add check for different scaler types

src/preprocessing.py

@@ -149,6 +149,11 @@ def custom_loss(
 
     # as far as I know tensorflow does not directly support the use of scaler objects
     # therefore, the backtransformation is done manually
+    
+    if preprocess.scaler_type != scaler_type:
+        raise Exception(
+            "Data normalized with scaler different than specified for the training. Compare the scaling approach on preprocessing and training.")
+    
     try:
         if scaler_type == "minmax":
             scale_X = tf.convert_to_tensor(
@@ -566,6 +571,7 @@ class preprocessing:
         return design_resampled, target_resampled
 
     def scale_fit(self, X, y, scaling, type="Standard"):
+        self.scaler_type = type
         """Fit a scaler for data preprocessing.
 
         Args: