Plot ROC Curve with Cut-Off Markers

gistfile1.txt

def plot_roc(y, probs, threshmarkers=None):
    fpr, tpr, thresh = sklearn.metrics.roc_curve(y, probs)

    plt.plot(fpr, tpr, lw=2)
    if threshmarkers is None:
        threshmarkers = np.linspace(0, 1, 11)
    for t in threshmarkers:
        k = np.abs(thresh-t).argmin()
        x = fpr[k]
        y = tpr[k]
        plt.scatter(x, y, c="red", marker="x", s=50, lw=2, alpha=1.0)
        plt.annotate("%0.2f (%0.4f, %0.4f)" % (t, x, y), (x, y), textcoords="offset points", xytext=(25, -10), ha="left", va="center", fontsize=7, arrowprops={"arrowstyle":"->", "connectionstyle":"arc3,rad=0"})
    plt.xlabel("False Positive Rate\n(1 - Specificity)")
    plt.ylabel("True Positive Rate\n(Sensitivity)")
    plt.xlim([-0.025, 1.025])
    plt.ylim([-0.025, 1.025])
    plt.xticks(np.linspace(0, 1, 21), rotation=45)
    plt.yticks(np.linspace(0, 1, 21))
    plt.show()