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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 | |
| from alepython import ale_plot | |
| # XAI (Explainability) | |
| import shap | |
| from sklearn.inspection import permutation_importance | |
| # 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(): | |
| """ 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() | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| st.write("1D Main Effect ALE Plot") | |
| perm_imp = permutation_importance(global_model, X_test, y_test, | |
| n_repeats=30, | |
| random_state=0) | |
| sorted_idx = perm_imp.importances_mean.argsort() | |
| ax.barh(X_test.columns[sorted_idx], perm_imp.importances[sorted_idx].mean(axis=1).T) | |
| ax.set_title("Permutation Importances") | |
| fig.tight_layout() | |
| st.pyplot(fig) | |
| fig, ax = plt.subplots(figsize=(10, 5)) | |
| ax.boxplot(perm_imp.importances[sorted_idx].T, | |
| vert=False, labels=X_test.columns[sorted_idx]) | |
| ax.set_title("Permutation Importances") | |
| fig.tight_layout() | |
| st.pyplot(fig) | |
| def main(): | |
| global global_model | |
| # Ensure the model is trained only once | |
| if global_model is None: | |
| train_model() | |
| st.title("Permutation Feature Importance") | |
| st.write(prompt_params.PERMUTATION_INTRODUCTION) | |
| # Explain the selected sample | |
| if st.button("Explain Sample"): | |
| explain_example() | |
| if __name__ == '__main__': | |
| main() | |