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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)
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