l1_1_ho_xgb_2.py 文件源码

def predict():
    saved = state.load('model')
    #saved = None
    if saved == None:
        train, y, test, _ = data.get()
        z = pd.DataFrame()
        z['id'] = test.id
        z['y'] = 0

        v = pd.DataFrame()
        v['id'] = train.id
        v['y'] = y
        cv, _ = run(train, y, test, v, z)
        state.save('model', (v, z, cv, None))
    else:
        v, z, cv, _ = saved
    return v, z, cv, _