auc roc

from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt

# Compute ROC curve
fpr, tpr, thresholds = roc_curve(y_test, y_pred)

# Compute AUC
roc_auc = roc_auc_score(y_test, y_pred)

# Plot ROC curve
plt.figure()
plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()