调用joblib.load时未知gbm类型

Question

我使用joblib.load加载经过训练的模型文件xgboost.m（其他win10计算机训练模型），但它不起作用：

model = joblib.load('xgboost.m')

我的计算机也是win10，但执行代码时出错：

2020-06-16 20:47:48 [1832] ERROR XgboostAlgObj.py:70: - Traceback (most recent call last):
  File "D:/iWork/pp/python-schedule/oritek/task/component/XgboostAlgObj.py", line 67, in execute
    return self.xgboost(chk_date, dest_equips, busi_type, equip_code,flag)
  File "D:/iWork/pp/python-schedule/oritek/task/component/XgboostAlgObj.py", line 37, in xgboost
    pred = model_predict.predict(busi_type,equip_code,chk_date[0:7],self.predict_time,equip_code)
  File "D:\iWork\pp\python-schedule\oritek\task\component\model_predict.py", line 156, in predict
    model = joblib.load('xgboost.m')
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\numpy_pickle.py", line 585, in load
    obj = _unpickle(fobj, filename, mmap_mode)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\numpy_pickle.py", line 504, in _unpickle
    obj = unpickler.load()
  File "C:\ProgramData\Anaconda3\lib\pickle.py", line 1085, in load
    dispatch[key[0]](self)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\numpy_pickle.py", line 329, in load_build
    Unpickler.load_build(self)
  File "C:\ProgramData\Anaconda3\lib\pickle.py", line 1549, in load_build
    setstate(state)
  File "C:\ProgramData\Anaconda3\lib\site-packages\xgboost\core.py", line 981, in __setstate__
    _check_call(_LIB.XGBoosterLoadModelFromBuffer(handle, ptr, length))
  File "C:\ProgramData\Anaconda3\lib\site-packages\xgboost\core.py", line 176, in _check_call
    raise XGBoostError(py_str(_LIB.XGBGetLastError()))
xgboost.core.XGBoostError: [20:47:48] C:\Jenkins\workspace\xgboost-win64_release_0.90\src\gbm\gbm.cc:20: Unknown gbm type 
Exception ignored in: <function Booster.__del__ at 0x0000020E4CC3F730>
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\xgboost\core.py", line 957, in __del__
AttributeError: 'Booster' object has no attribute 'handle'

什么可能导致问题？谢谢大家!

Answer 1

这可能是因为模型是在xgboost版本中训练的，该版本与您环境中使用的xgboost版本不同