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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()
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