Rename folders to tidy up

Code/models/width_optimization/2W/cv_summary_per_output_B29_H30.csv → Code/models/width_optimization/2W/ml_models/cv_summary_per_output_B29_H30.csv

 output,best_model,cv_rmse,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel
-exymax_tw2,SVR,0.003898200989668,0.003898200989668,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/per_output_models_B29_H30/best_model_exymax_tw2.joblib,372.41,
-exymax_tw1,SVR,0.0043375719853902175,0.0043375719853902175,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/per_output_models_B29_H30/best_model_exymax_tw1.joblib,294.69,
-eyymax_tf,GradientBoosting,0.006096959350644509,0.006096959350644509,,"{""learning_rate"": 0.07929606546342034, ""max_depth"": 1, ""max_features"": 0.7452453063052724, ""n_estimators"": 1273, ""subsample"": 0.8474562788291421}",../../models/width_optimization/2W/per_output_models_B29_H30/best_model_eyymax_tf.joblib,269.21,
-tfmmax_frame,SVR,1.855708614995125,1.855708614995125,,"{""svr__C"": 869.6420997068749, ""svr__epsilon"": 0.005970174820257551, ""svr__gamma"": 0.013624631381822645}",../../models/width_optimization/2W/per_output_models_B29_H30/best_model_tfmmax_frame.joblib,390.64,
-tfmmax_tw1,SVR,6.514565955904576,6.514565955904576,,"{""svr__C"": 6450.78941362485, ""svr__epsilon"": 0.05487965008895495, ""svr__gamma"": 0.01812192186911149}",../../models/width_optimization/2W/per_output_models_B29_H30/best_model_tfmmax_tw1.joblib,201.26,
-tfmmax_tw2,FlexibleMLP,9.543413656978952,9.543413656978952,,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 5.7116133827395183e-05, ""mlp__learning_rate_init"": 0.00551761396114586, ""mlp__n_layers"": 5, ""mlp__n_neurons"": 193}",../../models/width_optimization/2W/per_output_models_B29_H30/best_model_tfmmax_tw2.joblib,249.26,
+exymax_tw2,SVR,0.003898200989668,0.003898200989668,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/best_model_exymax_tw2.joblib,394.57,
+exymax_tw1,SVR,0.0043375719853902175,0.0043375719853902175,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/best_model_exymax_tw1.joblib,302.92,
+eyymax_tf,GradientBoosting,0.006096959350644509,0.006096959350644509,,"{""learning_rate"": 0.07929606546342034, ""max_depth"": 1, ""max_features"": 0.7452453063052724, ""n_estimators"": 1273, ""subsample"": 0.8474562788291421}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/best_model_eyymax_tf.joblib,272.33,
+tfmmax_frame,SVR,1.855708614995125,1.855708614995125,,"{""svr__C"": 869.6420997068749, ""svr__epsilon"": 0.005970174820257551, ""svr__gamma"": 0.013624631381822645}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/best_model_tfmmax_frame.joblib,413.62,
+tfmmax_tw1,SVR,6.514565955904576,6.514565955904576,,"{""svr__C"": 6450.78941362485, ""svr__epsilon"": 0.05487965008895495, ""svr__gamma"": 0.01812192186911149}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/best_model_tfmmax_tw1.joblib,195.88,
+tfmmax_tw2,FlexibleMLP,9.543413656978952,9.543413656978952,,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 5.7116133827395183e-05, ""mlp__learning_rate_init"": 0.00551761396114586, ""mlp__n_layers"": 5, ""mlp__n_neurons"": 193}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/best_model_tfmmax_tw2.joblib,261.39,

Code/models/width_optimization/2W/cv_summary_per_output_B34_H30.csv → Code/models/width_optimization/2W/ml_models/cv_summary_per_output_B34_H30.csv

 output,best_model,cv_rmse,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel
-exymax_tw1,SVR,0.001189142247241681,0.001189142247241681,,"{""svr__C"": 404.07318534699954, ""svr__epsilon"": 0.00016710787988155612, ""svr__gamma"": 0.004514122336435558}",../../models/width_optimization/2W/per_output_models_B34_H30/best_model_exymax_tw1.joblib,267.15,
-exymax_tw2,SVR,0.006084823423825638,0.006084823423825638,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/per_output_models_B34_H30/best_model_exymax_tw2.joblib,436.07,
-eyymax_tf,GradientBoosting,0.011193199924578985,0.011193199924578985,,"{""learning_rate"": 0.07921529866641679, ""max_depth"": 1, ""max_features"": 0.612242984332449, ""n_estimators"": 1057, ""subsample"": 0.7050910309575588}",../../models/width_optimization/2W/per_output_models_B34_H30/best_model_eyymax_tf.joblib,239.43,
-tfmmax_frame,SVR,3.7496112763926472,3.7496112763926472,,"{""svr__C"": 888.0357335845127, ""svr__epsilon"": 0.00027560887878157734, ""svr__gamma"": 0.011255115229536036}",../../models/width_optimization/2W/per_output_models_B34_H30/best_model_tfmmax_frame.joblib,288.43,
-tfmmax_tw1,FlexibleMLP,7.568684427554417,7.568684427554417,,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 0.007095015676184179, ""mlp__learning_rate_init"": 0.0010350129402744886, ""mlp__n_layers"": 1, ""mlp__n_neurons"": 93}",../../models/width_optimization/2W/per_output_models_B34_H30/best_model_tfmmax_tw1.joblib,207.07,
-tfmmax_tw2,SVR,11.314099136094795,11.314099136094795,,"{""svr__C"": 1932.2768280341643, ""svr__epsilon"": 0.0002731420424984291, ""svr__gamma"": 0.002631082857769719}",../../models/width_optimization/2W/per_output_models_B34_H30/best_model_tfmmax_tw2.joblib,275.25,
+exymax_tw1,SVR,0.001189142247241681,0.001189142247241681,,"{""svr__C"": 404.07318534699954, ""svr__epsilon"": 0.00016710787988155612, ""svr__gamma"": 0.004514122336435558}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/best_model_exymax_tw1.joblib,265.79,
+exymax_tw2,SVR,0.006084823423825638,0.006084823423825638,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/best_model_exymax_tw2.joblib,389.17,
+eyymax_tf,GradientBoosting,0.011193199924578985,0.011193199924578985,,"{""learning_rate"": 0.07921529866641679, ""max_depth"": 1, ""max_features"": 0.612242984332449, ""n_estimators"": 1057, ""subsample"": 0.7050910309575588}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/best_model_eyymax_tf.joblib,235.39,
+tfmmax_frame,SVR,3.7496112763926472,3.7496112763926472,,"{""svr__C"": 888.0357335845127, ""svr__epsilon"": 0.00027560887878157734, ""svr__gamma"": 0.011255115229536036}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/best_model_tfmmax_frame.joblib,224.52,
+tfmmax_tw1,FlexibleMLP,7.568684427554417,7.568684427554417,,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 0.007095015676184179, ""mlp__learning_rate_init"": 0.0010350129402744886, ""mlp__n_layers"": 1, ""mlp__n_neurons"": 93}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/best_model_tfmmax_tw1.joblib,199.94,
+tfmmax_tw2,SVR,11.314099136094795,11.314099136094795,,"{""svr__C"": 1932.2768280341643, ""svr__epsilon"": 0.0002731420424984291, ""svr__gamma"": 0.002631082857769719}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/best_model_tfmmax_tw2.joblib,217.53,

Code/reports/.gitkeep

- 

Code/reports/width_optimization/2W/rbf_optimization_curves_2W/.~lock.optimization_results_2W_B34_H30_TFD_W90.csv#

+,cimne,cimne-Precision-3660,22.01.2026 15:57,file:///home/cimne/.config/libreoffice/4;
\ No newline at end of file

Code/src/width_optimization/ml_optimization_de.py

@@ -110,7 +110,7 @@ def save_optimization_results(
     # Save to CSV
     csv_path = os.path.join(
         output_dir,
-        f'optimization_results_W{w_val}_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv'
+        f'optimization_results_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv'
     )
     df_results.to_csv(csv_path, index=False)
 
@@ -407,7 +407,7 @@ def main():
     input_models_dir = os.path.join(
         BASE_DIR,
         "..", "..", "models", "width_optimization",
-        f"{w_val}W",
+        f"{w_val}W", "ml_models",
         f"per_output_models_B{b_val}_H{h_val}"
     )
 
@@ -416,7 +416,7 @@ def main():
         BASE_DIR,
         "..", "..", "reports", "width_optimization",
         f"{w_val}W",
-        f"optimization_curves_W{w_val}"
+        f"optimization_curves_{w_val}W"
     )
     os.makedirs(output_graphs_dir, exist_ok=True)
 

Code/src/width_optimization/ml_surrogate_train.py

@@ -377,11 +377,12 @@ def main():
     # Path configuration
     data_path = f"../../data/width_optimization/{w_val}W/FEMdata_B{b_val}_H{h_val}.csv"
     output_models_dir = (
-        f"../../models/width_optimization/{w_val}W/per_output_models_B{b_val}_H{h_val}"
+        f"../../models/width_optimization/{w_val}W/ml_models/"
+        f"per_output_models_B{b_val}_H{h_val}"
     )
     os.makedirs(output_models_dir, exist_ok=True)
 
-    best_models_data_dir = f"../../models/width_optimization/{w_val}W/"
+    best_models_data_dir = f"../../models/width_optimization/{w_val}W/ml_models/"
 
     # --- Load and prepare data ---
     df = pd.read_csv(data_path)

Code/src/width_optimization/rbf_optimization_de.py

@@ -103,7 +103,7 @@ def save_optimization_results(
     # Save to CSV
     csv_path = os.path.join(
         output_dir,
-        f'optimization_results_W{w_val}_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv'
+        f'optimization_results_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv'
     )
     df_results.to_csv(csv_path, index=False)
 
@@ -400,7 +400,7 @@ def main():
     input_models_dir = os.path.join(
         BASE_DIR,
         "..", "..", "models", "width_optimization",
-        "models_rbf", f"{w_val}W",
+        f"{w_val}W", "rbf_models",
         f"per_output_models_B{b_val}_H{h_val}"
     )
 
@@ -409,7 +409,7 @@ def main():
         BASE_DIR,
         "..", "..", "reports", "width_optimization",
         f"{w_val}W",
-        f"rbf_optimization_curves_W{w_val}"
+        f"rbf_optimization_curves_{w_val}W"
     )
     os.makedirs(output_graphs_dir, exist_ok=True)
 

Code/src/width_optimization/rbf_surrogate_train.py

@@ -127,7 +127,7 @@ def main():
     # Path configuration
     data_path = f"../../data/width_optimization/{w_val}W/FEMdata_B{b_val}_H{h_val}.csv"
     output_models_dir = (
-        f"../../models/width_optimization/models_rbf/{w_val}W/"
+        f"../../models/width_optimization/{w_val}W/rbf_models/"
         f"per_output_models_B{b_val}_H{h_val}"
     )
     os.makedirs(output_models_dir, exist_ok=True)