Update app.py

app.py

@@ -28,14 +28,13 @@ def load_data_predictions(github_token):
         'DNN2': 'Neural Network 2',
         'DNN3': 'Neural Network 3',
         'DNN4': 'Neural Network 4',
-        'DNN_Ensemble': 'Neural Network Ensemble',
         'LEAR56': 'Regularized Linear Model 1',
         'LEAR84': 'Regularized Linear Model 2',
         'LEAR112': 'Regularized Linear Model 3',
         'LEAR730': 'Regularized Linear Model 4',
-        'LEAR_Ensemble': 'Regularized Linear Model Ensemble',
         'Persis': 'Persistence Model',
-        'Hybrid_Ensemble': 'Hybrid Ensemble'
+        'Hybrid_Ensemble': 'Hybrid Ensemble',
+        'Weighted_Ensemble': 'Weighted Ensemble'
         })
         df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
         df_filtered = df.dropna(subset=['Real Price'])
@@ -87,10 +86,10 @@ start_date = end_date - pd.Timedelta(days=7)
 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']
+               'Regularized Linear Model 4', 'Weighted Ensemble']
 
 def conformal_predictions(data):
-    data['Residuals'] = data['Hybrid Ensemble'] - data['Real Price']
+    data['Residuals'] = data['Weighted Ensemble'] - data['Real Price']
     data.set_index('Date', inplace=True)
     data['Hour'] = data.index.hour
     min_date = data.index.min()
@@ -108,8 +107,8 @@ def conformal_predictions(data):
                         hour_quantile = quantiles[hour]
                         idx = (data.index.normalize() == date) & (data.Hour == hour)
                         data.loc[idx, 'Quantile_90'] = hour_quantile
-                        data.loc[idx, 'Lower_Interval'] = data.loc[idx, 'Hybrid Ensemble'] - hour_quantile
-                        data.loc[idx, 'Upper_Interval'] = data.loc[idx, 'Hybrid Ensemble'] + hour_quantile
+                        data.loc[idx, 'Lower_Interval'] = data.loc[idx, 'Weighted Ensemble'] - hour_quantile
+                        data.loc[idx, 'Upper_Interval'] = data.loc[idx, 'Weighted Ensemble'] + hour_quantile
     data.reset_index(inplace=True)
     return data
 
@@ -137,9 +136,9 @@ upper_space.markdown("""
 with st.sidebar:
     st.write("### Variables Selection for Graph")
     st.write("Select which variables you'd like to include in the graph. This will affect the displayed charts and available data for download.")
-    selected_variables = st.multiselect("Select variables to display:", options=['Real Price', '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', 'Persistence Model'], default=['Real Price', 'Hybrid Ensemble'])
+    selected_variables = st.multiselect("Select variables to display:", options=['Real Price', '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', 'Weighted Ensemble', 'Persistence Model'], default=['Real Price', 'Weighted Ensemble'])
     st.write("### Model Selection for Scatter Plot")
-    model_selection = st.selectbox("Select which model's predictions to display:", options=['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', 'Persistence Model'], index=8) # Adjust the index as needed to default to your desired option
+    model_selection = st.selectbox("Select which model's predictions to display:", options=['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', 'Weighted Ensemble', 'Persistence Model'], index=8) # Adjust the index as needed to default to your desired option
 
     st.write("### Date Range for Metrics Calculation")
     st.write("Select the date range to calculate the metrics for the predictions. This will influence the accuracy metrics displayed below. The complete dataset ranges from 10/03/2024 until today.")
@@ -152,7 +151,7 @@ else:
     st.write("## Belgian Day-Ahead Electricity Prices")
 
     # Call conformal_predictions if 'Hybrid Ensemble' is selected
-    if 'Hybrid Ensemble' in selected_variables:
+    if 'Weighted Ensemble' in selected_variables:
         df = conformal_predictions(df)  # Make sure this function modifies df correctly
 
     temp_df = df[(df['Date'] >= pd.Timestamp(start_date))]  # Ensure correct date filtering
@@ -164,7 +163,7 @@ else:
         fig.add_trace(go.Scatter(x=temp_df['Date'], y=temp_df[variable], mode='lines', name=variable))
 
         # Check if conformal predictions should be added for Hybrid Ensemble
-        if variable == 'Hybrid Ensemble' and 'Quantile_90' in df.columns:
+        if variable == 'Weighted Ensemble' and 'Quantile_90' in df.columns:
             # Add the lower interval trace
             fig.add_trace(go.Scatter(
                 x=temp_df['Date'],
@@ -234,7 +233,7 @@ if start_date_pred and end_date_pred:
     models = [
         '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',
-        'Persistence Model', 'Hybrid Ensemble'
+        'Persistence Model', 'Weighted Ensemble'
     ]
     
     # Placeholder for results