all issues were handled

src/Class_Mod/RegModels.py

@@ -93,8 +93,8 @@ class Regmodel(object):
   
 ########################################### PLSR   #########################################
 class Plsr(Regmodel):
-    def __init__(self, train, test, n_iter = 10):
-        super().__init__(train, test, n_iter, add_hyperparams = {'n_components': hp.randint('n_components', 2,20)})
+    def __init__(self, train, test, n_iter = 10, nfolds = 3):
+        super().__init__(train, test, n_iter, nfolds = nfolds, add_hyperparams = {'n_components': hp.randint('n_components', 1,20)})
         ### parameters in common
         
     def objective(self, params):
@@ -114,14 +114,9 @@ class Plsr(Regmodel):
         params['deriv'], params['polyorder'], params['window_length']  = a, b, c
         x2 = [savgol_filter(x1[i], polyorder=params['polyorder'], deriv=params['deriv'], window_length = params['window_length']) for i in range(2)]
 
-        Model = PLSRegression(scale = False, n_components = params['n_components'])
-        # self._cv_df = KF_CV().process(model = Model, x = x2[0], y = self._ytrain, n_folds = self._nfolds)
-        # self._cv_df['Average'] = self._cv_df.mean(axis = 1)
-        # self._cv_df['S'] = self._cv_df.std(axis = 1)
-        # self._cv_df['CV(%)'] = self._cv_df['S'] * 100 / self._cv_df['Average']
-        # self._cv_df = self._cv_df.T.round(2)
+        model = PLSRegression(scale = False, n_components = params['n_components'])
         folds = KF_CV().CV(x = x2[0], y = np.array(self._ytrain), n_folds = self._nfolds)
-        yp = KF_CV().cross_val_predictor(model = Model, folds = folds, x = x2[0], y = np.array(self._ytrain))
+        yp = KF_CV().cross_val_predictor(model = model, folds = folds, x = x2[0], y = np.array(self._ytrain))
         self._cv_df = KF_CV().metrics_cv(y = np.array(self._ytrain), ypcv = yp, folds =folds)[1]
                 
         score = self._cv_df.loc["cv",'rmse']
@@ -147,15 +142,15 @@ class Plsr(Regmodel):
 
     ############################################ iplsr #########################################
 class TpeIpls(Regmodel):
-    def __init__(self, train, test, n_iter = 10, n_intervall = 5):
+    def __init__(self, train, test, n_iter = 10, n_intervall = 5, nfolds = 3):
         self.n_intervall = n_intervall
         self.n_arrets = self.n_intervall*2
         
         
-        r = {'n_components': hp.randint('n_components', 2,10)}
+        r = {'n_components': hp.randint('n_components', 1,20)}
         r.update({f'v{i}': hp.randint(f'v{i}', 0, train[0].shape[1]) for i in range(1,self.n_arrets+1)})
 
-        super().__init__(train, test, n_iter, add_hyperparams = r)
+        super().__init__(train, test, n_iter, add_hyperparams = r, nfolds = nfolds)
         
         ### parameters in common
         
@@ -166,7 +161,7 @@ class TpeIpls(Regmodel):
         arrays = [np.arange(self.idx[2*i],self.idx[2*i+1]+1) for i in range(self.n_intervall)]
         id = np.unique(np.concatenate(arrays, axis=0), axis=0)
 
-        # ## Preprocessing
+        ### Preprocessing
         x0 = [self._xc, self._xt]
         x1 = [eval(str(params['normalization'])+"(x0[i])") for i in range(2)]
 
@@ -180,35 +175,35 @@ class TpeIpls(Regmodel):
 
         params['deriv'], params['polyorder'], params['window_length']  = a, b, c
         x2 = [savgol_filter(x1[i], polyorder=params['polyorder'], deriv=params['deriv'], window_length = params['window_length']) for i in range(2)]
-        # print(x2)
         
-        # ## Modelling
-        folds = KF_CV().CV(x = x2[0], y = np.array(self._ytrain), n_folds = self._nfolds)
+        
+        prepared_data = [x2[i][:,id] for i in range(2)]
+
+        
+        ### Modelling
+        folds = KF_CV().CV(x = prepared_data[0], y = np.array(self._ytrain), n_folds = self._nfolds)
         try:
-            
-            Model = PLSRegression(scale = False, n_components = params['n_components'])
-            yp = KF_CV().cross_val_predictor(model = Model, folds = folds, x = x2[0], y = np.array(self._ytrain))
+            model = PLSRegression(scale = False, n_components = params['n_components'])
+            yp = KF_CV().cross_val_predictor(model = model, folds = folds, x = prepared_data[0], y = np.array(self._ytrain))
             self._cv_df = KF_CV().metrics_cv(y = np.array(self._ytrain), ypcv = yp, folds =folds)[1]
         except ValueError as ve:
-            Model = PLSRegression(scale = False, n_components = 1)
             params["n_components"] = 1
-            yp = KF_CV().cross_val_predictor(model = Model, folds = folds, x = x2[0], y = np.array(self._ytrain))
+            model = PLSRegression(scale = False, n_components = params["n_components"])
+            yp = KF_CV().cross_val_predictor(model = model, folds = folds, x = prepared_data[0], y = np.array(self._ytrain))
             self._cv_df = KF_CV().metrics_cv(y = np.array(self._ytrain), ypcv = yp, folds =folds)[1]
-        # self._cv_df['Average'] = self._cv_df.mean(axis = 1)
-        # self._cv_df['S'] = self._cv_df.std(axis = 1)
-        # self._cv_df['CV(%)'] = self._cv_df['S'] * 100 / self._cv_df['Average']
-        # self._cv_df = self._cv_df.T.round(2)
+
+
         score = self._cv_df.loc['cv','rmse']
         
-        Model = PLSRegression(scale = False, n_components = params['n_components'])
-        Model.fit(x2[0][:,id], self._ytrain)
+        Model = PLSRegression(scale = False, n_components = model.n_components)
+        Model.fit(prepared_data[0], self._ytrain)
 
         if self.SCORE > score:
             self.SCORE = score
             self._ycv = KF_CV().meas_pred_eq(y = np.array(self._ytrain), ypcv=yp, folds=folds)
             
-            self._yc = Model.predict(x2[0][:,id])
-            self._yt = Model.predict(x2[1][:,id])
+            self._yc = Model.predict(prepared_data[0])
+            self._yt = Model.predict(prepared_data[1])
             self._model = Model
             for key,value in params.items():
                 try: params[key] =  int(value)
@@ -231,4 +226,4 @@ class TpeIpls(Regmodel):
 class Pcr(Regmodel):
     def __init__(self, train, test, n_iter = 10, n_val = 5):
         super.__init__()
-        {f'pc{i}': hp.randint(f'pc{i+1}', 0, train[0].shape[1]) for i in range(self.n_val)}
+        {f'pc{i}': hp.randint(f'pc{i+1}', 0, train[0].shape[1]) for i in range(self.n_val)}
\ No newline at end of file

src/pages/1-samples_selection.py

@@ -658,6 +658,6 @@ if not sam.empty:
         zipname = json.load(f)
     if os.path.split(recent_file)[1] == os.path.split(zipname)[1]:
         with open("./temp/"+zipname, "rb") as fp:
-                st.write('Download the Analysis Results')
+                st.subheader('Download the Analysis Results')
                 st.download_button('Download', data = fp, file_name=zipname, mime="application/zip",
-                                args=None, kwargs=None,type="primary",use_container_width=True)
+                                args=None, kwargs=None,type="primary",use_container_width=True)
\ No newline at end of file