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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 3382 additions and 20 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_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/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_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/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_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_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_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_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_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_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/ml_surrogate_train.py
...@@ -46,6 +46,8 @@ from skopt import BayesSearchCV ...@@ -46,6 +46,8 @@ from skopt import BayesSearchCV
from skopt.space import Categorical, Integer, Real from skopt.space import Categorical, Integer, Real
from surrogate_validation import ( from surrogate_validation import (
bootstrap_confidence_intervals, bootstrap_confidence_intervals,
build_local_oof_error_table,
compute_oof_error_envelope,
compute_metrics, compute_metrics,
get_inner_cv, get_inner_cv,
get_outer_cv, get_outer_cv,
...@@ -591,6 +593,30 @@ def run_nested_cv_for_target(X, y, target_name, n_samples, outer_cv, output_dir, ...@@ -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=[ agg_preds = aggregate_repeated_predictions(outer_predictions_df, pred_cols=[
"y_true", "prediction_supervised_selection"] + [f"prediction_{m}" for m in SUPERVISED_MODELS]) "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 = {} bootstrap_results = {}
for model_name in SUPERVISED_MODELS + ["SupervisedSelection"]: for model_name in SUPERVISED_MODELS + ["SupervisedSelection"]:
pred_col = f"prediction_{model_name}" if model_name != "SupervisedSelection" else "prediction_supervised_selection" 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, ...@@ -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_predictions_df": outer_predictions_df,
"outer_summary_df": outer_summary_df, "outer_summary_df": outer_summary_df,
"bootstrap_results": bootstrap_results, "bootstrap_results": bootstrap_results,
"validation_envelope": validation_envelope,
"local_oof_error_df": local_oof_error_df,
"model_selection_freq": freq_summary, "model_selection_freq": freq_summary,
"fold_selection_info": pd.DataFrame(fold_selection_info), "fold_selection_info": pd.DataFrame(fold_selection_info),
"final_model": final_model, "final_model": final_model,
...@@ -795,6 +823,8 @@ def main(): ...@@ -795,6 +823,8 @@ def main():
all_model_selection_freqs = [] all_model_selection_freqs = []
all_final_selections = [] all_final_selections = []
all_fold_selections = [] all_fold_selections = []
all_validation_envelopes = []
all_local_oof_errors = []
print( print(
f"\n=== Starting nested CV surrogate model training " f"\n=== Starting nested CV surrogate model training "
...@@ -816,6 +846,8 @@ def main(): ...@@ -816,6 +846,8 @@ def main():
all_model_selection_freqs.append(result["model_selection_freq"]) all_model_selection_freqs.append(result["model_selection_freq"])
all_final_selections.append(result["final_selection_df"]) all_final_selections.append(result["final_selection_df"])
all_fold_selections.append(result["fold_selection_info"]) 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( print(
f" ✅ Final model for {tgt}: {result['final_model_name']} " f" ✅ Final model for {tgt}: {result['final_model_name']} "
...@@ -867,6 +899,24 @@ def main(): ...@@ -867,6 +899,24 @@ def main():
fold_sel_df.to_csv(fold_sel_csv, index=False) fold_sel_df.to_csv(fold_sel_csv, index=False)
print(f"Fold selections saved to: {fold_sel_csv}") 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"\n=== ✅ ITERATION {iteration_idx} COMPLETE ===")
print(f"Models saved under: {output_models_dir}") print(f"Models saved under: {output_models_dir}")
print(f"Results saved under: {it_dir}") print(f"Results saved under: {it_dir}")
......
Code/src/width_optimization/rbf_surrogate_train.py
...@@ -31,6 +31,8 @@ from sklearn.pipeline import Pipeline ...@@ -31,6 +31,8 @@ from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import StandardScaler
from surrogate_validation import ( from surrogate_validation import (
bootstrap_confidence_intervals, bootstrap_confidence_intervals,
build_local_oof_error_table,
compute_oof_error_envelope,
compute_metrics, compute_metrics,
get_inner_cv, get_inner_cv,
get_outer_cv, get_outer_cv,
...@@ -187,6 +189,29 @@ def run_nested_cv_rbf_for_target(X, y, target_name, n_samples, outer_cv, output_ ...@@ -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=[ agg_preds = aggregate_repeated_predictions(outer_predictions_df, pred_cols=[
"y_true", "prediction_RBF"]) "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( ci = bootstrap_confidence_intervals(
agg_preds["y_true"].values, agg_preds["y_true"].values,
agg_preds["prediction_RBF"].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_ ...@@ -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_predictions_df": outer_predictions_df,
"outer_summary_df": outer_summary_df, "outer_summary_df": outer_summary_df,
"bootstrap_ci": ci, "bootstrap_ci": ci,
"validation_envelope": validation_envelope,
"local_oof_error_df": local_oof_error_df,
"function_frequency": func_freq, "function_frequency": func_freq,
"epsilon_frequency": eps_freq, "epsilon_frequency": eps_freq,
"smooth_frequency": smooth_freq, "smooth_frequency": smooth_freq,
...@@ -394,6 +421,8 @@ def main(): ...@@ -394,6 +421,8 @@ def main():
all_smooth_freqs = [] all_smooth_freqs = []
all_final_selections = [] all_final_selections = []
all_fold_selections = [] all_fold_selections = []
all_validation_envelopes = []
all_local_oof_errors = []
for tgt in target_features: for tgt in target_features:
y = df[tgt].values y = df[tgt].values
...@@ -411,6 +440,8 @@ def main(): ...@@ -411,6 +440,8 @@ def main():
all_smooth_freqs.append(result["smooth_frequency"]) all_smooth_freqs.append(result["smooth_frequency"])
all_final_selections.append(result["final_selection_df"]) all_final_selections.append(result["final_selection_df"])
all_fold_selections.append(result["fold_selection_info"]) 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( print(
f" ✅ Final RBF for {tgt}: {result['final_selection_df']['selected_function'].iloc[0]} " f" ✅ Final RBF for {tgt}: {result['final_selection_df']['selected_function'].iloc[0]} "
...@@ -470,6 +501,24 @@ def main(): ...@@ -470,6 +501,24 @@ def main():
fold_sel_df.to_csv(fold_sel_csv, index=False) fold_sel_df.to_csv(fold_sel_csv, index=False)
print(f"RBF fold selections saved to: {fold_sel_csv}") 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"\n=== ✅ RBF ITERATION {i_iter} COMPLETE ===")
print(f"Models saved under: {output_models_dir}") print(f"Models saved under: {output_models_dir}")
print(f"Results saved under: {it_dir}") print(f"Results saved under: {it_dir}")
......
Code/src/width_optimization/run_ml.sh
#!/bin/bash #!/bin/bash
## python ml_surrogate_train.py --W 2 --B 29 --it 1 python ml_surrogate_train.py --W 2 --B 29 --it 0
## python ml_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 1 python ml_optimization_de.py --W 2 --B 29 --TFD_W 100 --it 0
python ml_surrogate_train.py --W 2 --B 34 --it 0 python ml_surrogate_train.py --W 2 --B 34 --it 1
python ml_optimization_de.py --W 2 --B 34 --TFD_W 90 --it 0 python ml_optimization_de.py --W 2 --B 34 --TFD_W 100 --it 0
## python ml_surrogate_train.py --W 3 --B 29 --it 0 python ml_surrogate_train.py --W 3 --B 29 --it 1
## python ml_optimization_de.py --W 3 --B 29 --TFD_W 90 --it 0 python ml_optimization_de.py --W 3 --B 29 --TFD_W 100 --it 0
## python ml_surrogate_train.py --W 3 --B 34 --it 0 python ml_surrogate_train.py --W 3 --B 34 --it 1
## python ml_optimization_de.py --W 3 --B 34 --TFD_W 90 --it 0 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_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_optimization_de.py --W 5 --B 34 --TFD_W 100 --it 0
Code/src/width_optimization/run_rbf.sh
#!/bin/bash #!/bin/bash
## python rbf_surrogate_train.py --W 2 --B 29 --it 1 python rbf_surrogate_train.py --W 2 --B 29 --it 0
## python rbf_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 1 python rbf_optimization_de.py --W 2 --B 29 --TFD_W 100 --it 0
python rbf_surrogate_train.py --W 2 --B 34 --it 1 python rbf_surrogate_train.py --W 2 --B 34 --it 0
python rbf_optimization_de.py --W 2 --B 34 --TFD_W 90 --it 1 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_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_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_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_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_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_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 ...@@ -175,6 +175,80 @@ def bootstrap_confidence_intervals(y_true, y_pred, n_bootstrap=2000, random_stat
return result 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): def aggregate_repeated_predictions(outer_predictions_df, sample_index_col="sample_index", pred_cols=None):
""" """
Aggregate repeated outer predictions by sample_index. Aggregate repeated outer predictions by sample_index.
...@@ -237,3 +311,86 @@ def get_cv_strategy_names(n_samples: int): ...@@ -237,3 +311,86 @@ def get_cv_strategy_names(n_samples: int):
inner_name = "KFold(5)" inner_name = "KFold(5)"
return outer_name, inner_name 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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