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2026_Article_RESILINK_ML
Commit ad8a49d0 by Joaquín Irazábal González
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Add out-of-fold error envelope and local OOF error table for adaptive acceptance

- Implemented `compute_oof_error_envelope` to calculate absolute prediction-error envelopes and safety margins for adaptive surrogate validation. - Added `build_local_oof_error_table` to create a one-row-per-sample OOF residual table for local safety margins. - Updated `run_nested_cv_rbf_for_target` to utilize the new functions and return validation envelopes and local OOF error data. - Enhanced `main` function to save validation envelopes and local OOF errors to CSV files. - Modified shell scripts to adjust parameters for training and optimization. - Introduced unit tests for new optimization logic, including safety margin calculations and local OOF error table construction.
parent ff86a892
Showing with 9669 additions and 101 deletions
Code/models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/local_oof_errors_2W_B29_H30_it0.csv 0 → 100644
output,sample_index,x1,x2,y_true,y_pred_oof,abs_error_oof,positive_underprediction_oof
exymax_tw1,0,15.22,18.46,0.0368687,0.0422112713272016,0.0053425713272016034,0.0
exymax_tw1,1,12.74,12.61,0.040946,0.04664465833913353,0.005698658339133529,0.0
exymax_tw1,2,18.03,11.3,0.0194026,0.019836574974328197,0.00043397497432819815,0.0
exymax_tw1,3,21.19,19.38,0.022359,0.010424563392394726,0.011934436607605275,0.011934436607605275
exymax_tw1,4,9.78,15.96,0.0678181,0.06031974063248685,0.007498359367513155,0.007498359367513155
exymax_tw1,5,18.64,13.8,0.022213,0.017890111602707953,0.004322888397292047,0.004322888397292047
exymax_tw1,6,13.46,20.99,0.0500821,0.049278116335851954,0.0008039836641480433,0.0008039836641480433
exymax_tw1,7,8.58,8.88,0.0615553,0.065016206698085,0.0034609066980849992,0.0
exymax_tw2,0,15.22,18.46,0.0396548,0.04094617686370043,0.0012913768637004344,0.0
exymax_tw2,1,12.74,12.61,0.0732361,0.07061740781854915,0.0026186921814508463,0.0026186921814508463
exymax_tw2,2,18.03,11.3,0.0889196,0.09013284113882811,0.0012132411388281056,0.0
exymax_tw2,3,21.19,19.38,0.0460981,0.0468183021372557,0.000720202137255696,0.0
exymax_tw2,4,9.78,15.96,0.0409845,0.05141413124142669,0.010429631241426693,0.0
exymax_tw2,5,18.64,13.8,0.0716685,0.07283469077034004,0.0011661907703400393,0.0
exymax_tw2,6,13.46,20.99,0.0286307,0.025572741649373336,0.0030579583506266626,0.0030579583506266626
exymax_tw2,7,8.58,8.88,0.0931119,0.08363436126032547,0.009477538739674524,0.009477538739674524
tfmmax_tw1,0,15.22,18.46,72.7038,102.48085063167565,29.77705063167565,0.0
tfmmax_tw1,1,12.74,12.61,72.6758,88.42409574013531,15.748295740135319,0.0
tfmmax_tw1,2,18.03,11.3,28.7372,32.25690969105291,3.5197096910529098,0.0
tfmmax_tw1,3,21.19,19.38,34.6226,31.08502008974129,3.53757991025871,3.53757991025871
tfmmax_tw1,4,9.78,15.96,142.7821,117.31009625159284,25.472003748407175,25.472003748407175
tfmmax_tw1,5,18.64,13.8,29.4176,9.518365948613175,19.899234051386827,19.899234051386827
tfmmax_tw1,6,13.46,20.99,104.2237,117.91729620679615,13.693596206796158,0.0
tfmmax_tw1,7,8.58,8.88,122.4554,122.22355713303452,0.2318428669654793,0.2318428669654793
tfmmax_tw2,0,15.22,18.46,62.4008,41.975242873630265,20.425557126369732,20.425557126369732
tfmmax_tw2,1,12.74,12.61,107.3854,113.77332363286199,6.387923632861984,0.0
tfmmax_tw2,2,18.03,11.3,142.6177,127.70012781262461,14.917572187375399,14.917572187375399
tfmmax_tw2,3,21.19,19.38,69.0521,100.388973834992,31.336873834992005,0.0
tfmmax_tw2,4,9.78,15.96,71.6787,82.37558559790746,10.696885597907453,0.0
tfmmax_tw2,5,18.64,13.8,104.9098,118.9917667434553,14.081966743455297,0.0
tfmmax_tw2,6,13.46,20.99,49.3735,60.29294757540451,10.919447575404511,0.0
tfmmax_tw2,7,8.58,8.88,161.7784,117.61283617073224,44.16556382926777,44.16556382926777
tfmmax_frame,0,15.22,18.46,86.3253,94.70708119618446,8.381781196184463,0.0
tfmmax_frame,1,12.74,12.61,85.5848,85.26050251415383,0.32429748584617357,0.32429748584617357
tfmmax_frame,2,18.03,11.3,105.3345,95.3461821597234,9.988317840276608,9.988317840276608
tfmmax_frame,3,21.19,19.38,98.062,96.97841104027135,1.0835889597286439,1.0835889597286439
tfmmax_frame,4,9.78,15.96,74.5182,76.40930698627149,1.8911069862714953,0.0
tfmmax_frame,5,18.64,13.8,96.9956,99.60648387113002,2.610883871130028,0.0
tfmmax_frame,6,13.46,20.99,77.0242,81.1267892806428,4.102589280642803,0.0
tfmmax_frame,7,8.58,8.88,79.7824,81.55810338009186,1.7757033800918691,0.0
Code/models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/validation_envelopes_2W_B29_H30_it0.csv 0 → 100644
n_unique_oof_samples,envelope_reference_n,percentile,small_n_threshold,abs_error_envelope,abs_error_rule,damage_safety_margin,damage_safety_margin_rule,abs_error_p95,abs_error_max,underprediction_p95,underprediction_max,mean_abs_error,output,method
8,8,95.0,20,0.011934436607605275,max_abs_oof_error_small_n,0.011934436607605275,max_positive_oof_residual_small_n,0.01038180957357303,0.011934436607605275,0.01038180957357303,0.011934436607605275,0.004936972421913356,exymax_tw1,SupervisedSelection
8,8,95.0,20,0.010429631241426693,max_abs_oof_error_small_n,0.009477538739674524,max_positive_oof_residual_small_n,0.010096398865813433,0.010429631241426693,0.007230685603507769,0.009477538739674524,0.003746853927912875,exymax_tw2,SupervisedSelection
8,8,95.0,20,29.77705063167565,max_abs_oof_error_small_n,25.472003748407175,max_positive_oof_residual_small_n,28.270284222531682,29.77705063167565,23.52153435445005,25.472003748407175,13.984914105834779,tfmmax_tw1,SupervisedSelection
8,8,95.0,20,44.16556382926777,max_abs_oof_error_small_n,44.16556382926777,max_positive_oof_residual_small_n,39.675522331271246,44.16556382926777,35.856561483253444,44.16556382926777,19.11647381595427,tfmmax_tw2,SupervisedSelection
8,8,95.0,20,9.988317840276608,max_abs_oof_error_small_n,9.988317840276608,max_positive_oof_residual_small_n,9.426030014844356,9.988317840276608,6.871662732084816,9.988317840276608,3.7697836250215104,tfmmax_frame,SupervisedSelection
Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/final_model_selection_2W_B34_H30_it1.csv 0 → 100644
output,N,selected_model,selection_criterion,inner_cv_rmse,inner_cv_mae,inner_cv_rmse_dispersion,model_path,best_params,gpr_kernel
exymax_tw1,9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.0033723157449567984,0.002889708896174529,0.49814236833394643,../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/best_model_exymax_tw1.joblib,"{""gpr__amplitude"": 1.491462267977867, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 0.9838602333915322, ""gpr__n_restarts_optimizer"": 2, ""gpr__noise"": 1.8553244846829496e-08, ""gpr__rq_alpha"": 0.12655202748193148}","2.24**2 * RationalQuadratic(alpha=172, length_scale=2.61) + WhiteKernel(noise_level=1.86e-08)"
exymax_tw2,9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.0027245597341881015,0.002360205024165106,0.3566448130047311,../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/best_model_exymax_tw2.joblib,"{""gpr__amplitude"": 100.0, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 0.899678518316048, ""gpr__n_restarts_optimizer"": 0, ""gpr__noise"": 1.719670533732525e-10, ""gpr__rq_alpha"": 5.086798780002291}","6.15**2 * Matern(length_scale=10.2, nu=2.5) + WhiteKernel(noise_level=1.72e-10)"
tfmmax_tw1,9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,8.72774045098701,8.219639448840784,0.8765218938499422,../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/best_model_tfmmax_tw1.joblib,"{""gpr__amplitude"": 1.4154650936878224, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 0.9654419830060187, ""gpr__n_restarts_optimizer"": 2, ""gpr__noise"": 2.220770054919255e-09, ""gpr__rq_alpha"": 0.14270342866012728}","1.81**2 * RationalQuadratic(alpha=1e+03, length_scale=2.5) + WhiteKernel(noise_level=2.22e-09)"
tfmmax_tw2,9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,12.165863669344043,10.257366290133891,0.346134169145658,../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/best_model_tfmmax_tw2.joblib,"{""gpr__amplitude"": 2.1166494238130023, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 0.6763351411917367, ""gpr__n_restarts_optimizer"": 2, ""gpr__noise"": 1.1741986684450723e-07, ""gpr__rq_alpha"": 0.45118356508626994}","3.16**2 * RationalQuadratic(alpha=1e+03, length_scale=3.93) + WhiteKernel(noise_level=0.01)"
tfmmax_frame,9,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band,2.6192348957645466,2.0447240530897703,0.7771515993126951,../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/best_model_tfmmax_frame.joblib,"{""svr__C"": 100.0, ""svr__epsilon"": 0.001, ""svr__gamma"": 0.022022232237845447}",
Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/nested_cv_fold_selections_2W_B34_H30_it1.csv 0 → 100644
outer_fold,selected_model,selection_criterion
0,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/nested_cv_model_selection_frequency_2W_B34_H30_it1.csv 0 → 100644
model,frequency,percentage,output,N
SVR,5,55.56,exymax_tw1,9
GaussianProcess,4,44.44,exymax_tw1,9
SVR,6,66.67,exymax_tw2,9
GaussianProcess,3,33.33,exymax_tw2,9
FlexibleMLP,7,77.78,tfmmax_tw1,9
GaussianProcess,2,22.22,tfmmax_tw1,9
GaussianProcess,5,55.56,tfmmax_tw2,9
GradientBoosting,2,22.22,tfmmax_tw2,9
FlexibleMLP,2,22.22,tfmmax_tw2,9
GaussianProcess,5,55.56,tfmmax_frame,9
GradientBoosting,2,22.22,tfmmax_frame,9
SVR,2,22.22,tfmmax_frame,9
Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/nested_cv_outer_predictions_2W_B34_H30_it1.csv 0 → 100644
output,N,outer_fold,sample_index,y_true,selected_supervised_model,prediction_RandomForest,prediction_GradientBoosting,prediction_XGBoost,prediction_FlexibleMLP,prediction_SVR,prediction_GaussianProcess,prediction_supervised_selection
exymax_tw1,9,0,0,0.0512763,SVR,0.05291468357857145,0.05154040210282981,0.0515877865254879,0.04451038266569829,0.05242531777528296,0.050841394777034185,0.05242531777528296
exymax_tw1,9,1,1,0.0480552,SVR,0.05608589716117206,0.06164175200486702,0.06999729573726654,0.05721697659781766,0.052650983725940154,0.056140038398526305,0.052650983725940154
exymax_tw1,9,2,2,0.0212773,SVR,0.033769371739999894,0.02525813712506521,0.0365205854177475,0.02522424365033943,0.02254134112331682,0.024142009905140958,0.02254134112331682
exymax_tw1,9,3,3,0.0319299,GaussianProcess,0.041381411330000104,0.0430973098966023,0.0453285351395607,0.02280753029730953,0.034668158387790235,0.036637405683495605,0.036637405683495605
exymax_tw1,9,4,4,0.0902318,GaussianProcess,0.05450122526999993,0.05777953644797756,0.05262457951903343,0.0641901253262041,0.0718023982617021,0.07454934547593797,0.07454934547593797
exymax_tw1,9,5,5,0.0266662,GaussianProcess,0.04011995614246528,0.02008418632967565,0.03429035469889641,0.025004478773979738,0.023477103051979836,0.024277095553180375,0.024277095553180375
exymax_tw1,9,6,6,0.067683,SVR,0.04489755837904766,0.053651783714729694,0.05319276452064514,0.07020093013457027,0.07404819794622902,0.07175672993792648,0.07404819794622902
exymax_tw1,9,7,7,0.0712047,GaussianProcess,0.05526451801385713,0.0706787491403021,0.05463815852999687,0.13951556309687074,0.061050451350197196,0.0619050614459514,0.0619050614459514
exymax_tw1,9,8,8,0.0509658,SVR,0.05545439567209796,0.05169378791641436,0.07160308957099915,0.04929048251911239,0.05030037299786258,0.05155194883757219,0.05030037299786258
exymax_tw2,9,0,0,0.051279,SVR,0.05405933136054436,0.052218701855175055,0.054793331772089005,0.054150154377073365,0.04974634853957369,0.05067969317171098,0.04974634853957369
exymax_tw2,9,1,1,0.0854114,GaussianProcess,0.08117379735666695,0.09215388153327486,0.10187952220439911,0.0815575752830815,0.08728518824139984,0.08959268566593241,0.08959268566593241
exymax_tw2,9,2,2,0.1170466,GaussianProcess,0.0884027683738095,0.09011166572733642,0.09434670209884644,0.1123674676181767,0.11210587436725383,0.11274468881674968,0.11274468881674968
exymax_tw2,9,3,3,0.0606494,SVR,0.05073376156311832,0.05179127091737506,0.05432504788041115,0.05247437635529684,0.06137979740108482,0.053074320794660235,0.06137979740108482
exymax_tw2,9,4,4,0.047305,SVR,0.07398712967571645,0.06944314070545629,0.08349835872650146,0.06166087919145857,0.052648564146285076,0.04893478118950726,0.052648564146285076
exymax_tw2,9,5,5,0.0926283,SVR,0.08579192188333332,0.0909087707051047,0.10953792184591293,0.09537983778903525,0.09417828684018936,0.09553638274717287,0.09417828684018936
exymax_tw2,9,6,6,0.0341728,SVR,0.058632289008097305,0.04814944566346872,0.05805160477757454,0.0683630897489785,0.03405621241755721,0.03932915972524253,0.03405621241755721
exymax_tw2,9,7,7,0.1104125,GaussianProcess,0.081146832455437,0.09051608234347557,0.08698471635580063,0.07328188116400053,0.10280860982533598,0.10243642661314761,0.10243642661314761
exymax_tw2,9,8,8,0.0551819,SVR,0.050685713000000014,0.05069257704688972,0.03762730956077576,0.0645699810070004,0.054587143605178495,0.054312242394231394,0.054587143605178495
tfmmax_tw1,9,0,0,99.6966,FlexibleMLP,106.49267906666661,99.22430892686742,99.1294174194336,98.74529583058015,99.97207195176631,99.63047640796168,98.74529583058015
tfmmax_tw1,9,1,1,93.0272,GaussianProcess,125.20760558193663,138.75424751620125,144.3971710205078,98.64598875812206,107.47074593226671,106.02403281056068,106.02403281056068
tfmmax_tw1,9,2,2,34.7023,FlexibleMLP,55.74039316666664,28.03074869247534,68.35953521728516,45.21487991216971,42.45055626093196,26.880518099010004,45.21487991216971
tfmmax_tw1,9,3,3,49.5951,FlexibleMLP,54.2624901200004,71.12682679853802,70.95980072021484,78.88003729856126,69.11315669730917,45.52373883047603,78.88003729856126
tfmmax_tw1,9,4,4,178.8235,FlexibleMLP,114.37278576666657,120.6119208844368,108.0477294921875,147.49262689301952,135.1050938732661,146.88608427423966,147.49262689301952
tfmmax_tw1,9,5,5,37.1978,FlexibleMLP,57.5849686400001,25.681847683283387,37.74962615966797,34.78878875740877,32.31036508620166,40.6820866834917,34.78878875740877
tfmmax_tw1,9,6,6,137.2798,FlexibleMLP,96.17095933333343,106.25990954526026,92.56334686279297,154.42268174868573,124.13515297778466,141.1587046149448,154.42268174868573
tfmmax_tw1,9,7,7,144.4093,GaussianProcess,106.39404027183575,163.06195521822497,106.58305358886719,137.31676469051428,107.99361995255732,126.71849807966457,126.71849807966457
tfmmax_tw1,9,8,8,99.1282,FlexibleMLP,105.18131000000002,99.84092745350313,127.07234954833984,96.84395707010358,99.04662518420895,99.27552415741373,96.84395707010358
tfmmax_tw2,9,0,0,79.5103,GradientBoosting,82.87918900666638,85.92289222582724,80.73748779296875,79.74008914688893,77.96632885403642,78.76608593780824,85.92289222582724
tfmmax_tw2,9,1,1,134.6891,GradientBoosting,149.58524509999972,150.27523534060862,167.59120178222656,146.09508699074354,142.8183214997212,145.31576922935898,150.27523534060862
tfmmax_tw2,9,2,2,183.3526,GaussianProcess,130.4721176666667,136.3095368280167,137.5691680908203,164.8521373794007,152.82465563399558,161.86123977396196,161.86123977396196
tfmmax_tw2,9,3,3,86.0991,FlexibleMLP,79.71728993333386,95.25846498394299,88.50909423828125,121.78230954025841,108.80734088543241,66.56694510021012,121.78230954025841
tfmmax_tw2,9,4,4,79.6009,GaussianProcess,95.82481163333328,124.00086487950816,132.9017791748047,97.36100082623932,109.01735175794946,95.5898321354079,95.5898321354079
tfmmax_tw2,9,5,5,132.7491,GaussianProcess,140.04755220449593,145.02418058010966,151.52195739746094,146.65377751685244,127.95885674307812,147.0854530714796,147.0854530714796
tfmmax_tw2,9,6,6,59.7863,FlexibleMLP,85.41879693333338,81.72808616352133,83.564208984375,78.47374884258716,61.56651298948418,61.47819776138209,78.47374884258716
tfmmax_tw2,9,7,7,187.2618,GaussianProcess,152.7176814885715,153.97671923839047,136.6938018798828,165.08343546178745,131.89644756986036,131.0613519789366,131.0613519789366
tfmmax_tw2,9,8,8,85.4217,GaussianProcess,81.72760486190475,79.22780250027719,68.9708251953125,82.0193153140293,86.58498525785822,81.77133011954638,81.77133011954638
tfmmax_frame,9,0,0,93.2656,GaussianProcess,92.19534678999959,93.72971898898409,93.73442840576172,92.31376250764684,94.58638984024161,93.78212561938936,93.78212561938936
tfmmax_frame,9,1,1,94.8678,GradientBoosting,88.35878920753383,86.04965238765881,84.63240814208984,101.39172170125084,92.13244149896977,95.44501022735054,86.04965238765881
tfmmax_frame,9,2,2,117.5466,GaussianProcess,107.14108477428582,113.41251624519202,95.82760620117188,104.14197088321052,112.04718485913472,113.22648446525749,113.22648446525749
tfmmax_frame,9,3,3,111.5967,GaussianProcess,109.5907045024882,107.68931817166859,109.46119689941406,93.26844045588057,99.19737550202436,101.36800116823335,101.36800116823335
tfmmax_frame,9,4,4,80.7235,GaussianProcess,89.57024156666674,91.58325388406436,91.9088363647461,76.84180154567086,83.83807835622466,80.08755597885222,80.08755597885222
tfmmax_frame,9,5,5,111.9409,GaussianProcess,111.36711841975344,116.81317686368673,112.22537231445312,106.37542934383104,115.58558500758288,114.64868211946867,114.64868211946867
tfmmax_frame,9,6,6,85.7607,GradientBoosting,94.31153991666667,93.25495327552639,93.55200958251953,89.69899825944536,86.29121200242011,85.44132448837046,93.25495327552639
tfmmax_frame,9,7,7,83.381,SVR,91.30258839000001,88.36116918998906,93.52702331542969,91.43414127794132,90.84863299224499,90.20790426930948,90.84863299224499
tfmmax_frame,9,8,8,93.7245,SVR,88.74345865031263,93.26560001952595,88.48162841796875,93.57830218996853,94.74989939444993,93.58563174679233,94.74989939444993
Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/nested_cv_outer_summary_2W_B34_H30_it1.csv 0 → 100644
output,method,outer_rmse,outer_mae,outer_r2,outer_std_abs_error,outer_max_abs_error,n_outer_predictions,N,outer_rmse_ci_lower,outer_rmse_ci_upper,outer_mae_ci_lower,outer_mae_ci_upper
exymax_tw1,RandomForest,0.01687750010458109,0.013779023773489113,0.3706776997348862,0.009746205088636118,0.03573057473000007,9,9,0.009377955194612198,0.023817463920646896,0.007906875438319345,0.020645337932174494
exymax_tw1,GradientBoosting,0.013413824494322024,0.009257592601454857,0.6024771272651195,0.009707093632491746,0.032452263552022444,9,9,0.005890376183339298,0.020269250424528634,0.003817402811934879,0.016157824288177688
exymax_tw1,XGBoost,0.019079306625147015,0.016424549391142528,0.19576668796384544,0.009708456035554694,0.03760722048096657,9,9,0.012643857078726813,0.025784882076918945,0.010600133388204312,0.023126877978758415
exymax_tw1,FlexibleMLP,0.024910869104975565,0.014356057099699341,-0.37099112093609254,0.020358168486271016,0.06831086309687075,9,9,0.004959431935628238,0.04068295419444211,0.0041474758801904685,0.028912954614465896
exymax_tw1,SVR,0.007640664749080667,0.005394497032979722,0.8710209506367559,0.005411022044772773,0.018429401738297907,9,9,0.0028556053187044615,0.011717454778934835,0.0023066175392888978,0.009345194644581046
exymax_tw1,GaussianProcess,0.0070788436778896485,0.005347003945617513,0.8892913572449801,0.00463891976882,0.01568245452406203,9,9,0.0033358119740574278,0.010316869386681271,0.0025806057657501405,0.008648121278430367
exymax_tw1,SupervisedSelection,0.006874177888688443,0.005124241197925806,0.8956005007046958,0.004582190937838907,0.01568245452406203,9,9,0.0029957665603104946,0.010161663374396411,0.002366108623655065,0.008392861125612291
exymax_tw2,RandomForest,0.018791576721643142,0.015257472823554781,0.5451900657073663,0.010969634393355495,0.029265667544563,9,9,0.010681112574431266,0.024516593154572573,0.007728883323513526,0.0227085571719988
exymax_tw2,GradientBoosting,0.014737417861247864,0.01174392255746594,0.7202653307121372,0.008903469446304074,0.02693493427266358,9,9,0.00777656914236425,0.019770668517027096,0.005726469816925192,0.017853400262916347
exymax_tw2,XGBoost,0.020714860226013574,0.018552318158960343,0.44732793460600306,0.009215037987549994,0.036193358726501465,9,9,0.014888703116906932,0.026222842575959554,0.01252089025198486,0.024645204820396633
exymax_tw2,FlexibleMLP,0.01813920813126891,0.013043949076998946,0.5762203017820825,0.012605009484572017,0.03713061883599947,9,9,0.0063515754605696405,0.026819616363794773,0.005514396070725457,0.022296244604751728
exymax_tw2,SVR,0.0036506767047962533,0.0026984830971177667,0.9828347454328361,0.0024587861593704592,0.007603890174664021,9,9,0.0013390528986698346,0.00525729411342721,0.0011795530924181886,0.004428532235811587
exymax_tw2,GaussianProcess,0.004664354330930324,0.003910837504150574,0.9719788104198515,0.0025419581705051282,0.007976073386852384,9,9,0.0028219964816274356,0.0061494020117613115,0.0022447205441541527,0.005615620399645477
exymax_tw2,SupervisedSelection,0.0038559233223070624,0.002925246006809443,0.980850374832446,0.0025121863918023553,0.007976073386852384,9,9,0.0018290747945173542,0.005365751041468919,0.0013395703618718341,0.004617380014537383
tfmmax_tw1,RandomForest,32.12375236296141,26.077451244826072,0.542429051366176,18.75905121402968,64.45071423333343,9,9,18.458115436297867,44.108304830032964,14.642522057190648,39.0475823693136
tfmmax_tw1,GradientBoosting,28.734762186065915,21.612824583793795,0.6338819683391942,18.936007271859665,58.2115791155632,9,9,14.723796676284639,40.52888819925669,10.464410370586968,34.855453337969976
tfmmax_tw1,XGBoost,38.65694034834036,32.085948366970484,0.33738561682040125,21.56040246579941,70.7757705078125,9,9,25.365606431814733,50.77851403049976,18.30396725809733,46.54844652119954
tfmmax_tw1,FlexibleMLP,16.115640498398935,11.84746160843472,0.8848401618202202,10.924812223114008,31.330873106980476,9,9,6.817332296595899,22.44315534329044,5.265769448158653,19.1781487722316
tfmmax_tw1,SVR,21.301429591106306,15.581452640917272,0.7988021171642874,14.524780074882768,43.718406126733896,9,9,9.172451107563987,30.4679333265348,6.42047386584069,25.686910780710953
tfmmax_tw1,GaussianProcess,13.361840958702741,9.121648063895442,0.9208340366709082,9.763930069603056,31.93741572576033,9,9,4.8112695038852,20.43782915447611,3.3643978616667654,16.19566974344356
tfmmax_tw1,SupervisedSelection,17.44015835022477,13.844829459911203,0.865132672364333,10.605650404708316,31.330873106980476,9,9,9.869036791936816,23.18738018186245,7.092602431617047,20.533031129421683
tfmmax_tw2,RandomForest,24.154060018597843,18.324488991927993,0.7060551751257108,15.7363184565748,52.880482333333305,9,9,10.8080842273069,35.253927213560914,8.636433287449798,29.900870837166533
tfmmax_tw2,GradientBoosting,26.52196185462192,21.810773956314847,0.6455974578304888,15.089884030189172,47.043063171983306,9,9,13.890702773531245,35.79950382144119,11.966717597016673,32.194745769127366
tfmmax_tw2,XGBoost,32.952038494076135,27.243692689344616,0.4529209188228689,18.536937437549554,53.30087917480469,9,9,19.938384520566515,42.565011303407935,14.863139336412214,39.22008785698783
tfmmax_tw2,FlexibleMLP,18.58059310184375,15.750269412039152,0.8260579108810082,9.857355297668205,35.6832095402584,9,9,12.155526048825063,24.822146224209654,9.289982673373999,22.451161099497053
tfmmax_tw2,SVR,24.66262666351814,17.26943595438611,0.6935467766319847,17.60715013173433,55.36535243013964,9,9,10.367574118356938,36.34310055186431,6.06602034684449,29.73018616517506
tfmmax_tw2,GaussianProcess,22.589866556006005,16.0291554763517,0.7428936119904261,15.917545216932885,56.20044802106338,9,9,9.724386414301085,34.71105475044236,7.030960418653471,27.36927380337059
tfmmax_tw2,SupervisedSelection,25.822970467454642,20.89298325374711,0.6640319850259945,15.17593669338659,56.20044802106338,9,9,13.58694265002858,36.93584691003305,11.939889611000908,31.68791423546378
tfmmax_frame,RandomForest,6.625579127359547,5.6516408365511035,0.730206604928809,3.4579263479042512,10.405515225714183,9,9,4.395434905260039,8.2822303250307,3.2904878647181497,7.920431851988078
tfmmax_frame,GradientBoosting,6.0844360440089496,5.10989837535614,0.7724775350566258,3.302923033794149,10.85975388406436,9,9,3.9395294895054844,7.986796871361745,3.0379561863978983,7.332252312884441
tfmmax_frame,XGBoost,9.986573109980917,7.68985892469618,0.3870619323958251,6.371633401198191,21.718993798828123,9,9,5.208139258351091,14.447474883348875,3.678177282036675,12.121427405395508
tfmmax_frame,FlexibleMLP,8.727373453608136,6.754828256936575,0.5318869549691942,5.52624127396146,18.328259544119433,9,9,4.3453294787843,12.568688784425689,3.4341512445617823,10.723947383791858
tfmmax_frame,SVR,5.510557981227182,4.19307730367059,0.8133728706203159,3.5755212193342065,12.399324497975641,9,9,2.538252083753599,8.106003187773524,2.09893319594033,6.801777441135452
tfmmax_frame,GaussianProcess,4.451642196395906,2.9190471542235787,0.8782064963511993,3.360994191627203,10.22869883176665,9,9,1.5032343617556825,6.730957213347955,0.9439041866491791,5.378290727704313
tfmmax_frame,SupervisedSelection,5.981764245886755,4.801611044564162,0.7800914026721528,3.5673568464745142,10.22869883176665,9,9,3.6787221229746723,7.744437726592566,2.5037771136108984,7.066453522549985
Code/models/width_optimization/2W/rbf_models/per_output_models_B29_H30/it0/local_oof_errors_2W_B29_H30_it0.csv 0 → 100644
output,sample_index,x1,x2,y_true,y_pred_oof,abs_error_oof,positive_underprediction_oof
exymax_tw1,0,15.22,18.46,0.0368687,0.039349987460320174,0.0024812874603201765,0.0
exymax_tw1,1,12.74,12.61,0.040946,0.041748432978490777,0.0008024329784907733,0.0
exymax_tw1,2,18.03,11.3,0.0194026,0.022016191315068847,0.002613591315068848,0.0
exymax_tw1,3,21.19,19.38,0.022359,0.032638595838958784,0.010279595838958783,0.0
exymax_tw1,4,9.78,15.96,0.0678181,0.05912795356200942,0.008690146437990584,0.008690146437990584
exymax_tw1,5,18.64,13.8,0.022213,0.020003620447742315,0.002209379552257685,0.002209379552257685
exymax_tw1,6,13.46,20.99,0.0500821,0.05039469029367061,0.00031259029367061014,0.0
exymax_tw1,7,8.58,8.88,0.0615553,0.04775714809119386,0.013798151908806143,0.013798151908806143
exymax_tw2,0,15.22,18.46,0.0396548,0.03882006400983205,0.0008347359901679438,0.0008347359901679438
exymax_tw2,1,12.74,12.61,0.0732361,0.06876053411884203,0.004475565881157967,0.004475565881157967
exymax_tw2,2,18.03,11.3,0.0889196,0.08926994950316429,0.0003503495031642878,0.0
exymax_tw2,3,21.19,19.38,0.0460981,0.04898500719758495,0.0028869071975849495,0.0
exymax_tw2,4,9.78,15.96,0.0409845,0.05251269208060104,0.01152819208060104,0.0
exymax_tw2,5,18.64,13.8,0.0716685,0.07206308943441321,0.0003945894344132145,0.0
exymax_tw2,6,13.46,20.99,0.0286307,0.024122136167031908,0.0045085638329680905,0.0045085638329680905
exymax_tw2,7,8.58,8.88,0.0931119,0.09231186502721295,0.0008000349727870504,0.0008000349727870504
tfmmax_tw1,0,15.22,18.46,72.7038,72.78968484152168,0.08588484152167553,0.0
tfmmax_tw1,1,12.74,12.61,72.6758,87.38797481516035,14.712174815160353,0.0
tfmmax_tw1,2,18.03,11.3,28.7372,28.919786048950254,0.18258604895025243,0.0
tfmmax_tw1,3,21.19,19.38,34.6226,50.08856179505832,15.46596179505832,0.0
tfmmax_tw1,4,9.78,15.96,142.7821,116.59352301889726,26.188576981102756,26.188576981102756
tfmmax_tw1,5,18.64,13.8,29.4176,29.47129572844358,0.053695728443578616,0.0
tfmmax_tw1,6,13.46,20.99,104.2237,113.81990187990658,9.596201879906587,0.0
tfmmax_tw1,7,8.58,8.88,122.4554,88.09047848136643,34.364921518633565,34.364921518633565
tfmmax_tw2,0,15.22,18.46,62.4008,53.66859256155374,8.73220743844626,8.73220743844626
tfmmax_tw2,1,12.74,12.61,107.3854,112.80598843647891,5.4205884364789085,0.0
tfmmax_tw2,2,18.03,11.3,142.6177,130.24032345730427,12.377376542695743,12.377376542695743
tfmmax_tw2,3,21.19,19.38,69.0521,59.70245703072726,9.349642969272736,9.349642969272736
tfmmax_tw2,4,9.78,15.96,71.6787,79.54373848301191,7.865038483011901,0.0
tfmmax_tw2,5,18.64,13.8,104.9098,113.51337642166618,8.603576421666176,0.0
tfmmax_tw2,6,13.46,20.99,49.3735,62.224593181415855,12.851093181415855,0.0
tfmmax_tw2,7,8.58,8.88,161.7784,147.0567488768353,14.721651123164719,14.721651123164719
tfmmax_frame,0,15.22,18.46,86.3253,81.87772308597414,4.447576914025859,4.447576914025859
tfmmax_frame,1,12.74,12.61,85.5848,86.78711242194424,1.2023124219442423,0.0
tfmmax_frame,2,18.03,11.3,105.3345,96.19372205178321,9.140777948216794,9.140777948216794
tfmmax_frame,3,21.19,19.38,98.062,79.07603304790348,18.98596695209652,18.98596695209652
tfmmax_frame,4,9.78,15.96,74.5182,71.47941565635551,3.0387843436444797,3.0387843436444797
tfmmax_frame,5,18.64,13.8,96.9956,102.85610781889989,5.86050781889989,0.0
tfmmax_frame,6,13.46,20.99,77.0242,85.56534804538171,8.541148045381718,0.0
tfmmax_frame,7,8.58,8.88,79.7824,89.29879468755053,9.51639468755053,0.0
Code/models/width_optimization/2W/rbf_models/per_output_models_B29_H30/it0/validation_envelopes_2W_B29_H30_it0.csv 0 → 100644
n_unique_oof_samples,envelope_reference_n,percentile,small_n_threshold,abs_error_envelope,abs_error_rule,damage_safety_margin,damage_safety_margin_rule,abs_error_p95,abs_error_max,underprediction_p95,underprediction_max,mean_abs_error,output,method
8,8,95.0,20,0.013798151908806143,max_abs_oof_error_small_n,0.013798151908806143,max_positive_oof_residual_small_n,0.012566657284359566,0.013798151908806143,0.012010349994020694,0.013798151908806143,0.005148396973195451,exymax_tw1,RBF
8,8,95.0,20,0.01152819208060104,max_abs_oof_error_small_n,0.0045085638329680905,max_positive_oof_residual_small_n,0.009071322193929504,0.01152819208060104,0.004497014549834547,0.0045085638329680905,0.003222367361605568,exymax_tw2,RBF
8,8,95.0,20,34.364921518633565,max_abs_oof_error_small_n,34.364921518633565,max_positive_oof_residual_small_n,31.503200930497776,34.364921518633565,31.503200930497776,34.364921518633565,12.581250451097135,tfmmax_tw1,RBF
8,8,95.0,20,14.721651123164719,max_abs_oof_error_small_n,14.721651123164719,max_positive_oof_residual_small_n,14.066955843552616,14.721651123164719,13.901155020000576,14.721651123164719,9.990146824519037,tfmmax_tw2,RBF
8,8,95.0,20,18.98596695209652,max_abs_oof_error_small_n,18.98596695209652,max_positive_oof_residual_small_n,15.671616659505418,18.98596695209652,15.54015080073861,18.98596695209652,7.591683641470004,tfmmax_frame,RBF
Code/models/width_optimization/2W/rbf_models/per_output_models_B34_H30/it0/local_oof_errors_2W_B34_H30_it0.csv 0 → 100644
output,sample_index,x1,x2,y_true,y_pred_oof,abs_error_oof,positive_underprediction_oof
exymax_tw1,0,15.22,18.46,0.0512763,0.05114693084532329,0.00012936915467670618,0.00012936915467670618
exymax_tw1,1,12.74,12.61,0.0480552,0.05400132617055037,0.0059461261705503685,0.0
exymax_tw1,2,18.03,11.3,0.0212773,0.024176457197253195,0.002899157197253196,0.0
exymax_tw1,3,21.19,19.38,0.0319299,0.042283411335063005,0.010353511335063008,0.0
exymax_tw1,4,9.78,15.96,0.0902318,0.0766605325923928,0.013571267407607196,0.013571267407607196
exymax_tw1,5,18.64,13.8,0.0266662,0.024796622548916472,0.001869577451083529,0.001869577451083529
exymax_tw1,6,13.46,20.99,0.067683,0.07134675587529823,0.0036637558752982413,0.0
exymax_tw1,7,8.58,8.88,0.0712047,0.053580599362685624,0.01762410063731437,0.01762410063731437
exymax_tw2,0,15.22,18.46,0.051279,0.047740247564823216,0.0035387524351767827,0.0035387524351767827
exymax_tw2,1,12.74,12.61,0.0854114,0.08831145173266461,0.0029000517326646102,0.0
exymax_tw2,2,18.03,11.3,0.1170466,0.11201921065892645,0.0050273893410735515,0.0050273893410735515
exymax_tw2,3,21.19,19.38,0.0606494,0.0602216830325355,0.000427716967464499,0.000427716967464499
exymax_tw2,4,9.78,15.96,0.047305,0.05638274159437359,0.009077741594373592,0.0
exymax_tw2,5,18.64,13.8,0.0926283,0.09595788796908056,0.0033295879690805608,0.0
exymax_tw2,6,13.46,20.99,0.0341728,0.03895568756784007,0.004782887567840066,0.0
exymax_tw2,7,8.58,8.88,0.1104125,0.10465367079458698,0.005758829205413013,0.005758829205413013
tfmmax_tw1,0,15.22,18.46,99.6966,97.6224382499142,2.074161750085807,2.074161750085807
tfmmax_tw1,1,12.74,12.61,93.0272,111.06109728079156,18.033897280791564,0.0
tfmmax_tw1,2,18.03,11.3,34.7023,33.267573967288,1.4347260327119997,1.4347260327119997
tfmmax_tw1,3,21.19,19.38,49.5951,64.43397804295296,14.838878042952956,0.0
tfmmax_tw1,4,9.78,15.96,178.8235,143.95438089845993,34.869119101540065,34.869119101540065
tfmmax_tw1,5,18.64,13.8,37.1978,39.75436806820997,2.5565680682099696,0.0
tfmmax_tw1,6,13.46,20.99,137.2798,151.66582179292823,14.386021792928233,0.0
tfmmax_tw1,7,8.58,8.88,144.4093,140.43634850876015,3.9729514912398542,3.9729514912398542
tfmmax_tw2,0,15.22,18.46,79.5103,71.44170014344601,8.068599856553988,8.068599856553988
tfmmax_tw2,1,12.74,12.61,134.6891,137.6080316972492,2.9189316972492065,0.0
tfmmax_tw2,2,18.03,11.3,183.3526,164.6299343616604,18.722665638339606,18.722665638339606
tfmmax_tw2,3,21.19,19.38,86.0991,66.29695957618617,19.80214042381384,19.80214042381384
tfmmax_tw2,4,9.78,15.96,79.6009,93.41807481546371,13.817174815463716,0.0
tfmmax_tw2,5,18.64,13.8,132.7491,146.18057970340942,13.431479703409423,0.0
tfmmax_tw2,6,13.46,20.99,59.7863,65.81620482916196,6.029904829161964,0.0
tfmmax_tw2,7,8.58,8.88,187.2618,179.2156098457276,8.04619015427238,8.04619015427238
tfmmax_frame,0,15.22,18.46,93.2656,93.35193348234543,0.08633348234542382,0.0
tfmmax_frame,1,12.74,12.61,94.8678,93.33075219479798,1.5370478052020218,1.5370478052020218
tfmmax_frame,2,18.03,11.3,117.5466,113.99975561681512,3.546844383184876,3.546844383184876
tfmmax_frame,3,21.19,19.38,111.5967,95.41632772640014,16.180372273599858,16.180372273599858
tfmmax_frame,4,9.78,15.96,80.7235,77.09880184765132,3.624698152348685,3.624698152348685
tfmmax_frame,5,18.64,13.8,111.9409,114.22129831394966,2.280398313949661,0.0
tfmmax_frame,6,13.46,20.99,85.7607,87.80746687257806,2.0467668725780612,0.0
tfmmax_frame,7,8.58,8.88,83.381,97.63717736190392,14.256177361903923,0.0
Code/models/width_optimization/2W/rbf_models/per_output_models_B34_H30/it0/validation_envelopes_2W_B34_H30_it0.csv 0 → 100644
n_unique_oof_samples,envelope_reference_n,percentile,small_n_threshold,abs_error_envelope,abs_error_rule,damage_safety_margin,damage_safety_margin_rule,abs_error_p95,abs_error_max,underprediction_p95,underprediction_max,mean_abs_error,output,method
8,8,95.0,20,0.01762410063731437,max_abs_oof_error_small_n,0.01762410063731437,max_positive_oof_residual_small_n,0.01620560900691686,0.01762410063731437,0.01620560900691686,0.01762410063731437,0.0070071081536058274,exymax_tw1,RBF
8,8,95.0,20,0.009077741594373592,max_abs_oof_error_small_n,0.005758829205413013,max_positive_oof_residual_small_n,0.007916122258237387,0.009077741594373592,0.0055028252528942015,0.005758829205413013,0.0043553696016358345,exymax_tw2,RBF
8,8,95.0,20,34.869119101540065,max_abs_oof_error_small_n,34.869119101540065,max_positive_oof_residual_small_n,28.97679146427808,34.869119101540065,24.055460437934975,34.869119101540065,11.520790445057557,tfmmax_tw1,RBF
8,8,95.0,20,19.80214042381384,max_abs_oof_error_small_n,19.80214042381384,max_positive_oof_residual_small_n,19.424324248897857,19.80214042381384,19.424324248897857,19.80214042381384,11.354635889783015,tfmmax_tw2,RBF
8,8,95.0,20,16.180372273599858,max_abs_oof_error_small_n,16.180372273599858,max_positive_oof_residual_small_n,15.506904054506279,16.180372273599858,11.78588633116194,16.180372273599858,5.444829830639064,tfmmax_frame,RBF
Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/final_model_selection_3W_B29_H45_it1.csv 0 → 100644
output,N,selected_model,selection_criterion,inner_cv_rmse,inner_cv_mae,inner_cv_rmse_dispersion,model_path,best_params,gpr_kernel
exymax_tw1,17,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.007386876148430646,0.005985287372716268,0.4457079072654744,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_exymax_tw1.joblib,"{""svr__C"": 15.192926398283221, ""svr__epsilon"": 0.0010023878240083964, ""svr__gamma"": 0.010311615261737522}",
exymax_tw2,17,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.007704919518744075,0.007198781617960337,0.5335953781531853,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_exymax_tw2.joblib,"{""svr__C"": 0.1500657521453483, ""svr__epsilon"": 0.001, ""svr__gamma"": 0.050770137520634445}",
exymax_tw3,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.0074451185013724025,0.006123488781111923,0.9068901062537463,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_exymax_tw3.joblib,"{""gpr__amplitude"": 66.36085077612337, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 5.537373996465696, ""gpr__n_restarts_optimizer"": 1, ""gpr__noise"": 4.650004290224827e-08, ""gpr__rq_alpha"": 0.26474549244554496}","2.21**2 * Matern(length_scale=4.74, nu=2.5) + WhiteKernel(noise_level=4.65e-08)"
tfmmax_tw1,17,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band,14.989166467509728,12.266247991039252,0.4379274754827781,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_tfmmax_tw1.joblib,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 0.0001, ""mlp__learning_rate_init"": 0.0002706510218672049, ""mlp__n_layers"": 2, ""mlp__n_neurons"": 21}",
tfmmax_tw2,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,10.3362106636413,9.512724608051858,0.6627630235447725,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_tfmmax_tw2.joblib,"{""gpr__amplitude"": 15.277161421708573, ""gpr__kernel_type"": ""Matern32"", ""gpr__length_scale"": 5.203753854397489, ""gpr__n_restarts_optimizer"": 2, ""gpr__noise"": 1e-10, ""gpr__rq_alpha"": 10.0}","3.52**2 * Matern(length_scale=9.08, nu=1.5) + WhiteKernel(noise_level=1e-10)"
tfmmax_tw3,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,7.329392311714703,6.011603551539829,0.6290079384381844,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_tfmmax_tw3.joblib,"{""gpr__amplitude"": 8.632012725909878, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 0.21242927029064568, ""gpr__n_restarts_optimizer"": 0, ""gpr__noise"": 4.013494019734207e-05, ""gpr__rq_alpha"": 0.5561085291185478}","1.82**2 * RationalQuadratic(alpha=1e+03, length_scale=3.26) + WhiteKernel(noise_level=0.00169)"
tfmmax_frame,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,2.6727226722174917,2.405484808993129,0.4052288548890203,../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/best_model_tfmmax_frame.joblib,"{""gpr__amplitude"": 67.8545812939654, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 7.279027436529991, ""gpr__n_restarts_optimizer"": 0, ""gpr__noise"": 6.334601653763827e-09, ""gpr__rq_alpha"": 0.19889397112163434}","1.83**2 * Matern(length_scale=4.8, nu=2.5) + WhiteKernel(noise_level=6.33e-09)"
Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/nested_cv_fold_selections_3W_B29_H45_it1.csv 0 → 100644
outer_fold,selected_model,selection_criterion
0,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,RandomForest,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/nested_cv_model_selection_frequency_3W_B29_H45_it1.csv 0 → 100644
model,frequency,percentage,output,N
SVR,10,58.82,exymax_tw1,17
GradientBoosting,5,29.41,exymax_tw1,17
GaussianProcess,1,5.88,exymax_tw1,17
RandomForest,1,5.88,exymax_tw1,17
SVR,11,64.71,exymax_tw2,17
GaussianProcess,5,29.41,exymax_tw2,17
FlexibleMLP,1,5.88,exymax_tw2,17
GradientBoosting,8,47.06,exymax_tw3,17
SVR,6,35.29,exymax_tw3,17
GaussianProcess,3,17.65,exymax_tw3,17
FlexibleMLP,9,52.94,tfmmax_tw1,17
GaussianProcess,4,23.53,tfmmax_tw1,17
GradientBoosting,3,17.65,tfmmax_tw1,17
SVR,1,5.88,tfmmax_tw1,17
FlexibleMLP,12,70.59,tfmmax_tw2,17
GaussianProcess,5,29.41,tfmmax_tw2,17
GaussianProcess,13,76.47,tfmmax_tw3,17
FlexibleMLP,2,11.76,tfmmax_tw3,17
GradientBoosting,2,11.76,tfmmax_tw3,17
GaussianProcess,16,94.12,tfmmax_frame,17
SVR,1,5.88,tfmmax_frame,17
Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/nested_cv_outer_predictions_3W_B29_H45_it1.csv 0 → 100644
output,N,outer_fold,sample_index,y_true,selected_supervised_model,prediction_RandomForest,prediction_GradientBoosting,prediction_XGBoost,prediction_FlexibleMLP,prediction_SVR,prediction_GaussianProcess,prediction_supervised_selection
exymax_tw1,17,0,0,0.0287568,GaussianProcess,0.022615004969746968,0.020390072529016385,0.026711033657193184,0.036961072803931516,0.029329608855591993,0.021183260084492048,0.021183260084492048
exymax_tw1,17,1,1,0.0053857,SVR,0.008469266030952386,0.01954804848750287,0.015043599531054497,-0.0004882853973155327,0.010758925938332144,0.006268230527412641,0.010758925938332144
exymax_tw1,17,2,2,0.0440541,SVR,0.045617750850836636,0.039491802024518026,0.03358830139040947,0.04088320479014046,0.039170567679535646,0.04225750506029751,0.039170567679535646
exymax_tw1,17,3,3,0.0781113,GradientBoosting,0.04732011286785717,0.055141607976206444,0.042160674929618835,0.05473444443204037,0.05453656300605149,0.05460020365289453,0.055141607976206444
exymax_tw1,17,4,4,0.0140436,SVR,0.017428703212669684,0.018147993409163933,0.02248404547572136,0.02570898376946794,0.01742763994125969,0.015706077758571496,0.01742763994125969
exymax_tw1,17,5,5,0.0112778,SVR,0.01968294343732896,0.018642694395268337,0.02107233926653862,0.01880428050439137,-0.00022754718332418378,0.008631596683711787,-0.00022754718332418378
exymax_tw1,17,6,6,0.0024325,GradientBoosting,0.007955673088183419,0.0030320878218712865,0.0023566887248307467,-0.0075419714178375785,-0.00039570405596306424,0.00040554277403713826,0.0030320878218712865
exymax_tw1,17,7,7,0.0024167,GradientBoosting,0.00845455068984126,0.0010655878082269814,0.0058835651725530624,-9.945760459749431e-05,0.001070293534974967,0.005692533645497978,0.0010655878082269814
exymax_tw1,17,8,8,0.0550528,SVR,0.050521735746550944,0.05644869860505926,0.053392231464385986,0.04368390062061578,0.04499451471285325,0.0428872154673858,0.04499451471285325
exymax_tw1,17,9,9,0.0081104,GradientBoosting,0.010969821096860479,0.0056393193384580995,0.008095316588878632,0.019545449329240128,0.008572977572354472,0.009506761109489954,0.0056393193384580995
exymax_tw1,17,10,10,0.0327732,SVR,0.02680297893333334,0.03298833445832741,0.02620396576821804,0.040073851069397926,0.04157688687729627,0.0445823016423068,0.04157688687729627
exymax_tw1,17,11,11,0.0553474,SVR,0.04430196249325395,0.04708434949217952,0.058577343821525574,0.049210889053378615,0.049832439992378665,0.05587849837513338,0.049832439992378665
exymax_tw1,17,12,12,0.0154791,GradientBoosting,0.015167960233686049,0.014620155884046724,0.012252707034349442,0.01489067756051704,0.015640653479711233,0.01431164102775761,0.014620155884046724
exymax_tw1,17,13,13,0.0221142,SVR,0.041023663885714275,0.026790469917227725,0.036579813808202744,0.03175118622644396,0.029891818493326103,0.027902305585589537,0.029891818493326103
exymax_tw1,17,14,14,0.0195449,RandomForest,0.011593174264793784,0.015016591012141782,0.0158791895955801,0.02458157584610482,0.024518074002819575,0.01834956791291119,0.011593174264793784
exymax_tw1,17,15,15,0.0107046,SVR,0.01761372089523809,0.014084900968956827,0.017018333077430725,0.018234248288356125,0.003931452459380591,0.009897548113851136,0.003931452459380591
exymax_tw1,17,16,16,0.0411245,SVR,0.054959737541666664,0.04450964707435878,0.052345164120197296,0.03941229896332349,0.04804295469995616,0.05006211311466298,0.04804295469995616
exymax_tw2,17,0,0,0.0384806,SVR,0.03843201356808854,0.03395881103923363,0.04178193211555481,0.040439109396770756,0.027535704648153636,0.02639833159004059,0.027535704648153636
exymax_tw2,17,1,1,0.071338,SVR,0.06710204364285714,0.06742304987744366,0.07628266513347626,0.07409017534084256,0.07231259772119213,0.07094140264831263,0.07231259772119213
exymax_tw2,17,2,2,0.0648756,GaussianProcess,0.07316357772246278,0.06150103896753963,0.06551148742437363,0.043476785276879204,0.06917543778188905,0.06274344614357764,0.06274344614357764
exymax_tw2,17,3,3,0.0638333,SVR,0.052823276730952384,0.04748098695401882,0.060324326157569885,0.0646371354161252,0.04941038805392761,0.04920448082254675,0.04941038805392761
exymax_tw2,17,4,4,0.0763849,SVR,0.07181128686470846,0.06743534675747452,0.09316051006317139,0.07979841557365311,0.08046000840336295,0.08038877057413882,0.08046000840336295
exymax_tw2,17,5,5,0.0581062,GaussianProcess,0.0493326214750567,0.04823419614028361,0.04435708001255989,0.07671317165365724,0.05268092136744272,0.054557722764884464,0.054557722764884464
exymax_tw2,17,6,6,0.0266335,SVR,0.035391559585396734,0.04274064105968848,0.034711409360170364,0.04454216333984179,0.03727698889521955,0.038950272133835145,0.03727698889521955
exymax_tw2,17,7,7,0.0199308,FlexibleMLP,0.029820528595238108,0.02447533972561915,0.030201442539691925,0.021215093584163847,0.015543359841712637,0.0127148950185152,0.021215093584163847
exymax_tw2,17,8,8,0.0172312,SVR,0.027076846715476208,0.02685903944855217,0.02732098661363125,0.0007545541032852321,0.018622156038261284,0.01770345213359093,0.018622156038261284
exymax_tw2,17,9,9,0.1076924,GaussianProcess,0.0766056043386511,0.09306899081100685,0.07253976911306381,0.08025647672821845,0.07897774928205853,0.09662915254406984,0.09662915254406984
exymax_tw2,17,10,10,0.0440125,SVR,0.042341882985714306,0.04145163600984208,0.05005715414881706,0.03202900463964137,0.05095455992027219,0.05059090650066826,0.05095455992027219
exymax_tw2,17,11,11,0.0315451,SVR,0.026523608202838826,0.02955498894020365,0.02996445633471012,0.03642322129033598,0.033147058587089995,0.035185763694304005,0.033147058587089995
exymax_tw2,17,12,12,0.0930859,SVR,0.07281058814571417,0.10657886748489022,0.07646698504686356,0.08615909019609558,0.09238262505122669,0.0930532395642705,0.09238262505122669
exymax_tw2,17,13,13,0.0185585,GaussianProcess,0.021925874721558027,0.015426401458054765,0.011077575385570526,0.011693473202858198,0.010968703456954026,0.018522505619030828,0.018522505619030828
exymax_tw2,17,14,14,0.0265998,SVR,0.03709959759156218,0.03949366765580314,0.03511132299900055,-0.038411027289952826,0.027287922411395327,0.026244130801652433,0.027287922411395327
exymax_tw2,17,15,15,0.0205985,GaussianProcess,0.02499697179123707,0.024726261534373982,0.023852718994021416,0.030317778057985467,0.018333399856419463,0.023151351907629875,0.023151351907629875
exymax_tw2,17,16,16,0.0438643,SVR,0.06358867559294869,0.06181848987798918,0.06330932676792145,0.061597917740257746,0.047621335524235586,0.05076100075519345,0.047621335524235586
exymax_tw3,17,0,0,0.0426347,SVR,0.04222425233739593,0.042843587999111504,0.04674408212304115,0.04546813603358962,0.033762868826775094,0.03642705088599796,0.033762868826775094
exymax_tw3,17,1,1,0.0471032,GaussianProcess,0.05462483257111028,0.0504715716411936,0.054168760776519775,0.06485898455513528,0.04843665486130559,0.05106398477722832,0.05106398477722832
exymax_tw3,17,2,2,0.0503902,GradientBoosting,0.04984400796073183,0.04927856645696552,0.05132384970784187,0.05034787754405723,0.04939751254728597,0.04660907828207994,0.04927856645696552
exymax_tw3,17,3,3,0.0292695,GradientBoosting,0.041481012229002585,0.03936980963641063,0.043223991990089417,0.02810924256005265,0.03278405722825638,0.028154208753179838,0.03936980963641063
exymax_tw3,17,4,4,0.0527733,SVR,0.050171524582857126,0.050250823966310997,0.048960838466882706,0.06371692491605355,0.05545098822198717,0.05436639617897929,0.05545098822198717
exymax_tw3,17,5,5,0.0324297,GradientBoosting,0.03702193279112553,0.030616059666895713,0.02851356379687786,-0.000903304944908645,0.026312427929204546,0.02582166249024606,0.030616059666895713
exymax_tw3,17,6,6,0.1034537,GradientBoosting,0.0857516363362727,0.09906170669932648,0.11332868784666061,0.10561340225730748,0.10835777130512915,0.10733098542321567,0.09906170669932648
exymax_tw3,17,7,7,0.1360301,GaussianProcess,0.1306113319466721,0.1203860972320994,0.14489765465259552,0.13196494767485323,0.1373105840365101,0.13323849051955494,0.13323849051955494
exymax_tw3,17,8,8,0.05162,SVR,0.05481435263721139,0.06158876831280212,0.07515127956867218,0.06657289237850389,0.07748410050437413,0.07796559987030609,0.07748410050437413
exymax_tw3,17,9,9,0.0846721,GradientBoosting,0.07601766394652931,0.08119294223066681,0.10005516558885574,0.07586354972534715,0.07707084141099241,0.07794498942652911,0.08119294223066681
exymax_tw3,17,10,10,0.02804,GradientBoosting,0.032006792737032254,0.03202262834830112,0.03169507905840874,0.02181694371633519,0.034967164198984185,0.03451127603139809,0.03202262834830112
exymax_tw3,17,11,11,0.0281899,SVR,0.0424836943807215,0.03551045916070281,0.03099033050239086,0.031425827743510604,0.020982321137166804,0.019012804686924463,0.020982321137166804
exymax_tw3,17,12,12,0.0712027,GradientBoosting,0.06393039435041785,0.05655971555357955,0.08122147619724274,0.0667087486518596,0.07358539834690103,0.07357792205373594,0.05655971555357955
exymax_tw3,17,13,13,0.1558781,GaussianProcess,0.11021038794999992,0.12979336149531104,0.1369742453098297,0.14631722575647216,0.13469147315828808,0.1333619016031155,0.1333619016031155
exymax_tw3,17,14,14,0.0326696,GradientBoosting,0.04396168323087309,0.032420594204743115,0.05324123799800873,0.04410361843647989,0.04219678623087489,0.03942796324778562,0.032420594204743115
exymax_tw3,17,15,15,0.1445584,SVR,0.1126906312690699,0.13495397738177276,0.13903595507144928,0.14401917375667622,0.14090010972995745,0.13648938577030192,0.14090010972995745
exymax_tw3,17,16,16,0.0479126,SVR,0.045759679100572014,0.046811883939557444,0.04987568408250809,0.05987345673833361,0.05221051359033754,0.05170084338380148,0.05221051359033754
tfmmax_tw1,17,0,0,42.4856,FlexibleMLP,22.894839557142856,43.24306889344056,47.0372200012207,36.739895769428344,54.07317639559974,41.19071837524553,36.739895769428344
tfmmax_tw1,17,1,1,2.7556,FlexibleMLP,20.613918160317485,23.549934089804726,19.225309371948242,2.0644867961464612,1.9188327238973741,3.062940400082219,2.0644867961464612
tfmmax_tw1,17,2,2,74.3944,GaussianProcess,90.59236527694789,80.75556532135688,83.27584838867188,74.64484033753318,72.68237161896704,79.45540227037814,79.45540227037814
tfmmax_tw1,17,3,3,153.736,GaussianProcess,99.31575172666663,107.40911599270893,97.92886352539062,119.28282594728742,77.70795632732134,125.02529678240012,125.02529678240012
tfmmax_tw1,17,4,4,11.0749,FlexibleMLP,21.014075631890353,30.87004081543133,24.917869567871094,9.006936044926643,10.550986296749358,12.704923776592715,9.006936044926643
tfmmax_tw1,17,5,5,7.9097,GradientBoosting,29.844182272493182,28.303339829661734,34.434791564941406,7.487912896894639,6.124374275481941,3.4265255099925724,28.303339829661734
tfmmax_tw1,17,6,6,0.6643,GradientBoosting,10.234619936394017,-0.48962482089890136,0.9710974097251892,2.1028993612417906,0.9265376702564367,0.8571122014207191,-0.48962482089890136
tfmmax_tw1,17,7,7,1.0671,FlexibleMLP,12.063739522936535,-2.123918156268989,0.17705309391021729,2.359595990964086,1.6349505282549757,1.5169709449174462,2.359595990964086
tfmmax_tw1,17,8,8,123.3135,GaussianProcess,114.02070599999992,100.71561172378455,106.46036529541016,119.50369671078052,78.90479750273974,94.1650835867149,94.1650835867149
tfmmax_tw1,17,9,9,5.8951,FlexibleMLP,8.468859917063494,-8.105893384524336,10.207292556762695,6.698024134854821,5.85222396330547,12.325680518584193,6.698024134854821
tfmmax_tw1,17,10,10,63.8575,SVR,40.053709607142835,40.97822341064199,37.396583557128906,83.1670167719412,78.23005379609639,77.34328872826,78.23005379609639
tfmmax_tw1,17,11,11,120.2943,GaussianProcess,99.15274581650785,103.30490492589126,137.6754150390625,130.6803435656933,99.40415737446403,125.07878937209851,125.07878937209851
tfmmax_tw1,17,12,12,12.5924,FlexibleMLP,14.234073667532472,16.490813875899537,14.845376968383789,18.544642516520035,13.666953507545415,11.987325027869815,18.544642516520035
tfmmax_tw1,17,13,13,30.8212,FlexibleMLP,81.87588671380935,65.40190916475306,68.83283996582031,64.20359488794193,50.58879059602525,55.8041108970138,64.20359488794193
tfmmax_tw1,17,14,14,25.1018,FlexibleMLP,14.534883398541405,7.13402024488804,16.25975227355957,45.57937995672612,32.52991911284177,18.87068474397775,45.57937995672612
tfmmax_tw1,17,15,15,11.0271,FlexibleMLP,16.96552051030636,6.087818848927775,27.940122604370117,1.7677049761580275,17.443103927869426,4.346260360318055,1.7677049761580275
tfmmax_tw1,17,16,16,85.9787,GradientBoosting,115.26758459999998,94.00969805911363,110.94416809082031,92.2042584102237,85.14299466828076,90.08386248580308,94.00969805911363
tfmmax_tw2,17,0,0,55.4009,FlexibleMLP,56.59558756293644,47.794286871564836,76.0320816040039,42.304036464777106,61.615812095786914,47.398588276328994,42.304036464777106
tfmmax_tw2,17,1,1,112.2407,FlexibleMLP,120.02497358714288,112.58500746223534,125.27998352050781,114.93469654516345,112.1160193727712,118.68652167868666,114.93469654516345
tfmmax_tw2,17,2,2,118.5858,FlexibleMLP,122.82732497976194,121.82034883184633,114.86471557617188,120.66190617714463,116.02520481581422,111.73856462412338,120.66190617714463
tfmmax_tw2,17,3,3,106.5364,GaussianProcess,88.67256316666666,79.35573020818977,112.79500579833984,107.72707803765039,93.53931938073562,92.55362533901994,92.55362533901994
tfmmax_tw2,17,4,4,132.0136,FlexibleMLP,121.72879960000009,130.32650019559614,165.76806640625,135.21499243051548,129.45136077237314,133.79188614532302,135.21499243051548
tfmmax_tw2,17,5,5,90.9036,FlexibleMLP,79.25825916666669,81.09583828101168,76.55889129638672,86.85349087922667,87.32567282214674,82.21041266553779,86.85349087922667
tfmmax_tw2,17,6,6,46.3963,FlexibleMLP,45.007927836190476,48.84637171191386,52.21295166015625,77.02300561839067,62.384481385458145,64.96182896613689,77.02300561839067
tfmmax_tw2,17,7,7,36.7154,FlexibleMLP,51.1337188357143,32.541774520820375,39.111209869384766,29.807611055980065,31.109497433619197,21.842624949733235,29.807611055980065
tfmmax_tw2,17,8,8,26.3489,GaussianProcess,47.78279545714294,42.768932559554486,45.54428482055664,23.248553523756133,29.44740203919801,23.112035548678136,23.112035548678136
tfmmax_tw2,17,9,9,201.8901,FlexibleMLP,132.9582133992834,168.345883549806,142.24986267089844,175.45692343384044,148.50671634029408,179.4602990875403,175.45692343384044
tfmmax_tw2,17,10,10,75.9879,FlexibleMLP,59.99125241136929,63.996394317978925,83.3016128540039,84.62516524925404,75.51790005712115,81.43209981014495,84.62516524925404
tfmmax_tw2,17,11,11,53.3086,GaussianProcess,43.1817964428572,51.32518470690914,48.405181884765625,56.46988632054106,56.1723526224539,57.339696128748486,57.339696128748486
tfmmax_tw2,17,12,12,168.2033,FlexibleMLP,171.47396071666682,199.9555642719607,155.97445678710938,175.29378872230583,149.1100079988766,173.50636145469892,175.29378872230583
tfmmax_tw2,17,13,13,27.8159,GaussianProcess,37.76249722142856,31.754120650116498,36.83064651489258,24.84037578869428,27.56713317448545,34.80740510548769,34.80740510548769
tfmmax_tw2,17,14,14,45.484,FlexibleMLP,41.936831360720454,45.223536140387246,48.46034240722656,44.58649022274663,50.79755791278164,37.07962969549532,44.58649022274663
tfmmax_tw2,17,15,15,37.9509,FlexibleMLP,43.279337894857676,43.622293480524114,37.338584899902344,47.18048074612094,36.030720405917116,32.9530826573369,47.18048074612094
tfmmax_tw2,17,16,16,77.2855,GaussianProcess,108.86787505070544,107.77968645620192,100.40499877929688,92.47400609463483,90.58512025200388,87.76615073482517,87.76615073482517
tfmmax_tw3,17,0,0,65.4914,FlexibleMLP,61.57439401547617,64.99689359189811,66.54412078857422,37.64215521109664,64.60191230550176,67.1684450251205,37.64215521109664
tfmmax_tw3,17,1,1,71.322,GaussianProcess,83.74913268209237,65.20541480136724,83.18019104003906,105.68817230771393,79.01878712852773,76.39580489702848,76.39580489702848
tfmmax_tw3,17,2,2,83.264,GaussianProcess,81.63376703880955,86.67555530897488,84.09208679199219,82.12837568624451,74.42923914062882,73.93176971485812,73.93176971485812
tfmmax_tw3,17,3,3,46.4819,GaussianProcess,65.55890796650725,63.6945835742782,64.27913665771484,64.78753314047151,55.38402823050774,54.52740699925274,54.52740699925274
tfmmax_tw3,17,4,4,84.0868,GaussianProcess,75.59956912106783,80.01690764317203,84.61798095703125,74.76783559396529,84.60224449521931,84.79491726297421,84.79491726297421
tfmmax_tw3,17,5,5,49.9149,GaussianProcess,57.613705895952386,49.927855626375646,48.99932098388672,67.9901851042709,50.75369315005277,43.50465421560391,43.50465421560391
tfmmax_tw3,17,6,6,184.0874,GradientBoosting,147.06304464493115,121.399491492647,235.4934844970703,189.550752860601,191.78226998798084,187.80996799097736,121.399491492647
tfmmax_tw3,17,7,7,241.1134,GaussianProcess,218.50862226496005,226.81483767565842,217.6211700439453,235.96023640615306,210.87013861176985,246.53328833595785,246.53328833595785
tfmmax_tw3,17,8,8,108.0964,GaussianProcess,90.08259573809522,83.12866602290863,105.70081329345703,130.3287751278129,122.73491006064611,127.68150458246026,127.68150458246026
tfmmax_tw3,17,9,9,122.4642,GradientBoosting,131.8612888468253,135.23574883597968,174.84979248046875,123.19895161194177,116.65930296139298,116.49540432655873,135.23574883597968
tfmmax_tw3,17,10,10,48.1235,GaussianProcess,56.40749712857149,57.74648206088317,49.9785270690918,48.01192796167846,47.10405839318665,51.590832153403,51.590832153403
tfmmax_tw3,17,11,11,50.6261,GaussianProcess,59.68334856750394,46.989147860457756,76.34642028808594,37.69193708772084,45.90791553818774,38.58096915030721,38.58096915030721
tfmmax_tw3,17,12,12,110.4781,FlexibleMLP,108.32491314523824,87.68355404123872,121.67630767822266,110.25037029851701,112.52635284940112,112.23333164602046,110.25037029851701
tfmmax_tw3,17,13,13,274.0096,GaussianProcess,227.59942644285707,240.59259780168878,243.2655029296875,297.37368046483397,189.58721784687054,255.13478268355954,255.13478268355954
tfmmax_tw3,17,14,14,59.1328,GaussianProcess,110.38442509682025,50.87601461101118,78.3691177368164,74.8078601050404,84.00830392377112,83.41767769569591,83.41767769569591
tfmmax_tw3,17,15,15,252.2847,GaussianProcess,211.64102718436507,228.51723029149377,240.1627655029297,231.96804291029486,201.11062910838638,232.53464082834364,232.53464082834364
tfmmax_tw3,17,16,16,78.3623,GaussianProcess,83.05572094047623,83.20107874990903,108.72232818603516,79.57659088772598,89.20999464288036,91.23059785596618,91.23059785596618
tfmmax_frame,17,0,0,63.6471,GaussianProcess,60.3280893744833,64.59638575945884,60.55095291137695,58.153423004920505,64.7672367400134,65.97988151708513,65.97988151708513
tfmmax_frame,17,1,1,64.5245,GaussianProcess,63.11556625268443,63.562509129192165,64.67369079589844,67.58285578620729,67.28221414688488,67.14337262177025,67.14337262177025
tfmmax_frame,17,2,2,56.9333,GaussianProcess,60.210398880813386,59.832834564762386,59.75285339355469,54.90180668173357,56.26262281150832,55.7810073746774,55.7810073746774
tfmmax_frame,17,3,3,48.37,GaussianProcess,60.70804320927851,57.72749880315331,57.37022018432617,39.78459979449182,50.889213578333,51.80015205697087,51.80015205697087
tfmmax_frame,17,4,4,64.072,GaussianProcess,61.254546940346465,62.375940213248455,60.40733337402344,67.84858189598772,61.630354414209386,61.8709261498414,61.8709261498414
tfmmax_frame,17,5,5,63.3658,GaussianProcess,59.98106187237372,56.47248062668159,58.79005432128906,67.43581338618488,63.40262490207172,62.56796825398455,62.56796825398455
tfmmax_frame,17,6,6,93.6778,GaussianProcess,71.7634961812951,64.08641413365115,92.8777847290039,93.89393935547744,89.71778200635895,89.42171654713536,89.42171654713536
tfmmax_frame,17,7,7,103.3849,GaussianProcess,96.66694194999995,99.89766566285074,100.11233520507812,105.35124325536938,105.74059270039105,108.0694894586854,108.0694894586854
tfmmax_frame,17,8,8,69.2869,GaussianProcess,54.75943378333331,53.45449280334032,63.065189361572266,99.59983632272142,73.12743449215556,71.9259113553859,71.9259113553859
tfmmax_frame,17,9,9,61.2348,GaussianProcess,71.5317804790419,69.55773963896277,87.68211364746094,72.40333606702161,63.465132582617244,62.653623728163765,62.653623728163765
tfmmax_frame,17,10,10,57.0745,GaussianProcess,59.63234232508655,59.83004350028866,57.900238037109375,52.57098221156644,56.09633921169024,57.50841287343505,57.50841287343505
tfmmax_frame,17,11,11,52.4444,GaussianProcess,62.44158997369251,63.972609800617434,70.12181854248047,58.55560521890841,51.567306362001204,50.454740822926695,50.454740822926695
tfmmax_frame,17,12,12,61.6913,GaussianProcess,63.23316921596321,56.6106661369821,63.92231369018555,64.42430710720889,62.60405382066704,62.97507927940286,62.97507927940286
tfmmax_frame,17,13,13,104.2669,GaussianProcess,97.69098419333324,100.36976975987126,99.87836456298828,111.86827843629936,98.96980103903678,101.29191529256306,101.29191529256306
tfmmax_frame,17,14,14,63.8419,SVR,65.70276458795314,72.41620170928644,67.00364685058594,63.53998969160034,66.18334057478239,66.05877341470642,66.18334057478239
tfmmax_frame,17,15,15,103.1451,GaussianProcess,96.56612207063576,97.22029652771083,97.59417724609375,94.83116446664673,101.50340298701141,100.4700905944952,100.4700905944952
tfmmax_frame,17,16,16,59.15191,GaussianProcess,60.63175265735077,57.0084753236613,60.361045837402344,61.84779078099143,60.23169924739485,60.25198238231588,60.25198238231588
Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/nested_cv_outer_summary_3W_B29_H45_it1.csv 0 → 100644
output,method,outer_rmse,outer_mae,outer_r2,outer_std_abs_error,outer_max_abs_error,n_outer_predictions,N,outer_rmse_ci_lower,outer_rmse_ci_upper,outer_mae_ci_lower,outer_mae_ci_upper
exymax_tw1,RandomForest,0.010828163339054552,0.008073782424709979,0.7392921985291951,0.007215480486820631,0.030791187132142822,17,17,0.006016095563100944,0.01592954684031464,0.005091737657912583,0.012025134924050444
exymax_tw1,GradientBoosting,0.007801371916383487,0.00545030523958485,0.8646723950016199,0.005581718066428193,0.02296969202379355,17,17,0.003911858303145958,0.011809325688090079,0.0030619671215834,0.008684105582766975
exymax_tw1,XGBoost,0.011203602976736279,0.0076626291246917316,0.7208999905532953,0.008173422450709376,0.03595062507038116,17,17,0.005684052698392216,0.01776290269672769,0.0043467644662484655,0.012349800374122533
exymax_tw1,FlexibleMLP,0.009349669854195233,0.00782667922570992,0.8056265325345549,0.005114627912203181,0.023376855567959626,17,17,0.006499399781318074,0.012634756820059594,0.005556312039263858,0.010469339687718775
exymax_tw1,SVR,0.008227451492408123,0.006171279983221193,0.8494866259123844,0.005441163609800947,0.023574736993948506,17,17,0.00491817136232375,0.0121598196548086,0.003842769600337118,0.009210884727633666
exymax_tw1,GaussianProcess,0.007841282393118923,0.00512782005772506,0.8632842277523458,0.005932214681232413,0.023511096347105465,17,17,0.0037252359161179813,0.011923415105018733,0.002573978894157178,0.008435375147706934
exymax_tw1,SupervisedSelection,0.008461296319740873,0.006751057691470211,0.840809099378539,0.005100662256697785,0.02296969202379355,17,17,0.005505807005714907,0.012084132800080218,0.004549690012674423,0.009597962330986292
exymax_tw2,RandomForest,0.012157456135951669,0.009498082727135183,0.7947560871225237,0.007588818366918868,0.031086795661348893,17,17,0.007555874710684957,0.01646272372127461,0.006277366955099319,0.013319650727632784
exymax_tw2,GradientBoosting,0.010252042414697255,0.008708232931283241,0.8540494783334374,0.005410272903215292,0.017954189877989177,17,17,0.007609769358184756,0.012582236758562411,0.006200584717810195,0.011362681530643185
exymax_tw2,XGBoost,0.01296853431145359,0.009967203771146606,0.7664571079881395,0.008296850677925327,0.03515263088693618,17,17,0.007907271462534747,0.017870859912968946,0.0063929836626145055,0.014206293244248134
exymax_tw2,FlexibleMLP,0.020396480746914275,0.013832736737447558,0.42230934450915836,0.014989056715207713,0.06501082728995283,17,17,0.010228455603674608,0.030360302611635793,0.0076557203155942,0.021558941814919564
exymax_tw2,SVR,0.009333263469183863,0.006401559630883689,0.8790371767843961,0.006791895264021838,0.028714650717941465,17,17,0.004429476043065191,0.013769355010022752,0.0034673800021799146,0.009911457879898094
exymax_tw2,GaussianProcess,0.0069892374757193985,0.005173724128379975,0.9321664639273186,0.0046991508951532215,0.014628819177453248,17,17,0.0043409306517860615,0.009101496213976193,0.002953217178076863,0.00753050510317158
exymax_tw2,SupervisedSelection,0.006297193360960808,0.004515378127526589,0.9449345844616441,0.004389305707145876,0.014422911946072384,17,17,0.0034442001902274745,0.00849772930473872,0.002413592033033776,0.006811589180937577
exymax_tw3,RandomForest,0.015658159171527578,0.010550987693915173,0.857399568988679,0.011569555191265728,0.04566771205000007,17,17,0.007078423261838213,0.023128790269989583,0.005551999300711862,0.01674248250591757
exymax_tw3,GradientBoosting,0.009572018151300592,0.006799664486546644,0.9467099094616251,0.006737068677045262,0.026084738504688953,17,17,0.005450286167235973,0.01356802126274085,0.003908928063298487,0.010411858685849902
exymax_tw3,XGBoost,0.01136368167303739,0.009110816320458582,0.9248934955110573,0.006791633613571289,0.02353127956867218,17,17,0.0075207865804393665,0.014521946410699857,0.005884316349797497,0.012409927180813685
exymax_tw3,FlexibleMLP,0.011663817641212399,0.008441331683421713,0.9208736972925934,0.008049134200516124,0.033333004944908644,17,17,0.006648698374971988,0.016792506581532647,0.004983991571352143,0.012732335026555967
exymax_tw3,SVR,0.009603749501686912,0.006961462575587638,0.9463560093132999,0.006615893235221103,0.025864100504374127,17,17,0.004949692999618808,0.01361407226818508,0.00421758062024169,0.01037481905820479
exymax_tw3,GaussianProcess,0.009827905300019155,0.00718605873814828,0.9438226359771049,0.006704348021845775,0.02634559987030609,17,17,0.0049378218604257135,0.014067183071036351,0.004399071128660873,0.010664162141085095
exymax_tw3,SupervisedSelection,0.010109317991456234,0.007153961673523053,0.9405594052344254,0.007142768554709297,0.025864100504374127,17,17,0.004993450886648694,0.01424965385668826,0.003991688147747663,0.010856398293426262
tfmmax_tw1,RandomForest,23.564287806639513,18.577081770805272,0.7583579912404523,14.497161539934433,54.42024827333336,17,17,14.459381018970095,31.724501827762317,12.203434146061529,25.95135138252886
tfmmax_tw1,GradientBoosting,19.674462108301128,15.568135956724188,0.83155057377997,12.029862928725684,46.326884007291056,17,17,13.429797104037606,26.119653928717828,10.253923384661547,21.561250268657457
tfmmax_tw1,XGBoost,21.860188908920037,16.662784340246986,0.7920439202446287,14.14989318560711,55.807136474609365,17,17,13.624346851831431,30.32354362173833,10.580010889114492,24.41708894995883
tfmmax_tw1,FlexibleMLP,14.180132190015922,9.174513928942241,0.9124967145963058,10.81223580458596,34.45317405271257,17,17,7.168685481726666,19.8681746682105,4.512487177540483,14.684517349136641
tfmmax_tw1,SVR,23.042257207079285,12.26705828136955,0.7689458141926636,19.505509434995027,76.02804367267865,17,17,7.7646546469264415,37.50685861722928,4.462085347157946,23.27944799521445
tfmmax_tw1,GaussianProcess,12.591430091788702,8.152011012272474,0.9310055880114416,9.596292420106336,29.148416413285105,17,17,5.873923683865663,17.479585226242172,4.0464747564441,12.91588352366569
tfmmax_tw1,SupervisedSelection,15.540653859900589,11.254561275263518,0.8949000824492916,10.716658662782066,33.38239488794193,17,17,9.671715333760396,20.414300853771806,6.492031276810993,16.557676704382896
tfmmax_tw2,RandomForest,21.098997478956267,14.057978113094274,0.8173474833505289,15.733434017682384,68.93188660071658,17,17,9.711130191295569,31.948150711466138,7.876315580229238,22.435113910675927
tfmmax_tw2,GradientBoosting,16.193856163802938,11.325905684240535,0.8924024023311291,11.57431803112504,33.544216450194,17,17,9.606741003574575,21.4545698660193,6.20018433211562,17.104813923461474
tfmmax_tw2,XGBoost,20.016027162563724,14.056840654081457,0.8356166664255178,14.249441188981821,59.64023732910155,17,17,10.46566246246686,29.217514552269733,8.307103059521848,21.41715061010921
tfmmax_tw2,FlexibleMLP,11.796334315558232,8.268077916041173,0.9429052514752755,8.413821418278909,30.626705618390673,17,17,5.897364247223205,16.72938594183976,4.603153070091704,12.62290548960618
tfmmax_tw2,SVR,15.265803895419966,8.783680807854587,0.9043816207091347,12.485660576803554,53.38338365970591,17,17,5.699628142363079,24.20684443339182,4.000809964602837,15.646827786620765
tfmmax_tw2,GaussianProcess,10.388120792996512,8.85336983413281,0.9557232228225075,5.43423372610653,22.429800912459683,17,17,7.077161165916017,13.411531917512955,6.330551011633067,11.613994438581914
tfmmax_tw2,SupervisedSelection,12.092807046812283,9.039051441981613,0.9399993069812383,8.033152015282852,30.626705618390673,17,17,6.531137878831338,16.882064322773182,5.463833800790649,13.270651649964309
tfmmax_tw3,RandomForest,23.729005138113518,17.810045148761695,0.8973238468675608,15.680177825639857,51.25162509682025,17,17,13.987404905688136,31.135743445231093,10.485531406843734,25.50254471090082
tfmmax_tw3,GradientBoosting,21.256773249209374,14.84579107781523,0.917604174729654,15.213576050428278,62.687908507353,17,17,10.926646623494673,31.044292864761523,8.063481036897564,23.3959365845108
tfmmax_tw3,XGBoost,23.737555414127126,17.288142436308014,0.8972498389087096,16.266273947671053,52.385592480468745,17,17,14.35130747761329,31.652478590508572,9.658381306269028,25.499002789055314
tfmmax_tw3,FlexibleMLP,16.50484152928345,12.73400708557304,0.9503255295664746,10.500231304658731,34.366172307713924,17,17,11.656064546353415,20.9964428161819,7.915743997787635,17.718283053803322
tfmmax_tw3,SVR,26.513075280344633,15.597910033121316,0.8718169095248666,21.439411452273475,84.42238215312943,17,17,9.116879000325488,40.76179614675808,6.5876676191290215,27.07453302248967
tfmmax_tw3,GaussianProcess,11.748004224773064,9.35229726621328,0.9748325788678758,7.109862102014735,24.28487769569591,17,17,7.86303573962511,15.104934561018514,6.07149894161155,12.85287518086968
tfmmax_tw3,SupervisedSelection,20.521733862002925,14.67069676610501,0.9232039945941728,14.34964170632401,62.687908507353,17,17,11.051269189650268,30.155892744314972,8.786360564909046,22.076332227248127
tfmmax_frame,RandomForest,8.536588921223801,6.505558159452631,0.775300078213384,5.527301642206644,21.91430381870491,17,17,5.077051911748978,11.75940028559634,4.072986859605406,9.3584223009365
tfmmax_frame,GradientBoosting,9.853777573180516,7.052689027019997,0.7006083430070269,6.881606640158027,29.59138586634886,17,17,5.249453142235398,14.44755662311224,4.204008488451171,10.809619430779208
tfmmax_frame,XGBoost,8.616435313281796,5.5936258392693015,0.7710769897171144,6.553953576142296,26.447313647460938,17,17,3.4707796079973803,13.020012162033941,2.8940174569361083,9.095389068930231
tfmmax_frame,FlexibleMLP,9.058893672772962,6.055312456554025,0.7469627210673002,6.737710711221407,30.312936322721413,17,17,4.31429857357525,13.823601537603789,3.551499254958866,9.571628072809975
tfmmax_frame,SVR,2.455916908243354,2.062401409029109,0.9814022021656014,1.3334272714439068,5.2970989609632255,17,17,1.7048491277942652,3.1329556066363935,1.425337946908326,2.740908288583647
tfmmax_frame,GaussianProcess,2.5186179971577425,2.2474002148410515,0.9804404531510275,1.1369383844075607,4.6845894586853944,17,17,1.9526215490032763,3.063810374579905,1.7263776335050438,2.81226252625927
tfmmax_frame,SupervisedSelection,2.525240101499053,2.25472769484552,0.9803374636240371,1.1371194274637797,4.6845894586853944,17,17,1.954263032302633,3.068250103858061,1.731238153977438,2.8173396982926358
Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/final_model_selection_3W_B34_H45_it1.csv 0 → 100644
output,N,selected_model,selection_criterion,inner_cv_rmse,inner_cv_mae,inner_cv_rmse_dispersion,model_path,best_params,gpr_kernel
exymax_tw1,17,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.010814534641938614,0.00862216923033217,0.3726567589934607,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_exymax_tw1.joblib,"{""learning_rate"": 0.02185353287218318, ""max_depth"": 1, ""max_features"": 0.8939212703144164, ""n_estimators"": 269, ""subsample"": 0.8016038962805814}",
exymax_tw2,17,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.009248645603192341,0.00821248537571313,0.5618056095988183,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_exymax_tw2.joblib,"{""learning_rate"": 0.0694978153873313, ""max_depth"": 1, ""max_features"": 0.9644709477075852, ""n_estimators"": 125, ""subsample"": 0.8381630604005251}",
exymax_tw3,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,0.011382395265212533,0.009495795098599751,0.9372634491217228,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_exymax_tw3.joblib,"{""gpr__amplitude"": 17.76576664980768, ""gpr__kernel_type"": ""RBF"", ""gpr__length_scale"": 1.570703295827246, ""gpr__n_restarts_optimizer"": 2, ""gpr__noise"": 4.5852845967856126e-07, ""gpr__rq_alpha"": 0.1552065618292981}",1.91**2 * RBF(length_scale=2.82) + WhiteKernel(noise_level=4.59e-07)
tfmmax_tw1,17,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band,19.150679889851585,15.4999501531671,0.42625011708649,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_tfmmax_tw1.joblib,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 0.057547661357822966, ""mlp__learning_rate_init"": 0.000697400151786318, ""mlp__n_layers"": 2, ""mlp__n_neurons"": 13}",
tfmmax_tw2,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,11.47801652066376,9.653648661504333,0.33885970321076103,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_tfmmax_tw2.joblib,"{""gpr__amplitude"": 100.0, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 0.1, ""gpr__n_restarts_optimizer"": 0, ""gpr__noise"": 1e-10, ""gpr__rq_alpha"": 0.1}","2.64**2 * RationalQuadratic(alpha=2.06, length_scale=3.59) + WhiteKernel(noise_level=1e-10)"
tfmmax_tw3,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,8.381010953036936,6.803657606519015,0.7857188531329555,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_tfmmax_tw3.joblib,"{""gpr__amplitude"": 8.632012725909878, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 0.21242927029064568, ""gpr__n_restarts_optimizer"": 0, ""gpr__noise"": 4.013494019734207e-05, ""gpr__rq_alpha"": 0.5561085291185478}","2.35**2 * RationalQuadratic(alpha=1e+03, length_scale=3.63) + WhiteKernel(noise_level=0.00119)"
tfmmax_frame,17,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band,2.758197829696905,2.3343985040582194,0.16421389739963982,../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/best_model_tfmmax_frame.joblib,"{""gpr__amplitude"": 0.010340016434251914, ""gpr__kernel_type"": ""RQ"", ""gpr__length_scale"": 3.0378441442185053, ""gpr__n_restarts_optimizer"": 1, ""gpr__noise"": 1.1236999294535673e-06, ""gpr__rq_alpha"": 0.3543477648512007}","1.45**2 * RationalQuadratic(alpha=1e+03, length_scale=2.84) + WhiteKernel(noise_level=0.000101)"
Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/nested_cv_fold_selections_3W_B34_H45_it1.csv 0 → 100644
outer_fold,selected_model,selection_criterion
0,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,XGBoost,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,FlexibleMLP,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,GradientBoosting,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
0,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
1,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
2,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
3,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
4,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
5,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
6,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
7,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
8,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
9,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
10,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
11,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
12,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
13,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
14,SVR,lowest_cv_rmse_dispersion_within_5pct_rmse_band
15,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
16,GaussianProcess,lowest_cv_rmse_dispersion_within_5pct_rmse_band
Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/nested_cv_model_selection_frequency_3W_B34_H45_it1.csv 0 → 100644
model,frequency,percentage,output,N
GradientBoosting,12,70.59,exymax_tw1,17
SVR,3,17.65,exymax_tw1,17
GaussianProcess,1,5.88,exymax_tw1,17
XGBoost,1,5.88,exymax_tw1,17
GradientBoosting,7,41.18,exymax_tw2,17
GaussianProcess,5,29.41,exymax_tw2,17
SVR,4,23.53,exymax_tw2,17
FlexibleMLP,1,5.88,exymax_tw2,17
SVR,8,47.06,exymax_tw3,17
GradientBoosting,7,41.18,exymax_tw3,17
GaussianProcess,2,11.76,exymax_tw3,17
FlexibleMLP,12,70.59,tfmmax_tw1,17
GradientBoosting,2,11.76,tfmmax_tw1,17
GaussianProcess,2,11.76,tfmmax_tw1,17
SVR,1,5.88,tfmmax_tw1,17
GaussianProcess,11,64.71,tfmmax_tw2,17
FlexibleMLP,6,35.29,tfmmax_tw2,17
GaussianProcess,14,82.35,tfmmax_tw3,17
GradientBoosting,2,11.76,tfmmax_tw3,17
FlexibleMLP,1,5.88,tfmmax_tw3,17
GaussianProcess,14,82.35,tfmmax_frame,17
SVR,3,17.65,tfmmax_frame,17
Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/nested_cv_outer_predictions_3W_B34_H45_it1.csv 0 → 100644
output,N,outer_fold,sample_index,y_true,selected_supervised_model,prediction_RandomForest,prediction_GradientBoosting,prediction_XGBoost,prediction_FlexibleMLP,prediction_SVR,prediction_GaussianProcess,prediction_supervised_selection
exymax_tw1,17,0,0,0.0300764,GradientBoosting,0.028822756037318018,0.02360364250931316,0.03751327469944954,0.023807456020811746,0.027226782802496824,0.02893429132740214,0.02360364250931316
exymax_tw1,17,1,1,0.0055782,GradientBoosting,0.013174287916666664,0.021810745101297425,0.01660875789821148,-0.00218857085074442,0.013087358677973449,0.011037234677081367,0.021810745101297425
exymax_tw1,17,2,2,0.0456976,GradientBoosting,0.06930213615410058,0.0656042687801499,0.06261151283979416,0.04810914485722073,0.06434917122394435,0.05534553796465337,0.0656042687801499
exymax_tw1,17,3,3,0.1001293,SVR,0.06534767961904764,0.07870216792871372,0.06707499921321869,0.05745412446295667,0.07598601466895231,0.07332316127201463,0.07598601466895231
exymax_tw1,17,4,4,0.0153468,GradientBoosting,0.017278229465223637,0.026043709550972724,0.026609130203723907,0.022106146765440537,0.013059467583230067,0.01256451241425665,0.026043709550972724
exymax_tw1,17,5,5,0.0128897,GradientBoosting,0.01644212422194803,0.02492864526963491,0.026237934827804565,0.008421096962940439,-0.006098752477915886,0.005507861413045722,0.02492864526963491
exymax_tw1,17,6,6,0.0018112,GradientBoosting,0.010753794266558439,0.004796384772830113,0.002256374340504408,0.031189986628891664,0.011989755899322219,-0.000751714397662806,0.004796384772830113
exymax_tw1,17,7,7,0.0018189,GradientBoosting,0.0079869672674531,-0.0024332191875986726,0.0009275486809201539,-0.00668128626153415,-0.0019432203927072511,0.0029856821892392527,-0.0024332191875986726
exymax_tw1,17,8,8,0.0823355,GaussianProcess,0.06760388851206314,0.0904077820261316,0.05336045101284981,0.05469989973073952,0.056834859606500296,0.06287333029947903,0.06287333029947903
exymax_tw1,17,9,9,0.0078472,GradientBoosting,0.012425591657142852,-0.0003793895846147963,0.0052122087217867374,-0.0012894116455061666,0.012937057867168589,0.010114840325527962,-0.0003793895846147963
exymax_tw1,17,10,10,0.0406728,SVR,0.03422894572619048,0.041246895661125195,0.045514121651649475,0.04361324214099083,0.058564031234548286,0.05546900098988004,0.058564031234548286
exymax_tw1,17,11,11,0.0817119,GradientBoosting,0.07363040275833337,0.07026430672738686,0.08449825644493103,0.06303778746420347,0.05620533400809942,0.07876831766377973,0.07026430672738686
exymax_tw1,17,12,12,0.0146728,GradientBoosting,0.016183364787445906,0.022004623216989025,0.00982130877673626,0.021680214056992386,0.007209641286327867,0.013361515992146102,0.022004623216989025
exymax_tw1,17,13,13,0.022831,GradientBoosting,0.04370937653690477,0.039182588790115054,0.03248251974582672,0.06813070221637958,0.03503565249794237,0.03805436956162163,0.039182588790115054
exymax_tw1,17,14,14,0.0246182,XGBoost,0.0209972907438095,0.01284189217432155,0.01465984620153904,-0.07029898217992445,0.020295086958533076,0.02554237285010527,0.01465984620153904
exymax_tw1,17,15,15,0.0098081,SVR,0.016835248272222204,0.009745209533927648,0.019067037850618362,0.0007668558986317908,-3.327361407262641e-05,0.008182624632741026,-3.327361407262641e-05
exymax_tw1,17,16,16,0.0420229,GradientBoosting,0.033338308081343006,0.03026100124215438,0.031842827796936035,0.04051180543579316,0.04532540245784672,0.04672785932048822,0.03026100124215438
exymax_tw2,17,0,0,0.0382345,GradientBoosting,0.04482646578979814,0.04575181822784942,0.045217834413051605,0.048823629029397075,0.03849909760163466,0.03555083070985882,0.04575181822784942
exymax_tw2,17,1,1,0.0762335,FlexibleMLP,0.07400915153809523,0.07351456018281778,0.08102526515722275,0.0740142746897745,0.07567741717112411,0.07428834270309598,0.0740142746897745
exymax_tw2,17,2,2,0.0687318,GaussianProcess,0.08048691788955702,0.07313907298212186,0.06792668253183365,0.07573704227341505,0.06905985397210396,0.07098666734389908,0.07098666734389908
exymax_tw2,17,3,3,0.0733091,GaussianProcess,0.06363125065833333,0.05286450362523523,0.06806330382823944,0.05381935398308152,0.050888495435731276,0.05938650538215653,0.05938650538215653
exymax_tw2,17,4,4,0.0804918,GradientBoosting,0.07331119110476186,0.07677484850885845,0.09748146682977676,0.08367598249865782,0.08134284850892001,0.08160288341538716,0.07677484850885845
exymax_tw2,17,5,5,0.0629427,GradientBoosting,0.05106179877551024,0.05905107194304356,0.07994253188371658,0.06687933366254745,0.067929874079321,0.06023085535685266,0.05905107194304356
exymax_tw2,17,6,6,0.0258942,SVR,0.029648553395634932,0.03279728893082777,0.03223464637994766,0.0442981544053585,0.04636430812985637,0.042191612679239446,0.04636430812985637
exymax_tw2,17,7,7,0.0244975,GaussianProcess,0.03244871605000004,0.029173995246829117,0.033305488526821136,0.022052344382658573,0.014762684604982507,0.0160707097916356,0.0160707097916356
exymax_tw2,17,8,8,0.0200708,SVR,0.04341648069880954,0.03890814674997664,0.038762517273426056,0.0005870259374991527,0.02519405604289883,0.024315297691029615,0.02519405604289883
exymax_tw2,17,9,9,0.1121302,GradientBoosting,0.07741132654240476,0.09340634526417821,0.06846103072166443,0.08076179772725706,0.0975142116478166,0.09717944998502195,0.09340634526417821
exymax_tw2,17,10,10,0.0456274,GaussianProcess,0.045930120925396854,0.048272950744787435,0.04730895906686783,0.09907438457102294,0.051285247198140343,0.05118187999518464,0.05118187999518464
exymax_tw2,17,11,11,0.03751,GaussianProcess,0.0402711809305859,0.029944404846401484,0.04423408582806587,0.045058669233192294,0.04219994868007283,0.0417370114664451,0.0417370114664451
exymax_tw2,17,12,12,0.0948819,SVR,0.08130757140230159,0.11393693468621699,0.08567647635936737,0.08066816149696523,0.09414048879704177,0.09705887956457657,0.09414048879704177
exymax_tw2,17,13,13,0.0200336,GradientBoosting,0.030251712446612,0.03303291389669486,0.022452598437666893,0.023151001807288754,0.022867963218290974,0.02751796421878115,0.03303291389669486
exymax_tw2,17,14,14,0.0585452,GradientBoosting,0.03373420725000001,0.03541208488297641,0.04048892855644226,0.02400670912581021,0.007358865740098364,0.02846363940338169,0.03541208488297641
exymax_tw2,17,15,15,0.0242279,GradientBoosting,0.024644031823734706,0.024374222631447114,0.019729452207684517,0.03256469428409525,0.020836013517438204,0.02502793122318714,0.024374222631447114
exymax_tw2,17,16,16,0.047137,SVR,0.057491218288402236,0.04700354690514628,0.036947671324014664,0.05712663447840685,0.04985466366232465,0.048451763700183614,0.04985466366232465
exymax_tw3,17,0,0,0.0411367,SVR,0.03868737938214287,0.04261468113240845,0.041174132376909256,0.0426492641827777,0.035801909987627833,0.040458693029650765,0.035801909987627833
exymax_tw3,17,1,1,0.0422645,GradientBoosting,0.060134862828373006,0.041913884972055136,0.046710118651390076,0.05561034803960774,0.04625703600179737,0.04822388209404263,0.041913884972055136
exymax_tw3,17,2,2,0.0453606,SVR,0.04671053899563421,0.04514567706744925,0.053307000547647476,0.04967953982519552,0.044568166912749035,0.04545357478651049,0.044568166912749035
exymax_tw3,17,3,3,0.0281703,GradientBoosting,0.04049359680389663,0.03712865465100918,0.04066455364227295,0.014427214200490701,0.033029968511853386,0.028616687295496913,0.03712865465100918
exymax_tw3,17,4,4,0.0491583,SVR,0.045672773018238695,0.047639325553419314,0.04843365401029587,0.054641742975489405,0.05351554336169523,0.04917184495905401,0.05351554336169523
exymax_tw3,17,5,5,0.0299352,GradientBoosting,0.03583520778882116,0.02850149917680987,0.02578466385602951,0.048774493671281116,0.021581239703821115,0.016824665046606048,0.02850149917680987
exymax_tw3,17,6,6,0.1242548,GradientBoosting,0.09255548008226658,0.07967734905245448,0.1441413164138794,0.14549716943370627,0.1363049521567741,0.1313604433616643,0.07967734905245448
exymax_tw3,17,7,7,0.1719441,GaussianProcess,0.1480759346392857,0.1479872751969786,0.14990681409835815,0.14413610783720476,0.1692884268430798,0.16557815660328043,0.16557815660328043
exymax_tw3,17,8,8,0.0528458,SVR,0.05879116823557619,0.06115425514380996,0.06850308924913406,0.1092155170483216,0.08890820473199738,0.07926068341056787,0.08890820473199738
exymax_tw3,17,9,9,0.0833631,GradientBoosting,0.07643695873968244,0.06912285044193046,0.11610560119152069,0.08257629380228063,0.07676708247504796,0.07801684153021257,0.06912285044193046
exymax_tw3,17,10,10,0.0278009,SVR,0.03871475653976251,0.030401053635401167,0.032133810222148895,0.015269172904211481,0.03589630346745587,0.03585135799887497,0.03589630346745587
exymax_tw3,17,11,11,0.0294146,SVR,0.03532784508844604,0.026988155668300328,0.035777654498815536,0.0254285932216885,0.01998875158796054,0.018201554838091744,0.01998875158796054
exymax_tw3,17,12,12,0.069075,SVR,0.06414655040742132,0.050655726007549896,0.08296283334493637,0.06727930370023583,0.06951709847667942,0.07077212684306344,0.06951709847667942
exymax_tw3,17,13,13,0.1829432,GradientBoosting,0.15795291699801564,0.16370423729077047,0.16513390839099884,0.1668160981120358,0.16710061891456862,0.16122944302379327,0.16370423729077047
exymax_tw3,17,14,14,0.0292621,GaussianProcess,0.06933483390416033,0.030050729874889374,0.05708389729261398,0.03231158706864659,0.053518422744335645,0.04484716902913738,0.04484716902913738
exymax_tw3,17,15,15,0.1771025,SVR,0.14904757596794369,0.15970673845695268,0.1714930534362793,0.1326486937066902,0.1646424450680009,0.17153093151271537,0.1646424450680009
exymax_tw3,17,16,16,0.0491903,GradientBoosting,0.042388977953939415,0.04641433392174516,0.04498186334967613,0.05371162421201631,0.05069840619934918,0.04819472770518478,0.04641433392174516
tfmmax_tw1,17,0,0,60.2223,FlexibleMLP,53.47936909027329,44.84396950514281,79.92381286621094,46.18261416339867,67.56538562034073,53.874777217987585,46.18261416339867
tfmmax_tw1,17,1,1,5.2262,FlexibleMLP,15.086531717568535,28.874157153601374,27.023996353149414,0.4607912657573799,-0.736060471743329,8.585521329035913,0.4607912657573799
tfmmax_tw1,17,2,2,91.383,FlexibleMLP,141.02241765450793,166.43752079376415,118.85387420654297,107.25520957610162,62.90937463298586,104.51766358141558,107.25520957610162
tfmmax_tw1,17,3,3,200.3137,FlexibleMLP,139.39157630171445,144.980485189857,130.07623291015625,158.63151301468858,96.43013193934576,160.06499908106434,158.63151301468858
tfmmax_tw1,17,4,4,18.4505,FlexibleMLP,22.571077466822807,46.30216227741362,33.85643768310547,6.289937020705797,16.791831690529378,17.28230750304676,6.289937020705797
tfmmax_tw1,17,5,5,14.4752,FlexibleMLP,43.53970944828575,49.99013170061331,53.98143005371094,9.319898109801255,22.98389972592276,8.946817714268796,9.319898109801255
tfmmax_tw1,17,6,6,1.0483,GradientBoosting,14.11776014192858,4.265344130288382,0.9481802582740784,1.7596502207955702,0.5399985060569534,-0.5757761339329477,4.265344130288382
tfmmax_tw1,17,7,7,0.8163,FlexibleMLP,17.99977530582335,5.079225633556837,-2.8157670497894287,4.537146034337158,2.1298087543271578,3.8407228226505836,4.537146034337158
tfmmax_tw1,17,8,8,167.3135,FlexibleMLP,130.28334746407964,129.861003693295,116.06094360351562,148.3659767984187,93.22919205573521,127.32909300383524,148.3659767984187
tfmmax_tw1,17,9,9,9.0268,FlexibleMLP,12.470539799261967,-6.440054229697098,13.52851676940918,0.3338928155319941,9.637186270300681,17.395778096881507,0.3338928155319941
tfmmax_tw1,17,10,10,88.2524,GaussianProcess,67.43190280839825,76.42797860861099,81.56634521484375,116.20135414962948,93.79087718879791,107.2198110088008,107.2198110088008
tfmmax_tw1,17,11,11,164.9613,FlexibleMLP,149.7608930163095,166.27661310577565,177.88780212402344,181.8708758358315,125.01888563332159,170.82156509632293,181.8708758358315
tfmmax_tw1,17,12,12,18.087,GradientBoosting,19.066121966919187,29.707723440816007,17.062889099121094,21.7313403140068,20.044879003016213,17.010247036201847,29.707723440816007
tfmmax_tw1,17,13,13,40.6132,FlexibleMLP,92.95483377666669,85.67435300546602,87.56587219238281,77.40134985244883,56.26229737027369,75.00642925416889,77.40134985244883
tfmmax_tw1,17,14,14,42.4084,FlexibleMLP,16.039069219709855,17.85494513925343,6.1982011795043945,60.25264530688394,44.90382411385385,37.66431917721738,60.25264530688394
tfmmax_tw1,17,15,15,13.1861,GaussianProcess,27.623456453873406,24.012063030135486,28.707921981811523,9.30127609970109,11.878099277430003,3.834843473365467,3.834843473365467
tfmmax_tw1,17,16,16,83.32143,SVR,69.43979321799432,60.10786077737473,62.80791473388672,91.58330621424884,88.54716425921313,91.32314359638556,88.54716425921313
tfmmax_tw2,17,0,0,72.8627,GaussianProcess,86.52603718595243,81.96896677877915,91.56952667236328,61.55723803959265,76.03009420476964,67.17975090997851,67.17975090997851
tfmmax_tw2,17,1,1,140.1637,FlexibleMLP,142.54357734642863,145.65685014448016,154.52760314941406,141.68981662817126,133.2577593255194,143.30309688153767,141.68981662817126
tfmmax_tw2,17,2,2,145.0081,GaussianProcess,156.59316602369879,137.7746129674065,127.97380828857422,142.45695349858406,140.63058726581048,137.847050466937,137.847050466937
tfmmax_tw2,17,3,3,124.4333,GaussianProcess,130.59540899213775,85.39382380234755,137.20542907714844,105.37875332939821,110.88234821723611,127.76213720922934,127.76213720922934
tfmmax_tw2,17,4,4,160.4279,GaussianProcess,149.40552856192048,147.26891765879856,203.04080200195312,163.37812304543854,166.50919147271125,156.77067862041727,156.77067862041727
tfmmax_tw2,17,5,5,117.4921,FlexibleMLP,93.78514771707137,110.79224654250486,145.8865509033203,123.87571165434393,118.82980725364234,115.032727228976,123.87571165434393
tfmmax_tw2,17,6,6,54.4047,GaussianProcess,54.50710744047622,63.05679007241047,50.19424057006836,85.03911357721519,79.19744608893566,79.7777933344295,79.7777933344295
tfmmax_tw2,17,7,7,47.323,FlexibleMLP,63.82522060079367,57.60474498557926,60.166969299316406,24.533920538367333,45.245489542132255,26.898592857415082,24.533920538367333
tfmmax_tw2,17,8,8,32.8734,FlexibleMLP,65.43008266486511,75.38595012595812,59.73665237426758,44.903951640019436,38.54304243892298,31.21948531732771,44.903951640019436
tfmmax_tw2,17,9,9,227.631,FlexibleMLP,169.10938774663066,192.02923705369352,168.91917419433594,209.41930119058497,184.55058092198988,214.9797651228567,209.41930119058497
tfmmax_tw2,17,10,10,97.9154,GaussianProcess,86.41652824897844,85.50761913985741,91.3265609741211,111.67821843731852,93.73039094820723,99.25338445625283,99.25338445625283
tfmmax_tw2,17,11,11,67.0263,FlexibleMLP,80.80505325890479,58.278426577902216,79.12257385253906,66.98612972579885,76.94883874146996,72.49620108901749,66.98612972579885
tfmmax_tw2,17,12,12,194.0625,GaussianProcess,194.52866579999892,220.96092584666462,168.2084197998047,198.58629349776302,181.5055997354841,198.86114190259474,198.86114190259474
tfmmax_tw2,17,13,13,37.3301,GaussianProcess,44.38673576666667,37.24907870998929,51.5335807800293,20.631757002741224,42.70392688954438,45.692391742157234,45.692391742157234
tfmmax_tw2,17,14,14,87.8423,GaussianProcess,62.20144511666669,75.47174103433181,74.91571807861328,71.5467027820909,65.36391290250322,52.821789209223866,52.821789209223866
tfmmax_tw2,17,15,15,46.0884,GaussianProcess,48.171012462793655,51.221753005950845,39.721343994140625,57.468766408268245,43.325580389482425,37.71721552699981,37.71721552699981
tfmmax_tw2,17,16,16,90.30767,GaussianProcess,105.01506545343136,120.65241149018773,115.32814025878906,91.95318391878288,98.94788569242661,95.02281011938913,95.02281011938913
tfmmax_tw3,17,0,0,75.235,FlexibleMLP,67.09440102803211,75.68937353325022,76.7556381225586,58.64013215605328,76.99224362503246,76.27394283001325,58.64013215605328
tfmmax_tw3,17,1,1,77.0429,GaussianProcess,97.5454086554158,74.2859735871865,88.3662338256836,110.37109132912775,84.51762214063636,81.24369166040215,81.24369166040215
tfmmax_tw3,17,2,2,87.44,GaussianProcess,90.45714211589491,86.8628369263326,102.37982940673828,88.23060553377452,81.7516871641331,80.60905613651059,80.60905613651059
tfmmax_tw3,17,3,3,48.8154,GaussianProcess,71.14261249203737,64.87306048091172,67.8333969116211,74.90304438712495,62.746661662961955,58.5915599448367,58.5915599448367
tfmmax_tw3,17,4,4,88.775,GaussianProcess,88.81237587366725,88.30530182582059,82.81697845458984,85.3615312078567,91.75642805354893,85.50194426936667,85.50194426936667
tfmmax_tw3,17,5,5,54.9076,GaussianProcess,62.20027267187162,55.76073109865297,57.6820068359375,67.93309673144893,57.89140520936854,50.26698301069749,50.26698301069749
tfmmax_tw3,17,6,6,193.3375,GradientBoosting,143.50762120801596,119.88458966212563,252.25144958496094,212.56868843529227,162.3633346692722,194.77312580045134,119.88458966212563
tfmmax_tw3,17,7,7,253.1506,GaussianProcess,233.39398930261186,223.5934547457598,235.50889587402344,249.54052421948703,230.85393192642084,254.14123465307634,254.14123465307634
tfmmax_tw3,17,8,8,103.9524,GaussianProcess,101.97404882706903,108.21264147633262,124.10604095458984,168.2043881542997,156.2853813225915,136.73997016907072,136.73997016907072
tfmmax_tw3,17,9,9,120.0323,GaussianProcess,122.69560066979192,128.9726753968552,181.36456298828125,120.25330265500506,115.06056534414066,114.13971116445191,114.13971116445191
tfmmax_tw3,17,10,10,53.6332,GaussianProcess,63.2839310364026,56.35744608562135,56.91250228881836,50.666463303671335,52.19206301493942,62.43097556132257,62.43097556132257
tfmmax_tw3,17,11,11,55.0584,GaussianProcess,87.34065617280116,57.367958236638124,78.95049285888672,50.62534737289003,55.763608450402245,41.867846161741454,41.867846161741454
tfmmax_tw3,17,12,12,110.4195,GaussianProcess,109.11360660702388,83.03279210950677,121.34801483154297,104.86791714296179,112.95180872922163,114.7751913855367,114.7751913855367
tfmmax_tw3,17,13,13,284.6548,GradientBoosting,242.93292165000042,262.53701405508275,247.10931396484375,246.29018361473007,183.97576022662355,256.13607494995983,262.53701405508275
tfmmax_tw3,17,14,14,64.2884,GaussianProcess,78.39707460388894,80.46074923904153,92.75839233398438,104.93719882180864,84.87935496583349,75.8644969428922,75.8644969428922
tfmmax_tw3,17,15,15,263.3107,GaussianProcess,230.0644560469037,240.94619269121443,256.86279296875,240.3503397722494,203.71509446041907,262.90360816251393,262.90360816251393
tfmmax_tw3,17,16,16,88.53941,GaussianProcess,85.02350396190477,84.8820448256987,82.91224670410156,84.78587285746654,91.65442959842264,89.37377081239858,89.37377081239858
tfmmax_frame,17,0,0,67.7267,GaussianProcess,63.374897038968264,67.16282905634553,64.03581237792969,61.359783447394584,68.32815040778237,67.20725637630865,67.20725637630865
tfmmax_frame,17,1,1,70.961,SVR,71.78718698431454,70.5512947545597,73.1802978515625,85.82668475169214,74.96412672221658,74.16036152535054,74.96412672221658
tfmmax_frame,17,2,2,58.5602,GaussianProcess,63.42070910118365,61.07898484059831,69.00497436523438,67.62159512457887,59.99236600638643,59.308606024147515,59.308606024147515
tfmmax_frame,17,3,3,51.4282,GaussianProcess,60.219799364833435,62.93756195462462,58.33601379394531,62.17924602648115,50.667650438266634,50.523389834671185,50.523389834671185
tfmmax_frame,17,4,4,69.1554,GaussianProcess,66.73819696432318,66.78079293953303,65.52872467041016,65.48321471321968,67.5021345433738,67.64378194503217,67.64378194503217
tfmmax_frame,17,5,5,71.8219,GaussianProcess,62.84740529336789,59.724842124579055,58.803611755371094,71.35790196912308,67.92041514471231,68.00785138042751,68.00785138042751
tfmmax_frame,17,6,6,102.1931,GaussianProcess,79.38811506484126,77.0410648252312,99.89038848876953,96.7260530269448,98.75157853292546,96.93392384763035,96.93392384763035
tfmmax_frame,17,7,7,114.4571,GaussianProcess,103.61192029523825,109.3873619159945,113.64540100097656,116.16620924783543,116.50700270831484,120.8571007276966,120.8571007276966
tfmmax_frame,17,8,8,70.3458,GaussianProcess,56.90985903085687,54.922814932275884,62.661312103271484,77.42717387988507,75.95206511031907,73.73626000197221,73.73626000197221
tfmmax_frame,17,9,9,67.6932,GaussianProcess,79.3656716554104,77.35027562485904,99.59899139404297,79.11912160060152,69.36750384367647,67.80167425163647,67.80167425163647
tfmmax_frame,17,10,10,59.26,GaussianProcess,64.54804061968255,63.30609183052919,56.858192443847656,49.19231680945347,60.08064472401605,61.165558723812886,61.165558723812886
tfmmax_frame,17,11,11,52.032,GaussianProcess,63.79781058456558,62.27813690185844,64.931884765625,60.956303995537965,53.01479087790516,50.68164888274271,50.68164888274271
tfmmax_frame,17,12,12,67.1494,GaussianProcess,68.53480191925829,64.09475569315552,69.80670166015625,67.82285511772609,66.70684343131973,67.54587234074731,67.54587234074731
tfmmax_frame,17,13,13,113.7973,SVR,106.12343641071426,111.48276265178079,105.7306137084961,126.04121276669981,105.34737146476436,108.79167945464914,105.34737146476436
tfmmax_frame,17,14,14,66.8746,SVR,70.24675896688869,80.76961774560594,78.57976531982422,67.8378070921009,70.70852176119263,69.9361524422429,70.70852176119263
tfmmax_frame,17,15,15,113.2492,GaussianProcess,103.9571663302669,103.99420682061248,108.6440200805664,102.88573493543514,110.03805661969717,111.36589478646935,111.36589478646935
tfmmax_frame,17,16,16,65.02994,GaussianProcess,61.416874867651785,65.97321108673526,67.15467071533203,62.755046431833925,64.83506677486199,65.7369905294429,65.7369905294429
Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/nested_cv_outer_summary_3W_B34_H45_it1.csv 0 → 100644
output,method,outer_rmse,outer_mae,outer_r2,outer_std_abs_error,outer_max_abs_error,n_outer_predictions,N,outer_rmse_ci_lower,outer_rmse_ci_upper,outer_mae_ci_lower,outer_mae_ci_upper
exymax_tw1,RandomForest,0.013019195897500571,0.009611020533385944,0.8038477331311401,0.008782240381834776,0.034781620380952366,17,17,0.007119373600426925,0.018464587685544247,0.005877814353464972,0.014241290535411918
exymax_tw1,GradientBoosting,0.011655634097623295,0.009977490107390711,0.8427839724880477,0.006025238366620801,0.021427132071286284,17,17,0.008909889378712798,0.014369980411821175,0.007181569527402207,0.01300804036018338
exymax_tw1,XGBoost,0.013600549154930648,0.010442401770501583,0.7859388290715406,0.008713850043414227,0.033054300786781315,17,17,0.00821941734617434,0.018500727376263442,0.006809589062899648,0.01499589753852585
exymax_tw1,FlexibleMLP,0.030026136258804063,0.019081927154561657,-0.043335320731007876,0.0231829444786522,0.09491718217992445,17,17,0.0128759669306667,0.04497403719290043,0.009035134059434116,0.031032474255884113
exymax_tw1,SVR,0.01425979128317933,0.011734893495841288,0.764684060548117,0.008101476537094893,0.025506565991900584,17,17,0.0102482621867953,0.017856107243878514,0.00802312962661004,0.015677896575258716
exymax_tw1,GaussianProcess,0.0102427410896797,0.007071052780082054,0.8785891784391867,0.007410395239898258,0.026806138727985374,17,17,0.005580294541235258,0.014401044008319893,0.0039155671535064645,0.010851329733614511
exymax_tw1,SupervisedSelection,0.013556001251562585,0.012294178673764632,0.7873388253433719,0.005711246857728815,0.02414328533104769,17,17,0.010787787447134527,0.016161587719425295,0.009728156202203561,0.015070760954857007
exymax_tw2,RandomForest,0.013974886280356468,0.010677564762772023,0.7327507769694004,0.009015933522697729,0.03471887345759524,17,17,0.008585708703780563,0.01896145373458519,0.006840350474199471,0.015006903355873296
exymax_tw2,GradientBoosting,0.0119723045751693,0.0092656398787114,0.803855831989491,0.007581819997774463,0.023133115117023588,17,17,0.008007428472376173,0.015190039236546767,0.005733319743804958,0.013002003594216521
exymax_tw2,XGBoost,0.014633595369822536,0.01071170283925393,0.7069633297909099,0.009970031882156187,0.04366916927833557,17,17,0.008111411141352444,0.021311025239110336,0.006564195184583631,0.016074742145625313
exymax_tw2,FlexibleMLP,0.019952010342588113,0.014665715229431513,0.4552550551846525,0.013527731277637192,0.05344698457102294,17,17,0.011178634539361583,0.02803775517585439,0.008674828270859532,0.021726329670674654
exymax_tw2,SVR,0.015308503877678747,0.008857128481137106,0.6793100253677737,0.012486054862987555,0.051186334259901635,17,17,0.005799947247581286,0.02343636444190589,0.0038359475377771,0.01511179548430935
exymax_tw2,GaussianProcess,0.010328140588442947,0.007069873997994723,0.8540298117258238,0.007529101517918372,0.030081560596618307,17,17,0.005072763584958026,0.01477127064749966,0.003769833597976822,0.010706889196142506
exymax_tw2,SupervisedSelection,0.010619031348879992,0.007987406596290297,0.8456915559946264,0.006997511175699134,0.023133115117023588,17,17,0.006298718780286024,0.013878346452341052,0.0046603256288180635,0.011247390960909282
exymax_tw3,RandomForest,0.017865333477463725,0.013734838999749043,0.89026717765211,0.011424724850601828,0.04007273390416033,17,17,0.011113056983512679,0.023261094872081747,0.008544270361777307,0.019262084550505952
exymax_tw3,GradientBoosting,0.015248641961538342,0.009922513037123673,0.9200577017439668,0.011578636219317828,0.04457745094754552,17,17,0.007895987458095034,0.02226530498499052,0.004865295386906135,0.016153918715304943
exymax_tw3,XGBoost,0.015044328285730074,0.011773838252331229,0.9221856133856557,0.009365284105501546,0.032742501191520695,17,17,0.009817010905812544,0.019389501529825756,0.0073125652550504495,0.01649858796489372
exymax_tw3,FlexibleMLP,0.021168403279157207,0.014700894645423785,0.8459397861590616,0.015231053608112074,0.056369717048321596,17,17,0.01104430154476084,0.029703746286404757,0.008090955852516577,0.022253331594745136
exymax_tw3,SVR,0.012845561550574048,0.009240311421122457,0.9432689941327683,0.008923289549838682,0.036062404731997386,17,17,0.006800624184589217,0.018269306844586407,0.0054630560998301545,0.013704112019434011
exymax_tw3,GaussianProcess,0.010751880126339151,0.007668244499345707,0.9602549431508768,0.007536640667394268,0.02641488341056787,17,17,0.006269424223856313,0.014642069286982976,0.004347102703193602,0.011416644081694965
exymax_tw3,SupervisedSelection,0.016435307775043415,0.011205681688387654,0.9071311692662964,0.01202297964561764,0.04457745094754552,17,17,0.008050915942478492,0.023712402138046953,0.00594182927836683,0.017616256079959047
tfmmax_tw1,RandomForest,28.21221961940831,22.065100153716408,0.7936020599484767,17.58012204338159,60.92212369828556,17,17,18.145513921811848,36.61068117445295,14.192609613681594,30.651256104866018
tfmmax_tw1,GradientBoosting,31.40266403291323,24.799678563976116,0.7442805084491918,19.264040372867928,75.05452079376415,17,17,20.422511503132487,42.30628964893874,16.367104371275225,34.85507026254626
tfmmax_tw1,XGBoost,30.097812195923737,23.14359731063731,0.7650904340062709,19.241938636846275,70.23746708984376,17,17,19.6305800455924,40.017822286222675,14.750302341680841,32.95365127180885
tfmmax_tw1,FlexibleMLP,18.25961565695971,14.178232248016487,0.913540223129712,11.506141588786116,41.68218698531143,17,17,11.732332588735066,24.354772550475346,9.166637241032928,20.287048131522297
tfmmax_tw1,SVR,33.57874645804137,17.90961406131675,0.7076118246119959,28.403836675141466,103.88356806065426,17,17,10.643928682007166,51.88799710480173,6.577739927387025,33.305029139388324
tfmmax_tw1,GaussianProcess,17.671341307750815,12.069610394859255,0.9190214722718937,12.907393560720369,40.24870091893567,17,17,8.62619164543768,24.418234408052044,6.3845334590334035,18.54113903962799
tfmmax_tw1,SupervisedSelection,17.83858412661418,14.409457222532561,0.9174814461554632,10.515827413679606,41.68218698531143,17,17,11.508660901880114,23.968627330763105,9.883276590441156,19.920821531427052
tfmmax_tw2,RandomForest,20.371965115699606,14.790230917934137,0.8629630707722861,14.009497923532695,58.52161225336934,17,17,11.368953085545076,29.13654249423037,8.83904853877747,22.08845376910432
tfmmax_tw2,GradientBoosting,20.60428373356024,16.103712880186976,0.8598197551973055,12.853285169385885,42.51255012595812,17,17,13.20550420201628,26.872049429266017,10.354441272294054,22.446257073718247
tfmmax_tw2,XGBoost,24.103500929732547,19.974752498204847,0.8081631548211022,13.490293536653564,58.71182580566406,17,17,16.06854043508964,32.06667173067594,14.309648034849284,26.764023303518844
tfmmax_tw2,FlexibleMLP,14.079341547873149,11.281379570597817,0.9345459596870853,8.423676952837479,30.63441357721519,17,17,9.668048178886448,18.078538397003715,7.190631245086729,15.531561199007056
tfmmax_tw2,SVR,14.74576661957101,10.409459619650455,0.928202969440006,10.444174626351346,43.080419078010124,17,17,7.428580752754861,21.610931633831182,6.039328909805703,16.141351354334486
tfmmax_tw2,GaussianProcess,12.813336579372008,9.035713616145646,0.9457878989498671,9.08501368976805,35.02051079077613,17,17,6.40302129402842,17.865651222427985,5.1470982905158476,13.373689773248618
tfmmax_tw2,SupervisedSelection,13.728760720065605,9.928831323428223,0.937765036091164,9.481412313559979,35.02051079077613,17,17,7.619722313243933,18.640096099176976,5.754783423995279,14.575184845422935
tfmmax_tw3,RandomForest,21.863469363345732,15.963366803541756,0.9179108468552115,14.939284216371895,49.829878791984044,17,17,13.092058869435101,28.929334055086642,9.064034812584708,23.395059430764796
tfmmax_tw3,GradientBoosting,22.571517912093444,13.771302654033882,0.9125078375446052,17.88364180101342,73.45291033787437,17,17,10.221696439902212,34.51138937367191,6.221320240579397,23.623557917642955
tfmmax_tw3,XGBoost,26.237568504748598,19.398014292782054,0.8817789561115108,17.667117550364537,61.332262988281244,17,17,14.828057074660727,35.84180924988052,11.474246022638662,28.660198334174655
tfmmax_tw3,FlexibleMLP,24.83428726838157,17.601953788265625,0.8940865731934945,17.518933956273145,64.2519881542997,17,17,14.454778010455625,33.89326626535026,9.75625288324815,26.04859782059271
tfmmax_tw3,SVR,33.125226345288304,19.650093938357113,0.8115633703908915,26.66747885797087,100.67903977337647,17,17,12.53356334447877,49.30093374295965,8.176997441124369,33.540761946206445
tfmmax_tw3,GaussianProcess,12.186174187732275,8.149836817926982,0.9744975219762566,9.059966952197682,32.78757016907072,17,17,5.530222257563265,17.47275372194979,4.301456684778326,12.714023470990726
tfmmax_tw3,SupervisedSelection,21.513342414743583,12.924676255940724,0.9205189861696451,17.198158201770546,73.45291033787437,17,17,7.9753822571974675,33.68082942497623,6.248908066277315,22.18620705081814
tfmmax_frame,RandomForest,9.391332397633777,7.727691052935791,0.7940068494681682,5.336657679973663,22.804984935158743,17,17,6.413585528497738,12.39512777975064,5.330336054697836,10.395362594371122
tfmmax_frame,GradientBoosting,9.989365094554794,7.560583192396654,0.7669365732331896,6.52878218224187,25.1520351747688,17,17,6.391840717085184,13.527665345121315,4.623671896679324,10.896684999284266
tfmmax_frame,XGBoost,10.426271677565982,7.474893131534351,0.7461036662845717,7.268639058761627,31.905791394042964,17,17,5.709733622426511,15.336571252881699,4.581170830207376,11.252769367047478
tfmmax_frame,FlexibleMLP,8.157638871485394,6.845623427631428,0.844572698541956,4.4367230975837595,14.865684751692143,17,17,6.096997188614518,9.852402792303533,4.731783425267534,8.856693881064027
tfmmax_frame,SVR,3.3036139094178796,2.53293501246401,0.9745095837554265,2.1208736608136824,8.449928535235642,17,17,2.0408241535319624,4.488661752230225,1.5963613513888741,3.609452413641969
tfmmax_frame,GaussianProcess,2.999316643417922,2.362688827006957,0.9789891805761517,1.8475935251619173,6.400000727696607,17,17,2.0057667168909346,3.8850723324027108,1.516777853736869,3.270057579001734
tfmmax_frame,SupervisedSelection,3.5179399291961015,2.6580089744011066,0.9710948522204257,2.3045367511574755,8.449928535235642,17,17,2.156738156846746,4.721367247902057,1.6101834104236774,3.8145848713580537
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it0/local_oof_errors_3W_B29_H45_it0.csv 0 → 100644
output,sample_index,x1,x2,x3,y_true,y_pred_oof,abs_error_oof,positive_underprediction_oof
exymax_tw1,0,8.93,10.69,11.92,0.0287568,0.020009428444123012,0.008747371555876987,0.008747371555876987
exymax_tw1,1,14.65,7.11,11.38,0.0053857,0.006816098464389908,0.0014303984643899079,0.0
exymax_tw1,2,6.29,6.52,9.56,0.0440541,0.05453286351687242,0.010478763516872422,0.0
exymax_tw1,3,4.58,8.02,12.59,0.0781113,0.0589104553422537,0.019200844657746297,0.019200844657746297
exymax_tw1,4,10.95,6.12,10.09,0.0140436,0.014404183734996634,0.0003605837349966341,0.0
exymax_tw1,5,13.03,8.96,14.9,0.0112778,0.006749212107130795,0.0045285878928692045,0.0045285878928692045
exymax_tw1,6,15.45,11.1,6.67,0.0024325,0.0025094407927199747,7.694079271997447e-05,0.0
exymax_tw1,7,14.25,13.56,5.6,0.0024167,0.0022721214779605203,0.0001445785220394796,0.0001445785220394796
exymax_tw1,8,5.22,14.05,8.76,0.0550528,0.04901804517317371,0.006034754826826291,0.006034754826826291
exymax_tw1,9,12.69,4.24,7.49,0.0081104,0.006977259463487928,0.0011331405365120721,0.0011331405365120721
exymax_tw1,10,7.82,9.84,14.42,0.0327732,0.04293035358199215,0.010157153581992151,0.0
exymax_tw1,11,5.72,11.78,13.37,0.0553474,0.05898387011219486,0.0036364701121948603,0.0
exymax_tw1,12,10.54,4.85,8.21,0.0154791,0.014921914094509381,0.00055718590549062,0.00055718590549062
exymax_tw1,13,7.07,15.26,4.16,0.0221142,0.030604750098959577,0.008490550098959576,0.0
exymax_tw1,14,11.94,14.8,15.93,0.0195449,0.013706738035229413,0.005838161964770588,0.005838161964770588
exymax_tw1,15,9.81,12.65,4.96,0.0107046,0.010072326373563048,0.0006322736264369518,0.0006322736264369518
exymax_tw2,0,8.93,10.69,11.92,0.0384806,0.026148500852803847,0.01233209914719615,0.01233209914719615
exymax_tw2,1,14.65,7.11,11.38,0.071338,0.07177157556747149,0.00043357556747149195,0.0
exymax_tw2,2,6.29,6.52,9.56,0.0648756,0.07490198610397525,0.010026386103975243,0.0
exymax_tw2,3,4.58,8.02,12.59,0.0638333,0.05321201762959052,0.010621282370409477,0.010621282370409477
exymax_tw2,4,10.95,6.12,10.09,0.0763849,0.07832771430727659,0.0019428143072765802,0.0
exymax_tw2,5,13.03,8.96,14.9,0.0581062,0.057523706674193376,0.0005824933258066212,0.0005824933258066212
exymax_tw2,6,15.45,11.1,6.67,0.0266335,0.040036030990703006,0.013402530990703005,0.0
exymax_tw2,7,14.25,13.56,5.6,0.0199308,0.012791045630606651,0.007139754369393347,0.007139754369393347
exymax_tw2,8,5.22,14.05,8.76,0.0172312,0.018121438342734705,0.0008902383427347067,0.0
exymax_tw2,9,12.69,4.24,7.49,0.1076924,0.09913416498626226,0.00855823501373773,0.00855823501373773
exymax_tw2,10,7.82,9.84,14.42,0.0440125,0.049713060778891816,0.0057005607788918125,0.0
exymax_tw2,11,5.72,11.78,13.37,0.0315451,0.034852866223823886,0.0033077662238238867,0.0
exymax_tw2,12,10.54,4.85,8.21,0.0930859,0.0922148639231953,0.0008710360768046932,0.0008710360768046932
exymax_tw2,13,7.07,15.26,4.16,0.0185585,0.018696933758731174,0.00013843375873117542,0.0
exymax_tw2,14,11.94,14.8,15.93,0.0265998,0.022061720872390556,0.004538079127609444,0.004538079127609444
exymax_tw2,15,9.81,12.65,4.96,0.0205985,0.02500681269581203,0.0044083126958120306,0.0
exymax_tw3,0,8.93,10.69,11.92,0.0426347,0.037469884592374214,0.0051648154076257835,0.0051648154076257835
exymax_tw3,1,14.65,7.11,11.38,0.0471032,0.052263877180894536,0.005160677180894538,0.0
exymax_tw3,2,6.29,6.52,9.56,0.0503902,0.04866364962823122,0.0017265503717687827,0.0017265503717687827
exymax_tw3,3,4.58,8.02,12.59,0.0292695,0.026392305919144654,0.0028771940808553463,0.0028771940808553463
exymax_tw3,4,10.95,6.12,10.09,0.0527733,0.05169623802311085,0.0010770619768891501,0.0010770619768891501
exymax_tw3,5,13.03,8.96,14.9,0.0324297,0.026558509712274248,0.005871190287725751,0.005871190287725751
exymax_tw3,6,15.45,11.1,6.67,0.1034537,0.10657355028968607,0.003119850289686074,0.0
exymax_tw3,7,14.25,13.56,5.6,0.1360301,0.13473046861674187,0.0012996313832581152,0.0012996313832581152
exymax_tw3,8,5.22,14.05,8.76,0.05162,0.0720294639081398,0.020409463908139802,0.0
exymax_tw3,9,12.69,4.24,7.49,0.0846721,0.07568947387698248,0.00898262612301752,0.00898262612301752
exymax_tw3,10,7.82,9.84,14.42,0.02804,0.03395944171544585,0.005919441715445849,0.0
exymax_tw3,11,5.72,11.78,13.37,0.0281899,0.019121970714370717,0.009067929285629283,0.009067929285629283
exymax_tw3,12,10.54,4.85,8.21,0.0712027,0.07260785478543141,0.00140515478543142,0.0
exymax_tw3,13,7.07,15.26,4.16,0.1558781,0.1177780046837299,0.038100095316270094,0.038100095316270094
exymax_tw3,14,11.94,14.8,15.93,0.0326696,0.04600196215791008,0.013332362157910081,0.0
exymax_tw3,15,9.81,12.65,4.96,0.1445584,0.14135371888759085,0.0032046811124091523,0.0032046811124091523
tfmmax_tw1,0,8.93,10.69,11.92,42.4856,37.25620515290575,5.229394847094248,5.229394847094248
tfmmax_tw1,1,14.65,7.11,11.38,2.7556,1.1893928665531348,1.566207133446865,1.566207133446865
tfmmax_tw1,2,6.29,6.52,9.56,74.3944,92.66454140600263,18.270141406002622,0.0
tfmmax_tw1,3,4.58,8.02,12.59,153.736,125.33160132569265,28.404398674307345,28.404398674307345
tfmmax_tw1,4,10.95,6.12,10.09,11.0749,11.952008091686366,0.8771080916863667,0.0
tfmmax_tw1,5,13.03,8.96,14.9,7.9097,3.7611553916094635,4.148544608390536,4.148544608390536
tfmmax_tw1,6,15.45,11.1,6.67,0.6643,0.1959566930201161,0.4683433069798839,0.4683433069798839
tfmmax_tw1,7,14.25,13.56,5.6,1.0671,-1.1149899554026206,2.1820899554026205,2.1820899554026205
tfmmax_tw1,8,5.22,14.05,8.76,123.3135,92.99599932193786,30.317500678062146,30.317500678062146
tfmmax_tw1,9,12.69,4.24,7.49,5.8951,9.770129437139985,3.875029437139985,0.0
tfmmax_tw1,10,7.82,9.84,14.42,63.8575,75.22016042714603,11.362660427146025,0.0
tfmmax_tw1,11,5.72,11.78,13.37,120.2943,128.23385804463555,7.939558044635547,0.0
tfmmax_tw1,12,10.54,4.85,8.21,12.5924,9.86003633234077,2.7323636676592304,2.7323636676592304
tfmmax_tw1,13,7.07,15.26,4.16,30.8212,53.76518914312169,22.943989143121687,0.0
tfmmax_tw1,14,11.94,14.8,15.93,25.1018,21.70383301093934,3.3979669890606594,3.3979669890606594
tfmmax_tw1,15,9.81,12.65,4.96,11.0271,-6.680332694108429,17.70743269410843,17.70743269410843
tfmmax_tw2,0,8.93,10.69,11.92,55.4009,48.55252595847179,6.84837404152821,6.84837404152821
tfmmax_tw2,1,14.65,7.11,11.38,112.2407,118.25595039278676,6.015250392786754,0.0
tfmmax_tw2,2,6.29,6.52,9.56,118.5858,128.16185182064692,9.57605182064691,0.0
tfmmax_tw2,3,4.58,8.02,12.59,106.5364,99.03118084437321,7.50521915562679,7.50521915562679
tfmmax_tw2,4,10.95,6.12,10.09,132.0136,130.5971643305479,1.4164356694520848,1.4164356694520848
tfmmax_tw2,5,13.03,8.96,14.9,90.9036,86.71107001653624,4.192529983463757,4.192529983463757
tfmmax_tw2,6,15.45,11.1,6.67,46.3963,62.20072861417884,15.804428614178846,0.0
tfmmax_tw2,7,14.25,13.56,5.6,36.7154,18.97543873359028,17.739961266409722,17.739961266409722
tfmmax_tw2,8,5.22,14.05,8.76,26.3489,26.116473629516406,0.23242637048359427,0.23242637048359427
tfmmax_tw2,9,12.69,4.24,7.49,201.8901,179.82129679784163,22.068803202158364,22.068803202158364
tfmmax_tw2,10,7.82,9.84,14.42,75.9879,80.13479121397904,4.146891213979046,0.0
tfmmax_tw2,11,5.72,11.78,13.37,53.3086,57.77972806088246,4.471128060882464,0.0
tfmmax_tw2,12,10.54,4.85,8.21,168.2033,170.39504596154916,2.1917459615491452,0.0
tfmmax_tw2,13,7.07,15.26,4.16,27.8159,33.7323795613481,5.916479561348098,0.0
tfmmax_tw2,14,11.94,14.8,15.93,45.484,44.68812324897135,0.7958767510286506,0.7958767510286506
tfmmax_tw2,15,9.81,12.65,4.96,37.9509,40.863341903323224,2.912441903323227,0.0
tfmmax_tw3,0,8.93,10.69,11.92,65.4914,72.05906895765474,6.567668957654746,0.0
tfmmax_tw3,1,14.65,7.11,11.38,71.322,76.80811876292552,5.486118762925514,0.0
tfmmax_tw3,2,6.29,6.52,9.56,83.264,78.4134523220564,4.850547677943595,4.850547677943595
tfmmax_tw3,3,4.58,8.02,12.59,46.4819,54.53888850955809,8.056988509558089,0.0
tfmmax_tw3,4,10.95,6.12,10.09,84.0868,80.89618865035027,3.1906113496497284,3.1906113496497284
tfmmax_tw3,5,13.03,8.96,14.9,49.9149,47.7717506866411,2.1431493133589044,2.1431493133589044
tfmmax_tw3,6,15.45,11.1,6.67,184.0874,182.63959030430374,1.4478096956962645,1.4478096956962645
tfmmax_tw3,7,14.25,13.56,5.6,241.1134,238.32914946941185,2.7842505305881673,2.7842505305881673
tfmmax_tw3,8,5.22,14.05,8.76,108.0964,122.68475849400238,14.588358494002378,0.0
tfmmax_tw3,9,12.69,4.24,7.49,122.4642,117.04819521445825,5.416004785541759,5.416004785541759
tfmmax_tw3,10,7.82,9.84,14.42,48.1235,49.51378520125763,1.3902852012576332,0.0
tfmmax_tw3,11,5.72,11.78,13.37,50.6261,41.638082367720926,8.988017632279075,8.988017632279075
tfmmax_tw3,12,10.54,4.85,8.21,110.4781,113.26366657930852,2.7855665793085223,0.0
tfmmax_tw3,13,7.07,15.26,4.16,274.0096,252.66233606551577,21.347263934484204,21.347263934484204
tfmmax_tw3,14,11.94,14.8,15.93,59.1328,66.03856036637558,6.905760366375574,0.0
tfmmax_tw3,15,9.81,12.65,4.96,252.2847,254.57906405487435,2.2943640548743645,0.0
tfmmax_frame,0,8.93,10.69,11.92,63.6471,67.56061291159473,3.9135129115947294,0.0
tfmmax_frame,1,14.65,7.11,11.38,64.5245,67.03209467571685,2.5075946757168452,0.0
tfmmax_frame,2,6.29,6.52,9.56,56.9333,57.819195044592334,0.8858950445923313,0.0
tfmmax_frame,3,4.58,8.02,12.59,48.37,50.71866464312211,2.348664643122113,0.0
tfmmax_frame,4,10.95,6.12,10.09,64.072,62.07009069719652,2.001909302803483,2.001909302803483
tfmmax_frame,5,13.03,8.96,14.9,63.3658,62.5910433468523,0.7747566531476977,0.7747566531476977
tfmmax_frame,6,15.45,11.1,6.67,93.6778,89.4969963545891,4.180803645410904,4.180803645410904
tfmmax_frame,7,14.25,13.56,5.6,103.3849,107.22020916831741,3.8353091683174085,0.0
tfmmax_frame,8,5.22,14.05,8.76,69.2869,72.22717989791522,2.940279897915218,0.0
tfmmax_frame,9,12.69,4.24,7.49,61.2348,60.7152295183796,0.5195704816203985,0.5195704816203985
tfmmax_frame,10,7.82,9.84,14.42,57.0745,54.48156657224058,2.5929334277594194,2.5929334277594194
tfmmax_frame,11,5.72,11.78,13.37,52.4444,53.176815311335076,0.7324153113350746,0.0
tfmmax_frame,12,10.54,4.85,8.21,61.6913,62.75019772186022,1.0588977218602196,0.0
tfmmax_frame,13,7.07,15.26,4.16,104.2669,101.81477529620292,2.4521247037970824,2.4521247037970824
tfmmax_frame,14,11.94,14.8,15.93,63.8419,64.35029276409932,0.5083927640993195,0.0
tfmmax_frame,15,9.81,12.65,4.96,103.1451,98.40876979213516,4.736330207864839,4.736330207864839
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it0/validation_envelopes_3W_B29_H45_it0.csv 0 → 100644
n_unique_oof_samples,envelope_reference_n,percentile,small_n_threshold,abs_error_envelope,abs_error_rule,damage_safety_margin,damage_safety_margin_rule,abs_error_p95,abs_error_max,underprediction_p95,underprediction_max,mean_abs_error,output,method
16,16,95.0,20,0.019200844657746297,max_abs_oof_error_small_n,0.019200844657746297,max_positive_oof_residual_small_n,0.01265928380209089,0.019200844657746297,0.011360739831344314,0.019200844657746297,0.005090484986918376,exymax_tw1,RBF
16,16,95.0,20,0.013402530990703005,max_abs_oof_error_small_n,0.01233209914719615,max_positive_oof_residual_small_n,0.012599707108072865,0.013402530990703005,0.011048986564606145,0.01233209914719615,0.005305849887523587,exymax_tw2,RBF
16,16,95.0,20,0.038100095316270094,max_abs_oof_error_small_n,0.038100095316270094,max_positive_oof_residual_small_n,0.024832121760172375,0.038100095316270094,0.016325970793289485,0.038100095316270094,0.007919920336434797,exymax_tw3,RBF
16,16,95.0,20,30.317500678062146,max_abs_oof_error_small_n,30.317500678062146,max_positive_oof_residual_small_n,28.882674175246045,30.317500678062146,28.882674175246045,30.317500678062146,10.088920569015261,tfmmax_tw1,RBF
16,16,95.0,20,22.068803202158364,max_abs_oof_error_small_n,22.068803202158364,max_positive_oof_residual_small_n,18.822171750346882,22.068803202158364,18.822171750346882,22.068803202158364,6.989627748052854,tfmmax_tw2,RBF
16,16,95.0,20,21.347263934484204,max_abs_oof_error_small_n,21.347263934484204,max_positive_oof_residual_small_n,16.278084854122834,21.347263934484204,12.077829207830357,21.347263934484204,6.140172865343658,tfmmax_tw3,RBF
16,16,95.0,20,4.736330207864839,max_abs_oof_error_small_n,4.736330207864839,max_positive_oof_residual_small_n,4.319685286024388,4.736330207864839,4.319685286024388,4.736330207864839,2.2493369100598177,tfmmax_frame,RBF
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/final_model_selection_3W_B29_H45_it1.csv 0 → 100644
output,N,selected_function,selected_epsilon_factor,selected_smooth,fitted_epsilon,inner_cv_rmse,inner_cv_rmse_dispersion,model_path,best_params
exymax_tw1,17,multiquadric,2.0,0.001,4.932832639351668,0.007941177226060381,0.35725013135904454,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_exymax_tw1.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 0.001}"
exymax_tw2,17,multiquadric,2.0,0.001,4.932832639351668,0.007921106895165462,0.3532319644711232,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_exymax_tw2.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 0.001}"
exymax_tw3,17,inverse,2.0,0.0,4.932832639351668,0.0076438457433181785,0.8385491048454318,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_exymax_tw3.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""inverse"", ""rbf__smooth"": 0.0}"
tfmmax_tw1,17,multiquadric,2.0,1e-05,4.932832639351668,12.186508249786684,0.41469564423804284,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_tfmmax_tw1.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 1e-05}"
tfmmax_tw2,17,multiquadric,2.0,0.0,4.932832639351668,10.955062765938061,0.29238562377971616,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_tfmmax_tw2.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 0.0}"
tfmmax_tw3,17,inverse,2.0,0.0,4.932832639351668,7.588602379089775,0.5783975957183825,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_tfmmax_tw3.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""inverse"", ""rbf__smooth"": 0.0}"
tfmmax_frame,17,inverse,2.0,0.0,4.932832639351668,2.596568929719396,0.3609696285583503,../../models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_tfmmax_frame.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""inverse"", ""rbf__smooth"": 0.0}"
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/nested_cv_fold_selections_3W_B29_H45_it1.csv 0 → 100644
outer_fold,selected_function,selected_epsilon_factor,selected_smooth,fitted_epsilon,inner_cv_rmse,inner_cv_rmse_dispersion
0,inverse,1.0,0.0,2.499599591812427,0.007294763779782031,0.7409897067631944
1,multiquadric,2.0,0.001,4.907966743727067,0.006062155946566159,0.5139738185288961
2,multiquadric,2.0,0.001,4.981509766475796,0.00738061755414862,0.47107736856115695
3,multiquadric,2.0,0.001,4.922368603768309,0.006068384059446909,0.5413462183539091
4,multiquadric,2.0,0.001,4.992028383446165,0.006943495005242431,0.44478398432572
5,multiquadric,2.0,0.001,4.906420121227111,0.0058339670288932695,0.4200865188396406
6,multiquadric,2.0,0.001,4.899300504089988,0.009653193986317964,0.295482971592605
7,multiquadric,2.0,0.001,4.912302816770661,0.006530112583006525,0.6795772400603018
8,multiquadric,2.0,0.001,4.987738310990956,0.005027542922515042,0.7294676266951837
9,multiquadric,2.0,0.001,4.997607992151069,0.007861529318729037,0.5122038686015316
10,multiquadric,2.0,0.001,4.993514249505538,0.006873156539603362,0.4675301914401366
11,multiquadric,2.0,0.001,4.952854861657599,0.008614160926710725,0.4845758654721142
12,multiquadric,2.0,0.001,4.997922056650458,0.007241785441812072,0.5764152884578679
13,multiquadric,0.5,0.0,1.2287046098641214,0.0055069439147512325,0.4960463641417422
14,multiquadric,2.0,0.001,4.966597313134793,0.008663670918704952,0.7479836135949223
15,multiquadric,2.0,0.001,4.9181128508873,0.00641347800897613,0.76397383171106
16,multiquadric,2.0,0.001,4.95803763244025,0.00804588511002657,0.37375807226403773
0,multiquadric,2.0,0.0,4.999199183624854,0.004942790521666911,0.20024051208035934
1,multiquadric,2.0,0.001,4.907966743727067,0.007400025823106553,0.34885967882054814
2,multiquadric,2.0,0.001,4.981509766475796,0.006760253054739401,0.48353693373216894
3,multiquadric,2.0,0.001,4.922368603768309,0.004432393430350481,0.34387244249887516
4,multiquadric,2.0,0.001,4.992028383446165,0.006105251179683566,0.29326730682753044
5,multiquadric,2.0,0.001,4.906420121227111,0.005439955723458166,0.5150983839264801
6,multiquadric,2.0,0.001,4.899300504089988,0.006506318911074224,0.4012872222848728
7,multiquadric,1.0,0.001,2.4561514083853306,0.008672333045832994,0.5271726695515114
8,multiquadric,2.0,0.001,4.987738310990956,0.006320182344977268,0.3628454888808105
9,multiquadric,2.0,0.001,4.997607992151069,0.007696634129883936,0.22656501720887684
10,gaussian,2.0,1e-08,4.993514249505538,0.0056413247778824786,0.4179527739963048
11,multiquadric,2.0,0.001,4.952854861657599,0.005913023988952044,0.6055378369555203
12,multiquadric,2.0,0.001,4.997922056650458,0.007436718282321357,0.4012822919955501
13,multiquadric,1.0,0.001,2.457409219728243,0.0051134382333111305,0.3602713220919777
14,multiquadric,2.0,0.001,4.966597313134793,0.007611222236191838,0.5727075509062384
15,multiquadric,0.5,0.0,1.229528212721825,0.006822427581730249,0.2568965989146628
16,multiquadric,0.5,0.001,1.2395094081100626,0.005881367032777927,0.24719875864414378
0,multiquadric,2.0,1e-05,4.999199183624854,0.005606222112594128,0.4752068307319293
1,multiquadric,1.0,0.0,2.4539833718635333,0.008251102308574346,0.6770555063769266
2,multiquadric,2.0,1e-05,4.981509766475796,0.006668672252541743,0.30230090657743525
3,inverse,2.0,1e-05,4.922368603768309,0.006783169096318685,0.39893305231229076
4,multiquadric,2.0,0.001,4.992028383446165,0.007788453908071686,0.6382173341072315
5,multiquadric,2.0,0.0,4.906420121227111,0.007311364108515297,0.43943221675892075
6,multiquadric,1.0,0.001,2.449650252044994,0.012082429713442543,0.6583899414233715
7,multiquadric,1.0,0.001,2.4561514083853306,0.007727840555886027,0.6430676071326894
8,inverse,2.0,0.0,4.987738310990956,0.006822131145202026,0.3739794052293172
9,multiquadric,1.0,0.001,2.4988039960755346,0.011692413150438763,0.8345032351808398
10,multiquadric,2.0,1e-05,4.993514249505538,0.007314403515075921,0.2864906310317952
11,gaussian,2.0,0.0,4.952854861657599,0.0055872092078645425,0.35824011088180546
12,multiquadric,2.0,1e-05,4.997922056650458,0.008668300702575996,0.16621522250187684
13,inverse,1.0,0.001,2.457409219728243,0.009483923568805983,0.5313780644482236
14,gaussian,2.0,0.0,4.966597313134793,0.009715017288105242,0.668027662945377
15,multiquadric,2.0,0.001,4.9181128508873,0.014416764438527404,0.8506403634005673
16,multiquadric,2.0,0.001,4.95803763244025,0.009656804856679147,0.5142738738414968
0,multiquadric,1.0,0.0,2.499599591812427,13.849918077644915,0.3776553186112016
1,multiquadric,1.0,0.0,2.4539833718635333,10.49444620275767,0.5115047421482448
2,multiquadric,2.0,0.001,4.981509766475796,12.19729438102777,0.6527852493590227
3,gaussian,2.0,1e-05,4.922368603768309,6.4230354614443215,0.7405368765542236
4,multiquadric,2.0,0.0,4.992028383446165,10.677028867302425,0.4304337967611355
5,multiquadric,2.0,1e-05,4.906420121227111,11.024373125653089,0.3694933626116376
6,multiquadric,2.0,0.001,4.899300504089988,18.380813402558083,0.4055616865689551
7,multiquadric,2.0,0.0,4.912302816770661,10.010051366307405,0.4060441767994139
8,inverse,1.0,0.001,2.493869155495478,8.21974983507706,0.7976577161127387
9,multiquadric,2.0,0.0,4.997607992151069,8.741110776742325,0.4333484172797072
10,multiquadric,1.0,0.0,2.496757124752769,14.55279195116951,0.5461382083975893
11,multiquadric,2.0,0.0,4.952854861657599,14.262053673638572,0.28582760834624804
12,multiquadric,1.0,0.0,2.498961028325229,12.071817227000292,0.7738906609888114
13,multiquadric,1.0,0.001,2.457409219728243,10.2237726894091,0.40517899486522224
14,inverse,2.0,0.0,4.966597313134793,13.323851536452358,0.6803277978830308
15,inverse,2.0,0.0,4.9181128508873,11.692802173090177,0.7604142722312058
16,multiquadric,2.0,0.0,4.95803763244025,18.1405753035094,0.38764245592566693
0,multiquadric,2.0,1e-05,4.999199183624854,11.787860109704008,0.376064904303838
1,multiquadric,1.0,0.0,2.4539833718635333,12.749406101032083,0.2857751490124589
2,multiquadric,1.0,0.0,2.490754883237898,9.651490585672683,0.4165957940402044
3,multiquadric,2.0,0.001,4.922368603768309,6.44685702618326,0.6035960072435813
4,multiquadric,2.0,1e-05,4.992028383446165,8.277184406006166,0.11664389995641683
5,multiquadric,2.0,0.0,4.906420121227111,8.313446990511153,0.6861092966479585
6,multiquadric,2.0,0.001,4.899300504089988,8.63100143372498,0.3740448233587711
7,multiquadric,2.0,0.001,4.912302816770661,11.836783634466041,0.47866626689258523
8,multiquadric,2.0,0.001,4.987738310990956,9.45977058199679,0.2629570622690539
9,multiquadric,2.0,0.001,4.997607992151069,8.063092036623633,0.39288333836277045
10,multiquadric,2.0,1e-05,4.993514249505538,9.557805143847734,0.09797794395045793
11,multiquadric,2.0,0.001,4.952854861657599,8.632936235806602,0.46663291134798435
12,multiquadric,2.0,0.0,4.997922056650458,12.293208310980301,0.354357534996678
13,multiquadric,2.0,0.0,4.914818439456486,5.961295562111376,0.3368369540546346
14,multiquadric,2.0,0.0,4.966597313134793,9.465783238258835,0.1888736844310189
15,multiquadric,2.0,0.0,4.9181128508873,10.33210739375231,0.30720949276212706
16,multiquadric,0.5,0.0,1.2395094081100626,7.6873972105166875,0.6555349341884174
0,inverse,2.0,0.0,4.999199183624854,8.680332498635423,0.4388038451272015
1,inverse,2.0,0.0,4.907966743727067,8.043419881247441,0.36599038864878414
2,gaussian,2.0,1e-05,4.981509766475796,5.693558813211786,0.2977989486249072
3,inverse,2.0,0.0,4.922368603768309,6.558767730333992,0.3497381937132648
4,inverse,2.0,0.0,4.992028383446165,9.347731481866424,0.1720304625717589
5,multiquadric,1.0,0.0,2.4532100606135554,7.491696470660051,0.42826725210897354
6,multiquadric,1.0,0.001,2.449650252044994,15.931949700719233,0.6794267881723468
7,inverse,2.0,0.0,4.912302816770661,8.559027398873651,0.33200106780975236
8,inverse,2.0,0.0,4.987738310990956,8.760338608608578,0.3006044853397288
9,inverse,2.0,0.0,4.997607992151069,13.889156690625091,0.9633865884274526
10,gaussian,2.0,0.0,4.993514249505538,9.034114351072855,0.3151769121073181
11,inverse,2.0,0.0,4.952854861657599,6.670184856164587,0.288430266935364
12,gaussian,2.0,0.001,4.997922056650458,12.05518285525234,0.4355458211720912
13,inverse,2.0,0.0,4.914818439456486,12.33357978445887,0.4622751616607607
14,multiquadric,1.0,0.001,2.4832986565673965,11.43667504643746,0.8546545365119633
15,multiquadric,2.0,1e-05,4.9181128508873,14.994489853625605,0.8440653226886491
16,multiquadric,2.0,1e-05,4.95803763244025,9.12736828415545,0.6600804754751396
0,inverse,2.0,1e-05,4.999199183624854,2.28628660385111,0.435299011486518
1,inverse,2.0,0.0,4.907966743727067,2.3161333268273774,0.393687616826496
2,multiquadric,1.0,0.0,2.490754883237898,2.3108433749337367,0.34089080348045064
3,inverse,2.0,0.0,4.922368603768309,2.155335226342321,0.2888591448883216
4,inverse,2.0,1e-05,4.992028383446165,1.586611916294921,0.46586949030220565
5,inverse,2.0,0.0,4.906420121227111,2.7029264571496183,0.39094373023014134
6,inverse,2.0,0.0,4.899300504089988,2.6540361287368137,1.063217031736749
7,inverse,2.0,0.0,4.912302816770661,1.5219751306630749,0.41947150788047965
8,multiquadric,2.0,1e-05,4.987738310990956,2.164921463022905,0.3705945061608508
9,gaussian,2.0,0.0,4.997607992151069,2.894503752654331,0.606728782263299
10,inverse,2.0,0.0,4.993514249505538,2.540806426515862,0.2524350133398182
11,inverse,2.0,0.0,4.952854861657599,2.2785521977315772,0.31763075356729303
12,inverse,2.0,1e-05,4.997922056650458,3.263293241819415,0.35736162978088615
13,gaussian,2.0,1e-08,4.914818439456486,2.7907418374630253,0.13129460365128598
14,multiquadric,1.0,0.001,2.4832986565673965,3.1563624600263105,0.6361081592386175
15,multiquadric,2.0,1e-05,4.9181128508873,2.0413222320657605,0.4566680082441417
16,multiquadric,2.0,0.001,4.95803763244025,2.719116963313831,0.4404792268091712
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/nested_cv_outer_predictions_3W_B29_H45_it1.csv 0 → 100644
output,N,outer_fold,sample_index,y_true,prediction_RBF,selected_rbf_function,selected_epsilon_factor,selected_smooth,fitted_epsilon
exymax_tw1,17,0,0,0.0287568,0.01994203664341826,inverse,1.0,0.0,2.499599591812427
exymax_tw1,17,1,1,0.0053857,0.006883167515882249,multiquadric,2.0,0.001,4.907966743727067
exymax_tw1,17,2,2,0.0440541,0.045821795429910495,multiquadric,2.0,0.001,4.981509766475796
exymax_tw1,17,3,3,0.0781113,0.05780053027215892,multiquadric,2.0,0.001,4.922368603768309
exymax_tw1,17,4,4,0.0140436,0.01462374186313209,multiquadric,2.0,0.001,4.992028383446165
exymax_tw1,17,5,5,0.0112778,0.007814915204205075,multiquadric,2.0,0.001,4.906420121227111
exymax_tw1,17,6,6,0.0024325,0.0010412985790588802,multiquadric,2.0,0.001,4.899300504089988
exymax_tw1,17,7,7,0.0024167,0.00484508418889007,multiquadric,2.0,0.001,4.912302816770661
exymax_tw1,17,8,8,0.0550528,0.046549200052289574,multiquadric,2.0,0.001,4.987738310990956
exymax_tw1,17,9,9,0.0081104,0.008192080120458023,multiquadric,2.0,0.001,4.997607992151069
exymax_tw1,17,10,10,0.0327732,0.04384595689639781,multiquadric,2.0,0.001,4.993514249505538
exymax_tw1,17,11,11,0.0553474,0.05890257659964648,multiquadric,2.0,0.001,4.952854861657599
exymax_tw1,17,12,12,0.0154791,0.014467251650041923,multiquadric,2.0,0.001,4.997922056650458
exymax_tw1,17,13,13,0.0221142,0.03246060001710466,multiquadric,0.5,0.0,1.2287046098641214
exymax_tw1,17,14,14,0.0195449,0.01513146698567347,multiquadric,2.0,0.001,4.966597313134793
exymax_tw1,17,15,15,0.0107046,0.006399441644009851,multiquadric,2.0,0.001,4.9181128508873
exymax_tw1,17,16,16,0.041948,0.050356574755481276,multiquadric,2.0,0.001,4.95803763244025
exymax_tw2,17,0,0,0.0384806,0.0254132241638505,multiquadric,2.0,0.0,4.999199183624854
exymax_tw2,17,1,1,0.071338,0.07191633343285986,multiquadric,2.0,0.001,4.907966743727067
exymax_tw2,17,2,2,0.0648756,0.06749193835760536,multiquadric,2.0,0.001,4.981509766475796
exymax_tw2,17,3,3,0.0638333,0.05053891540282862,multiquadric,2.0,0.001,4.922368603768309
exymax_tw2,17,4,4,0.0763849,0.07830555971528419,multiquadric,2.0,0.001,4.992028383446165
exymax_tw2,17,5,5,0.0581062,0.057390927024931315,multiquadric,2.0,0.001,4.906420121227111
exymax_tw2,17,6,6,0.0266335,0.03717446521936374,multiquadric,2.0,0.001,4.899300504089988
exymax_tw2,17,7,7,0.0199308,0.013993071026726422,multiquadric,1.0,0.001,2.4561514083853306
exymax_tw2,17,8,8,0.0172312,0.017198074653880013,multiquadric,2.0,0.001,4.987738310990956
exymax_tw2,17,9,9,0.1076924,0.10029003598434771,multiquadric,2.0,0.001,4.997607992151069
exymax_tw2,17,10,10,0.0440125,0.04733061631029045,gaussian,2.0,1e-08,4.993514249505538
exymax_tw2,17,11,11,0.0315451,0.03493723827197037,multiquadric,2.0,0.001,4.952854861657599
exymax_tw2,17,12,12,0.0930859,0.09190728439048534,multiquadric,2.0,0.001,4.997922056650458
exymax_tw2,17,13,13,0.0185585,0.0190051058557561,multiquadric,1.0,0.001,2.457409219728243
exymax_tw2,17,14,14,0.0265998,0.02388789519299106,multiquadric,2.0,0.001,4.966597313134793
exymax_tw2,17,15,15,0.0205985,0.0212614495231688,multiquadric,0.5,0.0,1.229528212721825
exymax_tw2,17,16,16,0.0448101,0.052070093047230814,multiquadric,0.5,0.001,1.2395094081100626
exymax_tw3,17,0,0,0.0426347,0.035389222446482904,multiquadric,2.0,1e-05,4.999199183624854
exymax_tw3,17,1,1,0.0471032,0.051170536180586534,multiquadric,1.0,0.0,2.4539833718635333
exymax_tw3,17,2,2,0.0503902,0.046587866323648086,multiquadric,2.0,1e-05,4.981509766475796
exymax_tw3,17,3,3,0.0292695,0.03275064477708156,inverse,2.0,1e-05,4.922368603768309
exymax_tw3,17,4,4,0.0527733,0.05611721424969218,multiquadric,2.0,0.001,4.992028383446165
exymax_tw3,17,5,5,0.0324297,0.02431159098702551,multiquadric,2.0,0.0,4.906420121227111
exymax_tw3,17,6,6,0.1034537,0.1086551301994576,multiquadric,1.0,0.001,2.449650252044994
exymax_tw3,17,7,7,0.1360301,0.13554843767552383,multiquadric,1.0,0.001,2.4561514083853306
exymax_tw3,17,8,8,0.05162,0.0742584109474187,inverse,2.0,0.0,4.987738310990956
exymax_tw3,17,9,9,0.0846721,0.0798742041255589,multiquadric,1.0,0.001,2.4988039960755346
exymax_tw3,17,10,10,0.02804,0.034794955702722064,multiquadric,2.0,1e-05,4.993514249505538
exymax_tw3,17,11,11,0.0281899,0.018055942667960068,gaussian,2.0,0.0,4.952854861657599
exymax_tw3,17,12,12,0.0712027,0.07369129097930482,multiquadric,2.0,1e-05,4.997922056650458
exymax_tw3,17,13,13,0.1558781,0.11996272152144682,inverse,1.0,0.001,2.457409219728243
exymax_tw3,17,14,14,0.0326696,0.07006263099547994,gaussian,2.0,0.0,4.966597313134793
exymax_tw3,17,15,15,0.1445584,0.1326797789941736,multiquadric,2.0,0.001,4.9181128508873
exymax_tw3,17,16,16,0.0475545,0.051709501584594975,multiquadric,2.0,0.001,4.95803763244025
tfmmax_tw1,17,0,0,42.4856,37.6288915407431,multiquadric,1.0,0.0,2.499599591812427
tfmmax_tw1,17,1,1,2.7556,2.382893618052549,multiquadric,1.0,0.0,2.4539833718635333
tfmmax_tw1,17,2,2,74.3944,84.07896954620446,multiquadric,2.0,0.001,4.981509766475796
tfmmax_tw1,17,3,3,153.736,127.47044710512773,gaussian,2.0,1e-05,4.922368603768309
tfmmax_tw1,17,4,4,11.0749,13.850144670563168,multiquadric,2.0,0.0,4.992028383446165
tfmmax_tw1,17,5,5,7.9097,2.973087057005614,multiquadric,2.0,1e-05,4.906420121227111
tfmmax_tw1,17,6,6,0.6643,4.391232247331573,multiquadric,2.0,0.001,4.899300504089988
tfmmax_tw1,17,7,7,1.0671,-3.41267015421181,multiquadric,2.0,0.0,4.912302816770661
tfmmax_tw1,17,8,8,123.3135,92.99766324332771,inverse,1.0,0.001,2.493869155495478
tfmmax_tw1,17,9,9,5.8951,11.363459988337127,multiquadric,2.0,0.0,4.997607992151069
tfmmax_tw1,17,10,10,63.8575,73.17504371688028,multiquadric,1.0,0.0,2.496757124752769
tfmmax_tw1,17,11,11,120.2943,127.40888280175568,multiquadric,2.0,0.0,4.952854861657599
tfmmax_tw1,17,12,12,12.5924,10.17156310862083,multiquadric,1.0,0.0,2.498961028325229
tfmmax_tw1,17,13,13,30.8212,61.13317910601654,multiquadric,1.0,0.001,2.457409219728243
tfmmax_tw1,17,14,14,25.1018,10.977403108321596,inverse,2.0,0.0,4.966597313134793
tfmmax_tw1,17,15,15,11.0271,6.540873385705709,inverse,2.0,0.0,4.9181128508873
tfmmax_tw1,17,16,16,88.2952,85.09474896291795,multiquadric,2.0,0.0,4.95803763244025
tfmmax_tw2,17,0,0,55.4009,45.15321472883079,multiquadric,2.0,1e-05,4.999199183624854
tfmmax_tw2,17,1,1,112.2407,118.8180299895048,multiquadric,1.0,0.0,2.4539833718635333
tfmmax_tw2,17,2,2,118.5858,112.10933221112373,multiquadric,1.0,0.0,2.490754883237898
tfmmax_tw2,17,3,3,106.5364,93.96685248164431,multiquadric,2.0,0.001,4.922368603768309
tfmmax_tw2,17,4,4,132.0136,130.16603976378974,multiquadric,2.0,1e-05,4.992028383446165
tfmmax_tw2,17,5,5,90.9036,82.17162219216698,multiquadric,2.0,0.0,4.906420121227111
tfmmax_tw2,17,6,6,46.3963,65.54910058889527,multiquadric,2.0,0.001,4.899300504089988
tfmmax_tw2,17,7,7,36.7154,21.796118012116494,multiquadric,2.0,0.001,4.912302816770661
tfmmax_tw2,17,8,8,26.3489,22.1402456300857,multiquadric,2.0,0.001,4.987738310990956
tfmmax_tw2,17,9,9,201.8901,181.69580261582178,multiquadric,2.0,0.001,4.997607992151069
tfmmax_tw2,17,10,10,75.9879,81.70017020982777,multiquadric,2.0,1e-05,4.993514249505538
tfmmax_tw2,17,11,11,53.3086,57.952145959148766,multiquadric,2.0,0.001,4.952854861657599
tfmmax_tw2,17,12,12,168.2033,171.80766546216546,multiquadric,2.0,0.0,4.997922056650458
tfmmax_tw2,17,13,13,27.8159,35.069550561393044,multiquadric,2.0,0.0,4.914818439456486
tfmmax_tw2,17,14,14,45.484,56.18137025874421,multiquadric,2.0,0.0,4.966597313134793
tfmmax_tw2,17,15,15,37.9509,29.28459495428615,multiquadric,2.0,0.0,4.9181128508873
tfmmax_tw2,17,16,16,80.418,92.25892337491547,multiquadric,0.5,0.0,1.2395094081100626
tfmmax_tw3,17,0,0,65.4914,67.24445294545512,inverse,2.0,0.0,4.999199183624854
tfmmax_tw3,17,1,1,71.322,78.81596407838515,inverse,2.0,0.0,4.907966743727067
tfmmax_tw3,17,2,2,83.264,74.39048781741621,gaussian,2.0,1e-05,4.981509766475796
tfmmax_tw3,17,3,3,46.4819,57.7159541709043,inverse,2.0,0.0,4.922368603768309
tfmmax_tw3,17,4,4,84.0868,80.28456702899712,inverse,2.0,0.0,4.992028383446165
tfmmax_tw3,17,5,5,49.9149,44.27358330653897,multiquadric,1.0,0.0,2.4532100606135554
tfmmax_tw3,17,6,6,184.0874,185.99205008813442,multiquadric,1.0,0.001,2.449650252044994
tfmmax_tw3,17,7,7,241.1134,248.78018286768202,inverse,2.0,0.0,4.912302816770661
tfmmax_tw3,17,8,8,108.0964,127.72169525388745,inverse,2.0,0.0,4.987738310990956
tfmmax_tw3,17,9,9,122.4642,114.23302966711344,inverse,2.0,0.0,4.997607992151069
tfmmax_tw3,17,10,10,48.1235,55.024757063663856,gaussian,2.0,0.0,4.993514249505538
tfmmax_tw3,17,11,11,50.6261,37.387494285056164,inverse,2.0,0.0,4.952854861657599
tfmmax_tw3,17,12,12,110.4781,97.150391896148,gaussian,2.0,0.001,4.997922056650458
tfmmax_tw3,17,13,13,274.0096,260.18512512856705,inverse,2.0,0.0,4.914818439456486
tfmmax_tw3,17,14,14,59.1328,82.40675999350219,multiquadric,1.0,0.001,2.4832986565673965
tfmmax_tw3,17,15,15,252.2847,227.20118532295237,multiquadric,2.0,1e-05,4.9181128508873
tfmmax_tw3,17,16,16,78.1291,88.96982940704765,multiquadric,2.0,1e-05,4.95803763244025
tfmmax_frame,17,0,0,63.6471,66.6935617202248,inverse,2.0,1e-05,4.999199183624854
tfmmax_frame,17,1,1,64.5245,67.23989441362437,inverse,2.0,0.0,4.907966743727067
tfmmax_frame,17,2,2,56.9333,56.22509206843171,multiquadric,1.0,0.0,2.490754883237898
tfmmax_frame,17,3,3,48.37,50.83845777626834,inverse,2.0,0.0,4.922368603768309
tfmmax_frame,17,4,4,64.072,61.9794692543536,inverse,2.0,1e-05,4.992028383446165
tfmmax_frame,17,5,5,63.3658,61.73077234250832,inverse,2.0,0.0,4.906420121227111
tfmmax_frame,17,6,6,93.6778,89.15699362965552,inverse,2.0,0.0,4.899300504089988
tfmmax_frame,17,7,7,103.3849,107.72583425358243,inverse,2.0,0.0,4.912302816770661
tfmmax_frame,17,8,8,69.2869,68.26663128274959,multiquadric,2.0,1e-05,4.987738310990956
tfmmax_frame,17,9,9,61.2348,59.26849579483496,gaussian,2.0,0.0,4.997607992151069
tfmmax_frame,17,10,10,57.0745,57.06970535635992,inverse,2.0,0.0,4.993514249505538
tfmmax_frame,17,11,11,52.4444,51.07894847315947,inverse,2.0,0.0,4.952854861657599
tfmmax_frame,17,12,12,61.6913,62.88656041581544,inverse,2.0,1e-05,4.997922056650458
tfmmax_frame,17,13,13,104.2669,103.52670213263718,gaussian,2.0,1e-08,4.914818439456486
tfmmax_frame,17,14,14,63.8419,67.63147256435525,multiquadric,1.0,0.001,2.4832986565673965
tfmmax_frame,17,15,15,103.1451,97.36652161160055,multiquadric,2.0,1e-05,4.9181128508873
tfmmax_frame,17,16,16,58.74176,60.77004081767382,multiquadric,2.0,0.001,4.95803763244025
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/nested_cv_outer_summary_3W_B29_H45_it1.csv 0 → 100644
output,method,outer_rmse,outer_mae,outer_r2,outer_std_abs_error,outer_max_abs_error,n_outer_predictions,N,outer_rmse_ci_lower,outer_rmse_ci_upper,outer_mae_ci_lower,outer_mae_ci_upper
exymax_tw1,RBF,0.007436424867734441,0.005408937432708658,0.8774396670184492,0.00510331369430709,0.02031076972784107,17,17,0.0042778938992673226,0.010727512818578339,0.0031312634111003686,0.008230182915061944
exymax_tw2,RBF,0.006157961847052086,0.004416286581969921,0.9473093785448177,0.004291492390260243,0.013294384597171374,17,17,0.0035144583237312014,0.008229820207481,0.0023972245010017365,0.006630193632618459
exymax_tw3,RBF,0.014825543381405121,0.010111602992618743,0.8722220511693421,0.010842150223714337,0.037393030995479935,17,17,0.006508907219633948,0.020927567501235613,0.005507935722115533,0.015552299772034426
tfmmax_tw1,RBF,13.535048705883563,9.638724182439873,0.9206684308043329,9.502238662835676,30.315836756672297,17,17,6.830377732009448,18.468627445372462,5.544589260142951,14.561799770224631
tfmmax_tw2,RBF,10.550600900172782,9.255531400866417,0.9542971847583606,5.064614283664893,20.194297384178213,17,17,7.736746122146911,13.126816556585856,6.917248734728647,11.77325486752261
tfmmax_tw3,RBF,12.616008083278315,10.748016553874871,0.970981304207366,6.6063454432065205,25.083514677047617,17,17,8.943462430079162,15.84161465119677,7.721736417200763,13.99843002126127
tfmmax_frame,RBF,2.7674325760511103,2.31861941266198,0.9764237351019516,1.5108564068818382,5.778578388399453,17,17,1.9610755060099727,3.493342040074415,1.660394406633257,3.035769088758838
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/nested_cv_rbf_epsilon_frequency_3W_B29_H45_it1.csv 0 → 100644
epsilon_factor,frequency,percentage,output,N
2.0,15,88.24,exymax_tw1,17
1.0,1,5.88,exymax_tw1,17
0.5,1,5.88,exymax_tw1,17
2.0,13,76.47,exymax_tw2,17
1.0,2,11.76,exymax_tw2,17
0.5,2,11.76,exymax_tw2,17
2.0,12,70.59,exymax_tw3,17
1.0,5,29.41,exymax_tw3,17
2.0,11,64.71,tfmmax_tw1,17
1.0,6,35.29,tfmmax_tw1,17
2.0,14,82.35,tfmmax_tw2,17
1.0,2,11.76,tfmmax_tw2,17
0.5,1,5.88,tfmmax_tw2,17
2.0,14,82.35,tfmmax_tw3,17
1.0,3,17.65,tfmmax_tw3,17
2.0,15,88.24,tfmmax_frame,17
1.0,2,11.76,tfmmax_frame,17
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/nested_cv_rbf_function_frequency_3W_B29_H45_it1.csv 0 → 100644
rbf_function,frequency,percentage,output,N
multiquadric,16,94.12,exymax_tw1,17
inverse,1,5.88,exymax_tw1,17
multiquadric,16,94.12,exymax_tw2,17
gaussian,1,5.88,exymax_tw2,17
multiquadric,12,70.59,exymax_tw3,17
inverse,3,17.65,exymax_tw3,17
gaussian,2,11.76,exymax_tw3,17
multiquadric,13,76.47,tfmmax_tw1,17
inverse,3,17.65,tfmmax_tw1,17
gaussian,1,5.88,tfmmax_tw1,17
multiquadric,17,100.0,tfmmax_tw2,17
inverse,9,52.94,tfmmax_tw3,17
multiquadric,5,29.41,tfmmax_tw3,17
gaussian,3,17.65,tfmmax_tw3,17
inverse,10,58.82,tfmmax_frame,17
multiquadric,5,29.41,tfmmax_frame,17
gaussian,2,11.76,tfmmax_frame,17
Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/nested_cv_rbf_smooth_frequency_3W_B29_H45_it1.csv 0 → 100644
smooth,frequency,percentage,output,N
0.001,15,88.24,exymax_tw1,17
0.0,2,11.76,exymax_tw1,17
0.001,14,82.35,exymax_tw2,17
0.0,2,11.76,exymax_tw2,17
1e-08,1,5.88,exymax_tw2,17
0.001,7,41.18,exymax_tw3,17
1e-05,5,29.41,exymax_tw3,17
0.0,5,29.41,exymax_tw3,17
0.0,11,64.71,tfmmax_tw1,17
0.001,4,23.53,tfmmax_tw1,17
1e-05,2,11.76,tfmmax_tw1,17
0.0,8,47.06,tfmmax_tw2,17
0.001,6,35.29,tfmmax_tw2,17
1e-05,3,17.65,tfmmax_tw2,17
0.0,11,64.71,tfmmax_tw3,17
1e-05,3,17.65,tfmmax_tw3,17
0.001,3,17.65,tfmmax_tw3,17
0.0,9,52.94,tfmmax_frame,17
1e-05,5,29.41,tfmmax_frame,17
0.001,2,11.76,tfmmax_frame,17
1e-08,1,5.88,tfmmax_frame,17
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it0/local_oof_errors_3W_B34_H45_it0.csv 0 → 100644
output,sample_index,x1,x2,x3,y_true,y_pred_oof,abs_error_oof,positive_underprediction_oof
exymax_tw1,0,8.93,10.69,11.92,0.0300764,0.026594928545530083,0.0034814714544699164,0.0034814714544699164
exymax_tw1,1,14.65,7.11,11.38,0.0055782,0.010531306327645207,0.004953106327645207,0.0
exymax_tw1,2,6.29,6.52,9.56,0.0456976,0.06018241725114393,0.014484817251143929,0.0
exymax_tw1,3,4.58,8.02,12.59,0.1001293,0.07101413691073068,0.029115163089269328,0.029115163089269328
exymax_tw1,4,10.95,6.12,10.09,0.0153468,0.011316493536164707,0.004030306463835294,0.004030306463835294
exymax_tw1,5,13.03,8.96,14.9,0.0128897,0.0022038197806511506,0.01068588021934885,0.01068588021934885
exymax_tw1,6,15.45,11.1,6.67,0.0018112,-7.125855220929136e-05,0.0018824585522092914,0.0018824585522092914
exymax_tw1,7,14.25,13.56,5.6,0.0018189,0.003626291607419757,0.001807391607419757,0.0
exymax_tw1,8,5.22,14.05,8.76,0.0823355,0.06613166635672524,0.016203833643274768,0.016203833643274768
exymax_tw1,9,12.69,4.24,7.49,0.0078472,0.009298850176982842,0.001451650176982842,0.0
exymax_tw1,10,7.82,9.84,14.42,0.0406728,0.05434285916505956,0.013670059165059555,0.0
exymax_tw1,11,5.72,11.78,13.37,0.0817119,0.08191747847886732,0.00020557847886731873,0.0
exymax_tw1,12,10.54,4.85,8.21,0.0146728,0.012488343356836573,0.0021844566431634266,0.0021844566431634266
exymax_tw1,13,7.07,15.26,4.16,0.022831,0.047025464160672215,0.024194464160672214,0.0
exymax_tw1,14,11.94,14.8,15.93,0.0246182,0.01952617315586736,0.005092026844132638,0.005092026844132638
exymax_tw1,15,9.81,12.65,4.96,0.0098081,0.010031040995206214,0.0002229409952062139,0.0
exymax_tw2,0,8.93,10.69,11.92,0.0382345,0.02985316935031325,0.008381330649686748,0.008381330649686748
exymax_tw2,1,14.65,7.11,11.38,0.0762335,0.06923763102505985,0.006995868974940145,0.006995868974940145
exymax_tw2,2,6.29,6.52,9.56,0.0687318,0.07743722365741514,0.00870542365741514,0.0
exymax_tw2,3,4.58,8.02,12.59,0.0733091,0.058265113968839426,0.015043986031160576,0.015043986031160576
exymax_tw2,4,10.95,6.12,10.09,0.0804918,0.08249963082323744,0.0020078308232374387,0.0
exymax_tw2,5,13.03,8.96,14.9,0.0629427,0.05982529683282589,0.0031174031671741126,0.0031174031671741126
exymax_tw2,6,15.45,11.1,6.67,0.0258942,0.044604648112610036,0.018710448112610037,0.0
exymax_tw2,7,14.25,13.56,5.6,0.0244975,0.015532986551153982,0.008964513448846016,0.008964513448846016
exymax_tw2,8,5.22,14.05,8.76,0.0200708,0.020977126984303266,0.0009063269843032663,0.0
exymax_tw2,9,12.69,4.24,7.49,0.1121302,0.10279782548161397,0.009332374518386033,0.009332374518386033
exymax_tw2,10,7.82,9.84,14.42,0.0456274,0.05431124238469259,0.008683842384692594,0.0
exymax_tw2,11,5.72,11.78,13.37,0.03751,0.041184458531179136,0.003674458531179134,0.0
exymax_tw2,12,10.54,4.85,8.21,0.0948819,0.09485286331481618,2.903668518382252e-05,2.903668518382252e-05
exymax_tw2,13,7.07,15.26,4.16,0.0200336,0.03264788307415479,0.01261428307415479,0.0
exymax_tw2,14,11.94,14.8,15.93,0.0585452,0.025367468228197437,0.03317773177180256,0.03317773177180256
exymax_tw2,15,9.81,12.65,4.96,0.0242279,0.01985379796846598,0.00437410203153402,0.00437410203153402
exymax_tw3,0,8.93,10.69,11.92,0.0411367,0.03724592286945594,0.0038907771305440597,0.0038907771305440597
exymax_tw3,1,14.65,7.11,11.38,0.0422645,0.05067627140437449,0.008411771404374484,0.0
exymax_tw3,2,6.29,6.52,9.56,0.0453606,0.044494600304120446,0.0008659996958795546,0.0008659996958795546
exymax_tw3,3,4.58,8.02,12.59,0.0281703,0.028541569853824278,0.0003712698538242791,0.0
exymax_tw3,4,10.95,6.12,10.09,0.0491583,0.04746862887124981,0.0016896711287501948,0.0016896711287501948
exymax_tw3,5,13.03,8.96,14.9,0.0299352,0.020917154749895417,0.009018045250104582,0.009018045250104582
exymax_tw3,6,15.45,11.1,6.67,0.1242548,0.12532392615047216,0.001069126150472166,0.0
exymax_tw3,7,14.25,13.56,5.6,0.1719441,0.16694506936267794,0.004999030637322044,0.004999030637322044
exymax_tw3,8,5.22,14.05,8.76,0.0528458,0.0754111898066745,0.022565389806674496,0.0
exymax_tw3,9,12.69,4.24,7.49,0.0833631,0.07500130924310647,0.008361790756893528,0.008361790756893528
exymax_tw3,10,7.82,9.84,14.42,0.0278009,0.03583586691415758,0.008034966914157577,0.0
exymax_tw3,11,5.72,11.78,13.37,0.0294146,0.019025846104975666,0.010388753895024333,0.010388753895024333
exymax_tw3,12,10.54,4.85,8.21,0.069075,0.07035956518129982,0.0012845651812998238,0.0
exymax_tw3,13,7.07,15.26,4.16,0.1829432,0.14217121125491977,0.04077198874508023,0.04077198874508023
exymax_tw3,14,11.94,14.8,15.93,0.0292621,0.05244195119984352,0.023179851199843518,0.0
exymax_tw3,15,9.81,12.65,4.96,0.1771025,0.16723441874810163,0.009868081251898364,0.009868081251898364
tfmmax_tw1,0,8.93,10.69,11.92,60.2223,52.70811323707596,7.514186762924034,7.514186762924034
tfmmax_tw1,1,14.65,7.11,11.38,5.2262,5.298560097628979,0.07236009762897844,0.0
tfmmax_tw1,2,6.29,6.52,9.56,91.383,118.49775658405474,27.114756584054746,0.0
tfmmax_tw1,3,4.58,8.02,12.59,200.3137,149.2715618652462,51.0421381347538,51.0421381347538
tfmmax_tw1,4,10.95,6.12,10.09,18.4505,17.71118622981976,0.7393137701802424,0.7393137701802424
tfmmax_tw1,5,13.03,8.96,14.9,14.4752,4.326219037075134,10.148980962924865,10.148980962924865
tfmmax_tw1,6,15.45,11.1,6.67,1.0483,-0.6367922717050902,1.6850922717050902,1.6850922717050902
tfmmax_tw1,7,14.25,13.56,5.6,0.8163,-0.47961867094204536,1.2959186709420454,1.2959186709420454
tfmmax_tw1,8,5.22,14.05,8.76,167.3135,126.37282294831377,40.94067705168624,40.94067705168624
tfmmax_tw1,9,12.69,4.24,7.49,9.0268,16.0415152090809,7.014715209080899,0.0
tfmmax_tw1,10,7.82,9.84,14.42,88.2524,104.41789868107149,16.165498681071497,0.0
tfmmax_tw1,11,5.72,11.78,13.37,164.9613,174.4952200070536,9.53392000705361,0.0
tfmmax_tw1,12,10.54,4.85,8.21,18.087,14.035114096087284,4.051885903912716,4.051885903912716
tfmmax_tw1,13,7.07,15.26,4.16,40.6132,89.74047897437504,49.12727897437504,0.0
tfmmax_tw1,14,11.94,14.8,15.93,42.4084,34.59463586073768,7.813764139262318,7.813764139262318
tfmmax_tw1,15,9.81,12.65,4.96,13.1861,-8.793883618219581,21.97998361821958,21.97998361821958
tfmmax_tw2,0,8.93,10.69,11.92,72.8627,67.92381671906043,4.938883280939578,4.938883280939578
tfmmax_tw2,1,14.65,7.11,11.38,140.1637,139.37575084591072,0.78794915408929,0.78794915408929
tfmmax_tw2,2,6.29,6.52,9.56,145.0081,139.39868123003225,5.6094187699677605,5.6094187699677605
tfmmax_tw2,3,4.58,8.02,12.59,124.4333,123.04484348467913,1.3884565153208683,1.3884565153208683
tfmmax_tw2,4,10.95,6.12,10.09,160.4279,157.9238321096626,2.5040678903373816,2.5040678903373816
tfmmax_tw2,5,13.03,8.96,14.9,117.4921,118.25913542676392,0.7670354267639254,0.0
tfmmax_tw2,6,15.45,11.1,6.67,54.4047,76.91186150444992,22.50716150444992,0.0
tfmmax_tw2,7,14.25,13.56,5.6,47.323,25.151084244706908,22.171915755293092,22.171915755293092
tfmmax_tw2,8,5.22,14.05,8.76,32.8734,35.39580600441649,2.5224060044164958,0.0
tfmmax_tw2,9,12.69,4.24,7.49,227.631,214.6318070948737,12.999192905126307,12.999192905126307
tfmmax_tw2,10,7.82,9.84,14.42,97.9154,98.12304343572896,0.20764343572895427,0.0
tfmmax_tw2,11,5.72,11.78,13.37,67.0263,66.27671774119952,0.749582258800487,0.749582258800487
tfmmax_tw2,12,10.54,4.85,8.21,194.0625,199.17803382482373,5.1155338248237285,0.0
tfmmax_tw2,13,7.07,15.26,4.16,37.3301,42.76364007036307,5.433540070363065,0.0
tfmmax_tw2,14,11.94,14.8,15.93,87.8423,60.74305467277145,27.099245327228545,27.099245327228545
tfmmax_tw2,15,9.81,12.65,4.96,46.0884,49.69728035802743,3.6088803580274273,0.0
tfmmax_tw3,0,8.93,10.69,11.92,75.235,79.60019383285089,4.365193832850892,0.0
tfmmax_tw3,1,14.65,7.11,11.38,77.0429,86.94100176115558,9.898101761155573,0.0
tfmmax_tw3,2,6.29,6.52,9.56,87.44,79.43868749255853,8.001312507441469,8.001312507441469
tfmmax_tw3,3,4.58,8.02,12.59,48.8154,59.118879984835985,10.303479984835988,0.0
tfmmax_tw3,4,10.95,6.12,10.09,88.775,84.23958165119271,4.535418348807298,4.535418348807298
tfmmax_tw3,5,13.03,8.96,14.9,54.9076,50.48385640495326,4.423743595046744,4.423743595046744
tfmmax_tw3,6,15.45,11.1,6.67,193.3375,187.68926495023652,5.6482350497634854,5.6482350497634854
tfmmax_tw3,7,14.25,13.56,5.6,253.1506,252.7412706649916,0.4093293350084082,0.4093293350084082
tfmmax_tw3,8,5.22,14.05,8.76,103.9524,147.01484687685115,43.062446876851155,0.0
tfmmax_tw3,9,12.69,4.24,7.49,120.0323,110.9372383157745,9.095061684225513,9.095061684225513
tfmmax_tw3,10,7.82,9.84,14.42,53.6332,58.4977373707934,4.8645373707934,0.0
tfmmax_tw3,11,5.72,11.78,13.37,55.0584,39.9298118674983,15.128588132501697,15.128588132501697
tfmmax_tw3,12,10.54,4.85,8.21,110.4195,113.39871525119997,2.9792152511999745,0.0
tfmmax_tw3,13,7.07,15.26,4.16,284.6548,220.80218835479556,63.85261164520446,63.85261164520446
tfmmax_tw3,14,11.94,14.8,15.93,64.2884,80.54369831684903,16.255298316849036,0.0
tfmmax_tw3,15,9.81,12.65,4.96,263.3107,273.1332483337436,9.822548333743612,0.0
tfmmax_frame,0,8.93,10.69,11.92,67.7267,68.48806694681167,0.7613669468116768,0.0
tfmmax_frame,1,14.65,7.11,11.38,70.961,73.55929751831422,2.598297518314226,0.0
tfmmax_frame,2,6.29,6.52,9.56,58.5602,60.74999784956253,2.189797849562531,0.0
tfmmax_frame,3,4.58,8.02,12.59,51.4282,49.75305482509815,1.67514517490185,1.67514517490185
tfmmax_frame,4,10.95,6.12,10.09,69.1554,63.52290739080763,5.632492609192369,5.632492609192369
tfmmax_frame,5,13.03,8.96,14.9,71.8219,68.04226925457942,3.779630745420576,3.779630745420576
tfmmax_frame,6,15.45,11.1,6.67,102.1931,97.43138615144039,4.761713848559609,4.761713848559609
tfmmax_frame,7,14.25,13.56,5.6,114.4571,118.58348177413723,4.126381774137229,0.0
tfmmax_frame,8,5.22,14.05,8.76,70.3458,71.22594124301804,0.8801412430180449,0.0
tfmmax_frame,9,12.69,4.24,7.49,67.6932,67.41522929758355,0.2779707024164537,0.2779707024164537
tfmmax_frame,10,7.82,9.84,14.42,59.26,60.57327518863406,1.3132751886340586,0.0
tfmmax_frame,11,5.72,11.78,13.37,52.032,51.1295798813278,0.9024201186721967,0.9024201186721967
tfmmax_frame,12,10.54,4.85,8.21,67.1494,67.47673718658916,0.3273371865891619,0.0
tfmmax_frame,13,7.07,15.26,4.16,113.7973,109.23851289066727,4.558787109332741,4.558787109332741
tfmmax_frame,14,11.94,14.8,15.93,66.8746,61.85817032361682,5.016429676383183,5.016429676383183
tfmmax_frame,15,9.81,12.65,4.96,113.2492,112.77925619445352,0.4699438055464782,0.4699438055464782
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it0/validation_envelopes_3W_B34_H45_it0.csv 0 → 100644
n_unique_oof_samples,envelope_reference_n,percentile,small_n_threshold,abs_error_envelope,abs_error_rule,damage_safety_margin,damage_safety_margin_rule,abs_error_p95,abs_error_max,underprediction_p95,underprediction_max,mean_abs_error,output,method
16,16,95.0,20,0.029115163089269328,max_abs_oof_error_small_n,0.029115163089269328,max_positive_oof_residual_small_n,0.025424638892821493,0.029115163089269328,0.019431666004773408,0.029115163089269328,0.008354100317043783,exymax_tw1,RBF
16,16,95.0,20,0.03317773177180256,max_abs_oof_error_small_n,0.03317773177180256,max_positive_oof_residual_small_n,0.02232726902740817,0.03317773177180256,0.019577422466321073,0.03317773177180256,0.009044935052894151,exymax_tw2,RBF
16,16,95.0,20,0.04077198874508023,max_abs_oof_error_small_n,0.04077198874508023,max_positive_oof_residual_small_n,0.027577885586152696,0.04077198874508023,0.017984562607538307,0.04077198874508023,0.009673192437633952,exymax_tw3,RBF
16,16,95.0,20,51.0421381347538,max_abs_oof_error_small_n,51.0421381347538,max_positive_oof_residual_small_n,49.60599376446973,51.0421381347538,43.46604232245313,51.0421381347538,16.01502942748598,tfmmax_tw1,RBF
16,16,95.0,20,27.099245327228545,max_abs_oof_error_small_n,27.099245327228545,max_positive_oof_residual_small_n,23.655182460144573,27.099245327228545,23.403748148276954,27.099245327228545,7.400682030104802,tfmmax_tw2,RBF
16,16,95.0,20,63.85261164520446,max_abs_oof_error_small_n,63.85261164520446,max_positive_oof_residual_small_n,48.25998806893948,63.85261164520446,27.309594010677387,63.85261164520446,13.290320126642419,tfmmax_tw3,RBF
16,16,95.0,20,5.632492609192369,max_abs_oof_error_small_n,5.632492609192369,max_positive_oof_residual_small_n,5.170445409585479,5.632492609192369,5.170445409585479,5.632492609192369,2.454445718593274,tfmmax_frame,RBF
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/final_model_selection_3W_B34_H45_it1.csv 0 → 100644
output,N,selected_function,selected_epsilon_factor,selected_smooth,fitted_epsilon,inner_cv_rmse,inner_cv_rmse_dispersion,model_path,best_params
exymax_tw1,17,multiquadric,2.0,0.001,4.933715377188262,0.010974605850825329,0.2768516141315062,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_exymax_tw1.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 0.001}"
exymax_tw2,17,multiquadric,0.5,0.001,1.2334288442970656,0.010275585278795919,0.4312695801469449,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_exymax_tw2.joblib,"{""rbf__epsilon_factor"": 0.5, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 0.001}"
exymax_tw3,17,gaussian,2.0,1e-05,4.933715377188262,0.010683144226339626,0.7956673559070129,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_exymax_tw3.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""gaussian"", ""rbf__smooth"": 1e-05}"
tfmmax_tw1,17,multiquadric,2.0,1e-05,4.933715377188262,16.10282195669235,0.38547564406338325,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_tfmmax_tw1.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""multiquadric"", ""rbf__smooth"": 1e-05}"
tfmmax_tw2,17,gaussian,2.0,0.0,4.933715377188262,10.851432788935238,0.40351631638512697,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_tfmmax_tw2.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""gaussian"", ""rbf__smooth"": 0.0}"
tfmmax_tw3,17,gaussian,2.0,0.0,4.933715377188262,7.342281503479441,0.5308762494648005,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_tfmmax_tw3.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""gaussian"", ""rbf__smooth"": 0.0}"
tfmmax_frame,17,inverse,2.0,1e-05,4.933715377188262,2.4784724856939366,0.17674636877785252,../../models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_tfmmax_frame.joblib,"{""rbf__epsilon_factor"": 2.0, ""rbf__function"": ""inverse"", ""rbf__smooth"": 1e-05}"
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/nested_cv_fold_selections_3W_B34_H45_it1.csv 0 → 100644
outer_fold,selected_function,selected_epsilon_factor,selected_smooth,fitted_epsilon,inner_cv_rmse,inner_cv_rmse_dispersion
0,multiquadric,2.0,0.0,4.931720895569254,0.009664310676838702,0.49460344883997276
1,inverse,2.0,1e-05,4.9240989677705045,0.008092309305842964,0.5037537715114043
2,multiquadric,2.0,0.001,4.926696317780376,0.008891262822694267,0.6476964502314119
3,multiquadric,2.0,1e-05,4.9176338973310525,0.009054357514143515,0.6778620320095747
4,inverse,1.0,0.0,2.481712272071719,0.011609391781283368,0.32786058023736303
5,multiquadric,2.0,1e-05,4.9378380678400156,0.009628513032027042,0.3957313025540563
6,multiquadric,2.0,0.001,4.912410346462185,0.013324347362877857,0.2898883810723782
7,multiquadric,2.0,0.001,4.8435723061900635,0.009188421156989555,0.5676281787873869
8,inverse,1.0,0.0,2.4803891001446434,0.008011966622100578,0.5143457240496966
9,inverse,2.0,0.0,4.99352695892402,0.009164497648786333,0.5705007623034545
10,multiquadric,2.0,0.001,4.929657320713167,0.010284000443418016,0.4852955254964288
11,multiquadric,2.0,0.001,4.928529056456992,0.014051143520869825,0.341946095596065
12,multiquadric,2.0,0.001,5.027511972806831,0.009975092900967928,0.6522015180105969
13,multiquadric,1.0,0.001,2.4448723932909178,0.008273742554334104,0.4732937638034557
14,inverse,2.0,0.0,4.932065943992928,0.012245970811248076,0.7784768026155378
15,inverse,2.0,0.0,4.897625284890629,0.011248427818504773,0.3661387369101165
16,inverse,0.5,0.0,1.2395094081100626,0.014589195123867294,0.8214024517340939
0,multiquadric,0.5,0.001,1.2329302238923134,0.010640011904477697,0.4329419678231551
1,linear,1.0,0.0,,0.012641322976868435,0.48472715793650556
2,linear,1.0,0.0,,0.009058212924126873,0.38940392154899084
3,gaussian,1.0,0.001,2.4588169486655262,0.00852559909685852,0.38253371847746137
4,gaussian,2.0,0.0,4.963424544143438,0.007523582525640738,0.6683192519760995
5,linear,1.0,0.0,,0.009761894513454711,0.3832941223360111
6,multiquadric,0.5,0.0,1.2281025866155462,0.009492978752621093,0.4835688818204183
7,multiquadric,0.5,0.0,1.2108930765475159,0.011250267529600455,0.4818562373484195
8,gaussian,1.0,0.001,2.4803891001446434,0.006716284936123136,0.7102924047177482
9,multiquadric,0.5,0.001,1.248381739731005,0.010634070036060688,0.6239465276059585
10,linear,1.0,0.0,,0.009486236109151919,0.3867949454768031
11,gaussian,2.0,1e-05,4.928529056456992,0.010447094961080212,0.6486538807547299
12,multiquadric,0.5,0.0,1.2568779932017078,0.009558116184952165,0.4808438105211512
13,multiquadric,0.5,0.0,1.2224361966454589,0.009087077371636092,0.4491942990039465
14,thin_plate,1.0,0.001,,0.00902623687404074,0.6124013607435248
15,multiquadric,0.5,0.001,1.2244063212226572,0.010529098008401109,0.16302291515673187
16,linear,1.0,0.0,,0.009135635440062038,0.388291090056172
0,multiquadric,2.0,1e-05,4.931720895569254,0.008395448326453295,0.3411265818129931
1,inverse,2.0,0.0,4.9240989677705045,0.012223536560736858,0.710953614250301
2,multiquadric,2.0,0.0,4.926696317780376,0.007426954944327433,0.34079092469551703
3,inverse,2.0,0.0,4.9176338973310525,0.009023218443079543,0.8867399591381336
4,multiquadric,2.0,0.001,4.963424544143438,0.010478255590583494,0.4962634578087626
5,gaussian,2.0,1e-05,4.9378380678400156,0.008828698171331254,0.5871941304306145
6,gaussian,2.0,0.0,4.912410346462185,0.012611288866635142,0.7424620999968251
7,gaussian,2.0,0.0,4.8435723061900635,0.008850072305550682,0.5663390022063169
8,inverse,2.0,1e-05,4.960778200289287,0.00702780349094527,0.41809300192250526
9,gaussian,2.0,0.0,4.99352695892402,0.014192011273315822,0.6498528575369056
10,gaussian,2.0,1e-05,4.929657320713167,0.009146000364141607,0.3783437831711946
11,gaussian,2.0,1e-05,4.928529056456992,0.0064996094606203925,0.46426829292149624
12,multiquadric,2.0,1e-05,5.027511972806831,0.010078141197931025,0.20827147107241145
13,inverse,2.0,1e-05,4.8897447865818355,0.01272942782494672,0.6055751722306844
14,gaussian,2.0,1e-05,4.932065943992928,0.006641732476583869,0.8951254967721338
15,multiquadric,2.0,1e-05,4.897625284890629,0.017752438910194256,0.8547962873831783
16,multiquadric,2.0,0.001,4.95803763244025,0.0134200119695199,0.43698383742771657
0,multiquadric,2.0,0.0,4.931720895569254,18.342143413095133,0.22144388743763532
1,inverse,2.0,1e-05,4.9240989677705045,13.552880394580438,0.5104069837893944
2,multiquadric,2.0,0.001,4.926696317780376,15.910650699692349,0.6920172698160828
3,gaussian,2.0,1e-05,4.9176338973310525,10.494227143022817,1.008373881434677
4,multiquadric,2.0,0.0,4.963424544143438,17.12976870081898,0.3476827070809829
5,multiquadric,2.0,0.0,4.9378380678400156,15.54400896812813,0.409423755077995
6,multiquadric,2.0,1e-05,4.912410346462185,23.062637770916137,0.11905311440663154
7,multiquadric,2.0,0.0,4.8435723061900635,14.261990814506344,0.5191598128117291
8,inverse,1.0,0.0,2.4803891001446434,12.550334612574053,0.6360328905960411
9,multiquadric,2.0,0.0,4.99352695892402,12.512916297961482,0.4653942287277051
10,multiquadric,2.0,0.001,4.929657320713167,19.43096204708788,0.5008405476308894
11,multiquadric,2.0,0.0,4.928529056456992,20.767691764419194,0.33816970459204493
12,multiquadric,2.0,0.001,5.027511972806831,18.40227710981457,0.6745781177768597
13,multiquadric,1.0,0.0,2.4448723932909178,12.733265349009462,0.30865825972600713
14,inverse,2.0,1e-05,4.932065943992928,19.934293573175246,0.8911654773410052
15,gaussian,2.0,0.0,4.897625284890629,17.16113875642555,0.40551394742551716
16,multiquadric,2.0,0.0,4.95803763244025,24.89432239029293,0.411482547192123
0,multiquadric,2.0,1e-08,4.931720895569254,11.909287584726533,0.7384874405647331
1,multiquadric,2.0,0.001,4.9240989677705045,17.531378300739814,0.4831886489987625
2,gaussian,2.0,1e-05,4.926696317780376,11.288255220044068,0.5398263444267609
3,gaussian,2.0,1e-05,4.9176338973310525,8.753023835512584,0.3227305051231936
4,gaussian,2.0,1e-05,4.963424544143438,7.714617980751439,0.4331764554651107
5,multiquadric,0.5,0.001,1.2344595169600039,12.77581962527089,0.4268061985391917
6,multiquadric,2.0,1e-05,4.912410346462185,7.109024804729115,0.34305767614988947
7,gaussian,2.0,1e-05,4.8435723061900635,11.43904276730198,0.4888193933308074
8,gaussian,2.0,0.0,4.960778200289287,6.728694351566292,0.5699225611747581
9,multiquadric,2.0,0.0,4.99352695892402,7.768393477986093,0.6874743532566795
10,multiquadric,0.5,0.001,1.2324143301782917,12.739836507980751,0.2707556071859926
11,multiquadric,2.0,1e-05,4.928529056456992,9.145737972308444,0.7062186653071283
12,multiquadric,2.0,0.001,5.027511972806831,12.453995686516942,0.7059999861741599
13,gaussian,2.0,1e-05,4.8897447865818355,9.64114433858887,0.421671369649702
14,multiquadric,2.0,1e-05,4.932065943992928,9.243078876378437,0.4290202152708926
15,multiquadric,1.0,0.0,2.4488126424453145,12.73882780853384,0.5300120267044158
16,multiquadric,2.0,1e-05,4.95803763244025,9.635246408723791,0.6025454838435148
0,multiquadric,2.0,1e-05,4.931720895569254,9.352178848205682,0.4009896420871044
1,gaussian,2.0,0.0,4.9240989677705045,8.603989770068992,0.20414866277357824
2,multiquadric,2.0,0.0,4.926696317780376,8.09570875367735,0.5233632868641802
3,inverse,2.0,0.0,4.9176338973310525,7.251720993619489,0.9487294042664276
4,multiquadric,2.0,1e-05,4.963424544143438,8.737853261227041,0.7169759294628053
5,multiquadric,2.0,1e-05,4.9378380678400156,9.214786230246261,0.2411361395728266
6,multiquadric,2.0,1e-05,4.912410346462185,16.976614881905213,0.6655755804304611
7,inverse,2.0,0.0,4.8435723061900635,9.212944795023152,0.6633684071307352
8,inverse,2.0,0.0,4.960778200289287,5.476379611549233,0.47617568254117804
9,gaussian,2.0,1e-05,4.99352695892402,14.280035723343374,0.854752327861911
10,gaussian,2.0,0.0,4.929657320713167,10.65174526057298,0.573436520334735
11,gaussian,2.0,1e-05,4.928529056456992,7.031588602541296,0.5693650580976728
12,multiquadric,2.0,0.001,5.027511972806831,14.156515989361953,0.6309539689402903
13,gaussian,2.0,0.0,4.8897447865818355,13.802960641958325,0.4199215425246384
14,inverse,2.0,1e-05,4.932065943992928,9.194059179569717,1.116211247306908
15,multiquadric,2.0,1e-05,4.897625284890629,17.418348738325484,1.0240538693430192
16,multiquadric,2.0,0.001,4.95803763244025,13.402563684822102,0.7447549051066337
0,gaussian,2.0,0.0,4.931720895569254,2.8364567213178433,0.10275449997822941
1,gaussian,2.0,0.0,4.9240989677705045,1.8962747136450795,0.45897815985823776
2,inverse,2.0,0.0,4.926696317780376,2.1367835386963314,0.24613694040380205
3,multiquadric,2.0,1e-05,4.9176338973310525,2.0887818709300854,0.2891024115470877
4,gaussian,2.0,1e-05,4.963424544143438,2.0318930022213424,0.3143661063848014
5,inverse,2.0,0.0,4.9378380678400156,3.149803447191478,0.3392591950681182
6,gaussian,2.0,1e-05,4.912410346462185,3.1420527911487257,0.7572673646058077
7,inverse,2.0,1e-05,4.8435723061900635,1.8748914329130517,0.49910840678405877
8,gaussian,2.0,1e-05,4.960778200289287,1.966382096125539,0.483086801893101
9,multiquadric,1.0,0.0,2.49676347946201,3.9368490824779245,0.7673171536959166
10,inverse,2.0,1e-05,4.929657320713167,2.6704636621423923,0.2668789712448423
11,multiquadric,2.0,1e-05,4.928529056456992,2.4800170494029743,0.4169701652607097
12,inverse,2.0,0.0,5.027511972806831,3.244742625775662,0.3679787530015053
13,inverse,2.0,0.0,4.8897447865818355,2.873600369873832,0.5586598653197663
14,multiquadric,1.0,0.001,2.466032971996464,4.076970529572945,0.540657797484287
15,gaussian,2.0,0.0,4.897625284890629,3.6554945598282194,0.6914332644554965
16,inverse,2.0,0.0,4.95803763244025,2.7627134200189913,0.2114133572172932
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/nested_cv_outer_predictions_3W_B34_H45_it1.csv 0 → 100644
output,N,outer_fold,sample_index,y_true,prediction_RBF,selected_rbf_function,selected_epsilon_factor,selected_smooth,fitted_epsilon
exymax_tw1,17,0,0,0.0300764,0.026586807179612038,multiquadric,2.0,0.0,4.931720895569254
exymax_tw1,17,1,1,0.0055782,0.011191203352655776,inverse,2.0,1e-05,4.9240989677705045
exymax_tw1,17,2,2,0.0456976,0.060057213717803926,multiquadric,2.0,0.001,4.926696317780376
exymax_tw1,17,3,3,0.1001293,0.07920346124778632,multiquadric,2.0,1e-05,4.9176338973310525
exymax_tw1,17,4,4,0.0153468,0.012412621876340235,inverse,1.0,0.0,2.481712272071719
exymax_tw1,17,5,5,0.0128897,-0.0012914267304324767,multiquadric,2.0,1e-05,4.9378380678400156
exymax_tw1,17,6,6,0.0018112,0.0033636227430076104,multiquadric,2.0,0.001,4.912410346462185
exymax_tw1,17,7,7,0.0018189,-0.00047659456267901135,multiquadric,2.0,0.001,4.8435723061900635
exymax_tw1,17,8,8,0.0823355,0.06395667017362827,inverse,1.0,0.0,2.4803891001446434
exymax_tw1,17,9,9,0.0078472,0.011610418179821558,inverse,2.0,0.0,4.99352695892402
exymax_tw1,17,10,10,0.0406728,0.055604513248216136,multiquadric,2.0,0.001,4.929657320713167
exymax_tw1,17,11,11,0.0817119,0.08165703673435232,multiquadric,2.0,0.001,4.928529056456992
exymax_tw1,17,12,12,0.0146728,0.012407387832920946,multiquadric,2.0,0.001,5.027511972806831
exymax_tw1,17,13,13,0.022831,0.04069752446903596,multiquadric,1.0,0.001,2.4448723932909178
exymax_tw1,17,14,14,0.0246182,0.022188374388266485,inverse,2.0,0.0,4.932065943992928
exymax_tw1,17,15,15,0.0098081,0.004244951223928872,inverse,2.0,0.0,4.897625284890629
exymax_tw1,17,16,16,0.0427069,0.052414792263378585,inverse,0.5,0.0,1.2395094081100626
exymax_tw2,17,0,0,0.0382345,0.03375676372586661,multiquadric,0.5,0.001,1.2329302238923134
exymax_tw2,17,1,1,0.0762335,0.07499886854411732,linear,1.0,0.0,
exymax_tw2,17,2,2,0.0687318,0.07450253396231848,linear,1.0,0.0,
exymax_tw2,17,3,3,0.0733091,0.058277934380900454,gaussian,1.0,0.001,2.4588169486655262
exymax_tw2,17,4,4,0.0804918,0.08093241050644995,gaussian,2.0,0.0,4.963424544143438
exymax_tw2,17,5,5,0.0629427,0.06933337673005445,linear,1.0,0.0,
exymax_tw2,17,6,6,0.0258942,0.04457859900376595,multiquadric,0.5,0.0,1.2281025866155462
exymax_tw2,17,7,7,0.0244975,0.015845819293434593,multiquadric,0.5,0.0,1.2108930765475159
exymax_tw2,17,8,8,0.0200708,0.02032573128286376,gaussian,1.0,0.001,2.4803891001446434
exymax_tw2,17,9,9,0.1121302,0.10277435343029834,multiquadric,0.5,0.001,1.248381739731005
exymax_tw2,17,10,10,0.0456274,0.054913808028125136,linear,1.0,0.0,
exymax_tw2,17,11,11,0.03751,0.03890721823318799,gaussian,2.0,1e-05,4.928529056456992
exymax_tw2,17,12,12,0.0948819,0.09669903068610741,multiquadric,0.5,0.0,1.2568779932017078
exymax_tw2,17,13,13,0.0200336,0.027026658504045287,multiquadric,0.5,0.0,1.2224361966454589
exymax_tw2,17,14,14,0.0585452,-0.002740347873172122,thin_plate,1.0,0.001,
exymax_tw2,17,15,15,0.0242279,0.023103616773173352,multiquadric,0.5,0.001,1.2244063212226572
exymax_tw2,17,16,16,0.046008,0.046191294315376955,linear,1.0,0.0,
exymax_tw3,17,0,0,0.0411367,0.03914483592103579,multiquadric,2.0,1e-05,4.931720895569254
exymax_tw3,17,1,1,0.0422645,0.048197700687856226,inverse,2.0,0.0,4.9240989677705045
exymax_tw3,17,2,2,0.0453606,0.04488379104084572,multiquadric,2.0,0.0,4.926696317780376
exymax_tw3,17,3,3,0.0281703,0.030167831528302713,inverse,2.0,0.0,4.9176338973310525
exymax_tw3,17,4,4,0.0491583,0.052502867611313064,multiquadric,2.0,0.001,4.963424544143438
exymax_tw3,17,5,5,0.0299352,0.017350293779657022,gaussian,2.0,1e-05,4.9378380678400156
exymax_tw3,17,6,6,0.1242548,0.1252704256726247,gaussian,2.0,0.0,4.912410346462185
exymax_tw3,17,7,7,0.1719441,0.1691131429233188,gaussian,2.0,0.0,4.8435723061900635
exymax_tw3,17,8,8,0.0528458,0.08184900551704533,inverse,2.0,1e-05,4.960778200289287
exymax_tw3,17,9,9,0.0833631,0.07728419101550443,gaussian,2.0,0.0,4.99352695892402
exymax_tw3,17,10,10,0.0278009,0.03552460507724042,gaussian,2.0,1e-05,4.929657320713167
exymax_tw3,17,11,11,0.0294146,0.01944620835277533,gaussian,2.0,1e-05,4.928529056456992
exymax_tw3,17,12,12,0.069075,0.07015123894615272,multiquadric,2.0,1e-05,5.027511972806831
exymax_tw3,17,13,13,0.1829432,0.16133954869952216,inverse,2.0,1e-05,4.8897447865818355
exymax_tw3,17,14,14,0.0292621,0.053637841310626655,gaussian,2.0,1e-05,4.932065943992928
exymax_tw3,17,15,15,0.1771025,0.17006192935238573,multiquadric,2.0,1e-05,4.897625284890629
exymax_tw3,17,16,16,0.0482397,0.04819563566038809,multiquadric,2.0,0.001,4.95803763244025
tfmmax_tw1,17,0,0,60.2223,55.34735313213605,multiquadric,2.0,0.0,4.931720895569254
tfmmax_tw1,17,1,1,5.2262,6.575292559939953,inverse,2.0,1e-05,4.9240989677705045
tfmmax_tw1,17,2,2,91.383,112.78110398194985,multiquadric,2.0,0.001,4.926696317780376
tfmmax_tw1,17,3,3,200.3137,163.4065992756823,gaussian,2.0,1e-05,4.9176338973310525
tfmmax_tw1,17,4,4,18.4505,20.772221435487154,multiquadric,2.0,0.0,4.963424544143438
tfmmax_tw1,17,5,5,14.4752,5.282909177534748,multiquadric,2.0,0.0,4.9378380678400156
tfmmax_tw1,17,6,6,1.0483,1.9780412465624977,multiquadric,2.0,1e-05,4.912410346462185
tfmmax_tw1,17,7,7,0.8163,2.230741305029369,multiquadric,2.0,0.0,4.8435723061900635
tfmmax_tw1,17,8,8,167.3135,122.03676679642876,inverse,1.0,0.0,2.4803891001446434
tfmmax_tw1,17,9,9,9.0268,15.843287072464591,multiquadric,2.0,0.0,4.99352695892402
tfmmax_tw1,17,10,10,88.2524,106.68589581024935,multiquadric,2.0,0.001,4.929657320713167
tfmmax_tw1,17,11,11,164.9613,172.78565114834055,multiquadric,2.0,0.0,4.928529056456992
tfmmax_tw1,17,12,12,18.087,16.953804335409586,multiquadric,2.0,0.001,5.027511972806831
tfmmax_tw1,17,13,13,40.6132,80.21242718055964,multiquadric,1.0,0.0,2.4448723932909178
tfmmax_tw1,17,14,14,42.4084,35.317889560346885,inverse,2.0,1e-05,4.932065943992928
tfmmax_tw1,17,15,15,13.1861,-2.483367260938394,gaussian,2.0,0.0,4.897625284890629
tfmmax_tw1,17,16,16,85.01217,83.83186706792185,multiquadric,2.0,0.0,4.95803763244025
tfmmax_tw2,17,0,0,72.8627,70.77858401586127,multiquadric,2.0,1e-08,4.931720895569254
tfmmax_tw2,17,1,1,140.1637,139.33314172361497,multiquadric,2.0,0.001,4.9240989677705045
tfmmax_tw2,17,2,2,145.0081,138.93040624215428,gaussian,2.0,1e-05,4.926696317780376
tfmmax_tw2,17,3,3,124.4333,129.15961336256805,gaussian,2.0,1e-05,4.9176338973310525
tfmmax_tw2,17,4,4,160.4279,157.1576219721901,gaussian,2.0,1e-05,4.963424544143438
tfmmax_tw2,17,5,5,117.4921,123.78982311362643,multiquadric,0.5,0.001,1.2344595169600039
tfmmax_tw2,17,6,6,54.4047,75.55129726594896,multiquadric,2.0,1e-05,4.912410346462185
tfmmax_tw2,17,7,7,47.323,33.4149720536152,gaussian,2.0,1e-05,4.8435723061900635
tfmmax_tw2,17,8,8,32.8734,34.0351194303621,gaussian,2.0,0.0,4.960778200289287
tfmmax_tw2,17,9,9,227.631,215.1224830343126,multiquadric,2.0,0.0,4.99352695892402
tfmmax_tw2,17,10,10,97.9154,98.97800807958828,multiquadric,0.5,0.001,1.2324143301782917
tfmmax_tw2,17,11,11,67.0263,67.29339950440408,multiquadric,2.0,1e-05,4.928529056456992
tfmmax_tw2,17,12,12,194.0625,200.04039278863092,multiquadric,2.0,0.001,5.027511972806831
tfmmax_tw2,17,13,13,37.3301,43.50627260133297,gaussian,2.0,1e-05,4.8897447865818355
tfmmax_tw2,17,14,14,87.8423,67.33029560783325,multiquadric,2.0,1e-05,4.932065943992928
tfmmax_tw2,17,15,15,46.0884,46.141505975329665,multiquadric,1.0,0.0,2.4488126424453145
tfmmax_tw2,17,16,16,87.38388,88.16030645924911,multiquadric,2.0,1e-05,4.95803763244025
tfmmax_tw3,17,0,0,75.235,74.36793881238555,multiquadric,2.0,1e-05,4.931720895569254
tfmmax_tw3,17,1,1,77.0429,87.75653921175763,gaussian,2.0,0.0,4.9240989677705045
tfmmax_tw3,17,2,2,87.44,80.49477114536421,multiquadric,2.0,0.0,4.926696317780376
tfmmax_tw3,17,3,3,48.8154,61.502007285311265,inverse,2.0,0.0,4.9176338973310525
tfmmax_tw3,17,4,4,88.775,84.17452587407024,multiquadric,2.0,1e-05,4.963424544143438
tfmmax_tw3,17,5,5,54.9076,50.278194442988024,multiquadric,2.0,1e-05,4.9378380678400156
tfmmax_tw3,17,6,6,193.3375,193.5692357691296,multiquadric,2.0,1e-05,4.912410346462185
tfmmax_tw3,17,7,7,253.1506,253.93783278441697,inverse,2.0,0.0,4.8435723061900635
tfmmax_tw3,17,8,8,103.9524,138.16039979614652,inverse,2.0,0.0,4.960778200289287
tfmmax_tw3,17,9,9,120.0323,109.4222619218126,gaussian,2.0,1e-05,4.99352695892402
tfmmax_tw3,17,10,10,53.6332,62.232469512000534,gaussian,2.0,0.0,4.929657320713167
tfmmax_tw3,17,11,11,55.0584,39.232329198450316,gaussian,2.0,1e-05,4.928529056456992
tfmmax_tw3,17,12,12,110.4195,111.91579446435344,multiquadric,2.0,0.001,5.027511972806831
tfmmax_tw3,17,13,13,284.6548,259.1204140136497,gaussian,2.0,0.0,4.8897447865818355
tfmmax_tw3,17,14,14,64.2884,83.74945647810273,inverse,2.0,1e-05,4.932065943992928
tfmmax_tw3,17,15,15,263.3107,250.32189733951236,multiquadric,2.0,1e-05,4.897625284890629
tfmmax_tw3,17,16,16,86.07454,89.19144179245995,multiquadric,2.0,0.001,4.95803763244025
tfmmax_frame,17,0,0,67.7267,67.06104520082863,gaussian,2.0,0.0,4.931720895569254
tfmmax_frame,17,1,1,70.961,73.87060676467809,gaussian,2.0,0.0,4.9240989677705045
tfmmax_frame,17,2,2,58.5602,59.03701391630557,inverse,2.0,0.0,4.926696317780376
tfmmax_frame,17,3,3,51.4282,48.899815089596814,multiquadric,2.0,1e-05,4.9176338973310525
tfmmax_frame,17,4,4,69.1554,67.75910078556558,gaussian,2.0,1e-05,4.963424544143438
tfmmax_frame,17,5,5,71.8219,67.9181799688198,inverse,2.0,0.0,4.9378380678400156
tfmmax_frame,17,6,6,102.1931,96.906033572061,gaussian,2.0,1e-05,4.912410346462185
tfmmax_frame,17,7,7,114.4571,118.18103006903834,inverse,2.0,1e-05,4.8435723061900635
tfmmax_frame,17,8,8,70.3458,72.14949358981903,gaussian,2.0,1e-05,4.960778200289287
tfmmax_frame,17,9,9,67.6932,68.82169488504462,multiquadric,1.0,0.0,2.49676347946201
tfmmax_frame,17,10,10,59.26,60.71342534564383,inverse,2.0,1e-05,4.929657320713167
tfmmax_frame,17,11,11,52.032,51.87756780453674,multiquadric,2.0,1e-05,4.928529056456992
tfmmax_frame,17,12,12,67.1494,67.55026390119542,inverse,2.0,0.0,5.027511972806831
tfmmax_frame,17,13,13,113.7973,109.52690635335648,inverse,2.0,0.0,4.8897447865818355
tfmmax_frame,17,14,14,66.8746,72.92596028087036,multiquadric,1.0,0.001,2.466032971996464
tfmmax_frame,17,15,15,113.2492,111.15327662792652,gaussian,2.0,0.0,4.897625284890629
tfmmax_frame,17,16,16,64.9438,65.38849479526789,inverse,2.0,0.0,4.95803763244025
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/nested_cv_outer_summary_3W_B34_H45_it1.csv 0 → 100644
output,method,outer_rmse,outer_mae,outer_r2,outer_std_abs_error,outer_max_abs_error,n_outer_predictions,N,outer_rmse_ci_lower,outer_rmse_ci_upper,outer_mae_ci_lower,outer_mae_ci_upper
exymax_tw1,RBF,0.010670212170058015,0.008253688153540916,0.8683735636946925,0.006762400433141512,0.020925838752213682,17,17,0.007175110354474918,0.013540299571116708,0.0051344646083193125,0.011520573422077993
exymax_tw2,RBF,0.016682171261364,0.008963491351628047,0.619656033565158,0.014069493977494999,0.06128554787317212,17,17,0.0051941631496028665,0.02689034142919865,0.0036968847139758547,0.01616972732642965
exymax_tw3,RBF,0.011809237611126676,0.00806411409445463,0.9520972128797998,0.00862717548376449,0.029003205517045327,17,17,0.006148168806023527,0.016243765987929436,0.004397818975194009,0.01247957380589518
tfmmax_tw1,RBF,19.254329760757667,13.024188803297717,0.9039833470662663,14.180963315376454,45.27673320357124,17,17,10.063514104327226,26.210091447924572,6.841183775673041,19.7931395188544
tfmmax_tw2,RBF,9.10565104899492,6.284520819497581,0.9726647526933214,6.58920929212024,21.146597265948962,17,17,4.9546354929372685,12.400981631191463,3.3391851871381273,9.51932195478056
tfmmax_tw3,RBF,13.691048879318714,10.19424731443798,0.9678605647323715,9.139044868473302,34.207999796146524,17,17,8.183820424401894,18.42422811444124,6.25550903986913,14.643045792561706
tfmmax_frame,RBF,2.877990177180913,2.2761622438336224,0.980659397125088,1.7612248294002992,6.051360280870355,17,17,1.9381134913932554,3.6364232819555453,1.4772941820903218,3.0898751325407443
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/nested_cv_rbf_epsilon_frequency_3W_B34_H45_it1.csv 0 → 100644
epsilon_factor,frequency,percentage,output,N
2.0,13,76.47,exymax_tw1,17
1.0,3,17.65,exymax_tw1,17
0.5,1,5.88,exymax_tw1,17
1.0,8,47.06,exymax_tw2,17
0.5,7,41.18,exymax_tw2,17
2.0,2,11.76,exymax_tw2,17
2.0,17,100.0,exymax_tw3,17
2.0,15,88.24,tfmmax_tw1,17
1.0,2,11.76,tfmmax_tw1,17
2.0,14,82.35,tfmmax_tw2,17
0.5,2,11.76,tfmmax_tw2,17
1.0,1,5.88,tfmmax_tw2,17
2.0,17,100.0,tfmmax_tw3,17
2.0,15,88.24,tfmmax_frame,17
1.0,2,11.76,tfmmax_frame,17
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/nested_cv_rbf_function_frequency_3W_B34_H45_it1.csv 0 → 100644
rbf_function,frequency,percentage,output,N
multiquadric,10,58.82,exymax_tw1,17
inverse,7,41.18,exymax_tw1,17
multiquadric,7,41.18,exymax_tw2,17
linear,5,29.41,exymax_tw2,17
gaussian,4,23.53,exymax_tw2,17
thin_plate,1,5.88,exymax_tw2,17
gaussian,7,41.18,exymax_tw3,17
multiquadric,6,35.29,exymax_tw3,17
inverse,4,23.53,exymax_tw3,17
multiquadric,12,70.59,tfmmax_tw1,17
inverse,3,17.65,tfmmax_tw1,17
gaussian,2,11.76,tfmmax_tw1,17
multiquadric,11,64.71,tfmmax_tw2,17
gaussian,6,35.29,tfmmax_tw2,17
multiquadric,8,47.06,tfmmax_tw3,17
gaussian,5,29.41,tfmmax_tw3,17
inverse,4,23.53,tfmmax_tw3,17
inverse,7,41.18,tfmmax_frame,17
gaussian,6,35.29,tfmmax_frame,17
multiquadric,4,23.53,tfmmax_frame,17
Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/nested_cv_rbf_smooth_frequency_3W_B34_H45_it1.csv 0 → 100644
smooth,frequency,percentage,output,N
0.0,7,41.18,exymax_tw1,17
0.001,7,41.18,exymax_tw1,17
1e-05,3,17.65,exymax_tw1,17
0.0,10,58.82,exymax_tw2,17
0.001,6,35.29,exymax_tw2,17
1e-05,1,5.88,exymax_tw2,17
1e-05,9,52.94,exymax_tw3,17
0.0,6,35.29,exymax_tw3,17
0.001,2,11.76,exymax_tw3,17
0.0,10,58.82,tfmmax_tw1,17
1e-05,4,23.53,tfmmax_tw1,17
0.001,3,17.65,tfmmax_tw1,17
1e-05,9,52.94,tfmmax_tw2,17
0.001,4,23.53,tfmmax_tw2,17
0.0,3,17.65,tfmmax_tw2,17
1e-08,1,5.88,tfmmax_tw2,17
1e-05,8,47.06,tfmmax_tw3,17
0.0,7,41.18,tfmmax_tw3,17
0.001,2,11.76,tfmmax_tw3,17
0.0,9,52.94,tfmmax_frame,17
1e-05,7,41.18,tfmmax_frame,17
0.001,1,5.88,tfmmax_frame,17
Code/reports/width_optimization/2W/ml_optimization/it0/optimization_results_2W_B29_H30_TFD_W100.csv 0 → 100644
Parameter,Value
Configuration_W,2.0
Configuration_B,29.0
Configuration_H,30.0
Configuration_TFD_W,100.0
Iteration,0.0
tw1_optimal,13.629422229207401
tw2_optimal,14.923874164345547
Objective_score,-1.370259603730532e-06
Exy_tw1,0.04079740234759202
Exy_tw2,0.057044011011291654
TFM_tw1,74.52799569983878
TFM_tw2,79.57444287070942
TFM_frame,85.38471341208209
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_feasibility_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
0,42,0,0.0,7.462619571258133e-07
0,42,1,0.0,5.822958225938523e-07
0,42,2,0.0,5.389508915819784e-07
0,42,3,0.0,2.526943907144004e-07
0,42,4,0.0,1.517756475884094e-07
0,42,5,0.0,1.4285714285714483e-07
0,42,6,0.0,4503599627.370496
1,43,0,0.0,7.921463676377414e-07
1,43,1,0.0,4.5461906890240316e-07
1,43,2,0.0,3.730213605252087e-07
1,43,3,0.0,2.3487620373267377e-07
1,43,4,0.0,1.8419508050337444e-07
1,43,5,0.0,4503599627.370496
2,44,0,0.0,8.213139517099173e-07
2,44,1,0.0,5.012907109066742e-07
2,44,2,0.0,2.584961734395899e-07
2,44,3,0.0,1.8173493854658964e-07
2,44,4,0.0,1.4739832416625305e-07
2,44,5,0.0,1.4285714285714343e-07
2,44,6,0.0,4503599627.370496
3,45,0,0.0,7.609848123833615e-07
3,45,1,0.0,5.914298477572671e-07
3,45,2,0.0,4.4857908018944014e-07
3,45,3,0.0,5.504526304155408e-07
3,45,4,0.0,3.2252295010289715e-07
3,45,5,0.0,2.1519276688850594e-07
3,45,6,0.0,1.4370353887625653e-07
3,45,7,0.0,1.4285714285722816e-07
3,45,8,0.0,4503599627.370496
4,46,0,0.0,8.967283112388455e-07
4,46,1,0.0,7.808822758653607e-07
4,46,2,0.0,5.288141540170702e-07
4,46,3,0.0,3.8368949669157666e-07
4,46,4,0.0,3.0924297256886237e-07
4,46,5,0.0,1.925669803043459e-07
4,46,6,0.0,1.597911502240165e-07
4,46,7,0.0,1.4285714285715187e-07
4,46,8,0.0,4503599627.370496
5,47,0,0.0,7.490651507835502e-07
5,47,1,0.0,6.151221056480334e-07
5,47,2,0.0,5.551923909725285e-07
5,47,3,0.0,3.9509897483368897e-07
5,47,4,0.0,2.8901209519607833e-07
5,47,5,0.0,2.3031621828686293e-07
5,47,6,0.0,4503599627.370496
6,48,0,0.0,9.050381550518258e-07
6,48,1,0.0,8.009871979169015e-07
6,48,2,0.0,7.202587116300975e-07
6,48,3,0.0,4.935275201891363e-07
6,48,4,0.0,2.084284055847487e-07
6,48,5,0.0,1.4285714285714396e-07
6,48,6,0.0,4503599627.370496
7,49,0,0.0,7.099710481745872e-07
7,49,1,0.0,6.370050505734338e-07
7,49,2,0.0,5.369270586901664e-07
7,49,3,0.0,4.3983172357323515e-07
7,49,4,0.0,2.6317248003311326e-07
7,49,5,0.0,4503599627.370496
8,50,0,0.0,8.020119145914496e-07
8,50,1,0.0,8.089129260618947e-07
8,50,2,0.0,6.965648540224376e-07
8,50,3,0.0,5.267387011770105e-07
8,50,4,0.0,3.770700483447223e-07
8,50,5,0.0,3.057623822361368e-07
8,50,6,0.0,2.1069406165222493e-07
8,50,7,0.0,1.6045973406613412e-07
8,50,8,0.0,1.4285714285715103e-07
8,50,9,0.0,4503599627.370496
9,51,0,0.0,8.602872770080198e-07
9,51,1,0.0,5.923340786084891e-07
9,51,2,0.0,4.5202659794207835e-07
9,51,3,0.0,2.9997030473194806e-07
9,51,4,0.0,2.0984958396181908e-07
9,51,5,0.0,2.0339882762780376e-07
9,51,6,0.0,1.4285714285714364e-07
9,51,7,0.0,4503599627.370496
10,52,0,0.0,7.333832611722988e-07
10,52,1,0.0,6.003144868839783e-07
10,52,2,0.0,2.381513287865446e-07
10,52,3,0.0,2.433800657319956e-07
10,52,4,0.0,1.4285714285714404e-07
10,52,5,0.0,4503599627.370496
11,53,0,0.0,7.015329242904531e-07
11,53,1,0.0,4.931901078251647e-07
11,53,2,0.0,3.6020434658143274e-07
11,53,3,0.0,4.1493636829034303e-07
11,53,4,0.0,3.5329347984846383e-07
11,53,5,0.0,2.816202968670453e-07
11,53,6,0.0,1.6841601117997134e-07
11,53,7,0.0,1.4285714285715351e-07
11,53,8,0.0,1.4285714285715351e-07
11,53,9,0.0,4503599627.370496
12,54,0,0.0,1.03114696782802e-06
12,54,1,0.0,9.342522655575961e-07
12,54,2,0.0,9.14553489231543e-07
12,54,3,0.0,7.862036321298453e-07
12,54,4,0.0,6.846597372511661e-07
12,54,5,0.0,6.368394198066339e-07
12,54,6,0.0,5.489820774685081e-07
12,54,7,0.0,5.06848792018731e-07
12,54,8,0.0,5.058633195565904e-07
12,54,9,0.0,4.785418663372941e-07
12,54,10,0.0,4.771697059438554e-07
12,54,11,0.0,4.327832047123037e-07
12,54,12,0.0,4.523903658748322e-07
12,54,13,0.0,4.4431012181668847e-07
12,54,14,0.0,4.1124045290867145e-07
12,54,15,0.0,3.6596868920584864e-07
12,54,16,0.0,2.9978343548994357e-07
12,54,17,0.0,3.085709562544909e-07
12,54,18,0.0,3.085709562544909e-07
12,54,19,0.0,3.0847708197667475e-07
12,54,20,0.0,3.018612765209892e-07
12,54,21,0.0,2.609043555565227e-07
12,54,22,0.0,2.584600384730822e-07
12,54,23,0.0,2.410259908939441e-07
12,54,24,0.0,2.0412171333403914e-07
12,54,25,0.0,1.4285714285714518e-07
12,54,26,0.0,1.4285714285714518e-07
12,54,27,0.0,1.4285714285714518e-07
12,54,28,0.0,1.4285714285714518e-07
12,54,29,0.0,1.4285714285715444e-07
12,54,30,0.0,1.4285714285715444e-07
12,54,31,0.0,4503599627.370496
13,55,0,0.0,7.837622572739846e-07
13,55,1,0.0,5.64561238033811e-07
13,55,2,0.0,3.7399038413383877e-07
13,55,3,0.0,3.7865797339757777e-07
13,55,4,0.0,2.377725872399742e-07
13,55,5,0.0,1.5037587197922822e-07
13,55,6,0.0,1.4285714285725391e-07
13,55,7,0.0,4503599627.370496
14,56,0,0.0,8.352726366366306e-07
14,56,1,0.0,7.527188718500786e-07
14,56,2,0.0,5.596908100054667e-07
14,56,3,0.0,3.103924932553156e-07
14,56,4,0.0,1.6239838624159497e-07
14,56,5,0.0,1.4285714285714303e-07
14,56,6,0.0,4503599627.370496
15,57,0,0.0,8.832909808350822e-07
15,57,1,0.0,6.727215475368182e-07
15,57,2,0.0,4.43563980573468e-07
15,57,3,0.0,4.902414582210139e-07
15,57,4,0.0,4.1282714364552406e-07
15,57,5,0.0,2.8237965660697967e-07
15,57,6,0.0,2.3963916469795e-07
15,57,7,0.0,1.4285714285714324e-07
15,57,8,0.0,4503599627.370496
16,58,0,0.0,6.308962053583987e-07
16,58,1,0.0,3.727150131648438e-07
16,58,2,0.0,2.5766842564432415e-07
16,58,3,0.0,2.391654947218015e-07
16,58,4,0.0,2.0298401536158835e-07
16,58,5,0.0,4503599627.370496
17,59,0,0.0,9.214406934983778e-07
17,59,1,0.0,8.005522132391445e-07
17,59,2,0.0,7.561549865498686e-07
17,59,3,0.0,4.717904517604787e-07
17,59,4,0.0,3.4180496066170044e-07
17,59,5,0.0,2.617336323470314e-07
17,59,6,0.0,2.0125238379203e-07
17,59,7,0.0,1.4285714285714483e-07
17,59,8,0.0,1.4285714285714483e-07
17,59,9,0.0,1.4285714285714483e-07
17,59,10,0.0,1.4285714285714483e-07
17,59,11,0.0,4503599627.370496
18,60,0,0.0,5.725190378877086e-07
18,60,1,0.0,4.0366346426564983e-07
18,60,2,0.0,3.412771154191128e-07
18,60,3,0.0,2.0993800210611265e-07
18,60,4,0.0,1.4285714285714385e-07
18,60,5,0.0,4503599627.370496
19,61,0,0.0,7.861027300031255e-07
19,61,1,0.0,5.995747053840084e-07
19,61,2,0.0,4.061838920638302e-07
19,61,3,0.0,2.8567151847478483e-07
19,61,4,0.0,3.148222599407356e-07
19,61,5,0.0,2.829032709391543e-07
19,61,6,0.0,1.428571428571435e-07
19,61,7,0.0,1.428571428571435e-07
19,61,8,0.0,4503599627.370496
20,62,0,0.0,8.035176032952625e-07
20,62,1,0.0,4.834893147919002e-07
20,62,2,0.0,4.88448794300042e-07
20,62,3,0.0,2.953646677755896e-07
20,62,4,0.0,2.403463077668706e-07
20,62,5,0.0,2.050757691618502e-07
20,62,6,0.0,2.0540089559716973e-07
20,62,7,0.0,1.42857142857145e-07
20,62,8,0.0,1.4285714285714862e-07
20,62,9,0.0,4503599627.370496
21,63,0,0.0,9.13102378091789e-07
21,63,1,0.0,6.327161916055252e-07
21,63,2,0.0,5.079621224772056e-07
21,63,3,0.0,4.759231128502931e-07
21,63,4,0.0,4.301340067831371e-07
21,63,5,0.0,2.3563499294149901e-07
21,63,6,0.0,1.638222365667998e-07
21,63,7,0.0,1.5962543695200534e-07
21,63,8,0.0,1.4285714285714616e-07
21,63,9,0.0,1.4285714285714616e-07
21,63,10,0.0,4503599627.370496
22,64,0,0.0,7.565093129907035e-07
22,64,1,0.0,6.669832629251487e-07
22,64,2,0.0,5.141886311888108e-07
22,64,3,0.0,4.047121778780196e-07
22,64,4,0.0,2.660901202187114e-07
22,64,5,0.0,1.7491168989816698e-07
22,64,6,0.0,1.876007604224469e-07
22,64,7,0.0,4503599627.370496
23,65,0,0.0,8.183235827352042e-07
23,65,1,0.0,6.004083383750976e-07
23,65,2,0.0,3.5609457543791506e-07
23,65,3,0.0,2.413132131963628e-07
23,65,4,0.0,4503599627.370496
24,66,0,0.0,6.670544851553919e-07
24,66,1,0.0,7.3739307070386e-07
24,66,2,0.0,4.812712888008011e-07
24,66,3,0.0,3.637516466786103e-07
24,66,4,0.0,2.3500630214921302e-07
24,66,5,0.0,2.086904020116935e-07
24,66,6,0.0,1.4285714286343021e-07
24,66,7,0.0,4503599627.370496
25,67,0,0.0,7.886909900354079e-07
25,67,1,0.0,6.932184574672754e-07
25,67,2,0.0,4.0950502880472337e-07
25,67,3,0.0,3.8290493586114497e-07
25,67,4,0.0,2.5355726566362e-07
25,67,5,0.0,2.0016837074401904e-07
25,67,6,0.0,4503599627.370496
26,68,0,0.0,7.522735884466941e-07
26,68,1,0.0,3.803150783511466e-07
26,68,2,0.0,2.981990855469346e-07
26,68,3,0.0,2.980358812841269e-07
26,68,4,0.0,1.4285714285714444e-07
26,68,5,0.0,4503599627.370496
27,69,0,0.0,7.968729787646904e-07
27,69,1,0.0,6.110615156076025e-07
27,69,2,0.0,5.637277085454125e-07
27,69,3,0.0,3.5037191165165535e-07
27,69,4,0.0,3.6503089618971415e-07
27,69,5,0.0,2.493004784452161e-07
27,69,6,0.0,2.302305749105946e-07
27,69,7,0.0,1.4285714285714703e-07
27,69,8,0.0,4503599627.370496
28,70,0,0.0,7.693637568461549e-07
28,70,1,0.0,5.520648563360097e-07
28,70,2,0.0,5.136578826110268e-07
28,70,3,0.0,4.132032723904936e-07
28,70,4,0.0,3.8861983444812514e-07
28,70,5,0.0,2.2360709726378528e-07
28,70,6,0.0,1.7185445177637033e-07
28,70,7,0.0,1.7185445177637033e-07
28,70,8,0.0,1.4285714285714343e-07
28,70,9,0.0,1.4285714285714343e-07
28,70,10,0.0,1.4285714285714343e-07
28,70,11,0.0,1.4285714285714343e-07
28,70,12,0.0,4503599627.370496
29,71,0,0.0,8.794025699605582e-07
29,71,1,0.0,7.175655131361048e-07
29,71,2,0.0,6.19626181758029e-07
29,71,3,0.0,4.272843261820857e-07
29,71,4,0.0,2.9867979568771306e-07
29,71,5,0.0,2.1117568030826207e-07
29,71,6,0.0,2.2351570345559696e-07
29,71,7,0.0,2.244080557351057e-07
29,71,8,0.0,4503599627.370496
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_feasibility_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,0.0,7,400,True,Optimization terminated successfully.,0.0,16.065883699483756,18.8523639126256,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,0.0,6,350,True,Optimization terminated successfully.,0.0,15.544849393328771,18.333020718656428,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,0.0,7,400,True,Optimization terminated successfully.,0.0,15.937411371590933,17.888089001485802,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,0.0,9,500,True,Optimization terminated successfully.,0.0,16.539730856016593,19.24506763265577,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,0.0,9,500,True,Optimization terminated successfully.,0.0,15.536015699474115,19.737163411437052,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,0.0,7,400,True,Optimization terminated successfully.,0.0,15.230534374646536,19.362632755058247,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,0.0,7,400,True,Optimization terminated successfully.,0.0,15.863943087351478,17.654013744916913,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,0.0,6,350,True,Optimization terminated successfully.,0.0,15.48711876619779,18.14752916816119,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,0.0,10,550,True,Optimization terminated successfully.,0.0,15.439120514599136,15.062782914489048,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,0.0,8,450,True,Optimization terminated successfully.,0.0,16.38540312056785,19.655583104425318,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,0.0,6,350,True,Optimization terminated successfully.,0.0,14.231473991890434,15.258524832472904,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,0.0,10,550,True,Optimization terminated successfully.,0.0,14.962579478214279,19.51719310356609,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,0.0,32,1650,True,Optimization terminated successfully.,0.0,13.913188125558234,15.188961494819827,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,0.0,8,450,True,Optimization terminated successfully.,0.0,15.359543588460415,16.698420676624472,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,0.0,7,400,True,Optimization terminated successfully.,0.0,16.506109248746636,19.467743263153793,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,0.0,9,500,True,Optimization terminated successfully.,0.0,14.93369684575638,19.552229838135784,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,0.0,6,350,True,Optimization terminated successfully.,0.0,15.86251874211767,19.60770858116002,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,0.0,12,650,True,Optimization terminated successfully.,0.0,16.38274064742792,18.907534933775683,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,0.0,6,350,True,Optimization terminated successfully.,0.0,15.148306440157462,16.006252701709542,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,0.0,9,500,True,Optimization terminated successfully.,0.0,16.308111178417406,19.498750968963833,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,0.0,10,550,True,Optimization terminated successfully.,0.0,16.46550056154523,17.97057011227143,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,0.0,11,600,True,Optimization terminated successfully.,0.0,15.766512270238547,17.638823839441912,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,0.0,8,450,True,Optimization terminated successfully.,0.0,15.6260778342502,18.22169597191216,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,0.0,5,300,True,Optimization terminated successfully.,0.0,15.406768441367104,17.55196448334218,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,0.0,8,450,True,Optimization terminated successfully.,0.0,15.05046646974749,18.232015919064843,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,0.0,7,400,True,Optimization terminated successfully.,0.0,16.30936440507958,18.57778106549875,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,0.0,6,350,True,Optimization terminated successfully.,0.0,15.447597652607111,17.58184547449495,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,0.0,9,500,True,Optimization terminated successfully.,0.0,16.108605847297994,17.184320332087477,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,0.0,13,700,True,Optimization terminated successfully.,0.0,16.41628292245235,19.051554344861337,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,0.0,9,500,True,Optimization terminated successfully.,0.0,15.026241910157331,15.46486890237201,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_feasibility_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,0.0,0.0,0.0,0.0,0.0,8.0,450.0,15.585463613789486,15.642056582824893,0.6633644952489619,0.9954481675375462,18.226855945488502,18.037233573454678,1.4561564748042326,1.7738067433272562,27,0.06666666666666667,2,6;21,6,0.0,"[15.863943087351478, 17.654013744916913]",0.06889399147709999,0.0487154085564665,0.09743081711293122,0.01074088516695594,0.007594952737502325,0.015189905475001098
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_lexicographic_optimization_summary.csv 0 → 100644
w_val,b_val,h_val,tfd_w_val,it,safety_margin_mode,safety_margin_factor,local_margin_support_pass,local_margin_k_requested,local_support_percentile,local_margin_fallback,TFD_tw1_margin_source,TFD_tw1_margin_in_support,TFD_tw1_margin_k,TFD_tw1_margin_nearest_distance,TFD_tw1_margin_support_radius,TFD_tw1_margin_neighbor_samples,TFD_tw1_margin_neighbor_distances,TFD_tw1_margin_neighbor_underprediction,TFD_tw2_margin_source,TFD_tw2_margin_in_support,TFD_tw2_margin_k,TFD_tw2_margin_nearest_distance,TFD_tw2_margin_support_radius,TFD_tw2_margin_neighbor_samples,TFD_tw2_margin_neighbor_distances,TFD_tw2_margin_neighbor_underprediction,TFD_frame_margin_source,TFD_frame_margin_in_support,TFD_frame_margin_k,TFD_frame_margin_nearest_distance,TFD_frame_margin_support_radius,TFD_frame_margin_neighbor_samples,TFD_frame_margin_neighbor_distances,TFD_frame_margin_neighbor_underprediction,min_de_reproducibility_required,de_reproducibility_pass,feasible_domain_found,minimum_domain_violation,feasible,feasibility_violation,tw1,tw2,TFD_tw1,TFD_tw2,TFD_tw1_margin,TFD_tw2_margin,TFD_tw1_conservative,TFD_tw2_conservative,TFD_frame,TFD_frame_margin,TFD_frame_conservative,min_window_tfd,max_window_tfd,mean_window_tfd,window_tfd_range_diagnostic,distortion_measure,stage1_Tmin_star,stage1_preservation_threshold,stage2_distortion,max_constraint_violation,stage1_observed_basin_spread,stage1_preservation_tolerance,stage1_preservation_tolerance_source,stage1_rep_run,stage1_rep_seed,stage2_rep_run,stage2_rep_seed,stage1_reproducibility,stage2_reproducibility,stage1_n_basins,stage2_n_basins,stage2_tw1_dominant_basin_iqr,stage2_tw2_dominant_basin_iqr,stage2_tw1_dominant_basin_range,stage2_tw2_dominant_basin_range
2,29,30,100,0,local_oof,1.0,True,-1,95.0,global,local_knn_max,True,3,0.24789283067959156,0.5881879819029009,1;0;4,0.24789283;0.38773862;0.3986428,0;0;25.472004,local_knn_max,True,3,0.24789283067959156,0.5881879819029009,1;0;4,0.24789283;0.38773862;0.3986428,0;20.425557;0,local_knn_max,True,3,0.24789283067959156,0.5881879819029009,1;0;4,0.24789283;0.38773862;0.3986428,0.32429749;0;0,0.8,False,True,0.0,True,0.0,13.629422229207401,14.923874164345547,74.52799569983878,79.57444287070942,25.472003748407175,20.425557126369732,99.99999944824596,99.99999999707916,85.38471341208209,0.32429748584617357,85.70901089792827,74.52799569983878,79.57444287070942,77.0512192852741,5.0464471708706355,1.370259603730532e-06,74.52799624080674,74.52699624080674,1.370259603730532e-06,0.0,2.269054306225371e-06,0.001,dominant_DE_basin_spread,2,44,0,42,0.5333333333333333,1.0,4,1,1.014534387167032e-06,3.6038872863741744e-07,3.834963873572406e-06,1.7603183355419105e-06
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_stage1_utilization_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
0,42,0,-71.34469917369044,0.0
0,42,1,-71.34469917369044,0.0
0,42,2,-71.34469917369044,0.0
0,42,3,-72.74531262461043,0.0
0,42,4,-73.41510280433832,0.0
0,42,5,-74.22584416454563,0.0
0,42,6,-74.41448641388838,0.0
0,42,7,-74.41448641388838,0.0
0,42,8,-74.41448641388838,0.0
0,42,9,-74.41448641388838,0.0
0,42,10,-74.41448641388838,0.0
0,42,11,-74.41448641388838,0.0
0,42,12,-74.41448641388838,0.0
0,42,13,-74.41448641388838,0.0
0,42,14,-74.4537764695946,3.0271419321817617e-05
0,42,15,-74.46728015607597,3.35367760399817e-05
0,42,16,-74.52382836015678,4.4421907316821285e-05
0,42,17,-74.52382836015678,4.518559335297276e-05
0,42,18,-74.52382836015678,0.0002309530145470271
0,42,19,-74.52382836015678,0.0002667992989320477
0,42,20,-74.52382836015678,0.0003264374568106679
0,42,21,-74.52410279639513,0.0003584719476326083
0,42,22,-74.52498828351288,0.0011583132383979323
0,42,23,-74.52798566926528,0.0015926971901052628
0,42,24,-74.52798566926528,0.0024343942798852863
0,42,25,-74.52798566926528,0.0024002954485673697
0,42,26,-74.52798566926528,0.002767964206058656
0,42,27,-74.52798566926528,0.004167328024728387
0,42,28,-74.52798566926528,0.004254603531857145
0,42,29,-74.52798566926528,0.004354694939432423
0,42,30,-74.52798566926528,0.0043557664479597176
0,42,31,-74.52798566926528,0.007802996146652824
0,42,32,-74.52798566926528,0.008481741760663184
0,42,33,-74.52798566926528,0.01472858694559392
0,42,34,-74.52798566926528,0.014739765754935229
0,42,35,-74.52798566926528,0.014504020505877577
0,42,36,-74.52798566926528,0.014714120312049841
0,42,37,-74.52798566926528,0.015122580594039328
0,42,38,-74.52799291328154,0.015193147145513902
0,42,39,-74.52799291328154,0.0150852364445633
0,42,40,-74.52799291328154,0.0359632385501374
0,42,41,-74.52799545718037,0.035750228682417555
0,42,42,-74.52799545718037,0.0356403369702901
0,42,43,-74.52799545718037,0.03552489888216786
0,42,44,-74.52799545718037,0.053840499809196606
0,42,45,-74.52799545718037,0.05376620582456305
0,42,46,-74.52799547533485,0.05354999832216771
0,42,47,-74.52799547533485,0.18395140234015567
0,42,48,-74.52799547533485,0.18554333657894953
0,42,49,-74.52799547533485,0.6941153415715016
0,42,50,-74.52799563893885,0.6878376597852455
0,42,51,-74.52799563893885,0.6928174020852685
0,42,52,-74.52799618318191,0.6912590444253441
0,42,53,-74.52799618318191,0.6902346774613389
0,42,54,-74.52799618318191,0.688551087207253
0,42,55,-74.52799618318191,7.762801274735458
1,43,0,-69.08328199007596,0.0
1,43,1,-70.54248765356385,0.0
1,43,2,-70.54248765356385,0.0
1,43,3,-70.54248765356385,0.0
1,43,4,-71.3478743284153,0.0
1,43,5,-72.85051512761578,0.0
1,43,6,-72.85051512761578,0.0
1,43,7,-72.85051512761578,0.0
1,43,8,-72.85051512761578,0.0
1,43,9,-74.0039507789148,0.0
1,43,10,-74.0039507789148,0.0
1,43,11,-74.26243914091643,1.551345217660329e-05
1,43,12,-74.46029158204428,1.5338717378710944e-05
1,43,13,-74.46029158204428,2.109290653699818e-05
1,43,14,-74.46029158204428,2.1754096602127013e-05
1,43,15,-74.46029158204428,2.4430834544441153e-05
1,43,16,-74.46029158204428,2.4957785619903606e-05
1,43,17,-74.50218234303307,3.3726506071686925e-05
1,43,18,-74.52726805372903,4.320353467795583e-05
1,43,19,-74.52726805372903,4.720841164753232e-05
1,43,20,-74.52726805372903,4.708068003271864e-05
1,43,21,-74.52726805372903,5.023860038640829e-05
1,43,22,-74.52726805372903,4.997609024983553e-05
1,43,23,-74.52726805372903,0.00011779838012354793
1,43,24,-74.52726805372903,0.00011894402691681238
1,43,25,-74.52726805372903,0.0001323061471851311
1,43,26,-74.52726805372903,0.00015433739748088777
1,43,27,-74.52726805372903,0.00033399704610659277
1,43,28,-74.52726805372903,0.0003501956260871032
1,43,29,-74.52799104546267,0.00044206827709473637
1,43,30,-74.52799104546267,0.0006379019871773151
1,43,31,-74.52799104546267,0.0006883789690414831
1,43,32,-74.52799104546267,0.0010391655169125183
1,43,33,-74.52799104546267,0.0011623852347314742
1,43,34,-74.52799104546267,0.001195634635927952
1,43,35,-74.52799104546267,0.0012312578459513874
1,43,36,-74.52799104546267,0.0012241704225852848
1,43,37,-74.52799104546267,0.0013886889786892784
1,43,38,-74.52799104546267,0.00285163058624284
1,43,39,-74.52799104546267,0.003182392215704275
1,43,40,-74.52799104546267,0.0031803773864163497
1,43,41,-74.52799104546267,0.003329974308448457
1,43,42,-74.52799104546267,0.01739423673606887
1,43,43,-74.52799104546267,0.01724537336016583
1,43,44,-74.52799104546267,0.017435877669851985
1,43,45,-74.52799104546267,0.017415733199815823
1,43,46,-74.52799104546267,0.017317971489437724
1,43,47,-74.52799104546267,0.01729980733365281
1,43,48,-74.52799104546267,0.038306023877217335
1,43,49,-74.52799104546267,0.03841844974919339
1,43,50,-74.52799104546267,0.047990324762869296
1,43,51,-74.52799104546267,0.10938704052561228
1,43,52,-74.52799397175244,0.10888074021533123
1,43,53,-74.52799397175244,0.10901379696109544
1,43,54,-74.52799397175244,0.7477862898232669
1,43,55,-74.52799397175244,0.7428229947119859
1,43,56,-74.52799397175244,0.865385418571556
1,43,57,-74.52799397175244,1.18011305580842
2,44,0,-65.22843152454679,0.0
2,44,1,-65.22843152454679,0.0
2,44,2,-65.68404251450063,0.0
2,44,3,-70.92905565259954,0.0
2,44,4,-72.10424263628146,0.0
2,44,5,-72.10424263628146,0.0
2,44,6,-73.63609515479776,0.0
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26,68,19,-74.52304580818644,8.393694724659894e-05
26,68,20,-74.52304580818644,0.0001680022807589246
26,68,21,-74.52536465044477,0.00020784808815670246
26,68,22,-74.52758636651292,0.0010203266716396807
26,68,23,-74.52758636651292,0.0010615101308794385
26,68,24,-74.52758636651292,0.0011400790096402307
26,68,25,-74.52758636651292,0.0011532460879172422
26,68,26,-74.52758636651292,0.0012708127417689174
26,68,27,-74.52758636651292,0.002183003338088544
26,68,28,-74.52777231353805,0.002805842316733303
26,68,29,-74.52777231353805,0.004987771554613857
26,68,30,-74.52796026258629,0.005061937757810835
26,68,31,-74.52799376163297,0.00641687615505008
26,68,32,-74.52799376163297,0.019471732987867786
26,68,33,-74.52799376163297,0.028706234181919636
26,68,34,-74.52799376163297,0.02849875524116284
26,68,35,-74.52799376163297,0.030988482132670095
26,68,36,-74.52799376163297,0.03240202667072886
26,68,37,-74.52799376163297,0.03666276477124189
26,68,38,-74.52799376163297,0.07705336781202582
26,68,39,-74.52799556471346,0.08010972013317327
26,68,40,-74.52799556471346,0.09470661657255419
26,68,41,-74.52799593143244,0.0946615155068065
26,68,42,-74.52799615788531,0.3106168022757001
26,68,43,-74.52799615788531,0.346841528870698
26,68,44,-74.52799615788531,0.35059980162984195
26,68,45,-74.52799615788531,0.36147492625118016
26,68,46,-74.52799615788531,6.616548478501701
27,69,0,-67.3944177496099,0.0
27,69,1,-73.6727345532299,0.0
27,69,2,-73.6727345532299,0.0
27,69,3,-73.6727345532299,0.0
27,69,4,-73.6727345532299,0.0
27,69,5,-73.6727345532299,0.0
27,69,6,-73.6727345532299,0.0
27,69,7,-73.6727345532299,0.0
27,69,8,-73.6727345532299,0.0
27,69,9,-73.6727345532299,0.0
27,69,10,-74.08741477023553,0.0
27,69,11,-74.39401944996001,1.9637127588809667e-05
27,69,12,-74.46410512873675,1.9052923871716092e-05
27,69,13,-74.46410512873675,1.8861430315436063e-05
27,69,14,-74.46410512873675,2.096252133225127e-05
27,69,15,-74.46410512873675,2.2275621710773154e-05
27,69,16,-74.46410512873675,2.3723302506483015e-05
27,69,17,-74.46410512873675,2.4927025589209578e-05
27,69,18,-74.46410512873675,2.639612717674983e-05
27,69,19,-74.51845341695335,3.653946122078142e-05
27,69,20,-74.51845341695335,3.759712032965903e-05
27,69,21,-74.51845341695335,3.795433997861194e-05
27,69,22,-74.51845341695335,4.366085863152124e-05
27,69,23,-74.52571659081761,4.597851954440098e-05
27,69,24,-74.52571659081761,4.798699723927849e-05
27,69,25,-74.52571659081761,4.798963786946564e-05
27,69,26,-74.52571659081761,4.8180328912314996e-05
27,69,27,-74.52571659081761,4.84717891913087e-05
27,69,28,-74.52571659081761,4.845207062319543e-05
27,69,29,-74.52571659081761,4.8386965861435936e-05
27,69,30,-74.52571659081761,4.8344129435769506e-05
27,69,31,-74.52571659081761,4.830629479024288e-05
27,69,32,-74.52719157397492,0.0012607276839786859
27,69,33,-74.52791393456343,0.0015931260080350672
27,69,34,-74.52791393456343,0.0016072136858058513
27,69,35,-74.52791393456343,0.0028854844537390574
27,69,36,-74.52791393456343,0.004266284225879133
27,69,37,-74.52791393456343,0.0043153454637624945
27,69,38,-74.52791393456343,0.004493766068768521
27,69,39,-74.52791902007739,0.007645761875900474
27,69,40,-74.52791902007739,0.00762602864840571
27,69,41,-74.52796491075974,0.010472294087201368
27,69,42,-74.52796491075974,0.0108251858481105
27,69,43,-74.52796491075974,0.01079173326055049
27,69,44,-74.52796889159212,0.013589074441746259
27,69,45,-74.52796889159212,0.013734228123229986
27,69,46,-74.52796889159212,0.09894551445523823
27,69,47,-74.52798472573635,0.11281276750601697
27,69,48,-74.52798472573635,0.12393795752579416
27,69,49,-74.52798512014014,0.15849266496687267
27,69,50,-74.52799550852846,0.15829082256138668
27,69,51,-74.52799550852846,0.15778598011570283
27,69,52,-74.52799550852846,0.3126429838968142
27,69,53,-74.52799550852846,0.314629015202082
27,69,54,-74.5279958531165,0.4084643591117904
27,69,55,-74.5279958531165,0.40782483769354455
27,69,56,-74.5279958531165,0.4138281475469843
27,69,57,-74.5279958531165,0.4145136925112238
27,69,58,-74.5279958531165,1.6968174366671558
28,70,0,-73.205249045694,0.0
28,70,1,-73.205249045694,0.0
28,70,2,-74.42864933579403,0.0
28,70,3,-74.42864933579403,0.0
28,70,4,-74.42864933579403,0.0
28,70,5,-74.42864933579403,0.0
28,70,6,-74.42864933579403,0.0
28,70,7,-74.42864933579403,0.0
28,70,8,-74.42864933579403,0.0
28,70,9,-74.42864933579403,0.0
28,70,10,-74.42864933579403,0.0
28,70,11,-74.42864933579403,0.0
28,70,12,-74.42864933579403,0.0
28,70,13,-74.42864933579403,0.0
28,70,14,-74.42864933579403,0.0
28,70,15,-74.42864933579403,1.8154478265797618e-05
28,70,16,-74.498533829334,2.65991217767693e-05
28,70,17,-74.51018556681444,2.703698366878322e-05
28,70,18,-74.51018556681444,2.7589593773404813e-05
28,70,19,-74.52601324614542,3.4682887111365606e-05
28,70,20,-74.52601324614542,3.7772375477756285e-05
28,70,21,-74.52601324614542,4.091499657473591e-05
28,70,22,-74.52679254112829,4.168091887232056e-05
28,70,23,-74.52679254112829,4.1649920930675835e-05
28,70,24,-74.52679254112829,0.00017101555434801235
28,70,25,-74.52679254112829,0.00022068130125284987
28,70,26,-74.52679254112829,0.0004312743070407409
28,70,27,-74.52680169224793,0.00043530638780305556
28,70,28,-74.52680169224793,0.00043523452965892615
28,70,29,-74.52742508456048,0.0004437330302143753
28,70,30,-74.5274893539446,0.0007303904367273813
28,70,31,-74.5274893539446,0.0007339675305688916
28,70,32,-74.52791557686356,0.0007789121372520197
28,70,33,-74.52795865209654,0.0007909065913618732
28,70,34,-74.52795865209654,0.00314539290790721
28,70,35,-74.52799018393036,0.012297319088059856
28,70,36,-74.52799018393036,0.017626272497232056
28,70,37,-74.527995375942,0.01757826265643769
28,70,38,-74.527995375942,0.023663852464083358
28,70,39,-74.527995375942,0.026216527742499444
28,70,40,-74.527995375942,0.027076611422269133
28,70,41,-74.527995375942,0.20241072315884698
28,70,42,-74.527995375942,0.20781185941119212
28,70,43,-74.527995375942,0.26813713046042725
28,70,44,-74.527995375942,0.36703691533735244
28,70,45,-74.527995375942,0.4264464935412434
28,70,46,-74.527995375942,0.4276617805040768
28,70,47,-74.527995375942,0.42352566041217593
28,70,48,-74.527995375942,0.49477412163949547
28,70,49,-74.527995375942,0.74474923211034
28,70,50,-74.52799593529488,1.4439056669909562
29,71,0,-72.02947871399552,0.0
29,71,1,-72.07284295656042,0.0
29,71,2,-72.07284295656042,0.0
29,71,3,-72.07284295656042,0.0
29,71,4,-72.87749147633696,0.0
29,71,5,-72.87749147633696,0.0
29,71,6,-74.29921213597243,0.0
29,71,7,-74.29921213597243,0.0
29,71,8,-74.29921213597243,0.0
29,71,9,-74.34578084253287,0.0
29,71,10,-74.38217830833632,2.1530300310587093e-05
29,71,11,-74.38217830833632,3.1770077537859355e-05
29,71,12,-74.46516053535886,3.2806079960169254e-05
29,71,13,-74.46516053535886,4.102191490708169e-05
29,71,14,-74.46516053535886,6.471873633893006e-05
29,71,15,-74.49229686168002,7.647597820603099e-05
29,71,16,-74.49229686168002,0.00012384605474867344
29,71,17,-74.49229686168002,0.00013842809482770516
29,71,18,-74.49229686168002,0.00016943558906046846
29,71,19,-74.49229686168002,0.0002654924884717348
29,71,20,-74.52599508633871,0.00029178085710351517
29,71,21,-74.52599508633871,0.00028851670509204324
29,71,22,-74.52668534430751,0.000367424626585932
29,71,23,-74.52668534430751,0.0004147944630445556
29,71,24,-74.52778819061054,0.0004887714016759973
29,71,25,-74.52778819061054,0.0006498492896363858
29,71,26,-74.52778819061054,0.0011013377605171573
29,71,27,-74.52792686402681,0.0012520145574261878
29,71,28,-74.52792686402681,0.002966854543742477
29,71,29,-74.52792686402681,0.003381479810174371
29,71,30,-74.52799069637983,0.003442289892429178
29,71,31,-74.52799069637983,0.01077220949915875
29,71,32,-74.52799069637983,0.012255685341286205
29,71,33,-74.52799069637983,0.013876565951669804
29,71,34,-74.52799069637983,0.01674516218051608
29,71,35,-74.52799069637983,0.018666555117529807
29,71,36,-74.52799069637983,0.018591508669045888
29,71,37,-74.52799069637983,0.046129322787850466
29,71,38,-74.52799310134654,0.05505506561945334
29,71,39,-74.52799310134654,0.08934089428105259
29,71,40,-74.52799310134654,0.21189112027376822
29,71,41,-74.52799310134654,0.28191061179502774
29,71,42,-74.52799310134654,0.3058919554875629
29,71,43,-74.52799310134654,0.4114805935485062
29,71,44,-74.52799310134654,0.44721537178718873
29,71,45,-74.52799455706182,0.44597053000230463
29,71,46,-74.52799467856057,0.4456307370433505
29,71,47,-74.52799560170855,0.4468660929549019
29,71,48,-74.52799594904417,4.812820392414958
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_stage1_utilization_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,-74.52799618318191,56,1045,True,Optimization terminated successfully.,9.60065660148217e-06,13.648648190973281,14.963514629823651,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,-74.52799397175244,58,1044,True,Optimization terminated successfully.,6.315323790600643e-05,13.63638152801098,14.938222718911298,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,-74.52799624080674,54,1073,True,Optimization terminated successfully.,2.4551529979931508e-05,13.661184570635703,14.989362347792675,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,-74.52799545310833,49,1047,True,Optimization terminated successfully.,6.581686988309696e-05,13.647452546939723,14.961049330237724,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,-74.52799614551677,60,1282,True,Optimization terminated successfully.,4.61107199375186e-05,13.804036867642658,15.283897506606115,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,-74.5279962265581,49,1074,True,Optimization terminated successfully.,5.7683908135691616e-05,13.719227856700812,15.109036930022002,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,-74.52799614555266,55,1175,True,Optimization terminated successfully.,6.193015639525097e-05,13.679934801815195,15.028021863893787,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,-74.52799606696344,55,1032,True,Optimization terminated successfully.,6.385474452873486e-05,13.663920641137953,14.995003597973968,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,-74.52799603024303,46,983,True,Optimization terminated successfully.,6.918908681530964e-05,13.697377781807813,15.06398606560873,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,-74.52799603966437,49,1074,True,Optimization terminated successfully.,5.883982176155323e-05,13.784055411370646,15.242699402797296,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,-74.52799607888747,66,1169,True,Optimization terminated successfully.,1.0574198762284652e-05,13.637572750438878,14.94067909324991,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,-74.52799540355305,60,1030,True,Optimization terminated successfully.,6.328244956848335e-05,13.64605995398103,14.958178052710865,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,-74.52799612681356,56,1237,True,Optimization terminated successfully.,6.240340417968342e-05,13.773401975770089,15.220733989402484,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,-74.5279956338989,51,1023,True,Optimization terminated successfully.,4.141126750401556e-05,13.744875463408565,15.161917497236798,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,-74.52799590490191,62,1143,True,Optimization terminated successfully.,2.5216139321645587e-05,13.679669888307481,15.027475627517733,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,-74.52799566083505,53,1157,True,Optimization terminated successfully.,3.8338368083396714e-05,13.838110861813274,15.354151750519389,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,-74.52799553184713,46,1073,True,Optimization terminated successfully.,2.7353686498208852e-05,13.78793675100189,15.25070194096391,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,-74.52799619944618,59,1319,True,Optimization terminated successfully.,4.0242805793559446e-05,13.829370202826098,15.336130193537794,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,-74.52799625101045,57,1282,True,Optimization terminated successfully.,1.0065409725627537e-05,13.787935052771264,15.25069853942514,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,-74.5279961445242,60,1122,True,Optimization terminated successfully.,6.836469261871684e-05,13.65014657805698,14.966604023131433,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,-74.5279962471426,50,1096,True,Optimization terminated successfully.,4.6195320622312175e-05,13.763019020312612,15.199326260942156,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,-74.52799601266662,50,1171,True,Optimization terminated successfully.,4.573277591028952e-05,13.808875250369763,15.293873343764936,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,-74.52799618245501,50,1094,True,Optimization terminated successfully.,9.898300065964188e-06,13.690993932110757,15.050823762392206,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,-74.52799623696862,49,1148,True,Optimization terminated successfully.,3.137955674758724e-05,13.707429217341375,15.08471030637004,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,-74.52799620600494,48,1130,True,Optimization terminated successfully.,3.0148404182379985e-05,13.848917516168536,15.376433160652903,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,-74.52799623276253,59,1064,True,Optimization terminated successfully.,6.045013147050023e-05,13.63330584893252,14.931881548218133,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,-74.52799615788531,47,995,True,Optimization terminated successfully.,1.1263877191104127e-05,13.717077484863893,15.104603249115451,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,-74.5279958531165,59,1139,True,Optimization terminated successfully.,4.392220955825571e-05,13.695662707271186,15.060449872637431,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,-74.52799593529488,51,1013,True,Optimization terminated successfully.,5.1615535278183885e-05,13.65837769214641,14.983575037972361,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,-74.52799594904417,49,1152,True,Optimization terminated successfully.,1.5485303025642046e-05,13.825488844786918,15.328127512091502,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_stage1_utilization_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,-74.52799625101045,-74.52799610285052,-74.52799594841355,4.4739994116004777e-07,2.8287995235132257e-07,53.5,1095.0,13.702403499574594,13.72221490632381,0.07068071348643956,0.12788573065237685,15.074348185989386,15.115195638517326,0.1457306494505607,0.2636768898407382,4,0.5333333333333333,16,2;23;25;0;22;6;19;10;7;8;28;14;27;3;11;1,2,-74.52799624080674,"[13.661184570635703, 14.989362347792675]",0.024070104950989813,0.03559518568903641,0.07412336840885381,0.049628159822886035,0.0733908276623847,0.152828758151907
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_stage2_distortion_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,-1.370259603730532e-06,104,785,True,Optimization terminated successfully.,2.0149982981262972e-13,13.629422229207401,14.923874164345547,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,-1.3702595722238786e-06,89,591,True,Optimization terminated successfully.,1.3330506337147343e-12,13.629422675244093,14.923874330891552,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,-1.3702595925435393e-06,109,730,True,Optimization terminated successfully.,4.557463913877453e-13,13.629422391296803,14.923874222885622,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,-1.3702595953794355e-06,94,741,True,Optimization terminated successfully.,3.455448661167971e-13,13.629422351239386,14.923874207881125,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,-1.3702595325066648e-06,92,672,True,Optimization terminated successfully.,8.041397205162346e-13,13.629422931741303,14.92387458978939,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,-1.3702595642683522e-06,89,670,True,Optimization terminated successfully.,8.112105506478228e-13,13.629422421333256,14.923874431622128,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,-1.370259581333837e-06,91,651,True,Optimization terminated successfully.,1.3414803924955825e-12,13.629422511287551,14.923874288336945,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,-1.3702595761545782e-06,96,610,True,Optimization terminated successfully.,6.968665996890264e-13,13.62942284457761,14.92387427409753,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,-1.3702595198779556e-06,86,596,True,Optimization terminated successfully.,1.3257932993716415e-12,13.629424160310144,14.923874488488476,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,-1.3702595079631552e-06,89,544,True,Optimization terminated successfully.,1.0109480568736709e-12,13.629423671556374,14.923874656716393,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,-1.370259531181806e-06,91,606,True,Optimization terminated successfully.,8.420260316701772e-13,13.629423589398645,14.923874494512885,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,-1.3702595729568085e-06,100,807,True,Optimization terminated successfully.,8.361380596042327e-13,13.6294227615568,14.923874311538587,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,-1.3702594838890916e-06,92,653,True,Optimization terminated successfully.,1.1585367952483373e-12,13.629424512476172,14.923874703912999,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,-1.3702594139877997e-06,95,643,True,Optimization terminated successfully.,1.1217778658098136e-12,13.62942505226545,14.923875145421322,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,-1.3702595351475852e-06,96,621,True,Optimization terminated successfully.,9.519260574469315e-13,13.629422633151954,14.92387461764435,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,-1.3702594891656351e-06,79,631,True,Optimization terminated successfully.,1.3053331577998324e-12,13.629423316314647,14.923874855552159,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,-1.370259514463929e-06,95,679,True,Optimization terminated successfully.,1.00899087453784e-12,13.629423344750995,14.92387465993741,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,-1.3702595781403279e-06,89,629,True,Optimization terminated successfully.,7.198751675413413e-13,13.629422617577273,14.923874295439584,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,-1.3702595772445029e-06,95,655,True,Optimization terminated successfully.,7.287978016428323e-13,13.629422719012176,14.923874285967223,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,-1.370259538888576e-06,106,664,True,Optimization terminated successfully.,8.889127866949808e-13,13.629422939521415,14.923874540345235,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,-1.3702595925141033e-06,99,882,True,Optimization terminated successfully.,3.940484637703064e-13,13.629422375076889,14.923874225704742,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,-1.3702595535712934e-06,92,597,True,Optimization terminated successfully.,7.654840512801832e-13,13.629422401492038,14.92387451558734,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,-1.3702595730789075e-06,92,644,True,Optimization terminated successfully.,5.71135220696442e-13,13.629422520946733,14.923874349134861,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,-1.3702595781599503e-06,100,789,True,Optimization terminated successfully.,4.880908469566583e-13,13.629422388876947,14.923874331903741,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,-1.3702594338113006e-06,81,533,True,Optimization terminated successfully.,1.272349322791773e-12,13.629426064171275,14.923874833714086,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,-1.3702593037056762e-06,95,681,True,Optimization terminated successfully.,7.30261755502044e-13,13.629425387400051,14.923875924663882,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,-1.370259585843043e-06,92,582,True,Optimization terminated successfully.,3.3237703789703443e-13,13.629422591302342,14.92387424147222,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,-1.3702594070484442e-06,95,587,True,Optimization terminated successfully.,1.1494675601716836e-12,13.629423214472332,14.92387549203658,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,-1.370259583264737e-06,98,803,True,Optimization terminated successfully.,3.717979257225227e-13,13.629422632513341,14.92387425434724,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,-1.3702595316314473e-06,98,645,True,Optimization terminated successfully.,1.3109968212303935e-12,13.629423939663269,14.923874435044477,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/ml_optimization/ml_2W_B29_H30_TFD_W100_it0_stage2_distortion_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,-1.370259603730532e-06,-1.3702595589198228e-06,-1.370259534122563e-06,6.838040737173141e-14,6.233760913471357e-14,94.5,648.0,13.629422740284488,13.629423172991157,9.85245986353117e-07,1.014534387167032e-06,14.923874433333303,14.92387453896452,3.949241942865998e-07,3.6038872863741744e-07,1,1.0,30,0;3;2;20;26;28;6;23;17;18;7;22;11;1;5;21;19;14;4;29;10;8;16;9;15;12;24;13;27;25,0,-1.370259603730532e-06,"[13.629422229207401, 14.923874164345547]",9.85245986353117e-07,1.014534387167032e-06,3.834963873572406e-06,3.949241942865998e-07,3.6038872863741744e-07,1.7603183355419105e-06
Code/reports/width_optimization/2W/rbf_optimization/it0/optimization_results_2W_B29_H30_TFD_W100.csv 0 → 100644
Parameter,Value
Configuration_W,2.0
Configuration_B,29.0
Configuration_H,30.0
Configuration_TFD_W,100.0
Iteration,0.0
tw1_optimal,13.286469317625878
tw2_optimal,14.134825855416723
Objective_score,-1.4559028116900094e-06
Exy_tw1,0.0411750319312881
Exy_tw2,0.0614481309994559
TFM_tw1,73.8114223210311
TFM_tw2,91.2677923894737
TFM_frame,85.19863096288157
Code/reports/width_optimization/2W/rbf_optimization/it0/optimization_results_2W_B34_H30_TFD_W100.csv 0 → 100644
Parameter,Value
Configuration_W,2.0
Configuration_B,34.0
Configuration_H,30.0
Configuration_TFD_W,100.0
Iteration,0.0
tw1_optimal,15.15300665087472
tw2_optimal,18.673421403771716
Objective_score,2.694164620607694
Exy_tw1,0.05211853321222765
Exy_tw2,0.049968477739541384
TFM_tw1,100.6200027812006
TFM_tw2,75.06793868153244
TFM_frame,92.6941646206077
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_feasibility_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
0,42,0,0.0,1.1953748343772714e-06
0,42,1,0.0,9.357729266230994e-07
0,42,2,0.0,7.794709624329533e-07
0,42,3,0.0,6.728379133739084e-07
0,42,4,0.0,4.609017463272338e-07
0,42,5,0.0,4.2862687429739706e-07
0,42,6,0.0,2.774011664924995e-07
0,42,7,0.0,1.9634723961809524e-07
0,42,8,0.0,1.5046118926814126e-07
0,42,9,0.0,4503599627.370496
1,43,0,0.0,1.2353260345966194e-06
1,43,1,0.0,7.368407874145206e-07
1,43,2,0.0,5.126738679137698e-07
1,43,3,0.0,4.2061890009859887e-07
1,43,4,0.0,5.110515313043564e-07
1,43,5,0.0,3.5571930404984183e-07
1,43,6,0.0,3.408866305845757e-07
1,43,7,0.0,2.897016589744347e-07
1,43,8,0.0,2.738471168693493e-07
1,43,9,0.0,1.4285714285715312e-07
1,43,10,0.0,1.4285714285715312e-07
1,43,11,0.0,1.4285714285715312e-07
1,43,12,0.0,4503599627.370496
2,44,0,0.0,1.3674082851264765e-06
2,44,1,0.0,1.2476801759882023e-06
2,44,2,0.0,1.1193253677939266e-06
2,44,3,0.0,9.80210200829847e-07
2,44,4,0.0,7.527553659376002e-07
2,44,5,0.0,6.014854438079431e-07
2,44,6,0.0,5.153748542862013e-07
2,44,7,0.0,5.725032992592167e-07
2,44,8,0.0,4.6865131831628154e-07
2,44,9,0.0,3.655853557071129e-07
2,44,10,0.0,3.2607780618621167e-07
2,44,11,0.0,2.744040965602011e-07
2,44,12,0.0,1.4285714285714396e-07
2,44,13,0.0,1.4285714285714396e-07
2,44,14,0.0,1.4285714285714396e-07
2,44,15,0.0,4503599627.370496
3,45,0,0.0,1.1327330779573628e-06
3,45,1,0.0,1.0495333448158737e-06
3,45,2,0.0,8.417392239458533e-07
3,45,3,0.0,7.103638920507424e-07
3,45,4,0.0,5.461229806728309e-07
3,45,5,0.0,4.4007341372334347e-07
3,45,6,0.0,3.7723857394259755e-07
3,45,7,0.0,3.0654423836921024e-07
3,45,8,0.0,2.465441247438568e-07
3,45,9,0.0,2.2778200883469586e-07
3,45,10,0.0,1.7807153670223644e-07
3,45,11,0.0,1.708662605585946e-07
3,45,12,0.0,1.5405864329023821e-07
3,45,13,0.0,4503599627.370496
4,46,0,0.0,1.045762796831212e-06
4,46,1,0.0,9.375969353370132e-07
4,46,2,0.0,7.910222938893348e-07
4,46,3,0.0,4.722600411513224e-07
4,46,4,0.0,4.4358910260180716e-07
4,46,5,0.0,2.4843586269707597e-07
4,46,6,0.0,2.0453273384358532e-07
4,46,7,0.0,2.312597260481258e-07
4,46,8,0.0,1.4285714285715523e-07
4,46,9,0.0,1.4285714285715523e-07
4,46,10,0.0,1.4285714285715523e-07
4,46,11,0.0,1.4285714285715523e-07
4,46,12,0.0,1.4285714285715523e-07
4,46,13,0.0,1.4285714285715523e-07
4,46,14,0.0,1.4285714285715523e-07
4,46,15,0.0,4503599627.370496
5,47,0,0.0,1.0988781807932479e-06
5,47,1,0.0,9.094825091514703e-07
5,47,2,0.0,8.92845919773246e-07
5,47,3,0.0,6.354877340162716e-07
5,47,4,0.0,5.094869472167738e-07
5,47,5,0.0,3.455055210045489e-07
5,47,6,0.0,4.0725582908118463e-07
5,47,7,0.0,3.7744238869791677e-07
5,47,8,0.0,2.7952363460725185e-07
5,47,9,0.0,1.5809049808873087e-07
5,47,10,0.0,4503599627.370496
6,48,0,0.0,1.1486834576327272e-06
6,48,1,0.0,1.1921243527213977e-06
6,48,2,0.0,8.099039445045066e-07
6,48,3,0.0,7.454606225286334e-07
6,48,4,0.0,5.623950653540499e-07
6,48,5,0.0,3.9408553757634437e-07
6,48,6,0.0,2.4792640387800947e-07
6,48,7,0.0,1.7646910051623475e-07
6,48,8,0.0,1.48244630213726e-07
6,48,9,0.0,2.1392644219292668e-07
6,48,10,0.0,1.8444757175120882e-07
6,48,11,0.0,1.4285714285717186e-07
6,48,12,0.0,4503599627.370496
7,49,0,0.0,1.2781330818476828e-06
7,49,1,0.0,1.1640819344474245e-06
7,49,2,0.0,9.576837339307555e-07
7,49,3,0.0,8.537515645742312e-07
7,49,4,0.0,6.248188286001971e-07
7,49,5,0.0,4.857601744719242e-07
7,49,6,0.0,4.895203844114451e-07
7,49,7,0.0,3.880034244764066e-07
7,49,8,0.0,3.744300560461742e-07
7,49,9,0.0,3.016045051652812e-07
7,49,10,0.0,4503599627.370496
8,50,0,0.0,1.3144093030170853e-06
8,50,1,0.0,8.270348950579167e-07
8,50,2,0.0,8.04247972885515e-07
8,50,3,0.0,6.212976794990664e-07
8,50,4,0.0,6.073428615771805e-07
8,50,5,0.0,4.0826061480469706e-07
8,50,6,0.0,2.8152743530152766e-07
8,50,7,0.0,2.8046169036970934e-07
8,50,8,0.0,2.2686322968502193e-07
8,50,9,0.0,1.7519569265088444e-07
8,50,10,0.0,1.7968983152433194e-07
8,50,11,0.0,1.428571428571517e-07
8,50,12,0.0,1.428571428571517e-07
8,50,13,0.0,1.428571428571517e-07
8,50,14,0.0,1.428571428571517e-07
8,50,15,0.0,1.428571428571517e-07
8,50,16,0.0,4503599627.370496
9,51,0,0.0,1.417863937282954e-06
9,51,1,0.0,1.4420918637005752e-06
9,51,2,0.0,1.1636469105255989e-06
9,51,3,0.0,8.476149775615331e-07
9,51,4,0.0,7.170411317806214e-07
9,51,5,0.0,6.232546179834738e-07
9,51,6,0.0,4.965211336171415e-07
9,51,7,0.0,5.241207408892311e-07
9,51,8,0.0,4.5456462599450097e-07
9,51,9,0.0,3.930171432387457e-07
9,51,10,0.0,2.67057232870577e-07
9,51,11,0.0,2.3673936813865725e-07
9,51,12,0.0,2.1125139116602805e-07
9,51,13,0.0,1.9804370262458378e-07
9,51,14,0.0,1.4285714285716087e-07
9,51,15,0.0,4503599627.370496
10,52,0,0.0,9.492967000465943e-07
10,52,1,0.0,6.596745860408291e-07
10,52,2,0.0,4.1234583235082615e-07
10,52,3,0.0,3.7563387321090274e-07
10,52,4,0.0,3.3370970129254423e-07
10,52,5,0.0,1.612045747905196e-07
10,52,6,0.0,1.4285714285714504e-07
10,52,7,0.0,1.4285714285714504e-07
10,52,8,0.0,4503599627.370496
11,53,0,0.5461736315879762,1.3620751072830874e-06
11,53,1,0.0,1.1090467914617455e-06
11,53,2,0.0,9.821404081036653e-07
11,53,3,0.0,7.462250688928713e-07
11,53,4,0.0,6.959814998309566e-07
11,53,5,0.0,4.906544761946574e-07
11,53,6,0.0,4.447331114496218e-07
11,53,7,0.0,2.86422299226242e-07
11,53,8,0.0,1.6731014893566e-07
11,53,9,0.0,1.4285714285714433e-07
11,53,10,0.0,4503599627.370496
12,54,0,0.0,1.1715421684987783e-06
12,54,1,0.0,1.0028238170246218e-06
12,54,2,0.0,9.258781880937363e-07
12,54,3,0.0,7.901681825711121e-07
12,54,4,0.0,7.348910502378059e-07
12,54,5,0.0,6.418529344626358e-07
12,54,6,0.0,6.660382083638104e-07
12,54,7,0.0,5.262977519339299e-07
12,54,8,0.0,3.449699857803502e-07
12,54,9,0.0,4.258183757292573e-07
12,54,10,0.0,3.2976122972215797e-07
12,54,11,0.0,3.7018165301013365e-07
12,54,12,0.0,2.0741333460633636e-07
12,54,13,0.0,1.723911081500591e-07
12,54,14,0.0,1.9861304204134963e-07
12,54,15,0.0,4503599627.370496
13,55,0,0.0,1.0198812417466953e-06
13,55,1,0.0,1.0162841458595738e-06
13,55,2,0.0,8.57828400354367e-07
13,55,3,0.0,6.910836624021062e-07
13,55,4,0.0,5.995099286786467e-07
13,55,5,0.0,6.315043800590307e-07
13,55,6,0.0,6.129279317233886e-07
13,55,7,0.0,4.826875853114347e-07
13,55,8,0.0,3.1871199672404245e-07
13,55,9,0.0,2.9151045096893257e-07
13,55,10,0.0,2.2201677546069847e-07
13,55,11,0.0,1.4285714285715044e-07
13,55,12,0.0,4503599627.370496
14,56,0,0.0,1.0482977463397712e-06
14,56,1,0.0,9.947712620631623e-07
14,56,2,0.0,8.45021516715177e-07
14,56,3,0.0,5.488919154843927e-07
14,56,4,0.0,5.007501236260636e-07
14,56,5,0.0,4.219531166748046e-07
14,56,6,0.0,4.0292362120196576e-07
14,56,7,0.0,4.881820699138857e-07
14,56,8,0.0,3.7549866062875065e-07
14,56,9,0.0,2.776793288843845e-07
14,56,10,0.0,1.469957557792691e-07
14,56,11,0.0,4503599627.370496
15,57,0,0.0,1.5467270918352294e-06
15,57,1,0.0,1.2960939097042437e-06
15,57,2,0.0,9.776398263678728e-07
15,57,3,0.0,5.892334220407305e-07
15,57,4,0.0,4.767465944007824e-07
15,57,5,0.0,5.663322332697817e-07
15,57,6,0.0,4.382456217904557e-07
15,57,7,0.0,3.517472342613631e-07
15,57,8,0.0,3.12226028465338e-07
15,57,9,0.0,1.9706938284526742e-07
15,57,10,0.0,4503599627.370496
16,58,0,0.0,1.2999714474936802e-06
16,58,1,0.0,7.481649285311626e-07
16,58,2,0.0,6.795665297960121e-07
16,58,3,0.0,6.314654661863002e-07
16,58,4,0.0,4.840319761093882e-07
16,58,5,0.0,4.1679140079779123e-07
16,58,6,0.0,3.845142286705037e-07
16,58,7,0.0,2.704597141335611e-07
16,58,8,0.0,1.4669731458615832e-07
16,58,9,0.0,1.428571428572221e-07
16,58,10,0.0,4503599627.370496
17,59,0,0.0,1.2315819927309866e-06
17,59,1,0.0,8.947228883429055e-07
17,59,2,0.0,8.240336976176695e-07
17,59,3,0.0,6.167839476612713e-07
17,59,4,0.0,4.5014433280961894e-07
17,59,5,0.0,3.764558138508323e-07
17,59,6,0.0,4.079988519201331e-07
17,59,7,0.0,2.539753839566123e-07
17,59,8,0.0,2.2928552562374705e-07
17,59,9,0.0,1.811098833818065e-07
17,59,10,0.0,2.081791163339353e-07
17,59,11,0.0,1.871711241119784e-07
17,59,12,0.0,1.871711241119784e-07
17,59,13,0.0,1.871711241119784e-07
17,59,14,0.0,1.871711241119784e-07
17,59,15,0.0,1.4285714285717432e-07
17,59,16,0.0,4503599627.370496
18,60,0,0.0,1.2967487779914155e-06
18,60,1,0.0,1.2668629200028423e-06
18,60,2,0.0,1.0319188359905517e-06
18,60,3,0.0,6.720457366013819e-07
18,60,4,0.0,4.881140258759904e-07
18,60,5,0.0,3.102418282921371e-07
18,60,6,0.0,1.9393324111086173e-07
18,60,7,0.0,2.6715197249366267e-07
18,60,8,0.0,2.0350272652710871e-07
18,60,9,0.0,1.4438029807676405e-07
18,60,10,0.0,4503599627.370496
19,61,0,0.0,1.3787296050308588e-06
19,61,1,0.0,1.121738857644412e-06
19,61,2,0.0,8.564452722239021e-07
19,61,3,0.0,6.319786485399471e-07
19,61,4,0.0,4.84682004656997e-07
19,61,5,0.0,4.704424699621156e-07
19,61,6,0.0,4.6165084422895046e-07
19,61,7,0.0,2.98045468592796e-07
19,61,8,0.0,1.4285714285716333e-07
19,61,9,0.0,1.4285714285716333e-07
19,61,10,0.0,1.4285714285716333e-07
19,61,11,0.0,1.4285714285716333e-07
19,61,12,0.0,1.4285714285716333e-07
19,61,13,0.0,1.4285714285716333e-07
19,61,14,0.0,4503599627.370496
20,62,0,0.0,1.279194099837192e-06
20,62,1,0.0,1.1917769458080695e-06
20,62,2,0.0,9.473553845281432e-07
20,62,3,0.0,7.287512772097177e-07
20,62,4,0.0,5.642167519057992e-07
20,62,5,0.0,5.653209002949466e-07
20,62,6,0.0,5.281058494804157e-07
20,62,7,0.0,4.601987205689845e-07
20,62,8,0.0,3.584752727148065e-07
20,62,9,0.0,2.7517883780722226e-07
20,62,10,0.0,3.101265741293608e-07
20,62,11,0.0,2.657757270335463e-07
20,62,12,0.0,1.8878486662733475e-07
20,62,13,0.0,2.037944973574323e-07
20,62,14,0.0,4503599627.370496
21,63,0,0.0,1.4372774009853409e-06
21,63,1,0.0,1.1467797577802003e-06
21,63,2,0.0,9.830557294710073e-07
21,63,3,0.0,9.44185548228934e-07
21,63,4,0.0,8.752222731466393e-07
21,63,5,0.0,7.687786303430271e-07
21,63,6,0.0,6.405413785001985e-07
21,63,7,0.0,5.824303194047101e-07
21,63,8,0.0,5.165907955407403e-07
21,63,9,0.0,4.255222963578776e-07
21,63,10,0.0,4.319556967837007e-07
21,63,11,0.0,3.3574531079962563e-07
21,63,12,0.0,1.9159935443393094e-07
21,63,13,0.0,4503599627.370496
22,64,0,0.0,1.1743457474537983e-06
22,64,1,0.0,9.092236399917353e-07
22,64,2,0.0,6.14148987306851e-07
22,64,3,0.0,8.097779307726807e-07
22,64,4,0.0,9.001942945021625e-07
22,64,5,0.0,7.035969110670836e-07
22,64,6,0.0,5.470298295170058e-07
22,64,7,0.0,4.056769786954258e-07
22,64,8,0.0,3.4331740531821116e-07
22,64,9,0.0,2.1862157129818605e-07
22,64,10,0.0,1.5361007275518467e-07
22,64,11,0.0,1.5361007275518467e-07
22,64,12,0.0,1.5361007275518467e-07
22,64,13,0.0,4503599627.370496
23,65,0,0.0,1.1668251364245426e-06
23,65,1,0.0,1.0059557627066222e-06
23,65,2,0.0,8.52468357069686e-07
23,65,3,0.0,7.643931046613637e-07
23,65,4,0.0,6.63016455153537e-07
23,65,5,0.0,4.407148743145767e-07
23,65,6,0.0,2.968624112452132e-07
23,65,7,0.0,1.9674825352706083e-07
23,65,8,0.0,1.7779386638030445e-07
23,65,9,0.0,1.4621936510144773e-07
23,65,10,0.0,1.4621936510144773e-07
23,65,11,0.0,1.4285714285727895e-07
23,65,12,0.0,4503599627.370496
24,66,0,0.0,1.2733736399874964e-06
24,66,1,0.0,1.1054335254957387e-06
24,66,2,0.0,7.046684358044919e-07
24,66,3,0.0,7.20124162006037e-07
24,66,4,0.0,6.791624888086279e-07
24,66,5,0.0,5.139605696541834e-07
24,66,6,0.0,3.119763370444509e-07
24,66,7,0.0,2.8483644538635727e-07
24,66,8,0.0,2.0609699898409862e-07
24,66,9,0.0,1.8357478493073016e-07
24,66,10,0.0,4503599627.370496
25,67,0,0.0,1.1791833998750768e-06
25,67,1,0.0,1.063755329102802e-06
25,67,2,0.0,8.603313502392962e-07
25,67,3,0.0,7.473989121046102e-07
25,67,4,0.0,6.004115500612272e-07
25,67,5,0.0,5.660671030869157e-07
25,67,6,0.0,4.0347324723969923e-07
25,67,7,0.0,3.5173075806649964e-07
25,67,8,0.0,2.8703345128804326e-07
25,67,9,0.0,2.0232785970784688e-07
25,67,10,0.0,4503599627.370496
26,68,0,0.0,1.3708640694082454e-06
26,68,1,0.0,1.2650213913050034e-06
26,68,2,0.0,9.214092773201559e-07
26,68,3,0.0,7.42881983709909e-07
26,68,4,0.0,5.936720357884758e-07
26,68,5,0.0,5.325129222729181e-07
26,68,6,0.0,4.7006741082941863e-07
26,68,7,0.0,4.533131303978133e-07
26,68,8,0.0,4.6038527009736215e-07
26,68,9,0.0,3.1955291697916476e-07
26,68,10,0.0,1.9818995289690399e-07
26,68,11,0.0,1.4285714285714457e-07
26,68,12,0.0,1.4285714285714457e-07
26,68,13,0.0,4503599627.370496
27,69,0,0.0,9.165579369147186e-07
27,69,1,0.0,7.746099021081321e-07
27,69,2,0.0,6.459145736922165e-07
27,69,3,0.0,6.739892868485805e-07
27,69,4,0.0,6.664411561112231e-07
27,69,5,0.0,7.626860346693714e-07
27,69,6,0.0,6.763153286973713e-07
27,69,7,0.0,5.144693649311672e-07
27,69,8,0.0,3.670087782953046e-07
27,69,9,0.0,3.6428485404770056e-07
27,69,10,0.0,3.041093357688433e-07
27,69,11,0.0,1.4786604916238688e-07
27,69,12,0.0,1.428571428574094e-07
27,69,13,0.0,1.428571428574094e-07
27,69,14,0.0,4503599627.370496
28,70,0,0.0,1.1800612797297286e-06
28,70,1,0.0,8.642129493044066e-07
28,70,2,0.0,7.548430651568748e-07
28,70,3,0.0,5.066288893535908e-07
28,70,4,0.0,3.849300350284382e-07
28,70,5,0.0,6.545368934173915e-07
28,70,6,0.0,4.097224180039436e-07
28,70,7,0.0,3.536026813459688e-07
28,70,8,0.0,3.1411538718461777e-07
28,70,9,0.0,2.5293024771079254e-07
28,70,10,0.0,1.4285714285715238e-07
28,70,11,0.0,1.4285714285715238e-07
28,70,12,0.0,1.4285714285715238e-07
28,70,13,0.0,4503599627.370496
29,71,0,0.0,1.4533821258229874e-06
29,71,1,0.0,9.784242330571732e-07
29,71,2,0.0,7.671755897196145e-07
29,71,3,0.0,5.868127090954391e-07
29,71,4,0.0,4.302783237308582e-07
29,71,5,0.0,2.391259504210874e-07
29,71,6,0.0,3.8694791455391917e-07
29,71,7,0.0,2.1195641413818463e-07
29,71,8,0.0,1.7263387542818202e-07
29,71,9,0.0,4503599627.370496
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_feasibility_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,0.0,10,550,True,Optimization terminated successfully.,0.0,14.854905570542464,17.50557045414938,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,0.0,13,700,True,Optimization terminated successfully.,0.0,14.424563676120558,16.926063164242706,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,0.0,16,850,True,Optimization terminated successfully.,0.0,15.088132051456707,18.382187178569392,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,0.0,14,750,True,Optimization terminated successfully.,0.0,15.019479426075817,17.827489824660585,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,0.0,16,850,True,Optimization terminated successfully.,0.0,14.32952687017877,16.752757694897532,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,0.0,11,600,True,Optimization terminated successfully.,0.0,14.04322559850818,15.668017708304259,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,0.0,13,700,True,Optimization terminated successfully.,0.0,15.336121864949245,18.13934588630849,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,0.0,11,600,True,Optimization terminated successfully.,0.0,15.00057997957197,17.600780366103308,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,0.0,17,900,True,Optimization terminated successfully.,0.0,14.142525505788052,16.81853936029035,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,0.0,16,850,True,Optimization terminated successfully.,0.0,15.25956794960161,18.27106799678458,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,0.0,9,500,True,Optimization terminated successfully.,0.0,15.23256894320041,17.76640407642462,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,0.0,11,600,True,Optimization terminated successfully.,0.0,13.4045770018225,14.183191419112674,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,0.0,16,850,True,Optimization terminated successfully.,0.0,13.652217234882214,14.724674416639632,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,0.0,13,700,True,Optimization terminated successfully.,0.0,13.699424794350268,15.011609517171873,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,0.0,12,650,True,Optimization terminated successfully.,0.0,13.518492415047628,14.477317222299684,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,0.0,11,600,True,Optimization terminated successfully.,0.0,14.181157505010022,15.762189124565664,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,0.0,11,600,True,Optimization terminated successfully.,0.0,14.289571732522095,15.898832725003798,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,0.0,17,900,True,Optimization terminated successfully.,0.0,15.44894781838174,18.14418698885836,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,0.0,11,600,True,Optimization terminated successfully.,0.0,15.39674550728013,18.459813102677757,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,0.0,15,800,True,Optimization terminated successfully.,0.0,15.231984040746429,17.668204549577457,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,0.0,15,800,True,Optimization terminated successfully.,0.0,15.364645830256789,18.435451985229236,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,0.0,14,750,True,Optimization terminated successfully.,0.0,15.199542874209277,18.210045675202508,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,0.0,14,750,True,Optimization terminated successfully.,0.0,15.174023240208408,17.669463096048876,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,0.0,13,700,True,Optimization terminated successfully.,0.0,14.401389279582949,16.903388569849355,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,0.0,11,600,True,Optimization terminated successfully.,0.0,15.025131597332967,17.76738530690026,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,0.0,11,600,True,Optimization terminated successfully.,0.0,14.730167946799494,16.77382725042058,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,0.0,14,750,True,Optimization terminated successfully.,0.0,14.847161709298636,17.240404441601317,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,0.0,15,800,True,Optimization terminated successfully.,0.0,15.280236131827747,18.214032707067663,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,0.0,14,750,True,Optimization terminated successfully.,0.0,14.544387755521992,16.928107974125815,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,0.0,10,550,True,Optimization terminated successfully.,0.0,13.95261041503366,15.206586000986906,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_feasibility_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,0.0,0.0,0.0,0.0,0.0,13.0,700.0,14.85103363992055,14.669120408870292,0.6238956771627078,1.0156126872241007,17.37298744787535,16.977897859469156,1.2866411649635985,1.9490679034192837,23,0.1,3,10;19;22,10,0.0,"[15.23256894320041, 17.76640407642462]",0.033633802066563856,0.029272851495999674,0.0585457029920029,0.056335726805817526,0.049099763423580356,0.09819952684716426
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_lexicographic_optimization_summary.csv 0 → 100644
w_val,b_val,h_val,tfd_w_val,it,safety_margin_mode,safety_margin_factor,local_margin_support_pass,local_margin_k_requested,local_support_percentile,local_margin_fallback,TFD_tw1_margin_source,TFD_tw1_margin_in_support,TFD_tw1_margin_k,TFD_tw1_margin_nearest_distance,TFD_tw1_margin_support_radius,TFD_tw1_margin_neighbor_samples,TFD_tw1_margin_neighbor_distances,TFD_tw1_margin_neighbor_underprediction,TFD_tw2_margin_source,TFD_tw2_margin_in_support,TFD_tw2_margin_k,TFD_tw2_margin_nearest_distance,TFD_tw2_margin_support_radius,TFD_tw2_margin_neighbor_samples,TFD_tw2_margin_neighbor_distances,TFD_tw2_margin_neighbor_underprediction,TFD_frame_margin_source,TFD_frame_margin_in_support,TFD_frame_margin_k,TFD_frame_margin_nearest_distance,TFD_frame_margin_support_radius,TFD_frame_margin_neighbor_samples,TFD_frame_margin_neighbor_distances,TFD_frame_margin_neighbor_underprediction,min_de_reproducibility_required,de_reproducibility_pass,feasible_domain_found,minimum_domain_violation,feasible,feasibility_violation,tw1,tw2,TFD_tw1,TFD_tw2,TFD_tw1_margin,TFD_tw2_margin,TFD_tw1_conservative,TFD_tw2_conservative,TFD_frame,TFD_frame_margin,TFD_frame_conservative,min_window_tfd,max_window_tfd,mean_window_tfd,window_tfd_range_diagnostic,distortion_measure,stage1_Tmin_star,stage1_preservation_threshold,stage2_distortion,stage1_observed_basin_spread,stage1_preservation_tolerance,stage1_preservation_tolerance_source,stage1_rep_run,stage1_rep_seed,stage2_rep_run,stage2_rep_seed,stage1_reproducibility,stage2_reproducibility,stage1_n_basins,stage2_n_basins,stage2_tw1_dominant_basin_iqr,stage2_tw2_dominant_basin_iqr,stage2_tw1_dominant_basin_range,stage2_tw2_dominant_basin_range
2,29,30,100,0,local_oof,1.0,True,-1,95.0,global,local_knn_max,True,3,0.16197909138076544,0.5881879819029009,1;4;0,0.16197909;0.39530479;0.47376864,0;26.188577;0,local_knn_max,True,3,0.16197909138076544,0.5881879819029009,1;4;0,0.16197909;0.39530479;0.47376864,0;0;8.7322074,local_knn_max,True,3,0.16197909138076544,0.5881879819029009,1;4;0,0.16197909;0.39530479;0.47376864,0;3.0387843;4.4475769,0.8,False,True,0.0,True,0.0,13.286469317625878,14.134825855416723,73.8114223210311,91.2677923894737,26.188576981102756,8.73220743844626,99.99999930213386,99.99999982791996,85.19863096288157,4.447576914025859,89.64620787690743,73.8114223210311,91.2677923894737,82.5396073552524,17.456370068442595,1.4559028116900094e-06,73.81142295014797,73.81042295014797,1.4559028116900094e-06,9.367664688397781e-07,0.001,dominant_DE_basin_spread,25,67,7,49,0.23333333333333334,1.0,10,1,7.166471061026414e-07,3.3854788838993954e-07,8.77271488519682e-06,1.8131439123436621e-06
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_minimum_violation_solution.csv 0 → 100644
w_val,b_val,h_val,tfd_w_val,it,feasible,minimum_identified_violation,tw1,tw2,TFD_tw1,TFD_tw2,TFD_tw1_margin,TFD_tw2_margin,TFD_tw1_conservative,TFD_tw2_conservative,TFD_frame,TFD_frame_margin,TFD_frame_conservative,safety_margin_mode,safety_margin_factor,max_violation,viol_window_1,viol_window_2,viol_frame
2,29,30,100,0,False,12.281693303517756,15.27262459137902,19.999999732172817,77.91677178488419,54.27476446383082,34.364921518633565,14.72165112316472,112.28169330351776,68.99641558699554,83.29572635136122,18.98596695209652,102.28169330345774,oof,1.0,12.281693303517756,12.281693303517756,0.0,12.281693303457743
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_stage1_utilization_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
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0,42,12,-73.79828076051251,0.0
0,42,13,-73.79828076051251,4.430766165868193e-05
0,42,14,-73.80779177422318,5.0148067062522e-05
0,42,15,-73.80779177422318,4.999100786603686e-05
0,42,16,-73.80779177422318,6.46340105430658e-05
0,42,17,-73.80904368011335,7.316201926328209e-05
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0,42,19,-73.81019617626538,0.0003172897373953167
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26,68,19,-73.8107002789481,0.0
26,68,20,-73.8107002789481,8.633188822694426e-05
26,68,21,-73.8107002789481,0.00010932664014158232
26,68,22,-73.8107002789481,0.00010896408526750213
26,68,23,-73.81108102539412,0.00011131479935070844
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26,68,45,-73.81139787175039,0.11906171835003188
26,68,46,-73.81142018442421,0.1195805417485831
26,68,47,-73.81142018442421,0.11829259620243364
26,68,48,-73.81142018442421,0.18781522529948466
26,68,49,-73.81142018442421,0.19487788090972627
26,68,50,-73.81142018442421,0.24089023633752504
26,68,51,-73.81142018442421,0.33747830973448395
26,68,52,-73.81142018442421,0.3400169445318016
26,68,53,-73.81142018442421,0.3703686642975762
26,68,54,-73.81142018442421,0.3859643582034354
26,68,55,-73.81142018442421,0.5678423966419539
26,68,56,-73.81142275745663,0.6627893927734743
26,68,57,-73.81142275745663,0.7827945364759934
26,68,58,-73.81142275745663,1.7457130776724883
27,69,0,-69.70377171157793,0.0
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27,69,4,-73.58423919118673,0.0
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27,69,6,-73.58423919118673,0.0
27,69,7,-73.70734565289871,0.0
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27,69,9,-73.72740098287346,0.0
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27,69,11,-73.7672741505404,0.0
27,69,12,-73.7672741505404,0.0
27,69,13,-73.7672741505404,0.0
27,69,14,-73.7672741505404,3.1153444187890033e-05
27,69,15,-73.8107668575422,3.36543310911014e-05
27,69,16,-73.81101054593762,3.4198704261491796e-05
27,69,17,-73.81101054593762,3.484125076292973e-05
27,69,18,-73.81101054593762,9.286919827421903e-05
27,69,19,-73.81101054593762,9.861051111813517e-05
27,69,20,-73.81113628442205,0.0002064943019891638
27,69,21,-73.81113628442205,0.0002475290285444641
27,69,22,-73.81113628442205,0.0002478697648703286
27,69,23,-73.81113628442205,0.0002564755969235917
27,69,24,-73.8112744842108,0.00025589948483414627
27,69,25,-73.811367948299,0.0004202112829521688
27,69,26,-73.811367948299,0.0004218184994840514
27,69,27,-73.81137188300528,0.006564072215372939
27,69,28,-73.81139075856106,0.00715581986195051
27,69,29,-73.81142226440761,0.007134987979029725
27,69,30,-73.81142226440761,0.07612846644871728
27,69,31,-73.81142226440761,0.08038762901990139
27,69,32,-73.81142226440761,0.09089094745037934
27,69,33,-73.81142226440761,0.1177126662036862
27,69,34,-73.81142226440761,0.12698977598483838
27,69,35,-73.81142226440761,0.26654935944096925
27,69,36,-73.81142226440761,0.29361335208208333
27,69,37,-73.81142226440761,0.3077400589873939
27,69,38,-73.81142226440761,0.31312378445273387
27,69,39,-73.81142226440761,0.3241535038571709
27,69,40,-73.81142226440761,0.6959623053526726
27,69,41,-73.81142226440761,0.7860107681453083
27,69,42,-73.81142226440761,2.2774339517231423
28,70,0,-61.785505800071064,0.0
28,70,1,-69.05046406446957,0.0
28,70,2,-72.37942427671766,0.0
28,70,3,-72.37942427671766,0.0
28,70,4,-72.37942427671766,0.0
28,70,5,-72.37942427671766,0.0
28,70,6,-72.98882831976329,0.0
28,70,7,-72.98882831976329,0.0
28,70,8,-73.32977439857274,0.0
28,70,9,-73.32977439857274,0.0
28,70,10,-73.32977439857274,0.0
28,70,11,-73.66682543061773,0.0
28,70,12,-73.66682543061773,0.0
28,70,13,-73.77818817234333,0.0
28,70,14,-73.77818817234333,0.0
28,70,15,-73.77818817234333,0.0
28,70,16,-73.80642137340794,0.0
28,70,17,-73.80642137340794,0.0
28,70,18,-73.80792682633825,0.0
28,70,19,-73.80792682633825,0.0
28,70,20,-73.81133465384295,0.0
28,70,21,-73.81133465384295,0.0006994477204337257
28,70,22,-73.81133465384295,0.0009050169115133775
28,70,23,-73.81133465384295,0.0009467450027071772
28,70,24,-73.81133465384295,0.001055549492685015
28,70,25,-73.81133465384295,0.0018945442291751938
28,70,26,-73.81133465384295,0.004584640274188515
28,70,27,-73.81133465384295,0.006043527179942892
28,70,28,-73.81133465384295,0.00798415276549817
28,70,29,-73.81133465384295,0.015239616866135857
28,70,30,-73.81140258497038,0.0160818957978468
28,70,31,-73.81140258497038,0.028597879357878032
28,70,32,-73.81140258497038,0.031891638138507525
28,70,33,-73.81140258497038,0.03449220253663716
28,70,34,-73.81140258497038,0.03446515708502949
28,70,35,-73.81140258497038,0.03875947664200599
28,70,36,-73.81140258497038,0.03883164925771472
28,70,37,-73.81140258497038,0.04008101985566897
28,70,38,-73.81140258497038,0.040160303328714404
28,70,39,-73.81140258497038,0.06884311527556775
28,70,40,-73.81140258497038,0.07544606395162291
28,70,41,-73.81140258497038,0.07627388009235381
28,70,42,-73.81141218977672,0.10652428037983662
28,70,43,-73.81141218977672,0.2114205219044698
28,70,44,-73.8114220133815,0.2275284906601272
28,70,45,-73.8114220133815,0.22603766976586434
28,70,46,-73.8114220133815,0.26808022540374243
28,70,47,-73.8114220133815,0.31642391032009753
28,70,48,-73.8114220133815,0.334616126227353
28,70,49,-73.8114220133815,0.3476183160335604
28,70,50,-73.8114220133815,0.36355328071753135
28,70,51,-73.8114220133815,0.37098203498264737
28,70,52,-73.8114220133815,0.3803929594932896
28,70,53,-73.8114220133815,0.7563743189839525
28,70,54,-73.8114220133815,0.7478777767393172
28,70,55,-73.8114220133815,0.797686907907782
28,70,56,-73.8114220133815,0.8752418672984058
28,70,57,-73.8114220133815,0.9214174732477851
28,70,58,-73.8114220133815,0.9214174732477851
28,70,59,-73.8114220133815,0.9401643051735362
28,70,60,-73.8114220133815,1.0535740999922203
29,71,0,-68.97754637948617,0.0
29,71,1,-68.97754637948617,0.0
29,71,2,-68.97754637948617,0.0
29,71,3,-73.1744603332603,0.0
29,71,4,-73.1744603332603,0.0
29,71,5,-73.1744603332603,0.0
29,71,6,-73.27746838648852,0.0
29,71,7,-73.75773095838832,0.0
29,71,8,-73.75773095838832,0.0
29,71,9,-73.75773095838832,0.0
29,71,10,-73.75773095838832,0.0
29,71,11,-73.77624631853286,0.0
29,71,12,-73.77624631853286,0.0
29,71,13,-73.78580282726722,0.0
29,71,14,-73.79100657159512,0.0
29,71,15,-73.79562220663911,0.0
29,71,16,-73.80788118085133,0.0
29,71,17,-73.80788118085133,0.0
29,71,18,-73.80979637696112,0.0
29,71,19,-73.80979637696112,9.097441999451268e-05
29,71,20,-73.8108504802558,9.403083431586723e-05
29,71,21,-73.8108504802558,9.454574825584994e-05
29,71,22,-73.81097887902166,0.0003219664340842584
29,71,23,-73.81097887902166,0.0003216420483252555
29,71,24,-73.81097887902166,0.00032500267278050043
29,71,25,-73.81097887902166,0.0003251503343214888
29,71,26,-73.81097887902166,0.0004062929795835651
29,71,27,-73.81097887902166,0.00040714393817715267
29,71,28,-73.81097887902166,0.0004066651651065402
29,71,29,-73.81097887902166,0.00040988600893115485
29,71,30,-73.81112265672999,0.0004109485558899661
29,71,31,-73.81112265672999,0.0004107814031466612
29,71,32,-73.81121082029085,0.0035290261257969846
29,71,33,-73.81121082029085,0.0035744941460548528
29,71,34,-73.81121082029085,0.0035839920096646383
29,71,35,-73.81139588719998,0.004029558726036958
29,71,36,-73.81139588719998,0.004011888017645546
29,71,37,-73.81139588719998,0.006945823886773927
29,71,38,-73.81141726850956,0.006936838963883327
29,71,39,-73.81141726850956,0.011756415599944899
29,71,40,-73.81141933006973,0.011812840932085835
29,71,41,-73.81141933006973,0.03493215836182797
29,71,42,-73.81141933006973,0.043110005646925235
29,71,43,-73.81141933006973,0.044048274536290934
29,71,44,-73.81142102867295,0.04557256072379855
29,71,45,-73.81142102867295,0.04672710501673293
29,71,46,-73.81142102867295,0.1629004028251966
29,71,47,-73.81142260259475,0.16327731984559668
29,71,48,-73.81142260259475,0.17633538576421473
29,71,49,-73.81142260259475,0.18794303156849101
29,71,50,-73.81142260259475,0.1945907143103856
29,71,51,-73.81142260259475,0.29877652949631395
29,71,52,-73.81142268177865,0.301302948032524
29,71,53,-73.81142268177865,0.3547164572429039
29,71,54,-73.81142268177865,0.35444572442692096
29,71,55,-73.81142268177865,0.3657957184564641
29,71,56,-73.81142268177865,0.37050751710356933
29,71,57,-73.81142268177865,0.37654487957785754
29,71,58,-73.81142268177865,0.9576411196176559
29,71,59,-73.81142268177865,0.9530093599169748
29,71,60,-73.81142268177865,2.1625288368415507
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_stage1_utilization_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,-73.81141984423931,48,1083,True,Optimization terminated successfully.,6.305705322517576e-05,13.91052747058177,15.542012454311795,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,-73.81142290443358,62,1092,True,Optimization terminated successfully.,2.811344186944699e-05,13.546581456463594,14.7257698816719,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,-73.81142285171734,63,1007,True,Optimization terminated successfully.,7.017446177585205e-05,13.58241816658772,14.80638770079582,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,-73.81142296845036,50,1185,True,Optimization terminated successfully.,7.039398135766791e-05,14.08831763711169,15.942685801778255,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,-73.81142146029237,53,894,True,Optimization terminated successfully.,5.397820184064959e-05,13.798995980524762,15.291959744682462,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,-73.81142261122619,57,1138,True,Optimization terminated successfully.,6.711954155145442e-05,13.800212798889612,15.294685703383443,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,-73.8114228999114,61,1405,True,Optimization terminated successfully.,7.365908547013644e-05,13.89500719180335,15.507179419104917,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,-73.8114221757832,53,978,True,Optimization terminated successfully.,6.674766349912201e-05,13.623582909630501,14.89884941459913,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,-73.8114222502494,53,956,True,Optimization terminated successfully.,4.301067182117006e-05,13.594981431818116,14.834621048416608,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,-73.81142297462287,50,1164,True,Optimization terminated successfully.,5.024354195553224e-05,13.959181021474032,15.651327994922822,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,-73.8114228709698,71,1192,True,Optimization terminated successfully.,1.8865637308860467e-05,13.529534649942724,14.687373853266958,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,-73.81142278829117,71,1607,True,Optimization terminated successfully.,5.723348351677716e-05,14.089423727259224,15.945190977994434,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,-73.81142132461213,68,1170,True,Optimization terminated successfully.,7.014524239445677e-05,13.51693313400218,14.658968136318922,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,-73.81142284246192,66,1083,True,Optimization terminated successfully.,6.3541271549862e-05,13.695917223602898,15.061059648433584,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,-73.81142059344927,53,1113,True,Optimization terminated successfully.,5.913239295255458e-05,14.075296230855326,15.913206917770337,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,-73.8114229033294,75,1264,True,Optimization terminated successfully.,4.422457924374614e-06,13.411513538646222,14.420455377538817,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,-73.81142278705772,59,1104,True,Optimization terminated successfully.,2.558713352526956e-05,13.673571724812923,15.010976959185246,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,-73.81142257114078,59,1339,True,Optimization terminated successfully.,5.5629213776751115e-05,13.842288414125397,15.388959925441242,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,-73.81142252963963,57,1324,True,Optimization terminated successfully.,2.0192710147094588e-05,13.888037005998951,15.491540547729006,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,-73.81142252144292,70,1209,True,Optimization terminated successfully.,7.129830270149922e-05,13.43453753822212,14.47269880793721,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,-73.8114227090386,59,1341,True,Optimization terminated successfully.,4.408958469519116e-05,14.122784592278816,16.020842736123974,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,-73.81142278492655,68,1017,True,Optimization terminated successfully.,6.0676727927831354e-05,13.629381561927323,14.911863860928893,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,-73.81142286060225,68,981,True,Optimization terminated successfully.,5.506855458846906e-05,13.315684287128464,14.201879603232337,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,-73.81142283240371,59,993,True,Optimization terminated successfully.,6.803076095800041e-05,13.350843072340325,14.28230246239921,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,-73.8114201186645,56,1179,True,Optimization terminated successfully.,6.433474191987376e-05,13.884200786235393,15.482934148106494,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,-73.81142295014797,61,1068,True,Optimization terminated successfully.,5.1474993641956627e-05,13.590574412976624,14.824718827573198,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,-73.81142275745663,59,1415,True,Optimization terminated successfully.,4.228150902703746e-05,14.021781166903342,15.792309627234147,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,-73.81142226440761,43,933,True,Optimization terminated successfully.,3.240989132074601e-05,13.854077470226361,15.415384743638786,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,-73.8114220133815,61,1115,True,Optimization terminated successfully.,7.005805041580016e-05,13.617537405841237,14.885278537269514,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,-73.81142268177865,61,1381,True,Optimization terminated successfully.,3.413198203656902e-05,14.077825275346749,15.918930661745868,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_stage1_utilization_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,-73.81142297462287,-73.81142273324761,-73.81142235487097,8.471116499802392e-07,6.045920741826194e-07,59.0,1126.5,13.74745660206383,13.74738497611859,0.23839944354368772,0.3221901727022196,15.176509696558023,15.176078517451177,0.5360130496320651,0.7223337130199106,10,0.23333333333333334,7,25;1;2;21;8;7;28,25,-73.81142295014797,"[13.590574412976624, 14.824718827573198]",0.028785940512109347,0.03406386795369798,0.08280010546372907,0.06469229120084541,0.07651071174981361,0.18609397925699334
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_stage2_distortion_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,-1.455902791918472e-06,124,911,True,Optimization terminated successfully.,4.4528728011257255e-13,13.286469545671402,14.1348259561469,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,-1.455902784642136e-06,103,511,True,Optimization terminated successfully.,1.4090763830930556e-12,13.28646963397643,14.134825992521522,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,-1.45590269592588e-06,97,630,True,Optimization terminated successfully.,9.162208775338513e-13,13.286470706959324,14.134826436601047,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,-1.4559027413860086e-06,111,671,True,Optimization terminated successfully.,5.496888359289679e-13,13.286470869989525,14.134826095741241,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,-1.4559027877803068e-06,100,685,True,Optimization terminated successfully.,3.1988794216663796e-13,13.286469749781757,14.134825952373678,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,-1.4559027656829166e-06,120,749,True,Optimization terminated successfully.,6.765304044239666e-13,13.286469978803146,14.134826069062244,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,-1.4559028074446269e-06,119,884,True,Optimization terminated successfully.,7.696394336980281e-13,13.286469416533288,14.134825869107775,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,-1.4559028116900094e-06,136,794,True,Optimization terminated successfully.,1.0959450114661894e-12,13.286469317625878,14.134825855416723,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,-1.4559028032598676e-06,111,740,True,Optimization terminated successfully.,1.4457557228714798e-12,13.28646956290083,14.13482587483535,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,-1.4559027932987726e-06,100,588,True,Optimization terminated successfully.,1.0727452929043281e-12,13.28646961322481,14.134825935846914,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,-1.4559027775268165e-06,114,815,True,Optimization terminated successfully.,3.510170230888161e-13,13.286469884573753,14.134826001985212,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,-1.4559027823692168e-06,105,593,True,Optimization terminated successfully.,9.651404084585735e-13,13.286469434987053,14.134826039896693,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,-1.4559027788073478e-06,117,743,True,Optimization terminated successfully.,4.69995804454716e-13,13.286469844348307,14.134825999507337,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,-1.4559027421336492e-06,98,720,True,Optimization terminated successfully.,7.14264877262881e-13,13.286469594099264,14.134826293357973,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,-1.4559027962629437e-06,113,652,True,Optimization terminated successfully.,1.2575237327422823e-12,13.286469559077268,14.13482592391768,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,-1.4559023487095266e-06,90,544,True,Optimization terminated successfully.,1.2777065294893471e-12,13.286478090340763,14.134827668560636,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,-1.4559028080873061e-06,125,871,True,Optimization terminated successfully.,6.704624869097583e-14,13.286469431595359,14.13482586226131,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,-1.4559026680980282e-06,105,581,True,Optimization terminated successfully.,1.3339345205079383e-12,13.286472269094624,14.134826381098561,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,-1.455902685083507e-06,88,463,True,Optimization terminated successfully.,1.251096491954297e-12,13.286470061942111,14.134826614239161,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,-1.4559026828346052e-06,108,637,True,Optimization terminated successfully.,1.4243145171367052e-12,13.286469903324077,14.134826655031038,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,-1.455902711610463e-06,90,573,True,Optimization terminated successfully.,1.0194110458028846e-12,13.286471297771483,14.134826234032058,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,-1.4559024591836569e-06,123,579,True,Optimization terminated successfully.,1.1709384229827498e-12,13.286477208326588,14.13482704338904,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,-1.4559027512718563e-06,103,650,True,Optimization terminated successfully.,1.3312638122502396e-12,13.286470160067502,14.13482614009081,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,-1.455902744078524e-06,113,651,True,Optimization terminated successfully.,4.056582664736873e-13,13.286470897774015,14.134826072671387,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,-1.4559028059872885e-06,118,870,True,Optimization terminated successfully.,6.837176700686869e-13,13.286469371782132,14.134825886317186,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,-1.4559027329603436e-06,118,682,True,Optimization terminated successfully.,1.140653955435112e-12,13.286469667512034,14.134826345241876,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,-1.455902794241674e-06,109,650,True,Optimization terminated successfully.,1.4533746867426765e-12,13.286469747740576,14.134825907933816,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,-1.455902763334424e-06,100,638,True,Optimization terminated successfully.,9.26950287940372e-13,13.286470315551185,14.13482603180823,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,-1.4559027767235337e-06,125,840,True,Optimization terminated successfully.,1.419645657476088e-12,13.28646976616968,14.13482602637015,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,-1.4559027572552441e-06,109,650,True,Optimization terminated successfully.,1.1975564440340438e-12,13.28646952687263,14.134826199281157,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B29_H30_TFD_W100_it0_stage2_distortion_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,-1.4559028116900094e-06,-1.4559027712032251e-06,-1.4559027383196318e-06,1.0042034730121496e-13,5.788693761732042e-14,110.0,651.5,13.286469757975718,13.286470480947226,2.0589996128803793e-06,7.166471061026414e-07,14.134826035852461,14.134826178821491,3.94171352669779e-07,3.3854788838993954e-07,1,1.0,30,7;16;6;24;8;14;26;9;0;4;1;11;12;10;28;5;27;29;22;23;13;3;25;20;2;18;19;17;21;15,7,-1.4559028116900094e-06,"[13.286469317625878, 14.134825855416723]",2.0589996128803798e-06,7.166471061026414e-07,8.77271488519682e-06,3.9417135266977894e-07,3.3854788838993954e-07,1.8131439123436621e-06
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B34_H30_TFD_W100_it0_feasibility_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
0,42,0,3.5514912098109903,1.7363405873177373e-06
0,42,1,3.5514912098109903,1.6621305689908327e-06
0,42,2,3.2925193466057294,1.2483250300868896e-06
0,42,3,3.2925193466057294,1.3553444747567593e-06
0,42,4,3.2076689437938626,1.0841647559686005e-06
0,42,5,3.2076689437938626,1.1627068986408966e-06
0,42,6,3.1772764958695916,1.801448496858823e-06
0,42,7,2.7896703480552105,1.9615414595053622e-06
0,42,8,2.7641165043569345,1.8415999280992153e-06
0,42,9,2.7641165043569345,1.95671614011128e-06
0,42,10,2.7641165043569345,3.849659406665858e-06
0,42,11,2.7641165043569345,3.7664966304738964e-06
0,42,12,2.6948421882829052,3.6102311730603503e-06
0,42,13,2.6948421882829052,3.6396825882178704e-06
0,42,14,2.6948421882829052,6.234779644506145e-06
0,42,15,2.6948421882829052,6.3824486498508055e-06
0,42,16,2.6948421882829052,6.3044395242603135e-06
0,42,17,2.6948421882829052,8.976020796594065e-06
0,42,18,2.6948421882829052,8.945858379050976e-06
0,42,19,2.6948421882829052,5.062620437072174e-05
0,42,20,2.6948421882829052,5.912742448778743e-05
0,42,21,2.6947276091150343,7.353481911982368e-05
0,42,22,2.6947276091150343,0.0001003951554927948
0,42,23,2.6947276091150343,0.0001621369158277626
0,42,24,2.6947276091150343,0.00017014125347769487
0,42,25,2.6947276091150343,0.0005874020429045337
0,42,26,2.6947276091150343,0.0007078669581312932
0,42,27,2.6947276091150343,0.0009986270238799993
0,42,28,2.6945265375698,0.0012089931788159469
0,42,29,2.6943703880773455,0.005229782171003993
0,42,30,2.6942089546203647,0.00598162072657085
0,42,31,2.6942044245682837,0.009218014342074188
0,42,32,2.6941732887436842,0.012977097066925859
0,42,33,2.6941719159185595,0.029445475269350433
0,42,34,2.6941719159185595,0.031691232149452596
0,42,35,2.6941711996215645,0.032803313709968654
0,42,36,2.6941711996215645,0.032969928617494045
0,42,37,2.69416884349738,0.07078671859344493
0,42,38,2.694166899438642,0.13323125439922703
0,42,39,2.694166899438642,0.16464213373530207
0,42,40,2.6941662483884556,0.3103829186552684
0,42,41,2.694165661199179,0.4675394463248222
0,42,42,2.6941653713827804,0.5311993998196094
0,42,43,2.694165001908459,0.7939747731484207
0,42,44,2.6941647032647325,1.5034429500450044
1,43,0,4.467632474931577,1.6456557118955353e-06
1,43,1,3.694132074891286,1.3238526443577114e-06
1,43,2,3.694132074891286,1.3786763665274742e-06
1,43,3,3.2435638510068543,1.2788074907940267e-06
1,43,4,3.2435638510068543,1.1749958534546986e-06
1,43,5,3.2435638510068543,1.4408264321966012e-06
1,43,6,2.861689417161301,1.3317146967766282e-06
1,43,7,2.861689417161301,1.4133494284098035e-06
1,43,8,2.861689417161301,1.5842757268923071e-06
1,43,9,2.861689417161301,1.8457638806649027e-06
1,43,10,2.7186845400879065,2.7854737781452054e-06
1,43,11,2.696818889580811,2.7101615734201e-06
1,43,12,2.696818889580811,4.456391460215976e-06
1,43,13,2.696818889580811,4.530354664319582e-06
1,43,14,2.696818889580811,4.192046384527944e-06
1,43,15,2.696818889580811,8.698714982826859e-06
1,43,16,2.696818889580811,1.3815014761300014e-05
1,43,17,2.696593386718746,1.3700374447827275e-05
1,43,18,2.696593386718746,1.5432258500980235e-05
1,43,19,2.6946836949644535,2.178371281379858e-05
1,43,20,2.6946836949644535,2.184338013989251e-05
1,43,21,2.694637675488252,8.58074787780601e-05
1,43,22,2.694637675488252,0.00028058311159504624
1,43,23,2.694637675488252,0.0003831520610634179
1,43,24,2.6945795230309386,0.00037078495658979997
1,43,25,2.6942128431792867,0.0004796664287269382
1,43,26,2.6942128431792867,0.0005944282880800598
1,43,27,2.6942128431792867,0.0014634436144970776
1,43,28,2.6942128431792867,0.0021586481693661678
1,43,29,2.6941756436899595,0.002987164882183725
1,43,30,2.6941756436899595,0.004048606988138313
1,43,31,2.6941756436899595,0.004268703011350119
1,43,32,2.6941756436899595,0.004334595858908692
1,43,33,2.6941756436899595,0.007900516389936463
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27,69,18,2.694654815461945,3.081432257154119e-05
27,69,19,2.694654815461945,3.544589449941868e-05
27,69,20,2.694654815461945,3.718387924858871e-05
27,69,21,2.694654815461945,6.689032246998904e-05
27,69,22,2.694654815461945,9.94648988112648e-05
27,69,23,2.694654815461945,0.00010358478305842275
27,69,24,2.694654815461945,0.00023388803577877525
27,69,25,2.6944496926989245,0.00025736333059816357
27,69,26,2.694312396970645,0.0010922448957686659
27,69,27,2.694312396970645,0.0024986170186720846
27,69,28,2.694212295707345,0.002619072178915278
27,69,29,2.694212295707345,0.0029966261002829526
27,69,30,2.694212295707345,0.0078048010753538795
27,69,31,2.6941900265436374,0.009559643305644348
27,69,32,2.6941768027402873,0.013190913582936309
27,69,33,2.6941768027402873,0.02469770601027476
27,69,34,2.694172764183861,0.02745845434389422
27,69,35,2.6941694657667767,0.03348429667521665
27,69,36,2.6941694657667767,0.03352090487300872
27,69,37,2.6941678245197522,0.033665743398381694
27,69,38,2.694166230966516,0.033325039966704786
27,69,39,2.694166230966516,0.1831029560558314
27,69,40,2.6941656825047176,0.23822286795612965
27,69,41,2.6941656825047176,0.2882521828908973
27,69,42,2.6941656825047176,0.38371261564163767
27,69,43,2.6941650642103525,0.4239734391864838
27,69,44,2.6941647599228986,0.6268248024141706
27,69,45,2.6941647599228986,0.6856736540241772
27,69,46,2.6941646463849906,1.7281377654670598
28,70,0,4.0927077213660255,1.5652840755536112e-06
28,70,1,3.381847938645649,1.4618783794358473e-06
28,70,2,3.381847938645649,1.3636761198703812e-06
28,70,3,3.288175894059691,1.1974987106631111e-06
28,70,4,2.964712833085173,1.1640446688125405e-06
28,70,5,2.964712833085173,1.1083424004754098e-06
28,70,6,2.8855455235892578,1.0899077969194336e-06
28,70,7,2.8855455235892578,1.0121520831843234e-06
28,70,8,2.8789451738270486,9.607852046382525e-07
28,70,9,2.842855234483011,9.208185400630749e-07
28,70,10,2.805122915701361,8.571266401422509e-07
28,70,11,2.7016420764439317,8.358886981398123e-07
28,70,12,2.7016420764439317,1.1857178550670014e-06
28,70,13,2.7016420764439317,1.1858236411709955e-06
28,70,14,2.7016420764439317,8.30702363642635e-06
28,70,15,2.6945963701113556,8.665411757498651e-06
28,70,16,2.6945963701113556,8.578955550328635e-06
28,70,17,2.6945963701113556,3.6892561295104856e-05
28,70,18,2.6943759823170694,8.825721870494319e-05
28,70,19,2.6943759823170694,9.850475170464278e-05
28,70,20,2.69419122923415,0.00018527432997107965
28,70,21,2.69419122923415,0.00039209817654041446
28,70,22,2.6941769057346505,0.0005452550440136044
28,70,23,2.6941769057346505,0.0006068585235166627
28,70,24,2.6941769057346505,0.0006449466962640565
28,70,25,2.6941769057346505,0.0010351781381233633
28,70,26,2.6941764204413516,0.001084071819655611
28,70,27,2.6941764204413516,0.001146896185503666
28,70,28,2.694171079899661,0.005391200396405003
28,70,29,2.694169230832415,0.006425415177072103
28,70,30,2.694169230832415,0.007646793837447525
28,70,31,2.694169230832415,0.03794646617004769
28,70,32,2.6941653274487862,0.05543856824864229
28,70,33,2.6941653274487862,0.07032444814246598
28,70,34,2.6941653274487862,0.16415440373521276
28,70,35,2.6941653274487862,0.2509075434581386
28,70,36,2.6941653274487862,0.3607553914190145
28,70,37,2.6941653274487862,0.45402141641790433
28,70,38,2.6941647179302777,0.7770811394724974
28,70,39,2.6941647179302777,0.9372911168592447
28,70,40,2.6941647179302777,1.4724210370457955
29,71,0,4.602770592363925,1.6900140638892482e-06
29,71,1,4.473404979724421,1.8190618553179833e-06
29,71,2,3.543919699139792,1.672988780309043e-06
29,71,3,3.543919699139792,1.2679307055782826e-06
29,71,4,3.5371449453950277,1.1261720921679714e-06
29,71,5,2.740265693222966,1.1281521562477543e-06
29,71,6,2.7159643703058123,1.0057960873165359e-06
29,71,7,2.7159643703058123,1.1314987753740504e-06
29,71,8,2.7159643703058123,1.4985259079735833e-06
29,71,9,2.7159643703058123,1.7056011125706228e-06
29,71,10,2.7159643703058123,4.136259130148006e-06
29,71,11,2.7034668639894335,6.559376036165809e-06
29,71,12,2.7034668639894335,9.09009546291752e-06
29,71,13,2.6972410998362193,1.3463675557527597e-05
29,71,14,2.6963965292849252,1.8447833799701725e-05
29,71,15,2.695425635702918,2.6453718271279404e-05
29,71,16,2.694275206707516,0.000184309990604315
29,71,17,2.694275206707516,0.0002451291059249667
29,71,18,2.694275206707516,0.0003808439038504556
29,71,19,2.694275206707516,0.0005336338873032824
29,71,20,2.6942240819131626,0.0009553235952423311
29,71,21,2.6942240819131626,0.0014232610192974591
29,71,22,2.6941926954256132,0.002916623077108747
29,71,23,2.6941814255776535,0.0036866858801761293
29,71,24,2.6941814255776535,0.0038600685004391497
29,71,25,2.6941814255776535,0.005722972019427585
29,71,26,2.694176268179831,0.009893946715704403
29,71,27,2.694169685931172,0.01004552960961899
29,71,28,2.694169685931172,0.010516133924554888
29,71,29,2.6941664841897364,0.0325168998390185
29,71,30,2.6941664841897364,0.03288160760260879
29,71,31,2.6941647515769915,0.12046972299087734
29,71,32,2.6941647515769915,0.1389692014899208
29,71,33,2.6941647515769915,0.1869164680045484
29,71,34,2.6941647515769915,0.6024593065116335
29,71,35,2.6941647515769915,0.6884664800577541
29,71,36,2.6941647515769915,0.7325890231029955
29,71,37,2.6941647515769915,0.7480652320865511
29,71,38,2.6941647515769915,1.4536887097432138
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B34_H30_TFD_W100_it0_feasibility_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,2.6941647032647325,45,2300,True,Optimization terminated successfully.,1.791998191780475e-06,15.152894980572478,18.673158697217346,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,2.694164752793583,45,2300,True,Optimization terminated successfully.,2.2192547306181015e-06,15.152774161259009,18.67287463050379,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,2.694165127136955,47,2400,True,Optimization terminated successfully.,1.8192969403371081e-06,15.152848105008358,18.67304811130475,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,2.6941646470109646,44,2250,True,Optimization terminated successfully.,2.1668219986961486e-06,15.152891022513273,18.67314946755903,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,2.694164620607694,43,2200,True,Optimization terminated successfully.,1.4150158240561412e-06,15.15300665087472,18.673421403771716,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,2.6941646261788605,41,2100,True,Optimization terminated successfully.,2.3342847286729934e-06,15.15318469529751,18.67384016822247,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,2.6941646736834173,42,2150,True,Optimization terminated successfully.,1.7250817521794752e-06,15.152665702639098,18.672619510434686,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,2.694164747117611,45,2300,True,Optimization terminated successfully.,1.6443005785244591e-06,15.152469902669255,18.67215902315798,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,2.694165132985063,43,2200,True,Optimization terminated successfully.,2.581493283378144e-06,15.151930832555953,18.67089119343217,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,2.6941647020118467,45,2300,True,Optimization terminated successfully.,1.7879295385735464e-06,15.153020634386602,18.67345422710853,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,2.6941648501561417,48,2450,True,Optimization terminated successfully.,1.3632012959003184e-06,15.15355891588239,18.674720261011046,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,2.69416484911153,43,2200,True,Optimization terminated successfully.,2.6648065891895155e-06,15.153648882874357,18.674931907709343,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,2.6941647091758796,55,2800,True,Optimization terminated successfully.,1.194200250657793e-06,15.153432483649228,18.67442295658387,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,2.694164719254701,41,2100,True,Optimization terminated successfully.,1.9120019519999843e-06,15.153474260690542,18.67452125114793,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,2.694164758941895,47,2400,True,Optimization terminated successfully.,2.4499911203497694e-06,15.152621277108995,18.67251496803988,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,2.6941646898566773,39,2000,True,Optimization terminated successfully.,1.6680900569227756e-06,15.152959756570624,18.67331105489128,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,2.6941649577263433,41,2100,True,Optimization terminated successfully.,2.64004766298356e-06,15.152301173173091,18.671762082380802,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,2.694164709606298,44,2250,True,Optimization terminated successfully.,2.6845258758074594e-06,15.152884600209282,18.67313427883543,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,2.6941647273517333,47,2400,True,Optimization terminated successfully.,2.202476783560344e-06,15.153058739561443,18.673543830049816,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,2.6941649133773353,44,2250,True,Optimization terminated successfully.,2.464624530710827e-06,15.152755630418648,18.672830802130512,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,2.6941646483990667,47,2400,True,Optimization terminated successfully.,2.4987458573504744e-06,15.152811168828173,18.672961629633733,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,2.694164682696112,43,2200,True,Optimization terminated successfully.,2.33198398079598e-06,15.152903919512426,18.673179737089946,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,2.694164730115858,45,2300,True,Optimization terminated successfully.,2.6625625826429303e-06,15.152792905452362,18.672918612094097,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,2.6941647461998173,47,2400,True,Optimization terminated successfully.,2.0620086907691642e-06,15.153224085191269,18.673932722121588,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,2.6941646911076873,54,2750,True,Optimization terminated successfully.,2.2243793569035083e-06,15.152659310447845,18.672604464038667,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,2.6941646942405697,49,2500,True,Optimization terminated successfully.,1.9254855833543274e-06,15.152944936455278,18.673276280085076,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,2.694165028066891,46,2350,True,Optimization terminated successfully.,2.646880183553402e-06,15.153011642927032,18.673432816774937,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,2.6941646463849906,47,2400,True,Optimization terminated successfully.,1.558999677952638e-06,15.153259884221281,18.67401700802985,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,2.6941647179302777,41,2100,True,Optimization terminated successfully.,1.829752827177548e-06,15.153076636401252,18.673585931425364,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,2.6941647515769915,39,2000,True,Optimization terminated successfully.,1.853331138647899e-06,15.15309963614603,18.673640141118256,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B34_H30_TFD_W100_it0_feasibility_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range
30,2.694164620607694,2.6941647185924893,2.694164765135584,1.3697770292538405e-07,6.72353870356801e-08,45.0,2300.0,15.152924427983852,15.152938884449926,0.0003596573704140951,0.00031503890248885114,18.67322800858751,18.673261972263465,0.0008459000427756782,0.0007409627936638685,1,1.0,30,4;5;27;3;20;6;21;15;24;25;9;0;12;17;28;13;18;22;23;7;29;1;14;11;10;19;16;26;2;8,4,2.694164620607694,"[15.15300665087472, 18.673421403771716]",0.0003596573704140951,0.00031503890248885114,0.0017180503184039964,0.0008459000427756782,0.0007409627936638685,0.004040714277174118
Code/reports/width_optimization/2W/rbf_optimization/rbf_2W_B34_H30_TFD_W100_it0_minimum_violation_solution.csv 0 → 100644
w_val,b_val,h_val,tfd_w_val,it,feasible,minimum_identified_violation,tw1,tw2,TFD_tw1,TFD_tw2,TFD_tw1_margin,TFD_tw2_margin,TFD_tw1_conservative,TFD_tw2_conservative,TFD_frame,TFD_frame_margin,TFD_frame_conservative,safety_margin_mode,safety_margin_factor,max_violation,viol_window_1,viol_window_2,viol_frame
2,34,30,100,0,False,2.694164620607694,15.15300665087472,18.673421403771716,100.6200027812006,75.06793868153244,2.074161750085807,8.068599856553988,102.6941645312864,83.13653853808643,92.6941646206077,0.0,92.6941646206077,local_oof,1.0,2.694164620607694,2.694164531286404,0.0,2.694164620607694
Code/reports/width_optimization/3W/rbf_optimization/it0/optimization_results_3W_B29_H45_TFD_W100.csv 0 → 100644
Parameter,Value
Configuration_W,3.0
Configuration_B,29.0
Configuration_H,45.0
Configuration_TFD_W,100.0
Iteration,0.0
tw1_optimal,6.529194041599833
tw2_optimal,8.003035071843449
tw3_optimal,9.154159911806682
Objective_score,-1.254328973833789e-06
Exy_tw1,0.04230598273951332
Exy_tw2,0.05245130908883433
Exy_tw3,0.05564387343168242
TFM_tw1,71.59560122795085
TFM_tw2,92.49478061429727
TFM_tw3,95.1494521950408
TFM_frame,62.27677574827612
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_feasibility_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
0,42,0,0.0,8.432159056986347e-07
0,42,1,0.0,4.5910105120028743e-07
0,42,2,0.0,2.5860840143223444e-07
0,42,3,0.0,1.7427767098628137e-07
0,42,4,0.0,1.3633535504595913e-07
0,42,5,0.0,4503599627.370496
1,43,0,0.0,8.602038005604697e-07
1,43,1,0.0,6.741355677351934e-07
1,43,2,0.0,5.485338379531098e-07
1,43,3,0.0,4.4343818350693205e-07
1,43,4,0.0,3.360268887142339e-07
1,43,5,0.0,3.130677979354267e-07
1,43,6,0.0,3.130974347120899e-07
1,43,7,0.0,2.867789404475224e-07
1,43,8,0.0,1.8418090598547532e-07
1,43,9,0.0,1.9093577749932258e-07
1,43,10,0.0,1.809158635400817e-07
1,43,11,0.0,1.7948736391066062e-07
1,43,12,0.0,1.1624763874382004e-07
1,43,13,0.0,4503599627.370496
2,44,0,0.0,6.466099160551599e-07
2,44,1,0.0,4.772274807502287e-07
2,44,2,0.0,3.8630992221625186e-07
2,44,3,0.0,3.626713566818545e-07
2,44,4,0.0,2.529628296544556e-07
2,44,5,0.0,1.1624763874382105e-07
2,44,6,0.0,4503599627.370496
3,45,0,0.0,8.222033564867002e-07
3,45,1,0.0,6.776196926832113e-07
3,45,2,0.0,3.746244611347158e-07
3,45,3,0.0,2.6982935357204323e-07
3,45,4,0.0,1.162476387438199e-07
3,45,5,0.0,1.162476387438199e-07
3,45,6,0.0,1.162476387438199e-07
3,45,7,0.0,4503599627.370496
4,46,0,0.0,7.88620680795734e-07
4,46,1,0.0,5.744393167940012e-07
4,46,2,0.0,4.6786098025190253e-07
4,46,3,0.0,3.5254836318561547e-07
4,46,4,0.0,2.47609718518277e-07
4,46,5,0.0,1.6674303325268924e-07
4,46,6,0.0,1.643258807775177e-07
4,46,7,0.0,1.5650517347810415e-07
4,46,8,0.0,1.1624763874382081e-07
4,46,9,0.0,1.1624763874382081e-07
4,46,10,0.0,1.1624763874382141e-07
4,46,11,0.0,1.1624763874382141e-07
4,46,12,0.0,4503599627.370496
5,47,0,0.0,7.070849458291968e-07
5,47,1,0.0,4.6006266775210474e-07
5,47,2,0.0,2.3457724689037347e-07
5,47,3,0.0,2.3363404361902627e-07
5,47,4,0.0,1.8811001437352219e-07
5,47,5,0.0,1.162476387438193e-07
5,47,6,0.0,4503599627.370496
6,48,0,0.0,8.588319552559368e-07
6,48,1,0.0,5.171448725442299e-07
6,48,2,0.0,3.00676852432595e-07
6,48,3,0.0,2.412082027403016e-07
6,48,4,0.0,1.8371358802461964e-07
6,48,5,0.0,4503599627.370496
7,49,0,0.0,7.728046412638317e-07
7,49,1,0.0,3.8906083142352893e-07
7,49,2,0.0,2.6808603156797425e-07
7,49,3,0.0,1.4135483261333212e-07
7,49,4,0.0,4503599627.370496
8,50,0,0.0,7.287365546207074e-07
8,50,1,0.0,5.073196807840417e-07
8,50,2,0.0,3.638163085364562e-07
8,50,3,0.0,1.6339040659646281e-07
8,50,4,0.0,1.3655553825878094e-07
8,50,5,0.0,4503599627.370496
9,51,0,0.0,7.392322554011814e-07
9,51,1,0.0,5.445168063782429e-07
9,51,2,0.0,2.431752384417202e-07
9,51,3,0.0,1.8276302096983025e-07
9,51,4,0.0,1.1624763874381998e-07
9,51,5,0.0,1.1624763874381998e-07
9,51,6,0.0,4503599627.370496
10,52,0,0.0,8.017765181376376e-07
10,52,1,0.0,6.027897308011628e-07
10,52,2,0.0,3.139639942501177e-07
10,52,3,0.0,2.484481552994595e-07
10,52,4,0.0,1.645488721068912e-07
10,52,5,0.0,4503599627.370496
11,53,0,0.0,8.45567703651883e-07
11,53,1,0.0,5.592765371048547e-07
11,53,2,0.0,4.7173004934110315e-07
11,53,3,0.0,3.262850539178529e-07
11,53,4,0.0,2.7526619302994305e-07
11,53,5,0.0,1.5122311453638236e-07
11,53,6,0.0,1.5122311453638236e-07
11,53,7,0.0,1.1624763874382002e-07
11,53,8,0.0,1.1624763874382002e-07
11,53,9,0.0,4503599627.370496
12,54,0,0.0,7.816304588173345e-07
12,54,1,0.0,5.441553093455987e-07
12,54,2,0.0,3.2402540200114907e-07
12,54,3,0.0,2.327639573626083e-07
12,54,4,0.0,1.4499510255781763e-07
12,54,5,0.0,1.1624763874381949e-07
12,54,6,0.0,4503599627.370496
13,55,0,0.0,7.543065060715749e-07
13,55,1,0.0,4.227442956581205e-07
13,55,2,0.0,2.4502860354275455e-07
13,55,3,0.0,2.0885182425189915e-07
13,55,4,0.0,1.715096774834056e-07
13,55,5,0.0,1.2213013172726624e-07
13,55,6,0.0,1.1624763874382748e-07
13,55,7,0.0,4503599627.370496
14,56,0,0.0,8.028262155953363e-07
14,56,1,0.0,5.891730245421811e-07
14,56,2,0.0,3.638028418492596e-07
14,56,3,0.0,2.7314758765187107e-07
14,56,4,0.0,2.10012196955084e-07
14,56,5,0.0,1.352360694630143e-07
14,56,6,0.0,4503599627.370496
15,57,0,0.0,6.030226062830058e-07
15,57,1,0.0,3.1963406578146544e-07
15,57,2,0.0,2.3783227019002063e-07
15,57,3,0.0,1.6810848775799944e-07
15,57,4,0.0,1.4346606584111462e-07
15,57,5,0.0,1.653230780171902e-07
15,57,6,0.0,1.162476387438198e-07
15,57,7,0.0,4503599627.370496
16,58,0,0.0,6.803838735522418e-07
16,58,1,0.0,5.24215849821965e-07
16,58,2,0.0,4.3689124312633713e-07
16,58,3,0.0,2.641489385702718e-07
16,58,4,0.0,1.1624763874381969e-07
16,58,5,0.0,1.1624763874381969e-07
16,58,6,0.0,1.1624763874381969e-07
16,58,7,0.0,4503599627.370496
17,59,0,0.0,9.117909045945911e-07
17,59,1,0.0,7.374430144811434e-07
17,59,2,0.0,5.668460476899503e-07
17,59,3,0.0,5.015729040625346e-07
17,59,4,0.0,4.0689726618761696e-07
17,59,5,0.0,2.798297516071873e-07
17,59,6,0.0,1.806606730993765e-07
17,59,7,0.0,1.4657633424677648e-07
17,59,8,0.0,4503599627.370496
18,60,0,0.0,5.791620981906918e-07
18,60,1,0.0,3.995033546885559e-07
18,60,2,0.0,2.936233492764085e-07
18,60,3,0.0,2.0549497175035716e-07
18,60,4,0.0,1.6551828571500628e-07
18,60,5,0.0,4503599627.370496
19,61,0,0.0,7.472933011656862e-07
19,61,1,0.0,4.777596070663009e-07
19,61,2,0.0,2.8700021903165766e-07
19,61,3,0.0,1.8225951757221926e-07
19,61,4,0.0,1.1624763874381961e-07
19,61,5,0.0,4503599627.370496
20,62,0,0.0,6.286405788202414e-07
20,62,1,0.0,4.5129581001739135e-07
20,62,2,0.0,3.3515052684209356e-07
20,62,3,0.0,2.954140443857463e-07
20,62,4,0.0,2.1366907685825898e-07
20,62,5,0.0,1.6320506263755667e-07
20,62,6,0.0,1.325913056871698e-07
20,62,7,0.0,4503599627.370496
21,63,0,0.0,8.049549294388211e-07
21,63,1,0.0,4.6717632642858045e-07
21,63,2,0.0,3.73753563417354e-07
21,63,3,0.0,2.8427875187578635e-07
21,63,4,0.0,4503599627.370496
22,64,0,0.0,6.987185851277053e-07
22,64,1,0.0,4.02627084801452e-07
22,64,2,0.0,3.198147586610237e-07
22,64,3,0.0,3.3382177092816777e-07
22,64,4,0.0,2.869895714132606e-07
22,64,5,0.0,2.354511788728886e-07
22,64,6,0.0,1.874480273561234e-07
22,64,7,0.0,1.5504226613865175e-07
22,64,8,0.0,1.2541722605915762e-07
22,64,9,0.0,1.1624763874381945e-07
22,64,10,0.0,4503599627.370496
23,65,0,0.0,8.18837178887769e-07
23,65,1,0.0,6.315667188897672e-07
23,65,2,0.0,4.6135873032683655e-07
23,65,3,0.0,2.2647920748791227e-07
23,65,4,0.0,1.504490984983643e-07
23,65,5,0.0,1.162476387438194e-07
23,65,6,0.0,4503599627.370496
24,66,0,0.0,9.079011122500244e-07
24,66,1,0.0,7.00696269169174e-07
24,66,2,0.0,5.396358095692792e-07
24,66,3,0.0,4.841960382568593e-07
24,66,4,0.0,4.221722657737355e-07
24,66,5,0.0,4.551414853474354e-07
24,66,6,0.0,3.7212071461680465e-07
24,66,7,0.0,3.147057189159248e-07
24,66,8,0.0,2.3125076771569928e-07
24,66,9,0.0,1.9793023926425826e-07
24,66,10,0.0,1.1624763874381969e-07
24,66,11,0.0,1.1624763874381978e-07
24,66,12,0.0,4503599627.370496
25,67,0,0.0,6.168173525856474e-07
25,67,1,0.0,4.294087775730618e-07
25,67,2,0.0,3.169852568817559e-07
25,67,3,0.0,2.506756126072435e-07
25,67,4,0.0,1.3424206594216262e-07
25,67,5,0.0,4503599627.370496
26,68,0,0.0,7.237569166226655e-07
26,68,1,0.0,5.118503061078941e-07
26,68,2,0.0,4.037690262901817e-07
26,68,3,0.0,2.720060829587563e-07
26,68,4,0.0,2.3032492307329062e-07
26,68,5,0.0,1.5111615185192883e-07
26,68,6,0.0,1.162476387438194e-07
26,68,7,0.0,1.162476387438194e-07
26,68,8,0.0,4503599627.370496
27,69,0,0.0,7.784980052610694e-07
27,69,1,0.0,7.275405692481541e-07
27,69,2,0.0,5.308460100077458e-07
27,69,3,0.0,3.736854930788015e-07
27,69,4,0.0,1.4979211433523548e-07
27,69,5,0.0,1.6162360958040744e-07
27,69,6,0.0,1.1624763874382072e-07
27,69,7,0.0,4503599627.370496
28,70,0,0.0,7.515297928960452e-07
28,70,1,0.0,5.783976587437266e-07
28,70,2,0.0,4.085411362570486e-07
28,70,3,0.0,2.8822753066753774e-07
28,70,4,0.0,1.7123062026678513e-07
28,70,5,0.0,1.1624763874382202e-07
28,70,6,0.0,1.1624763874382202e-07
28,70,7,0.0,1.1624763874382202e-07
28,70,8,0.0,4503599627.370496
29,71,0,0.0,6.516433169932485e-07
29,71,1,0.0,6.050162246914873e-07
29,71,2,0.0,4.7499658559953675e-07
29,71,3,0.0,4.104769057161796e-07
29,71,4,0.0,3.255554908945694e-07
29,71,5,0.0,2.988376330786678e-07
29,71,6,0.0,2.5115327681026573e-07
29,71,7,0.0,2.3137506123712955e-07
29,71,8,0.0,1.1624763874381937e-07
29,71,9,0.0,1.1624763874381937e-07
29,71,10,0.0,4503599627.370496
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_feasibility_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,tw3,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,0.0,6,525,True,Optimization terminated successfully.,0.0,11.174150137331889,10.687534582953951,12.014694291649047,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,0.0,14,1125,True,Optimization terminated successfully.,0.0,9.19152419196501,8.108034821402732,10.039534646752873,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,0.0,7,600,True,Optimization terminated successfully.,0.0,10.155752192375719,8.970601276850276,11.40587002894916,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,0.0,8,675,True,Optimization terminated successfully.,0.0,11.474514872268399,11.814394401763003,13.021042987337953,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,0.0,13,1050,True,Optimization terminated successfully.,0.0,7.939167103683044,12.056894534842051,12.335280508765564,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,0.0,7,600,True,Optimization terminated successfully.,0.0,8.234494833209553,8.492558603674022,10.610761151377147,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,0.0,6,525,True,Optimization terminated successfully.,0.0,12.347028715349623,12.99749701336649,13.427106488055184,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,0.0,5,450,True,Optimization terminated successfully.,0.0,10.41216849664563,10.587571607995258,10.756026474632204,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,0.0,6,525,True,Optimization terminated successfully.,0.0,8.911227323344477,12.226386351247932,11.460774053803068,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,0.0,7,600,True,Optimization terminated successfully.,0.0,9.513669223694748,10.505189234814223,11.73880049153873,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,0.0,6,525,True,Optimization terminated successfully.,0.0,13.57465180799608,12.484013761680794,12.950813595285702,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,0.0,10,825,True,Optimization terminated successfully.,0.0,12.596369851808078,11.260885371057173,10.827516832666497,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,0.0,7,600,True,Optimization terminated successfully.,0.0,8.210095666482019,10.022750428527393,10.98277888842702,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,0.0,8,675,True,Optimization terminated successfully.,0.0,7.558856951332707,9.37690999310906,11.821647591302575,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,0.0,7,600,True,Optimization terminated successfully.,0.0,9.673186436825947,10.889597875123748,12.271919249022055,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,0.0,8,675,True,Optimization terminated successfully.,0.0,9.39003803924594,12.763397702906135,12.82093993858448,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,0.0,8,675,True,Optimization terminated successfully.,0.0,8.9255081799879,10.015603093634308,12.21998219882257,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,0.0,9,750,True,Optimization terminated successfully.,0.0,10.55683544877956,8.943540620865285,10.361702454275155,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,0.0,6,525,True,Optimization terminated successfully.,0.0,13.056035708580877,10.534043563873398,12.582569334152524,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,0.0,6,525,True,Optimization terminated successfully.,0.0,13.546399752239537,9.303722397656896,12.267089755108481,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,0.0,8,675,True,Optimization terminated successfully.,0.0,9.80939501668398,8.551532503509659,13.562800413583357,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,0.0,5,450,True,Optimization terminated successfully.,0.0,13.693612343311138,10.382500915640675,11.410614633442112,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,0.0,11,900,True,Optimization terminated successfully.,0.0,10.07981532574443,9.325157154256473,10.902970939499848,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,0.0,7,600,True,Optimization terminated successfully.,0.0,12.75262775406484,11.22402326504208,12.728811561401438,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,0.0,13,1050,True,Optimization terminated successfully.,0.0,9.041458796896983,7.992653739929237,10.794398181345834,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,0.0,6,525,True,Optimization terminated successfully.,0.0,11.60520656110254,10.154676417415345,13.165188171886886,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,0.0,9,750,True,Optimization terminated successfully.,0.0,9.577142499507492,13.627560578316487,12.169749587364446,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,0.0,8,675,True,Optimization terminated successfully.,0.0,7.85195950528264,9.309737261286935,10.038672023456906,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,0.0,9,750,True,Optimization terminated successfully.,0.0,8.25645447185958,9.519918102483198,12.10572990343829,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,0.0,11,900,True,Optimization terminated successfully.,0.0,9.423696947466686,9.793881373633914,12.399075849113814,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_feasibility_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,tw3_median,tw3_mean,tw3_std,tw3_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range,tw3_dominant_basin_std,tw3_dominant_basin_iqr,tw3_dominant_basin_range
30,0.0,0.0,0.0,0.0,0.0,7.5,637.5,9.741290726754965,10.2844348051689,1.8729737425017308,2.618037804678833,10.26858866652801,10.39742561829527,1.5009648009933627,1.938077610024079,12.060212097543669,11.8398287408347,1.0012560271192728,1.6137730361612057,30,0.03333333333333333,1,0,0,0.0,"[11.174150137331889, 10.687534582953951, 12.014694291649047]",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_lexicographic_optimization_summary.csv 0 → 100644
w_val,b_val,h_val,tfd_w_val,it,safety_margin_mode,safety_margin_factor,local_margin_support_pass,local_margin_k_requested,local_support_percentile,local_margin_fallback,TFD_tw1_margin_source,TFD_tw1_margin_in_support,TFD_tw1_margin_k,TFD_tw1_margin_nearest_distance,TFD_tw1_margin_support_radius,TFD_tw1_margin_neighbor_samples,TFD_tw1_margin_neighbor_distances,TFD_tw1_margin_neighbor_underprediction,TFD_tw2_margin_source,TFD_tw2_margin_in_support,TFD_tw2_margin_k,TFD_tw2_margin_nearest_distance,TFD_tw2_margin_support_radius,TFD_tw2_margin_neighbor_samples,TFD_tw2_margin_neighbor_distances,TFD_tw2_margin_neighbor_underprediction,TFD_tw3_margin_source,TFD_tw3_margin_in_support,TFD_tw3_margin_k,TFD_tw3_margin_nearest_distance,TFD_tw3_margin_support_radius,TFD_tw3_margin_neighbor_samples,TFD_tw3_margin_neighbor_distances,TFD_tw3_margin_neighbor_underprediction,TFD_frame_margin_source,TFD_frame_margin_in_support,TFD_frame_margin_k,TFD_frame_margin_nearest_distance,TFD_frame_margin_support_radius,TFD_frame_margin_neighbor_samples,TFD_frame_margin_neighbor_distances,TFD_frame_margin_neighbor_underprediction,min_de_reproducibility_required,de_reproducibility_pass,feasible_domain_found,minimum_domain_violation,feasible,feasibility_violation,tw1,tw2,tw3,TFD_tw1,TFD_tw2,TFD_tw3,TFD_tw1_margin,TFD_tw2_margin,TFD_tw3_margin,TFD_tw1_conservative,TFD_tw2_conservative,TFD_tw3_conservative,TFD_frame,TFD_frame_margin,TFD_frame_conservative,min_window_tfd,max_window_tfd,mean_window_tfd,window_tfd_range_diagnostic,distortion_measure,stage1_Tmin_star,stage1_preservation_threshold,stage2_distortion,stage1_observed_basin_spread,stage1_preservation_tolerance,stage1_preservation_tolerance_source,stage1_rep_run,stage1_rep_seed,stage2_rep_run,stage2_rep_seed,stage1_reproducibility,stage2_reproducibility,stage1_n_basins,stage2_n_basins,stage2_tw1_dominant_basin_iqr,stage2_tw2_dominant_basin_iqr,stage2_tw3_dominant_basin_iqr,stage2_tw1_dominant_basin_range,stage2_tw2_dominant_basin_range,stage2_tw3_dominant_basin_range
3,29,45,100,0,local_oof,1.0,True,-1,95.0,global,local_knn_max,True,4,0.1728952080764362,0.59278440308231,2;3;0;4,0.17289521;0.43891902;0.50471262;0.54393562,0;28.404399;5.2293948;0,local_knn_max,True,4,0.1728952080764362,0.59278440308231,2;3;0;4,0.17289521;0.43891902;0.50471262;0.54393562,0;7.5052192;6.848374;1.4164357,local_knn_max,True,4,0.1728952080764362,0.59278440308231,2;3;0;4,0.17289521;0.43891902;0.50471262;0.54393562,4.8505477;0;0;3.1906113,local_knn_max,True,4,0.1728952080764362,0.59278440308231,2;3;0;4,0.17289521;0.43891902;0.50471262;0.54393562,0;0;0;2.0019093,0.8,False,True,0.0,True,0.0,6.529194041599833,8.003035071843449,9.154159911806682,71.59560122795085,92.49478061429727,95.1494521950408,28.404398674307345,7.505219155626804,4.850547677943595,99.9999999022582,99.99999976992407,99.99999987298439,62.27677574827612,2.001909302803483,64.2786850510796,71.59560122795085,95.1494521950408,86.41327801242964,23.553850967089943,1.254328973833789e-06,71.5956013132062,71.5946013132062,1.254328973833789e-06,5.808918785987771e-07,0.001,dominant_DE_basin_spread,0,42,29,71,0.06666666666666667,1.0,24,1,1.2672039026995208e-06,4.1114188853441647e-07,5.626177870254878e-07,1.9901961145762925e-06,1.9733604812444128e-06,1.7222444039788343e-06
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_stage1_utilization_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,tw3,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,-71.5956013132062,114,4430,True,Optimization terminated successfully.,6.194433289789758e-05,7.162980283472351,9.071489186778065,10.824527491834264,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,-71.59560092319805,168,6020,True,Optimization terminated successfully.,5.509237214273995e-05,6.816046254885113,8.511206604553829,9.761214158682861,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,-71.59560112112729,176,5551,True,Optimization terminated successfully.,5.8447830826045275e-05,6.718269597415892,8.57743244454149,9.39882514281931,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,-71.59560121475147,110,4095,True,Optimization terminated successfully.,6.183110294111742e-05,7.021606879212942,8.799702205221843,10.354312037109278,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,-71.59560073231432,133,4970,True,Optimization terminated successfully.,6.959855232807453e-05,7.167349133501979,9.134601988939075,10.801612509403265,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,-71.59560119115645,138,5335,True,Optimization terminated successfully.,5.126734348498234e-05,6.985843079250031,8.562415887377279,10.354908677224277,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,-71.59560084875363,132,4607,True,Optimization terminated successfully.,6.728343493805375e-05,6.863035959240232,8.333991513536951,10.02625879760508,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,-71.59560120775791,128,3974,True,Optimization terminated successfully.,6.441519402152394e-05,6.82615011939599,8.841314814087154,9.587098992033818,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,-71.59559922928895,123,4469,True,Optimization terminated successfully.,6.587909222747428e-05,6.91465407177314,8.326723471286682,10.215665484608207,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,-71.59559897383588,138,4702,True,Optimization terminated successfully.,6.884028233636735e-05,6.786673051404128,8.753540948315477,9.510802368023418,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,-71.59560126356689,97,3507,True,Optimization terminated successfully.,6.243086673158898e-05,6.9466042500336105,9.004522237714221,9.913427516341528,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,-71.59560113679981,125,3975,True,Optimization terminated successfully.,6.338034008119436e-05,6.980217494868272,8.05007184883686,10.59529467753997,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,-71.59560062893308,139,5121,True,Optimization terminated successfully.,6.903061738481193e-05,6.876205259056352,8.824928869059148,9.774234817892717,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,-71.59560059003752,115,4309,True,Optimization terminated successfully.,6.496263066773825e-05,7.0166748721490375,8.508619328637147,10.510083678527138,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,-71.59560087596071,130,4791,True,Optimization terminated successfully.,6.207492489129756e-05,6.8352225598104885,8.318407216508817,9.938019305857093,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,-71.59560040072813,144,5444,True,Optimization terminated successfully.,6.915524617728641e-05,6.9296757946967364,8.989529879835828,9.859297740031415,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,-71.59560048151172,121,4299,True,Optimization terminated successfully.,7.084894424027815e-05,6.805869836783867,8.538030262545716,9.710444406898176,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,-71.59560073164312,106,3766,True,Optimization terminated successfully.,5.700730756676588e-05,6.672435827243351,8.319121143514796,9.407556363819705,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,-71.59560129276394,145,5407,True,Optimization terminated successfully.,6.737264632412548e-05,7.118459482602686,8.922483905565251,10.70582930885702,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,-71.59559905750382,169,5554,True,Optimization terminated successfully.,5.027414334204276e-05,6.861819696041696,8.072068672100025,10.157759297950925,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,-71.59560115997874,100,3951,True,Optimization terminated successfully.,6.339830590216774e-05,7.149458851373995,9.165938553006814,10.686070756055804,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,-71.59560059303487,107,3514,True,Optimization terminated successfully.,6.818240581492438e-05,6.87876111001825,9.018220673267933,9.651331347715487,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,-71.59559798921941,143,5178,True,Optimization terminated successfully.,6.618346890828906e-05,6.816394553524583,8.401117670859307,9.826942513365392,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,-71.59560083917108,127,4973,True,Optimization terminated successfully.,6.318076536060043e-05,6.9370788633373515,8.714775680211574,10.071600878111632,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,-71.59560096277403,139,4759,True,Optimization terminated successfully.,6.784471004382617e-05,6.744205212055821,8.112953311523718,9.74566678249268,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,-71.59560124478514,119,4246,True,Optimization terminated successfully.,7.1120725808895e-05,6.907956124410639,8.201876740579355,10.256480522311126,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,-71.59560065847666,146,5194,True,Optimization terminated successfully.,6.745157222816346e-05,6.949788875848146,8.241238078532996,10.390611612381525,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,-71.59560125930852,153,5701,True,Optimization terminated successfully.,6.81171426378915e-05,6.988069098196383,8.84055810281764,10.189645013401357,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,-71.59560102160845,172,6073,True,Optimization terminated successfully.,7.155509017917678e-05,6.848168338414652,8.209613979104194,10.041193150095484,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,-71.59560126119118,145,3721,True,Optimization terminated successfully.,3.991026526763978e-05,6.723913511111675,8.651390724241656,9.370995763552399,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_stage1_utilization_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,tw3_median,tw3_mean,tw3_std,tw3_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range,tw3_dominant_basin_std,tw3_dominant_basin_iqr,tw3_dominant_basin_range
30,-71.5956013132062,-71.59560089957938,-71.59560067347957,8.08262641160859e-07,6.015981171003659e-07,132.5,4730.5,6.893358617214444,6.90831960137098,0.13099973091669231,0.16560323816215572,8.569924165959385,8.600596198103364,0.3369107626689106,0.5201039108120078,10.03372597385028,10.054590370418078,0.4298446464818715,0.6052058906553004,24,0.06666666666666667,2,0;4,0,-71.5956013132062,"[7.162980283472351, 9.071489186778065, 10.824527491834264]",0.003089243481937284,0.0021844250148141953,0.0043688500296283905,0.04462749038773509,0.031556401080505836,0.0631128021610099,0.016203339467729904,0.011457491215500326,0.022914982430998876
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_stage2_distortion_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,tw3,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,-1.2543289412061643e-06,324,2145,True,Optimization terminated successfully.,2.0442479995858788e-13,6.529194497523315,8.00303524305427,9.154160051019819,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,-1.2543288703899944e-06,381,1669,True,Optimization terminated successfully.,1.2250901698101617e-12,6.5291945876491155,8.003035363229227,9.154161311088533,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,-1.2543288918881548e-06,322,1785,True,Optimization terminated successfully.,8.561770641960674e-13,6.529194921480267,8.003035412806215,9.154160555836716,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,-1.2543289587061589e-06,284,2067,True,Optimization terminated successfully.,6.699435736811177e-14,6.529194295264915,8.003035124144144,9.15415996251053,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,-1.2543289537312428e-06,427,2156,True,Optimization terminated successfully.,5.591866888291407e-13,6.529194257941312,8.003035251423158,9.154159992472005,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,-1.254328864897061e-06,254,1519,True,Optimization terminated successfully.,1.1219513142676478e-12,6.5291957251822055,8.003035519920864,9.154160339990955,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,-1.2543289281762941e-06,234,1695,True,Optimization terminated successfully.,1.1803560319048685e-12,6.5291943538176636,8.003035579408142,9.154160165731355,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,-1.2543289670330709e-06,267,1861,True,Optimization terminated successfully.,4.181004583097061e-13,6.529194100871442,8.003035116742312,9.154159963507697,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,-1.2543287806747263e-06,301,1837,True,Optimization terminated successfully.,1.044789849454672e-12,6.529195236737502,8.00303654276855,9.154161634051086,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,-1.2543288097387766e-06,203,1370,True,Optimization terminated successfully.,1.0051233610981968e-12,6.529195748869749,8.00303622168629,9.154160873003189,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,-1.2543288481480953e-06,190,1252,True,Optimization terminated successfully.,9.890349971774107e-13,6.529195644977029,8.003035923294572,9.154160422788198,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,-1.2543289355660285e-06,242,1304,True,Optimization terminated successfully.,5.70130556675185e-13,6.52919439211583,8.003035343214513,9.154160173428405,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,-1.2543288749070473e-06,356,2084,True,Optimization terminated successfully.,1.1640672397178826e-12,6.529195050686532,8.00303573659835,9.154160531033424,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,-1.2543289578474475e-06,357,2409,True,Optimization terminated successfully.,3.5322422231536225e-13,6.529194230333807,8.003035110941102,9.154160045062854,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,-1.2543288158637682e-06,300,1694,True,Optimization terminated successfully.,1.1844988727458842e-12,6.529196031795948,8.003035170970973,9.154161353563158,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,-1.2543288610741704e-06,338,2145,True,Optimization terminated successfully.,1.2011415882070613e-12,6.52919581383224,8.00303498085816,9.154160774920781,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,-1.2543289403244648e-06,468,2217,True,Optimization terminated successfully.,2.780900108542062e-13,6.5291945670558205,8.003035196293885,9.15416004786966,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,-1.2543289315755166e-06,393,2845,True,Optimization terminated successfully.,8.095203096762344e-13,6.529194268433941,8.003035705349378,9.15416006781873,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,-1.25432893498989e-06,365,2049,True,Optimization terminated successfully.,3.638818613843775e-13,6.529194272736227,8.003035316691637,9.154160306732333,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,-1.254328864066292e-06,377,2036,True,Optimization terminated successfully.,1.2359793037332648e-12,6.529195936544566,8.003035220640069,9.154160418984034,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,-1.2543289733546565e-06,423,3081,True,Optimization terminated successfully.,2.5862928613811944e-14,6.529194055094787,8.003035062944093,9.154159917280607,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,-1.2543287873694454e-06,370,1598,True,Optimization terminated successfully.,6.722511247902537e-13,6.529195806016114,8.003036954218642,9.154160690081675,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,-1.2543289350206691e-06,366,2507,True,Optimization terminated successfully.,8.906555666536031e-13,6.529194660194246,8.003035153282317,9.154160111293603,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,-1.2543289551067036e-06,233,1908,True,Optimization terminated successfully.,8.052509613585695e-14,6.529194182323613,8.003035153730783,9.154160106560559,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,-1.2543289634276857e-06,481,3190,True,Optimization terminated successfully.,8.930805751683725e-13,6.529194091153769,8.003035228253694,9.154159955196757,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,-1.2543289463724293e-06,362,2128,True,Optimization terminated successfully.,2.2972603618127504e-13,6.529194213783571,8.003035266648345,9.154160166222955,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,-1.254328874671716e-06,404,2417,True,Optimization terminated successfully.,1.0480745973202473e-12,6.529195546852483,8.003035495669534,9.154160311707464,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,-1.2543288478078836e-06,264,1648,True,Optimization terminated successfully.,9.945425349855066e-13,6.52919547051604,8.003035856712366,9.154160629754411,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,-1.254328872960125e-06,349,2276,True,Optimization terminated successfully.,3.4520027268516337e-13,6.529195462715939,8.003035070760347,9.15416075772411,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,-1.254328973833789e-06,382,2733,True,Optimization terminated successfully.,3.0945828786342905e-13,6.529194041599833,8.003035071843449,9.154159911806682,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B29_H45_TFD_W100_it0_stage2_distortion_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,tw3_median,tw3_mean,tw3_std,tw3_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range,tw3_dominant_basin_std,tw3_dominant_basin_iqr,tw3_dominant_basin_range
30,-1.254328973833789e-06,-1.2543289298759054e-06,-1.2543289020243156e-06,5.79743696334954e-14,8.7617554990897e-14,352.5,2058.0,6.529194577352468,6.529194848803328,6.902955409429391e-07,1.2672039026995208e-06,8.003035259035752,8.00303544646998,4.614499522554156e-07,4.1114188853441647e-07,9.154160240080369,9.154160384968076,4.5397604691078615e-07,5.626177870254878e-07,1,1.0,30,29;20;7;24;3;13;23;4;25;0;16;11;22;18;17;6;2;12;26;28;1;5;19;15;10;27;14;9;21;8,29,-1.254328973833789e-06,"[6.529194041599833, 8.003035071843449, 9.154159911806682]",6.90295540942939e-07,1.2672039026995208e-06,1.9901961145762925e-06,4.614499522554156e-07,4.1114188853441647e-07,1.9733604812444128e-06,4.5397604691078615e-07,5.626177870254878e-07,1.7222444039788343e-06
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B34_H45_TFD_W100_it0_feasibility_de_convergence.csv 0 → 100644
run,seed,generation,best_objective,scipy_convergence_measure
0,42,0,0.0,1.256956074427095e-06
0,42,1,0.0,1.0633202155564365e-06
0,42,2,0.0,7.914223120386386e-07
0,42,3,0.0,5.651644651674987e-07
0,42,4,0.0,4.5306778189836687e-07
0,42,5,0.0,3.115082416903974e-07
0,42,6,0.0,2.2665962417304007e-07
0,42,7,0.0,2.115405999902157e-07
0,42,8,0.0,1.1624763874382052e-07
0,42,9,0.0,4503599627.370496
1,43,0,0.0,9.454514568442789e-07
1,43,1,0.0,6.742648897492821e-07
1,43,2,0.0,4.954154749115381e-07
1,43,3,0.0,3.5698186272430893e-07
1,43,4,0.0,3.3507546377452715e-07
1,43,5,0.0,2.1637051651416837e-07
1,43,6,0.0,1.6125925777794278e-07
1,43,7,0.0,1.1624763874382004e-07
1,43,8,0.0,4503599627.370496
2,44,0,0.0,1.1475258485803853e-06
2,44,1,0.0,9.115050668927326e-07
2,44,2,0.0,7.184424017928703e-07
2,44,3,0.0,5.010674360775986e-07
2,44,4,0.0,2.0529620989318688e-07
2,44,5,0.0,1.3951321320448518e-07
2,44,6,0.0,1.6326876877757512e-07
2,44,7,0.0,4503599627.370496
3,45,0,0.0,1.072151329185799e-06
3,45,1,0.0,8.353342567840484e-07
3,45,2,0.0,5.364472142857773e-07
3,45,3,0.0,3.880710041828315e-07
3,45,4,0.0,3.2084944495107963e-07
3,45,5,0.0,2.903262329376938e-07
3,45,6,0.0,1.7586540205416466e-07
3,45,7,0.0,1.1624763874381961e-07
3,45,8,0.0,4503599627.370496
4,46,0,0.0,1.0750922184880092e-06
4,46,1,0.0,6.516917534304131e-07
4,46,2,0.0,4.638451569924034e-07
4,46,3,0.0,3.569176257662181e-07
4,46,4,0.0,3.0050765577405455e-07
4,46,5,0.0,1.493020245574937e-07
4,46,6,0.0,1.2568299483363858e-07
4,46,7,0.0,1.1624763874382194e-07
4,46,8,0.0,4503599627.370496
5,47,0,0.0,9.284604211764257e-07
5,47,1,0.0,7.056656641190581e-07
5,47,2,0.0,4.488095535697207e-07
5,47,3,0.0,4.056999202821772e-07
5,47,4,0.0,3.595593126512236e-07
5,47,5,0.0,3.0356218498955363e-07
5,47,6,0.0,1.8014949029016489e-07
5,47,7,0.0,1.5691403807471908e-07
5,47,8,0.0,1.408762416075266e-07
5,47,9,0.0,1.408762416075266e-07
5,47,10,0.0,1.408762416075266e-07
5,47,11,0.0,1.1624763874381934e-07
5,47,12,0.0,1.1624763874381954e-07
5,47,13,0.0,1.1624763874381954e-07
5,47,14,0.0,4503599627.370496
6,48,0,0.0,1.0759944778069648e-06
6,48,1,0.0,7.0616605350969e-07
6,48,2,0.0,5.176695890293848e-07
6,48,3,0.0,3.680136109223358e-07
6,48,4,0.0,2.2671862220608202e-07
6,48,5,0.0,1.525316741684165e-07
6,48,6,0.0,1.1624763874382485e-07
6,48,7,0.0,4503599627.370496
7,49,0,0.0,9.044091399988245e-07
7,49,1,0.0,6.002870105138291e-07
7,49,2,0.0,4.831850236624306e-07
7,49,3,0.0,4.314176639564509e-07
7,49,4,0.0,3.065554680356464e-07
7,49,5,0.0,2.0085024554013225e-07
7,49,6,0.0,1.6460064702664363e-07
7,49,7,0.0,1.6008623356115133e-07
7,49,8,0.0,1.1624763874382004e-07
7,49,9,0.0,1.1624763874382004e-07
7,49,10,0.0,1.1624763874382004e-07
7,49,11,0.0,1.1624763874382004e-07
7,49,12,0.0,4503599627.370496
8,50,0,0.0,1.0446018424020724e-06
8,50,1,0.0,8.77547937785097e-07
8,50,2,0.0,6.745231613522296e-07
8,50,3,0.0,4.6886810253792224e-07
8,50,4,0.0,2.5673769511762785e-07
8,50,5,0.0,2.1028007222857467e-07
8,50,6,0.0,1.673546956800787e-07
8,50,7,0.0,1.3570640461652275e-07
8,50,8,0.0,4503599627.370496
9,51,0,0.0,9.593669100146891e-07
9,51,1,0.0,6.164732262063648e-07
9,51,2,0.0,4.861443108487652e-07
9,51,3,0.0,3.579467156030976e-07
9,51,4,0.0,3.336694712387409e-07
9,51,5,0.0,2.694098846703094e-07
9,51,6,0.0,1.2087827507034574e-07
9,51,7,0.0,4503599627.370496
10,52,0,0.0,1.0620187586153443e-06
10,52,1,0.0,8.351350482902679e-07
10,52,2,0.0,6.379096731930126e-07
10,52,3,0.0,3.160886587089081e-07
10,52,4,0.0,1.6145895848717573e-07
10,52,5,0.0,1.45251092963135e-07
10,52,6,0.0,1.162476387438193e-07
10,52,7,0.0,1.162476387438193e-07
10,52,8,0.0,1.162476387438194e-07
10,52,9,0.0,4503599627.370496
11,53,0,0.0,9.412647469533054e-07
11,53,1,0.0,7.261613878305201e-07
11,53,2,0.0,5.078066445170623e-07
11,53,3,0.0,3.583131087688407e-07
11,53,4,0.0,2.477542858669608e-07
11,53,5,0.0,1.3897054279433118e-07
11,53,6,0.0,1.1624763874382093e-07
11,53,7,0.0,4503599627.370496
12,54,0,0.0,1.0306638983584254e-06
12,54,1,0.0,8.312065150565974e-07
12,54,2,0.0,4.771707090996299e-07
12,54,3,0.0,2.1607747011961076e-07
12,54,4,0.0,1.9410030520517482e-07
12,54,5,0.0,1.7099012852795586e-07
12,54,6,0.0,1.2916528560832168e-07
12,54,7,0.0,1.1624763874381978e-07
12,54,8,0.0,1.1624763874381978e-07
12,54,9,0.0,1.1624763874381978e-07
12,54,10,0.0,4503599627.370496
13,55,0,0.0,7.739561286674233e-07
13,55,1,0.0,6.178573471395e-07
13,55,2,0.0,4.990655148501581e-07
13,55,3,0.0,2.9877054296129677e-07
13,55,4,0.0,1.6654486965922471e-07
13,55,5,0.0,1.58387148578977e-07
13,55,6,0.0,1.58387148578977e-07
13,55,7,0.0,1.1624763874381992e-07
13,55,8,0.0,1.1624763874381992e-07
13,55,9,0.0,1.1624763874381992e-07
13,55,10,0.0,1.1624763874382024e-07
13,55,11,0.0,4503599627.370496
14,56,0,0.0,9.16640331477294e-07
14,56,1,0.0,7.14002724981467e-07
14,56,2,0.0,5.961676219331998e-07
14,56,3,0.0,4.366761368874973e-07
14,56,4,0.0,2.859159576883136e-07
14,56,5,0.0,1.8311303518517456e-07
14,56,6,0.0,1.1624763874382075e-07
14,56,7,0.0,4503599627.370496
15,57,0,0.0,7.554020014000888e-07
15,57,1,0.0,5.362615969452385e-07
15,57,2,0.0,4.5151172032933725e-07
15,57,3,0.0,2.991789845507268e-07
15,57,4,0.0,2.2194627592647065e-07
15,57,5,0.0,1.825129547219044e-07
15,57,6,0.0,1.1624763874382089e-07
15,57,7,0.0,4503599627.370496
16,58,0,0.0,9.387411038985561e-07
16,58,1,0.0,6.919192992756343e-07
16,58,2,0.0,6.3421088244052e-07
16,58,3,0.0,5.487868654838638e-07
16,58,4,0.0,5.037441616275953e-07
16,58,5,0.0,3.0474903201924497e-07
16,58,6,0.0,1.8056009367325875e-07
16,58,7,0.0,1.2107165799612786e-07
16,58,8,0.0,4503599627.370496
17,59,0,0.0,1.0110439956139678e-06
17,59,1,0.0,6.441765177799063e-07
17,59,2,0.0,5.948049502306746e-07
17,59,3,0.0,3.018592912187054e-07
17,59,4,0.0,2.3291452965554593e-07
17,59,5,0.0,1.1689752402118759e-07
17,59,6,0.0,1.1689752402118759e-07
17,59,7,0.0,1.1689752402118759e-07
17,59,8,0.0,1.162476387438717e-07
17,59,9,0.0,1.162476387438717e-07
17,59,10,0.0,4503599627.370496
18,60,0,0.0,7.019440350411797e-07
18,60,1,0.0,4.55284616575274e-07
18,60,2,0.0,4.445230932939937e-07
18,60,3,0.0,3.04099575279544e-07
18,60,4,0.0,2.0015883520544487e-07
18,60,5,0.0,1.6350663654440008e-07
18,60,6,0.0,1.162476387438194e-07
18,60,7,0.0,4503599627.370496
19,61,0,0.0,1.00631761084886e-06
19,61,1,0.0,6.715408621450435e-07
19,61,2,0.0,3.689948720468301e-07
19,61,3,0.0,2.0231405611845934e-07
19,61,4,0.0,1.5171681374756366e-07
19,61,5,0.0,1.4285402584394382e-07
19,61,6,0.0,4503599627.370496
20,62,0,0.0,9.146674761267096e-07
20,62,1,0.0,7.841026291625986e-07
20,62,2,0.0,6.255825408677254e-07
20,62,3,0.0,3.86808696901425e-07
20,62,4,0.0,3.8196951119092996e-07
20,62,5,0.0,3.536598340092125e-07
20,62,6,0.0,2.8068997845571403e-07
20,62,7,0.0,2.456715319203227e-07
20,62,8,0.0,2.295804889438153e-07
20,62,9,0.0,1.960047501353119e-07
20,62,10,0.0,1.6470682902236327e-07
20,62,11,0.0,1.1624763874382609e-07
20,62,12,0.0,1.1624763874382609e-07
20,62,13,0.0,1.1624763874382609e-07
20,62,14,0.0,1.1624763874382609e-07
20,62,15,0.0,1.1624763874382609e-07
20,62,16,0.0,4503599627.370496
21,63,0,0.0,1.0777247779119615e-06
21,63,1,0.0,6.787637768154609e-07
21,63,2,0.0,5.448234169953427e-07
21,63,3,0.0,5.030430167392632e-07
21,63,4,0.0,3.586790739815558e-07
21,63,5,0.0,2.662077446252526e-07
21,63,6,0.0,1.436187360195252e-07
21,63,7,0.0,1.4994985698697724e-07
21,63,8,0.0,4503599627.370496
22,64,0,0.0,1.1338076494610358e-06
22,64,1,0.0,8.321913778475534e-07
22,64,2,0.0,5.400672981782292e-07
22,64,3,0.0,3.786950502376539e-07
22,64,4,0.0,3.7347130983039056e-07
22,64,5,0.0,2.1808068040400365e-07
22,64,6,0.0,1.4612986718413177e-07
22,64,7,0.0,1.4280399676414095e-07
22,64,8,0.0,1.2172386076170256e-07
22,64,9,0.0,1.1624763874381933e-07
22,64,10,0.0,4503599627.370496
23,65,0,0.0,7.663727957264237e-07
23,65,1,0.0,5.738386567763472e-07
23,65,2,0.0,3.1859458778122803e-07
23,65,3,0.0,1.482461012450608e-07
23,65,4,0.0,4503599627.370496
24,66,0,0.0,9.140501003421255e-07
24,66,1,0.0,4.6068062347636106e-07
24,66,2,0.0,2.944914215886358e-07
24,66,3,0.0,2.740508710233652e-07
24,66,4,0.0,2.4163062357858386e-07
24,66,5,0.0,1.6126323376722715e-07
24,66,6,0.0,1.1624763874381942e-07
24,66,7,0.0,4503599627.370496
25,67,0,0.0,1.0754086808936547e-06
25,67,1,0.0,7.605054960313384e-07
25,67,2,0.0,7.023189826412958e-07
25,67,3,0.0,4.535453416284334e-07
25,67,4,0.0,1.7367040479217235e-07
25,67,5,0.0,1.44591949347712e-07
25,67,6,0.0,1.1624763874382045e-07
25,67,7,0.0,1.1624763874382045e-07
25,67,8,0.0,4503599627.370496
26,68,0,0.0,8.549899324235246e-07
26,68,1,0.0,7.764058953931228e-07
26,68,2,0.0,5.712653035025528e-07
26,68,3,0.0,4.3395020057321756e-07
26,68,4,0.0,2.7432477068232017e-07
26,68,5,0.0,2.2882875505674573e-07
26,68,6,0.0,1.4464972832144578e-07
26,68,7,0.0,1.440936828246672e-07
26,68,8,0.0,4503599627.370496
27,69,0,0.0,1.0416545908156014e-06
27,69,1,0.0,7.517919766960045e-07
27,69,2,0.0,6.122969478247315e-07
27,69,3,0.0,4.298324128153388e-07
27,69,4,0.0,2.580570366920575e-07
27,69,5,0.0,1.6687333845114833e-07
27,69,6,0.0,1.1624763874382617e-07
27,69,7,0.0,1.1624763874382617e-07
27,69,8,0.0,1.1624763874382617e-07
27,69,9,0.0,1.1624763874382617e-07
27,69,10,0.0,1.1624763874382617e-07
27,69,11,0.0,1.1624763874382617e-07
27,69,12,0.0,4503599627.370496
28,70,0,0.0,1.0364684497858276e-06
28,70,1,0.0,8.878948885827211e-07
28,70,2,0.0,7.205048610725526e-07
28,70,3,0.0,5.306443668648118e-07
28,70,4,0.0,4.14881047064861e-07
28,70,5,0.0,2.68717227788315e-07
28,70,6,0.0,2.6002946328982e-07
28,70,7,0.0,2.0097202537293311e-07
28,70,8,0.0,1.5865484537082968e-07
28,70,9,0.0,1.5926510218255756e-07
28,70,10,0.0,1.1624763874382194e-07
28,70,11,0.0,4503599627.370496
29,71,0,0.0,9.257271641885841e-07
29,71,1,0.0,7.732697920911685e-07
29,71,2,0.0,6.429307677300393e-07
29,71,3,0.0,5.16496979368823e-07
29,71,4,0.0,3.370079902468378e-07
29,71,5,0.0,2.491914602330666e-07
29,71,6,0.0,1.8993592185740266e-07
29,71,7,0.0,1.400011262608375e-07
29,71,8,0.0,4503599627.370496
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B34_H45_TFD_W100_it0_feasibility_de_raw_runs.csv 0 → 100644
run,seed,objective,nit,nfev,success,message,final_population_energy_std,tw1,tw2,tw3,de_strategy,de_mutation,de_recombination,de_popsize,de_maxiter,de_tol,de_atol,de_init,de_updating,de_polish,de_seed
0,42,0.0,10,825,True,Optimization terminated successfully.,0.0,10.418777406165528,10.904332586717414,13.231124235848181,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,42
1,43,0.0,9,750,True,Optimization terminated successfully.,0.0,10.451201167865932,12.925539696277058,11.99812296848252,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,43
2,44,0.0,8,675,True,Optimization terminated successfully.,0.0,12.578133967595326,11.04392106955783,11.346251616786416,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,44
3,45,0.0,9,750,True,Optimization terminated successfully.,0.0,10.124906709519564,11.92060226990638,12.124970367240914,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,45
4,46,0.0,9,750,True,Optimization terminated successfully.,0.0,12.743724785235583,10.606171735209305,11.106224884284119,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,46
5,47,0.0,15,1200,True,Optimization terminated successfully.,0.0,10.444408728569934,10.698361894151823,11.096094375397817,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,47
6,48,0.0,8,675,True,Optimization terminated successfully.,0.0,13.376294371930026,12.855671623378878,13.714536428223639,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,48
7,49,0.0,13,1050,True,Optimization terminated successfully.,0.0,8.530706960804409,12.558742105858977,12.892959204014788,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,49
8,50,0.0,9,750,True,Optimization terminated successfully.,0.0,11.550815784379013,9.988212493813577,11.215463477224887,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,50
9,51,0.0,8,675,True,Optimization terminated successfully.,0.0,9.70888246747979,13.592381521865667,11.708175451772144,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,51
10,52,0.0,10,825,True,Optimization terminated successfully.,0.0,8.293137014420644,9.945023693430967,11.191165935911398,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,52
11,53,0.0,8,675,True,Optimization terminated successfully.,0.0,11.616010737087754,12.039170638187928,12.445808042409173,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,53
12,54,0.0,11,900,True,Optimization terminated successfully.,0.0,8.548980888131311,11.815619515667784,13.248321041461072,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,54
13,55,0.0,12,975,True,Optimization terminated successfully.,0.0,10.369173449704583,10.827263907870522,12.635387975603273,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,55
14,56,0.0,8,675,True,Optimization terminated successfully.,0.0,9.604512056778177,12.819370018379898,12.100945082691405,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,56
15,57,0.0,8,675,True,Optimization terminated successfully.,0.0,13.439973170035133,12.476744211784407,13.80560977298645,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,57
16,58,0.0,9,750,True,Optimization terminated successfully.,0.0,11.284112068612398,12.14905977910317,13.63722679725812,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,58
17,59,0.0,11,900,True,Optimization terminated successfully.,0.0,12.701436161416396,10.361320828290019,13.621773180149187,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,59
18,60,0.0,8,675,True,Optimization terminated successfully.,0.0,11.238412115730446,10.116385177503153,13.364684756057919,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,60
19,61,0.0,7,600,True,Optimization terminated successfully.,0.0,11.309737324575691,12.351155624967038,11.093994267465963,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,61
20,62,0.0,17,1350,True,Optimization terminated successfully.,0.0,9.536154272451078,9.925435536587786,13.341842696568289,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,62
21,63,0.0,9,750,True,Optimization terminated successfully.,0.0,11.248988699878963,12.895896083093685,12.27381759946967,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,63
22,64,0.0,11,900,True,Optimization terminated successfully.,0.0,9.261807659650698,11.258109417655092,12.71408649471115,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,64
23,65,0.0,5,450,True,Optimization terminated successfully.,0.0,9.640554050252105,11.141558256791948,13.25235702125808,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,65
24,66,0.0,8,675,True,Optimization terminated successfully.,0.0,10.880015312257225,10.382882345571987,12.844355547467746,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,66
25,67,0.0,9,750,True,Optimization terminated successfully.,0.0,12.769038449953381,11.030613127511918,11.62876080441135,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,67
26,68,0.0,9,750,True,Optimization terminated successfully.,0.0,13.784177105046208,9.555493905495275,12.200467135192321,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,68
27,69,0.0,13,1050,True,Optimization terminated successfully.,0.0,12.399046211001417,10.623477029578872,11.007586843636135,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,69
28,70,0.0,12,975,True,Optimization terminated successfully.,0.0,10.166471121985008,10.670664211053534,11.070268945038555,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,70
29,71,0.0,9,750,True,Optimization terminated successfully.,0.0,10.283674316552073,10.303317604758302,12.556019115947052,best1bin,"(0.5, 1.0)",0.7,25,500,1e-06,0.0,latinhypercube,immediate,False,71
Code/reports/width_optimization/3W/rbf_optimization/rbf_3W_B34_H45_TFD_W100_it0_feasibility_de_summary.csv 0 → 100644
n_runs,min_objective,median_objective,mean_objective,std_objective,iqr_objective,median_nit,median_nfev,tw1_median,tw1_mean,tw1_std,tw1_iqr,tw2_median,tw2_mean,tw2_std,tw2_iqr,tw3_median,tw3_mean,tw3_std,tw3_iqr,n_basins,reproducibility_rate,dominant_basin_count,dominant_basin_indices,best_basin_run_idx,best_basin_objective,best_basin_x,tw1_dominant_basin_std,tw1_dominant_basin_iqr,tw1_dominant_basin_range,tw2_dominant_basin_std,tw2_dominant_basin_iqr,tw2_dominant_basin_range,tw3_dominant_basin_std,tw3_dominant_basin_iqr,tw3_dominant_basin_range
30,0.0,0.0,0.0,0.0,0.0,9.0,750.0,10.665608240061578,10.94344215116886,1.5215623781211878,2.3903988145332686,11.037267098534874,11.32608326366734,1.1140638254161135,1.8619269705197539,12.359812820939421,12.348946735498988,0.9366447143204514,1.8271429263652017,30,0.03333333333333333,1,0,0,0.0,"[10.418777406165528, 10.904332586717414, 13.231124235848181]",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
Code/src/width_optimization/CHANGELOG_LOCAL_OOF.md 0 → 100644
# Change log - two-stage lexicographic optimization with local OOF margins
## Final optimization objective
- Removed `window_tfd_range` from the optimization objectives.
- Removed `mean(window TFD)` from the optimization objectives.
- Stage 1 is `maximize min(window TFD)` using nominal surrogate predictions.
- Stage 2 is `maximize distortion_measure` while preserving Stage 1 within a DE-repeatability tolerance.
- `mean_window_tfd` and `window_tfd_range` are retained only as diagnostics.
- Dense 2D grid verification now reproduces exactly this two-stage ordering.
## Separation of safety margin and lexicographic tolerance
- The local/global OOF safety margin is used only in the upper damage constraints: `TFD_pred + margin <= limit`.
- The Stage-1 preservation tolerance is independent of surrogate error and is derived from the spread of the Stage-1 criterion inside the dominant DE basin.
- The safety margin never enters `min(window TFD)` and never defines the Stage-1 preservation threshold.
- When a predicted TFD is far below its upper limit, the conservative constraint has positive slack and therefore does not govern the solution.
## Local surrogate-error safety margin
- `LocalOOFSafetyMarginModel` uses normalized design-space distances.
- Default neighbourhood: `k = max(d+1, ceil(sqrt(N)))`, capped at `N`.
- Local margin: maximum positive OOF underprediction among the `k` nearest FEM samples.
- Default support radius: P95 of FEM nearest-neighbour distances.
- Outside local support, the default fallback is the global maximum positive OOF residual.
- Local margins affect only feasibility constraints, never the lexicographic objectives.
## Surrogate training
- ML and RBF training save `local_oof_errors_*.csv` with one independent/aggregated OOF residual per FEM sample and output.
- Global validation envelopes are retained for adaptive error validation and comparison.
## Optimization outputs
- Final summary now records only two lexicographic stages.
- `stage1_preservation_tolerance_source` is explicitly stored as `dominant_DE_basin_spread`.
- Final dominant-basin geometry diagnostics are stored with the `stage2_...` prefix.
- The exact local margin, neighbour sample IDs, normalized distances, support radius, source, and support status are retained for the selected candidate.
## Adaptive acceptance
- DE reproducibility is evaluated for two stages rather than three.
- Inter-iteration geometry diagnostics use the final Stage-2 dominant-basin spread.
- FEM feasibility, OOF error validity, local support, and DE reproducibility remain separate acceptance checks.
Code/src/width_optimization/README_ACCEPTANCE_CHANGES.md 0 → 100644
# RESILINK surrogate optimization - two-stage lexicographic formulation and local OOF margins
## 1. Optimization logic
The optimization no longer tries to equalize the TFD values of the dissipative windows and it no longer maximizes the mean window TFD. The final lexicographic order is:
1. **Feasibility**: every conservative window TFD and the conservative frame TFD must remain below their prescribed limits.
2. **Stage 1 - activate every window as much as possible**: maximize `min(TFD_window)` using the nominal surrogate predictions.
3. **Stage 2 - maximize the distortion measure** while preserving the Stage-1 optimum within the numerical repeatability observed across independent DE runs.
`mean_window_tfd` and `window_tfd_range` are still saved only as diagnostics. They do not enter the objective function or any lexicographic preservation constraint.
The resulting problem is therefore
```text
Feasibility:
TFD_i_pred(x) + safety_margin_i(x) <= TFD_window_limit
TFD_frame_pred(x) + safety_margin_frame(x) <= 90
Stage 1:
maximize min_i TFD_i_pred(x)
Stage 2:
maximize distortion_measure(x)
subject to
min_i TFD_i_pred(x) >= Tmin_star - stage1_preservation_tolerance
```
## 2. Two different tolerances - do not mix them
The code deliberately keeps two quantities separate.
### Local OOF surrogate safety margin
For a TFD output at candidate `x`, the local margin is based on positive out-of-fold underprediction residuals of nearby FEM designs:
```text
positive_underprediction = max(TFD_FEM - TFD_OOF, 0)
margin_i(x) = max(positive_underprediction among the k nearest FEM samples)
```
It is used **only for the upper physical damage constraint**:
```text
TFD_pred(x) + margin_i(x) <= TFD_limit
```
If the nominal TFD is far below the limit, the constraint has large positive slack and the margin does not govern the optimization. No extra `if` statement is needed.
The safety margin is not included in `min(TFD_window)` and it is not used as the Stage-1 preservation tolerance.
### Stage-1 preservation tolerance
After the independent Stage-1 DE runs, the code identifies the dominant basin and evaluates the spread of `min(TFD_window)` inside that basin. The Stage-1 preservation tolerance is
```text
max(CONSTRAINT_TOL, observed dominant-basin spread)
```
It is therefore a **numerical DE-repeatability tolerance**, not a surrogate-error or physical safety factor. Stage 2 uses it only to prevent the distortion maximization from materially degrading the Stage-1 optimum.
## 3. Local OOF neighbourhood
Distances to FEM samples are computed in the normalized design space
```text
z_i = (x_i - lower_i) / (upper_i - lower_i)
```
The automatic neighbourhood size is
```text
k = max(d + 1, ceil(sqrt(N)))
```
capped at `N`, where `d` is the number of design variables and `N` the number of independent OOF samples. Thus the expected defaults are `k=3` for `d=2, N=8` and `k=4` for `d=3, N=16`.
## 4. Local-support / extrapolation check
The code also computes the distribution of each FEM sample's distance to its nearest other FEM sample. By default, the local-support radius is the 95th percentile of that distribution.
If a candidate lies outside that support, the default DE behaviour is conservative:
```text
--local-margin-fallback global
```
so the global maximum positive OOF residual is used instead of the local k-neighbour value. The candidate is flagged as outside local support. After FEM evaluation, such a candidate is used to enrich and retrain the surrogate rather than being accepted immediately.
## 5. Safety-margin modes
```text
--safety-margin-mode local_oof # default
--safety-margin-mode global_oof # global one-sided OOF margin
--safety-margin-mode none # nominal surrogate constraints only
```
`--safety-margin-factor` multiplies the empirical margin and is intended mainly for sensitivity analysis. It is not a structural/material partial safety factor.
Optional local-margin settings include:
```text
--local-margin-k 3
--local-support-percentile 95
--local-margin-fallback global
```
## 6. Surrogate-training output
Both ML and RBF training save the global validation-envelope CSV and additionally
```text
local_oof_errors_<W>W_B<B>_H<H>_it<it>.csv
```
For every output and FEM sample this stores the design coordinates, FEM value, independent/aggregated OOF prediction, absolute OOF error, and positive OOF underprediction residual.
## 7. Adaptive acceptance
The previous fixed criteria `error < 5%`, `|eJ| <= 10`, and fixed `5%` geometry stabilization are not used.
After FEM evaluation of the optimized candidate, acceptance requires FEM feasibility, surrogate error within the independent OOF validation envelope, local design-space support when `local_oof` is active, and adequate DE reproducibility in both lexicographic stages.
Inter-iteration geometry changes remain diagnostics. In the current two-stage formulation they are compared with the dominant-basin spread of the final Stage-2 solution rather than used as a hard percentage threshold.
## 8. Typical run sequence
Train the surrogate:
```bash
python ml_surrogate_train.py --W 2 --B 29 --it 0
# or
python rbf_surrogate_train.py --W 2 --B 29 --it 0
```
Run the local-margin optimization:
```bash
python ml_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 0 --n-runs 30
# or
python rbf_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 0 --n-runs 30
```
Neighbourhood-size sensitivity can be checked without changing the formulation:
```bash
python ml_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 0 --local-margin-k 2
python ml_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 0 --local-margin-k 3
python ml_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 0 --local-margin-k 4
```
After FEM evaluation and appending the candidate to the FEM dataset:
```bash
python adaptive_acceptance.py --W 2 --B 29 --TFD_W 90 --it 0 --surrogate ml
# or
python adaptive_acceptance.py --W 2 --B 29 --TFD_W 90 --it 0 --surrogate rbf
```
## 9. Interpretation
The local OOF margin is an empirical model-error guard, not a probabilistic confidence bound or material/manufacturing safety factor. Its role is only to reduce the chance that local surrogate underprediction close to an active TFD limit produces an FEM-infeasible candidate.
The Stage-1 preservation tolerance has a different role: it expresses the numerical repeatability of the DE Stage-1 optimum and defines how much of that optimum may be sacrificed while Stage 2 maximizes the distortion measure.
Code/src/width_optimization/adaptive_acceptance.py 0 → 100644
"""
Adaptive FEM Acceptance for Lexicographic Surrogate Optimization
================================================================
Evaluates a surrogate-optimized candidate after high-fidelity FEM validation.
Hard acceptance gates:
1. FEM feasibility: every window TFD <= TFD_W and frame TFD <= 90.
2. Surrogate validity: candidate FEM-vs-surrogate errors for all active TFD
and Exy outputs must remain within the independently estimated OOF error
envelope saved by nested cross-validation.
3. Local-support validity: when local OOF safety margins are used, the final
candidate must lie inside the empirical design-space support radius. A
candidate outside support is FEM-evaluated but then used to enrich/retrain.
4. DE reproducibility: every lexicographic stage must reach the requested
dominant-basin reproducibility rate.
The old absolute objective-error threshold and fixed 5% geometry-stability
threshold are intentionally not used. Inter-iteration geometry changes are
reported only as diagnostics relative to the observed DE basin spread.
"""
import argparse
import os
from pathlib import Path
import joblib
import numpy as np
import pandas as pd
from de_utils import TFD_FRAME_LIMIT
BASE_DIR = Path(__file__).resolve().parent
def infer_h_and_start_rows(w_val):
if w_val == 2:
return 30, 8
if w_val == 3:
return 45, 16
if w_val == 5:
return 60, 64
return None, 10
def infer_paths(w_val, b_val, h_val, tfd_w_val, it_val, surrogate):
method_models = "ml_models" if surrogate == "ml" else "rbf_models"
method_report = "ml_optimization" if surrogate == "ml" else "rbf_optimization"
models_dir = (
BASE_DIR
/ ".."
/ ".."
/ "models"
/ "width_optimization"
/ f"{w_val}W"
/ method_models
/ f"per_output_models_B{b_val}_H{h_val}"
/ f"it{it_val}"
).resolve()
fem_data = (
BASE_DIR
/ ".."
/ ".."
/ "data"
/ "width_optimization"
/ f"{w_val}W"
/ f"{surrogate}_FEMdata_B{b_val}_H{h_val}.csv"
).resolve()
report_dir = (
BASE_DIR
/ ".."
/ ".."
/ "reports"
/ "width_optimization"
/ f"{w_val}W"
/ method_report
).resolve()
prefix = f"{surrogate}_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}_it{it_val}"
optimizer_summary = report_dir / f"{prefix}_lexicographic_optimization_summary.csv"
validation_envelope = models_dir / (
f"validation_envelopes_{w_val}W_B{b_val}_H{h_val}_it{it_val}.csv"
)
return models_dir, fem_data, optimizer_summary, validation_envelope, report_dir, prefix
def model_path(models_dir, target, surrogate):
if surrogate == "ml":
return models_dir / f"best_model_{target}.joblib"
return models_dir / f"rbf_{target}.joblib"
def load_candidate_row(fem_data_path, row_index):
df = pd.read_csv(fem_data_path)
df.columns = [c.strip().lower() for c in df.columns]
if row_index < 0 or row_index >= len(df):
raise IndexError(
f"Candidate FEM row {row_index} is not available in {fem_data_path}. "
f"The file currently contains {len(df)} rows. Append the FEM-validated "
"optimized candidate before running acceptance."
)
return df, df.iloc[row_index]
def evaluate_candidate(
w_val,
b_val,
tfd_w_val,
it_val,
surrogate,
min_de_reproducibility=0.80,
candidate_row=None,
fem_data_path=None,
optimizer_summary_path=None,
models_dir=None,
validation_envelope_path=None,
geometry_match_tol=0.01,
):
h_val, start_rows = infer_h_and_start_rows(w_val)
inferred = infer_paths(w_val, b_val, h_val, tfd_w_val, it_val, surrogate)
inf_models_dir, inf_fem_data, inf_opt_summary, inf_envelope, report_dir, prefix = inferred
models_dir = Path(models_dir).resolve() if models_dir else inf_models_dir
fem_data_path = Path(fem_data_path).resolve() if fem_data_path else inf_fem_data
optimizer_summary_path = (
Path(optimizer_summary_path).resolve() if optimizer_summary_path else inf_opt_summary
)
validation_envelope_path = (
Path(validation_envelope_path).resolve()
if validation_envelope_path
else inf_envelope
)
for path, label in [
(fem_data_path, "FEM data"),
(optimizer_summary_path, "optimizer summary"),
(validation_envelope_path, "validation envelope"),
]:
if not path.exists():
raise FileNotFoundError(f"{label} file not found: {path}")
# Training at iteration it uses start_rows + it samples. The newly appended
# FEM validation candidate is therefore the next row by default.
if candidate_row is None:
candidate_row = start_rows + it_val
fem_df, fem_row = load_candidate_row(fem_data_path, candidate_row)
opt_df = pd.read_csv(optimizer_summary_path)
if len(opt_df) != 1:
raise ValueError(f"Expected one row in optimizer summary: {optimizer_summary_path}")
opt = opt_df.iloc[0]
env_df = pd.read_csv(validation_envelope_path)
env_df["output"] = env_df["output"].astype(str).str.lower()
envelope_map = {row["output"]: row for _, row in env_df.iterrows()}
feature_names = [f"tw{i+1}" for i in range(w_val)]
x_fem = np.array([float(fem_row[name]) for name in feature_names], dtype=float)
x_opt = np.array([float(opt[name]) for name in feature_names], dtype=float)
geometry_diff = np.abs(x_fem - x_opt)
geometry_match = bool(np.all(geometry_diff <= geometry_match_tol))
if not geometry_match:
raise ValueError(
"The selected FEM row does not match the optimized candidate within "
f"{geometry_match_tol:g} mm. FEM tw={x_fem.tolist()}, "
f"optimized tw={x_opt.tolist()}, abs diff={geometry_diff.tolist()}. "
"Select the correct --candidate-row or increase --geometry-match-tol only "
"if the FEM geometry was intentionally rounded."
)
tfd_targets = [f"tfmmax_tw{i+1}" for i in range(w_val)] + ["tfmmax_frame"]
exy_targets = [f"exymax_tw{i+1}" for i in range(w_val)]
active_targets = tfd_targets + exy_targets
detail_rows = []
all_surrogate_valid = True
fem_feasible = True
for target in active_targets:
if target not in fem_df.columns:
raise KeyError(f"Required FEM output '{target}' is missing from {fem_data_path}")
if target not in envelope_map:
raise KeyError(
f"Validation envelope for '{target}' is missing from {validation_envelope_path}"
)
path = model_path(models_dir, target, surrogate)
if not path.exists():
raise FileNotFoundError(f"Surrogate model not found for {target}: {path}")
model = joblib.load(path)
pred = float(np.asarray(model.predict(x_fem.reshape(1, -1))).ravel()[0])
fem = float(fem_row[target])
abs_error = abs(fem - pred)
rel_error = abs_error / max(abs(fem), 1e-12)
env = envelope_map[target]
abs_envelope = float(env["abs_error_envelope"])
local_valid = bool(abs_error <= abs_envelope + 1e-12)
all_surrogate_valid = all_surrogate_valid and local_valid
is_damage = target.startswith("tfmmax_")
physical_limit = np.nan
physical_pass = True
safety_margin = np.nan
conservative_pred = np.nan
conservative_pred_pass = True
margin_source = ""
margin_in_support = True
margin_nearest_distance = np.nan
margin_support_radius = np.nan
margin_k = np.nan
if is_damage:
physical_limit = TFD_FRAME_LIMIT if target == "tfmmax_frame" else float(tfd_w_val)
physical_pass = bool(fem <= physical_limit + 1e-12)
fem_feasible = fem_feasible and physical_pass
# Use the exact position-dependent margin that was applied by the
# optimizer at this candidate, rather than the global envelope.
if target == "tfmmax_frame":
opt_prefix = "TFD_frame_margin"
else:
window_num = int(target.replace("tfmmax_tw", ""))
opt_prefix = f"TFD_tw{window_num}_margin"
safety_margin = float(opt.get(opt_prefix, env["damage_safety_margin"]))
margin_source = str(opt.get(f"{opt_prefix}_source", "global_oof_legacy"))
margin_in_support = bool(opt.get(f"{opt_prefix}_in_support", True))
margin_nearest_distance = float(opt.get(f"{opt_prefix}_nearest_distance", np.nan))
margin_support_radius = float(opt.get(f"{opt_prefix}_support_radius", np.nan))
margin_k = float(opt.get(f"{opt_prefix}_k", np.nan))
conservative_pred = pred + safety_margin
conservative_pred_pass = bool(conservative_pred <= physical_limit + 1e-12)
detail_rows.append(
{
"output": target,
"surrogate_prediction": pred,
"fem_value": fem,
"absolute_error": abs_error,
"relative_error": rel_error,
"oof_abs_error_envelope": abs_envelope,
"oof_error_rule": env["abs_error_rule"],
"surrogate_valid_at_candidate": local_valid,
"physical_limit": physical_limit,
"fem_physical_pass": physical_pass,
"oof_damage_safety_margin": safety_margin,
"safety_margin_source": margin_source,
"local_margin_in_support": margin_in_support,
"local_margin_k": margin_k,
"local_margin_nearest_distance": margin_nearest_distance,
"local_margin_support_radius": margin_support_radius,
"conservative_surrogate_prediction": conservative_pred,
"conservative_surrogate_pass": conservative_pred_pass,
}
)
# Local design-space coverage is part of surrogate validity when the
# optimizer uses position-dependent OOF margins. Outside-support candidates
# are intentionally sent back to the adaptive loop after FEM evaluation.
safety_margin_mode = str(opt.get("safety_margin_mode", "none"))
if safety_margin_mode == "local_oof":
local_support_pass = bool(opt.get("local_margin_support_pass", False))
else:
local_support_pass = True
# DE reproducibility is based on the two actual lexicographic stages.
de_stage_rates = {
"stage1": float(opt.get("stage1_reproducibility", np.nan)),
"stage2": float(opt.get("stage2_reproducibility", np.nan)),
}
de_reproducible = all(
np.isfinite(rate) and rate >= min_de_reproducibility
for rate in de_stage_rates.values()
)
# Inter-iteration geometry change: diagnostic only. No fixed percentage is
# used as an acceptance gate. When available, compare the shift with the
# observed final Stage-2 dominant-basin range from the current/previous iteration.
adaptive_diagnostics = {}
if it_val > 0:
prev_prefix = f"{surrogate}_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}_it{it_val-1}"
prev_summary_path = report_dir / f"{prev_prefix}_lexicographic_optimization_summary.csv"
if prev_summary_path.exists():
prev = pd.read_csv(prev_summary_path).iloc[0]
for i, name in enumerate(feature_names):
delta = abs(float(opt[name]) - float(prev[name]))
curr_spread = float(opt.get(f"stage2_{name}_dominant_basin_range", np.nan))
prev_spread = float(prev.get(f"stage2_{name}_dominant_basin_range", np.nan))
finite_spreads = [v for v in (curr_spread, prev_spread) if np.isfinite(v)]
de_spread = max(finite_spreads) if finite_spreads else np.nan
adaptive_diagnostics[f"delta_{name}"] = delta
adaptive_diagnostics[f"de_repeatability_range_{name}"] = de_spread
adaptive_diagnostics[f"delta_within_de_repeatability_{name}"] = (
bool(delta <= de_spread + 1e-12) if np.isfinite(de_spread) else np.nan
)
accepted = bool(
fem_feasible
and all_surrogate_valid
and local_support_pass
and de_reproducible
)
if accepted:
action = "ACCEPT_FEM_VALIDATED_NUMERICAL_OPTIMUM"
elif not de_reproducible:
action = "RERUN_OR_STRENGTHEN_DE_BEFORE_ACCEPTANCE"
elif not fem_feasible:
action = "REJECT_CANDIDATE_ADD_FEM_AND_RETRAIN"
elif not local_support_pass:
action = "ADD_FEM_AND_RETRAIN_OUTSIDE_LOCAL_SUPPORT"
else:
action = "ADD_FEM_AND_RETRAIN_SURROGATE"
summary = {
"surrogate": surrogate,
"W": w_val,
"B": b_val,
"H": h_val,
"TFD_W_limit": float(tfd_w_val),
"TFD_frame_limit": float(TFD_FRAME_LIMIT),
"iteration": int(it_val),
"candidate_row_zero_based": int(candidate_row),
"geometry_match": geometry_match,
"geometry_match_tolerance_mm": float(geometry_match_tol),
"fem_feasible": bool(fem_feasible),
"surrogate_error_validity_pass": bool(all_surrogate_valid),
"local_oof_support_pass": bool(local_support_pass),
"surrogate_local_validity_pass": bool(all_surrogate_valid and local_support_pass),
"safety_margin_mode": safety_margin_mode,
"de_reproducibility_pass": bool(de_reproducible),
"min_de_reproducibility_required": float(min_de_reproducibility),
"stage1_reproducibility": de_stage_rates["stage1"],
"stage2_reproducibility": de_stage_rates["stage2"],
"accepted": accepted,
"recommended_action": action,
**{f"candidate_{name}": float(x_fem[i]) for i, name in enumerate(feature_names)},
**adaptive_diagnostics,
}
return pd.DataFrame(detail_rows), pd.DataFrame([summary]), prefix, report_dir
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--W", type=int, required=True)
parser.add_argument("--B", type=int, required=True)
parser.add_argument("--TFD_W", type=float, required=True)
parser.add_argument("--it", type=int, required=True)
parser.add_argument("--surrogate", choices=["ml", "rbf"], required=True)
parser.add_argument("--candidate-row", type=int, default=None, help="Zero-based FEM data row; default is the first row not used for training at iteration it.")
parser.add_argument("--min-de-reproducibility", type=float, default=0.80)
parser.add_argument("--geometry-match-tol", type=float, default=0.01, help="Numerical/rounding tolerance in mm for matching the FEM row to the optimized geometry.")
parser.add_argument("--fem-data", type=str, default=None)
parser.add_argument("--optimizer-summary", type=str, default=None)
parser.add_argument("--models-dir", type=str, default=None)
parser.add_argument("--validation-envelope", type=str, default=None)
parser.add_argument("--output-dir", type=str, default=None)
args = parser.parse_args()
if not 0 <= args.min_de_reproducibility <= 1:
raise ValueError("--min-de-reproducibility must be between 0 and 1")
if args.geometry_match_tol < 0:
raise ValueError("--geometry-match-tol must be non-negative")
details, summary, prefix, inferred_report_dir = evaluate_candidate(
w_val=args.W,
b_val=args.B,
tfd_w_val=args.TFD_W,
it_val=args.it,
surrogate=args.surrogate,
min_de_reproducibility=args.min_de_reproducibility,
candidate_row=args.candidate_row,
fem_data_path=args.fem_data,
optimizer_summary_path=args.optimizer_summary,
models_dir=args.models_dir,
validation_envelope_path=args.validation_envelope,
geometry_match_tol=args.geometry_match_tol,
)
output_dir = Path(args.output_dir).resolve() if args.output_dir else inferred_report_dir
output_dir.mkdir(parents=True, exist_ok=True)
details_path = output_dir / f"{prefix}_adaptive_acceptance_details.csv"
summary_path = output_dir / f"{prefix}_adaptive_acceptance_summary.csv"
details.to_csv(details_path, index=False)
summary.to_csv(summary_path, index=False)
row = summary.iloc[0]
print("\n=== ADAPTIVE ACCEPTANCE ===")
print(f"FEM feasibility: {'PASS' if row['fem_feasible'] else 'FAIL'}")
print(
"Surrogate error validity vs OOF envelope: "
f"{'PASS' if row['surrogate_error_validity_pass'] else 'FAIL'}"
)
print(
"Local OOF design-space support: "
f"{'PASS' if row['local_oof_support_pass'] else 'FAIL'}"
)
print(f"DE reproducibility: {'PASS' if row['de_reproducibility_pass'] else 'FAIL'}")
print(f"FINAL ACCEPTANCE: {'PASS' if row['accepted'] else 'FAIL'}")
print(f"Recommended action: {row['recommended_action']}")
print(f"Details: {details_path}")
print(f"Summary: {summary_path}")
if __name__ == "__main__":
main()
Code/src/width_optimization/ml_surrogate_train.py
......@@ -46,6 +46,8 @@ from skopt import BayesSearchCV
from skopt.space import Categorical, Integer, Real
from surrogate_validation import (
bootstrap_confidence_intervals,
build_local_oof_error_table,
compute_oof_error_envelope,
compute_metrics,
get_inner_cv,
get_outer_cv,
......@@ -591,6 +593,30 @@ def run_nested_cv_for_target(X, y, target_name, n_samples, outer_cv, output_dir,
agg_preds = aggregate_repeated_predictions(outer_predictions_df, pred_cols=[
"y_true", "prediction_supervised_selection"] + [f"prediction_{m}" for m in SUPERVISED_MODELS])
# Independent OOF error envelope used by the adaptive acceptance logic.
# For small N we use the maximum observed OOF error; otherwise P95.
validation_envelope = compute_oof_error_envelope(
agg_preds["y_true"].values,
agg_preds["prediction_supervised_selection"].values,
n_samples=n_samples,
percentile=95.0,
small_n_threshold=20,
)
validation_envelope.update({
"output": target_name,
"method": "SupervisedSelection",
})
# One-row-per-sample OOF residual table used to build spatially local
# one-sided safety margins during DE. Distances are evaluated later in
# normalized design space; here we preserve the original design coordinates.
local_oof_error_df = build_local_oof_error_table(
agg_preds,
X,
pred_col="prediction_supervised_selection",
output_name=target_name,
)
bootstrap_results = {}
for model_name in SUPERVISED_MODELS + ["SupervisedSelection"]:
pred_col = f"prediction_{model_name}" if model_name != "SupervisedSelection" else "prediction_supervised_selection"
......@@ -704,6 +730,8 @@ def run_nested_cv_for_target(X, y, target_name, n_samples, outer_cv, output_dir,
"outer_predictions_df": outer_predictions_df,
"outer_summary_df": outer_summary_df,
"bootstrap_results": bootstrap_results,
"validation_envelope": validation_envelope,
"local_oof_error_df": local_oof_error_df,
"model_selection_freq": freq_summary,
"fold_selection_info": pd.DataFrame(fold_selection_info),
"final_model": final_model,
......@@ -795,6 +823,8 @@ def main():
all_model_selection_freqs = []
all_final_selections = []
all_fold_selections = []
all_validation_envelopes = []
all_local_oof_errors = []
print(
f"\n=== Starting nested CV surrogate model training "
......@@ -816,6 +846,8 @@ def main():
all_model_selection_freqs.append(result["model_selection_freq"])
all_final_selections.append(result["final_selection_df"])
all_fold_selections.append(result["fold_selection_info"])
all_validation_envelopes.append(result["validation_envelope"])
all_local_oof_errors.append(result["local_oof_error_df"])
print(
f" ✅ Final model for {tgt}: {result['final_model_name']} "
......@@ -867,6 +899,24 @@ def main():
fold_sel_df.to_csv(fold_sel_csv, index=False)
print(f"Fold selections saved to: {fold_sel_csv}")
# 6. OOF validation envelopes for adaptive acceptance and conservative constraints
envelope_df = pd.DataFrame(all_validation_envelopes)
envelope_csv = os.path.join(
it_dir,
f"validation_envelopes_{w_val}W_B{b_val}_H{h_val}_it{iteration_idx}.csv",
)
envelope_df.to_csv(envelope_csv, index=False)
print(f"Validation envelopes saved to: {envelope_csv}")
# 7. Local OOF residual database for position-dependent safety margins
local_oof_df = pd.concat(all_local_oof_errors, ignore_index=True)
local_oof_csv = os.path.join(
it_dir,
f"local_oof_errors_{w_val}W_B{b_val}_H{h_val}_it{iteration_idx}.csv",
)
local_oof_df.to_csv(local_oof_csv, index=False)
print(f"Local OOF error data saved to: {local_oof_csv}")
print(f"\n=== ✅ ITERATION {iteration_idx} COMPLETE ===")
print(f"Models saved under: {output_models_dir}")
print(f"Results saved under: {it_dir}")
......
Code/src/width_optimization/rbf_optimization_de.py
......@@ -6,9 +6,8 @@ This module performs structural optimization using RBF surrogate models
and Differential Evolution with a lexicographic optimization strategy:
Priority 1: Feasibility - No window/frame should exceed TFD limits
Priority 2: Maximize minimum window TFD (best utilization of least-used window)
Priority 3: Minimize TFD range among windows (balance)
Priority 4: Maximize distortion/Exy measure (tie-breaker)
Priority 2: Maximize minimum window TFD (activate the least-used window as much as possible)
Priority 3: Maximize distortion/Exy measure while preserving Priority 2
Author: Joaquín Irazábal González
Date: September 2026
......@@ -27,7 +26,9 @@ from de_utils import (
compute_run_summary,
find_dominant_basin,
LexicographicPredictor,
LocalOOFSafetyMarginModel,
create_constraints,
load_damage_safety_margins,
run_single_de,
run_de_stage,
run_de_sensitivity,
......@@ -252,6 +253,56 @@ def main():
parser.add_argument("--sensitivity-runs", type=int, default=10, help="Runs per sensitivity config")
parser.add_argument("--verify-2d-grid", action="store_true", help="Dense grid verification for 2D")
parser.add_argument("--grid-resolution", type=int, default=250, help="Grid resolution for 2D verification")
parser.add_argument(
"--safety-margin-mode",
choices=["local_oof", "global_oof", "none"],
default="local_oof",
help=(
"Safety margin for TFD constraints: local_oof uses the maximum "
"positive OOF residual among nearby FEM samples; global_oof uses "
"the previous global OOF envelope; none uses nominal predictions."
),
)
parser.add_argument(
"--safety-margin-factor",
type=float,
default=1.0,
help="Multiplier applied to the OOF damage safety margins (default: 1.0).",
)
parser.add_argument(
"--local-margin-k",
type=int,
default=None,
help=(
"Number of nearest OOF samples used by local_oof. Default: auto, "
"k=max(d+1, ceil(sqrt(N)))."
),
)
parser.add_argument(
"--local-support-percentile",
type=float,
default=95.0,
help=(
"Percentile of sample nearest-neighbour distances used as the "
"local-support radius (default: 95)."
),
)
parser.add_argument(
"--local-margin-fallback",
choices=["global", "local"],
default="global",
help=(
"When a candidate lies outside the empirical local-support radius, "
"use the global maximum OOF underprediction (global, recommended) "
"or keep the local k-NN margin (local)."
),
)
parser.add_argument(
"--min-de-reproducibility",
type=float,
default=0.80,
help="Minimum dominant-basin reproducibility rate required for DE acceptance diagnostics.",
)
# DE parameter overrides (optional)
parser.add_argument("--de-strategy", type=str, default=None)
parser.add_argument("--de-mutation", type=str, default=None) # Can be "0.5" or "0.5,1.0"
......@@ -269,6 +320,15 @@ def main():
b_val = args.B
tfd_w_val = args.TFD_W
if args.safety_margin_factor < 0:
raise ValueError("--safety-margin-factor must be non-negative")
if args.local_margin_k is not None and args.local_margin_k < 1:
raise ValueError("--local-margin-k must be >= 1")
if not 0.0 < args.local_support_percentile <= 100.0:
raise ValueError("--local-support-percentile must be in (0, 100]")
if not 0.0 <= args.min_de_reproducibility <= 1.0:
raise ValueError("--min-de-reproducibility must be between 0 and 1")
h_val = None
if w_val == 2:
h_val = 30
......@@ -359,8 +419,62 @@ def main():
frame_model_path = os.path.join(input_models_dir_it, frame_model_name)
model_frame = joblib.load(frame_model_path)
# Create predictor for lexicographic optimization
predictor = LexicographicPredictor(models_exy, models_tfm, model_frame, w_val, b_val)
# Configure one-sided OOF damage-underprediction margins. These margins
# are used ONLY in the TFD feasibility constraints. The default local_oof
# mode makes the margin position-dependent in normalized design space.
damage_margin_model = None
if args.safety_margin_mode == "local_oof":
local_oof_csv = os.path.join(
input_models_dir_it,
f"local_oof_errors_{w_val}W_B{b_val}_H{h_val}_it{it}.csv",
)
if not os.path.exists(local_oof_csv):
raise FileNotFoundError(
"local_oof safety-margin mode requires the local OOF residual file: "
f"{local_oof_csv}. Run surrogate training with the new code first."
)
damage_margin_model = LocalOOFSafetyMarginModel(
local_oof_csv,
bounds_vals,
w_val,
k=args.local_margin_k,
support_percentile=args.local_support_percentile,
factor=args.safety_margin_factor,
fallback_mode=args.local_margin_fallback,
)
window_tfd_margins = np.zeros(w_val, dtype=float)
frame_tfd_margin = 0.0
elif args.safety_margin_mode == "global_oof":
envelope_csv = os.path.join(
input_models_dir_it,
f"validation_envelopes_{w_val}W_B{b_val}_H{h_val}_it{it}.csv",
)
if not os.path.exists(envelope_csv):
raise FileNotFoundError(
"global_oof safety-margin mode requires the validation-envelope file: "
f"{envelope_csv}. Run surrogate training with the revised code first."
)
window_tfd_margins, frame_tfd_margin = load_damage_safety_margins(
envelope_csv, w_val
)
window_tfd_margins = window_tfd_margins * args.safety_margin_factor
frame_tfd_margin = frame_tfd_margin * args.safety_margin_factor
else:
window_tfd_margins = np.zeros(w_val, dtype=float)
frame_tfd_margin = 0.0
# Nominal TFD predictions define the lexicographic objectives. Conservative
# predictions (nominal + local/global one-sided margin) define feasibility.
predictor = LexicographicPredictor(
models_exy,
models_tfm,
model_frame,
w_val,
b_val,
window_tfd_margins=window_tfd_margins,
frame_tfd_margin=frame_tfd_margin,
damage_margin_model=damage_margin_model,
)
# Build DE settings from CLI args
de_settings = {}
......@@ -393,6 +507,26 @@ def main():
print(f"Number of independent runs: {args.n_runs}")
print(f"Base seed: {args.seed_base}")
print(f"TFD_W: {tfd_w_val}, TFD_FRAME_LIMIT: {TFD_FRAME_LIMIT}")
print(f"Safety margin mode: {args.safety_margin_mode}")
print(f"Safety margin factor: {args.safety_margin_factor}")
if args.safety_margin_mode == "local_oof":
print(
"Local OOF margin settings: "
f"k={args.local_margin_k if args.local_margin_k is not None else 'auto'}, "
f"support P{args.local_support_percentile:g}, "
f"outside-support fallback={args.local_margin_fallback}"
)
for output_name, data in damage_margin_model.output_data.items():
print(
f" {output_name}: N={data['n']}, k={data['k']}, "
f"support_radius={data['support_radius']:.6f}, "
f"global_underprediction_max={data['global_margin_raw']:.6f}"
)
else:
for i, margin in enumerate(window_tfd_margins):
print(f" TFD window {i+1} fixed OOF safety margin: {margin:.6f}")
print(f" TFD frame fixed OOF safety margin: {frame_tfd_margin:.6f}")
print(f"Minimum DE reproducibility for acceptance diagnostic: {args.min_de_reproducibility:.0%}")
prefix = f"rbf_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}_it{it}"
......@@ -427,10 +561,23 @@ def main():
print(f"\nFeasibility optimum violation: {v_star:.6f}")
if v_star <= CONSTRAINT_TOL:
print("FEASIBLE DESIGN DOMAIN CONFIRMED.")
if args.safety_margin_mode != "none":
print("CONSERVATIVELY FEASIBLE SURROGATE DESIGN DOMAIN CONFIRMED.")
else:
print("FEASIBLE SURROGATE DESIGN DOMAIN CONFIRMED.")
feasible = True
else:
print("NO FEASIBLE DESIGN IDENTIFIED WITHIN THE CURRENT DESIGN DOMAIN.")
if args.safety_margin_mode != "none":
print(
"NO CONSERVATIVELY FEASIBLE SURROGATE DESIGN IDENTIFIED "
"WITHIN THE CURRENT DESIGN DOMAIN."
)
print(
"This is not, by itself, proof that the physical/FEM domain is infeasible; "
"the conclusion includes the selected OOF surrogate safety-margin model."
)
else:
print("NO FEASIBLE SURROGATE DESIGN IDENTIFIED WITHIN THE CURRENT DESIGN DOMAIN.")
print(f"Minimum identified violation: {v_star:.6f}")
# Save infeasibility report
......@@ -447,7 +594,13 @@ def main():
"minimum_identified_violation": v_star,
**{f"tw{i+1}": feas_optimum[i] for i in range(w_val)},
**{f"TFD_tw{i+1}": preds["window_tfd"][i] for i in range(w_val)},
**{f"TFD_tw{i+1}_margin": preds["window_tfd_margins"][i] for i in range(w_val)},
**{f"TFD_tw{i+1}_conservative": preds["window_tfd_conservative"][i] for i in range(w_val)},
"TFD_frame": preds["frame_tfd"],
"TFD_frame_margin": preds["frame_tfd_margin"],
"TFD_frame_conservative": preds["frame_tfd_conservative"],
"safety_margin_mode": args.safety_margin_mode,
"safety_margin_factor": args.safety_margin_factor,
"max_violation": v_star,
**{f"viol_window_{i+1}": violations.get(f"window_{i+1}", 0) for i in range(w_val)},
"viol_frame": violations.get("frame", 0),
......@@ -491,6 +644,7 @@ def main():
predictor=predictor,
stage_criterion_key="min_window_tfd",
stage_num=1,
derive_preservation_tolerance=True,
)
stage1_prefix = f"{prefix}_stage1_utilization"
......@@ -501,65 +655,62 @@ def main():
print(f"\nStage 1 optimum Tmin*: {Tmin_star:.6f}")
# =================================================================
# STAGE 2: MINIMIZE WINDOW TFD RANGE (SECONDARY)
# STAGE 2: MAXIMIZE DISTORTION WHILE PRESERVING STAGE 1
# =================================================================
print(f"\n{'=' * 60}")
print(f"STAGE 2: MINIMIZE WINDOW TFD RANGE (Secondary)")
print("STAGE 2: MAXIMIZE DISTORTION / Exy")
print(f"{'=' * 60}")
print(
"Stage-1 preservation uses a DE-repeatability tolerance only. "
"It is independent of the local OOF safety margin used for upper damage limits."
)
constraints_stage2 = create_constraints(tfd_w_val, predictor, stage1_tmin_star=Tmin_star,
stage1_preservation_tol=stage1_preservation_tol)
constraints_stage2 = create_constraints(
tfd_w_val,
predictor,
stage1_tmin_star=Tmin_star,
stage1_preservation_tol=stage1_preservation_tol,
)
stage2_results, stage2_histories, stage2_summary, stage2_rep, stage2_observed_spread, stage2_preservation_tol = run_de_stage(
(
stage2_results,
stage2_histories,
stage2_summary,
stage2_rep,
_,
_,
) = run_de_stage(
predictor.predict_stage2_objective,
bounds_vals,
args.n_runs,
args.seed_base,
de_settings=de_settings,
constraints=constraints_stage2,
stage_name="STAGE 2: MINIMIZE WINDOW TFD RANGE",
predictor=predictor,
stage_criterion_key="window_tfd_range",
stage_name="STAGE 2: MAXIMIZE DISTORTION",
stage_num=2,
derive_preservation_tolerance=False,
)
stage2_prefix = f"{prefix}_stage2_balance"
save_de_results(stage2_results, stage2_summary, stage2_histories, output_graphs_dir, stage2_prefix)
stage2_prefix = f"{prefix}_stage2_distortion"
save_de_results(
stage2_results,
stage2_summary,
stage2_histories,
output_graphs_dir,
stage2_prefix,
)
range_star = stage2_rep["fun"]
stage2_optimum = stage2_rep["x"]
print(f"\nStage 2 optimum range*: {range_star:.6f}")
# =================================================================
# STAGE 3: MAXIMIZE DISTORTION (TERTIARY)
# =================================================================
print(f"\n{'=' * 60}")
print(f"STAGE 3: MAXIMIZE DISTORTION / Exy (Tertiary)")
print(f"{'=' * 60}")
constraints_stage3 = create_constraints(tfd_w_val, predictor, stage1_tmin_star=Tmin_star, stage2_range_star=range_star,
stage1_preservation_tol=stage1_preservation_tol, stage2_preservation_tol=stage2_preservation_tol)
stage3_results, stage3_histories, stage3_summary, stage3_rep, stage3_observed_spread, stage3_preservation_tol = run_de_stage(
predictor.predict_stage3_objective,
bounds_vals,
args.n_runs,
args.seed_base,
de_settings=de_settings,
constraints=constraints_stage3,
stage_name="STAGE 3: MAXIMIZE DISTORTION",
predictor=predictor,
stage_criterion_key="distortion_measure",
stage_num=3,
stage2_distortion = -stage2_rep["fun"]
stage2_preds = predictor.predict_all(stage2_optimum)
stage1_threshold = Tmin_star - stage1_preservation_tol
print(f"\nStage 2 optimum distortion: {stage2_distortion:.6f}")
print(
f"Stage-1 preservation: min TFD = {stage2_preds['min_window_tfd']:.6f} "
f">= {stage1_threshold:.6f} "
f"(Tmin*={Tmin_star:.6f}, DE tolerance={stage1_preservation_tol:.6e})"
)
stage3_prefix = f"{prefix}_stage3_distortion"
save_de_results(stage3_results, stage3_summary, stage3_histories, output_graphs_dir, stage3_prefix)
stage3_optimum = stage3_rep["x"]
stage3_distortion = -stage3_rep["fun"]
print(f"\nStage 3 optimum distortion: {stage3_distortion:.6f}")
# =================================================================
# FINAL LEXICOGRAPHIC SOLUTION
# =================================================================
......@@ -567,9 +718,9 @@ def main():
print(f"FINAL LEXICOGRAPHIC SOLUTION")
print(f"{'=' * 60}")
# The Stage 3 representative is the final solution
final_optimum = stage3_optimum
final_rep = stage3_rep
# Stage 2 is the final lexicographic stage.
final_optimum = stage2_optimum
final_rep = stage2_rep
# Final feasibility verification
v_final, violations_final = predictor.predict_feasibility_violation(final_optimum, tfd_w_val)
......@@ -586,19 +737,100 @@ def main():
print(f" tw{window_num + 1} = {tw:.4f}")
print(f" min_window_tfd = {final_preds['min_window_tfd']:.6f}")
print(f" max_window_tfd = {final_preds['max_window_tfd']:.6f}")
print(f" window_tfd_range = {final_preds['window_tfd_range']:.6f}")
print(f" mean_window_tfd = {final_preds['mean_window_tfd']:.6f}")
print(f" window_tfd_range (diagnostic only) = {final_preds['window_tfd_range']:.6f}")
print(f" distortion_measure = {final_preds['distortion_measure']:.6f}")
print(f" frame_tfd = {final_preds['frame_tfd']:.6f}")
print(f" frame_tfd_conservative = {final_preds['frame_tfd_conservative']:.6f}")
print(f" max_constraint_violation = {v_final:.6f}")
# Per-window TFD
# Per-window TFD: nominal prediction, local/global safety margin and conservative value
for i, tfd in enumerate(final_preds["window_tfd"]):
print(f" TFD_tw{i+1} = {tfd:.6f} (limit: {tfd_w_val})")
conservative = final_preds["window_tfd_conservative"][i]
margin = final_preds["window_tfd_margins"][i]
diag = final_preds["window_margin_diagnostics"][i]
print(
f" TFD_tw{i+1} = {tfd:.6f} + margin {margin:.6f} "
f"= {conservative:.6f} (limit: {tfd_w_val}); "
f"source={diag.get('source', 'n/a')}, "
f"in_local_support={diag.get('in_support', True)}"
)
frame_diag = final_preds["frame_margin_diagnostic"]
print(
f" Frame margin source={frame_diag.get('source', 'n/a')}, "
f"in_local_support={frame_diag.get('in_support', True)}"
)
print(
" Local-margin support at final candidate: "
+ ("PASS" if final_preds["local_margin_support_pass"] else "OUTSIDE SUPPORT")
)
# DE reproducibility is an acceptance diagnostic, separate from physical
# FEM feasibility. It does not alter the optimum; it indicates whether the
# same optimum basin is repeatedly recovered across independent runs.
stage_reproducibilities = [
stage1_summary.get("reproducibility_rate", 0.0),
stage2_summary.get("reproducibility_rate", 0.0),
]
de_reproducibility_pass = all(
r >= args.min_de_reproducibility for r in stage_reproducibilities
)
print(
"DE reproducibility diagnostic: "
+ ("PASS" if de_reproducibility_pass else "FAIL")
+ f" (required >= {args.min_de_reproducibility:.0%} in every lexicographic stage)"
)
# =================================================================
# SAVE FINAL RESULTS
# =================================================================
# Flatten local-margin diagnostics so that the exact neighbourhood used at
# the final candidate is permanently recorded in the optimization summary.
margin_diagnostic_fields = {
"local_margin_support_pass": bool(final_preds["local_margin_support_pass"]),
"local_margin_k_requested": (
int(args.local_margin_k) if args.local_margin_k is not None else -1
),
"local_support_percentile": float(args.local_support_percentile),
"local_margin_fallback": args.local_margin_fallback,
}
for i, diag in enumerate(final_preds["window_margin_diagnostics"]):
prefix_diag = f"TFD_tw{i+1}_margin"
margin_diagnostic_fields.update({
f"{prefix_diag}_source": diag.get("source", ""),
f"{prefix_diag}_in_support": bool(diag.get("in_support", True)),
f"{prefix_diag}_k": diag.get("k", np.nan),
f"{prefix_diag}_nearest_distance": diag.get("nearest_distance", np.nan),
f"{prefix_diag}_support_radius": diag.get("support_radius", np.nan),
f"{prefix_diag}_neighbor_samples": ";".join(
map(str, diag.get("neighbor_sample_indices", []))
),
f"{prefix_diag}_neighbor_distances": ";".join(
f"{v:.8g}" for v in diag.get("neighbor_distances", [])
),
f"{prefix_diag}_neighbor_underprediction": ";".join(
f"{v:.8g}" for v in diag.get("neighbor_positive_underprediction", [])
),
})
frame_diag = final_preds["frame_margin_diagnostic"]
margin_diagnostic_fields.update({
"TFD_frame_margin_source": frame_diag.get("source", ""),
"TFD_frame_margin_in_support": bool(frame_diag.get("in_support", True)),
"TFD_frame_margin_k": frame_diag.get("k", np.nan),
"TFD_frame_margin_nearest_distance": frame_diag.get("nearest_distance", np.nan),
"TFD_frame_margin_support_radius": frame_diag.get("support_radius", np.nan),
"TFD_frame_margin_neighbor_samples": ";".join(
map(str, frame_diag.get("neighbor_sample_indices", []))
),
"TFD_frame_margin_neighbor_distances": ";".join(
f"{v:.8g}" for v in frame_diag.get("neighbor_distances", [])
),
"TFD_frame_margin_neighbor_underprediction": ";".join(
f"{v:.8g}" for v in frame_diag.get("neighbor_positive_underprediction", [])
),
})
# Save lexicographic summary
lexicographic_summary = pd.DataFrame([{
"w_val": w_val,
......@@ -606,35 +838,53 @@ def main():
"h_val": h_val,
"tfd_w_val": tfd_w_val,
"it": it,
"feasible": v_star <= CONSTRAINT_TOL,
"feasibility_violation": v_star,
"safety_margin_mode": args.safety_margin_mode,
"safety_margin_factor": args.safety_margin_factor,
**margin_diagnostic_fields,
"min_de_reproducibility_required": args.min_de_reproducibility,
"de_reproducibility_pass": de_reproducibility_pass,
"feasible_domain_found": v_star <= CONSTRAINT_TOL,
"minimum_domain_violation": v_star,
"feasible": v_final <= CONSTRAINT_TOL,
"feasibility_violation": v_final,
**{f"tw{i+1}": final_optimum[i] for i in range(w_val)},
**{f"TFD_tw{i+1}": final_preds["window_tfd"][i] for i in range(w_val)},
**{f"TFD_tw{i+1}_margin": final_preds["window_tfd_margins"][i] for i in range(w_val)},
**{f"TFD_tw{i+1}_conservative": final_preds["window_tfd_conservative"][i] for i in range(w_val)},
"TFD_frame": final_preds["frame_tfd"],
"TFD_frame_margin": final_preds["frame_tfd_margin"],
"TFD_frame_conservative": final_preds["frame_tfd_conservative"],
"min_window_tfd": final_preds["min_window_tfd"],
"max_window_tfd": final_preds["max_window_tfd"],
"window_tfd_range": final_preds["window_tfd_range"],
"mean_window_tfd": final_preds["mean_window_tfd"],
"window_tfd_range_diagnostic": final_preds["window_tfd_range"],
"distortion_measure": final_preds["distortion_measure"],
"stage1_Tmin_star": Tmin_star,
"stage2_range_star": range_star,
"stage3_distortion": stage3_distortion,
"stage1_preservation_threshold": Tmin_star - stage1_preservation_tol,
"stage2_distortion": stage2_distortion,
"stage1_observed_basin_spread": stage1_observed_spread,
"stage1_preservation_tolerance": stage1_preservation_tol,
"stage2_observed_basin_spread": stage2_observed_spread,
"stage2_preservation_tolerance": stage2_preservation_tol,
"max_constraint_violation": v_final,
"stage1_preservation_tolerance_source": "dominant_DE_basin_spread",
"stage1_rep_run": stage1_rep["run_idx"] if "run_idx" in stage1_rep else -1,
"stage1_rep_seed": stage1_rep["seed"],
"stage2_rep_run": stage2_rep["run_idx"] if "run_idx" in stage2_rep else -1,
"stage2_rep_seed": stage2_rep["seed"],
"stage3_rep_run": stage3_rep["run_idx"] if "run_idx" in stage3_rep else -1,
"stage3_rep_seed": stage3_rep["seed"],
"stage1_reproducibility": stage1_summary.get("reproducibility_rate", 0),
"stage2_reproducibility": stage2_summary.get("reproducibility_rate", 0),
"stage3_reproducibility": stage3_summary.get("reproducibility_rate", 0),
"stage1_n_basins": stage1_summary.get("n_basins", 0),
"stage2_n_basins": stage2_summary.get("n_basins", 0),
"stage3_n_basins": stage3_summary.get("n_basins", 0),
**{
f"stage2_tw{i+1}_dominant_basin_iqr": stage2_summary.get(
f"tw{i+1}_dominant_basin_iqr", np.nan
)
for i in range(w_val)
},
**{
f"stage2_tw{i+1}_dominant_basin_range": stage2_summary.get(
f"tw{i+1}_dominant_basin_range", np.nan
)
for i in range(w_val)
},
}])
lex_csv = os.path.join(output_graphs_dir, f"{prefix}_lexicographic_optimization_summary.csv")
lexicographic_summary.to_csv(lex_csv, index=False)
......@@ -695,25 +945,42 @@ def main():
print(f"2D DENSE GRID VERIFICATION (Lexicographic)")
print(f"{'=' * 60}")
grid_result = verify_2d_grid_lexicographic(predictor, bounds_vals, tfd_w_val, resolution=args.grid_resolution)
grid_result = verify_2d_grid_lexicographic(
predictor,
bounds_vals,
tfd_w_val,
resolution=args.grid_resolution,
stage1_preservation_tol=stage1_preservation_tol,
)
print(f"Grid feasible: {grid_result['feasible']}")
if grid_result["feasible"]:
print(f"Grid min TFD: {grid_result['min_window_tfd']:.6f}")
print(f"Grid range: {grid_result['window_tfd_range']:.6f}")
print(f"Grid Stage-1 Tmin*: {grid_result['grid_stage1_tmin_star']:.6f}")
print(
"Grid Stage-1 preservation threshold: "
f"{grid_result['stage1_preservation_threshold']:.6f}"
)
print(f"Grid final min TFD: {grid_result['min_window_tfd']:.6f}")
print(f"Grid distortion: {grid_result['distortion_measure']:.6f}")
print(f"Grid point: {grid_result['point']}")
print(f"DE min TFD: {final_preds['min_window_tfd']:.6f}")
print(f"DE range: {final_preds['window_tfd_range']:.6f}")
print(f"DE Stage-1 Tmin*: {Tmin_star:.6f}")
print(f"DE final min TFD: {final_preds['min_window_tfd']:.6f}")
print(f"DE distortion: {final_preds['distortion_measure']:.6f}")
print(f"DE point: {final_optimum}")
diff_tmin = abs(grid_result['min_window_tfd'] - final_preds['min_window_tfd'])
diff_range = abs(grid_result['window_tfd_range'] - final_preds['window_tfd_range'])
diff_dist = abs(grid_result['distortion_measure'] - final_preds['distortion_measure'])
diff_stage1 = abs(grid_result['grid_stage1_tmin_star'] - Tmin_star)
diff_final_tmin = abs(
grid_result['min_window_tfd'] - final_preds['min_window_tfd']
)
diff_dist = abs(
grid_result['distortion_measure'] - final_preds['distortion_measure']
)
print(f"Differences: Tmin={diff_tmin:.6f}, range={diff_range:.6f}, dist={diff_dist:.6f}")
print(
f"Differences: Stage1 Tmin*={diff_stage1:.6f}, "
f"final Tmin={diff_final_tmin:.6f}, dist={diff_dist:.6f}"
)
else:
print(f"Grid infeasible, min violation: {grid_result['min_violation']:.6f}")
print(f"DE min violation: {v_star:.6f}")
......@@ -725,11 +992,23 @@ def main():
"tfd_w_val": tfd_w_val,
"it": it,
"grid_feasible": grid_result["feasible"],
"grid_stage1_tmin_star": grid_result.get("grid_stage1_tmin_star", np.nan),
"grid_stage1_preservation_threshold": grid_result.get(
"stage1_preservation_threshold", np.nan
),
"grid_min_window_tfd": grid_result.get("min_window_tfd", np.nan),
"grid_window_tfd_range": grid_result.get("window_tfd_range", np.nan),
"grid_mean_window_tfd_diagnostic": grid_result.get(
"mean_window_tfd_diagnostic", np.nan
),
"grid_window_tfd_range_diagnostic": grid_result.get(
"window_tfd_range_diagnostic", np.nan
),
"grid_distortion_measure": grid_result.get("distortion_measure", np.nan),
"de_stage1_tmin_star": Tmin_star,
"de_stage1_preservation_threshold": Tmin_star - stage1_preservation_tol,
"de_min_window_tfd": final_preds["min_window_tfd"],
"de_window_tfd_range": final_preds["window_tfd_range"],
"de_mean_window_tfd_diagnostic": final_preds["mean_window_tfd"],
"de_window_tfd_range_diagnostic": final_preds["window_tfd_range"],
"de_distortion_measure": final_preds["distortion_measure"],
}])
grid_csv = os.path.join(output_graphs_dir, f"{prefix}_2d_grid_verification.csv")
......@@ -741,8 +1020,10 @@ def main():
inp_best_arr = inp_best.values
print("\nPredictions at final lexicographic optimum:")
print("Exy:", [m.predict(inp_best_arr)[0] for m in models_exy])
print("TFM tw:", [m.predict(inp_best_arr)[0] for m in models_tfm])
print("TFM frame:", model_frame.predict(inp_best_arr)[0])
print("TFM tw (nominal):", [m.predict(inp_best_arr)[0] for m in models_tfm])
print("TFM tw (conservative):", final_preds["window_tfd_conservative"].tolist())
print("TFM frame (nominal):", model_frame.predict(inp_best_arr)[0])
print("TFM frame (conservative):", final_preds["frame_tfd_conservative"])
if __name__ == "__main__":
......
Code/src/width_optimization/rbf_surrogate_train.py
......@@ -31,6 +31,8 @@ from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from surrogate_validation import (
bootstrap_confidence_intervals,
build_local_oof_error_table,
compute_oof_error_envelope,
compute_metrics,
get_inner_cv,
get_outer_cv,
......@@ -187,6 +189,29 @@ def run_nested_cv_rbf_for_target(X, y, target_name, n_samples, outer_cv, output_
agg_preds = aggregate_repeated_predictions(outer_predictions_df, pred_cols=[
"y_true", "prediction_RBF"])
# Independent OOF error envelope used by the adaptive acceptance logic.
# For small N we use the maximum observed OOF error; otherwise P95.
validation_envelope = compute_oof_error_envelope(
agg_preds["y_true"].values,
agg_preds["prediction_RBF"].values,
n_samples=n_samples,
percentile=95.0,
small_n_threshold=20,
)
validation_envelope.update({
"output": target_name,
"method": "RBF",
})
# One-row-per-sample OOF residual table used to build spatially local
# one-sided safety margins during DE.
local_oof_error_df = build_local_oof_error_table(
agg_preds,
X,
pred_col="prediction_RBF",
output_name=target_name,
)
ci = bootstrap_confidence_intervals(
agg_preds["y_true"].values,
agg_preds["prediction_RBF"].values,
......@@ -300,6 +325,8 @@ def run_nested_cv_rbf_for_target(X, y, target_name, n_samples, outer_cv, output_
"outer_predictions_df": outer_predictions_df,
"outer_summary_df": outer_summary_df,
"bootstrap_ci": ci,
"validation_envelope": validation_envelope,
"local_oof_error_df": local_oof_error_df,
"function_frequency": func_freq,
"epsilon_frequency": eps_freq,
"smooth_frequency": smooth_freq,
......@@ -394,6 +421,8 @@ def main():
all_smooth_freqs = []
all_final_selections = []
all_fold_selections = []
all_validation_envelopes = []
all_local_oof_errors = []
for tgt in target_features:
y = df[tgt].values
......@@ -411,6 +440,8 @@ def main():
all_smooth_freqs.append(result["smooth_frequency"])
all_final_selections.append(result["final_selection_df"])
all_fold_selections.append(result["fold_selection_info"])
all_validation_envelopes.append(result["validation_envelope"])
all_local_oof_errors.append(result["local_oof_error_df"])
print(
f" ✅ Final RBF for {tgt}: {result['final_selection_df']['selected_function'].iloc[0]} "
......@@ -470,6 +501,24 @@ def main():
fold_sel_df.to_csv(fold_sel_csv, index=False)
print(f"RBF fold selections saved to: {fold_sel_csv}")
# 6. OOF validation envelopes for adaptive acceptance and conservative constraints
envelope_df = pd.DataFrame(all_validation_envelopes)
envelope_csv = os.path.join(
it_dir,
f"validation_envelopes_{w_val}W_B{b_val}_H{h_val}_it{i_iter}.csv",
)
envelope_df.to_csv(envelope_csv, index=False)
print(f"RBF validation envelopes saved to: {envelope_csv}")
# 7. Local OOF residual database for position-dependent safety margins
local_oof_df = pd.concat(all_local_oof_errors, ignore_index=True)
local_oof_csv = os.path.join(
it_dir,
f"local_oof_errors_{w_val}W_B{b_val}_H{h_val}_it{i_iter}.csv",
)
local_oof_df.to_csv(local_oof_csv, index=False)
print(f"RBF local OOF error data saved to: {local_oof_csv}")
print(f"\n=== ✅ RBF ITERATION {i_iter} COMPLETE ===")
print(f"Models saved under: {output_models_dir}")
print(f"Results saved under: {it_dir}")
......
Code/src/width_optimization/run_ml.sh
#!/bin/bash
## python ml_surrogate_train.py --W 2 --B 29 --it 1
## python ml_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 1
python ml_surrogate_train.py --W 2 --B 34 --it 0
python ml_optimization_de.py --W 2 --B 34 --TFD_W 90 --it 0
## python ml_surrogate_train.py --W 3 --B 29 --it 0
## python ml_optimization_de.py --W 3 --B 29 --TFD_W 90 --it 0
## python ml_surrogate_train.py --W 3 --B 34 --it 0
## python ml_optimization_de.py --W 3 --B 34 --TFD_W 90 --it 0
## python ml_surrogate_train.py --W 5 --B 34 --it 0
## python ml_optimization_de.py --W 5 --B 34 --TFD_W 90 --it 0
python ml_surrogate_train.py --W 2 --B 29 --it 0
python ml_optimization_de.py --W 2 --B 29 --TFD_W 100 --it 0
python ml_surrogate_train.py --W 2 --B 34 --it 1
python ml_optimization_de.py --W 2 --B 34 --TFD_W 100 --it 0
python ml_surrogate_train.py --W 3 --B 29 --it 1
python ml_optimization_de.py --W 3 --B 29 --TFD_W 100 --it 0
python ml_surrogate_train.py --W 3 --B 34 --it 1
python ml_optimization_de.py --W 3 --B 34 --TFD_W 100 --it 0
python ml_surrogate_train.py --W 5 --B 34 --it 0
python ml_optimization_de.py --W 5 --B 34 --TFD_W 100 --it 0
Code/src/width_optimization/run_rbf.sh
#!/bin/bash
## python rbf_surrogate_train.py --W 2 --B 29 --it 1
## python rbf_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 1
python rbf_surrogate_train.py --W 2 --B 34 --it 1
python rbf_optimization_de.py --W 2 --B 34 --TFD_W 90 --it 1
## python rbf_surrogate_train.py --W 3 --B 29 --it 0
## python rbf_optimization_de.py --W 3 --B 29 --TFD_W 90 --it 0
## python rbf_surrogate_train.py --W 3 --B 34 --it 0
## python rbf_optimization_de.py --W 3 --B 34 --TFD_W 90 --it 0
## python rbf_surrogate_train.py --W 5 --B 34 --it 0
## python rbf_optimization_de.py --W 5 --B 34 --TFD_W 90 --it 0
python rbf_surrogate_train.py --W 2 --B 29 --it 0
python rbf_optimization_de.py --W 2 --B 29 --TFD_W 100 --it 0
python rbf_surrogate_train.py --W 2 --B 34 --it 0
python rbf_optimization_de.py --W 2 --B 34 --TFD_W 100 --it 0
python rbf_surrogate_train.py --W 3 --B 29 --it 0
python rbf_optimization_de.py --W 3 --B 29 --TFD_W 100 --it 0
python rbf_surrogate_train.py --W 3 --B 34 --it 0
python rbf_optimization_de.py --W 3 --B 34 --TFD_W 100 --it 0
python rbf_surrogate_train.py --W 5 --B 34 --it 0
python rbf_optimization_de.py --W 5 --B 34 --TFD_W 100 --it 0
Code/src/width_optimization/surrogate_validation.py
......@@ -175,6 +175,80 @@ def bootstrap_confidence_intervals(y_true, y_pred, n_bootstrap=2000, random_stat
return result
def compute_oof_error_envelope(
y_true,
y_pred,
n_samples=None,
percentile=95.0,
small_n_threshold=20,
):
"""
Compute an out-of-fold error envelope for adaptive surrogate validation.
Two complementary quantities are returned:
1. ``abs_error_envelope``: two-sided absolute prediction-error envelope,
used to decide whether the FEM validation error at a candidate is
consistent with the independent outer-CV performance.
2. ``damage_safety_margin``: one-sided underprediction envelope based on
positive residuals ``y_true - y_pred``. This is intended for
conservative damage constraints, where only surrogate underprediction
can cause a nominally feasible candidate to violate the FEM limit.
For very small datasets (N <= ``small_n_threshold``), the maximum observed
OOF error is used instead of a percentile because high percentiles are not
well resolved with very few independent samples. For larger datasets, the
requested percentile is used.
"""
y_true = np.asarray(y_true, dtype=float).ravel()
y_pred = np.asarray(y_pred, dtype=float).ravel()
if y_true.shape != y_pred.shape:
raise ValueError("y_true and y_pred must have the same shape")
finite = np.isfinite(y_true) & np.isfinite(y_pred)
y_true = y_true[finite]
y_pred = y_pred[finite]
if y_true.size == 0:
raise ValueError("No finite OOF predictions available for error-envelope computation")
n_eff = int(y_true.size if n_samples is None else n_samples)
abs_errors = np.abs(y_true - y_pred)
underprediction = np.maximum(y_true - y_pred, 0.0)
abs_p = float(np.percentile(abs_errors, percentile))
abs_max = float(np.max(abs_errors))
under_p = float(np.percentile(underprediction, percentile))
under_max = float(np.max(underprediction))
if n_eff <= small_n_threshold:
abs_envelope = abs_max
safety_margin = under_max
abs_rule = "max_abs_oof_error_small_n"
margin_rule = "max_positive_oof_residual_small_n"
else:
abs_envelope = abs_p
safety_margin = under_p
abs_rule = f"p{percentile:g}_abs_oof_error"
margin_rule = f"p{percentile:g}_positive_oof_residual"
return {
"n_unique_oof_samples": int(y_true.size),
"envelope_reference_n": n_eff,
"percentile": float(percentile),
"small_n_threshold": int(small_n_threshold),
"abs_error_envelope": float(abs_envelope),
"abs_error_rule": abs_rule,
"damage_safety_margin": float(safety_margin),
"damage_safety_margin_rule": margin_rule,
"abs_error_p95": abs_p if percentile == 95.0 else float(np.percentile(abs_errors, 95.0)),
"abs_error_max": abs_max,
"underprediction_p95": under_p if percentile == 95.0 else float(np.percentile(underprediction, 95.0)),
"underprediction_max": under_max,
"mean_abs_error": float(np.mean(abs_errors)),
}
def aggregate_repeated_predictions(outer_predictions_df, sample_index_col="sample_index", pred_cols=None):
"""
Aggregate repeated outer predictions by sample_index.
......@@ -236,4 +310,87 @@ def get_cv_strategy_names(n_samples: int):
else:
inner_name = "KFold(5)"
return outer_name, inner_name
\ No newline at end of file
return outer_name, inner_name
def build_local_oof_error_table(
aggregated_oof_df,
design_matrix,
pred_col,
output_name,
sample_index_col="sample_index",
true_col="y_true",
):
"""
Build one-row-per-sample OOF residual data for local safety margins.
Parameters
----------
aggregated_oof_df : pandas.DataFrame
OOF predictions aggregated by original sample index. For repeated outer
CV, each sample must already have a single averaged OOF prediction.
design_matrix : array-like, shape (N, d)
Original design variables in the same sample order used by the CV.
pred_col : str
Column containing the OOF prediction to use (e.g.
``prediction_supervised_selection`` or ``prediction_RBF``).
output_name : str
Name of the response variable represented by the OOF predictions.
sample_index_col : str
Column holding the original row index.
true_col : str
Column holding the true FEM value.
Returns
-------
pandas.DataFrame
Columns: output, sample_index, x1..xd, y_true, y_pred_oof,
abs_error_oof, positive_underprediction_oof.
Notes
-----
``positive_underprediction_oof = max(y_true - y_pred_oof, 0)`` is the
one-sided residual used by the local conservative TFD constraints. Only
underprediction is dangerous for an upper-bound damage constraint.
"""
import pandas as pd
df = aggregated_oof_df.copy()
required = {sample_index_col, true_col, pred_col}
missing = required.difference(df.columns)
if missing:
raise KeyError(
"Missing columns in aggregated OOF predictions: "
+ ", ".join(sorted(missing))
)
X = np.asarray(design_matrix, dtype=float)
if X.ndim != 2:
raise ValueError("design_matrix must be a 2D array")
sample_indices = df[sample_index_col].astype(int).to_numpy()
if np.any(sample_indices < 0) or np.any(sample_indices >= X.shape[0]):
raise IndexError(
"OOF sample_index values are incompatible with the design matrix"
)
y_true = df[true_col].to_numpy(dtype=float)
y_pred = df[pred_col].to_numpy(dtype=float)
abs_error = np.abs(y_true - y_pred)
positive_underprediction = np.maximum(y_true - y_pred, 0.0)
data = {
"output": np.repeat(str(output_name), len(df)),
"sample_index": sample_indices,
}
for j in range(X.shape[1]):
data[f"x{j + 1}"] = X[sample_indices, j]
data.update(
{
"y_true": y_true,
"y_pred_oof": y_pred,
"abs_error_oof": abs_error,
"positive_underprediction_oof": positive_underprediction,
}
)
return pd.DataFrame(data)
Code/src/width_optimization/test_new_optimization_logic.py 0 → 100644
import tempfile
from pathlib import Path
import numpy as np
import pandas as pd
from de_utils import LocalOOFSafetyMarginModel, LexicographicPredictor, create_constraints
class ConstantModel:
def __init__(self, value):
self.value = float(value)
def predict(self, X):
X = np.asarray(X)
return np.full(X.shape[0], self.value, dtype=float)
class FeatureModel:
def __init__(self, feature, offset=0.0, scale=1.0):
self.feature = int(feature)
self.offset = float(offset)
self.scale = float(scale)
def predict(self, X):
X = np.asarray(X, dtype=float)
return self.offset + self.scale * X[:, self.feature]
def build_local_csv(path, n=8, d=2):
# Deterministic sample cloud in [0, 1]^d mapped to bounds [10, 20].
if d == 2:
X = np.array([
[10, 10], [20, 10], [10, 20], [20, 20],
[15, 10], [10, 15], [20, 15], [15, 20],
], dtype=float)
else:
rng = np.random.default_rng(7)
X = 5.0 + 9.0 * rng.random((n, d))
rows = []
outputs = [f"tfmmax_tw{i+1}" for i in range(d)] + ["tfmmax_frame"]
for out_idx, output in enumerate(outputs):
# Increasing residuals make nearest-neighbour selection testable.
residual = np.arange(1, len(X) + 1, dtype=float) + 10.0 * out_idx
for i, x in enumerate(X):
row = {
"output": output,
"sample_index": i,
"y_true": 80.0 + residual[i],
"y_pred_oof": 80.0,
"abs_error_oof": residual[i],
"positive_underprediction_oof": residual[i],
}
for j in range(d):
row[f"x{j+1}"] = x[j]
rows.append(row)
pd.DataFrame(rows).to_csv(path, index=False)
return X
def test_auto_k_and_local_margin():
with tempfile.TemporaryDirectory() as td:
csv = Path(td) / "local.csv"
X = build_local_csv(csv, n=8, d=2)
model = LocalOOFSafetyMarginModel(
csv,
bounds=[(10, 20), (10, 20)],
w_val=2,
k=None,
support_percentile=95,
factor=1.0,
fallback_mode="local", # force local branch for this test
)
assert model.output_data["tfmmax_tw1"]["k"] == 3
margin, diag = model.margin_for_output("tfmmax_tw1", [10.1, 10.1])
assert diag["k"] == 3
assert len(diag["neighbor_sample_indices"]) == 3
expected = max(
model.output_data["tfmmax_tw1"]["residual"][
np.argsort(
np.linalg.norm(
model.output_data["tfmmax_tw1"]["Xn"]
- np.array([[0.01, 0.01]]),
axis=1,
)
)[:3]
]
)
assert np.isclose(margin, expected)
def test_outside_support_global_fallback():
with tempfile.TemporaryDirectory() as td:
csv = Path(td) / "local.csv"
build_local_csv(csv, n=8, d=2)
model = LocalOOFSafetyMarginModel(
csv,
bounds=[(10, 20), (10, 20)],
w_val=2,
k=3,
support_percentile=50,
factor=1.0,
fallback_mode="global",
)
# Far outside the design domain -> unsupported -> global max fallback.
margin, diag = model.margin_for_output("tfmmax_tw1", [40, 40])
assert not diag["in_support"]
assert diag["source"] == "global_max_fallback_outside_support"
assert np.isclose(margin, 8.0)
def test_stage2_uses_distortion_not_mean_tfd():
# Both candidates have the same Stage-1 minimum TFD (=85). Candidate B has
# LOWER mean TFD but HIGHER distortion. Stage 2 must prefer B, proving that
# mean utilization is no longer an optimization objective.
predictor = LexicographicPredictor(
models_exy=[ConstantModel(1.0), ConstantModel(1.0)],
models_tfm=[ConstantModel(85.0), FeatureModel(1, offset=90.0, scale=-0.5)],
model_frame=ConstantModel(70.0),
w_val=2,
b_val=29,
)
candidate_a = [5.0, 0.0] # TFD=[85,90], mean=87.5, lower distortion
candidate_b = [5.0, 10.0] # TFD=[85,85], mean=85.0, higher distortion
pa = predictor.predict_all(candidate_a)
pb = predictor.predict_all(candidate_b)
assert np.isclose(pa["min_window_tfd"], pb["min_window_tfd"])
assert pa["mean_window_tfd"] > pb["mean_window_tfd"]
assert pb["distortion_measure"] > pa["distortion_measure"]
assert predictor.predict_stage2_objective(candidate_b) < predictor.predict_stage2_objective(candidate_a)
def test_safety_margin_only_protects_upper_limit_and_not_stage1_preservation():
# The OOF safety margin protects the upper TFD limit. The separate
# Stage-1 preservation tolerance is applied to nominal min(TFD) only.
predictor = LexicographicPredictor(
models_exy=[ConstantModel(1.0), ConstantModel(1.0)],
models_tfm=[ConstantModel(84.9), ConstantModel(86.0)],
model_frame=ConstantModel(70.0),
w_val=2,
b_val=29,
window_tfd_margins=[4.0, 1.0],
frame_tfd_margin=2.0,
)
constraints = create_constraints(
90.0,
predictor,
stage1_tmin_star=85.0,
stage1_preservation_tol=0.2,
)
x = np.array([10.0, 10.0])
# Upper constraints use conservative TFD = nominal + safety margin.
upper_window_slack = np.asarray(constraints[0].fun(x), dtype=float)
assert np.allclose(upper_window_slack, [1.1, 3.0])
# Stage-1 preservation uses nominal min TFD: 84.9 - 85 + 0.2 = +0.1.
# It must not subtract/add the 4.0 OOF safety margin.
preservation_slack = float(np.asarray(constraints[2].fun(x)).ravel()[0])
assert np.isclose(preservation_slack, 0.1)
def test_safety_margin_constraint_is_inactive_far_from_limit():
predictor = LexicographicPredictor(
models_exy=[ConstantModel(1.0), ConstantModel(1.0)],
models_tfm=[ConstantModel(70.0), ConstantModel(75.0)],
model_frame=ConstantModel(60.0),
w_val=2,
b_val=29,
window_tfd_margins=[4.0, 5.0],
frame_tfd_margin=6.0,
)
constraints = create_constraints(90.0, predictor)
x = np.array([10.0, 10.0])
# Large positive slack: the margin is formally present but does not govern
# the design because the nominal damage is far below the limit.
assert np.all(np.asarray(constraints[0].fun(x)) > 0.0)
assert float(np.asarray(constraints[1].fun(x)).ravel()[0]) > 0.0
def test_auto_k_three_dimensions_n16():
with tempfile.TemporaryDirectory() as td:
csv = Path(td) / "local.csv"
build_local_csv(csv, n=16, d=3)
model = LocalOOFSafetyMarginModel(
csv,
bounds=[(5, 14)] * 3,
w_val=3,
)
assert model.output_data["tfmmax_tw1"]["k"] == 4
def test_build_local_oof_error_table_repeated_predictions():
from surrogate_validation import build_local_oof_error_table
# Two OOF predictions for sample 0 emulate repeated K-fold; they should be
# aggregated before this helper is normally called. Here we pass the
# already-aggregated form to verify coordinates/residual construction.
agg = pd.DataFrame({
"sample_index": [0, 1],
"y_true": [10.0, 20.0],
"prediction_RBF": [8.0, 21.0],
})
X = np.array([[1.0, 2.0], [3.0, 4.0]])
out = build_local_oof_error_table(
agg, X, "prediction_RBF", "tfmmax_tw1"
)
assert list(out["x1"]) == [1.0, 3.0]
assert list(out["x2"]) == [2.0, 4.0]
assert np.allclose(out["positive_underprediction_oof"], [2.0, 0.0])
if __name__ == "__main__":
test_auto_k_and_local_margin()
test_outside_support_global_fallback()
test_stage2_uses_distortion_not_mean_tfd()
test_safety_margin_only_protects_upper_limit_and_not_stage1_preservation()
test_safety_margin_constraint_is_inactive_far_from_limit()
test_auto_k_three_dimensions_n16()
test_build_local_oof_error_table_repeated_predictions()
print("All new optimization-logic tests passed.")
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