app.py

import streamlit as st
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import VotingClassifier, StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc, classification_report
import matplotlib.pyplot as plt
import seaborn as sns

# Set Streamlit interface title
st.title('Classification Model Comparison: Stacking and Voting Classifiers')

# Allow user to upload data
uploaded_file = st.file_uploader("Please upload a CSV file", type=["csv"])

if uploaded_file is not None:
    df = pd.read_csv(uploaded_file)

    # Define features and target variable
    X = df.drop(columns=['Target_goal'])
    y = df['Target_goal']

    # Split dataset
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Standardize data
    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)

    # Define base models
    estimators = [
        ('lr', LogisticRegression()),
        ('dt', DecisionTreeClassifier()),
        ('rf', RandomForestClassifier()),
        ('gb', GradientBoostingClassifier()),
        ('svc', SVC(probability=True))
    ]

    # Stacking classifier
    stacking_clf = StackingClassifier(
        estimators=estimators,
        final_estimator=LogisticRegression()
    )
    stacking_clf.fit(X_train, y_train)
    y_pred_stack = stacking_clf.predict(X_test)
    y_pred_stack_proba = stacking_clf.predict_proba(X_test)[:, 1]
    
    # Stacking classifier accuracy
    accuracy_stack = accuracy_score(y_test, y_pred_stack)
    st.write(f'Stacking Classifier Accuracy: {accuracy_stack:.2f}')
    
    # Stacking classifier classification report
    st.write("Stacking Classifier Classification Report:")
    st.text(classification_report(y_test, y_pred_stack))

    # Voting classifier
    voting_clf = VotingClassifier(
        estimators=estimators,
        voting='soft'
    )
    voting_clf.fit(X_train, y_train)
    y_pred_vote = voting_clf.predict(X_test)
    y_pred_vote_proba = voting_clf.predict_proba(X_test)[:, 1]
    
    # Voting classifier accuracy
    accuracy_vote = accuracy_score(y_test, y_pred_vote)
    st.write(f'Voting Classifier Accuracy: {accuracy_vote:.2f}')
    
    # Voting classifier classification report
    st.write("Voting Classifier Classification Report:")
    st.text(classification_report(y_test, y_pred_vote))

    # Confusion matrix visualization
    st.write("Stacking Classifier Confusion Matrix:")
    conf_matrix_stack = confusion_matrix(y_test, y_pred_stack)
    fig, ax = plt.subplots()
    sns.heatmap(conf_matrix_stack, annot=True, fmt='d', cmap='Blues', ax=ax)
    ax.set_title('Stacking Classifier Confusion Matrix')
    st.pyplot(fig)

    st.write("Voting Classifier Confusion Matrix:")
    conf_matrix_vote = confusion_matrix(y_test, y_pred_vote)
    fig, ax = plt.subplots()
    sns.heatmap(conf_matrix_vote, annot=True, fmt='d', cmap='Blues', ax=ax)
    ax.set_title('Voting Classifier Confusion Matrix')
    st.pyplot(fig)

    # ROC curve
    # Convert y_test labels to 0 and 1
    y_test_binary = (y_test == 2).astype(int)  # Assume 2 is the positive label

    # Calculate ROC curve
    fpr_stack, tpr_stack, _ = roc_curve(y_test_binary, y_pred_stack_proba)
    roc_auc_stack = auc(fpr_stack, tpr_stack)

    fpr_vote, tpr_vote, _ = roc_curve(y_test_binary, y_pred_vote_proba)
    roc_auc_vote = auc(fpr_vote, tpr_vote)

    fig, ax = plt.subplots()
    ax.plot(fpr_stack, tpr_stack, color='blue', lw=2, label='Stacking Classifier (AUC = %0.2f)' % roc_auc_stack)
    ax.plot(fpr_vote, tpr_vote, color='red', lw=2, label='Voting Classifier (AUC = %0.2f)' % roc_auc_vote)
    ax.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
    ax.set_xlim([0.0, 1.0])
    ax.set_ylim([0.0, 1.05])
    ax.set_xlabel('False Positive Rate')
    ax.set_ylabel('True Positive Rate')
    ax.set_title('ROC Curve')
    ax.legend(loc="lower right")
    st.pyplot(fig)