summary.py 文件源码

def print_performance(cm):
    tp = np.diagonal(cm).astype(np.float)
    tpfp = np.sum(cm, axis=0).astype(np.float) # sum of each col
    tpfn = np.sum(cm, axis=1).astype(np.float) # sum of each row
    acc = np.sum(tp) / np.sum(cm)
    precision = tp / tpfp
    recall = tp / tpfn
    f1 = (2 * precision * recall) / (precision + recall)
    mf1 = np.mean(f1)

    print "Sample: {}".format(np.sum(cm))
    print "W: {}".format(tpfn[W])
    print "N1: {}".format(tpfn[N1])
    print "N2: {}".format(tpfn[N2])
    print "N3: {}".format(tpfn[N3])
    print "REM: {}".format(tpfn[REM])
    print "Confusion matrix:"
    print cm
    print "Precision: {}".format(precision)
    print "Recall: {}".format(recall)
    print "F1: {}".format(f1)
    print "Overall accuracy: {}".format(acc)
    print "Macro-F1 accuracy: {}".format(mf1)