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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
# 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")
# ale_plot(
# global_model,
# X_test,
# "Age",
# bins=5,
# monte_carlo=True,
# monte_carlo_rep=30,
# monte_carlo_ratio=0.5,
# )
#
# st.pyplot(fig)
fig1, ax1 = plt.subplots(figsize=(10, 5))
st.write("2D Second-Order ALE Plot")
ale_plot(global_model, X_test, X_train.columns[:2], bins=10)
st.pyplot(fig1)
def main():
global global_model
# Ensure the model is trained only once
if global_model is None:
train_model()
st.title("ALE (Accumulated Local Effects)")
st.write(prompt_params.ALE_INTRODUCTION)
st.write("now has bug, waiting for fix")
# Explain the selected sample
if st.button("Explain Sample"):
explain_example()
if __name__ == '__main__':
main()
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