Added new evaluation method

pipeline.py

@@ -54,6 +54,18 @@ def prepare_user_data(df):
     users = df_sorted['user'].unique()
     return {user: df_sorted[df_sorted['user'] == user] for user in users}
 
+def prepare_data_for_model(user_data, sequence_length):
+    X, y = [], []
+    for user, data in user_data.items():
+        features = data.drop('user', axis=1).values
+        labels = data['user'].values
+        for i in range(len(features) - sequence_length):
+            X.append(features[i:i + sequence_length])
+            y.append(labels[i + sequence_length])
+    X = np.array(X)
+    y = np.array(y)
+    return X,y
+
 # === Training & Validation ===
 def train_models(user_data, user_data_val, sequence_lengths=[20], tuner_dir="./working/tuner"):
     best_models = {}
@@ -65,25 +77,8 @@ def train_models(user_data, user_data_val, sequence_lengths=[20], tuner_dir="./w
 
     for sequence_length in sequence_lengths:
         print(f"\n=== Training for Sequence Length: {sequence_length} ===")
-        X, y = [], []
-        for user, data in user_data.items():
-            features = data.drop('user', axis=1).values
-            labels = data['user'].values
-            for i in range(len(features) - sequence_length):
-                X.append(features[i:i + sequence_length])
-                y.append(labels[i + sequence_length])
-        X = np.array(X)
-        y = np.array(y)
-
-        X_val, y_val = [], []
-        for user, data in user_data_val.items():
-            features = data.drop('user', axis=1).values
-            labels = data['user'].values
-            for i in range(len(features) - sequence_length):
-                X_val.append(features[i:i + sequence_length])
-                y_val.append(labels[i + sequence_length])
-        X_val = np.array(X_val)
-        y_val = np.array(y_val)
+        X, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
+        X_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length)
 
         if X.shape[0] == 0 or X_val.shape[0] == 0:
             print(f"⚠️ Skipped sequence length {sequence_length} due to insufficient data.")