XGBClassifer，反序列化时，给出 'XGBModel' object has no attribute 'enable_categorical'

Question

我有一个序列化的 XGBClassifier 对象，使用 xgboost=1.5.2 训练和生成。

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.5,
              colsample_bynode=1, colsample_bytree=0.30140958911801474,
              eval_metric='logloss', gamma=0.1203484640861413, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.1, max_bin=368, max_delta_step=0, max_depth=6,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=100, n_jobs=6, num_parallel_tree=1, random_state=42,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
              single_precision_histogram=True, subsample=0.976171515775659,
              tree_method='gpu_hist', use_label_encoder=False,
              validate_parameters=1, verbosity=None)

我使用以下方法加载对象：

clf_model = joblib.load(model_path)

我想使用该对象来预测我正在使用也具有 xgboost=1.5.2 的 Azure 环境的某些数据。但它给出了错误：

File "score.py", line 78, in score_execution
[stderr]    clf_preds = clf_model.predict(clf_data_transformed)
[stderr]  File "/opt/miniconda/lib/python3.8/site-packages/xgboost/sklearn.py", line 1284, in predict
[stderr]    class_probs = super().predict(
[stderr]  File "/opt/miniconda/lib/python3.8/site-packages/xgboost/sklearn.py", line 879, in predict
[stderr]    if self._can_use_inplace_predict():
[stderr]  File "/opt/miniconda/lib/python3.8/site-packages/xgboost/sklearn.py", line 811, in _can_use_inplace_predict
[stderr]    predictor = self.get_params().get("predictor", None)
[stderr]  File "/opt/miniconda/lib/python3.8/site-packages/xgboost/sklearn.py", line 505, in get_params
[stderr]    params.update(cp.__class__.get_params(cp, deep))
[stderr]  File "/opt/miniconda/lib/python3.8/site-packages/xgboost/sklearn.py", line 502, in get_params
[stderr]    params = super().get_params(deep)
[stderr]  File "/opt/miniconda/lib/python3.8/site-packages/sklearn/base.py", line 210, in get_params
[stderr]    value = getattr(self, key)
[stderr]AttributeError: 'XGBModel' object has no attribute 'enable_categorical'

我们在管道中具有与 produce/serialize 模型相同的版本，并且在反序列化模型以预测新数据的管道中具有相同的版本。

Answer 1

以下是一些可能的解决方案：

以其他方式保存模型，例如这里指定的 JSON https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
将 xgboost 版本的允许范围限制为已知可与我们的模型一起使用的版本。这可能会导致将来出现问题，例如，如果我们需要的 xgboost 的旧版本不再受 Python.
使用 save_model 保存 JSON 值得一试。

XGBClassifer，反序列化时，给出 'XGBModel' object has no attribute 'enable_categorical'

XGBClassifer, when de-serialized, gives 'XGBModel' object has no attribute 'enable_categorical'

python

azure

azureml