File size: 4,239 Bytes
c52a337 6fc156b c52a337 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
import io
import pickle
import streamlit as st
import joblib
import shap
import pandas as pd
import matplotlib.pyplot as plt
# Load the LightGBM model and other necessary objects
with open('lgb1_model.pkl', 'rb') as f:
lgb1 = pickle.load(f)
categorical_features = joblib.load("categorical_features.joblib")
encoder = joblib.load("encoder.joblib")
# Sidebar option to select the dashboard
option = st.sidebar.selectbox("Which dashboard?", ("Model information", "Stroke prediction"))
st.title(option)
def get_pred():
"""
Function to display the stroke probability calculator and Shap force plot.
"""
st.header("Stroke probability calculator ")
# User input for prediction
gender = st.selectbox("Select gender: ", ["Male", "Female", 'Other'])
work_type = st.selectbox("Work type: ", ["Private", "Self_employed", 'children', 'Govt_job', 'Never_worked'])
residence_status = st.selectbox("Residence status: ", ["Urban", "Rural"])
smoking_status = st.selectbox("Smoking status: ", ["Unknown", "formerly smoked", 'never smoked', 'smokes'])
age = st.slider("Input age: ", 0, 120)
hypertension = st.select_slider("Do you have hypertension: ", [0, 1])
heart_disease = st.select_slider("Do you have heart disease: ", [0, 1])
ever_married = st.select_slider("Have you ever married? ", [0, 1])
avg_glucosis_lvl = st.slider("Average glucosis level: ", 50, 280)
bmi = st.slider("Input Bmi: ", 10, 100)
# User input data
data = {
"gender": gender,
"work_type": work_type,
"Residence_type": residence_status,
"smoking_status": smoking_status,
"age": age,
"hypertension": hypertension,
"heart_disease": heart_disease,
"ever_married": ever_married,
"avg_glucose_level": avg_glucosis_lvl,
"bmi": bmi
}
# Prediction button
if st.button("Predict"):
# Convert input data to a DataFrame
X = pd.DataFrame([data])
# Encode categorical features
encoded_features = encoder.transform(X[categorical_features])
# Get the feature names from the encoder
feature_names = encoder.get_feature_names_out(input_features=categorical_features)
# Create a DataFrame with the encoded features and feature names
encoded_df = pd.DataFrame(encoded_features, columns=feature_names)
X_encoded = pd.concat([X.drop(columns=categorical_features), encoded_df], axis=1)
# Make predictions
prediction_proba = lgb1.predict_proba(X_encoded)
# Get SHAP values
explainer = shap.TreeExplainer(lgb1)
shap_values = explainer.shap_values(X_encoded)
# Extract prediction probability and display it to the user
probability = prediction_proba[0, 1] # Assuming binary classification
st.subheader(f"The predicted probability of stroke is {probability}.")
st.subheader("IF you see result , higher than 0.3, we advice you to see a doctor")
st.header("Shap forceplot")
st.subheader("Features values impact on model made prediction")
# Display SHAP force plot using Matplotlib
shap.force_plot(explainer.expected_value[1], shap_values[1], features=X_encoded.iloc[0, :], matplotlib=True)
# Save the figure to a BytesIO buffer
buf = io.BytesIO()
plt.savefig(buf, format="png", dpi=800)
buf.seek(0)
# Display the image in Streamlit
st.image(buf, width=1100)
# Display summary plot of feature importance
shap.summary_plot(shap_values[1], X_encoded)
# Display interaction summary plot
shap_interaction_values = explainer.shap_interaction_values(X_encoded)
shap.summary_plot(shap_interaction_values, X_encoded)
# Execute get_pred() only if the option is "Stroke prediction"
if option == "Stroke prediction":
get_pred()
if option == "Model information":
st.header("Light gradient boosting model")
st.subheader("First tree of light gradient boosting model and how it makes decisions")
st.image(r'lgbm_tree.png')
st.subheader("Shap values visualization of how features contribute to model prediction")
st.image(r'lgbm_model_shap_evaluation.png') |