Update app.py

app.py

@@ -84,7 +84,7 @@ max_date_allowed_pred = df_filtered['Date'].max().date()
 end_date = df['Date'].max().date()
 start_date = end_date - pd.Timedelta(days=7)
 
-models_corr_matrix = ['Persistence Model', 'Neural Network 1', 'Neural Network 2', 'Neural Network 3', 
+models_corr_matrix = ['Real Price', 'Persistence Model', 'Neural Network 1', 'Neural Network 2', 'Neural Network 3', 
                'Neural Network 4', 'Regularized Linear Model 1', 
                'Regularized Linear Model 2', 'Regularized Linear Model 3', 
                'Regularized Linear Model 4', 'Hybrid Ensemble']
@@ -182,12 +182,12 @@ else:
                 line=dict(width=0),
                 fill='tonexty',  # Fill between this trace and the previous one
                 fillcolor='rgba(68, 68, 68, 0.3)',
-                name='Conformal Prediction'
+                name='P10/P90 prediction intervals'
             ))
 
     fig.update_layout(xaxis_title="Date", yaxis_title="Price [EUR/MWh]")
     st.plotly_chart(fig, use_container_width=True)
-    st.write("The graph presented here illustrates the day-ahead electricity price forecasts for Belgium, covering the period from one week ago up to tomorrow.")
+    st.write("The graph presented here illustrates the day-ahead electricity price forecasts for Belgium, covering the period from one week ago up to tomorrow. The forecasts are made every morning on day D at 08.00 for day D+1.")
 
 
 if not selected_variables:
@@ -204,16 +204,12 @@ else:
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=plot_df['Real Price'], y=plot_df[model_column], mode='markers', name=f"Real Price vs {model_selection} Predictions"))
 
-    # Calculate the line of best fit
     m, b = np.polyfit(plot_df['Real Price'], plot_df[model_column], 1)
-    # Calculate the y-values based on the line of best fit
-    regression_line = m * plot_df['Real Price'] + b
 
-    # Format the equation to display as the legend name
-    equation = f"y = {m:.2f}x + {b:.2f}"
+    regression_line = m * plot_df['Real Price'] + b
 
     # Add the line of best fit to the figure with the equation as the legend name
-    fig.add_trace(go.Scatter(x=plot_df['Real Price'], y=regression_line, mode='lines', name=equation, line=dict(color='black')))
+    fig.add_trace(go.Scatter(x=plot_df['Real Price'], y=regression_line, mode='lines', line=dict(color='black')))
 
     # Update layout with appropriate titles
     fig.update_layout(
@@ -241,7 +237,7 @@ if start_date_pred and end_date_pred:
     ]
     
     # Placeholder for results
-    results = {'Metric': ['MAE', 'sMAPE', 'RMSE', 'rMAE']}
+    results = {'Metric': ['MAE', 'rMAE']} #'sMAPE', 'RMSE',
     
     p_real = filtered_df['Real Price']
     
@@ -251,12 +247,12 @@ if start_date_pred and end_date_pred:
         p_pred = filtered_df[model]
         
         mae = np.mean(np.abs(p_real - p_pred))
-        smape = 100 * np.mean(np.abs(p_real - p_pred) / ((np.abs(p_real) + np.abs(p_pred)) / 2))
-        rmse = np.sqrt(np.mean((p_real - p_pred) ** 2))
+        #smape = 100 * np.mean(np.abs(p_real - p_pred) / ((np.abs(p_real) + np.abs(p_pred)) / 2))
+        #rmse = np.sqrt(np.mean((p_real - p_pred) ** 2))
         rmae = mae/np.mean(np.abs(p_real - filtered_df['Persistence Model']))
 
         # Store the results
-        results[model] = [f"{mae:.2f}", f"{smape:.2f}%", f"{rmse:.2f}", f"{rmae:.2f}"]
+        results[model] = [f"{mae:.2f}", f"{rmae:.2f}"] #f"{smape:.2f}%", f"{rmse:.2f}",
     
     # Convert the results to a DataFrame for display
     metrics_df = pd.DataFrame(results)
@@ -286,12 +282,6 @@ with col2:
     st.markdown(r"""
     $\text{MAE} = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i|$
     
-
-    $\text{sMAPE} =100\frac{1}{n} \sum_{i=1}^{n} \frac{|y_i - \hat{y}_i|}{\left(|y_i| + |\hat{y}_i|\right)/2}$
-    
-                
-    $\text{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_i - \hat{y}_i\right)^2}$
-    
                 
     $\text{rMAE} = \frac{\text{MAE}}{MAE_{\text{Persistence Model}}}$
     """)
@@ -313,4 +303,4 @@ fig.update_layout(
 st.plotly_chart(fig, use_container_width=True)
 
 st.write("## Access Predictions")
-st.write("If you are interested in accessing the predictions made by the models, please contact Margarida Mascarenhas (KU Leuven PhD Student) at margarida.mascarenhas@kuleuven.be")
+st.write("If you are interested in accessing the predictions made by the models, the models themselves or how these can be incorporated into your workflows, please contact Margarida Mascarenhas (PhD Student, KU Leuven) at margarida.mascarenhas@kuleuven.be or Hussain Kazmi (assistant professor, KU Leuven) at hussain.kazmi@kuleuven.be.")