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import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
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
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 = {'Feature': feature_names, 'Random Forest': rf_importances}
xgb_importance = {'Feature': feature_names, 'XGBoost': xgb_importances}
cart_importance = {'Feature': feature_names, 'CART': cart_importances}
# Create DataFrames
rf_df = pd.DataFrame(rf_importance)
xgb_df = pd.DataFrame(xgb_importance)
cart_df = pd.DataFrame(cart_importance)
# Merge DataFrames
importance_df = rf_df.merge(xgb_df, on='Feature').merge(cart_df, on='Feature')
# Save to Excel
file_name = 'feature_importances.xlsx'
importance_df.to_excel(file_name, index=False)
return file_name, importance_df.head()
# 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
excel_file, preview_df = calculate_importances(uploaded_file)
# Display a preview of the DataFrame
st.write("Feature Importances (Preview):")
st.dataframe(preview_df)
# Provide a link to download the Excel file
with open(excel_file, "rb") as file:
btn = st.download_button(
label="Download Excel File",
data=file,
file_name=excel_file,
mime="application/vnd.ms-excel"
)
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