import sklearn.metrics as metrics
import matplotlib.pyplot as plt

def plot_roc(y_test,preds,edge,extension=''):
    fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
    roc_auc = metrics.auc(fpr, tpr)
    plt.title('ROC Curve')
    plt.plot(fpr, tpr, label = " ".join(edge)+ " " +'AUC = %0.2f' % roc_auc)
    plt.legend(loc = 'lower right')
    plt.plot([0, 1], [0, 1],'r--')
    plt.xlim([0, 1])
    plt.ylim([0, 1])
    plt.ylabel('True Positive Rate')
    plt.xlabel('False Positive Rate')
    plt.show()
    plt.savefig('metrics/aucroc'+extension+'.svg', format = 'svg', dpi=1200)
    plt.clf()
    print("AUC:", roc_auc)
    return fpr,tpr, roc_auc