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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 sklearn.inspection import PartialDependenceDisplay | |
| # 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(features, kind): | |
| """ 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)) | |
| PartialDependenceDisplay.from_estimator(global_model, X_test, features, kind=kind) | |
| st.pyplot(fig) | |
| def main(): | |
| global global_model | |
| # Ensure the model is trained only once | |
| if global_model is None: | |
| train_model() | |
| # Define feature names | |
| selected_feature = st.sidebar.selectbox("Select a feature for PDP/ICE analysis:", ("Age", "Workclass", "Education-Num", "Marital Status", "Occupation", | |
| "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss", "Hours per week", "Country"),) | |
| print(f"selected feature is {selected_feature}") | |
| kind = st.sidebar.selectbox("Select plot type:", ("individual", "average", "both"),) | |
| st.title("ICE and PDP") | |
| st.write(prompt_params.ICE_INTRODUCTION) | |
| # Explain the selected sample | |
| if st.button("Explain Sample"): | |
| explain_example("Age", kind) | |
| if __name__ == '__main__': | |
| main() | |