software.amazon.awssdk.services.machinelearning.DefaultMachineLearningClient Maven / Gradle / Ivy
/*
* Copyright 2014-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package software.amazon.awssdk.services.machinelearning;
import java.util.function.Consumer;
import software.amazon.awssdk.annotations.Generated;
import software.amazon.awssdk.annotations.SdkInternalApi;
import software.amazon.awssdk.awscore.AwsRequestOverrideConfiguration;
import software.amazon.awssdk.awscore.client.handler.AwsSyncClientHandler;
import software.amazon.awssdk.awscore.exception.AwsServiceException;
import software.amazon.awssdk.core.ApiName;
import software.amazon.awssdk.core.client.config.SdkClientConfiguration;
import software.amazon.awssdk.core.client.handler.ClientExecutionParams;
import software.amazon.awssdk.core.client.handler.SyncClientHandler;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.core.http.HttpResponseHandler;
import software.amazon.awssdk.core.util.VersionInfo;
import software.amazon.awssdk.protocols.core.ExceptionMetadata;
import software.amazon.awssdk.protocols.json.AwsJsonProtocol;
import software.amazon.awssdk.protocols.json.AwsJsonProtocolFactory;
import software.amazon.awssdk.protocols.json.BaseAwsJsonProtocolFactory;
import software.amazon.awssdk.protocols.json.JsonOperationMetadata;
import software.amazon.awssdk.services.machinelearning.model.AddTagsRequest;
import software.amazon.awssdk.services.machinelearning.model.AddTagsResponse;
import software.amazon.awssdk.services.machinelearning.model.CreateBatchPredictionRequest;
import software.amazon.awssdk.services.machinelearning.model.CreateBatchPredictionResponse;
import software.amazon.awssdk.services.machinelearning.model.CreateDataSourceFromRdsRequest;
import software.amazon.awssdk.services.machinelearning.model.CreateDataSourceFromRdsResponse;
import software.amazon.awssdk.services.machinelearning.model.CreateDataSourceFromRedshiftRequest;
import software.amazon.awssdk.services.machinelearning.model.CreateDataSourceFromRedshiftResponse;
import software.amazon.awssdk.services.machinelearning.model.CreateDataSourceFromS3Request;
import software.amazon.awssdk.services.machinelearning.model.CreateDataSourceFromS3Response;
import software.amazon.awssdk.services.machinelearning.model.CreateEvaluationRequest;
import software.amazon.awssdk.services.machinelearning.model.CreateEvaluationResponse;
import software.amazon.awssdk.services.machinelearning.model.CreateMlModelRequest;
import software.amazon.awssdk.services.machinelearning.model.CreateMlModelResponse;
import software.amazon.awssdk.services.machinelearning.model.CreateRealtimeEndpointRequest;
import software.amazon.awssdk.services.machinelearning.model.CreateRealtimeEndpointResponse;
import software.amazon.awssdk.services.machinelearning.model.DeleteBatchPredictionRequest;
import software.amazon.awssdk.services.machinelearning.model.DeleteBatchPredictionResponse;
import software.amazon.awssdk.services.machinelearning.model.DeleteDataSourceRequest;
import software.amazon.awssdk.services.machinelearning.model.DeleteDataSourceResponse;
import software.amazon.awssdk.services.machinelearning.model.DeleteEvaluationRequest;
import software.amazon.awssdk.services.machinelearning.model.DeleteEvaluationResponse;
import software.amazon.awssdk.services.machinelearning.model.DeleteMlModelRequest;
import software.amazon.awssdk.services.machinelearning.model.DeleteMlModelResponse;
import software.amazon.awssdk.services.machinelearning.model.DeleteRealtimeEndpointRequest;
import software.amazon.awssdk.services.machinelearning.model.DeleteRealtimeEndpointResponse;
import software.amazon.awssdk.services.machinelearning.model.DeleteTagsRequest;
import software.amazon.awssdk.services.machinelearning.model.DeleteTagsResponse;
import software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest;
import software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse;
import software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest;
import software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse;
import software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest;
import software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse;
import software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest;
import software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse;
import software.amazon.awssdk.services.machinelearning.model.DescribeTagsRequest;
import software.amazon.awssdk.services.machinelearning.model.DescribeTagsResponse;
import software.amazon.awssdk.services.machinelearning.model.GetBatchPredictionRequest;
import software.amazon.awssdk.services.machinelearning.model.GetBatchPredictionResponse;
import software.amazon.awssdk.services.machinelearning.model.GetDataSourceRequest;
import software.amazon.awssdk.services.machinelearning.model.GetDataSourceResponse;
import software.amazon.awssdk.services.machinelearning.model.GetEvaluationRequest;
import software.amazon.awssdk.services.machinelearning.model.GetEvaluationResponse;
import software.amazon.awssdk.services.machinelearning.model.GetMlModelRequest;
import software.amazon.awssdk.services.machinelearning.model.GetMlModelResponse;
import software.amazon.awssdk.services.machinelearning.model.IdempotentParameterMismatchException;
import software.amazon.awssdk.services.machinelearning.model.InternalServerException;
import software.amazon.awssdk.services.machinelearning.model.InvalidInputException;
import software.amazon.awssdk.services.machinelearning.model.InvalidTagException;
import software.amazon.awssdk.services.machinelearning.model.LimitExceededException;
import software.amazon.awssdk.services.machinelearning.model.MachineLearningException;
import software.amazon.awssdk.services.machinelearning.model.MachineLearningRequest;
import software.amazon.awssdk.services.machinelearning.model.PredictRequest;
import software.amazon.awssdk.services.machinelearning.model.PredictResponse;
import software.amazon.awssdk.services.machinelearning.model.PredictorNotMountedException;
import software.amazon.awssdk.services.machinelearning.model.ResourceNotFoundException;
import software.amazon.awssdk.services.machinelearning.model.TagLimitExceededException;
import software.amazon.awssdk.services.machinelearning.model.UpdateBatchPredictionRequest;
import software.amazon.awssdk.services.machinelearning.model.UpdateBatchPredictionResponse;
import software.amazon.awssdk.services.machinelearning.model.UpdateDataSourceRequest;
import software.amazon.awssdk.services.machinelearning.model.UpdateDataSourceResponse;
import software.amazon.awssdk.services.machinelearning.model.UpdateEvaluationRequest;
import software.amazon.awssdk.services.machinelearning.model.UpdateEvaluationResponse;
import software.amazon.awssdk.services.machinelearning.model.UpdateMlModelRequest;
import software.amazon.awssdk.services.machinelearning.model.UpdateMlModelResponse;
import software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable;
import software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable;
import software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable;
import software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable;
import software.amazon.awssdk.services.machinelearning.transform.AddTagsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateBatchPredictionRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateDataSourceFromRdsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateDataSourceFromRedshiftRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateDataSourceFromS3RequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateEvaluationRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateMlModelRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.CreateRealtimeEndpointRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DeleteBatchPredictionRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DeleteDataSourceRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DeleteEvaluationRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DeleteMlModelRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DeleteRealtimeEndpointRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DeleteTagsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DescribeBatchPredictionsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DescribeDataSourcesRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DescribeEvaluationsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DescribeMlModelsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.DescribeTagsRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.GetBatchPredictionRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.GetDataSourceRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.GetEvaluationRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.GetMlModelRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.PredictRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.UpdateBatchPredictionRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.UpdateDataSourceRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.UpdateEvaluationRequestMarshaller;
import software.amazon.awssdk.services.machinelearning.transform.UpdateMlModelRequestMarshaller;
/**
* Internal implementation of {@link MachineLearningClient}.
*
* @see MachineLearningClient#builder()
*/
@Generated("software.amazon.awssdk:codegen")
@SdkInternalApi
final class DefaultMachineLearningClient implements MachineLearningClient {
private final SyncClientHandler clientHandler;
private final AwsJsonProtocolFactory protocolFactory;
private final SdkClientConfiguration clientConfiguration;
protected DefaultMachineLearningClient(SdkClientConfiguration clientConfiguration) {
this.clientHandler = new AwsSyncClientHandler(clientConfiguration);
this.clientConfiguration = clientConfiguration;
this.protocolFactory = init(AwsJsonProtocolFactory.builder()).build();
}
@Override
public final String serviceName() {
return SERVICE_NAME;
}
/**
*
* Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
* add a tag using a key that is already associated with the ML object, AddTags updates the tag's
* value.
*
*
* @param addTagsRequest
* @return Result of the AddTags operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InvalidTagException
* @throws TagLimitExceededException
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.AddTags
*/
@Override
public AddTagsResponse addTags(AddTagsRequest addTagsRequest) throws InvalidInputException, InvalidTagException,
TagLimitExceededException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
AddTagsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams().withOperationName("AddTags")
.withResponseHandler(responseHandler).withErrorResponseHandler(errorResponseHandler).withInput(addTagsRequest)
.withMarshaller(new AddTagsRequestMarshaller(protocolFactory)));
}
/**
*
* Generates predictions for a group of observations. The observations to process exist in one or more data files
* referenced by a DataSource. This operation creates a new BatchPrediction, and uses an
* MLModel and the data files referenced by the DataSource as information sources.
*
*
* CreateBatchPrediction is an asynchronous operation. In response to
* CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the
* BatchPrediction status to PENDING. After the BatchPrediction completes,
* Amazon ML sets the status to COMPLETED.
*
*
* You can poll for status updates by using the GetBatchPrediction operation and checking the
* Status parameter of the result. After the COMPLETED status appears, the results are
* available in the location specified by the OutputUri parameter.
*
*
* @param createBatchPredictionRequest
* @return Result of the CreateBatchPrediction operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This can result from retrying a request
* using a parameter that was not present in the original request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateBatchPrediction
*/
@Override
public CreateBatchPredictionResponse createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
throws InvalidInputException, InternalServerException, IdempotentParameterMismatchException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, CreateBatchPredictionResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("CreateBatchPrediction").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createBatchPredictionRequest)
.withMarshaller(new CreateBatchPredictionRequestMarshaller(protocolFactory)));
}
/**
*
* Creates a DataSource object from an Amazon Relational Database
* Service (Amazon RDS). A DataSource references data that can be used to perform
* CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
*
*
* CreateDataSourceFromRDS is an asynchronous operation. In response to
* CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the
* DataSource status to PENDING. After the DataSource is created and ready
* for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in
* the COMPLETED or PENDING state can be used only to perform
* >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction
* operations.
*
*
* If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* @param createDataSourceFromRdsRequest
* @return Result of the CreateDataSourceFromRDS operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This can result from retrying a request
* using a parameter that was not present in the original request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateDataSourceFromRDS
*/
@Override
public CreateDataSourceFromRdsResponse createDataSourceFromRDS(CreateDataSourceFromRdsRequest createDataSourceFromRdsRequest)
throws InvalidInputException, InternalServerException, IdempotentParameterMismatchException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, CreateDataSourceFromRdsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("CreateDataSourceFromRDS").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createDataSourceFromRdsRequest)
.withMarshaller(new CreateDataSourceFromRdsRequestMarshaller(protocolFactory)));
}
/**
*
* Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource
* references data that can be used to perform either CreateMLModel, CreateEvaluation, or
* CreateBatchPrediction operations.
*
*
* CreateDataSourceFromRedshift is an asynchronous operation. In response to
* CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the
* DataSource status to PENDING. After the DataSource is created and ready
* for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in
* COMPLETED or PENDING states can be used to perform only CreateMLModel,
* CreateEvaluation, or CreateBatchPrediction operations.
*
*
* If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
* by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to
* transfer the result set of the SelectSqlQuery query to S3StagingLocation.
*
*
* After the DataSource has been created, it's ready for use in evaluations and batch predictions. If
* you plan to use the DataSource to train an MLModel, the DataSource also
* requires a recipe. A recipe describes how each input variable will be used in training an MLModel.
* Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
* be combined with another variable or will it be split apart into word combinations? The recipe provides answers
* to these questions.
*
*
*
* You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
* Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing
* datasource and copy the values to a CreateDataSource call. Change the settings that you want to
* change and make sure that all required fields have the appropriate values.
*
*
*
* @param createDataSourceFromRedshiftRequest
* @return Result of the CreateDataSourceFromRedshift operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This can result from retrying a request
* using a parameter that was not present in the original request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateDataSourceFromRedshift
*/
@Override
public CreateDataSourceFromRedshiftResponse createDataSourceFromRedshift(
CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest) throws InvalidInputException,
InternalServerException, IdempotentParameterMismatchException, AwsServiceException, SdkClientException,
MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, CreateDataSourceFromRedshiftResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler
.execute(new ClientExecutionParams()
.withOperationName("CreateDataSourceFromRedshift").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createDataSourceFromRedshiftRequest)
.withMarshaller(new CreateDataSourceFromRedshiftRequestMarshaller(protocolFactory)));
}
/**
*
* Creates a DataSource object. A DataSource references data that can be used to perform
* CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
*
*
* CreateDataSourceFromS3 is an asynchronous operation. In response to
* CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the
* DataSource status to PENDING. After the DataSource has been created and is
* ready for use, Amazon ML sets the Status parameter to COMPLETED.
* DataSource in the COMPLETED or PENDING state can be used to perform only
* CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
*
*
* If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* The observation data used in a DataSource should be ready to use; that is, it should have a
* consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
* one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
* describes the data items by name and type. The same schema must be used for all of the data files referenced by
* the DataSource.
*
*
* After the DataSource has been created, it's ready to use in evaluations and batch predictions. If
* you plan to use the DataSource to train an MLModel, the DataSource also
* needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will
* the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
* combined with another variable or will it be split apart into word combinations? The recipe provides answers to
* these questions.
*
*
* @param createDataSourceFromS3Request
* @return Result of the CreateDataSourceFromS3 operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This can result from retrying a request
* using a parameter that was not present in the original request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateDataSourceFromS3
*/
@Override
public CreateDataSourceFromS3Response createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
throws InvalidInputException, InternalServerException, IdempotentParameterMismatchException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, CreateDataSourceFromS3Response::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("CreateDataSourceFromS3").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createDataSourceFromS3Request)
.withMarshaller(new CreateDataSourceFromS3RequestMarshaller(protocolFactory)));
}
/**
*
* Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set
* of observations associated to a DataSource. Like a DataSource for an
* MLModel, the DataSource for an Evaluation contains values for the
* Target Variable. The Evaluation compares the predicted result for each observation to
* the actual outcome and provides a summary so that you know how effective the MLModel functions on
* the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or
* MulticlassAvgFScore based on the corresponding MLModelType: BINARY,
* REGRESSION or MULTICLASS.
*
*
* CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon
* Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After
* the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.
*
*
* You can use the GetEvaluation operation to check progress of the evaluation during the creation
* operation.
*
*
* @param createEvaluationRequest
* @return Result of the CreateEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This can result from retrying a request
* using a parameter that was not present in the original request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateEvaluation
*/
@Override
public CreateEvaluationResponse createEvaluation(CreateEvaluationRequest createEvaluationRequest)
throws InvalidInputException, InternalServerException, IdempotentParameterMismatchException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
CreateEvaluationResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("CreateEvaluation").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createEvaluationRequest)
.withMarshaller(new CreateEvaluationRequestMarshaller(protocolFactory)));
}
/**
*
* Creates a new MLModel using the DataSource and the recipe as information sources.
*
*
* An MLModel is nearly immutable. Users can update only the MLModelName and the
* ScoreThreshold in an MLModel without creating a new MLModel.
*
*
* CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon
* Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING
* . After the MLModel has been created and ready is for use, Amazon ML sets the status to
* COMPLETED.
*
*
* You can use the GetMLModel operation to check the progress of the MLModel during the
* creation operation.
*
*
* CreateMLModel requires a DataSource with computed statistics, which can be created by
* setting ComputeStatistics to true in CreateDataSourceFromRDS,
* CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
*
*
* @param createMlModelRequest
* @return Result of the CreateMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This can result from retrying a request
* using a parameter that was not present in the original request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateMLModel
*/
@Override
public CreateMlModelResponse createMLModel(CreateMlModelRequest createMlModelRequest) throws InvalidInputException,
InternalServerException, IdempotentParameterMismatchException, AwsServiceException, SdkClientException,
MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
CreateMlModelResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("CreateMLModel").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createMlModelRequest)
.withMarshaller(new CreateMlModelRequestMarshaller(protocolFactory)));
}
/**
*
* Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the
* MLModel; that is, the location to send real-time prediction requests for the specified
* MLModel.
*
*
* @param createRealtimeEndpointRequest
* @return Result of the CreateRealtimeEndpoint operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.CreateRealtimeEndpoint
*/
@Override
public CreateRealtimeEndpointResponse createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, CreateRealtimeEndpointResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("CreateRealtimeEndpoint").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(createRealtimeEndpointRequest)
.withMarshaller(new CreateRealtimeEndpointRequestMarshaller(protocolFactory)));
}
/**
*
* Assigns the DELETED status to a BatchPrediction, rendering it unusable.
*
*
* After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation
* to verify that the status of the BatchPrediction changed to DELETED.
*
*
* Caution: The result of the DeleteBatchPrediction operation is irreversible.
*
*
* @param deleteBatchPredictionRequest
* @return Result of the DeleteBatchPrediction operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DeleteBatchPrediction
*/
@Override
public DeleteBatchPredictionResponse deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, DeleteBatchPredictionResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DeleteBatchPrediction").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(deleteBatchPredictionRequest)
.withMarshaller(new DeleteBatchPredictionRequestMarshaller(protocolFactory)));
}
/**
*
* Assigns the DELETED status to a DataSource, rendering it unusable.
*
*
* After using the DeleteDataSource operation, you can use the GetDataSource operation to verify
* that the status of the DataSource changed to DELETED.
*
*
* Caution: The results of the DeleteDataSource operation are irreversible.
*
*
* @param deleteDataSourceRequest
* @return Result of the DeleteDataSource operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DeleteDataSource
*/
@Override
public DeleteDataSourceResponse deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
DeleteDataSourceResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DeleteDataSource").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(deleteDataSourceRequest)
.withMarshaller(new DeleteDataSourceRequestMarshaller(protocolFactory)));
}
/**
*
* Assigns the DELETED status to an Evaluation, rendering it unusable.
*
*
* After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation
* to verify that the status of the Evaluation changed to DELETED.
*
* Caution
*
* The results of the DeleteEvaluation operation are irreversible.
*
* responseHandler = protocolFactory.createResponseHandler(operationMetadata,
DeleteEvaluationResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DeleteEvaluation").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(deleteEvaluationRequest)
.withMarshaller(new DeleteEvaluationRequestMarshaller(protocolFactory)));
}
/**
*
* Assigns the DELETED status to an MLModel, rendering it unusable.
*
*
* After using the DeleteMLModel operation, you can use the GetMLModel operation to verify
* that the status of the MLModel changed to DELETED.
*
*
* Caution: The result of the DeleteMLModel operation is irreversible.
*
*
* @param deleteMlModelRequest
* @return Result of the DeleteMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DeleteMLModel
*/
@Override
public DeleteMlModelResponse deleteMLModel(DeleteMlModelRequest deleteMlModelRequest) throws InvalidInputException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
DeleteMlModelResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DeleteMLModel").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(deleteMlModelRequest)
.withMarshaller(new DeleteMlModelRequestMarshaller(protocolFactory)));
}
/**
*
* Deletes a real time endpoint of an MLModel.
*
*
* @param deleteRealtimeEndpointRequest
* @return Result of the DeleteRealtimeEndpoint operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DeleteRealtimeEndpoint
*/
@Override
public DeleteRealtimeEndpointResponse deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, DeleteRealtimeEndpointResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DeleteRealtimeEndpoint").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(deleteRealtimeEndpointRequest)
.withMarshaller(new DeleteRealtimeEndpointRequestMarshaller(protocolFactory)));
}
/**
*
* Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover
* deleted tags.
*
*
* If you specify a tag that doesn't exist, Amazon ML ignores it.
*
*
* @param deleteTagsRequest
* @return Result of the DeleteTags operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InvalidTagException
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DeleteTags
*/
@Override
public DeleteTagsResponse deleteTags(DeleteTagsRequest deleteTagsRequest) throws InvalidInputException, InvalidTagException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
DeleteTagsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DeleteTags").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(deleteTagsRequest)
.withMarshaller(new DeleteTagsRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a list of BatchPrediction operations that match the search criteria in the request.
*
*
* @param describeBatchPredictionsRequest
* @return Result of the DescribeBatchPredictions operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeBatchPredictions
*/
@Override
public DescribeBatchPredictionsResponse describeBatchPredictions(
DescribeBatchPredictionsRequest describeBatchPredictionsRequest) throws InvalidInputException,
InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, DescribeBatchPredictionsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler
.execute(new ClientExecutionParams()
.withOperationName("DescribeBatchPredictions").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(describeBatchPredictionsRequest)
.withMarshaller(new DescribeBatchPredictionsRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a list of BatchPrediction operations that match the search criteria in the request.
*
*
*
* This is a variant of
* {@link #describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)}
* operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will
* internally handle making service calls for you.
*
*
* When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no
* guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response
* pages by making service calls until there are no pages left or your iteration stops. If there are errors in your
* request, you will see the failures only after you start iterating through the iterable.
*
*
*
* The following are few ways to iterate through the response pages:
*
* 1) Using a Stream
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
* responses.stream().forEach(....);
* }
*
*
* 2) Using For loop
*
*
* {
* @code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client
* .describeBatchPredictionsPaginator(request);
* for (software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response : responses) {
* // do something;
* }
* }
*
*
* 3) Use iterator directly
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
* responses.iterator().forEachRemaining(....);
* }
*
*
* Note: If you prefer to have control on service calls, use the
* {@link #describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)}
* operation.
*
*
* @param describeBatchPredictionsRequest
* @return A custom iterable that can be used to iterate through all the response pages.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeBatchPredictions
*/
@Override
public DescribeBatchPredictionsIterable describeBatchPredictionsPaginator(
DescribeBatchPredictionsRequest describeBatchPredictionsRequest) throws InvalidInputException,
InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
return new DescribeBatchPredictionsIterable(this, applyPaginatorUserAgent(describeBatchPredictionsRequest));
}
/**
*
* Returns a list of DataSource that match the search criteria in the request.
*
*
* @param describeDataSourcesRequest
* @return Result of the DescribeDataSources operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeDataSources
*/
@Override
public DescribeDataSourcesResponse describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException,
MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, DescribeDataSourcesResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DescribeDataSources").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(describeDataSourcesRequest)
.withMarshaller(new DescribeDataSourcesRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a list of DataSource that match the search criteria in the request.
*
*
*
* This is a variant of
* {@link #describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)}
* operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will
* internally handle making service calls for you.
*
*
* When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no
* guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response
* pages by making service calls until there are no pages left or your iteration stops. If there are errors in your
* request, you will see the failures only after you start iterating through the iterable.
*
*
*
* The following are few ways to iterate through the response pages:
*
* 1) Using a Stream
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
* responses.stream().forEach(....);
* }
*
*
* 2) Using For loop
*
*
* {
* @code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client
* .describeDataSourcesPaginator(request);
* for (software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response : responses) {
* // do something;
* }
* }
*
*
* 3) Use iterator directly
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
* responses.iterator().forEachRemaining(....);
* }
*
*
* Note: If you prefer to have control on service calls, use the
* {@link #describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)}
* operation.
*
*
* @param describeDataSourcesRequest
* @return A custom iterable that can be used to iterate through all the response pages.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeDataSources
*/
@Override
public DescribeDataSourcesIterable describeDataSourcesPaginator(DescribeDataSourcesRequest describeDataSourcesRequest)
throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException,
MachineLearningException {
return new DescribeDataSourcesIterable(this, applyPaginatorUserAgent(describeDataSourcesRequest));
}
/**
*
* Returns a list of DescribeEvaluations that match the search criteria in the request.
*
*
* @param describeEvaluationsRequest
* @return Result of the DescribeEvaluations operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeEvaluations
*/
@Override
public DescribeEvaluationsResponse describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException,
MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, DescribeEvaluationsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DescribeEvaluations").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(describeEvaluationsRequest)
.withMarshaller(new DescribeEvaluationsRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a list of DescribeEvaluations that match the search criteria in the request.
*
*
*
* This is a variant of
* {@link #describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)}
* operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will
* internally handle making service calls for you.
*
*
* When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no
* guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response
* pages by making service calls until there are no pages left or your iteration stops. If there are errors in your
* request, you will see the failures only after you start iterating through the iterable.
*
*
*
* The following are few ways to iterate through the response pages:
*
* 1) Using a Stream
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
* responses.stream().forEach(....);
* }
*
*
* 2) Using For loop
*
*
* {
* @code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client
* .describeEvaluationsPaginator(request);
* for (software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response : responses) {
* // do something;
* }
* }
*
*
* 3) Use iterator directly
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
* responses.iterator().forEachRemaining(....);
* }
*
*
* Note: If you prefer to have control on service calls, use the
* {@link #describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)}
* operation.
*
*
* @param describeEvaluationsRequest
* @return A custom iterable that can be used to iterate through all the response pages.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeEvaluations
*/
@Override
public DescribeEvaluationsIterable describeEvaluationsPaginator(DescribeEvaluationsRequest describeEvaluationsRequest)
throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException,
MachineLearningException {
return new DescribeEvaluationsIterable(this, applyPaginatorUserAgent(describeEvaluationsRequest));
}
/**
*
* Returns a list of MLModel that match the search criteria in the request.
*
*
* @param describeMlModelsRequest
* @return Result of the DescribeMLModels operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeMLModels
*/
@Override
public DescribeMlModelsResponse describeMLModels(DescribeMlModelsRequest describeMlModelsRequest)
throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException,
MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
DescribeMlModelsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DescribeMLModels").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(describeMlModelsRequest)
.withMarshaller(new DescribeMlModelsRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a list of MLModel that match the search criteria in the request.
*
*
*
* This is a variant of
* {@link #describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)}
* operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will
* internally handle making service calls for you.
*
*
* When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no
* guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response
* pages by making service calls until there are no pages left or your iteration stops. If there are errors in your
* request, you will see the failures only after you start iterating through the iterable.
*
*
*
* The following are few ways to iterate through the response pages:
*
* 1) Using a Stream
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
* responses.stream().forEach(....);
* }
*
*
* 2) Using For loop
*
*
* {
* @code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client
* .describeMLModelsPaginator(request);
* for (software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response : responses) {
* // do something;
* }
* }
*
*
* 3) Use iterator directly
*
*
* {@code
* software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
* responses.iterator().forEachRemaining(....);
* }
*
*
* Note: If you prefer to have control on service calls, use the
* {@link #describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)}
* operation.
*
*
* @param describeMlModelsRequest
* @return A custom iterable that can be used to iterate through all the response pages.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeMLModels
*/
@Override
public DescribeMLModelsIterable describeMLModelsPaginator(DescribeMlModelsRequest describeMlModelsRequest)
throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException,
MachineLearningException {
return new DescribeMLModelsIterable(this, applyPaginatorUserAgent(describeMlModelsRequest));
}
/**
*
* Describes one or more of the tags for your Amazon ML object.
*
*
* @param describeTagsRequest
* @return Result of the DescribeTags operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.DescribeTags
*/
@Override
public DescribeTagsResponse describeTags(DescribeTagsRequest describeTagsRequest) throws InvalidInputException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
DescribeTagsResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("DescribeTags").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(describeTagsRequest)
.withMarshaller(new DescribeTagsRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a BatchPrediction that includes detailed metadata, status, and data file information for a
* Batch Prediction request.
*
*
* @param getBatchPredictionRequest
* @return Result of the GetBatchPrediction operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.GetBatchPrediction
*/
@Override
public GetBatchPredictionResponse getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, GetBatchPredictionResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("GetBatchPrediction").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(getBatchPredictionRequest)
.withMarshaller(new GetBatchPredictionRequestMarshaller(protocolFactory)));
}
/**
*
* Returns a DataSource that includes metadata and data file information, as well as the current status
* of the DataSource.
*
*
* GetDataSource provides results in normal or verbose format. The verbose format adds the schema
* description and the list of files pointed to by the DataSource to the normal format.
*
*
* @param getDataSourceRequest
* @return Result of the GetDataSource operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.GetDataSource
*/
@Override
public GetDataSourceResponse getDataSource(GetDataSourceRequest getDataSourceRequest) throws InvalidInputException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
GetDataSourceResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("GetDataSource").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(getDataSourceRequest)
.withMarshaller(new GetDataSourceRequestMarshaller(protocolFactory)));
}
/**
*
* Returns an Evaluation that includes metadata as well as the current status of the
* Evaluation.
*
*
* @param getEvaluationRequest
* @return Result of the GetEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.GetEvaluation
*/
@Override
public GetEvaluationResponse getEvaluation(GetEvaluationRequest getEvaluationRequest) throws InvalidInputException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
GetEvaluationResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("GetEvaluation").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(getEvaluationRequest)
.withMarshaller(new GetEvaluationRequestMarshaller(protocolFactory)));
}
/**
*
* Returns an MLModel that includes detailed metadata, data source information, and the current status
* of the MLModel.
*
*
* GetMLModel provides results in normal or verbose format.
*
*
* @param getMlModelRequest
* @return Result of the GetMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.GetMLModel
*/
@Override
public GetMlModelResponse getMLModel(GetMlModelRequest getMlModelRequest) throws InvalidInputException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
GetMlModelResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("GetMLModel").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(getMlModelRequest)
.withMarshaller(new GetMlModelRequestMarshaller(protocolFactory)));
}
/**
*
* Generates a prediction for the observation using the specified ML Model.
*
* Note
*
* Not all response parameters will be populated. Whether a response parameter is populated depends on the type of
* model requested.
*
* DataSource.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws PredictorNotMountedException
* The exception is thrown when a predict request is made to an unmounted MLModel.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.Predict
*/
@Override
public PredictResponse predict(PredictRequest predictRequest) throws InvalidInputException, ResourceNotFoundException,
LimitExceededException, InternalServerException, PredictorNotMountedException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
PredictResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams().withOperationName("Predict")
.withResponseHandler(responseHandler).withErrorResponseHandler(errorResponseHandler).withInput(predictRequest)
.withMarshaller(new PredictRequestMarshaller(protocolFactory)));
}
/**
*
* Updates the BatchPredictionName of a BatchPrediction.
*
*
* You can use the GetBatchPrediction operation to view the contents of the updated data element.
*
*
* @param updateBatchPredictionRequest
* @return Result of the UpdateBatchPrediction operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.UpdateBatchPrediction
*/
@Override
public UpdateBatchPredictionResponse updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(
operationMetadata, UpdateBatchPredictionResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("UpdateBatchPrediction").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(updateBatchPredictionRequest)
.withMarshaller(new UpdateBatchPredictionRequestMarshaller(protocolFactory)));
}
/**
*
* Updates the DataSourceName of a DataSource.
*
*
* You can use the GetDataSource operation to view the contents of the updated data element.
*
*
* @param updateDataSourceRequest
* @return Result of the UpdateDataSource operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.UpdateDataSource
*/
@Override
public UpdateDataSourceResponse updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
UpdateDataSourceResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("UpdateDataSource").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(updateDataSourceRequest)
.withMarshaller(new UpdateDataSourceRequestMarshaller(protocolFactory)));
}
/**
*
* Updates the EvaluationName of an Evaluation.
*
*
* You can use the GetEvaluation operation to view the contents of the updated data element.
*
*
* @param updateEvaluationRequest
* @return Result of the UpdateEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.UpdateEvaluation
*/
@Override
public UpdateEvaluationResponse updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
throws InvalidInputException, ResourceNotFoundException, InternalServerException, AwsServiceException,
SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
UpdateEvaluationResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("UpdateEvaluation").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(updateEvaluationRequest)
.withMarshaller(new UpdateEvaluationRequestMarshaller(protocolFactory)));
}
/**
*
* Updates the MLModelName and the ScoreThreshold of an MLModel.
*
*
* You can use the GetMLModel operation to view the contents of the updated data element.
*
*
* @param updateMlModelRequest
* @return Result of the UpdateMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws SdkException
* Base class for all exceptions that can be thrown by the SDK (both service and client). Can be used for
* catch all scenarios.
* @throws SdkClientException
* If any client side error occurs such as an IO related failure, failure to get credentials, etc.
* @throws MachineLearningException
* Base class for all service exceptions. Unknown exceptions will be thrown as an instance of this type.
* @sample MachineLearningClient.UpdateMLModel
*/
@Override
public UpdateMlModelResponse updateMLModel(UpdateMlModelRequest updateMlModelRequest) throws InvalidInputException,
ResourceNotFoundException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningException {
JsonOperationMetadata operationMetadata = JsonOperationMetadata.builder().hasStreamingSuccessResponse(false)
.isPayloadJson(true).build();
HttpResponseHandler responseHandler = protocolFactory.createResponseHandler(operationMetadata,
UpdateMlModelResponse::builder);
HttpResponseHandler errorResponseHandler = createErrorResponseHandler(protocolFactory,
operationMetadata);
return clientHandler.execute(new ClientExecutionParams()
.withOperationName("UpdateMLModel").withResponseHandler(responseHandler)
.withErrorResponseHandler(errorResponseHandler).withInput(updateMlModelRequest)
.withMarshaller(new UpdateMlModelRequestMarshaller(protocolFactory)));
}
private HttpResponseHandler createErrorResponseHandler(BaseAwsJsonProtocolFactory protocolFactory,
JsonOperationMetadata operationMetadata) {
return protocolFactory.createErrorResponseHandler(operationMetadata);
}
private > T init(T builder) {
return builder
.clientConfiguration(clientConfiguration)
.defaultServiceExceptionSupplier(MachineLearningException::builder)
.protocol(AwsJsonProtocol.AWS_JSON)
.protocolVersion("1.1")
.registerModeledException(
ExceptionMetadata.builder().errorCode("InvalidTagException")
.exceptionBuilderSupplier(InvalidTagException::builder).build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("ResourceNotFoundException")
.exceptionBuilderSupplier(ResourceNotFoundException::builder).httpStatusCode(404).build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("InvalidInputException")
.exceptionBuilderSupplier(InvalidInputException::builder).httpStatusCode(400).build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("IdempotentParameterMismatchException")
.exceptionBuilderSupplier(IdempotentParameterMismatchException::builder).httpStatusCode(400)
.build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("TagLimitExceededException")
.exceptionBuilderSupplier(TagLimitExceededException::builder).build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("InternalServerException")
.exceptionBuilderSupplier(InternalServerException::builder).httpStatusCode(500).build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("LimitExceededException")
.exceptionBuilderSupplier(LimitExceededException::builder).httpStatusCode(417).build())
.registerModeledException(
ExceptionMetadata.builder().errorCode("PredictorNotMountedException")
.exceptionBuilderSupplier(PredictorNotMountedException::builder).httpStatusCode(400).build());
}
@Override
public void close() {
clientHandler.close();
}
private T applyPaginatorUserAgent(T request) {
Consumer userAgentApplier = b -> b.addApiName(ApiName.builder()
.version(VersionInfo.SDK_VERSION).name("PAGINATED").build());
AwsRequestOverrideConfiguration overrideConfiguration = request.overrideConfiguration()
.map(c -> c.toBuilder().applyMutation(userAgentApplier).build())
.orElse((AwsRequestOverrideConfiguration.builder().applyMutation(userAgentApplier).build()));
return (T) request.toBuilder().overrideConfiguration(overrideConfiguration).build();
}
}
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