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python - Amazon SageMaker 如何预测新数据

问题描述

我在 Amazon SageMaker 中构建了模型，代码附在下面。现在我希望能够将新数据上传到 s3 并基于此模型获得预测，而不必每次都重新计算。

sess = sagemaker.Session()
bucket = "innogy-bda-germany-dev-landing-dc3-retailpl"
prefix = "sagemaker/xgboost-upsell"
role = get_execution_role()
container = sagemaker.image_uris.retrieve("xgboost", boto3.Session().region_name, "latest")
display(container)

train_path = 's3://innogy-bda-germany-dev-landing-dc3-retailpl/UPSELL/LIST/train.csv'
test_path = 's3://innogy-bda-germany-dev-landing-dc3-retailpl/UPSELL/LIST/validation.csv'
s3_input_train = sagemaker.TrainingInput(s3_data=train_path, content_type='csv')
s3_input_test = sagemaker.TrainingInput(s3_data=test_path, content_type='csv')

sess = sagemaker.Session()

xgb = sagemaker.estimator.Estimator(
    container,
    role,
    instance_count=1,
    instance_type="ml.m5.4xlarge",
    output_path="s3://innogy-bda-germany-dev-landing-dc3-retailpl/UPSELL/LIST/output",
    sagemaker_session=sess,
)
xgb.set_hyperparameters(
    alpha= 1.340343927865692,
    colsample_bytree= 0.525162855476281,
    eta= 0.06451533130134757,
    gamma= 0.9683995477068462,
    max_depth= 10,
    min_child_weight= 3.851108988963441,
    num_round= 987,
    subsample= 0.8725573749114485,
    silent=0,
    objective="binary:logistic",
    early_stopping_rounds=50,
)

xgb.fit({"train": s3_input_train, "validation": s3_input_validation})

我要一个代码示例，现在如何将此模型从 s3 提取到新笔记本并使用它来预测新数据。

另外，我想知道为什么您在使用 sagemaker 中内置的 xgboost 模型时不丢弃目标变量，因为在对新集合进行预测时，我不会知道目标。

train_data, validation_data, test_data = np.split(df_smote.sample(frac=1, random_state=1729),[int(0.7 * len(df_smote)), int(0.9 * len(df_smote))],)

标签： pythonamazon-web-servicesmachine-learningamazon-sagemakeraws-code-deploy

解决方案

您需要按照此处列出的步骤将模型部署到 EC2 实例，以便进行批量或按需预测。 https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-model-deployment.html

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