Merge branch 'master' of https://src.koda.cnrs.fr/cefe/pace/nirs_workflow

src/Class_Mod/RegModels.py

@@ -98,6 +98,7 @@ class Plsr(Regmodel):
         ### parameters in common
         
     def objective(self, params):
+        params['n_components'] = int(params['n_components'])
         x0 = [self._xc, self._xt]
         
         x1 = [eval(str(params['normalization'])+"(x0[i])") for i in range(2)]
@@ -130,6 +131,10 @@ class Plsr(Regmodel):
             self._yc = Model.predict(x2[0])
             self._yt = Model.predict(x2[1])
             self._model = Model
+            for key,value in params.items():
+                try: params[key] =  int(value)
+                except (TypeError, ValueError): params[key] =  value
+
             self._best = params
             self.pretreated = pd.DataFrame(x2[0])
             self._sel_ratio = sel_ratio(Model, x2[0])
@@ -197,7 +202,11 @@ class TpeIpls(Regmodel):
             self._yc = Model.predict(x2[0][:,id])
             self._yt = Model.predict(x2[1][:,id])
             self._model = Model
+            for key,value in params.items():
+                try: params[key] =  int(value)
+                except (TypeError, ValueError): params[key] =  value
             self._best = params
+
             self.pretreated = pd.DataFrame(x2[0])
             self.segments = arrays
             

src/data/models/model_sd_2024_06_07__on_Xcal_and_Ycal_data_Wavelengths_index.csv

+;from;to
+band1;353;409
+band2;501;882
+band3;1727;2020

src/data/params/Preprocessing.json

+{"deriv": 0, "n_components": 18, "normalization": "No_transformation", "polyorder": 0, "v1": 752, "v2": 735, "v3": 125, "v4": 1198, "v5": 461, "v6": 522, "window_length": 1}
\ No newline at end of file

src/pages/2-model_creation.py

@@ -293,9 +293,11 @@ if not spectra.empty and not y.empty:
         
         M1.write(Reg.best_hyperparams_print)
         a_Test=Reg.best_hyperparams_print
+
+        with open("data/params/Preprocessing.json", "w") as outfile:
+            json.dump(Reg.best_hyperparams_, outfile)
+        
         
-        with open("data/params/Preprocessing.json", "w") as outfile: 
-            json.dump(Reg.best_hyperparams_, outfile)      
 ##########
         M1.write("-- Model performance --")
         M1.dataframe(metrics(c = [y_train, yc], t = [y_test, yt], method='regression').scores_)