import streamlit as st import pandas as pd import numpy as np import plotly.graph_objs as go import requests from io import StringIO import base64 #@st.cache_data(ttl=86400) # TTL is set for 86400 seconds (24 hours) def load_data_predictions(github_token): url = 'https://api.github.com/repos/mmmapms/Forecast_DAM_V2/contents/Predictions.csv' headers = {'Authorization': f'token {github_token}'} response = requests.get(url, headers=headers) if response.status_code == 200: file_content = response.json()['content'] decoded_content = base64.b64decode(file_content).decode('utf-8') csv_content = StringIO(decoded_content) df = pd.read_csv(csv_content, encoding='utf-8') df = df.rename(columns={ 'Price': 'Real Price', 'DNN1': 'Neural Network 1', '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' }) df['Date'] = pd.to_datetime(df['Date'], dayfirst=True) df_filtered = df.dropna(subset=['Real Price']) return df, df_filtered else: st.error("Failed to download data. Please check your GitHub token and repository details.") return pd.DataFrame(), pd.DataFrame() github_token = st.secrets["GitHub_Token_Margarida"] if github_token: df, df_filtered = load_data_predictions(github_token) # Your existing logic to use df and df_filtered else: st.warning("Please enter your GitHub Personal Access Token to proceed.") #@st.cache_data #def load_data_predictions(): # df = pd.read_csv('Predictions.csv') # df = df.rename(columns={ # 'Price': 'Real Price', # 'DNN1': 'Neural Network 1', # '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' #}) # df['Date'] = pd.to_datetime(df['Date'], dayfirst=True) # df_filtered = df.dropna(subset=['Real Price']) # return df, df_filtered #df, df_filtered = load_data_predictions() min_date_allowed_pred = df_filtered['Date'].min().date() 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 = ['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'] st.title("Belgium: Electricity Price Forecasting") # Sidebar for inputs 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:", ['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','Neural Network 4', 'Regularized Linear Model 4', 'Persistence Model'])#options=['Real Price', 'Neural Network 1', 'Neural Network 2', 'Neural Network 3', 'Neural Network 4', 'Neural Network Ensemble', 'Regularized Linear Model 1', 'Regularized Linear Model 2','Regularized Linear Model 3', 'Regularized Linear Model 4', 'Regularized Linear Model Ensemble', 'Hybrid Ensemble', 'Persistence Model'], default=['Real Price', 'Neural Network Ensemble', 'Regularized Linear Model Ensemble', 'Persistence Model']) 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)#options=['Neural Network 1', 'Neural Network 2', 'Neural Network 3', 'Neural Network 4', 'Neural Network Ensemble', 'Regularized Linear Model 1', 'Regularized Linear Model 2','Regularized Linear Model 3', 'Regularized Linear Model 4', 'Regularized Linear Model Ensemble', 'Hybrid Ensemble', 'Persistence Model'], index=10) # 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.") start_date_pred, end_date_pred = st.date_input("Select Date Range for Metrics Calculation:", [min_date_allowed_pred, max_date_allowed_pred]) # Main content if not selected_variables: st.warning("Please select at least one variable to display.") else: # Plotting st.write("## Belgian Day-Ahead Electricity Prices") temp_df = df[(df['Date'] >= pd.Timestamp(start_date))] #& (df['Date'] <= pd.Timestamp(end_date))] fig = go.Figure() for variable in selected_variables: fig.add_trace(go.Scatter(x=temp_df['Date'], y=temp_df[variable], mode='lines', name=variable)) 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. It incorporates predictions from three distinct models: a Neural Network, a Regularized Linear Model, and Persistence, alongside the actual electricity prices up until today.") if not selected_variables: st.warning("Please select at least one variable to display.") else: # Plotting st.write("## Scatter Plot: Real Price vs Model Predictions") # Filter based on the selected date range for plotting plot_df = df[(df['Date'] >= pd.Timestamp(min_date_allowed_pred)) & (df['Date'] <= pd.Timestamp(max_date_allowed_pred))] model_column = model_selection # Create the scatter plot 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}" # 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'))) # Update layout with appropriate titles fig.update_layout(xaxis_title="Real Price [EUR/MWh]", yaxis_title=f"{model_selection} Predictions [EUR/MWh]", title=f"Scatter Plot of Real Price vs {model_selection} Predictions from {min_date_allowed_pred} to {max_date_allowed_pred}") st.plotly_chart(fig, use_container_width=True) # Calculating and displaying metrics if start_date_pred and end_date_pred: st.header("Accuracy Metrics") #st.write(f"The accuracy metrics are calculated from {start_date_pred} to {end_date_pred}, this intervale can be changed in the sidebar.") st.write(f"The accuracy metrics are calculated from **{start_date_pred}** to **{end_date_pred}**. This interval can be changed in the sidebar. Evaluate the forecasting accuracy of our models with key performance indicators. The table summarizes the Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE) for the selected models over your selected date range. Lower values indicate higher precision and reliability of the forecasts.") filtered_df = df_filtered[(df_filtered['Date'] >= pd.Timestamp(start_date_pred)) & (df_filtered['Date'] <= pd.Timestamp(end_date_pred))] # List of models for convenience #models = [ # 'Neural Network 1', 'Neural Network 2', 'Neural Network 3', 'Neural Network 4', 'Neural Network Ensemble', # 'Regularized Linear Model 1', 'Regularized Linear Model 2', 'Regularized Linear Model 3', 'Regularized Linear Model 4', 'Regularized Linear Model Ensemble', # 'Persistence Model', 'Hybrid Ensemble' #] 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' ] # Placeholder for results results = {'Metric': ['MAE', 'sMAPE', 'RMSE', 'rMAE']} p_real = filtered_df['Real Price'] # Iterate through each model to calculate and store metrics for model in models: # Assuming column names in filtered_df match the model names directly for simplicity 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)) 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}"] # Convert the results to a DataFrame for display metrics_df = pd.DataFrame(results) transposed_metrics_df = metrics_df.set_index('Metric').T col1, col2 = st.columns([3, 2]) # Display the transposed DataFrame with col1: # Assuming 'transposed_metrics_df' is your final DataFrame with metrics st.dataframe(transposed_metrics_df, hide_index=False) with col2: st.markdown("""
Equations
""", unsafe_allow_html=True) # Rendering LaTeX equations st.markdown(r""" $\text{MAE} = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i|$ $\text{sMAPE} =100\frac{1}{n} \sum_{i=1}^{n} \frac{|y_i - \hat{y}_i|}{\left(|y_i| + |\hat{y}_i|\right)/2}$ $\text{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_i - \hat{y}_i\right)^2}$ $\text{rMAE} = \frac{\text{MAE}}{MAE_{\text{Persistence Model}}}$ """) st.write("## Correlation Matrix") models_df = df_filtered[models_corr_matrix] corr_matrix = models_df.corr() fig = go.Figure(data=go.Heatmap( z=corr_matrix.values, x=corr_matrix.columns, y=corr_matrix.index)) fig.update_layout( yaxis_autorange='reversed' # Ensure the y-axis starts from the top ) 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")