Gau*_*sal 5 python pickle xgboost amazon-sagemaker
我正在尝试打开在 AWS Sagemaker 中创建的腌制 XGBoost 模型，以查看模型中特征的重要性。我正在尝试遵循这篇文章中的答案。但是，我收到如下所示的错误。当我尝试打电话时Booster.save_model，我收到一条错误消息'Estimator' object has no attribute 'save_model'。我该如何解决这个问题？
# 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.
小智 2
您在笔记本中使用哪个版本的 XGBoost？XGBoost 1.0 中的模型格式发生了变化。请参阅https://xgboost.readthedocs.io/en/latest/tutorials/ saving_model.html 。简短版本：如果您在笔记本中使用 1.0，则无法加载 pickled 模型。
这是在脚本模式下使用 XGBoost 的工作示例（比内置算法灵活得多）：