evaluate_ensemble.py 文件源码

def plot_roc_auc(predictions, ground_truth, name=''):

    # Calculate ROC curve
    y_pred = np.asarray(predictions).ravel()
    y_true = np.asarray(ground_truth).ravel()

    fpr, tpr, thresholds = roc_curve(y_true, y_pred)
    roc_auc = auc(fpr, tpr)

    # Plot
    plt.plot(fpr, tpr, label='{}, AUC = {:.3f}'.format(name, roc_auc))

    # # Return index of best model by J statistic
    # J = [j_statistic(y_true, y_pred, t) for t in thresholds]
    #
    # return thresholds[np.argmax(J)]  # TODO test this out!