chrom_hmm_cnn.py 文件源码

def plot_roc_per_class(model, test_data, test_truth, labels, title):
    # Compute macro-average ROC curve and ROC area
    fpr, tpr, roc_auc = get_fpr_tpr_roc(model, test_data, test_truth, labels)
    # First aggregate all false positive rates
    n_classes = len(labels)
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

    # Then 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])

    # Finally 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
    lw = 2
    plt.figure(figsize=(20,16))

    colors = cycle(['aqua', 'darkorange', 'cornflowerblue', 'green', 'pink', 'magenta', 'grey', 'purple'])
    idx = 0
    for key, color in zip(labels.keys(), colors):
        plt.plot( fpr[idx], tpr[idx], color=color, lw=lw, label='ROC curve of class '+str(key) )
        idx += 1

    plt.plot([0, 1], [0, 1], 'k--', lw=lw)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC:'+ str(labels) + '\n' + title)
    plt.legend(loc="lower right")
    plt.savefig("./per_class_roc_"+title+".jpg")