我正在尝试打开我在AWS Sagemaker中创建的腌制XGBoost模型，以查看模型中的功能重要性。我正在尝试遵循this post中的答案。但是，出现以下错误。当我尝试呼叫Booster.save_model时，出现错误消息'Estimator' object has no attribute 'save_model'。我该如何解决？
# Build initial model
sess = sagemaker.Session()
s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train/'.format(bucket, prefix), content_type='csv')
xgb_cont = get_image_uri(region, 'xgboost', repo_version='0.90-1')
xgb = sagemaker.estimator.Estimator(xgb_cont, role, train_instance_count=1, train_instance_type='ml.m4.4xlarge',
output_path='s3://{}/{}'.format(bucket, prefix), sagemaker_session=sess)
xgb.set_hyperparameters(eval_metric='rmse', objective='reg:squarederror', num_round=100)
ts = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
xgb_name = 'xgb-initial-' + ts
xgb.set_hyperparameters(eta=0.1, alpha=0.5, max_depth=10)
xgb.fit({'train': s3_input_train}, job_name=xgb_name)
# Load model to get feature importances
model_path = 's3://{}/{}//output/model.tar.gz'.format(bucket, prefix, xgb_name)
fs = s3fs.S3FileSystem()
with fs.open(model_path, 'rb') as f:
with tarfile.open(fileobj=f, mode='r') as tar_f:
with tar_f.extractfile('xgboost-model') as extracted_f:
model = pickle.load(extracted_f)
XGBoostError: [19:16:42] /workspace/src/learner.cc:682: Check failed: header == serialisation_header_:
If you are loading a serialized model (like pickle in Python) generated by older
XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. There's a simple script for helping
the process. See:
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for reference to the script, and more details about differences between saving model and
serializing.
您正在笔记本电脑中使用哪个版本的XGBoost？模型格式在XGBoost 1.0中已更改。参见https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html。简短版：如果您在笔记本电脑上使用1.0，则无法加载腌制的模型。
这是一个在脚本模式下使用XGBoost的有效示例（比内置算法灵活得多：]