performance.py

from sklearn.metrics import accuracy_score, roc_auc_score, classification_report, roc_curve
def performance(y_true, pred, color="g", ann=True):
    acc = accuracy_score(y_true, pred[:,1] > 0.5)
    auc = roc_auc_score(y_true, pred[:,1])
    fpr, tpr, thr = roc_curve(y_true, pred[:,1])
    plot(fpr, tpr, color, linewidth="3")
    xlabel("False positive rate")
    ylabel("True positive rate")
    if ann:
        annotate("Acc: %0.2f" % acc, (0.1,0.8), size=14)
        annotate("AUC: %0.2f" % auc, (0.1,0.7), size=14)