plt.title('ROC')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
for i, filename in enumerate(image_list):
    with open('plot_cache/' + filename, 'rb') as file:
        y_pred, y_label = pickle.load(file)
        fpr, tpr, thresholds = roc_curve(y_label, y_pred)
        roc_auc = auc(fpr, tpr)
        plt.plot(fpr, tpr, 'k--', color=colors[i], label='%s AUC:%.3f' % (legends[i], roc_auc), lw=2)

plt.legend(loc="lower right", fontsize='medium')  # after plt.plot
plt.savefig('ROC.jpg')
plt.show()

plt.title('Precision/Recall Curve')  # give plot a title
plt.xlabel('Recall')  # make axis labels
plt.ylabel('Precision')
for i, filename in enumerate(image_list):
    with open('plot_cache/' + filename, 'rb') as file:
        y_score, y_label = pickle.load(file)
        precision, recall, _ = precision_recall_curve(y_label, y_score)
        aupr = auc(recall, precision)
        plt.plot(recall, precision, 'k--', color=colors[i], label='%s AUPR:%.3f' % (legends[i], aupr), lw=2)

plt.legend(loc="lower right", fontsize='small')  # after plt.plot

plt.savefig('PR.jpg')
plt.show()