Plot a good-looking roc curve with thresholds

plot_roc_curve.py

from sklearn.metrics import roc_curve, auc

def plot_roc_curve(true_labels, scores):
    """ Plot ROC curve with associated score thresholds """
    # compute fpr, tpr, thresholds and roc_auc
    fpr, tpr, thresholds = roc_curve(true_labels, scores)
    roc_auc = auc(fpr, tpr) # compute area under the curve

    fig, ax = plt.subplots()

    ax.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
    ax.plot([0, 1], [0, 1], 'k--', )
    ax.set_xlim([0.0, 1.0])
    ax.set_ylim([0.0, 1.05])
    ax.set_xlabel('False Positive Rate')
    ax.set_ylabel('True Positive Rate')
    ax.set_title('Receiver operating characteristic')
    ax.legend(loc="lower right")

    # create the axis of thresholds (scores)
    ax2 = ax.twinx()
    ax2.plot(fpr, thresholds, markeredgecolor='r',linestyle='dashed', color='r')
    ax2.set_ylabel('Threshold',color='r')
    ax2.set_ylim([thresholds[-1], 1.05])
    ax2.set_xlim([fpr[0],fpr[-1]])

    return fig