metrics.py 文件源码

def cv_reg_lr(trX, trY, vaX, vaY, Cs=[0.01, 0.05, 0.1, 0.5, 1., 5., 10., 50., 100.]):
    tr_accs = []
    va_accs = []
    models = []
    for C in Cs:
        model = LR(C=C)
        model.fit(trX, trY)
        tr_pred = model.predict(trX)
        va_pred = model.predict(vaX)
        tr_acc = metrics.accuracy_score(trY, tr_pred)
        va_acc = metrics.accuracy_score(vaY, va_pred)
        print '%.4f %.4f %.4f'%(C, tr_acc, va_acc)
        tr_accs.append(tr_acc)
        va_accs.append(va_acc)
        models.append(model)
    best = np.argmax(va_accs)
    print 'best model C: %.4f tr_acc: %.4f va_acc: %.4f'%(Cs[best], tr_accs[best], va_accs[best])
    return models[best]