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| # Import Libraries | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| import src.prompt_config as prompt_params | |
| # Models | |
| import xgboost | |
| from sklearn.model_selection import train_test_split | |
| # XAI (Explainability) | |
| import shap | |
| # Global Variables to Store Model & Data | |
| global_model = None | |
| X_train, X_test, y_train, y_test = None, None, None, None | |
| def train_model(): | |
| """ Train the XGBoost model only once and store it globally. """ | |
| global global_model, X_train, X_test, y_train, y_test | |
| if global_model is None: | |
| # Load Data from SHAP library | |
| X, y = shap.datasets.adult() | |
| # Split data | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) | |
| # Train XGBoost model | |
| global_model = xgboost.XGBClassifier() | |
| global_model.fit(X_train, y_train) | |
| print("XGBoost Model training completed!") | |
| def explain_example(baseline_number, example_idx): | |
| """ Explain a given sample without retraining the model. """ | |
| global global_model, X_train, X_test, y_train, y_test | |
| if global_model is None: | |
| train_model() | |
| X, y = shap.datasets.adult() | |
| X_base = shap.utils.sample(X, baseline_number) | |
| explainer = shap.TreeExplainer(global_model, X_base) # Use the TreeExplainer algorithm with background distribution | |
| shap_values = explainer.shap_values(X_test) # Get shap values | |
| shap_values_exp = explainer(X_test) # Get explainer for X_test | |
| # SHAP Summary Plot (BeeSwarm) | |
| st.write("### 📊 SHAP Summary Plot") | |
| st.write("This plot provides an intuitive way to see how different features contribute to individual predictions, making model interpretations easier!") | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| shap.summary_plot(shap_values, X_test, show=False) | |
| st.pyplot(fig) | |
| # SHAP Summary Bar Plot | |
| st.write("### 📊 SHAP Feature Importance (Bar Plot)") | |
| st.write("It helps understand which features the model relies on most.") | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| shap.summary_plot(shap_values, X_test, plot_type="bar", show=False) | |
| st.pyplot(fig) | |
| # SHAP Dependence Plot | |
| st.write("### 🔍 SHAP Dependence Plot for 'Age'") | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| shap.dependence_plot('Age', shap_values, X_test, ax=ax, show=False) | |
| st.pyplot(fig) | |
| # SHAP Waterfall Plot | |
| st.write(f"### 🌊 SHAP Waterfall Plot for Example {example_idx}") | |
| st.write(f"Visualize the SHAP values for an instance of interest") | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| shap.plots.waterfall(shap_values_exp[example_idx], show=False) | |
| st.pyplot(fig) | |
| def main(): | |
| global global_model | |
| # Ensure the model is trained only once | |
| if global_model is None: | |
| train_model() | |
| # Streamlit UI Controls | |
| baseline_number = st.sidebar.number_input( | |
| label="Select the number of baseline:", | |
| min_value=20, | |
| max_value=1000, | |
| value=100, # Default value | |
| step=1 | |
| ) | |
| example_idx = st.sidebar.number_input( | |
| label="Select the sample index to explain:", | |
| min_value=0, | |
| max_value=len(X_test) - 1, # Ensures the index is within range | |
| value=100, # Default value | |
| step=1, # Step size | |
| help=prompt_params.EXAMPLE_BE_EXPLAINED_IDX, | |
| ) | |
| st.title("SHAP") | |
| st.write(prompt_params.SHAP_INTRODUCTION) | |
| # Explain the selected sample | |
| if st.button("Explain Sample"): | |
| explain_example(baseline_number, example_idx) | |
| if __name__ == '__main__': | |
| main() | |