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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import seaborn as sns | |
| from sklearn.ensemble import RandomForestClassifier | |
| from xgboost import XGBClassifier | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.model_selection import train_test_split | |
| import numpy as np | |
| # Function to process data and return feature importances and correlation matrix | |
| def calculate_importances(file): | |
| # Read uploaded file | |
| heart_df = pd.read_csv(file) | |
| # Set X and y | |
| X = heart_df.drop('target', axis=1) | |
| y = heart_df['target'] | |
| # Split the data | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) | |
| # Initialize models | |
| rf_model = RandomForestClassifier(random_state=42) | |
| xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42) | |
| cart_model = DecisionTreeClassifier(random_state=42) | |
| # Train models | |
| rf_model.fit(X_train, y_train) | |
| xgb_model.fit(X_train, y_train) | |
| cart_model.fit(X_train, y_train) | |
| # Get feature importances | |
| rf_importances = rf_model.feature_importances_ | |
| xgb_importances = xgb_model.feature_importances_ | |
| cart_importances = cart_model.feature_importances_ | |
| feature_names = X.columns | |
| # Prepare DataFrame | |
| rf_importance = pd.DataFrame({'Feature': feature_names, 'Importance': rf_importances}) | |
| xgb_importance = pd.DataFrame({'Feature': feature_names, 'Importance': xgb_importances}) | |
| cart_importance = pd.DataFrame({'Feature': feature_names, 'Importance': cart_importances}) | |
| # Correlation Matrix | |
| corr_matrix = heart_df.corr() | |
| return rf_importance, xgb_importance, cart_importance, corr_matrix | |
| # Streamlit interface | |
| st.title("Feature Importance Calculation") | |
| # File upload | |
| uploaded_file = st.file_uploader("Upload heart.csv file", type=['csv']) | |
| if uploaded_file is not None: | |
| # Process the file and get results | |
| rf_importance, xgb_importance, cart_importance, corr_matrix = calculate_importances(uploaded_file) | |
| # Display the correlation matrix as a heatmap (static for now) | |
| st.write("Correlation Matrix:") | |
| plt.figure(figsize=(10, 8)) | |
| sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", cbar=True) | |
| st.pyplot(plt) | |
| # Plot and display Random Forest Feature Importances with Plotly | |
| st.write("Random Forest Feature Importance:") | |
| fig_rf = px.bar(rf_importance, x='Importance', y='Feature', orientation='h', title="Random Forest Feature Importances") | |
| st.plotly_chart(fig_rf) | |
| # Plot and display XGBoost Feature Importances with Plotly | |
| st.write("XGBoost Feature Importance:") | |
| fig_xgb = px.bar(xgb_importance, x='Importance', y='Feature', orientation='h', title="XGBoost Feature Importances") | |
| st.plotly_chart(fig_xgb) | |
| # Plot and display CART (Decision Tree) Feature Importances with Plotly | |
| st.write("CART (Decision Tree) Feature Importance:") | |
| fig_cart = px.bar(cart_importance, x='Importance', y='Feature', orientation='h', title="CART (Decision Tree) Feature Importances") | |
| st.plotly_chart(fig_cart) | |