scikit-learn - MLflow 网络服务器返回 400 状态，“列 X 的输入类型不兼容。无法安全地将 float64 转换为 我正在使用MLflowand实现异常检测 Web 服务sklearn.pipeline.Pipeline()。该模型的目的是使用服务器日志检测网络爬虫，而response_length列是我的功能之一。在服务模型之后，为了测试 Web 服务，我发送了下面的请求

Question

我正在使用MLflowand实现异常检测 Web 服务sklearn.pipeline.Pipeline()。该模型的目的是使用服务器日志检测网络爬虫，而response_length列是我的功能之一。在服务模型之后，为了测试 Web 服务，我发送了下面的请求，其中包含训练数据的前 20 列。

$ curl  --location --request POST '127.0.0.1:8000/invocations'
        --header 'Content-Type: text/csv' \
        --data-binary 'datasets/test.csv'

但是 Web 服务器的响应有状态码 400 (BAD REQUEST) 和这个 JSON 正文：

{
    "error_code": "BAD_REQUEST",
    "message": "Incompatible input types for column response_length. Can not safely convert float64 to <U0."
}

以下是模型编译 MLflow Tracking 组件日志：

[Pipeline] ......... (step 1 of 3) Processing transform, total=11.8min
[Pipeline] ............... (step 2 of 3) Processing pca, total=   4.8s
[Pipeline] ........ (step 3 of 3) Processing rule_based, total=   0.0s
2021/07/16 04:55:12 WARNING mlflow.sklearn: Training metrics will not be recorded because training labels were not specified. To automatically record training metrics, provide training labels as inputs to the model training function.
2021/07/16 04:55:12 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "/home/matin/workspace/Rahnema College/venv/lib/python3.8/site-packages/mlflow/models/signature.py:129: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."
Logged data and model in run: 8843336f5c31482c9e246669944b1370

---------- logged params ----------
{'memory': 'None',
 'pca': 'PCAEstimator()',
 'rule_based': 'RuleBasedEstimator()',
 'steps': "[('transform', <log_transformer.LogTransformer object at "
          "0x7f05a8b95760>), ('pca', PCAEstimator()), ('rule_based', "
          'RuleBasedEstimator())]',
 'transform': '<log_transformer.LogTransformer object at 0x7f05a8b95760>',
 'verbose': 'True'}

---------- logged metrics ----------
{}

---------- logged tags ----------
{'estimator_class': 'sklearn.pipeline.Pipeline', 'estimator_name': 'Pipeline'}

---------- logged artifacts ----------
['model/MLmodel',
 'model/conda.yaml',
 'model/model.pkl',
 'model/requirements.txt']

如果有人能准确地告诉我如何解决这个模型服务问题，那将非常有帮助。