def plot_auc_roc_curve(y_test, y_test_pred):
    """ Takes y_test and y_test_pred from a ML model and uses sklearn roc_curve to plot the AUC-ROC curve."""
    from sklearn.metrics import roc_curve, auc, roc_auc_score
    import matplotlib.pyplot as plt
    auc = roc_auc_score(y_test, y_test_pred[:, (1)])
    FPr, TPr, _ = roc_curve(y_test, y_test_pred[:, (1)])
    auc()
    plt.plot(FPr, TPr, label=f'AUC for Classifier:\n{round(auc, 2)}')
    plt.plot([0, 1], [0, 1], lw=2, linestyle='--')
    plt.xlim([-0.01, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc='lower right')
    plt.show()
