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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()
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