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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) |