Update app.py

app.py

@@ -113,10 +113,37 @@ class StockPredictor:
 
         return mse, mape, rmse
 
-    def cross_validate_model(self):
-        df_cv = cross_validation(self.model, initial='730 days', period='180 days', horizon='90 days')
-        df_p = performance_metrics(df_cv)
-        return df_p
+    def cross_validate_model(self, n_splits=5):
+        tscv = TimeSeriesSplit(n_splits=n_splits)
+        cv_results = []
+
+        for train_index, test_index in tscv.split(self.data):
+            train_data = self.data.iloc[train_index]
+            test_data = self.data.iloc[test_index]
+
+            # Train the model
+            model = Prophet()
+            model.fit(train_data)
+
+            # Make predictions
+            future = model.make_future_dataframe(periods=len(test_data))
+            forecast = model.predict(future)
+
+            # Calculate metrics
+            y_true = test_data['y'].values
+            y_pred = forecast['yhat'].tail(len(test_data)).values
+
+            mse = mean_squared_error(y_true, y_pred)
+            rmse = np.sqrt(mse)
+            mape = mean_absolute_percentage_error(y_true, y_pred)
+
+            cv_results.append({
+                'mse': mse,
+                'rmse': rmse,
+                'mape': mape
+            })
+
+        return pd.DataFrame(cv_results)
 
 def fetch_stock_data(ticker):
     try:
@@ -319,6 +346,15 @@ def test_model():
                             cv_results = predictor.cross_validate_model()
                             st.dataframe(cv_results)
 
+                            # Calculate and display average metrics
+                            avg_mse = cv_results['mse'].mean()
+                            avg_rmse = cv_results['rmse'].mean()
+                            avg_mape = cv_results['mape'].mean()
+                            
+                            st.write(f"Average MSE: {avg_mse:.4f}")
+                            st.write(f"Average RMSE: {avg_rmse:.4f}")
+                            st.write(f"Average MAPE: {avg_mape:.4f}")
+
                             # Feature importance
                             st.subheader("Feature Importance")
                             feature_importance = pd.DataFrame(predictor.model.params['regressor_coefficients'].items(), columns=['Feature', 'Importance'])
@@ -331,6 +367,8 @@ def test_model():
                         st.error("Failed to generate predictions. The predicted data is None.")
                 else:
                     st.error("Failed to train the Prophet model. Please try a different dataset.")
+            else:
+                st.error("Failed to fetch stock data. Please try again.")
 
 def predict_stock_prices():
     st.header("Predict Stock Prices with Enhanced Model")