code snippet to calculate the common performance measures, area under the precision-recall curve and area under the receiver operator characteristic curve

auc.py

import pylab


def plot_roc(y, out, label="", out_fn=None):
    """
    show or save ROC curve
    """

    pylab.figure()
    plot_roc_noshow(y, out, label=label)

    if not out_fn is None:
        pylab.savefig(auc_fn)
    else:
        pylab.show()


def plot_roc_noshow(y, out, label=""):
    """
    create area under the receiver operator characteristic curve plot
    """

    # plot auc
    from sklearn.metrics import roc_curve, auc
    fpr, tpr, thresholds = roc_curve(y, out)
    roc_auc = auc(fpr, tpr)
    
    pylab.plot(fpr, tpr, label='%s (area = %0.2f)' % (label, roc_auc))
    pylab.plot([0, 1], [0, 1], 'k--')
    pylab.xlim([0.0, 1.0])
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('False Positive Rate')
    pylab.ylabel('True Positive Rate')
    pylab.title('Receiver operating characteristic example')
    pylab.legend(loc="lower right")



def plot_prc(y, out, label="", out_fn=None):
    """
    plot precision recall plot
    """

    pylab.figure()
    plot_prc_noshow(y, out, label=label)

    if not out_fn is None:
        pylab.savefig(out_fn)
    else:
        pylab.show()


def plot_prc_noshow(y, out, label=""):
    """

    """

    # Compute Precision-Recall and plot curve
    from sklearn.metrics import precision_recall_curve, auc

    precision, recall, thresholds = precision_recall_curve(y, out)
    area = auc(recall, precision)

    pylab.plot(recall, precision, label='%s (area = %0.2f)' % (label, area))
    pylab.xlabel('Recall')
    pylab.ylabel('Precision')
    pylab.ylim([0.0, 1.05])
    pylab.xlim([0.0, 1.0])
    pylab.legend(loc="upper right")