AUC ROC Pytorch

auc_roc_pytorch.py

def auroc(model, loader_name='val', N_classes=4):
    model.eval()
    y_test = []
    y_score = []
    with torch.no_grad():
        for i, (inputs, classes) in enumerate(dataloaders[loader_name]):
            inputs = inputs.to(device)
            y_test.append(F.one_hot(classes, N_classes).numpy())
            
            try:
                bs, ncrops, c, h, w = inputs.size()
            except:
                bs, c, h, w = inputs.size()
                ncrops = 1
            if ncrops > 1:
                outputs = model(inputs.view(-1, c, h, w))
                outputs = outputs.view(bs, ncrops, -1).mean(1)
            else:
                outputs = model(inputs)
            y_score.append(outputs.cpu().numpy())
    y_test = np.array([t.ravel() for t in y_test])
    y_score = np.array([t.ravel() for t in y_score])
    # print(y_true)
    # print(y_pred)

    """
    compute ROC curve and ROC area for each class in each fold

    """

    fpr = dict()
    tpr = dict()
    local_roc_auc = dict()
    for i in range(N_classes):
        fpr[i], tpr[i], _ = roc_curve(np.array(y_test[:, i]),np.array(y_score[:, i]))
        local_roc_auc[i] = auc(fpr[i], tpr[i])
    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
    local_roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

    # Compute macro-average ROC curve and ROC area

    # First aggregate all false positive rates
    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 += np.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
    local_roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

    # Plot all ROC curves
    plt.figure()
    plt.plot(fpr["micro"], tpr["micro"],
             label='micro-average ROC curve (area = {0:0.2f})'
                   ''.format(local_roc_auc["micro"]),
             color='deeppink', linestyle=':', linewidth=4)

    plt.plot(fpr["macro"], tpr["macro"],
             label='macro-average ROC curve (area = {0:0.2f})'
                   ''.format(local_roc_auc["macro"]),
             color='navy', linestyle=':', linewidth=4)

    colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
    for i, color in zip(range(N_classes), colors):
        plt.plot(fpr[i], tpr[i], color=color, lw=2,
                 label='ROC curve of class {0} (area = {1:0.2f})'
                       ''.format(i, local_roc_auc[i]))

    plt.plot([0, 1], [0, 1], 'k--', lw=2)
    plt.xlim([-1e-2, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristics')
    plt.legend(loc="lower right")
    plt.show()