preprocessed spectra plot visualization + report

src/Report/report.py

@@ -340,9 +340,16 @@ def report(*args):
             \label{fig:importance}
             \end{figure}
             """
-
-        elif "LW-PLS " in to_report:
-            """"""
+        elif "LW-PLS" in to_report:
+            latex_report += r"""The average of raw and preprocessed spectra is visualized in \cref{fig:importance}. \par
+            
+            \begin{figure}[h]
+            \centering
+            \includegraphics[width=1\linewidth]{Variable_importance.png}
+            \caption{Visualizing the average spectrum computed for raw and preprocessed  spectra}
+            \label{fig:importance}
+            \end{figure}
+            """
         elif "TPE-iPLS" in to_report:
             latex_report += r"""
             Many research papers have proved that interval selection methods, with different number of intervalls, helps reduce noise and model overfitting,

src/pages/2-model_creation.py

@@ -186,8 +186,8 @@ if not spectra.empty and not y.empty:
         #M2.dataframe(Pin.pred_data_)
 
     elif regression_algo == reg_algo[2]:
-        M1.write('KFold for Cross-Validation = ' + str(nb_folds))
-        info = M1.info('Starting LWPLSR model creation... Please wait a few minutes.')
+        M20.write(f'K-Fold for Cross-Validation (K = {str(nb_folds)})')
+        info = M20.info('Starting LWPLSR model creation... Please wait a few minutes.')
         # export data to csv for Julia train/test
         data_to_work_with = ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']
         x_train_np, y_train_np, x_test_np, y_test_np = X_train.to_numpy(), y_train.to_numpy(), X_test.to_numpy(), y_test.to_numpy()
@@ -260,14 +260,15 @@ if not spectra.empty and not y.empty:
             ## plots
             Reg.cv_data_ = KF_CV().meas_pred_eq(y = np.array(y_train), ypcv= Reg.cv_data_['YpredCV'], folds=folds)
             Reg.pretreated_spectra_ = preReg.pretreated_spectra_
+            
             Reg.best_hyperparams_print = {**preReg.best_hyperparams_, **Reg.best_hyperparams_}
             Reg.best_hyperparams_ = {**preReg.best_hyperparams_, **Reg.best_hyperparams_}
             info.empty()
-            M1.success('Model created!')
+            M20.success('Model created!')
         except FileNotFoundError as e:
             # Display error message on the interface if modeling is wrong
             info.empty()
-            M1.warning('- ERROR during model creation -')
+            M20.warning('- ERROR during model creation -')
             Reg = None
             for i in data_to_work_with: os.unlink(temp_path / str(i + ".csv"))
 
@@ -481,8 +482,8 @@ if not spectra.empty and not y.empty and regression_algo:
     if regression_algo in reg_algo[1:] and Reg is not None:
         fig, (ax1, ax2) = plt.subplots(2,1, figsize = (12, 4), sharex=True)
         ax1.plot(colnames, np.mean(X_train, axis = 0), color = 'black', label = 'Average spectrum (Raw)')
-        if regression_algo != reg_algo[2]:
-            ax2.plot(colnames, np.mean(Reg.pretreated_spectra_ , axis = 0), color = 'black', label = 'Average spectrum (pretreated)')
+        # if regression_algo != reg_algo[2]:
+        ax2.plot(colnames, np.mean(Reg.pretreated_spectra_ , axis = 0), color = 'black', label = 'Average spectrum (Pretreated)')
         ax2.set_xlabel('Wavelenghts')
         plt.tight_layout()