visualize.py 文件源码

def plot_roc_curve(y_pred, y_true, n_classes, title='ROC_Curve'):
    # Compute ROC curve and ROC area for each class
    fpr = dict()
    tpr = dict()
    tresholds = dict()
    roc_auc = dict()

    for i in range(n_classes):
        fpr[i], tpr[i], tresholds[i] = roc_curve(y_true, y_pred, pos_label=i, drop_intermediate=False)
        roc_auc[i] = auc(fpr[i], tpr[i])

    # Compute micro-average ROC curve and ROC area
#     fpr["micro"], tpr["micro"], _ = roc_curve(np.asarray(y_true).ravel(), np.asarray(y_pred).ravel(), pos_label=0, drop_intermediate=True)
#     roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

    # Aggregate all false positive rates
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

#     print("Thresholds:")
    # Interpolate all ROC curves at this points
    mean_tpr = np.zeros_like(all_fpr)
    for i in range(n_classes):
        mean_tpr += interp(all_fpr, fpr[i], tpr[i])
#         print("Class_{0}: {1}".format(i, tresholds[i]))

    # Average it and compute AUC
    mean_tpr /= n_classes

    fpr["macro"] = all_fpr
    tpr["macro"] = mean_tpr
    roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])


    # Plot all ROC curves
    fig = plt.figure()
    ax = fig.add_subplot(111)

#     plt.plot(fpr["micro"], tpr["micro"],
#              label='micro-average ROC curve (area = {0:0.2f})'
#                    ''.format(roc_auc["micro"]),
#              linewidth=3, ls='--', color='red')

    plt.plot(fpr["macro"], tpr["macro"],
             label='macro-average ROC curve (area = {0:0.2f})'
                   ''.format(roc_auc["macro"]),
             linewidth=3, ls='--', color='green')

    for i in range(n_classes):
        plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'
                                       ''.format(i, roc_auc[i]))

    plt.plot([0, 1], [0, 1], 'k--', linewidth=2)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Multi-class Receiver Operating Characteristic')
    lgd = ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

    plt.savefig(pjoin(FLAGS.output_dir, title.replace(' ', '_') + '_ROC.png'), bbox_extra_artists=(lgd,), bbox_inches='tight')
    plt.close()