From c7b89505c2ea2a4fe5fb1acbc75242231a83b525 Mon Sep 17 00:00:00 2001
From: Hannes Signer <signer@uni-potsdam.de>
Date: Mon, 24 Mar 2025 20:26:28 +0100
Subject: [PATCH] add check for different scaler types

---
 src/preprocessing.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/preprocessing.py b/src/preprocessing.py
index be783d6..d7ecd3e 100644
--- a/src/preprocessing.py
+++ b/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: