所以我试图使用我在SageMaker Studio中使用自动驾驶仪创建的模型，但我不断得到不同的错误。最终我想让它变得简单;取一个数据框，并使用该数据框(显然是pandas)预测输出。以下是到目前为止我所得到的，然后是我得到的错误。
import sagemaker, boto3, os
bucket = sagemaker.Session().default_bucket()
model = sagemaker.predictor.Predictor('Predict-Low', sagemaker_session=sagemaker.Session())
df = pd.read_csv('s3://sagemaker-studio-xxx/Sagemaker Data Predict Low.csv')
y = df['Low']
del df['Low']
y_hat = model.predict(df)
---------------------------------------------------------------------------
ParamValidationError Traceback (most recent call last)
<ipython-input-43-18ff980cf441> in <module>
----> 1 y_hat = model.predict(df)
/opt/conda/lib/python3.7/site-packages/sagemaker/predictor.py in predict(self, data, initial_args, target_model, target_variant, inference_id)
134 data, initial_args, target_model, target_variant, inference_id
135 )
--> 136 response = self.sagemaker_session.sagemaker_runtime_client.invoke_endpoint(**request_args)
137 return self._handle_response(response)
138
/opt/conda/lib/python3.7/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
384 "%s() only accepts keyword arguments." % py_operation_name)
385 # The "self" in this scope is referring to the BaseClient.
--> 386 return self._make_api_call(operation_name, kwargs)
387
388 _api_call.__name__ = str(py_operation_name)
/opt/conda/lib/python3.7/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
676 }
677 request_dict = self._convert_to_request_dict(
--> 678 api_params, operation_model, context=request_context)
679
680 service_id = self._service_model.service_id.hyphenize()
/opt/conda/lib/python3.7/site-packages/botocore/client.py in _convert_to_request_dict(self, api_params, operation_model, context)
724 api_params, operation_model, context)
725 request_dict = self._serializer.serialize_to_request(
--> 726 api_params, operation_model)
727 if not self._client_config.inject_host_prefix:
728 request_dict.pop('host_prefix', None)
/opt/conda/lib/python3.7/site-packages/botocore/validate.py in serialize_to_request(self, parameters, operation_model)
317 operation_model.input_shape)
318 if report.has_errors():
--> 319 raise ParamValidationError(report=report.generate_report())
320 return self._serializer.serialize_to_request(parameters,
321 operation_model)
ParamValidationError: Parameter validation failed:
Invalid type for parameter Body
对我来说，它似乎需要一个字节串来做预测，所以这就是我所做的。我将数据帧转换为字节串，但仍然得到一个错误。有人知道我哪里做错了吗?
顺便说一下，这些都是在SageMaker Studio中完成的。这是数据。
Date Company High Low Open Close Volume Adj Close
0 7/13/2020 LIFE 4.380 3.880 4.21 3.88 62400 3.88
1 7/14/2020 LIFE 4.210 3.721 3.95 4.16 80800 4.16
2 7/15/2020 LIFE 4.550 4.053 4.17 4.50 212500 4.50
3 7/16/2020 LIFE 4.550 4.350 4.40 4.51 44600 4.51
4 7/17/2020 LIFE 5.170 4.410 4.54 5.09 257700 5.09
.. ... ... ... ... ... ... ... ...
255 7/16/2021 LIFE 4.590 4.440 4.46 4.50 156300 4.50
256 7/19/2021 LIFE 4.490 4.220 4.36 4.22 211700 4.22
257 7/20/2021 LIFE 4.546 4.230 4.23 4.47 212500 4.47
258 7/21/2021 LIFE 4.800 4.369 4.45 4.48 487500 4.48
259 7/22/2021 LIFE 4.510 4.260 4.44 4.45 235200 4.45
Sector Specifics
0 Health Care Biotechnology: Biological Products (No Diagnos...
1 Health Care Biotechnology: Biological Products (No Diagnos...
2 Health Care Biotechnology: Biological Products (No Diagnos...
3 Health Care Biotechnology: Biological Products (No Diagnos...
4 Health Care Biotechnology: Biological Products (No Diagnos...
.. ... ...
255 Health Care Biotechnology: Biological Products (No Diagnos...
256 Health Care Biotechnology: Biological Products (No Diagnos...
257 Health Care Biotechnology: Biological Products (No Diagnos...
258 Health Care Biotechnology: Biological Products (No Diagnos...
259 Health Care Biotechnology: Biological Products (No Diagnos...
Open Difference from Yesterday Yesterday Open to Low
0 0.00 0.000
1 -0.26 0.330
2 0.22 0.229
3 0.23 0.117
4 0.14 0.050
.. ... ...
255 0.01 0.080
256 -0.10 0.020
257 -0.13 0.140
258 0.22 0.000
259 -0.01 0.081
Yesterday Open to High Yesterday Open to Adj Close
0 0.000 0.00
1 0.170 -0.33
2 0.260 0.21
3 0.380 0.33
4 0.150 0.11
.. ... ...
255 0.100 0.00
256 0.130 0.04
257 0.130 -0.14
258 0.316 0.24
259 0.350 0.03
所以我发现您需要为您的模型指定一个序列化器以便进行预测。在model.predict(...)之前添加这段代码就可以了。
from sagemaker.serializers import CSVSerializer
model.serializer = CSVSerializer()