%) [ Info: [ 52%] LOSS: training ≈ 0.2631 validation ≈ 0.2451 ( 93%) [ Info: [ 52%] LOSS: training ≈ 0.263 validation ≈ 0.245 ( 93%) [ Info: [ 53%] LOSS: training ≈ 0.2629 validation ≈ 0.2448 ( 93%) [ Info: [ 54%] LOSS: training ≈ 0.2628 validation ≈ 0.2447 ( 93%) [ Info: [ 54%] LOSS: training ≈ 0.2627 validation ≈ 0.2445 ( 93%) [ Info: [ 55%] LOSS: training ≈ 0.2626 validation ≈ 0.2444 ( 93%) [ Info: [ 55%] LOSS: training ≈ 0.2625 validation ≈ 0.2443 ( 93%) [ Info: [ 56%] LOSS: training ≈ 0.2624 validation ≈ 0.2441 ( 93%) [ Info: [ 56%] LOSS: training ≈ 0.2623 validation ≈ 0.244 ( 93%) [ Info: [ 56%] LOSS: training ≈ 0.2622 validation ≈ 0.2439 ( 93%) [ Info: [ 57%] LOSS: training ≈ 0.2621 validation ≈ 0.2438 ( 93%) [ Info: [ 57%] LOSS: training ≈ 0.2621 validation ≈ 0.2436 ( 93%) [ Info: [ 58%] LOSS: training ≈ 0.262 validation ≈ 0.2435 ( 93%) [ Info: [ 58%] LOSS: training ≈ 0.2619 validation ≈ 0.2434 ( 93%) [ Info: [ 59%] LOSS: training ≈ 0.2618 validation ≈ 0.2433 ( 93%) [ Info: [ 60%] LOSS: training ≈ 0.2617 validation ≈ 0.2432 ( 93%) [ Info: [ 60%] LOSS: training ≈ 0.2617 validation ≈ 0.243 ( 93%) [ Info: [ 60%] LOSS: training ≈ 0.2616 validation ≈ 0.2429 ( 93%) [ Info: [ 61%] LOSS: training ≈ 0.2615 validation ≈ 0.2428 ( 93%) [ Info: [ 62%] LOSS: training ≈ 0.2615 validation ≈ 0.2427 ( 93%) [ Info: [ 62%] LOSS: training ≈ 0.2614 validation ≈ 0.2426 ( 93%) [ Info: [ 62%] LOSS: training ≈ 0.2613 validation ≈ 0.2425 ( 93%) [ Info: [ 63%] LOSS: training ≈ 0.2613 validation ≈ 0.2424 ( 93%) [ Info: [ 64%] LOSS: training ≈ 0.2612 validation ≈ 0.2423 ( 93%) [ Info: [ 64%] LOSS: training ≈ 0.2611 validation ≈ 0.2422 ( 93%) [ Info: [ 64%] LOSS: training ≈ 0.2611 validation ≈ 0.2421 ( 93%) [ Info: [ 65%] LOSS: training ≈ 0.261 validation ≈ 0.242 ( 93%) [ Info: [ 66%] LOSS: training ≈ 0.261 validation ≈ 0.2419 ( 93%) [ Info: [ 66%] LOSS: training ≈ 0.2609 validation ≈ 0.2418 ( 93%) [ Info: [ 66%] LOSS: training ≈ 0.2608 validation ≈ 0.2418 ( 93%) [ Info: [ 67%] LOSS: training ≈ 0.2608 validation ≈ 0.2417 ( 93%) [ Info: [ 68%] LOSS: training ≈ 0.2607 validation ≈ 0.2416 ( 93%) [ Info: [ 68%] LOSS: training ≈ 0.2607 validation ≈ 0.2415 ( 93%) [ Info: [ 68%] LOSS: training ≈ 0.2606 validation ≈ 0.2414 ( 93%) [ Info: [ 69%] LOSS: training ≈ 0.2606 validation ≈ 0.2413 ( 93%) [ Info: [ 70%] LOSS: training ≈ 0.2605 validation ≈ 0.2413 ( 93%) [ Info: [ 70%] LOSS: training ≈ 0.2605 validation ≈ 0.2412 ( 93%) [ Info: [ 70%] LOSS: training ≈ 0.2604 validation ≈ 0.2411 ( 93%) [ Info: [ 71%] LOSS: training ≈ 0.2604 validation ≈ 0.241 ( 93%) [ Info: [ 72%] LOSS: training ≈ 0.2603 validation ≈ 0.2409 ( 93%) [ Info: [ 72%] LOSS: training ≈ 0.2603 validation ≈ 0.2409 ( 93%) [ Info: [ 72%] LOSS: training ≈ 0.2602 validation ≈ 0.2408 ( 93%) [ Info: [ 73%] LOSS: training ≈ 0.2602 validation ≈ 0.2407 ( 93%) [ Info: [ 74%] LOSS: training ≈ 0.2602 validation ≈ 0.2407 ( 93%) [ Info: [ 74%] LOSS: training ≈ 0.2601 validation ≈ 0.2406 ( 92%) [ Info: [ 74%] LOSS: training ≈ 0.2601 validation ≈ 0.2405 ( 92%) [ Info: [ 75%] LOSS: training ≈ 0.26 validation ≈ 0.2404 ( 92%) [ Info: [ 76%] LOSS: training ≈ 0.26 validation ≈ 0.2404 ( 92%) [ Info: [ 76%] LOSS: training ≈ 0.26 validation ≈ 0.2403 ( 92%) [ Info: [ 76%] LOSS: training ≈ 0.2599 validation ≈ 0.2402 ( 92%) [ Info: [ 77%] LOSS: training ≈ 0.2599 validation ≈ 0.2402 ( 92%) [ Info: [ 78%] LOSS: training ≈ 0.2598 validation ≈ 0.2401 ( 92%) [ Info: [ 78%] LOSS: training ≈ 0.2598 validation ≈ 0.2401 ( 92%) [ Info: [ 78%] LOSS: training ≈ 0.2598 validation ≈ 0.24 ( 92%) [ Info: [ 79%] LOSS: training ≈ 0.2597 validation ≈ 0.2399 ( 92%) [ Info: [ 80%] LOSS: training ≈ 0.2597 validation ≈ 0.2399 ( 92%) [ Info: [ 80%] LOSS: training ≈ 0.2597 validation ≈ 0.2398 ( 92%) [ Info: [ 80%] LOSS: training ≈ 0.2596 validation ≈ 0.2398 ( 92%) [ Info: [ 81%] LOSS: training ≈ 0.2596 validation ≈ 0.2397 ( 92%) [ Info: [ 82%] LOSS: training ≈ 0.2596 validation ≈ 0.2396 ( 92%) [ Info: [ 82%] LOSS: training ≈ 0.2595 validation ≈ 0.2396 ( 92%) [ Info: [ 82%] LOSS: training ≈ 0.2595 validation ≈ 0.2395 ( 92%) [ Info: [ 83%] LOSS: training ≈ 0.2595 validation ≈ 0.2395 ( 92%) [ Info: [ 84%] LOSS: training ≈ 0.2594 validation ≈ 0.2394 ( 92%) [ Info: [ 84%] LOSS: training ≈ 0.2594 validation ≈ 0.2394 ( 92%) [ Info: [ 84%] LOSS: training ≈ 0.2594 validation ≈ 0.2393 ( 92%) [ Info: [ 85%] LOSS: training ≈ 0.2594 validation ≈ 0.2393 ( 92%) [ Info: [ 86%] LOSS: training ≈ 0.2593 validation ≈ 0.2392 ( 92%) [ Info: [ 86%] LOSS: training ≈ 0.2593 validation ≈ 0.2392 ( 92%) [ Info: [ 86%] LOSS: training ≈ 0.2593 validation ≈ 0.2391 ( 92%) [ Info: [ 87%] LOSS: training ≈ 0.2592 validation ≈ 0.2391 ( 92%) [ Info: [ 88%] LOSS: training ≈ 0.2592 validation ≈ 0.239 ( 92%) [ Info: [ 88%] LOSS: training ≈ 0.2592 validation ≈ 0.239 ( 92%) [ Info: [ 88%] LOSS: training ≈ 0.2592 validation ≈ 0.2389 ( 92%) [ Info: [ 89%] LOSS: training ≈ 0.2591 validation ≈ 0.2389 ( 92%) [ Info: [ 90%] LOSS: training ≈ 0.2591 validation ≈ 0.2388 ( 92%) [ Info: [ 90%] LOSS: training ≈ 0.2591 validation ≈ 0.2388 ( 92%) [ Info: [ 90%] LOSS: training ≈ 0.2591 validation ≈ 0.2388 ( 92%) [ Info: [ 91%] LOSS: training ≈ 0.259 validation ≈ 0.2387 ( 92%) [ Info: [ 92%] LOSS: training ≈ 0.259 validation ≈ 0.2387 ( 92%) [ Info: [ 92%] LOSS: training ≈ 0.259 validation ≈ 0.2386 ( 92%) [ Info: [ 92%] LOSS: training ≈ 0.259 validation ≈ 0.2386 ( 92%) [ Info: [ 93%] LOSS: training ≈ 0.259 validation ≈ 0.2386 ( 92%) [ Info: [ 94%] LOSS: training ≈ 0.2589 validation ≈ 0.2385 ( 92%) [ Info: [ 94%] LOSS: training ≈ 0.2589 validation ≈ 0.2385 ( 92%) [ Info: [ 94%] LOSS: training ≈ 0.2589 validation ≈ 0.2384 ( 92%) [ Info: [ 95%] LOSS: training ≈ 0.2589 validation ≈ 0.2384 ( 92%) [ Info: [ 96%] LOSS: training ≈ 0.2588 validation ≈ 0.2384 ( 92%) [ Info: [ 96%] LOSS: training ≈ 0.2588 validation ≈ 0.2383 ( 92%) [ Info: [ 96%] LOSS: training ≈ 0.2588 validation ≈ 0.2383 ( 92%) [ Info: [ 97%] LOSS: training ≈ 0.2588 validation ≈ 0.2382 ( 92%) [ Info: [ 98%] LOSS: training ≈ 0.2588 validation ≈ 0.2382 ( 92%) [ Info: [ 98%] LOSS: training ≈ 0.2588 validation ≈ 0.2382 ( 92%) [ Info: [ 98%] LOSS: training ≈ 0.2587 validation ≈ 0.2381 ( 92%) [ Info: [ 99%] LOSS: training ≈ 0.2587 validation ≈ 0.2381 ( 92%) [ Info: [100%] LOSS: training ≈ 0.2587 validation ≈ 0.2381 ( 92%) [ Info: [100%] LOSS: training ≈ 0.2587 validation ≈ 0.238 ( 92%) [ Info: [ 0%] LOSS: training ≈ 0.4215 [ Info: [ 1%] LOSS: training ≈ 0.3806 [ Info: [ 2%] LOSS: training ≈ 0.3564 [ Info: [ 2%] LOSS: training ≈ 0.3399 [ Info: [ 2%] LOSS: training ≈ 0.3279 [ Info: [ 3%] LOSS: training ≈ 0.3185 [ Info: [ 4%] LOSS: training ≈ 0.311 [ Info: [ 4%] LOSS: training ≈ 0.3048 [ Info: [ 4%] LOSS: training ≈ 0.2996 [ Info: [ 5%] LOSS: training ≈ 0.2952 [ Info: [ 6%] LOSS: training ≈ 0.2914 [ Info: [ 6%] LOSS: training ≈ 0.2881 [ Info: [ 6%] LOSS: training ≈ 0.2852 [ Info: [ 7%] LOSS: training ≈ 0.2826 [ Info: [ 8%] LOSS: training ≈ 0.2802 [ Info: [ 8%] LOSS: training ≈ 0.2782 [ Info: [ 8%] LOSS: training ≈ 0.2763 [ Info: [ 9%] LOSS: training ≈ 0.2746 [ Info: [ 10%] LOSS: training ≈ 0.2731 [ Info: [ 10%] LOSS: training ≈ 0.2717 [ Info: [ 10%] LOSS: training ≈ 0.2704 [ Info: [ 11%] LOSS: training ≈ 0.2692 [ Info: [ 12%] LOSS: training ≈ 0.2681 [ Info: [ 12%] LOSS: training ≈ 0.2671 [ Info: [ 12%] LOSS: training ≈ 0.2662 [ Info: [ 13%] LOSS: training ≈ 0.2654 [ Info: [ 14%] LOSS: training ≈ 0.2646 [ Info: [ 14%] LOSS: training ≈ 0.2639 [ Info: [ 14%] LOSS: training ≈ 0.2632 [ Info: [ 15%] LOSS: training ≈ 0.2626 [ Info: [ 16%] LOSS: training ≈ 0.262 [ Info: [ 16%] LOSS: training ≈ 0.2614 [ Info: [ 16%] LOSS: training ≈ 0.2609 [ Info: [ 17%] LOSS: training ≈ 0.2605 [ Info: [ 18%] LOSS: training ≈ 0.26 [ Info: [ 18%] LOSS: training ≈ 0.2596 [ Info: [ 18%] LOSS: training ≈ 0.2592 [ Info: [ 19%] LOSS: training ≈ 0.2589 [ Info: [ 20%] LOSS: training ≈ 0.2585 [ Info: [ 20%] LOSS: training ≈ 0.2582 [ Info: [ 20%] LOSS: training ≈ 0.2579 [ Info: [ 21%] LOSS: training ≈ 0.2576 [ Info: [ 22%] LOSS: training ≈ 0.2573 [ Info: [ 22%] LOSS: training ≈ 0.2571 [ Info: [ 22%] LOSS: training ≈ 0.2568 [ Info: [ 23%] LOSS: training ≈ 0.2566 [ Info: [ 24%] LOSS: training ≈ 0.2564 [ Info: [ 24%] LOSS: training ≈ 0.2562 [ Info: [ 24%] LOSS: training ≈ 0.256 [ Info: [ 25%] LOSS: training ≈ 0.2558 [ Info: [ 26%] LOSS: training ≈ 0.2557 [ Info: [ 26%] LOSS: training ≈ 0.2555 [ Info: [ 26%] LOSS: training ≈ 0.2554 [ Info: [ 27%] LOSS: training ≈ 0.2552 [ Info: [ 28%] LOSS: training ≈ 0.2551 [ Info: [ 28%] LOSS: training ≈ 0.255 [ Info: [ 28%] LOSS: training ≈ 0.2548 [ Info: [ 29%] LOSS: training ≈ 0.2547 [ Info: [ 30%] LOSS: training ≈ 0.2546 [ Info: [ 30%] LOSS: training ≈ 0.2545 [ Info: [ 30%] LOSS: training ≈ 0.2544 [ Info: [ 31%] LOSS: training ≈ 0.2543 [ Info: [ 32%] LOSS: training ≈ 0.2542 [ Info: [ 32%] LOSS: training ≈ 0.2541 [ Info: [ 32%] LOSS: training ≈ 0.2541 [ Info: [ 33%] LOSS: training ≈ 0.254 [ Info: [ 34%] LOSS: training ≈ 0.2539 [ Info: [ 34%] LOSS: training ≈ 0.2538 [ Info: [ 34%] LOSS: training ≈ 0.2538 [ Info: [ 35%] LOSS: training ≈ 0.2537 [ Info: [ 36%] LOSS: training ≈ 0.2536 [ Info: [ 36%] LOSS: training ≈ 0.2536 [ Info: [ 36%] LOSS: training ≈ 0.2535 [ Info: [ 37%] LOSS: training ≈ 0.2535 [ Info: [ 38%] LOSS: training ≈ 0.2534 [ Info: [ 38%] LOSS: training ≈ 0.2534 [ Info: [ 38%] LOSS: training ≈ 0.2533 [ Info: [ 39%] LOSS: training ≈ 0.2533 [ Info: [ 40%] LOSS: training ≈ 0.2532 [ Info: [ 40%] LOSS: training ≈ 0.2532 [ Info: [ 40%] LOSS: training ≈ 0.2532 [ Info: [ 41%] LOSS: training ≈ 0.2531 [ Info: [ 42%] LOSS: training ≈ 0.2531 [ Info: [ 42%] LOSS: training ≈ 0.2531 [ Info: [ 42%] LOSS: training ≈ 0.253 [ Info: [ 43%] LOSS: training ≈ 0.253 [ Info: [ 44%] LOSS: training ≈ 0.253 [ Info: [ 44%] LOSS: training ≈ 0.2529 [ Info: [ 44%] LOSS: training ≈ 0.2529 [ Info: [ 45%] LOSS: training ≈ 0.2529 [ Info: [ 46%] LOSS: training ≈ 0.2529 [ Info: [ 46%] LOSS: training ≈ 0.2528 [ Info: [ 46%] LOSS: training ≈ 0.2528 [ Info: [ 47%] LOSS: training ≈ 0.2528 [ Info: [ 48%] LOSS: training ≈ 0.2528 [ Info: [ 48%] LOSS: training ≈ 0.2527 [ Info: [ 48%] LOSS: training ≈ 0.2527 [ Info: [ 49%] LOSS: training ≈ 0.2527 [ Info: [ 50%] LOSS: training ≈ 0.2527 [ Info: [ 50%] LOSS: training ≈ 0.2527 [ Info: [ 50%] LOSS: training ≈ 0.2527 [ Info: [ 51%] LOSS: training ≈ 0.2526 [ Info: [ 52%] LOSS: training ≈ 0.2526 [ Info: [ 52%] LOSS: training ≈ 0.2526 [ Info: [ 52%] LOSS: training ≈ 0.2526 [ Info: [ 53%] LOSS: training ≈ 0.2526 [ Info: [ 54%] LOSS: training ≈ 0.2526 [ Info: [ 54%] LOSS: training ≈ 0.2525 [ Info: [ 55%] LOSS: training ≈ 0.2525 [ Info: [ 55%] LOSS: training ≈ 0.2525 [ Info: [ 56%] LOSS: training ≈ 0.2525 [ Info: [ 56%] LOSS: training ≈ 0.2525 [ Info: [ 56%] LOSS: training ≈ 0.2525 [ Info: [ 57%] LOSS: training ≈ 0.2525 [ Info: [ 57%] LOSS: training ≈ 0.2525 [ Info: [ 58%] LOSS: training ≈ 0.2525 [ Info: [ 58%] LOSS: training ≈ 0.2525 [ Info: [ 59%] LOSS: training ≈ 0.2524 [ Info: [ 60%] LOSS: training ≈ 0.2524 [ Info: [ 60%] LOSS: training ≈ 0.2524 [ Info: [ 60%] LOSS: training ≈ 0.2524 [ Info: [ 61%] LOSS: training ≈ 0.2524 [ Info: [ 62%] LOSS: training ≈ 0.2524 [ Info: [ 62%] LOSS: training ≈ 0.2524 [ Info: [ 62%] LOSS: training ≈ 0.2524 [ Info: [ 63%] LOSS: training ≈ 0.2524 [ Info: [ 64%] LOSS: training ≈ 0.2524 [ Info: [ 64%] LOSS: training ≈ 0.2524 [ Info: [ 64%] LOSS: training ≈ 0.2524 [ Info: [ 65%] LOSS: training ≈ 0.2524 [ Info: [ 66%] LOSS: training ≈ 0.2523 [ Info: [ 66%] LOSS: training ≈ 0.2523 [ Info: [ 66%] LOSS: training ≈ 0.2523 [ Info: [ 67%] LOSS: training ≈ 0.2523 [ Info: [ 68%] LOSS: training ≈ 0.2523 [ Info: [ 68%] LOSS: training ≈ 0.2523 [ Info: [ 68%] LOSS: training ≈ 0.2523 [ Info: [ 69%] LOSS: training ≈ 0.2523 [ Info: [ 70%] LOSS: training ≈ 0.2523 [ Info: [ 70%] LOSS: training ≈ 0.2523 [ Info: [ 70%] LOSS: training ≈ 0.2523 [ Info: [ 71%] LOSS: training ≈ 0.2523 [ Info: [ 72%] LOSS: training ≈ 0.2523 [ Info: [ 72%] LOSS: training ≈ 0.2523 [ Info: [ 72%] LOSS: training ≈ 0.2523 [ Info: [ 73%] LOSS: training ≈ 0.2523 [ Info: [ 74%] LOSS: training ≈ 0.2523 [ Info: [ 74%] LOSS: training ≈ 0.2523 [ Info: [ 74%] LOSS: training ≈ 0.2523 [ Info: [ 75%] LOSS: training ≈ 0.2523 [ Info: [ 76%] LOSS: training ≈ 0.2523 [ Info: [ 76%] LOSS: training ≈ 0.2523 [ Info: [ 76%] LOSS: training ≈ 0.2523 [ Info: [ 77%] LOSS: training ≈ 0.2523 [ Info: [ 78%] LOSS: training ≈ 0.2523 [ Info: [ 78%] LOSS: training ≈ 0.2523 [ Info: [ 78%] LOSS: training ≈ 0.2522 [ Info: [ 79%] LOSS: training ≈ 0.2522 [ Info: [ 80%] LOSS: training ≈ 0.2522 [ Info: [ 80%] LOSS: training ≈ 0.2522 [ Info: [ 80%] LOSS: training ≈ 0.2522 [ Info: [ 81%] LOSS: training ≈ 0.2522 [ Info: [ 82%] LOSS: training ≈ 0.2522 [ Info: [ 82%] LOSS: training ≈ 0.2522 [ Info: [ 82%] LOSS: training ≈ 0.2522 [ Info: [ 83%] LOSS: training ≈ 0.2522 [ Info: [ 84%] LOSS: training ≈ 0.2522 [ Info: [ 84%] LOSS: training ≈ 0.2522 [ Info: [ 84%] LOSS: training ≈ 0.2522 [ Info: [ 85%] LOSS: training ≈ 0.2522 [ Info: [ 86%] LOSS: training ≈ 0.2522 [ Info: [ 86%] LOSS: training ≈ 0.2522 [ Info: [ 86%] LOSS: training ≈ 0.2522 [ Info: [ 87%] LOSS: training ≈ 0.2522 [ Info: [ 88%] LOSS: training ≈ 0.2522 [ Info: [ 88%] LOSS: training ≈ 0.2522 [ Info: [ 88%] LOSS: training ≈ 0.2522 [ Info: [ 89%] LOSS: training ≈ 0.2522 [ Info: [ 90%] LOSS: training ≈ 0.2522 [ Info: [ 90%] LOSS: training ≈ 0.2522 [ Info: [ 90%] LOSS: training ≈ 0.2522 [ Info: [ 91%] LOSS: training ≈ 0.2522 [ Info: [ 92%] LOSS: training ≈ 0.2522 [ Info: [ 92%] LOSS: training ≈ 0.2522 [ Info: [ 92%] LOSS: training ≈ 0.2522 [ Info: [ 93%] LOSS: training ≈ 0.2522 [ Info: [ 94%] LOSS: training ≈ 0.2522 [ Info: [ 94%] LOSS: training ≈ 0.2522 [ Info: [ 94%] LOSS: training ≈ 0.2522 [ Info: [ 95%] LOSS: training ≈ 0.2522 [ Info: [ 96%] LOSS: training ≈ 0.2522 [ Info: [ 96%] LOSS: training ≈ 0.2522 [ Info: [ 96%] LOSS: training ≈ 0.2522 [ Info: [ 97%] LOSS: training ≈ 0.2522 [ Info: [ 98%] LOSS: training ≈ 0.2522 [ Info: [ 98%] LOSS: training ≈ 0.2522 [ Info: [ 98%] LOSS: training ≈ 0.2522 [ Info: [ 99%] LOSS: training ≈ 0.2522 [ Info: [100%] LOSS: training ≈ 0.2522 [ Info: [100%] LOSS: training ≈ 0.2522 [ Info: Baseline mcc: -2.8367742685852565e-17 [ Info: Optimal 1 variables model - mcc ≈ 0.7309 [ Info: Optimal 2 variables model - mcc ≈ 0.762 [ Info: Optimal 3 variables model - mcc ≈ 0.7788 [ Info: Returning model with 3 variables - mcc ≈ 0.7788 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 ┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead └ @ SDeMo ~/.julia/packages/SDeMo/JWEc2/src/variables/vif.jl:24 Test Summary: | Pass Total Time Package | 299 299 22m04.3s Testing SDeMo tests passed Testing completed after 1379.47s PkgEval succeeded after 1438.97s