load model and compute mape

src/model.py

@@ -73,17 +73,23 @@ def scale_train(train_ili):
 def train_(train_ili_scaled, val_ili, save_model_path='./models'):
     model = ExponentialSmoothing()
     model.fit(train_ili_scaled)
-    
+
+    return model
+
+
+def save_model(model, path):
     model_name = str(model).split('(')[0]
-    path = f'{save_model_path}/{model_name}.pkl'
+    path = f'{path}/{model_name}.pkl'
     if not os.path.exists(path=path):
         os.makedirs(path)
         model.save(path)
     else:
         model = ExponentialSmoothing.load(path)
 
-    # print('Exponential Smoothing mape:', mape(val_ili, scaler.inverse_transform(exp_preds)))
 
+def load_model(path):
+    model = ExponentialSmoothing.load(path)
+    
     return model
 
 
@@ -108,4 +114,8 @@ def plot_results(train_ili, val_ili, preds):
     return fig
 
 
+def compute_mape(preds, val):
+    metric = mape(val, preds)
+
+    return metric