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import streamlit as st
import shap
import lime
from lime.lime_tabular import LimeTabularExplainer
import pandas as pd
import numpy as np
import joblib
import seaborn as sns
import xgboost as xgb
import streamlit.components.v1 as components
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from imblearn.pipeline import Pipeline as imbPipeline
def load_dataset(name):
if name == "Financial":
# Replace with your dataset
data = pd.read_csv("datasets/loan_approval_dataset.csv")
data.columns = data.columns.str.strip()
data = data.drop(columns=['loan_id'])
# Remove leading/trailing spaces from the categorical column values
data['education'] = data['education'].str.strip()
data['self_employed'] = data['self_employed'].str.strip()
data['loan_status'] = data['loan_status'].str.strip()
# Encode categorical variables
data['education'] = data['education'].map({'Graduate': 1, 'Not Graduate': 0})
data['self_employed'] = data['self_employed'].map({'Yes': 1, 'No': 0})
data['loan_status'] = data['loan_status'].map({'Approved': 1, 'Rejected': 0})
elif name == "Healthcare":
data = pd.read_csv("datasets/healthcare_dataset.csv")
data.columns = data.columns.str.strip()
data = data.drop_duplicates()
data = data[data['gender'] != 'Other']
def recategorize_smoking(smoking_status):
if smoking_status in ['never', 'No Info']:
return 0
elif smoking_status == 'current':
return 1
elif smoking_status in ['ever', 'former', 'not current']:
return 2
data['smoking_history'] = data['smoking_history'].apply(recategorize_smoking)
data['gender'] = data['gender'].map({'Male': 0, 'Female': 1})
return data
def load_models(dataset_name):
if dataset_name == "Financial":
return joblib.load("models/loan_models.pkl")
elif dataset_name == "Healthcare":
model_path = "models/healthcare_models.pkl"
model = joblib.load(model_path)
return {"Random Forest": model}
def main():
plt.style.use('default')
#st.set_option('deprecation.showPyplotGlobalUse', False)
st.title("Model Interpretability Visualization with LIME and SHAP")
# Create different sections for each dataset
st.subheader("1. Select a Dataset")
dataset = st.selectbox("Choose a dataset:", ["Financial", "Healthcare"])
# Perform different interpretability methods on the first dataset
if dataset == "Financial":
# 1. Load the dataset
X = load_dataset(dataset)
st.write(f"{dataset} Dataset Sample")
st.write(X.head())
# 2. Select interpretability method
st.subheader("2. Select an Interpretability Method")
method = st.selectbox("Choose an interpretability method:", ["LIME", "SHAP"])
if method == "SHAP":
st.subheader("3. Interpretability using SHAP")
# SHAP analysis
loaded_models = load_models(dataset)
model = loaded_models['XG Boost']
sns.set_style('whitegrid')
X = X.drop(columns=["loan_status"]).copy()
X = X.astype(float)
explainer = shap.Explainer(model)
shap_values = explainer(X)
idx = st.slider("Select Test Instance", 0, len(X) - 1, 0)
# Visualize SHAP values
if st.button("Explain Prediction"):
st.write("SHAP Force Plot for a Single Prediction")
fig = shap.force_plot(explainer.expected_value, shap_values[idx].values, X.iloc[idx], matplotlib=True, show=False)
st.pyplot(fig, bbox_inches='tight')
st.write("SHAP Summary Plot")
fig =shap.summary_plot(shap_values, X, show=False)
st.pyplot(fig, bbox_inches='tight')
st.write("SHAP Bar Plot")
fig =shap.summary_plot(shap_values, X, plot_type="bar", show=False)
st.pyplot(fig, bbox_inches='tight')
elif method == "LIME":
st.subheader("3. Interpretability using LIME")
# Choose model type
model_choice = st.radio("Select Model", ["Logistic Regression", 'Decision Tree', 'XG Boost', "Random Forest"])
loaded_models = load_models(dataset)
model = loaded_models[model_choice]
sns.set_style('whitegrid')
x = X.iloc[: , :-1].values
y = X.iloc[: , -1].values
X_train, X_test, y_train, y_test = train_test_split(x, y,
test_size=0.25,
random_state=42)
target = ['Rejected', 'Approved']
labels = {'0': 'Rejected', '1': 'Approved'}
idx = st.slider("Select Test Instance", 0, len(X_test) - 1, 0)
# Explain the prediction instance using LIME
if st.button("Explain Prediction"):
explainer = lime.lime_tabular.LimeTabularExplainer(
X_train,
feature_names=list(X.columns),
class_names=target,
discretize_continuous=True,
)
exp = explainer.explain_instance(
X_test[idx],
model.predict_proba,
)
# Visualize the explanation
st.write("LIME Explanation")
st.write('True label:', labels[str(y_test[idx])])
exp.save_to_file('lime_explanation.html')
HtmlFile = open(f'lime_explanation.html', 'r', encoding='utf-8')
components.html(HtmlFile.read(), height=600)
st.write("Effect of Predictors")
fig = exp.as_pyplot_figure()
st.pyplot(fig, bbox_inches='tight')
if __name__ == "__main__":
main() |