train_triplet.py 文件源码

def plot_roc(fpr,tpr,figure_name="roc.png"):
    import matplotlib.pyplot as plt
    from sklearn.metrics import roc_curve, auc
    roc_auc = auc(fpr, tpr)
    fig = plt.figure()
    lw = 2
    plt.plot(fpr, tpr, color='darkorange',
             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    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")
    fig.savefig(os.path.join(LOG_DIR,figure_name), dpi=fig.dpi)